diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml deleted file mode 100644 index 2710e3d..0000000 --- a/.github/workflows/static.yml +++ /dev/null @@ -1,43 +0,0 @@ -# Simple workflow for deploying static content to GitHub Pages -name: Deploy static content to Pages - -on: - # Runs on pushes targeting the default branch - push: - branches: ["main"] - - # Allows you to run this workflow manually from the Actions tab - workflow_dispatch: - -# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages -permissions: - contents: read - pages: write - id-token: write - -# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. -# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. -concurrency: - group: "pages" - cancel-in-progress: false - -jobs: - # Single deploy job since we're just deploying - deploy: - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v3 - - name: Setup Pages - uses: actions/configure-pages@v3 - - name: Upload artifact - uses: actions/upload-pages-artifact@v1 - with: - # Upload entire repository - path: './01_Docs/build/html' - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v2 diff --git a/.gitignore b/.gitignore old mode 100755 new mode 100644 index 2d41d43..9377675 --- a/.gitignore +++ b/.gitignore @@ -1,31 +1,67 @@ -### MacOS ### -.DS_Store - -# Default ignored files ### -/shelf/ -/workspace.xml - -### Python ### -# Byte-compiled / optimized / DLL files +# Python __pycache__/ *.py[cod] *$py.class - -### C extensions ### *.so +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# Jupyter Notebook +.ipynb_checkpoints +*.ipynb_checkpoints/ + +# Virtual environments +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# Simulation outputs and temporary files +simulation_output*.txt +temp_output.txt +**/simulation_output*.txt +**/temp_output.txt + +# Generated figures +Figures/ + +# Development notes +DEV_NOTES/ + +# Run outputs +runs/*/ +!runs/Processing/ +OFF/03_Code/runs/ + +# Figures +Figures/ + +# OS +.DS_Store +Thumbs.db +Desktop.ini -### Docs ### -*.doctree -*.pickle - -### onwards files ### -.runid -off_run_* -*.m -*.asv -wind_farm_3d_layout.svg -*.obj -*.db - -### directories ### -.vscode/ \ No newline at end of file +# Documentation builds +OFF/01_Docs/build/ diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 26d3352..0000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml diff --git a/.idea/OFF.iml b/.idea/OFF.iml deleted file mode 100644 index 454c8df..0000000 --- a/.idea/OFF.iml +++ /dev/null @@ -1,18 +0,0 @@ - - - - - - - - - - - - - - \ No newline at end of file diff --git a/.idea/dictionaries/MarcusWork.xml b/.idea/dictionaries/MarcusWork.xml deleted file mode 100644 index b27ac02..0000000 --- a/.idea/dictionaries/MarcusWork.xml +++ /dev/null @@ -1,3 +0,0 @@ - - - \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml deleted file mode 100644 index 105ce2d..0000000 --- a/.idea/inspectionProfiles/profiles_settings.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml deleted file mode 100644 index f3c2011..0000000 --- a/.idea/misc.xml +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml deleted file mode 100644 index 5eab17f..0000000 --- a/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml deleted file mode 100644 index 94a25f7..0000000 --- a/.idea/vcs.xml +++ /dev/null @@ -1,6 +0,0 @@ - - - - - - \ No newline at end of file diff --git a/01_Docs/build/html/.buildinfo b/01_Docs/build/html/.buildinfo deleted file mode 100644 index 02b120a..0000000 --- a/01_Docs/build/html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 21bda27f4dc57f2b8d989b1a449002e8 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/01_Docs/build/html/_images/9T_ueff_2x2.png b/01_Docs/build/html/_images/9T_ueff_2x2.png deleted file mode 100644 index 9055097..0000000 Binary files a/01_Docs/build/html/_images/9T_ueff_2x2.png and /dev/null differ diff --git a/01_Docs/build/html/_images/OFF_Logo_light.svg b/01_Docs/build/html/_images/OFF_Logo_light.svg deleted file mode 100644 index 96c068b..0000000 --- a/01_Docs/build/html/_images/OFF_Logo_light.svg +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - - - - - - - - - - - diff --git a/01_Docs/build/html/_images/OFF_Logo_wide.png b/01_Docs/build/html/_images/OFF_Logo_wide.png deleted file mode 100644 index 9e491ac..0000000 Binary files a/01_Docs/build/html/_images/OFF_Logo_wide.png and /dev/null differ diff --git a/01_Docs/build/html/_images/Results_T1_T2.png b/01_Docs/build/html/_images/Results_T1_T2.png deleted file mode 100644 index bd70296..0000000 Binary files a/01_Docs/build/html/_images/Results_T1_T2.png and /dev/null differ diff --git a/01_Docs/build/html/_images/Results_power_9T.png b/01_Docs/build/html/_images/Results_power_9T.png deleted file mode 100644 index 69ba07f..0000000 Binary files a/01_Docs/build/html/_images/Results_power_9T.png and /dev/null differ diff --git a/01_Docs/build/html/_images/turbine_effective_wind_speed_at_0700s.png b/01_Docs/build/html/_images/turbine_effective_wind_speed_at_0700s.png deleted file mode 100644 index 6f9f240..0000000 Binary files a/01_Docs/build/html/_images/turbine_effective_wind_speed_at_0700s.png and /dev/null differ diff --git a/01_Docs/build/html/_sources/index.rst.txt b/01_Docs/build/html/_sources/index.rst.txt deleted file mode 100644 index 3154bdf..0000000 --- a/01_Docs/build/html/_sources/index.rst.txt +++ /dev/null @@ -1,52 +0,0 @@ - - -.. image:: ../../99_Design/01_Logo/OFF_Logo_wide.png - :width: 100% - -a dynamic parametric wake toolbox which combines and connects OnWaRDS [1]_, FLORIDyn [2]_ and FLORIS [3]_ - -Documentation -============= - -Run the main.py in the `03_Code `_ folder. - -To change the simulation, you have to change the .yaml file OFF calls. This is defined by one of the first limes of code in the main function. The .yaml structure is showed in `run_example.yaml `_ . This is where you can change the wind farm layout, the flow conditions, the wake model etc. - -.. image:: media/9T_ueff_2x2.png - :width: 75% - -.. image:: media/Results_power_9T.png - :width: 75% - -Code ----- - -The documentation is handled automatically using Sphinx and available here. - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - -.. toctree:: - :maxdepth: 1 - - off.off - off.turbine - off.windfarm - off.states - off.ambient - off.observation_points - off.utils - -License -------- -The documentation for this program is under a creative commons attribution share-alike 4.0 license. http://creativecommons.org/licenses/by-sa/4.0/ - -Publications ------------- - -.. [1] FLORIDyn - A dynamic and flexible framework for real-time wind farm control, M. Becker, D. Allaerts, J.W. van Wingerden, 2022 J. Phys.: Conf. Ser. 2265(2022) 032103 - -.. [2] FLORIS Wake Modeling and Wind Farm Controls Software, National Renewable Energy Laboratory, 2023, GitHub - -.. [3] A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction, M. Lejeune, M. Moens, P. Chatelain, 2022 Front. Energy Res., Sec. Wind Energy diff --git a/01_Docs/build/html/_sources/off.ambient.rst.txt b/01_Docs/build/html/_sources/off.ambient.rst.txt deleted file mode 100644 index 8a536ef..0000000 --- a/01_Docs/build/html/_sources/off.ambient.rst.txt +++ /dev/null @@ -1,10 +0,0 @@ -ambient -======= - -.. automodule:: off.ambient - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False - \ No newline at end of file diff --git a/01_Docs/build/html/_sources/off.observation_points.rst.txt b/01_Docs/build/html/_sources/off.observation_points.rst.txt deleted file mode 100644 index b42b1ba..0000000 --- a/01_Docs/build/html/_sources/off.observation_points.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -observation_points -================== - -.. automodule:: off.observation_points - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False \ No newline at end of file diff --git a/01_Docs/build/html/_sources/off.off.rst.txt b/01_Docs/build/html/_sources/off.off.rst.txt deleted file mode 100644 index 7ac0714..0000000 --- a/01_Docs/build/html/_sources/off.off.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -off -=== - -.. automodule:: off.off - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False \ No newline at end of file diff --git a/01_Docs/build/html/_sources/off.states.rst.txt b/01_Docs/build/html/_sources/off.states.rst.txt deleted file mode 100644 index c67111a..0000000 --- a/01_Docs/build/html/_sources/off.states.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -states -====== - -.. automodule:: off.states - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False \ No newline at end of file diff --git a/01_Docs/build/html/_sources/off.turbine.rst.txt b/01_Docs/build/html/_sources/off.turbine.rst.txt deleted file mode 100644 index dc591b7..0000000 --- a/01_Docs/build/html/_sources/off.turbine.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -turbine -======= - -.. automodule:: off.turbine - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False \ No newline at end of file diff --git a/01_Docs/build/html/_sources/off.utils.rst.txt b/01_Docs/build/html/_sources/off.utils.rst.txt deleted file mode 100644 index 28c4a17..0000000 --- a/01_Docs/build/html/_sources/off.utils.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -utils -===== - -.. automodule:: off.utils - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False \ No newline at end of file diff --git a/01_Docs/build/html/_sources/off.windfarm.rst.txt b/01_Docs/build/html/_sources/off.windfarm.rst.txt deleted file mode 100644 index 766d921..0000000 --- a/01_Docs/build/html/_sources/off.windfarm.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -windfarm -======== - -.. automodule:: off.windfarm - :members: - :undoc-members: - :show-inheritance: - :exclude-members: - :special-members: False \ No newline at end of file diff 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Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; - console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - // array of [docname, title, anchor, descr, score, filename] - let results = []; - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // lookup as object - objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - results = results.reverse(); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/01_Docs/build/html/_static/sphinx_highlight.js b/01_Docs/build/html/_static/sphinx_highlight.js deleted file mode 100644 index aae669d..0000000 --- a/01_Docs/build/html/_static/sphinx_highlight.js +++ /dev/null @@ -1,144 +0,0 @@ -/* Highlighting utilities for Sphinx HTML documentation. */ -"use strict"; - -const SPHINX_HIGHLIGHT_ENABLED = true - -/** - * highlight a given string on a node by wrapping it in - * span elements with the given class name. - */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); - } - } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); - } -}; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; - -/** - * Small JavaScript module for the documentation. - */ -const SphinxHighlight = { - - /** - * highlight the search words provided in localstorage in the text - */ - highlightSearchWords: () => { - if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight - - // get and clear terms from localstorage - const url = new URL(window.location); - const highlight = - localStorage.getItem("sphinx_highlight_terms") - || url.searchParams.get("highlight") - || ""; - localStorage.removeItem("sphinx_highlight_terms") - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); - - // get individual terms from highlight string - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do - - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

' + - '' + - _("Hide Search Matches") + - "

" - ) - ); - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - localStorage.removeItem("sphinx_highlight_terms") - }, - - initEscapeListener: () => { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; - - document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; - if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { - SphinxHighlight.hideSearchWords(); - event.preventDefault(); - } - }); - }, -}; - -_ready(SphinxHighlight.highlightSearchWords); -_ready(SphinxHighlight.initEscapeListener); diff --git a/01_Docs/build/html/genindex.html b/01_Docs/build/html/genindex.html deleted file mode 100644 index ca36875..0000000 --- a/01_Docs/build/html/genindex.html +++ /dev/null @@ -1,635 +0,0 @@ - - - - - - Index — OFF 0.1 documentation - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Index
    • -
  • -
    • -
-
-
-
-
- - -

Index

- -
- A - | C - | D - | F - | G - | H - | I - | L - | M - | N - | O - | P - | R - | S - | T - | U - | W - -
-

A

- - - -
- -

C

- - - -
- -

D

- - - -
- -

F

- - - -
- -

G

- - - -
- -

H

- - -
- -

I

- - - -
- -

L

- - -
- -

M

- - -
- -

N

- - - -
- -

O

- - - -
- -

P

- - -
- -

R

- - - -
- -

S

- - - -
- -

T

- - - -
- -

U

- - -
- -

W

- - - -
- - - -
-
-
- -
- -
-

© Copyright 2022, Marcus BECKER and Maxime LEJEUNE.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/index.html b/01_Docs/build/html/index.html deleted file mode 100644 index 5a6d9ae..0000000 --- a/01_Docs/build/html/index.html +++ /dev/null @@ -1,154 +0,0 @@ - - - - - - - Documentation — OFF 0.1 documentation - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- - _images/OFF_Logo_wide.png -

a dynamic parametric wake toolbox which combines and connects OnWaRDS [1], FLORIDyn [2] and FLORIS [3]

-
-

Documentation

-

Run the main.py in the 03_Code folder.

-

To change the simulation, you have to change the .yaml file OFF calls. This is defined by one of the first limes of code in the main function. The .yaml structure is showed in run_example.yaml . This is where you can change the wind farm layout, the flow conditions, the wake model etc.

-_images/9T_ueff_2x2.png -_images/Results_power_9T.png -
-

Code

-

The documentation is handled automatically using Sphinx and available here.

-
-
-
- -
-
-
-

License

-

The documentation for this program is under a creative commons attribution share-alike 4.0 license. http://creativecommons.org/licenses/by-sa/4.0/

-
-
-

Publications

- -
-
- - -
-
-
- -
- -
-

© Copyright 2022, Marcus BECKER and Maxime LEJEUNE.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/objects.inv b/01_Docs/build/html/objects.inv deleted file mode 100644 index 0335ef0..0000000 Binary files a/01_Docs/build/html/objects.inv and /dev/null differ diff --git a/01_Docs/build/html/off.ambient.html b/01_Docs/build/html/off.ambient.html deleted file mode 100644 index afd4f3f..0000000 --- a/01_Docs/build/html/off.ambient.html +++ /dev/null @@ -1,573 +0,0 @@ - - - - - - - ambient — OFF 0.1 documentation - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

ambient

-
-
-class off.ambient.AmbientCorrector(settings_cor: dict, nT: int, states_name: List[str])
-

Bases: object

-
-
-update(t: float)
-

updates the corrector buffer

-
-
Parameters:
-

t (float) – current time in s

-
-
-
- -
- -
-
-class off.ambient.AmbientStates(number_of_time_steps: int, number_of_states: int, state_names: list)
-

Bases: States, ABC

-
-
-abstract create_interpolated_state(index1: int, index2: int, w1, w2)
-

Creates an AmbientStates object of its own kind with only one state entry, based on two weighted states. -The returned object then still has access to functions such as get_turbine_wind_dir()

-
-
Parameters:
-
    -

  • index1 (int) – Index of the first state

    • -

  • index2 (int) – Index of the second state

    • -

  • w1 (float) – Weight for first index (has to be w1 = 1 - w2, and [0,1])

    • -

  • w2 (float) – Weight for second index (has to be w2 = 1 - w1, and [0,1])

    • -
-
-
Returns:
-

ambient state object with single entry

-
-
Return type:
-

AmbientStates

-
-
-
- -
-
-abstract get_turbine_wind_dir() → float
-

Returns wind direction at the turbine location

-
-
Returns:
-

wind direction (deg)

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_turbine_wind_speed() → ndarray
-

Returns u,v component wind speed at the turbine location

-
-
Returns:
-

1 x 2 vector of [u,v] wind speeds in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_turbine_wind_speed_abs() → float
-

Returns the absolute wind speed at the turbine location (first entry)

-
-
Returns:
-

absolute wind speed

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_turbine_wind_speed_u() → float
-

Returns the u component of the wind speed (x direction) at the turbine location

-
-
Returns:
-

u wind speed

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_turbine_wind_speed_v() → float
-

Returns the v component of the wind speed (y direction) at the turbine location

-
-
Returns:
-

v wind speed

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_wind_dir() → ndarray
-

Returns all wind directions

-
-
Returns:
-

m x 1 vector of wind direction states in deg

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_wind_dir_ind(ind: int)
-

Returns wind directions at an index

-
-
Parameters:
-

ind (int) – Index

-
-
Returns:
-

m x 1 vector of wind direction states in deg

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_wind_speed() → ndarray
-

Returns u,v component of all wind speeds

-
-
Returns:
-

m x 2 matrix of [u,v] wind speeds in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_wind_speed_abs() → ndarray
-

Returns the absolute wind speed

-
-
Returns:
-

m x 1 vector of the absolute wind speed in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_wind_speed_u() → ndarray
-

Returns the u component of the wind speed (x direction)

-
-
Returns:
-

m x 1 vector of the u wind speed in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_wind_speed_v() → ndarray
-

Returns the v component of the wind speed (y direction)

-
-
Returns:
-

m x 1 vector of the v wind speed in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
- -
-
-class off.ambient.FLORIDynAmbient(number_of_time_steps: int)
-

Bases: AmbientStates

-
-
-create_interpolated_state(index1: int, index2: int, w1, w2)
-

Creates an AmbientStates object of its own kind with only one state entry, based on two weighted states. -The returned object then still has access to functions such as get_turbine_wind_dir()

-
-
Parameters:
-
    -

  • index1 (int) – Index of the first state

    • -

  • index2 (int) – Index of the second state

    • -

  • w1 (float) – Weight for first index (has to be w1 = 1 - w2, and [0,1])

    • -

  • w2 (float) – Weight for second index (has to be w2 = 1 - w1, and [0,1])

    • -
-
-
Returns:
-

ambient state object with single entry

-
-
Return type:
-

AmbientStates

-
-
-
- -
-
-get_turbine_wind_dir() → float
-

Returns all wind directions

-
-
Returns:
-

float of wind direction state at the turbine location in deg

-
-
-
- -
-
-get_turbine_wind_speed() → ndarray
-

Returns u,v component of u & v wind speed at the turbine location

-
-
Returns:
-

1 x 2 matrix of [u,v] wind speeds in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_turbine_wind_speed_abs() → float
-

Returns the absolute wind speed at the turbine location (first entry)

-
-
Returns:
-

absolute wind speed

-
-
Return type:
-

float

-
-
-
- -
-
-get_turbine_wind_speed_u() → float
-

Returns the u component of the wind speed (x direction) at the turbine location

-
-
Returns:
-

u wind speed

-
-
Return type:
-

float

-
-
-
- -
-
-get_turbine_wind_speed_v() → float
-

Returns the v component of the wind speed (y direction) at the turbine location

-
-
Returns:
-

v wind speed

-
-
Return type:
-

float

-
-
-
- -
-
-get_wind_dir() → ndarray
-

Returns all stored wind directions

-
-
Returns:
-

m x 1 vector of wind direction states in deg

-
-
-
- -
-
-get_wind_dir_ind(ind: int)
-

Returns wind directions at an index

-
-
Parameters:
-

ind (int) – Index

-
-
Returns:
-

m x 1 vector of wind direction states in deg

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_wind_direction_at(location: ndarray, op_coord: ndarray)
-

Returns the wind direction at a requested location

-
-
Parameters:
-
    -

  • location (np.ndarray) – m x 3 matrix where the columns are [x,y,z] locations in m

    • -

  • op_coord (np.ndarray) – n x 3 matrix of the OP world coordinate states in m

    • -
-
-
Returns:
-

m x 1 vector with absolute wind speeds in deg

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_wind_speed() → ndarray
-

Returns u,v component of all wind speeds

-
-
Returns:
-

m x 2 matrix of [u,v] wind speeds in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_wind_speed_abs() → ndarray
-

Returns the absolute wind speed

-
-
Returns:
-

m x 1 vector of the absolute wind speed in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_wind_speed_at(location: ndarray, op_coord: ndarray) → ndarray
-

Returns the absolute wind speed at a requested location

-
-
Parameters:
-
    -

  • location (np.ndarray) – m x 3 matrix where the columns are [x,y,z] locations in m

    • -

  • op_coord (np.ndarray) – n x 3 matrix of the OP world coordinate states in m

    • -
-
-
Returns:
-

m x 1 vector with absolute wind speeds in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_wind_speed_u() → ndarray
-

Returns the u component of the wind speed (x direction)

-
-
Returns:
-

m x 1 vector of the u wind speed in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_wind_speed_v() → ndarray
-

Returns the v component of the wind speed (y direction)

-
-
Returns:
-

m x 1 vector of the v wind speed in m/s

-
-
Return type:
-

np.ndarray

-
-
-
- -
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/off.observation_points.html b/01_Docs/build/html/off.observation_points.html deleted file mode 100644 index 0358909..0000000 --- a/01_Docs/build/html/off.observation_points.html +++ /dev/null @@ -1,332 +0,0 @@ - - - - - - - observation_points — OFF 0.1 documentation - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

observation_points

-
-
-class off.observation_points.FLORIDynOPs4(number_of_time_steps: int)
-

Bases: ObservationPoints

-
-
-get_vec_op_to_turbine(index: int) → ndarray
-

Returns a x, y, z vector pointing from the OP to the turbine location in the OP coordinate system. -OP coordinate system is usually the wake coordinate system based on the OP data.

-
-
Returns:
-

1 x 3 matrix where the columns are the x,y,z coordinates

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_world_coord() → ndarray
-

Returns the world coordinates of the OPs

-
-
Returns:
-

[x, y, z] coordinates in world coordinate system

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-init_all_states(wind_speed_u: float, wind_speed_v: float, rotor_pos: ndarray, time_step: float)
-

Creates a downstream chain of OPs -Overwrites the base method of the States class

-
-
Parameters:
-
    -

  • wind_speed_u (float) – wind speed in x direction in m/s

    • -

  • wind_speed_v (float) – wind speed in y direction in m/s

    • -

  • rotor_pos (np.ndarray) – 1 x 3 vector with x,y,z location of the rotor in the world coordinate system

    • -

  • time_step (float) – simulation time step in s

    • -
-
-
-
- -
-
-propagate_ops(time_step: float)
-

Propagates the OPs based on the u and v velocity component

-
-
Parameters:
-
    -

  • uv_op (np.ndarray) – m x 2 matrix with wind speeds of all OPs in x and y direction in m/s

    • -

  • time_step (float) – Time step of the simulation in s

    • -
-
-
-
- -
- -
-
-class off.observation_points.FLORIDynOPs6(number_of_time_steps: int)
-

Bases: ObservationPoints

-
-
-get_world_coord() → ndarray
-

Returns the world coordinates of the OPs

-
-
Returns:
-

[x, y, z] coordinates in world coordinate system

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-init_all_states(wind_speed_u: float, wind_speed_v: float, rotor_pos: ndarray, time_step: float)
-

Creates a downstream chain of OPs -Overwrites the base method of the States class

-
-
Parameters:
-
    -

  • wind_speed_u (float) – wind speed in x direction in m/s

    • -

  • wind_speed_v (float) – wind speed in y direction in m/s

    • -

  • rotor_pos (np.ndarray) – 1 x 3 vector with x,y,z location of the rotor in the world coordinate system

    • -

  • time_step (float) – simulation time step in s

    • -
-
-
-
- -
-
-propagate_ops(time_step: float)
-

Propagates the OPs based on the u and v velocity component

-
-
Parameters:
-
    -

  • uv_op (np.ndarray) – m x 2 matrix with wind speeds of all OPs in x and y direction in m/s

    • -

  • time_step (float) – Time step of the simulation in s

    • -
-
-
-
- -
- -
-
-class off.observation_points.ObservationPoints(number_of_time_steps: int, number_of_states: int, state_names: list)
-

Bases: States, ABC

-

ObservationPoints is the abstract base class for a list of wake tracers / particles -The class inherits get, set & iterate methods from the abstract States class, init is overwritten

-
-
-abstract get_vec_op_to_turbine(index: int) → ndarray
-

Returns a x, y, z vector pointing from the OP to the turbine location in the OP coordinate system. -OP coordinate system is usually the wake coordinate system based on the OP data.

-
-
Returns:
-

1 x 3 matrix where the columns are the x,y,z coordinates

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_world_coord() → ndarray
-

Returns the x, y, z coordinates of all OPs

-
-
Returns:
-

m x 3 matrix where the columns are the x,y,z coordinates

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract init_all_states(wind_speed_u: float, wind_speed_v: float, rotor_pos: ndarray, time_step: float)
-

Creates a downstream chain of OPs -Overwrites the base method of the States class

-
-
Parameters:
-
    -

  • wind_speed_u (float) – Wind speed in x direction in m/s

    • -

  • wind_speed_v (float) – Wind speed in y direction in m/s

    • -

  • rotor_pos (np.ndarray) – 1 x 3 vector with x,y,z location of the rotor in the world coordinate system

    • -

  • time_step (float) – simulation time step in s

    • -
-
-
-
- -
-
-abstract propagate_ops(time_step: float)
-

Propagates the OPs based on the u and v velocity component

-
-
Parameters:
-
    -

  • uv_op (np.ndarray) – m x 2 matrix with wind speeds of all OPs in x and y direction in m/s

    • -

  • time_step (float) – Time step of the simulation in s

    • -
-
-
-
- -
-
-set_op_propagation_speed(op_propagation_speed: ndarray)
-

Sets the propagation speed of the OPs, meant as a temporay storage, which ensures that different lengths of OP -chains can be used in the simulation

-
-
Parameters:
-

op_propagation_speed (np.ndarray) – m x 2 matrix with [u,v] velocity component for all OPs

-
-
-
- -
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/off.off.html b/01_Docs/build/html/off.off.html deleted file mode 100644 index 37c1fbb..0000000 --- a/01_Docs/build/html/off.off.html +++ /dev/null @@ -1,206 +0,0 @@ - - - - - - - off — OFF 0.1 documentation - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

off

-
-
-class off.off.OFF(wind_farm: WindFarm, settings_sim: dict, settings_wke: dict, settings_sol: dict, settings_cor: dict, vis: dict)
-

Bases: object

-

OFF is the central object which initializes the wind farm and runs the simulation

-
-
-get_wind_farm() → WindFarm
-

Get the current wind farm object which equals the simulation state

-
-
Returns:
-

Wind farm object with turbines and states

-
-
Return type:
-

windfarm.WindFarm

-
-
-
- -
-
-init_sim(start_ambient: ndarray, start_turbine: ndarray)
-

Function which initializes the states within the self.wind_farm object. -Assigns turbine & ambient states and distributes the OPs downstream. OP -locations are not necessarily correct but the wakes are “unrolled” and -do not have to first develop.

-
-
Parameters:
-
    -

  • start_ambient (np.ndarray) – 1 x n vector with initial ambient state

    • -

  • start_turbine (np.ndarray) – 1 x n vector with initial turbine state

    • -
-
-
-
- -
-
-run_sim() → DataFrame
-

Central function which executes the simulation and manipulates the self.wind_farm object

-
-
Returns:
-

Measurements from the entire simulation

-
-
Return type:
-

pandas.Dataframe

-
-
-
- -
-
-set_wind_farm(new_wf: WindFarm)
-

Overwrite wind farm object with a new wind farm object. Can be used to restart the simulation from a given state

-
-
Parameters:
-
    -

  • new_wf (windfarm.WindFarm) – Wind farm object with turbines and states

    • -

  • ------- –

    • -
-
-
-
- -
-
-settings_sim = {}
-
- -
-
-settings_vis = {}
-
- -
-
-wind_farm
-

alias of WindFarm

-
- -
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/off.states.html b/01_Docs/build/html/off.states.html deleted file mode 100644 index 32edb8f..0000000 --- a/01_Docs/build/html/off.states.html +++ /dev/null @@ -1,264 +0,0 @@ - - - - - - - states — OFF 0.1 documentation - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

states

-
-
-class off.states.States(number_of_time_steps: int, number_of_states: int, state_names: list)
-

Bases: ABC

-

Abstract state class -Provides basic functions for state lists, such as get, set, initialize and iterate

-
-
-get_all_states() → ndarray
-

Returns state matrix

-
-
Returns:
-

m x n matrix, columns mark different states, rows time steps

-
-
-
- -
-
-get_ind_state(index: int) → ndarray
-

Returns the state at a given index

-
-
Parameters:
-

index (int) – index of the state to return

-
-
Returns:
-

state – 1 x n state vector

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_state_names() → list
-

List with names of the stored states

-
-
Returns:
-

List with the names of the stored states in the corresponding order

-
-
Return type:
-

list

-
-
-
- -
-
-init_all_states(init_state: ndarray)
-

Copies a given state across all state entries as initialisation. -For more advanced initialisation, use set_all_states()

-
-
Parameters:
-

init_state (np.ndarray) – 1 x n vector of init state

-
-
-
- -
-
-iterate_states(new_state: ndarray)
-

shift_states shifts all states and adds a new entry in first place

-
-
Parameters:
-

new_state – 1 x n vector

-
-
Returns:
-

none

-
-
-
- -
-
-iterate_states_and_keep()
-

shift_states shifts all states and but keeps the first entry the same

-
-
Returns:
-

none

-
-
-
- -
-
-n_states = 0
-
- -
-
-n_time_steps = 0
-
- -
-
-set_all_states(new_states: array)
-

Overwrites the states with the given matrix.

-
-
Parameters:
-

new_states – m x n matrix with new states, columns mark different states, rows time steps

-
-
Returns:
-

none

-
-
-
- -
-
-set_ind_state(index: int, new_state: ndarray)
-

Overwrites a state at the given index

-
-
Parameters:
-
    -

  • index (int) – index of state to overwrite

    • -

  • new_state (np.ndarray) – 1 x n vector which overwrites the state

    • -
-
-
-
- -
-
-state_names = []
-
- -
-
-states = array([], dtype=float64)
-
- -
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/off.turbine.html b/01_Docs/build/html/off.turbine.html deleted file mode 100644 index 07767ec..0000000 --- a/01_Docs/build/html/off.turbine.html +++ /dev/null @@ -1,668 +0,0 @@ - - - - - - - turbine — OFF 0.1 documentation - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

turbine

-
-
-class off.turbine.HAWT_ADM(base_location: ndarray, orientation: ndarray, turbine_states: TurbineStates, observation_points: ObservationPoints, ambient_states: AmbientStates, turbine_data: dict)
-

Bases: Turbine

-
-
-calc_power(wind_speed, air_den)
-

Calculate the power based on turbine, ambient and OP states

-
-
Parameters:
-
    -

  • wind_speed (float) – Wind speed (m/s)

    • -

  • air_den (float) – air density

    • -
-
-
Returns:
-

Power generated (W)

-
-
Return type:
-

float

-
-
-
- -
-
-diameter = 178.4
-
- -
-
-nacellePos = array([  0,   0, 119])
-
- -
-
-turbine_type = 'name'
-
- -
- -
-
-class off.turbine.Turbine(base_location: ndarray, orientation: ndarray, turbine_states: TurbineStates, observation_points: ObservationPoints, ambient_states: AmbientStates)
-

Bases: ABC

-
-
-abstract calc_power(wind_speed, air_den)
-

Calculate the power based on turbine, ambient and OP states

-
-
Parameters:
-
    -

  • wind_speed (float) – Wind speed (m/s)

    • -

  • air_den (float) – air density

    • -
-
-
Returns:
-

Power generated (W)

-
-
Return type:
-

float

-
-
-
- -
-
-calc_tilt()
-

Get the tilt of the turbine

-
-
Returns:
-

tilt (deg)

-
-
Return type:
-

float

-
-
-
- -
-
-calc_yaw(wind_direction: float) → float
-

Get the yaw misalignment of the turbine

-
-
Parameters:
-

wind_direction (number) – Wind direction (deg)

-
-
Returns:
-

yaw misalignment (deg)

-
-
Return type:
-

float

-
-
-
- -
-
-diameter = 1
-
- -
-
-get_rotor_pos() → float64
-

Calculates the rotor position based on the current yaw and tilt

-
-
Returns:
-

1 x 3 vector with x,y,z location of the rotor in the world coordinate system

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-nacellePos = array([0, 0, 1])
-
- -
-
-orientation = array([0, 0])
-
- -
-
-set_rotor_pos(pos_rot: ndarray)
-

Sets the base location, based on a rotor location and the nacelle position -:param pos_rot:

-
- -
-
-set_yaw(wind_direction: float, yaw: float)
-

Sets the orientation based on the given wind direction and yaw angle

-
-
Parameters:
-
    -

  • wind_direction (float) – Wind direction in degrees

    • -

  • yaw (float) – Turbine yaw misalignment angle in degrees

    • -
-
-
-
- -
-
-turbine_type = 'base'
-
- -
- -
-
-class off.turbine.TurbineStates(number_of_time_steps: int, number_of_states: int, state_names: list)
-

Bases: States, ABC

-
-
-abstract create_interpolated_state(index1: int, index2: int, w1, w2)
-

Creates a TurbineStates object of its own kind with only one state entry, based on two weighted states. -The returned object then still has access to functions such as get_current_yaw()

-
-
Parameters:
-
    -

  • index1 (int) – Index of the first state

    • -

  • index2 (int) – Index of the second state

    • -

  • w1 (float) – Weight for first index (has to be w1 = 1 - w2, and [0,1])

    • -

  • w2 (float) – Weight for second index (has to be w2 = 1 - w1, and [0,1])

    • -
-
-
Returns:
-

turbine state object with single entry

-
-
Return type:
-

TurbineStates

-
-
-
- -
-
-abstract get_all_ax_ind() → ndarray
-

get_all_axInd returns the all axial induction factors of the saved turbine states

-
-
Returns:
-

Axial induction factor (-)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_all_ct() → ndarray
-

get_all_ct(index) returns the Ct coefficients for all turbine states.

-
-
Returns:
-

Thrust coefficient at all turbine states (-)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_all_yaw() → ndarray
-

get_all_ct(index) returns the yaw misalignment for all turbine states.

-
-
Returns:
-

Yaw misalignment at all turbine states (deg)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-abstract get_ax_ind(index: int) → float
-

get_ax_ind(index) returns the axial induction coefficient at a requested index of the turbine state chain

-
-
Parameters:
-

index (int) – Turbine state list index at which a should be calculated

-
-
Returns:
-

Axial induction factor

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_ct(index: int) → float
-

get_ct(index) returns the Ct coefficient at a requested index of the turbine state chain

-
-
Parameters:
-

index (int) – Turbine state list index at which Ct should be calculated

-
-
Returns:
-

Thrust coefficient

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_current_ax_ind() → float
-

get_current_axInd returns the current axial induction factor of the turbine

-
-
Returns:
-

Axial induction factor (-)

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_current_cp() → float
-

get_current_cp returns the current power coefficient of the turbine

-
-
Returns:
-

Power coefficient (-)

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_current_ct() → float
-

get_current_ct returns the current thrust coefficient of the turbine

-
-
Returns:
-

Thrust coefficient (-)

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_current_yaw() → float
-

get_current_yaw returns the current yaw misalignment at the turbine location

-
-
Returns:
-

yaw misalignment at the turbine location (deg)

-
-
Return type:
-

float

-
-
-
- -
-
-abstract get_yaw(index: int) → float
-

get_yaw(index) returns the yaw misalignment at a requested index

-
-
Parameters:
-

index (int) –

-
-
Returns:
-

yaw misalignment in deg

-
-
Return type:
-

float

-
-
-
- -
- -
-
-class off.turbine.TurbineStatesFLORIDyn(number_of_time_steps: int)
-

Bases: TurbineStates

-
-
-create_interpolated_state(index1: int, index2: int, w1, w2)
-

Creates a TurbineStates object of its own kind with only one state entry, based on two weighted states. -The returned object then still has access to functions such as get_current_yaw()

-
-
Parameters:
-
    -

  • index1 (int) – Index of the first state

    • -

  • index2 (int) – Index of the second state

    • -

  • w1 (float) – Weight for first index (has to be w1 = 1 - w2, and [0,1])

    • -

  • w2 (float) – Weight for second index (has to be w2 = 1 - w1, and [0,1])

    • -
-
-
Returns:
-

turbine state object with single entry

-
-
Return type:
-

TurbineStates

-
-
-
- -
-
-get_all_ax_ind() → ndarray
-

get_all_axInd returns the all axial induction factors of the saved turbine states

-
-
Returns:
-

Axial induction factor (-)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_all_ct() → ndarray
-

get_all_ct(index) returns the Ct coefficients for all turbine states.

-
-
Returns:
-

Thrust coefficient at all turbine states (-)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_all_yaw() → ndarray
-

returns the yaw misalignment for all turbine states.

-
-
Returns:
-

n x 1 vector with all yaw angles

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_ax_ind(index: int) → ndarray
-

get_all_axInd returns the all axial induction factors of the saved turbine states

-
-
Returns:
-

Axial induction factor (-)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_ct(index: int) → float
-

get_ct(index) returns the Ct coefficient at a requested index of the turbine state chain

-
-
Parameters:
-

index (int) – Turbine state list index at which Ct should be calculated

-
-
Returns:
-

Thrust coefficient

-
-
Return type:
-

float

-
-
-
- -
-
-get_current_ax_ind() → float
-

get_all_axInd returns the all axial induction factors of the saved turbine states

-
-
Returns:
-

Axial induction factor (-)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_current_cp() → float
-

get_current_cp returns the current power coefficient of the turbine

-
-
Returns:
-

Power coefficient (-)

-
-
Return type:
-

float

-
-
-
- -
-
-get_current_ct() → float
-

get_current_ct returns the current thrust coefficient of the turbine

-
-
Returns:
-

Thrust coefficient (-)

-
-
Return type:
-

float

-
-
-
- -
-
-get_current_yaw() → float
-

get_current_yaw returns the current yaw misalignment at the turbine location

-
-
Returns:
-

yaw misalignment at the turbine location (deg)

-
-
Return type:
-

float

-
-
-
- -
-
-get_yaw(index: int) → float
-

get_yaw(index) returns the yaw misalignment at a requested index

-
-
Parameters:
-

index (int) –

-
-
Returns:
-

yaw misalignment in deg

-
-
Return type:
-

float

-
-
-
- -
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/off.utils.html b/01_Docs/build/html/off.utils.html deleted file mode 100644 index 1715a89..0000000 --- a/01_Docs/build/html/off.utils.html +++ /dev/null @@ -1,255 +0,0 @@ - - - - - - - utils — OFF 0.1 documentation - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

utils

-
-
-off.utils.lg = <Logger off.utils (WARNING)>
-

Utilities for the OFF toolbox -functions which are handy in multiple places but do not have a true parent object they could belong to.

-
- -
-
-off.utils.ot_abs2uv(wind_speed_abs, wind_dir) → ndarray
-
- -
-
-off.utils.ot_abs_wind_speed(u, v) → float
-
- -
-
-off.utils.ot_deg2rad(deg) → float
-
- -
-
-off.utils.ot_get_closest_2_points_3d_sorted(ref_loc: ndarray, points: ndarray) → List[int]
-

Function to find the index of the closest 2 points to a reference location in 3D. -The function can expect the list of points to be sorted / trailing each other.

-
-
Parameters:
-
    -

  • ref_loc – [1 x 3] np.ndarray Reference location

    • -

  • points – [n x 3] np.ndarray Points

    • -
-
-
Return type:
-

[1 x 2] int array

-
-
-
- -
-
-off.utils.ot_get_closest_point_3d_sorted(ref_loc: ndarray, points: ndarray) → int
-

Function to find the index of the closes point to a reference location in 3D. -The function can expect the list of points to be sorted / trailing each other.

-
-
Parameters:
-
    -

  • ref_loc – [1 x 3] np.ndarray Reference location

    • -

  • points – [n x 3] np.ndarray Points

    • -
-
-
Returns:
-

index

-
-
Return type:
-

int

-
-
-
- -
-
-off.utils.ot_get_orientation(wind_dir: float, yaw: float) → float
-

Return the turbine orientation based on the wind direction and the yaw angle

-
-
Parameters:
-
    -

  • wind_dir (float) – Wind direction in LES degree (270 deg pointing along the x-axis, 190 deg along the y axis)

    • -

  • yaw (float) – Yaw angle in degree

    • -
-
-
Returns:
-

Orientation in LES degree

-
-
Return type:
-

float

-
-
-
- -
-
-off.utils.ot_get_yaw(wind_dir: float, orientation: float) → float
-

Return the turbine yaw angle based on the wind direction and turbine orientation

-
-
Parameters:
-
    -

  • wind_dir (float) – Wind direction in LES degree (270 deg pointing along the x-axis, 190 deg along the y axis)

    • -

  • orientation (float) – Turbine orientation in LES degree (270 deg pointing along the x-axis, 190 deg along the y axis)

    • -
-
-
Returns:
-

yaw angle in LES degree (clockwise)

-
-
Return type:
-

float

-
-
-
- -
-
-off.utils.ot_isocell(n_rp: int) → tuple
-

Isocell algorithm to discretize the rotor plane (or any circle) -Masset et al.

-

https://orbi.uliege.be/bitstream/2268/91953/1/masset_isocell_orbi.pdf

-

We choose N = 3 here, 4 or 5 are also viable options, 3 is close to optimal

-
-
Parameters:
-

n_rp (int) – desired number of Rotor points (algorithm can not work with all numbers, takes the closest one)

-
-
Returns:
-

[yRP, zRP] : np.ndarray location of the rotor points with values between -0.5 and 0.5 -w : float weight of the RPs (1/number)

-
-
Return type:
-

tuple

-
-
-
- -
-
-off.utils.ot_uv2abs(u, v) → float
-
- -
-
-off.utils.ot_uv2deg(u, v) → float
-
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/off.windfarm.html b/01_Docs/build/html/off.windfarm.html deleted file mode 100644 index 2fdba88..0000000 --- a/01_Docs/build/html/off.windfarm.html +++ /dev/null @@ -1,257 +0,0 @@ - - - - - - - windfarm — OFF 0.1 documentation - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

windfarm

-
-
-class off.windfarm.WindFarm(turbines: List[Turbine])
-

Bases: object

-

Wind Farm Attributes

-
-
turbinesList[Turbine]

List of turbine objects which form the wind farm

-
-
dependenciesnp.ndarray (with boolean entries)

Row i describes which turbines influence turbine i. The main diagonal should always be ‘True’.

-
-
nTint

Number of turbines in the wind farm

-
-
-
-
-add_turbine(turb: Turbine) → int
-

Adds another turbine to the list

-
-
Parameters:
-

turb (Turbine) – Turbine class object which describes the new turbine

-
-
Returns:
-

index of the newly added turbine object

-
-
Return type:
-

int

-
-
-
- -
-
-dependencies: ndarray
-
- -
-
-get_current_turbine_states() → ndarray
-

Collects and returns the current turbine states of the turbines

-
-
Returns:
-

[n_t x m] matrix with current turbine states at the turbine locations

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_layout() → ndarray
-

Gets the current wind farm layout and diameters

-
-
Returns:
-

[n_t x 4] matrix with wind farm layout in the world coordinate system and turbine diameter

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_op_world_coordinates() → ndarray
-

Collects and returns all OP world locations

-
-
Returns:
-

x,y,z coordiates of the OPs (in m)

-
-
Return type:
-

np.ndarray

-
-
-
- -
-
-get_sub_windfarm(indices)
-

Creates a subset of the wind farm with the turbines at the given indices

-
-
Parameters:
-

indices (int[]) –

-
-
Return type:
-

turbines array

-
-
-
- -
-
-nT: int
-
- -
-
-rmv_turbine(ind: int) → Turbine
-

Removes a turbine from the wind farm

-
-
Parameters:
-

ind (int) – Index of the turbine to remove

-
-
Returns:
-

removed turbine object

-
-
Return type:
-

Turbine

-
-
-
- -
-
-set_dependencies(dependencies: ndarray)
-
-
Parameters:
-

dependencies (np.ndarray) – boolean array with dependencies - true if there is a dependency, false if not

-
-
-
- -
-
-turbines: List[Turbine]
-
- -
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/01_Docs/build/html/py-modindex.html b/01_Docs/build/html/py-modindex.html deleted file mode 100644 index 2674522..0000000 --- a/01_Docs/build/html/py-modindex.html +++ /dev/null @@ -1,158 +0,0 @@ - - - - - - Python Module Index — OFF 0.1 documentation - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Python Module Index
    • -
  • -
    • -
-
-
-
-
- - -

Python Module Index

- -
- o -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 
- o
- off -
    - off.ambient -
    - off.observation_points -
    - off.off -
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    - off.turbine -
    - off.utils -
    - off.windfarm -
- - -
-
-
- -
- -
-

© Copyright 2022, Marcus BECKER and Maxime LEJEUNE.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
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- - - - \ No newline at end of file diff --git a/01_Docs/build/html/search.html b/01_Docs/build/html/search.html deleted file mode 100644 index 2c92392..0000000 --- a/01_Docs/build/html/search.html +++ /dev/null @@ -1,123 +0,0 @@ - - - - - - Search — OFF 0.1 documentation - - - - - - - - - - - - - - - - -
- - -
- -
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    • -
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-
-
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© Copyright 2022, Marcus BECKER and Maxime LEJEUNE.

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
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- - - - - - - - - \ No newline at end of file diff --git a/01_Docs/build/html/searchindex.js b/01_Docs/build/html/searchindex.js deleted file mode 100644 index 0d55c66..0000000 --- a/01_Docs/build/html/searchindex.js +++ /dev/null @@ -1 +0,0 @@ -Search.setIndex({"docnames": ["index", "off.ambient", "off.observation_points", "off.off", "off.states", "off.turbine", "off.utils", "off.windfarm"], "filenames": ["index.rst", "off.ambient.rst", "off.observation_points.rst", "off.off.rst", "off.states.rst", "off.turbine.rst", "off.utils.rst", "off.windfarm.rst"], "titles": ["Documentation", "ambient", "observation_points", "off", "states", "turbine", "utils", "windfarm"], "terms": {"an": 1, "unifi": [], "framework": 0, "onward": 0, "floridyn": 0, "flori": 0, "turbin": [0, 1, 2, 3, 6, 7], "windfarm": [0, 3], "state": [0, 1, 2, 3, 5, 7], "ambient": [0, 3, 5], "observation_point": [0, 5], "util": 0, "1": [0, 1, 2, 3, 4, 5, 6], "m": [0, 1, 2, 4, 5, 7], "lejeun": 0, "moen": 0, "p": 0, "chatelain": 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found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.https://www.sphinx-doc.org/ - exit /b 1 -) - -if "%1" == "" goto help - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/02_Examples_and_Cases/00_Inputs/00_OFF/05_Turbine/README.md b/02_Examples_and_Cases/00_Inputs/00_OFF/05_Turbine/README.md old mode 100755 new mode 100644 diff --git a/02_Examples_and_Cases/00_Inputs/01_FLORIS/02_Wake/gch.yaml b/02_Examples_and_Cases/00_Inputs/01_FLORIS/02_Wake/gch.yaml index a23c250..4907043 100644 --- a/02_Examples_and_Cases/00_Inputs/01_FLORIS/02_Wake/gch.yaml +++ b/02_Examples_and_Cases/00_Inputs/01_FLORIS/02_Wake/gch.yaml @@ -14,7 +14,7 @@ wake: ### # Select the wake deflection model. - deflection_model: gauss + deflection_model: jimenez ### # Select the wake turbulence model. @@ -26,11 +26,11 @@ wake: ### # Can be "true" or "false". - enable_secondary_steering: true + enable_secondary_steering: false ### # Can be "true" or "false". - enable_yaw_added_recovery: true + enable_yaw_added_recovery: false ### # Can be "true" or "false". diff --git a/02_Examples_and_Cases/00_Inputs/01_FLORIS/README.md b/02_Examples_and_Cases/00_Inputs/01_FLORIS/README.md old mode 100755 new mode 100644 diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/00_Logging/warning_none.yaml b/02_Examples_and_Cases/00_Inputs/02_PyWake/00_Logging/warning_none.yaml new file mode 100644 index 0000000..5b4b46b --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/00_Logging/warning_none.yaml @@ -0,0 +1,7 @@ +logging: + console: + enable: true + level: WARNING + file: + enable: false + level: WARNING diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/01_Solver/turbine_grid_3.yaml b/02_Examples_and_Cases/00_Inputs/02_PyWake/01_Solver/turbine_grid_3.yaml new file mode 100644 index 0000000..4bb4978 --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/01_Solver/turbine_grid_3.yaml @@ -0,0 +1,3 @@ +solver: + type: turbine_grid + turbine_grid_points: 3 \ No newline at end of file diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/02_Wake/gaussian.yaml b/02_Examples_and_Cases/00_Inputs/02_PyWake/02_Wake/gaussian.yaml new file mode 100644 index 0000000..240955b --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/02_Wake/gaussian.yaml @@ -0,0 +1,85 @@ +### +# Configure the wake model. +wake: + + ### + # Select the models to use for the simulation. + # See :py:mod:`~.wake` for a list + # of available models and their descriptions. + model_strings: + + ### + # Wind farm model to use (PropagateDownwind, PropagateDownWindIterative, or All2AllIterative) + wind_farm_model: PropagateDownwind + + ### + # Deficit model to use (IEA37SimpleBastankhahGaussian, BastankhahGaussian, NOJDeficit, TurboNOJDeficit, GCLDeficit) + deficit_model: BlondelSuperGaussianDeficit2020 + + ### + # Optional wake deflection model + deflection_model: JimenezWakeDeflection + + ### + # Optional turbulence model (CrespoHernandez, STF2017TurbulenceModel, or null) + turbulence_model: CrespoHernandez + + ### + # Select superposition model for multiple wakes(LinearSum, SquaredSum, MaxSum, WeightedSum, SqMaxSum) + superposition_model: SquaredSum + + ### + # Select rotor averaging model (RotorCenter, GridRotorAvg, EqGridRotorAvg, GQGridRotorAvg, PolarGridRotorAvg, CGIRotorAvg, or None) + rotor_avg_model: CGIRotorAvg(9) + + ### + # Select site object to use (UniformSite, WeibullSite, or UniformWeibullSite, etc.) + site: UniformSite + + ### + # Configure the parameters for the wake deflection model + # selected above. + # Additional blocks can be provided for + # models that are not enabled, but the enabled model + # must have a corresponding parameter block. + wake_deflection_parameters: + gauss: + ad: 0.0 + alpha: 0.58 + bd: 0.0 + beta: 0.077 + dm: 1.0 + ka: 0.38 + kb: 0.004 + jimenez: + ad: 0.0 + bd: 0.0 + kd: 0.05 + + ### + # Configure the parameters for the wake velocity deficit model + # selected above. + # Additional blocks can be provided for + # models that are not enabled, but the enabled model + # must have a corresponding parameter block. + wake_velocity_parameters: + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 + + ### + # Configure the parameters for the wake turbulence model + # selected above. + # Additional blocks can be provided for + # models that are not enabled, but the enabled model + # must have a corresponding parameter block. + wake_turbulence_parameters: + crespo_hernandez: + initial: 0.1 + constant: 0.5 + ai: 0.8 + downstream: -0.32 diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/dtu_10MW.yaml b/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/dtu_10MW.yaml new file mode 100644 index 0000000..b62238d --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/dtu_10MW.yaml @@ -0,0 +1,125 @@ +# DTU 10MW adapted to FLORIS v4 turbine library +# Based on https://github.com/NREL/turbine-models/blob/master/Offshore/DTU_10MW_178_RWT_v1.csv +turbine_type: 'dtu_10MW' +hub_height: 119.0 +rotor_diameter: 178.4 +TSR: 8.0 +operation_model: cosine-loss +power_thrust_table: + ref_air_density: 1.225 + ref_tilt: 6.0 # Copy from IEA 10MW + cosine_loss_exponent_yaw: 1.88 + cosine_loss_exponent_tilt: 1.88 # Copy from IEA 10MW + helix_a: 1.719 # Copy from IEA 10MW + helix_power_b: 4.823e-03 # Copy from IEA 10MW + helix_power_c: 2.314e-10 # Copy from IEA 10MW + helix_thrust_b: 1.157e-03 # Copy from IEA 10MW + helix_thrust_c: 1.167e-04 # Copy from IEA 10MW + power: # in kW + - 0.0 # 0.0 + - 0.0 # 3.99 + - 280.2 # 4.0 + - 799.1 # 5.0 + - 1532.7 + - 2506.1 + - 3730.7 + - 5311.8 + - 7286.5 # 10.0 + - 9698.3 + - 10639.1 + - 10648.5 + - 10639.3 + - 10683.7 # 15.0 + - 10642 + - 10640 + - 10639.9 + - 10652.8 + - 10646.2 # 20.0 + - 10644 + - 10641.2 + - 10639.5 + - 10643.6 + - 10635.7 # 25.0 + - 0.0 # 25.01 + - 0.0 # 50.0 + cp: + - 0.0 + - 0.0 + - 0.286 + - 0.418 # 5.0 + - 0.464 + - 0.478 + - 0.476 + - 0.476 + - 0.476 # 10.0 + - 0.476 + - 0.402 + - 0.317 + - 0.253 + - 0.207 # 15.0 + - 0.17 + - 0.142 + - 0.119 + - 0.102 + - 0.087 # 20.0 + - 0.075 + - 0.065 + - 0.057 + - 0.05 + - 0.044 # 25.0 + - 0.0 + - 0.0 + thrust_coefficient: + - 0.0 + - 0.0 + - 0.923 + - 0.919 # 5.0 + - 0.904 + - 0.858 + - 0.814 + - 0.814 + - 0.814 # 10.0 + - 0.814 + - 0.577 + - 0.419 + - 0.323 + - 0.259 # 15.0 + - 0.211 + - 0.175 + - 0.148 + - 0.126 + - 0.109 # 20.0 + - 0.095 + - 0.084 + - 0.074 + - 0.066 + - 0.059 # 25.0 + - 0.0 + - 0.0 + wind_speed: + - 0.0000 + - 3.99 + - 4.0 + - 5.0 + - 6.0 + - 7.0 + - 8.0 + - 9.0 + - 10.0 + - 11.0 + - 12.0 + - 13.0 + - 14.0 + - 15.0 + - 16.0 + - 17.0 + - 18.0 + - 19.0 + - 20.0 + - 21.0 + - 22.0 + - 23.0 + - 24.0 + - 25.0 + - 25.01 + - 50.0 diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/dtu_10MW_old.yaml b/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/dtu_10MW_old.yaml new file mode 100644 index 0000000..d67c94c --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/dtu_10MW_old.yaml @@ -0,0 +1,97 @@ +# FILE NOT COMPLIANT WITH OFF YET + +turbine_type: 'dtu_10MW' # https://nrel.github.io/turbine-models/DTU_10MW_178_RWT_v1.html +generator_efficiency: 1.0 +hub_height: 119.0 +pP: 1.88 +pT: 1.88 +rotor_diameter: 178.3 +TSR: 8.0 +ref_density_cp_ct: 1.225 +ref_tilt_cp_ct: 6.0 +installation: fixed +rotor_overhang: 7.07 # in m +shaft_tilt: 5 # in deg +yaw_rate_lim: 0.3 # deg/s # TODO Find true value +power_thrust_table: + power: + - 0.000 + - 0.000 + - 0.286 + - 0.418 + - 0.464 + - 0.478 + - 0.476 + - 0.476 + - 0.476 + - 0.476 + - 0.402 + - 0.317 + - 0.253 + - 0.207 + - 0.17 + - 0.142 + - 0.119 + - 0.102 + - 0.087 + - 0.075 + - 0.065 + - 0.057 + - 0.05 + - 0.044 + - 0.000 + - 0.000 + thrust: + - 0.000 + - 0.000 + - 0.923 + - 0.919 + - 0.904 + - 0.858 + - 0.814 + - 0.814 + - 0.814 + - 0.814 + - 0.577 + - 0.419 + - 0.323 + - 0.259 + - 0.211 + - 0.175 + - 0.148 + - 0.126 + - 0.109 + - 0.095 + - 0.084 + - 0.074 + - 0.066 + - 0.059 + - 0.000 + - 0.000 + wind_speed: + - 0.0 + - 3.9 + - 4.0 + - 5.0 + - 6.0 + - 7.0 + - 8.0 + - 9.0 + - 10.0 + - 11.0 + - 12.0 + - 13.0 + - 14.0 + - 15.0 + - 16.0 + - 17.0 + - 18.0 + - 19.0 + - 20.0 + - 21.0 + - 22.0 + - 23.0 + - 24.0 + - 25.0 + - 50.0 + - 100.0 diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/iea_10MW.yaml b/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/iea_10MW.yaml new file mode 100644 index 0000000..96b5f0c --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/03_Turbine_model/iea_10MW.yaml @@ -0,0 +1,253 @@ +turbine: + iea_10MW: + name: IEA Wind Task 37 10MW Offshore Reference Turbine # Source: FLORIS v3.4, adapted for OFF + performance: + rated_power: 10000000 + rated_wind_speed: 11.0 + cutin_wind_speed: 4.0 + cutout_wind_speed: 25.0 + Cp_curve: + Cp_tb_values: [ ] + Cp_tb_tsr: [ ] # in (m/s)/(m/s) + Cp_tb_bpa: [ ] # in deg + Cp_u_values: + - 0.000000 + - 0.000000 + - 0.074 + - 0.325100 + - 0.376200 + - 0.402700 + - 0.415600 + - 0.423000 + - 0.427400 + - 0.429300 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.430500 + - 0.438256 + - 0.425908 + - 0.347037 + - 0.307306 + - 0.271523 + - 0.239552 + - 0.211166 + - 0.186093 + - 0.164033 + - 0.144688 + - 0.127760 + - 0.112969 + - 0.100062 + - 0.088800 + - 0.078975 + - 0.070401 + - 0.062913 + - 0.056368 + - 0.050640 + - 0.045620 + - 0.041216 + - 0.037344 + - 0.033935 + - 0.0 + - 0.0 + Cp_u_wind_speeds: + - 0.0000 + - 2.9 + - 3.0 + - 4.0000 + - 4.5147 + - 5.0008 + - 5.4574 + - 5.8833 + - 6.2777 + - 6.6397 + - 6.9684 + - 7.2632 + - 7.5234 + - 7.7484 + - 7.9377 + - 8.0909 + - 8.2077 + - 8.2877 + - 8.3308 + - 8.3370 + - 8.3678 + - 8.4356 + - 8.5401 + - 8.6812 + - 8.8585 + - 9.0717 + - 9.3202 + - 9.6035 + - 9.9210 + - 10.2720 + - 10.6557 + - 10.7577 + - 11.5177 + - 11.9941 + - 12.4994 + - 13.0324 + - 13.5920 + - 14.1769 + - 14.7859 + - 15.4175 + - 16.0704 + - 16.7432 + - 17.4342 + - 18.1421 + - 18.8652 + - 19.6019 + - 20.3506 + - 21.1096 + - 21.8773 + - 22.6519 + - 23.4317 + - 24.2150 + - 25.010 + - 25.020 + - 50.0 + Ct_curve: + Ct_tb_values: [ ] + Ct_tb_tsr: [ ] + Ct_tb_bpa: [ ] + Ct_u_values: + - 0.0 + - 0.0 + - 0.7701 + - 0.7701 + - 0.7763 + - 0.7824 + - 0.7820 + - 0.7802 + - 0.7772 + - 0.7719 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7675 + - 0.7651 + - 0.7587 + - 0.5056 + - 0.4310 + - 0.3708 + - 0.3209 + - 0.2788 + - 0.2432 + - 0.2128 + - 0.1868 + - 0.1645 + - 0.1454 + - 0.1289 + - 0.1147 + - 0.1024 + - 0.0918 + - 0.0825 + - 0.0745 + - 0.0675 + - 0.0613 + - 0.0559 + - 0.0512 + - 0.0470 + - 0.0 + - 0.0 + Ct_u_wind_speeds: + - 0.0000 + - 2.9 + - 3.0 + - 4.0000 + - 4.5147 + - 5.0008 + - 5.4574 + - 5.8833 + - 6.2777 + - 6.6397 + - 6.9684 + - 7.2632 + - 7.5234 + - 7.7484 + - 7.9377 + - 8.0909 + - 8.2077 + - 8.2877 + - 8.3308 + - 8.3370 + - 8.3678 + - 8.4356 + - 8.5401 + - 8.6812 + - 8.8585 + - 9.0717 + - 9.3202 + - 9.6035 + - 9.9210 + - 10.2720 + - 10.6557 + - 10.7577 + - 11.5177 + - 11.9941 + - 12.4994 + - 13.0324 + - 13.5920 + - 14.1769 + - 14.7859 + - 15.4175 + - 16.0704 + - 16.7432 + - 17.4342 + - 18.1421 + - 18.8652 + - 19.6019 + - 20.3506 + - 21.1096 + - 21.8773 + - 22.6519 + - 23.4317 + - 24.2150 + - 25.010 + - 25.020 + - 50.0 + generator_efficiency: 1.0 + hub_height: 119.0 + pP: 1.88 + pT: 1.88 + rotor_diameter: 198.0 + TSR: 8.0 + ref_density_cp_ct: 1.225 + ref_tilt_cp_ct: 6.0 + turbine_type: HAWT + installation: fixed + rotor_overhang: 7.07 # in m # TODO Copied from DTU 10MW - Change! + shaft_tilt: 5 # in deg # TODO Copied from DTU 10MW - Change! + yaw_rate_lim: 0.3 # deg/s # TODO Find true value \ No newline at end of file diff --git a/02_Examples_and_Cases/00_Inputs/02_PyWake/README.md b/02_Examples_and_Cases/00_Inputs/02_PyWake/README.md new file mode 100644 index 0000000..d6ea20f --- /dev/null +++ b/02_Examples_and_Cases/00_Inputs/02_PyWake/README.md @@ -0,0 +1,6 @@ +# FLORIS files +The files in this folder have been retrieved from the FLORIS GitHub. +Full credit goes to NREL, FLORIS v4, 2022, https://github.com/NREL/floris + +OFF assembles its own input file from the given "building block" yaml files. This means that e.g. 02_Wake only stores the wake part of the yaml input, other parts, such as logging and solver settings, are stored in the respective folders. Settings also relevant to OFF, such as the wind farm layout and the flow conditions are generated from the OFF input file. +Update to FLORIS v4, April 2024 \ No newline at end of file diff --git a/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step.yaml b/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step.yaml index b3ce9c4..59775d8 100644 --- a/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step.yaml +++ b/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step.yaml @@ -76,22 +76,30 @@ wind_farm: layout_x: - 0.0 - 5.0 + - 10.0 + - 15.0 layout_y: - 0.0 - 0.0 + - 0.0 + - 0.0 layout_z: - 0.0 - 0.0 + - 0.0 + - 0.0 turbine_type: - iea_10MW - iea_10MW + - iea_10MW + - iea_10MW unit: - D diameter: # Only needed if unit was set to D - 198.0 boundaries_xyz: - -2.0 - - 12.0 + - 20.0 - -4.0 - 4.0 - 0.0 @@ -225,12 +233,12 @@ controller: orientation: False # Checks if the data is given as orientation or, if false, as yaw angles path_to_orientation_csv: "/_.csv" orientation_deg: # in deg - - [270, 270] - - [270, 270] - - [240, 270] - - [240, 270] - - [270, 270] - - [270, 270] + - [270, 270, 270, 270] + - [270, 270, 270, 270] + - [240, 270, 240, 270] + - [240, 270, 240, 270] + - [270, 270, 270, 270] + - [270, 270, 270, 270] orientation_t: # in s - 0.0 - 600.0 @@ -555,9 +563,9 @@ vis: time: [656, 696, 736, 776, 816, 856, 896, 936] #[600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920, 940, 960, 980, 1000, 1020, 1040, 1060, 1080, 1100, 1120, 1140, 1160, 1180] # Master-switch to generate & store flow field data - plot: False + plot: True # Generates a mountain range like visualization of the wind speed based on the OPs - mountains: False + mountains: True # Steps between the OPs -> 1 every OP, 5 -> every 5th OP mountains_stride: 10 # Offset in OPs, e.g. 0 -> first OP, 1 -> second OP, etc. Is treated as variable if mountain wandering is True diff --git a/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step_pywake.yaml b/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step_pywake.yaml new file mode 100644 index 0000000..5808d9d --- /dev/null +++ b/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step_pywake.yaml @@ -0,0 +1,649 @@ +# //////////////////////////////////////////////////////////////////// # +# ____ ______ ______ +# / __ \| ____| ____| +# | | | | |__ | |__ +# | | | | __| | __| +# | |__| | | | | +# \____/|_| |_| +# //////////////////////////////////////////////////////////////////// # +# Copyright (C) <2024>, M Becker (TUDelft), M Lejeune (UCLouvain) + +# List of the contributors to the development of OFF: see LICENSE file. +# Description and complete License: see LICENSE file. + +# This program (OFF) is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU Affero General Public License +# along with this program (see COPYING file). If not, see . + + + + +# //////////////////////////////////////////////////////////////////// # +# Welcome to the OFF input file. This file is used to set up a simulation. +# The yaml file contains multiple comments to explain the setup choices and the structure of the file. +# If you experience issues, create a new issue on the GitHub page https://github.com/TUDelft-DataDrivenControl/OFF +# //////////////////////////////////////////////////////////////////// # + + + +# Simulation settings +# The time step is given in seconds, the start and end time in seconds as well. +sim: + name: Test sim + description: Test simulation with 1200s runtime, features a wind direction change from 225 to 235 deg + + sim: + time step: 4.0 + time start: 0.0 + time end: 2000.0 + + # The logging is split in two, one for the console and one for a file. The level can be set to DEBUG, INFO, WARNING, ERROR, CRITICAL + # The console logging has a feature where the log messages are colored based on their origin. + logging: + console: + enable: true + level: WARNING # INFO + file: + enable: true + level: WARNING + +# OFF stores all results in a run folder, which is by defalut located at "/runs". The user can specify a different location. +# The name of the default folder is "off_run_" followed by the current date and exact time. +# The run folder contains a copy of the input yaml, the yaml used to init FLORIS, as well as the log file, and outputs. +io: # Needs to be written by the user + data folder: '' + simulation folder: + + +# Wind farm settings +# The layout is given in x, y, z coordinates, where locations can be defined in meters or in D (rotor diameters). +# To distinguish between the two, the unit parameter is used. If set to D, the diameter parameter needs to be set. +# Note that the layout refers to the BOTTOM of the turbine, not the rotor center. That is calculated internally based on the trubine information. +wind_farm: + name: turbine array + description: Four turbine case with IEA 22 MW turbines at 5D distance + + farm: + layout_x: + - 0.0 + - 5.0 + layout_y: + - 0.0 + - 0.0 + layout_z: + - 0.0 + - 0.0 + turbine_type: + # - iea_10MW + # - iea_10MW + # - iea_10MW + # - iea_10MW + - iea22mw + - iea22mw + unit: + - D + diameter: # Only needed if unit was set to D + - 284.0 + boundaries_xyz: + - -2.0 + - 16.0 + - -4.0 + - 4.0 + - 0.0 + - 5.0 + + +# Ambient conditions used during the simulation +# Currently the code only supports time variations of the wind speed and direction. If a single value is given, it is assumed to be constant. +# There is also only one ambient corrector as of now. +ambient: + name: ambient_conditions + description: Constant wind speed at 8m/s coming from 270 deg (0 deg = north) + + flow_field: # This struct is passed on to the AmbientCorrector to feed in the new flow properties + air_density: 1.225 + reference_wind_height: 119.0 # Needs to be defined as FLORIS expects different turbine types (-1 might still work) + turbulence_intensities: + - 0.03 + wind_directions: # a single value indicates constant conditions, multiple lead to linear interpolation + - 270.0 + - 260.0 + - 265.0 + - 275.0 + - 285.0 + - 270.0 + wind_directions_t: + - 0.0 + - 200.0 + - 250.0 + - 600.0 + - 800.0 + - 1200.0 + wind_shear: 0.12 + wind_speeds: # a single value indicates constant conditions, multiple lead to linear interpolation + - 8.0 + wind_speeds_t: + - 0.0 + wind_veer: 0.0 + corr_overwrite_direction: true # All states are overwritten instead of only the first particle state + +# Wake solver settings +# Here we select the Temporary Wind Farm (TWF) solver and the FLORIS wake model +# As of writing, the TWFSolver is the ONLY wake solver available. It can use any FLORIS model, as well as a build in PythonGaussianWake model. +# For future wake solvers, the initialization of the solver needs to be adapted, as it is hardcoded of now (see OFF.__init__). +solver: + name: TWF PyWake + description: FLORIDyn with PyWake backend for wake modeling + + settings: + wake_solver: "TWFSolver" + wake_model: "PyWake" + op_propagation: "frozen turbulence" + extrapolation: "pair" + # Number of OPs per turbine. The chain of OPs should span long enough to cover the whole wind farm. + # n_op * time_step * u_free_stream = covered distance + n_op: 200 + + +# Wake model settings +# These need to fit the wake model that the wake solver is using. +wake: + name: Gaussian + description: PyWake Gaussian wake modeling framework + + settings: + # TURBINE LIBRARY SETTINGS + # Set use_pywake_turbine_library to true to use PyWake's built-in turbine models + # Common PyWake turbines: DTU10MW, IEA_22MW_280_RWT + # OFF automatically finds turbines in PyWake's data folder using introspection + # Specify the PyWake class name for each turbine type in the turbine definition below + use_pywake_turbine_library: true + # The yaml path is used to create an input file for the FLORIS model, before it is moved to the run folder + yaml_path: "02_Examples_and_Cases/00_Inputs/02_PyWake/not_relevant_.yaml" + # The FLORIS input file is generated based on the settings linked below as well as the info in this yaml (e.g. turbine type, layout) + floris_logging: "02_Examples_and_Cases/00_Inputs/02_PyWake/00_Logging/warning_none.yaml" + floris_solver: "02_Examples_and_Cases/00_Inputs/02_PyWake/01_Solver/turbine_grid_3.yaml" + # This is where the floris model and settings are changed! + floris_wake: "02_Examples_and_Cases/00_Inputs/02_PyWake/02_Wake/gaussian.yaml" + # Settings not used yet + # Settings not used yet but kept for compatibility + nRP: 10 + rotor discretization: Isocell + + +# Controller settings +# Multiple controllers are available, each with their own settings. Below you can find examples of the controllers, along with one that is selected. + +# Contoller used in "A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies" Becker, Lejeune et al. 2024 +# Can be used as baseline and as LuT contoller +# controller: +# name: Dead-band LUT yaw controller +# description: Chooses yaw angles based on the current main wind direction and a lut for it. Only corrects if offset to the averaged wind direction is larger then a given offset of if there has been a mismatch in wind direction for long enough +# settings: +# ctl: "Dead-band LUT yaw controller" +# wind_dir_thresh: 4 # Dead-band width in deg - if you set 2, the ± width is 4 deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml +# orientation: False # Checks if the data is given as orientation or, if false, as yaw angles +# # LuT data can be given as a csv or a pckl file. +# # The pckl file is a dictionary with the keys "wind_speed" and "wind_direction", "turbulence_intensity" and "yaw_angles_opt" +# path_to_angles_and_directions_pckl: "02_Examples_and_Cases/04_LuTs/yaw_offsets_HKN_10T.pckl" # Leave empty if using csv instead +# # The csv file is a table with the first column being the wind directions and the following nT columns being the yaw angles for the nT turbines +# # IMPORTANT: As of now, the csv file is not sufficient to run the Dead-band LUT yaw controller, provide a pckl file instead - the code will throw an error. +# path_to_angles_and_directions_csv: "/_.csv" +# k_i : 0.01 # Integral gain (per second) +# baseline: False # Activate Baseline mode, yaw = 0 + +# controller: +# name: Ideal greedy baseline +# description: Follows the main wind direction, disregarding yaw travel costs or other turbines +# settings: +# ctl: "IdealGreedyBaseline" + +# controller: +# name: Realistic greedy baseline +# description: Follows the main wind direction, only corrects if offset to the averaged wind direction is larger then a given offset +# +# settings: +# ctl: "RealGreedyBaseline" +# misalignment_thresh: 5 # in deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml # Not used yet +# apply_frequency: 6 # in time steps, has to be an integer. Time step length is set in the simulation yaml # Not used yet + +# Use "Dead-band LUT yaw controller" instead +# controller: +# name: Look-up-table based yaw steering controller +# description: Chooses yaw angles based on the current main wind direction and a lut for it. Only corrects if offset to the averaged wind direction is larger then a given offset +# +# settings: +# ctl: "LUT yaw controller" +# misalignment_thresh: 2 # in deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml +# orientation: False # Checks if the data is given as orientation or, if false, as yaw angles +# # The csv file is a table with the first column being the wind directions and the following nT columns being the yaw angles for the nT turbines +# path_to_angles_and_directions_csv: "/optimized_angles.csv" + +controller: + name: prescribed yaw controller + description: Sets the turbine orientations based on a set of prescribed yaw angles along with time stamps + + settings: + ctl: "prescribed yaw controller" + # Can be "csv" or "yaml" + # - csv: The file needs to contain the time stamps in the first column, and yaw / orientation angles for each turbine in the following columns. + # - yaml: The orientation needs to be added below + input_method: "yaml" + orientation: False # Checks if the data is given as orientation or, if false, as yaw angles + path_to_orientation_csv: "/_.csv" + orientation_deg: # in deg + - [270, 270] + - [260, 260] + - [265, 265] + - [275, 275] + - [285, 285] + - [270, 270] + # - [270, 270, 270, 270] + # - [270, 270, 270, 270] + # - [240, 270, 270, 270] + # - [240, 250, 270, 270] + # - [270, 250, 270, 270] + # - [270, 270, 270, 270] + orientation_t: # in s + - 0.0 + - 200.0 + - 250.0 + - 600.0 + - 800.0 + - 1200.0 + +# Turbine information +# When using PyWake's turbine library (use_pywake_turbine_library: true), specify the PyWake class name +# for each turbine type using pywake_turbine_name. OFF will automatically extract Cp/Ct curves. +# The turbine_type name here must match what's specified in wind_farm/farm/turbine_type above. +# +# Example turbine types: +# - DTU10MW: pywake_turbine_name: "DTU10MW" (rotor_diameter: 178.4, hub_height: 119.0) +# - IEA 22MW: pywake_turbine_name: "IEA_22MW_280_RWT" (rotor_diameter: 284.0, hub_height: 170.0) + +turbine: + iea22mw: + name: IEA 22MW Reference Turbine (from PyWake library) + pywake_turbine_name: "IEA_22MW_280_RWT" # PyWake class name to import + # The following will be automatically populated from PyWake when use_pywake_turbine_library is enabled + # Cp/Ct curves extracted from PyWake: + performance: + Cp_curve: + Cp_u_values: [] + Cp_u_wind_speeds: [] + Ct_curve: + Ct_u_values: [] + Ct_u_wind_speeds: [] + hub_height: 170.0 # IEA 22MW 280m rotor + rotor_diameter: 284.0 # IEA 22MW 280m rotor + shaft_tilt: 5.0 # Typical for large offshore turbines + pP: 1.88 + pT: 1.88 + rotor_overhang: 7.07 + yaw_rate_lim: 0.3 + turbine_type: HAWT + installation: fixed + +# turbine: +# iea_10MW: +# name: IEA Wind Task 37 10MW Offshore Reference Turbine # Source: FLORIS v3.4, adapted for OFF +# performance: +# rated_power: 10000000 +# rated_wind_speed: 11.0 +# cutin_wind_speed: 4.0 +# cutout_wind_speed: 25.0 +# Cp_curve: +# Cp_tb_values: [ ] +# Cp_tb_tsr: [ ] # in (m/s)/(m/s) +# Cp_tb_bpa: [ ] # in deg +# Cp_u_values: +# - 0.000000 +# - 0.000000 +# - 0.074 +# - 0.325100 +# - 0.376200 +# - 0.402700 +# - 0.415600 +# - 0.423000 +# - 0.427400 +# - 0.429300 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.430500 +# - 0.438256 +# - 0.425908 +# - 0.347037 +# - 0.307306 +# - 0.271523 +# - 0.239552 +# - 0.211166 +# - 0.186093 +# - 0.164033 +# - 0.144688 +# - 0.127760 +# - 0.112969 +# - 0.100062 +# - 0.088800 +# - 0.078975 +# - 0.070401 +# - 0.062913 +# - 0.056368 +# - 0.050640 +# - 0.045620 +# - 0.041216 +# - 0.037344 +# - 0.033935 +# - 0.0 +# - 0.0 +# Cp_u_wind_speeds: +# - 0.0000 +# - 2.9 +# - 3.0 +# - 4.0000 +# - 4.5147 +# - 5.0008 +# - 5.4574 +# - 5.8833 +# - 6.2777 +# - 6.6397 +# - 6.9684 +# - 7.2632 +# - 7.5234 +# - 7.7484 +# - 7.9377 +# - 8.0909 +# - 8.2077 +# - 8.2877 +# - 8.3308 +# - 8.3370 +# - 8.3678 +# - 8.4356 +# - 8.5401 +# - 8.6812 +# - 8.8585 +# - 9.0717 +# - 9.3202 +# - 9.6035 +# - 9.9210 +# - 10.2720 +# - 10.6557 +# - 10.7577 +# - 11.5177 +# - 11.9941 +# - 12.4994 +# - 13.0324 +# - 13.5920 +# - 14.1769 +# - 14.7859 +# - 15.4175 +# - 16.0704 +# - 16.7432 +# - 17.4342 +# - 18.1421 +# - 18.8652 +# - 19.6019 +# - 20.3506 +# - 21.1096 +# - 21.8773 +# - 22.6519 +# - 23.4317 +# - 24.2150 +# - 25.010 +# - 25.020 +# - 50.0 +# Ct_curve: +# Ct_tb_values: [ ] +# Ct_tb_tsr: [ ] +# Ct_tb_bpa: [ ] +# Ct_u_values: +# - 0.0 +# - 0.0 +# - 0.7701 +# - 0.7701 +# - 0.7763 +# - 0.7824 +# - 0.7820 +# - 0.7802 +# - 0.7772 +# - 0.7719 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7675 +# - 0.7651 +# - 0.7587 +# - 0.5056 +# - 0.4310 +# - 0.3708 +# - 0.3209 +# - 0.2788 +# - 0.2432 +# - 0.2128 +# - 0.1868 +# - 0.1645 +# - 0.1454 +# - 0.1289 +# - 0.1147 +# - 0.1024 +# - 0.0918 +# - 0.0825 +# - 0.0745 +# - 0.0675 +# - 0.0613 +# - 0.0559 +# - 0.0512 +# - 0.0470 +# - 0.0 +# - 0.0 +# Ct_u_wind_speeds: +# - 0.0000 +# - 2.9 +# - 3.0 +# - 4.0000 +# - 4.5147 +# - 5.0008 +# - 5.4574 +# - 5.8833 +# - 6.2777 +# - 6.6397 +# - 6.9684 +# - 7.2632 +# - 7.5234 +# - 7.7484 +# - 7.9377 +# - 8.0909 +# - 8.2077 +# - 8.2877 +# - 8.3308 +# - 8.3370 +# - 8.3678 +# - 8.4356 +# - 8.5401 +# - 8.6812 +# - 8.8585 +# - 9.0717 +# - 9.3202 +# - 9.6035 +# - 9.9210 +# - 10.2720 +# - 10.6557 +# - 10.7577 +# - 11.5177 +# - 11.9941 +# - 12.4994 +# - 13.0324 +# - 13.5920 +# - 14.1769 +# - 14.7859 +# - 15.4175 +# - 16.0704 +# - 16.7432 +# - 17.4342 +# - 18.1421 +# - 18.8652 +# - 19.6019 +# - 20.3506 +# - 21.1096 +# - 21.8773 +# - 22.6519 +# - 23.4317 +# - 24.2150 +# - 25.010 +# - 25.020 +# - 50.0 +# generator_efficiency: 1.0 +# hub_height: 119.0 +# pP: 1.88 +# pT: 1.88 +# rotor_diameter: 198.0 +# TSR: 8.0 +# ref_density_cp_ct: 1.225 +# ref_tilt_cp_ct: 6.0 +# # Determines the class of the turbine, other types like VAWT are not supported yet +# turbine_type: HAWT +# # Determines if the turbine is fixed base or floating, currently only fixed base is supported +# installation: fixed +# rotor_overhang: 7.07 # in m # TODO Copied from DTU 10MW - Change! +# shaft_tilt: 5 # in deg # TODO Copied from DTU 10MW - Change! +# yaw_rate_lim: 0.3 # deg/s # TODO Find true value + + +# Visualization settings +# As of now, the visualization is mainly used for debugging purposes and many settings are placeholders. +vis: + grid: + generate: True # Master-switch to generate & store flow field data + # As OFF isn't grid based, the flow field plots need a grid to be generated. + boundaries: + - [ -2, 20] # x in D or m + - [ -4, 4] # y in D or m + - [ 0, 3] # z in D or m + unit: + - D + # Needed for plotting also if boundaries are given in D + diameter: + - 284.0 + # x, y, z in points + resolution: [ 301, 301, 10 ] + volume_3d: False + slice_2d: True + slice_2d_xy: [ 119.0 ] # z-coordinates for xy slices + slice_2d_xz: [ ] # y-coordinates for xz slices + slice_2d_yz: [ ] # x-coordinates for yz slices + slice_2d_p: # x,y,z coordinate of a point on a plane + - [ ] + slice_2d_n: # x,y,z coordinate of a normal vector on a plane + - [ ] + data_vel_background: True + data_vel_effective: True + data_dir_background: True + data_ti_background: False + data_ti_effective: True + data_op_markers: True + + + turbine: + generate: True # Master-switch to generate & store turbine data + plot_together: False # One plot of one QoI with all turbines above one another + plot_seperated: True # One plot with a subplot for every turbine + plot_selection: False # One plot where you can choose which turbine to view + input_yaw: True + input_Ct: False + output_Power: True + output_EffU: False + output_Red: False + farm_interaction: False + farm_layout: False + store_data: True + + flow_field_plots: # Plots are not generated online but rather stored in the run folder + # Times at witch a plot of the flow field is generated + time: [656, 696, 736, 776, 816, 856, 896, 936] + #[600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920, 940, 960, 980, 1000, 1020, 1040, 1060, 1080, 1100, 1120, 1140, 1160, 1180] + # Master-switch to generate & store flow field data + plot: True + # Generates a mountain range like visualization of the wind speed based on the OPs + mountains: True + # Steps between the OPs -> 1 every OP, 5 -> every 5th OP + mountains_stride: 10 + # Offset in OPs, e.g. 0 -> first OP, 1 -> second OP, etc. Is treated as variable if mountain wandering is True + mountains_offset: 5 + # Mountains wandering through the farm, i.e. the mountains are not static but move with the wind + mountains_wandering: False + # 3D mountains + mountains_3d: False + # Triggers a debug plot, which creates a voronoi "prism" with the TWF models + effective_wf_tile: False + # Generates the tiled landscape in a 5 color scheme + effective_wf_tile_5color: False + + + + debug: # Plots are generated online + # Triggers a debug plot, which creates a voronoi "prism" with the TWF models + effective_wf_tile: False + # Generates the tiled landscape in a 5 color scheme + effective_wf_tile_5color: False + # Triggers a debug plot during runtime of the wakes passed on to the wake model + effective_wf_layout: False + # Times at witch a debug plot of the wind farm is generated + time: [400, 452, 500, 552, 600, 652, 700, 752, 800, 852, 900, 952, 1000, 1052, 1100, 1152] + # Turbines for which the debug plot is created + iT: [0,1] + # Generates the effective wind speed across the farm based on a ghost turbine + turbine_effective_wind_speed: False + # Plots the data, requires the turbine_effective_wind_speed to be true + turbine_effective_wind_speed_plot: True + # Adds dots for the particles + turbine_effective_wind_speed_plot_ops: True + # Plots the data, requires the turbine_effective_wind_speed to be true + turbine_effective_wind_speed_show_plot: True + # Stores the contour plot as .png + turbine_effective_wind_speed_store_plot: True + # Stores the data as .csv in the run folder + turbine_effective_wind_speed_store_data: False diff --git a/02_Examples_and_Cases/02_Example_Cases/003_two_turbines_yaw_step_plotting.yaml b/02_Examples_and_Cases/02_Example_Cases/003_two_turbines_yaw_step_plotting.yaml new file mode 100644 index 0000000..2555105 --- /dev/null +++ b/02_Examples_and_Cases/02_Example_Cases/003_two_turbines_yaw_step_plotting.yaml @@ -0,0 +1,598 @@ +# //////////////////////////////////////////////////////////////////// # +# ____ ______ ______ +# / __ \| ____| ____| +# | | | | |__ | |__ +# | | | | __| | __| +# | |__| | | | | +# \____/|_| |_| +# //////////////////////////////////////////////////////////////////// # +# Copyright (C) <2024>, M Becker (TUDelft), M Lejeune (UCLouvain) + +# List of the contributors to the development of OFF: see LICENSE file. +# Description and complete License: see LICENSE file. + +# This program (OFF) is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU Affero General Public License +# along with this program (see COPYING file). If not, see . + + + + +# //////////////////////////////////////////////////////////////////// # +# Welcome to the OFF input file. This file is used to set up a simulation. +# The yaml file contains multiple comments to explain the setup choices and the structure of the file. +# If you experience issues, create a new issue on the GitHub page https://github.com/TUDelft-DataDrivenControl/OFF +# //////////////////////////////////////////////////////////////////// # + + + +# Simulation settings +# The time step is given in seconds, the start and end time in seconds as well. +sim: + name: Test sim + description: Test simulation with 1200s runtime, features a wind direction change from 225 to 235 deg + + sim: + time step: 4.0 + time start: 0.0 + time end: 1200.0 + + # The logging is split in two, one for the console and one for a file. The level can be set to DEBUG, INFO, WARNING, ERROR, CRITICAL + # The console logging has a feature where the log messages are colored based on their origin. + logging: + console: + enable: true + level: WARNING # INFO + file: + enable: true + level: WARNING + +# OFF stores all results in a run folder, which is by defalut located at "/runs". The user can specify a different location. +# The name of the default folder is "off_run_" followed by the current date and exact time. +# The run folder contains a copy of the input yaml, the yaml used to init FLORIS, as well as the log file, and outputs. +io: # Needs to be written by the user + data folder: '' + simulation folder: + + +# Wind farm settings +# The layout is given in x, y, z coordinates, where locations can be defined in meters or in D (rotor diameters). +# To distinguish between the two, the unit parameter is used. If set to D, the diameter parameter needs to be set. +# Note that the layout refers to the BOTTOM of the turbine, not the rotor center. That is calculated internally based on the trubine information. +wind_farm: + name: turbine array + description: Nine turbine case with IEA 10 MW turbines at 5D distance + + farm: + layout_x: + - 0.0 + - 5.0 + layout_y: + - -0.5 + - 0.0 + layout_z: + - 0.0 + - 0.0 + turbine_type: + - iea_10MW + - iea_10MW + unit: + - D + diameter: # Only needed if unit was set to D + - 198.0 + boundaries_xyz: + - -2.0 + - 12.0 + - -4.0 + - 4.0 + - 0.0 + - 5.0 + + +# Ambient conditions used during the simulation +# Currently the code only supports time variations of the wind speed and direction. If a single value is given, it is assumed to be constant. +# There is also only one ambient corrector as of now. +ambient: + name: ambient_conditions + description: Constant wind speed at 8m/s coming from 270 deg (0 deg = north) + + flow_field: # This struct is passed on to the AmbientCorrector to feed in the new flow properties + air_density: 1.225 + reference_wind_height: 119.0 # Needs to be defined as FLORIS expects different turbine types (-1 might still work) + turbulence_intensities: + - 0.03 + wind_directions: # a single value indicates constant conditions, multiple lead to linear interpolation + - 270.0 + - 270.0 + wind_directions_t: + - 0.0 + - 1200.0 + wind_shear: 0.12 + wind_speeds: # a single value indicates constant conditions, multiple lead to linear interpolation + - 8.0 + wind_speeds_t: + - 0.0 + wind_veer: 0.0 + corr_overwrite_direction: true # All states are overwritten instead of only the first particle state + +# Wake solver settings +# Here we select the Temporary Wind Farm (TWF) solver and the FLORIS wake model +# As of writing, the TWFSolver is the ONLY wake solver available. It can use any FLORIS model, as well as a build in PythonGaussianWake model. +# For future wake solvers, the initialization of the solver needs to be adapted, as it is hardcoded of now (see OFF.__init__). +solver: + name: TWF FLORIS + description: FLORIDyn - A dynamic and flexible framework for real-time wind farm control, Becker et al. 2022 + + settings: + wake_solver: "TWFSolver" + wake_model: "FLORIS GCH" #"PythonGaussianWake" # "FLORIS GCH" + op_propagation: "frozen turbulence" + extrapolation: "pair" + # Number of OPs per turbine. The chain of OPs should span long enough to cover the whole wind farm. + # n_op * time_step * u_free_stream = covered distance + n_op: 200 + + +# Wake model settings +# These need to fit the wake model that the wake solver is using. +wake: + name: GCH + description: Gaussian Curl Hybrid wake model + + settings: + # The yaml path is used to create an input file for the FLORIS model, before it is moved to the run folder + yaml_path: "02_Examples_and_Cases/00_Inputs/01_FLORIS/not_relevant_.yaml" + # The FLORIS input file is generated based on the settings linked below as well as the info in this yaml (e.g. turbine type, layout) + floris_logging: "02_Examples_and_Cases/00_Inputs/01_FLORIS/00_Logging/warning_none.yaml" + floris_solver: "02_Examples_and_Cases/00_Inputs/01_FLORIS/01_Solver/turbine_grid_3.yaml" + # This is where the floris model and settings are changed! + floris_wake: "02_Examples_and_Cases/00_Inputs/01_FLORIS/02_Wake/gch.yaml" + # Settings not used yet + nRP: 10 + rotor discretization: Isocell + + +# Controller settings +# Multiple controllers are available, each with their own settings. Below you can find examples of the controllers, along with one that is selected. + +# Contoller used in "A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies" Becker, Lejeune et al. 2024 +# Can be used as baseline and as LuT contoller +# controller: +# name: Dead-band LUT yaw controller +# description: Chooses yaw angles based on the current main wind direction and a lut for it. Only corrects if offset to the averaged wind direction is larger then a given offset of if there has been a mismatch in wind direction for long enough +# settings: +# ctl: "Dead-band LUT yaw controller" +# wind_dir_thresh: 4 # Dead-band width in deg - if you set 2, the ± width is 4 deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml +# orientation: False # Checks if the data is given as orientation or, if false, as yaw angles +# # LuT data can be given as a csv or a pckl file. +# # The pckl file is a dictionary with the keys "wind_speed" and "wind_direction", "turbulence_intensity" and "yaw_angles_opt" +# path_to_angles_and_directions_pckl: "02_Examples_and_Cases/04_LuTs/yaw_offsets_HKN_10T.pckl" # Leave empty if using csv instead +# # The csv file is a table with the first column being the wind directions and the following nT columns being the yaw angles for the nT turbines +# # IMPORTANT: As of now, the csv file is not sufficient to run the Dead-band LUT yaw controller, provide a pckl file instead - the code will throw an error. +# path_to_angles_and_directions_csv: "/_.csv" +# k_i : 0.01 # Integral gain (per second) +# baseline: False # Activate Baseline mode, yaw = 0 + +# controller: +# name: Ideal greedy baseline +# description: Follows the main wind direction, disregarding yaw travel costs or other turbines +# settings: +# ctl: "IdealGreedyBaseline" + +# controller: +# name: Realistic greedy baseline +# description: Follows the main wind direction, only corrects if offset to the averaged wind direction is larger then a given offset +# +# settings: +# ctl: "RealGreedyBaseline" +# misalignment_thresh: 5 # in deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml # Not used yet +# apply_frequency: 6 # in time steps, has to be an integer. Time step length is set in the simulation yaml # Not used yet + +# Use "Dead-band LUT yaw controller" instead +# controller: +# name: Look-up-table based yaw steering controller +# description: Chooses yaw angles based on the current main wind direction and a lut for it. Only corrects if offset to the averaged wind direction is larger then a given offset +# +# settings: +# ctl: "LUT yaw controller" +# misalignment_thresh: 2 # in deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml +# orientation: False # Checks if the data is given as orientation or, if false, as yaw angles +# # The csv file is a table with the first column being the wind directions and the following nT columns being the yaw angles for the nT turbines +# path_to_angles_and_directions_csv: "/optimized_angles.csv" + +controller: + name: prescribed yaw controller + description: Sets the turbine orientations based on a set of prescribed yaw angles along with time stamps + + settings: + ctl: "prescribed yaw controller" + # Can be "csv" or "yaml" + # - csv: The file needs to contain the time stamps in the first column, and yaw / orientation angles for each turbine in the following columns. + # - yaml: The orientation needs to be added below + input_method: "yaml" + orientation: False # Checks if the data is given as orientation or, if false, as yaw angles + path_to_orientation_csv: "/_.csv" + orientation_deg: # in deg + - [270, 270] + - [270, 270] + - [240, 270] + - [240, 270] + - [270, 270] + - [270, 270] + orientation_t: # in s + - 0.0 + - 600.0 + - 700.0 + - 800.0 + - 900.0 + - 90000.0 + + +# Turbine information +# The turbine information is used by OFF as well as by the wake model. +# Current issue (FLORIS): The turbine information is NOT passed on to FLORIS, so FLORIS needs to have the same information in its turbine library. +# In OFF, the turbine model is used to calculate the hub height & the power generated +# The turbine type needs to only be saved once. You can also add other, unused turbines to the list. +turbine: + iea_10MW: + name: IEA Wind Task 37 10MW Offshore Reference Turbine # Source: FLORIS v3.4, adapted for OFF + performance: + rated_power: 10000000 + rated_wind_speed: 11.0 + cutin_wind_speed: 4.0 + cutout_wind_speed: 25.0 + Cp_curve: + Cp_tb_values: [ ] + Cp_tb_tsr: [ ] # in (m/s)/(m/s) + Cp_tb_bpa: [ ] # in deg + Cp_u_values: + - 0.000000 + - 0.000000 + - 0.074 + - 0.325100 + - 0.376200 + - 0.402700 + - 0.415600 + - 0.423000 + - 0.427400 + - 0.429300 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.429800 + - 0.430500 + - 0.438256 + - 0.425908 + - 0.347037 + - 0.307306 + - 0.271523 + - 0.239552 + - 0.211166 + - 0.186093 + - 0.164033 + - 0.144688 + - 0.127760 + - 0.112969 + - 0.100062 + - 0.088800 + - 0.078975 + - 0.070401 + - 0.062913 + - 0.056368 + - 0.050640 + - 0.045620 + - 0.041216 + - 0.037344 + - 0.033935 + - 0.0 + - 0.0 + Cp_u_wind_speeds: + - 0.0000 + - 2.9 + - 3.0 + - 4.0000 + - 4.5147 + - 5.0008 + - 5.4574 + - 5.8833 + - 6.2777 + - 6.6397 + - 6.9684 + - 7.2632 + - 7.5234 + - 7.7484 + - 7.9377 + - 8.0909 + - 8.2077 + - 8.2877 + - 8.3308 + - 8.3370 + - 8.3678 + - 8.4356 + - 8.5401 + - 8.6812 + - 8.8585 + - 9.0717 + - 9.3202 + - 9.6035 + - 9.9210 + - 10.2720 + - 10.6557 + - 10.7577 + - 11.5177 + - 11.9941 + - 12.4994 + - 13.0324 + - 13.5920 + - 14.1769 + - 14.7859 + - 15.4175 + - 16.0704 + - 16.7432 + - 17.4342 + - 18.1421 + - 18.8652 + - 19.6019 + - 20.3506 + - 21.1096 + - 21.8773 + - 22.6519 + - 23.4317 + - 24.2150 + - 25.010 + - 25.020 + - 50.0 + Ct_curve: + Ct_tb_values: [ ] + Ct_tb_tsr: [ ] + Ct_tb_bpa: [ ] + Ct_u_values: + - 0.0 + - 0.0 + - 0.7701 + - 0.7701 + - 0.7763 + - 0.7824 + - 0.7820 + - 0.7802 + - 0.7772 + - 0.7719 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7768 + - 0.7675 + - 0.7651 + - 0.7587 + - 0.5056 + - 0.4310 + - 0.3708 + - 0.3209 + - 0.2788 + - 0.2432 + - 0.2128 + - 0.1868 + - 0.1645 + - 0.1454 + - 0.1289 + - 0.1147 + - 0.1024 + - 0.0918 + - 0.0825 + - 0.0745 + - 0.0675 + - 0.0613 + - 0.0559 + - 0.0512 + - 0.0470 + - 0.0 + - 0.0 + Ct_u_wind_speeds: + - 0.0000 + - 2.9 + - 3.0 + - 4.0000 + - 4.5147 + - 5.0008 + - 5.4574 + - 5.8833 + - 6.2777 + - 6.6397 + - 6.9684 + - 7.2632 + - 7.5234 + - 7.7484 + - 7.9377 + - 8.0909 + - 8.2077 + - 8.2877 + - 8.3308 + - 8.3370 + - 8.3678 + - 8.4356 + - 8.5401 + - 8.6812 + - 8.8585 + - 9.0717 + - 9.3202 + - 9.6035 + - 9.9210 + - 10.2720 + - 10.6557 + - 10.7577 + - 11.5177 + - 11.9941 + - 12.4994 + - 13.0324 + - 13.5920 + - 14.1769 + - 14.7859 + - 15.4175 + - 16.0704 + - 16.7432 + - 17.4342 + - 18.1421 + - 18.8652 + - 19.6019 + - 20.3506 + - 21.1096 + - 21.8773 + - 22.6519 + - 23.4317 + - 24.2150 + - 25.010 + - 25.020 + - 50.0 + generator_efficiency: 1.0 + hub_height: 119.0 + pP: 1.88 + pT: 1.88 + rotor_diameter: 198.0 + TSR: 8.0 + ref_density_cp_ct: 1.225 + ref_tilt_cp_ct: 6.0 + # Determines the class of the turbine, other types like VAWT are not supported yet + turbine_type: HAWT + # Determines if the turbine is fixed base or floating, currently only fixed base is supported + installation: fixed + rotor_overhang: 7.07 # in m # TODO Copied from DTU 10MW - Change! + shaft_tilt: 5 # in deg # TODO Copied from DTU 10MW - Change! + yaw_rate_lim: 0.3 # deg/s # TODO Find true value + + +# Visualization settings +# As of now, the visualization is mainly used for debugging purposes and many settings are placeholders. +vis: + grid: + generate: True # Master-switch to generate & store flow field data + # As OFF isn't grid based, the flow field plots need a grid to be generated. + boundaries: + - [ -2, 12] # x in D or m + - [ -4, 4] # y in D or m + - [ 0, 3] # z in D or m + unit: + - D + # Needed for plotting also if boundaries are given in D + diameter: + - 198.0 + # x, y, z in points + resolution: [ 301, 301, 10 ] + volume_3d: False + slice_2d: True + slice_2d_xy: [ 119.0 ] # z-coordinates for xy slices + slice_2d_xz: [ ] # y-coordinates for xz slices + slice_2d_yz: [ ] # x-coordinates for yz slices + slice_2d_p: # x,y,z coordinate of a point on a plane + - [ ] + slice_2d_n: # x,y,z coordinate of a normal vector on a plane + - [ ] + data_vel_background: True + data_vel_effective: True + data_dir_background: True + data_ti_background: False + data_ti_effective: True + data_op_markers: True + + + turbine: + generate: True # Master-switch to generate & store turbine data + plot_together: False # One plot of one QoI with all turbines above one another + plot_seperated: True # One plot with a subplot for every turbine + plot_selection: False # One plot where you can choose which turbine to view + input_yaw: True + input_Ct: False + output_Power: True + output_EffU: False + output_Red: False + farm_interaction: False + farm_layout: False + store_data: True + + flow_field_plots: # Plots are not generated online but rather stored in the run folder + # Times at witch a plot of the flow field is generated + time: [656, 696, 736, 776, 816, 856, 896, 936] + #[600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920, 940, 960, 980, 1000, 1020, 1040, 1060, 1080, 1100, 1120, 1140, 1160, 1180] + # Master-switch to generate & store flow field data + plot: True + # Generates a mountain range like visualization of the wind speed based on the OPs + mountains: True + # Steps between the OPs -> 1 every OP, 5 -> every 5th OP + mountains_stride: 10 + # Offset in OPs, e.g. 0 -> first OP, 1 -> second OP, etc. Is treated as variable if mountain wandering is True + mountains_offset: 5 + # Mountains wandering through the farm, i.e. the mountains are not static but move with the wind + mountains_wandering: False + # 3D mountains + mountains_3d: True + # Triggers a debug plot, which creates a voronoi "prism" with the TWF models + effective_wf_tile: False + # Generates the tiled landscape in a 5 color scheme + effective_wf_tile_5color: False + + + + debug: # Plots are generated online + # Triggers a debug plot, which creates a voronoi "prism" with the TWF models + effective_wf_tile: False + # Generates the tiled landscape in a 5 color scheme + effective_wf_tile_5color: False + # Triggers a debug plot during runtime of the wakes passed on to the wake model + effective_wf_layout: False + # Times at witch a debug plot of the wind farm is generated + time: [400, 452, 500, 552, 600, 652, 700, 752, 800, 852, 900, 952, 1000, 1052, 1100, 1152] + # Turbines for which the debug plot is created + iT: [0,1] + # Generates the effective wind speed across the farm based on a ghost turbine + turbine_effective_wind_speed: False + # Plots the data, requires the turbine_effective_wind_speed to be true + turbine_effective_wind_speed_plot: True + # Adds dots for the particles + turbine_effective_wind_speed_plot_ops: True + # Plots the data, requires the turbine_effective_wind_speed to be true + turbine_effective_wind_speed_show_plot: True + # Stores the contour plot as .png + turbine_effective_wind_speed_store_plot: True + # Stores the data as .csv in the run folder + turbine_effective_wind_speed_store_data: False diff --git a/02_Examples_and_Cases/02_Example_Cases/004_HKN_pywake.yaml b/02_Examples_and_Cases/02_Example_Cases/004_HKN_pywake.yaml new file mode 100644 index 0000000..23b8643 --- /dev/null +++ b/02_Examples_and_Cases/02_Example_Cases/004_HKN_pywake.yaml @@ -0,0 +1,907 @@ +# //////////////////////////////////////////////////////////////////// # +# ____ ______ ______ +# / __ \| ____| ____| +# | | | | |__ | |__ +# | | | | __| | __| +# | |__| | | | | +# \____/|_| |_| +# //////////////////////////////////////////////////////////////////// # +# Copyright (C) <2024>, M Becker (TUDelft), M Lejeune (UCLouvain) + +# List of the contributors to the development of OFF: see LICENSE file. +# Description and complete License: see LICENSE file. + +# This program (OFF) is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU Affero General Public License +# along with this program (see COPYING file). If not, see . + + + + +# //////////////////////////////////////////////////////////////////// # +# Welcome to the OFF input file. This file is used to set up a simulation. +# The yaml file contains multiple comments to explain the setup choices and the structure of the file. +# If you experience issues, create a new issue on the GitHub page https://github.com/TUDelft-DataDrivenControl/OFF +# //////////////////////////////////////////////////////////////////// # + + + +# Simulation settings +# The time step is given in seconds, the start and end time in seconds as well. +sim: + name: Test sim + description: Test simulation with 1200s runtime, features a wind direction change from 225 to 235 deg + + sim: + time step: 4.0 + time start: 0.0 + time end: 1000.0 + + # The logging is split in two, one for the console and one for a file. The level can be set to DEBUG, INFO, WARNING, ERROR, CRITICAL + # The console logging has a feature where the log messages are colored based on their origin. + logging: + console: + enable: true + level: WARNING # INFO + file: + enable: true + level: WARNING + +# OFF stores all results in a run folder, which is by defalut located at "/runs". The user can specify a different location. +# The name of the default folder is "off_run_" followed by the current date and exact time. +# The run folder contains a copy of the input yaml, the yaml used to init FLORIS, as well as the log file, and outputs. +io: # Needs to be written by the user + data folder: '' + simulation folder: + + +# Wind farm settings +# The layout is given in x, y, z coordinates, where locations can be defined in meters or in D (rotor diameters). +# To distinguish between the two, the unit parameter is used. If set to D, the diameter parameter needs to be set. +# Note that the layout refers to the BOTTOM of the turbine, not the rotor center. That is calculated internally based on the trubine information. +wind_farm: + name: turbine array + description: Four turbine case with IEA 22 MW turbines at 5D distance + + farm: + layout_x: + - 0.0 + - 1.6403662 + - 5.03577465 + - 3.28073239 + - 10.07653521 + - 4.92109859 + - 16.05464789 + - 6.56146479 + - 15.37656338 + - 15.03752113 + - 24.99439437 + - 10.34577465 + - 30.58859155 + - 23.56842254 + - 12.00109859 + - 36.108 + - 19.39022535 + - 13.66140845 + - 32.20901408 + - 41.74208451 + - 15.67073239 + - 27.51228169 + - 22.62109859 + - 40.71 + - 47.34126761 + - 18.31825352 + - 34.5623662 + - 49.206 + - 20.96577465 + - 41.88169014 + - 38.0375493 + - 32.65276056 + - 28.21030986 + - 23.61329577 + - 26.2608169 + - 47.95453521 + - 45.07267606 + - 39.89729577 + - 34.83659155 + - 30.49385915 + - 51.29011268 + - 58.42495775 + - 33.2111831 + - 46.26929577 + - 43.3824507 + - 62.60814085 + - 35.92352113 + - 41.25346479 + - 57.75684507 + - 38.63585915 + - 67.34476056 + - 61.04256338 + - 56.79456338 + - 52.61138028 + - 71.42822535 + - 50.70177465 + - 71.25870423 + - 49.82425352 + - 43.69157746 + - 71.18391549 + - 71.26867606 + - 49.35557746 + - 71.55287324 + - 68.69095775 + - 66.21794366 + - 63.69008451 + - 61.16222535 + - 58.6343662 + - 54.93980282 + layout_y: + - 0.0 + - 4.15825352 + - 0.45870423 + - 8.31650704 + - 0.92239437 + - 12.47476056 + - 3.60980282 + - 16.63301408 + - 9.77239437 + - 15.61588732 + - 8.18188732 + - 26.23588732 + - 3.45025352 + - 14.60873239 + - 30.43402817 + - 4.13332394 + - 26.35056338 + - 34.63216901 + - 13.57664789 + - 4.70670423 + - 38.6508169 + - 25.40822535 + - 34.24825352 + - 12.57447887 + - 5.07566197 + - 42.32543662 + - 23.34904225 + - 11.66704225 + - 46.00005634 + - 24.39109859 + - 29.52160563 + - 36.09304225 + - 43.96580282 + - 49.66969014 + - 53.34430986 + - 27.98594366 + - 36.14290141 + - 43.13814085 + - 52.79087324 + - 59.22270423 + - 38.75552113 + - 32.36856338 + - 63.00202817 + - 47.51577465 + - 53.5836338 + - 34.35295775 + - 66.78135211 + - 60.68357746 + - 39.5183662 + - 70.56067606 + - 36.89078873 + - 45.54633803 + - 51.72388732 + - 58.62439437 + - 40.1316338 + - 64.87174648 + - 44.62892958 + - 71.39332394 + - 77.5908169 + - 49.13121127 + - 53.65343662 + - 79.05169014 + - 58.14574648 + - 62.12450704 + - 66.10825352 + - 69.88757746 + - 73.66690141 + - 77.44123944 + - 80.76684507 + layout_z: + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + - 0.0 + turbine_type: + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + - iea22mw + unit: + - D + diameter: # Only needed if unit was set to D + - 284.0 + boundaries_xyz: + - -2.0 + - 80.0 + - -2.0 + - 90.0 + - 0.0 + - 5.0 + + +# Ambient conditions used during the simulation +# Currently the code only supports time variations of the wind speed and direction. If a single value is given, it is assumed to be constant. +# There is also only one ambient corrector as of now. +ambient: + name: ambient_conditions + description: Constant wind speed at 8m/s coming from 270 deg (0 deg = north) + + flow_field: # This struct is passed on to the AmbientCorrector to feed in the new flow properties + air_density: 1.225 + reference_wind_height: 170.0 # Needs to be defined as FLORIS expects different turbine types (-1 might still work) + turbulence_intensities: + - 0.03 + wind_directions: # a single value indicates constant conditions, multiple lead to linear interpolation + - 270.0 + - 260.0 + - 265.0 + - 275.0 + - 285.0 + - 270.0 + wind_directions_t: + - 0.0 + - 200.0 + - 250.0 + - 600.0 + - 800.0 + - 1200.0 + wind_shear: 0.12 + wind_speeds: # a single value indicates constant conditions, multiple lead to linear interpolation + - 10.0 + wind_speeds_t: + - 0.0 + wind_veer: 0.0 + corr_overwrite_direction: true # All states are overwritten instead of only the first particle state + +# Wake solver settings +# Here we select the Temporary Wind Farm (TWF) solver and the FLORIS wake model +# As of writing, the TWFSolver is the ONLY wake solver available. It can use any FLORIS model, as well as a build in PythonGaussianWake model. +# For future wake solvers, the initialization of the solver needs to be adapted, as it is hardcoded of now (see OFF.__init__). +solver: + name: TWF PyWake + description: FLORIDyn with PyWake backend for wake modeling + + settings: + wake_solver: "TWFSolver" + wake_model: "PyWake" + op_propagation: "frozen turbulence" + extrapolation: "pair" + # Number of OPs per turbine. The chain of OPs should span long enough to cover the whole wind farm. + # n_op * time_step * u_free_stream = covered distance + n_op: 600 + + +# Wake model settings +# These need to fit the wake model that the wake solver is using. +wake: + name: Gaussian + description: PyWake Gaussian wake modeling framework + + settings: + # TURBINE LIBRARY SETTINGS + # Set use_pywake_turbine_library to true to use PyWake's built-in turbine models + # Common PyWake turbines: DTU10MW, IEA_22MW_280_RWT + # OFF automatically finds turbines in PyWake's data folder using introspection + # Specify the PyWake class name for each turbine type in the turbine definition below + use_pywake_turbine_library: true + # The yaml path is used to create an input file for the FLORIS model, before it is moved to the run folder + yaml_path: "02_Examples_and_Cases/00_Inputs/02_PyWake/not_relevant_.yaml" + # The FLORIS input file is generated based on the settings linked below as well as the info in this yaml (e.g. turbine type, layout) + floris_logging: "02_Examples_and_Cases/00_Inputs/02_PyWake/00_Logging/warning_none.yaml" + floris_solver: "02_Examples_and_Cases/00_Inputs/02_PyWake/01_Solver/turbine_grid_3.yaml" + # This is where the floris model and settings are changed! + floris_wake: "02_Examples_and_Cases/00_Inputs/02_PyWake/02_Wake/gaussian.yaml" + # Settings not used yet + # Settings not used yet but kept for compatibility + nRP: 10 + rotor discretization: Isocell + + +# Controller settings +# Multiple controllers are available, each with their own settings. Below you can find examples of the controllers, along with one that is selected. + +# Contoller used in "A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies" Becker, Lejeune et al. 2024 +# Can be used as baseline and as LuT contoller +# controller: +# name: Dead-band LUT yaw controller +# description: Chooses yaw angles based on the current main wind direction and a lut for it. Only corrects if offset to the averaged wind direction is larger then a given offset of if there has been a mismatch in wind direction for long enough +# settings: +# ctl: "Dead-band LUT yaw controller" +# wind_dir_thresh: 4 # Dead-band width in deg - if you set 2, the ± width is 4 deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml +# orientation: False # Checks if the data is given as orientation or, if false, as yaw angles +# # LuT data can be given as a csv or a pckl file. +# # The pckl file is a dictionary with the keys "wind_speed" and "wind_direction", "turbulence_intensity" and "yaw_angles_opt" +# path_to_angles_and_directions_pckl: "02_Examples_and_Cases/04_LuTs/yaw_offsets_HKN_10T.pckl" # Leave empty if using csv instead +# # The csv file is a table with the first column being the wind directions and the following nT columns being the yaw angles for the nT turbines +# # IMPORTANT: As of now, the csv file is not sufficient to run the Dead-band LUT yaw controller, provide a pckl file instead - the code will throw an error. +# path_to_angles_and_directions_csv: "/_.csv" +# k_i : 0.01 # Integral gain (per second) +# baseline: False # Activate Baseline mode, yaw = 0 + +# controller: +# name: Ideal greedy baseline +# description: Follows the main wind direction, disregarding yaw travel costs or other turbines +# settings: +# ctl: "IdealGreedyBaseline" + +# controller: +# name: Realistic greedy baseline +# description: Follows the main wind direction, only corrects if offset to the averaged wind direction is larger then a given offset +# +# settings: +# ctl: "RealGreedyBaseline" +# misalignment_thresh: 5 # in deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml # Not used yet +# apply_frequency: 6 # in time steps, has to be an integer. Time step length is set in the simulation yaml # Not used yet + +# Use "Dead-band LUT yaw controller" instead +# controller: +# name: Look-up-table based yaw steering controller +# description: Chooses yaw angles based on the current main wind direction and a lut for it. Only corrects if offset to the averaged wind direction is larger then a given offset +# +# settings: +# ctl: "LUT yaw controller" +# misalignment_thresh: 2 # in deg +# average_window: 1 # in time steps, has to be an integer. Time step length is set in the simulation yaml +# orientation: False # Checks if the data is given as orientation or, if false, as yaw angles +# # The csv file is a table with the first column being the wind directions and the following nT columns being the yaw angles for the nT turbines +# path_to_angles_and_directions_csv: "/optimized_angles.csv" + +controller: + name: prescribed yaw controller + description: Sets the turbine orientations based on a set of prescribed yaw angles along with time stamps + + settings: + ctl: "prescribed yaw controller" + # Can be "csv" or "yaml" + # - csv: The file needs to contain the time stamps in the first column, and yaw / orientation angles for each turbine in the following columns. + # - yaml: The orientation needs to be added below + input_method: "yaml" + orientation: False # Checks if the data is given as orientation or, if false, as yaw angles + path_to_orientation_csv: "/_.csv" + orientation_deg: # in deg + - [270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270] + - [260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260, 260] + - [265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265, 265] + - [275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275, 275] + - [285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285, 285] + - [270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270, 270] + orientation_t: # in s + - 0.0 + - 200.0 + - 250.0 + - 600.0 + - 800.0 + - 1200.0 + +# Turbine information +# When using PyWake's turbine library (use_pywake_turbine_library: true), specify the PyWake class name +# for each turbine type using pywake_turbine_name. OFF will automatically extract Cp/Ct curves. +# The turbine_type name here must match what's specified in wind_farm/farm/turbine_type above. +# +# Example turbine types: +# - DTU10MW: pywake_turbine_name: "DTU10MW" (rotor_diameter: 178.4, hub_height: 119.0) +# - IEA 22MW: pywake_turbine_name: "IEA_22MW_280_RWT" (rotor_diameter: 284.0, hub_height: 170.0) + +turbine: + iea22mw: + name: IEA 22MW Reference Turbine (from PyWake library) + pywake_turbine_name: "IEA_22MW_280_RWT" # PyWake class name to import + # The following will be automatically populated from PyWake when use_pywake_turbine_library is enabled + # Cp/Ct curves extracted from PyWake: + performance: + Cp_curve: + Cp_u_values: [] + Cp_u_wind_speeds: [] + Ct_curve: + Ct_u_values: [] + Ct_u_wind_speeds: [] + hub_height: 170.0 # IEA 22MW 280m rotor + rotor_diameter: 284.0 # IEA 22MW 280m rotor + shaft_tilt: 5.0 # Typical for large offshore turbines + pP: 1.88 + pT: 1.88 + rotor_overhang: 7.07 + yaw_rate_lim: 0.3 + turbine_type: HAWT + installation: fixed + +# turbine: +# iea_10MW: +# name: IEA Wind Task 37 10MW Offshore Reference Turbine # Source: FLORIS v3.4, adapted for OFF +# performance: +# rated_power: 10000000 +# rated_wind_speed: 11.0 +# cutin_wind_speed: 4.0 +# cutout_wind_speed: 25.0 +# Cp_curve: +# Cp_tb_values: [ ] +# Cp_tb_tsr: [ ] # in (m/s)/(m/s) +# Cp_tb_bpa: [ ] # in deg +# Cp_u_values: +# - 0.000000 +# - 0.000000 +# - 0.074 +# - 0.325100 +# - 0.376200 +# - 0.402700 +# - 0.415600 +# - 0.423000 +# - 0.427400 +# - 0.429300 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.429800 +# - 0.430500 +# - 0.438256 +# - 0.425908 +# - 0.347037 +# - 0.307306 +# - 0.271523 +# - 0.239552 +# - 0.211166 +# - 0.186093 +# - 0.164033 +# - 0.144688 +# - 0.127760 +# - 0.112969 +# - 0.100062 +# - 0.088800 +# - 0.078975 +# - 0.070401 +# - 0.062913 +# - 0.056368 +# - 0.050640 +# - 0.045620 +# - 0.041216 +# - 0.037344 +# - 0.033935 +# - 0.0 +# - 0.0 +# Cp_u_wind_speeds: +# - 0.0000 +# - 2.9 +# - 3.0 +# - 4.0000 +# - 4.5147 +# - 5.0008 +# - 5.4574 +# - 5.8833 +# - 6.2777 +# - 6.6397 +# - 6.9684 +# - 7.2632 +# - 7.5234 +# - 7.7484 +# - 7.9377 +# - 8.0909 +# - 8.2077 +# - 8.2877 +# - 8.3308 +# - 8.3370 +# - 8.3678 +# - 8.4356 +# - 8.5401 +# - 8.6812 +# - 8.8585 +# - 9.0717 +# - 9.3202 +# - 9.6035 +# - 9.9210 +# - 10.2720 +# - 10.6557 +# - 10.7577 +# - 11.5177 +# - 11.9941 +# - 12.4994 +# - 13.0324 +# - 13.5920 +# - 14.1769 +# - 14.7859 +# - 15.4175 +# - 16.0704 +# - 16.7432 +# - 17.4342 +# - 18.1421 +# - 18.8652 +# - 19.6019 +# - 20.3506 +# - 21.1096 +# - 21.8773 +# - 22.6519 +# - 23.4317 +# - 24.2150 +# - 25.010 +# - 25.020 +# - 50.0 +# Ct_curve: +# Ct_tb_values: [ ] +# Ct_tb_tsr: [ ] +# Ct_tb_bpa: [ ] +# Ct_u_values: +# - 0.0 +# - 0.0 +# - 0.7701 +# - 0.7701 +# - 0.7763 +# - 0.7824 +# - 0.7820 +# - 0.7802 +# - 0.7772 +# - 0.7719 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7768 +# - 0.7675 +# - 0.7651 +# - 0.7587 +# - 0.5056 +# - 0.4310 +# - 0.3708 +# - 0.3209 +# - 0.2788 +# - 0.2432 +# - 0.2128 +# - 0.1868 +# - 0.1645 +# - 0.1454 +# - 0.1289 +# - 0.1147 +# - 0.1024 +# - 0.0918 +# - 0.0825 +# - 0.0745 +# - 0.0675 +# - 0.0613 +# - 0.0559 +# - 0.0512 +# - 0.0470 +# - 0.0 +# - 0.0 +# Ct_u_wind_speeds: +# - 0.0000 +# - 2.9 +# - 3.0 +# - 4.0000 +# - 4.5147 +# - 5.0008 +# - 5.4574 +# - 5.8833 +# - 6.2777 +# - 6.6397 +# - 6.9684 +# - 7.2632 +# - 7.5234 +# - 7.7484 +# - 7.9377 +# - 8.0909 +# - 8.2077 +# - 8.2877 +# - 8.3308 +# - 8.3370 +# - 8.3678 +# - 8.4356 +# - 8.5401 +# - 8.6812 +# - 8.8585 +# - 9.0717 +# - 9.3202 +# - 9.6035 +# - 9.9210 +# - 10.2720 +# - 10.6557 +# - 10.7577 +# - 11.5177 +# - 11.9941 +# - 12.4994 +# - 13.0324 +# - 13.5920 +# - 14.1769 +# - 14.7859 +# - 15.4175 +# - 16.0704 +# - 16.7432 +# - 17.4342 +# - 18.1421 +# - 18.8652 +# - 19.6019 +# - 20.3506 +# - 21.1096 +# - 21.8773 +# - 22.6519 +# - 23.4317 +# - 24.2150 +# - 25.010 +# - 25.020 +# - 50.0 +# generator_efficiency: 1.0 +# hub_height: 119.0 +# pP: 1.88 +# pT: 1.88 +# rotor_diameter: 198.0 +# TSR: 8.0 +# ref_density_cp_ct: 1.225 +# ref_tilt_cp_ct: 6.0 +# # Determines the class of the turbine, other types like VAWT are not supported yet +# turbine_type: HAWT +# # Determines if the turbine is fixed base or floating, currently only fixed base is supported +# installation: fixed +# rotor_overhang: 7.07 # in m # TODO Copied from DTU 10MW - Change! +# shaft_tilt: 5 # in deg # TODO Copied from DTU 10MW - Change! +# yaw_rate_lim: 0.3 # deg/s # TODO Find true value + + +# Visualization settings +# As of now, the visualization is mainly used for debugging purposes and many settings are placeholders. +vis: + grid: + generate: False # Master-switch to generate & store flow field data + # As OFF isn't grid based, the flow field plots need a grid to be generated. + boundaries: + - [ -2, 20] # x in D or m + - [ -4, 4] # y in D or m + - [ 0, 3] # z in D or m + unit: + - D + # Needed for plotting also if boundaries are given in D + diameter: + - 284.0 + # x, y, z in points + resolution: [ 301, 301, 10 ] + volume_3d: False + slice_2d: True + slice_2d_xy: [ 119.0 ] # z-coordinates for xy slices + slice_2d_xz: [ ] # y-coordinates for xz slices + slice_2d_yz: [ ] # x-coordinates for yz slices + slice_2d_p: # x,y,z coordinate of a point on a plane + - [ ] + slice_2d_n: # x,y,z coordinate of a normal vector on a plane + - [ ] + data_vel_background: True + data_vel_effective: True + data_dir_background: True + data_ti_background: False + data_ti_effective: True + data_op_markers: True + + + turbine: + generate: True # Master-switch to generate & store turbine data + plot_together: False # One plot of one QoI with all turbines above one another + plot_seperated: True # One plot with a subplot for every turbine + plot_selection: False # One plot where you can choose which turbine to view + input_yaw: True + input_Ct: False + output_Power: True + output_EffU: False + output_Red: False + farm_interaction: False + farm_layout: False + store_data: True + + flow_field_plots: # Plots are not generated online but rather stored in the run folder + # Times at witch a plot of the flow field is generated + time: [656, 696, 736, 776, 816, 856, 896, 936] + #[600, 620, 640, 660, 680, 700, 720, 740, 760, 780, 800, 820, 840, 860, 880, 900, 920, 940, 960, 980, 1000, 1020, 1040, 1060, 1080, 1100, 1120, 1140, 1160, 1180] + # Master-switch to generate & store flow field data + plot: True + # Generates a mountain range like visualization of the wind speed based on the OPs + mountains: True + # Steps between the OPs -> 1 every OP, 5 -> every 5th OP + mountains_stride: 10 + # Offset in OPs, e.g. 0 -> first OP, 1 -> second OP, etc. Is treated as variable if mountain wandering is True + mountains_offset: 5 + # Mountains wandering through the farm, i.e. the mountains are not static but move with the wind + mountains_wandering: False + # 3D mountains + mountains_3d: False + # Triggers a debug plot, which creates a voronoi "prism" with the TWF models + effective_wf_tile: False + # Generates the tiled landscape in a 5 color scheme + effective_wf_tile_5color: False + + + + debug: # Plots are generated online + # Triggers a debug plot, which creates a voronoi "prism" with the TWF models + effective_wf_tile: False + # Generates the tiled landscape in a 5 color scheme + effective_wf_tile_5color: False + # Triggers a debug plot during runtime of the wakes passed on to the wake model + effective_wf_layout: False + # Times at witch a debug plot of the wind farm is generated + time: [400, 452, 500, 552, 600, 652, 700, 752, 800, 852, 900, 952, 1000, 1052, 1100, 1152] + # Turbines for which the debug plot is created + iT: [0,1] + # Generates the effective wind speed across the farm based on a ghost turbine + turbine_effective_wind_speed: False + # Plots the data, requires the turbine_effective_wind_speed to be true + turbine_effective_wind_speed_plot: True + # Adds dots for the particles + turbine_effective_wind_speed_plot_ops: True + # Plots the data, requires the turbine_effective_wind_speed to be true + turbine_effective_wind_speed_show_plot: True + # Stores the contour plot as .png + turbine_effective_wind_speed_store_plot: True + # Stores the data as .csv in the run folder + turbine_effective_wind_speed_store_data: False diff --git a/02_Examples_and_Cases/02_Example_Cases/README_PyWake_Turbine_Library.md b/02_Examples_and_Cases/02_Example_Cases/README_PyWake_Turbine_Library.md new file mode 100644 index 0000000..facf23a --- /dev/null +++ b/02_Examples_and_Cases/02_Example_Cases/README_PyWake_Turbine_Library.md @@ -0,0 +1,154 @@ +# Using PyWake's Built-in Turbine Library with OFF + +## Overview + +OFF has been enhanced to support loading turbine models directly from PyWake's built-in library. This allows you to use pre-defined turbine models like DTU10MW, IEA15MW, V80, etc., without manually defining all turbine parameters in the YAML file. + +**PyWake Turbine Structure:** Turbines are stored as classes in submodules: +```python +from py_wake.examples.data.dtu10mw._dtu10mw import DTU10MW +wt = DTU10MW() # Instantiate the turbine +``` + +## What Changed + +### Code Changes + +1. **wake_model.py**: Added new functionality to load turbines from PyWake's library + - New method: `_load_turbine_from_pywake_library()` - Loads turbines from `py_wake.examples.data.wtg` + - Modified: `set_wind_farm()` - Now checks if PyWake library should be used + - New settings: `use_pywake_turbine_library` and `pywake_turbine_name` + +### YAML Configuration Changes + +The YAML file now supports two modes: + +#### **Mode 1: Use PyWake's Turbine Library (NEW)** + +Set these flags in the `wake > settings` section: + +```yaml +wake: + settings: + use_pywake_turbine_library: true + pywake_turbine_name: "DTU10MW" # Case-sensitive! Must match PyWake class name +``` + +The `turbine` section becomes minimal - it only needs a placeholder: + +```yaml +turbine: + v80: # Must match turbine_type in wind_farm section + name: Vestas V80 2MW (from PyWake library) + performance: + Cp_curve: + Cp_u_values: [] + Cp_u_wind_speeds: [] + Ct_curve: + Ct_u_values: [] + Ct_u_wind_speeds: [] + hub_height: 70.0 # Placeholder - will be overwritten + rotor_diameter: 80.0 # Placeholder - will be overwritten + shaft_tilt: 5.0 + pP: 1.88 + pT: 1.88 + # ... other minimal parameters +``` + +OFF will automatically extract the correct hub height, diameter, and power/thrust curves from PyWake. + +#### **Mode 2: Use YAML-Defined Turbines (ORIGINAL)** + +Set the flag to false or omit it: + +```yaml +wake: + settings: + use_pywake_turbine_library: false # or omit this line +``` + +Then provide full turbine definitions in the `turbine` section with complete Cp/Ct curves. + +## Available PyWake Turbines + +Common turbines available in PyWake's library (actual names may vary by PyWake version): + +- **DTU10MW** - DTU 10 MW Reference Wind Turbine +- **IEA15MW** - IEA Wind 15 MW Reference Turbine (if available) +- **V80** - Vestas V80 2 MW (if available) +- **NREL5MW** - NREL 5 MW Reference Turbine (if available) + +**Important:** Turbine names are **case-sensitive** and must match the PyWake class name exactly. + +### Finding Available Turbines + +Turbines are stored in: `py_wake/examples/data//` + +Each turbine follows this pattern: +```python +from py_wake.examples.data.dtu10mw._dtu10mw import DTU10MW # Module name is lowercase +from py_wake.examples.data.iea15mw._iea15mw import IEA15MW +``` + +To find available turbines: +1. Check the `py_wake/examples/data/` folder in your PyWake installation +2. Each subfolder contains a turbine class +3. Use the **class name** (not folder name) in your YAML configuration + +## How It Works + +1. When `use_pywake_turbine_library: true`, OFF loads the specified turbine from PyWake +2. OFF automatically extracts turbine parameters (hub_height, diameter) from the PyWake object +3. OFF updates the `turbine_library` dictionary with this information so the rest of OFF can access it +4. PyWake uses its own turbine model for wake calculations +5. OFF uses the extracted parameters for its internal calculations (hub height, layout, etc.) + +## Example: 001_two_turbines_yaw_step_pywake.yaml + +This file has been updated to demonstrate using PyWake's DTU10MW turbine: + +1. In `wind_farm > farm > turbine_type`: Set to `["dtu10mw", "dtu10mw", ...]` (lowercase) +2. In `wake > settings`: Added `use_pywake_turbine_library: true` and `pywake_turbine_name: "DTU10MW"` (class name) +3. In `turbine`: Simplified the `dtu10mw` section to minimal placeholders + +## Troubleshooting + +### Error: "Turbine not found in PyWake library" +- Check the turbine name spelling - it's case-sensitive +- OFF will list available turbines in the error message + +### Error: "Cannot load turbines from PyWake library" +- Ensure PyWake is properly installed: `pip install py_wake` +- Check that `py_wake.examples.data` is available + +### Turbine parameters seem incorrect +- Verify the `pywake_turbine_name` matches a valid PyWake turbine +- Check that `use_pywake_turbine_library: true` is set + +### OFF can't find turbine parameters +- Ensure the turbine name in `wind_farm > turbine_type` matches the key in the `turbine` section +- Even with PyWake library mode, you need a minimal turbine entry with the correct name + +## Benefits + +✅ No need to manually define Cp/Ct curves +✅ Use validated reference turbines from PyWake +✅ Easier to switch between different turbine models +✅ Ensures consistency between OFF and PyWake turbine parameters +✅ Reduces YAML file size and complexity + +## Migration Guide + +To convert an existing YAML file to use PyWake's library: + +1. Add the two flags to `wake > settings`: + ```yaml + use_pywake_turbine_library: true + pywake_turbine_name: "DTU10MW" # Case-sensitive class name from PyWake + ``` + +2. Replace the full turbine definition with a minimal placeholder (see Mode 1 above) + +3. Update `wind_farm > turbine_type` if needed to match your chosen turbine + +4. Run your simulation - OFF will handle the rest! diff --git a/02_Examples_and_Cases/02_Example_Cases/run_example_pywake.yaml b/02_Examples_and_Cases/02_Example_Cases/run_example_pywake.yaml new file mode 100644 index 0000000..8aa4b22 --- /dev/null +++ b/02_Examples_and_Cases/02_Example_Cases/run_example_pywake.yaml @@ -0,0 +1,229 @@ +# //////////////////////////////////////////////////////////////////// # +# ____ ______ ______ +# / __ \| ____| ____| +# | | | | |__ | |__ +# | | | | __| | __| +# | |__| | | | | +# \____/|_| |_| +# //////////////////////////////////////////////////////////////////// # +# Copyright (C) <2024>, M Becker (TUDelft), M Lejeune (UCLouvain) + +# List of the contributors to the development of OFF: see LICENSE file. +# Description and complete License: see LICENSE file. + +# This program (OFF) is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU Affero General Public License +# along with this program (see COPYING file). If not, see . + + + + +# //////////////////////////////////////////////////////////////////// # +# Example simulation of two turbines - using PyWake instead of FLORIS +# +# To use PyWake, simply change: +# 1. solver.settings.wake_model to "PyWake" +# 2. wake.settings to include PyWake-specific parameters +# //////////////////////////////////////////////////////////////////// # + + + + +# Basic simulation settings +sim: + name: Example PyWake Simulation + description: Two turbines in a row with yaw step change + + sim: + # How often the OFF simulation updates states + time step: 1.0 + time start: 0.0 + time end: 400.0 + + # Log levels options (see https://docs.python.org/3/library/logging.html#logging-levels) + logging: + console: + enable: true + level: INFO + file: + enable: true + level: INFO + +# OFF stores all results in a run folder by default at "/runs" +run_info: + name: "Test run" + tags: + - "PyWake" + - "Example" + notes: "Testing PyWake integration with OFF" + save: True + + + + +# Wind farm definition +# The wind turbines in a wind farm have a global coordinate system with x pointing east and y pointing north (0 deg wind is from the west). +# Each turbine knows its own position, turbine model. In our case, the turbine model is passed by the yaml, as it is assumed all turbines are the same. +# The wind farm should also define, where in the wind farm area measurements should be taken, called observation points (OPs). +# An OP per turbine is created between each pair, with n_op OPs in total. +wind_farm: + name: turbine array + description: Two turbines in a row with IEA 10 MW turbines at 5D distance + + farm: + layout_x: + - 0.0 + - 7.5 + layout_y: + - 0.0 + - 0.0 + layout_z: + - 0.0 + - 0.0 + turbine_type: + - iea_10MW + - iea_10MW + unit: + - D + diameter: # Only needed if unit was set to D + - 198.0 + + + + +# Ambient conditions +# Define the background wind that does not come from any turbine +ambient: + name: constant_wind + description: Constant wind speed and direction over time + + settings: + # Option 1: All wind turbines in the farm share the same ambient conditions + # Select the ambient condition with one number + ambient_selection: 0 + + # Option 2: Each wind turbine has its own ambient conditions + # Select the ambient condition with a list of numbers: [0, 0, 1] for 3 turbines -> 1st and 2nd turbine have the same ambient conditions, 3rd has different ones + # ambient_selection: [0, 0] + + # List of possible ambient conditions. Ids need to be defined with: amb_id: + ambient_list: + # Ambient id 0 (see ambient_selection above) + - amb_id: 0 + # Fully describing the ambient conditions with a list of time steps + definition: full + # 1 x 3 array with [t, u_abs, phi] - time, absolute wind speed, wind direction in radians + u_amb: + # Time: 0s, wind speed: 8 m/s, wind direction: 270 deg (from west -> turbine 0 is upwind of turbine 1, as x is 'east', y is 'north') + - [0, 8, 4.71238898038469] + # If you set the time to 400s, this will give you a constant wind speed and direction over the whole simulation + - [400, 8, 4.71238898038469] + + + + +# Solver selection and solver settings +# Here we select the Temporary Wind Farm (TWF) solver and the PyWake wake model +solver: + name: TWF PyWake + description: FLORIDyn with PyWake backend for wake modeling + + settings: + wake_solver: "TWFSolver" + wake_model: "PyWake" + op_propagation: "frozen turbulence" + extrapolation: "pair" + # Number of OPs per turbine. The chain of OPs should span long enough to cover the whole wind farm. + # n_op * time_step * u_free_stream = covered distance + n_op: 200 + + +# Wake model settings for PyWake +wake: + name: PyWake + description: PyWake wake modeling framework + + settings: + # Deficit model to use - options include: + # "IEA37SimpleBastankhahGaussian" (default, simple Gaussian) + # "BastankhahGaussian" (full Gaussian model) + # "NOJDeficit" or "Jensen" (Jensen/Park model) + # "TurboNOJDeficit" (TurboPark model) + # "GCLDeficit" (Gaussian with Curl) + deficit_model: "IEA37SimpleBastankhahGaussian" + + # Optional: Turbulence model - options include: + # null/None (no turbulence model) + # "CrespoHernandez" + # "STF2017TurbulenceModel" + turbulence_model: null + + # Site turbulence intensity (default: 0.06) + site_ti: 0.06 + + # Site shear exponent (default: 0.0) + site_shear: 0.0 + + # Settings not used yet but kept for compatibility + nRP: 10 + rotor discretization: Isocell + + + + +# Controller settings +controller: + name: yaw_step + description: Yaw step change at t=100s + + settings: + # Select which controller to use + type: "yaw_prescribed" + + # Yaw prescribed controller settings + # This controller sets the yaw angle of each turbine at specific time steps + yaw_prescribed: + # List of yaw angles for each turbine at each time step + # Format: [turbine_id, time, yaw_angle_deg] + yaw_schedule: + # Turbine 0 starts at 0 deg and stays there + - [0, 0, 0] + - [0, 400, 0] + + # Turbine 1 starts at 0 deg, then steps to 25 deg at t=100s + - [1, 0, 0] + - [1, 99, 0] + - [1, 100, 25] + - [1, 400, 25] + + + + +# Visualization settings +# These control what plots and data are generated during the simulation +visualization: + settings: + # Plot wake field (flow field) + plot_wakes: False + + # Create mountain range plots (wind speed profiles) + plot_mountain: False + + # Plot effective wind speed at turbines + plot_ueff_all: False + + # Export flow field data + export_flow_field: False + + # Time steps at which to create visualizations (list of times in seconds) + # Empty list means no visualizations + visualization_times: [] diff --git a/02_Examples_and_Cases/02_Example_Cases/tmp_floris_input20260128102752342695.yaml b/02_Examples_and_Cases/02_Example_Cases/tmp_floris_input20260128102752342695.yaml new file mode 100644 index 0000000..2bfc862 --- /dev/null +++ b/02_Examples_and_Cases/02_Example_Cases/tmp_floris_input20260128102752342695.yaml @@ -0,0 +1,81 @@ +description: File to initialize the FLORIS simulation with the correct turbine types + and settings +farm: + layout_x: + - 0.0 + - 5.0 + layout_y: + - 0.0 + - 0.0 + turbine_type: + - iea_10MW + - iea_10MW +floris_version: v4 +flow_field: + air_density: 1.225 + reference_wind_height: 119.0 + turbulence_intensities: + - 0.03 + wind_directions: + - 270.0 + wind_shear: 0.12 + wind_speeds: + - 8.0 + wind_veer: 0.0 +logging: + console: + enable: true + level: WARNING + file: + enable: false + level: WARNING +name: OFF FLORIS Init Simulation +solver: + turbine_grid_points: 3 + type: turbine_grid +wake: + enable_active_wake_mixing: false + enable_secondary_steering: true + enable_transverse_velocities: true + enable_yaw_added_recovery: true + model_strings: + combination_model: sosfs + deflection_model: gauss + turbulence_model: crespo_hernandez + velocity_model: gauss + wake_deflection_parameters: + gauss: + ad: 0.0 + alpha: 0.58 + bd: 0.0 + beta: 0.077 + dm: 1.0 + ka: 0.38 + kb: 0.004 + jimenez: + ad: 0.0 + bd: 0.0 + kd: 0.05 + wake_turbulence_parameters: + crespo_hernandez: + ai: 0.8 + constant: 0.5 + downstream: -0.32 + initial: 0.1 + wake_velocity_parameters: + cc: + a_f: 3.11 + a_s: 0.179367259 + alpha_mod: 1.0 + b_f: -0.68 + b_s: 0.0118889215 + c_f: 2.41 + c_s1: 0.0563691592 + c_s2: 0.13290157 + gauss: + alpha: 0.58 + beta: 0.077 + ka: 0.38 + kb: 0.004 + jensen: + we: 0.05 diff --git a/03_Code/inspect_pywake_modules.py b/03_Code/inspect_pywake_modules.py new file mode 100644 index 0000000..7f0c42f --- /dev/null +++ b/03_Code/inspect_pywake_modules.py @@ -0,0 +1,24 @@ +import pkgutil +import py_wake +print('py_wake version:', getattr(py_wake, '__version__', 'unknown')) +mods = [m.name for m in pkgutil.walk_packages(py_wake.__path__)] +print('modules:', sorted(mods)) + +# Try candidate imports for Points +try: + from py_wake.utils.grid import Points + print('Found Points in py_wake.utils.grid') +except Exception as e: + print('py_wake.utils.grid Points import failed:', repr(e)) +try: + from py_wake.utils.model_utils import Points as MUPoints + print('Found Points in py_wake.utils.model_utils') +except Exception as e: + print('py_wake.utils.model_utils Points import failed:', repr(e)) + +# Try flow_map API +try: + from py_wake.flow_map import FlowMap + print('Found FlowMap in py_wake.flow_map') +except Exception as e: + print('py_wake.flow_map FlowMap import failed:', repr(e)) diff --git a/03_Code/off/controller.py b/03_Code/off/controller.py index 3a95a50..ecdd7e0 100644 --- a/03_Code/off/controller.py +++ b/03_Code/off/controller.py @@ -373,6 +373,7 @@ def __init__(self, settings: dict): elif settings['input_method'] == "yaml": self.lut = np.array(settings['orientation_deg']) self.t = settings['orientation_t'] + # Debug logging removed else: raise Warning("Orientation-input %s is undefined!" % settings['path_to_angles_and_directions_csv']) @@ -401,6 +402,7 @@ def __call__(self, turbine: tur, i_t: int, time_step: float) -> tur: ori = np.interp(time_step, self.t, self.lut[:, i_t]) wind_dir = turbine.ambient_states.get_wind_dir_ind(0) + # Debug logging removed turbine.set_orientation_yaw(ori, wind_dir) def get_applied_settings(self, turbine: tur, i_t: int, time_step: float): diff --git a/03_Code/off/off.py b/03_Code/off/off.py index b7cc44b..d985fb7 100644 --- a/03_Code/off/off.py +++ b/03_Code/off/off.py @@ -56,11 +56,12 @@ def __init__(self, wind_farm: wfm.WindFarm, settings_sim: dict, settings_wke: di settings_wke['sim_dir'] = self.root_dir # =========== FLORIS =========== - # Move the FLORIS.yaml file to the simulation directory. + # Move the FLORIS.yaml file to the simulation directory (only if using FLORIS) # tmp path is updated for reinitialization of the simulation, as a result, the yaml file is only available in the latest simulation folder. - settings_wke['yaml_path'] = self.sim_dir + '/FLORIS.yaml' - shutil.move(settings_wke['tmp_yaml_path'], settings_wke['yaml_path']) - settings_wke['tmp_yaml_path'] = settings_wke['yaml_path'] + if settings_sol["wake_model"].startswith("FLORIS") or settings_sol["wake_model"] == "PythonGaussianWake": + settings_wke['yaml_path'] = self.sim_dir + '/FLORIS.yaml' + shutil.move(settings_wke['tmp_yaml_path'], settings_wke['yaml_path']) + settings_wke['tmp_yaml_path'] = settings_wke['yaml_path'] # =========== Solver =========== # self.wake_solver = ws.FLORIDynTWFWakeSolver(settings_wke, settings_sol) @@ -389,7 +390,7 @@ def get_sim_dir(self) -> str: return self.sim_dir # Print iterations progress - def _print_progress_bar (self, iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = "\r"): + def _print_progress_bar (self, iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '=', printEnd = "\r"): """ Call in a loop to create terminal progress bar @params: diff --git a/03_Code/off/off_interface.py b/03_Code/off/off_interface.py index 9a1eb39..2d29db8 100644 --- a/03_Code/off/off_interface.py +++ b/03_Code/off/off_interface.py @@ -84,14 +84,47 @@ def init_simulation_by_path(self, path_to_yaml: str): # Convert run data into settings and wind farm object self.settings_sim, self.settings_sol, self.settings_wke, self.settings_cor, self.settings_ctr = self._run_yaml_to_dict(sim_info) self.settings_sim['path_to_yaml'] = path_to_yaml + + # CRITICAL: If using PyWake turbine library, populate curves NOW + # (BEFORE creating wind_farm, so Turbine objects can read the curves) + if (self.settings_sol.get("wake_model", "").startswith("PyWake") and + self.settings_wke.get('use_pywake_turbine_library', False)): + + # Get unique turbine types from the wind farm layout + turbine_types = set(sim_info["wind_farm"]["farm"]["turbine_type"]) + + # Populate curves for each turbine type that has pywake_turbine_name + for turbine_type in turbine_types: + turbine_def = self.settings_wke['turbine_library'].get(turbine_type, {}) + pywake_name = turbine_def.get('pywake_turbine_name') + + if pywake_name: + self._populate_pywake_curves( + turbine_type, + pywake_name, + self.settings_wke['turbine_library'], + sim_info["wind_farm"]["farm"] + ) + self.wind_farm = self._run_yaml_to_wind_farm(sim_info) - # Generate an input file for FLORIS - tmp_yaml_path = self._gen_FLORIS_yaml(self.settings_wke, - sim_info["wind_farm"], - sim_info["ambient"], - path_to_yaml.parent) # This might not work on Windows - self.settings_wke.update(dict([('tmp_yaml_path', tmp_yaml_path)])) + # Generate an input file for FLORIS (only if using FLORIS wake model) + if self.settings_sol["wake_model"].startswith("FLORIS"): + tmp_yaml_path = self._gen_FLORIS_yaml(self.settings_wke, + sim_info["wind_farm"], + sim_info["ambient"], + path_to_yaml.parent) # This might not work on Windows + self.settings_wke.update(dict([('tmp_yaml_path', tmp_yaml_path)])) + elif self.settings_sol["wake_model"] == "PyWake": + # PyWake doesn't need a FLORIS yaml file + pass + # PythonGaussianWake also needs FLORIS yaml for parameters + elif self.settings_sol["wake_model"] == "PythonGaussianWake": + tmp_yaml_path = self._gen_FLORIS_yaml(self.settings_wke, + sim_info["wind_farm"], + sim_info["ambient"], + path_to_yaml.parent) + self.settings_wke.update(dict([('tmp_yaml_path', tmp_yaml_path)])) # Visualization settings self.vis = sim_info["vis"] @@ -320,6 +353,7 @@ def _run_yaml_to_dict(self, sim_info: dict) -> tuple: settings_sol = sim_info["solver"]["settings"] settings_wke = sim_info["wake"]["settings"] + settings_wke['turbine_library'] = sim_info["turbine"] # Pass turbine definitions to wake model settings_cor = {'ambient': sim_info["ambient"].get('flow_field', False), 'turbine': sim_info["turbine"].get('feed', False), @@ -331,6 +365,101 @@ def _run_yaml_to_dict(self, sim_info: dict) -> tuple: return settings_sim, settings_sol, settings_wke, settings_cor, settings_ctr + def _populate_pywake_curves(self, turbine_type: str, pywake_turbine_name: str, turbine_library: dict, wind_farm_info: dict): + """ + Populate Cp/Ct curves from PyWake library BEFORE Turbine objects are created. + This is called early in initialization to ensure curves exist when Turbine.__init__ reads them. + + Parameters + ---------- + turbine_type : str + The turbine type key in the YAML (e.g., 'iea22mw') + pywake_turbine_name : str + The PyWake class name to import (e.g., 'IEA_22MW_280_RWT') + """ + import logging as lg + import inspect + from py_wake.wind_turbines import WindTurbine + + lg.info(f'Pre-loading PyWake turbine curves for: {turbine_type} (using {pywake_turbine_name})') + + # Generate possible module names (lowercase variations) + # "DTU10MW" -> ["dtu10mw", "dtu10mw"] + # "IEA_22MW_280_RWT" -> ["iea_22mw_280_rwt", "iea22mw280rwt", "iea22mw", ...] + import re + module_name_lower = pywake_turbine_name.lower() + module_name_no_underscore = pywake_turbine_name.replace('_', '').lower() + # Extract base name (e.g., "IEA_22MW" from "IEA_22MW_280_RWT") + match = re.match(r'([a-zA-Z]+_?\d+(?:mw|kw)?)', pywake_turbine_name, re.IGNORECASE) + module_name_base = match.group(1).lower().replace('_', '') if match else module_name_no_underscore + + # Try possible module names in order of likelihood + module_names = [module_name_base, module_name_no_underscore, module_name_lower] + + turbine_obj = None + for module_name in module_names: + try: + # Import the module package + module = __import__(f'py_wake.examples.data.{module_name}', fromlist=['']) + + # Find all WindTurbine classes in the module + for name, obj in inspect.getmembers(module, inspect.isclass): + if issubclass(obj, WindTurbine) and obj is not WindTurbine: + # Check if class name matches (case-insensitive) + if name.lower() == pywake_turbine_name.lower() or name.lower().replace('_', '') == pywake_turbine_name.lower().replace('_', ''): + turbine_obj = obj() + lg.info(f'Loaded turbine "{pywake_turbine_name}" from py_wake.examples.data.{module_name}.{name}') + break + + if turbine_obj is not None: + break + except (ImportError, AttributeError) as e: + lg.debug(f'Module py_wake.examples.data.{module_name} not found or has no matching turbine') + continue + + if turbine_obj is None: + raise ImportError(f'Cannot load turbine "{pywake_turbine_name}" from PyWake library. Tried modules: {", ".join(module_names)}') + + # Get diameter - use YAML value if available + if turbine_type in turbine_library and 'rotor_diameter' in turbine_library[turbine_type]: + diameter = turbine_library[turbine_type]['rotor_diameter'] + else: + # Get from PyWake or wind farm layout + try: + diameter = turbine_obj.diameter() if callable(getattr(turbine_obj, 'diameter', None)) else turbine_obj.diameter + except Exception: + diameter = wind_farm_info.get('diameter', [178.4])[0] # Default to DTU10MW diameter + + # Extract curves + ws = np.arange(3, 26) + power_w = turbine_obj.power(ws) + ct_values = turbine_obj.ct(ws) + + # Convert to Cp + rho = 1.225 + rotor_area = np.pi * (diameter / 2) ** 2 + cp_values = power_w / (0.5 * rho * rotor_area * ws ** 3) + cp_values = np.clip(cp_values, 0, 0.59) + ct_values = np.clip(ct_values, 0, 1.2) + + # Populate turbine_library + if turbine_type not in turbine_library: + turbine_library[turbine_type] = {} + if 'performance' not in turbine_library[turbine_type]: + turbine_library[turbine_type]['performance'] = {} + + perf = turbine_library[turbine_type]['performance'] + perf['Cp_curve'] = { + 'Cp_u_values': cp_values.tolist(), + 'Cp_u_wind_speeds': ws.tolist() + } + perf['Ct_curve'] = { + 'Ct_u_values': ct_values.tolist(), + 'Ct_u_wind_speeds': ws.tolist() + } + + lg.info(f'Pre-loaded Cp/Ct curves from PyWake: {len(ws)} points') + def _run_yaml_to_wind_farm(self, sim_info: dict) -> wfm.WindFarm: """ Generates wind farm based on loaded yaml information diff --git a/03_Code/off/turbine.py b/03_Code/off/turbine.py index 9a3079d..7fd1a66 100644 --- a/03_Code/off/turbine.py +++ b/03_Code/off/turbine.py @@ -312,7 +312,7 @@ def set_yaw(self, wind_direction: float, yaw: float): self.turbine_states.set_yaw(yaw) lg.debug("Turbine yaw angle set to %s deg, resulting orientation %s deg" % (yaw, self.orientation[0])) - def set_orientation_yaw(self, orientation_yaw: float, wind_direction=orientation): + def set_orientation_yaw(self, orientation_yaw: float, wind_direction=None): """ Sets the orientation of the turbine in yaw direction (opposed to tilt), calculates the effective yaw angle and updates the turbine states @@ -324,7 +324,9 @@ def set_orientation_yaw(self, orientation_yaw: float, wind_direction=orientation Wind direction in deg """ self.orientation[0] = orientation_yaw + # Debug logging removed yaw = self.calc_yaw(wind_direction) + # Debug logging removed self.turbine_states.set_yaw(yaw) lg.debug("Turbine yaw orientation set to %s deg, resulting yaw angle %s deg" % (orientation_yaw, yaw)) @@ -575,8 +577,10 @@ def set_yaw(self, yaw_angle: float): """ if self.n_time_steps > 1: self.states[0, 1] = yaw_angle + # Debug logging removed else: self.states[1] = yaw_angle + # Debug logging removed def get_ct(self, index: int) -> float: """ @@ -608,7 +612,7 @@ def get_ax_ind(self, index: int) -> np.ndarray: if self.n_time_steps > 1: return self.states[index, 0] else: - return self.states[0] + return self.states[0, 0] # For single timestep, access [0, 0] def set_ax_ind(self, ax_ind): """ @@ -639,7 +643,7 @@ def get_yaw(self, index: int) -> float: if self.n_time_steps > 1: return self.states[index, 1] else: - return self.states[1] + return self.states[0, 1] # For single timestep, access [0, 1] def get_all_ct(self) -> np.ndarray: """ @@ -698,7 +702,10 @@ def create_interpolated_state(self, index1: int, index2: int, w1, w2): """ # TODO create check for weights t_s = TurbineStatesFLORIDyn(1) - t_s.set_all_states(self.states[index1, :]*w1 + self.states[index2, :]*w2) + interpolated_value = self.states[index1, :]*w1 + self.states[index2, :]*w2 + # Debug logging removed + # Reshape to (1, n_states) for single timestep + t_s.set_all_states(interpolated_value.reshape(1, -1)) return t_s diff --git a/03_Code/off/wake_model.py b/03_Code/off/wake_model.py index acc1b5f..1ff2a5e 100644 --- a/03_Code/off/wake_model.py +++ b/03_Code/off/wake_model.py @@ -25,6 +25,7 @@ from floris import FlorisModel, TimeSeries import matplotlib.pyplot as plt import yaml +import os from typing import List from .turbine import TurbineStates, AmbientStates @@ -595,4 +596,533 @@ def get_point_vel(self, x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarr return self.fmodel.sample_flow_at_points([x], [y], [z]) else: return self.fmodel.sample_flow_at_points(x, y, z) + + +class PyWakeModel(WakeModel): + """ + Interface to PyWake wake models + """ + + def __init__(self, settings: dict, wind_farm_layout: np.ndarray, turbine_states, ambient_states): + """ + Initialize PyWake model interface + + Parameters + ---------- + settings : dict + Configuration including deficit_model, turbulence_model, wind_farm_model, + site_model, superposition_model, rotor_avg_model, site_ti, site_shear, + and optionally floris_wake which points to a separate YAML config file + wind_farm_layout : np.ndarray + n_t x 4 array with [x,y,z,D] - world coordinates of rotor center & diameter + turbine_states : array of TurbineStates objects + ambient_states : array of AmbientStates objects + """ + super(PyWakeModel, self).__init__(settings, wind_farm_layout, turbine_states, ambient_states) + + # Import PyWake modules + try: + from py_wake.site._site import UniformSite + from py_wake.wind_turbines import WindTurbines + from py_wake.wind_turbines.power_ct_functions import PowerCtTabular + except ImportError as e: + raise ImportError(f"PyWake not installed or import failed: {e}") + + # Load model settings from external YAML file if specified + if 'floris_wake' in settings and settings['floris_wake']: + import off.off as off + yaml_path = os.path.join(off.OFF_PATH, settings['floris_wake']) + lg.info(f'Loading PyWake configuration from: {yaml_path}') + + try: + with open(yaml_path, 'r') as f: + wake_config = yaml.safe_load(f) + + # Extract model_strings from the wake configuration + if 'wake' in wake_config and 'model_strings' in wake_config['wake']: + model_strings = wake_config['wake']['model_strings'] + + # Override settings with values from YAML file + settings = settings.copy() # Don't modify original + settings['wind_farm_model'] = model_strings.get('wind_farm_model', settings.get('wind_farm_model')) + settings['deficit_model'] = model_strings.get('deficit_model', settings.get('deficit_model')) + settings['deflection_model'] = model_strings.get('deflection_model', settings.get('deflection_model')) + settings['turbulence_model'] = model_strings.get('turbulence_model', settings.get('turbulence_model')) + settings['superposition_model'] = model_strings.get('superposition_model', settings.get('superposition_model')) + settings['rotor_avg_model'] = model_strings.get('rotor_avg_model', settings.get('rotor_avg_model')) + settings['site_model'] = model_strings.get('site', settings.get('site_model')) + + lg.info(f'Loaded PyWake models from YAML: deficit={settings["deficit_model"]}, ' + f'deflection={settings.get("deflection_model")}, ' + f'turbulence={settings.get("turbulence_model")}') + else: + lg.warning(f'No wake>model_strings found in {yaml_path}, using settings from main config') + + except FileNotFoundError: + lg.error(f'PyWake config file not found: {yaml_path}') + lg.warning('Falling back to settings from main configuration') + except Exception as e: + lg.error(f'Error loading PyWake config from {yaml_path}: {e}') + lg.warning('Falling back to settings from main configuration') + + lg.info(f'PyWake model initialized with deficit model: {settings.get("deficit_model", "default")}') + + # Store settings + self.site_ti = settings.get('site_ti', 0.06) + self.site_shear = settings.get('site_shear', 0.0) + self.site_model_name = settings.get('site_model', 'UniformSite') + self.deficit_model_name = settings.get('deficit_model', 'BastankhahGaussianDeficit') + self.turbulence_model_name = settings.get('turbulence_model', 'CrespoHernandez') + self.wind_farm_model_name = settings.get('wind_farm_model', 'PropagateDownwind') + self.deflection_model_name = settings.get('deflection_model', 'JimenezWakeDeflection') + self.superposition_model_name = settings.get('superposition_model', 'SquaredSum') + self.rotor_avg_model_name = settings.get('rotor_avg_model', 'RotorCenter') + + # Ensure turbine_library exists in self.settings (the original, not the copy) + # This is critical because OFF Turbine objects read from self.settings['turbine_library'] + if 'turbine_library' not in self.settings: + self.settings['turbine_library'] = {} + self.turbine_library = self.settings['turbine_library'] + + # Support loading turbines from PyWake's built-in library + # Read from self.settings (original) not the local settings copy + self.use_pywake_turbine_library = self.settings.get('use_pywake_turbine_library', False) + # Note: pywake_turbine_name is now specified per turbine type in turbine_library + + # Initialize deficit model + self.deficit_model_class = self._get_deficit_model_class(self.deficit_model_name) + # Initialize turbulence model if specified + self.turbulence_model = self._get_turbulence_model_class(self.turbulence_model_name) + # Initialize deflection model if specified + self.deflection_model = self._get_deflection_model_class(self.deflection_model_name) + # Initialize superposition model + self.superposition_model = self._get_superposition_model_class(self.superposition_model_name) + # Initialize rotor averaging model + self.rotor_avg_model = self._get_rotor_avg_model(self.rotor_avg_model_name) + # Create site using the specified site model + site_class = self._get_site_class(self.site_model_name) + self.site = site_class(ti=self.site_ti, shear=self.site_shear) + + # Initialize wind turbine model (will be updated with actual turbine data) + self.wind_turbines = None + self.wake_model = None + self.shaft_tilt = 5.0 # Default shaft tilt, will be updated from turbine data + + # Note: If using PyWake turbine library, Cp/Ct curves are populated by OFFInterface + # before this __init__() is called, so they're already available in turbine_library + + lg.info('PyWake model interface created.') + + def _import_pywake_component(self, component_type: str, model_name: str, return_instance: bool = True): + """Dynamically import PyWake component by searching common module patterns""" + # Module search patterns for each component type + search_patterns = { + 'deficit': ['py_wake.deficit_models', 'py_wake.literature.gaussian_models'], + 'deflection': ['py_wake.deflection_models'], + 'turbulence': ['py_wake.turbulence_models'], + 'superposition': ['py_wake.superposition_models'], + 'wind_farm': ['py_wake.wind_farm_models'], + 'site': ['py_wake.site._site', 'py_wake.site'] + } + + if component_type not in search_patterns: + raise ValueError(f"Unknown component type: {component_type}") + + # Try to dynamically find and import the model + for base_module in search_patterns[component_type]: + try: + # Import base module and search for the model + module = __import__(base_module, fromlist=['']) + if hasattr(module, model_name): + component_class = getattr(module, model_name) + return component_class() if return_instance else component_class + + # For structured modules, search submodules + for submodule_name in dir(module): + if not submodule_name.startswith('_'): + try: + submodule = getattr(module, submodule_name) + if hasattr(submodule, model_name): + component_class = getattr(submodule, model_name) + return component_class() if return_instance else component_class + except (AttributeError, TypeError): + continue + except ImportError: + continue + + # If not found, raise clear error + raise ImportError( + f"Could not find {component_type} model '{model_name}' in PyWake. " + f"Searched in: {', '.join(search_patterns[component_type])}. " + f"Please check your YAML configuration." + ) + + def _get_rotor_avg_model(self, model_spec: str): + """Parse and instantiate PyWake rotor averaging model (e.g., 'CGIRotorAvg(9)')""" + import re + + try: + # Parse model specification: ModelName(param1, param2, ...) + match = re.match(r'([A-Za-z0-9_]+)(?:\((.*)\))?', str(model_spec).strip()) + if not match: + raise ValueError(f"Could not parse '{model_spec}'") + + model_name, params_str = match.group(1), match.group(2) + + # Import from py_wake.rotor_avg_models + from py_wake.rotor_avg_models import rotor_avg_model + if not hasattr(rotor_avg_model, model_name): + raise AttributeError(f"Model '{model_name}' not found") + + model_class = getattr(rotor_avg_model, model_name) + + # Parse and convert parameters if provided + if params_str: + args = [] + for p in params_str.split(','): + try: + args.append(int(p.strip())) + except ValueError: + try: + args.append(float(p.strip())) + except ValueError: + args.append(p.strip()) + return model_class(*args) + return model_class() + + except (ImportError, AttributeError, TypeError, ValueError) as e: + lg.warning(f"Failed to instantiate rotor averaging model '{model_spec}': {e}, using RotorCenter") + from py_wake.rotor_avg_models import RotorCenter + return RotorCenter() + + def _get_superposition_model_class(self, model_name: str): + """Get PyWake superposition model instance""" + return self._import_pywake_component('superposition', model_name, return_instance=True) + + def _get_site_class(self, model_name: str): + """Get PyWake site class""" + return self._import_pywake_component('site', model_name, return_instance=False) + + def _get_deficit_model_class(self, model_name: str): + """Get PyWake deficit model class""" + return self._import_pywake_component('deficit', model_name, return_instance=False) + + def _get_deflection_model_class(self, model_name: str): + """Get PyWake deflection model instance (optional, returns None if not found)""" + try: + return self._import_pywake_component('deflection', model_name, return_instance=True) + except ImportError as e: + lg.warning(f"Deflection model '{model_name}' not found: {e}. Continuing without deflection.") + return None + + def _get_turbulence_model_class(self, model_name: str): + """Get PyWake turbulence model instance (optional, returns None if not found)""" + try: + return self._import_pywake_component('turbulence', model_name, return_instance=True) + except ImportError as e: + lg.warning(f"Turbulence model '{model_name}' not found: {e}. Continuing without turbulence.") + return None + + def _get_wind_farm_model_class(self, model_name: str): + """Get PyWake wind farm model class""" + return self._import_pywake_component('wind_farm', model_name, return_instance=False) + + def _load_turbine_from_pywake_library(self, turbine_name: str): + """ + Load turbine from PyWake's built-in library for use with PyWake wake models. + Note: Cp/Ct curves should already be populated in turbine_library by OFFInterface + + Returns + ------- + WindTurbine + PyWake WindTurbine object ready to use with wake models + """ + # turbine_library was already populated in __init__, just load the turbine object + lg.info(f'Loading PyWake turbine object: {turbine_name}') + + import inspect + from py_wake.wind_turbines import WindTurbine + + # Generate possible module names (lowercase variations) + # "DTU10MW" -> ["dtu10mw"] + # "IEA_22MW_280_RWT" -> ["iea22mw", "iea22mw280rwt", ...] + import re + module_name_lower = turbine_name.lower() + module_name_no_underscore = turbine_name.replace('_', '').lower() + # Extract base name (e.g., "IEA_22MW" from "IEA_22MW_280_RWT") + match = re.match(r'([a-zA-Z]+_?\d+(?:mw|kw)?)', turbine_name, re.IGNORECASE) + module_name_base = match.group(1).lower().replace('_', '') if match else module_name_no_underscore + + # Try possible module names in order of likelihood + module_names = [module_name_base, module_name_no_underscore, module_name_lower] + + turbine_obj = None + for module_name in module_names: + try: + # Import the module package + module = __import__(f'py_wake.examples.data.{module_name}', fromlist=['']) + + # Find all WindTurbine classes in the module + for name, obj in inspect.getmembers(module, inspect.isclass): + if issubclass(obj, WindTurbine) and obj is not WindTurbine: + # Check if class name matches (case-insensitive) + if name.lower() == turbine_name.lower() or name.lower().replace('_', '') == turbine_name.lower().replace('_', ''): + turbine_obj = obj() + lg.info(f'Loaded turbine "{turbine_name}" from py_wake.examples.data.{module_name}.{name}') + break + + if turbine_obj is not None: + break + except (ImportError, AttributeError) as e: + lg.debug(f'Module py_wake.examples.data.{module_name} not found or has no matching turbine') + continue + + if turbine_obj is None: + raise ImportError(f'Cannot load turbine "{turbine_name}" from PyWake library. Tried modules: {", ".join(module_names)}') + + # Read shaft_tilt from turbine_library (YAML value) + if self.turbine_library: + turbine_key = list(self.turbine_library.keys())[0] + turbine_data = self.turbine_library[turbine_key] + self.shaft_tilt = turbine_data.get('shaft_tilt', 5.0) # Default to 5 degrees if not specified + + return turbine_obj + + def _create_wind_turbine_from_yaml(self): + """Create PyWake wind turbine from YAML turbine library""" + from py_wake.wind_turbines import WindTurbines + from py_wake.wind_turbines.power_ct_functions import PowerCtTabular + + # Get turbine type from first turbine (assuming homogeneous farm) + # TODO: Support heterogeneous farms with different turbine types + turbine_type = list(self.turbine_library.keys())[0] + turbine_data = self.turbine_library[turbine_type] + + # Extract parameters from YAML + D = self.wind_farm_layout[0, 3] + hub_height = turbine_data['hub_height'] + self.shaft_tilt = turbine_data.get('shaft_tilt', 5.0) # Default to 5 degrees if not specified + ws = np.array(turbine_data['performance']['Ct_curve']['Ct_u_wind_speeds']) + ct = np.array(turbine_data['performance']['Ct_curve']['Ct_u_values']) + cp = np.array(turbine_data['performance']['Cp_curve']['Cp_u_values']) + + # Calculate power curve from Cp + power_curve = 0.5 * 1.225 * (np.pi * (D/2)**2) * ws**3 * cp / 1e6 # Power in MW + + self.wind_turbines = WindTurbines( + names=[turbine_type], + diameters=[D], + hub_heights=[hub_height], + powerCtFunctions=[PowerCtTabular(ws, power_curve, 'MW', ct)] + ) + return self.wind_turbines + + def set_wind_farm(self, wind_farm_layout: np.ndarray, turbine_states, ambient_states): + """Update wind farm layout and turbine/ambient states""" + self.wind_farm_layout = wind_farm_layout + self.turbine_states = turbine_states + self.ambient_states = ambient_states + + # Create wind turbine model + # Check if we should use PyWake's built-in library or YAML definitions + if self.use_pywake_turbine_library: + # Find a turbine type that has pywake_turbine_name defined + # (currently assumes all turbines are the same type) + pywake_turbine_name = None + turbine_name = None + + for t_name, t_data in self.turbine_library.items(): + if 'pywake_turbine_name' in t_data: + pywake_turbine_name = t_data['pywake_turbine_name'] + turbine_name = t_name + break + + if pywake_turbine_name: + lg.info(f'Loading turbine from PyWake library: {turbine_name} -> {pywake_turbine_name}') + self.wind_turbines = self._load_turbine_from_pywake_library(pywake_turbine_name) + else: + lg.warning('No pywake_turbine_name found in turbine_library. Using YAML definitions.') + self._create_wind_turbine_from_yaml() + else: + lg.info('Creating turbine from YAML definitions') + self._create_wind_turbine_from_yaml() + + # Initialize wake model + # PyWake distinguishes between literature models (complete wind farm models) + # and deficit models (components that need wrapping in a wind farm model) + # Detect this by checking if the model is from the literature module + + is_literature_model = 'literature' in self.deficit_model_class.__module__ + + if is_literature_model: + # Literature model - complete wake model + kwargs = { + 'site': self.site, + 'windTurbines': self.wind_turbines + } + if self.turbulence_model: + kwargs['turbulenceModel'] = self.turbulence_model + if self.deflection_model: + kwargs['deflectionModel'] = self.deflection_model + self.wake_model = self.deficit_model_class(**kwargs) + else: + # Deficit model component - wrap in wind farm model + self.wake_model = self._get_wind_farm_model_class(self.wind_farm_model_name)( + site=self.site, + windTurbines=self.wind_turbines, + wake_deficitModel=self.deficit_model_class(), + superpositionModel=self.superposition_model, + rotorAvgModel=self.rotor_avg_model, + turbulenceModel=self.turbulence_model, + deflectionModel=self.deflection_model + ) + + def get_measurements_i_t(self, i_t: int) -> tuple: + """Get effective wind speed and measurements for turbine i_t""" + if self.wake_model is None: + raise RuntimeError("Wake model not initialized. Call set_wind_farm first.") + + # Run wake simulation + sim_res = self._run_wake_simulation() + + # Extract results for turbine i_t + WS_eff = sim_res.WS_eff.values.flatten()[i_t] + TI_eff = sim_res.TI_eff.values.flatten()[i_t] + Power = sim_res.Power.values.flatten()[i_t] + CT = sim_res.CT.values.flatten()[i_t] + + # Calculate axial induction factor from thrust coefficient + # Using momentum theory: CT = 4a(1-a) + # Solving for a: a = 0.5 * (1 - sqrt(1 - CT)) + # For CT > 1 (Glauert region), use empirical correction + if CT < 0.96: + AI = 0.5 * (1 - np.sqrt(1 - CT)) + else: + # Empirical correction for high thrust (CT > 0.96) + AI = 1.0 / (2.0 - CT) + + # Store measurements in pandas dataframe + measurements = pd.DataFrame( + [[ + i_t, + WS_eff, + CT, + AI, + TI_eff, + Power + ]], + columns=['t_idx', 'u_abs_eff_PyWake', 'Ct_PyWake', 'AI_PyWake', 'TI_PyWake', 'Power_PyWake'] + ) + + return WS_eff, measurements + + def vis_flow_field(self): + """Visualize wind farm flow field using PyWake""" + if self.wake_model is None: + lg.warning("Wake model not initialized, cannot visualize") + return + + # Run wake simulation + sim_res = self._run_wake_simulation() + + # Get ambient conditions for plotting + wind_speed = self.ambient_states[0].get_turbine_wind_speed_abs() + wind_direction = self.ambient_states[0].get_turbine_wind_dir() + + # Plot horizontal plane at hub height + import matplotlib.pyplot as plt + fig, ax = plt.subplots() + sim_res.flow_map(wd=wind_direction, ws=wind_speed).plot_wake_map(ax=ax) + plt.title("PyWake Flow Field") + plt.show() + + def compute_wake_flow_map(self, x_grid: np.ndarray, y_grid: np.ndarray): + """Compute PyWake flow map on horizontal grid""" + if self.wake_model is None: + raise RuntimeError("Wake model not initialized. Call set_wind_farm first.") + + # Run wake simulation + sim_res = self._run_wake_simulation() + + try: + from py_wake import HorizontalGrid + # Don't pass wd and ws parameters - they cause PyWake to recalculate + # without the yaw angles, losing deflection effects! + fm = sim_res.flow_map(grid=HorizontalGrid(x=x_grid, y=y_grid)) + return fm + except Exception: + lg.exception("Failed to compute PyWake flow_map on provided grid") + raise + + def _run_wake_simulation(self): + """Run PyWake simulation with current turbine states and ambient conditions""" + # Get ambient conditions + wind_speed = self.ambient_states[0].get_turbine_wind_speed_abs() + wind_direction = self.ambient_states[0].get_turbine_wind_dir() + + # Get yaw angles from turbine states + n_t = len(self.turbine_states) + yaw_angles = np.array([self.turbine_states[ii_t].get_current_yaw() for ii_t in range(n_t)]) + + # Reshape yaw and tilt for PyWake (n_turbines, n_wd, n_ws) = (n_t, 1, 1) + yaw_in = yaw_angles.reshape(n_t, 1, 1) + tilt_in = self.shaft_tilt * np.ones(n_t).reshape(n_t, 1, 1) + + # Run wake model simulation + return self.wake_model( + x=self.wind_farm_layout[:, 0], + y=self.wind_farm_layout[:, 1], + wd=wind_direction, + ws=wind_speed, + yaw=yaw_in, + tilt=tilt_in + ) + + def _sample_flow_at_points(self, x, y, z): + """Sample PyWake flow at specified points (internal helper method)""" + # Run wake simulation + sim_res = self._run_wake_simulation() + + # Convert inputs to arrays and handle z broadcasting + x_arr = np.atleast_1d(np.array(x, dtype=float)) + y_arr = np.atleast_1d(np.array(y, dtype=float)) + z_arr = np.atleast_1d(np.array(z, dtype=float)) + if z_arr.size == 1: + z_arr = np.full_like(x_arr, z_arr[0]) + + # Sample using PyWake Points + from py_wake.flow_map import Points + grid = Points(x_arr, y_arr, z_arr) + fm = sim_res.flow_map(grid) + ws_eff = fm.WS_eff.values.flatten() + + # Return in [3 x n_points] format: [u, v, w] + result = np.zeros((3, x_arr.size)) + result[0, :] = ws_eff + return result + + def get_point_vel(self, x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """Get velocity at specific points in the flow field""" + if self.wake_model is None: + lg.warning("Wake model not initialized, returning ambient wind speed") + n_points = np.size(x) + result = np.ones((3, n_points)) * self.ambient_states[0].get_turbine_wind_speed_abs() if self.ambient_states else np.zeros((3, n_points)) + result[1:, :] = 0 + return result + return self._sample_flow_at_points(x, y, z) + + def vis_tile(self, x: np.ndarray, y: np.ndarray, z: np.ndarray) -> np.ndarray: + """Get datapoints to visualize the turbine wake field""" + if self.wake_model is None: + lg.warning("Wake model not initialized, returning zeros") + return np.zeros((3, np.size(x))) + + try: + return self._sample_flow_at_points(x, y, z) + except Exception: + lg.exception("vis_tile PyWake flow_map sampling failed; using ambient fallback") + wind_speed = self.ambient_states[0].get_turbine_wind_speed_abs() + result = np.zeros((3, np.size(x))) + result[0, :] = wind_speed + return result diff --git a/03_Code/off/wake_solver.py b/03_Code/off/wake_solver.py index 542d689..8617e03 100644 --- a/03_Code/off/wake_solver.py +++ b/03_Code/off/wake_solver.py @@ -268,7 +268,7 @@ def _get_wind_speeds_location(self, loc: np.ndarray, wind_farm: wfm.WindFarm) -> raise NotImplementedError("_get_wind_speeds_location() is not implemented in the base class, please implement it in the derived class.") def vis_OP_mountains(self, wind_farm: wfm.WindFarm, sim_dir, t): - """ + r"""" Goes through all OPs and plots the wind speed along the OP location Creates a plot like this ASCI art: | | | | | | @@ -412,6 +412,99 @@ def vis_OP_mountains(self, wind_farm: wfm.WindFarm, sim_dir, t): np.savetxt(sim_dir + "/mountain_plot_v_" + str(int(t)).zfill(6) + "s.csv", data_v, delimiter=',') + # # Also create a WS_eff contour map at hub height with OP lines overlay + # try: + # # Build grid from visualization settings + # x_bounds = self.settings_vis["grid"]["boundaries"][0] + # y_bounds = self.settings_vis["grid"]["boundaries"][1] + # nx = self.settings_vis["grid"]["resolution"][0] + # ny = self.settings_vis["grid"]["resolution"][1] + + # x_grid = np.linspace(x_bounds[0], x_bounds[1], nx) + # y_grid = np.linspace(y_bounds[0], y_bounds[1], ny) + + # # Scale to meters if unit is 'D' + # if self.settings_vis["grid"]["unit"][0] == 'D': + # D = self.settings_vis["grid"]["diameter"][0] + # x_grid = x_grid * D + # y_grid = y_grid * D + + # # Ensure PyWake wake model is ready (set wind farm states via rotor-plane query) + # _ = self._get_wind_speeds_rp(0, wind_farm) + + # # Compute flow map via PyWake adapter + # fm = self.floris_wake.compute_wake_flow_map(x_grid, y_grid) + + # # Helper to bilinearly sample WS_eff from flow_map grid + # def _sample_ws_from_flow_map(flow_map, gx, gy, xs, ys): + # Zgrid = np.asarray(flow_map.WS_eff.values) + # Zgrid = np.squeeze(Zgrid) + # # Normalize grid orientation to (len(gy), len(gx)) + # if Zgrid.ndim != 2: + # raise ValueError("FlowMap WS_eff returned array with unexpected shape") + # if Zgrid.shape == (len(gx), len(gy)): + # Zgrid = Zgrid.T + # elif Zgrid.shape != (len(gy), len(gx)): + # raise ValueError("FlowMap WS_eff grid shape mismatch") + + # x_flat = np.array(xs, dtype=float).reshape(-1) + # y_flat = np.array(ys, dtype=float).reshape(-1) + # nx = len(gx) + # ny = len(gy) + # ix = np.clip(np.searchsorted(gx, x_flat) - 1, 0, nx - 2) + # iy = np.clip(np.searchsorted(gy, y_flat) - 1, 0, ny - 2) + # x0 = gx[ix] + # x1 = gx[ix + 1] + # y0 = gy[iy] + # y1 = gy[iy + 1] + # denom_x = x1 - x0 + # denom_y = y1 - y0 + # tx = np.divide(x_flat - x0, denom_x, out=np.zeros_like(denom_x, dtype=float), where=denom_x != 0) + # ty = np.divide(y_flat - y0, denom_y, out=np.zeros_like(denom_y, dtype=float), where=denom_y != 0) + # f00 = Zgrid[iy, ix] + # f10 = Zgrid[iy, ix + 1] + # f01 = Zgrid[iy + 1, ix] + # f11 = Zgrid[iy + 1, ix + 1] + # return (1 - tx) * (1 - ty) * f00 + tx * (1 - ty) * f10 + (1 - tx) * ty * f01 + tx * ty * f11 + + # # Plot + # fig2, ax2 = plt.subplots() + # fm.plot_wake_map(ax=ax2) + + # # Overlay OP lines (center points already computed) + # # Draw thin lines along each OP mountain line for context + # for i in range(0, data_x.shape[0]): + # ax2.plot(data_x[i, :], data_y[i, :], color='#ec6842', linewidth=0.8, alpha=0.9) + + # # Note: data_u and data_v already contain accurate point-sampled wake velocities from vis_tile() + # # No need to resample from coarse flow_map grid - the point sampling is more accurate + + # # Overlay yawed turbines + # for i_t, tur in enumerate(wind_farm.turbines): + # x = tur.base_location[0] + # y = tur.base_location[1] + # yaw = ot.ot_deg2rad(tur.get_yaw_orientation()) + # ax2.plot([x - 0.5 * tur.diameter * np.sin(yaw), x + 0.5 * tur.diameter * np.sin(yaw)], + # [y + 0.5 * tur.diameter * np.cos(yaw), y - 0.5 * tur.diameter * np.cos(yaw)], + # color='black', linewidth=1.5) + # ax2.plot([x, x + 0.2 * tur.diameter * np.cos(yaw)], + # [y, y + 0.2 * tur.diameter * np.sin(yaw)], + # color='black', linewidth=1.5) + + # ax2.set_aspect('equal') + # ax2.set_title('WS_eff contour with OP lines') + # ax2.set_xlabel('x (m)') + # ax2.set_ylabel('y (m)') + # scale_grid = 1 if self.settings_vis["grid"]["unit"][0] != 'D' else self.settings_vis["grid"]["diameter"][0] + # ax2.set_xlim(self.settings_vis["grid"]["boundaries"][0][0] * scale_grid, + # self.settings_vis["grid"]["boundaries"][0][1] * scale_grid) + # ax2.set_ylim(self.settings_vis["grid"]["boundaries"][1][0] * scale_grid, + # self.settings_vis["grid"]["boundaries"][1][1] * scale_grid) + # plt.savefig(sim_dir + "/wake_map_at_" + str(int(t)).zfill(6) + "s.png") + # plt.close(fig2) + # except Exception: + # lg.exception("Failed to generate WS_eff contour map; continuing with mountain plot only") + # Don't plot if the 3d data has been collected if self.settings_vis["flow_field_plots"]["mountains_3d"]: @@ -508,8 +601,11 @@ def __init__(self, settings_wke: dict, settings_sol: dict, settings_vis: dict): self.floris_wake = wm.Floris4Wake(settings_wke, np.array([]), np.array([]), np.array([])) elif settings_sol["wake_model"] == "PythonGaussianWake": self.floris_wake = wm.PythonGaussianWake(settings_wke, np.array([]), np.array([]), np.array([])) + elif settings_sol["wake_model"].startswith("PyWake"): + self.floris_wake = wm.PyWakeModel(settings_wke, np.array([]), np.array([]), np.array([])) + lg.info(f'PyWake model initialized: {settings_wke.get("deficit_model", "default")}') else: - raise ImportError('Wake model unknown!') + raise ImportError(f'Wake model unknown: {settings_sol["wake_model"]}!') def get_measurements(self, i_t: int, wind_farm: wfm.WindFarm) -> tuple: """ diff --git a/03_Code/project_tomato.py b/03_Code/project_tomato.py new file mode 100644 index 0000000..1e8ac95 --- /dev/null +++ b/03_Code/project_tomato.py @@ -0,0 +1,132 @@ + + + +import os, logging +logging.basicConfig(level=logging.ERROR) + +import off.off as off +import off.off_interface as offi +import numpy as np +import sqlite3 +import shutil +from pathlib import Path +OFF_PATH: Path = Path(off.OFF_PATH) + +name_database = 'off_simulations_database' +filename_database = f'{name_database}.db' + +yaw_rate = 0.3 # deg/s +path_to_output = Path.cwd() / (name_database + '_results') +path_to_db = Path.cwd() / filename_database +path_to_WindDirOffset = Path.cwd() / '02_Examples_and_Cases' / '02_Example_Cases' / '005_WindDirOffsetData' + + +# connect to the SQLite database +if not os.path.exists(path_to_db): + print('Database does not exist, aborting') + exit() + + +def retrieve_settings_from_db(db_path): + """ + Retrieve the first entry from the database where sim_done is 0. + + Parameters: + db_path (str): Path to the SQLite database file. + + Returns: + tuple: A tuple containing the wind direction, yaw start, yaw end, and yaw sigma. + """ + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Read the variables test_wind_direction, test_yaw_start, test_yaw_end, test_yaw_sigma from a random entry from the database where sim_done is 0 + cursor.execute(f'SELECT * FROM {name_database} WHERE sim_done = 0 ORDER BY RANDOM() LIMIT 1') + row = cursor.fetchone() + + if row: + # Set sim_done to 1 for the first entry + cursor.execute(f'UPDATE {name_database} SET sim_done = 1 WHERE rowid = ?', (row[0],)) + conn.commit() + conn.close() + return row[0], row[2], row[3], row[4], row[5] + else: + print('No remaining simulation found') + exit() + + # Close the database connection + conn.close() + return None + +def main(): + + # Create output directory if it does not exist + if not os.path.exists(path_to_output): + os.makedirs(path_to_output) + + while True: + # Retrieve the next set of simulation parameters from the database + simID, test_wind_direction, test_yaw_start, test_yaw_end, test_sigma = retrieve_settings_from_db(path_to_db) + + if test_wind_direction is None: + print('No remaining simulation found') + break + + print(f'Running simulation for wind direction {test_wind_direction} deg, yaw start {test_yaw_start} deg, yaw end {test_yaw_end} deg, sigma {test_sigma} deg') + + # Retrieve Wind Dir Offsets + wind_dir_offset_data = np.loadtxt(path_to_WindDirOffset / f'WindDirOffset_std_{test_sigma:.0f}_len_1800s.txt', delimiter=',') + wind_dir_t = wind_dir_offset_data[0, :] # Time in seconds + wind_dir_t += 100 # Offset to start at t=100s + + for i in range(1, wind_dir_offset_data.shape[0]): + for ii in range(1, 2): + wind_dir_sign = 1 if ii == 1 else -1 # Sign for the wind direction offset + # Create an interface object + # The interface object does mot yet know the simulation environment, it only checks requirements + oi = offi.OFFInterface() + + # Tell the simulation what to run + # The run file needs to contain everything, the wake model, the ambient conditions etc. + # Example case + oi.init_simulation_by_path(OFF_PATH / '02_Examples_and_Cases' / '02_Example_Cases' / '001_two_turbines_yaw_step.yaml') + + print('Created simulation object') + delta_t = abs(test_yaw_end - test_yaw_start) / yaw_rate + + # Get and modify controller setpoints + control_data = oi.settings_ctr + control_data["orientation_deg"] = [[test_wind_direction - test_yaw_start, test_wind_direction], [test_wind_direction - test_yaw_start, test_wind_direction], [test_wind_direction - test_yaw_end, test_wind_direction], [test_wind_direction - test_yaw_end, test_wind_direction]] + control_data["orientation_t"] = [0.0, 100.0, 100.0 + delta_t, 2000.0] + + # Get and modify wind direction setpoints + ambient_data = oi.settings_cor + ambient_data["ambient"]['wind_directions'] = np.hstack((test_wind_direction, wind_dir_sign*wind_dir_offset_data[i, :] + test_wind_direction)).tolist() + ambient_data["ambient"]['wind_directions_t'] = np.hstack(([0],wind_dir_t)).tolist() + + oi.init_simulation_by_dicts(settings_ctr=control_data, settings_cor=ambient_data) + oi.create_off_simulation(yaw=test_yaw_start) # Initialize both turbines with the misalignment angle, but T2 changes back to 0 + + # Run the simulation + oi.run_sim() + + # Get the power output and save it in a file + results = oi.get_measurements() + + # Append the power output and yaw misalignment angle to a CSV file + data = np.concatenate((np.array([simID, i*wind_dir_sign, test_yaw_end, test_yaw_start, delta_t, test_wind_direction, test_sigma]), results['Power_FLORIS']),axis=0) + + output_file = os.path.join(path_to_output, f'Results_sim_{simID}.csv') + + if not os.path.isfile(output_file): + np.savetxt(output_file, data[np.newaxis], delimiter=',', comments='') + else: + with open(output_file, 'ab') as f: + np.savetxt(f, data[np.newaxis], delimiter=',') + + # Cleanup the simulation folder + shutil.rmtree(oi.off_sim.sim_dir) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/99_Design/02_Presentation/.DS_Store b/99_Design/02_Presentation/.DS_Store deleted file mode 100644 index 5008ddf..0000000 Binary files a/99_Design/02_Presentation/.DS_Store and /dev/null differ diff --git a/99_Design/99_Ideas/README.md b/99_Design/99_Ideas/README.md old mode 100755 new mode 100644 diff --git a/OFF/03_Code/createSimulationsDB.py b/OFF/03_Code/createSimulationsDB.py new file mode 100644 index 0000000..3ff448a --- /dev/null +++ b/OFF/03_Code/createSimulationsDB.py @@ -0,0 +1,181 @@ +### Example script to create and manage a SQLite database for storing simulation settings. + +import sqlite3 +import numpy as np +import os +from pathlib import Path + +def count_simulations(db_path): + """ + Count the number of simulations in the database. + + Parameters: + db_path (str): Path to the SQLite database file. + + Returns: + int: Number of simulations in the database. + """ + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + cursor.execute('SELECT COUNT(*) FROM off_simulations_database') + count = cursor.fetchone()[0] + + conn.close() + return count + +def count_remaining_simulations(db_path): + """ + Count the number of remaining simulations in the database where sim_done is 0. + + Parameters: + db_path (str): Path to the SQLite database file. + + Returns: + int: Number of remaining simulations in the database. + """ + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + cursor.execute('SELECT COUNT(*) FROM off_simulations_database WHERE sim_done = 0') + count = cursor.fetchone()[0] + + conn.close() + return count + +def reset_simulation(db_path, id): + """ + Reset the simulation with the given ID in the database. + + Parameters: + db_path (str): Path to the SQLite database file. + id (int): ID of the simulation to reset. + """ + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + cursor.execute('UPDATE off_simulations_database SET sim_done = 0 WHERE rowid = ?', (id,)) + conn.commit() + conn.close() + +def create_simulations_db(db_path): + """ + Create a SQLite database for storing simulation results. + + Parameters: + db_path (str): Path to the SQLite database file. + """ + + + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Create table for simulations + cursor.execute(''' + CREATE TABLE IF NOT EXISTS off_simulations_database ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + sim_done BOOLEAN DEFAULT 0, + test_wind_direction REAL, + test_yaw_start REAL, + test_yaw_end REAL, + test_sigma INTEGER + ) + ''') + + conn.commit() + conn.close() + +def insert_simulation(db_path, wind_direction, yaw_start, yaw_end, sigma): + """ + Insert a simulation record into the database. + + Parameters: + db_path (str): Path to the SQLite database file. + wind_direction (float): Wind direction for the simulation. + yaw_start (float): Starting yaw angle for the simulation. + yaw_end (float): Ending yaw angle for the simulation. + sigma (float): Yaw sigma value for the simulation. + """ + + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + cursor.execute(''' + INSERT INTO off_simulations_database (test_wind_direction, test_yaw_start, test_yaw_end, test_sigma) + VALUES (?, ?, ?, ?) + ''', (wind_direction, yaw_start, yaw_end, sigma)) + + conn.commit() + conn.close() + +def check_if_entry_exists(db_path, wind_direction, yaw_start, yaw_end, sigma): + """ + Check if a simulation entry with the given parameters already exists in the database. + + Parameters: + db_path (str): Path to the SQLite database file. + wind_direction (float): Wind direction for the simulation. + yaw_start (float): Starting yaw angle for the simulation. + yaw_end (float): Ending yaw angle for the simulation. + sigma (float): Yaw sigma value for the simulation. + + Returns: + bool: True if the entry exists, False otherwise. + """ + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + cursor.execute(''' + SELECT COUNT(*) FROM off_simulations_database + WHERE test_wind_direction = ? AND test_yaw_start = ? AND test_yaw_end = ? AND test_sigma = ? + ''', (wind_direction, yaw_start, yaw_end, sigma)) + + exists = cursor.fetchone()[0] > 0 + + conn.close() + return exists + + +def main(db_path): + + # Create the database if needed + if not os.path.exists(db_path): + create_simulations_db(db_path) + + # Define test parameters + test_wind_directions = np.arange(250, 291, 20) # deg + test_yaw_start = np.arange(-30, 31, 30) # deg + test_yaw_end = np.arange(-30, 31, 15) # deg + test_sigma = np.array([2,5]) # deg + + # Insert test data into the database + for wind_direction in test_wind_directions: + for yaw_start in test_yaw_start: + for yaw_end in test_yaw_end: + for sigma in test_sigma: + if not check_if_entry_exists(db_path, wind_direction, yaw_start, yaw_end, sigma): + insert_simulation(db_path, + float(wind_direction), + float(yaw_start), + float(yaw_end), + int(sigma)) + + print(f"Database '{db_path}' created and populated with simulation data.") + + +if __name__ == "__main__": + # Define the path to the database + # If the database does not exist, it will be created. If it already exists, it will be used as is. + db_path = Path.cwd() / 'off_simulations_database.db' + + # Reset specific simulations by their IDs, if simulations were not successfully completed. + to_reset = [] # IDs to reset + + main(db_path) + + for id in to_reset: + reset_simulation(db_path, id) + + print(f'Current number of simulations in the database: {count_simulations(db_path)}') + print(f'Current number of remaining simulations in the database: {count_remaining_simulations(db_path)}') + print(f'Percentage of remaining simulations: {count_remaining_simulations(db_path) / count_simulations(db_path) * 100:.2f}%') \ No newline at end of file diff --git a/OFF/03_Code/main.py b/OFF/03_Code/main.py new file mode 100644 index 0000000..31c2ac1 --- /dev/null +++ b/OFF/03_Code/main.py @@ -0,0 +1,68 @@ +# //////////////////////////////////////////////////////////////////// # +# ____ ______ ______ +# / __ \| ____| ____| +# | | | | |__ | |__ +# | | | | __| | __| +# | |__| | | | | +# \____/|_| |_| +# //////////////////////////////////////////////////////////////////// # + +# Copyright (C) <2024>, M Becker (TUDelft), M Lejeune (UCLouvain) + +# List of the contributors to the development of OFF: see LICENSE file. +# Description and complete License: see LICENSE file. + +# This program (OFF) is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU Affero General Public License +# along with this program (see COPYING file). If not, see . + +# //////////////////////////////////////////////////////////////////// # +# Welcome to the example OFF main file. This showcases how to run a simulation using the OFF framework. +# The settings are defined in the run_example.yaml file, have a look to see what is possible. +# If you experience issues, create a new issue on the GitHub page https://github.com/TUDelft-DataDrivenControl/OFF +# //////////////////////////////////////////////////////////////////// # + +import os, logging +logging.basicConfig(level=logging.ERROR) + +import off.off as off +import off.off_interface as offi +import time +from pathlib import Path +OFF_PATH: Path = Path(off.OFF_PATH) + +def main(): + start_time = time.time() + + # Create an interface object + # The interface object does mot yet know the simulation environment, it only checks requirements + oi = offi.OFFInterface() + + # Tell the simulation what to run + # The run file needs to contain everything, the wake model, the ambient conditions etc. + # Example case + oi.init_simulation_by_path( OFF_PATH / "02_Examples_and_Cases" / "02_Example_Cases" / "001_two_turbines_yaw_step.yaml" ) + + # Run the simulation + oi.run_sim() + + print("\n---OFF Simulation took %s seconds ---" % (time.time() - start_time)) + + # Store output + oi.store_measurements() + oi.store_applied_control() + oi.store_run_file() + + +if __name__ == "__main__": + main() + diff --git a/README.md b/README.md old mode 100755 new mode 100644 diff --git a/analyse_OFFpy.ipynb b/analyse_OFFpy.ipynb new file mode 100644 index 0000000..75292c3 --- /dev/null +++ b/analyse_OFFpy.ipynb @@ -0,0 +1,775 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "12468403", + "metadata": {}, + "outputs": [], + "source": [ + "import py_wake \n", + "\n", + "# setup site, wind turbines and wind farm model with the corresponding wake models\n", + "import numpy as np\n", + "import ipywidgets \n", + "from ipywidgets import interact\n", + "from ipywidgets import IntSlider\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from py_wake.flow_map import HorizontalGrid\n", + "from py_wake.deficit_models.gaussian import BastankhahGaussianDeficit, BlondelSuperGaussianDeficit2020\n", + "from py_wake.deflection_models import JimenezWakeDeflection\n", + "from py_wake.turbulence_models import CrespoHernandez\n", + "from py_wake.utils.plotting import setup_plot\n", + "from py_wake.wind_turbines import WindTurbine\n", + "from py_wake.wind_turbines.power_ct_functions import PowerCtFunction, PowerCtTabular\n", + "from py_wake.site import UniformWeibullSite\n", + "from py_wake.rotor_avg_models import CGIRotorAvg\n", + "from py_wake.superposition_models import SquaredSum\n", + "from py_wake.flow_map import XYGrid\n", + "from py_wake.wind_farm_models import PropagateDownwind, All2AllIterative" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6ea5861e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define turbine iea_10MW\n", + "Cp_u_values = [0.000000,\n", + " 0.000000,\n", + " 0.074,\n", + " 0.325100,\n", + " 0.376200,\n", + " 0.402700,\n", + " 0.415600,\n", + " 0.423000,\n", + " 0.427400,\n", + " 0.429300,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.429800,\n", + " 0.430500,\n", + " 0.438256,\n", + " 0.425908,\n", + " 0.347037,\n", + " 0.307306,\n", + " 0.271523,\n", + " 0.239552,\n", + " 0.211166,\n", + " 0.186093,\n", + " 0.164033,\n", + " 0.144688,\n", + " 0.127760,\n", + " 0.112969,\n", + " 0.100062,\n", + " 0.088800,\n", + " 0.078975,\n", + " 0.070401,\n", + " 0.062913,\n", + " 0.056368,\n", + " 0.050640,\n", + " 0.045620,\n", + " 0.041216,\n", + " 0.037344,\n", + " 0.033935,\n", + " 0.0,\n", + " 0.0]\n", + "\n", + "Cp_u_wind_speeds =[ \n", + " 0.0000,\n", + " 2.9,\n", + " 3.0,\n", + " 4.0000,\n", + " 4.5147,\n", + " 5.0008,\n", + " 5.4574,\n", + " 5.8833,\n", + " 6.2777,\n", + " 6.6397,\n", + " 6.9684,\n", + " 7.2632,\n", + " 7.5234,\n", + " 7.7484,\n", + " 7.9377,\n", + " 8.0909,\n", + " 8.2077,\n", + " 8.2877,\n", + " 8.3308,\n", + " 8.3370,\n", + " 8.3678,\n", + " 8.4356,\n", + " 8.5401,\n", + " 8.6812,\n", + " 8.8585,\n", + " 9.0717,\n", + " 9.3202,\n", + " 9.6035,\n", + " 9.9210,\n", + " 10.2720,\n", + " 10.6557,\n", + " 10.7577,\n", + " 11.5177,\n", + " 11.9941,\n", + " 12.4994,\n", + " 13.0324,\n", + " 13.5920,\n", + " 14.1769,\n", + " 14.7859,\n", + " 15.4175,\n", + " 16.0704,\n", + " 16.7432,\n", + " 17.4342,\n", + " 18.1421,\n", + " 18.8652,\n", + " 19.6019,\n", + " 20.3506,\n", + " 21.1096,\n", + " 21.8773,\n", + " 22.6519,\n", + " 23.4317,\n", + " 24.2150,\n", + " 25.010,\n", + " 25.020,\n", + " 50.0] \n", + "Ct_u_values = [\n", + " 0.0,\n", + " 0.0,\n", + " 0.7701,\n", + " 0.7701,\n", + " 0.7763,\n", + " 0.7824,\n", + " 0.7820,\n", + " 0.7802,\n", + " 0.7772,\n", + " 0.7719,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7768,\n", + " 0.7675,\n", + " 0.7651,\n", + " 0.7587,\n", + " 0.5056,\n", + " 0.4310,\n", + " 0.3708,\n", + " 0.3209,\n", + " 0.2788,\n", + " 0.2432,\n", + " 0.2128,\n", + " 0.1868,\n", + " 0.1645,\n", + " 0.1454,\n", + " 0.1289,\n", + " 0.1147,\n", + " 0.1024,\n", + " 0.0918,\n", + " 0.0825,\n", + " 0.0745,\n", + " 0.0675,\n", + " 0.0613,\n", + " 0.0559,\n", + " 0.0512,\n", + " 0.0470,\n", + " 0.0,\n", + " 0.0]\n", + "\n", + "Ct_u_wind_speeds =[ \n", + " 0.0000,\n", + " 2.9,\n", + " 3.0,\n", + " 4.0000,\n", + " 4.5147,\n", + " 5.0008,\n", + " 5.4574,\n", + " 5.8833,\n", + " 6.2777,\n", + " 6.6397,\n", + " 6.9684,\n", + " 7.2632,\n", + " 7.5234,\n", + " 7.7484,\n", + " 7.9377,\n", + " 8.0909,\n", + " 8.2077,\n", + " 8.2877,\n", + " 8.3308,\n", + " 8.3370,\n", + " 8.3678,\n", + " 8.4356,\n", + " 8.5401,\n", + " 8.6812,\n", + " 8.8585,\n", + " 9.0717,\n", + " 9.3202,\n", + " 9.6035,\n", + " 9.9210,\n", + " 10.2720,\n", + " 10.6557,\n", + " 10.7577,\n", + " 11.5177,\n", + " 11.9941,\n", + " 12.4994,\n", + " 13.0324,\n", + " 13.5920,\n", + " 14.1769,\n", + " 14.7859,\n", + " 15.4175,\n", + " 16.0704,\n", + " 16.7432,\n", + " 17.4342,\n", + " 18.1421,\n", + " 18.8652,\n", + " 19.6019,\n", + " 20.3506,\n", + " 21.1096,\n", + " 21.8773,\n", + " 22.6519,\n", + " 23.4317,\n", + " 24.2150,\n", + " 25.010,\n", + " 25.020,\n", + " 50.0] \n", + "\n", + "hub_height = 119.0\n", + "rotor_diameter = 198.0\n", + "TSR = 8.0\n", + "ref_density_cp_ct = 1.225\n", + "shaft_tilt = 5 # in deg # TODO Copied from DTU 10MW - Change!\n", + "\n", + "fig = plt.figure(figsize=(12, 5))\n", + "ax1 = fig.gca()\n", + "ax1.plot(Cp_u_wind_speeds, Cp_u_values, label='Cp')\n", + "ax1.plot(Ct_u_wind_speeds, Ct_u_values, label='Ct')\n", + "ax1.set_xlabel('Wind speed (m/s)')\n", + "ax1.set_ylabel('Cp and Ct')\n", + "ax1.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "33c30fad", + "metadata": {}, + "outputs": [], + "source": [ + "# define turbine and site \n", + "# ws = np.array(Ct_u_wind_speeds)\n", + "# ct = np.array(Ct_u_values)\n", + "# cp = np.array(Cp_u_values)\n", + "\n", + "# power_curve = 0.5 * 1.225 * (np.pi * (rotor_diameter/2)**2) * ws**3 * cp / 1e6 # Power in MW\n", + "# wind_turbine = WindTurbine(name='iea_10mw', diameter=rotor_diameter, hub_height=hub_height, powerCtFunction=PowerCtTabular(ws, power_curve, 'MW', ct))\n", + "\n", + "from py_wake.examples.data.iea22mw.iea_22_rwt import IEA_22MW_280_RWT\n", + "wind_turbine = IEA_22MW_280_RWT()\n", + "hub_height = wind_turbine.hub_height()\n", + "rotor_diameter = wind_turbine.diameter()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "0f781071", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# define site (HKN)\n", + "wd_site = np.linspace(0,360,12,endpoint=False)\n", + "p_wd_site = np.array([0.066,0.063,0.063,0.064,0.054,0.052,0.072,0.129,0.150,0.116,0.091,0.080])\n", + "a_site = np.array([9.56,9.21,9.38,9.78,9.23,9.20,10.96,12.73,12.75,12.17,11.22,10.59])\n", + "k_site = np.array([2.18,2.36,2.40,2.34,2.30,2.20,2.11,2.33,2.42,2.20,2.15,2.11])\n", + "site = UniformWeibullSite(p_wd=p_wd_site,a=a_site,k=k_site,ti=0.06,shear=None)\n", + "\n", + "wf_model = PropagateDownwind(site, \n", + " wind_turbine,\n", + " wake_deficitModel=BlondelSuperGaussianDeficit2020(rotorAvgModel=CGIRotorAvg(9)), \n", + " deflectionModel=JimenezWakeDeflection(),\n", + " turbulenceModel=CrespoHernandez(),\n", + " superpositionModel=SquaredSum()\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6df20a94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0. 1420.]\n", + "Array shapes:\n", + " time: (501,)\n", + " ws: (501,)\n", + " wd: (501,)\n", + " yaw: (2, 501)\n", + "Simulation complete!\n" + ] + } + ], + "source": [ + "# Run simulation of test wind farm\n", + "# Wind farm layout and initial settings\n", + "x = np.array([0, 5*rotor_diameter]) # x positions of turbines\n", + "y = np.array([0, 0])\n", + "\n", + "print(x)\n", + "\n", + "# Simulate yaw changes over time\n", + "time_series = np.arange(0, 2004, 4) # Simulate for 501 time steps\n", + "\n", + "yaw_table = np.array([[0, 0], # Same as in OFF example\n", + " [0, 0],\n", + " [30, 0],\n", + " [30, 0],\n", + " [0, 0],\n", + " [0, 0],\n", + " [0, 0]])\n", + "# yaw_table = np.array([[0, 0, 0, 0], # Same as in OFF example\n", + "# [0, 0, 0, 0],\n", + "# [30, 0, 0, 0],\n", + "# [30, 20, 0, 0],\n", + "# [0, 20, 0, 0],\n", + "# [0, 0, 0, 0],\n", + "# [0, 0, 0, 0]])\n", + "\n", + "yaw_table_t = np.array([0.0, 604.0, 700.0, 800.0, 900.0, 1000.0, 90000.0]) # Time points for yaw changes\n", + "yaw_table_interp = np.array([np.interp(time_series, yaw_table_t, yaw_table[:, i]) for i in range(yaw_table.shape[1])]).T # Interpolate yaw angles for each time step\n", + "\n", + "# For PyWake time series mode:\n", + "# - time: array of time values\n", + "# - ws and wd: must have same length as time array\n", + "# - yaw: shape (n_wt, n_time)\n", + "\n", + "ws_array = np.ones(len(time_series)) * 8.0 # Wind speed constant at 8 m/s for all time steps\n", + "wd_array = np.ones(len(time_series)) * 270.0 # Wind direction constant at 270 degrees for all time steps\n", + "\n", + "# Yaw array shape: (n_wt, n_time) = (n_t, 501)\n", + "yaw_array = yaw_table_interp.T\n", + "\n", + "print(f\"Array shapes:\")\n", + "print(f\" time: {time_series.shape}\")\n", + "print(f\" ws: {ws_array.shape}\")\n", + "print(f\" wd: {wd_array.shape}\")\n", + "print(f\" yaw: {yaw_array.shape}\")\n", + "\n", + "# Run PyWake simulation with time series\n", + "sim_res = wf_model(x, y, # wind turbine positions\n", + " h=None, # wind turbine heights (defaults to the heights defined in windTurbines)\n", + " type=0, # Wind turbine types\n", + " wd=wd_array, # Wind direction (time series)\n", + " ws=ws_array, # Wind speed (time series)\n", + " time=time_series, # Time array\n", + " yaw=yaw_array, # Yaw angles shaped as (n_wt, n_time)\n", + " tilt=5.0 # Tilt angle (constant)\n", + " )\n", + "\n", + "print(\"Simulation complete!\")\n", + "\n", + "# Extract power data\n", + "# In time series mode, dimensions are: (time, wt)\n", + "power_time_series = sim_res.Power.values/1e6 # Shape: (n_time, n_wt) = (501, 2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "76c6f5b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 1002 rows from OFF measurements\n", + "Columns: ['Unnamed: 0', 't_idx', 'u_abs_eff_PyWake', 'Ct_PyWake', 'AI_PyWake', 'TI_PyWake', 'Power_PyWake', 'power_OFF', 'time']\n", + "Power time series shape: (2, 501) (turbines x time)\n", + "Time range: 0.0 to 2000.0 seconds\n" + ] + } + ], + "source": [ + "# Load OFF simulation results\n", + "import os\n", + "import pandas as pd\n", + "\n", + "BASE_DIR = os.path.join(os.getcwd(), 'OFF', 'runs')\n", + "if not os.path.isdir(BASE_DIR):\n", + " BASE_DIR = os.getcwd()\n", + "\n", + "# Specify the OFF run directory to load\n", + "RUN_DIR = os.path.join(BASE_DIR, 'off_run_20260226150850823295')\n", + "\n", + "# Load measurements.csv\n", + "measurements_file = os.path.join(RUN_DIR, 'measurements.csv')\n", + "if os.path.exists(measurements_file):\n", + " df_OFF = pd.read_csv(measurements_file)\n", + " print(f\"Loaded {len(df_OFF)} rows from OFF measurements\")\n", + " print(f\"Columns: {list(df_OFF.columns)}\")\n", + " \n", + " # Extract power time series for each turbine\n", + " # Assuming columns: time, t_idx, power_OFF (or similar)\n", + " turbine_indices = sorted(df_OFF['t_idx'].unique()) if 't_idx' in df_OFF.columns else [0]\n", + " n_turbines = len(turbine_indices)\n", + " \n", + " # Get time points (assuming same for all turbines)\n", + " time_OFF = df_OFF[df_OFF['t_idx'] == turbine_indices[0]]['time'].values if 't_idx' in df_OFF.columns else df_OFF['time'].values\n", + " n_times = len(time_OFF)\n", + " \n", + " # Initialize power array: shape (n_turbines, n_times)\n", + " power_time_series_OFF = np.zeros((n_turbines, n_times))\n", + " \n", + " # Fill power array\n", + " for i, t_idx in enumerate(turbine_indices):\n", + " if 't_idx' in df_OFF.columns:\n", + " df_turb = df_OFF[df_OFF['t_idx'] == t_idx]\n", + " else:\n", + " df_turb = df_OFF\n", + " # Extract power column (check common names)\n", + " power_col = None\n", + " for col_name in ['Power_PyWake', 'power', 'Power', 'P']:\n", + " if col_name in df_turb.columns:\n", + " power_col = col_name\n", + " break\n", + " \n", + " if power_col:\n", + " power_time_series_OFF[i, :] = df_turb[power_col].values/1e6 # Convert to MW\n", + " \n", + " print(f\"Power time series shape: {power_time_series_OFF.shape} (turbines x time)\")\n", + " print(f\"Time range: {time_OFF[0]:.1f} to {time_OFF[-1]:.1f} seconds\")\n", + "else:\n", + " print(f\"Measurements file not found: {measurements_file}\")\n", + " power_time_series_OFF = None\n", + " time_OFF = None\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "fd736b75", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot power over time for each turbine\n", + "fig2, (ax3, ax4) = plt.subplots(2, 1, figsize=(12, 8))\n", + "plot_colors = ['blue', 'orange', 'green', 'red']\n", + "# Power time series\n", + "for i in range(len(x)):\n", + " ax3.plot(time_series, power_time_series[i, :], label=f'Turbine {i} (PyWake)', color=plot_colors[i])\n", + " ax3.plot(time_OFF, power_time_series_OFF[i, :], label=f'Turbine {i} (OFF)', linestyle='--', color=plot_colors[i])\n", + "ax3.set_xlabel('Time (s)')\n", + "ax3.set_ylabel('Power (MW)')\n", + "ax3.set_title('Power Time Series')\n", + "ax3.legend()\n", + "ax3.grid(True)\n", + "\n", + "# Yaw angles time series\n", + "for i in range(len(x)):\n", + " ax4.plot(time_series, yaw_array[i, :], label=f'Turbine {i} (PyWake)', color=plot_colors[i])\n", + "ax4.set_xlabel('Time (s)')\n", + "ax4.set_ylabel('Yaw angle (deg)')\n", + "ax4.set_title('Yaw Angle Time Series')\n", + "ax4.legend()\n", + "ax4.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "932ce81e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Array shapes:\n", + " time: (501,)\n", + " ws: (501,)\n", + " wd: (501,)\n", + " yaw: (2, 501)\n", + "Simulation complete!\n" + ] + } + ], + "source": [ + "# Run simulation of test wind farm\n", + "# Wind farm layout and initial settings\n", + "x = np.array([0, 5*rotor_diameter]) # x positions of turbines\n", + "y = np.array([0, 0])\n", + "\n", + "# Simulate yaw changes over time\n", + "time_series = np.arange(0, 2004, 4) # Simulate for 501 time steps\n", + "\n", + "wd_table = np.array([[270], # Same as in OFF example\n", + " [260],\n", + " [265],\n", + " [275],\n", + " [285],\n", + " [270]])\n", + "\n", + "# yaw_table = np.array([[0, 0, 0, 0], # Same as in OFF example\n", + "# [0, 0, 0, 0],\n", + "# [30, 0, 0, 0],\n", + "# [30, 20, 0, 0],\n", + "# [0, 20, 0, 0],\n", + "# [0, 0, 0, 0],\n", + "# [0, 0, 0, 0]])\n", + "\n", + "wd_table_t = np.array([0.0, 200.0, 250.0, 600.0, 800.0, 1200.0]) # Time points for yaw changes\n", + "wd_table_interp = np.array([np.interp(time_series, wd_table_t, wd_table[:, i]) for i in range(wd_table.shape[1])]).T # Interpolate yaw angles for each time step\n", + "\n", + "# For PyWake time series mode:\n", + "# - time: array of time values\n", + "# - ws and wd: must have same length as time array\n", + "# - yaw: shape (n_wt, n_time)\n", + "\n", + "ws_array = np.ones(len(time_series)) * 8.0 # Wind speed constant at 8 m/s for all time steps\n", + "# wd_array = np.ones(len(time_series)) * 270.0 # Wind direction constant at 270 degrees for all time steps\n", + "\n", + "# Yaw array shape: (n_wt, n_time) = (n_t, 501)\n", + "wd_array = wd_table_interp.flatten()\n", + "yaw_array = np.zeros((2, len(wd_array))) # No yaw changes, only wind direction changes\n", + "\n", + "print(f\"Array shapes:\")\n", + "print(f\" time: {time_series.shape}\")\n", + "print(f\" ws: {ws_array.shape}\")\n", + "print(f\" wd: {wd_array.shape}\")\n", + "print(f\" yaw: {yaw_array.shape}\")\n", + "\n", + "# Run PyWake simulation with time series\n", + "sim_res = wf_model(x, y, # wind turbine positions\n", + " h=None, # wind turbine heights (defaults to the heights defined in windTurbines)\n", + " type=0, # Wind turbine types\n", + " wd=wd_array, # Wind direction (time series)\n", + " ws=ws_array, # Wind speed (time series)\n", + " time=time_series, # Time array\n", + " yaw=yaw_array, # Yaw angles shaped as (n_wt, n_time)\n", + " tilt=5.0 # Tilt angle (constant)\n", + " )\n", + "\n", + "print(\"Simulation complete!\")\n", + "\n", + "# Extract power data\n", + "# In time series mode, dimensions are: (time, wt)\n", + "power_time_series = sim_res.Power.values/1e6 # Shape: (n_time, n_wt) = (501, 2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "6330728a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 1002 rows from OFF measurements\n", + "Columns: ['Unnamed: 0', 't_idx', 'u_abs_eff_PyWake', 'Ct_PyWake', 'AI_PyWake', 'TI_PyWake', 'Power_PyWake', 'power_OFF', 'time']\n", + "Power time series shape: (2, 501) (turbines x time)\n", + "Time range: 0.0 to 2000.0 seconds\n" + ] + } + ], + "source": [ + "# Load OFF simulation results\n", + "import os\n", + "import pandas as pd\n", + "\n", + "BASE_DIR = os.path.join(os.getcwd(), 'OFF', 'runs')\n", + "if not os.path.isdir(BASE_DIR):\n", + " BASE_DIR = os.getcwd()\n", + "\n", + "# Specify the OFF run directory to load\n", + "RUN_DIR = os.path.join(BASE_DIR, 'off_run_20260226162925406491')\n", + "\n", + "# Load measurements.csv\n", + "measurements_file = os.path.join(RUN_DIR, 'measurements.csv')\n", + "if os.path.exists(measurements_file):\n", + " df_OFF = pd.read_csv(measurements_file)\n", + " print(f\"Loaded {len(df_OFF)} rows from OFF measurements\")\n", + " print(f\"Columns: {list(df_OFF.columns)}\")\n", + " \n", + " # Extract power time series for each turbine\n", + " # Assuming columns: time, t_idx, power_OFF (or similar)\n", + " turbine_indices = sorted(df_OFF['t_idx'].unique()) if 't_idx' in df_OFF.columns else [0]\n", + " n_turbines = len(turbine_indices)\n", + " \n", + " # Get time points (assuming same for all turbines)\n", + " time_OFF = df_OFF[df_OFF['t_idx'] == turbine_indices[0]]['time'].values if 't_idx' in df_OFF.columns else df_OFF['time'].values\n", + " n_times = len(time_OFF)\n", + " \n", + " # Initialize power array: shape (n_turbines, n_times)\n", + " power_time_series_OFF = np.zeros((n_turbines, n_times))\n", + " \n", + " # Fill power array\n", + " for i, t_idx in enumerate(turbine_indices):\n", + " if 't_idx' in df_OFF.columns:\n", + " df_turb = df_OFF[df_OFF['t_idx'] == t_idx]\n", + " else:\n", + " df_turb = df_OFF\n", + " # Extract power column (check common names)\n", + " power_col = None\n", + " for col_name in ['Power_PyWake', 'power', 'Power', 'P']:\n", + " if col_name in df_turb.columns:\n", + " power_col = col_name\n", + " break\n", + " \n", + " if power_col:\n", + " power_time_series_OFF[i, :] = df_turb[power_col].values/1e6 # Convert to MW\n", + " \n", + " print(f\"Power time series shape: {power_time_series_OFF.shape} (turbines x time)\")\n", + " print(f\"Time range: {time_OFF[0]:.1f} to {time_OFF[-1]:.1f} seconds\")\n", + "else:\n", + " print(f\"Measurements file not found: {measurements_file}\")\n", + " power_time_series_OFF = None\n", + " time_OFF = None\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "0fda7b15", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\daanvanderhoek\\AppData\\Local\\Temp\\ipykernel_6828\\3197713988.py:19: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + " ax4.legend()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot power over time for each turbine\n", + "fig2, (ax3, ax4) = plt.subplots(2, 1, figsize=(12, 8))\n", + "plot_colors = ['blue', 'orange', 'green', 'red']\n", + "# Power time series\n", + "for i in range(len(x)):\n", + " ax3.plot(time_series, power_time_series[i, :], label=f'Turbine {i} (PyWake)', color=plot_colors[i])\n", + " ax3.plot(time_OFF, power_time_series_OFF[i, :], label=f'Turbine {i} (OFF)', linestyle='--', color=plot_colors[i])\n", + "ax3.set_xlabel('Time (s)')\n", + "ax3.set_ylabel('Power (MW)')\n", + "ax3.set_title('Power Time Series')\n", + "ax3.legend()\n", + "ax3.grid(True)\n", + "\n", + "# Yaw angles time series\n", + "ax4.plot(time_series, wd_array)\n", + "ax4.set_xlabel('Time (s)')\n", + "ax4.set_ylabel('Wind direction (deg)')\n", + "ax4.set_title('Wind Direction Time Series')\n", + "ax4.legend()\n", + "ax4.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "OFFPy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..aa8e813 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,20 @@ +# Core dependencies +numpy +pandas +scipy +matplotlib +pyyaml +networkx + +# Wind farm simulation packages +floris +py_wake + +# Jupyter support (for notebooks) +ipykernel +ipython +jupyter_client +jupyter_core + +# Development tools +ipython_pygments_lexers diff --git a/runs/Processing/plot_mountain_range.py b/runs/Processing/plot_mountain_range.py index 76571c2..03466c9 100644 --- a/runs/Processing/plot_mountain_range.py +++ b/runs/Processing/plot_mountain_range.py @@ -9,17 +9,17 @@ import matplotlib.pyplot as plt # Path to data -path_to_data = 'runs/off_run_20250513115216959633' +path_to_data = 'OFF/runs/off_run_20260128104823422709' amplification_factor = 10.0 #m/(m/s) # TODO # change storage type into something like a pickle file? Or a hdf5 file? # Load the csv data -data_x = np.loadtxt(f'{path_to_data}/mountain_plot_x_000700s.csv', delimiter=',') -data_y = np.loadtxt(f'{path_to_data}/mountain_plot_y_000700s.csv', delimiter=',') -data_u = np.loadtxt(f'{path_to_data}/mountain_plot_u_000700s.csv', delimiter=',') -data_v = np.loadtxt(f'{path_to_data}/mountain_plot_v_000700s.csv', delimiter=',') +data_x = np.loadtxt(f'{path_to_data}/mountain_plot_x_000736s.csv', delimiter=',') +data_y = np.loadtxt(f'{path_to_data}/mountain_plot_y_000736s.csv', delimiter=',') +data_u = np.loadtxt(f'{path_to_data}/mountain_plot_u_000736s.csv', delimiter=',') +data_v = np.loadtxt(f'{path_to_data}/mountain_plot_v_000736s.csv', delimiter=',') # find the largest value in the data_u and data_v arrays max_u = np.max(data_u) max_v = np.max(data_v) diff --git a/runs/plot_sim_res.ipynb b/runs/plot_sim_res.ipynb new file mode 100644 index 0000000..09312ae --- /dev/null +++ b/runs/plot_sim_res.ipynb @@ -0,0 +1,315 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d5772bea", + "metadata": {}, + "source": [ + "# Plot measurements.csv from OFF/runs\n", + "This notebook lists run folders, loads any `measurements.csv` files found inside, and lets you interactively plot selected columns." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "35a95d9c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Runs base: c:\\Users\\daanvanderhoek\\Nextcloud\\PostDoc\\Software\\PyWakeDyn\\OFF\\runs\n" + ] + } + ], + "source": [ + "# Imports & setup\n", + "import os\n", + "import glob\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ipywidgets import Dropdown, SelectMultiple, Button, VBox, Output\n", + "from IPython.display import display\n", + "\n", + "BASE_DIR = os.path.join(os.getcwd(), 'OFF', 'runs')\n", + "if not os.path.isdir(BASE_DIR):\n", + " # Fallback: assume notebook already in OFF/runs\n", + " BASE_DIR = os.getcwd()\n", + "print('Runs base:', BASE_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "75ad9ef1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 1 measurements.csv file(s) in off_run_20260227111343795525\n" + ] + } + ], + "source": [ + "# Selected run directories\n", + "RUN_DIRS = [\n", + " # os.path.join(BASE_DIR, 'off_run_20260216135343112309'),\n", + " # # Add more runs here, e.g.:\n", + " # os.path.join(BASE_DIR, 'off_run_20260216135154878136')\n", + " os.path.join(BASE_DIR, 'off_run_20260227111343795525') # Example placeholder\n", + "]\n", + "\n", + "# Loader utilities\n", + "def find_measurement_files(run_dir):\n", + " return sorted(glob.glob(os.path.join(run_dir, '**', 'measurements.csv'), recursive=True))\n", + "\n", + "\n", + "def load_measurements(run_dir):\n", + " files = find_measurement_files(run_dir)\n", + " if not files:\n", + " print('No measurements.csv files found in', run_dir)\n", + " return pd.DataFrame()\n", + " dfs = []\n", + " for f in files:\n", + " try:\n", + " df = pd.read_csv(f)\n", + " df['__file__'] = os.path.relpath(f, BASE_DIR)\n", + " dfs.append(df)\n", + " except Exception as e:\n", + " print(f'Failed to read {f}: {e}')\n", + " if not dfs:\n", + " return pd.DataFrame()\n", + " return pd.concat(dfs, ignore_index=True)\n", + "\n", + "\n", + "def load_measurements_multi(run_dirs):\n", + " data = []\n", + " for rd in run_dirs:\n", + " if not isinstance(rd, str) or not os.path.isdir(rd):\n", + " print('Not a valid run directory:', rd)\n", + " continue\n", + " df = load_measurements(rd)\n", + " if df.empty:\n", + " print('No measurements.csv files found in', rd)\n", + " continue\n", + " data.append((os.path.basename(rd), rd, df))\n", + " return data\n", + "\n", + "# Preview counts for selected runs\n", + "if RUN_DIRS:\n", + " for rd in RUN_DIRS:\n", + " try:\n", + " cnt = len(find_measurement_files(rd))\n", + " print('Found', cnt, 'measurements.csv file(s) in', os.path.basename(rd))\n", + " except Exception:\n", + " print('Unable to list files in', rd)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c973a678", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] Column not found: Power_FLORIS\n", + "[off_run_20260227111343795525] 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+ "[off_run_20260227111343795525] Column not found: Power_FLORIS\n" + ] + }, + { + "data": { + "image/png": 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IZidmw5z5mkO24qFtc8ykdyJCE0RohsTGTA02aIYLBtwwDBdgOAHYIMAOwbBBZL0dIqwQQfsZy4qsr0k4eXuMx4fmDR0p6NCgCSpT9OlQobFegy5Qo1GE/lFvbg8dyTrKnjdk0xrxjMq2lwknLbXN74ps7Ob1Mz2eRuY48+/Hcy9mzGlZHzl7HjbPmDmfj/ZOh26Ck193rHdy4jx6qnd8oo0w+70NHTcA3YBIv3tdI5dJtm86VtGdZD7FdDAw/0Kn+UcEdBGg4YCcwXRRhCZlf+ds/kp7k1EvGaJ57AyFn/Epp+nKnmo9Mj7M3+rwr4f95iRDhcx+VaN/Hzqbi9PXQhSgZSjCxn476TuPff00LpEXiMTWBYZu9mMxNHdLtCxg67VTXaGhO5xeS4BObeguHHNRNsrxUe/TTDea7H22+ksvxU589tDKcMSzZeg7Tnsipv+f6ak44gcztE/3lXjCs8Ws4wIkSKA1kfymDBw6G70SUIQ4UkIcScHcTgpRJIQoFCHKxrBTM/LZTj6adRHHyamfO6rX0fDWyaPdydun+K7P9K2ewKRZS/HPABe6LwBKqqbhZ4l5I1NrliucOeiNQAu58TyOC0RDZy4iafmW2TusEuwWCU6bzBptm8dEtm2XDdglAzZBh1VUYRN1WGjVrsagJ6OsGUoEhhIGlBDERBhiMmI248wtIxoEhAwrBg0bBjQLBlQLgpoVAykH+hUH4ikrZFWCVRVZO1sEUYDV74Tgs0Pz2qB47FCstE6Jw6rH4dZj8GkReBCCU47CakvAcOvQvYDhoL9XITkHAGqjIIYBuUOApUMw+3YBcqcAUTnNYOt0Qs7Ph6WwEJYJlbBOmAhrJfWVsFRUQrdZ8eyzz+KqK66ErIvQkzoMRYWuaDCSGgxFO3mb+oQGPa5Cj6VG+liKGa9oirNpEmvZeMZ8n7oUhWoLQrWFmMJCtQahWUPs2Mh+nPWGnDzp7xOqDQnNBkW1QdGsUDTaN3tqKc0CVZeR0mUmdLBeN3s6rhrpnjWJ9fQ83RDTLS240IKILVZMl9jMx5mIZWQfMydWUwgbzd44pOAyR4hM12AdsqFBNlRIhgabmoQrFYdDTcCuKbBrSdj0JKxaChZqeoo9X9I19nxJ1yEaZqNz0eKZLa9GBiIzwmJo2mXrmfS7YEqcM5xFBQM2bxL2XAU2bwwOX4rt27wpWFwp0g+NG10VoCoStAQJ8CJruipCT6WbKkJLZe/rKQG6JjJhWieBOt2GjrFtEpJpJXy2Gr93CCTEOGQPnLLXbJIHdskFu+SGg/UuuGU3ZNF6xufWDBWKlkDSSEChhhgUJBAX4ogLCUTFGKJSAiE5jpAlhqA1joAlgZAkISLIiIoyFMEOAw4ImhMCCfo6NS903QJdswKajW1rmhW6bmVCh6Yz2W9c0F3sFRLIFWLIFWPwCQl4BIU1UoCcDlLgxg0LFMhQDBmJdJ+EjAT1hgQVElIQkUpvC2IKspyCRUrCJitwyAnY5URW77Yk4HeKyHVZketyIM9lQ57bBY/DC4vVD4vFD4vsgyx7IEnOrCaexXd1OpKJOHqaGtDdWI/upgb0t7ZgoLMNqqK8qfOyMcRqg+7yQsjNg2Z3sGt2uoDHpJhEQkogLsWRkBNsW5EUdjwlplhvNywoVHNRnipApVaKyd5q1BROQW5pEaylbshFTohWkvp1oHMv9MaNSLXsgtK2H8l4CAqsSIK+WysSsCMKB6JwIWbNR1T0IgonIroF4aQ5Yo8FKbQK7RqK5QiKjG4UJZtQkmyEAydfO1LHN9osqLN50GEtRFAugUtwwydIcMgqLK5+WFx9kB2BDIPHCdeU5pdQDewDS+AOT4crXgRbjJTjp3iXsoCErMJbmgPdY0NPXEX3YBKdPTGEgyfPoYTLZ4W/yAl3rh0unw1On5X1Lr8NTo8FNlmAmNJh0PzP5vuM1xcFCDYJIjW7DNFrNb+Lc0wsFUNDoAF1g3WsNQWb0BHuQGe0A0mDtMGnwQBshhV+1YMczYsczQe/4YVXd8Oje+DRnfBqbrh0F1yaAw7VyppNk1mj9c3piBs6IrqBiKYjqgFhDQhpBhL/pHMPU4QyZanGDCymMtXc1gUVmph+jI6LQz09T8NlNyzAPwM8pvsCQNMSaGj8GRrq61FdXQVRHNK+kqrYOGE7bTJh/+nZx9Oz1dC2bmjQNRWarkLXNWhaCrqmQdPpuMaOqele13Vomg6DnqenoKoq+1smHDANqsT60zYSIjSyXFGzZPQW6KfQwo4H0sy6XC643W54PB7WZ25T7/f54LZLEBIhJowjEQRoOz4ARHvNFkn3Qy3cSSv4U39HVhcivlIMugvQ6fCiVbLjqGhFR0JBJDyIcDgAQ0nBmhJgS0mwJ0XYFQmO5Mi2PTXOicUwYE+pcCkpuJUU6x0Q4HY74SxxQiyxQS+UoOZqSLkSSFnDSFpDSMmhMTWJ1oQPjlA+7AN+2DudkJpS0Hr7WNNjZE0+NVJ+PsI+H0pWrIBzxgzYp0+DraoKgnWMRR7di8E2tqBBTy3QcxjoOQL018PQUjBggw4PdIOaGwmLF3GrD3GLDwk5D4bhZVb0Yff6pAeCcWb3TwwaBgQVvTDQDaDXANumFpSAqEVAQhJhkwQ4BQF2gxpg1w04NMCm6pBVA5a0XY96smSoooKUGIcixVhLSDGkpBiSIi3+YkiIccSlGFsUUh8T49BFmuTJ68GAqOuwqQasGmBRAYsGWFMGXIoId1yEUxHhUAR231hTIiyaAEkja4/ZhrT4zJZ5pkLvW4gkq3DkKnDmKbBTy08wYVu0jL0AJKFYDctQYzL0mAgtJkGNS+Z2XIIWF2GQcJ0kcxl9flJEyYBA44wAi9UJQbBBIM+OISWFaYo2rXFsbCTJzOwNUtDpGmCo6f4EfX368oqiDkk0IIkaOYhAokbHBDUd9zce0ooWyNAgw2CWZBIyaNw0VZIGe5yszKb3jaaTNw69XRrPDVAKFp29b3MOGFbCDG+zSZ31FtEKl8UPp8UHh+yFw+I1+7SQTcI1janjIaUrSGhRxLUoFNZiTKhW9BiSWhyKHofCenNfHc+i9nRXizxvBAGSKEIWJVhkGbJsgWy1wmK1weZ0wuZ0web2wO7zwZGTC0d+AWx+P2wuFyx2FyxOF2S7g82U3d09aG9rQ3dPN3q7u9Df1wtNHWPcFwTYnG7YXD5YnG6INhdgsUOT7FBFO5ISKetEKJqOREpHPKkhpsQQSQQRTUQRTyqIpQwkVDviqh3aGY5do2GVReQ6rchhwrgFhR47Cr02FHvtKBpuNnacnnu2pJIK2msPo+XgPrQc2MsE7dEkYVGS4Sssgie/AJ7cfLhz82B3u9l3YnU4IcqU18S8Z7VkEuFgEF09PegZDCAQiSKqkwfCKO/TMCCqKbhsVhQUFKCyZgoqJlXB7/ezOT6BBPpifeiIdjAhqj3Qho5gOzrC7ehMdKFX6x/zsxUmczEtUYFZCQML0I95icOwacETPpgFKJwGFM0Gimaa27lVgK8CkGgWGEHTNITDYQQCgaw2MDCA7u5uKGMoJhLyICxyNwqELixIdmOONgAvElnPobGiIzkDTcpSNCWWQHWXwZfvhLdAgrsgBId/gAnhMLphdCQgtrpg762GNVZ88iUl1a9fh60kF9YSLyxFLsj5Dkh+G8IxBc/8+TW4tGJ0HguysSbTSJBf7kbhBA8KKs1GwrbVTuOXAS2oINkaQaotjFRPDGp/AupAgrJf4UwgIVzyWiHn2iEXOCEXOGBJ96LbctpxKpQM4UDvAezv3T8sZLeGWzO8Wk54PUNAnupDYSoPhalc5Bs5yHPmI8+bh3x/IfL8BcjLKUR+fhGcPg8Ecg84AwzNgBZJsutjtpFtuj5qXwJGYuw1p2E1YOSkoHpjUL0hJD0DUNz90IQ4dD0BXVPM3kjCMNI9FBhs3DXnvBGvmdEaQfONmW8IrDe36Rj5NAIW1pNHE1l8BGQ0wQ4BLojwQhS9kIR0L3kgyxJESYQoCaxJGdsibct0LL2d1aefL2cfJ/nkueefwzXXXA2LJfv3906M6eZC9wVCKpVilsSrr75wbiwaVOlHrOsKUwzQj1xjP3japh+9MryvaTGoWgSaGoGqRdM97UdZr9J+KoKEEoOixNLupORaaoOasiJFvWo1W4q2bUixbfOxVNI+bsu9LMtsgs7JyWGNtnNzc9kETvviidnhabEd6TGFxGBrurUBgRag7xgw2GQu1EdB95UjUDgfDTkzsdU1ES8LHhyN9MLZXw9/qAU2pQtJaRABlwKSue0ZQrhLkeCMy/BHJORFLHArVsgpspCe4rNZrMivnIDCidUonEStCvmVE9kilL6jaPQYItGjiEbqEInWIRI5imSyZ5RrRNdnKXJzViDHsRiWqANqby9SHZ1IHj+OZMtxpI63sG0tMEYsjcXCBG/7tGmwTZ0CZ5kMq9QJqW8/0LYTiHSN+meGxYOO4hlo8OUgaItCsrXBJo/uZh9QvOiKFqIzUoRguByJaAmQyIU15UWBxYUS2YI8QUAuDf864FZJYaFDPsvcJ0mkEJIj6JMDaLf24LitE8dtHeixDWBADiEkRrKvo0rCsQRbUoQtJZp9UoQnLsETSwvQTHiW0oKzdH48UAwDkk5NZ71I+8Puw6ZsPhRxwOT2ITdPyYAmGlAlsyUFDXExhbhIrvrk/o/hx1IykJKHts3HUvQ3FnqODp/TQKlLQ4VDR5VDQKFEAunJb1UU7XC7psDhnAiHoxJORyUcjgls22otGF5gGYaOZGoAitIFRemGkuhEPN6CaKwRsVgjEok2U2geBbutFD7fArP5F8Ljnj4siI+JqgBdB8x7t3Mf0HcUGDxuKuzG+P1nIwBWJ+AqAPwTgcLpQOl8c8HuLQFchYB8biyRqm6gPaGguS+K3u4Iwr0x6P0J2IJJ5EZUlMcM5CbHYfKlBZDPBslrgeCSIND6ymoAFh2GRKEeSajJKNRwFGowhlQwilQ4hv5wN5qV4+jUuxBFGCKFnlCjEAENcKgWOFUbbIYM+ieRcCWQ0oGUujpT/Kq0aIcBNe1qq5KLrSjCoBv2HGO6idNboHAaEYYkw5At0O0OOAuL4C8tQ35JKQpKy1BcUYm8ggI2j5wKuveCwT3o63sVff2vsrH3ROz2cng989F43Ipps6+BYKlE0vAjouiIJFREFBVhRU1vpxBOqBiMpTAYTWIgmsRgLIn+aBLJMxRi8t1WJnwX++wo8ztQmetEBWsO1nvtI2sMQ9fR1XgMx/fvZYJ2R90RaKlspQkJ1EVVNSiqqkZB5STklpXDV1gM6RTXiNYPJHgePXoUDQ0NaGtrY8r9TOga5+flwmuzwZJKQOnpRP+xo1CVeNbzckpKUb1oGaZdtBqFE6tOKYSRhbMx2Ij6QD3qB+pR31uH+mA9ulO9Jz3XoeuYm1AxP56HxfpsTC2/HNb5F8E2uRCCdGbjdFe0C7u7d2NPzx7Wjg0egz1lhz/phy/pY422XarrhAsFyCkP3Ek7Ko1eTLUcRKX9MDw4Ic9P+RJgwZ3AzBugJa2IH+pD/GA/lMbACbm+DOg5cSRymhB27kTc1YSkq4N5fgmCFXl5K5GXcyViXQtRty2AlkP9WWsOEqorZ+aifFouymr8sDpGvmMtlETi2CCUY4NI1AegR8ZQrtF8Q1ZspwzRIY9cS5p/NIN5yTEreFyFkTr1vU3nsRQ6YC33wDrBA2ulFzFnEju6d2Brx1bs7N7JLNqjCdg5qhcTE6WYpJShUilBsZqHUm8ZSksr4Sr1M48HUkCMR7A/G+g3kEz2IZFoZ/MV9Uqylx1LhcPQBw0IQQvEsBvWaBFskXJYoyWjGhgMQUXCcxwJfwPivgYkfA1IOfousNA5ERaLDxZLLmy2QtishbAO9wWwWYvgcJTDZitm4/A7VTYaDS50n+GFeFtRItAfuhGDg4PIyc2FSKYUnLBSzuzZzZqxTdpXyQpItpFtOWM7s8np3uIALC7ASs0JWN2Ahfr0MXrOebKi0WJFVUNIpQJIpQZH7ZOpfigKDU49rNf1FFIpG5JJh9kU58j2cHNCUSjecOz3LUkS8vPzmQBOjbaLioqYUH6SMJ65GB9ohNpzFP0dBxHrroWj7wgKgw1pd+ARSEkcH7Qi3mVFtNuGeC+5PZLDLtCfK6NnRhG6J/nQUSChyRVDnd4JRU9muzQlRUzQCjFLqkJFKg+eiIh4zwACXR2jWmYEUUReeSWKJk1G2fQZqJg+G74iGtTM65BMDiAcPsAWiMHQXtZrWrbw6HLVID9vHQoKLoXXOy9rQNSCQcQaG7Hzsccw1WZD6mgdEkePQlACcJcpcJcm4CxUINuyrwVZYjVPNZoKV2KvZSaOGzGI8hGUOPai0JmtCCBX7bZwKZpDlWiLTEBSmAS7YyKK/AUo9ZFFx45Cj23YukMWH+YRohtQe2NItkWQ6opC7YszjbtG2nbj1PFWw/Ffp0FBEgEMImQEEUsFoSRDSCbDiCthxFIhJE64luNF1jTImgFZ1yFrpCjQQUZgsuhZLFZYbTZY7U7YHA5mOSLLntXlgcXthtXjgcXjgdXrg83vg8Xrg+R2Q3S5WBNOIzCcDhKKomqULWAjyQgiqQiiqShrtB1I9CMZrYWk1MOldiIHAVhHiU8LqALaUwI6UmS1EdGeEtGrCvDbcjHBO4G1ib6JqPJVYUrOFJS5y8a9+NH1JBPCSQAPhetw7Nh6eL2DiMePn3ytZVK8rWBKpry8NbBbC4HeWqB9lylkt+82vTHGCk2h8dA/AfCVAXafaQ3TkqYnTagDGCDF3GiWCwHIqwaKZwOlC4CKJUDJPGY1HS+Gqg/f12pvHKneGFTaJmtJ8tShNHG7iB6nhOM2oMUKdNtFdDkEdFFvFxCzi5jtcWKB14WFPicu9nuQZx393umN9eLJhifxTOMzTKDJpMoxCYusczE3ORVTBirg7bVCj44yVtklWCs8sE3wwlaTwxbSLLtDIgE9HmeNFqPJwQEkAwEowSCSoRCSEWoRJGMxpKgl4sztWUkmkUqRUJpCUlOZ4J6SJKiiiJRE8cJnp+Cisp0uuxNuj5dZcb1FxfBPnARvRSUs7jAiykb09D4DJdk98tkECR73TKbk8fkWwu9bAJut6E0vGmnhHk9pTAgPxFKs748q6Akp6Aol0M2agq5gAj3hBFKZrrxjkOMQMUfsQ1W0Ce7uWiAWOknInjB7HipnzUXFrDnsGowHEqpJuD548CATtmndlQmtxSZNmoSKigqUlpaisLCQzclZ59A09B5vQuuh/WjauxNtRw6xY8PvvbQc0y9ejVlrLzv9+yLvql0PAvv+jLASxGGbFQdsNuz35GKPRUYA2e7TuSkfFkdnYkVyPi6qWgXf3BLYJvlHtXSmtBR29ezCa22vsXY8dPK4U+GpwPz8BZganw9naxFCdQaikShSliBS1iCSFA5liWa/h9xczJo1C7MqclDYtwU48jRSR7YhEZShBC1QgjYokVyoKQ/0FCXKSEJweCDn5cFSWgjH3Jlwr1wO+8yZzGU3ENiBgYFN6OvfgPBgFwbr12Cwfh00ZWQ9bPWrmL+mBpPnFzGhOxMtpCC2vw+xfb1ItZ6gHBcFWIqd7HdsKXFBznNAzrMzC/p4lRa6ojJhnlmByVLeG2dzeqo3Dm1w9Hm8Xw7giKMRhx2NOORsQL29BSWWYkyLTER1sBQTE2VM0PZrHlhKXWyssVf7Ya30MJf2cw2tWSPRY8zYQcq3WLw5LWi3M+PU+BBYyIgs5sAenwB7pBLWUAnkYD7kgRwIiVEUtg4NQpkOqRKQJlog5cvM84uta1jM/pDcMBLHPrSuN3QVhkFesGQtJ6/WFDvGrObsMTK4paDpMWgqGcqizLhmGtiiUNXw8DpeVYPs+HgRRSvs9oq00t1UuDudE+F2T4PVWji8BiDl4z/2teH5bYfw4Keu5EL3O4F3hNAdHwT+ZyIuKMhlk4Rymwdw+AE7Nd/INut9I9uOXMBdYFpzSIg/h9Cig37UitKDZLIXiUTH8IAWZ30HFKUzPYCIUBQXEgl3dovnIBbzsMdHw2azsUUAtbIy0oSWAk4Xdobj2BaIYHswin3hGJKqhqr245h7rBZzmw5j8eBBlFl74chLwZ6XhMWRrbU1IEP1zAKmXQN5xfsh5JRmPa7qKtPSHuw7iIP9B3Go7xDTkKsnLOBJKFlVugqL7TNREHGg73izGWfX1IB46AT3OFo05eWjYvoslM+YhYoZs+EvLh0eyGggDYcPYmBwCwYGNiMY3JllMSQrYWHRNSgqupYtIunvhheOy2fAUv8CjKPPAC1bWRzwELoqob3Hh+3RadhnVOOgYxIilRbMqziApcU74beHsoXsaDXC+mxYnYtQlLcQlfk5qMhxMuGaBOpR74P+BHNlIyE7SS5tHREYydE15YJFZC50oXwFx9ytOCI24EiqDk3hBqRCEbjjFhTHfShO5KIg6Ud+yo88zQ+fkAOX7IOLueKOHV+eGb8aSwYQTw4goQwgmehHKkauq0GIggKbywGHxwu7PwcOvx/O3Hw4cvNgyc2B5PNB9Hoh+fyQfF6I9vELY28ldH+Ew4cwOPgGBgbfQCCwE7qebY0SRQe83tmwu2cgZalASMhDT1JBd6ybWYE6Ih1oDjWjJ3ay58UQboubCd/zCudhfuF8zCuYBz+NMachU7ARBAWh0H4Eg7sRDO1GILALRjIMfzCFnEAKvnAK3ogBKWMhP4zNCxRMBfKnAgVTgIJpQP4UwF/JEg+OiZYCBptNwZ2s5Z37ga79ZujKiZDAXjLXFMDLFwMVS2F4S6FHU0y4HhGszf6UCiQBkLw2ttClBa9EPblophe/QwtMZnVMqjgQjmEPtZDZAurJ12C224FVuR6syvFgsdeJPd1b8cjRR7CxbSO09DghCzKWli7FZZWXYWX5ShQ6C0++JMz1NAylNYxkSwiptshJVi0Swm3Vfjim58E+PReS68yF0p6eHuzbtw8HDxxAtL8fViUJW1KBXVVR6vYg32GDSxAgJpNIhIOIh0OIx2OIJ+JIJBVyyoQiS0iyJrOkVqeDciDYRBV2u46cXDcKJkxG2ew1KF14MWw+/9tmqSHXYLKOkzBOQnlnMIG2wRhaBmJoHYgh3t6Akt7DqIk2wKGPuDMnBQtaHeVod5ZDL6lBWWUlZpT5MLPUi5mlPua2fiplWG9vL/bu3cuE7UxBmyzZVVVVmDJlCuvJ0+xMLYpKLIbj+3fj6Buvo3HXdqip5LCyuWbJCsy/4lqUTTfnKQaZbetfBjb/FGjeNHIicg+f8z5g1o1A4QyWYI/m3h1dO/BG2xZs69qGeMY1oTjdFeG5WJtahotmrYF3SRkCjgg2tW3CpvZN2NKxhSkhh5AECdNyp6XHrfmYkJiKnr0J1O3shpKhgLLaJWZFrpiRi4rpuRBsKRw7doy1+vp6Fto3RKGuo/roMZQePMCUtGcCKV9dK1bA/773ATMXYs9LLTi8qR1qOr5bdgzCN+EN+CZtgeS0YNbML6GkhMZPiVmjE0f6EdnaCaUhkJXNzFLmhr0mB7bJftgqPRAs5z4ee4ieUDc27H8Z9UcPwdtnx4x4FaoS5SzN2YlK9OFEeyJgn5ILx6x82KflQHKfuzwHNI7SupPmllBoH/MmJCGb1qVjIzDlm91exhptW635rNmsBentPFgs9NsY/VoyV/5BhY2jyZYwFBpPO6InJbCg+HhSLtCYSt+P7LfhrYKUC6kUCeGDzGiWVHqgMKMZtW52jRSlC8FIHwYVJ4KKF7GUE0ndgqRmTTcLUvAjohagI5yDjrAT0ZQTKd2CJz6xFPMnFuBChVu6z/BCvJ3okRC6v/E59PT2orCocMTiOqKUGvKNS+9QFtqhbNs0+RjM4G168OnpfbPGoNk01kTKrM1iEjWIQhIC4hD1GAQtDiQjQDJqWm/eLFbPiAA+3BeabpeeEsBfYU6AJLSfI2s6CQX0w47FmtLup03MAkY9DZIsqskQkEi4EIv5EI/5EIvlIJEoQiTiZPGgJxKz2NDlzUUSIoq6ezCr7gjm1dfCHcvWTFOGcNuUKXDMmwPnjEo4c2KQwwcgNG7MdrGmL2jixebEP/1dgH30+zGhJrCvdx+2d21nCwKKUxpa7BK59lxcNuEyXDHxCswvmI94MMAE8M5jR9F6+CC66utYPP6JQnjV/EWoWrCYWS8sthHhLpUKor9/I3ORJFdJ0mgO/51rKkq9a1HQHoSy8+/wJVqzzqsWzcW+4vdgozYbGztl7G8PQRJSWFKyG5dUbsREb9vwc5MJC2LHfHDsF1FyzEDelNlwLloI58KFsM+ZA9F28gSh9seZGxtN/EpjcFR3NsEqsoWAtcQNId+Kensr9oR2o6HjELram6ANRuGNyfBEZXhjlvElwjMM2FQNDpWSLbnhEb1wUyys1Q+HswBWZx4sthwI4mlKuJFQ5MsQiphAlCEU2S7cXJbkAmfeFxswMPA602pnQi5kOTnLkONfBp9vPlyuKRBJWXcayHreEm5hAvjx4HHWk+WUFsAp/eTvd5JvElvIUltcvJhZw09kVMGmrx6ofRrGsZeAtm0QSDDOQJUEhNwylPwyWCZdAd/0u2DJnXpuPXwofwQJ39TadkJv2Qc1YoFqlJlNL0WKepSbmcFPEfNoKXRCLsyOd5Rz7Ey5dDbQQq4pnsTuUBS7QzFsDURwOJoWOkiojO2AK/QUpFTL8N+QEuTGmhuxrnIdfDbfmb2eZiDVHWULR6UhyH7XRjxjnBIB20QfHHMK4JyTD9E5toBKgkltbS127NiB48dHLIxWqxXTp0/HtGnTmJBHytTToUejUPv7ofb1Id7Tg+bjrehobUWktwfJ4AD0eARSKskW9nGrjLjFMrYLvGHApRnItTqQl5uHwsqJyJ86HXu7erDutlthexvWIJHBARza8DIOrn8Jge4RJZDk9AATZqE7pwaHhULU9yUQHcNzIs9lxYxSL2vzyv1YOCEHOQ4Jhw8fxq5du9DS0pL1HdD1nzFjBvsOaP9cQQJ4/Y43cODVF9Fee2j4OLm+r7jpFkySmyBs+amp/CJIiJl6FbDwQ0D12lMqzpJaEju7dmJj60a82PgC+lIjseEU93tZYBlKkvl4xbcde1y1bGynuXhV+SrWlpcsh5yyofaNThza1IFA94jlz+G1YtLcfFTPK0DZ1BwWt3oiWiiE/hdexKFNm3AsEUdncTGM9FrQkkyiurUHM0JA/gQHnI5ayIF9EC06q+SgFa+EOundUKM64nv3IrptO/RQCKpkx/HKy9BacQl0UvZR6EGFG/Mvq0R+dSs6u/6M7p7nYBjmus8tz0L54P+DcDgHekYSNesEL5xzC+CYnQ/Jc+6T9WVdB13DhrYNeKzuMWzu2MxyWAwpNRYVLcJlJZdgUctUOPYmx3RvJ4u2fUoO7DPymAX+bF3HyQocDh9GMLgLAVLiBnePGq5HkEBNHoOsOavZ/pAr9flIcGikNCTbI1CaglBojXQ8ZFYOyYDmC8eMXNin5zEPI4rRP5/QnNIXSaK5P4qmPrORsq8nrKA33SLKqfMnjcWvbs7HVQsv3AzmXOg+wwvxdkKDbd2St+9momRYosMBweGA6HRAdDkhsd4OyWGBZJdAY4ZoNSBZNJYgSRQVSHICshSFaIQgkLWe4qI15cyE8yEBfKj3lZtWJXLlJEH9HCyAKdY5FmtAOHIEkfARs48cRrdqwQHMwxF9BrpiE2CJiCgIB1A62AtfInqSKOWIxVDY04PiQAATSkpQOHcuHPPmwT5rNiT3KAtm0rh3HwRqn2WLf7bwHkK2M+s3Fn0YmLDilJ+TkoSQVv211teYhj2gjMRYk4Xp3dXvxg01NzBXNiKlJNBRV4u2IwfRdvggOo/VZrmlU1z4xHkLMWXZRaheuIS5LmdeKxK0ujv+hr6B11iJHkLUDRT1KKhoVxDNuRSvea7BxvgkbGqOIpROCOKQY7isciMumbAZbktaQBMo6c8K5A5OgXWfDmXfISQOHWLupJkIFgvss2fDsWABLGXTAbkCyeMKs/xlIQss+ywTsss9EAotaA02YOuBV9DccBDxzl54guJpk9ZZSKBOqixhnSOlwp40e6cowVNYDG95BWzl5bCUFEMuKoaluAhycTHL8J7pus0SpjC3uLiZIIXc4/rj0FhCmfiYVvghRJclSwgny/ywQH4KoeN8TZj0uzBjVDcwTX6miVWS3KaQnbOMuWnT4mK8cVnjgQRuyjBL3h5DcZEkkJ9IubscC4oWYG7BXGZZIqVHPBnH5jc2Y+HUIhjHX4Path2pcCcrLkclAVOCANXhRypvMlRvESJyEiG9E7F4CxOmWO4ZQYbLNRVe3zzIlhwzpwVGEpcNb7PACRGiMHqTdBHumA3usB3usA3OkAWusBWOoAxb/NRKCU3ohS52wJA6IPg0yCU+yDVTYKlZDKszBw7JAZncB89T6E+PksS9tc/g6dp7EWPKSoq3tiPhXo2Eaw3m5E3BtYV+XFvgwwTHm7OiUGhIqj2CeO0AEof7kerMUGZKAuzTcuFaUMT6IffeZDKJnTt3YsuWLYhEzLAOuhZTp07FnDlzUFNTc0bW5KSuMy+mjQPhYW+mxBjpzy2pFCpDvagYCKCwuw25vZ1wBfrhiIchqgpUkXIdjD7u0PiSE40jX7SgJL8IeVWTYZ86BbaaGtbknBycS+h+JffsPc8/jYZd21jcNvsMdgdqFi/D9IvXoHL2PIgZrt30N2Qhr++J4GhXGIc6QjjUEURDbxRaxjWxI4Vpcg9myD2wpucH+g5qaqZg3ry5Z/wdnC3kgr73hWdweNOrUJOmgFhkD2NFwXFMyklBWPRBYNn/M9cUZ4iiKnjw0INM8OuJdkNNh85Q6bLl4bm4IrUSM5YtwuSlc5l7fF9bBPtfbcWxHd1Q094cslVE1fwCTFtagrJplEtGGFXhE351PULPPYfopk0wMmLplfxJaJ42G/XFLkTSSUrotebPn4+LLroIOckO4PUfAwcfM3NOUHjhik8BF38euujA3r/twa5NA8ySSHiDjagJb8X0z7wP3ssuHR5DYrEebHnheyju8sPbugyinv5dOwy4l1bAvaSEec+cb8hr4O/1f8fDhx9GW6QtS9H37snvxiUVl8Bam0LohWZoAWVYGelcWARrhRtqdwyJ2kEWZpYJzamOOflwzimApXhsxeYQsdhx9A9sZOugwcGtLJdRJoJggcczEz7vPLjd0+Fym0K2LJ99+dRzJYQrx8OmgaI+wDwBMz2kaK1BY6ljei5zt6fs8W+G/oiSHiNCONIZYgI25RihHBWnQ6YqPrKIlKojNcZ4S/Ue3JYIvLYwPNYIvnPlFZg3ZzYuVLjQfYYX4u1ETyTQ84tfMNei6qpqVkdxKDstE9zYhGlmAWXuJOwx2jfroxhqig3Wwy2ZzN4fOhZPx80lEjDGkbF63FgsbNEg5eZA9vsgeRyQ3VZIThGyXWe1dWVbAhZLBKLSBSHUDsT6Tn9ecvWkeMi8GiC/BsibbDbappj0MySh6dgajGD9QBgb+sM4GjMHU3JFnFd3GEsO7cWKw7tQ3NMPVZIQ8PvRU1SI7pJi9OXls5qimVA8Gmn0acFH7uinXQhTUqYDjwL7/wr01WWcaCaw+MPA3FtP65pPgsmOzh144fgLePn4y0wgH2Jp8VLcPv12rK5YzRb/w3+TVNB26AAa9+xA4+4dCPWOaGoli4VZv2euvgQTp0+DVP+C+f4a1rPa2V2FNrQXOxB1jwzQB/qm44XmdTgyMIWZcgtcOu6Ysx3TPU9BpHrazF2/GOVld6Cs7H3MbSoTuh+VY8cQ27UbsV27ENu5E1rfifeDANFbBqlgCmzT5sB90RI45k5CRA6is+EoGg7tRnPtPqj94dHL2BsGnEkVzmQKTsVsrvS2JycPzooKWCoqYK2sgKW8AtaKclgqKyH5/edMoGGZpiMpUyBPZ3XVhoXz+Kgxr5lQEhopzwELxciRIM4EclNAp8fOxfvUdYr5246e3hfQ1/tSVowqQeEFeflrkZ+3Bh7P7NNasln8qRofjgEnq3ZmLDjbV839zEZ/o2gKW+wmKCu2pjCPD2oxNTaqBfzthBbfBalclKTyUZYsNJtSxPriVB6r9DoWASmMdms3S9TXZu1hPe13WvuQZFnuT41ElbFlB+yynTUHbUsZ2yccc1qczG3fZXGx3m11D+97rB7WO2Uny/j7Pzv+B7u6d7HX8Vq9uGryLfDnX41XAwazhGeqkOa4HbimwI8bivyofJMCOEG/i/jBPsT29GQJ4BQfal2cj8NCK97YuQ2x9NxFFSsWLlyIBQsoYd74re69yRRe6Avhhb4gNgciiFGdsQx8YgpV+iGUGU0oQxtqXE7Mq7gek4tWwX5i9mrDQIeSwvG4grpIHPtqjyK4dzfy2puRG+iBPU7j4cnWY6uqIScSR240gbxIHDkeH+xpAdw+aybsM2fBOnECc6E+E2isr319I3Y/9xT6WkYUViVTpmHOuiswdflKWM4whCWR0pgQvqe+DQ0HdkIYaGFVHIiIYUWdWoB6LR+izYllVblYWVOAlTX5mJR/9hbGcdO2C7Gn/g07DvRi72ApVJaoEpg4ew7W3vVJ5JaemcDdGmrFX4/+Ff9o/AcGEiNlOUtdpZB1CS3xEU+vmbFqvFe5BrmpeTjUMBLrnFfmxuw1ZahZXMSyfI9G/OAhDP7lzwg98yyM+IhiWcotg1S4EJbyxZDySuFaUgznsmI09bVi06ZNLF6eIG/IxYsXY/Xq1XCGGoAXvgY0vcYeCzjmY33q39GRllv9hXbMzuuA/e+/hNZpejqQ23nR3XdD8pcg8GIT4nt7hz22FG8bBiqfRbhoBzw5s1Az+Svw+xfhfEFzwsNHHsbvD/0e4WR4eOx5z5T34IbJN7CcH+QVE3yuiSnp2Od3W+C+uAzupSUsUVsmalCBcnTQVObVDWZlUqf4btfCIjjmFQ6HstCcFY3WoafnefT0PndSQkRav5hJOSlXwwJ4PbMhSRdmGFgmlKQuUTeA+OEBJI4OZmdLlwXYp+YyRQSF9ZyuZBvlkdh9fBD724M43BFkgjaFrowG/eRLfQ5Mynciz21jVTcoNrulL4y2YHLsko6GgdzUAGpiTVjmjWJKeSFLGuwvKcGhpr249oaPsjw3Fypc6D7DC/F281Zn6GOWG0pekxbAzSQ2CVY+So+EoVFmxRD1IejhiNmz/TD0cJhZ57WBAaapPROoHrSluBiWwgLIuW5YvBbILgMWEsqtYVjEPoik5STBfKxARhIoSfimch6szTIbabQzJnn6jPUxBesHQkzQpkVjPK20mNjZhqWH92Ht0UOYfPQQpMxsrZIIfYobsakxxKfEkaqg2oIiQqFCxGNzEAyWoq8vXbYnDd1jM2fOZAlQSkpKTr3YoL+jUlo7f2cK4am0AsSZDyz/F2DxR8Z0PT/RHe7V1lfxxLEn8EbHG8PZOyk51funv59ph2nxnf3SBrMQ1G3djLqtr2OwcyRDqlNOYbq3G3NzOiFbBGzw34CXLeuwvs+NAlsDLp+wHguK9rHao0TcqIbPNxd6bANU1VygkPVz0sRPoaDgCohpd7ZTLbKju7oR3dEFtaMNWv8xqP3HoA/UQ4/0sE8TcNrQ73Yg4LRj0OtkFssTsaY0eBIKvPHkcE8Ctp2s1dXVsE2uhpX66mpYJ1WN7pXwNqAn1GHLuFlGJD4soOuh5Gkzusr5Y1jIXafOxEoucxTP39vzAnr7XkYqNbK4pFq/OZTRPncVLO75iMOOYDKIoJLRkkGElNBInz5GPS2axirTcq4YsjJT4jKHpsNt6LAaBmTWyCPHBbu7GLIrH7Jkh0WysBhk1ouUSdtcYAxZpocS6qVS/YhGjiKRaGFZ32VdRpExHWXSEuQqxfBEHXBH7XBHbHBGrZAoBfwYpCQVAVeUtQFXBIPOCAacIQw4w4jJCRYuQomYSJlAjX7L1FOOB7atxpFU4+Zz6LlvYYZauhYFzgJM9k9Gnj2PuZH7bX5IsgcNigX7ohIOxCRoohu66AZEGy72u3FLSS6uLvDDeZbJyzJJdkYR292N6M4uHFPasd1Sj5hgWrf8Xh9Wrl6FuXPnnjbD+BB9SRVP9gziHz0BZtHOFLMLrDJW53gwz9oNf89P4E9sY+IHWbGqqj7PEkyeifBImeUpbv4vnf14vHsQaiKB4t52zG+vx4Lu40h0tbFSUydawgtCURSGYsiPxCDRfex2s2RYJIQ7yAto9mzIYyh3ye1630vPYtczf0csaHpCyTYbZq66BPMuv5pVuDhbqATWa6+9xmK2h7KPF5eUIr96NlqNHOxuCWJPS+Akt1HKmk7C98XUJufD7zyHLrbRfuCV/wB2/8FcJ1iciM39CLb3lmDPSy+y8Coqabbg6ndhxXtuO6WigbxXyJPsT0f+hNfbXx8ev/Id+XhX9bvYPEo5VQjKt/L7vb/Dsy3PQk0rU6bHJuH6rutQVrgYU66chOIq76jfEa21Qs8+h8G//AWJ/SNeb6T0tVQuAeyzIHnLWLiUe0UZPKvKsrydaO6mUAr6LhobG9kxu92OVatWYcmSJZCOvYj9f34OW3uuhgobZEnF8usnY9a6iaz8Eq3t+n7zGwzc/4BpUbfYYJ97Gyzly9m5rNU+eNdUQJoooaX1PrS0PDCct6Ok5D2YXP1vsFpzz9EXaK5fKFfEbw/8dljBMdE7ka1drqu+jikLqexW4KkGxPf3DVu2PWsq4Lm4dFyx5JSkLXFkgCWBYwL4UJJBSYA8W0R06k70Ks8gGhtJDCkIMhOw8/NWIzdvNQuxO+/Ko/OMoelQmkPsWsSP9DNPvCEoPInc8JkAPjWHXRvKAbGjeRA7mwewo3mAebuMBinWWNhJiRfVBS5IgoC2QBy7mvuxtaEPfbGTFY42qnbEqlWY44EdKi4vVHDb/CLU1FSxSgWZHjg8e/k7DC50n190RWHCtzowYPb9/dAGBqEN9EMdGITa3we1uwdqV9fYpacyEQTmxkuWR2uhDxa/DKs7Bas9BKvQCTFwzEw8Nxo2H9SSeWjOm4XXnNPwZ3ESDsBMhCWrKrNmX3ZwN5Yf2A1PX3biC0tpKVwrV8K98mI4ly1jmaBZSZjQXuZm1E+ZP8MjMWSplBXh8EyEQ7PR2SkhRVlE0+Tl5TEXMFoUUl3RUxIPsMyq2PZrMxETQbHuy/7FdBWjTPLjgJJUkYb+0bpHh7XFtGD+8OwP4+YpNzOLVxa6DuPYi+h9+V4cPtiEI8FCxLSRRVGrowL7PDPR7JzAaqlSXdhqp4LbV+ajyvUs+nsfy8rKaZH9qJ78ZZSWvOeUpZlo4aAcCyD8ejsrOzIkn5EQ6ZiVB1RZ0RlqQOO2zTh+cB+UZHa4AiVuy4kqyIkm4KcWV1jstVxYCMeC+XDMngPH3Dmwz5gB0XluE/q9lehJjQnfzDJOrS8tnJPrekas3WhQcqoThXExV0JA2om2wScRHnwdhj7i6ZKCDV1CMY4lPTgc19CbCDAB+s1AwqxLdmVZWWmb+hMbWVmHLLQ2yTZsobXJ5jY7NvyYDZbm16Ft/AFSLTuQhAVJWFFv9eAluxO7bG4osKLIXoQlhUswM3cmJENiMcBDjQSe4W3KnKuo0JMqtKRm9kqK1RU2NIGVRDcdy0dqIgxZhJioTsn6rDIsNissDhusThusbhssThtTnjocDrYwpv7ENl6BkTAGmpBqeBVK80bEW7YgER9EXBCQEAUkqPdXIl46B/HC6Uh4S5HQk8xLIK7Fh70NMrPQU0/75CmTmS/ibDAECxO+DdENUfZhgrsQC3PKUO0pRJ4jjwkvQ40E+EwvnFPR0dGB5559Fq1p655bt2OBWoXJKIZ7QTG8ayvYvX0qz6bn+4J4tGsQGwbpc448NtfjwNX5flya70UNS2T1n+jqeoI9ZrHkoWbyV1Fc/O43HT4RTKn4U3sfftrQhkDaS2SZ24YvWRRYmo+lQ4AODLtHs9cnIWcwgtK+IHJiiawwJ7mkBM5Fi8y2eBG0gnzsefZJ5kaupHONePIKMP+q6zB77eWsbvbZEo/HsXHjRmzfvn1Y2K6urmZCXmVlZZYgQu7n5GK66VgfNh3rxc7mQSQzPAgkUcDSSbm4alYxrphZzKpRnBWksN77R+DFu0fWAXNvAy79FuApYrsDHe3Y8IffomnPTrbvLyrBlZ/8PMqmzcg6FSm0nml6Br8/+Hs0BBuGj19cdjHeN/V9rCclXSad9QFse6oRdU3HcbB0Aw4Wb0Iq7Z0yPzoNny/5JOZevSrLdZfKbg7++S8IPPEE9KEkcxYLvJdeBsuUtVDafGDe66IA9/ISeNZWnDb5F5Vge/HFF1lJNqIgvwD52kz0HTGvebl1H9Z6fwlvkRd438Nm+cK08BV4Yid6f/4/0LrNuHfbrLVovPZSrL79uiyDDyXAamz8MTo6H0m/5RxMrv4KSkpuetNCKBkJ7tl2z3DGdwqN+5d5/4IrJ14JSaRa0Qbi+3qZwK3HVJbvwbW0BN5LKs86MZoWTSG2twc9R19Cn/NpRPL3Udwce0yABbm5K1FUdDXy8y+BxXLhGujeLHRtU10xxPf3MmUES9SZRhGB10UNT6gJ7D3BQ2dyoRvzKvzDyRWnl3hYNYWNdb14ra4H2xv6EFCyvYZEQ0OR0oNipRsupNDom4aO9Jo8xyHjk2sn49alE+A+RW4bLnS/w+BC99sATdC6OtJoUadr0GMRJnynqJEg3t2DVHcv1J4+pKh19jBr+5iIIqzFebCW5MCWb4PVq8LmCgNiB6yxDkjZxSoZg6DM5S5IbQoSnQKUgAyDMpjLNjgWLIbropVwrVwN66RJp51IaBLq73+NCeD9A68NJxyjmuPR6FwEBuejvZ0W9FpGnFsNcwOjxcqYJcnYSVQzNmvTD836wIS7GFh3NzDvtlNnTs6AFthU1ofi0dojphW7wFGAj835GHPXkilZHlkHdtzHhPwew49ntaV41nI5evoVzAodxsT48eGFnuHNx9RLr8O6a67Ey6+8jCuvvAydnQ+iselnw8lXMhNrVVR8kLmVnzhp0WQf29OLyOttbMAfgrTr8UoFbYNH0bx/F7oa682FVRrJMJAXiiIvkmCCtjehsJq7FLJtOUFWIMGbLUaXLIZz8WJYq05dz/WdLJDTRJkpjCu9EaT6YhDCI78BgxI/5dQhVLwVkaKd0Kwjpc20pBMDgVI0RryoU4BeS5CVYYmK8WHpkoRKcj8mQcln9bHea/MOb7N9q3d4m47T4yRIk7B8umtPwm80GmWNXIYTiQRb6A/1mdusDw0gGY8gqQtQceEmoBsvTqeTKeXIAyuz9/v9THFH+6OOGfT7oFJI9S8BdS+wKgJZpc5IaVdzBTDj3cDkS0YNxSGr+m/3/xa/2f8bJnTTGPG5hZ9j1m0SykkYJy8Gyh9BjZQwo22TZf5MII+DXEcue71iVzFrRc6irO0caw7eeP0N5k5LC0QSBFauXImFJTMQ29jJ4hUZEgkppfCuq8iyCB6NJvDHjn482jWAwYzs7CRo31iUw1ziy+3mwp3KJx489DlWQ5fu+LKy21Fd9QVWa/ZcQYvGJ559Dk0zF+LX7X3M28oiCLi7ugQfKy9g2bgp/Kdh9w407HiDJT4bwuPyYILdjdKuPsh19ZRBzjynKKK5wIemwhxWJo3IKSrG0ptuZbWsT1U/+3TQ75KSo61fv5799ggq87VmzRpMmDBhXOeIJzVsa+ofFsLrukfGHhoWFlbm4MpZxayV54xTORruAp76DHDsBXOfvNuu/gEwwbTUnkjDru14+f5fItJPtYwFLL7uRqx47/tZfDYpp8mdeaiSAikGKS/KLVNvQaW38qRz9RwPMWG75ZD53YiygJkry1C52omHjt6Px5ufYJZv8n65IXoZPrXu83AZQfT95rcIv/DC8JxGyn3KJu5cdjlCL/exSgUEZZr2v6uaJUscL6QIIe+DF198CYlEnCmwnYlyXH7lpZhb3gDhqU8B4Q6zHOwN9yJhvRiBJxtYuUEKTdS6XkF8x2NsvaYUF2PKHx6Es/Lkz07Jw2pr70Ykaq5L8vLWYvq078JmO/NM0lR28Ps7vo/nmp9j+6SI++S8T+L6ydfDkvaMI7fogb/VIXHITGRHcdg576lJlxc8OzRNQXf3k2hp/R1zJR/CHqyGr20lPN1LYLF64VpWwsaU850s7u2GQka2Nw3gtaM9aDnShykDKayDBYVDGeDJO0gG2sqdcC8swtwZhchxWdnfbWsawMajPXj1cCeaB7ONIrKeYgJ2WaIDVVIICyYVoHjKdDwdKsCjh4PMtdxhkfDRlZPw0VVV8NhP79XLhe53GO8IoTuVgLb55zh6tBZTp0yBxBZa6djtIVPg0PaZ9CwGPEP41bW0AHzCsax9/dSPpwXo0Z+T7s/SvZTerqaISEYkpCIy65NhmW0rIRl6agyhVTBgIwHcn4I9x2y0fWLd6DGhwd7mNsujUXI36of3qfea+1S6yJkHuPKg2T0IJOvRFX4DvYGNwzUKVdWCeGwdurqq0dUVzqq9uXTpUsybN+/UGXXp+h9+Anj5P4BAOitv0Wzg2h8DFYvHfS1pUf1U/VO4d/+96IyacVyTZS++3NWBySEVz2tL8LRxMbZrNZQLf/jvFlT6cWmFBeXde9G65VUkouZiyenLgbO6AJMvOoJE0py0cnMuRk3N3QgEd6Dl+G8RT7QMJ9wqL78DlRUfhEXOYzGaoVdahjWq5D6XrAHatDoc2/sGBjs7st67FwLyuwdQFIrCF1PYNBD2WSEtmY+qS2+A56KLWOK/2N69iO3YgdiOncxdLzMJDXsfeXlpi5AphNtqJp9xjOSFAilTyJuBFClDLXN/KLbfqsuYK3qw0KljkmcQNuuIEktSvPB0L4anawkcgRqKmj/pdXRaq3skyD47rF4zNwO5q4su2eydFkhuC/NMIGuOYJWyMqIyC0U8zsZbGnsp2RW1IeE6c3toQf9mIMHeKlpgESRmLbKLVsiaCIkaRMjkim6Y21ktfUwUBEh2CyuZIzmtZi4KjxWyxwbZa2WPqWofOjofNT1dDIFZQsvKbmPZ2lkpl7TlnBYHmT0l/SJlQaYSYUiBkBmaMhZkCacSSySADzXKI1FUVJQdgkQWv/pXTAGcBPFMTyAq+zj1StMamM7e3Bxsxtde/xoO9B1gT7l60tX42tKvnXk2csNg8fZDAvhgIoDNfR14pacFDeFuiFqQNQdCsOkhVuN+fCcG7JodDtXBBPEFVQtQlVfFEuiVe8qRO+BC/NVOKOQuSveAQ4ZrXQU2T3Hjvo5evBEYcYUss1nw3uJc3FScg8nOEesqCR3Hj9+LxqYfM48mqhk7a+aPWMzmuSZz0dinG/h6XTue7TOvxSW5Xvx0eiXy07XRdV1D68EDLDnYsW1bWFLMIUqqazBl0hSE6+pwoKUeqfQ864krqOkeRFEwCltVFdzksbV6FRyLFkE8w8zhra2t+Mc//sHKsBEFBQW44oorMHny5Dd1DVr6Y3juYCeeO9iFva3ZHm/zK/1476IKXDunZOxFOCmkn/4CkAgAkhVY+zVg+acB6dTKBZq/Njz4Wxza+Arbl4tz8MK8drSKpqcbKX/eP+P9zBuMFIwn0t8RwfanmtC413w+JUObtqIEi66eCE9GcrHWcCu+t/672DDwGqa3Au/dLGJm88h8RF50ObfdCtfyixFa34bIa23sPhc9VuS8uxr2mXlnpSCmxG0vPbQPQUc9FIf5ndE4cfPNN6PYYwH+9kHojTsQUD+CmHaF+RlcMnxXTmIJyGLbt6H9i1+C1t/PPAwrH3gAtqpJJ70OlRltbf0d+71QeBIp2KdP+w4KCi4b93t96fhL+NaWb7G5irxdSMHxqfmfyrrulI27/49HzLWCJMC7rhKe1eUQRsn2zp6f7Ec02sBKxpqlY7vNetK6wjzxSHGsJHtZ2JCWdpUXRRuKi69HZcVdcEgTEdvVjfCWjhGXa1mAa3Exc2OXfRduDPGZQnHVLx/pxguHupkiTMmIdadbb26ZDzcW+LA0BribQllJYAcL7XhJTOH+nkFE9WzPwxKlC5WxVlQJg1g8tRzVc+aiYqZZpva1Y3342uMH0B4wr/275pbi7mumn5GnCxe632G8I4RucjH+n/FpkN/RkKseuWuR+zH14lAvZ+xnHjO3qQJjRFURDKmI9CaRGlQhDuoQAjqEAQ3pUL+TkB0a7HkpOAs0OPIV2PwJZISKnDMM2Q7N6UFc1hCVIlBsEhSriIBcidbkOtR1uKAkzQmYXE1J+KZGlq4xURVg+2+A174PJGiBJgBLP2FavkkBME5SoQ787dUv4xcDexFMZwBWw9OR6Ho3DNU/LGhfM6cUV88uRolvxCqWTMRx4JUXsPPpJxAZSGudXSmULo5h+XWfQ2nZjRm1v1X09DyD5uO/Gk5EIsIGf89a+GsvgyWRB9WhoiuvHfWtO9HdPBJDJUkyimUbchtbUBAIw562TjUWAwNLp2Lxez6JqYsuO3WcMgk2e/elhfAdiO/bB0PJvjEoSZpj0UK4hoTwqVMhnI8b4iyhZGItIbOUFglG5HpHjTK5Zib1GY1CWccKr4x5jiT84ogHAmWeljyLkFdwJcpz18ETc5uZ1TNiyfWQAi2UNN34TgNZc8JCAlEhgUi6j0pJREUFESQQNRLDsY7jgRZFDnIbF62wCRbYYIHNsMAKGTZdhDVpwKJZYaN/hsyOy4YEC2RYmNhsxmOPcXJodqBPHECz0I4eywD6LAFMKK/CFXOvQV5xMROwx1NGheVC6HsRdXXfZgs8orLyI8wqSou4M4EsVCR409wUDoezemoUQztA+TLSLr0nfSxBYMIQ5Y4YasXFxaYyjxSfrduAI/8ADj8FhEayABueEjxavRg/iBxBXFPYYvcby76BqyZdhXMNJRa7r60Xf+wcGE5UVm4T8ZFiK5Z7VAzEe9EV60J3tJv1VMe9ZaAF/cl+6MJpMv4LIrOIV8plKOjJQa9QhJ0Fleh2l8OQPCwR6eV5Pry/NA9rcmk/+/slBemhQ19Ab99LbL+o8FpMm/ZfkOWzt6SdyaKR7qU/dPTj3+vboegGiq0W3D9rIhb6skOJUokE6nduxeFN63F83x4zcWoGvsIiLFm2GiWhKGKbtyC2Zw99uKz8Ka6lS+G59FJ4Lr0E0imSzdH9+Morr7ASbASFP6xbt44lqaOM2eeSzmAczx/sYgI4xYsO6Z/IAnb17BK8d1E5lkzKNcf7VAJ4/t+AXb83n1Q8h1ltUZTtKn66ElR//sfP0Pq3l2FNCkhYNBxcbuCmyz7CYratJMRnQrWYB/pw6Kk30LPvIJziABxiCCUlKoqKNVgRN/OwUJlV8lAzNBi6hkizip6dCSS7zA9kCAbUSRImXLMMvjkzoQp5CL2RQnLADdUohHN+GfzXVZ11lYq9L7dg89/MeXTCrDxMWm3FCy89x8YQ+s6uuuoqzPRMROBPu6AptNbQ4So4Bt/HbofoGZnnY62tqLvtdth6eyHl5KDivt/CMXPmqK8ZidTh0OEvsioXBCkfp9TcfcoxkBTG39vxPTx27DG2Pz13Or654puYmZf9GpTbZfBJ8uYwIOXYkHf79CzrNgn7odB+DA6+gVD4AFOCUt3ns4UUbX4/JUdbiBzfMojNXoRfa2N1sBlShvD9Fta7Ppd0hxJ48VAXnj/Uha2NA1lVCIq9dqyaks8SH1LOBbJkD9HeE8GBV5thOzKImoxlVBA6XtJj2BM7BrfShKWVXkybMxsTZs9D4cSqYaNGUtXxvedrcd/rTWy/PMeB/7p+FtZMLTzjz8CF7ncY7wihOxWH/vQXWexaRUUFRBI22ULhhELdWcfG6ofqebMC3pSe+hRCbloIPlHwHX7+iX8jZT+Hzs2E6DHOk/m37JzjtzI2xRS8OhDChoHwSRlmRU3D9R1NuPrQHkzaugVid9dJJdAoW/uJsLJUkyvhKLXD4euHSzwCSc6wttF7pNinkjlA/hSWnAVKCFAigBI2teyxfjOJC/WUgX0cdc0VWLBPmIltwiL06+YkQjlAFtcU46KLV8JVMnVsjT291otfN+O+CSqr9u5fAFWrT/2ioU7om3+Gbdu24LHUUjxnzIZa8DosOVvN2u2GHWvyP4QvX/TBU7r3UY3K/fs/j/Z9vejaVYBUxFwg+ItLmKvetOUrs6zHtDDsangWTXX/i7g9nQVUF6F2V6Nhs4B4fzqJlSihNCcPhU2tyD/eDjk9GbQUAJtmirBctga3r/s8JuecnYVFJyvjgQOmEL59B1uQZmaJZe/B42E1wk1L+CIWB55ZDuy81bGM97GEPE2hJiZcs3rVoePDHgljQa7cVKOaWqm7FBUOF4q0Fkix3UjFR2ISRdGO/Px1KC66Dnl5q8ctFJLbOiVwUwaj6O+gGt39GAwMYiASQCBOrsZhRNLWgtNhNyxwGXY4DRschgUOWOEw0i1jm4TsU9Y5z4SGGYcM0S6blnaHzKzuZC2CU8KBhsOYv2IRrH4HE6ZpMTtUampvz17m9UGJkgiKIf/wrA/jzpl3npRs8FSQwHbs2HfQ3mH+Ht3uGZg188dwud6cJfDk19HYnEXfAQng1Pf19aGrq2s4e/eJkPBN7r8TJ05kvYOSR7XvZtUIIgcfwTfcEl52mb/1pboF/zX1DhTP/yDgOLflqjIJpFQmYP62rRe9SVOpU2634PMTivG+4lxWOoa8Akgo3b17N4ueL68px/xV8xE0guiMdDKlU1vYbOTVQdntx8JqeDDZWon5VXMxs2AWZuTNYMmZKE6UIEvYvv0fQzh8EIJgxbSp/8mSRJ3PMJSxFo1HInF87FAzjsUUWAUB351SjttL8076+57mRrxy/6/QUXck6ziNu+RKTm7TBRMmseSm0S1vILLpNURf2wS1NyNvicUC90UXwXvNNfBcdinEjMRiVDXlySefZIofgryxLr/88lMrhc8RPeEE/r6nHX/d0ZqVrGlinhMfngnc0nQ3LL0HzfXMyi8Ca75irjvGyZb2Lfjhrh+yzPyuuITL95bCNyiyWP2VN78XixZWQeg9DPTWAYEWGIHj0PuPQ0qHjp0OUhhEu2zo3e9BYtAUWgTRgG9SDHnTIrB6RldAUp4UgeZyqs5SMH0kKSytP2TbaUvtbXmiAXtfMj3L5qwtx8U31zDlIY0NTzzxBI4dM+feaq0IK1PTYXMZyFX/HTZhPzD1auA9v6P6ccP35wuPPIIZjz0O5fBhiC4XKu79NfMSGw2yIDc2/hTHW37D3FK83nmYPet/YbeXnPTchkADPrf+c2yOo3H+rll3sdhtSmqZ+XmCLzQjstFUEFJZq9z3TmHjdyLRyZJ99vW9gkBg53BitxOFZ4e9DHZ7KauakkoFWNlLJdk17Hnn882DzVqEeKIVsVjzqLW2nc7JKMi/FL7kRdA3uZBqSleGkQWW2M67pvwtL+N5NlCm8af3d7Df1e6WbK+S6SVeXDGziOVVmFbsyRr3ekIJPLm3A//Y24b9HSNemqUQcJOawuWCAznSyFxprfbCs6IM9ml5w/MsQWXDPv3nPTjQbnrz3Ll8Ar5y1TQ40948ZwoXut9hvCOE7nfQjXW+iKgaE65f7TcF7eOJbGG2VNBx2/FjuGjvDuS8sRlGRlI2wW6H66KL4Fm3Du61ayDn5rIs64mDBxHfu5dZQMnyeVIiN0GAraocrko7nM42OG2NkKwZbp8keFNc5Oz3AgVUHmuUGZe03dFeINxtxk+FOoFwJ4xQG1L9h2EEW2CJJ5gjrw4BRzAZm7AEXTA1fjYoWCHswfL8CKwFNAFPNV+XJmAqlTYkjJP76NOfYwsDtgBZ9SVgNS1AThjI4oNofOEXeHxXG55ILUM7RuKuKnOdWDNLxaHUfagLmJrq+QXz8dWlX2VlOShR1VCSIxKeW1ofQEPDD2AYKVitBRjsfzdKxKnY8dRjiIfMAbWgciIuuuUOVC1YAiOlI/RyCyKvt0PTVLS4X0So5mXYC/uHL1e8oxDOQ5XIe/4obAlThRp1itgw08CG2SJ8M+bgK0u/itkF57YeI7meU33w6JAlfNfukzLvU+I1qhM+5I7umDWTKXDejOWaFh204CMhe6gfVAZPKVjTd0GCwiTfJJaJnpLMkJBNj5E7XU/Pc+jq/geCQTNR0FDGVUoEQ4I2JYIZT81QcnXu7e1lrqRDPQl4NFaeCqvVyuKOfR4vPG4PvA43PDZqVJLKCY/NyUpmUQ1zKtdCiyqzNwVnNj0PWZdp0qf/JJElJRPUEITdv4HQ+ho5BkLIq4B42b9BmLzUfHwM4Wi84yfV/v7Bjh9gf5+ZPbjEVYK7l92NVeWrcCb09r6EI7VfY5nfJcmFWTN/whQd5xtS2tCcRsJ3Z2fncKNjJ0LWbxLAHYUO/Kjph2iMNEGGgM8PDOL9wZAZXEAKhznvBZZ8FCg+fzVQSWn6cEcf/relBz1p4bvGacOXijzoeek5tLeb+ScuvfRSVoN4rO95MJnCjxvr8OeWw0gp7ZBSHfDonXCpXQiNYfUipQrVc6/xlMAZXo8KaQBFdj/mzvn1eS2FNJ57k+a9z9a24Jle8zf3gdI8fLumDFZRRDQwiNf/8hAObniJDZ5U3nHh1e9mAvb+V15gdbiHmDRvIVbcfDuKJ08ZSVhZW4vw+vUIP/c8K9GYqWz0Xn01XNe/G693drJEaUMhUNdeey2qqsxM3W8l9H5JQHh0Zyv+sa8DS9Sd+Knlf+EV4ohIPvRe9r+YtOxd4z4feQxR+bvX2l6DYBiYCRs+WbAUiw03NmyoxaFWU4k/L6cDa4sahoejTBLwQ8ybAGthpVlZhELLnLnpsDMXYvU96H34WcQOmtZmwW5DzlUXI/e6FbB4rDjefgCv1P4NMILIV3XMSeSgDBJksRfCUMWSEyGre+l8oGIpULnc7F0jihhdN7D+D0dQu9W815ffUI35l2cntUsNxPHK/U9jW+Qws7YXOfJw+0fuhLd/G/DIHYCaACatBm75EwyrC/GogueffRFrly1Dz93fYGsm0eFE6Q9/yLLnSxYBVlJwnlCVoK9/A/MYUdUgczefNetnyM0Zia/f1LYJX37tyyxpY6GzEP+98r+xuHjxSbleBv92jIWgEd7LJsC6woruXgpxIKv9vqzn0+vk5CyH37eQ1cp2u6cNz3VKsg/1x76Dru4n2T55rkya+GmWp+HEEl+qGmb5HALBXQgEdiAY3AXDGPH0oioshdZrYd81B3p9OnGmXYZ3bTncF5WN6e7+dqGoGtbX9uCx3e3YcLQHqYyskeTJSLkTSNCekJftTUMx2i8c6sJjO1vxen3/SFYkw2Bu45OjjVjoS2LRojmomr8Efi0fsW1dZib4oUTwPivzCKBEd691BPDpP+1hlQz8Tgu+d9McXD6z+P+EbBQap6wpGOMJLnsHw4XuCxOq3XcoEmcCNlm0dwZjSGXcipRo5mILcEPdQcza+QbEN7bAiGXUs/T54F67lmntqeYkxfieNmNjSwvi+/cjtns3s34mG0asgwxRhH1CPtylKbhdDbDnJEYcDUrmmYvTWTcBnvEPIvS6g/2vo+voz5HoegOOhA57XEcgMRfbAjPQpZo/TheiuASbMQ+HRiJtKeN44QzT8k5udSSE7/+Lmb2VqFwB3HQf4CtDMBzBk089hkfq2nFEdkC09kK0BGG1BpDji8NijUHRIwinwqxEylhQQplcux8OPQiHPogC2UBVznQsnvxZHN/VgxuuuQGGpmL3s08x4TsZNxcPxRNqMMu+Ar5EHpojB3E0vhORmOkS7S5OYuLFcch5I7HbtoMCcLgAf/aG8cZUwOvOxxcXfRHXVF0z7uzGbwZDpVIitcPu6FQvXD9BcBEoy/S8uUwAJ5d0+5w5EMeIxyf374N9B3Go/9CwgE0Lv9HKZ9Hnq/RUsjI0QwL2UJ9jP9nqSAsEEvK6u/+BgcHNLAY1/Q7h9y9BUdG1KCq86qR66EOQmzJZSocENBKuqQ1ZtsYSrCk2kBbi1DK3Xa7zVH+3cQPw2EdMJRZ5x5Bla+WXANl6Tidm+k0+3/w8frLrJ+iImvckuVh/ZclXkGsff0kcSqxISbgCgW3su5hc/WVUVn70bUneR/MclRNqbm5mjb7vTDRBQ9AVxLqF63DJ9LnwND0L7HkY6DGVb8PjyYpPA1OuPCOvpDMhrun4Q0cffnq8G1owiGv3b4ZHicNis+N9N79nzNhhEk7vbe3FvW09CKVjEWe67fh0ZRGuK/QzF3JyY63duxcHNm3FMaEJxxytaHS2I2GcbBkvdORjYfFiLCxciIVFC1Hlrzpv487p7k2aC+l6fK+pi40Wa/0ufLbzMHY88jCSae+cqStWYdVtH4S3YMQ9s6vhGHb843Ec20pjgnlNqhYsZsJ3UVX2dVTq6xF69lkE//4kUh0dCPh8eGP5coT8psv54gULcNmVV7Lf/duKYSD5+s9heeXfWdm+HfoUfDr5aXQhD4sn5uD/ranG2qmFY/7GSNH55+0/wqF9D2J6Io7ZySTmpQzYKFxr5CWwZ7AU67tJuSBgSomIuVWVaGp2IaQWIG4pwYyrl2DGmmpWautEErW16P3JTxHZsIHtk2I259ZbkffxjzGFf2ZJyNaHd+N/Iw/g+ZzN7NgyYQF+cONP4DMUoL8B6K83kyJ2HzTbaJVZaN6vXApj0lqs31WDI9v6mVV73R3TMG15tnWZykENPFIHI66iyx7Gy/JBqHERHks+Zk9dCKO/D5H6A4im/IgLuUjqLibIjwfJIsLmlOHy2eDy2+DOscHhS2Aw9gA0eQdsnkFMn256jjx0+CHmYUBrDfp9/XjNj0+a23RFY/HblJtBl1Iwrm3HgOMVDAzQtRpaowiQjRlAfDmQWABdqWQRNBabBKfXyhrF1guuzWg8/h9Ipej6CSgtfS+qq74Iq/Vkz5HRSKVCLEEuhZyQlVzX0/lnBAvy7JfAt28d5Gbztyfl2eG/porVt347E7XSPEb5Ef62qw1P7+9EMD6SQ4AyjN+4oJzlSSg6IX6a/o6SqD22uw3P7GtHNDXy/ZckOjE1cgwLvQoWXbQUU5evQm5p2ejlXrd1spKOetRUVlB+4uf0JP6CJPIn+vCzW+dnhSueLfu69uGx1x7DN274Bhe63wlwofvCoS2RxGsDYbw2GMamwQj6M8psERPsVlwlqrjswG6UvvE6EqSBz0iORYk+PJdcAs9ll8G5aOGbdgUm97vo9u2IbaO2jZX2yETyueCeYIHb0wxXUdy0gtPCbNIqYM77gBnXA9bxu+BRDFJj00+Zq1T6E0FTb8Phg04EQqbVtcyZwtXuwygLbDet6CdCr+8thRHuhqCnEJOd+FbOQjwjqRBsPRDS5UvOB+QeRtbXWfmzWJtqr0LwtQPY9/wz0DTzdUVBgp4WCu0eL2bPXYiiXfuhb92OVKmOyBU64ov04QzZDQkRAecKfHTpT5BzHt1cT4ehaVDq6oYTs8V27oQ2mL0AosWVY+5cCIvn4fisfNTnJXEoUMuE7aFM8SdCQtyUnCmoyakZ7qt91SeXcBtF0CZLQk/P8+jvX59Vns3jmYXionehsOga2G3FJwnY5IZMlsMhIZusorTwHw3SyFJ8MCXooj4/P//8CtajQbHLm34ArP+OmUWrcCZw471nZHk9G204CWm/3PtLPHTkIbY4pFrU31z+TVw64dIzeOsp1NX9x7C7eXHxDSzBkJiuQfp2znsPvv4gdhzZgaJ4EVxqtoWDSj7NnDEDM3xxeA4+aMaAD2UiJ0+bFZ8xx7hxKDzOhvq2Njz88MMUTIyAw41nZy/HtVUV+GpVCQqslqya13/s7Mf3m7rQl54vprvs+PKkYlyZ7xv1HtXCSQw8cpSVJtSgo21BA7a4f4bj8QTaNA+OJ1JQTyiTRknkFhctxkVlF7FSUZTA7Vwx3nvzxb4gvr7xDaxe/wRKes3xhCzXaz/wUZROMUs+jcZgVwe2Pf5XHH5t/bDwTUL6ylvvhK8w+3MYuo7tTzyBF/fvhyYIsMfjWLJ9O8rjCfhvvQW5d9yRJTi+pahJ4Nkvpmtvk4nuA9g/5248sLUdzxzoHLbekVvsJ1ZXM4GCwhPQdww4vhm9tU/COL4ZhalRQr7Io6NkrtnIhbtwBmqbBvHcvb+CrmkQ5UpY3O/C5IXlWPm+GiZYnkiyrQ29P/4JU14wyV2S4L/xBuR/8pOwlJScJJT0/f4QyxROHjobZx/CD6O/RlJMoUQvwC+u+hVqiqdmvwCdc6DRzMlAFQmopSuZ0EObwh/FgdjVEKDj8pWtmHzZctMzThCYINX/YjNaXmzBoGYgZJUQEkVEAmMkvBkL0/GI3Sek9GCvfYoSoFl/KqZg9Xag3xfHXuEgujxNWD13Kb6+/OtZ7uRDoUx9DxxEpLsBwcoNCE3cAtUYmW9jfZMROr4U4fb50BJj5yIQrVEUzf8TfBNMbw0jORHFOd/AlLkXw2o/u/Uhzb3kSdbR8QjCYTPZJOGTF8O3/1LY26awtZBtSo6ZcT7/zQuWZwJVBnhybzse2nochzpGDAVFXhuun1+GG+eXY2rxyfkpwokUHt/djt+/3oCmjDJh3lQIUyN1WGTtx4rlC1jISn7lxHHN/4aqI7yvB8eebURRdGRMtdb44V1ZDluN/6zXEZqu4f6D9+NXe39l5sG48g+YUzQHFyrc0n2GF+LtRFNVVi5k565drLwUm5gFgd2sQw1ZPblZ0qbpbkluZ5LFCpn1I02WLW9rtmaK5yOXcRK0SchujGdPAE5JxMV+N65IhLBk93ZYNm5grk2ZJaOs1dXpRDCXwk7uvucz9q6rC9HNWxDZuBHRzZuz3Y8lEc4yC7yF3fCUJyDbdbM0z7z3A4s/bMZmjRNKBtLQ+CMMDGxi+6LoRzx2J/bujbKMx8SiRYtw6cJq2PuPAF37Eevch/39h7Ebcey229Avirinrx8zkinQUvR7eTn4s8fN3HrLPBWYnFPNXJKHSvGwGrnpkk5kzaZJUBIkVi7op7t+igcPP4g5DhVXelXEdSAh5cNRcAO6FIXFGzcGG9Ebz65tTsi6hKXNZZh8jLJHj1jQC8srsSCYgPj6G+knyvDfeCOOXjMD32/+AZY5QljiUilciuFxz2TlxoqKrjnjxFTnA1p0kCdEePt2HDm4AXv79qHOE0VDqYC2PMAYxR9xknciZuXPNl1Zc2pYo+s+XigWjeLXenteQP/A61kl2ZzOahQVXYfiomvhdE7KchFva2tjWYepJ2FbOSGB3FAmbHI3prhfyn49JGRTwqS3PYnkYx8G6l829+ffAVz9/VHLXJ1psirKzhpVVMSSGtum5C5UO5h6csdj+6qOxvARPN7yE/QoZtKXeb6rsTrvLoiClVkhWREIw2DlTqgnaBiiBT/Lfi4AXuMp+FK/YAvipLwCMee3IMt2WCURdosEmyzClu7tGb2drEeyBOs5dFekygX3bL1nOHHRrVNvxYcmfggN9Q04cuTIsCu3+TkEZl2eO2UCpg68BMvuB8w8FgTFnJK3wbzbz6nwTRb5P/3pT+w+zS8qwtGla/Fo0FwEemURX5lUgg+W5eOV/hD+s6GDxTwTVQ4b/q2qGNcV+Nl1PxUU0hDZ1IbuLa+hdcH/QLdE4XXNw/yFv0fSkFjm9t3du7GrZxf29+5nFtJMqHTaRaUX4eLyi7GgcMHJybbOsdBNSdO2/O1P2PnM35kSKmG1o27V1fje+29HkWN84yHVp9762J9xZPNG0x1dljHvyuuw7Ib3sXrd9D6ef/55Vg6MqJ4wAWsUBYlHHoHa0TkcquW/+Wbk3fWhkwTJ8wrlTPnL7UDTRlOxfPk9wLL/N5ynhhJCPfB6E/60tQkVqSYsEWux1nEMy8SjsCWzk0zS0j+WOxHuiashlC0EyhaYcdMZoViaqmP7003Y+fRrSIafom8A+RVTcNs932FeF1nni0TQf++9GPj9g8MVMrxXX4X8T38atkknZ/pOtoWZwK1HUhC9VuR/YCasZW7s27MNX9r1FXRZ+uA2nPjpmp9iycRlp74usQEYLdux5Zku7D1G5bx0XOr7GaY6NkLRnWi3rEOr5VK095RhMDa6kOnwWKAghKgaACxJLF25EJO8IThe/Sxs+gAss6/Gc5bLcM211wzfn/SZW+76MKsKIhQUouzBP8Lw5iERTSEaTCIaUBAZSCDYF0egO8aampHtegiySBdX+1BS7UPpZD8KJ3lBInzLI4+iQ/ob4oV7WfUZIhXzI9h0MYLHlyMVKYTLZ4Unzw53rp1Z1mWLCEkWIUoCknENscQB6DnfhWjtY2Vg+2uvQt/ha1kJDnouJZebsrQYE+fks6zzYzGQUtEQU9CaSKI1nkRPMsW8Lmm8dydrMS32KPJj69nYTrj1OcjZfR2cAzUs3ptqh3tWjp1h/VzR0BvBw1uPM8t2OGEqIGneuGZ2CW5aUI7l1XmQRvmc9T1hPLi5GX/b1Yq4mi5dpydRE23A7GQz1i2ehtlrLkFJzbQzXl/TPPqpP+3BS4e7MQsSvlmch5LuxLDruVzkhGdlGZzzCs/o+rSF21iFDQoHI2ZZZuHn7/o58t3jX0+91XCh+wwvxNsJlbX4xV23nJdzi5IM2WoK5Va7HVa7Axa7A1aHw9x2DG07h4/RBE1WSofbA4fHC7vHwyai0/0go6qGnaEYtqQF7X3hWFblbFqczvc4scrvwtqedlRs34rYK68wC2Mm5MY7JGiPVr7irYASscV272ECOLVkY+PIg6IAVwngKR2EpzxuliarvgRY8jGg5rJx19Tu79+EY/XfGa4bKUuz0Nl5HWpr26BDRyIvAft0O+oSdagdqGUCciY5KR3f6+vFsnRsdFgQ4DAMyKTVr1gCTFoJTFwJlC445aKZrCMb9n0e+sDTbL8h6cCK+fdjVtHS4efQgu2vT/8VxfOKURuoxf7je9DReBQz6uwoGjQXKYqsIWbXkBMxX0vSdFT1hzB/xWoUfOLj+HXP40xzSdBC9rvLv4xo75Noa//TcIIUKsdUXnYbSstuOcmK+1aQ0lLMRXx3z262KKdBf6gcVyZ5EQHVbRomdxqY3AlUdRlwS044Fi+Ca8lSOJcthX3atNNmR4/HW9HfvxG9vS9iMLA1w3WcBO1JKCi4krmOU8Iu+v2RS/iQCzEJL2TVPhFaOJWWlrI2lN2a3MNPWSf+7SDYBvzxZtPFme7Za39k1qRnWm6DaeYHYykEYkkE4ikEY7SfRCCWYq501EiojiZV9tzu/iBEq2NY0CYr6fhRYSt8Eda818zXTxQj0X4b9OT4s63Oyj+Mf5l7P6xSCgf7puEXez+CpD4+YY0WT26bPNLsMjzpPnPf57Qi12lFjsuCHNp2WVnsHAnuBNXY/sKGL2BLxxbmLv3lxV/G7dNvz3otyo5++PBhHDp0KEsAp8oK82bPwCJrI/L3/QqIpGOk/ZXAqi+b3804x7axaGlpwUMPPcTGE0r2duutt7LX3R6I4OvH2nEgYo4DPllCMF3FINci4UsTi3FHaT4s48g0P0Qs1oQd22+CqgdhD0zGhGNfQeH7FzMB6EQlxeH+w3ij4w2WaI8E8swQHIoJJ+v35RMuZ7H/TkqweQ6F7qY9O/Hy/b9CqLeb7RcvWo6fzV2L4xYnqh02/H3B5Czr/+mgxGsbH34ALQf2mu/f68OSG2/BrpYO5vlCrF27ltU+pzGBvHzCL72M/t/+luW9GEo66r/lFuSTy3T+eV7oRvuAh28COveaJTopydeUy83HSMlFbtcN64Hm12G0vAFhSCmUJkGJSmUPdrk05Ey9Gu9a/W24XGPXkCYB8YX7DqKv1fQkK5+WRMveB1io1IQ583H9v34DMiVj1TQEHn+cuZJTSS3CuXwZiv71X1nSzdFQmoJM4DYUDZZSFxO4pQyrec/xDnz6hU/isK0BFkPGfy37Nq6edu0pL8+el1qw5TEzbnzRuhyIgQa0HFPQHS6EwUTYEbz2KIpq8lA4tRwFlR7kl7thc1rYPfjII4+wBGukgL3lllswWTtmKjoMDccKr8bEj/4h6/6k8I/jd9zJ1me26dMx8Y8Ps7wnY82b//Hid7DvSB0KYqWYq1TAFpwCLZk9/lGSObuvDa6yXXCXHIDN34po93QE6tfALq1A+bQClFT5UFTlHdXbgCCFKnkWUSUJUkw7HBNRM+l7iPVNRMexABp29yCYrn/OrkmBA/MuqWCu+KQE6Egk8epAGNuDEewKxtBwgjFoNPKNHlyNf2AtXoIVpuJFDi9A+f6bYIuWMeEy58Ya2CacWxmDXP9fre3B77Y0YXN9f1Z+nvcvq8TNCyuyso5n/h2VByOr9pamkVxGOclBzAkdwNoSEYsuuQRTl10MS0ZSxTOB4sE//tAubKzrZfPXz2+dz+LGqSpKZEsHoju6YSTNMVx0W1j9c9eyEkiuU49lzzY+i//c+p9sLnNZXPi3Rf8G4bCAa64ZUQpdiHCh+wwvxNuJEovhkf/8GoLBALwer+nfo+tMWUQWN6R7toQ0dDbosFB80sZpOjQ1BS1lNlVNZVmKzxWkNbe7PayRIE5N8ngx4HCjRXbgiGTDQcGKsNODmN0FI73Ap2Q5q3I8WG0TMffwfmibNrHsqlpvhrAgSXAuWWwK2pdcAkvxWy9snY5kSwvCL76I0HPPDy9MGALgKkrANyEOT0UCYn4FsPijwMIPAvbT329Uaquj8xE0Nv4InfEAahMSjiqVqI32ISVku9/rKT+02ETWKuNWfEDbi+vnlsFLcxNzyaPCnxZ6YvaL0CJxwgpg8qXA5MtMq3xagaJpCg4f+VdW7ovYqRTgjz0R2GUnvrvyu7ik8pKsheNVV16Frn/swxvP/AVtMdP1TZAlJOYWQOs5gqteGkBKsuFwaT4CLnMwV10SmuaK2OyrZ9frjhl34PMLPw8LvVd27kG0t/8Fbe0PZ5QAEZGftwalpe9DXt4aiBTjex5QdZUtuLd1bsPWzq3M6nVidmRadM8pmIM5+XMwO382c60nC3ayqQnRrVuHwxNOTNQn+nxwLVkM59JlcC1byrw2aJEwGNjBBG2KIYvFMpQ5LBv2NCZoFxZcwZK5UD3rISGb2mhCNrmDU9WD8vJy1siKfa7L/JwLaCFAQnNPWEGsZR+mr/8wnIluhC35+GXpd7E7WYHeiIL+SBKhROqcDWPMyiyLsMpSxna6SWZPxyySiJBwEHX6b5BCGBIcmG35FxRIc5nCg+S9IQsrWbw1arrZ2L5uIM9yEKvyvweLqKAzPh3Ptn0e4aSNWQQSKZ0tVMjqPtSfK1xWCT53Ckr+vUhKx1m5vlX+z2FRwcUo9NhYXVRyPyzw2IYFdILup3379rGWmYytatJELM8NYHLtLyFETWGQ5Ze49D9MxeJZeBxRmMPvf/97Vp6qurqaLfwzF1GktP3IoWasHxjJNbDI68SDs6uQd4aZb8lrZMfOmxCPN8PjnI2yrV+A0SUwV9+8O2bAPmXsUJagEhwWwDd3bGbVBoawS3ZTAJ94OVaXrx6XAD6W0B2PhPHqA79GLVmmydsnvwCXfviTLC67Oa7gpj31aFdSzJ3+8fmTkWMZ/zWg9UHzvt3Y8If7MNDeyo5pDheMCVNw0x0fGDV2nv4mumUL+u/9DWLp5GpUdiz3zjuQ95GPQHKPv1TluKHEoA/dYMY2U6Ky2//GwqeYkN243uyjJ2SatrqhlC7CIwE7/t47Dwe06UjCgmmlVtzz7oVYOGFs9/ij27qw4U9HoSoa7C4L1tw+FdULCtF+9Ageu+cbrC46Xf9LLr4Ufd//AUtGx15ywgQU/tu/sUStYxkfKLlU/0OHWUJRW5UPeR+YAdF28ncW7ujHvz75eWx2mla8L8/5Eu6Y/4FRz9mwpwfP30vZ2wGbS4aSjp8dwmtRUCodRaX9VZRa9sMlpV20KURnzs3ArPcA/gp2iKoEkOBdV1fH5gf6/dVEtgNPftJ8/OofQV7y4azzp9rb0XTze6ENDLCwvrKf/uQk70lyA/7Kpq+wPBnkPXf3/A8gr/8+aKoCG66GR/gsGvfVo7clATWR7fYsyjoKKvyYtqwYNUuKYXOc+h6nMKva2m+gs8v04ikouBwzpn8vq9wfqxDSGmE1zA9v7oCSLoVp2CUcnOvGUxUi9Ixs20SZzYJKhxUVdisr4UeJDOkp9KyBlIZOJYVOJYmeSBsu0/6K1XgVEplGDBHu9nUorbseouaGd1U5Swb3Zq3e5IH11L4O3LuxAcd6TOUQ3XaXTCvE+5dNwKqaglGt96qms/jun71Ui8a0CznV0p4Ua8YitRFXLZuJOZdcjrxy8po4e0ix/ZEHd+KNxn5W6u++DyzCRZOzlXN6XGVl4CKb26EF0157sgjXwkKWjM5SmD12krfR/2z/n2EPLTLMfGfld1BoK+SJ1N5JvBOE7nOZoY8GHF1TTQF8SBBPJaEmk0gl4iwxS3Koj8fNY4mRbVIAJKJhJMJhtihIhEPM/f2M3oMgMoHc53LDpWqwDQzC0tEJR1yBI6nCkUzBbrPDfdEKuClGe80aVkP5nQIJ4KHnX0Do+eegHB4p4yJaDHgrY/BXxWAvdUKgjMDkIuca3VJAk9Xe3r1Y37IeG1pfxfGwuTgawqo7UBwtYDGZtngZ9iamYZWwB7dKr2JuiQvCdT8GytPZd4+9DDxyJ5CKmnFec24xLQfNr5vlzTLxT2AL59TEZTgQ/hMGY/tZwhCqVevJuwJf2vglZimjuCXKbH7rtFvN+/ORxyHtqUdd6zYYzIdBwMzV67Bo0QpEfvhjliGeSFSVYv0NE7Er1IIp+yW4E+ZE2utX0LRQxpql78Ka8jXMDTtzAUPxsZTIpK31DwgEd4xcB2s+CguvYZm5qTzJmwkxoN8Hlera2rGVCdk7u3ay5HJZl8fmx/zC+SwBDA380/KmDSsIxjyvrjOLABPCt25jseEUnmCIBlJlBpKTDSRny1AmqzDkjFq6gsTqhJKCoaDgCtjtlcxNnCwSVMpnyDI1WlZqaiRsU/z1hQDFmrUH4mYbpD6W7uPoCCSYiyhZn5cKR3Cf9QfwCHHU6WX4YPLf0IHRfyNk5fU5LMyaS5Zdn9MCf3qfjrvSlmGbCBzcuwvrVq2A12lnx1yUbMcqj+pydyp6Y73sN0DeDkPW4tum3Tbu+45K2+zd92FoWgQ+3yLMm/sAZPnk72jIBV5J6YgkVUQSKiJKirkOUvZXWtgMbdNjtB2IJzEYTWGAWfyTzBOAhH1BDsFRcT8kezd01Yl464egJ8zF9omQdZwEcUqwU+q3s5KBZX4bbLFedDUcwvHGkQSTxUWFuLgohhlH/xfiUNZ98p656n/MCgvjhLLiP/DAA4hEIuyeveOOO7KSdm0ZjOBLR1uHQ5DyLBL6U+bvZI7bgZ/PmICpaSXe6aB6vnv3foh5jtjt5Vi86DHIuh/9f6plSZtoNZ136zQ4Zp3egksW7yMDR/BS80t48fiLaM0Yo6naw8qylazW88rylZDHUAyONreTdfuFe3+G6OAACxNbcM27seLm25inWWbZzHfvOcayvZOH2CPzquHJUJiMh8MHD+Kp+34FuauVLb5JYKISY8vecyssVpuZ6FMZZEkfqWRhT6yH1Uy37z2GuY8dQNFxUxET8ch45eoS7F+UB5fNzcZIagXOApbng5JAUqWFM3LDp0Riv7/WrPpB2cGnXAF07MlO7jesNL7ILJM54SLskTR8dfM3zDwamh3TxE/hQEMhU2oRV80qxteuno6K3JFFfSqp4bW/1KF2izmelk3x47K7ZjK35SEoG/xj3/kmM2KUDIYxr6UHEuW8+JdPskRpp6pkET/Ux+4vaAbsU3OQ9/7pEKgu6BgkesK459Fv4O9uM7/Ll+Z9ER+Y+8Hhxwc6o8zCPfR+hyD36orpOaiY5IVrVzds0RSzpOffWQVL4HXgwKPAsRczSpkKppJs0V1AzeVs/H300Udx9OhRZvH+wAc+gNL6P0Ha+B0YkhXCh54bWVOkocSzLR/4IHOtz/vEx1H4uc8NP0b3zz3b7sFfj/6V3f8/WP0DpqgfHNyOPXs/jPhgLiRZhcXdxZSoSrAMgwffDSgLEQ1TGP+I4pGSw5XW+FA9vxBV8wtOsnRTsrP9Bz6RTlwpYnL1l1BZ+bExx2VS4j3V3o/Nr7WhaF8IuRHztQbcIo4v9aNmQSEW+t1Y6HWOW6FFita6aAJ7eo9Aaf8pKpNmCUpNdaHk2I3wt66FXuBEyS3TT/KoGQ805v95ewvuf70JnemQG/Jwum1pJRO2M+/pE4V0Soz28xcPoyNijptWTcGs8GGs8gRx6dWXs1ht8uB4s5DC+M77t2N78wCbZ3/3ocVYPHFsRRdlqI8f6EN4UztS7SN5iqg0nHtlGVNQ0Zrsixu+iPpAPVt3fnzux/HxOR9n9xTPXv4O4/+a0H0uoIGlKa6w7OJ7g1Hs6w/gWN8AhHgEjkQM9kQczkQUzlgEpckYipUYvPEwxFAASjhkWuFPA7mrUyZWX2ER670FRaz2c25pOfxFJcyy/k5AaWoys8I+/gTTCg9h86WY8O2dbEC++C7gos8B7gIk1ASzorza+io2tm7MKh8lCzJK7RMR6M1HZ89i6EoJioQo1tnqYYMKydBwuWU7llx6I4QlHznZ1ZNq8pK7LtUPz5kIvP9xIGcS0HsEaHgVOPYS0PJGVm1xXQACOQ7Y5n0UrvmfAjxFzPr739v/m02kxOfmfAZTdluw++WnoOrm31ZWzcbqD38IwrPPo+83vyU1OqvvWfD5zyPn1luYWzXV2P3osx+G70AQsxt8sGim9rexJIpd0wbhzstn1qLVFauxtGQpW8QOEY02oqPzr+jsfJyVZcqsyVmQfwkrz+T3Lx5XwipFU7Cjawc2tG5gJWROrIftsXqwpHgJew/U0yLybDMZa1qClTkZHNiGwY6NCCmHoYvZiX3EAGA/LMLZkYvcvJWQ5y5DR0EBGnt60NDQwCyBmVAM9pCQTS65b0X93LEgl+6mvigaeyNm3xdF64ApXPdHT1+zfpFQi4ds/wMHFByxzcHDE74Dd04+CtymFbbQY0e+m1ymrUyoHm+s87keP5NaEt/e+m38vf7vbP+9U97LFFBjCVaj5W3Ys/dOlpSHMszPm3s/JMl5XrwHjvYfx2c2fAJdsXb4LHm4rfIeQC1CT0hhig7yLGB9SGEx7acjV05igaMfpWonxHS4g8Plwar8fixpvR8SZfal5EqkVFzzVcBxaoUphUTcf//9zK2d7uUPfvCDw7kEQqqGbzd04KEOUzFYZJVxT005rs734qneIL5a14ZBUtyKAr5WVYKPlhecMp6b5p7a2q8x7yGqz7to4SNwu6cOJ/0Z+OtRtgCkEhE575kC14KicV9rdu6BWiZ8v9j8IlrCZp1kosBRwITvG2puYMLnWPemoaaw4aH7ceCVF8xrXVqOq/7lC8Olvk6kNhrHjXvqmbVtmc+Fv8ythn2UjNqjQaXA6HWJCSXF8IV60LTTtGDrfhvqlko45GhDODlGFQPDwOI6A7ev11GanqaOlQAPXC6x3BYnQlbO6bnTsah4EVNYLipaBDe5i58Iee8dfQZ44hPpZKEsdVfGEwQz4Vn1OqB6rVk2S7YxBcj9B+7HL/b+goValbnL8F8X/Rd7Pbq/f/xSHR7Z2cpicclzhTKdU8I1JZDEc/ceQH9bhFkLF187CQuvmphlKSSBcuDBB3H4d/djR2kuy9kxs6AUl377fyDnnDrBZ7x2gFm4SeB2zM5H7vumjsvSmeqN4Qd/+Tb+5DXDur4w84tYmricWWe7h2pEpy2c1QsLUbOwCBUzcmH0xVkSMj2agpzvQP5HZkH2ZyikKAP64adMAbzZzBvD8JYDCz8Adc5t+OuzG5lil36Hd95xB4y/3oFSKkHpKQU+vhFwZ4fVBP7+d3R+5atsu+wnP4H3yivY9n0H7sNPd/+UCUrfX/19XDHxCuZ9eXjX39HW9b+wuM3ktFrKhlTjOhQfvwpT37MQ7kXF0FI6OuoDOH6oHy0H+zHYFcu+BapHBHCLcwB7992FaPQY+11TXfC8vJVjJuy9r60Xf+zoRzg93km6gZt7BUzZGYIRMY1I5dNysO7O6Sz7+dnS1vs6DtV9G7Jiuv/LoSqUHfoALJEJUNaWoebSSUyZcDpIgUo5Cx584/hwFnKaD++6aBJuX1YJr90ypgD8l+0t+MXLteilZDy0RtLimBfcj3dVWXHxtdehYuacc5YLiSzpn3h4F14+0gOPXcZDH16KeRX+cY+fyaYQwq+3I3Gkf/gnHy/U8b/2h7HBuQ05jlz896r/xrKSZRekbHQquHv5GV6It5u348aiH0FvUkVdLIEjkQQOR+M4HInjaDSBxCjxkG5JxDyPEwu8Tsz3OrHQaYW79giib2xF9I03mKVTV1UkZQmKRULCbgMmVyNVUY6Ex4VILMri1iKD2YlPTkSUJJZ1NbesHDklZazPLa1gpQvIrf1ChCyd5JIX+NtjzA2dYsKHYpgsVXEcXCpi/ZQp2JzqR0IbiSGi2stLiy+GEZmJLQfz0J0ukSwKBpb7d+H/Kc9hvtaEp4TLcRRmsrYZM2bg+uuvH728C1kPyF0vcBzwlAAfeBrIz3AlVCJI1D6CwR3fhr83AIeSuQgXzFjwadfAmHYtfn78Gbz04h+xqDYH7rgpbOQ4i7HmQx9BaVEuOr7yFShHTEu/5/LLUfT1r8FSZC5iu6Pd+ODzH0RbpI0tSH+y5HtoevoV1L++iQ22qmjgUFUQB6pCUGWDuXCvKF2BNRVrWNzkUOkmslpRCZGu7qfQ1/cyNG1kYqbJN8e/hNXa9fkXweuZPSyEk0so1QklQfuNzjeyEiVZRSvmF81nAzs1WihKZxiryqyUSicikaNmi1Jfy9zFM+t9EuT65vXMhztaBtsREdrGBgzU1aOtqAhtFeXoy88fDscgbJKE6qoqTJk5k7nhejwnZyI9n9DE2twfQ31PJEvApnY6wZo032V+B8pyHCf1lbFDyHv8Fgi0yK9aC9z6F8By9gue8z1+0nf84KEH8aNdP2Kl3yiml2rNnpiJdyyCoX3Ys+dOZvHO8S/D3Ln3QZLObeI6SjjzoRc+xKyT5e5y/Pby36LcUz7m56F4+O4weR0o6A4mmBdC62AMbYNxtA3E0BlKDLv1k6JvmtSN6XI37IIpfEd0C5bKh/Ae/RkmKiWsuehYejf8S9+PXPfJMZjk0kou5eS9kZOTg7vuumv4ft40EMbnaluYCzVxZ2kevl5VAl+G1alLSeHztS3DLudrcz342fTKMWOcW1ruZ3kySKqeO+c3yM9fm30NKMTh8WOI7TRd5nNuqmE1Zs8UupZHB4/i6Yan8Y/Gf7CSgUOQwHlTzU0sCz6Na0P35tyqiXj5Nz9DsMd87QVXvxsX33onszifCsqN8p499UyAeF9xLn4yreKUi2h6bxs2bsDGDabbulam4WDBQWZNKuu0YtmhXDgV8xofnBTEnqlBFHqKWeJNqqdc7Cxm4y8JzJR006HLcP59PZwPPwMxrjCBtPvaJTh84xx0qP1oDjajKdh0kscQjbOUDf7KiVditW8KXC3bTOVvwysnl8YigbB6jSloT1qTVZt6aDz/6qavMu8kgkpL3r307pOE+tquEL711CFsbTS/j2KXDasDIiqjZkKxKz4yC2VTs4VoSt7a+e/fHM4t07tgNnak55kr/t/nMGvN2NUMEg0B9P3uEMUpwTGHBG7K4zF+AUdpD+OnD/8ETTEdU3oXw6pnj4cuvxU3f2URXGmhOtkeQe9vD8BIqGbM+F2zILlPoXim9cCu3wF7/gjE0/eoICE55Ro82D8f7X3m+nhiaQGu7/0phL46s3TgB56iGmFZp+r+3vcx8MADrM77pCeewHOJnbh7893sMSq3SN5AR/dsQnPLf8PiNUPPdNWGRH8NLO42eJViTHP+ELlXTxv1rVIcduOeXuZSn6l0IJx5rXCXb0F+dRMWLf8pPJ6Ts/kficRZ6b1/9AZI/8GY5LDitpI8vKc4ByU2K5IJFXtfasGeF1ugpnRY7BIuvrkG01eUnLVgSiGCFGN+rOEHMLQIDENA7vHLkV9/I1qKfKi6fTpK80e3elMukgdeb8Z9mxoRVsx1w8Q8Jz6+uho3zC9jyTZHg7ybHt3Zgu8/ewj9CfPDutQoFkUP4n0LS7Hs6utGLfX1ZqBx5V//tp8lciOlFgncSyadXaWDVF8c4U1tCO/sgJQ2xvTbQyi6ZAqKllVleYlwofsdxv91oTuh6ehOptChpJj1ujmmMBe+5niS7UfHsHw4RBHT3XbMcjvSQrYL1RKQPHQIsV27Edu1E/EdO6HHYtkW7LIyuFYsh2vVKriWr4DkPtmtktzew32U7KKHCeGh3h62EBnsbMdAexuLqxoLErrzKipRUDkJBRPMRvunW7i8lVACkq6nn8Crb/wJGz3t2F0tIGUZGdBLZBfWVV2LSudybD/iw7MHeoctUGTlu3V+IW6P/A7FR37HjsUcImprctDj+iK2b+tnZaHIYkQxWbSQPYlwN/CHdwG9tScJ3qHQAezd9yEWR+10VGFBxb/D1rQDqH0aaDez2hLdcRfWD8xGe8i8F6N2FfrEUnz5Mz9G7LFH0Pvzn7NybhQWUPytbw1rvon+eD8TBGghRoLAfVfcx6wS7LyN9djw0H1oO2zGqeluC/bPiGJvXudwGTGyMs8rmMcEcGpkeWbXVYux5HN9/etZGa1kMju+WRBkpKQ8tCk6DoUDGNAEBDUBAU2Aw1aAZaVrsLpiLZaULGGL4VOGaOhxFhdKr5FM9kNJ9iARb0M80YZEvBXRWANU9eQEa4TVWsgUAWSJ9/sWw+2ewtzIycWWskdTEqvMBFaEf3AQpR0dKOnoRO7AAETDgG3KFJaQzbVsGZyLFjFXx3PNQDSJ2s4QjnSFWV/bFUZdd/iU8cYUGzwp34VJ+W5U5btQmedEeY4D5X4nvA559MULuY0+SG6FQdM9+bZHzqjc3uk4nxPzy8dfxr++9q/MA4Q8M3645odZXhmnIhjcgz17P8gE79ycizF37m/OWXZ+cq+96/m7WK1xcu994IoHmLvvm4HcFDuDcSaEkwcD6/uCiLcfRX60Gda08B3XBdyAF7BMNPNbrNfm4vvyx+EursKMEi9mlHpZX79zI/bu2Q2bzYaPfexjLKEf1eq+p7ED97WZv9+JDit+NLUSK3JGX5iycjEd/fhmfTtTBpM1/JczJuCinGxlVG/fK9i//+PMalpTczcqKz40+vl0A8GnG1myHxpzcm+dBuecs79ulERqY9tGPH7scRYDPpSEjTxo3jf1fbix8nq8+PNfI3D0ILMek2fXlf/vc8wCNV4oMekt+xpYUM89NWX4cHnBSdeIKkxQ2MzBzQdhazfvscP+wzjiPzI8tpIwPcs9DdV7AOOAOQblT5yEaz/zZeSVVZz6c/b0oOd730foadMya6moQMm3/5ONT/T6pPihbPAUsrOjcxtaIm3Df+vQdbwrEsVtoTCqhkqFksC88gvAtOuA/Jox8wTs6t7Fwj1I8KZx+6tLvorrJ18/ppBE74XiWr/1+AH0pwWZeRYbfvLxxZhYPlJ+So/H0fvTnzELN8v27vezuG3f9e/G5r8+jG1P/JUlon3P3d9GxYyTyxcqLSH03XeQJYsiV9m8O6ZDGKcXAr3HlsMDTABsqx1RQAw6ulCYm4dUuwVWh4z3fm0RfAXmOJnsiKDvvgPQYyqsE7zI/9BMiOMti5VKAEeeAnY+YHq70ZwOB+6X7sSA5oTDYce/3HoV3H+81qxesPxTwBX3ZL/nVArN738/Evv2Q505GR+6thWKqOFDsz6Em5zX4PDB/x5ORKmrVli16zFn5icw8PhmtMy4B7qcQG7uKsyd8+vTjoHhgQQa9/aibsdx9DTRetC8rvSVk4V6ypJiZgGnsmAkbP+wuQtP96YtFgBW5rjx8YpCrMv1jOoZQ8n0XnnwMLoazTl8wuw8XPqBGbC7z37uUJRu1B27Zzg/jhwtRunBj0KJTcahy0tx00WTzBJ36VCsh7Y241cbGliI0FApvM9cUsOSkY0VEkX3zau13fjPx/fgeDg9zqhhLIkexG0X12D5ddfD6R27xNqb4bvPHcG9GxvZe7v3/Qtx6YzxewmdSCgZwpc3fhkHW/bhusHVeE/octiSlpGkaytK4V5WAjGdBPCZZ57FNddwS/c7gneC0E1JeAYSCl588SVcctmlEGWZWRs0pMvUpJ/z/9n7CjCryu77dbumu5PpGYahuztEQlRAQAQxwPpU7P7sxk4EA0FauruGmWG6u7tu1//Z75luEP3093fpec6tudx77jnv++69116L8ll0X2MyMVoebQ20N5rQaDAytVeqXJdp9SjV6dmeqHk9gYYyEo8ItZCxIDvMQoZQhQzeMjFMtbVQx8ZCHUNBdgw0iYktlhnNoImKFD0Vw0ewYFvs2fPE3RtoUGmsrkJ1cSHbaoqLuNtFhWio6mxZRaCeOFtXNxaAO/v1g7NfANtL/mIaLlHHSXyHBEWIxty2uupcY8bIZDOGpZkQqDRAHMTDR+7zsINPFCkeIj1tsGKkN2ZK4iHZ/2iTcjAPxuGrkeRchYoarmohEMzDlctOUCpVjGa8aNEiRjvuhMYK4IfZ7QLvOlEj4q6tYJRXK8v+iCSlZXGbTGVdEZQxO3D298NILOR6toU8I4bYF8LWpQwXjDL0v+wJRTZHBbWYOBGur7zcTt2WRIhWHlqJ9Jp0ZlW2cfrGloC77W+cefkCTv34bUvVx9bPG6qxHjhtiGU9lG1BAcUEzwksAI90jGRVaVJcJ9/z6ppLyC49BI0yERL0Tm+m6jjRfPk8IXh8EQvUSTGcqtO0GY1qFiC1VRHvDhRIy+V+TPzMQhHE7S2CIJG0Zs1JCC0xMZEJVRUXF7f7e+ptJdZCSEgILPl85g1OgmzKi5egTeMqBS3g8yENC2OCbHJSRx80sFsl2W6FlapUiC+sRXJxa5BN1OOuIBcL0M+JC6opuPZ1VLDbPg4KVs2+LpQmcL2bmlquirL0N0B8c/vQ/+xsOF3Xj5x4hLUqDHMZho8nftxnFWuux/tuljQibYLwsA/YufNHUNxYzK4zCrxvVsDdG8ie7uips4i9conphhCsRVrcpf8JDqiD0izBm4Y78aNxMszgI1BQjpGiPDZvqT1HIDAwAHJ7KT6tq0WWhrtWl7vZ4wV/Nyj60KtMi+t7k3KZhRjNW4/7uuARb2e2qG5oTMXVq4tgNCrh5nYHgoNe67UaXLsrE8pLpazH22FZKKRBf9yfmgLP3Zm7sTNzJ9dzTJeuiQe/YjnCsq0wZsgsjF+2+obmps/zy/FyVjGzWdwa6Q8PQQ0ulV7ClZIruFx6mSU7B1cOhncjR29PckyCQ5ADE4EMtQtFiH0IYx01H5eMKxdw+MsNTLdFKJZg0sr7ED5hSq+fo+HkSZS+9DIMpZzopd3y5XB89BHwq5JbxM/M+ReRIQAOKeQ4ZCFHXptrcpRKjTU8O0TddaBTRbvjb/Rz6s9498q7MJgNzMaNeob9bXq25yQ7MBJLiz9fjHNSA65KDewctJIK8fzsUCwc5AHV5Ssoef556PO5FgHruXPh9NT6Fio5Mdd+/+htpF88C6nCAkvf/LCd57m+XIXyz6/BrDZA4m8NhxXhTKSvNxD1OvNqOWIO5zO6O4F+Dk8fKyTqD+OcPAEz0lezx2fcFwG/Adw1rS9TouKreJho7eBpCYd7wvsecHdEWTJw5Rsg7mdUG8T4FrdDCQVCnMVYNCoIvB0rudeRqB31g3fwKs++9VaYG5XYOYIH9cIFGK00gW+zA3whd00LtJMQNex5WNl4oPLbBGiz6qD1y0d+4BssmU3aJeFhH/cqjkrXdGzsXVDXG6CrnANt6SyU5bTauPJFfFT7yHDQhYcsFxFjYMxxssFDXk4It5T3qTXn2tECXNqTzc4ZCzsJpt8bAWefPxYnVFadRGrKM9DqykjgCHY5s2GXNRd7QqwxenYAEtKq8MnxzJa5189RgUcnBzLrr56szRIKa/H8louIq+TWJhKjBiNUCVgxJgDDZt/C3Ib+LBCF/akdnGf52wv7Y9HgG1/nFzQUYO2xtSxJSMKUL418CTM8pnOia2eKYGzylueJ+ZAPcUGWzoiEpBwseWpC1+zOvwn+pZdf54H4X4L8rIPPcpW/PwNSPg/OYhF8ZBL4yiWMcuNLt2USptgo4fNhqKiAJjkZ6qQktqet2buzLQQODpBHRUE2cCAUw4ZCQrZIf5EVEXmZUgBeWZCHiryclk3d0HXF0dbNAy5+/eDiHwBn/0AWiJOX+c0ELcLPFZ1jgTb1aKsMrZV/Cjapx4k26yoZ4j76Ch6n90HclLjgi0zghVvA+NDbCImMAg4+DcRv4f7YPgC49TNG96YAMz//G2Rlv8uCQQE/Aikpk1FWVsksX+bNm4eIiIhuAu85rKfbpHDApf5SqEQaVn0lqqtQ2FpZIvGYq/t2s+w+ieoR/CzsMMq5BA6SWNSmiVF+zRJmIx88MeCyeAysV/4HPCeuX5Kg0qtwz6F7kFiVyNS9KeDu2N/YFiTud3XfLlzaubWF3RA2fjKC5s7ApfpYnCg4wRaTVGFshq3EltHPSeCMBu/fc35nC12qbtkKzBhs64qRDj7wkUrAN9UxNXSNprTFkux6QAG5WGQPscSBiblJpe6QST2YOBPZeSkU/l1m7CkAJIVYCrRJDI2YCdz78ViChALt4ODgHmnjhupq1q7QLMymy81t/wKRCLL+/aEYxtmTyQYMAL/NhEQ9jnEFtSzIji+sw7WCWtQ3eXt2hLe9nGXZQ1ytEOxihRBXS3jayntcAFzXAo+SPyTm5zEEuGsnILn5i4O/goJGugC0WKBrnNoSPp30aZ+Fo8h3/dq1VTCb9fBwvwuBgS/eMJ2RhN6WHVjGWje8LL1YwO2suPGqw43MqcePH0dcHGdJJRYJMV6egeF1u8GHGVlWw/Cx4G5Y16Sz+1f1Hog3usLoIYch2IYFuQKdCcMbgRlONojyskGYm3W3VMq2UBqNeDa9CFtKOarsdAcrvOtvgbTYBdBoi2FrOwIDIr8Hvxfhw+aKN+vxvlbBAiYKZCQ+N6dKZDAasPnQBvyU8QvKbFrHHvL+Xh62nJ0/1/v7FzcUY23MfiRUXIVEkwyesU2blhkYVjUMHg0erKo9aNIgTB85vVcBSEpwH/j0feQnciKY/SdPx4QVa3qdJ7U19ch/4z3UHjsLEVn7WDbAa2gpJNZtxhhrL9aTbfabgCsCEzafexmnhCaYm743iWk+NPAhBNgGdJnAfuXCK4y6T5jhM4MtzntLdGka9ax/m6yj6J8ZvSgQ/EBLrN8ej6Tiesj1GjxffBwDYo+z1wtdXOD68kuwGDeu03vpdVpsfekplGZlsET+Ha+8zY6LsUGH8s/iYKzRQky2XPdEgC8R9BrgkZr2lX05qCvnzgehRICw0W7oP9EDVvYyFP2ehe37MyAwiZHkegZrHriVJZmJilvxxTXm+y1yt4Djqgjwe1H57hOUVSz4zr+wHRu102CCAFOEVzDKSQUUx3DidvefZzovLcfEqMdbb83F/E3ZUA01oXqhHAKLprYCXSjCI16EszsnxFZ/sgD1B3PZteX0UBQaBLGIY2OgDu7uSxAU+HK310BzwE16LpaWEYgaQHZmVqirUOHaxRLEni+GsKa1AGSQ8uEd5Yhho9yZL/j1XFuVhY04+GUCo7fzhTyMuS0AYWPd/1AftF5fh/T0l1Fatpvdl9R7IT/xbryjckRN03qA2GEPTwpgNHJhDwyJ4lo1Xt56CYeyuYQD6WwMUqXi3tGeGDN7DksK/Zm4mleNO766CL3RjMemBLJq/I0ipiyGJa9Jy4haWT6Z+AlLBrYTXYuvRMOpQuhLue9LBcdCvRmut/WD74ibS5m/mfg36L7OA/G/BFWsA89wWaRm0CXILGrQalXT/JiYx4eVUABLYdNeQLcFsBLyWa8bBdguEhGcJUJmf0CepzSAUObYUF7BrI50OdnQZtM+h1XVKOjuCmI/P1ZVk0UNZHuRl9dNE2W4GWA2J7U1LPguz8lCWU4mmyQbKjt/HxJno8nTLSgE7kGhbH8jVByaeEjd+1DuIRYYNupbFRldFa4tgTZVF67m12Lj+VwcSixlyqESgxaLyqNxe+ZhCKqbBlGRCXahWtj510Ig4XHUrgnPACJZp4pZQuKDjPLM5zuiuOhuZGRwiZFp06ZhxIgRnT9sYwWM302CoDoPSpkAGWMnInzID+3UlGnBdezbz1lCg2Avc8MAm4lwdvOH9TRbVLz7ElRXOOq5zFkD96G1EClMrdYkYbfCFDoXjyZ8xgTiSNX2+2nfo59tZ1uartBQXYmzP/+A5DMn2H3yix8273bW86gxaxllk44zsQe6Ev2hbCn1T64IW4Egu9YkQEerEarw00bVbKpqU784JTGo6kgBNu2pCk7JiOaK+PWc61TJvnr1KvM/biuGRj7ZkZGRCA8Ph8UN2u7oS0u5KvglCsQvdE6IiSWo9wtGupMfTovdcFbiCnWHfmkSJQtzs0K4mzUXYLtaIsjZkimA/ymoSAc2zuIsf9yigLt29Sq6daP4q/q+rlVcw+rDqxmLZYr3FLwz9p0+6wGUlf2OxCRS/jXDz/cR+Pquu+5/n5gkpJVAKq/UuvH99O8Zo+R/ATrf6ZhTvzbB1UqEOcqfYWuswGdYjgYo4BcQDKv+Y/FhRRVSm6jp/AoNRAk14DWpTROIdkmUdBLlGeRti2G+9nCx7r7f/+eSKjyVVgid2QwPfiUeNr6MfnIJBg/aDpGo72M6LfKqNiVDk1YDnlQIpwcjIWqi894oyO+ZfLdTmsYz3gAPXPKoQqo5vYV6HmofigcHPMjUz7sbYyi5QklHSvaQpSElWdqBJ0SUY38McxkKcbIYRRlF7L1uu+02ltjrK6iqS4nPc9t+YjRr135BmPPY07C0d2hH9828VIDsy3morTJA08F/mSAwqGEvqYBHqCWc+wfCLSoIUuo3pv7t72cB5UkosPPCt5EzsCvvMBNDIwGu+QHzmYWktcS65Xs/dPwhlrwlcbbHBj3GbCZ7G4vrK9XYu+Eaow5Tr+601eHwDrNv0an47YttcPvmQziqOWvH+slzMPjNF3q0QquvLMfm9Q9D09iAqOlzMH7JKtZTrS9ogMBeCqcHBvToO8xo/7EVuLQ3BzUl3HxPFObIiR4IH+fBbMuaK+A73olBWW49GmXl+Ln/m7CSWuDHcZsg/rEKxmoNRK4KOK6OYJTbmwm9qh6/f/0qrtUowIMJd2EH/EDnmhnwHA6QonlTYeX1i68jLToGSxXZ4HtzdG6T1gG+vk/CP2h+y29E1HtKFBBN03ZhABSDuTGqvPwQW8PQe/v7PQEfn/s6fR61Oh9XohdCr69qF3DT+mlTcSXeyilFnd4A1xojppea4Zujgb6xNQC3dpQheKQrsyGzsO2bZohWbcCxjcnIuca1vISMcsW4O4Ig6AN7oSeUlR9AUvJzMJtqoTcJsDtzJi7lToZziD0+WTAA/hayHlt9Pt4Xg68uFEPX5McerMrCfUMcMWPB3L+EyVlap8GcT86iokGLmREu+HTxwBte/x/MOYhnzj4DvUnPxr8NEzewwLvLfzenDtFfJcJdZ4CjiFK3Zjg+FgWp01+rb3M9+Dfovs4D8b8EDcwqnQ6HDh7ErBkzIBaJbujEJuo3Bc+0QCfql760DPrSEhhoX1zMAmyyMeoSPB4LsKWhoZCGhXL7kBAI/mIRp5sFVV0t6x+mALw0OwMlGWlQ17f2/DSDhNrcg8PgFd6f9dhZ2HZNMaSBghY+NHBQYNk2+KOBoznQJi9n6ofdE1fMgu3kktYq/FAfOywf6YOpYc4QwoyG335AxYfvQldjbg2+Zw6D3bOfdNu/q9EU41r8GjQ2kq2KGDXVDyIxkZsoRo0ahcmTJ7c7d0hFOfHinYi6WsxE08xuUeBRj7fEggW7pzZ/h7TzXB+WTGGFCIsx8JGFMQqbyCYNFR+8y84ZnkyK0mnTcH6qEark3zBNpcEojRZ8UysNO10kwhFLS0yc9hFCgm657t+sOD0VJ374CqWZnKANqdmPX7YKvlFDcLb4LL5O+Bpx5Vx1rSs4y52ZGNtI95EY4TqiZRH3Z0Kn07Eg+8qVK+3o4zTW9O/fn23km31T/02DEYnRycg9chrmmGi45STBRtM+GUHL2WIHTzQEhEI6cCC8x45AYJhfn9XA/zCqc4DvZwANJYBLBLBsDyD/4/Td7vBXiq2Q+8CDxx5kYwIJZr04ou9V64LCTawCQggJfgtubgv7/O8Sk2T1kdXMS55owptmbOpWNO2vArE4YmJicOTIEWi1WnYcHPgNqDBawA41mDDQBmscFyFbo4eIx2NCaXc42iCpqJ4xMWLzaxFXUIPKxs6tIV52cibUQ9swXzt2v+1xjqlXYnlcPCqMMsigwhfBDpjmGnj930FnZL2yuvwGCB1lLJC60UpiWU4W9n30FmpKilnb08hFSxA1ay4OHjyEyLGR2JKxhfV+N7ceEfV77YC1GOE2AjWaGhZgU6BNG2litAUFoGH2YQhyGIRfal1QI/THPe6uGJQcjfj4+BsKuNuCbMz2b3gXGmUj5NY2uPU/T0NbVIuY45Uoqey8DhBCC6HQCK1RCrO587hCTBmPIGv4q3+Gn+oXSK0UXABn78+opZ/EfoIjeUfYa+2l9swdwNPCEw+deAhlqjI2fr8/7n2mwdEbKvIb8Psn16Cq1zGa8OwHI2HfZNlE81fZW2+jdutWdr/SygHvRCxEvGM/3DbIAy/PDWPWgt0hO+YKdr7FXbPjBy6Fc407+HIhHO/vOUFDYmBnt9H6g1t3SORCDJjihf4TPFgvcltE78/BpT05rI97lBsfz1u+iQJxKT4sWg8vpQsX4N8XCYHlzafXcj2z+2AyGhGfkAAZX481ph9gg6Y5hcbveV9id0EOiqO3oZ//RfAFBkDHg+UBPtztbof7i6+2vJ9JY0DZx7EsUSCLdITdHUHtrtuCgo1Iz+BeHxr6Hlxdbm15TqerRvTV26BW58LCIhQDo35iAfel2kY8k1GIpEYumR2qkOKFfm4Yb2fFEhaFaTVIv1yGrNgK5sHeQtsPtUPISDf49nfoNYCmdTjZtF3cmcXaO0k9ffqaCMitbuyYl9dr8M6hNBxOTMaykC0Y4NTkt14TCLuk+/F2iDsWjPNnlPiOOB6XjWe3X0OJnvu33TQlWNnPjDuXLoTCpmc1/ZsFUka//csLuFZYx5hw2+8fecMJ+h+Tf8RbV95it8lS7vXRr3fJWqHfIPFUEc7+lgGTgfQvZJg02wu5sQkYcs+kf9XL/wn4JwTdJo0G5Z9/gcz0dKZaTOKXlIEnaw3KQnN7I9fQbTLBpNXAVN8AY309TOSjTbcbGmDuIGrWJfh8iDw9IPH1g9jXF2I/X0j8/SENCmKWT/9XwXr4SotRlJaC4vQUFKeloKqw1fKlGWTh4hkeyYJw1+AQJChTmT3M0fyjrMrUDFr0TvWZygJtooCR+FdhjQqbL+bh1ysFTCWYQCqPtw5wx7KR3oxC2YLSRGD7KpjLUtBQKEVFsg10tdykwJcKYL9iBezWPAh+k7VOW1BvaFLyf1BRcZhNDmrVA7h6lZsgBw8ezAIPop2TqvbVmDthMNTBRRCO0Ivx4KmrYfSdiFibxTi/YyvzZqfFYWjEOATW9odYIIXYWwjttc1oPMlVamSDBsHp1VdwJCEB06ZPwzMXnmGLJReeBBt9boMobR9si66hXajjHM4q4Aid1149vbffyWRiFe8zP29EY201cl1USA3XoUzMJS+ILjnDdwZTSpUKpYzaT6yD6LJoRvVvBv0e4fbhLAAnSme4Q3if7Z76gsrKSkRHRzOKbXNVWyAQsAVvVFQUo5HTb3AzQNTElNJ6nM+swvmsSlzOqYZS16bn3GxGkKYSU81lGFCbB+e8VAjKOreGkPCRfOBAyAYRa2UQS7L9KawVEvL7bhpQkwM4hgAr9vXYu3kz8FcrnNL5T+JOVLlcHbGa0WT7iqysd5Gb9zl4PBGiBvwAW9thfWLXrD2+lp3r5HhArRtd0XL/VyBbsAMHDjCRwGb4WObglcg1UAnkcBfx8HX/fhhIgVcXYzMJtlEQHpNfg+jcGiQV17HprqOA3yh/B4wOcMDofg6A9hTOJjyNj/A40nihbN58K9ATS92u/1xjlOFP4mCs00ISaAuH5WHXpULNFuwHf8fpH7+F0WBgdoizHnoCHsFhnc5NCq6/T/ye9Ss3j1lyobxdaxKBqsDBdsHMxpACT2qpaVbrPlxZh2Xx2RiTcQ1hJbl/OOBu+hKoTTmPPZ9+gorKBuLYQaiYCYGYS2S4iZLQzy4FroEOsAgZDEnwGPDktoymr1XpUPjVj8jbew51lr6odw6FUtAaHPChR0CkJcKnh7Ge2eZxh+imL114qSXBQOM2XVMknknUUy8rr14/dkFqNQ58kQC9xgh7DwvMWRvZ4r9NyuRF69dDn8fN9bbL7oLN2ofwyYUifHoyk82f1FO74c6o9vNzB5z+eSOu7P4NQp4YUz3vht8DYyHx7fr1ylotzu/IZEEgQSjis2B7wGRPSLqoUlcWNmDbG9EwGc2YfHco/PtZI+XTUyg0FyNCHQClSAPfh0ZC7PjnrM+az88pU6Zg8+bNKCkpgYejFe42b4WgktNXqbcQ4lqAI3SW3LpGYByEUNkSVK16it33+uEH1m5IqN6aBlVMOQS2Ejg/PLDL3vOMjNeRX/AtGwMHDPgedrYjGAMtJvYu1NfHslauwYN+g4Zvx3QMfi7hWilshAI85eeKu9zsIehi7iJ18qyYCqReKGEtBs2QKIRMfI1Uyh09ey4kkYXZ4W+SoFMbYGErwcwH+vf6Nx2DVfLZ/uxEZss8PS/KDfcOTkVZwRswmpQQ6CzhnLAGm+wGQjzGHc/3c2dsn9pGNR7/6hCOlnPnicyoxnybCjy2YnavQoc3EzSePb4tnnl/28hF2PPgaCaYer2ga/nDmA/ZeEdYHLwY64eu79KSlX67kz+lsTYMAukZTFweAr7Q/K9l2D8J/4Sgm4Ln9KG9L7z6BJGI2TYJXZwhcnaByNUFQmcXdl/i68vo4W17P/9/hrqxASXpqShITkBBUjyrUtAgUWavQY6rCnnOKmglrRRIUn4lSilZoEQ5RTUJeplxIauKVbWPppS1LBTJJmnZCG/cPsSTeQ63gJIol74Ajr7I+WUrnIBbP4fZczgaPlyHih1noavnJimhvTWcnnoWVrNmdeqbpz7vzKy3kZ//Nbuv163AxYvcAD9o0CBMnBiGmNg7GRXdyiqKLe6FpSko/OxOHC30QJWWm8BdA4IxrN8cSFK59xfal6J+98cwVlaCJxLB8ZFHYLdiOQwmU6vXLN+MB449wCr/pNCr0WsgM2jwou1gTCUmRfZJki9t/bDUnx40g9s8hhLPv8ffhY7psezDeO/sWygE1yYgNPAwVjAAj896GZ6OnJp5xx5AWsARFZ0CE6LftgVZ35BvLHlx00aiPNcbcDKqYHY2zp07x/bNsLGxYcmOAQMG3DB9vKss+cn0CpxKq2CBdrPCaTNs5SKM8Ldn7InBPnYsE922L0xfVtYigKiKuQptahp37nUQQZSRPkNkf9YfLo2I+OPMFk0dRyUtSwBsvIF7DgOWfz79+X9hK7I9fTsLGAiUuZ/jP6dPf0fXbmLSwygv3w+h0AZDBv/GNAK6f70ZT599Gvuy9zH1ZrIFo0Tf3w00x27YsIHZhBEMfD7O9usPR0kFPs/6AA6z3wBC5/bpvchK52peDUsw0XatsJb1FLaFh0UpQuxTMDrQBxccZmFXNZcUfcDTCc/5u/bo590VmB3TF9dg1ptgMcoNNnP8+/Z3ahUOffExE90i+A8exqymmoWNms/N8VPGI6Emgatkl1xGclUyo022BSVUqBI0znMcG696Yuu88ttumBJj2TuMnjMXUwZF4bpRXwLknOLG7OyTMNeXIKZxFs6U2cKo5zyWvVwDMf3WAbCMHAvYdiHa2QaNp0+j6PEnYKqvh8bBE5pIH+TxRqDK0Krt4ehliagpXsx7mqrhOqMOD594mIkVNidWySGAxDN7A9lLUYBEAat7oA1m3N8fEpmQsf4qP/8ClV9+SVlq1rvt9uYbTGm9GTRvP/prHErrNRAL+Hh+TiiWDuu6fU4ZX4bf3n0RldpC1na1+L33we/QVkLJUarSXdydxRIABKI4D5vrz4K3rkACXhRwVxU1wjfSgYmnEUgVXZtVCzVPi/XeH2DqiDm4L7IzFftmj50k/PnFF1+wRDIx50Zmvo0iRRIK3KWsdCzUm+Bf6QT3wS+DFzgNJS++xBgEtK70270LugIN8xAnXQHH+yIh8bbq8xiYkfkmswUVCq0xeNBWnFU7Yn1aIRMFJix1tcfTfq6w74GV0BbUZkDBd+rFUpYIaYaDpwULvgOHuHSrVl5TqsT+zxO4VgWJADPWRLCqeW84k1GB53YlIq+KS6BRu8wLc0Ix0ItLQKlUuUhIXIvGxhQmsmafPRvn1bfh5EgnzKqowPsnilDH54LbofxivHLnCARHhOOvBnmGv/J7Mmtn3bRyGEt0Xi9Ii+fF8y9iT9Yedv/hgQ/jnvB7ury+qkuUrK+evNrJ13zkfH9ETuKsEf+1DPuH4Z9S6S59623k5ufBx9cPAlo08nlcoEUDO92mzJCAzx7jicXgW1lBYGUNgZUl+JZWTXtLCKyt/zJhs/8roMGBArb9mftwNO8I6oytVF2Jlg/vMjl8ixXw1trDp/9A+A4YBKeQSCZs8cOFPOZn3AyqwFCwPSnEubPtQ0MpsOt+zquUEDgDmPsJoGgd0MyFMah/Zw3Kz9TCoOImF2l4GJyfeYZVKTsiL/8bZGa+wW5rNLfhymWuh8ndvRC+fieYnyVRtEx6IaseXztygD0vE+gxZsIguEgWQ5tcA7NRD2iOo/HIdva8uJ8/3N99F9JgzlOz48Cn1CtZb2lqdSp7nujcX0z5gstgqqo5C7KknUDO6fYBuMwWCJgGBE0H/CcB0vbXJP0O7119j1FoCQqhHEPrfOFyvg4SvQBSSyuMWrQU/SdNY37u3YHE1YgGTEE47cmioi2IzkjVIxI0oiC8o8J6WxiNRlbBo2C7tEmxl/18gYEs2O7Xr98frmqT7ybRbE+kVuBkejkSi9p/XoVYgGF+9hjpT5sDC7KvR+jM2NgIddw1qGOuQhV9Fer4eJjb9J03g6rfFIBTIC6N6A9pUCBLvvQJejXw4wIg7xyXTFp5kFFJ/wr8rybmj2I+wjcJ3zBP4h9m/MAYFX2B0ahBTOxi1NdfYwE3VXVEoq773T+O+Zi1Vgh5QibeRuyNvxuIZr5p0ybk5uZCY2OLCp4QnjVcwixCXok5ql8ghgEY+wQw/pmWHtHrqR5REH42sxJnMsoZPd3c7IPVpFfg4mqBLAsejI5SzPV2wIYQL4iv899RJVSi+ieuumd3exDkUT23hpAOxu53/4vqogI2Ho1buhJRM25hi0VKBJIGwMWiizicehjFpmKmwt0WpDwfYhfC+rUTKjldF0uRJdYNXIdFgYu61Qs4f/48Dh8+zG6fCoiEKSAEBwcFQtabZZWykrs+c89yYzO5WzRBa5LjWP0jyNEMYUGRRHAa9VUx7LkB02Zj4op7+7S20OXno/DuO6EtqgZPYIL7E8ugGrcOCacKkRldzgJNgo2zHFHTPHFItBXfJX/XknSgsZqq/Gsi1+C+/vd1ewzSLpbg2A8prFrtP9AJU+4OZRRi0qopfvJJ5rbC3nPOHLg8/1yXLVs1Sh3zHqaEOWF+lDv+Oy8CMrGgvVL5J3FQqmpwsPR76PUajFm8AkPntraGVBQ04OSPqSjP49YOTj5WGHdnIJy6CTqbQcrZ0ftzWW/3nS8OY1TmhtOFqNvPVf6vypPwnPen7HhQDywlY/7ssZPmuq1bt8LGthhBflchVnAVY0W5CVE5jZBom1pBnMJgHPgAstd/A0NZGWyXLYdZMJEJzPUlacXGwJg7Ud8QD5HIltmY8vli+IZ9j7fKXbCznPt3/WQSvB/sieE2N5bQpmRIQUo1Us6VICe+gtGWCSSa5tvfkfVve4bYdZpPtSoS5UtEURp9Lh4mLgtG0HDXLv+NykYtXv09GbvjuBYzFyspnpoRjFsi3Tq9r9GoRUbGq8zbm8Cr6I9vUlfhfJPeorWxEU+NtMcdt076n+gnncusxLLvLrM1yXOzQrBqjN91vwcxs9afWc8YYdQWQyKIZPPXLbPg60ToNEYorMWYujocbv1a58J/g+5/GP4JQfc/6cT6vxRoU/8cDQrH8o+hWtOqBEvVhclekzHOYRTsi83Ij4tFXnws63VrixKJM7Llvii28ceU4RFYPtIb/boTesg4Auxcw6k4C6WcB+bge7r2JjVoYTr8Kqo3fo+qZAuYDNxCx3L6dDg9/h+IPdr3cZaU7EBK6lNMFEylmoOr0VQd4cHDsxR3LX0VBfFZOPbtZ2is4b5jRJg7xuh/g1RgQqXuJaga3aBP3wx9bgZ73nbxnXB68knwpdJuz0+qvpEKJfW3EwJsArB55mYoRIrOlc/Mo0DaQSDjMGcb1QxS1vUZDQTNRKHHAHyQ8SsO5x1uEUdbHLIYd4fdDRupDfLi41i/d3NLgKOXD8Yvv5e1AfQGo8mI1JpUVpmnjQJ7jbF9wEmiVBR8UxBOwTixGqhfOzY2li1wafwg0HcfOHAghg8f3rVH+nVApTPgdHoFDiaWsqp2c0sCgU6L/u7WGB/khLGBDujvYQNRHz1g+wKzTgdNSgpUsbHQxCdAnUDiQAWdXseTSJi2g7R/RJPOQygkfr6dA3Gqom9bznnBSqw4Srlr332I/6njJzFjSPSJfJqdZE7YMntLn627tNoKXImeB622BHa2oxAZ+V0nG51t6duYijPhlZGvYF7APPwd0RwEGgUCbB04ARqFBZ7WVKDs0nk2VjjIzLhdvQmOqOaSjfO/6pRw6ysyMt9AUuYvyKiLQjnvfpzPVqGotr07gclCCA8va7w7JgDDvTsvqHtC3ZE8NBzLB08sYKrLIoeuxY4yLp/Hwc8+YG4PCls7zHz4CVTa6Vt6sq+VX4PO1L5X3U3hxsYXRhl3GdpOdZ7GpTcvv9limUjB+PPDn0eEY3tnCnJF2LlzJ7s9fPwEPCGyZ1ahqz0c8GqAR2cHi+Ygm7aK9naMrCTpGolK+9k4GD0IdbVcMDJ6YQDCx7oj9tBenPjha0Y9Dx41DtMfeJQJkvaIlL0wbr4LRedsoSyTsgSL87PPwG7JEqgbdawafO14AbRKLgFRJ6nEFa99mDZpJO4KXcp6P8lyjUAaHe+Me4cF422ReLoIp37mbBWpajl+aTAbM2u3bGH925RQpMKE60svwmrmzB4/Lp2fX5/JxlsH01igQQnNL5YOYvaI1J9c/mkcDBVqiH2tUR5QhkNffsSOwdI3PoStuxdiDuYhel8uC+6oJ3vErX4IHePe6zlHfejb3oxm9HwSfes3yAnq1GpU/ZDENMysZ/lCGV2GD3nfYp/tGXYMtt+y/aYLJ3YcO7Pjs3A18VHYuSRxz2skGJxRDrtaI/hLtnO2cOT3rePWQw2N/ij8XQ3w+JCPfxZi735wfnRQr4ruBI22FJcuzWQtcASV+6tYXxGBar2RCQff7+WEx31cek8m9RGkbp9+pRQp50tQWdC6nqN2BDqPaLNqc71TgogSO82U5xHz/BE1tZUNQb/51ugCvHEgFXVqPasMk27Pf6YG9WqtWVS0AzvOfY+NybehSsO1xMhcZXjtlhAs8O06uP+zUVCtwi2fnGXMOkpAvbco8roDf0o2PnryUcZcIdYK2fxN9JrY5XWXcLIQZ7dmcD30/ayZZVvbHnp6TVbMFZw5sA9L1z/3t46N/hVSu84D8b/Gv0H3X3CMTXpG62sOtGu1te0CbaL1EX2cgq+2Vis0ER9PLsFvh86jNj0B3qp8OOvaq6Pbe3ih35Dh6DdkBLMmaxmoyM/2xGvA2Q+4+84RwMJvAceuVbbbIe88DD/di4qz9ajNJsoRjwU79qtXwX7NGvAlrZS1ispjiI9/gCJ2lJf5Ii1tFHgGAzyMKtRmNwXTrm6Ysnot3H1DoP14KWSagzAaJcg95ghdtQk8GxtYPPkkpGM5RV2q3koo6JJKWbW37cS8M2MnXjj/AgQQsD7DOl0ds/L6eMLH3as507EouAikHQDSDwJVmdDwePjKxgo/WFlBx+cU+uc5Dcfakc/DgSxn2oCEXq4dPYDzv/7YkgDxHzwcY+5cDnuPvvc7EZ2RVaBKLrIgPLEykSnpNkNoEmK4bjicK52BpjiYPNGHDRuGIUOGsNs3ijqVHsdSy1igfTqjApo2Cs7WMhHGBjpiQpAj2ztYdE1J/LNANmVUAWdBeHw8C8RNTcmGtiCmjSQgANLQEEgoIA8Jgaz4V/CufA6QhRbZgvmMZseZBKOob1Vr0HL7po2SHvQ87Ts+R1lyqghSsoR+F0qQ0b6r+82vo/cqKy+Drb0tTPSf2cSouzRpt/xn7nyf0PLaNo/Rns5/qi6TFgBtlLFvvt32cWJ30AKDKnQuchcsCVkCK4kV69MlsRiihNPeSmQFa6k1q2Q2jw8NDSlMOIgs7by97kW/futbjvOZwjNYd3wd+673R96PBwbQ9f2/h9lggLGuDsbqahiqa1BaVIgfExLYMTsVMABlDi547+ppRJQVo4TPxwkLBVQCAcRmIxbidwTysmHgOaDB+V7wHAMgdHSAyMMTYg93dm71hJqai4iJXcrUj/tHfAFHxyns3yW20dGUchxPLUN0Xg1bxDXD3kLMPHBpo1aMTgykjt/PZGbCatrsOqYYTcJqbT2YaRw69+tmXN79G7sv8LJH+mgRohvjWwTSmkGJmEHOgyAtk2LllJXw6YWeTefy1vSt2BCzAQ36BnZu3RVyF9ZGrWUaFjk5OazvlpgF5FYxdepUHK9uwJJ4rt1le4AVRtVdawqyz3URZNOHCgW8R3EJT9+xyM0y4eBXiTDqTbC0k2LaveHtvIpTzp3CwU/fZ9/bb+AQzH5kPUSSblShS64B300nSWyYB65EySUF6rZz7CmHBx+Ew9oH2blf36jEu5u+gWWKD+R6yxba78h5/RiN9/fs31myiY6nn7UfPpn0CTwtuTE+7mg+zv3GtQ+RKNno2wKYtk3Jc8+h4chR9rhi5Ai4vvEGa7XrKy5mV2HtzzFM1I88vT9dMhChlyqgTqyCwEoMp3VR4FuIsOvtV5i4mp27DxQOS1GRz1GJ/aMcMeaOQCisex+3KVjb/lY0q4xTsE1BN3lxl392DWatEYqhLrCZ1w+GMhUKP7mC/3i8gwxZPms5+GbqN312TLieteeEsZNx+chGGC2/glDSyK6h4qJgXCl1w3LJOQyuKeLWL6uPA3olcOkr4OKnLLFeeM4WDQUyiOyd4Pb1NshD+yYeqlRm4/KVW9j4F4NBeI/3DHs8WCHFB8FeiLL68xS6KemRcqEE6ZdLWxJAlIPyDLZFyCg3+EU6MuYEjQcXdmYxkTXCwGleGH6rP7IqGvHMjkRczuWKGeQM8sb8CJYg7w2ZCfF46odTiBZyVWQHaRVWhP2CAfWReNJpIVZGeWOVR9+StzcLxCaa/9l5Jv7b38MaW9eM6JONY0exT9IeoYIWzXsfTviQJc46wmg04eyvGSx5Rgge4YLxi4Pbid2R9tKZX35AYTLHWFn8xodw9eu7PtBfjX+D7us8EP9r/Bt0/0nH1ahnwRVVUI/nH29HMybP50neXKA9xGVIJ09Top5RFpPE0Ujoh0Br5UnBzrgz3Ap2lRnIir7I+sFpQdIMSwdHBI0Yg+DIEDhdfhm8ggvcE0NWA1NfAzpYOfUIqhTvfRias3tRFmcNVRk3oZMInusrL0M+ZAi7T1Xuq1dvR119LJssi+JGoyK6FjwS4OPx4DVsNGxCB6Cuth6VGSUwNlZjie5r2Fg1QlsnxKVroTg/ZCQ0XQi3sX9PLGaLJRcXFwjlQhwqPYR6fj2mBE9BpHckHrv8GAuYloYsZUIZfcHZ5F/x39iPUGjgKHnD1Bo8UVWDIPIxp+DNazjgPxHwm8ApqDYtNMiX/fy2n3HtyH4mvEatF+ETp2DkwsWwsLt+EaVGXSOull3FhcILyEnMgV2JHSQm7jg3ChuRZZMFK18rDHUfimEuw1g/rUgguq5A+0BiCfYllLA+QrI+aYannQzTw1wwNcwFUZ42Pfp1/tWghX1tVjJqYi5DlZAAY1omhFkFEKha++MINv5KuA7hgvNzhc44I7JEtp0BefZGlNoCppvh9/1/CBSwU1BO1nq0hUqUGIxY9lyF1XzIbcew4OuNy2+woGOu/1y8OurVv4xqaGxUQl9cxBwvaDM07fVFxdAVF8FYWcUqn+y1fD72zJkDnUyKXDtnpNu44b9fvAuXas5RgaCRSHB+1EhUODmBZzZhkv4sRomvwqjho+C0HTTV4laRTzc3SIKDIItoanEIj4DAgmPP6PX1uHR5JmMGuLkuQkgI11bTEdVKHX6IK8An0fmsQslropMSHC0lmBHugpkRrhjSQwBurNei7KMYmJQGKEa4wnZuP5acSciPwanPP4M+p5y9LsmnHtHBNWgW7yaGTLN2BM0pRB+nHvfrZWFUqavwbvS7LPgkeFt545GQR3Bl5xWmEh8WFoYFCxaAT+4ABRdx4dph2BVdRJAqt/Obka0jBdg+o7hgu007U258JQ58lcAot16hdpiyMqzLPtfs2CvY+/6bMOi08AgNx/z1L0HUhgnVIqD49QSgvogbtxdvg5kvQOXnn6Py4w3c8VmxAvJHH8C6E+vYmGsBS/xH8jqqL/Ja+qB9IuwxamEAioW5WHdsHcrV5WyupgU8P8GhJeAeON0bw+f6cWJp//kPZ6MoEsHpP4/BbtmyG2qzK6vX4L4frzJVfdLRe8gsxXy+mCmHS7y49WNDdRW+f+R+6LUqCKTDYWE/FmPvCETAEOc+X6NU4Tu9JR1iqQCLXx7O6OxlGzjFb7GvFRzviQCvyWmi8VwRkg5exFq/N6Dha/FQ1ENY3X81bubac+fmXyBXbIfMkQtweAYfVCqmIf5oPYRmIUaN7o8pV/8DqKuBic8DYx/n/lhTB/OFL6A7sAE5+xQwG/lwmyaD9epnORHVHn4Dg6ERV6IXQKXKRCZC8ApIH8OMNc46PBUy4rpbQ24UlGzKvlaB5LPFKEytaXmcKP9Bw10Y/dzezaIl2WOEGbnBcuytqGE6EzKRAP+ZGogVI316nb+1KhW2bdyMDzOkqJRw1+EsHz5WDDqB+gYuOSUpG4pXpeswJNwLr/Rzv25tihvFMzsT8POlfNgrxNi7bjTcbLq3M+sKNFetPbaWsXxIQ4daoQY6d26J1Cj1OPR1IneseU3sgSmt7AFq2Tn90/fIir7E7lO7rYVfEBY98gSsbmB991fh36D7Og/E/xr/Bt03D0RvoUCbKtrk79zW3osWRUQdJ+VxqkB0pWidWFSHTRdyWX8O2X8RSMGRRNGWDvOGp1377CtVXXNiriDjygXkxF2FQdsalNiKVQi2rUPwwnWwG7vixr4QLXCjv4P5wNNoyOOhNNYOxiahW5vbFsLp8ceRWfYBCgs3Q1snRdZRH+gquQHMKJVD4+oDk7T1M9vU1GDkufOwMdTBd1oFRDITriEE+8W3sL5EqhxRwEVbsyhSbyiUF+KSMzdILrBegNuDboe7u3uX1xxR+d+49AYO5h5ssfp6asA6TNIBvOzjQOZxoK6DsrzUurU6Q5tzOKqKi1kmlBIfBKFYgkGz5mLILQsgkfdd6ZW+I/lrnzlzBo2NXAVdaiWFxluDi+aLKFJy2dhmUAaXhPRocT3YZTBTSe8YhKt1RlbRpnPoZFp5OxEooi9SkE3Bdohra9XzrwJViivVlShXlTNrnjJlWcu+WlvN1JXZpq1hr20LntkMp1rAp8zMtpFqDSYEVBGzEBUJlqhMat9eQZ0RxfY8lDgKUO4kRoWLFNUuCjQ4WUAokUIikLRuQm5P/dGssswXsACV9lRl7ni/ufrMqj4mICkhCQMHDIRYJGaP85r+4/7nsaohe6TpeLd9rPk1jOHB+Bb0lib2/Vll3WSE3qxvud38OFXaabwh5em06jRGMydEOESwyiRl/uk50kCgcahjJbQZt1jrMNHKAK0J+KBcilJ96+KNKuNEQ6aN2iAokCNVZwrE3CzcOiULewJd28ba2qYgujWwbgmwi4pZFbsvIP2QXZOmQCUWQC0SI71fJN4pSoc1aY7IZKw1gUeLUB4fFE4dLy5Ccj2X9ByATMzB7+CZ+CjPDUFtoqZr9w2BALIBA6AYNRJlbldQZnEaMoUXhg75HUJhz9d4ulKDRbGZKC9uhE2lFoJyDRo0hnYB+Jz+bpg/0J1VqTpeh42pFYj+5QgSFOlIDSpBblEyhl5SwEIjhF5gwvmIKlT6CDDEeQijjFOQTQKNHZV5/8jcfrrwNF6+8DK7VolyHFgXiGmIxN2uuRAVXgLqCztTRK0D4RE0ATzfMYDXyG6dA7LjKtjil0TIWE/0PaEQ9BA0FKYmYeebLzPhuE6Bt14DbJwFFEVzwpmrjgKy1opf9abNKHv9dXY7ergd3hlXB4XEklWwaR5WN+gQfSAXiSeLWBWYL+AxtW+vcXI8dv5RJjongggTU5fBt6Y/Bs/0wZBZ3qj+9jtUfPQRE0sjMS/399+HLDwMf7Ti9/TPsdjZ1Od9m5c9Xl8zlLX3kKL1yZ9SkXr+NPTK/QBPgNtffB8eIX3XriBbs59evMjea8ztgYgY7855xadUQ2AjYRX1tt7fVGklb/B91YfwvtsmNraRfsTNEFNU1atx9sD74Nn+CL5QB7NJBGe7e+ERsgxzd8+HbYUtoqqiIBQKcd94LzgcXce1xz14qUVUr/FSCep2XoM5cwMaErMhlBnhP7McfLcgYNx6IPTWLoPv+KTHUFG2G9Www3N4G3YiAe7VvwQ/YTWGDtkDmax35fqbDfJ6J+p5yrliKOtaW0Nc/KxY9TunSolXTmegTMjN5xODHPHKreHwsO29Ik+Jq/c37sUhyUDo+SJY8I344I4BmNKf+5752ZuRkfMqwDNCVO+Nr4xPwjokBB8Ge0H0Jyeu914rxrpfYllR6Ye7hzKm3fWACi6UILtQcoG1GH415Stmh9gRtWUq7Pssnu2FEgHTYSCV8uZ19MXtWxB7cC8rYlExJWz8ZAy59TacuXT5b996+48Iuk+fPo133nmHLXjJpoD6lG69tX2zfUpKCtavX49Tp06xxTFZYmzfvh1eXn27IP8Nuv//AFUGaIFysuAku/DbLm4dZA4tgTbZrnRFzaKJlii/VNUmwZ5mhLpasQzmLQPc+kS10auVyPnxGaReuoTsRjsY2/iXOvn6I3TMBISMmQC51Q14SBN179e7YCzPR3mCHWozuGqswUKMukUqFAhtUHzFmQUgZh4fWkc36O2cW/rGvfmO8E/JhMO1o+CZDOA7O8N1/RJYXnmSVaBwywZg4LJ2/yRdc1RZoWCUvHhz5bmIK4iDjdkGo+xGQaPUoLa2lr0mzToNiXaJbHE4qmwUXNQusLS0ZMF385ZmTsOb0W+ywJsWD0TFJdpsu15wGpKqsoCsY0DmMUazR5vESbsg3GsEigyuOH3kIoozuB4/ElsbPu92RE6ZAWEPdFWizFN/JI0tzT3bpEQ+btw45q9NFmCEwobCln7wS6WX2vX/s39PIGULoIHOgyDWByAh2wpHk9rbelGgTefQjHBX+Dr8udZ8NKSTvR2JMxU0FLDPT3t2u7GQLeCpatdXEE3aVmrLEla0p+os9Rd6qxsw/8xXEBu0KPIciULbWyDPq4CooAz83CKYcvJhVncdZFIwRVVNsacHRJ5eEHt5chRj2nt6tVQ3/2lJy9cuvoZf035lx4r6L2ns6bg4qdXUstYW+o0osUH7Wk01HGt/hpWxCJUGAd4uFUNv5ndSt+4ISkJQ4O1j7YMg2yAEWfVDkNERDvWAsbQM+pKSdlVrfXFJn6wlqR+Wfh+Ruzu3b7u5ujDl+w0xCajav5vJmdWOmoi3Jo2GpIfqFJ2XFy9eZL3fdDtIUY+Fyo0QwQTzrPdg8J4DXU4uNMnJUMdfg+ZaPPvMbWGwM8N65hw4zb+btTb0lrDKVWuxIDYTRVo9vCUiPG5pi8upFTicXMZ6MJsR5GyJW6NcEOGnQlbDNUSXRrNKLFG8CQH5FhiebAeBiQedlRCOd07AyMgp7Jj3RvW94XNTXQMURqM65zTuz7yCZAWX/AvS6vF2RQX89AYW9DEWkNcI5DoPwewqB1QKrfFpiBcWuHSvtpwTX4mDXySwALffYE6EjN8Hlk1xeiq2v/5C+8Cb2px2PQBc+xmQ2nD04y4EFAt/+QF1r7wJvhk4N0CKkRs2I9QxvJNqNPV45idzYyx5bo+43Revp76Iq5qL4Jl5WKFYh3Xjb0XJ+qegPH+evY4cPlxefgmCm+AeYdYbUbohFpvLa/E5tOwKHBfoiFfHB+L09ymoq6D+ZUChOIyqgkR4hvXHbc//t8/J0yPfJyH9UhlTcV/41GAozxSi7kAuGbHD6f5IiD0668LoK9Uo/TAabzl9h1PW0Uz4k8aXThoqff2OZjOSL59DXtFLkNhwom08fQQGD38XVtb98N+L/8WWtC3wt/LHvIZ5yMnOYfP3SuFuCPLOcGKoi3+FSWtE6TvRMCn1sJrugfJXVzFGjH2EDk5hTWwXx2Bg/FNAyNyW4DupYCdKMx6HCXy8ilcx2mM0nvV1QMq1pYytRx7dgwdtg0BwHazAmwjy/qZzkKrfuQlVLPFB0PHMSBEZkSYyIFwrxNwoN0y+O6zH/n1VfR0Ofv8NvkoHUi05YdooZwm+uGc0nK3af7/q8ouIv/YAjII68HWW2KH6D2r6jcLXYT43ra+9I3IqlZiz4SwatQasndAPj0/rQ+tjG7R1H6CCxJdTvmRFiY4ozqzF/s/ioVW1t2KjczHp5FFmy6eu59Zh1MYydulKZpP2d5nb/08E3eTrSYrAZHE0f/78TkF3VlYWhg4dinvuuQd33nkn+yJJSUlMxMjJqW89I/8G3f93Qd6eVMmmQDuuPK7dApXERiZ6TmSB9gDHAd0ujKgX8JfL+cyPsFnIivwSiX5Iwmhk9dDnSiTR67atAPK5hYA2ciUyrach9dIFJsTGPNdpQSsQwn/QUIRPmAKfyIE9qnC3e/uGBuSmxMDp9FNwbkyGqlyMrBgnKLVCJHg4oV7OBeEyF3f4TQyHBpsgk9WjKH8ZinNUGHYlBh75HP3QYvx4ZqFCi2eceQ849gqXwV51DHDprMBMA98Huz/AZuVmdp88VJuVVGkIIauRiooKvB3/Ns7WnoXILML44vGw0nGDj46vQ6x9LAotuMqMm8gNT0Q8gfEh41kWvUdQL3hpc5/iWSDvQqcg3MwTIUsYhTN5Vqiu45gGlvYOGD7/DpYt7Sj+Q2PLwYMH2Wdmr7W0xNixY5nHdk+fh75rRm0G61miRTltHYNws0kIo9oTCnMgxnoOw4pB49Hf/eb3Z9FnoYp1Vl0WsmqzkF2bzazSsuuy2+kVdBeskeAXsQxYFVXuDCe5EwsWm4Pr5j1Vn7u0GvpmEkcl9RkDLN1BVIP2n4+YEiUl0GZlQZuZBW1WJnRsnwVTE6OgOwhsbSHyoj5fzyb7Q2cInZwhdHaCyMkJQkfHdv2/f5eJmared+67k/0Oo9xH4bNJn3XpSdoVdLpqHDs3AWJzI6JVEtw6cidbXLewEarzUV6YjpriHChLC2EsL4d1rR729YBDvZkF2raNYEFNbxA4OLQPpN2bb7uz2z0FL3TevZtZiOLtW2CtUcLcLwgvLrmjzzTI1NRU/Pbbbyyh52Whx52NX0MGLTDpBWD0Y+3EJXWFhag/fRiFv78PUZIBfG3rc5KgINguXQLr2bNZZb075Ku1WBiXhXyNDp5SMXZE9YOzUIjTGaXYFHMBV0quANIsCOS54AnaCyxa8hSYFOsGm2Ku6uU3eChmPvif62LS9Onc1KmA0gSg6CpQHAMUxQDVWWxG24WpuIYwVFDC0zEa9XwepDwBngm4A/MGrQPErZ/l/dxSvJ1TyqqGp4eGwKELeyXqZd3x7lUYdCZGiZ68IqRPAXe3gfcUX4gOP8kYDUzPwW98p7+hcWrVoVVwupCBh/aaIDABNnfeAZcXXug0v9L5RYHOmV/T0VDF/R5mmHDOZycSXU+z+8svSjHrRCN4UilcnnsW1gsW3DTGUM2uTCgvlrD+7YQZHnhkVyI8lWbMUktABU4KGKauCofcUo2Njz0Ag16HmeseR8jozt+7I0gJe9cHsSxoX/jkYNiYTaj4JoElyqmH22JY9wJa9ScLUHI4BQ/6v44yYRXuDL4TzwzjeqCvBw01dbhw9L/g2+4Ej2+CySCHumEOps95EWKxhDmS3P777Swx++3UbxEkD8Jnn33GEuuThkdizOXVgEkP3P4janPD0XiqEEJHGZwfGYjGE8dRuHYd057xe2EOxJmbAW0Tc8Y1Epj4Ao7IPaBJWwQ5lDjAX4Sp4U9hqgNXhNBoSliPt15fDVfX2xAa8ib+10jNqcFXmxJgV6aDranzdRI4lK6hUGZ11REZl87jp+9/wi75CNSKbcGDGesm+OHhKSHdtraoGgsQd24l1KJswCTAmca7Eee1EJv6+8FKePN6+Tv2cZP96M+rh11Xixu1bz528jGcLDzJig+fTf6MMX46gtpYDn7N6UaQsv/M+yOY9kF1cRGOfv0Js+0l2Ll5YPzy1cwh6O82t/+fCLrbggbMjkH3HXfcwQ4yCYfcKP4Nuv/vgOicJIB1quAUC7Zz69v3r5HaK3l7TvCawCoQ3U3CWgNX1ab+lUs5rQGTm7UUdwz1wh1DPOHUIQPZKwouA1uXAdRjJ7YE5m4Awua1y3amXziLpFNHUZrFCZsRSPU2dOxEhI+fzAactiB6d2FhIdLS0liQ2GxXxYcJk3EWIbJ47NP2Q1kS0Qd5EBmMCKtVY/D6Z2A9dSoKCjYiPeNV8PMFsPzUBoqGBph4PNg//DCc713d2vNGyYCfFwGZRwA7f2DNaUDSftFdXF+M+bvmQ2lWsur0U0Of6nYQvu/ofayvh0Sl3ol8h1XGvy76GvXmelapCKoNQkhtCKPxUoBLrBVfX1/4+/uznvFe7bfaBuF03GlTcj2WlJBOrHXBhUovNBq4YNHaSoKRk0cheOptqDHJWaWNjilBJpOxYJusv65nQG9mRmy5ko/LhSkQyHMgkGdDpMgBhO0TAkSDJrox0dEHOA1gVfGe/He7Ai2AqFJNNMuUqhRuX53SyQqtLRxljkx8yMPSg210mzZSUKaA+obFeLSNwPczgNJ4wCGQ8+ImK7g+gqYcQ3kF9AX50OUXQFeQD31BIbfPL4CxppVp0hME9vYQOnFBON/RETnVVQgaPBhiBweWTKLAnW3W1uArFH+ZlWJmTSbu2HcHq2rTdULXS5diZHQ9kiBZQwOMdfX4vfw4ftH9grWOWqaC63ZlEGTxYhgqK2CsqISpDxVqgl4AVFkClVY8VFnRHqi05sHs7AC3fgMQHDIag71H9miT19Nv93JWMeJOnsCAwkzw5Aqsf2gdE1u8HuTl5eHnn39mC3knBXCX8ktYQgWMXAdMIYpl69idkPgQysv3wUIciODGdWg4cBiNJ0/C3NTGQ7+vzaLbYHf33RDadV3dLdLosCAmEUV1KXA0ZiNSVIDUqgQ06tsnf8xGCYwqXxhUfnDUeODWkkQIqotY60G47RiMemAFFP37lvDvdtFI9ozlKW0C7FigPJn+8U5/e1E+GQdVEexw3DVnPCwCg/Hs+edZ+xRhXr95LPCiVgaCzmTC9Oh0JCs1mO9si89CW/2xCeRXvO2NK4w6Sz3csx7sf10Bd1eBt69FDeZ6JEEw9WVg1MOdXkvMmnsO3cPma0rsfWVeCt2L7zBGE/V4O61/ssu5Wq814uBXCchP4uZoElmqdT2Gn9x2saB1aaItHlnzHST9bp64ElMP38ipdjusDIeknw32/pyCgrPc3FsiBRY/EoVQH268u7jjVyaqJ7e2wcoPv+wxGUOsgl9fu4zqYiXCxrpjzBxflH0cA1ODnlnT2S4K7DFxYDaamHXZpfpoPOu1gZ2TG6dv7LJvtsu/N5sRf34/Sqpfg0jBzZdCwxhEDXsNJ05eZeenQCjA8gPLEVcRh+k+05lyPCEuLg67du1ic/OaAYBzzHswW7ihuOZjmI1S2C8PhSzEnv0bBfesYgwEyxnT4fH6i8DFz4ALn7aonRdaW6PUB0i1DcO4ob/Bs8Mxq64+h9g4asUzITT0Pbi6dG019WeDvsuPF/Pw+v5UqPVGWIiFeGqwD5wrDciKrWABZDOsnWQYvzgI7oG2LPim3u0TG7/CtquFOGk/Bka+EA5yAT5ZOgTD/XrvSzbolLh29EHUSs+w+ymqyfjd6SH8OCCoy0TajeK135Pxzdkc2CnE2P/QGLhYS69LmPiJU08wUWJKzFOrCLm/dETqxRIc35TK2ALeEfZMNJDPN+PK7t9wceevMOr1rDVw5G2LMXDm3E4Fkn+D7r8o6KaAg7IGTz75JM6ePcuse2hh/vTTT3eioLcFTeK0tQ26PT09UVlZ+bfv6Sb67pQpU/7W2Zy/GsWNxThfcp5Rxq+UXWm3SKJgZrDTYIz3GI+x7mN7tdIgGs2v0YXYEVvMLBEItLgdH+iIO4Z4YGyAQ6/Ktp1AKscxG8E//Ax4Jj3MDkEwLPwBsO9+IVBZkIfkU8eQeu4UNA2tQZN7SDgiJk2D2NkdaenprCLU3GPcDApKKThVGGsQs/07aOu5AcrfRo/AUjF4aZzIjNWC+bC+Zx2yP3kIwiPx4Ol5UMvlOD9iOKyHD8eiRYvaB7eqagi/GQ9eQzGMUcthmvleu4DvgWMP4HL5ZQRYB2DT9E1dVz+bQIHgisMr2CLLUeqIKm0Vew8vSy88HfE0xDVitugmT1+qkLeFQqFg17mfnx/bW/SFLkh5w9o88IqugFd4BfzCKzCUpiChxgmXKj2hMoph5vPBd3JEo407TDw++90H+ztg9PjJkDn5dW3d1gWyKpT46VI+dl8rQX1Tfyj96Sh/e9w20B0Tgx1Rpi7E1XKuCk57EgLqCF8rX8bA6O/QnwXh3pbe7RZcVKmmBFNsRSwLsMnyrGOAQKAqKvX5ksIvvSft/a39Wb8vUb1uOkxGCH5bBn7GIZjlDjCsONjS23fT/onGRugLC5mFGe0N5eUwlJXDWFHB3S4vp76H635fnkLBgm++hQX4Fgrw5bTJwZOIwRNL2uwl4EvEjAJPdEgWrNNG/d904vB4bAFMx8JMAooGI8ympr1BD7Nagz2yNHzqkgCJkY9ProTDudbMvpeJAuz6+k4U73Q34KUlAhiEPDxWp4VXuBE8FeD4XxGENa3nBVX2qEottLeHwMkRQhdXiEjc0JU2VwhdXGCysUR+YwE7Z6jP/FrlNZagaavQT3BVuGK4y3CMcR/DBAJ7O19MZjOezSrBofRszIs9xTrfaRwJCAjAjaCsrAy//PILGwPs5XysUH3OAm/TgLtgpPGHx0dl5VEkp6ylkAtRA36FpSXHwqEkRf2unaj7ZQsMRRztmieXw2bJEtgsXw6BtRUqVBWIq4xjAQRdS6k1aTB1OAYk+EMtRzSPUPAiNXvi92tlOHPqAgbn7IfMpIGaL0We7yyMM3ljrFwG54ciwW/Tc9sj9GoYSxKQenIrwuwMEJQngVeRAp6hfUWdYFY4wewWBbPbQJhdo5Ctc8AvO39ni39aFxDrj/sdTPgu6Tt8kfAFu00J5rdHv92i7n2tQY1brmVT8RQbQ70wyY6jKxt0Ruz5MJ5ZJdm6yDH3sUhmcXWjKL52AbveexMGEx9BHiJM/e8v4HVgbVHb16qjq5DXkMeSsF9O+pJ9zvodO1D+IglnAbZr1sB+7YOd3j/nWiWOfpcC6oQhsXRt0yHTm5Lw6+BtaJTU4NGoR5m6+82ASWVA1SfXuCB4pAtkk71wYlMqcuKq2PMZ1sBuqGGjEOGbuwYyhWeDXo9fnnkUNSVF6D9lJqvSdQfyiT6zJRMSuRCLnhsEzY4s6DLrIHCSwX5NOLOo6w36okZUf5mID1w247DNBabv8MuMX3qcjwl1VRWIPvsSRHbH2H2jzhY+Hs/CN2h2u7XnocJDeOHCC2ws2DF7B2NAEegcJHZKeno63FxdcI/qMwjq8tBgmA+196OwWU6Wbdw4pU1LQ8Fti9i87LFlC6RhoaiqLcXlA29gRs4WiJquQa3PKPAnvca1R3RAXt6nyMvfAIFAjoFROyCT3dw5pjdUKXV4akciTqZzFPkRfnZ4Y14Y3JvExcjDOzO6AteOFqKxpjXesHKQwi2Aj/TLP2G/2R0JVtx3Gxdgh7cW9GciZX2FyWBC5uE3UWqxmcqjKNaGY6PNs/g2PAzu0r6/T3c4n1WF5RuvsttfLBmAScFO11UAe/b8sziSf4Rpinww9oMuVcrjjxfi4k6ufSFgqBPGLQ5ATWkRjnzxMcpzuLWqd/8ojF+xBtZOzv/o2IhiTQcHh39upZuqeq6ursye57XXXsOECRMYHfSZZ57BiRMnWN9lV3jppZfw8ssvd3qcsup/xOrnX/w10Jl1yDHkIEOfgUxDJipNrSq4BDlPjn7CfggRhSBAFAApr+fMHLXVxlfzcLGch4z61iDTRmzGcCcTRjiZYXODzkx8kw79CzbBu5qjvBXZDEGc1yoYBH0LdmjBrizOR31WGlTFRLtusjASiqC3cYTexgE8iZQln+giJgo032xCZcwFNDTZgInkRkxyykGYrAQNAiek5A2HxbmrTPCK0cWbFneaUBMKFtghNnMai1GdnZ3h5ubW7vM4NCRjVCZH57ro9xjKrAew2+c157Ffs58J2Txg+QAcBb1TpfP1+fhG+Q0ToyJEiaIwWz4bEl7rwaahR6PRsMQCUedpo2RbW1Almr43fX8KyHutgjdBYNLCSp0Pi/psJOUpkS1wh7mJ/ixTVWOK+TSi5DksYNYKLVEr80Gd3Ae1Mm/Uy7yglDhxdMmm6nlSDQ+nS3lIr2v99+0kZgx1NGGYkxl23ZxD9B2rTdXINeQi15iLAkNBp3OaQOexLd+WVf+VJiVqzZ3p4UII4SJwgZvArWWj30LE++smotCiXxFQvg9GngjnAp5GjeJ/YOFhMkGgUkFYXw9hXT2E9XXstkCpAl+pZM8JVEp2X6BUgn8DAfof/ogAXl4iQIoXD/1zTHh2i4n1PneEUSJBpb0Yz92mQZ3cjKgSBdYk+UF7SyrM9nVAnTOMeQthsLSGwdISZqLU3wCNVmvWIt+Qz8ZW2oqMRS3XZvO55S/0R7g4nI2tHcdVeuUvUjucFSqw4OoJ2KsamE+9j88fWwxTgjwjI4MtrCyERtxr+BZWUCLPfhziPG+H3OId8PkN0GknQqeb3fkNTCYoUlJge+wYKnXFSPXgIcVbiFR/KarEnfUELHnWqJMGQykNgp3QB4/pxLDktb9ea5LjUH0tmt2vlzlgp/001Iu4RZQ9eJgi4yMsVIs2drLcMTSqYK3Kh7U6FzaqPFir82ChKWbMpI7QC+RszKlR+KFW7ocauR80ItuW35ZpZKSlMd0JOzs7xgbqWAXN0mdhq2orYx/R73Wn/E74i7he6t8kNjgisYatyYAXG4shpXEoTgp1qQh8kQlOI1UQym986cczGzEi8200lpZiZ0EYzODBOiAUDoNHtnxOlUmFbxu/RZmpDNY8a6yyWAVbQSsjxub8eTjt3sNul916K+pGtFbJNJUCVEbLqG8IbrgG/8s/ochhNHJ8ZsDMF8EkMOC09zakOl1k88pwSecK2/XCJ0MB+0oJNFIjEkPqUBEng65GyAIe23AN4GzAl6kC5DXyIOGbsTrYhABrM1SlRSg+TqJqPHhOnweJbedKJulRlp5SwKTjwzpYA38pH565CpioR7h/HTTyvmtseOTIIS834V7/V1ArrMdYyVhMlU3t8rVMFLUhEQr77RBKuQS/snIMTKLp4PFlndZeH9R/gAZzA6ZIp2CctP0am67R5ORkNkeHW/KxsOE9mM1CnPV9C9W27dcELlt+hVVsLJT9+uHUfQ/ia5kDJLwKfKR5CAH5dXAt1YEUKwiFNsOR6joPSmlbar0JMtnnEAizYDR6QK16iI1SfwXS6nj4MYOPej0PQp4Zc71NGO1iZon6jqC1VH2GCA1ZreOlkmfGHrkWhSLu+013N2KaZ9d/3yvIv7oiE43e38Is1KJW74EPhOtxl8oMlw4ip9cDlQF485oAdToeRjqbcLtf388/SvTtUO1AnD6OWcYuVixGkCio83FJF6Mhm1sYWfjoYBWkQV1aAhtbKUnNF0vgOGgkLHz8/3Ix2T8DKpUKixcv/ucG3cXFxUy4gXq5KWBuxi233MIW35Qh7wr/Vrr/WaCMGfUQRZdHs2o2VSWIttIMEtsiau5I15EY4ToCwbbBvdJi6ZSOya/Fzrhi7EsoYwIRBBr0qJpNVe1xAQ5/zJ6prhCC7SvAL4ljomWmCc/BNHzddS2Iq6qqEB8fj4SEBDRWV0FUUwFRbQX4RJ+ma4LPZz7UkVNnwTUwmKl0n/rha6jquIDMMbwBM1Z/BBuTEMJf7wCvroBVSyrl96Hyk++oqZa9TjpkKKrWqlGnvIqa6oFITOTUXW+//Xb060DN4x95DoLLX7D3Mdx7Bjn6eiw+uJhRZW+R3YJnZz/ba7aRqkpPnn0SFepWL/O+VCRogUl0+uzsbLY10+mbQf+ut7c3q4LTRovRngbrmpoa7Nu3j1XVCXKREIL8TJhrK1kA5GJpwEjbNPjIqzv9bGaRHAb7YKTDGwcrHXBB6Yo0sxeUPDlTLF08zBOj/Ox7FFDp9nNpapggG2WJ4yvjUaXhKikdIeFLGC2cKuLj3MdhqMtQiMlK7X8EXvJOCHdylRzDvK9hJkuYvwl6yoabNBqYlEpuo2qzUglzYyOMjY2sKm3WaWHW6tjeRHutht1nlWzKuFAiiK4lk5mrbNNKiC9gVT0e9dg13YaQ7gvBk8rAl0pRKFVhFX8T9DDiactFmOUwHnwrS/AtLSGwsmIVdx3PiFVHViGpOokxFDZO3cjEkdTqfMTE3gqjUQUf70fg5XXfTT1eJDQZWx6Ls8VncbroNIqVrYJlVLmg8Xa232yMdRsLAV/IKtw/ltZgcG4qBuelsgT2mjVrbkoim67TH3/8kVUKyFN7eeOnsEIjanwCEeNZBZncD4MGErVV0q6CSscssTIRiVXc1pEJQr3t/lJPRHmNQJRjFGOUEBsqm3q843NRoTdgoKUMv4T7QC7gQ6tS4siXHyP76mX296HjJrPKZZnKyBhSWy8XooqJr5nhza/G8n5KzHaugkNDGnhlCeDVcBWdjjDL7VEhcIVt6Hjw3AbATJU9W9+WpF5HUK/7pk2bmLgsFR6WLVvWrc4E9fs/eeZJJFQlsLnyycFP4raA26A2mjAlNgt5Gh2WudpicRFwblsWUwWftTYCrv1uQMiz7bE99hIEFz+BWaxAcuQ7OLh5C1tpD1twB4bNu539Fvcfu5/9Rg5SB3wz5RvGdOqI6i++RPWnnzImieuHH0AxYQIqCxux96N46NV6RGlOwPbSDvZaSVgYZE/9F+dPNqA0iwsgs+2u4ZTfFqwf/Thu9b9xGrImuRp1v6Qz2rr4jgAc3ZWLmhIVRFIBpt0bCrcATomd1hIP/ByHC9nVEAv5+HhRf0wKccKBT95DxsWz8AyPxK3rX+w0L135PRexhwpg5SjFrSuDUfdNMmA0w3KOD+RDe2bodYRJbUDlh3E4x4/Gq55fsd9987TNCLbjRLqaUV9TgJiLz4BvcYXdN6jdENjvFXj4je5y7Mx1y8U3yd+w9qPts7d3WT0n0WMqfgl5Aqw2nIOz4AJMQbNgJHZf2/csLETenFsYI+nJh5/BlaBwvMp/FX6ma7CxGYkIt2cgPP0W+Mk72evNPAHMkXfCOOYJwIpre9FqS3E1Zi4Mhjp4uN8NP7++WZHeKPRGEz4+noUvz+SwoLGfowIfLuqPIJfOwnYdcXFnEuKPV6OCb8J2hRoNAj7EZmCWUoz+chkChzsjeLgzrByvn31Gc1DZvqPIlj4Pg7QWaqM1vpA8i3cjp8G/Scfnut7PbMajWxOwL7EUPvZy7H5gOOTXQVn/IOYDbE7dzHRh3h7zNmOatgW5IZz9NROpF7j129A5Pug3RMHG1oLEa+wx78iBmLTqQVjYdi/22Ix/K91/UdCt0+lYcP3iiy/iueeea3kdKZkT3ZwE2PqCf3u6/16ggJrosiREFV0WjdiyWGap0xbUa0hUlVFuo5gViyX1SPcBRbVq7IwpxPaYIkYlb4aHrQwLBnpg0RDPFnrQH0LOaU4wTVXF9bIu/I7zJe0D1Go1EwOkHikKMJtBPZEhISEICgyEobwYCUcPojg9peV56hWjRSG7baOF59gSjJj6Hpwcp3EvaCgFNs9HfXQWii/bwmzgURMc1z9I7z9oAEqXFkMlKUR+/izk5dqxKjItnEmxuwVk/fLVOKAiFabg2ViiMCKxOoklPGaqZmLWrFndBt00mG9N24o3rxDl0MCozqQa/1XCV4wG/fnkz7ukIHUHqoBT8J2Zmcl62jtS0elzE9WeEgdERW/uK6VM/KVLl3Ds2DG2gKXPO378eAwbNgx6tQrRe3cg5uDeFms3dz9vjB7iCQ9BIVCWCFNZCvjGzvRPgsHKE0LXCGZZBucwjhpHC+heKvCUHabz/lzROZwvPs8SE23pvpQxdlI4saCagvKuerVJNTzMIYwlocimjG53VMf+00AiT99OBfQqrm9zyiv4O+Hv2vf1TcI3+CjmI6b2vvvW3Z1+r5fOv4TtGdtZj/8vs35poQgTSkq2IzmF+l0FGDRwK6ybmCc3G3TdkvDb0byjzMaPhPiaYSOxhcz9CcQbPGGjasSdV48zgTzyiI6I6EwLvVFUV1dj48aNbL4mK6+7Gz6CHGoUO0vAW7gdxSY5C7DjK+LZvm2SoBlEiaV2jdAqKTx3XIJfeiPkOsB63jw4P/M0BJat80hKoxrzYjNRazBisr0V3rXmY/8Hr6OmpJj1FE5ceR/6T5rOKYiXJbOea2NpIqqToiHTZMOCuP9dwdoTcOnPiUa5cnu91AH7Dxzo87lJicIrV650PT53AUqI0nnU7Ol9R9AdWD90PS7WqZmAnHOtAWuONcBsMGP0ogBETmw9x24Iybs5/RLCbT8AYbci7vB+HPv2M/bQ5AcewvuqnxBTHsPGrO+nfY9+tv26PfdKX3gBtdt+Y60TDh9/ib37dNBXVGFg3o9QFHG+0baL74TTU0+BLxazICT2aD4u7c5mC3ylqBYnAn7G+nlrMd6zdzGzjjAq9Sj74CpMjXqYBznjeEwFowzLrcWYsy4SDh3UxEnP46FfYpkCPomufnxnFEY58/D9o/fBaDBgwdMvw6eNEFRDtYZZhFEP8LR7wmBxpgCGcjWkIXawXxZ6Q1W+xsslqN2Ridc9v8UZi6ssobRpxiY2z5rNJsRf+QrltRvAF2pgNgkgMS7G8InrIRLJuhw7f/n9F2xQboDGqMG7497FNJ+mdUUH0Nz63RffoLC8GF4mOVbw3wQzBFy+F/Ad2/I6g8mM39c/g6C9u5Dm5Yvk127FsMa3WOJs2NADkMubtAZK4oHjrwEZh7j7FOgPWQWMeYz5yVdUHEV8whr21IDI72Bv3zXD9Y+ioFrFLLPiCriCxp1DvfDC7FDmn94TmpW3j333BXJkk7DP1ht60uNQSPCojyvqr1VD09haRHIPskXoaFdmkyXsgxtOy79jMqN8zwVk4FlorfJhMIvwo+gxvDh4GfrJr09PY3dcER7eEsdaKbffPxIDPHseX9rih6Qf8G70u+z266Nfxxz/Oe2ep3P88HdJyI6tYIWM8UuCIbcowYHPPmDFIqFEggnLVyNi4rQ+n/d/17n9/6SQ2siRI9miuq2Q2rx589hk1Lb63RP+Dbr/tyA7nITKBKYuToEGbR29amlBSiJTFGBToE39qH29IFU6Aw4lleK3q4WsR6X5bJaLBUyBnILtYb52N1SR7AR6cxIEOfICJ3pDi6vbfwRsvXv5MzOrtkZHRzMLPKroEug7UsA4YMAABAYGdhpQyrIzcXzjlyhOaw6+zXAaUAWXQZXw9lmNwMD17YSZyl77L2q2bGH3ZU56uLz2OjRVYpS9+ioTYRI42KFyZQOUXmqkJN+J6mo+Y5PcTQJEbSspZE329SSmUPq0oz1O2blg28xtiD4Z3e3AR0H2m5ffZHZJhCneU/DqqFeZ3dQL51/Arsxd7HfeMmsLPK2uf9FHkz31gFLwTUF4fn5+Oyo6HUvSbvDw8EBOTg6rEhEoGJ8zZw6rireFsrYGl3f/hmtH9jMhD4JdYATiHIdjXwkP3ihFCC8fo63KMNaqFK6aLPBIqbsriOSAQwBnjeIY1LQPhsHaHdEVcSyYOZ5/vF3ln0D9eKRwTYkIOv/l9D5N5wsFFVSJpOuGgoy0mrROntnNfbnhDuEIsw9DqH0oExO0IduemwlVNfDVeNY3z5JLS35j1d2/E/6uEzMlGRfvW8zYPG2FiQi7M3fjuXPPMUEkslkZ4Tai3d/SeZCY9DATEZNJvTB06F4IhX/cEqknNKvz78vehz1Ze5ErmQq11XRW9b8zYT+sa41sTl66dOlNpwRS4P3d99+hsaERAlk19PIDSJLwkSkRdyJo0zGjpB6d+5SEinCMQKBtINP5IBhqalDx4Ueo3bqVjdukzO721puQD2lV1r1Sp8SiuEx4pcdj9qmd4Ot1sLSS45bxHnAx53HBdkPn4J5gMgtQLPBAjN4NiSYfJJl9YHSKwNKJUZgZ7tpuvrmec5OYTzt2cJXdJUuW9Llfnn63bxO/xccxHzMXD6o+vT3ubfwnsQROP+fDsd4E73B7Jpz2h363ijTg64mcKBaJ3k19reWpUz9+xxKaJPB8cGgpVM4ifDvtWzYu9fjZDQYUPPgglKdOQy+xQIr/HQjO3QGxqho8mQyur7wM6zlzOn+U/AYc/jaJef4SEt1P4d5V8xDlen3JqeotqVDFVUBpLcG5ai3UDXrYOMtZwG3l0HWS3mA04cnf4rEjtogFLx/cPgBW1/bj6r5dcPDywV1vfQR+0xh59PtkpF0qZeyC8f5WUF0qBd9SBOeHB0JgcWPMJRaEfRKL0vIirA54BWpo8Nqo1zDWLggxlx8HxNyaQVffD2Fhb8IroLOFU9vzc+W2lYwuTPoGJM7W3TlC51nap2exteI4o8Yv9FcjPOsLLvl87yk2LzQYjFiTlIuY3EL89MIjkGs1qL9XgsYBDfD3+w98fB7o/MbkSEIOKk3OLxBbACMeZOdYWt57KCzcDLHYEcOG7odY3Ht19Ho9qp/ZkYAGrQFWUiHeXNCfrR17g06jxrFvPkPymRO4ZhmOMw6jWZuFp56PBQYZlvxnEOzcFEybgHy/81OqmzsIWV9/0DAXhI52g71738Z0OvZVexKRqXkRSkeuYrxXsBxrBz+Bfoq+FZOqGrWY/P4ppmn06ORAPDy573oce7P24pmznFr+Y4Mew93hd7d7Xq8zMhtCslrjC3mYcncIStIP4fKubex5Ry8fzHpkPbMB+78wt/8jg26qZNECmkBWPe+//z7r3W7uYaIgnCiwn376aUtP9yOPPIKTJ09i9Oj2FJnu8G/Q/dcuMMm2KKkqiQXXFDC0rZo0g6o6g5wGMWsB2gJsA/psq8P+HaOJBdg0WJKCdDN9nDDCzx4LBnlgRrgLFJKb2AOkUwJ71gGJ27n7/e8A5nwIdJE5bgb1K5MPNAXbzdZUBEdHRxZokxc09St3heriQhz+cgOKUjklVQs7WzgNj4eVZyPq8xUoPhOCyCmzETV9NiRGEwofegTqq1wfonWIAq4RGeCJJMAdP0Mr9EfRQw9Bm5EJCPiom69H1XAp4uNvg1ZrZBZ806dPb/fvVxxaD8cLX6CGz8eVhZ9iQsBt3Q58DboGpmJ5rvgcWww/PPBhrAxf2SqsYtRi5cGVjErdz6Yffpr5U0uAeaOgNhISYqMgnDai6ncE9ayTKjklNrobBOsrK7Bz4w8ojz7N+uUJ2ZSFHzgdy2ePwhCfNpZxFHySynBpIquIs41UiLsQRSLoeDzkCoXIEouQLRKhUGYBK/ehCPCbihFe465LPZq8MNNr0lkCi4LwpMokdm115eNMFFpqwwi2D27ZE2Xwhhbb1Orw00Ig+wQnmLb6BCC/uYuem4G/88RM4yEF3kwAa9p3bMyj33LJviWsskQ+9fdH3t/l3+r19bh8eRY02mK4usxHaGhr0P5n463sInyQx41b4dk/YHSBNYw8IzJDMrFsyDJM9Z564wr4TQlZSkbQxsQCq1NRVVmFscVjITaJUSorxXnn8zDzzHDiSxHhMaolyKYkkwUtzHuBKiYGxU+uZ3RXKr04rLwDDgsmgFeTDWN5Gg6dSERKHnfde8lrMMs9DXJha2WqpXrtFAo4hwJOYVBVOaP6kIGJ7qnvDsUP14qw7WoBNE1qxgFOFlg3KQCzIlxZQNbXc5PmiK+++oq9nhwVJk7sG3uqLY7lHcP6M+vZmEvVz4Vlj6PoUjUapDx4rA3F0n5dixX12bXg6wlAZTpnE3jXLkDQOsfqDFq89ewyyHOV0IpNGPfUYxgVNrlPb62rbUDirNshq8phIxqNVGIfH7h//BGkgYHd/h0t9M9sS0PKGY7KWmNRgrn3DkZEYN+8hjUZNaj8NhG1RjMuGgCt2sCCoFseHgB5x6b9DjCazHhqezxTqKYcy9tz+qH0mxegVSox7f5HmCMJUeVJsZxwy9Ig8H7PblFGlwb23fGhK2hz61DxRTy22R/GJqedmGMrxFi5Cjy+EUa9BBLdPRg5dR1EJArZA+JK43DXIa71i9g2dI31drxiRDmIEWTDQqHAOv3nkOiqgFs2IC/kdixLyEGaUgMZn4fNl47C/rtvYHAyQ/mWN4YO2ws+v5vPQ6FI5jHg2MucMwZB7gDT2Mdw2bQLSk02HB2nISL805uS8CMXm//uS8GmC1z72WBvW3x4xwB42Pa+NqnMz8XeD95EVXEhztmNRKx1JHv8toEeGFFsRElqLRTWYubDbmHLVaLrq9RIPV+ClPMl7cTXyEIrdJQrs+8TS3teszLNiZ1pyKn7ALXeR9ljF/jTcOeQd+Cv6N3G8JEtsdgVV4wQVyvsWTsKoj62WJIH97pj62AwG3BX6F14YvAT7X4DncbAPLiL0mshFPMx+W5/xB34GrnXYtjzA6bNwril90DYxubz/8Lc/o8Luil4pmC6I5YvX86oZoTvvvsOb7zxBqPiBgUFMZG0uXPn9vnf+CcE3QZDI2Jil6G2tha2Nk2L/JYTuv2egprOzzXfa7rPnm/z+rav7TBYdX4/HhsUBXwpowLxBRJuz5dC0LSn+waeAIVqJbIaqpDZUIr0ujxk1uZCZ+L8TNuCKJPUl0qLALJMut4gu9lu40puNfZcK8aBxFJUK1v/HS87Oatozx/oDk+7P0Esrzob2LIUKE8CqJIy7Q1g6Opu+7epykr0QOrVpgGDQIMF0THJk56Cwe4mDaKnXdmzHRd3bGEVWJFEipG3LwHfZQdq6s6Db3ZA7v4QVBdyYlz2WiMGF1ZCQCrnQgnko1bB/dW7IDz+AJD6O0A9wHf8DJPbSBQ/9xwaDhxkf6caakTOTA8kpnKUsLvuuotVsJqTJ0v33o5Xkk4jSKeHOWIRDLd81uXAR+ryDxx9gHlFE73zjTFvYJLXpC5tY8j7k/xaiXL+/vj3b1qljJgD9Nmo34xA79txWHNycmLBN31HSugJBELsTyzBJ8czkVraACt9PYbVXUUQ9Wc2BbKBw0dj5G1LYO/RQ2bWZISpOgvZGQeQk3UQhrIkeGtV8NUbIOtuaKVzyM4PsA8A7P04mzZ7f25v6dorVb0ZjbpGpkpNQTgF4xS0kK1YV6AWDer5o40qgpT8oEphr8mPw88D5z/mqvmrjnKU+r8h/u4T86sXXsXW9K1MaZoC7yX7lzB1f2I5UNtFT+NhTe0VxMQsZuJC4WEfwdm5C0Gxm4zP88uZNRj77N6OqNn+E1RKFdLt0pFgndDC1FjdfzVm+s5sqTB3BWJo5NfnI702ndmpZdRkMOZGUWPXzBFPnS0GF48F3yyEo6MJC6o+gQupw098Hhj7eO8fnl5bkwtUZrAA0VSaDP21kxDoyyCUcIGx0iDC3sIQFKm53uah9gUIda2CwSkEzh6RLQE2nIIBqXWnKmPFl/HQ5dVD1t8B9otDUKPU4YcLufjubE6Lq0E/Cr4n9sO0EEccOtgzvZzO36+//hrl5eWMnUPjcV9FIzsipiwGa4+vhX2pD6anrWKj2Y/jLVHvIcW5YSGwFd1gMnrXg0Dcj9wYRbaSFq1qx0aTkVXBDmUewMyLLrCvE8PWzQNL/vter57mNLcf/jQa8i3vwLGKO7fICtD3970Q9nHNlhpTiIMbr0Gkk8HI12PUYn8MGt2zyKNZb0TphzGoLFPhosYMncEEJ29LzHloAKR9VKenz/7srgT8crmALQde8C5D9YkdsLCzZxZih79NZx7F/aIc0b9GDWOtForhrrC99eYIUFZtSUVZ3kkkRn4IezG31lBXDEB4+GvwCQvp9e9pnlxxcAVrBZjlMwtvjnuzx9dWfH4NuvwGSEc44+e8g4ydMsJbhml5r0Mnd8TIoT+h0CyBs1iIHyL84NVwFSW3rAZfyYPNKw/AddG63r8UMdhSdgPHXmVe9ewhG3ckuzaizEGI0NB34eo6H38E1Ir4wE8xuNZEJ39wgj+r/Pam88PYRyeO4Pj3X0Kr0+OE+wykiDmtgiemBeGB8f7QaYzY8c5VZg1n72GB+Y8PbBdM0zlTkFKNlLPFrApO9wlCiQABg50QOsoNzr5W3bMNTGZU/5qG4pqfUR70MxP6S+YPwcxBX8DfsnuW26n0Ciz/7jJLEO18YBQi+0grp/XFykMrGUOVxnpa47Wdr0jJfe+GayjLqWcaCKNvs8OFXz9GbVkJswKbet9DCBk17v/s3P6PCrr/CvwTgm6qZpw+0z39538Jgxmo0PNQauCjVE8bj+0rDDx0pccr5ZnhSYOHwhJBVs4ItfWFk4U3JBInSCTOkErcIJN5Q9CLxQWBBqPYghrsTyjF7/HFKKtvzQ6S9QJRgOZEurWvRt5spB8GdqwCNHWAwglY9APgPbLLwI8svi5evIiCgoJ2VW2qtkZGRvbqZVuamY5DX37MsqgEn8iBmLzqQVTUb0Re/lfg82UYMng75PJ+yLh8AbkffgCPa8lMMKhRIkLVgMkY/vxjsOnnQf4wwG93twbet/8Ec8AUVFNV99136QND72FC9K1jkF/pwSru999/PxNGau5DHWYS4ev8HPDMJhhu34J96bp2Ax9V6+4/cj+zxHKSOWHDpA09UgmJ+XD3obvZInztgLVYE8n1av0RUI/8tm3bWO83gfq2qUJEImzNVHQSZWwLvkCIKp4VMtQKFJmsYRApsHiYN1aO9oVEWYXz235G2nlOkZ7H4yNk9DiMWLgYNi7tKWcF9QXYm70Xe7L2tAsg6FhM8pqImbbhiDCLIKjKACrSWZ88o2fq2vt4twPZNrGAvDkY79cakNMit5fznAJxCmiaK4i0Ub9uV9R0Sri5WbghwCYA/jb+bKOEmK+1Lyeik/AbsP0e7sULvwfC/9hC588C0VO1tbU4tncvxg0eDJ5aDbNaDROJpGk1bE9iahq1FiqNFmqNHhqNDmqtgS20DSYTjEYz60Nkt01mbqPHmvoTaZakMYbbOFFGZifGbvOY6CHdFvD5kIgEEIuEkEhEEItFEEtEMAuM2J67HUqeCnWhTkgQFcJJZIkfw96Cg4MH6zcmgbWOlkvNyMp+H7m5n0IotMbwYQfZePpn4efiKjyWxo1hT/u6IjAlhmkkEANtyT1LsC1zGzYnb27RHaDz5ZGBjzBKMwl7ET2dAmvaU5BNjIy24phtQWwPaokIsQ9hrAxT6ZcwNlyEXjcJly97sBaSsX5yTMz+L/cHM9/lEp4GLVCTB5B4WTVt2a23qQ3C2Dn524y8ahvsrwiGykS/jxDTF83BUf9heLlUzyrTP/X3w3i7ntcKuuJGlG+IZXRRh9URkPpzC9h6jR4bz+XimzPZLcG3n4MCo2zq8exd0yHtpupIi8rLly8zHZv77ruvWwZUX5FSnI79b6ZCqrNAuvdFXB0zEpl6Gyxzs8fbQTfQ0520k9MxocX28t8Bn1EtT9ES8vVLr2NL2hZOXGnQq8j+9Dc0VlXCb+AQzH3iuRaqdUfQ357/5Dgk378Ghaq0yZqPx+Ynh7Vr4diFlVh3KCorw9cf74NjFaeoHzrGFWMXBTF/765QdygXBUfycV5pgJ5Uov2tMWttJCTXaaNG65TndycyK0mR2YC1NTtgqKvCgGm3I/WyO/s6s0a6QJBUBRmvRdQAAQAASURBVIGtBM6PDAJf8sfbc/T6WiRceQk1mr3c9zECO2ukeGbMdoS4thdV66mCef/R+5l7wd5b98LD2qPb16rTqlH1fRJ4Ij5cnhyC7NI8/PTTT2xMXC4/DB9lIt7wWYXjYffihwhfuEqEiI5eANPWRFjtFULs7w+/Pbu7HeM6gfRorm4ETr0FKDnGTZ2lEDn+9giadgIyWd+ZYm1xOr0CD2+JZRRra5kIH9weiYnBzn2ikx/95jOknDkBLV+MY363IctoBZGAh3cWRuLWqNbPQ1Xt3966CnW9jrV1zLw/AvwuAnpVvQ5pF0uRfK64pU2CQLR0Cr4DhzlD1kULAllWVm1OQUXVUZT0/xxmgR65vCCMG/gd+ll3FuZTag2Y+sFplmy4Z7Qvnp/dc8tHM3LrcrHswDLUaGuYrs+nkz6FiLSCmqBu1GHPR3HMhpAo84Om8XDm54+h16hh5eiEuY8/BycfP/wR6P8Nuv9Z+CcE3SaTHuXlxxF9NZpVQpuzbS3U0Za0CD3SfKf5ufb32fOd8ihtnuv0N2bojHqUqGtQrK5BiaoaxcpKFKuq2GOlmnoYu8nLKAR8eEmE8BCZ4C7UwF2kh72wL9YIPEil7pDLfSGX+0Auoz15MgeBL3TExewqRhsnoZKKhtZA21IqxPQwFxZoj/S3/2Pq433Jtp55FzjxOnecPIYCizbBbOnCee3W1cGkVkPb0IC0hASkxicwz22B0cj8a909PeEfEAhHN1cIyA+4ySNYYGsDgY1NuySBXqPBua2bEbN/LxNBkVpaYeLy1QgePR7lFQeQmMhlh8PDPoaz8ywWQJS+9DLqdu1ij1fbOiDa3RIGAZ+JAIVPnIahcxfCytYG+G0lkLKHC7yXbAP8xkN56TKKHn0Exuoa6Cz4ODLvFjTqJQgNDcXgqYNx297bGGOBCWVkXAAufgqztSf2eT+PaXPms6C7uZpC1HKqmlK1rjefdML29O146QLnz7ph4oYbEr5pS8ckFwPKttNnIj2IsLDOlVgSYMvKysaZqwkoys+F2Nx+QW5pZYWApio4qaKTZkRFXg7Ob/sJmVcustfwBQKEjZ+MAbfMxbmGq6xHnaoDzSDFaaLbkrDIIOdB3VcumZdGMReAU6BQlQlUZXEZfQoiOvgItwPRae18ARtvwMarzb5pk3Y9vumNesZEaA7CKSCiQLxaU93l6+mzjxXa4r3MeIhNRqSFzkDjuCeY1oK91P5PS3BR8GysrYWhuhrGmloYa2hfg4bKGlRVN6CqXo06lQ4NGgMa9CY06s1oNPHQCAGUIhmUQilUtBdJoRGIoRWIoG3a6wRC5jLwdwLfZITcoIXUoIPMqIXMoIXMbIScb4JcyIdCLIBCKoKNQgxrCwlU9schlBTAzSoAQwa+BltLKSwlwpv6exyurMPdiTkkrIwHvZywWsHHl19+yYKjZjaMSq9i5xHpN5BWAVHk2fchF4emFo2OIBYMJXdIUIvGC2JbEOuCWo2aUV5+CAmJD4DHE2PYkN+RnlyK3fuPsOfmO+agfwU33kFuz7V6dNFa0QKySyQmCWktOASyvdk+ANFHL+HM77th5vFgodVj+m13wXvJUvb91qXk47eyGlgK+NgzMAAhFj33SdbsyoTyYglEHhZwemAA5+HeBAq+f6Dg+2wO6pjiORDsYolnZ4VgTEB7eyWyBmt2Y7mePu6ecGxTCqOyNiqq8Uv4f2Ept0G27RMwidxwdEgQwnr5bu1QWwB8MYpLPI95HJj0fLunv0/8Hu9ffZ8l8d4a+xZm+M5AaVYGfn1xPQx6HYbNW4TRdzQJr3XAtTc2gb/5PQiJIWfrAO9PP4IuJxclzz7Lnvf4ZAMsJ/eNok7IrM7E2198j8j8SS3U3en3hsPSrn3CW1+mRMZ7V3GuwQBdU8A9e11kr/TengLvp3bEY2t0IUKU6Zhcfgx8gRQiy3sQGO6K0CIu0do2QXOjYOJzpXuQkvwqzLwa9ph1wQR8p1PhjDiBBUdfTf2qT+9D7DNiS42SjMKGBRt6FEltrnJbjHGHzSwukNqyZQsrNmjlAryuehdqoQXMD8dBYemI0tLdSEp+DEKdHC7PS2FqaIT7B+/DasaM6/vC2gbg/Ccwn98Anp4TU611doL1vB3gdeHx3dNvtOF4Jj48ls6m4XB3K3y+ZFCf2JFVRQXY897rqC4qQKPIEkcDFqNAzYeFRIgvlg7C6IDOgqZU+d31fgwMehMTLyQRw+5Ax7ckqw7JZ4uRdbWc/Q2BeqNJdI0CcI8g23ZjDDE1Kr9PQm1VNAoGfgizSIlynjuGDCDhQo612IxXf0/Gt2dzmJDw4UfH9qn1skJVgbsO3MUKClRMIYYWrXOaoazTsoCbKvoySxFChtfgwm/fMKFNz9AIzH70Kcit/phLAuHfoPsfhn9C0P1nnFh0ERMdpFZby3rnKFNFFN/mrUxZxqoSdJsW3131hjaDLjRWCbPmqmG0aKK9s9y5ZcHH/CBNauj1ddDrq6HVlkOrLYNWR/vSlvtkhWM0trd2URskSKkKQkx5f1yrjIBK37ogsJAIWBZydn9XjAtyhIQsev4kkIWQvqgI+twM6A9+CH1OGowaPgwSLxj4DixIpYAATZTxG4ZIBKG9PYTOTqhysEV0QyWUWm7hGjxyLCbcvYYNVipVDi5fmQujUQkvr9UI6PcUdIWFKFz3ELQpKazqIAmdB1HINGgnCBB9endLDzhVc/tPnobhcxdAceRRruJNA+ayXYDnUOhLS5G39l7oEzNQZW+DY1OmMhGQcv9ynDGdYQJfn0/6HDxSq/5sOFCbjyzHafC69yecLTmLJ04/wfoGo5yiWPDcdvHcG167+BpbsNN5RT1kVCm7XlAVe+vWray3m65vshZ0cekc9NN5eSS5DB8czUBKCVXmzHCX6DDbVwBncy2KCul8bB/okk0PBd9E85QYdLiy81fkxHHUdSPfjDTPBiT0q4NGYsZw1+G4pd8tjFJPgcUfAmX2a/Nbg/C2ATktfHsKMgikpN8ShLcNzD05GxaiybYJ0Oi6z6rNYgE4VSTZvjYTPHUNthSXwsNgxDmZFA84O8LU9HcWIgsWfNNG9GLae1l5sYolKRV3FQCadDoYyitgKC+Dobwc+rJyVJVVoaSyHmW1apQr9ajSAbVmAeokFqgXK1BHm8SC7bVN/uo3BLOZ+SRTv37zXmQ2QcYzQsozQgwzRDwThDBBBDOE7LYZAqpa82hP7TZckw5LUZq5qndL2pLuM2Et7hGjiQcaHXQmPrRmHsiNVmfmQwsBtDwBdDz6l/gwUYDatP8jyQD6PpYwwlpohoNCBEcbBZycbOBkawEHSwkcLSRs72AhhoOFBNIeFHOv1imxMC4TapMZCx3lWOdqxuY9m5FTkwOxkxgCJwGrfNC80RNIMJHs7WihRnMFsSeIUdEuEUVaASRSVlfINlNNDsoyvoRIWQ8rsw3EKg1jhBzBaJzDEAhgwHL8Bi9wAoktSShyDrDzadrT5sfdpopdm+oqq1J9/SlSzp5k9z3EcoReTYTQZIbTk0/CfuXd0JpMuD0uCxfrlPCQinBgUCAcxd3PxcZGHUrfjoZZZ4TdncGQR7YPpgkNGj2+P5uNz09kQG3kro1xgY54emYwgl2s0NDQgM8//5x5vHalrXEjKEiuxp6P41i32MS1/ngm8xF2XYuENihzeBIjnUOwNbKP3rhE1f9hDpB3DnAfDKw8yLliNOFAzgE8efpJdpv6PJeFtQbXVBHc/8l77PbsR9YjaMSYlufMOh3Sn3gFpkOcRoopMBJB330CoQMXvJT+93XUbN7M2B++O7ZD7Nn36vzJgpN4Z/vnmJixFFKjglHFp94TBs9Qu5ZrNvujWJxIq4XWDDh6W2LuI1HXXeHuCGLH/GdrHHbHFmJ50TZY6qshlI3ALP9JkCr1sBjpBptb2gdD1wu1ugBJSc+hrv4su6+tc4Wg+kGE5XmhxFyONQGvQm/WM2HG3pxCjuQdwWMnH2Nipw/LH8Zts2/rdu3Z3D9OA6PrU0MhsBSzMe/la2kw7NkGkcmISYpYjFGeZOJnxknP4sLFyWzt5+/3OBT7TKjc8AkkAf3gu3s3YwZdNxrKoD/2HARxW1lhg9YrvAGLgQnPcNd7D6D2j0d+jWMU62Z18hfnhPY4HjYj48oFHPz0fejUaqjsfbHHZRYqVEY4WUrw/d1DEObW/donK6YcB7/iFPgnrQhB8PDeBdqIrp1xpQzJ50qYWGAzLO2lrPc7eIRrS5+4SWtAxdcJaKxJR/6g92CSVqEetoiM/AZ+9pygIFHo5312jhwvsfHuIRgf1DtLihKrxEokrQ1qD908YzPsZfbtFPl3fxiLunI15FYieAVnIq7pWg4dOxFT16yDQHhzqOD6fyvd/yz8E4JuCl62JG9BcnIygkODWS8XC2JhalrU0ULPzKoIdJuoehqDhgXVtLW9TZS/5kC7O0pfV6AAiC4u2sgbuPk2LawpuCY0Gk2o0RtQZzCiVm9kdiu1BgO7rTKaoDGZoDWZoTOZW2/TZyZbW2odBw8i8sGEDtraOlQU1aCsSIOyCgFM5tYFgJW4HgMcEzDQOR7BdhlQSO1gYzMEdrYjYGs7gtHTb7S6w6rT2dnQZWdDm5UFXVY2dAUF0BcXw1Tf2aapOxgEAhiEQhiJKiUWQ2ZlBZmNNQRSGRMrg9HEPH3Nej1MKvIHbvIIpt5rWgQK+Ehxc0CRHUcjlOr0CC+shJNKC7GvD6ThoSi3u4Q6z0JYBg5BVNSPUJ27gKLHn4Cprg48uRWkUasgdAmBw4owSAM4UZaCpHhc2L6F7Qlk0TB4xhwMVu+CJP8EF3it2MfURikYynx8MYyHk5AUGorE/hHQ8/Q4430GWxZuYQtlhsyjwI8LWDvBpqlP4f2Mn9l5OM5jHFNjvt5gkyqvqw6vYpVi6iv+edbP7TKovYF65Ulkkain1J9NYotEy+wImmxe25eMK7lcNYCy0neP8mH0Khu5uMWakJTlm6nolZVcr3wzKLOss9ShpCEVzkVKONQ2LcyEAoROnoJxC+66KdncXsHotLncRoF58755U3ddtW4H6sumfkwrN25rvs327oCVK8xSW+h/WgBx3jk0KBzwzfAlSNOUs/5j6t/vKTkn40ngYrKEjUYCWb0Y/DoRzPVS6FSWUJtcUCd0QJXUGlVSKxjaCDAxdWmzHmKTrmnTMzYC3RbRbZMOMrMOltBBDgMXLPNMbGMBM88EgdkInUoJmUgAnskAs14Hk0HPts7Mn78ZKLAXisCjYIbGE56AC8jBh8HMh56Cd7OABe5qIR9qoRgasxQanhQ6nhg6vgg6vrh147XeN/CE7RItpM7rai2Ds40ANpZqKOT14ImqUWMqw+naHBiNlZCaK2E01PX4kSnBQnODj7UP29tKbHGq8BSOFxyHxGSCn0CBB/3nY6x1EHiNZUBDSUuAzTYKuLupireFSeaAreaZSNXYQy4CVlufg23lZUBiBdxzhOu57gVVhfnY8/4brEpFi/2xi1dg4My5qHj7bVT/sIm9xm7FCjg9+QRqjSbMvpqBLLUWw6wV2DbAH+IeAoT6o3moP5oPgb0ULo8OAk/I73LRuG33fqSL/PDT5QLojRwbbOFAdwQ2xKEwP5clDFetWtWtH3dfQYJGW165zBbFERM8MPb2QGZBeO+Rexk7wcS3QK3Ts/h+0BhMdejDuHX6XeD4q1yC474zXFKjCWT7uebIGk4DJGQpsynriGZFc5qHFr/2HlMwpoRv7gMPwZDM9W+rxizEwM9fZB73zaB5M2/ZcqhjYyENC4P3Lz8zu7C+4uv4r/H9hR8xLX0lHJSeLAERFSVGiF0Z6i6lIe9KBvj6RkiEJji4SMAzG8GXyyGwtADfwpIlxEnITeLjA7GfHwR9XDuSqvlDv8TA4eRlWDfsZ/o3t3isgdzRFs6PDAS/FwuqntiQBQXfIyvrQ5ihhckoRE3abIT2X4ewMd5oOJbPzsOvvXdhh/wwa9nYMntLt4wr6sGfv2c+a/1YHb4a3oXePRZ8Kn9IgialGoohLrBdEAC9yYxHUzlmyMC8NAzNTYGFTIR16g9YYSRn4SPILv6GtRMOH34EUOqQOWkyYwm6f/QRrKZNxY2iJPFt8E++A+dKXSurZdgaYPRjgKwziyC5uB73bo5GYY0aEiEf/50XgYWDeg7SCSaTEee3/oRLO7ey+9rAUfiFH4UGrZHpNVAA2xfRtUt7shG9PxcCIR/znxgIJ+++xyEUdBP1PP1yGXRqrl2FhnOvcHtW/faOsAc0RlR8FQ9NbRFyBr8Pk6IAWsgQHP4pPO3HYM6Gs0y35tYBbvjwjt7bWCnOoGTMsfxjbJwn4VtKrDeDxhaq4NdXamBhK4GDWzRSz3KMpOEL7mA6ODeTfaX/N+j+Z+GfEHRToDzql9YeqZsJEV/ELhyqRFKvqZ3cEdYSR8glDpCK7SAU2YMncoAOCtQZTa3BtN7A9nV6I2oMXKBNlMMbhsEEfrUW/Cot+BUa8NXtq4smmQAmJymMzjJY2vHhLqyFpzkb7vp4eCMbPsiGiHVYAhKJK2xthzcF4SMhlXbOHhL9W0sBdXYWtJlZ0GZzATYF1z0twgUSE0RyA0TWYogGTYfQLxwCO3vUms2Iy8lGemkptBIJjEIhE+ai6gRRLvs6yBi1WiQf3o/TO7ZA0+S7HWjrhFCVEcb0dEav7Qi+nQ1ETs7Qpqax+yKvQIiDV4KvsIP9khDIwjtTmyjoPv3zRtYnTpBaWGKYWy0G8C5AaOkArDzEeoVpYkl8cRoEvxXj2KRJqHawh8JFhsfXPNnuO2l/WYq3K89iqxWXJFgQsADPDX+uRwGlnkCCarfvvZ31g1+PsNqFCxdw6BDn6UlUcrIQ7LhQLa3T4M0DKUylk313ER8rR/li9Rg/2CrEvY4X6ZnpOJ1wGhWFFZDoJe2DQ60SlrUV0NdwaukiqQyDZs3FoFm3Qqr4c+2ceoSmHqgraB+IU28rUdbptqbzedUjaLHmPYotss1yBxiNEigb9MipqUJibTWSdQ3I4ilRJlWhQaGBTtq1gntbCAwCSLRiSLUiyDUCWGj4sNTwYKHmsftyrQAyrQD8Ngm4PwU8HgQCAestpF5TvlDIEp3UQkAMEeq94/EF3Z+PPYwf1B5iMhoZe8JkMLDbtGl0atay8md/N5aUFZqhkhqgkhrRyDYzGmT0mJE9rpHooW8SXeoJIpMYCr0cthI3BLr3R7iNG/pb2sGPL4UNKVk3lLZujbQvg6G+EEJyeugLqOXFyh1GSyeUaxMYc8TedymsvGZyiuGUCBLLWWKMBFVJp8HVxRkr+dshKr7CMTlWHQcsOleYm5F8+jiOfPMpDFotFLZ2mP3Qk/AI5dSZKZFd/d33KH+HU4S3mj0bbm++gSydATOi09FgNPXaA23SGlH6zhXm8UxVTKpm9rRoLK7X4e2DadiXUIJgQRmGi/JZRX7Vqnvh4fYHlMWbcGZrOuKPFzI69R0vDG2hS1MSngJkUtI38q1h7fMKTo+aAFFPvWCFV4HvpgKkBzH3MyBqSctTxJAh6im1F9H4Td7OXSnZ0/yy442XkBcfy4TV5s29HeVPPQtTbQ30Ahkqpj2ICe/c3WW/q76kBDnz5rM50XbJErg8/1yfj4OuuBg/fP8oTFdTYCFchFo7rurrUBmP0JSNEBpb29b6AklAAGSDBkI+aDAUo0dBaNu98nh2QiX2f3IN2obNgLESIdYjMPGJB1oS49eLuroYpKQ+D6Uyld1XlgdBV7wGkxZPhq2LoqXiSawLSrCsDH4JKpMKb415CzP9Znb5nqRD8uzZZxkrZe8te3Hm6Jlug259uQpl719liQvnxwZBZyfB6sRcHK9uYIyg9/q5oWz7L6ipqcFYqyKMUm/DheFOzOkgLPQDuLjcwt6n4uMNqPzsM0iCguC7c8eNVbubxtirMbfDXHAJwQVCWDY7l5BVJgktDllNEzN7iLSAntgWD7XeyAR3v7xrEFPu7g3qhnrs3/Bui/o2xtyOL0sdmAYIaQh9vWxwS+K+189rMmP/5/HITahiQeptTw/pVRm/K5X+7JhyVv0uzmidy2VWYoSMcEFgf0fotqdDV1+NrEEbYLZOgRFCFJofwytHPGArF+HoY+Ngb9G7lhK1ilDLCMUO30z9BgOdB3YZcFs6SGHvfBmpZ4+yOXXK6gfRf9IfZ+p0xL9B9z8M/4Sgu06nxIMnnkF1TQ3s7ey57GRThpJHRJomNfJmJXIejxaGUvB4EvD4EoA2nhgmngRGvhx6ngJaWEADGRpMIjSaTGg0mNBAi8E/WPSR8HmwEQpgIxI27QWwFgqgEAgg5fMgJSEhomOagIqyRhQUNSC7sB5FZY2M3tIMomw6u1jA1k0BsbMcSgkPRTo9qvVd97RKeEYE8wsQaLyECPNV+CGLRBvBrwOs6v3YJq2wgCmvklWyjR0qlm0hsLWF2N8PEj9/SPz9IPLyhLhgD0RpG8EXmTk7lNs2AgoH5gd9+vTpFmu75mBvzJgxXdKZe0JtWSmOfvMpW4AQ7D28MHXNQ3ALDG5ZBBL9tuziTyg+8QXEWTxI8sTkxdL+8zsEQugxFA5rFsJqQvfWKPR+mVcu4Owvm5gFGcFSYsJIu0yE+sjBX3WELViJ9v/ha5Mw6qgCxydPhUkgwPzp09F/+HD2N7SweuzIg7hYGUvKi/iP+xQsm/zH1cfJVo6UU0nki2zGVkWs6vG7HD9+HGfOnGH3hw4dyqiYbRV+id636UIu3j2UBqWOO49I0Z5URam61xvoexLtnUSiWL+zGXCGMyYoJsBN54bywnIm3EZBl0BZB0l5MQRaTvxEKJWi/9TZGDl/ESSyP0FB/49Cp+KqjbRRTzltbW6bm243K7cT9CY+VAYRlEYx26sMYqb6rGq632gQo9EogdoogtrMg1JmRKPMwLYGObdvbNprmlSje4UZkBmEkBvFUJilsIQMFjwFrIQWsBZawkZkBWuRNWzE1iyRaCWxhkJiAZ5AiJi4OIwcPRpSmZyppgrFIrYXiERMrZ4v5ILsG13s3Sg2JW3CO9HvQMwX48eZP8Jg0OOufUshNPOxefpmeMrdYdTrYCAavk4HvVbTclulaUS9tg712nrU6xpY8FSlLUFR7RUoYYJObAMVH2iEGiq+FkqhHiZSVuwDBEYeFGoBLFVCWKqFsKC9SgQLtRBiEimy8wZfr4VrQTQs+RoohDq2kaUW2wua9kI95AIdhG3+XYNAhFIeUCbgo1YkgY/7cPh5jQGPWh0ooCY6KAlT8vlISFyH8vL9sLEZhoFRnDBTR9D8/cUXX7DrLyoiFHMLX+eE0zyHA8v3MOeGttDrtDix8SskHOMSdF7hkZj10BOQW3euhNXt3Yvip58hGxFYTp8O93fexrF6Ne6KJ0s+4K1ADyx375zYbEbjxWLU7soCXyGCyxODwe/QF9zVovFUfDaO7fiR8Rku6r3QaOWDl+aEYXLojQfelYUN2PrfKywnRB7TXmH27b+ntg4rDq5EZm06jAJ7rBq2AY8FRHTfR/vFGO4Yh83jhBSbfhdqSVu6fylKlCXMleTrqV9DStXGbqCqr8Pm9Q+jsboSrrWNGJBXhkYLT+SNW4tbXp0BCVEYukHj6dMouJcT3HT/8ANYdUO/p/mBWq7q9u1D48lT0GVxqtfNKHYZgfTAO2DiCyHW1sClMh6Dbw2E3MmKWb/xREKYlCqYGhtgrG9gyXldbi7bDGUd2ikEAub3bjllMqymToXQsX3SZ+d7MSwwskAWKmt2g8cT4bb3v4KnW/fJoa6g01UhM+sdlJRwPsdGrQLl126Db+AdGH6LP6uctkXDuSLU7c3Gr26HsdF6FzwsPLDn1j3thK+amWZzds1hvbqPDnoUdwXd1WNrY832DCivlEIaag/+nYHsurhar2KWYF+F+WCKgzVSUlLw66+/Mp2d6Z6bUedlgpU8GIOH/d5yPVPyhFW7lUp4fPYZLCd2di7qK5TKLFy+MhsmanFTLIHd1SNABedNDmsvmCY8i3dL+uOzUznsoTEBDthwZ1SfAuWynCzWv11fUcbNH7Puwwfxera+mBrqjI/vjOoTLb0tyIrutzejmVga+bVTO0PH36+vqClVMtux1AslzFO+Ga6+VnCr1cDZrEPakG8hsuX0aH5NuxXThj7KrHR7w46MHXjx/IvsNtP08Z/TfcDtdAmp544xsdnpDz6K0DE3/nv2hH+D7n8Y/glBN1WVg89yfR9/BehSp0DZWiSAjVAI26bAuSWQbnrOVihseg0XXNNrZd2Il5FYTFxBLa7m1eByThVi8mqhM7ZfaHvbyzHS3wETg52YEFpXYg6NBiMKNDqkqzRIalAjqVGDa3WNEJWWwLu0CN4lRWzvX1YIr5ICSNXdq9QKXVwg8fdvF2CTgqbQro3PMAnykNgY+RATRqyFedJLyC0oZMF2Tg43aNPEQZZfFGyTIvn1gGzAru7bhQu//QKDTsuCgBEL7sTgOfM69b2o1YW4fGUODIZ6eBoXgPd2HAykhk6VOGs7mGraJBMEAliMHg2bRbfBYty4dvS8tqBKW9KpY0wYrLGaywo7ShoxIVwIz0f24GxFDFMwDc03YX5yCFKCwiHV6XDf0qVo9LTC2mNrmRCXxMzHW+VlmMS3AtZGdyvcdT34Lf03vHzhZZZQ+mzyZxjtPrrLBdWxY8dw9izXx0bq5PQ7tF2cJxbV4ekdCUgo4mixUV42eOWWcER49E6hrFJX4ceUH7EldQsa9Rz9n3qUV4StwC3+t7TYahGdvaysjJ0TpJael5sLc3UFxBVFEOiaKr1CEezCo5iYnV+/fkzx+U9T1r9BaMrKUB0Xi+rUFBTn5qGgshZ8cyWW+p2DiG/C+WofnCv3bvEtvx6IqFdaoIdUYGCbrOk2T6Rvqr6a0CAXokEuQo1EiBoxH1VCoJJnRKVZB0NvfetdQMATsPYEvoEPRytHZo9GG/k4Uw86PScVSCERSpgqe9uNgoXm22KBmL0XJT07bh0fJ3qm0dy0kW0cKfybDWzf9rm06jS8cfkN9vhsv9no79gfWoMW2zO2M8q+t6U3BrsMZgkfOvdo33bryoKxL7DkCeEEERxMfDjozbDVGmGjNsBapYOF2gi5RgiBTgAtJUyMImjYJoTaKISKp0C9XwSrwEoLsyBq4NozeoORL4RRLAcU1pBa2UFsLUeKMQX5/CyoZHpE+Q/HMxNfhq1FazBYXXMBsbFL2aw0dMgeWFp2b3NELSCbN29mt2+ZMAwDz6+hplag/x3AvC9agsKa0mLmoVuRm80eG7HgDkZ77E5Bm9Bw/AQKH36Y6XVYTpsG93ffwSfF1fhvdgmEPGDbgH4YYdM1k4WUhMs+iIGhUg3LSV6wnuLd46KRWBBUuS8qKoKVkwd+q/VBcZMzx+QQZ7w8N4wJHl0PaIykYK8ksw7+A52YeFh3Y93835ehWpUPs9AJu2ZvRj/rztV57HoAiPsJsPIA7j/L6UVQsKNXskQpUdWprYB6PW2owtgDKNiK/8+jONFQzgTs3OstoQq8B/OfGQkb594TlOXvvY+qr78G38ICfrt3QeTeqhJNVPXa7dtR//s+6JrmagY+H7LISGgGBeMt5Q6kOxqxuOx1oIyrDIslAsxaFwm3fr2Lmhmqqpjfu/pqDJQXL0KbylWcGQQCFjzaLLodilEjUZbbgO1vX2Ui7JMUApwq3YR6XRny3YfijTefgVzcOzPMbDaiqGgLsrLfZesAQm32KCjz7sTEJcPgEWzX9d8ZTIx1oaxvwKqwV1FlrMbTQ5/G4hCyG+wcWJEo5oEFByA0C7sNuo31OpS8dRlUrdGvCsWyqnJkqLRsTbi5vx+GWCtazr//x955gEdVbW//Nz29956QhJ7Qe5FeBRRQERB7xd696r12sV3FigpiAQXsCNJ774QeQnrvPdPne/Y+SUggVPHe6//z5TnPOTMZppw5s/dea73rfb/88kvS09Px90+jTdstdK3sgtcEJWHQgMK336bks8/ldxP53bd/aG5MT/+EU6lvotV60Kv7cgzH1ijCt6J1RQju2yN53TqFtv0m8MSI1hclvCs0H1Z9MlsKAHoGBmEcfg9vbVE84IUt7ayJHS9bwFcEyyLwFpZiHQaGMnDKxXnJnws2m52MpBJJP888UtJIwBItnGE6FZVtV+Df+nt5X3DozbSN/4cMkM+FnXk7uXv13XIeuyvhLmZ2nnmOgNuAt98Okrevl883auYjtO13+YK4F8LfQfdfDH+FoLvGauPa/SlUVAifbi8p3iP/yT7o+q2+J1oca1UqnDQqDGo1etXp6rJercJNo8FDq8FNq8ZdTAhasTU51qhxEfTJPzDYCRXI9JIa9mUqQfa+jDKSC6vOYl0GehhkkC0C7N6tfC/Y/yLEVcwZGZIW3kAHl33XaWk4TC3TwWwqFUUBAdhCnHHxz4YgI9Ygh9wMniEEBV9LcNC1uLg0XwhJ5B1U/LcrMmW/q+Pq2Zxy6SyDbVHhFhCV1E6dOtGvXz8ZQF0qBL171afvSzVsgfD2CZKG4x0c2mLf1t59U6is3I9PUjTO80twGI3oQkLwe/glqrfZsFcXo3Y6ifnUDkxH6zO74prw98dz4rV4T7kRXWDLQhmi+nNgxW/s/PFbTHVKkBgXouHLTiaSyWFMYDTDS46x/8DVVLl6oK84xoquWZTZqvB39uc69TXclTsPlVDd7j0TRtTb+PxB/Gvbv2QQIqhuog9NaAk0QEzma9asYevWrfK2qG4LSn/jZ7LZpSLph+tTZCZaKNw/NaoNU7pHSDbF+SDEBOcdnidfW+gqCAjhp1s73CoVeC9EnbdarXLhnHrqFCe2babqRBJqs/I8dq0Os28wTuHRREZHExkZKTc/P7/L9t+9GIgqaWVREVXFhVQW5lN+8iQVWRmUFxdTXlMrnQokDaUJRJA8LWo/nnoTadXe/JTVXorUyM8h+oudXNG7eeHq5YW3ry/+Ab54eHri4qzGVWfHWW3CiVqcbJVoTSUKlb2uDOrEvuG4DOqVZ88F8a5K1WqKtRrK1GrKNBrKNGrK1PV7TcP9ynG5WgiR/W8lNP4MCHaJm92Bm8OOt82Gj82OT8PebsO7/ravzY53/e1zesQ3gUVjoEjjQZnOgwCLBufiasyFVfzuexUnvWMJqCygz4ENWAMicLRqiyU8hjKdEyXFZVSWlVJXWYGtphKNqfqSEjQqZxc8fQPwDvCjzrYb9EUEhHUnvt0duPn64uHnj87QcuVUjM2C8SLaA24bkUDI77coiv9Dnof+j3Ji+2ZWzXkfc10tzh6ejL7/MaISLs6Os2r9enIeeFD2E7sPG0bI229xz8lcfiksx1+vZU231gQaWq7K1h4qonTBcVQGDcFPdkfdpHp75qJx48aNrF+/HoPBwL333ovO2ZXZa1OkzZiwpxOq9WIMm9oz8oJjWANO7MhjzfxjaPVqbvxXr7PUupsitzqfUb9Ow24pwMs1ht/HLZAJqkYc/lGxmzzDHkz0bt+/9n625m7Fx8lHsjaajtUtoe7QYXIeekgKlKYG+nA8SATvaobc/iydhvW4aFeDjOk3yf5ul27dCP9iHrW7dlH27bdUr98g7cUEVHo9blddhcfIEbj27YvG07MxyFy58AAJeSIwcEhtj2qTTSpDD5raRgpTXQqEBkzV6jVUrlyB8aCinSKgCwsjr9VwDts7EubiRGdnDdntKtm67GMsKi0ZQx7mw9sGnPc7rag4wInk56mqUgRRjWXh5O+bSmhEL66a1uaC/uHV23Ip//UUv4dsZ7bn1/J7Wn7t8kbNFMEqG/fzOLKqsnis22PMaD/jvEFNxYp0qjZkUdjKnTs76Mg2Wgg26Pg2MYY2rs0TQ3l5ecyZ84lcsfaPWcqQtFS4dyf4xzc+xlpcLKvdYj0X8eWXuPa8uGtAQLzP5ORkDh8+LFtOXF1dqKpag0aTRrv2MXTu9CGpuUWs+/JFrjf9gIeq3oIr5ioY+gKEKMJiLUEobm/57it2/aIEqVGJXcnsfB3vbVTsW4UWzHNj2l307/FcEJ7tyz5OkoyuQdPa0K5fCwmvy4AIikXl+9jWPHncAJVzBQHtfsUjYheBIUPo2P5NqTNwJtIq0pi6fKpM9I6KGiVdCBpihDMDbi+frZzcuUkyxkbPfJQ2f8CD+2Lwd9D9F8NfIej+X76wRA/LycIqjuRWSkGKI7kVHMurotp0tu+vqGR3jfCmS6S3DLKFP+mZwb0IomwlJTKQNgn6VprY0hQqlwh0z1CSboCYUKWwSWwr1NExJAcEsdbDj+90blTWizIJutO1XpVczTIo/72ZSrqXVw+CgyYSEDAKrdYVDn4HSx8EqxGHVzTJvd9k48G0Rk9nsajr0qULffv2xcvr0i0+xKJv66Jv2L/iN8UGzM2dgdNvo/3AIS0mPMR5OXTybY6k/wAb/DGecKXCzZ26jonY+g2l/HglJpUDW4Azjgh3KVLnmpVBp/WrSdy4FtdKpcIres3TBwwiY+L16OOF+q6WAL2OUCcdoQY9WrVK0v22zX2bpB17leBKZScl3saLD83hxLGpFGdYWZrdnj3+e7Gr7cTrw5k9+jP2bNjDmNYGtN9dL8WeuGcrBJy7MnXR58pm5pYVt5BUnCSthET1RFSXzwy4R40aJX24G5BcUMUjiw9wOEepBozuGMS/xrUnwP38fuiCZjn38FwWHlvYGGx39Oso6e3Cwuycdl8XgLGujq2//MCR1cuwVCuqo3atHrNfEBZPP6X64uzcGICLTbQoXEoQLijHlTKgLqCiqJCKogLluCCPivw86up1Ai4EsQis1rpSp3HmzpCttDbkUq7yZXXsKwSExxATEURAgD96Z+crV6kX3vHNAvKy07cFnVVs5moQPcGiZ1j4mct9jXJ/w331AZ4IK+tUKqrVaqrVKqrUamrUaqoa72vYVJhUZ2z19xmb3GdWqbAJgr2wCZZJABV2lZIMsNcn+KRSuRgfxCaV0UEr2lyE4nn9fcpePMaBweHA2a7snRo2edt++rYMqu24i6SR3DfZbHZc61+nEcJDXe+CXWugzlqMTeNA7xaFk0e8IpQoGChy76kIjjW7z0ve90uljbtOKBWcf7cJZ0qwUn3OSU/ns/nz5fHYtDRcd+1u1sMu7A7dBg7AfcRI2dsqxK3E71RU11Iy80jNzCM7r5CiwhIqxThfWYbBXIWbtQY3W430Mb4YCBq4p3+g9Hr1DBD7QLl39wtgxYaNnDx5Em9vb+7q5oTT6idkK8Q619s5vE9JRIa2aceYB5/A3efctPCWULVhAzn3PyADb49xV+P5yiuM3X+K4zVGenm68n2nWDmGttS3WTh7P5b8mrOq3U3n9pKSEj799FPJmrn22mtJSEhoNp4Jto5IZAuI3tFZExOI8Xe7IHV1wT93SE/gXhNi6DpS8ak+H77LPMpLG+9Eba+gc2AP5g77RKEhC5eEj/sqDIIz7MFe2fGK9OIWwpnCPqiDX8vVdHk+HA7KFy2m4JVX5LlUBYawI2w6ZaoD2C0psop406zZ6FtoxTHa7OSaLOSazJRYrJQJa6TiEvIXLyYyI50uman4FCrXrkBhhwTyRoymbsBAXD098NFpZWAYqNfhq9NweFMOm75VtE12xCzh7eueZ+/KYlL3K0rWnYdF0OuaVpcVUBmTkylf8j0Vv/zSKMRq1rmjbzUEn+ET8L+7O5899iDVOWns8upKp/E38PToti1SyU+deovcPEW0y2ZxpujQNVRnDWbA9a1lYuBixmFhI5X3xm7MVUbuTXydLHMOD3V5iNs63ib/vix1GU9tfkrq/KycuFLOs+dae9rNNvJe3UWm2sa9Az3Js9uIcTawqFMrwp3OpmnX1qbz1VfPkp8fS5BzDXfWfYq643Uw8bNmj8t/8UXKFn6La58+RMybe1FreJFsE8G20diydohWa8QQ3Z75yf7UmGy09bTwddxm/I59BbZ6tlDHyTD4WfBu/vsw1day/IO3SN27S97uPm4SGz27M3eLEnA/NDSOB4fEXbF5cM/yNHb+miaTPtc80oWgmCsnxCrGoRNJRSz94jCuptMuGyqNCY/wPYQlltN36L/Q6RRtHgGhAyACbpGIEUws8dsWzK+WAm5P7y2k7NosA+4xDzxB695nMxP/f4mNLjfWVDnE6Ph/GH8H3ReHOiHUUFzNqaIaThWK/enjM2niAkIFMiHMky71QbbY+7sbTgfW5eVYsnOwZGfJgNokAmsRYKenSwXLc0F4WQsKuCFGUMFjMLSKVfquw8Kk8NFZ36/Vxvf5pXyZW8KJGmVAFlX/qUFe3OB6GFvxEkpLBS1Zucy1Kmc65Hjgm3JELp6PBU1ik70zBYUKbVuIcnXr1o0+ffpcdpLm1N6drJ37CVUlyqTetv8getx4C4V6Z7KNZmVB0bCvX1zkGU3SYuhyoLVa6XtwD9euX0HCKUVsTWBbxy7MHzuJkxGKJZegSUY4GYh2NtDB3Rnv5FXYl/1IbaEINcDVy5vEsT340foBKyqVSTW8wp8X5xfT6qXX2GS1KgPfDzcrFmSi933G0mbqyJeL/Jp86RkqeqlFlVmIwIj+bVHVEhCvK/q4G66vr3dk8PKyYzIp5OWi46XxHaR/+4VsMBYcWyBFQqosyjXYJaAL93a6V1ocXalJ1WqxcHjdKnb8+B015coCWu3kjMknEKO7jwy+GyCqXeHh4TIADwsLIyQkBJuxjrK8XEmTLc/Po6IwXwmuiwqpKbuwSrlQr67UulOtdZOBdY3GVR7rnFwI8PUlOi6K+OgQ2gR70DrpDXQ7P1Ds5ESPf+DZPuf/UxDTldVYH4BXKwsqqxGrsYadWzfSs2sntA6LovYuHic3U5O9EDSzKZuokMpjqxLINx43/F3cZz19LLU2RJJKfH+qJsecdX9uTQEHig5gEfTm8IEEuIUqfcdSoVwv98XmKuYe/QqzCm5LvIcQz0jl78INQOcMeldFcV5QtsX3I+4Tt5sov2dlf0Vy8guo1c707LGsZUbPGThYVcv4fScx2h3MjAjg2VYhjb+rr776SrZPiFaaiRMnSmpt9ebNVG/cSM2Wrc3GbmHnJDyUPcePw6VHjxZ75cVzFlSaZOJ2X2YBP+/fQHWREZc6A25WI262alzrA3I3azXu1mr04vs73yWg1lAX2wGbRoevXkvrmmOcSiuSWgPi3PecMJnek25Ec5lK4KLinT3zfpkA9plxE1UPPMTIvSele8e94QE8Hxty/mq3k6h290Bdb0HVsGgULJ358+fLimDbtm257rrrzhpzBItMjG2zVhyn1myTIpBPj2rL9F7nrnpvWXKSg2uz8AxwZspzPdHoLpzEE9/LqG0ryTr1HGqHkdHRo3mt78uovxwHmdvOsgdbfGIxL+14SXLt3h30LoMjBp/zue21teS/8AIVv/wqbxv6XsUGp3HU2QzE9/Aibe/7VBUXEdd/EL5T7+RYjZHk+k2oxhedoWGiN5u5evMarlu7jID68a/GyZmVvfqztP9Q0kPOXW0Xs5hHpQ2fKhsORzr5zhsYEhDJ+30eZ++yNPb+niEfF5Xgx7Bb212+T3ddHTuf+BjDph9wNinvUe3lhf+991IUHcZvH7yNUa1nfvh0XprUlRt6RJymkucukgG3td4xoDytD0VJE/EJDJVWZxdDwW+Kqs3ZVCxLY13IXt70nCudBVZMXCFbaa795VrZKvZA5we4I+GO8wY11bvyOLAylbt7uFCsVxHnYpBtFkHnYHscOfoYmZm/s3fPRKxWNdeynATVybOq3ebsHE6NGCF/X1FLluDc8dzJG8EkEx721fWuL2JNJhJVvr6+1NTUyO3QoZ3sLPdnjzVcFhE6BBiYf2d/aZEohUTXvQyHlGQGah10v10RXHP1kzo7P7/xonQ4EG1/Q++8n68KfaXfusDzY9txa79LtzS9UGC84rPDMunj4qnnume64+p5YYGzi8W/fj3C/G3pPGZwpUMFZJjtVDdZwjt5lpI4qD3t+7ZC4wJ3rLpDOsmIljrBXvFz9js74PbV4eG1lVN7tkjBUZHQjO/554g/n4m/g+6/GP4OuhWI4CSvoo6csjqyy+vILVeOc8rryCiplftzQVB224d4SD/Chn20mxpHfp5ityWD62zMOdlYsrLlsRDLOCdUKtmXJSrX+uhoaZElrTlEv3VAwGUFP2IRsamsmrfT89lVoby2s1rNI1GB3ORvo6zwF4rTvyV23xE5CR8mnnX6fpSblcBar9fTvXt3evfujZub22W9fmZBARu//IyifTvlfRYvX44Om8T+4OizFhLngs5ixqumGn8vD/zcPXFNq8St1oaLiw7vToHSEqmhtUAkF9SI6pywk0P6zGqPHCZ0ySKCt21GbVdG2v2de/DZmIkcCz1t+9AUPsYyQnPTCchOo9RlI9kByqKhTVUr2hUn0mn/QdqcPEn+tdfS75/Po6vOhQ97KIHMpHnQYSJXAnvy98gJQPYUed9F8T4lETJixAj5vTRoBzz5fRIrjiiVjkGt/WU1KMDj3NVtIRwjKOSfHPyEEqPS0y4q6kK8rX9o897wKwlB5xfBt6CsNfTSO7l7EJDQlTpnd3Iz0rHWVKE2GyUtvWGvEkHeeeBwqKhVu1Bi8KZM7y0D7Cqtu9xXaj2wqnW0ctfQIdybjrFBdAj1pHWQOx5OZyyWDn0PPygVECkcKAST/qL4X5qYs6uymbx0suzRvjPhTu7vfP85H/vkpidZnrZc2u99MOSDS34twaLZv386ZeU7pJND505fnfd6LjJbGLEnWSb7hvh48FVCtGxnEhBikd98841k+cycOVNWkpu9ltUqKb6Vq1dT9fsKrEVKUlFAGxKM14QJeF5zzXk9lcU4KVgm7+39AJvJjyBHOP1C7ietyCIZVMVVRgx2Mx7WSjysVXLvaa0mWFOLl60KXW05DqsZq7MbdZGt5VzilJOKrvJ0Mkrv5IxXcAjeQSF4B4fg1WTv7O5xUb/3il9/JfcJxQLL/6GH2H7t9dxxRKl8zesQxWh/rxYX0wXv7cNaUIvH0Ag8hkY2uzZFe5LQpnBycuK+++7D3f10telMZJfVyqr35pPKGNgv1o83JyecJQhZmlfDdy/tkq/dknja+bCzvJpJ237Cs+htOYvc4tmeRw78fpY9mLAGu3PVnXJcbhqstQRTaho5Dz6I6eRJ2e/sc/+DrM5pT3mREX07T1yvjeTA4YMEzn9Xtk38NmQyx+ISz3oeMXdHaWDk5rUM/mUJbvXJy1pXN46ER5Oe0BnD9OlS9NNst1Njs0uHFeG2Umyxkm+yyP25oFM56OjuSq9sK76rCmTPsl+4G2NnJl5WAFRZUsc3z20XnmEMqNyPIXc11kIlcBNrnSO+7qRgZotvHw55d+ab23vSxjed5OSXqKpSrNOsNZHk7LiBupJYEoeE0/uas8XSLgaiQp0/azeWGiN3J75GtjmXR7s+Kq1gH97wMO46d1ZOWil1L841dorf6dZP93NnJJQa1LR2deL7Tq3O6VtfU5PKjp0jJA/IbnuNrVuP4qWzMNPyMdqO18LEz5s9PvfJJ2VSRrRxhL0/u8XnFJXtn3/+WbZxCR0dwXSLiopqxgwT7WXP/5zEt7tz5O04TRG9tBkMHzpYtgQ2/tZFK+Gaf8EpJYmP3p3yuOtZtDKT6qo66Www5pFneH1PLcuS8qSln1hXTO528f7wl2rtJ3r/S3NrCI33YtyDnVpU8L9UHMurZMzszXI9+M2tPWh3sIyaPfmIUeRn92oC81Q4bMr1LSQu6sKKWO3yPRV+OXw1+itivWPl32oqTPz01j4qiupw99Hi4rqOjKQ9MuAe+9CTxPU4vwf8/9W5/Xz4u9J9iSfivwmR3S6pqmP16tUMHjpUVlvtoqLjEJ55ig2M3AvPboeg49qpNdmoMVupNVslnUZUqgXlu7zWTEmNmZJqM6U1ZoprTHJfXnthixhPZx3xfs60d7YSpzESaa8hxFqNW3UZ1sIiqeJpLSrEUlB4UZ7Wos9YVKj1kZGNwbUMtCMjURuuXGavKcQ52lJWzay0PPZUKj09ghL1kmcFA3+bwaEaHzbTk1KURZNGYyYiIpuePYX1143odOemktscDrlYzagzkV5nJr3ORFqdiczqWjx3rKfr7nXorWbsKjW7E/uyresgrLrTNCwfnYYwg54QJx0hBr2kwAXrtRiXPE3QkuP4VFbiERlJxPuz0QaFUjQnCUtuDVpfJ/zvSUTjdvE2E4JZUPzxx1JgRlgVyUTH1VdTeNudJLt5sjIvhc3FuVj1wsNUh8pWhWfxu+hMyQgya0B1b3qZdegqfNGLqsgvv+JsNOL35BP433ILbJgFG14F9xCYuRsMV8YuS1Siv17/NT0Le8qqihBMGzJkSKPv9n0L90m/TZ1GxVOj2nJr36jzLqI3Zm1k1u5ZkjolIBRdhUCIqKZfLo38UgT0RKW6MCOVE1s3knn4IJZzaBM0heBkOLR6THoXKnUelKk9KFT7kGfwl4G1Ue3UyC7Q4SDeU0vHSF86tgqgQ4gSYF9QXTUvCeYOVyq//R6Gof/ir4z/lYlZ9L0KoamkoiQS/ROZP3L+ebUBRC/dhF8mSKG1haMX0tH/HGrS50FtbQY7d43GbjfStu0sQoIntfg4EZxMPnCKnRU1xLoYWN41Xup/CAi685w5c6RQoEhwiUTX+eCw2ajbt4+K35ZRuXz56Qq4SoVrv354T5kiaegtsZOMxjzmrBvKV8XCf1wlBbk+HvqxDAoKq4xSFPFgVoUURUzKLqe4uomYnKTnGwmxFjOAEyD6dm1WvAsycKotpNoizvW5xwODq6sMxpVAPBSf0DDpIiGOtWdcN6VffknBa6/L46AXX+DdLn2Zk1UkdVHWdG9NpPPZc1jtwSJKvxXVbi3BT3WXSubi2vzpp59kP6oIIMaNGydbly5mXfDNzgxeXX4Mo8UuPdZFIDCq4+ke5GUfHpRWRKJSO+be01T1i4VQot6UuRyPkjny9ktFJUwY+hZ0urExgTRl2RTKTeWNDKRzjbeVK1aQ98w/ZKVb4++Hx1vv8OlJPVtNRtKDdVQ4nx5v++xeS9+96zHrDaTe8RQxYWHEuxiIdXUiTKdBvXwZxe9/gDUvTz5e6Jr43n0Xrv37kz5psmxR873jDgIefaTF92KqtfDtrD1kV5uweGvx8NJQ0MGH9eo8si0GHOrTyYvQYivXb6nC1eTA6K7F5cZo+sb60t7NuTEhdSFs+i6ZQxuy8deq6B/mSsADiVT+9ovyGeqTUyWuTiTFRPJF2/FM6fA7iX575f0qXClMGk/JiQE4uTgxZEZb+X3+EVRuyKJyRTprQvfwtsc82dstdFlOlJ04KxHY0ti5/2QRU1KzKNeraefixOLOsfidRwTu8JGHKSj4FT+/obRt8z7vv/8+VVVVjGADvTkA9+1qVu02paSQOvZqOV7E/LZUCt42hWC5iQSVQFxcnGTdiGRVU1TUWrh34V62ppTIX/zk+J+JtVaQm9tO/l38xsaMGSOTiI04tR7W/FMJwqVwr44jju7E3j+Xp1flsupogVxbCKXzkR0urdf/coTVlry2B4vJRrfRUfQcpyS5/sja97o529mdXiZb7T6a2lWKPBZ/eRRTchlmZw0zO5UyuXAV9rREjKWnK/g6D+g0IJq2fYLR6tT89M5+yvJqcPVWodeuIC/5CFqdnrEPP0Wrrhffh/9/aW6/EP4Oui/xRPw3IQaPxBdXXZHnUjnsGGwWDDYzTlYzTvV7Z5sZH2sdYVoLwSoT/g4T3tZa3E21uBir0ddW4aioUILpi+w4EGqiuvBw9GGh6MLC0YWFog8Lk4G2yO6qzxgk/5MQA9APBWW8eCqXwvoqc8f8FLqnHEdvs+LkbKBNazUenr/icChVU7XaCb/Aa1EH3kqew1cG1TKwrjWTYTSRWWfGfMa5Cc9JZeiWpfiVKRNrYXAUOSMnExAVTZSzgUhnvaRzi+OGBW4DbNU1nHrkRmyblH4z19GDCHvlbVQ6A8VfHsF0slza0ATcm4jW99LUbBsghOiKPvhAVqYEVM7OeN58E3cGLCfdnMfNHe4g2rcfb226jypHNXaVM5X+D2BxUihfXjUVxBdkM6Ioh8Hzv5D3+d0/E787b0X1US8oS4e+D8KwF7kSENTW+V/NR+VQkeOVwwu3vUCIewiL92Tx7E+HZcJJ+G1+cGNnEsLOnSARi8VZu2axIXuDvC2UWu9OvFv6i59poXIlKtpluTmU5GRRmp0p9yXZWZTn50rl+PNBZO19vHzwVRkoLDFxFE+SfNuQ6haCtYVgzUNVR4C6hmgPFYlhHnRpFUxEWCiBgYEXPyEJxf5PByr+3bFD4cbFStr7L4z/lYl59r7ZfHboM1lNWjJuiaTsXQjCL1f45vYN6csnw4QQ0aUjI2MOKafeQKv1onevlej1Zy/anzyRJdtwRND4e7d4Yl1Oj88HDhyQVSWxsH3ggQdwcbl4SqvdaKR63TrKf/iRmnr9hYZAyfum6XhNmozGTRFyaqCh5uf/RIW+Ax/n1ZJfmy8DgjnD5hDnHXfWOJ5XYZTBd1K2CMIrKDu2j55563Cx1VEb2Qa7ixuFNleOOrWnS/UmEq3HiYxNxCU4QeociDYNsVWXnNtGUqjwegUF4RMagW9YOL6h4TIYt/74C5Wffy6rtiFz5jDNOYDdlTV0dnfh1y5xZ/lcy2r3u3uxFtbhMTwSj8ERUvRp9uzZkiIbHR3NTTfddEnsGtHq9cjigzLpKDCtVwTPjmlH8akKfnn3gKSdC0/uBs/mS8HR6jrGbd/PzSfuYJG7Gi0q5o74gi5BXaVSubAGSylPoZ1vO74c+WWL1mBCALXgrbco++pryt3c2THhOrYMH8OOahO2Jh9Tp1LR2cOFPl5udHE1kP/+qxSdPE5IfFuuf+F1+R3UbNlC4ZtvYUpW5kRtYCB+99yN17XXSl0Xgaq1a8m+b6b8TqIWL8K5ffuzvgMhVpVxqARXFy39teDi50zQI12xqmzcuHwqhyvLaBU0gm4RUzhUXUd6bhXj11fgW22nVq9iUX83KoMM9PJ0o6+3G4N9PGSiqqXvTQT485/citVip4+rhja3tse5vV8j7bx0/nyK53wqRVEF96wkUYd1Rg12gwpHzTBOrRuOzehJSJyXpLi7ef/xdZP07Z61G3OtibsSXyXXrCQvRD++6OX2dvI+59gp2k8m70qmUg3tLSq+H9Qeb925A27Ry719x1CZLlYcCNqzd+9eli5dirPaxgP2OTh3HHdWtTtr5kyq16zFa/Ikgl96qfH+BvsxAdHmN3To0LN0T9KKa7ht/m5Si2tw0WuYfUNnIvXzyMz8nMKCHiQnt5FjhwjYr7/+elnIkufFZmPDl59Su/0L+vln4KU3YnTouId/sN4Uj16rll7eg1q3LEZ7pZG8O5/Vc4/K47H3JxJ5CUyVM/HT/mweXnRQsiHXPjqQkHoHBLvJRtGnSVhyqqny1jO1czUzta8QWG6jJLUf1ZkDcJhPz5k6Jw0Wow1ndysax28UZ6VKbZcJTzxPeLtLTwr/X5nbL4S/g+5LPBH/TZSVVLB8wk1y0FI7RFeKshfiPKr6fcP9YpNCPWKvEiI+Isg2o7eY0VlM6IRY0R+FRiOr1ILqrQ3wRxcQqBwHir0/OrkPkD19/2t2SE1hqS5jy8KXedupK3vC2sqsqoepjodcoG+HduTYHKTV1nK0NIWUykJybe4U44dDiISdA2LhEOGkJ9ZaR+sNSzEc2i3v17t70H/qLSQKobSLEMYSXuKZ992FNS0bh9qB832jibr3bfm3ssXJ1O4vRKVX439nAvqwc9MQLxZ1Bw5Q8PosuRco9IQfrvZh0ozXeGrLU1K1MtTmYFZBBafa38cS72FsMdqwaJWFjrgOO+ZncuvC+SSkHMf35psJuCYB1bc3KH1S92xrlsm+HAiRoc8//1x68VZ6VbLaazXtfNsTZ3uCb3Yoi4Zh7QJ5+7rEs6nS9RB2TV8e/ZKPDnwkRdK0Ki03tb9JWmA0WH/9kV7t0pwsqUQvesDEVpqTTbkQ9TlHokrn5IxvQzUtJFRaOenyCkg5eJDjJfnYVUpCyKTSc8ijPQc9EqjVKu/TQ+OgY6Az8cHuBGhrMdTkU1aQIysIZ0IsSkTgLXrCGzZByWtYbJw+QVb45lpI26gIyty5odEO6K+M/4WJWdBwb1t5m2QmvTngTUZGt+wpfCYEC2PcT+MkfferUV/ROeDi1Labwm63snvPNVRXHyUw8Go6tH+32d+X5Jdy/zHFkeHrjtHSW7fpuROVKTFXDhs2TIpHXi6EGGbZd4uo+OEHbBVKj6qYJ7yvvw7vm27C6FwiLRHFXNe920+YtMHcufpOGdgJ94IPh3xIp4CWVYaNNdXSe/voJoUiqvMLoajT1ThyDqDFxh5LGIdtp6tT7noVnSJ96RHlQ69WvrTzd6a2pIDyer2EsryGRFkWpvMIELpodLiWleNuV+F90808q/clw9OPO1uF81x9P3xT1B4opPS7E6hdtAQ92YN9h/bz22+/yd+iUCu/HBcMQaN9e1Uyn2xU/KfbBLpxY4WBqvxaOl4VxoAbLn/s3TF/Oj3Sf+X+4DA2OallD7Do7Xxrz1usz1ovezy/G/Mdga5ne4gLy67jTzzFao0T67v2Zl+7jtiaMIh8K21c5enG5IRgaS/l2qTqWFFYwFdP3C9FR/uMHEfoll2NSRu1hwd+d9+N99QbW2TFZT/8sEwkG9q0IXrJYlRNfvM7f01lz/J0Sc3u767B0wE+N7bBJUGx+kwpS5H6IcKO7+W+LzM+drxUjd+fX8muz49Cbh1WDfzYy40TYafZZZFOeob4eshNJA4a7FMPrMlk6/cpuKthZFd//G/t0GxdJPq2s/d/Ts5zH+N5SmE6GT01zOlyNZnOAxldp6PH6Gi6jYn+w+rYTVGxOoOqtZmsjt7DO07z5H1T20zlqZ5PnXPsPFRr5oaDp6i02elQbmNhpxgCos8/Pxw/8Rw5OQvx9R1Ep0QlsBa2eJ988glFRUX0YxdDVdvh/r2NLQsCwoYt48apqAwGYjesR+vt3Sg0aDKZzsm42X6qhLu/2StbzUI8nfh8RnfahXhgs9WxY+dIjMZsNOrb2LrVIT9b586dJbvEYqzjt/feIG3/Hvk8/a+7kQ6BVdy5opot1jY4YeLz4KX0G397o2L/fwIbFp7gyKYcqUx/3T+6n9d54FyoMloY/PZGiqpMPD6iNfcNUmjiDbBVmCj48AD2SjNZIXpuam/hQdUs2nIUh80FL9XbZB7wlZaDAg57FZaaH7BbS3FydWfSsy8RGNP8Of9/mtsvBn8H3Zd4Iv6bsFZUcLLnaRukKwVR1VQ3bC7OqD09pfqs2LT1+7M28RjhLdwCLfCvguo6I6s2r2XD0RMUGTypcnKhxNOHbO8ALOehHjbA4DASSD6BqgJiXNxJCOxKa88QSScM1Kg4tGqZ9Lw219XJ7Hzi8FH0vW46ThfZC165ahV5Tz8j+95tng7sj3Wi4yTFs7LBokPIFfvOaI9z60tfoJ0LIvOb8fO3ZL/6Mr5VSpC4q7WaeUNVRLTqzHvxM/BZeL0iHjXydXLjhvH6T5+wxXMIuX6nF7Nt0lO4bvUyxkaHEt72BKqUVRAzCKb/dNmiaiLQFgG3mHRDQ0MZPmk4U1dMpcJcgaW8K8a8STw0NJ4HBsedc2GSUZkhq4YHixTqWM+gnjzT8xlivGIu+TyJHuyizDSKMtIpzkyXgXZpbra0FmkJTq5u+IQ1VMkiZKDtExqOvqqaup07SdtxgB3pZSQ5B3LYN4Zs9wDJSomtOUX38r34WsoaRaLcOvah94SJdGzTsqKuCLqFyr7YhNCM2NfW1tujnBGIC4syoZAuAnKxDz82B/2eOX8d4bS/yMRcbixn4tKJFNYWck3sNbzY98XLss0Tgn5zR1xY0bclVFYeYveea2VfZWLiXPx8r2qsZo7Zm0yd3SE1Lp6Ibk6b3LJli3QJEPPj/ffff0XOn6h+i77o0i/mN/oni0qlZbAPxQMy8YsfQ8cOsxvdBO5be5/83YpK3LtXvUuf0OY9g2kH9rJqzmxFG0GlotvYa+h73TS0ej379+/nl19+kclOl4SRHDhyhP1VHsLErtlzCFGyrpHe9Ir2lUF4YpiXrGxJ9fWy0kaGSmnOabaKsEVrCSL9Xe7hQ3RMDG1j4/CPjMIvIgqvgCBJGC54Z6/07daPCOGLXd/L8W3w4MEMGDDgD53XjclFPLLoAEFlNkbX6lHr1dz8ah+cL6H1qBkO/wDf34oNNRMS3sShXkFGxQkZeJeZytCr9Xwx8gupanxmq8LPW3fz3aET7I5ti6XJNdPe2UD4/kpanaqjT8cABt/U9pzJ+aQVy1j9xceo7Q76nszGw+bAe+pU/O6+S65HzgUh8Jc6eoxM7Pg/9KAM0AVSDxTx+ydKj3Tvjj4EZFWhD3fH/97EZu9h7qG5vLvvXZno+WXCL43iUYLqu+rzw5KyL5cKw4LZFKNne3l1M5abcErp6+3OMB8PquecgCITia4aej7dA52fUmEU11VJyQbpt11dfRybRUXhR+G0SSvGtZ59ty6sC44ZM5k5XdEsuZKwVZvJe303FquZia0fwaK2cl+n+yTjq6Wx06vPQG45lkmtzU5imZWPctXE3Nf5vIUVk7mYbdv6Y7eb6dL5W7y9T1OPT5w4IUXQtCo79zvm4tl1Elz9XuPfxfkRrQLGI0ekboLHrbcwd+5c2eISERHBjBkzmlPDgUW7M/nHT4dlkiQx3IvPburazK2kuHg9B5NuR6XS4uf7CT/9tEm+Tt9ePclbu4yizHRJkxbe0qGde3Hbl7vZkVqKi8bGPP3b9HIoRQniR8KQf0KgQlX/M2G12GR/d3FWtVQyn/BoZzSX2N/98m9H+XxLGtF+rqx4qD+GM1iVAuacago/OSh6oPg5MJ9ZiRHM5D26sRO71UDx7ncozdKjUpVjrPgeh60SVO7o3ScS2jpa2pu16hKATq/5/2puv1j8HXRf4on4b0LQs8p+W8aBpIN06txZ6S0T1VKRMVarlMrpeY5lQC2sfZoE2Sonp4uquP4VISaFHKNZKoFnm8TeIo8za42kVtVQ6hC2PxcO/jy1Gvp7uxHn4iTp31H1VHB93WHSM96npGRj/SPVBAWOw2AZydYFv8ggTCA4tjVDbrvnojOAQoio6N13KflcWVSb4uxU3+1Bj2Er0Ot9qd6eS/kvSiXDe1Icrt3EAu7K4rGNj7EheQU373Dmqm3V0trI7KQl5B/P4jfpOlQ758CKJ0FQm29eRrr9AAd2fsyGtCkkhbfmREC4tCUTCCou5N6MXdxRNxuVsN667itoN/6S35PIigsBJ0EtF7/RO+64g1KzmqnffE2550eoVA4mRj7Av65qWcBHTKqLTizi7T1vY7QZcdO58UT3J5gQO+GCTAyL0Uhxdsbp4DozjeKMdFlVO1dw7ScW2OGRMrgWgbUItIXFkXgtS0EBNdu3k79jL9uSC9ir9eeAfyw57s3paoKxEuehpWd8ID3i/AmpSOPE6l/ISz6u/F2lJq5nH3qMn3TB60t8fjG+NQ3EhULymfYqHTnORH6Xx5uCbsPUapQMxgMCAqQa7P/yhPa/PDGL8//g+gdlVVD0Jy8au+iSWRW51bmM+WmM9NGdO3wuPYIvr2/u5MlXycyai5MhhJ49V1DjMDBybzJpdWYG+bjzTUJMsz5Vkax57733ZGVpwoQJdOp0bi/by4FIUlVv2EjJZ59JETZ5n9aBx8RxBN3/BFo/v0ZngUc2PCI9oEWgJ0Tleof0lnY+G7/+nEPrlPYrIYg24p6HCW3dttn5X7hwobQREwm7W6ddh+OzoRwvNrHXdzy7fCewI61Uap2cGYR3i/ShV4zYfGW7igjCm0LYK0p2S/Jx0ud+ToXVTI27K8ZGM57mEP7iYmzwdA7AJddAuh9kqMslbf+hhx46qy/1cpBbXMN3L+zEYIGNzhYGXB0jK1uXzDoT7SUf95P2YKvb3cl0/6n0cjNRmPKwZD4JvNL3FcbFjmv8L6m1Jr7JKea79FxKtad/Z/F6DdeE+jPW15ODHx2hIK1SCpNNfLwr2hYW6eI7q1y6lII332Knq4ZCT1e8NDpufPltnGMuLknaIHYnqtzRP/1InWhDem23pMZ26B1EqxOlUhTH/64EDNGeZ2kv3LjsRo6XHmdk1EjeHPhm49/sNjsbv0vm6GbFPrTLiAg6jo1ia0UNa0oqWVtSKbVdmnwYIoqtTHF3ZfKgWMKc9JSX75GK5OUVChNOq/UgIuw+Nn9aRmnKeuILzUQW5UoGY4mTB67Pv0Dnay+OGXMpKPvpJDU783k/8FuW+2yW7S6/XfNbM50JMXbOWrWOz9yCpBVpj0o7b++qIXh8LG49zt/XfCr1HdLTP8TDI5FuXX84o8Lv4IsvviAzM5NOHGaCZiM8mAQewWd9h4I5efiB+zl4+DCurq7cddddzdbrNruDN1YcZ86mVHl7bEIwb01ObFG7JOnQPRQVrcLTsyt226MsX67MeU45p/BUOZjwxHO4hcVwyxe72ZNRhrtBy/xbu9PVR/ygZsHe+YqDhVhnJ94Ig54GzzD+TFQU1bL4ld2YjTZpYddn4sVXlYXV4Kj3NstzNP+W7lx1Dmq8+D4+XvI2Y/d1R42aFV1cec7Pwe22ecRsbkttYTtU6nwcpt8wVlfi5hNEYOx0clOE8r/yHHonDfE9gmQA7h/xxxmYF4O/g+6/GP4KQfdf6cL6MyHE40rqlUfzTBYKzPV7k4V80aNkNsvbpZbz98kKaOw2Ai2VtPILINzNXfpKKoG1gb2V1byYkicz1/EuTnzZMZpol7MpbBWVB0lLe5+8zM3k7fSn7KSSeRcV7f433kLHQcMuOrFhLS4m57HHqd2xQ96uHmKjcoKNxC5KRarucDElC45JFS2PYZF4DGlZafyP4EDhAab/Pr3xdkShg+c2+uGZUiBvC5Ga4Bf+hW7rP+DIj+AejP2ONew6djvHjhnISO+MUaXmhIsbSe16UOOiVPafTZnDzJyF2DxC0czco1gcXQKWLVvG7t275XV/2223kWMycMdXe6QAoF/INkyev8pFwhcjvjiLelppruSfW//Jmsw1jdXtl/q+RLDb2YsFQSEtTE+lIDWFwrRTFKSdktXrlqjh4nv1CQnDPzJaVrBEJcs/Iho3H99mCwtRaanZsZOS7TvZdSSb3TZ3DvrHkuIViqMJzVK0iLT30dO7bQg9Y/3lYt/TRXfWpJhz/Ai7f/2B1H27T39PHTvRY9wkIjo2r9acD3JRW1lJfn6+3EwZuxmU+jo6LGymO2s5219TqFULSnrTTVTKha3Z/zr+m+Pnd8e/45Wdr6BT61gwegFtfS/Pu/7lHS/L5JGwsRMCbJfTumOz1bJj5yhJsQwPu5nXTNP4vbiCUIOO1d1bSw/jplixYgU7duyQyRex0L0Uz/hLgRBqO/DVaNRL0jCcUl5D7eKCz6234nvLzdIqUrgMPLLxETZkbcBJ48Qr4Y+RvniFtJcS6DzqavpPmSED2zMhrvUPP/xQJg8kRb6VJ3w+RHFYGPJPHP0eJqWwmh2pJbKyJfZnBuGiN7RntA/94vwZEOdHbIBbs+9AiD+l3zAFe3U1uhuu56n2XanLySShpoR2VSWy3cRmOR2M2ZxdqY1SrgX3/HRiYmIIiokjILoVgdGtpGLy5XzHgjotKNRWZzWz9TWyb3pMx2DZdnNBAcXGL8QG88c22oNlTvmFvntOYXE48M//J5iV4ObJ7k8yuc2N/F5UwTe5JWwpP52M9C0vY3x5IdPGj6SdjxLUbvr2BIc25mBw0TL56e54+p+tRWI8doz8l16WQnzyPEVFsCHAHZPJSO9JU+gzeepFj3FZd99NzcZN6Dt3Y3f7mZRk1xAc68mAUFeMB4swxHtLundLOFpyVAbeNoeN9we/z1XhVzV77r2/i/OssDSEwNRVU1tLhWnxN+HbvrqkkoUHckj3aP6bidcU0tm6iu7sIFRdSnjYDNwN01jzeRZleYWYKgQF287k6XeR9fp7+BQrytvON9xIxFOPX1EtnJyMdGwfZ2BRWbmpw3NUWqt4Y8AbUhSvAT/nFXPfsSxsKhVDnZx5YWmhvI6C/9ET9XmqmlZrDVu39ZdWZx07fEhAwNlJg6ysLFm9Fknm+5iPX+9pMOKVZgWnk0OGYCsqZnvvXmRFRUnNA6F90IAak5WHFh1g9VFlnSI8s4V39rl+O0ZjrlRSF2Ohm/ke1q1Iw+TlL1vkpkyeRGCrNtw0b5fUSBDihF/f1lNWzRtRnALrXoSjvyi3hWd1z7ug/yN/aivWqf2FrJhzWB6Pvqcj0YlKO8T5IK7FKZ/tkGPa8HaBfHpTt3M+9vNDn/PevveYXDKcWwsnSCbH1/28KdhTSlyeFYcjA0vN99gtKgKiYpj4zIuymFBdZuL49jyObcuV9mENEEm1jgPDiOseiM7w51W/LX+R2OjvSvclnoj/Nv4qF9alQthYlZit0r6juOleHlua3VdotmC9SNd4N42aEL0Wl9pqbAV5uNRW4W6sI8yYx3DTZvp36YFu2L8afUbPxN6KGm49nEaB2Yq3VsOChBi6eLqeJZC1Z+mP7Pp5MVazWEw58G1bTljvGlq1voOI8FvRaC4scFazcxc5jz0qJxbBRqiYoaI6oZywsOm0jv8XpvQKij4/hPjwrj2D8JpwGVWLixicRR/bsdJj8rYIEAQFdkzkKCn0UvTebDkBCnG8oGefwjN7FhSfkLTxsrFPsXvvTezZPR6z2ZVwZz1FB3eRHtme3Z36UerrzabdMwg35bOy3R1EjXlZWoxcDA4ePCiVfQVuuOEGsuzezFy4D5PVTodQD+be1I03DzzHyvSVkgIoqogBLkom90jxER7d+Cg51TkyKH+k6yNMbTtVqpLXVVfJwFoG1yLITj8lva9bgphYGoNrGWBHywr2mWrG8jza7ZIOV7lpM0k7j7C1XMU+/ziO+kRhbeKfLNDKXU2/tiH0bR1Izxhf6Q5wsRA0OBF8H9+6sZHSLire3cdNlHYdwrrjolFTAp9eBRWZ2GMGkzPw3+QXFkkan9hE392ZVfGmEOOmCMBFL2rTTQTpZ/WM/382fiaXJTPltymyP1SwK6a3O53UulQU1BQw+sfR8rnmjZhH96Dul/U8JSWbOHDwFulC8Dyvka2O45cucVLEqinKyspkL7cIiKdNm0Zs7J/Xs1dUtJqkQ3ejVjnRWfMWZbPnYTysLDA1fn4EPPQgntdei8Vh5dFl92Nbf4JWuUpSzzMgkBH3PHRBEZ8GmrmgpN599934Zy6HpQ+A0Oi4bRWEdWs2Hl4oCA/0MNAv1p8B8X70jfWTvr9V69aTfe+9ygNefImRgXGyOvh6fBg3BXnLXnHBmClIT2Pb0RSMGjXa8mKc8xR21Jnjjgi+A6JjCYwRgXgs7n7+5x37jdUWvn52m6yICdGtPQ4z//z1MBabQ1LnP7upGz6uF0E13/gmrH+5mT3YQ0dP8V1BFTrjUTqbvpPK+iqVBlvws5RolWtDtMT0OHyQq3duYsKk8fhdM6HxKU/szGfNF4oo1Jj7Eojq6HeWcGjRe+9RtmCBdNQQc6HfPffgc/MMTu7dyW/vzpLJzqmvvHPR7DFLbi6nxl7N0bAJ5IX0xdldx7V3dKD6i8MygR0ws9N5NVHe2fMOXxz5gkCXQH4e/zNu4nw0gQg01n99XOZlYzr7M/zW9o0+6EWnyln85j6pyq4b68ZqQwmHzP7Nkq2tnbX0shhwXZGHb4kVN28D7p6bSdu/hbb9rqLf9LtYeOvjDDyqsOr0sbGEvv0WTq1bcyXwxu43iFrpTJ/qTixO3MgX5kW09Wkr51FxnS3KK+Xh45lS4O1qPw9e3F+H7Wgpbn1C8BrXXFH8TGRlzSf55Es4O0fSu9dqea20BMFCEcr9HTjOJN0mePgwuJxum8t//33KPvyIEh8fTC++wJChQpRNgbC2ve3LPdIKS7BQ3pyUwPhOFxanzMj4nG0/zCF3Z4BsBVG370qlXYWLlx+b1Ikczq3E20UnA25hqdkisvfA6ucho14c0skT+j8KPe4E3eUJ214Imxcnk7QuWyathH+3R32rwrnw68FcHvh2PwatmjWPDCTcp+WCx6bsTcxcO1PqjTzX8zmGHelM1a489hjt5JnsmO15WGuXoLZYcQ2qpfu01nTq8i4akXBoIlCYnVzGsS25nDpQhL1+sa531tKmd5AMwC/VU/7/Umz0d9B9iSfiv43/xQtLLExqhQemVfHBrLbZqBZ7q63RF7PcYqPMam08Lhc2SXKv3K47Rw/suSCWG/56LUF6HYHCUsugI1BfvzfoCDLo8LSYOLxrJ3v37JHnTSBQXcYA+1baavNQT3gfOojexvNDVNBnHErjQFUtLho1X3SIZqCPu/zcJ7ZtYtOC+VSVKFWW4Pg2dLumD+W2rxp9NQ2GIGKiHyY4+JoWJxwRLJXMmUPR+x/IRYaYUGvu9aLIaRsuLrH06P4L9hI7hR8fxFFnxamtD77T2qESCnlXGA09bAKeek9mD55Nl8AuzYTdcp9+GuPBJOUxI64i0PMnNOo6GPoCSR6nOHT4BCeT+0iK5DWDB7L568+xpWdhdwuioHcw/yr9BJNKx6Bu8+kc3ZFHo4KIaYFB0Hj+Cwr47LPPpI3OwIEDKfWI44kfkiRNakibAGZP6YyrQSupp1OXT5ViS538O8mAZGnqUl7a8ZKk44a6hvJCu6dwLbCQc+IYOSeOUiYq2C1ALGrF4lYsdsXCTlSdXL28L8hSqN6yhbwtO9mUXMRutzD2BrSmzKn5eBLkBH3jA+nfNpg+rXzP6x1+sRBiQ3uX/SzptVazIsLj7utPpxFj6DhkBM5uF6B4SeG0ayBtE3hHw53rz8rWy57WmhoZfJ+5ifvPBzG2nhmMe3l5yfudRZvLf0ho8b8xftZZ62SlTFyX/UL78dGQj/7w531p+0ssTl5Mn5A+Us37crH5wAOYS5eRQSSOuAXcFn428+OHH37g0KFDsgIrqkt/FoSQ1M5dY6ipOUlU5D20avWYHBurVq6k8N/vYslUBN4MHTtQMnoYOzetlawUsUBMiTJy3wNv0zH0wrR3cR0vWLBA+o2LntCbb74ZtfChF6wdIRp49xYwuJ/Tnut4fhVbUoqkN/autFKZ+GuKtsEe9I/zI+HwZsLnz8ag17H33Q951OEie3yFjVirekX47du3s3LlSgwOLeNru3AyJIeQWF8ZkItEoOgXFx7rZ8LJzZ2gVnEExbYmOC5etjAJb/EGbPshhf2rM/ENc+P6Z7qjUqtkwuDOr/ZQabTKns4vb+lBhO95Fr9Zu2HeCIVCO+ET6DRFWtbdse4ZlqkmSgvJ3h4Gjqb+G33tdmxqTzT+/2Tkui2M3rKOcHcXwmbPbhYYluRU8/3re6SCd7cxUfS8ujlFvGrNGlndFrajAh6jRxHwxBPogk63UC19dxbJ2zfjHxUjA2/NRSb09sxaxM40fzHhMub2ONyO1WA8WoJzRz98p7a94G/42l+uJbs6WyZsn+rRXGRMIHV/ESvnHpZBRlgbb0bd3RG9k5aVr+wiozSV8IQVGCK243BYqcCTZPdb2aseyPYqW7MiQojRwZSYAAZRw8bnH5GJ09s/mEuu2cALz37KfbsX4WOqknR5/0cewWfGTX+oRVCwwIYtGUZUZRBvZzxGha6GGfHPYrKbZAvLEXsMTycr82RfcxVfdelE2TsHZLIi8OEu6ALPrYZvt1vYvn0wRlMurVu/RFioYjHXEkSrk7AjFE98D18TOPAOhbJdjw2//ILf08+gsdsJ/forPLoryUZRib79qz1SHMzPTS+ruF0ivC/KqnPN5x9yeP1qeTuiuw+j7/6I2Z98zg8lwRQ73GTAvfCOXvI3fV6IbMvJ1YrNWKGSUMIjFAY9A4lTrrjrh81q58e39lGYXklAlAfXPtblnF7tggEw+O0NFFSaeGRYPA8Mae780IDUilSmLptKtaWa6+Kv47nez2Gz2Fj+/A4yy0w4rLnU1v2ExmqiOtiH3qN2o9GZ8fLqSWLCHLRa9xaTf8e253F4Uw6VRXWN94vfhxB2jOroe0V8x/9XY6OW8HfQfYkn4r8JIQqxr7ySbdu20bt3H9RarVxA2Bs8e4Vfd30PmeLb3cS7u56W3TC2m+0OSZs22kX2yiEFT0Qm/pz3i/tkQK0E1aJfutqqHIvA+iILz+eFVgV+Op30efTTaeXet8mx2PvWB9r+et1ZNiwNKC8vZ+vWrezbt0/2AQsEu6sZWPUL8aSg9mut9BUHtLno91ZjtXHr4XQ2llVJZfJ3XK1Yf/2O3ORjjQHOgKk307rPALmgFgulgoLfpDiK0ajQwtzc2hAX+ww+PqcVL62lpeQ+/kSjGqvnNddgv70tJzL+hUqlo3u3H3BxxFL40UFs5Sb0Ee743d7xvHSuS4a4cGwW1mas5JEt/5DXkI/OnW96v0q4R7jSt63Rg95VVjzEQrj4o48p/uQTmSTQBXgRmngSZ38HNTfMZ1vm4+zfN5LaWi+pcHzVgP5s/+E7jn2/iG4nswgdUk2QVxlrfXoytcMsNGoV1wX58HBkIBFneNqKyqpQKS0tLaVVq1ZYovvw4m9KP/PELmHMmtgRbZNBO7Mykxt+u4EqSxWtPGMoy8wiqMSJDsZwfEo0GKvO9o33CgyWQXVAQ4AdFYOLh+eFT5vwID5wgMqNm9m75yjba5zYE9iaZO8I6cHeAGe1gz6RXlyVGE6/WD+ifF3+tCBT9JYeWPkbB1YtbxR30hoMtB8wmM4jx8m+8hax4mnY8VG9cNqaSxaGET2/xcXFMgAXlVHxfTVswgrpfBATpBh7z7W5u7tfsUn0vzExNwTIwo7uh3E/4Ot8+XYvTZXMr/7pakl5/W7sd7T3vXShu0qrjQm7dvGA8R7cqSIm5jGio+45x0IYSSsPDv7zPGlz877n2LEn0Wo96dN7AzphClsPwa4p/WYBpz6bQ5KvGxX1DBnRF72rUxWbbPvl+RVq2sLH+0IQc4SgmYvrYfz48XRuEw2f9IOKLKU/85qPL+o9Gy029qSXsfmkEoQfzWs+vjg7rHTKO0YvSwFbb53GJq2GTu4uLO0SJ/UgBINA/D4Gh/UkJsWNci8zrR8d0HhtWkxGijMzZItLYVqK3IvbdpEkOwNeQcEy+PYObcOBdS5S5/LMSrLo6xQ9qjnldfi66vny1h4tV/CMlTCnv2L12GEiTJwrhek+Ofgp75zYRrXPDOya+sBG+KEXvoDFnE2HdAfPfmvDa/Ro6VmuaSIaajZapd9weUEtEe19GHNfYqMApCUvj/xXXpHWUAK6iHCC/vEMbr26ySBZbmIe0jlTW1XFF4/cg7G6in433ETPa6674PdUmlvDktd3YzXbiU5bRmIHAw7dKJm9D3y4K7qAC1fetuVu467Vd0mG1OKxi2ntc3aVOet4Kcs/PoTVZCMw2oPeo3Ts2v0m7pE7UamV5ImPT39axTyCh0cClcV1/Dj3MNscJo6H6UkN0WNtMjVEVBbT6tBOpsRHM+66G/lhbzYvfrOFh/YvoVe+Ety59O5FyKxZ6AIuz75q3uF5/Hvvv4n1jOWTnH9iyapiTtel/Fz7O+E+Pdjnpnh13xrsQ/cT+7nK0JGaDTnooz0JuOv8nu/5BUs5cuQhdDof+vbZjEZz/gTz4sWLOXr0KG05yfVOW+DhI2Bwk/OI+L122badGKHpMnoUoe+8w29JuTy6+KBMfLUJcufzGd0I877wdyl+e0vfeY3MwwflXBzSJw//DmXEtVvIXQtrOFZYhwELb42J4Or+p4sOF9WOkbQI1r0ClfUJff+2MOR5aC2utys371eW1Mn+blOtVeoJ9L6mZdbHO6uTmb32JOE+zqx+eGCLrSUi8SIC7vTKdNm29Pnwz6Wjy7pvjnN8Wx52ay626h+xOcyUhsbw1cip9HdN5Q7ra9htNbi5taNT4jwMhpap7qL6nXmslMMbc0g/VKwEJGJN7G2gff8Q2vYNwdXzj7WnWf4Ouv9a+CsE3eUWK222KFS7/1W4atSS0u2m0chjV60aL60WL51GCpJ5a7V46jR4iWOdVrmv/m9i+yPBSGFhoVTYFVUZkYwQCA8JkpXt2PxfFT1ysaAa85YSQF4GBf7hbfsxrfiRdicPNgY0QsRKqOS21ENos5nIzvmK9PSPsFqVBVlAwBji4p7BfiibnEcexVpYKAXtgp5/Hv3IztIqR/QZxbZ6gvDA2yj6JAlLfg1aP2f870lE43oRwYL4/LUlUJYB5elQVQA1hVBTBDXF9XuxlYClhgUebrzh441dpaJnnZG3C4vwFNmaMyGCSVEFcvahtsKb3OWlWMrNUkU9sFM5Xl29WBvXi3LTKY4cGSwpnELpWFQ1hdLvlo9nE73+NzoOzpVszldjn2F2qGL3IZIZU0N8eTAygGCDXn6HYhIWfpziN6lpN4x/b1CqXbf0jeK5Me2aqXaLxwvP6983fMv2HctksO1kaT7BaHQ6WSEKbd2OELHFt2lWIboQbFVV0iO2YMNmNh7NZ5tHFHsCWlNpaE43FOJngxLCuKpNIN2ifM4SXvoz4LDasRTWymvFWFBFyuEdHDmxkbIqxUZNIMg1hta+3QnxikOl06A2aHBRrcK99DX5d2P32ag7T0Tn74Kqnh75h95TfXW8aRDesImx9kIV8gaIfnE3N7fGTQTiTW+LTXhGi6r5+YLp//TEvDZjLQ9teEgei4q0qExfKTy9+Wl+S/2NYZHDeOeqdy75e7n7aAa/FJYzTruZ6y3volYb6Nnjd1xcIhsfJyrCQnisY8eOTJw4kT8LYpzcvmMIJlMesbFPERnRXAxRCKUJJ4j9v/8q37vWZic+v4QY9Hg9+TD32r8hufykFKgTdmpN/YXPBZGYXb16tbxmZs6ciWvxQZg/RgnwJs1Tgs1LRHG1ia0pxWxKLpYK4uJ2U6jcdVj8DFyXEEL//CMcPHhA2vbdfM00Ct/ZKxejvvcn4HwuKquscllk4J138gR5J4+Tl5Isbc0aoHUZitaQgN2ag0/gASkmF9qmHaFt2stkYmGlkVvm7+ZIbiXuTlopqtQ18gz3i5/uhoPfgmeEpJWbDB68dmw3n+WUY9M1twR7y1pOyHvP8MT4Skx6FXfqBjNzyrtniWWtmX+U1F0ZBHmXMfI6dwzGHBwl6ZStPUjRmkzsZofgpePbwYJfm1LUmnOw3zR6jlaF8ntGGBq1g5sGu+MTFqmwFMTmGwc+0Y3VRaH6LKrrJTk1hIRoaL3wHrkWcBnwJO5De+Fz3cVTtIWI3+qM1efVUxDicEvf34ep1o7BI4fwgf9G61yBr89AoqPvx9NTsfpLTyqW50QETQZXLcNubY9XvCfLiyv4uaCczWVVsqBSfwLp7enKtcE+7N6Wza+7s5lSsJcZB36Wvt4aX19JN3ftdWnuNkIobtQPoyioLZAaJ8Pr+lK64Dh5nmXcGvKsLNuUBr3G/XFdeSTMl9+X/U73o4HYqyzN7NXOhT17r6OiYi/RUQ8QE/PgRa3hPvroI3l8JwsIGfko9LpbqpsLlfP2rq50mDtPKM6x6cXPeW1XsXzs4HrGm5vhwqyH8oJ8fnr9X1KnRVh1jn3wCUwuP3Mq82fe2/8wJ8uCEUusIarDBDvbueeee+T65ZJgMcKuT2Hz22AsV+4L7aYE3zEDueL93SoY92Anwts0/x3nVdQx6K0NGC12Pp7ahVEdg1u0T525biZbcrYQ5BrEt2O+lQnMTd8mywq1GEdsxp+xWUwEOEUw4Ko7uK61ngyThaucsrnH+iJWSwnOThF06vQFLi5R533PItF0ZHMuR7fmykq4gFqjolVnfzoMDJNaC6rLiAP+Drr/YvgrBN1VVhuDdh2XtiKuQolcpUKNSk4g4hqVe9GX0ngsxMvP/rvY69UqZVOpcdKIvbitxtDkfnGs3FbL+5zUSkAtA2utElQ3BNfifuFHKV7vP43s7Gw2b94sB+UGCCpkv1hPorc+gaomH7TOMOZt6Hxx4istVRB3/rSYg6uWSVqSwNH4Toybfgvj48/f0yRgsZSRmvYe2dlKn5r7WifcfxHUBAf6mBhC3/03hrhW7N03RU5SgrLTueOXlMw/hulUBWo3HQH3dkLb1JtRBNbVBVB0AoqToSSlPsjOUPaWCwc04pPM8vXmOw+FGnRtVTVPl1biJFSVxaJFZG5tFhCq4y3QHG1mFXm7vKjKVnqK3MPrCOpRSY2/gdTaMNLNUXjE92fI9ffIvnmx+Dq1cR3Oc28ntFUp5moNc7mJlRNvY4dRYSWIa+7WUD8GlWSz7vffpWiTc4ehfLhLsct6WFiCDVH62UXGOiPpAGkH9pB56GAjzb/xvGvs+MXHktB5gFx0ikp2Sz3Y54M5K4vq9etJ3riTDQVWdga04ZBfDNYmyq5uGgd9Y3wY1DGMAfH+hHj9Ob1cTWGrMmNKq8CUWoE5vUIG3KdXaQrE+S4yZpFcuYec2pON97vrfIjz6EprDw3Bhn+iUlmptE6h0lr/+1CBLtBFVjMMUZ5S1VfjcZl2QxeYKMXYK8bdMzdRjRR/E20FlwLRP94QgDfdxH16vV4GkV26dJH3iWBe3Ce2psdXQigsvyafib9OlFWEW9rfwiPdHuFKQvgIX/PrNXJEF32ml2J5tzCvhEeOZ0l20c+dYtGk3Utp2VZ8fQaQmDhP/rYaxY1UKhmUCuX6PwuZmfM4mfKKbMXp3WttY0VMjLVJa1ew/ftvG5kb8b3706tTDyrfeAtzqiLkpRvQh6d6p3NCXShtq0SlRtiKnQ+CBSWq+GKhL9TYhSo7616GTW+CwRPu3faH1IgFFV1UvlduPMzqjUmc8Apr7OP1VVVztUFhScX1H8fY3h0xLzmB6VgZzt0D8J14ab26QpuiICWZ1IMpHNsuKtsqTJXf4bA116fwDgmTCUffVq1595CFLQXgYtDy+U3d6BNbXxE/9D0Iur1KTc1Ny1mgbcUHGfkUWpTBRY+ZOyPCKKwz4fb5p0xd+YtU1940NJAPupegUWmYf9V7dLI6cOQfwpq1H1PGEdRVObhoFLVzAWOZlrzdXhhLlXHF2c9McPdyDJ4X/r2Lqe/HrPak1/gQ6lzB9ZFJzYuIYv4SNodBCWzKGMKhQ86yj/v6Z3tQ+o9nqF6zHLVnODG//YTO/+IT8HnVeYz/Zbykm7/a71WubiW85E+jouIAGZmfkZ2SRNamh7DWeaNxqqB9bwP9rx/dqHq+c2ka+1ZkyNuCHjzyzg5n+S4XmS38UlDKpzv2kul32udd/GadyiyY0iuZqqnjzrWfYxJrH7Ua//tn4nvXXRdNN1+Vvkrqnfg4+bB60mp06Mh/Yxe2CjM3tp1LGXuJDxrBDyPekmP1tq9X0yrZXa5Hgp/qgeo8yeSqqmPs2j1W2nL17bMJg+Fs//aW8OOPP5KUlEQcqUz1OsDJ0UtYsPBbOSaLALjq/gcwHTjAl21H8l3rodzWL5pnRreVjLkLQTATf37zZTmWuPn6cc0Tz0tmW2lVMdd9+D0p5eG4G+wsvL0fO1Z8Lx0+hFibaKu5rIJQXRlsnQ07PwFLvV1n9EAl+G6iHfFHsH7Bcamg7+Kp54Zne+DsfnqefnjRAX7an0OPKB8W3dWrxc/w7t53mXt4LgaNQSYsRS//1h9SOLgmC7s1G5vxFxlwh7VqR0/7KLRosQ8M5WqPWql1NNi1nHut/8JkzEKn86VTp3l4uLcsStgUIhl2al8Rhzdmk596miHkG+oqg+/4HoGyPeNi8XfQ/RfDXyHo/itdWH82RDCRmpoqg+309NPiM23btqVfnz6Epn4LG15TAkX/NjB5PgRculqwoPftW/4ru375HnOdMmiGt09gd59RfK1xl9XZ+R2jGeJ7cddMedYOsh67H+1BRd3V3MeVsJfewTd0ABmZn5OS8hoajSs9ui2jbmkNdQeKUOk1+E8PRq86BQVHoChZES8Te1PL/rCNcA8B70ipMI5bALj6gas/uAZQbXDj8ePz2FK4Vz5UY3cQ79OG78YtlhS6s1Y5QuFX0A6NFUoVXVRQq/JxlGdR9vsuCtbkyqBP724hrF9Zs8WTQ6NHVb8IIjgRi0sYjoXT0etMFB12Y3NpNBnTbmFN+97srTXjVVPFpH0b0Npt6KI68dlx5Vp/amRrJkZrSN2/h/QDe2VfdlNPbJvaQaGXicogNV7x0awwbsPV4CbVoi82KBHPV3fgIJVr17J71zG2WD3YGdSOdM/TCx+BKDcNwzqFM6RdkBQn0l2h3qTzQQTWdUdKqDtSjCX7bLsylbMWXZCLrFRrPA1oPPVo3PWoDBoqK4tJ2raKo9vXYTbW4a41Mi3mIC4aMzUe/TFHvo2lyIiloFZqB5wJXZgbzu19cW7nizbgz6PIn/k7F0rTwnO8urq6xa3hbyIZ2cBw+aMQgXvDJtga59uLxWDDuRB72V6Cg/l188mwZRCqCeVO9zvRoFHagex2uW/Yzne7peOmexEE1FhqcNe542PwOesxLf2/Ur0zi7sMlIJ+vVKP0DU3FVfXajom/IhabSctdThVVfGShSASHkKbQQTcIinRkJhoOG5gIAjmgZg3xV7cdynXhtVaxbbtg2Rism2b1wgJuU6+z5O7trHl2y8bhQ2FDdigm+8iulNXedtuNlMy51OKP/1UTIzg4c4nw+2sizMxPHI4bw1864LvQ9gUzZs3Tx6L3u6o8FCYNxJy9kDMVTDtJ8WW8w+i9KuvSH7rPfaGtGfRkBvoUH2EAFUNp2y+bLbEyICha6A7vfJMDNDp6fKPPqgvYcHZgJWfHyZlTyGRHX3pO9FfWguKMTLn+FGpmn4mLHpXMrSBFLqEcOukIYxO9Ec1ZwCVVitf9H2HOfr2jS4gamsp0bZ9LB18H+6lFaQ+/Aj2/fvROtnQjepEcK8Ini7eyGqtkRCLlSW5eXi0wJaqMWspPOSJMUUPDhV2LVS398XRvQMeEW0I79QL9+BoRYRKJDZlxVoFdotSQbTWgamaypxU5r/9KRaLlSF9wukUXAOlaVB8UnmM8G03dmd5+TPyeGz4HCITQyk5FknhV1+CpY6gF17A+/oL09Ob4rOkz5i9f7YU7Fw6YSmuOheKi9fK+buiYs/pB+YO4vi2yWDX4eKhZ8IjnTG46Fg19zA5J5Tqp+hr7Tsp9pz9uAJCq+OXH78nq+sAMrsN4EhNEzFLi51BGjXPrFiIdunSRneRkDdmofW+MNvj5hU3s7dgL3cl3MXMzjOxORwsWXKYfvvK+d03ndkBb0gxVRGQe2g9OPnmZjwqdbgPCsdzxPkrmseOP0Nu7iICAkbTscP7XCxKSkr44IMP5BhwK9+x3OsW8svr6N27N137XsVH//iAySs/p8jZi+yPFnJj79MK5ufD8W2bWPHRv6VzgGglEwG3cBmpNlmZMW8XezPKcNHW8Hj3z7lh+Dzq6lxk1V2MgbIFpbPCULgsCKbh5rdgzxfKdSzQZiwM+scf9vi2mG0seXU3Zfm1RCX4SUVzMe6JXvfxHypti7/O7CutDs/EivQVPL7xcXk8q/8sRseMlo4HwvnAbsnGZlICbuGMMuHxZzEnlVP2g5K8r7smhqvNpVKXabinmXutL1JTfUyuXxM6ftysjfJCKMqsksF38q4CqfcgoHPS0KZnkAzAfUJc/8/ERn/3dF/iifhvQi6y8vJkoNm/f/8WFYHFfeKCE5tYHImF4f8liHNw/PhxeQ7EuRAQi96EhATZP+yvq4Mf71IsTgQ6TYXRb14yndxus3F4wxq2L1lAdVmpvE8Itwy48WYiEzrLguI9RzP4tbAcJ7VKqpr39T6/WFXtvv3kPPII1vx80GupukFNVc9quZ7w8xtMSckWHHYTHQLvx22/AduJvejVqRhcMlEZFRrVWRDBsaDUiT51v1jl2EvQ7CLBM1yMXC3+t6zKLO5fdz+nKk6h1+ix2qzYsf8hNWTR35x9921Yy2tRax24jFGh9jDha6nCibP7eh1qLSq7VeZFcrZ7caQugGORoTjG3Uh2di7uVeVkefuzPK4HAccyuaXmJM55x6guLWn2PEJB3CUujG9NK0n3KCfKJ4ZPhn0iKVJ3rL5DLirC3cNZOHohXk5e5/RGr929m9JVa9i8J4VNrhEy0G4qgibsvLoGuzK8cyRD2gYQ49+cUv5nwV5roXZ/ITW7CyR1vBGiGh3kKqvQhhhP9OHuqD30Fww0RPLo6NplhO94DF9tOYVGV75NTySwdQKJQ0cSK1TPTWDKqJIVdFFNt+TVNPZhCeiCXXHtFohL5wDUZ1ia/bfQEKCL4Fv0mIt9w9ZwWwSSooIrFNXFJC0eL3pqxSaOr1TQfszrGEe9j6K1axmSMwQ363/mWrkQhN3Pz50GUOThTWhZEWOTtiptN6J/NPIgkZFJmEzO0oHAbr+871XMPQ1BuBDMEwG72ITPu6BpnskiOJX6b9LTP8DFpRU9eywnLzmZjQvmNXrRO3t40mfSjVIQsCXRLGNyMrlPPYXpqFI53tpezefDVNzW50HuSGhOU28JS5cuZe/evVJ5X6iZa8rSlP5uEbiNfgt6XPg5LgRxXWXfcy/VGzaQ0bUrO+Jisag1pMUMpqTIxomC0xVggdYeTozoHi7tfdqHeFxUEkOIlH330i55LCq6fmFuZ1XDc+sFJEUQXnAquZG11QCbqzPHOnVmU5tB1NQrL7uranCULCLQksSSUfPxXL+SmoVvY3CtwNnfgt7ltDVnlUrF5NBgcnRaBlUauS/NRrHJlRKzNzV44WZzJuxgCk41SgI738eDw0HemM+wqBOMpPhe/egwaNh59TX2/b6U9fPnYHBx5ZZ/f6KIXQp2VskpqlMOsehbV4xmPYmuv9LP/YvT5+qEF4X7RWLSjVYrV6G5gEhmU5htZq799VoyKjO4NqIHI50zqK1VLMOEDktQ0HgCrBPZ9mkNWRYHemcN5jqbVJoWNNq6Kgtag4bB09pIG6ULQbC55tw9QwpkXv/CLGrDW/F9filfZBZR1WRQnrFnC9O//gyN2Yw2KIjQf7+Dy3kCxROlJ5i0dJLs3V0xcQU+zgHcfyyD9dmlLN9Yg8EOj/Z4n6NVx3ig8wNM87uektkH5bwT9GR3tF7n7s+2WCrZsrUPdnsdXTp/i7d3Dy4Fwl1AuAwEUkQB/nItO3rK7dy36DCFJZV8s/JlPMw1hH30Ie6DB1/wt7fr5yVs+e4rebtVt16Muf8xdE5OMuC+ed4u6cMtbMGe6/8rPqoV+PsPl4GjaFdcs2aNZEXdd999MsH4hyAYiMLjW7RuSOagChKug6ueks4Al4vi7CqWvL5Hivj1vz6ejleFMvmT7fJzXdsllHeuay4wKfSddhYeY+bqGZhtRrpGXE/n6DuwbC3AeXMxdms+5rofwGrCuU0Hus58knB3VylQXLksjeotOaBVUzo1jglF+VLjaZyPlrtsr1FevgOVSk/7dm8RGDjmkj6HqdbC8e35ktYutB8aEBrvRcdBYUQn+J1TeO3voPsvhr9C0C0WjbNmzbqk/9NAq3R1dW0UJhILH7EYEosMMYj8p5SD/wjEoljYRgmvWJEJbUgwdO3aVWZAZc+NoMX99ohS/RWWHiLY7nRutcyWYLfbOL5lI9t/+JbyfCWo9/APpN/102jTd2Az2pYQmbvtcLr04fTQqvmtSzzxLVhgCcGtkrnzKJo9W5R10EdFEfreu2hCXcjZ/0+MGStwr7biVmPDrcYu1TnPhgp8YyGogyLM4R+vBNq+rURj+SV9xl15u6TPbYWpggDnAELcQjhQdIDB4YN5b/B7/BHU5WaTc+NwLPnKYqBsiJo1vhPwVNdww4C2BNrzIe+gstU2TyQI4Z+Kcmd+cR5EumtrbCo1P3ToQ6mPQnv0KSuk/87VtM1NJbJDAtGdu8uq1yHLSR5c96C0UBK9dkJx3VPQQ8XrG8uYsmyKtAsTyYQ5Q+egq7eHE+JM1Vu3UrxqHRuSMtniGcOO4PaNi00BNw3SCmi46M9u7Y+Xy5WnWJ8L5txqqjfnUCuERxoUkjUqDK28cO7gi3NbX1nFvmSI62vxdDj+GzaDN2s113N4/7FGxoCTuwcdrhpKx8Ej8AkJbaSy1x0rwXikBGNKOdjqF3taFS4d/XHrH4o+5H8jsDwfzjcxi8WZqGo0BOHiWGyChnyuvdjOrFSfrD3JW9lvyWr3jIAZ9HTrKR/TUAVvqIyfeXy+2y39n4b9W3vf4mjpUYZEDOGWjre0+JiG/Xv5FcwpqMBTo+a3DhEE6TSNn8FqreNE8hRMphyOHplKaamaNm3a0KNHj2aJiaabEDpsYBuI7XyWcgJi8Sy8vkUfc1hYGIFBrhw5IgL8WsJ8XuT4mnRSdm9v1MsQWhndr74WvfP5xZHEb7no448p+fQzwRun0BNmj9dw//QPmvkqtwSRkBGCZmJ+HTVqFD179oSdc+D3JxSaslAzF+PsH4S1rIzkiZNY2q0rdS4u7Ixux9Go1qzr3hptnY2Vh/P4cf0Jko3Nu0RCPJ0Y2i6Q4e2C6BXj00w4silWfHpI0jVbdQmQdOULvh+zmfzUk2QdO8LPWw9wODyUpLbdseiUMcW3tJA+R7fStmIFIe4lDPfxw6s4GbXqdJAtIIrZIrDOq3OnSh9Kemg4b3rvl4KcU0w347G3Ix7ODgbpN1L9q2L7qA0MJOj553AbPJiaslIKM1IpSk8j8/ABso4cblRsF9dAp+Fj5HXQknuEmK8X/uMxClJPyvl5zAOPNwo3/Tr7ANnHy6RP8KQH49Hk7KDu14XoqzejdpSSusIfc6UOn452Au+4DrrfpiStL4C6umyWHnmLl46slYnYp4OMBDu7ERo6lfCwm9DrAsj+915+O1kpv8ex9yeyccFxqkobXCWcGDszEZ/giy8ErPxkNofXr2r2Gc02O1d/u4dDKisEOWNXq4jJzuCFz94lrDAfh0aDz2OPEXjzjBbXeP/a9i9+OPkDI6JG8HK/N7jraDoriyslff23LC1+R8rY1OEor9k+kL2+C7TvY95RhKGNN/43n//6ysz6gpMnX8bVNV4m0i51jSlai2bPni3HJQG38Hi+yPChxmyTyvuf1GzD/u3Xsqof8dmn59U/WP3ZhxzZsEbe7jpmPAOm3YparZGq3jd/sYvd6UrAveD2XkR7FUhKvHBS6JT4BV5efaVzSn5+Ph06dGDSpElcEYiWwPWvnPb4FoyOLjfBgMfBozmj7mJxcF0WWxaflKwJr0mRPLXiKE46NesfuwqbQcOuihr2VYqtlmMVwp7wOTTWQsxOHajwf5yeyWaGH6jFbiumpnYJOmsdmSHR/DD6JqxaZa4UbaQdXZx4bEclkdl1ok+NrOlx3JCWLQWXpwS6cYvt3xQVrZBr1vj4fxIedun2mA5hO3aijEMbsqX2gaOJ8FqHgaG06xeCs1vztc/fQfdfDH+FoFssaMTiQCx2BIXvTIgFn1hAicXRxVZsxPOICoRYBAllWrH38/O7Ij2NV+p72bVrF3v27Glc0Am6o1gIisWRSCZIuvPyxxXVyAbBiomfXVLmUEzeJ7Zvkb2DDTZSosLSc8J1JA4f3WIfsFBjLSmq45m9aWSV1xHs0HC7nzd6m0i2O2TvlvAcrdu6BXtOJmq7BfdQZwITVLiZjuFWk4SbugSVqvl3ZVNpsNijUYd2Rd+lNwR1VKjxlyH+diYWHV/E67tex+qw0tGvI9PaTuPJzU/KbPfPE34m0uO0iNLlQAx8m5Z8SvvFb1F2XElAlEa5s7HzEHyjorjjjjuUa0tcn6IHPWkJbHqjXn8f0gjjS8TEpuIa2zLsNdXMD76GpR3HUmtQFt5d3J15LjaU3l5ubMjaIMVthCCMWFy/OeBNnLROZ/W+Tvt9mqThToqbxOOuEyj5eSlrdp1knU9rdge2wdgkceFnUDEiIZSRCSH0jPb9j4igNUD8bs1plVRtzMJ4QulhFxAVbdceQbh08v/jlWWhrCrOuVCkn/EbRPSU7IFD61dxaO2qZn3xQmxOBOCte/drDHwaK+97CpQKeD0M8d64DwyTVff/1UTenz0xi0SWqB6Jfu6xMWN5rb8iUPdnYmfeTm5fdbvsyRMVK0F7bQnby6u5dn+K/KV91j6KqwO8WvTuXrf+Hxw5PAStVsMDDzx4SfOhmHsagnCxcBYJUiGa16Bs3+Am0RQGWykuZQVYi5RrSaVS02HQUPpMnirpn5eCuoMHyXnscSxZWVjV8MNgJ6a9uJhYn5Ztchqwe/duli1bJueWBx54ABcnJ/h6vGKhF94Tbvn9itj+rFqwgG0nT+JaXU1NfEc+iUukp6crP3WOlVXnFUuXE3PAl21mE7siXdiSW0FdPb1bQHhrj2gfxNiEYHpGnw7Ai7OrWfTyLpmbFX2dvqEXlwDLqDPxYWYh3+UWY67nPISU53Jz1mrGZf9MqFM5unrV7QYYLRpKqlxJs3hT6RLNvsCurPKJoU1CIp/2UDzSPz7wMR8d/Ai9xYkHV11Nj8yVOMoVxpj3jVOk1VVTZfOmqK0o5+Su7dL+UATTAlq9gS6jx9F74hS0+uaL7YLUFBY884gM1Cf942UiEzqRtD6bzYuS0erUXPeP7ngHuWLKqKTo44OgdhA0TYtxxadkfbpT3m41qhC9hwPaXg29Z0J488qsCMLEbyM7ZwElJRvkfDWnyMAxo4be/rF8NPxrtFrl89TsLWDPN8c4XGfHJ9iFkDgvDm863Vuvd9Jw9YOdCIq+sENG08/4zdMPodZouevj+dK7vUEoa+S7myk3WxkwMIIqPwNJ+UU8/s2nXLVvp3xM+oBBxL/2Kq18vZqNU0OXDMVoM/LJ8C94r8CLTWXVUk/l8/ZRDDRpKHx/P2aNlZs7PE+ZuZznCu+mT0kCXtNb49b+3Erp4nvYvmModXUZF7QJOx8afLtrHVqWmBJxoKZ3jC8fT+uCS3E+p4aPkGJFrVatRB9+tiuHsbqaX995lawjot9fzeBb76bTcKWvXgTcQsV/V3qpFBP85raeJIYr5yf55CtkZc3DxSVaJgzy84tl4C3m5qlTpxIXd/6x5JKQu1/RkUhRkgKItYtg1vR9GFwvbewT72/ZR0mcOlTMfG8zZQ47XbsEURrlyrGmLQkOOx5Fb2MwJqHS+hMT/xZtUrSEbyzGbivHZFqCylSFOTSS9Gn3k4eGHKOZfLOlMdfuanUwb2ctrartpHtpWTAygF/qhf/uCPVmqv1zcnIXysdGRd0nLXMvd01QVWqUle+jW04Lr4nEQly3AFn9DohU5qe/g+6/GP4KQfelXFiiEiOCVJHFF5tYCDWIEzUshoS1T0vBuaiOR0ZGyk2ISIjKxH96ES0ELERV+8iRI43ZTkEJFYG26K1pTDpk7oAf74DyTIVqLTKFYquvZl4IorqXvHMr25YspDQnq9EDtfu4iTLYNteppd1IaV6NVF2sLjXKjHV1mVGqjv5RaFUm3A05OLvk4hVYh813GwRlo1W5Etf2WUKCJ1+Rcy+C0lm7ZrHohJKYGBMzhud6Psf0FdM5WXZSCb57PHnFrs+xAflUz/mnFMpx2FRUu7mypV8/ht52m1RCbrrAqlo0k8DcpZRZXZjDNIxaVzrZDzNBrfhnCpSr3Xg//j7mBY6gDmXx28nFRHbqS6jMGbKH8/UBr8v+s5awLulnHtz7HDZTKLGHOpOu70yl4XQSI9hZzejO4TLQFh6fFyPKcqVhzq6i4vc0KZwnoQLnBH/c+4XKfuor8hs8/CN8f4tyPP6js4QFRfIpbf9ektb8LvdNK07xPfrQ/qphhLfrIBkfYuwQfeVVm7Opa2IDYmjlieeoaPRhF/AG/y/gz5yYxfkQCaA1mWuIcI9g8dWLcRUWbH8yxOtOWz6NpOIkbutwGw91VdTSz7Q8vGr3CbKMZm4I8uHdthHnfK73Zj9HeZmW6Jhibpo+Wy5YrwREwC3mHVE1EuKXaUl7qD55HG21IqIjLh+ruzcebRNo362HrLKLhPClXvfCYSD3+eep/l1UW+BYaxeGzVuGu+9pr+czIeYYIapWUFBA9+7dGTNmjDKnfNQHzFUw9AXod/Z5vdQ1hqjeibm5z5athNfUMO2Z18lx8+DVuFCmB3rJa7Ofox11O/JxauuD241tpBr6qiMFrDqaT1ltfT+oqES76hnZIYgxCcGUr84j7UARsV0DGHHHhavcJ2uMzM4s4MeCssaFdI+KJGZmLmRY6fbGlgOBOouW/Ep3Uk3eZNZ5U2oWTCDlEUKMyqttRz5yCiAzNIbfB3ahjaszxXmV3PXVdYzfkkn3k8oLCMHQ4JdexKWr0pN/IYhrUQhkikR4fkqy8pnDIhh13yOSft4Ua+d9Iq0SRd//1Y/O4sc3DsreUEG1TRikiOEVzTuMKbkMl26B+EyKl/dl3nEHNZu34BbnRnhX5TUkIvrAkOcwB7cmL+97snO+xWhU1gcCPt79sHgO4Y4tb0vvcqFk3jWwKw6Lnby3drMmq4YqO3j6O1NR70/cZUQkeSnl5J2qQGfQyGq3CMgvFgueeZj8UyfpN2UGPSdMbrx/xeE87v5mnxSSW3B7T0JC3FmcV0L5Nwu48bv5kjmXEhbJsiee45ouHRjq68HXR77k7b1vE+fdGkvoq+yurMVFo+arjtH0q2+TK/zkIOb0ShZ2W8/XNUtIrGnNi/kzCftHH/SGczOsRHLiwMFb0Gjc6Nd3G1rtpY+B4rv/eM6n5OXlo1E5WG2OZ0CHSF68vnejdkrm7XdIJxHf228j4LHHzqtQfvVDTxLdWREuqzWLCvdudqWV4m7Q8vXtPelUH3A3aEyIpIHZXCwdZCIj72LlypVs375dxgiCZi4YO1cU6Vth7YuQtUO5rXeHPjOh932KW8xFIrOkhmdf38YmjVmKI1cODpI0cHHGEt1d6OrpQln+Qjalfo2TxomvR3+N47gn674STLcasH6PqaZEWjFe98/Xmjm7WOwOUmqNJFXVcai6lpM5FTy7tgxvi4M1gVqeTnTCUT9W3xPmx3TtT6SlvStvh4RcT+v4F1E3EZ+9VFgtNqlXIRJqoge8AUExHjL4jujgzcpVK/7u6f6r4P9a0H0xEJO/WASJhUZubm7jdqZasOjPi42NlRk+sb/iA06TzyY8GkXVQSzKGiCC/169etG6devTFXirWanWCUsGERh4RcC1n0FEr4vu2T6xbZMUSCvOUlRE9U6uhLUfgqtfdyoKbZTl1WAxnV2ZaQrhEubmbERHOTZzIT72QvQOI8ZCDeYy8V7taJxVOIc6sLv5Y9QFUWv3oc7iQnWVwvQ9E2qtCZeAo7iH7SesrTMdO/0TZ+fLV9EtN5ZLhdJd+buk0vEDXR6Qi/OfU37m+W3P46H3YPm1yxsp2Vfk+hw1Ct3iKRh3byBrewDCLc2q0XB04ADGvPoqaft3cWLbZqk4rsHCjJh9bNP3Yhed0RqNDF67ikD3alwHhKK3JOOjU0TDCvQ+vB15MwuCx0pGgPju41RpfNNzFJEuzSmoDouFqrVrObnkF5YVqvgpvitlutMLb3+DigndI7m6UygdQ/971VlrqZGKVelSNE9Co5L90u79w6RN3BVD9l7FEkn0qopqzohXzvtwUf0+unm91DZoYH8IeAYE0rb/INr0Gdjo+20tqaNqcw41u/MbqefOCX54joxurrj/fzjoXnxiMS/teAmtWss3o76hvd+le2dfLtZnrueB9Q/IIH/VpFXyN90UT57I4svcEsKcdGzo3ka6T7QEYc+3aNEi1Bor3bv/SELH5wgNnXLF3qfVYuHkji3sWfYzhWmnlDtV4BXTGrNfCAWVzXubRS94YmKi1Oy4FNsesWjPWfglJa/NQm+FSn9XEj5fgHPrc6uCp6Wl8eWXX8pxQPR2i2Qz+74WKkRKBerurYpuxmWioU81PCyMq35bhvn4cUp79WHiTTNx1WpY07kVSevWMLz7oNO9s493b/z9WG12tqeWsPxQHisOnw7AA6wqZlQ7yaRFu1taM7B7yDmThoeqankvNZtlpTU46gPngaW7eTDza3pXHJT31NgNZFe7k1fpjmuKFbd8sR5QYWnfBsPdd1FcViKrh8KyrKlfuHg+c3A4fbt2o/a3o4QeXo3OZpaMg5LJAxnwj9moL2PtIL5L0XKw5vOPZJJWJPx6XnM9vSfdIGnCAqbaGr54+G5qysvwDLoKk6kLYW28GfdAJ1RqFabMSoo+ElVuCHq0G1pfZVw1nTpF6rjxsiUh4u3ncbVuxZG0GJVw6xABpLeeU1EuVLkLUUUPgoMnycqtqIIKvLj9RZYkL6GDbwcWjFlAzZZcUn85xZbq0+sGIQg17JZ2RCf6y/WEqEbmnChDq1cz5t4Ews6weToXxDi88uN38fAP4LbZnzV+doGnf0zi211ZBHk48fuD/fF21cu+3a1rN+L8zFO4VlZQ4erGi7c9QEGnLtjLlmIu/RXPwKmk6vpKu1ahS9PN83SAXJtUROnC4xR7VjIj+GnsKgcvVT/OmDunnHfsPJh0F8XFawgLu4nW8f/kcrDvSDL3fLOXcE05HbQF6Bx1PN3NhvrqtxofI+b27PtmovHyInbjBtT1hZicE8f45c2XqKuqbKZQLlBntnHL/F3sSFUC7q9u60HniLPbFvLyfuToscfRaFzo1Ws1apWP9AkX8cHAgQMZNGgQVxyiACYq3mtfgPxDyn0uvtDvEaX1oUnb25lIqqrl/YxClueUoN9YIOnZo2p0uA0LpWePYAb7euCj0zazsBQsrPiSbqyZdxS73YyGn6ktz8bTz48b7pmGm6NccccR7jWi90+ss0UCQArx+oFHGPklXpi/PI7a7mBeKz0fxZ5mDHZ2d+Eln+1UpL8s18H+fsNo3/49NJo/5sftcDikLZ8Ivk/tK5SsUgFnDx3agGom3DoAD58/P9l9ufhbSO0ST8R/G382hUJUI4RAWUZGhlQFF5t4zQaI1xTBd7t27YiPj78iAbigHgr6+IEDBxop5CK4FhVRUdkWlPdmyD0AP98LhUeU2wk3wOg3wOnCQaPFbCJp9Sp2//ojNeVKkKNSG9Dou6Bx6oJK1XxAED7QngHO+Pg68HQqxd2RjbvpOO5V+3EzHkOvbt7DKGxQsnb4Yq1QJkSfsb0IeGAmqpAOZ/VeJye/RcrRH7HXxuJ0aDplVVpK7aJ9twn7QGXDNfAk7foF02XgOLS6S6M4nio/JQXTsqqycNG6MGvALEnDNtlMjPlxjPTnfKzbY8xoP4Mrfn0aS+CjXtjKy0neFwxKboO00CBO+LjIHjSBwJg4Ilt5szZbyUBkGg3ccPgg4SmKF7rXrbdQEOFKxfYFhOtyCXepINM1hNejbmdpgDL5GRxWbtGX8EC79rgb1ZQsWsy6dXtZ6t2encHtsddX6zQqKyr3w7h6H+aLa5+kS2BzgZH/JBw2uwxUK9dkNvZsC/q4x4gotN5XOFAtOQVzhyt99HHDYcp3F02XFZOc8AMWiz6RqDLXKZWbBnHBtn0H0rrPADz8/GUCoXJ1BrUHCmXpUnh9uw+JwL1/KKr/gLr7f2v8FGwRoR0gfldX8vd0sRDVNmFPllKewv2d7+fOhDsb/7a5tIrJB5UAd0liK/r7uJ+z2vvJJ59IC63ERA88PD9Eq/Wkd69V6PUtU9YvFhWF+Rxcs4LD61c3Wn+ptXZ8Wpdz1fXPEdl6lLxPCN0J60chlnnq1KlmdPSoqChZhRYV8IsVCD2w5ScqH30G/wqwG3SEv/Y6HqMVimlLEAkHkXhotAkSd35zLZxap1Q/b152WWrmorovzq3Abbfdhn9dHekTJ8nE4A+338cHXfsx0MuNKZlHGDN6NOVfHseUUi7bNQRr5ExYRAB+qoRlSXnYNhcSbVJzTGflN1cL/u4GxiWGcE3nUEWEzW5j96l9vJtTzlpO04JHFm/mwcxv6FR5nGKTC8lVgaRWe1JodCOwvIb2OSU4iQS86MN/6km8p0xplpgUzh5CkC09aT8n9u+lOicTV6OZjllF+NQq82JhgAuzxpkoCnTih/E/EuHRMsPiYm071837hBPbN8vbMV17MPaBJ6QglsDxrRtZNvtNMcrjFnAbN74wvNGGq/jLIxiPleLSNRCfyfHNv5tXXqXs66/RtY7GOqsH5Vk/E5aaR0i+UTDPJWra9MdpzCdo3Jsnv4vriuU8Wmut5bVer9JlkR97io1kCc9xMXcFukg1aUFvb4DVbOP3OYfIPFKKRqdm1N0diWzve1Frl0/vniGF1a558p/EdDkteCqqt2Pf30JqUQ2jOgTx0dQujd+VJS+P1Jn3Yz9yRAopfnrNjSweKvzoLaDS4KPX832nWNq5OZ81P+W9vkt6cr8YNoft7gfprenJh9d/fM6x02QqYuu2vpKO37PnCtxcL52KnVJYzeT311Jm0eKpsTDRkCTHpus1K2n76DJw8WkUP00ZOkwK0wq1ds9x486pUC5gtNi4/cs9bEkpln7eIuAWrLaWIBhee/ddT0XFPgIDr6ZD+3dlQWjx4sVy7BEWioJ5+adAVGKO/qz0fIsWvAYXmoFPQOdpzVicO8qreS+jgPWlSrJSe7QcbVYNwU56puarcXLVyXYTN28nUstT5RwlrlXBbJyouUUq6YsClN62iKqqfJy1FqZEHsBbf35tjkboXKlxuZGygvHy5rGhfjzpYifXdDpmuMX1IENrhJOQBS+vHiR0nINOd2VirJoKk6SdC/p5bYUi1jv5H10JCP/jBaQ/C38H3Zd4Iv7b+E/3LYjXEwF4SkqKXAgJanoDRMDdvn176XEaERFxiTYxVvl8IthuavklvgMhjiYo5KLC3vw/mWDjG7Dl32JGULKAQl22w7XnfB2bxU5xTjU5J/I5vmUVBac2Y7fV96GqnNEauqAxJKJSO+Hh64R/kAoft3J8tNn4WI/gWbsbTcmJ0zYPZ0L0jQd2wBHQnpLtxRQsXIHaaqPY0wvnl16m2/AhLf434ee5Z6+giNkJT38Ul+SOaP2d8bsrgdISoxSPSNmXS1neadVvnXMd7ftH0mlIHK6eF84WbsrexBObnpC9zKFuobw/+H3ivJVJ8MsjX/LWnrcIdAlk2bXLZD/olb4+VYIe+NvHHF++kNQqbyLKrbTOyJEL2WpPd+x33kr8mHG4+frz/gcfUVlRRmcO0UV3isBHtlH2wYeUzlXsfNyuugr/l15g+YoFpKxaQay+ihi3UmpCA3gn9ma2eneRj3O3VjPj1E/4puexXNWbw44o2QvWJdiVyb1jGNHen2e3P8rmnM14G7ylL2WU54XFc640zFlV0nqjQY1c+GF7jfmTKNk1xTB3GJSmSrs2bl4OhssTPRML7ZTdO+QCN/3gPjlhNyCsbQfa9B1AbPfe6Gp1VPyWKj3EBbSBLnhfEys9v/+vjZ/Cs/fGZTfKgLdvaF8+GvLR2ZZ7/wEsS13GU5ufktf1ykkrpU91laCV7zpOjsnCzaF+vB5/bsbMoUOH+OGHH2TrzgMPzOTwkRuprj5KcNC1tGsngplLg7ClO7lzG0c3rSXzyCGlklNPSQ5J1KIP34R/UHe6dFnQ4v8X2iVioSsENJvOEWJe6Natm9ykpscFMH/b+zi/+DEJ6crr+917D37339/ifCX6z0VFSwT7U6ZMkewqqTj8UW+w1Fy2mvnXX38tkwhivpw8WaEGl8z7gsI33hD9XMx4dhaZPv7cXFfMS8MGYk2upOTro6hddQQ/fW4/ZEGzXPzqbnmc09uTZeklVNRZCFcV0F99iLAgM6vCerLDK0E+Ru2wMb5wPfeUrMOzykhSSg1p1T5YHEoSwzswANeT+0lIUxbdJz1D+XXMXbz35DW4GrTnFbL75LkX6fvbz+iE0KBazYlgH9J93VnZs5B8PxOhFW48qL2OqITO0gHkfKrk58OxLRtY9clsrBazTNhe8+TzUmStMKOSBf94Crslk8BWnZj2qqiwIcfYgnf3SeZA4CNdpZ1iA4TqeMHJH6m7eS6qOgdlN1up62FHr/cnzHUgYcnp6I4pbQoI5wvhr9z15mYJy0+TPuX9/e8TqPbnoyP/YF25Cls97XXs/Z0wOGtbXJes+OywnOfVWhUj7+ggK+EXwoavPmPvsl9kwC0C76Y4nFPBhA+3yqT97CmdZeKlAXaTifx/vUDFT4qQ3frOCbwx42GMBidJPR4f4MXDUUFnCcFWrEynan0W+12O80zkbPToWTN5Dd4uLQecGRmfknJqFh4enene7XsuFZtPFnHP13uoNttxU5n46vbelKYdkSriQRRy16BoVAMVITmBoo8+onj2+zh37kzBdRNaVChvCLjv/Hovm5KLcNFr+OrWHnSLOj/DoKrqCLt2i2DSQZfOC2XA+NVXX0lGjLCmvf766/lTIVgkQuV8w+tQWc80846WNmOprcbyYmoeK4qV1hyNCobonNnyW4oUNvz2tp5kf59GYUaVbGEYcl8cU3+/kfTKdPr5JvKoZTyrV3ljs6twMc2nrK4MncrGdZFJBLkaFTE34X7jLujpTsr1LuY0YRlbU6RsYlyst+Yrt9xMtU1o8Zjxb/UzC0LjeVHfA2P9urKt4zBPqt5A56jB1TWOxIS5ODuHXsFTZSd5Tx67Nh7ixkeG/G0Z9lfA30H3RVa88vLkQujw4cPNAnChhi6qECIAFz3h5/r/wq4nKSlJ/v+GqrZY/IjquVhECfp6iyJuOfuU6naRYgtD+2uUBZCgudRDCJeV5tXKybcwvVIOOEWZuVhq92EzCbpOfQCr9sDVszthkXEEeZQRoD1BgHkHTuUHwXa2tZWEUEMXft9C1EwoiAd2VPwVDe6YMzLIfepp6vbvlw9N7d6LRybfgpOvD2u6t8Zf33xxb7PVsWv31XLC96ocQOCOW1G76wm4N/GsCmd5YTW7Vq4gba8aq1GhV2q00GFAOJ1HRLQYfIvzPP/IfP69999SQblbYDfeueodvJ2UibLKXMWoH0dJMZUX+7zINXHXcKVQXVHBz/Pn4m41kpG0H4vxdFXUw2BE5RxCt+0HMZjNqD09CZn1Outq6ti/eyd1Di2PquYRQAkMfg4GPEbFr7+S9+xzclHniArjqbHlZDmbGF2WQMgxG8bqSnwNtRTHRfNzwjhSPJQAOtBUzGMZ8xlbtB113DA8E69WfHf1rtRaarl15a0cKTkikxHfjP7mnOJTVxqieiAq21UbsmQlWO2ixXNMDC5dLr139aJgroUvr1Z8h0ULxm1rwP3CNjUXW3kSQZUIwLOPHT79B5WK0NbtiOvRm1CXeOxbKrDXWOWiV1TuPIZGnjOI+LPxZwTdDRRTYVH3/bjv/2PX0pmw2q1c/dPVZFdn82T3J5nWbhqPn8ji69wSIpz0rO/eWtKYW4IIMoUnrWg3EtRJQaFUEoOTGhed3t49Lyopk3Zgr7wuUvbswGpSaLoCItASOhkhbUPZuXs4DoeFLl2+w9vrwhaFYq7Zt2+ftPYS1XAB8f2JOaNPnz5nJ2jPYAHct/oeIhdsZvxOJfD2GD2K4FdfRV2/KG+K1atXs3XrVunucc899yjzUYOauZgH7t0BXmcLN50LImH9zTffnFUhE3oimTfNoHbPHko6dWHSnY/hgp3NPdsS4uRcX2U04zutLc4dWr6mln+cRNrBYuK6eDO8fw62lLUYT6xhiyGMdyOms99D8QDW2S0MEA4VRWmokjMoK6zXjRBJEIODtsOvJcLJlbw3X8Grwirss1HfOINp1gSKTQ6GtQtkzrSukvl1Jmr37yf/+ecxnVSqcjvadyZ4xL10HREq54B9R7bwUcAabBoHfZJ8iM92l2NEcKt4ojp1JbpzV4Ji4po5g1wIucnH+PkNhUIs6NbjH/8na7/Ml61i5qpvJBV24jMvEpXYhZLvjsvWHeeOfvhObUtNTSqFhcspLPqd6mrFls5thRqPX7U4ApzwX/QGvoGDTvefCt2YZY9BQT3lVzAervm4UencaDUy5ocxFBoLmZxzLb6ZA6U92K1v9junvZGAzWZn9dyjkiIrzuuw29rLnvzzoTQ3hy8evkuevzvenys/e1O8uyaZd9ecxEv4gT88gAB3p2brgu1z3sd99sdo7ZARFsaSx55nWX3PsPhmrw305pGoQFq5KP/PUlJHwZt75Dri7o6vkWnN5pnuzzCl3dktJ+L5d+wcQW3tKdq0eZXQkEsLSr/ens6/lh7FZncQoKrivkQdM26YKDWJ3n3nLcxWOzc4baTNYysaWYOWwkJSBg+RrjCb48OocjY0UygXMFlt3PPNPtYdL8RZp2H+Ld3pGXNxImXHTzxPTs4CqcLeo/uvFBWVSsaK+KzTp0+nVas/7mpwQYhik/D3Fj7fItgV78s1mllRt7LKrz83hvhxX7g/z317kM0nixnZPohPpneVlluLXt2N1WSjqMNRDunf5+Y6Gx2LYvm9+GHs6HC3LKSoOh/xs54wsT/RA8Yq69yLYcGJpEDJSchLwpG1i5Ld0RhNiagpI8DwMCaDha8DRvB18HhOuYQR4UjnCV7GmzJ0en86JX6Oh/uF9ScuFn8Lqf3F8HfQfWkQdJ/MzExZhRBiZ0K1tsHGS/TfCRsvsWhpqB6Ix4lgW4i3NUAslLp06SI3UeFuERaj4mu49T2lui36Sca8jaPNOMoLRYBd1RhgF2dVSfEUad1jzcZq2o/dImiVykLLxeBMO18bnV334aE9/T6aQVjE+LdWbLkC2pzee4SdRSsUr1P+3XcUvPEmjro61K6uBD7zDNpx4xi17yQna0309XJjUWIrtE0WK8knXyYr6wu0Nh+iNr6EVuOB/10J57Vcqig/yraVn5CX1IG6EqWvUKNTkXBVON3GRKF30jZW3YQVyPK05fL25PjJPN3j6UabLAGRlRfZ+RjPGH4Y94PsQf0jEH10p/buJGXXdjIOH8TeRBPA3def1j160CbrMwKsp9ju3ZotOQPpu207PiUllHt6snKkokJ6QN+eL4eY8Pz9PkVZ+86NMrFRd+gQGffeg6OohGonWDW9NQ9e/x5lCxaxcNcJlob2JNslVH7LrsEOrHGeVDorC4nY2gyeTvuM0cWbUYmsa/QAiB9BWVgXpm5/lqzqbNr5tuOLEV/gIr77PxHW4jpKFp3AklXVSCX3HBuD5gzriysG4Ve7aDqcWKZUaW5brVjN/QmoLC6S1HPhANCgONyAwKhYwtziCSgLxl3nK6v5Pte3Rhfw557v/8TEvDpjtRRPE5gzbA59Qvrw30RDX3mIawhPXPUdUw8pPR0/dGpF33qBpJYgeo1Fz7FImD700EONQpXHjz9LTu63uLjE0rPHUtTqs69V0Webun8PqXt3kXZwb7NAWwhbtes/mHYDBjcGCMeO/4Pc3O/w9u5Dl85fX9LnEwwpMd8IkU2RABYQwayYQ0Si4Fw+uqXGUib/Opn2O/K5a6UDtc2Bc2Ki9PnV+jZfgAvrsPfee08mhcePHy9ZV5Ly+cUoRegodihM/V6OWRdCU4E2MSeOGDGi2d9FwjZ1/AQcRiPf3nIPn/YYwDg/Dz7tGEPFijSqNmTj1Nobv1vOWJzarBTt3cPiebWosDPF/yHcNbn85j+Q9yKmccxNCQb0ditRBflkJosqlTIPauxWWhkzGGrbyWSP/YTfvYiiz5dQvmSJ/Huhj4a4N98jpO8Q9maUMeWzHTLguWtgDE+Panv6LVTXUPTuu5QtWCBZDFZnDz4cN52fB/blkchAnmh1utL6RdI83tn/b5zQc/PxLhhTle+uAcIpRATIwgJS7JsKOJ0LZfm5/PjaP6W1p7P3MBx0xNldR3j8EZLW/IZ3SBhTn3qDwncPYHLLhLEFlBjXUl1zovE5VCqNvA79PYZQe/Nn2AqLCHjqSXxvvvms883uz2HdS2CuVpIvI16VNk81lWbmfPoeXwR8jbPFlSn7nqf/1e3oNurCDCpRKFj75TGSdxXIy2nIze1o3fPcgn8Ci198RvbU9550I30m33hW28H4D7ZyNK9SJko+nd61MZm7payK2ze9RcdDP/DoTyo8a6wy8W155VXeDozm9+L6tg9gaogvT0QH4Z5RTfFcJaG6tPMuPjLOp71Pe767+ruz3pegYgv2nlrtTP9+29FqL461Ja6tfy09wsKdmfJ2rLaE3po07rrjNmkrKLBm1Sq2bNtGEAXcNa4Pqi7TGxXKD1wzHs+sXDJ9PQn85/ONCuUNz33vgn2sOVYg7bPm3dydPq0uPilqsZRLUTWLpYy4uGeJCL+F33//nZ07d0qXH5GUu9hWlz+KDXl5HF77b6anLcDTpmjcGAMTcRr6HOttCdwyfw96jZrVjwwg0ldhAB3fnievL7Ax0ecZzA5nlpc9gw09vm67yMnaIh834u4H6TBo2B96f3ajhaIPdmMptqHTZuGveRi1yogdFT8FDmdZ+7vZYbXzBK8QTiaonUns8D5+flemP97yH2YBXy7+ppdf4on4b+N/8cISNEBBTRTWXqIfsAENQbewimmAeM+iH1wE5qJv7rzWZEJUYvnjOErSqLAFUxQ0hSK/ayjMs1CUUYXZ2FzkzOGworIewm7ai9mi0G4EIlzK6OKTK+nIjeskQU0XvtcNm7DkEptnxEX17FkKCsj7x7NSPVM+XY8ehLz2KrpQhTKTXGNk5N5kam12HosK4rFoZSItK9vJvv3KRBm672Hcyjrhd0t7nGIv3B9ks5k4deptju/ZRvGRcY3Bt6unnr6T4zDEm2QAcKLshLQAe6LHE9zQ+oZmFVTRgzb6x9EyOH/3qncZEtky/f18EMmGwvRU0vbtJnX/bvKEsmwTFXydhyeJA4fSuldfAlvFKa+fvgXmj5UZ8w9cbqCsKoAhOTnsc3ahxN8Pj8Iyxt3/MLFtIuHbGyB5BYR0lpXZMksV9y6awg1fZhBf77xyyjOUWV2nkOWhnFctdjqoConN2YafpZikdt3Z1WMo1XolY59Qm8rDqXMZUbJVeqsKWD1DWaYysl6vQhUzkDeGzzmn+vkfhehxLvsxBYfZhspJi/fEWOlv/adBfB/LH1MWiiLZcNMvENmb/wQqiwtlAkbY/mQfP9Ls2nDVeRHsFEOIeyvibxiMZ8+Lrxj+r42fudW50h5MMEdu7XArD3d9mP82RE/58O+HU2yqwxH1MaU2DbeF+vHKeWjlosotrChFNXnYsGH07du38W8WS0X9orOUVjGPExV1t/z9F2WkkSp+//t2nfX79/APJK5nH+J79iU4rnWz8Ud4HG/fMUSO1V27LMLLS1EUvixxrZQUNm3aJNlTDe1OouotgtuWLDW35WzjrjV30S7DzvNLnVFX1aCLjCBi7lz09Yv7BohKt6h4i7XA/fffr1wrRcnwSV+FDTVpHnSYeMH3KRLNP/74o3w/Dz74IC5niD0KlMyfT+Hrs3C4uHDDM7Mo9PXju8QY+tp1FLy1RxFUe6oHWnu+0lsutrSNLMu7l3RTD1o5baagbQofRU0nQ68kNkTFvF9uCnFrf8KlpooqjRvJHvGc8kmgwH6aiTaw/AQPHPwJl7JieXt5NzV9XviQ3q1Oe5v/vD+HhxYdkMdvTkpgUtcwqlavpuDV12Q/rYCt13C2aoZwKM6X73u64q3VsKdPO1zrAxKb3cb036dzqPgQV4VdxSudnifj4H6pTp6RdABzXe3pEyKq4LHxRHfqJoNwoVR+riq4YNt8+/zb1NUNlNfZ4JtiiOnkz9wH75TaAbHdvPFqfwyr0+kku0qlxdu7N4EBo/H3H4ZOp8y9ZUuWkP/c82g8PWm1ehWaltaBpWkK4y5zm7xpihjOsuTb6OwwcFerl8kzFNI9azTv3ftCYz/5hWC3O9jwzXGObcuT3/Xg6W1p2yf4vPT65e+/hbufP7e//3kzQTX597xKxn2wBYvNwb+vT+SazmH8XlTOXYdTcct5CI2tjNdaP0X7d37FmJQk1zsBjzxM9nVTeCu9gNUllY2ezHeUq5m0owyDHSrcarkx/Ans2GWyPt67eQL32LGnyc1bfEntKIWVRu5ZsE8md8QwMTlOj3PGVsLCQqW9aAMEu+Xdd95EuOdN8dxH64d+oTQvl5/ffAn1iWR6iiSOkxPxW7agcXNtTEDcv3A/K47kS9vPeTO60y/u0llIOTnfcfzEP6Qae+/ea7HbXOV4KSrwI0eOlCK/fybKLVaeT8lhcb5yDXfUGvmschlRB+cpCSDRWqBpwyt1E+nY72qeGV2fGMvcQdnyx9l9fAgnjQNwVpdixgObXUtgZBVZh76QQog9Jkym/5Qro0FiLTNS+OEB7NUWnCKseLl/iPaU4kJjUWk42fp6HguYwHA+oSNJsvUvJvafxERM+z8ZG7WEv4PuSzwR/238r15YDdRzkQEUvdoiEG8KIYYmBichgnM+8TVBuSo7kUrRii8pyq6h2BJDsTUai+NsyroWE366VNw5Ro0xndwaG0abMgFpVTbaeZfQpZUTvhHRzQNs31aNYhyX8zkrly4l/+VXsFdWohI+548+gve0aWctDIQdy71HM2S/zdLOcSS4Oti5a6y0HvHMHkDQ0VvxuaE1Lp3OTyk7E6Wl2zhy9HFK0/0pOHADlmrl/+d7nWJt9DfovODtgW/TLejsBe0rO17huxPfkeCXIGnVF0tpFouijEMHSN23Ry6UasoUz9UGiN46QSeO6tyNHQcPtXx9rnoWtr1PmiaYL203NN6tsVgYvfx33J2cCHn9Ndw6x8NHPaX/uuWqp7ml5hD23QcYvccL5xJ32oleIjHJe0fyfq+pjBzWlRl9YwjydJLKpXuW/sipPTsxanXsTuzLnsR+WHTKNdeWah4q+o2xx+ehsZ++RkX6JscziPBOM1C1GgKhXRUe/x+Ew+aQNmDVW3Iae7dFlVfrdWV66M+JLe/+P/beAjyq6/v+/ozH3YUIEII7BClaXIpDDUrd3d1b6m6UttDixYtbcdcgAUJC3F3H3+ecG4UEqfy+7f/t4rncmcnolXPP3nvttWCT6PlTwaTZ0EoROvm/RjULQgTgKSeOSS/iamhUWgIDo4gafB3hHTrhERD0tyvI/1Xjp6ByixaFI9lHpN/97GGz/7aEzbVCeCTPSKmk0qU/4Q56NndrURP8NARB2165cqXsjxaB4cXjs1DyPX7kWcoy3HE0TSAlNq6el7uAEC2K7NSNZl26y9uN7cfqibmwXOrYcfaf/q3SVioxkU2bNknnDQFR7RbJA5HYvfh7vLn3TWmb2Kbci9cWa7CmZ6Dx9aHJzJk4REfXO06++OILOR+4/vrr6d27t/KH32fA72+Dsx88eAAcPS5blRf94YLdNXDgQK677rqGf4PVStJNN0uP8XMt23D3Q88T7mRga7sgjDPnos3ZhaPrCTQVSbXBirkpi/M+kEnMBcNdiHdVxhMXi5lOsbtpf2QHDialfSugWRStrusvxQ4dtVZOfjGFVbkRuJ6oZGDCfvmcDCcvPo3pi9+AKD4aeTOuDvWP5Q83nOHzLfGEVuTxZd5WdAcVayNdaCiODz7Dyo1qbBY7vac05x6HEi5UmHizeTB3hvjWExuc9Nskee683+d9hkYMlY+LMUHQxUVbQuKRg+Qm1/bvV1fBI0QVvFNXIjt2Qe9Ym7gwVVqY/9peSgsE0+4YAW02E97Dk4T9Z0j+3Q+1zkrLyQkYXLR4efeSCspKoH3pfhPCXIJ1YDp/Hu+77pLX9UYZRHu/wrbpddQ2ExXWAIotzzPHs4g5/jMx2B3ZfOPGa3IDEWrT2xac5eR25TrR7+YWtL4uuFFBtW/vnYqxrKyGQn8xvthyjg82nMXNQcuDN7fj1dQstOWHcM/9BA+DJ5snbkJrhczXX6fo1yXyNW4jRhD45hvsM1p5JT5NWkMJhJTbeCHBQtc0I8+1+Iqj6hOXWIxareXs2BmD1VpGp47z8fSs73PeEI4kF3DvL4fIKjZKr+xPJrXjyJq50t523Lhx8vyti41rf2PXvoMEksUNPTvw64ItchsIobR+59KxpaYS8MrLUvBPKP0/suAoq2MzZPX3u6md6dfi2uZZ1RCicAcOjqekJLYmoSC0iH777TeZRHv44YdxaKBN5a/AoaIy7j55QWpyiJHs7hBfnokMlPZuUqtl1ydY9n6HtmouYwm7Dm3MfRC7SBFjEy1/Nifm5n+DxaIwDwKbQcbpmVSWlsjE6MhHn7mm1o4rwZhUTM53x6WLiWu/UPStcjm16gU65yhjhsXBg+XtHyPWcII+bJWPOQVMJ6bl83/KntL8D42NLsZ/Qfc1boj/Nf5JB5ag3wmRtYSEBKk4W7fHu3ryIzKU1V7gTcOb0L9LNCEuNuylOZTlFFCQWUF+ro38fC25Ra7klftiRddggO2tu4CPNhE/XTxemvPkVhg5URpCWkntgOfq5kzH62JoM2gUjv6Rf0hptjGY09PJePVVyrYr6qkObdpI1UxDpGJH0RDuO3mBZdmFRDoa+Nb9F7IzFqCt8CZ895t4DWmFa58/ZgVmNhcTd+ZFMtLXc/D4MJzPD0Vr12PRmugxKYJufS61xhEK5qOXjcZit/DDkB/oGtB4L6UQyBKeoMmxR2WwnX42rp49jPBtDmvbgciOXaX/pau3z5WPT9Gb9F1/qTr/nXoC6TalyhkQ1JzBa37FGKf013lNm4bv9cGoVz+IFRUrjzVlk24oG8K6Ytbo6JF+gieOLsTZVIHazU0yDFwHDryk2hq7ZQMntmwgu6KSQ+16crhNDKaqynczBy2POOYxNmMtxrhVOBfVWmJJGNwUKnrTAcridamC8JVgLTOTP19RIRZw7R+K26AwaWHzt+L4Ylh6p3J7yDvQ437+CTCL8eLEMcmQOL93L2Vl9ccL0YrQpE07Qlsri1BD/6eOn18c+YJvj3+Li85F+nGHuv7fVuwvh+UZqdwbJ3zTbcwItzMtsvNVVbkHDx4sK8XVE3uhTF19/tfYe1VBqzfQpG17mnbqRkSnLrh6XbmCVFGRIqvmssrdeREe7lfn13w1ENcYQTvfvHlzTQuTsJoUftvC67saQs9h4qqJJJckM9l7EDd9dx7j2bOoXVwI/fornLrWjonCTWP58uVyQi0m1rJKLcawr3spvYxdboeRHzf6nQTzSxxr4jooXn+5ZLOwrkocOxaDSxknRnTAxymbbsWn0NjrWHKpNKhCu5EdOZS5uzuhT7ZwPEzPihgX3MuK6XJkO23iDqG3mCXboNV1/aS1n1dQ1TVGXIcXTaVk83oyD3tjUYpkbGjZjq+bTqBSqwSzou91eNtAJnUJoVuEl0xcWCoq+fnRN+m0Y4WkrKPT4XPXnbjddgdLPoqlILOc8HY+UqlbaAg8fTaVYIOOfTGt6rVWfXX0K74+9rXUPVg5ZiWuwo/4IpTk5dYE4MknRBW8VhdEo9XSpG0HmnaNIaRNEPtWJZF40BmdUwHhQ15Go6usdV9a1pyyHC1hoeGMnfHhVVkVlWzZQur9D6BycKDZxg1oq9h6F0OoJZ9avIZhHu/hqsnGZtez03QfL4fuJ8857Q8xX8QxvGPROWK3KteiPlOiaNuv4flBtS95VExvRj327CV/F0Hn2K92E5tWhNXXgLmjN9HFn5FXdJDprafzeJfHa1vkFi6URQTRF+3QqhUhX3yOJjCQubsSea+kkBwHZQ41Ms1Mt9yDfO75lUwobJm4Bb1oA5OJuSWcOv00jo5h9IjZfMXk6aKDKby47AQmq41mfi7MnNqF0owEKeQozhfR4iJaFetCVrs/fA+zTcXAyk3sT/SQTJobnnwR44qVZL3zLoaoKJosW8YTi4+x4mg6Oo2Kb2/tzIDoP6djouhbKMyWLp1/xcWlXY0GhkimiaTaXwmxX2am5vD6+XQsdohw1PNZyzC61rF0k9+rwszE95Zwk3kJt+q21BsvRCFhqasLh7xuIPzwJCneZ7eb0KmXUpqfTkDT5tKLWyd8b/9ilB3JpmCh0sbhOSkKc1tvXtm8mDtOfEi7UqX9rCCkJ1+GR9FD/Zu8X+p6PSM6fopWCLf9y2Ojy+G/oPsaN8T/Gv+zA8tmxVJRRPqFeNIunCMjKZ7i7BQcqMQBo1ycVCb8PRzxddHipgetuYSCkkp+L2hOnLEVaosLGosTbhYVWFwx2xtWntWrymRw7euUjq9HKT6+Njz9nVC5+ZNd4Ujs6SzijsVhrLoYi+yYEGNpO2CIVPVU/8U9NkLwpmD+fHI+/AhbeTkqMeF44H6877hD3r4SNWjAgTN4V+7nWRQ11ZCDT+PfZjAew689kKuLUlMpT22ezs7sONwqvbkh8VacC5X3bN7Vn743RmFwqv1+QtlYKBz3CurFN4O+qf8b7XYKMtKkb3ZS7BFSTsZK79O68PAPlBNsEWgLpWptA5PIKx6fmSewfdePX22DOYVCTxPWPOHBwWR/+CEFc5QeT21QEI5NEwkJziPWFs440+uY0dIxwJl7ro+mn7uFjCeeUOhxVYG6qEyoLvpOInlw/vB+jm9aR9zpUzLwPtS2B5UOygQzWA0PNg3CPXsFR3e/Tc+KSvqbwSAEyOpCqIZG9oWIqsX58kIs5pxycn88iTW/EpVejefEqL+XTl6Ns+thwU2Kr2bMAzD0bf6JkD7Kvx8jbslm0kviyTOmScGruvAICKRJ6/YEtWgpF3H8/dlK+F8xfh7IPMAd6++QVcb3+rzHsAjF7uqfgOIqtXJh2eJYvI5Rrul8NuCzK1e5DXpGD+xHdvxZ0s6ckgk3YbtTFw5eRlyDS2nbazptek1Dp782tkZNldvrOjp2+Im/A6K6vGfPHrZt2yZvi/YlQZcX/d7Vk/ij2UeZtm6aPN4+7fI2kW8vpOLQIclaEgGHS1VFuq6FmkhGiKSEROIOmD1SYZHcsQFCL63qCabXZ599JgMFEfgLkdEGUZgM57dKyrjt9HrU9toAU8DkEYGpoDWV5g7kTpnILL2GbSeymLapGJsK5vcwE312B9Hnj+Pk6EiLmOto2ac/wVEtL6leWTZ9TuaMDylJUVhjohVq97SOvG9fh7MqkAmB77H2eCHnc2rH/XBvJ+5xyqHT8lnYUpSe28O+zdk6ZBpfPjuGPQvPcXJHOk7ueqa81A1HFz0VVhtd9pwiz2zhu9bhjParrSqbrCbGrRxHUnESN0XfxHPdn7vs/rRazKSfOc35I/tIPbcZqyYZZ/8KnAPLsVREkLz1afm80L4f4RKQSEmqnqJkB4KChuFzqgWbk3+WY8at732Ob5PwqxqXkqbcKFkHnrfeSsALz1/ynEPrLrB3eYK8PTDcTljZ2zhqDsr7P2kH8GFoPA4aB9aOX3vNoori83ctiefYJqVlovek5rQfcGlCT7R3/fzMw6g1Wu75ZvYlSvDifZ49lMiCJadR2aFrDzfOFD4gx6zVY1dfYt1WfuAAqY88ijU/H423NyGffUrpPg35aSV8P8iXufZK2ZTlabThUvEjlcW/837f9xkarrAVDh2+kcLC/TSNfILw8MaTvILy/cZvp5izR2FtDG7lz0eTO0gLr++//57U1FT69esnl4shGBFzPn6LpDI7QWTSwsOTXve/Juch1qIizvXth6XSyHePfMHKpAqZ7BHWaYNbX75H/mpx6vQzZGT8iqtrW7p2WcqZM2dZsGCBHFdEC0qjukTXiDKLlYfjklmdo/TYj/L14KPoUFwbEMF8a/UpZu5IpJmXjnVOL6PNrRIarsI6XUeSs1/GbBRJbQN5SUuxmc/g5ObJre99iovnH2N8Xg2qle8F1dP37nYUBTgy8XAcA8/9wtMXfsDBZsKud2Zup3H46jeixUK+riVDuszCzfHakyT/Bd3/Mvwrgm5zJda930gf0ZbRLdDIi6q9qp+u7ppLHxcTWmF7ZTEpPWlyMde5XbWIv5vLsJvKsRlLsBvLUJnL0QhPx0Zgs6sptXlTbAmgSFCtrAEUydv+shfbbG9MMMmGu2MJ3rpkPK1x+OhEoJ2D28DbUHW7G3TKpE4IpQiF5NO7tpGfplyMBNz9/GnTfzCt+w28qkrLH4ExIYGMl16WkzIBx06dJAXrctXti7E9O42sE2PwIp/y7EH0Uj+G54SoP1XxFJ6Lj/3+GAlFCbJ/+0Z/FzppMsk7PZy8U6Ox2xV/8RH3t5MeoWfyz8gKj7joLhy5kJae0eSmJpN6KpbU0yel+rQQRKoLB2cXQtu0kxXtsLYdcfcPuGLQc6WBr6DMxOyPnqHQWnuBCgry46677hMvJvOdd6XHtsZmpVKjw79zEYERhfzmMgHf8e/RvaryIiAUzbM/+pj8n36qZR7MeBdDI4qiwitYVL8P797FzsCmknpe4agkflytZjrYkziV8TFaWxFftLqL6ypMSv9k6n4liK0LoV5fHYSH9axnwWVMLiZv9kmp2K3xcsBnait0dXxa/zYkbodfJoDVCG0mwLiZfynT4++ASE7kzT5FZXYxefYMiiNLSU89Q9b5eOmVejHNNCgqmsDm0TKo8G/a7Jqz9H/2wlxQWcCElRPIrshmbLOxvN7rdf5JeOZMCrPT8wg1qKmIn47abmbV2FWEuYXVPMdms1KQnkb6uTNsXPorlqI8NMZLfVkFbVOc+6Ky2KRNezLzZ5GU9A0GQ6D07tZorl4Ir6IiuarKbZWVInf3jvydENXudevWSQaWgKh2jx07lsBApVf2k0OfMOvELHwdfVk+dCFFT71E6bZtsoIb8vFHuF5/vXze2bNnmTdvnpxYi+pbjVCb6Os9Ohf8WsM92+p55wqIoH/r1q1SqVwolteILQlKaOI25VxN2AYFifVeZ7VoKMvQEefWgQcHPUJYUBT3J5n5piSP7UHO2FUqJu8oISrdTIEuFZfCFTTt2IUWvfrIZGhDiVCRNC788QuyP/0Sm0mNkCr2mj6dpPEx3LlDCcSEu8WgsEEyWDucXMivh1LYtec0Nx1eRt+0Y/J9Slw8yL7lXp4v8KfYaGVUUz+iD5XI3MPoRzoQGl07gX8/MYMPL2TR2c2J1Z3r9/7uSd/D3RvvlrZ680fMl0KWF8NozKa4JJaioiNSoKu4WHg11yYkbFYtietfwVwagM7lADbTejy8OuPbJJKj61fL53TzGUGmLZHk/FNSJX38c69d1bFTtmcPydNvlwn1phvWo6s6ZqQ69/LzHF6vJB+69Qsm8Hi2FNmr9FxKs4qf5NTr1qYtOWYr4+aWN/Nst0ur0FfCxZ/Tc3wzOg661N/8l+ceJSshnn5T76TziDE1jwvLsKfPpjAvIx9NQgm6c8XodTZ0Ye/So0lLvh/8fYOfa05LI+XBhzCePi0UcTG0mYI+og+Bz3fniM3Mo3vPEV91eDmUbOJ6xwt8P+gLeW7v3iPEsNT06rkdB4eG+9Gziitlj/X+C0pr2mPXR/HQAOFWoyItLY2ZM2fK8+Sxxx67RBBRqNSv+ugdks6cprJ5a6wqHTc1KyfqlvdqnpP63Au8dkHLxrBuaNQqvrixI8PaNt4bf60wmnLZs2cgVmspLaPfJTBwAj/++KMUFBZii0J08c8i02jm1uMJxJZWoFOpeLVZELcH+zQ477qQW8agj7fJ3v0fHT6mP6LdxZM9Ed3ITdpBt0J/VuW/jtHuQpBHFkHtStm5YoXcTwEtbuPGV8ZcVl3/z0K0TOTNPU3lyTxpfej3YAfynDSMOxKPJT+Br869T+cCxfHnSLuhpLifxllVRrHKl87tZ9LEq+01fd5/Qfe/DP+KoLuiEGbUTp7+L2C2i8y1OxU2D8psnpRYfSix+1Nq86PM7keZ1Ysysyu2Kp/PhiDm/e4+eryCXNG5qUjMiCMj/wJWbQWuqhKGsI3WxKPqcptiFeXkRWlBPmf37JCBdqYQ6qmCRlwIu8TQbsAQSUX9K3tR6sJuNpM36wdyv/xS3lY7OeErerdvvPGaPzP28GNkF64kg0A+sn7Ilj6d8DD8cZaCqFa/tuc1KYYmJo1iwtTWuwVnz74uK0kVeRFk7HsIU6kregeNtCJ5P/VFzp4+SC9VWzpYIkk7fZLKsip+YRW0Oj2BUdFVQXYH/CKbXiLS8mcGPmEFctsP+3BN3oGfuoxWnOEcEZjRMzwoiIxV2/jZpxMJboE8dWgeHXIVSqtbk3L8OxejvXuFQvm+CCVbtpL+3HPYiopkpdv3kYfxuu02VI0wHsQkVFTzjuzazpK8MvZEd6bITZkwaqwWAjP241C2jreGP0HfJn0Vb8qkXcokWUyYs0/Vf0Oh/h7cRQbhRm1Hcjc4Yjdr0IW44HNb679PnbwuUg7AnBsUL+EWw2HSnEsCgX8qBA1fJClMySVCjAGvydFomjrKRJBgXKSfiyM7Ib5eP3g1w8UrOAS/8EhliWiKb3gkji6uf8uFWVRGH9ryENtTtxPuFi6TV3+34v214HBRGSMOn5MTf6FWPu/ws+xI3s5k31GMcR0oJ+hiyU5KqKcwXg3PwGDJKAiObiUt38T9upM9YXUoLIEqK9MID7uPpk2fvOYqkbdXHzp0+PEv+81X/NxTp2TvpRA9ElVvUfEWdFCz3cz4leNlxXVyi8m80Olp0p5+hpJ164QcOkEzZuA+coQMgEQFTgQFQpNECCdJlOXBF12gIh8Gvwk9H6r5TPFZQv1cVLvHjx5OW5eC2rEjq461noBKAyFdZAuLJawPW1bsI/Sb77CoVDz65Msk+wZQ4lpbKe50Kp4RsWKsstNjjI62/brVeBE3mjR+8UUqDiuTW4cABwK/+AVj8xBZcc4uz2Z88/G82vPVer3N+b/8Qs5nn2MvL8emUrEisje/RA+hXOcgq5KlRuVcHFau49Z+kfQcp4h6ViPHZJbVbqPNzm+dmtPlIlrs09ufZm3iWtp4t+H7Ae9RXhZHcckJ6Y1cUnICk6m+ZoCAVusmkzXubh25sLcdp7ZZ0RlsuLr/TvqZo7WbVKWS+02FmoEj72LL2lmyNaqx/ueLIV6bPO02yvfvx2PSJAJff02KnW2bf4ZTOxTdAPF7wworKD+cjaaZOyuP5ROp2cL1Xl+xT6/mrkB/DGo9a8evw9fp2hlO4jvsX5XIwTVKf3vMmEg6D61fqT+2cQ2bvv8K75AmTPvgS/m7hXDrPScvSDE0MUt5u3kwy1ec4WhKERrnM3xxc0uGRTbOzBFMvvQXXqBkreJN7tBxMOFzPpAJiNxjmbx7KJlfIpTrmcacxrwObWlSvoqEhI/w8uxFx46KT/bF2BWfyyMLjpBbapLHz8eTO0h19WqsWrVK2gG2bduW8ePrCxTmpSZLe7jCrAx0Do6E9OzG8YwyglXZ3PnUW6icPOX+eWbWNhafL0Ntt/HxmJbc0KP+MflXIDl5Fufi30an85I0+szMImbNmiW3/b333ou//x+nsZ8srZABt2Apeeu0zG4bccl5Uxf3/rSHdXH59FEfY45+BjQdyO+dJ/PQgTfxKgvixjNPYDVqCdCdppPhI5alRCPOCjf3bpjUvaXrTfdRV188+iOwmazkfH0Mc0YZugAnfO9rTxY2GXgnl5fzeurP3JGgFE1yglqypakaH1WW5M4GNH+fTqG1SvRXwn9B978M/4agW1SdLSseIy0tg6DgIOkjKU4ikWqWazk/UtfcF70vwsnLaIZKs7BP0GC0aKg0ayirVMulvFKNyarHanfAajNUrR2x2Ryx2xzAfnUTU7VGhau3A+6+jrj7Osm1m7jt4ygrrppqX15RPd/1GWd3rWKdNYYClAlFZIg/fa7rT/75M8Qf3EfqqRM1lS4xuRb9gy1796NZ1xgMTn9v1bBs334pMCIEVQSc+1xH4KuvoguqtUC5WmReWM3JhIdFQx7fWN5khz6aqUHevNci9A8pE7+3/z0WnV0k73cP6M67fd6tR2HLzFzJ6dMvUZxhIOvQvZjLxIXajql8BzajQoGrhqgSikm2oIqHtGpDQNMotH+yZeFyA9+7a+NYv2M/ffUJMqP9iOZnDpki2UYMRrOKBdZOUs1SZbdhcDnEw3nH6L/xnJjto3GwEthHg+u7uxoUwZNq8i+9VNNv79ihA4HvvI0hIuKK9MXzRw8zL/Y0K518SPer7aELS4ljlK2AqV17EdKydW0CojS7qlL1uzKRFhTROrDZDVgcO6DtMQx18/4Q0O7vrTinH4U5o6XwnKy837QIdH+PsMvfeXEW/e+Vp/PlOCaYIM6daycwFrOZ7MR4STUVQbjQGLhYzK8aQtVXUEm9gkNlP6tcgkOkDdGfuTB/H/s9nx7+FL1az9wRc4n2qhXe+l/DaDIzacseCtJS6GsuobuxmNQLZyjOzEQjeMgXQWgyWPSOmHQG2vTozcAbxuLk3rgoWDVycjZwPPY+VCo9Md3X4OR05RaZ8vIL7N03uKrKvQR39w78X0IIM61evVoyxKp7vcXE/kz5GSmGp0LFnGFzaO/VRjpSFIlqkEolWTPuo0fX89kWQnM184PDc2DlQ4p91IMHwS1Q9nxvXjGfHbEX8NOVca/lB9R1+iwl/Nso56lgyjTpAQ7K+xmNlcz/+WeOFpWzrmkbCt2VFha11UKr+FiGn0kh2Kk7uXk66ec85K42lw2ecr/7jvxZP8iksUprw6+zHc9Pfwf3QOlysSl50yXJo7K9+8h65x2MVQwBYasW8OorZPo24ddDqXLJLK7Pini4f1Pu7tdMBlN18Xhcsqy2jvR15/s2EdJ+qbTsHGWlZ0ktiOXho2upsNmY4Gmit8tF2wg1zs5NcXPrgId7J9zcO+LsJAT61OQkl7D43YOykjbs3rZSrVxaFu7ZQdzObWRfqKM/oFLhFRhMfnoqPk3CuXXGp1eVSC4/fFiK24mKb/jKVWzfUkr8wWw5NvW/OZqoaE8yPzooyHoUXhfCtlWJePg7cdM0o7RpnOrlwFEHA1ObTeCpXkLQ8o/hwOpEGXwLdBsVQdcRteebaP/65p6pWExGbnzjAwzhzZgam8Ch4nIc1Cq+bR3OEB93fj2xkyfnCo0HHW+Obckt3SOvaGOWNPUVKg8JgTU7Tl26EPzZp9icnEl7ay+H3HQ80cmGUe2CFiv36ZcSY1xAy+gZBAVNuCTR/sWWeD7ZfFYSLqMDXCXlO9K3tpIt7GY/+OADuZ42bZp0tamGsKBc//UnmI2Vktk45qmXcPD0lr3dFjTc3MmdZqMe5aUVJ/hlb7IMuJ88NJ8p04bjdeufV8S+ZNvYzFIMt7w8ntDQ6UQ1f5GFCxfKsaV58+bcfPPNf+h9t+eXMP1EImVWG82dDPzSLpIwx8bbd/Ye2M+UJTmosbHO8WWiht5HXLPrmLpuGs6F3ow/+xgqoxa/cDcGDitn0YfvU2a0E+2WzbDgBE6WDeRQ+UQGP3Y9Qc2uPO7/GVgKjWR/eQRbiRmHaC+8p7Yiw2Rm3NF4Kbg4ueQQH51+E01FPhXuAfzaLoIg1TlpNeYY/Ai9oh68qray/4Lufxn+DUF3UX4pvzyvqI3+X0IEzI5uOpxc9bh4OeDiacDFs/5a2FZdlqpirlAmKdvfhzIli20K7MJG7Shij5xCXVyAxli/n01UXVv26ivFQpw9rmyp9Wdhzs4m+733Kf5NEXbQeHnh/+wzuI0a9Yd6SSuLstm7dyhWXRHeWaMp7fcKE+KUzPWyjs3o4dG4L3dDImhP/P4Ep/NPy0ni3e3u5r7294kQVWaAhYWPqGIJ+57M82cwV4pKlhqtU3+0hvbK77MeJSK6UlJ0RRDpF95UCtP8lWhs4Fsbm8GDcw8y1hCLi8pEjLc36n0bmNskhgCdHUeVhb3mJpT4lFPosphIHzfmjZiH9swF0p95BlOCMvFwb++F/7er0Xg0oD5rt1O0dKm0srGVlSnK8o8/JnvzroadYKwoZ9Xho8zKLuaIux9UKWn656TR8+wRRvl50LJbD8mw0GjrBGz5iRi3/Yb18HoM6uNoVEovVg0cPSH8OmWSHdYLfFr8dUF4+hGYMwYqCyGkG9y6rB7V/d8EofReuCKesv2ZSuA9rjnOXRvuxxP7urQgj+zEBHIuJMgeRzHZLsrOavT9RdDtERhEqclCdPsOePgH4ObjJ/2jRaB+uR7l/Rn7uWvjXbLa/VrP1xjXfBz/lxDsDEGxLMrJkr9RLMViLe9nUiisGoWqcgMwa2w4BPnQvu110n7JP6IpSVk5rFy1SoqDCdr05US+6n0Pu52jx6aTn78Db+++tG+nVHkuh1OnniIjc6l8fof2P/C/gPjewr5LBN9ici9+t1BInpM1h2Xxy2jm0YxFIxehVWnIfO11KS4lzlFR8XYbOYIffvhBWpN169ZNjm0Swrv7+4GQflgJpJ19KU86wifWmzBhYBKraEU8eIbXBtnhfcDFt14ySYiF7T18mMWVKg41bYepqmXCsaKMdmePkefqztCQNkzYVMTmEiU4nfxiV3xCXBv8nSXrN5A1YwaWKh9z58BKArsUobtnMTQbyJKzS3h1z6to1VrpYNHauzWmlBSy33uPko2b5GuEf7PQyPCYMKHe2CmCqDlLTjNvTxLndLaqRD846jWMaqeIrwlGb2VlEvH5J5mfeFB68rbXZWA1169e7yjRsqRQj6PKztvNQgjyaIerWxtcXVvj6tKywfYF4W7y67sHyU0ppWknP4be3eaS33/uvY2cjd/L2ZKDl7SoCGG5gbffe1WJ++S775ZJ3OIW13EwcIosLAy6vbVMeOQvOiOr3CKI2F1kIuV0QW1QnB3HzgVjuc9di6Md1g5fgLdoQ/iDqNtD3mV4uPyc6nNu7RcfcmrHViL7DeaDjoM4X2GUdm1z2kXWiG49s/0Zlh8swZg9QqqEb3q8L/5ul2NHFEn1aUveCYyHZ8lrqTYokMBPPyVuawYB6Y6sbX2GN7x1mByVuUV/tvBdzztwraPYnltq5LGFR9lxTrGjm9I1lFdHt8ZBVz/pUS1YKFoxRG+0YKQILZYd82dLJxIB0d4y4pGna3rX18/5kD0JJQSr80lrfxc/7UmWlmOvBZXR9ctXpKBaxIrlf4sbRl7+To4enSY93rt1W01lhSc/fTELL6szA9tfh0ulDmuxSdqD2kw2cdKgdtKhdtWjcdGh9XPC0MRNMuHUeg0bc4u48+QFyQrp5eHCrDbheOgan5fZji1i9MIcTtjCudlpH2/dM5k8twBuXH0jliwtY+IeRmPW4x/hxvD7WrHig1dJP3MKn0A/bmqXiy55m3wfi13PGdsImj38Ngbfay8oXQtMKSVkf3tc9D7g0idE6hmlVZpkxTup0kQP8lh04ll0eWexOrgyt3NvgjUH5GtVXqPp1+5d1OrLa4j8F3T/y/D/atBtF6lYlR2V2i6ZbGqtSi6CduzgpMfZzQEXdyccnfXoHbUYnLRSDMXRTY+jqxJo6xw0f3zwEtW3A7OkxYYItsstOpJVUSQ5x3AhKY/S/Lw63xWsTq64hIQx/OZpRERX+Q3+zRB0uoJ58ySdzlZaKrPjnjdOwfeRR6Rn5x+B1Wjh8LrbKHbdg6E8hG49V6L3cufJuBR+ycijmZOBTV1a4HAVPTVbkrfw4s4XKTGX4KZ15R7XCQRm6mWgnZNyoUGqqKhiuwZqKHROJr4yhlb5o+TjLboH0H9qNJq/qZenoYFP9B6N/Hwnzc2JtNdnojOaOZLlyUHhiw600mTQTZeKXWtjRfBKDHoD80fOJ9JdycjbjEZy3n6R/IWrJItD6+FM4IwPcenbt3GV+RdfpGz3HnnfsUtngt58E334lYV0qhGbn8s921aS5NoGa5VCq0tpER1P7qfrhZO0btWapp27Ed6+M9Zz5RQsOiMPYOeufnj0NKO6sF2pgl/YBaaS+m/u6KX0gVcvoj/8j9iTpR2Gn0XAXaQE3Lcsqama/Vsh1XRXnqdsjxIweIxthkv3q+/LEy0T4rzIS00hPz2F/LRUWeUqyb2UrnoxRM+4s7sHTu7uOLopaydXd6yOGr6I+4Z8WzExYb15oOtDGJycJNVR2BeJxNXVjI8icBYUeUF1tZhMslJlLC+Xa1PVWtwXwbWwWisvKpBr5XahnIheDiadHufAEKIiIvEOCZW006O2c7xy4h18nHxYP369VBsWiuXCDkv0PV/sy301KC9PZO++YdjtZtq1+w5fn4FXV+XushR3N2WS/r9Cbm4uixcvJitLSc70uK4Hb+e8Tb4xnwc7PMg97e+R+ynzlVcpXLxYBt7BH7xPXsuWzJ49W1a7H7qhKx65hyBpN6TuU7RRqrCZnuygO/4OJu4Z1AJ1ZD/wrN8SJvZx4pEDnDuwl13JaeyN7sKZpq2xV1VgAyrLmFSYzqB338LJbueWF98j3y+AGSsKyKuw0STak1GPXtoTb4yPJ/OttyjfU2XnFeiPX3Qyrn55qHo9DIPfILEokcm/TZatSUJde1r4ZPK+/Yb8n2bLirig1ntOnozPQw+i9bw00V2YVc7CN/djo5Qmg2FR8hHcdVn4OeXg75yNv1MOzrr6yfO6cHAIxtm5OS7OUTg4NeWR/bM5W3SBiVETebnHy1cdgIp5yk2vxuAkFFvrboOEQnK+iwWtGvsEVxa984KcWVRTzgU0ej0te/Wj/aBhUsG5MZQcPkbqTVMka/Bgz5fp+8Qgwlp7Sx2KrI8OKeP91FbM//yYrOLe8kYPyfATsOed56blN3BCq+L2Snhs0m+XHAfXgiMbktm9NF7e7jQkTNLNxW9KPnGMxW+8gNHgwJe3PkOAsxPz2zclylkJqktMJfRf1J9Ki4mgwg85m2ni+pb+zJzaudExK//Xs5QfzMKpiz/OHTVSzd2UlCQV3bNHjifS0hejxsTNrV/Cy7MPRwwTpbJ+WxdHfmobQbCDnv2J+Tw0/7C0AxNq+G+OacP4zg0rsYu+aOGC079/f9n+IbzXV386g+QTikhq19Hj6T1laj2B3JL8LD759HP2WiI4aVUSs++Nb8f4KDfO9emL3WgkfOECydT4O3D8+L0UJsXiVzwOz7z+mNPri85eFYTrl78js9xsrPfX0inUk29ah2FoLCEvDrItb/Dr7/t50nwfrmoTvz/eCzdPT+7ccCep5/MYFXc/OosDAZHujHqoPTsXzuLI2lUyyXTzOx/jGRAkhSBtm99ALcYuEXyrHNH0uh9Vr4eUAsHfhPJj2eTPr1I0n9Ac5y4BpFaaGHsknpRKEx20RlacfQVDyh5sah1zu40lQP+7rObbnDrQp9O3GPSNazf9F3T/y/BvCLrFZCkxPol9+/fTIyZGVieEGJfIDAoxCkG9EtVmtUolHxd/d3J2/Nu9bxtEaQ7s+xrz3lmkFUBSmSdJlX7klOsvCQ6FR2/Tzt0p0ejZumOn7IUTwjViQigqC3/n9xc0sszX36ixq3Jo146Al1/GsU3rP1Wxi1/yDck+H4BNQ8dmc/EKV9Rri8wWrtsfR7bJwmNh/tJzsSFP7Pz0NHLSLvBD8nw2WZWMn2+BgX5HfHCu1F7Si+0dGoZfeAQBzVpIGw0x6RbHw7hlg4kvyeSGko4EnpwKdjXhbb0lNVGr/2tV3hsa+CrNViZ9to2IgxvxaqbDrlGzzRRJos0bDXbGt/fjzpSHWFveQ7YanPQ4yR033FHj4VoX5XNeJOPzhZhKlN/vPn4c/s8+i8a14YpP4cJFZL33nuxLFL3e3nffjfddd6I2XJ3qcpGxiFs23M8pcwRm1yGYtMrnaC1mWp47RqfYPfgX5OBjCCbIqSnNuvegya096jM+xIRcVKNFT6cIxEXvteWiSamwzREKyDII7wXBnQT/9/JfLvUQ/DJWCbhDuysBt6HxXuZ/EyRj4bcESnel/6HAuzG7svyMNHJTkjiwcwdBPl6U5uVKWmpxTrakL/4pqMQ4rJETQ1EZFMeAOP9EgG2zWGWwfXHV7Y9AqM26+QVIqqVcfP35rszOWpue6KAgVnRuLsf/mt9tMzN0yVDZu/t6z9cZ23xsTWXpWqvcdRF//n0pqubgEEpM9/WN2jGdPPUkmZnL8PbuT4f2DQs4/V9DjFHr16+XXrsCXmFe/MAPaHVaVoxZQbBLsAy8RbtKycpfcfSz4D+lF/Nzvbhg9KAzxxnF5to3FBY3lkrKnJvwqWkyJrOFyZMn07Jly3rOEAmHDyg2WHGnOBvSjIPte5EWWJsI7KK10b0wm6eHDECv05F8++0ygD7duTvP3fgQD64uQm2HoQNDaDqxVpzMWlpK7hdfyj5sYfskx7o7bsdbvxJ11kEI6gS3r8esUnHL2ls4lXeK7n7deL9sGDmffIo1V6lEOvfsif9zz2JorgSiFksZFRVJlFdckImW8rJEUuJPotJloHW4KJF4EfIrPcgsC8CsjeCAOoRs96b8GjMYT4f6SexDWYe4bd1tkr0lBT69G0+0F2SWsfDNA1gtNgbe1pLomEvHhNzZJ2WLinP3ADzHNmfbLz/IaqlguQgBQeHrXBeirarrqPFEdOhcr6Iv/L9/++IYPsvewzfvOLr+Q2n2tWIPl7/wDOVHlCp3aogru36Nl0HO+KfrW+Bti/uVB/e9hqPNxvoiFZ63rQWPP24teGxzCjsXK3ZLHa4PlQJr2/OK2frMA7iWFnJo5C18NGk8AXX0YhafXczre16nqXtT3u0+h1Ff7JTCW5/f2JFR7S+tbtrNVtLf3IfdaJWK04ZId6kMnvbEk5Tt3Cmf49DlBrTBw5jZfQ1tXZaQrm3DN9qXKLBq8NNrGWHUsmhTgmRFCDswQSeP8m/42iQst4RloYAQUKvIyWLlR+9Qkpcj54ZD7nuUFj16X/o97XbunPElWysMqLSl3NojmJhmbpitZkoXLMa65yCePa6j6X2PSd0bTwdPKdz3ZyE+t/JMAUVb4rEk1yl2qCBfVUoeJQR3iCC0dQQqvUZZNCqpWyLo1dZiI+b0UozJJdiKhbd8LXRhbrjGBOLYzgfVxUURMY9Y+RDlR5fS3/ghWXjx3NAW3N23qdT32XXoMCPi7kVndSCwmTsjH2zP+YM7WfP5B/LlY55+Sc6x6/wQ8netwrL2NWnBKyFYCj0fhO73/m3J+6KNSZRsTlYUze9siyHCneQKo6Sap1aaaaaH9Skf4xy3HLtKzS/d78JDvxYnyrHr/Oja/ptGE7f/Bd3/Mvwbgu5/+oElqW0JR0lf9w0ZJw+TXuZIdqULNtlnXgvfsAipiCvEukJbta2nuCq2vxDVEH10AqJPRqhCXqxm+We+o7igGJNSyfvme8p27RWpb9RunniMn4Rz9x5S3VVRgK8PkchQBlI1Kl3tWi3WDlr5d/H+2Uv2ccr1bmy6MkI97iaq0zP13ue37ELuik3As6yImX4OOOZny2qcCLQL0lOliFyxk5ltHXPJc1cG5laJrnSO88Td00duP7k0Ccc3LBLPwKAGbdI2JW2SCudOWgfeaOJERZIXaXvuwW7V06SNF8PvaYdGp/7bjk+N1covL36K5eB+jnbuhJermWybMxtM0YzrFMKDA1vQxNuJrDOryJr/OksZgVpl4fEnnm14f9vt2OZNJWfh7+SfFdQ5FRpfH/yffkZSQBtKzphSU8l8+RXKdu+W9/VhYfi/9BIuva+uuieClalrp5JamoWX7xh0PhM5WVZ7sQxNS6Bz7B6aJsWhttulzVVkp27Suk5Q+OvR0AWEO0DGMUWYTVTKkveC8SI6ughgQrpWBeE9lNt1A2rRS77gZjCVQmgM3PLr/zMBd73Ae00ipTvS5ITG66bov8RyraHxU06kSkukP7CoslQUFcq1qC7vT9hFStZ5HC06mjqFix4N6RtsrqjAYq4/abpW6B0d0Ts5Y3B0kpUIvZOTvO3o5oazuydOHp6yraZ6EZX3i4+nPYWlslIgzuJNXVvQykWptNXFDyd+4ONDH0sK9a8jf62pcl9//fX07n3phPZqIAIyUcE2GjOJjHiMiIgHL3mOCNT27BU2Wza6dlmGm1s7/kkQdmlCZE1Yg5mcTWzy2sTwkI68GjIMUvZhv7ATMmJRCc8lIIlgfmQSaqw80iIH96gYJUkmmCtfdGFTRUt20o2AgADumD5dCgGKIDvhyAGKsjKp1DsQG92JI61jKHJXdClE+nCcvyf3NPEjyqCtd2yKynXCmLEykJ497h1C893QO6gZ1cQF/8c6S62LwiVLyfn885rA2WXgQNkSpT/5lcIuExNpoa7uFcFHhz7ix9gf6JHqxJMH/LGeUa6x2tBAHB8YgaWDc1WQnST3ncmUfdntp9f74uQYTkKhJ1vOGSg0BfD0yOs5ne3GwoPZHEutHdfsDhqua+PPOwNaEOrl1KCoWke/jsweOrvBcVz0by/76DAZ8UU0aeXFyIfaX/I8WYH+UHEZ8X+iMzpfJ8koEdZa4voaFNWS9LOn5XxDBNnnD+2vYY8IVkiXUeNo2bsvVotKBtziszxN6XTc/ZZkPESu/g21i39NlVuoMS+bfVpS3YU9Z5u+9Su5YlyZvGIcp4viuauwiIfVvjB9Xb32gmtF7O+pbF+gCMsauvnwaoSVmP2b6HF4G6HtOzPp+foK7besuYVjOcd4ovMT3NbmNj7eeJZPN5/D21nPxsf74uWsb7AaKVw3Ap7sUuOwYrdayfzgAwp/VESvNAHtyJjQDkurH6i0QVjn7dwbV0R8pXDBsaM7UcCEQC9Z4Xa+qNe/LjZv3syOHTto1qwZ7YP82DzrK5mgFCKONzz5gtwv8nuZyzmSfYT9mftlouZ07nlM9vpCsJeDcHgRbB8/Rz+pgSPE7URyLcQ1pGbtpm983i/2ZcXxHIq3pGDJUuxE7SobZV4nMIacp+WI19hz5LB0LPDw8JCOBRf7jNfFhpxCnj2YQM9sCzflQ5NsY82cU+Oux6VXMM7dAlA7aKUOEoumwfnNfGSZyGeWsYR6Oco2gaXxi/hx40KGn74Hnc1AcAsPRtzfnqKsFOa++IRkQcaMm0yvybc2+D0Orkkke/0CurvOx1ur2LjJ8azv09Dl9isn/68R4jwW2i0VsbmonbT4PdABrbejrHRPrOrxDtZr2Jr5FW6x82TgPaf7U6j06whGzAV0tGzxBkFBE/9VsVFd/OfTfY0b4n+Nf8qBJQYhMSmVFOcLCWQc20nG+fOUVl4arbp6+xDWrqNiPdO63RX7s6Vy5/79bNiwQVb3nZ2dZf9d00asoGxGq8wg2kpMWMVSrKyFZZOt3IytvP66oYD6T0OF7NsRgWG5OgGTcwZ2jQZ3/95UUE6pMZ+C0kzy8tJkH2ZBTjbqBnowhX3L+eAy9rYpwKKx4WQ3cIfbOIZGjZBCMBf7cTYGq83KhFUTiC+Ml/3f97aZxqnTT5F8KpmUHQ9jtxoIb+/F0Lvb/aVUc1lFWriQzjm5bFuzm0UhMZwPaMIN+hMyl2GM6MP9o2JqJl6iEjdt7TS6nduJU+Fw0gmgU6sQRk+6s+EPMJbCzAGUn0ok40gApgKlx9Gpe3cCXn6pQbswmQxau5asd97FkqPQjJ379sH/qacwNLuywmlycbIMvPMq8+jo14l7un/Mj/F5rC0pxVo1KfGuLKP90Z20PnUAB5NSNRXU47B2HQhv10mu3f0a6E8Wx0DWyaoAfLeyrtI8qIHI0Pu1UoJvoZR+6CfF/k/0iU6Z96/t4b4SJFthWVWPt0YlleAdmnv+n42fv6f8LtXKBT7o+wFDwofU+7uYsJsqK7AJyrjNJu+LCqmoqIm1WEQyTATKYq3WapXbWg1are5POy/Y7HaGHjzL8dKKy4ozFpuKGbR4EOWWcl5u8jInt52UVW4hCma4StZHQxCijSdPPSZ7b3vEbMFgqB9MnDz5BJlZy/HxHkD79jP5x8FqJuPIBk6u/4kA8wWCSceTSyfyZqsbZUkWygudWTfgPpILS+r3dgsBst2z+GR9AmaVjnCtlaKzp2rafrK9/DnWtgenojpgqmojEX2304J9mB7sg39VZbKhYzNrxntkzF3C7pg3sKu1bO3mzAdnjLh3LSP/xy9rxD5lMvGF53Hp0wdOrYBFU5UvNmUetqjB7E3+jUXLnmf4Jjuh55Uxy+aoomSYhbJ+ViX6bwA6nacMrFW2YM7t1WIq8aPTgBiiu3ZGq1XGHVHRvPn7vexNyKd9qAdL7u2BVqPmdEYxCw+ksPBwKhWVyjgt4uReTX2Y3DWUwa39MWg1ZJZlMnr5aEl5n3HdDIZHDm802NQaNNz4cjfcvC9NLhUsO0fZvkwcWnrhM62WqZYRf4b5Lz4lmSaCXluQmU7bgUOIGTeFw2tXErt5nUykCTh7eKF36Ux5aZRMhI1+uAOmGc9Q+vvvuI8Zg0OHaZQfzZGfoRoUxoLX90uW4fT3euPgcul4sjlpM4/+/iguNjsbklNxFb3/034Dxz8uXnViexrb5ik03YNNDbh109LkqzfkeHLP17Nr5leilUBsV41Kw8YJG2WgabLYGPX5Ts5klTCmQxCfTKnfppD700kq4/JxHRCK++D67Vji+Nzx5lsELVsm7Tpt3o7kPFTEbicNOfa3WHfUjaJoN2x+yr55OTKQ+8MaV/MWc7tPPvmEkpISmrk6kLVfqaQLd5phDzwmr5/7Mvex6MwitqZsxXKxbac4Pq0GHK0GnFQaQkOaodPopBd80anjVNiMlPm6UMDVUb9d9a6EuITUBuIuIQS7BhNg9MZhYzkkKsG2yqDBuXsgTj282H9qBEZjBhERjxISfA+fffaZFG4cMmQIPXr0aPBzRKL0xmPnqbTZmeDvyWctm2AvNlF2MIvSPenYSpV2FbWzFrfrfHE+cy+qjAOkaUIYUPkeRiuSPeDhlcgbSz9iyOk7ZMAdEu3J8PtFYtPC3Ocek6rvYt497rlXGxUPFKrvKz4+Qvq5fDqFHiHGdQGq/KrKt0cTxU1I2I/+hUKwUtH82+OY00plf7vf/e1lckHYpYnA+1y5ET+dmu0ZX+BxcqFsXfi226sUGXbTRdiiAcHBN0sRO7Va/4+Lja6E/4Lua9wQ/2v8Xx9YMsMnBHyyMslLSyE3OZGcpAvkJF+goviiCl2Vbrqfu4rAVh0J6jxAqmO7+fr/IYq46LtbsmQJ2UIkCOjVsivd/NtgKzRjLajEUlCJtVARrLj232UT6VvUTnrUTgZUOlG1VqMSKuv1vmqdOzZ7lTiGFZvRIm/bLXZU18AatdoslFmK5FJsKSRdW4mDj4GgSG/0wV78XP4bW3OVi0/XgK683fttApwbFpO6HNYkrOGZHc/IC8m68etkFlfsy6Tk7zi++zdSdzyI3aYjooMrQ+/q/Jf4NVbGxZHxw0+sPprKiojenPNUgoD+unOEaQppEtmM26fWVxQVatBCFdpd58JX6XaWlg6VPTwPPPAA3r6NXLBzz8F3/bFVlJBf2p/cLRdkD5fw2PW+7TZ87rtXWrxdDGtJiawKFcybL6tH4kLiMXEivqJ/0efyPu9x+XFMXzedUnMpfXx788z+mxFH5bIObiz1VlFgUY5BR+x0L8igxd6NuCUpVMBqiCp4WLtOhLfrSGjrdrIvuMG+rbz42kq4WIpqvelroNYp9mlNYpRgPLjzv76f+0qZccEs8b2rHfpQ1799/EwtSWXSb5NkT+QtLW/hmW712Sr/BCzIyOPRuBRcNWp2x7TEV9/475mxfwZzT81ldOZotJXaP1XlroYYTw4emkhx8RGCAifRsuU7NX8rK0uQ9mJKlXs5bm7X5rn6t6AkC1IPQOp+pc1DCKBZ6rcVCMXcFDH5bjceTVhvyTaxO/iQ8sCDlO3YQXZ4OFtjussq1v333E3+hQSSYo9w7Ox5ih1cUFeU4XThNDa1mtQ23TjW4TrOONUmSls6O3BHiK+sbjtdNOY2dGwK6vja2z4iyac3Rk0xy7tYefeXX/BMU9qhhKCkz/334zllMnYtlKdvp3TNHZTpTZSGRFHmrMGUnIrbSnA8pEy87Vo7ZX1slA61YnMRAt2uODqG4+QULgNsoUjvWHVbp3PHbLKy6K0Dsp+7WRc/Bt/R+pLreUZRBUM+3k5xpUX6Lz9yfW2vdH6Fia4rD2NKLkWTV0vJ9XDSMaZDMFO6hbI9ewGfH/kcPyc/Vo1ZVc+KryS/kvmv7cNstHLd5Cja9b+0N9haaiLjXdG6Y8P37rYYIusHtUKUa//yxTi6ulJRUiJbP6a+95lMZAsthWMb13J4zUrKCqscEVQOdBg8mutumoj1XDwXJk6S/e7OA15D7eyH30MdObQ/i8Prkwhv58MIGexcCiG8OG7FOM4XnefRUjN35GQo7UC3Lgf9tdsNVlhtPHw6meR9WYw6UCZnJy17BZIR9z2Z8Wfoe8vtsmJf14u+T0gfvhz4Zc17HE0pZNxXu8R0hh9u68KAaOU6KyjQGW/tk/Mc/8c7o/NzavD4HBAWRvrdD2ArzsfqCDN6DmaHq2C0QNcIL5r0CmJuToG8/0iYP89GBDQ4/zt79izz5s2TxQens0elKGzPSTfTZfQ4lp5fxs+nfuZCsSI8KxDkHISzvQXH432wVQbzRP/u3OqTzqe/bseKlqlTxhMZrYwzeT/+RPaMGTi0akXw4vnkV+aTU55DdkW2XGeVZ5FWmiYXMdaLv18OKrsKb4sHwc5BhAVFEuIeKgNynTGR/JTP8dTp6RezgdOnsyRT09HRUSY1HS6y8ztRUi6ZSSVWG4O93ZjVJgJdVeJewG62UX40m5JtqVhylUSQVpWOu9OvPBN4LyvjK+kW4cWbE715dt6b9Dl1EzqbXrI/hJK/aBncNOtrjm1YLd0opr3/xRVdKcT5JXQajOUWOg8JJqbJXvj9HShRdFWk5sygV6Ulmcya/QWwFhnJ+vKopNg7tPDEe1pryarINVmYcuw8J0or8NbAjqwv8Tq5SCYc3+7xKSma04xnIWrsuLt3pm2bL2uSvf8F3f8y/P816BbVGUGlFPY7ZUUFlObnU5SVQWFWJoWZYp0he4wbggiwPfQV+BrK8HcxE9S+F/5D7kUX1PqaJm6iAl0TROcblbW8b8RYUMYeexxxWqW/M9jqRT9zaxypT4sStG+Nm15RiHTTo3HVo3bRoXbUYoo/TeGyRZiT47GbytCF+OP35GO49O1TczEQlCaRRJC0UkExlfTSIipKFJppebEQN8qX1G+xrUQVq3Y7qDFoHDGonXDQOGGoWhw0zrg6eePm4I2jygWdVS971xpCnEMi74X+RIY2Bw1q7vK/jTs634XBx/maExYiIzxmxRjpQVstDlQXeXk72LPhG5J2TAOblsguOobe0fsPJUYE7ax023YS58xnSb6O3yJ6keeoTDKFfUdzVzM9LMfle9933334+fnVU4QWAiCiuv9h3w/ppXdg6XezOE8E0Z4VTHlkRuMffHoVLFQCeFOnZ8lakyyrEQJCaTXg+ecl1bKh32RMTCTno49qlXqdnGSvt/D2VjteWkGpxsHMg9y78V6MNiPXF8bwjP5+/O5oh1GjYmlWATNTc4grq53Et9Wr6J+fRujh7eTEnap/zKjVBDaPlgG4yEYLQZ+GWgQkitJg3bNwemXVizUyYVQfKvBtAUEdaxdRVfkDE7t/GuwWm+zVNJ4rlNl/v/sVStrfNX4Kaz7BbBC9r+182/HTkJ9kBeWfhDKLlZ77TpNlsvBiZCAPXqaiJCAmlvfOuZcuOV3QO+h54rEn/lSVuxpFRYdl4C2Ov25dV+HqqvTknjz5OJlZK/DxuZ727b7l/xzl+UpQLfQU0o4o6xLlGlIPDh5ST8Ec0JGNZ4s4mqWhEi0BXQO4f8T9NU+zVVRw4fY7JD15d//+lLu4oc/LxJCdik2jpaxZW+GbiVtRPKlNw9nUZhzZdiWo1qhghK8Htwf70N298fG8oWOzsszM7Ke3Y7GqaHPiG/xyY7Gr7Fj9NRhu6YumdwTllmRKS89SUXEBex17MnURuKzT4LxDjcqmEq6V2K7zx+G2vjhFtK0JsIXn8OXGflFhFpVm4VAy5eXuODg3fC6sOJrGIwuOolGrWHJfTzqE1k72Xz6XxnepOfQ2ONCzVMXiQ6lkFNUZK0OcKPR6i0JzlnTluL9D7bZf8/VxEo/lyr7pcU92qqE810Xx5mSKNyahC3aRtO+Lf4+gmf/05P2yeCCoy6LHPrxDZ8Y/91pND/eqzw6RenofNtNBbBYlCBO94F1vmIDfirWU79yJrkkvPG55HO9bWjLnhd2UFhilPopQNG8MK8+v5IWdL+Ctd2d9UjIGocMRPRIm/XxNFUThfX5bbKK0BNOpVLxldKZ0ebLM0/qFWUg++jm+TcKY+v4Xkuk2+NfBMsj8qN9HDAobVO+93lp9ipk7Egl0d2DDY31wddBRui9DMot0gc74P9Lpssdn9v4lXHhlBl6plVhR8WObYXjcPp7nh/aQ+//zpCzeSlCCNsHCeScqBE2dfSLmfd999ikZBYXo8rLwsVYw4qEnyfE28/a+t2WSW8BJ68SopqOk0N7qQyo+26wksp8ZGs19/ZrKJPWa925nf0UYTTx1TH/4ebnvLQUFxPftJyvyYfPn4dTxUuHBuhD09bpBeEpuEoln48iwZpOhz8WovnIrkRBA83MKxlxoRlOpIdw/nDZN2+Bh8MDDwQOTyoWXE4rJtznRxcOPBR1b46Rt+JpvL82j7Ku3KM7vhw2FubALMx9TyQd3t2bWmo/oeHIkGrtGtgoOu6ctWp2G+AN7WfHBm/L5V+tLLxB/KJv1M0/IacSYxzoSHG6Qmkzs/ASMxcqTRJL/+leVBP9fAFNqiax4i0SDS+9gPEYqwrmFZgs3HU/gcHE57mo7u9M+wvvsSux6Vx7r+R1nbOk8wKc4Uo5B70/btl/i7t5RHp+/rVzJyNGj/6t0/xvwbwi6hafwmX27OXL4MB06dECjVis9ymLUrVpLWqP4Z7PL5wvalAiaZR9ipbhdIa2RRFApVXFFtbpK1fNycHF1xNNgwseehq++WAba3oZydBE9oePN0OoG0DtfIag21gbXBbXBtV3YKlwOKjjvlMMO60ksWHHVOzO22zCCwoLReBiUYNuglYGzVAAuK6WitISKXbsxzV8ISYqPss3JiaIeXclrGia3gXieUDsWlfyLBVauBJEpd/LwwMXTG0e1M5psMDc5it07HQ+/MLr0+lyKHNXtwbRbbVgLq353vpHy7DK2n83kuGoNKzx/w6qy4Wfy4tn022lZoQxAKkct+hAXWd3Th7jKtUgoXA7L45fz0q6X5GAvqtzOukv3S0VFMttWfcj5raOkuFqLPsUMvPGGqw68zWlpFC5dxuF121nhEsXm0M4Ytcr30tstmFRaQj0cuM0vmZTkJHm8jhkzpub1hZWFjF81XvZMj28+nld7viofj11+G0uOCpVXFXcO60BI99rXXIJdn8HGl5QDZOJPlGS5kfXmW1K9XMCpWzf8nnwCx3YNVyHKDx6U9M3K2Fh5X+vvL6ve7jfcgKqBgEwwHFbOms0rjp9iU9m4NeoWnop5umabiWN9d2Epc9LzWJNThLnqvBJU0gk+bgwoycJ24hBJx49QkFE/ABBUOtEDLjzThc6BX0RTJQgXVnvL74OTy5QnXvcE9HkGsk8o1brqyt1FXuE1wblvdFUQ3kERVPJv/a/z8K5uIRE2NhdT0v7qoFvsQ3HurDi/Qp4/wkYq0OXPibj9HZiRkMHHSVmEOejZ3j26ccXbOlTO1z54DXWFGntzO6/dXL/3888g9sRDZGevwcuzFx06zKa8XFS5hRCijW5dV0oLqL8VIojJOF4nyD4MhVX9iZckpqIhtKtSbRSK/97NagIfi8XC5798TtEFhcHVs28POkQ0J+VkrLT1Sjl5HLPRiMXFnYrQ5rKnOrg0B3tIJIfUjiRERBPn5oVZnHeAj07LrUHeTA32JtCg/0PH5p75xzm8LRcnayrR3m9gCbdTEa5Go2+Y3aUVljw5apwOR8HGVDApQfjRSBUdXv6AljGXUrcvh+STeaz6/Ji8Perh9jRppXiHN4aH5h9h1bF0InycWf1wb5z0yjmaUG6USSIxUu6JaUmoQc/2czksOpDCxlNZWGx2tK7HcQyZhwYD78XMY1BUcy4cz2XN17GSwj3pha54B1/aSiMm7Rkz9ktarteUFjh1aDgAjj+4jxXvCxq22D/KXGnSK+/gH9mypodbOLiMfKgdhWlH2LNkfs04HaQx0OHwKdnq02TOEoo8/Fn24RHpAjP9/d4y4GkMooVq+NLhkkb/UtQtTNr0AVhN0PMhGKwESFfC2bJKbjmeQHKlCQ+tRtpK9fJ05dyBLDb+KBK6dmzmOEyla6UX+VldOvdtug93gztbJm6RrgV1UWGyMvTT7STllXNz9ya8NbatHF+FXZj7sHBc+4Y2enx27j2A5xYtZm+CN/cfX8rQJMVRJ7lHBIO+Xoq6qrr7S3oeT51Jkd184/09+TS6CVq1Ss4513z1MSfKbfLci3LQMOieu/jk5OcyQSEgGHoi+SKsGUXg/eGGs3yxVaE9Pz88mrv71LaRFe/4jk83JyvV7ltvJbKqxSz9+RekhajbiBEEf6gIil0NymNzKfj1rNT+EUleIeRZ0VRNamkqaSVpyroqQM8tzyW7PJMS87Wrl+vVenmNcXdwx13vLveVXNDgdnIl7sXpuGm90AY+DSe1eFqdMdid2OqYBKnhstgT3tmLobcrLYIl+bnMeeohqU/SeeRY+t16xzV9ny1zTnN6d4a0AJ78YjcluSaSlzs+hP3fKcesQOuxCu3cu+F2z2tB+fEc8ucpCRaPcc1w6aZca0stVnm87y0qwx0L+889j3v6PmyugUzs/C3nbWU8p34PH1sKKtHSE/IkcRuKSTx9kjs/FNXvf+4c5z96+TVuiP8lKktL+fKOKX/5+1YHkEK8x9nDQ6riejhacbek41lwGLf8g+hUdfppxMSl9ThoNxG7W7jSQ11YiaXQKINKGVjWuX019G+7o+gxE4sdq8GKWWfCpDZSqSqnwlaKyVRBUWk550oqMNkF3ceOT2Ux+rIiTJWVMqFgFZYndjvepRVEZebjWa5Q2SxqFRd83Enw88RyGRq12A5CxEj0TQv7oJq1sA4Saw9PXD29cfbykttKBEUVJ3PJm3uagpCNZEfPRa12pHs34X8bccXfLHqu7t/6LKlFp+T9Pt79edHrIRxyVDK4MKWXCjPFS16n9XZAH+EulUXFovWoHWCEeueo5aPkBeHxzo8zvc30Rj/fai1n0+JviP+9g7wffX0s/cbe26gSsc1konTLFlJ/Xc6aNBPrm3SroZALtPJ1xMPVkd0J+TgbNHw9Kpgtvy1RbHYeekgKjMh9bbdLgbfNyZsJdwuXqrXVdEKzuYBFHz3IuYrmhKnTue2xN1C5NlLFE0HtmqfgwExFfGzaKmy+7cj95lvyf/xRZrkFXAcPxvfRRzFEXrpPxMSreO1acj78qCZY1zVpgs/99+E+ciSqKjGUujTnzX4H+MD7R/n4I50e4c62dzZYlZiXns+c9FzSjLWWQn08XWQvZ3dbJeknjpJ07AhJJ45ekvQRIltNo8Loo92MS3kCdrUO1ahPlQRXY9RZEXBkHK0NPMoaEEISPeHi/BVVcBGAB4h12z8l7vN/hbqUNEOUp+zbFMqwf2XQ/cupX5hxYIZUuv36+q/pGdSTfxoyjCZ67D0tewLancQuAAEAAElEQVS/bx3OSL8r94ceO3aMZcuWYVQb2RK+hXWT1klF378CFRUpUjDNbjfRvt33ssKdlbUKX59BtGv3DX8ZxPlecAGyTkDmiar18YYTTgJeTZWEk3ADEOuAdpfVPxBJ2/T4M3wz+13csm1oyktRXaS74ejiimduASlNW1Hu5oZD6/bMdvKlwKn2fTsXn+L2tCWMHHQ/huaN26k1fGyupFfvEIozd5B5cj3HdjyMzexMUMy3uDVR1NYFTHY9ztYm+IS0w9klCpfMNBxWfU7JcScKL3hirxpz4oPVzO0DwyY8zbTW07gWiCr7/Nf3UV5kom2/EPpMqVVMbwyF5SaGfrKDzOJKbo0J440xtT7aNx07z5b8Eu4J8eW15sH1/JyXHk5lwYFkMp0+ROOUhLmwM6GmaYzJUEO5lU5DmtBjbMP6G2UHMilYcg6Nu4GAp7tcqvxcBXHdWfruq1w4eghXb1+pkB0Y1RIH9yk1AffoRzrgH+5WwwI8tX0LuxbPlU4HXc+n41tagXbw9aTFPMjJ7WlE9wxk4NQrW5vOPT2Xd/e/K/uEVzWfjnbpXcofRnwEXS8fHP2eX8w9J5Moslhlkm1u+0iaOTnUq1JunHVS9udaTXG0uU7H2sizrLuwjpuib+K57s81+L67z+dy00zFNmruTZ0Imx8v9W4Cnu1ab05RjYpKI8//tJ4N6XrKTKK+bWOirpxhGTvx3bUJjR0MrVsR+uWX6AKUlrgV2QU8cCpJTmPG+nnwtL2IDV98SIFNRWVQBC6ODoy7exKPbntUzocEE1AE2g93ehgvBy+5z95bf4avf1e0C14c0ZI7r1MKEjUwlbF6xnQOWFsR5ufK9PufkA9XnjpF4rjxon+CZps3o/NvnI1QfY0vWn+B0m2p8r4+3A2vG6PRul+ZEXT81IucTl2AUReKd5MnWPP7WrJLsvEM8sTVz43deRkUGgvQ2srQ2UtkIuaaYYdOaYPoljJS3k0NPU5ZTILs1fd29Ma+6AiV5zPwaBLKzW9+iIPh2lhugu2x6O0DFGVX0LSTr2Rw1BRhxBi79W04tkD5ImIe0fk26PsMuFx+u14J1SwVIfrjM71Wt6XcauP22ER+LyjB11LK7lOP4FoQj8mnJX3afEKmzc6L+m/xSz1A0uZgTMVKYmns868TeZUV/v8F/gu6r3FD/C9RVlzI3GcfpaKiEkenaisw5aRQzo2qE6R6pVJLD1mxqDXaGjEfsdbqdGgNDtJuSjJcyvOgNBt7aTa2kmxUVmFB5oZW5YZG5YpG74dW74tW54UOJ3R2PQa7IHk7NEqZrosKSwllluKafma5mJV1ubUY2yV02YYhvEwrgiOxuigUZn1OOvrcdPkNvEoraJ6Zj3cVxdeqUZPfPJLizu3RevtgcHbGwdml3lqIpTi6iuDaTU6orkXcqPJcgRQeMRrSSOr1KnaViRZRrxESUr9vuaE+r/lx82XPVaW1ErXaiULPqYyMGMGXrcPr0WrNWeWYUkqUJbUES3b5JUJwGk+DtF4QAfhG9U5ein0Nbwdv1o5fi6P28jRccVHbMGcJ8Xu8QGWhxZDV9B76LA4OgbXKzrGxFP22ml3bjrDGuzU7g9rVVLW1KjuDWvozrXck8dklvLj8pGw7+OqmDiTt/o3MzExiYmIYOnToJZV4rVrL3OFzaeXdqt53iov9isVL0mXm+kb/s7S455fGaXhiUixo5mfWKFTRaSshsL0MoHO++JKi5cuFWojsx/OYMAGfB+5HV4fiXvM2RqPs9c6bORNrvkItFL7ePg88gNvwYRRvSa21urirLQvLVvD+wffl817p8QoToiY0+PWsdjub84pl9Vusq3ddgF7H5EAvpgR4EeagJedColQ7TjkVK9e+9jRGBsfhrDVTYdWyJqs9hPeWlXBBSw9o1hy9g+PlAxTRkyUC8LqLOM8bgrNfVQBetYjb3s2haj//U2BKKyXnm2OyuuXcIxDPG64shHe1Qfee9D2yOmS1W3mqy1NMbV0lRvUPw9NnUuTx1NXNmZWdml2RnSJE3r788ktpz5MVlMVOw85Gk0V/FPHxM6RehLAQq6xUNAgUunn9c/uqUVkMuWfrB9hCcLCa6ngx3EPrB9iB7a/oOWs2Gck8d4bU0yflOZd+Lq5G/KwadrUa18AQOvUbKNtAhDXj73HxLFy9jqDyAiq1OuZ2HyyvGxOCfJka7EOH3W8oyuG+LeHenVAlntYQbDYjRUVHKSjcT0H+XgoKD6FSKRPyvLjB5ByfiN4lkzbDf0KfZIftSWSqm3PXza8xIhO+ndAR2/m95D1/CwVn9dityjhpaNeW77uXsso7mZigHnw76NtrskyS14XvT8qAzsPfSVaZdVdpMbnjXA63zlKqnwvujiEmUqmOb8orltUrN62aIz1b43xRO434zEWxO3nzyP3Y7SpaxT1K38JwCtU2Yts4MapzMCPaBuLhpK/3mqyPD8vrovvwCFz7NOwFXQ3hEDL7yQeklZ+YCwkRRJ3LWBxdmzH60Y41AXddVOYUs+OVb8lI3kzX+CSsKhU7Ow3E4nw9ox/tfcXqfzV9eciSIRQaC3m/z/sMTY6FrW8qbKSbF0Gz6y95jfht36bk8Pr5dAQXUJzvP7aNwKeKPVAXCUdzWPddLNKV0H6B2THfUKGukAnti6+vdfHc0ljm70+miZOeH8oNuIW543df+wb36Zu/neJMliI0GOl+gTvab6Xv3kclA+xttxeYvjgPtwrQ+PgQ8tlnOHVSKN3rcoq462QiZjtEx8cyYvNirE1bU6kz4Bfjx8/5P0utFH8nfylY2cGvQ83vf3dtHN9uT5D3Xx7Zitt7N1zMKFr1Ip8dQs4Zpk2bRkSE8rwLN99CxaFDUvfA92FFFLMhiLmWqG4LkTwBl74huA8OazSBczHM5kL27L1eFg2imr+MydSbn3/+WRYcrKMm8k1BJQ5qFcs6NqeDq6MUDSwwFsjjodhYLO1JixK3UhS7iCKVjSInL4pCO1NgMXE8Q8yFyumVNpD2Gf3l5x0MXsfB0LU1c/0WSS70OOmNRW1jZe8MSl1tBDgFEOoWSqhrKE1cmxDpHikt+YSFWmPXjOykYpbMOCQTOP1vjaZVr4ts5cRYvPk1OLdBuS9YlMJmTLA2/qCDitjPwoav4miObBMVmgz6EOW9jDYb959KYnVOEaGVmWw7/iBOFTkUNh1Cp+BnaHd4O9cd3CLnOzoXM6H9yhgw+nvc3aP5p+K/oPsaN8T/EkIAZM6999UJtJWgu/Z/+WjNCaVGg0atRaPSoVFppW2CRq3c1qn16NQO6NUGudapDfK2Xu0o+5K1dVQBr0YcTATOIqguv2iRj9UJqqWar06PRgT9cqm6rddLKrZWr5N/F/dFUKFzcEDn4IjeoKx1DgY0egOxCUnEJSoiG809POi6Zw/WY8eVLaDX4zF5Mj5334XW9++p4BkvFJE76wQ2i5nUvu9Srj+Hl2dvOnT46bKT4IzSDBlwClVOgR6BPZjS/jluOlUk9+DGLlG0cW08Q2mrtGC8UCxpYMbEIsxpJYLFWQ8XDOlom7rQIaYnhgg3aWt2pUFv9TfbSDpmQ62toOmQr+jgPx12JHN86z62aAPYFtKRtDrV0GaeBqb0jGRsx2C8XQzsjs9l6g/7JU1wRKiV22OCpaCI6Bt9+OGHpQK9gKDYjV0xVl5kG5v422xm5s26m/i0MPzI4d7+4aj7PtX4DzCVw5wbFJq1CLynrlDo1GLCdPYsOR9/QunWrfK+ysEBr1tvxeu2aWi9L50s2crLKZg3j7zvZ2EtLJSP6YLD0AQORhvcGa+J0Th38a8nAicms2KycHHf3MUQfpSCcjcvI59ccy1zJMbdmSmBXozy9cBZo8a26xNUm99AZbdSrPZhZWorsorqZ1pEQs0nLJyg5tFSrFCs3f0bFqypF4gXpUJmbFUQUxXQ5ItJTQMtJiKb7RUJPlFKv7iokIvbPs0bbCX5v0LFiVzyfjktb3tOiKrZH38m6BYK9TeuvlEqfY9uOpo3e735hzQO/m5cqDDSe99pWTla1rEZPTyurFx//Phxli5dKsV9mo9uziv7X5ETXNF6IhJffwUslhJ27xmA2awkrHx9B9Ou7ddXfqGgL4rgOicOcs7UrovTGn6+oMmK41BUrWuSRK3BSbHgutwYV5yTRfq5M2SciyPj3BmyExNk8FUXDq5uhES3Zo/mFCmVJTSv7Cgz2s27diOvdSfmZeZLWxvBtJp0YDOeFaVkunpyz4ihdIxuobxJRQF81lFZj/xYsd6pA6Mxh7y8reTmbiG/YJdkHNWFugS0Z/WcTpyB2eZCv1ua0rp3mEwknh82XIpGvnjP4ySEhPHz7+vRbltDdSu3Q7t2skVmtssxvjn+rRTQXHbDMilQdi04uz+TjT+ckv3Twn+6oWD0cqgO5sK8nVj3SB8c9Rqpti8o5mL7vRcVIhMUDeGpbU/JKm1wURQjT93PUhcTCVrlIqfTqOjfwo8xHYMZEO0HCUXk/nhSKkoHPtftqlpOts/7iQMrfkWtdcBmqUSt9WPSKx8QHNXwMVS46jylu9KxBqkpWP4ihvRMzvt5cCYogN6Tb6LLqLGysHElfH3sa746+hXRXtEsGrEQ1YoH4Ng85ZolbN08w+sJpglq9q9ZiiDZ5AAvZkSF4HCZIDDhSBZrvzkuA/lsp7Oc7v07C8fOv+w4VlxpZvBH2yUz4Ub0vHBDa1x61AZa57JKeHvNabaeUYJRJ42daZ2O0cnjRyLD78Xr+A1SMf7n1hvYmL+M11c44i0Yejodga+8LBPdqadO8MnyFczrNkTqH3Qpyqbzsb2cdznLcZ/jsh1S2MWJ3nNh51V9vr61+jTf70yU918b3ZppPeurqddDwQVWf/oYB2hPeLAft92laAIIFlvaY4/LRECzLZtR17Gnrdu6lPfLKakZIqqtnhOa49zp6q8p1UhNm8eZMy+h1boR030j8+f/xoULF4gLaMLvLTrxbeswbvDzbPi6vG2GIl4mEDUMxs+UQexHG87w+eZ4Rpu1RJUr1yunmGKaadVkXEgiX1tMoS4f7eGzqCx2EjprOBCcIYP6xiAYBCL4buXVSq5berWUau3Vx4kQB9yz7DxavZrJL3STSbdLkLgDNr0CaYpFH04+is1Y5+l/KFEvdVtEASu+UGox+d0nCmWONYWLF8+l8WNaLh2KT/PbsYcwmewsLhtKbrrSCuTSrgVR3fdhpZjuMb/h6qJYzf0T8V/QfY0b4n8JY24eOR8oVOT/C9jVdmx6OzaDHbsBueCgAke1XFQualSuQqxMVNA1tdV0UUkXQbQInnUiiK4NrhsVi7rW72YysWvOz2xOScauUuGfmUnPffvxu2E0PvfeW0Nv+tuqbTOPY6+0Uth1A1me86QCbPdua2sqxJd8X7td9okKFWERcIoKtKB/T24xWQ529568wPLsQvp5urKgw9X3yggVdVNSiQzC007E45KrQ13XF12rklVwhyhPHFt5NypAZTFZWfbuNrLTQWvOIzVvL9uDWpHkVrsdHTUwSqjNdg+jY6hHzSCdmFvGmC93UVRhZlS7APoZkkhMTJDWGYMGDaJXr1412+DujXezN2OvFKiaM3QOmkasLNLSNvHTrM2YbQbGsIEO0z9UPKsvVxn7ZZzS43xR4C1QfugQ2R98SMWRI/K+ymDAY/w4vG6/HX1IQ2q4ZRT88gt5s37AVqJU1zR+Ifg9/gDuI0bInm/xe17b8xpLzi1Bp9ZJOnL3wO5X2GNgstlYl1vM/Iw8tuWX1ORMmlgK+DHhI1pnbFceaDdZTtrtWkdyU5JkFTztzGkyzsZJauTFEK0QQVHRshIe1LwFfhHNGlZIb8iGTQQ7NcH4yctXFQXcm1QF4i2UINwzQgnQ3YL/UnuRK1HShOOA8PrUBTj/4aC7zFzGzatvlgrD7Xza8cPQHzA00mLxv8aDp5LkRLy/lyvz2195nKhb5R4wYAAxvWIY9OsgqdYrxAsHhyuqw38Fzp//iAtJikpyp06L8PSoEtyxGKEgCfLPKwmevPNVgfaZhlsgquEaWBVgC+ZFOyXAFsfaVYjaCR2TzPPnZHAtKtiZ8WelIObFcPb0kuwRZWmNd3CorFqfyT/DhFVTcLcPwOo0kCTvAKlILuCiUUv1cb8NKynNy0FttXJv8yj8ptbxwt33Lax9Wk5G7Q8dotSSTm7uZnLztlJcrPRIV0NrdkR30oI+zob+nApnj5bkD3uY/YfBxcvALW/0qLF1zP7kE/K++RajYKmZTWiqxBkdfMH3pfdxHjSC2NxYKQQoGBuyqhpRyzK6Ggg14wVv7MdUYaHbqAi6jrhyq1RDwZxQMxdiaXf0juClkUq19ZvkbF49n05bF0c2dIlqMCBMLkph9LLRWFUWbjc/zZSJE1h5NJ1lR9KIyyypeZ6rg5b+BgPXF9np1TMU79FXx3oxlpfzzb13YpHjm7j+WBn12LNExfRuUBU9c8YByazxub0N5tQjpN7/AGa1hq2tQrFoNPg2CWfQPQ8R2Kwq6dIIhI7J4CWDZUD07fXf0tO/M/w4HNIOQkBbuGMj6Bxl+8j02AscLSmXInyvNQvmjmCfq0oCrvjwR1LOBqJS6VEHGJn+1PWNCt9VY/2+FO5ZdlzOGpbc3o2OUb4k5ZXx6aZzLD+aJlXORS/2Ld1DiTKfIMD7Nex2s5zv6IuCyP78CEmOmdwb/jrOZg3zjvbAuEkRNS3r3IHt5mI5R0tr05VFvUdjQUVY2mLKrUr/tmCJPd/t+RqxSnFdfeO30/ywSwm4RYuCaFW4EormTOXThDBsaLjtttsIDw/HbjYTf/0gLFlZBL03A/fRo+u9xlZhIWdWLObUUnktEQJ5Di0un8BrDHa7lf0HxlBaeoqgoMnkqm9n87xf5PXd+YZJPNOxAcaBSPqtfgwOz1Hu93oEBr4iRRlTC8oZ8sE2BhVpaW7RYBO/rH8290++ST614lQeBcvPsSnuJ3KMqQQGNGfK++/LIouwNxWJ5JSSFJJLkkkpTuFc4TkSihIk0/JiCGZkZ//Ocunq35UTs0tIP1OIbxNXmXTTCFefS3+wIu66+XXFcUVAJI5Ev7doP73GeYAoKEndlvQyNN4O+N3bvka/SBwTXyRnS3G+aSfnE7nnMKUWAyqthtW9byChZWeWt/Ekec9yhg697z8htX8D/g1Bt60gj4wZB6uqUsoiqLw0uIiatwWVyoQKo7KI2xpRCdYpNllubqg9vFH7BKL29ELloJVq3xpnnVQBVxv+mgD5r4QIhgoXLyZ/9mwsmZlkBASwu3cvLFotvl5e3DJtmtyPfxdMGWXkzjyuiMO1yOF8+HNSMbZVqw8JDGhY9Ev0V7+x5w12pe+S99v7tuet3m8R5lZ7IUmSFaw4Kb61qH1T+nhdG1VHXMiHLRmGsayCN4NeoGNpNMZzBViL6qtuChEqEXw7tPJC66OlfP9+in/fxrGDp9mnC8bFf7BoHuC81spSZxNalZU+zX0Y3SmM61v542Kon9EXgfbYr3aRkFMm1Wp/md6ZX+b8JGnloof7wQcflPY6AgvjFvLmvjdx0DiweNRiwt0vk7kGFi9+hJMnPXGjmIdcN6K7b9vlK1oy8B5fVfF2h5sWKZZaVRADt6h4i57vyuMKK0LQzt2GDZPq5Q4t6k+ahKd75ke7qTi4BnPiJuxGpSKlCw7G+667cB83FrtWw1Pbn2Jj0kYp+PLDkB9o7XP1wlHplSYWZxaQdXQxT5x4D29LEUaVjk9bPoZDtzuYGOjVoAhTSV4u6UJd9dxpuc5OPC97Ui+GZ2AQ/pHN8Y9oin9kMynQJloqrghxQRXVRhEYVQdI8vaZxinqAiJY9QxTAvDqRQbkEYrv51+kAi7672Rm/GwBWl9H/B7seFXj1cVBt5iAPLr1UekDK2h3C0YuuOaq4P8VTpdWMODAGTm6r+8SRfvLMGIaqnJXW9h8ceQLvj3+LZ38OjF72Oy/5ssZSzlz8B4q07fhWGHDXxOFu9VdCbIFu6KBiV49ang1k6Iuo+IqvYxFgJ2dlCgr1+I8yEqMl0mqiwVCRVLYLyKSwOYtZGJKBElCv6RuMCPGiKMlFfyamc/ctDQqqU2++BfnM8JRzQsDeqOxWPjwww/l8STQfd8+rnvuOcUnW8BqpnxmNzL1GWSGBVChqg0WBVwconE86wiLz6A9b5ZMNUPLliR07ULvRx9j4ZuHKc03yj5q0U8tvlf5vn1y7Crfu7fmfZwDKvFsbcTl+d9QBXeSNGZhdSecK4ZHDGdGn8s4QDRyXq387CipcQX4hbsx/qlOf9hOcmtcNtN/OiBb3369tyedwzzJN1vouPskRpudtZ2j6Oh26TEslNJn7H6fY8FbaOrWjKVjltRQ4+Myi1l+JJ2VR9NIr6N+7uusZ2jbQIa1DaBbuJf0CW/w99nt7Fx0jsPr1mAp34Raq8dmMeEVHMq0D764xM+4aN0FSn5PQRfiIpN7FpOFY72G4FyaQdmgYeyryKRSJGVVKjoNHUXvG6eiu4yIk+jrFv3dvYN7yyStdKb4tg+U50L7mzjY/31uP3mBbJNFCnDObBNOb8+rnwvs27+eXZ8sRu86FpXKEe9gZ0Y93AHny/QliwTmExtPsxELkT7Ocj8tPZIm/dcFBrfy59lh0YR6GNiw8WUcHBbh4tyC7t3XyL9nf3lUtr892v4jzpjiea7Ls3RffBbTgkXy77kuDhhvmcJ1dz/IljIz9++dg0v+TPm36a2n81jnx+qJkb626hQ/7VZYjG+PbctN3a+yapnwO7/N+ZSDotrdJJTbbld65XO/+YacTz6VLJCIRQvrVbhzfziBKalYCqb53NbmT9lRChQWHuTQ4clU4MhbDr8QdWgvEXmZtGzZksmTJ9d/sqkMFt+mULXF8T3iw3qsmEdnH8JlfwGBVrVMQBX1OcmLUx6tN14dXL6UbfN/QKvSMyR4Op5hwXhNboHO37nReeK5gnOczjvN6fzT0qFDBOMXe6D7Ovjhmx5JUG40Iztfz8Bxl7Yc1MBqhiO/KJX60izlMZEkHfQaNB1wTdvPWmIi++tjWPMrpROBoJoLkeRq/PDrYnKX/ILGZsVTX87Ipmm8NmgmS0xuRDrqeTgrnvHDh/0XdP8b8G8IugWN1vbb46SmphISEoK6oUyS1kGxCRK9FsK3V/RqClqwWHuE/uG+i/81jAkJFCxYQNGy5dhKlAmMxtcHr6lTqezfnwXLl8vKqtiHU6dOxbsB6vCfhTmzTFa4bWUWNE0MJHV8mbKKc/j6DqVtmy8uyUQL2455cfOk/6gY7IRa5X0d7pMXmoYqvC+eS+X71FxZBRCTavU10Ft/PPEjHx36SNKEhM+pyBqLC5glp4LKswVUxuXLarhMW1fBaC7nZGU+vxkc+F2vR4Tn/hYVN5Ua0KKiPOQI0V1/JNA7mrZtv67xQ6yGxWqTk6od53Kl9ciKB3uhMZXJypqosE2cOJHWrZUAVGRahVq52A7PdnuWm1s2IghWB4WFZ/n6q5kYTc4MYCd9orzhxgWX94oUgffcCZCyT6GiCvGxDkpmuBpyArv/gOzfLtupeKILOPe5Du/bb8epe3eZt6oO6jReDvjc1oyi5b+S/9NPNT3fWj8/vG6fjvP4MTy450n2ZezD0+DJT8N+kv1TV4XSbFj/PMQulnfTPFpwR9TzHHVUEhLiDL/O05Ux/h7SdsitEYsRYYmTfeG8DMCFrZGo8pXkXloNrw7ERRVcBOEiGBdetUIo8KpRlqcE39WBeHUFU1QzLysQo1Kql2Iccg+pWsTtOvevMtCqrkJlf3ZECjk6dvCVk40rVYMuDrrFufnd8e/kufnT0J9o6/sP8JNuBNNjE1mbW8RIX3e+b3Pl6qM4B7/66ityc3NllbtPVUAoHAOG/DoEi90i1dkFxfCyEMGrSLQIMR3hGV+YogTS8nbVY4JKfTnoXWqTMEL1Voic+VUF19dwTRJOEyK4FoF19gUlyC7ITG/QgcPN108G1jLAFpXo8KaSgdUQUitNLMks4NesfM5VCXAKqK1FGMp285BLNMU7lAp1z549cXFxYcOGDTg5OVFeXo57YSHDduwk+OdvKHI7Q2bmMoqKj9S+j9oBL6/eeKraw8oLlC9YJ6twAo7t20vhRn2PHqxdu5amXp3YNvccjm56bnmxI+Ub15E/dy7GU0pLhaggaR1MhF6Xj4OnhRktX+XpScpk/M29b7LwzEKZOFo6eqlUQ74WHNucws7F59Dq1FLBuEFq6TXg8UVHWXo4jaa+Qs38Ohx0GimstSSrgJsCvfgoun5AVVZkZN4reykxl7Cw+5uU28t497p3GRE5ot7zRM/plvknWBGbwe9qKyV17Bi9nfUMbh3A8LYBsp9cVycA37cygYNrhLWaFQ3zKS/MlseEGD+H3v8YrfsOrFcFzXh3v1Sx9r61JY6tfTh/JJujr82iVdwcSVkOXraEbQt/5vQOpX1J2JENf/AJApo1LDonroMjlo2QlOoVY1Yo14mEbfDzGJmYer75E/wQNFr6uf/UNoIwx2tj3Hx44EPKvtiIi8kft4BpGCvEeeDIDY90wM3nUpab0hN/iL3ZxTylrsRcZ37QN8qXxwdF0b7K+k2MnVu2jkKrPUfTyKcID79X2WeHsihYfJaVQdv52n0BTWx+DFjniH9RGe1TstFabTJRHfLVl/xmiuW1Q68LJVzKXQZxb8eneCqyVj/mlZUnmbMnSV7i3xnblindroEmbLdT+FlfPivoK6vd06dPJywsDEteHvH9+svzLXzhAnm+CeZC7k8nMJ4vkg4xvne3Qx/417RMxZ54jJezm7JH1ZsWpnL671H6n++66y6Cg6sEBCsKlQKBYDkI3Z0JP0B0rbPA9oPp7Jx1Cne7mgpNOSm9dvHB5NfrWVcKS1+hT2Axm+gzbCrByaGyGCTYje5DI3DpGdSgvV5DFpknck9wKOuQtEU9nH1YPlYNtU1DR8/ODG85hAFNBtS0AFwCkUTY+zXs+rSWJRfZT7EZEzobVwlzbgU5Xx/DVmbG0MwDn9tayyr/1p++49hGJdGTENmK2zy307X8NBUBHejX5jOSzHY6mstY0a8r+kbG+X8C/qOXX+OG+F/j32IA/1dADJIlm7fIYLtudl8IXHndcbu0dqru0SksLGTOnDnk5+fL/uFbb72VgL+QYm7OKiPnu1g5EIisd9HANaRk/IBO501M97Xo9fWD/LMFZ3l196uS6icgaDtCcCvCvfHJcq7JQszeU5RabXzZsgnjA66O5iTosUOXDJWiHG/0eoMxzWor7qbUVFkhKdp/gOOnsyhwjcbNN5pogycudQKUSuxccNGgaeGJu5czexYraqFN+szCKWAvBkOA7NF0c6u133p15UmZkXbUaVh8bw/aBLvLqpqoromk0B133CEnguJiKvy492fup2tAV74frPRBXw02bnyLXbvMiJTAQ/yI65CXoEeth2ujg/+yexQvb4Fej1ZRti79TKFwmvf99xSvW68IrgkRoubNcOgwFGtFFGpHJ3zva48+yKXGr7dw8a/k/fCDZFoIaITy/5SJvOS/iwOmswQ4B/DzsJ/lulEIWtnBH2DLm2AsUjLd4nv2e44yNKzMKWRBRj77impVzQ1qFQO93Bjr78n13m44XqECJaxZshPiyRLVP7mOpzinYTqvk7uHDL6Fz6tPaLi87R0SetmqTYOidiIYEwF49SLUpuXtRLhMn1ntj3QDtyBw8VcWoVzvEnDRbT+FyaBSSW0FQUkTHD5h7eLSPfCqx88taVt4ctuT8nHBPBG93P9UHC4uY/ihczIJ83u3aKKcr7xfYmNjWbJkiaxuP/roo3Jdjae3P83ahDXcFDaU51pNh5JMpUohxPeEEn5ppvJY9eOW2qpiYzBrVZhdPDE6O1Kozkfr35HQdq8ogbaz7+WTZRe/l8lIfloqeanJsmqdl5JETvKFRhNJLt4++IVHyiSSCK5F0OPiefnxs8Ri5becQsk0EVZ/1XBUqxjq486EAC+OJMxi9qkfaOHZgmeCnmH1b6vlc8S2rKysZMiQIWzZskUeVx2dNuPSMV1hLEuo8ap0IuBCBq7mHhQmRFCycWPNOOPUpYsMtp169JDjpHiP1avXUHrIj6KcCtp6pRKw+SusRUU1ehQe48fjOaQTut9uRq22sb2yI5OGfMLsthE4G2OlEKDAd4O+o0fQZdpxGkB+eplUL7ZabPS9MYo2fS8vSnY1EGrmgz7eTk6JkUevb86j10ext7CUMUficVSrOd6rNa51Eonrvz9B/MFs/MJcKRh+hM+Pfi6FoESAKlp4qiHEuzLe3idbvFyntuSIxsba2EzWn8qksLw28efhpKNflC8DWvrjkVJJ7G9KBVUwCHS686z+7H2pJSPsVQXrYfrH39TYfBZvSaZ4QxJafyfpWy0CGCFWlnAwkz7HXkdTlEvAa6/hOXkSiUcPseGbTyktyJctdDHjp9B9zKQG2+ke3vKwZNZMiprESz1ekv3bm1a+zqhjH0uW07sDfubJHoNxbiTB2hhEtVK0joQfttAm0Z2IjgMoK42hOLcSZw+DVGb3qhNYiqT52l1J/LTmDIeoFbIVZ+mHk9ozrlP9/V9amsreff1Qqez07LENR0fl73azlfS391NgKuCm5s9KH/mx24LoHTOSHjF9yHriKcwpKdgdDHwyzMqeaPDTdeVkwIPyuic8vKcFevPyyhP8sjdZDhMzxrVjUtdLbcuuiP0zWbVmLYdoJ8XUhKiaQPqzz0lRVbdRowh6512pCSIKEVK46662f7rCXRczkxJ4KaEYjd3C9+E5VBytkHOiyMhIWRCSOhY/j1WcRoTYo2DkhXareX3y6XyWfn4UnQ0KtOXE917Jl+M/rmf9KuZUS95+WdqPNmnTngkvvomtxEz+r2dloUDA0NQdz4lRDSrRXw4i4BYB+K60Xaw7tYlsau1NNSoNMYExDI8czoDQAbiIZGpDSXlhMyZcZaptxtpMgIEv1dMtuKKH93fHpZ2wqqUTuy4sIeWU0CtQ0XvKVBwGDOfJ/XtYvHcaHpZSTnV6gKFukzHZ7cxvE0Z/37/GmePvwH9B9zVuiP81/v8QdJszM2VgU7hoEZacqkmWWo1L37543nQjzr16NagyLirdQjEyKytLTopuvvlmQkND//z3yS6XA4DwARWUF93ESo6cEv17dtq1/RZf3+vrDViicvZD7A+ykuSic+HxLo9LL+qrCTQ/uZDJu4mZNHHQs6t7S3RXkan89ti3fHH0CyKdw5jXagam4ycoOnqMI6dSOII7sT5NOeUVXqM4LiAIO0OdDIx2dyOq2Iq2rA69SKPitE7D2cwK9A5qmo/8Aqv6CGq1gejotyWNfu6+JF5YdkI+/ZtbOjG0TSDp6el899138rHqniqBpeeW8sruVyStfOkNS+Uk6qq3vbmUzz9/ieJidzoRy2j1Nrh9HYR0ufwLxcRWqMOKwV8gsj/c8IVSTW0ApqQk8n76iaIVK7GXVwkbaR1xHTQSv0dul4meem9vMlG0YgV5M7/HnFxlWaTXs7eDA/M7lOEQGcnsobMvtWUSFbnzWxQREtFDLSCUloV1TAO/KbHcyPLsApZmFdSrwIm+0mG+7oz185SV8Ks5TmoC8TpBuFBNL8zObLBSKC5wHv4BVUF4GF5BIXIRlXLhK35NEPujLKe2QnrxWlRPKxQGwVVBMHoEe8fJC4vRCVOODpvKDcduUWh8/cHRC5w8lbWY2Ihg3uCK2a6S42d493Du2nyXZF5MbTWVp7peRqjvHwCh+izUn4Wg0qctL6r+iH1nLgdjibJUFmMry2XTqsXYSrNpFeZPEx9npVotKtLleZhFUF2ezzVdQUTCQ7IUQuusm1CmM3Pg3ENYtRq6d1uDHRv79wtbGztdOv+Ku3vjVQ6L2UxhRhq5qckysM5NSSYvNYnCzEzsjVDSPQICZWAtWiX8wyPlWiSNrgZlFisb84pZlVPIlrxiKupU9np5uDAhwJORvh41gaDowx26dKhMbH7c72Oc051Zv369/JtOp2XKjX5s+30/yckheHhk0LbdJgz5LoR2eRD/gNFY1m8g78NXKM+urVg69+2Dz5134tS1a73vZjIa+e3tX8jIikBrLqPn3pfQWo2ySiiue+7jxqHVmmBmf9n6UZpuIGFPAJPf+AwvXx/0aY+QW5EtWUSCTXQtEIH2kvcOkZNcQpPWXox8sP1fJiT42/F0Hpx3BL1GzdpHr5MU5j774+R49m5UCLdVCapVe4KLj534XFecAzUMWzpM6g+8FPMSk1pMqnnP0n0ZFC6Ll72fAU90qanoma029iXks+ZEButPZJJXVttepbJDiFUthdim3tCCSG9Hfnn+MXIuJKA1GKRy/fV33k/7QcNlUJ/57n5ZOaz2/haWSj88tROr2caolucp+/ojdGFNaLpmDSphH1pawqbvv+Lsnh3y84ROwIiHn8LFq35C/kDmAW5ff7u8Hn4/cjWPnc3nVGkFs0++wJC8Xdi9mqISwmrXyErckbqD+zffT0SFD323OksdnVvf/4F1352hIKNM9naPfKg9JjctS4RF2/4UKaAmII72ke2DyCyuYF9igaxuL72vJ5o615XECzNJSHgXN7dOdO2iMLMEBLMq6YfdhBLFM4Hvc9wjkcmBY3hx8Bvy70KQNPGRBzHvU0S31nVzo9+jX7HZM5gPLmSittvpm2Zhz8lsue/fn9CeCZ3/YMLHWELhB135zDxRVrtvv/12mjRpQsWJk1yYMEEKvPk98wsVJ4pEozq+t7fGEHn17KorIa6sgiEHz8r2iZvsPzFWv4/oFov5+usfJPNo2uSxRPz+AGTFgpM3TF2paFZUv35PBpvnnJZMu1SthZMxv/DThE+k+Fm9z9m1TUkY6XRMe/8LybCoDsaFsF3R6gRZzRcCgx6jm+LUye8Pnc9mo5VvPljJMet+0kJOkapRlOQFhC6REI8Vc1shhHfJ+wvm29a34PiiKpsxHXS7C/o8dUXhSwHB0EycuZPt6YsotRRIIeURDz9J087da1oyZ63/jtePvogVNd8O+IG0EhdeHdzvHx0b/Rd0X+OG+F/j/9WgW/Rql2zYQNGqlZTv3VcTBGi8vaUCpuekiXLycSVUVFQwd+5cScEXFBMReAuK0R+FOacq4C4xowt0xvP2CA7EjpW2OIGBE2jVsrZnbnfabt7Z/w4XipVsev/Q/rzQ/QX8na9eCbPMaqX7ntNS3frDFqHcHNQwTV5Sx7OyyD+8j19+fRXnbBfM1kjiXQI56xHKeY9gTBf1z3rqVXRr6kOPZr70a+FHuJiIV72XOaOMylN5lMfmYskql0qzO0utFFjteDlraN9zL1kuv2DTlaN1vZk7l3XDbFPx5OAoHhzQXL7H7NmzpVqnp6cn999/vzw+c8pzuGHFDZSYSniyy5PX7BUrcOzYYpYtE1ZkNu5hLgFuBrh729V5Sx9bCCsfAkGXEoHXkLeh4y2NVt2MSTmkPfc1pjObsdcReRLUc88pN+LS57oa72657SwWSjZtkoJrwlqtGgeaqzg5uBkv3zMf52qlb6H4KS5CyXuU+6JaO/BlRfGzEUG5ms+x2zlVVsmyrAK51PX+9tJpGO3nyWhfD7q5O0vRm2uBubKS3NQkcpPFcoHclAvkJCdRUaxU2BqCqCJ6yiA8GM/AqnVQCG6+vpf0RV41BENB9DiKXnJRXZWV1+qqa9V9sVxO4O0KsGsMGNGSpzZTrLKjdfCgqb/oB3dTFNl1jkpbggjqtYaL1tW3DYrVjziGxG8VyTR5X13nftUiXBtEwkGuxWK59DGxFmJj5gqloiwWs1iL+0byykvYmJmFo83EIFc1TpYypY1CBtlV66u0XGwIlXonHDzCq9gEgQqjQKzl/SqWgWAfiN/dAI7H3kdOzgb8/EbQts1n8rFTp58lI2MxHh7d6dB+DiW5uRRkpFGQkV61TqMwM53inJxGg2sHF1d8QsOkTZdY+4Q0wTc84uo0CRoJtIVtn/A3r0ZzJwMTA7ykMFqIQ8OUxOoe+GYezVgyegmffPw+xcUVhIScICLyCJWVzhw8MAa7Xc2A41vwPZWDc79+WNLSMJ47p7yJyo57Kxe83p57iXaEtbRUtk3lzZ3Lbp8bKXUJISJxNW1CivC8+WZc+vaRQZ08RmaPkq0zdu/mJO1uQsWR06zpfT3v33wHrrlf0VKXKVsGHMSxeg3Yu+I8h9YmYXDWcuPL3S/bA3ytEGPXbT8eYNvZHGIivZh/VwwzU3N4OT6d1i4ObOrSAovZxoLX98mqbPsBofSe1Lyev7XQW1g9brWc6Iv3y/70MObMctxHROJ6XcPzAlHJPZxcyLLtF9ggAnBhJF0HoV6OtPMAy96VRBhT0FmMcly7/bOZVO7LlYGLDOofF97fKs7sy2TTj6dw93PkxmfacX7AQMlCCP7kY9yqLDHFd4vb+TubZn2FqaJCJoOGP/QkYW1rRT3Fc0TffVx+HGbPKRS6jsBHp+X7SDdiFgxVxr/2N8LYa/O4f27Hc/yW8Bs3tbgR/4XJFKSnSsp80859WP7ZEfKSSxGOciscjZwXZVQxJ1CpGGnXMfWGVjTvEUxmUSWDPtpGidFyiSf2/v1jKCmNpVnTlwkLm0ZZYQE75s3m5LZNuOq8GB5yF9tcD/JuyA8EOgdKdwRRaDBbzdy+5jbaLzrCiAPKPnDq34/g99/nqaRcft2QgCarQgiHywr72I5/kmGx5mlW7j/PYdrWVpeF88OUG7GU+ODQdpIs53tPa41j9B8TTWtMIHXEoXPECu0NL2ceKLubyspkwprczZkzbThw4ADB2kLutPyISiSMhb2pX8uaY2L/qkTZ+iBwWmdhf/PNrLj9UQJd6rO3KstK+fGxe6UwZM+JN9Njwo0NUrQLFp3BlKy0Yjq08sZzXDM0LtdOuxaJuF9nHMRmtdNioivxPodYnbC6Zp4rEO4Wzo3RN0qWpZPuooR8xnGlyCCKDQKi5aX3oxBzn3K9bQQZ586w9K1XqKwoxVnrztAbHiR8Un32TpHZwrE50+iT8hsphgA+aPMh7w8Z/B+9/N+A/4Luq4PI1lksFhn8i3Vjt+s+JgYU8TqxrrtgtWKIi8Nh7z60hw+jMtUR/WrdGocxN+A6aBBObm6yct1gD3sDMJlMzJ8/n8TERBn43XLLLX8o8FYC7lhsJSYZcPvc2ZazKa+Snr4AB0OQFBIRquXppem8f+B9NiVvqlGCfCHmBa5vcv0fyi5WK7yGOOjY3b0lWosF0/nzVMadoeR0HGfPZ3A2p4wErTvnPEI45xlKWQODl5deRUwzH2Ka+8netma+LqivIiATlf2K2FxyDmWxObFE+ms2M6hp5ayizCuWEv8D7NeUkqh5lPcn9Za/MS4ujgULFkjRtBYtWjBmzBi57R///XEpMia8QoUn9x+xKBLHysyZT5Oe7ky4JpVp1sWowq+DW5df1gO3BjlnYfl9Sv9UddVbUJ2CO9f/HIuN7G+OSSVTbYgzzq1LKJg/l7LtSuVCQOvvL1XPBc2zbhJIfEfhB5r3w4+Ubqm6uABZYc50ntQPF9tRVGkHasXGut4J1z0Ozo30R10GIiFysKiMZdmFrMwuJK+O/ZgIwAd7uzPc150+nq6XtZi5EsSkSlB7RTAuqo/C41YESw2pQFdDWOeIXlo3X39ZJRdrd78ASd0UlmYOzi5/voImLOJE8C0q54KqV5GPrSCbsm2nUVkKMfhZ0LkZa/4mq7tXQY/+10ME+QZX7AZXckutFFt1uPqF4xceXVX591YqDFXrTTmHefrwh3g6K/Zhdem7V4uSkpPsPzAai1FDdPhMzKUGac1VmH8OW8C3qNQ2EtaGU5zc+ORK7+ioBNYhTeoF2SJg+aPHSml1oJ1dyJb8+oF2hKNe2vON8vOgjYvjFT9DeOgKkUpnirjPuzPH90SiUlnp1m0ZZosDTUJv49w5X06ePE3TSiNdli+vea3a2RmPMcPxKp+JzlCuUEmjhsi/VZw8KUVBi1euklaFud5tON72PileOeXBCNxb11HjFtfLVQ8rKsdi0nrXFsoS8kmeOg2bWsOtr3xAlqeKtR2DaedbWzm7GmScL2LZB4fkRwy5qw3NOv/1QoIp+eUM+ngblWYbH05sz4B2AXSoElRb06k5xq2ZHF6XhIungRtf6Y6+yvrLZDUxatko0svSpdvH9DbTpTaJSIQLtWlpE+bU+HGbmVDEio+PyKDeu4sPFS1d2RKXLavhJmttskdttzI9da5MaAUNmkSnrOY4l1jwHNcc525Km9DqL49xITaPLsPD6T46kpwvviT3iy9waNWK8CW/1juOxDi56uN3yUlKlMm5nhNuovu4STIhKYKzu/bM5nD8R1g1njRv+S3ftmlGgEEHSbvhpxGK8OCYb6DDpQFVQxCMnX4L+1FuKZetTRXbT7N78VwcwltxMHoC+87lMqxYR6RUwbaT2ERPz64BdNqcgcGgJejF7jXWosLqTVi+OejUrH+0D2HezlRUpLJ7T1/pn965/Xritu3n4KplUsRQoE3/QbS3X0dZcgE3t3yeMnu5FBUV7WRCRHbR2UU4qhy5Y2sEvfeflIr/umbN+HLA3SzLVmNXgUMnX9aPbE/4NfaxX4LccxR8MYDPua1etTvv5/WUn3CQdpvuw8Jw7fvX2krNSMjg46QsKYAnWoA0JTs4dvwuVCodbZr/zMxZKzHbNUxx2E70Hd+Ar9L3LxgUW+bEcf6wkuTfYzCz1yNdaiC08L5UkV+wKURvs0hyT33vc+kQ1BDsVjsl21Mp3pQkfLekWJzn2OY4trn2OceRjcnsXhKPRqdmwjNdpEDfsZxjksUo7P2qLcpc9a5MjJooA/BL2utE0L3x5VqGn3A66f+8kmC6KFEff3Afqz99D4vJiI9fE3oaRuGodcF9eASufeonZSwVRRR/2ROv0lQW+w3Ca8JMBjZkzfYPwX+V7mvcEP9LiABW9IYcPXqUdu3aodFoagPYqol/Q/cFRNBrtVprAuGGlsb+XjeAFs/5MxCDrV92NsFpaYSkpOJgrKXMFru6khQeRlJYGGUul/aKiMBbqPCK/SOUsUVFte7a1dW1JjD/s4G3FHMQFe5iE7oAJ3zuakdB5S6OHVMUMTt2/AVnt078dOIn6dVcaa2U/S5isLm/w/1y8LlWiIqHKTGRooREBup8yTdruGHPHnzOJJLk4scFtwDSnH3kJOti6FQ22vg60b6pPx2aeNIuxJ0IH+c/HeCc/T2VjQvOytsxzhr8dcr2tassVPom4t29C67tovj6x+9kP72wBxPCQoKJsT1ju1SGFttl4ciFtPC6vKXK5ZCefpyZMxdjFxct9WqibWeh58NQRWG7IkRFcffnsPVtpeot0GyQ4i1Z1U9VsCKesj0ZUljF/+GOaD0daqjnBQsWUrRsWY13t5hIOV/XG89Jk2Tbg7AQq4bxfAKp372Iy4VteEeUo3VQJnd2NNhaT0Yj+tJF5fAvgPBF31FQIu3mNuQWUWCpPT+dNGoGeLkyzMdd9oC76/4aT2aRaS9ITyM/PVVOLuVaBOSZ6VirxKEag6ClywDczx8XLx9cvX0k/dLVy1v25YrbOr3hz/l3q5AJMoemdWiDVgvmigKe2/Qg53OOEqLz4LUuT+Kl0lZVjEvBVKpUE2W1+TJrcfzICrVdqTCLCbJY5GNVFWz5N1ttxVtWwDWK93l1NbzmMU1VBd0RdNUVdWXJQ8uPWWVUqvXcFRGKv6uXQjuVi1sNbV4uolKvUjWoWN4QREAz+NfB0l7mctZSggJeXlhAaUGe7FkVVWsRWBflZJOVfIjywkpspkvHpKAeWfi1y6c8x4Hzq5rjGRCMR0AQnoIVIdaBYgn+U8H1xZoYopK9NreQrfklMqCrRqSjQQbZo3zdaX0VgXZdlJaeYdPRR3E1nSXudG9yciIICSnB28ebY0eNBBSX0MlqY42nByqbjVErV+FYWSnHhCY/z8GpQwdlsrnrU6werSjyfZDCJUtqhdHE+N20KQdaPkh+qZ7214fQe8JFQlz7voO1TynHzU2LobnS0nRu+jQse/azo2MML9/9CB9Fh3JT4NWLiIoJ/8K3DlCcU0FUd38GTb9654Vrxde/n2fGujg8nXRsfqIfLydlSPu7qTpnIualSnG0Yfe2JbJDfQbTivgVvLjrRSkKt3bcWkyLU2VSWATDIihuDAWZZSx9/zCVZWaatPZm+P1ta6zXSo0W9ifmsSs+j99PpHK+0EzLktNcn/s7FWoHZofeTJDWka7tA+gY7klbX1d2fXQMm8XOlJe64R3sgqWggPgBA7FXVBA663tcqqwx6+oSbP3xW2K3KEJaYe060uGuh3gkpZBDRUV4pz2G2lbE273fZVTTOkJx295TGFGih/feHYrw4BWw/sJ6ntj6DF6qjtwQ9Cz7j52h28HvsKFiVpNpVGocae7rzBiLIyQqOiGtIt1olleOc2d/vCbVXpvFnPGmmfvYk5BHj0hv5t3VneSUWcTHv0NlkT+Jq5pgLFM0EIQY58Db75OOAOXHc8ifF8enofNZ57KDsc3GSgvNZ3c8K9X5h5QNwTnbmfHt2qH/+BNsOTmU6Bx5r+dtZIzszXkXNc2cDKzu1PzPX6t+HsuK82qO0JamTZsyZdA4qYotRPFMidvxurk9HqNFC8xfg0NFZYw6fE7ag33XOpzRfh5yOx47fgcFOb/TJU7NvtwodtINXy937nvwETlXFcfo2m9iKcgsx6ayscHBTKzBxtuTfbmpY22fdzUy4s8w78Un5fVl4ktv06RNrcZOYzCll1Kw6KwUAhZw6ugnKefCqehaXA1Wf3WcpBN5Ulxx4nNdahJjovVm5fmV/HLqF2lPJqBVaaUdpWjdqufmIhheQjRWaNkUVbXl+bWCQa9Ds+vl9St26wY2fvuFZEBFdOjMyMeepXJPDsXrk+TTPW5oWs9LXiJ5H7Yfh6K227DctBRtVK0g4j8N/wXd17gh/pcQ1OkZM67NAuTvhBg0REArqptiqb5d9zG5mEy4njuH2+k4XM6eRVMn0LY4O1PUtg25bdpQ6u+PtSo5IIJm8XvFIm5fDUQSwtfXF39/f7mI27t27ZKUZ/GdhLiayHhelUr597Gyh1uIqAihDZuhnL37hmEyZRMachuZTn0l7U34IFYLpT3f/XmiPBtWLa2mIpszszCnpVGYlEpaSjbp2QVkFJSTXmYlDQfSnb3JcPahyNCAQEUVXDV2orwdMDtlcKbyd5r561g28Uv01yi8crXYvvAssVtTMWug0NnKCL2aQEstTemENpm92nM4GRy596572LprGz0H9mTymsnkVORwZ9s7eaTTI3/6eyxe/A4nTxpxNRTzqPFHEcLCxNnQumGrtgYhPIK3v6/0GVVTcr2bY/YZQMHxKEz25njf1rZB2pno4xZCSEJvoK6wn1DRdx82CI8uIRhs8RC3SlF1roKxUk3xOWcKzzthMetxHTgQz1tulj2df1XfZHUAvreolLU5RazLLapHQdeqoLeHK0N93Rni49agDdmfhc1mlQFZUbYIyDIpFuvsLNkzLm6LyvnVQNCKawJxL2+c3D1xcneXCusiQKteO8gkW/1jvmDJOcoOZKJxN+D/WCfUVRMDYQ32zPZnZFZej545w+bQ2u/vCzD+Ktx/Kkn284/wdWfWVSqW1/XlrlYsvxh2m00mT2bt/4ZlxxfR0qEpdzWbJoPq0vx8ykSAna8E2UIx/GogfOLdq1gOgu3g6utEof4N7PZK2rT6DP+A+grUfxZiUhtfbmR9bhEb8ool+6MuUb1pdaDt50ErZ4drPtcqKpI5n/AxWVmKn7DR6Mj+/ePAruau227D8fAR4j//HNeMDPn3LQP6k+PnR7egIFrt2EnFgQNSCyJs0UJMxw5S+M7dFCdpsVuV7yGCctfBg/GYOIF89yhWfnpUlFy55Y0Y3L3rUOjPrIMFNypJnEFvQK+Ha47pF366makzjsr7d77wLpVNm7E7piWGq2SFbZ0bx6kd6bLCLIJJw2Wqxn8Wot965Gc7OZNVwuQuoYy/PpLRh88xfWsJITkWwtv5MOL+S4MI4QIybuU46TF8V9QdjFvVWQon+j3SqVHFaaGCLnrUS/IqpSjbmMc7oWvEUlAcR9+/9AIH00oJs+ZiMBazx6MbBz1rmVCtTBpGlOspN6go7e9DswA3mvu5EPjz15TPn4tTTAxhP/3Y4Puf3LZZ0s1Fz3iZsxsrrp9EaWhTRmk3s/7sLGkh+svwX2pfIJJ3c26ACzsgpCtMX3cJo0t859SCCk6mF3MoOYdfjx+hoMgN7LX7b1Lar/ibctD0Gs/IKRNo5ucqX3do7QX2rVQ8sIN1Kgbd1w7nVvUTNcKne8gn2yUz4Y2RLQgpvxObNoWUHf7knfKSFmuC1hwV06tmDLZbbVLt/ZjlFE+HfyxbAQR7pthUzNRmUynbWCbnaHc/+ChPfredccs/JbogBbtGg8vLrzA2tJW8ZvXzdOWXdpH/H3tnAR7Vtb393/hkMhN3T4CQBHcpbi1WSqlB3d3bW9fb3rq791YpLYUWaHF3h+AJcXeZJOPzPXtPEhJIILT0lvv/7ttn95yZDKPn7LPXWu96X063TaoVDv9Oxfc3y2q3WtDndeNQ1DhQ6MzUzHkQr57dpZL5mUC908W4bYfJaLDKNpX3U44Vd+prj1D35TCCy63UqX15R3kzFpud6dOnY3JHsPzLA9gtTpxeVn7wKiHfFcSwrhq+uXZCm3P2d088QFH6EVKGj2biHfd3+D0KFl/N8hxq1+TK9mqVjxb/ixLRJ3a8Imwx2/nh+a2YK6106R/C+Ou7tZpTxXy0JncNXx34iu3FjazCxrXxrb1ulQmYZoj2KSG0tvZVsDQWMuKGs0N7Hqt/WdzMnhh3w+2SPSdQvSSL2lWe9bb/jC54D2hdSXeufpn96dkkX/UGmv+pl5/9+G8Iuitq6rnkrcWy8qzTaqWllDjkhZqkomnfU4STf1Mp3GiVoFWBuN7o1UoMGhVeGiUmIbDgpcbPILYadI2BslZzbBwfQB8fVLdH9xaTg/XQIeo2baZu40bqtm4VzeitghTT6DGYxo3FWyi3nqI3XVTYhVKsCMBFFVX8RkKtvLKysnkr7muq6h8P0dstAnfxvmfOnCn7fE6mmii8G4WAiiZMUMq7yz6YffvuprhkIVWqGJbburK+0XNb9Jrd3+9+hoeOobq4nOrCUiqKyygtraasspbymgYqzFYqLE4qXSrKdT6UeflSf5JeliYotAqcBg19AvVMSowgKdyHrqEmQn10sjolKI+iwv7e2PcYEdX24vpMwGZ18vYj6/Cqd5Gld3HrY4MJs1WTs/J7XFmh/OLIxqpwMMyeRBJRVJmsbIrey6fW7wgNCP9DPYZtoa6uijfffBm7Xcuo0AxGFf/isSK6caXH3/d0IILvda/D3h9a2Vy5lXoUYcme7KuoMAgqrqDmiv5r0Y8rPCmdVmxpB6lauomqTRk4hU1HI/T+Nnzj6/HprEDdbSwHIlK4Nn02PY44uPJAICGHj6kvazt3wv/ii/E5/3zU/meWDiXOhb3mBhmA/1ZazZH61vRqEYSMDfRhTKAP/X28OyzE9mdgt1qkeronKC/2BHXlZdSKbUU5tRVlcmHaUQiaoJePjwzCRcCnNxrR6Q04D9WhtmkwJgTjf06C7AH+IWsuCwsW49IqmWG6hLsuvPes7vsSyKi3MmzLQRlILuufSI/jfLmlroPNirWuDmt9Pdb6Og4ePsy6HbvQqtWMTOmC22bFYq6VAnoieG6oqZH7ltradnup24IQ7BH9rt7+nkSICK5rrMuwK/cRET+CvoNeRdNGRT0j4y0ys97GYEhg0MDfUf6B9pKWsAihrOo6KYK2tLyazIbWCVlhtzg+yEeKoQnbpT+S1LLZysnMeo/8/O9wuz1zQ0jIJJbvDqY63YXWUsGMlVtw1XiSES6lkuyYGLIHD0K41AqGwV3XXEPuRRfjLC2VquNuy7HzTxegwO+mB/GZdkHzeT//jV3kH67EO9bG5Q+MPabXkr8DvpziEcrrfYVHDLLxM4nK0kvbXuKfP5nomlbJ1u79eOj2B6Qa9LWN4mQnQ9beMlm9Eph2bx+iuv71lMxtWRVc/OEm+RGEUNebazIYuKEaNEquemYwpoC2rxPLs5dz7+p78VLo+ezw04TGRBFyc9vewbYGB/Ne30lZrhnfYC8ufLAfBp+Tn+sZu7Yx78VnCDHEUFKfg1qpp+v9L7Ov3M7u3CrCUmuJtyrZqLOzwevYfB9cX8nny15A7Xax6Lbn0aT0kNfnUF89YT56Qn30aLRKntm4Hc33HxFUWYpLoaTv5dfSc+wwJsydIFXHT7Duq8rF/cEQFNZa8vs9yJ6468kqryOrrI6MMjMHC6ups5643lGozKi8j+LnX8CImhoid1bg3ymOa59/p9W5kDo/nXWLc0T8RUQXP8kwEEJrLfHhykO8uPQoodoS/jXqOUneOTK/HyMvvoXkYSPb1O4QgVH1qmyuS3qaYkWZvC85IJkbDTeyYd0GIhOS+LkykoOFNQTr4IvixahWe9ryXFdfw9TB5yII69dHBvF84p/o7RaJi7d7M7+yK/72S+nkCkPlpyPw8hgyJk3w2If9OAevHn/eIvKZ9Hw+yC0lXKdh1YCu+DVV6cUX9usd0sNa9NIf7JdIhellVq5YQ4AjCVW5p43DEVrDl6blVJZNxEsLax8cR7BJ12byZvH7b0hBsevf+hhvv9M/X605NbLq7Sjz0MG9B4dLyrZSBAkdgGjXmPfqTslKGTmrK91HtK2nIPy/vz7wNYszF0tBYQHRanBH7zvoG9r32ANF69e613Fv/ojNxSFsLPMI1vYbN4aRNxzzb2+63lUvzMC8oUAGOrL1o0Xg/d+id/W/SvdpfhF/J3Ir6xn+kscP8j8BsQ4X6pUa4QuqUki/S7FVK5Xyfs++CO4VhNeUkFhwiM55h4jPO4TBcszqSKAiKIKspP7kJvenIrozahHUKxUyOSCf4/ghkgaqxm3jffLxTX877vFK3FiEnVNVNZVVVVRVVVJeXkF9g5hc3IQpzegVDpxuBTWGcIIiYggLC8fP31/2KVmdLupL6qjaUiR7vZwmDapkf2w2K1U1GRRZdlCgO0yNTvRoCeqoEnX5IGwlY6jHiPsPLO5MOAjRuAkzaggP8CY+Opj4yABiAw2yj2p2aSWPp+UTodOw6bjqxUtbX+Kbg9/QM6inzJKfyYppS4iJ7pkFB1i0Lpsraj3+3aOvTCLlnAhcLhuzZz/PkSMKfNROpisHoqk5Rum1KewoOhkI7Z+APjkApe7P05tXrfqGNWvSUast3BO6E2P+FlmploG38KU/3c9nqab2ky9QlyxHr9qBUl7yT+PfO4XLkp7qTC/MhV6yAiOhVmMaPUoG1BtjLTy0+XGZCb7dNIVpuzVUL1goaYkCCq3WU/G65OIzXv1uwtF6S3MFfEdNvVxsNcFHrZT93yIAH+lvIrIdQam/GuJYE4GjCMQ9QbgnGBdBogwaq6s8+9VVUim4TcX1jkKhkDR2ESjKoRND59nqveS+CDRFll1YCCnlVt3mbRH8e56z+cmP/YbNG4WsQruczsbhaLHf+j5B5xbJh21lFRSY6whVuEnUKuV9Isi2i63Vis3SIB/f/P2JykpCN1w6L7QleejKPZZ2J4NISAj18QpFLX7+wQzoPMwTXAcEYPIPxFuwDfwDJPug5XFZXbOH7dsvRCEsZAYtwWBouwrvcNSycdNo7PZKkpNeICLimAJ1R4+Jow1WVpXXsqqihk1V5laK41qFgnP8jUwI8mVCoM+fOnZdLgf5+d+QkfmmfN8CAQHDSYi5B/u6HD7evg2bSsU569YTlZ+POiJcJs2YMIEvfv6Zuro61CoVDqeToYVFRK9Z0/zcIrHse/4U/Ow/ovcqQTHlNY+uQ+NCVlRlhQJ36Ihazp8x0bNoFDZ7n433aBd0GguzfhDZD/lvMqoypBiXcMt4Nvwmku7+QJ4PNz/8HNVdurJ5cMpJLQUbam18/8+tNNTY6DU2mmEXt0/TPtO474fd/Lwrn/5hPozOdkGDkz0DfPjoun7tzn2S8rxoJvvK9zOtYhSPjn0SQ8/gNlXYF767h7xDlXiZNMz4Rz98g0/ttCCe/5uH76G3dTibyxZQYy/nnEuvZPCFl8ogXqqWO1wEXBhLlsNGWomZtJJacisauH/H94zL3cH6iB48P7ANoVClArdGgQYHE0vXEFd+SN5dHtWXDd3zqFBuw885jFDrldTZnNQ02Kmx2BlvW8nr2g+xuVVcYPsnB9zH2y05UOpKUOnzURmyUXll42+0U233VA4NFhUXr4yUc8/m8xWM7T6ZixIvkj7LFXMOk7WliG0WFw6nG/8wA5Nv7yWTFKJPe9fihWxdOJ9vTOPokXiAmUnz0NCFiuqbmTxlSrtBjaPSQtHL2/hnxMds9NktX/vHqT+y7LtlZJXWsEnbj/xaB0FGHd/cMJCuIUZK33mH8g88onHm0WOYccHV2LRaXk6M4qoOJI/axYa3Kfl9OzbHjdLnWTkjkqgBncn/xz+kjoKwD4t85eU//vxCRLK2nvO2H5GX/a97xDM+yPfYH4VzyopncSuUHOgZQZGvhbCgB1nxRRiqBs9axdatiM/171KbeR+4vHjm/G5cPfREWy3xm3x+7y3UVVYwfNY1DJx20R9+z0KZv2ZxFuaNHiswIRYYcHEiurgW770D/d1KtYKL/tGf4Jj2WymL6or4LPUz5qbNxd5Y3BgaMZTbe99Oz+Cezefeui/eYdsSTxvG0KAsBocUoRh4g0fp3DuwdeC9IMPz3o8LvCvmzWfXvlRGPPzw/4Lu/wb8NwTdJbUWhr20EpfT5bHMErFf42LLs9/Yy918+6+B0VZPl6o8ulbm0KUyl6TKHAJEX2QL1Kt1pAYmsCe4M1tDk8k3nXlxltOBuOCdqz1MkLKeOreG323JmN0d6B1VWtEGrEUbuBaF0jNp2Gu6Yy05D7e99QVB6XLi7bJhdDvwVzrx10CAl5ogPwNBgb4EhwcSHhlMuJ+BcF893qcIQkVVZ8iWgxRa7bzaNZorGpXMi+uKmfTzJGwuGx+N/0hOYn8VPl2XwXOLPL2H/0qOpXJTiaTpXfr4QGyY+eCDD2Qw0aPHKvz88zDah7Btfyz9qnoTbWtB/1Er8erqL1U09V39/5CKpoBoPXjzzSeprdXQOa6UyytWoRBqr8lT4ZKvT8sLWMC8qYCqX47K9xd6ew80mmIoOQDFBzxWVk1iXJZqj+WFWPQKdWshgCY8J/1iPT7EUf1wNLipWbhI+oEK/+8mKI1GKgYl8l7QblLjFNzc5zZu7nQlNQsXUjnnR6wHj/V2CjqqoJuKBYEm5K85Z8ptDtZU1spqoRCZqrC31mkQtNxh/kZGBJikjVJz5v4sggg2ReW2KQgXW6vZLCnTInCv2Z9PfVElhZoSStzFaO0qfNxeuK1CPfyvmhn/HoigX2cw4PQPpsIUiAo3KQY1Bm9vGVTrvI1SjFKwAbxMx5gBXiaTTCCklqYy67dZkgq69KKlclF+KuzefS3lFWsJD5tBSsrJF645OZ+Tlv48Ol0YQwavRCWEBE+CUpudDZVmNlSZWV1RS66ldTU7TKthdKBJetaPCjBhPANtNRWVmzhy5Bnq6jxq40ZjCrHaWSh+z6L61wUcDghgx4D+eJvNhBz+nUPDInn+/t9k8kWIoOXMm0f6l/+mxtubvb174VtVxblLl6Hv1KlZwTz6448w6g/Dbw94FOHv2iX78JsEuhIHh1Lvn+6p1NhqPAF3xVEI6wnX/tZsISXUoC//7XIOVhzknIhz+GDcB2RffjsNO1exO6U39975EM91ieSGqLbdHcTCVfSRZu4pwz/cm0se7Y+6UUTrP4GSGgtjXlvD8AoF3e1qSvxUfDzehwX9E+nv274y/ep1v3Nnxj/QuNUsmL6AyOPsH8XnEuriR7YWo9apmH5fH0JiO76WO/rrBnQbXWTW7mVr2e8y0XTju5+RlVrDss8PyF7WWU8PapUYqLc5OLo1FfUNs2Tiff69r5OmCaC4xkpmZT0NlhZWnJ43SZ/q3ZxTuVnm4/aFGtnebz9ulwZz2iPgapkgcPOR9k3OVW4jUxnNpV7XUqXIRaktw99kZWSnroyKGcacQ3PYVrxNVhFv7nUz9fZ6jlYdJa0qjUPvf4+uoIHtXSvZ16lG2pTN6DyDSUt7ElBvQnlhZ5b+nEFdlRWdQU1st3KObPoRS2NLiT0yCf9z9tDZP4MG/R04SxNOWUlM+3IjV7nuxayql0H3T2N+4r1P5rHMnki9W0uEr55vbxwsNWeaUDVvPoVPPikZkVVJyVx37V3U+voxt3dnBvm132p3MtizCij+UFyHdWxVp9GQrJNuNs32YWo1nVcsRxPacXeZ49u5Ju08wt7aBtnDLXq5m7FvLvx0nWd/0qsURgexfd0HFG65EXtdIG6Fk9rANH7o9DHmouk4qvvTLcKHX+8Y1sqmrQnrvv83W+f/iF9oOFe/9n674mmnA0t6JZU/puGs9rDLvIeE43te3CmLI+I8++2DVMmU8QnSc8ljA9Gdoj+80FzIx6kfMz9tfnPlW7Azb+91O2VLtrDt17nyvlHTJ9PPvgyOrvD8Q6FZIpTOB90KWsOJgbfo8Raq7PpSsq+4ErfNRsQnH+M7fDhnK/5X6T7NL+Lvxh+hUHgE1RoDdKEk3higi/uErUZlvZ1ys1V6WpaarRRWNVBQZaGwog5bfj66ghxCKouIry6ga1UuUebSE9+XSk1+RBcquvbE2asfuu7dUGo0OF1u7C43TqdLTlJiiPtEdtUh+rfFbbf4e+O28e9Codlx3H1yiEqQ1S6rQU6b2DrkcDqccgjBJBq3CofYF0NQgp1SFbZLiBkvjZN6p5qt9mhsSo2skov+YLXLgU+dGf+aKgwNtZREZ3O0Wzo2lWfRF1IbwMiiAXRVRuPj441PoB++wf74hgXjFxGCd0iQXISdSXycWyKtVYTi7vpBybLK/9zm5/jh8A/0DenLl+d9+ZdVuX9PLeS273bK4+TRSUncMCyB+a/vpDC9mvDOvjSEHyItLY3ExEQmTupCaupt2O0V1DphjS2Jf/X5DltqJQ17S3GUt6A3K0AbbUKfFCCHUIY/nc+wb99mfvppsVQQvmKcD51WvgxOG4x7Gobd2+HnESrtxW/vEldPfKcmYDrn1JZ0HYXl8BHp413z2284io5VHGu8YFOSgpjpM5l+0WMySWDZ16hivHChXMBLKJV4DzsH32nTZA+4sh0xrD8LcX7tqalnRUWNFJ/aXVPfqi9W/Co9TV4M8zcxxM8oLcl8/iLtgDMJl8XB1x++w6v+n8vbN/W8iTv73Cm9kBctWMCYUSNxO52S8m63NGC3WI/tWz37Yo5xCfFIh6hAe7Zy32Fv3vcIx3nm0qY0p2xzaRHYN90WiVKlSi3nCNGaI6rkSpWqcRzbV2m0rDdbWW22Em0ycm9ijKzKCy/hpq1aq0Wr90Ln7S2r8+I1OtLL3R4uX3Q5e8v2ygrELb1uOeljq6t3sn3HxY1V7mUYDCcXqHQ6rWzaPBartZAunR8lJsYjRtmESrtDVrDXNwbah+tat0KIavYgP29GB/gwOsBE0h+kjbcFm61CJgSKijyK42qVL+Glw1B9m4Pt4GF5n/glF0+dSo23gRGD+vFIxXPYG+p433ADUduyMa9e08xasWk0/DrtfJxqNbPOP5/Evn0pfOYZqr6fjcrfn/i5c9DMmQRV2dIusCz+Jn54bpvHm/rxfmzcvppJ40ej+W4G5G0F3xi4YZnHuq0R7+x6h4/3fiyFxX4+/2dCDCHUbTlAzjUXy77v2x98hoqkbmwenNyme8HBjYWs/OogSpVCKhGfrFL1V+HjH/djX1Es1yEZMyL5Tm1p24O+BYrf38X9PMce7yPSmuif57QW0dyyIIPti7IkG27y7T2leNrpQAinCmX0Q1VbOeLYIZN6oqpYVpAoExT9JsYyeFrboma5t9yKefVq6adue/Ah7jqYw+bqOtHoyrlGb24LDwKHmzqbg3pRaTyyl6JfPsVla2DByFIqvBuYEnUT50Zdiq+XBh8vjdyanOU43uuHt62ez3x9+Cwsmpt73iwFW7UqLWUNZYz9caxkUS2avogYn9bf394Vi1n28btowwLYNMFBaplHOVrtUjO1fjT3X/8Urmr45fUt1FWrZcuJo2EVPgFlDJlxGXH9E9m4yTOXPLv1BW7qrOOSaSdfez6w8F6WlC9H59JiVdqY6LqD+YfDsKGWffBfXT+QcN8T2+tEG2LenXfhqq6mJjiEu295gIa4eJb170qIUHY/zWpuybu7cJQ0oFTu4RNNqUyK3HjjjURGRpJ1xRU0bN9B4E03EXJfx9cMba3NBFNs/cDkY+8xd6unJUQIbg6+Def459m2MJMdizMlQ1LvU0eB6RBOZx17jHnsKpsu/9nPtw2lb8yJlPGq4iK+vP9Wea2Z9sDjdB4wmDN5naxamEH9dtEYg6Tgi+rxqXq9hTjhnOe3UVthoVOfYM69qXuH5mShgSTmrgVHF+B0O+X6olOeN30P+zHl8jvoc26juN3RVY1K5572F0wRHqXz3rOk8GjLwNtlqcay5SWclWWYU1Lo9f13aHVnzvLwTON/QfdpfhF/N85034Lwx3YUF2EvKsIhRL4KCrBlZkgFZltWFu52eiyr/EM4GhjLLkMEB32jSPeLauULLZJ1nUOMdIvwpU+Mn5xMksN95P2iv81ZW4vLbMZVW4uz1ozLXIuzukb6Xjqrq+TW1Xy7cdTU4G4KSv4gLP5+LB8xkjovPUFWK32qLWS7teQE6LFohBO0ixxjDmkBR6hRear3wWq4f+CjTOp82V8W4J7Mt7v/xgNSlfrDlFgGeVuYNG+S7ANrsuT4K7Aju5JZn2zG6nBx5eBYnp3mEc2oKWtg9nNbqXOVUh2wTwYPwpM7KCiI7/e9hzXvDaK1YpmqomvXp4mKnNXsAy4UZy2HKuR+S6h8tei7BqDr7IcuwfeUVXCPhdgLFBTYCAou4pZ+3VEvfqxR2XcOdBnfITXO0g/3SB9LXRc/gq7tLumdZxpC36Bh504ZfNcsXoKzoqL5b3Z/I8HnTcFH2OINGCCztNW//Ub1z/No2LWrVaXcZ+J5MgD36tc+BfNMQPhebqqqk4roaytrSatvff6LVxbqz4P9vBnka5TbYO3Z1z/1W8Zv0rdWnM/nV4ziiQufQx/r+1/R9yV8pfttOkCVw8kX3eOYGNxChb0ddFSxvD0I31WhMiw0KpbMWIKmxVx+PHbtupqKyvVEhF9CcvILHXr+goI5HDz0CBqNP7F9lrKjTsHW6jq2Vpk5WGc5gZUlrLwEy0IwLob6G/EWPtVnEGIOEYG2CLgF9V14FvlmROP1cSlKkTVspIQbx46lfPgw5qWmYlCpuKF/f3b9+B7+24/i1aIAr4mOll7Nh0NDWJGeLu+Ljo7m+uuvx2W1So9gwWgxDBpEzD3novjlFqkTscRvPum7q+jcP4QxV3flt0ULmdrwE8rDCz06Etcva6VXIWx6rvr9KhlkvTLyFc6LO+YPnXHBrdgOr2FfUnfuvPsx/tUlkuuOq3Y3zd9CvGnwBQn0O+9EKutfDafdxff/3EJ1SQO7tA5UE8L5xduJl1LB7qHd2lSuFlorJe/u5qB3JvfFvCL9n+dNm0eCr0efpclDW6Cp/el00PT8boWbhdkf4NA6JaVXb/JBqbsGl1PNJY8NIDi67QRF3eYt5FxzDU69nstfeI9ivQGjSin76y8K9W9zzi7Py2HeS8+wVZfOph4VhOtCWXzpUvnZBKosVTyw5gG8j67irZIyqURed80CTMIusxHfHfyOF7a+QPfA7nw/5fsTXkO04Xx405UyaXjVK++Spirg3RVvkOo+hNIJfQpD6XXUD1edA41hPCqdp688aWgYI2d2pbDoG46kPUt+XWee3HAXfQNd/HDPee3OnTuKd3DN4mtQuBVcVD6O73V52PKuwImKpCAts28biZ+h/eu7NTOT3FtuwZ6dQ4OXgUduuR+vAQP4sXfn09IdEYGkeX0+SoOCUOcsflUMYQ/JdOnSRVa7a5cvJ++OO1H6+tJl1UqUhlO3ILSEYN+M3HpIiqi1ZCFSnQ8fjYD6Mug6iapRH7Psy0OUZHvWkj6xG9kQP5fcsggGlA3AjoofLT2Z3j+ely5qW4n819f/RdqWjcT06M1Fj/3zL7n+y6r3z+k4KzwJT0PfEHwnJ6A6rs+/JYoza/j51R3Sv3vYJV3oNSa6w6+XVZ3FM78+yHaXp9VCq9BwQ68buabbNVKAr1npfN9PsOKfrZXORXGlywR5zahakEbJi/fhqjiKKjSaw7dez3kzLjxrr+2nE2ueffzC/w8hxB/qN23GcCSNOt8NsrdaLOpl6VocgsILu+m22yUv9q6aWpzm2tbb2hocpaUyyBaB78kg+k218fFoY2PRxMagS+iENiaaOI2GnvUNTK6rI6+qgUNlFg5WWTlYr+CQTUulW8ORYrMc83bly+fyclglFT2lPFMOsW8QFjynC9HT7eMjJ0xVq9HiPh9fVH7H/qb08fFsdTqiSkv5/PPPETIfhwIjGG/viTYlgIVxm/n66DdUuD2BkVHpZryPnX7qi+lnGv0fD7gFxGLz+qhgXs0q4t2cEnbZvpcB96CwQX9ZwH2kuJbrvtwmA+6xSSE8NTWl+bP7BHnJxdovy7bI27179JUBt1CWfW33F7hceu4PDiJMl8vhw09grj1AYuITaCOMcvieG4ej2iqDbzGs6SLBYqNua5EcAkLATtfJF10nTxDepEDdBPFezj//Kj766BPKSsNYY85mbN+rPB62gtJ1w/JTCqvVbSmUAbdCp5Iqnn9FwC3fq1KJoX9/OUIffZS6zZvZ9NUr+G85gnelWVbAxBDHrWnUKEwTxhPzxeeyOl79669Uz/9FJsKEYroYwhvcZ9JEfCZORJecfMaPSbHgFQrnYggUWe0yABd03y3VZilctc/cIMeneWXNdkx9fQyNw1sG5f8JYbb2IESXHl3/qAy4p6rGc0vxBVT/chTdHX34b8D3RRUy4BbsFtGrfCqI9o41jf3DQ4YMOe2AW2BC7ARe2/6adBtYlr2MSQmT2nxcVdV2GXArFGri4m4/5fNaXS4OmC3scI5ksfpxDtijqdh2TNm/CV0MoqXBJINswagI+AtbGqzWYg4efITyCs93pilQ4fu1Am22Z/7R9+yJ7wXT8J00yWP78/zznLNrNxElJRR/+x1N4VyJL/hNnETijGvRd/ckJYNcLrK+/ZajR4+Sm5tLdna2tKqMfO01Mi+6iPotWyjfNJCgkG5UFlSSniUU/RWe4Nftpnv+dyhLl3paWGbObjWPCdrwo+selQH3pPhJzQG3gHht/8uvp/ip9XQ/tI+eaQd5V6fh8ojAZi0QIX7UpJYcluBLnwkdt9A8k9ixJFsG3GpvNetUDdg25BA/NZ5Mq40fiyvbpMWbN3kU4vt2GciokFGszl3NR3s+4qURL0mf8ZVfe1p0+oyPOe2AW0D4GQuIPnFVtZ768lLZgtFQU43asZvA6OEERbVPc67s1ZvCuATCszIYs2YZGZfM4u3kGGJP4jkdGBXDzH++iurVp9luX0UhxcxZ8TGXjbuFtMo07lx5J/nmfAy+QRR7dyU0cwOm3x+Bm1Y19/b/nvm73E6Mn9jma3gZTcT17kvGjq0c3riOc2bMIirtLtbULSG7chde9aLQYMFiUtJ3RiA+ini2/JLJoY1FlOfVEXnOevk8ibHno9wIO8uVrDhUwnk9IttUmRdaMwLn+07EdHgkDdIhREm0sorvbpp20oBbQBcfT9zs2eTdeSds38HL77zIs9ffyfM+Bp7u3DEmmjWrGvMGz5rT/5IUVBtTGJGzmb0kS2Zefn4+EaNHo4mJwZ6TQ9X8+QTMmsXp4Im0PBlwD/b1ZlZ4o9OJWMvOuVIG3O6QHuwPe54NL+zAYXNJ6v7wmV14u+Q1NpQ5UBhz6GsejsZioZ++hIcmtv375R8+KANu0UI0+qob/rI1qL6zP6H39KVmiafXu35nCZYjldJazKtHUJuvGxrvw9AZnVk/J032eIvbYfEd6wsvWrGF7gsbCPUN48goLWn2HN7f/T4/HfmJe/rew+SEyR6h5p6XQPL5sO1Tj+uMaP377hKIGQJjnqBh2xIZcKMxoO9xE+HFPu0KKv+3QeH+v/JJ/osr3fbSUtKH/wUq1SqVDK5FZl8hKgqCkuZyS/qlCNxpobzaEYgDpULvw1HfSNL8ojgQEMfBgFgahAdtCyhdLrrWF9HPUkR/VwU9vJx4+Rg9gbJfy2DZrzmolveZTJ6e9j8IcSgfmruNn1IX41A4cYdY2Rm2j+xajw+gv86PEV4NDPWupLosjsOHhsmFUXx8PMOGDZPq5//JAFzQL0XlS0zy/qWvoW7YzdcTv6Z3SO8z/lq5FfVc9OFG2ZMmGArf3jAIg7b1Anjrlq389vtvKFxquvmN5/y7+8jqy/7y/QwOG8ykhon07FlIVtYb8mjw8elNj+7voNefuBhy251YMqqxHqnEerS62UuyGUrQRBjRxfigjTWhjfGRFCjx/f/y69fs2nkUL68arrlyAqGL34KcjeAf7xFWM5xo+yUgepiKXt8hPTvFRcU49Mx4Zp/O8ffa5pfY/fvXDDriZmSmF5rqYwwOhZeXVPU3jhqJ9/DhOPLy5MKgdvESXHXHvh+RCDM1BuD6xPat6s4kiq12aUu2uaqOLe1UKfVKBT1NBvr4GOhtMtDD5CUDcyGa+FdDBIz/WPMP2Td2fqfzebrXE5S8tgu3xSF/a92A4LO60i3o/kM2HyTHYuPFxCiu6YCQ0J+tcjfhwz0f8t7u96TAzbeTvm3zMTt3XUll5UYiIi4lOelfrf4mWoKO1lvZVVvPrpp62aqw39yA7bilgxKnVBkf5Ocr2xXEOF366B/VAcjb+i4ZNR/i1NjADqZFKozLlWhCwvGdMhnfCy5AodNhXrWK2uUrqN++3VNxaYQmIgLT+PHMjSnmA/syhkQM5eMJH7d6HeGU8eqrr8qt0WiUv4k41sQ5XPjwI7J1JPa529i0roBDDWOJSzYy+e6BONe+jmrlM54nuegL6H5hq+f956Z/MufIHEINocw9f66kl7eEo7yBrMvvxZ61htTkHtx116O8lBjF1Y3H0M6l2Wz6+WizJocQzfpPo6q4Xla5hd/1hBu68erBXJbsLyauXyiHgtR09dazekDXVtdXZ52dwhe2yjag4Ft7cdSYx6ULL5UV4W9G/MC290ppqLUT3yuIiTf3OO0Eqvjeil7dLhcuwoZsz/YlrP3mc7z9A6RwFQoDgy56hmEXtVAXbzGXiyTZU2n5DNmwmkf+/QHWwCB6rFyOqoMUV9HKcvvns9hkSCOqxIt/dL2Lpxs+ocJSQZQxirfHvE0XjR+8N8Cj9jzmCRjxAAXmAs6de67sm15+8XLZZtAWDm1Yw6K3X8E3JIwhoy5m47zvqbF7EqYKk55t8aXsj6hAq9FJa8+RTGTZ5welRZRSW0fEoE85d8YHvLKsgs82ZEt19mX3jcRH3/qc/TntZ57a+BQmjYkLAt7i/dUl8v4uqlKGhefy1B1PdPg3cVks5D/wAOblK3AqFLw+6wam33ydtP87Ja387V1Sndsg/McvTmzur/5ZdQF7nfGyHW7WrFlUfPMtxc89J4tJnX7/vcNrytUVNVy2J0PacK4YkCSPWYkFd8OOL6nXRLPK+1OyDnmu6VFJ/pxzeTzP7H2C1XmrxZKGaUYvNu6+k2GqoyhUau67525MJtMJx9bspx6i4PABeoyZwISbPVaBfzWkwvlPRyQ1X0C0APpNTUAdeOJ8Id7jko/3cXRXKd5+Oi55dMApnQJ2/r6AVV9+JPdHXXUDfSdNY0n2Et7Y/gYFdZ4+7ZTAFB4Z+EjrNa449te/AVs+AoeFyjQDRTv8JK026K7nsWZ55jm/q5MwJretZ3E24H/08tP8Iv5O2AsLSR895m99D2JBovTyQmHwQullkPtyeHvLYFhlMqI0mlD5mORWaTKiMplwG4xkOLXsqnKzu9TKjvwacis9J3UT9BolA+MDGdZZjGCSw1sr5p4JCC9JQaOp2pnHj34r+CVgFbVaD/XHT+cn6S19FAeoLJmPThdBZMRHbNu2j4MHDzZn0CIiIhg+fDhdu3Zt1zbtTOOp9Hw+yi1FYznEZM1qPhznUfs8kyittXLxhxvJKq8nMdTInJuHnJCZFrZtb7/9ttz6mLugM4ejGFHEB/YXMGlN/DjpR7av3i6Dmurq9ew/cD8ORzUaTQDdu71JQMA5J30PTrNN9tVZj1bJKnirXvBGKH206GJMKCMMzNs4n0J7LTFdDjNz6nOov5jq8cgWFLwr5zVXBFqi/OsDNOwvl33lYhH3V1W5Txl4b3+Nfx/4NwqXm+d8rmDgYZekvTkKPFWdJuhSkjGOHCkDcUdZObVLlsj+wZatH8J+zOfc86QNny4p6T+WFKqyO9hZUy/Hjpo6GWyJKu3x8FYpJWVYDBGEC/urzgZdh/2EO0opFxVu0SsmKj8vDHsBlVKFeXMBVfOPSlZD4N29WLJu+VkbdC8oqeLG/VkEaFRsH9INw0kUqI/35R47dqycl/4oRH/ohJ8mSJXZ7yZ9R4/g1nY6lZVb2blrJgqFhj4Dl5PlDJBBdctR5zzRhsxfraK3j0Fa0/kUvUhEw1K6RM8iscvj/EfYYdu3U7VyETmm+dR391xzNNkKAucHEthvMqZx42SCuW79esxr1sq2qlaf288Pa+/eDLnnbnRdPQGhCHgm/zxZJne+n/w93YO6t/o327dvZ+HChXK/d+/eXHDBBXK/4KGHqP7lV+xRXdnQ+XZcqJhxzjrCkqJg0X3yMc6xz6Aafk+r51uXt47bVtwm9z+Z8AmDw9vu6yx8aTlVX94tmW53PPAMVSndpPNFTUE9P764TQa7o69IImXYfzbR2DTn/fLmbmmNFpMSwJQ7e5FdXs/4N9ZgE21n4yJlgmZB3y4MaCGoVrsuj+pFmVL7I+SuPvL7v3PFnTKA6WYezPDUmQRFG7nwgX7tenGfDJW/pFO3qRBdoj/B13WXYowf33YtdosdhdIbt6ua/udfycjLL2317wqtNu4/lMvKCs/6YZBBw4v33wJlZUS88jK+U6d2+D1kVB5l2q8XyMD/olWRHImpxTYgnE/P/exYcmXPbJh3MwghwlvW81nROt7c+aZkvIlWs/Zgt1h474aZjfoTHmi1BgZffCm9z5tCub2Spzc+zYZGG9T+of15JOVJtn15mKoCz/W/t2AQjItg/BsrKbMomDkwhhcuPDY/1NpqmTJvikwU9NBfycZd3eT9fdRF9FTlkh6Rxtc3fd1Mne8I3A4HhU8/TfVPHpGtf0+/jGsf+wddjV6npJULH+rQe/uhFAJfwubzje6Uma28xzUySXzTTTcR5utL2ugx0vov6v33MI059dpa6BON2XZItl3dFBXMs10aq+87/o3717s4bBnNBtudWBoUUt17yAWdiDvHh7tW38Wukl3oVDoe7n4hXy1XsbesGxcbj+DtqGHAgAFMnjy51WulbdvEr68+j1qrkxZhwqrxPwXp671S+HrniUywFJr1GR2NaWQUCnXr31Co+//44naZUBP2c+ff0xtVO9etJtszAeHzPvTiy5v/JpwYhA3iJ6mfUGf3FBeEdoOofAd6tfjsNQXUf/Ew2R9ulW1BIb1qCLxoPOaAGzhwsJI+N7ff/nA24H9B92l+EX8nXA0NFDz5JHn5BURFR0vRHdFk6cnQCXNuZbNRt7SxURy7T9xWaDUoNNpjW1HZPv4+raY5kFaIoFoG14239XpPJfwMoaCqgQ3pZXKsTy+Xk2JLCJXLMckhjE0KZUinQPR/UmHVZXWS9e02fi5fwK8Bq6lUexQ6tU4tXaq78I/x/yAypIh9++6U9Zi+fb/D389D4RZ+4Js2bWLHjh3SN1wgLCxMihaJPqG/OsjZWprB+aki667mzQQll8W23f/zRyEsSmZ+vJn9BTVE+Xvx0y1DCfM9sWK2ePFiNm/eTHBwMEMSJ7NpbgY2VQM/9HqBJ8c9yvjo8a0qiQ0NuaSm3k6teb+HLRB3B3Fxd3TYr9dRZcGWXYMtu1ZmYO0F5mO2XC3fv6IebWQtIbHxaHc9gca5H2WfqSimvdNK0VwE2yLoFtnRkDv7oA1vXy33P7EIfX3H63y5/0t5W1xcrut+nez9NK9ZIwWaGvbubSXKJcSYvIcMxqtff1mBq9u0ibp162Rw0QRBQRfBt+hHNfTti+IMi/ud6jNlNFgbg/B69tV6qp0tbZ6aP4vCQ01P8vaS4lhJRr2sGsTpddIe8HQwP30+T254UgoziQr3s0OflQG3fE8uNyXv78aeZ0bfK4gNhiNnZdAtvrvJO9Pkd3dvbCgPJYSfVpX7nnvuQfcnBWQeW/8Yvx79lSkJU3hh+AvYXC7ZUnCkzsLq9G9Itygo0vQg1+HT1mko+3IFy6F3I9NBjBi9tnl+LC9fx+4916BQaBk6dBV6XQuHgzMEUSWr27iR2mXLMa9cSYN/JZXXOXCKQogTgjJSiA68Eix26jZskC0fTUJoEmq1PG/0I0fyXXoa1TodV199tWQ6tfVdCZq36K9uCSH4+dprr1HfqEEyZcoU+vfvLzVUsmbMIFU7iPzIkURp9zAt8NnGc9zNkdCpxN/wRatjs9pazfRfpkvq/+XJl/PwwIfb/ezmzYUUPfUk9uz17O7em3tvf4iXO0ei/TKD8vw64noGMenWHn9Lq1RT37VKo2Tmk6LS7umj/ddvB/l4bQb6/sFUBWq5PDyA15Jims+J4td34ChtwG96Z4yDPOfEvpJ9zPx9puwdvuboU9x03xSM/qd/7Iskb9FL23DbXQTd2AN9J08lddWXH7N7+SGUmlgc9cukJ/L173wqhQwFo+ObgnKeyyigxuFCp1TwUHw4N0cHU/nRR5S++Rb6lBTi5v50Wt/zNb9fw46SHfRK86VPmh8p553Hedfcfuw5xDHy7UWQvhyiB3NxkJFDVUd4csiTXJx4cZvPWVtRxrpvv+Tg+tXytjBXTfIdxLD7r8On67H5RXzPP6X9xCvbXqHB0YBBbeDp6AAqtvajKt0TjIrERl5QMe9kegKq724cxNBOnuqiSCCL65jeHUbpoTtkR+r9Q6Mp3/mznJMXRS/i/UnvMzB84Gn9Pp7f/w0qP/lE3l4+YQrXvvovjG3oiFiza6ROi4iqA6/thlfXFky31S/B6n8x13A5qfUhsmAyc+ZMSl59lfJPP5OaKrFff9Vh8bRAjZqNg5I8+gMFu6j66CpWV15Pvs2zLguMNDL+uhTsfmZuXX4r6VXpkgHwzth3yC0I5Z4f9qBWOHh28AaO7DLI4s2dd96Jv79/Myvn3w/cTkVBHoOmX8Kwy67i74AQm636JV2yEAXUQV6SMXa80FplUZ0MvEXrSo/RUYy49ETmXfr2Lfz62vOyBVZUt0WVu02tg4Zy3t71tmROCIhizt197pZ2d+KaLtrtMi+6WOrj+PQMJiJ5LwqFOMoUWNUmVLesRRPUtoXl2YD/Bd2n+UX83fhvEAL6IxCTq+j/Xt8YhG88WobFfmxZ56VRcU7nIMYmhzAuOZRg0+ldYHOLs/n0l7f5TbMGi9IT3Id5h3Fl8pUYM4zs3bEXtajI9F6BlyGXuNjb6NTp/hOex2w2s2XLFjkEfbBJMGfcuHGyd++vwkNrH+KH6nAsxlFMDPLlix5nblKx2J1c9flWtmZWEGTU8uMtQ1vZeTShrKyM999/X1bXrrjiCsKiw3nryfn4VYdTF1XEPx6f1ebxKdSLjxx5moLCOfK2r29fuqW8gZdXa8uXjkDQx0TwJAJw0ZMtgnBnVdu6AEqqUfsr0HRJRB1iQBWgp2peOq5am8zY+k6MPyuOe3GB+TT1U3n72u7Xcm/fe5svRo7ycszr1skAXFTijtdgEHoLol9cJMRsuTnUb94ihQpbBunG0aM9lfKhQyTr5O+gTKfXW0mtrSfV3EBqregJr5cL1ragUSiI9dIS76WTQXmcwbMVPc7Ch1ko+LdEk5iQgLgwPzH4iRMqKrbcWhl4iwXZ4ZQaRsyccNbNnzuq62TQLdS6dwxNOaVA3ZmqcktRMZudzHobG8oyeX//fNyacCICB5NjcQjR5TYhqvE9jAZSjHrJYOhm8qKzl/6kCRPxWjt3zqSqehtRkVdKscUzAXtxCea1a2Syqm7jJim4KUSxzONc1J7vRGkG7yO+hJQMwL4r7QQ2iTo4GO+RIzCOGCEZJeI82bp1q5zLhGbF7be3CH4acbjiMBctuEgea79d+BuRxtY9p6LPftWqVXJfLKqvu+46oqKiqNiWyg+fFOBSapigf58ufsvkY5wDbmKh/RwmTZ7c6tgUc/9vmb8R5xMnPY/16vbbBwQVO//RBdQtfUJWu2956Dmqoztz/dxyvI0aLnti0Cnpn38FhNrxd09vljTwQecn0H9SXKuE76hXVlOmBdvAYMmK2XtON6lnYjlaRdknqSi0KsIfG9hsZ7R29hFeKnqCHP8DjA89j9fPa5306Ciql2VTuyIHTZSRkNt7N//GQjH6q0fmo9R0wmX5ErulmjHX3ozX8HH843CeFAEUEMklobjeRDF2VFaSPmq0ZCGJIE4Ecx2B0Gm5ZMEl0uZL41Qyc2kUSreCXhMmM/a6W44de4LF9d5gsNfxVFAAv/r4seqSVfjpW9OuhbPCjkXz2Tx3tqSvN0GvMjK9x72EPzywzYBHqEs/vv5xjpTt4NmIBil8a9J+z955Fqx1DhQqN+WJRj4rKCM2yMDiu0dQbi1k6rypkvVRn3Mtamsyb17amyBLHosWLcKpsjE/ZgHTO0/n2XOe/UO/U87nX1D3ssea8OCI0VzwzhtSn6clg1E4kTiK64/Ryluithje6Eapyyir3QI333wzQQoF6ePGS5ebuB/n4NWjNbunJcpsDoZuOSCvW03iafaaKna+/BK7ykbiRCsTSgOnxNNrXDSHqw5JRkZJQwkhXiF8MP4DAjWxjHt9jXQMmt75N6YkLCYr805yc6vo2bMnF17oaSnZu3wxyz55Vwr53fD2J9L68e+CmLOFA41gEbhqPcl90eftNyUBle+x3yBjd6m0IhQYe3UySUOOJXUKjhzkx2cfw2G30W3UOM69+a5T0vl3l+zm+S3Pc6jiUDPl/NHeD+Jz1wvSklUwAOO+/RZl6rew9DFJORdwTH0Pdb8rOFvxPyG1/+GsgLgAdA0zyXH9sHgZCG46Ws7yg8WsPFRCYbVF7ovxqCKVgXEBTO4Zznndwwgx6dudLLYXb+f7vd+xomAlLp1nkd/FuzPX9rmO8+LPk960ziQn9dV1pKcfZe/egQwfHkx8fNv9M6JHTyxwBw8ezIYNG+TCTAjmfPHFFyQlJTF+/HgCA88sDUh4bgrBFC+1J+heXFZNVoOVuJOItHQUwjLuju92yYDbpFPz5bUD2wy4BZYuXSoX+qKy37lzZ57Z9AwrYzdwUeqDeOeFkbm3jKjkE4U0hC+vUDn29x/CocNPSMuhLVsnk9T1n4SGTj2taoBSq5LiamI0oTAzn4Vf/ESgy0QXPxdB9MJZ1oALX2yVYGsUaGuJhoMVMourDtDLYFxs5b6/Xr7Gfwris4s+Oh+tj6x6f7HvC2qsNTJwFFlddWAgfhdcIIeoZovKt6zObdhIQ2oqtsxMOTxfjlLSXzXh4bL323LwIM7KSqp//lkOWcHr0wfvEcMxjhiJLvGvZ2gIiCBZLErFuOi4QO+Q2cKhuqbRICuqoiougnQxjoe2RUAuthllW9iatwKVtjMz4kfy6IC72qQwilYC74Fh1G0pIjrLIKvfZxu+yPf0WV4Q6tchRfjU1FQZcIsq98CB7VeQhE2j+K4LLDYKrHbyrcf2xTwiRismgq+HFpvR4GH0CBXmSHIJcxwi2TeUYXHnyyA7VKs+7eNHPD4+4W527bqC/IIfiI29Gb3+1BX94yHo4OJckIyQNWtbed3L14kKomGYA0VROcH/UqMpFMdEAw2s9TxAo8HQq5fUTDCOHNFMG2917RD93IJuK5JabXzOrgFdGRI+hE2FmyQt8qGBD7X6e9++fWXgLeZMMX766Se50D+U4yUDbv/6dDoFewJuUYN09b8eNntsylqKAoqAWxzTzw97/qQBt4BQGzb0S8J6YBCO3E1c9/s8Hrr1AQ5GaXlgcte/JeAW2Dz/qAy4/cMM9JnQ2tZK9AbfOz6Rx+fvQ9XgoM5LzaLSai4JC2gW1zT0Dm4OuFNX58nR3/s8GXSvLFlGTk3OCXZZHUng1m3y9JCaRkS1+o29/YJR6zrhdqvwC4+lNHMvK+b+wLuaCCxKlWz7eCQ+nOuiglolAdX+/lIXoOqHHyj/9787HHR/svcTGXAL2FUugq8cR/nXK9mzdJGscMvAWwQpfjEe66Slj3FvRRUNncecEHCXZGWw5IO3KMk6Km9HJCYz8orr+OnZx7E4zNSG1hLRznkbbYqWVPV5O+5FWbuIHJuST4vv5bFrnsayNIDCtGoCDtZxuV7Pr6UNvLzkECW6r2TA7TB3wV/Rk09v7k/vaD++/nq5fM54Z0iz3sajgx495THcFmKuu5Zd3ibUzzxN8tpVbLzxFoZ+8mFz4G3eUCADbqVBje+kNpLpplBImUbwvp/o7mdhX5VenpuXXXYZvpMnyZYPUfGOeuvNdt/DS5mFMuAWehSXhflzZGsRG7/dTp3V45YS3dXEyCu6S62EFTkrpHuGYA108u3E++PeJ8IYwd2zd8mAOynMxE3D4yjIh/CIZeTmDpCspXPOOYcAX182/ujR1BC2bX9nwC0gzgtDrxDpMFOzLFsKrUknmsOV+IyPkZo4CpWShN7B9J8cJ237Vn97mIAIb0JifWS1ft5Lz8qAO6HvACbcdGeH+ud7h/Rm9uTZ0h733V3vcqD8AFcuv5YxMS6urPCj82svoFz6AOz6Rj7ebQonQ9eNmJ6t20D+W/G3qpevXbuWV155RVJ7CwsLmTdvXnOP1PG45ZZb+Oijj3jjjTck3e5/+O+EoJKPTgqRQyyADhTWsPJgCcsOFrM3r5otmRVyPPXrfgaIALxHOBNFAO6jl/1FwgdwzuE5HK32XHgExb6PNZkbht/K8KRRrS6wKpWK/v3LKCiopL7enwMHRjJ4sIeZ3x68vb2ZMGGCVAsWk7c4Ng8dOsSRI0cYNGgQI0eO/MOCRsdDqDoK8sx5ESkUBZikn/KneaU81+X0K8UtIdRsH5qbKhMZOrWST6/uT/fIttUnhRqv+GyiYiM+t7ioCKVJhbeCiCF6ijbaWTf7CBc92rfd1wsLOx9f3z7s338v1TW72H/gXkpKF5PU9Vm02lMLRrWH8PhIwgfGsn37btKc1VwwvYIucffj+PUVHHs2YicOW9gMrLnHflBHSb0cbUFp0qAO8ELlr5OZXLWPVm6bhtKoOeN94KLCLQLvZzc/y9y0uZJiJZR5DZpjViaiHcTQr58cwXfdJS30BDVWUGlFdU8osYrgozkAUSikQqvS2yB7wZ2lpdRv2yZH6Wuvow4Lwzh8GIbBg/EePFgG+P8piPMvXKeVY3TgMWaRoG6KoDCz3kpmg1VS1Zv2sxtssudT9NMdszLrBEGiHQQ+a4Av1u4lVKchvHFE6DSE6bQEalT4DQiEzHJ8zQq8dhQRNST6b6HZtoVSm51fS6rk/nWRpxaBES0uS9dvoMrLm6ghQ1lda6G83Ey53UGZ3UFhi8BaKNC3zSloTfWP1nuSGUp7EZtzfiZAYWbuuW/g3bCL3XvuQqnUMqS7oIT/OSaYv99g/PwGUlW1lazsD0nq2igedgrY8vI8x/qmTdLFw1nl+b6aoI6KRGU0Ya8oxZVXhtds+ck8f1Qo0CcnYxgijvUhGPr1PalNUE5ODiUlJbLi3KtXr3Yfd3W3q2XQLaiQt/a+VZ7DTRDCSMnJyezfvx+tVitblObPXUDttlD59yERc4TlLOYiA4agelQb3gLVMUVy0R/7z80eL2rRdiIE7joCQ59g6rdOwpG3mYF7d9A5N5Od/ROk0NjfgaKMavav8wS3I2d1RXVcT6jAzAHRfL0piwN5ddDFl+8Ly5lhNNKwz5OI8m6klecdrmTdHE9wOm3CGEpV21iXv056/z437LnTel/Cm9hV75BJV6/urb+b7H3lMuB2OasozTlAg7cJr5oquh7cQdjIcfyrS5Rk3bSFgKuvkkG3ecVKbNnZUvDyZBA2Wx/u9Wi0jIgcwdr8tWzTp3Pbrfew+IM32bPsN+mfPe7622Sw4h50Mxlr/0UnSx13lJY2P4/TYWfTT7PZ9utPkp6sN5okhTdlxBjpER5h6Ex2zX5y6w/TlXHtvh+R7O2krkJ4uBQSTo2tkod23sf5A86ni2oQlqM+RFhcXGvRsWbtQY6mLJZunWHOC/j69qFE+Ruk5ktmYzJ4kFdPQmwBlFDBmrw1nBt3Ln8EfS69iJ80ehKefpzArZs5cNPNpHz4AW6bkprlHkcEwV5r1+Zq0M3SgmpkzTz2MVOu14qKigi4/noZdNcuXSr1HLRxJ9roHTQ3yJYCgX8oTcx/Zae0zAIDJlUxwy6IJn5cf/n3z1I/462db8k12zkR58jWE0GRXrq/iF92F0j2wEszepIY0Z/ysl+E1B0xMYPJyXGycuVKOhk01FVV4hsSSq/xbaua/x0QLjJ+UztJJkHV/HTJNBRaC3Xbi/G/oDO6eF8GTo6nLKeWrNRyWfWefFtn5r30FBZzLWGdE5ly90OettgOQqVUMSt5FhPiJvDC7NtYqjzIij5Ktvd1cO/vFzG9JAelWNwPvhXHiIfZt2w1p5d6O3vxtwbddXV18sIn6FlN9Iu2IIJx0W8qhK7+h/87EAtj4fctxp1ju0iF7d/3FbIotYg9uVWySivGs0uXERWzm1rNVuwuD9VE59IyunoAF2jPY/CVk1CZTrxQVlRsIL/gPVK6eZO69yIKC8skrfD8888/5aJcLKxEv56oNIlKcHp6uuz9FlUoEZz26PHn+ucEhXFp9lKpUHpb79socIfIoPv7wgoejAtr09O0IxCJDJGwmLszD5VSwbuz+jIooe2gS/QnLlmyRO4LwQ+84ekVHlqoEJ47v8dQvj+0hdoKCzsX58BJinReXtH07TubrOwPyMp6l9LSJVRVbaNr4tOEhEz6w9/V2LHnysVtQ4MvmzdtICJ8MqYL/4HWeRUc/Deu8rmUKF5EEduTgBldcFZacVQ04KiwSG9KsRWibULRXFCobIJG5RGzPxFKUJlEAN4yGG/cFwG6SYvSqEV5mqI+MxJn4KPz4eG1D0uRIOF3+u7Yd9tVpRW2eT4TJsghYC8q8gTVWz2BtVhAiEC81VsXyv86nayAC1uyJisyAV1iouwXF0G4qNCojO1b5PxVEArnIvgTYwSmE2jq+RYb+2ureCN1DmnmGlyqQKL8e2FT+srg0u52y4BTjDbRpzERZq1AvaYCf41a2lMJqrS/Wo23WolBqZTUVkFzFUNUtTz7nvu8VEpZ3RLqtWqFQr7ntvbFfyKJIN6Tw+2WejRiX3wOuXV57heJhNmFFXIbrdOyucrM8vIa6oS4l9Mlh9npxOxwUWF3yMC63ObAmTK08YsR5b9GtkM7EJT9MJ2GSJGI0GtlMkIMYWkkqPtRem2zzZvdFcN5GU9KWuTewpWEVf1b3h8RMfOM9GCLczwh/m527rpc+nfHyWr3iddsQdUVFlsioSQCbXtubuvn0Ynzzhen2Szp5I68fDy1eaGv48YZrcF30Dj8zjkPw6CBsgrZUTRVubt37y6ZBO1haMRQOvt1lj2bIgkpguOWEFVyMS+JSrf43Lm76vC2OgnUZBOn20ZduQ+5a73xT9AQqvkB7+Q+zfPzc5ufk4F3F/8u3Nrr1g6/d6+kQFSBEagjB+LI28JVv83nyZvvZW2lmZEB/9n2EqfTJateAklDwog8rhe0CWqVkn+cm8T1c3bh6OzDpqo6Dm4vxM/pltRvbaRReowLpWTBUkkcFCor5reW3SqD7oUZC7m5581E+3TMK9jtdEuBNgHT8MgTkqjpOzzK23ZdMdQ4yAuKpEvdISbt28DtN1+DWtM+Y0CXkCBbFerWrKXi628Ie/yxdh8r+vUfXvewtIETWhQ39rhRBt3r89fz+IWPM/G2e/n9/Tck3VgcP2Ovv439lYd50d/IN4V1xBxdA9kbqdLFs/CtlynO8CQkEgcPk3R40Ysu0HCkgmh9Vxl0H92/jbHieGynqmCzVVBZtVnu3zHsC7yPLJSB5K+ZvxIQuJ7nx75C0W9KGXROqA2geP/d7Ijdzfc3zWxWNBfrIHHMi9aMmN5dGb1lAD8ELZHFkD8adAvMmD6Zpx0upj3/JF5btpB50834jL0Ht82JNsYkA8J2ETUAwnsRXLiH7qEa9hXbZcHk0ksvle1XgjVT/vkXhD97YhLwhYxCQiscXHLYzpEcD91ZrbDQz3suvSclox4zU4p/PbHhCVnRF7is62WS/aJWqik3W3l0nod6fePwBHpFe9gJXROfZG/qrYSELCA3dzKHD+ynLPeI/JsQGVOpz64WKAFh/Rp8Sy/qdxRT/XumZBiUfrQXr17B0hJ23HXd+OnF7VQWVfP9k49jqy/GLyyc6Q89heYPFqL02w9xwyv7GRrl4uvLvDmqsvC0N/wcHcNjgx4jpftloveW/0v4W4PuiRMnynEyCO89IUQggoPjVQD/h/9biA4wcNOITnLsK8rnw+0/sblkKVZVjszOirKO2xpCXOVQLq0ezLk9EggWXsxtCLFZbWXsPyBUY910SphEctIsvvnmG3bt2kV4ePhJaZstERISIvuchQ/k77//TkVFhRQ4EhXwqVOnyovPH61yC4gLlVh8dXa7peiUoON+W1jBbTFtB2Qng1jQPbPgAF9vzpYaYy/P6Mn4lPYvVjt37pRVH7H4HDFyBPdtuI8qaxVJAUnc0ecONCoVIy5LZNH7e9m7Mp+QoSenDgkRtYT4OwkOGsuBg//AbD7Ivv13EVQ0n8TEp/5Qr7d4bxMmTOKXX34hO7s7O3Y+wYjhP6C88BNcn0xBWbKdYO2TOEf/hibYIAf4n/C9uBscngBcBOOVVpw1VmkxJrzE5bbWJo8vz32i2upRrm0LCo0SpUmLyqiRQbhnq2kMyjWoRGDeeFsoa4tF1fjY8QSfG8xdK+/iYMVBZi2axXtj35NU1lNBExYmFXObVHPtJSUy+G7YsYOG3XuwHD6Mq7YWxGh+kwppFyh6EK1HjshR8e+vpI2g8B429O2HoX8/vPr2Pa2g5a+ApHHai/lw450UVmcQpNLLKsKo6CHy7yLAFX13orpbYPVUeUXwLWzORLBaYXdSbrNTXmejXq2QvcqlNoccZwtyrTaePuqpCnYEetwE63VS2EcOrUpuPQG2lgi9ZxusVXfYsk203FyadCnv7HqHf6d+yM2mg7JFJC72Fs4U/P1FtXsQVVVbGqvdz2IvLqZ+23bqt3uOWWtaeut/JI5Vnc6j2i/OVasVR4knOBKaBiQGUBOZi62TC+++g+k+4B00Gv8/lOQ/cODAsSTjSSDOWVHtFovtbw9+KzVCNC0cE+Li4uTcL/QwuiYmUbHOs9ju7/0DivjhMO5+WHYbleneGCMtJPr/Kngvsp1ILN7VCjXPn/M8WuHb3UGIeccRYUSbOFEG3cP2bCO6qIB3/Y3/8aB778o8yvPN6LzV0tP3ZBB6LQNCfdhYbsUVpGd2fhniiBPiaXabk98+TJW94cExJkZf7nFoEAr750Sew4b8DVL1uKM9ww37SuX8rvRWnxCo1VvsHE31VJA3xpsYUgoJVSUY/PypryznwNqV9Bx7jJHQFgKvvloG3VU//0zwXXfKBOnxENcboRpeVFdErE+spF57a7wZGDaQrUVb+Tn9Z24fcbs87n9/73X2LPsdrZeBzV1K2KPXsTk8icGFh7DMuZlv9ydiabDK6vb4m+4gcVBrl5D61FLCvOLRaHSYK8ooTD8saedtobRsGW63E5OxGz7GTrL9SSSXhEd8UX0Rt++4nWjfB9CZFAyrNxJqjmPi/lh2zcuQ/cxeJq2sIguIljvv/qGMWTWIH1gikwmVlkr89X/sWiJ+8/umT+IWi537X38etm2jIuMpDEPuxO+CPidnoIn5b+BN8MvtjKhbyD7Ola40otodeOMNMuiunj+f4DvvkBoPTVixpxD/+fncWOgJ6pRKBd0Ct9KP9/Hu1ANGfUxGVQb3rL6HzOpMGWQ/NOAhLku6rPl3FgF3mdlG11CTbKVoQlCQaEccTTmriIqupnhHLbaGegIio0kaNpKzFeJ79h4Qhj4lUHp7120romFPqWSmGM+J5NyruvLtM8/gsBai1noz45FnMfh0zMP7eNiys8m//37UejtThyg4PyeN731MvBcYyF61i5k7X+Cyukxu7n4z/5fwtwbdp4LIqF155ZU8+OCDdOvmsSr4H/7vwua0SZqSUI5dn7de9hMJFqFKoSJGO4jSjJ4U1sSyDwX7gDeP5jFtsZtLB0TLnvEmiAvLgf33YbOV4e2dSGLik6hUXlIUbdmyZVKpWwTTYuHUUYh+Z6Fyu3HjRtkWkZ2dzQcffMDo0aMlFV1Q2TsK4Xu9Mnel7OcT1MWmi46wqrjvcC6f5ZXK/dNRepYVlEUH+XKjxxbnpQt7MqNf+0GuoIk1iQGNGjWKeVnz2FiwUVpfvDT8pebFoFDFFT09Qkyjcr+u2V7tZDCZUhjQ/2eysj6Qle+y8pVUbNko++ljoq+VdNbTgWDDbN+xhfw8oUDvQ2zMh8TF3kGZ7Un8XXeiUWajWnoFRC0B7xOTIOK7VRg0aMWIMrVbIXGZbThaBuI1LfarrbjMdqmGK4aoootxSqgUsh9N6aUhwqDmW+3rbKrcQr6zmJ///TmjEsfSK0ZQYtUoDRpUcquWVh7tsQM0ISH4Tp4sR5P7gUWwAfbskUF4w+7dOEpLW1mPNcPpxLJnrxwVX3wh79J26tRIb++LV+/ekrr+n6Rnby3cyn1r7pPVIVH9F/613QKPzfciqBR+z2L0pm3qsBD62/DNMmKOmKj2UqK6vhs1BpWsHlfandTLyrKnwuzZP3a76T4xnC2q1837jdX4pvtbVpnVjRVwUU0WyQNxX9O2weWSyQHx91EBPvioVbKPWlTYjY3VdaPaU2kX9ltlGUfZsnwp/iolD9x9159WLG8LQpDuoz0fcag6m2ydkhGdr0CnO/0E38l6sqOd07Gt30ZdxhzSxDyeV3iKf+RuFglUBQdh6N0br7790PXuRqbiS0oqFsu/xURfT+fOD6FQ/DFtBpFwFewewZjrCGtuUvwkSSctqS9hcdZipnY6ZhUlzg8RuItEbH52Lnp3MAHqHAyGfKwzVuNtCsT/iiuo/OYbCrf6ERe4kYqC7VJASOCmnjeRHNh2cNQe6qqt7EyrZoBPBKrwXjgL9zBz2QJeDrtZeqcL+7b/BATzaetCDwNj6IWd8TKefD4X39XDE5OYPm+3DLoX+Cu4SadC3zOI5V8dpDzPjJdJw8RbeqBuobshWAAi6BZVVPF9RZlOnrQV1yZphSQC+iERrTQ81lTU8uGyNIbZ3VR6K6kf0gv1vgVQXUHC6AnsW7WUbb/Opfvo8Sgb3RHagmHIEMkcEknMqh9/JPD669t0XFies1wGaaKVSATcTeeeDLrTfpbV+5Tho3HYbCz7+B352und7BALDSMfwjbnDvR1OXTzUlEUey6T73oQU2Dra5u4DlkOVqBSqonv3p8juzZweNP6doPukpLf5TYk5FhiQdiS/TDpB26Y8wi7swawz2pEH/sVebo8BqddQ2JNJ/atyZcK9b3HRZF2+Ghz0C1oyck9+tC5KJp0r1yWZC1pDkj/CAS774Fp5/Gg081Lb7+AsTwd6973URnbttFrhe4zYOkThJgP0C36MvbnVsp12sUXX4xXr17y2ijYCcH33kPOgQp2L82RLQ0iTHYroOvAUAaGLMd3279A74t7+kcsyFzE85ufp95RL69Lr496nV7Bx1pS5u3Kl170GpWC1y/t1cqFR2oZJT7J5i0bCfZbSWWFhxzdacS4kx5fZwsEld//wi6y/aP6twypcm5em4dzQzaJXiGk2fJR6KZQcNSN3x8gSTnNdeTefjtehhIih5lRKWyg8+WqCW9yXsIwXt32Kr9n/c53h75jadZSRitHM9F99lDy/88G3S+99BJqtZq77uq4ebxVVHZaLDaFolzTokyMsxEul53S0pWo1PsoLlahasMKSKXUo1IZUatNqNV+MtN/tvQt/hkI79idJTvlRWpZzjJqbJ7fSyAlIIUp8VMY4zUcxY8lOGssHNbAygQvfs+tpNxs4/MNmXL0jvblkn6RTOoeRlnxJ1RUbkCp9CI56Q1cLrX8jsUiqaCgQNICf/zxR66//npJIz8diAA7JSVF0tQzMjJYvny5fD5BRReBfEfw7s535XZi7ESiDdHNx+XUQCPPa1Sy9/XXonKmBncsgygWGy8tOcJnGzy86eempTC9d9hJj/fVq1dL2xtRrfGO9eaNZR6PxXv73Eu097H3JDD4wnhyDpRjq1RzaHMhyUM60uahIDr6NgIDzyMt/Wmqq7dy9OjLknaaEP8QAQGt++9PhfPOncznn39GWWkcO3f9iHfuIGxFSsr0zxFmegRFeTqury/Eefk80P/B3lSD0mOlF+7VJpNeVsxtLhl8N4+647Yt7heUdhGlCVp7kzqo+MRDadHDWQwVtBaLklCJ6p8KpV6FQq/27IuqeePtY/uN9xti0Y9MwGv8DBEN4qoqw3poP9YD+7Du24f18GFJ1W0LtqNH5aia41GhVxiN6Ht0R9+rF/oePdB3744qoIVNyxmC+D6/OfQNb+9+W3pwdwvoxusjXyfYK/i052rx+Gp/O4ZYE9qsWnSrCki+tMtf8p5F3N2R6vLM1CwZdN8cGcTDcaGn7OX+cP1qjNYGho8ZIzUW/orrlUllYmRYd5YV7GRdnZ6ZkTf8qdcRyR3L3lQse0UiZw+WffvERRi/xqWFg/YDbnFM6bp1Q9ctRVoxiX114xxqs5Wzf/8t1JpTpX94l85PExY2A4dUxj9VJ/uJaCmg1qdPnw59ZtFGcGnipby35z2+2v8V50af22rOEteB5UuXYrbWodLU0Mm4kn+7ptBlySrZwuR/152Y16/DnpVNyQ4TOQtvpcboJMk/iauTrz6t7128/xX/PkCB2Y7VX4Ouy0TqC/cwYet6Pp9yEe9kFfFhcsco2H8Wa74/jMPqJCzBh879gzr0OXpFmhjrb2SN3UWxl5Jd/fwpXpFD+vYSWV0bd10yepOq1XOl+KUwOGwwm4s28/Gej3li0BMnfQ0RGNgL6kQGDN0AzxySa7HxbGYRi8truTDdM/+F9ghgXv+ubEgbx67ffqG6tBi90UhVUSEHN6wjcXDravLx8L3iCkqefJKKr77GNHOm1ORogqj2Cpstgdt63kaiT2LzZxoeNhwfrZ9M4ry5/hfivPpjMSSiO+868lfMJ/5ADXXKQAqyNrEyP4LzItIYFl6I8/Y7UPr4nvA9i4BbXGOUvlq6jBgug+4jm9dzzmVXnUAxt9urqKzcJPf9/ce1eq51h2o5kD4Vl1OB2jsNjekA4psq72Nn9nYrY6waQiywbWE2RmUvXP6l+PsGyufQDwhmzLcDZdD96+FfmNFpBn8GPQ1aJib05L57HufVt/6FT85hsq+9joiPPpQtJ+1DjbL3Fag2vc1w53r2002yWsR6z/faazHf9yD7lqZTWLWJqhJPgs+pgP3xOu65JJnOqizUX3jYFOaxT/P0rjdYkr2k2d/8xXNeJEAf0Py9CQHgJ38Rdqlw5+hOJAYbTvh91OpwYqJvZePGb1G4XDh1XhwoKmWwzfZfs3ZXhOjwvToJ25EqyuYdRF2noof/cJIDziGt3s36bw/j5aMmKqnjDAe3y0XRPx7AR7+LoH5mSVRwh/XEceHn4B8neYrPD32eqfFTeXH7i+TU5vAjPzKqYBRDIj3st7MRHZ3PFe6OlK7+AxAHYUshNUHfFXRyQYFtykqLyqQQUTuZkNrTTz/NM8+c2Lvx3XffYTiJwMrfiwaMpvb7g9qC263G7fbF5fLD7QrF5QrD5QrF6RTZ4PZ71c4GiAp2uiOdA/YDHLQfpMF9zEvVR+FDb21vOUJUIfiVa4hLN6JyKbDonBxNqsVicOF0wcFqBVtKFOyrFD2WnklMp3IxKGwzw6M2Eq45B6dzwAnsicOHD2OxWKRiuVDr/iMToDhtBNVctD+I6ol4DuHvHRoaetLny3Xk8pH5I5Qoudt0N4Gq1v3WC3S+LNT5Ee+w8nB9UQfeByzMUbK8wHORvTjeybCwk5/S4rMLmpj4DLEJsXyr+JZiVzFd1V25wvuKNt9/zVEtNUd0KLUuwkbUoTytliQ3avV2tLoFKIXHjwwyErFZz8fl6rhOg1CTF3ROvb6WGZbheFkCyYuppy4wk2Fpz6Fz1FLu3YVNnf6BU3Xmq4SnC4UT1A4laodCDpXY2pXN+6WWIurstZic3gQ6/Qhw+aFxCPmQM3tBdok+WKUTZ30pzpo8XFW5UJWHuzIPRX1Ve+8e1HoUGj2odLgNJuyBQVgjIrGGhWANCcLu441bqcCldONW4tmKt96Bt29xW/i5/mc5Bwj00vTiAsMFaBR/rtfNq05F8l4f+R0e7FFNvVHUqf/zKFSqedoYKS6wPG/OJ9B98vdRWlpKXl6eTDILVpcIuv8aOKlQv8jrZQ1SqOYBnwfxUXYgSeV2o66sRJ+Xhz47G31uLrriElQtbOxOBofRiDUiAktUFJaoSKyRUTh8fTzU0OOgUJTiZfgYpVKIXgnxpmtwOjrLUFs8+o9oHdbW1sp+VPG9in7ujjKT6lx1vFLzCg4c3Gi8kVj1MfEs/7p00g4Ukqs24m31ITrJh0OZnkprTEyMdLvQ5+QQ/f4H8jgIH1rBVUODmBJwN6Gqkydhjoc5R0PVfj0o3QxIcBFRpqd68ysoi9L4ccxEPrjoSp6pKyDU9de2VDQUqynf6SVWjoSeU4/G1PEESEW1gh2uMH6K1dK9soHpS0Vop8AvxYIxtu0Fa44jh4/NH8vr5b2me/FXtb+473LAhE+1hpIwC2nxDSzW+bJca8KuUKK1u3jglypUTggZUofWz4W9tobsBT/If+ub2I3qI/vR+gcSfd70k17DFXY78S++hNpspnDmTGp7H6t+ijltp20nYcowbjXdSqVVxe5yBUdrFOTWKbD5L0IbuB57TTcs+Vee8Ny+9moS6jPpYsnmoU7rCHQUkBU4ij0xrTUFBOLSvAks01EcbiEnuobMud/gdtiJHH8+XsGtjy+1eit6r9k4neE01D8o77M5YX62kg3FnrkmweRGH/MxuS4Pi8EtJvPc2zGboxikUDK8ToHC7mE1KDVuvGNtGGPshGY6uS30KXmdEb/R8Wua04HSoSBlly/39/Km1JLP6289j4+5FktEOHk33IDLu33Fby9bGeP3348CN++aHqas1oqfPgx/VywNmS6cKs+6WNij7U3QsqqrgaFqMxc2lDLi8NP4WnJJ80nmukA1VVTLY260fjQjdSNbOWcIQ4j3DihJr1ESZ3RzV3enFKxsC46GGrJ/nY3bqcAS0wm7t79kTPr5tVamP9vRUFJEwcrfiPLqQs/QsRjdHl0Ym8tNht1JbW8zSr+OzQWhyxfS3f4LxjBPcTQzcDT7oi7H1QYDUsQK66zrKHQWMtMw86xOVogi1qxZs05pT33WVrrXrVsn+03FxasJIri5//77efPNN8nK8tBoj8cjjzzCffeJXt5jlW7htyzEr85Wn26Hw8ze1DnyxxKe4sdXUURtxeVswOE043SacThqUCgcKBTlcmECjUreEgoMhk74mHrj49sXf/9h6LRnjj74RyGoo1uKtkghKSGQUufw+GEK+On8GB01mgmxE+gf0l8qGwrqVO2SbBqOFMvHaBN8CL60C7GGY4vyJrJfaa2VebsLmLM9h+wKK2vzh8qRHGbi8kHRTO0ZhkGrblWt/vzzz6U3t0jECIr4H4VYzAmKoej5Fgr8IqgXVW8RgLeF21beBmaYmjCVKwefeNEdYHOwZNsRMtU6okaMpqex/QSKCJrfWJHO8gLPRfKpKUlcMejUGo+iyi/+badOncjrkkfx4WKZxX134rsEerV9wRR9ZcKP1VGvJNCZxJBpCZweJuNw3E9u7kfk5X+JWn0Etfo1QkKmEhtzB15ep37fgsHy4YcfICyt9zkq6Wcw0veaUSg0Y6BoAO5vLiCwLo1JNV/hvOQ70JzdySeBVbmreHLTk/J8MGlMPD7wccaGjsZtceKyOHFbHR4ROLEvhwOXVdzvbP0YsbWJ4aG/i31ZkhULGbcCpVONRhcOwWIcS0S5rLW4GgNxZ3UuLnMRrtoicFrFigG3ozEhZi5CU5KG5iCY9H4ovYNRGIJQegehNAShaNrqfVGIgEZU6kV01HIrViZKBVZs5Nfnc6NrMk7lRCJ8Igk1hoK15b9RCkUbz20xZDCv8MRoLW837gvBovSMozKJZg+vwlFYT/eCUAyDQj2BXePjhVievHC3jN5OdiE/7k8nPLKdO+YUVzKuyEwvby8uiuzn+S1EhqzFVqa7BWXd4WTN3jX4OKJI7pxMdEBjxbLp8a7Wj2/1N3l/i+eVf6PFtvFvjX+ur0unqmoiDzdoqHFCrM2bbgFJLd6+ApfVgqM4F0deJo6ibBxlhbiqSnA31OC218FJAjuFtzfaTl2kbV2p9yZq/TMJ7D2NpH4vNr4Ht+yBLKqxyB58sS8YS559KyXVFZRVO7A6bsDmMuBSBCEcziyipaPR/kz8XKL1RtU4xL5OrcKkV+MjhpdGbn29NPK+IKOO7OI9VLi8GdS9K5Mmnyf/XUdxYPMBfsn4heyAbG4d1tgOlLkG1w/vcKDmOQhIp15v5tzzryV8717ZtiMqbOeddx7BkyaRaa7A+e/ZlGz35ZVeQSRcfS2ng+qSBuau2Ckr/IOndSI52Y+K91Px7jyZhqI3mbZhJd9MvIBDnbpzbZe/TmzWbnUy5/kdYiam97hoBp7fhoXTSVC3Jp/oLfky6D7oo+c8rYU+/UMZftnJE997Vu6Ra4eMkIx2q92iwl2xKVWe3wfHJfNCVSVldk+ia4ivgbvq9RxwVmH01zFt5rDm1/sl+wjZe3cRGxvLweyj2CrL6RYVTlyv9t06BCoKCql4/33iUlOJeuRh+XyCsbdz+U7cDgODQx/ni0wlqfnHmHsC6pr+ELgejekgPTtp8Fb7Ulpr40BxMS6nF9UaX3b59pajkp58wmPElq8h8oInIewYQ0rM8aU7dyCaX5Km9KNnjIkluUc5vHEtwSo3IyZNavW6+/YvoKICEuIvIjZ2EmklZu6ds5fDxZ4k+NgIF9ecH8BtazJlO9/13a7n60NfUxfxBcrMu9niMJEYvA9vsy8hyhQaqpzUpuuoy9Sjizcxongsq0OXUx9dx5W9T1zXdBS1S3OodxTwXL6bmf0TuPveJ3jvnRcwFBSSMudHIj/9FJVI1rUDt3059Qc30RMjO8uT0dh9xHJLtinqLeXEVGyh9F+3M7+0Gj+1ilf7D8Jv3YuoLLnUaby4wa+GKlREGaN4buhz9Aw60VngvdUZpNek461V8ckNg4kLbD8RsPbrz8hyKjCENOCblENerr+MSYSl2V+XWD2zqC0vY/YTD+B2OdF1CyD+ttHYDlZiXpWPtrSBJJ0a+0E/vAeF4jM8opXH9/FomP8BXl4/oQl04VZocU59k6gel3CyxpGJ9olSzFjEcMJ14mxFE6v6v7bSLXxKRRDTEueee67s8b722mvp2vXUAkSnY1h+NlATBGV50qRJpzywXC4bVmsJVmsRDQ251NWnU1eXjtksKritlWAFjMZkAgNGEBxyLj6mnv+RbJHT5ZS9yxsKNsi+rNSyVLkwbkKIVwhjY8dKgak+IX1k/1MTHGUNlH930EMVE5TIkVH4TIiVnoHtQVhv7N59PZuOFrCxaDzbirphlVREESSoubhfNFcMjiEh2JOh27dvn/RXFZg5c2aHj6e2X9stVc1F8C16pcVkKjy/RXDfcmIVFiJCuVqI6CyYvqDdHrXbDmTzc3Ell4UF8GZyTLuv+ebyNN5a4VE1fXJKCtcNO/UiSFDiv/rqK3kMDJ4xmAd2PCDvF6JeI6JGnPT4/PnfSyjbbpCBzqWPDyAw4o+pYDc05JB+9BVKSn6TtxUKNeHhFxEXe+spxdYOpO5nztwfUbgVDI8qoPeMqwgIaKQD5m6Dry8Amxk6j4fLvgX131/xPhWEF+1Dax9iX7lQKoALOl8gBVuM2j+uMi6ndYcLlwjCbccCcuFh2xycN+63eoz4NyLAqSzFUZSLoyQPR0kOztJcnFWFuC2nuLAoRU96YHNArvAKQKn3Q+HlLwNypd7fUz3/H/42uB1W3A2VuBoqcDdU4KqvaNw/dh/Ok1PlxO8pEi9K7xCUpnCUPpEofSJQ6Hwl26EQN9k4yVM2kK90UKwyUOB2U+BwYPmbvdRFwB1q0hHmqyc20FsumuOCDI1bbxmst8ShikNcvOBiGYwsmbGE0Jyt8NN17KmZwPra66kJ2YNVWS3nfOHH++2330orxuDgYG688UbuWX4HF724lvASBd4RFqJ/WIoiuGOtDy6ni59f9dgYRXb1Y9rdfWRep/iNHdiL67HufhV7dhpfTp7B91MvZtuQFGmt91dgw9x0di/LwRSoZ+ZTg9C06Jk+FYQyedGr27GUNzBtsIESPzVT0+18eE0/VJqTBx87i3dy9eKr5Rrh9wt/J8z7xIR2+feHpODThigdd3fzVMyEev9jncKZFOTLss8PkLatmF7johl20bHv/uiOLcx/+Z9SqEwIXO1evJColO5c+pQnSdQeHBUVpI8ajdtmI27296h7dOOCuddyJCsad/UQHE7PdyNyO0M7BTEmKUQqW3eL8OHqJbOkN7GY469IuYLfN/zAP9KfQ9XgxYjU0RxShJFliMOm1PKW5l2mqTZS5N8P/9uWomt0NWnYX0751weku0bYQwPlNTl922Z+efU5jIFB3PTu580Uc4ejjnXr+8t144ABi5i7V8eLvx+S66Mgo5aXZ3Sn9shW5uvms714OzO6zODpoU+TW5vLMxufYUNGEYacK5iqO4Twwnv04YfJSa1k17IcSrKOXQ9qdGWUBh/lH5ffRmicz2mvMx1VFnmMCCXMwKtS2Bam5dI9R4kqyufzt5/3MG169CDmi89bOXCIa51ITAk7uMwtaeTniHWf57O7caEJtHLepQOw3X8Fjtxcvp95DR+POJfHE8K5XVOI+5PRKN0u7gkJYoW3QX7+B/o/0Ob1d3tWBZd+vFkmAF+/pBcX9m1/vWKurOCzO2+QPtaDrk6kTv0b27ddiN2ulSzeU4k5ng0QmgOzn3pIqucHx8Qx85+vNiuVi3O6ZlsRRfPT8W6a0pUKvHoEYTwnAl1M63jL/tuLqDe/IG3oHASgvnURhKac0djo70RHY82/tdItKo2C8tUE4f+3e/duAgICmulZLSG+cFFB/DMB0v8FCCEqEZyI4efn8RBsghAPq67eTXX1DiorN1NTmypVpMXIzvkIvT6a0JBJhIVNw2g8c9+jmPiEwuOOkh1sK9zGxsKNsrrdEp18OzE8ajhjY8ZKb9KWlJ0m1O8tpXJumqdXyaDG/9KueHU9dT9pdvaHVFSuJTlIx1XnTcWhjOenHXlSyTu7vL6593t4lyCuHBzL2JRuDByYw9atW2WyR/jA/1HKj7i49OzZk4SEBBYuXCip20KwTSy8pk+f3tw3/t7u9+R2epfpJxWFuTYySAbd80sqeapzhLQ/Ov67FhfNj9ZmyNuPT07uUMAtmCJCRE6ge5/u/Gv/v5otME4WcDdBH+wkrmcgWXvLWfdDGtPu6f2HEjiiqt2j+zvU1N5MRsbrlJevoaBgNoWFPxIaer5UU/b2blsRN6rYSJwzmCxVKTurtWj2P8igQQvRagMgegDMmgPfzID0ZfDDFXDJ13CWB3kxPjF8NekrPtj9AZ+mfiqFeISonbDMGBMz5g89p/xdNCpUQtylPX/Tk6LtuUF4iNsyM7FmZtKwZy/WI4ex5xfgrKzCbbXIKqjLXAzmYilA1hZcSiVOLz16vxA0gaGogsPQhISj8g1EafRB4e2L0uCD0ssHhVK00bSu+rqbq7+NFWCXYAI5ycnOkawmQQu05dTgKG2Qc4iuk59HrKtF1bj5dsuc88liwQ4+TjyszG4nrc6KTqmgr6+3p5Deotre8rZgxogKvTg3wyMj8Pf3O+ExTdvmCn0b94mko8tcjauuCldtJc7aapxVZTgrS2R/v7O6EqdZBNRmsZKiI1DoTSh9g1AHhKMOjUYdHo86SFQyBHVVLRM4xUIRut5GutXGUZudDHs9WS4nzaRz2YKtbvWaYtYPQCGHv9wq8VeASVOLSVeNyUdBeMwAjIEmTMHeeAfo8dap0aqUUsne2WTL5vRsxe0Gm5Mai52aBgc1DXa5X93gGYezCzlaWIFVqafOpZb/pqDaIsfOnBPbKwK8tcQHeUtxziQ5QugZMIi9FVvYu/xhxu+ej8OlYpfVIxrVs3tvth1YI3vGRdAt5vwPP/xQtgx8+vOnrLNuIud8La984aCuQE/V6/fj/4JQMz81dizOlgG31kvN2KtTmlWcDb1CqFmWjb7XFOzZb3DxmqX8MH4KH+eV8kSnM1/tLsurZc8KT0JfOFqcTsAtYE2vwlHewC6Liz4ZVpb0VbMiQulhv5wCfUP7SsGvbUXb+GLfFzwy6JFWf9+TU4nf3lLp3P5+tEraBN4fF8ZVEUFS4NBhc5K11+ML3rlva9ZffJ/++ASHUFNagl9IGEqVmrwD+yg4crBdQTIBdUAAPpMnUz1vHoVf/JsHew5lf9rFzUvq5HAfZg2KYWL3MMm0aAmRVBVBt5jn+1dG8+/l70AcpLiDeOXVJ1n6wdsc3PIFaf4pfBpxBROc2wmr3ME/X3+J6VfcQfdIXxoaVdiFD3nTMSGq81ovL8zlrVXMKyrWy4Db7ErhltlVbDzq8aUekRjMaxf3wk+v5J39R9letV06HAiRN4FoUzSfTPiERZmL+GbORrBoybIbufm353l27PVc3K8/pbm1HFhXwKGNBfhYg/DJC2LuSzskoyAqOYDIRD9pJ2cKOPU1uGZxlgy4dQm+6JMDGKFQcFdsKG8B99z1KO++9TyW1FRybroZvxfeprjARuHRaimIVlN6rEVRzDBhmoNEdvdnWUE2ToUVpzGZoJtupOiJJ5mwaD6/Dh/HaO9Kcr6cTqzbxWJvA0fCuvLZkKcZGN62s011vZ27Z++W88f0PpEnDbgFti/4WQbc4YlJDBr/FFu2biUmZjdHjw6UbBhhO6v/g1Zb/wmIdeayT96VAbfe5MO0Bx9vZQ0mjjvfQeG4Yn1Y9+oOYnATpFZ61M73lKKNNmEcFoFXij/u3x5Cs+szOfnXmSMxPL0ejGdeJ+a/AX9rpVuIObVF7b366qv58ssvT7i/Iz3d/z9Uuk8HQpBG+FWXla2QKtJO5zExJV+fPkREXkZoyGSp7n06cLgc0mtaVG8FrWpXyS7pPdoSgi47OGIw50ScI+0/2spQt6RLVS3KoG6zh92gjfMhYGYS6pNQVZpQXr6O3XsEZc9NUtfniYw8pqDpcrlZk1bK15uyWXW4pHntHBNg4Noh0VgPrKSosEAmecRxdzoq5G1+Drdb6hGI4FaIIwn6umBvVJoquWHpDfKi9tuFv538u3C7Gb/9CPvMDTzVKYJbW9iHiQn/8fmpfL81tzngvmF4x6jewutevC9hw5XeLZ3N5ZtJ9E/ku8nfSdXyjhyfwwaO4cd/7cBpdzHhhm506X96vYltobJqG1mZ71JRub7xHgVBQWOJjr4Gf7/BzYG9U9BSX9mO2VrPz8atWB02Ejpto1evSHr2+OhYAuDoKvh+pqRHkzAaLvsOtIYzJ6TVqGAuDya5EdvGIY4voTwuqNEalbT5OandyXHYXrSdJzc+KasMAoIJ8vDAh9v19D6b4HY4sGZkUL99hxTUsmVkYC0qwFZRjsrm/EOd6sJKSunjI23NVEFBqIOCUJlMKE1Gz9ZoQmUy4vLyYktqKueMH4/O3x+3W0PJu/vAqSTwyhS8uv3xPsPTxYW70tlYZeaBuDAeiA875TVw9apVBPv5ceOVV4LFiqu+ToreuerrcdbV4aquxlldIxMezuoqz355GY6KSnnbZa7D3dBy0dkxiEPVqoEGo4aIzr2lD7GwkxMK9trYWJRa7QmLzr35VezJrWJ3bjW7c6skJbwtaNVK4v0NRBhsGKybCVPb6BtxAbFqEyF2UJrt0qbPVWPDWSuswto/OoT9k0ZYZUUY0UQa5b46QN+h80qcmyIALi4ulvak/foPkJT2wuoGKYaUVV5HVpkY9WSW18lWpfZgVFYwgGy6KnKJ8h5AdU4C3n46Zj41gDfffkOynJpYU6LdSFS8BdaGraWXfy8ezfOj7JPZKNUu4r/7FG3P4Sd978VZNcx9eYdMKo27NoWug8JascFEVdCtcGHd9jz2vFzevOxaVow5jx1DUqQS9JmCeP25r+yQwX+nPsGcd3OP036Osq8PsG9HCfstLixeSl6d4iv1IB43+XNH/2N98u1hc+Fmblx6o7xOLZ6xmCCvIA7VNfBKZhFJa4uZmWNna6CKgxfGcWdsqHQKaIJw3vj9w1QZCF71r6EnJIq3zJvD+tlfEZmUgn94lFQyT+g3kOn/ePKk76l6TyoFl16CQ6Hk6gmPUeHlS5dwF49PHMyILkHtJqRFMWL0nNFSRHbq+nBW9iuhzsvJ6yNeY3z8BJwOO/NfeY6s3TtQm/wYOjiIAXlfkOcO4lz7a9w0KoUL1peisrkIvrUXuthj69pFb7/CoQ1r6Dd5GqOuulHet2//A/y0s5A5Ry6l3q5Cr1Hy2KRkrhgcK9+jzWbjgjkXkOvMZWbSTGlxdjze//B9SopKWG+P46hCi2/Ch1zZcxo39LgBX50v1horz7z7AvV13nSu6oXC2fr4M/hqCYo0EhhpxC/MgNFPh7e/Dm9fnUzgOIrrKH13t3xs8O29UIZ6Y6t3UF1p4fFd2ZSU1TOgpJbOB7Op04dg17SuQitVCiK6+BHbPZAExQp81t0DgZ35rdM/ZWFF6EJdc+VVbBk3nqDyMn6/uC+u4I08UFFBjVLJ92Pv5apBD+Kl9mp3Hrn1m50s3l9EXKCBhXcNx6hr/xyrq6rkU1HltlmZ8cgzxPXuR1nZSnbtvomdO6bS0ODLsGHDpKPO2Yqdvy9g1ZcfScbEjEefJbZH73YfW5BWxS9v7cLodjMg3gdjpUWKyAoo1Wa83b9jUK2gJseN7+vrZdKqo/i/Vuk+a+jlfxX+fw+6W8LpbKCsfBXFxQvkBOAWllxSZMOHyMhZREdd3aZ9jKCKiyq28BcWGVoxxH5DU79nI8QFUVSw+4b0lUF2j6AerWjj7cFeXEfF7MPYCxvp5KOj8Rkn6OSnXlQJev3WbdNwOKqJCL+EpKR/tXuxy62o55st2fywLZeqeg99Mkzv4DxlqqzQjRw58k/1d7eE0CMQ9HWxFagKrWKV1youTb60zYva8fimoJwHDucS56Vl46Bk2edvc7i4b85uFu4tlLS1Fy7swaUDTt0L3cQqeeedd2RftF8fPz6r+kxeYGZPmU2Cb8JpHZ+7luSxbWGmXMQImqFWf2YWeDU1e8nKel/6iTbBaEwiKuoqQkOmYF5cjHl9vlx45wx2sGjRIpRKB337LaBXr/uJjrrq2JNlroPvLgXRgxo3HGbOBl37dG1BqxaVGOnjXWXFUWWVAYGrweEZ9fbm/Va+UR2B6FEWgbhWidLLYx/m2TYOYRPm3ejzbdLiMLj5LPNLPj/0hVT1Fr/Ttd2v5eqUqzFozlYxyNYQrSQLMxby+vbXKbeUo3a4ucB7KNd7T0CbWYAtIxN7nqCul+CsqpI0zb8Egk2j1qLy8Uap10vf51ZbrRaFWKCr1ShUYoh9lWdfuEg07ssTrrm67mmmbk62tLiv2mpnUUEpGoeD83wN6F2Ctm/DbRd2c/Zj+1arDKgtVVWoHA6Uf9FlWCQtVP7+OP3c1HkXQKQPncc/jz4phTp/PRPmT8TqtPL1xK/pHXJsUWV1ODlQUCMD7D151XKbUXZMh6MlVTshyJvEUJMcXcOMdAk1ERtgQN3YDrRz15VUVm4kIuIykpM8lllNyMn9grTDL6CxBBLmdQnhXhfjLLfK81AElo5ywZ448bsRav6ikiKqYmIIK0Bxjh0PIXT5ySefyGTqAw88IBOOJ4PZ6pBB+NFSM4eKajksRw35VccE45RuuKFGh69bySqDHWu8N72FbWFpGuEx8Vx3tfD0VvLYx4+hKdBg09jo07UP06ZMpfD8YdTn1GGI8yHmt00nqEw3QfhXz3l+G1XF9XTuH8KE67udcF0rfncX9jwzKu9Uqr59h5LQcC578lUe7RwpA88zhf3r8ln97WE0OhWznh4s5/3TgbBeTP3nZjbWOmWiR1TK73JVclDhxK/Ywt6LBsokzckgzrUrfruCvWV7mdH1Ksp8LmFucSU+NjcL15rxErSaK5OI6nbMh7kJyz7fz5GtxfQaE82wS06k9Zsryvn49mulqvL0h59i3kvPyvP56lfeJSimbVvR1YdLeHz+Pu7+9VW6l2fyfbeBbL/Ql4Wz3myTwXc8bpx7FZvNu4grMJAVUY9BbWDNpWvQqz2VRLvFwpxnH6HoaBoBIYFcGbMFdV0hr9kv4h3nhaSg4mWTL90eGdwq+dREMTcFBnPju5+RXWHm9i++YH+5hznWJ8aP1y/pLZkcTViVtYq71twl12+Cvh9sCD5hDf3666/L/XWG/hytUKD0ysYQ8zEmnReXJ1/OlSlXsvn3ZTxQ/QwBTn++GPgTxWk15B2uojS7phVRqD2INInIvXWk+0ThsuNHFTHjehHZNZDIrv7H1iHWWngtGWy1mC+azdu/7pKJhZAxoziw6CNu/2U3lSboNbEQb6Wb8vFPEXjOMR2otvD+6nReXnxY2oPNvXUoPaNOzopc883nstId1jmRWc+91nzupu67i4MH93Bg/2g5J915551npaiaYHr88PTDkkE26qob6DfZ0/Z7MghbueVfeIRRR83oRLS1GvPGXFyuY3GXOkiNcVgchp5CA0bz/2XQfdYKqf0PZx6imi2o5WJYbWUUFs6lIH82DZYcSc/Ozv4cXeC5OHxGUWCpJ6M6Q1azD1cePiHAFjBpTbIfWwTZ/UL7SW9djarjJ4XwRq5dlydpciKQERWNgEuT0Cf6dziJsDf1Nhlwi171xMSnT0p3jg4w8MjEZO4e24W5O/P5fH0mmWV1rFHGMFKbweo1a3AbQxgz4M97wgv7MNHPJ2jmItPqV+zHGO0YZoztmKXG9FA/nj2aT1aDjdUVtQwxeXPbtztYdbhUTvxvXtqHyT3DO/x+VqxYIQNu32BfvqzysEhE8N+RgPt49J0Qw6FNhdSWWyT9ccgFnTgT8PHpSc+eH1JXd5TcvK/k8Wk2H+LQoUdJO/IcxqqB+PqMIHD8FPol+kurNiGoeOTIUAyGF/DzHYDJ1EgJjB8OV/4M31wEWes8lPPLf8StM8lg2pZvlsNeYMZRUi8X+SelF7eHZmEvIerlof2KAL5VYO50S/9iKYgmrcNOXZW8kL5M1/ejRFlOASVUZFfz9cq36B7fm56d+6Lz90blp5MB+9mk6CkWx0Ic7t3d75JW6dEbiPOJ45GBjzA0cmi7/85ZW4s9P18G4vaCQuwlJTgKCrAXFkpbKmdlJa66uj/whlxiBYuz3NIu3f1Mo8lNVKQ0PTJF7ePPLCGURqO03VIHB6EOC0MTFYU2LAx1cDCayEh5W7ABhF3Qxk2jcDicdEt5Et+wc+W/F6HT5ITJzD0yjw+2zWNCWBB78jyV7AOFNdjbSC7FBhroFeUn+1N7N/aotvSnbQsJ8Xezo3KjPJ/j4+5Ar/fMWzk5n5GW/i9JOQxPmkbnTveccCwLRolIyjadq7aCOuyFZnkuCcqyGAKCUaLr7Ic+KUCOJoaUcD9psvc6VcAtIKpXgr4rxjRxh60O5t5AdcNqDrujedurKy7rWHyrY6hTutmtceDIryZLoWeGDvKzM+n/1AICA7XkWZX0dBuJtDrJyyuQCZ3wF54n4+q7qM+qoerz9/G/4Y4238emueky4Pb21TJyZtc2z3FDr2Cq88woffuh9PUlpLiQoak7+Viv5caoYPQn0UDpKOprbGya5xFqHXR+wmkH3AKla/PZbvYE3IkDQ+k+MpIHS7y57kA2Vf5avtqcxQ3DTn4dEp//wqRr2bv+Xn46MofyiCG4VUYeLVfKgFsT4U1ISmsfawGH3UlmI7W8U7+22ULGgEAS+g7g6PYt5OzbS+LAoRzZsoGtv85l0h33t3qsYHb8c+EBftldIG+v6jaS7mszmZS5lSnDPu1QwC2e27S6APpDXriHWTEyamRzwC0gaLzTH3qK7594kIriQjb5JDJcU8jd+kX81jCaAy5/brLV8lVZHZ1DjiWTmyjmVeXlvDZvC5/ursBi74xGaeeBc7tx/bBOzcmwZiZI6ody/5LES04IuAUEa0MgMjKST6ePZtq766lpiEVXeRW1AV/w0d6P+ObgN1wddTl+5SYq1JVkOncxdrpnnrFZHFQU1FGeb5ae7NVlDZgrrdRVWbHWHxNllPNziylHJBMMPlp5DtQalCzHSpmPiod9rYQ+9zDK+lqMqtFEXfAmihZCuehM0HsWbP0I494v6TXgKrZt2EbGukVs7LKPy0wQWAu2DC+8x/YhcMjJmbOCIfnKksNy/5nzu58y4K6vqWb30kVyf8hFrRW3ExOfpLx8Avl5RVRXh8l12YwZf85m7UxDvP8Fb74kA+7EIcPpO0nOhKeEYOLUlDWwdUEmhxYsomv4q5g01dRZBlFRcR7KwN44yhxUzU+nauFRvJIDMfQJQd/FT7IC/3/B/4Lu/89gd9opqi+i0FxIoT2MAsPFpNdvI61iHwWWBqy5K0SIdsK/E9W25IBkUgJTSA5Mlh7aCX4JHbrItPk+Suqp+PEI9txaeVsslPwv7ILK50TbgLYgLhaHDj+O2XwAjSaAHj3eQ9VBmyihZC76ui8fGMPKQyV8uj6D9NxqOqvK+X3hfD7ZXsZ1I5MYmxSC8o/40zRCZOUEpfHnip8JzgjG1+bL3K/nMm3aNGkLdDJ4q1RcGhbAJ3llfJJbwodbStmaWSGpYR9e0Y9RXTtONxZWRLt27ZL760zrZPVULLanderYZHo81FoVwy7uIil7QlgneUg4fqFnrgLr7d2JpK7P0CnhPgoKfyQ//3saGrKojlgtR1nVt4TlTOPcc0fz+ef51FSHkpcXj5fX3QzoPw+1ujGLHzMYrpqP48tbsWT6Ynv1a6zKfjhr2haJEtUzdZCXVN9U++tQ+XiCWlGNVjRXpT1VarHIb+6pbYeSKYJvSUNv3IoeWFEpdzdXz8XWLrdO4fNda8Mp6bY2GagrLG5CCZBDQmjWFEP15iPH3rNWicpP73m/froT9sX5dDr09j8KwYZZm7eWT1I/kaKJAkaNket7XM9VKVehVZ38vBbBoSopCX1SCyXt4+Cy2XCWlsog3C4C8fJyD9W6uhp7ZSUFR44Q7OWFS9KwBSW7WvjjcTbCqtWybvhwrHod/bZvJ6yqWvrQShq9ry8qP7/G7bF9EVhpgoOll7Wg2YsqfUeQnfMJDkctRu+uhIZOobjGIqnhsoqdORJzbk+WHNKzhD0n9DaLwNoTZPvKrb93x16zJYTuiJ/fQKqqtpKT8ymJiU/I95Se7hGriou7g4T4EwNuAXGeiSq2GC2TteL6YcuqxprhGa46u/QtFkNAE2VEk+JH6l7Psdi378nVqNtEdT7MngmFe/BV60iZeg+H9r7BhTs8vhnjp3dmZr9gUvOr2ZtXTcWuPAy2SmLdJewtFn3VgxHO4GKoipwseWs1gxOjSBg/hvB1++GdjzBOuhBNoyVqE7L3l5O6Jl/uiz5ufTuaDIaewVT/lom9wIbvtAup/OoLLl/5O7f16s9PxZVcEfHnWyrW/5gmA6PgGBM9RkWe9r8XQe/qZTlY3eDnr2PU5Unydz43xA/fAzlUa5W8nprLrIExrRxGWqLQauP9nBK+yg/CoIlBbc8h2bGa1/vcQ8j6VCkbIMRW2zp+cg9UYLc4ZRtAWHz71aceY86VQfeBNSu44KEnZWAsaNrnXHI5viFhcr3x654Cnvp1v2TJiSn12nPiKdesomwXBNVC+K5SOAVTXgi3LXrrZcKdOnxcBmqUnna/8XHjT3iswdePCx99hu8ff4Ct6dUkp0QQ5CzgB82PXGC9hTyrnRkfbOTTq/szIM5zjVBrtbi6jeSHQh/Kt3nOha7+aTw4soJxg0+sVgrtkAMVB9Cg4Zrka9p8z8JiVT5P166yQv7OrL5c+8VWyku6clmXV0hzf8KRyiO8n/kx/Swp7DAe4MfdsxnTc4L8TUQFOizBV47jIdrUij5LpSGjGm33IPynJKDWKKW4nlhntFx/7TuUw8rCCu7XGfn97Teouv12zKtWkf/Ag0S+/pqHmdQI94AbUGz9CNeRxXxSf5gk5XkY7d5E2fphOs8OP+6k7IAJ3xdfPqmKeEapmbu+3yUr9aJHX4xTYfvCeTisVkITuhDfu7Xmkk4bRGKXR6mueolduyZLAd7BgwfLhMbZAJfLKVsUhC6Af0QU595852kl9ftPisOQ/Qtdi59DbbVjqdZSuDYd3yucBN44mPrdJdTvKMZeVE9DapkcMmGa6I9XSqCMA1R/SIPmvwf/C7r/SyEuAqInSFSg6+31nq2jniprFZWWSs+werai11oMEWiXNpRKC7K24XEIDlK7CBVDq6Rb6BBGJN5J54Du0srrT79vlxvzunyql3lEMxR6FX5TO2HoG3JaJ3de3lcUFc1HoVDRvfvb6PWnLx4jJvRxKaFy7M7qzNzv/o23zYy2YDc3flVLfJCR64fFc1G/qFNWc9rDmrw1bHFuwS/Wj6scV5Gfmy8tu0QgLPp5TtZDfk1kkAy6V1XUoi2sxlen5vNrBzRfYDsCIdQkqDkCtlAbae40YkwxPDH4iT9VIY3vFURMtwBy9lewbs4RptzR64xXXDUaX2JjbiAy4CoyP/qayrCV1IXvor7+qBRgg9dJTBzF/v3RZGX2JSBgAYcOP0ZK8uvSMqphfxkN+xQ4zB5qnKfsaPdYUIcYPIv5CG/Uod5oQgwoTZoz9hlEoKsQYkOnKTjUdG7LgFwE4Y2BuK2mgYPZ+ygqysXPYiLY7o+/00eqkMtKfckxrYZWELZKvtpjwbi/HrUIyP11qEVQ7qdrk5bbUZhtZikI9N2h75r70EWCTlAOr+l2jez3O1MQPcZKUcGNjMSrDQrajt9+o89xFDRHTT2FL6zDba7H59wIdLEGXPUNuC0NuBrEsHj26xukVRYOp7RGwenybOVtwVxout9z20M/F9Zo6kZ7MxVHrQ4WVJilwvCtceGoNWoUei+UBgNKgxdKLy8UXmJrYNm+VMoPHyYqPJwRzz+P6i8S1CmvLmLp7nUcrRpHBRdwz8pVspe5NfSgsBEZ6GRiUkpzFTvK3+uMnQ9xsbexu2or+QWzUamNZGW9K++Pj7uLhIS7T+u5RNuRNtxbDuOQCM+1sKAOy+EKLIcqsOXWStr1gYI0bFobPkoDQUU6XGEOmTDrEPJ3wPezpFUehkCpC2GMGcy0PeX4WoNw6mx0HxElKdeCQTWpRzi7wy3Mnz+f3sYCjnivxtuVTCev0ezNqcLiVHGozMahsizQTYFxUzDa6un2ym8MmTSCPrH+9I7yQ+eClV8dlG+hx+goolPan+tFclAb54stsxp9twmg+YbkIwdIzD7KJ956Lg8P+FO/n1CDForf4ilGXd4V5R+onG/88iDlVpdw/2PSHb3k9yXfu0LBFdFBvJdbSlWgjm82Z3PTiNaMqZwGK+/mlDC7sAJbIz+5S9jFFOe+hrXyd8IPX4a9ziHnMq/uJ1ZoBY7u9AiOdeobfNLkY3zvfhj9A6TitAg4Ynv2kVZi2xbMo99l1/P4L/tYtLewWSTtxQt7oNDncPlvSzD1UzJrtYvKr7/Bd9q0dr/z/EMHWPiGp4KYcs4oKjtXsiBjgSxcDIsc1ua/8Q+LkBXvOc8+yu8ZIVwRX0CQYjVfe5/PvQFJMnl2+adb+ODyvvSI9OXlJYf5qTwetODlsjKr90oGhy6mV6cP2nz+j/d+LLcDdAPw15/IMBTzqnA8EUhMTJTbkYnBPDwxiX/9dogfNjp5//J30Poc5NuD31KU5bkGbHPt4ZLvZzAsaSST4ifRxb9ttX57Tg3urBpZTAibmoDav/158J9dItlaXUd6vZWHg8J55523yb/9DmqXLqXwiScJf/458usKWJSxSLY2/cNLz/AGC5fUlPJjSCW9isLpX5NEV+N35Ho5cdSrqFqxg4Cru7f5erUWOzd9vYNai4P+sf48PfXUDEiL2czuJU1V7svaPBbCw2cQHTOf/PyjlJR0YsmSJdKR6WxgrG36aTY5qbtR63Scf98jaL1Or5ii2PgO3Uqekmus6gIjhRuNqPuNIPjuuyTTxzQ8CuOwSDlf1+8spmGfSJxbsewvl0P8OzGneQJwf1kE+b+G/wXdZwFqbbVc9OtF0lz93fnvNqrRitDYfcK2qVotAmxRsfwj0Cq1hBvDCfcOJ8IYQYR3hKxax/vESyXl+tq9HM14naqqLWBZRfHBPXgn3EtkxKUyyP2jsJfWU/njEWw5jdXtrv6e6nYHxNKOF96StEShRtrpIQL8h/Bn0TsumOCrZ/LZZ58RTyVlqir2lylk35aw5RLB9+WDYjDpT4M+73Y3K5Zf2P1Crut9naQTbdy4kU2bNsl+w4svvrhZ3fx4qOqdeNfYqfPRSC/M78d1l7TH04FwAxCesUq1kqXapbLH/uWRL+Otad9bsiMQF4jhlyTy/T+3yMA7c08ZCb3bXvj8WdRtKMCrNBEfbV8CLulEadlSiop+obJyE/4Bq/H1HS+pWmmHhxFWUkfugiWoqlr0byvFIl2FrnwuOudWtGFKlFfPAePZKU4mvluR7RVDE+b5ncT/hxKHxWFhXvo8Xtz3FuW1ZTL4DrUHMsQ4gAH6PkS5wlDUuHBWWXBW22SDnLPSKocts60XQ/aRewLxpiq53hOU+3uCcuVxiQMx/wgrQLG4WZ27GovTE8T5aH24KPEi2d8nhI7OBqh9DPiOS5YtLJYjanwndf/LKv/37jnKyopa7ogJIfQkCtKVlZXsnvez3B87YcIZC7gtdqfsQ5aVVyl2VkV6SS1ubm4hJW6RVTrRfy2r2NF+WFSHeC31UVR6Xx6cuOyUoop/BAEBwzCZelBbm3os4I6/m4T4u/70c8tqWqRRDp8xMTJRJRJuR1ftFZbSdLaGUv3LUaoXZeLVPVAG6toYU/uL3NSf4JfbwWGBkBSPHoR/rBTljEzrQQMudoUtp54h+HJsPhYU9oW/LcRhdRDhX8iL0+6WSsj19Q08++aH5DeocPpEYjOGsy+7BLPWwBYMbGm0fRQI06gJsrnpFKRn+JBgHE5XKzrw8TD0DpZBtzXLhe+kiVT/8iszV/7OM7GdWFdpZkRA29eWU0FQgld/d0ju9xwbTUgLsa6O4uiuElJ3eDRNhvQJxj+ytabG5RGeoNsVpOP9jVlS2EtUuzPqrbydXcxPxRUiLy8xyNeb++LCOMe3O9N/nUtWTRazd37HDEZjGhbZpv6LqKJm7mkKuk8+1ytVKrqNGs+WeT+QunIpA6ddLIPuvSuX8nRuBHkWtdQvuHNMZ24f3Vl6w1+7xJPM1U6bhGLTMiz799OwazeGvn1OeP7yvFzmv/ysVLMWVPaJt9/H1i3PNq8T6ux17Yp4hXfpypR7/sEvrzzPvqpQevgVE637lO9vWMuds/ew/GCxDA5F25nwsxfoUX+EQaUb6NnnIGqVTp5/x6NJBFeIuw7TtR30C0chIQgrelRDQ4/pBNw4PIHMsnq+35rDPbP38O0Ng/j83LGyHfHOebdSqC4luMwk3TjEEIn+IRFD5BBK9OJaIT53zdJs+XzeA8NOGnDLx6hUfJASy+QdaSwuq2FuYgpTX3uJ4nsekCryi0pX8/awGo+zA/C9fyDDG/K5uN7Bs91mkVC9FVNDHVsIpeeAAorWuij78CN8Z1yEyth6PST0c275ZgfpJWZCfXS8f0XfU+oOCAhaud3SIO21Evq2rYIu5p2krs9RXDyDsrJYcnJypONNcnL7avn/CYjWis0/z5b7E268g6DoUwscNkMkxZY9CRvfljerymIpXGvDbIjggP/FBJZa8G9cyyhazNe+UxJkAN5woBzLgXKp6yTmMzGqF4nEohZNgi/+tVpcFoeoxPDfjv8F3WcBRDBdUOfpEaKdgtXJICZNMWGLISpLImMZoAuQW7mv9+yHGcJksB2oDzxpVk3r15++fb6lomIdR9Kep74+ncOHn5A036Sk5/D16XV6n8/uomZ1LrVrcj3VbZ0KvykJGPqHnnZ2z2ItYt++O6QInBDXio6+jjMFQfERYmrCzmGYLpcLhvXiy+2l5Fc1SIuu91alc9WQWEkrO94GpC2syFkhPV6FSIqo+omq9oQJE4iKipIVETHZfvTRRzLwjo2NPcEP8savtmMzqaF3ALoEH5LCT2/hI9R0ly9fLvf3+u7FqrbyUL+HZO/9mYCglPceF8POxdms++EIUUktxEzOEISAmXmj59zwGRONRuNDRPhFclitpVKR36jYwrJ1UF0TTEl5Z8KcRlxKG/bIQjRdNfj2TMIU1A1lmT989SuUFsPn58JVv4Bfx4TozhaIvj+hMCuC2xXZK/gp7Se2FG5hJwfB8pVMqgxKGMSo6FH0De5DvCoGV5XNIw5XaZHBtxCJc1ZacFRaPV7espJug8Zk2PEQWgsuHxUVuhqyyGOPbT/ZynxKNBWoNAoSAhJkZXtKwpSzUuhNeIYK7QjBBmjYVyZpuWcaRVa71F4QmBV+clrvmjVrJAMl/v+x9xXQcV1J0zXMI2YGiyUzMzMndhxmZtxNskk2uLthdMAhO+Y4ZoiZmcTMjCMN8/yn75NkyZZsOXHyZfd36dzz3qBm5tHt7uqqiAg2fgvIIiu7RouMyhakV7SwQDu/Ts/cDTqDBy9pI/qF+WNgVBT6hHggKUjdicprdwZiZbEfqg3V2F68nVkaXWvQeZ40GyjoJoSG3ndNAu6uQIKE9jg5KnZwAVe/UYMgzDLCXmuE6Xw9GyTGqBwWyPqi25ke5E2+61XgOJcoRcwUYP4SrkeUeltP1cLU4IRNZEaq735sKuzNEkxtcPKdqFJVwbfRF6Mwqt16SCQSok9kABT5+XAYmjBjbBh6jxHjxFtv4FhjAnJ8o1CYMBglGjNqbHbUSIAMuwEbvzgKuViAlGA39A31QN8Qd/QL8+h07SHLqOaNhWyy6j7rJhZ0jzp9DL6zb8I3FerfHHSf2FQEfZOFeXIPnnn1uh/Uj77nB65iHy3hI66L94iUSzBALcdprRENbkK8f7QI1b4SbKjVsPQQYZSHEk+F+2Oo+4WAnRSzXz7yMn6W7cAsxWjIB3btEFCe3QQrUcvdxAjogtp8MZLGckF3Sdo5jLrzITi8QyFoKENI1WlI4yYwX+a2ft6jlUdZ0EoJqvtGPQPnTAlafl4HzU/LLgm6dU0NWPfOKzAb9K0B9AuMHXOy+mT7/G9L4RbcmdQ1vZsQ1X8wxt/5IA4v/QgxqgZIjFngZW/AlKQhOFxQz4JtOvZ7+Srx7xtSUL0hCzm1ZjQXqhGe2LtLdxpqBSLMjJwJdUPXc4u8vLz2KnfHuRqtvzE7EfU6M3Zn1+GeH08zgbFY31jMDp+NLyuWQCsyYKr/ZOyu24syXRnKcsuwOnc1ez0F4ZMdozCvdBicAhfKk7WwGmrZ/JV+07b/RYE5OeXobXrUGGpQZ6jCZIUFm/WBeDG3GO82foYx04BHtwAjDmtQxReg4oYh7Fo0PmQcXN9OgqguCzfXboMycQi0p1NxBAMw4P5+EJd+C2tpKZqW/gifhx9u/26UXHv+51QcKWiEQizAt3cMhK/qyolRm8WMs9s2svWBc2687NxWLg9HYsLDqKrchvLyZOzcuR29evWCsANF/s/u497+2XsseE4aOwnxI69CUNhhBzY/AZz/id1sMQ9C9e4K8N09UDH2Geg1Qmz86DzmPtMPbj6d98NOAfjEMKatwwLw7EZYSqhNzArHuXpEQgmn3gao/vsr39eD7r8AKCj7cdKPOHb0GPP5FAlFXAUKfLZTcqRvbgelv/YgW8QF2nT7WoP+l5fXKAz2GIrKyuUoKv6Y9U+fPn0Ds3KKinwKAsGVJ9jmPA00GwuYkBGBejc85kUzauvVwum0ICP9UeZFTv2J8fHvXHNKDtk4kHAI0b9FFWew79nbsCm1Gl8eKGRZz8/3FWLJoWLcNDCEWXURtbC7HtfPznEVnVsTbu1E3aKKCAmtrVmzhqmbkz0eBePU20PfZ+P5Sjy3Ng1WhxO9veQoFArQYHdgb5MWk7x7XukmOyJiT5gkJuSp8phYCwVH1xLUw0OTURJVO7W1BMPnd+2v/VuhO1LFRJOo4iuN7xzMCMxqyE/1R/DJIAxFBQ6LcnBaVAhp6DHwwg/CKWrNYGWQiKCS9Zb6TLodAbuXgt9UBHxLgfcGwOfa+dX/WaBjfkrEFDbKtGWM4r27bDdzGaAqNI022z5yFIh2j0ZYUBjC48OZ/ZhKHAhPoRICYlQ3mWHTmGBpMsDYoGVLV4sNQh0gsgngNNgBgx2ekMATUeiHzjRQSqIJiiQwuBfB4iZppbPTsrW33O3SavmfCb5UCOXwIOj2lEG7p6yTt+21wrrWQGGgWsGCie5A1lWpqVzv9Pjx43v03o16C3JrOSXtjEou0C6o7yrABrwUYiQGuaFPsBvcHKvggfWIDOiLPn2+7/b9KVFzU9xN+PDMh1iRvYJpPVzr82p1DdEpV7TfFvD/2ORMWlpau81o8OR4uCa5GOVcf7waxtQ62Cr1jHWl3VkC1ahgKOL54G28ByjljhuMeBoY9zKVQNlNp8PJHBsI7oMcsDktWJO7BrfG39r+W32Z+iXSJekYj/EQ1AtY0rNNvI2W5IxBSVCik0Y88ACGDwYCV+yHpfgo5B5N2IAZKLPbwEtQo1YCnC9rhs5ix/GiJjY6itn1C/VgAXi/UHd4RbvBntcMh9ET8iFDYDx+HHP3/YqvPG9hVePL7Y9doaa4BWn7Ktg60crbKOE9hc3iwPav0tnSS8BDSrSatQJ0hQX+nizotkep8LnLCNS29jh7qfFkmB/6u136uqkRU/HZoY9QI2zA3qR03CMe3eV7F57lquyR1LrWg+Pd3c8foUm9kZpbihu+OQk+P4G2CPobs3DfPc/BzU3dHgguTuXo2jfG3MjsP8233sqCbu2vO+FbUwORP5cIsJqMWP/Oa9A11MMjIAhznn8FIomUOcBU6Csg5Alhd9nZ+fuOxDsue9zFhA5GlXI0TjTWwOrlhf+sbUGeg9vPJUI+LHYnqppNrAofO3Qk60lvLlbDy+vS80xmYyaOVB5h1PY74u9A+iEuGdYR9D07Bt0XgxgYny7qh0XfHGesmju+O4nVDwzBrIHzWNCdJsvDP3kv4tWb/sk81ql/nEaptpRds+JLud9ovdseLDnEMX8INMel5LKAJ2BtkxczOl00B/Z5BjZZbzR7PojMQZ/htNIdA1alYcFBB/xGjYHnJE6vprD3vYja9TTurVwHT14RfoA3auCHQ/VKDHvicVQ+/QyavvseHosWMT0Nwr935GDD+Sr2Oy6+tX+P2YUZ+3bBpNPCzdcPsUO6Zg50BM2jk5L2oKbGBI0GOH36FIYM+f2szasFbeedX33CWis8A4Mx7s77e/5imwn4+W4gdxtzCtGp5qNq1RHmBhL88ccITeiD9R+cg6bagI0fnWOB9+U828kKkpgrNEgDx1qqhTG3EdUZpfD1+ut6ml8NrgfdfwHQpIfstcqF5ZwC+F+IQsHni9jJwc9vJvLz30JN7UaUl3+H+vpdSIj/Fzw8hnT5OluDCS1bi9qFbfhqMatuy5K796+80okhO+dFtGjPMYuz5OTFPQr6rxZUjZ47dy7zdiVl7NMnT+CGYcMwr28QdmXX4ov9hUyA6MdjpfjpRBlm9w7Eg2OiGF2zI7YVb0NhSyFTeKeL6cXw9vbGvffei82bNzMxDZqIEd282i0R7+/heqgmJ/oxlfJ/l9Xgy/J6ZiPW06CbJvekmk447XEaPgofvDH8jWvfdy0WMAuYrZ+nIXVPOVOw9A7u3prrauC02KE/wlW5VeNC2idOLpsD2v0V7cwJQnJ4HGpFNuSXF+KMVoHefAsU0hAoFNFoaTnNhKQaG/ejEftRHO9A33QhFLoq2JeMQvP0v0EePRcyGedbei3AZehdLDDinKVcraO1Z7vDfW12KmQLRxRG+pqkN0B9j3QftQxfWL/081FLyOP9HmeDHAf2lu1lvrZp9WnQ2XSdgvCL0Tbp6wRp6/ADFA4Zo6/7273QW5yERGEsgh1+UJikcJKtmsHOkiJtfeXduRyTEJ2wPQhvDcrV3G12v5uEE6f7g6AaHsjs5li1M7MR8uRrR3+n7bmmhjvPLQi4vPMCBV30fEq8EeOl43s0GazMNzq/Vs+C7DwWaOu79cL2VorZhJB6OduWAW5Stg+3tJzF6TNLaa9CdPTfrvgd5veaj8XnFzMrSKKckhvFtUJ9/W5kZT3HpsueHiPQpDmM8oofERp6zwXRw98Bh8MJbb0J2kYzS/7pm804kUXyZUCAeyQaKnTwIp/vEBU8Q1RwmxoOw6ka6I9Ws0pK8+YiaLdooRL4QinzBG/ep0D8jE7/I/dELVrqTZAqRZgzdzQWb5QzijMFE1TRJkbTD5k/wCF2QOGpgKHJwAL/wYMHt7/HoEGDUFBQwK4rGzZuxF2jnkJA3sMo2eUN446t8O4djaB+AzDvgX6sf5oqbmRddq6sGWfLNGyZV6dDaaORjfXnOLE1uZCPePCQcrQYA6bfAp8zqZhzZC+WTZuLJRX1eDsmuOe/pd2JfctymIp07BB/hCZcvRjbwdV5TK1aIuBhgEIA1aBLXTZofz/WbMCWuhbujtZjPx5CfDIgEsmq7q/tzhIj5tdOwOcBq7DStgG3Ox64xDWFvkebanl0v54xW+gzVUePxSqdHnaTEB7evSBxpsLSUI28g7sxcOY89jw6t56vP88qsncncUw7EoCUDxwI46lT0KxcBd+nnmTaD9s//xD1ZSVQuHswr2O5mrt+7yrlbDFHBI/A8arjbK5AgXCSd9f9xQRjWj20HkPxb14USmzcfEMttOOB8YlYNDAUDy0/gxPFTbjr+1NYfnsI+CIHbHoRHC1hwEU6Xd+mf9uewAhRhSAd6V3OIcgGiaqv3TFyZKwSPAA3fnmMWQre/M0JrLp/CHorkpBqyMCWrM14bExvxryiQWg2N6MoNRO+OU7Y+Q7kxdYiwBaAOmMdC7Cp8t+VWw4xNqkVktiaPioTfjA40SIOwdDeS3DbghDUe32Ghs8/R+2bb0LgpobbzJl4Uz4M/xF5INBSDxTuxEReBJa55uDU6dMY/MgjkMTFwZKTg8YlS+D33HNYcqgIXx3k5l//np+CUTE923ccdjsTUCMMmDmftStcCdSqmZz8DgqLHkNebj/s27cLvXv36ZHLwrVE6s5tTERQIBRi+hPPM+X8HsHUDKxcBJQdBQQSGGOfQsUrdM0B/F9+GYrBHNNn9pN9sP79s2ipM2Hjh1zgTcKGVwIl6qW9PGBUmZFenY8Y3ij8L+B60H0dPYJY7IXExA9Y8J2T+w+YzeU4e+5WhBFNMPIp8Pmcqi2pMmv3lnGUYLKc4YP10KknhrGK029FScnnF4TTEj+BXH4V/SZXCS8vL0yePBlbtmzB3r17WZaXguTJif6YlOCHY4WNLPg+XNCAX85VsjExwQ+Pj+uF5GA3JnD3xfkv2HvRRZn6l7qCWCzGvHnz2OSbgu6MjAw0OIsgRzRuGRmHv02NZ0HYLQFeLOje3ahlSq4BEvEVJw8knkbLSnklmhRN+H70910KpVwLhCd7I6qvDwrP1WP/8hzMf67/NakkGk7WwmW2Q+gjY9VJgimnCc2bCuEgiy/6DcPVzNNdEuWGOcZeWLx4MfR6oLRkMCKjjrJ+/5EjTsNgyIVGcwKa5hMsIDnTm4ekdB14OiGa13+JPRHrUK8IAk8cDRAlmx8IB98XNpcaJqsLRqsdBqsDRgst7YzOR31fxEawOVrXW2+3Lf8g6+X2wJyW5AdMVQ6xgM96zmiIBLEQC+MRLuDB7jLD5NDB4jTA7NDDaNfC6jLA5jKDx7PDwrMDPDt4fBKYs0Ms5MFdKoe7VAE/hRciPIIR7RGGGM8IeMiUkIj4kAoFbCkhD2uiNbZYuEG09ZZW+nr7fVa4rA6m2G6jUdO97RfzKm+rkFNgTiry7be5+3i/0QaJVOcZzXxvOXR7qdp9+Rabq0GG3oRcgxkSPg+zfLq3k8kvKMT5vBIYoYIloDc+3JXHgmzyhaYJKwn2dIdQTzli/JRICOSCaxrUa9jVd6DjPj+f07ygNgyl8spMDqJ1Mvuw/HVMEOlaBd3NLWeQkUk0cicC/OcjLu4tHD8xGSZTKSqrVjKhxN/ac8xpSdQzwa+O1kM2kRYGLy3xvZH3qxkFO05BIhciINodIfEeiBnkD/XYUKiG+sOw+ifostVwuPzQYr8HetwHtSEacqer/RxGQf3pbVyVu9+kMLgr1Yy+uiZvDaPL0m/1z6P/ZAHDpPBJGCUbhe3btzPHiI5BN22rOXPm4IsvvkB5eTlOxk/CkOggeBRroClQIK5gFSLfv6ldsIwSbOR5TmPBwBB2n9ZsYxXwM6UaFoi3VcPPUI+uyYDvaWX6GwjR1SLwUAFWaoAbFEr0CXDrkRPHuZ2lLGCWqUQYcUPX4leXQ+7xauQcrWZttf2lfMgkAkbhb4ODrk31LUyN/Jyucy8dv9KAlmIjIod172BA0B+swKSWoVgV+CtqTbVMjGxeLy4gbkNlrobtEzK1GP5RV/ZBpt/15fUZ2JRqAfgiBJsq8dHc/pAYb8TOLz9htOF+U2eCLxAyRgOBWnw6Wmx53HYrC7qbV6+G90MP4sSW9Sg4dYwFM7OeeYlVQNsriyU72fr0iOlMX4X0Maja3VXQTYnbnenV+Ci1BLk8qvqqIIYd9wi24k7er1DGH4BCKca3dw7ETV8fY2yYh1bnYlGoCbpCJYrPZiA04UJ/MbGhdpdybWf3JnV//LVZhUVGRl62EOSllGD5fYNx09fHWTLo5m+O45bxM5CanoG9suO453Qta+Vog7vUHSFpKljQArfBwfh89pft7EAKtkkjhFW4nY4LjE6B7JLEypBGLRalFeH7qkaM8VJj0qOPwKHVQrNsGar+9ndUCUTYLvNHUuAsPFP6I3tN1OBpiKyLZOJwe/ftw6Qnn0DFgw9B89Ny7IoZhTePcYma5ybHYn7/nierco8dgra+jinOJ47pGYOJQPPYESMWoqL8FIxGd+zevQkzZy7EnwVKCO1ftoStj7z5LviG97CVRFfD2bDWZgASNSwj/oOyJ95jD3ncfDM8brrwHRRuEsx+si8XeNebsOHDc+x2T+wHSXxw0wdvw2w0onnKNPiE/He1A3aF60H3dVwVvL3HYoj7Dlb1rqpeg9Kyr9GkOYKEmPfgOi9nvds0wW4TSnObHsmUoX8Pamo3o6j4Q7ZOXtxeXiP/8K3Wv39/ZGdno7CwkPVf33333cxagiZOw6K92UiraMbi/YXYkVmDXVm1bIyJ9UFidBmjjlFm9ua4my/7f+j9YpP74quTjfBtOAtvvhGL1Pm4LXkAC6oIvRRSDHFT4HiLgSm5Uo/b5UDBe2lpKRw8B9I80/DcgOfQx7cP/kiMWNALZVlNqC3WIutIFRJH/j4LDJfDySqTBOXIIFZNbdpQAFOrME5XzAmxVIYRE6fjp3WbUV7WDzk2D9gKtcDpDbDzI9BiSkCLqReajQvRYrRAZ2nrHASQ1dWnoAswdxH+o0AfnSrYhK7owl2BnkdTL/JRpuC/607si0/zbq3j8tuFaqr0fqRBe6H20dg6LgWJ91DwTeqzkg7BOLut5EPqLoSYL4LYBYidLojt3BDZnBBbnRCaHRCZHdxjBhskBiPEleQhzSMBXrakS7O4dUmVRrmbFBJm6yZtp7C7FAKILHxmJ9Wd+TVRzMk5gfpfLfnNkMZcmyTUyooGwGTHUKUC54qaWMWaRr3egupmM6pbTEwXorrZBBe443Dr1oIu3yvQTYooXyVi/VSI8VexJfnwKiQ9v1TX1//KGEF8vgyRkZf3oO2Im+NvZkE3MSXI6YIqSr8HBkMhUlPvZ21B3l7jEBf3Nvh8IcLDHkR2zt+ZfVhw0G09tnps840mm8L0g5WwWy7QToUSAdy8pYy6WGGpRLMW8JIHIyjKCw3lehaAlaQ1sHH0l0LE9FYi2fQpfBo3QCERwBj8ErQNw+FosUHzcx7TACBXDWm0OwsitQ1mFsAltdpmLYhdwIJu+q2+y/gOGY0ZzCLvb4P+BgUU2LmTxB5rmIilj8+FwMzd3Z21ElFCd8++fQjufS8Cav+BlnI55IZaOLasAh58sNvvr5aKWPWtrQJH5wJqezq4NgvnKluQKeOh1GRFucqP8zxO12Be+hGopULWF0609P5hHswC7mJRUE2NAae2lbSfz+lYuxrQ6/ev5OjIiRFq+DSZ2PmZku1GhxOra5rwVXkdSkxW9hwpn8fo5UlKGZ7PqwDPV4bGzGYsPVaKh8Z0bmFpg7WaVOo1EPNEuCP+dnyQ9TET6poVNYsxBi+mlkf18blisuFcmQaPrzqH8iYTu+bOdG9E0JnNqD+lw8QHHseRVcugb2pE7rHD0EcrGBOExGjbqtxtUI0bB2FgAOxV1cj//BMcPXmQ3T/h3kcQGHMhkUD2WtTjTJXyUcGjWMKLgm5ixz038Ll2IUNK9K47U4FvDxejpJFLUFAN9LaRkbitryeUix+Fh0CLzM9vRczLu6GUSLHk9oGY/flhFDcBxwSDkIQs5B0/glG3XFDHXpa1jFWTqfIc7RHNFMovF3RHR1+5ZSzATYaV9w3Bwq+Psc+6fJ87hN5CFEkrkHb0OIYOntOeMLWUaWEpbGECp2T11gZyx1GKlaC/nmCslxoPhPjgq/J6PJVThj0DY+H/97/BqW1h2gb2v72A3o+8gBDvDg4A0eMwISUBX3/9NWMYDrz7bsgHDIDx9GloP/0YGHALHh0bjYe72f+6AjEaTm5Yy9b7TZ0Fkfjq2jlCgm9G7z6HcOwocPZsFgYOrIS//x9vIWazWph9ncNmY+r9/abN6tkLWyqAH2cC1KKn8IV91vcof+hVuIxGyIcOgd/fL2VW0Xl5zlN9sf6Ds0zvgZZ0+3JU89Rd27H3+6/gdNghdvfkXEL+B3A96L6Oq99phErWT+3lPQbZ2S9Cp8vEyZNz4J95F9SmIcyOyW16BGSxPbe26g5UlczOfp6th4bcg+Cgywex1wp0gZo5cyarSlB/9/HjxzFs2LBOzyFRFer5oUnPF/sKsDG1Cvtz67E/VwaB/F4sGOnfrSJpG6g/k1QyKzQu+EqScaN7GQzNjazPe8aMGe3+srcEerGge0V1E54I82sP1C6G2WzG9h3b2XqOWw5Gx4xmwlt/NJQeUgyeFcl8XY+tL0REbx/Ie+i53hVMaQ2sUspXiiD0kqH247MwUQDDc6I5wRNNESpUVTWiIrMCFRojKjUmFuRw1eVWobiyjn1oNd3+LznfBndnC9Q8I2TuKgjkPAihgRCNEPMNkAqtkAgsbEgFFogFVshEfCjkflDJ/aFWBEAhC4BKEQC1IhBSsYKrPgv5nWjibQE2o4+33r64UklVEBZUt9LO29aJaupsvd1GS7eTXzFV1jtW2u1OWC663elxh5P1/rU/1+5gSzOt2xyXLNljNgd7TduSaPNtoMDf5rCjGwb0tQfZvumpKob2YLwtOBdDiE/P7oFEwIdIKGB+r+T1Sp6v1AYhpMBV5YCzyQzx2lQo4z3ZJJu2j4DPZ9uFfmv2m7eOthYB+h30FjvHeLA4GNuBLS12mGwOxsY/zgZHTewaPPDhgr+bFMGeCkR4KRDurWDetzSoV/e3WhO2wem0oqDwP2ydqsgSyQXF4SshxiMGg/wH4WTNSVbBfbJ/zwP2i2Gx1OL8+TthtzdDre6DpKSPWcBN8Pefg6LiT2CxVKO6+mcEB19ZZ8JssDHBxvQDFbBbuWSZ2kfGHBPIvpA8gOlYowDivfc4i8TpC8awKh31Y9eX6VGZr0HeyVo0VuiRfVqLbNyBcFkyRsyPhNuIeZDbnNAfq4J2XzlrQ2hYkg5xkhdOZ2jY+/WfHMb2I0KsZyx6+/RGan1qe+WT2jvaKp+kREzJz7NnzzLWVEfQOZ0eI5r5itNOPCxwh3/fZlQd90DD4i+hnj4d4hCusn0l0P4b669C6LgoTF2WDYFIDOEjcdh+5+PIFHliW9II6JwyaM12HMirZ4NApx1K6FBfeH8WjLvj7LIcOO0uhCV5odeAnu83BLvVgV+/yWCJkKBe7ohq5oJES38fvFdcg+8q69Fk45IkHkIB7gr2xl1B3vARi2B1OvGv4mpQgwbfS4KvDxbitqFhUHaRaKIqN4GC+YV9+uO7wh+ZVeGvJb8ypgaBtnfReS5ZGnkZajmdU4lK/P7OXHack0XeJ4v6wt9cg5VnViPv5FGMv+ch9J0yE4dXLcXpTeuwbxzznWSVddLG6Ajyifa8+WbUvfc+mpevBKID0XfqLCbQ1hH0WQlkE0bCk4MDBjMnGRIy3Fe2D/HqkVhxsgyrT5UzT3CCSsDHXIcQdw0MRcR0jrmin/4vYMfDiOFlYt8nr2HiM2+zc8uXN8di4denccg2DImCfGjra1FXXAi/yGhmHbupcBN7/R0Jl7a9tYH0CIiNQSCBr54g0L018P7qOErqTfBQJACyNOzmHUHv1OFQ9OP2KWIbEeR9fdu1fQx2B/OWJ8ZQpcWKCjOXmBnopsQwdyWGuivYvnIxXowMwFGNHul6Ex7LKsPqPlEIePNNGFq0JGqDtxe/i5hJOqCt7nPmRwQuXIa+ffsyJsqOHTugmnIbUk6fwdiKcxDfeBPumdRZNO5KKDp3Co0VZcxeq89kbh+8GtD/Gj36NeTnvYqGhgBs2PAtHnjg91m69gQHln7LPjdV56c8/FTP/p+mlAu4m0sB9zA4F65B+ZOvwlZVBVFYKII//BC8blgRam8Zo5YTxZzagTZ8cJZVvOn+jnDYbSzYTtu9g92OHjQMzvCYdqbIfzuuB91/AVD/Uc7RGhjKhcg7Ucv5u/JbhdNa+zjZbVryeBCK+ZAqRO2DJpV/NkiRXF6QjIiTb6Ey7DMYvbJRnfIlXAMaEDfkdQiEv992xmQqR2raA2wi6e09AdHRL+DPBFUlaMJEfdcdaeYXgypRHyzsgycm9MJT63fgbIEIDmM0vv0VSMs9hkfH9cKoXpf2sq85Xc4syWhCH+Ipw9e3DUCUlxTr169nVfZNmzaxagl9hhk+7ngpvwLlZisOanQY49k1ZX3P3j0wGozQCXVwhDnw2tDX/jT/x+QxQcg5Xs0qS0fW5WPiXVevkk5BZ02LGWd3FaIIVlQJnCj+5gTK4EANuMATmQYgs/tJKPW68sw6CO1GeEitCPM7C3eZDUm97oefRzDUMhHcZCK4y0WsciTmOYFNjwGpKwBqJRv9b2AIKeQ7YDKVQafLgk6fDaOhAAZjOaPG0mPtMAAOA9lkAM2sbcAbMlkoN6QhkEgDIJX4QyIJgFQaAKGge0VhdnwLKOj/64JsjDoG4R3XL152DOC7ut3d67p6bzpO2kBrtKmodsyhA0vAQb7adq5s3x10NuBk91T3qwYP8FNJ4KmQMDEzT4UYXkox67H2U4pxZNcWuIxNmDF+JEaP+uN600isjPZP2gdJIfxqQdVuCrpJFf/B3g8yQaOrBeknnE+9G2ZLFWSycPRO+bqT/ga1IoWF3Y+8vH+itOwrBAYuYNoh3aEkvYH1GVOVm+AbpsLAGREsOLz43JabmwuLxQI3Nzcmosb+n4APvwg1/HzM6NvwIWqMRUg3TkOBeThKTP1QvoqPvk1F6Dc5jBNVG+CHll2lMByvRu6ZOuhNTshkAiQM71z5J3oxBd1WpxUJnglYELPgksCaqmnjxo3r9DpiTM2aNQufffo5jC4NdjgXYH7YV2iu8IKxwoKaN95AyFdfXdV5WxrrCZ5UAIfWCk+tC1MnD8TAzz/HMEshHnj6Nbzq7wu51s4o6URNr9CYmL0cjRUnyth7yJxAsEqAaZESnCxuYkll6tntCQ6tzUdjJUdLHz7AB4X7S7EiWYFNFeUwtybpQqViVpm8KcCT2T+1QcznY66vB76tbIAsQg3NqXosPVaCh8d0rrDam80wtjKdaDuJRXKmHv/puU9ZtZv6k0kYrCq/mSVpaG5ECYCuUKc14+k1qaxFjDAjJQBvz0tm1wOXy52JnmmqK5F34ihSJk7FifVrGBW3MrMWIj8R7km+p8v3lUydAscHH0JtNCPWNwijb7vnkutbWz/3pLBJbEmfeXr4LCw+dhgvr61GQ9P+Tm0ldw0Nw4hd1ZA5nPDpf4Hlphx8Myxnvoak/jwCK3/BwRUxGH3r3fCXnMS9yT/hi9R7USANRS9DIfKOH2ZBN4n/WRwWJHglXLaFhOjX9FlpvuPRKjDWEwR7yJmY2u3fnkRZYwpkwWnY6XYS9+4vg7yPL2svMuc0sfOlakwwC7a/r2zAF+V17UmZjsjUm/FDJbeNJnip8VCIDwvC244NCZ+PxYlhmHgqD4eb9axt4bEwP6x74gWEVtWhb14Wqn/lQzw7CBLK1OZsATQl7JjMzMxkTJRDVglmhQ7ApLJTGL9nOXD/zB5/X/qNTrRWuXtPmgaJ/LdpVND8YMrUaVix/BRqaoQ4eXI9Bg/u3DJxLZF/6hhSd3HJSbKvo8D7imgq5gLulnLAIwKu2zai6tX3YE5NA9/NDSGLF0Pgfvn3UXvJMOdpLvAmqjlRzmc90afdTszQrMGmD95BVW4WywyOWHgb+k6fw9p1/lfwV57b/X8Dg9GKt9Zngabamwu4aIKdUlytS1x+KRELIJeLoKC+RbUYbu4SqN2l8PCSwc1DArFQwKpuMpGAG/R8EikQCdopzFdl4XSyhglcOXVW8KFAqOUltIz4FdWOZaix/gzT+UIkJ38BieS3+yDbbFqcT70XNlsTVMpEJCZ88Ls8wn8raPKUlZV1Cc28K3irgQblx1BEO9FH9DxO5YlwqkTDVD3JD/exsdEYH+/LAoh/bs7EypNcxndcnC8+XNAHbnJu4kkWYgcPHmTq4ySGVl9fz+6b7+fJLlBU7e4q6K6ursapk6fYeo5fDj4b99mfauFEk9sxt8Th53+fZsmj+KEBCI7z7PZiVau1IKu6BdnVnDJzUYMexfUG1jvdjladnTaQhQcpxlNVgi7wQe6y9vUAdyk85WKWpGpqamJ2bDQBj1K4EBi4CzLkYWDULxCJLr4w8IHZnwNSN+DEYmDHC4C5GbzRL0Auj2DDz+9CBpvoskZjKQzGAkahNRoLWXBOw2bTMHV9GsTS6AqkpC6R+LMAnC0ltPSFSOwJsciL6SeIRJ5MMPDPSphcDUi1lsbVUJ6vBagyRZX6CwE9VegdbKk3W3DoyHEkxfWGxWCHVW+D3WiDTW+DzWSDzWiDnXrKzXZGzW8bFLyTdA+3BAStg/YhkVQIEXmlK0SQKMVQuUuh9pAxGyWlVMS+/xsl1dirM+D+cF+83o1g1ZEjRyA31UKlVmHokK6FJ68F6JxZ3MED+7eIlI0JHsPEisjCkuiuF/fKXgl0bKSlPQi9PocF/n37/MD254sRGLCA6XSYzZVMnJN6zy+GxWTHkbX5yD5azW57+MsxbH50l8H2xarlKSkpF87TRBk5vxz49SXwzM0IkIoQME2NARGDcGhtISpyNDi9rQR5J2sw6d4k+IWr4TE7GpK+vtj5LncMR8MFzXcZ8Lghpr1dqqNWx/yY+Ywe2wYK+Clp29zczDx4L4a5kQdZSzgM6kLkytygFQTAP6UaxTWBMBw8BN3OXVBP5oKynoBsz2SJ3jCeqWWBqcfNi9D4zTeIKcpHYkkBfvVUYcOwXrhjWHh70EkB+NmyZpwsbGS2cyY+kA8HPj5YyAapNycEqjsppdP59uLfntwrsg5VsclIxMIoPFNZh90jFXDS85wupKhkeDjElyWO6T27wo3+nizoNnmKwRfy8PXBItw+NLxTtVt/uIq9H+l3iIO5xCWp7n+f8T0Kmguwr3wfxoeOR+FZLjCP7OPd3h/fEdQG9sK6NNYCQvOhf85KxI0Dgtu/Fy0TR49n1e2sA3uQNGYCksZNxLntm5FUrIbH8DFMsfxiOB0O7PjhK6jcFQht0iHZKWL93J1+q+Z8JsBH9HSilpMo6y9nK7DhfDTMpghwSiVg7QO3DA7FhHg/WHOb0GithEAthji0w3Wfx4Nk9vvAkvFIdKvF0p3L4BEQCPjuRn+/NNzZvxGHD0SyoDvjyCEMXHATVuasbK9yX+7acjXU8otB1+KfHxqGO38Aihw/o0XUjB/1Z/FkdiRzDmhjKuxwWfHC8cL2YDtCJsZMH3cES8UIkophcjhxvEXPKtlZBjPTtKFB+9Oz4f7torLRcineignC0znl+HdxNQaoFVjaqIPq7ruw8j9PwdIkRNkeNcIXRULUcAg48RVE49+ERh0FRUM2+osqIL7vAfDeyYA5LQ3aLVvhNrOzkGJ3qMzJRHVeDgQiEaOW/x5ER81GfMIpZGYAe/eeRFLSCCgUv30O3R10jQ1Mp4AwYOY8hPfm2JSXRWMhF3BrKwGvaOCOzaj97Afodu1mle2Qzz+DJLJn/eAqoppT4P3ROUY1/+W9s5j5WG84bTXY+P5brJWDWAPTH3+Oedp31/7w34rrQfdfAFaHC8el3YvoXBlU1bFwlR1iwpX2/JVEg2VBeHswLmQXOpVUyCqCtKTeLyX1XJbpISzVQmkHlOBBrRTDf0QQ/AYHIUg2FD4NI5GV9QzrJzx1ei56p3wDlSrhqr+N02lDRsZjzB9cIvZDSu+vr4nK7bWgmZ86daqTOE5HUK+UxqJBhFcYfpw9DY16O746UIQVJ0vZxfXepadZVZwqdmVNRkbxe3pCDB4ZG92p74wmi2PGjIGfnx9++eUXFBcX45tvvsG0WXNAxj+/NrSgxWaHm+jC4Uu+vyt+4Sx5yhXleHLSk4hw+20ewL8HNGFNHhWE9AOVOLAyDze9PAgOHhgFP7tai6xqLVvS0LRS5y4GTV0DwEco+Ajh8RHXxx9xAwIR5aOAj6pr8aiL4enpyapJa9euRWGBN1SqFJqSIz3jUfTp/f2llTWaoE95B5B7AvveAva/A5g0wOR3uMc6PVUCpTKGja4CH6o0tgXhFFSYLdWwWGpgNlfDbm+BgwTNjAVsXA48nggikQfErcE4BeV0WyhUsYBcJFRDIFSxJXffhUGf8X8NdIxI+Vyy8GLQhbku04WJ/QIvK/pDPd+WomY0fJvBbiupp7BVDM7eKv7mpCo4ReHEkDU6gXpLh253LkdDLQ8uHxniLHoIFHzcFCWDy+684PncCrLsO3ToEFun6gqJJ/5RKC39kiV95PJoFtT+FlDgSO0o7595nwmqzY2e2+PEj8vlRFbW89A0H4dAoECf3t9BJuuaJi0QSBEacjejwtPnDvCn/3NhuzZVG7D181TWS03BXJ/xIax9hdoFugP91qQO3hZ0t08WyUO2hNsG8E8G5nwJ+CeB0oFUZSk6X4/Da/LZ//rl3TMYNi8aKeOCkV/QApPNCYVCiHCFANYyHWo/Ocv8ZIVDvfHuqXfb/zd5NlOfd8dzOFFY9+3bh/PnzzNxzjZYTXbs/j4bMmMgBD7N0Foasd1tEW6yfACv/hI0nDCi9u23oRg+HAJlz6978j4+LOg2pTfAfVYU3GbPQvPan7Fg7za8GhWDNJ0RKa2q4L5qKaYkBTBx0O1fpiOf9v8QGVQj/Zj9E1XDKSmaVtHCxg9HuV5vEu+jCjinmK9GqFiMfT9xSYXiPmq8oasDWFzIw1i1Ao9E+mN4h8pkd+itkqGXXIJ8owUBUe7Q5Grw49ESdm1sS/YbTnLJF9XoC/sUJT5ofyXP6a/TvsaYoLEoPN8adPfrHLBQa8ibW7PbK/vxAWp8uqgvuyZfDPIpPrx6Gcqz0tFSVwvP4b3h3L4JQQ0yTHKb0OV3OLTyR5SmnYN7oA8Luo0HDnC028ALImI7S3bBYQqCB2Zg2ken2DygDWKxCVCdwEMjU/DUkAtJXlMGp6chTfS6VKA0eACQMAe8rA0Y6VuMDUu+QNS0SiiDgCcn90WppgX2vXthbKjF0uPfM3q5n9wPE8M7U94vToi3HUc9pZZfDGL6rLpvBKavGIQGHMQPbsch/iUOC43c+fHXeAWezixpD7ZJp2aer8clSZkZvlyCvNhowVcV9Vhd3Yg0nQm3pxdjob8n3uwVBJVQgEX+ntjfpMOmumbcm1nMAvm361chdHQDSg8Ew9rYgrL1EoQN5IN/dikeKBqHQxVyzJVIoOJZEONphvf9D6D+o49Q9/77UE0YD34PVMTberkpSaP0+P3tlDNnPIeiwndgMkmxceN/sGjRu9c08U795zu++BBmvY4xH0bcdNuVX9SQzwXcumrAOxa4YxOafvkVmqXL2MOB//4X64u/Gig9JJj3bD9s/jQVdaVarH1rKayGPXDabcy2bPZzL7Pl/yKuB91/ASgUYtw2JJT1eIWFkW0Rdf5xPZ1EzOL6VKmvExfd74Kdqj1mO4xmO8wWB1unJevHtDlhBye6RIEPrdt4YKMNbRTOZvyGbJLeAOzQADsyWMaYTrRq6fsQOnIhF9RDmbYcUYEjEeQTBw+FGB5yETzkHPWSll1R1+g75ea9xmxlSAiod+9vGPXm/xJUsZgwYQJTBN+zZw/i4uIYfbEjWiwt+DGTU8h8pM8jTNTFTy3EKzMT8PDYKObt/cORYhZ8Eujacv+oSDx8UcDdEdQXSLZiK1euhEajwaGVyzE8eRCOKL2xub4FtwZemMjtOrILunodbDwbYofEYmJY9xfVPxJUPUCKO3CihmUxn/znQexwGVnv78UglkWkt4JVU6gvMcpHicAWCVCx4wABAABJREFUG5SbiiEit06pAN63J0IS2XNv8o5ITExkxxQlSnJzBqJ3nypoNMeQl/8G4mJfv/QFdHEb/TxX8d7+PHDiS8BQD8xZTEpNPfqfIpEaIlEy1OrkLh93OIwwm2tYPysLxs01bMlVxxthszXCam1igbnLZYPVWsfG1YIovHT8CAQy8PlSthTwpeALLr5PBr5A2r6k1/F4QvB5IvD4tBS3LkXsfh5f1OExuo+GgJ2zKBrlJgj8zrd5fNbNzC15HR6jJe+S13L3Xd1Eg6P7k2K8Ay7XZdpt+IAkWg1JnBvMORo4TRZ4zO08saTgmWi65F/uaDTD3jZab1Nrjb3eBNSb0C6llJaFSgrGvWUQhyghCuIsqg5mHGU6C76+vhcCwT8AJlMlyis4L+5e0S+090//FsztNRdfpH7BBJ9O157GQP+BPXpdQcG/UFu3he0nxHRSqS7fXhIUdAtKSr+C0ViMuvpf4ec7jd1fnt2EHV9nsOCUWAUT7kpAYPSV6Y/ESKLko7+/P0vOYfdrwLEvAIcFIG2NsX8HhjwMdFBBpv0sqq8vgmM9sHdZDorO1TNdivKcJtSVcEmWgXOiEJjsDc0v+bDkadCyvQR1Z9JhUuvgqfREk6WJqUHTNYBEsdrQp08fxlYqKyuDUnkhsDu0Jg+6JjPcvGUYe+t8fPf9EuQ0OpEtSEBsaBZaylJgq65Fw6efdilK1B0kke5MA8Opt8Gc3wyP225jQfeI86fg3dyEbyrq8Wl8Z9cP+r7FqQ2M4n3rnSnwClK2X4erWsycSnqrUnpWlZYF4m2ioW1QSAC1XIByngGiWjFGmXh4WKLA8LE9D9hoO5Co2ltF1VBGqVnQ/c0hqnaHsaS//kQ1XFYnRP4KSC6ijN+acCt+yv6JeV9vPbMLJq2IqdXTNm0DVfKfWH0ORfVcSwlde5+ZFMNEH7uC2tsHoYkpKMtIRdbBvdjin4GWACMiqhWo2HcMyQmd/ZSzD+/H6c2c1/SIZ/4G6VffMb90sg9TP/4EczzZk1OL1We9YbM+Bi6kNTLByUkJ/kwpu9K+F++c2oHjDZQUuK1dUNSczQXdbQ4el2D8K3DlbEGkUoNgaROKdvoi8UYF3FRJ+OAWG944HYHAlnz8kLWcCWGQt7zoMu0c1NKm1+tZ8pLmo78VVMB5Z9KduG/XQYhU6eBVU+JShlJ/KZ5u5iwW7w7yxuvRQd0yINoQIZfgXzHBeC7cH5+V1TI3FxLmO9qsx6fxoRjirsS7McE422JAhcUGpV2PWVVbwZc4Efrpf1Dy3LuwVlaj1BqI8FGViDasx1npbIwcPR7nD2zD0aNH0ee++yBcs5oJ4RFLxOdxcl3oHnUlRSg+f4Zdv6hifC0glbph0qTx2LjxCPLzZcjI/AbJSVfhm30FnN+1je3TQrEE0x57DgLhFcQS63O5gFtfC/jEs4Bbe+Q8av/1b/aw73PPQj2NO29fLWQqMaY+EIMVL/8L+iZOttXHLwrTZ8yCpKwSVpsDoqA/XlDuz8b1oPsvAAo+X5keh23bijBtWvw18+mmXnEKfBoq9KinLH2xli3tdgfVxrkAHC4IlEJ4RbrBI0IFhb8cxiYTGgqb0VSuhd5qhx4uNkwKEUwKIfQ8F7O3oUHiQgQSEyKFXk5vmk7U3Ml6TylNhrtuwKULjpdCAm+VBD5KMVsXOtLgNFZBJe6HvnH3ocYYChvfCneZqEeWJ38UBgwYwPrzSFxk69atWLRoUafAgBRs9TY9EyOaHN5ZOIf6xAjUy0qgr0Ftbl8eKMKvmbVMqXVu3yBm/3QxqNp93333sYotBZDJZw7DGB6PtWp5e9Dd0NyAQ/sPQQghtKFa/HP4P//gX4MT9CK7I1a9rmqrXutQo+UIcrF8AWZBjOgGB5QqwK4QsspCQuug9V5+yk5VS1utAbUrz3NBl0QAv0f7siDm94D64YmhQNT7kuIFiIr+EpWVy5l/d0jw7V2/aPADgMwD2PAQkLEO0NcBNy3ngvHfCeptVSgi2bgcHA4La62w2hphsza1BuRNrJJpt+tht2tZ76zNroXDrruw7uCSOqSDQIMq6/+/gPazQ4ef7dmTyXmkzX1k3xWeS7tgcOtwAUKLB8T6QBw23AWZPgAD9bXw18sgcMhgrzOxgTP10PFMOCk+ySq1vSy1OLPqNhg9c+CQdNwmnRuGLpxTultyuaGLHyN2EEeQ5yMj84luX9uT/0GP95fZcFgHfHLoXtznL77iZ3Q4THA4uCBVwJcjO/ti/Y3W17a9pvU9qDpOyMp6FiUlX6Ixvy+KD48EXHyoAxqRNOMstPZt0OdTQkjKsTpEbhAJ3Ti2h4iWbowNQudnQrKHBfi0H5cwI0SOBWZ8AHh2f8xJ5CJMuT8JGQcqcfjnfJSmN7ZXZOKGBkAg5MP7rkQYT9eiaVMBfOtU+LzxJegmSPCe7gvkanKxpWgLbom/IApHidmoqChWNWxs5N6PLM5yjtWwrz/hzgQEhLhj+PDhjA2xjT8JEYIC+I+WonwV0PTTT3CbMxvS+PhuP3enX1jAY7Rdw7Fq5vLguTC2XZl5xqE9WO7hhX9EBsJXwl2PLEYbDq7i1Mb7TQlrD7jbtjFRyWnM6s1Vak1WB3PsOFvRjC0F9SgtbGY9uQY+YHA5ICzizj1HKAiVtiBhiZGd42PI9sxXyazPSEujO8zz88DbRdXId9gRHahCRZWOKZk/PCKStbMRSO364oQcuYRQfz0xzb7P+RYT8CAiUrzZNqPr1NcdxNKoUv/Bgj4YHt1NANsBVL2kACXtwC5sG3gObhF8FnTnHDmAETfdDpUX9x4NZSXY+dWnbH3w3AWIGDgcJ6vM2NUkR2qeGFmv7WRtMRyUAM+KifEBmNMnDGPjfFhwStCYJ+Hd0/9iyQOy9SKmmqWoBU6jndkpSiK6uf54RYE34G7g5NcYG1yKpXkpKNjqjeGjmuHh7oFJ0ydh58506CQtEPFkmBdz+QCxjVpO3tzk0f17MCigPxOJa9FqMKPVVuKjRg349Wq8NCQSD4f4XFWC1UssxKvRQZjs7YbHssuYxs28cwX4d2wwbgv0Zj3fLxVUQS9UYp3PBNzoIYSo/1QEfROL/AWLgHotKg974N7Ru3DTA28h2t8dLWXZjE24+8ABTH3+BVQ++SQav1kCt1mzIG7VhegKpzatY8uYIcPh4X+BzfB70afPBJw5cxYVFSbs3XMaIcED4O7eAwr4FUAaBQeXc4nZkTffCc/AKwS0dTnAjzO486hfEnD7Rhhzy1H1/POsEkgtLJ53d1bwvxIcLS3MVs9w4iSqT53ACZhhoPMRXVdlw6G1DMKpf21EaPkudqUg6rooNBQBUils/fpB1EOByb8yrgfd/8Ogiw5dSGnEDvZvD8Try3WoymtmXpZVBc2w65ywpGpQk6phO7qPkIdEER8BIgGkKikUA/yhGOTPKJUXgy5qerMdLSYbmoxWaAxWaIxWNOrNKKzYi+qmYuitStgFMTA7fdFktKHZaG23O2KBejNJIrWBstOt/X3pNJHkbDcoE0oVcm+lhAkU+Si5YN2r9b62dapw0PO6CmB/D4guSDTzL7/8Enl5eayqQpVUQoOpASuyOWr3Y30fY8IobShuMOCJVecYRY9w8+BQPDWhF1MmXXK4mD3+/M9p+GRPPgu+b+wfwvrvO0KhUOC2225jSpuM3l6SjVyTHoUxQYhUyvHR6o8gdAihk+rw8o0vd7JO+b2g7UsUuNyqZuyq5GHfz+koajQiv1bPEi1dgcRfIvxVcBZaIaw14wVvHyx4tn+X/XVtIIGV+q/TgNbEBE0Yf2/ATaBJA/XDU393TY0OPj73w92DtuEbTOjM22tM1y9MWQAovIHVt3HU1O+nAbf8DKh/n41ST0E2SgIBJ7x2NaBKr91uYMG3w2GG00nBEHmemuFkgZEJTqf5ovuM7etOlw0up51bdlx32lrvs7MAr9NjrLrsbK0009LV4barw/0dLNr+W0GMIakGTVIHPvQOhZMnwPuu/0DvqoHQ4gmJNhRSbQRkLZE4pXPAyXMh0OGJuNo+4NVytFSLvBomz2zofVJh9MyGS0ACYRwTpCfe7pd/jpNtz9+LEQoeDutkSDU6UGvSwFPYc9N5u0PLxtX2gpedCURd6mh2Wx16Av4Df0BTi/0SXYeuYDHLUVpKwQQPSdmk3q6HVeUJ7ZAF4MVOhVwmhNTluKwuCE3+k8cEwytIgfUfnGObhFS5dY1muPvJ2ePyAX54pfxtzEgbjF7mULj9CjyTeBcedP0dv+T/wiwiOwYRpAnSFnSb9FbsX5HL7u8zMZT5hhNGjRrFRJ1Ih2IvhmMa9kE19gbo9h1FzT9fR9jKFT0OTEisigXdmY1wWh1sYkxB99yj+/DT1Ln4oaoBz0dw5xSyTiNxOvpu/adeuaJpgAv7YcV3PCN8JXw81CiGFS7UTfJFuK8C2Vn1SC9qQgmcaDLbmUhZm1BZGyjo5YJwFfOdp0A82kfJ9Eyoj3eEhxKHNHrED/RHxUYdvj9SjFskMla9J3tAWUrXwfKdiXdiVc4qFDizkaAqwLR+KahqNuHpNedxvIirqk5N8sfbc5MZ664n6DVoGHZ/uxj6ujp4NAkQGpeE4GZ/VGRl4Oz2TUy0zGIy4scPP0GuKBTG8EQcaoxE1qu/ckF2QmsF0OFkgorBfhpkmFZhaKQXvpn61SX/z0PqgWFBw3Cw4iCzEHu076MwZXC/n6wranlHjHoervMr4GPVIsmvGhm1gdjw7htY8MrbGDtxDD4seI09zVmfiOomHtSXIQ/+Xmp5RzCRuMjpaNpfDDmEKOI5ccpmg/hsI/KdEmhmurP52tWCKtt7B8bi73kVTPX8udwK1FnsSNcZ4WXVoFHsgb/1egoDYhTQV7Tg5e1VMA24E/86/CVQC6iPGxF9417wAuZjypQpbF5H4rWD7riDtXUYjhxBzZtvIeSbr7s89ppra5B7lGtZGTj7Uj2K3wP6f3Pm3IMvvvgUGk0g9u17HTNmfM9ay34rnE4HdnzxEewWC0ISU9D3SirrmhJg6Wwu4PZPYQG3tU6LiocfhstigXLsWPi9+GKPzksuux36g4fQ/PPP0B84AJfDgRJvN+QGeMLJF0Fqs6Ofzo4mmQwlPB4Ko+bA4heFXplLAZMR1sJCkIJDT+j+/w24HnT/fxiI+0e4cb23fbyhz2hExakaVFYbUUuCRE6gzu5Cnd2BVAsQGuqGBH8llG5d02uJIkwXTBqhXp1Fu1yuKJSWLkZh0fvsdnDwbYjp9QqbGFGFnKjIDXoK0C0orj6H7OIt0FoVcIj6weQKY/fT4xTQU5a6TmdhoycgZWoWoHcI1DlFYQm8W5d0n7dCArVM2KOTB1FER4wYwUTOSE2R7GhkMhnrJTM7zEjxTsHoYG7SSMHG2jMVeG1TJoxWB8vw/3t+MuujI5Ci+V3DI/DT8VJGoyM12ZfWZ+DzvQUs+F4wMKQT9U0gEGD69OnM83Xr9u2IrS3H8mXLEBArgLBayNoOpk6dCj+l328KrMlLuLTR2DoMbEmV7KIGQwfVaAFQxvXVtTEVYv2pcq1qr14TTbzN/1XbYMLK10+gsVjHxJC68+5mAfc36Syjz/6LjwzS+N/fH9VVf3d6ugEjRy4EeKuRkfEEBvRfA6WSs2C5BFHjgLu2AT/dANRmAN9OBG5dB/h08/y/ACio4CjuXavb/1/j4iC88+0LwfrVgnq6d+3ajYkTJ/SYKWRIrUfLxkLw1SL4PdaPVQp7iuU1OjgLmpCsEGNB3y2XPF5VVYui71ex9XFjh0Kuk8NWYoS9xgyJMYAN94pxgJAHcaQckgQVxHFK8GVcYoqOZ26lC3X29t+RqxRnZD4Jne48vL0nIqbXSx0C+Itf2/V7XPx42//ec/A1nKpLQ5FqEaYm39r5ta3PofaIzKynGPPC03MkoiKfZefSSz9/F/+/9TYJqhWcsKIuleuJjhtpQeyYSLicL3IJIsbaoESRkWN42LSw2VvYus3WApG2AY4MiiJ4CEMF5EID8kIVqAjkwWX5GUj7mb0vjyeGQh7BaO8qVRJU6iSolAmszaIjaoq07KMSs8pssGPdf85gxqO9mQL6hoIN2Knbh2NRp7BG+RVwogUhmWq8rnwU/3IuYVXKRO8LtHpyvJDL5azffPuqozC2CFiQO2jGBb0N2l/JHnLp0qU4id5IQRb8hvGhPyGH6fx5aLdtg9v0nlkRiUNVzLfe0WxhKtGqCRMg8PGGur4BI8+fwo8yKbOcbChoQRYJk5Hf8a2xEF7Gqq7MZMHi8nqsqm6EyemCwuzEnFNcYqffuBCMnsdpW9TnGmCBFeLRQahK8kRejQ55tTrk1emRX6tDdYuZ0dNpHMpvuOR6HealAE8ugNBmxUl3C7s+N+qtqNpdytrElSOD2r2eLwbZd03ynYatNRtxLnQXTpmn4I0Pz0BnsTPR2NdmdhZL6wlEUinC+w9A/pFDCK+SY/rEOyCy2YGsDJzasRVL9FE4W9oMg2Q8QO3jVCMo5zJE1ErXh69D/JFtGKS0Y9Tq73HXr3dBWJeLiREvdvs/p0VMY0E3iRg+nPIwS560Bd2XhdIH5v43QHbsB4z0L0ORIRo1BXnY/vkHiL1jHqq8TeC5gPBSbzy5+jw2PDKsS2r9b7EKuxJmhE9HcxOnobEiBhioVeNMjRYbzlex/eDJiTFYOODSYsOVQL3cRC0PkYrxYWkt3i3hbEE/K/gMPwXOwnH33piV74J+72HQZUXpH4nG5/+J4H+/CG2ZHIJ33oHf8nmMTdi/f3+cPn2aFTbueulFlMyeA8Phw90KGp7esp6de8P79IdfRM89vXsKUo0fMmQwjh49idzcKISGPYt+fb9pbcW6epzevB5VedkQy2SY8tCTl/e81tUCS+cA+hrAN4EF3HYzUHb/A3A0N0OalISg999jFnmXg9NoRNOyn6D56SfY6znWkUkkQHpcJBqE3HEYHp+EqY8/B7knt3+n7avA4TV5qFAmA7d/g/GzvGEvL8L5Hb8i+qKWzv9WXA+6/z+Cw2CDtbgF5sJmdkF2aLgAlvJnHgoBBvTyhi3cDRV6GwrPN6CxUo/SjEY2yAqEquWJo4Lg3qreeiXQBS48/GFGA6Q+7YqKZYwGmxD/HxaY0aALrUZzAqL6pxEdZUVg4ELExS7sdHGkoI8L0C3Mi5kuxLTeoLOgse3+1nV6HgWR5G9J4/JSVWivonu10tu55YWg3F0mZgFz2whL7A9FejZ0TfVMJKfPqD5Ym7e23aeVPneL0YYXN6RjaxoXoA6J9MSHC/sgwK3z5I7Ujx8YHcUUZUnc5csDhayP7h8bM/H5vkI8ODoSNw0K7UTBHjRoEDJ5IuTt2ApJXQ3K65zggw91pBoTel8q8EJKz8Q8oN+HrLiI/s2Wres0ESKP6wv0t0tBwTX1XstsLRjVOwaxAWpE+6qYt/Dl1O/Jf3HI7CjWI0lVlfBkbyjcOydvSLyq/rsMOA02FoDA7oJqWOA1V+0mVkJpaSlTgz992g3DRwyHxXIEqWn3YcCAXyARd0M3DOgN3LsLWDYPaCoEvp0E3LwGCO1aTO86Lg9uuwr+ACcCmvEqWDWgp0G3Wx83GLc3wdlogz3fAXly956+F2NLAzcZnuXndUkFgoLZffs2snXq444ed6EnmgShLMVamPM13Dm42QJrnoENCHjM+okEsWQJXpeIsnWF+vqdLOAm4bzYmFcglV47muPtSffh1N7HsKlkLx4f+CJk1BfdAcSoIOFMCn4piE1JXnxJANsjND+MmjNcEJY8zg0jb+zXc8/Yg/8Bzq/Ely7u3BcXJELj+I8h5hsQaKllAoacqGEJa7fQG3LZqK7h+m+Jjq9QREGtSobarS8U0kE4u5MLRoff2Au5x6tRV6rD5k/PY8KjvfDBmQ/YY/f1vR/BSSkwRtZD83Me+uvj8VHxC9h9ZhsSJyd2YtokJyfjxIkTKCrJhZqXgLG3xV0iCEcJXNpXSH19G8bh3uJf4H3786j/8ltO2Gn8ePClV7ZvY9X43j7QHahgKubyFB943LgADV98gRsP7sK+AUOxvVoDw3KOQpwwIhCBvbquoBUZLfiwtAa/1GrQJsnRWynFwnN6mMxOxggYPpcLOOwaMywFZJgIeA4OhK+nFH1COvdea802xpCiADyPlnWcawUl0rnrdXP7pLSlTbSQ/NaNGniAh8AzRfAvrIKfWgpflRRuMiFL9lP7Fl2bw2vHgefajHJ1Dv62bTuclmD0CXHDRwv7Itxb0en4bHM/sJD7gd3JEuMsyU/zCR0l+7lx1hgKTeCNaOG5Ye8yI0si3SJyh6etGbrUIzC49YbAZUecrwIDewWgb6g7+97E9nJqtchf9xZcJhNqj+zFubpz7P+PDRnb7fajx+g4I+/xc5nH4ad3MG0TSdSVNQ2qwvwQcpoHudWGhfP6YumKE8g/cRSbvTlF/7AaORJb6rG8WosPduXh71MvbVsglxb6fSi5T1o21wLKEiUkdi80C3So5mfis5ohqF3UHy/tyUNurQ7/2JDBvNmfHB+DOX2DrspRh/b3FyID4CMW4sV8rrlxp3oo+macwYnByagX8SGIUmOeXIl/TI9nAoItMj2qXnkLmrMGCN97Dd7P/RNjx45lFn+1tbXIaGpC6L33oHHxl6h95x0oRwwHX3Fh/zEb9EzVnjBgxlz8URgzZgLS0tOh1wGpqYXw9FyMiPBHrvp9mqoqcHTNT9x73nEf1D6XUUQ3NQM/zQM0xcyHG7eth5MnQ/lDd8JWVsZ6rEO+XAy+vPsYwGWzsap2/RdfwFHPndcFnp5oGjUUp6pLYTWbIJRIMOa2e5EyYUqnc33K2GCovaX4dUkmKnKbseknGyLvSMKOUXYMx/8Grgfd/6NwOV2w1xthLdfDWq5lyqtUUexUbBDyII1yhzTeC7IkLwiUHM2HarEDZ0YyBVmadFAPGtHQzu8uZyM00ZPR8MKuRHlqRXDwrSzwzsp+DjU1G1hFggJvytrpdNlITbufTYh8vCciNub1SyZclAH1d5Oy0RNboWaTrf2iSQE6XUwpIGej7b7WYJ360qmK3paB7xmozycMKw/aID55DEY8CLVUgi938PCh7RijktNFnL7FsGgvjOrlgz3ZdSzjTrR3CvKpitK2FPB4iPNX4b0be+NAXj3Wn61kAfFrm7Pw0e58TEsJwMhob3Yxos8qhhc2BAzFsIo0hKAFNhcfxbZ+eGzlOUbdp8QDTWJo2R0F/GKIBXwEe8oQ7qVgE4YwLzlbJ2VX6utzOOxMSG7amMir0hxIHhvMrHho4kr9g1MfvCAw5rQ40PhDJpxaKwQeEpYE4on5kPe99jYZhEmTJrEsPvV352QPR1x8FczmYqSlPYB+fZczReUu4REO3LMLWLEAqDwNLJ0FzP8WiO+Zrch1/DVBQS21zej2lUN/tLrHQXe91cYEfAizWtV1L+6JJP0FYqdc4tEsF7GqFQ0mhFlrZNUsY1o9WzdnNbJBgliKgf5sCD273i/pnJlf8C+2HhpyzzUNuAkjg0YiWBmMCn0Fo7tS32wb6LNn5/y93RosJfnL3xRwk3L4kVVkucGHe/Re+KYQ66h7/+B26uORT4CzSwGnDfXwRA382Lm09y3vs8ryxaAWCHIPoIBbp82ATpcBrS6DiRQaDPlsUCDekDUNFsNcyNz18I45i+iBI7BjcTGqC1uw+ZM0uOJEiA6IZuJdBApwhT4yVH1/DkE6X0w+qIQuqh6q6Av7UlxsAgu6LdJGxPf27VYUbuLEicxerMrqjzRnJFISLND4+zNhp6aly+B9f89812WtQTcldZwmO9wXLkDDV18hPj8bEZVlOJpvR1itEXK1GMPmRXUbbK+r0bRzTkZ7qPBYmC/cUltwMK+KMeYm3p3YXiE3nK5lcwtJtHu3+ysFx/3DPNjoCIPFzphVZU0GlDQasaKgFsWNRqgsTlgMNqZU0AQXmqq1yKi+fNuCNKA3RO7nIPbaB3PlbThf3oLxHxxg104Rnwcb2Q62M7d6AjUTHyNQDizMSwmLaiSQvhlDNKcQo8vDnBtmYticSxPeAjc3TkF+1WqUffclXGNcSPRK7NJurA1k8TkudBw73jZnb8K9mA5ZfM8ScA3aY3CEyBFTZIBn3gpMuv99rPv2A5x0ZDIByaQSN3iaG6C2aVmf+9hYXwyJ9OqSWv5brMK6gpOU0PeUMIWfrR4HIUYeeK4hiMzTYvNjI7DqVBk+2VOA8iYTnlmbynrvZ/cNwry+Qaz1oKe42d8Lb58pgaVCj1+rEsBzxEOYqYGtjxeckSos6B3FAm6C24JbYT+xGnVbC1D/7RoII3vDff485hhDle69e/fi0fvug3bTZtgqK1H/+Rfwe/659v+VsW8XbBYzvIJDEZrUG38UyOliyuTp+Pnnn1FRnoSszG/g5tYPnh6dRfyupFa+6+vP4LDbmTVY0pjLCOxaDdwch1h9xJi8fQNcch9UPfnkBS/ub76G0Lt7PQTDyZOo+ccrsJZyFkrUg62+/16crChC7vHD7L6A6FhMffRpeAR0zXyk4gwpm2/4IhW/eDlxtLAcfKkXbjdakOB2bfSu/i9xPej+Lw+sqUJIWWaHhtR1LbDXGZkgFanrksruxRD6ypnPpbSXB7tA8i9jweIZoMDQudHMqqU0swmZBytRmtmIsswmNogm12dCCGKH+F+Wnkbw958FvkDCrMBqatYz5eOwsIdwPvUu1n/q7jYQiYkf/S7VXQJNvIhGToN6x64EynRrDFyQ3jEobzBwS6K2U+WaLVsHF8jyYIUQVjN93mA0m4F9za3CPa2g/MaRgkY2fisogUBV8DabkzbIYYG/RMd6TE/Yw1CQz/yNLlvJ93eTwV8tYRV3qhZQnxktQzxl7L7LZZgdPYvdu9weY2+Lx9q3T7EJduG5OqYWTPtu06oc2KoNLMCg/ZKCbgq4+dI/5rTUsb+7oqIagYEPQqF8G1rteWRlP4+kxI+6p28pvJhyJ36+G8jbAay5DZj2HjDwnj/ks17HnwPFkADoDpQzBpC12gBxwJUtmrbUt7BgpI9KjjBZZ+aGw+HArl272PqQIUMuWy2i5CKpMdNQjw9lSVHj+XoYztQw2zJKBuj2l7MAXTkqGJKOHr0AYw6RPZ1Y7IOwsAdxrdFmH/bu6XeZfdj8XvPbE6IVFUtRV7eVUypP+vyqtQcIDRU67Pouk50LIvrxIY5aheoaKaKinoRI1MXvVpMBHPkIyPiFZJ25+yLHIF19I3C+AFFR0V0G3ARiVshkwWz4eI9vv99iqWXBt06bjvra88jL5UQwPeJWIjv3JHtd0Ihh0OpugKFOjhlZj2Do6IBO6s/iQCWCHh+Ewx/9jF6GUGi+y4bgJrAqM6HkmAECmxwOkRGqqDYn5kuhUqlYf/fu3buxGyMQf/4n+D7xFqr+/hIav/oK7vPmXnbC2wZRgAJCX07Uz5TZwDRZqFKu27kT8w4ch0TIBYijbophAnJtKDSa8WFJLatst80cJnip8XS4H/qpFdDUGLB6HReUDZ0bdUHp3OmC8RznsKAYcPXtTcT4IgcLGoSEZF8sSC1EgtaFD47pUAsn3vIGHpkWx6ritS1mxnjTmjgtGWJyFdcbYCTHlsYxLOgWqrLAF9fBafVlzDcapJxwMWh3lgrJhpDfrhlD+jDUgqZzVmFr6Sr0rhIitlKP4aOGYcoDj8LQ0htfPbgVIqeN2aYNndV9tdPz1ltZ0K08ngmvvgKM69s5CdcdxZyC7j2GQ7gLU1hB5Eqg/Vivz4IxQIpedQrwWsqQIC/DD5PD4LRXwlcjQbw6FvWaYszz0uAHrRrPrEnFr0+NgqTdzt7VLqJ2rajlG85WYFCjDVaeC1s9DkODFlSJ6hF4lgf1hFDmxX5D/2D8eLQUXx3kmH6L9xeyEeunYqyB5GA3JAaqGZuBWH80qKBQrjGivMmI82XN+DW7FnazndmNEpwqEaYlBUDhr8SKmiY8ml2KPQPj4N0qWuf1/L/gKJyMxhwVql/5BwSeHhg4ahSjmDc0NODA8eMY8fJLqHjoYTT98APUkyZC1qcP640+/yvXTkS+3NeakdcVQ+/MmTNM6K2wcABU6icxeNBmSCQ9K0yk79uFiuwMVlmecO8j3X9euxVYcztQfoITjL1tPROeJOvCnnhxO/QG1H/wPjQrOB94gbc3vB9+CC0xUVj/7RfMe5so7UPnL2Jig3zB5eOFPCXw7UwPlFg4V6VelVaY/QyA26X2fv9tuB50/wVAgifNq/MQWa1EszaPiXa1o/UYoQCaDasDTrOdBdusB/Yy+jY8ER+iYM66hoYk3A0C1dWLVpAAFqmB0mipNzKF16wj1UwZff/yXJzYXIze44JZ9Vt8mYDJ12cyEhM+QEbmU6iqXoO6+u2Mbq5UxCIl5evuK41/IKivyd+NRs//N2XKS6vr8PmS72F1CVETYEJ50zBG4SYkB6mZOiqJxVHF22S1c0ubAzaHs30SQJQ9h5NucxV6u9PJVM1pyefx2CBvUbrAtFluUQCtkJvQz5EHkcuJWpEaIe5uUDVUQAgnEqPDMGpwf3gqJfCUi+GuEEEl6VnP+h8F72Al+k4OxZntpTi4Mo9ZuZj2lsOc3cTKBx7ze6FxWTZ7rmLwHytU1rG/++TJDMya9RqaW55nAUSRPAJRkU91/2KxAli4HNj6FFdl2/o051059qWOktLX8V8EIYkzJXozb2PDsSqI5115srmxliqzwOwuqtzkyVxfX8+0Hkj/4WpAwbfbFAXUE0NhymqC4UQ1o+ySTy8Ncbga6rEhkMR4MBX74hJOMTkq8hkIhT33c74azOk1B5+d/wwFzQU4WXMSgwMGo6XlLPIL3maPR0f/De7uV+fRSjDprNj2RTrsVidC4j0w+e4UnD4bB70+G5WVK1hbUntPeOkR4PBHQAGXzGjXWxj5DFxhw5HxKfc7EI37aiGR+MGHhvd4NGQWwWkrgdqXh6RhfdCo0bAKuN50CAFDzqFs3/NQ6QJQssyAAS9ooXC7kAQRqSRIm1yPul1NGK7rg6aVOcx2TuevQNahGkgVvjCISpCZnYGBg7v/vShRc+bMaWg0wGFjOMaFGtGUlARzRgbqP/0MAf/kBLGuTDH3hXZXKaOYU9DtcfPN0O7cBT9jL+hUgC1aici+XFKgwGjGRxcF2xNZsO2PvmouieF0OLH7+yw4bNz2IhpoG6ylWjiazMxxQppw5QDxShjuoUSARIR5BS1kJIhTPCfON5hYO9rEhM5V4t1ZtfjHxgwWcFPP8gRZNERBY3Gwch9uGJ+PF/rfzK61VOG2O5wQCviQCvmQiASQCDnWWXfXxod3fwGxxykMDpkFrKlC4ckjsN/9APZ8u5hVEAkkCnW5c78kOhrSwQNhPnEKk845Me62KwfdQwOHwl3kjmY047xbHmbFjLziaxobOcFZhXsKeCOnAdufg/Hg+zgayGmjJBapoWni6Ne9zCUI9ohjOjLvbMvGazPi2q3CDAYDq7CGhrZZO/x2ECOo5Sj3P+tj3RAflIijVUdxKDwNC/PHMzaGx+xopt5OOjZ3DQ9njMD15yqwP7eeUc9prD7N9ZhfCVKeBRN4Z6GOC8Z3wYOxkWfFKwpJu//70zll+DE5gtvefonwmdsf9pXn0VIiR+VTTyP0u2+ZLs6yZctYG1rfBx6AeuZMaDdvRtXfX0TE+l9QlJHKfNulCiXiR3YjwnoNQZ912rRpWLx4MZqaglFdXcA0PPr2WXrFApWhWYODy79j68MX3Ao3324SYk4HsP4BoGA3IJIDN69lv0/jt9/1yIvbeOYMKp97jjFyCO4LF0L5wH04tG4lst/lBIapqj3t0WfgH83pP3QHjc2ONwqrsKKaEz/0FQkxv8COgOwWxM+93tN9HdcKDhcsmU3wgBiWJm5n6zF4gEAtYfRcoYeUKT6L/OUQ+ikYzasn9O+rgZuPHMNv6IWBMyKYEEvqnnLoNRYc31CEc7vK0Gd8KLsgi2VdnxD8/GbAZtMiN+8fLOAWCJTo0+f7v6zwU1cgurtD0Ywa91TEtcRBWivEebMJfmoZ/jUvBWPjri09mgJ1opx/tq+AKYkrjWaEiS1wgof9if3w0fA+kGWnMVoUSmpRLdJg2Pz5kPagB/DPwoBp4Sg8W88SNQe/yUB8NUfP9VwQA1uVgXmoicPUrGr0R6Njf/euXdmYO/cVlFf8AyUln0Euj0CA/5zuXywQAjM/AdRBwP53gIPvAtpqYOZHnfx/r+O/B8qhgSzopmqd25RwRgHvDjUWG060cF6/My8Kuq1WK9N5IFDFkgLv3wISi5Ine7NB1W/doUoYz9fBWqJFw/eZLPhuid8Hu0MHpTIBAQHXxiO2K6jFasyKmoXVuatZtbuvVzTSMx5jKva+vtMQEnznVb+nw+FkPtzMq9pHhkn3JkEgFDCKfFb2syivWIrQoDvBz98NHP0EqDjFvZBYKAlzgBFPcloLtD2qq5nqN7FYYmN/u8Ch2WBj1zLC0NmJiI4Zi154gfWDH8snm8G1CBn9AUr3vgB9kzfWvLcaQ26qQnDYQqiUXNAyrdd03JA1Hw/WLcDMptFo2VKEklbhL3d3DxgcJaztoKWlhdmJdQX6HpMmTcbq1atxFP3R79gP8Hv+HZTefjua166F5623QNKDKiTR3inopqSNQ2eFfPAg1KTMhU4VBgfPgdW9JZhqMOOTsjqsvyjYfibcH31ag+02nNlRylqE6Lo+7vb4TvMK41muyk12ZZdjzvUU1G51j0iBUfVN7HPpUzyB1EpGiW6jQ5Pw5z83ZWFHJiee5QU+JuuFuOvGWJjD7mNB967S7Xiy/6MIVF9920WlvhKHKzk67Pzx92L7r/+AsaUZ+374ivVJ8/gCVoxoKC9FRVY6U4TuDhWTUuB94hQmpvEQIb+y5RGxKMYKh2G9bRsOB6Vj9hVYhITGxgNs6eU1Ggi9gzFCNrta0GIVshaRIR5JqK7lEts1edl4628P4o5V2Vh+ogyTE3za+7mvlVUY4YPMCtxXxVUqU8ZFYIZtBgu69ypPYAHGwXCqFupxoe2FIKpgT08JYIMKDSeLm5Be2Yz0Si0T5TNY7UyjhgoQCrEAIZ5yNvw8ZNiiL8WxnAcg41mBiQfhZfZjwmpvFFbjjehAvF5YjZ2NWnxX2YB7grnvyxv2GAIK58NuE8NQaUb5gw8h7KdliI+PZ0rm1E53+0svMr91a3Ex6j/8COfsnNZA8vjJEEn+nPkV9de32QoWFQ6Cu/tGFBW9j+joi20ZO2PvD1/DYjDALzKaVeW7BCU1tz0LZP4CEHtn4TKmV6NZuxZ17757WS9uYkY0/fgj6t59j1Ehqd/b/43XUWIx4Jd/PAuzXseYg32nzGAWeyRM2B3ovTbWNePl/Eo0kFghaYoEeuHlqEAI+1nx67bSS3Qw/ltxvdL9FwBVpFUzwpltCAUE1AvYWVTWRWdidkGj5/IkQgiUIubdSBPEq1HdvVaginafCaGsZ7fgVC1Oby9lAdWJTUU4v7sM/aeGI2VMMASiznRd8hiuruH8DQlELa+t24bQkLvw34RXD76PPPdchGmjoXYBC0LN+PudU5iwy7UG9YGTmnlkcAPuWvsThjRyyrdZdj8YTxvxnjkf6+YMhZeXF9avX88oYt999x3zEvfw+O02E9cS1H5AKrnr3z+HvGwNfBQCREwOgyzJGzX/OdVO9f2z0LG/+8ABA0aNuh8VlV8jO/vvkEmDL1+9o0z5mL8BKn9gy1PA+Z8AfS2w4EeuGn4d/1UQR6hZlZkCXMOZOqi6UdknbKlvZqflAWo5gqWdWUPHjh2DXq9nlPKBAy+Ip/0e0OfyvDEGbpPDoDtYCf3xKhZ8y0r6I8j7KfjM6f8HiNJ1BtlgUdB9oHwfzqRx4mRyeRTi4975TQyaw6vzUZXfDJFUgGkPp0Cq4M6Zfn7TUVjwb1is9ahZ3g+BJa1OCQIJ0PcWYNhjl3htk3VjWw+qRNK1w0ZPQNcsq9nBKNNRrRVgglAcgPcLUlGmk+LBhJkYuagFR5YqYayPwulNtagcNB2enkMRGnovoj1Ho5dnDL7AaqSE90XYWTXCicbkJkJ9HCDQhaKsrIwJNtEkujvExcUhIiwYxaUV2NXgiwV+dqgmTmA0z7qPPmY0zyuBJd+DlbBV6FlCyRXniXzvscwYQKo/gWLhDIw5lds+xZhEwXaEP3qrLqXn15fpcHprSTslXelxYfLssjmYHgFB0e/aJZuns3Yp4ICfEFPGRWJxWiX25tQhs7IFx4oamdYJOaBQS9QtfYLhsbceMpEAYUleEEv9GCPjRPUJ/JD5A14c3L1aeHdYl7eOqesPCRiCcPcIxAwZwWjFaXt+ZY+Puf1eNFWWIXXXdpzZtumyQff2oHpMVQHeOif0v/7K/J8vBwpARlSnYL37NhzmnYLNYYPoMgldp9OOJg2XIGAWmEIJnCOfwbJULmi6NXYh5k6ah5X/eBaa6io2n1TXZOGWwWEs6H5xfSYej7kQdF8LavmpFgPs5+ohdgE2fxmkISqMt49nInFl5goUh9UjstQXusOVcJ96Qcm/DUT1n5Lkz8bFIOYC5Xzazj0fldTg0V3vcQF35FiWkHva5UKt1YalVY14p7gG94f44LOyOrxeWIWh7kokKGVA9Hjw/OIQPDQXZef6wlRYg/J778P4L79kve10rGaWliLqzTdQ/sCDKF29EmWxISyQ7DOpZ24C1wqUxKXzBjFgykp7Qyj8GgplbLcFgqJzp5B37BCjdE+8/7Hu6dwH/gOcpmo4D5j3NRA9AdodO1DzyqvsYa977+nSi5vo5NUvvwzdjh3stnr6dPDuug2bVi5FdQFni+gTFoFJ9z92xep2hdmKv+VVYHcjp9dAzIT3YkMw2J0rwCxf/Qtjj9G19a8yn/09uLZmxtfxm0AiGfLB/qj3t7AlVV7YGNY6hgdBOSiAeXASFVIa7c4mY5Qh/L8IuDtCIOAjdkgAFr06GBPvToCHvxwWox1H1xVgxT+PI/9Ubbs1DVm+pKbey3pohUJ3BAffwe7Pz38TNTWb8N+Aono9FvywElktx2HjOVEk4TLX6uZ88B3d9+v9XtQZ6/DcoaeR4NRDxbNDJFOgVhUFnt2F/PN1GP6vvdhZJcK8m26DUqlEXV0dlixZ0m7/8VdAQLgake5coJJqd0E+KhiWwmam4EwKrfIe9K5d6/5uYgNUVlaisDAZPj6T4XJZkZb+EKtwXRH97wRuWgGQqjNRX3+YAeg79/Vfx18fNHlTDOUSPoaT1R2stC7F1vrmLqvcWq0Whw9zE9/x48dfk0pRRxCbyX1GJPyfGwhTdA5cPDuUDb1h+t6Blu3FTJTwj0KkeySGBQ7DWJUNRu1p8PkyJCd9BqHw6lkpdD3IOFjJ5niT7k5kuiEM1Wngb34KwQUV7Ga5pwkuuRejkOOpDGDGh5cE3LSdKFFNoGT1bwVR3dP2cv930MyITlVcqu6X6crgLfPGnX2eQ8rA+zDtwUGs6K4tHYamnBnQaI4hNfUenDg5FQsDw8DjufCZaAXSTdw2CecB4RXy9s9ICuVX2h+nTJsJMl7LQgxK9i+Fz5NPkkAG9Hv2wHiOU8HuSbWbYDhfhwMrcmF38mEQ6fH6bVzfOu3lk73V+HVADJamRHYZcBOdfPcPWaz9iejoMYM6U1RNWY1wWRyMaScOvzb0T3K0EGVyjL8fwsVIc9gwJZELvm76+jje3JrNAm5SCt/y2AhMlyohBg+hCZ7t7W33JXOic+Sd3mDqbE92JdicNvY6woJYzsYuvE8/7kGXC9GDhrHqXd/W6mHhmRPQ1FR1+177qw9hd19uqq1ZvuLK37/OiLjqYHja3aB3Glh1+HJo0Z5jrEFyUVCrueD/gE8oSkUiqBxOzG1ugkylxry//ZP19hKOrVuJFybHMJHUimYzNpaAXQevhYgaib2+mFuOeeVcF73vMM6qrU0kjrA/PJUtyVOeHB2uBpRoaQu4Sahta2kRbq7eyj04/Am2oMff6hWMEe5KGIgpWNuEUR5KWJwuPJhZCiMlw+g9hjwMvtCFkBH1rBXAXleHlqeewug+fdj77Ny5E8LBg+E2fx5KvDk2ZlS/AZdXAP8DQOK1RDMnVFUlQK/3QE7O39HScum5gETe9n73JVvvP31O95Zm55YD+7k2IUx/D0iaB/2hQ6h87nm2n7svWACfZ565JLFqq65G6aJFXMAtFEL17DPI6BWClW++zAJukVSGUbfchVve/vCyAbfD5cI35fUYdTKHBdxiHg/Phvtj98DY9oCbGAdURGpubmZ2dv8LuB50X8e12ZH4PMQM8sdNrwzGuNvjoHATQ9tgxs5vM/Hzv8+gIrcWqWkPoLnlFKOU9+3zA2J6/aM98CYhq8bGQ3/ZrUGCLW9uycLkjw4g3cj57/aSjcHSp+9BYGAgo5YyevcfAIvDgqf2PQVzixmxLRyFct6sGdj37Hio+nvDqRAyFfaP9+Rj/o/ZaAwbB6lXIOvP+uGHH5Ceno6/Apo3FyHO5YSUTz7tTsaOMJypZY9RQonXAxrdte7vnjuXE8EhqrnLeQ+zPrLZmnA+9V7WBnFFxE4F7tgMyDyBqrPAd5OApqI//sNfxzUFBSiknE8ClFRJ7goNVjtONHPU8qnenQOMPXv2MK/w4OBgJCUl/WFbp8V+AmWR/0LJ8Fchipay1iTqjax9/zRM2b9dsPFKWBQ+BNPcuMlxRNTfoVRevnrRFbQNJuxfnsPWB0wNR3i8AkhbC3w3FfhqJHDuJwRV6iFw8qBXCtF05xJg/CuAsusJLtn7ELWcmGHkh/1bcXp7CWwWB3xCVYjofUGojIK1L9O4yesT/Z6AQsQlCMISvTFqYas3dfpsSEzPsWsa9X97ajfgBX8zhLazOI8aVPnIWYLBp1aKoGIZ+6z0ual/9nJg3sEJXJJhRzEfIm8Z3OZyVa369z+4bGKoDbJWIbesohZm+2nnAz9OCIKdRJFqqphczDu9grsMtttwYnMRmqoMzDJ0zM2xl0zA26jlJIB5rVrZ9Ecq2X5dHyhDprsAy3NrmIAagXy3yQP7X/OSse7BYYgPUKPwHJfojOpQaR/kPwgp3ins2kmJk6vBvrJ9aDQ3skTLmJAxTDzr7LYLRYFeg4ax38ErKAQRffqzAOXcjs1dvtfZ2rPQWXU4M9iTBSim1FSYWhNF3YH0G8gGdIyAU6neUcJVE7tDY+N+tvT0HNHOevkxh/vON+p0kB/9DLDo4e4fwCqP7H9oW3Bs6Vd4Zx53rjpSJ0S1Q3lNrMKWVjVAUapHiMkFSAWQ9bnAHJkRyTl+7G7ZD56/hGkU6Y50nbDoCchFYlLRGsidFjj9ezNhxTaQWv03SeGIkIlRabFDZ3cya7E8oxmvFXAJBqQsBOTeEFjKEfLsHAgDA5jyduC338FHSce0gbUMuT32GKo8uaA7pLiyR8fftQYxEBISEhjxtaR4EhwOG9LSH2SuDB1xcsNa1neu8vLB0BsWdf1m1L+9+XFufcTTwMB7YTh6FBWPPgbYbIxO7v/qK5cc7+bcPJTctAiW/Hy4fH1Qe98dWHdwO1N0pw+WMGoc7v7oKwycNR+CyySecwwmzDiTj38UVLIEyGA3BQu2n43wh6RV08pisWD79u3t50PaN/8XcD3ovo5ru0PxeYgfFohbXh+KwbMiIJIIUFeixcYPM5G+uQ/shnD07fM91OpkdkDH9HoZvr7T4XLZkJ7xKPR6jpryVwFRmZafKMXY9/ZjyeFiOCX5ECqKIOSJ8MX0v0EmFjLxDcK5c+fas8XXCnRyf+v4W0irT8OgpkHgu/js5EsURBKFWdAvBNbhvkgeGcQUPkmsbeWZanxTHYxz0j6os0mwbt067N+////kQtEGoiAaTtawC+GImWS5BpzbVYqqNtXb/levenstQH2gbYJXW7bsQFDgvyGR+MNoLERGxqNwOnuQhQ8ZCNyzE3AP5QJu8vKuPPvHf/jruGYgxXwSnyLQftoVdjZyquXJShlCO6iW0zGfmspVbqZM6ew7ei1BNNI2ATP/2CnwvWcAvG5PgMBTykS7Gn/MQtPaPCa0eS1BySdR/Q9kI46zBgFOGoS/qY+bErBE4Q4IEWIgbzHwfizwy71A2VGARIES50F0+68ICL2dvaasaull3/NaUMspEcAq762K3B233afnPoXBZmA2T9TX3hFJo4ORMo4TE8vaGY/EqF2IinwWQqEb/EUu3OltRa+J7yL0JiPU86MZVRnnWxCu5PqLe5IIHTv9Bkh4dtTAB+e2fgefRx8FTyyG8fRpGA5dOUFtU4qwPEmB01auY/tQggzBauD9j97EV++9ApHVipWtgkVdobqgmem0EMbcEgfZRSKs1CtuzuNEBeX9rs35myzODCe4488x0A/CdA2yfi3BmVJNm6YsZvYOwE2DQtlcgxICGnLAEPAQnnIhYULb8Z5kzlliVc4qFvj2FGvy1rDl3Oi5rL/62M8rUZZ+HrxWim7R2ZPtz+03bTZbZuzbDYuRS8h1xP5yLiDuEzsa6skcw0Cz4vLVblMGV5mfEsnNK/aV72PJgyv2c3uOZsvMhkycqT0DIU+IRXxPwNgInPyaPRY3fDQUHpy4Wsa+nZDlHMKC/lw7zRFbOEIjf1+V2+Bw4IOSWtxQzl03lf38OvX5E13fU+oJjUWDzP5cwl1/uPKqq91tWFtWjrsrOVYCf8QTl4jaeYiEWJocCbWQj3M6I+IVXGsE0c63EWtJJAUGc84PouzvEbpkCQQeHrBkZWH8qdPgOxwsIX9s1zY4eDyozFZI9x2EZunlz01/FOj6Quc6jUaMhvpRsFobmOUptW22eXKf3Mi1b4698z6IpV3oilSnAmvuAJx2IHkBS2xSwF3+0MNwWSxQjhvHhNPa9vc2GI6fQOktt8BSV4eyuCjs7xWEM0f3w26xIKBXLBa98S6mPvI0lK37V1ewOV34sKQGE0/lse2hEvDxn5hgrO8bjZjWbdMGSnYQg4ySQP7+3dvs/bfhetB9HX8IKNgeMC0CN72aAt/4TIDngL6qL4p2vIScA+6wt/pHU39MYsJ7cHcfzPq7U9Pug8V6dXSwPwpHCxsw/ZNDeGl9BhP2iPSRIz7hCHtsYdwCBCg5SmpISEi7ci75PF7L4HZFzgqsL1iPSH0kPEwejGY0nfpnWi8uTEGZx0OGkodVDw3FT/cMxshe3ixZkNoswmZrIn61xmD5nrP4+ed1rBr3Z4MmZ80bOKsZ1ZgQxE6NQK8BZBsGnNXZwfPh+g//rzB27FiEh4ez32b9+t2Ij/sCAoEcTZojyMl5qWfb07sX5+XtnwwY6jmqOQlBXcd/Dcizm2BMr+9yErijvoUtp3SoctO+Qcc8ISUlhVW6/yhUVa1m1VSikYaHc/YvsgQv+D/VD0rqQ+eRkmwtaj88C3MBR4P/vaDvl5P7EszmCtgFHlijEWN5zoqrPsed/CUbtcVaSAQmTDDfDf6ZrwFzC+AWAoz+G/BkBnDj90zEh9P34KOp6VC3SdhrRS0/uaUYTrsLwXEeCIm/MFnMaszCxpz1kFr4eDz8XjRXV7EJLY2WuhqY9XoMnRuJwF7urEq+a0kRAv3vQ3LMr8gp6Q+TE/CQNyMz904Ui/6J0l61bPuE17u3B93OVgXs7qBQKDAmmduf9ubr4XBTweOWW9jtug8+bFfQvhhEuV1X04QRJ7JRVG6E3OpCs0qAu+fHYvvI3hika4LMYMDI86eworqR0TwvhtVsx+4fsxkHPW6IPyI7VCvbQOJ+9Lg4VMUq8dcC+hPVqLXY8LHMjpu3ZkBYZWTBdkykB96ay1Vl152pZMwzAllQEkISPCG5SLyVqtTR7tHQ2/RMk6AnKGkpYb3gPPCYLz31xh5fxzHbhszlqOZFZ0/BbuWo02EpfZlfs81sQvrenZfso21B99iQsfC45Wa2rt2yFY7mro9Pe6OJ2WjSzHxAv+Hwk/uxxE+bqFvXVmEkkMaDlxencv5j1o9sOSViCvxH/Z17IgkSmrXsnJEwcmz76w8u/x43eDVBwbNC55Jif+PvE7T9sbIRAq0VI+u4xJ9icOdgScgXMks0wi7nQQj95Kw9gXq7rxYtNjvcM1bBy94Ci1sYEM8lQC5GL4UUXyeGs6ThQY0eQ904xsrTOeWoNFs5209isdSmQ+IsQsjXX4Mvl8OVmopJWdkkRIS0XVzFNWXwCLY/1r77Hoxne9bmcS2hVquZHg2hoCACNlswdPpMVrByOKxMWd/psCOi7wBED+zCz7u5DFi+ALDqgYhRwOzPYTh27ELAPXYsgj/6kFmEdYRu7z6U3H8/CqUCHEiJQoYEMOl1TJV81tMvYtEb7yEwJv6ynz1dZ8TUM3n4d3ENbC4XE208ODgOtwd5M6eejqiqqsKJEyfaEw2dHJ3+y/G/802u4y8HplJedB88kz9C9LR3ERgnYu4Ep7eVYNXrJ1FOllGsOi5GSvLnkMnCYTZXIi3tQTguk9n9o0FZ9du+PYGbvzmBnBod1FIhXp2ZgL/Pd6LUkMPEQO5NvrfTayZMmMACYuqhJsGLa4GDFQfxn1P/gcwuQ7/mfu39oh3pX1R1I/qUyenCriYdRvTyxrJ7BrNet1m9A1n/U7XTDbtssXjzLPD4J2tR18QFD38GaOKhWV/A7O3IP5a8OQmjFsVCKuRB7wRyBYL/U0szonzecMMNrBeePDr37ctHYsLHjKpHon9FxR/17I1IWO3ObRzFzWYAVi7k+qau478ClPihfRR2Fwytk/k2GOwOHNBw1bJpPheCbjrW6ZinY5/OAX8UqGezbT+MiHgcItGFz0BtGe7TI+HzQAoEXlI4Wixo+DYd2j1lzEP594BZO9ZtY37cKUmfgC9QoKilCMerj1/5xTYzkLURFV8+h7N7OArkWNUnUItbgMS5wK2/AE+kAmP/DqgviCjKZCHMXpJQVs5Z3lwM0qxobGz8XdTymsIG5B7nqqoyeQ62fvIuVr36Ar559G5sfex53PZrKG7aE4Jjb3yE7596sH0seexefH7PTfj41jmoSKfzhBGaGiNWvrYFmz9YA8vRcfioyB0HdRQE8tHQuAvm8JdgG1mCEJcXxC4hq+CQg8KVMHD6HfDitcDgkuLghh/hdf994CuVsOTkQLuNCwQ64nizHtPO5OOR7DJISwxIKeWCw+kuYJJEDr5QCPd589l9s4/uQ6XFhgNNl1aBj/1SCG29CUoPCUa0UukvhvFM3TWtcpfXG/DqnlwshB5rTUZmzRkepIJlsA+cfb2waFAo8242WB1YcYKrwLdTyzuI37WBz+Pj7iROBGpZ1jKY7FfuCf0572e2HBk8EgqjANs/fZ/d7j1pOvMYVnp5swC7NJ0LuOi61W8ax4IgirmTLMRaQcdJhb6CVcvJBkzWty8kcXEsuGn+ZX231HKCJNIdIqUEk8O54+DXYk7ArTurMLUqGWKxN6r11dhZwgX/tyfcDiTfAHjHACYNcHwxu78tGOOTEwdVuJd8ghEOjjXyS2YTTpVcpYNOh3PkZ2W1mF1pY37Z4gg3iPwuFRZto5hTBZ8/ltNx0R+puupq99baJtxdzrESxMMf45xFusEYTzVej+Yq+sdaDGzO1Gx3MP9uh8wD6M+1OeLIx5AlJyH4s0+pkRpumZmIS0+Fy2KCUCpD/2de4JS87XZUPvUU7I1/XEtPd+jXrx9TmLfbHagovxE8nowlKPetexRlGakQisQYd9eDl86paB/46QZAXwP4JgALf4L+SIeAe8wYBH38EWPTdET9ls3Y/8Y/sC86ANlB3jDDBZW3Dybe9yjueO9z9BrMtVt0B4vTiX8XVbOAO0NvgodQgC8SwrA0OQIBkkstjCkZuWXLFjZ3pIRqVFQ3Pen/pbgedF/HHwKLpQ5nzy2CVnuOUe6Gjn4fc54YgSkPJLF+75Z6EzZ9fB67vs9kQjZUvenTewl7Lr0mO+eFP50OfbZMg9u/O4n5i4/iUH4D8/C8fWgYDjw3FncMC8MXqZxq7C3xt7B+r44gC5g2mvKuXbtYj/fvQXZjNp498Cw7AU01TYWLqjHBwRg0aFCn59HJbo4vp+i4sY6j+hGSgtzwyaK+2P/sGNw5LBwyER8tLhm217thxLsH8OKa0yhr5BRi/0iYUuthzmokHxh43BjD7JAIAp0VfSTcenaOBpW5Fz77/wUo4CZhNfo9KZAqLlYiNvYN9hhZiVVUXlkAh0Gq5nwuibZF9K2NDwM7X+a8MK/jry+o1lrtJop5x/PP3iYdE+EJl4kR10qDo2OcjnUCHftUhfijUFT8MdMaINXwoMCu+/Qk4W7we6If5AP8WAWSLKMaf8yEw/Db2C16Qz7y8l5n61GRTyPAaxjmRHN9xSuyuzkeHHauX3D9Q8B7vWBddT/2plPCkI8Ez1OImjMHeCYXuPEHph4Mftc6DqGhHDWYBDYtlvpuqeU0IeuJNSIFQ1V5OTi9ZT0Lrr978n6seYsLfBzWXKTvWY6cIwdQmZMJbX0d+B0OV7FMxnx5aUgUCjapbYPV1Ahzy0a4XA7om9XQ1NTBpduCqXsCIF4biayjKeBbibbuQJH0NWj77ECEg2tjOPPrsSt+bqFEhsmJXGByPK8WWpcLXvdwgWT9xx/D1XqdKTJacHd6MeacK8B5nRHuTmDReS5xHeMrhbeQ364y7j5/HmNHJedmIqiuGj9VdQ4cyrIa2yn34+6Iv6SCzL53lZ6p/dN5Xd6B1v1bkF7RgsdXnsOYD/Zjvc0C2lsHhXtgxb2Dse6BoRB4iNlkPc9owf2juD73744Uo65Kh8YKPaOZR7SKxl2MqRFTEaQMQpO5Cevzuw5020AU7g2FG9j6DRHzsPnDd2A26JkYFKmVkxJ0r0FcwEq2YW2IHzkWUpWa7TcFpy8koyioJJCSOomI0fnF42bu2NWsWtUlU6GNWi5rFRWdEj6FLfdX7O8yadDQ2s/NrMJamXEOl4P1tMd7xXPHFzltEI59zgKvgOgYRjGnimhArzi27FVxEn2VetYv/PzPaTBZr/56RXZcLRY75ldyVW7lkK4pwQleCQhXh7Pf+6jiPBMFZtXuQ1dX7S5N3YgwczXMEnfw+nAMkMvh7iBvZkNFqLbYmLbMsWYDPimtBYY+wrW4FB9krWGKYcMQ9J9/s+PESAl02jYqD+gMBvi//jrEkZGw19ai/OGH4fyTBb5oP5o5cyZL9FZUNEDAfxFOqwiZ27lE1OC5C+Dud9Fvb7cAq24BGnIBVSBwy1q07D6M8kcevRBwf/Ix+B0C7qaqSmx79e/46cfFyPN1h0UkZH3iFGzf8/HXSJkw5bJ924SzWgMmnc7Dh6W1lMvGDB83Vt2e5+fRbaBOFW6qdBONnqrc/2u4HnRfxzWH0ViC02cWQK/PYdnXfn2Xt/dwR/X1xc2vDWFWY8TTyTtRi+WvHUfWkSpW6U5O/pxVVWprN6OsjOtD+qNxvrwZd3x3EvO+OIqDefWsOrxwQAj2PTsGr89OgodCjO3F21HQXACVSIU7E7v2ph02bBgLvqmK0UaN+S2oMdTgkT2PsIvsWPFY8Bp4jF4za9asLmk2s1qVlPc26hjlqiPIx/K1WYk4+dIEPDsuDO4CK6wuAVacrcXod/fh3h9P40BePVOnvdagPlPNRs6GhPw4O3pwG07Xwk/ER6QfR0vcszQbVtO17UW9WoSFhWHixIkX2gScwxERzgnP5Oa+ivqGPT17I6EYmPsVMPJZ7vbRT4HlNwDG31ZBuI5rA9rHyedVZ7axdpFarRnlTUbmSJBbo0NGZQsK/STIETiRVqvDqVOVLBFHzJcVWdXgaSzoaxcw/9jjRY1YsukAcjUu6GT+kATFs+emVTQjs6oFOTVaFNTpUNxgYP+jstnE/l+D3oJmo5WpL9sczh4lFnX6HFRUcD2EJD7JJz/VbkD9k543xMDjhl7E5YQ5V4O6T89xAdJVwOEwIzPjCTidZnh6jkRoKKcGvSiOCxoOVBxAubbVGcFuBQr3AlueBj6IA36aD6SuACxaHLc+CJ3DDyo3Pob/42lgyEOAvPuevza4ufWFWt2XuQlUVq3sNugmYaHuQJPG05t/wS/vvIrP7r6JWSYdWPYtC66b6wCBOArU4+Id1IDeE6cyxd1Jjz6JE2Nt+HlMJeRPT8ZTKzfisR/W4pHvVrHx6Her8cRPv+DJ5evx0DfLcef7izHvbw8jsjcXQAllIyF1S6AyK6Q2AazpFpz9XoSS3YGwmwSo91kLr0COFp9bXYiW45xq+uUQM+keRKEUTvCxZ+sv8Lz9dgi8vWEjy8PVa/BqfiVGn8zBtoYWNqGjwOKTOil4WhvU3lIMmBTWngAliAICoBjJJYinHt3PtArqLFxixmK0Ye9STuwueUwwQuK63lbt3twJXpf1te8OtO9vS6/GTV8fw8zPDmNTahVpp2EABPhucBTWPDgMw6K94SUWYVyrgBX5ic/sHYgANynqdRZs3cYJVgbFebTbzl0MojPflcjZkZJ9GKmJd4ddpbvQYmmBv8If1t1ZqC0qYMH0zKf+BmEr3TZmEGf1RsG1w869l0gsQe8JXP/1ma0b29+vI7W8DW4zZoCvUsFWVgZDq9tBR9V2a7mOzYvIpYaQ5J3EkgY0FzhU0bmPn/RGmpq49/DyGsNo6G2VelblbkPCXK6yaWlhgTclD6IHDGEPeQYFQ6D2AM/hwLCSrfBRiNg564NdV6eto7M78EVZHYY0OuBldoIvF7Z/h4tB88C2avfW4q3t7Deqdvc0QVhqsmBU3k/c79bvdkDcvRhgx//bpmhudrogbZ1LvVdSg9PwAJJv5J54hGMUqadOhfjZp9CklIHnciGyog5bNm8GXyFH8GefQeDmBnNqGiqffgYuu/1PF4EdN45Tgj96tBS67GmwG4WQuFng1/ci5goldzY8BJQeASRqFnA3bT2IqueeYxV79YwZCP70ExZw0/WoPCsd6//zOr5/6gFk56TDyefDQyTB1Iefwj2ftAXblz/mTQ4nXi+oYmJpuQYzvEVCfJMYjiVJEfARd/9aYi+RKCmB5mIqlQr/a7ju030d1xQ6XSbOp97NBB5k0lD07fsjZDLupNoGsUzI1F9jB/lj/4ocNJTrsW9ZDvJO1mL8HX0RE/MqcnP/gYLC96BUJcLLk5sgXEvQyYWq2d8eLmZBJ4GC7fn9gvDo2F4I9bpwEqcL9Rfnv2DrdybdCTdJ19YolHmkEyF5ZZN9UP/+/SGXX/li0BF6qx4P73kY9aZ6xCpjEVQYBBNMzKfR17drFd94pQyxCik7ue1o0GJhwKUTJZVUhEcnJeHO4RF4+7sN2F/NQ5XTDbuza9kg65CFA0Nw44BgBLhdm/48zYYCuEx2iIKUUI250OvqsjthbKXvDp8bhfq1BdA1mnFwdR4m3Nn9JPrPwNChQxldmKwq1qxZgwceeABmSw2qq9ciI+Nx9Ov7EwsIrgi6oI//B+CfBGx4mAtKvhnHWYz5/d9+x78qLHYKiO3Qm+1sScExqRUbrXaYrE62pKDZaOVGx3Wj1YaqWgEWFx2F2e6Exe5kE3uiqNocLrZOVjZXhV84cbQ2kFTXdjRgOzh9Ag5xgAZYt4Tzmr9akOCzVCSARMiHRCiAVNR5KabA2XgOPMdtUCl8sV/rAYUkB0qJAAqJkA1l61IhFly4He8Jd1859KtzYW80o25xKrxuiYc0pmc+p/kF70BvyIVI5IWE+HeZ9gYh3C0cI4JG4Gz5QZzd/wpCbAIgbyc3oW8DWX0lzkWlei7Sl3OV2LF3pkAsu7rgLCTkDmRmkjjlcoSHPcjakNqo5eTbSglIEkLseE6vLcxH7vHDKDx9AprqzpUzqlQHxSfCLzIGRan+aK5zIGFUMMbe8o/25yxJX4JsWRXrpb2z/73gd1OJp0mnXO3GhldwCIrTqK+2mv1OSq/ZuOXfL+OOVbMhqbNgimgotHkVyF6tQODQOnjGboGs4RbW95266ThizAnwGx3fPUVTHYCJkQIUFrmQWViBYc3N8HrwQdS9+SZKP/0MP/pEwyaRYpynCq9EB8K91op1B/PZS8fcHAd1mAqGbcWsV9hWb4TIRw73G26A4eAhzDxxCN/PvBGra5rwWJgfDq3Oh6HZAjdfGYbO65rW6XK4uH7uVtXyq0GFxoi1pyuw8mRZuyI5XXenhXlhXrEJMVIxAqZ2FvOiqtjORi1+qdXghQh/xtx6Z3sOKjMaKVTqklreEXN6zcHi1MWoNlRjW9E2zI7uuvd3be5atpxtGowM8uPm8TD9sWeh9r7wHQPj4iF3c4expRnlGWkIJ/VyEkqbNA2nNq1DVW4WagryIAryYuKnhFHBo9pfT73C7vPmounHpcw+TDnqwmPm1iq3OEzN7GAJtE9MCp+E7zO+ZyrmtN4GsosiHRzOKiwZS7N+Yv3rVEUmevyFf8oHxvwdWHMbRzEf/BCiBw1F6q5trD+92TcccrMJAl0TxjcdxCrJUCYaOyUpAP3Dena+WFJRD43dgVuqHRfU7IXd1/SmR07HZ+c/Y/3zLcO51jPaP/WHKuE2hRNavRwOZB3D7S3n4eAJoBzyAHqKNkVzCgYLTRZGdabP/WBWCXYNfgweqSuBrE1AYyHgFYUCG8cG9G/WI7msDhmbNuNsUhKb2wUv/gJld90N/b59qHnjTfi/9uqf2iY3ePBglnyszMtBcUkeuy94RA2KSv4FkViG4OBbuSeSLVjGOlbJd924FA1r9qLhC67VwOPmm+H38kswGw3I3r0DGXt/RX1ZSfv/8G0xICkmEb0/+Ii1pvQEJ5r1eCqnHEUm7vie7+eBN3oFwVN0+dcTq3PTpk2w2+2MPk+/8f8irgfd13HN0NCwDxmZjzM/bqUyHn16fw+JpPsLol+EGjf+bQDS9lXgxKYiRjFe9cZJjLppLPz9b0RNDQU6T2DQwA2sz+9agGhT689V4vsjxciv07df9Of2DcJj46IR5nVpD9K6vHXMq5VUN2+Nbz2RdQMSVDt69CizhTl06BAmtyqW9gQU3D9z4Bnka/IZfX2eYx7yjHnMKqGNut4dZvq4I9dQw3yEuwq626BUKPD6QwuxdetW7DuTjhyHL0rhyypxH+zKw0e78zAm1hc3DQzB2DhfiFrp4FcLsi9itHI+D54daOUEUrx1GmzgK0VQJftgglKMDe+fZf2V5LVK1nP/V6CL5uzZs9vtiH7++Wfccss/YbVSD+kBJvQ3oP9ayOURPXtD6l31igZW3QxoioElE4B5XwHxM/G/Bgp6qN9SY7Ci2WiDxmhlo22dxI/ag2oLt94WXGvNdhYg/z7wAD13TPc04KV5oZDHA+2efLiY+C3P6YLL5mSCOTwJnwlTmZxUZwQUrfux3W5j9FABnw+RWAyni8eUzSmup0HhvaNt3cVjYlVttzuCbrclDsCItV2Bzn2t57+iK/cBXzzJlPN5UFkA5XfH4emrgIe/EmqpCG4ybqhlwgvrUhEcppMoL/4FchEPiQnvXziHNxUDhXvwVmkelOUVEJd2qNIqfIDYaUD8LCByNGx2Pva+yak8J4wI7CRS1lP4+kxBgdgPFmstauu2IcCfo7ZTQowQGRkJmUyG5toaZB3cyyrYHQNt6lkNSUxGeO9+CElMgU9YOAui80/X4uyuTAglAgyaceE4bjQ1sqC7zSKMtDt6gurCFmQd5nrWFe5i6JssOLamFEP6TsKK3BUIC+NjeMIiDO3bG5VZGajM3wQP32yYahJRKKhF2A5P/LJxOfwGxyJmyAj4hEVcMnn3H3UXehd9jFQkYMO2rdiZPAyPefsiqKEO9x/ehaFPPYGxXmrmq716WVq7ABoJjBEk0e6w5GlgSmuAaHwoVGPGQODlBXVjI4akn8NypRxTm3jIPVHDjgFKfoo6qE53hDlfA6feBr5CCGnslYMySpbtyKjBz2cqcLTwApXdWylh15mbB4dCuDIPVlihHBLAnAQ6YpK3G+QCPsrMVpzRGply+Xc78+Fh5g757qjlbZAIJLg98XZ8eOZDtn2pyiq4KJlC19yzdWfho5XCfoJjIgy78Wa273QE7T/RA4cgbfcO5J082h50Kz29EDtsJLIP7cOZbRthncop1sd7xrPKeUd4LFrEgm79wYOwVlRA3Cq+aMrkfpuLK8REMaegmyrdRpuRUdUJjU2cajkxUexOJ5ZmcWyYu5LuYv3snUDXG/8UoCaNiaqFjH0ZErmCWYcJPMxwxaRAXVsCVJ1Hn7AQnOcH47mfU7Ht8ZEsKXg56O0OfFleDy+LEwNruCRbW5tOdwhWBaOfbz/2m+8o3oGFE2aicVkWs4tTDg9sTzp0d53xOMuxIKsipyDE7erEK0nRfFlKJKafyWMBt5zPR4XZhkfr1VjWazL4+b+yardxzJvIPsyxFfqSSv2X3yApMxOZ772HyC++gEe/fgh8711UPv4EmlevhtDHBz6PPoI/C5R0nD1rFr5/jmtx8o5NQNLw0Sgt/RK5ea+CxxchqM4JHHyXPe6c8h6qPt8E3XZO+NProYdgHDsSWz99DwWnjsHRKrIrFIoQ1NCMsJpGBIwag6APP7xEybw75fq3C6tZmwFd5vzFIvwnNpgdvz3BqVOnmM4FFa+I1fl/qfPzR+J60H0d1wTl5T8iL/9NOrTh4TEUyUlfQCS6co8jX8BHnwmhCE/2xu4fspjC7e7vsxHVbxHcEkpgtJ5CWvpDLNARCH57Bba6xYRlx0qx4mQZCwIIVBm6cUAI7hoe3mWwTSDKFmXJCQ/1fqj9gtft9+HzmaDS8uXLmdUEZSN74nvZZg12tOoom+y9HP0yDm3i6GR0AhJeIcs43ceN0aT2N+kY1Usl7P4kSe9F7+nt7c16Uge4ytHiGYlKaTjOlmuxN6eODR+VBDNTAjGrTyAS/HpesXfZHMyTm6AcEcR6tjqivUpC3sgCHgKj3TFgWjhObS3B/hW58Itwg5vPtam2/xZQj+jChQuxZMkSFBcXY/fufZgw4VOcPXcLdLp0nDt/FwYM+BkScQ97GUnR/L79wM93cj1jq2/lFJtHv8BVIf6CoP2RKNCNeiujRDcarFwwbeIC6GZD67I9uLahxWRlVeXfCzouiZmhkgqhlFIFVwiZWMB0CSQCHgRwgO+0tw4beE47XDYzmupr4KlWsnWH1Qyr2QSHzcKCaQHPxZZ8RtJtXe/umk6HTsfDh5538WbqWLTt+JVZpN79dyM2OT2dQnoH+Nxw8cAXSVj/Lg2BWMoGZQI02hNwwAKlOgkKdRIcPCFIpsjs4BIcBmICWBxsWxmsdnab1s02Z7tFSwtotKJOx40r4l/g81xwP6SFB28NPB318LDXw5Ongyei4MnzhUNggDQoEr373gDPiN7wUEpZlZ0mSyd+yW8X4ho+/7fZEBGNPij4FhQVfYDy8h/g7zebvXdODkd/9pJJsO7tV1CSesGeTyiWIKr/IPQaPJwFTJKLmEYUlB5bz7W89JsUCoXbBasxYjO1WYRRJa6nVmht3uPxwwKQNDoI6/5zBkXn6tEvaDxWYAXrxx2oHAjPwGD4hUWgH2aiuCAVP/60HuWCeths8egnG4Gd25bhxPo18A4NR/LYiaxXWKZqvYaGDcNoz5eQ0RSD+soqVHlUYvnshXj+208xf8cmRD3zKHvamR0lzEKLfLWH39ir/XPKU3xY0E193erxoUwsyX3uHDQu+Razj+7Ha3H9sWc39z36TgqDf2T3E2XjWc7qiSz2OiZTL05u78utw9a0auzJqW3fHwnDojhRtMmJ/ozJYSlpQX2plvWHK4dxYlcdQQH3NG83/FyrwbpaDd6JCcY8H0+gUQ+tSgC5uvsArQ0LYxfiu4zvUKItwbbibZgZ1TnpuTZvLWRmASafC2LBB6k/D5m7sMv3on2Lgu6CU8cx4d6H29kQ/afNZkF33vHDqIgobVdQvxji8HAohg+H4cgRaFauhN9zz8Ght8JS3NKpn7sNFLiHqEJQritnbR3Up97Rn5v6uek71RnrWLK+jbrdCRTAjH2JE/c8+TUEQx9BZL+BLKgU6pqhjojGnLvuxLo3XsLA8p0oCLsFRfXAh7vz8Pepl1elJtu5FrsDT9W5wGtTs+9CQO1i0DFGQfeWoi24Y+YdEIWoYCvXQbu3DB6zuz9nnK8px8Sq1kBzJLffXy0i5RJ8mxSBhamFMFLylAfsadLiw6Tn8QwF3edXIt2QwvYF34goxD3xFBoUKjS8/wESz53HiRdfwqTPP4N64kTYX3oJtW++iYbPPoPLZoPPk0/8aQFj5fnT4Jv0cPH5KOdJ4aa+EyEhVpSXf4ea488hMEPPLkeOfg+i7L0dMKelwSiTQjt3Bg7np0J79ILivk94JOKT+0H2xdcQNGkgHzoEge+/16OA+7BGx9TgKTFGuDnAE69GBcLtCtXtNmg0GuzevbudVu7h0TOGxX8jrgfd1/G7QH1F+QVvoaJiGbsdEHAj4mJfb6cC9hTufnLMe7YfzuwoZcFX4dlGyAsfhm//L4mwwnpqExL+c1XvaXc4cTC/ntHZdmXVttNLQzxluHNYBKNSU2XncqAMM4mwhKnDMD+GU329Esg3liyoSkpKmNfg3Llzr/iabzO+xbr8dSxD/fbQt5G2kaOmUdBOlmRXAok7RcslKDBasKtRyyh5lwNdFIYPH84o67/88guEukIE2SrxyLzpONkoxLozFaxvjgRraIR4yBAn5yO6VofE4MtXrbT7K+BoMkPgJmYTvI4gD2FTVtMl1EQKuityNKxqtOu7TMx9th8Ev7HKfi3g5+fHthtRzI8fP46AgAAm9Hf69I0wmcuQmno30yoQCnvYc6TwAm5dz4mqnVgMHPgXUHUWmLMYUPw+IaKr6WmmALlBb0Wj3oJ6CqZbg+qGTuvckijavwVElfaQi+EuF7Uv3VuXFEirJML2oLo9uBYLwHNYYDPqoNdpodPpOo9mbtmd5R1NLNivSNWv1piZnYFadyHKnotEYojF3KDbHZdt65Q0o2Er18NW1AKBuxRL/YTQOp1YFOiNKJmYtY6YzWZGgaNBtLiuhsPhYGJrlxsOh4378A4rYNQBrdqGbTJGavYtxEBTKazgJvL0ldxEIgSrVKznTeWtYiJubF3FrStUaggkMphsTsYmaDFaUX2sCnVpddDBBWu4GpYgBVpMdmjNlDCxsec0amugswphcUhZ9b7JDDRBgULQJPoi2ie1MVJurYiCBU5FWSzgw40qlVobZAoeeoXJUbI7l1U1fZQSeKvE8FLQUgJvpZhR6C+HoMCbmJAhJbtIYNNqDkR1dTXLXmSsWw4+Cbe1WjeRFRJVIcWy7hOExKqiVhYS86RkbxsKNAX4OZ/rh31u4HOXVgq7wfldZcwrWqoUYdi86Pbl4bX5KN5hRNKg/shwnEGWLQtzceE6EBHdG35+HCMqT56FFGNvTIiahz01K9BQVoJ9P37D7JxIaTpx7CQc9Q5Bvu94DG06gWMYgMnl2Xjw4fthPbKLKZk3frME/JsexJnt3D4ycmFMpz5nWaIXNOt5sNcaYa02QByggNv8+Szo7pd5HnOPaWA38OAVpOxU/e/KR9tEDCY6f/fvrFpepzWzZO3u7DocKWiAqdUWlG0fLznm9wvGvH5BCPbovH10raJtin5+EHQTQNP1jILuTXXNTIU6xMi6OnDSZmLaCdG+lz8PK0QKpsXy8dmP8XXa1yxwpX5vAlWPt+ZtxrgzPhAa7PAMCsH0x59jvc9dISQhmbUqUJW4MjuTsSgIfpHRCIpLZGJ8zVQt79V10E0g+zAKult+Xvf/2PsK6KjO7fs9nmQmmbi7J0SAENzdtUCFUgql9NXl1d3b1/bVnRq0SEvR4hS3QAwS4u46E5lMxue/znczIYEkJEh/ff2z17prJj6ZufPdb5+zz95weeghtKYrWTWOEhSEDlaXXaup2/1t2rfYW7SXPXYaeSLPHFo8HBxG4MeEe9n3khJPLOhm7xU6BfCKA8qTmEt30KBp7aSb1g17Nw8seP51/PracxhTfRA73abh26MFmBblgf4+XTcOaE/1TVktez/OK29rZsT3TqlGzuxvn3kb2cps5DbkwneqP+q+TWMmlrYjvSB06rrwXn3yGwww61DsGAU/vy5isXqJ4Q4yvBfmjUezSpkKifC+QogBEcsxNuN7pO7byT43cBrXdXVZuRKtjY1oWf0d/A4dQupbb2PgC8/DcckdMGs1qHnvfdR//TVMajXcnn2m2/PnekHd1Ijj67l4ONvwWKjMfDbauHz50xA01sD35E9MtdXiGIb8DxNQptegMtwXDRIRcIFz3ye1A2W3R4+fDCdbOQoXL4ZBoYRVdDS8P/2sk7FaV6AGz2v5FVjbZsboJRHhg3Af5hbfW5hMJmzdupVd28lXZ9CgQfgn4ybpvomrBs1tp6U/jIYGzjQsKPBJ+PmtuuoqH3W942cEwLefEw78kIGGajWKDt4Dh+BAmGI2wcFhKDw85l/x9+RWNzMp2+aUckYcLRgS4IjlIwMwMcKNScqvhFp1bbtk69GBj7Loj96A/n+q1n377bc4d+4cmxN2d+/+QrSzYCfbDBCejn8a+mw9GhsbmSmbxSyjN39zpos9PiquZhLzK5FuC0JCQtjc8m+//Yby8nIc2/U7k7I/+uRYHM9XYMe5ClawKFW2olTJx/7PTiHUTYaZMZ6YFOmGcHfbTq+3oa4VzUc4gyX5zEDwJYLLI1EMJggpm9tL1um1n7g8EhvfOMvUDmd2FGLY3P/bqAgyaRo1ahQbE9ixYwdcXJajf//vmUkgeRecO38vG6EQCK7snsxAkSbT3uE63388BuTuA74cAcz/hklyr3YOmkzB6povkmcLqe5IoOlW0aK9TN7cm66zk0wCJ5m4E5F2aCPSlvvy9s+LWVe6S+l5SwsaGhrYQZXthrIGVLTdp/OdSGpvlQi0SaQcY/JMoIOcTqnIRXEq9DWSHdNBX6Pv72vOp7FJi8q3zyBTx8dRbynrtt06MgonDh9mhJuI7W233cbI+rWA/metVovW1lZ2qNVqdiiUeSgs2AC9XgypdCQMBmv2eSo80N+nDQqNP9DRHShOi9YQ6hrQ4ehrD0+xCMKkZsiKVHDykMN+cAN4ZLBTdBwVSEZmrIRt1PonN8PQYg2lLBQKj1FQOsWhXhYMpU7AzrdalRr7C07BYJDAQegDlYbHijQ6owm1Lbo2tYAZJUX1AB3dgAouRMiJgHO33LlmuU+fN1ovgqbxNyQdfxVJ+zwBBzcIWlXstY0aO5GZWNm7X4wc6w4alR6Ju7l5xSFzgiDqsDZ9kPQBTGYTJvpORJxb72YJKYGDCsSEkQtDGOEmxIz3RmmWAsVp9RiZvQgZwalI1aVe9vOU605Ko1IXAyKrlZC0umFcwAwo5zQj+1AhaorycSojE6+6hKLUkwcb5wm4X/ADUoxR0DQ2oC43B8GPP4bSe1eh/pf1ONcynBXWaMY5OK7zrDXfWgjrcEcmYVanVEPsEQhJQABsBg1CQSkPgTU8GPnA0CWhEIi6f69Qhj3ZEFO+stZJghPZNTiRW4cT+fXIrGzq9L3eDtaYEeOBmdGeiPKy63JfoK9Rc2NIPEA2+vIutwWjHWzhJBKiXm/An0X1UJaomGIkR2TEd8eL8Pb86Cu+XmQC+NOFn1i3m4xRLd1uut8/yQoujRJYyWwx76mXGBnpDuTYHBQ/FBcOH0BOwsl20k2ImzGHke7AIiuU9HNlXequIBszBiJPT+grKlj0m76eu9bZRLt0S1CJdJPEnHxfmixRYXYxOFObyUxeqbCwKIzLEu8SrNv9HGd0eHY1rGbPgJnHA1+vhUjP7ZNcfP2x6MU38dvrzyNXlYMcWSie2JiCXY+O7rJAtruuEaUaHcY0AzaNevBIkRTTs9zfAvLGGe01GgdLD7I90GNxj0ESYg9tbgOaDpTAcfFFvwYLNDoNBmZzyQma+Hu5/+kacKuHE8o1erxXxEUH0jn1gPtSfJd8FCq1ATa2tggbfnHu3ueJJ3CuugaSHTtg/fPPKHVzhc/KlXBasQI8a2tUv/Y6lGvXwtTSAg+a8b7G60NPOPnrL8xdn16z2Q8/jq++/pq5fp848AdG5x4Hz2CGUifB9v02qHORAjxuv0W+Ez79otBvzEQW9yWSWDEH9uI7l8JQUcmUGD7ffA2BrGe1wp/1TXgyuxQVbSaMy7yc8UKgB2RXKKReihMnTjBZOV1LabTvn5TJ3RVuku6buCo0Np1DWtr90GqrIBBIERn5Xnu26rXCzd8Oi56Px6nf85B2pBzKvPFQ14XAqP0UduNjIJVeLj0ikrGL5sYSS3Gu7KKpj5NUjLkDvHBLnDciPPoW6fPFuS+Ya2isSywm+E7o0896eXmxjMELFy4wN8Y77ug60oIcTp8//nx7lXqEzQj8kPAD+5hiIYhM9BYkMSfSfbC+ic3XSHshCyKQ/P3uu+/Gvn37mCSeOnllZWUsu3pS5AA2k7cvvRLf7U9FVpMAOdUqNv9NBxmwTYhwxchgZ1bU0G3PZ5syunhaR13ewe1owHPpJszOyRpj7wjDvtUXkLynGB5BcjZ28H+JcePGoaqqCrm5udiwYQPuvfde9O//A5KT70BDwxmkpT+AmOgv+6bsGHAH4BELbFrORXismQOMepyZ3Zj5wk6ybgtpvijz1nYi2DQL3VcQSe6K3HCf60yAuiLQPYEIZEVFNXMhpYNyzy33rxSjR+cDkUQ6OnZuOx4U7dYV0SUSumvXLkZkqGN9rRDYSZjp2FED5/o91sEWjXV17L1BoCiTayXc7O8IBO2Fg47qobOJL8A/IBPubnPRr9/9nX6GnsdLlQCUmNDxPh1E6Lsk5m1LijhZCPskHdx42XC0aoY5jopHZvip/eE45lYgaDxcnYK63di+e+Ygfs78FkO8RuGLiV8wSfGpQyU4vD0fOgkP/ecHQWU2MUUFnbPs3O1wn0YRLHP95JjcPSgmcTDEfB0m2eXABa0ot49Ec/ydUNtZoSBfA+fqik7nMc2nU5RUR5zdWcgSEpy8ZQjrEGd0ovwEjpcfZ51P2vj3BlREOrohm8nVvcMdEDrYrdN5PG5JODa8dgaaemCQZBrO+u1kRVxPuWcn/w8i3ZXVDRAtCIBxYw2sFaHQ5h3F2Ecm4IfqVfhJqYGBx4dIr8Pgs4dRWm+DkfZncQCjcPDgQfR76CFYDxyIdKUnFNVaJisfc3tYlwTXZqArR7pTayGfGgAenwfxrIXI2cd979FIK9iLDZfqGRiMJjNya5px9EghzqMVWS06FLy677IiXqyPPSaGu2J8hCsiPbom2h3RfJTzBbCKcGIGb92B4jvnuNqzedETp8tBr56ttxQtqlZsTi7Dk1PC4Cjt+f1IpPSufnexAvdX575q73af+O0XBFZKmcnD7Mef7VUBJ3TICEa6886cxPhl97Z3NYMGDYGJ1o4mLcY3dW+OR5Jd+1tvRe1//wvFmrUQhT8KHniw7uZ6F+oQygzSqGBAUWR+6jZpueMYvJr2Hbu/KHQRbMVXUF4FTQB8hgKlp8E/9QkMUjlEqgaoyy96RZCnwKKX3kLrG6+i1OiN/Drg/Z1peH5O/8t+3Vel3PX8wVru/7Tp73JZob0nzAya2U66yUdBPsUfNbmpbJ8gG+3NFBkdkZn4Gwbo6lErdkJI/K24Hnjc3w0qoxFflnKGukoTD3+0DoQTKhETaN3uXE+g1zP6nbdxtKYa7glnoPrgv6gVCOCyfDkcb78dfGsbVD7/PBo3b4auoABeH30IUQ8Nl6tFbXEhG3EgjLt7FewdHNh+cfO6n+B37CnwRNVo0kuwoag/1Dbc+8LBW4b+E29H2LBRkNpfbMqQN0nFU09Bk54Ogb09I9zCHuTdSr0BL+eV49cqLuaVYjT/G+bLlAN9RXl5OVODEqZNm8Zc2f/puEm6b6JPoM0Gucnm5L7J4lxsbAIZ6eiKCF8LyMRl9G1h8Itxxp8/ZKC1wQeF+56AWf0xJt/yHzbfTXPae9OrsDu9CmeLFO0bALpAkwkYEe1xYa5sbqyvKGgowObczez+43GPX1X3nrrUZPpDhI1mg0mK2hFnq87iicNPsFxNmsN6OOZhfP3V1+xr/fv3ZzL1viBKZg0/KzGKNToWHzarLUqsN6A57+nTpzMpOzlIUtfwq6++woIFC9jjnhHtDl6pCSPHTcCfOfXYd6EKx/PqmAHbmlPF7KBnKBR8xPKEGBoiwyBFK5PyW547Y6MW2vwGdt+mf9eutyGD3FCZ28CKLaR2oOILkfH/K1DVdf78+Uy1QOSFFAFLly5FbOxqpKYuY3N1FzL+jah+H4LH63qzYWyXdXeUcEtRF/Ad6nUnUVdXg/oDctQd2oQ6yKGlQMs+gM73jgSa7pOUt6vPUfzd1ZrjdZr3VqmYizQdFmJNt/T5nkBkmoo8loM6sJb79DUioX8XkHz2eAGXfTrR0RY7dmxjUjhyzI6I6HnO8VpA83gqVSaEQnuEhDx32deJ7Ds5ObGjOxjVDWjKP4uG4jQoKwrQUFcDpY4HJeRogBwqSKHjGVDD46MG4YgOK4W90IzGRhesPTcScnkLnHNOwckph3k/kJkjjaKQuqBj5/CXzF9wrPwYipuK4QIPFOwthZuRj5EzQhA70qfHc6ip1cAR8kvGHKjQVNvUiuKyatQ0tkDNs4KeLwLPxIOTiMvEPd7sAFVS97m+pGQi1QUVXYmMufMECEpuZmtUa6Qt9lyoYl+ztxHinVOfMrM7+n987TqPw3SHwtQ6lFxQgC/kYcxtl5NcmhUfuyQMe75Ox4CKCShxyMCe4j1YHsNlbBPofKe1la4N+Uol4m6PgmJNNuzLRuPL/Wp856sDeHyMtZfi3uZGVLbU4ly9K+60T8EZc380NQH7tm9Dv8X3oWQXN1cxYro7rLsxobIKc2RRTqYmHVuHyVwtudYHBpESts3F0PAEWF/phKm2MuTVqpBf24L8GhWLv6MsbfIQaIdK397NpoLriGBnNqtNRbu+qEksKRa2Y65shkUuyES6DZlcUT12qAeiMnVIL2/CuoRiPDj+4gx7d7g9/HbW7SZjVJqDFl+oh9d5rhg4fOmyTl3rnuAb3Z+NMaiUClTmZcMzNKK9i5gV0IzIc2LYpTcxQtOdzNh+4S2o+/xzaLMywXfJg3XsAAgdu1ZNMYl5wFRWLNhbtAe3CE+wz1fyvJBcs5oVD+6IuHJWdXu3e81seFbug8h2PqACVKUXHasJ5Cuw7KVXUfbOl9giG4nVJ0sx1l+GEbEX9yRnG1uYsZ2jAQgoUvVJWm4BObtTDGu1uhpJ1UmI946HdYwzM/xr2lsE52X9On2/TcqP7PZC2CKMFfb+XOsJ9Ny+FOSJFqMJayrq4VpXAafqSqYCiNYdAFrqOo2A0TVqwMcf4+jKexGSloa6/7wHnk4H5/vuYz4JAns5Kp56Gq2pqSicNx9eH7zPcr+vF2jtPPTjNzCbTQgdOgLWMluc+n098s6cwlztHvjbV0NnFGBLaT+IWoB+0c4wx5+C2NYAoYsjrGwndvp9Ne9/gOb9B8CjkaXPP4PYt/s1kFSUz+SUoVZnYGvpvd4ueDrQgynB+gqdTofff/+dXVNJVUh73v8fcJN030Svodc3IDPzGdTWtZlYOE9Ev8j3ez/XehXw6+eERc8Pxp5vU1BdABQcmoe15WuwTdwPKR062gSSsM3t78U620Q0rgUfJn/I5IbjfcZjoFtnB9PegjbFFHtArozU0Vi5cmX75iy9Lh0P/vkgdCYdy/F8fcTr+GP7H0x6SwSEOml9Bf3uGS72+KK0Bn/UNvSJdHfsvpAUfuPGjYxE/fTTT0xubnFPt7MWYdEgH3ZQZ4tm98gw51R+PQrqWpANE7LNOvy6KwPYlcHkyGFutghzt4VfsxGOZgN8vGzhYifqdvEZcUsIqouaUFPcjL3fpGP+v+N6lDzeaJBMmaTERLwLikrw2469GDB0FHT2n+B81pdQlSmwOfNriKXj2o3F6LahlSTd3NG9rJs2m20bTta0NneSdVPnjrttuy8Vt83Ddu7q3SjjFpI8UzyThWDTQXOo9PnuQMTMQgrpINJGt0Swr2QI+HdCY5AdMusFLKPVJSUdJ0pLGeGl4tSNe75LUVD4CbsfEvIsxGKnXvyQEqg8D1SmApXnmAOxQJHP4pToaC/10Zyyaz/AJx469xBUVPqh7FQNVL5/QmRfDZNJhJJiGmfhM7k/Hfn5nOmYBaQ0IPJNngd0O85+HA4rDmN91nrEZ8yGVm2As48M0WO7lwq3qxraxhKCXWWdNpT5SWdwZM1qRFRzjuA0YxsxOwypRUdRVRQDG7kjnhs+qI2st5H0DvdpPp0KXRYST2+phS1i8MwC5IqM2HqmAOCM1dtwJ7O2W18qwYHjRxhJp4Nzdr/o8m45bAQCHNmYCRPPjBGT/JgfSVcIGuDKHMSzTldhfN4d2Oe1tRPpJpAyg0j3+fPn0X/VcJyKc8DwpAaszLJBlqwB44Ny8K+YFeDzQ2AeNZpJlxWb7sA440lswxScTTmPYkUczDxbuFWfhfzYCWDci10/50I+rKKcUHGmEqUnSpGbUY1jObVQWZkgVteiPs8dFZX5GGzoGIl3EbSxDjfyEO0gxfCZIRjgYw9Xu16O1nSB5mPlzOJf7G8Hid+VVWgD7WwQYRTAu9bA5OjBcW5Y4cTHYxvP4adTxVg5OvCKHgFkhEqz3R8lf4TPznyMidutIAAPLXHOGDatd54tBOp+khEZOebnnD7RTrozFZlIca1CiNAbqFGg6HwKAtoczi/7HQ4OsJs1k8116/P/hNOSST3+zSl+UxjpJmXGFE8V5BJHrC/gOt5UtHeTdp6x7xaBY2DwHgZh2SkMl2XjFM8eugYFGmuq4ex1sfjh5O2Ll194GNnvb0eG0BuPrE3AVhszvENCOnW5/60WtY8c0Ex6X0DO8hSDRn42O/J3IN49HnaT/NCaXgdNlgLaggZIArl9jLoqA2G1icx80mXoClxP0Hr0Tqg31EYTVIe4ZktWUBRW+/rj8VOfARNf6fT9tEcLee1VpL34EqLT01H70ccwtWqYiZrtuHEI+H0Tyh55FNrMTJSsuAeOS++E84MPQnAdcqfpfKMsbSrmVOXn4KcnOTO5eKdSRLtWs73GufOeCFQLMOjzz2AdGorKqq3IzHwWtbV7kZRchpiYb2AlcUfD5i1QfP89+3mPt96CTTcxXbU6PZ7NKcMftdyeO8RGgg/DfTFIfmXDvO6wZ88e1sig4uPMmTO7vaYeKDmA/a37MR3T8U/A/84O6Cb+T6FUJuBCxuNMTs7jiREc/BR8vJfdcJdGMvk5VaFEarAN0FwKj1ontOSEYJBVPfIlIoT622NqlDtzQvVx7FsmdndIrEpksm8BT4BH4h65pt9F+dqpqals1oYyFUlyTqY99x24D2qDGkPch+C9Me8hNzuXfR9h7ty5bA71ajDTVc5I94H6JrQaTbC+igokdbWoQECLYkpKCpPUFhQUXObCTtLjiZFu7FAlVCJvSw5SxWYU9ndCamUTMiuaGPlMKFSwox3lauCFPXC1lcDLwZpJ1D3trTttbB0meULxSx4j3vvXZWHowmAIBXyIBDyImNlV9+cdzTVSRBPNmGr1Ru6WHUZo9SaW72z5HN23OD6zOCtygab79Lm2j7ncaD0U6v7QUBf6NB3cTB1wd4e/zDm2dwV6m1DnjSPOnJEUEWhyiGdEmtcIp4R34VxzCs68JljHzufmv63/GhdPysbsSKwtB8mUuwNJwYh40fliIdZ0UJHin4ADjZzLd/86Nc5mnWhXr5D8/UaACGdW9oswmTRwsB8KD/cuSIBawZHrijaCTfeVnbtU7ZD7cGMMnv0B78GA10BAwm36qBdKMmIb2+NI1//JSj2B1g9j0qP3s9l7i3LBclDhhWbvSclAB60HBAc4YC7mornQgKwabi4ycJQN1K0UG9m3DTjFff35/VcoPs+Z/JAEcuStSxE5hgoBZiSmc9nTkSFCzBzSfTeGoueo8EXdcyp4laXXoX5vBcx8QDbEGZNNbT4IKg1KGpQwGWmt5UNJjvwt3cW2dfX8Ap+dyoJtcl47OSf3dloXbdghhETGQ5G9EbxWORqTxuIjhyT4yt2Y+krI54PHdwJfIGDP8czNR5ArkuFNJz4m15vwn9RWJJs3Y6+6DP7+j5EmGSa5D8zj3wL2vggNzBC3RMBssoXB1Iqy1jz8lADYbEtGi9iGpQnQ+ktFCMttbbMGOnq1s9oUKZZLpifXUaQ5UFqr/BxtEOQiYwWRIFcZYrzkkK3JApRaOE4Og02/vuVzXwpjix4tCVxRxXZc7+JAaa8xq45b95s8JMwZf4atJ97elcUyv/84V4kFcVfumJOi4Yfz36FSV4N8T5Kz8nDPnS/3+X8giTmR7twzJzHmzhXs8R0pPQK9yAxNhANEaY1I3rWtW9JNsL/lNka6DZWpELr0PCoU7BCMYPtgNr+d1ipAjDwWRwqOMNO/FVF9I6H5vosRVnYKQ4TpKA6+C+W5echPPA1nr1s6Pz53D3z9+DxM/fg46oT2+PfHv+Lte6aCHxaF3W0EbEIpNw8ujXO7qv0gFQyIdO8v3o/nhjwHKxcbSAd7oOV0JRp2FsL1gf5sFKLyxNegyffjriMx2vP6KisJfB4Pb3vK8VUeZ2KbHDUMf7j7IiLrdUwb1rnbTSC1U8Hdy5C6Zi36nzvHTNSMDQ1wf+lF1i32X78O1W++iYbfNrGIuMY/dsL1iScgnzunzyZrRoOe5cJnnz6OC4f/ZJ8jFUVTbS1bP+LslBjtyl0HatOdkWs3ADn9A2GuqsLo0FAWt2ht5c2SgMiPJvHsfIQJHkf9K6+zn3F+4AHIZ83s8ppEiQEv5pazeDVyeX/I1w2P+rnB6hoUc+fPn0dyMpc6QYa1HcerLGjUNjKjPRo9IJASYqj3UPyv4ybpvokeYTC0IL/gvXZ3csonjur3MWxtO8t+rheIBKWVN+JkXj2O5dYipbSBdS042GBpyDF4FMXBXmOLh/h8TBkdxozXrhdokaE8T8KCkAUIlAde0++jOdThw4fjyJEjbLZb5iXDvfvvZQtKjHMMPh7/MfStembURaDvJefzq8UAWxvmIFmu1eOIohlTXa6OINAsOZlakMSdHhsVDWi2OTExkTmqdzS7MGmNaNpfDGfwsWByIHMeJRDJza1WIbuqGRkFClxIqkQVTKgWgbkq00aJjpQSTnJ+KQL4fNwCCfJPVeGz1BKkS4yd5KNEwNnfN1HesbntwA0HD2ZIYICLvRQudjaw5tfBrEuCVKSGr+sABHiN7GQ6Ro7NjjZiVjToEQN/AA69BRz/EDi/AaAuxqyPgbC+qx56Ahlx0WtJLtB0SweRKpJ5dQWqRBO57ngQ0b4es9N/Z+yv4woOQwrSoDPp4eHmjsGDab74xqCqajMUimPMHyA8/A3w1PVt5DqlrYN9Dmjk5O6Xwd7vIsH2oCP2iq74NDteJH0f5mY9bOqiIE6JQqtDPaSRTkyt4HuJzJDm9ek8IaWDRfHAVA/qVrgoo9j3tFpXYefBo9h5kFv7PD09mfM/Hd7e3p3k6R03lIk7tjCJJEX0kFFV3Mx5GDJ3YbsTOf1thYIzaLKyPgSz+V/dbvCJ0LrZWbHDoDdi/U857PODJvthaAdzxk+SP2HmVL6yAHwzfh2aNCZG1GnenOLxyNmdubq3Uo48d6ts1qKKzL94Zujb/jwV6OigUZtuQXUooxcy91JhgitOWDBGZIcAgRJuaTkoMfiCMjoCIEWIXgaHxGdwP1qgw8lLfuHr8DTwcXsrJyXfbstHfn8uxxynODLbE2xNgA3J9a1EGBDnDusdm+BalIVDI0fgwsRxODAyio2tWMBivZRaZpRldR2ut6qTFTDrTBB5SJl3Qm/hnNcCKoWd9BSgXmeAk1iIu4b747292fjueCFzR7/iHHlpBSKzbHAquBlJ4Q3wdPTBIPe+Oyb79x8IoUSCptoa1BTmM/dymrcmRE6ajNL0TSzOrr6shHWNu4JJ6wiBcxiMddlo3vU7rCP/3ePfJEO1vNQ8pKqFKGlbn2g23V/etz1DisIaQvgiCCUIta0HDWpQVvOQOZ1JN8HH0xVvLhyARzdl4Iw0Gl//93PYThgDk08MFoptICirYcqD7kbGrgRSEnpIPVDZUski0eh/tJvoy0YP9OUqdiuNlsEji0sXqIy+84Y1ezIO7WOJCC3uPqhw5Qo494f8G9tO/IiYyZe/NpMnT8a3xcVIFAkRl5jE8rqNSiXL7+ZbWcHj9ddhO2UqI9+6wkJUPvccFD/+CIcld0A+cyb4XZBNCyjysig1CblnTqEwJRFadQffCx4PgYGhcK9VwiXzGALHcvPoKl0/OH6+GeEVFcjZvp3NS9MaTPs4e/tBiB+0GefOr0RrZS6q33kBAh0Psgnj4fxAZ98QQoVGhyezy1iUmmV88cNwH0TbXltzq6ampn2/S0a1l45dEig698UTL7IYPGp+jRKPYvvlfwJuku6b6BZK5WlkZD4DjYZzo/b0XIyQ4OchFF69pORSkLt4UrECScVKJBYrkV7eeFnWb6CzFKNCnDEqxAWDA8Yh9cS9yDkwAdoGP+z47ByGzQvCgEm+12Uhpmrr+brzLCv7X/3/hesBItIkMScpzUu/voRaSS2rWJP5kI3QBut/W8+ciYnM9Nat/EoSc4rxIIn51ZJuC6gzT5tlihUjh8m9e/ciOzubEXJLliKZ4ZhUegicrCAbetGEhqR+UV5ydkysNaAZjbCKdILTnREs17lMqUa5spVtVqsaNe0bXMtBneezMCK+RYDJrSIoBGZUCDliSIWYi8WYnkExRhRjJREJ2m7p47b7Qj4XWSVpy4SmOCuJ5WOuc2Xb9nl71s0SYvf2LcjJzoIN3wYrb1vJnofikhrk5b0DYAtCvV+BjzdJVvsIgQiY+DIQNg3Y+i+gPo/LVY1eBEx5C5D1zhW2I8hYy0KuLbfUsewK1KG2yIY7HleruvhfBqlEjimb4V9XAWiqWP7sxODhN8xZVdOQjZqE5+DXoIaH2QM2KbO7J9iOgRypJnJNJNs9BrDpuwFNUfFXLIpLKLRDoPAZ6M0GKDZkweW+WIg9ZV0W4mgtoKNjkfKHXzej9ZAdjHw9fOOtUKNwZp1bOvdoraCj/aE7OjLfCMthbFRi/zefoq60uD36a+KK+y8zsyJfDKoJWVsT5UpGQ8NZODhcuQCSeqAUTXUaSO0lGDjVr/3zVS1V7ckUT8Q/Ci8HGXoWxHP/6/aPU1HWZIJftBMm3RvFyDYj5W1rlrotO53isui+mill9Eg4mw1TixRavgHuwU4w8IAGnQEFLRrk6d0QYFQiUKhApSyUxRe9pzfgfTUf4RDgMb4AXwqVEPAp1s6OdTZF2ibMaLJm5lt5Yg1osHagVg2bwgJIjDo0yISQySQIj4lCeHQ/lqFOaQOkqqneUoCEszWso73o0Xg4e9uivjEJNSd/hVtiK1aNG40jymZMcLoo+VYnc1JiMvri99Fc8VKY6Dk5WdHe5e7tdZvSTJrLWmDiARe8xNhe24C7vZxxxxBffHowFxmVTThdoMCwoO6LAjVFBdj0xgsIapUg1UeAVokR/g6BV7V3INdn6mLnJpxETsIJmN1kTF5Or8mEmJk4PqiMEdnkXdsx6d6u86Rb0+ogCprASLfyt01wvv/+HonYWI+B+DwVyNLwYdZwf+veaC4urC+qJlKqqDCcke7g5v04hEGoys2GSlEPmePlz9/cQQHYm1mP3Reqscd5PG7dvQlTwvKxPJYzM5OEOHQb93Yl0PlMmd2r01bjj/w/GOkWyMSwG++Dxt1FbLabp8uCjV6FIitPxPSfgRsBo8GA1P272P1Zcxcg0U6G3LoKKMX2WGroj1315fB06rxKUOGZDGe/USiglUgwPOEMmvftQ+nKRnh/9imTk8tGjoB021Yo1v7MzfDn5KDqpZdZxJh89myW2W4zKA4COzsWA1aQdAa5Z08xxQ8VIS0gZ31tSwub5e5f3wLP1F0Q2hjhP6kOfKEZRs8RkK3YzlJSBri5obS0lCkVN23axBJqaJ9ibe2DuJh1yH1nBngNDdC7mVG/pAXuxkaI+A7t69zPlfV4La8CzUYTxDwenvB3x/2+rhD1IvWnJ2i1WhbDSsanRLbJqLYjKL6Pml4bsjewjymq97Whr6H0TClEtD/6B+Am6b6Jy6DV1jICUVW9lX1sJfFEeMTbcHLk5nqvFjXNGmRUNLGL44UKzpylRNEWTNsBNKc6OMCBkWwyablUNh437BXoefNRmTQfjQWjcWpzPhTlLcy4Rii6+g2B3qhvj+66u9/dcLa+Ps7ZtFmNHxGPI/uPwL3aHX4Rfvhm0jcsMiMpKQk5OTnMnIMMu67HzCu5mBPpprxuvcl8zQslyWnJfX3NmjWss0Uma1988QVbMOMjB0LV5kBLzqM0N3gpaBFXn+cqsTaxLmyDY5mbjPHuee7cbDJj77fpyE+pxT08GaY/MgBiOzH0RhOTkdJeiWRh1Pm23Gcf83iMXBPh7kmKfjW4ZcF8fP/994zArlu3DitWrICf70oY9E0oKv4COTmvMFM1b6/br+4P+AwG7jsOHHoTOPkZkPYrFy826TVgwJ3k7nbZj1CXmsh0R3JNtyQV7u41pdl96kBabqmjfaPHRf5XcFzZDJNWi7G559jHMUY/2BZcJxmFVtUmEU9hh7kiBVaKAsS2f0Paxe91Cr5IroloE8G27rtXw6UgiSHlXxPCQl+F64ghqFNcgDavAfU/XYDrAwN6tYnWa43gJVOHS4+z3rtx7+gFWOQ9n22uLOcgHaSUoS65xU2dohQZjEYIIIaVpx+GTZiMYdNmdmmml5VFmcSAn58Ne5+Xlv10RdLdrNAgqS0ibPj8IIgpP7wNn6Z8Cq1Ri0Fug5inRm+Qn1yLsiwlBEI+Ri0K4Yp4IsEV/UNog7lZn4HcE/VwUHnAQWNEzgx/fF1WBzNsIRe5Q3i8CNYaDX5a6I+gIK4br8lWoO7HC5hlkiI+fD0a3Y/DxWUKovp9ghO/pOF8tRJWfCXs7TMwSm/E/Q/ej5YndkKTnIhyd2ecc5MD5UdQl+iKwDkL0W/sROhazdh7gRv1CbXiw0HOPXbquhEJCC3IhW9lOTa42LeTbjMVECzr98Brk5UTWhKqYG41QOhs3WXCRXfITaxmt/wAGdRWfGyqUjDSTcUEygD/JaGEdbu7I93k+PzbGy+wmCVHf1/oBCXtstVmXfOVnb+7kZgT6aajYgBXnIxxiYGTtRPips9hpDvj6EGMvG0prG07z60bVZyhndA9BkJPLxgqytG4fQccbl3c7d+zNRTCU2RChZ67BlB+d6B939R4JSUlzMSqQRYCs/sk2OXth5OtGfXNPEb2Bky5XGZMeGt+DJJLj6K6yQHHHYdjXNZR2GpHMAWi9JKIuquRmBPpJlPGutY6tveSDfeCKqEKRoUGzYfy2STEbr95uE92fcYIL0Xe2dNQ1dfBRm6P6JFj8AuPj1tTzaipyUeptScWJ2dj9zj3yyKxqDA9e/Zs1pg4LBZj3KnTUCckoPj22+Hz1VcQeXmx6DCnFcthv2A+m6NWrl8PfWkplL/8gopfN6JKLkWthwvq+eY2VxcOMpEYXkIruCmbkausQKWDLRxaWuFRWgmhsx38J9VDaDYBrpEQLN3AxZK2NV/Ie4S6yuQQTskrtE8h5HzwBaoabWAOskfTXD0MpUUoqrkTQYHLoHccidermnCqiduXx9nZ4L/hvgiTXnvhnRUtt29nBVlSQZFJb8ci9rnacyzJh0w5LWMglCYhNAtRCq7x90/ATdJ9E53khmXlP6Og4CMYjTTzxYOX120IDnqqT2ZpjWo98utUKKhtQV6NiuV3EtHumJltAW2gyGgrzs8Bg/wdEOfr2MnxuiuQU3po2JMw4xVYO1ShOmURshOq0FCjxrT7oplz7NXgt5zfmKupk5UTixa5XqCLyCfVnyBYGAypQYoHnB+Ai40L23zS3DSBOtw9ZXn3BWRuYck0Pd2gwijHazfvoNeDJMV0cdm5cyfrelPEWPLRsxhuCIK3r3e3cSckEaMLJ0/Eh1VE3zpyNMs1/q4INNS0or5chaM/ZGLevwdCdIV4mBsJMtOyGKsRkaBKMn0cGPg4TCYtSkq/Q3b2i6wLQe+fq4LIGpj8BhA5D/jjEaAqDdjxMJC6DoYp76BW4N4uDbeQ7K4iueh1oznrjuSabruaobqJi6CC1Yi887DSaeHs6IyBlYHQFTbBoNB06zDcLRrLgJLTQOkZFtWDqnTAfHFUwrLStVoJIPQbB5HvGMBzAOARA1jJb8g6Twoms9kAF5epcHObxZ0nt4ej5stzMNS2om7NBbjcG3PFrmbKvhKoG/Uw2Wpx3uMwfsnSYJT3KFZo9PPzY4cFZL5HUYS5mRlISzyDVhqyFghglMlBpaEDiSk4lpbBxmuoCxIYGMjWHIo+o8IkYeDAqSgtW4fa2n3QaCpgZXUxgutSHP8tFwadCR7BcoTEXzSZylZkM9Mmwr8H/btXhSadxsB+H3sMU/0g7yHeqitYCSVQj8qC3T5XKLN0OGVbDnOgBLe6O+K1EC8cbShmozs062gh3eQ2Tp3g5oOlcM+8BxrbEtRiL07ufxfnTwxj3zNR/ilOOY1CllLApKSzHnkExUvuhFddAxyW340zxw8y+fOB1Z/j9JaNkLvfDq1aCLmVAKFiHtTnatk4kNDZGbLRo6E6dAhTTh/FT57eLBrIQSREa4YCZo0RAnsJJAHXdj6a9SY0H+OKtLZjvdn63qufM5uRe5Yj3fHDPME31DHn7AK1FoE2EiwfGcBI959Z1Sx+LsC5sxqvuiAPv7/1EjTNTXAPDkXGOBGM5WDO2U26Jvx44Uc8NOChPv8/ZKYmEImYH8H585zB7FifsezWK6IfXAOCmPSc4p2GzOucoU3xbcSwxN52sIlYgpp33oXi57WwX7yo23Oyvv4IgiXGdtJ9b0zfutwEy3uJJMe8wROAvP3oZ12Eo80BrHjQHemm5Iv3F8bizu/OIN2uH0bzDLCCDfQmLcpb8xCCqyfeQfZBTD5MKkPqdi+LWsb2C/Kp/lCsy4KhaRTUVuth7r/khhWGU/Zwa0LMhCnMKI92Tb8NCMErh9KwS6dArtgRC86mY+eQmE6jF+xnYmLY2kaRq4cmTsCE0wnQ5uah8NZb4fPFl7CO5sZvKJLL8e5lME0Yh8zfNyAvNRlKraXxxNFtu1Yt3Bpb2GGr0bHrg8LGCpUhXvRGwODgKHg//CRsa1aDl5cFSF2B2zcCVp2LOtTAIfn7L7/8wpol7733HisAMowexd3SXEE5YODzsVqhR4pvOYx8AYRGI6Y0VmK2ygw1vwUKPz/WKb+W5/7UqVMsQpeI9sKFC9s9P6jZ9eW5L/Fd+nfMvNjVxpUZCw/35Bzf2x/zPwQ3SfdNMLlKTc0u5Bf8F62tXJXJzjYGYWGvws4upssLILnEMnlwQyvKlK0orqd4kRYU1KnY17oCvV9JKh7pKUc/TzuW49nf1x52Vn2XjXh7LUFd3Z/g8fZD7mqFosPzUV3YhE3vJGL6v2Lg4ts3okkz1pTLTbi///3M5fR6gGZSVuxdgaLmIli7WiO0IhRpZ9IwdshYbNmyhS0otDEdNozbRF0PUJd3irMd1lUqsLuu8bqQbgto4b3rrruY6dv+fftQp1FiuzgRUbIWTGjgFuZLYemSEOG+Gmkidaim/ysav72TiNqSZhz4PgNT7o267h3svoA6xUS0f/jhB+Tl5bECBOVMBgc/C6pVU+xTVvYLzDHay7P7zsWVoHWJQvXUtag8/TuqshNRWeqA2tW/w9jF0k0dQpKHdyTX9PH1yJP+/wm0vqVlZmJITRlbtObOnwvJbgW0+Y1Qn6uB3bgeYqXMZqA+Hyg8AhSfAEoSgCaOZHSCnRczNjO4hiKjcR2U1lr4hT0Jf//LZ+uuN0pKVkOlymCRZLTGWzZSfMpvX9YPNZ+nQl+mgnJzLhwXd535TGiqb0XKfq5bOGSeP77NNTFn5cLGQgTIA7ocX9BVlCB30xoItVo4SaUYtGgp+A7OzMGbFDTkN9BRkk4dETqPqaBEm7Tg4GFQtQyHUnmS+YwEBz/d5WMrSqtDQUotI3WXRnqRfJHeo9P8p6Gfc++8SRJ3FqGlQQs7ZysMnNy7WLGOoO00L2gCDkeZMSFNh6mpajw8JhAzgl3aN+1EuilicsaMGe3vWbuJftCVNDMFQkDmK8js9yQuHAhjX4uMNsGvNgXWLQpkYT77WZqRtBk2FOpTp+GZW4h7PvsOaX/uxdltm6BucoDBROuGCR4+NTBVu7J5WYsHh3zeXEa6p589ju/mLMbWGk6+rU7myK7NANdek+Tu0JJUDVOzHgK5pE8zwPXlLVBWqZnKIHaQO8bkaHFI0YzfqhQssohM38aHu+JgVg1+OFGI1+ZwJIdAc9Xb//s29JpWuAWGYOLjT+C/u2azr90TfQ9LKlmbsZZ11vqqbiPPARqJIEmwMi0XCAbGenOkm8456nbv/vy/SN37BwbNmgeBUNRJWk6gYrU0bgHqPvkUurx8tJw8CdkI6iB3hsmkg0JxAnU0m8CKdTw4Wvd9rIRGNQihoaGAZyRMYTMQmnYAR2sCUJaRziTONnZdF1fs3KUw+MkgLFbB1X4YYARKVJlI/HAvBkybhVG33cVk91eDuSFzGenekreFNT3o+aPnRiirg0HljGz+k5jsd20eO92hujCfJQOQKVnMpGmdHPvfGD8fw35cgcd878M5jQQzknKwOy7kslEjIrhUACdZ9/FZMzH2+AnocnNRfOed8Hjnbaj8fZGXeJoZ1ikrufEKS8ycR2AwfF3c4WPrAGutDkaFkinahI4O4Nvb48zxA0BdNaInTEHUqoeB3c+wYgmEVsBtGwD7i2sSKd5of0YFvI6jZBbyKtZqIbe2hsTTkz3Hmda2+MPRCw0SrlDlpazB6JxUyDVqkKVlSlJS+1pMhdDw8HBWGOzLvoIKPbRHsjxPFr+QXGUunjv+HLIUWe2Kh2eHPAs78ZXTDP5XcZN0/3++uaSc4YKCD9GsusA+JxI5wd7tAWhEM3C8WI+apkIWx1LTpEVVk4aRbJrDJefnnuBmJ2EXwkAXKcLd7RDpaYdwd1vm6Ho9QItFRPhbOJ0wDUbbHRh2V3+kbglnc1+b30vChGWRCO6D5OmL1C8Y8aZZ6/kh86/LY6S5QSLc1D0no5C3Z7+NrWu2soWQuqO0ONPCRW7l13tWdJqznJHuPXWNeDPkyuYyfQE91oEDB8ItV4TD6SeRK6hEekEmMj7NxqBBg5hju6WKSecYZW4SbGL6PpNsgZ2zNaatisa2j1NQkFqLE5tyMWpRKP4v4eXlxUYCaEYpISGBOXjHx8cjJPg50sWjtOxHZGU9Bx748PRceMXfR1LwS+evyUX6IsLb70mggTtPCQ9vP7j3nwQPb1/29/9OOdf/q0iuVSAqg9toDBk6jM0wtwwQcqQ7uQa2Yy+ZQyX38MJjQOFRoOgY0HyJkRXlt7tHA75DAZ8h3CH3Yu+N9PMrUG/Ww9Y2Br6+fe9a9RUtLfkoLOIiyUJDXoBE3JlkCJ2s4bQkArWr09CaWosWPzvIhnXdTU7YVgCj3gSvUHvEDwvHGM0YlvpA8WHkQtwReq2GOZNfOHyAfewbFYupDzwGW0fu75M5I41I0HlPBJwOUtPQXDgdBPK9oFzXgAAiNSdRXrERAQEPQyDo7Jav1xlxdAPXzes/wQdOXhfn009XnsaJihMs1/ihgb3rbCoqWnDuT07eOGpxKIR9LBw2G4z4ztoZZ+ul4IWbEVbWDG+lBMadZTA/7MzOJZpvp3QIiowkKT2RcAKRXMdbw1D9SQpMCh2cEt9FdqsZIlk1PMaWAft94dpQjGhvW6SVNbNu97yHHkLxqdNo2LIVTqtWYeC02QgZPAHrXj0Ngw4wtJ5B6pmTyBJIEd48GLZl/rDxdoDt2LGsE2evVCAuKw0b5FIstbODJld5XaTlZqMZzUe459F2tFeXo0jdwdLl9otygsRaiEXujox0b6pW4skAdzZStGJkACPdvyWW4YlJYSyOLuPYIez98iOYjEZ2zs1+4nlsLNzEojojHCNYfBhFEqXVpeHL1C/x4rCu49auJDEn0u1dKUFNf2/WtbUgbPgoHP3lB5bnnXPqOCJGjWt3b6c4LIJNtDMEttaQz5vH5MbKNWu7JN2Njckoam1BhoY736lwdKD4ABaFde6g9wRS19E1ha7hRKDYYxnzLOyyd8HVqhk1Glsms6Zub1f4iXLSQ+3g0ajH8AbufSCKcQQOASm7dzDjryn3PQqv8Ej0FSSV/8+Z/6CgsYCR71iXWPB0LbAzfQAF3oSLOgIuNVrA3+qGdblDhoxoX5MsEPP5mD1hJay3v4rl/V7HOVUrxiXmYE9caKd0GOosUwf3m2++QalKhdOzZ2Hgvn0wp6Wj4tHHkOvqgFx3B1bIJbNIKtYExw9DUNxgJmnvDmmH9qGurpoVeCjNAWe+BRK+5L447yvAO46tnbRukHcQrZ0W0OtMa4tEJEJOHhcFOESpxPjXX0eJ3shcyffVc0ZpbiLgLv5GRMo2QxNhB4MhAELBONTVidh4EK3DNBZEB/2vRLxjY2NZ8aansUjqstN+lzBw4EC21htNRlbo+iTlE+hNethL7PHi0BdZfNw/HTdJ9/8gaLNGHlJ0S9FIGj0XjUS3GgPdtt3XG5mhC/exEU1tbqxNrRrIzIcRaL0FDmLuIqgxWGFf8QTsKRoLrZHk2Qk9Pgbac7rZWsGbIp8crOHbFjFCJDvQRcYMqG40SFoYGvI8MrOeRU3Du5j20HacWGeFkgwFmwNWVgVg0HT/KxJOivDamL2R3X968NNsQ3atKFeVM8JNt14yL3w35Tt2O2bMGGzdurU9/5a6o111h68VoxxsWZW2QqvHueZW9Le7vnJiQ30rzOcaMMYUieFzxuFYxmlmzkLyKppT79+/P+ve27aIYWxoc70Nu7b/0zPEHhPvisS+7y7g/MEy2DlZI3ZC7+JmbhQiIyPZaMDBgwexa9cuZhRFF6OQkBdghgllZWvY+Ul9CU9PzhmWLpCUgXzp/LWFXFwKS7evvYNtqoTD8VfBqzoHNurUsAYY+wzgvOQv/u//eaA1dc+unZDqtNDJ7DBxPLdJptlT5dZ8Jr3WF9dCrEvh5uxz9wMNnDqoHQIJN5fvPwrwGwZ4xQHiy80ny8vXMbkouZVHRrwH/nVYd3r+30zsXKSOmZPjaLi7t7ldXwLKxZVPC0DjzkI0/FEAkZcMEt/OnQdSnOSc4YjQiFtC2Bp7R8QdjHRvy9vGpLqWGVlFRTm2f/Amc3Gmrs7whbdj8LyF4PM7k1faIFIhi46RI0e2Gz5RUYvu0/uG5IkXLpgwKF4Ga+tGJCV/gQH9H+7kpJ+0qwjN9RoWKTVoxkVXZ5Iu/jfxv+z+4rDF8LH16dX5cHRDNoshDIh1hn83IzTdIa1ZjZXpRSgSSUH/7WBxJo76fYfbmp5BaaYSGccr0G8UVxQlon306FGkpaW1k24CmUo53RqGmm/S4NIqhJfYANHQb1BeXQKP4OGQJ5ZgrOEQ0nnxrItZN2oUpMOHs45p3ZdfwvPNN3FySyEMOh4cPW0QPmQAzm7PRXNdLVIVh5D1fCIGL7gFsZOnw27GDEb8pp06gtciY1GYUA4rEyD2sYWoj5L6rhRPRqUWfKkINvG9H6Vi0vK2eW7LmMAUZzlkAj5KNTqcaWzBUHsZhgc5scJ+VlUz1p0pQv/6RJz89Rf2/eEjxmDq/Y+CLxCyMTLCLaG3sHPu8bjHcffeu7EpdxMjsGGOnJKgtwiKG8Li6BybxRgrjeu036DOdv/JM3Di15+RtGs7wkeOZV9vvVBHggOIPKWs0EUgR2t67lVHjkBXVATxJSkmdXWHsaOB6y6GO4az7uDeor19It2WLjd1GtsNMl3CUeo4AqF1xYx0UwRaV6S7UW/AlmrqwPLwXowfbI5WoRRGnA8fgvlDwrDv609YB3fDK0+zDv+IRUsg6oMJJ60Xk/wmYUfBDmzJ3cJINzK2wsaUhlrxcQh0o9GwNR+uDw0Ary255HqAOvsU/UYYOG1Wl98j8BuOqQ42eC/nAzwR9hSyWzQYmZCJ7QND4GV1seMrMBkRHxyAw6lpyC0vR7G7E2Ir5Qisa0RIjRKeNjLInvo3AoeNbE9m6AnkWH58PWf4OOyW22BTmwjsblP3THgJ+tCZSD17lkm3qaBiARVUBgwYwAgxjfpUvvQytZuREx6Gk56eSExKw5caPjQmM4Q84F5vVzzu7wapIBqVlQOZ6lWno8JzEtzcgzF58nLo9f2Rm1vIyD0VBy2KJDqPoqKiEBcXx/YnHUHxkuR7Q0olGhuaMWMGylRleOH4C0iu4SLDxniPwSvDX+lSZWIwNKOi4ndIrGgM82ZO901cJxARHvHOQegNAjyb9CdTKFL8kbntgtP546v/O/aSRoz0PI3R3ifhZM1VrzUGCQ6XjcDuwglQ6blNkrVIAFc7CctRphxhF5kErnZW7L63vTW8HWzgLrdi0Sz/1/DwWIjqml0saqeg6FlMv38DTm0pZJ2JMzsK0VSvwdg7wiDoJq6Jnt93z74Lo9mICb4TMNTj2nMAS5tLGeGmCAza2H0/5Xu4S93b56joQk8bSDLgIHJ6I0AZiuMdbfFHbSPrdl9v0t10sJRtGiShDvAeFIqAQaGskEDkk4w7iHjTESD3QhDfCWERYeBdg8mdBbTpalZqmHne8U25zJW4L4qGGwGSdJI5CMm5iCDcc889bBY1NOQlaFrNyMvbjwN//gCxqBwqlZSZm3Q1f00g0n7p/PXleceRQORYIO034OCbnMv1jkeAEx8Do/4NxCzinNBvos+g11BfVAAjj4eAiVPayRxfXQIHjwMQVB2B6Kc0wNzh9SOy7DUICBgFBIwGvOO5mfwe0NJSgNy8t9j9oKCnIJPdeNUG+XU0NiZBQB3O8Dd7LEbKRnoxWTNJYBU/Z8L14QGM/Flwamt++/vRMsozxH0IguRByG/MZ8R7SeQSFKScxa5P3mebR8rdnv7Qk/CN6l30C3VPaMNIhJs2drfffjuTKdKmr6IiDEFBSaiq+gXvv69Fv35RLM5NbLZtl7xTV7qjedruwt3MXVoqkvZ6FjYvsQblOQ0QivgYuTAEvQVzdS+vwyt5FdCZzXA0GfB9/xCIDSbcVVCDJL89GFQwAyc25cEnwpEpeSykm8ZVaLPa8X2vthEhT2dCiJiPgbYiGEJuRWH9f3BeeBIj+QI4VR3DgPBbkJxVxNbghQ8+wEh349ZtaB5zB/s/qGs+4a5IuPrZIWbCRKSu246zezejRdPIurFntv+O2EHDYcfnYdS5REjVLWi5oIXV9ehym8xoPlzafm71ZcyoMq+BFVFEEgFzjSdQQXmmiz02VCmwqUrJSDedzzTb/eLGM8j/+ROom7mOX9yMuRizZDnLRj5TeQZFTUUsNYQcswkUF0Zkj5JL3kt8D99O+rbTe4NeS5rXpWIIXePoa3RekkqNCkTxg+NR62yAa40QwXWXdyxJrpyw5VdUF+SiPDsD3uH9OkjLL6q/JAEBkI0Zw0i34udf4P7C851+z9GSvcjRCiDiCfDM4GewbM8yJFYnthuP9WWeOySk87mc5T4PcTWv4ngtUJKWwszmrKSdrzu/VinRajIjXGqFfnmtIIeePdDj5305iF81DHe9/wUOr1mNC0cOIGnnVmSfOsae97Dho3uttJsXMo+R7j1Fe/BU/FMQJq0BrTq/+bVgcZkA+qoWtJyugGzElbIGeg8avyCXcIp88wi5qCa7DJNexR1fjkCt2AHvBKxksayjEjLxnoMIPoUZTDpemZvNNukSmT003kHQObmjbuFChAvE0H+zGtKiUoje/S9MH/sC4T38rTac3rwR6sYGOHh6Y8CAQOCnGcwPxBx7G87bTsD+jz9mawWB1khS2hH5JdWMBU179qLh118RLeCjbORoqOuqodi/G5IBoxHn6Y63Qr07GaWRKs/NbQZKSr5DcclqqNV5yM17DmKxK0LDbsOoUXPR3GyF9PR01vWmZgGNxtBBqjB6DJR6Q14c69evZw0G2teQCmBr/lb85+x/oDao2XuQmlzzgudddn6oVDkoK/8FVVVbYDS2gC7DKlUWHByi8b+Om53uvwlUWgNnqWO8aLDTF9ColZVIwB1CPrslZ1WZWI9gu3SEyU/CyzoJfF5b5BLsoZUsgNjlFiwIdsLyaRSHxB0Uj/S/go4y88amFJRX/ISRC1fA3s0GR9dnI+tkJdSNWkxZGdVpA2YB5WqS5FDMF+OJQU9c8+Mh58Xle5ezWW5/O3+snrwablK39os3mZBZ8pCpWkgzjDTreCNAEnMi3btqG/FMYOcK5LVAX9faPucnn3TRKIk6vFRhJXfUkydPsipoYWM5CsXlOFGUg5jdJawzTHKna5HTUzwcbcLSj5Rj//cXILYSXNes9qsBOblTx5oI9erVq1lBhSrPnHn4pLbvokIXV+yi/5++pyO5poM2c70CdQkprqXfPCDxe+Doe4CiANh2P3DkXWDkY0DsbYDo/7+4r6sFjX3Q+5OQ6huChyXlwJ6fuY52fR5Yr5q4AqmM5N7ghUwBQiYD/iMByeURWz0ZmV3IINM9DRwdRsDH+/qZNnaH1tZy5Oe/x+6TMWZPBmSWddVhQQjb5FJ3X7EhG87LoxhxK81UoDRDAb6AhyGzAzv9zO0Rt+P1069jXdY6BGWLcXLjWrYJ9QyLxOzHn2XEuy+wuJZTx4a6c3RMnDgRlZXTkZExE1JpI6yti5GcrENyUjLc1INhMloxGTJ1pi3QGXXMsZywImoFHK2uPAtLMvWTm/PazdOIGPcGLUYjHs0sxY5aTj48ydEW04oyEGcXC4GwPxs1SnLdj5G66dCUGXFwbRbmPNKfjYcQiaOiJW1ohw7lCsAk4Sd1T73aCA+pEDK9CdLjQ+A5djEqqjeixkkMt9pWjBal4ZzAns3GV48eDenIkVAmpODsFipCiDFwii8j3JYO7MDb5sI1zxVF9WnINiehsb4aCYf2QBQVCP9qBe44lQgnw0AyCWGpE9cCTaYChmo1eBIBZMP6di3KPMGNa4QMcoWoA1lf6O7ASPf2WiVeD/FiUt9hcjVur/wdMn0TeEIRJq98AFFjJ7b/zK85v7bPjlLxxQK69h8pPYKEygS2JxjvO55dr8+2dRG7i1kkKe/JUyehdvKESdEATdbl/g00Hx0xaizSDu5D8q5t8PAJZa7l7GuXKCcclt7JSHfj5s1weeRhFjlFaFGX4tdqim3jY3HYAsS5xbUbj1GxgObRrwQq9NK50T7P3QGtEhfYD7sVTsWJqNdJUZCYgMgxE9q/Ts/FmgquUHCvnRzaAu59YYhwhDGzCg+vT8Guh0cxNUHosBH487uv0FRbjZ2fvIfUfbswduk9cA+6ctGK/i9vmTfrhh5I/xmzyxJgBB8pYTNwX78ANGzNQ+P+YljHuEBgK76uMWEDpnKGkt3CrR/Q/w48mPIL8mwiscltBFoNRuxdvw5hBRcufltgMIIGDYHK2g4nzpxFobIZAxcsQPCYcSh7+GHoiotRtGgx3J57FvaLF3f7N0khRHFzhHELb4Hg1zsAXTO0HvH4pa4/Ss5tbfeXoVha6mxfOmetr6hAxUsv4VTUAPyw/H4Uiq0wO/U4XFUNWJqVgAdH3gN5F87kAoENAgIego/PMpRXbEBp6Y/QaqtQWPgxO+R2AxAWPhtDh85FdTVYNFlGRgYrTtFB89u0l6E9EO1vZy+cjRfPvsjOVcJA14F4c+Sb8Lb1bj+/WlpyUFOzBzW1e9h9C6ytA9GgjIWV1cWoyv9l/O+wq38wSIq9/9EROHrkCMaOHQuxSNQef9R+27ap6fgxu+WTM6oAIgGv/c2r09VBoTiJ2rr9qKs7BJOptf1vyeWD4OV5K1xdp0Eg+GdsyGkDGRL8LLKyn0d+wYdcrMpob9YF3fdtOkouKLD1vymY8UBMJ2dziox57yy3ESXjjt7IDXtCRn0G7j9wP+o19azjs3rK6k4VaLqAk9kNES6KZyLSTRf0a83m7g4TneyYdChHrUG+WoMgm+vzejcfKGbEwyrckckOO4LOQYtrcUVqIU5vOow8YRXUWg2be6aDnLOp0k5df/o+ei76Avob1MVqbdYjP7kGu79Kw6yH+zP5+Y0EzZTSPJxlLs5y0McdO9cUl0Tz+hZQldfahubo0iCVNiAiYjGi+t1zfeavhRJg6L+4KLHE74CTn3Jy5z8eBQ6+AQxeCQxacVUZ3/8/gQphW37bwF5HW2Ez1pc+AVlxhzhDvhBm32FoKg5Dq2Yg5NOmwjr86go9hUWftuVjyxER+R8mub6RoA0NrY1Goxr28nh49TLKjm8lZPPdNZ+lMiMv1bFyyEZ5tRPRqNFekLt0JqJEZj5K+oipfTae/Qo+ZhvETJiK8ctXdTKR6u3jpvWSQOY9HeHhEYimpkUoK1+LIUNbUFbaDwVnlTA1W8HEMyCv9ThOnlSxGULa9NH4EI36uFq7sg58b5C8txgqpRa2Tlas0NcbkNx5WVoBLqg0EPF4eDHIA8vc7LG7KL09k3hawDR8n/49smIOIrh6EsqzlbhwrBxRY7xZt5tINykuLKT71LZ81JWqYCUTwXNlNJq/vwB9aTM8Su+Bxr0C5e5/MtItz16HuP5f4UxSCv7880/c8cD9OFO/D1qzGA4uYsRP72xuR8ojaYwbAhKByLjxqHGvxKnNG6GsKEOuuyPG1BkBe0DpKYS3zdUrZ+h1bLJ0ucl5vIvid3fQtRqQ15YRHjGic6FomL0MXhIR6zjuqVbA9dhunNm2CTKjEQ1CO+RFL8BjHYgjdYT/LP6T3b9Ukk2jX7QH+DbtW7yf+D4G2A/Arh272uXYpHihc5A6eEQmaI2n60FycjIjGU7wRUuQN0oryXBPeVlxaeD0OYx05505jZroLE5a7iFlsWkdQWMB4uAgZqhGxNvxLq4gt+nCl8yx3JrPx6pYzouA5l+JdO8p3NMr0k0FAuo+UheUCjyXwjTiMYTsmYX6Gily/tzciXSfbWxBrlrL/v6kMh20ZhpDkeOZxRE4/mkTiuvVeHLTOXx9ZxwCB8TD578xSNqxBQlbf2MGZb889xgC4wZj+C23M1LaHej9MTd4Lj5L/QxbstaB7O4OOg7BaL8QSL1c0HK2iqWhNO4uhOOivo0BXCkmjDry3aGprgZFqckozHRASc4w+GftQdRYK6SHx+GPiYtQl3QYYjs5bhs/DrNDLua+G3h8tuehOLFFixYhZPPvqHzmWVZYqXrlVbQkJMDjlVcgkF9uXHdk7WqYjAYExA5AQDop2krRYuWBzyoHoRVV7JykcUVaJ7qaqaaCwrrPv8HqB55Gri9XHJULBYiYNRemvduhVCiwdu1a3H333ZBKLx9/IlBqEcWhUmG4pmY3Kqu2MDM/anDRQZBI3BEZORxR0WEoKpQgPb0SKlVLe1ypNEiKVSdWoaKlgo1uPtz/AdwSOA46bRGKi3eioTEZjY0p0Osv+tfweEI4O42Dt/edkMnisXv3bgiFvS9s/51xk3T/DUAZw/5OUmRYgc1Gd5xP682cnlpdCEVzOpqa06BUnmIyjI6gChGRbA+PBZBJey+R+1+Cp+ciVFVvQ0PDGWTnvIzYmNUIiHHG3McHYucX59gM4u//ScKsh2Lh4M4tMGTkQBVV2oiRi+m14GT5STx2+DEmmwlzCMPXk75mWZ0W0Mzu3r172f1JkyYxovnbb7/h9OnTbNG8ERFOcpEQI+xtcUTZjD11TXjA99pJN3W+KGaGYDex542odZERgw3BGB0zDMoBAraRpE0MbVYshhwE2gRQB8vitk0HXQR6qjqTc/mk5ZEsJ7jkQj3++Pwc5j42oL2T0+f/S69nFwmSSpEcqquDIo+6Az1W+j/odaVNM0liqWu1ZMkStumnjWde3tssTqyu7j8oKaEZpwevn8EddVpHPALErwSSfgROfc65Zh9+Gzj2XyBqPhC3jDPxupnFzaFVCRQdBwqO4Hh6GUpawyGGFssNv0IGNSBzB0Imcd3swLHgWdnBtC0PhlOVaD1Xd1Wkm4qhRUVcSkJ42OuwklyfmMCeUFW1mY3f0Ox4RMTbfSL5Ijcp7GcFMSfzxn1FKNUYGQEUWQmYX8al4GmN6FfrgtP2zcgKUOHuOf9G7KSrm8Uj5QgVJmlDSQW6S+HtvZSRbrX6NMYMewWKY2UwwAS9YzmaWmqxf/9+HD58GP1i+uGnhp/akymshVfuWDNn9n2cTH3EguBemaclNKiwPL2IRTU6i4T4Psofg+1ll0XekKyZSPefjbtxy6xlSNxcihOb85lah2Yj6TphyTVX1/Bx7gBHWMcvjYCdrx2EC4Kh+CULqiPlCFv5DtK8l6Ml7zSkrWqMtK9AslDI1qBE7/6odosHz2zEAPMZCEQjL3vM5EiuTqyG5oIC4XPHIGzEaGTt241Tn38KP1/ODCs9cQOy3vgDcTPmICA2jsm0+wLq6lKRAEI+ZCN7VlhcCprlptg3B3cbuAV0Xtup6XCLuyM+Lq7Gx0dOYNZWzpfFL24oflBGQdUgQmKxEvH+nKpha95WGMwGlqPd1dz2iugVzDlbU6PBZ198BiPFpAkETFlBxZuuVEjUXbzzlzshL5PDReMCjWcA9u3Yhnl3Luv0fc4+fsw4q/h8ClL370Q0fwSsYy4nvnQ9cFxyJ6peeYVJzB2WLIEOBvxAahvq7vsNgr0VV1ye4j+FFQhSalJQ3VLdrqjr9rlsKyBQwbvL647UBWFjpuL0b+dQlFMETaMSVnKuePBLJTcvPNtFDuMuS2a7G6RWInx220DM//IE9mVU45ujBVg1JggisQRDF9yKyDHjcWLDWmQeP8LM5uigmDXqKvtF9+/yXJoTPAefp36ORF09SoRCrPeYjjdd7ZnKxn5OEGq/OMcMLW3i3GAVZH9dY8IsoHGYsswLKL1wDkXnUpgfxUUIYCPQ4tGM9dgQGY0DEONkPFegOFzejPXqAqa8CJFaYcqUKWzfQHsfMhO79dZbEfzlF1D88CNqPvwQzbv3oDU5BR5vvgnZyIvmeYUpiShIPsvc1Me65gNlydDwbPCdZgpaYYXo6Oj2feSlaDWasLVGiS9Ss5A7kfPtsOEBd/u44kFfVxYD2OC2FN99R3uROhYpRok0dH4bGxuhyciAtrAQuoJCliVu0mhg1ulg1mrhYG0NR8dx0Fgp0SqtRbNrFXS+VajUbmZ/h+qqUulQqFQhzNMm364Am1WbYeaZYWeWYBn5P9W+gzO1b1/2uOn65Og4Gq4uU+DsPB4iEffa3owMu4nrDpIb0qZIIMiEQmkLIQu4b1sUaXEkwzSjGgZDEzu02mq0akrR2lqG1tYi9rVLIZNFwslxJCPbtrbRNyzb8O8C2kiGh72BhDMzmSM7RaDRXApdqOc/GYc/Pj2HxtpW/P5eEmb8KwYCTx2+Of8N+9lH4x69poiw7fnb8fKJl9kFfYjHEHw49sN2EyECLbobN25kVWaSdRHJJhJG5JKcHUmKTRf2G4GpLnKOdNc24gHfa599Vu0vZV1u2jCIvW17nOFrTefkaLJYVziHOLKLPT0H1AUm6TlJ3SwbazrowmQByaTIYM5CZKkoQUScbulrVJiiI3aWI1pUatQXt2LrR8kYdpsX5O4S9vxS15I6EpfekqSfCHbHo7sZ665MzSj3uuNBnWx6rJZqM214f/zxR3ZLFdp587iZJYoTEwhtUVj4EQoKP4LBqEJw0DPX970ptgGG3Q8MvhfI3Aac/AyoSAbOrecOlwhg4J1A1C2Abc+btH8cdC1cVjbFeRUcBirPMZf5YnjhEDiTu37WefjSbRGmDluIgaGXFygo5qiFSPeFeph0xj7Npmp1dUxWTm8gT49FbH260dBqa5GT+ya7HxDwKGxsLo/yuhJs4t2gyVagJb0OZ3YUsM8NnOwH60vknQ3VVdj8zivwUmrBGwuUO7fCNv7Kc4tXkpYT4e4qnkYqDWSGcHX1R3Hw5/Mw6OxZJvfMh+5A+oV01mGi9TU1KRXDMAwKJwXGunBxTlcCeUZYnNkDB1xZJbKuoh5P55RBbzYjSmaNH6MD4N3BYKkjQh1CWUpGXkMeyvzT4Bnij4rcBhxck4k5jw5g/y/N3iadTUHFIa5AED3GixWRCTbRLtAMVDDi0bipFNGrvkRl7ngE5dVBlPQehgz+CidOnsSxU0cgxwD4lRwA79Qe6B9eApFr52sA5W5TfJexUYvWrHr2uyOnzoRsRzL4Aik0xlaUtxaCl2ZESVoqmy3tP2kaMwTrLlbqUlDOOEE22L2TL0BvkHmSk5aHD/e4bJ0k+a3zvi1A2Ehku3pjpqsnZi+5i7mJn910HhsTS/HdsUJGuskteVMO56C8KLRr4zGSmy91XIqijCIYaQDP0R63LrqVFYO7AxXtzxnPQeAhwAMNS1GubMK5/CLYHzrEVIsdHzOZixHpzi09g3CfQZ3muTtCPmc2I2REeFSHD2ONYwbq9VrYC0xYFv2v9u8jn5gBrgMY6SbZbk8KDmZG14F0dwfnWc/Aacd81GskyN/0PvqteBNNBiO213Dy+rthDUNdK8vQtm6br4/2luOlWf3w4tZ0vLsnC9FecgwP5s5VO2dXTHvwCQyZv5jNJ2cR+U4+yw4HD0/ETJyGiJFjOykD6P8aLg/BicYcrJM7oTFgEjwk3HlDho7SoR5oOV2Jhs25cHt04FV7xXSMCSNXeSK6pRlpKL1wHtUF+ayh1SnWKyQM/v0HIiAyHG7b5oOnUWCq9XmsspmAnbWNbPKIXu3DymaMPZuFua4OjOTOmTOHFeFJfk37wDvuuAMBK5bDZlAcKp56msnNS++5Bw633wbXf/8bZrEIh9asZn93QKQzHMs2MYn9OvMMaGy8cOvs2ZcpfwjFrVr8UlGPnyvrodAbASspbFrVWMrX46HxI+DUITmI9lVLly7F999/zwp8P733PsZduAADNULaxh+vBCpBSdh/LIbBlQd9lBjJfv1R3eoPHV+DnID9yDZx+yrPFk/E1cWhvEgIvWsh/ANoPtsBUmkQ5PKB7LCVRUFAJqT/cNzsdP8NYDS2Iv3CSpCZYXr6t33+eT7fCrayCNjaRcFePggODsMgFv/fzrj+X4DewP7+/2IzJzm5r8HRcSREIjnsXW2w4Kk4/PH5edQUNWHbx6moHZ6CVkMrq3pbDFX6CrqQrU5bzWIPCPR7Xh/+OkQdTKyYbHXLFkYqaaGzEDA6aA54w4YNbHNIRPxyw6xrx1RnOzybAyQ2taBWp4eL+OplgrJGIXQ5DcxAwG7y5V2ujtAVNcGk0oNnJYSkQzWaOgfkYkkHgQiwZQ6I5qFpk0ySbSLBdJ+OK4FnEkAuigZa7XD4hwI0OqbBIOLMRfoCkv0TqaYZqUsPIv5ErnuTTUkdbpKSkWsnFRLoZ6moQq95YMBDEAqkyM17k+UlGw0tbVnJ1znqiwp3UQuAfvOB8iQg8Qcg/XegNhPY+xyw7wUgYAxnuhY6FbDpe97r3x6aRi4nm/Ky6ahIAUzknXERLY79sKl5Isx6PvzCQrDKbQ6kQgGeCInqUhEg9rWFwF7CHPmJiBJJ6Q1oA5dx4QnodLWQSkMQGvoS/grk5LwKg6ERtrb94Ouz4qp+h2W+OztTgRYyNRPzL0sNqCkqwO9vvcRMfzyc3DHcxQcn6k6z2e4Xhr5wVX+3O2l5R3j73IWCZD3qi+whEPIwbkk4xBIx60xSFzIpMwk/bf8JThonuNS74IvPvmBGPxRr2J26qDxHibykGvbyj1wU2mNRzGAy49X8cnxbxhUYZ7nY46MIH0ivMDpC14qPkz/G7sJd+GDpJ9jw+hlm2JZ2pJxJzIl0JyemwK55EJw8ZRi+oHOn3352ELQFjTAqNND+qYPj+M9hzF8M64Z6BNsew2meBHq+CiL3ZkQ0VkBXqGPdNbenn+r0e6h7aDPABc2Hy7juYdv5LA4cBUMtoK9Mxre3PoIHKzPAO3OMSc8P/fQtjvz8A4IGDUa/MRPgFzOwU5ewIygWix4nzYXLRvfN/Iqi2qoLm9hjDB96cQ5cWVWBM1s3McMus8kEdwd/VLl6Q/roSwgN4uY+V4wKYKR7X0YVShVqFKmT2HgBFcOpQ9xdkafkRAnLvy6WFaMmrIYVxnsCzYET4tzjMG/8AnzzzhvQuXjhyJEjbH/QcXTMP3Yg7B3c0aCsQjEvCwHOXRfa+dbWcFi0EPXfrsa5DV/iu9GcaeF8Jwmc7Qd2+l76X4h0k/FYT6Sbrq2k1qLCsOXa2yUktgiL64+TJzKRdfo4+t2uxGaFkRmohdpYwS+7ES1taQ78Dt4/S4b4IqVEic3J5XhofQp2PDQSnvYXFSWOnt6Y/uATGDr/VpZZfuHIn8zp/Mja73Dk5++ZuRzFdQXFxcPOxQ1zVWqcIHWCrS1Wune+Nsmn+rOip6Fewwxd5VN63ot0JbumzvXBH75mH4usrPHjE/ezc6kj7N094BMZzRQKvtH9YS3r0GRoegbY/RSEh97Elw/Ox72UelHXxMb5+suskdrcit+rleyY5GSHO8ZMRLDegLzcHNZZpv1BaGwsArZsRs0H/+Wi4tatR/Phw1BNm8TeZ9Y2EgzTbmE+ItswGZKQcbh/zpxO+8Q6nQF/1Dbg9yolzjZxcm6CW6MSc//chVulYkS88+blz4GqBcI//8S4lFTsDwwApYbvdXTAaD4fIjcZ9O4GaJ2aYXA1w0QCSaEZZiHA0/AgaAb4TTwIawFRMR/Ceh74tcD5lliUtfpDIa5HqtMxKE1GCHl8rAqbgWFWw5CcXILi4lrU1AShri6Erc+REaPZ/uqKr1nv6gD/E7hJuv8GoCoadaZpUZRT9Zhd4y025eZ2YwOh0A4ioRwisROsrbxhbe0Da2tfWFv73/C4mf8V+PutQnX1H1Cr85GX/x9EhHMLDnVlSH5MhjRF5+tgfyQKocHxeHbG42yOqK+gyvnbZ95ujxq7O+puPDrw0ct+14kTJ9gGisgmLbQdTdPCwsKYiRZJz+n7SIp0vUEV4hhba5xvbsWB+ibc5nF1xRjaQHiVcJtU6WB3iK5gKsQiUeh5j3TsMY+VHDeps9NRPkpyIkv3mwxsSPJNknTLQV8nUk639LhoU2y2K4apPBD8VinslbEw+RZBbG9sd5m13NJBf5O65pce9Pnr1XWmbsLs2bOxbds2HD9+nBFvclcm+Pouh0AoRVbW8yivWA+DsQWREf8Bn38DHMfp//EexB1T3uQcz89vBMrOAgWHuIMIv99wIHwmJ6d2DPzfk6CbzZBqq8FL3wRQlFrJSaCKXMYvuVrbebe5jI+BKWA0tuw4hGZFHptxbBg0GiirxygHGSTdSGjp/CBTqeYjZSzHureku7j4KyiUx1mBNCrq08uypW8E6uoOoqZ2NyvoRIS/c03XCJOIjxw9dy0KFgCG/AaIIrm1pCwzHVvefQ26VjVc/AIw75mXMUBbgBP7TjMV0MMDH4aduG9jH+STQESBnu9LTZ86QsIfgppUbrMZOqqxfXSIQD+7Xbkdhz0OY7jVcAxrHcbUNTTSQ8Y/FEtGxc6O41wUDXbsV64jSFFezt7dF0Ib9AasulDMlESEJ/3dWexOb9YQmusm0n2m6gy0UhWGzw9m2eKntuThlmcHQCgQQWdshcmqGZNXDLlM3k5z0Y6LQlH7bRpazlTBKTwemqB4SPPOov5oEiRNC6G2LUarvAQuq1ag/L5EKDduhNO9KyG8JKKSZMJEuqmIZGzWsTgmg4KeEzP4uQcRUu6JnUMmY/uy5cg4egjph/ejuiAPuQkn2UHRR2QcRR1m3+hYiK3acqTNZmZ6RZDGu0No37fxpsyTRAUA/2gnSGx4zA37/IE9KEnnxpIIJFVeFh6MdxQabFG2wNIHDnWzxagQZxzLrcMPJ4pQJ+ViwuYEzYGV8PLHQY7xNO5FjzkwIhDbtduhr9Izx/vpgd2PR1BEniX2yMnbF25CoKaqGFp3P+ZET6ahls4ySanDPYbhtHILsuoSMMpwH8tr7goOS+5E3Y8/4nPvTOhNfIRbGTHJd9JloyHkuv7umXdxrvYcKlWV8JB1bVJn6XIHBARcsXAcNv9BnDzxAEqapGjd9w7WeS5nn7/T1R7q3ZwjvM0liSF0zr81LxpZlc3IqGzCv35Jxq+rhkIi7HzeOnp6YfzdqzDytqXIPHYYFw4fQGVeNltD6Dj049ewdXSEp7EesV42qLQzwoXNDk/tdO47zAlC/c+ZbB2m9VjU4X1vAZHoZkU9lJXljODXlxWjOj8PNcUFzK3cAm0LV6CXu7rBp18MI9rekdGwc+5hbR+0nCtk12ZCfOQdfDv1XdyfUczME9NVrXg+0IPtuYgQ769vYoezdyTmtaiBijLWbKEGDMnE3V98AbYTxqPyhReZ8ZnVd2swUC6F04BmSARGHMFQuE9+hEWwqk0mnG5Q4YiimeXUn2tWtzMFfltU7JyThxDz2YeQuLggcAdnxGaBgfxovvsODes3wKRWg8oIY6sqcWT0KNS5uGD/naMRNeBPCIX0/PC5LrRdLGSyMEhlYZCI3di1i65jtD7o9Uqoqgqwc8ufKGzQId82B+kOaTAKAFelGQ+d1MK9eAtEszRYsGAZmpudWLoCpQBQuk1qaioGDRrEEmC6ajoZjCa8tjMLKTl8TDeZ8U/IZLnJ1P4GIIOAgQM2s5zfcWOn92mm+yY6g8+XMJl5csptqKjYAE+PhZDLuVguihyZcm8kXnt3NdxKQzE+bwlMaXKAi+LtNTQGDZ4++jQOlh5kVXGKPaCM2ktBGbO0wBAon9DTs/M8m6XbTR1RMlkjB0rqtF5vUKX1Wkm3NlMJmUoIiPiwm9DzLDdtXFrTOVMMqoj3FXT+U+QWHX2BTmPAzs/PM6mmpDIUM+bFwPsas8GvBVTJbWpqwqFDh9h7m17biIgI9jUvz8Ws430h4wlUV29nIyJR/T6+seaG1vacsRod5HR+/jcgYxtQcwEoOsYde54G5D5A4BjAfzTgEw84BPy9SDjlJjaVA1XpXBe/PAnC8iRM1DQAGZd8Lz12/xGAX9th79v+v5w8fpxttqn7Q3EmK8ub2OfHO/ZMEK0tpDtbAZPGcEVjKIXyFBsnIISFvvqX+GrQ+ZSd/TK7Tx1uW1tuPvdqceFYBVqa9bCxEsBfzINySy4k/nYoyk7Fjg/ehkGvg1d4P8x7+iVIbKSINzu1S6gpc5dMqvoCS7QRGS1215GmdebQ2mwYddawciiClQ+RpjntpDdXmYtt+dvY/fvH389yf+n1pllvUtCQ2Rht/KZOncoKoPRzlJtdX6aCxEaIwbMDejRMu/1cfru51KcRvpjp2vv5UjLuskiDidgtHb2UmUJSt3vf6iwIVU4wWFdBHtEKJy9Zt3nqZGynOlrOZu5dF74KTc4ipBUvh7XZDjr7CjQ0KpBPap3ICGgzMqFYswaujzzS6feIXG2YgoMi4qjbTQ7jMJoBngqmxlJMPX0Ub0TGoMTMR/8pM9hRW1yI9MMHGBFuUSqQeewQO0iqS7FLfjH94SUPA7+whXW5bcf1zaTUaDQh6xRHupvrTuLLe9+Ets2Yid6/Af3jMHT+YniGRrBu3/sn09k1jvKTLfFHK0YGMNL9a0oaBP5cR3ph6MLL/halbRAJotEnWp9vueUWtKa3MjOvd868g2Gew+Bgdfl1pEnXhKRqyjIGxvmM40aI4oehYcdmyP2CUaPVM5XbqlWrWDePChre6kBI+DZoUSmRc+oYkzV3BZGbKxLvGIB0jxSIjcBCBx2cnS4fjXC1ccVAt4Hscewr3tft+4zGua4kLbfA0dsPLh4uqK2sRc7hHWiYNAZiGy/MUfLYNZbGEejcuxSUmPPVkjjM+uw4zpU24LnN6Xh/YUyXRSgqzMROmsYOMiljBZwzJ1nkVrNCgWy4YkAjMIDex6c/w1f2v0Dm6MRk6GR6Rj+v5TXAVKeD4P2TEPWTQ6/VQKdWQ9WghEpRzw4yIusKZOpoNOjZ7xq37F54hoTDzqUP43ekZpz2DrBmDnB2NURxy/BlZATEWTzW3X67oBLvhfngmUB3/Fheh201DajRGfBd0ECMMwAhNWXY9Pvv+LmoAu7RsfAMCIfn2vUw/PshuKach3tjC3jHgOSYMPz54Fuos3fG02eykK/WkgdfJ1BTZYGbA5Ozy5ITUfLJB+zzNCcuaJv5NjY0oP77H6D4+WeY1dw4qjggAMIZ0VCGnUQ/9R6kpU1Ec6sLLpyfh8ERThAZPKAqU0Op1cKgK4ZBn8cULRQlZyWzZc+dzNUNx5IzkdPUgiS3JFTYcO/Z4Q1OWPlLHaTNPOA8YPxjFzIm74BgaiQmT7kPreqROHToMIqLi5nSkwwJhwwZwgqhlvz4Zo2eqSYOZ5OHAB8JhQqMCb/xHig3GjdJ90384+DgMBju7vNYxh+ZqsUP2twu3/2j6A9s8foCo423ILJiJI5tzIVWbWCmQL3pUFAU2KOHHkVaXRqLGXt71NvMRfRSUIfWUjmnLG6SO3YFughStiHJq6kjOm3aNFxvTHaW44OiahxWNENrMnXbxesOZqMZqrbsW+lwjytGdejLVGxGkCcWwCrkryO9FAk386FY7PriPMqylNjxaSomLotEyKD/u9llkrES8abNPRmp3Hnnne3yPje3mUzBkpb+AOrqDiAlZQliYr6BWPwXSL2pmz32ae5QFALZu4Ds3UBpAnNJRcrP3EGwceY65e7RgGsk4BYFOAbc+Cxwox5oqgDqcoCaTKA2G6jN4m51XHfRAnrnGnki8DxjwWed/Xiue2/XtXETFcSIdBHoPSdxdEJiBjc/Ot6pZ9LNXIddrFmUFskcpXHdn18aTQXS0x+G2WyEu/tcZmb5V6Cg8BNotBWwsvJi0S/XAjIrTNrNRQ3Fzw2CJKmK/e8l35/GtpPvw2Q0so7jzEefhkjCbZhoLaVC5KunXsX6rPVYErEEAoq56yUsJIHIcHegyECKLhOIePAevg6tmkIolSfh6MgZEn2U/BFMZhPrBhLhJpCihmINKXOZXn9S0xDhos+PHzsJCdu4mfX4mQGw7mb++IKqlRHuap0BHhIR1kYHIMq2754g0wOmM9K9q3AXI0tklLbutQQoylsgEbtCY12FCkURU/R0V4iXT/aHJksJQ40aTSkuSNY9CrXJETLrcrgEVyIr25l1XO+6dxUqH30Uyp9/gdPy5e1RVBbYDHJjpLslsYqNBLHPDXBG8xZgdOpZZtJGediW6ElSNIy7ayXG3rkCFTlZyD1zgjlBN9ZUszlZOsZ73AEXK28UazKR9O1hNt9r6+TMyJPIygoCgRB8oQgGnRatzU3QqJrRXF+L2pJi1JaYYeZPgNmkQlk6xfiZIXVwRPS4SYgeP6UTQXIWC1mhbF99EzZVKfB8EPeeHxPqgmBXGUpM+yExmzDIbRAC7S9G3BFIRfXrr7+ymVs6BxYsWMBUacujljPJNhWNyKyM4o0uxYnyE8zHJVAeCB87rqgQPGgoEndshjE3De6DxzLPkt9//50ZVanP10LAEyLcdzjOFR3A2e2/s9n4rvYdDZoGfOuTDxiABceMcJ3Mg5PTqC7Pgan+UxnpJhfzrkg3eZZY0jR6ej91RNjYaahdvwY5DQ54ofBr7Br7MfhnOfUaZbaT5L8r+DrZ4NPbBmDZD2fwe3IZe/7/NTaox79F5wVlqdOh12hQ8eF0lJbW4RA/DObmZkg1QuYIT0eXaKQqWNdfoiKQ3NWdzY+THwE5p7v6BWDTmy9BpajD6DvuRngPruU9InAsEDELyNzBitXCpdvxSYQvJHwe1lUq8ER2KZ4N8MBrwV54JdgLJ5QqZnB20mYYNGmJiK4ohHXSKRytqsHpoH5wqa/CUlML7EK9Mba2GEalANYpzZj4+EP4bs5i5A8ZBRNFjYqFGG4vw1hHW4x1tIO7hFsbjM3NKHiey3a3v3UxM2aj/Wfz7t2oeu11RrwJVv36QbZyHkpddkKh3ISWKmuoq53gqq5GpcATTSoJ/jxeB5vSk+Dru/e6MYnEaPUJQZ1tKxI8E6AWqSGEAA+Gr8Ld8atgvqsJDVu2ov7H1UB1PeS/CWHcm42CaQ+DPzUUM2Y8gqamUawxRXPltP8l8k2NKNeAcKxcm4ycahWsRHzcFqDH8KB/xsjsTdJ9E/9IBAc/g9ra/WhuTkd5xUZ4e90OpUaJDxI/AHhmxM3zQXRJAM7sKGQHEe8RtwT3SLwv1F3AwwcfRk1rDeQSOT4a+xEGuQ+67PvIrIs2cmSgRt1t6nJ3B/p7ZLjy888/M2LWnczmWhAts4abWMg2iacbWjDGsW/ddHVSNYx1GhiEJtiMvHLGqrrNQM0q3IEZrvyVoBxXioY78EMG8pNr2TiBulF32QzqXwV6fadPnw6VSsWIBKkaaANGc98EcunsH/sTzqetYhEcScmL0D/2ezY28peBCPSwB7iDzMZKTnFGYyWnObMxdR2Qs4c72v8xASD35n6WOuMyN+6QOgNWdgAZCZKjOhmjUJHHIm0mIm3UAQYNoG0GNE2AtglorgKaKzmSTV3spkpARfP8FvHcJaDf5xQCeA1kh94tFruTSjBt5mzwr6AUIqJlKYjFxsaygtj22gbWQaC5xe4MsC6VmDcdKEHr+dpuSbfRqEVa2gPQ6xVsfIgUOH+FoWWzKgulpd+z+2Ghr7DCzrUg7XAZi+ezc7ZCxChPGALtUPNFKoRlPLhLAmA3wAtT73/sMqkszS1/mPQhm6U9WnYU43x7JymiddOSJ9wdSWioVuPk71x02bB5wbDyHomyskKUlq1hpPts1Vn2NwU8AR4e8PBl3g30utOs+LFjx5iRJXXAC/ILYA1feLqHIWpM1/PHx5XNWJZWCJXRxDqq62IC4XWF86U7ULGWOqkUNVnYWAg/Bz/Y2kvQUNMKkU4OmVQGVYuKdf0pqqor0OiOwy0hqP3yHNSptWhQ0fXIiDGOXyDPqR4Sye2sAFzo6gK7oCDo8vPZ7Kjzqns7/R6bGBc07ihgxRTuFwN2U2KgXBME5OVjbNIpbHKQ46kAd+Ya3v73+Xx4hUeyY+zSlcxMjwzXFElFcFF6w2gyILXyT2jK+uaxIZItgIDiUG2qMHDSrfCPjYN7cAj43RRuFro7MtJNXcZnAz3a4lZ5uGu4D95JP8u+55aQzl1u6mwTIaa1mVRVNP5lMcMkX5ZXh7+KJbuWsBGJ8T7jMcHvYoRWR2n5WJ+LHWiP0DBY28nR2tSIsbFR2KFQsE46uehHZ3GkIXbydGT8dBy1JUXMWI1mvTuC1qV3zr6DBkMTfFpEmJVggNnKBaLbui5gT/SbyEbd0uvTUdZc1p59fKlqhEbZejM/SwgbNhrH169BqdoeqypPIKAlB5ocm3bH+54wOtQFL8/qh5e3X8B/9mYh0EWKKf1616EUNRXBT5sETzcB7hv2CUI1n6OoJhsP+9+DUQ5D0NLQAHWDknW16fXTljcxd3yhQAL7kX6wcrJj3XAq7Ng6UWfc8bJ1idQZRLivFBPWK0x+E8jdDxQeZeRbEDkbH4T5wEkkxKclNXi7sBK1ej0j3qMdbdlBqB4Qgm1/HkTF2dOILc9HkFYFcc45Vjz2cmuE1VA+vre6D1O2bIFLbTWeWfMVnji0C/b33QefObPA62Isofrtd2CoqITIxwduTz4JQ20tql57Dc37D7CvS0JC4PTwg6jxP4eE5HegSJShIT8EerXld9XAWtwEjV8oTBIraENi0N/bHU72cghFYghEYlYgIzl+Tb0SF+obkG2XhzTHNOZObtsixJgUZzTs2onv3ZIQPnwMIiaNR/Adt6Nx8xbUff0lUFkN+41C6I/lIfuW+2E1OBaLF7+MykoxDhw4wNzUv9txBEcNlVCbBHC1leCrO/qj9BxN+P8zcJN038Q/EhKxM4ICH0NO7uvIz38fri5T8d+kD9GgbUCIQwju7LcEomgRxNZCHP81F+f+LGWZoGOXhLM4qktBEsAXT7zIsr2psv3Z+M/aq9uXXjBpjpfkizQnvHjx4iuOCwQFBTESRm7XtAGcPPnyzvm1gDYglNlNsR/76hr7RLrJobmRcrkp9sy7FV5XkNLS/69pI91XIy2/HhCKBJh8TxR7XYkwHP8tFyqlBsPmB3f52t5oUOeEJItkoEJkggosy5YtazfpIWVGXNyvOJe6nMX/nU28Bf1jV8POLuYvf6wQS4HgidxBMGiByvOcA3r1Be6oyQD0ai4PnI4bCZpzdwoGXMMBFzrCOAd26tQLO5Admu/nc9K2nkBeAJaCGG1CZ86cyTbnB+u5zvkEp969N6zbSLcmtwHGFj0EUtFl74OcnFfQ1HweQqE9YqK/+EvmuMmwLSvrBdZZd2mLXrkW0JqYvI97jQfPDIBAwEdOcRLKG5MQLh+CoR6z4bN8eJezqRTNtSB0AX5I/wG/ZP3Sa9JN86f0/BERIvPCS2EymnDgxwwY9CZ4hzsgZqw31K1LUVa2FnV1f0KtLmFkn3BL6C3wl3dttEQ+D2RwSEqk7Vv/QElZEVpsi1Bvr0J1td9l40BbqpV4OLOEOZQPs5fix6gAFst4tXC0csRwz+E4Vn6MdbsHFk5hhJtxWjMP1jp3qJDHzBi7I93s//C1g80wD6hPViLWRoBa014EGjOgaJHD0zMJhYVxOHrsGJbeuxJVTz8DxY8/wvHOJeB3kO3TiIR1tDOTlxOsQh0glEsgnzMHtR/8F1PPnMDOkROQ0NjC8rG7g72bO+SuU1Cbcw46ZTNzmp4Z8gwU5aVorq9jh0pZD6NOzwytSOIrEIlgbWvHJKtEmKztfJG0V8Seh0Uv3gk7J+tejVDZCfmo0OpxskGFkQ7c+9jZNR98URNMBinMLZ2fQyLCtB7T9ZkI96WzzmSyuqzfMvxw4Qd27Q91DIWPLXfN15v07HW7lHRTUSAobgjSD+1DTWY68/UghRN18Zw18XDi2cJxsD+iSycjefd2nN2x+TLSTSMROwt2soLRCgcfCE05wLFGRqKEXYxcOVs7I94tHglVCdhbtJdFn/VVNdLV6yj2DYSupAC5Tc4IOrQbjab5EHnJ2DjClXDXcH/k1aiw9nQxHt2Qit/uG4Yor14Q/vMb2M2fjkMR6eqNuZJ5eFv5Nnaoj+CuCQ9dVrSkdaLu+3RocxsgbrSFy22xzJOgO9D3J/2xld2PmTi1WwPAXsPBj4vqPPIusOdZIGg8eBIZU1u4iIV4Ka8Cq8vqUKnV49MIP9hQJYlUblZi3DtjKjICfNkIgn1BBqyryyDkGTHMvQryFXvxilsETA/cB+XPP6P+m2+B0hK0PP8cCr79Bk73rIDdzJngW6TYBw+xXHd603i+/RbUiYnMGZ0iwCASwfm+VbC+YzqO7XwChdsa0Vrr1/4vkFzcv38cvCOi2CGyk7O9Cu1hz1crcMuo8e2+GmQMTHvUlII9OON6BpVSTh02ynEobpGNRJ1PNiqyM9BYXYWELRvZ4eIfiAFTZiJsxw6otm1H7SefQFTRBOdP+GiNTUfy4vnw6Hc7Vqx4BO9uScO+8yqYwIMjrwXLvBrhIdF3J2T4n8RN0n0T/1h4eS1BReVvLLf8dPrT2Jp3ms1gvzT0JYjaTKtix/tAYi1kcS0UT0KbzEkr+kHQZv5FF1fawFGmN2GU1yj8Z/R/IBN3vfGgzglFQ1A3hQh3byrLdCEhGfL69evZbPeIESMYYb+emOQkZ6SbDD3eaDMf6w1UJytgatKBby9GrRuX1dkT9FVq5ipKmaxWYf93jthErkctDoHUXozTWwuQeqAUymo1Ji/vxwotfzVoY3fbbbdhzZo1rLiydu1a3H333SxyjEBzvoMGbULquXugUmUgKfl2REd9CmfnPhoOXG8IJdxcNx0dZ6qpM60s5KTp1J2mrjQdLXWc9Ju62FoVYNITS7roGk6db3JWp1uJLdcVl9gBMlfA1oOTg7cfXpy0vY/jED1tuHbs2MEknzQnbCmI0efJmIYw7grz3BaIXGzYBlRfrmLReLIhnRUg5eXrUFH5K5tFi+r3ETO9/CtQXrEBTU0pEAikCA158Zp/3/nDZdC2GFhOcshgd2QcPYg9X3zE1tEA5xhI9NZo2lkMx8Vdb+hvC7sNP134CQmVCchT5iHY4fK87UtxJZJwdmcRc7Wm9zFJsknqSvFhjo6jWPTmicw32fgPkf77Yu+74t+j96BzSyyUDRKoHQqhaKjDt99+y9ZkOmgt/7K0Fq/lV7Q7lNMMt1XbBvpaQCZdRN6ST+YB5zm/h5GLQtia1VopB5y5IgRJhHu6JlxoNsDFaIZMwEOYawSgAkIaPaHwykFZWRTrdheNHAlbHx8WRdXw229wvKuzFJlyjy2k2zqGI3fymTNR+98PEZWbCbf6WmyqcuyRdBO0OUomVSeVk8OkQDjbilkmc29xZD29/uXwj3HuFeEm0Gsxx9UBayvq8WuVop10b8v/nd3qGwbhp5PlmBnj1979pWs1gYhxd/4hDw18CMk1ycyo7N9H/o2109ZCLBAjpToFzbpmOEgcEOPcuTgaHD+Uke78xARmGkb7ATpOC3Mwz288G88iKXXK3j+YKoBM6Uj2TMhvyMdbCW+x+/fH3gcn5efQBZggpqV2zVq4PkGRg5djSsCULkk3jSaQaVVfSTd7joKi4V9SgEyVOzwbuJ+16d97n5WXZ0WiqL6FzdUv//Esfv/XcPg49kDYyT38PK2ZwCa3yZjhYo8ZzjOYvD9HmYNMRSYinSK7SFYIRfWHSdCVNqP5WBnsxna/1lZkZzLjNir09J98nSIbRzwKnNvAFaAPvQlM5TKo7/VxhbNYhEcyS1ikWLkmDz9FB8CtTQ5OIA+BrIwMlG85CR14iHcqQ9mgp2BPxWW6ekgkcFqxAvaLb2UO54rvv4euqIiZrpHrucNtt8J22jRUvsSlYTgsW4aWU6dR98UX7DotiYyA+5tvIiVnN1Ke+he0jbT3sYZAJEBw/AhEjBzDij40494RtDeh4jQVpUidN2HCBFac3Lp1K86WnUWCOycnp330U/FPYXHYYm5PuQBsRCA/KQGZJ46gKDUZtUUF2Pf1Jzi27kem8oj57Ve0rCW39nWwPgdIcnionL8Jr8mtcLKCxoB4GOQKRDTnoKbIgG+/zWNrNJno9lap8XfGX6v9vImb+AtBbr2hoa+w+zrlQfiIjKzz0d+18wYgfJgHpt4bDb6Qh/yUWuz+Og0GvRF1rXVYuW9lO+GmOa9Px3/aLeG+cOFCJ+M0X9/eS4Spkkh5oHSRJHfd641RjuTIzEOJRocctbZXP2NS69F8uC1jdYIPzL1YLSzZ3NQp4ZMhz/8h6CIQN9Ufk6mIIuKjOK0em/6TxPLa/y9AnTXK6KQON0kaiYBTYsHFr7shbuB6Rh5MplYmOafRiL8d6OJq58HNTA+4AxjzJDDjfWDxWmD5buC+48Aj54Cn8oFnSoDnyoEXqrnj2RLgqQLgiUzgwTPAPQeAOzcD874CJr7MmbyFzwA8B3BE/DoRbgIlBNAsL50XZJxGEX6EbLUGVTo9rPk8DJb3vthFEnMCyXo7or7+GHJyX2X3gwKf6HYW83qDcsDz899j9wMDH4OV1ZVHQXoCmSadO8C9/+Om+SM34QQj3NRNj5owCd4rKMccUKfUQJPX9bwluSlP8OVkudTtvhJotpak3t1FhZVlK5HYNl8+9vYw2DpeNB709r6T3WqVByHimXF3v7tZF/BKKLmgQGmmEjZ6dyy/ayXrKrNCzJEj+O677/ByckY74b7X2wVf9/O7LoSbQLJlT00gotM5ddOASb6IGefDRp2EBimEBhnrLtG1pTuQceT5YxVIURvZx611gdAY+8OutABO1n7w9k5nnz96/Dgc7uEIWf1338Oku2Rek4pplrttGT0iDw/YtKUuTDxzgrkza3rI7+nkWD70yv4fl0LbakDW6Sp2P2ZcZ5n0lXCLGye/JoLTYjSitLkUJyraZKnNQ3C2SInzZQ1s7aXuIoESJchBujsQqXh/zPtsnIzGANh4GoBDpYfY7Wjv0Zd5FZCDu1AiYfPpNYX5mDRpEgTgo1LQgDI3TmZPM+lhw7h1IfEP7rFQhCkRe7od5jEM872jYDSp0TqNI6rK9ethbOLMHi/FRN+JrDNOxLS46aL6qLCwkO0pKD2jp7zxS1HSqsUeL850rUHrCZ2ZVAIm2AT1XikmFPDx2e0DEeZmi5pmLZZ+fwZ1qh72HmTk2VSOBqEMB5yGYZqznD3vlvVjc+7mrv+OvQT2s7i58ab9xdBXXYzOuhRJO7kud8TIcZ2ywa8JYhtgJqesQcJXnLlnG+a7OeC3/kFwFAmQ2qzG9KQcZKi4/Qe9r8lgtenQGugMPNiJNKh2CsXmlDr8+OOPLMHBAoFMykZCgv78E65PPgmhpweMSiXqvvgShbPnwFhXB4GrK7TZWaj7/HP2Xna4/XYIXnoe675+HafXHWSEm4z7B82Zjnu/WIOZjzzFVBmXEm4CmZktWbIEcXFx7GPyv/jwow+xu3Y3S4Mgwk2qj5+n/4xbw2/t1MQhv4bwEWMw76mXcN/XazBmyXJ2vpNvw+nfN+CHF55AXpAPPNf9AqvoaJQK3fFqxeOMcPNhxMpBuVj/0Cg88sB9bF9Mz1NtbS3zxvkn4Cbpvol/NBzs49EkjmQ8YaGT+bL5PgsCB7hg5v2xELaRsw0fncBtW+9gBiVSkRQfjv0Qj8U91q0ZEBmhWS7k5MJoWax6C0u3m3DmzBkmf72eoNzYEW0div11F4leT2g6XAazxgiRuw2sYnonFbeQbuuov4/pRUi8G+Y9MRBSuRjKyhb89s5ZlGd3Y8pyg0EdVjJTI9ksEW4i3rQJ7JhkEBvzLTzc5zOJcFbWc2w8gojOTVw9iLTQzBiB3KopOscCS5d7qL2sT2TKuu09oStqZM7EBFVLLtLSH2w3TvPzW/WXvWy5uW/CYGhimdw+3kuv+felHy2HpkUPuas1BIIS7Pr0PY5wj5uMiSvuh8RfzogVoWFLHsx6jvRditvCb2O3JJklx+eeQG625IlBXd1L5d3qJh32f3+BjflHjvBg7+uOIHdno8AB1nwTxsiteuWYTlL1E22z4THjfeDl78IKMmSoRRtPMvgx7tyMyIpCvBTogddCvDrNNF8r+FoRpueshMgkgcGzEUPnceQhcqQnfCIcIFFz87Pnzp3v8ucNOiMO/ZzF7rsNdYd0GPd6KE1PwGzgIcowDF7eJKHWsG53cVAQhG5uMFBu82buemWBpcvN7qdcvC+fPZvdTjt7HE16I1NLdQdNtpIZaVKX23ZM30gzIft0JQxaIxw8pPDqY/IEFcz8rcVoMZoY8d6Us4l9foTnCMyI4Ij198cKsHPnTnZ9peJnb0a53KXueGsk132m3Pk/8v9oJ93kWn4pRGIJAmK5639e4mlI1SLEGLgO+9G8BEaCCfGzF7TPGNMsPM33k3Gbk5UT3hr1FpRKrhNvO348xMFBMKlUrDvYFchdfYjHEHafut1dqUb64ifxW5USzTJ7NPkGwU/KdZcl/DQIznDPQ28htxZhzYrB8LK3RmFdC+7+4SxU2q4dxVm3mNQJLuPR38ERrm0dYRpRsawfahpr6gIUYWYV7shc9xUbs2E2XH69bKiqRO7ZU+x+3Iw5uK4IngDELOYiKrc/wnmXtIGuKzsHhiLYRoJyrR4zknKxuUrBVFeKk+vQUM997+ixsfCf9wIbcyAfgC+++IIln9BIVEfy7bRiOYL37YPXRx9CRKasbcUyY00N1CdP0TwbpEuX4IwNsOntl9FY2QSB2IjIqb6478v1GHP7/bChaOIrgPwNKFGH4jR1fB2OOx1HmhM3v0358L/O/PUy5cGloLGRQbPmY8XH32LW48/CLTCEzeOT7HzNx29h48Rb8cjEJ1Ak94CDpglvJn6FMeWf4cyZGRAIC3H77bfjtqlTEW4w9Klo9HfGTdJ9E/9oUNX30+IyaE2Ar0gHbWP3hgw+kY6Y9kA0IDShMd+AQSnzECINxboZ65hZSU/GTCQNpy4NuZFfbd42dXZI5kabTiLe1xuTnLmFtqdNkwWGBi1UJ8vZfbupAd26lXaEvlYNQ7WahshhHfH3Id0EN387LHw2Hq5+tkwuu/3jVFw4xv1/fzXIKG/p0qVMKlVfX8+k5h2LLJTXHRHxH/j7P8g+Lir+EufT/gWDoW9mRDfBgZx7OxbE6OgIcvUnkBtsX0C5wyIfW0YCycVcp6vHuXMrYTSqIJcPQkT4W3+JcRqhXnGcRc/RJZ0zbLs2lYleZ0RqW2KBfxQff3z0NnMppw7GpHsfYAZaBPkUf/DtxGykpOlg15N35BpN8WHUwduWx0V4dYeOJIFk3RaYTWb8+VMmM0UkqfvIxZdnd7catDjYyG22pzqImbz8SqCRIirESaRCxE29OOcY2q8fCsbPRJm9C0QmI0bnnoNzwpHrWgylWKy9q9MhbLFBo6QOO4O+gbktEIjFSd4ZAZnZnZ1f5eVlbK3oSmpPpnI2cjFGLAiGfGoABPYSGI0OaDTcBUnaHwgLfRLe3lyn/Mixw7BfzuUu13/7LcxtBNCkNbYXTAm6wia2nhNsp0wGTyKBV2U5QksKsala0W2XmzqNBOlwTwi6cX/vDvQapx3m1uSYsV59fu/Q99/qzo00rauoa++MLgxbiOUjuCJb2oULyMzMZOfW3Llz243TrgTqaK+I4lQCL5x4gZkDUnoJxYl1BZKYE/LPnob6XA0j3TKhNRoaG3HqFEf8XP0D4RczgOVJ/7jmdfZ4aWzjndHvMIVGfT0Xc+bkMhbO93LGd4off2L5yt25mBPIdZ1AHUKLiVpfpOUmsxkbq7jXOGDYGPjJOGJlzT8MpP4MlCSgL3Czs8LaFYPhKBUjrbwRq9YmQnNpgY4MPDO3d5KWWzDYfTDrqqr0qk4Fhctl5iHgS4XQV7agcQ+nhumIpF3bGEGl+WVnn4vv9euGKW8B1o5AdRpw6rNOXwqwkeCPgSEY42CLVpMJ92eW4KcmJaQ1hTCaBfB2tULo8vdYzOi//vUvlnBC+0lS23z22WfM14Fez/b/VyiEzaBBrNvNIOiw3huNaFnzM/x+2ICYkmqE2lRhxgNTMe3uLyCx6d01jlz99+zZw4h/blMuDnodZPPbfBMfAxUDsUC8gDWjegtykQ8dMgJ3vPVfzP7387DxDsQOm6H4qlACrZmPgQ7At2Xb0b+sEE5fiiB7rQZFd9+JzGFxMC27GzG/b+72vP9fw03SfRP/WNAm4I3Tb6DWYECOmbvo5uW/C6NR0+X316pr8VrJ09ga9il0fA28m0Jxe9Ez8JZ0LxPXaDRs5oVm7qhyToZZHTeLfQH9nKXbTRJzIt/XE2SmRjjb2AKFvptqcxuayDzNYIY4wA5Wvew4aDK4C7UkSA7+/8Hc9JUgtZewjjd1yEwmMw7/ko2jG3JYt+uvBkmbiXhTR48MS8hcjc6ljpsIMgKMjHgPfL6YRYolJt2C1laOCN1E70ASPUtBjKRqlxbESCp7uoErZvTV1Z9g02YWqE6rxvm0+6DRlMLayhcx0V+Cz5f8JS8TuaRnZ3Mzfd7eS66LAV/GsQrmWG4jFyDpj/dh1OsZkSCX8o4O0mTA5TCb685Sdrm++nJpJ53Llm73xuyNLMaru/U6KyurS5KQcqAEJRfq2ZjIlJVRLKXgUvyS+QsONuigN/MgNFSjoeHMFeXzCTsK2f346QGwajPDUxtNzKF8e4sB+/uPgN8Ibq6b5nK//vprpmq6VtD/SmsPRRsKxXycjN6ISkMZzlRdfMwknR9zSxREOm79TTh5UbZKqC1pRkpbYWTMbWGQ2IjYSA+RD0KLcRa0tRJ46/3QL8oNIlErGhtVKA4LgsDREfrycjTt2sW+lwi3WWeC0MkKkrb1vuUMJ/MWyGSwncAZ8k1KOIY/65tQrzN0uf6TxwFFRdqO7nuXuzRLwQoIYivaoF9dV4tczImqn25Uo94oZl3qMd5jEO0txzAfKeIFXFGAUkLISLEveGjAQ8yR32jmCGOYYxhsRF3PKAcMjGeFKXIor03IgQgCjIvj5OQ0S26RysbP4rq4mpRCSHR8PD34aQz1GMqiBltactkW3clxFOymT2eu1BT7pPyVm3u+FON9x0PIF7J8+oKGAlRWVjLyRJ1TS0xlb3CqQcXG0GwFfMyKGQG52AVGswEtAW2u5TseJolFn567QBcZfrw7HlKxACfy6rFyzSXEm+IqdSoUWXnirF0Uprtc7MTyeXwsCOGeJ4t6oSvQKAPNdxNUx8uhyb2oZtOoVEg/vJ/dHzRjHm4IKLmDiDfh8DtAHb1+F2EvEuKXmABM13NF3pPe/fH22CehsrHFuMfebi9kOjg4sIQTUtxQYZ7Olc2bNzPynZiY2K6UqHrzTZhoPI2KU0YjeJERqJg6DiWOttAJ+BAbTfBWqhB8qgWGZV+hYM5cVL76Khq3bYOuuJgVey4F/S0i2x9//DFOnT6FXJtcHPE6ghZhC7ykXrhLfBcCGgOwd+9eZrbWcTyuN+DxeKi08cM+wWA4N2mxOPsA3jn5CZ7c8Dyss7gxGIK4mA+rTB6gVJOnJHQuTsxI8J+APrED6uj98MMPWL58ORusHzZsGDOhePnll5mj3U3cxN8J5Ah7uvI0q0jPHfQFJBJ3aDTl7XE6HUHV4Xnb5zHDn0bHSnjdamQX/+q8Zuz45BwzWLsUtJEns4mamhrWvSQpDM3tXgtonpBMI6ijQqZq1xM+VmJESK1YL+VQD91u2jhTTBhBPi2g1x2H1gyuE2Md+ffqcneEUCzApOWRGDqXy2sld/OtH6agpeH6Fjh6A3qdiXhbW1szc7VLiTfBw2M+Bg5YD7HYlW3CzpydB4WS65TcRM8g2T49p2TAQtI0kgxfWhA709iCVpMZ7mIRwmwuzgf3FpYxCm1BI1S1uRAK7RAb++1fk7XehuLiL9HaWgyJ2A1BgV0bLfUF5GdhcSxXKw7DoG1lHbkZjzzdpUu5VT8nWEU4UosMys15rGN5KWYGzoRMJGPKo1MVXZ+/ZHBHmz7qPlKWtgVlWQqc3sKZQY1cGAInr8s9NRq1jcwlvdXMA8+W6z5SfFhPIMLa2qSD3MW6PSKs2WBkGdyHFM2w5vPxc0wQ7p40nu15qFBGe6Dvv/+exTteCygtgwobxBApaWFIv/7t16yOiBjuAS8n7rlITU6F0WBs75IfXJvJnuvgOFcEdjC4sgpxgDSeI61K/SMwn12D6Oh34OfHFRgOHz8A+VJu/KB+9XesAKBO5tZ7m4Fu7aaAdA0w67mNud2sWex2ctIpmAxGbKvpPJ5Dj4MVaknJQ13uS9z8e4Pzh7hiRvhwD4ivkJLRHSi+zaJY0UhHYVHoIkZECcPEJbDiGdAIG8QP5bLc+wIaLXtjxBuwE3PF6yxFFstZ7wrWMlvmBE0oqclg2ecDJg6BDxnZ6fXMzZxwWpyNejsdREY+luknsWx7Ql09F0cml/eHSGTPOptO99zDPqf4/ofL5/HpeyVyNgtOoI6wRTVC+eO97egTLF1uMqYT5HLdxQp1PjJ5kYDUBajNAo63zTD3ATHe9vh+WTxsxAJmrnbPT4lo1bUR77Tf2M1m14kYYCe9LIZvbvBc9jqerzuPbAX3f3UF2ntYRl4Uv+awZAnCuQO7YdBq4eLrz2bubxhib2UO5iwSc8sqwHhx30jvswP79iHy5HZ8nP4mJDoNSj0DsPaOx5Fq1zmGjfZctBd88MEHMX78eDbqQgXkP/74Ax9++CF2rF6NooQzXKCm2QzjqOE47uWA1MoSpPu6Im2JDIpHRZAtnQ1JBGfQqM3ORsP6Dah4+hnkT5mK7EHxKFy0GOXPPocLb7yBXc89h5+efwHnDh6ETqdGhl8GUpxTYISROfT/OvtXPHb7YywClc4n8t74/PPPWYPI0oU363QwKJXQ5uZCdewYKxCRW3nFs8+h4K5lSBg5Ho63TMJb29/Ei2d+wrLMPYitKYF9qxZ8SjPg82BydwPEbdnjUjPqHtGj9kUDDC5djy/9I0k3zTXdc889rDL4xhtvMEJATnZEvL29vdncAZlFREZGYuPGv6Hxz038fwfahP3n7H/Y/VWxq+DvEIrgoKfb5bpabU379z115Ck8eeRJdj/MIQwbZmzAotGzMPvRAZDYCFFV0IhtH6dCq744p0OLDElWyd2RKslEuK+HsyKRAqrCE6iQ1XGe53pgci8k5o17i5mkkTbTFEfTG9BMq66E+51Wf2PS3dFgbdp90RBZCVCZ14iNb55hm/u/GqSOsBBv6qBdKjW3bLzi47fAzjYGBkMDUlPvQmnZWnYRv4muQc8hPZe0UaH3ZXcFMYu0nLrcVyMFFzhawejYAJ6ZD9vaeDaPL5Ve2aH7ekGtLkZxydfsfkjoCxAK+96tvxSZJyqZjBtogaYpCe5BIZj9xHPdxuvQ82Y/Jxg8MR+64iaoUy/OA1tAHUHaOBPWZ63v8vdYSAJFKFpiFpsVGuxdfYGNLYYPdUe/UZ3nvC34Lv07NOubEeoQimERz7HP1dXtZx3DrqBSapG6j+sSD5sfxNIqlHoDFqbm43RjC+vybYwNxKg2Akf7nFWrVjG3YVr7aR6TDiq89hUFqbXtc+QkCQ+IcWYu5oQDxQdYLGXH53b20jHgmQXQmdU4uIUj+yn7SlBXqmKy+FFdSO3lMwIgkPFgMHuiKU0OiR4YN+4u1u1WqczIC3UCXyplm+OmXQdY0ciSwUypEwK5GCa1Aa0XOMm5bORICBwcYNfUiLisNGyq7ky6NRn1TNbLkwggG9V1xnlPqCtTMS8VegtGX8UseEeMkHLKFY10NOYEc11NkpQrygtYwfmI1h9bz3FRR30FRY5afAko2YSMVqn72tVabJGYl6tzYRPrDL5IgHHjuBnw5ORkfHb6M7yZ8CbSA7nnXpBaBb2Oe+3bpeWOY9p/n3ze3G7n8S2YGnBRYp6V3bVqpCeoDEbsqOEez63uDmhtM4ksUWUgKyEBpimcMzeOvgfUcL+/LxgS6IQf7x7MiPfxvDqs+Oks1A21QB7nt7HFdQJmdOhyW+Bk7cRMBwm/5XAEvTvIpwdA6GoNU7MOyk05MOh1SN2zg30tbua8GzvyQ7979meAlZwzVDv+X/ZpOj/279+P5NNHcQe2IiLvPJb8/iXc66ugEoiwNK0QL+SWXWZSSOsgqR8fe+wx5kVC1zIqIieVleHA5EnYOXMGEubNxm6hGUqFElQPCplTCJ8RekTfsQ4+z72LwC2bEXL8GLw+/hiOd98Nq/6xjNSa1Wpozp9H05Yt4P/8CwI2b8HE/fsxe/sO3PbrFjzzUTo+/dKAH9Y54PGvaqBc+TBKV6yA+1df4dasLEw9cQJjd+wA/8GHkBY3CJkxsciKiUXusOEomDUbpSvvRdVLLzOzt8YtW6BNSIBdXSUEZhP0EmuIo6IgnzMbLo8+CtmLzyN9/HDsiwrAHjcZshfNAUKCIGjhwfkzEaSnmsHn970o/j9LumnOgIx/qLpL8QMk1/vggw8YASfN/9GjR1mo+YsvvoiPPvoI77///o1/5DdxEz3go+SPoNAoWKY2udgS3Nxmwc5uAIxGNfILPsDRsqOYt20edhftZs6f98bci/Uz1iPQPrB9DngOEW+pEDVFTdj2USozFqIFlOQ1ZM5kiQa71PTnWkBuqtRVYYvrNXZUusozJVAnR99FR0pb1Mg2UNSBoXnN3kJDhNUMiLxlLN/1fwHUHVr0bDzrnJGUlua8z+4s7LJTdyNBxUySk1k63l0RbyuJOwYOXA93t7nMpIsyoLOyn4fJdH2LMv8EUKGKRj5Itk/yfSpqkHtvVziibLqqeW4LyC1c4cjJFt1blsDefhD+SuTkvs7OAUeHEXB1mXbNv49GLVLa5nL16gQ4eLhj3jOvQGzV83w0OQjbjufGcBp3F8KkuZyMUqwMgdbdsuayK0aFUcd9z9dp0Kj0cPaRYcztXZtB1ahrsC6TM5gio0w72wjY2w9h75Oy8q6NpxJ2FLCcb48gOVsHanV6zE/JYw7D5DT8+4BgDL4kGoven5TnTM0GAq3N5DJMEt7eoqa4qd0Mrt9oL8RO4OKNBrgOYFJomlul56cjHF3t4OfFSfiTk5JReL6WrVOEUQtDYGN3+ew0yf7t53MdLpVhNvRHN8PLawbCI7hN/fGziZAtnM/u1335DXs8kkA5hI5WLOfY0ilXJXDklCcSMYkzYXLCcSQ3qZGv5lQ5ZqMZjfu4GVrZiKvrcidToZcKLnGusHe7cg50Tyit3ASeUQWT0BEXWsVsTGv37t3sa3L/aCjMUvxwopCNGPUVR0o5MhzpGMnmvKlA8uqpV/H00aeh0nX23AiM5WIW6zRl4IVx7x8ycHT1cGXFmhOnTsAMM0aMmwc7Z1eoGxuQceQgTCYtlEpOOerk3CEDXCxmJlqE+tWr2+fxO4KM3chxvaCxALmKXPZ+IZ+Z3mJ7bQObOSbTryiFAcZGLXhWAigEtWhpUKLEFAiETuWiIElm3oVE+UoYHOCIn5YPZlLzk/n1uP2bE6g3WuOCNBi5Un/MdL04z90RlDxzJUM1Al8sgOOt4YCAB02mAud+2A6VUgGpgyPCR3DjezcUci9gOudyz/K7K1JYYzLh5DEswh9wMNbjeF0AHBvr8bVUh1XenEqF8rwnJ+Ygpeny/40KxkOHDsXDDz+M8VVV8CkugcBgQItMhiKJNVrcfNESEoNGvyjkVkxCcdEqHD5UwDrj27Ztw45jx7C9tgYbra2wNjISv86di13Tp+HEiOFI79cPZcHB0AYFQu9kB1PbEmujBdwaAGlxLSPn6oQEtJw8xQ59YhLkpWVwUDbAVqWCmPYrHQ3f5HJIwsIgGjEKaXET8GPENLw38Da8P+0RKH/cjOjUJARt+g2e777LMsR97vh/7F0FWFxn1n7HhWFwd9cEosRdGm+TpkldUm+37t3Kdtvu/lv31C2VuDXauCcQSIK7uzMzjM/8z/kuQ4AAAUKqefvchymQYeTOd79zzis3YfH7n2LM0huZ/js/NQUHvZygnTwePBPg9KMZlpSSv0/RTVqm//3vf2yS3RPogkQ5tGQSQRlvV3AFvxeSqpLatT8vjHkBIgG3CaALUHjY8+x2ReV6/PvQfajV1iJQGcjyN0mzZftdG9z87XH1o8MhsxcxDd3W98/g4IFDOHmSMxMhMxaazAwmBAJB+7SbYo5sGp7BwDClnG0qm01mnGpWXxj30mZAYjfSEyL3vm9+yEiK8EczULsYaIN37dMjEDXei03TTm0txC8fnoVW/dsWs0R/psKb3M2JWUSu5tR06QiBQIro6DfbGBs8VFSsxunk5dBqfx9DuD8iaDNLbCsyTyNKHjnF27LQu6JGb0S6Wsc0oJPacn37g8LCD9mUWeWRxP7fWiJgMXu/FWrr9qK+fj94PBHCw18alAlO7ukqqOr1sFq0kMjKsOS5V/rkdEuwn+DDNMEWlbFbU7VAh0CM8x7HCo012Z11qaQNJA0qgbT3tBYd/jkHNcUq1vScc88QJg3pDp+e/ZQVP/Fu8awQIvj5cs7l9BkhzXtH1JWpkHWc+1vjrg1FtcGEq5PzkKnRwUMsxIZhoRhq3/3aR68xrc0U/UfnF7FTvvjiCyYxuhhaarVsbTEZLMy0c+KysPb3jHSrc4K4psn2gs4Uc8LkmRxtWCeuxa4vU2ExWeEf49Kr9pmotjIf0tgL0HhMymLAZs96HCKRHlqtFGlhRlZMG/LSYK7Ph3z4eYqrnIpuXpuhWg23DtFUij2Ws0mQ6bRYV8VNu4mabqrRgi8XDkjL3VTTirwkjt4+fPalGVxRo31P0XZIWrmi9eeqBjYUItkCNbJvXzIHCokQ+bUaHMztv0b0QOmBdv00xYc+NuIxCHlC1ri/duu1zFeAGHMEUbUAjmJ3dr6XVWcyz5jvMr7DTj5ndBbSEoJXR7+Kp8c8gxHzORbI6W0b0dBwig0GxGI32LeZmNnguHQpp8cvK0PzFm562xH2YnuM9+Go82V2ZSy2lK4pfcX35dx1nAzpbFNuWawrwsaOY7czjxwA5r0FUGxq6Ukg6UsMBKMCnfHdigTmbn6mQYRrDS/jG8d5iFFIESjrvmlP7uwXM1SzQeytgOOCYLaOnDnMfZ6GzZ7fbTzWZcGQa4GYawCLCZpVN+H4oT1YjB0IRTGON4RAaxLCxdcfI2fOxb/CfLBqaDBcRULktOowPzkH/ymohL6bhkbdu+/C7cBBjDl+HFFlBZCW5UPUUAMpXwsezwKTSQqVyhEFBZWMTUEa8JSUFJw9e5bRwYn5RUwdgVgMt/h4RNx2G8a+9y6mbd6E46/Nx413t+L6pwV4/7WRcNywCgE//gj/r76E78cfwfutN+H9xhvth887b8Pv88/g9sUXKHj4IWxdMB8bFl+DdcuXIf35f2Lzfa9ikc9iPOU3B+uiZiDs5qX48P/uwrgxUd1eq0i6NG7pDbjh1bfg7OMHTXMjDjTXoGzBbDTHx0E2prMB6l+66KZNS39MnXra5FzBFVxukEPui8c4UyEy3xjp2XnylKrWINugAA9WLHAw4Kaom7B2wVoMces5q9PVV8Em3mS0U1yTjQMHubgQihsZOvTSTYu6Q1xcHJvOkS6VFs3BgoDHw/S2aXdXijl1hQ1FLYCQD+WMvmeMWwxm6PKa2G1ZzJ/vs0+b+Wk3R2H6rVEsMq4kowFrXktksoLfq/CmAqS7wpsuVgEBdyMu7gsIhWSychanEhewAuzvDtpMrF27lrGxiJZHlPLeYkYONXITyiH2MriI+6chLSpaiYJCTtcYEHc7RJ52TNOsbTMTvNwgM8icnH+z2/5+t8POLmRQXr/DPyWz21ZzGhY/8wIc3PtuaMUT8uHQlperPlre7n7dETZDtQ15G6Aj3WMbbC7LpHklf4xz+8qQcbSS0zyviIHStftJe2lLabtL9cPDH27fzLm6TodE4gWjsQE1Nb+0/z5two+uy2OT3dCR7hD4yLH0TB7ytXr4SETYNCwMkXYXdz2n6eFdd93VHv1Hed4FBQU9/r5Zz8P2j9MYo4am9lfdHQtBl3i6eUHz2FeadHeNVgsICIBS6QAr3wwNrxp8AQ9Tbrx4DJTDstHgQQuDMQiaXUegUHhi1Chu8pmS2wLRFI4CbcjfBdmQ87GQxFZiEUwdDNUoU1ccEACRQY+JZxKxvroRFoOp3bHcfqofm7D3F6Stp4ZnQKwL3CgN4BJA54LBYkC0kGNSHC8qbXcLnzNnDpzt5Vg2imMXfHWEYwv0Z29xvJK7r6n+U1mj5PbY2/H1VV/Dy86LOZpT7Nf0tdPZ5PvjMx8hM1yPcyHNeCHvf5ixbgbeTHoThaJC6OQ6CK1CKKu4a/GQqbMgVdijsbICaYc46riL8yTweB02KA0AAQAASURBVJ3PEb5MBpcVnIt63SefdDvttrmYU9FNDay+Il2tRYqqFSIeD9e5OaI1lZMWyOPdED2Rm7jnnjoOo8QVmPEy949+fRGo5/wW+osRAU5Yf1MIfFCLQqsX1pUkYJil5/Ono6HaxSjmBLsELzT5NKPJUAMBT4TYMRxD5TcBfS7nvQ2D2Bl2rWW4Gz8iBrmoNypxpoHTnE+59a52jwwyuT04OhJXuztS6hneK67GzMQcZmpnQ/Mvv6Dh8y9g5vFwNiYExdoWiNXNGDZSjVET1mHCxC244YYpbPg5f/58TJkyhR2kCSf574IFC9j+gqjqzz33HGOAUQPR1dMVzxx5Bh+f/Zj9nZtibsEH13wNr+gRkA8fBrtx41hsncO8eXBYML/9UM6ZAwX9+wnjMe+++3DL44/DPyoahWZHvHhcj/cOFENjMCPGU44tD47Hs3OiIOuhcdoRHsGhuOn1dxAzeTqLqDxXkodkdyW0Lb/tfux3N1IjLQHpUV555RXmvjiY07cruILBwvvJ76NUVQoPuQceH/l4+/cr1ZV4dP+juG/PfVhXb6L9MWJkFtwdNgVS4cW1IkRBjpgvhtqB0+K5CUMxcvjoy/bGkVHFhAkT2G0yXRmIdrAnzHLhJld7OhTdRKlu3sVNue2JItgPirg+pxEwWZi+VXiJ1MDfE5FjvXDtMyPZ9Js0nxvfTEbS9sLf1N2cNN633XYbo0WTsdS3337LnPG7gjKJR4/aCqUyDiZTM86duxu5ef+BhWh/f0PQ9YiKHjqo4KaNB015ekN7VFg/p9zFJZ8jv+ANdjsk+AkE+N/VXrB0jF26nCgu+Yw5pZM5pC1a7lKx96vN0GvlsFpNmLFiCjyC+l/IyyKd2/Nym7YWXKB1negzEd523mwauKOQo/x2pZYXpdbh6Lrcds2zfy8eER+e+RAmq4lN9zo2WPl8IXx9bmo3VLM9DmqokWM4X8hDxPwALEnJR24rV3DThJuiffozXCCvG2oU0FCCTPtootQVRr0ZdadlaKnTwd5FivkPxnVrFEZ6dIpWo6Jxb3HnJhrJmGIiOWMunayaPZ/uzD27QujuDGUQ19BoPmqAWW3AlCk3QSKxQKezR0oUXQN4MFWcgbG06IKixTbJJkM1KvCVbdPu2aeOMofrrH3FMLcYWEyZYkz/JVa0ztpYB8M7RLYNBGaLuZ1BcXf4NMTYSTAm5wxrJtF5ZZMt3DYukFItmZlXdlXfpQEnKk4wRoWPwgdhjucp2/Hu8diwcAOeS3iOvYf0O2SI9x1/A3b5n0NyRBPyxFXMtT/OLQ4vjH0BN8y9gf1bYszRuSOSSjF8DvfaZu7LZk2IjtTyjnC6fjkELi7ctHvzhRF8Y9zGsGgntVgNgVffowNXtTEXyDncvkgNq9YEvr0YkmBHeIVFwtHDi2Us5546BoxcAQRNIg0KsOHuToZh/UFo9XZslLyEIGEVrEZg85ZcrEnqPnqQQL4QRJ9PrUtFeh0Xg9cT6HzNUXEspCDFELRuLf9NpWPJWUVYbeCYN25ohBV8HLDOY+djyMgEBA4d1un3qfG7MiYQX8YGtk+9r0nJw8OZJSg+fBQVTz0NE5+HpBBvVAq5yfCw63wh8T0KHk+MYfEfITx8CjvPR44c2V50kyZ8/PjxGDFiBJM3UC1nMxQl9sXtO29nHgDE2PjXuH/hqVFPtZsP9gdlOjG2aIKxzxgGlVUKGQyYICrAyMaDOLZtDZu667oYxfYE+jxQUgYdQokEurpqtP7diu6VK1eybis5d06ePJlRdah78p///Ie515nNfw1nuSv484KcRIneRXhp7EuMamU0G/Fl6pdYtHkR9pTsYdrtq8JvhZf3de0RYn0xpCKJxd5DHC1MofeFtcyLUQVpQ3W5QF4KNPUhahzlNA4WSL8q5AF5rXoUtHIMltbkGpaxzZMJYd9PI5uOruW/VS7x5QI1V5Y+OxJhI92Z5u/klkJsfCsZzd1M7S4X3N3dWeFN7z3pkkk32l00h0zmgxHDf4afHyfnKSn5AsnJ1/doHvVX1nCvWbOGaWup4Cbqb0f36+5An/mDbZPu/kSFUcGdl/dfdjs46BEEBt7HbtuKboqp6U7PPJjQaktRXLyS3Q4LfRZCYd/zUntC9vHDyDzOTem9gsk1e+BUPof5wUxPSc04Ys90dYBeFrms3VCN3gcqOgoLuamjh4M/drcZp0VP8G7XPHf7mBuy2wv3h4c9fMHPvb2vY3F7KlUaWlpSWPPsWJuBWdA0X9xRUs42tuRcvy4+FAE90Fp7A7FSaGIUGxvbbq5J+yEbzEYLfv0iA8ZmAaPJL/hHHOx6aGjS2jk3iNNNbyvcdmEGdjbX0DSKm2CCjmnDSfd+MSjmjYeIlw+rWYLmLVnM+HP8eK6hm9fiDXMIx2ao/7Jzqoc03IkV02So1prKUY0d2lzM47PTEFDXAPExbgqunBkAnqj/UZln9pYwqrxXqAO8Q7vX8vYVB8sOolJTyVy8adq7SFMP7+Z6mPkCZkJlg5+zHLOiuedM2u6+Yn8px3AjJ+eu1zmFWMFYHOsWrGO+MDcpl2J+wyTMM05FdK0rhuU44OOw17Fq7ipcF3EdYqNjWdOGihBbSkn8VfMhkkrQWsuDqlQJZyfuPeoKvlze7mRe98lK5hjdEZXFlfDUcs/vROP5c7E3kMhmUy13jbnZ26XdDFEe5wYen8e5aU/mJsWp+3dTFwi4+pM2w7Ak4HCbhrm/SF0Hd14ThoeVQewpg9FswVPrzuHZDecuzPJuM1SbHcjFPv6Y1b1fgw31ZSUoOneaTZ0jXEcxs0AbK+Nyg4z7yGgxEOelXwUaNxTll7FiefLNHFuhO1BG+eGESPY+2NzkZzRbsW7yTJwM8UG9nRQiqQxjbx8OiyPR7PmIjX0Xzs79c+TPrM/E9duuR1p9GvvMfDbrMywO43we+oO08mbc+W0irl15HIlFjZAI+bh3cgi23jcKV8d5MUYPedWQvvytt95i1+rU1NQ+FeB0zi1/5Q14jJ16ebLVfwf0eZWkTSBt/sitmbQBH3zwATOPomKcuiiULTdvHkePuoIr+K1BVMUXj77I9FOLQhZhou9EnKo8xXRWZKpG1LDh7sOxZsEa1skLC3kcAoGc0XNras9PXLoDZceuW8c5lFIhfNPdSyGRCpnr9baPzsJoi70YZFABQbF8Nm23LZbhUmEvFGBsm0nQnvpmNsWwXYyUU/zAl/dd90QmOsxEjRXdv11M0uUETaFmrojBjNujWWxcVUELfn41EemHy38zx3A3Nze25trb26O2tpY1O8mssiuoqAgP+yeGDPmYOVc3t6TgVOJC1NVxG8S/OogFQGwAui5R954m3H3JpE1Va1FrMEEu4GOkw8WLVnrf8/PfbC+4gwL/gaCgf7T/nPwPhOSBYLZC26XQHGzk5L7KzJacnMbC3f3Sr7lV+bnY+cl3EIiC6Jlixh3db/b7CpGrDPZtDtZNvxS0x07ZsDh0MSQCCTIbMnG2ltMaUtPe2d4dx34qZY1MnwgnTLo+vNcm3gcpH7D1ngqsKBfONKwjKLbNw2Nh+7Q781glGio0sDgK8YaPGRkaHdzFQqwfFtKvCfcFz1ckwuLFi5nREYFybvft2weTyYxdX6ShLKsJPIEVV90TAyeSIfQCm66brl00hbIh63gVarL1EBscGeXe7FiH+nINjm24OLWX5zsMjp5k9mdB67lm6PKbkJAwGRIRHzqdEpmzRO30VWObrp79Oz4PdqPbDNXaptFiPz/Ihg8H32LBk8mVkBosEHjImet5f6FVGZBOsWlENb6q76adPeHnrJ/ZVyoceGYe9Ke5gjPJPxyF/M5mcysm0rkObEgpR71a36cpOhX1tqK7J7Di1CUGtxbOwwPVy/HC0Odwj8tyxOU5ovVcQbcpJTTtpvOfYsZCxnLNwrqzfr0mETgtXwaBqyvLWm/atKnTz4g14qfhmlWkfe7LNStRZAe12YIQmQRjpdJ2mQxRy22ImTKD0d3LMtLQWFkOOPgyCnW7YRi5dfcHdXlA5RmYeQLs9JyMa+eE4YlZ9JkHfjpViqUrjyOvprPvTEeJys7CnUzD3xNOb+Nel9CRY+B/PddEVO0vhfYyp5RQA5H2i+OtJzERXEPF5BiK/ZXcezLiqvlw8uydFeIkEuKNCD9s8lIgtKIUKjsFPrruNrx1x1MoChuKaffPhZr/LfvdiIh/wd2Na0T0FcSkuXXnrahurUaQQxB+nPsjRnlyxn99RXJJI+74JhHzPziCPZk1EPB5uH60Hw4+ORXPzIlEaIAvrr32Wjz22GNsQOtK56vRyAZY69evZz5hdO0+cOAAu373xJ4mfbfCn/u8/hXQ/9YkdfiDg1lupe0Fe/bZZ9liQxeb/oAMLkhnQMU7/ftNHRYPegOefvpp5uRMVEv6Heook8nQFVxBdxuwopYiuMncWNYlxYCt2L2CuXg6S53x2oTX8M1V3zD6F0EidoW//13sNm2me6LlUseSOnNU8NI0g85XzyAHLHgonsVNlec0YfvH52C6TIU30YTIsKe+vp4V/4PtYr67rgXq4xXMpZRiYhTjOEphX2EobmaTEDLREQdcemTaHwW0HkUkeGL5iwnwCXeESW/GgR+ysf2TVLS2/DYma3SRWrFiBZuI0KSbCm/qGHcHuuiOHrUF9vaxMBobcfbcncjO+RfM5s4u6H8lNDY2Mi0tvSZk5El5tET17Qv2tkkrJjvZQ9Ilu7srSFeWnfMSixokhAQ/ieDgRy74vXaKeZsW8nKAmil1dXvA4wkHxTyNXH03v/kqeALOmyJkuDsc3C5dImI/1R98pRjmBh1URzo7lTtKHdt1pz9n/8yKBJ5FAFl1BNQNeji4y7rVPHdlNVERRMylB4f1TK/38+XyqGtqdiBpdwp0Ih7WznJCequOUThpwh0ygHz2rqAiavbs2e2RUIcOHsKq/+xD4dk6CIQ8uAzXwiPo4vGLvva+zBCOmgm2Kb6mWd9Ot4+J5rxHLE517HdS95eh6NzFzzfJhJmwE3D317QxD2KBCPF2XKOiUB8Fc5Q9bbrQ8A23kbeBFd0CHoylKhhKOWaIw8IF4MmcMdrMFQ75Y7lpaH+RuL2IratkVuofc2kN28LmQqa35oHHsrlJkqVRq2FW2OOsXyi+r+TYWDaMDHDCUF8HGEwW/HDy4q7IRGemAs9eZI8RHiN6/V1jhQYmMp8T8iAb6sqoxIT806dgsZzfJ9B+gpgSxNCxySvchtSDJ7CgpcKC0vSe2W2k7Xa9q23avfL8tJuK99zcXHi2ekLCl6BMXYb0+t5p2IRDZIxGel5vF+io4DZZIHSTQeRz3sHf3sUVgXEcJTpt/6/nDcNil1D3naOZ6/tO10caZ3Z7zHkU6sWOmOPmiAenheHb20fDUS5Cankz5r1/GF8cLujkND/UbShiXWKZDGN9zvpu75qc1jMOc43nkfOvgTzOHXZj2/K7V2fD1NA3mnN/QfUJpTuNMJ/GDBzlvjnzFSS634tmowwKoR4JkkSWrX0xUN6168MP4OPXn8HinWsg16rR6OiGtdOvwwOtMmQghrHcfH04qUJf8U3aN3jkwCNsEEXGlsS+8Ff2zcOH3odfM6qx/LPjWPzxMezLqmFSjavjvbH70Un4z+Kh8HTovJ4SY48Gsw888ACT45BskvY2tKemBgXVkDTQJdb0J598gg0bNrAhE+13ydumr5T0v2zRXVJSwoptcignfQAZSVGn7oknnmC2+P2dUpBhFAWsdwUZCJH7HsWQ0Vd6I2hhWriQ61xfwRXYkFiViO8zvme3o12iccP2G5ibKF2Al0csx9ZrtmJhyMILNqj+fisgErlAqy1GZSV3AehacJMxk63gvuaaa9q1MJ7BDljwj3iIJAKmEdyxMrVPVL/+gqIiRo/mtOPkpTBYk9aZbbrutDoVWvZzGirlDKII9l0DRrB1xEnHSVEzfzXYO0uZid64JaFMB0ob3J//fZJl7f4WIBkPNTip6UhrIl2cemq+yGT+GDliDXx9b2b/X1b2HZt6t7QMnjThjwLa3JBrtC2Hmxqy1JztK2xFt81UsCfQRDk94zGUl5NshYeIiH8jMPDebn9XbqOY5zTAoh98ijm5cOfkvtLuzq2w63sUUHcwGY3Y8vbrUDdqIZBwBdiwmYND4eNLBHCcy00nVPtKYWrqPE28PoqbVu0u3I3srBwoG2NgaOFBphSzdZVMK3sCrYHvnn63XeMZoOz5Mdvbx8DBYQTTqQs992D1dCVyeGaW4LA2PgThdoOX/UrXF5LezblqDhQt4dCUCwCeFTNWREHq2vdrgy2zm3TBBHJx17eaWHE6e8l4Rg9XaVrgn8BN5/d+l8kK814RuwQO8g3goxGmOi2afy1GRI0rROCjtdUR2eO5z07j2rUwN3GmmASBQgz5UG7aqT7GDTyUV10FSewSpptPszPiG7v+n+sk10k/VN6ek36pzaPV2avZV3KvtzPZ4dgxzr181JRpsPAF2FTdBJXp/HtAf2/FBO78/O54MfQdftaba/kE3wlMU9wbNElV7XIrMpbzjYqFxM6OGUFV5nL6eptvC+lsCadOkWO5HhrjSbhEcq//yY3cc+oJjsuWQejmBlNFJZo2bGjfn1ORopQpmdmbLWKrN5xTa1EikEBMBmqezp2p5V3el9hps9jX9EP7YLFJSsnNXOkD1OcBWx7qU0HJfieNe8xrXKfCQSjAmDb23aRwN+x4eCImhrlCb7Lg1W2ZWP7ZCeTXqi9YP+h9N1kuPP9Ob98Ms9EIr7AIeEdwa5vjvGCI/eyZVr3+h0xYTYPr1UJMNPJ1iDakYC7aaqFJT6El/Hqc2sENJCd7FEGc8TNw7INe78ui06HsvvthKC5Bmq8bQorO4Z6f38fNEi1EMCIbkXiN9wqeUy/DyQ5ma73ep9WCNxLfwFun32pnDHw0/SMoKdz7ImjWGvHN0UJMe+sA7vouCScKGiDk83DdSF/se3wK3l0+DCFunSMWu4LOJV9fX8yYMQMPPvggO4gdHRMTwwpz2meTnI6klJRp/vPPP+PTTz9llHTyyqAm+9+q6KaNH024qcimyR+J9SkLlV4IeoGoOCbBfn9AbpKU9U3FTFfQRorul3Ix6W8RdevDDz9k2Zi0sFzBFRBUBhWeO/wc6/rT1IOmH7QIj/Uai5/n/4znxzzf46JCWkibJrOw6MNO0TJpaWntBTexLegcpSivjqCMVzLFEYo5x+udn6YxDd9gIyEhgV2gqevXm0Nuf0B0yjC5BDcUGNhFiKix8uEe/b4fG7Vc+ieLCusPaIozbKZ/p0xvarLs/Cz14pvdQQAVk+Q6SusvMYDoYtRTA4bPlyAi/GXEx30NidgDra0FSDp9LQoK3vvLmKzRZ5Om/tS0JeM5YgNQ57yvqDeYcLotC3VaL3pug6EBySm3oLp6K5ssx0S/3etUgUwEheSybTovuRhMlJR8Dq22BGKxO4KCHrqk+6JzZ88XH6EyJwsSBRmQ8eEZrOzTNLavkMW5QRyoZPRyyu7uCKLg0sTKUeMISXUwxEZHJuUgzbODW+/u4UfKjyC5Jhlivhj3xnXfAOkID7cb2Vd5+GGUKy1wFAqwJi4EUYqLu5T3F2azBc3pCsi0nuya1OyQidSi4/1qlpJmla5lNKE8fiQN+Sm14PN5mHpzJKQyKdukEkwOtXDxVbAc8z1fZ/SeOy2xB3/EEjiKvmD/qz5cDrFFiKFyrnFTYB0Coy8f1tZWNP70U6d/qhjHTbRbz9XCrDLA1MKHyGcUxwCpO45fG1SdCtq+4MTmAljMVvhHO8OvzSV9oKDM5s15m9sLid27d7OJLw2F5g+PQ7hcyrKnN1R33rTPifWCh1KCOrUeW8+ep9X3VnRTDnZvsBrNaE3hGrK2rHPS8AbFcyZ/+UknLmCyUTFC9Nr8fMro1sF3FJ9lFZeknUNFTmaPf4svlcLlnnvY7bqPPoZFq22fmJNr+fzg+ew2MSa6K0xt+KGyzUDNVQlHvQX6tiQSefyFkoGQEaMhUzpA09iAwjOcSRlkTsDSbwAy30rfAJz6DBdFTSZQlw0TX4SdrhOYe7eoA1vCy0GG7+4YjdevGcLyvE8VNWD2O4fw6i8ZaNEZGVOGGIxEj7Zp7W3QqdU4u5trNCRcc11744DSFZxvjGSsPGO5Gk1bBua63h2Iifb9998jrDURi9DGAhhzPzD1ORz4/guYDHr4RsciYumj513fsy6MBiRYLRZUPPMstGfOINvHFZWOlLYDLHnoEVwn+h/ett6POcITEPGAI00aLErJw4LTudhV1wxLD+sMeRs9e/hZFllHeGLkE8z4rzfDNFqzThc34Im1Z5Hw+h68vDUDRfWtUEqFuGdyMA49NRX/uzYOga4D8xRxdXXFqFGjsHTpUjz++ON45JFHmDyM2EK0xtGgwRZ3R/vw/jTV/xJFN01Y6Ik///zz+Pe//82o36Q3pWLgtwKd2PQBounPFVyBwWzA3bvvRlUr11k2W81sM/f5rM+ZKQRNvS8GH+8bmAOwXl/VNs0Ca+yQJsdWcFMWd9eC2wbvMEfMfyCORU0Vp9Vj5+dpMA9yB5UWG1tHnGhzg4WFUjluKOZoaQ6zA/s9qaZIIJqaEP1QGvbX/0wyk7VnRrIsWSrE85Nr8ePLJ5F26PK7ohLjgQzC6CJF2Lt3L2P/9KSDcnGZhISE7fBwnw+r1YzCoveRdHopNJrB22j81qDP4549e9hnk9z8Q0JCGOOKovX6AzJQo3cr2k4Kb2lnracNGg01K5aguTmJaSvj476Cp2fvLCu6Nl0uijllsdvo7Zx5Wu9ThYvhzO5tSD/A0dSlSu6cGjq1b9T8voJdqxeGMA2y9mwt9AXnJ6iEZeHLMaFwKSR6V4Bvxdz7hl40MoqmNe+nvN9eYHnaXTzSrDApHC1mJygFTZjMP4nV8SGI7SGH+1JAEqOdK1ORm1jNiuSoWUoYZfVsSkODgr56clAxMdZ7LCRGOZLXc9PlYbP9218b8hUhZGSkY/JNIazpS2yrxG0XMQVLuAcywWFI+Cks3o59a/RoNjnXaJxROI2baDd8v4pN2myg6SAd5FegPlmJ5ra/Yyo5jlGntsNgMmN7mwlXX1Bd1IK8pBp2XtCU+1LxS8EvLLvZ394fXgYvxlCjc4/M04iZdpM3V9SvquhMMRcL+bhlbGB7fFhPjZGSlhLkN+czd2dbBnZP0KbVw6ozMQM6Scj5a2LoKE7vn5d08oLhUmRkJLudmHiKffUOnIjoSdPY7RPrOZ16T3C6bilEvr4w1dai4bvvOqUAjPMZB0eJI+p19cwjoDtQs8RmoHajpxNaz9ayOD16v1kDsQso59r22FL37T7/A7/RwKxXudu7ngdKOS1zj8jgpKTHXRKgEiow2/VCaRq9hzck+GPnI5MwPdIdJosVXxwpxNQ3DmB1YiUWBS9pN2TsiJRdW2HQauHqH4jgYZ11ykJHKZyXRbBzj6Lw1Kd6b7b0BcRAowl3cPNRXI1fWRwtRt4BzH4dRalnkHvyGHh8Pqbdfi94Y+4DRpDxqRVYfydQlXrB/ZFcQLVzJ4pdHVHQ9rpMX3oTjIpf0KI6B3ehBR+PnIdjY6Jxk5cLYygktmhwa2ohJp/KwtfldVB3aIIRjfwf+/7BmDN0Dr8+4XXcGnNrj8+nqE6Dd/fkYNpbB7Hkk+NYd7oMOqMFER72eGVRDI4/O53Ff3k7Dl7TktdW19F5S2whKsTvvvtuPPXUU1BNUKEmsAbNpr+Ge3mfK2ZayIg+Tvx7GveT4yhx8+kFIlv64WSucRFt3KWAKDNU6FMnpLdNFj2ujpni5PxMoM3pHznmzPbY/siP8Y8CjVGDjXkb8WX6l2g2cB9ED5kHHh/xOKb7TWcf4L6/jnz4+92P3LwX2aa2qCgQ+/Ydat/c0IWbNku9bZjcgxWYfU80dn6awejHNAGdcUck+L1oEvsLKrbI5ZQ0MNQV9/HhjIouBfNSVZBagHQnASaHKnp9zbo7PzXpXGFB0yyzwMroXH8HjJzvj6B4Zxz6KRe1JWoc/DEb2ScqMfH6MDh5Xt7INMqGJ433rl27mAMo0bGIhdH9pNcOERFvwslpKvLy/wWVKpVlegcFPg5v7xvB4/VPSvB7gqba5AZLGdwEavjSdYeuOR3X9r587n+t5Yq/qU7dn/MNDYeQlf0ETKYWSCQ+iI35FHZ2oX26b1GkI9kcQ5vdCINGB14fckn7gpzc19kUzEE5Es7OV13SdaIyNwsHvuUmnpGT70DhWSvkDmL4D3Ec9OsPz00C2Uh3aBNr0LglH873DWENK2pSSY8EwqPFjk2E7cY0snX0Yn9/V/EuZDVkwU5oh1sib7no76sbdfhPcwsCHGdjKX7GndI9iJbeM+jPk+K7dn+egYrcZghEfMxcEcV0yi7BImzevJnJIMi9lzxB+kKlnu0/G8KD/oBWCEcPGeJm+rY/Zsqep8870VmLq3IxYVkoDnyfg6TtRXDzt4NfT4aWCh8IIubCMfMTVBsok1fIJufU0KUs6zxxLIJcqoD6BjSsXQuH5cvb/6k0wYNputVHymHVmQERH8bSPXBqbEBcbibWOdtjsevFUwBYTvp6Tp8eNsodDh7SS3ov6P5+zORcrK8NvRY722i8tB+lDHW676td7PFaPo+ZJ55ubMHQDgyH64Z744N9uciobMHR3BokBF342u0t4uLbyIhVxpP1+nhVbUWcdLgbTBSj1Vb7+EQPBV8gRGNFGWqKi+Dkff76TY+V9tcFBSa4uong6DgBrvOHIP3gXhSeOY3SzHR4hvaQt83jwfmBB1D97LOo/exzaGbNhEAu5+ISzcBM/5lYm7sWW/K2YJT7hUZZaysb0GqxwMtswHCZCKrkavZ9yVCXHp9n1MSpOP3LRhQkJ6Kpphp2Tm2v2fAVEBQfBz9zM6xrb4Xpjr2AXTfXJasVwrQNbHr7s8tkVjROtO/5PPC0F2HljfE4lFuH17ZnoaCuFS9sSoObfSRM8gk4ZTmB9Jp05tdj0GmRvJ1jPYxcsBgmosB3SVYSBNtDMd0P6j2laNqcD56LBGJ/+wEnZ/zwww/wq92HhdjDvmcesQKWWf+FWafFvq+5lImhM+bA0csHRop+nfk6BPX54BcdgvXHZTDduhNQcnpzzf79qHv/A9TYy5He1iwa6h8KpwQrcnNJbsBDZOSbEAo9Qe3G/4R44hE/F3xd0YDvKxtY/OGzOWV4Nb8CS9wdcI2LFJ8kPomU2hRIBVK8MfENjPcef8FrXVinwe6MGuzKqEZq+fkoWZmIjzmxnlg+0hfxfg5ta5f1N6tTCpoLsLt8NzPXU+lVf+j6qK+PjWcdoEiUHOgOHjzICnEyRKOimIpwurAMBPRmUtwGTRW7ezJLlixBWVkZK/p7K7pffvll/Otf/7rg+0SFt1EVruDPCY1Fg+P64zhpOAmt9bxBVKgwFDfb3cwoeQODGTL5/6G01AelJUPbY5tsBn99ha5WgLpkGWDhQeZphHOcDrxB7EMVFxe3a1gvFol0Mcg0AkSdUzLd++0JciwQ1iDA0j+DsPB0e9i3iFASqEGt1+WnWf/RQCunuliElhwJrGYe028qQwywDzbgctezZL5DzRea+FLhSVopKsZ7Ao/XBIl0NYRCbhJiNvtBr7sOFsulN28uN6hxSuc+PVf6PNKGkjbUAwG1zp5Q+ELDF+AJTRXCOkhK6Kdi8W6IxL+Cx7PCbA6ATnsHrNZ+bMisQGyKAyR6AfLDVWhyufRNAl+QB7n8Y1itPGhbH4fF0v88ZBtM2laU7twIs7aVOcLy+AtgbBZCGaaHMvTyGAQKjDz2mgjNfBSFqFHnZkBTugSaUnEbBTsDibF7cbuSi77rCcRkel/1Puot9ZgunY6p0t6pvoSfW1yw31cBB2sjPrTeCz7PhFbNo7BYBm+qb9LyUJckg0ktYC7lriO0kLic3+iTBI8+q7ZUAmqYXuy6oqq1oDnJAVZYYB1VBH/X8y7ShJqamnYDQZqUNqZxrydfZIX7eA2Esu63dc7qHEzMfRXNxpuhMi+DQWzGmZg6pGZRTJsVIyx7EbqmDkYnJxQ++QTQxvDiWYAhpx0hMnEXtApfLSwnV8EhMRG/TJiGt2+4E/+nLoODtfdpvrZGgPrTcsZs8JzU8+PsKwqMBfhK8xVEEOEu412oLqtmrLTo6OhOLMwvpS44JVZggkGFm7s4Xq8p4ONoNR+xThbcFXnh4/9C9QWKzEWYJ5uHsRIuTaQ7SLR8xJ5x5AzuhjfDKOl8X+X7tkNbVQ6X+NFwio5r/z697llZadDpTAgOToSDA5n/SVF94iBUBTmQe/nBe+r5yLMLYLEg4P33IamsQmZkJIomTWQsIEKJqQSfqT+DGGI84/AMxLzzzB565V+z80KpQIylugbMb2pFzFkHWHhWnBvRBLOo5/embPdm6Opq4BI3Ck4x8e3fF5q1mJz9EhT6KtTbheFY6NOwdHGOt9eWYlrW8zDyRIgatxmBVj7+oeV05BeD2QIcreZhTwUfzYY22rhABV+XMtzuEwZBUSrqkk9ApFDCf/5SNmHuFlYgOEcBpwYxDCILMoc2wyTu37lIgxiS+0WpDmEuOPlBvtsspPncyJohjZnnUJ9yEgKJFP4LroNAfD4hQWTSYFLOv9jrpJJ640joc0CDDn4ffYRWWHAs3A9mPg9eWiMUt18FuXIleDwT9Po5MBpmdvt4tODhuEiBg2J7VAnO+w4IDMVQaI7jdgQiSsAV90TGLFDxkNnIQ0YTD1Xa8+sRTeojHKwY6WbFUGcrJL9Tb55vMWBv46fYK6jGlFY95rq9AINo8ORPgw1iPNxwww2Mkd1bjTpgbjgtarTJo6gwOkhnuGNH79FLAwEV3KTrpk0XxXBcjEpITupkUd9xw0aOtjQh6i8N8bcEPU/SsJO1PkWQXMF5VGgqsCpzFTblb4LOzNHeKHJGb9Yj3DEc383+DmJB9zTRvoD0X+vXl6K0hNM2TZyYgIkTuYl5f1EysoFNPLRVIsj9fDDlpvABObt2B4qO+uyzz9iHmszV+qNj7Qi6yDd9mwUDmnHOX4pURwGuCojDXL/OG7vezk+L1oTak5yma9g14yF0HjxDoj8bVA06HFmdh9KMRrTkSYAmB4y5JhgBQ5wva245Fd5btmxhG3qir1K8GK1zPTUXrdbrUVm1GoWFZKRSCrndu/D1uRUBAQ+y+Lw/GqjIpsaubbpN5zs1ZUnHPdD1M7mlFZpzhVAK+Hhg1jQI294fcnzPynoCjU2c46yX1/UICX6WRbL1FyphMVqPVCJaHAjHuZdmdkbSgOSUz6DRUO70coSFco7FAwEZH23878us4Hby9sW0W5/Ftg+zmEHgwtsmQa4c+Bp6MWhcK6DeWYKgakcYJQ4oL+UkQSqHbJQ6pyPfko+IcREIceyZbrw+bz3qT9XDSeKElxe8DDtR7xq/D7Mqsb+OK7AedAyGp3guamq2IDCoAJERnBb2UlFT1IJdn2XApDay148YT2R41hE2Hwb6jNIaTnpbYmn0BIpMW/s6xS/pkeZ5GO4RVtybcOsFG7z3338fWq2WMbLcZnpgyztnUVeqhrnQC/MeHsom7l1hNcyG4Y01UApXQyNYCLFBhqnK4ZCOkjMzrzzHoQhS7IWosRETRWLYX3U+iqihJgPGwhYmJ4q/fRK0w+xQkZiIaSmn8P51t6F16Chc7+PSp+dFUoYxV196DNCjBx8FNMCCgAVQneKcs6dPn94uw7HBuVmDpalFSJY64NNJo6AUnq8kImo1uOr9o0hv4iMmYRICXM6vhU36Jry04SV2+76Z98Fb0XPDS7W7BK2ogCTMCTOvubA4Pyfi4cC3n0Hc2oK5czmzPBskkmKcPNmI6uqhWL78GnbdaBoxDN8/+SBaK0sxPDy052k3fb6UDqh84AGE5eYi4L77MHTa1PZr/bYt21CuKYcsRtaecU1IUbWi9GwhmzSPNWgwQh4BHaohi3TG7EU9NxcI6XZi7P38IxgrSnDV40+Dz+9QmY0dAus3V8FFk4t5xm0wX/0pGaO0/5h/4D/sa6L7WKiFdrgxxAtzvTjNe19ASfFksEZxbx/sz0atyh6lNVF4rdqMuyrTQav/6MU3YMSs3mO0LDPMaPg0DeJaLUbW+sHp9mim++5rwb1p40YMV/2KqTjOvmdOuA/+01+BP4/HNO/fbeDMfafeciei2zLOO6EpAdbv5sFeVYHZ1StRtE0GtdGI5JggmPmAi1qLWS8+jyzTS9DrTXBxnobo6LdYbFtPWNL2nu+qrcYz6YdRLwyDWRyAZnEASJTjZ+FDXq9HVXYDtJrzOn8yRRsb7IxZ0R6YEeUGV8XAIxQvGVYLeBkb0XLgNfzLgRpXfFyrFWP0qBgIvbn0hj8ibKzqi6FfRTd1V2nSbKOZ5+TkMD0QFQCPPvpoe1zGYBfcFIFAf7O3SU5H7SMdXUEbsT9DMftneZyXG6TdO1V1Cmuy12BfyT425SCQTjtIGYRthdsgE8rw5pQ3YScduMECMTTIGLCgoJFNt0JDTyAwSAmxmMtL7S9C4j0w+04+03bnJtZAJBZgyo2Rg1J40+SdphrkXk2JAd2xQvoCbXYDDPnNbPOknuIDVNfgUJMGjwd79/n8bM1oYmNDMmCTeQyMmvVXgbOHiDku552uwZE1uWip07HGi2+kEyYsDWNa8Mvyd52dmWs3marRepyens7kB7ShIyOS7gr+AP+b4ekxGzm5/0ZNzXaUlX+FuvpdiAj/F1xdB3f9vhRQgUINBVs2ORkOUUOBrjeXsn4eaOYM1Ka4KCFru6+6+gPIzHwGBgMZVkkRGfkavDwH9tkiKOI8WNFtyGmEEPx+JwJ0RFn5Wmg0WRAKlQgNeeySrg2H1v6A8sw0iKQyLHrieSTv5pqMYSM94OByeU1qHCb4QXOqGqfLNCgt0zJNpTCgBnpdDRx8HQATsC5/Hf455p/d/nudSYfPUz9nt+8eejcc5b17SJBp1v+1FdyLa3j4x9QANLfcxoru2todiAh/DmLxwJqWNuQkVmHfd1nMPJMMzeY/MBQKp+6bj7R3iYqKYrIQisOhGEhbTnNXHN9QyKLTxA48nPT/BdISMZ4b8xykwvP3TWwnuj8yFSTNONHW59wzBGteT0RtsQpH1+Rj2q1RF6wBrZlN0BoWwUX8BhxFX6LB8CA0Ryow7u5RzMukucUNpVNdEbi1Hk3ffg2n+fPYfZia9TCVtbkkm62w1hmgHDMGNe7ukNfUYFTGWWx2dsC9gT1r7E9uLGLPixIhEhYEQyS6ND+gouYiHCrnpGDRLdHIbM1kjTkyHu3qwTLBxQERdlJka3TYWK/Cnb7nG8yR3o6YEuGGA9m1WHWqDC8v5IzqCCdKTrC9B1GXA5x6dsm3mq3QtSVa2Cd4dfs5DU8Yx4ruytxsGFs1kDucP4dd3c5BIPCERiNhyQyBgYFw8/Vn+mnyXkjctAaLn72QwWmDOT4ONW5ucK+thcO+fRDN5lzGbW749NnZWbIT88M4czXCTzWcNG++qxJ2TRYY0rjPi2Kk50XXmZiJU3Hkx2/QUluNsrRzzGCtHV4xwLJVwKrF4GdsBN81FJj2z/P0sCyO/v29E2e8PNfDud/rGv36LeOCcX1CIBb88Bxyiz0QWWmCSK+CWmCH244CE+rOsiJyfJgrfLrTH4tEcL01BjUfpsBYooZmVymcrg696N+movaXrVsQkLUSY5DCfXPq8xBMehKCts/bsTWrYNTp4BUagaHTZnU/cXcLAW7ZAuvXc8CvTYNHoBjHxcOh4QNSgxGTEyaiQrYG+roKyKT+iI19G0LhxYvhBm0DPj/1EPgtefAWeEMofBxlUkdYHMQo5lsANxHg6g6xyoQIgQjzvZxwc5QXnOWXr+naZxQdAXb/E6hIwToHJfR8R0RJPdAYdD8ruP/ItVFfH1ufya+0wHt5eeHmm29mWkIKPSeXSKJO0TSCaN2k7+4P1Go1zpw5ww4CbRjpNm24qOCmv5GUlMQ0EzSNrKqqYgfpKK7gr4kmXRO+Tf8WCzctxF2778Kvxb+yi94YrzHMIO35hOexq2gX+91nRz+LIIeBd8ubmprw9ddfM4oQfWDmzo2Ep1ceSku/hdHY2fSnPwge5oaZd0QzHUrG0UocXpM7aFFfJOEgUKwCPf7+gjYHNiMccqUdE8Q1spJaNGg29j36RZfJmdJIoy7NefavAtqYUvFy4ytjMPyqAAiEnLnR6ldPMc23Vn151iyiltO6Sw7eRF2lCRgZja1evbrHiA2JxB1DYj9A3NAvIJX6QKcrZ7neqWn/gF7Pafp+L1ATbNu2bcydnApuMhFctmwZ5s+ff9GCuy/Y29AWFeashNnciqzsF3D27ApWcNvZhWHUyA2XVHATRL4KZqRkNVigyxl4zInR2IyCgrfZ7eCghyEWD/yzVpCSiMTNXCzi7HsfhszegzOzYgZqvrjcIN+uFPBRarAyLefYhX6o1HHRd/MS5rGvW/K3sDSK7vBz1s+o0dbAy84L10Vc1+vf2l/fgocyitnt0Tk6vDKek+I4KOOgVMbBajWgvKL3OKbeQEX2oZ+y8euXGex24BAXLH5ieI8Ftw3UOCImhs0IMSWlbcPeAZV5TUg9wOWaz7plCNyVrswkjBrPXWEz16T9GPnYKF1lmLWCmm1A1okqpOy+MOVFk1QNrWU8LGIPyIw7IfHQsiLadKCm3aAtz3s4rCIr9GkZ0CZxbKbm7YXMhZ6v4DaW6kNl4AkEULZNbGckHWOT08LW7mVGVYXNONsWTTnlxgiIpZduwEvsN6JyT3OehuwznGyGPFi6Mz2l9fl2H67J8k153QXXY1t82JqkUhaP1NW1fIpfz8wEgi67ARaVkb0+FJ/ZHSjn2iM4lBWe+cnnjc3MZh3U6uNwdeMkCLb9MGHMNctYwUbabirWewI14s/FcdI4zdat0LUZqhFsLuZHy4+iUcetRy1koFbd1G6gpmwSwaI2MmdvacTF1xmRRIrYqVxhf2ZXN5LS4MnAgve424feAJK5qS+q01i0mFkgxm6X8RiulMNDMvBCSiTg47FJ06Dw/QoJmoPse8U+o6Gz8LAnsxpPrT+H8f/dh6lvHsBzG1PZ+5tTrYK5zUhQ5CqD8/JIzljtRCU0iRwDpzcc2LsbAcmvny+45/wPmPwUo5QTyrLSkUkZ4Twept1+T88Ud4JbOBp5S2A28HCO54kqiQh8iwWjNSYIbvRBbR1JnUSIjX2fGXp2hdZgRmpZM9YkluLlLem49rM9mLzqehS25MFiskdjzvWoOWOE+EQtvE43ILbFAh/SvvF4MChFSLUD/tPSiPGns3FPehF+rqxHpf53qK9qs4Gfrge+mccKboNYgZ/cOCr8jcP+AV5HJsWfHH1e+WiqRpNs2vQPljaaCuqO03EbLZzicaiIp0kHIT7+vGaEQFPv3uhZV/DnAl0AU2pSsDZnLXYX7YahTVtM9EG6YCwNX4oI5wioDWos3boUJquJ0aQoo3WgoMYOFSZk0EQbe3KG9vLyxKlT30OtyUZJ6VcICT4vU+gvqAAjF/O932ayTZRAyGNZz5dKNybtLnXBiVJM5jcUu9cfUIaoqaaVXVyVU/3gKBOx6DAy4DjcqMZ894u7kLOuflsxIbtSdHcCbSbHXh2CmAneOLY+j8X9kLt5blI1Rs0LQuxkH1aQDzbovLjnnnvY1JsO2oQRQ4iiFmmiRpO1rqDJtpNTAgoK30Np6dds8l1Xtx8BAfcgwP9OCASDH6nUE6ipSpM2auDSZ9K27vdGl+8vavRGnFNxXhDxvHScPPkitDquMPHzux0hwU9AILh0mQRzMY91ZaZT5GYsixnYRLWw8H1Ge6dmgA/pBAcIVX0ddnz0DrsdP3s+IsZOwOmdRSyyiSLC3AMur+zKoDNh56epKC1oARF+RsoE0JTktrN3JoZMREhaCHOIpsL7xqgbLzDO/DLtS3b7vrj7epUSpbS0YkV6EQ3OEVOsx4Nie7j6nt+s+vregoyMx1Fe/iMC/O8G/yKZy91JSXZ+lsZo5YQRVwVg9MJg5lbeF4wfP55RwimFgvY3dG6TYy/BZDRj/6ospjeNHOuJgBhXLDQuxMqzK7E5f3N7frcNAQEBTNpHzTViuZAhl3+MCyZcF47Dq3NwfGM+HN3lrAnM7r9ZD30urdtCWEfeA/6xV+BofRfV/Oegy2zAmGuHIpmfjPpmZ5RPdILvvibUfP4RvFzeZe7zVJg4LgpBww9Z0GbUw1inhXL+fDR88w0mnEuGTKdlDIPHgzpPu+k6uP977nlFJHiyxzgYzXlbTFh4bThqLbUICwtDaGjPk8prPZyYwVRe27VuUoe4wAmhrsydObtahdWJJbh7UghLR6F4ur5EhZETNoFiN3ujKIeMSEB1QR7yk05iSFvR2th0AhaLHn5+TaiuAnsv6bpOjE1HT6/2afextT9gyXOvdHu/ZMRW7+oK4+jREJ06her//hf+X33F1qJgx2BEOUchsyGTDSyWRy7HuqoGFqNGcWqjlHJk1YrbY/76SrGOnzkXp7dtQtHZZDRUlMO5gzkcw7CbgIYC4PBbwNaHAJGMiwojmY/7eGiEclzVjWt5fzHdfzpG1vrCTgcI7GVY+eoDKGw0YntqJQ7n1uJsWTMzCqPjx5Pcek8xZGEe9ghzVyDUXQHPoU5QnK2Dx8YchLrLIA3o/nElHtqNoCOPIhDlsPL44C36GIjnMsMJZpMJ+77izNOGTJ3ZqySA0LJjB6q/3IocH38cc+V8JqY4FsLn7geRVvI2TBY+XH2eQ16TL0ryy1HS0Iri+laUNGjY1xpVhyYXvxXygC8gkFTAalLAXfUwRg2NxKhAZ3aQbMK2/yzXGXCwQYV9DS041KhCo8mMzTVN7CD4SEQY6WCHEUo5RirtEGsvg/hyGGU3FgMH/w84+xOjlTMznBG3YWfQcNQl/R/c5e7MDPDXzLYYtr9T0f2f/3A6jMEEFc69TQAHazp4BX9M5DTmYHvBduws2olydXn79+kCQdOMuUFzIRfJ28+FF4+9iDJ1GbztvPHi2BcHXMBSJ5mckGmjT/pQcsS3xdBR/m1q2gNs2u3vdwdEooFHYUWO8WKTkAM/ZOPMnlIIxQIkLLw0AzQCFVFUdCcnJ2PSpEl9zi+06E1o+ZWbANlP9wdfzm04pzorkdtai/0NLX0qug2lLbC0mljhLvb74/ok/J6gqdNV9wxBeXYjDq/NRX2ZGkfW5uLs3lKMmh/INp+D6W5PIOMgamKS38bOnTsZc4iorDRRo2YpTca6Sm9Iy00RVJ4ei5Cd/SKaW1JQWPguKip+RkjIk/D0WNirhuxSQdo42jDS5I9MAm20eZpsX6pZYE9T7jBhLcoyuHxniguMjvofnJ17jwLqL2QxLlzRndUAq9kCXj/fa7UmF2Xl3HQoLOyf/S4OO+q4t73/P+hULXAPCsHkm1cw5/D0w1wUFTWBLifUjXps+5jTGgslAsy6NhSibQXYXVYACLhoI1rHqRB47eRrbKJ9Q+QNndb2VRmrmLY2UBmIBSGk6OwetJG8JbUArWYLgiuNuCapFWNe5qZ/Nni4z0Fu7ussIpKmSB7unQvZ3lCSXo9fv8qATmOERC7EjNuiETi0/w0V0hyTHwPRwteuXcuGDOQ7k7StCI1VrUwbPv5azgtgYTBXdJ+oPIFqTTU87Dw6sVyo0KbPDl0L6LaNudBUpUHqwXL8+nU6rnEezhorreRMTXFQQUoIJt0FnH4PopajUETpoE6XwLS/GvFD4pB8NgV5YePhu28btIcT0RDKMRLsErwgH+KG1sgalkNP57fjomiIg4KoQ4QJZ5Ow0dkRjwV6dHr/kncVo6FCA6lChPFLL07f7QvW5Kxh/i6jBKNQW0iyED5mz+5dw6sQCrDU05lFKtHRseimx3vHhEA8vT4V3x4rxh3jg5BYlYhWUyvcZG69xo+aGnRs0s1eo1EX+k10RMjIBFY8F587A6NexybG9fXcND0keBgKC1xQX1/f3kSxTbtpckrFbVlGGst77qolJZNhgt9zz6J66XVoPX4C6v37YT+Ni/ei4QUV3Vvzt2JZxDJ83xafdouPC6x6MxwbuKLbbkTvj78jqCEQPGwkczGnXOypt9194S9N/SegqQOSvwU23N3uZv6tIyetGIyim9bX2Fx7WNCKrDAthGIxIjyliPC0x6Mzw1mu98mCBiQWNeBsaRNSy5uhMZhxprSJHZ1gASSfHIG3sxw+znJ4OUjhrBBDKRWBX3kGi7KfhjevDga+HLXzvoTFfwqEzVoI+XyIBDyk79yM2uJCSOwUGLnkJva3zWYrjBYLTMQoMVvRajShRWtCfV4hCj9YjcaQCcj080ErhBBLeTgjH4/qAwJU8l+Cymg7R4/1+Pyd5CKEe4lQJfscDeYKKEWO+Hzel4h27bng95GKcYO3Czsoio1YKvvqW7C/QYVzqlaU640o71CES/g8DFXIEaeUIdpOhiiFjMk15APdw6hrgENvAklfAZY2ZknkfGD6S7C6huG7rUvbYyFFHUzh/lZF9yuvdN9h64oXX3zxUh7PFfzFUaoqxY7CHezIa8pr/75cKGfTayq2KWu7a0H9Q+YPjGou5Avx5uQ3oRQrB2TMRGZLpIcmkD6a4pY6FiJubrOgsItom3Z/jZDgRy/p+cZM9GGd/sOrc1mkC028R869NAMZKkZI6lFZWcnMb/rqpaA6UMYoZEIXKRQJHHWHMNXZHp+VUdGtYs2NizUztJncBkMa7tTvbO+/G3winHDdc6OQebQCp34pZJMy0oEm7yrB6PlBCB3hPmhGezZQI4m03jTpJg0pbeJICkQTcJp8k8lQ1+mxvX00RoxYi5qabcjL/x+jnNNEsKz0W4SFPQ9Hx74b3fQFJB+iouPYsWPtxTY9JmrEUnOgO4ropYCo5JtKiLbpgiHGfSwuzdf3VkbbvtTM6+4gDlCCbyeERWOCvqgF0g6ZvRcDfQZzc19jJmqurjPg4sxJSgYC2uCXZ2VALJNh/iNPQygSoTi9Hqp6HSscQ4e743KhtlSFbR+dg6ZJD5m9CPPuj2OT9eqyJpRnce85mYoRqJh+N/ldFLUUsQKTcqoJzfpmJjci3B9/P1v/u4PGZGYFd63BBG+NFdceUyFuoi9rfnUEny+Bj8/1KCr6EGWl3/Wp6KYmReL2Ii4H2wpmlHbV3bEX3HdfQevrwoULGaMjLy+PJassnrccyW108EnXh0Nqx200/ZR+LKoquSYZWwu24s4hnY30iA1CzD8qukgLTMwBwoTrwtBUq0VpRgN++fAsFj06DMYkTjpiN8ITkCqBUSuAI29DqX0brfYvwVSvw+joSKTwzqC6WYGq0Y7wqxsOcwNHO3aYxWmaFRN9WNFNVHXlDH8o589D3QcfYmbiUTyVMJFFcw1ty0Evy6b8cI42PfG6MMgUly4RoQk0ZTPzrDyE1YRBDz3TcffFWPQ2H1dWcO+qa0aZzgBf6fnHsyjeB/+3MxvlTVrsTK/Cmdb97PuT/SaD30vjkbLL6byQhDlC5NY7K8ctIAhKN3e01NagOPUs00LX13G0aFfXyYiPl7ZLD2xFNxW3sVNn4tyenTiy+jsse/n/Ol2jqWlJoMaNU2QkjLfdivrPv0DN//0PigkTwBOLGUvi7dNv41zdOWwpS0emxgQpn8em/7qkavCtPAjcZBD103+EmDNUdKcd2IPxy2+GWNrlM0HT0fnvkiYDOPsjoK6GmS/EDuexCJZJGMvuUpG2/1dYWlqhlVpwwq2UsRMm+XJ6cQIVzDOjPdhBIGp5Qa0auTVq5FbTVxVKG1pR0aRFrdoAmh0XNrSyw4Zx/DR8IPoALjwVyq0uuEP7JLLX0lCQO0cI9sYW3Fi+mhm5bZONxJtvcgZrvWIoV1x2gs303nze5MzNXgI/ZzkC6HCRw9/Fjt32d5ZDLDLi/r33o6Emn+Wyfzn7S+ZB0FfQ/Y9ysGPH08FeLOP7jKoVSc0anG5pxekWDRqMZpYFTocNdAbSexilkCLKToZQOwlCZBIEySSw62BU2AnaJuDYB8CJTwBj230FTWLFNny5PcapypPIbsxmnk3Ecv2roc9FN9G9aUGnKKWeJtC0EFwpuq+gqyFaZn0mDpYdZEdGfUb7z0R8EVsc5wTNYV/pQ9YdztaexVtJ5LgMPDHyCQxx67+DIXWDaapQWsrpymhCbMv57XwO8ztMu7+Bv9/tlzTtJpBTq9loxbENeTi5pRACoQDDZvkP+P7oc0aTS3o+1EAYN25ct+aBHWFq0kN1mGMTOMwJ6kQhG+OoYBfgCr0R2a06RNrJ+qjnvnSa4N8BRD2l5kt4gifSDpSzyU9TdSt2f5nOaL6jFwQjKM51UJ3O6b6ooKHoGCpuic5KxS1t0Kn4pmk4beqIomr7u/TVw2M+K/SIbk659S2qczidvAzu7nNZgUp51ZcCMuO0+XiQ/pxA1HfaNPflPO4vLBYTKivXIqvgY5wwvsF2ChPtWjAqehNrNFwuUCNFGumC1tPV0KXX96vorqvfh4aGw+DxxIyFMFCUpJ3DyU1r2e1Z9zwEJ0+uIEs/VN7OxCH2zeVA4bk6dn6b9GY4edkxkzFbkVofboU52wKFVQqHOiHgxUmJFoYsZMUUHbaimwpulVGFUMfQTs7LHWG2WnF/ZjHS1To48/hYsq8BdgIBRswJ7Pb3fX1uQHHxSjQ1J0KlyoS9fVSPz4Om2r9+mY6SDK5JED3RmxWPwkswxyNQU4lMYr/99lsW/bV23WrYW+MQNswbIcM6N0JIRkVFN9GpV8Su6LROUGIBfZbJUI0asDZzTWLRzL4rFpvfSUFtiQpb3k7BOJ4VCpkAsiFtxemY+4DjH4FfcRSO44xo2AeYT9RheNRQnM45i7IhsxFaNY79qt14l3ZmlCTYgRVnxnI108E6zOOK7hGZqXBsacb66kZWdFODcdfnaaxpQcyesItMgfuK7YXbUaetwzDtMOhb9Izp1Vc/IZrOjXdU4GiTmk17nw0+33yWigS4KcEf7+/Lw5dHCqByO3BRajnp3FvbdMCKMRc3IqX3jqbdKTu2Ij/pBLyj3ZnEhXS7Tk7jEBdnZik9tE8hTwtbI2HMkuXIOLiPNdCKzpxG0LCRFxTd5LtEcLnnHjRt2AhDcTEaf/oJzrfeCleZKyb6TMSBsgP4OGM9IF6ExR5OcBQJUd1mACeL7/81KHDoMNYUaKqqZNP4uJndNLFoj7XoQ6DyDFCTAZ7FjNn1R+ER1JnRMhCYjEac3LSGe/zjI2AWlOKrtK86Fd1dIeDzOGo5GcB22Uqqy1XI+CQFVSYzVENd0OghRVDWp7iq5ksIeFZk84LwisNLUOsdoNQZYaTptcUCo8mCKfWHIbKaUCb1RqYistP90tMU8fnsb8vFAshUjZA1N7CIPxPPBKlZh8D6BvhMCwJPvAfDK6rgr2+Au0ADp7kvgD/y9nbNeEdQgs+Dex9h64O9yB6fzvy0XwV3T4yQCU727CBQvVeoNTDfn3S1FplqLVtr640m5Gv17PilljPls8FTLEKwXMKKcvoaIjAgOGcjAk69C7GWM0eFzwhg+otAcGepML1/hEUhi+AgcfhDZ3Nf1qKbNCa0GJAZyB133MHof10Lliu4ApsG70TFCVZkHy4/zC6QNlDHOMEzgRXa0wOmX3RiTdqtJw4+wXTcswJmMephf0FGaevXr2eTBdrU03Sbptw9YbCn3QQqss0mMyu6qfimOJdLMTCiCyw54tIUk7SwVLD0hpZdRSycURyohLSLpk4m4GOso4JNuvfXq3otuk21WphqtMz5nCbdV9B3kJM9nQcxk7xxbl8pUn4tRX25BjtWprLp2fDZAUx/2Vd9aF8391Rc00QsIyODFd9kRklGfHQQlZvOJaL5ki6c1nTSNQcG3gcvr2uZkVdFJU3At6OmZgfc3a5CYOD9fS5Y6YJNMUk00SPDJ2JndHRgHjt2LDNwGuximybbFRVrmTeDTleGMxgGPU8KN6EJy0a93zni5jKBKOZUdJP+1WFBcJ82mKTtpCk3gRp+cnn3hePFoFW1YMdHbzHTpiHTZyNiLEfnVDfqUHSurr2AHGxQgXVqWyGjShPIwZ+mwpK2go2QW8ZFwPmbXdG8swiyKBfWBCSKORXcdN2oUFewWEgyyyI8OOzBHqeNpNPdVdcCCY+Hm5J1cGi1IH5uYI8RaBKJB9zcZjNWR1nZd4iK6l46R0Xj1g/OorFSA6GIj8k3RrBGxWCBjAFJ2vTR+59Aa9BA5ZKJsddeyGqYGTATr598nbEAqAEd797Z44YaVlR00+eLjNpsciOJTIgFD8Vh09spjN59jA/MjHMF3xa8q3AH4m8ATn8NWc17EAf+E4aiFozQB+E0ziKwcQh4IhnMDfmoO74KDtM/Zf+MzmP7Sb5o+CkL6uMVUEwaDemQIdClpmJK8klsdnXFc/4eTMevUxvh6qdg5mmD0VSkJj41YqQmKULqQ5iR2owZM7r1rOgJZKhGRfeqinpGhZd02MfeNDYAKw8W4Gx1BuzsqtkgIMErocf7aj1Xy6RWZJzYV1NR0nWzovv0KUTO4SbLTo6jIRTagVJtSZdOLCVqTNJzI9g7uyL+qvlI2roBR37+HoFxw5lBF+1pKE63Y9EtUCjg9vBDqHrxJdR+9DGUCxdC6OSEhaELWdFdVrsX8F6AFb5uMNa2wlisYq+jNL7nyNCeQI8hftZ8HPjuc6Ts/AVDZ8zp/n2m9bZtWMeHFR9nvYpSyrIPebrbYrKvSNu3G+r6OiicXTD/usewausBnK4+jTM1Zy74nPQFCh97RC8Mg/eGPPDSquCk/gzy2l9ZszZfORah96/DD9IL2QBZRw9h2/sl4AuFePTl5/Cyhw+EAh4rssnsjb7a0PDtt6j+z3/RpLTD8WBvWK0WDCmtQeiIKJRF/8p8PALH3o6Qs9lA5hZg26NARTJn2CY+z6QwWox44sATjBlE5+nHMz7uVQYxUDBfACqgu7ASag1GZKh1yKBCXKNFQaseBVo9m4pXGYzsONbUlnjAMBH8UePgQ89PoUCAizcCBBIE1jQhUCZGoEyC0uZsHKs4BgFPgFtjOsck/u2KbnKUJfoSdWaffPJJZtpDFEYqwG1GIFfw90SrsZXRlpKqkpBUnYRztefYgtCROj7OexzrPk70nci6rn2ByWLCU4eeQpWmCgHKAPxr3L/6deEmzTZFKdFkz0a7JSdkKjR6A027A4P+gbS0B1FW9i0zlerOObK/IFq5yWjB6R3FzOiGqOY0AR0IqDgiUx4y4yFDNYrtI01vdzCUq9GawjkVO87rfvM/zVnJFd0NLbjPv2fKqb6NWi4JcQRfdukOtH9XszU6F2In+yLl1xJWgNM0iqZCDu4yDJvpj4gxnpc8Tet6vsTGxrIoMZqskQaUNuk0/SbdNx1E7yaTPiq+iaro6enJChIynyoseh+1tbtRU7uDHTSV8fW5Ea6u0zvpjUnCQdNsKq7p71DGNuXKd3wcZHhETQCaxA82jZxo8SWlG1Fe/gPbvBBEImcUyO8FWoA57h6/ScFNkIQ6gifiw9ykh7FSA7H3xamb5CWh1RZDLHZjzY2BgBoduz/9AOqGepbHPfWWu9p/RmkKtPf1DnOEs9fgxoSxqfBXGUz7TBgy2QfjrwuDoIPujzT8FDVKCJR4wlyvY4Wb/URfBDsEs5QK2kRSVCRdQ7QmLZMbTfPjtKld8WNFPT4p5SZ1z4iVsOQUMtp8/AzOlKgn+PneworuqurNCA19CiJR5wZifbmaFdxEjbdzEGP+P+I6GbINFoxqHuTVUdA5pMAgbMK+Q7vZtLrjGq0QK9iUn8zUVmevvqCYoM+rTW5En+uOUWRE555/7xCs/9dJaMxW7E9vxMLaVjjYaNDj/sH0try83XBc/DxqigFLtgozvUchoEIJCyzQnVkFU3IFjI/UQ6TgGrZkFChwksDcqGeTXof581jRPSvpGDZNnon132agsVjFaPIUZTZYjApyEydZ2pjmMbCarPDx8UFcXFy/7oN0xN4SEWN2baxuxHKv801od3spFsR5Y2vJbu7l8R7Hmj89gc5dgt0Yrz7LhHyjYiGxs4O2pRmVZTvY91xczk/qqQlJRTcxlEg6ZlsjRy1cgnN7dqCmKB85J48xQ8Ts7Gz2eaf3n0z1bHBcsgSNP/wIfXY2at9+B17/fgWTfSdDLLSHwdSIoYJ8xCiGo/kwl2TS7GiEZw9NqoshZsp0HF39PerLSlB8NhmB8ZyrfifU5QG1mbDwBPjGcwHuqNwEv6P/AQy1XDE5gDWZNPEnN3IpBAlXXwcfRz8sCF6AjXkb8WXql/hg+gcDej52ozxhPncAstIXIK6ohRl8nHRajFH3fQJBNwkaOrUa+7/9rP1x+Af13CjVnjmD6jfehInPQ2pMKKytang1qeGrM6HpJiOMmkbYK2IQFP4UECECjrwD7Ps3kPI9UHwUWPQREDCO7Y2fOfQMa6LQ+fnR9I8G1GS4FLiJRZjsTEfndbHRaEJhVSEK0nYgv6YEBVIvFMp8kS8PgEYgRanYFaUG4HAlt5fsCAl0MHu8BH+pED/UChCoboCviI9mnuAv4/HVr10z0cufffZZdhw6dIjFLZE+cMiQIdizZw9kst/O7fYKfj9Q7ERaXRrrKFKRnV6XzibRHeFn78cWeSq0R3iM6NVxtie8l/wejlceZ128t6e8zTYffX6MjY0sOok2/gSa9lGcSF9jh9zdZkMuD0Vrax7Kylaxyd9ggIzUyFyNjNUO/JjNJt4DnZ4MHTqU0YVtpjy2CJmOoIWqeVsBuy2Ld4PYr/uN41QXeyAPONGkgcZsZvTM7qBro1nSFO8KLg20ISWn8/jpfji3v4y53DfXaJnx3smthYib5ovYST6dpoSXCtrM0yadDjIeos0dbdyoECK6N03D6ehIX6XNnKPjTAgEE6HVnoNOl4mSkhakpHwGHu8niIRhMBpdoFYbWYFNza6OoE0jFfNUZFPh31fjv77CbNaitvZXSGWf4lQiFXTcxZmyTf3974Sn52I8eoo+A0bMdPntjP/4YgEkYU7QZdSz42JFt95Qh8Kij9jt0JAnB6w1T923C3mJx8EXCDHvoSchapsCWswWZBzhigRiWwwmqgqa8etX6Syjnta0qTdGIKKbdY20xzbGUeS0YWjZWICWvSXM9VlgJ2LTbiq6KcmCsrkJ/6DImG4ahUcbVXgqh5MLPebvDuV3JSDbn/gZ/hf9zDg4jGCbW5U6HRUVa5hjvw3lOY3Y/kkqDFoTnDzlWPBQPMuWvhysAHIr5+vlCFGORr7mBFvHiVLcNcObXhcqusl9+qlRT8FJer7AoteGpt2bNm1CYmIiYz11bGbxS1owzk6Ao61mtDTqsf5/pzH3/qHwDHIAXEKAqIVAxiaIcz+CXcLTjDIeUMV9TtIEpXByVMOxxIrib/6J0Ac/4f6mgAf7yX5o2pQH1aEyuN52Far/+3+IzM/G1cfr0VjKZwPMWXfGDFj7fsHrZbXii9Qv4Kxzhk8L16yeO3duvxmXpGG9w8cVrxZU4tPSWizzdO50fpGh2o6t3BoY59KzwaKhVAUjZZcLeLAb2XfqvEAoRFD8SOSc3A+N7hyborq4nKfY0jpJDVC6rlPT0uZ7IFc6YMS8a3B83Y84umYVwkaPZYZrHafcNlCcm+eLL6D4xpvQtG4dHK9dAl7sEOjlY8Fr2Q13/TFYzddCQ+Z61GRy7z7qrS+Q2ikYmyZ5+2ac2rK++6KbJrYA8j0S8Fz4o3DyDMc1KW8AiV9wcVGLPweU/dsHJe/YCnVjA5RuHoidxjnB3x57OzblbWLFaG5jLsKcOEPCPsNkAO/gf6Esfwc8ngWNUGKv6DrMu/tViHrYOx7+8Ru0NjfB2dsXo6/uWYNsamxE2aOPUWcaBWOHQ9XaDKnRjNjSWgj+PQ2Nmu3McyI65i3w+W1/a+JjgPcwYNP9nBP813NhGX03XrIDdhfvZh4X7059F6M8R+F3BxXFZYlwOvkpnNI3YDi5kRP8xgDDHoc1dDRqjGYUafUo0hrY12KdAcVt/090dT2k1K1GgRV4p7hDfKm9L0ZrDYgchNjQ3xsDHlVRsU0OyrRBI9MH4t1fKbr/mgU26bBtR3p9Oio15ymiNnjIPTDScyRGeoxkC4C/vf8l0cnI1fyb9G/Y7X+P/3efdSosfiwlhbk3U5470c4WLFjAJnz9AZt2B9yLjMwnGEXVz++2QYlQoteEosOo8CZ32X3fZrIIKYoY6y9osk30XDLJokkldci7bkC0aXXQFzQDQh4cZvfcgSUDDD+pGKU6A441qjGzG1dRkZ4PU7mGbRJk0VeK7sGCzF7MmjFEPc88Wokze0qY6/OJTQWMFRE13osV306eg1usUuFDRTAdVCiTjpAOKozoKxXhtPGjgyL2OFDxweX5ngc13M5fICUSIby9feHt7cM041RwD0bGdtcCtaH+IGpqd6Oh4QgsFh1sRA+awvt4L2cUYj5fiLQ2N1YZn9euU/utQJ8TKri16fVQzuCMqHpCQcE7MJvVsLcfAk/Pawb09xoqyrD/m8/Z7YnX3wKPoJD2nxWn1bPJLblIh8QPjoGaxWJF8s4inPqliBWRSlcpc+1366G5Z9OfUiFhP8ob2hPVjAWg2lsCx4UhrFFLWdy2awyZiNG0sSuIynhnWhFMVmCRuyMW1AD7qltZI2voNN8+Np9uQWbW0ygr/4E1Z8hcL+90DXP8tpis8ApxYMWpzdRssJFxtAKVec0QivlYeNskZBc6M0YhGWkRy4QYITbEusYy6ihdg2mSd0fsHZ3ui65vdB0g7xJqopHO2wYyPJPzeZgzNxCHztQxVs3mt1Mwc0UMgolSPOERVnQjbT0c7noWmiQ+kyIZ+WYkCwsQMG0GRn6zHto1B6BfUQuJzK3d6bplXwnMzQboSy2QjRmDlPpADCnlrkHjloXBbxAjJU9VnUJqbSqmN0xn/0/XO5p0DwQ3ebvgraJqZGp0ONKoxsQO0zoXBy0E0gpYrTwUlfoDnQ3wL5hyy4e6QdBPg7jQUWNQUfoLwDNDKvWDXB7c6bpOgyzyayEJkK3oJoyYdzXLxW6sKMPpnVuZdI7Q3f5GPmIEHK6+Gs2bNqHqX6/g1Psr0SQbD6eW3ciqOYzG9HKWLc6zE6LZ6dJ0s7bHVZp+DlV5ORdGZbUV3avbTCHl4x8EQmO4YrLoMLByArD4UyCUo9NfDFq1Comb17Hb45fdxAwiCUEOQZgRMIOZ7pI2+D8T+5G6VHQU2PY4m8jTzjUF0ThqnYNZqnGwpLdQ1uEF/4Qyuc/t3cluz7zrwfbH0RVWiwUVzzwDU2UlmkMCkdfKsb+GlFRDMiEEFS77mIFaaMhTUNh1aRSETAUeOAHseh7WlO/xWv4abFHaQwAe3pz4f5jgM3CjzUGBXg2kbwBOfQ5UnTv/fXovJz7OJvMEek09JHyWy57QjcXJ88dew4aiY4jwmIIZobewgpwV6K16VDDTw7+Gi3m/i26isn711VdYs2YNWwxuv/123HDDDVCSGOUK/pRgukttLQqbC9uPguYCdtS0crTkriC6d7xbPJtiU7Htq/AdNCOorIYsvHTsJXab3Fp7MtDpCtpwUBQYTe8IRJFdsmRJexxYf+HhsYBlGOt0paioWM0K78EAvU4Tl4UzV3OiexIlkyiYtjzV/oCm20SfJ5owbWg7XnwtBjOaf+HoYzSVEDpJe31M5GL+XUU9o5l3V3Q7NojanZkF9n/+juMfkXYeN90PsVN8kJdYzdyMSYt5bl8ZO0gfSxFPQUNdBz1uzDaNpsO2JlDRTYwROmiCrdfr2UHNLE77zYPBWA6jIRcWSzpE4mZIpTTF1EAolMPRYSSEouFobomGvSIKEonXgNYI0mdrNHlQq3PQ3JKMpqZEtLZym00bJBJvqFqiMX7801AqO0eNkd6XQDQ48i/4LcF0njywwpKihYQ9TEzV6mw2cSWEh/1zQDFtFosZOz9+ByaDHv5D4tlGuCNsU+6osV5sGn2paKnTYs83GaxwJJBR1uQbIpiWuDvQOdXR9InouA7zglD3RRrUJyoYRVfkLsdVQVfh67Sv27XcXc+ZZqMJN58rYNmyw+zleCvMFxv/nch+Ro0r+hz1BWQamJf/XyZJqKvbi8q0aBxek8OIEmRsOGtFzGUzmtM063FsA6dtp4YbTYNHuY5qp4iTB8ndd9/dSQq1PGI5i80k6v2t0bdC0IGSKxKJ2q8FVKzZim5TnZbptOkcdBvvjaun+WH3F+msAbPj01QMn+WP0fPjIAiaDBQehOnwT4CJM6ESCYQQQoACvQgRbvawr1GhaO0/EXFLm7ZbxGfa7uZfCtC8vxTpAUtRQd4MVgs2J9gjOMYeg0l4/Tz1cwSqA+God2QNQ4peGyjIQOx6L2d8VV6HlaW1nYpuorATzFp/bExswlMzTLCTdD6nzGoD03MT7Mb2n6kWGDcCZ05zRpIK6YgLznFb0U0NFFpzbZ4XErkcY5Zcj/3ffIrj636C1Tccnj4+zN+lO7g/8ThUe/dCl5GB7O9XwZQwBU7yADS2FqPsSCacIWXZ3FZr9/u8vkLp6obI8ZORcWgfTm1Zh4WPPXf+h00lQEUKy7X+2WEcM22dSM1P16sBjxhg7W1AdRqwagkw5gFgyjOcu34vOLVpLfStGrj5ByJqfGcTvRVDVrCimxJyHoh/AL72vhePr/r1RS4rmgp6nhxbrVNRphyJ66KuAg7Wo2lLHsQB9p3c6c0mI/Z8zjGTYqfOuiDKrSPITV5z8BBMMinO+rgAzU3wr2uGq9GAxpsNsBh1cHaawBqB3ULqwMzoPlCIsKZsD3hWK16vrcP0LU8DU5qA6KsB4SAkA5gsyKlWIbOyBZmVKuTXqtHYamBHc6uRnacykQByEQ/jhJlYaD2IePUhiC1a7g6EUiD2WiDhbsCr77KP2tZa7MhfD6HFiBdip2GEx/khFA10t27fDgm/59f3L1l0/+9//8M333zDHBVvvPFGtrgTvfUK/vggh8MGbQOqWqtQqa48/1VTxW6XqcqgNnY0PLiwwKYuO2nr6GukcyTsxZdnYkSmaw/ve5hlcFIH78H4B/s83aZOv06nYwXEtGnT2BT4Usz+aEoWEHA3srNfQHHJ5/DxueE87ecSQRvOyTdGwmyyIvtkFXZ9kYY59w5BoM1Zto+gizHpuQ8ePMhMsmizZbuAqw6UwtysZyYv9pMvPv0hXTdXdHNFSlc4tWV5ymL6n017BX0HNWCImktu5+SanHawHEWpdSjLamSHnaMEMRO9ET3em92+HKBziGjgdBAV/WIwmVSoq9uP2ro9aGw8DqOxAfUNh9hx/nkpIJV6s4PysYk+LeDL2xkkFosBFqsBJpMaBkMt9PoalqdMRZGNMt7hEUKhiIKb63RmfCiRhGLHjh2QyS7U8u6u54rCWS6XngnbXxBlmswLDYUt0GbWw368Tw8RYa+zrBh3tzkDjmdL2roRlbnZEMvkmH3vw8zkyAaacFOhRSDmxKVOt1P3l+HE5nyYDBaIJAJMvj6cna+9NVXIwK+pqYlN8sgsiiANdWKNCV1mA5p3FML11hh2XbJBKujcpLBYrXggs4Q55vpIRPh2SBBKEmvQUqtlsWRDpvTdnJIMA729l6GoaCWOrk9H+RluO0SskonLwwfV0LArjqzNZfR1MlDsaKhJdGnyRCC2yc8//4wVK1a0F1zUjHgj6Q2Uq8txtOLoBQ7NZHJL1wAy1qLXmqblmtMcA4VMLwUOEopGx9z7hrAIy7RDlKRQgqLUekyf/gjcCo6i4SzXdONJhbDqTJjqNAw7tKeQN3MGhv24EbrVB6FdWtr+ObMb7Yny3cVILtGg0SxhztTRmd9g1cRlWF/tjjlul5b8YQNNuJPLkjG7gWvAU/KIQnFpUX93+bqx+LC9DS3I0egQbiftVHTbW+JRozNhfXIZbhnbmSWmPl4JolmIfBU9SrZ6A0X4OQVzlG5NhSPApYO1gyb41HChZjoV3h332nEz53DT7spyiOsrETOLo1Z3B6GrK9weeRjV/34VV635AetjR2BZ+DX4OelLOJS2Xc9HuAFJuGSQ5pyK7txTx9FQUQ5n77a1LnMr+1LuMRJ1YifMcLI/n+/sGgbcuYdNcZH0JXDiIyBtHTDz38DQ67o1WWupq0XKTu4+J95wW6d1jkD71LFeY5k0kUz3nh/zfPcPuLUBOP4hcPJTwKCGFTxkK8ZgszoOPLkL7rjlFrg4u6CuNJWxBck40P3++Pbkl8QtG5iOXaZ0wKSbbu/xddGcOoXa995jt4tmT4G6MBdykwWRlfUwvTgUGmMihEJHREf/r9dm64+ZP+Lzsj3s9kte0zG3dhtHOd9wF7DrOWD4LcCI2wDH/iXj1Kh02J9Vg31ZNTiSW8cyzLuDCCaM5adjljEJMwWn4cE7n3FeYPHET+ZpSLSbgxhrEKY3u2Oiu4WZyPUF32d+zzw8aJBH7Kau+G1cWP5gRfczzzwDf39/FnNBF1YqwLvD22+/PZiP728BOtlOVp1EnjGP6dmoaLTa/mszD6Db7KvV2m4wYztI+2a73WxoZo7fjfrG9q/0/YuB3AJJhx3oEMgoOkHKIPY1xDHkshXYXUHPgwruCk0FK/T/O/G/nbr53YGaQDTdtjl4ku8AGdFQtN1gwNtrCYoKP2Sb/8qqjfDxXobBAm3qpt1ChbeF0Rp3fpqGeQ8M7Tclj/R8lHdMUxLSgNGGliYcqoNl7OeO84OZvvRimOCkIBY6i4cobNUjqINbpUVjhKKFWy6u6Ll/G9A6GxDjwg6aKqYfqWB531RAndpayPJvA2KcWYEeONRlUI3X+gsyGvT0XMgOcmNVa3JY8a1SpUOtyoCmNZ9RpzWaHHb0FyKRCxSKcNjbx8DJMYFpckWi80V0T7EiVXojzqq49W/Gb6jn7ghZtCsruik6rLuiu77hIBoaj7CIsJCQJwf0N+pKinBsDef0PfW2u9nkqSOyTnAGal6hDpckU6ivUGP/91moLuQac2TIRmtYuzFXL8jKymJfaX3qKDdwmBsEXXYjK7yLz2Yx3bIN5GbeMSLy7aJq7KlvYdOyr4cEwUUgwM42p3Ry/6cGQH/g7XkDjq/Vo6Uorn3qPGLO+Ri9ywFqfuQl1bB6YupNkZ1YK9SQoD3WZ599xopvMsq89tpruQmTUMbiw77P+J5Nu7sW3ZQGQE1X0vmS3GjxNYuZez5BPuL85Ij+HjES/KKdceCHLMamWfcDD+F278EPfrCXGeC0LBr132bAu9EeLmIF8vitCLeXwy6/FQU7XkTM4q/ZdSv51xIk1elgsZBWGhjOPwVFbTKmJ/rie78AqExm2PeU2dsPkJY7qikKEouEad6p0XypoOsbmartqGvG52W1eCPCjyWv0F6MsDx6Nt4vbcHXR4twU0JAexOGGGSaExxrhCb9AzlXWlvzIZBqYDHxUJzYgHHzO/+c7pOm3dRMJ4p5x6KbNOGjl1yPXR++CXFDNQK8PHv9W07Ll+Pcqp/gUZiPf/+yGmPfeRn1ezMgsPJh8RZB6H7xz25f4OoXgODho1hud9IvGzDr7n90Krq3u07qfh0WyYD5bwMRc4AdT3GF5Ma7gcTPgfGPcN/vsA8kTbvZaGST5W71420MSSq6SYpx99C74SbvsB42lQJJXwGnPmPFNsHqFY8d/Bk4VW5mTa7bbr65Pa7NeXkEqt9NhrFCwxqDjgtCWMPjxIaf2c+n3nInZIru98imujqUP/44dSqhnTUdOYUcC3NIUSX4Yz1Q5X6a/X9k5KssVaEn7C7ajf+e+i+7TYOoJXH3AFNeBU6uBBK/BFSVwOG3uIOiuMKvAsJnAx5DuMi2LqCs8gPZNfjxZAn2Z9fA0qGn7SATIdpLiRgPMUbLKhCsOQO3ulOwr0kC35atTddckT1yXGdhv3Q6drf4I62iBZZ64Ex9CX44WQJXhRiL4n1w7QhfRHkpe5Ww/pz1cztL4XKuvX+qoptyjenFsBk3dIe/+ot1Od2/79vHGXV9s7/7ZsalggwXSHdNejlPO89OX70V3kyDLRL8fpoJigJ5/sjzzAWdsvnIjZG+9gSiuNLGgjr7pEcleh11v8eMGTOojshkbEF6v9y811i2q7cXbYAG8f4FfMy4I5ptYArP1mH7x+dYzIt3WN/juMh0haiFJ06cYK8HbWqbfimgAFtIwhwviAjrLZ9xtIOCxTzQtDuow4VKn9UIHngQesl7pMheweUD0U/JdG30vCDkn6lh02+i9dKUig6xTIjQEe4sD5cKq99zLaZuvb0ikh02mM16Ft2l01VAp6+AXl8Ns1nDqON00OSazxOxz5tAIIdY4gaJ2J1tROTyIIjFA/MQoAKNQDRkd8nvs77Jop2ZmaG+qBmWVmN73rEtRzw3l9Md+vndArm8d913dzCbTNjx8TvsK216YyZPv5DWfZTTSEeNG9iUW99qROIvRTh3oIxpt0VSAcYvCWVsi766Ntuo5V3jGomyqRjjBfWxCjT8kgf4WDHMYxhSalKws2gnnhj1BJylzthd14w3i7gp+P+F+7EsaJrWUrQXxYPRhLo/MOhM2PtVLVqKxjFtbdSMHIyc271L+mDBqDfj4I/Z7PbQ6X5s0t0VJNWjwpsGG7TfokYyJVUQrgu/jhXdh8oOsYm3j6Lzc6bfo39DEWLjg0bA3GIAXy7s1oOD9Ny0Vhz8MQf5yTXIVnkjGyYEGgsw1BgFaawL9Gn1mCIYivXmY8ifNR1D1m9Fw/oSHNCfQPE5M/OeIHhK+Bgq4cMxJBx1+4DZp4/jy4XLsK22qZM7+ECQ05iDxPxEzGiZ0R5fO1jX+Hv83FjRvbaqAU8HeSGp4jBzhqam/z3jxuLrg3tRWKfBnsxqzIrhitvW5BpYNCbm3j5Q1lddPTdNV1fKUZGdx4y45A6dWQG2opsa6Wq1utNkv1Uih0mugLBVjbSdW+D/4OM9/q0ivQn/uu42fPi/FxF2cB+kScuxSMOd54meWViI7gvXgWDUomtZ0Z1xcC/GLb0RCqERKDnBfvapIqH35mfYTCDoBMuOx6E3mCkXVt8IOAUBCfcCsYtR29CK9AN72a9PvP62Hq9z5CtEU9MztWeYtvvpYQ8BOTuB5O+B/H3nmVOeQ2Ce9DTWp7YiIzOT7SOJyUtu8DYIlBI4LQ1nTSj10QqIA+2xe/0HrPAPGDoMkRM650zbYDWbUf7EkzDX1oEfGoLkNgp2QF0znExaNN4kY1IM8u/wcJ/T42tKqUDPHn6WDd6WRSxjTQQGiT0w6UmuMZG9nTOlKzwElJ/mjv2vASI7jsLvGQu4hMEgdcHeEjPWpKlRp9ZDAAuGwYzhbmaMdTciRqmFu6EcvJp04EwuPYnOD8bOHYicC0QugChoEmKEYpCgkfiozVojThbU40heHbadq0Sd2oAvjxSyY1K4Gx6YEoKE4AvXgu8yvmODwSjnKObp8VdHn4tuil66gsv0JvCFCHUIZYZFdMGlPFLbYkKFDvvKo1vcf/T71PWmQyqUtt+mg3KvHaWOcJI4MYdT+kr/rxAp/tBNkQ9TPmx3Y3xnyjvsotcdaANJmwqbaQyBisx58+Z1iswYTPj4LEdR8cfQakuYcVNvC+RA6cSz74zF9pWpLG7nlw/PYeHD8fAM7jsdlqj0p06dYuaG+UczIMlqoFB01pXtz/tOum6u6FbhDt/zRTdNoQiS6MEzxrmC/oO0uOGjPNnRWKVB9okqJk+gzS9pdukgM6vw0Z4IGe4GF58/xudeIJDAzi6EHb8ldtVx1PLZrr+f54jQRQaRpxzGqlZosxpgN/z8RIO8IighgSKrAgMeGND9n9q8FjWF+cxFeObdFzp9V+Y1oblWy6bAIcP7xwAix/OsE1U4sSkfWhXHJiC986Tl4VD04hHRFfX19WxyS3KfjsZQNthP94fqdCXcVA6Y0TwGdy18DP869i+k1adhQ+4GTA+5GQ9mcmym23xcsczLGSajGad3cFNumk73R3/d2mLAto/OoqZYBeo1eyV8BJ5LHozGmyESXb5z5dTWAtYkIDf00fODevw9YhVScUnGapQMQ1TxkJAQxkSzxapRfNhjIx7r9O+oQKffo0LtyMHDGINAyOPd2ymxXUGRYrPviEZqjQbZVa2oMVlRpAtB0cdZkPKAGQ4iOGlliNWPRLnJjJaxo2GQOAMHOT0yUfonXhcO9yYtVHtLYWryAd/ODq51tYgpzMVGF4dLLro/TvkY8fXx4IPPGjb0/AYLCQ52iLOXMTbMN+V1qC7hCrpp/tOYjvvGBMrtzsdnhwpY0U0NJ/URLhHFfoIPc3EfCOrrD7KvVk0gK7zyk09hyNTONHGatNL7SVG9tOchRpsNZGCsd/eDsCgTmYf3Y/ichfAM6d6pe2VpDTIDQ5E4ZyEStm9GzZtfwCX6Tuh4enxqXIXZ5msxWPCNjIF3eBQqcjKRuGU9psbQZ9KKBvc4lEvcEWknha+0F4meUMI5dsddD5z6FEj6GmgsBHY+DeuOp7G/eiysViHCY8PhTRN6olh0neRaLOCpKvC0+3jsyz2IsfvfgXXXm+CZDed/J3AiK+Qt4XMYU5IKbmrkLF++nH32ukIW5QLFBB/23p/87CeU1aRBJJFixp0P9Hh9rfvoY7SeOAGeTIaKuTPQfHAPpBYrIirrofunP/TWfEilPogI5/yLugP5Kz28/2EYLAbM8J+BZ0c/e+HfowUsehF3tFQCubu5I38/QJPpslPcQbIGalq1HeioTFO1HRc8cSfAfxwQOIE7PGK7nZzbJuT0GaHjhfnROJhdy6QZuzOqcSinlh2jAp3w5OxIjA7i9pHExiXaPOHeuHv/EHuVy40rQbt/ANiJ7LBm3hps376dabqo2/Z3wvqc9cwkhfDy2Jd7jD8gnduvv/7aTiUnOt2sWbM66ZgvB2jy5utzEwqLPkBJyedwd7tq0P8eFVNz7onFto/PMd3u1vfPYNGjw+Ae0LcNIL0WREE7c+YMDu87iBmIZRcJMibqD6a5KPFaQSVzdNVbLJDw+bDoTTC0mSUxY6gr+EOAaMJjrg5hlNjy3CZWfOefrmGxTUnbi9ihdJMhJN4NwcPd4BGg7PNU8q+AVrMFhxu5ncSsbowBf0tIo11Y0U1O5raim3TwBYXvsttBQQ8PqNirKSrAifUcNW/aHfdC4XTh59M25Q4b6d5nkzFqbhak1OLklgI0VnEFFsVnTbguDP4DSC6wTbnJqI+YOd1p3w8EnsXU7KG4u2EpwuzCcH3U9Yz99HPWavyoHYsWkwUjlXK8EsrFnVGDiZpNCicJoif0PQKtubYVW94/y3Tg5OROkp6iGj00Gi0qq9bD369nfealoK5UjbP7OMnPpOvDL/pekEabCi7yK6H4SzJWo8by9ZHXs6J7Xc463DP0HrZ/6AiKG6OiO7OpEEPhBfdRvdOPW/YUw1ltwHgPGUTD8pG2NxWlphFQGV2QozUjSiZAnEiJOq0JBgnAtxjhyDuH6JsSEDNmHJO1WHQmqI9VwtxogmzEBGgO7cL0U0fxQUgEavTGAbNMyK09JzMHo3WjGSuMYg4HE3Qdv8/PHfdmFOOLslooK7jihAocwu3jA/HlkQIkFTfidHEjolVmJt3iyYSQj+z9de0J5FlBZpAED8+ZKMQu5CedvKDotk276RxITU1tL7ppOEPNdcjsEJIwHvknj2Lf1ytx/StvXKBvrjOYsLqKa5iHPfEYRGeTwJNHsP9Pcs5Epaka+0v3YzAx9trrsf71F3H21+0YaeCBuByHPKb0T+JD8WEzXuYmuWd/BpK/Q152KUobhRDyzJikXQW8+yVAGer0u0wLzeMmsy0VgNkAst3qZL3l4AcMXQYMuxFwDmZr3K6dO9meic4DknH01tBxmBOIupxCnD3FvV4Tl98GR4/uzwH1kaOo+4SL1xM/+hBSdmxgt2OKq2CZoECTJ5ko8hAd/RaTZnWHBl0D7t9zP1oMLYhzi2NO7BeTW7LXYsSt3GE2AfV5yEg5ijOJh6HUV8IZLfAQquEl0UEmFoFH90fMTbkzYO8FKDwARz+uuKYJOX1vAHtd0nLPiPZgR0l9K1Yeyse6pDIkFjXiuk+PY2GcN56dG4m1+d+h1dTKfKKm+k3F3wF9Urn/97//ZU62fQE5LlJ39gquoC/YX7Ifr5x4hd2+a8hdWBS6qFvdNrnlf/HFF6zgJt0bUckffPBB5tb9W3THfH1vYiZqLS1n0dQ8CK4j3YAmNXPvG8oofwadGVveO4O6su7aj93DRkEsMlejWWGAcvqFplIXQ7SdFO5iIbQWC041ac5Puc1W6KRmCNwHJ3P1CgYPVEj7Rjhh+i1RuP2NCZh5RzSbRlIjhwqLlF9LsP7/TuPb547h0M85KM1sYJF1f3UcaVRBZ7Eyw62oNpOk3ws2eq8upxHWtte+qOhjZjgnl4ewiLP+gtxzd370NixmM8JGj2PuwV1BZl15yZwzcdT4ixemNMkrPFeHdf9Nws7P0ljBLbETYvy1oVj2z9EDKrg76rm75gnbQFGU7+ALVIhqYaeXQnWwlKVWEFOrurUKebXH4SIS4vPYQIj5fJgMNOXmmq8j5gT22c+gpriF5VTT54IYIUueHMHyqn19b2Y/Lyv7nnkSDDZIT3/451z2+pIMpC+GmXRdoyY8mWpptVpmrEayqil+UxgTTGVQYWPuxgv+HcX0eSndYeZZkOFUDbFXzxp+fWFzu/eH0zWhcJu9GFO91+EWlzux4vZaRKyIgVkmZHF7/o71ULmmYlTiixh67Evojf+DgExAaCMpFZ4365Rx+uMZKSfBM5uwuea84VJ/sTJxJYbWc/c3ZfKUy8JmW+DuyCIzm0xm1EvGwF3mziLaCB5KKa4ZxlH4PzuUD9VhbspNcgh+P/0DbGhsPAarlSJ2AxA2fB77XvG5MzDquVz6jqA4RzoPaOBApmq2KTeBDC6n33YXRFIZM1BMP8hN6Tvi6/JatgbSNH+clxs8XngZIh9uqKFw4ZgrG/K4gnCwQJRr36hYRr8+cbqUfe8zu9ED89UQ2wGjVsB4+24c0HOa8JERdnDwDgD4IsCsBxqLOB14Qz53mybafCGjpdcHjsOrLk5Y4h+A2rv3AdNfYAW3jb1L9Qph0aJFPa5NNlh5Vpyq2w6z1QQPaQD8VaHtnksdYayuRsWTT7IPvfK663A8+xyLDPNs1sAVGjQt5yQZAf53w8lxVI/mx+RvVKYuYxKS96e9z1it/UG1xoQH9rRi7n5PPKdeilekTyJ/7k/wff4M5M/kgvdYBvBIKvDwGeCufcDyHzht/YRHOaq/0ntABXdX+LvI8fo1Q3D46am4IYGihIEtZysw/Z0d+CaN8yG5d+jfY8rd56KbPuS0kN9///3MIba2lotKIJhMJmb08PHHH2PcuHFYtmwZ7O1/2yzUPzsMZgt21jUhRSDF9pombK2qZ8eWqnpsrqxjx966ZiQ3a5DfqmPmJH8FkGbvyUNPMj33NaHX4B/D2ow32kAXmc2bN+Ojjz5qv9DEx8fjH//4Byu6f0tGgFjsCk/Pxex2SckXl+3vEAV0/oNx8AhSQt9qYoU3Gd30BU4iJQKtHH0006sW/C4xJ30BFx3GXRj3tbmYt6bWsa+NLoa/zcL4Z4VILGDUcmre3PHGBMy+K5ZFOZEGlwzYUg+UsXPqi8cP4ZePzuLc/lI0Vbd2u3n4s2N3W1TYbNffV+NOEPkoIHAQw2qwQJffBK22FCWlnH9HWOiz4NMGsp84vu5n1JYUQWavxIw77+/2OeYmVTOHcZpS05rSG42cmBI/v3qK+UoQ7VooEWDk3EDc/Oo4xM/wh6AHivLFQDIgKhoIERHclK0rPkj5AEa+CeeGcJt09eFyCFRArPdc9v8y1R58FO0PLwlHTSUtN1HEiabdV516cXo9Nr6dwmjyrn4KLH5yBBw9uKm7l+fVEAqV0GqL26m/gwl1sQi1JWrmvUBsgb6CrnGk76YUgerqarb/IvnZLdFctBDpu0mD3BXxBk6elWEsYgV7d6DpdMPqbCZvJaM1WawrZ2pF+lliZ5x+F/5DXOG+hHu8MTx3iPhaFCUMA9/Ig3VHNqprzg9YFOO8wVeIwJMEg690gkLVghGZadhQ3YiBIK0uDS1pLZBapHB0cWT7y8sBAY+HBwO466ZWOQcT/aaz19iGuydxRdru9GrkFTfSP2DPdaCoazu/XFymwC0gCEo3dxbzV5x69oLfpb10UBAnQ6BpN8Hmq0QDB3tnVzZZJhz68Rvo1OpOTB9yZyfc7+/O1ge+IhQ8kRQWdTXC1u2G2MxDUk0S6szc7w0G6O+MX841sdIa3VAlC0eyxBeOQgFGKgdm4nh660a01NVB4eKK0c+uAh5MBJ6vAh46A9yxG7hjF3D7Tu7rw+eA56tZMel863Zkh0xEjsCKr9K5GEJb9DHp5Qkk46B95cWQvG0zKvOzGa18lNscaFNqoUk8n7RAsJpMKH/scZgbGyGJikL16HiWWy60AlFlNVA/5gwTSJ8fjeDgR7r9O3QtfuHoC0yPbi+yx8fTP2aeFv3BppRyzHjrINNWC/g83DkhCPufmIKbxwZCMgjGhgMBNbBev2YItj44ASMCnGCyPwijVQuJxRchduelE3919Okq+t133zFdETnEUiY36YvIfZQWBHL6GzZsGMvuvuWWW1hHm0zXrqDvaFSrcFdWOVbaeeCenHLclVnKjrszS3FPVhk7bkwtxNzkXIw/mYWww6kI3X0CE7YdxPId+/HqwWPYnZGFFk3PsV9/NOQ15uGBvQ+wjh6ZJ7w49sX2TSPp/zZt2oQPPviAUetoESId4H333cecyYlK/XvA328FowTV1e2BRsNlrF4OEO1wwT/imMkObRA3v5vCCqOLoXl7AYYaOD1SRnkOy1YeCEjXTSBdN1HLyVnYVnRfwZ8HdB7RVI3yhqkAJxotFShyBzErxIpT61l00A8vncCqF44zgydy0adi5s8OipZqjwr7HfXcNtDaRhRzArmY5+X/D1arAc5O49nmu7+gjRxpuQkz7nrgAhMmGzKP2QzUvLstyum9TtpRhO//eRx7vs5gDT5q/A2b6Y+b/z2WSRd6yt3u75SbJnPkWdIVydXJOFp+lCVozJ59DcRBSsYGKN+ej93GUSzKR6xPhz+/rt2MLHkXN+UeOS+wT82AtINl2PbROZj0ZvhFOeGax4fDzkHSSULk7bWU3S4r+w6DCXWDDi053N8atzik09/tC+h6t2TJEnabrodnz57FwpCFjAVASR97irkYIRsMhc3wbrKHs1UBg8nIvD66Q9OWfJib9BA4S+G4sEO2/agVgFgB1GQwbSgZcUpCHZnbdYIpDDne3jCIRLA7KEBB1lss6o9ACRn2U/0YZVXozZlzzUg6ihRVK0vD6C8+O/AZy+UmLF60mDHcLheudlNCaG6EReAIq5KjltsQ6m6P6ZHuzHrrZxggH+YOgf3AokNpL1PfZqLm6jKZfSZDRnIFR34SZzjWHcWcQOZ4FLlXUlLC/t+WxU56bmcfP2hbmnG0LcGAQLTyBqMZ/lIx5rly64MmkXOzN9WmwFJQjEdTuIZVkmFw2Xuk7Q7yFMMCPnbXcfGAU5ztIRyAvElVX4eTbWvdpBtvh0jaNvEVCAHnIMA/AfAfAwSM5b46BXA/s8kH4jmTYnL8r2mtQXJyMnbt4hISpk6d2kkr3xNqiwtxZPX33PO49S54zY9r/wwZys/vuykaTHv6NPgKBZxffQXH2qQ/EeW1wHQRNO7VjDEZE/1Wj/GzJLWkjHEhT4h3pr6DYMcOn82LQK034bE1Z/DI6jNQ6U2I83PElgfH45/zoy/Imv+9EOvjgC9ui4K9+3H2/00VUzDv/SNYnVjyl2z+d0WfW9dxcXH4/PPPWUF0+vRprF27lv0/nbzUgU1KSsK9994Lqe0DcQX96rR6VZWww5sdxfCppKMIvhXc4V5XAaWqEWIDR0FSi6TIkzvggNQJH1rkuKVah8jjWUhYuw33/bgGq7dvQ1lWBnO0/aOhuKUYd/16F6PHkVbljclvMAM1moYQjZyKbdLZ0AeQNDaUVUrNHg+PniMVfgvY2QXD1ZVzBi4p/fKy/i2JXISFD8XDxceObYyp8KbYqJ6gy2uC9lwd3OGAAG8/WCwWFiM2EExytmcLQ5ZGh8L0WsBkgcBFCq38r8Gw+DuC6LdEaZ12SxRu++94RhUee00IfCIcwRfwmA6cpoe7Pk/D108dYYX4/lVZbPpJxk9/NpAxUo3BBDsBH2MdLy3Pd7Ap5vVlx1FTs51dfkPDnuv3FN5kNDK3cqIsRoybhPAETlbSFWS0R9FeJD+IGHNee0gU59KsBvz6VTq+fe4oTm4uYNpoMsWiIvuW18dh3JJQ5gg+GLBN5mxFQkfQGk9TbgLFYfk5+MNxHrfJFJyrh7/KEQolR8H8OZvbwKYfLmfNSKKHd3xePWWKH16Tg4M/5bDnHTnGE/MeiOtWT00SImqqUrZ8a2shBgtH1xXAaubBM1jJ3N4HguDgYMbuIvzyyy9QNaqYtpvwdfrXnTarVFiR4eroAK4woOuARtOZLUXsJXLhJhms83XhnVlRZJ5Eeb+EI++y89NxQTDbLQZa3OFqdEDe8GEQtPDAO1SOysr17f9UMdqLMTqE7lzW/OQzSZAY9P2edp+uOA1RFsf+iIyL7NbcajCRXncW0uYt7Pa2ZjsYO2YoAbgjiitOd8IIw6iB70MoLpHiR/l8KRwduWIvdOQY9jX/9ClYLBdeY8k8jgwIiWFKE1qbN4Jt+EARYtPv4NgJZ3dvZz4P9Pg/KeFkJff6ubFi11jbCkNxC3vPXW7h9LPD95YivMyKFEMKjObuYxcHBJMB45Xc576mRAXXhuoBRzYe+PZzmPR6eEdEI3Jc/wd6lNk9zH0YMyP7z4H/MOM0AjEn+jIgNBr02Pb+G4wuT+kQQ6bNYlFxzN/GZEX9qgyWSqHavx/1n3MsSK/XXsOJQ3ugb9VAqdXDW9CM5oXcdTQk5CkWf9mT3NK2Hj435jkkePV9Apxe0YwFHxzBhuRy8tDFozPCseG+cYjx/n39TLrDV+lfwWDRIlgZhmEuE1g2+NPrU/H4mrPQGf/a+8x+tz7ow09UjL7QMa6gb3BRKHDoqgnMJGzmzJkQiTpsdmg/ZqU1TA+DTgejTou65mbkNjWjQK1Fnt6EbAhRaOeEVqkcxa4+oBkAKb0UubWI+fUgplu1GBschNARo+Hk1b9YlcEGxZzcuftO1GnrEOYUhvcmv4eC7ALWjbcZpNkcySdPngw/v/7rki8nAvzvYpPuqqqNCA5+DBLxwCJD+gIy+Vn48DBsejuZaSs3vZPCJjREqewIq8mCps157LbdGC9MivXG999/zzq6dFEhamJ/4CwSYphSjtMtrfi1sA4UIUrxMTBw2aRX8OcGbaJdfRXsoGxjik4qz2lCaUYDKnKbUF+uZswKOsisikDnnHugEh6BSrgH2jMWRl9NuX4PULyUbbpCZoB/BEiCHECW0NX+3MTE23tpp1i1viJx8zo0lJey6bZts90dyHWcQFnuVFDT+5qbWN3udm8Dva9DpvgwVsRgZ70Ttdy2rndXdFMmclJ1EkR8EXOvJYh97ZETpkB4rhpP5uhhve42PH3wFDbnbcb9Qx5Ayu6Sdi03JT/0BDqvd3+RznKxCWOuDmbne09NDpnMH64uU1FXvw+lZd8jIvzFS37+RefqGKMEPCsmLg+9JCNDWsvptSwsLGRDj6U3L8WXaV8yszF6DcmAlDb/NjnQsBkJOLM9H1VVVTh8+DCuuuoq9n1zix5NG7m8YPvJfpAEdrMpH/sAcPJToOQYUHISIv8EKMZ4s1i3saZwbAtqQdjpZNjttaJwyofw8lrM4v54Ij5zom9crwdP4QaxuhbjziVjo6MSjwV69KnBRA2E77Z+ByeTE3NYvnrO1bjc2FuyF1LNQZicl6FMD9YkIId8G8LTGxENATJgxqqsKjwRMLBCxjbldnKiaFPuOu4TGQOJnR2bVFfmZMMnsvPnRCaTseFDbm5uO8W8Y243wT82DhFjJyL7+GHs+eIj4L5nUKIzwFUkbHeP1yRxU25phDMc5sVAc2gRmjdvxkPbgMdvV2Nf6T7MD+sSGD5QFB+Bh6AKoU4eyGtUYuKpXzF1Qecow74gL+kkck4eZQZxtNYNRCZE/+ahYQ/h9l23Y2/NXswUzMTkoZPZXrsv93fw+69QX1bC1tvZ9z7M/RtqVi0NR/WHZ2Bu0KHuu3No/Po59vtON98MdZAf0r96n/1/dHkNWp6Vw8JrZswmP99bu3+ujXl45vAz7DZFgy0N55g3fcGO1Eo8uuYMdEYLvB2keHf5sHaH8D8aqjRV7Y7lT4x6FOO8x7F0gDd3Z2NDSjlya9RYefMI+Dj+Nf2D/ri7pr8RTCYrTmyuRHOpI060VoLfzUJAFDrS2JFmUyR1RYTCG/GuIjaNoA0xmd3kNDRhe0EJ9jepcZYvhVrhgJOxY0FWEa71VYj7/CtMUtchdsRIhI+dCDd/jrr1W6FaU407d93JPnTh8nDcKb8T36z8hrlx2ho6RKWiDuTvPdXuCQ4OI6BUDkNLSwqjIYYEd45sGWzQ+7vokWHY8FYyM//ZTIX3E52pkaoDpTDVapmezmFmABxlQpYzWVlZyZoZRKHqL0jXTUX3EaOOFd2SGGcgZZCf3BX8IUDFc9BQV3YQdBojKvObWQFOR22Jik276aA8XwYe557uwQpwJWNkUDyZ1O6Pkbzwa/15PfcfBRTbpItLg86hAHyrDMFBj/b7PhoqynBy42p2e+qtdzE9d3egqW72CY5aTq7PP758spNEhbTF5GZO5mrUSLlcsLmWE7Xc0dHxguKKoiIJ10VcB087bmp9sEGFR7yt2JAPDGkww1kViUBlIIpaivDV3h8haYlgjuWUSd8T6FylSLD6cg0zFJxxWzRrKlwMvr63sKKbpre0tguFA2dJkNkbTdkJikADnHoxNOsL6Pq4ePFirFy5ksWvHd9/nLEDKDrsm/RvWNHdmlLDmEkiLzuI/ewxY8YMrFq1il0HiEZL70HDulxYWk3MZ0A5o4cJMpkoxS0DUlYBR98F/H9iv9t6pgZOrQqEmLyRFxODqLNnSR+AiqC1bUwBwG6EB9Pki7xHwZCzHTOTjuK5kWNxTq1FnP3F0zQ2Jm6EYyV3rlw156rLzp6k83BfyT7wrEZc7WzGzw3A+8XVWOLhxCbEhlIV9DlNuJEnxvNWLb4/UYz7poQMiK57Xs993vSQJtVB8SORdfQg8pJOXFB02/TbVHSToTFFW3Vn+jX5lhUoPJPETNVSNm8EokbjAX93yAV8WM0WtJ7mim67kdzeyuP556A5cQLu1dW44QAPP3n/NHhFdxan9fcdHoLs/fUILcpCS8ZZuMT3PRNc39qKvV9+zG6PXLAY7oF9p1l3havOFV5aL1TKKlEeWI758+f3qeDOP30SZ3dzz2XO/Y92kvHw5SK43BiFmk/OwFDUCqHHWPD9y+D2+KP48WWuePZtaIHoKjNUzmoIhQ6Iiv4feB38Amxo1jfjof0PMSdv+hw/PfrpPj0vxhTal4e3f+XWGcrDfn95PBzlg8NSuhxYeXYlYx2M8BiBCT4TOAnAlBDE+TnggR+SkVrejIUfHMEnN434wzYOLgVXiu4/ACwmC3JO0IIoQk45tzD2F0IxH/YuMkS6yzDO2wv2nnLk2FmxXlWPA1oT6lw8sXfiAhzW6zAk+zSGv/ICwlycEDNpOqImTO5REziYBfddO++CpdqCadppcFY746SVc46kSezw4cMxatSobjV/fyTQAkHT7tS0+1FW9gMCA+5lesDLCTtHCa5+dBg2vpnM8nY3vZ3CCnHaeBJlrGU/Zz5EFEC6EBAmTJjApiG02aImBnkv9AeTne3xZlEVEp2E4LnKIPS8vM/xCv44oMK5YxFOE8OaohZmrlVNX4ta2KS0sVLDjqzj581k7BzEcPFVwMVbwQpxKjQc3OWXrAvuD8p1BqSptUwiMb3NFPCPALNZh0qn7wAL4FqxCBKJW7/+PW2w9nz+EZMMBcaPYNTyrqD3pTynEVnHK6Fp4rS2BWdq2xu3ftHOCB/twdztB3uq3R06mj51xdGKozhbexYSgQQrYskvA6g3mPBQZjFqpHycG+qA0Wea0byjCDfMvR6vn/4PtlRuxLV4GsNmBfSo5SZK/bZPzkHbYmBNy7n3D+1zY8HZeTzk8mC0thagsmoj/NpczQeC0zuLmWzDzlEMZWjfUyh6A/nokL6bfHYYk2nOJKzBGhwqO4SMunS4JHIUVrvRnuxaRYwxoqYXFBRg3759mO01FvqcRkDIh/OyiB7zuxnGPQyk/ABkbwdqssB3j4RydiCaNuZhuCkIWyKHIDQ9HYo9FhSN+BheXkshEEjAE/DhMDcI+vzRrOgenXYW9ho1NlY3XrTo1mg1OLXnFKSQQuIrwZh4jnp9OZHVkMW08VKBFM9HxGN3UgHytXqsqW7ADV4uLFKNMGuYDz4vrkBRfStWJ5bijgk956x3B6OxGc1tySeuLtM6/Sx01BhWdBPFfPJNd1zwb8mAkN5PWgPI1Jim311BpmqTbryDTbpjju5AXkAEbvHh9ODky2JRG1lj3hb7KVAqGQ269M47Mee0FUlhZ5CWkNbu3D5gkNQhi+QzwP7AKUiOrcCoc0ex/5tP4ffGRxD20QD3yM/fQd1QD0cPr3azuIGAmCE//vgjonnRqPSpRLo5HZmNmYhxuXBN6gj627s+eY/dHjHvarbmdoWYDDJFOTCbwiCOWgTH+V5IPbQfNYX5EJotCBbXoWUatw5HRr4KqeTCRiEZCdOEu1RVypzK35r8FmP+XAwGkwVPrjuLzWcq2uPtnp8bBWEv7J/fGwXNBdiYxyUuPDL8kU6Nj3Ehrtjy4ATc8/1pZFS24KYvT7IGwvSIy8cm/T1wpej+A4A68aMXBiIrKxuRkRGsk9kVZpOFdc6Negv0WiN0KiNaVQam921tNjBjJNsmuPDseSfKSRIBFvkp0OAmwh6ZCaccxDg9dDxSYscgJjsFYzeuxaEfvkb4mPHMkMMrrHt32YGC3O1PZ5zGd/u/Q2xzLMQWrgNnhZVRx6nQJtrh5TRJGWy4uc1gVESttgQVlevg58s5yV5OEJuBcrs3vZPMJlYb307GokfioduQx+K8JOFOkA09v4mnTrizszNzgKfN2dixY/v194bZy6GwAM1iHgqGOsL9imv533oS7hvpzA4baN2h4ru6uIXlDxN1WVWvg6bZAE1zA0rSuXgbG2RKMRzdZcwt2tGdO+xdpOyQyIWD6i6+u23KPdLBDi7iP866UlpKOrZqCHXOcMicBmNNK0TufW9mUSRQaUYqhGIJZqy4j10TaJJLTARqhlTkNLIiryNIr0/Nk5Dh7ggY4vKbSgKIWt7V9MkGKh4+SvmI3V4esRxucjf2vSezS1FtMCFMLsHshaFozk9m9M2ZtQl4mydDvaQSdV6FiB7fvflcTmIV9n2XxSLxiHlB5oFd5Ti9gaZQFB+Wk/MvFh/m63PTgM5NWqOTd3PF2rglIcis4CjugwEqokl6Re7LJ/aewLzh8/BL9S/YdGgNbqmawQpqedz5awFNuz/77DNGTQ5JkcMVCjjOCbz4uecWDkTOA7J+AY6+B1zzCexGeUJNDIpKDYaYQpAfHobIjEy05NWgIuAn+PlxWnBppDPkw6KgO+0HNJdiUspJbHJywgsh3szDpid8tv4zSA1S6IV6/GNZ5zSTy4U9JZwR3Xif8XCT2uEhfw+8nF+BtwqrME8v4IxE+YDTdH/clSfF8xvT8OWRQtw8NoDlEfcV5IpvtZphZxcOmawtXq0NgXEjwBcI0VhRxtgszt6df05Nc2I6mM3mbgtuG6KmzsKPO3fAvawAy45uhXwu15jTnOIao/Lh7qwpYoNiwngol12HltVr8MAvFqxL+Bqxc97CJaEiBVBVwCpW4HNhFLQjIzChOB2NlRU4/ctGJFxz3cXvIicLZ9omzGQUKRL3b2hgA+XV//TTT2wfOjx4OMQBYuwo2oH3Tr+Hz2Z91uO/o8bm1nf/D1pVC9wCgzHh+u4p4S27d6PppzcgGXoDxMFTULezBEcrOCPGsNp6aB8jKroVnp5Xw8OdS2Loii9Sv8CR8iOs+fje1PfgJL14LJ7WYMa9q07jYE4tY2P8++pYXD/68voeDAY+TPmQNRko9jDe/UKJsp+zHOvvG4eHf07B7oxq3P9DMl5eEIU/Dl/t0vHH2ZH8jUETh/iZfqgwprKv/Y3CMhnNUDfomdEWaX8bKjXMhbauXM2cXmvymoE8gLYpU3hAi6sY51z5yPGOw5fLhyEmOxktiXtZp9UzJIxRecISxoHPH9gkhBY46qxTzFdGZgYMegPcwG0C5HZyDB82nHkCuLr+OTtYPJ6AOZln57yEkpKv4OtzI/ve5YaDmwzXPDacM1Wr1WLjf5IwlmeFnVQAp6tDO20O6QJNud1kGkJGOtTc6E9jg280Y1SdCfvdhTjlJcblnzdcwZ8JNEEMHOrKjo6Z0LT2UAFeX6ZGfYWGFR9UoGvbjkpai7qA4syoMFJSEe4shcJZyhzW7ZQS9pX+Fk3f+6qHtem5Zw7QuOdyQK+vRVHxSnbbS3Ub+BYxtBn1fSq6iSpeV16Hg6u2QSAZBo+wcdjxaTEaKjPZzzqClgBXX3vUVahhNVuZJ4RP+OVlMfUEW8wjNVe7Jk7QZDatPg0yoQy3x97OvremqhHb65oh4vHwcXQA7Ii1MzsAjetyoT9YjREOE3HUeTeKIk5BKL6z0/2ZzRYcW5+Hc/u4aDJqMJBr/0CaDF6ei5Gf/xZaW/PR0HgULs4T+vXvqXlwaHUOLCYr/GOcERjngsxBtsOgopv03UVFRfDM84RQIYR7NleMyYe4tjOeCN7e3hgSE4vU9DSc4uXg6tCpsBvbR0O3CY9xRfe51cCUZ8BzCoDTohDUrjyHCLM3dkSPQ2h2Duz2mlEUuhLe3suZVpmuRQ7zgqHaORr65lLMTjyGbROm43iTGhOcuo+UTctOQ3Pb+hA2IQwu9gPLhO8viFpOmO7PaY5v9XHFp2W1KNcb8VVSCUhZKx/mAaGLDEuUvnh7dw7Km7TYnlqJRfF998gh2QLBZsbaERK5HP6xQ1F0Nhl5iScwetG1nX5O7zUV3AQyLu4JG2qbsHniIty+9kMI87OQuncXokdMgS6ba4JS06QrXB9/HJV7d8O5rglBK3eidvIzrAl2qdTyGv/JaOaJ4W0vwtSb7sDOj97GiY2rETVxKpSubr0al+3+9H02MY+ZPB0BQwbmH5WTk4PVq1ez1y0sLIxF71XrqvFrya84XnkcJypPYIxX9zubQ6u+QkV2BsQyORY88nS303lDSQkqn/8nuy0fIYPAxREpJ9dD16qGQmeC4+wWaO0NkEp9EBH+crd/hx7DR2e45uPzCc8jwvniQ68WnRErvklEYlEjZCIBPr15BKOV/9FBEYC/Fv/KTB5JY98TZGIBPr5xOF7YnI6fTpXgxS2ZmOPLw5y/iLN5v65IFBlGXTZylo6NvUQKyhUMatHOJkgecvjHuHRybqXJN01BqgqaUZHTxOjJyloDJtQCEzJ10Eh4yPOKwsHpsfAsO4K4c0dQ9e7/wcnbFwlXL0Xk+MlMc3QxUDwVdRVth053fuKiE+jQ4tiCFTNWYFjEMFYQ/tnh5bUEBYXvQqcrRU3trh67mIMNpasMVz82nJmr0VTrCA+4arIvhN1McyhxYP/+/UwzT1MOivbrK3SZDUhoK7qPGPXoeYm8gis4rxP2DHZgR0dQMd5U08od1VpWiDfXtDLdLblQG3Vm1iTsLY+eZHBShZhNxcnXgthBXJPJytY5kug0NcuxLi0ZoWodiPjp5GjEJnEFBEIeK77EUgFEMiHk9lwhTwW9wlEKpZv0slOtCwrfgdmsgVIZBy/ZIjSnFLLPmGKSL/QaI7RqI3RqA3s9iCLeXKdlTVRqrrXU69jkFsIFEAmB+nK6R+61IoM0Nz97uPrbwzvUEV4hDig8W4s932SyJp132O83I+iJWs6m3G0bzeWRy+Eic0GN3oiX8tgTw5NBnhjSRkOWD/eA+mgFjJUaLNZMZ0V3iuEkKtWV8FJwjtKaZj1z3bc1dEZcFYDRC4PBH6BpGem4yRiMPDvo6G/RnZ9cy0wJif7+/+xdBXhU5xac1Ww27u5CEkgCSXCX4l6cQr3UXan3VWmpUKdG8eLuLsEtxIi7e7KS1fed/2ZDgDhJgDbTd99eopvdK/+cM2dm4Ez/dsmIp3soycxpvru0qBSTRBMxuCK8Vlp+MyJ4vojRxyBHUIrcEC3sm/vauIYD3kOAlCNA5BJg3GJmvCYJsYUyqghh2i5I8faG34VEVOYVIjt7NdzdOXm02NkUZmPGoDpmI4ITYmFXUozN+db1ku6qqips3MS5oBfaFOKdQRyZ6YgklaSyJBbPNMiV6wobC/h4xdMRr17LxFJTDcaKeHAcxhm6SkQCPNTPE4v3J+DXoymYGFp/FN/N0OnUtfnvtrb1e6z4hPdmpDv5/JlbSHdUVBR7pN9VVFTEZvrt7W/0KNDo9Pg2PR9lFjYwHnM/1NvX4ujKP2BdZsfMeCn2TWR3a5GPb2yMojnzIPnxB/S6psOJXz/ElJc4r4VW4dpu9nDEbiB7HG5jjiD/IFYAyI6PwdEVf2DCS9zMc304vnpZrXHZ4Hnc2ElrYgopCYdSXEiaP336dNZ0cBW5Yob/DKyOX42vz3+NNePWQHBTcynu5FFc3M052Y955uV6zYd11dXIfvEl6CorYdyjBxxefgElmdlI3H+RfT7EeQgUvSgXnIegwK8gFJrVO3L5xrE3WOd3iu8UTPGb0uTfVSJTYd4fZxCTUwEziRDLHu6JcI+7f+6ZrvlfnvuS7U/wmcBMlBsDSeQ/ndINdmZGWHIwEbuzBJiRXIKhgY2nVfzrSDd1YCm6wVBx68TdDVp0kMSONkNUCS10aeYvI7oYGbElgFyD0DQVQtMAhSgCmSG9YVp6Gkg/hj0/fYNTG9dgwMx5zBmTHCQNqK6uZlV2A8mmKLm6MJIaIVGciFRJKiwcLPDbqN9ur3p6l0EgMGbSw9S075GR/hvs7ca0y+KqPlA3cKiXGQ6WKFGlA/Ydz8Hkno6s6FIXdJMhWTm54p84cYKR8OYWPORRRehTxMXNna+Qo0rTec53ovVk3N7DnG03Q63SMnJZlFmJoqwqRsyriqsZCSWyTioevY66vajtljcMAUrKZTDclstLK9HcpHqREZ/F9BmbiRmRlVoYsfl0IZF7Po+d22zjUwGAxzrMRPbpkdvH9Y9p9SwnnB41ah2U8lIU5FtDr30eJsbdkabJgbxCDVVUMVTPHGYL4qag1+ug11XAxd8RroFOzD3ezs2U+T3cfN0xuJZTnFZHXZPqK8JmZmbWKy0/nHkYcSVxkAqleLjrw2xB9mZCFso0WoSYGeNpt+tkgl5rmg8u+iMaAXxTDNIOxDHBcaxPWI/nw55n5Hb/slh2XFBRZfhDQfDufvv3GRoZIsJdVHSIjRHROFFzQP4HJ9ZzzuBho9zZGAU1K9oDNN9NxmqUVIFcHvJ45RCIylBsYoZQXO8OKmKKIDhfie4CT1wUpWLf0QPw69YFUmkzRxsGvsKR7osrgEGvA2YOLNYtJ6YIjlpLJAePgi45GSaHBUhz+AUuLtTt5n629YwIlP7tD21hAoZeOIUd9vb41F93Q6IAEaNV61ZBX61HhagCU8dNZRGiHQFyLSeQeZWF0fUC1Qw7K3wXlYFMIz429bfGWzbXJd0P9PHAT0eS2dzpyaRiDPBrWrFXXn4RGk0FRCJrWJjX37mlvO6Df/6MnMR4yMvLan126PgxqEbIkJDOKypo3Uy6V+UWI02hgo1IiIdnzsD2a5eRmxCPgzt/w1D7WTDpzRWp6kO1iwtK54+B7V+74PnXIcgmJcHEm8vXbhHKMoCCGOh5fPwm6c78KygqjK5D5D6+4o0XkHD6BBLPRsKvV79bvj318gVc2s1Feo1++qUGjSIbA702GzduZMcVXXuoOFV3ZHNB6AJsT97OrkFbk7diqt/U2s8VZaZzXXaAyeBp1r4+5H/6GZSxsRBYWsLlm6/BE4lwfMNyKgHDSeIBN4v+KI/Rw2gsH1ZWvW75frVOjdeOvYYSZQm6WHXBwt6c83ljKJer8cDvZ9hxZ2sqxt+P9Lor48Dqw770fbhYcJEpm57r0byxETpmXr7PH9ZSIY5diMEA345RvrQ3Wnxle/vtt7Fw4UJ2oaeZ0U7cWyDCFtDHiW0kyctLLkfypULEnMuDcZUG/ulEtCKgdImAUBGF8sIz2LHkS5zcvgXu/QZDyRMgOzubSZzqZoPSCeLi4sLmzSrMKvBJ/CdQ6pQIsQ3BD8N/aNacyr0GcmtNz1iKisoolJWdq/fi2h6QRxUCiWXoby7CGZEQZSQ1X3yRzXxb3+SQGxERweJiqChC1d/6Yntuhq5aA2VCCVxJHikSIUOtxumK687HnehES2HodpcXKGofDY7ostJqRlabBI+KeUKIjIQQinhcAYnHkV26lskq5aiGEDq1DuIW1ojIK0NdXX1DlFbbIoz9vwz0xKpu+Sz9XYzwm3KEn7rUlENtYinE/t8+Q0V+MrqPGovhj1xfINaHqlIlsmgGlUh3I+7e7Q0DSaAifV1zTOrq/HSZcySeGziX3Re2FZQxWbmQB3wT4M5mFOsiR6ZBsVoHJxEfz1bNwDHz49h0bTPC0kbjyv4sVrSwdjbBmAXBtxQeWwup1AvW1gNRUnIcWVkr4efX9KKYcG5HKmRl1TC3M2bxZO0NipIi00wqqh4XxaParBh5Vw7g1/t+ZZ/XlCpRUlME6Ne7HzIyKlmndN++fZg8uZlRXJ4DAddeQNZZ4NQPwMj/QWhhBPOhbqg6kInuOn8k+fjD6+Q1VI4tZq+Xh8cT7FsF5kYwu280ylYnYPTZk1h333gcLq7EaLvrZIFm03MzcqHhaaAN1mKY140mYx1Bug3ScgNUF/PxZLwSb4ca40+pBk+oNLX+EFYmYszs6YZlkWn45Whys0h3UdHBWtfyhkbRzGxs4eDti/yUJCRfPIvgoSNrZdLU5KDzKDw8vJZ0U267oahWqdFiUSpXbHvJ0wFmIjHGPfcqlr/6HIrkWYiVn8KooMYzqcOe+xD7jxxAl1QVEp5bgNBNu8BvoQErEvayB4VLL0TrjGHE52GAFZcAYOfhhZ4Tp+Ls1g3Yt/QHOPkFwNSqrk9IOWv0EHqMmQCvFjidG0CKvk2bNrG1KSXh0DF+s0eStcSaEe+vzn+F7y5+h5EeI2EqNoWsrBSbv/iIZYK7B3dHvxlz6/0dpevWoeyff9gsj/OXiyBydERG9BVmgsfT6+Hqlw29TAuL3AEwy3YD6mnq0kz5pYJLMBWZ4ushX0MibNx3olKpxvy/ztYS7rVP9IWvfeuTFToS1dpqpiogPNzt4dqUiuZibi83WBVxUXn/SdL9ww8/ICkpic0JkYvizRnAZNrUiXsDlHHq4m/FtoHT/ZCcUIJNB5OAxHwIdDIoTcygsewNjagcVXw9Mi9evuH7rays2E2fiLaXlxcbPdiQsAEfn/4YWr0WA10G4qvBX0Eq+nc6X4vFtmz+LztnDTIyf+8Q0q2VqVG2NZnt2w1zw9S+ztj67WU2R0uS8wnPd2dy07oGLL169cKxY8fYwowM1prqfpHsFRo9hLbGGGxrjhW5xTheWoXe7f7XdeJeBnWlGammjG8mJ1cwGTk9Nt6h5jqappZGzFjN1NqIFQcN890mNV3nxua6qRu0ddcuvGnlhSqtDju6+6Kb2IiR/WqFhsmzqauuVelY95lMKQ2Pyio1kyiTIaWiUgUFyb0r1Zyk+yaIjQWQmhnBxIp7TnwhnymK6Hlxj9zfYviYWpONvMLVEAi18PV7DlJTByaPV0UXQXOpAOYBVnB6qGuDedMn1q5ARX4SW5wOmFW/mU9dJJzLZyTUydeCjaLcKURHR9crLSeSc630GkxEJniw64MoUWuwMIGbw37O3QFdTW98zrR4vrA7DVUKLRwpoSNHgMmYAGT44EoV931dB7lgwDRfCMVtOyJAebpEunNy18Pb+8UmUyroGnylZqZ80Ez/Nn8+DaF/QE9cOxaFQn4FytV6nMo+hXN55xBhF46SNfHQKzUQuZnBZqwPJuZMxJ9//slGBCnrme7dTYLuF9TtXjMTOP8nMOAlQGoNi8HuKIvMhIlcAmHXidAnfQnpST7SzZfCxWVObdya3TMzUbb2R3hlpcE9Nxub7C1rSTetJYl0E6LtovHjsB87TJ1B8t6oQk62PdT9uuRbp9SgYn867qvSYGV3IeK0GnyRmotFXTiJOeHRAV4sOuxEUhGuZJYh1M2y1fPcN3e7Gek+f6aWdF+6xGV1klItICCAkUiDxNwQrfp9ej6K1Rr4GBvhQWeuCGBh74g+/pNw7OpaxBREIijuKsvzbghikRFKX5+P8ld+h0VyDvL+9zGcP/4fWiMtv+zEEfx+lqYwqUN6icimXbmEgrRk7P35W0x968NaV/a9v3zHOvw2ru4YOIcz5GsJ6JjeunUr+1n0Wk2aNKlBZd+cgDlsrUoxhEuvLsVzXZ/GlkUfoaIwn7mlj3v+tXo9jRRXriD/fx+zfbsXXoDpwIHQ6bQ4tJST4ztJyqDul4fCzHWwj5uNyr2ZENubwTjoepf2QPoB/B37N9v/uP/HcDdvXEUjV2nwyLJz7DizlIqw8rHe9wzhJqyIXcHSARykDnioa8vf138bWky6m10d7cRdC1qgkrNsWVkZkwGSwzVdwAsLCyEvLQXqu3/o+RCqJBAoyiFU5CJ88GAMnDar1lVSo9Pgi7NfYGXcSvbvST6T8H6/95sVfXAvw83tEUa6WypDbC3KtiVDJ1ND6CCF+TB3FvlCcWLbllxmLsZbFl9kETlUSDGA8lnJTC0nJ4fFZzS10CJpOcE4xBZDbMwY6T5W1km6O8FBp9Uxbwhyzi7JqWKz2GSaRh+72dirLki2TZ1IihAzdHLNrGqItSW5897eYjteKGGE21EsQpilCfg8Xqtzw2nhRl14ip9ifhiJZcylXaXQQqXgigpkAOceZAOfHnbMUE5kJLhFEn7u/MuwsY5mjthd/ENqP6eykaAgqhC8tArwidvXw89I6nhuGzfnOuzhJ5nZUlNIOJt/x7vcpKohNRQtpuuS7rpd7gcCH2ByXooHK1Jr4C+V4EVPjkDURWZcCYuqI5m/8QBnxB7IhEvcCApsgEZYjXEPhzcrf7s1oK6kIaUiL28rXFxmN3q8HF1zjR3/3j3s4NGt46SQyguFGKbuhs3G52BZbYnAskB2L/6N9zlUGZXgSYSwmR3A7hWkPCBTzXPnzjGTzaeffrp5xq3+owCHbkB+NHD2N2DIG+CJ+LCZ5I/yNQkIgDcS/LvD+fAV5A0tZc7vnp5PsW8V2lhD2rMv5GdOYPj5SKx2dmXjSqqKciYDJqSapWL8wPEsMqmjQGMOhFC7UNhLrx9DlUeyWLyW2NYYnwS7Y2pUClbkFOMBZxuE1HgNkMvypFBnbLqUjR8PJ2Hp/IgGf49cnso2Hk/UpD8AzXVHrluF9KjLUFcroahWsfE9ApnPUmY5NTqo+01qEiLdWUoVlmZxsYDkDi+quY5SnKhTlQe8zUKQUhmFXT8sxvxF30Nq3rAkeUrfR/HilFV4bZUC5Rs2QBoWBsupTc8aM1RXAmnH2e5qs14gUQ/Nc9eFQCjC2Odexco3X2Dz65f27EDYmAk4v30TUi6cZf5BRHhb6lZ+4cIFdjwTKH6WcrgbG6UTCUR4NeJVPHvoWayKWQmnfcXIS06ExNQMU978oN7XSFNYiKznX4BerYbZfSNgs4BTc8Qc2ofi/FwIdVrYTOfeB7thfWFi7gjZmTyUrL0Gu6dCIXYyQUZFBt49+S77mgeDHsRwj8aLMGqtDk+uvMhM02iGe+WjvRHgePcYhDaFQnkhfov6je2/FP4Sk5f/19Fi0v3++++3zzPpRKtBsytEpFUqFRQKBeRyOWQy2S2PBpJNpiWNgTrWtnZ2yDQywUWlEI5FJgjMFEPMjfhCr69C1J4LSD3zMkY99TQsvN3ZfMrJ7JPs8890fwYLQhbcsXnCjoSJiXerZIitAc3mKa4UsvgS6+n+tRmrElMRiw/b9fNVRg62L7mCkY91rZ1tNOSgU2Y3dbsbI91U5SdpOcE42A4DiAxRR0KhQkkHOLR34u4CdYCpmMMcyRnJlqE0X8acmesDyaSJVFs6GLN5Vgt7w2P7Z3VfFnILYuqiEeG+HdC1y9zGmG1+ERwZVFSpkH2tDBmxxUi7Wsy698kXC9gmFPMZ8aavJeds6lzn5+9AZWU0BAJTeHneOMcmcjaBwFwMbYUKyuQyGNeJYzOQuIN//AydVgPv8F7w7dV05F9xDucaTzFhFBF2p0ASTwKRA1PT6x2ZfWn7mGmVmcgM84LmMSdrciynd+qbALcb5nwNOL8rjT3S63oyoQJZihr1gaQMa4O+xRD37wG0z9/K4sNc5iEx6RNkZi1nztwN3dOuncljRm50HAyY3rhJUFtCp9JCfqkAZnpjjO47HNtO7kFAWQAqEvMhK8xlX0P3irpGm8OHD2ejRqWlpdi9ezcmTpzYzG73y8CGR4AzPwN9nwGMTGEaYo+8AwkwLuTDLGAKkHAZxhf5SDf6jY1fGQykLKdPZqR71JkT+Gv8NGxPz0PRrk1svVIqLkW5VzkjIR2J+qTlmmIFKk9wagXyEuhnY44p9pbYXFCGtxOysS3sekrI00N9sPlyNos2upZXiS6O9buyU0GeYGXZq15DrbogCba5nT0qCgsY8c5Vqtm1gIolNjZcIYcKWUS6DRLzz1NyodTp0dfSBKNsrxMyInyEvn2noyymFCXZmdjx7Re4f+FHDZrjWkosETxmLtZl/olZx3TI+/BDSIICIQkIaPoFTT4MaFXQWnlhs9a2wQQJG1c3DJr3CA79+QuOrfoTfIEAx1dznd8h8x9nr0FLQOuaXbu4XHAqKI0ZM6ZZ3jVknNfPqS80B+KRk34FApEIk197F9bOtxZ+9CoVsl58CZr8fIh9fOD02efsOCCn9RN//86+xjGkBAITLWxtR7BjH856aIoUqE4uR9Ff0TB9wg8vR76MKnUVwuzD8EL4C40+P3rf39p0FccSCplLOc1wd3O5N2a4DSD5vlwjR4hdCMZ6dYzZ8N2OVq2CiLxt2LCBVeBee+01NttNsnKqutFcbydaBprXoRl5uglSx9kgtyHQY919AhnZEcE2EG2K6GopqLpNMS6WlpZsI1MOOzs7thFJM9xYjpZU4rm4dOytUqFXqgrDklTQVZlCJB0MhUKOFYvX4kifSOQICyARSPDJgE8w0pOTRf1X0FIZYmugrVShdBM3m2c2yBVi1xtv3mQCNeG5UOz7I4bltO/59SqGPBBQa6BHhmrU3aAoN+pCNXSeKuNrpOV2xhA5SiHm8dDDXIoLFXLENTF31Il7F6yzW6xEIRmaZVYxok37JLmuD0IjAfMPoFlaG2fDoylzBL8TxTatXo8rNaR7nG37LEyMTcWsq0obdTQpo5zOtaTz+SxJIOl8Aduoo+/f2w7V0r8BCeDpsQBi8Y2dT3qNJEE2kJ3OhTKu+BbSfS3yGLLiolkmNxkQNec1NXS5qcva2g5/WxxHBqdlmqk0QKvT4ucrP7P9eV3nQSoyx5tXrnH/drZBuMWNY2qEnMRSRmRJsp94Pp+NA5ATfZCIBw8ja+zVS7EqdhVCBzcsmb1dODlNQ3LK15DJElBWdgZWVrcaKyllahZXRug5zqtFueC3C0VUIfRKLQTWEvQYHoEMWR6T2TpVuEMJNcwHusC4TqIJgbqlpFikNQet2+heQLPCTSJoMmD9MVCSAlz4C+j3HOflMjMYRT9EwZXniNRug2F56CQKIshIbxm8vLhik9mwoeBJJHAoLkBgWhIuXj4Bk4piyAVyRDpE4qd+P7HuY0ehvLoc5/PO30C66dgt3ZjI7n/k9C0J5M7J93ydsbe4AucqZNiQX4rpjtzHfe3NMKabI3ZdzcNPR5Lw3az600EKa+a5bW2bnlWn15Mk5mQmlnT+NBLUHHmsmzxCbtwGifmJtAz2nAjv+7jUXieoGCO7wF0PLPp7YMLIN7H6nVeRGRPFzNrue/zZBq8p84PmY/SAlfDPrkZYcjXr7nqt+4eZhjVnnjvNbSi04MFPagQP4/o71t1HjkPqxXPMOI2eD8WDBQ8bidCRLSNmp06dwt693O/t06cPRo0a1aL7z9iMACSlc3l+nrPHwCWgfs+b/C8WQXHhAvimpnD9/nsITLnr1dk/foVcVQ2xSA3r3sUwEjsgMOAz7jkIeLCZG4iCX6KgKKjAws1P45r4GpspXzRoUZMq0G8PJGLDhSyQcOHHuT0Q5n5v+SJdyL/AjOoIb/R84z/RhGsX0k031BEjRjDCRu7Vjz/+OCPdZF6QkZGB5cu5YPhOtKxTnZXFVVepI307oC41uZIScabHuvsGkk2P9O/mnASDrc1wsGcXvBCXgUPiSkT6GuGpcjHcL5Whsggwx1CMuByBWIdDmD9qJIb+xwj3dRmiBxSKdOTmbYGry5w2/flsMbApETqZBiJHE5iPqN+gh2YIRz/RDUdWXUNcZC4Or4hn86o9Rrqz+XtaBNP5e/LkSZZZ2ai0PNi29vigY4BId2ynNOhfgbrSaZLuknM4kexqeT3FOx5g5SCFLc2EuhC5NmUkm4hFc3OzOwLnKuSo5AtgIRSgj2X7z7vR3+7oZcG2PpO82etH89REfKkDfuUALeSegYl9KlxNR0Lrprtlbts40JqRbkVcCSwn6WtfT7VSiaMr/2T7vSZPg7lt051cKgIk1pBuv563yrQ7ClTQo3ElKurS/KkBe9L2IKU8BeZicyYt/y2rENdkSliLBHjLu35X5ZM1RJac6zU6HZz9LDH0gQBoD6RDcbUIz+XNxpuS75AfkQ8Hk/b5m0Uiczg5TWFRWNTtro90n9mWwjwArBylCB1+fe63I1B1mutmm/Z2ZMfP6KEjkXolEeWQYbvkDFQ2PLyBW+OZSIUwbNgwHDp0iHUJHR0dm26Y0IwrzXNvew6I/AHo+TggkkDqaolKTx4s0gBr7/HA9uMQJ/CQIfoTbm4Pse4uXyqF2YgRKN+xA4MTLlP2FjR8DU46nsTYwLHMPbwjQTnxGr0Gvpa+tTO1srN5qE4pZ7J5qynXO9pORmK85OGAT1Jy8VFyDkbbWsBMyKm+nh7iy0j39is5eGmEPzxtbyweqdUVKC8/32zSTfCN6MNId3z0VZTZubFzqa4BKhVNfH19ce3aNfwdeQ5w9sU0Byt0N79e7JdfLoBeoWHFGIm/FYz51hj/wuvYsuh/LLrLxsUN4ePqHxWllJnJflPx/YR/8O1yISwyMpD98stwW7oUvIbiYym+IZEjv7us+zJfCXItbwjMzfzRp/DnS09Cp9FAaGTE5rhbQsxItXfgwAG2379/f8ZLmvv9bBxkxR9I2s+pEE51LcZ+5SaM0sy7RQJdtmkzSletYvvOixbByJvrxMsLCnD+8F6mAnEcUAgy3A/quhhi8fUCKl8qgtXDgfh47bO4II6BRG+E7wZ92+T1at25THx3kGuyfDw5GMMC7tw1vTVQa9XM24kwzX8a63R3opWk++WXX8ZDDz2ERYsWscgKA8aOHYs5c9qWbPxXIBaLWawBVZ2p4kxRTwRDTI1h3/BIVU66ENP31X2krT2qSXZiEVaGeGNxWh4Wp+XjBysZfHtuhWNCMcKzRsG82hYRWZMQ81s55Gc2YfSTE5uV7f1vAZMhus5DYuLHLGbGxXl2m74P8nP5nLmZgAfrWV1qZeX1gS/gY+i8AOaEfHFvOk5tToa8UoX+U33ZjYlIN82CUZXc1ta2QWm5NOR67M4QKzN8nZaPOIGERSF14t4CxRgVpFUgnzaaUU6tqNfYjGTJ1LEmIz4WSeVORNv0llnluxF7iivY433WprUzjR0FOtcNkWh9p/gg5UomTu3cisqcQMgKvHDgz0REbkhH0EBndB3gAlMrrvtj5GMJnlgAXYUK6pyqWvXKmS3rUFVSDAt7B/SccH+znkNuSjkrpNCcuVdI027K7QVDl5sIN5k4Gvw+frnyC9snI50KnRG+SkutnUG1ovDxOqgoVuDo6msoSKusHVnoP80PAX25CDTNeG8oE0oRqPDGmJIB+OfaPyw+rL1AEnMi3YWF+6FU5kAi4dRDhIL0CkQf4/LFB8/uwrK5OwqqzEqos6pAtu/SCEcWUVe1KQVDlV2xzegcylGNK+euIDkgGT6WPrd8P7meU5GEyBtlGj/xxBO3GOPegpBZwJHPgYps4PIqoCeXo+w1swdyFp2FBcyR22MyLA7vRGGXCmRlrYKn55Psa0zHjcXBvDxGuLXQI9I+EmZWZni95+voaJCZVd0ut6a8GuW7uGPSfKQnhHUiwghPuNlhTW4JUhTV+CwlF5/6u7KPk9x3aBc7HL5WyJzMP7//RnJRXHIUer0WJiZ+zfZ7cQnoCiMTE5SLJLVycsO5ZAB9jN43k+w0mLv54R0f5xsIpSyS696a9nWqLeZ5h/XE4HmP4Mjy33FkxR+wdHSGT3j95q/kMr0xcSM+nqzBotVGkEWeQsGXX8Lhrbfqf9LZFwBZIfRG5viD78fmuRsj3RqVCvt+/Z4RbiKt5BhOzuWTXn2Hyc2bApnvHT7MzeQPHjz4Bif3pkCvD70GF3dxXdhBDz+GPVU/Iq8qm12naPbYAMXVaOR98AHbt33mGabYMODIBwuhIcWSlRJWvuXw8HgK1lZ9b82nvvY1jkrPQagX4J3Mx+G63wj6uXrwBPU/31PJxVi4mRvReWaoD+b0bl+foPbA8tjlbJSIuvovhr14p5/OXYUW3yFIorpgwYJbPk5V0jy6oHaixSASTYsU6kKTdIj2aaN9f39/tvn5+bGNKpzkFE55jSQJpw4m3SiJeLenfINmJF/zcsKn7lrY5L2P8rLduGZ/FqpZ2eg33QMCoQo8vgXSoi3x24vbkHyJm8X7r8DZaRqTlctkiSgtPdVmP5dmzMp2cEYqFqM8Wae7KdBxQIv//tO4nM0rBzJx4O9Y2FrbsWOJQN3uxqTlZNRmQA9zE5gJ+JDxBYiWKdvsb+tE24Oit2j+OvZEDg6tiMOaj87gt5eOMYf701tSmByaCDeZlhGpJudn6h7OWNgTT3w7GDPf7oVh8wMRPMQVjt4W9wThpoXNnmKOoI1uZKHXEWDdbPM1cOn/HYKn/4qIse4wNhdDXqHC+Z1pWP52JPYsvcoMwvQ8QOLPSTYVscXssSwvl5kKEQbPfwxCsbhZv9fQ5fbpbtdhrtk3g8aeDK7l5IxtwK7UXcwl2NLIEnMC5+C9pGzItTr0sjDBzBqprkGmfWpzEla/fwYZMVzxj8z25n7UB4H9nGrvbxRXZTGG6zY9VDARR68egFLTftclU1N/WLHFtA5Z2VzHy3CuUXGAunr+vRzg0sXqjnS5pcF2EJiI2H1Cea0UtgILDOszmH2uW0k3fHbwMybvvxk090oyc1IqktfL2rVroVQ28ToKxUC/mgLHyW8BLaeOkVqZoiKIO+5sXIdCFC+CMBfIyPwDWq2cHRsHiouR4uvDpMT5OItC4yK8qX6SOdl3JORqOSJzImtJN10/yjYnQV+thdjdDKY1I1l1QX4Dn9UQ7T+zi3C8hLveEJ4dxt1nN17MQk6Zot55blub62StKVDDwqN7BNRm1rdIyw2QuHtCy+fDSl6FN60lcDS6LlVWpZZDnSdnHXuTiBsNFcPGTkLw8FHsPaD5boq6qg+uZq5sBjfdgYf98zjFSsnfy1nXt14kcK7lZR6DkaflwVzIRy+L+hVHWo0a27/5jP1ukZEEI594DkKRGCkXz2Hf0u+h0zac90jvFakzDISb1BpDhw5t9tqXzOl2ff9VLeEmmX3P0ZOxsBfnxbM8ZjkSS7kOs6a4GFnPP8/muU2HDIHtM0/X/pzcXbsQX+OZ4NynABaW3eHt9cItIwxvnXiLFQV54OGjwPcQXt0VythilG1LuiFy14DMEjmeWX0RGp0eE0Od8erILrjXkF1TvCCQWR0ZZnbiNkg3VdzI+fpmkLEDzQN34t8Jcp5dFr0MP0Q+Dr46CxBYoNzuVazRjIAy3AGPLr4PXiF0UVRBq7HEnl9TsPXbE2yx+V8ASegcHbkMXep2twX0Wh1K/rkGvUoHsZcFTAe0zC+h+wh3jHgokFW6E87kM4fzXuFcJfbKlSu3nMf1ScsJ1DnsWzNzeay0cRO+TnQsKA4rPboYp7YkY8s3F/H7S8ew9n9ncXhlPOJO5jLjMyIFFMNF5lpUiJn6ahge/3YQI9pD5nRB0ABnRsAFoo7r0rUlrlYpkF2thlivw6AOkJY3BoUiG1lZnClQUMgz6D3RFw9+2o8ZG5I8mmTgyRcLse27y/jztRM4kytHpkqH0ppz78iK36HVaOAR0oPJTJsDyihPulDA9v173TnXcvJ4IdNOKgIbzBrVOvUNXe7TFVrsLCwn0Q4+93dlxVwagTm9JZkVJC7uzYBWcz2qbdTj3dgs/c0w6eUIsZc5k2s+lD4Bu1I4I6X2gpvrfPaYk/MPtFqOmFJhi8YzxBIB+t3PEa+Ogk6uhpxMNem16OuEypPZkJ2qMU6b2QX9Rw2Gm5cbBHoBzOPNsSxqWYPjaDNnzmSSZcp/pjlvMjdrFGHzAaktUJYBRHPu44SAqT1RzK+EmCeGpudcmB8xh1pdgozMtVi/fj2uRkezHOO+kacQGpsOufk4SBJsa/+OjpSWK7VKuJm5IcA6APKLBVzBWcCD1f1+DY7N0JjVQy6ciuSF+AyUq7mCQ7iHNfp4W0Ot1WPpsZTar9fpNCguPtqsqLCbIXLxpAoeBFo13NxuHFkgsrYwtQAZVtzYiWcBp7QwoOok1+WWhtmDf5N5JZN1P/IU63prVNUsl5rmvOvDo8GPMrL4u20M9A9PZx/Le/99yGsizOqb5z5hP5B7razM61UcEaHeteQrRrCJaE958302yz32+VdZxzvmyAFs/epjqJS3HoP0d5OcnOJPCffddx8GDWo8e7wuKooKsfb9NxB/8ijrpo988nmEjBhdGxk3zG0YGzn46NRH0CjkyHz6aWhycyH28GB53LwaczYi48d//hZ6irl0lsHSk4duXb8Bv86M9tHMo5iydQp2puwEn8fHO33ewYTeU2E9M4CNbJHJXeWhzFuiwZ5YcQElMhW6uZhj0bSQe24Omt6jz858xs4vGhkZT+MmnbgBLV5lkdPlRx99xEy8CHRQ0Cz3G2+8wSTSnfj3IaUsBQ/veRiLLyxmi6ghbkOwaeImBDv0RZlGi5mXk7GzrAJjnx6GaW90g0icwb4vK16Fvxcew5WDGawr8G+Hm+u8WuMUhYKb0b8dVOzPqIl8EXBu5a2QzXbp44Txz4SwhSE5m59emQcXJ1fmI0AmJA25lt+MQVYcmaHosE7cWTdxIljH/knAP5+cxe8vH8OOH67g4p505qytrtay7rRLF0uEjfLAmCeD8dAX/fHgp/3ZvD8VYpx8Le9YN7Q9QCSO0FWjgHEDedcdhZTUr6HTqWBl2Qc2NkPYx0hyTO7bU14Jw6x3ezF1gZGJkM3QpyZX4KJci51JFfjz1cPIiLWCUBKKHmMeZMWS5iAzpoR1iamjTu/7nZaWd+vWjam3CDuSdyCzMpPJDKf4zazN5H7M1Q6uSuDEhkRGti/sSYdaqYWNqym8u3PEhooUJNmvD3QttLrfH1qBDj3kAUg+ernezlFbgUiTROICtbqUudJTnjsVCgi9J3mzLPmOBDPJ0uggcjKBtkqF8h0c2SMFgLSmaDrz/pkQSoQwV5vj+KHjuFbCGdfdDDLAnT9/PiPgJDcnXx4qnjQIsRToW9P1O/E1N89LJM9EClk4J4m2tO0B41gnqIuMsXXLZeaWTseE2iIN7pmZ6J3Ag0o6AfscRcyvRJ1/e142LcG+9H3scaTHSGjy5SjbwnkHmA93h8ih8a77uz5O8DIWI6dajbcTr5PdZ4dyjvVrzmagsLKa7ZeXX4RGUw6hkHx06jdZawhZpdw1TVCcj8K060Se/Y7cEpwoq0KmPdd5Jxdzw7GvKVPWqmZM+97asWc/UyjEhJcXwqtHBCPe2xd/AnnejcSdQCMJBtL0ZXAGTEeMYHFZ1P1V53PKGu4PzeKi5Hh8/GXcnX2oPmm5Rq3G7h+/RsKZk+w5THr1bbgFcWaLfr36YeLLb9V2vP/54E1UlXLrEQLL8d67t1ahN3r0aDYu11xkREdh1cKXUJCaDGMzc0x75+PaHHQD3ur9Fpvnvlx4Gb8vfhDKK1HgW1jA9eefIagZpdXrdLj22mvINOYItnPvAgQGfFQ7OlChqsDbJ95mUWSFikJ4mnti+ZjlmNGF89Chc9NyAjfqQVnw5CNg+PteWx+FuNwK2JqKsXReBCSie+8evTt1N45mHYWQL2SFhnutaNARaPEKZfHixSxyiqTNVBGleQqSPNN89yeffNI+z7ITdwTV2mr8ePlH3L/9flwsuMguSB/0/QBLhi6Bn7k9NnT3xTg7C6j0ejwVm46/s4vg6O2KR76aA7cuqdBp8qDT8HFifRI2fH4WRTR/9i+GiYkvrK3618gQubzy1kKZWIrKo1wl1Gqq3w2RLy2Fe1cbTH09nJlfUZayMplzsz1//nzt4koRXcRJyx2kzLX8Zgyy5BYj5ysUkDUi/+pE24HJHgvkiIvMwcHlcVj57ikse+Mk9v4WjauHs5jLOBEzcztjNu9KMnEidY99MwiTXwpjIwYUG9fRhKCjX6PtBWVsP1zdCFHoAFRWxrA8Z4Kv75v1LjhoRp7UBY98ORBTXwtH2GgPWBpziytFlR4CoyAIjYdjz9IM/PHqcVZQubAnjTl5a1T1n3dk4Ebwi7Bnng53KoGDiFVdaTkVaH+N+pXtP9LtEfyRW4l0RTXCinUI31fEjmcafaFCka2bKSsQTXm5B7Kuce9n9/san2UU2RpDOpxT/0xMHYALiWfb7e/j8QRwdZlbq2SK3JzEiib0vLsN6tjEFlJLkAEfwcjPEqVrOIk7df9N6zwXimubPX029NDDs8ITn2//HCpt/cozZ2dnPPjgg8xgNTc3F3/99Vfj44I9HwNINloYD1zbWfvh0NG9kSzMZ8d+bq9JuBAzAWVl1hCJeND20GKb9QUUm/NgotShd2wM9noYQVetRdGyGFY86AhpOXW6Cfc5jUDRiljo1Tr2OpoNadoEz0QgwPeBHmzhTK7hO2quPf19bRDqZolqjQ5/nOBmw4uKDdJymjduPoHKz8+vNdYVlRcj8ez14niyXMnGMwhTw7uzQkZxcTFLvSGw40JPfhEWjY6iCUUiTHzl7RrirULu0b1IOH3ilq97rsdzMBIY4XzhBaQ8Pw5G/v7QFhYh65lnoTOMIiRx8/Eq53BEqoxYBOAwmxvTVeQV5djw8Tusy0wd43EvvgHP7jc65hPxnv7ep4wUEzle/fYrSL5wljUIyOzv9OnT7OvGjRvHnMqbA/q9NCu+/n8LIS8vY3Fkcz/9ppbs14WjiSOe78GNTvzuEId8WyFclyypNU4jlCz7G5fzk1lX3tyzEt4hY+DoOIl97njWcdbd3pa8jSkEKAJv/YT1LAe+Lkz7Odcea6WbE9na69djKdh5NRciAQ8/PxAOZ8t7L8+aMrk/OcNxwCdCnoC3RcPRtP9ltPgOTc7X+/fvZ0H0S5YswbPPPstOCDI2aNKEoxP3BJizY+ZRTNs2jUkDyQiHMg23TtqK+/3vr11MUldpaVdPPOJiy5oybyRk4ccMyqwVY+JLj2LIHHtolYeh11PWrwzrPj3LZLBa9XX54L8Nrm5c1mhOzjpotU3I9BqJByNZOVtI9Xa8wdSstaA4p2lvRsDByxy8CnMI1SZMrUIZlwT5ZU7iJ+1uVy9Z8JSIYaPTQK3X41RZx3Ul/ksgNUhRViWiDmcxYr3szZNY9d5pHFoej/jIXFYwoRUNdQNp5poky9TFnve/vhj+YBCTiROpo3nt/wpiZUpmbmTE5yFY07rzra2QlLSIrp5wcJgIc/NbF3V1Qe+Rk48F+k72wcQpPhhrIUQXUSGgvwRnP3OW90ykjkYHaBZ/8+JLbDZ/wxfnmat3yuVCNrqjVmmRWiNNv5Ou5TTLTdGVlCVMBI6wNWkrm++zkdigq9EoXNmehud3lGPcgTJkRhXTWCncgqwx7pkQNupABaL4U3lQKTSwdJDCs9uNUVf1wXaIDwqtKmCiM4ZqcxYjpO0FZ+cZ4PONUFkVg/RrJ2rN0zq60FGdVAZNsZLmfhjJYqTR3wqWk3xuuXaTS3l4b47cOKU74ftIyjWvH+RgTia5RNYLCwuxdOlStq6jeexbILEAej3O7R9fzGaECbQGVIYb44QwDkctcqHmGcFUWoxuYQexuXgD64Yaj7mPfe3IcyeRbASkuEqgLa1G8Yo46OuMFrQHiHBTM4Gk5fb7AG2xEgJLI1jPCmi2kizCwgTPeXDn2qvXMhkRptf92aHciMHK0+kol6uvz3M307XcACqGE1zt7cDXqJF4huvuyjRaPHw1DVVaHfpYmOBJbxfW8Krtdqu1tZ1TInZNwUC8PXtEQK/VYs8Pi5nBWt2ZaidTJ5Y2QFgc+xOcfviORYcpo6OR++57XIe9hnTHOXKd5+5mUma+a0BRZjpWv/0ysuNjIDaWYsob78Ov542GYwY4+wdgzseLYeXkgsriQmxZ9BGWvvoszp88Uau0pSzupkAJEJf37sRfLy5AzNGDjCSH3jcGsz/6khlUNoSxsRIEZuhRLebh9yc9Ydwr4gZTtcRfvkeeKVdQ8B5ojC7+H6JSVYn3Tr6Hpw8+jQJ5ATzMPfD3mL/xas9XIWkgZtV8lAek4Q5sjVe0Oh6H93Bqi/cndEVPzxvjI+8F0HFAsnzq9AdaB+Kx4Mfu9FO6a9Fii2ky2qD5H3K+pK0T/y4klCbgy3Nf4nQuV1WkBRPJbkiKVR8ZE/B4+MTPhUVofJeej/8l56BKo8XrXo4IHjYCtu5u2PLl11CrwiAQ+zEZbFpUEYY/GNigdPBeBlW1JRI3KJWZyMvfBhfnmS36flo0lqy7Bl2VmnWdLce3XbVQai7G5Jd7MPOfy5fcUGkZjxPHIhHWpQeqk7mKvTS0/ngieu+DNAocF5vhaElFo86knWgeqPhE7sc5SWUsjzg3uZwRjpsdxek8Iamtk68FI2qUyd4JDoYu91ArU0jK7twIS2npaZSUngCPJ4KP98st+l6diwAiHuXaOsBxog263RfB5rSLs6pqjgvu+CCSTe7ztF3ez30v5XFryADKmJOrk8z8TmR0X6qZ8yTTJ7pWKFXV2HB0F/rkTkSYYhDOHI6FYZltZCxkBQIqHJFbfu3roNXhykFO3dN9hFuzSBB9jcUMPyiWpsO91B5Z+6PhNqrxgkdrIRJZwcF+InLz1sPK7xC8AgYys8GOhsFADTo9cyynTGnbeYHgNUD+x40ch8TkRFQWVSL1RCr2uuzFKO9R9X4tKRjJKHfHjh3MHZsMq0jBQFJeMnalhJRa9HkKOPUjkHMJSD6ESodebGTpfOxlqIXc+GFgtQPcsBsKoyJEmIjRP+BldO3VF6n/7EPfqxchVchxdLALfDdmQ5VewXKyrWb4t5ssdW8aN3s8RN8H1fGlzPndZl4QM6JrCV7xdMCJ0koWpTk/KhU7w/0wPMAeAY5miM+rxOrISPjzk8HjCWFjM6hFihHyWyEMHDoMu0/uR0kOqZrSsbBCjwS5Eg5iIWt2CPm8WhdzSiTpZRoInVzDigiSwKYLVgbiPf6lN7Hy8/+hLPYKLuzYzLrM455/DSaWVrWz3ZsSNyG1PBXbFWcx7ttvkfHoo6jYvh0SPx/Y1MytbzWPIJFf7dqApNhRB/fg2Kq/oFIoYOHgiCmvvwcb18YVLJaOTnjg829xauNaZiopy86ACcVyBQbDTFsNtaoaIvGt6i36fYUZabh6aC9ijx2GSsEpn6i7PeKxZxihbwyy06eR/8GHeNpUi9eelOCKNg2r4lZhXtA8aEpLWWxaXDczQAZY+cjQZ+gfOJt/Be9Fvod8eT7rbj8Q9ABTB9wcO3Yz6PgmBaNSpoIuvhSfwBibfKWYew86lRO2p2zHkawjTFb+yYBPmswg/y+jxaSbHLZ79erFZOXkGti3b182C9SJexvpFelYGrUUO1J2MNM0OmnoAvJ48OMwE98oFarvAkJZq6YCPsuy/CY9n3VE3/Z2gpNvFzzw6ScsH7IoOw4i6QiU5AAbvriAiDEeCB/reUt+7b0MJkN0fQBJSZ8hK/NvODvNaNECovJQBqoTy5jzqM2cAPDaeK5HKBIwd2qSRW47nAYNlFjx/U4M1bjC1su8URl7kEZZQ7r/3WMC7RndlZdSzggUzddThNfNqg+ax3b0sYAzEWxfSzh4Utfz3pvt6mhp+Vha6KXeueeRnPIN23d2nglj45ZlNUfuWQMPlTfMxTbwsuPyeOmaaIgho+xn+h0VRUrkJZchJ7kcecnlKMmVMZJNoGLNju+v1Dp+k+KBcqMtHUxg5SRlWevtVayhrijJYek6py+yxtZvLyEruQQD1VyHjI5wLQ9IdRJh2ghv9O7pyK5DNyP5UiGLPTM2E6FL7+Ybwnl7+ePngC2YENcX+iMlUHWtrI1fa2vIc0YC/PUwc72IsLD2+R2NgWZ2yf2YgQi3twVs5gc1ep8gCfLDcx7Gkp+WwLbaFn9v/xuOcxxvkb0aQKOCs2bNwtWrV5mKkeTmGzZsYCa6RPIoqYYaL7TuU3o9guzEK8havxNZmsjarriJ1AThZe7w1zujImEYFAPWYbK1GOOC5rN7pNjHh5z3MPDyOWy1tMCrcwJQsiwG8ksF4In5sJzk2yoPk6ak5cezj7P9XlFch9hqsh/ELi03XxTz+VgW7IXR5xOQrKjGY9FpWBPqg2eG+uK5NZcQm7oL/j60Xu7JTFabC3rNVSoVU4z4BwQgNqQHm3FevW8/tnlHUI0Av3fzgn2NWzmlkdD7SxGgGUfiYQ4hTPu7tOi14/MFsO3eC/1GjML+pd8zY7U/X3wCvafMRNiYiWz9tyB0AT4/+zkbNxw3dRccFr6F/P99jIJvvoOonwpmXWzwN7jr3ghbcxSkpeDAbz8iN4nzEXAN6oYJL70FqXnzilQCkRhFEnPIvIJglJ8JoawC+bFR2BYbBZHEGPaeXkyGLjE1h15PBcoMFGdlQl3HgI1IPj3/7iPHNRlDVp2cjKznngc0Gvj1G4tX+/TCx2c+xncXv0N/+94QvPg/ZNnnokTmAfD0CJ/xEL68spzFqhFIOfG//v9DuMONkvnGoOMBC9VVGA8N+kCIOZkUHSlr1fF4J5Evy8fnZz5n+890fwZ+Vpy/QSfaiHQb3AOPHDmCb775hknKIiIiarPyyFGwE/cOqHr5+9Xfa8k24T6P+1hWIV1IWgKSXEkFfGYw8kNGAevevOHtBDMbW8z68Avs+mExks4vY8RbIPbHuZ1pSI8pwX2PBMHS/tY54nsVzk7TkZLyLapk11BWdhZWVr2b9X3KayWoOMiZ0NGioylTl9aCFsehw9xRqOiHE+cOoViQhqMl9uhjKoadXt9gkaCLRsnmUajanqNUwVnSvDij/yqoM8m6lInlrJtdlFlpUGHWgggGkWtnX66Tbetqesfmcu81xMmUbMFL0vL7rM3ATWp2PEpKjqG8/DyTHnt5Xo+VaQ5yEuIRe+wQhFY6Rrqr40tg0v1WtQmdkxZ2xmwjc0RCVZkSK98+Ba1WD7dAa1QUKdgIApFz2lKv3Kp0Idk2eTuYWBnB1NIIxmZiSExFkJgIIZYImYO9UMQHX8hnqhudltvU1RrWSa+WaSCvVKGyWMkIMv3OpMLLgAgQKawRtccwB8yDUiiDiQ8fBxwccc6Gh6f9nNDfm3vuN4OKCpf3c9c+mpFuaaGp27C+OJ51EQMrw1C0Jg6OL4SD38bFKllZNS7uEMCprx+kdokoLtsAC+uOzaAt33M9ilPsaQ6bh7o26++kWLApE6dg86bN8Cv1w7s73sWP035s8B5PxxvN5lM8KY0gkUkexYpdvHiRbddB94CeAPMO0zKnbXKUJqn6psUr4K90hqnrSEgungAiclBQsAuOjhNhMX4cCr9bgpHnI7G372BE2YsRPNUfpRsTmLMzJXZYTfNvMMu4NSCDJ5KWO6vs4FPtCrNhbjCJaP1IBkmoV4R4Y8LFRGZs9nZiFj7t5gJvWxN0sYxqsWs5nQMGaXl4eDh7D3x79WWkO/vCacA7Ah/6uqBnTZIIgYofNEJA6UFJ5RkIN/KDSSvHTOh32Xt4shnovOREHF+9DFEHdqPP/bMxKXwcVsetRkZlBpZcXII357wJVXIKSlevRs5pS5gFBkOm1SGkIAOZf+7DwdMnWOdZbGyMAbPmI3TkWEbumwMq3GzcuJF17/nGJhj/ytuwEgmQcPo4Ek5HMtl5dnxsvd9LBm3e4b0QMmIMPLqF1jqONwZyI89c8CR0lZUw7tEDTp99ihliMQ5mHMSp3FN4bcMjWFhWiIQuTkAWoO5ljNeSViFXxilO5gbOZbPgUlHL1rA/HU7CkeRiXBLysc3BAYJsGYr+vAq7BaEQ3SPrYYoiXHhiISrVlehm040lVHSijUm3QVa+cOFCRrgpt/vXX3/FokWL8Pnnn9c//9OJuwp0cT+Vcwor4lbgRPZ184zBroPxVOhT6GrbtdU/+1FXOzbf/U5iNut4C3k8vOLlCJFEggkvv4nDy5bi8t4d0Kr8IbEcg4K0Cqz75BwGzfJHlz6O/wq3Q5HIAk6Ok5GdswaZWcubRbo1JUoUr70+x307i4HmYvDIvrgUcwYyuQwy4zxERvJRqNaz96I+iaoJdAg1M8alSgWOllZitlPzJGz/BdzQiUwsQ05SOcrybzX2MrOR1BJskowTCfo3HPN3VFpubQZToeAOdrm/ZvuuLg/AyMihRd9LEWEEoy4WQD6giC9lUYENSYXrgoo5RLjJSG/C86GcrFumRmFmJUpz5SjLk6E0X47SXBlk5SpWBGrrCEc9dKiy4yKKrEXu8IqwR7ZpEv4u+QkiWx2GhP2FE5nFcJWIaudg6/1bkspZ/BaR/m6DOVfmlqC/a3/M9l+CwCtesC22QtmmRFjN7NKm51bkpiTmsK4rHwfYfcuu756eT4PP75jiI0VrKWq8N4SOUtg+3K1FhYXQkFAkJiUiOioaAVkBeG7Pc/hzwp/MWb4hUNd7+PDhTNWYnp7OiBCRbxozJCNd6rK6aNLgUnQMru7esHv4ffaaXym8goOWp9GlwBmusIFzxjykRHyBtPSf4OAwHubjONLdPT4aVhVl2JRfij4RbuCJeCj5J4F1vHUqLWxmB4AnbJsi5K7znMnhwIowmA/3gPmI25fyBpka4+cgDzx4NRXLc4oh5vPw5CB7WFRwM7om5s2XlpNzPJnX0WvavTvnAp7sGQgdjw/74jzMEWuYf84tzyEoiJHuVEEhBvUeBL6kxcv6WpD0m2aq404cYaS7vCAfe3/+lpHZ+/39sJNfgqOFm9Cb3xVhjz8CxZmtKCrlo2J/EZ6M/QpmsgoYPPL9+w7E0PmPwdS6+esE4hQUL0eSeXodpk+fjoAAThZO8vDBDzyK/NRklBfkQVFZCWVlBbuO2ri6wcbNA1aOzk12tetCp1AwUzh1VhZEbm5w/fEH8I046fpH/T/C1PXjkWBcjjVjjGF7Uorz3YpxzaaKScxdTV3Z11A0VktxIb0U3xxIYPvvTO4Gt25OKPz9KtRZVezR/snQ2zLP7SgsvboUZ/POMjn9pwM/ZfLyTjSOVr1CdIJTp9uw0RzK+PHjWae7E3cvyOSBOtpbkrawDjeB5lAGuw3GgpAF6GbbrU1+D8XBqHV6fJicgy/T8tiNiBZcVOkc9vCTMLWywYm1yyEvyoGlyxwoZaY4+HccMuNKMGRuAJPY3utwdZ3HFmVFRfuhVOZAImnY2IQMUIpXxUGv0EDkalobKdHeoPm8no7dcCTlDNQWWTCudkLiuXzkJJRi6LxAeNRjZEQ5yES6j5X8t0k3zZ+SG79hFps62vLyWwkNzawyku1HknFLmFrd/TfSe0ZaXsiR7gl2dy4mi87vyspoCARSeHg80aLvTTwbidyEeAiNjNBj/lRU/pjAZjKr0yog8Wn6bzJkc/uF29eSSyqWuQVYs60uSH5OBJwKQdSxrSqlTclIOuVk06NKqeUysusZjSdjN/rZRlIh64xTt5ylIWjzcPKymhloPf7yeKj1aozd9DxKTArxZNeP8EUWF/vzka8LU0E1hMsHuC43FV6pI99SUBbu5OCpWFS4DJ9lvMCMIUUupjAb2HICXx/IPT7hbD4zMuwzcg4Ss9eiujoPBQW7a92L2xOyc3ksWotByIP9U6Hgt+I+OWHcBJbHjVLALtUO83bOw0/3/cTMnxoDn89nXW/abkFxMvDDr0BGDJAXhQOqQrxz8h2ozdU4VdoV91f3gcg0EJYxPVHW9RwKi/bD3n0UJKEhLJZpyIXT2G5tg4/9XJinCEnli1fHQRlTjIJfrsB6Rpfb6vyRO3rOtmhEVp1l1sGjA8cxwt1WBZmRthb4zN8VbyZk4fesIjxjdhH9+DrkVDkg5QoPjzeTdxu63BS5Ry7yW/JL8Ux6Me539oRHdgpmFqWDx7tu7GWAl5kLeHoeSvlVqA68/aQK6g4HDRrG3MQv7t6G2OOHUZKdidLYJPQDd8+/dG4pmIuDqSNQo4Ymws03kiB40FAEDxsFB++WZdeTseu6deuQmJjICDeNOPj5+d3y3Bx9/Nh2u9CrVMh6/gUoLl8G39wcbr/+AqH19eum9OhFPLFbhsVjhSjMMsOJgTmQSbmm4uyA2Xgx7MUWd7cJlUo1XvznElkyYFJ3Z0yP4NQmVEQrXBrFYuwY8V4QAsFdnDxyLu8cM1omvNvnXXhZ1HNt6MTtk26a56EKJxFs2iifm2RInd2auxPFimLm2Lk3fS/rbhsk5FKhFFP8pmBOwBy4m7e9ecNT7vbQ6PVsxps2G5EQc5xt2HHSe8oMmFhZY98vS1CW9Rsc/WegvNiFLWqIyIxZEMw6gPcyTE27wMqqL0pLTyErezV8fV5tkDyUUgcluwp8qRA2DwS2WWW/KdDv9s23xhm9GAqeEkGTJMiJFLCFOUUVBfZ3Qv/7fW+YBR1oaYLvMgtZp1un14P/H+nS0jw2GVjlkukZzdSmVjADq5tNz+zczWpItiUzPbsTplb/BcTLlEiSc9JyWvSi5rrWkaBZQsMst5vrgxCLb+1CNQStRo3jq5ax/YjxU2FmYwN1gDXkFwugjCtpknQTiSZnc4JvRP3mh3VBRmvkD0Bb438TJycn8k0O6yTvpXO8oRnR1as5w83Q0FC2UF4du5rl0zqZOCFSEwyVvgpDrMwwht6jBkDXG4MDe/fhLRtpqotJPpPw/cXvsbR6A57Kn4HyXaksNknixxlC3U6B7dharivVdYAzHDxtIMccpKR8zZRM7Um6SeJfsTcNlUe5CCmC6SBX8I1a11GiueyZ02fi999/h4vcBQU5BZi7ay6LAQ1zCGvdk7TxAbpOBaI3IH7bArwk4dIterv0RoBLCKKPZSJE6wG7jLkoD7iCtLQfYWc7EhbjxjPSPfp8JDYPHY2jJZW4z9YCxkE2sH2oGytEU+cvf8klWIzxZLnTLZ3zVmVWsiSQI6rjULmo4Sp0RtjowW2+Xn3IxRbmQgFeiMsAKo6wj10p7oYjUcmY28cdUnHj75dMJmMJAISw8HD8llmI95OymR+CWfdeQHYKks9Govek+2/5Xs3ZYrjorJElKMa1zCQ4eDXtXN4ckDqR1mq00cz0tVMnkHktGrGZlyFU6GCsFsKcr4SFVAB1lgaOpRXwCAqB70MLwBO27PikOfa1a9ciJSUFQqEQs2fPZrL59gK5tWe//gZkx4+DZ2wMt19+gZH3ddPaqpMnkfnBG7B9Rg0njQgXA7gCr73EDp8N+hy9nHq1+nd/sC0WmSUKuFga46NJ1xtdZOZn92gwCn69wlz1C/+4CrvHQyAwa18lDUWyVVZWorS0FCUlJexYJMUBFUHocwb/BioEWVlZMbNFmU6GN4+9yfjERJ+JmOAzoV2f478JLb5y29nZMTdLksHQRpmCRMLpDenEnQflcF4tuorzeeeZdJxkXpTTaUAP+x7sJBnlOapJg7TbBXW3KzRafJ9RwKI1aC5nTE1XqtuQEWzeZ+d3XyIv4R+4dr0P8qowlOTIsP6zcywCybvH7Udl3Um4usxjpDs3dz28vZ6vV4ZIiymS0lEF3np2AISWHdcJJaKPYhVCxJ44w0vAlbhzePLNp3F+ezquHM5E3MlcpF0tRv+pPvAK4yrcYWZSmAj4KFFrEV2lQIjZv++8J+JBnUAyPSPDKiLZ9c1jE5EhYm0wPiPTq07Ts47Bthpp+RBrM5acoL4DMYTU5ZTJEphRkrt7TXxSM3Fl/26U5edCamGJnhOnso8R2SDSrYgrhsU4r0aJAZFUIsZUnCTTtLYC/U6BkLamC3+0UKOulMG1nIyqyB+E0NfvRfxaUsV8PT72d2n0b7lMjuV6wDPEFlaNZAs3Beo6USF5uWo5+vDC0CPPFyVr4mH/THcIbVpv9hp9LBvF2TIYmQjRZxJHBCiVIjX1B1RUXEZ5xRVYmNdvSnY7oA4tEcZa4zQCDzDrXf9cfHNBkW7kvbN3716EloTioOQgHtv3GF7v+Tqm+0+HoJmzt3WR22M2nKI3wD83Dl4uThjc43E8H/Y8VAoVfjz7HbxlDjAVWMI+eiryQ9eiuPgILMeMRv7nn8M/JRFOhfnYXGDFSDdB4msJxxfDULIhgRmLlm9PgSKqCGYDXZgzd1Oz3uoCOapO5bD5cGopnvDk3PXHBI5jXfv2wFQHK1gJtJBFcTPv51yGIr+Mj5Wn0vHE4MYJ5IULF7jIPQdHvFikxKlyTiEy18kab4SOwW+7/mGmZJXFRcwjp66xnuJqIbx49ox0k/yfZurbGiQ77zd9DtundeVTB55iedzLcvKhD3wUL5X1xPfffARNZCTyPv4Yju9zYwbNASll16xZg7S0NKa+mzt3Ljw9PdGe9/e8Dz5E5Z49JPdjWdzSsB61n1dcvYr0V5/F7gUqbOBLIBPqyTsNDsVGsAtwZGvo1mJHVA42XswC1Y6+mdkdFsY3FuUF5mJGvAt/vQJNgYJ1vO2eCGmxu35joBFgGmWgcRF6zUn5QkWPlkAj0cBT4Ak3aze80PWFNntu/wW0mHRfvnwZZWVlzEyNMhxptptOdJpBobmfTz7hwtE70QEzpKoKZFZmspgv2uJL4hFdFM3MQuoiyCYIQ92GYqzX2HbpajeGhd5OKFJrsCa3BE/GpmNtqBB9LblFon/v/hC+Jsa2xZ8iK2Y/3LpVwdxuFPKSK7D716sIG+WO3hO971ljKcrnFIvtoFIVorDoABzsx97weUVsMetiEEhSfrsdmZaCFviEHv7BuJqTxc7r+GuxGDCjO7x72OLwymusC3VgWRycTphD68CHiM/DACtT7C2qYJ2JfwPprpar2TwpuYlTN5t8BuqbfaV5bCLZZHxGj9ZOJm3usNuJe0NaTl1u6tgR3FwfZj4OzYVSVsXicAj9ZzwAsYQjhEZ0/gt4rMuhKZA3aqRokJZTl/tOqcxoLUDvBZlnUTF+WfQylChL4GTqgd1ykiurscDNDr7ShguJikoV4k/l1saE3S7I1Ihift63WILNvJ+AXA2K/o7hFq6mLe8Y0XXgzDZuFIsIN0nrCaRqcHAYh7y8zcjM/AsWXb9FW0JTrEDxyjioc2VMTm7kacHyuakw0xaS0969eyM5ORlJSUkYVjoM2+2245Mzn2B9wnpGvns7Nc/8M0+Wh1+jfsXmxM34WmqMYXIFfjEOgHPEK+zzIhMRwvv1RuSROIxUh8Ii7z6Uep9gs93hYetg0qcPZJGRGHY+EpscnCDTamFSM5NLf6ftI91YFjmpFihSrDi9gn2czMJEzqYQ2hqz2Vcay9CUKKApVEAeVciIugH6biY4r49lNvojPUeiPRHMi8YVKFABayQIu0DfnY9PiosRUeqAHpYm9Z6rRLZPnz3L9rfYuCKmXM5GMd7xdsLDLrbse5z9A5FzLZaNpJAjtwFVJ3LY3+Xn6Y0T+VwzjLqVZJzXXhjgMgBTfCZjc/IWvGFvg1BpIBIsvJG48D10/eAdlK39B2IPT9g83LSpFnkDrF69GhkZGRCLxXjggQfg7t6+a9TCxYtRtn49zUzA5ctFMB14Pfq4OiUV5954DD8+Xo1YgZi9tlYVIgyMc0BkRDlraH169lO81+e9Fl9388qVWLjpKtt/eogvennV/x7R8Wz7eAgKf+Wk5kVEvB8PBv820ifoOk1Em9zxSVFB3ey6oL+FkqnouCEfByp+kOKAClT0HsnlcraRSz59r1AphBu51cuAn777CU5OTmwsgnggjRp1omG0SqNEbw6F1FN2Y79+/bB161ZWqTpz5kwn6W4FFBoFPjvzGTLlmbh49iKrNNOsNcFwYtPXVKoqIVPLmGScnBPlmluNmghkjBLhEIFejr3YvLajSfPjV9oa9Py/9HdDqVqDPUUVmB+Vgu3hfggw4Raa3j16YuqbH2Dzoo+QGX0KXj20CBk6E1GHs3FxbwYjQiMf7daqOb87DT5fBGfnGWxxnp29+gbSrcqVoWRtPGec1seJSec6EnqNDvIr3MLdoqcL+hX2q00mCA4OhrOfFWa90wuXD2bg/M405CZVAElSHFJdw8BeVqC0UyLdjZkj3Y3Q0Px8lowdV0Su6bE+wzMi0zYuJrUE28mH5rHv3vmq/xJIYUHScgmfh1GNyJbbE0VFB1g6gUBgCje3ljm2nt2ynhkAWbu4odvQ62kfNKNr5GOJ6oRSKOJKGiTdVCTKiKmRloc3LS1vD5Ds0DCDGhYWxrrcf8X8xf7t4fkKoirUcBSL8FIT1wfqIlNsnr2HGTMWvF04mzqz9I09aXuwMmgfHpGNZh2jot+jYft4cIs7Rqe2JDMpP42NBA248Rrt7vYII93kyK30eb1R346WQH61EKUbEqGv1oJvKmIzzcUrObdmk7631+U2gBbTkydPxs8//8wW0Q8KH8QG4QZWvKeudx+nPhjmPgwDnAfAzfzGYggVVs7knsHJ7JPYnbobKh1XoDzrNwjDruyFc/IxoDQNsOK6lRQreyYyEmnaAnjq7OEY/TAyTD9GWdk5mI8fz0j3mPORWDV6MvYVVWCKg9UN6we6N0qCbBj5lp3Nhba8GhU1HgANggfWETft54SD/EhUH69mc+tdrLqgPVFYuI89+jmNwotiR3yblo9qKzHGXk6Cu0SM4TbmrOlAqzqVTodSjRanL16Ca1UVZGIJ4myc0c/SFN8EuMHD+Pr9xr93v1tIt1amhuwMV7CyH+oDz9OeSE1NRVxcHFubtydecxmOS3HrkSYWIatkB2DfFWETxsFCXoGCL75AwaJFELu5wmzEiAZ/BilkV61axeIGaexh3rx5cHVtGw+GhlC09DcU//4H23f88AOYjx5d+zlZWgp+/noW1syQQ8UTMHLUJ98d3heBXuPvx6ghvnj24LPYkLABfpZ+mBPIdf6bS3rf3BSFCqUGoa4WeGFE4zPpIltj2D1BHe8oVngr/CMado8Fg2/cMspGBR1KHjh16hSLdjSAJOPkz+Dh4cE2kozTeFBTIE+oD458AAuVBWbazwSKgJycHBYrSNvBgweZ8V3Pnj2ZWqFz7LgNSPemTZtqDdSow02VEXIzX7x4MYsN60TLodapsTl5M9s/n8QtZJoLItiUi0c3E38rf4TYhcDT/O462IV8Hn4O8sSsK8k4Uy7DA1Ep2B3uzyI3CO7dQjHl9few+fMPkXrpLJM2jnjkURxZlYDsa2VY9+k5jHkyuMl5xLsRLs6zkJb2M5OZy+WpkEq92KKheFkMi0Ux8rWE5YTrs0QdBWVCKXQyDfhmIkh8rdDTsydOnjzJquQxMTHMp4GchMNHe8K/lyNOrE9AyqUiJJ0rAO9iIcZ4inHZXwtZyPXOxN0GMocijwCShhdlVqEoi3N11pGDyU0gB2jDzKu9pznLMRd15mPfldicz3WxaAFL0vKOBi2gSFpMcHOd16Iud0VhATMnIgya+/AtTrvGQdaMdNNct/mQ+ju/KZeL2Nw1mfTZON+ZTFeSlZOLNS3eqMOxPH45I2P2FsHYV0UdHD0+8HWGSSPvD8njo49ms/3QEW5tds96sOuDjHRvyN2CRx94BPwV2VDnyVBEM5KPNb9jROMl8ZEcqaFEB5pxrwszs6Ba3w6a7fbzffO2nrderUPZrhTIajr/Yg9zNnKkiCpk9wohZa03w2CvuTA1NcXUqVOxYsUKlCeX47sp32Gfch/WXVuH07mn2UZwkDrUGkZpdBqmrquLMPswvBD2AjcTXjkZSDkMnPwOGM/5HdDoYe9+/RB57CxclFYwrvSBRfYgpNv8jOD7liDvgw/gkpMFn+wMbLS1uIF0GyC0MILFKE+YD3NnnWxlfAk0RQqmCKDXhliswErCuoSU0W7Sy7HW/Xn/4f3scaTHyHZdF+n1WhQWHmD7Tvaj8IaNM0yLVPgkNRc6OwkylCr8lV3EtjrfhPsTYthuqXcXfBXkgVmO1rd4pZCp2ZHlvyM7Lhby8jI2llJ1IpsdM2QYaORvhaCKIEa6aV3e3qTbLC0S3xYUYrarKxTVcXCu2gBvaTj0Dz0IVXoa63Znv/Y6PFasgHG3W9NwqGtKxx0RNZoXJsJNYw/tidK1a1H4NZc0Yf/aa7CaPr32c2ej9+LDA28goyf5tPDgY6THApvHELvrAMsF7znxfpYxTlG6X1/4GovOLYK3pTcrTjUH689n4ci1QoiFfCyeEQpRM9SbIjsp63AXLr3KRgEL/4yG3aPdmuVOT5J9ivo7ffp0bVebutddunRhaztvb2/WyW4JSEX7QeQHqBZUY0LPCXginDMOraqqYiPHFCVIBJyOP9pIsUCckH7X3cRH7jnS/eSTT7KZkSeeeIK9oNQV68TtwUhghKdDnsa1hGvw96vJpqzhBfqa/8iS31RkyjZLiSWcTZxZB1sivDfckI0FfPwV7IVxFxKQqlBhflQqNvbwrXW0JeI96fV3sWXRR0g6d5o5nd//2jPY93ssSvPk2LL4Iu57pOs9N+dN3Q9bmyEoKj6E7Ow18HF9HUV/RTPiLbQzhs2cgGbFA7U15Bfz2aO0uz073ugYpI7EoUOHWLebFtKG2TdyKR7xSCC2rNkDcYkLsuLLEJFczbZ/Yi+g/zB3eIXaspzfOzmDTeS6MPM6yaYc4fpAElEDueYezWDcCulpJzoeZN63taCU7U+x79hxDANoHrWyKoY5lru5PdKi7z3xzwpo1Wq4dQ2Bd9itUTPUnQN1VzMqoK1S1SuJrpWW36EuN4GiQg2z3NX66tout9jxWVQr9KxbN8m+cYKYfKmAybelFmL4hLXd30IpHEQELxZcxJrCDXj28QVcxyhHxhautg91bVJqToU5g3laQD8nOHrXX1hxd3uUke6cnLXw8nwWQmHriiDV6RUo3ZDA5NEEsyGuML+PJPo8VEVykWxm/RufjW8NyKyKCBoVXA/sPoCnn3yaSfQPpB9gs7uXCy4jX87dK+qCCvx9nfoyJR2p6mqf16BXOdJ9aSUw6HXAnOvMkyLyzMmTOC9KRV+NP+wSZiDV7i3IfNJhOmQIKvftw/BzJ/GnmweKVRrYNGA8xhPxYRLuwLZa4z+ZmhGR+gxISRl4POs42ycfm/ZEefklqNXFzOPBEBP6RIQ71h9LRerVUowd6glTNzPEVimY1wGlulgUF8CuqpxFci2ZOKpBbyRzO3vmBp6fksTWR90GjGAz6+xzQ7mCVWBgIHbu3MlkxDQqRorUdkPifvioNRhtNhSbKw9CXboDu1MHYYzXGDi+8w7UWdmQnTiBrKeegue6fyByuq7QIBK4fPly5gdFf+/8+fNZrnt7onzHTuR9+BHbt1mwADaPctftUmUpvjr2Mbbl7gOsAClfj4mWejzR52fs+uIf9jWkLCDCTaAc6sTSRGxP2Y5XjryCVWNXwdOi8fnz7DIF/reDU6q8OtIfvvbN91IitRNHvKOgpnUNXb+IeDdgpEgKpEuXLuHw4cOMDBPMzc3Rp08fpkiiImlrQGMkLxx+gY2uUrTwcz2eu6F4FxERwTYabyAFFD0HGhmgwgqNH40aNardVQz3Clq8Si4o4G74nWg7EOF5rNtj2JWxC2ODx7KK1L8R1iIhVoX4MOJ9qVKO5+LS8VtXz9qqrmdID0x65W1s/epjJJw5CbFUivtffxr7/ohlcsrdS6+i31RfNvt3L1XOXFzmMNKdk7sR5kdGQp2nYB1mlrN6G3M6rYVOrmbyVYI07Lr8s1evXoiMjGRzO1SpJOJdF2ILHcbODkZBShVWbUuAcYoMivQqHPgrFnwhDy7+VvAMtoVrgBWsKH+6jeedaYFFi3SSg9NGEUjFlGuZWYlqmabe76E5bDs3M9a5tqVHV1MmE7+Xjp9OXMe5chmyq9UwFfBZp/uOdLnTuC63i8tciMXNn52kBXPc8cNsf/ADj9R7DFJHjzpX1Nmgbp5JxI2LUYr3yqo5d+8U6SY1DM0CE2ihtTp+Ncqry2FlPQKXFFIaS8cnfk0TxKuHOUfuboNcIGjjwiN1u4l004wyxWHWLlyzqlDw42XYPtiVOZs3hNgTOSjMqGRmiX0nN2yCZWMzGFKpN+TyFOTkroe728Mtep6URV2xLx1VJ7NZoZ3k5FbT/WHcxbpWaq4tqwbfRAhpOxWcyYuHOqTUpSIl44MPPohHgx9lW5WqiknOtfrrSQ0UDWRr3IBTv0d/wL0vkHEKOPUDMIrz+KFuZp9+/XD8+EkEqOxgpbGCfcIspLv+Ao/xExnpHnXhFH6bNAs7CsvwYD151A0a/zVSQDmaeZTJ30laToWCjpCW29oMqzVNpY7mm2MC8OTKizh8NB1HXh0KR4vrxGfNmnMs17p7aGiTZsTU7aZrCK2NPAVB0Cu1ENpLmfTeQH5IKkwGWSQxpyJ6u0BGmmLOmO6ExSTIecaQVuzAeyffY40gMhpz+fYbpM+eg+rERGQ++RQ8Vq2CwNSEEcG///6bSZ1p9peONZI2tyfKt+9AzhtvMFWB5exZsHvxBea6vSlxE7499zXKNZXs6/qYaDDRSoABYX8gP5aK+OkwkpogYvyUG4639/u9j/TKdEQVRmHB/gVYPmY5HEwcGpaVb4xCZbUGYe6WeHRAy1WNdJ2yfSwYhb9dhSqjEkV/xTCvA/5NSjwyRdu1a1ctRyOncUqYonVcc6TjDaFMWcb+Tooc9rbwxucDP2/QbJGKJxQfTY1ZKuSRQSAZtVFaAhVoR4wY8Z+f+Ra21v1uy5Yt7MQmBAUFYdKkSbf1xnbivwFvqRH+DPbCjMvJ2FlYjkWpeXjT+3oV1KtHBMa9+Aa2L/4M0Yf3Q2JqhrFPPYgT65OYFDFyYxLKCxUYNNPvnjFYs7EZBCMjZ1RX56BIfhCWRgMZ4TbI3zoaJM+DVg+RkwnETtcXnlQFpYoojY6QSSKd1/U5vbp0sULAg13wyrkUjMjWol+mBhWFCmTGlrCNIJIIGNmlzdxOAjMbY9Yxp5xfWshSFrtBrsm6FRo9qhUaNj+plKtZ5jXlCctofq9IyRHtAjnUyhtjugygn2XlZMLINSPZrkSyTW+IO+vEvY/NNa7lY+wsmHqmo1FScoI5VvP5Eri7P9bs76Nj/OjKP9l+4IAhjWbYGgdaM9KtiL2VdKdcLmRdWBtX09ty+r4d0ELK0CUVm4mxLGYZ9BCg3HIOoAEedbFDoGnjbuEF6RXIS6lgMXs3z0q3BYa4DWFEK70iHZuTNrPurd2CEBT9HQttiRIFP12B9ewuMCZlwU2gwsbprclsv/dEr0b9RHg8Ppvtjr/2DjIzl8HNdT54PEGzjgfF1SKU706FtpQzPpWG2cNyvPcNhVhmlEWz3L2dWH51e4BkptOmTcMvv/zCulPHjx9ni3WCqdi0ZVFiVGgZ+Cqw6n7g/J/AgJcBE+417tu/P06fPImjkkRMqu4J89x+yIz/EvwR7uCbmsK6uAjdkhOw2cqs2aS7KexN29tB0nI9Cgs5Gbud3Y1mbaO6OiLCwwrn00vx9f5rWDSNc7ovLi7GtWtEucHuu03Br3d/nFi7HDmxcajUZl/vctcpbtM9m0g3Fc3bjXQnH2IaTIVdV8TyrWBiPRMDzMpxIvs4njn4DP4a9Re6WHeB2y8/I3XmLFRfu4bsl1+C1aJF+HvlSlbUJ7MuIty2tm3zPjeE8m3bkPPmW9QChsX9U1kXnszQvjj3BSPNBBe9DtMcVPA1Nkb3Hn/B3LQ7Nq9/mn2OCLfE1PSWJhlF7D2450F2fSFCumz0MqZAvRlrz2XieGIRjIR8fDU9FIJWNiLEzqZMWk5u5qq0CqaWJMUOdbxJSr5///5ajw1ax5EKmeaqWyohvxnk1fH0waeRUp7Cxkx+GfELuyY0Bequjxkzho0ek1fQlStXWPc7Li4OI0eOZAT8v9r4aPE7QhXusWPHMgkLzQcQPvvsMyYhIGlLe2brdeLfATIS+TrADc/FZeDb9Hx0MzXG+DpSRL+efTFywXPY+8t3OL99EyPeg2ZNg6W9FCc2JCLmWDYqixUY9Vg3RuDufvBhXXYfco3/RrnbYfiMe4JdRO8UDK7ltMirz9XWYLpBczp0E68PA61MUSXlY7MfH+/OD4FxmQZpUcVIjy5i5mREjnMSy9jWEOiae3MMV1Og7zGzNWbHgqWDMYtLIpJt5SSFsJ0WpZ24O6DR6bG9hnTfKWl5WvrPtV4NRi3I5U69dB6ZMVEQiEQYMGt+o19LEnMyiqpOLIVerb2BbCVd4KS+fs3I5m4PUHYrze4RaFG3MnYlM/g0tpuPTI0IdmIhXvVqWipq6HJTt96kDdy4bwafx8e8wHn4+MzHWBG7ArO6zGJSTYoPK1kVh+qUchQvj4XZEDc2J0yyZQOIcJNyhq4t1IVvCo6OU5CcshhKZRYjXvb2182Z6oMqqxJlO1LY4pkgsBDDcoofjAOsb8mYJsdukg6Y9mnfeVfy5qEOFXW6qeBqMFlqFXyHA07dgdzLwOmfgOHv1na7+/bpg6OnTiFJlwI/gQ8c4uYhI/BvWI4cifJNmzDi3El84xeALKUKrpLbG/khaTlJ5DtCWk6migplBvh8I1ZkrwsiFwvHBWLqT5FYfyELD/f3QqCTOVOVEXx9fZn7f1OwdnaBrZsHrMvtoJdrILCWwDjkxu8jifnu3btZd7GiooKRnzZHEje3HuXQjz0OtrbA14GL8eT+J5m6xND9dXdxh9vPPyF93nzIjh1HwuNPoCgoEOYWFoxw29jcWvBqS5Rt3oLchQu5Dvf06dC8+hheP/Em83sgSNTAOCMV+jlrYCQ0Q48ey2FuHoKYowdRmpsNiZk5wsZed4qvCxtjGyy9bynm7Z6H5PJkVmz4beRvtd4HhPwKJT7dyTUmXxvVBd52t7fmI68C6nAX/RENVWoFiv6MQfkQY+zYs5P5axDCw8MxfPjwNolwVmvVeOnIS6xIYWFkwf5eJ9OWGTlScWXKlCnseVEXPi8vD9u2bWPFpgkTJjB1xn8NLW4VPP/884xY00lNN1/aqDpKF2n6XCc60RxMd7RmcTLsmIrPQFwVN8tmALn6kgSTcGLN36zrHTrcDWMWBEMo5iMjpgSbvrrQ4Nzu3QKqgFPGqPRMD0AngMIyERrHOzeioS6UM4kSnfk0z30zaGFExJtAiy+aEaoPViIhutfEhR0vlbGuW4+R7pj8chge/2YQZr3bC0PnBbD3jObwyf3X2EzEOlsG1Ee4xRIBTK2N2Lw1zYkHD3ZB3yk+zEhv9vu9sWDJEMz7X19MeC4UA2f4I6i/M/vZnYT734/jpZUoVmtgLRJgoFXz5+LacmazrOwMeDxRi7rcOq22tstN84E0n9kYRM4mjIiRSZIymVtMGeK1sq6V3VFpOXXQyHWYFvMOHg6M0GoFViiQDmeff9fHGeZNmNvR35F4nrsGBg9tvzm/ib4TYWlkieyqbBzMOMg+Ru7ltHA16e3I5NyVhzORv+QiqtPKazvwMTXdZWae1gw1hUAgYaMGhIwMLqe8PpC0neLLCn64zAg3EX3zEe5weCXiFsJNqCTJORVHQ+xYfm97gwyWQkND2T1r48aNt8QKtazbzUWG4exvgPL6Mdx38GCQvitSmgm1Tg6x3BHqSC0k47h7zohLZyDUaLAln/NtuB0cyTzS4dJya+uBzOvhZoS5W2FciBO75326K44RYorcI1A3sLnwjeiHLha9auf+b84rp/OSGmAEgxK1TUHrgSTuXNpgFs4eR9iaM8+h74d/zwx9i5XFeGL/E8ityoVxcDBsP/mYWRS5XL2K4NS0jiHcGzfWEm7+nClYOcEUE7dNYoSbkoEGp/LwtqUCA100kIodEB6+jhFurUaNUxtWs5/Ra+L9EBtLG01KICJKhDSqKArPHXqOdYYNeH9rDJOVh7pZskJLW8DI3ZyZQeolfJzMvohV/6xmhJvm92k2nohsWxBumt1++ejLiMyJZO/tT8N/YsZxrQWZqpEP2IgRI5h6kkg3JSckJHC+Gf8ltLhNSAtxcsSrmwNIJ9Dnn3/e7o6Jnfh34V1vZ0a2j5VW4aGrqdgT4c/InAERE6ZCUVXJInb2//YDzKxt4N09HFNeCcPOH6NQnC3Dhs/PY8Lz3Zmc+K7MUt+TxoxwhLCEtXgQSjSHmaFaly4f3NEuN2WCC8zqX8iR1I3OcTI6oYsjVc/rw2BrMzabf7S0EjOdrl8PaKFKXSLa6gNFBKmUGiaTpS4Aj8/Jw0USyoX8b0qOOtE0NtcYqFE2N+XFdzTS039lj46OkyCRNL/if3nfTpRkZ7LOSa/J1x1zGwKdE9TtpogkZWxxLSFLvlQIvU7PikwWdre/sGrN9YwccQ2z3KviV6FSXQme0+uo1vPR09wE0+pxnr4ZRGrJuZxiwhy92i/yjRaLM7rMwNKopVgeu7w2o5kMt6ym+LFrYOnWJGZeVvhLFKQRDjiTUMbIuH8vhxZFmLm6PID09KUor7iE8vKLsLDgJNn0flFmtG+sKUpORXNfzOMKnuajPdkMf30go01FFOdybTqg6W57W4FUjNRQobl96nrPnTu33hGjJhEwHrALAArjOeJNBms10tf+vXvj4JkzOIVYDEIErFPHoCjkPAS2tpAWFSEiLgqbLU3x7G3GUXaUtLwu6bazux4BeDPeGBWAfTF5TG68budBNqZJZISilZoLb2kwtMJyyDQVsAusv/BI6jR6D6lAZiigtxlIvSAvgk5sijVCLvZqhDXXTTcXm+OX+37B/N3zmcP97J2z8fXAr3EhJwcm3bujx+XLCDp3DiIaTxnZfnnppevWIe+996EWAMceC8cax6OoiONUJRF8b0w+lACzaSrojQETY19077Gs9noeffgAygvymTt891HjmvxdPpY++Hn4zyxm72zeWVZs+GnET4hMkGFPTB5L7vl8anCrZeX1QWmhxz7HOGTmcWqhrkaeGD9/Goyt22YNTPHELxx6AadyT0HMF+PbId+yVKTbBV1HBgwYwJQddG2h2XPKaB84cCDzlWjVdeYeRIv/SsrTq6zkjAfqggwSKNy+E51oLuiC9EtXT5Zfma5U4amYdGhvan+SFDNo4FDodTps++ZzFKSlwN7DHNPejGA5ymSsteXriyze5a4j3PvSUXmUuzBaTvGFRzfOZCc3bzO0WnnHPyed/rpreR0DtZtBlVIyVSPQfHdD3W4i3ajJ6yZX6eaCosiMzcRMVkrzkuQcTrPXnYS7Ew1BqdVhdyF3jk9tBrFra8hkSSgsoplNHjzcH2/+95WV4uQ/K9n+wFnzITFp3sLIuMYciQwP6bytKy33vUPSclK00VgZebf4dPXByriVUBkFolgUzBYSn/q73BJ1dDN0Wh0bD2rvLrcBswNmQ8QX4UrhFebEXRfG3Wzh+FI4I9sE+fl8dC9XopepEL36OrJreHNhZGQHR0dOipqR8SdTFJXvS0PeF2dRtjweFuViTl0UZg+Hl8JhPbNLg4SbUEWxYTo9xF7mEDdQvGwP0Ppu5syZbBY0OTmZNVlaBVpA0zw3gSTmqutd8z5kpqTTIcG4HOX6HPD0QvAPO0I6YSj7/H3nIhFTpUS87Eb1W0tAxn7HsznXcnLUbk8oFJmoqopjs/x2tpzioz6420jxUD9PGEGN9GtX2ceIcDQXumot9FHcuiGm9CRSrtQfL2soktNsd31r9dtCTZc727kfNHwhQsyMYW903YOADPb+HPUni7Gljvej+x7F2dKzyOweCqPJk1jnOef1N6CI4uap2yMWLPPD97G/Ow8vv2yBX62voEJVAV9LX3xaNhxPn4yD6VyOcFuYRyA8Yn0t4daoVDi9aS3b7z1lJkRGzfPcCbYLxu8jf2dFB7rOPLT7Eby7/Qz73JODfdgoQVuBzNLIe4EIt1gkxgh+d/Qt90HZ73FQF7X+fKk7kvHUgacY4WYd7hE/oZ8LN0bQVnB0dMTjjz9eWxAiDwki3xQj919Ai0k3zf2QTODMmTPspkQbdcUoSmzixPrnHzrRicYczZcFe8GYz8OR0kp8m3ZjPAlVqEc++TyL2FErFdj8+QeoKCpkplzU8aYol2q5Blu/u4zMGlffu0JSviOFSRcJFuO8YdrbiWW6Ght7QKutQn7+jg5/XsrEUmjLVeBLhTDu2ri8iyJeaAFG3W6qmNeHcHMpTAR8JvmlGJROdKK9sL+4ApVaHVyMROhp0fEGYukZv7FHO1tyX23YBO1mHFv1F1QKORy8/dBtWMNdsJth5G0BnlgAXaWKmaqRoWBOwp2VlhtmUEmCvCF9A6rUSqhtOZn9fBdbBNeMmzSGtKvFLN6PYvs64u8gEjDOm+tYUbf7ZpBpmfU0f1g81BWFeu5+4yTkofLvWOR9cQ6lW5KgiCtmnWdD8eNm6DU6qPNlsFdwLscFBXuQ9eMeVB7KZNdbnrEQ+Y5K2LzQHdYzukBkL23S0Vx2Nrc2Jqyj4eDgwGSqBCLdrZaAdrsfsPIE5MXAhb9rP0zpLAMjerKO/35eHHQCJSTlnqh24v7WAVHnIalWYnN+w34gTYEizyhTnMgfbR3R5ba06AmRqPGC4PPD/RBuXAwBdBCaWrOuX3NBijldlRpqIzXSqqIRH3ms3q8jqbGLC/daki9LmyKJM4s7bM0V5UfUkyBBDuZ/jPgDXvCChqfBaYfT0PbTwfnDd2EyeBD0SiUyn3r2IpqcAAEAAElEQVQaqiyuIdFWyPr7d/y54yM896QAv40RIF8og52xHd4PewtfH3CEbdlOVMzQMtbj6Hg/wsKWQyS6/vyjDu5BVUkxzGzsEDKicV+G+og3FRusJdZILLsGmc0SeNgr8Oyw5r+/TYHGEShqjcgpEdcFTy5An6dHsdl+Mocs/Pky84FoLcid/NG9j+JC/gUWTUzS+d5ObayUqHMNIKO1qVOnsgIfeYX99ttv/4l0rBaT7iVLlrCZbnJGJKkQkwv1788uHt999137PMtO/KsRZGqML7pwc0hfpeXhWMmNFw6BUISJryyEjas7qkpLsPmLD9lClrqjE1/oDrcga2iqtdjx4xWkXCrEnQQtzMq2JKHqJDcXaDnRB2YDXWqdbl1cZrP9rGxubqgjIT+Xdz2bu55M05u73Qb3U8p8rK/bLebzWR4v+5qb3rNOdKItsT6PK6jd72DVZDe1raFU5iIvbyvb9/BY0Ozvy4qLRuyxQ2zGdcSjT4HfQMxKfaDzU9KFW8ArYorYdY0arw5e5jC3adwZvD1AjsMGp+Wg8CCsilsFhdkIyAX2bMb+jWaYpxGia7rcQf2dOsyHYX4QZ1xHc90ke60Ply4XIbJcjfNiIYy725EMi0V1kcS/+O9Y5H52FtnvnUTe4vMo+Oky8n+4hPzvLiJ30Tlkv3sS+d9chHKdGtKibgBPj1LPAzDyt4L17ADYvRaGLC95s9Mq5JcKoKsxyjLEQXU0qLBCRnkEkoKWlrZixlogBAa8xO1HLgE0nEs7oee4sTBXqVAmAdL53OyxKN4Pwq4hEFdXo1/UBWzKL22R2qAudqXuYo9jvcaivVFQKy1vWjItghYBQq6xcKzCBgWV11+TxqBTaGpVc9KBDtBDh7TLF6GUcVnMN8NggNpQwbxVUJQCWefY7u+S7g2Sbo1Gg12bd6F7anf4V3Gz9GtS1+D+nTOQ/dpMGAUGQltcjMwFT0Jbwcm+bwdp5Wn4YOls3K/8FsvuE6DEnAd7qT3e7PUmNvf4CcEfL0NmyH7IB+oAPQ++vm8hKPALZnpngFqpxJnN69h+n6kzIWxFbC85tr8eugQ6tQUERoWodvgGUUVc0sPtgNZeBw8eZIlRtE/v7SOPPMLGeoW2xrB/KpTFTOpkGhaLqIgpbvHviC6KxuwdsxFTHMN8MH4f9Tu623PvcXv7SDz66KOsUETXmD///JMpNP7NaDHpphdn69atrPq5YcMGttHNePPmzbCwaL/5rE78uzHD0RpznayZ4cZTsenIrVbd8HmSZU598wM2a1OUkYZdP3zNJOcUPTXuqRD49LBjsVN7ll5FPEnz7gCo41G6PgGyM3msim81zQ+m/W50nXVyvB88nhiVlVdRUdE+Eqv6oK1SXc/m7tm8BTLNdpOxGkWbREfXzCPehCE1EvNO0t2J9kKRSoNDJdzibJpj83Ox2wqZmX9Br1fD0rI3LCx6NNs87eCfv7D94GEj4ejbciMnkj8TKFoqucaLwaeexIGOACUaECixZFveNsh0Iigtp7GPveXtdIMXR0MoL5RzkYI8IKgD55Sp09nfuT/L5iW39ZtRlFWF6BpCEz6nC2xmBcD5vT6weTAIJn2c2MKWrZQ0ejb/TUaUZIqmzpWxDhPdtHhGAohcTeFsMof9nAqvE7Ca7wFpqN0NzujNKdpWneAKE3TvqBsH1dEYNWoU65gqlUqsW7eOOde3GKGzATNnoDIXuLyq9sM0ojAolIvOOqrNh9w6BXydCKJQOqZ4GHk+EplKFS5UtFxymi/Lx7m8cx1CuqtVRSgvv9DkPLcBFOukU6ugFJggQWWBL3Y3rxNdeSIbeqWG5XLbDwtiDQidVoOkc6cblZiTHLnVhng3I/kwHaCQWfsjQWQH2zpmqgYQKaQiTWJiIsRCMRaPW4yvBn/FOs4ZlRl44uQL+HO+I/R2VlAlJyPr+RegV9241mtudvT6hPWsMzthywRsNIqGXMKDi84C7/R+B7un7sbEXCekfjwD2fOSofbRQ8CTIjT0N3i4P3bLjP+lvTsgLy+DhYMjug4Z0aqXR6vT46d9FZCnPQ1zvhebu6cZ77XxnGS9VT9Tq2WvJ0mwDeMIFO9Xd5SXvHnsnghhRT4y3yxeGYuKw5nNLljtTt2Nh/Y8hAJFAZPhrx63Gl1tuqI9oa2qgvLaNcgiI2Fy5Qpm2NvD0ciIXWuWL1uGMz//grJNm1F56DAUly5BnF8AfWuuP/eykRqdTF9++SWze1epVMyW/v3332eL8k50oi3wsZ8rrlQqEF2lwJMx6djY3ZfNfRtArr+TXn0H6z56C8nnT+PkulUYMGsemxEe+VhXHF51DfGRuTj4dxzLfA4dxnXPOwI6pQbFK+NQnVTGFmg0sycNvXWBLBZbw8F+DPLytzJDNXLM7AhQ94Rlc7ua3pDN3RhIxULGF5QBSRf9+iJkqNL9dmI2zpZXoUKjbdK5uBOdaCm2FJQS30GomTH8TTo2216trkB2Drdo8mxBl/vSnu2sOEhxh01FhDUESYAV67jKChXIqdSwj1FxsaNBfi0Gp+Wu4V3xzblvILOcAy1PghBTY8xxal43NuYYp/5xD7KGhV3Hrhvmd52PkzknWWb3092fZo7DBFqYnlifwFQEPmF2cKsxreOLBSzD25DjrdfqmcRcU6yAXqUFBHxGiIlQC22MwTcTsYW8Xt8duWf/gkyWiOycf9gCvyWgmDgi9kTiTWpmze8USPY5Y8YMNkOam5vLoqhaPEIoNAL6Pw/seRM48S3QYz7XASdH72nTEPnmmygxMUGcKg09BE4Qyh0h9B+GiJgjMK+qxMb8UkS0cJyEHKr10CPMPow5TLcnigopPksPM7NgSCSN/y4iFCdPnmT7pA79Z38pNl3Kxtw+7gj3aLiYqJWpawsx5vd5sOOuS9+BiFy/CtdOHUe3ekgiGR07OTmx940k5hTZ1Fbz3FccOAXcMBuzG1RHdC7t2bOHddepqDJ79mxmEkf/UdHr+0vfY038GmytOI7LE/T4eCUPOH0a8W++BJ8vv4FYIG7UTZu6sSR9poLK+bzz0Oi5ayJPr0ePZPJvmIWRcxaCp9Ygb/FXSKv6C/IHOYWemXFXBHf/EcbGt64Jq+VynNu2ke33vX82BK3Mtl51Jh0xORUwl9hg3cQVWHLlU6a4+OTMJ7hceBkLey9kc9/NBRW5qNhFBQwyGaORD8q3rg98IwFsHwxC2fYUps6p2JsGTYEcVlP9Giz6kdP6l+e/xIaEDezfg10H4/OBnzcrh7u50KlUqI6NhTIuDkp6jL8GdUYGtDURZ3XRXyDA6b59kO3qit15ucjbtQv+iYnsc2Q1qBk9CmJ3d9zraHYJ9pNPPsHChQtZrhpVP0lK/swzz7Tvs+vEfwrGAj5+6+oJMwEfZ8plWJzGyaHrwtk/ACOfeI7tn9n8D+JOHKl1zB72ABdRRTixLhHndqZ2yPPWlFWj4OcrjHDTHKbtg13rJdwGuLhw3ZC8/O3QaNpflk03Q9k5TtJmEtG8LrcBJDE0MTFBWVkZc7S9GR7GRvCVGjFSdPNYQCc60ZbScooZ7Gjk5KyBViuDqUkXWFvfmL/bEMry83DinxVsf+CcByE1b50CjG8khMTfCnlq8k4BbN1MYU5d1w4GOZZTx4Xu+3vK9qCC7wilyWD2uU/8XSFohtxfo9YiLpJTIDUn+7qt0depL4uNImfef679U/vxlMuFyL5Wxgq3/aY2PH9J0UwkDyfHc+OutsxRnt4bIy8LFudl6JzRo7sbF3WZmbkMOl3LujOVNWNJRLj5ktYt/tsSpF6kzhqB4mEvXbrU8h8S9iAgtQXK0oFoboFP4FO3u1sw279SrUGBD0fqJEFTIZLaY/ClM9haUApVA0aed4O0vLCIk5bbN0NaTmoRitsjWfDYwb0xI5xbq7y/LYZ1SRtC5cEM6Ku1LErQ4MXSpR9nwJZx9TIUlRXtLzGnC1BNPvc605qoMJsbr2tUUDCkG1A2M42hGkBE7q3eb2H9hPWY6jcVBS5SfD2JBx2dNrsO4fNnIvDArgfw8pGXsfD4QnwQ+QHePvE262SP3TQWfVb3Yd1YIu6nc08zwu1dZYI5h7X4fikPS3p/gdFz34EmOwcJz05DguOfkA/hjht318cQ0XtjvYSbcHH3ViirKmHl7IrAgUNa9fIUVlbjy73XajO5XSwsGIF9MexF8Hl87EzZialbp7Ln3hxUV1dj1apVjHBT8WvWrFkNEm4DeAI+rCb7wnKSD2N21Ggp+PUKNPXE6kYVRmH69um1hPuRbo/gu6Hf3TbhprWm4upVFP26FBmPPIKEnr2QNms28j78CGXrN0B59Wot4RZYWsLIzw/GEeEwHTYM1mPGYLSlFYJI+cDj4VJ4GJKGDIHIzQ1aY2MI/iVK6mZf1WmA/6effsKCBVy1/8CBAxg3bhx+//33/4zVeyfaH15SI3zZxQ1Pxqbju/R8lsfbz+rGC0HQoGEoysrAua0bsO+XJbB2doWDty+rAPef5gsjqRBnt6eyjdBzXNtkJNYHVVYlipfHQluhYt0O24e6Nek2a2ERDhMTP9YNIeLtWkPC2+05Zlayqid1zaQ0r9gCkIxp0KBBrMuRl5fHqq9kglEXw63NkSQvxMGSCoy3b37MTic60RQSZEqmfhHygMn2HetaToQpM4sz33Jzf6RZsUO06Ni/dAk01dVwCwpG8NDbi8aRBtsi5yxXMKO8+44GqdrOneOkut0iuuHZmOdRZfs6WxRRPFhzTe2SLxZCKVPD1MoIHsGcbL4jQe8dLSzfPP4mk5jPC5oHkU6MkxuS2Od73OfeZgUNB4dJSEr+CtXVuSgo2AUbm+aRPzJjq04oZfL7m8eS7iSIPA0bNgyHDh3Czp07mYkTdVGbDbEU6PsMcPBD4PjXQPAMzt2cZjpnzUTkG2+gwNIS8QUCmNpEwbQ4BJLwhzHm/FZsHziCjS6Nsm3egju1PBWxxbEQ8oS1EXHtBSqYl5ScatY8N0m8DSMa9FpSJ/i10V2wKzoX0dkV+OdcJub0vrWLRy74Vae5YpXFWK/acQNa89h5eqMwLQWJZyMRMnx0vRJzmgVOTU1l5lu3ld+cHw1U5UEnkmKTJJBdjw2jZYQrV64wTmAYS+jWrVuDc88f9vsQr0W8xoojh/A3RqxPwfQjanxnfgn7uzbMJcikLNwhHGEWXeH5027YHI8BTyKB65LvYDpoEMp27kDK9oUomyKD3ggQ6s3QrfsS2Ng0XCylSNrz2zez/X7T57TId6MuPt8dj0qlBt1czDGnt0ftNefR4EfZc6YCAsnrH9/3OGb4z8CzPZ6FlaT++xkVZlauXMmSImj9NWfOnBbFypn2dYbQzhglq+PZGEz+95dgPcOfqXYqVZUsQnFF7Apo9Vo4SB3wyYBPbsswTa/VQn7+Air370flgQPQ5N3YLBNYW0PSrSskgUGQBAZC7OUJkYsrBKb13ztc9XqWmkMmjhccHWAyYzpz4e/SBvnjdwP4LYkLoQxHAyjknA6qnByuMtuJTrQVJjtYYZajNahO+WxcOkrVnIyoLkhW7h3WExq1Ctu+/hTyCq56Rsckkex+93NdCyLe53e1T8dbdj4fBb9cYYRbaG8M+6e7NyvehZ6js/NMtp+Tc73r0l6gKBzDAr413ROSplHHgwi3YQFeF8NrzFQOFle0KDqsE51oChtqutzDrM1hK+7Yzh8RpurqPIjFdnB04Nycm8LVQ/uQER0FodgIIxc8D95tFqR5HuYoJBkJSezc68/lbU/Q+U6LQCsrKxxRHUGFJBwaIz+WWvCOT/OJoSEmrOtA5zsWDTjKcxRcTF1QWl2KzYmbcflAJiqLlawQEDbq1tGZ1kIgMIK720NsPz1jabNnK6tOcGspMk8jyfrdBBoz8vf3ZyZZa9euZSMHLULPxwCJBVB0DYjfXvthvpERBtd0ZJMVImT774VWWAWBpSdC+d6wKymuVbo0dz6V0Ne5b4Okpq1QVHwEer0KUql3k4kGJ06cYAUsKlYY5q1tTY3w0gjO6+HLvfEok98621xOaxedHhJSVvje+PeQxJxwLZKb970Ztra2zImeRkMNJoitRk2XO8uxN1R8MXpZmNaOkpHzNPk8GVJPDAasjYE6qjO6zMBz/9sJq4ceZB97fhcPH5vOxSvhr+CZ7s/ghbAX8NnAz7Bs9DLsvX8vjsw4gkVd30K/D3cwws03M4P7n3/AuHt3ZLzxImKvvoTSGRzhtpSGo8+AfY0SbsKFHZuZKa+tuye69BnQqpfmXFoJNl7Mojok/jep2y2Z3GRIRh3+mV24Nd+6hHUYt2kc/o75Gyqt6pYRBOpwE+Gm0d0HH3ywRYTbADpW7J/rAZGbGfQKDQr+jsbyTT+z37ssZhkj3GM8x2DjxI2tJtyq9HQUfPcdkkbch4wHH0TpypWMcPOkUpiOGA6Hd96B984d8Dt5Au5Ll8L+pRdhPnoUJF26NEi4Detjyu2m4hTh2LFjjGe21lTxbkOzVwR0saUZz7qgjlerzDU60Ykm8ImfC3yMjZBTrcYr8beaQlBFcsyzr8DS0QkVhQXYueRL6HTa2s9T56LvFE7edGZbKs7vTmuz11yv1aF0axJKNyQwcx1JoDUj3EKr5s+bOjpMqjFUi0ZlZQzaC5TtKb/CObob8mhbCpI3DR48uDY26OY8xd6WJpAK+ChQadg8fic60RagAs6G/NI7Ii2n601G5h9s39V13g1Otw2hsqQIR1dw39N/5gPs2nS7yEgsY+aSZnzAKK+NDJGaCSIJhhnU7n26Y13iDlRZzmL/fsnDAY518nkbQ3F2FXKTyxnZDux/5zq4Qr4QD3XlyPA/Fzbiwh7untB3qg8z5GxLuLjMhUBggqqqeJSW1k+K6oIKt7KLXHHUkHZxN4HUjCQZplnh8vJy/PPPP2xN2GxIzIFeNZ4Ix77i5Mo1CHzgATgXFEDP4yEjIRj5gdxohth/LOaei8K+ogqU1VN4r++crZWWe3eAtLyZruX0ehlk10Qk6ipD5/X1gL+DKUrlanyz/8ZoNmVyGZRkfsrnutw3w0C6M2OuQlZW2r4S80SOdB+w4qLC7qsptBMZorljIvbBwcGsGddSOLz+OszuGwGeRovARVswx3QIngx9Eo8FP4bx3uNZp5hm89XZ2Uib+wCq4+MhsLWFx8oV0KvUiF8wBik9tkPRi3Mn9/Z8CWG918DIqHHTSWrUXNy1je33n/FAqwqkGq0O727hTGZn9XRDD/f6Cz1SkRTv9HkHf4z8AwHWAahUV+Kr819h4paJLAmC8rFJUk551VlZWbWE2xD91hrQetT8UX8cCo/GU97/w5eVP7GCo6eJB34c/iMWDV5U623Rkq525aFDSH/oYSSPGo3in3+BJjcXfHNzWEydCteff4L/qUi4/fADrB+YCyMfn2YpxOoDKSzvu48zJ6ToWlJs/BvQ7KOMLmgPPfQQy1UzbFSVoXzuuh/rRCfaAiZCAX7u6gERj4ddReVYmXtrDAI5mk965W0IjYzYbNPJf250pqXuRZ/J3mz/zNaU2kXW7YCMdAp/iYKsxiHdfIQ7bOYFtbiDTIZqBrfTnJz1aC/IrxSweTChjYRl/7YWJBejGwHdGAxOmgYY8fkYVDMCQN3uTnSiLRBZVsWKbuZCfu0ir6NQVnaWFcP4fAlcnLmYv6bujwd+/4l1TcipPGxsCw2nGoDBtdxJxIf8ahE6ustNBTbqch/XHkeZ2VjoBZbwNhbjcbfmS91jjnMdXK9QW5hYNF28aE9M9p3MZKqe8b2hUeng5GMBv3YwLBOJLODizBUoMrN+b/Lrq05mM6NLsYc5jDzvztlFuv6T1JWaL5mZmcxUt0Xdpz5PASITIC8KSOTyngl8qRQD/bswIp4uE6HAIhsVDmfA4wswXekFgVqH7YVNZ3ZfLbqK9Ip0GAuNMcyN65K1F7TaahQXH20W6aZOHXkiuLu735LLLRLw8cEEzil6xel0xOVW1LrYl+9IYfsmvZ3qzXa3dHCEo48f9HodEk6faJR0Jycns/V6q6CsADK5WeS/jK9HhZHHC3VlqTjn5eWFSZMmtWrUlMiu86JFkISEsHnfjAULoLkpok6ZkID0OXOZCZfI1RXufy9D2datiF0+D3kP50FrTxGmtgiP+Ade3s+Cx2u6iHZ26waoq5Vw8PaDT0Trur1/n0pHfF4lLKUivDYqoMmv7+XUC2vHrcVH/T5iju7ZVdn4/OznGPnPSHyx9AumKDYyMsK8efPYGEdrQOdkYmkillxcglFbRuNL+U/INMqHqVaKJ/Om4/tLr6F7khczh2wudHI5SpYvR/LoMch6+hnIT59mI0YmAwbA5evF8Dt+DM6ffgKzoUOZeqWt0L9/f4wcOZIpNugY+zeg2WcIVV3s7e2ZzNSwPUAVSmfnGz7WiU60FULMpFjozXWL3k/KQZri1kxLkgWNWvA82z+7ZT2bb6qL8NGe6D2JI96nt6Tg4t7WZwDKLhUgf8klNiPNkwhgMz8I5iM4N9HWwKVGYp6XvwVaraJ9DNRq5sHoxt3aiuN1STzXpaKqPRmrNSQx70Qn2gJrczlZ6UQ7K0gEHesbYuhyOzlNZQWypnAt8hhSLpwFXyDEqCdfaPVsYF2olBpkUMQWGUga8aHJl7MZz47ucof2DsU/6ZFQmI1i//7Iz5UV2poDjUqLhLPcjF/QwDs/pywRSjDX6jH4F0Uwh+v+031v67rYGNzcHgaPJ0J5+Vnw+emNJl8Y5nbNBrvibgZJlqdPn85es6ioKCabbjak1kDEw9z+8Ru73X4PzodHNjeCkBk3EHmBy6ERlsBIYoP/XSjGhrymc8K3JXNdy+Huw1lnsT1RUnKcGSwaGTnC3Iwzg6sPhYWFzICOPa/hw+s91vr52mJssCOpyJmpGt235RfzWSQdrTPMhzfs2BzQn1OgxZ3kCgA3w87Ojr1ntyUxTz0K6DSQWXghUeIMD4kYztCyuWOaVSdCNHPmTKaIay34xsZw++lHiJydoU7PQNYzz0JXza33FJcvI33efGgKCmDk5wvHjz5C5sIXkGS0FBXTtMyZytZqOPr03wdLi+a5tFeVFOPK3p21qqTWXAMKKpS16oQ3RgfA2qRh9/W6EPAFmOI3BTum7GDxZp5mnuiW3Q26Yh3UPDXOu5zH8qzlOJJ5BHmyvCYLW/T5AnkBjmUdw9cXvmYRalO3TcVvV39jnW1nE2c2Q79n8m7MdLwfQi0fFXvSUPAjt5ZtKtqLTNGSho9A/qefQZ2ZCb6FBWweexS+Bw/A/fffYD52bJsS7frMfGnt2V7X6Y5Gs8+Sv/76q32fSSc6UQ8WuNlhX3E5TpXJ8HxcBjb38L3FLZduPHnJCbiwcyv2/vwd7D29YWF/vUoYMcaT3eBJZn5qczIjySQ/by4osqNsWzIUNTJtsac5rGd1gdDy9uKLrKz6QiJxhVKZhYKCPXBymoK2BMuSzZGBXE+k4bffzTEzM2PzRZT9efjwYSY3NIBmbgmUq1qs0sCmg+dvO/HvAsXP7azpbs126lhpuVyeiqIizknZ4ETd6NdXlOPQX7+y/T5TZ8LWrW3mgzNiSqBV62BuK4GdlxlUiWVQRBdBNLT9Y1OosGboch/RHEWZxSyAJ2SKA+pyNRfJlwpRLdfAzFpSG8d1J0EdRPPzfiiGHPH2pxEnEMABrXMsbgoSiRMcHSYiN28jxOJDAJ6q9+tkZ/I4NZK9MZvdvdtBxmrk70OmamTURaTOMKfcJPo9B5z9Dcg8A6SdALw4ibTQygoDvL2RqVAgXyaEncweku6/wu3cGxhSZoT98WVID6xmaRn1gWZjKSqMMMG7ef4Lt4OCAm523N5+DHi8+gtQhggteqR8+/oiNw14e1wQDsUX4GxqCbadzkDPAzVKuqHuEJg2TOa69BuEoyv+RG5CPEtNoO53fd1u6raTxDy0Jhu9NfPcF+25We0RliZMUk6dbkPz7ebR09ZAaGsLt6W/Im32HCguXkTuWwthPmUysl94EXq5HJLQUJgNHYLkrx5DyQNK6CzJc1AIf/932ThHS0jZmS3rmB+Qc5cgeIaGter5frIrDlXVGoS6WWJmRMvjaakwRHPtRvFGuKy4DD1fj3NO55Crz0VCXAKTnRMoZszbwptJwWkW3kRoAqVWiYrqClSoKpBWkYYS5Y2+ByK+iPkaTPSZyIpQNFpD0M+3gvxCAcp2prC1YcFPl2HSyxEWozzBl4pu6myvQPGff0JXwTVSyEXc5tFHYDFxIlOndKJ16LQd78RdDcqB/C7AHaYCPs6Wy/BTBie3vBkD5zwMJ78uqJbLsOPbL6DV3Og1EDHWCz3Hc/KUyI1JuHIws8nfzSrOlwuQ//V5jnDzuZxMuydCbptwE+hm7ew0ne3n5K5DW8PQPZEG20Fg0rz5y8ZgMLgwuJVSBqgBLhIxAk0kbP70aGlndFgnbg+b80uh0OnhL5UgzLxjb/CZWX+zR1ubYZBKm5a0HV62lMX2kOqm12QuXqktkHyJu9b59LCHSTAn51ZcaX+JOY2QkHcDIaR3CP7JTYXaOARCnh4f+bZsxjC2xhwssL9TqxVBbYn407kozpBDL9LirNtO/H7193Y16HH3eJw9CoTRkMs5uXBd6DU6VNZkMJsNcr0rXqPmdp969eLmezdt2nTDvaBRmDkCYfOud7vrwPORR+CflMz2MxL7Qm6VgDJLznTtrRgldifUf+9nPyrrOMqry5lk93acmJsrLS8sOlBLuhsCdZZJ1k1O5eTo3RhcLI3x/HA/tl+4MwU6mRpCeylMm/BAMLWyhnswR6Tja+JTG5KYk+EZndstjwrjCpBrKSpMr4fb1XNIT09nMui5c+eyYnxbwcjXl7mRQyhExa5dyFrwJCPcxj17gmcsRnLiYhQ9zRFuYyMP9Oy5Ba6uLetUkwdQ1IG9bH9AK7vcp5KLsfVyTo15WtdWm0NSIsDly5fZc5g9Yza2PrwVS4YuYbFqflZ+zIWfiDXlfB/NOsqix8iIjVQdR7KO4GLBRUa4KZbMx8KHFZy+HPwljs86zua2yTzSQLgJ9HsojtDxlXBIw+wpYp4V/XK/PI/KY1nQyZUoWb0aSSNHofDbbxnhFnt7w3nRF/DZvQtWs2Z1Eu7bRGc76i4AnVTkKlitqsaiDYuY7I37H/efodplI7GBjbENnEycmBkDbb5WvjAS3Nk5ufaGu7ER/ufngpfiM7EoNY9JmYNMb3R3FQiFGP/iG1jx+vPIS07EsZV/YehDT9zwNb3Ge7EF1vmdaTixPpFFiwX0dWpwdrtsewqU8VwFUegghfU0f4jd2tZB2MnpfqSkfsdmSKnD1pxFfnOgk6uhiOI68yZ9bt/QyQCS+dB8d3R0NKvik8+D4aZF70ucTMkk5lMdOjbeqRP/LqypkZbPcbLuUFkZxQDl5m5i+241DtSNIfnCGcSfPMoKaDTmIhCK2uZ5qLVIv8r5WHiH2cHYQcrMG9V5MhYtJXJoXlRXa3DmzBnW5SbTrIPqYyi34Gban3JzYJGOzUVZvhw5iWVsYdrQdbYjoVJocGoLR3xDR7tAW1KNK4VXcCH/AiIcI9rld5qa+MHaeihKSg4jK/svWFh8fsPnKUtXV6kC31wMaffGjZ/uNhCRLC4uZsRyzZo1ePzxx5tHwPq/AFxYBqQcAbLOA67cay9ydERvLy+kVFejAkbIz+sCXs+tMN0axF7HLnuyoAtyBl906+jG9hSOnJPxFsl32xMlJTSjXcWk5Rbm9Wcnk8Hw3r0csSM3bzqXmsLjA70RfSYHo0u56x1lLvOETffFAgcMQXrUJcSeOILeU2fecr0k+Tf9fupMJyQkMMOzZqPwGlCeCZ3ACLtMuiEsNxV5idHsd1B+O42ctjVM+vZl3dTyTZsotxFCZ2fIc2NRPL0MKj9uPezkOA1durwPgaDlBdlTG9dCp9XAvVsI3LqGtPj7KVP9w+2c+e2cXu4IcbVstZrI4I8zfvx4BARwM+FD3YeyzaDgSC5LZnFjVaoqFvkl08iYbwFxAtocTRwZQaePNReknrCe0YUR8NKtyWx0qfivbch5dR10Ffm1nW2755+D+bhxt53CcdvQ41+Dzk73XQAigmXVZVDoFYyA04lF7oZV6irmakhbriwX0cXRrNq19tpafHDqA8zaOQt9VvfBY3sfw7LoZezk/LfY6t8MihAbbWsOtV6PZ2PTodJRoNiNMLe1x+hnXmL7F3dvQ+I5LhfzZuIdOpyTAh1aEY+UyxwxrTtfV7Y7FXlfX+AIt4DHutsOz/Voc8JtkCDa2HBzWTk5bdftpvlzvVoHkaMU4jaOGiKHUprfomp3XVdUw1z3oeIKaGhArROdaAXiqhS4XClnJorTOti1PCd3A5vVNDHxg5VVv0a/llQ1ZJ5GCB8/mRmotRUy40qhrtayOCsHD3Mm/ZP4cYUsQxpBe4CioAxzusG9grG2SA2dyAE2QuBFj5aNqMRFcl1u9642TF5+p0EJFooKFSzsjdFvVAAm+U5iH/8jmpvfby+4uXLd7vz8LaiuLrhB6k7dJYLZAJdmEay7CdTBJeJF8vKKigoWJdasNBtLdyBk5nUn8zpwfvQRdI3h7imZaeHQ6vnI8/wVKl01/Eo1SNySeMuPK1OWsXURYYJPR0rLxzYoLadM7tLSUlaEGDiQk9A3pwP2Go87T3ZChctonju8b8++EIrEKM3JQkEqpxSoCyLIhm53TExMq6Tl6Q49YVNegZ6JV9m/yVXaz4/rzLc1iv9axhHuGmhyclA+oJwRbgHPGF2DvkZQ0BetItyleTmIOcr9Tf1m1CguWoh15zOZeZqFsQivjuzSqp9BxY/du7njiNSDFMtaH8QCMQJtAlnH+n7/+/FQt4dYnNoj3R7BNP9pLIs+xC6kRYS7Loy8LWE1xRaa9GVQnPqeEW6ekTmM+8yHwztLYTZq7B0l3Op8GSp2pKJLjNm/htt0drrvApiKTLFh3AY2dzN40GAWxcaj/3js/1m3m6RTxYpiFCmLkFGRgfiSeLYRWT+Td4Ztiy8sZrMfU3ynYLzPeNga2+LfAnotvuzihrPl8YiVKfF9egFe8bp1fsknvDciJkzF+e2bsPfnb+Hg5cPIeN2f0/9+X1TL1Yg/lYd9v8dg/HOhcPGxgOxcHioOZkBXxS0cjPwsYTnBp17n0LYEGaoVFx9GTu5GeHu/DD5fdPsGamfaxkCtPlhaWrLc1iNHjmDfvn0sv5WO2Z7mJrAUClCq0eJchQx9LZvOLO/E3Q+KBqKOFnVKaCFJG5Ez6oaSkQ6ZbpE7L30dGfZQQcawkeOxiYkJ22gBSh0X2mxsbGBqWv/xsbomqWCkbcdmc+v1WmRlLmf7rq7zmzxvjiz/gxnyUDRYv+lz2vS5pNS4lnt3t6uVHEtD7VghkEZdqBDYHgoAOqfp/SRFyzbVSVSZcyTmIz93lijRXGi1OsSdqjFQu4MxYXW77oaRogHT/SAQ8vFw14exMXEjTmSfwLWSa+hi3boFdFOwsAiDVuMFgTAVmZnL4Ov7Ovu4IqYImkIFeBIhm6u8F0Hn9+zZs/H777+zbOGNGzdixowZTbtYD3gJuLwaSNgN5EUDjt3YhyliqLuHOxIrK1FlZoaszK7w6HEF8l/+gNj7aZhcKITcz/oGVQDNcmt0Gqb8o45f+0vLObm1QwPScipAGDqYVKAmGXZzUHUyB6KSaiiEPPykqYbt5qvY9cJAGDVx3hlJpcx9+9qp44g7cRgO3rdmhpM6jYppiYmJzMW82TPYSZzL/E5pT4yIO8dWo927d29WFndr1i1FP/yIoh9/5D5gZopqmwoYpfFhsU4AsasbAuf8Cam05bnVBpzasAZ6nQ5ePSLg0qWZPgR1UKlUY/E+zpCOxgGsmmmeVhd5eXnYsGED+3t79OjBorHuBGhuu2jpUpT8+Rf0KhWT9JsOnQy+1TDoVUKU785E5dFcSMMcWEdc5Nh+6qq6oGaRIr6YJQRVp5Szj5lCBHVGFcS+d7/nRVPoJN13AUgORWQ5XhAPLwsvRmCanSVbmcEWDcezj+Nc7jmklKcw8v3dxe8wxG0Iq4gF27VATnQXw04swqd+rngyNh3fpudjrJ0FAm+SmRMGzJqPrLho5CUlYPcPX2P6e5/c4CZMi9ihDwQwg5/UK0XY+f0VDLQ1grmSqywLbY1hMc6Lmdp0hLTVxmYIxGI7qFSFKCo+BHu7xue/moIqtRyaAgV4Yj6kPdpHstivXz/mykoZpDT/STneQj6PmSxRtvLeovJO0n0PgkgzOe6SioEyWGlWk/5NH28u6na7iKA3BCLdTk5ObHOlGBh3d/DE4lqn4jlONuhIUASQQpkBodAcTo6TG/3atMsXEH14H4tNIbdykVHbdXKJsKZGcbPbPmHXo7kkQTbgifjQFCuhzq6C2LVtFSwFBQW4cOEC2w8dEIpPUy4CJkboKkWLx0XSo4pZV9nYXAyPkI59H+vDyY1J0Gn1rOvuGcwVo93M3TDKYxR2p+3GH1f/YLm17QWVahiMhX8gK3sVPD2fgkBgispDXBGA5nZbGjl5N4EKaORevWLFCsTHx2PHjh2YMGFC4/dOWz+g62QgZjNwfDEw/bpRr/3jjyPk5VcQOaA/srO6wdExERXh53FJlouhKieUbExkBMBAArYnb+8wA7Xr0nInmJtz8Vk3r8nIYI6ugXRNCwlpnnxZXaRAxX7O4d5qrBdEh2KRXCjD0qMpeK5m1rsxBA4cwkh3/MljGPTAI7ekJ5DEnN4nKp7SrHmzDNVUMiA9EmoIkJJjDHONAvbOzkwK3dbrIubA/cUilCxbxv6tM+KjdF4pqoP0sFwmgPS8ACY/loHXuxJo5XK2OCsDcTVz75TL3Rr8dCQZRVUqeNmaYF6flhtmVlZWslEMKmySKe04km53sCs3vdaVe/ci/4tFLGObYNKvHxzeXsiKXnq1FrIL+ag8lg1tiRJVJ7LZJnIxZYVfWhcL7Yzb9Hnr1Vook8qgiCqCIraYGUsy8ACjACtcRToGuP87mjj37pW+E+yg9zD3YNvcwLls5oOqvpsTNyOqKAoHMg6wra9TXzwR8kS7za11JCbZW2JLQSn2FFXghfgM7ArzZ2Tv5vnucc+9huVvPM/I97mtG9F7yowbf1C1Fn39LVB1rQSFSh1O5isw0NEYriM9YNLTsUOlftTZptnu9PRfmMT8dkl3VU2GOHUD2msxJxaLWX4iVWypqk/Vb3IyHWVrUUu63/f598Q8/JtBnRmSu5HRDpFtheLW+Drq1hi61ORoTV1r6l5LpVL2Oepqk9yUOlyGrjctPOlnUTecNirQULecFn60T91y6rzQRqBjxdjWDv4SC8gcXDDIsmMq6wZkZnIGas7OMxuVLlbL5di39Ae232P0eLgGcl26tkL2tVJWEDQ2E8HR5/q8IN9IAEmgNVuYkMS8rUk3qVZoQUZO1L8WnYTSZBgzTvo2qEuLz+PYkzUGan0dIejguLebkRFTjLSoImZ2NGD6jV3AR4IfYaR7b/pePFfxHCPi7QGtNhBSqS/k8iRkZ6+GvWI6FwklFsC0351XAtwuiEDcf//9WL9+PSvGUkFt2LAmsrIHvsqRbtqGLuSIOHXPQ0Lg7+qKxIICFNrbIzU1DAHhJyBdsRJnur2J3sVaFK+Mg/2z3ZFRncXWOgKeAGO9x7b735lfsKtR13LyOiFSS9fBJgsPNaC85NJ/rrEOn5G3BWz6OONdKfDC2sv4/nASJoQ6w9O28WshOXBLTM0gKytFRnQUPENunDWn50Hd7qNHj7Ln2CzSnXoceq0KW4WTYS5XQC02wtzbjAarD3qtFnkffICy9RvYv7XmOhS9qoLWltZGEjh9+gE0H+yBLDISmU8sgMfqVTBqRWZz5LpV7HpGcvz61ABNIbNEjj9OpLL9hWMDIW7hGpHuhzSCQfc+Q6GqrV/LJp8DFdLfex+ymhEiimizf+tNmI0YUXus8kQCmPZxhkkvJyivlUB+Ph+K+BJW6C2nbVcqBNYSSPws2cilyMWMqUF5gubdI/R6PXQVKqjz5ahOr0D1/9m7DvAoqi56tmbTe+8NUkhC770XpQkiCiq9KKCgYMGuv4odBVFBERBEUEBAOkrvLb333stme/m/+yYJLQlJSEjQPZ/jDputszPvvXvvueckl0KVXgHc4hsuII0LqrD3dILeVICKv5L+NWtJQ9D9LwLZCVCfB20JJQnYGLWRqR2eyznHtn6u/fByt5dZNf1hBV14H7dzx7nSWIRXyPFtRj4W1dJrSJTPITPn4+DaL3Dmty3w6BDGei7JJqHyYg5kV7me525GfJzTASUqHc7LtZjYzrpVeutcnCexoLuo6CQUimxIJE1biGlKFYy2SGjpxVxwcDATA0lPT2f9STSJDLIxh5jHQ4pchQSZEu1MW7+X04C7QdVr6u+jyhTR3W4FMW3c3d1ZpYaq0E5OTqyloDknPcr05+XlsUo6VdQp2KequKwgH52QD2Qk4LPI86xvkPoR/fz8GswAagqklQkoLqGFCB9urvVXQU5u+REVRQWwdHRCvyeeafbPQjZb1dTyO1VxTULtuWpAeAEsR3k3m9o1JVxoo2DBv0c7LIuTAGLgEVsBQswb115TWaZkgS4hsJUDSmINkGgmIWSQG6zvoEgSJbmPax+cyTrD5ss3er3RQp+EDze3mYiPfw3pGRthFNG5RuSyOZwl2gLoOqXKHVW6qVWOEnI9e/as+wlEKW83iqOYn/4CGM/pIxDs585Bp6XLcHjEcBQUeMPFJQ7OvjF42UOO7+TGsCqUo3hbLP4M4ajeZI/U0u10Wq2ixkrQ0eHuAJ+SiH/9xQXlRBmm6nJDUPFPBvNLJk9u68fbsWt6bJgLdl7JxKmEQqzcHYnNs7rXO/6SgGP7Xv1w48hfTMX8zqCbUB10k/AdJUOpNaBeJB7FVXRApMYbxHOyHDiMJdabE3q1GtmvvIry/ZxnttpFh8JlGuiNSe/GHaGh62BuFgDt6tFIf+YZKKKikDF7Djy3bYWoESJu+anJiL9whjGTej/+VJM+60cHY6HS6NDb1xZDAxvHIKRAk64LasGg4/7kk0/e+/g3I4hSX7p9O/I/+ZTRyolRZjtnDmznzAa/jlYDOg+NA23ZppWquHknpohRvqkCTqrntDEIeczRh4JlgaURO5fZ3ESBuI4T9tXJNOx1qKWmppJ9CwSWYhgH28E4zJ4F89VzW4N0Ih4itGoKmgZmygZWG5/v3r37rhP1zTffZAs/OkGpP6a6KmJA/aDepg/6foB9E/bh8XaPM9sAoqBP3DMRn1z6hIm1PaxwNBLVWNd8mpqLRJmi1scF9R/MfCzFkCDy233I+/IK8r++xvmiksiYsykcHm+P8e/2hLWTCSpLVfjzq+uQlase8DcCUy23siKrEx3r7W4qKskmTEcCGZYt3oND1yz5tdJCnYI3ElUzEwrQx5qjAVG124C2A6K2UV/ft99+izVr1rD+3eqAmwJsEnSZNWsWXnnlFTz99NOsUkVVT6psN3eWmZgSFNiT7dD48eOxZMkSPL7gORwL6IJ4R3cYGRuz3sOIiAhs374dn376KXbt2sUWjI2hujcUmZmb2a29/VAYG7vV+bi08OsIP8b5AZNauagZ/GlvhU6nR0qVuKNPp5vU8mpI2tuAZySAtkwFVRrnn3q/IFZCtdIy/R7vpF6CRuwJkV6JjwM5AabGII7GVz3g5GMJK8fW9XON/CcLJbkyxhroNqb2XtDZHWaz212Ju5BXySn3tgQc7B9hitcqVT4KNYcAIR/m/RpnwdbW0bVr1xpbSXK3qG5XqBP9X+Juw7cDpek1d5v06gUXLy/4JHNq80lJXSHroUO/6MN4IUxCRsRQxJXA9AQ3V5OOzYOhllfWSS2nxDMFsxRsk+ZJQ6DKrED5sSpa+Ti/GitSGm/fG9eBVVNPJxbizxscc6Q+BPbjjnv8hbNQK+5eE9nb2zO1cRo/Y2Ji7vl6WbGX8Re417zoHYRRIY0fC+qDTqlE5uIlNQG3IkiLgte4gNvKqju6d9vFAm6CwMyUeXiLPD2gzspCxpy50JY1fH1xZjs3vgf07g97j8b3hF9KLcb+8BzmxLByTFCj50MqTpDNKj1v8uTJrNL9oKBKS0P6M88i9513WcBt3KkTvHfvhv2i5+sMuGtTO6cijv2sELi82Qu204Ng1s+VrTNpPoJGD02hnAXk5MhA/dikUSA9yVHTqchFeiTqTCkXcPPBKOoUYFtN9IPjS13h9Ep3WI31hZGnxUNjnfjQBd1EOSSaCy0Aa8OqVauwevVqrFu3jtmYEJ2RbCpoMWZAw+Bm7say97vG7kJ/t/7Q6DXYFL0J43ePZ96WDysed7JmVVWlTo+X4zLuUjbUsF6UbHQVDsU4j+cRKO4OTZ6cZeSMQ+1gNycEDos7wbSLI0ysJBi7pCNT2C3Ll2P/2nCmHPygQYJqhJzs35ioU2PBenEuckHUvfw9mwtUBa1WZ6VeNrqmiWJOMATdrQ+ielNCZOvWrfj8889x9OhRVmGmRAlVj8eNG4eXX34Zs2fPZn35FAgTTbw18LtUjQRHd+h7D8SKl1/GjBkzmGCPhYUF85elRQv1jdKcQBUbouk1BzQaKXJzd9cIqNUFlZxo5avZftjwMU2ym7kXcqivrULN7Axd29/dR0093cYd7JpVxZw0GYj5QJVJy2A3XNRwi+sFrqawbaSQHY3DJFBJCOjVuuJg8goVLu7j6KA9xvrAyKT2inIXxy7o7NAZap0aP0b+2GKfh88Xw8N9Jtsv9voLJt3sIDBvvBBTWwdVeauFtvbu3ct8iOsE2YV5DwB0GuDMVzV3U3Bi99xChIRHQKhWQyq1Q36RD4ZJjiDSnIfwoZwF3YiCnphUMYLp17Q0cvP21VS57wy6KOFMzCG6n8bUhtCGdSotirfHsSQ5rUmMO96eZCNK+eLBHA36vX3RKJXVXwxwaRcAa2cXqBVyrqpbC6jaTSCKeX2ozIzC9orO0EKIdBsH5PkFoUMt+jn3FXA/9zykf//N/i3roUXx81ww5uw8CZ06/gyR6PbxT2hrC4/16yGws4MyLg4Z8+ZDV1l5z/fKio1G8tVLTIW716Spjf+sOj07/oQpXd0R5MK5tDQUqampLAFVrfru4+ODBwGi7ZMSfPK48ZBdugSesTEcX3sNnls2w8in6WxXanMyDraF1Rgf2M8NhctbveD0clfYzw2BzZT2sBzlBfPB7jAf6A6z/m5ssxjpBeuJ/rCdHgjHFzrD9d0+cFrWFbZTA2DW3Rkiu+btEW/LaNWge9SoUXj//fcxYcKEWifvL7/8EitXrmSDGAlSbNq0iVER76yIG3BveFl6Yc2QNVg7ZC08zD2QL8/HwmML8fbZt1kv+MNJM3eDMZ+Pc6WV2JmUD3lkIUp2JSDnk0vIXXUJZfuTocmoZI8tUmbjcuEhKMeIYPtkICS+t9NlzawleHRxGIxMhchPLcfhDVFssH2QsLcfwUScFMpsFBefbfTzZdcLGIVHYG0ESeCDy6RS0E0ZdFKzpslleJV12JVyGQpU/y5q0MMCClIpUfn111+zPjLq2aYxlYJqYhe99NJLmDZtGlNPpWRma0Op02FblWr5s652LCng6enJkqwvvPACC8Cpikb946Wlpfj777/Z/EA9pJmZnO1SU5Gbu4dVsExMfGFtVTcd9uTWn1FekA8Le0f0f+reHt73Qy33DrWrsxeaxGwI8ogC6LX3V/UvLCxkCQzCyJEjsSIxCnqBGSxRguX+wY1+vfy0CpTkVEIg4sOva+MsxpobF/5MZt7cdu5mCKwnCUnzwIKOC9j+zvidyJfdtPVqbthqR4OvMoXaNA+KkJt2i/8m0PEkvQ9iTRD27NnDGCv3rHZf3QxU3Gx1Me3bF1bt2iG4yuYqJbUTtB1kCM2KwlozDU77c/fPyBoLXdK9g6/7gUZTeZNa7vjIbX+j5B8lFwhU4SbmZoNsYnclMqot31zMeXLXEnTM7e8LPwczJt710YHYel+Tnh88YCjbj/yHUx2vK+hOSUlhCfLaQJXw33ftQTksYC6Q42hgNwyzs2y2oEinUiFz8WLWV0zOPNKBWpQ+wxUZfH2WITDgI5agqg1id3d4bNgAvqUl5NevI3PRIhbA13ecT//KOVJ0GDgUNi51s5jqwu7rWQjPLIOpWIClwxtnC0nnxm+/URFFz/zRW0L1vTYoExOR+uSTyP/4Y+gVCpj07AmfP/fA5unp4DVzUp2q0kJbY2Y/RsK95gPcYTncC5YjvWA12pttFgPdmUODcbAdY2A+bPaI/4mebhoUiPpIlPJqUD9Jjx49mAfiE088Uedik7ZbRYKq+wLaam8A+XUqssshkQkgzyqHhnoXaXyr2aoEDqr3q8e+qn06gdlJLODdk5bR07Ento3ahm9ufINtcduYZcq57HP4uO/HCLZt/CLrQYOJMEjV0OTJYJEnw9ZELXhZlXCTx4NbsleBD4g8zCEJtoVRoDXi98Ug6cB15PyUiqfa+8HY/O5spZmtGCPmBmH/1xFMeOfEtlj0mez7ADNwAjjYP4rsnF+QmbUNFhb19MPVpkh5OovtG/dwhEarAZqxWF997dR1DVEv38aNG9niKiAgACFmEkRIFTiYV4InnBqnfGxA00F9hZcuXWJiRtWMIGrNIUYRbeSpW422NB7uyS9FsVoLZ7EQ/S2M7/pstJCljSjvVLmn6hlpCVB1iTZXV1dGWb917G/odZNRRS13dprCqNa1ITM6EjcOczTIIbMWgicQNvvxo3kg+RoX8HmG2tT5+nwPU/BNRdBVqlEZUwijWiriDXo/vR5//vknY0NQ9SXVkof4Qn82p7zlZQ+9VgN1I8eQ6KoxyDvMFnyhvtXOscJMKaJOc5TcXhN9oNVqoK3nu3S27YyO9h1xveA61t9Yj5e7vtwiY2fl0QJY64ejyG8X0vK/g51b3V7PDzto7Ubf+dq1a/jjjz/YtVUd9N0G154QuHYDP+sStGdWQzfknZo/Wc+fB/9Fi5Hs54cKMzOk5YbiScUveLXEA1nC76CwfApDy3qgaEs0rGcEQeTWMgrHefmHoNOR1ZYnJJKAmt+Urh0KqohWTqwvcvVoyDkvO5fDaLi0TrGc5AutCNDW8jxaebz7aCCe3HAJv17KwCMhjujhXbdtkn+vfji9fTMbrwqzMmDpcDvbhAQwqV2TtDRoriZ/6Dvndmo7Si5SkUkT4CqESijCICvTZrmWqYc758WlkJ04yQXcw3SomEAXJg9+vm/CxWVqnWNwNQQ+3nBZuwZZs+eg8uw5ZL64FE6ffgJeLZofqTeuMjFdgUiEruMmN/o7yFQafHyQS3YsGOADa4mgwa9RfW5QMYJaDqjIeK/v1hzHt+Snn1C87jv6QcE3M4PtS8tgMXEiW8e2pTm/udaebQUN/Xw8fRtxHKcTgnr2qL+vmvLWp08fVtmmQaIa5AFJj6U+v9rw9ttv4513bg7a1SB6JdHn2iIEGh46XmqewETH00PP10PHA3fLB3R8PfSkZyCgf1fdJ9CjFGWI0cWhnFcBJU8NPyN/uIk9oBdwz7n18dqafbq/ajZobugBvpYHkZoHoZrPbkV0q+LDSMGHkVLAboWauxcpVO/JMQVE5gqUW6pRYaGG7paUkk6rQebBXVCVlcLU3RtOfYfUGUzLcoQovk69LjxYtlfA3OfBXex8fhZMTD+DXi9AZeVbgL5hiwizMiHaR1tAy9cjoksptMIHf1mTSAjZDhG1LjOkG3ZbOCJMLcNCefPQYA2oGxRg07EndfDqIZ2qwsRAIMXxe/rmtjJWmTgiSSjBWEUpxqgaRhunxQzRokmArfo7U+Bd/Z0bQpPnC5JhYvIN9HoxKqVvUYrirsfoNGqk//U7NNIKWPgFwKE7107R3FCW8FFw3hQ8gR4uQ6Tg1fPx3VJN4JgjQYmNCsntm8ZUIhV5SlzQudE+IABvWkhQZuQBB3kM3lM3fq6kjpjs42bQa3iw6yaDxO7Bt+iwz6EHCi4YQ1UihLGzGrYdG9aOlqhOxMbKjRBCiKUWS2HBbxyN9F4wLRciIMoCGlElEgcsA4+vgFw+E1pN86rftyUwW9P0dDYuEYhpc2virxoOZTfQK/kzaPhGOBz8OdTCKmV+vR4eX3+DEq0WJwcOYDN95+D9eEk0FkZlO+HJ88CqrGWwKBNBI9QhLrgCCpPmP+8kxj9AKIyBSjkcKtXIu+Y8Gmvat2/fIE9umqvbRZuDBx4yPCuR73LvROH2ZD7O5vFhJ9FjRagW4nrGhqzjf0GemwXrDp1gG3q3Yw21F9G6mhTmSajyVhCLiApehMfwFxZ1WYEkU298XpEJMS3Q7gdaLVy2/AKz6Ggu4B6uQ8V4LfR6PpSKJ6DRNM5dxzgxEa4/bQRfo0FFSAhypj4B3DLm07lHaz5lSRGsAkJg17nhRYxqHMjg4WCmADZGerzWUUtSAg1GU86N+4FRVhYcd+6EJJtzr5EGBiB//ARorJpX/M6AutcjJJBH7AZqiXvoKt1NxauvvoqlS5feVummgZ7oTvUdiNaETqFBYfR1qJQqiFm2joJBPfuP27jBjt3Q/6rHPrq9gwLNp+hay0NDCCS2cIAvmujjTFV1EZ9ZnrBbtk+3AiYQUxPP3lqVr/oSJGKm1+jYLdg+3aeFTq5hggz3BIki2kggdDSB0MkEGXZiPFaYjwoRD1uCO2BAlZDXncjvEIzf3l6ByowU+FmbM5G1uhB+PAvndyWjLE6CLr3C4NelZfyua8PVawcglUYjLFQBV9c7rM7qQOnWOChRArMujhgxtleLZPGOHDnCepLqUpGmx/z0008sEOpQkIk95g6IMzLFoP5dYNzKtkH/5gXt+fPnmfJ0NajqS6rB7dq1a/PBNiGmUoGka0kkt4A3+3eHo1jUaIE4EqqhCj+pohPdnAJKOgZESadAvM73jn0JBQVU5R6Ldu0eq/UxJzatZwG3ma0dpi5/A0YtlLw9vzsFBciET5gDhjxa99hEILup4rURsC4zwsiBvcE3ETb6mH3//fdsn9gD1xwtUJZJMrNKfNOxI3rZN95SJ+lqAbI0sTC1NsL4aX3vUl5/UEi+VoCsklhGcZ8wvzfMbCQNvp6uH73Oqt0Zzhl4ucvLzTp2dqh0hQYVMA/zhofnM8jI+A729hfRqePL/+p+RjquJNRHomoZGRksACHG4u0PGgX9hsMQ5kVghFUqdP1X1PxJamwCyZIlcMvOQaaLMxIzusPeZj/KecATXZ6E78TeKP05BsiQIiTZDtazgyFs4G/eEKjVJTh/YTlbevXu/QJMTLieXLIGoyo+gYpFxPC6F7SlShSti4AeGkhC7dBlUo8G/fb9FGqMWn0WeRVKJBj54eV6aM7x1uY4uOZzaHIyMWr5StbLfCsoKPjmm28YK4ro8MSEovOT6M/kIU3ogWvwMilFrKkPhtqYY3y/UbjfCmzu8hWorAq4KwdyATePJ0RQ4BewsxvWpNet7NgROUtegHlEBCvOOX78EXhV/fTx588gqaQIIokxJi95qVZ2Y33ILVfglS/J0UKHt8aFYXRIwzUqqJ2rsedGU6FXqVhlu+THH1lig6j39q+8At8xd2sPPIxQN2Dt2RZQzaq+F9ps0E1Uneqs3K2Vbvo3eQLXBcom1ZZRoh+rzf5gIhEcXunK7CZIDboxn5NVeHT6miCWAljQ/p2bumpT0a2W3ZKQB3efFgn5cUgqTIRYJ4KtwBr+Zn4QaPg1f69+Xk3Ar9UzoQa9ovmzyqSGKDATgW8mBt9MxPaFNsYQ2kq4YNvOGPxbUr3EEXgiQY8fMgvxZnIu/u7eHka1BBuu7QLQc+ITOLvjF7aQ9unYBSaWN31wb0WXEV6QlakQfjwT/2yJh4WNCVzbPRiatIvL44iPfxt5eb/D03PmPQdOdYEMytgStm/R161Fz/P6riO6n2zDaDFfmJmBQWIzHHdvj9MVcoy2r/04G9B4EG2N1GeJDUQVi2rQxE70Rg8Pj4fqsP6Sx/VxjrSzhJtp4wNaqmoPGTKEZZpprqDgmypr1PdNfe3EmOrWrdtdwbdKVYjCwsNs3919Wq3nNVETq2nlpFZu1syWObeO46k3OKs/vy6O97yGRR5WqHAyhTq3EuqYEuar2lBQzybRyokdQccrrEcPTD91AeCZoaMgDv1d6haTqw8JFzlGS0BPJxgZtY5AmEalZckLQucRnrB2bJyXOfV2zzsyD38k/oE5oXNgb3K3gnxTYFYuhCa1giWrLYd6wtxkFrKyNkEqjURFxTnY2lIV99+LRx55BBKJBGfOnGFijtQGQsme2+a2fkuBnTMguLwegr4vAEZc8txq+DCUBASg4+XLyHlkNMrLHWFpZIcy81xY2Q6EkZkE9jM6IP+7cNZ2VroxBvZzQpst8M7LPwq9XgNzs2BYWrZn91FimSygCJTco4D1XiDbpOJf4qCv1DDnFJtJ7W5bx9QHG5EI708IwZxNl7HhTBrGdnRDB9fax6L2Pfvi743fMVvDnPgYeIbcvl4mpgHNEZSwpVYd6r2nZCW1AFCA42upw/CyEzhqN5YVS4bbW93XmkKv0SDr9ddRefQoC7hlvXUof5zWjXwEB30OR8em+6tbDRkCwdermQq69PBh8AR8uH7yCfQ8Hi78ziUQuj4yARY2jde4+eJYFORqHbp6WmNsJ7cGB7DEFqju8afkUkPOjaaC+tqzX18JVVIS+7f5yJFwWvk6hLWwSR52iNpyDFf1+R7qoNvb25sF3seOHasJsimTQIuoBQs40ZN/C2jBRYIvOjWglKmhFVYF0/RfVZArNhZASFXkO8AGAqo6UyXxPtgr3eEHXu4lLP1nKUqVpXAxdcG3Q7+Fj9VNpUX2maqr0rcE7zW31UE8bdWVevbEqv/RLZ/H9aCLbtnYvwWsWkMBNquWNxLLvZ1ZX2iyXInvMwpq9e5m33P8ZCRcPIuCtBQc3/g9HlmyvM7X7DPJH9ISJaucHFgXgYkvdYGNS8uLTjk5PorExP9BWhmHiopIWFjUP2iTJQMdW0mADUSOrSuKRRM6CXX9/vvvaJccg3hTK+zNtzIE3c0AWqhS9pwq2zSxE4jKT+MjCbQ8SBuS5oJUo8XOvJIaAbX7AdH4qLJNi0hS56XeRKKeU5a8ul3p1sp3dvZO6PUqWJiH1nqNqZUKHFrHKSp3GDQcXmGct3JL9SCXFypYddYjuO6ezVth0sUBZftTILuS36ig+9SpU0xRlxYJjz32GN6Jj4GcZwa+pgBfdO3TpM9P3twZ0ZyqRkDPm0nyB41rR9IhLVbCzNoInYY3PvnUy7lXTW83KZmv6H6z4no/cMng2hZMuzpW2UJJ4Ob6JNIzNiAl9RvY2PT/V1Sl6gJ9N+rxpoLI8ePH2TlIY9htSt9B4wAbH6A4Gbi6Cei1sOa5dgsXQLl4CYJj4xAeFITQ4lAorIXYU6jGI04A30TE7Izyv7sBbZEC+etuwH52CEQO989Kycvbe5uAGrFEtmzZwpJWxKK8VXeoLuiUGhT8FAV1rowJp5HlUkMD7moMC3LEmBBn7I/IwYrfw7HnuT4Q1sIgE4rFCOg9gHl2R/1z9K6gm0DCxBR0h4eHs/kjOTmZ9aWTrdhk7RYIoMcOsy7c+9o1nSFKhZnsFa+g4gCn3i3vokPZNK5QExjwPzg6jsH9wnzQILh99RUylyxh75Ol0aB4zAiU5GSx6nbXR7iW1cYgPLMUf1zl9CneeKThFmGUEN+5cyc7N0iDhKqzLQGtVIqCz79ACTET9Hqm6O705huwGD68Rd7PgOZDqwbdRG+5lRZJvSQkkEOVC8rEkWotqZtT3wkF4W+88QY7kav7vv8tUMo02Lj8HA0f+Pno+TofJzISQGImYgsKWxczFgDauprBwcu81oC8sejm1A1bRm/BwqMLkV6RjmkHpuGrQV+x+wls4BFxtHJ+G2uPNxcK8KavC56PScfnqXmY6GgNV8nd1RaBUIgR85fgl9eWIu7sSQT2HQDfLndQ3apA9MhhM4Kwp+w6cpPLsO+bG5j0SleYWLRsFUcksoK93XDk5e9Dds7OeoNurVSFyiuct6x5/8Yrc7YEKLOblpaGy5cvY0jsFfxlZgF5gIeBYt5E0CKPko10PKvF0UifgoJLquC2BfXxpmJ7bjEqtTr4mRihj1XziCBR8E2icSTaRItKUuemBf7hw4dZpY3U9rt06YSsbK4S4ur2VJ3erqW5OYxWPvDpWWhJUGKP4BFkA7GkYdOySUcHlB1IgSqjgrFdRPb3HpTpuqRkRHX1sdLUHFvzVKTGiYHieARaN22RmHApr8qb26LVvLkrihW4epDzPO79mB9EjQxqapTMwxZg3tF52BG/AzM7zLzvarcquQzm5SKWHDcfdDMR4OExC5lZm1FWdhWlpRdgbd34ntOHCXRsyU6MeompSkxCXkR1JlFcprfDFwB9lgB7lwDnvgG6zaYIkj3XnAJ2f3+0i4xCuL87JDDHkGIrfF9QjCKVG7O2E1iI4TAvFAXrI6HJl6HguxuwmxkCsWvTxxWFIhulpZdqgm4af3/55Rf2uWmdSp/9XvZgVJQo+jka6owKVliwn92hyVX4t8cGM9/uqOxy/HAqBQsG+tb6uOCBQ1jQnXDhLJSzKmFkcvscERQUxJiVxBytFoKj3+DJMQMg2fgadDwBTlp3RoiZMZybyFqhIk3u2+/U+HAr/bUoncUF3P7+K+HiMhnNBfPBXOCdRYH3kaOovHQBQjc79JjwOMTGJo3+3NUWYRM6uSLMveEsPSoSUnsTJZfIj7sh1nGN/WwVR44g7/0PoMnnRDctx42D46uvQGBlYBM+DGjVhj9aRJJlDW0E6sWm/TfffJP9e/ny5Vi0aBHmzp3LFpcUpJMlEdGU/k1oaIKbvKMrihTISSxD5MksnPw1Hrs+u4r1S09hz5fXcOVgKoqz7886w9PCkwXeYfZhqFBVYP6R+TiRwVnKtHU85miNHpamkOt0eCfpJu32Tjj6+KHro5xN3dH1a6GU1X3MhGIBRi8MgaWDMVvUHVgXDk1jJX2bAOeqCSkvj9SF6xZakZ7NZuwDkbs5xN5tR7OArJ6IumqsVmHw9TM4nM0lBgxoOGhBRHY7ZI11+vRptuCjhR4pxb/44osYOHDgQx1w6/R6bMjkKNWz3OybvdJHwTfNJzSHjB07FlZWVswmh+aQ1as/R1qaGAKBJRwdbrcAImTFxeDKX3+y/eFznr9r0drcSLrKLaB8OzdcO4I8niXtuKq4rOr59YHo98RAoYVbtZr9C1Ex0PGEECsi8b+OTa86xV/kru923VvPm/vcriRo1Do4+1nelwZHL5debP5TapX37dtNx1p6nLO0M+7qAKHVTTqakZEjnJ05zY6U1DX4r6Bz58546qmnWGBC1db169ezsY4hbCpg5gSUZwERv9U8h/qSqdot0OnQ48Jldp+60Bv9Sv/GrnyOKUMQWBjBfl4oUzHXVWpQ8H04FAk3/95Y5OVTwKiHlWU3CIUOLEAlVx0ad8ly8V7jL7XwFW2JgTK5jLXN2c3scF9sNHtzI6wcE8j2vzwaj5TC2tcuTr7tYOvmAY1ahehTnBf2raAAu1pEjRJxNPZSkGhdwBV9Em1CUS40x9Aq+8+moOCLL1G6YwfbVzvoULyEoz56esyDh/sMNDco8Hb/4QfojYxgVVqB3ql5CO5ce0GlPhyIzMWl1BJIRHwsH8m1EzQE1ONPjCoCMTisrZu3HVGdk4PMhc8ha/ESFnCLPD3g8dOPcPn4I0PA/RChVYNuWjTSpHTnRtZDBBoI3n33XTbI0YKTeoFIHOjfBrGxELM+7wPXERWY/WUfLFg7CAtp+3YQnls3mN3O/rwfnnq3Jx5b3gVDZwSxfjWvEFtWddWqdciMLcH53cnY9u4F/Pr+RVw9lAZpScNUW++EtcQa64evxyD3QVDpVHjhnxdwNO0o2jrofPlfOzd2Uv+ZX4rTJRV1PrbX5Cdh5eQMaXERTv7yU72va2wmxiPPhcHIRIjc5HL8vTm2Ri25pWBj3QsSIxdoNOUoqOo7rW1CrzzPKVWa93dtU/REoq5OnToVMDGFtVyKC7t3tXnLh7YA6rclEZZNmzbh22+/ZXRyoqwRjZH65Z9//nmWgGzLvU0NxbGictYOYiHk43HHltNLoOCbFvp07ChhQZW2igol4uP64NrVsUhISL3telarlDj07ZeMtke+t96dGqeq21gU51SihGinAh4b0xsDk6ognYJushyrC2RVQ57m1KJFiRvSDvm7qBznaIjUa/CoWRa8LL2a9vmzK1GQXsGYQX5dH5zg5K3ITixl1XYS2ez3eLv7GgvpuQvDOGozVbsL5VxiqClQJpVCnVbBXEVM+7ve9XdPj7lMTKqk5CyreP9X4Ovri5kzZzIbWNJfoMCbeXkLjWpo5Tj9JQ2INc8xHz4c+Q5G8MrMh1VpMlNUDYrPwbb03NuuX4GpiFHLxd6W0Cu1KPwxEhUnMps0Z+flctRyO/sxLOAmGjaNvZQ0oOuoPmgrVCj4IQKKuBImLmv3TDDEbo3TGKgNk7q4oZ+/HZQaHV75PRy6Wq57OodDh3LiZ+FHDtT63WkcrAbNL25ubkAcRwP/05JjNw63a5qGRdFPG1FUJdSoNdGj+DUB9Hwd7O2Gwde3ype9BSAMDcGlQC8ohQKYSWXInP40FPHxDX6+Qq3FhwdiajzSnS3vdrOoDcSkIvel6j5uYhI0F4iiX/zzz0ga8wikf//NNKBs58+Dz549MH1Avt8GNB/avrTtfwA0QFIvH9l18gV8tnghv+3qhQPdGpmIYOVgAicfS7Tv4YReE3wx5rkwPPtxH0x9qwf6TfGHZ4gtW7gVZUpZ1n/T6+dw6IdI5KY0zILnVkiEEnw28DOM9BoJjU6Dl068hL+S/0JbR7CZcU1v6GvxWVDXsRAViY0wfO4ith9+9CAyoiPqfV2iTI6Y24H9LlTVuXKAozG2FHg8AZydOTXlnOydtT5GdjkPOpkGAlsJjIPbnnAGuQUMmjwFSqEI4qJ8/LZzJwsqDbgbJGRDAmBr1qxh9oa0uKPrniZvWpzOmjULgYGBD4UaeUPxQyZHqX7K2Ramwvtvj7kXiOpHCYu5cyfBy/sqhEIlysqAX3/9FRs2bGB9zoQzv25i/YCm1jYY+PTsFv9c1d7cbgE2bJxvDIwDbcGTCKEtU7IArzbQgpvovNS+RQEDs90UifByLAUugIn0KF7p+GSTP3/8RU4Iz6ODLUtQPmhQ0HFqO7ewDurjAnuP+w9sqNodah96X9VuOu7lR9LZfqGjktGf74SxsSucnDjWVUrqWvyXQN7FxGIkj3hKyBIL48CBA9CETQcklkBRAhDHUZMJ0SWx2NaTS9wOOBkBkVAOldwMPnGHESGV3/bafIkQ9jM7wKSLI9M7oTaM4l/jWKK6oZBK41AhjWJJkX/+LmfJUBpDKPlJbY71QZ1Xifw1129Symd2gJGPZfMVFyaEwFgkwIWUYmy/nFHr44L6D4JQbITCjDRkx3GBZDVofrl69WaSh4kPqyqBlJPs33ttesNeLESYecOCzltRtmcP8j/+mO2T7WzJayJoxUqYmQUhKOizFvWlv7J/Fwr1GkT37gKRuxvUmZlIfWIqyg/XXri4ExvPpiKjWA5HCyPMH3BTz6i1+rgrL15EymOTkPfhR9DLZDDu3Bk+f/wOhxdeAP9fxvj9r+Dfs4L7j4IGYBtnU4QOcmfV2Bmr+mLgU+0ZxY4qH4lX8vH7x1fw+6rLSI8qalS2V8QX4aN+H2Gs71ho9Vq8cuoVHEg5gLaOFd5OsBUJES9TYEPVwr42uAeHInQI57l5+LvVrMJVH9wDbND/CY5pceHP5BpKaEuhOuguLjkDuZwT9aiGXqtHBQmoUQWgrytLBrRF9Pdyx9VOfaHl8ZEQF8fo0jRJGcCBKo/E4Pn888+xf/9+ZnVFCyASRlu8eDELkB42NfKGIEYqx8kSKZuAZro1j0J0Q5FfsBPu7lEYOiybWeYwX/nMTMawWr/uW1w8doQ9jpJykluqQS2FpKp+bt/OjT8OpK9h0pF7XuUlLvi9EyRaRVop1RRSEij9MbMQmdTKrS3DFFsN3M3dm/TZaY65SS2vXbyypRF7LgeFGVLGGOs5rmEL5UZVu+OaVu1WJpZClVYO8sLLdb09KLwVXp7z2FKsqOhvVFRE4b+Eapo2XYcE0q74YfN25AZVJbtOf1GjJkusg7OBPJQ5mkIiUyA0kQsaXdPL8GsMp95857VhPckfVuN8mYCr/EYB8ldfY1TvhiA3dze7rajwRkpKARNgpM/q51e3nR6tr2ThBcj/9gazByPHFfsFYc0WcFfD3cYEy6psw/63Pwa5ZXezGiWmZgjow1kP3jh6c91G3tFUtafPSi03BGIb8FJOAFolis3cEGfixajl/EYyRirPnWNq2gQS0y2fJ4LKRgax2AFhod9DKGy5Nh1ZeRku7+N+sy4z5sDrt99g0rMnC1aJkl2w+mtWNa4LhVIlvjnOaUy9PCIAJuKG9WOTRkZ1H/ekSZOapY9blZmFzCUvIP3pZ6CMjQXf3BxOb78Nzy2bmbaBAQ8vDEH3vwwSUxGC+7kype0pK7sjoJcT+DTpJ5dj79c3WO93XkrD/OQIAr4A7/V5D4/5P8YG0ddOvdbme7wtRUK87ssp6H6amos8Zd205v7TZsDM2oYJJp3bsfWer92hvytCB3GCZUd/ikY+LapaCMbG7rC2InEdPXJy/7jtbzSxa4sV4JsKuWx+GwUtXvsG+ONYYBdm43Hjxg2WFSa6638ZWVlZrLJza7829YCNHDmSaVtQT3xz94S1JayvSoaNtreEey2Chy0FnU6F7GyuV9TH+0mmPLxkyRKmak4sgszcPMi8g2DUqTcsPZsngKsPZQVyFjBS0sw7rGlsFdOqPmp5VBETVrwVJCRHatGEUaNGsfasfKUaH6dwCTuLst/xfFjT+ytzksqY1oVIIoB36INn2yjlGpzfzQVc3cZ4wdi8+c6l3i69EWoXCoVW0ehqN1fl5thQJt0coRbXnew2MfGuUcb+r1W7CXTd0XVYLahG/d3fXwdO83tCl3WVVV9JX+avlL+g5/MgmfMMe57PhUzYmGcCej5Up/dDptbUOv+Y9XKB/dwQ8C3E0BTKWZ93ya4E6BR1z0F6vRZZ2dycm57mwj7Xs88+Cy8vr3rp5MVbYlC8NZZZqYo9LWC/sGODBA6bghl9vBHmZokKpQYrd0fUWlAJq6KYx587xYJSSvJWK68TnZzaTKrp0fq4Q2z/iE1vziqskf3cirh4ZC5aTL0s7N/ygQLIOsgYay8k5BtIJC3ranDhj+1QK+Rw8PZFux69IbS2hsf6H2DzDGeBWLh2LdKeeQaqTE5j4U58fiQeUqUGIa6WmNjp7laQ2kD98DR/E8i15V4tB/eCpqgIuf/7H5JHjULFoUN0ccBq6hPwPXwI1k9Muctz3YCHD4Zf8F8MOzczDHkmCE9/0BthQ91Z8J0VV4qdH1/GofWRzOalIeDz+Hiz15sY4zMGGr2G2YpdzLmItownnGzQ2cIEUq0O79UjqkYCSUPnPMf2L+/dhbzkm2r6daHPJD9m60OiPX+tDWe2Yi0tqJaTQ/ZGuprqUsVxjrZo1te10dYjDxpjHayQbO+Kf4K7s/5a8pjetm0bo1T/l0Dflyh95GP+ww8/sB5Gott7enoyyiIJfpHnK6P6/YtRpNLg9yqbsDkPvMp9CGp1EYzEjrCzG8LuMzc3Z0reYXYWEJZxtleFChWj+hMtmxaqLYWkKmq5i79lk6nZYhczJqQIrR6Vl/NuC7ir+wzJv53U7gkfJOdAruNBqEzCU872cDVr2AKzPmo5CcCR6OSDxuX9KZBXqGHtZIKQqmRoc4FVuzty1e7tsduRW1k7k6A2KONLoEqv4JgI/e5t5+blydmgFhQchFTa8B7UfxMCAgKwcOFCtG/fno2LR3W98AOmIv3od9ifvB9yjRw+lj4IfWIBjPz9wNcDXS5cgUCgglm5ApuP3S0YVg0jL0s4vdilJkFVeSEXuZ9eZr3eZOd1Kyh4PXvuJ2g0hVCrxdBqgzBjxow6KeVkkSo9n4O8L66wxBdV1c2HeMB+TgjrL28pCPg8rJoUBpGAh6Mx+dhz/e51jpNfOyYcq9VocOPoQaa8TuMZ2UuS5gpV7am3m9hnCfEcBX2HVU+ISWneuuFtGuq8PGTMmwedVMr920WHsse54+rrswxWlpz9WEuB2oGuH+ZaEfo/OaMmOOUJhXB89VUmNsY3MYH88hWkjB2Hkh07bktSxOaW49eL6TUWYdTieS9Q4oK8zauFKckto6nQlpYi/6uvkDhsOEo2bYZerYZJr57w3rULzm+9xRIIBvw7YAi6/wMwtTRC30n+eOqdngjo6cTEZhIv52PrW+cRSQIj9Qjw3Bp4U8W7Wlxt0fFFiCiovw+6NUG0qP/5u9FXZT7A50u5yaA2kGVY+179WFBLvrw0QdX72gI+hs/uAGtnU1SWqfDXt+FQN6JXrDFwsB8BgcAMCkUmSko4ZVF5ZCE0BXLWy0lZ/LaOUDNjeErEiLN1htfoR1lfaVJSEhMLa8mApq2AKjdEHf/ss8/w559/Ijs7myUfyCuVehppQfdv69euD5uzC6HQ6RFqbozulg9WfT0ri2OzuLhMAZ9/kwaYfO0SEk8chXF2CsYNHcwWo7TwJ4eN1atXsxYAstVpbiRd4YJuv/tkq5j1qAomLuay8fzKlSu3LQirvYSvllUymzaCVdk2zA2b0+T3JAFPal9qLWp5SW4lwquUwftM9oegFs/i5qh2d3bozOa8dTfWNeg5dMzLjnILeNMezkxl/l4wM2sHe/sRbD817b9X7a4GBYBU8Sb1ZyOxGDlwxI/Z7XDu4GlINBI83v5x8IVC2C1ezB5vHS6HrzOnZp598exNFfRawDcWwnqiP+zmhEBoZwydVM16vXM+uoSyI2nQFMnZfLR9+3akpf3KnqNWd8K8ec8x/+o7oZOpUf53OnI+vojS3YlMX0XkbAqH5zvCcpgneMKWH8/bO5lj0WCObvz23ijkV9xNM68WVDtPRYXcXHaMq5XXac4hi0/CdaUblCIzXLAMRR9rswbrbOgqK5ExfwE0ubmsMqsT61H2kin00MDWdiA8PJo+xjQUp7b9DJ1WC++OXeAZercvOdlqef+5B8Zdu0AnkyH3jTcZdVseGcWu1/f3xYCWwaNDnNDdu2HVaprTyTqOKPrEImoK1Lm5yPvoYyQMHoKib9cxKrykQwe4b1gPjx9/hKT9v084+r+O/8YqzwAGC1tjDHk2CI+/2g0OnuZQKbQ4sS0ef3x6BaX5sgb1eH8y4BP0cO4BmUaG5449h4zy2kU82gI6WphgmgunBvxafCY09SQXBs+YB4mZOQrSUnBlP9cXVB+MjIUYszCU+aaTcu+xjdEtomguEBjDyfHRm9XuW6vcfVyYYExbB1WMqNpNOCq2wPTp05ntH/VBfffdd0zU5d8GpVLJlMdJoItUyEkkje4j+hkJrRCFfOLEifcU5Pm3QabV4Ycqm7B5LWATVh+oglhaepHRHV1cp9TcL5dW4PB3X7P9zqPHoVPf/mxRSnRSomBSKwRRCL/66it221wMDaKW56dVMMtIn6q+7KbCONQePImAtZyc2ncce/dyqstEm6cAhhbXZNH2WgIXpBpJT2KqZyc4mTbd4istqghKmQamlmK4tnvwlZgzOxOZiBoJiHoGN071vaGg8/OFLi+w/d2Ju5FWfm8BTUVUERPQoiq3+YCGV9+9vTjGVV7ePlRW3t2j/F8BHXNm9bd4MTrbkCWWHtaldhiZORImCSYoKSlhvt2S4GDwwEPA/jTY2GSCp9Pjtz/+uKdmiMTXCo4vdGb93hR86+UaVBxLR+4nl5H+0VlYRCnhrbeFUbkH+nZ5CRIjCbSVakZNVySVsgA9f90NZL9/AeWH0ljwLrA0guUjPizgJubJgwR5dQc5W6BUpsabu7kg8la069UXPKEIOpkURio5U16/tXWJkr+EBHjjb+u+UPNFGGPfMM9nvU6HrBUroIyJIaVKpjZfucACKkkZjIycEBT4SYsKpxGyYqOZHzm9T/9pM+t8nNjNDZ4//wyH5cvBE4shu3QJqZMm4eqCFxAXngCxgI9XRnJ2bPcCsYiIqUbn6mOPPdYoG2PW93/1KrJXrGCV7eKNG1mwbdS+PVy/+gpeO36DWZ8+bcqNxoDmgyHo/g+C1F0fW9GVKZ5TLx71e2//4BJizmbfM3A0Ehhh9aDVCLQJRImyBAuOLUCponbV3LaAV32cYS0UILpSgc05HHW0NphYWtUoFZ/7fRvKC+4tkmZpb4xR80KYYnzS1QJc/otTP24pink+UQ+j0qDOlTHPT/M+D0/ANtmJyx4fLSqH2NEZc+bMYeq15Ju8efNmnDx58qFXNqfFHincUs/6J598wkTjMjIyWMBDKuSUbCDbqj59+jzU/tr3g205RShSa1gf9ziHBxuoZWZtZrdEK5cY3Qw2j/+4DpUlxbB2cUPfqVz/H4H6N0k1nipvVOmqtq2kyvf58+fvO/iuFmJ0bW/NrB/vB9RiIg6zxWlhDI5fPcXuo/OMLNKqGRS/5hbjeoUcPJ0c1uW7MDtk9n0LmBH8uzs1iI7ZnEiNKERaZBEbe4nF1ZLo5NAJ/d36MzHRNdfq99MmgcuyQ6k1rT8NqXJXw9w8mFkqUZCZkvoN/uugiuzYKTNQav0XCo0KIdALcOPqDXb9EYtD/vR06Hg8SGJ4aOdwnrkRFOXlsbnkXqAqtGlXJ5jNC0BBFx7yROXQQQcrnSlC1T5wD38eXuffReVXlch67TRy3jvP6OiFP0SwAF2VWg4qjVJl22ZKezgt78oJmrYA2+JeEAn4+GRyKIR8Hg5G5WJ/BHddVuPk6TNQmnNBtLtQD2fn23ur7ezs4MwrhB58HNWHQsTjYYx9w4TfClavhvToMVbhpl5uZQ8jVLSndRYfHYK/glh8fz3O9wKtV09s3sD2QwYPh527Z72P5wkEsJ05A74H/oLFo1wxw+Sfw/jxyIdYk7QTdqkx91wDU/87VbkJ/fv3Z3ZrDa1qF2/axOjtaU8+hbI9fxKVAiZdu8L9++/gvXsXLEYMNwTb/3IYgu7/KGiRRIrnU9/sAdd2VtAotTi+KRaHfoiCUla/n7KJyARrhqyBs6kzy/wv/nsxs1Zpi7ARCbHch5tkViXnoKQWsZVqBPUfDLfADtAolTi+8bsGvb6LvxUGTG3P9i/uTUHy9brV0psKC/NQmJr6Q6dTIvPGdnYf0cr5jbQXak20M5UwKrFWD2zPKWY9ZbNnz0bHjh3ZJEdiT9TnTAJjDxPos9Nn/uuvvxh9nOy+IiMjWXWUvuPgwYPx4osvMhVy8qb9r1DIawPZ932bwQWaCz0c2CLxgb23uhw5OVx/s5vbzcA67txpxJ45wXoARz33IrMSvBVUbaBe0wULFmD8+PHMV1gqleLgwYNMBI8W+E2lnVdTs/263L+3NakP78w8hlhhNrNIGtR3IKOUV1dLqI/+vUSu59OkbBem+o+Eo2nTKeFyqYoFvQTWsvQAodXoWJWbEDrYndk5tjQWdeLsJQ+kHkBscWydj5NdzWOtP2QT1ZgqdzW8vbn3ycvb+5+udlcjzdgUx60UOOF8AiN885m9GI25VGX87do17Js4Adc7dgJvnw28vTmaOV2TJHBVF+j6jY6OZgren3/xOfZEHcVewSXsdQiHrK8pFJ7xkFskQ298+zqIJxZAYGUE41A7WE30g9PLXeG4pDNMOjm0SrB9K4JdLLFwEKeq/sbuyBqa+blz53D27FmobByZOFpefAzyU+9glpWmo5ueU4LXSUUYaG0Ga9G9GXRle/ehaF3VOkmng85Ej/KnufHGw2MWrKy6oqURf/40chLjIDKSoPfjTzX4eSJXV7h+sgrXXvsC1+38INTr4Hb9LNKemoaU8RNQsHYtFNF3sxepMEAJH2KtEQuKgu66oNdoGH29aP16pEyZgsSBg5D3vw+hTEgATyKB5cSJ8Nr+K1MkN+vf3xBs/0fQ9rmpBrQozG0kGPtCJ1w7nIaLf6aw6kthRgVGLQiBbT00KXsTe6wdshZPH3ga1/Kv4fXTr2NV/1Ws97utYbqzLTZlFSKmUoFPUnLxv3a1L4ZogTpk1gJsXrEYSZcvIPHyBfh17XHP1w/q64LCTCki/slkiuaPLe8CW9fmo5jR53JxnoyExP+h2PQoLER9Ydb34alyV4P8mC+WVeKXnCIs8nRgvd0UyJCQGAUxOTk5LPAmSiwFq6QY2xZBATUt6uLi4thGfV3VoAo2CaoQZY+o4waK2E3szi9BpkINO5GQCR0+SFBrhk4nh6lpuypHAKCytARHN3D9sz3GT4azH5c8qw2ULKEEEf22ZL915swZRnOlZBFZchEdloTKqGrUEFA7D7WlkGq5T6emU8tpEUiOAHT90EJQwhNjoCoIQXyf2869d5KyUKLRQqBKh5XsBGZ22If7QcKlPOi0esaaas6xriG4fjQdpXkyGJuL0HV03WrSzYkAmwCM8hrFgu7VV1dj7dC7+671am2NYrn5IPcmtf5QtdvObigKC48iNXUNgoM/x38ZW6K3UA4J/eVy9Cr4Db0WrUCWbAjTLKDAmdJd8QF03bYHEvRMVE2rFTOF7m7durFqOTFUKDEmk8mYngZdt7eCxunOnTsz7QONJh9nz33I2Aa9e52EROgEnVLLfssH0aN9P3h+kB+ORuchOqccK3aGY1k3ExwiBWwAQ0Y/gtIrNog7exIXd+/AIy+sqHkeP+EQghGP/RgCC7kMnfR394XfCdm1a8h5/XW2zzM1hb6yEvKXXaHmpcLExBc+3i+ipaGSy3BiC+cq0G3cYzC1ahxzKq9cgQ8Sgcq+87G6mym6XT/GEgnKuDi2Fa7+GkIHBxh36sRsuoza+eNKRQXS09MhFokwYexYQK6ARiFnfeLq7ByoMtKhTk+HIioa8uvX2f014PGYz7bF6FGwHDsWAvOGC9UZ8O+BIeg2gFW9u4z0gluADQ59H8l6Dcnbe+iMoHp7Df2s/fDFoC8w/+h8HEo9BBczFyztsrTNHVGqqL3n74pJ15Pwc3YhprvYItDMuNbHEj2pyyMTcGnPThz/aR08O4RB1IB+nT6T/VCcI2Xq8CSs9tjyrvdNGb0VjnZjkZDwMRSWyeD3VkLQRKXj1sSjDlZYmZCJdIUKZ0qk6GfDTToUsJBw1ZEjR1ivFIlXUSDRpUsX5ldN1cXWBi3YEhISWJCdmJh4G7WYfDlJCI0CbarEkEiaAbeD+om/TuMqu/Pc7WH8ACtDZP+TmclRy93cprNglCoYh79bDUVFOey9fNDzsSca9Fr0W1NSiM7ZqKgo1uNNvrcXL15kG53HtIAna676/Fqrq9xuAdZNVi2nxR8F2xRIEMjPfXTAQGj+zIL0XHaNs8Hpkgr8llvC/I7Ni3/EE+0nsaTp/SD2HKfkTZaUDxIUbF/ax9G3+0zyZ9oaDwrPdXoOh9MO41TWKWabOcB9wG1/l57NgbZcxfp7zXo2PSnq472YBd25eXvh5fUcTE198V9EmbIMe5L2sP2nTXwA3QXgn4/gOuFbuLq6MrsrGosvbdiAXL0elUyFm7uW1Go1q/DWBQcHB3h7e7PrmHzrq5Ga+jPXQ27dG8bGnKq/QPRwjOdiIR9fTOmIR78+jeSEOOxM5yralAykVpMCNxcWdMefP8PUvq2due/HSzgII6ihNdMDUsAkJQHoGFzn+ygTE5lwml6lgtDJiQmoqYfaoMyRrktqpfoEAkHLO3Cc2rYJFYUFsHRwRNdHJjT6+R8diEWlSouO7lZ4ZEJv8B8bCIdly1B+5AikJ06g8uw5aPLzmXUXbcXW1jg5bCij0nekcX/zFtyrCZH8tY07d4L5wIEwGzIEIof7ZzUZ8HDDEHQbUANHLwtMfq0rDv0QyYLHA+siWCWh2yPedfbskajau73fxWunX8NPkT/B1dQVUwJuihS1FfS1Nmd9SvsLyvBGQhZ2dPStswrZa+ITbHKivu4zO37BwOmz7vn6pJw7ck4Idnx0CeWFCuz9+jrGL+3cbItC5UUFzPI7Qup4BeVeJ+GMwXjYYCLgY6KjNX7OLsKWnKKaoLvatomExWgRRNn53Nxc1jdLgQwFtGQj4+/vD2Pj2pMlzQnmsVtezoIZqminpqYyVdxbqWZUQaHAij4XLd7E4ocvCfIgcbiwHPEyBcwFfDzj+mD9nIuKTkCuSIdQaAFnp/E1nq7JVy9BIBRi1HNLIRA2rlWjWn2elH9TUlLYuUr9/BQE0EbnKf2Nzg/q+bvz/GgqtZxYFpT8oaRUbCxHcyaLOaI5kt0cX89D7qlCaEuUqDyfA1EfF6yI48TTJNJjsOMVYm7oXNwPirKkrEpP/dT+3R6cajldf/9sjWX0cvcgmweumO5p4Ymng57GT1E/4YMLH6CbUzfWalWjZP0PJypqQcrVoqYnlW6tdqekfo0OwV/iv4gd8TuYTVh76/bo3vkVYP0QIPxXoM9iwCGQJbWo9cN36VIkDh4ChViM1ClWyHdwRG5WEDtfSDeErj+6HmkjbQaiBdc2j2g0FcjK5tq3PNzrFuRqyyA18+c7ilEYmcTaTHzaBWLkyJFsrePg5QOfzt3YuHfpz98xfN5ioCwTvBSuB/68ewf0jIlFbHQUlKNH1Wpdqc7JQfrsOdCVlUHk5QV1aiq0NjyUPCZj7+fpOQ+WFmEt/j1JPK3aImzYnEWMXt4YXEwpxq5rWUzE8t1xwTXrW4GVFawnT2abTqmE/OpVKGLjUB4fj/PQQ8/nwz0rC14pt+j38HjgGRtDZG8PkacHxO4eEPv6wKRLFxj5+bE+cgMMqIYh6DbgNlDVZezijjj7exJuHM9g4mDZCaUYNjMYZta1Zy8f9X0UWdIsrLm+Bv+7+D+miHtnFaAt4E1fFybkdbpUyoLvR6oUte8EVbYHz5iP3aveZUrmzIYi5G4bijtBSuaPLurI1OALM6TMw/vRxWEQ3memXFOmZIrlVuYDWdCdk/87fPyWQCR6+LwbSU2egu4DBWWsz9RWfPsQRAHsvHnzmKUYVREp4KWKIm20cKAFE9EBqTpBCypSYSXl0MbSuEn0jHr7KioqGN2weisoKGAbUXXvBL0fBVG0kRjNf7k/u7FV7i/TOCufZ13tYNFAK5rmQkbmJnZLLRoCgQlrGznz2xZ23+CZC2Dv0XSKMp13xG6graioiCnWU0BM51V19ZvOE1r407lL562xwAKFmeUQ8AXwCbO/53lKlXRqvaDKNgXaRJetBlXVqRWDkkDVsBjsgZLfE1BxIgNbnPlIkish0JbBtHQHFvdYBkuj+2OOxJ7nqtxeIXZNrtI36X3P5bJksFDEZzoardG6MT+MY3VlV2Zj7fW1eKnbS+z+0n3JTAVb6GgCk873X82qrnZTb7ery1RYW9+7zenfBLVWjW0x29j+08FPg+fWFQh8FIjZCxx/H3jil5rHipydYT58OHDoEPz/yIPluxngWdoiO8qBXTukbUAJ23uBAm6tVsr0U2xt2976pSGgsack+jRZhSNBY4fwEldM1d8Ub+o+bjILuqNOHEevSU/CPGobeNDjomUYrtu3x8DsXCjKStl8S2PLrdCUlLCAmyrbFFxqS0qgF+lR/polNPoCmJkFwqdKk6AloVGpGEuJmDsdBg2r1SKs3udrdXhzTyTbf6KbB0Ldal8H8o2MYNqrFyTdu2Pfli2oSElhrLupy5dDIhQyxXHqzeYZGRnayAxoMAxBtwG1+lD3fdwfDl7m+OeXOBZ0b3//IoY8G8gWWrVhXug8ZEuzsStxF14++TI2DN+AEHvO/7GtwNPYCAvdHfBFWh6zzulTj2CIb5fuCB0yEuHHDuLgms8xfdXXMLG492KVRH0o8N79+VV23A6vj8LIuR3YMW0qyv5KgV6lg5VZb5Sa/QWpNAYZmZvZwuxhQ4i5CfPtDpfKsTOvGPPc716g0mKaaLq0UbWZeveI1k3BMKmB03YrqDecKuXUT037tFEVhCodtFHfK9ENKZC+tb+vPtBnoKoIUXZJxZpuLSwsmv14/BdAPfzXK2SM6TDX/f5ozY1FZWUyiotJzZsSNtNQlJmBA998yv7WccQYhA7hvJGbAyScRwt8CoIpaUSCelQFJ9YEMSZuE3dyJAtGCTZtTWABM52vFJzTeUetC6TqTxs9904LJDrXqYpOPeZEk70TJp0dUfFPBq5oVVidyVXUTUo2IdjaE4/5P3Zf31Gn1SHuAhd0t3+AAmqychXO/J7A9ol5Rc4RrQGqbL/e83Vml7klZgvG+IyBV449ZKREzwOsJ/ixPv37BVW7KdjOyt6G2LiV6NF9H/j8lqfsthXsS96HfHk+7IztWC89w+A3gNj9QOw+IPMyQIF4FZzffw8VR45AINXB9KAQPuMOwbjdUiTFF+DXX3/FlClTGDOpLuh0amRk/FRT5W5pm6vmBs1xJCD3zz//sH+37xCK3yNMUJFRhrf+jMIH4zuwscU1IIiJxWbGROLKvl0YWMolLzY7jWbJ0J5du+CfY8eYABuNL9WJZepNzpy/AKqkJAgdHSGwtoEqLQ0Vi8wgNylgBYDQkHUP5By9sGs7irMzWQ/3gGn3ZiHeie9PJSM2twKWxiK8PKJuHQ8CrR9IJJXGcVpXTJ069abWjIHdZkATYAi6DagT7bo7wcHTAoc3RDE64f414eg80hM9xvrcRTenAf2NXm8gtzIX53LOMSuxn0b8BH/rlrVzaSwWezpiX0EpEmRKvJ6QhbVBdVtMkIUYTU40wJOP77iXXm9QRpPEhUYvDMXe1TeQcqMQRzfGsP74ptjqkC+o/EYBt6Ab5wcI5iMyagkyMn6Gh/ssCIUPn/XUUy62CI/PxJbsIsxxswe/nmNKVW3aKJihSnQ11Zvo51TFoOCZAmpSb6atMaAFBQU8VkQps7ZmGwVOFMjQbX09uQY0DPlKNd5L4nqOX/F2gr34wSruZ1ZVue3sBoOns8GeT5dCJZfDLagDBj49p0Xek84rqqzRRos2Oi/pvKVqNZ27mRnZ0PO0UOsVNf3Y9YFonnQNELuCXpOEB+tjWfAEPKgGu+HV/ByQV4OJ9BKMZBfx6oBNrLp+P0iPLoa8XMVYPZ4dWsYb+07QMTy5LQ7KSg1s3cwQNrRhFj0tBbIPG+E1glW83znzDj65sYiGZ6ZWbuTVfPoTvr7LUVB4FDJZMlJT18HHZwn+C5CpZfj62tdsn+j8IkHVmGHfHgh7Eri+BTj2DvAM50VPIFEq8xEjUHHgAMyP8qHoyoO1+zYE8BciNjaeBd7kIEF09NqQn38ASmUuxGI7ODqOw8MESiTv2rWLJaYJPXr0wIgRI2AXVIC5my9j64V0eNuaYk5/H+7v4yezdc31w/sQ5pkDYxNT7LcfgEdsLdDDxx/nz5xhCW5KGlILjV6tRtaLSyG/cQN8S0uYDuiPst92QDZCAGlgCXg8AToEr4axcePV+huL7PgYXNyzk+0PnjkfklsYPg3BlbRifHY4nu2/PjoQNqb1M3UuXLjARPsI5Md9a/+/AQY0BYZVpQH3rNw+9nIXnPkjERF/Z+LqwTTkp5Zj+Ozgu6iFIr6ICavNPTwX4YXhmHdkHn4e+TPcLVp3kXQrSMDpq0APPHIlAX/klWC0nWW9NPPRi1/GtpXLkHT5PMKPHkDYsNENeh/XdtbsGJEwHSn9Ulw55NnGBd46lRaluzlrHNMezhC7mMFBPwrGyZ9DLk9Dds5v8HCfgYcNExyt8X5SNkt87Csow9g6jv+dqA6MbwVVBYnKSxtVsOnfFIRT7yslSGijAIWy1ERDpwCGbqlaSH19Bop4y+KNxCyUa3QINTfGLLcHW+VWKgvYNUJwdZ2O/as/QUlONszt7PHoi6+yfu6WBp1/lMChjZARW4w9X16DwFiL0UsCoVDKWJsDna/VrAw6V4m1QRuxKygp1BgqtVavx0uCSuRL+PCUajE6IQXybmOY3/T9Iq6KWk791IIHpOYcdSobSdcKWA/54OkBTD+jtbGi2wqczTqLqJIo7DQ6iCctJsBiaP0ewY2FSGSBdv5vIDJqMVLT1sHR8ZH/hKja+oj1KJAXwM3MDU8F3mEDNfAVIOI3gPqQE48CfkNr/uSwYgUqDh4ET6eHxU9iaFbkwLNTBvj8oBqLsEmTJiEoKOi2l6TrLj19Pdt3c53+QETAmguUeN6+fTtrbSGdiUceeYTpohCGBTli5ZggvLcvGv87EAMPWxOMCHaCZ1hneIZ2Qlr4NRzN8Ye2hwdkAmOMd7Bkc2Lv3r2ZKwNVzYODg5H3xptMWIyo1Hbz5yH/41VQBOpQNpazX/XzexU2Nr1b/LuS28Tezz+ETqtFu1790K5Hn0Y9v1SmwuJt16HV6TE2zAWTu9afJCCNjmr192HDhtWZsDHAgMag9WcvA6DT6VFeKIemkseUw0mhlbaS3EoU53Ab/Z28UTVq7V3egS0NgYiP/lPaYdisIAjFfGTGluC3/11i1e87YSoyZXYqflZ+bOKcc2QO8iq5fs62gs4WpqziTVgen4ECVd2+5I7evuj35LNs/5+f16MoM73B70PK78PncCId8RfzcOznaPZbN4ZWzjxfLcSwHM4t6Cir7OnBCSHRQkGnu6mi/bCAaGzVNONPUnJYkNBUkEAVBTREASfBNbJ9IXVpEpWijD8pt9K/6X7qxabHUbaaAhpDwN2yOFZUjj35pWyS+bS9OwQPuAc3PWMD87a3sOiIqEOJSL1+BUKxEca9tLJBrSItAUpc8sBDcA8veHq5s3OSVPrpXKVzlha8ZHVEgQFVtCnJ1Nje5c9Tc3GyRAqxXodV1xV4onAgXgxc3CwU7+TrBWw/oJczHgRItO30Do5W3nO8L2NetQWQ+vvzdrPZ/s8OfyJ3pK5FLKUcHEbD1nYg9HoVYmNfh16vw78ZpA3zcxQpiAMvdX0JYsEdlUgrd6B7lRDggVeowbfmT2InR5g/MobtS3L0sNgtQG7Wjxg1irP6o4QWBd5UvbwVJaXnUSGNAp8vgZtbw72eWxtkXUgWmxRwU3Ju5syZNQF3NWb28cL0np7U/owlv15DeGYpG0+GTn8GQp4O6TIrHJEHwlGrRh9LjjVHYxEF38TQObNqFcp27yblSDitXMl8uVUeOpQupMWIDs5OE+Huxq2PWhJajQZ7v/gQ0pJi2Lp5YMS8xvWO05p5+c5wZJXK4Wlrgg8mcHT7ukAMpB07drDnEc2exmUDDGgOGILuNgCVXINf37mM3JNm2P7uZfzy1nm2bX37Ara9w22bV57Djy+dxneLTuC7xSeweeVZJth1eH0kLuxNRvylXBYEa9UtNym36+aESSu6sn46abGSvX/1IuxWkFDP98O+h7u5O5tEyVKsQnV3gN6aWOrliCBTCYrVWiyPy6w3kdF51Fh4dewCjVqF/V+tYkIeDYVvJ4ebgfeFhgfe8ugipj5MsJncDnyTm7RcZ+cJEIsdGB0uN5ezVHnYQL3clkIBq3ZTYGbAvwuVWi1WxHO993Pc7RFq/mA911WqYmRlcf2KQll/XN7zO9sfPn8xS6S1BsqL5EgNL2T7IQM5u57mxvGicnyeyiU5zUs2Apo0mOqMIT5Zed+vHXM2m3lzO3pbwN695T1m1SotDq2PYnOaR7ANOg5pO4wpdV4l+p9qhz7lnaDl6fB69NuMFt3coMCgfbt3wOcbo7TsErKyOHGxfyu+uPIFVDoVujt1x2CPOhw6BiwHTO2BogTgwre3/clhyQvM0olg9o8AxlFaRMe9jgkTxrPkFuHAgQOsgklBOCE15Rt26+z82EMhTkpsrt27d7ONWF0k4jh37lxmo1bb+fPWo0EY2N4eCrUOs36+zAJPq8Kz6GnHaUx4XY/A0PK8mjYvYoP17duX7V8oKoKWz4fTO++g9PffoRSXongJoBNoYGPdFwEBHzwQEbETWzYwxXKxsQnGLnuN3TYGm86l4XB0HkQCHr6Z2hnmkrrbnKiNbevWrTXH9tFHHzUIpRnQbDAE3W0AJLxCFWSeQA+RkQAiiQBiiQBGJsKajRRbq0GLELKlykksQ8LlfFzen4ojG6JZ9fn7F05gx4eXWA9c/MVcVJbdrcJ8P7B1NcPk17rBI8gGGpUOB76LwPWj6XcFrVQJ+GH4D0wIJbE0ES/+8yJTJG0rEPP5+DrIEyIeDwcKy/B7Xkmdj+Xx+Ri54AWYWFqhID0Vp7ZubNR7scB79s3A+/jPMfUG3toKFVMfJpDXrsT/9oUAiZV4eHCWJmnp3zMf4oex2r2gqtr9aUouNI1gABjQ9vFBUg4yFWq4Gomw3OvB98FlZG6EViuDROyHkz+cYPd1GzcJgX1aT5U46mQ2qziRN7e1U/NrMaTJlVgYnUbOPfDSRYEv/RvHAq+zv1VeyoUyvbzJr63X6RnNmxDcr2USBneCKtwlOZUwsRBjyDNBzSJQ1hygtp+iX2IBtR7LTRbAycQJaeVp+N+F/7XI+1GvrJ8vp5KemPQR5PKGs60eJlzJu8L65Pk8PpZ3W153oCOxBIa9y+2fWAWU39RFELu5wmrSpJp/W24WQZZ1Fdk5Wxn1esiQIex+EgrbuXMn8vJPsEo3jyeGp8c8tHVQBfb7779nVW46PoMGDcK0adNucy+4E0IBH19P7YQAJ3MUVCgxa+MlaK9sRlfbLOjMjWCikMHv4rHbntO+oAASuZx5nxfOnQNtcTGkSVdRtFgLnbGGCf2FhKwBn9/y7gVRJ47h2gGuf3/U88tg49K43vHIrDJ8sD+G7b86KhAhbnWznKhF7ZdffmEtP+RWQjoARNs3wIDmgiHobgMgL+eZn/WB63ApZnzaG3O/HIA5Xw7A7M/712zzvh6IBWsHYfYX/TH9/V6Y+FJnjJjTAb0n+iGojzOc/SxhZCpklYj8tApEnMjCkR+jsXHFGfz63gW2gMmKK2kUvbm+zzvmuVAE93dl3oxndibi5Lb4u17b1cwVa4eshYnQBBdyLuCts289cGp8fQg2M8YyL45mTqJqOcq6K9iklDliASdkc/XAn0i+dqlR7+XbmQu8aeFI6r91Bd50fEp2xkNXqYbIyRSWI2u3MyJlW/IdJpGdgoIjeBgx280eNiIBkuVKpmRuwL8Dp0sq8GMWV9H9PMADpg/YIkytLmdCg4TUE2LGTCHbv75PTEdrgdqCok9zwUHIwOYXHJJpdZgRkYJSjRbeYgWkmZ/CSGCEmaMWchZWeqB0TxILnpuC9JhiVBQpWALYr+v9W2LdC+RjHk1BPg9MhJIC77aCsr3J0OTLwDcXwXNKF3zY70MWKO5J2oMDKQda5D3d3J6GlVUPlkiKjlnxr6OZa3QafHTxI7ZPCvvtbepXlUboE4Bbd0AlBY68edufqO8YVXoNwnLA6mchEuNXQaHIQr9+/TBx4kTWWhQdHYVLF19nj3N1fQLGxg8mmdQUkIPBiRMnsH79ehQWFrIg++mnn8aAAQMa1CZFld0Nz3aDvbkR1HmxEGRdZIyAAwMmQM/jQZaSgJRrl9ljpadOoWDlGwiM5gLVy0olsn5Yg+IFGmhtdTCWeCAs7EcIhY0TMWsK0iKuc/Zg1F7y2BPw69o46zypUoNF265BpdVhaKAjZvSp2x6SKtskuEfHlzRfnnzySab/YoABzQlD0P0QgSqlFPBa2BnD2c8Kfl0c0Gm4BwZND8TEl7pg1qf9WEA+fFYwwga7MxVtWrQUZVXixrEM7P7iGjauOI3jm2OQEV18XwE4WWANmNoOfSb5sfeIPJnFqO530tsDbQPx+cDPIeAJsDd5L765zlG52gqe93BER3MTlGm0WBqbUW9SwKdTN0Y1Jxz69ism7NHYwHvErYH3prsDb+nJTCjiSgAhHzbkRVtHnyBNeG5uXBCRmvZtm0pmNBRmQgGe8+CSHkSJVRuq3Q89KjRaLInhKnFPu9higE3L05DvRGbmz8xvV1NpgdxwHaydXZggIv8+lbvvB4mX86GoVMPMxgheIc2r+k3X/ktxGYiuVMBGxIc28x3woMHCjgvhZekFy1He4EkEUGdJUXmRE0JrLCJPZLHbgJ7OEIlb9jiSfsnfW2LZfufhnnAPtEFbgexGPmMN0JxnM6U9BGZidHXqijkhnBL+O+feQWpZarO/L1lYBQV+zHzmS0svIiOTSyr9W7Atdhtii2NhIbbA852ev/cTKNAc/QmzAkTEDiD1TM2fRC4usJrMVbt1PB4k0XyYHFRW9cTrmSL3M888AxeXIhib5ECrFUCp4CrgbRHk1PHjjz/i77//ZpR40ntYuHAhvL29G/U6rlbG2PBMV8wUcUn6I6KuiHUNhv8QzpLtyHerkX/yBDIXLwE0GnQMCIClhQWkcjluTPCB2lPP6PcdO/4EI3Ht1rHNicL0VPz52f+YcFr73v3Re9KTjXo+/dYrd0UgpbASzpYSfDIptE72BB3X33//ndk6Er3+qaeeYp7cBhjQ3DAE3f8i0IBCAbl/N0fms/34a90w85O+rCIe0MuJVcLlFWrEnMnBn6uvY/PrZ3F+dxITbWvq+3Uc6oGRczqAL+Qh6WoB9q+9AZWCU7WsRh/XPnir11ts//vw77E36abVR2tDyOdhdaAHjPg8/F1cga059VdcSVTN3sMLsrJSHPz2S+ir+sKaFHifvz3wJnuwsoPcgs3qUR+IHOunoLq7PcPEXyoqIlFSchYPI551tYWdSIh0hQqbs7nqqAEPL95OzEKWUg0PiRhv+bo88PfXaKRIz+DaPzLPmbHev3EvvQGJactXZepDxD+Z7LZDf1eWsGxObMgqZE4MAh7QWf0X5IpMBNoEMrslgsCchBi5Ck/ZoVRopY0TX6woViAtgrs2g/u37G+q1epw5McopnNCvePdxzYusGhJqPNlKPmDc5MwH+QOid/Ntp/5YfPRxbELKtWVWHZiGRQaRbO/v7GxO1OKJiQlfcI86P8NIJvRb65xyfgXu7wIG0kDkywuHYGuVe4df71Mals1f7KbNw88kQj8qmS0+V4hpBfPICeH03bw8HBHSEgS28/OCsAffxzFnj17GL24rUCpVLLe8++++w5ZWVms6kpV+smTJ9/0im4kQu34mCI+zfY3SYcgMF+N4U89CyNrWyikFdj/6QfQyeUw7dsXHh+8jx5V+jWpmiAolZbMi9vEpO5qcXNBWlyEPz56Byq5DK4BwRi58EXW5tcY7LiSid3XsyGgNd7UTrCuwx6MgvP9+/cjNjaWUcnJi9tgDWZAS8EQdP/LQbZeVBGnnriZq/pi3AsdGS2caILSEiWuHExjom17V19HakRhk+iHFEg+8lwYhEYCZMSU4M+vrrOqzq2Y4D+hphrw9tm3caPgBtoK2plK8Io3p8b7ZmIW0uV198ELxWKMWbIcQpGYqSET1byxYFTzWTcD7783xUBdqkTxtlhGAzXp5ADT7vfugxWLbeHiMqWm2v0wwlQgYKJ2hE9Tc1Gqvj1hY8DDpVb+S1XS6stWoJUTcnJ+hUZTCkWpGKXJFhj1/EuwdWtdAa7c5DLW8kMWW0F9mjdoPVcqxVuJXBV6qo0UN9J/gZAnxHt93oOQf9MSzZQq1C6m0Ms1KN3XuGCNaPEUu7i2s2qRXvRbcWlvCnKTy5mmCY2RbcEejKBTalC0JRp6pRZib0tYDLndHoyO9ar+q1jAGF8Sjw8vftgin4PaikjAilT5o2OWP5R6Hndi1aVVkGlkCLMPw0T/iY178uA3AGNrID8KuLyh5m6RkxOspnBzY6WZBXh6wPpHIRKvvgeFIpv5csvk8RAIzODqyumjXLt2DWvWrEFMDEerbi1Q1TU8PJx9Fuo9p6CQnDkWLFjAqvT3JVx2YxuEGhkSRe44qwtG0vUC7ArPh4d3AAQ6HYqNxUjrEgK31V9BHhEBcdxPsLTMhU4nRFEhtTh0RUtDXlGOPz58CxVFBbB2ccO4l1dCKKpb+Kw2xOSU4609UWz/xaH+6OZVdyKHrNFu9eImhxMDDGgptI0ZzYAHAqqwuAXYYOCT7fHsx31YBdwj2JYxtNKji7F/TTgLwCNPZLIexMaAKIDjX+jEqul5KeXMi/bOwJtoY4PdBzN10iXHl7AMd1sBWVj1sDRFpVZ3TzVzsqwY+AxnF3Pyl43IS+Ey5o0BJUKqA+/Y87k49PFlJqAmcjKB1QS/Bk+snh6zweMJUVJyDmXlbSeR0Rg87WKHdiackvwXaW3LXs6AhiFfqa6hlc9xs0Nv69aoLKtq/Hbzr9miz+Tpje4BbAlcO5Je421tbN58vcmkQTEnMhVaPfCInSkuxaxk98/oMOOunlgaZ6wn+LMZX369APKowgZXnqPPVAmokYZHC4I8zK8c4hSVqWWKWFttAZzORgI0+XIILMSwfTIAPKIV3AEHEwd83P9jZgn3R8If2JPY/M4SNC8EBn7IgsXy8mtIqzrfH1aczDyJI2lHWPvZGz3fYL3xjYKJDTCkqqf7+AeAtOC23m6eiQlMpeXIt7KBoIwHi/UKREcsR3LKl+wxHh6zMWzYeMyYMYNZT5KAFvlek3p1Xl7eAz/PyBuaKtt//PEHysvLYWVlxajOU6ZMuX+6M7HyLn7Pdjd4Pgab9lwgumXjAXj9sh0dMrhjF6OVIz06Aslfv4CyqRr4+F4GLUeSkkqRmtr8rRN3Btw73l/JBGtJR+exV9+GsZl5o/24522+Arlai37+dlgw0K/Ox166dIn1yhPGjBlzl4e7AQY0NwxB938UQpGABX6PLgrDtHd7oeNQd4iNhcwn/MS2eGx+/RyuHU6/iypeH4gOOGFZZxibi1CYIeUCb+nNwJsmVBKdaWfdDkWKIiw+vrhFbFaaAvIQ/jzAndHM/ympwI561MwJoUNHwa9bT+i0Gvy1+hOoFYqmB948IK1EietKPaymBoDfiJ5JicQFTo5cn3la2jo8jCCK/zt+XAXwx8xCJMuaV3HfgJaFTq/Hoph0FKo1CDSV4DWfB08rJwh4p6DVlUJZLoKN5XD0mMhVuloTpfmyGltFasVpLih1OsyOTGXHnKwP7ct+QbGiCN6W3pgXVrsKs9jdHOb9uap/ya5EaO9IitaGtPAiyMpUbEz36ci5DbQEyAP86I/RjOkT1NeFjY1tBdJTWZATvV7Ag820QEbXrws9nXuyXnrC++ffZ33KzQ0a89v5v8H2k5O/hFQah4cRNPdXK75PD5p+b/G0utD5GcA5DFCWAcferrlbaGcHm6c53RORWAyFWASjWD7kp84zAVKRyAYe7pzHtKenJ+bPn8+ssiixQcHvt99+y/p8yQe7pUXSIiMjsWHDhppgn/qKBw8ejOeeew7+/v7N80Yp/wBFiZAKTPCb4wi8MzoQCxzkePf09xCplHD08EdQnwEs+P/zsw+QPqgQEALe7l3RuTNnt3bw4MEaq7XmhlxagZ3vv4GC1GTmFDP5zf/B0qFxzhdanR6Lf72O9GIZ3KyNsfqJToxeXhuioqIYrZxAgnTdunVrlu9hgAH1wRB0G8B8t/tM8sezH/VBvyn+MLM2Yougs38kMn9wEmFrqP+3rYsZxr94M/De/eU1yG/pITQRmeDrwV8zGl5McQzePvd2mxEB8zWR4KUqe6M3E7JQoKp7UUoT8/B5i2FmbYPi7Ez8s7lpFQcnlQZdjAVENkC6QovTB9MaLXDn6UmLbB4KCg5DWslZjT1sGGRrgcE25lDr9Xg3iaPLGvBwYE16Pk6UVMCYz8d3wV4wbgVKsFajgEhwlO1XJPtg5MJlbcJblcZOCiQ9Q2xh49J81Ow3ErJwpVzGvO6fdyjBvqTfWYX1nd7vMNXyumAx1ANCBxPopGqU/nlvhk5UleJ6YG9nRo9vCVBL07GfY9icY+1syvRI2goUCSUoO5BSo7Nh5GFxz+fMDZ2Lvq59odAqGKOrRNE4wc2GgDyl7WwHQ69XMZq5Ttd27Dgbii+vfoksaRacTJ2wIGxB01+IBBJHf8rtX9sCZHIq3ATbWbMgsLSEdX4uDvUcCJ2xHio/bn51d3saQuHNKqpIJMLQoUOZSFl1xTMiIgJff/01tmzZwmjnFCA3F8gP+uTJk/jqq6+YfVlmZiaEQiH69OmDJUuWoH///uwzNRsu/sButjuOgLOFNQbnpGH81o9hqlHghp0vZrefCu+JM+Hg6AS1TofEEx4QVHoiOPRzlgCgRACJul28eBHNDRKl3fneSuSnJrGA+/E3P4Sta+Pbgj47HIeT8QWQiPj4bnqXOvu4U1JSGJuAQP7tAwcOvO/vYIABDYEh6DagBuQRHjrIHdPe64VB0wNYME6VarIb2/LWOcSey2lQzzctLlngbSFGUaaU9XgrZTcXBS5mLkzRnHoPyWLll5hf2syvsMDdASFmxsx657X4+oM/Y3PqGV3G9sOPHqyx3GgoFPElKN2bBFcxH317OXFU83O5TLm3Mb31pqZ+sLcf9lBXuwlv+7kyMaiDheXMdsqAto8rZZX4OCWH7X/g78r0EVoDF4++CaGxEiqpCAMmfA4jk5btPW4IKNkYe5Y7Np2ascr9U1YhNmUXsUTd5+0c8d1lrro3NWAqOjl0qve55IZgM7kdRzO/UQB5ZGG9Amrp0VyVL7B3y7EXbhzPQHpUEQQiPhOZbGl19IZCXSDj/LhJZ6OzA0x7cLof9wIxuj7q9xE8zD2QXZmN5aeXQ9vMvdeUUAoIeB9CoSUT0nzYxv2LOReZYjmBEkWUjL8vuHcHOj7F7e9fBui44y0wN4ftXE5LZnDMDVya5wWdGSDIBYqvHq61J97e3p75M8+bN6+mypyYmMho51988QX+/PNPViWVyRrH0tNoNEwQ7dSpU4xCTsH28ePHGY3c1NSUVVsp2B42bFiThdLqREkq9HGcnd1PrhPwsrQAmbPnQCeVQtylCzYMnYk8NR9L1p+Gh9sFGFkpoa4UIemYD1QyDft8lJAgHD16tFmr/0Ql/+X1pbcE3P9rkg7HvvBsrP2HSyR+/Fgogl0s6/Q6J2swSqBQrzzRyttCgtaA/wYMQbcBd6Fa8Gfq2z0w8Kn2MLUUQ1qsZNWIHR9dRk5SWQMD7041FW/qF1crb05wpPS6rCsXsH52+TNcyeOELNoC1Zlo5hT87S0oxYGC0nof79EhDF3GjGP7h75bzXqSGqqEW/RLDKDjFnQhTwdi2MwgLvA+m4PjjQy8vTw5SmNe3l7IZFxf5MMGCtieceGsSF6Nz2QUWgPaLorVGsyLToVGD4x3sMJU59axdkqLuIpKHbegtDIZByfvALQFkM2WRq1j1o0u7aya5TVPFVdgZQKnhP6ajzOupXzPAjtXM1cs6bykQa9xO808Adqy2ts5YihhUCWgZuXYzEFAFfLTynFuF7dQ7jvJD7aurasyXw2i3hdtjIJeoYHY04L1wzdmYW5pZInVg1fDRGiCK/lXcEDe/P7dRkaOaN+OcwVJSf3modH0IIX3N89yfdiT201Gb5fezfPCQ98GjCyBnOvAlZ9q7rZ+6ikIHRxgrMuDix93rln+IYT4swSkXud6u2uDs7Mz66detGgRqz5T4Ek931evXsWOHTuwatUqfPPNNyyAO3LkCOsPJjE2qo5TUE7/JpsvCtLJX/vDDz/EDz/8gGPHjiEnJ4edTyTaNX78eLz44osYNGgQ84duEVz8ATzo8Y91V7gVqBGwYhkLuE26dYPb2jV4toMA3jbGmKX5DJquMviOSoeJlTlKc3Lx+//egrSkmFWEyaaMkge7d+9uFpp58tVL2PbGy6goLIC1syumvP0x08xpLK6kFWPpb9z5P6efN8Z1rF1/gir1mzdvZsrw1FJQ7dlugAEPCoazzYA6Qcqxwf1cWeW71wRfpihbkF6BPz65gsMbolBZWn/vrY2zKR5d3JH1ilOgfmBd+G009acCn8Jo79HQ6DVY9s8y5Mvy28SvEWJugoXuXE/hK/GZKLuHonafJ56Gjas7KkuKcWzDvVXEaZFb+FMkp4TrZQHridyCzr+rY5MDbwuLENjaUj+W9qGretyK5d5OsBcLkSBTYrVBVK3NQqPTY35UKjIVangZi7GqvXurVAtoMXh63ysQm6uhURqhY5/X0RZAQpTVNmEdhzXPsSGtgzlRnHDaJEdrdBelYXvc9iZVC4lmTmrmukoNirbFQk8veguoxSWmSkCNeqxbAsR+OvRDJHRaPesXb2mhtoZCr9Gh+JcYaIoUEFgbwXZ6IHiixi+VfK18mYYJ4bzqPH5P5OyqmhOOjmPh4DAaer0GUZEvMMu8tg5KshOtnBJF1Yn3ZoGZAzCE63XH0XeBCk4IjS+RwO6551A+TgsBX4d4VXvI8y0hLOah/JUfUJpXP12aBNao+kyBMQXhPXv2ZNVwQmFhIbOaOnPmDOsPJtsx6gOnoJz+TSJdFKQTdZwqq8bGxqx6/uijj2LZsmV49tln0bFjR0YrbzEoyqC/wvm6H5d1w8ov3q8JuN2/Wwe+iQnMRMCXvkdhO5hj98XkjcQjK/4HiZk58pIT8MtrLyI/JQnjxo2DWCxGRkYGLly40OSPpNWocea3X7B71XtQK+Tw6BCKJ9//DDYujR8DUgsrMfvny1BpdBgW5IhXRgXW+rj8/Hxs2rSJ2cK5uroya7Bmpe8bYEADYAi6DbgnhGIBOo/wxFPv9kJgH2emdp5wKQ9b3z7PFpb1BYb27uZMrK3aTuzwj1E1Pcu0ECX/bn9rfyas9tKJl6BuI71pS72c4GtshDyVBu8mcYvPuiASG2HUc0uZj2TcuVOIPcOpYdZVQSnYEAltiRJCO2PYTg9ilM9q1ATePDQ68Pbyeo7d5uTuYrYoDyOsREK8789NvKvT8hFb2XZ8Uw24iQ9TcnCyRAoTAR8/dfCGRSvYg+m0Wuz/6iNYB3Dq4BrtUAiFbUPxmqwA5RVqmNkYMYvA+wUl/p6OSGZtL10sTPCery3eOnezWtjDuXEq7Yxm/mQgeEYCqFLLUX70dnZMRnQxs5QkNwqfTs0voEY6HsScKi9UwNxWwtqZ2gLFkymV70qEMrmMHRu7Z4IhMGu64vxgj8FYEML1K3906SOczuI8kpuVZt7+A0iMXCBXpCMunqt8t1WcyTqDHfE72P67vd+FqaiZ20C6zgRcOnGiaoc5NX+GoZ5QdNUxZpn2rDsWL3gbGjMhxGk8pC2ZCzU9/h6gwJgC5pEjRzKBs6VLl2LatGkYNWoUE+Fq3749/Pz8WPXaw8OD/bu6X5gqqlQxX758OQvc6X4zswfE6riyETxVBWLzPTBpyz4IlEqY9u8H9++/YwE3QSQPR6E5l8DLuuGL1SnDsexwLia89TFsXNyYb/b2t1YgJ+oGRowYwR5HFXtKOjQWuUkJ2PLqizj/+zbo9TqEDBmBia++C0kTjkdxpQrP/nQRJTI1Qt0s8dUTHWsVTisoKMDPP//MWgJcXFzY70a+5wYY8KBhCLoNaDBMLMQYPD0Qk1/pCgcvC6gUWpz8NR5/fHoFRVl1Z9idfCwxen4I+EIekq8V4NT2+BrxNKrOfDnwS5iJzHAt/xrWXFvTJn4REoP6NICjYJL38L16jJ18/dFz4hNsn6rdNEnV5vVKFW5NvoxZz9jN6gCB6d2ZVhZ4V6maU+Dd0B5vK8susLbqCb1ejbR0zhrkYcRYeysMt7VgomovxWYwdWwD2g725Jcw8bRqP+5As9YJdE9v3wyp+hyMLNUQ8C2g0fZFWwDZbF2tsr7qOMTjvr2miVUwLyoNiTIlXI1ELMmx7vrXrFrobOqMpV2WNul1RXbGjGVDqPgng2lM3OrNTWjfw4k5XbSEwFzKjUI2J4yc2wGSWsbB1kD5oVTIruSxxLLN1ACImsGXfHaH2ego6sj6uonR1dyK5iKRBYKDv2DLudzc3cjNbX6rsuZAgawAr51+je0/GfAkujt3b/43IVG1MZ8zYVFE/AYkn4BOp0F80rvszybn+Bi06xw0QiHeWfgy9EJAfF2JhFeeaLSgq4WFBQuye/TowfqCqXJKwRxVr2fOnMn+TRVtCrrJX5sq5g88saRRQX9+HUqTjaH9WwuRVgvzUSPh/s034Btz43alNBESi01Mqdwk2QphE7bAXCLExdRiLDuYjQlvrYJ3p67QqFXMqSXt6D64u7owmjkJwKlUN4Vy60N5YT6Ob/wOW19fhsL0VBhbWOKRF1Zg2JznIWhCpV+q1GDWz5eQWiSDq5Ux1j/TFSZiYa2U8o0bN6KyshJOTk7sNyLGgQEGtAYMQbcBjYaDpwUeW94F/aa0g0giQG5yOX773yVcOZgKnVZXp4/3sBnBbC6kXsfqRSnBw8IDb/fmxIA2RG5o9mpAU9HLygzPuNiy/WWxGZDV8d2q0WPC43D08YOiUsr6u2+dxPVqLYo2RUOdKQXfRAi72SEQWtedaeUq3lzgHdOIwNvL+3l2m529HUrlTc/Shwm0MPmonRvMBHxcLpdhY1bjs+kGtAwiK2R4ISaD7T/v4YCxDs3Tq9xYJF6+gEt7dsCxE3duuLvPpC5XtAXEX8hjFVzSswjqd//U7HeSspiNIanDbwzxRnrJDWyN3cr+9navt2EmbnrFzCTMHqY9nFjvdvH2OGhKFagsUyI1vLDFqOXUanTuj+o+bn82n7QFVJzOQkVVSwAlI4wDbJptPBtvMh5dHbtCppHhuaPPIbcyF80JK6uu8Pbixv7YuDfbnK6HVqfFq6deRbGimFmGvtjlxZZ7M9fOQPc5NaJqmek/QSqNhVBoBafsnuCr1Xh532847R2Ks89PYw/jHUhF6udNS161aUT+jpIrZci5aA2+Xg/Txx6D66efgifm2BtKVSEiLk2DXqKDKJWPsIG/IMzLAZtmdoeZkRDnkovw3M5oDF3yKlvfEJsv5eollJw4BCGPxwLavX/+WWfCQqfTIichDvtXf4L1i2bj2oG9rLod0GcAnv1sLdr36tekRIRMpcHMny7hWnopLI1F2DijGxzM715PEQ3+1oB7+vTpzS9SZ4ABjYAh6DagSeDzeQgd5IYn3+oBr1A71pd3fncyfv/kKkpyK2t9Dnmv0iKLQI+Nu3Bz4THCawSmtOd8dV879RryKrl+rNbGSl8XuBiJkKZQYVWVSnNdoGztqOeWQSASIfX6FUQcO8Tu1ym1KPwpCsqkMvDEAtjN7ACRw70Hfv9udwTev9w78KZKt6VlZ+h0KqSncxYhDyNcJGImFEX4IDkHKQbv7lZHlkKFaeEpkOt0GGBtjlerfp8HjbL8XBxc+zksvStgbKNitj8uLlXKxa0MSjpePpDK9jsN87xvJe4t2UX4IZMLgL8J8oCfMa9GhGqi/0T0dr1/ESqrR3yr+rs58bC4U1msBcjJx4JZQDYn5BUqro9bp4d/Vwd0GNA2+rhl1/JRti+Z7VuM8IJpt8b5A98L5NTxWb/P4Gvpi3x5PhYcXYBSRf0inY0FtRdZWnaFVitFZOQiaLX1a648SHwf8T0u5F6AsdAYnwz4BBJhC1N7B68EzByhrEhCcvJn7C4/35fhvOR1yoKg84UzCE6Kx8r2YyB9lvNnVvxwEDk/UJX83wG9Tof8L1ch7yqn4p3+2ONwf/898ATcmKTVynD94tNQCUshyAf8hS/CxI8ToezkYc0CWVOxAGcSizBtwyUEPjKFBcr+PXqDp1ZClBpD/RiIII/xjz/A+d9/xcU9O3F57x+sX3vnB29gzcyp2LpyGWu5o89DvduPvfYuxix+GSYWtauL3wsKtZb1cFMl3txIiM2zusPf8W4BuqSkJNbDrVAo4O7ujmeeeYaJ4RlgQGvCEHQbcF8ws5Zg9IIQDH02kAmm5aeWY/sHl7he71qyn2FD3NFxKEfbPr4pBpmxxTV/e7nbywiwCUCJsgQrTq2ARle/gNmDgLlQgI/bubH97zMKcKOifpsQsrroN/UZtv/PpvUoSctE4Y+RN3sEZwRD7NZwhVIKvIdW9XjHnLl34M0ponK93ZlZW6FS3Ty+DxuedbVDT0tTVGp1mB+dCpVBzbzVUK7RYlp4MnJVarQ3leD7YE8IWqEHV6NS4c/PP4SyUgq3XlxLi5vbM7f57bYmSOuivEAOiZnovgPKsyVSvBLPsQpWeDthjL0Vvr72NTIqMuBo4oiXur7ULJ+ZRMJsnw4G31wEda4MgjPZzI6suavcNG4d/SmaCXCSGvrAaW2jj5ts04p3xLN9sz4uMB/IjffNDXOxOdYOXQt7Y3skliZi4bGFTMm7ucDnC9Eh+EuIRNaokEYhIfF9tAVcyr2EdTc4cc83er4BH0ufln9TiSUw4n9I8DGFFmpYmATAxeVxSAICYPnYRO6z/LmNBY2vDX4F6olcArH0sx9QtG0zHnbotVrkv/ocis4r2L93Pzoefd95s+Z6I8HVyIglkKriwJMCxvv9YDtl1m2v0dXLBr/M6QkbUzHCM8swed05VEpsMHbpa5j63qcICA6BSTFXOMmUq3Fi3x6c2roRJ7b8yPq108KvQSWXQWQkQWDfgZj20VeY/Mb/4BXWucnfS67SYt7mKzibVMQSAj/P6o5Qt7vZVtevX8fWrVuhVqvh6+vLKtwGSrkBbQGGoNuA+wYN5O17OmPqm93hHmTDFMqp1/uvbyOYV+2d6D3RD35dHVh1/OD3kSjN4wJZI4ERPh3wKRNXIQux9RHr28SvM8zOEuMcrEiDBcvjMqC9R+9X51Fj4RbUATw1kLvuGlRp5eBJhLCfHQIj78Znd9t1c7ot8P7nHoG3rc0AmJsHQ6eTIyPjpnXKwwY+j4c1QZ6wEgpwo0KOj1Oal5JpQMOg1ukxJzIVMZUKOIiF2BLqA0tRC6rt1oN/Nv3AVHRt2+sgMi+DQGAKD/dn0RZA1dvLBzhab6dhHhAZNb3KHV+pwIzIlBo7thc8HXE9/zq2RG9hfycBSgrimgtCKyPYPR0M8kq01esRaiaEXxdHNCeIAZAeXQyhiM/6uMWS1jmHboU8qhBFW2MBnR4mnRxgOcanRRMBLmYu+GH4D7AyskJEYQQWHV8EhYYLjJoDEokzgoO4nuasrK3Izf0TrYkcaQ5ePvEydHodxvuNx6O+jz6w9y52cUKegxELrANS9eCxVBJgv3gxeCYmcIyPxbjLZxFRqcLVOWsgG8G1p+S9+z+U7tqFhxU6mQyZS5ageM8/7N9Jg7zguGQpTKvELqkYEp/wPgqLjwNqwG6LFQqGT2PU8TvR0d0KO+b3Yj3TyYWVeOzbs7ieUQqXdgEY99LrWPr5N/D38mLsAZVXALx6D0Rgv0EIHjgUg2fOZ4H28z9tx+hFL8HR2/e+vlehVIknfjiPE/EFMBYJ8NOM7ujsYX37d9fpmI84WZqRWnxQUBDrrSfFdQMMaAswBN0GNGvVm5TK+072ZwI51Be4/b2LyIq7KdBDIEusIc8EwtHbAkqZBvvXhkNRyamWe1p44vUenO0PZcdvFLQN79F3/VxhLuCz4O+ne/QY0+Q1fOpzGOo6HeZ6K2iFOtjPDWH+uE0FC7xncIF39D0C71ur3RmZm6BWN8w7vC3CVSJmvukEEu/6p/jh/S4PI0jEbmlcOk6UVDClcgq43SWts4CJOfU3bhw5wHQh/IZw+gpurk+xyl5bQOKVPJZAJMXv+6ly5yvVeDI8CWUaLbpamOCLAA8otUq8ceYN6KHHWN+x6OfWD80NGp8yHDj6pZeQB8WF+ttpGoOM2GJc3JfC9gc82b5N+HHLo4pQ9AsXcBt3tIf15HZsbmppkJXYumHrWHKZqsDLTiyDStswMaqGwNa2f834Hxv3OiorE9EakKllLKlAziTUx/1q91cf2HtrNJWIiePUy91yVDCPPQPEcAkIkYMD7ObPZ/sLd22FqVyG/2WqYfP6t6gcoANPD+S8+hqKN3MJrocJ6rx8pE2bDunRY+Dx9XDsVYbvJi/DU1XaNASyFM3M3MT2rTcK4f7Mu9Ba1K2r4Gtvhj8W9kZ7R3PkVyjx+Lpz2HI+jQXvQpEIjz/1FNzc3FgxIlGhRafHnsTIBS+g04hHWKDNr6Kz3w+SC6SYuPYsbmSUwspEhE2zuqO79+2aCyToRlZtp09zmkD9+vXDpEmTWtaOzQADGglD0G1As4ICPqKQT1rRlVEIK8tU2PPlNSaydmuQSIq4o+aHwMzaiC1Uqc+PVH8Jj/g8glHeo5ja6ysnX4FU1freo45GIrzmy9EtP0rOQY6y7kWSMrUM8u2ZMBfaoFJTjmOZm1Ghvz3x0BS0635H4L01rs7A295uGExN27H+vsxMzqPzYcVoe6saQbtFMekoULUNW7l/O2hR9VpCFnbkllABFN8FeSLUvHVEaIoy03H4h2/Yfrcp3aDUJoLPl8DD43ZKZKv2cu9PrVEsb2oVt7KKxk/+597GYvwc4sOcFNZeX4vU8lRGTV7ebTlaAgqpGtcTShEl17J/l/2VAukZzrf3fkB08iMbophYW2BvZwT0ah0tgFshiyhA0dYYLuAOs4fN5PYPJOCuRrBtML4Z/A1jd53MPIkX/3mRJVaaCz7ei2Ft3Zv17YZHzH/giVeqbJNwWlxJHGwkNuy7NsZH/n6RmLQKCkUGJBJX+LrM5u488Aqg5FxIbJ59BmIvL4hLirHs8B6Ua3T4tNAFdq++BOlA7vzP++ADFHz9TaNVzVsL8vBwpE6ZAkV0NPjGAngMLsTBHgMwNbATjKqq2FlZ25CU/Cnbt9gpgJPXYzAbPvyer+1oIcHOBb0wItgRKq0OK3dHYtmOG4zuTV7XpAhO3tfkgU191Dk5zZewO51QiInfnkV6sQzuNsb4fUFvdPO6PeCm9/vuu+8QExMDPp+P8ePHY8iQIWzfAAPaEgxnpAEtAvLnfvy1bgjo6UTsLiac9te6iJqKNsHU0ghjngtlHt6ZsSU481tCTeC+sudKuJi6IFOaiQ8vftgmfqWnXWzRydwEUq0ObyTUvhiVXc9HwfoI6Co1ELmaIc7qGkrkuTi07ium5NkcgfeQZ6sC79PZdQbePB4fXl4L2X56xkZoNK2fuLgfvO3nynqJC0i1NCIVSkN/d4uCFprvJeUw5XgKRb4J9GRtFq0BlUKOPz/7HzRKJTxCOsLEI5rd7+pKtEE7tAWQ0GFJroxZX5HAZFOtweZGpSFcKoeNSICtob6wFQsRXhCOn6O5xNmbvd6EpVHL/A4kbKnT6FFqbwzzQRy7pHRvMqTnOfuwpkCj1rJxnzzLqbrd/4l2aG1Iz2WjmCjl2qqA+/H24FFW6QGjq1NXfD34a0gEEhZ4Lz6+GHKNvFlem8cToEPwFzAycoZMloKo6BdZH++DAmkPHM84DhFfhK8GfQVnsweXaCkuPoOsLK5KHRjwIYT9XgGsvYGKbODv/7H7+WIxHF/nGHUDj+yHT1YGfs8rQYLpZBg/Pwrlj3B6MoVr1iDvvfdZj3RbHquLt25F6lPToMnNhdjDBd5DsiG202FvwGw87sgFqHn5fyE27g22b3aQD+t4Tzi+zlm4NQTmEhHWTeuCV0cFgPJTf1zNwqivTuJsYiHzvKa+6VsD77S0+1PQJ8G0d/dGY9qGCyiVqRHmZoldC/uwyvut3/38+fNYv349ioqKYG5uzgTTOnbseF/vbYABLQVD0G1Ai4F6Ggc/E4iBT7WHQMhndPMdH15CUfbNANDOzRzDZgSx/YgTWYg5yy3wLMQW+LDfh+Dz+Pgz6U8cTDnY6r8UCUd90t6NVf32FZThaNHN6oFerUPJnkQU/xoHasSUBNnCfl4oBs6fB7GxMXLiY3F1f/P4p5J37m2B97baA29Hh9EwNvaCRlPK+vseZlC1b0MHL1gKBbhUXomX4jIemgrEw4jPUvOwNoPz4v6svTsmOLYOhZt+48PffY3i7EyYWdug37ODUVZ2GTyeGB4eVRWsVoZaqa2hTncd7cUEJZvyPV+Jz8Sx4nIY83nYEuIDbxOjGlo5VQ7H+IzBQPeBLfANuPePqvLmDu7nCovhnjDrzyUPSncnQdoEqjm95j+/xDFxTSMTIUbN7wDhfaq53w/o85QdTkXpniRWdSerNJsprRNwV6OXSy8mrkaq3mezz+L5Y88zWnZzgBJSoaHrwOcboajoHyQlcSreLY2tMVtr9Fje6f0OOjo8uABIo6lATMwrbN/VdRpsbPoAIgkwhqvu4sI6IIdrWTPr1xfmw4aCp9Xiw91bWO/38oQsuPv/D/wnQlE6RQM9DyjZuhUZc+ZCU3L/bLWW6N/OXrECee++B6jVMB82DPaTzCA202Kn4zA8F9YdQj4PRUUnERVFlmh6mJziw3yfCC6rPobArHFtHlQQmTfAF1tm94CjhRHzyH5y/QUs/e06ZFr+bYE3WXWdOXOmSfN0ZFYZxn5zGj+e4cbVaT098OvcXrAzu2kLWVhYiC1btuDgwYOsf7tdu3aYP38+PD09G/1+BhjwoGAIug1oUdAgTYs48vW2sJMw/9rfV11BasTNvmifjvbo/qg32z+xNR75aVww29mxM2aHcAvr9y+8j0J56/s1dzA3wRw3e7b/anwm8+7WFMmRv+4GKs9xC1Pzge6wnRYIvlgACzt7DJjOfYcz27ew4KHZA+9TtQfeVO3w8lrA9tPS10OrbZ4qSmvBz4RUs71Y0oMoz9TjbUDzghZI/0vKxqepnGjde36uePKWfsAHjRuH/0Lc2ZNMJ+GRF15BTsFGdr+Ly2RIjJrX1qmpuHEsA7IyFcxtJejQv2m93KvT8rElp4hNyN8GeaGzpWmNrkVyWTJsJbZ4pRsXTLQEcpPLUZJTCaGYD//uTmzcthzlBbO+3Pcp3ZWIsiNcH2djjkvc+VxG2x4xtwMs7VvPH5clRXcmoOI4pwZvMdQDVuP9HiilvC50c+qGdUO5Hu+LuRcx69AsFMmLmuW1Lcw7IDDgI7aflv5diwur7UrYVcNMmx82/4EKpxESEj+EQpkNicQdfr63tGH4DQWCJ5KEPrDvReoHYXc7vvIKeBIJHCLD8cSNi8hRqvFBajFCQ9ZBO9wRJbPU0BvxUXn2LFInTYYiJgZtBfLr15EyYSLK/9wLCARwWL4cLsufhkX2P9BAgBudFqKvtTmKi8+yFgO9Xg3jcCNY/iqA3bx5MOncdBXx3r52OLJ0AJ7u5cnWIFT1HrDqb3zxdwpGT5iCkJAQNlYcOXIE27Ztg0zWsERSfF4FFv5yBY98fRrxeVLYmYnx47Nd8f74EBhXJeyod5vE0tauXctswQQCAUaNGsUE0wyWYAa0dRiCbgMeCOw9zDH5lW5wbWcFtULLxNOuHU6vWcR1HeXF/L61Gh0OrIuArJzrmZ4fOp/ZiJUpy/DuuXfbRHXzZS8nuBqJkKFQ4eNzSchbfQ3qLCn4JkLYzgiG5Uiv2xZzIYOHwzO0EzRqFQ592zw089oC7xO1BN5OjuMgkbhBrS5CZtYveNgxwMacidpV+3cfKGher9v/umjayoQsrK5KZrzp64I57lyCqTWQmxiPv3/mvOb7PzUDZs5KFJecAY8nhKfHPLQFkDvD1cMcjbLnOB8IRI2fUnfmFuPDFC5h976/K0bac/TxqMIo/BT5U43VkpXkbmuc5kL0aa5dxq+LA4yqKvUs8B7jXUM1rziWjpLf4qHXcNob9SE9qghnf+cEvPo85gf3gNt7MB8ktGVK5H8fDtmVPCbCZzXBDxZDKVho/YC7GpRg/n7Y90zVPLIoEtP+mobUMk4j4H7h5DQWnp6ccFhM7CsoLbuClgCx0d4+9zbbnxY4DQvDuPamB4W8vP3Izt7O9oMCP4ZQeIcn88gPASMLIOsKcPlHdpfI1RV287mxZM72jbCsKMem7CJclkkQFvoD1N2MUbBMCb2jMdRZWUid+iRKft3equsQvUqF/K++QuqTT0GVlgahoyM8fvoRtjNnoOAYR5/f7TQU88N6orT0Mm6Ez4VOp4RJhg2sftDBODgE9gvv/7exkIjw7rgOrMc60NkCFUoNvjuRjEFfnMF5rS/8uw1gAXF8fDxWr17NBM4oYL4TlUoNDkbm4LlfrmLElyfxV0QuW9OM6+iCgy/0x+AAzkWBqudUOf/666/Za5FSuZ+fHxYuXIgePXq0qevZAAPqgiHobgOgQElbroJIxYO2QsVtUhW0lWq26WRq6OQa6BTcRouethB8NhbkXfvo4o4I6ufC6H1n/0jE8c2xzGKMglQSCSPxNWmJEofXRzJxIpFAhPf7vA8hX4i/M/7G/pT9rf01mPXGe84ObH+9UooEkR5iTws4LOkM4/Z3Ly5pMhg+bxGjmWfHx+Dagb3N9lmqA29aTEadysaxTTHsuFWDzxfB2+t5tp+W9h0T1nnYMdPVjgmr0RUwPzoNJ4o5cRwDmg5Snn0xNgMbqnq4yZt+oQd3jrcG5NIK7P3yI+i0Gvh164UuY8YjJXUN+5uT0wQYG9+fB3Zz4fJfqSyJSElF/66Nt9g6WVzBjjthobsDZlaxaIhWvvLMSiYmOdJrJIZ4DkFLQSlTI/Eyl2gJqqpsV4MF3iO8YD3Rn60WZNfyUbAhks1RdYGEMQ9viGJaHgG9nRE6uGV8rxsCErXM+/oa1BkV4BkLYTejA8x6tL6QW20ItQ/F5lGb4WrmyrRMph+YzmzimgO+PkthZzeEBV83bsxFZWUymhNH0o4w4TRqg5jUbhIT+3uQQRD1rcfEcv3JlGCwtu5x94PMnYAhb3L7x94FKvLYru3MmTBq1w780lJ8tu9Xdt8LsRnQG7dDhw5fQ+suQO7LZUAXJ+gVCuS+/TbSZ8yEKrN5WGuNQeWFi0iZNBlF364jfyxYPPoofP7cA9Pu3aHMuAzH1GPQgo/ini/CShWD6zdmMetQc5kPLD+pAF9sApdPVoEnEjXbZyLbrv2L+mL9013RzcuaCa3tuJqF10/JsE8ZCJXQDAqFglWnV332Jb7feQBrD4XjvX3ReObHi+j07hHM33IV+yNy2JgxqoMTDi7pj6+e6AQbExEyMzMZhfyLL75glfOKigpYWVnhiSeewFNPPQVb29ZjYhlgQGNh0NJvA9ArtSj85CpCYY3CK1cb9iQ+DzyxAHwjAXhGfPCMhOCL+eCbiMA3E0FgJuZuTW/+W2BpBF4TKjHNCertHvhke9g4m+LMjgTEns1BWZ4MI+eFwMRCjFHzQrDz48vIii/Fhb0p6DXeF+1t2rOK9zfXv8GHFz5Ed6fucDBxaDWaovRsFsKOpmNgsBj/OIqwqo8l9vYLYt+tLljYOWDA9Fk48v03OL1tE3w6d4O1s2uzBd6EYz/HMDon2bCNmBPMFOIJTk7jkZq6FnJFOjIzN8PTs21UCZsKWsy97++GfJUGBwrL8GxEMn4J9UVv69a3IXoYIdVoWfKCNAqIuv9VgAcmObVeZVKv0+Hgms9RXpAPK0dnjFz4AioqIlBU9DfLE3tVVe1aG6X5MkSe4CrEvSb4NpqqfKWsEs9GpkCt12OcgxVW+t4MBr+59g0SSxOZ8vOrPVrWainuQh40ah1sXEzh5FO7dZBpdycIrIxQ9EsMVCllyPvqKhMgk7S7vddfJdfgr2/D2RhErzVwavtWqUDptTpU/J2B8uPpgA4QOZnCdnoghLbGaMvwsvTCltFbWG93VFEUZh6aiVe6v4LJ7Sbf13HkhNW+xNVr01BefgPXb8xE1647YdQMQoTbYrexeZns7Eh3YGWPlQ/0N9dqlYiIXMycOqwsu8HH+8W6H9x1JnB9K5B9FTj0KjDpR/DEYjh/8AFT/vY9+Tce7dQDewPCsCIuA2uDBiA46DNERr2A7BnpcO/cD7rNNyA7fx7JY8fBftEiWD/1JBNma0lQlT1v1SeoOHSI/VtgZQWnt9+GxcgR3AP0euTuXQHqZt7vMhKPuvNw7foz7JhYCDvA9I0k8DQ8OK5cASNvrpWvOcGnokmQI9uupBVj55VMnIgrQHYZsE0aAB9+EToKs2CulCE78gJ7TpHOBOU6S3jACOaWZujk44T+ftawFetQlByBnaezGX2cKtzVcHBwQK9evRh93WAFZsDDCEPQ3RZA6T0+jy022WTVkCK2Tg+9QgOtglPZbChYAG4tgdDKCAJrIwitJNytvQmE1pIHIirDbMUGu8PayQSHfohCTlIZdnx0CWMWhsHOzQyDpgfg8PooXD2YBmdfS3iF2GFmyEymhhpdFM1o5qT6+iAndmIWyMMLUHYgFdpSztrltUoRLvF5uKrTYFt+Cabdo/c1ZPAIxJ07jfSI6zj47VeY8vaH4PMFzRZ4k4DToe8jmWDdvq9vYPSCUHYfq3Z7P4/omOVIS/8Brq5PQSh8uANUEZ+HdcGeTMmcxKemRSRje5gvulX1whrQMGQrVJgekYwoqQISPg9rgzyZRVtr4uKenUi+egkCkQiPLn0VRiamiL7+Ofubk9M4mJh4obVB48Gp7fHQafXwCLKBe2DjkhTRUjmeDE9mmhADrc2xOtAD/Krx7GreVfwcxamVv93rbRZ4txSYgNopLnEQ3M+l3jGVAmyHhWEo2hoLTZ4MhT9Gsp5v1k4j5EOn0+PIj1FMxd3UyoglUptCt79fqPNlKP4tDupMTrCTeXBP9GcaGw8D7Izt8OOIH/H66ddxNP0o3jv/Hm4U3GCOHiS41lQIBCYIC/0ely9PZgnYGzdmoXOnrXfTsBtx7qy+trpGNI0SA6/1eA2CZprTGoqEhPcglUZDJLJBcIcvwefXs6ylz/bIF8APg4DI34GOTwF+Q2Ac0gE2M55F8YYf8cLWDTjx6ofYlQ8MtLHAFOdHmEBbbNxKZHQ6Ba+eMyD4Ng7yy5eR//HHKN60CfbPLYTl+PHgNbMnNNHHi378CWW7djFaOfh8WD8xBXaLFkFofTPhlXhtN/zyL0POF8Oy9yOIjaAKtwJWZl1h/kY+tHItzEeOhNWUx9HS6OJpwzY6P5IKpMzyK7tMgcIyGQryk2BWmQ2JqhS2fBnbGJSALiYS/9TSMm9kZARfX1907tyZ3Rpo5AY8zODpH0aeciNQXl4OS0tLlJWVwcKi9ix+W4BarcZff/2F0aNHM99DAvtp6Ndhv9DNfcriU3Vcp9RCr6q6rdpndHSpGjqipleouFv6d4WKVWnrhYAHoY2EC8DtjSGyM2a3Qjtj8E1FLTLYleRWYv+acJQVyCGWCFig6NreGie3xTE1cyNTIaa83h3mNhIklCRgyr4pUOvU+KDvBxjrOxYPgvqviC5C+T8ZNYs4gYUYFiO8YNLJAd9nFeCtxGxYCQU41SMA9uL6aVtUvdv40nNQK+QY9MwcdB49rlk/b1Z8CeuXr6a8ProoDMbmYuh0Gpy/MAJyeSp8fV6qEVi7n/OzLUCh1eHpiGScLJHCTMDH+g5ebKFkwL1xo0KGZ8JTkKtSw14sxM8h3uhs0bpJi/TIcOx8fyX0eh2Gz1vM9BBKSi/h6tUnWC93r55HYGzs0ernZ9K1fBz8LhJ8IQ9T3+jB2mIaihSZEmOvJTD7u24Wpvi1ow9MBVygQsrVj/35GKMXj/Mdh/f7vt+C3wIs4fnHJ1cgFPHx7Md9YGRy72OnV2tRuj8Flee5PnSaH6zG+uJadDFLlFKgPfGlznDwfLDXIX2uilNZKCexNI0OPIkQ1hN8YRLWem0S93Nu0vy/MWojvrz6JaNtt7duzxw9/K3975uKffnKZKjVJbCy6oGOYRsgEDQumKfz9N3z72J/Mtfu9XzH5zE3dO4DD4iys3ewPnXqr+oY9hNsbfs17IkHXwXOr+WsxBaeA0TG0CkUSB43Duq0dGSMGI2nx0+HiYCPo13bw8fEiLVnkf83wd11JuxvtGOWYpo8jqYu8vCA1eRJsBw3DiKHpp9zeo0GskuXULL9N1QcPsxo5AST7t2ZxZekffvbHq9Qq5D7ZVd4VabhjN8IKF0joNerYGszADY/GaPy0HGI3NzgvesPCMzN28TcXllZiYSEBGYrRnRxqVTK7qP3J9sv2mxsbFiQ7ebmxnrDDfhvQt1G155NjTUNle42DDaB1cxhNyczRhGXCNGYYYgmcJ1Mw6q02hIFNHRbvV+sgKZQzoJyTYGcbXd9FmMhRBSAO5hA5GgKkaMJhI4mLAC9n4nW2skUk17pysTTshNK8efX1zF8ZjD6TPJHbko5CtIrcOiHSExY1pktNhZ2XIivrn6Fjy58hB5OPeBo2vg+yoZAp9JCfqMAFScza44HT8yH+QB3mPVzramazHK1Z0rakVI53knMxjdB9dtVWNg7YMC0mTi6fg1ObdsE705dm41mTnBtZ40JSztj79fX2bH749OrGLukI0taeHsvQnT0MqZk7uY2DULh3RPwwwaJgI+NIT6YHp6MM6VSTAtPxqr27njS2dDnVd9Y8EtOMV5PyIRSp2f+51tCfeAuaVmK5L1QUVyI/atXsYA7eMBQdBg0jH3W5CqrIxeXx2sNuB80VAoNTv+WwPY7D/dsVMCdo1Rh8o1EFnAHmUqwOdS7JuAmfHb5MxZwO5s6Y0X3FWhpVFe5/bo6NCjgJvBEAliP94PE3xoluxLY3HHl23Bcl3MCkYOnBzzQgJuxkCILUbY/pYaFZORvBZtJ7VhL1cMKmldndJiBINsgLD+5HHElcSzpvCBsAbufdE6aAhMTb4SFbcC1a0+jtPQCwiMWIDTkOwgEDTtWccVxeOnES0gtT4WAJ8Bbvd7CBP8JeNAgP26qPhO8vRY1POAmDHoNiNoNlKQApz4DBq8EXyKBy/vvI23603A/9BdmB4VifbswzI9Oxd7O/qwti8cXIyHhfWRk/QhtyONod3A/SrfvQNF330Odno6Czz5HwZdfwaxvX5gNHADjzl1g5E8q+fUzPtT5+VBERkF64gQLtLW32JOZDugPu9mzYdy1a61rrWNH12BMZRqkImOoHa6y68HefiRcwruj4NBHgEgE1y8+rzXgbi2Qyjh5aRv8tA34r8EQdP9HQIM19XfTBlezOsXcNAUytoiiQFNN+wVypv6ql2ugSq9g222vaySAyIELwOm2Jhin/vEGBuMSUxJYC8ORDdFIvl6Agz9EYsDU9hg5twO2f3AJeSnlOL8nmangPhv8LI6nH0dEYQRTSl07ZG2zZddpsiIV8spLuZBdL2DsAfYdJUKY9XaGWW8X1ht/K8gDc1V7N4y5koCdeSWY4mSDfjb1T26hQ0ci/vwpVtU7tO4rTHnro3tOyo0BVbgpSfHnV9eZqBFVsijwdnJ8FKmpayCTJSMj42dGOf83gKoRW8N8mCDVH3klWBqbgQy5Csu9OesjA26C6Mwr4jNYoogwzNYCa4I8YSFs3UqCVqPGvi8+hqysFPYeXhgyaz777YqKTqG07BL4fDG8vJ5DWxFPI7FHsgjrPLLhnrBFKg0ev56ETIUa3sZibO/oCyvRzSn4TNYZ/Bb/G9t/r897MBe37CJZUalG4hVOQI1sHRsL42BbGPlaInZrLG5c5F7H35gPu/RyqH0sGVOqxVlIMcWoOJFRMy8JLMWwHOUN4zD7f82138O5B3Y8uoO1VZ3IPMEo3cfSj7FgN9A2sEmvaWkRxirc12/MQHHxKUREPofQkLXsOqsLVG3fHrcdn176FCqdiumqfNTvI2Z59qAhlcYhPGIh9HoNHB3Hwtt7ceNewMgcGPUx8Nt04PSXQMhkwL49TLp1g+3sWShavwHTNqzFP69/hHAAK+Iy8UWAOzzcZ0AoMEdM7KvIzvmNsQWCpn0K68mTUfbXXyj7Yxfk166x4Jk2At/cHEZ+fhDa2UJga8u8sbWVldBVSKEtLYUyLg6agoLbPh71bJPntvW0aZC0b1fn17hWkI9uV79i+1kefGiFejg7PQZPxeNI/2gmu9/xpWUwDglp/EE2wAADmh2GoNsABhIBoj5v2uBvfRdtT12ogCZfxvrl2G1eJTSFChaYqjIq2HYrSOTttkC86pYF47UIDpHoF3m5ku0Vs7/aGoduY7ww+OkARuO8fiQd7oHW8AiyZWrmk/dOxums09iduPu+suy0cFOllUMeXcRo5JoiRc3fqNfdrJcLTHs4gW9U96VClNxnXe3wU1YhVsRn4ni39qwCWxc4NfMl+Pml55AVG41rh/ah86jmpcoTg2Diy12wd/V11mNJFe8xC0NZRSAq+kWkZ2yAu/sz/4pqN8GIz8eaQA94SsT4Ii2PbcQ++CLAA3ZiwzBHiKyQ4bmYdMRVKphtxas+znjOw6Gml7g1cWLzj0zZn/q3H132GkRGEq4nMJmrcru6TmsTvtzF2ZW4cZRTG+8/pR1EDewTrtBo8WR4EhJkSjgbifBbR7/bWlHIEvHNM5yy8pMBT7JAq6VBoovkHGHragZH76ZVpovy5Th9o4h1PnnaGCFQq4Xsch6z5zIOsYNpNycY+Vo1qx82OXjIwwtRceomCwlCYiG5se1h6d1uDCjAJR2Tvcl78dHFj5jIGlW9yQd7UadFcDJt/LVhZdUVoaHfs95uEimMiHiOqXULBJK7HhtREMHeN7yQQlBggNsAlhiylty+VngQUCrzcePG7BrhtKDAj5qWYAl8FPAfASQcAvYtBZ7dR5Mz7BcvRuX5C1BERmLN1u8xZu7L+DW3GEFmEsx1d4CLyyQ2b5K4WkHhEVy+MgkhHdaywJs2ZXIKyg/8BfmVq8xLW1dRwQLxesHnQ+zjDZNOnWA+YiRMe3S/p8I4JfHC/3oTndQlkEn4yHI2Zt7kzpLxSJs0mXi5MB8+HNZPP934Y2OAAQa0CAyrUQMaRCcUO5uy7VaQdZmmSA51XnUgXhWUF8hZfznZtNB222uJ+VwATtVx2qiH3IbE3CTM55qUzU0txLi0P5VtZC9GAj9kh3V0YwyeWNkdPlY+bKHx//buA76peosD+C+z6d57t3RQStl7D5migIJbcQ9U3BtFRRBRnuLAjQMRQUVlg4DsDaVA9957rzRJ8z7nH1oBGQWaJi3n67svo+s2uQ0593/GB0c+wIJDCzDAa0CL33TQGzZNXg3UGZWiCy9d6uvPmJstl8Ay0gXWvd1hEdTyN4sUwKwvKkdqnRofZxbg+cCLj6Wxd3PH0Dvvw9ZvPsOu5d8b0sw9vNCaKKWcVrzXfnIchRlV+ON/x3DdvX1hbR2CmpokZGYtRdDlrhCYMXrj9WKQJ3wtlXg5MRtbSiox8lA8Pu7sL+Z7tyXq4lxWUIvq0nrUVKhRU65GbZUGGsoYoWOQejKcbrFA7xelMomYj6xQyWFhJReNqGzppI+jSqQvW9lfeRmHtlEvjskP0vOh1UPUb38REWA23d7jdv+DYxsNY/TGP/5M899BcTGNhzkhGkAFmEHHfRrFt+3HONEwLCDKRWwtzS6450QajlfVwUkhEw3/zk3lp4CmsK4Q/nb+eKrXUzC2y2mgdiGVJXXitYWOZZ9wR4x/vBu0OdWia3h9fKkIjGmjEiTL7m6wjHCC0sdWNF27XNSrRJ1SjtrjRahPKIU4kEUWkgw2/SkLyVv8nI6MniPqY0JlVfRv34a0Dfgr5S9sSt+EW8Juwe2dbxfjxi6Hk+MAkVoec+JhFJdsEx2vqdmaQmGYFZ9TnYPPoj8TP4dYya3Ev713dL7DJJkEDQ3FiI6egXp1rkiTj4paAqn0CksIaP8nLAQ+3Qlk7AaOLQN63iW6mXt/8D7SpkyF8ng0vjrwN+4dMAZzknMRYqXCCGc7uLmNRS+Ln3DixOPi39JDh6egS8T7cHW9DhZBgXCdObO5Prs+IQGarGxoS0ugKy5BY00NpDY2YpPZ2kAZFARVeDikVlaXNeLx690f4vn0n8Xt1GAnRHX/Cs52g5Ex416xck5p7V7z53WYjA/GOgJupGYm2kuzgJagRm+0YixWw08H4iIwL66jfy0u+HW0Oi46qTuqkFrZgEOxhhRYP39byOo0KCtRw6WTA0bdHwG9HHhw20OILo7GCO8R+GDoB4CuUTSVo5nmorM7zTsvb4C2vB66Utqf2uaav7N+rqUcluFOUEU4iQ69F1vVvpg/C8vw8KkMKCUSbO8bhmAr1cUfp8ZGrJr7GrJOxcCncySmvz6vVdPMm1CQR3NzM06UiNYAfW7ORpXkTXG2fuCAHc1vsDrS8UndoR85lYHEWkPmwv3eLiIgb+00agrAqBlgcWYVijKrUZRVJYLtusoLzzG+EhSI00gnFx9beATbwTPYQZxUacnjQOn20VWGLrETXe2xINTXbFb/izLTsfy1Z6FVq9Fvyi0YfOtd4v7GxgbsPzBeNP4L8H8MwcHPmvz4pLTyA3+liokAt842NHdsWcCdil2nG/391qMTutme/eb674y/8fQ/T0MqkeKH8T+gm2s3GBv1z1j9wVHIldRAbbA44XO5qelUtkJZNM7e1pjyXK+zvkdDbjVqDuShNqZYlCY1k0th4WcLpa8tZHTC1VElUsIhk4oYqCnA1lU0iLImTX6NyEQ6t8+I3M0S1r09xCgzqco8juW2PjZPFp8UPQAOFxwWt+n4oRXoW8NvFWM1L6fmu6zsIGJOPCS6dFtbh6HR8wmsSN4g0tkprZxQY79ZPWfB1cowS76tqdVFOBZ9lwhylUo39Oq5AlZWLS/vuKA9HwFbXgeonOPRPYCj4XtW/Pkncl98SaxCr31jHj5wozIcKdb1DEWItap5n06efEKUwBBPz5sR0uklKBTGywDQaqux8sgrGL93ORwrNChxcYDFPZthYxuG/LfeQtnyn0VKe+CqlVAGBHS4f9vZtUXTwRqpcdBtJtrLgdU6wfjplfGiWujK1KKRG3VXP1duQyOO1OpozCpc5RL0tZZB3gpnbaV2Slj42kIZaA+LADsoPG1aZVQarR7RGKDtpVUY4miDld0uPd6iojAf3z/3ODTqeoy892H0GDcJxlql2/lLEk7tpNWtRoRPmQcoMhAQ8DiCgy4y17QdH591ukbMSc7B97kl4rabUo43gr0w1d3xis/+63SNKEyrRE5SOfKSy5GfUoGGMzMlzkBz5+1cLMWqtbWDUtxWquSiS7/CQn7WMUdpvnRyhFbIKaCpKVOjirbSelRRk8PznKui7+vmbytq+Gmj5lX0M0iFRov30vJFyQP9/djLZZgf6oMpbg5ms/Khrq3BspefQnl+HvyjemDqy3OaR+hlZX2HxKS3oVA4Y+CAbZcccWfs45OaEv767mFxkmX0vRFiRN/lBNzW1HcgKgj9HM7+PYrrijH1z6koU5fhga4PiKCmLdBor8SDBeg8yBMj77q8umA6Vv9aHC0CdzoGb36xl8jIOB/KhqJV79qYIqhTK8RkjStFkzQsu7iIem2Fh5XZHMeXYsxjU4yuy9mFZbHLsC9vX/P91A9gsPdgDPUZiiiXKLECfrFRXlTecDDjd2izP4ASdSjVSvBNsQVyNFIM8BwgVre7upquLlitLhAzxqkfiYWFB3r2WCZWultFow5YOgHI2g/4DTSkmZ9+rHJfehkVf/wBqZ0d3n3jPWywsoeXhQKre3SCv6Vhhb2xUSO6mmdlLRXjZWh0WWjIbLi7T2rVY1Sv1yEvfzVikxfBMzsDXRKqoZPJgUf3QeYSitJlP6Fg7lyxgu+z5DPYDh/eou/bHv9tZ9cOTTs5Prl7OTM7EpnUUOPt9t80KtE5nVakKQgvqxdN3UJqNbDJqcauk2Uo0uqxr1aHPpZSqC62GiyXQqqSidUPMZOc6tTtaSa5StSU0yZtYZfey/79JBIR2Aw/GC/eaP9RWI4p7hc/423v5oGhd9yLrd8uwc7l3yGwRx84uLd+7apUJsWw20Jh56zCvtUpyDk4Ht6DPkdW5lLRHEahMO1sZmOwlEmxIMxXzJ2mdHNK/aea5mV5JZgd7NXi8VjUNCv9RDEyT5UgJ6HsP0G2wkIGF18bQ/DraytWpB3crMSKaGvQanRiRZHqiQszKkWgX5xVLVLW02g7Xtz8uZSG3uCuwj6VDin2Elg5yjHCzwlvhXjB08J80m8py2PDp/8TAbetiysmPPFcc8Ct0ZQjNW2xuB4c9IzJZ8rT479laawIuIN7uCK0r3urBNwUMFFzLAq4Qx1DRVfqtlBfrUHK0aIraqBGPTD+/j5WBNx08ohGEl4o4CaUSk7lOrTR70sr1hR800lXMTWjzHDCVU8ZUKdHZNLrM61+U/8PubMllP62UPrZGZqAsrMfX4lEBNa0pZanYnn8cmxM3yiCaEo/p40oqRGhfYCoDacVcIVUAV2jDnk1ecityRWfT5xkejzqKoGrQo9nPTRwC3gW3YJMW9pRU5OM4zEPoa4uAyoLL/QQAXcrrHA3odedKZ8Dnw8GMvcC+z4BBhlOfnm8OQfqtFTUH4/By58sQM4LbyFGDdwcnYI/enSCt0oJqVSB0JBX4eY2DvHxr4qVeOqbkpX9neh47upyHSSSK89go2kOJSU7kJKyENU1CVBoGhGcasj8kA1/FXAJReXmzSh45x1xn+vTT7c44GaMtS3zzsti1wwag6ZwtRLbmShkdUqtEA3BSut12C6XoKFOAzdfG0x+qjs0ugbctekuZFRn4ObwaXhpwMswpQBLCzzp7y5WGd9IzsEoZ7tLpjR3u248EvfvRlbsCWz+/CNMm/2OUdLM6Q1az7GGEUdblkpQX+YDlWM2EmI/Q2S3V9BRUT03pfsvySzERxkF2FdegwlHkjDexR4vBnkg3Pq/XZYrimqRcqwIqceKRPf8M9HseJ9QR3iGOMCrkwOcfWwgbcVGUedrMkjBPG1NK6xUR1uUSSP1qlFIlxmGtPbaigagogHUT7ipp7CVXS1iQqtRGuYI385OYvXd1A7+9RtSDu+HTC7HDU+/DCu7f0sc0tI/gVZbARvrMHh5TYOp0eSEsrwaWNopMeyOsEuuXlHAPeMSATehOtntWdtFEDRv8DwoZW1zUiR+fx502kZxoogyJVqKgua9vycj+XCh6EEw7pGuoglbS9HjdqGTruzqUa+T1/q/hpf7viwanu3I2oG9uXuRWpEKtU6NxLJEsV3w6+2DMNBrIDzdo2BTugLlZbtRnP4eErR5COn08pXXTl+FoqK/cSr2WdE0TaXyQc8eP8HS0qf1f5BTIDBuPvDXE8C2uUDwKMAjElILC/h8/DHSp02HNi0Nny37HHffPwup9Q24OToZq3uEwMPCcDLIwb4X+vb5CxmZX4kpIZWVx3HixGOwsqJZ03fB1WUUVKqW922pq8tBXv7vyMv7DfX1huaNNbCGQ5otVJpS6F07QzLwCdQePYrc518QJ60cbr0Fzg8+0PqPD2OsVXB6uZloLykUpkIrfDQCS12rBZ00pjIz6m7ed1IQ9uftx4ObH4QEEiybsAxRrlEm3Vd1YyNGHkxASp0a93m7YF7opd8klBfk4/vnZ4ra1pH3PYIeY6836j4WZ1dh66qlcOn2ERq1Svg7r0Zor/AOf3xm1Tfg/bR8rMovFWnXktN1zo/5uiGgBkg5ViiC7ZLs6rO+ziPIDv6RLvDr4gQXX1ujBtmXe6ytLijD51lFSCmvg3u5Fp0q9RjaIIdDsQbleTX/SU2nky5B3V3F5hZg2+Zpuhkx0fht3utiBee6Bx8XI/Sa1NamYf+BcWIUUPfu38PZaXCLvqexjs+Uo4XY+OVJcX3izCgEdHVpUcC9s6xajLL7+QIBd35NPqb8OQXVmmqRUk6p5W2BAuflcw6IUYLDbg9D5NCWr3QfXJuGQ2vTxPXRMzojrP/Fm0Uy83jtpBVtWs2mlXDKqtA2asVG/15SA1JPG094WXvBRmlzViozZZukp38ibltbhyKi8wLY2bXNv6302kCBa2rah+K2g0NfdI38GEqlizF/KLDidiBhPeAaDjywFbAwPCZ1J08h4847oa+vh2LaNNw+/hZkqg2j/36MCkKnc/q3qBuKkZ31HbJzlok6+SY2Np3h7DQEVlZBUKm8m4Nwna4GWl2tSJ+vrIhGReUxsWLeRCK1xmb9aJQVeeKL2LmGO+/bBLXWDRm33Q5dRQVsRoyAz8eLIZHL29XxyVhHOD45vZx1KFSvOvmZHvjzw2iRHkkOrU8XHYT7+/fHpKBJYpzKm/vexIrrV4j0OVOOr1oQ5iNS0KimdrqHE7rbXXx1h1LKh94+A9uWfoFdP32HwO69jZJm3oQacl1/38PYvXM95NZJiDn8PoozXkL/G4NEKnpHRV2jP+rsh8f83LAgNQ87sspQtLsAv6dnwrP837Rx6lrvHeog0okDu7mK2lVzUqDWYHleCb7NKUZRg6FRlZ2lDHeGeuJBH1dYn86u0DToxCp4dkIZsuNLUZBaKQKuo5syxGbjaIHA0wG4Vyd7oz/3ZXk5WPvhu+JNdZdho9F11NizPp6UTB/Twtl5eIsDbmMpya3G39/HievdRvu2WsBNjalm75ktAm46QTijywy0ldzEcvH8U0lES9Lkm9Cx0hRwD54ewgF3O0K13L62vmJrKYlEJnp92Nl2FTOpa2oScejwTfD3ewCBgbPOO1astVTXJIk07YqKI+I2rRKHdHpVpHEbFZ18nLQYyDkKFMUDfz4GTPte3G8Z2QVe785HztPPQLNqFX6yUOH2kTcira4BE44k4qsugWdNyLBQuiA4+DmRXp6buwqFRRtRUXEM1dVxYmspR8cBqLa7HjNzOkFWW4adSfcbPjDgcagbXJBx7z0i4FZFRYmO65cbcDPG2hb/hbJ2gwLFpsBbdIfWQ3Tlpk7Cz/V5TjSUofS5H2N/xH2R95l0Xwc72oqGXb8XlOGFxCxs6BUK2SVWFLuPmYjE/XuQHXfSqGnmTaztLdBr4Bs4fvxOOATvxMkNo1GYXonR93YRwVhHRYGoJLYcU/ZXondcuciaIDopkOKhQKafCpE93TAk0BVdbCzNpmETrWpvLq7EL/ml2F5a2TwIgOY+U3f2u7ycYa84+yWd5kh7URp8iAP6Xh8oGrVlnCpBanSR6GZP9eontmeLTWWtQKdebggf6CnSjlv7966vqcbq994Wl56dwjD6gcfO+hlFxVtRXPw3JBK5SGc1JWpmt2HJCWhPj8MaOCX4op9frdVhxok07C6/eMBNfkn4RWTnqGQqvDPoncvqMn21msaEhfR1F039WiJme7boA0H6Tw5Ct5EtD95Y++bqOhr29j1FU8OCAkqd/hL5BWsQGPA4PD1vatVAWKdTIz3jU2RkfAm9XiNGBYaGvCHmYrcZG1dg+g/AdxOB2D8Nnc0HG0b42Y0bB11VFfJnvw7tsh+xysoSTw6/HgcranB7TAre7OQtXofPfE2jCSF+fveJraGhVNRm08mEuvps1NfnoL4+V7ze0e9Km8rCA3b2PWBv1x12dt3wTYEe76TmQt+ow4akd2GnqQA8u6Pe73Zk3n0PdKWlsAgLg+/nSy5r5BhjzDQ46GbtirOXDaY+2xOrFx0V9asVhXXYtTIJI+4Ix7O9nxUrSEuil2CM/xj42Bqh9usyzAn2wt8lFYipqsP3OcW4z+fio1YowB77yCx8/8Ljor47est6o6eZuzgPgLPTUJSU7oRb1Brk7LsfK+YeEB2NafWzo6C0WqrNjt2Ti+QjhdCc0QzNPdAOnr1dsctDih3llchv0GBfSRm+KimDn0opar/Hu9qjl501FG2cVl6m0WJrSSU2FleIrvg1utNnCAD0srMS5Qs3uDm2eL+ouVtIb3exUYOw7LgypEQXIf14sQg0T+7MERs1gwvv74nQfu7i5MzVatTpsO6j91CWmw0bZxfc+PxrkCv/rWHW6WqRmPimuO7nez+srTtd9c+84n1t1GPLt7GoKKoTY8HGPNDlohkA9BzdfjwVx6pqLxlwU33tosOLxHWax03NrdpKXVWDKJ0gkS1soEZ/L7t+MdQB954QgF7j2m5/mXlQKp0Q2eV/cHe/HgkJr0OtzkN8wqvIyPgCAQGPwd19oggYr5RGU4GcnOXIyv4eDQ2G49PFZTTCQt+4rBroVuPXDxj/LrDuWWDrm4BnFBA8UnzIcdo06NUNokt43Zdf4kulAvNGXo+VBeV4LSkHW4orRZYb9XY53+Po6TlFbJdCJ/Eej8/CmqJycfvLsj/QreQwoLCGuueryLzvQejKymAR0Rl+33wDuaPxRpQxxloPB92s3aGa1KnP9cJv7x1GXZUGsbty4d/FGTd2uxFrUtbgYP5BzN0/F0tGLzHpKqWbhQIvBXrilaQczE/Nw/WuDuK+i3Hw8MSQ22Zg+3dfYOdPS42eZk4oDY6Cblvf/fAom4z8eGds+PwEIoZ4YfDNISIVtb2qq25Awv58xO7JE82wmtg6q0RTMtroeCKDADyv1+Of0iqsyCvFlpIKZNY34IvsIrFRQNXXzhoDHW1EwEur4A7nrCxf7YkB+nl0kuZARTX2l9fgVHUdJXQ0o1Xtae6OmO7p9J86witp0EblGbTRSLmchHLE7csTq+DUKZ2aZu37IwX+XZwQPsBTfJ5MfmWZFzuWfYv04zQX2gKTn3sN1g5nv0lMS/tErPxQnWNg4OMwFXoOdvwULzrVyxVSjH+kKyxtLtzgLF+twS3HU5BQUw9HuQw/dQu6YFd8jU6Dl3a+hHpdvRjDdFv4bWhL9Nw26vTNY+YuJfFQPrYvixfXu43yRd9JrTSiibVL1AjMyXEwcnKXIz19CerqMxEX/xISk96Cm+s4eHhMhr19b8hklz5Jp9PVoaz8AIqLtyE/f7U46UZoHFhoyOtwdR1j2gyj3vcDOceA6GXAr/cB928BXELEh5zuvAN6tRqFCxei7JNP8VJePrrc9yjmZRdjR1mVmF7ylL87HvF1g+oyy3U0jXoxXeODtHwUa7SQS4CvrLMxbuen4uP1wQ8g49FX0VhdDVXXrvD7+ivI7P9tQskYM28cdLN2yd7VEje/2Bs/v3UA2oZGbPrqJG6f0w+z+8/GTX/dhD25e8S4lAlBE0y6n/d4u4h04ONVdaKb+ZIul14p6jF2IpIO7kF27Els+vxDTJ89z6hp5ra2XcRM0YKCNQgatgGefq/g2OZMcTIjK7YUw28Pg2eoHdoLGmuUFV+K2N15SDteJAINQkFUcC83RAzyhGewg6jbPheVAFDHedpqdDrsKK3ChuIKsdpcqtHhn7IqsTXxtlCI7ud+lkpRL+6jUsJJIRNzsalrPY23o59C7x9pN6q0OrFV6nTIU2uQU69BjroByTVqxNXUofqMlewm4dYqjHOxx1gXe3SztYTUCG9GaSXXN8JJbOo6LZIPFyB+Xx7yUyuRfqJEbNS9u/NAT3QZ7HVZHdBjtm7C0fV/iuvjZz4N96CzV7GrqxOQmfWNuB4a+sZVrZpdbcC9Z1WyOEFDD/GoGREXDU4z6tSYHp2CjPoGeCgVWNE96Lyd8Jt8Ev0J4krj4GDhgLmD50J6FWOErmT1/uQOQ2p5lxY0T6Ou/X8vjRMlPPT5g27uZDZlFsx0KKCmEZPeXrcgO/tH5OSuQF1dpqHLdv7vkEgUsLEJF6nRlipvyOQ2kMusoYce6vo81KvzRLPEigqaed/Q/H3pa/x8HxCr5lKpGYw2pGN94gdAYSyQexT4YTJw30bAwVBa4Xz/fZAolSiYPx8Vv/2GcdnZuG7BQryUXyF6Oryblo8vs4tEPxcq+wm+xAnSkgYtNpdUYHFGgagTJ9Sk7UvXenRd+Rig16HesgfS3lwl/iYte/eC75IlkNm2fPoAY8z0uHu5mWgvHfrMTVFWFVbNOyQaj9Iopzve7I/vU77Fp9GfwknlhDVT1sBOadqA8XhVLcYfThTdsld2C8bQMxquXKyb+Q/PPw6Nuh4jZjyMnuMnGXUfa2szsP/AmOau0bWFEdj2QxyqS9Xi4yF93FBtl4pJk8eb7fFJ9clxe3MRtycPVaX1zfdT4BQx2AshfdxhcYWzsxv1erGauae8GvvKq8VqNHVCb21KiQSh1ir0tbdGfwcb9Le3vmR2hDGV5deI4Dt+Xz5qqY8CkQB+EU5ixnNAV+eLpl7TiaNVc19Do06LgdPuwICbz17dpYZqR47eJt6E0zzbqKjPTfb6eeCvVBxeny6uj7y7szjBcCFx1XW49XgKChq08FcpsbJ7MPzPk1La5FD+Idy/6X4RfHw4/EOM8h+FtkQnn9YvOSFeI2fMHwS58sLZKzSTnjJd6GRVWH8PjLq783lPULGW6cj/ttOJqorKo8jPW43Cos3QaEpa/LUWFp5wdh4GN7fxcHIcZJ4ndWqKgW/HASVJgHMn4N6Nhrrv06r++Qe5zzyLxtpaKAMC4LnwPWx088E7KbnIURsavpKuNpaIsLFEZ2sV/C2Voulila4RRQ0a7CytxuHKmuaMJheFHM8GeuBOGw0U314HVGSJLuVpq2XQ6yRwmD4dHq+9KoL+1tCRj0/W/mnayfHJ3cvZNYFmF4+4Oxzbvo+HukaLX989jDteuBvrUtchvTIdn0V/hpf6vmTSfexma4UZ3i6i0/TLidnY1jdMdDi/GEopH3LHDGz79nPs+vk7BPXoLVLPjcXKyh8+3nciK/s7JCXNRd8+a3Hb6/1EIEKNlJIOFUIit0aMXTa6j/SHTGEeHc4pLTrjZAlid+eKy6bxWBZWcoT29UDnQZ7iGLlatLrcmd402VjigdO1+RUaLWJr6pFUU4/s+gZkqzXislyjQ6VWhwqtFupGCrPE4gToEbOVy2Ajk4pVcJrv6m2hhLdKIQK2CBsVgi1VbV43fjGOHtYYMKUT+t4QJOq+qRFXVlwZMk+Vio26utNjHDHIS9Q/n6miMB9/LponAu6wAUPQ/6Zb//P9s3N+EgG3aJoU+jpMFThQZ+6mgHvoraEXDbiPisZJqSjX6sSb6BXdguF+kRMjFeoKvLL7FRFwTw2Z2uYBN6G/YULP08UCbiotoKwhCriDe7ph5F3hHHCzC6JAmeZT0xYW9rZoDFZZGY3Kqhg0NJRAp62GVkdlPXqROk6NwixUXnBw6ANrq3aQPWHtAtz9x+nAOxlYNgW4Zy1g6SA+bDt8OPx/Xo6sRx5FQ3o6Mm65FYNmzMD+x2die60GP+SWiCypE9V1YruYSBtL3ODmIJqxWetqoV86RQTcDTVKZGySQi9RwGPOq3C89b+vo4yx9oHTy1m713mAF7Jiy5B0qACVxfVY//FJPH/bS5i56xH8HP8zpnSagjCnMJPu40tBnlhbVC5md3+aWYhnAi5dp939uglIOrAXWadisOnzjzD9deOmmQcGPon8gj/FfNDc3BXw8bkTQ6aHihXi7T/Gizrf/avTELsrD/1vDBap2qaaV12aV4PEg/mI35uHmop/V5ypSzetatOor4sFF62BOoUPcLARW0cnk0lFEEZbeWGtOMlBK+A15WocXpeOI+vT4d/VBV2GeMGvizM09XX4gzqVV1WKdPKxj876zxtsSjNNTl4grgcHPWeSpkk6bSP+WRaP+P35zd25uw6/cANGKje492SaWKmiuv5lUUFwvERd/zsH3hFzuf1s/fBinxdhir+V7PgykTEbOezCqeXUYHDLN6dEKjp1sR99X0SHHh/IWhf9fVtaeouN0sQ7DHsf4O4/DYF3/glg6QTg9l+aU81VYWEI/O1XFLwzD5Xr1qH0229R9fff6P/M07hu9Gjk6xpxpKJWlA/F19Qjt14DG7nhxKutTIYedlYY7WwHb9XplevaUjR+eyOkxTHQ1kuRuc0BMu9g+LwzF1Y9e5r2sWCMXRUOulmHMPyOMOQklIkU2OKsashW2WFMz/HYnLsB8w7Mw3fjvjPpWXX6B5ZGijwam4GPMgowxc0RgVYWLehm/iS+f+5xMUbs2Ka16Dn+BqPto0Jhj6DAp5CQ+AZS0z4Udd50n0egPaa+2AO/fbUF6kx7cWKDRrXZr0lFj+v8RAoqNeUyNpovnHKsUKy6l+RUN99vaasQc4OpVptWZplxObhZYeDUTug3KQipx4twamcOchLLkR5TLDYaN6fXxqIkuxjWjk6iU7nC4uxVcL1eh9i4F9DYWCdm0dIs3rZG3do3fnFC7DulT9MKd+RF6p2X55aI8X9aPTDM0RbfRgY0z0O/kLWpa0VvCZlEhvlD5sNK0fb16if+MaxyUyM8O+fz15wnHMjH1u9iRaYIdaynlHIOuBk7zTkYuGs1sOwmoPAU8PUo4LYVgLchCJY7OYk52XbXT0T+nDehycxEzlNPQ+7lCac77sS4qVNwfeDFM9X0DQ2oWrMclkdegUJZA12DBNn73GF/3xNwfvBBSFspnZwxZjocdLMOgWbOUh3m2k+Oi9s0HqqPfgr2ee3B0cKj4s3vpGDj1kVfymQ3ByzPK8Gusmq8kpSN5VFBlzwRYO/mgWF33Ye/v/4Mu5Z/j8DuveDo2bJxP1fCy+tWZOcsE6vdaWmLERo6W9xPK9rWvlpMntEbp3bk4fjWLDGu7Z+fEnBgTRrC+rqjUy93uAW03nxnmqddkFoh0sapkRcF3U2kMomoK6ZgO7DblXfVZleOSgyaxo9R7fepXbmI358nauuBYFjYB8I9xBpl+RLYOOjPSlPOzPwaFRVHIZPZoHP4AkjasKkYyU+twN/fxYpjWKGSYeyDkWICwoXq+Rek5YuTZeQmd0csCve9ZIlITnUO3tn/jrj+cLeHEeUahbZGTfGaVvGjRpx/BZ/+lnevShLXaUb7iDvDTZbBwpjZ8ogEHtwKLL8FKDhpWPGe/BkQObX5U2xHjIBV794oXboUZT+vgDY3T3Q5p03ZKRiW3btD1bkzpBYWdFadun5CnZSEuhMnoc85Bt/+eZBbNkJTK0OJ7gZ4fvcGLAJ5agBjHQUH3azD8I90FqnQlGZOcV9Jeh3u1L6ML71n44PDH2C473DYKk3X7ZOC0XdDfTDiYIKYubymqELUcF1K1OjxSNy/B5knj4s081veeNdoaeZSqRwhIa8hOvoeEXx7e9921sxkGh/WZ2KgGCFEDcui/84UQVb031lio7peeh5o9jVttCrakiZMlOZLQXVJbrXIVMhLrkBhRmVz53GxbzKJSB+n1FdKc1ZZm29TjWsNZRgMnhYCrXoPjm06BblFFKRyb+Qm1SE36TjsXFQi7b/zQC80StOQkvqh+LrQkNdEOmpb0WkacXBtqujOT6u6tCo/cWY3uPicv0SgXteIp+Iz8UehYV7u0/7ueCHQ45InlnSNOryy6xVUa6rR3bU7Huz6IEyByi+0ap2Yu+4d5vifWvb9f6Ti6KYMcbvrCB8MmRbCNdyMXSzVnLqYr7oXSN4C/HovcPI3YMJCwM5QHkMdxV2ffBLODz2EyrVrUbrsJ6jj49GQnCK2inO+pUSqh3PnajgPrYJUBmhlHsDDq+AR3PYn6RhjxsVBN+tQ6I1/ZmyJaKpGQVpjthVuaHgUfwZ+Kpqqvdi37Wsqz0SjQx73d8Oi9AK8npSDEU62ornWxdAb/LGPzMJ3z81ETnwsjqz/E72vn2K0fXR2GgwXl1EoLt6KhIQ30KPHsvNmFlDgHTncGxkxJUg+Woi0mGLRNfzkzhyxEblSKpptWdtbwMpeKWqDCTWVaqjTiXKA2ko1assbRC3puehrfcIcRWosjbO60u7jzPiOrPsDR9b8Kq6PfGAcPEL6itVvmpNOJQkU4B1aH49OE96FRNkAZ+cR8PS8uU2eGjEDPbYUe39LFr0JCM1oH3JLCCyszn/yplSjxb0n0nCgokbMy30/zBe3ep5/Nfxc35z8RmTYWCusMW/IPMilcpOMzmtKLac69TNPFFADwu0/JYigvKmWvedYf/NvbMWYqVnYGlLL/5kH7PkIiF8LpO4ARr4K9LwHUBpKSKQqFRxuvlls2tJS1EVHo+7YMajT0gCtTkxusFAUwNnlOOQwjKHUh4yB/KavARXP3masI+J3sKxDsbJTYtBNIWLcFZHKJXAv7IThjXfgZ+nPmBIyBaGOoSbdxyf93PF7QRnS6xqwMC0fb4VceqXPztUNw+++H1u+/AS7V/wA/6gecPW79MzvK0UrkKWlu1FWvh/5+avh4nL+1HwKooN6uIpN26ATgU1ecjkK0itRmFElZqhTCi9tl6JUyeDsbSNW5dwD7eEd6gBbZxUHAu0AzeH+54evxfVBt9yFLsMMHbqH3hKKAVOCxdxv6nwudf0QEmUWNHX2iPl1Cirjk9Cppys8gh2MktJMwTY1ETu4JlXMHG/qATD89nBxzF5IWq0ad8SkIrVODTu5FN90CcSQFoz6I9GF0eIEH3ml3yvwtTU0XGprGadKUFFUB6UldfJ3P6tsY/NXJ0XJBsXYw+8MF13NGWMtJJMDo14HIm8C1swCsg8BG18Cts8Duk4Det4FeHY3zPs+XfNtO3Kk2FBdCJz4FYhZAeQZyuFg7QaMfxeSLlObv4Yx1vFw0M06nPABHqKzNb3Zdva2RlleLUKLe0Mjq8e8/fOwdNxSkwZyKpkU80N8cFtMKr7OLsI0D0d0tb10g6WuI8ci5fABpB49hPUfv4873lkEuZGaq1ha+iEw4EmkpC5EUvJ82NsPvuTXULfwoO6uYiM6mkVaUo/aCjVqymlF++zVbEpVt7ZXwtJOCRtaDXew4AC7nQbc27//SlzvN+UW9Jsy/ayPK5QykVZuF7AL8fH7Ab0URUceQVWRJU5szxYblQr4dHYUdfo+4U4i7ftq/kYri+uQdLhAlJqU5NQ016BT9+5e4/xhaXPhv5s9ZVV48FQ6SjU6eFso8FO3IIRbn78B2fnGg72w8wXo9DpMDJqISUGm6yPRtMpN488oM6Wpedy6T2NETTs9HmMf6ILAbhc++cAYuwj3LsB9m4EjS4G9i4GydODwN4ZNYQ04Bxnme1OmS2UeUJULlGUAep3h6+n+HncBo98ALM8u/2CMdTwcdLMOh96sD7s9DCvePijecHcZ6iXSXLsUDEb0ITXWha7H9cGmHWkywtkOk1wdsKaoHC8mZmNtzxAxC/pSv9eYh5/E988/juLMdOz+5UcMv+t+o+2jn9/9yC/443RTtQ8ADLysr6dVcKrppo11TEfW0Qr3vwH3oFvuPG+wXFUVi8TEOeJ6cPCzGD70AWTFlyKFyhKOF4tgMPlwodgIlSK4B9jBzd8ODu5WoiacOm9bWMv/kyZdX6NFRUkNarLk2PVLEooyDH0BmlCTPRpl1nOcvyhzuNiq+NfZxZiTkgNqJRBla4llXYPgdpEZ3Od+/Zv73kReTZ5Y3Z7df7bJTiJRfwSaow4JpZYbMmmqy+rx1+LjKMurEXPsJzwWBa9Ol+4pwRi7COqv0ud+oNe9QPpO4OgPQNxaQFNjGDFG27m8ewFRtxqasNEscMbYNYGDbtYhUaDX9/pA7FudIubPDpraCXt+S0b33FFY99t2DH9qGGyUpp2v/FaIF7aXVuJoZS2W5Zbgbu9L/+Nr7eAoxojRDOQja1eLbub+XbsbZf+kUgXCw+biyNFbkF/wK6TU4IUxUS/ciF0rfsChP3+9ZMDd0FCKEydmorGR6riHw9//IdGtPKCri9gocKZpA1SaQFtRZhVqKxpEME7bWSSAXC4Vq7REXas944OWKEN+8+d5hzqKtGrKvLhU0z2au/1CQhZ+LShr7lC+MMwXVpcxp/rXpF+xJWML5BI5Fg5dKOq5Tb3KTU0N7V2txGz1vz6MFj0XKLtk0pPdRSkHY6wVg++g4YZN2wCUZwAlyYaNujZSozVbT8DR39CQjTF2zeGgm3VY3Ub7IpHSS7OrUZhZhf43BWL/b2mITB2Br5b/hqdn3GPS/fO0UOLFQE/MTs7BO6l5GO9qD1flpVfVgnv1Q9TocYj5eyM2fvY/3LVgMazsjNN4xcGhN7y8bkFu7i9QWayETvcAFAruGn4t02o02LTkQ8Tv2dFcw00p5ecLuBsb1Yg58Sjq6jOhUvmgS8T7/xkPRvOgPTs5iK3fDUHQqHUoyqpCYXqlCMCpCVtlSZ0IxKGnn98otmYSiJVbvUU9wnv4wyPQQXS5v9iq9pmy6htw34k0nKiug0wCzAn2xgM+Lpe1Sp1clowFBxeI67N6zkIXly4wlYZ6LeL2GRqkRQ33ETPt//ooWpR32LtZ4oZZ3S84r5sx1grkSsAlxLAxxlh7CLp1Oh3mzJmDZcuWIT8/H15eXpgxYwZee+01rv1kLUpvHnlXOH5997Co7Qwf0A3uQ6Uo2NkI5X5f7PI5gSGju5r0kbzX2wW/5JfiZHUd3kzOxScR/i36uuF3PYCs2JMoy80WAdDkF1432t9Ep+AXUVy8DQ0NhUhPX4Tw8DeM8nOY+aurrsKaRfORdSoGUplMlDs0NU07X7p1XNwrqKg4LOZxd+v2NRSKS9ctUq0/pT2fm/pMjfoa6nXQanRi9BctHlnaKETATSO61q9fj/4Tgi7rpNDusio8dLp+20khw1ddAjDI8fLGCtZr6/H8zueh1qkxyGsQ7u5yN0yJusVr6nUiLZ+aqK1edFRMc6CVbQq4qdkkY4wxxtqWcYb9tpIFCxZgyZIl+OSTTxAXFyduv/fee/j4449NvWusnaCaUJo/S3auSMTkm4egOCRR3D7+a6FIPTcluVSC90J9aLFOpLZSE6eWUKhUuH7WC5ApFKKxGjWzMhaFwh6hIe+I6zm5P6C0bJ/RfhYzXwVpKfjp5adEwK20tMSUl+ZcMOAmaemfiJ4AEokMXbt+Chvrq1v1oUZ9FDDSKi3NBXfytIalrVKslF8uOiHweWYhpkeniICb6rc39w677ICbLDy0EMnlyXBWOWPu4LmQnrOS35bo92pKLfeNcBQr3BRwuwfaYfIzPTjgZowxxkzErIPuvXv34sYbb8TEiRMREBCAm2++GWPGjMHBgwdNvWusHek7KUi82aSxVdFbs3DX/WMQ774PEkiw6RsanXNO3Wgb62lvjbu8DPN/X0rMRkPjGamzF+EWECRWvMnOn75DfkqS0fbRyWkoNA0DxPW42Beg1bbs5ADrGE7t2IoVs59HRWEB7N09cOub7yEgqscFPz8nZwXS0j4U18NC3xKz380F1W8/FpuBOSm5oL80mh7wZ48Q+KgufwWYarhXJq4U12ket4ulaZsiZcWVoiy/VjSPi92VK1L1vcMcxQr3peraGWOMMXaNppcPHDgQX375JRITExEaGorjx49j9+7dWLRo0QW/Rq1Wi61JZaVhNqtGoxGbuWraN3Pex/aKpnL0nxKIbd8n4PD6dEzv2QveY2VIWncEISW9sPHLExj/aKSoAzWV5/1csa6oHEm1anyano/HfVs2xidixHVIPxGNlEP7sObDd3Hb3A9gYdX6DZzouFSrb4CNbRbq1dmIT3gLYaHzWv3nMPOiqa/Hzp++xantW8TtgG69MOaxp6Cytrnga1VBwR9ISHxNXPf1eRBubjcZ/XWtpa+fmfUNeDAuC7E19ZBLgDcCPXCPpxMkjTpoGk+P8Wmh3JpcvLHHUGoxI2IG+rj2Mfnr97HNGc3j+qj+3b+rE0bd2xkSmd7k+3at4n/bmTnj45OZM007iY1aun8SPeWjmanGxka88sorIqVcJpOJGu933nkHL7/88gW/hmrA33zzzf/cv3z5clhZ8eiiaxUd5UUHLdFQKofKXQPr7mX4sOIjDEm4FQFlXcWbUte+tVA6tGyV2Rj2K6yx1NIFCn0j5lTnwUV/ZmfmC9M1qJG14Xdoa6ph7eMPjyHXGa2+WypLhaXlp5BI9KivuxNabU+j/BxmevWlRSjYsx2aqgpx26lrTzhG9rzosSWXH4OFapk4PhoaBqNBPcXQ6cwMxMpU+NrSBTVSGWwbdXiorgihun9P0F4OrV6Lr6q/Qo4uBz4yHzxo8yBkEhlMqaFSisI9dMKN/kmXwMpLA8eu9TBhtjtjjDHW4dXW1uL2229HRUUF7Ozs2mfQvWLFCjz//PNYuHAhunTpgujoaDz11FNipfuee+5p8Uq3r68viouLL/pAmMNZki1btuC6667j7tBGUppXg9/ePQp9IzDukS44qNyGBQfew6SEx+BRHiwaMk2aFQUnL9OM+qE/xVtOpmNfRS1GOtrguwi/FgfPBSlJWPX2K2jUajFw+p3ofcNNRjs+c3I+Q2bWEkilVujRfSWsrTu16s9iptXYqMPRdX9i/68/o1GnhbWjM8Y88iR8u0Rd9OuKijchLu4ZOg0ED49pCOn05n86lZvi9bNRr8cn2cV4P6NQhKPdbCzxZWdfeLVw/vb5LDy8ED8n/gw7pR2Wj18OL2svmNrv7x1rnk/eebAHBk/rBInUPE54XMv433Zmzvj4ZOZM005iI4o1XVxcLhl0m3V6OQXcL730Em699VZxu2vXrsjIyMD8+fMvGHRbWFiI7Vz0ZJnzE9be9rM9cvdzQLeRvoj+Owt7f0vF9Nduxq/Jv2Jt6OeYkfY6UGSLdZ+exNRne4rOv6awIMwPow4lYFtZNdaX1WCy+6W7PROf8AiMuu8RbPnyE+xbtRxeIeHwj2r9+d10bHbq9DSqqo+jrGwv4uJnoU/v3yGX88zfjqAkJwubPvsQeckJ4nZIv4G47qEnYGlz8QZj2TnLkZBAqdaN8PCYjIjO74gGaqZ+/azQaPFEfBY2lxjKjKh3wtudvKG6guZrTTanbxYBN5k3eB78HVo2ccCYJ+t2r0pqDrjD+ntgxB2decKHmeF/25k54+OTmTOFmcdGLd03qbkv10ulZ+8ipZlT2jljV6LP9YGwtleisqgOMX9n48U+L0Ira8CywPmw9VSgrrIBf354TMwFNoVQaxVm+buL668kZaOkoWUp5iRq1DhEjhgDvb4R6xa/h8pi43Rmp2Aqssv/YGHhgdraFMTFvyLe+LP2vbp96K/f8OOLT4qAW2lphbGPPoVJT7980YCbnvfU1A+RkDBbBNxeXreic/gCkwTc54qtrsPYI4ki4LaQSrAo3BcLw3yvKuDOrMzE63tfF9fvi7wPw3yHwZT0jXrsWpmEmG2GjuW2zhYYPSOCA27GGGPMzJh10D1p0iRRw71u3Tqkp6dj9erVIrV8yhSqE2Ts8ilVcgyaZhhddGRjBsIVXTHabzTqZTXY22OFWOGuLlPjzw+jUVNxZfWeV+tJfzeEW6vEKKPXk3Mu62tptds9qBPqqirx58J3RCMsY1AqXRAZuRgSiRyFheuQkfmlUX4Oa5vV7RWzX8DOn5ZCp9EgoHsvzPjgM0QOH33R4K2xUY34+FeQlm4Y4RgY8CTCw+ZCSp0LTezX/FJMPJKI9LoG+KgUWNMzBLd7GiYEXCmax/3sjmdRo6lBT7eeeKLHEzClRl0jtv0QhxPbDQE3GTwt1KT7xBhjjLF2GHTTPG4aE/bYY4+hc+fOeO655/Dwww/j7bffNvWusXasUy83MUZHp2kUq0TP9H4GSqkSe8p2wmV6LexcVGIlnALvuuqGNt8/pVSKRWG+ov3UbwVl+Pt0amxLyJVK3PDMK7C0s0dhego2fLYIeiNlhjjY90JIp1fE9ZSU98SYKNbOV7cfmYWpL82BrfPFR1/V1+fiyNHbkJtH47KkCAt7G0FBs0y+wkrj9l5JzMbjcZmoa9RjhJOtmL8dZXv15SILDi1AfGk8nFROeG/oe5Cb8OQCvXZt+uoU4vfnN/eps3ezRGCUaUeWMcYYY6wdBt22trb48MMPRR13XV0dUlJSMHfuXCiVlz9PlbEmoUOFrQAAWTRJREFUFBgMvTUUUpkE6THF0KZZ4u4ud4uPfZTwPsY/ESlS0MvyarBm8XGo61qe4t2as7sf8jGMDXsxIQvV2paPM7JzdcONz74KmVyOpAN7sXfVT0bbT1/fe+Dv/4i4Hp/wGvIL1hjtZ7HWU11agpVvvvLf1e0Rl+58X1q6BwcP3YjKyuOQy+3RrdtX8PG+3eRPT5lEhukn0vFtTrG4/bS/O5ZFBcFJcfXB8ZqUNfg18VdIIMH8IfPhbm0oATEFmr297rPjSI0ugkwugcra8Pt1H+XLjdMYY4wxM2XWQTdjxuLkaY3uo33F9d0rEzEj/D64WroiqyoLfxX9ihuf7gFLWwWKMquw9uPj4o1uW3shyAN+KiVy1Bq8k5p3WV/rHR4hGmCR/b//grjd/xhpL4HgoOfgLYIuPWJjn0Nx8Taj/Sx29bJOxeDHl2YhJ/4UlJaWLV7d1unqkZQ8H8eiZ0CjKYWtbRf07fMnXJyHm/xp2Vdeg3esPXGkqg52cil+6BqIF4M8IWuFlfeU8hS8vd+QXfVIt0cw0GsgTEVdq8FfH0UjK64McgsZokb6or5aK16rwgd4mmy/GGOMMXZxHHSza1av8QGwdrBAZXE9knaUYFbPWeL+L2O+hM6+DpOe7C7GiOWnVmD9khhoNW0beFvLZHg/zHBiYGlOMQ6UG7oTt1SXYaPQ58abxfVNSz4UwZYx0MpoWOibcHe/AXq9FjEnHkVuLqUdM3NCZQYH//wVq95+DbUV5XDxC8Cd8z9s0ep2ecURHDx0PTIzvzY0TPOcjl49V8HS0nB8mgo1cluSWYjbTqajSipDhLVKpJOPcbFvle9P9dvP/PMM6rR16OfZDw9HPQxTqa1swOpFx8TrEb0u3fhkN6SfKBEf6zbKF3Kl6ZvXMcYYY+z8OOhm13RTtYFTg5ubqo1wHoNI50jxRnvx0cVw9bXF9Y93g8JChuz4Mmz68iR02rbtnD/UyRa3eTqJ688mZKFed3k/f8itdyOk70DotFr8sXAuCtNTjbKfNI85ovN7zYF3XPzLSE5ZKDqpM9Orr67Gnx+8g13LvxPPCZ2QuX3u+3D09L7o1zU0FCM+4XUcOXILamvToFS6ISrqS3TuPB8y2X9HM7YlKrl48FQ63kzJBZ0O699QjT+iAhFgadFqAf3sPbORWpEqsmDeHfIuZFLTBLZVpfVY/cFRlGRXw9JOicnP9ERtlUaUwChVMkQO8zHJfjHGGGOsZTjoZte0kD7u8Aiyh1atw8E/0vBi3xfF/X8k/4FTJafExyY+FgWZQipWlTZ91faB9xvBXnBTypFcq8b/Mgou62slUikmPPEcfDpHoqGuFr/PfwMVhZf3PVpKKlWgS8QiBAYY0tozMj7HyZNPQqutMsrPYy1TkJaCZS/PQsrhA5ApFLjuocfFODCFheqCX6PTqZGe/jn27huFnBzqCaCHp8dN6N9vI1xdRpn8oU+sqcf4I4lYW1QBhUSCucGemFFfAsurGAd2rm9PfostGVtEw7RFwxfBxdI0TcqKs6vx24LDKC+ohY2TBaY+2xPO3tY4uilDfJwCbgtL03eMZ4wxxtiFcdDNrmmUVjvkFsMIsYQD+fCoDsTEoInQQ48FBxeI1S7qdD7h0a6QyaVIO15sCLwvc8X5ajgo5JgfaljJ+jSzACerai/r66mj+Y3PvybSiWvKy/DbvNdRW1lhtMczKOgpRHReCIlEgcKiDdh/YDxKSnfDHNHzSGUDmgadqNtvqNeioU4ramfpOs1Bbs/p5EfX/4mfZz8nTrTYubrjtrcWinnuF0on12qrkZn5DfbtH4mU1IXQ6apF7XbPHj8hIuI9KBStk7Z9NVbml2Ls4UQk1arhoVRgdY9OuMfTqamJd6vYm7sXi48tFtdf7vsyurt1hynkJJRh9ftHUFPRACcva9z0fC8x1jA3sRwFaZXiNSlqJK9yM8YYY+aOT4+za56bvx3CB3oifm+eGCE2a+YsbMvchmOFx7ApfRPGBY6DX4SzCLzXLzkhAu/NX5/CmAe6QNaKK2sXM9HVARNd7bGuqAKz4jOxoVeoGC3WUiprG9z08ptYPvs5lOXlYNXbr2La7HdgZWecIMrTcyosrfxFY7W6ukxER98DL69b0Sn4RSgUdjAmCqAp7ZZq9StL6lBVUo/6Gg0aarWiE7369CUF1zR66VKoYZXSQgaFSiZKEqjcwNJGARtHlVh5FJeOdGkBK3sLSKWmHZtFSnNzsOnzj5CbECtuB/Xsg/Ezn4XKxua8n19Xl42c3BXIyVnWnJlgYeEhmuR5eNwoygdMrUanwyuJOfglv1TcHuxggyVd/OGqVECj0bTaz8muysYLO19Ao74RUzpNwbTQaTCFpMMF+Pu7WDRq9fAKcRCvPxZWCvGxplXuzgM9YW1v2jR/xhhjjF0aB92MUT3ojUFIOVqIwvRKVJz0xn2R9+HT6E+x6MgiDPMdBku5Jfy6OGP8I12x/vMYpB4rwpavT+G6Ngy83w31wb7yapyqrsei9AK8FHR53YptnJxx86tzsfKtl1GcmY5f574mAm9LWzujzfHu13edqO3Ozv4BubkrUFi4Hn5+D8DX5x7I5ecPAC8HZSJQ2i3V3NPKX1FWlQi49a24QE2lB7ShBePSKeC2c7UU3fEdPa3g7G0jTurQ7Pe2mGFNGQzRm9bh0J+/QqtpgEJlieF33Y+uo8b+5+drtTUoKtqMvLxfUVa+v/l+K6sg+Ps9KIJtqdQ8Arq46jo8dCpdrG7TX9tzgR6Y5e/eKt3Jz0QN057+52lUqCtEf4dX+79qktnjx7dmYfevSZTVj+Aerhh9XwTkCkM9eUF6JTJjS8V4sO7X+bX5vjHGGGPs8nHQzRh1Cre3QO8JAdj3ewr2rU7B7a/fhd+TfkdeTR6+O/UdHu32qHic/COdMf7hrtjwxQmkHCsCvonFmPsjIG2DwJtW9BaE+ormUYszCjDG2U7M874cTl7eItBe+ebLKMpIw69zZ+Pm2XNhaWNrlH2WyawQFvoG3FzHIiFxDmpqkpCaughZWUvh43O3qBO2tLx4M69zUdp35qlSpMUUiWC7tqLhP59DI5TsXS1h60ybCla2StHxWWkp//fS0nApVqbpf5KmS7qQoLFRL1LONWotGup10NQb0s/pPuokXV2mRk1ZvbisKqtHTXmD+Bo6CUAbov/dH5W1Am4BtvAOdRTlCq5+tq26Ik4r25RKfmrHVmgb1OI+/6geGPPQE2Ju+5kr2iUl/6C4eCtKy/ZDr2967CRwchwIH5874eIy2ixWtptOqvycV4pXk7JR16gX6eSfRfhjoKONUX7W2/veRnxpPJxUTvjfiP/Boo2bxVE5w74/UnBsc6a43XW4DwZPDznrWDm8Pl1chvZxF8c4Y4wxxswfB92MndZthC9id+WioqgOp7bk45lez+D5nc/j2xPfijRTD2sP8XkBXV0w/qHTgffRQmyRAtfd2zaB9yQ3B0wpcsDqwnI8GZ+JLb3DLrt5lLO3L6a/Pg8r33oFhekpWPXWK5j68puwcTR0STcGR8f+YtW7oGAd0tIXi07YaWkfic3BoR88PabAyXkIVBaGx/hcVHedFl2MxEMFyIotPauZHTW58wy2h2cnB7j52YqA1speedUrlLSuSKnkgLJFn08Bd22FGmX5tSjNrUFpXg2Ks6pEIyxKb6cTBbQRCvb9ujihU083+EU6Q3EF457KC/KRuH+32ApSk5vvdwsMRp9JUxHctxdqa5OQnb0F5RWHUV5+CGr12fPeLS0D4OkxGZ6eN0Gl8oI5oe7kLyZm47eCMnF7hJMtPu7sDxelcf7ZWh6/HGtS10AmkeH9Ye83/723FSp12LYsDokHDI0O+08OQs+x/mcdx8XZVUiPKRYnh3qN92/T/WOMMcbYleOgm7EzgrdB00Kw/rMYRG/NxG2DhqKnW08cLTyK/x35HxYMXdD8WAVEuWDcw12x8YsTSD5cKNJAR98bIRobGdu8UB/sLa8W3cznpebi7ZDLb6Tk7OMnVryptptWvFe8/jxuevVtOHoYL/CSSGTw8LgBbm4TRJp5bt5KlJXtR3n5AbE1pTY7Og6AnW1XWFuHoK7MAwl7K0R9K9ViN6EU7uDuriLzwD3Irjn11pRoNdJQ362Cb2ens4Kp4pxq5KdUICexDDmJ5aKenI4b2uRKKQK7uaLHdX7ihMGFaBrUyIk7hfTjR5AecwQVxemQq7SQqXRwCNLDI8wdHmFukFnVoLR2DnJ2GVZLz30O7Oy6iw7kLi6jYGUVbJL06UuhMoon4zKRVd8AmQR4KdATM/3cIDXSvh4pOIL3D70vrtPJtj4efdCW6qobsOHzE8hLrhDH0Yi7wxHe/7/lI02r3CG93ODocXlZLowxxhgzHQ66GTtDQFdn+EY4idXUvb8l48XpL+LWtbdifdp63BZ+21ldjAOjXDD2wUjRzTz5SKFIQR73cOQVrVpeDkeFHIvC/XBHTCq+yi7GCCc7jHS+/LpsF19/0c2aupmXF+Th59nPi2Zr7kGdYExSqVwE37TV1+ciL3+1qC2uqjqF2tpUseWc8flae3t4DrKHXusIWwdPOLg5w9rOHnKZFbQSGXJyz/jkpqBMrxfzqGlm+L+bTmyN/7mPupSfcf30peHzzr6v6frpH3Y6DVt6OnCVXuC24fMkTlJ49peIjY6V2kqtSI3XqPWohQR7/qG0eAvRndrCGtCoa6CuqxKbpr4aWm09pHId5K46+E5ohO9/HtkslNdRBPfvPTRX28YmDPb2PUWNvb19D5Hyb65oDv27aXn4IquIzmPBV6XEp5390Neh9dPJm+RU5+Dp7U9Dq9diQuAE3BVxF9oSZUWs++y4aPxHGRDjHowUr0Hn+zxR0gJa5Q5o031kjDHG2NXhoJuxM1CwNPjmEKyYe1B0Ke86vDumhEwR9d3vHnwXyycuh/SMeteg7q6Y8FgUNn5+ApmnSrBmcTQmzuxm9Lm5o5ztcJ+3C77NKRYrgtv6hMHNwtDZ+HI4eHji1rfew+/z54hU81/mvIRxM59GaL9BaAuU0hwYMFNsdbWlOHVgE7IzdkFqkQkLu1zILSuaNyATWhxHcRnE1u4pAJULcO607Op6w9ZMCSho+883kEChcIRC4SA26jZuaekHS5WvuKRgW6l0RntxoqoWj8dlIqHG8Mvf4emENzt5w0ZuvJNY1Q3VeHzr4yhTlyHCOQJzBs5p05V/es2gk3Z0Eoaa7V3/eLcLrmAf3pAuMmqCeriKBn2MMcYYaz846GbsHDQPt+swb8Rsz8buVUmY+czjYnTYqZJTWJOyBjd2uvGsz/fv4oxJs7pj3acxIj30j0VHMemJ7rCya1kt8JV6PdhLpOHG1dTj8bgMrOgWfEXpt9YOjpj+xnz8tWgeMk9EY82i+eg3ZToGTr8DUqnx07ZpxNepnTk4tiUTtRXuAG6GykaByKHeCIuyhtSiCA3qIqgbCqFpKIFWVwudrgY6Xe0Zq87k7JblEolcpFMbLuWQNt+mTfHvx6T/fg7dJz3zY3Qp/fdjzZeQiJV0+pmGy8b/3G5spCZsdairqkB9dSXqqumyCnXVleJSXVOF+upq6HSGZmYSqQpShT+kck9Ar4S+UQ4pKuHooYNHoA9cfINh5+wFpdJJBNtyuZ3ZNDy7GtpGPT7JLMD76fnQ6qlhoBwfhPlijItxZ4LrGnV4cdeLSC5PhqulKxaPWCymFLSVE/9kixGF1DzNs5O9mIxgaXP+1wxqzJd82FDr3ZtXuRljjLF2h4Nuxs6jz/WBSDiYL1I6C4824KGoh0Rd90dHP8Jo/9GwVpy9GuXVyQGTn+khVrqLs6qx+oOjuGFWd9g6nbuO2XpUMim+6BKAsYcTsLOsGp9lFuJxfwpaL5+FlZVILd/507c4su5PHFi9EoVpKRj/xHNG62zeqGtE3N48HFyb1tyBnGZdU/OozoM8z6jTdgeMswtX1em6oa4WVcVFqCotQVVJEapKDJfVdPv0/Zr6M3K9z4uOI2tx4sPFLwCufoGwdQlAXqoN0k9Ui/MIFVkSWCo9EdY9ENYO5jHCqzVHgT2bkIWjlbXiNs2ify/UF85GapZ2Jvp73pm9U3QoXzxyMdytr+xv50qO+92rkkXQTcIHeGD47eGip8SFHNmQLsbgUfnLxer+GWOMMWaeOOhm7DxoxFOfiYHYvTIJB9ekYvobt+LXxF+RVZWFr098jVk9Z/3na1x9bTH1uV7486NjYmXq94VHROBtzIZHodYqzA3xEYEL1cL2d7BB78scI9ZEKpNh+N0Pwj2wEzZ/8THSoo/g++dmYuzDT8InslurBqyp0UXY/0eqYbQWIE5O0Mi2sP4ebdKM7lLUtRRQF54VUFeXFqOq5N/t0gG1gcrWDrZOzrB1dhGbjZPh8t/bzlBY/PfkTElONfb/mSq6VVNX/cT9+eg2yhc9x/lDqWrfL921ukb8Lz0fS7IKxeq2nVyKeSE+uMndsU3Su6lc5PvY78X1uYPnItIlEm3VMG3LN6eQFWeojxgwJRg9xvhd9HemaQoJB0+vck8IbJP9ZIwxxljrat/v3Bgzoshh3ji5I0cEhjGbc/Fc7+cwa/ss/HDqB0wNmQpf2/+2sqImWBR4//VRtPg6WvGmOk03/8tvdNZSt3s6YUdZFf4qLMcDJ9OxpU+omOl9pToPGQEnHz+sW7wQZbnZ+P3dOYgcMQaNLv/tpny5KopqsfPnRGTGGkZnURo5BduRQ7wvutLXmijor6+pRmVRISqLClBZVGS4LC5ERVEhqooKxcdbQmVje04A3bKAuiWobnfiY1HITS4X8+PzUytwZGMGYvfmof+NQeg8wBOSVpz13VaP/ZaSSryWlIPMekN2wwQXe8wN8YaXyrjlGE0O5x/G2/vfFtcf6/YYxgWMa5OfW5BeiY1fnkB1qVp0rKdpB8E9/p2hfiFHN2WIFHRqruYeaLzXEcYYY4wZDwfdjF2ATCbFoJs6Yd1nMTi+LQu3De6Pfp79cCDvABYdXoT/jfjfeb+OVm2nPtcTaz4+jqLMKhF4X3dfF9F0zRholWxRmK9I1U2qVeOhU+lY1a0T5FcRkLkHBuOuBR9h9/LvcXTDXzi5fTNkltaIc7JH1+GjIZFeXoBMY7OObs7AkQ0ZYsa2VC5Bj9F+IpWcOjYbI7irq6pEeX4uyvJoy2m+rCjMR0Nd3VUF1IbrVx5QXw4qXZj6fE/R2I866tPK5/Yf48UJocHTQuAV4oD2IL6mDm8k5YoTRMTLQiFWt8e5Grd2+0yUqfL0P09D26jF2ICxeKTbI0b/mXQsxu7Oxc5fEtGo1cPezRLjH+7aomZoVaX1iN9nmK3eZwJ3LGeMMcbaKw66GbsI/67O8Al3RHZ8GfavThEjxG5eczP+zvwbB/MOoq9n3/N+naWtEpOf7oGNX50U48c2fHFCBPCUHmyM9Fnq8PxtZCDGH0nEvvIavJ2aKzo/Xw2F0gIjZjyE4N79senzj8Rq8JYvFiNmywYMv+t++ES0LCU3K75UrG43pZLT4znstjCRFdAatA0NKMnORGFGKorS08Tc8eKsDNGs7GKs7B1g7+oOW1c32Lm4iut2dP30plS1XVOtS6FjJuj0XHJq8Hd4XVrzCZ3gnm4YODUYdi7ms79nyq1vwIcZBViWWwJqMaeUSPCQryue8nc3amfyc1U1VOGJrU+gXF2OLs5d8Pagt42eyq5t0GHnikTRu4AEdnPBqBkRLZ5ucGxTBhp1eniHOcCzU/s4ucIYY4yx/+Kgm7FLjRCbFoJf5h4UM3KjRvbAtNBp+CXhFyw4tAArr18J2QU6fNMK7sSZUdi1IhGnduViz6/JIvAcckuoUeqWQ6xV+KizH+4/mS7mHPewtcJkd8er/r5+kVG4c8Fi/LL4fVTGn0RBahJ+efMleIaGo/f1U9CpT//zdjmvqVCL3znpkKEe1dJOicHTOiGkt/sVBzs15WUiqC5MTxWXtJXmZkPfSOHcf9k6u8LR0wuOnt7Nl/buHiKoppMK7Q0dNz2u80NYPw8cWJOKuN25SDlaKOq+qTaY6r2NPSe+pQrUGizOKMCPuSVooC5gpxulzQ72QoBl2z72ap0aT257EikVKXCzdBON04zdqbyyuA4bvzwpTo7Q4d7vxiD0HOPf4pIA+vuJ3WMI1rmWmzHGGGvfOOhm7BIoDTRisJcInKnr8MynZmJD2gYkliXit6TfMD1s+kVT1IfdHgZ7Nyvs/T1ZfA/qiD72oUhY27d+4DHR1QGP+7nhk8xCPBWfCT+VEj2vsLHameRKJRwjumPKg4/h0B8rcXL7FuQlxovxYvZu7ggbOBRBPfvCMySUWrKJEWDUBKyhTkvjpNF1qLcIOiysFC1uZEb15KV5OSjOTG8OsCnovlCzMjf/QLiesVGQ3Rbp36ZA4+hG3BEuRtvRWLuchHIcXp+OhP35GDw9RKyotuW86TMdr6rFt9nF+KOwDOpGQ7Dd394aLwZ5YoBD28+XplTyF3a8gMMFh2GjsMEnoz6Bm9Wla6mvRlpMMbZ+Hwt1jVb0LRjzQBf4hjtd1vc4tjlTlGJ4BtvDO5RXuRljjLH2jINuxlqg76QgsWJLq1YFx+vxWPfH8O7Bd/HJsU8wLnAc7JQXbnBEwQ+tTjq6W2HLt6eQl1KBVfMOYdwjXeER2Pr1rC8HeSKhpl40rLr7RBrW9wqBXyutLFJK9ugHZmLAzbcjetNaRG9ej4rCAhz8Y5XYVLaBUFiPhkZtGGvk4K5A/xt94BXiDLlSAq1Gg0atRlyqa6pRU1GO2opyMWKLVqzLcnNEoF1TZmi09h8SCRw9vOAaEPRvkB0QCBtHZ5MFmaZk52WNwY91RXJ0EWL/SBM1wBs+PwG/Ls4YcksIHNxaJ4X/UooaNNhQVIGV+aU4fHr8F+ltZ4UXAz0x2NHGJM8P1VPP3T8X27K2QSlVihXuzs6djTpzfu+vyTi5M0fcdguww7iHIi97dGB1mVrU7JPeEwOuyWObMcYY60g46GashSuLvcYHYN/qFOz/IwXT59yElQkrkVqRis+Pf44X+rxwye8REOWCaS/3wfrPT6Asrwar3z8qRga1dp23TCLB5xH+uPFYMk5W1+GuE2lY0zMEdq1YP0tzpQfdchf6Tp6G5IP7kHToKDLj5NBLu0CjlkKvV0Nbtwf58cfxR7xhtfNKAnwnbx84e/vC1T/IEGD7BUCh6pir1+dTrtEiva5BdPrOrFOLyyxxvQG56gbUnV5JJoqRVhgcK8GAhHpknirBj3NKIB3gih5j/RDpZNOqz39DYyNOVtVhf0UNtpRU4EB5jajXFvshkWCSmwPu93ZBTzsrkwaMHx/7WGSjSCVSLBi6AH08+hjtZxVlVYlxYGX5hpMO3Uf7ov+NwVfUlf/whnTDKncne/h2vrwVcsYYY4yZHw66GWuhbiN9cWpXDiqL63Hi71wRaD/y9yP4Oe5nUecdaH/pGbrUPOzmF3th2/dxokacap6pSdvIuzuLwL61WMtl+KFrICYcSRKr3g+eTMePUYFQXmbX8UuRKy0gt4xASYECEplhBJSztwZ2TumoKGhAZaGjSBXXqOv/87UKlSWs7R1EcG3j6ARHL6q79haBNl2qrNs+FdlU6nSNSKytR3x1PeJq6sRlfE098hs0Lfp6K5kUDZJGbI+ywvFAC4w7WoPgfC2wpwjbj5XgpZ5WqAm0QqiNJYKtLOBtoYSPSik6iFMwbi+XwVYug+x0fKw/vU/lWh3KNDqxkp1Sq0ZqnRpJNfUihfzMgJ90t7USNdu3eDjBzeLKR9a1lh9jf8RXJ74S12f3n43R/qON8nNonBdNN9j3R4roTm5lr8ToeyLEiK8rQd3pqVaf0Gg4XuVmjDHG2j8OuhlrIVqxGjClEzZ9dRLRmzNxx+D+GOozFDuzd4qmaktGLWnRG2SlSi5qug014knIOFkiGrWNuCscAV1dWu35oLnHFGjTijeNaZoZm4nPu/iLlfDWQE3hdq5IQFacoc6aRiFRV/LzrczptFo01NdBKpVCJldAKpedt/na1WrUNyKnKgdZ1VnQNeqgF+Ej4GjhCC8bLzipnEwexNRodThRXScC15iqOsRU1YqA9vyt4AB3pRx+Kgv4WSpFjb7v6UsKmh3lMtEBnJ5TSqWu1TWiTKtD2uB6RB8ugGZzLhyqdbhldzWSUtTY1EON7bat87g7KWToY2+NQQ42GO/qAN82mrPdEmtT1+K9Q++J60/2eBI3h95slJ9Dzc62fh8nJhQ0ZbOMvDscljZX/lgcWpeGxtNzub1Crr4RImOMMcZMj4Nuxi5DcE9XkfKZl1whVraen/o89ubuxZ6cPaJudJTfqBZ9Hwr8Iod6iyZJm785JZqrrfs0BqH93DFkWqhovtQautpaYWlkIO6KScWaonLYJkjxQdjVpbNrNToc3ZSJoxsNM7epo3av8f6ie7Zccf6ATiaXw9LGUOfdmiiwPlZ4DFsytuB40XGR7l+nvfAMbupY7Wvri0iXSDE2qqtLV4Q5hYn0Y2MG2DGnA2wKtJNr1adPBfw3iO1sbYlwaxU621iis7UKodYqsQLdEvScUoYDbRSQDxljB82wIJGqHL0lEyF5GoQUVqKurxNSutsiW6sTK+mVWp3Y9BdYQafA3lkhR6CVBYIsLRBkZYFutlboZGUBqRnWGu/K3oXZu2eL63d2vhMPdH2g1X8GneBIPFiA3SuTUF+jgVwhxaBpIegyxOuq/rZK82qQeCBfXO93Q1Ar7jFjjDHGTImDbsauYITYqvmHkXigAFHDe2NGlxn4+sTXWHBwAQZ4DoCVwuqyOqNPe6k3DvyViuNbs8T3pFUz+hkhfa58tNaZhjnZYkkXf5FivjyvVARxc4IvPzigqU/JhwtxeF2GSLEntBo39NbQNmvY1SSrKgs/xf2ETembUFxXfNbHqGGWn50fFNJ/T1yU1JegqLZIBOTUdZ6235N+Fx+j1e9BXoNE1sIg70GwVdpecSCWUqfG4YoaHK6oxaHKGiTW1J83mKW07ihbS0TZWokAtquNJVyV8lZfhVdYyDBgcjDC+3tg18okcWxZ7ivBgMQaMbousJchs6JRr0eNrlFcNrGQSqGSGedkhLHQiZdn/nkGWr0WEwIn4Pk+z7f6Y1pZUocdyxOQecqwuu3ia4Pr7usCJ8+rnxJwcE2q+Duj7vPuARduzsgYY4yx9oWDbsYuk5u/HcL6e4jxTHt+TcIDsx7AutR1yKvJE8H3kz2fvLw/QqUMg24OQXAvN2z/MV6sem/5NhYn/skWq2et0eGcRoktCvfFU/FZYoY3Nbt6NcizRQEJBZOUQl64xwo5VQniPqpbpRMDnXq5tWm6Nq1kfx3zNdanrYdOrxP3UZA8wneECJrDHMPESvb5Zqc36BqQX5OP5PJknCw+KbaY4hiU1pdiTeoasVnILMT3uiH4BgzwGgC59MIvkZTKHV1Zi8OVNThUUYMjlTUo1Rj26UyepwNsCq4NQTYF2G1b8+zoYY1JT3RD6rEiUdJQVVKP9Z/FIKCrMwZPD4W9q2WLV9TNVYGuAO/teA/1unpx8mTuoLmtmsFAKd8ntmdj/1+p0Kp1IsODOotThgeNBrxahRmVSDlaJEbs8So3Y4wx1rFw0M3YFaCuxClHC8X4r9yYarzY90U8tf0pLD21FJOCJ7Woqdq5KLie/nIfHN2cgaObMpCfWonfFhwRK959JgaIwOlq3OrpjGpdI15LyhFzvKlR1tsh3hdMEaYgIy26SOxLYUYVJYlDoZKh51h/0VSOVlHbSoW6Av878j+xOt1Up02B1e3ht4vsAoXs0kGsUmZYAadtpN9IcZ+mUYPowmiRkrw9azvSK9OxMX2j2Nws3XBb59tEkzwaCZej1ohVbAqwKdA+VV0H7TnL2BZSiWgo1tveGn3srEX3bnNoKkbo5EhwTzcxTuzw+jREb8lC+okSZMUfQK9xFy8PMHd0wuv76u9Rqa9ElEsUFg1b1KJjoqVKcqqx7cd4FKZXitteIQ4YfkfYVf9Nnnlia+/vyeJ6aF93kQHDGGOMsY6Dg27GroCNowV6jPHHobVpYozYbW8Mw2DvwdidsxvzD8zHF9d9cUUrwNSsrc/EQHQe6IUDf6Ygfn++mA+edLgAQd1dRXBEK+1X6gEfVyglEryYmI1vcopR19iIhWG+ZzVXq6tuEGnuNGuYmqU17Zeldz2mPjwUto5tl0pOwcimjE1498C7IkWcDPcdjoejHhZ12VeLUtBpjBRtT/d6GrElsfgz5U9sSNuAvAY93k2IxrxMLSTW3VCjt/zP13soFaKZWG97KxFkR9patnqH+NYmUs6ndEJYf0/sXJGInIQyHFyTJo61obeEwj/SGe1JWX0ZZm6fKQLuQLtAfDrq08sq8bgYjVqHIxvScWxzpjgJpVTJMPCmTogY5AWJtPUyPChVPSehHFK5hFe5GWOMsQ6Ig27GrlCP6/wQuztXpOrGbMvGKwNfweQ/J2Nf3j5sztiMsQFjryqoHzUjAlEjfUU347TjxSI1mDb3QDuED/AUqd0q68tfzbvb2wWWMilmxWWKGm9qorUoyAfFieVIPJgvflajzrCEa2ElR9fhPug82B3bd/3dag3eWrq6PXvPbLECTSh74I0Bb6CXey+j/Lw8tQaxWk8U2N2JOr8bUao+Y1wXPRx6Hdxk1Rjt5o2hTs5iNdvbQmHybuhXimqQb3yqO5KPFGLPqiRUFtVh7SfHRT3x4OkhsHP+70kGc1OrqcVjfz8mMhTsJfb4dMSncFA5tMrJHjrZtff3FNSUq8V99LhQd35rBwu0Jgrm9602rHJHjfBtF487Y4wxxi4PB92MXVWTqiD8/V0cjmzMwJ0DB+D+rvdjyfElYlwRrXxbK64u/dTVzxYTHo1CSW41jm3KROKhAhSkVYqNOidTIzPvUAd4hznCxdumxatvk53sIbFzxbIjWbDdUYXvitIg0539czsP9BS16zTiTKNp2bzo1nKq+JRoiJVbkyvqqh/s+qDoQk0p4q2lpEGLXWVV2FNejd1lVUirM8wZbyKXAD1srRCgqERm/hqk5K+DXq/G7mxLBHS+E2OcHoBEYj5jsq4EnTAI6e0uVrcPrUtHzNYscdKFGq5RR/ruo/1EzwFzpNFp8PQ/T+NkyUk4WDjgHuU98LD2uOrvm59agb2/JYvSEWLnohI9FyjTxBioN0RJTo04wUWZLIwxxhjreDjoZuwqhPb1QMz2bFHzTB3I77v1PvyV8hdyqnPwxfEv8EzvZ1rl8XX2ssHoeyMwYGqwGFWUsD9PvFFPjykWG6F6a2qIZediCVtnFZQWMkhlEhGIaxsaUVuhRm1lAypL6lGWXwt9ox7XnfEzqmxkiOjhhoHDfODi0/rjvVq6wrgqcRXePfiuqLempmiLhi9CuFP4VX9vnV6P45W12FpaiW0lVYiuqj2rszglhVOjs8GONmL2dF97azF+S4jqjQN5E7D42GLEFMXgqxNfiTT0Z3s9i/GB49vtancTOrEy6KZOCB/ggZ0/JyI3qRwH/koTs+T7Tw5GKHXSb8V06taYx/7q7lfFuD4aA7d42GJkHsy8qu9JpRT7/0hByrEicVuulKLXuAB0v87XaLXu2gad6FhO6GddSeYKY4wxxswfB92MXQUKRKjD+Or3jyJuT65IxX6l3yuYuXUmfoz9UXTB7uTYqdUeY2t7C5HW3n20r2julBlbitzEcuQml0NTr0NxVrXYWsLSTglXX1tYBtrgA3kNjil1sJJrsECuwTS0PZq5TRkCy+OXi9vURXzu4LmiidmVqtBo8XdJJbaWVuGf0sr/dBenWdhDHW0xyNEG/R1sYHeRDt79PPthmccyMY/9/UPvI7s6Gy/uehG/JPyC1we8jmCHYLR3dHJn8jM9RA8B6lVQXarG30tjEbMtSxznXp2uPnW7NU7M0EmZDekbRBbEh8M/FPX9mbiyoLuiqFZkqsTvyxcnoqh7OJVv9JsUCBtHFYzp+LYsVJepYeNkga4jvI36sxhjjDFmOhx0M3aVKBChrtDUzZxGiN0wa4ho9vVP1j+Yd3AevhnzTauvhNL3o9Vo2nqO8UejrhHlBXWoLK4Tc4RpNVunaRT1olSfLZdLxZgvKzulqEmlr7N2UDbvVy+NVszx3l1ejSfiMkXa9fwQn39Xeo2sXluPl3e9jL8z/4YEEjF27f7I+6/ocStQa7CxuAIbiiqwu7zqrA7jtjIphjrZYpSTHUY428LT4vLSw2l/RvmNEqUDP5z6Qax4Hy08imlrponmbvdF3teqXbNNgX7H0D4eCOrmKoLCIxsM3evpxFJwD1eRbWHv2rZz2c/0ZcyX+Dn+Z3GcvDPoHQz0HnhF5Q9l+TXid6OSDRFsA/Dv6izmmrdF9/CaCjUOb8honobQXjvHM8YYY+zSOOhmrBUMnBqMtJgiZMeXiTFML/V9Cftz9+NQ/iGsTV0rxogZk1QmhZOXtdiuhKNCjl+6B+PD9AJ8kJ6PlfllOFpZi8Xhfuhp3zpjkS7WMO3JbU+K4JW6ic8bMg/jAsZd1vfIrFNjXVEF1hdViHFeZ6aNh1mrMMbZDiOd7dDbzhqKVkiTpnneD0Y9KJ7Xt/e/jZ3ZO/FJ9Cei0zrNh45wjkB7R7XclPJMnfQpBZqaBlLqdVpMMSKHeqPnOH+RedGWViasFI8zoTF9E4ImXPb3oAyRw+vTkXy00NAgD4BfFyf0Hh8AzzZcyadUdpr3TY0RKX2fMcYYYx0XB92MtQKqo+4+yhdHN2WKJky3zu6Lh6IeEjXACw8tFCujjipHs36saWzYs4EeGOBgg5lxGUiuVWPi0STM8HbBc74uRvmZpfWleGDzA0gqS4KtwhYfjfxIjO9q0ddqtFhTWI7fCspwsKLmrI/RfOzxLvaY4GqPYCvjpQhT465PRn4iRoxRyjP9HnesuwMze8zEvV3uhUza/lcvKTti+B3honSCjm0qaaA+BhSERw73Qc8xfrC0NX5Duc3pmzF3/1xxnf627uh8x2WlpOcllyP6b0OjuCbUkbz3hICrGsN3JQrSK0U6OxkyPdSs6uUZY4wx1vo46GasldCqYNzePNGQ6eSOHMwYPgPr09YjuTxZBN60gtseDHS0wdY+YXgjOQer8suwNKcY64rKcb3cGuP0Z64hX52SuhIRcNPj42LpImabhzqGXvRr6nSN2FJSid8KSkUzNM3p/aGQZaCDDSa62mOciz28VMo2TcemFdf+Xv1FULglYws+OvoRdmXvwjuD34GPrQ86Akq5nvRkd2TFlYqmgdRBP3pLJk7uyEbnQV6iz4Cxxl0dyDuAl3a9BD30uDn0Zjze/fEWz9mmMXgn/skWjQcFCdCppxt6jQ+Ai4/x08jPdwKAJg+QsH4eYqWbMcYYYx0bB92MtRKlpRz9bgjCPz8liNnaNG7rzYFv4s71d2JN6hpMDJqIQd6D2sXj7aSQ4+PO/rjFwwkvJGQjtU6Nb61csPtYCl4M8sQEF/urqlMvrivGA5seQEpFClwtXfHN2G/EHO7zadTrsb+8BqsKSrG2sBxVusbmj3W1scRUd0dMdne47Prs1uakcsIHwz4QXc3nH5jfXOtN6eaj/Eeho/Dt7ASfcEdknCzBwTVpKMqswont2eJEU0hvN3Qd4QP3ALtW62NwquSUKD+gbvaj/UbjtX6vXfJ7U3O0EztyEL83D+pabXM3cgpyo0b6ihnlpkJN6mgsGe0PdYZnjDHGWMfHQTdjrYhW/JpW1Q6tTcOQW6JEGuyyuGV4a99bWH3jalgpTNeE6nINdrTFtj5h+DQjH5+m5yGxVo37T6YjwlqFu7xdMMXNAQ6Ky3sZKa8vbw643azc8O3Yb+Fv99/5xCm19fg1v0wE29n1/zbK8lEpMNXNEVM9HBFubZyV1StFweDkTpPRy72XaAx3vOg4nvrnKdwTcQ9m9ZolatY7Avo9A7q6iPne2XFlOLo5Q/QzoHF2tDn72KDLYC+E9nWHhdWV/84ZlRl47O/HUKutRV+Pvnh36LsXTNlv1AHJhwuRfLhIpMA31WvTGD1KjadxaFezL62hoV6Lvb+liOs0k9vGsW1r4hljjDFmGhx0M9aKpDRC7OYQ/PVRtFj5ixzmjSd6PIGtmVuRW5OLj499LBpAtScqmRRP+rrC68RhpHfpha9zSxBbU4+XE7MxJzlHpHNTo7IhjrZws7h4UFOrqcXMbTObA+6lY5fCz86v+eNlGi3+LCzHqvxSHKmsPavr+A1uDrjZwwn97K0hNfO52DRffOm4pfjwyIf4IfYHfB/7PWKKY7Bw6EK4W3ecplkUfPtGOImtMKNS1HonHylESXY1dq5IFGnU3uGOCOruKuqnL6fxWmFtIR7e8rCo++/s1BkfjfhINLA7k07biOyEMiQeykfeIRvk6hKaP0YnBCjY9otwMpua6YNr01BTroadiwrdR/973DPGGGOsY+OgmzEjpN8GdHUWXcyp8dTEmd3EHOdH/34UP8X9hNH+o8VKaHtjBT2e83fDw/7u+C2/DMvzShBXUy+CZNpIqJUKve2txCV1DQ+ysoC7UiECd0oPfn7n84gpihGzt7+87ksRcFdqddhbVi1WtLcUV6LhdJ22TAIMd7TDNA9HjHWxh6VMivaEVrWf7/M8erj1wOw9s3Gs8Bimr52OBUMXoL9nf3Q01Ixs9IwIDJ4WgoT9+Yjdk4vS3BpkxZaKbcfyBDi4W8ErxEFsLr424rbsPM8rdbSngDunOgd+tn74bPRnsFEa6q/rqhpOTwkoRnpMMRrqm2avS2DrZIGw/p5iVduUY83Opzi7GjHbssX1obeGie7wjDHGGLs2cNDNmBEMvKkTMk+VisCbGk8N7jwYUzpNwerk1Xht92v47Ybf2lWa+bn13g/6uuIBHxfEVNfhz4Jy7C6rwonqOiTW1ovtXPZyGSTaMlRrRkLmPgRBzuF4KqURqXUnUaIx1Nw2ibSxFIH2FDfHS66ctwd0kiXEMQTP/PMMEssS8dDmhzCz+0wxckwqaV8nElpCZa1At1G+YqOmgqnRRWKGfWFmlbhNG3U+J1KZBI4eViJAplRrG0cVFLbAl/Gfo6ZajxDLKLzsORs5e+sQW5woOpAXZ1X/p7u6f5QTijWpmHz7YChNXNt/PjQHnE460CXNOqdVeMYYY4xdOzjoZswIHD2sRWo5pdvu+TUJ01/tixf6vID9efuRXZ2NRUcW4bX+r7X71OJutlZiaxrhta+8Gqco+K6pR0JNPTLqGsTKdYWWViPtAAs7UIh9QMRN/4758rZQiPTxaR5OiLAxrzrt1kA16z9N+AnzD87H70m/i1nTVO9Nq962Slt0VLSS3XOsv9jqazTIS6lAblI58lPKUZJbA029TvQ/aO4sfloYRouNHDxsCNDP7aROKe2Utu4RaAetTov165PMJo38XHH78gzN0yxkGDw9xNS7wxhjjLE2xkE3Y0bS5/pAJBzIFwFF3J5cdBnijbcGvYUHNz+IXxJ+wUjfkRjoPbDDPP60Aj7R1UFsZ45H+jp2BRZFf4dGmR1ui7gf3dz7oUbXCBuZVKSfB1pawFbe8VNtVXKV6Gbf3bU73jnwDnbl7MLt627H4pGLL9i5vaOtgAdGuYit6dioKq0XKehVJfWoKq3DnsRDKC2rgIXOEr4WAZA3KsRKtq2zpaiDFsF2Zydx31maMszNEKXD7/vd0Dyt7/WBYjWfMcYYY9cWDroZM2KQQYE3NZOiucYhvd1FLe9t4bfh5/if8fre10Waub2FfYd9DjZlbMLHh+dDDj2e6HoTHooah2vdlJApYh75rO2zkF6ZjjvW3SFWvIf4DMG1hDIlaK43bRSA04mIX6p+gcxLhv8N/x9G+A1HR7BrZZJY5Xf2tkbUyI4xs50xxhhjl6fjFRQyZkYoxZxSbOuqNDiyMV3c91TPp0RzqILaAryx9w0RcHREB/MO4pVdr0APPW4NuxUPdn3Q1LtkNrq4dMGK61eIVe8qTRVmbp2Jb09+22GPhYuh3/l/R/4nsj8kkODtQW9jhN8IdATU6C3pUAGo2f6Iuzqft2kcY4wxxjo+fgfAmBHRm+xBN3US16O3ZqGyuE40UHtv6HuQS+VilNiKhBUd7jmIL43Hk9ufFB3Lr/O/Di/1fUmsbLJ/uVi64Jux3+CmkJvEiQkKPF/c9SLqtHXX1MP0ecznWHpqqbg+e8BsTAqehI5AXafFP8sNI8xoPJh7gJ2pd4kxxhhjJsJBN2NG5t/VGT7hjmjU6rH3dG0nrXQ+0+sZcX3hoYUiSO0osquyxXi0Gk0N+nj0wfwh8yGTdvya7SuhlCnxxoA38Fq/1yCXyLEhbQPu2XAP8qrzcC34/tT3+Cz6M3GdGg1OC52GjmLv78liJre9qyX6Tur4NfuMMcYYuzAOuhkzMlrhpdnFtNBLo5Nykw0zre/sfCeG+ww3zK/e8TxqNbXt/rkorS/FI38/guK6YoQ5huGjER/BQmZh6t0y++PjlvBb8OWYL+Fo4Yi40jjcuu5WHC04io5sZcJKvH/4fXH98e6P466Iu9BRZMeXInaXoev6yLvDeSY3Y4wxdo3joJuxNkBdlzsP9hLXqbEazeulYIvqV92t3EVDrdf2vIZGfWO7fT7opMHMv2ciozID3jbeWDJ6SYceh9XaKCuA6rzpZAWdvLh/8/0iMO2I1qSswdz9c8X1+yPvx0NRD6GjUNdqsPX7OHE9cqg3vEIcTb1LjDHGGDMxDroZayP9JgVBoZKhKLMKCQfzxX0OKgcsHLZQ1HdvydiCz49/3i6fD41Og2f+eQYnS06K1drPR38OVytXU+9Wu+Nl44Ufxv+AsQFjoW3U4u39b+PtfW+Lx7ej2Ji+UZxgojp26uQ/q+esDlXvv3NFIqrLDGnlA6YGm3p3GGOMMWYGOOhmrI3QbOHe4wPE9f2rU6BRG4YL93Drgdf7vy6uLzm+BBvTNrar54RW52n82Z7cPbCUW+LTUZ8iwN7we7LLR432Fg5daAhGIcHKxJV4YPMDKKkr6RAr3C/ufFEcM5M7Te5wDfaSDhcg8aChW/noeyOgVPFUTsYYY4xx0M1Ym6I5vbbOKtRUNODY5oyzZjffE3GPuE6rgCeLT7abZ4a6bq9NXSsagS0avghdXbuaepfaPQpEH+j6AD4e+TGsFdY4WnhU1HnHlRjSltuj1Umr8eruV0XAPaXTFMwZMAdSScc570ur2ztOdyvvNT4AHkH2pt4lxhhjjJmJjvOOh7F2QK6QYeBUwwixY5szxQixJk/3ehpDfYZCrVOLuc3m3tGc5itTOvx3p74Tt98a9BYGew829W51KMN8h2H5hOXwt/NHfk0+7tpwF1Ylrmp387yXxy0X2RCUUn5L2C2YM3BOh+por9M24u+lp6Cu1cLN3xa9J3KmB2OMMcb+xUE3Y20suKcrvEIcoNU0Ysu3p9CoMzRPoyBkwZAF6OzUWTTSum/jfYgujDbL54eCvg8Of4BPoz8Vt5/t9WyHma9sboIcgrB84nJxQoNOyLy17y08t+M5VDZUwtzR/s7ZOwfzD85v7tj/ar9XO9QKN/0t/LMsHjmJ5ZBbyERauUzWcX4/xhhjjF09fmfAmAlSh0fd0xlKlQz5qZU4tD69+WM2Sht8M/YbUeddpanCQ1sewv68/Wb1HOkadZizbw6+j/1e3H6xz4uYETnD1LvVodkp7UStPM12pzT+zRmbMe2vadiVvctsV71pZX7Ghhn4Lek3EWRTjTrN4u5INdzk0Lp0xO/Ph0QqwbgHI+HoYW3qXWKMMcaYmeGgmzETsHOxxLA7wsT1I+vTm2d3ExqzRd2/B3oNRJ22Do/9/RiWnlwqgl1To2ZeT21/Cr8n/S4CqbcGvoU7I+409W5dE+jxvjfyXtHd3MfGB7k1uXhs62N4cMuDZlXrTScBqGHa9DXTRTd7ewt7MT6OatQ7WsAdvy8Ph9amievDbguFf6SzqXeJMcYYY2aIg27GTCS0jwfC+nmAFir//jZWzPc9s4M1NdG6zv86aBo1WHRkEe7eeDdSK1JNFkhFN0Tj5nU345/sf8SIM+qwTQ3gWNuiRnWrJq3CjC4zoJAqcCDvAG5Ze4voCm6q46NJclky7tt0H17Z/QrK1GWiVOKX638RJ5A6muQjhdj+o6HvQs+x/ugyxNvUu8QYY4wxM8VBN2MmNPTWUNi5qFBVWo+/l8aisfHfVGGlTIkPhn0gVpNtFDaIKYoRKcVUR12jqWmzfaRAbtaOWfi19ldUNFQg3CkcP0/8GWMCxrTZPrCzURnCs72fxV+T/8L4wPGiQdn6tPWY/MdkvLDjBaSUp7TpQ1ZcV4z5B+Zj2pppOFxwGCqZSqST/zThJ3jbdLxgNHZPLjZ/fVL8vYb2dUf/G4NMvUuMMcYYM2McdDNmQkpLOcY8EAmZXIr0EyXY+1vyWR+ndFxaTV5942oM8hqEhsYG0TF8wu8T8FPcT9Do/l0db2151Xl4fc/rmPLnFOzO3Q0ZZHgs6jHR1IsCb2Z6PrY+eG/oe1h5/UqM8B0hgu8N6Rsw+c/JeOTvR0TNN43oMpay+jKRhTH+t/FYHr8cWr0WI31H4s/Jf4p0coVMgY7m+NYsscJNGSoRgzwxakaEqOdmjDHGGLsQ+QU/whhrE+4Bdhg1ozM2f31KvKF3cLdC5NCzVwc9rD1EXeyWjC1YfGwxMioz8O7Bd/HtiW9xU+hNmBoyVXxOq6SRF0Xjt8TfsCFtgwjyyTDvYehW2U00TKOUZmZeOjt3xuKRi8WYOTopsy1zG/bk7BFbgF0Abux0I8YFjBNBemugOfK/JPwijhHqUE6iXKPwZI8n0c+zHzoifaMeB/5KxZGNGeJ299G+GHhTpw5Xp84YY4yx1sdBN2NmIKS3O8oLanFwTRp2rkiEvYslfCOczvocenNPKd0j/EZgddJqLDm+BIV1heLyi5gvMMR7iJjrTPWzl5PSSw3aYktisS9vH9anrkdKxb+pyX08+ohAqotjF6xfv75Vf2fW+igD4cMRHyKrMgs/J/wsjpP0ynR8dPQjsUW5RGGk30j09uiNCOeIFp9AodVyCuhp5Xxr5lbElf7buI3qtmd2nylmzHfUAFSr0WHb93FIOlwobve7IRC9xgd02N+XMcYYY62Lg27GzETvCQEoL6xF4oECbPjyBKY80xOufrb/+TwKlKaHTcfkTpNFALQyYaWoo92RvUNsxM/WTwRgfnZ+8LX1hZPKCRL6TyIRKenU+Tq3OlesmNMscBpP1sRSbomxAWNxU8hN6ObazfA1GuOlsbPW52vnK8ZzPd79cWxM3yjqvQ/lH0JMcYzYmp7nri5dxTFCJ2looz4Cgh4oqS8RATsdI6eKT4nbZx6DdALo1rBbm4+Rjqq+WoP1n8cgL7kCUqkEw+8MQ+eBXqbeLcYYY4y1Ixx0M2YmKHAZeWdnVJeqkZtUjjUfR2Pq873g4GZ13s+nAImaaNGWWp6KTRmbsD93P44XHUdmVabYWorGlPVx74PBPoNFGjLdZu0fdcGn0gPaqNkZlSfQMXKk8Agq1BU4mH9QbC36XnIr9PfsL46RUX6jxImcjq4kpxrrPz+ByqI60X9h3MOR8A3v+L83Y4wxxloXB92MmRGZQooJj0Vh9QdHUZJdjTWLDYG3tb3FRb8uyCEIjzo8ike7PYqqhiocKzyG9Ip0ZFVlia2yoVLUa9N/MolM1H9723rD29obXVy6iBRhmVTWZr8na3suli64Lfw2sVG6eFJZkkgTz6nOQXZVNvJq8s6aBW9nYSfqwf3t/BFkHyRWtDtiY7QLSTpcgG0/xEHb0AhbZxWun9kNTl7Wpt4txhhjjLVDHHQzZmYsLOWY9EQ3/L7wCCqL67Hm4+OY/HQPqKxbFvDQKjXV19LG2PlIJVKEOYWJjZ2tUdeI/X+k4tgWQ6aIT7gjxjzQBZY2p1PvGWOMMcYuE48MY8wM0cr2DbO6w9JO2bzira7lumrGjKmyuA6rPzjWHHD3GOMnToBxwM0YY4yxq8FBN2Nmyt7VCjfO6g6VjQKFGVX466NoqOu0pt4txjpsOvkv7xxCfmoFlCoZxj4YiYFTO0Eq438mGWOMMXZ1+N0EY2bM2dsGNz7VXaSWU+AtVrw58Gas1dTXaLD1u1hs/voUGuq0cA+0wy2v9UWnXm78KDPGGGOsVXDQzZiZc/GxxQ1PdYeFtRwFaZX483/HUFfVYOrdYqzdS48pxs9vHUD8/nxAAvQc548pz/WEnYulqXeNMcYYYx2I2QfdOTk5uPPOO+Hs7AxLS0t07doVhw8fNvVuMdamXH1tceOsHiLVvCizCr+/fxRVpfX8LDB2BWorG7Dl21NY91kMaisa4OBuhanP9cKAycGQcTo5Y4wxxq6l7uVlZWUYNGgQRowYgQ0bNsDV1RVJSUlwdHQ09a4x1uZc/Wwx9bmeora7vKBWdDef9GR3OHnyGCPGWtqZ/MSOHBz8KxUN9TpIJEC30X7oNykQciWPzGOMMcbYNRh0L1iwAL6+vli6dGnzfYGBgSbdJ8ZMydHDWsztptrusnxD4D3u4a7wCeMTUYxdTFZ8Kfb8miymARA3f1sMvTVM1HAzxhhjjF2zQfdff/2FsWPHYtq0adixYwe8vb3x2GOP4cEHH7zg16jVarE1qaysFJcajUZs5qpp38x5H5l5UNnKMGlWFDZ+cQqF6Yau5oOmBSNisKfRfiYfn8ycXez4pCD7wF/pyI4rE7ctrOToMykA4QM9IJVK+DWXmezYZMzU+Phk5kzTTl4/W7p/Er1er4eZUqlU4vKZZ54RgfehQ4cwa9YsfP7557jnnnvO+zVz5szBm2+++Z/7ly9fDisrK6PvM2NtRa8DSk+oUJenELdt/BtgH66GxOw7NTBmfJoqKSpTlKjLo3PLEvrXDjZ+Gth2aoBMabb/7DHGGGOsHamtrcXtt9+OiooK2NnZtc+gW6lUonfv3ti7d2/zfU8++aQIvvft29filW5KUS8uLr7oA2EOZ0m2bNmC6667DgqFIYhi7FLozzd6cxYOrc0Qt92D7DBqRjhsHC34+GTXjDNfPysL1Di2OQup0cXA6X/dgnq6oO/1AbBz5a7kzHTHJv/bzswNH5/MnGnayesnxZouLi6XDLrNOr3c09MTERERZ93XuXNn/Pbbbxf8GgsLC7Gdi54sc37C2tt+MvPR9/pguHjbYev3sShIrcRvC45i9IwIBHR1afWfxcfnldE36qFp0KFRpxcnSvSNgEwuEc27pDIJJNTRi13V41tXKMOmz+OQm1jRfH9QD1f0Hh8gmhAyZkr82snMGR+fzJwpzDw2aum+mXXQTZ3LExISzrovMTER/v7+JtsnxswRBRfOPn2w6atTYqTYuk9jEDXCB/0nB0NhwV2ZW5tWoxOjpmoqGlBboW6+pFFU6lot1HUaw2WtFg11dFvbvOp6Loq3LawUsHawEBkKNk4quPnZwi3ADk6eVpDyCKsLqqtuQPy+fJzamYOKIiofqoBEKkFwT0Ow7ext0+rPPWOMMcbY5TLroPvpp5/GwIEDMW/ePEyfPh0HDx7El19+KTbG2NnsXa1w0/O9sHd1MmK2ZSNmezbSTxRj5F2d4c3dzVusoV6L6lK1mINOWzVtZRRY/xtcUzDdWqjAp75GI7aSHENn7VOnPyZXSsUqrXuAnQjC6dLWWXVNr4w3NuqRE1+GuL25SIkuQqPWcDZDItej6zBfdB/lB1snQz8QxhhjjDFzYNZBd58+fbB69Wq8/PLLeOutt8S4sA8//BB33HGHqXeNMbMkU0gxZHoo/Ls4Y/uyeFQW1+OP/x1DlyFe6HdjECxtlLiWUcBGK9TVZfX/BtUl9agqUzcH2C0NqGVyKazslbC2V8LK3gLWdnSphMpaIVaulVZyWFjKRbdsuq1QGVLJpRQwS2hmtB5aTSO0ap0IuKvL1agpU6OiqBaFGVUoTK8Us6TzkivE1sTSVtEcgDcF4/QzOzJKyS/OqkbCwXwkHSoQz2ETGv0VNsAdKSXH0P+GQLNOQWOMMcbYtcmsg25y/fXXi40x1nJ+XZxx2+v9sPf3ZJzalSu25COF6DMxEJHDvSHrwCnLlPpdUVgn5piXF9SivLAWVRRYl9aLoJYC70uhQJnSvGnF1PZ0yjelf4sg285wSZ9zNSvOVNNNgTsF5vS9z02FpjrlsoJaEXwXpFeKy+LsatRVaZBxokRsTexdLeHiawMnLxs4eVrD2dta3NeeU9PpeSpIq0TKsUKkHisSz2ETOsnQqbcbIgZ5iUwAaraStv6YSfeXMcYYY6zdBt2MsSujtJRj+B3hCOnjjl0rk8S84t2rknBqVw76XB+I4J5uYk5xe0VBKQXUVMNemFmF0twaEWRTcH2h+mlCNb82VD/tZCGC6ubgWly3gK2jSjx2pkb7SQE0beEDPJtPKFDg3RSIU1BKJxgqigxbytGi5q+XyiVwdLcSX09du+2cLWHrohK/O63MK1Uys0tTp+cuO74UmadKkRVfCnXNv1kHlGrvH+mCsP4e8ItwEicsGGOMMcbaA9O/s2SMGZV3qCOmv9IHcXtyceCvVLECvPnrU3BwT0Ov8f4I7eNu9iui5wbYRRlVKMqqgqZed97Pp6DZwd1KBJ0O7pYi6KRgumnFur2ebJArZPAItBdbE0pNL8yoRElODUrzasTJB7qktHW6j7YLpsefTom3tDVc0m1Kk7e0U8LGQQU7FxVUNgqjBOfUXI5OBBVnVyE/pQJ5KRWidv7c5zGgq7NoFEjZGwolNwVkjDHGWPvDQTdj1wAKMrsM8Uan3u6I2ZaF41uzxKrw1u/isP+PVHQe5ClSdc2hAdVZATYF15kXDrCpht3Fx0Z0+3b2sYGjh7UItqnu2dxWcY2FUq39IpzFduZjSKvGIgjPqxGp2VTfX1VSJ5rBUUd1nbaxua79YuQWMtg5UwBu2XxJwTkF47QpVXLIFVIRxNPqOjU20+kaodM0ivp46jBeX22oWad9qCyuM2QknJEufubqPqWL00o2BdnuAbZmf0KIMcYYY+xSOOhm7BpC9cNU191tlC9O7shB9N+ZqClX4/C6dBxZnw7fCGcxbikwykWsfhpbo64R5QV1IqgWwXULA2xXf1u4+dvB0YNHap0PBa8iSHaxPO+8dm2DTow3O99WV0mj0NTNHdtpxVysnueef8X8atCINBdfei5t4dnJQTSG4xF3jDHGGOtoOOhm7BpEq5M9x/qj20hfpB4vEnXeOQnlyDxVIrZ/JBBBEI0a8+xkD2cfmoF8dd2nqQEYjcQSG6UV51SjLK9WrLiei1ZOqTGYq5+dWPmkoIwD7NYjV8qag/KLoRpysUpOjeiK6wwr1SX1qKtqEGntddUaaOpp1VwvVtfPDPplMono4E4d82lFnFbHqbkbpazTz6XGcR296zpjjDHGGOGgm7FrGK0eh/R2Fxul/CYfKUBqdLFYcc5NKhcbkUgBmaUVNmadEinclIZO3bspeKdRWBRwibRirSGlWKyYVjWcTik2NPm6UP01rWxSt20OsM2zhpyeb9pakrVAY9CklGbeTmvmGWOMMcaMgYNuxphAtdC9JwSKjQJlWvHOpRnRKeWoLlVDWyND5slSsV0RyenRVt42ov6aVjppozphWhll7RvVXku5zxljjDHG2H9w0M0Y+w9K/40c5iM2Ul5UjU1//YPwwK6oKjasYFNaMXWg1qh1kMokIuhqSim2sv23I7YhpdiQVkwrp4wxxhhjjF1LOOhmjF0SjdlSuegQMcQLCgXX4TLGGGOMMdZSPIuFMcYYY4wxxhgzEg66GWOMMcYYY4wxI+GgmzHGGGOMMcYYMxIOuhljjDHGGGOMMSPhoJsxxhhjjDHGGDMSDroZY4wxxhhjjDEj4aCbMcYYY4wxxhgzEg66GWOMMcYYY4wxI+GgmzHGGGOMMcYYMxIOuhljjDHGGGOMMSPhoJsxxhhjjDHGGDMSDroZY4wxxhhjjDEj4aCbMcYYY4wxxhgzEg66GWOMMcYYY4wxDroZY4wxxhhjjLH2hVe6GWOMMcYYY4wxI+GgmzHGGGOMMcYYMxIOuhljjDHGGGOMMSPhoJsxxhhjjDHGGDMSDroZY4wxxhhjjDEjkaOD0+v14rKyshLmTKPRoLa2VuynQqEw9e4wdhY+Ppk54+OTmSs+Npk54+OTmTNNO4mNmmLMppjzmg26q6qqxKWvr6+pd4UxxhhjjDHGWAeMOe3t7S/4cYn+UmF5O9fY2Ijc3FzY2tpCIpHAnM+S0ImBrKws2NnZmXp3GDsLH5/MnPHxycwVH5vMnPHxycxZZTuJjSiUpoDby8sLUqn02l3ppl/ex8cH7QUdVOZ8YLFrGx+fzJzx8cnMFR+bzJzx8cnMmV07iI0utsLdhBupMcYYY4wxxhhjRsJBN2OMMcYYY4wxZiQcdJsJCwsLvPHGG+KSMXPDxyczZ3x8MnPFxyYzZ3x8MnNm0cFiow7fSI0xxhhjjDHGGDMVXulmjDHGGGOMMcaMhINuxhhjjDHGGGPMSDjoZowxxhhjjDHGjISDbjPw6aefIiAgACqVCv369cPBgwdNvUvsGjB//nz06dMHtra2cHNzw+TJk5GQkHDW59TX12PmzJlwdnaGjY0NbrrpJhQUFJz1OZmZmZg4cSKsrKzE93n++eeh1Wrb+LdhHdm7774LiUSCp556qvk+PjaZKeXk5ODOO+8Ur42Wlpbo2rUrDh8+3Pxxapfz+uuvw9PTU3x89OjRSEpKOut7lJaW4o477hDzZx0cHHD//fejurraBL8N60h0Oh1mz56NwMBAcewFBwfj7bffFsdkEz4+WVvZuXMnJk2aBC8vL/Hv+B9//HHWx1vrWIyJicGQIUNELOXr64v33nsP5oaDbhP75Zdf8Mwzz4jufEePHkW3bt0wduxYFBYWmnrXWAe3Y8cOEVDv378fW7ZsgUajwZgxY1BTU9P8OU8//TTWrFmDVatWic/Pzc3F1KlTz/rHnQLuhoYG7N27F99//z2+++478QLKWGs4dOgQvvjiC0RFRZ11Px+bzFTKysowaNAgKBQKbNiwAbGxsfjggw/g6OjY/Dn0hm/x4sX4/PPPceDAAVhbW4t/2+lkURN6E3nq1Cnx+rt27Vrx5vShhx4y0W/FOooFCxZgyZIl+OSTTxAXFydu0/H48ccfN38OH5+srdB7SoptPv300/N+vDWOxcrKSvH+1d/fH0eOHMHChQsxZ84cfPnllzAr1L2cmU7fvn31M2fObL6t0+n0Xl5e+vnz5/PTwtpUYWEhnQbX79ixQ9wuLy/XKxQK/apVq5o/Jy4uTnzOvn37xO3169frpVKpPj8/v/lzlixZorezs9Or1Wp+BtlVqaqq0oeEhOi3bNmiHzZsmH7WrFl8bDKTe/HFF/WDBw++4McbGxv1Hh4e+oULFzbfR6+nFhYW+p9//lncjo2NFa+lhw4dav6cDRs26CUSiT4nJ8fIvwHryCZOnKi/7777zrpv6tSp+jvuuENc5+OTmQoA/erVq5tvt9ax+Nlnn+kdHR3Pet9Jr9NhYWF6c8Ir3SZEq4N0RoZSKZpIpVJxe9++fabcNXYNqqioEJdOTk7iko5NWv0+8/gMDw+Hn59f8/FJl5RW6e7u3vw5dIaSzjrSWUnGrgZlYlAmxZnHIB+bzNT++usv9O7dG9OmTRMlNT169MBXX33V/PG0tDTk5+efddza29uL8rEzXzspTZK+TxP6fHoPQKs9jF2pgQMHYuvWrUhMTBS3jx8/jt27d2P8+PF8fDKzktZKr5X0OUOHDoVSqTzrvSiVTFJmkrmQm3oHrmXFxcUiPffMgIXQ7fj4eJPtF7v2NDY2inpZSpmMjIwU99ELIb2A0Yvduccnfazpc853/DZ9jLErtWLFClFyQ+nl5+Jjk5lSamqqSN+l0rBXXnlFHKNPPvmkeL285557ml/7zvfaeOZrJwXsZ5LL5eKkJ792sqvx0ksviRPfdJJcJpOJ95nvvPOOSNFtOvb4+GTmIL+VjkW6pB4G536Ppo+dWfpjShx0M8bEiuLJkyfF2XDGTC0rKwuzZs0S9VvUFIUxcztJSasu8+bNE7dppZteP6kmkYJuxkxp5cqV+Omnn7B8+XJ06dIF0dHR4qQ6NbLi45Mx0+H0chNycXERZyHP7QZNtz08PEy2X+za8vjjj4vGFNu3b4ePj0/z/XQMUglEeXn5BY9Pujzf8dv0McauBJU2UDPJnj17ijPatFEjP2q2QtfpDDYfm8xUqMtuRETEWfd17txZTHI487XvYv+20+W5DVNp6gN16eXXTnY1aIIIrXbfeuutovzrrrvuEo0naWIJH5/MnHi00mtle3kvykG3CVEqWq9evUTtzZln0On2gAEDTLlr7BpAPS0o4F69ejW2bdv2n9QcOjapO++ZxyfVx9Aby6bjky5PnDhx1gsirU7SWIdz35Qy1lKjRo0SxxWt0DRttLJI6ZFN1/nYZKZCZTjnjlek+lnqnEvotZTe6J352knpvlR/eOZrJ53QpBNMTeh1mN4DUD0jY1eqtrZW1LueiRZ46Nji45OZk8BWeq2kz6GO5tSH6Mz3omFhYWaTWi6YupPbtW7FihWiS993330nOvQ99NBDegcHh7O6QTNmDI8++qje3t5e/88//+jz8vKat9ra2ubPeeSRR/R+fn76bdu26Q8fPqwfMGCA2JpotVp9ZGSkfsyYMfro6Gj9xo0b9a6urvqXX36ZnzTWqs7sXs7HJjOlgwcP6uVyuf6dd97RJyUl6X/66Se9lZWVftmyZc2f8+6774p/y//88099TEyM/sYbb9QHBgbq6+rqmj9n3Lhx+h49eugPHDig3717t+jUf9ttt5not2IdxT333KP39vbWr127Vp+Wlqb//fff9S4uLvoXXnih+XP4+GRtOYXk2LFjYqOwc9GiReJ6RkZGqx2L1PHc3d1df9ddd+lPnjwpYit6Tf7iiy/M6onmoNsMfPzxxyKwUSqVYoTY/v37Tb1L7BpAL37n25YuXdr8OfSi99hjj4lRDPQCNmXKFBGYnyk9PV0/fvx4vaWlpfiH/dlnn9VrNBoT/EbsWgq6+dhkprRmzRpxwpFOmoeHh+u//PLLsz5Oo3Bmz54t3gjS54waNUqfkJBw1ueUlJSIN442NjZizOK9994r3qAydjUqKyvFayW9r1SpVPqgoCD9q6++etY4JT4+WVvZvn37ed9r3nPPPa16LB4/flyMcqTvQSedKJg3NxL6P1OvtjPGGGOMMcYYYx0R13QzxhhjjDHGGGNGwkE3Y4wxxhhjjDFmJBx0M8YYY4wxxhhjRsJBN2OMMcYYY4wxZiQcdDPGGGOMMcYYY0bCQTdjjDHGGGOMMWYkHHQzxhhjjDHGGGNGwkE3Y4wxxhhjjDFmJBx0M8YYY9eAf/75BxKJBOXl5abeFcYYY+yaItHr9XpT7wRjjDHGWtfw4cPRvXt3fPjhh+J2Q0MDSktL4e7uLoJvxhhjjLUNeRv9HMYYY4yZkFKphIeHBz8HjDHGWBvj9HLGGGOsg5kxYwZ27NiBjz76SKxq0/bdd9+dlV5Otx0cHLB27VqEhYXBysoKN998M2pra/H9998jICAAjo6OePLJJ6HT6Zq/t1qtxnPPPQdvb29YW1ujX79+InWdMcYYY+fHK92MMcZYB0PBdmJiIiIjI/HWW2+J+06dOvWfz6MAe/HixVixYgWqqqowdepUTJkyRQTj69evR2pqKm666SYMGjQIt9xyi/iaxx9/HLGxseJrvLy8sHr1aowbNw4nTpxASEhIm/+ujDHGmLnjoJsxxhjrYOzt7UU6Oa1eN6WUx8fH/+fzNBoNlixZguDgYHGbVrp//PFHFBQUwMbGBhERERgxYgS2b98ugu7MzEwsXbpUXFLATWjVe+PGjeL+efPmtfFvyhhjjJk/DroZY4yxaxQF5U0BN6Ema5RWTgH3mfcVFhaK67SaTanmoaGhZ30fSjl3dnZuwz1njDHG2g8OuhljjLFrlEKhOOs21Xyf777GxkZxvbq6GjKZDEeOHBGXZzozUGeMMcbYvzjoZowxxjogSi8/swFaa+jRo4f4nrTyPWTIkFb93owxxlhHxd3LGWOMsQ6I0sQPHDiA9PR0FBcXN69WXw1KK7/jjjtw99134/fff0daWhoOHjyI+fPnY926da2y34wxxlhHw0E3Y4wx1gFRgzNKAadmaK6urqL5WWughmkUdD/77LNi1NjkyZNx6NAh+Pn5tcr3Z4wxxjoaiV6v15t6JxhjjDHGGGOMsY6IV7oZY4wxxhhjjDEj4aCbMcYYY4wxxhgzEg66GWOMMcYYY4wxI+GgmzHGGGOMMcYYMxIOuhljjDHGGGOMMSPhoJsxxhhjjDHGGDMSDroZY4wxxhhjjDEj4aCbMcYYY4wxxhgzEg66GWOMMcYYY4wxI+GgmzHGGGOMMcYYMxIOuhljjDHGGGOMMSPhoJsxxhhjjDHGGIN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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Simple manual plot (multi-run, multi-turbine, no widgets)\n", + "# Configure selection\n", + "SELECTED_T_IDX = np.arange(0, 69) # list of turbine indices or single int\n", + "SELECTED_COLUMNS = ['Power_FLORIS', 'Power_PyWake'] # columns to plot (check your measurements.csv for exact names)\n", + "\n", + "# Resolve run directories\n", + "try:\n", + " RUNS = RUN_DIRS\n", + "except NameError:\n", + " RUNS = []\n", + "\n", + "# Fallback to single run_dir if defined elsewhere\n", + "try:\n", + " if not RUNS:\n", + " if isinstance(run_dir, str):\n", + " RUNS = [run_dir]\n", + " elif isinstance(run_dir, (list, tuple)):\n", + " RUNS = list(run_dir)\n", + "except NameError:\n", + " pass\n", + "\n", + "# Filter valid directories\n", + "RUNS = [rd for rd in RUNS if isinstance(rd, str) and os.path.isdir(rd)]\n", + "if not RUNS:\n", + " print('Set RUN_DIRS (or run_dir) to one or more valid run folders under', BASE_DIR)\n", + "else:\n", + " runs_data = load_measurements_multi(RUNS)\n", + " if not runs_data:\n", + " print('No measurements.csv files found in the selected runs')\n", + " else:\n", + " # Determine turbine indices to plot\n", + " def any_has_tidx():\n", + " return any('t_idx' in df.columns for _, _, df in runs_data)\n", + "\n", + " if any_has_tidx():\n", + " if isinstance(SELECTED_T_IDX, (list, tuple, set, np.ndarray)):\n", + " selected_tis = list(SELECTED_T_IDX)\n", + " else:\n", + " selected_tis = [SELECTED_T_IDX]\n", + " else:\n", + " selected_tis = [None]\n", + "\n", + " # Time axis detection\n", + " time_cols = ['time', 't', 'timestamp', 'Time', 'T']\n", + " def time_series_for(df_):\n", + " for c in time_cols:\n", + " if c in df_.columns:\n", + " return df_[c]\n", + " return range(len(df_))\n", + "\n", + " # Create a single plot for all timeseries\n", + " fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", + " \n", + " y_total = np.zeros(251,)\n", + " ylabel = None\n", + " # Plot all turbines and all runs on the same axis\n", + " for ti in selected_tis:\n", + " for run_name, rd, df in runs_data:\n", + " if ti is not None and 't_idx' in df.columns:\n", + " sub = df[df['t_idx'] == ti]\n", + " if sub.empty:\n", + " print(f'[{run_name}] No rows for t_idx={ti}')\n", + " continue\n", + " else:\n", + " sub = df\n", + " x = time_series_for(sub)\n", + " for col in SELECTED_COLUMNS:\n", + " if col not in sub.columns:\n", + " print(f'[{run_name}] Column not found:', col)\n", + " continue\n", + " y = sub[col]\n", + " # Scale power-like columns to MW if applicable\n", + " if 'power' in col.lower():\n", + " y = y / 1e6\n", + " ylabel = 'Power (MW)'\n", + " y_total = np.sum([y_total, y], axis=0)\n", + " ax.plot(x, y)\n", + " \n", + " ax.set_title('All Timeseries')\n", + " ax.set_xlabel('time' if 'time' in time_cols else 'index')\n", + " if ylabel:\n", + " ax.set_ylabel(ylabel)\n", + " ax.grid(True)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "OFFPy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/test_pywake_integration.py b/test_pywake_integration.py new file mode 100644 index 0000000..d219635 --- /dev/null +++ b/test_pywake_integration.py @@ -0,0 +1,104 @@ +""" +Test script for PyWake integration in OFF framework + +This script tests that the PyWake wake model can be loaded and used +within the OFF framework. +""" + +import sys +import os + +# Add the OFF code directory to the path +sys.path.insert(0, os.path.join(os.path.dirname(__file__), '03_Code')) + +import off.off_interface as offi +import off.off as off +import time + +def test_pywake_integration(): + """Test that PyWake can be loaded and used""" + + print("="*60) + print("Testing PyWake Integration in OFF Framework") + print("="*60) + + start_time = time.time() + + try: + # Create an interface object + print("\n1. Creating OFFInterface object...") + oi = offi.OFFInterface() + print(" ✓ OFFInterface created successfully") + + # Load the PyWake example configuration + print("\n2. Loading PyWake simulation configuration...") + config_path = f'{off.OFF_PATH}/02_Examples_and_Cases/02_Example_Cases/001_two_turbines_yaw_step_pywake.yaml' + print(f" Config path: {config_path}") + oi.init_simulation_by_path(config_path) + print(" ✓ PyWake simulation initialized successfully") + + # Check that PyWake model was loaded + print("\n3. Verifying PyWake model...") + wake_model_name = oi.off_sim.wake_solver.floris_wake.__class__.__name__ + print(f" Wake model class: {wake_model_name}") + if wake_model_name == "PyWakeModel": + print(" ✓ PyWake model loaded correctly") + else: + print(f" ✗ Expected PyWakeModel, got {wake_model_name}") + return False + + # Run a short simulation (just first few timesteps) + print("\n4. Running short simulation test (first 10 seconds)...") + original_time_end = oi.off_sim.settings_sim['time end'] + oi.off_sim.settings_sim['time end'] = 10 # Only run 10 seconds for testing + + oi.run_sim() + + print(" ✓ Simulation ran successfully") + + # Restore original time end + oi.off_sim.settings_sim['time end'] = original_time_end + + # Check that measurements were generated + print("\n5. Checking generated measurements...") + if oi.measurements is not None and len(oi.measurements) > 0: + print(f" Number of measurement rows: {len(oi.measurements)}") + print(f" Measurement columns: {list(oi.measurements.columns)}") + print(" ✓ Measurements generated successfully") + else: + print(" ✗ No measurements generated") + return False + + # Store output + print("\n6. Storing output files...") + oi.store_measurements() + oi.store_applied_control() + oi.store_run_file() + print(" ✓ Output files stored") + + elapsed_time = time.time() - start_time + + print("\n" + "="*60) + print("PyWake Integration Test PASSED") + print(f"Test completed in {elapsed_time:.2f} seconds") + print("="*60) + + return True + + except Exception as e: + print(f"\n✗ Error during testing: {e}") + import traceback + traceback.print_exc() + + elapsed_time = time.time() - start_time + print("\n" + "="*60) + print("PyWake Integration Test FAILED") + print(f"Test failed after {elapsed_time:.2f} seconds") + print("="*60) + + return False + + +if __name__ == "__main__": + success = test_pywake_integration() + sys.exit(0 if success else 1) diff --git a/test_pywake_sampling.ipynb b/test_pywake_sampling.ipynb new file mode 100644 index 0000000..81a9dd3 --- /dev/null +++ b/test_pywake_sampling.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "0bbd7d03", + "metadata": {}, + "outputs": [], + "source": [ + "import py_wake \n", + "\n", + "# setup site, wind turbines and wind farm model with the corresponding wake models\n", + "import numpy as np\n", + "import ipywidgets \n", + "from ipywidgets import interact\n", + "from ipywidgets import IntSlider\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from py_wake.flow_map import HorizontalGrid\n", + "from py_wake.examples.data.iea37._iea37 import IEA37Site, IEA37_WindTurbines\n", + "from py_wake.literature.gaussian_models import Bastankhah_PorteAgel_2014\n", + "from py_wake.deflection_models.jimenez import JimenezWakeDeflection\n", + "from py_wake.flow_map import XYGrid" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "94712f95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "110.0\n" + ] + } + ], + "source": [ + "site = IEA37Site(16)\n", + "x, y = [0, 600, 1200], [0, 0, 0]\n", + "windTurbines = IEA37_WindTurbines()\n", + "h = windTurbines.hub_height(0)\n", + "print(h)\n", + "wfm = Bastankhah_PorteAgel_2014(site, windTurbines, k=0.0324555, deflectionModel=JimenezWakeDeflection())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8508ee1b", + "metadata": {}, + "outputs": [], + "source": [ + "# define function that plots the flow field and AEP history of 3 wind turbines\n", + "def plot_flow_field(WT0, WT1, TILT):\n", + "\n", + " ax1 = plt.figure(figsize=(12,4)).gca()\n", + " \n", + " sim_res = wfm(x, y, yaw=np.reshape([WT0,WT1,0],(3,1,1)), wd=270, ws=10, tilt=TILT)\n", + " sim_res.flow_map(HorizontalGrid(x = np.linspace(0,1400,200), y=np.linspace(-200,200,50))).plot_wake_map(ax=ax1)\n", + " ax1.set_xlim([-200,1400])\n", + "\n", + "# def get_flow_map(model=None, grid=XYGrid(x=np.linspace(-200, 500, 200), y=np.linspace(-200, 200, 200), h=70),\n", + "# turbulenceModel=CrespoHernandez()):\n", + "# blockage_deficitModel = [None, model][isinstance(model, BlockageDeficitModel)]\n", + "# wake_deficitModel = [NoWakeDeficit(), model][isinstance(model, WakeDeficitModel)]\n", + "# wfm = All2AllIterative(UniformSite(), V80(), wake_deficitModel=wake_deficitModel, blockage_deficitModel=blockage_deficitModel,\n", + "# turbulenceModel=turbulenceModel)\n", + "# return wfm(x=[0], y=[0], wd=270, ws=10, yaw=0).flow_map(grid)\n", + " \n", + "def plot_flow_profile(wfm,WT0, WT1, TILT):\n", + " ax2 = plt.figure(figsize=(6,4)).gca()\n", + " grid = XYGrid(x=300, y=np.linspace(-200,200,300))\n", + " # sim_res = wfm(x, y, yaw=np.reshape([WT0,WT1,0],(3,1,1)), wd=270, ws=10, tilt=TILT)\n", + " fm = wfm(x, y, yaw=np.reshape([WT0,WT1,0],(3,1,1)), wd=270, ws=10, tilt=TILT).flow_map(grid)\n", + " ax2.plot(fm.y, 10-fm.WS_eff.squeeze())\n", + "\n", + "def plot_particle_vel(wfm, WT0, WT1, TILT, xpart,ypart):\n", + " ax2 = plt.gca()\n", + " grid = XYGrid(x=xpart, y=ypart, h=h)\n", + " fm = wfm(x, y, yaw=np.reshape([WT0,WT1,0],(3,1,1)), wd=270, ws=10, tilt=TILT).flow_map(grid)\n", + " ax2.plot(fm.y, 10-fm.WS_eff.squeeze(),'*r')\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fe64a28a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Run the plot_flow_field_and_aep function when moving the sliders\n", + "plot_flow_field(0,0,0)\n", + "\n", + "plot_flow_profile(wfm,0,0,0)\n", + "\n", + "plot_particle_vel(wfm,0,0,0,np.array([300, 300]),np.array([-100, 100]))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "OFFPy", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}