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CEO_Compensation_EDA.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="author" content="Afsar Ali" />
<title>CEO Compensation EDA</title>
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      header.appendChild(paddingCol);
    }

    if (columns.number + columns.visible < columns.total)
      header.appendChild(renderColumnNavigation(columns.visible, false));

    if (typeof(clear) == "undefined" || clear) clearHeader();
    var thead = pagedTable.querySelectorAll("thead")[0];
    thead.appendChild(fragment);
  };

  me.animateColumns = function(backwards) {
    var thead = pagedTable.querySelectorAll("thead")[0];

    var headerOld = thead.querySelectorAll("tr")[0];
    var tbodyOld = table.querySelectorAll("tbody")[0];

    me.fitColumns(backwards);

    renderHeader(false);

    header.style.opacity = "0";
    header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)";
    header.style.transition = "transform 200ms linear, opacity 200ms";
    header.style.transitionDelay = "0";

    renderBody(false);

    if (headerOld) {
      headerOld.style.position = "absolute";
      headerOld.style.transform = "translateX(0px)";
      headerOld.style.opacity = "1";
      headerOld.style.transition = "transform 100ms linear, opacity 100ms";
      headerOld.setAttribute("class", "pagedtable-remove-head");
      if (headerOld.style.transitionEnd) {
        headerOld.addEventListener("transitionend", function() {
          var headerOldByClass = thead.querySelector(".pagedtable-remove-head");
          if (headerOldByClass) thead.removeChild(headerOldByClass);
        });
      }
      else {
        thead.removeChild(headerOld);
      }
    }

    if (tbodyOld) table.removeChild(tbodyOld);

    tbody.style.opacity = "0";
    tbody.style.transition = "transform 200ms linear, opacity 200ms";
    tbody.style.transitionDelay = "0ms";

    // force relayout
    window.getComputedStyle(header).opacity;
    window.getComputedStyle(tbody).opacity;

    if (headerOld) {
      headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)";
      headerOld.style.opacity = "0";
    }

    header.style.transform = "translateX(0px)";
    header.style.opacity = "1";

    tbody.style.opacity = "1";
  }

  me.onChange = function(callback) {
    onChangeCallbacks.push(callback);
  };

  var triggerOnChange = function() {
    onChangeCallbacks.forEach(function(onChange) {
      onChange();
    });
  };

  var clearBody = function() {
    if (tbody) {
      table.removeChild(tbody);
      tbody = null;
    }
  };

  var renderBody = function(clear) {
    cachedPagedTableClientWidth = pagedTable.clientWidth

    var fragment = document.createDocumentFragment();

    var pageData = data.slice(page.getRowStart(), page.getRowEnd());

    pageData.forEach(function(dataRow, idxRow) {
      var htmlRow = document.createElement("tr");
      htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd");

      if (columns.hasMoreLeftColumns())
        htmlRow.appendChild(document.createElement("td"));

      columns.subset.forEach(function(columnData) {
        var cellName = columnData.name;
        var dataCell = dataRow[cellName];
        var htmlCell = document.createElement("td");

        if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell");
        if (dataCell === "__NA__") dataCell = "NA";

        var cellText = document.createTextNode(dataCell);
        htmlCell.appendChild(cellText);
        if (dataCell.length > 50) {
          htmlCell.setAttribute("title", dataCell);
        }
        htmlCell.setAttribute("align", columnData.align);
        htmlCell.style.textAlign = columnData.align;
        htmlCell.style.maxWidth = maxColumnWidth(null);
        if (columnData.width) {
          htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width);
        }
        htmlRow.appendChild(htmlCell);
      });

      for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
        var paddingCol = document.createElement("td");
        paddingCol.setAttribute("class", "pagedtable-padding-col");
        htmlRow.appendChild(paddingCol);
      }

      if (columns.hasMoreRightColumns())
        htmlRow.appendChild(document.createElement("td"));

      fragment.appendChild(htmlRow);
    });

    for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) {
      var paddingRow = document.createElement("tr");

      var paddingCellRow = document.createElement("td");
      paddingCellRow.innerHTML = "&nbsp;";
      paddingCellRow.setAttribute("colspan", "100%");
      paddingRow.appendChild(paddingCellRow);

      fragment.appendChild(paddingRow);
    }

    if (typeof(clear) == "undefined" || clear) clearBody();
    tbody = document.createElement("tbody");
    tbody.appendChild(fragment);

    table.appendChild(tbody);
  };

  var getLabelInfo = function() {
    var pageStart = page.getRowStart();
    var pageEnd = page.getRowEnd();
    var totalRows = data.length;

    var totalRowsLabel = options.rows.total ? options.rows.total : totalRows;
    var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,');

    var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows";
    if (totalRows < page.rows) {
      infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : "");
    }
    if (columns.total > columns.visible) {
      var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total;

      infoText = infoText + " | " + (columns.number + 1) + "-" +
        (Math.min(columns.number + columns.visible, columns.total)) +
        " of " + totalColumnsLabel + " columns";
    }

    return infoText;
  };

  var clearFooter = function() {
    footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0];
    footer.innerHTML = "";

    return footer;
  };

  var createPageLink = function(idxPage) {
    var pageLink = document.createElement("a");
    pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index";
    pageLink.setAttribute("class", pageLinkClass);
    pageLink.setAttribute("data-page-index", idxPage);
    pageLink.onclick = function() {
      page.setPageNumber(parseInt(this.getAttribute("data-page-index")));
      renderBody();
      renderFooter();

      triggerOnChange();
    };

    pageLink.appendChild(document.createTextNode(idxPage + 1));

    return pageLink;
  }

  var renderFooter = function() {
    footer = clearFooter();

    var next = document.createElement("a");
    next.appendChild(document.createTextNode("Next"));
    next.onclick = function() {
      page.setPageNumber(page.number + 1);
      renderBody();
      renderFooter();

      triggerOnChange();
    };
    if (data.length > page.rows) footer.appendChild(next);

    var pageNumbers = document.createElement("div");
    pageNumbers.setAttribute("class", "pagedtable-indexes");

    var pageRange = page.getVisiblePageRange();

    if (pageRange.first) {
      var pageLink = createPageLink(0);
      pageNumbers.appendChild(pageLink);

      var pageSeparator = document.createElement("div");
      pageSeparator.setAttribute("class", "pagedtable-index-separator-left");
      pageSeparator.appendChild(document.createTextNode("..."))
      pageNumbers.appendChild(pageSeparator);
    }

    for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) {
      var pageLink = createPageLink(idxPage);

      pageNumbers.appendChild(pageLink);
    }

    if (pageRange.last) {
      var pageSeparator = document.createElement("div");
      pageSeparator.setAttribute("class", "pagedtable-index-separator-right");
      pageSeparator.appendChild(document.createTextNode("..."))
      pageNumbers.appendChild(pageSeparator);

      var pageLink = createPageLink(page.total - 1);
      pageNumbers.appendChild(pageLink);
    }

    if (data.length > page.rows) footer.appendChild(pageNumbers);

    var previous = document.createElement("a");
    previous.appendChild(document.createTextNode("Previous"));
    previous.onclick = function() {
      page.setPageNumber(page.number - 1);
      renderBody();
      renderFooter();

      triggerOnChange();
    };
    if (data.length > page.rows) footer.appendChild(previous);

    var infoLabel = document.createElement("div");
    infoLabel.setAttribute("class", "pagedtable-info");
    infoLabel.setAttribute("title", getLabelInfo());
    infoLabel.appendChild(document.createTextNode(getLabelInfo()));
    footer.appendChild(infoLabel);

    var enabledClass = "pagedtable-index-nav";
    var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled";
    previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass);
    next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass);
  };

  var measuresCell = null;

  var renderMeasures = function() {
    var measuresTable = document.createElement("table");
    measuresTable.style.visibility = "hidden";
    measuresTable.style.position = "absolute";
    measuresTable.style.whiteSpace = "nowrap";
    measuresTable.style.height = "auto";
    measuresTable.style.width = "auto";

    var measuresRow = document.createElement("tr");
    measuresTable.appendChild(measuresRow);

    measuresCell = document.createElement("td");
    var sampleString = "ABCDEFGHIJ0123456789";
    measuresCell.appendChild(document.createTextNode(sampleString));

    measuresRow.appendChild(measuresCell);

    tableDiv.appendChild(measuresTable);
  }

  me.init = function() {
    tableDiv = document.createElement("div");
    pagedTable.appendChild(tableDiv);
    var pagedTableClass = data.length > 0 ?
      "pagedtable pagedtable-not-empty" :
      "pagedtable pagedtable-empty";

    if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) {
      pagedTableClass = pagedTableClass + " pagedtable-empty-columns";
    }

    tableDiv.setAttribute("class", pagedTableClass);

    renderMeasures();
    measurer.calculate(measuresCell);
    columns.calculateWidths(measurer.measures);

    table = document.createElement("table");
    table.setAttribute("cellspacing", "0");
    table.setAttribute("class", "table table-condensed");
    tableDiv.appendChild(table);

    table.appendChild(document.createElement("thead"));

    var footerDiv = document.createElement("div");
    footerDiv.setAttribute("class", "pagedtable-footer");
    tableDiv.appendChild(footerDiv);

    // if the host has not yet provided horizontal space, render hidden
    if (tableDiv.clientWidth <= 0) {
      tableDiv.style.opacity = "0";
    }

    me.render();

    // retry seizing columns later if the host has not provided space
    function retryFit() {
      if (tableDiv.clientWidth <= 0) {
        setTimeout(retryFit, 100);
      } else {
        me.render();
        triggerOnChange();
      }
    }
    if (tableDiv.clientWidth <= 0) {
      retryFit();
    }
  };

  var registerWidths = function() {
    columns.subset = columns.subset.map(function(column) {
      column.width = columns.widths[column.name].inner;
      return column;
    });
  };

  var parsePadding = function(value) {
    return parseInt(value) >= 0 ? parseInt(value) : 0;
  };

  me.fixedHeight = function() {
    return options.rows.max != null;
  }

  me.fitRows = function() {
    if (me.fixedHeight())
      return;

    measurer.calculate(measuresCell);

    var rows = options.rows.min !== null ? options.rows.min : 0;
    var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0;
    var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0;

    if (pagedTable.offsetHeight > 0) {
      var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight;
      rows = Math.floor((availableHeight) / measurer.measures.height);
    }

    rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows;

    page.setRows(rows);
  }

  // The goal of this function is to add as many columns as possible
  // starting from left-to-right, when the right most limit is reached
  // it tries to add columns from the left as well.
  //
  // When startBackwards is true columns are added from right-to-left
  me.fitColumns = function(startBackwards) {
    measurer.calculate(measuresCell);
    columns.calculateWidths(measurer.measures);

    if (tableDiv.clientWidth > 0) {
      tableDiv.style.opacity = 1;
    }

    var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1;
    var columnNumber = columns.number;
    var paddingCount = 0;

    // track a list of added columns as we build the visible ones to allow us
    // to remove columns when they don't fit anymore.
    var columnHistory = [];

    var lastTableHeight = 0;
    var backwards = startBackwards;

    var tableDivStyle = window.getComputedStyle(tableDiv, null);
    var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) +
      parsePadding(tableDivStyle.paddingRight);

    var addPaddingCol = false;
    var currentWidth = 0;

    while (true) {
      columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount);
      currentWidth = columns.getWidth();

      if (tableDiv.clientWidth - tableDivPadding < currentWidth) {
        break;
      }

      columnHistory.push({
        columnNumber: columnNumber,
        visibleColumns: visibleColumns,
        paddingCount: paddingCount
      });

      if (columnHistory.length > 100) {
        console.error("More than 100 tries to fit columns, aborting");
        break;
      }

      if (columns.max !== null &&
        columns.visible + columns.getPaddingCount() >= columns.max) {
        break;
      }

      // if we run out of right-columns
      if (!backwards && columnNumber + columns.visible >= columns.total) {
        // if we started adding right-columns, try adding left-columns
        if (!startBackwards && columnNumber > 0) {
          backwards = true;
        }
        else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
          break;
        }
        else {
          paddingCount = paddingCount + 1;
        }
      }

      // if we run out of left-columns
      if (backwards && columnNumber == 0) {
        // if we started adding left-columns, try adding right-columns
        if (startBackwards && columnNumber + columns.visible < columns.total) {
          backwards = false;
        }
        else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
          break;
        }
        else {
          paddingCount = paddingCount + 1;
        }
      }

      // when moving backwards try fitting left columns first
      if (backwards && columnNumber > 0) {
        columnNumber = columnNumber - 1;
      }

      if (columnNumber + visibleColumns < columns.total) {
        visibleColumns = visibleColumns + 1;
      }
    }

    var lastRenderableColumn = {
        columnNumber: columnNumber,
        visibleColumns: visibleColumns,
        paddingCount: paddingCount
    };

    if (columnHistory.length > 0) {
      lastRenderableColumn = columnHistory[columnHistory.length - 1];
    }

    columns.setVisibleColumns(
      lastRenderableColumn.columnNumber,
      lastRenderableColumn.visibleColumns,
      lastRenderableColumn.paddingCount);

    if (pagedTable.offsetWidth > 0) {
      page.setVisiblePages(Math.max(Math.ceil(1.0 * (pagedTable.offsetWidth - 250) / 40), 2));
    }

    registerWidths();
  };

  me.fit = function(startBackwards) {
    me.fitRows();
    me.fitColumns(startBackwards);
  }

  me.render = function() {
    me.fitColumns(false);

    // render header/footer to measure height accurately
    renderHeader();
    renderFooter();

    me.fitRows();
    renderBody();

    // re-render footer to match new rows
    renderFooter();
  }

  var resizeLastWidth = -1;
  var resizeLastHeight = -1;
  var resizeNewWidth = -1;
  var resizeNewHeight = -1;
  var resizePending = false;

  me.resize = function(newWidth, newHeight) {

    function resizeDelayed() {
      resizePending = false;

      if (
        (resizeNewWidth !== resizeLastWidth) ||
        (!me.fixedHeight() && resizeNewHeight !== resizeLastHeight)
      ) {
        resizeLastWidth = resizeNewWidth;
        resizeLastHeight = resizeNewHeight;

        setTimeout(resizeDelayed, 200);
        resizePending = true;
      } else {
        me.render();
        triggerOnChange();

        resizeLastWidth = -1;
        resizeLastHeight = -1;
      }
    }

    resizeNewWidth = newWidth;
    resizeNewHeight = newHeight;

    if (!resizePending) resizeDelayed();
  };
};

var PagedTableDoc;
(function (PagedTableDoc) {
  var allPagedTables = [];

  PagedTableDoc.initAll = function() {
    allPagedTables = [];

    var pagedTables = [].slice.call(document.querySelectorAll('[data-pagedtable="false"],[data-pagedtable=""]'));
    pagedTables.forEach(function(pagedTable, idx) {
      pagedTable.setAttribute("data-pagedtable", "true");
      pagedTable.setAttribute("pagedtable-page", 0);
      pagedTable.setAttribute("class", "pagedtable-wrapper");

      var pagedTableInstance = new PagedTable(pagedTable);
      pagedTableInstance.init();

      allPagedTables.push(pagedTableInstance);
    });
  };

  PagedTableDoc.resizeAll = function() {
    allPagedTables.forEach(function(pagedTable) {
      pagedTable.render();
    });
  };

  window.addEventListener("resize", PagedTableDoc.resizeAll);

  return PagedTableDoc;
})(PagedTableDoc || (PagedTableDoc = {}));

window.onload = function() {
  PagedTableDoc.initAll();
};
"></script>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css">
div.sourceCode { overflow-x: auto; }
table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {
margin: 0; padding: 0; vertical-align: baseline; border: none; }
table.sourceCode { width: 100%; line-height: 100%; }
td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }
td.sourceCode { padding-left: 5px; }
code > span.kw { color: #007020; font-weight: bold; } /* Keyword */
code > span.dt { color: #902000; } /* DataType */
code > span.dv { color: #40a070; } /* DecVal */
code > span.bn { color: #40a070; } /* BaseN */
code > span.fl { color: #40a070; } /* Float */
code > span.ch { color: #4070a0; } /* Char */
code > span.st { color: #4070a0; } /* String */
code > span.co { color: #60a0b0; font-style: italic; } /* Comment */
code > span.ot { color: #007020; } /* Other */
code > span.al { color: #ff0000; font-weight: bold; } /* Alert */
code > span.fu { color: #06287e; } /* Function */
code > span.er { color: #ff0000; font-weight: bold; } /* Error */
code > span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
code > span.cn { color: #880000; } /* Constant */
code > span.sc { color: #4070a0; } /* SpecialChar */
code > span.vs { color: #4070a0; } /* VerbatimString */
code > span.ss { color: #bb6688; } /* SpecialString */
code > span.im { } /* Import */
code > span.va { color: #19177c; } /* Variable */
code > span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code > span.op { color: #666666; } /* Operator */
code > span.bu { } /* BuiltIn */
code > span.ex { } /* Extension */
code > span.pp { color: #bc7a00; } /* Preprocessor */
code > span.at { color: #7d9029; } /* Attribute */
code > span.do { color: #ba2121; font-style: italic; } /* Documentation */
code > span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code > span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
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NOjMKhUpWZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%2BYDIuVagZDxeFHOF1MEKbsBMEQS%2BKJjOVdXJ1BKw61EH%2BfeqSTzTz3I7ZA3Zuv%2Bwhshy3sDFL2TjctJR6n2SDsfFJ3A0I5ewXfAgugw7s%2B0XQG0SAfFVWHOEsr6TyphSHW5NHFc9J6Wa%2B7B3Dfp42HguHAUINniPlZCpQ%2Fl0CogDIrW%2F8u85iv7sGv8ZzGzYAxjwV%2FMCxTwobJQCTWU8HRPQeruaaXpRqestVdUOXso7dupeF7px4Z8%2Bed3arKFc44AIg51W9ch4kIIiUEocmSk4sBpCcj15oUDRJXYYExl37RmirrkIv55rLASYJJF%2BS3t0nopeptU%2BE%2BmLrLK%2BlPgQyid3mCBU6UP1rVz8R2n770zc%2FXf7x8s%2FNn9fvaFi3rmFHPfmMLWRP4lycho%2FjNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ%2Fhbrd7GuYBwhgAIYtbTx%2F3%2Bd4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB%2FcXYlcQshhyMsZrI6PYLWc3lOG%2FvlA4rHr%2F3uTFD3r38%2Fr%2B3fMKOke9W4oJ9G566u7au84CpOz%2Fct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY%2B0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq%2BBLL299am8j%2Bzv774zj995%2FdgTOZsOfWr3rnTWPj2h8qGbo1%2FM%2F%2FkYYvmxfms7TtPrM54E7ns4vwBw0rFy%2FaNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk%2BhMQ5oJDqhrC%2Fl%2F%2FFxmAUlGYeK5Z6Jl5MDec2yJQdc%2Bl5ViNduL1avoZ805eGll04jy6COKheT8S%2BU6kQwdw%2BlW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M%2BWgUwldSkjbL1HPLrCf51d8MHbv66zu%2FmcGl5Kz0YNZ0%2Bmcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn%2F9i8n08OU3L%2F760UX2E369YuvqVUPrI9VryFR8CXc5V%2FrYefbW7svv%2FYNdxUHv%2FOnFVQ1V8yse2Dde0UcAIY%2FzU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF%2BmpaKOYunHhldNAlZhEyFGpz4R20C%2Bc47Vmu%2B6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf%2BvWjNvnr22b%2BbsfDJR7%2Be%2BcL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg%2F48%2B8C5U0euLuu%2Ff8ozr1xteHTRssdGru8V3kwfeHTMsN937%2FzksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0%2FIwe1eO%2B%2B6%2FRVHpg2mv%2FuPbBuguPMtfKLU%2BtuXfjkIFraEVzg2tlMuZg6O57%2FvXBP1C3kZ3H9od2PPV81RMVE%2FaNAy3HEcaokRS34Ta%2BLAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL%2BY3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj%2FT6FDccpXsmn6Rjlxv%2BknyrTFMR8%2BU%2FcF9%2BDiRwh%2FUCiChwdeXD58cDhSwsRjeikNNcTo83%2F0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up%2Bg5GIBDhGEr2BkRNVlMZTa%2FP3HKVyrMMKrF3H%2FKPYUAWjlGsXaRnXrxTIhrJwqp%2FbMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ%2Baq%2FctSSXgtmD6gf2emV91%2F9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj%2BzqPXn0w4On3e9nZ%2BNJLYFZ1yqkQ2ITFEM5zzwyA%2B1KLJ1kVwpAjsvSTgx3S%2BrQQeiisxv5Ky%2B9kGbnqUmllmSFEhOP6%2FG4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH%2FudKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt%2FDeaWvZt3%2B8wA7swe6Y%2F5cvjv3I1rHJn%2BAyhLM44ODVn14%2F7bBUDpq%2Fhpxb8c388XfdM%2BrU3veu%2BTws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz%2B5W8H0OIlBsz%2FtURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9%2FgxBMbluwe4SAjxpj%2FmcgN0ef3cCt2IAhVVLsR%2F7%2BTIjjZjU9PTeY1ew4I9%2FOvhn8cCeI%2FNf9BnK2Pk3%2FkZ7TF00%2B6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi%2FHK2DL%2B4emRymUNIE3%2BRo3WokKfbtNP37Cs0%2F7rxjQ0X2Cvs2Rex%2FNNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA%2BZuAPZTBx2Az2Uo2CY%2FhIHysic%2F1z59PI%2FdU5CtWz%2BaJB9gi9gKmYebVKZgHgMq89Bc%2Br1GJWSSDAQXQoWAyS%2FreEUlCQsTeEUKRr3B03DZmUZBwxy%2F6S%2FMZmh%2BdTYZHt5OF4oH1LKc%2BeilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4%2B8%2FHaphRyV9%2Brq5lT1xe9nfPc0a2IrDuKQL%2F%2F9bve3DrL%2Fso%2FQj0kbVrGXCYuWZWXjUhzzD7xn%2F%2BD6GvYau8Q%2BZe8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG%2FQbydi5f%2FDYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15%2BJJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC%2BUjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4%2BjCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0%2B0aio0pV%2FbIp9V%2BKIgpPrUZJOFCUev%2FJSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo%2Bbh4oGrVQLPOume7Uev%2FBCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj%2BWvNn%2BtsW4%2FRVB2xkGeEk582NR%2FnE3ZMwaxy2guAqFp99FZ5bu%2BIXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9%2Bv1%2Ffvn5h%2F9Eeb77LHuYa%2B94HIt1bArbxs6yU1iIuRjEAnYqZp%2BE8erqdUBRONnA%2Bc75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW%2FmlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp%2BZp8MeAIEa%2BOqmZtns6x0xC7KTL2yZM%2BMtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf%2FDnN%2BWMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7%2FVXTzl2vfEZGRLeJB94%2Fzf4%2BLjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D%2Bo5jgx%2B6jmBbwy4BEI%2B9d3rHnZ0I%2FGN%2B7usnL1ey%2BxM389WLx%2F1%2BINHRbWXfoDLjz%2B6Z07su%2BYN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij%2F2F%2FcJfFxrfee3ld8QDzf2vsC8wo5nuaa44%2BMabh%2BghQAAA4XW1%2FpMcNqJgMuooCJQqiPLlrxWvQhjgF8%2F%2FSgXTwej3O6M%2FNmF1x8zWHdVaFh%2F5uU3bnwXkmg1yXz6aT6km%2BQwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u%2BnL9p6UYpSa0Nne7yy%2B1EQ%2F7PaW6%2Fdbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj%2FPIY0oTXmKdjuAkfHg%2F60QWROeQZnI4%2Bgq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE%2BqKL%2BGCJdcNEhlrSb%2BQ6T8%2BR887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82%2F0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B%2BdkS6svyKX6HWC0A%2Bi1c2Kd5c2XRy3h0mgYbo%2F4spg%2FKNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA%2BKF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO%2BG5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU%2FLQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ%2Bs1ZoxwDW1OMStBHU83G1fm5MZ0%2B4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz%2BRw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o%2F98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc%2FVveH0F3HR77xCrNs%2FmPDWy89tOWB3js3Y1%2Bb1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0%2BD1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv%2F3%2Bk0l%2F7GwKzGzQ6Wa811i%2FqXFjfb0wlJ1jP%2FDXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9%2B2mlNGRBlVqGU88fB%2BdM97E%2BVvGCx2CV7ht%2FhtgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi%2BJOiG7ST%2F%2Fn2W%2B%2F%2B%2BTCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA%2Frh0V%2B%2Fmfny%2B4Gv3r54%2Bi%2BfxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632%2B%2BJsUuivNbh53Kb%2Bx%2F2JYp%2Fe%2F%2B7qFl8eecf%2FzBk65bfb7WQLstc2AZl1GMH9v3fJxx%2Fp2pttp%2F%2Bc%2FeGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE%2BzrxreeANH3lObN6LH8KHopW83l9G3%2B3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn%2BQzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm%2FJekTY2T7teEU9KnHUa%2Bzj%2F8pXd%2BrzbxD1uragaVBdAqDC%2BjaAUkrJv%2FOXKcGMXmJOnbhQXF%2FF3QsHJVnf87VhB3sSqoa%2Fte5X9jf3r7FdPzMgtC%2FccNOnTtwb3ZPb6ZWdOPLzh7amPD50%2F4z8%2F1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY%2B2PS66ypEhs2ZFOn5IO08%2FZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm%2F%2BGZhgadoWD%2FjBMnyJuLfn%2Fkk%2BjrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04%2F3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv%2FeNGZnLt9g16B6h%2BaqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW%2FXTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz%2BgdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv%2Fzx9HjQmV99dDDg8e8%2BheuMZq2cnxdUBBOApeiri69x23S22xcWW02g%2FV2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ%2Bq8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf%2FwOTZcvobX5lZ3PPffii88%2F10Cy2I%2FswyeR%2FAFNmMfeZ1f%2F8rfzH545p1j5vdyW1apU%2B6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y%2BgRHrThpU8Niry7c%2BPBf%2F%2Bf7yzvryabGFc8%2B6xowcMRg1kUqqh9azT5h%2F1GcNr14%2BGTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz%2F%2BY2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf%2FfJ57P0SQsajObpM%2Fd9mHXp3YunT59birloRDO2a6z%2F9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk%2FfTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq%2Frj%2F5t8pdueG5kbsG25Hfpq50%2Bj%2Fe%2F%2BtjA%2FbXzF82%2BdmN88r%2FevSPL3Z6ftEjj7Yds%2BJ13jSzsaHnpjbt7h4Uvrdr2aAH%2ByzaXLm4R1W3O7p2KO71FCCkX%2FuG7BQrwKPWJlwu3jPioEKS1%2BC0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9%2F83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS%2F4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau%2BHAQ0ms6hHK6jhiJZl%2BNX0NFTicIYQt7ER%2B76ptuiMte%2FtYyP4oI%2F8o0cx9iPtrx6K5UpSgI%2FWinsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY%2B3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR%2FdK5QyUaz6j5l%2B4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m%2FJktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z%2FsVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS%2FtYTsWfl%2FStKu2xq3cXzuCVn9wf%2Bpn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2%2F5cz0oeZ%2FksHR0%2BTL6D5y31Q6eN685sPxrixetlPl5%2FYlJxu9AFbZRbmnpqlpTq09K3F7TdV%2FbpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58%2F49MClCxdM44aRZaRxE%2BaPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN%2BtlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx%2BZ%2F9jjp45OnvHwVFIePIvB49LSPRvZ%2ByPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS%2FXAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK%2FsMJSfuLFn%2FUKvsVinhsvqH%2FRkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1%2FXt0PNKk%2FGszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg%2B07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY%2BtHv7My8sX%2FFdifTO%2Bxlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI%2BdGD1OMS3dlORL6h%2FR%2B3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2%2B99f%2FEEzUL%2B%2F1uGTs397MxS%2B7YtDz%2FxwtzsfO%2BU4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4%2BOLx8%2BeOq7t%2FUVXVgmF14%2BYuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD%2FCHvfj7JZN%2BN2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW%2FYmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg%2B33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd%2BARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p%2FYebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK%2Fn1%2FmCviN%2Btt%2FWK6OGGznh%2Fs4t9I39VVFmLztSUlwuwZdCiRC2l%2FKk33lG0dHD%2FqprTbw5%2FZmTxqMV9Z8yYvelw%2FcCqjf%2F%2B6K9P9H9t4KLl7R%2BcvmJR99W%2Ff6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH%2BklDl%2BfctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI%2FFxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t%2F2dq9X3n575qfMjIXZI%2FQ7b%2Fu6brOGD0zj0rT%2BwD%2F%2BwB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0%2FedPztWvHjM%2BbtnB%2BHauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI%2BCBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1%2Fdk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc%2BCdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS%2Bhp0Bqm51Fnr%2BL2UjHz5YPXLwfRNx36B%2Bl3eeXrwWxYbNVy%2F8n%2BpGrtwd7tNtSfXsNFaLo9jTdPZ89ub%2FpXB47YrkEiRpzW3r%2BoJ09UfBJLnmAoG5dBi5LJ5U83Z%2F2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c%2FfPn39g4Oqa%2Bfun195VPX3qwLunC2vmH9i%2FoGZlTdOCgdOm3l0zdZoiv%2FGASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz%2B0JMt%2Bsjy645vIQH91edGont0XbPj9msiaPXiIVI2%2FNHhk35IePbMLh0yeP6V6%2FZPPA4KflKlzBqAsnGkVRaCONIPUOstxn%2FMhJ%2BnrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd%2F2PpPpA3vsPbRzw4w1sz%2F8snbmA6Or7%2Bw%2BpUPP8mXDl2wVvqx%2BwJu%2F%2FYmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6%2BLeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz%2BZ%2F0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa%2F8RJkG7MortqVTEvILI6Z9PL1rzacn%2F%2Fov0pY1S3t%2FraYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz%2B7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog%2F71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu%2BbHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2%2FhFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V%2F3qF3eu3%2FB8lN07fsKwYRMeGCZM3nHw8LPP7T%2Bw%2FTH%2Bb%2FYjjwCBau4hdsY9BF%2BZRr1AgMrEoJdu5R%2F4fBhELEUxdqM72c5aTGef1%2BIQVnvjPTGxCb3wfhzek01IufGW24c%2BAOIZzq8gnCYLACAbHrsGKMNHNDV6EPR%2FosTBA8ziYuCw7Tjs%2BThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w%2B9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9%2BdbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB%2FyIz70jFvcU%2FeGRvmVKrdoPJ0bltbq9R1v%2FYaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA%2FfAdPM%2BwIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y%2FSAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG%2FAvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC%2FhhXgUA%2BgFip9id54Z5wod3t1glmAKcgCUk%2BrogS11erXC6%2FJJ%2BWL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf%2Bbn%2F07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS%2B3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0%2B0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa%2F3fbaLyfPTsaR%2BCIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0%2Ba32f2945YHTpRoDazQHnjnES1lrm3%2BFq4%2BYgL%2Fygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu%2F9zy%2FNwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO%2FfrHjvH9%2BpMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM%2BzzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq%2FB6ei%2FV%2B5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj%2Bvm0xpaZCBL2vZIQjWCo6Q2%2F2lnOTKUqE%2F1UYJv5ZAOKb36Lxv32p%2BOTCrfUnn27ofnjujZq094yVz2TcPf%2Fv7%2B58IPi6dX3OnPyC0L3b917LZdPTcF8w%2F0mVQxcHZN%2BcTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t%2B7FP1eajFc%2FGz689fe%2BUW3xc%2FvP58whegruiOKsCNGRZehzj%2BcwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF%2BWlJTe3ghi5y4pbYNtKyK%2BAqGgV6RD66BdECyZQU%2BxzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy%2F%2BqHSkOv7bLFExMz5TiAMaaVIb%2Fwg7NmPnUc0VVb4%2Ba%2F3xO8a6Hj%2F0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY%2F%2ByddrEUqp%2Fie%2B4eMYP%2F9%2ByRWGwjyVpav5k5sXH9%2F5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU%2BJFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4%2F9rD7PafGcS1R7PsEQk1d7TaLX%2FgqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0%2B9LqSttY3QmLJw6%2F3b430QyITiIlAqxdlBMcj%2FlHpUk%2B6gRVqnV4kwil39%2Be%2FsK5T%2F9sUYXdkp9n3vr4YN77ll3OW%2Bpzc8v7NpC3vppe0vPUtC7Ev2FzR%2FcQmlWcInr25%2BcGHXgtrefZ6cNHMlm8b%2BtaaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk%2F1HquySb%2Fe8drD6PPN2z4%2Bp45Ngi%2Bd8fu35a9%2Ff4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ%2FQQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP%2Fq3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M%2Bb2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt%2BDw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs%2B5ZLXnE%2FyxtZarrfrYDqw6wr2xGWIjpKsAWu%2BI2t%2BVyXex0jOkFJfNZpfsrQMOsKeYPHqqT%2BNdjB7q5euvRZPnb3oYUWsXUUomXo%2FW9JUVbx7J4HugOKR748Sz333%2Fyd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce%2FZMxo1nF6Nmgn7Y%2FTmxejV%2BpuEyuv9TaJArLfsb%2BIw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj%2BVwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8%2BRfs09fmXXEH%2Becs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs%2FNzup6pu8B%2BROnrBae6djz2%2BInL0aAOq4Y%2Fe8%2BQDVf9G154buPm5xvWCb3mrjKRjN%2B7vp4xEwtQh3q8Y%2Ba0KbPYz19MYDO5tw1mkLIPz3985rOPP%2F10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE%2FIgzx1QzhIE0DR3nLfB89CcmUMWLuFF2u%2BWPJGTu3C%2Bt3TBoiIAgpP5iG2lhdp%2BkEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn%2Bk78TN1N5wPn%2BSzg2gC%2FnKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB%2FTOLAxZRpmn39tucP%2FKjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff%2FFEyCCePMvngykw%2BK%2FeMIh5f8VUtYgffQ49lB7%2BR0HUNTpQenhP6WBBkscHEs5y%2BQZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk%2FbGKDPGARzmLjqmfcouq%2Fe4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1%2Bx3pUgbyrhA%2FvjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe%2Fei9JQJ4ELUkjOsxJ7m6%2BQYbnXvbTY2Ow6D6FHh%2F7lTTBZZSVLOtqB8g4iCCHzeZK%2BdC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD%2FURrIP3Wr6u%2BOqQ9OmDF94qRp5JtZj%2F9u9sx5C%2Ficym8TiHvgB8gGOwAEwU4c%2FM4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ%2B16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4%2BZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb%2F%2BILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT%2FdUDsCHUKOz4AWfRHQvA065Z1snHLxtW7%2FoddaNewgZANO4LY%2Bn9OPN%2BrQSxmD80rC7ed1%2FRm9%2FpuaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y%2BEuJtrjkUwGEVlI650ylKvE%2B5ABA%2FHNTwuf9lc%2BBgItUcf0%2FAgZwQedwuks0ypTyaYjSqY%2BiqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO%2B0ESk9LSjDup6913x%2FKzVKdeX9THFGzb1v5TDDfpQ45bECoJ9%2B43cBcf0nCXXr%2FF8%2F43notvxJ6rVEnqc1TWG05X9cp%2BAAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK%2FV%2BGK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua%2FdUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n%2FqjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786%2B2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d%2FYbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS%2Fohgnt9rG%2BooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc%2F4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8%2FsMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW%2BjOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX%2BV%2F3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr%2BPdo7x4dIHTKe5DFn%2BO%2Fj%2BW2VnE3ooW6isf0LIUENvZs1gf%2FLHojJwdpplCP5gn%2F5gi26FoYa19ZVFOJ6Sxuoz%2Fq2Ti20IKVJdnqvYJwnhfPH%2F2f6YHoQF30aZaK9J8T026RxH5fA%2FWPW%2F8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM%2BhdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe%2FuYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H%2FDXNOTeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznO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Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAAADBQ8CvAAFAAgFmgUzAAABHwWaBTMAAAPRAGYB%2FAgCAgsIBgMFBAICBOAAAu9AACBbAAAAKAAAAAAxQVNDACAAIP%2F9Bh%2F%2BFACECI0CWCAAAZ8AAAAABF4FtgAAACAAA3gBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ2Bg3QYkS1m3sZ5lQAEscUDxagaG%2F29APAT5TwRIgnSJ%2Fpny%2F%2FW%2F%2Fv8P%2Fu0Bigj9C2MgC3BAqKcM3xgZGLUZLjNsYmQCsoGY4S3DfYZNDAyMIQAKyCHTAAAAeAGNVEd320YQ3oUaqwO66gUpi6wpN9K9V4QEYCquKnxvoTRA7VE5%2BZLemEvKyvkvA%2BtC%2BeRj6m9Iv0VH5%2BrMLEiml1XhzPdNn3n0rj6%2FEKn2%2FNzszO1bN29cv%2FbcdOtqGPjNxrPelcuXLl44f%2B7smdOnjh09crhe279vqrpXPuM%2BPbmzYj%2B2rVws5HMT42OjIxZnNQE8DmCkKiphIgOZtOo1EUx2%2FHotkGEMIhGAH6NTstUykExAxAKmEqSGMFl6aLn6J0svs%2FSGltwWF9lFSiEFfO1L0eMLMwrlT30ZCdgy8g2S0cMoZVRcFz1MVVStCCB8raOD2Md4abHQlM2VQr3G0kIRxSJKsF%2FeSfn%2By9wI1v7gfGqxXBmDUKdBsgy3Z1TgO64b1WvTsE36hmJNExLGmzBhQoo1Kp2ti7T2QN%2Ft2WwxPlRalsvJCwpGEvTVI4HWH0HlEByQPhx468dJ7HwFatIP4BBFvTY7zHPtt5Qcxqq2FPohw3bk1s9%2FRJI%2BMl61HzISwWoCn1UuPSfEWWsdShHqWCe9R91FKWyp01JJ3wlw3Oy2Ao74%2FXUHwrsR2HGHn4%2F6rYez12DHzPMKrGooOgki%2BHtFumcdtzK0uf1PNMOxwDhN2HVpDOs9jy2iAt0ZlemCLTr3mHfkUARWTMyDAbOrTUx3wAzdY%2BniaOaUhtHq9LIMcOLrCXQXQSSv0GKkDdt%2BcVypt1fEuSORsRUwgrZrAsamYJy8fu%2BAd0Mu2iYFhexjy9FIVLaLcxLDUJxABnH%2F97XOJAYQOOjWoewQ5hV4Pgpe0t9YkB49gh5JjAtb880y4Yi8AztlY7hdKitYm1PGpe8GO5vA4qW%2BFxwJfMosAk2X9n9X2cVVfnA36pzHNHJGbbITj75NTwpn4wQ7ySKfAu9u4kVOBVotr8LTsbMMIl4VynHBizBEJNVKBAfMNA9867j0InNX8%2BranLw2s6DOmqIHBIbDfQR%2FCiOVk4XBY4VcNSeU5YxEaGgjIEIUZOMi%2FoeJag4mEB3PUOweCaG4wwbWWAYcEMGKn9mR%2FsegY3R6zdYg2jipGKfZctzINQ%2FvxkJa9BOjR44W0OpTKAskcnjLTcKyuU%2FSVIWSKzKSHQHebYW9mfGYjfSHYfbT3%2Bv877XhsIwGzEUaleEwITyE2u%2F0q0Yfqq0%2F0dMDWuicvDanKbjsB2RY%2BTQwOnfvbMUhiNPFyDCRwhZhdjE69Ty6FjoOoeX0spZz6qKxxu%2Bed523KNd2do1fm2%2FUa6nFGqnkH8%2BkHv94bkFt2oyJj%2BfVPYtbzbgRpXuRU5uCMc%2BgFqEIGkWQQpFmUckZe2fTY6xr2FEDGH2px5nBcgOMs6WelWF2lmiKEiFjITOaMd7AehSxXIZ1DWZeymhkXmHMy3l5r2SVLSflBN1D5D5nLM%2FZRomXuZOi16yBe7yb5j0ns%2BiihRdlFbd%2FS91eUBslhm7mPyZq0MNzmezgspUUgVimQ3kn6ug48mntu3E1%2BMuBy8u4JnkZCxkvQUGuNKAoG4RfIfxKho8TPoEnyndzdO%2Fi7m8Dpwt4XrnSBvH45462t2hTEX4Bafun%2Bq8jIzK%2FAAEAAgAIAAr%2F%2FwAPeAF8egd8lFXW9zn3PmX6PNMnPZNJMRRDMkzmDYgZMRRDCEmMMUPJIgZEepHlRYyIiNhRUdYuS4ksy9reLDYsdOmLLC%2FLy7L2CgKrrCJkLt%2B9T2YyYPl%2BD8804J5zT%2Fn%2FzznPBQKbACSTvAEoqJAdtUhUJpQYjBJVAUrKSkIOJ1ZUOEKOUGkfV8ARiPB7E72m87WJZF58ibzhXPVE6QsAAnMufI4H9XXsUBh1UpOJSJLmQNWqNsasLkKhsrKnA%2FT1HCF9PQzSAPYtD5V5PW4lmFeIK86EcCRbObLp2lGjGxpH4%2Bf0wLkjjU3NDSNGxYSMxbSdDkzomhE1SypQalCISvniob1lDuTL7injC1O%2BMr%2FxmeJtxeRt%2FiJviJ8mmrjFOr0BJCZ3QAbkQFu0ypCZ45HcRqNJQkiT%2FLKsOO02s2Ryudze7CxVUnw%2Bv9%2BtmKTcgEEymzPRlgN2e5rHaeOXyeeiisnJFagMOSsqSkr45kL8Tr450SfM5%2Fy1V66pGvBwTV1BcYcDEX67QjQkbo8cigTplyVI2OHh%2F6zdXHO4%2BiR6SjoxMPzo8O21h2tPx7O2lmylNV%2FtY5Nwubj3fXUA%2F8BuFveBr74CoNB84V6pSnFCLhRCL7g7OijfR7Oy3FalR49AcXYRFBnsQUcgkAYO6H15j6wiAGu%2BI%2BAo6pleFDAWKJZMX%2BaImNunWOpiskIVH796ewAqEzvV9gqX9nQ4Qd8S%2F1V%2FScSM%2FrmsTP9FfNUNIvzuVlRPMFxY5PB6fY6iwsJw3%2FJIOOTx%2BlT%2BWzaR%2BxYWecrR7fWFFanqi%2F33nnn9%2Bv%2BMvXr7mk933%2Fv5Gy3PrN6yZjg7WFV1D5s2oGoh7nx%2Bk2vvTrkeDT0HKlieXvvakkfecj%2F5uKnhm6iNHRk27a6bevTL%2BclH3ulVkX3cBTJUXjip%2FCDvBiO4wQ95PB6qo%2Flen0%2BWTRpofo8nLa04mB3UgpeX5PbMLEzzKz4%2FtapOlXt5a1llpXhN7FF7r8zJ37o%2FiN15Q2XhvsE8RdajOqwFyrwFGETXr%2F0F9u9dNnZsWW9869X1azow9qe%2Fkpc7D52mPRf%2F%2FHcJFrR1npvf9sWX336EO7%2F9x7lqeUMn6frt8y%2B%2F%2FZD%2FJjzecOGEAnxvWdzjpTAzWtHbGjRhlhdMXqvLVZSWnl5kpSoChLJVtcwXSPea8vNLSrT0dEnTegyPaZIUqIlJLnSKhAV%2FpfBuhb9EbE53bYVIM%2F3S45hfiZ%2B7th8IFPHN5QuXcscms1vF8kiAZ2qBsEEEFQX7FnJDeNy%2B8nIF2JLZ7%2F77DPtk3rJhVV9vefPD%2B57CzCF98cr82%2Bs631s4%2FvbxrKPf1XjT0Iqrh%2F%2BuafTMxR%2B9e%2B%2BmxqZnxzzx5l8embstxo7PeX0Ju3DjoqYJA7C611hyd3hAtH%2FzpD5jAAVm4DM6Zjj5C5WIAIu9DuxCIB0kuvEBAKGBbSTz%2BL%2B3Qm7UZjaZqCSBqtrN%2BVQgmAMTua3joeaMhBTicTt9wULS8PSj5x58eNk9Z5c9RUrRiPte3MTKzvyHRd5Yh9vFygP4yq3JlfmyfHG%2Bso1LyP%2F5yqgRNVjuDPclRSGvk7Q%2B%2FejZJY89%2FOA5sTT7ifVb%2Bzru%2FOEM7tv0EisFhErSJGUpbrBBOOo3ms0ypVZUVc0umUyqilarYrDxpN1aJrKQuykJwvwz%2FyPMUOCTXSqlRa6CiEzJy8U4J8DWf%2FjpM%2FeeOMZeLMKpxYqbPTyx088Oz8MKtnMuFqefm4gzAKEZPpUqpG1g5qivGRSjkSKAxWo2giJRKOFCysqS4vjNhQXCAa4Bxz1HEI%2ByNlx0FBextqOk9SjezW49yhaIHbGzuBtOggKe1wgFWVapDCXbdSNt5ghfoNCgMxLA3X1v%2B%2BdV%2Beg%2FvIsdR9MJYWVcS5rISqDg%2BCuVQQLkSiTc7QoHPANIGq49dw6wi7GwgmvujZoUrrSRNsaMLqjsmfjnkYu4aU6SlJZ28xECNyqt0mMrM2pBricBidueiNS5iDcRA0ir4h%2By4yQgGJP%2FDwLVF05IQ%2BW9XLoPLou6LYoTFPCnGT0jYkaV2kfEaBok8y%2B1kkYCeeDQnIEyQI2nUrlDE3kkDT3PzsfZhXMoxZHGw2OmTRl7w%2BSpLeQoW8gexttwNi7C6ewO9hD7%2FusTaELr8eOAMA%2BA1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae%2F8Tw%2F1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD%2BEQLt4TkOo6CU5f1drrhvrrVly%2FdspDayfe%2B8EtQx7fuJG0HcbZLyyc1r%2B5qXbojtE1xa0dt4x%2F5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8%2Fvq4tZ%2FGilfr8pX7VqJm1EzJQGeg3j5%2FxX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j%2Fo2ykPlEmSr335KxvYPvbZydev29P65KNrX58%2Bc92zfxv6%2BKil76PnU1Sl6fe%2Bl694%2F%2FzIweMjUO1ZPnH2TU3fxqa09%2Bl%2F6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5%2B98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4%2FHXxjyKYhkRU9LgYsIJ6e%2BpgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT%2Fn6efcC%2BPLN8xActmMGOhu%2B4bH6EpsV%2FyAgOoO0n9%2F%2BHnR2B5h7hr455LAPJ1%2Bwc%2B1i1AYGhXOs6eQf4IR%2BuigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd%2Fgvy8Q3E6TmsbErv%2B%2BZ2tRuuN%2F7f1X%2BzsNyv%2FvYhoN066sbVlcRuZiq%2FiWvuP7rEb%2F7LuhyPfsFPLMffdxfMnz7%2B1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U%2Bo3%2BQo%2FHnp1ttBtL%2BihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0%2FLqoW8qk7kShFiku3kK9cfCPVHyDedt%2FqpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg%2Bflxtq7zz3ZUzXhrU%2FO6sjqN73mrbXD2iY%2FKzm89vbBp7Y%2F3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR%2BNfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP%2F3%2BJIOl3xOz3v0nmr9Y%2Bf2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r%2B83%2FH0fEE%2B3DTXbdNum1%2BHfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU%2BxG7xcf6mPNQAQX6%2FIUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK%2FVYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304%2FFkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA%2BB2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m%2F7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44%2BjrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o%2BUfT%2Fqt9NdGau79%2FZhf73%2BexCP2T2Pz%2FZefZXez6I%2FgIyv%2FEkRs7Yf3IFpM1FG27n5x%2B%2BNQ9Q%2FotPPTGQSQBH%2FPd%2F9Yf%2Fvjjne1sx152gh0p6f3eKHwYW3%2FEZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik%2BLHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI%2Ft4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn%2FC%2FfPZh2fjx7C84%2FaZ8xev2nXHraxT3vDKpkVrHaacdQ%2B%2B%2FxGdXTuy8Zr4NrZo3PgNgDCXI%2FUBnh9eKI36VZeLN%2BNWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2%2B0Iw4l2BUdoTI%2BZiikBS%2B9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U%2FHmW846xyGi3DSZ3j9azd1FvUDImwoz%2BE2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO%2FxGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt%2Br%2BzlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr%2ByVfg43vTxgZtW%2FGXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu%2FUg004jIzW%2BQJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr%2FNdmE5uRrDZG78Xj5t2EIGAOCFiawBT%2BozgRw%2BbSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS%2BtfxVS0wMJpjIcRkNiOAzUBl2cq%2FUrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6%2FHf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI%2FEnAKwL1KEcM%2FJO9nv43fpSiwh81U7%2BqQGdrQtXseFv4FZvycdQPQ8%2BVKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj%2Bkj3BXubXE3gz1vNr%2FPlDb76Bs9nSNzaSY%2BxxdivejVP5tZCj0mP%2FOYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr%2FTSzoRn%2FZLXHoEyAo4ckJSx%2Bau%2BBBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb%2FgchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe%2BLX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C%2FWaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm%2BPz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n%2BRlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq%2BuhvwVHHWrRpn%2BIvGGoVFl%2FMhDadWMcJP9LZen9cr%2Bdin7JuOx%2FZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f%2Flkz5g4srx3e8%2Borqypveeqmzf8qL%2F13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k%2BdXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6%2B6CYLnlEXbyLfYFex%2FD9zz7BAf0ztqVZ%2B7EwHn6YufCPz33%2FDraBqjXfyHBI2K%2BRonRKAOiVZYkC3BDJ%2Bq9VNpUJOaj%2BsXtVx6h57CC2dmLTMMKdPlKFXO0a4DY%2BdTwvZeN%2FqJLhrqRy8gSsx%2BT0e52yQh%2Bv2ynlszMrKwci9mcnemSzdRvt6NJiOSi%2BEtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy%2Fnm1hKoRixV%2FmpUJxwVT85trLi1EAa6twb%2BaS%2B9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG%2BooUZ3VpjZj8Izex59h6fnXg56wfNmF%2FDGMtC5Pi%2BGHyHdka%2F47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA%2FLwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m%2FRXi%2F0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt%2FZIKYnGpfnBa6sUmAHY9%2FmJhqI4S4fJ%2BQL55xoKIY%2BVYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd%2FUoR8AzFIA%2BR3QIHeUTdBWVYkSTznFd6SVJko0DW%2BxLKLeyTRZYcwiGjADQ%2FjqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl%2F%2FvMAAGKNdIQ%2BqATAwK1gBjVKRVTIdwCUpB%2FrioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ%2Fz7GYIdmjju9pfm6E8Zq6OFTovBQvLy%2FP78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az%2Fy%2BZVYSeXlcUJWdMvMcN9%2B1u8h0omny9N6YT%2BhuGr1r0xzd%2BOr%2F5xbv%2FOn7T8Y9PswO%2FX3znY5MWPHHDsNfXvfono1K6rn7f%2BK3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX%2B7YNsMT%2BhpLLZbJM2ZOJJNvaZk%2BQ5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM%2B%2BlZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf%2FBTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi%2BobTZhqFy7xwIOIyi6fTTdPt5ft4%2BoT4Q%2BecShOXlPGioU%2FBLkji3iOnVPiAnZ9vHnOw9ON%2Fmw7Jv%2B1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y%2BDMEKROSUpzOLKcJlOm3%2BOkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs%2B%2FY3zDt7LdoiJfID985vmNc3Qb61PyZM%2Bd3NmAPdGAahth3Jx%2B789Eel5%2B4rCjB7nSOkgMeuCKa7SZElSn1%2BqwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l%2B9eu3bj%2Fufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz%2Bn4pxIOWz79gRHv0LneqJs2FQ76ewKfPao%2BpSsqEvmsj%2BykQFfCF6ZeRcGFyUQK8v26El%2F4WGzqS33OfxjpXbL2ndc3sTfYvm9%2BvP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5%2Fdrrmura10otT%2FceNqZjJ5Xzew187smt%2F1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D%2FJfU5336yUOPaKqJJEuCQeFQirWX7O%2B6YxfZjqapqE%2F61bQ958LsXt8S%2F40CwpeDekav%2Fvh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ%2FZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd%2FL0JgaDT%2FNcE9PAaBctOk%2FsjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd%2BeJXN6mThXRIWPMH%2FUn%2BNdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7%2BG7krl7%2BOC8ePqbjJDCRIiEfKFykdziVfBd8q%2Bke9n%2B%2BuvnTGL7vy529F437Xwso%2FdL097ZwvbVXz9jOnlw3rz12%2BLfSS1Lh1%2B%2FurZpy%2BF4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV%2FXLRG8TS0ierkyof%2BZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu%2FxUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl%2FI6Xt6%2F70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl%2BRVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p%2FtaZv8wfaPbt58TGf7vePdSx%2Fwsv5K9SPtbB87%2FT%2Fs7H10mU722JDgM67pTN1euaIq8dIsyh%2BTpOUZ%2Bfg6PcNnz%2FZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook%2BLIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9%2B6xqU%2FbcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy%2BJIqpdWewrCD%2B6iu3%2Fsre97yvSbCP7xLR8SXyH1LKxZTYkqp%2F1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX%2BvJ2%2F2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1%2FbDrDtW395oycuG8yJd%2F3%2FXu6MZHa5Zcv2zRrf2wZn1HILfzsvKx%2Bb0rCstHz73%2B8VXN%2F8y%2F%2FJriK%2FqHR%2F%2B30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu%2BTD4aeiNN0jGwgknTn4e%2F%2FyqK4UOT%2FGc4zM%2BcENZ1E8cDrfby3t%2Fj9NoJ7JNtumyPcmJ1sVDgItr7tQYgH%2BgrxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt%2FEpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X%2Fx0SbTfiOtaElf%2FINyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin%2FpGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH%2FPGvvE03jMkcer4XnuKKI5gApOW6bWqi%2BYoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu%2FmD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy%2BDRewhcJLzK17ycs1KTC73ZrXK0koahm%2FJgob%2FpNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu%2BvcfR9RErqqfl%2Bfkfr2%2FYcZNo8ic866XXnR8Z72xNZI450HXce2MIn%2BoKqkIYDYgmvQhAm8c7YR%2FMwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6%2B3xekDI4T38Id7D54ipCHUbcnIcfn%2BuNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm%2BREWRw08Coc18VTQ4xFQ6%2BEJhDmL2m6%2Fc%2FOZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg%2BknbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl%2F3%2F2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2%2B9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG%2FAg4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv%2FRAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii%2Bnphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA%2Bn4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8%2BePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN%2BrL%2FfHcyhjZk77c8arjmflv%2FBn9kZObzbAuFFEB4A0ST%2Bd2BztZXeaidFqTfd6iV%2FzO51ado7Fn%2BavjxnT0sDFqcleG3P6QR7xs%2BNNXUfUIJTSVqjbjT%2BpBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3%2Fmvbhr%2B6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM%2FOyYp9LSycWo2xQPZ4ctYBHz%2FYyHc11H2qb9S%2BiA4oURXyC3SM%2B0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt%2BNn93Y9siC8v1T6%2BqnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo%2FiNtYyYOnz6ttm7aDBHpCoDEp%2BPghZnR%2F7I53U6Plce2UaYyMYkJqxeRED%2FHBp%2FidDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448%2FvsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw%2F0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO%2FObSe%2B8uy39mVlqEzr76oeyi%2BbG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb%2Fbpj257c%2BOS5s4tvmrim7appHXPputbn8kPlVdURssit194%2FxklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0%2FYrQaWIvIdGcQ4pBB7dzU9snCdpLZJF%2FSOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0%2F7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7%2Fc7yzPc%2BbV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul%2Bc91p4c3j55tf%2FfvilPddGFx5b8zJqf5X9DCi9v%2Fm10vvcrj6U09uHsg%2F0Ke%2F29invHSBfX7VJ%2BTAv99nwkcNvfNd82xjlI%2F4%2FSu%2BrLyi3%2FObXaPaLTJb0b6xlBfCX%2BDHKMLqgAOoieZk65HLlmXXU56PLK%2FRmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy%2FKbYvMvxsYvGRLrPu%2FBCWegef0l%2BcNcmpeGP%2FqIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI%2BK5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12%2FoayC49HL1E%2Fs9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW%2BS5kUqBYFHUgcViJEbamxB9uT%2FROLFE8QLBIegdsp5%2BnaSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h%2FcO3K%2Bs89eFTJuceP%2Bdft9utd0xUFqDpyj3kqh3K1%2BH6uhrlzX%2FZctHQEckuSNLhJG8MjPTGCNLRbwWDZH%2BFr%2F6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg%2B7W0766bZL10emd02YWJ7G%2BNFAYSwiCGdcx%2BZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2%2Fh3kG0RVlFY12UNylYiiCAo%2FgZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF%2BJnjK%2F5%2BbriipGXeqiuf3np9%2B4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo%2BIr4APJkRy7enY7QbIl%2FLTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9%2Fn1P7N541ards5oXtJ3REgwFWsOdE%2Fb9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn%2BqLI0JEDyCnCdwXdyjui3uFjR%2FVNMjMIUk6ao6YiGZWHZ0i%2FDX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR%2FdrXNlOGl7yiCq4Y2Z%2BzTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF%2Bvigy%2Byj67YoVdz%2FPrAF7Z8wo%2F9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1%2F72IW5Tjkmn%2F3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr%2F%2B2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1%2F3PjAIiZPQYprQ3RWgnr3Xd88KXuOu%2FGW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD%2BWfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw%2FxTGK3kxc9YlKPgt969IarH3%2FwwP4nFG9dY%2BPEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79%2FOej1B9UBL%2B6NL0aoFonqQehRdg4ip%2FLxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y%2BTuAYvg1fgUA6eAP5Nrjj4L8IMFW%2BuJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as%2BEb9qmXpVwDBeWUH%2BLLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6%2F%2FYLru%2Bj0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2%2F%2FheIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj%2FJFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9%2FghbKIUT%2BhoLlq5jiPvcYqZDnXNrC6WKXZGjNP8%2BVlGYAXOBfY556p5%2BZaodTT0KC89ZE%2BUXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia%2BgnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0%2BBgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9%2Bd%2BCzzzahATIdd1Du%2FG5GoOPWnR9%2BofQoyl1qHsRXeDuriLez36eUA%2BdUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL%2BuB%2BVocqr6Rhetkf5tfL7vfj3qKxH%2BSMavZf%2B%2BVuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW%2BTMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO%2FyqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1%2FZBiOG0%2BWqzyvXWXXHDpANrEwNWGNfM3DSi%2FfHYJ%2Frbsp%2B8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv%2FagMUij5c2Vc18rz5%2FNZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH%2FaTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1%2FRVfoo3fmXTau%2BubIbfnTo2vnNjQ0TVjXsWQjbb4%2BhL9FfuGvkV%2BcNqai1JldVTJn7srmu%2B7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv%2FLO%2FazH5ASbVEBr5VQ%2BUtQfAPb2jbbEazY1vfvCE6Xna%2BkHfxhi6RUj001a%2BkAasPTikemClt4lAX%2B3T%2BGCYcUDmqJ%2FlKrwqwogTCEpQjeUQBBOgS2RydU1JDM%2FP2g3GoNBuabG7%2FGMKZPlsC%2FfW50fjVVXsyDp7OxQNJZtNo6aSoF3p%2BS0NFDHPHgbYiBJgQZGv%2FERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19%2F03NdfPRtr2nHzvKc%2Femdx%2Fd3LDyM4XkaJq%2Bcfm%2FbY8bqFq1fv6FyOvX%2B1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T%2F05acLVrNap952xtZhP0xWx0%2B0iY%2BfnCrjtT1FbQ2389oqStRWanr34n%2BeflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA%2Fy5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ%2BkEY0tOTnr6qEKLpcQNmZh%2BSJ2ImdjppB56CnnSKS02%2BRpiJifBU2MEnYC8izsQ2clwI9I%2B1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5%2F0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n%2Fm5lz4fKh8pLyPrJR8gyV%2Bhdtuva4%2FMv2Lj1ih27%2Blg74MwMf2tPV9%2FaEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS%2BgUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2%2BK1tf%2FPFpXNzVaf2ojO%2BKOwcEvTpva%2FPOG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx%2FC%2BzhmCNsJ9hQWsvOLVn0n5GBm1eUrt%2FzK5jR21o%2FOiJKy9AhwzKa%2F6alefjSoYJlXV2dVyL7IwUqpp%2BQes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh%2BBQ8XJOgmziYWsLhqD%2B1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL%2BCQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf%2F%2FuZLf5xv0ulUfvGjOONEDrXMYEgzK%2BCtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc%2F0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF%2Bvs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd%2BThplmiAy%2BBMMx2M6XcOLuERnVGvx%2B3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd%2FXVrfR7t8jpaBvqQuejTSZZXeCR145%2B8%2B1PDivZbnPyN%2BhT3SphMXhgNARhQWRMoMKEHQ6%2FX19RkWu3V%2BXr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk%2BLtyhf7dl5pPfHLSgb4QUvT7nsi2%2BR%2BbhTt2fL%2BU90tDx99FwN5Pu4fbWMBnC3%2FZprdiD9%2FciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h%2FFXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU%2BdFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy%2B67Pqa0tKaiorSGvGlCzavv%2BiCDZu7ykKhsrKqKkDwa%2BHPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET%2B%2FfAk7F%2Buray7vKyjOr%2BKHAr4bGHqI3IN7%2BG5S%2BAS7SU0nbeih999Xlbp%2FqtQllG7Sj%2Fp4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L%2Bp1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl%2F%2BgzwRczUIGsiggSSZNFlkHRtI0Z%2BoT8E%2BbOoWSnwxY%2FoUzVPdILhSZyRP8ezp2Vz%2BE4SGJn%2FndpNDXwrMFMaMYjsRi%2BqN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6%2FavnXvvku37cGf9hzou3r%2FLbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v%2BAps89kZzPacGPqPmctKUf%2BVkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W%2FVN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN%2F%2FIKMjge2AeK7BW0S%2B6EYdkQaJaL3%2BXI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO%2FCtQOhvro%2FH5DgKdA420DrVfS4oWELdb%2F7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG%2Be6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT%2F%2FQ4%2BXsWgfzjp%2B0n6MTYDbdHRriMbs%2FSh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn%2B8DhpfWI1CfA%2BzWrZnHP5%2BzefKBrTh0zXKHkmuzliH39q3rwfXHT%2FUN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL%2FEV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6%2FwCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo%2FbuCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv%2Fbnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna%2FoRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN%2BU5LpOW1pswUpmU0hZj%2BTGOmWnQ2lPNyV2rEoO%2FA%2BmUw0CwATG8cNjkwyXzEYZrG9Of5NUyy%2BXBY7Q4Hm9a8tgCH%2FWU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D%2BzHRXUbqzi96bmrEQNEeim4V965jWnB%2Bho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts%2FGdwsk5fIecd011vh9O%2FOaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe%2ByRgh%2F6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui%2FGeGFqDOZLiPj7iXnpkDsKJ5%2BTwO3LmEe8JYecb2fcazoXMC%2FEd4z0J7EFS3MdH3EuPJJX07gom%2Bff4%2FDMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK%2FS1jzBBfUZbwUv5X%2F7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk%2Fown8RLYvLrQAAeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDlNxQAADArI3Ydv7Vtm3btm3btm3btm3bD7VvBoIgLXVVqCf0ztXT9dzd3j3cvcX90CN5Snmae%2Fp45np2e356gbeH94HP8Q3x3feH%2FX38NwJwoHigQ2Ba4GBQCK4NfgxVDE0OnQr7w1nCI8P7wi8jdqR4ZGzkRDQSLRmdH%2F0UqxTrEVsbux%2FPHe8b3xh%2FlgglzESJRJfE6MS6ZChZJzkj%2BRouCA9GJKQuMhI5hsZRHR2A7kZ%2FYZWxldhtPDPeFd%2BIPybyE0OIy2SIrEy2IneSX8mvFKB6UpfodPQYeiOTjmnK3GOzsCPYpexaLjdXiRvBHeJ%2B8BX5Lvxe%2FqOACmWEnsJ60SsyYjqxiLhE3CoeE6%2BLL8RvUlRqJXWThkszpJXSbjkq83JaOZ9cXm4gd5IXKZACK4qSSSmiVFWmq0lVUtOr%2BdXyagO1oxbRSM3UsmnFtOpaC62nNkqbo7M60HPppfXaemu9j77X4IwUI49RxqhrtDWOGzeM92Y985lFWWWtcdZia4d10%2FpiU3YZu6%2B91j7rME5xp5szGVAgDcgBioDhYDpYDjaDE%2BAmeAW%2Bp8R%2FA5ajfCcAAAABAAAA3QCKABYAWAAFAAIAEAAvAFwAAAEAAQsAAwABeAF9jgNuRAEYhL%2FaDGoc4DluVNtug5pr8xh7jj3jTpK18pszwBDP9NHTP0IPs1DOexlmtpz3sc9iOe9nmddyPsA8%2BXI%2BqI1COZ%2FkliIXhPkiyDo3vCnG2CaEn0%2B2lH%2BgmfIvotowZa3769ULZST4K%2BcujqTb%2Fj36S4w%2FQmgDF0tWvalemNWLX%2BKSMBvYkhQSLG2FZR%2BafmERIsqPpn7%2ByvxjfMlsTjlihz3OuZE38bTtlAAa%2FTAFAHgBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFFSzVCLEEQ7fpjH113V1ybGPd1KRyiibEhxt1vsj3ZngE9AIfgBmMR5fVk8qElsRjHOHAYW%2BQwyumxct4bKxXkWDEvx7JjdszQNAZcekzi9Zho8oV8NCbnIT%2FfEXNRJwqmlaemnQMbN8E1OE7Mzb%2FP%2F8xzKZrEMA2hl3rQATa0Uxs2bN%2B2f8M2AEpwj5yQBvklvJ3AqRcEaMKrWq%2F19eWakl7NsZbyJoNblqlZc7KywcRbRnBjc00FeF6%2Fenoi05EcG62tsXhkPcdk87BHVC%2BZXleUPrOsUHaUI2tb4y%2F8OwbsTEAJAA%3D%3D%29%20format%28%22woff%22%29%7D%2A%7Bbox%2Dsizing%3Aborder%2Dbox%7Dbody%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dfamily%3A%22Open%20Sans%22%2C%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A16px%3Bline%2Dheight%3A1%2E5%3Bcolor%3A%23606c71%7Da%7Bcolor%3A%231e6bb8%3Btext%2Ddecoration%3Anone%7Da%3Ahover%7Btext%2Ddecoration%3Aunderline%7D%2Epage%2Dheader%7Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23159957%3Bbackground%2Dimage%3Alinear%2Dgradient%28120deg%2C%23155799%2C%23159957%29%3Bpadding%3A1%2E5rem%202rem%7D%2Eproject%2Dname%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A%2E1rem%3Bfont%2Dsize%3A2rem%7D%2Eproject%2Dtagline%7Bmargin%2Dbottom%3A2rem%3Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E5rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%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</head>
<body>
<section class="page-header">
<h1 class="title toc-ignore project-name">CEO Compensation EDA</h1>
<h4 class="author project-author">Afsar Ali</h4>
</section>
<div id="TOC" class="toc">
<ul>
<li><a href="#executive-summary">Executive Summary</a><ul>
<li><a href="#age-and-gender-distribution">Age and Gender Distribution</a></li>
<li><a href="#ceo-average-compensation-by-gender-and-age-group">CEO Average Compensation by Gender and Age group</a></li>
<li><a href="#male-vs-female-average-compensation">Male Vs Female Average Compensation</a></li>
<li><a href="#average-compensation-for-both-genders">Average compensation for both genders</a></li>
<li><a href="#older-females-observed-to-have-significantly-higher-bonus">Older females observed to have significantly higher bonus</a></li>
</ul></li>
<li><a href="#final-thoughts">Final Thoughts</a></li>
</ul>
</div>
<section class="main-content">
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Course: BUAN 5210</span>
<span class="co"># Title: CEO Compensation EDA </span>
<span class="co"># Purpose: Final Project StoryTelling</span>
<span class="co"># Date: Dec 7th, 2017</span>
<span class="co"># Author: Afsar Ali</span></code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Clear working environment</span>
<span class="co">#m(list=ls(all=TRUE)) </span>
<span class="co"># The tidyverse package contains ggplot2, tibble, tidyr, readr, purr, and dplyr among others</span>
<span class="kw">library</span>(tidyverse)
<span class="co"># The gridExtra package contains grid.arrange function used to combine plots</span>
<span class="kw">library</span>(gridExtra)
<span class="co"># The GGally package contains ggpairs which is a custom correlation graph for ggplot2</span>
<span class="kw">library</span>(GGally)
<span class="co"># Load gridExtra so can plot more than one graph with grid.arrange</span>
<span class="kw">library</span>(gridExtra)
<span class="co">#load other packages</span>
<span class="kw">library</span>(psych)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co">#Load and Review data</span>
dat <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"ECON5100_project_data.csv"</span>, <span class="dt">header =</span> <span class="ot">TRUE</span>)
<span class="co">#Make a subset of relevent data</span>
dat1 <-<span class="st"> </span><span class="kw">select</span>(dat, <span class="st">"AT"</span>,<span class="st">"NI"</span>, <span class="st">"CH"</span>, <span class="st">"DLTT"</span>, <span class="st">"PRCH_C"</span>, <span class="st">"PRCL_C"</span>, <span class="st">"TDC2"</span>, <span class="st">"SALARY"</span>, <span class="st">"BONUS"</span>, <span class="st">"OTHCOMP"</span>, <span class="st">"AGE"</span>, <span class="st">"GENDER"</span>)
dat1<-<span class="st"> </span><span class="kw">mutate</span> (dat1, <span class="dt">ageg =</span> <span class="kw">cut</span>(AGE, <span class="dv">6</span>)) <span class="co">#grouping age by 6</span>
datm <-<span class="st"> </span>dat1 <span class="op">%>%</span><span class="st"> </span><span class="kw">filter</span>(GENDER <span class="op">==</span><span class="st">"MALE"</span>) <span class="co">#male only </span>
datf <-<span class="st"> </span>dat1 <span class="op">%>%</span><span class="st"> </span><span class="kw">filter</span>(GENDER <span class="op">==</span><span class="st">"FEMALE"</span>) <span class="co">#female only</span>
<span class="co">#attach the file for use</span>
<span class="kw">attach</span>(dat1)</code></pre></div>
<div id="executive-summary" class="section level2">
<h2>Executive Summary</h2>
<p>The focus of this analysis is to determine compensation of Chief Executive Officers (CEOs) in US publicly traded companies by age and gender. The data set is consisted of 114 factors (columns) and 8300 data points (rows). Following are the initial exploratory data analysis.</p>
<div id="age-and-gender-distribution" class="section level3">
<h3>Age and Gender Distribution</h3>
<p>Let’s explore Salary and Gender Distribution by Age</p>
<ul>
<li>The CEO Compensation Data shows:
<ul>
<li>93% of the CEOs are middle-aged men</li>
<li>Only 508 Female CEOs</li>
</ul></li>
</ul>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">grid.arrange</span>(
<span class="kw">ggplot</span>(dat, <span class="kw">aes</span>(<span class="dt">x =</span> AGE, <span class="dt">y =</span> SALARY, <span class="dt">color =</span> GENDER)) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span>
<span class="st"> </span><span class="kw">stat_density2d</span>() <span class="op">+</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Mostly Middle aged Men"</span>, <span class="dt">sub =</span> <span class="st">"Small number of Female observations"</span>),
<span class="kw">ggplot</span>(dat, <span class="kw">aes</span>(<span class="dt">x =</span> AGE, <span class="dt">y =</span> SALARY, <span class="dt">color =</span> GENDER)) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span>
<span class="st"> </span><span class="kw">stat_density2d</span>(<span class="kw">aes</span>(<span class="dt">fill =</span>..density..), <span class="dt">geom =</span> <span class="st">"raster"</span>, <span class="dt">contour =</span> <span class="ot">FALSE</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Mostly Middle aged Men"</span>, <span class="dt">sub =</span> <span class="st">"Heat map, light color indicates more observations"</span>),
<span class="dt">ncol =</span> <span class="dv">2</span>
)</code></pre></div>
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" /><!-- --></p>
</div>
<div id="ceo-average-compensation-by-gender-and-age-group" class="section level3">
<h3>CEO Average Compensation by Gender and Age group</h3>
<ul>
<li>Male CEO has larger outliers</li>
<li>Average female compensation seems lower than men</li>
</ul>
<p>Let’s look closely to find deeper insight</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">ggplot</span>(dat1, <span class="kw">aes</span>(ageg, TDC2, <span class="dt">fill=</span> GENDER)) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_boxplot</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.text.x =</span> <span class="kw">element_text</span>(<span class="dt">angle=</span><span class="dv">65</span>, <span class="dt">vjust=</span><span class="fl">0.6</span>)) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">labs</span>(<span class="dt">fill =</span> <span class="st">"Gender"</span>) <span class="op">+</span>
<span class="co"># Add and improve titles and labels</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Average female compensation seems lower than men"</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Average Compensation by Thousands"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Age Group"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">labels=</span><span class="kw">c</span>(<span class="st">"Under 40"</span>, <span class="st">"40 to 51"</span>, <span class="st">"51 to 62"</span>, <span class="st">"62 to 73"</span>, <span class="st">"73 to 84"</span>, <span class="st">"84 +"</span>, <span class="st">"NA"</span>)) <span class="op">+</span>
<span class="st"> </span><span class="kw">ylim</span>(<span class="op">-</span><span class="dv">5000</span>, <span class="dv">30000</span>)</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="male-vs-female-average-compensation" class="section level3">
<h3>Male Vs Female Average Compensation</h3>
<p>Let’s look at the difference in compensation by Gender</p>
<ul>
<li>Older females has higher compensation although they account for very small share of our observation</li>
<li>Middle age males has higher compensation</li>
</ul>
<p>Why are older females observed to have higher compensation?</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">grid.arrange</span>(
<span class="co">#Male Data</span>
datf <span class="op">%>%</span>
<span class="st"> </span><span class="kw">group_by</span>(ageg) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">Avg_Com =</span> <span class="kw">mean</span>(TDC2)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> ageg, <span class="dt">y =</span> Avg_Com, )) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat=</span><span class="st">"identity"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>, <span class="dt">color =</span> <span class="st">"black"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">labs</span>(<span class="dt">fill =</span> <span class="st">"Gender"</span>) <span class="op">+</span>
<span class="co"># Add and improve titles and labels</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Older female higher compensation"</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Average Compensation by Thousands"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Female Age Group"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">labels=</span><span class="kw">c</span>(<span class="st">"Under 40"</span>, <span class="st">"40 to 51"</span>, <span class="st">"51 to 62"</span>, <span class="st">"62 to 73"</span>, <span class="st">"73 to 84"</span>, <span class="st">"84 +"</span>, <span class="st">"NA"</span>)) <span class="op">+</span>
<span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>, <span class="dv">6000</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.ticks.x =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.ticks.y =</span> <span class="kw">element_blank</span>(),),
<span class="co">#Female Data</span>
datm <span class="op">%>%</span>
<span class="st"> </span><span class="kw">group_by</span>(ageg) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">Avg_Com =</span> <span class="kw">mean</span>(TDC2)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> ageg, <span class="dt">y =</span> Avg_Com, )) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat=</span><span class="st">"identity"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>, <span class="dt">color =</span> <span class="st">"black"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">labs</span>(<span class="dt">fill =</span> <span class="st">"Gender"</span>) <span class="op">+</span>
<span class="co"># Add and improve titles and labels</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Middle-age males higher compensation"</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Average Compensation by Thousands"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Male Age Group"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">labels=</span><span class="kw">c</span>(<span class="st">"Under 40"</span>, <span class="st">"40 to 51"</span>, <span class="st">"51 to 62"</span>, <span class="st">"62 to 73"</span>, <span class="st">"73 to 84"</span>, <span class="st">"84 +"</span>, <span class="st">"NA"</span>)) <span class="op">+</span>
<span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>, <span class="dv">6000</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>( <span class="dt">axis.ticks.x =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.title.y =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.text.y =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.ticks.y =</span> <span class="kw">element_blank</span>()),
<span class="co"># panel.background = element_blank()),</span>
<span class="dt">ncol =</span> <span class="dv">2</span>
)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="average-compensation-for-both-genders" class="section level3">
<h3>Average compensation for both genders</h3>
<p>Let’s compare total compensation side by side<br />
+ There are no female executives over 84 + Female age group has larger growth from 62-73 to 73-84</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Average profit by region and category </span>
dat1 <span class="op">%>%</span>
<span class="st"> </span><span class="kw">group_by</span>(ageg, GENDER) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">Avg_Com =</span> <span class="kw">mean</span>(TDC2)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> ageg, <span class="dt">y =</span> Avg_Com, <span class="dt">fill =</span> <span class="kw">reorder</span>(GENDER, <span class="kw">desc</span>(Avg_Com)))) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat=</span><span class="st">"identity"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>, <span class="dt">color =</span> <span class="st">"black"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_hline</span>(<span class="dt">yintercept =</span> <span class="dv">0</span>, <span class="dt">color =</span> <span class="st">"black"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">labs</span>(<span class="dt">fill =</span> <span class="st">"Gender"</span>) <span class="op">+</span>
<span class="co"># Add and improve titles and labels</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Middle age female age groups has a high compensation"</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Average Compensation by Thousands"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Age Group"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">labels=</span><span class="kw">c</span>(<span class="st">"Under 40"</span>, <span class="st">"40 to 51"</span>, <span class="st">"51 to 62"</span>, <span class="st">"62 to 73"</span>, <span class="st">"73 to 84"</span>, <span class="st">"84 +"</span>, <span class="st">"NA"</span>)) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"blue"</span>, <span class="st">"grey70"</span>))<span class="op">+</span>
<span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.ticks.x =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.ticks.y =</span> <span class="kw">element_blank</span>(),)</code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="older-females-observed-to-have-significantly-higher-bonus" class="section level3">
<h3>Older females observed to have significantly higher bonus</h3>
<ul>
<li>Older females receive high bonus compared to males and other females</li>
<li>Older females high bonus makes the largest impact on why older females observed to have higher compensation.</li>
</ul>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">grid.arrange</span>(
<span class="co">#Male Data</span>
datf <span class="op">%>%</span>
<span class="st"> </span><span class="kw">group_by</span>(ageg) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">Avg_Com =</span> <span class="kw">mean</span>(BONUS)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> ageg, <span class="dt">y =</span> Avg_Com, )) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat=</span><span class="st">"identity"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>, <span class="dt">color =</span> <span class="st">"black"</span>) <span class="op">+</span>
<span class="co"># Add and improve titles and labels</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Female 73 to 84 gets a large Bonus"</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Average Bonus by Thousands"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Female Age Group"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">labels=</span><span class="kw">c</span>(<span class="st">"Under 40"</span>, <span class="st">"40 to 51"</span>, <span class="st">"51 to 62"</span>, <span class="st">"62 to 73"</span>, <span class="st">"73 to 84"</span>, <span class="st">"84 +"</span>, <span class="st">"NA"</span>)) <span class="op">+</span>
<span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>, <span class="dv">2000</span>) <span class="op">+</span>
<span class="st"> </span>
<span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>(<span class="dt">axis.ticks.x =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.ticks.y =</span> <span class="kw">element_blank</span>(),),
<span class="co">#Female Data</span>
datm <span class="op">%>%</span>
<span class="st"> </span><span class="kw">group_by</span>(ageg) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">summarise</span>(<span class="dt">Avg_Com =</span> <span class="kw">mean</span>(BONUS)) <span class="op">%>%</span>
<span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> ageg, <span class="dt">y =</span> Avg_Com, )) <span class="op">+</span>
<span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat=</span><span class="st">"identity"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>, <span class="dt">color =</span> <span class="st">"black"</span>) <span class="op">+</span>
<span class="co"># Add and improve titles and labels</span>
<span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Male Bonus similar accross age group"</span>) <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Average Compensation by Thousands"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Male Age Group"</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">scale_x_discrete</span>(<span class="dt">labels=</span><span class="kw">c</span>(<span class="st">"Under 40"</span>, <span class="st">"40 to 51"</span>, <span class="st">"51 to 62"</span>, <span class="st">"62 to 73"</span>, <span class="st">"73 to 84"</span>, <span class="st">"84 +"</span>, <span class="st">"NA"</span>)) <span class="op">+</span>
<span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>, <span class="dv">2000</span>) <span class="op">+</span>
<span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span><span class="st"> </span>
<span class="st"> </span><span class="kw">theme</span>( <span class="dt">axis.ticks.x =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.title.y =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.text.y =</span> <span class="kw">element_blank</span>(),
<span class="dt">axis.ticks.y =</span> <span class="kw">element_blank</span>()),
<span class="co"># panel.background = element_blank()),</span>
<span class="dt">ncol =</span> <span class="dv">2</span>
)</code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="final-thoughts" class="section level2">
<h2>Final Thoughts</h2>
<p>Analysis of Chief Executive Officers (CEOs) compensation seems to show us that Males and Females has different average compensation distribution. Older females having higher average compensation and Males having a normal distribution, due to higher bonus observed for older females</p>
</div>
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