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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="Team K - Zichen (Zoe) Huang, Jiaying (Claire) Wu, Chen (Cici) Chen" />
<title>Advertising Response Measurement - Final Report</title>
<script src="data:application/x-javascript;base64,(function() {
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<link href="data:text/css;charset=utf-8,%2Edt%2Dcrosstalk%2Dfade%20%7B%0Aopacity%3A%200%2E2%3B%0A%7D%0Ahtml%20body%20div%2EDTS%20div%2EdataTables%5FscrollBody%20%7B%0Abackground%3A%20none%3B%0A%7D%0A" rel="stylesheet" />
<script src="data:application/x-javascript;base64,(function() {

// some helper functions: using a global object DTWidget so that it can be used
// in JS() code, e.g. datatable(options = list(foo = JS('code'))); unlike R's
// dynamic scoping, when 'code' is eval()'ed, JavaScript does not know objects
// from the "parent frame", e.g. JS('DTWidget') will not work unless it was made
// a global object
var DTWidget = {};

// 123456666.7890 -> 123,456,666.7890
var markInterval = function(d, digits, interval, mark, decMark, precision) {
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  if (!/^-?[\d.]+$/.test(x)) return x;
  var xv = x.split('.');
  if (xv.length > 2) return x;  // should have at most one decimal point
  xv[0] = xv[0].replace(new RegExp('\\B(?=(\\d{' + interval + '})+(?!\\d))', 'g'), mark);
  return xv.join(decMark);
};

DTWidget.formatCurrency = function(thiz, row, data, col, currency, digits, interval, mark, decMark, before) {
  var d = parseFloat(data[col]);
  if (isNaN(d)) return;
  var res = markInterval(d, digits, interval, mark, decMark);
  res = before ? (/^-/.test(res) ? '-' + currency + res.replace(/^-/, '') : currency + res) :
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  $(thiz.api().cell(row, col).node()).html(res);
};

DTWidget.formatString = function(thiz, row, data, col, prefix, suffix) {
  var d = data[col];
  if (d === null) return;
  var cell = $(thiz.api().cell(row, col).node());
  cell.html(prefix + cell.html() + suffix);
};

DTWidget.formatPercentage = function(thiz, row, data, col, digits, interval, mark, decMark) {
  var d = parseFloat(data[col]);
  if (isNaN(d)) return;
  $(thiz.api().cell(row, col).node())
  .html(markInterval(d * 100, digits, interval, mark, decMark) + '%');
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DTWidget.formatRound = function(thiz, row, data, col, digits, interval, mark, decMark) {
  var d = parseFloat(data[col]);
  if (isNaN(d)) return;
  $(thiz.api().cell(row, col).node()).html(markInterval(d, digits, interval, mark, decMark));
};

DTWidget.formatSignif = function(thiz, row, data, col, digits, interval, mark, decMark) {
  var d = parseFloat(data[col]);
  if (isNaN(d)) return;
  $(thiz.api().cell(row, col).node())
    .html(markInterval(d, digits, interval, mark, decMark, true));
};

DTWidget.formatDate = function(thiz, row, data, col, method, params) {
  var d = data[col];
  if (d === null) return;
  // (new Date('2015-10-28')).toDateString() may return 2015-10-27 because the
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  // i.e. the date-only string is treated as UTC time instead of local time
  if ((method === 'toDateString' || method === 'toLocaleDateString') && /^\d{4,}\D\d{2}\D\d{2}$/.test(d)) {
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    d = new Date(d[0], d[1] - 1, d[2]);
  } else {
    d = new Date(d);
  }
  $(thiz.api().cell(row, col).node()).html(d[method].apply(d, params));
};

window.DTWidget = DTWidget;

var transposeArray2D = function(a) {
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};

var crosstalkPluginsInstalled = false;

function maybeInstallCrosstalkPlugins() {
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  crosstalkPluginsInstalled = true;

  $.fn.dataTable.ext.afnFiltering.push(
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        return false;

      var ctselect = oSettings.nTable.ctselect;
      if (ctselect && !ctselect[iDataIndex])
        return false;

      return true;
    }
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}

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  type: "output",
  renderOnNullValue: true,
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      data: null,
      ctfilterHandle: new crosstalk.FilterHandle(),
      ctfilterSubscription: null,
      ctselectHandle: new crosstalk.SelectionHandle(),
      ctselectSubscription: null
    };
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  renderValue: function(el, data, instance) {
    if (el.offsetWidth === 0 || el.offsetHeight === 0) {
      instance.data = data;
      return;
    }
    instance.data = null;
    var $el = $(el);
    $el.empty();

    if (data === null) {
      $el.append('&nbsp;');
      // clear previous Shiny inputs (if any)
      for (var i in instance.clearInputs) instance.clearInputs[i]();
      instance.clearInputs = {};
      return;
    }

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      instance.ctselectHandle.setGroup(crosstalkOptions.group);
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    // If we are in a flexdashboard scroll layout then we:
    //  (a) Always want to use pagination (otherwise we'll have
    //      a "double scroll bar" effect on the phone); and
    //  (b) Never want to fill the container (we want the pagination
    //      level to determine the size of the container)
    if (window.FlexDashboard && !window.FlexDashboard.isFillPage()) {
      data.options.bPaginate = true;
      data.fillContainer = false;
    }

    // if we are in the viewer then we always want to fillContainer and
    // and autoHideNavigation (unless the user has explicitly set these)
    if (window.HTMLWidgets.viewerMode) {
      if (!data.hasOwnProperty("fillContainer"))
        data.fillContainer = true;
      if (!data.hasOwnProperty("autoHideNavigation"))
        data.autoHideNavigation = true;
    }

    // propagate fillContainer to instance (so we have it in resize)
    instance.fillContainer = data.fillContainer;

    var cells = data.data;

    if (cells instanceof Array) cells = transposeArray2D(cells);

    $el.append(data.container);
    var $table = $el.find('table');
    if (data.class) $table.addClass(data.class);
    if (data.caption) $table.prepend(data.caption);

    if (!data.selection) data.selection = {
      mode: 'none', selected: null, target: 'row'
    };
    if (HTMLWidgets.shinyMode && data.selection.mode !== 'none' &&
        data.selection.target === 'row+column') {
      if ($table.children('tfoot').length === 0) {
        $table.append($('<tfoot>'));
        $table.find('thead tr').clone().appendTo($table.find('tfoot'));
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    }

    // column filters
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    switch (data.filter) {
      case 'top':
        $table.children('thead').append(data.filterHTML);
        filterRow = $table.find('thead tr:last td');
        break;
      case 'bottom':
        if ($table.children('tfoot').length === 0) {
          $table.append($('<tfoot>'));
        }
        $table.children('tfoot').prepend(data.filterHTML);
        filterRow = $table.find('tfoot tr:first td');
        break;
    }

    var options = { searchDelay: 1000 };
    if (cells !== null) $.extend(options, {
      data: cells
    });

    // options for fillContainer
    var bootstrapActive = typeof($.fn.popover) != 'undefined';
    if (instance.fillContainer) {

      // force scrollX/scrollY and turn off autoWidth
      options.scrollX = true;
      options.scrollY = "100px"; // can be any value, we'll adjust below

      // if we aren't paginating then move around the info/filter controls
      // to save space at the bottom and rephrase the info callback
      if (data.options.bPaginate === false) {

        // we know how to do this cleanly for bootstrap, not so much
        // for other themes/layouts
        if (bootstrapActive) {
          options.dom = "<'row'<'col-sm-4'i><'col-sm-8'f>>" +
                        "<'row'<'col-sm-12'tr>>";
        }

        options.fnInfoCallback = function(oSettings, iStart, iEnd,
                                           iMax, iTotal, sPre) {
          return Number(iTotal).toLocaleString() + " records";
        };
      }
    }

    // auto hide navigation if requested
    if (data.autoHideNavigation === true) {
      if (bootstrapActive && data.options.bPaginate !== false) {
        // strip all nav if length >= cells
        if ((cells instanceof Array) && data.options.iDisplayLength >= cells.length)
          options.dom = "<'row'<'col-sm-12'tr>>";
        // alternatively lean things out for flexdashboard mobile portrait
        else if (window.FlexDashboard && window.FlexDashboard.isMobilePhone())
          options.dom = "<'row'<'col-sm-12'f>>" +
                        "<'row'<'col-sm-12'tr>>"  +
                        "<'row'<'col-sm-12'p>>";
      }
    }

    $.extend(true, options, data.options || {});

    var searchCols = options.searchCols;
    if (searchCols) {
      searchCols = searchCols.map(function(x) {
        return x === null ? '' : x.search;
      });
      // FIXME: this means I don't respect the escapeRegex setting
      delete options.searchCols;
    }

    // server-side processing?
    var server = options.serverSide === true;

    // use the dataSrc function to pre-process JSON data returned from R
    var DT_rows_all = [], DT_rows_current = [];
    if (server && HTMLWidgets.shinyMode && typeof options.ajax === 'object' &&
        /^session\/[\da-z]+\/dataobj/.test(options.ajax.url) && !options.ajax.dataSrc) {
      options.ajax.dataSrc = function(json) {
        DT_rows_all = $.makeArray(json.DT_rows_all);
        DT_rows_current = $.makeArray(json.DT_rows_current);
        var data = json.data;
        if (!colReorderEnabled()) return data;
        var table = $table.DataTable(), order = table.colReorder.order(), flag = true, i, j, row;
        for (i = 0; i < order.length; ++i) if (order[i] !== i) flag = false;
        if (flag) return data;
        for (i = 0; i < data.length; ++i) {
          row = data[i].slice();
          for (j = 0; j < order.length; ++j) data[i][j] = row[order[j]];
        }
        return data;
      };
    }

    var thiz = this;
    if (instance.fillContainer) $table.on('init.dt', function(e) {
      thiz.fillAvailableHeight(el, $(el).innerHeight());
    });
    // If the page contains serveral datatables and one of which enables colReorder,
    // the table.colReorder.order() function will exist but throws error when called.
    // So it seems like the only way to know if colReorder is enabled or not is to
    // check the options.
    var colReorderEnabled = function() { return "colReorder" in options; };
    var table = $table.DataTable(options);
    $el.data('datatable', table);

    // Unregister previous Crosstalk event subscriptions, if they exist
    if (instance.ctfilterSubscription) {
      instance.ctfilterHandle.off("change", instance.ctfilterSubscription);
      instance.ctfilterSubscription = null;
    }
    if (instance.ctselectSubscription) {
      instance.ctselectHandle.off("change", instance.ctselectSubscription);
      instance.ctselectSubscription = null;
    }

    if (!crosstalkOptions.group) {
      $table[0].ctfilter = null;
      $table[0].ctselect = null;
    } else {
      var key = crosstalkOptions.key;
      function keysToMatches(keys) {
        if (!keys) {
          return null;
        } else {
          var selectedKeys = {};
          for (var i = 0; i < keys.length; i++) {
            selectedKeys[keys[i]] = true;
          }
          var matches = {};
          for (var j = 0; j < key.length; j++) {
            if (selectedKeys[key[j]])
              matches[j] = true;
          }
          return matches;
        }
      }

      function applyCrosstalkFilter(e) {
        $table[0].ctfilter = keysToMatches(e.value);
        table.draw();
      }
      instance.ctfilterSubscription = instance.ctfilterHandle.on("change", applyCrosstalkFilter);
      applyCrosstalkFilter({value: instance.ctfilterHandle.filteredKeys});

      function applyCrosstalkSelection(e) {
        if (e.sender !== instance.ctselectHandle) {
          table
            .rows('.' + selClass, {search: 'applied'})
            .nodes()
            .to$()
            .removeClass(selClass);
          if (selectedRows)
            changeInput('rows_selected', selectedRows(), void 0, true);
        }

        if (e.sender !== instance.ctselectHandle && e.value && e.value.length) {
          var matches = keysToMatches(e.value);

          // persistent selection with plotly (& leaflet)
          var ctOpts = crosstalk.var("plotlyCrosstalkOpts").get() || {};
          if (ctOpts.persistent === true) {
            var matches = $.extend(matches, $table[0].ctselect);
          }

          $table[0].ctselect = matches;
          table.draw();
        } else {
          if ($table[0].ctselect) {
            $table[0].ctselect = null;
            table.draw();
          }
        }
      }
      instance.ctselectSubscription = instance.ctselectHandle.on("change", applyCrosstalkSelection);
      // TODO: This next line doesn't seem to work when renderDataTable is used
      applyCrosstalkSelection({value: instance.ctselectHandle.value});
    }

    var inArray = function(val, array) {
      return $.inArray(val, $.makeArray(array)) > -1;
    };

    // encode + to %2B when searching in the table on server side, because
    // shiny::parseQueryString() treats + as spaces, and DataTables does not
    // encode + to %2B (or % to %25) when sending the request
    var encode_plus = function(x) {
      return server ? x.replace(/%/g, '%25').replace(/\+/g, '%2B') : x;
    };

    // search the i-th column
    var searchColumn = function(i, value) {
      var regex = false, ci = true;
      if (options.search) {
        regex = options.search.regex,
        ci = options.search.caseInsensitive !== false;
      }
      return table.column(i).search(encode_plus(value), regex, !regex, ci);
    };

    if (data.filter !== 'none') {

      filterRow.each(function(i, td) {

        var $td = $(td), type = $td.data('type'), filter;
        var $input = $td.children('div').first().children('input');
        $input.prop('disabled', !table.settings()[0].aoColumns[i].bSearchable || type === 'disabled');
        $input.on('input blur', function() {
          $input.next('span').toggle(Boolean($input.val()));
        });
        // Bootstrap sets pointer-events to none and we won't be able to click
        // the clear button
        $input.next('span').css('pointer-events', 'auto').hide().click(function() {
          $(this).hide().prev('input').val('').trigger('input').focus();
        });
        var searchCol;  // search string for this column
        if (searchCols && searchCols[i]) {
          searchCol = searchCols[i];
          $input.val(searchCol).trigger('input');
        }
        var $x = $td.children('div').last();

        // remove the overflow: hidden attribute of the scrollHead
        // (otherwise the scrolling table body obscures the filters)
        var scrollHead = $(el).find('.dataTables_scrollHead,.dataTables_scrollFoot');
        var cssOverflow = scrollHead.css('overflow');
        if (cssOverflow === 'hidden') {
          $x.on('show hide', function(e) {
            scrollHead.css('overflow', e.type === 'show' ? '' : cssOverflow);
          });
          $x.css('z-index', 25);
        }

        if (inArray(type, ['factor', 'logical'])) {
          $input.on({
            click: function() {
              $input.parent().hide(); $x.show().trigger('show'); filter[0].selectize.focus();
            },
            input: function() {
              if ($input.val() === '') filter[0].selectize.setValue([]);
            }
          });
          var $input2 = $x.children('select');
          filter = $input2.selectize({
            options: $input2.data('options').map(function(v, i) {
              return ({text: v, value: v});
            }),
            plugins: ['remove_button'],
            hideSelected: true,
            onChange: function(value) {
              if (value === null) value = []; // compatibility with jQuery 3.0
              $input.val(value.length ? JSON.stringify(value) : '');
              if (value.length) $input.trigger('input');
              $input.attr('title', $input.val());
              if (server) {
                table.column(i).search(value.length ? encode_plus(JSON.stringify(value)) : '').draw();
                return;
              }
              // turn off filter if nothing selected
              $td.data('filter', value.length > 0);
              table.draw();  // redraw table, and filters will be applied
            }
          });
          if (searchCol) filter[0].selectize.setValue(JSON.parse(searchCol));
          filter[0].selectize.on('blur', function() {
            $x.hide().trigger('hide'); $input.parent().show(); $input.trigger('blur');
          });
          filter.next('div').css('margin-bottom', 'auto');
        } else if (type === 'character') {
          var fun = function() {
            searchColumn(i, $input.val()).draw();
          };
          if (server) {
            fun = $.fn.dataTable.util.throttle(fun, options.searchDelay);
          }
          $input.on('input', fun);
        } else if (inArray(type, ['number', 'integer', 'date', 'time'])) {
          var $x0 = $x;
          $x = $x0.children('div').first();
          $x0.css({
            'background-color': '#fff',
            'border': '1px #ddd solid',
            'border-radius': '4px',
            'padding': '20px 20px 10px 20px'
          });
          var $spans = $x0.children('span').css({
            'margin-top': '10px',
            'white-space': 'nowrap'
          });
          var $span1 = $spans.first(), $span2 = $spans.last();
          var r1 = +$x.data('min'), r2 = +$x.data('max');
          // when the numbers are too small or have many decimal places, the
          // slider may have numeric precision problems (#150)
          var scale = Math.pow(10, Math.max(0, +$x.data('scale') || 0));
          r1 = Math.round(r1 * scale); r2 = Math.round(r2 * scale);
          var scaleBack = function(x, scale) {
            if (scale === 1) return x;
            var d = Math.round(Math.log(scale) / Math.log(10));
            // to avoid problems like 3.423/100 -> 0.034230000000000003
            return (x / scale).toFixed(d);
          };
          $input.on({
            focus: function() {
              $x0.show().trigger('show');
              // first, make sure the slider div leaves at least 20px between
              // the two (slider value) span's
              $x0.width(Math.max(160, $span1.outerWidth() + $span2.outerWidth() + 20));
              // then, if the input is really wide, make the slider the same
              // width as the input
              if ($x0.outerWidth() < $input.outerWidth()) {
                $x0.outerWidth($input.outerWidth());
              }
              // make sure the slider div does not reach beyond the right margin
              if ($(window).width() < $x0.offset().left + $x0.width()) {
                $x0.offset({
                  'left': $input.offset().left + $input.outerWidth() - $x0.outerWidth()
                });
              }
            },
            blur: function() {
              $x0.hide().trigger('hide');
            },
            input: function() {
              if ($input.val() === '') filter.val([r1, r2]);
            },
            change: function() {
              var v = $input.val().replace(/\s/g, '');
              if (v === '') return;
              v = v.split('...');
              if (v.length !== 2) {
                $input.parent().addClass('has-error');
                return;
              }
              if (v[0] === '') v[0] = r1;
              if (v[1] === '') v[1] = r2;
              $input.parent().removeClass('has-error');
              // treat date as UTC time at midnight
              var strTime = function(x) {
                var s = type === 'date' ? 'T00:00:00Z' : '';
                var t = new Date(x + s).getTime();
                // add 10 minutes to date since it does not hurt the date, and
                // it helps avoid the tricky floating point arithmetic problems,
                // e.g. sometimes the date may be a few milliseconds earlier
                // than the midnight due to precision problems in noUiSlider
                return type === 'date' ? t + 3600000 : t;
              };
              if (inArray(type, ['date', 'time'])) {
                v[0] = strTime(v[0]);
                v[1] = strTime(v[1]);
              }
              if (v[0] != r1) v[0] *= scale;
              if (v[1] != r2) v[1] *= scale;
              filter.val(v);
            }
          });
          var formatDate = function(d, isoFmt) {
            d = scaleBack(d, scale);
            if (type === 'number') return d;
            if (type === 'integer') return parseInt(d);
            var x = new Date(+d);
            var fmt = ('filterDateFmt' in data) ? data.filterDateFmt[i] : undefined;
            if (fmt !== undefined && isoFmt === false) return x[fmt.method].apply(x, fmt.params);
            if (type === 'date') {
              var pad0 = function(x) {
                return ('0' + x).substr(-2, 2);
              };
              return x.getUTCFullYear() + '-' + pad0(1 + x.getUTCMonth())
                      + '-' + pad0(x.getUTCDate());
            } else {
              return x.toISOString();
            }
          };
          var opts = type === 'date' ? { step: 60 * 60 * 1000 } :
                     type === 'integer' ? { step: 1 } : {};
          filter = $x.noUiSlider($.extend({
            start: [r1, r2],
            range: {min: r1, max: r2},
            connect: true
          }, opts));
          if (scale > 1) (function() {
            var t1 = r1, t2 = r2;
            var val = filter.val();
            while (val[0] > r1 || val[1] < r2) {
              if (val[0] > r1) {
                t1 -= val[0] - r1;
              }
              if (val[1] < r2) {
                t2 += r2 - val[1];
              }
              filter = $x.noUiSlider($.extend({
                start: [t1, t2],
                range: {min: t1, max: t2},
                connect: true
              }, opts), true);
              val = filter.val();
            }
            r1  = t1; r2 = t2;
          })();
          var updateSliderText = function(v1, v2) {
            $span1.text(formatDate(v1, false)); $span2.text(formatDate(v2, false));
          };
          updateSliderText(r1, r2);
          var updateSlider = function(e) {
            var val = filter.val();
            // turn off filter if in full range
            $td.data('filter', val[0] > r1 || val[1] < r2);
            var v1 = formatDate(val[0]), v2 = formatDate(val[1]), ival;
            if ($td.data('filter')) {
              ival = v1 + ' ... ' + v2;
              $input.attr('title', ival).val(ival).trigger('input');
            } else {
              $input.attr('title', '').val('');
            }
            updateSliderText(val[0], val[1]);
            if (e.type === 'slide') return;  // no searching when sliding only
            if (server) {
              table.column(i).search($td.data('filter') ? ival : '').draw();
              return;
            }
            table.draw();
          };
          filter.on({
            set: updateSlider,
            slide: updateSlider
          });
        }

        // server-side processing will be handled by R (or whatever server
        // language you use); the following code is only needed for client-side
        // processing
        if (server) {
          // if a search string has been pre-set, search now
          if (searchCol) searchColumn(i, searchCol).draw();
          return;
        }

        var customFilter = function(settings, data, dataIndex) {
          // there is no way to attach a search function to a specific table,
          // and we need to make sure a global search function is not applied to
          // all tables (i.e. a range filter in a previous table should not be
          // applied to the current table); we use the settings object to
          // determine if we want to perform searching on the current table,
          // since settings.sTableId will be different to different tables
          if (table.settings()[0] !== settings) return true;
          // no filter on this column or no need to filter this column
          if (typeof filter === 'undefined' || !$td.data('filter')) return true;

          var r = filter.val(), v, r0, r1;
          var i_data = function(i) {
            if (!colReorderEnabled()) return i;
            var order = table.colReorder.order(), k;
            for (k = 0; k < order.length; ++k) if (order[k] === i) return k;
            return i; // in theory it will never be here...
          }
          v = data[i_data(i)];
          if (type === 'number' || type === 'integer') {
            v = parseFloat(v);
            // how to handle NaN? currently exclude these rows
            if (isNaN(v)) return(false);
            r0 = parseFloat(scaleBack(r[0], scale))
            r1 = parseFloat(scaleBack(r[1], scale));
            if (v >= r0 && v <= r1) return true;
          } else if (type === 'date' || type === 'time') {
            v = new Date(v);
            r0 = new Date(r[0] / scale); r1 = new Date(r[1] / scale);
            if (v >= r0 && v <= r1) return true;
          } else if (type === 'factor') {
            if (r.length === 0 || inArray(v, r)) return true;
          } else if (type === 'logical') {
            if (r.length === 0) return true;
            if (inArray(v === '' ? 'na' : v, r)) return true;
          }
          return false;
        };

        $.fn.dataTable.ext.search.push(customFilter);

        // search for the preset search strings if it is non-empty
        if (searchCol) {
          if (inArray(type, ['factor', 'logical'])) {
            filter[0].selectize.setValue(JSON.parse(searchCol));
          } else if (type === 'character') {
            $input.trigger('input');
          } else if (inArray(type, ['number', 'integer', 'date', 'time'])) {
            $input.trigger('change');
          }
        }

      });

    }

    // highlight search keywords
    var highlight = function() {
      var body = $(table.table().body());
      // removing the old highlighting first
      body.unhighlight();

      // don't highlight the "not found" row, so we get the rows using the api
      if (table.rows({ filter: 'applied' }).data().length === 0) return;
      // highlight gloal search keywords
      body.highlight($.trim(table.search()).split(/\s+/));
      // then highlight keywords from individual column filters
      if (filterRow) filterRow.each(function(i, td) {
        var $td = $(td), type = $td.data('type');
        if (type !== 'character') return;
        var $input = $td.children('div').first().children('input');
        var column = table.column(i).nodes().to$(),
            val = $.trim($input.val());
        if (type !== 'character' || val === '') return;
        column.highlight(val.split(/\s+/));
      });
    };

    if (options.searchHighlight) {
      table
      .on('draw.dt.dth column-visibility.dt.dth column-reorder.dt.dth', highlight)
      .on('destroy', function() {
        // remove event handler
        table.off('draw.dt.dth column-visibility.dt.dth column-reorder.dt.dth');
      });

      // initial highlight for state saved conditions and initial states
      highlight();
    }

    // run the callback function on the table instance
    if (typeof data.callback === 'function') data.callback(table);

    // double click to edit the cell
    if (data.editable) table.on('dblclick.dt', 'tbody td', function() {
      var $input = $('<input type="text">');
      var $this = $(this), value = table.cell(this).data(), html = $this.html();
      var changed = false;
      $input.val(value);
      $this.empty().append($input);
      $input.css('width', '100%').focus().on('change', function() {
        changed = true;
        var valueNew = $input.val();
        if (valueNew != value) {
          table.cell($this).data(valueNew);
          if (HTMLWidgets.shinyMode) changeInput('cell_edit', cellInfo($this));
          // for server-side processing, users have to call replaceData() to update the table
          if (!server) table.draw(false);
        } else {
          $this.html(html);
        }
        $input.remove();
      }).on('blur', function() {
        if (!changed) $input.trigger('change');
      });
    });

    // interaction with shiny
    if (!HTMLWidgets.shinyMode && !crosstalkOptions.group) return;

    var methods = {};
    var shinyData = {};

    methods.updateCaption = function(caption) {
      if (!caption) return;
      $table.children('caption').replaceWith(caption);
    }

    // register clear functions to remove input values when the table is removed
    instance.clearInputs = {};

    var changeInput = function(id, value, type, noCrosstalk) {
      var event = id;
      id = el.id + '_' + id;
      if (type) id = id + ':' + type;
      // do not update if the new value is the same as old value
      if (shinyData.hasOwnProperty(id) && shinyData[id] === JSON.stringify(value))
        return;
      shinyData[id] = JSON.stringify(value);
      if (HTMLWidgets.shinyMode) {
        Shiny.onInputChange(id, value);
        if (!instance.clearInputs[id]) instance.clearInputs[id] = function() {
          Shiny.onInputChange(id, null);
        }
      }

      // HACK
      if (event === "rows_selected" && !noCrosstalk) {
        if (crosstalkOptions.group) {
          var keys = crosstalkOptions.key;
          var selectedKeys = null;
          if (value) {
            selectedKeys = [];
            for (var i = 0; i < value.length; i++) {
              // The value array's contents use 1-based row numbers, so we must
              // convert to 0-based before indexing into the keys array.
              selectedKeys.push(keys[value[i] - 1]);
            }
          }
          instance.ctselectHandle.set(selectedKeys);
        }
      }
    };

    var addOne = function(x) {
      return x.map(function(i) { return 1 + i; });
    };

    var unique = function(x) {
      var ux = [];
      $.each(x, function(i, el){
        if ($.inArray(el, ux) === -1) ux.push(el);
      });
      return ux;
    }

    // change the row index of a cell
    var tweakCellIndex = function(cell) {
      var info = cell.index();
      if (server) {
        info.row = DT_rows_current[info.row];
      } else {
        info.row += 1;
      }
      return {row: info.row, col: info.column};
    }

    var selMode = data.selection.mode, selTarget = data.selection.target;
    if (inArray(selMode, ['single', 'multiple'])) {
      var selClass = data.style === 'bootstrap' ? 'active' : 'selected';
      var selected = data.selection.selected, selected1, selected2;
      // selected1: row indices; selected2: column indices
      if (selected === null) {
        selected1 = selected2 = [];
      } else if (selTarget === 'row') {
        selected1 = $.makeArray(selected);
      } else if (selTarget === 'column') {
        selected2 = $.makeArray(selected);
      } else if (selTarget === 'row+column') {
        selected1 = $.makeArray(selected.rows);
        selected2 = $.makeArray(selected.cols);
      }

      // After users reorder the rows or filter the table, we cannot use the table index
      // directly. Instead, we need this function to find out the rows between the two clicks.
      // If user filter the table again between the start click and the end click, the behavior
      // would be undefined, but it should not be a problem.
      var shiftSelRowsIndex = function(start, end) {
        var indexes = server ? DT_rows_all : table.rows({ search: 'applied' }).indexes().toArray();
        start = indexes.indexOf(start); end = indexes.indexOf(end);
        // if start is larger than end, we need to swap
        if (start > end) {
          var tmp = end; end = start; start = tmp;
        }
        return indexes.slice(start, end + 1);
      }

      var serverRowIndex = function(clientRowIndex) {
        return server ? DT_rows_current[clientRowIndex] : clientRowIndex + 1;
      }

      // row, column, or cell selection
      var lastClickedRow;
      if (inArray(selTarget, ['row', 'row+column'])) {
        var selectedRows = function() {
          var rows = table.rows('.' + selClass);
          var idx = rows.indexes().toArray();
          if (!server) return addOne(idx);
          idx = idx.map(function(i) {
            return DT_rows_current[i];
          });
          selected1 = selMode === 'multiple' ? unique(selected1.concat(idx)) : idx;
          return selected1;
        }
        table.on('mousedown.dt', 'tbody tr', function(e) {
          var $this = $(this), thisRow = table.row(this);
          if (selMode === 'multiple') {
            if (e.shiftKey && lastClickedRow !== undefined) {
              // select or de-select depends on the last clicked row's status
              var flagSel = !$this.hasClass(selClass);
              var crtClickedRow = serverRowIndex(thisRow.index());
              if (server) {
                var rowsIndex = shiftSelRowsIndex(lastClickedRow, crtClickedRow);
                // update current page's selClass
                rowsIndex.map(function(i) {
                  var rowIndex = DT_rows_current.indexOf(i);
                  if (rowIndex >= 0) {
                    var row = table.row(rowIndex).nodes().to$();
                    var flagRowSel = !row.hasClass(selClass);
                    if (flagSel === flagRowSel) row.toggleClass(selClass);
                  }
                });
                // update selected1
                if (flagSel) {
                  selected1 = unique(selected1.concat(rowsIndex));
                } else {
                  selected1 = selected1.filter(function(index) {
                    return !inArray(index, rowsIndex);
                  });
                }
              } else {
                // js starts from 0
                shiftSelRowsIndex(lastClickedRow - 1, crtClickedRow - 1).map(function(value) {
                  var row = table.row(value).nodes().to$();
                  var flagRowSel = !row.hasClass(selClass);
                  if (flagSel === flagRowSel) row.toggleClass(selClass);
                });
              }
              e.preventDefault();
            } else {
              $this.toggleClass(selClass);
            }
          } else {
            if ($this.hasClass(selClass)) {
              $this.removeClass(selClass);
            } else {
              table.$('tr.' + selClass).removeClass(selClass);
              $this.addClass(selClass);
            }
          }
          if (server && !$this.hasClass(selClass)) {
            var id = DT_rows_current[thisRow.index()];
            // remove id from selected1 since its class .selected has been removed
            if (inArray(id, selected1)) selected1.splice($.inArray(id, selected1), 1);
          }
          changeInput('rows_selected', selectedRows());
          changeInput('row_last_clicked', serverRowIndex(thisRow.index()));
          lastClickedRow = serverRowIndex(thisRow.index());
        });
        changeInput('rows_selected', selected1);
        var selectRows = function() {
          table.$('tr.' + selClass).removeClass(selClass);
          if (selected1.length === 0) return;
          if (server) {
            table.rows({page: 'current'}).every(function() {
              if (inArray(DT_rows_current[this.index()], selected1)) {
                $(this.node()).addClass(selClass);
              }
            });
          } else {
            var selected0 = selected1.map(function(i) { return i - 1; });
            $(table.rows(selected0).nodes()).addClass(selClass);
          }
        }
        selectRows();  // in case users have specified pre-selected rows
        // restore selected rows after the table is redrawn (e.g. sort/search/page);
        // client-side tables will preserve the selections automatically; for
        // server-side tables, we have to *real* row indices are in `selected1`
        if (server) table.on('draw.dt', selectRows);
        methods.selectRows = function(selected) {
          selected1 = $.makeArray(selected);
          selectRows();
          changeInput('rows_selected', selected1);
        }
      }

      if (inArray(selTarget, ['column', 'row+column'])) {
        if (selTarget === 'row+column') {
          $(table.columns().footer()).css('cursor', 'pointer');
        }
        table.on('click.dt', selTarget === 'column' ? 'tbody td' : 'tfoot tr th', function() {
          var colIdx = selTarget === 'column' ? table.cell(this).index().column :
              $.inArray(this, table.columns().footer()),
              thisCol = $(table.column(colIdx).nodes());
          if (colIdx === -1) return;
          if (thisCol.hasClass(selClass)) {
            thisCol.removeClass(selClass);
            selected2.splice($.inArray(colIdx, selected2), 1);
          } else {
            if (selMode === 'single') $(table.cells().nodes()).removeClass(selClass);
            thisCol.addClass(selClass);
            selected2 = selMode === 'single' ? [colIdx] : unique(selected2.concat([colIdx]));
          }
          changeInput('columns_selected', selected2);
        });
        changeInput('columns_selected', selected2);
        var selectCols = function() {
          table.columns().nodes().flatten().to$().removeClass(selClass);
          if (selected2.length > 0)
            table.columns(selected2).nodes().flatten().to$().addClass(selClass);
        }
        selectCols();  // in case users have specified pre-selected columns
        if (server) table.on('draw.dt', selectCols);
        methods.selectColumns = function(selected) {
          selected2 = $.makeArray(selected);
          selectCols();
          changeInput('columns_selected', selected2);
        }
      }

      if (selTarget === 'cell') {
        var selected3;
        if (selected === null) {
          selected3 = [];
        } else {
          selected3 = selected;
        }
        var findIndex = function(ij) {
          for (var i = 0; i < selected3.length; i++) {
            if (ij[0] === selected3[i][0] && ij[1] === selected3[i][1]) return i;
          }
          return -1;
        }
        table.on('click.dt', 'tbody td', function() {
          var $this = $(this), info = tweakCellIndex(table.cell(this));
          if ($this.hasClass(selClass)) {
            $this.removeClass(selClass);
            selected3.splice(findIndex([info.row, info.col]), 1);
          } else {
            if (selMode === 'single') $(table.cells().nodes()).removeClass(selClass);
            $this.addClass(selClass);
            selected3 = selMode === 'single' ? [[info.row, info.col]] :
              unique(selected3.concat([[info.row, info.col]]));
          }
          changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
        });
        changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
        var selectCells = function() {
          table.$('td.' + selClass).removeClass(selClass);
          if (selected3.length === 0) return;
          if (server) {
            table.cells({page: 'current'}).every(function() {
              var info = tweakCellIndex(this);
              if (findIndex([info.row, info.col], selected3) > -1)
                $(this.node()).addClass(selClass);
            });
          } else {
            selected3.map(function(ij) {
              $(table.cell(ij[0] - 1, ij[1]).node()).addClass(selClass);
            });
          }
        };
        selectCells();  // in case users have specified pre-selected columns
        if (server) table.on('draw.dt', selectCells);
        methods.selectCells = function(selected) {
          selected3 = selected ? selected : [];
          selectCells();
          changeInput('cells_selected', transposeArray2D(selected3), 'shiny.matrix');
        }
      }
    }

    // expose some table info to Shiny
    var updateTableInfo = function(e, settings) {
      // TODO: is anyone interested in the page info?
      // changeInput('page_info', table.page.info());
      var updateRowInfo = function(id, modifier) {
        var idx;
        if (server) {
          idx = modifier.page === 'current' ? DT_rows_current : DT_rows_all;
        } else {
          var rows = table.rows($.extend({
            search: 'applied',
            page: 'all'
          }, modifier));
          idx = addOne(rows.indexes().toArray());
        }
        changeInput('rows' + '_' + id, idx);
      };
      updateRowInfo('current', {page: 'current'});
      updateRowInfo('all', {});
    }
    table.on('draw.dt', updateTableInfo);
    updateTableInfo();

    // state info
    table.on('draw.dt column-visibility.dt', function() {
      changeInput('state', table.state());
    });
    changeInput('state', table.state());

    // search info
    var updateSearchInfo = function() {
      changeInput('search', table.search());
      if (filterRow) changeInput('search_columns', filterRow.toArray().map(function(td) {
        return $(td).find('input').first().val();
      }));
    }
    table.on('draw.dt', updateSearchInfo);
    updateSearchInfo();

    var cellInfo = function(thiz) {
      var info = tweakCellIndex(table.cell(thiz));
      info.value = table.cell(thiz).data();
      return info;
    }
    // the current cell clicked on
    table.on('click.dt', 'tbody td', function() {
      changeInput('cell_clicked', cellInfo(this));
    })
    changeInput('cell_clicked', {});

    // do not trigger table selection when clicking on links unless they have classes
    table.on('click.dt', 'tbody td a', function(e) {
      if (this.className === '') e.stopPropagation();
    });

    methods.addRow = function(data, rowname) {
      var data0 = table.row(0).data(), n = data0.length, d = n - data.length;
      if (d === 1) {
        data = rowname.concat(data)
      } else if (d !== 0) {
        console.log(data);
        console.log(data0);
        throw 'New data must be of the same length as current data (' + n + ')';
      };
      table.row.add(data).draw();
    }

    methods.updateSearch = function(keywords) {
      if (keywords.global !== null)
        $(table.table().container()).find('input[type=search]').first()
             .val(keywords.global).trigger('input');
      var columns = keywords.columns;
      if (!filterRow || columns === null) return;
      filterRow.toArray().map(function(td, i) {
        var v = typeof columns === 'string' ? columns : columns[i];
        if (typeof v === 'undefined') {
          console.log('The search keyword for column ' + i + ' is undefined')
          return;
        }
        $(td).find('input').first().val(v);
        searchColumn(i, v);
      });
      table.draw();
    }

    methods.hideCols = function(hide, reset) {
      if (reset) table.columns().visible(true, false);
      table.columns(hide).visible(false);
    }

    methods.showCols = function(show, reset) {
      if (reset) table.columns().visible(false, false);
      table.columns(show).visible(true);
    }

    methods.colReorder = function(order, origOrder) {
      table.colReorder.order(order, origOrder);
    }

    methods.selectPage = function(page) {
      if (table.page.info().pages < page || page < 1) {
        throw 'Selected page is out of range';
      };
      table.page(page - 1).draw(false);
    }

    methods.reloadData = function(resetPaging, clearSelection) {
      // empty selections first if necessary
      if (methods.selectRows && inArray('row', clearSelection)) methods.selectRows([]);
      if (methods.selectColumns && inArray('column', clearSelection)) methods.selectColumns([]);
      if (methods.selectCells && inArray('cell', clearSelection)) methods.selectCells([]);
      table.ajax.reload(null, resetPaging);
    }

    table.shinyMethods = methods;
  },
  resize: function(el, width, height, instance) {
    if (instance.data) this.renderValue(el, instance.data, instance);

    // dynamically adjust height if fillContainer = TRUE
    if (instance.fillContainer)
      this.fillAvailableHeight(el, height);

    this.adjustWidth(el);
  },

  // dynamically set the scroll body to fill available height
  // (used with fillContainer = TRUE)
  fillAvailableHeight: function(el, availableHeight) {

    // see how much of the table is occupied by header/footer elements
    // and use that to compute a target scroll body height
    var dtWrapper = $(el).find('div.dataTables_wrapper');
    var dtScrollBody = $(el).find($('div.dataTables_scrollBody'));
    var framingHeight = dtWrapper.innerHeight() - dtScrollBody.innerHeight();
    var scrollBodyHeight = availableHeight - framingHeight;

    // set the height
    dtScrollBody.height(scrollBodyHeight + 'px');
  },

  // adjust the width of columns; remove the hard-coded widths on table and the
  // scroll header when scrollX/Y are enabled
  adjustWidth: function(el) {
    var $el = $(el), table = $el.data('datatable');
    if (table) table.columns.adjust();
    $el.find('.dataTables_scrollHeadInner').css('width', '')
        .children('table').css('margin-left', '');
  }
});

  if (!HTMLWidgets.shinyMode) return;

  Shiny.addCustomMessageHandler('datatable-calls', function(data) {
    var id = data.id;
    var el = document.getElementById(id);
    var table = el ? $(el).data('datatable') : null;
    if (!table) {
      console.log("Couldn't find table with id " + id);
      return;
    }

    var methods = table.shinyMethods, call = data.call;
    if (methods[call.method]) {
      methods[call.method].apply(table, call.args);
    } else {
      console.log("Unknown method " + call.method);
    }
  });

})();
"></script>
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aoServerParams:[],sServerMethod:null,fnFormatNumber:null,aLengthMenu:null,iDraw:0,bDrawing:!1,iDrawError:-1,_iDisplayLength:10,_iDisplayStart:0,_iRecordsTotal:0,_iRecordsDisplay:0,oClasses:{},bFiltered:!1,bSorted:!1,bSortCellsTop:null,oInit:null,aoDestroyCallback:[],fnRecordsTotal:function(){return"ssp"==y(this)?1*this._iRecordsTotal:this.aiDisplayMaster.length},fnRecordsDisplay:function(){return"ssp"==y(this)?1*this._iRecordsDisplay:this.aiDisplay.length},fnDisplayEnd:function(){var a=this._iDisplayLength,
b=this._iDisplayStart,c=b+a,d=this.aiDisplay.length,e=this.oFeatures,f=e.bPaginate;return e.bServerSide?!1===f||-1===a?b+d:Math.min(b+a,this._iRecordsDisplay):!f||c>d||-1===a?d:c},oInstance:null,sInstance:null,iTabIndex:0,nScrollHead:null,nScrollFoot:null,aLastSort:[],oPlugins:{},rowIdFn:null,rowId:null};m.ext=x={buttons:{},classes:{},builder:"-source-",errMode:"alert",feature:[],search:[],selector:{cell:[],column:[],row:[]},internal:{},legacy:{ajax:null},pager:{},renderer:{pageButton:{},header:{}},
order:{},type:{detect:[],search:{},order:{}},_unique:0,fnVersionCheck:m.fnVersionCheck,iApiIndex:0,oJUIClasses:{},sVersion:m.version};h.extend(x,{afnFiltering:x.search,aTypes:x.type.detect,ofnSearch:x.type.search,oSort:x.type.order,afnSortData:x.order,aoFeatures:x.feature,oApi:x.internal,oStdClasses:x.classes,oPagination:x.pager});h.extend(m.ext.classes,{sTable:"dataTable",sNoFooter:"no-footer",sPageButton:"paginate_button",sPageButtonActive:"current",sPageButtonDisabled:"disabled",sStripeOdd:"odd",
sStripeEven:"even",sRowEmpty:"dataTables_empty",sWrapper:"dataTables_wrapper",sFilter:"dataTables_filter",sInfo:"dataTables_info",sPaging:"dataTables_paginate paging_",sLength:"dataTables_length",sProcessing:"dataTables_processing",sSortAsc:"sorting_asc",sSortDesc:"sorting_desc",sSortable:"sorting",sSortableAsc:"sorting_asc_disabled",sSortableDesc:"sorting_desc_disabled",sSortableNone:"sorting_disabled",sSortColumn:"sorting_",sFilterInput:"",sLengthSelect:"",sScrollWrapper:"dataTables_scroll",sScrollHead:"dataTables_scrollHead",
sScrollHeadInner:"dataTables_scrollHeadInner",sScrollBody:"dataTables_scrollBody",sScrollFoot:"dataTables_scrollFoot",sScrollFootInner:"dataTables_scrollFootInner",sHeaderTH:"",sFooterTH:"",sSortJUIAsc:"",sSortJUIDesc:"",sSortJUI:"",sSortJUIAscAllowed:"",sSortJUIDescAllowed:"",sSortJUIWrapper:"",sSortIcon:"",sJUIHeader:"",sJUIFooter:""});var Kb=m.ext.pager;h.extend(Kb,{simple:function(){return["previous","next"]},full:function(){return["first","previous","next","last"]},numbers:function(a,b){return[ha(a,
b)]},simple_numbers:function(a,b){return["previous",ha(a,b),"next"]},full_numbers:function(a,b){return["first","previous",ha(a,b),"next","last"]},first_last_numbers:function(a,b){return["first",ha(a,b),"last"]},_numbers:ha,numbers_length:7});h.extend(!0,m.ext.renderer,{pageButton:{_:function(a,b,c,d,e,f){var g=a.oClasses,j=a.oLanguage.oPaginate,i=a.oLanguage.oAria.paginate||{},n,l,m=0,o=function(b,d){var k,s,u,r,v=function(b){Sa(a,b.data.action,true)};k=0;for(s=d.length;k<s;k++){r=d[k];if(h.isArray(r)){u=
h("<"+(r.DT_el||"div")+"/>").appendTo(b);o(u,r)}else{n=null;l="";switch(r){case "ellipsis":b.append('<span class="ellipsis">&#x2026;</span>');break;case "first":n=j.sFirst;l=r+(e>0?"":" "+g.sPageButtonDisabled);break;case "previous":n=j.sPrevious;l=r+(e>0?"":" "+g.sPageButtonDisabled);break;case "next":n=j.sNext;l=r+(e<f-1?"":" "+g.sPageButtonDisabled);break;case "last":n=j.sLast;l=r+(e<f-1?"":" "+g.sPageButtonDisabled);break;default:n=r+1;l=e===r?g.sPageButtonActive:""}if(n!==null){u=h("<a>",{"class":g.sPageButton+
" "+l,"aria-controls":a.sTableId,"aria-label":i[r],"data-dt-idx":m,tabindex:a.iTabIndex,id:c===0&&typeof r==="string"?a.sTableId+"_"+r:null}).html(n).appendTo(b);Va(u,{action:r},v);m++}}}},s;try{s=h(b).find(G.activeElement).data("dt-idx")}catch(u){}o(h(b).empty(),d);s!==k&&h(b).find("[data-dt-idx="+s+"]").focus()}}});h.extend(m.ext.type.detect,[function(a,b){var c=b.oLanguage.sDecimal;return Ya(a,c)?"num"+c:null},function(a){if(a&&!(a instanceof Date)&&!Zb.test(a))return null;var b=Date.parse(a);
return null!==b&&!isNaN(b)||L(a)?"date":null},function(a,b){var c=b.oLanguage.sDecimal;return Ya(a,c,!0)?"num-fmt"+c:null},function(a,b){var c=b.oLanguage.sDecimal;return Pb(a,c)?"html-num"+c:null},function(a,b){var c=b.oLanguage.sDecimal;return Pb(a,c,!0)?"html-num-fmt"+c:null},function(a){return L(a)||"string"===typeof a&&-1!==a.indexOf("<")?"html":null}]);h.extend(m.ext.type.search,{html:function(a){return L(a)?a:"string"===typeof a?a.replace(Mb," ").replace(Aa,""):""},string:function(a){return L(a)?
a:"string"===typeof a?a.replace(Mb," "):a}});var za=function(a,b,c,d){if(0!==a&&(!a||"-"===a))return-Infinity;b&&(a=Ob(a,b));a.replace&&(c&&(a=a.replace(c,"")),d&&(a=a.replace(d,"")));return 1*a};h.extend(x.type.order,{"date-pre":function(a){return Date.parse(a)||-Infinity},"html-pre":function(a){return L(a)?"":a.replace?a.replace(/<.*?>/g,"").toLowerCase():a+""},"string-pre":function(a){return L(a)?"":"string"===typeof a?a.toLowerCase():!a.toString?"":a.toString()},"string-asc":function(a,b){return a<
b?-1:a>b?1:0},"string-desc":function(a,b){return a<b?1:a>b?-1:0}});cb("");h.extend(!0,m.ext.renderer,{header:{_:function(a,b,c,d){h(a.nTable).on("order.dt.DT",function(e,f,g,h){if(a===f){e=c.idx;b.removeClass(c.sSortingClass+" "+d.sSortAsc+" "+d.sSortDesc).addClass(h[e]=="asc"?d.sSortAsc:h[e]=="desc"?d.sSortDesc:c.sSortingClass)}})},jqueryui:function(a,b,c,d){h("<div/>").addClass(d.sSortJUIWrapper).append(b.contents()).append(h("<span/>").addClass(d.sSortIcon+" "+c.sSortingClassJUI)).appendTo(b);
h(a.nTable).on("order.dt.DT",function(e,f,g,h){if(a===f){e=c.idx;b.removeClass(d.sSortAsc+" "+d.sSortDesc).addClass(h[e]=="asc"?d.sSortAsc:h[e]=="desc"?d.sSortDesc:c.sSortingClass);b.find("span."+d.sSortIcon).removeClass(d.sSortJUIAsc+" "+d.sSortJUIDesc+" "+d.sSortJUI+" "+d.sSortJUIAscAllowed+" "+d.sSortJUIDescAllowed).addClass(h[e]=="asc"?d.sSortJUIAsc:h[e]=="desc"?d.sSortJUIDesc:c.sSortingClassJUI)}})}}});var Vb=function(a){return"string"===typeof a?a.replace(/</g,"&lt;").replace(/>/g,"&gt;").replace(/"/g,
"&quot;"):a};m.render={number:function(a,b,c,d,e){return{display:function(f){if("number"!==typeof f&&"string"!==typeof f)return f;var g=0>f?"-":"",h=parseFloat(f);if(isNaN(h))return Vb(f);h=h.toFixed(c);f=Math.abs(h);h=parseInt(f,10);f=c?b+(f-h).toFixed(c).substring(2):"";return g+(d||"")+h.toString().replace(/\B(?=(\d{3})+(?!\d))/g,a)+f+(e||"")}}},text:function(){return{display:Vb}}};h.extend(m.ext.internal,{_fnExternApiFunc:Lb,_fnBuildAjax:sa,_fnAjaxUpdate:kb,_fnAjaxParameters:tb,_fnAjaxUpdateDraw:ub,
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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%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCV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<section class="page-header">
<h1 class="title toc-ignore project-name">Advertising Response Measurement - Final Report</h1>
<h4 class="author project-author">Team K - Zichen (Zoe) Huang, Jiaying (Claire) Wu, Chen (Cici) Chen</h4>
<h4 class="date project-date">5/8/2019</h4>
</section>
<section class="main-content">
<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>The ultimate goal of any marketing activities is to increase sales, either in short-term or long-term, and ideally each campaign or marketing channel should be evaluated based on the incremental profit, which is the additional sales we produce with advertising over what we would have sold without advertising, relative to its cost. While marketing efforts seem intangible as it is hard to land causal relationships between marketing events and transactions, advanced statistical methods could definitely shed the lights of tangible marketing impact on business growth, and guide the direction for future practice.</p>
<p>By analyzing the correlations between marketing activities and the transactions, weakness and strengths of various channels, seasonality of consumer’s response, the marketing team could evaluate the cost efficiency and thus move forward to optimize the resources allocation for marketing spending, and strategically plan marketing events to improve the effectiveness of marketing efforts.</p>
<p>Among diverse marketing activities, advertising is a major mean for branding and informing. Both online (emails, social media, displays, etc.) and offline (direct mails) advertisements play an indispensable role in marketing. As placing ads is expensive in terms of the cost of time, recourses, and budget, one most frequently asked question from the management team is that: how does my advertising work? With this question in mind, our team leveraged four methods to tackle the advertising effectiveness measurement problem. The methods are last-click attribution analysis, holdout testing (experimentation), marketing mix models and model-based attribution analysis. The main goal of this project is to evaluate the <strong>effectiveness of different advertising channels</strong> from four different perspectives. Meanwhile, we would learn from the advantages and disadvantages of the approaches and gain a more comprehensive understanding of mix modeling methods for continuous study.</p>
</div>
<div id="sources-of-data" class="section level1">
<h1>Sources of Data</h1>
<p>The full dataset could be found in the submission files. As dataset related to advertisement responses and transaction records is very sensitive, we could hardly find real or original open data. The dataset we used for this project is a synthetic one simulated by the Elea McDonnell Feit, Marketing Professor of Drexel University, and is organized from three perspectives: customer, impressions, and transactions. Data generation method could be found here: <a href="https://github.com/eleafeit/ad_response_tutorial/blob/master/R%20code/AdResponseDataGeneration.R">https://github.com/eleafeit/ad_response_tutorial/blob/master/R%20code/AdResponseDataGeneration.R</a>. The whole raw digital advertising dataset describes 10,000 customers as well as potential customers of a retailer, and the retailer uses four different advertising channels - display ads, social media ads, email ads, and direct mail ads for its marketing promotion.</p>
</div>
<div id="examination-of-the-data" class="section level1">
<h1>Examination of the Data</h1>
<p>We will be working with a simulated data set related to social media sites. The data are stored in following three files: <strong>customer.csv</strong>, <strong>impressions.csv</strong>, and <strong>transactions.csv</strong>. The first row of the data set includes the column names, and each subsequent row includes one observation of values. Here is a selection of 10 lines from each data file:</p>
<div id="inspect-customer-file" class="section level2">
<h2>Inspect customer file</h2>
<p><strong>customer.csv</strong>: Information about the users with some fields from their profiles. Each row in the file represents a customer, 10,000 rows; the columns describe some basic information about each customer including id number, whether the customer has made a purchase prior to the observation period, and whether the customer is eligible to receive emails or direct mails.</p>
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<ul>
<li><p><strong>past.purchase</strong>: in the original dataset, the type of <em>past.purchase</em> is <em>int</em>, but this variable records whether the customer has made a purchase prior to the observation period so we decided to convert it into <em>factor</em>.</p></li>
<li><p><strong>email</strong>: in the original dataset, the type of <em>email</em> is <em>int</em>, but this variable records whether the customer is eligible to receive emails so we decided to convert it into <em>factor</em>.</p></li>
<li><p><strong>direct</strong>: in the original dataset, the type of <em>direct</em> is <em>int</em>, but this variable records whether the customer is eligible to receive direct mails so we decided to convert it into <em>factor</em>.</p></li>
</ul>
</div>
<div id="inspect-impression-file" class="section level2">
<h2>Inspect impression file</h2>
<p><strong>impressions.csv</strong>: Information about which users are connected to other users. Each row is an exposure of marketing communication to a specific customer, 501,336 rows; the columns describe the information about the customer’s impression towards an advertisement including id number for the customer, date of impression, the channel of the ad exposure, and whether the customer clicked on the ad.</p>
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<ul>
<li><p><strong>date</strong>: in the original dataset, the type of <em>date</em> is <em>chr</em>, and we decided to convert it into <em>date</em>.</p></li>
<li><p><strong>channel</strong>: in the original dataset, the type of <em>channel</em> is <em>chr</em>, and we decided to convert it into <em>factor</em>.</p></li>
<li><p><strong>click</strong>: in the original dataset, the type of <em>click</em> is <em>int</em>, but this variable records whether the customer clicked on the ad so we decided to convert it into <em>factor</em>.</p></li>
</ul>
</div>
<div id="inspect-transactions-file" class="section level2">
<h2>Inspect transactions file</h2>
<p><strong>transactions.csv</strong>: Information about history of the user’s account registrations (logins) over time. Each row is a transaction made by a customer; columns record the basic information of a transaction including customer id, date of the transaction, channel of the last ad impression the customer saw before the transaction, and channel of the last ad the customer clicked before the transaction.</p>
<div id="htmlwidget-e3b1a3fea7b58b984a25" style="width:100%;height:auto;" class="datatables html-widget"></div>
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<ul>
<li><p><strong>V1</strong>: in the original dataset, <em>V1</em> only represents the row number, so it is safe for us to remove this variable.</p></li>
<li><p><strong>date</strong>: in the original dataset, the type of <em>date</em> is <em>chr</em>, and we decided to convert it into <em>date</em>.</p></li>
<li><p><strong>last.touch</strong>: in the original dataset, the type of <em>last.touch</em> is <em>chr</em>, and we decided to convert it into <em>factor</em>.</p></li>
<li><p><strong>last.touch</strong>: in the original dataset, the type of <em>last.touch</em> is <em>chr</em>, and we decided to convert it into <em>factor</em>.</p></li>
<li><p><strong>last.click</strong>: in the original dataset, the type of <em>last.click</em> is <em>chr</em>, and we decided to convert it into <em>factor</em>.</p></li>
</ul>
</div>
</div>
<div id="method-1-last-touch-analysis" class="section level1">
<h1>Method 1: Last Touch Analysis</h1>
<p><strong>Attribution rules with last-touch analysis</strong></p>
<div id="investigation" class="section level3">
<h3>1. Investigation</h3>
<p>Based on last-touch attribution, we are able to find the last ad the user clicked on prior to the conversion so that we can get the sales attributed to each channel. In this case, information about the last click and the last touch is stored in the transaction file. By doing a quick crosstab on the transaction table, we are able to calculate the number of transactions attached to each channel by last touch.</p>
<pre><code>last.touch
direct display email none social
2594 5252 7145 846 6596 </code></pre>
<p><img 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" /><!-- --></p>
</div>
<div id="results-and-interpretation" class="section level3">
<h3>2. Results and Interpretation</h3>
<p>Seen from the result, the incremental sales for social media ads are 6596.</p>
<p>Please refer to the dashboard for last touch analysis result for subgroups of transactions to see if some channels are not available, which channel of the rest is the best choice.</p>
<p>Also, we can see if the incremental sales for different ads would be different if we take time into consideration. For example, we can subset out the transactions based on date and then crosstab.</p>
<pre><code>last.touch
direct display email none social
1669 2682 4081 258 328 </code></pre>
<p><img 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" /><!-- --></p>
<p>Seen from the result, in February, the incremental sales are far lower attached to social media ads. Probably, this is because that social media ads were ended at the end of January. Please refer to the dashboard to see the difference in different period of time.</p>
</div>
<div id="assumptions" class="section level3">
<h3>3. Assumptions</h3>
<p>When we do this, one assumption is that we ignore all the customers who didn’t make a real transaction. Also, we assumed that all the sales are counted as incremental, which means that consumers who saw ads would not have bought if they hadn’t seen the ads.</p>
</div>
</div>
<div id="method-2-holdout-test-analysis" class="section level1">
<h1>Method 2: Holdout Test Analysis</h1>
<div id="investigation-1" class="section level3">
<h3>1. Investigation</h3>
<p>Holdout testing, also called randomized controlled trial, is an experiment that randomly select the customers for the control group not exposed to an ad and see if there is any difference between people who received ads and those who didn’t so that we can know if the ads is effective.</p>
<p>There was an email on 2017-01-03, 2017-01-17, 2017-01-24, 2017-01-31, 2017-02-07, 2017-02-14, and 2017-02-21 that included a holdout group by randomly selecting the customers for the control group to be not exposed to an email ad.</p>
<p>Here, we picked 2017-01-31 to analyze the result of holdout test for the first 10 days.</p>
<pre><code> consumed
group FALSE TRUE
control group 702 470
test group 2613 2216</code></pre>
<p><img 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" /><!-- --></p>
</div>
<div id="results-and-interpretation-1" class="section level3">
<h3>2. Results and Interpretation</h3>
<p>Seen from the table as well as the plot, it is obvious that the proportion of people made actual consumption in test group who have received the email ads is higher than that in control group who didn’t receive any email ads. Below is a proptest for more detailed information about the comparison of conversion rate between these two groups.</p>
<pre><code>
2-sample test for equality of proportions with continuity
correction
data: ttable[, "TRUE"] out of xtabs(~group, data = total.tab)
X-squared = 12.541, df = 1, p-value = 0.0003981
alternative hypothesis: two.sided
95 percent confidence interval:
-0.08978269 -0.02595789
sample estimates:
prop 1 prop 2
0.4010239 0.4588942 </code></pre>
<pre><code>diff.prop 2 ci1 ci2
0.05787029 0.08978269 0.02595789 </code></pre>
<pre><code>
2-sample test for equality of proportions with continuity
correction
data: ttable[, "TRUE"] out of xtabs(~group, data = total.tab)
X-squared = 12.541, df = 1, p-value = 0.0003981
alternative hypothesis: two.sided
95 percent confidence interval:
-0.08978269 -0.02595789
sample estimates:
prop 1 prop 2
0.4010239 0.4588942 </code></pre>
<pre><code>diff.prop 2 ci1 ci2
0.05787029 0.08978269 0.02595789 </code></pre>
<p>Seen from this example, the test group had a 45.89% conversion rate in the first 10 days after the email was sent, while the hold out group had a 40.1% conversion rate. The email on 2017-01-31 produced incremental sales. The incremental increase in conversion rate is between +2.60% and +8.98% (95% confidence interval). Please refer to the dashboard to see the difference about how much advertising increases sales with different experiment date and different window period. One inference we can get from the dashboard is that ad response is often greatest just after exposure and then falls off over time.</p>
</div>
<div id="assumptions-1" class="section level3">
<h3>3. Assumptions</h3>
<p>By randomly selecting the customers for the control group, we assumed that the two groups are the same on average. Both the treatment and control groups are assured to be similar in their propensity to transaction and response to ads, which is also called probabilistically equivalent.</p>
</div>
</div>
<div id="method-3-marketing-mix-modeling" class="section level1">
<h1>Method 3: Marketing Mix Modeling</h1>
<p>Based on our datasets, the data points are usually categorical response or logical response, but before we go deep into the logical regression, we want to do linear regression first to find the correlation between total sales to advertising spending on that same day/week/month.</p>
<div id="investigation-2" class="section level3">
<h3>1. Investigation</h3>
<p>We did some research about marketing mix model before we started to build our models.</p>
<p>A “marketing mix model” or “MMM” is a regression relating advertising spending or total impressions to some response such as “sales (transactions)”. Marketing mix modeling (MMM) is also a statistical analysis utilizing marketing time series data to estimate the impact of various marketing strategies on sales and then predict the impact of future sets of marketing mix or tactics. It is often used to optimize advertising mix and promotional tactics with respect to sales revenue or profit.</p>
<p>In order to investigate the relationships or correlations between total sales (transactions) and impression factors (four different advertising channels - display ads, social media ads, email ads, and direct mail ads) within a specific time period, we implemented linear regression model for marketing mix modeling.</p>
<p>A simple marketing mix model for our case could be represented as follows: <span class="math display">\[sales_t = \beta_0 + \beta_1display_t + \beta_2social_t + \beta_3email_t + \beta_4direct_t + \epsilon_t\]</span></p>
<p>the <span class="math inline">\(\beta\)</span>’s represent the unknown relationship between advertising spending and sales. For instance, <span class="math inline">\(\beta_1\)</span> means the increase in sales we will gain for each additional display impression unit while holding other impression factors fixed.</p>
<p>We have built five different linear models to see whether there are some thoughtful results which may help us understand the relationship between sales and advertising spending better.</p>
<ul>
<li>Model 1: Basic regression including email.holdout</li>
<li>Model 2: Add in a day of week variable</li>
<li>Model 3: Taking the advertising effect into consideration</li>
<li>Model 4: Interactions terms (<span class="math inline">\(Email \times Social\)</span>)</li>
<li>Model 5: Interactions terms (<span class="math inline">\(Direct \times Social\)</span>)</li>
</ul>
<p>When we fit models, it will be more convenient to put the transactions and impressions data together in the same data frame. Also, it will be easy to do regression when we put the necessary variables together, so we did some data preparations before modeling.</p>
<p>so let’s take a look at our summarized data, exploration plots, and the corresponding correlation matrix.</p>
<ul>
<li><p>Summarized data <div id="htmlwidget-f6e6b0cb067ac237513b" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-f6e6b0cb067ac237513b">{"x":{"filter":"none","data":[["2017-01-01","2017-01-02","2017-01-03","2017-01-04","2017-01-05","2017-01-06","2017-01-07","2017-01-08","2017-01-09","2017-01-10"],[325,357,589,479,403,498,564,586,556,660],[0,0,0,0,0,4974,0,0,0,0],[3786,3792,3656,3731,3770,3611,3719,3780,3744,3685],[0,0,4798,0,0,0,0,0,0,4765],[0,0,1203,0,0,0,0,0,0,1236],[7481,7416,7505,7648,7620,7614,7552,7504,7446,7595],["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday","Monday","Tuesday"],[0,0,4798,2399,1199.5,599.75,299.875,149.9375,74.96875,4802.484375],[3786,4927.8,5134.34,5271.302,5351.3906,5216.41718,5283.925154,5365.1775462,5353.55326386,5291.065979158],[0,0,0,0,0,4974,3730.5,2797.875,2098.40625,1573.8046875],[7481,9660.3,10403.09,10768.927,10850.6781,10869.20343,10812.761029,10747.8283087,10670.34849261,10796.104547783],[0,0,49914025.82,25834655.873,13015388.38095,6518804.7571425,3242476.71357137,1611502.50703571,799942.688555356,51848123.4015943],[0,0,0,0,0,54063417.86082,40337005.0186845,30071080.129204,22390725.9665709,16990959.944041]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Sales<\/th>\n <th>Direct<\/th>\n <th>Display<\/th>\n <th>Email<\/th>\n <th>Email Holdout<\/th>\n <th>Social<\/th>\n <th>Day of Week<\/th>\n <th>Email ad.effect<\/th>\n <th>Display ad.effect<\/th>\n <th>Direct ad.effect<\/th>\n <th>Social ad.effect<\/th>\n <th>Email&Social<\/th>\n <th>Direct&Social<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,8,9,10,11,12,13]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script></p></li>
<li><p>Exploration plots <img src="data:image/png;base64,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" /><!-- --></p></li>
</ul>
<p><img 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" style="display: block; margin: auto;" /></p>
<p>Those plots gave us a general idea about how’s the relationship between impression factors and sales, the Display channel tends to show a different trend compared to others, which will be explored more in the following model building part.</p>
<ul>
<li>Correlation matrix <div id="htmlwidget-246a695e27233b7744db" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-246a695e27233b7744db">{"x":{"filter":"none","data":[["Sales","Direct","Display","Email","Email Holdout","Social"],[1,0.07,-0.028,0.529,0.533,0.395],[0.07,1,-0.235,-0.077,-0.077,-0.007],[-0.028,-0.235,1,-0.068,-0.071,0.142],[0.529,-0.077,-0.068,1,1,-0.025],[0.533,-0.077,-0.071,1,1,-0.02],[0.395,-0.007,0.142,-0.025,-0.02,1]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Sales<\/th>\n <th>Direct<\/th>\n <th>Display<\/th>\n <th>Email<\/th>\n <th>Email Holdout<\/th>\n <th>Social<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script></li>
</ul>
<p>Based on the correlation matrix, it can be easily found that Email, Email Holdout and Social seems to have a big impact on Sales. They have positive correlations, which are 0.529, 0.533, and 0.395, and the other two factors seem to have a slight impact.</p>
</div>
<div id="results-and-interpretation-2" class="section level3">
<h3>2. Results and Interpretation</h3>
<p>By utilizing two datasets <code>impressions</code> and <code>transactions</code>, we can have observations of the sales and other advertising data for different impression channels, which will give us the data to estimate these unknown parameters of the model, and the process will be shown below.</p>
<p><strong>Model 1: Basic regression including email.holdout relating transactions to impressions</strong></p>
<p>The model 1 can be written as <span class="math display">\[Sales_t=\beta_0 + \beta_1Direct_t+\beta_2Display_t+\beta_3 Email_t+\beta_4 Email.holdout_t+\beta_5Social_t+\epsilon_t\]</span></p>
<div id="htmlwidget-0b71cc1f8f3420c0e55f" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-0b71cc1f8f3420c0e55f">{"x":{"filter":"none","data":[["1","2","3","4","5","6"],["(Intercept)","Direct","Display","Email","`Email Holdout`","Social"],[438.501,0.015,-0.035,-0.162,0.805,0.013],[940.623,0.014,0.252,0.322,1.29,0.003],[0.466,1.049,-0.14,-0.502,0.624,3.747],[0.643,0.299,0.889,0.618,0.535,0],[-1405.085,-0.013,-0.53,-0.792,-1.723,0.006],[2282.088,0.042,0.459,0.469,3.334,0.019]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Variable<\/th>\n <th>Estimate<\/th>\n <th>Std. Error<\/th>\n <th>t value<\/th>\n <th>Pr(>|t|)<\/th>\n <th>Coef.Lower.95<\/th>\n <th>Coef.Upper.95<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<ul>
<li><p><span class="math inline">\(Sales_t=438.501+0.015Direct_t-0.035Display_t-0.162Email_t+0.805Email.holdout_t+0.013Social_t\)</span></p></li>
<li><p>From the above output, there is only one statistically significant effect, which is social impressions, and we gain 0.013 additional transactions for each social impression.</p></li>
<li><p>The estimated effect of Email and Display is negative but they are not significant.</p></li>
</ul>
<p><strong>Model 2: Add the Day of Week variable</strong></p>
<p>Besides advertising impressions, we introduced a new variable into the linear regression model - the Day of Week, which enable us to inspect the relationships between sales and marketing channels across the week rather than within the same day.</p>
<p>The Model 2 can be written as <span class="math display">\[Sales_t=\beta_0 + \beta_1Direct_t+\beta_2Display_t+\beta_3 Email_t+\beta_4Social_t+\beta_iDay.of.Week(i\ is\ different\ weekdays)+\epsilon_t\]</span></p>
<div id="htmlwidget-aa186246d072cfcfcb70" style="width:100%;height:auto;" class="datatables html-widget"></div>
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<p>When the new variable added into the model, it changed the coefficients of our model. Also, it can be noticed that there is no Friday, and it is just a coincidence that there is no Friday in the subdata which we merged from the original data that meets our assumptions in modeling for transactions and impressions.</p>
<p>In Model 2, we found a significant association between direct impressions and transactions, as well as social impressions and transactions.</p>
<p>For instance, Direct impression has 29.5 additional transactions per thousand direct impressions, and Social social 12.8 additional transactions per thousand impressions.</p>
<p><strong>Model 3: Taking the advertising effect into consideration</strong></p>
<p>Now we have Model 1 and Model 2, and these two models assume that an impression on specific time t has an effect on the number of transactions on a specific time t and that those impressions have no effect on sales on other days.</p>
<p>However, this is not really happened in the marketing area, just like the results from the email holdout test, it suggests that email impression last about three days! It is easy for us to understand that an advertisement had its biggest advertising effect right after it is shown to the users and then the effect wears over time.</p>
<p>The following picture shows how’s advertising effect looks like, and we usually use an exponential decay function to describe how the effect of the advertisements drops.</p>
<p><img src="data:image/png;base64,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" 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<p>Additionally, markets defined a term named advertising adstock, which describes the prolonged or lagged effect of advertising on consumer purchase behavior. It is also known as ‘advertising carry-over’. Adstock is an important component of marketing-mix models. An ad stock variable is created by computing the exponential decay of the impressions on each day and then sum up the total stock from impressions on previous days.</p>
<p>In order to make the report easy to understand, we will simply call this phenomenon as a advertising effect. In Model 3, we will count advertising effect into consideration.</p>
<p>By using R, we can transform the original impression factors into the new variables corresponding to four different channels, and their plots could be shown below:</p>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>The Model 3 can be written as</p>
<p><span class="math inline">\(Sales_t=\beta_0 + \beta_1Direct.ad.effect_t+\beta_2Display.ad.effect_t+\beta_3 Email.ad.effect_t+\beta_4Social.ad.effect_t+\epsilon_t\)</span></p>
<p>In order to make the results more precisely, we will remove first few observations to allow for the “Warmup” of the advertising effect based on the theory of advertising effect.</p>
<div id="htmlwidget-c1a1b5007938619c5a1c" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-c1a1b5007938619c5a1c">{"x":{"filter":"none","data":[["1","2","3","4","5"],["(Intercept)","`Direct ad.effect`","`Display ad.effect`","`Email ad.effect`","`Social ad.effect`"],[199.476,0.056,0.004,0.052,0.009],[704.798,0.007,0.132,0.004,0.001],[0.283,7.777,0.028,11.931,6.658],[0.779,0,0.978,0,0],[-1181.902,0.042,-0.256,0.044,0.007],[1580.854,0.071,0.263,0.061,0.012]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Variable<\/th>\n <th>Estimate<\/th>\n <th>Std. Error<\/th>\n <th>t value<\/th>\n <th>Pr(>|t|)<\/th>\n <th>Coef.Lower.95<\/th>\n <th>Coef.Upper.95<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<ul>
<li><p>Positive effects for all forms of advertising.</p></li>
<li><p>Email and Direct appears to have a similar influence on sales at about 0.056 and 0.052 additional sales.</p></li>
<li><p>All effects are statistically significant except for Display. Display still has a high standard error after the adjustment of advertising effect and it indicates that we do not have a precise estimate of its effect. This happened because daily display impressions are pretty much the same every day.</p></li>
</ul>
<p><strong>Model 4: Interactions (<span class="math inline">\(Email \times Social\)</span>)</strong></p>
<p>Until now, we have three models, what’s else that we need to consider? Interactions occured when there is an extra effect to have two advertising channels or impressions active meanwhile.</p>
<p>We model this by adding an extra interaction term into our basic model, and we take <span class="math inline">\(Email \times Social\)</span> as an example, which could be shown below:</p>
<p><span class="math inline">\(Sales_t=\beta_0 + \beta_1Direct.ad.effect_t+\beta_2Display.ad.effect_t+\beta_3 Email.ad.effect_t+\beta_4Social.ad.effect_t+\beta_5 (Email \times Social)+\epsilon_t\)</span></p>
<div id="htmlwidget-3eab6ac91a6ffce4e22c" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-3eab6ac91a6ffce4e22c">{"x":{"filter":"none","data":[["1","2","3","4","5","6"],["(Intercept)","`Direct ad.effect`","`Display ad.effect`","`Email ad.effect`","`Social ad.effect`","`Email&Social`"],[204.787,0.056,0.003,0.05,0.009,0],[710.729,0.007,0.133,0.006,0.002,0],[0.288,7.494,0.025,8.066,4.625,0.542],[0.775,0,0.98,0,0,0.591],[-1188.217,0.041,-0.258,0.038,0.005,-0],[1597.791,0.07,0.265,0.062,0.012,0]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Variable<\/th>\n <th>Estimate<\/th>\n <th>Std. Error<\/th>\n <th>t value<\/th>\n <th>Pr(>|t|)<\/th>\n <th>Coef.Lower.95<\/th>\n <th>Coef.Upper.95<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<p>There is no significant interaction effect between Email and Social at the significant level at 0.05 as its p-value is larger than 0.05.</p>
<p><strong>Model 5: Interactions (<span class="math inline">\(Direct \times Social\)</span>)</strong></p>
<p>Similarly, we did the interaction factor in Model 5, which consider the interaction between Direct and Social impression factors.</p>
<div id="htmlwidget-21a757a85230c8177782" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-21a757a85230c8177782">{"x":{"filter":"none","data":[["1","2","3","4","5","6"],["(Intercept)","`Email ad.effect`","`Display ad.effect`","`Direct ad.effect`","`Social ad.effect`","`Direct&Social`"],[359.094,0.053,-0.027,0.058,0.01,-0],[796.468,0.005,0.15,0.008,0.002,0],[0.451,11.448,-0.178,7.441,5.423,-0.446],[0.654,0,0.86,0,0,0.658],[-1201.955,0.044,-0.321,0.042,0.006,-0],[1920.142,0.062,0.267,0.073,0.013,0]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Variable<\/th>\n <th>Estimate<\/th>\n <th>Std. Error<\/th>\n <th>t value<\/th>\n <th>Pr(>|t|)<\/th>\n <th>Coef.Lower.95<\/th>\n <th>Coef.Upper.95<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<p>There is no significant interaction effect between Direct and Social at the significant level at 0.05 as its p-value is larger than 0.05.</p>
<p>There are different combinations in our case, and we will show the entire combinations in our reporting engine that you can choose whatever interaction terms you want eventually.</p>
</div>
<div id="assumptions-2" class="section level3">
<h3>3. Assumptions</h3>
<p>In our case, we assumed a decay rate for each channel when we created the advertising effect variables. We selected a small decay rate for display and social and a larger rate for email due to our intuition, which is reasonable as people normally have a short memory in display and social ads while having a slightly longer memory in email impressions.</p>
<p>We assume that there are no holiday effects in our modeling process. Take a holiday advertising as an example, the causality is actually reversed as the retailers are buying more ads as they know there will be a high demand than usual, so sales now have an influence on advertising impressions. As we want to know an ordinary result, it is reasonable to think that our simulation data does not include the holidays’ factor.</p>
</div>
</div>
<div id="method-4-model-based-attribution-analysis" class="section level1">
<h1>Method 4: Model-based Attribution Analysis</h1>
<div id="investigation-3" class="section level3">
<h3>1. Investigation</h3>
<p>Different from the MMM method, model-based attribution analysis focuses on the <strong>user level</strong> by connecting the transactions of customers to their prior advertising impressions. By implementing this method, we could get a sense of how the advertising impressions could impact the user’s purchasing behavior. To investigate this relationship, we firstly used the visualization to have a rough but quick idea.</p>
<p><img 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" style="display: block; margin: auto;" /></p>
<p>The above four plots are telling the purchasing tendency in a very straightforward way. First, users of the online retailer convert more on days they get <strong>emails</strong> or <strong>direct mails</strong>. To be more specific, the channels of email and direct mail are more likely to bring more transactions to the store. (Email may bring even more transactions than direct mail.)</p>
<p>Additionally, we could also see that with more Display and Social Media advertisements, transactions may increase accordingly, and too many ads (around 10) on these two platforms would decrease transactions. However, there are fluctuations as reflected from the plots for Display and Social Media. We then implemented model-based analysis to further verify our findings.</p>
</div>
<div id="results-and-interpretation-3" class="section level3">
<h3>2. Results and Interpretation</h3>
<p>By implementing the logistic regression model on the dependent variable as transactions and the independent variables as the different advertising channels and whether or not the user has purchased before, we obtained the model summary as below:</p>
<div id="htmlwidget-99557df47be3ea9b6925" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-99557df47be3ea9b6925">{"x":{"filter":"none","data":[["1","2","3","4","5","6"],["(Intercept)","direct","display","email","social","past.purchase1"],[-4.023,0.413,0.101,0.762,0.18,0.957],[0.014,0.042,0.007,0.02,0.005,0.016],[-287.078,9.896,15.291,38.099,33.621,61.079],[0,0,0,0,0,0],[0.018,1.511,1.106,2.143,1.197,2.604],[0.017,1.392,1.092,2.061,1.185,2.526],[0.018,1.639,1.12,2.229,1.21,2.686]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>rn<\/th>\n <th>Estimate<\/th>\n <th>Std. Error<\/th>\n <th>z value<\/th>\n <th>Pr(>|z|)<\/th>\n <th>Odds.Ratio<\/th>\n <th>OR.Lower.95<\/th>\n <th>OR.Upper.95<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[2,3,4,5,6,7,8]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
<p>From the statistical significance perspective, all the four channels and the binary past purchase record have significant impacts on the online retail transactions.</p>
<p>From the odds ratio perspective, we could tell that <strong>email</strong> has the highest positive impact on the transaction - each email could increase 114% of probability for the transaction. The second highest channel is <strong>direct mail</strong> - each direct mail could increase 51% of probability for the transaction. Another notable fact is that the customers who have purchased before will significantly and positively impact the transactions - they are 160% more likely to purchase.</p>
<p>Relating these findings to future marketing strategy, we would recommend the retailer to encourage the users with more than one purchase record to register for memberships, and design the email sending and tracking system specifically for those super users. In this way, we could match up the regular customer group with the most effective advertising channel and would likely to maximize the efficiency of the marketing efforts.</p>
</div>
<div id="assumptions-3" class="section level3">
<h3>3. Assumptions</h3>
<p>In this analysis method, we assumed that direct mail, email, social media, and display are the only four channels that the retailer applied for its marketing promotion, such that the effectivenesses are only compared between these four channels. Furthermore, there are no special events (holiday, big discounts, etc.) happening in the same time period (as the date in the dataset) so that the impact of the advertisement would not be influenced by other factors.</p>
</div>
</div>
<div id="limitations-and-uncertainties" class="section level1">
<h1>Limitations and Uncertainties</h1>
<p>For these four methods, each of them has its own advantages and disadvantages:</p>
<table>
<colgroup>
<col width="28%"></col>
<col width="35%"></col>
<col width="35%"></col>
</colgroup>
<thead>
<tr class="header">
<th>Method</th>
<th>Advantages</th>
<th>Disadvantages</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Attribution rules</td>
<td>Easy to compute and understand</td>
<td>Ignores non-transaction customers</td>
</tr>
<tr class="even">
<td>Holdout testing</td>
<td>Simple analysis</td>
<td>requires planning in advance</td>
</tr>
<tr class="odd">
<td>Marketing-mix modeling</td>
<td>Radically reduces data size</td>
<td>sensitive to model assumptions</td>
</tr>
<tr class="even">
<td>Model-based attribution</td>
<td>More flexible</td>
<td>requires large dataset</td>
</tr>
</tbody>
</table>
<p>Limitations and uncertainties of this case mainly come from four aspects:</p>
<p>Firstly, this dataset is not a real one but simulated for training only, which means the result may not be a good reveal of reality;</p>
<p>Secondly, this case is limited by marketing channels provided in the original dataset so that we cannot exclude the impact of other marketing channels, such as friends’ recommendation; also in our analysis, we count all the sales as incremental, assuming that customers who saw ads would not have made a transaction if they hadn’t seen these ads in the first place;</p>
<p>Thirdly, each holdout test was conducted on Tuesday. Therefore, it is hard to see whether there would be any difference if the holdout test was conducted on other days during a week.</p>
<p>Last, the dataset is not large enough and especially after combining the data, the informative data is not enough.</p>
</div>
<div id="areas-of-future-investigation" class="section level1">
<h1>Areas of Future Investigation</h1>
<p>By using last-click attribution, experiments(holdout testing), marketing mix models and attribution models, we are likely to find out the reasons behind the increasing or decreasing sales as mentioned above.</p>
<p>However, there are still rooms for us to make additional contributions based on this theory when applied to reality in the future. Correlation is a complicated magic that people may spend a long time on it. Also, it will be interesting to focus on the trends on different channels besides the impression factors we mentioned before.</p>
<p>We may be able to explore more about the connections among different channels. With the rapid development of the social media and the internet, the traditional way of advertising has been changed a lot. What we studied in our project is just a small projection about what is really happened every single day in our real life. The simple theories used here is still useful in many areas of today’s world.</p>
<p>Moreover, breaking down each advertising channel would give us a more explicit and detailed overview of the effectiveness across different promotion platforms. For example, having data aggregated from social media channels - Facebook, Instagram, Snapchat, LinkedIn, etc., we could investigate the advertising platform with the highest cost efficiency, or ARPPU (average revenue per paid user), and the time slots with highest response rate.</p>
<p>Additionally, adding back the users’ profile data would also largely increase the potential of learning the user response behaviors at a granular level. Customer information such as age, gender, education level, and personal income would help us do customer segmentation before implementing the analyses, which would distill more insights of advertisement response from different user groups.</p>
<p>There are still a lot of potential areas of future investigation, such as Marketing optimization. Marketing Optimization services and tools can help improve efficiency and profitability by pinpointing the level of marketing activity required to maximize your sales and profit and minimize costs. Methods we utilized before could be excellently generalized in this service in reality. It helps the retailers plan budgets, manage multi-channels, create pricing strategies and produce optimal revenue for a single brand.</p>
<p>Marketing data could be quantified by using these different methods and thanks to these methods, we will be able to understand that why every dollar spent in advertising is actually working for your business.</p>
</div>
<div id="references" class="section level1">
<h1>References</h1>
<ul>
<li>Marketing mix modeling. (2019, April 24). Retrieved from <a href="https://en.wikipedia.org/wiki/Marketing_mix_modeling" class="uri">https://en.wikipedia.org/wiki/Marketing_mix_modeling</a></li>
<li>Lohse, A. (n.d.). TV Effectiveness Analysis is Incomplete Without Ad-stock Consideration. Retrieved from <a href="https://www.martechadvisor.com/articles/ads/tv-effectiveness-analysis-is-incomplete-without-adstock-consideration/" class="uri">https://www.martechadvisor.com/articles/ads/tv-effectiveness-analysis-is-incomplete-without-adstock-consideration/</a></li>
<li>Marketing Optimization. (n.d.). Retrieved from <a href="https://analyticpartners.com/marketing-optimization/?gclid=CjwKCAjwk7rmBRAaEiwAhDGhxMBUlY0olF0IYV-oplCVBS1f4HbT8dV4p2aRmEaUghp50RSXM1-sExoCXLIQAvD_BwE" class="uri">https://analyticpartners.com/marketing-optimization/?gclid=CjwKCAjwk7rmBRAaEiwAhDGhxMBUlY0olF0IYV-oplCVBS1f4HbT8dV4p2aRmEaUghp50RSXM1-sExoCXLIQAvD_BwE</a></li>
</ul>
</div>
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