olalidmark/xgdemo.ipynb

## xgdemo.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Expected Goals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importera skott"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Pen</th>\n",
       "      <th>Goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Pen</th>\n",
       "      <th>Goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"events.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lite statistik kring skott och straffar..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Pen</th>\n",
       "      <th>Goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.257800</td>\n",
       "      <td>49.824800</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0.117400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.604926</td>\n",
       "      <td>13.411938</td>\n",
       "      <td>0.093404</td>\n",
       "      <td>0.321929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Pen</th>\n",
       "      <th>Goal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.257800</td>\n",
       "      <td>49.824800</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0.117400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.604926</td>\n",
       "      <td>13.411938</td>\n",
       "      <td>0.093404</td>\n",
       "      <td>0.321929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisera avslut och mål mha deras koordinater. Målet ligger vid [0,50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f13a2249190>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hbAwdCx+27cA6evvPeaVvebdlGQ6FkRBWMuWVJOel0XDbQdiJg9lBUGsEUA/8mvupo505RkLDeMmwb9N7LnQdazlU7/G3oRoIB8pmcK0VIz5YN0Ihc1C94VlTMsJ65nc/28XnZqb8vJlRNcJo5M//q2s5hyrCdmfTiNK3wDYmfgW++ngogr1V5WTXMRFDOMiQRiJYTh0XL80y3EtYrI+y3FFCAvYOjSFnz1LZfwAT3fo3Q0SZTZT9Za41Ag2FcCFVErd9N2zPKpf63kZ5I3sbH2RhS6lj12fsBi70fMDdVzIcusucrIjwXCPgQk+YSRwCPDbYQYRG+LbxnjWrmc8Wn2kEO87nVVVOdPxDY2PH0gjBZsqFnuVr2zwk3EDBMx7Jtc81HPqO35m+21YKGYpyNXFMlSAeXINGCGs9pW6g42B808/nzn+ukS/om/B4LaAWfLqLO1AJOFwxnOpYFjNlc7kjcUo58HMVLva2XutzPUvFyLVdTi3wvOzlvjeNuxFOhVvUABT4CuKhCPaJ8wE/lOtTp2ogrKysMmtDbKbUAj+k5GRf2J8qo/3+bQV7VWFXJDRzZWigmW9bqIVCvMM2vOd8ofXGbXpsYN3Crh2O9dFA1TEa+S/4uZ5lKVW+NRLcVSonMsKjtWBbHfp4bBi/RQlLrp7muZF+qQWwtoMaJxt0ww7dQK9UAljNHGzjPJqrsKvkNfdDoRKK33WUDTxRC/m4o8z2HZOxvy/mEviFkeCeUzgbMDvsyA5VAq4kOe3c34d95/stnm8EfNxxXL7hWi9nStu6reqmQGjlynjkqZyxSED8OR7/AnsqCtx/eCiCfclAgPjW+02BsOeUoDpENjvP6JCf7hMKBIFltu84FsW3dYLKAeytGKw6ZhPoqudKK4Ydg2LZiLfvvZHHVWjskIW1c+8EOX0jJ5sqK5kycRfVLbnzO4+ZgarjYMWwv+x7AFYzx9muY90qo6HXi2/XHAX+vJYDWEgdi4nSdZ5jboSe2tgOkUAk3lZ4M53Vs17llAwUJ7OJIxI4VDFMRcKeOGA8UuYS6KnfdewpCUerIceHhX+9mnGq46gHwt+cMHxjKERVmU+8iipR2BX7ZrcvSv/eCIUXh8Pr1DjfHAqoBoZ329mWa72YKhGeOqxvukn71tNCzw+FnO85LvV9T8WhSsCRwoP/vkEr12sP7UYgHK0axnboUblbeCiCfSC+cPVxxzEaKbFAy4KoUNm9C2Yu4NabSGMIsgxprsGBo6RhzO00z5aMl1me6wpfb0AgsJ77P6d3aPapBsL+shlorz2Pu5YrNSNM7vCA2NgN3IhQlLZVJm5jzTeDqvJuy7KYuWsZ4ccdy2qm7CvBm+vePqAW+DX/fC3npZFw24AvIoyF8P8u5kyGQjWEuUQ534Pnd+D5jfhGrfdbjpFIr3XQehtow+vNnN7ADC1XeLfleKxqeLTms+GnGly71tb5wnclEH5zm5bo813LRx3HaOi585nEMZ8qL42EX0izFcBwJHxj+PpzsZg6tmPgjSiTseFK3ze71QJfMG/myjeHAsKb7MgKfLlo58orazmh8TvbjVrL8aHgjqW2t4KHItiDz/piA2e7jpaDqYEaZzkznHzmG9SuXsDNz0O5hDn2BEzuo2R8UfHcoLN1V8lwqGxumu09VjVUDJzr+Q7J3YMOyZsFjCfqAdXA+9nnDvaWDEerwY7F2Y3dwBadvROqg238uZ4PykOBcOQm/jMbuNRz/HwtYzZRdpeEl0YiGiEsZt5SYQPTsTCXOJYSzxVveO4Phd4o7mzX8tw2E8xVlbUcnm8ELKRKK/e1iZHQsJI5JmLhUs9xZZClH6gY9pYM+8sBAXC251jKvBrn+aGAdu4L4RsPxBCYMsrZruO7YwEXuo4/ms9p5vBkTfi3pko79jpkzrtrNpOcV9e8Nn/j+LOJu6tFcFVfU7jYc2Tqr/WBm3Tv7nStrfP2F/vLhlMdv1saCuGFoYDJexgwCnx+XOhZAoHhQVIUBj4hPNFxTMb3bia03A+62+PHj+ubb775pRy7b5WfDfxShgLPLa9kcLRqKAl81HEMh55SaFqlJMKLX2C2txPeWc+v88ZZy5WqEZ6qB7zWtEQGasZzvx2rfGs4YDzePghc6jl+/2qfWiAMh9DKfbfp3xiPWc3dlh3GfN+yZtnSuZqr0rHwK2Nb6xyZU360nDNZki2vJ06JxdC0fgex4XNztGp29KP5ZOANdOMuYjFRQnH81ZplckChLWWeDvoP9sSMbLNbauXK/zjT93YOoa+XrFgQp/zd3TEvjsbbruFOcKJjBz5I/gu+litDgeFbIzu7aL69nrOQ+uL25mu9kw9SgfsbP1nJiA1bxBSLifLy+O11UovIW6p6/FZ+9nNl9iLynwP/Ib6d8wPg3wd2A/8cL3p4C/gtVU0/z3HuJcqB/9JsdkJ8qm7YHQs/XrXXKXjGjZch3otsby75NLPfM8j2bqas+Vo9oN6Ta66Xe0uGR2oBJzqWXGEtcZzJ/bCV0VA42XG8tEOw//FKxlAgjA+oplIAkjjeaGY8so0eUYxhRLxaaDlTOjk0IhgOPPWUO+Vy4pjpOYJBlr479kqQGx0sew5Uve59QxceAdNGOd+zHBxQLzeiHviH2Frm1xEa2BMJmcJbbcuBslAK/O/bG/gH2rvtnO+ObQ3c6hwfdSz7SxANJoxNB3CuY7k88Mff7Hp5uLK9fcNnoWf9Z5repLya3KS8mt4hI/96PeD8Ntf6fg/0q5njfNfRsspE9Kmn0cOOoUhYzZTNm+10oLy6l6fnjisCIrIX+M+A46r6NF4a8XeA/w74R6r6CLAK/P27sdB7iVogPDcU8m+Mh/y1sYiDlYBUBQfXFXTBqx3u9rCPU13H2y0/kjA0cKZnebNpsTfZdW1wsi+PR3xvPOSpRkjJCDM9x5luzkqulIznB093PT2y00SqK4lj9IZkfDTy2fVwKCyn/r1WleXUSyGPlIVX13JWMkcU+OLmW+s5k5HwTsvySccSGJ8FvNdynOg4Hq0aVjKfyYMP1olThkOzxZdGRBBkUJ/YivFION91nOpaAvGdxK+2LB2rIFwL9BsYCuFSf/vf1XbCcCB0VbBOUfUe/9UAOrnyWjPnUt9SMp5ffa2Zc/kOOlt7TjE7Ka/ynd+307W+n7GYWF5Z8x3BZeM7hF9Zzf31echxuByQOL12LhLnhRWPVu8dhQOfn7MPgYqIZEAVmAV+Bfi7g///+8B/DfyTz3mcLwSbT3RsPEvqVK9TMGwnBbxVbGSIixsZYtVQNcK5nmUqlmvHmbiFbG+nda9b3zjTs8pV6y0NygKL6c4DxscjYTFTEnX0ci9tLBuYjIRvDAec7cJMXxHxuvCjFcOb6znPNAKWM8+/NwLhQMlwoe/oWq7r3pw2XtFzqBoxGVr+ZDFjKVUOV4TvT5doBL42sBXeuGwlc1zoetfLyZLhYNmwlnsHzCuJ8n7LERvhiZpQNopTr5/fPPSkY+FoxSt4LvUcc4mjHAiHKsaPg4yFsigrFnLnP08cGUqBv+YbmXyEb5Y70XHsLt3evNuSkcED9wYqS32h7lZwq8HADWoDM32HqvfG2VO+8/m8zUw537e0MmU0Eg7dpItZ1Q9lH97knTRifH/Exa7lyZs03j0MGI78jISTHcdiqlQNPNcI2HOPmyDu+Kyr6hUR+YfAJaAH/BmetllT1Y085TKw93Ov8ktAbHwgONt1jEefauYNN59UtRP61lfgLUo9ENoDl8hDZYMgWwJxbKCZc9te+oF6HXY9FBqB0MwcMzk8N2S2PLg28Gzd8N/PpExGMBIZFlLHcgr/6f7YB9F6yOM1H4xFfMBq5rC/ErC/Ak59a3+uytmOZaJk2BzQRAQReL+Z8cOVnLFI2FeG1Vz4o/mUv7cnpmK8Fnw4BIvvlN0dG9q54611Sy3wYwBn+parfUfDwFzqCI3wdMOgCuvWH+frNcM7bce+sqMcGJYTv2t6thFcU/A0Qr9reL2Z83Q94PmhkDealiNlX59ZzSE1wnQcbKHTQiPY3Bftq7fx/awF3tPo6qDWEuCvccWwo/LqTvFh23K5788nAh+2HUuZ8mwjuO3scTVzvNbMKRlfA5lPlauJV15tF/Bz9b0NN9ZnaoH3KSoAo5Hh2yM7fyfvBe442IvIKPB94DCwBvxfwK/exvt/B/gdgAMHDtzpMu4pHq36YPN609J1cKQs/OJYdEcOljN9R656TW8fG/+f52GVGxuEMvW8dHfQYbuYOBqRcKh8c2WNEz+1KkPoOv/QOlBW3CD4XulbLg008/tKg2zPGF4eDTjZcyykPhD+9TGD2ZQZbw4QRoSq8Tz46a5v5toVC0dqAeOxwbqt58dayyttb818pudYz5Xp2PPxbzVzXp6IOd21zCVefnq0EnC4LPys6YedbNAWY8bTSjN9RzODQzVh48ESO8dMH76/L2SiJLzSdPQTx96y4TfHI3L1Q+OtwuW+JTbCVCyc6jh+ZTSgHsDrTUffKQfKhpfHI1pWudS311RH4Bv0BOFO2hmeqgdUe8KFAf++qyQ8Wt36QPk8WM/9XOPNct9yyfv5H64YRm7TG+dExwLCfOJoDwbr1ELD2R3mEITi6z6Z0+s+V99xzXajgMcX2ffwefZTLwPnVXURQET+EPgOMCIi4SC73wdc2e7Nqvq7wO+CV+N8jnXcM1zuO+ZS5Yl6QCieY/6kY/l2KLf95VzO3JbiVGwEEWUiMixuUtY0c0+j1ALh52s5OtgNrGbK1X7OC0MBEzvQO5VAqEWG4cFg7ly9umYoED5s5cwk3nJAgA8G2d5y5vjaUMjXhz8dAaiqN83CKqL8P/Mpo5GfgnWm63i/bfkvDpZZtd4aeST07o2rmeflL3VTZhJPLfks3ZE7R9WE/Ma08Ewj5Gt1RfAPl8T5cZCNG2izWijMJUIl9NekavxxmjlMRIIxAd8ZDXlx2JHzqW3CW82Mcz3rH1aDB+knHU9LZIT80ljAL4x4S4INCqgzoN7ag2J37pTlzEts78R/foN/f6RqUO7Nl71rdQdvHJ9xj9yGN45T38B3NfH9KSUDS7kyl+aoBjzT2PoeGfS1vN9yjEX+fupZpW+Vww85hfNl4vOc+UvAt0Wkiqdx/jrwJvCXwN/GK3J+G/gXn3eR9xqp8z7j86mjJMLBig+WJzte/71RpK0EwmLq+d79t6nGaQTCXKrXeZPkqgQiPNsIuND3GbxVr2U/VgsG3Y96rbMuNr5J6ETX8Z0d9Lh7SwZRZTH3PGvJwKGKt2u4mup1SpByAHOJw+CzrkrANaVM3/kO3g3u93LfN/fsLwu7Sob32sruEpzrKSc7/kG1vyR83LX8rcmYU4MsXYADZcOhWPlHl5RyoAxFn6p+Zrqwkn7qG7Q5+IXCtU7dxVTpORgNvfZ8VywMhZ5aW838bmB/SYgDuVbsNcawWXvTd0rbwoGKXDufkfNWBGYwH8AYc51qoRYI3xoK+aRjfdeqwBM1w8HKndEuqspC6v30c1X2lLye/24OLikZ2C7cK3LbA1qMCIsJhCj1wUM3MrCQeIvqnbCvZBDgTNd5+5AAXhje2TupwL3H5+HsXxOR/xt4G8iBd/CZ+r8E/rmI/DeD1/7nu7HQe4XMKW80c9rWZ24t633Fj1QMlm3UOEZYzpT9lds7zoFKwKUko2t91+xGhvh4zVAKDMdqnjZSuFZEW0zclmEk5UBYTHZ2Qjxa9buEJ2pCKGCdsprDnpIwm2ydcGUEpiPhSuoDZsn4jLqZK8cbhg9aliuDhh1Q3m0pexLHiU7OhQQi8UE/cXC+76i2LP/OLp+lPz3I0o0I3dwxHsFSJpQDR2wMvdxLM2vB9hEoEGEkgH+1ZJmM/Wc/31NSB39vT8ypjmMkgsdqGx77PsjsRLOFxlMvXatUA8GqLy5PRHKN5toOw5GX51pVDLdeJN0OZ7qOU11HI/TE3ccdy3yqHB+6e55GI6EwMlBRjUZybd7w0B3MO3Dqr/1SJtdomdT5Yn3lJj47IsK+csDeksHBfS8TfRjwufZUqvoPgH9ww8vngBc+z+/9IjGfem+Xa12ixme853texbCaKQupl/aNRYZQfPZ8u2iEwrP1kJ+sZlwdBPFvj3h2iR7qAAAgAElEQVTHww3c+GWvh16V03GOZu6oGWE8NkRmZz3uWGR4smb46WrOQgpjMbw4HDJVMlzuZZxoZZzuelfEx6rCcGTYVwmZLnmHyfVMqQS+07UcGK4m1/uylI0yO1BmhAKjA+1+Fbjasyxlfs7rbOK40veF7X0lYTKCR6oBE5nlQiL+84jwZBUeq2+/S1L18rSjFeFE19G1vsHrcMXQt/Ct4ZATHctS5ndIRyqGR270Cd6EidBwsCxc6jtOdpRYhCfqwvg20s/tsF3AOtnJeXvd0smVJ+qG5xoh1dCwljreaOac7/tdzwvDEVOxp7um4k+vdTnwnkbLmTJ5lzyNRLyK6lQHriT+Wu8uGY5Vb/+BYkTYWzbUAm8f0Rk0Fh4p+2Lzrayl6Oe9P/DQE2jLme9G3IxQBCNg1fGzNcv0YEj2yY4PcN+5Az/cxCknu5bRSNhVDsidt6Kdjt2Ofhi7YsMPlhLKgefFFzPHya7lNybDHb+0Hauc6Q085yveJ/18X5mKvVPmB13L7kFQ+cEyPFJ1/I3xkCgO2FUy5OrpExHh6sB3ZTNEBKuOuhFaVsisEgVCkvugUg/gvZancIZCSBTebilHqoZfHA3502XhuyOOMAjoZY6l3PDS8PbnM1Xvn7OSe18bI5A5WEodV/uOR2sBx4dDcud3JZ8VyCZj4UoCDuFYze+uzndh/+jW2bS3gp+tpvzZcs5Y6OmRv1i2fNJRfmMi4A/mcnrOT5261Fc+aff53liIyDbKK/FKpMm716hLbISnGyFP1D3V8nky60erhndb8PW6V47kA+uLI3dIZRX4cvBQBfu1zLsAdgeZ/L6yoW6Eeeeob9rGqypOFQccLAmnut4JcXds2F32VM/wTYpcy6ljJnGk1qst9pQMMz1H6j5V4zCYx/rJwA9jeeDRvuGns6dsaFrlaFVo5n6gdyheS97K/Rq3oxPOdS2t3HG5b1lIfXPU4XLAK2tKR5XHK4bmQC1zrC4kVplNhfHYH38tc4xE3nMlNt50bClxLAyKtdORYFR4rB6ynOSc6kLX+Z3Ko1VhbznkUi/ncs/xXkcJgOeHDOqEXx6LWMwcfzjvWM0te2PDb+8OOVgNru0GrvZ9Z+H+smEkhCupI3PKua6jbWGq5PsGNjfnbMd3b3et1633/ZnpKyfajnIAj1YMqfrAvz5QPvWtsmvApW/QFpf7joXUXRtAEgv8eMUyGQlNCzaHocirfP5o3tLKNwrJvjZRC4WfNC1P1wxrmWE+9d3SE7Ef5F4JvDx3pu9YzpzvW6gEOw51uVXcDfpkbzlAgdNdR2I9lXl86NbtrQvcH3hogv18Ynlr3VIJPG97umu5kji+Vg842/PBtDbgcZcyr5A51bU0rbC/YgiARIXlxBc1dxoxeKln+bDtpXqheG+dq4lidasap2yExdxxom051/cBMxD4sK1+y5z74xwWH3Q3stebcfanO5a/WM0wItQMXOzDmW7OvlgIVDhYD9iYo2KMn9d6oZtxsmNA/C7nct9bHRwfMiylXo0xMXi4vd6DwxXDi0PC710VHq0o5TCgkzsWMuGXRxx/NO+YHahxVOEP5x3HaobpcsB7LccTNT/gu5MrP20qR2uWC30/xLwRQi+H1wZ1k35u+VnTe+BUjHCi7YvYX29sPwzmZtc6EmUp9UXrx2sBFj8zIEq9gduZnr9GkcCJttfzP9sIeKtl6VqviFrOlMv9nOnIF45Xc68sCgSu9H09Ya5vqYQBHeeLpa0cVhyE4ljPhVPrOVOx90Z5v+Wbj14aDnilmZM5fx/OpcqlJOPbwyGj99j69lawwb9v3vkVeLDwUAR7pzqwN5ZrahNf6HSs5cq3hkM+aucspj4TPVoJ2FOCHy47dpchGsjwqnhteW+Hlu/M+WlM4zcoeBYSpWK8Bn7zZEKrih0UNnvW8X5byRwcKguZCvXA0M6VjlPWM5/9XZva5Cw/ayo/WclIFV4aNvzyWMTprmU981bHK5kyFAmTIVxIYH/ZX+5N8nmc88W3Sqj0cmU2V+qhX/cHbUdDLI3WGieWW4jA4bEGtdooYDg+ZHir5egnSiMSXhgyzKXC+b5lf8WPHTQCE5HyQSvnjxdhKv60T2C8BFf6jh+uZFQDP/TljXWf2T9aEc52vWplfwkSvEHaaCz0c+/J088d77VyPun6OsPxoZDDFbPjtW7ljjWr1ylpMue40PPNR1Pxp6qYDS79/XZOJ4fJAT9dxstiz/csC4ljd8WwmvvGskYgrGSWiijr1jGM7/SNBWJRFjMhNspjNWEp9Z26ewa7yxNdR8f6bH8u8Z9nKBQ+aVteGv3ygz34AF+IaR5cPBTBPnHb2xzUQmEpVQ5VhJdGI1LnC4qBCO3BYOaWhRHxXGXf6qAZavs7vmvZ1k+nHEDFeCvfvvhAkquynMJ0bPjL1ZQz3UFmb+DnTWU4MPzNCXht3VE23jxpPVVOdOA3JiP+YC7jz5Yt45HPKn9/1vHmumM+sby9bgkN1APhcs9xTuF4I2A0UK72HbsG3PB8BiOR5+DPdv1nLBmYS8Gpo2KE7Nx5ktU1jlRiUOhfWGKuucbykSOExvDCkN+NWOdppdPdjFT9eMZqIKhTVq1/EM72lMdq11+D0RA+bOXkGFYzx3AkuAzOd5V9JS8TbYQBtUBREZxTVsWwnin/bD5jpu8Yi2Atg/+tnfDLowGJyrbXei7xmXZ/YCPhVGlZYShUMpUtdFDVwLmusv8Gbjo2ghFvpfBByzEdQWCEc4kf/v5YPeC1dUcndNRDYS1XlnLlyYoQmYCDZcPhqoJ6CmotU061c1rOK2fKAXQy7yezKzJbmpMKFLgTPBTBPhQQZEtrcupgfNMZ2BzEI+PdGhdTx8m2I1VlKhIOV4MdJ8pEBlBlPYf5xPuVj0eCKBxuGMZC4X+fSznddYwEwvenYnaVhA/aypGKEGyamXqqbbnYFw6UvdvhbNtRNsIjFcP5bs4Pl3PGIpjPBFWYjOCDtmUltQNawT+oYhGqKFdSxz+cKvFHpxf4+NwMqHLowB7+9tP7+OOlnMT5tS85b42MQGe9TX9plbAxxAUCRGBPrcTC8irxWJuhUBhZnsV0WrihERbGdmE1IBRhKBR8o64wGinzzg/0Xs8cbSv0nadFVL3+e6Zv2VsKaA8agkYGfQ77K0I3t/xsxdK2XkL6tbrBqnK2m9PNlXfXvdnWo1XDT9csX28ErOeOpVQHXZve4nh32VBJlQ/aOReSgSdJPWDPoPh7Yx0kHUy56ubQzP2s3bJsZPm+azgwytmuN4mbDOFwJaAW+qarxcw3egUIj1fEd66qYy0TFlL/YJgYNJ4lznsa7RpM7SoZ6OSO2dTdUyfEzVBVkoV5ktk5RITynj1EExMFZfMVwUMR7CPjG6XOdx3jsZfp9a2SqbK/sv0pKBlvCXCx79hd8oG4nSuXE+V749vf/NVAQOCnK7m3RjbCh21HJRCeqgv/+HLKauq9zBOn/NPZlF+fCKga6DmhJj7Y9J13XLzUc4xGhkZgGI08b990cLnrmE0sV9OAkvEK8eWMgVujV7HUjO9aza2joyCqXDpxit2nL7C/WkWN4M59yIxbJ9jzGGe6lkbgjavmU6VllcNZyiUtsZLFDBsHCq9rzKSLeGZ1gXDmPBoZKFfQixcoX7jEvq8/x5mggsGPDnTOsZwLu0sBL9QN/9+aZVcMVWO4kFqaObw4JHzchUt974HjVJm33nDMWsufLCm1gST2Qk+5lFh+a1p5dU3pKdSNsJ7Dhb7lcBmeqgX81Yq3p/YqKk+7fX8y5B8vZTQzx1TkdeM/b+b8rTjkyXrEzKA2YcR3fDoVnq4b/s/5FKd+F7TulBNdywuNgJIxCMozded3fk5IVSmbgD1leLLulT+BKB0r1AIhUeW9tYyJWAhEeK+nDIXCdGxo9/RaFm9V6TthJPy0XnOv0f74Y3ozlwjqdVAlmb1K5chR6o89du8PXuCe46EI9uCzvq5V3m56SdyuWPiFkZBG6DngD9o5p3pKw8CzQxF7y0KqvknnbNeRqddB74q8idl2apzc+a357jKc6/pW/elImIoM/3IxHbhYGroWKiFUnOMHK5bdkVINA1ZzBTz9MRUZQpRuu8PU5bO4+VlkeJjekceZD4dYymAsslzpe754NIK1FE/rGE9r9FM/9WkqhqF+lytXzhHWaiw0W6jC+FCdK+cv0qvvYthUudj3wXc4hIMlQ1cMnSCkgnIl3/B5d7SDmOHZGeJKiUtBlZ6DajXmQLLOvpWrDB96jD9ZyHm9aTF4P5h/czJi2MC+2PGXKzkdB5MxvDzq7Xpj50iAK30f2PaVPGXzXkvZG/uHXNdBNYJI4fWWYzGBfRVD3/k6xKhRPu4ov+py9pUDznUdKY7xQNhXEU62Leosl3qOn656A7LnhoSzPeXXJ4VADJf6iuIptReGA9ZzZZKMk3OrnFpZoxTHPL5vkp7WsSjOWX7SdKQKh0rwaC1gNBTKzvGj5YxLidIwwssTEVOVABCmY+FcX7HOsadkmIiF4dBwuOo7hdvW04n7y8LQoGh/u3DOcbrneLflrZifrgc8Ub3e7+i6e3d9nf7lGeKpqWuZvNZq9M6fp7x3L2Gttv1xVFlIHLOpX/Pe0merdNQ5v4OYm8dEIaXde4jHxm7/Qxa4LTw0wX4l9dK5A+XA67VVOd9XhiLL/zGfc6XvM+6rCu+0+nxvLGA59e34e8oGEc87r+Teu33vNmqcjoWlTOk434gieG+a1Vx5rWnJHb7l3njFiVWvU5+sB3Sc8HgVZMBHJwYO5R2u/OgHpP11wnodtzBPfvIku77zMj07wUeJp1wkgKWOz4SfrXjFTEW8FDDJYS6BZ1yPhZUWzeU25ShEgKX1DkOa015b56NymViE0dBbJXzUdTxZKtMMSvRzqA6oq5nUUZWIuks5F05QD5RR8fLEK1Gd764v8Gb/UYYjw0sDuqNlhfm+clWVH69aBP9QamXw5yuW7404Fq2SWKERgXPK6R5Mh0rb+catIfWDs1U9vXKhA7mBxcxRCQwudyxZEIW5vpc77h607FuUZi58tJ7wZ8tKy3q3yraDH60oy2nKb+8p80Q94NGqVzqVjC9IfrjS4fxrbyKtFrtrdchS5i+cxH7jG3wiQ7zVVmqB74b9pAtXUsuzdeH3rlh6zmftq1b5/asp/9G+ErVQ6DlvLYH4Iu1yphwsK30Vnqp7F1QRZXUwMe1OOmv/dNk/bEcGPkgn2o7nhgy/MbW9jWreaYO53oJDxK/RdtrbBntV5cO2ZabvB6QrMNPPebwacHQHz2Z1jtYHH5DMzWJqNXCW/swM9SefonKfGiJ+VfBQBHunykcdL9/7tHnG+9z81UrGxZ4jwnuUl40wXTL8xaoF1Gutr7XzCxe7Xru9HQTlUs+yp2II5dPs5kLXgnqrWJM5lvMNZQ30nOHfngr5warjrabDknOoZPg7e0qc+9cf0kvWuDi8mxULtXqdI0mT4J1XCZ/5dSoGlnNwg2y8ZMCGwtGKV35sZLxHywptw2KzTTQ1zWXntd3j5ZjlhVmW+znVqp/6NK9QExiLYC43dEanKbdXmM3855soR3SrY7TmrzIlGXM2pOe82mgvGfNBlW7uDcw+avvA/mTNsJILP17OIMnopV1WE0e9FLAa13gtCKgDs4nySdtn9kcqXiLZzSE2lnXrH0IjxtNU1cArY1opXHXe32dPGWJgIVWO1ZXypus203N8uK6sZLBr0GwWA2VR3m5DZjOgRGjkui/FlctztNdaMDFBK1fiSolGVOLMh5/wyZEXGA+9fNOqr7V0cvjjRT/mcm8lwA7O56iz/OlSwjcaIYfq1zfFne84ysPC8w3Dia4jcY4A4bGq4dCgOLyS+UYyi3+ITUS+OStxvui+OrBD2Fs2tKzy5lrGZOx7AMDfa++s5zw/FLGvvDXzNmGED9c33tSChNs3lazlvv9g16ZO2loAp3qWvTvMas5WV0nmZomnpq69ptUa7VMnKe3ahYnvYmdZgevwUAT7xLG9e2Ig/HTNcqrjSNU7Ia7myoVezkQkHK6In2Jk9NoXKzJeebEdFG8/0MlhKPT8ezJQfuwuG/5i1REFPhtv5nC6A780Ci01TMXwq5Pel92I13PXzpzktBlC+j0mRcgdnDIVDq8t0Ov3aQVlKgII9AZDN7qZ8mgt5LnIF6DLg3pAOyuxGFVorXWJ676FbLnVpR7UWDFlriaeK64Hgyy9r0yVoGsC1oYm2cjrruJ3DUt7DjJy9hMaIxOMRAEuz+mtr/PB9NO80sxZTH1BVhVeazqu9B1zrYRmr0eojlCg3bO4bossrdGMItrWm5MpcLLnP9N0DO+0/TmLA7iQ+S7a3xyDH677nVMj9BLSS304VvGa8K7zMkcz2HWEAqsDaX5rMBIuVz8W0QCXEsPBbZiK9bl5LgdlXN+rolo5XHEhlTSln1t6GhCLnzDWzr019QcteGbYMBQJTkEEAon4uJWj+N1iI/D3R88qpcAX1HeXA6ZLhsT5HooNddC5rp/8VQ28K+rlvh+7eLRieG3dkjl/jy2ljgs977uznMH6phGQK5mSKiwkbttgH46OEpQq5K11wsYQAFmzSVirEY2MbHu/N3Plxjk+RryiqDW4729EtrqC3BDQJQgQlLzdLuice4iHIthvqHHaufcg6TtlJBQiEXrOsZA49lYCnwkLVI3jXE/4zmhA1yqvrlr6zhccn2p4NU7uvHvhfOo5dt9t6R0eNxQ8GV7Bc7BiWE6U8QiWU08VBfhA1nPKiVZG2wnvruckCk9WDak1BBoSp13Ol3axGJRouIxj/VWWc6FHSEW83Sjqg3rifNCpBfBR23mdfQBfqxmGSyEzB48Rzl5mcXEVVGkM1WnvPUQ3LPks2sJa7iiLUAn8SMNyAJGDlvN0QGMwJ3NueA+SWbJL5+ikOfVShB55itPVCWZWlSmT0elaEKERxZzpCkmnS6iOJIrpIoQoJktYy/q0JaIxsFcQoA7MJH4I/J4Y1gaZfSwwWfIDFEZCaK+3SDpdMIbJ4QZKmYnIe9af7g48jULvm7Mrhos9aGfQUx/kRwPAwHiktHLfQJY4f92mSoZ+VCLMVnAV75gZAA1xtK0jUWEihBwf4GoGVlKvke9aGInlWmE1zS2BER6rCR31TVsWL709WvHqpdT5LuKVTGkEvovaACe7108yqwU+o+7ket2MhFogrOfKuZ5lNVeOlj6lgMoGTne8b1HfeifTZu6/B7tLhnIYMvSNb9D++CPSxSVEhHBslMaTTyE78PwlAafbJz6ReFuL99ZzFjLlQNmb4wXlCmq3jnRUBya6De/lAreNhyLYR0aoG+VHq5axyMsRZxNv2TsaefXL1cRSDQzOOZq5MBYqqRVeWbPUQj/Q42riaDv4ldGQN9f9F6pqPOd6vmc53giIxH+R/JfT0LPKQgpLifVSyMDrt62DjoOlVPnJasrHXaEyKMad7Fr2lyxHJg/z/vplus4QkdNU4b0kYmLXIayE5OqLjOCzXQWGDPz5Sk4kvjFqLVN+sGL5dyfK/DBssLD/GYbSPgCtuMSoZhyMK8z2oRwqZfGKkWYfDlf8ZK4oFEYGQaOnijpFFX7U2EvliV1UNaMtMZkxxJmjn6ZccZaS8bTAStZHjUFEWQvKhOowqG+UCmJK1v++nvXyVYcPxqq+KPvscEigSqp+7OBaplzuWZKlZartdRphiOaWfL5Nf3oapcylxDEZ+qJr3ykzifLiiPCny35NcQDWwlwOR2IoG8NPVzMi4xuHrvaV8UTJd+1FT1zAZCmNuIRai2uuEu7ax2hsSCyMhkoQeBfPyMBfGwt4q6M0MksjCshzy7me8itjAbUo5HxrMHTcCN1cWUq9XfOra148UDbCUuY413M8Ut06yUzESz7P9ewWbrwRwJXBBK21TBkb+CA1M6USeIXZz9dycvV+QIup43zP8e2RkGqtxsg3X8D2/f0RlMvcDBOxIZZPvf7BH3Mo8Mf7g7kUI34neK4Lb687fmtiHDEG2+0SVKuoKrbZJBobJWxsY45f4K7hoQj2qkqisLcMl3pK4hwjoR+N10y9I2PLCQuJlzFOR75t/mLiOFQVes5H1Hrog/Tb6znpYKfwfqLUjHCoanivbbHqawQ/WE7pWXik4qdbrVqwaU55ZQHTXEMqFXRimgVX5XWnDMfKxa6nAfaUDSfbjivjh1jPahy+fBrnvLJlaWo/b+466IO8ge6AZi2J38GsK4zgmG+2WUwy4jBg13CdD9ZDFktDlPs9VmPvz1xVx3K5wWgGVYHldpd+ZilHAZOVKoLvfp1LlHOpP87eEuyNDcvWf6l7ErKsISXxcs9WBwKbMtrvEnVaqDHUq3WWy1UyCahkfcJehyBNyUplsnKVoBQNBpZ4zlvEP8TsoMjcy3JOdHzgHwvgkSo0Ouu02usk9SE6EmJUGbV9kvl5ksN1qkHEG+s5LQsHysJztYBzfWFIlDWFlh3sVAYUzNutlJKEnO5a+s43dGUqaL1B+uSz2Ivn6LR6ROoYmt6DO3SUX64a3l/LmWmlOJR6HPKLYzG/vqvE8V7OH8xZZlN/3b47GvKfHIj52ZrX7l9OfAf1VGwYGUzLaltfhL6aeOltxXgKB4Qrfce5rh9Cf7gaUhafgHRz6DhHK/c2C8OBUI8M3x0L+aSjzPYtqjARBxwfNizkiiLXdgN1fEJwpmv5+mCwyGcF+Q1ERvjmcMgHrZzFwUyCycjwVD3gn15NqRkYH/D543gp8RvdgO9+4zitjz4kXVwCIJ6aovHEE3f69b6r2NixLw4MEveUzRabkwcVD0Ww7w8UD10rjEc+W81R2lZQBCuG0VCZLhlUHW274YkuPFrzropOfVBYTZU32pZ27m1660a4ovBx1/J4RbjYzfhJ03OZIfBWW7ma5mS9jMYn71LptHClCrK2CLMXWTv2LHPDI5xOfLYJsLDuKAnUaRCOGc6OT1G2CVkQ0iMki8rEBlwAuwaZfTLgixfbCc3FRdxA1+0Sx+L/z96bxViWZed53x7OdIe4MUfOmTV1VXU1u7qb1a0mRXMQLZiQIdswZUJ+8gi9GIIfLfjRT3rVowXDhgHbsk3DBgE/yLJpCqI4NdkDu7prrqycM+aIO59z9rD8sE9EZFVXNcWuptnZrAUkIjPi5j0nzj1n7bX/9a//X8w4Ha0yzSwn2Sq5pIbxTBs0geliwV7jMMHRUxrnGh42jp31IXfR7LWpoQwJF3c+8opVNDH1ObRKvPA2KrRb8syj97h68AAp+xiJ+LrlwbWbPCo30ONjiqYGa1DLKfPegHx7ixngOm49pGTc03DVwD89SbBAoeCehwcO/q35AW8UQxyWUgSP8MD0uRLm3Dme8201QJPMRt6dR45b4bSNOAUF3Y6o8/6eOfjWOHAaIraTBHh/kQbi1jLhwXAD/coGVXS0KPZUxld7hstuzu70lFs6ElHIMmJO+rz8fI9fXu/x7+x47tZpcG+ztBy7yNi3tKLYzgGliDFVwW+4wJLE+CqM4tjB2eDWo8bzzUlktXMy+96s5dlK8xvbhv/j0FPqNONx2EamAf72puXEG3YKEOkSuEq/7jIImx/pga7YJNHwxR+hsB5axc+tWuoO5itN8i543Eae+Yjc9GYGby8if3NrxNrP/TxxuUQZgy7+nEbLf0Hho6Qdu0uL7b5E3l9Gvjb65EHKpyl+6pJ97Oz09ptkRn2l0ORK+GAprGdPMjSS7WAlyVv2fpOkEzSa6wVs5iAIC5ceoFbSjb2IMHcJ9hkaxYMmUmjFyKQhnzcXwsgkjHsuSV/+7iJSPNylt5gyW11FvEBVUDZLBnfe4d5LX6WyinGbIIyBTTTOfpmDiYzrml3dJ5PIKM/QVcXnC3ingcdpx81qBi9VcLA/YRnAZBlTUeRW0L5lPp7jR6tkTUPdzQPkxuPyjOlyQSURn1c4NJpI3i55fNyyHG2xalMjE5Vw8rmkpLHiW9zjA1jOMcMVemubbIQ5L+29y/fXbjAjZdS1wvHLD77H/9u7zizLGffXERS6CGwe72IyQ3N5laPgmUuSSu6Rsa7gnRaGXUN7QWLQVAru2QHVosXbjFMxaCWsxIYI/MFEKIaBA5d2A1sW7vvAxKXPsTTpq1KQC5wA70wDoxzebRK8djWD0yZJK68ZeNzAbsjIFFwvhYULbO3f45JZ4V0piMBWLrw038ef9qizDd6YRz5YChu54gs6Vb53lsJmrph6IQAjq9hthIFJvPuNekZYzCnzguVgxG1nmAThepmGtpRKu6tlFB61mqtlmthOvQ7FjTI15l8bWV6fBU7alIVXtOKLQ8u3Jp55EGYhMvfQt0nTp/wUU1uq6/GcRa4THNfGeG4JCQmmO/OgVUpher0f+Zh/EfG4iRz7J7170/Dl69PAL66pp36S+Kcq2UcRvjsNPOqEpIIIHywCL/Y1IwPLoCh0YkH4mETPjFGISpCOQqMlYcmjrMM3x57tXGG15lETcALXMuH+MoJKOLuPSX+mp4RpC/ukSlTrC8u9y0e7NFmGaR0KkBZanTFanBJ8y74qSCM3cOzS1xc0vEdJVpYUOjCPhmPgawV0qApXuh23C3AaQRYLjuwAK2AQ5qJpTY+NekqY1wQBQzqnJioIEWlbbFUywJOstIXaWhZ1jVqFgdEMs9hdY83URarFnP53/pD1doEUJXJnyXF/lc8/c4V9Y3ieGpUZNOBdYCDCpdMD3t++xdryBKUtEgMxK1g72ufe9jVMjJQ6SUtLjNRolk1GC2QmJfoINMDb+RrER+i6oW80SiC0Dcuyz0HeY3+admJGwX6bbvRSpaQYfHo/F1KDu9LwsIE/np+xYNIiWuqkEfSoBUda7KMID1vohcjj2gNznu0KCFULYx94f/eI/9OtsO8iQwPvLOD3TwN/a8PiRfjuNLLSVemP64BCsaoC813qla0AACAASURBVHsPWLg5Nstx/pTJ/jH+6g1MnvN839B2i3Sm4X4tfHfu+PooA5V6NplKv++Bgy/rZO6yDN3i2UERlwvFb+4F+iaxZXbbNN377+38+FKB1ZqvDg3/8tRzs4pYrWlj5NgLv7b1k2tlstemxviTUXZWpGe2nU9z/FQl+2MnPGqEneLiA3Mx8ecvl4q9OvLH40jtha0iTRV6gZ5W1AITl6YAt4u0nb7W09weB775YEIbI5cHJb+w3efAp6RTKsU8JNrcio4c1zAVMN5x1DiESK+weHKCUuRtzTwviaQx+9w1SONotMGSEgpARnr/OsK6hgPnmAbBEFnPMqIkrL5yDafzGhUj/V4JtsJrS+Yd3ha0CrRAKY4GjfWeYC2tSft4GzzGOYxS9KJnETMCiePdwxOyZM04DpGDRTq3nSJptTz33nfo+QVvrV/Bocj7kZcP7nLt4ZzVnVX+ZCkcLAJKRa7khucub/KNxw15CMxMEoTRGFZ8gytyliGijGKGRqPo6cCMgPcZlU7JOXayzlMHkQLZuMzKyQE+CEpAlzmnKxucLmAlB9M9nKWGEwc6JAhHVKp+UVAIqAiPXKJwBi52V2MH9xak6wichkThHBq474W1WWB9XTPWOR7NZhZ4dLTgj+ucaRPYyNW5zLFS8NvHjhhT834R06I2tJoownBySt/POB2sJYZVDjvNjHD4gL0rzwIf1m6KkiZzvSSpbGvO7vck1nf2yo9aNC5D6kdMQno2rFJcLdQnKrn+qPFL65ZG4DvTQJSA1Ypf27C82P/JTTmlTjMTT4aIgPxoU8w/afGTe+V/hDjsDLafjEwnWlwbhN+fBAqEwmp2m8giRv7GqmHeqV2eMRdOPFzK4M3dKe/dP6JA6GnN+GDOt+YzVi5tkryWEwtBojAJml4RqccLfOtQpAQ0d56oPfXqBtXhHta30CW7ajFjurENRmjhvLJvu6+nDSALBiLnjlHROQ6aHvV8Qjg+otQmVXazMZPBkGVvQJzVaAJWFAHBBUGKAtEGpS2FJFmGoC2ik5H0tG7oWY/RhhADcx/48s4q//cSpqSqWoD3lzBSwsu77/H+5i1eCvPEV40RtzKiPLjPB1dfpOhnXM4KRIFtFuxWPR5eW8M8vAOjzcQuCQFVz9i99nlqk9TXFCnZTjBYPCWRadSUknZKi5CuTw9os5LZ9nWsONDQkKFJ2L7p5hmUStX4XKWeSK6gIk0dS0iJ3Ajni22hUmIOkqrkmXQLhUoLTh1SP2Er05z0RrzX5lRl2sE8cJYiH4AZshqERZMMZ87yqA+RjUKzlunOFByIaQBua3LArKq4YRq8UlhJzKVrk32cucVeo87hhVMXyTX8a2uWd5awpZPeU5Q0NPb5wceb0UNSNH15oImS5EByBVoJuy188VM8ex8NqzV/ayvnF9ciM5/gv/KjpPyfsLhWau7Vnp5OeUM6SPhaqT9R6fZpiqc22ddB2G1jwgFtUiLMFfgYOXVJTjhTsJGnyun9ZbLjc2ecaGMIMb3HMLmC4CQpLm5l4ELg9x+dUpaaCQUNmk0VuLOoeWa8ZKvsMY6KaSsohO1ccHVAL2uU1rRZVz1LxLiWujdk/8bzrD+6B5JYP+PRBrPL14ioD1X2Z7vFVmpM8KzOZ+jFgpgXNMMBh85hpjOk6HOSlYlyGVp0XeNX16EUpGlpO/zdZBmuV6Fqh0ig0Smx2hjRRrHS76Gt5k4NrWTkOG70LaE/JHooIpxZheQkd/l7xSobsabOS0DACFVTM+6t8KfPvMrNO2+wnJyggGxtxDevv8R7pxlrLrB58CA1J5Xm3vWXmI02zj/XrmeKAWK0lDoZVo+FVHYDq6TKfeoTpDOVDEKaIrYqYfT95QR77wOK5ZzJ9hWuXb5GmVuKGvYCLF3a9Wznia5ak+CcSUiUz1wrKtPtGBSUnTqlNWk3MGvhaOMKK4tjFkuX3KY0zNYvURc5pyGypjUzn3pHRiLHXvH1Vc3jBt6YejxwpdC81E3KhmbObVlJ5uci3JCGW8bz/OWS3zoMaRIbGFnN372Uc71UGB15bxGJpHv3uZ7mRjc0NXbCXpsYPju5YjVL7mPLQMLsQ5rJGBj1A0XSjysGVjN4SrLMaqb50tDwxjzivSDdBP1LnyD98LTFU/IxfDgmXvjGqSeSpj7vLGGtFl7qKd6vhXnwDIwiAm/OPc+VCifw+aGljWngptDpYbhfR0a5ZjODiMao9DDsThwnQXGYDbCARtiVDGM1W4sFsRqwZUFnGiXCIoI0E0IMtHmJCQI6EkQh2rJsZ9SrO+yPtildi7OWaDNWTQQsjgQVKFIy1UDualYe3mFluYAsA+9pDw2TzR0e2wGzaoCRlIbntqDUOTvNknG1jqkcVgJeGWoyNlRgD0dQ9vz/eJWkgme25C0Z4jJHhjBF8Y7KqBepul3NLwbpFTALireefZVf/97/A1euEa3FOE88fsw7X/tVHuYDbj//NYbtkqhgmlWYKDQGHl//HAeXbpJ7xzIvccZ2kIOjY5MCXV7v/jF74tgCjIFngEcxXauzm/jUw7aFn2sPib/326xKS7A5dvdt7u/d5HO/8kv81wuDhOSVS4RxSENbawYetbDRsS6cpIr0ag5e0Tl1pTOYB/AGrDVMV3foEyEGWpO8hYcW7jXCPCT5DeciYw+v9FNV/q1pctFSCj5YJLru37xxlfY73+TquqY2GRkRfXRI77nn6JeW//SaZb+NxJgWqDNBs2d7hutlEoMr9AXU88Ei8NY8nO9s318IL/QMVwrF/77vqHSCfx41wjLC39n+bKAJ4Eo3xbwICTL8uCngpzWeymT/vakn06lqgEQd228id+okbXDYCB/Mk4HHsz2D0QnvjR9hB7QibBeKTat4axnZbSOFEl7qW66VcKRzKiIBjaDoEZmicSbjug28OXOc1pFMKZ4b5QRrQbXkoUliVkEQpYkScKYg5AWZb2lUhe6aaeM8LSaBlLggpRQFrI+PqNqaw5UNGmWwRDYXE/qTQ5Y7G5jgEZUYLzoGWmVoSkVOSz1bEEJAjKHoVUSt0cqAeEQnT1EdIlobbrcRQeiRqpmsuzaPG4UxUC3G5EeHoBTt5hazbEj5zDM4vszjt99hqnJWo+PSV76Ke+ZzzPccUi94Z9liFGwOhMZUXar0tGgaW6C1YLpaPnUkUgMcugY3GadcLAB0fxcSxq4QdHS0Po3d5pmmiZa//f3f5rf7Bd+sruHQjPD8u4dvML+9TbbyBbIizUugE3NkGuBXRorFWLi3jOeY/Vd6sFMZzDTwQQOLmCr9m1VSGc21phE4ahVCRoViOxfWbBJgO3SphxNRXMmTYNqDJrG1HrcRLyT83gVOVjbYefnzLN57h7IbkCuvXad365nz3337Y9QkfRQO28ipT4vMdq4JAm8tAmU3VCckGOW9RWDDwqU8LditpH7TpTxN1X4WKYxSDJ/KzPjD46n7ldooTAJs5R9ecVes4q1Z4J1lGjm3nXzA2/NApQy3epoHtXAjzRPRhMgsCL+6mfFPdlsO2khhFMsI35h4XhtkFLmldpFelh6EtJcAVfV562DKuNuiR4R3jmpurqxgZMZwOkF3MIXxjnlR4YYbkJWQ5djufYLOICb4pusXAulBj8D6yTG3BxtIZjFRcMrwaLjJ9izBI0hEyYUXq6CZOUOYnmJJCVCCx3lHXeagLIUxHSaTaB0+JoikahdE1cHvAlocbZFz/dFDdh6+hy5SeR8f3IYbL/LXX7rFb+m/hr36RUpX83beQ5cFX83geG+PuUtNOSdwd7ZktTenV44S/k3EkFgxAc9apjjp9k+644O77no8We2fXRuAIxHsYk6UgDnD+luFk8Cd0yW721cZJloPWmm+P7xC/513ufQLX2Ar0zSkm19rxaNl4NinneDlIl03JTAWxS9Vwu+cpHMYdU/LYwevrmi8KB63yeRGAJFIG9NU9ijXfMkmXaICoUUx8ZHH9cWxtIa9FhYW3ps5nrl5k/LKFeJyiS6KP5N/3kThj8dpcKzQcLcWSh25WelkuRkSrAXwoE6N5VOveGVgCZKej0Knz/xRC1/4oUf7LJ72eOqS/RnU8VHXKS9J1+X2InC1VNQBtE36K380CfxXzxX87knkvXlIuh8K/s1NS2YUIURW52Pa6RijLaubGzysCzZW+pxMppy0CSvNFaz3Srz3zEJgNbO0khKNjY4HJyesKU+DoTYppdtc048eyTxzIqFpENeitMH2wJOdJ7CP1lbzokfhWuqmhtahrSXLLM5YojFkbY3IGbihcJnFzaZYCYjJiEqhBZRvaOeOohpgFkvy2SkA7WAVyh7iY2pKKk1LZ2xOZFBP+frJbV5f2cR1Kp55Gfj60bs8Hl+m0gUnWcG+LhiYNLn77YOG2keiyRhri0LoR8e0biiYY3WOU9kZ/E4RPQMCpwzOF7mzMFxAWk/MQCVqZttCaNG2IKjU2jXBEeoFvydDyuDokxqtpYJHQTMarFAI7LeRozZ9ntd66ee3l5G+SVCQC8moZNwKd2rNSMN+TMNXWiUd/oVPC8OkjtyftEQRVsuMZwaGS2WGSORbU2GvCfS14isrms1M8zvHkZ1cMYlJvG1Fw/1l5DRkBBEOgmZs+vQFtp8QMvu4uLNMCqwXFX+q5N+dBe7WKemfPSNRhHvL5Ijmu57E2ZxQ2zF4IEGbZ9Ow24VmpVstXDdZOgtJr2jrCb/ez+LpiKcu2VvdWfUtI5t5YlsESYYPpVLMAtyr00MiXlhGSY5Hovn3r2QcdpzZzY4d8JsPFxT3buNmEzKT+H12fIi/epOeHnJQDVkvIfFaDE4Di1OMUmiStO9ZlZ448w2PhkOq1iFKAAM659XlCX/QzKmaBWiLlkiYWbL1dWa2+oFEH4E7Kxtcfv/79GMgaouOAUVkfPUWeE9AYwhdAlQgAdvUhKICEYykwR2xGXnbUBztcv3RBwRjEKUwu3fZvXQDvbXNIzsgaYIJDoVTlmdOHzHMND870sy6LDzQhuJU8fbBmPFomzomsa9JSFDUg4nj1BS0WYkmjeYvKanaOTiP5CVIwNB5ygLjZUCqH/yszxL92a7nrJ8BkLkG6Y6gI6AiURtcVvBw/TK39u4xWd9O59Z4ytmEvVe+zHt12jVYleiruxP4Yj8VC/dazmUbHjeJZvlHJ4HQwT15npq3USVJA9u0HJ9O6J2dWy0ctCXDNcs/OUw2hrlSnIhw/yDw69vJv+B7M6G0Hc/eJ5hFYuAbpyEZlGvwIryjIn9t1X7iuP7jJjKyH/7ZyMI786Qq2jzBDXcdu+hWmaZzd/I0byIdg+dnBpr354F3Fgn+FOCdReCVgWEr13yjM/3JlaKNsGIjXx3ZnwqWyl+VeOqSPcALPUMT6cTM0sP5hYHhW1HItZCphFuiksBUEwSt0rZ3GVJDaqYVuQjh9JSTkzHD0ZCpF6yBAZ7Tu3dZee4Vem3D41mNSKSXZdxYG1DmBmrPrG6pl0u0zRgNSpwyfPn0PuPNFzkpqoSUSOSLJ/fQWUEpJfP+Cq22qBgZ1HPy00Nk8zrWNZT1AkRoi4o2L5HFgqgUeQxkzZJoDHVWEOdzVs0Rk/4abVaCgJLAaHZKkIh4jxQZHp26DW2LaRuu7X7AZDg6J6BLiFzZvcu0X5ENS2plOQOTSnEMVcCQpBhO68QVKqqMvlbcd0CIGKVZxKQPdOoiu0HR2AJDxOt0HBsDi7xTOxSP1lmaoEWIIiz4QSz6LDLAuxaJiSZjlIEsoy+RqUoDYFGl/Z6JAdGG9269zFY7o//obupNZBlvvvRVio0rDBo4CWlCWZOgjTbCvkuV/jKmRavUiWZ5DNQeLlcaH0BpcCFyeylkzSkZll0yPIoNE5gtFnzzMGfhcwrvmDaBwhgGZcY3xh60ppDIuE3ib5VJdM9Dr1jxke0npjcnXnh7FvjKyLJbR95betoIz/csNyqNVR9uUEPayRRG8UJleOySHLIC1jLNC32TPJQDfLCIKNJC81xPs5Ypfu/EU3XKmZCGyN6cR/baSJAP9wyO2sjdZeSFnxKmyl+FeCqTvdWKL61YPhcE1yn8ZToZVPS14nIBqOQuNfUw6DjIv3uc1P4yrXhvATuFopicshc0j2aeXKdKsw6KSxGmDx8xOZpRKpWe8hDYPzFcvXmV4+NDetNTtEkmoXunhp2tTULZ5/nZPkflAJSi9DWrvuYdyZitreJshhbAGObZCHNySLGYsDI+xsaAaI1MT5iubLBx+BCFkLk2aaKHgM4Va0d7THqr6OCxnVysEBGl0DHgunOyJBldjCVzLUaEPAQkpjJdSUSJsGwDWYhoE85hlCxEjjYu8fDwIXv1KaETx5oezLiiBRmMeG8hFCaQac1eDLQBtClxtqZVBt3tV5y2EAOCRRTnMwjhzP5Of7JhRdXMGB3v4ZQmYMlCizIGv7UJjRA7nB8UURuMgsIqvrfzPKvDLYwElrZgUvQZNgGjDQOVjE80KcFPQkr4R52EMqQEH0iQRStw3ERyo4ghibXZ4Dhu4WFeJFVKIsdYViSH/SW1rql9QGlFi9AuYTxYoyENy52plZ71J45d5EvZh6vkoYF9J/zxScP/dRzSblLB7554fn5k+fzQ8J1pZDtPkOZ5ld43fGvqWfrIsKv86xipA2zmGZe14plKU4ckHVFoxaM66dlMA+cDRGEJK0Y4dYbnex/dQSgeNp8l+6cpnspkDwlnPHUx6YZnivUsOfh8fdXy+iziJfHf163hZ1YUby8ErVIlOfXJyGS3jpzojEwip63ntE0CZju5wUvg7u4x2mbU/T4BQy+01IsZu/fvsTqeczDaoNEGLcLqcoqcHvOdjevcPNnl+niPAGRGc3vjOgdB4ZWiDA0EIWpNRNMWFZcf32Wysskiz0GBCZGNgwesTY8p6wXHW1c468P2Z2MG02Nu33qZLAacTZh/FjpEu5fMopUoQurioiTSDoao3UDUiqA7cSwJqBhZZhlRK7R35zV2NIYDu8JbN19m8+67hMUy/R+r+f7Nz7GDpRc97eERcblABkPK1TXG2iCZJbrYVdxp1yGFTTKWzoEkCEdLJGgN2Scn+/5sjBKFN1knWldQuppe23CcpX5DkIToWyJ5r+TWwW2OdMbu6iVEQYawMz/GNX0e2jXWiuTuZTpG1N4ScptmCGbddHShk27OIAMbkp7+qZfUqNXQt4ZHUlASCRgCmhLhNFpKHE3QZCZjLJacyIiG+WzOcNinp5NmDyod0yo+NHx1FhGofeSfjQOXC0XRyTL4GPmDiefzA8OzleI700AdEnT5Sl9ztdR8b65ZsRcaQBZFoRIDJyMl+Ce59Y0ID2rhZnUhpxxEuLcQnu8LgQ87eHkumr+fxdMRT2Wyn3nhG2NPK2lC0S0SO+eFnuZGZXixZ5iG9ABZlV5z1Eb2neBJD7irAwMDH5gR7/t9TmyGyZLb1NgJN0JkaXNOV7fQKoEby6zAkDPee5/x5RsJl+4apNOiZOX4IRLgYTakshUKwSlNK4qmv0I1GaNRiYbhBRUDLitQolAqUrUtSgsSI8pmjJo52WKOPt1DTIaRSDYZI3lGNJbQLTSKlEfRhmo0ZOIV2jmsRDyKmGWsSs0yKyFEQkdZ1a2jsTm+HCbjE7ngsvsImpb9wRoPXv458maOoGiLHqXyDMZTyre/x7CpEWPQztEMV1g8/ypR5SmLdWNiQifgYwBjUdFBgGAA/UP43c5hnGNSdhZSAl4pnIaNekm+tkHMIkLosP0eJY4iRqpeSa4S60kDFAX5smbWg7pOiUpIFf2Zf+qTo/J1B+f0JbGVBoqUJVXC9yub1L5mXmM7n9cliqCEGsMdVaYxe4SI4T4ZN2RGLn2wMOj2PYpUsGzlmomHUl8QD45dUsAULhI9pOlUqyLvLzxWG/paUenE25+E5HNcGfjZkaGbw6JnkvLrpJNC/mgoSdfEyYXyqItpx3y9SDj/VnaB8586+NLwJ3si9rP4cDyVyf6NeUisiCdkR/fayI5XvNrX/OEkcuqTXO21QvPqUPPfPgoMzYetCe8vI7dnC47yAblvWeiki14ReL9YZapzbAg0xiJKkUkgZIbT/hpOoEQIRJQoxAeOVcl69AQJHC8dxICpCoosMFCRic0xrkFCJyqmNNoaSiLzICyylAAyBblvWV9bZZzl6MWCoFqQgOsNGa6MaMs+yrd4k4HSGB9o8orSe2wAExyEgNEarRXRGu7depnr999hND5GFHhjuXfjJXyMGBzBJnkDJWB8S1xG5mXBihZUlRKuRRhj0Hdvk7cts8EQCRFd9RlMJ9hHD6DTcknZ8SIyOpqpzj7EpzxbYH4gFMxtYqn4LE/9ixCIQbNQCXJpJe2QIp2mv0CTFxAD007ArG8hNxZdZBSkRL7sDtHrft8zFyrXnYvpkrpTsGXgsU/Qjib5ua5YiPkA6+fMXSoSCiJkJQs8iQirCKhuwYmMVc6LBdSiU2Mb0Gi2siRQ9kJP8/4ighJEhMuFptLwzY8KtpCYPIet0M8iV6uLi3nihLt1BFE0EaYhdlaXuuuTfHxURvFcT3M0XTIdj5PI32iFFwY9Xugb9tvkjnVGjX2up7nyFzV2+1n8hcRTl+zbKBw7+UGevVE8qAOrmcYqYcV2mjgxTbeWKtnHnUWibsLh8ZTjaoDXBhNjYnhoxdAtCRhaY6EbQvIqsV9mW5cYHO4TCo+yNjVilzNcr480S+qT01TYCkhb0zaOayrynXKTkBeYEEArGpuz6ZcsqorWaLQ2CEJEUduC5sUvcPzwMUfZgMwv8bag7x39SlPbnFD203sh1GUPFSJuOaHftkSVlCBBUE3NvKpY9Fd486XXqOpFmvqtUn1p24Zg85SUJB1fbIZtl+gqIyPtjhRp2MqKIp8csgSy6QTRChWFWkF19AB95VkqEgxxRplccpHsn/zkhE9I9AA2Y5b3kKzohsciPk/yzWOVEmGpumnbjpOZmYyprTgcz1LzGqjrwCI4NjdKmnhxXgC1pMU1dud4xvppJSX/wwb6eUr+PX3BCAoCRsOp6mOKiCbQSFKgtDGJzOVGnY+MRQSPpTKKW4XGKE0UyHXkcZPMRV7oG25UmkUHyfSNoo2RgVWctJG1rkE69xGtITfqY9k4B60wcZHvz4VKq45Z49nJFGvrH//Ir2WK1d0HbN1+C2fzJFlx1xFffIWN7RvsFIrnekIT03v+NE2W/lWJT5XslVKrwH9DmscQ4D8G3gb+F+AWcAf4DRE5+VRn+UScPaQi8iGxpyDQiuJkGblcXAhB1VF4fRa4Vir2Zg1v3z3Atw391VWe3V5lATQ6wwK+kw603jM2Oa4oyeuGGFNlpQWaPKdX9plF2Nq9i20botbMV9aZj9ZRt7+PshavukuroFrOyCpLqWocmhA8EjW5NOTS8GD7OmuHu2TzcWpcGsvh9jX+dFAR24yNgwcJOmhq5qvr/OnmFtprgsSuyanSgJUGVddEoOeWKO8Ra1lmOdQtfmuFfNnQlH0gTa+GqkBciw6BaDPCGYPHeyzCraJgv6mZdeYUhYJbRYFxNZkoXJUcLwQoZsdIXtEnadaE7vs5cOaLofHQBlBJTJks60CYj4+mN8C6Fi2JdKlEaLIClZds2MR3b5LaNAOdEvebvU2GsxYvELWiVLA/WGfJ4JzGeZbwNanSr0mL0dk5a1JSnwSILmnXzX16YIoID5YJ1880NGiQ1CgeaMhNup/E+fN5gixTaFPwCyPLuw3sNml31zOKnx/pcxZOwtIv7utca35jJ+c391ruLGL3Pfj1LctB10R+cv8Uu/PPlKKvpRPsSw3hwnQc+4+5zqZe8sLjd3l9ZYOuVGFDNbxy/23085egKOgZdS6V/JcRYyec+KR5tZnrD12nz+LPjk9b2f8j4J+KyN9RSuUkMcL/EvhtEfmHSql/APwD4L/4lMc5D6uTJOvjJrKRXwyMzEIaYRc+bDJQasUUCNMpd//lN8AlNsfSOd6/coVWrSBa0SrdpRxFbTU2+jTlWRToLtlHpdDaECRQxRZjNUoXaRLTavrNApFA1UaC8l2lHEECx21kbbGLco7Q8ew1sFjfRkUhFAVKd5OY2qCC56FXZGuXORquk7kGbzJ8VgGB6Fqy6BNLSM7YOBrtA8P5hCyeaWcKmTbMhutI5JwWCUkLSIAiz/EhNa+VRKJSCV7Pc0aFop9XuJBgCWO6hJdbypMJbVkiSkGMlPWCbLRxXqmfETnPEqzGoWvXseNTSAioMutEGj7uA8/wNkM5d/5eZBkl3ZqhoOjkQluBTGBucqaXb6Fci46RoBNdcxEvzumsIBa5OL+GiwXgLEm7AEeAdxduT8fAhkmV/VAndU3pdgJKwapOmvI5F6J2DYorNpnZXArCqtFEpMPo9cdKIZzFlVLzn13PeVgnWeSrpSLXmt4y8N1ZPOfMQ8L5163iVMFrvYx5p2ffN6pzypKPTdh+MubUFtSDEaaDGZemYjqtceNTiu2dTzy/v+gQEd6aBz5YJnltETAq8trIsPZT4CD1/1f8yMleKTUCfhH4DwFEpAVapdS/Dfxy97L/Hvjn/BiTPcCLfcPcC2/PAyEmE4ZXBwYF3AlPCg90IcLud1+n0oLODdo7wrCP7D4iX6+I1lAtZxRtTdCauuwTshzTthSuZe3oEUXTcLq+zclog+3FPut7jzkcbXaVvaEMjvVHHzCPyfwidI5LGoiiYDGjcND0+yk5K41xNeXJHkMUIuC07pgqmqqe4YylLnsYUzAzxXmi8kRs9MSzRU2BksRuqdolJrY0Ra+DciL5cklvPiMLgs8UsVvWBMgDuLLELVtMFIJWSZpBG2JZ8GtbBf/zw4bTNjVb1/Ocv3vZ8kdXrjPXj8lPj+hqdJqty/Qub7NhYDdcyD4Y4HIO9yYNSkCsRQSUEpQP2LbF5R+f7A1dtZ1dTBpDunEzlRK+k5QA+jYl2zP6HArgJQAAIABJREFUqGQ5T6LdjlQFnxWEQkr6ZywY253vk0NckJrVmUmvU6Qd3tjBRplYPHW3s9Ckr33XsBJb5tmFl2uJZ6edoM02WguTxhNFYXNFpROFWLRw4hNbrDKKdavOp1SdKKxO9phtVOQarpaaWegw+u43upxrLhWKb08Svj58AuYRfuDJOI+FKN6Wkm0dzn0AvATepOKWaP4yjQNPvHB7mSaPz3fsQfjTzkHqyUn6z+KT49NU9s8AB8B/p5R6Ffgm8J8DOyLyuHvNLvBjLwm8JKy1UAqxiVY2j/BMpXlvGfFRzh+SqRdkucQf7PHi7BjXOmLXbJ0Xffpqlet+zM7+PXyeY6LQasP9my+zNjngb/yz/wndSRFUiwXvP/sK8xd/hp3dD3j+zhs0RYkJHi1CWBnx/mCLaUjwBCLUImQ4irZlagrEe0zXFPTaMphPmBV9MtdiO/47AsuqT1/G3CsvrNvO3KlKnxqAogymk0s+S2pVPWPeW0lMna7ocdbSX8zJTKTCnL/WAN4ISwSqHiE4iBAywGQsgQcHp+jxnB42JdHllN2iz9rODgdZn+p6QFqHFDlNhHx9g6s9xU0RDn1KrDsGJm1AvMcVVVpqVJr6VUbTm09wef9jP+szzP8szuCXSBqA8orzpnIdUyL+pNBcYO/S4e+tT4uzxHR9P/pASHeuZ01bumttO4ZWHS+YPUi6N3t+xovaMjRzxhgqBbkSnGt4PK253VoaSW82qwWrAntN4N0F7LeC1UKIir6Br44sUx/51iSmxRGIEnixG5B6eWC5WaVhwdIknN9FIdORuvsepIFCi2I9+/jEuBisofU9lHPQLbyqbRFtmA9H/Aj2tD+22G+FUn9kx26SvPgspGb5Z/Fnx6e5TBb4CvD3ReSPlFL/iATZnIeIiFLq44kWSv094O8B3Lhx48914DdmASfCSpaocz2ThkK2csUXh4ZvjJP5htaKS7nii6sZ9x7fJ/RKnDZpkrOoMEcHfG4uSGO4e+kW0RiUwHAx5stv/iE333udkJeMByMAZoNVXnr3O0wyRb6Ys7u6hZeIspaR82wdPuTNzetI48C3qXITYVH1sc0Czgb8YyI/SxSUCDt79zjZuEJdpgEdYmTz8BHjS1cBAedJqVEjmSaKpte2zApLo1O9byRQuppYVkl3PXgIaXsfgFD16Ck5x9IhJauKyAID3rM6PqRczqmrPqejLbwVfvegZicTXEiVfabgdw4W/Os31hm4lt2Jo8lyyhi5slKyudrn4UK43jP08g4yAR4tA0oEJKZuQZcsRTxGPamIcxFnOkjn9xMflkA+2zWk+ykdZ/7xbwWkQaqtLNkUuo59MzDdrmPZyV5wsaBkQNVJkjpJlb1WqW+RkxYNvMMs5olGW/SoqoodIkLDI9XDi2KBMIqBS7T8yVyxWQoSkuDc0MCbc3h95smVMA2ah7VnI7ds58Lr0+S/W+m0uEXS/f72PLCVawZW/QCWnmnFayuG3z/13FkGBFizmq+vmk+UNzB5RvnMs8S77xIW83RNraX/3EsY+5ebTT+64J9HRzv+LP7V4tN8ig+AByLyR92//zdSst9TSl0WkcdKqcskS9YfCBH5x8A/Bnjttdd+SD324Whj0qA/bIWlJIO/KIkzf2+p2Mw1RiV+MqQteKXSIMyDOw/RVUqoIQbsfM6NUeB76y/QU0KIIcEAZY+t+2+xdbLL40u36Pk6TWpKpOn12bzzFntrO7i2TXg1UC/n1EaxMCVHaxtkPqDE421GLkIsKorTw/NqVkWwrqHOctaWM4r9+7RFBSisW5KFwJ6yZPUiyQB0UEn0imhzapM43bpznYokttB8dYPV3ftIiCitz1Uxxzs30sBWZ4oIZ1CFUNael77/BwznY0RbVPRM+yu89crXWErkzlIw8oQImRYezmp2+9tMrSPGQKMNpswStXAGj5eBPEv9hIWPKJNAbh2FqNI5n1EBXfHJVf0Zg+cs8QcuIJzmLAHrdF5ePnxDp47FRawoWD4B2UByvioM9DJQDujsD02EoJNQ2TikHYPt8OImwjCDS+2Y5997PS0EStMPLXe2b3Lz+mW+eTgBFcl08jXeD5HPVQXfR/PtqVzsbmJyV3tnJtyukxdukkFoGBnFL60JA5vUKqUbkBJg1SYMfnDmR/iROLMZzFVaVUUJ9Q9ZCNcyRTUaUbz6ZewieVD6Xp+IZvUveXrqUqF5bxHS56suduwrViVfgs/iXyl+5GQvIrtKqftKqRdF5G3gV4E3uj//AfAPu6+/9WM50y4U8KCOWCWsP9GceVRH7tUxCUkpxTgk7XoF/OksUpU5Ni+Qpk28Y4R8uILvjyDPkx9r9/5aKdr+CkW7QBPPPVu1CGWzpBmN2F/dYWu8T9Y2iDGcDte5PdxgXFRYJbiqIpKUGJfGsFDCaLmkX89RHZ7ttWY83GT7eBejA2sne+Ady+EqogyiNdY5fGfvpwDtW/J6SZtVmJjmDZJiceKi+2aZdhRaJVmEjnonKAam5Cg0uC7ZZ0RWVMb6wzfoLyYsqwFIAFXSX0y58fBdFtvXGCCE4AGFMZq5T+YYbRa56mboekkoe+zrEQ8bxZcG8O0ZNC5BTJWG10Zwe1GCC1iJ5wNFQWlCZc6hmY8LEwJ5vYDoEZvRln3OsBWDAxfSwqaSrnxJYtc8meg10LOpSjakZqxViZe/38AgJovCOqYFJAP6KiX1KiYmjqer7IGeirx27w0O85x9VeIj9IvIFw7vsv3MJv1BH7dIqqQqCn1rYGOdk5mgJTKLihCFoYV5UHx3FjhoYK0wTF2gygwTF/gXx8LnB5arlSLTF4SEO4vI11aEGCMf1MJxE1jNNc9UmoDiu7PIRnZhZehFeH0WWc/0D/jSQmIBfXmo+fYMQsewyoDXVsz5cf+yYsUqfmZoeGMWkycsqT/z6or9RPvFz+IH49Puz/4+8D92TJzbwH9Eeq7+V6XUfwLcBX7jUx7jQ3GGncoTH/LZDTDxkbtL6br2CiEp+H3ReqreiC98rs9MWbzz9MqMMJnw26ObVHunCAqvMxRCfzlltnOFk63rrB3tMl3dPG+o9pYz/uQrv8zq0V4aqLQZClitp9zfeYZl0aMpe1313Gm3eMdocszxpessmzmZa4jKMuutUDZzisWM63fewsaUUFHwePMa5YuvMtUGiT41oQSCMpRuRmMt+VkC7tKaVwX5YsZ0tE7makxMzd46L+nNJ0zDJZpuFCxdy5ylghf3b3NUlKzMTxGlUCLMqhGfu/sO31ndoZ1PMV2CaAWoBsyc58UPvkm1mBK1wgRhNFrn3vNf4AurJc9Unlln8beWJSs/q4S2yLA+IF0jmsxgnMNlKQl/dItnmwWjo12S+IWkJrLNYPsao+UJW5M9WltiohAlcrJ9Ba/71B95H0WSKG65GJxyHRNn0TVgDZw3Is/x+Zgq/TOY6GwuIKvn0Dbcs+vJGAfwUbMnhvXjY565+jzj1lG3Hqstmz3LoRdmPnDoOecjnXjomcRF2mvhbpt2l2qZvu7kkRcH8qGFMApoLTQx8j88DtypE0slxMBOrvnVzcS3elKC2CoFkoqhj0v2AJuF4Vcyzbjja65mCvMTkkyvl4btXDP1yS96ZD9rzP5541MlexH5DvDax/zoVz/N+/6wUMD1SnPYRI6ddLhv8vKcusD3Z5FndUO2XCDGMMv7/GENf/36LdTeHXqPHiDeo3s9/K3PMV1/lt18wuUP3koTrQghK7nz3BcZTI+4/q0/YH3/IUkxXbj7/Bdptq+gj3dps5ygdEflEwpX0+YVmfOUzQIlkToraMoeRkG/ramVxZs0uKXEsTk+4Oqj92nLMilDAgTHpaNHrBzvc1AOISu740RU9EkAzbcY7yldDVGoiyrpwLgloGmq3jmembUNRbPkEYqsq4TTDyKTmLF2uIcvBywGq5gQCMYwnBxy+fA+e4e32Fvdpu30dHQIXD28TzXvUdZz6vWt88+mf7SP23/IyeBZekahO0u8UsORi/RCIGSKgEE6nn3hAlbCeQI+n6PovvZODtESOEfvtSYLLaPDe+xM93h/6wYOiyKy0ix59d4b/PNbP4s6X24v/tSkij7jCQ0gMk4DDNUFVBRJE6pFBo3qKJ0XYpQogeNgGDtBZ7BQSWi6J4E2woFXnHhhoyxweY7RyWz8qE1Wh8QL/L3szumk2z1Yk+ApC+gAxy5Nqz5YRt5ZJBetW6Xixb7hrUXgzkLYqTRt1yg+bAO/dyJs54Y2JrgDks9sV0f80PDCuVuYl4tF7ychCq0o8p+gE3rK4qnrYyetDk2p0t+9pCnKcYA6RHqHu+RH99P2ToSesejrL9LLDAfvfcBgOUlNwumUU9unuvllpnOLuf48xrWI1tS9IdFompUt3v6Ff4NiMkZ7jxusEPOMm/ND7u3cRPKSzCVtmLktWV+c0muWDKYnZDHx7PvAeLiBWRmijo/oPfG4KRFWJkdkwXG6up2yiABUOO9Y33vA7esvEVTCtkWpBO+IUC6XjCZHGEkNuMFUMV5ZQ2nDaHJC6xuCsejoyduayWCd2DYQzmzNFeI9UUfaEHjt9d9jUQ1pspzCNfSWMw63r7DWLlmbHTHrxMr6rkE3c24u9ni8dZMtq84bmgeDVZ47echtf4t5R4cEYa4S9j2yAbfwGEnwjY4BbzJGlWVJqqrlIgvjvCOPDb3JKaJTe1vHQNAZQxupswotoDsNnNZmRImIeLTKP0Q1VHSDXq5FE8+b1JGAygzKJNcw1fHldSqEmXYvfILlSlSwyCruF0P2o2GeJznrsYrkMsUM/j/23qxH0uy88/ud5d1ij8i1srL26p3sFtUkTXLGI2E0lmYs2APYgIG5MOAL+yv40pf+Fr6x4QvDNgYejA1IGkkzHo4oiWR3s3d2d3XtlXtGxvpuZ/HFeSOziuyWqY3sbvBcsFjRFdsbEc95zv/5LwP2S8/cGGTTXS+tJyPYax/XoTv3wAJoNSeLiQVnL3B54eCKho+mJX8wbsRjwDuzkGV7KVUU3vPJstkMPSTScTcPVh4fLjjvzK3zbKUhRevz1pPC8vbMnZ86pAjQzmbya2D8q7C+dMUe4KWOIp/AxIbM1NrBzUwiZ0vunTzhrD2gaDq7vsnZ3r/Plcc/5YmpeGwkzjjiLOb68UO2D+4zmgnmrQ7SBJGSEp7No8fQaeMXir2dNYzStMucDpY1rTiZVhwjKHQaJPLAZq/F9ZOHHCUdjAh8PqsiNicHOIbkQoJz5zxvJwTzpI2uTbgtgO+wwuqFoFSKTpkjncEhKZKUIk65de99TvvrgTPvwUnF1sEDysEQE8V0pqdoW2OVJs+6mKyFrCuUCJbJeALGbz21inFCkpULsjIwMRwBHhsJw+FTtBjvBUMMr6QCE3n2iwZ/F3A98dzSirdsEO44BAiBFI5xDVfqBTMbkasEZNi8etWC3axDroLP/MrdUwFbEcRFDt5hdBYgJueIyhyEYNofUdWeUjUnCOc5bq9RNQVuxeJYDXYTKsBSnQcZeiSOqLbkKkITcgO88/g4wkYpUgWhlvWhyAsasZQQ/ODSK9w++IStYtoI6Cxvbz/Ha3GPYQyfLi1TEzx8rkdhCDxuZgYrLru3gUGUiWYw/FRddQSF8P87CUZmK18nj+NHM8urzqOEIFah6MdCUJtgzSCyYFg2c6FwJwKkl3gfPtczEwa2qQzD3sLB23PHMApGaxDSqd6aOX47kr8OKfn/WVPjWVhPLMKw+4sIMX0pi30iBd8ZKCbGU/mgDmwrgbo/5g+SNiZpnQtjjpOU3aOHyA/fZf9oycw2LedsSSfxtD96l07rCmvFE4yKgse7EMhuFxdHfHj5NrVofoFCsFZMuBLl7Dz6CTGCXCVEzjMoZ+idy2Tlguee3Av5UUIS1SXz4QYnOkKhkM5dtInSM+0MWcYZvdNDylaI5lN1RVQXPLr+Ejv7D+hPj3E6RlpDHqcUaZtsPuHFo0cYHeO9IDYF81YXb0qyqmDr6BEOj/SC0+E6ttsjF4rd/XvBM5+Qj/t46xromHlnQBknKGtwShNVJaY3pEpb1N01rAoD0CIzWFlz/YVbxA+fsNdfZ2EDM2Jndoa+/lwopA3Ozeqw4iFdLthJeixdjrGCWEAn8lwqz3gv6TyDS1vAq4jE5Ez6I5Aa3/ScdRwhcezLlEnaC4NZAVNrKCpFcPK/6OpXkJDCUKFZEfbCHEAhqFE2h9kUt8J8cg9xRnvQYyIEsQ98fkmAeCIFyzjj/rWXyeoSgaPWMRMXszRwb2p5Ul08+WkNr2YNVCUuXpOQoYMvbBggx+JiU7HNy6gsDFNF7cL7j4NtHcel49CKc1dWT8DtX+8FO20tAxQEK/FYiBbcrzwnlUcIj/eCjViwGYeT8KrQQzg5W8LGsPlr+OQzl/Oe9+aWh8WqiQsModd7+nNnI7+q9aUs9gDOOYrxhLKqiDot2oMenShYHT9sKr2gUW9GgrcfHOHilK0qxzkHccTdPELv7THYbDOTEXK5DK1VktA7O+KT26+F4a+r8Ui0N4zbQ54YRRJnDGYn9Mw8cNnjiH0fcfnuhxwNt6iTkLNnk5TN/XuctQeQpHilzmmU3lmEkLz3yvd4+f3vY72k1pLeYsYnN14hdZ7uyRMON64ESAZL/+SA9eN9NsYHzDt9rA7wSilh7fSQ+PgJLeF5snUtFDUB6yeP6T38kHiwzTJpUTWQjLY1O8cPcWnCvNUmK8sQkm5rFq0O880dzAtfRz64z8pMQuEpn/8a8vYQ5iX9k2NaTgTR0PYWyc4ugyPLuIbcWbwIASDDKJxkIjw93fgLCIGoYSolBY2dQvP5ejRnBtLeWoA2fMCrhfNYnfCot8YszsjyKaqpwjWKR511IsS5R8xqCYKv/aK59nAxHzAe4rMxtdLB2I6QIqXLJb1SMo27nDzFzW8rGOmA41deMYuD8M374H9/YuCghoECocJzLwy8t+Rc7v/0i1MiQJFSNsPj5jiyCgOfW9gQHtkYqyk8sypEa6rV1iUaKqsQGAJJYSeVjJoibXxQoe4sg/d9piWlC+ElR6WldPKZ63W+fmFS9Fd/OWMw4zHeWXS3h2q1OCgdD4uwGa6YQWe154OF5Te/YGqvL9ar+QXXYpnz79/4kLO8bIqm58bWkNbGFs+LQ15tR4x9iG1br3P2bc2+jOg/ukexIis7R5R1WLY71O0F7cWCoP+SWK3w7TanOmWCxngR/ptPSZzjIC9ptfqYvEBR4YVi2RmQjY9pLydBORtneCmJy4KkzunMT1mkVzDIcxzVCU9iag4vX+fD53+DrJgjnSPPOsS25tX3/gPv3fpN8nYfhMN7SbGV0Zu8ifSBYy8azF42YS1ZvSBNW6wtxhih0DgyYVl//Cn3+9uYJEFI1Vg2CITK0Trm8PJtbJSg6wITpai6xI42UZ0eJzdexC1zvBToLCNNNR8bxclzr3IwmiJNiYszdtd7CAQLG4q9bTrYykA7g6jbZTSZ4NIWjiA4s2XOvc42igpq35QuH65rJJj21qjrxq/UebxWKOtYJhkOybLVDzoEIbAiUGp+ppZC8/cKTeQtrunAgHOPIhDsRB7RpHvFOMZeQLXkTHRZPPVYxsJlwgYWirTEACmOwxpyE4qoUhd00raAszqodXVT2CF09UsHtzvwqIKBbvJim+u2mwVq6OPCkTae9pV1geuvBbstRSLledYyHs6caOYlz14AgeBBETbNWcP28R66GixBExDUtw3zygXmy6+aZ/9FWGY6ZfLWG7iyPJdVt557jofDK3Q1z1BA+xoOS98EuX9xrt2Xsti/8f5dFnnJtrRgDC5JubM/Zrs9INu9SvTem4wOD5A6Qly/SXt3F1uWOA9F0gq+6MbgjKFa5LTGJyzaXZQ1QdnqPfHhHmMX+N+ZDMdnJxxLoanKiu3xMcvuAO0MXkiEtah8SVzVzDLJIutgpaRnLZ3lhE4+5zGCVj4nLRdYFTHPehghWLR6OKVYtgcAOBxOa47WLlFmLZQNG4rwgSlztrZJqwpiK1GuaJSwbHXougqnVcDlRSioXihcpFAqcL5xK6a9REvJYmuTfFnSnk0wOkY6y6I3xA43mdUQ6YiiEyT0kQhF6b2pJVaCK9uj88/lQW45qy0zC9IabF0HGmGcMLGSK2sbuPmc6PEDZF1j0pRoZxeDpqo9CStfGoHAUdaCuNWBxTwoglUQ0NVpFl5IDcrUpMUSLyTLVgdvJUY2yoKGdeQFwVCNBCdKVMPzhxX8IbkkKoxPmQLWC2Ih2BI1E1LmPuD0q9+z8XC3gP/+Rsy/Oqw4q8LkNAa+11e8PbXkdejKzwek1qM13EjhzjKI/VbrSgzf6gmqqee4vmAEdXTgue9X8BdTy7R25x48L7cEO4lEK8GkCS2vPfRV0FjcygSnNjwvhM3jZktwXDkcQXy4WkeVQwG/ty750dQya15cTwm+1Q+qW+89E+ObEHNB7xfYAFazgZXKvfsL3Mc196ldgGc7T90ntyF8RTW4+C+LFuq9Z/qTnyCUJl7vhdusZfnRR/iXetDtfeb9vjhlPqwvXbEvy4onByes79/H5QsQEu8d/cEa816H4tEj9J/8G6Q1IGB552Oy3/1PyfIF9zevoZxBeI9BBCGOg/X9B1zLp9RxgnQObWoOt3ZpLaeY3rBRqYJCUAEiL8mWUzbPDjAqQjqHEzDr9DkZbfL+zVcpk+z8PH7zwQccXrrOP/jhH7B+8IAybRHXFbWU/OE/+6+plMaJkKEKIJxC2JJPr75MtpxTZO2wCSGIiyUnw0uMTveh8eDHe6xSRKZmduky8d07nK3vnF+ztdMD9navo72nOz2h1gkIT1QbtIaTS9c4sRLpbejoowQnFKNOxFnZJDit2Cg+OD0eG8eN+FmWRibhQe4o53MW0yWGgCdbJiTDITqxDO5/hJ/NQ56ABEzJxq2Ue3TJ8efq3qDzDX+Pu32Uq8L1lAqtYlyxoDWZsL13D4XAC0+pEx7t3qYmQa7SwQkFRFiLSxJ8lOCoz20WLBESz6aBO8YF7iOBvVJZx6TdOi++qx1CEcRXzjte7kgelwLrPQPtuZ4pIiz/6iQwkGgSpE6sZ1vB8y3Nb3ZduA+enQTmRjCIBf/5pmRp4aj29JRgU3sKIZDS81sS9qpQ1LdiwUArXu7A988sibwwtjuqPb8zVAxizWUZaJwQTiALGxTDc/Ns239OAoPGBVSwMjkRhEHtmzPLSR3sPTxB1fpqV31uwS2d582pZVyvFBKwm0pe6ajPHV4W1vPG1DAxQdntBVxLJS+1Ffdyx4dL23w7BKkMuPgvsoH8bZedzbB5Tryxfn6bUAoRazZnx3yQ9cjkhfPoxMBmIn7lYrSfXV+6Yg8gHz/AmxI5bLpK72F/jzjPyX70Ax5cfZ6ZiFEC1uan7P6f/wvTYcB+jYppzrRExlBKTX+8j41jsmbzsAJGxwd0ioJJy9A63kNXJbP+Ou1Ol4ErGeQTxnE3UAg1tKuctXzOv/72P6V3+BhV1XglyRYzPnz+ddaOnnD93vucDjaInMXqiPZiwstv/xl//E/+BUm+oFUVgSUjJXnWxSqF84J2PqUhX5LHKVW7T7m+SftwH2VC9yydYrG2SZUmmFnO4OyQWmmUs5y0+zy6dItplHH54D6JrRAECGFveBV6A+rCIqocF8VI7zFRxmnW4cxCXNfochZ++GmXiY7Q0tOW8HjpKL0n1YKREojKMp4XtDDoxl5XKsnRZM43Dz5gkeecDDeokLScYe3kiO3WfeTaixiln+m4E2foEOGEpBTBT0dJSSJgNDshuvcxh5u7WKUReJJ8wcsf/Zh3nn892D7IULSEAOEMorDEaZuK6Bl/oKEQsLGN3z/GlCUegcZhux1K1cLZix/KCvrwwMPCoITmUhKw9ER6HpeOfziI+TiveH8O1pUIJBux4r/aihjEih9NDY4QErO08Bs9zU4ChRfcPyvIyhoVR2RJzEhJPqgtmZbcbuIPjQdH4NJ3teewDKEisRCsxYJbbc1uKnlr5prZhWfpBa91w3doXzylUSGIp9a04K2ZYz0WXGqKVOk8b84c20lQ6DohmRvoR4L90jOKHNeyz6ZlfrSwTGpLpiRVQ49+WHiGkWM3/ez7vL+wLF0olBBOE3dzj8Lyae7oNA6jsnlPb80M/3Dwq1XRrseSq6ngYeHOB95dDS99AYPYv3TFPpKCtTpnqmL6zW1CCGYy5son7/FYRNwpJLUIUv2JaNEtPYukh60KdBU6RKd1CCcxBVFV0J2fnZ/TjdKUacbo5CG33vkPZOUyDFW94+HVl3i1VTEmQlRF0yEKCgnohDxpc3TrN8BbhPM4qZDC8dLb36eMEgaz0+Bxj2AZpdy49x5xWYSC6kyYW0pB4YLMqEozNA7pXQgoj1IqpYm0Yn9zF13VSBxlnNITlrFOObzyIt3RDtpUWK2ZZ31ynVGkbT66/jXSMoTylXHwok+M40zF6Gj18xdYrXFEtM72uHbnA5yQeCFQznHvuZfpb23zwcJT+aDulLXnRHh65QxblEzg/GTjrSN2M5b7B3ww2qVUARJywLgj2T46IN54AY2hcY1B4oiBgVIcFrNGPKewwmIixdWTR+zLoPYVzbzCK0V7Oae9nDPvDQF/rrR2SNomx9AOrBVWQ1uINJyqhJPBNmVRIKyDJEa2E+KQLrmCac9ftwK8kExrz7SBSrQI8YKV81xdjjmaWnIpiZzjSuZ4IdnmEMXDwjJpgs2XNVxLLb8z1PzP7+7zeF7iGobOcaz4Z89t0dWaGxHUCJwPm8pB6dkrPT3p2Ws+A0s4ETwqHd/oaX57JBk3CqlBFEJRVrqBa0pQuRCEUjZsqonxRE+xcRIpmHjHm1PLp7llv7zY7C4nEAv9mcXees+93HFUB8WwaD6HgYL7uf/MYl+5sGmtP5WsIoSgqz3vzi2583ySh++UJ+RUbEQcqqrNAAAgAElEQVSCuQ0zh7/PpTodVJpi8xyVBeKFdw5f1aQbG3ytq7ma/Zp6+Xe/vOe5K1u8fTjlaJqfi192Rl3q44T3dMSmzdHOgoC5jHgz7pOrwF1f9EY4qYjqAuk9rcWMzuyMRXcYbG+9QNUVa6cHvPjOn3E0vMRksBGgEjy37/wE0YupAC9VCBrBQ22YAYsowUkBIhQ0j8eiEM7RW55x2ttEexssBoxheHrC8HSfRXeEt6HYGhXRm46ptMbHLYq41exDAmkqerNTjokRwlF10jA4LWqmKsbUlsTULLv9hvXjUVWJLnJI29RaseyFE5E2FYm1FJVBaIlNYvyKyWQN1WLMi3feJ087+Mb5UNY1Nz55D3t1hFExhRNUPlAGYyVwVQGmxukIv4KlvMdXFfeiNoXUZNjzweVEZ2jrkZFC5gWqKhFCUCcpMk1o2wnGRdRK4wVoL5FlRe3CrCMrl2E+IQV53GLa6Z9T4FbV2RM8cBQS2xTSFStu1fm/vwCJIm63A7XSw/0i5M9mNF47/iLcZDuGn84d65GgdpLaw0g7PlkKWrMFD86WXM8SvAw5s/O85gc/3WN/6zLaeS6boB+wScqbM8Hz1ZijWc5UtzgRiq5yKJPz4PEhN6/uclx7bJPOVjjB9Uxwd2F5WMKNtqJuchQOC8vbM8t/tgGxFGwlzxadG5nkzFxQLwsrWIsl27FgOv/5rFtP6LgntefyU4X9wdIyWrrPlMp7D09KR+08oziINLz37FeeXvVX0HsEP9elC2BuPY8Lz24mkA00l1vPndzzj1fUpb/HJaSk+9prjN/4MWeTGc4LWtLTf/55okGYs/X0LzbH+FWuL12xl3FM78ou32qfMPOS2hiyNKVVLvnT+Juof/OneFtiZHBaSauSSdLjrNdjsvscliDMcUoxODshrgqyumaOxwkdhmrWgHVkixk7HqrJEU4oYlOirOHEtaE/IM+6Ad8EMlNROx9+wOJCheQB4S3zdo/WfIqTDVnOe+IyZ5m06M0mPHfnbVyU4IQgKRbsbV+nbnc5Gia0XYURIni1O0svn1FHMfPBIDyLB9qQFnPi5ZwyipDq4qOtYkHkDKVSFEkH35ij1TrGFjMiY3BxFmYTjS2Dk4ru0R7CWZQEXBMLqMImdbq3j7h8hZPKn+exdgQ8sGFjE9biG3N5YQ1Oao56m7TKBUXaWV0Z4mLKg82r9I8PSSdjjA4RIpGx5NtXmMYeEkWNOt84O7Xl0+ElRp/+lKTKcVqD86xbw9HaZeq0RWQqnAppJsI5pHNEnYSahsPvL9SwpQnCppogclpdUgl4BZtJYNiUhI6+KwPjYlrDu3OPxZ5rCQaRp1XMWIugkrIJHPdsJJo35hVdNebWvfcp6sAgir3l9Nrz/F9I3lBd9nxyjpd3iUmnJ/wXwnKpoykaiUiqILfw7rRmYT338wuevXOe48r9XGznakVS8K2e4sx4cguZuhBVIZ7NgqhdYHwtnaf1M814puCg/GzruvMAGfH0beEU9nkrbjr1s9ozeErlOzOwGQkeF8/eN6SJ/fJ4oWW7x3svf4fi9AzhLbS7vLzZ5bP9Wr+Y60tX7AE6L76IfePH9BaL0KKVS5KdHWRXUmz+lMGDj88z5LxUzF9+kWJRUztQJiQ8ydow7Y+YLi9xsLlPd3pKlWShMHjLoyvPE9cV1jqEChQ9JwRZmTPPuiziNhbBJOuR2AptK/I0C51iE++H93gpcULhtWbRHtCaT1CNQKeMU85Gm7z80x9ztLHDMgtujkZrbn/6DtPdW8xG2+y8++cMpycskhbvPv86PVuy174YHEPgoS9aXUSZExE2kqguMTpiGWfUUpJnPYS1xE3snEVQpT2i+TicAYwBbxpKpiapDamtWEgZ1LAEt9CeLzmtPLaCtTjAAamEx7Vn4SIqFaGbQTgQqKg6YrZzk+sPPyRZToM7v3fMWwNOBmts7D/AeYlRCtCoOqd99IT9y9epkWRYLAKJ4yRKaKuIvlQ4HLWIEMqiqwKvwqmAqj6Pk8QHjn9JRu6agHHRFEgPc9eEiPOsEMsSuPWpgqVoDMgIf7ZUYNXUFqRqfHU8nFZwWguciuh4KBqWjhWCmZesf/gOtCJMtx0Cx70h/vg9/vLKq+ylCf0mR0wAZ0j+mD7/Q1vyfhE6XAe0PLzW0Xx/LHHeUdsmCF6EQB/r/+oOUwiBFhDJJkheCDIFX+9IfjgxnKwGwYngm13JX0wlxvmGDRQahZ4SnxsJGGAeyX5e8+kysHGyJllr7a+IX3ypo/jRxHBYeqQITcRuKomE4rCG49JRNU1CT8HtTP1S8HrvPW/ODDKK2Ly8CQTdwnsLyyCSX/iOfrW+lMVeZRnD73yX+vQUW9fodhvd6/HcD9/m37/4GubWdcT4BBnHzNcukRiPNVMqJ8k7WVCWCkF/Pub42k2KsmCe9UjKHKcUTim6iym6WDJudbFJMCjzziGqinxjm7trV9jbvtoUXEFcLNg53sNEEcliihAibCrOUmUdtHXM211sFBFVBU5p5q0ukTFEVTDkPfetNxZlDf37H/O7P/k+65NjvBBI73n1wx/y5ivfxrz0PbySgbAtmtfmHDZtc/unP6Y3n2CVRHlHnrZ5+6VvI4Qnq4vziqY95Fph0jTwh7W+kHeWJbaTofYssirxjXhLmhJtPPNWi8MKXBVYE/jg+V75BKsLapk8JZDyCOuoum3ee/6bDCfHKFNRpS2mvRHbkwPmccay1T1nV4l2j8HsDFtVLOLgRXk+UbCe9XzJ8XADp3ebEwkYBO1ihjQ1VRIHmmmjJyBRzALgRu45755XlgoXXjkXyxMIPYclnPmLbn8GtOugbJXywjhsBSmWcYuPihKrUoQPz0lteNnPoC55s+4QcgjDKxgawaL2yNhhnnaqxHFKQi1Dt1w3r8F7QSKDsOuostRC4lz4Tkd4bqbyc4ug9Z53Zpb9aqUx92zHkq93FdPa8c7cMW9mEIeV4GsdzW90Fe8vLDtR4OEnUnBQOL7R++whpJaCDQ3/roBx5c5PfpX1/Pbg8weXLSX4B0PNaR046h0l6Wk4Mx7nTWPSFqY6tQwb7i/Dz35mYWE8G8nFRqWFQAvPQenofU6mwBdtfSmLPQTqU7yx8cxtt4cZ380MfxGNkHEfLyDRmn+aLvgf3Sb16Sm95RjvBQjBeLBJKT355ozByR6pMThbM2/1eHj9ZbKTQ2JTkC7n0BxNj4db3Ll8mydrVxiOD2myIVgmbT64/Rprh0+Y9IbBhhePdZrO5ARhK5RzLFodTG+IthZVVSBg0usjnaM9nwBBxj9t93nhnR/QEY7D/iY+jhHOMRof8Oo7f85b3/hdsmqJdGHS54SnijN64z36s1POeuvUSYIyNcPxIeuTYyb9jQb7Vg38Y1EmBKD7Jnu2eQHYSLNQfR5u32Dj6BEBUA0d38OdG2RRQhXs8kOAiAsRgVWcUUZlgFHEihLoKdI2ERG1hll3iLQWqzQIiSoN86SF9wFMFwic8czSLGQJeNB1jXAOoxQ20pRSIlUUYDElGs8Xh0Xi6hofpRdhs4CrwUQVgvgZZ81VydNchKTw1O0TA1Mugspp/t2nRbhN+cYRUwQIqBIhjMUgsXWFJ4SXWCGJO0NwdzGuEVU1w06LRCqBkoLaOqS6UNFGRPx4YthOY7aT4G2TiOBZY7yn9uE+BtDSNwP+8DorF9SdAJdjQaol93PHk8qxdd5hC55UDjHz/N/Hhq0YbjTFa24c/9tBxX9zKWKv9CEyUYRM2BstxXf+CoXow9IzrT1rsUQIsNaxV3uOa8f1z/9Zo0Swb3h6ScK8LFKQCYn3HuOfnsr8/a7wWfz8MwXA88uzvrTF/rNWvL7Bf2x/yI3TKY+cJhGO61oQ7WzhkGTCI5uioBCISFPXJR3n6OfzYIQmJUJrxkCRZhSqjbYGYQx1nDZUxhHTzoAibaNNiZeKMkrAeQZVEeATEeIAY+NI65zWfMrDSzc4Xd8hrguMjNB1yWh8QJl2UFWJi6IAORA6B5cmTHTMsr92PhswCC4dPUEXYxatEUldggo5s7aVsvbWXd5/7huUWYdg46h4tHWd2/ff5pMrL2Ki+KIaeA3WoCsDcYRTkpWVA96xQNMdDNnr9qAsQgcfZ0ipmKExLqhknQ9YtvABzjntDajrAlUHh00TpdgoIjE1l/fvE5c5RkpiY5j01xinbbypMVkrCNS8B22RuaNUMa3JhKQK6UkCwbQzoNQRg+oMpw211ShocPowQvVNF//0yqqaKg4nFAvnYTUr18424f3QwDUVMG/+7c+OLgugRcCuVycOqQKsYx2stTNmdUJlw8xjI4p4guWG1lzTFVMZYx20pSPFsNbKmBCzlnlqa5ASZiZilICQER8uHJUDL4Kbz3oEj3KL9Z6z5jm1gqGC08rx6cLyL48MRbOBxxL++brmSQ1rPwM7jLTgh1Mb4Cl90b12tOSocowN/Le7MXdyx7RyjBLFjVQg5WdDMpVzfLx0fHegKLzAOEhkgILemnm+Ofjcn/BnroPKcz3TxDJsQEpI+jrg+78MNk5Xh+/105m+znsK9+XyDPpKFXuhNa4q2cSwnYQvuS0rCge7T+5wmHQp+sNQTASsHT9BOsfGyROm3UHgsguIq4LN40c82rhKq5jTnZ+hjGXecYwHm1Q6xqkmy5ZwxvZS44THRBohJElZIkTAWGudctZfYzrYpDc9xkYR2taUacb7L7zO0douX3//z1hECVYrutMxD669yM6jT6jjlLhcBN64t9RxwrLTJh5PKXtbFDq6uACVY5l0KVpdxGpD844qbTPubQZY42c6FO0swju8gLTMiauKKo7JkxTwTLMuo8UU2+4gvEd5z2mrQ6cJLtdNe2Ubf/ZEQuzASk1igzulUYoUGO3fI1tOmbd6CB9EUJvHj7l7+QZlu4uuygsrCTyzzpD2coo2FXnWbYbd0Jmf4b2n1jFZXRI3UexOqmZDNp/5/ZDOBxsCLoq8B7qE7nzZBJWsVgrnNgmirklNgROaMgmU1a0YxnXA+xHhdDOMINWQA21X0ymXoBNMElE4xf7NV7j+6Tv0jcVJSeod+1du8e3tFosxHJqGEGqDB8+/2NZ8vHBsxFB70UA4nk9yz8Pcc2aCB49uuv6zynE/t/wfBxW9SLCdhoK8MI5/eWR4qSVpZ88WaSkCd/8zf1PNddJS8kJb8otMJF2DUGkRsP3Vmhv/uc/zV60V3yYYHj4FmYhfjnWPFILXuoofTywz686vyXMt9aWykvhKFfvy6JDs6jVkkmBmM4RSRL0++YMHbB89JOltUBcJpRB06pLaWrYPHrCvEpKigIaqaFVEbzmnNR+zNj4ksqEs9M9OwVjsi9+mtZgFB8mAbjBr9dhb38V5QX98RJG0QApUXRN5wyc3vo62FaiXSYsFNooplEY5x/7WVZ5cvklaBTpeHaVYpdj99H0une1xsr7DCjHuTY7ZH11mtnuDi59Bs7znyeWbrB3vsez2G4th6MxOeXDtpWDhm8+oG/w9MhUKSQVcefAJG2cHq1hzjgebHG3sUHd6HPXXglsn4KTEedA6DkZfDYyjdfOng+3ZCVfvvIdtkrdEnHD/5ssMj/colaZtz7BKoY2FuqI7n5F3h1RKh0ohRDh9COgsJlRpB20tTohgdSAUWbXExglLrYltjRMSoxVORhStNrGtQCqsIHjwAKQXoq3VMHYFz6QSZg0jB0JnHwtIPYjJCRun++eXOU9aHG7sMsjCRju1zUanoR/BeuS5d+c+ydGTAAd5h+/0SG69xLK/xg9e/C7R2RjpLGVngO60+S+HCV/vGP7tWfCVH8WS7w0Fv9GN+ZOx4b1TS9VMlaXwXNKCuQud/ChVGB/sjiPgxELhPJeji8LY1pKDyuFlMOpae6ojPas9r3YUf3xaU7kQ5wlQ2uDkeCX9/KHqZ61US263JA9Lx6Xk4vbDCn5n9Nd7LAjmhneWDUTVNAOF9aTyl4PZAwwjyT8aBbsJ48Pffxnq3b/L9ZUq9piQ/GB1zDJKkVIRaY0zNd88vc8fdteJZhNSU1NkbbbyMc8f3iNPRhyPdhA4vJBIZ3hu/z7pbMz+6BJ1rMJAT8Bgdspr7/4Zh+uXOV2/1JCvJelywXP33mH/0nWy432uPPkE5Qxn3RGT4SZnnRGJrenMxuRN522TFsetTkPnUBRN9udq/eiV7/Ef/fTP2TzZC+IsZ5m1+7zz3G+GSSASZUIXaxuq5fFwm2w5pzsbh/tYS561OR1eQria7iJQLQlvh0XWYevkCWvjfSad4TmffzQ+IFJwOBpQVo6Lr4onSSTDOLBYArQQvF5iCWuuoH33bcZZlzqKQyEtcl688zaTqqROg8eProPhmPQeYUzDpRPhWkDAWCykZUnV7oEIp4oAsXg6xrBIUmztWSYZOIEQjrTTopVEVIXBNf/eCo/2Hkn7HI5ZvRtLI5JqusT0qdsrD93FlHh8SJ6mIUYRSMqC0eQY+pcobehgHc2A2kJrfsLg4DHLbg8hQ95AvJjQenyX5Y3nmcsE1rbPP4MW0JKeMxHxYtuwlUS0FWgRLCP2Ckv1FCXSec/jOtgxRFIwabJ+PZ6WCIKjz1qiYcnUDg4rjxIe6wV9JfhmP6iQ/+jEoGXD1vKe31/XwaX0r7l+dy3if92ruLt0aBm+H9dTyet/A8xlqAU3MtnEjYa5kULwzf7nWy/8faxYCnY+R/37ZVhfqWIfra9z9wd/yePjs/Nc2jjSXH/uGoNWxj//f/4njuI2lU4YFHOGGu5/7dvs3rmLVjJsEM4yWkxIFxMimbAxP2MWZzgpSWxFZzFh7Sd/whuv/hZrs2OmrR66runMx3SWM/JWl2+99acMzw7Be2oV8Zff+j1OO0OWcYLojRDWhOKuNF5FoYt1F14ueAdCUo7W+MtXf4vnHn5AZzahyNp8fOU5XNYlLRdkkwIThe5SG0ORZOSdLo93btKbn6FNHRS07T5WazSSqdJNeImnjhOE0qyd7oUuuphjpES7IP3enJ4wVillXGOq4DWko4hYRYyiJvRKBJw6ViGA43o15th5Sh2fQyI+yVibztCuIisCrOSEIPGepFiEjr52tBbTRojlkQ4W7S5F1qIzHTNv9XBSEHlHb3pKujai7PaZ5BbfiKriVsQmlgmeRRoTJteABENEG36us1cEyCUzcDOFcRX+W1uHjyQdHzJPUiJnmvt4TJzQX4yplyMWJOTNY0oCrTN/cBfdzugrKBtKpO500Uf7sHuDTEVMG6QpkcFK4K1JzaGBH039uVr3VgqxUNROkKjQzXpCMbcerrckH+SegZbBLkEGA7nXewop5DNdunEOLzw3MsUwEpzWQfHZVoJRYyr23WHMrbbmzsIggVtt/Yxh2l9njWLJf3c55pPccWbCQPhGJp/xy/9FlxCClzqancQzMQ4tYC2WJF8w75kv+vpKFful0Dw8W5BZg05i8I58MefOEvxyTjqfcCUuEFLi6+bXJuBk8zIbpwcgPMILyijm4NINXFXhpaDtDc5LtDXUOuHy/n0un+xxd/c50qoMAikheP7uu2weP6GKU+5dewkQxFXBd3/0Bxx31zjb2KaIEmQUQ4OnR3UVfNSBc+KfCNxe5SzEKR++8M0LrN05snJBujB4HTUiqHDPrFwSu5oiSsMMwhGgJFMznB4zXlsHJK7BPT0SIhjOJyyiBGMNae2wUqI8ZMsZCwueiCgOm4olGGp1FexVoVhqHZgoRkHLW3IfhryrU67zMPeBLYS1tJezkPfrHcs4QwO9sxOqrHP+Pp2wdKYnLEdbREePWZud4GXYFGe9EZ1+n6lRyI7GNTHhNYJ5VQQMPeRBXdBr4FxQpbi4+XzwKgM98JLIkdZSi4y5UozqCbJQnPbXzzdk7SxXjx/xk52buDj49UM4WB4YuGkcifQUKiNaETa9pV/OuFs6yGCwsgVwwf/+h2eeYx/SuQokiYSHheMPTy0tKcELYtlQLwGD46Wu5m7lOK0C/31pPR0t+SejmFjBvz6sWTY0ypb2/Cdr0Xnx7qjw+aTyWffIzViyGT/lWfC3WKmWfK37N9ssPmv1I0E/+vJ21r/q9ZUq9k8eHyCvXiNpJbj5DCE13V6fk0/vIPYfwwuvIM+O8ZVBrXeo4gSWC2w2gHqJL0Jx160Oy9EWdnzKWW+Nqt3D41HO0z3ZJ9raJimWzLsjyihBOEe7WDCsC8xiyt2NKwGzFwKnFZXUXD28x3S4gVRBYCPwSGNQdUUdp6EkNG2nA5ASZyzKW4yIGo8XT2xqnPe4JGHZ6uEb+EZ4R7qYsXW8x6w7wuqISmuUtcR1SbtcMh5uAi5QTyHgv2iWWZe1g7sUWQ+DRONI8ymnWzfPu9+n4Q9PyFHdTWC/CnBHGsF2BHejAcI5cA7TdHGRNXihORttE5clufABfopiamfpmhItQ7iIURECj64NcbXE9QfEV6/hlgtcXaNbKSpqc+IsxlYsic9pkhGWMYoFn12s6qf+/6poyua9ZbZidOcOWT7Hi0B/LTauszPs0n7nXYyQFDpCWMfG/DgM2HVMS12cFpQCYWC8ucuNj9/ASk0lJMp7evNTZKdLlSQhmWq1r/swHH1chwHx47L5BvhwUsotvNwJDJSiOaikIkQMGi94vSv484lgZh0tIXitLZlYz8upRknDuAg605ZW7CQK5z0fzC0Pywue/dVU8mL7lwuJ/Hr98tdXqthbY5BKYdI2E91GiyBrF7UjKXJMq8NiYwfvQvxaenZM1BQbozWin4FzKFPSy2IebH6N+viMdDZBAFYKxpuXuffa13jTD9h9/CkoifCOs86QP/vu77Nz932MUrTzGSDBOSajbXrVsgm6Vo2Nr0coTa9c4CZHLIdbIVvVe8o0QxVLTBRRERHVZUNJtHitOcnaRFLhhUTZ4AFkPZRZm+58QpF1kHjSYhmsBLD0FhO0tSGuzobe1+gIgWHcGtAXms7sFCcUyhmKOGOStc8L6TmpoqGG5i6oUNdigUMicUysZ550OLt8i93HdzBxgsQha8PH118h6Xfh/h2wFhMnCGuRaYubHc3e6ZzHgxaqgRucFlw/PaXU2yySjKKKEQhqmTCIJaeFZKYgxpwnyhrgLE7Ouc+rHnDFuql4duNarTbwndkDHhQ5h1ljQeEst0/v0xoN+enmVbaOH2KjwPQxQnHn9mskUlDbcB1Wm7UWINa2WRxu0jp6QpRmqKrGRhr/yuvEIiRPrZgkwgfoZ2lD4HimLtC8WR0ee6Rhr+a8GC+dZyMSPC4dD0r4Rk8hZIR3jsel50dnhvdmjqEWbA3Ciax2nv/9oOL31zUPCoilZ2Ghozz3c09bBQdL5z2z5tDb1TyzAeQ20A0zKc4piH8fa2E9tQs5xl+k8I8v+/pKFfutnQ1+/PZHfFxljaDH07E1r4zW6aQpx2cTJlkP5wVaWHamEy7diHmwdY2RsBTTGVIpGI7wZYHYvc7l9RnFowfYypCurzNe2+YHa1dx0zOOWi3kYo6PImi1uBdl9I+e0Mcyzzoh5NobsrMFp+ubIBVVHMK9cRCbEhfHXDp6Quv+h43ZmETXJbN2n7s3v85UR0RVibQVTinqRizkigLtDLaBZKR3SA95u8PG/kNQ4jypyjuLy7pIZ2kXC1YMnqTIqdMWXjqOhlt0innYEJRkkXaJquKZ6D4IXxhNKJrGwdT6ENOmAhNEAgfb15n0N0nyaZhNtDpUcca3OvD41osUp2fouoB2m6Q34KV0xujwMQvTYy7DUHe9WvByVrOXaR6/+w79ydE5xDO7fIvk0lU8ESVctOzR+f88I7h5WjD1dHcPYSOI6oLtcs5i2KPKA3OnEysuqYQHi5K9ay9wvHMdvVzgdIRttzAqZP8WhI12tWJgp6M4fOVbHE4nyPkcEyUMhgOutmN0GRhLCxtm+4kKr8/6AHdVvkGLmserHfRjRaoFh7XHeViLJD0tOawdLUUYAhP+7GjLJ7ljJ/FIK6mcR4gGUvOC75/WzBzcyV1Q9wrPrSxYEvS14K2ZPT91pBJ+s6dpK/hgbnnUnAa8h5styfOtz1fq/k1W7TzvzC2HjbpXAM+3JDd+1pjn1+tvtL5Sxd50Bny0dpns8UMiGQyo5kLx4KVXyF//R9h/90f0l3s4odDW8GS0w62tTW7Egk91HxcFEnErinhNlcyuXWbx8CH+0hWUM9gkZX19yKQ3QhUlqbdUrS0iGyLe5p0BJ7dfZvuT9xhWeeiSTcXZzjWOtq9RJBna2kbuE7JUx0nG9p33SOoSYYrA6BCSzfEBj+TLlCIOXi/nPBEgEtQuJqlKomb24AVUUYKXCuUNtdcI6XEOtHUY64LtgSfYLADCW4SpSBcLYluzaPVQeCwCXVe0J6dEAgrviBruupERGYJYBY65OOfZe2ofzNAksMhalFnIZjUExsnlBO6UEWxshKLbeAW3ej1+snmbnaNHQfQqoJARf3HlFa49vE86PWY+2sRJiSxLhg8/okjb0FsLDxCtevVmWN089GoIu4JrYlbF3iDxzacQUXoYz5eUoz7bbX9OaD2aWRZeUqsYrQQ2zRozNkkVRSRcnBJWcxMHLGp45CTd7hDfHxILGDugaE4YLvjt0BR4I6Al4IzA5kGEU4InYOvWgpeCK2lQoxZN+lNHBY78Kr3JugDxZBqeFI4rrYvOWOB5VDmWxrPwgt0kiKKcc/x06Rq3U0kmodvQMpfW8+Op4VIseFh4NpqcVec9nywdHQWX/w7ZKR8tLMeVY6OZK1jveX9h6WrxNx4U/3pdrK9Usf8wd7Qv7TBcnJJPZiglWNve5GGrw/76VaLv/R760T3ifMlifRu1uUn71jXW336DO7TJbaD1rWnH7pUB/WGfo7d/Qn5yBni01ojNbV5jwR931zlpDZDW4iU4oWkJS7KxwaeD34bxKcqUlMM10iThpDPC6hRrTfiFCwFCEFdT2uWCtVIFoAEAACAASURBVLNj0uUUCNTR4+Emw5ND9i/fBuqLyeKqc5UeRREc+HzgvwsVES+mFHFKmbXDABZPUixJ51OcJwShrxgRzqNsTVRXQWcgJF4KIudJihwZaTqipreYUTbmaalWuE4H68LrOK3D29ES+gK8hLYMIqVVh9hSUDp4UkJPBHzaEwai1yX86algOhxRe0e0mAcuf7dHoRKGjw+Y71zjLMpCCHjXk0UKNT6C7Gfj4GqIICWiJsA2K9LoyppY+JqoCmZ4eI+ShlJrDgqJHh9zHLXxCGLv2D49Jr/5GjZKEELifBDRWTQeSQQMGn6+J+TMJh7ulkEucCIaYpWHBBiLYAiWyuCy6YCk6boTCbEJni9V83gtwrXsxpJLiWIRvPVopyHjdDuWHNTBPz23wbNmYSVbEfygdjwq3LmBnfKe3AmUFvQiztWvUkraynK/dHzNw/ApeKalBAel472FZze9CNSWQjCIgmf931Wxr53nYelZe8rxUglBR4VB9a+L/d9+famLvc1zXFWhWi1kFFEslkT3PiXt90mTpDneOvzD+9xtrXNFHpJcu0XZOHcd6Taz9iW6us/25JBJb52kKpFnZ/zRN76D/Ld/xMxEqNE6wlkKqdHv/ITbN6/yx72XKIQicxVWaMo45mpxQrV+icnZFJ22QvF0jipLWcTB0RIHicuxKsJEbeTMcWnvHlXWYTbcCl2oMVw+uMf9ay807zS6AKABheP/Y+9NYi3LsvO8b3enu/27r4s+MjKysq+GLBYlllQSKZmC1RiCYQOCBcMWCNAzGxAMGwI80MAeeGR7ZEOAB/LAoKyJBdmwRNMySVMkq1gts6oyszIzIjIiMuLFa+67/T3NbjzY572IKFaJrGyKDLIWkIj3IuOec8+556699r/+9f+d+Qll3kXo6LzlERSLKUm5ZjbcbaddfdSlSTsUq3kcGJOxYQhxgZAObJphS01Wl+fnaIyiHm5TTE9YZjlJCxc1CPrTE6zZZ+5iMpIyVpXzACMJfR1hChPnomg8jA28V8bKei+Nr/Ee7jaQuZKmrNBFl6YTZw28c9SbDcfFkDrNyK2jEZA5z1F3C7dZ/uCHooFRDg/t41t2VqlranQVJTGMj85LAYG2De9ce4kXb32TvippVEKxXnN3a58w2iJvYB4M5zsHARdSOCnj4NU4aRu+PjZUcXGhyUVc0Dxx4VM+wjVBRn9ZISKDZ+NhS4GVcUHq6giR1UDPwNUsmo4YEe9b5eFiJvnzA8lvnjqOmjjGP7NR4uDnh4rvrkseVJCp6EhVOthLAj0tSUX0ctUiwnO5BNuSBn6Q2kztn3r84j0gCsp9XOHbT+pMr/4slIiOWT+Jjx7PZLL31rJ8602qB5EfLqSg88KLXFtZfm96SvPNN8FGGYBqsIW+dB3X1DzojRgcP0K4hibvctjdorusufWFX+QoaOR6hdeKbrfL/uEd9u7fY39VsURgpaLXlAwSw8Mk5eZA0j1+RGUSlLMobTi5+gKLrI8a50zLiuA9WWroZTm2hO70EYPZaZQ/Bso0Z5V3Kao1ZdF7rM8twDQNifrBD7loLN1ySeI9lUkIAkxT06nWlJ0B/cUJ6+4QJxQyBHrLCadbe0g8pmnOcX7ta7RzHO/sQ6LI1xtUXeKSjE2RU3cH9MsV2lbMVZRPGNqKzNfUbsh2kmFDhGkyHaV3rYBRAk0Vq1dBHOcfaDgq24pfcc4ylQFc3VAiKaUmtHKUSmikd0wGQ6oGqrTgDJAoVjMYbEHrZmXbzoIiTjcG69hCMWlPkwFDwFRrVk6S1RYno0CZ8p5Ga5adgt99+c8TNhuUc9gkI2QFn0sFJxaW7XUKYvU+FnBMTN6tr/m57MBAxeQ4TgR1iBINDVF6wbQwS9VW6V0VNdutjOymOsRmbd/E4wQB20ZyvVAsbMTs++3rL+ea/zhXfG9leVjDTgIvdTQnTeBypnm5A4+qx3LFCxepncdNNNvYtNLDcyu4WcgWVgrnTVnX/nyjiIJwwyeUOWY2/FBLwg8TqRT0lWBpw1Mm4wsXeO1PoMXfsxjPZLJfv/su1YMHmO3tqK9tLYvvfodxgGtv/C63929gVLTRE8sln/393+Lh8HmWm5KDcZx6Dc7TOz1iMRzzXjpgV1g6bhMhlLTDSvXoHJ+SeMcwbX1rfaCczNl0d9hZVtRZQSIlShiy5Qz38H0eXrqBMoa+guADTiWceChWE4rpBKsTgow6JllVop3ldPsiyXqJsBanJGm5ZjoYU5knKYTnOA4AWV1Tm4y8XgMBLyS6bqjyHrJp6C2mWKXRzlJLzWKwRa0TtLWodrGJg1WGze4l/HrBpqdBDCE4vNSUOxco5hO8k4zKU4IQNDpBWEcBrFo0SgOtMgM9BR/UMUl1Wlpi5aNBSKFjtVq2WfPM+ckoEA5qKdABCIJaQMfXnA7HpMslabmK07XesewMSHJDhqdBI9tFMiDphooVEqEU26LF7X0UNNsvV3SDYmUKJB6kIkjBtekBR53nWMsU0Ymc/TMW0rSBiY94f9buVCoPxw62DBw154gctYuV/sUEZiFqwWgZFwAl4VprgrIIrX6MiFV9V8ekuwZ6QjBK4k7JAT0leKEjubX2BKLd3doFPtVRrY664KWe4bqPUFC0HvRcyQQzB5eLmDgbD5ezwJ8bGH7txDK1gVzB1MJAC35xbDhpAm+vXcvnjzo2r3QUO4nkKzPLUe1JpKBycbG4nn94aCV4j1utQEp0J/bKXutpvjKzPChbyYwQ2EslF9KfQDgfRzxzyd5bS3n/LmY8PscQhdaoToflN7/Fy/MDzHDAocwwwXPNL9mZPESKEdN8RBk0AY9UEkyDywsG997jxbe/Gh2qCGyPdvjGzc/TeB8NK8pNm5gkjTIki1MWl26y7G+dY6I6yRjPTwjlCrlZsnKeQNz4++GQrdkRa6lRweFb/NtKSVJXlN0e71z+FIFIr3TKcHnygE1/G2ieoJD4OA0qBJmvaMoFG5MjEKTNhtSWTDtDFnkX7RqSqqTRCU2aUJsk8tqfEE4LRPy1b2omz79GNj9Grla4Toeqv81lVSION+we34Z26jakGW5nlxGOWYgVn2vpg30VJRMksGygbJN5R0YOei/EAaKGVqWxTZIDbWjKNSmKjYhNyEFocECVpFS9FO2a2B/RhkYbvHLkZRXNSojWkDkNXeFZYqhdlCaGmKAlIHXC3vEBD7cuUEqNcJ6tzYKOr7hPVCltnniNAu6Xj9k89RlLhdir2EkiJHXa9i0GJmLwL/UER3Vg5SWTxpNKuJAIdhP4zipwuoHZ2bFErNSvF3BsBQMtWflAJgQOT18LLmWa+6XlUeVwHvZSxW4Srf7eW3ve28RnLQR4rpBcSQW7qeSqhFkrtj9o3aiuZIpfuiR5a2V5VMNeuxsotGSYRAPtwzo+1bvpY2OOnxtqDmvPysXPeSeR5/INP2rUkwnLb7+Bq2qC9yRbI3qvvU6RZgzb5nLtYaAluwkf+jw/iafjmUv2ONeqCTy92gutqZzjtLfF/vyYy4mJlbh1nPS3maRdKqL5t5Ca4Brmukd/PuPV736NRZrh8x4g6J4c85r5DmiJc4FGmqhs6RxJtSEET5XEhqEKUY/H6gSrDMXshKVOUcYgCVQe5PSU3nJObXJsUiC9jz61QVCUp3zn2mt0ljMyW0eTEtvw9tWXOdUpNCCdJa1LrDY0JgUF07SHtg1FU8YdjLessi6TnV2K+RzfSJwyOClwOmXZ7UXmyJmWPTHZuxC4hOedJKPqbpNkXRqdIZKM627JwaP7eCEInR4QEOUGc/AQfeN5pjZWu0ZHKGNqwVq4s4lV5kjHpFl7uL2G17ux6pciVsDOR0OOcZZxXDuwDbmQiBCHmnynAAzCgDPmnO0C4FBY5RAhkLczsxaBTLJzzv2T+jcNRCvH3pj+asFW7F2z1obj3g4VgYanufmWuCMIxAaraZlGVsRKORWxSh+066drC9JXCviKVzgbeL6jqF2gDPCloeJ3Zg2bFr4562ecNPBiBtSSU+tJg6D0gVRJ/vJQ89W5RUvB5VTFSWkR+OrM8UIheHsdGOl47YbArbUnFZKXCsV3lxYlIoUx7gZ0C5EIfmrwgwfPftiUaiIFu4mk8vEz/7AJ2G02zL7+NXS3S9KL/ZlmNmP+rW/x8NWf5rgJvNKNDlQ2BL6z9PS1Z+uHuGJ9EuFCYN32SD7JeYIfdzxzyV6mKbrfx65W59s/ALdYYF9+HXE4Jb24T1gsQCuKJOX9yYJT3aFMM9amoBUKoLeaMbz7Lr7TRTYWv14hEKxGY3ZOD6AquX/hOXyQCG8hSRkefsBquEt3s2CWZbggEEFQlEsqnRCcY8yGldNYIRl6ixWgvGO0OGVTOBoVK/x8s8YKyaw3pk4Lss0S6T1l1mWVd6Cs2Zk/5Pr99xBEkPdg+xL3r72E1Qnz/ggnWp49gbxcoZ0nrdbsP3wfhEAEx9H2JebjMdrFMfvwhH2HFlCrjBvvf4fN6fRcG6cYDpmNhugAi8FWFBVDYJKcbHbM0armC5dg2fq3DtKYCO9sHk+nrlssW4uIRZ9aeLGAe2VM8j0FF0yU1bdJh6Vp8E08T2KgL83ZMGn7bh9HCuRpzto3VMTE2UXSSEXt447Kta85k0eYJAldU5NpgW0sQkpGWnEkEhqiZMUZlbJdC56q9H1o25ftYFkKOAkndfy9o2NTdh00v3RJc3vtuFPG5vTrXcVby+ja1FURHkLEIapCQo3iS0PJG/PAkQ2MFXy6q7iSSw6rwBtLy0kTwaWuErzeCyydoAlwe9Pq6QTYTQXvbQI/29cYIblbxsbJpVSw/yHhEB8C31t77qz9OVz3QhG1bn5Unn19dIQQ8Xt8FmYwoDw84t7xnPFW//yYWgg6Cu6WP75k/6B0fHfl27mSwMVU8kpH/anYXTxzyR6g+/LLzL72e6w3EWM3TUkyGCFfeB0OTnC33kZ2O9A0hPWa+md+nsU7p9jgY/WsFMI2nHZH3Hx4C1VtWO9cJgsWJyTWOYbHDznYu8wm75E4i5c5wVpOLj/HcmuPqcoYTg6xSYpyliAk7z//aW5O7vHgQpQr1o1lk3foLidkSlLLdrJWyJaTJ1j1RxCgMlmEjIKPUgtAf37Ci7feYDrYiXo2IXDl4H2CEBzvXyMYjWrll61QrLIu45OH7D+8y2wwJsiA8HDx+ANk1uH+1n6ENLxt6ZoKK+DkdMrO4QEq70GSoOoaf3TAolqjWk2cs4fdhVhlJ8LTUxIjYRRita4EqLWP+LWPdEyI1asiYvUigas555js2sdm7iJEzrtsq+SKqKrZI9oAfn8kxEUmEYa0xdJtiK/R7eufTA/xLqUouSJ4QWKiPnODgE4KLfIvnvj3EBeNhsdDWWeLQZ/oL7vysJ3G699YmHtYW09XS35qqHi1hasSCV+fN8xsxO/Habuz8hEKW3jP0mmudwTXiP68Kx9Nun/j1EU536aOi26a8BsTz2tFpMoOjTj3xz0oPUsrkFhSJXi5q853cL83s3xppNFS0Pi460xb5cx/U7y/8dxaR019KQQuBN5ceQoJ+z8i9dLb5jH194lwQHAWKQS1j/2CVMbqunxyau0TjGnj+dbSMdIizukEeFhFT+DXPmmHlB9DfOQrEEIo4KvAByGEvymEeA74FWAMfA34D0MI9Uc9z5Mh+30OXv9z3Lt/gNisEfsjXr2yy15ieP8X/xbZweu4W+9ClsMLL5GrLpt7X6OWGqtMm2gVptlwsrXPX/zGr7Kul5wmGcZZxvWasmlY7V0i746YoPDOkWeGrckhj64+x1vdC2ydHDCYn1CZgun2Lk1nwKV3v8KN995gLg2NThhUS4RJePS5n2PeBFiv8Y1FSI3pZGRGIW3NYDGJcr8COus5886QKwd32aQdvDEtVBOYdYdceXCLye4VvAvUqk3GwSFDYDg9ZpXmBB/Oq+J53mP70R3uj/bJN4vzClkAZdFHHD7ig50rLDrDsxxMbzXjysEtEqURStPE3T/Gg5aGC1sdHtWeuZP4EI2rO9LzqQLu1rHpWrUlfqFiws90TG4nIZqTGCIHvWpZO09Ot579boAukclyVqVn7f+vaRefsx0EMUkn7f+vnjiWBsZSYrOM/vSQqnZICWlRUIqEdsbpPMlLHjN5CmIztQzxOB0Zq/wAvNSRrF18HxcSwaMqHvf2xjF3j6dr+xKGWmCJDKQz1UvZsnxqH8hF4GsLHyEEBa93BHfKwKP5mvWDB2zKGggkxtC5eIGJ6RCk5KAW5/zSTAQaBFuNYGoDy5a22JEwNJKTxrOwnOP8Ang+lzz/Q6ZhQwjcLj1j81g6QQnBUAdubfyPnOzNcMTqe+8QQjg/n7cWoyR5r8u3F5Z5uyVTIu7+vrRl/s0H/ZjifulJpThf/IQQjA3cLwOf6oRnXrrh41iu/jPgTWKxA/DfAv9dCOFXhBD/E/BLwP/4MZznPN5Ze+6KhNH1azii2ca3NoGfTQKXOoYPLj1PtnMBKyQuyXghlDQ7e5TTJSbYKFDmLauiz3L/Elopto/u0Su6qMaSlmvuvPxTLD/zs/De99gOdQSmyxXzSzcIWxcQNZxsX2I2vogTEWMe+5q03tB/dJ+s1ycIhd6sEMCq3yU9mlHlBSp1BCFpAmx3Epp7Ufvc+ciJdkLSn0/ob6ZMu+NoJt5eu0tSilDjpcILgXaRDuKCwJoE0dS4JO4MBG0CkxpVzejNjrF5gWineK3SdOcnNCEw7Y5IbX2uHTPtDhnmPeqdPTof3CZN2gnepmR+5Sb/1k6Pf3rkEW0j2opALQV/ZUfwO0vP3LberESBr90ExhpuVdEPFh055srEP8+qgSf7CRURKgkKBq5BA1YobHhcGUacvSEgqNGkcE51PDMmOZumveSXbObHHCVdTBJwQiCd5Wc2D/m/86usaAeveGxVONZQych777R/BgF7GnbTs6ZtZN3UPrCdCLpS8KgO1NZReYGRgbmUXDaSnoBJiwcLEa/biNjQ/vrSsZ8JbBBoArdKj1l7Dj94iKgbOkXsR5S15dHdB9zs32CtBJmMfqguBCatbPHba8+WEYzaIaW1DbyzclxK4bgR7CQxabsQeHvtyRRc/gGJOxCv8fu9xXXLJPpRw4xGZFcuU927h8xzgneE2tJ7/XXyNOH+0jLQsUewcoGNi9f344jSP1ZqPQshBAjPj2lz8YnGR0r2QojLwN8A/hvg74u4VP8C8B+0/+QfA/+QjzHZWx+4vXYsree91jNOSdjVcaT7lWZG79tvcLrcoIRg7+Ie4vmbVIMxIclYrSuCd6gkxRUFfbHk/vOfYdntk5wcxgboaMTWZk73wgWm3SGTgw+grlCjMZ3dXZgv6VpPjaQRCoWncDHRWym4++mfo1nOo/1h3qFrS3Ye3uZQdGgWG2ppUN7RTQXZaU1er+kcLVh2hngp6ayW4C226DJeTni4cwnXMk62Zse4YoDNMvAeJ9qPMIAQktPuFjvTQxa9rfMKPi+XzIsBvdUUc3pIk6QEISJbxyQsB1v0FxO8SfBCYYIj3Sw53LnEizeu0t/fYnF4REDQ39tBdIfc2XiyzZo3XYRCEgKfNfB+WbCfxIQ5bymW2yri1j6As7Gyd01Myrsu4vdn8Qe/U47eZo2vapwQFCJQdgq6MmflG0TjaVodlUTUSJ3QEMXNNjw9QetXc65T8lD0mItYy19XjkvlKb1sFyEyVu1rDHGHsJvEqdZpEx2pEhH/bsvE/+6sPYdtld5Vsbq/WBjenFf8P1N/vmv5XE/Q7wk6JnLoj9uh6J0kLnZlgHXjubeReGJzYKg9zWqDbywXunGmIQBFbni4qlkulwzHI2ZNYONDNI8xAufBEp5qLuYqKpTeXgeuFo9ljZUQjExs7P6gZC+FYNcIZi7wpL/4zAWufojpWSEEvVdeJdvbpzo8RBpNurcP3R7TieUvjTQTG9i4wLVMooXgYR3YS//wY3/U2EsEb6w8nSfuW+kChfzxLTifZHzUyv6/B/4LIrQKEbqZhhDOTEDvA5d+0AuFEL8M/DLA1atX/8gndMAHVcR6e4rWiDtwp/QUvuHyW99gWBSMR/3oQj+dMHnzbXrpDR4AOu9EM+kACEF/a8AH114grFeYayO8d6ylYfvqZVICj9Kczo0XkEJQBR+34XWNd5JUBjrB4UTruVpuKEXCSWeA6fTaihvsYk5yeEyZOtIkoZCRjWM3DfVygQqStKlJ6sN4b4imJ/NiQCITdo8fYk2Cdg21Sblz4xWE99jW1hDAKYFygcOdiwxXM/rzCXWSkjQVTkqOti9y+f73KLtDVLVpPwMYnD7i9niPfLVEuw2NSdBNTUCwHu5ytatYFTuI/ijCIUZzWVq+drLhXgMXpEdKgfeBb5eC5eGGqU4xAnbSFm4JMG+gcnDiIcOhhacJmkMrnpgeCLSOrpxNq16aH3JocjAJEk+JJFmsGPYsD5oEkGQmwkPWCURdYZL0HAJ6EmefezhQIwaiZigkIQQWwvD10KdwlpmJkJESrfBbiINE95pYfe+2k7JzB5/ra1aN5cjGgTEjYVHDUR24syz51UngYgpGKYJ3fGcVmDeBXQMP6nhvlIiTxv0WfzqwgY52pELi8RxUgo71bIeKlcjPG85SwLZwGFszs5BpQZf4nqc2MMoFO1pE7aLWbzkAF5K4KzHfV70aEXsdPyw+1VF8eWY5qSPMUfpAJgXXP2QGFEKQbG+TbG+f/13lA55AriWX9NPJ9seF2V9IJR9UgcMqziA0ITp5fWGgPlbBtz+u+NDJXgjxN4HDEMLXhBB/+Ud9fQjhHwH/CODzn//8H/nTVASmjWflA/fbb7ESoAJslqdRJiDLzt4jZjTCHDwi7VxjnORMm4YqxAGfRBnW6ZgrO1PulUOWyyVSafZGHdZFwUJIdkzggyo+iF0F2wkcBE/mHUYZKiSaQGE3bEzGRhuskDzKeggEmXd0T494mOZIAbUwlEIgBeTGwnyBL7aodQqqbRKGyMH3InD38gvsHbxPsZ6zyoec7F7kdLCDcZ50s6RpDc/T4HAmgTznrRc+y3A2obucsi66nA53yKoNeVVRdTx1GllMWbUmLdeopuaDSzeQ3qOcxymJl5JRkrJpPP9q4lm3978rLH9tBAeNQ0nJBBUtAQmgPPedOxdJq9pPtZCgVaxmc7/B1j4ylKhJU82KlKfnCYi/y0BvekzdH/Ew6eFQpMFybT1Blwo9vIg2UCOREnIcjQ3UNFjMU1LGCpgVPar1hgM9wIkoDtBxFVblNDpnW0b+uw2R/65baYItBe+UcacggSsZHNWWXEj+xo7i/Y2jCoHXewpnPf9qGpvUiyDxNiCCpCM9t0p4KYebnZhUmgCf6SpWNs5Opy4QEMx9QAVBVwVUmtMRcCgNh6QEYIjlRphxadhlpgPTVgs/lbCjzyQcJGXledhO0F5IBFtacDGXzL9vGnZu4UL6w5NZVwu+ONQ8rDxzF9jSkr1UfqwYdiKgq6LGT/5EZb2wgZd+TEazWgo+31cc1Z7jJpBJycVMPlXpP8vxUSr7LwL/jhDirxP7WH3gfwCGQgjdVveXgQ8++tt8HI6o5HdrE1X3TPuAVC5AXSKMIfhAWVYorUgSg5OavvA8DNBLDUZF5kTUgO9zN+uzYyest7fRvsFbz3vDC6xcHIzZ0b7F0uGwgSJVmMZhnccEhxdQq4SdzPPu85+he3Cf7fIIZGzs3rr4PHm5xlkHoSGIDKylEhoVFGm5ZtUfIV2DEAInFbopUcuSvFhxsnuRE3EFgkc1NcXshFJnICWJd3gZEAhMVVJ1eliTcLx9kePtfc7EftOmYjoYo5yjvzolMikls+FOFFNLUjwOf45WRz7+r53GWnsgYibeBMO/nETJgEooDD7SQhE0SKx3zOpIuOi0NL0yxEnTQIVvAkqJ836CrRxClvxB9RWgcZwkHeYyZVStOVOjf5D22d1MY9XeiPO0Ho1XamrUU7aDlljdz1TBJlUYZ0kROAJrkbAuMnaVYiOi+qSQEVoJMuLS92roJ7DTHufEglvBK33BTirJVdy1jBQcIFnY2Mdozoj3bYUoCbzYlTyykqvx46O0gRsdzbRysahwAdMqS66c5IUi8HDvCqezkl25RgAzL3g4vki/l/L+CgotGClB7QITG7hcCDbOs3RwNY9DapM6sPTwakfx1bk7r9IrH5vrz/8hMsKZEp+o1LAQgle7iq/MHBvvSYRg7QMDLbj0Y5yg1VJwIVNcyP7wf/usxYdO9iGEfwD8A4C2sv/PQwh/VwjxT4F/j8jI+Y+Af/YxvM/zUARcgFc6grJlcuwaydoFjO4xvXXE+995h3pTEgIMx0PG+zvoPOWnJUxsfM21QlC28/pvjy7zu/QJTUMwgk6RMlJ9dAPlasqDyuMFZASKvMOFJCNnziNpqINEEtjyDYUW3B/tovG4kyOUtTSjMbPdy6jZI+arkpOti1GhEugtTtlbLVFNEx2rdEoQoL1DVzUoSSMEm7TAyWh6noUFndkptj9mnXexacQWZHAUm2VcULSMlBpxxhb3bExGmXVQ3nLmjhFCIChJmfdJ601ktkiB8hYlYJ5kZMFi6vq8GZfIGpskcVhns6EMsaGsQoi7lTyhJBKe2pZKKwXsYVPjpcT6VsoiBAiBwlYsVIIXTz+OUnjmSc7GC2ZZn0C0csyrFcvuFjIESuFb+xJIsWghcCTn8sOBiL0HYInCJilNY85ZN0gQyhBaE5JZiFIGCTDykVqpXYMsS1ZtA31gNCtTsLGOf37gOI49fzIBl1PBSwV8YxGfs5rQMogCPQOf7ijerwXfWzuaBq7nihcKyX0Jb6899yvY+NjwvWw8lRdsbw25Piy5N1nifODlYZcqSbm7gec6EiNiM3OsJTINrBrB9TwmrKNWQvPmQFG5QBXgiyPNg9Izs4GhiXIEfxL8XEdG8hdGggelZ+UCop9CWAAAIABJREFUzyeSvUT+odTQn8QfLT4J8uh/CfyKEOK/Br4B/M8f58EdgsuZ4FEtKWTES0sXH1qT5Lx77xBVbkgHA3AN89vvMx3t8kKheVAHdlMRedg+0BewKR3vry0665D2IvZ7YKEuF2z7hqNakhkFItA4wXSxZLPV4VAWGNdQiCipMBeaIs/oz6YsTIHcv4Yk0CAx5Yp12uUo38aUG4wUeO9ZdocEfZPe6SHKO4yzCB+VKZeDMcFbNp0+yloMFg/UaYGuStZ5F2cMxtrIrJGKddbB1g3oBJ7CGOM0bZnmdDcLpG1iE1Ap1kkHrzTCWXJAuchSaQDqigrIA+StBn7jPVXt6OjAwguE8CTOUkuJ84IRgWl71jNV2tqDwDOsVhwVI0LwqOBphEQCw2rJvNhpX/WYGCqlYtIdoBuHtjVCxES8zjo0SdJKAQdMKyFXoxAmSg8HnmjM8piRAwr/fROigQjRLEPE+RWRJXMaYGA9lGtsoklarZ+ycXRZMak73K1a4TMRB8ze9YG/tyf48iIQfGyMVh4mAb7QkQipKH2INoAIqhB59kJEymqhooBa5QIP6gi39Ezg4qDDVjdObedacnvtEAq6UuAR7CYiMoNC3IU47xkkioF+fK1HIcTJX/nJVukfJTpK8MJPhM8+kfhYkn0I4deBX29/vgV84eM47g+KRMRGylAHli6w8XCxxRuTR6dMr97gVGU0kwnBJAxufobees6esOhUc7eK29mxgW0j+P2jBcaDyDRrBFJCV1lWG0/a1DR5h2OftBVibJa9P1shij51XVG2OjdpnjBxsFNVdDYljXd4BB0CvugwSwrKJKMxCTIEPBIvYZMViKYmrytCuUYET5PmVGlKmRWYpsIpjWur9KTasOkMCFKS1yVOKIKAzFVU0qDx2Kgn0d6xFjAJnlWasXN0n+HihIBg2R1wMtghrzYIERgsZ+1wjmfWHeFUwzrJEU9UVkJGrFtP5mwZyVwl1DJCI93gYO1RRU6qotyvIIp8VV4jhaC7WTLPOtRSorynt1niVYIJPjbNZfyiC28JUuCSDCfiQkjwBCOxStMoQ6agpqFpsf7UQGj9Z89avWfcfIgP+w8b+Fi2d2r9xGvyAL4piSKdgikSRaBnPL6u8KHDZ7uCu1XcbV5MY7Pzm+vAXxgqFtbzsI72es9nMi6i3lP5wO8vAyFEqeT9vubLM8/zuSCVkqWDkYHnRKy+jXN8Y+qZtf6HHdlgpORvjVNSLXEEFjZaFeZSUGhY2fAUlz2EeL7u93MLfxJ/ZuKZGws7w/a+PIsONmMZOcQdLVDliq/mWwy7BWG0jQqBY6k4LS3XleetEvZTQSYFpza0Ql0WpxJ0EyhEiNVzUCA9pypljaHAIhHUwIEp6NQl61STppqk1WXfBNB2Rf/ogJOij5QaIwIVgs7kiGr3Kk4bAiEai4uAE5IQBOtun3R2wrrbByQqWOo0Z90dkM9nmKYmSI0IFpA4k8TjOAfCI0KLzBsfjcnF92OcAoLj+v13SK1lsnUBCKTVhhv33sYOxxz1xiyyLspZnNKYasOrmwnf3rnOyuTnKLhD0a+XFM2SWbpFH4v2lkZAHQRJvSTtDKl8XJhDiPzlngBrEko0WbM55/NvTAbCUROrWxls5DQLiXM+0lyMpJFxqCxofb5rKQGBQZvH0gaC2Dw65TFn3rY/d3iaz/8kK+Ds77W36BCopGYlBJZo/VgDORaHZIrhU6Jk7mGoJM9n/lyL58hGD9druWQ/U7wq4vl9AOs8vzVzICSvdKJT1Kzx/ObUsraeoRZoKdhKIp8fFEJ4jIgGIkMVkFJwv4IrKXxxIPhOCSvr6QiBElGS+LM9zQeV5721JxGxGKmD4Ln8sbDZh4nae9Y2qnTqHzAF+2chGh8X9mdRM+eZS/bQYnvDaLi8avm4F1LJ18djDu8u+G6Ws0AiEezZkpFK2e+k/J2R5p1VnGx8tSvZSST/+CTH145SSOZBtuqJFi8VQUsMjg2qncyMhiB1nsXUJ1qOeMsGch4GsyPq3oDDoo8Tgn5TMn50yv3tS8TRK4EXRGa4j43N1dYu1qTIukHgaHSK6/UwtmE63MbYBm1j0rFKRcaRVDRK4lu7bYlHSIUU4GwTvWdRCFr1zXJDXq5Z9keEVk+nzAp681OGp4/YWZzw1vY1yiwnWy946fh9/kIzwQ2G3NKSiYgV85iKG3bBREn65RyX5lihyIIlqyq00fQU1DbKHAgiL7cj4VAZrBBYEd2gJAHpLBYBPhCUbsXa2kQsPPiGdFOivMWjkDiqJIc0O8fkLX8wMuJi8OTvKY8VMM+YOkn7s3OW7nJGUlcgIBWSVXdAmaZcEktu0eURGgk8R0lhBI8cvL1wzFv4JJURuvribvRz7ZuoId8XceGbNoJba8/N7mPXp4GRHNaWi6lk4iKEs3GBro6JpZCC3TShZzxvrRyNh0/3JBcSyYMGZo3lnzx0LH1suP7tHU0hJFdTwYMy8NY6fv6fyiXXPqSrlPee35pafnvm8D6QSslf2pJ8/oeIqf1pjMYH3l457rdj4X0leLWrGXw/j/VPcDyTyR7idvSmktgQv0hCCHx/yDu9PcxqSWoMIjjet1DtXUMow14qGUpPXVuKLKFCstXPqOdVO77vooco8UOsgLp2GB89SxshsVLSVSkGojIhMVk5NNsycLpzmVIb9teTqAeD4mj/WiuzFRk9IsQdBFIhg4e6ZpX1ENkTich5fGJwTcvO0RYrFCgNOrA1OWZWDNHeRskBlZBVFRjRyi4LVLD4FhfPmlYuTKhz8xSEBCmwtmHRG3Ft+jBOqQKTrItZ3KUajlEby00/JQAzmWIHW+zPJ9yvIKlKCuKIvgM6ieKhi3zuAa2JNnFqtJaGhrigaqKxt1UG6Zp4Xd8fUjJez9kEiRcShccFSb5ZkfmG4/xsvOOspo/HWLfXEBVv4s9rIof+LLmntNo0xJ1As5yjXENjDJLIhsmXM+p0xD3ZoUvDdrt0HnlDkke6xqM6LiRKEt2kHPx0FyZeclJalBKxUSsEX9pKWLiauQXdVtwWGGnJz/Yl/9/Uc9oEBhpWFpog+Nvbmv/r1DEyki+N4tht4+Go8vzGScWXF4LnCsi1oXKO//PY0Vc141QTEHyhHzniMxv42tzxxdHjgao/avze3PL/ThxXckikonSef35k6UjBy70fj5TBH3d8uzVC3zYCISQrF/jKzPIXR/qZqfKfyWTvQ5z4u73xOAK5FLzSkRxbkFtb3F12mdbRv+jyMGGdGayt+Y3vPuKdkxWeQEdLXr24wyZk9NKUlW8o25zR12CUoXSOfr3A1TUByKRk2R0w1pKB3bBerVkHSUFgnAqSoks13GILS+kUNgTyRLEOkqTbi4InNhBa/F1KEEpB3aBx2HOwxCN9rMw76xOGkwlxL+BZ9rqsiyEXNzOGwXFkCoIQjKs5vfWcadJhWgxokoymdapK6xItPDJ4grcg24/dO6QNLEfb5NWaB6NLWKnQ3rE/+YBf33uR5zqK3cO7nM6XEODGcEB39zoL+txYrVjUjo2PMruJMayLDqFN8GdsnIwziWFJZHCKpzjwXv6wilPQXy3QOmWW5DRtb2CrXuNtBTpD2wYnI5VThhqbJpHS2p4z8DScs6vgUSvPIIARMKJmUpcRZlM6Si2EgHCOsFgiR2NcaJg4jxCSTmI4cHF+YDeBoxp8a9HY13CnhJuZ43+bWI5tNBf/62PNa53Ab50KNt7zsIpN44izw+eGhr+yI/i1k5rvrQIvF4K/Ok4QwD85bBABNiFq2OdKsHSBby6jZEPeNmFTpbiaO/7ZiePf39XsPMGdH2jBYRWYNNE4/I/8XfOe3556LqSQtNBNpiQ7ied3pp6Xe3/IAf4UxNoFHlWenScooB0lKJ3noPZc/xgduz7JeCaT/a215+21Z2SioYgNga/OHfc3ju+VgcwYLhUp1nsOGqg3ngf3Dvn64ZKdwmCkZFU7fvvOAYvtC6x8ipGGTh4r0bLdlu/Mj5gJgzaapMVxu4spN03K20tLIQRj7WhCoCzhZrpitbfN8s776HZwyjYW3R8wHHT5oFZg4k13tLK4VJQiQjEGFzVOiHTGYrXgwuEDahOryBAk26cTVlUTWSyuZuSriD+3g1jjZs2R2UN5Rxo8TkjqJONCNedo5xJ7x/dj0xXIy5Lj8QU2W2OS9ZrueobQimAdR4M9DvKUV2/dpi8D470tAKr1hsW7t3j+5Re5XW4YF5LUSDa1RwuJTvusIxJCR8RZBgutDLHCG/eUGmXEzc1jb7+nQiCco84MA1u2LBnJShk6riSpS5AS6cO5qJcuK2yuz329znZKZ5j+IkQHrUxE2G0dwAZHbiumWYHBIQKRKaQUibNUAVJl6JpW495H2G7mIlf+Qqt6Wbo4XfutqeNXfeByIXnVKGrr+NdTz8A07CaSfz3lXJvmuA7sp4IrqaDQir+zn9GEqNEiheCwcgy04G4ZGJmAEoIPNp6hgZXzXFJPJ5quVtxeOergAN1i/5GNK0Rk40AsmOwT53kymlY8zcg4QbFy4alEB5E1NHlS5+JPcdT+7Ol6OrQU0XP4GYlnLtm7ELi9cdQ+8M1F2ywRkVnz3sZhBOxnktLFCuSqCTxYWL63XDEsMt52CttErvS+gtvzBUqlZDKO9QsBXQlr17B/+pBdoTmSKVZI9nzFsCnJ6PBCd8gjK5k4QQJ8KgskriLvDhgf3yW/9Tb4QD0aU33pr3FPK7Za96aaSPEbCKhCiqOO8wMtb0QBiEBvPsUF0E113lBsgM56gcgSTrMOp61TVddVDOYTsk7BpWbORMdKWOPZr+Y0WYd7N6+wGIwZTA4QAe5eeoHl9kXyzQIjNOu8iE1QGT13c+domoa6W7BqgXGTZNSLFS+qkq3nL/Kbj9bct46tnubn93J+dw6iivfQEpNJGmKSVdEvCv/EqKzAkACVcaja4lsleS3AGcVSZ3SrDas0o0RigiMvV9Rak7gGJwyNiLpBxjWIxrJOEowyT+HyZ/6xRUuFnEXPGYYSGpeQysDFasHcpLHXYmtUuSIbbbFsHbhKF+GakYwwSwXsmMjU8QEGCWw2gbc3cLXg3AQk0YobueNfHjv+kyuSf3cv4c2VxXr47JaiZwQTC0vneHPlI89eCG7mklRGttmNIk7q+gAvDmLDuHGCwwquFI+/H4el43ohCEHw1tIyaSlJIxPNyDtK8EHp+N46soJSKfhUIbmUKTYu8ObKcVjFjv9+Inipo7iUSU4qz/iJhH9Sw6eKZwO++KjRUSKaw7fqrmdRuWjQ86zEM/RWY9gQtXHmNjAwsTqqXeC9jWftBEMdP4RCxaqydIJd6fjAaQyGi4lHiUgLvNMksZnZiYk+IVZ7lYA+gcI1nAq44BqkjOdpfECtVyzSAeNEckEHnIeVE2RlzfDL/wf2+Ah34QpSCNRmzejX/ndO/+2/i0n6pCI6HLkQE3c3RPNmFyTSW2TwWBUbX2lZInzAGx2TsIiSuKraMN+/xCmarK6QAtZCYYc7PNfv4hdLdmxJKqK+Ry0VutvFI5nsXmE+3o/3UpmIaUtN0AbnZbvoxD5B1y3JZODERulciENHIxVpqO9azUu7HUSQSOF4y2o62pIQG9cKoL3OjPiwRatAc85/D0CXBmpHEIK0neF1QWIax7o7RK0mFNUahCR4T51mhCRSWB1QuCiSZqVm2CzJVMG0PcfZBK0iShWvLCQqcuN9O/iVSsXVnRGzh4/YsSVSSlxjkWnCzd0+36ji9RQy7hLmDp5LYC2iy1Sq4uI0ayIvXknofh+UnWhFHSyVhRt9ydX8cXNz2gQOqsBhHW0Ihzrq9nxnFeGTS5li6eAzPR0/axdJSq91FP/Lw4Y7a8dIR76+BX75Ssrvzy23K8+uiWPMd0u4mHqW1vHNpWdsBH0taXzgm4uogvreJlD7KAkCcNx4vj4P/MKW5n89qClLT1/FhVIBXxz92cDrjRS81JF8e+noqFjAzF1gO5F/YMfzJzmeuWSviLhjTwdUSzFMlMA3gcspnFoZ8ckmUCi4oWEiE05mmiENqmWi5AoeOU8vz+grWLqoiyKJHPyBC3ipkGnOfZ1hg6SDZWczA6WQruFUFyyQaBnYcRvq0wnj229RFn0m0wmNkPRtRX85ZfPeGzx4+QuUTc20kSTAVhJ55EkIbD28E60KW1vC+XgPYRvSakOpFF4phPeYaoVVBj8c48qSExslJjWSopNSporMS5blhpnzaCUZZBmNSjCbkr2j++iqBAI+yXi0e5mi2WB9nLhthECGgPIBiWCkYBqiZADAWAWGWvAuKY9Ky6+uoAmOVMDPdC17uaavLI7YsBRENooWsPCPWTJnE6wJkNUlFoV1npro11eEBiUFZa5ZqG1EtUF4TzCaOukwFDV7xwfM1ZCliBaQ+82CLFQoYygCHLbJryBWtr12cGlpH8vw5ESbwZ+5NOINrXn/0RTvHMWgz09d3eb6wLCYWN7ZwKMmJtmbOVzLIEjFtHG8W8bi4UoCW0awawJ3msd2hQCzJppiDHWEUJ6ETioPcxsLiTeXPurmt5X1gzL+mcj4TLsQp3Qd8GpP8w9zzb84rnlv7fl8X/JXxwmjRHBvI9lOJYd1XKR/Kos7kG8uPEPzWLPdyPievjmPgnZP4vkjIziqAi9rwd+7kPL1RTQD/1xX8jMDzVby7CS6jxrXckVXCe5VntrB653IAPxRm91/nPHMJXuH4GIKh3XU4k5k/KLlSvCZrmGD587Gcy2N+OSplfzCruZbtse94ykb20rT156uUezsjPj6KSAi9lrZWLlcTGDZHXLkFH3XRCeiIHmQjxgXkonposqaLRV3Aw+D5nqaI9crpJDsqxKpBNZ6Qrlh/+AW1fXPgJHsmsgrn9SC18OC8O5bWG3wUrWERMflu+/SCI/XirRaE2SkUQohmBc9jp0nmA7aPDb6eOBgaGEiDVlhGLS7nJMAFzRcv/cOwllckkZu/nrN1btvsy6GzPIuIng0ASsEQUq0ddxSPfxizn6axCbhpua9/pB3N4a3yghFGQ21g9+ew5eEpW/gUQVjFZP6KsDzKWw2cVI157FVoQNMuUYKg09zusESAlRSsbOekg5GHKQ5Ks3Pq/RANCkPRYdBuWZbx55F7SDdHrPXMbyxhMtprOKXTWQHbWfwVrtoZe3uak1cgFYejrMe15/vYZTgtAm81cArykdtHANXckFpA0cNXM/g013BvziRvNaNU6kndZQV/k+vG/6rdxreWzuGKlbiawd//6rh5V7CW2sfRc5EXBB3E8FB5Xhv7RkmgpEUOB+4VwaGGn66r7lTBq5kAkkULruY/v/svVmMZVl2nvftvc9wz51vzBE5Rs6ZVVlzd3VXj2LTFClKtKymCVOWYMIC6BcbFqAHSfajBViAAVkGZNgWIAESIMiSRdqiYdJqmWKz52J3VfVQQ85zZmTMceOOZ9h7+2GdjIycqqu7KluVzVwv1R2RJ+65Z1h77X/96/8VnUAxEWr+872VHfxdKcXt1IkyZqTphJLsjVKsZo6NwjN7n+hZrGHLeiY1PIhNS7U/V9H8ucqfHqrlw2Iy0kw+wQvcE5fsZYLW0Ak9m7lM0O6J5UE/VA04kjj+/rUxb/VEhvVXpjS/ORuxmbcZmZD3tlLG3jFTNxzt1DCB4lDV07OCQ0YBnI4htzEbUZXJdEg6LrDeUQ0DRmHERr1GrTbBcNBjOXUYDfP1ClngsM6RKPBGJk2TQOGylLEJOWF7nAnb3CYixHMsHJCvrNDaXmd19gDbtRZeKZLxgNbWGr7WYLvexviCKE8pdEgeR6RhlaK8dbs9Ux3CDJmIRQOom0klOh0Agy5z67dYn5hhpANJmC5nYmOZ5TBiHIb0kwYjbTDOUhv1UF6xMb9Io7bG1sYGKEW4MEO3McXSQBLsqiq16ctZgzf78GvT8G/X4FZZPh+twBda8I9HkuidYsd2rnCQRwl7V65xY3o/fRNJosoHdDbXuNWepoNw9i3lTkAJhh7MzLHa6zPIRG9gqhpSr1dph5pXW56zQ8+wkEV8IdFc7rm7LlYlzNRAsPcbKSxWxFwlyzxToeibv77l2BMrqkazbT3tSHMwcaxYxYGq5lfQfLObs1V4TlQ1r7UDGmHIf3dM879eHXNm6JkI4a/tqfCrs5Ism6Hi2kh8Tp+tKeZjze+NhPZ7R0nSaEWsHVuF4mRNMxV5boylsn+uIceosoF7ZiDuVhUj3rDNQJN5z7lBwXpJSZqMhPWzJ5IJ3d0uez0LC5F6wIzEe4EOq08ItfBpvH88ccleKaFZfm/bMhMpgrJxFmpFK4B/cqvgcDXgpZZUR7cy+H83LA3jueQi9k1GJIFmLfNczhVHA4/ynrnYcKBk4wwt5M6iTUBWWOKK4L+5BZOmpOEcfQdhrcF0QyrE2xaOBCFq/yHMlQtQqwlVYzCAepO8OcnVqM4MOQfIyYF1YhIiuhNzpHGF5lAQ7cKEbE7NEyhDv9EmLAqGAbiiHDxKatxfgd15T8eIQUiioBNLZb9pYX+WEdqUXAd0UnF1TU2EcRavFcOkTmwzjJWkOkzqJGOF0RrXmaHSmQSgi2GYSaPZAUE551CUVbLO4c2eYj5RnKxLo2+9ULw+cLQDWLdyTKzuTraqSp3VyQVqxZhJOwQPAxWwNrsXbUJyxOfWlE3f3AmL9VYOrlJnsiEqlWsOagUkWhharzTLwTcndMc70E1g5Po4BEIJvEA0nVBzKJHp0EHhWMocgZKknwSK2bj0J/CGOp5LQ08jNPz6XIxWimHhWco8N8aObQu/uSchMYrCedZzuDJyLFYN05FiMpRi4A4M0AgUsRE4p6JFTyhzirlQYVHMxprpyN9zzHrm+N62pRlAJ5Ln/fs9ywt1meK9MvKlbyxcGnr2VDRf6Gj+ZNthc9FsH1m5d59qBVwZOW6mYlLikR3EgYqm8VRi4bFF4WSH97PQy3/ikj3I9nRfxfHtrqVfiAP85zsB54cF65mnWzjWSl/j/ZHnrZ5Cefhky3Bm6NlKPXOxZk+s2c7F0q6bi5WeUTAdeCIFUaAxrRabucWihEoYBbTzMS4K2SzgZmkxtzeCNKlxaHGBYrrJyuVr+CynduQI9ckJVhbmiZRHKc2WVwQK2t6yVm+SjnMsim6zA0CQF4SpQ09NEid1hsOxJPMYGkmIVQnwsA23JM9EC6yzWcgNbmjoxwmLowErRcFqrQ0oOqNtJsddzjYmiF2BR1ynDJ7E5WTVGpUsw2xvMBjLPGpYTUojk2hnNzEuq+Q7Pq79zHPYbuGHQ1AwmdS5GDRoBqCLlJXcM0aasVNRQLsSckM1qWcj8vFI3IzqCctUaShZvDYRJc2w/D6FE6cokMakUXAoLpk2WUEGvDeQRNYOYC42zISwVNJCB4UkwTusobSAWuwIyhmEWqBZHjuOVCA2mqlA03eOilHESvoZuXO0jEaXvaNqIFDJtWHBTCXY0WUPtGIy8lwYORZixeWx5/pYrO5mI8XxmmFvRZN5EejrFnLOnVBRM8Ktf69vuT6WYbz5WHGsarg0cuTOc2YIo8IRa5iNBZfvhIqp0LCcyQLxcrOk9CLa9NdGlq71zEeK/YmhHihOB4rJyHFzLOqeLzVkkXkaH32spZazQ0fPQkXDkarko8eZ9J/IZH9l5Lg6htN1Q6gVQ+v5fr9geWx5p1/QDDUTpV7KxbEC7+gEik5seLEpldHYebYyERvYLhwWSR6pkybeczFU/YjvV6dpklMBtjGYwjLDFmfyBqEWXDhzcDODaiXk1MkjnH33As988kW0MfQGI9r1Ku8cPEKxXQirA0fuoes0jVaL1Qx0nhKkKQqF1YpBvUm12iBTIUkjItGCSY+BhofQ36UTKiTpGqQJOXCypW8jibjnYCqp88PDp2lsrrE/HwAKm+e8t+8Z0moLPxoQINCKLQ08fBITb67Rt444EF+44TgnyldpT+5h1coDdIfaWCDWfK3BGpteEUcVPJCOxkyEBSascCOHduipAEOn2M5yTtUMyyZgO2mQVBs4RHFySsuuqVt+xh2cf8PBLJKsa4FiOpaJ16EF7T0DB5dTYQ3FgbBkLgwtL9bgrRJ+agXyt3tOGqtHa4qVXFF3lkBB3yoaoeLzE4ZzY8WmdUyForC6msNfmjZsW8VWAdUSfx9YaIYaqzx3lAky5wiQ3YL1ju/3LJuFZyIU/H0jl0nMFxuGm6n0oKYj2b2kDo5XFT/sOzZySeAKccPq5gWruePaWBg8nUiRO8+VkUwnL1alQTu1y85vNXMl20ZxqvHgq6+VYm/FsPen0HJ33u/8jafx/rGVO/5k29IMpCGeOc8PerI3f5g15EcVT1yyd15ollOhVEwgmGLm4NZQpjn37tKrmDSW8yNHpDRzuB02TkULRllVcKwmzbXlXLDgFxtCcQxxHMl7nKVCgWaKjGNpl812h5aBRigveDUQXHzbw+FPvcRStc3r71ymGOcc3HeYX/zMs7y3oWnkOSujlIF1hAYWkhBlAlx7gsqwix8MwDuCuELaamN1SLPk/3fLHcREmd07udAGh0iiT4CmhskKVIYjtntjNpXw0ierEUVQZ3n/KWxriWR5Ce09w7n9LHfmCXSADhV+MCSzDq01ul4lwNEoRoyCGitayuiGypnOBwR2hDIJG+UCFAGzBhb0iKlswFK1xcUiQuPZF3pmhlt0iTldTbhOzBBFQzum7BD6KbO1DtdGcLNkRE1p4TBfzYRN00MgtgC5ZyMPVQ/tQJVG0YqpwHHTCjRxtAK3cxjlYpYdlHIGhyqwkpa7HgVHKsKPP1U3LFjPj3qe1Ikb1cGK5hPtiF+JNf/0ZsrbA0s7UPwn8xH/wUTIVzct83iWU08OYkiCYiKASwPhsq9nntjAiZpib6TZ8IqZ+O4L3Q4leQ+s50RN87XNgqXU0QoUn2kFxMawmuUYAbWmAAAgAElEQVTM7KqwOyGslFRN0eS5y6wJlWjgPyzlei+U5I8yxtZzdmi5nYqfwb6K5nDyVIP+/eLyyFE1amfnF2nFZOi5MHSPtbp/4pJ97svhhnJ0+w6NrWKkKdoKNGuZuFg5B12r2B9rjlQNNzJP0zliI1v/mlbUtKLwMFvRHK7L9GDPCm98uTZJurbKs+GIwBiG45xbYcxU0qCtFbFSTJZV2Mgp5rTj99cdS9MHeObPHiTwnjWr+D+6irqx3CogjGPmtUzp3nBwOIBmoNGtDlGnI5OYHlQ5nZlaiI2M4RelmcbeSKwL+xZmtFT2fSfOSgv5iDcGQyo4OgpSNNuDlGnAU2e1s4eosyDXEoV3kkw3iAnrMVGJF+ce5vNNVl1Apg1tKzSWgQ5ZcZp95KyqhKlQdgPDshI97FKuUGEjN+zVKd7DrTwksQEVN6JQdU6SESBQzigMKeyYayP5zCkti9eWg7A0mhkii5kpK/uhl+8/GSk2M5kszT2s5orn6p7lDMZoFmK/M+U4tEJpHCO9jMVQKuHNAuaUYl9Fc2XL8cm2IVaK1cyTeZHTPjP0/MJkxJ+fAes8G4WY2x+tat4bOBarSpg1hadhFFOB4h/dzKgamI41Q+v4403Hr04pJs2D2TZUcDv1LGeOfRXN8Zohc55LY0/mi4e+/EZ7IqXIlcgb38H57/g9zMeKW6no7IDg73Ox0JI/qiic57vbBWkpGe6AqyPLwHpebj5xqeVnFnfu1+4ItWKrpNY+rhbJE3dHonIo6fqoYCUTVkXDSHP2mZphZOWhW0o9kVa8nIgl3AutgBPWc2bo6RdepgYjxUpWcHlsUcD1sVjCzYaWbuHZiGpMzAT0BwMKa6l2Gti4Tj2EqUATaMVGJto8BxLPINdcHxYs9tZg5Ra2sEx1OtyY3Mt5q5mO4GYq4lkhsD+WBmKzVEtcL8+9Y6AwMBFIwu8WsJVLdTodyCK2J4Z5JZWvc3CiJtdlsLzBdGbpOs2GMgTeM6VzgmJI3IqZ7G0xyAUAmgg1W7U2nUpEwwsbaWQF81+IwRNiipwJm5FqQeSnXMrYWkbacKoKN8ZSMTeMcM9HtsLC9hYBATdciPZwWI/p6ILNsEpiM3o6JMVQwxGmY3pxTcTElCR5pWBCi3XkGLlWKVKZ7jYk+U/nQ/7dRs67fVkQv9T2vNTQ/OMlD95xLZXFu1UOwtTL61cPxYw71opTNc9WIbS6vzRreLtvGVrPC03DQqQ5N3QMnSwgW4X0cuZiuDiUYaOa0VwdeXLvOVoV7P0PVjOO1gxGKXqFox0a9idwdQSNwOH9vdVb5oT6WDFqh/lyp9q7lfpSi/7eY6xTHKnKsNWgcGwXXuCcUNMK4LlGwFTkuD52eA/P1sVP9aOsGjcKT7+AmZLKaRCIaCW9ez5P48GYDBW3M097V8Ifl0qnj5P49MQle6UUzQD+eNMxHSoagWI9d9xO4S/PhtzOYC33fLotuPNyBp/vGE7UDd/vWb44oYjKCbhIKVphwFc2Hd7BQigOTTczxYFYUzOWDVWhPh2TAL1CEutiohh7zch7nq1Llb6SKb40oVm+fB2/eRvdFCqI6/epdM9yrXOIm2lEbKQKSh0sl43FE3V4oy+iWqESv9bJAE7W4fdWhSs+F4nMwnoBrRjqFcVkaDiOVPYZsFU41kZjetaQaMuEz8k89JxhId9mf5YzcDAVijTYaFCwMOoxf2CRy06ztyoV9AhJ+lFcYxRViEY94rA0BckyxnGDKKnh0BypiQxv33rxAg4rbEd1WumAF+MQlGcwKrjV6LDYqPLOWo+mzpgOFd1MDNhr1QbDkXzPyfJhH8OOvk2GPKh3xM0sAp0NneZ0M+LPTEi/4HbmiSPDnnjM17ZhKoB2pNjIZcr5hToobTAKphKPQ9Fzir2Ro/DSKN3t4NQvPJeGlo3C0wiUTN0iLJdmYMkRJdXZXbg4CCwj8gQK782OaVg3t+yJNbczaAWC83dzTyfUpKVE8e4IS4G3hYriZirHKCUT19OR5mhV852uZTLS7E/kORQXrGAX/v74MOCh9Rj9oG6MUuJt+zA9macBB6uGpbRgK/fUSvmOofW8UiqUPq544pK9856tQvFaJ2Al9QxLPfuq0fQc/JWFiNe3Ct4bOCoavjytebYRoLX4bF4eOYbWs79iOFDRvLmdc6yqWUmFIRFrxbNVRawVVQOzkWfLanLvWago+rnjUD3gM62If3B9zFc3LS2j+PJsxJdbln+wvspGvcl2KudaDxIG4wF6axNTnSUsB2lMWdGv5vA39onGyte2Bap5vg5/YTrk99dyZmKp6tdy2dXMReA01L1nmBWczUSsak8kpixRnjFhPSaKGOmQxFvqWY/MeZ4zfW7Fda5Zgwf2RZqFrEvCgCxscHYstNOqgRMVqIeaN2fnqQw3SLs90XmfmcZXOsxWpLm0ZRWDQoSyEgUD57len6JaHdAbjgBFZaLNlqnSaRo+Exl+sD7gVu6YSjSvTDW5kEE+lO8+LjmkVSSpB4ia5FDgaRLK/2phpVwbifxsqBWnqgrrLE4bPtd2XBlJ8jucaGpa0Y4UiSu4tbHNxdwSAycbFSYnG9SDB7VPxs4Ta8/WIGVjc4Pt0YhQG6Y6LYatFoaHJ7Q9Fc3rWxatHX3rCZXIM9S04rm6ZiGHK2NH4eBYzbCvojk34KHVXtXAs3XDVOS5OhILzJM1w95YdpafaSsujywbuZhzv5j87DTW60Zh3b2fJbsQ/5Sb/z5RM4pPdwKuDC1rueyAnm8Y2uHjZT49cck+96L3Mh1pJneNo4+dZ9t66oHmS1MRX5p68NipSDMV6Xvs2kDRL6ARKj5TMcJ/LycmX2wZ3u7JUE5FKzZymIwMe2PDV9YLTtUDPtvSZGiWUnhrY8SCz/hm2mQmcETacy3X1J1mZtzl7eoshRc4IfMyWdo0cDkPeKYd8NqUQBQ9K5DB0EoDuB7CrBbIql+IeJfScGEIc7FUiNdSz1yk2GdyLlpFJRvTYUjmFSmKCVUQGE1HeQ5VBX/fLEQWN7EFl50sOLWKfOalHH695tmuGy4E08xMikfsSgYnq/BrMzF/0i3YHytqVTnnkfMsRI5zA7jpanQmazjgennO7UCzFMe8eqBCojw9qxg4R9tacZ1ydx/IPpLY90Sw7gTSqpRMI+thNoarY0cSKF5qGZwXuGtjLMYSsQnYWwGjPKkXDvyEz3lneRmN5hOR6Mzf7PZ5Lko5vWcf7w4czUCS83Yh8FzHZdy4dZtIaTqVmLH1vLeyxSnvsCw89Bl9pmb4neUMg0gQ9y1cGcFfmQsIjWHewPx9FffBquFWWe3VjSx6g7LaM1qzpyIaOfdHPVCcbgT3PdM/m5gIFZMlbNMOpejo5rC/IoJrPy7+fZzzxyVqRvHMQxhRjzOeuGQfKkm8mfM704Yg26B977Nl9d5zM3VcGDrG5Yj60ZqoJc5G0hnvFvL3T9ZkovZz7YgjlYI/6cnff7ahebaq6RWernVsjT3fziDWlsWK5+vDkOftiF+Ix5xzEanXPBsWzA+3+Ep7kdlQ8Oiek6bmTCSLV1Y4OtW7gxXNAC5nksQTLc21ZSvHTAeQK1gMoVZXvD30ooRYVeyvaFSnQX9tyFkfM9CGxFuO6owDcYWWKsjjkLdzmaA9FBVMesX5oMoUcDuD66kk1YVYJCD+5QsRf+fCiD/aELbFl2c0/+3hCmcGioZRnB851jIZwT9cMQyc48gwpVt4rqdCjzwciaywBfaMtrl5eZVbWU6zFjMzN8ebWUgVmfYdlpV9B4FvDtbgqEK45FZ2Nvsq8EwtYGh34cUKWoHj0kgMJhyea2PP0MFsJD2F9qjHJ9nmvXiCCzYiUY5PVsY01peZ1/NUmwFXRp6x8xxIZOf3h9c3mKWgmsR0nSYJPKeqmvHGOqqYBvOghEDfen6lMub6pavc3howEUe8dmiBQM89oItzJ2pG8el2wOWRZT2XRu/phmHix1R7m7nj7MCxlcsw1JGqvoft8zhDK8WLDcP1seLGWAQGn2toFn4MN39sPReHlpupPOOLieZA8mTpzDyJ8cQle60Ux6uaN3uWRiAJsF+A8pLsHhVXRo53B6KBXzfQtZ7XtwpiDe1QYzRMxyX3uoB9FcWhxBBozbNNWQS6Jc7/Ti/n7b6lbhQTpcb5OwOoasWpfYss3rrE4WYLggDf22ZYTzi6f5a3NoQiuL+kbG4V8OkGzCdhKe4mlX3fQmsXsygKZHcxsjLpeSyAdQyTFc2v1gXXHVjo5o6pmTnOr9+iasSkYpgpLtqY5+anWer12RykHEvkmLWxxSUtVohZGssOYjqSuYWlMTRDx0QU8fdOPZjQjmPZKjyfaQdUSv2X1MGhUPFv1kSa97WWJPiVVP52o7vJudsbtCuG+VpEN7MsXbuJak5TNTXCAKYQXHzkIXLwStNwYaT4lSnxPl1JRefmC52Q90aebu6pBXfv2/5KQKwcv79umYsVexUs557UKZ5P+5wzVfYHlpPBUBQs0SxlAaNxymw7fgB/D0Z9moEmVp5pk5OjGCrNLDlFlkP84LXpbveYevu7LFRi1Hwd8gx3/kdsu5x84hDxI3LanSr9g8ZW7vjOVrEzazCy4uvwSpOfWcIPtcgvH/qAnM7CyVxB6j2dUKShzw5cWUw9cenoiYon8urOVwyvasXFoTwkM5E8cI/CCQsnHNapiB1MthnARi76JAcriszDei67heNVhUJxvCYj6pfHjpGD/RXDwYrme92MsYOFcnGJgFZguZkq1IGD6IkG/vpVGI/Re/eTz+zj5SSimRR8fbPg8ljw1T8/FXKyYcisYzVzvNsX5cPDiWIhNmTeM1eRJC/sEeFypx62U5FbvTxSMiQUKratZ4Uae/YvUGysMRqm1OOYmekOb8QNpqpNjo66dNe38N5zaLrNcr3D1hAmR5vMXLxE0N+mqDdZXTjEIOkwLBx/vFnwo76U3C/WNZ/rBExGhqOJ5+tbBauZNBE/3wkYOcWRxGAV3BiLOv2rLY1zlvH12xyvaAZhQuo1k7Fo4CTDdS7Xa1SMSDtoYEHDSMNvzFa4mjr+xVLO+aHnRE3xW3sqTIaKbVtwY2x5a+CJNaIhE4LWAX9hSvHuQKCQE1XFgcTQt01G3Q1mSrnmAGgUBTcJCeL4wQcHWGg3ON9b4x2dcNWF1JXjBYZMU5AkDz+mtnKL2ypkO5lkyxkqJmGuGVK/egn9zAEujxVXxo7ciYLloar5qaztLg5lCvfy0NGznppRTIdwbuh+Zsn+J43VzDFwMFMKimkF05FITByqPsX6H2c8kcke7uLvHyREXdETqHv/faLVji5F7j37Ek3hpOI+WdNERjNnYO4+eKiuDTXl2MykGs8dbBeaxQrsrYSsBXO0Z+ZE97rwVJU0YHKnWKyG1AOh220XntN1wxvbnhupDHeFKG5nsJp7QoRuOBHBPn0Hx4W2kgnZc0OYCUVL/fLQU9WwYR2LnRqV6eY95/zWluDrTE4xPzONQqCppvV01taovfsmvlZDtSfQoxFz776JefEl/vflhKWxY76cqny9a1nKPL80oTk38hyrGZ5ryHm927fMRzAVi8/psaouNVY8yiocDqVD2j4j0LITSKOARZ1ypqJYLzwnE2HcLKXw2bamFUuD4784EBMr2fUspQK9LWXC1X++Kb6s18bQDhWJgmP1kGP1u99/aD39Vovq6iZb/TGNSkTuHL1Rxv65aQgfrujYnp/mu9czfJZxOhAO+fdyzYE9+wiCh78+tXGf9+IJIqvoaMvIKd6izhdNn/e2M27YkIlQoK2bmWO98Hy6FewMCX7QuJk6Lo0stZJ6nHnPxRHMWvdIuOjfd/SsEA12h1IKlPTdnib7xxdPbLJ/VIyt0OVuZZIEDyWa+TL5FM7f80INredQIk48V0aSxCoKXmpo5t4HdzxW15wZas4NCt4bCPXw+RocSEJO1xRvDx2vdx0jCydrilc6Aa1QczDx/M5Kyvm+UPN+dSqgE2q881SV5w/XRPr4ZKI5XDE0Q8VcLE2sfonZT0WewmvmI5l8ensg9nLHEsXBRBMqw2bumd91ZzcyEY2biBTv9nPeFa0xTiRwshFwbP06abvGZlhl7KBSrTIRQbJ0hZsH51is3r0WB2tweWD5xhbMV/RORVovP2+jbNDdTB3f7wmOe7JqmIoNWRJitKPrDWPnqQeKTmE5ONXkfzxU4W+fH/H9niSDX5qA//5IxBsDMdq4M5HZCGAz97wzsBxMFKlTrOVyXz/RFAhI5B7uZdaMrOdgPaJy5AAXb6/zVi+lagJO75tkYbpNhOO7W5bvdIVBc6ym+UIn5EwWcnDvDKq7xVZ/RDUI+NR0h+u1OsPcslbIRGTuYT5SHK4athqTLPY3sGHAdgGJ9pw2GUumzXamUdrxw56nAGYjzUh5VjNHLdBcGAhDo25+PP7eLzzKszOJGStIlWMz//jKFjSNFDq7w3sPpZf003h88XOV7PNdeGArEIXCH/UdQ6c5XtX8qO9oB55QC75rlGJPRZMYxcl6wMkP+DkHK5oLQ4tWik80FAMPZ0bwXBOu54q1XPFyy4h7UeH5Yd9yrOr5V8sZoVZ8dkKmOr+6ZcnIeLPnWM48R2qaUIk88de3LPtjzZWxNO8OliyZzQJerMNSali1nmfrGqOEJXN5BP/xrOF/uVHAyDIdyi5lNYf/es7zPy87Lo5gvtTAPzOEbVvwV4stvllpsi/UVEv8vZ8nHCy65A99/8RIY7F27y8rBrrOsZZJU/l0Q+OcVK/NwPDqsQW+9fYlZpOIJIrojcakeA7sneP/3PK81on58pwkg6XU85VNaIaeUN+3IzNwdeSZjzULgWJx1+9WU8+eSlnl33evT9Y1b/ZCgplZXtsrO6XrKSwazTe2LF/fsszHov1/aei4Osqk91GLabfn7zmHqyPLD/qOzULkCxIFtzLHWu5hcpbW7VUqxYi9SYLLc+xgwMr+Y1xLHQ5No2QX3U5FzGwhUqyXYmadcg7ju9uWlxoPMnfuRD1QLOcwLESLJ3VQeMVs9KBBysclpiJNM3CsZ45Widlv5nAw0TuL1tN4PPFzleyXM8fQwXQJ7xglPPkrI8cXOwEvN+DSSNQtZ0ucPzHS2Lo0LCt7LeyAO4yCW6nj8sgxdlK5LVYNl0eOZxuG6yPHxbEn0fCJumItc5wdFMzHeudFmwhFCfEr6zmZl+GeM0OpXlvG88frBVfGjpkQbqelTZ/RbBSW0zXNr01F/NFmzrsDMbL4hZZiIQm5kmacqBq2ClE1PJzI5y9UQv72wZD/7caIH/Q906Hirx+oMBtpiqUBp+tSCXvgVE3RtR4/Oc0vpkPettIoboeKT8UZtWSSt/3DrrRnJtSMrL/nBR1ZSaoToSfznrd7HgOcqGkqxjMxPcPCCP7g8iqrXcvBRoP/6PQcq2GV9dzu7CACDYva8W7f8lxTcXtsWc48Yw+dQFE3slvrFbKwrOWeAMV87NFojtVCpkP3wL1ezRyHq4pxuRuItOKVJmznjh8NHAcTtSPDMV+Bq0OHxjFymvYumm/mHB6x7ZvfpWVy517HcYw58QzByk3yrS10XCE6foKk2uL8dk4z8FwcClWxHcgifmVUMBEHO3LCiRE8+9zQMfcIvZS9sWYj87zezbmde6YCxautgL0V87FM9CB6Vq+0Ai6PHDfHjlDJdO/e9yFXPI2PJn6ukn2vENmB3aGUAi90urmKeQB/z5zn9W5BXu4Gcg/f7wkEoxW8N3C0Q2noLmWelbzgwtDSt579VcPxugwDDZ3g7GP34BY61orzfUvPeaqBJtEy3Xsrg+3ckjvF1VTRDmWs/3apTljRiuu551PtgE7J4FnNZNBmIpBJ0P0VjVJSjTdDxVZu0VrzXx5IqBkYOejlnjP9gkAr9iSGPcndcxsNLcWeA0yeeYNfbmgqtYTxYMSoN+SlTz1L12quDBwLifQPllJYrBo+19a80fc7A06Dkmc/Hyne7lu00jzflGN6hefGGN7sZnyVBnuPNzlhBPL5v0dw0LoHtEK01gQaNJ7vbjtmIhlyW0o9mXP85lzIv14tGDrPbCS7uje24ZMtud4Pu9cXCk8r0OwJFId3/fxs35J5dhL9nagbMenOclhKHdOh7AaWUvh0ywAPJuFYK5pGsVFJsAeP7MAWW4XnaEXxg23N2ZIsECq4NpLnrO80e++7BrFWbOcC0z1sTirSij/cyGkH8HxNFDi/ullw8mNuBB5rMTI/Uft4NpF/XuPnKtk3Arg6vneq0Xsx0Kg8Ag9cSh2p8zvNXlOyA84OLcrDdIn3g2yv1zJH34oz0B17t9BABctaBhH+gWGR1HnqoeL20DNVHhMpcM5x2yoC5dgTabpOWEFzkWIjk2356bphu/B0C2levdKUn0+EMu5/oZyq3BvDZKhJncY6eGuYs5rJAnaiZsi8qEbeH87ByT1TnJ7/NOffOc/2+haNTotnXn6WmZkpvpxZ/q/VjG9tOZSSpulfnA6ohYbjzvLHmwVrmd9h4wydlzHwwLM0KBUsI8Xtcc5GZpiLFbVArvVMLNd/KbWMvb7vvMT1yxPwyabhdi7MmrlYUTNwaexoa887vZz/Z0XYOJ9uaayV632/9ABAK1Asp476rudDppxLMxXn7kn4Awcv10Ry4HeWU76x4WmH8GcnDZ9pB3yt6x56r2ermk6o+N2VlHPljuxXJwPm4wBNxrFEsWEVqYf9iUwg15TMBLR2XYbUCTzzKImZH/ULFhPDau44M5RhsiNV+NHA85nJhx/zNP70xs9Vsp+NNBe1YzP3O3rlG7lo2TyK2rZV+AcWAqNEEXG3K9CdqGhFI9DEyrKRiftS6kTO4Jm64mA14GbqmShZMt1CjjlVD7g8ztgsjxl7WMsVp2qKvtWsF549sbCDNgoxrgiMSBLsua/sXU2lmfVO37E/UWgFt8aySA1swTe6jnrp3NW3nj/ayHmx6jleU5zrWfaXlf3NVKCA11qKSjDJ9BfvzRDee86NPNOR4bf2yCDWZiE/2+sKzg4dh6uaZxuys3h34JgNPZu5YyUTRVDv4b2+p2Egx3MwuLea6wTQK8R39cbYMReVu57Uc7IuMNt0pJndpbE+dp53+jn/Zt1SOMXRmrBxvtfzbBeWz04+PNnPx5pLw7sDSEWJF5+sBdS04htblvnYEWmxd4yVmIS83Xc83wj5bEeYV93Cs1YIznxp6Ojcd6818K9uZyiteK0tzfV/t2VxStRVM6c4GpcVfSldvVgNWCuk6XpHL2Wr8LzUeLReysWho2ctE6HAWmnJ1ro2tg8sXE/jafxcJftQKz7ZCrhUTudFGp6pa/a9Dx74qGov1PLf1bFlKRfz8slIEyk4XDVUFLzVs7zZ89S14pNNxYlawKmamDxfHjl6TvRbjlQNV8eOkY04P7QsZY6GUXy2LWJmhbOsZJ73hiKreyhR7KsYJgPR3N8NTWXO4/Ecr2nw8FZfmCDP1DWn64Yf9iwB0C4Pamnw3nIx1fydwxH/w5UxX90UOsSn25q/eTCm8ggK4VbhWc38PVrqs5EYWq+lIkJ3hyrXCIDCc3nsaYeiVbSRy27geFWzVXgMitVU3HnGzlMvjz1WNXy+HfC7Kynf3HJUtOILbcOfmw74dtc/wKIaW1gdO7qZ52hDFo/YQEVZ3hk6eoXs1i6NHKmDmVBxsJzD+FQ74Hypvx5rOF3ixYcTEdj7ZlearEeqmi9OhPRyz8BKz+biUHD+2RDODh1f7BgSrXfu9UIsbJw/2sjZLuRnb217alqxP1G83nU8U9c4Lzo4hZdzi2PNnormqNGcH96ZoJWBstn3YeN47xlYSL1nbGWB017UMz/OiT5zovNzMxXbx4PJ43dpeho/Z8kehIb2TCPgmcYH+/e7q71WWe1t5HCkarg5snxtyzIXiXXYxZEk0i/PhLzZdSxUDM+VsMrtVFyTIqM5kMCB5N6XdF9Fc3Xk+GwnoG6E+7+ZS7LZLjSR8TzXlGpvq/DESnG6bnh9W6wX60HprpV79seKb3YV1Ujzy9NmR09nORdhuFBBaj2xkR0KSqOBKyl8YbLCr80J0LVVeG5kitnKwzVKRhaUelDsy3vPqvUcvy8RJQaG5cBYpEU/3SMV76wxzAbwu6uW2UgWo5tjS7+AvzgdcmHkWawGvFDa53VzuDoWA+0f9aV6jspK2AKh1tQCVyY5gahSNHXteXcgw3LNQEy2b2We21nBa+2AqlE83wh4/r7nQynFJ9oRn2jf+/Pzg5xLI1n8q0YG9M6PoBNYch9wINEP3Otz/YJ3BpaKUdSN3Is3txXzkeMLnYStAl5o3HuvpyO907z8oDFf0XyrW9AMFO1Q0S2EdvmLnY8vG8d6z/e6BT1bMuaAH/YcfctTDP8xx0+d7JVS+4B/ijjEeeAfeu//J6XUBPAvgIPAFeA3vPebH/5UH0/EWvFqO+BCWe3d2Q0sRIorQ3ixqVhOxRxkb6yoabiWOo7WNEPnxYlIw0tNqcKt9w/V+LijfXJuYFkrm6wvNjTzscZ6z3ZR8PqWI/OeZ+qGV9uGZqj5VAvODx2rqWDLLzcMKMVqmnOwerca6mi4NBDjdOdlyrZXQlSzoSTzlfuq9JlIsZJ6qboVXBrZctETqYhYI9oE94XSmgkl1eRueGxsBUqTQTPPciaSvPtiTaCE9/65tuc7XcfFsWU+Epx/NXcMrdo5txCII8/VseMLnZAXG3BhKCbeU5HiWM0wzi0/7IvgVq88/wnjxbAmdxxIzM5uoKPFnPvm2HH4J0woY+cZOs++cqcUGNDKsZSC9o6lsShYZg4WYs3+RNMtZEcxV7nLLlJYro0Uz9VhOZfdgPWwJ9YcrpqfeKAKZCjwdM1wfmg5UxqVnKoqqkHwsUz0AKupYz33WO+5NvIYDXOhUF0PVJ7SLx9nfJjKvgD+hvf+TaVUA3hDKfVvgZ3KrfkAAB8rSURBVN8C/tB7/3eVUn8L+FvA3/zwp/r4olZWe8/V71a4/cLjlWJvLOqJdxpx/UJGu/dWNPuMxlfZ0StfzeQlf5RMSDOQyu3+pt6FoWOzEG6+Lqdu3+47Xm0r2qHmE617lTpXM8dEJOyLZiDm0H0rUMrLDc0fbVqmItHW6RdivPFnOpqRe/CctPIsjR1XU4FPqlqqzW91c15tBrQCxXomqoYgA02ToWZ/rHij52gDFSN6OgPr+WQr4OzAMnRwINE4YCPzTISay0NHhuYzE5oAR+o1g1LK4uB91fGd7zpynoWKYaFi7rkGn50I+YN1S+Y8i7FM3V4ZwctNWSTvT56JUWwWD+WRvm9EWlFRwmVPjOz8eoViNlKcH3qulUk21nB5ZFnOHFWjCZUT56pAkRbifrYnAasMh6r6A2vJvF8YRK/pcM3wnPKMvIj5Bcp/bCv7zUKo0ErJBLEDzo9kMHHk/NNk/xjjp0723vslYKn83z2l1HvAHuA/BL5Y/rN/AnyVj3myvxO7E3BU2v3deWnu/C51ntlQsV3i2WuZo2IUc5FCowjx3B47ro49mfPMxaLot1uhc/fnjK3n8sgyG939jKmy4l7L3A5mu/uYilbsrxgKD0uZOBFNl9jviUbAniTgD9dzro7E2PrXZwx7k4Bvd+0D39l5xVLqqGpFvaR9NDXoQmYSXm4ZLg7FxQsEnrrjMXqsKPh617KWCd/88+2AqUjTCBQXBtI3UUogsQMxfKdribWnajRgiIDlscdr6VXcH97f22jdfQ0mIsN/tT/i718d8fsbcv6/PKn57YWAb/Vkgbqd+pK5JBDTTDUgLfHi5dQRacViot53SnUmksTcLzwbhSdW0vDXwNWxwHoXh7KTmYp0yexSHK8FXBlZ3h05ahpO1hXToaH6EULptqTeZsDQKpkRqXgs6mOZ6AEy69gq/D1T2VEIV4buoWyxp/HRxUeC2SulDgIvAq8Ds+VCAHAbgXmeuIi0yA9cHDomQ4EJelY49Cdqmn+2lIGSJDuwjm9teb40EXA9Fcy4Fcj2/dLIspI5Xn2E9snQeRTqAcw8Ku0I71dhBGgEioWKZjV1vNwwKMR5K9aKyVAxEwk04JyYh4MkzsnwbpWuoGQtacFP78t3NXNXGO5kPeBETd7EO+e5mTsujh2HEs3JqmLoFGeGjoky2T/TCDhVv3tM7jxToTCQolKeemQ9WsNsrGVHcYdFBWzknoX40bro48LxtU3LwWrISw1PoRSrGbzeg6r2/H8b0htIjOLyyOO90FZf3yoYO6m4My8c/mfrD/ZY7sSdXku7ojhuFIX3bOQyYPdWr6Bn5VoZDTdSoWIeTTRf38yZCuVZGTrPrTF8oaM/0sZp3SiSQDEXiPtaXip/toz62Fb2odbUtKVXiCSEQ57zqUhYZU/j8cWHTvZKqTrwO8Bf995v705a3nuvlHroeq2U+m3gtwH279//YU/jA8V65rhSOlXNxJoDu7RdHhZHq8K+uVxisjOh4MWrmeNIVdMvTUZCpXihIdjx2YHo0JjdVXrmWcmkoXt/xFrhnGMtU9xORS9lKhTKZ92Ifs/VkeD89UCxmGjaoeb5uuGiUVwbS0W0EGuOVs09/QK9K7GoUnv84khxfXyHm685UjO8ue0Z3Qc/jZ1owO8+fnecHTiqRpXJWBEZoSReGlmeqRlupk5ULxUcqGhmI8WeiqFpHEuZQD51A4cTw75Ec7xqOFf2TbSCI4nAHY+KM4OC1fzeCrFuHN/uFjxX15z2fVYu3qI/Spma6tBcmOf8UN8zYR0hi+q5gWNP/CD0A+wweM4NhCUTapn4bBr4/TUvBinldW5rEaQbWMeXJkPOD4VllWixrKx/xEl4IdbgPeuFLJQVrTiUQCvQH8tEDyJUd7hqpN+Vy73eF2tCJcXKRu64OnL0S/ezpzj+RxcfKtkrpUIk0f8z7/3vlj9eVkrNe++XlFLzwMrDjvXe/0PgHwK88sorj30Dd2tseasnGvSRFm2TpdTx6XbwUE42SBW/WDUcTIRVcucFOjeULfv+8uVVSDK8MrKEPIybL1X0QuWBj6BmFIWH73YL5iPh2b/bF0roay3Dt7aK8t9JMv32VsErTcN0bDheMzvKkh/k5Q7LycXj9x1zJNH8ybZFl8NnY+fZLsQl6WEh1pCil3/vd5F+wvcdrORuR5/orZ5jMVEcqWp+0IPTDZGaHjsxf1+sCJ/++UbA6frd6/l+cSOVz9sdgdZkzrK2usX+G+foJAm0Qvz2KuNhlyuHTrC3fu9WKVAKiyx2jUesLXd6LbvvdTf3TIWKbuFphR6NcNyrRjFyisWa5kTdUDj5ndYipZB7Hqln/5PG4apmNXMcr8oznXt5zo59lFjRRxzTkaYVOupecTgRZsdG4ZmPDP3C8ca2qHhGWuYFbo0dr7WDn0oC+mncGx+GjaOAfwS8573/e7t+9XvAfwb83fK///pDneFHEM573huIDseNVDjTk4HwfW+O3Y9tliml7iEf1oyYnFfNXWzUeqnwFQ/arWVO7BIfFmMr7J1naoaV3JM5OJAoEq14d2DxoxHN5Ru4zQ2Sep1gfh9ngxZTkTBx7j+3DxL3HzMdG05VPV/fsqzljslA87mOeSSWrZU0csfu3oG01AFexL20gnMDi0Z46VeHis9PhrzQEHZR13maBl5ti2fquHB8r1fwbl+w9Jfqasc7eDN3XB07BkW5yFY0nVBMa8CzlTsCrZgMhCGjbt+EZhMTlo93FDLa6tPa3iCtzj0wU4GXBuuPi90LaqRhb6LIrMheOC+DcPORJPyxlUX+zm7hjgrno6Zhf5qYCDWfaBrODR0buXzeS40HZSI+TnFnFubsQHY9WsGhxHCoovhm19IO1U7xNakFdrw+dhx9Ssv80PFhKvvPAH8V+JFS6vvlz/4bJMn/S6XUXwOuAr/x4U7xw8fYwe3UspZLoy5QMj3qPEyEPz7Z3x97K5qrY7fDfy+cZz0XI+jUea6ljslQ2BI9K9XjoySTh85jtGKxojnoBcM0Srbl19Z77D3zBg6PqlZxm5uY20t0T75E0Zp9qF7KTxPb5VTsnormUFVkg8+PPFOxmCE/LI6WbmETofQ3xlaq9PlI8a1ti3WiLZPjOTeCinYMbcCeimEh1uX3LBOhc/zz5ZzrY8dMJFO/v7MKyxk83zR8b9tS3V3tpY7jiWYly7mRemZDmeA914fXagWn/IBreooJL0M7A69QUYVT43UuMX/3vnnPegaHqvc20D9IJEZxoGK4ljperkgzf+hExfH5huG720Vp3i79ivUcTtU+euu96Vh2edbLDuJJGEyqGsWLzQBb7pS0kudHein3vid3CqujPE32HzY+DBvnG9w/bXM3vvTT/t3HEQEixDUdeSJT4rUabowcAyesmRtjx3LmSqaLZvJ9jFFqRvFqM+C9gWU186VvrbBuPIKzf3fLknnP4arhc+8DFcVa4b1jM9cspY6iNFNXHibXbpJbRzw5AYCKYnITEFw5iz46w6Mv/08WFwaWWFMqLioSIxz9CwPLC03D7dRxfSwv5r6KYjbWzFcMLyKqjN1yxP+VpmGzkNmD3Xh7rD1XhjIxCpKQdr+654dSvR26D39/fbsgdZ6JWO9cv7is9i6PxPDjwsiynEqj91NtxZ4oYg8ZFZVy1cfkDlrKccJuMtvaw1Q74Ex53wIFx6r6fXsD7xcn64ZYi+uU8zAZCkxWDxSfagW815fPiUqc//1sMz9sPIn+rbvPOdTy/+/3IRg7mdp+Gh8+fu4maB8WBaIx07fQ0gKbjKw024xnZ6KvHgil8vVuwemGeV8D81YojbvC+R23K4ArQ8tq7jlV6swPLJwZOD4RqIc2AGtGkTvFd7sFs7HckLf7lqZR/NJok3OVGmE5DVs4TzeosDjaQFkLHxGzYy0XM5XdUTewkjl+1IObqWjJeOCtnmd/Ln6hCxVTDoWxcw22bUGtXCzqRo7pFdAMNYaHt2ZujB3JfV8l0BrrLSu5Zz65rzcQKK6OLPOJ4UTDkDmxPwy0ZjX1qAOLLFw+z/7OBC4MUKMh1hXEC3sIAoERCicQwodpZBolpvWHyx7I7uTVCTWvdfQDz8fTeHgYpTicaN4bOKZCkccYWk/uPQeSPxVp6rHHn4qrGCnRqBk7kSm2TirRw4lBadi2dxkaaDHhONN3zEcPZ2jsjt2/z53n7NAxvYuNUwtgJRUnooeZUKRl0jlZV6xkIqOwNxaWS9BocKK7xXUiNjNPaOBwaJlSEV4p3t7OeKvvKRw8W1c83wyIfooFoF4Kb+1mH6YOFIqbqWN2FwSVaM/11HEgkUr/2sjStTI0ta+iaRjN4cTsTBcrJZO6FQXxI85tsjTreCCU+Krm3S3UrRv4wQA1Oc14ZoGpIBQpCNTOdxaFU09n8SA+1IyuXMYXlqDRoP3KJwhqtZ0//dNMrD4q3m/B+Cg/5+c9FhONQuY78lKs7pOtuxr/T+PDxZ+KZB9ooXtdGDpebIBGONaDsol2fwr6IAyNh8XQ8nClTCO88fmHsHEGVqqYQ5WAA4kobQZKsZV7Rgv7mVxf5rlqBRfFaFuQb2xRO/UMX9l0fKdrS5qm0ADPDj1/eS68h3L5QeJwVfO9bYtRgr9nTtg2eyPFUn7vv1VKobxnqTR1CZR8v2tjy/WR4xMtQztUNLziUCKV/HYB7VDTfMTTdqwW8NVNx0rqmIk1hXPcHMPhJODFfIsLr3+PZjUmqFQYXb5Efu06L3z+07xdhDv4u/V+h5tfDQ0sLlI9cABvLToMH/7BT+NjFapkvx1IZLcYPl0oP9L4+HK0PuI4UtUcK7nxm4U4KL3aMsyEMvW4O8QT80EjlB8X8u/9zvF3Inee2iOqk0gpfAlvGKV28MrcexoTbVovv4y3Frexjh0OaJx6huHsHt7YLlhMoBNpmqHmcM1waeS4PP7JWayzseHFhiH9/9s7txi7zquO/9a397nNzJnx2GPn4vE9aYjbJk7iOiENpUQqhDaiIKGoQNQ+IPUFpCKBUOEFgdQHXrg88BJBRZEoUAGFqAqUUCJRXpI4TdLcY6exHTt2PL6MPbdzzt77Wzx8ey4Zz0zmduacmb1+UjRzduac8+3veK/z7fWt9f/7YI7S9HBPPWJ3LWKhfQFFONPIUEKfwYmJjLE0lP6daXiODcQMxsJoKlxLhVsrjiNLSPX2xY7Hby0zVHKcmgx6M3fXHb+yM2bH6bfYt3MbU739jLoyMrid2+OU+uXz3D8QU4uEkaZyLYH9tYjDc6o2xLkVBfqriefHYynPjaacnsryOwdjo3EiFujbQCFW9hD+Ad3WGxp1UmWm+qLilHemPBNZSBlkGpo99s7ZFFwutSgEtvfzahyXa+k4hJsX+eboi0PH6+WWZ7AUSjnHUqUs4Xh5aCflTw+hSYLEMeIcZyZSkBtlbEsOzjczDq1Cd2U6/57krkgiYS56o9nOVgjBvR5Nu3Z5hNxLteXJVFEiPlmPuXcgJvEh1bOcVMausuPLuytMpp7YQdk5fLPJ5FSD4Z1D7NbgTxAL+Eo/ycglth08xLH8fZysbZPyXCPjpbFQ9VMSeH08CKcdW6Tz2TA2G4UJ9gDXEuW9RsZkBrsqITD3RMG387XxlJFWKF87WIu4bZUVGod7I0oStGQ8yrZYuKc/ohqF9Mi5Zq5gGcFwLaI/Fu7qi3huVHn+Wkbq4VCP8NBgaeYLSUSQcnnmPWrOoQusOlMPfWu4VxMJbffTRCIc6XM8dTHh+xPBuevYQIkHd0T83fkEVOmf04060lKuJrPjWs3qrGdO6Z3EMRI5NE2ROJ6pUffNJuUds0YrK3mfxCsXmsHgveyCtkw9Fl6f8AyVZjfRa1HofL7Q8gx3cd26YSyXwgT7i82MF65nlJ1QcWED9my+chsoCQ8OlmjllRNrWSHGuZbM7T2KZ/YOoumVZ0dTpny4gzjfgjPNhKP1iLEMRjPljp4IkVBudnLKc0+8sKDVcAVuqUacbXhuLQdZhItNT18kfKx3fT/S/76S8uaUztyZvDqeUZGQJ7+U6EypXOJDc9IiEjOrQqKI2oEDTJ44QWn7diSO8c0mvtGkumflEhupV45fyxjNwlyNZXC2kXJbniOev4LvcUEfaHiBvRbD2GwUIth7zcXJ5nTnhVyv53zTz4hgrbSxZinmB45zDc/UHK/bWhT0318ay0iVD1XwDBAqeK5Ug4LifJxzPHZzif+8lHBy0uPxDFccn99Z+tDKeK2cbXh+PJYFlcc8ZTRU8rw4lnG417G3Klxo5t3DLjgO3bKEguRq6Nl/AICpd99FM09Uq9F/372UBgZW/FoXW57RTNlVnltdFL5YIVgofqjzWZWeLnZ8MoyVUIhg3/Qs3J0XCyMtZV+t/WO42PIzNnzTVCPh/aanInLD3UTZhQqeoTIL0h87Hrs55Lg9LCrHsKYxNz2Rkw9V9zjnEDz1kpCo457+UP6oqlxNg6XieiLO0XvwED379od0Trm86pr1kURvkBguuaC2OBiHfoPpvZapTPEa1EUNYytQiGAfS6gZn6842Mo1clZD6kP54Uii9ETC7opbsh64JxIuJ8rcjECmYSN2odiVeL2h0WjB121DkJ+mFoEuUP/ugf3ViLITTkx5fJ7KOVJ3S2rDrwWJIiRa22v3OOGieuZWGKkGT9+P98a81/ScaShK+GI+NhAtKrFsGJuNQgT7khP21YIV3FApBPyGD915w6vozkt82Ey9lnl6I+FqopyayrivHnRKFmJfNeJcI6HiQst/luuyHO6LuJYql1ue7aUgbDZde79rOepcbeRQj6Nekpn6d4DzTc9QWTjYE7TZ99QcSW6K3q2yutPcWnX8ZMrP2Cl6DY1fw1VHX8lxZ8lxW4+S5cJo1vVqbCUKEewhCHcBnJ7yKEED5756tKjQ11JcaHquZX4295t3oL424flMeWEt8YGScF9/xOsTnuupRwgB81BeCvrGBJxvKIinPxKODCyup/NRNPO7jquJUo+E3cvQBG9kyvvNoHMzEIdKpWrkePyWMt8bSTg1Gerq91cjvrCzNFP2GYkwf8GtqowkYQwRIchuL3U+HdIbCZ8aiHh1LGOk6RER9lQdd8ypzS85oVMtWK38c7uSKH3559azie8sJrOgKjueBce0WyorF5wz1g+Z3wDUCY4eParHjx/fkPdKfBDkqq5h5faj6ynjeV3+XEZans8Mlpa8QL2GpqWS3LiJ2/KKV9ak3T2VBW2fZu7n2chCnf/92xZvO5/IQqVQqmHFOy27/MC2eOYcR1se58JewVKoKq+NZ5xu6IzH6FSm3NkbrYvv6nqgqjR8SO91S/POdLXWZKb0xELTB3et+/Nqsc3GtST8O5TclGQyDenO+5cQBTRWjoi8oKpHl/O3nV9ubSDjqfLulOftyWBckq6yQ7IqkMzLZasq6EfrlTsRatHComhlJ2s2aTg1lZFoqPrpjYQdZUfs4K2JG/1np3knX7XvmPMcgJOTs8/ZVnYfGeghGKy81/DcVA4qmgOxsKssvD0Z0ifdgOSfQbcEeoAzU56GD+5MvZGwveSoOHhjic+tm3kjV1LdXgrns7PiaHjlzEKu98aGUJhgf7mV8X+jKacbGaOJ8vK4z5uYVh6AhmsRLa8zMguqyqVE2VOVjt+mftBS+ud9YfRFodLEL3IXdyFXtZxLfwwfNFc+N6Np6Gade9c0ndYa65Jg341caPob7rz64rAftNpFSadIvDKa6IyB/TT1WLiwoOKdsREUItirKq+Oe+pxWGn05avN0SzU2a+U/jjk34OWjOdSK/ho3tEFbjqV3J5uLkEeYnH1++oCz0nyVNeK319C6mY+msswGAtTjWSBzy30L3TRDciyiCQYsKfzFhctz6beg9jsFCLYNzxMzbPQA+iLhA9aq1s17apE/OxgzM8Mlnh4R8zH692hoXKw5riWzl5oXpUrCRyquUX3KA71OK4moRQUws+rCasy9dhRdpTziqJpRnO52gGTql2UAzXHWDq7iveqXGnB/mr3mocvhpPgvHa5xczdZOqV8UzZt5x6YqMtFKIaJxJA5QZv2ERhcA3/9pzIDabXneamSsQn+uDtCU+moeLk9p6gNb8Yt1YcTU/oxlWPE+HOXsfuVZR+lp3wqYGYV8ZSRloeNHwBfKJvcdVLA4bKjrvqEW9OeLLE53K/q3fR6jQHexwpefWbhua8u+rRTAe5sfEUItiXnbCnKrzX8AyVQz458UojU4brXRat14F9tYjdFUfDh7TOR21EiggHeyL2Vpf/nKXoj4UHt8VM+ZA6+qiyTyOwJ/fnbeTVWp3e/1kLToJF48Gam0kJbkbrxK1EIYI9wB29EZ5gsYd6YifcU4/Y1gX13+0gdrJiBczVPGcxRIQuqbTsCJkqF/Oa+Z4oGM4v50sv6sK7xbVQdsIiih/GBlOYYB874ZP1mI/1KkluwWcrDaMdJF554XrG1USpRpA0PScnZxVWDaMTbM1l7RJUnNAX3yg8ZhjrxftNz2jq2VUR+uPQt1CJ4LXxtNNDMwpM4YK9YbSb801PfV7KpjcSrmd6gwWmYWwUFuwNY52pOCGdF9O9KoKwhdLxxibDgr1hrDN7q47xTGd6HTRX19xbXVgmwzA2gi23QRtMNJRLucforvLmVg40Nh/TfQVvTXg8Cii3VBy3F7k8yeg4WyrYTysunmmEQO9VeXPCc1/dLaozbxjtYF8t1MxPZkGqwnoNjE6zpdI4VxLldEPZVYbBUqiCGIjh5XE/IwVgGBtFyQkDJbFAb3QFWyrYX0r0Bp36cr5ZZlVvhmEUmU2bxhlPPW9PpIymsLvqOFRzREBQZp+3klLddMqBhmEY68mmDPYXGp5vX2gxnikVBz+8mnGg5nh0Z8zJqdDBOK3tci1V+mNHn6XsDcMoMG1J44jIIyLyloicFJGvr/fr/8flBIdysMeFVX2v41Qj48RExt19EWMpjLSUkaanKnCk3xQXDcMoNuu+sheRCPgr4HPAWeB5EXlSVV9fj9cfTz3nmp7983Sxh0rCK+Oe+wfL7Cw7xnIf1Xq0eq9ZwzCMrUI7VvbHgJOq+hNVbQH/CHxxvV48BlDw/sN+SImHSl71EDthsOToj8UCvWEYBu0J9ruB9+Y8PpsfWxeqsePOPsfZ5uyx1HuupHBvvyXmDcMwFqJjG7Qi8lXgqwB79+5d0XN/YUeJiTTh1GQGCIry0EDMnZvU1ccwDKPdtCPYnwP2zHk8nB/7EKr6BPAEwNGjR1fU8dQXO768u8L7Dc9E5tlZcmwzuzPDMIxFaUewfx64XUQOEIL8l4Bfb8P7cGvVscX6wgzDMNrCugd7VU1F5LeB7wMR8E1VfW2938cwDMNYPm3J2avqU8BT7XhtwzAMY+VYDsQwDKMAWLA3DMMoABbsDcMwCoBoF+i8i8gIcPoj/mwIuLQBw+lmij4HRT9/sDkAmwOYnYN9qrpzOU/oimC/HETkuKoe7fQ4OknR56Do5w82B2BzAKubA0vjGIZhFAAL9oZhGAVgMwX7Jzo9gC6g6HNQ9PMHmwOwOYBVzMGmydkbhmEYq2czrewNwzCMVdL1wb7dFofdiIh8U0Quisirc45tF5GnReRE/nOwk2NsNyKyR0SeEZHXReQ1Eflafrww8yAiVRF5TkRezufgj/PjB0Tk2fya+CcRKXd6rO1ERCIReVFEvpc/Ltr5nxKRV0TkJRE5nh9b8XXQ1cF+jsXhLwKHgV8TkcOdHdWG8LfAI/OOfR34gareDvwgf7yVSYHfVdXDwAPAb+WffZHmoQk8rKp3A0eAR0TkAeBPgT9X1duAq8BvdnCMG8HXgDfmPC7a+QP8nKoemVNuueLroKuDPW22OOxWVPV/gSvzDn8R+Fb++7eAX97QQW0wqnpeVX+U/z5GuNh3U6B50MB4/rCU/6fAw8A/58e39ByIyDDwBeCv88dCgc5/CVZ8HXR7sG+rxeEm4yZVPZ//fgG4qZOD2UhEZD9wD/AsBZuHPIXxEnAReBp4BxhV1TT/k61+TfwF8PvAtOn0Dop1/hC+4P9LRF7IHf5gFddBx2wJjdWjqioihSijEpE+4F+A31HV63MN5IswD6qaAUdEZBvwXeCnOjykDUNEHgUuquoLIvLZTo+ngzykqudEZBfwtIi8Ofd/Lvc66PaV/bIsDgvCByJyC0D+82KHx9N2RKRECPR/r6r/mh8u3DwAqOoo8Azw08A2EZleqG3la+LTwC+JyClCCvdh4C8pzvkDoKrn8p8XCV/4x1jFddDtwX7G4jDfcf8S8GSHx9QpngS+kv/+FeDfOziWtpPnZv8GeENV/2zO/yrMPIjIznxFj4jUgM8R9i6eAX41/7MtOweq+geqOqyq+wnX/v+o6m9QkPMHEJFeEalP/w78PPAqq7gOur6pSkQ+T8jbTVscfqPDQ2o7IvIPwGcJynYfAH8E/BvwHWAvQSH0MVWdv4m7ZRCRh4AfAq8wm6/9Q0LevhDzICJ3ETbfIsLC7Duq+icicpCw0t0OvAg8rqrNzo20/eRpnN9T1UeLdP75uX43fxgD31bVb4jIDlZ4HXR9sDcMwzDWTrencQzDMIx1wIK9YRhGAbBgbxiGUQAs2BuGYRQAC/aGYRgFwIK9YRhGAbBgbxiGUQAs2BuGYRSA/we98wIVTReg7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f139e2ae8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "colors = np.where(df.Goal == 0, '#01baef', '#b80c09')\n",
    "plt.scatter(df.X, df.Y, c=colors, alpha=.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"uppsnyggat\"..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='https://s3-eu-west-1.amazonaws.com/scisports-production/2a8d8a_b0010c24664a4bd08a88c20f71256de3.png'/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Träna en modell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n              learning_rate=0.1, loss='deviance', max_depth=3,\n              max_features=None, max_leaf_nodes=None,\n              min_impurity_decrease=0.0, min_impurity_split=None,\n              min_samples_leaf=1, min_samples_split=2,\n              min_weight_fraction_leaf=0.0, n_estimators=100,\n              presort='auto', random_state=None, subsample=1.0, verbose=0,\n              warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "knn = GradientBoostingClassifier()\n",
    "knn.fit(df[['X','Y','Pen']].values,df['Goal'].values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Klassificera ett avslut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rakt framifrån ca 10m från mål"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.83415947,  0.16584053]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba([[10, 50, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Straff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.26380705,  0.73619295]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba([[10, 50, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exempel hög xG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"480\"\n",
       "            src=\"http://www.dailymotion.com/embed/video/x52kuq6\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"480\"\n",
       "            src=\"http://www.dailymotion.com/embed/video/x52kuq6\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('http://www.dailymotion.com/embed/video/x52kuq6', width=640, height=480)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28227869,  0.71772131]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba([[1, 50, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exempel låg xG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"480\"\n",
       "            src=\"https://www.youtube.com/embed/MTRFWAKXUpo?ecver=1&enablejsapi=1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "\n",
       "        <iframe\n",
       "            width=\"640\"\n",
       "            height=\"480\"\n",
       "            src=\"https://www.youtube.com/embed/MTRFWAKXUpo?ecver=1&enablejsapi=1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('https://www.youtube.com/embed/MTRFWAKXUpo?ecver=1&enablejsapi=1', width=640, height=480)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.95187783,  0.04812217]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.predict_proba([[19, 43, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}