{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PRIMAP-hist \n",
    "National historical emissions time series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from zipfile import ZipFile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.pik-potsdam.de/paris-reality-check/primap-hist/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File ‘PRIMAP-hist_v2.0_11-Dec-2018.zip’ already there; not retrieving.\r\n"
     ]
    }
   ],
   "source": [
    "!wget --no-clobber ftp://datapub.gfz-potsdam.de/download/10.5880.PIK.2019.001/PRIMAP-hist_v2.0_11-Dec-2018.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_file = ZipFile('PRIMAP-hist_v2.0_11-Dec-2018.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(zip_file.open(\"PRIMAP-hist_v2.0_11-Dec-2018.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>scenario</th>\n",
       "      <th>country</th>\n",
       "      <th>category</th>\n",
       "      <th>entity</th>\n",
       "      <th>unit</th>\n",
       "      <th>1850</th>\n",
       "      <th>1851</th>\n",
       "      <th>1852</th>\n",
       "      <th>1853</th>\n",
       "      <th>1854</th>\n",
       "      <th>...</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HISTCR</td>\n",
       "      <td>ABW</td>\n",
       "      <td>IPC1A</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Gg</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>...</td>\n",
       "      <td>0.22300</td>\n",
       "      <td>0.23400</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.24600</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.29100</td>\n",
       "      <td>0.30200</td>\n",
       "      <td>0.31400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HISTCR</td>\n",
       "      <td>AFG</td>\n",
       "      <td>IPC1A</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Gg</td>\n",
       "      <td>0.031700</td>\n",
       "      <td>0.031900</td>\n",
       "      <td>0.032100</td>\n",
       "      <td>0.032300</td>\n",
       "      <td>0.032400</td>\n",
       "      <td>...</td>\n",
       "      <td>9.16000</td>\n",
       "      <td>9.98000</td>\n",
       "      <td>10.80000</td>\n",
       "      <td>11.20000</td>\n",
       "      <td>11.80000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>12.30000</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HISTCR</td>\n",
       "      <td>AGO</td>\n",
       "      <td>IPC1A</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Gg</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.60000</td>\n",
       "      <td>63.60000</td>\n",
       "      <td>65.80000</td>\n",
       "      <td>67.90000</td>\n",
       "      <td>69.90000</td>\n",
       "      <td>69.90000</td>\n",
       "      <td>72.80000</td>\n",
       "      <td>74.60000</td>\n",
       "      <td>76.50000</td>\n",
       "      <td>78.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HISTCR</td>\n",
       "      <td>AIA</td>\n",
       "      <td>IPC1A</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Gg</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00159</td>\n",
       "      <td>0.00194</td>\n",
       "      <td>0.00252</td>\n",
       "      <td>0.00287</td>\n",
       "      <td>0.00303</td>\n",
       "      <td>0.00304</td>\n",
       "      <td>0.00251</td>\n",
       "      <td>0.00254</td>\n",
       "      <td>0.00256</td>\n",
       "      <td>0.00259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HISTCR</td>\n",
       "      <td>ALB</td>\n",
       "      <td>IPC1A</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Gg</td>\n",
       "      <td>0.060200</td>\n",
       "      <td>0.060600</td>\n",
       "      <td>0.061500</td>\n",
       "      <td>0.063000</td>\n",
       "      <td>0.065200</td>\n",
       "      <td>...</td>\n",
       "      <td>5.09000</td>\n",
       "      <td>5.14000</td>\n",
       "      <td>5.14000</td>\n",
       "      <td>5.04000</td>\n",
       "      <td>4.95000</td>\n",
       "      <td>4.91000</td>\n",
       "      <td>5.21000</td>\n",
       "      <td>5.27000</td>\n",
       "      <td>5.33000</td>\n",
       "      <td>5.38000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 172 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  scenario country category entity unit      1850      1851      1852  \\\n",
       "0   HISTCR     ABW    IPC1A    CH4   Gg  0.000366  0.000378  0.000390   \n",
       "1   HISTCR     AFG    IPC1A    CH4   Gg  0.031700  0.031900  0.032100   \n",
       "2   HISTCR     AGO    IPC1A    CH4   Gg  1.750000  1.770000  1.800000   \n",
       "3   HISTCR     AIA    IPC1A    CH4   Gg  0.000120  0.000120  0.000121   \n",
       "4   HISTCR     ALB    IPC1A    CH4   Gg  0.060200  0.060600  0.061500   \n",
       "\n",
       "       1853      1854    ...         2007      2008      2009      2010  \\\n",
       "0  0.000402  0.000415    ...      0.22300   0.23400   0.23900   0.24600   \n",
       "1  0.032300  0.032400    ...      9.16000   9.98000  10.80000  11.20000   \n",
       "2  1.820000  1.840000    ...     61.60000  63.60000  65.80000  67.90000   \n",
       "3  0.000123  0.000124    ...      0.00159   0.00194   0.00252   0.00287   \n",
       "4  0.063000  0.065200    ...      5.09000   5.14000   5.14000   5.04000   \n",
       "\n",
       "       2011      2012      2013      2014      2015      2016  \n",
       "0   0.24300   0.24300   0.28000   0.29100   0.30200   0.31400  \n",
       "1  11.80000  12.00000  12.00000  12.30000  12.70000  13.00000  \n",
       "2  69.90000  69.90000  72.80000  74.60000  76.50000  78.30000  \n",
       "3   0.00303   0.00304   0.00251   0.00254   0.00256   0.00259  \n",
       "4   4.95000   4.91000   5.21000   5.27000   5.33000   5.38000  \n",
       "\n",
       "[5 rows x 172 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1850</th>\n",
       "      <th>1851</th>\n",
       "      <th>1852</th>\n",
       "      <th>1853</th>\n",
       "      <th>1854</th>\n",
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       "      <th>1857</th>\n",
       "      <th>1858</th>\n",
       "      <th>1859</th>\n",
       "      <th>...</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
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       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scenario</th>\n",
       "      <th>country</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">HISTCR</th>\n",
       "      <th>ABW</th>\n",
       "      <th>IPC1A</th>\n",
       "      <th>CH4</th>\n",
       "      <th>Gg</th>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000378</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.22300</td>\n",
       "      <td>0.23400</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.24600</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.24300</td>\n",
       "      <td>0.28000</td>\n",
       "      <td>0.29100</td>\n",
       "      <td>0.30200</td>\n",
       "      <td>0.31400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <th>IPC1A</th>\n",
       "      <th>CH4</th>\n",
       "      <th>Gg</th>\n",
       "      <td>0.031700</td>\n",
       "      <td>0.031900</td>\n",
       "      <td>0.032100</td>\n",
       "      <td>0.032300</td>\n",
       "      <td>0.032400</td>\n",
       "      <td>0.032600</td>\n",
       "      <td>0.032800</td>\n",
       "      <td>0.033000</td>\n",
       "      <td>0.033200</td>\n",
       "      <td>0.033400</td>\n",
       "      <td>...</td>\n",
       "      <td>9.16000</td>\n",
       "      <td>9.98000</td>\n",
       "      <td>10.80000</td>\n",
       "      <td>11.20000</td>\n",
       "      <td>11.80000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>12.30000</td>\n",
       "      <td>12.70000</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGO</th>\n",
       "      <th>IPC1A</th>\n",
       "      <th>CH4</th>\n",
       "      <th>Gg</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>1.880000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.930000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.60000</td>\n",
       "      <td>63.60000</td>\n",
       "      <td>65.80000</td>\n",
       "      <td>67.90000</td>\n",
       "      <td>69.90000</td>\n",
       "      <td>69.90000</td>\n",
       "      <td>72.80000</td>\n",
       "      <td>74.60000</td>\n",
       "      <td>76.50000</td>\n",
       "      <td>78.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIA</th>\n",
       "      <th>IPC1A</th>\n",
       "      <th>CH4</th>\n",
       "      <th>Gg</th>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00159</td>\n",
       "      <td>0.00194</td>\n",
       "      <td>0.00252</td>\n",
       "      <td>0.00287</td>\n",
       "      <td>0.00303</td>\n",
       "      <td>0.00304</td>\n",
       "      <td>0.00251</td>\n",
       "      <td>0.00254</td>\n",
       "      <td>0.00256</td>\n",
       "      <td>0.00259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALB</th>\n",
       "      <th>IPC1A</th>\n",
       "      <th>CH4</th>\n",
       "      <th>Gg</th>\n",
       "      <td>0.060200</td>\n",
       "      <td>0.060600</td>\n",
       "      <td>0.061500</td>\n",
       "      <td>0.063000</td>\n",
       "      <td>0.065200</td>\n",
       "      <td>0.067900</td>\n",
       "      <td>0.071200</td>\n",
       "      <td>0.075100</td>\n",
       "      <td>0.079500</td>\n",
       "      <td>0.084400</td>\n",
       "      <td>...</td>\n",
       "      <td>5.09000</td>\n",
       "      <td>5.14000</td>\n",
       "      <td>5.14000</td>\n",
       "      <td>5.04000</td>\n",
       "      <td>4.95000</td>\n",
       "      <td>4.91000</td>\n",
       "      <td>5.21000</td>\n",
       "      <td>5.27000</td>\n",
       "      <td>5.33000</td>\n",
       "      <td>5.38000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 167 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           1850      1851      1852      1853  \\\n",
       "scenario country category entity unit                                           \n",
       "HISTCR   ABW     IPC1A    CH4    Gg    0.000366  0.000378  0.000390  0.000402   \n",
       "         AFG     IPC1A    CH4    Gg    0.031700  0.031900  0.032100  0.032300   \n",
       "         AGO     IPC1A    CH4    Gg    1.750000  1.770000  1.800000  1.820000   \n",
       "         AIA     IPC1A    CH4    Gg    0.000120  0.000120  0.000121  0.000123   \n",
       "         ALB     IPC1A    CH4    Gg    0.060200  0.060600  0.061500  0.063000   \n",
       "\n",
       "                                           1854      1855      1856      1857  \\\n",
       "scenario country category entity unit                                           \n",
       "HISTCR   ABW     IPC1A    CH4    Gg    0.000415  0.000428  0.000441  0.000454   \n",
       "         AFG     IPC1A    CH4    Gg    0.032400  0.032600  0.032800  0.033000   \n",
       "         AGO     IPC1A    CH4    Gg    1.840000  1.860000  1.880000  1.900000   \n",
       "         AIA     IPC1A    CH4    Gg    0.000124  0.000127  0.000130  0.000133   \n",
       "         ALB     IPC1A    CH4    Gg    0.065200  0.067900  0.071200  0.075100   \n",
       "\n",
       "                                           1858      1859    ...         2007  \\\n",
       "scenario country category entity unit                        ...                \n",
       "HISTCR   ABW     IPC1A    CH4    Gg    0.000468  0.000482    ...      0.22300   \n",
       "         AFG     IPC1A    CH4    Gg    0.033200  0.033400    ...      9.16000   \n",
       "         AGO     IPC1A    CH4    Gg    1.930000  1.950000    ...     61.60000   \n",
       "         AIA     IPC1A    CH4    Gg    0.000137  0.000141    ...      0.00159   \n",
       "         ALB     IPC1A    CH4    Gg    0.079500  0.084400    ...      5.09000   \n",
       "\n",
       "                                           2008      2009      2010      2011  \\\n",
       "scenario country category entity unit                                           \n",
       "HISTCR   ABW     IPC1A    CH4    Gg     0.23400   0.23900   0.24600   0.24300   \n",
       "         AFG     IPC1A    CH4    Gg     9.98000  10.80000  11.20000  11.80000   \n",
       "         AGO     IPC1A    CH4    Gg    63.60000  65.80000  67.90000  69.90000   \n",
       "         AIA     IPC1A    CH4    Gg     0.00194   0.00252   0.00287   0.00303   \n",
       "         ALB     IPC1A    CH4    Gg     5.14000   5.14000   5.04000   4.95000   \n",
       "\n",
       "                                           2012      2013      2014      2015  \\\n",
       "scenario country category entity unit                                           \n",
       "HISTCR   ABW     IPC1A    CH4    Gg     0.24300   0.28000   0.29100   0.30200   \n",
       "         AFG     IPC1A    CH4    Gg    12.00000  12.00000  12.30000  12.70000   \n",
       "         AGO     IPC1A    CH4    Gg    69.90000  72.80000  74.60000  76.50000   \n",
       "         AIA     IPC1A    CH4    Gg     0.00304   0.00251   0.00254   0.00256   \n",
       "         ALB     IPC1A    CH4    Gg     4.91000   5.21000   5.27000   5.33000   \n",
       "\n",
       "                                           2016  \n",
       "scenario country category entity unit            \n",
       "HISTCR   ABW     IPC1A    CH4    Gg     0.31400  \n",
       "         AFG     IPC1A    CH4    Gg    13.00000  \n",
       "         AGO     IPC1A    CH4    Gg    78.30000  \n",
       "         AIA     IPC1A    CH4    Gg     0.00259  \n",
       "         ALB     IPC1A    CH4    Gg     5.38000  \n",
       "\n",
       "[5 rows x 167 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index(df.columns[:5].tolist())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "emissions = df.loc[\"HISTCR\", \"EARTH\", [\"IPC1\", \"IPC2\", \"IPCMAGELV\", \"IPC4\", \"IPC5\"], \"KYOTOGHGAR4\", \"GgCO2eq\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1850</th>\n",
       "      <th>1851</th>\n",
       "      <th>1852</th>\n",
       "      <th>1853</th>\n",
       "      <th>1854</th>\n",
       "      <th>1855</th>\n",
       "      <th>1856</th>\n",
       "      <th>1857</th>\n",
       "      <th>1858</th>\n",
       "      <th>1859</th>\n",
       "      <th>...</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scenario</th>\n",
       "      <th>country</th>\n",
       "      <th>category</th>\n",
       "      <th>entity</th>\n",
       "      <th>unit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">HISTCR</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">EARTH</th>\n",
       "      <th>IPC1</th>\n",
       "      <th>KYOTOGHGAR4</th>\n",
       "      <th>GgCO2eq</th>\n",
       "      <td>2.720000e+05</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>285000.0</td>\n",
       "      <td>295000.0</td>\n",
       "      <td>334000.0</td>\n",
       "      <td>341000.0</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>365000.0</td>\n",
       "      <td>370000.0</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31400000.0</td>\n",
       "      <td>32100000.0</td>\n",
       "      <td>31700000.0</td>\n",
       "      <td>33300000.0</td>\n",
       "      <td>34200000.0</td>\n",
       "      <td>34700000.0</td>\n",
       "      <td>34800000.0</td>\n",
       "      <td>35100000.0</td>\n",
       "      <td>35100000.0</td>\n",
       "      <td>35300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPC2</th>\n",
       "      <th>KYOTOGHGAR4</th>\n",
       "      <th>GgCO2eq</th>\n",
       "      <td>2.380000e+04</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>31300.0</td>\n",
       "      <td>34900.0</td>\n",
       "      <td>38600.0</td>\n",
       "      <td>43900.0</td>\n",
       "      <td>46200.0</td>\n",
       "      <td>47400.0</td>\n",
       "      <td>48300.0</td>\n",
       "      <td>52300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3510000.0</td>\n",
       "      <td>3520000.0</td>\n",
       "      <td>3450000.0</td>\n",
       "      <td>3720000.0</td>\n",
       "      <td>3960000.0</td>\n",
       "      <td>4050000.0</td>\n",
       "      <td>4100000.0</td>\n",
       "      <td>4230000.0</td>\n",
       "      <td>4230000.0</td>\n",
       "      <td>4320000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPC4</th>\n",
       "      <th>KYOTOGHGAR4</th>\n",
       "      <th>GgCO2eq</th>\n",
       "      <td>9.470000e+04</td>\n",
       "      <td>95500.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>97200.0</td>\n",
       "      <td>98100.0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>99800.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>101000.0</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1470000.0</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>1510000.0</td>\n",
       "      <td>1540000.0</td>\n",
       "      <td>1560000.0</td>\n",
       "      <td>1580000.0</td>\n",
       "      <td>1570000.0</td>\n",
       "      <td>1590000.0</td>\n",
       "      <td>1610000.0</td>\n",
       "      <td>1630000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPC5</th>\n",
       "      <th>KYOTOGHGAR4</th>\n",
       "      <th>GgCO2eq</th>\n",
       "      <td>1.270000e-11</td>\n",
       "      <td>776.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>3880.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>5430.0</td>\n",
       "      <td>6210.0</td>\n",
       "      <td>6980.0</td>\n",
       "      <td>...</td>\n",
       "      <td>149000.0</td>\n",
       "      <td>148000.0</td>\n",
       "      <td>147000.0</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>157000.0</td>\n",
       "      <td>161000.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>162000.0</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>166000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IPCMAGELV</th>\n",
       "      <th>KYOTOGHGAR4</th>\n",
       "      <th>GgCO2eq</th>\n",
       "      <td>1.780000e+05</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>197000.0</td>\n",
       "      <td>207000.0</td>\n",
       "      <td>216000.0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>233000.0</td>\n",
       "      <td>241000.0</td>\n",
       "      <td>249000.0</td>\n",
       "      <td>257000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2690000.0</td>\n",
       "      <td>2730000.0</td>\n",
       "      <td>2770000.0</td>\n",
       "      <td>2830000.0</td>\n",
       "      <td>2850000.0</td>\n",
       "      <td>2870000.0</td>\n",
       "      <td>2800000.0</td>\n",
       "      <td>2810000.0</td>\n",
       "      <td>2840000.0</td>\n",
       "      <td>2850000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 167 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        1850      1851  \\\n",
       "scenario country category  entity      unit                              \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  2.720000e+05  275000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq  2.380000e+04   28800.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq  9.470000e+04   95500.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq  1.270000e-11     776.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq  1.780000e+05  188000.0   \n",
       "\n",
       "                                                    1852      1853      1854  \\\n",
       "scenario country category  entity      unit                                    \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  285000.0  295000.0  334000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   31300.0   34900.0   38600.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   96400.0   97200.0   98100.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    1550.0    2330.0    3100.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq  197000.0  207000.0  216000.0   \n",
       "\n",
       "                                                    1855      1856      1857  \\\n",
       "scenario country category  entity      unit                                    \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  341000.0  360000.0  365000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   43900.0   46200.0   47400.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   99000.0   99800.0  101000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    3880.0    4660.0    5430.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq  225000.0  233000.0  241000.0   \n",
       "\n",
       "                                                    1858      1859  \\\n",
       "scenario country category  entity      unit                          \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  370000.0  390000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   48300.0   52300.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq  101000.0  102000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    6210.0    6980.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq  249000.0  257000.0   \n",
       "\n",
       "                                                   ...            2007  \\\n",
       "scenario country category  entity      unit        ...                   \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq     ...      31400000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq     ...       3510000.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq     ...       1470000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq     ...        149000.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq     ...       2690000.0   \n",
       "\n",
       "                                                      2008        2009  \\\n",
       "scenario country category  entity      unit                              \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  32100000.0  31700000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   3520000.0   3450000.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   1490000.0   1510000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    148000.0    147000.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq   2730000.0   2770000.0   \n",
       "\n",
       "                                                      2010        2011  \\\n",
       "scenario country category  entity      unit                              \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  33300000.0  34200000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   3720000.0   3960000.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   1540000.0   1560000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    151000.0    157000.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq   2830000.0   2850000.0   \n",
       "\n",
       "                                                      2012        2013  \\\n",
       "scenario country category  entity      unit                              \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  34700000.0  34800000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   4050000.0   4100000.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   1580000.0   1570000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    161000.0    160000.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq   2870000.0   2800000.0   \n",
       "\n",
       "                                                      2014        2015  \\\n",
       "scenario country category  entity      unit                              \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  35100000.0  35100000.0   \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   4230000.0   4230000.0   \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   1590000.0   1610000.0   \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    162000.0    164000.0   \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq   2810000.0   2840000.0   \n",
       "\n",
       "                                                      2016  \n",
       "scenario country category  entity      unit                 \n",
       "HISTCR   EARTH   IPC1      KYOTOGHGAR4 GgCO2eq  35300000.0  \n",
       "                 IPC2      KYOTOGHGAR4 GgCO2eq   4320000.0  \n",
       "                 IPC4      KYOTOGHGAR4 GgCO2eq   1630000.0  \n",
       "                 IPC5      KYOTOGHGAR4 GgCO2eq    166000.0  \n",
       "                 IPCMAGELV KYOTOGHGAR4 GgCO2eq   2850000.0  \n",
       "\n",
       "[5 rows x 167 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>category</th>\n",
       "      <th>IPC1</th>\n",
       "      <th>IPC2</th>\n",
       "      <th>IPC4</th>\n",
       "      <th>IPC5</th>\n",
       "      <th>IPCMAGELV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>272000.0</td>\n",
       "      <td>23800.0</td>\n",
       "      <td>94700.0</td>\n",
       "      <td>1.270000e-11</td>\n",
       "      <td>178000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>275000.0</td>\n",
       "      <td>28800.0</td>\n",
       "      <td>95500.0</td>\n",
       "      <td>7.760000e+02</td>\n",
       "      <td>188000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>285000.0</td>\n",
       "      <td>31300.0</td>\n",
       "      <td>96400.0</td>\n",
       "      <td>1.550000e+03</td>\n",
       "      <td>197000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>295000.0</td>\n",
       "      <td>34900.0</td>\n",
       "      <td>97200.0</td>\n",
       "      <td>2.330000e+03</td>\n",
       "      <td>207000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>334000.0</td>\n",
       "      <td>38600.0</td>\n",
       "      <td>98100.0</td>\n",
       "      <td>3.100000e+03</td>\n",
       "      <td>216000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "category      IPC1     IPC2     IPC4          IPC5  IPCMAGELV\n",
       "1850      272000.0  23800.0  94700.0  1.270000e-11   178000.0\n",
       "1851      275000.0  28800.0  95500.0  7.760000e+02   188000.0\n",
       "1852      285000.0  31300.0  96400.0  1.550000e+03   197000.0\n",
       "1853      295000.0  34900.0  97200.0  2.330000e+03   207000.0\n",
       "1854      334000.0  38600.0  98100.0  3.100000e+03   216000.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emissions = emissions.reset_index().drop([\"scenario\", \"country\", \"entity\", \"unit\"], axis=1).set_index(\"category\").T\n",
    "emissions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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30L59e7744guSkpIICAhg3bp17Nixg5tuuokOHTp4tR88eDBvvvkmf/zxR77vLyIiUtooESgiIpe8zCVphmEQHBxMixYt6N+/P7169cq1fSbDMJg2bZpn77rz0alTJypVqsSUKVPo3bs3kydPxm63eyX3spOYmMisWbMIDg6mU6dOnuMNGjSgSZMmfP/992zevJlGjRpl6ZuQkOB5FqvVSkhICLfddhuDBw/2LBU8efIk48ePz9I3NjbWaylunTp1CAwMzNIu83/0T5w4kWsisH379jz55JM8/PDDLFmyhLZt2xIdHU29evWKpTronj17snyvtWrVyjMR+OOPPwJku1zw8ssv57LLLmP37t0kJCQQFBTkOWez2WjWrFmWPpmVeX/66Sev43v37mXMmDHEx8ezd+9ezpw543V+//79ucaZXytWrChQdeBrr702y7EaNWoAcNVVV3klhuGfJNa+ffvyvPY999zD8OHDqVevHt27dycmJobo6GgqV67s1e5Cx05u36HNZuOmm25iz549/PTTT1kSgdk9/7ljPtPdd9/NG2+8QceOHenatSutW7cmOjo62wTv+chr/GYmXm+88cYse+9BRkL334nA0mTChAmMHTuWunXr8uGHH3qdyy4JCv/8wmb9+vVAxpLl7PZ8PXz4MOnp6fz22280adLEMx5iYmKytLVarTRv3lyJQBERKdNKLBG4bds2FixYwO7duzlx4gSPPfZYlt+a5ubTTz9lzpw5WY77+Phk+QeCiIhIbsxsijPkp31ycjLr16+nf//+DBw4kFq1ap33/lEOh4PevXszbtw41q9fz5w5c2jfvj1VqlTJtd9HH31EcnIyDz74IL6+vl7n+vbty8aNG5k8ebLX3oOZatWqlWeBjZMnT2a7d1fmXn2ZctqfL3Mvr/T09FzvU6tWLb7//ntGjBjB4sWLmTt3LpCRVHnssccYOnRorv3zo1q1amzfvj3b2UctW7b0fK8ulyvfez1mFoXIaSZo9erV2bt3LydPnvRKBFaqVClLkiwzxnOvCxlFIpo2bcqJEydo0aIFbdq0ISgoCKvVyp49e3j//fdzLUpSlM59pkyZ33lu5/JTHOPRRx+lUqVKTJo0iQkTJjB+/HgMwyAmJoZXX33Vk4S70LGTn+8QMv4s/Ft24z67Md+0aVPWrFnDiy++yJw5czz/Vr3iiit47rnn6NGjR64xwoWN38xnrFq1arbXzu545nMXNMmcOYYzC8bkJrNNZvI4O2+99RbDhg2jXr16xMfHZymclF0SFP5JBGYWi3n11VdzjeXUqVNA3p/Vv2fFioiIlDUlVjU4NTWV8PDwPDcfzkn79u2ZPHmy1+uyyy7jhhtuKORIRUREsufv70/r1q354osvSE9Pp0+fPpw+ffq8r/fAAw8A0K1bN1JSUhgwYECefTKXBb/77rteFXQNw2DIkCFARrLw3zPI8is8PBzTNLO8wsPDz+t6ubnyyiuZNWsWx44dY8OGDbz88su43W6GDRvGtGnTLvj6mVVW4+PjL/hamTKTXZnLNv8tc5nkv5NiR48ezTY5mnmdc9uPGzeOY8eOMW3aNFauXMmECRN44YUXGDFiRIGWSJZFvXv35ttvv+XYsWN89dVX9O/fn9WrV9O2bVuOHDniaXchY+d8v8OCuvHGG/nyyy85ceIE33zzDc888wyHDh2iZ8+e+ZqNdyHjN3O27qFDh7I9n93x5s2bAxkVevNK5J9vv8znzqkC8vjx4xkyZAgNGjRgxYoV2SbhMpOg/35lyvzeEhISsm2X+cqcAZjZPqfPKqdxIiIiUlaUWCLw6quvpnv37jnOAnQ6nXzwwQc8+OCD3HvvvTz55JNs3brVc97X15fg4GDP6+TJk+zbt0+VvEREpNg1atSIBx54gH379vH666+f93Xq1q1LixYt2LdvH+Hh4dx66625tt+wYQM//fQTNWrUoH///tm+GjVqxMmTJ/n000/PO67iZrPZaNKkCf/3f//HzJkzAfj888895zNn0hUkOQEZsxhtNhtz5sxh+/bthRLr1VdfDWQkPf5t586d7Nu3j4iIiCwzx1wuF+vWrcvSJ/M6mdfNvA5Aly5dsrRftWrV+YZepgQHB3P77bczZcoU+vbty/Hjx1m9enWWdnmNnezk9h26XC7WrFkDwDXXXHOBT5HBx8eHZs2aMXLkSCZMmABk7FOYlwsZv5nVtdevX4/b7c5yfu3atVmOxcTEcMUVV7Bv3z6mT5+e6/XdbrdnlmdMTAy1a9fmwIEDnj00s7N161bmzZuHzWajX79+Wc6PGTOGRx55hKuuuooVK1bkOTs6J5mTBDK/x7xkfs/Z/dlKT0/P9rMSEREpS0osEZiXadOm8fvvvxMbG8urr77KDTfcwOjRoz2/lf235cuXU716da688spijlRERCSjWISPjw+vvfaa195gBTV58mTmzZvH3Llz89zfbPLkyQAMGzaMqVOnZvsaN26cV9vSauPGjV7LYTNlzsopV66c51jFihWBjH3zCiIqKoqnn36atLQ0brvttmwTcZD9EtCcZCYwRo0a5TVDLT09ncceewy3253j6of//e9/Xkt6jx8/zqhRowC89pvMnH3570TVkiVLmDp1ar5jLWtWrFiR7bL9w4cPA/+MiYKMnex07NiRkJAQZs6cybfffut1bvz48ezevZvWrVtn2R+wINatW5ftrNz8xggXNn5r1qxJy5Yt2blzJ++++67XucWLF2c7I9FisfDuu+9is9kYOnQoM2bMyPb72LZtG23atPEsIbZarUyaNAmLxcKwYcOYN29elj7bt2+nffv2OJ1OnnnmmSxFmV544QWeeOIJmjRpQnx8fLbFYfJr8ODB2O12HnnkEX777bcs59PS0ryShM2aNeOKK65g9erVWRK0b731lvYHFBGRMq9UFgs5evQoK1euZNKkSZ59QNq3b8+mTZtYsWIFPXv29Gqf+R/wjh07lkS4IiIihIaGMnDgQN544w1eeeUVXnrppfO6Tt26dalbt26e7U6dOsXMmTOx2+306dMnx3atWrUiMjKSdevWsXXrVurXr39ecRW1Dz/8kHfffZfmzZsTFRVFhQoV+OOPP/jiiy/w8fHxKtpx4403Uq5cOcaPH8+xY8c8ywWHDBmS5/LNZ599FtM0eeGFF4iOjqZJkyY0bdqUkJAQTp48yZ49ezxJkXOr9uakWbNmPP7447zyyis0aNCArl274u/vz6JFi9iyZQvNmzfnv//9b5Z+1atXJzU1lQYNGngSInPmzOHgwYMMGjTI696DBg1i+vTp3HXXXXTt2pUaNWqwZcsWFi9eTLdu3Zg1a1a+PuP8eO+997KdGQcZs8qK899anTp1onz58txwww2eJepr1qzhhx9+oEmTJrRu3Roo2NjJTvny5YmLi+Ouu+4iJiaGu+66i5o1a7Jx40aWLl1KtWrVsiTPCuqVV15h+fLltGjRgoiICMqXL8/WrVtZtGgRFSpUyNc2AHBh43fixIlER0czaNAgFi5cSKNGjdi1axefffYZHTp0YP78+VkKicTExDB37lzuvfde7r33Xl544QVatmxJ5cqVSUhIYMOGDXz33Xf4+/t7VUS+9dZb+eijj+jXrx+dO3emadOmREdH43A42Lp1K0uWLMHpdPLf//6XZ555xuue77//Ps8++yxWq5UWLVp4Zk2eKzw8PM9CSpnq1q1LXFwc/fr1o379+rRr147LL78cp9PJ3r17WbNmDZUrV+bXX38F/in+dOutt9KlSxc6d+5M7dq1+fnnn4mPj6ddu3YsXrw4X/cWEREpjUplInDv3r2efV3O5XK5sq04+P3335OSkpJtdS8REZHi8r///Y8pU6YwYcIEYmNjc9xsvjDMnDmTU6dO0alTp1zvYxgG/fv356mnnmLy5Mm88cYbRRbThejRowepqamsW7eOjRs3cubMGUJDQ+nevTvDhw+nQYMGnrYVKlTgs88+4/nnn+e9994jOTkZgF69euWZCDQMgxEjRtCjRw/eeecdVqxYwccff0xycjIBAQFERUXx0EMPce+99+Z7KeiYMWO4+uqreeutt/jggw9wOp1ERUUxatQohg8fjsPhyNLH4XDw9ddf8+STT/LJJ59w9OhRIiMjeeKJJzx7O2Zq1KgRK1as4Omnn+arr77C5XLRuHFj5s6dS3BwcKEmAt9///0cz/Xp06dYE4Evv/wyS5Ys4ccff2ThwoX4+vpSq1YtxowZw0MPPeQpiFGQsZOTDh068M033zB69GiWLFlCQkIC1apVY+DAgTzzzDO5FrPIj0GDBlGhQgW+++471q5di8vl4rLLLmPQoEEMHz48y4y4nFzI+K1Xrx7r16/nySefZPny5SxfvpxGjRoxb948tm/fzvz587Ot/H3nnXfyxx9/MGnSJBYtWsScOXNITEwkICCAunXrMnLkSAYMGJBl6W737t1p3rw5b7zxBosXL2bKlCk4nU6qVatGjx49ePjhh7PdImj37t1Axqza7CqWQ0aCMr+JQMj4u6Fx48aMHTuWFStWsHTpUvz9/alRowZdu3bl7rvv9mofHR3NmjVreOqpp1i0aBEA119/PStXrmTJkiVKBIqISJlmmAUtlVgEunXr5lU1eN26dUyYMIFx48Zl+c1k5t6A5xo5ciR+fn7Z/sZdRERERERyds899/Dxxx/z66+/csUVV5R0OKXaiBEjeP7551mxYgUtW7Ys6XBEREQKrFTuERgeHo7b7fb8Nvbc17+TgIcPH2br1q0qEiIiIiIikgO3251txdv4+HhmzZpFvXr1lAQUERG5BJTY0uCUlBSvf4wcPnyYPXv2UL58eWrUqEHz5s1566236N27NxERESQmJvLLL79Qq1Ytr6UOy5cvJzg42Ku6noiIiIiI/CMtLY2wsDBuvvlm6tati81mY+vWrSxbtgyHw8HEiRNLOkQREREpBiWWCPzjjz94/vnnPe8/+OADIGPPj4cffphBgwYxd+5cPvjgA44fP05gYCB16tShSZMmnj5ut5tVq1bRsmXLLEuIRUREREQkg91uZ+DAgSxfvpzvvvuO06dPU6lSJe666y6eeOIJ/VJdRETkElEq9ggUERERERERERGRoqVpdCIiIiIiIiIiIpcAJQJFREREREREREQuAUoEioiIiIiIiIiIXAKUCBQREREREREREbkElFjV4BMnTuByuUrq9kWqcuXKHDlypKTDEMmVxqmUBRqnUtppjEpZoHEqZYHGqZQFGqdSmthsNipUqFDwfkUQS764XC6cTmdJ3b7IGIYBZDyfCjJLaaVxKmWBxqmUdhqjUhZonEpZoHEqZYHGqVwstDRYRERERERERETkEqBEoIiIiIiIiIiIyCVAiUAREREREREREZFLQIntEZib5ORkXC6XZw1+WXPmzBnS0tJKOowyq1y5cthspXJoioiIiIiIiIiUWaUu25KamophGAQFBZV0KOfNbrdflIVQioPb7SYpKQl/f38lA0VEREREREREClGpWxqcmpqKn59fSYchJcRisRAQEMDp06dLOhQRERERERERkYtKqUsEAmV2SbAUDoulVA5LEREREREREZEyTRkXERERERERERGRS4ASgSIiIiIiIiIiIpcAJQJFREREREREREQuAUoEioiIiIiIiIiIXAKUCCxCY8eO5dZbby3pMERERERERERERJQIvJQ4nc6SDkFEREREREREREqIEoF5cLvdTJo0iejoaCIiIrjuuut44403AHjxxRdp3rw5UVFR3HjjjbzyyiueZNusWbMYN24c27ZtIzQ0lNDQUGbNmgVAQkICjz32GA0bNuSKK67grrvuYuvWrV73HT9+PI0aNeLyyy/nscceY/To0V6zC91uN6+//jpNmjQhIiKCW2+9lRUrVnjO//XXX4SGhjJ//ny6dOlCZGQkH330EVdccQVffvml170WL15M7dq1OXXqVJF8hiIiIiIiIiIiUvJsJR1AaffSSy/x8ccf89xzz9G0aVMOHz7Mzp07AfD39+f111+nWrVqbN++nccff5zy5cszbNgw2rdvz44dO1i5ciWffPIJAAEBAQA8+OCD+Pr6MmPGDAICApgxYwZ33303a9asoULS+2MTAAAgAElEQVSFCsydO5c333yT0aNHc+2117JgwQLeffddwsLCPHFNnTqVd999lzFjxlC/fn1mzZrFfffdx/Lly4mMjPSK/9lnn6VBgwb4+Piwbds2Zs2axR133OFpM2vWLP7zn/9Qvnz54vhIRURERERERESkBCgRmItTp04xbdo0Ro0aRbdu3QAIDw+nadOmAMTGxnrahoWFsWvXLubPn8+wYcPw8/PD398fq9VKlSpVPO2+//57fv75ZzZt2oSPjw8Azz77LEuWLOGrr76iV69exMXF0b17d+6++24AHnnkEVatWkVycrLnOu+++y6DBg2iQ4cOADz11FOsW7eOqVOnMnr0aE+7+++/n9tvv93zvkePHnTo0IFDhw5RtWpVjh49yvLlyz3JShERERERERERuTgpEZiL33//ndTUVJo3b57t+fnz5xMXF8eff/5JcnIy6enpec6q27ZtG8nJyTRo0MDreEpKCn/++ScAu3btok+fPl7nr7rqKr755hsAkpKS+Pvvv7nuuuu82lx77bVs27bN61jjxo293l999dVcfvnlzJ49m8GDB/PZZ59x2WWXccMNN+Qat4iIiIiIiIiIlG1KBObC19c3x3MbNmxgyJAhDB8+nJYtWxIQEMD8+fOZPHlyrtdMTk6mSpUqzJkzJ8u5oKCgC4753/z8/LIc69mzJ++99x6DBw/m008/pVu3bhiGUej3FhERERERERGR0kPFQnIRERGBr68va9euzXJuw4YNXHbZZQwbNozGjRsTGRnJ/v37vdrY7XbcbrfXsYYNG3LkyBFsNhsRERFer5CQEAAiIyPZtGmTV79z3wcEBFCtWjV++OGHLDFdfvnleT5X586d2b9/P9OmTeO3337jrrvuyrOPiIiIiIiIiIiUbZoRmAtfX18efvhhXnzxRex2O9dddx3Hjh3jt99+8yT+5s+fT+PGjYmPj2fRokVe/cPCwti7dy9btmyhRo0a+Pv706JFC5o0aUK/fv14+umniYyM5O+//yY+Pp7bbruNxo0b069fP/773//SqFEjT7GQ7du3U7NmTc+1Bw4cyNixY6lVqxb169fn008/ZevWrbz55pt5PldwcDC33XYbo0aNIiYmhho1ahT6ZyciIiIiIiIiIqWLEoF5iI2NxWq18tprr3Ho0CGqVKnCvffeS48ePXjggQd46qmnSEtL45ZbbiE2NpZx48Z5+t5+++0sXLiQbt26kZCQwLhx47j77rv58MMPGTNmDI8++ijHjh2jcuXK3HDDDVSqVAnImLH3559/8sILL5Camsqdd95Jt27d+OmnnzzX7t+/P0lJSYwcOZJjx45Rp04dpk+f7lUxODfdu3dn3rx5noIkIiIiIiIiIiJycTNM0zRL4sZHjhzB6XRmOZ6YmEhgYGAJRFR47HZ7ts92Ibp3707lypXzNeMvP+bMmcOIESP48ccfcTgchXLNwnQxjIPSzDAMqlevzsGDBymhvwJE8qRxKqWdxqiUBRqnUhZonEpZoHEqpY3dbqdy5coF7qcZgaXQmTNn+OCDD2jZsiVWq5XPP/+cNWvWMHPmzEK59qFDh5g4cSK9evUqlUlAEREREREREREpfCoWUkotX76czp07065dO5YtW8aUKVO46aabLvi6kyZNIiYmhsqVKzNkyJBCiFRERERERERERMoCzQgshfz8/Jg1a1aRXHv48OEMHz68SK4tIiIiIiIiIiKll2YEioiIiIiIiIiIXAKUCBQREREREREREbkEKBEoIiIiIiIiIiJyCVAiUERERERERERE5BKgRKCIiIiIiIiIiMglQIlAERERERERERGRS4CtpAPIj/TXn4Vjh4vvhhWrYH1kZPHdT0RERERERERECo3FlYTbFlDSYZQ6ZSIRyLHDcOhASUeRo9jYWBITE4mLiyM2NpbZs2cDYLfbCQ0NpWvXrgwZMgSbLePjNk2Tjz76iE8++YQdO3Zgs9kIDw+nc+fO9OrVCz8/P3bs2MFrr73G5s2b2bdvHyNGjOCBBx4oyccUERERERERESndTBflj3yJ76ktHA8bjNseXNIRlSpaGlwEWrVqxU8//cTatWsZMGAAY8eO5e233/acHzp0KM899xxt2rRh9uzZLF26lNjYWJYsWcKqVasAOHPmDDVr1uTJJ5+kSpUqJfUoIiIiIiIiIiJlgsWVQIV9kymX+B2GOxlH8o6SDqnUKRszAssYh8PhSd716dOHxYsXs3TpUoYMGcKCBQuYO3cucXFxtG3b1tMnLCyMNm3akJSUBMBVV13FVVddBcDo0aOL/yFERERERERERMoI+5ldBP09E0v6KUwMTKykBtQv6bBKHSUCi4Gvry8nTpwAYN68eURFRXklATMZhkFgYGBxhyciIiIiIiIiUjaZJn4J31D+6CIM3JhYMHDjtvqVdGSlkpYGFyHTNFm9ejWrVq0iOjoagN27dxMVFVXCkYmIiIiIiIiIlG2GO5XAQ58QcPSrjOQfVgzcJR1WqaYZgUVg2bJl1KlTB5fLhdvtpmPHjgwfPhzISA6KiIiIiIiIiEjBGO5ULM7jWF0nsDpP4Jf4A7a0Q5iAiRUL6SUdYqmnRGARiI6OZvTo0TgcDqpWreqpFgwQGRnJzp07SzA6EREREREREZHSz+JKxP/YUmxpB7E6T2Bxn8nSxsQATCUB80lLg4tAuXLliIiIIDQ01CsJCNCxY0d27drFkiVLsvQzTZPExMTiClNEREREREREpFQy3CkEHZiOX9JG7KkHPEnAjEIgFkysmBgYZ1OBkj9KBBaz9u3b0759ewYNGsSECRPYtGkT+/btY9myZdx9992sW7cOgLS0NLZs2cKWLVtwOp38/fffbNmyhd27d5fwE4iIiIiIiIiIFCHTRdDBGdjT/s6S5stI/LkxSMdA268VVNlYGlyxykVzP8MwmDhxIjNmzGDWrFlMmDABm81GREQEXbt2JSYmBoBDhw55VRZ+5513eOedd7jxxhuZM2dOkcUnIiIiIiIiIlJiTJPAw5/hOPOHZ9mvFB7DLKHqFUeOHMHpdGY5npiYSGBgYAlEVHjsdnu2zyb5dzGMg9LMMAyqV6/OwYMHVcBGSi2NUyntNEalLNA4lbJA41TKAo3T4uN/dDH+J1edTf9ZzrsKcLo1gOM1h2JayxdmeKWG3W6ncuXKBe6npcEiIiIiIiIiIlLi/E6ux//kKiCjCvD5JgElZ0oEioiIiIiIiIhIiXKc2kr5o18A4MamKsBFRIlAEREREREREREpMbYzfxJ06BMMTExsWHCVdEgXrbJRLERERERERERERMo2043VeRxr2mFsaYewpR06+/NhDNLPLgdWErAoKREoIiIiIiIiIiJFxzQpf2Q+fkkbMczsE30ZSUAtBy5qSgSKiIiIiIiIiEiR8U38gXKJ3wF4qgGDAaRjnG2jJGDxUCJQRERERERERESKhMWVQPljCwFwYz1bBETVgEuKioWIiIiIiIiIiEjhM00CjizA4k7FxKJKwKWAEoEiIiIiIiIiIlLofJK34JO8DRMwNAuwVCgTS4Ofjd/LkWRnsd2vsr+dkbfULLb7iYiIiIiIiIhcTIz0ZAKOLDj7zgqaDVgqlIlE4JFkJweSii8RWFCxsbEkJiYSFxdHbGwss2fPBsButxMaGkrXrl0ZMmQINlvGx22aJh999BGffPIJO3bswGazER4eTufOnenVqxd+fn589NFHzJkzhx07dgDQsGFDnnjiCa6++uoSe04RERERERERkfwof3QhlvRTmFhUCKQU0dLgItCqVSt++ukn1q5dy4ABAxg7dixvv/225/zQoUN57rnnaNOmDbNnz2bp0qXExsayZMkSVq1aBcD69evp0KEDn376KQsWLKBGjRr07NmTgwcPltRjiYiIiIiIiIjkyXH6N/ySfjxbIVhLgkuTMjEjsKxxOBxUqVIFgD59+rB48WKWLl3KkCFDWLBgAXPnziUuLo62bdt6+oSFhdGmTRuSkpIAeOutt7yu+dprr7Fw4ULWrl3LXXfdVXwPIyIiIiIiIiKST4Y7lYDD886+s2HgKtF4xJtmBBYDX19fnM6Mpc3z5s0jKirKKwmYyTAMAgMDs73GmTNncLlcBAcHF2msIiIiIiIiIiLny//YUqyuk2eXBCsJWNooEViETNNk9erVrFq1iujoaAB2795NVFRUga/14osvUrVqVVq0aFHYYYqIiIiIiIiIXDDbmT/xS1h/9p1ZorFI9rQ0uAgsW7aMOnXq4HK5cLvddOzYkeHDhwMZycGCeuutt1iwYAGzZ8/G19e3sMMVEREREREREblg5Y8txsDE1JLgUkuJwCIQHR3N6NGjcTgcVK1a1VMtGCAyMpKdO3fm+1rvvPMOEydO5JNPPqFevXpFEa6IiIiIiIiIyAWxOE/gSNmDCUoClmJaGlwEypUrR0REBKGhoV5JQICOHTuya9culixZkqWfaZokJiZ63k+aNInx48czY8YMGjduXORxi4iIiIiIiIicD99Tm8/+ZC3ROCR3SgQWs/bt29O+fXsGDRrEhAkT2LRpE/v27WPZsmXcfffdrFu3DoCJEyfy6quvMnbsWMLCwjh8+DCHDx8mOTm5hJ9ARERERERERMSbjycR6C7ROCR3ZWJpcGV/+0VzP8MwmDhxIjNmzGDWrFlMmDABm81GREQEXbt2JSYmBoAPPviAtLQ0BgwY4NX/0Ucf9ew3KCIiIiIiIiJS0qxpR7CnHji7LFhFQkozwzyf6hWF4MiRIzidzizHExMTCQwMLIGICo/dbs/22ST/LoZxUJoZhkH16tU5ePDgeRWwESkOGqdS2mmMSlmgcSplgcaplAUap7krd/xryh+Px8SKQXpJhwNAujWA4zWHYlrLl3QoRcJut1O5cuUC99PSYBEREREREREROT+miW9S5rLg0pEElJwpESgiIiIiIiIiIufFlnYQm/PI2WXBUtopESgiIiIiIiIiIufFJ2nT2Z9ULbgsUCJQREREREREREQKzjTxPaVlwWWJEoEiIiIiIiIiIlJgtpS9WF0nMTG0LLiMUCJQREREREREREQKzPdUxrJgU+mlMkPflIiIiIiIiIiIFIyZjs+pXwCwaFlwmaFEoIiIiIiIiIiIFIj9zG6s6acwtSi4TFEiUERERERERERECiRzWbCqBZcttpIOID/Wr0zizGmz2O7nV87gxpYBxXY/EREREREREZEyw3Thc2oLAAauEg5GCqJMJALPnDZJTnIX4x0LNlEyNjaWxMRE4uLiiI2NZfbs2QDY7XZCQ0Pp2rUrQ4YMwWbL+LhN0+Sjjz7ik08+YceOHdhsNsLDw+ncuTO9evXCz8/P6/rz589n0KBBtG3blri4uMJ5RBERERERERGR8+A4/TsWd8rZasHFN3FLLlyZSASWNa1atWLs2LGkpaURHx/PU089hc1mY8iQIQAMHTqUhQsXMmzYMEaNGkXFihXZtm0bU6ZMISwsjHbt2nmu9ddffzFy5Eiuv/76knocEREREREREREP36TMZcEWUKGQMkWJwCLgcDioUqUKAH369GHx4sUsXbqUIUOGsGDBAubOnUtcXBxt27b19AkLC6NNmzYkJSV5jqWnpzN48GAee+wxvvvuOxITE4v9WUREREREREREPNxpOJK3A2AoCVjmqFhIMfD19cXpdAIwb948oqKivJKAmQzDIDAw0PP+9ddfp1KlSvTo0aPYYhURERERERERyYlP8q9YzDRMpZTKJM0ILEKmabJmzRpWrVrFfffdB8Du3buJiorKs+/333/PzJkzWbZsWVGHKSIiIiIiIiKSL47Tv539ySjROPLi1taF2VIisAgsW7aMOnXq4HK5cLvddOzYkeHDhwMZycG8nDp1iqFDh/Lqq68SEhJS1OGKiIiIiIiIiOSLPXUfULqXBf+4z49v//SjYw0nNmtJR1O6KBFYBKKjoxk9ejQOh4OqVat6qgUDREZGsnPnzlz779mzh7/++ou+fft6jrndGVWTa9asyerVqwkPDy+K0EVEREREREREsmW4U7GmHS7pMHLkTIcvtgbx475yAGz6ZTtNrmtWwlGVLkoEFoFy5coRERGR7bmOHTsyaNAglixZkmWfQNM0SUpKonbt2sTHx3ude+WVVzh16hQjR46kRo0aRRa7iIiIiIiIiEh2bKn7MTAxMTAoXWtvj56y8slPFfg7yY6BSVQlqHtlo5IOq9RRIrCYtW/fnsWLFzNo0CCGDRtGTEwMFStWZPv27UyZMoV+/frRrl076tat69Uvs4jIv4+LiIiIiIiIiBQHW8q+sz9ZoBQtDd5y0Jd5vwSR6rJQzu4myL8+TktjUlIM/MuXdHSlS5lIBPqVMyjOAscZ9ysahmEwceJEZsyYwaxZs5gwYQI2m42IiAi6du1KTExMkd1bREREREREROR8Ze4PCO4SjSOTyw2Ltwfy7Z/+AFQJMLHZ2uLjqI5pujlxDCpWKuEgSxnDzE/1iiJw5MgRnE5nluOJiYme2W9lld1uz/bZJP8uhnFQmhmGQfXq1Tl48GC+CtiIlASNUyntNEalLNA4lbJA41TKAo3TDBX3vILVdaJY73k6zSAxxUqqyyDVZSE13cj42Wnhl4O+7EtwAFCzgj9Y78Bq9SHdfYZ0M5k7Okfi41t8E8uKk91up3LlygXuVyZmBIqIiIiIiIiISMkx0k8VexJw419+LNgSRLqZ88pNX5ubCuWvwOq4EYBU51GsFn8C/AueJLsUKBEoIiIiIiIiIiK5sp/dH9DEglEMS4O3H/Lh81+CMDHwtblx2EzsVhOrBawGGAaY2MFyC76OMEzTJMW5H197DQzj4pwFWBiUCBQRERERERERkVzZPPsDFl1dhUx7jtuZ9VMFTAxCg/04lRaFxeIAw45p2Eg37FgMOz72ilgsDtLdKThdifg5Livy2Mo6JQJFRERERERERCRXdk/F4KKtFnwoycaMDSG43AbB5YKxO+4gxCfnGX6pzmNYLX74OqoUaVwXCyUCRUREREREREQkZ6bpqRhclPMBT5yx8t73IaS4LAT6liOo/O2ASarzKAYWMCyAgYEBWDAMCw5bBS0FLgAlAkVEREREREREJEcW10ks6cmYFF0iMDnN4P3vQ0hKtVLex0FwwJ0YWElxHsTPUaOI7nrpUcpURERERERERERyZPfsD1g0aaQ0l8GHG0I4mmzDz26hQsCdWC0+pDgPKAlYyDQjUEREREREREREcmRLKZpCIW4T9p6ws/z3APaddOCwGlQIuAOb1Z8zaftU/KMIKBEoIiIiIiIiIiI5+md/wAsvFGKa8NdJO78c9GPLQV+SUq0AWC0QEtgGH3swZ9IO4GsPveB7SVZlIhE4b948kpKSiu1+AQEBdOrUqdjuJyIiIiIiIiJSKpnuc2YEnr9jyVZ+2FuOXw76kpDyTzrKx+amcnkb6UZrfB1VSUk7hK+9GoZRlGVJLl1lIhGYlJTEyZMnSzqMHMXGxpKYmEhcXByxsbHMnj0bALvdTmhoKF27dmXIkCHYbBkft2mafPTRR3zyySfs2LEDm81GeHg4nTt3plevXvj5+TFr1iweffRRr/v4+Piwa9euYn8+EREREREREbk0WZ1HsJhpF1Qo5OQZC29/U4kUV8Yeg3armxpBLk6nhZBOXSz22tgtDlKdR3HYK6oKcBEqE4nAsqZVq1aMHTuWtLQ04uPjeeqpp7DZbAwZMgSAoUOHsnDhQoYNG8aoUaOoWLEi27ZtY8qUKYSFhdGuXTsgY2bi6tWrPddVNlxEREREREREipPdMxvQCuexNNg0Yf4vQaS4LFTyd2ExAnCZV+K2RFLev5ynXZrrBDZrIBZDqaqipE+3CDgcDqpUqQJAnz59WLx4MUuXLmXIkCEsWLCAuXPnEhcXR9u2bT19wsLCaNOmjdcSaMMwPNcRERERERERESluNk/FYPO8+m/c58fvR32xGOBwtMPnnCrAbncaaenHsRi+2K3BmgBVDDTXshj4+vridDqBjP0Oo6KivJKAmQzDIDAw0PM+OTmZpk2bcu2113LfffexY8eOYotZRERERERERMSe8hcABu4C9z15xsKi7Rl5jiD/a/Bx1MBtOklJO0Sa8wSGYcfXXg2HTUnA4qJEYBEyTZPVq1ezatUqoqOjAdi9ezdRUVF59o2KimLs2LHExcXx5ptv4na76dChAwcOHCjqsEVEREREREREwHRhS/37/LqeXRKc6rLgZ69AULl6nEk7iIEVX0dVHPYKSv6VgAtaGvz555/z8ccfc/vtt9O3b99CCqnsW7ZsGXXq1MHlcuF2u+nYsSPDhw8HMpKD+XHttddy7bXXer1v2bIlM2bM4PHHHy+SuEVEREREREREMtlSD2KQjomBUcClwf8sCTYICbwJl/s0PvZKKgRSws47Ebhz506WLVtGrVq1CjOei0J0dDSjR4/G4XBQtWpVT7VggMjISHbu3Fnga9rtdurXr8+ePXsKMVIRERERERERkez9UyjEQkEKhZy7JDjY/xrs1kDSnMexW8sXfpBSIOeVhk1JSeHNN9/kwQcfxN/fv7BjKvPKlStHREQEoaGhXklAgI4dO7Jr1y6WLFmSpZ9pmiQmJmZ7zfT0dH799VcVDxERERERERGRYvFPoZD87w/otSTYEUJguStJcR7Ax1GxaIKUAjmvGYFTp07l6quvplGjRsydOzfXtk6n01MoAzIKYvj5+Xl+vtS0b9+exYsXM2jQIIYNG0ZMTAwVK1Zk+/btTJkyhX79+tGuXTtef/11rrnmGsLDw0lMTOTtt99m//799OzZs6QfodhciuOjuGR+tvqMpTTTOJXSTmNUygKNUykLNE6lLLgYx6mRfoag/XG4reVIqtYD0+qbpY39bCKwIMuCvZYEB7TA5U7Gx1610OIuCMMwLqrvrDAUOBH4zTffsHv3bl566aV8tZ83bx5z5szxvI+IiGDMmDFUrlw52/ZnzpzBbrd7HQsKCirWLy4wMDBLDLmxWCwYhoHdbsdiyZhkmVv/yZMn88EHHzBz5kzefPNNrFYrkZGRdOvWjdatW2O320lKSuLxxx/n8OHDBAUF0bhxY7766ivq169/wc9XFjgcDqpXr17SYVz0qlWrVtIhiORJ41RKO41RKQs0TqUs0DiVsuBiGqfpu2djpmZUBPY5/B7WRv+HYSvnOW+6zpD++5ECXTPrkuAAUl0nsVsDCi/wfLJarVStWhW/chdUHuOiY5j5rV4BHD16lP/97388/fTTnr0BR4wYQXh4eI7FQnKaEXjkyBFcLleW9gkJCQQGBhbwMUoXu93u9cxScImJiQQFBZV0GBctwzCoVq0af//9d74L2IgUN41TKe00RqUs0DiVskDjVMqCi22cWlyJhOx5FcN0YgIG4PQJJSH0Ac/MQPvpPwjeP6VAhUJm/hjM1r/98LOHULXC7aQ4D+LnCC26B8mFj69By3aB+PhenMVJbDZbjpPscu1XkMa7du0iISGB//u///Mcc7vdbN++ncWLF/Pxxx97ZsRlstvtOc6Ouxj+8EjR0fgoeqZp6nOWUk/jVEo7jVEpCzROpSzQOJWy4GIZp37H4s8mAa1nqwKDPXU/QfuncLLG/ZhWX2wpf51tnb9CIafTDLYfykgiVghohiv9FD72kp1BebF8X4WpQInAhg0b8tprr3kde/vtt6lRowYdOnTIkgQUEREREREREZHSw5p2FL/EH86+y0jwGeBJBgYfmMrJ0PuxeSoG569a8Na/fXGbBg5bBRy2YNJcCdgNa2GHLxeoQIlAPz8/atas6XXMx8eHgICALMdFRERERERERKR08T++DAO3ZzZgJq9k4P6pWFxJnuP5selARmFYf98IzjgPUq6ElgRnOuXOXwLzUqMdE0VERERERERELgG2lP34ntp8dl/ArImyc5OBnP05P4nAk2cs7DnuAMDfpyZWq39hhVxgaaab9e5E/jydynXJ/oT5+pRYLKXRBScCR4wYUQhhiIiIiIiIiIhIUfI/vgQA07BimNnPmMtMBmYkAC2AO8/rbjnoBxj42KuQbqZgN0qmCOwhM42V6QkkkY4BbD6cTFhFJQLPpRmBIiIiIiIiIiIXOfvpP/A5/TsmYMkhCZgpMxmYUTE4b5nLgsv7RmCUQKrJbZr85D7Fz2YyJuCPhQCrleYRJZOQLM2UCBQRERERERERuZiZJuWPLT77xga48uxikP3y4X87fMrGwUQ7YODnqIGtmJcFJ5guVqYncAQnADUMB4fNNByO/O5ueGlRIlBERERERERE5CLmk7wVe+q+szP88k4CFsTmA74A+DlqkO5Oxm4LKNTrp5lunJjZnttnprLenYQLEzsGlQ0bB8y0Qr3/xUaJQBERERERERGRi5WZjv+xpWffWCAfs/zyfWkTNp+zLNhi8Su0awP85j7NN+7EPCOuhI3TpHPAdBbq/S9GZSIRGLR/GlbXyWK7X7otmITQ/sV2PxERERERERGRouCb9CM255GzswELLwkIsD/BzvHTNgxs+DgqY7OUL7Rr/+E+wxp3Yq6Vi20YVLfY2evWLMD8spR0APlhdZ3E5jxabK+CJh1jY2Pp16+f5+cqVaoQGhpKeHg40dHRvP7667hc/0y9NU2TGTNmcMcdd1CnTh2uvPJKbrvtNqZMmcKZM2cAGDt2LKGhodxzzz1Z7vf2228TGhpK165ds5w7cOAA4eHhtGrVKsd4v/nmG3r37k3Dhg2JjIykWbNmDBw4kG+//dbTZt26dYSGhmb7Onz4sCfGW2+9Ndt7tGzZkqeeeirbc7NmzSIyMpITJ07kGKOIiIiIiIiIXCAzHf/jyzN+LIIUUOay4HK+l5HuTsYwCmdfvj/dKax0J2AClxmOs4VLsr6cmEoCFlCZSASWNa1ateKnn35i7dq1DBgwgLH/z96dB0l613eefz/Pk3ddXX1Kal0tCSBoLDYAACAASURBVCGEsIUBexgkjjEGPCNjA1qQZzfCMZhgdhwbNhMez84EDoaJ2PDMeMMxY8dgdpfZWY9PSWBkwCDUGLAuJIFAt1p931d1131kZWXm89s/MqvUpa5WV2VlHdn9fkXIkZXP8/zyV91pRejD9/v7/sEf8MUvfnHu+m/+5m/y7/7dv+MDH/gAX/7yl9m5cyef+cxneOihh3j44Yfn7tu2bRs/+MEPOHHixLz17733XrZv377gZ99///380i/9EuPj4/zkJz857/qf/Mmf8IlPfIL+/n6++MUv8sgjj/Df/tt/4+1vfzuf//znz7v/kUce4Zlnnpn3z+bNmy/6Z3DPPffwN3/zN1QqlfOu3XfffXzoQx+iv7//outIkiRJkqTW5CdfJqmNEIiI21wNmAZ4/kQJgO7CDSRRe6oBj6cVvpeOEGgM/jjmmX9tZRC4AnK5HFu3buXqq6/m137t17jzzjvZubPRj//1r3+dr371q/zxH/8xv/mbv8ntt9/ONddcwwc/+EG+/OUv8w//4T+cW2fTpk28+93v5stf/vLcez/60Y8YGhri53/+58/73BAC999/Px/72Mf4yEc+wr333jvv+vHjx/n85z/Ppz71Kf7wD/+QO+64g6uvvppbb72VT33qUzz44IPnrbl582a2bt067584vvjX5u6772ZycpJvf/vb894/ePAgTz31FPfcc89F15AkSZIkSa0rjjzRfNX++OfgYI6JmZg4ypFJeshmlh8EngozfCcdoQ5cEWUd/LECDAJXQaFQoFptHFj5wAMPcOONN/LBD37wvPuiKKK3t3fee/fccw/333//3M/33XcfH/nIR8hms+c9//jjj1Mul7nzzjv56Ec/yte+9jWmpqbmrn/zm9+kWq3yG7/xGwvus10lvNAIEN///vefF0bed999XHPNNdx5551t+yxJkiRJkjRfUjlJbvpg84y99lYDAjzXHBLSVbieNC0ve72zocpD9WFqBLZFWQYc/LEiDAJXUAiBRx55hIcffph3vetdQKMi7sYbb1z0Gu9///uZmJjgySefZGpqim984xsXrKa79957+fCHP0ySJNxyyy1ce+21fOMb35i7fuDAAXp6eti6devce9/85jd5wxveMPfPrl275q359re/fd71973vfYve+z333MNjjz3G8ePHAUjTlK985St8/OMfb2voKEmSJEmS5iuNzlYDJm1fu1qHl07NBoE7yCQblrXeUKjyYH2IKoHNZDgbqqTt2KjO0xFTgzvNd77zHd7whjdQq9VI05Rf+ZVf4bd/+7eBRji4FNlslo9+9KPcd999HD58mBtuuIFbb731vPtGR0d58MEHeeCBB+be+9jHPsa9997LJz7xibn3XhvAvfe972Xnzp2cOnWKu+++m3p9/v9K8MADD9DV1TVvP4v1vve9j61bt3L//ffzL//lv+Thhx/m9OnT8/YjSZIkSZLaK6pPURh/tvF6BaoB957JU6lFJHGJOMqSSQotrTMYquxNy+wNZSoENpJhhNoK7FizDAJXwLve9S5+7/d+j1wux7Zt28hkXv1jvuGGG9i3b9+S1rvnnnu466672L179wVDtAceeIDp6WnuuuuuufdCCKRpyv79+7nxxhvZsWMHY2NjDAwMzFUFdnV1sWPHjnl7PNc111xDX1/fkvY7K0kS7r77bu6//34+85nPcN9993HnnXdecNCJJEmSJElavsLY00ShSiAmWoHautm24O7CDsISW3gnQ539YZq9aZlhanPvbyBhgto572gl2Bq8AkqlEjt27GD79u3nBWy/8iu/woEDB3jooYfOey6EwNjY2Hnvv/GNb+SNb3wju3fv5iMf+ciCn3nvvffyz//5P2fnzp1z/3znO9/h537u57jvvvsAuOuuu8hms3zhC19ow2+5OPfccw9HjhzhwQcfZOfOnQ4JkSRJkiRpJYWU0uiTsz+0ffnpasQrA40KwK789eQy/Yt67nha4cH6EPfWz/DDdJxhasQ0hoJsIGGEOo4GWXlWBK6yD3/4w3z729/mN37jN/it3/ot3vOe97Bp0yZ27drFl770JT75yU/yoQ996Lzn7r//fqrV6oLVeS+++CIvvPAC//W//lduuummedd++Zd/mf/yX/4L//pf/2u2b9/O5z73OT73uc8xMjLCxz/+ca699lpGRkb467/+a6BRxXeus2fPUqlU5r3X398/1yI8PT3Niy++OO96d3c3119/PQA7duzgne98J7/zO79DqVRa8HeTJEmSJEntkZt6haQ2TCAiWoEg8OXTBeppRDbpI5ASx7mLPnM2VPl2Ojy3m01kSCI4G2qccijIquqIILCeWd6hk+vp86Io4gtf+AJ//ud/zn333ccf/dEfkclk2LFjB3fffTfvec97FnyuVCpdcM17772Xm2+++bwQEOAXf/EX+d3f/V2+973v8YEPfIBPfvKT3HTTTXzpS1/i05/+NBMTE/T39/O2t72Nv/iLv+BNb3rTvOff/e53n7fm17/+dd72trcBjQEkr52AfMcdd8xVIUKjKvC3fuu3+PVf/3VyuYv/C0KSJEmSJLWmNDI7JCSGFTofEKCrcB0RFx8EWguBv6+PEoCtUZbRUGOQ2koUK2oRorDU6RVtcubMGarV81PfsbExent712BH7ZPNZhf83bR4l8L3YD2Loogrr7ySkydPLnmAjbRa/J5qvfM7qk7g91SdwO+pOkGnfE+TmQE2HfnPBFhERNea//N7VzA6HbGt730U81cRRa8/lfjJ+hgvhikKRKSEVWv/7S8m/OE/3kFfoSNq4JYsm82yZcuWJT/nGYGSJEmSJEmXgOLobDXgyoRfk5WY0enZiDG6aAh4PK3wYpgCoC9KPANwHTAIlCRJkiRJ6nBROk1h7CeN1ys0e/fYaGNeQDbpI0kufIQZQCWkPJKOAnB1lON0cB7wemAQKEmSJEmS1OEKYz8mDjOEFYx6jgw3zv3PZzeTSbpf994fpGNMktJNwslgLeB6YRAoSZIkSZLUyUI61xYcVnAKx8GhLgDymQ0krzMteH9aZn+YJgKyrMTIErXKIFDr0no+fFWSJEmSpPUkN7WXTHWQQES8QkFgGuDkWCNGymQuHAJOhjo/SMeARkvwsDHgurLugsAoipiZsWT0chVCYHJykkzm0pzqI0mSJElSuxVHn2q+WrmYZ3AyoVoPRCREUe+C94QQeCQdpUKgnwxHbQled9Zd2tLd3c3ExATT09NrvZWW5XI5w8xlyOfz5PP5td6GJEmSJEnrXlSbIDe1u/F6Bavvjo40qgBz2Y1k4uKC9+wKZY6HGRKgSrpie1Hr1l0QGEURPT09a72NlkVRxJVXXsnJkydtb5UkSZIkSSuqMPEcESmBmGgFw7cjwwUA8tktCw4KCSHwXDoBwJVxjmOpBVLr0bprDZYkSZIkSdLiFMafAVZ2SAjAoaFGFWA+00UUnR8nnQwzTJKSJTIEXMcMAiVJkiRJkjpQUjlNtnKcACs2JASgWofBycb6cbRwW/C+0DjibUu07ppPdQ6DQEmSJEmSpA40Ww240ie/nRjNEoAkLhJH57cF10LgYDMIHA+eDbieGQRKkiRJkiR1mpCeEwTWVvSjZgeF5LObyWRK510/EipUCZSIGV/BgSVaPoNASZIkSZKkDpMtHyCpjxGIiFb4sw4NNcK/fHYjyQITg/eFMgAbbAte9wwCJUmSJEmSOkxh/CfNV8mKf9bRkSwA2aRAFM2PHadDytFQAWA4VFd8L1oeg0BJkiRJkqQOEqUV8hMvNV6vcFvweCVmcqYxKCSKzz8f8ECYJgAbSCiv8ORiLZ9BoCRJkiRJUgfJT7xEHGYIqxDrHJurBtxAJu467/q+tNEWXIiMmDqBf0uSJEmSJEkdZHZISFjx0wHhyHAeaA4KiecPChkLNQaoEgGDtgV3BINASZIkSZKkDhHXRsmW9zder8KE3oNzg0L6iOPcvGv7wzQAm6MMxoCdwSBQkiRJkiSpQxTGnyUiEFZhSEga4NRYIzpK4vy8ayGEubbg4NGAHcMgUJIkSZIkqROEcM604HTFP+7MRIZaGoiiDHHUO+/aWWqMUicBBld4YInaxyBQkiRJkiSpA2QqJ8jMDBCAaBUm9M4OCslnNpFJivOuzVYDbo2yzgruIAaBkiRJkiRJHWC2GjBEmWWvVU/hb17o5e/2dF+wtffQcCP8y2c3k0lenRichjB3PmA5rHxlotpn+d8cSZIkSZIkraxQpzDxHABRWH4r7o+OlHj6aCPcq6cRH7xl/Lx7jgwXgEAuUyKKXq0lOx5mmCYlT8TIKgwsUftYEShJkiRJkrTO5ab2ENcnCUREy1yrXI343t6euZ8fPdDN4we75t1TqUUMTjZKBeP4NW3BodEWvCnKLnMnWm0GgZIkSZIkSetcYfyZ5qvlTwv++33dTFVjskkvfaW3APDgrl6ePV6Yu+fEaCPkS+ISSfxqaDgTUg4124JH21CZqNVlEChJkiRJkrSORfUy+cldjdfLnNA7OJnw5KFG9d/GnnfQ33073YWbAPjq8xvYeyYHwNHZQSHZzSRxae75w6FCHeghYXIVJhervQwCJUmSJEmS1rH8xAtEoUZoQ4yzc3cP9RBRzF1FMXcFgZTNve+kmLuaNET81U/6OTaS5eBQIyzMZzfOmxh8LFQA6I6MlDqRf2uSJEmSJEnr2GxbcFjm6YCHhrK8dKoR6m3seRvT1dPEUQIEtm14D/nMZmbqMX/6o40cGW7Ml80m+XlrDIQqAOPBISGdyCBQkiRJkiRpnUqqg+SmDxGAeBkTetPQOAcQoKd4M0lcIp/ZAtCcCBxxRf8vkE16marGVGoBiIjOOR+wHOqMN/cwYVtwRzIIlCRJkiRJWqfybRoS8vyJIsdHc8RRhv6un2amNkQcvzr1N4pioijhyo0fImlOCc5lNpA553zA2WrA3jYMLNHaMAiUJEmSJElaj0KgODYbBLZegTdTb5wNCNDX9RbSUKWQ3XbefVEUE0d5rtz4i3Tlr6O/+3YySffc9dkgsOT5gB3LvzlJkiRJkqR1KDt9mKQ2RCAiIrS8zuMHuxmbTsjEXfSWbqUeZprtwOeLoohM3MXWDe8hn91CHGXmrp2hEQRWQut70doyCJQkSZIkSVqHXh0S0nor7vh0zKP7GxOA+3t+hmptmEJ28+s+E0URIQSqtZG599IQONOsCJyg1vJ+tLYMAiVJkiRJktabtEp+4nkA4mUEb3+3p4eZekw+u5lS7jriKLeo56IoIn9O+/AINaoEMkTNukB1IoNASZIkSZKkdSY/9QpxOk0ganmN4amEZ443Bn9s7H47ldppspneRT8fRa9+9uz5gBsiB4V0sszFb5EkSZIkSdJqKswNCYmBektrPH6wizREFHJXkstuJITWa/lmg8BkGcGk1p4VgZIkSZIkSetIVJsgN7W78brFEHByJuLHR0sAbCjdRqU6QBIXW97TQJgBYCq0th+tD1YESpIkSZIkrSOFieeISAkkLQeBTx7qoppG5DKbyGY2EMeLOxtwIZWQMtLcxzhpy+to7VkRKEmSJEmStI68Oi24tdBtphbx5OHGpOC+rjdTrY8QL+Nsv9lpwV3GSB3PikBJkiRJkqR1Ipk5TbZynADEhJbW+PGxIuVqTCbpoZi9kihaXoA30JwT3BMlTAYrAjuZUa4kSZIkSdI68eqQkNZqt+ppY0gIQF/pVmbqQ8Rxdll7mj0fsBpaCya1flgRKEmSJEmStB6ElMLEs80fai0t8cLJAiPlDHFcoKtwPSzzTL8Qwlxr8GSL5xVq/bAiUJIkSZIkaR3Ilg+R1EYJREQtPB8CPHagG4C+4i1Ua0PLmhQMMEadCoEEmG6xVVnrh0GgJEmSJEnSOpCb2gPQjN2Wbu/ZPKfGs0RRhu7izSRx77L3NNCsBtwQ2VR6KTAIlCRJkiRJWgdy5b0ARC22BT+6v3E2YE/xZmr1MbKZrmXvafZ8wExLNYpab4xzJUmSJEmS1lhUmyBbOdF43cLzx0ayHBzKAxF9pTeRhtbCxNearQgsOy34kmBFoCRJkiRJ0hrLlfcBEFqMah490Kj+6y7cQD2dJpfpW/aeqiFlqFmdOOGgkEuCQaAkSZIkSdIay03ta75aej3g2YmEl04VAOjrejOhTW28Z6kRgCLxMmcPa70wCJQkSZIkSVpLIZCbmj0fcOmVdz841AVEFHNXExGRz/Qv4iMDz6UT7EnLF7xn9nzA3qi14SVafzwjUJIkSZIkaQ0lMwMk9TECS68HrNbhuRNFAPq6bqWeVshmLr7K/jDNj9KJ5k+Bm+PSeffMng/YqAvUpcCKQEmSJEmSpDU0Oy0Yll55t3ugQKUWk8RdZOJu8tnNF32mGlJ+mI7P/fxoOsaxtDLvnhDCXBA45aCQS4ZBoCRJkiRJ0hqabQumhZP4nj3eqAbsLuyglk4QRRePep5LJ5kipYuYCAjAd9MRBpvBH8AEKWVSYqDsCYGXDINASZIkSZKktRJq5MoHAYiW2II7OROx50wegK7C9YuqBhwPNV4IkwB0R/HcJ1YJPFQfZjI0ziicPR+wr4UqRa1fBoGSJEmSJElrJFs+TBSqLU36feFEkTRE5DKbCNSIo4uPgvhhOkEd2BJlOB1qc+9HwBQpD9WHmQkpZ5rVgflFVBiuN1umh7lr33cgeLbhazksRJIkSZIkaY3MtgWHKCE6J5hbjGdPvNoWHC0i4jkZZjgYpomA6deEZLODSoao8b10hOnmuYDTHXQ+4BVTZ/noke/z3tM/JhNSyi/9DLz9H6z1ttYVg0BJkiRJkqQ1MjsoZKkh4NnJhGMjOSCilN9OJul93fvTEHiiPgbA9ijHsWbr77lmo8Fzr41TX9K+1sL2ydPcffh73DHwLEnzt9jXczU7ivk13tn6YxAoSZIkSZK0BqLaBNnKicbrJT77XHNISDF3JfW0TDbT97r37wllhqiRI5o7/+9i8kRUlnhuYbvFISW+QFXi1VMDfOzw93jnmReYPe3wlb7r6J6Z4qZ4ivjaG1Zzqx3BIFCSJEmSJGkN5Mr7AAjEREuYzBsCPHu8BEB34QaSuOt176+ElKfTCQC2xlmOpYsLAjdGGU6eM0l4VYXAXcce41cPPUSxfvH97urbwYbKKLeMHm68kdu4whvsTAaBkiRJkiRJayA3ta/5amn1gEdHsgyXE6IoQy67kWym53XvfyadYJqUXpJFh4DAmoWAPTOT/G+77+cdg7te976UiF0bdrC5PMybRg+u0u46m0GgJEmSJEnSagthblBItMRz+J5ttgV35a8lTadf996RUOOlMAVAroXJxKvtzcP7+cyuv2LTzBjVKOHFjW/g9sFXFtx5TODNIwdWfY+dzCBQkiRJkiRplSUzAyT1sblpvYtVS+H5k6+2BWeTDa97/1PpOAHYFmU5vVZtvosQp3U+fvjvuPvw94gJnCpuokbEWwdfWeutXVIMAiVJkiRJklbZ7LRgSGAJFYF7BvJMVyOSuEgc50mSwgXvPROqHA0VImAirN/pv5unh/nMrr/i1tFDADy78WZuGT5AYYmTlHVxBoGSJEmSJEmrbLYtOBCWVBH43IlGW3B3YQfhAtN05+5tDgi5MspxYpGTgldDoVbhjWOHuXX0ILeOHODmsSNkQ51ykmN/77XcPrRnrbd4yTIIlCRJkiRJWk2hRq7cGG4RL2FacLka8crpRgVgV+F6spkLtwUPhxqHQgWA0XVQWXfdxEneffonvHnkIDdMHCPzmhDzUNeVdFWnuG143wVWUDsYBEqSJEmSJK2ibPkwUagSiIgIi37uxZMF6iEim9lAICWOLhzrPN+sBrwiynJqjc8GvHHsKP/Hs18kn74aSA7mejlT2kS+Os3W6SGunzy5hju8fBgESpIkSZIkraK5tuAoIVpCtd6zx18dEhK9TqQzHmrsC41pwlMXaR9eaRsq4/ybF/+UfFrjQPdVJGmdbdNDbJoZY9PM2Jru7XJkEChJkiRJkrSKZgeFLCUEHJ5KODycA6CU2/66bcHPp1MEYGuUZWANqwEzaY3//aX/waaZUU4WN7GtPEhXvbJm+xHEa70BSZIkSZKky0VUnyBbOdF4vYTnnm0OCSnkriClQhQt/PRUqLMnTAFQWctqwBD4X/d8lTeOHWEyKRClqSHgOmAQKEmSJEmStEpyU41hGGEJkUwI8Myx2bbgG0mirgve+2I6RR3YRIZR6sva63LcdexR/tGpp0mJOFbayhWV4TXbi15lEChJkiRJkrRKZoPApdQDHh3JMjSVEEUZ8tlNZDM9C95XCSm7mtWAa+n2od382v5vAvDsplt44/iRNd6RZhkESpIkSZIkrYYQ5gaFREuo1vvJsUZbcFf+OtJ0+oL3vRymqBLoI2GQxZ8/2E5XTp3ht1/6CxICz268mbcO7lqTfWhhBoGSJEmSJEmrIKkOkNTHCEt4plqHF04224KLN5JNFh4SUg0pL6aTAOSXdPpg+5SqZf7tC39CV32ag91Xcevw/jXaiS7EIFCSJEmSJGkVzFYDQrLoZ145XaBSi8jEXURkSJLCwveFMhUC3SQMrFE14Kf3PsDV5TMM5XrYUBklF9bujEItzCBQkiRJkiRpFcwGgWEJNYE/Od5oC+4u3kAULfxcPQReaFYD9kRrE/VcMXWWOwaeA2AiU6K/Orkm+9DrMwiUJEmSJElaaaFGrnwQgJh0UY+MTcfsO9OoAOzKX0cus3HB+/aGMlOklIg5Gart2e8SffjYo8QEdvdex7VTp9dkD7o4g0BJkiRJkqQVli0fJgpVwhJOzXvuRJEA5LNbSEOVKDq/pTiEwEtpY1LwhgWur4bemQned+pHABSraz+1WBdmEChJkiRJkrTCXm0LXlxYFwL85FhzSEjhRuJ44bMBB6kxTI0YOLVG1YAfOvEE+bTG0a5tXFs+syZ70OIYBEqSJEmSJK2wXLkRBEaLHORxYizDmYkMEQnF3DaySe+C9+1NywBsi7KLbDhur1x9hl88/gMAynFuDXagpTAIlCRJkiRJWkFRfYJs5UTj9SKfeaZZDVjKX0M9LRNF5z9ZD4H9oREElsNaxIDwvlM/pq86ydl8HzeNH12TPWjxDAIlSZIkSZJWUG5qHwBhkTFMLW2cDwjQXbyRTNK34H1HQ4VpAkViRqi3Z7NLEIeUXzr2CACnSpsNmTqAf0eSJEmSJEkraDYIXGw94J6BPOVqTBIXSeICmaS44H17m9WA/Ws0JOQdZ1/iqvIgk5kCN48cWpM9aGkMAiVJkiRJklZKCHODQqJFVu29OiTkBkJY+JlySDkSKgAMh8WdO9huv3y0UQ24v/cachfYp9YXg0BJkiRJkqQVklQHSOpjhEXeP1mJ2XMmD0BX4Xpy2Y0L3rc/lAlAPxnKi169fW4ZPcQtY4epRgk3jB5b9c9XawwCJUmSJEmSVshsNSAsrn33+ZMF0hCRy2wiUCOOMgveNzstOL/AEJHV8MtHHgbglQ076K6X12QPWjqDQEmSJEmSpBUyGwQGFjfVd7YtuKd4IzH5Be8ZClUGqREDZ0K1LftciqumBnjH4MsAXDk5sOqfr9YZBEqSJEmSJK2EUCNXPghAvIj23dPjGU6OZYGYQu4KspmFpwXPVgNujbJrMCsYPnz0UWICu/uuY/PM2BrsQK0yCJQkSZIkSVoB2fJholAlLHJa8CunGxWAxdxVpGmZaIG23zQE9oVpACph9c8G7JsZ572nfgxA18zUqn++lscgUJIkSZIkaQXMtQVHizsf8JWBRltwKb+dTNK74D3HQoUyKXkihln9acHvP/lDcqHG4a4ruLp8ZtU/X8tjEChJkiRJkrQCcuVGEBiFiwd25WrEsZHGYJBs0k0m6Vrwvr3NasBNUbZNu1ya24f2ADATLzzEROubQaAkSZIkSVKbRfUJspUTjdeLuH//2TwByCZ9RBcI+aZDyuFmEDiyiHCx3XL1GW4eOwLAdoeEdCSDQEmSJEmSpDbLTe0DICwyetlzpgBAMX8VmXjhasADYZoU6CNhapFTiNvpTaOHyIY6Q7keSunMqn++ls8gUJIkSZIkqc1mg8DF1AOGALsHigAUs1vIZC7QFtycFlyM1ibOuW1kPwCnS5vX5PO1fAaBkiRJkiRJbRRXh8lPvgRARP2i958ezzA5AxEZoji34D0jocYZqkTA2VBt53YX7S3DjXCz6LTgjmUQKEmSJEmS1C6hTu/p+4jTaQKLmxa890wegEJuGzGFhe9pVgNujbJrMCsYSrUyN44fA+CK6aE12IHawSBQkiRJkiSpTbqGvktu+jCBaFHVgAC7BkoAlPJXkc32nXc9DYG9oREEVkNo32aX4E0jB0kInMlvoJCuTUWils8gUJIkSZIkqQ2yU/spDf89AGFRs4JhuhpxdCTTeD7TTRydX0V4IswwRUqOiKE1qQeEtzTPBzxb7F+Tz1d7GARKkiRJkiQtU1SfoPf0fUQEAgnxIqf6HhjMEQJkkh6iKL/gPbPVgJujTNv2u1Szg0K6ZibXbA9aPoNASZIkSZKk5QiB3tNfIamPE4gX3RIMsOdM40zAUm47mej8acEzIeVQmAZgNCx+3Xbqrk5yw8QJAK4oD67JHtQeBoGSJEmSJEnLUBx9nPzUbhqn9y2uEhAgBHileT5gIbeFTOb8IPBAmKYO9JIwuYS12+nNIwcAOFXcRG6Nwki1h0GgJEmSJElSizLTx+k++20AAskiTwZsGJjIMFGBiIQ4vkBbcHNacClauwjnLcONtuDhfO+a7UHtYRAoSZIkSZLUgiit0Hv6r4ioN88FXFq13N4zjfCvkNtGTOG866OhxmmqRMBgWLtJvbPnA/ZUJtZsD2oPg0BJkiRJkqQlimvj9J34H2Sqg0s+F3DWbFtwMb+dbPb8arvZasAtUZa1igE3VMa5duo0KbDN8wE7wviIEAAAIABJREFU3tqNm5EkSZIkSepA2an99J6+l6Q+QSBiKecCzqrUIo4MN2KZXKaL+DUTgUMI7GtOC66HtTkbEOC2kX0AnCxtYfvUmTXbh9rDIFCSJEmSJGkxQkpp6Ht0DX2XiNCsBGwtpDs4mCMNkEm6gdx510+GGSZIyRIx2EK1YbvMtgWP5boNAi8BBoGSJEmSJEkXEWbG6Dv+38mVGxVyjcEgrQd0u880zgQs5raTjXvOu743vNoWfCLMtPw5yzU7KKSvMrZme1D7GARKkiRJkiS9juzUAeqH7yc3M0IgIhAveTDIuUKA3aeb5wPmtpLJdM27Xg0pB0MFgPGwdtWAm6eHuXJ6kDqR5wNeIhwWIkmSJEmSdAG5yV30Hf8SzIw0W4HDskJAgLOTCWMVgJg4Pr8t+GCYpkagm5jxFWwLvmLqLH0z4xe8PtsWfLxrG8mK7UKrySBQkiRJkiRpAVFaoWfgb4gIEGVaPg/wtfaeyQNQyG0jpnD+9TANQE+8cvHbjvHj/OGP/oAvPPX73DJycMF7bmu2BU9miyu2D60ug0BJkiRJkqQFlIa+S1IfIxBDqLVt3VcGGm3Bpdx2stm+edfGQ42TzTMBB9Nq2z5znhD49X1fIxvqlOoVPvf8/8ubm6Hfufe8pVkR2D89sjL70KozCJQkSZIkSXqNpHKK0sjjzZ9C29adqcOhoSwAuUw3cTR/fMNsNeCWKMtKjQi5Y+BZbh09RCXOUomzFNIZfveF/85PD+2Zu+eK8iBbKiPUopit08MrtBOtNoNASZIkSZKkc4VAz5mvEZE2pwO3Lwg8OJgnDZDEXcD88wFDCOxNy3OvV0KhVuHX9n8LgJf7byCfVqkTkU+r/NsX/oSfGdwFvHo+4NGuKwyPLiH+XUqSJEmSJJ2jMP4TctOHCEREbR7Wsad5PmApv51M0j3v2gBVxqmTIeIs7WtFPtdHj3yfTTOjnM1v4M1D+wBICKRALtT4Ny/+Ke84+xJvGWlcqyTZFdmH1kbm4rdIkiRJkiRdHqL6FN2DDwI0pwS3Nwh85XQXAMXcVjJJ17xrR9IKAJujDKdC+88H3FYe5JePPgzAYL6PzZVXz/6LgZSITKjzOy/9GdW4ERlt9HzAS4pBoCRJkiRJUlPX4E7i+iSBmLjNIeDgZMLoNEBMHOeJomje9aOhEQRWQnumE7/WP9v3DbKhzp7ea7l57PB51+NmZWAmpGTqM8zEGbZURldkL1obtgZLkiRJkiQBmemjFMd+2Pyp/WHcbFtwIbuVOCrMuzYZ6gw124FH2xxAArx1cDc/O/gytSimrzJOdIH7GpWBDYe6r7rgfepMBoGSJEmSJEkhbQ4ICYQosyIB2CunSwAU81eRzfTOu3asWQ3YT6btEWQmrfHP9n0dgBc3voFtldefAhwDdWD7xOk270RrzSBQkiRJkiRd9opjT5GtHG8MCAntH9RRrcOh4cbgjVymhziaP4Rjti04H7U/gvzHxx/n6vIZxrJd3Dx8cFHPJEBX88xCXToMAiVJkiRJ0mUtWz5I1+BOAMIKNcMeGspRTyGJS0Tk511LQ+B4mAFgLLS3LXhDZZyPH/o7AA53X0kpnWnr+uosDguRJEmSJEmXhlAjP/EycX2cSvdPkWZ6Xvf2KK3QNfhtSqNPNh4nJl6BswEBdg80zgQs5q86b1rwaapUCeSJmGrz5//PBx+kVK9wpOsK3jy8r61rq/MYBEqSJEmSpI4WV0cojj1Fcexp4voEAN2DDzLd8zamNtxBPbflvGeyU3vpHfgqSW0EgJSk7VOCz7V7oBH+FbPbyCTd864dbbbgbowynAzVtn3mTWNH+PlTTzd+CKltoTIIlCRJkiRJHSik5Kb2Uhx9itzUK0SExtvN1t4o1CmO/ZDC2A+pdL2Zqf53UytcS1Qv0z34LYpjTzfvj5v/d+VCwKGphOEyQEQS54lecw7g7PmA06F91YBRSPnU3q8B8Hz/G/ip4b1tW1udyyBQkiRJkiR1lGz5EL2nv0xSG5p7L5AA9blAcFYEFCZfojD5EjOF60mqQyT1seYzGSLaPxjktfaeaZwJWMhuJY6L865NhjrDzT2MtDGMfO+pn3Dz+FHKSY7rxo+3bV11NqtCJUmSJElS50hn6D19L0ltiEDUDAAhov66Yz4CkJs+RFIfIxATYFVCQIBdp0tA43zAbNI779psNeBGMq+JMFtXqpX5Xw58C4BXNuygrzbVppXV6awIlCRJkiRJHaNr+PsktVECUbP6b3FVdLMhYeO5lRkIspBaHQ4NZQHIZ3qJ4+y867NBYC6KaFcS+D8d+i791QlOFzZy26AtwXqVFYGSJEmSJKkjJDNnKQ0/uqw1Xts6vNIODeeopZDERYjy867VQ+BEmAFgNLSnLXj75AD/5PhjAIxlS2RXMfTU+mcQKEmSJEmS1r8Q6D77DSLqBJJVD/RatWegEf4Vc1eRibvmXTsdZqgSyBNRbkdgFwKf3Pd1MiFlV9/1vGH82PLX1CXFIFCSJEmSJK17ucld5Kf2NM/2W7kJv+32ykAj/CvltpFJuuddO9qsBtwYtefktncMvsxbh/dQjRI2l4fbsqYuLQaBkiRJkiRpfUur9Jz92+YPnTPuYKQcMzQVARFxXCCK5o8zOdY8H3A6LL8aMFuv8s/2fQOAFzfexJaZ0WWvqUuPQaAkSZIkSVrXuob/nqQ2TCBetUm/7bDnTAGAfHYzUVycd20i1BmmRgSMtKHC8ZeOPcoV00MM57p509D+Za+nS5NBoCRJkiRJWrfi6hClkUeAtg3VXTW7BxrhXym3nVzSO+/a7LTgfjLL/r02TY9w9+HvAnCitJVC6JywVKvLIFCSJEmSpMtMlE7TdfZBCmNPr/VWLqrnzN8ShRqBhLiDJuDWUth/NgdAPttLHGfnXZ9tC86+pl24Fb968CEKaZWD3Vdx68iBZa+nS1fnNNZLkiRJkqRli6sj9J38H2RnThGAWn47tfyVa72tBeUmXyE/tavjBoQAHBnOUUshjgtAYd61egicaA4KGV1m9d6m6RHePfAMALl6heXHirqUWREoSZIkSdJlIjN9nP5jf0x25hQAEdB95m/WdlMXklbpPvuN5g+dV8e0ZyAPQCl3FZmka961U2GGKoECEdPLbAy+69hjZELKvp6r2V4eXNZauvQZBEqSJEmSdBnITe6i//j/TVIfJ5wTB+Smj5Cb3LOGO1tYaeRRMtWhjhsQAlCtw/MnG+FfMbeNTNI97/psW3B/tLyAs1Qr8wsnnwIgSTurYlJrwyBQkiRJkqRLXHHkcfpO/hlRqBJIiF5z1l7PmQcgLPL8vbDygVNcHaZr+O8bH7fin9Z+397Vy9h0RBIXSZIS0WvOAZwdFFJe7J/5BfzCiaco1SucKG7m+smTy1pLlweDQEmSJEmSLlWhTveZr9Nz9m+JCAQyC561l9RGFjU4pDT0d2zZ/zny48+vxG7ndJ/95lxo2UkDQgB2nc7z1JFGNeCW3ncRkZ93fTzUGaFOBIwu49zDTFrjrmOPAzCW6/JsQC2KQaAkSZIkSZeo3tNfoTT6BABplLxui2334Lcgnbng9eLoE3QPfZeItHF23zKr2S4kN7mHwuRLzUrAzmp3HZuOeeD5DQD0lm4ln9tKLtM7757ZtuCNUWZZ1Y53DDzHpplRRrLdvGH0yDJW0uXEIFCSJEmSpEtQXBulMPEsAUiJiS/S0hunFUrNdtzXyk28RPeZxuCOACT1CXKTu9q7YYBQmxsQ0qhe7BxpgK88t4Gpakwu08/G7rdSqQ4Qx7l59822BWeX89uFwIePPtxYr/sKko5soNZaMAiUJEmSJOkSlJvc3Xy1+PbaruGHiWtj897Llg/Rd/rec1qLG7oHd7Zvs02lkcfIVM8SiIg7bEDI4we7ODCYJ4oStvS9m0r1LIXslfPuqYfAidCouhwJrf9+bx3aw/WTp5iOc9w0enhZ+9blZUnjaXbu3MnOnTs5c+YMAFdffTV33303b33rW1dkc5IkSZIkqTX5yVcACIRF155FpHSdfZDxKz4BQDIzQN/JPyUKteaQkVfDq0x1gEz5MLXidW3Zb1wdoWvoe809R0QdVOV2fDTDd3b3ALCp+2eJoyxJJn/ekJCTYYYagQIx08s4+3C2GnD3huv56aH1N/FZ69eSKgI3btzIP/2n/5T/+B//I//hP/wHbrvtNn7/93+fo0ePrtT+JEmSJEnSUqVVcuV9AEsetlGYeJakcpq4NsaGE/8fcVomEC84ZKR76Dtt2S40zijsxAEhlVrE/c/0k4aIUv5augo3UkunSOLCeffOng/YHyUtf94N48f46ZF91Im4bvx4y+vo8rSkisC3v/3t837+1V/9VXbu3MnevXu55ppr2roxSZIkSZLUmlx5fzNUW3plXQT0DPw1UaiS1EaaIeDCwVy2vJ9k5iz13OZl7Tc7tY/CxAsdOSDkWy/3MjiVIRMX2dz7TirVkxTz2xe8d/Z8wHJovdrxw0cfAWBX/w3cNry/5XV0eVpSEHiuNE154oknqFQq3HzzzRe8r1qtUq1W536OoohisTj3+lIz+ztdir+bLh1+T9UJ/J5qvfM7qk7g91SdwO/pyshPzZ4PGNNKsJarNDr/GkHihavzIqBr+HtzrcQtCTV6mgNCIPO6k43Xmx8fLfLjYyUANvfdST2dopC7csF7x0ONUepEwEiLv+OW6WHeNfB843V5qKU1LidRFPnvltdYchB45MgRPvvZz1KtVikUCvyrf/WvuPrqqy94/wMPPMBXvvKVuZ937NjBf/pP/4ktW7a0tuMOccUVV6z1FqSL8nuqTuD3VOud31F1Ar+n6gR+T9snhED9SOPcuIXaeRdvcdWEhfHn6HrzJ4lyvS19SnrsQdKZASDumBCwWocHd/XywyNdAPSVbiOf3US9XiGKFj6F7WhzSMjGKMNgi4NC7jr2KAkp+3qv4aYxj2l7PXESs23bNpK+/rXeyroShbC0etRarcbZs2eZmpriySef5Lvf/S7//t//+wuGgReqCDxz5gy1Wmf8P/hSRFHEFVdcwalTp1jiH620avyeqhP4PdV653dUncDvqTqB39P2Syon2XjkDwmw6CEhC1nK81Mb3sPkll9cwuKBTOUY+bFnKI79iChUSYk74mzAwcmE+57p58RYFoC+0pvp734r09XTFC9QDQjwUH2Yo6HCtijL6VC94H0X0lWd4v954vcopjMcLF3JjqmTLf8Ol4W+jSSf/yOinr613smKyGQyLRXZLbkiMJPJzP0vNTfccAP79+/nW9/6Fp/+9KcXvD+bzZLNZhe8din/Sz6EcEn/fro0+D1VJ/B7qvXO76g6gd9TdQK/p+2Tm9jVfJWwnPP2lhIiFkcfZ2LjP4I497r3xdVhCuPPUhh/hkz1zNz7jQEh6/9swJdOFvjqC31UajG5JKK/5z2U8tdSnjlFIXvhqtZaCJxong842mI14C+c/CHFdIbjxS1cbwi4KCEE8N8r87R8RuCsNE3nVfxJkiRJkqS1k596pflq9arrolCjOPYjyhvedf7FUKcw/hyFsafJTR989W1eDQCX18K88mopPPRKL08carQC9xS62dD9QTJJF9Mzp8lnNr7uWXSnwgx1oEhMuYW/lyikfODEkwCMZ7uIymcu8oS0sCUFgX/5l3/J7bffzubNm5menuaxxx7j5Zdf5rOf/exK7U+SJEmSJC1SVJ8gM904O26p04KXq2vo+5T73gmzZ+SFGoWxZ+ga/j5JbbjxFtCoVEyJCOs+AASYqMT82dP9HB9tVDtu7LqR3q53AlCeOU4he9VFB1LMTgveECWUw9KDwNuH9nDF9BCTSYEbx44s+Xlp1pKCwNHRUb7whS8wPDxMqVTiuuuu47Of/Sw/9VM/tVL7kyRJkiRJi5Sf3ENEIBC/7rTflRCnk+QnXqTS9SaK409TGn6EpDYCNKYPB+Jm++/6D//O9fUXezk+mmu2Ar+bUv46avUp6vUyxdz2Ra1xrBkEthICAnzoxBMAHOi9hrcM721pDQmWGAT+i3/xL1ZqH5IkSZIkaZlyc23Ba6P77DfoPvtNkvoYMBsARsSkHVH991p7z+R4+XSRCNjc90/IZzcyXR0gm/SSz21a1BpjocYodSJgpIU/g83Tw/zMYOPv9aqJU0t+XjrXwjOtJUmSJElSZwk1clN7AFa9GnBWUp8gqY8RiEiJm3WA638S8EJqdfjblxoTZ3tKbyKX2UB55hj5zBaSuLDodWbbgjdGrY1p+IUTT5EQ2N9zNZuq4y2tIc1a9rAQSZIkSZK09rLlw8RphUC06ucDzppfAdjZ01ofO9jF4FSGJC7Q3/XTVGpnKeauXvI6x8IMAMmS5jA3ZNIa7z/5QwCnaqstrAiUJEmSJOkSkJ/c1Xy1dv+p38kVgOcaLic8vK8HgI3db2emNkQhu3XJ69RC4ESzInAs1Jb8/DvOvkx/dYLRbBc7Jo4v+XnptQwCJUmSJEm6BMyeD9iJZ/GtNw++3EM1jShkt1HMX00m6WtpnZNhhjpQJGa6hQrJ2SEhR7qvImlpB9J8BoGSJEmSJHW4ZOYMmepghzfjrg+zA0IgYlPvzzJTO0smKba01uy04A3R0mO87ZMDvGVkPykR144fa+nzpdcyCJQkSZIkqcPlJmenBVs3thznDgjpLd1CREIhe0XL680OCpkKS2+X/uCJJwHY03cdfbVyy3uQzmUQKEmSJElSh8tPzQaBnX8+31p67YCQNK0SRa1FJ6Ohxhh1ImB0ie3aufoM7z39YwCK1amWPl9aiEGgJEmSJEkdLKqXyZYPNV7bHNyyhQaE5HObWl5vthpwU5RZ8rN3DDxHd63MmfwGrp0aaHkP0msZBEqSJEmS1MFyU3uJSAn+Jz4A9RSmq9GSn5s3ICTX+oCQWbNBYCt/K7NDQk6XNrP030S6sKXH0pIkSZIkad3IT77UfHV5R0aVWsTTR0o8fqiLyUrMHTdM8p6bxsld5NjE8emYv325d/6AkPpZirmrWt7LgXSa42EGgNGwtLbgG8eOctP4MapRwg2jR1reg7QQg0BJkiRJkjpUMjNAfuIFAKIlnkN3qRivxDxxqIsfHi4xXXu1/u7h/d08d6LAP7l1jDdtq5z3XBrg6SMldu7uYboWEwH93W9b9oCQ8VDjsXQUgGujPEfC+Z/9el4dEnI9bx7Z3/I+pIUYBEqSJEmS1KG6BncSEQgkl10QeGYi4fGD3TxzvEg9bVRD9uRjivlbqIdeRid/xEgZ/uLHG7ll6zT/+NYxNpYaf0anxzN87cU+jgznAOgtFOgq/jyF3CYqM4NEmdbarNMQ+H59lBkCG8ksOQQsVcvcOfAsAP2VkZb2IL0eg0BJkiRJkjpQZvoohcmXCFx+1YBPHS7xty/1Eprt0P2lLNns7ZTytxBFjfe6izsYHHuSyemDvDJQYN/ZPO+5aYJ6Co/u76YeIjIx9HfdRlfxdqIopjxznGJue8v7+nE6wQBVskRMtzDB+V1nniOfVjlR3MxV5bMt70O6EINASZIkSZI6TQh0D3678ZIMEbU13tDqGZxMeHBXIwTc3F0kk/lZivnrAEjTGaarg+Qzm4mimC19d9Jbuo2zY99npjbBd/f0zK3TX9pAd+nnySRd1OpT1NKJZYWAx9MKz4VJALZEWU40zwhcincNPA/ASL7HIFArwiBQkiRJkqQOkyvvJVc+QADiyygEDAG+/mIftTSimNtGd+kDRFFEPS1TrY2Qy26hmLty7v401Mhn+7lq40cYK+9mbPJpMgn0lX6OYv4mAMqV4+SzWylkt7a8r3Ko8/fNcwGvjnIcayEE3FAZnzsT8JqJky3vRXo9BoGSJEmSJHWSkNI1+FDzhwQuo7bg504U2T+YJyJmU887qdUnqKVTFLJbKJwTAM6Ko0bsEajTV7qF3uLNAERRTLU2ShpSivnWqwABQgg8nI5SJqWPpKVKQIB3nnmehMDhriu5btIgUCvDIFCSJEmSpA6Sn3iRbOUEgeiyOhtwaibiW7sarb0bun+aQCCbdJPN9FzkyUYgGMLsmX2B8sxxCtkriaLWhoKc64UwxbEwQwIEaOFkwIY7Bp4DoJzJL3tP0oUs/xsvSZIkSZJWR6jTNbSz8fIy+0/6b7/Sy9RMQjbZQG/xTYSQLinIi6K4+U9CMbe9LSHg6TDD0+k4AFfFOcZaDGY3TY/wprFDpMC1tgVrBV1e/9aQJEmSJKmDFcaeJlMdJBARX0bVgAcGc/zkWAmAzb3/gEptgHy2f832E0Lg5XSKb9WHSIEroyxH09ZagqExLRjgUM9VlOqVNu1SOp+twZIkSZIkdYJ0hq6h7wI024LDGm9odVTrjQEhAD3Fm8kk3cRRds32Mx1SHk1HORwagd22KMvZUF3WmrPTgqtr+Hvp8mAQKEmSJElSByiN/ICkPk4gJm75JLrO88j+bs5OZkjiAv3dP0O1PrKsCb/LcSrM8P36CJOkxMDVcY4jy6gEBNhWHuQN40dJibh24kR7NipdgK3BkiRJkiStc1F9itLIw82fll8JOFGJ+evn+njsQNey11pJAxMZHtnfDcDGnp+lWhtekxAwDYFn0gm+WR9ikpRuEnpJlh0CAryrOSTkQM/VFNPlVRZKF2NFoCRJkiRJ61hm+gi9Aw8Qp9MEYqJlVgOeHMvw50/3MzrdiASu7Z/h2v71F0ClAb72Qi/1EFHMbaeYvZKwBpWQ0yHlu+kIJ0Mj9LsqyjEQZqi1af3ZIPDyaPTWWrMiUJIkSZKkdShKK3Sf+Tr9x/4vMjOnCESwzCBs1+k8X3piE6PTGaLme9/a1UtYhynUj48WOTycJ4oSNvX+HDP1QTJJcdX38Xg6xskwQ4aI7VGOE20MAbdPDrBj8iT1KLYtWKvCIFCSJEmSpHUmN/kyG4/8Z0qjTxARCGSImlFgK0KAR/Z38Zc/7memHtOV38xVmz5MFGU4NpLjhZOFtu5/uUbKMd9+pReA/q63EtIZCtkrV30fR9MKB8M0EdBFzPGw/Fbgc81OC97fcw35cPlMgdbaMQiUJEmSJGmdiGtj9J78Czac/DOS2iiBuDkhuPUatFod/vr5Pnbu7iUQ0Ve6kS19HyKX2UBf6TYAHnqll+oicqjT4xm+8WIvI+WVixNCgK+92EelFpPPbqaneDNRlCGKWo1BW1MLgR+kYwBcHeUYpc1BXQjc0WwLTtJ21RhKr88gUJIkSZJ0WchN7iY7tX+tt3FBycxpNh75zxQmXyRAswowJVrG6XETlZj//sNNPHu8RARs6nkHG3v+f/buNEiy6zzv/P/cLfestfeFBNDYdxAkKIICKYqWRFkbJVmybEmWg4pxSJblGU8oZMfExHyaiJkPXsLj8IQ9lkOSKVvUShAESVEEQSyNrRtooPe9u6q6a1+yKiv3vPfMh8yq3mvJyqzq6n5+EQxkVd57z6mqDkbkE+973uephTnqYZls8mFcJ8ls2eXti0sPDsmVHP7wvV7eHUzxnRPZlve0nEOXE5yZiGNw6M9+hnJ9DN/r3Hq33Ec0T56QJA7Dba4EBPhYYZTdxXFqxmXv/Ejbny9yMwoCRURERERE5I5mogqZsT+je+QP6Rn+L6Qnvgm3WxumtWQmvnHVQBDWVAXYfCR/dKCXwZkAz3HY1v1FssmHKVVH8NwsnhvHdXx60k8D8IOzaeYrN48JKnXDVw/2kq+4ABwdjTNZcNe0v5uZKzt863gj9OtOP4nBIeFvb/s6y5mxNQ7bAgBZ47S7FhCAz45/CMC57B78DRiCIncnBYEiIiIiIiJyx/Iqw/QM/T8k8ocW6+qSs/vpvvxfcOr5Dd3b1WKFowSl81hY81TgBaN5j5E5H8c4bOv5aeLBDorVS8T97TjGW7wuFbuHwOujGjp8/0z6hueEEXztUDejeR/PiRHztwCGN88vXUG4WtbCN452Ua47BF4f2cTDRDbEmPYHjkvvw/JmOIcFthufUduBtl1rF6cFB/VK+58vcgsKAkVEREREROTOYy2J3Fv0DP1HvNoU9qqPvxYIyhfpGfr3eKWBjdvjgqhKevJlAOxVAd1aHR9tDACJB7vx3CSV2hjJYPcNZ+05jkNv5lkADgwmGctfu4dvn8hyeiKOYxy2dv8ovelnADh0KUm+3L5Y4fBInJPjccCwJfsZKvUxYn5P256/UqdtiTFqeBhmOxECAvvyl9henqbi+HysMNqRNURuRkGgiIiIiIiI3FFMWKRr9KtkJl/CEGJxm2ftNd9v/tcN5+m5/J9I5N5qlKNtkNTMa83BIAanjcHT0dFGdV8y2EmtXiAe3LrFNu5vJRnbi8XwnZOZxe+/fTHJOwONyr/+7A/ju1l8r5eYv4XQGt5a5lzBlZqvOLx8rAuA7tQTGMcj5m9ry7NXo2Qj3osalaI7HJ/SGs5nXMpCNeD57G7cDq0hcjMKAkVEREREROSO4ZWH6B3698QKx7FAhItZ4oQ3gyUz+RLZsT+DqP0DIZbj1KZJ5l5f3E27TBddJuYbJw16bpKY373k9caYZqWfw5mJOGcmAk6NxxbP6+tJP0MytodafRbX8RenDb87kKJcW/u+XzqWpVhzCLweupKPEUW1a9qX18t70RwVLN24DHXo34OxEc9PNILAZLXYkTVEbkVBoIiIiIiIiNwZbETX6J80q+saH3dXMubBAvH5D+ka+WqHN3ij9OS3MLa+WLXYLifHYgDE/W04TnxF9/helmzyQQBeOtbF1w51YzGk4/voSj5KuTZCLNgCQCLYhe92UQ0N7w0m17TXoyNxjo0mAEN/9jNU6qPE/N41PbMVw1GFM7bc8XX25S/RX5ml5AbsLo51fD2RqykIFBERERERkTtCUDyz2GJ7dSvwcgyNMDBWOoNfOt/BHV7LL54lXjjWHBDS3rm0R0aabcHx3fje0tWAV+tKPo5jAqaLHtXQIR5soz98XbN+AAAgAElEQVT7aUq1yySCXYvXOY5DV6pRFbj/QppaC9uvh/D+UIJvHGtUHXalHsNxYmtqCZ62NfaHc3w3nGHK1lZ8X9GG7I/mANhtAnIdmRPc8NzEUQAuZnaxvmNQRGD962xFREREREREOiA+d7D5yoFVBjkLoWF64pvM7PlnYNrXpntTNiQz+VLzCw9o39mAharhUq4RMQVuZlUttp4bpzv9JNP5AwRehq1dn6dSGyfh77rh2lTsY8w4hyhUi3x4OcEn95ZWtEa5ZjgwmOStiynyleY+vR56Uk9QreXw3RsnFy8ltJaLtsyJqMgoV8K/obDC4ybFM04a7xZ/z8haTtoSB6M8VSxxDKO2gy3i1vLcZCMITNTUFizrT0GgiIiIiIiIbHomLBArnGi8XkM1l18dwS+dpZa8v11bu6nE7Dt41fFm9WJ7J9OeHIs3JiN7vRgTW/X9mfiD+G6WmN9PGBUI/P4bJg0DOI5HV/IRpucP8sb5NJ/YU8JZIj+dKzu8dTHFgcEklXqjQTHhG5LxR0nHH6dcGyMR7FzxPudtyMmoyClbotRsqzbANuOTs3XKWA7bAhfDMs87WXY51/4uJmyN/eEsk83ff3dzbEe5g9WAu4rj7CpNUjMuuwvjHVtH5FYUBIqIiIiIiMimF89/2JwQ7Kz5rL3MxEtM7/1fOlYVaMJ5UtPfA2gGge2dGntstHFmXzK2B9/rWvX9juOQjO2iHs7jmPiSFYXp+D5yhcNMF+H4aJzHdtx4xt5syeHVsxkOXUoQ2sbvNBN3iQePkYo/hjEu9XCemL9lxXs8GRXZH80t/uYSOPQajwlbZfSqlmAXmCPk29EM99s4zzlZDHAwmueEbVTk+Ri2OT6X1mFYzKcnjwFwIbOLB+YGO76eyPUUBIqIiIiIiMimF597HwCLXfPsXa82QVA4STX98No3dhPpqe/iRGUsDk4bB4QAVOuGc5MBAHG/G9dZfUUggLUR4OK5iSWvc92AbPIhcoXDvH4uzaPby4v5aalmeP1cmrcvpqhHjW/2JGME/jMkYvswxhBFNcq1MQKvF8f4K9rbrK3zdjME7G/GGpPUuXyTlt6QRqN4BJyxZYbCCgazWEG40wRM29q6hIDAYluwY9v7dxdZKQWBIiIiIiIisql5lWH86ggWcNpUXZeefJnp1ENtrwr0ypeuOsuw/WHQ2cmA0ILnpqGFtuAFxjjLhoALMokHmS0cY3gOzk8F7O2p8u5AitfOpSnVGi3A3Yk48dhnSMR2AxBGZaq1GWJe/6raga21vBHOEgJbjc/4CgaCLPyWXaCMBSwZXGIYhjt5HuB1+so59uUvEQG750fXbV2RqykIFBERERERkU1tIVizxsPY9py359WnCOaPUc081pbnAWAjMhMvYrBExsWx7T+L7kpb8F48N9v259+M5ybIJPYxVzrFy8ezVOoOs+XGEJBMzCMZf45E7F6MMdTDeephgZi/hUSwY9VrnbAlRqnhYZhf5e9v4epuPGapk1/16mvzqWZb8EB6J/fMD6/z6iINCgJFRERERERk84pqxPMfAuC0KQRckJl6man0I2CctjwvPncQv3IJi+lICBhGcHI8DkDC78d3k21f41YyyUeYK51mfN5vru+QTjxJKv4oxjjUwjxhWCLmb2lUK7Zg3oYciBrx3fY1nOmXa/NwlpV6rhkEVpyVtUCLdIKCQBEREREREdm0YoUTOFGpI0M33HqO2PwRKpkn1/wsExZIT/0NQHOgSfuDwIGZgEodHBPDtHg2YKsCL0NX6jGK5TN0JT9OKvEMjvEIoxLVWo64vw3fzbT8fGstb0az1LD04a3bmX7tkq4VeDR3HoDdhbEN3o3czRQEioiIiIiIyKYVzy+ct2egzUEgQHryW1TSj6+5KrAxIKTYHBDS/hAQGlN7oTEt2HNaq7pbi57UU/SmnwYgiqqUasPE/K0ttQBf76wtc8lWcYBKB85W7LRnp07iEjGc3MLO4sRGb0fuYu2pbxYRERERERFZZ04tR1A8C4DpUDjkhnPEmq3HrWoMCDnQ/Koz+7QWjo6mAEjGti/bfjtqq/x5fYJXwhmsbU+AaozB2pBSdRgLJIKdOGbt9UdFG/JONAfAbidgfhMGgQvTgnNB61WRIu2gIFBEREREREQ2pXj+EAaLxe3oOumpb0OrZ/pdNyCkvTOIrxiZ85ivgMHDcWKYW0w7ttZyNCrwcjjNLCEXbIXztty2fRjjkgh24jpB2575dpSngqUbl8FN1hIMEIRVnpo+DcD2wvgG70budgoCRUREREREZPOx9qq24M5WiLnhPInc/pbu7fSAkAULbcGJ2E4ck7rpNTUb8Wo0yztRHgukmpHAgShPvU1Vge12MSpzwZYxdPqv3DlPT58mFtWYinXRW5vf6O3IXU5BoIiIiIiIiGw6fvkiXm26I0NCbiYz9W0SM2+s6p7rB4R00tHRRitwMthJ4GVveD9n67wYTnO+GartdQIKzWhtnohjttDR/bWiYiP2N1uC95iAuQ6drdhpn2pOCx5N9G/wTkQUBIqIiIiIiMgmFJ9rVAN2OmC7WmbqW6Qm/6ZxIN8KrMeAEICpgstkwQAGz01grhtsciEq82I4RY46CRx6b9Ji+2FUoLSCisW6tQxGZaJ1qCB8O5qjREQWl0t287UEA7hRyLNTxwHoL01v8G5EFASKiIiIiIjIJmOiCvH5IwAdDdhuJpX7AemJr4NdulF1PQaELDg51mgLjgfbcJzENe99GM3zSpSjhqUfD4tl6ia/sxqWD6Kl21br1vLtcJrvRjneX+batboYlTnbrF502bxtwY/MnidTL5H3kmytzGz0dkQUBIqIiIiIiMjmEssfxtjaulYDLrBAcu49smN/CrZ+w/tuZYzU5LfpHvmjxiCTDg4IAYgsHLzUbAuO7cb3uhffK9toMbDbawImqVNeoo36pC0xY2u3WMfy/SjHGI33T9gi1WXC0FaVbMibC1OCTcDMJm0JBniu2RY8mNnR0X8HIiu19jneIiIiIiIiIuvEhAVS098DGqHceocrprlufP4IJiozu/1XMbZKPP8R8fwh/MrlxWstBtPBASEAh4fjTMw7OMYn5nXhmCsf8wdtI/brxmVwBa21Fng3yvMTbu+137eWN6M5Bm0Ft3ldFcspW+LxWwwmadXCWmUiujZxSzAA1i6eD9hVntvgzYg0KAgUERERERGRzcFaMuN/jRvONc/d25iG0YUwMFY8Q9/gv8Gp5zHNvTTq7Vwg7PgQk3oEr5zOANCVegzwr3n/oq0AkDAOuRUGkpdslUtRhd1ObPF7B6J5TtsSBthmfIabVYNHowKPmiSOaV8ce9aWGbAVHBq/y9tzlvHK7Mtfor8yS9kJ2Fma2OjtiABqDRYREREREZFNIp4/SLxwrBkObeypcQthoFufxRBhcYgwNEZ2hOtSqXhwMMlMycN1EqQT+65pC67ZiMvNIDB3kxbmpbwb5ReHgRyJChxuThTe7QSLISBAgYjztrzWH2PRvA15a6El2Nm8U4IXPDd5FIAL2V0KX+S2oX+LIiIiIiIicttzq5NkJl4CwNLZc/dWylzzOsJZx/q1St3w6tnG2YDdqSeo1WdwnWDx/SFbJQTSOJRWua8Z6py2Jc5EJd6N8gDsdQKGohvbdI9EBWwbJghba3k9mqWGpRfvhqnGm026VuBzYx8AEK+1LywVWSu1BouIiIiIiMjtzYZkx77WHBDirvuk4NvRWxdSFKounpshGduDc1UICDDQrNTLGpf5FoZ6vBflqTUDxL0muGUwN0WdYVtll4nd9P2VOmFLDNsqLlDZtDOCG9wo5PeOfZX+yiwTsW52F8Y2eksii1QRKCIiIiIiIre11PQr+JVLjeEbCgEpVBzeON8Y0tGTfopaOHvNkJDQWgabbcGzLQ4rqWKxwC4TLDto5EizdbhVs7bOe83Kw51OQGGTB4H/+OxLPJ47R9kNqDou/ib/eeTOoiBQREREREREblt+6QLJmR8AjSm8Aq+dS1ENHQKvl5i3hbi/7Zr3h22VGpY4zppCtcZgkOVbdC/ZKlNXnR24GnO2zg/CWepYthjvpu3Hm8mPDb/DTw6/RYThXGY3u0pTG70lkWsoCBQREREREZHbkglLZMf+DIPF4m3YlODbyUzJ5d2BhWrAZwijAsZc+9H+YrMtuNe4a1przNZWfLrg0WjlVYGRtQxEZb4TTvNn4SQT1PAxzLdYvXi7eCR3nt8883UADvU9xKO58xu8I5Eb6YxAERERERERuS1lJl7EreewOBhWN/n2TvX902lCa4j72/GcJL7Xdc37kbUMNNuC8+sYrJ21ZZ61IaklwseSDTllS5yMisxfFepuMz4VG5HbxG3fW0rT/N6x/4ZnI4723MczUyc2eksiN6UgUERERERERG47QeEE8fmPmhVpqgQEGMt7fHg5AUBv5hksdYy5tl16nBplInwM+XX8vVkaVYHPudkb3svZOh9F85yz5cUdBRi2GI8pW2esxbbi20W8XuFfHf0jumoFhpLb2JcbUBO73LYUBIqIiIiIiMhtJ57/sPnK1YCQpu+dzmAxJGN7wTgEbs8N11yMGm3B/cZjZJ0DtpO2xNM2TdBsVZ62NQ5FBS40W5UBevHwgHHqXN7kASCAsRG/e/JrfLwwwqyfIlkvEbeqXpXbl4JAERERERERub3YOkHxNIBCwKajI3FOjMUBQ0/6aayNbqgGtNZysdkWXLLrX0VZw3LSlthJwKFofrFFGWC78SnYiOk7qMXbD2t85ew3+PTkUWrGZTLWzX3zlzd6WyJLUhAoIiIiIiIitxW/dBEnKmMxmBWPq1heuWYwBmJe+57ZaaNzHt85meXsZAyAdOI+Ilsh7m+94dop6swT4sKGnbf3fpTnvau+3mF88jZk9A6o/rvarsI4/+L4n3BPYQSAYz37eGr61AbvSmR5CgJFRERERETkthIrHG++cqBNgdaHl+N842gXvmv59Wdn2NV9ewdT8xWH751O8/5QEovBMZBOPEJ36kmiqHTTewYW24L9DTt3LwQMsMME5Gx93duTO85avjB6kN8883XiUY28l+RycotCQNk0FASKiIiIiIjI7cNaYoXGxNV2tAVX64ZvHs/ywaVk4+sQ/uDdXn712Rnu7auu+fntVgth/4UUr59LUw0bZ+31JPtJJX4Y38tQro4SD7bf9N6FtuC63biKxyQOYBm2t9/vdq0S9TL/5PRf8cJ44/zKM5k99JdneGhuYIN3JrJyCgJFRERERETktuFWR3HrOSysefLq6JzH1w71MFFofPTtTj1GuTpBuTbGHx/o5ZefnuHhbZVlnrJ+Zoouf/BuL7lSY7/pWJJ04odJxLZhbUSpOkLg9d703llbZ4Y6BpjZwHP4infohOf75ob4F8f/OzvKU4Q4fNT3IE9OncDd6I2JrJKCQBEREREREbltLFQDgkurbcHWwnuDSb59Iks9MvhuQF/28ySC7UQ2ZCL3OsXqEP/jgx6+/PgsT+++eavterIWXjqWJVfyiHke2eSnSMXvwxhDuTqG66RIBDtuef9Fe2Va8ISm1rbVM1Mn+P2jf4xvQ6aCLLN+imemTix/o8htSEGgiIiIiIiI3DaunA/YWghYqhm+fqSLY6MJAFKx7fRlX8AYj1JthLi3ja3dn2Ny7m3my+f4y8PdlOuGH/p4sU0/QWtOjsc4PREHHPq7/i6B10WlNokxPvFg27L3D0S3T2XjnSRbned3Tv45vg050XUPe+ZHuLc6t9HbEmmZgkARERERERG5LTj1WfzK5TW1BX/1YA8DMzGMMfSkPkE2+TC1cBZwSfiNijprLf3Zz+A4AXPFE7x8vIti1eEL989j1tqP3IJqCC8fzwLQlXwEg6FcnSIe9K/o/oINGacxlGNG1YDtYy3/5PRf0V2bZzjRz31zgwR2Y6Yxi7SLgkARERERERG5LQSFk81XrbUFX571GiEghh09XyLm91OqXCYWbMcxV05zM8ZgraU3/SyOiZErfMirZzNY4IsPzLflZ1mN186myZU8XCdFNvUwWIgH2RXfP9AcEtKLx/QGng94p3lh/BA/NHmUunEIMQoB5Y7gbPQGRERERERERODK+YCW1qbeHhhsTAZOxj+G52Yo18ZIxHZdEwIuMMY0qgbTT9CX+RQAr59Lkyut78fkyXmXN8+nAejLfJJafRrPTaz4fmstZ6LGGYf+RpQz3qF6K7P85pkXATjc+wB7ShMbvCOR9lAQKCIiIiIiIhsvqhCUzgHgtDB5tlI3fDScAiATvxdrQ+L+8mfrAWQSDxIPthNZw+vn0qteu1XWwjePdxFaQyLYie92EfdvPRDkZi7aChPU8IAZW+vMRu821vJPT/456XqJgdR2npw6tdE7EmkbBYEiIiIiIiKy4YLiGYytY1v8mHp4OE4tBN/NAg6em1zxvcYYulNPAnBwKLluVYHHR+OcnYwBDn2ZT2FtHWNWvnZkLQeiPADbTUC1Q/u82/zY8Ds8PXOaquMRCyu4LVaoityOFASKiIiIiIjIhovNL0wLbq299d2BRiVfJnE/nrvy8/UWJIJt61oVWK0bXj7R2Gd36jHqUZFY0LeqZ5y0ReYIiWMYtooB22FbaYp/dO5lAI717GN7eWaDdyTSXgoCRUREREREZENZGy0OCjEtDgkZzXuAQ8zvw/daC/LWsyrw1bNp5sounpMmk3wQ3+1a1f1VG/FBVACg13gtNFPL9Rwb8c9Ofo1EVOVsZjdPTp1c/iaRTUZBoIiIiIiIiGysubM4URHbYjXgwpCQVHzvqlprr7deVYHj8x77LzTOM+zNfJJafWZVA0IADkcFykRkcBnW2YDXsrbxv1X66aE3eGT2IiU3oKsyp8BE7kjeRm9ARERERERE7gBRDbc2ReT3YJ3Y6m6dOgSANQ7Grq4i8NohIfcQeKtrr71ed+pJRqujHBxK8sJ983Qn2ltrZy1881iWyBoSwW4CL4vnZlb1jIINOWIb1YAJDPm27nDzMTZib2GMR3LneXT2PI/kLuBHdb678zm+secFZoOlf79d1Tw/M/Q6P3XpTQBOd32cJ6dPr8fWRdadgkARERERERFZs67RPyFWbExXDb0uQn8L9WALYbCFur+Vemwn9hZVb7YZBK42BITrh4S4GOO2/DNAsyrQ3065Nsrr59L8zGNza3re9T68nOD8VAyDQ1/mk0RRFeOtrvbsg2ieEOjDY5x6W/e3WcTrFb448h6P5c7x8OwFMvXSDdd8eeg1fvLyW/ztjuf4+t7PMR27tv26tzLLzw2+xt8ZeZdY1KiqPNp9L08oBJQ7mIJAERERERERWRMTFgiKV8ITtz6LW58lKJ1d/J41PqXspyj2vEDkXRnm4VYnoDSCpbUxIe8ONKq9GkNCVnfO3q10p59gdKb9VYHzFYdvLQwIST9JGBWIB9tX9YwZW+O0bYRe4V06zdbYiN8/+kc8mbvy76vs+FxKbScyDr3lHH3VWYpunFRY5qcuv8mPD7/NKzs+xV/v/TwAXx58lR8dOYDfDJ8HUtsJjcOjufMtNqiLbA4KAkVERERERGRNguJpDBaLg7lubMXCuX/G1kjO7icx9w6l7Ccpdn+OyO8mKCxMC3ZhlYNCGkNCXBpDQnrxvdTafxggEWzvSFXgN49nKdUcAq+HTPx+MKsP8t6L5rHADuMzcpeeDfily2/zZO4sFcdnIL2D/tI0vbV59uWHrrkuFZYBKDQDwZ8YfpsvjrwLgGcb/07PZ3Zhwoh7CiPr+0OIbBAFgSIiIiIiIrImscKtp6uaq6rWLI323+TsOyRm36Oc/QRe5XLz3dVX3V07JKS9H2/bXRV4fDTG0ZEEYOjPfoZqOE0i2LmqZwxHFYZsBQPMtdBGfSfYUZzg185/C4AT3ffy1PSpZe+5EgjGSIUVAM5k9xCrV7g3f3mpW0XuOBqCIyIiIiIiIq2z4WJb8PXVgNdbaLm0zWsTcwfwK8PN91ZXHXf1kJD0CoeEjNoqEyusoluoCoys4Y01ThAu1QwvHWu0LXclHwUg7u9Y1TOstbwXNcaC7DIBhRaC083OiUL++YmvEYtqnM7u5YkVhIBXWwgBC26M++eG2Fsc78Q2RW5rCgJFRERERESkZX55ECcqL7YAr8QNV7Yw4OPqISEGF2Nu/fG2YiN+EOb4ZjjNN8IpzkY3Dpa4me70EwAcGEqSK7X+8fk7J7PkKy6em6Er9QiOiWHMyn9fRRvyVpRnkjoehnFbbXkvi6zlsZmz7ChOrP1Z6+TLQ6/xQH6QohujvzzTcqCxEAiK3I3UGiwiIiIiIiItC5ptwdY4LU39bdy8+vuuGRLi3XpIyKWowhvR7GIFnQV+EM0SAQ84N59ivODqswL//MNufvXZGRL+6ioXz00GvD/UaGHuz36Gan3lLcHzNuRwVOCULS6enrjTCRiM1hZkJWslfuv0X/L8xGHmvQS/86nfYy5YW9Vjp318fphfuvi3AJzN7uWJmTMbvCORzUkVgSIiIiIiItKyWLHRntlyCNiCG4aEuDcOCanZiP3hLN+JZigQkcallyuVh69Hs5yMisuu1Zv5BMb4DMzE+IN3+pgrr/xjdDWErx9phJSZxAO4TkDcX35KcN7WeTOc5c/CCY43Q8BePHrx1hwCPjA7wL9+/9/x/MRhANL1Ev/gwnfW9MxO86I6v3viT/FtyPHue3lcIaBIyxQEioiIiIiISEuc2gxedax55t/6sBZeO9uoXkvF92Ju0ug2aqv8dTjFCdtoAd5jAkqETF83lfjNaI7jUWHJ9WJ+Hzt6fhzXiTGa9/nPb/cxOb+yVuZXTmeYKXm4TpKe1FPLtjDXreWNcJY/Cyc5aUtEQD8e3bhMU2ea+orWvRljI748+Cr/56H/l23lGSZi3YzGewH44sgB7s1fWvGzgnB9pxX/8sW/5eOFUeb8JLvmR9ft35rInUhBoIiIiIiIiLTkyrTg1Z/x16oDQ0mOjzWm72YT9xP41w4JORoVeDmcZo6QJA5b8BiyVW5Vr/hWlOfIMmFg4HWzo/cn8dw0uZLHf36nj0s5f8l7LuV83rrQqFTsz36aajiF72WXvOf9KM8pW8ICW41PFy6T1Mndcvcr013J878f/gN+7fy3cYk42nMfyVqJ7eVpABwsv3nmxUbKuoxfvvBd/vSN/40fHju0pj2t1IOzF/m5wR8AcDm1ja768lWcInJrCgJFRERERESkJUGxeT7gOk2wHZnz+NbxRpjWm34G4JoKu7KNOBDlsTQm61aJmFhBFd27UZ6Povlbvm+Mg+ek2Nn7JQKvl2LV5b++28uZieCa62ZLDu8NJvnqwR7+4J0+LIZU/B58N7NsS/C0rXHUNkKurXiM2xqzawwAAR6bOcu/OfhveWrmDBXH46PeB3h05hypq1qMLfDQ3AAvLBPuvTD2Ab888D0A/v7F7+LYzv7dY2GVf3bya7hYDvfs4+HchY6uJ3I30LAQERERERERWb2oSlA6DzQqyjqtUjf86Qc91CNDIthNKn4fXLfuGVsiBLpxubzKyboHonkcDI87N543CI0w0CHGjp4fZyz3KuXaKP/tYC8//mCeYs1weiLOyNy1VYKB10Nv+hOEURWzxGRkay37wzkssN34jNr2tN4GYY3fP/rHpMIyl5NbcKKQJ6dP3/izNf/76+df5r3+Ryh78Ruu2Tc3xG+f/IvGfoEdpSk+OXmMd7c83pa93sBafuvUX7CzNMV0kOG+uaHOrCNyl1FFoIiIiIiIiKxaUDqHsXXsOnystBZePNLFVLFx3t6Wrs9Qj+bw3MRV19jF4R8x09opcgejPMUlhp4Y42CMy/aeHyUVv4fIGr59Mstr5zKLIWA2nqQ3/RA7e3+Knb0/RS2cIeZ3L7nuGVtijBoehpxt/RzA6z0zfZJUWGY6yNJfmmZHsxX4ZizQW83ziwPfv+G9nsocv3/0jwhsneNd9yzGrz839NqK2olb8eXBH/DC+IfUjcNUrItUuLYhKSLSoCBQREREREREVu3K+YCdH91wcCjB4ZHGuYBbu16gWpsi7m+95poRW2WWEA/DeIthWgjLnhfYaEV22JL9LF3JR/Fdn55kP/2ZT7N3yy/R1/WLdKU+ReB1U6mNEfO3Lfm8so14N8oDsN3xKbexuvL58Y8AGEltIbbMVOeFv+LPXHqDHcWJxe/7zarCvuocw4l+PpYfxqERHD44N8hDcwNt2++CZyeP8w+bk4wP9z7A/asYZCIiS1MQKCIiIiIiIqtjLUEzCDRtOMduKSNzHt883gVAT/ppjPGIBztuuG5hQvA2468pSjthi5SWOfvONCsOezOfYHf/r9Cd+UkyyQcAS7k2RrWWAyAebMcxS5/IdSDKU8HShculaHXtzEuJ1ys8O3UCgG2FiWWuvsKzIf/47EuNL6zlt07/JQ/kByl4cdwoXDxbcCE4/NnB19q2Z4DdhTH+5xP/AwfLod4HeXrq5PI3iciK6YxAERERERERWRWvOoLbPNOuk/WAlbrha4d6CCNDIthFOr4PiK4ZEAJQtCEXbRlgza21dRqThz/pZpa8biEMrIcF6mEBz03juUlcJ7HkfVcbs1VONQPMds9dfnbqOLGoxni8m63l3Irvs8Cz0yf5xNQJdhXH+fzYB4Q4XEpu5cG5wRuu/+TUcXYWxxlObr3xYauUrhX5V0f+kGRY4WxmN49Nn1mHelORu4sqAkVERERERGRVgsKp5qvO1ZZYCy8e7WKysHAu4PPNcwGTN1x72pYaZ9zhUWjDBONjtkh5hRNxPTdFPNh6030tJWoOCIHGhOPpNldWfrbZFjwR713VfQvB22+f+gt+7dy3APio/8GbhoDQGBTzM0NvtLzPxedEIf/rsa+yozzFZKyLLaVp/HWaRi1yN1EQKCIiIiIiIqsSK5xovmrfYIvrnRiLcXj4qnMB6zeeCwiNQO1k1PSBLlYAACAASURBVN6qujqWo8ucFbhWx22RaeoEGCZWOeF4OclaiaenG2HtzsL4qu+3QE81j4vlw94HeHryxJLXf370fbqq+Va2uug3zn2TJ3NnKTs+Jcenq15c0/NE5OYUBIqIiIiIiMiKmXAer9IY3tDJts1Dl1MAZJMPY4xL3L/xXECAy7bKPCE+hok2BpOrqQpcrYINeT+aB2CL8WlvDAifmjqGb0NGEn301OZXff/C3/VsZjePTp9d9u8c2DpfuvzWqtdZ8MXhd/mpy/sba2b3sqc02fKzRGRpOiNQREREREREVixWOI3BYnEwHWrdrIVweiIOWJLBLlwnecO5gAtO2Ebl2Fbjc7mNlXU1LMeiAp9Y5qzAVrwT5alh6cNr654XLLQFz8S62FGaavk59+Yvrbh66EuX3+Kv9/4IFTdY+kJr2VGa5JHZCzyaO88juQtsrcwA8H7fw3xiaunqQxFZGwWBIiIiIiIismJBsfNTXM9Nxggji+skAQfPvfkAjnkbMmQbU2ynba3t+zhmizxmU8RuEUK2YiAqc8GWMTTCxnbLVAs8MXMGgF2F0TU9a6U/tQUy9RI/MnqQ7+z6zI3PiUKenzjMJyeP88jseXqvayMOMRzpfYCnFQKKdJyCQBEREREREVkZGxIUGyFTp6oBAY6MpAFIxfYS83tued2pqIgF+vGY7MB5hVUsx2yRZ0y6Lc8r24g3o8aAkN0mYKgD1YCfnjyCZyMuJ7ewqzjR9uffzELr8M8Ovc53d36aqBmcelGdz4++z88Pvsr28vTi9TXjcim1jYob0FOZZUt5hqemT93kySLSbgoCRUREREREZEX80gBOVMZiMB2oZgOILJwcjwGWeJDFcW7eahpZyylb6sgernY0KvCYSRK0oSrw7WiOEhFZ3I60BAM8P34YgNkgvW5BIDSqAreVp3lu4igH+x7mR0cP8OXBH7ClkgMg7yUZzOxga2GS/uos98wPr9veROQKBYEiIiIiIiKyIrHCMQAsLqZDE4MHZ3wqdYtjAhzTdevrbIUiETFMR6oBFyxUBT69xqrAC1GZc82WYAc6Uk/ZXcnzaO4cAHvmRzqwwq0tVAX++vlv8ZWzLy62/876KQbTO3gwd4FHZ86t655E5EaaGiwiIiIiIiLLMlGFeP4DAJwOBm9HRxrTgpOx3fhe9y2vWxgS0m/8ju1lcU9RgeoaJgiXbMT+q1qCc4Tt2to1fmjiMC6WgdR2MvVyR9ZYykJVYG81z3SQ5Wj3fSRrJR6fOUtgO/Mzi8jqKAgUERERERGRZcXn3m+2BXfuY6S1cGw0CUAy6LvlkJA5W19srZ3swJCQ61WwHIjyWNtaO/Rb0RxlIrpwudShlmC4Mi245MU6tsZSDDAV6+JY9710VfM8ljuH38GzJEVk9dQaLCIiIiIiIkuzEYnZ/Y2XXGkDbbexvEe+YjG4GCdzy+uORAUAthqf8XUIAgFO2BJhBJ91sjhm5b+B81FpcUow0KGTFaGvnOPhuYtEwN75tU0LXtM+KrP0VWY3bH0RWZoqAkVERERERGRJQeEkXm0ai8HpYIXX8bFGBWA82IHv3rwt+EJU5kRzSEi9xQq9Vp22Jb4f5Va8btGGiy3Be0zAbIdaggGen2hUAw6kd5IMKx1bR0Q2NwWBIiIiIiIisqRk7s3mq85+hDw83KgCTMW34Xs3DueYtXVejxrVZntNwHQHzyq8GQNctBW+G80se2agtZb90RwVLN24DHWwJRiuTAuuOp0/M1FENi8FgSIiIiIiInJLXvkyQflCsyW4cxVtuZLDZKFRaec6qRver9mI74U5alj68RjscLB2Mwtt0cO2yrfDGcq3CAOrNuKYLTJgK4sTgjtZu7itNMX9+SEiDHvXeVqwiGwuOiNQREREREREbik526wGNC50cPLridE4ADF/K57Tc817C9V1M9SJ41DoYCC5nIUwcIIa3wyn+JLbi4dh1Fab/6sxSW0x+NvlBAxFna4GbLQFn8/sZl9+qKNricjmpiBQREREREREbsqpzxHLN1pOTQdDQICPRrIApGI78b3sNe+dsCXONgdupHGYXOeW4OsthIE5Qv4ivDr2uyKFQ4/xOh4CxsIqPzH8NoDm84rIshQEioiIiIiIyE0lZt/GEGFxl20Lnim6DMwEbE3X2Jap467iIKpi1XA515ir63spzFVTecdtlXcWBm44MQaj22MQxkIYuBACZnBJG4eSjcgTUiCisA7tyz8/+Cr9lVkmY118fH644+uJyOamIFBERERERERuFFVJzL7b/GL5asD//kEPI3ONQRWeY9mRrbGrq8au7hq7u6r0pUIcc/N7T03EsYDvdeOaK23BZRvxSpgjAnYY/7YJARdYrhy8nyck3+GqyettK03xs4OvATAZ76G/Mruu64vI5qMgUERERERERG4Qzx/CiUpYHMwyTadTBXcxBHQdh3oUMZQLGMoFMNC4pi9Z5+eemOWe3hur5BanBcd2E/hdAETW8mqUo0BEBpcJW2vjT9c+G9mO+xvnvklg65zJ7uHB2YsbuBMR2SwUBIqIiIiIiMi1bEQyt7/xkkYL7FKOjzUGfcSD7Wzv/jvUwzyV2iSV2hi1cIxKLc9U0eO/vtPLZ+4p8MUH8vhu495aCOenPMAS99MY03jjjC1x2VZxm+tv7KmAt58np0/z3OQxQgyZ6vyyfyMREVAQKCIiIiIiItcJimfwahNYDM4Kat4ODy8M+thBPSpijEsq/nHSiXsBiKIqU/mDzJfPsv9CmtMTMX7xiVl2ddc4NxkjjCyukwTTvfjM87YMwDbjM3ybVgNuFDcK+crZbwBwpPcBnpo+tcE7EpHNQkGgiIiIiIiIXCOZe7P5ymG58wELFYeRxiwPPDeB76aueT+MqkS2xpauz5CK72Vybj8T8/Cf3u7jc/fNM1GIAZCK7SHmNYLAio0Ybg7amLKqBbzely7vZ3dxnLyX5P7cxY3ejohsIquY4yQiIiIiIiJ3OrcySlA622wJXn74xcnxRpAXeL24Tv+Nz3OCxXAwEWxnV9/Pkox9jMgaXj2b4ehIAEA86MJxGq8HbQULZHGpNKfySkNXNc8vX/weABczO0ndZgNUROT2piBQREREREREGqwlPfWtxkuzsgayjxbbgncSeNklrzXGwxifbd2fY0vXD+OYxoARxwQ4TtfidQPNtuCU0UfW6/3D898hFZYZTG3j0ZmzG70dEdlk1BosIiIiIiIiAATFk8SKZ7CAs4KW3GoIA9MuYAm8FGYFwZ3THAaSiu0l3reNueIJ4sE2fLcHgLq1DNlGlVvOLl+ReDfZNzfEF0YPAmCtVWWPiKya/n9DREREREREIKqRmfhm8wt3Rbecm4wRWovnpHCc3lUtZ4yL68TozXwCz83guY3Jw5dshRBI4lBawaCSu4WxEV85+yIOliM9+/hYcXyjtyQim5AqAkVERERERIRkbj9ufRqLWdHZgAAfDWca98b3EPg9q17TGBdrLa6TWPzeQltwl3EpWgWBANtKU/zSxe/x4NwgZSdgz/zwRm9JRDYpBYEiIiIiIiJ3Oac+S2rm+wDNISHLCyM4PREDIhJ+GmeFZwpezxiDaxpDQiJrGWi2BecVArKzOM4vDLzKC2OHcJvVkSd67uXpqZMbvDMR2awUBIqIiIiIiNzl0pPfxtgaFgdnhe24g7mAaj3CMQFmlW3BtzJiq1SxxDDMr7Aq8U60d36UXxh8hc+MH8ZtTk0+nd1LslZSCCgia6IgUERERERE5C7mly4Sn/+oWQm48iq8YyMpAJKx3QTe6tuCb+Zisxqw13iM2FpbnrmgpzLHT1x+i7IX4xu7XyB0VnYO4nqK18v801N/wfMThxe/d6Lr43RV5nhgbnADdyYidwoFgSIiIiIiIncrG5GefKnxEnfFZwNaC0dHk0BEMujBdWJr34q1i+cDFtrYFtxXzvHlwR/wxZH3CJqTkD81eYx/+/A/YDzRnkrGdvmVi99dDAGPd99LX3mGh2cvbuymROSOoiBQRERERETkLhWfO4hfGcZicFbRijs+7zFfiTA4OE62LXuZoEaRCA/DXBvagreWpvn5we/zI6Pv49vG8wZS29lRmuTBuUH+9cF/x3988Bd5e+sTa16rHXYXxvjJS28BcDazm0dy5zd4RyJyJ1IQKCIiIiIichcyYYn01N8AYHFWXA0IcGw0CUA82IHntaeq7mLUaAvuNx6ja2gL7i/P8PcvfJfPXTVg41xmN15Y42OFUQCqxiUVlvm941/luzOf4r/u+xmqbrD2H6JV1vKVMy/iEnG8+x4eyV3YuL2IyB1NQaCIiIiIiMhdKDX9tzhRsTkgZHUVeEeGM0BEKrYV322cFVi0IdO2Tp/xSRhnVc+z1nKx2RZcWUNbsBOF/B8f/X/sKk0CjQEbiVqJ+/KXrrkusCERjenIPzbyHg/NDvBvHvmHDKa3t7z2Wjw3eYwnc2epGZetxckN2YOI3B0UBIqIiIiIiNxlnNoMidl3m1+tLnibLTlMFBr3eG568fvfC3OM06jk68Zju/HZYQK2m4CUWXowxwx15ghxgJk1tAU/N3mMXaVJ8l6COS+55ICNhaiyZlz2Fsf4vz/49/yHh36J/Vufann9VgRhjd841zin8WjvPp6eOrWu64vI3UVBoIiIiIiIyF0mVjiGIVrVgJAFJ8bijWf4W3DdPgDytr4YAgLkqJOzdU7aEgBZXD5m4jzjpPBvUi04YBfagn3GW20LtpafG3oNgAuZPTwxc3pFt/k2pGZcYlGd3zr1l3zU8wDzfrK1PbTgZ4deY1t5hukgwyPT59ZtXRG5O62uXltEREREREQ2vdj88ear1bfhHh7pAiAV24HvZoArQV6fadSaGCCBQ6r5kXOOkCO2wNfDKSZvEvRdjBptwZG1q97PgkdmL3B/foiq43HfEpWAN+PbEAskwwo/2wwT10N/eYafH3wVgLFkP7HmVGMRkU5RECgiIiIiInIXMWEBv3yx8ZrVBW/lmmEo13jte0mMMcCVIG+hAdgCJSIKVwWNLjBLyIvhFB9G84uhX97WmaKOAaZpPQhbCPBOdt1DKiyv+n7T/O/fvbSfbHW+5X2sxj869zKxqMa5zC4e0oAQEVkHCgJFRERERETuIrHCSQwW28LHwVPjMawF3+3CdfoBKNmIsWZb8MwSFW0hjQ+gFjgYzfOtcJp5G3LxqmrCVseE7C6M8cmpE0TArvnRFp/S2Fs8qvLlwR+0/IyVenTmHM9PHCbCkKiVF4NIEZFOUhAoIiIiIiJyF4kVGm3BrTThvn+p0RacjO0i8LoBGLRlLNCNy3Kn+y0EfQ4wSo2/Cic5FhUXv9eqnxl6HYBTXffQV8u3/JyFMO5Lw2/RU5ldw46W5kQhXzn7IgCHe+9nZ3mqY2uJiFxNQaCIiIiIiMjdIqoSFM8A4Kyy/m5i3uX8VOMjZCLowTSHfixU9MVvMgTkltug8WG0imW+OaxkqWrCpfRU5vjc2AcAZKuth4ALLBBEdX6heXZfJ/zYyLt8vDBKwYuzb3Z15xmKiKyFgkAREREREZG7RFA8g7G1ltqC919IA5CM7cY4jcrAqo0YbgaBuVUGeVfHkP3GW7aa8Fa+dPktfBtyIb2DXaXJFp9yxUJV4N8Zfpf+8syan3e9LeUZfuXC3wBwLruHdAvnGYqItEpBoIiIiIiIyF0iVjjRfLW6E+kKFYdDl5IAZBP7iHm9AFy2VUIgjUO5pWbjhskWqwHj9Qo/Pvw2AGGbP976NuQXB77f1mfGwir/8sgfkqmXGExt49HpM219vojIchQEioiIiIiI3A1suBgEmmY77kq9O5gktBB4fVi8q9qCG9VsGeMudXvH/OjoATL1EuPxHu6bv9z2539h9ADbSu05v8/YiN898TXuKYww56dI1kpszG9NRO5mCgJFRERERETuAn55ACcqYldZDVgL4e2LGQC6kg8S8xvTgkNrGWq2Bc/Z1QWL7eBEIT899AYAk/Hujkzd9WzE3xt4pS3P+sWBV/ihySPUjctEvJv+6lxbnisishoKAkVERERERO4CsfmFacGrq0M7PJygVAPXSeI6CVwnAGDEVqliiWMorHLwSDv80MQRtlZmmPOS3J8b6Ng6nx99n53F8TU947mJo/zKxb8F4GjPPu7Lt796UURkJRQEioiIiIiI3OmsJVZoBIEOKz+Pz1p443wWgGzyITyve/G9hbbgHuO1caMr39jPDb3W2Ed2F34Hg0gHyy9d/F7L939sfoTfPfGnABzqe4inpk+1a2siIqumIFBEREREROQO51ZHceszqx7ncXYyYLLgYIxH3OvGd1MAWGsZbLYFF+36VwM+ljvHffOXqTge9+UGO77eZ8c/4t78pVXfl63O86+O/CGJqMrp7F4en1IIKCIbS0GgiIiIiIjIHW6hGpBVtgUvVANmEvfjuOnF749To0iEj2F2lYNH1ipeL/OVs98A4FTXPaSiSsfXdLD8Xx/8B3775J+veHiIF9X5vWP/ja2VGcbjPewoTOCtYbKyiEg7bEANt4iIiIiIiKynK0HgykO7sbzH+SkfMKRiOwmuagseaIZvfcZj1NbauNOlGRvxz098jY8VRsn5afbMD6/LulXjEdg6Xxw9wI+Mvs8b257iL/d+gcuprTfsb09hnEdmz/PpiaM8OnuBkhtQNw6ZsLQuexURWYqCQBERERERkTuYU5vBrwxjYVWTdd8836gATMb2Av7i9621i+cDVta5LfjvX/xbnps6Rs14zAQZ7imMrMu6gW2cq1hwY6TCCp8f+4AXxg7x1pYneGXHJ9lTGOPR2fM8nLtAtl5cvC/CcCGzi0dyF9ZlnyIiy1EQKCIiIiIicge7ti14ZRWB+YrDR8MJALKJe4j5/Yvv5agzR4gDzKxjW/Dz4x/y9wZeAeB4z708OX163dZekAqb5yK6MZJhhc9OfMRnJz665pqK43MptY26cemt5BQCishtRUGgiIiIiIjIHSxWONF8tfLqvXcvJoksxPwtGONjzJXj5S82h4T0G5/xdWoLvjd/id85+ecAvN/3MJ+YOrHMHZ2VvCoQrDseY4lerIX+So6eap77WhgsIiKyHhQEioiIiIiI3KFMWMQvNSrSzAoHVdRCeGcgA0BX4gGCq6oBAQaiRltwZNdn8EVXNc+/PPpHxKIap7If46mpk+uy7kokwwqEFbK1wkZvRURkRTQ1WERERERE5A4VK5zEEGFX8dHvnYEU5Tp4ThrjxHDMlfMB523IJHUMMEO9Azu+lhfV+f2jf0x/ZZbReC+7CmO4mrwrItIyBYEiIiIiIiJ3qGDxfMCVmS66vHK6UQ3YnX6cwOu75v2FISG9eJ0/HdBa/qfTf81DcwMU3RhgSYflTq8qInJHUxAoIiIiIiJyBzJRhVixMVDDrOB8QGvhpaNZ6pEh7m/Dc9J4buKq9y0noxIA3mrGD7fohyaO8MXRA0QYBtM72F6e6fyiIiJ3OAWBIiIiIiIid6DY/DGMra24LfjISJwzk3HAoS/zLL6Xveb9S7ZKjjoehknb+bbgL4weAOBo7z4emr3Y8fVERO4GCgJFRERERETuQPH8IQAsy5fvlWqGl493AdCdeoLQlvHc1DXXHLaNgRjbjd/xtuB0rcCTM2cA2Jsf7vBqIiJ3DwWBIiIiIiIidxinPotfOtd4vYLY7jsnsxSqDr7bxf/P3p0GWXbe933/nnPu2t23l+nZAQz2hQRBEKQIiqYWWrJNK2YS06lEkiM7sSXbkehQTsVJJeWKbLlc5dIbvcmb2BU4NCWbBEESIkiQAERQIAEIIIh1ZjD73rP09L737bucJy9uzxCNGcz0XXpmuuf7edV37nOec7rqFmbuD///8+/O30Ihu2PF+6OhyrlQIVr+ea39/OheMiHldNcW+p3IK0kdYxAoSZIkSRtMYfZtIgKB5KprT0xkeWOoC4DB0qcIxETRyq+Ke9JGGLcjyrF0Dab2fmbkHQBmsj1rfi9JupkYBEqSJEnSRhLCxbZgrjIkpFaHP9vTD0CpeC9EdfLZgRVrZkON48vTgmeuwdmAfZVZPjLVqGa8dX54ze8nSTeTTDOLn3zySV577TXOnDlDLpfjvvvu47d+67fYuXPnWj2fJEmSJKkJmco5MpXzBCC6SvXej4/1MDafIYkL9HU9QBJfWoG3N10gAFujLCPXoC3406N7SAic6t7OLoNASeqopoLAffv28bnPfY67776ber3OV7/6Vf7Nv/k3/PEf/zGFQmGtnlGSJEmStEqFmTcBCFGG6AoVfKNzCT860gj+NpU+ST1UycbZFWvKIeVgWASgEq5cXdgpvzDyNgDzGb9jSlKnNRUE/ot/8S9WvP7iF7/I7/zO73Ds2DE+/OEPX/aaarVKtfqz/2sURRHFYvHizxvNhd9pI/5u2jj8nGo98HOqG52fUa0Hfk5vQqFOYa5xvl58hRAwBPj23n7qIaKYu4UkKlDIbr1k3YGwQI1APwlTaz4rGAbLU3x4+gQAu+asBpTUniiK/DvwfZoKAt9vYWEBgJ6eDz7A9cknn+Qb3/jGxdd33nknf/RHf8SWLVvaufUNb/v27df7EaSr8nOq9cDPqW50fka1Hvg5vXmkE++Q1ueACK7QFnxiMseJiRwRGTaVHiGOipesqYXAu2njO18+iiGsfRD4V0Z3A3C8Zyd3zp1d8/tJ2rjiJGbbtm0kfQNXX3wTaTkITNOUL3/5y9x///3s2rXrA9d94Qtf4POf//zF1xeS2NHRUWq1tT9o9lqLoojt27czPDxMCGs/TUtqhZ9TrQd+TnWj8zOq9cDP6c2ndO4HFKAx+fcKFXy7zzaKOboLt1NPl8jlNl2y5mhYZJGULmLOX4OzAeFn04IrSVs1K5JEWk85f/480UL5ej/KmshkMi0V2bX8X9fHHnuMoaEh/vW//tdXXJfNZslms5d9byP/YySEsKF/P20Mfk61Hvg51Y3Oz6jWAz+nN4eoXiY/v6/x8xVCwDTAu+eKQEpXbhOF7KUVoyEEdqfzAPRHGRZCZU2e+b22Lk5w3+wQKRG7Zm0LltS+EELjLARdFLdy0WOPPcabb77Jv/yX/5LBwcFOP5MkSZIkqUn5+T1EoUa4yte8M9NZFqopUZQhTvoue37WUFhimjpZIoavQQgIP6sGPFa6hWJ6be4pSTebpoLAEAKPPfYYr732Gn/wB3/A1q2XHiYrSZIkSbr2CjNvAVc6GbBh99luALpyt5LLXNoSDFysBtwWZbk2s4LhM6ONIDDFg/0laa00FQQ+9thjvPjii/z+7/8+xWKRqakppqamqFT8vzWSJEmSdL3E1Uly5eMEIL5CdBcC7DnXCAK785tI4sIla86HCsNUiYHRJqoB8/UKu+aGSdLmh4rsXBjhrrmz1KKY2+fONX29JGl1mjoj8LnnngPgX/2rf7Xiz3/v936Pz372s516JkmSJElSEwqzby3/lMAVzgccns0wt5QSkRAl/Ze8XwuBF+vTAOyIcpxpIgj83/d+hUcmD7EY5zjQdwf7+u/k3b67ONJ7G7X4yl89f9YWfCv3zZxa9T0lSc1pKgj8+te/vlbPIUmSJElqRQgUZt9cfnHlRt4LbcHF/C3kkkvbgl9PZ5miToG4qWrALeVJHpk81Ng7rfDI5KGLr5fiDId6d/Hmpgd4bufPs5h5XxViCPzCchAYt1BNKElavZaGhUiSJEmSbgyZpSEy1XECEF3lhMDd53oA6M4Pkkm6Vrx3LlTYGxYA6I9imjkA6q9cqOjr2QnAQpxjNN/PfKZAPq3x0NQx/odj3+Pfvfpv+fXjz9FdXbh47a75YW5bGKEaJbYFS9IaMwiUJEmSpHWsMNOoBgwkV1w3MpdhejEFYpK4b8V7lZDyo+WW4FujHMOh1tQzXKjou9AC3JVW2LI0RXetDEA5zjKfKdBTW+TXT/6Af/fqv+W/P/Z9eitzF6892nsb1240iSTdnJpqDZYkSZIk3Tii+uLF8wHjK5wNCLDnXKMCsJjbQTYZXPHeT9JZ5qjTTcxwEy3BANsXxrh77gx1InbNnr3smkJahbRKABaSAt31Mv/Nqb/gb51+iepyeJirOYRSktaaFYGSJEmStE4VZ35KHCqEVXy1232mBEB3YSuZTPfFPx9KlzgYFhv7EdNcLSB8ZnS5Lbj3VgpXqSSMgO56mQDMJwUKaZVSbZGlOMuueduCJWmtGQRKkiRJ0noUahSnXr7w4opLJxYSxhdSICKJey7+eTmkvJg2WoJvi3KMNR0D/qwtOApXfob3em8gOJXt5nD/HWSu8jtIktpna7AkSZIkrUP5uT0k9RkC0VWHhOw5VwSgkNtGNtl88c//Mp1hgZReEs402RIMcNv8MLfPD1OLYna1MOgjAvqr8/RPHG76WklS86wIlCRJkqT1JgS6Jl9s/LiKr3XvnOkFoDu/nWym0SJ8LF3kWCgT0fhi2MqYjs9cGPRRuo1cuPIZhZKk688gUJIkSZLWmeziMbKVcwSuPiRkejFmZK5RMZhNGm3BSyHl5XQGaEwJnrrKHpcVwsUgMJNWm79eknTNGQRKkiRJ0jrTNfXi8k/JVde+O9xoC85nt5AsTws+GcosESiRcLqFlmCAO+fOcsviGJU4w6654Zb2kCRdWwaBkiRJkrSOJJUR8gsHCUC0ikq+t8/0AdCd30k2abQInwhLAPTEccsjOi62BffeRralxmJJ0rVmEChJkiRJ60jX1EvLP129GnBuKebszIW24G6iKKISUs4sB4GTafNTgoFGW/BoIwgsVBdb20OSdM0ZBEqSJEnSOhHVZinMvNn4eRXVgPuG8wDkMoNkki0AnA5L1IEeYsot1gPeOzvEtvIk5TjLrvnzLe0hSbr2DAIlSZIkaZ3omn6ViDphFdWAAG+f7Qegp3AL2czKtuBSlGn5OS60BR/rvY2k5eZiSdK1ZhAohXPscQAAIABJREFUSZIkSetBWqE4/eqFF1ddPrcUMzTZ+DmbabQF10JgaDkInAmttQVHIeUzI7sB6KnMtbSHJOn6MAiUJEmSpHWgMPsWcbpAICZaRRXeO2eKBCCf2UwSbwbgbFiiSqBIzHyLAz4emD7JYGWahSTPrQsjLe0hSbo+DAIlSZIk6UYX0otDQlbTiBsCvHaq0QrcU7ydXKYxOfhCW/BAG23BvzDyNgDHS7f4hVKS1hn/uy1JkiRJN7jc/H4y1TECEfEqKvnOzWQYX4CImGzSQxTFpCFwMpQBmG2xLThO63x6dA8AvZXZlvaQJF0/BoGSJEmSdAOLq1P0jj554dWqrnl9qAeArvwuMskAAMOhwhKBPBGzLbYFPzh9jP7qHHOZIrcujLa0hyTp+jEIlCRJkqQbVVqlb/hPievzy2cD1q96Sa0Ob5/pAqC7sP2SacGDbbQF/+q5nwJwsrSTqOVdJEnXi0GgJEmSJN2IQqB39Ftkl84QiIhWWcV3cKRApR5I4iJx1LO8VeDEclvwXGitGvC2+WF+YeQdAAYXJ1raQ5J0fRkESpIkSdINqDj9MoXZt1c1HOS9XhtaHhJSuItcdhMAo1RZICVDxMwqqgov5zeO/zkxgX39d7K9PNnSHpKk68sgUJIkSZJuMNmFo/SMfR+AQEK0yjhwdinm2FgCQDHXTxIXADieNtqCt7TYFnzH7Fk+PbaHlIjBxamW9pAkXX8GgZIkSZJ0A4mrk/QN/2ciUgIJcRMVfO+cKRKAfHYzUdQHNNqCL0wLLodm6wsbfvPEswDs77+LbUtWA0rSemUQKEmSJEk3irRC37k/IU4XVj0c5IIQ4LVTF9qCbye/3BY8QY0Z6iTAJLWmH+memVN8cnw/KRFbF8aavl6SdOMwCJQkSZKkG0EI9I58k2zlXFPDQS44O51lYgEiYrJJN1HU+Lp3Im1UA26Osi091m8efw6AdwfuZktluqU9JEk3BoNASZIkSboBFGbfojC3u+nhIBe8PtSYENyV30Um2XTxz0+ExvmAtRbagh+YOs4jk4eoRTE758+3+GSSpBuFQaAkSZIkXW9phe7xxjl8zQwHuaBWh3fOFgHoKWwnm2m0CE+HGpPUiGitLfjC2YD7+u9msDLb9PWSpBuLQaAkSZIkXWddUy+T1GcIxE0NB7ngwEiBSj2QxF1EUenin58IF9qCM002GsNHJo/w0NQxqlHCrrmzTT+TJOnGYxAoSZIkSddRVJula/KF5VetNQb/bEjIneSy72kLTpdae6gQ+LvHl6sBB+6mvzrf2j6SpBuKQaAkSZIkXUfdE88Th8rylODmg8DZcsyx8QSAYq6fJM4DjbbgUaoATIbm2oIfmTjEAzMnqcQZ7pgZavqZJEk3JoNASZIkSbpOksp5ijM/XX7VbPNuw9tnGmcD5rNbiKK+i39+KF0EYGuUbe50wBDeczbgXfTVFlt6LknSjccgUJIkSZKuk56xZ4hIlweENC8N8MrJC23Bt5NfbgtOQ+BwaAR49SanBX9m9B3umT1NOc5yz/SpFp5KknSjMgiUJEmSpOsgu3CU/MIBAhC1MCAE4N3hAjNliKMcmUw3UdT4incmVFggJU/EeBP1gH2VWf7RoT8DYH//XfTUyy09lyTpxmQQKEmSJEnXWkjpGf/e8otMa1sEeOFIoxqwt+sBsvHPhoQcCgsAbI6yTW34uwe/SW9tgdNdW/nIxOGWnkuSdOMyCJQkSZKka6ww+zbZpbMEIqLmTvC76Oh4jvOzCREJhdwWspkSAOWQcjI0pgU3MyTkrw6/waPj+6hFCYQ62RbPLJQk3bgMAiVJkiTpWkqrdE88B0Bo4yvZj5arAUvFe4gpXvzzI2GRFBggw8Iqw7zN5Un+4ZGnANg9eC+3Lo63/FySpBuXQaAkSZIkXUNdUy+T1KYJxMQtng14ZjrD8YksENFduJVcdgCAEAIHl6cF51Y5fSQKKf/0wBN018sc79nJw2MHWnomSdKNzyBQkiRJkq6RqDZL1+QLy6+am+b7Xj8+2mgD7i7cAeSIokbqN0aNSWrEwOgq24J/7cwrfHTqCEtxlq7qIknLTyVJutEZBEqSJEnSNdIz/n3isEQgJmoxCByfT9g3XACgt3gn+ezmi+8dShtDQrZFqzvhb8fCKH/vWGNoyb6Bu9i2NNnSM0mS1geDQEmSJEm6BrKLJyjOvrUc/7U+iOPFYz0EoJi7BchcrAashcDRUAZgIVx9/zit86UDj5NPqxzuvY2Hxw+2/EySpPXBIFCSJEmS1lpI6RltDOMIZFjl8X2XmF2KefN0FwB9XfeSz265+N6JUKZCoIuY6VWcPfi3h37E/TOnWEjyDJan/HIoSTcB/1svSZIkSWusOPMTspVzBCJiVnd23+W8crybNEA+u7kRKEY/O9HvUGgMCemPrn7K32B5il8/8ecAHOndxabKbMvPJElaPwwCJUmSJGkNRfU5usefAyC0XAsI5WrEqyd7AOjreoDCe6oBZ0ONs6ECwNgqhoR8enQP2VDneM9OHpo83PIzSZLWF4NASZIkSVpDPePPEadlAjFxG2cDvj7URaUO2aSXKMoTx9mL7x1KG9WAW6IslVUMIfnU2F4AKnHrbcqSpPXHIFCSJEmS1kimPERh5vXlV62HgLU6vHisF4C+7g+Rz2y6+F4IgcPLbcEhXD0E7K3M8cD0CQBunT/f8jNJktYfg0BJkiRJWgshpTT6bSJCWwNCAH461MV8BZK4SCbpIUmKF987GyrMkZIlYmwV5w/+3Ph+EgKnu7bSXV9q46kkSeuNQaAkSZIkrYHCzOtkl84QiIjaGBBydCzH9/dfqAb8CJm4f8X7B5erAbdG2UuuvZwLbcGz2e6Wn0mStD4ZBEqSJElSh0X1BXrGnwUgtPG1a3gmw39+c4A0RHTn7yCXGSCb+VmANxNqHA9lAKZWMSSkUCvz8ERjOMj2hdGWn0uStD4ZBEqSJElSh3WPP0ecLiwPCKm3tMdMOeZPXt/EUi2mkN3GYOmTJPHKKr7d6TyBRjXg/CrOIHxk4hC5UGM0389Ada6l55IkrV8GgZIkSZLUQcnSMMWZ15ZftTYgZKkW8Sevb2K6nJBNetnS94vU6nPkMqWLa+ZDnUPLbcFLYXX3udAWPFIcbOm5JEnrm0GgJEmSJHVKCJTGniYikEatDQipp/C1t/o5N5MljgtsG/hVqvUp8rktK9btTRs1gINkmF5F1WEmrfGJ8QMAbFkcb+HJJEnrnUGgJEmSJHVIbuEQucUjBCBexZl97xcCfOfdPg6PFohI2N7/K9Tq0xRzO1esK4eU/cvVgGGVe39k6ijd9TLT2W62Lk01/WySpPXPIFCSJEmSOiHU6Rn73vKLTEtb/PhoN68PdQERW/p+iTRUKGRvuWTdu+k8NQL9JEysciLxo2PvAnC6e3tLzyZJWv9a+9tJkiRJkrRCceanZKojBCKiVYZz73VoNM+fH+oFaAwGSfLkM5uJopUNxpWQsi8sAJBdZfNxFNKLQWD/0nTTzyZJ2hgMAiVJkiSpTVG9TPf4DwCWg8DVNuw2VOvw1N4+AErF+ynktpOJu4iiS5u4DoRFlgiUSBhdZeB478wQmyqzLCZ5diyONfVskqSNw9ZgSZIkSWpT1+RfEKfzBGLiFiYF/+hoD1OLCUncRV/XA8RRnjjOXbKuFgJ70nkAuqPVjyL51HI14ImenX4JlKSbmH8HSJIkSVIb4uoEXVMvL79qrhIQYHQu4cdHS0CjJThNa2SS4mXXHg6LLJLSRczwaoeRhMCnxvYCUKwtNP18kqSNwyBQkiRJktrQM/4MEXUCSdMtwRemBKcBirlbiKIM+dzgZdemIfDOcjVgf5Ss+h63LZxn5+IY1Sjh1vmRpp5PkrSxGARKkiRJUosyiycpzO0hABH1pq/fc67AsfE8EQmbSh8jn930gWuPhjJz1MkTcS5UV32PT4022oKPl24h00LFoiRp4zAIlCRJkqRWhJTS2HcbP7L6Cr0LytWIp/c1BoT0dT9Evb5IEl++JTi8pxpwc9xcnPfocltwnDZ/dqEkaWNxarAkSZIktSA/t5vs0mkCEXEL1YDPHy4xX4nJJCW68tvJZTZ/4NpjocwUNbJEDKerrwbcXJ7knrkzpETcOj/c9DNKkjYWKwIlSZIkqVlphZ7xZwAILXytOjud4dUT3QBsLj0KZIiiy+8zGqq8mM4AsC3KNhU5Pro8Lfhkzw4Kqx0uIknasAwCJUmSJKlJ3ZMvkNSmCcRNVwOmAb69t58AdOfvIJB+4NmAU6HGs/UJagS2RlnOhkpT9/rUchBYibNNXSdJ2phsDZYkSZKkJsTVCbqmXlx+1fzwjTeGipyZzhJFWfq7HyRJui+7bj7UeaY+QZnAABkmQpVmTvkbLE/x4aljANwyf77p55QkbTxWBEqSJElSE0pjTxOFGoGEqMkgcG4p5tkDjQEhAz0foxYWSeLCJeuWQsoz9UnmSCmRsEidZht7/8a5n5AQOFq6lZ56ucmrJUkbkRWBkiRJkrRKuflD5Of3EYCohQEhzx4oUa5F5DID5DP95LPbL1lTC4Hn6pNMUqNITEqg3GTgmKR1/tq514DGxGFJksCKQEmSJElanVCjZ+y7yy+Spi8/MZHlrTNdAAyWHiUiTxRFK9akIfB8OsV5qmSJyBIx31RDcMOjY+8yUJllOtvNnXNnmr5ekrQxGQRKkiRJ0ioUp14hUx0lEDVdDVhP4am9/QCUiveShjL53MoBISEEXkxnGApLJEAfCTMtVB0C/M2zrwBwqmdnC5GlJGmjsjVYkiRJkq4irs3SPfE8wHIQ2Fy77SsnuhmZyxBHeXqL95JJei9ZcyAscjgsEgHbolzTE4IvuGV+hIemjpISsWv2dEt7SJI2JisCJUmSJOkqusefIQ5LBGLiJlt1pxZjnj9UAmBT6ROkoUoc51asmQ91XktnAbg1bj0EBPjc2VcBONS3i77aYsv7SJI2HoNASZIkSbqCTPkUxdk3l181f17f9/b1Uk0j8tmtJHHXZQeEvJrOUiUwQIahtPUQMFev8NnzbwBQrBoCSpJWsjVYkiRJkj5ISCmNPtX4kQwRtaYuPziSZ9/5IhAxWPoEcdx1yYCQU2mZ46FMBKRNthy/3y+MvENPbZGxfB+7Fkba2kuStPFYEShJkiRJH6Br6sdkl84snwvYXAhYrcN33u0DoK/rQ6RhiWzSvXJNSPnLdAaA26Ic0y0OB7ngwpCQ4a7NRFdZK0m6+VgRKEmSJEmXUZx6mZ7xZwEIJE0HgT862sPUYkISd9FdvINcsumSNW+kc8yR0k3c1rmAAHfPDHHP7GmqUcJd00Nt7SVJ2pisCJQkSZKk9ylOv0Jp7LtAIwSMmwwBx+YSfny0MSBksPRJCDFRtPLr11io8m5YAKA3avYOl/rZkJDb6WrjnEFJ0sZlEChJkiRJ71GYfu195wI2164bAjz1bh9pgGLuFqIoSz67showDYGX6tME4JYox7nQXgzYVV3kF0feBmBgabqtvSRJG5etwZIkSZK0rDDzOr2jTwKtDQcB2HOuwLHxPBEJAz0Pk016L1mzLywwRo0sEROh2vZzf/b8G+TTKmeLm9m5ONb2fpKkjcmKQEmSJEkCCjNvUBr5FtDamYAA5WrE0/uWB4R0PwSkxHFuxZq5UOeNdA6AW+Ici21OCiaEi23B07lSe3tJkjY0g0BJkiRJN7387FuURr5JRCBtoR34gucPl5ivxGSSEl35HeQyW1a8H0LgL9MZqgQ2k+FEutT2sz84dYzbFkYox1nunjnV9n6SpI3LIFCSJEnSTSuqz9Mz8mf0nn+CiEAg0/RgkAvOTmd49UQ3AIOlR4mjIlEUrVhzIixxKiwRA2m7D7/svzjzlwAc7rudXGgtwJQk3Rw8I1CSJEnSzSfUKU7/hO6JHxCniwCkbYSAaYBv7+0nAN35O4iimGymZ8WaSkh5JZ0B4PYoz/HQfjXg7XPn+PTYHgC2L4y2vZ8kaWMzCJQkSZJ0U8kuHKE09h0ylREAwnJ9XqshIMAbQ0XOTGeJoix93Q+SzfRdsub1dI4FUnpIGOpACAjwGyeeA+Dd/rt4cOpYR/aUJG1cBoGSJEmSbgpxdYLS2NPk5/cBEIgIRMRtNunOL8U8e6AR/A30fIxAShyt/Ko1EirsCwsAlKKYuQ608N41e5pPjb1LSsTWxfG295MkbXyeEShJkiRpw4urk2wa+r/Jz+8jAIEMEaHtEBDgmQMlyrWIXGaAfGaAQnblgJA0BF6qN1qCb41ynAvVtu8J8JvHG9WA+wbuYsvSdEf2lCRtbAaBkiRJkja2EOgd+SZxWiYQEwFRG23A73ViIstbZ7oA2FT6JEnSc8maPWGeCWrkiRjrUAh43/RJPjFxgDoRO+Y9G1CStDoGgZIkSZI2tMLMa+QWjxKIiDo2qxfqKTy1tx+AUvFeoE72fUHgbKjxZjoHwOYoS5nQkXv/5oWzAQfuYbAy05E9JUkbn2cESpIkSdqw4uokPWPfA1iuBmz/bL4LXjnRzchchjjK01u8l2xmYMX7IQReTmeoA1uiDGdCpSP3/fDUMR6ePEwtirlt7lxH9pQk3RysCJQkSZK0MYVA78i3iEOFQELcwRBwajHm+UMlADaVPkEaAlGUrFhzLJQ5HSrEwELoUCViCPzm8WeBRjXgQHWuM/tKkm4KBoGSJEmSNqTCzE/JLR4hQEcrAQG+t6+XahqRz24libso5FYOCFkKKa+kswDsiHPMd6gl+eHJwzw4fZxqlHD77OmO7ClJunkYBEqSJEnacOLq1HtagpOrrG7OwZE8+84XgYjB0sfJZfovWfNaOkuZlBIJZ9LOtAQ3qgEvTAq+m/7qQmf2lSTdNAwCJUmSJG0sIVAa/RZxWCIQd7QluFqH77zbB0Bf14eohwqZpGvFmuFQ4WBYBCDq2J3hExMHuG/2FEtxhrtmhjq4syTpZmEQKEmSJGlDKcy+QX7hcMenBAP86GgPU4sJSdxFd+F2itmdK96vh8BL9WkAdkRZZjoVQobAbyxXAx4YuItSbbEz+0qSbioGgZIkSZI2jLg2Tc/Yd4HGlOBOGptL+PHRxoCQwdInCSEmilbeY3eYZ4o6BSJGQrVj9/6l829x99wZynGOu6dOdWxfSdLNxSBQkiRJ0sYQUkoj3yROO98SHAI89W4faYBi7haiKKGQG1yxZjrUeDttTPHtjZKO3f1To3v54sEnANjffyc99XKHdpYk3Wwy1/sBJEmSJKkTilMvr1lL8N5zBY6N54lIGOj5KJmktOL9EAIvpY1G4C1RhpFQ68h9PzPyNv9s39dISNkzcA8PTRzqyL6SpJuTQaAkSZKkdS9THqJn/Fmg0RIcdbAasFyNeHrf8oCQ7odIQ5UkLqxYcziUORcqJMBc6My9f2n4Tf7nA4+TENg9cC8PTh4hIXRkb0nSzcnWYEmSJEnrWlQv0zf8NSLqBJKOtgQDPH+4xFwlJpOU6Mpto5DdseL9ckj5SToDwI44x2IHwrpfOfdTvrQcAr696T4enDxsCChJapsVgZIkSZLWrxAojT5JUpvoeCUgwNBUlldPdAOwufQocVwgiqIVa36SzrBEoI+E02ml7Xv+9bOv8ruHvgXAW5vu5+GJg1ZwSJI6wiBQkiRJ0rpVmH2dwtzu5Vq5zp4LeHwix5++PkAAuvN3QATZTN+KNWfSJQ6HxvCOToR1v3b6Zf7RkW8D8ObgA3xs/IAhoCSpYwwCJUmSJK1LSeU8pdHvABCihLhDZ/MBHDif52tvDVBLI/LZrQz0fJQk6V6xphYCLy+3BN8W5RgK7VUDfnpk98UQ8I3NH+LjY/uJrnKNJEnNMAiUJEmStP6kVfqGv0oUqo1zATsYAr55usiTe/oIIaIrfyubez9DtT5NNupfse7tdI4Z6hSJGW4zBOxfmuWfLLcDvzn4gCGgJGlNGARKkiRJWnd6xp4mUzlPIOrouYAvHevmmQO9jXsU7mZT6eeo1WYo5LauWDcZauwO8wAMRAlnQxttySHwu4e+QW9tgaGurTw0fsgQUJK0JgwCJUmSJK0r+dm36Zr5SUdn6IYAzx0s8eKxHgB6uz5MX9eD1OoL5HNb3rc28FJ9mhTYHmU5G6pt3ftXhl/nk+P7qUYJSaiT7fBZh5IkXeC5s5IkSZLWjfzsbnrPP7H8KiHqQBy4WI14ck/fxRBwoOfj9HV9iDTUyGcHLll/MCxynioZImbabEnesjjBPzzyFAB7Nt3LzsXxtvaTJOlKrAiUJEmStC7kZ9+i9/wTRAQCSdstwaNzCa+e7Oat00Uq9UaNxObeT1PIbgMicpnSJdcshDqvpbMA7IxznEqXWr5/FFL+6cEn6KovcbxnJw+PH2h5L0mSVsMgUJIkSdINrzDzOqWRb7UdAoYAR8ZyvHKim0OjhYt/nsv0MdDzCTJxF3GcI4kLl73+1XSWCoEBkrZCQIBfO/OXPDR1lKU4S3d1gaSt3SRJujqDQEmSJEk3tOL0q5RGvw1ASoaYWkv7vHOmwAtHSozO/+xrUFf+Vnq7PkQhu52l2iiZpEQcZy97/VC6xLFQJoK2G5J3Lozw9459D4D9/XfxsYmDbe4oSdLVGQRKkiRJumEVp16mNPZdAAJJyyHggfN5nnincd5fHGXoKd5Lb/EBMkkPS7VRqvUZCtmtH3h9NaS8nM4AcGuUYyhUWnoOgDit86X9j5NPaxzq3cXDhoCSpGvEIFCSJEnSDalr8kf0jD8DQCBD1GIIOF+JeHJPPwA9xXsY7PkkEFiqjRNFmSsGgBe8mc4zR50uYs62EQICfGHoBe6bHWIhybO5PEnU1m6SJK2eU4MlSZIk3XA6FQKGAE/t7WO+EpNN+unv+giV2gRECcXcDjJJ8ap7jIcqe8M8AL1R3NaIkjtmz/LfnfgBAEd6d7GpMtvGbpIkNceKQEmSJEk3lOLUKxdDwHbOBATYfbbAu8NFIGJz76chBAq5bau+Pg2Bl+ozBGBHlOVcqLb8LJm0xu8f+BrZUGdf/108NHm45b0kSWqFFYGSJEmSbhiFmTcojT0FtHcmIMD0YsxT7zZagvu7HyZQIZvtW/X1IQReS2cZpUqWiMnQ+rMA/Mbx57h9fpiZbBe3zA3bEixJuuYMAiVJkiTdEPJzeyiNfBNohIBRG024IcCTe/pZqkXkMoN05XZQyO5Y9fW1EPiLdJq9YQGA7XGWchuzgu+fPsF/PfQjAE53b6OvttDyXpIktcrWYEmSJEnXXW7+AL3DXyMitB0CArx2qosjY3kiEjb3/jxxlCeKVlcHUQ4pP6hPMkyVGNgZ5RhKWx8Qkq9X+NL+x0kI7B64l4/aEixJuk4MAiVJkiRdV9mFo/QN/yciUlIS2hvHAePzCd/f3wvAQOnjpGGJfHZwVdfOhhrP1ieZok6WiAESTrc5JfjvH32aHeVxJnIl7p451dZekiS1w9ZgSZIkSddNZvEkfee+QhRqy2cCthcCpgG+8U4/tTSikN1OLrNp1S3BY6HKU/UJpqjTRUyRmJE2zigEeHjiEL929pXG/vl+uutLbe0nSVI7DAIlSZIkXRdxdYL+c18mDpWOtAMDvHism6GpHFGUZbD358gmJaLo6mM5htIlvlufYJGUPhICgZk2n6erusgXDz4BwNub7ue+2aG29pMkqV22BkuSJEm69kKgNPoUcVomEHckBDw1meX5Q42W4MHSJ0nTCrncpg9cXw+Bk6HMwbDImeX2361RlslQpdr208BvH3mKzUvTjBQGeGDyaAd2lCSpPQaBkiRJkq653Pw+8gsHCUBE2vZ+80sxX31zgDRAd/4OkriLQm77ZddOhioH00UOh0WW3jMJ+NYox5lQaWM28M88OrqXv3r+DVIi5pICW8NkB3aVJKk9BoGSJEmSrqkoXaI09p3lVwl04FzAJ97pZ3YpIZv0MtDzMHFcuGTd8bTMnnSekffU+xWJ2RRlGAvVtoeCXNBXmeN3D30TgLcH7+fj4wc6sq8kSe0yCJQkSZJ0TXVN/JCkNt2xluAXjvRwZCxPRMKWvl8iDXWycX7FmqF0iefTKQAiYFuUZSmkTFK/2BbcESHwTw59i77qPGe6tvDQ+KHO7S1JUpscFiJJkiTpmkmWhumaemn5VftNuEfGcvzwcAmAwd6fpx4WyWdXngu4GOr8KJ0GYGeUJQMMhyqTHQgh3++Xz7/Jz4/tpRYlpAGyHWh7liSpUwwCJUmSJF0bIVAa/TYR6fKU4PaCwJlyzNffGgCgp3AP2aSHYnbn+24Z+HE6Q3l5GvBwhwaBXM5geYrfOfxtAHZvupfbFkfX6E6SJLXG1mBJkiRJ10Rh9k1y5RMEorZbguspPP7WAAvVmFxmgL7uD5OJu4iiaMW6A2GRobBETKP+cM3q80LgiwefoLte5kT3Dh4eP7hWd5IkqWVWBEqSJElac1F9gZ6x7wMQOvA15M8PlTg5mSOKsmzp/QwhQBznVqyZCjVeTWcAuDXOMbMGrcAX/M2zr/CxycNU4gyFepmkI7OHJUnqLINASZIkSWuuZ/xZ4nSeQEzcZiC391yBl471ALCl969QD2Xy2f4Va+oh8Bf1KerA1ijLqbSDA0HeZ/vCGH//6NMAvDtwD9vLk2t2L0mS2mFrsCRJkqQ1lSmfojDz0+VX7TXnvn2myDd39wHQ2/Uh4jhHIbv9knVvpnOMUyNHxEyotXXPK4lDypcOPE4hrXKkdBsPjx9Ys3tJktQug0BJkiRJayekywNCAoEMEa2Hcq+c6OLpfY0QsLtwF6XCPWQyPZecC3guVHgnzAMwGCWcW8Mg8L8a+jEPzJxkMcnRX5m25UqSdEPz7ylJkiRJayY/t5fs0tnlASGtBXIhwPOHei6GgL1dH2JT98chyhBH2RVrl0LKC/UpAG6NcmsaAu6aO8dvHn8WgEN9d7B5aWbN7iVN76pCAAAgAElEQVRJUidYEShJkiRpbYSU7snnGz9GCVELoVwa4Ol9vfzkZDcA/d0fo6dwJ4GUXKa08nYh8HI6wzwpPcQMh7U7FzCT1vjSgcfJhjr7++7goxOH1uxekiR1ikGgJEmSpDWRn9tLpjJCICJuIQSsp/DNd/rZfa4IwGDpUQq5HURRQibpumT9u2GBY6FMBGSJmGv3F7iC//bk89w1d5bZTJHtC6NEV79EkqTrztZgSZIkSZ0XUronf9j4MWr+a0elDv/pjYHlEDBiS98vks9uIYkLlw0Bz4UKP0lnAbgtzjHZ5mTiK7ln5hR/5+RfAHCyZycD1fk1u5ckSZ1kRaAkSZKkjsvPv0umcn65GrC5UG6pFvGnrw9wfCJPRMLW/s8SR1mymd5LzgQEmA91flifIgA7oxyn0rVrCc7Vq3xp/+MkpOwZuIeHJo+s2b0kSeo0g0BJkiRJnRVSuicunA0YEzURBJarEV95fROnJnNEUZbt/b9KoE4+u5noMpWF9RB4vj7FIil9JJxfw3MBAX7r2Pe5dXGUqWwPd8ycXtN7SZLUabYGS5IkSeqoVqsBF6sR//GnjRAwjnLsGPjrhFChkN1+2RAQ4NV0lhGq5IioE9awIRg+MnmEz595CYDzxUFK9fIa3k2SpM4zCJQkSZLUOe+rBlythUrEl1/bxNBUIwTcPvDXqadlCrlbiKLLj+I4lC6wPywAsClKmCNt//k/QLFW5p8eeAKAdzbdx/0zJ9fsXpIkrRWDQEmSJEkd00o14Hwl4v97bZAz0zniKM+Ogc9RTxcpXiEEHAtVXk5nANgV5xluYSpxM/7Bke+wdWmS0Xw/902dWNN7SZK0VgwCJUmSJHXGe6oBV/tVY24p5j/8ZJBzM1mSuMCOTZ+jHubpyt/6gSFgOaT8oN6YC7wtynIqXerM83+AT4zt468N/5SUiJlsN8U1HEYiSdJaMgiUJEmS1BHvrQaMVnFa32I14rGfbOL8bJYkLrJ94HPU6nMUc7d+4DULoc736xPMkdJDwkSodvJXuERvZY7fO/RNAN4ZvJ+7586s6f0kSVpLTg2WJEmS1L5LqgGvHgQ+e6CX0bksSdzFjoG/QS2doSv/wSHgZKjx7HIImCciBtYyBrxv+iT/y/7/zEBllnPFQR6cOLyGd5Mkae0ZBEqSJElqW7PVgCcmsrw+1AXA1r5fpJbOXLES8Fyo8IP6JEsESiQEAjNrNCM4Cil/e+hH/N1jz5KQMprvJyUi18QEZEmSbkQGgZIkSZLakpvfR2nkz5ZfXb0asFaHJ/cMANBTvIdA7Yoh4NF0kR+l06TAIBnmqLNE6MzDv0//0ixfOvA1PjbZqP7bO3A3d86cZkt9bc8hlCTpWjAIlCRJktSatEJp7GmKM68BEIhXVQ3442M9jM8nxHGBvuI9ZDObL7suhMCesMBr6SwA26MsI6FK2rnfYIWPTRzkS/sfp786x1KcYX//XTw8cYjLjyyRJGn9MQiUJEmS1LRM+TS95x8nUx1brs3LEFG76nWjcwkvHCkBMFj6OQJZoujSGYZpCLyazrIvLACwK8pxKnR+Wm8cUm6fO8dnz7/Bf3n6JQDOdG0hTut8bOJQx+8nSdL1ZBAoSZIkafVCStfkj+ie+AERKWG5Xm41IWAI8Gd7+kkDFHM7icmRzw5csq4eAi+kUxwPjXbcXXGeU2lnWnOTtM5dc2d4cOoYH546xoemT9BdL198/61N9/PhySPkPQ9QkrQBGQRKkiRJWpW4OkXv+cfJlU8AkJIQNzGw483TRU5O5ohIGOj5KNnk0hCwGlJ+kE5xJlSIgR1xriMhYCat8T8d/CafHt1DMV1ZWbiY5DjTtY2UiEcmDrZ9L0mSblQGgZIkSZKuKrtwlL7hrxKn8wQiAnFTIeDcUsz39vcB0N/zMAGI4+yKNeWQ8mx9klGqZIgYjBLOpB1oBw6Bf3LoW/zK+Tcaz5IpcLZrG3GoM1ieYqA6xz2zQ+3fR5KkG5xBoCRJkqQPFgLFqRfpGX+GiLA8ECRd1VCQ9/re/l6WahG5zAD57CYK2a0r3p8PdZ6pTzJJjRwRPcScD1dvN16Nv3XmZX51+HVSIo703sZ9M6e4b+ZkR/aWJGk9MQiUJEmSdHlphd6Rb1KY2w1AIGk6AAQ4Mppj99kiEDFY+jkySf+K96dDje/XJ5mjTpGYLBETLdznch6eOMT/eOQ7ALy9+QE+Pra/I/tKkrQeGQRKkiRJukRSGaNv+E/JVM4TaISAzbQCX1Crw5/tbZwF2Fu8n0AgkxQvvj8eqjxTn2SRlB4SAoGZDoWAOxZG+V/3/ScSAu9supdHDAElSTc5g0BJkiRJK+TmD9B7/nHitLw8FTi0FAICvHG6i6nFmCQu0l28g3xmy8X3pkONp+sTVAj0k7BEyiKhI79DV22R/2Pvf6Sntsjxnp18aPLY8nxjSZJuXgaBkiRJkgBIKqN0TzxPfm73ivMAW1Wtww8PLw8I6f4ISVQgihpxXAiBF+vTVAhsIsMcNTowFgSAOKT8s31f5baFESZyJfqXZsiFzlQZSpK0nhkESpIkSTe5pDJG1+QPKcy+TbRckZeSIaa9YR2vnexivgJJ3EU26SGb6b343v6wyDBVEqBM2rEQEODvHnuGn5s4QCXOMJvp5vaF4Q7uLknS+mUQKEmSJN2k4uoE3RN/QWH2zYuVfxcGgrQbAlZqES8cvVAN+BDZzOaL782FOj9NZwHYGecYSjsXA/7S+Tf5O0MvAHCg7y4+OnmoY3tLkrTeNR0E7tu3j6eeeorjx48zOTnJP//n/5xHH310LZ5NkiRJ0loIdXrGnqY4/ZNLAsBWpgJfzqsnu1isQibpIZN0XxwQEkLgpXSaKoFBMh0NAT8+vp8vHngCgDc2f4hPOBxEkqQV4mYvWFpa4o477uC3f/u31+J5JEmSJK2x7vHn6Jp+hYiUQALQsQAQoFyN+PHRRhtwf/dHyGd/Vg14JJQ5HSrEwFIb5w++30cmj/C/vfsnZEOdPQP38DFDQEmSLtF0ReAjjzzCI488shbPIkmSJGmN5eYP0D31YwBSopanAV/JX57oplyDbNJLEhdJ4gIAC6HOq+kMALd0sCX4vumT/J97vkw+rbG/704emDy2HG9KkqT3WvMzAqvVKtVq9eLrKIooFosXf95oLvxOG/F308bh51TrgZ9T3ej8jGo9eP/nNK5O03u+0TobOjAM5HIWqxEvHSsB0N/9IPns1ovvvZLOskSgn6RjIeCds2f4v3Y/RjGtcKh3F3fNnCLbwUpDSdL6FUWR/1Z7nzUPAp988km+8Y1vXHx955138kd/9Eds2bJlrW99XW3fvv16P4J0VX5OtR74OdWNzs+o1oPt27cTQp36O/8B0gWIEqLQ+RAQ4KVj3VTqkE36ieMukjgPwIm0zPFQJoLlucTtu2X+PH+w+/+lu17mWOkWbps7Rz50vsJRkrT+xEnMtm3bSPoGrvej3FDWPAj8whe+wOc///mLry8ksaOjo9Rqa/OPj+spiiK2b9/O8PAwIXTqnzhSZ/k51Xrg51Q3Oj+jWg/e+zntGv0+XTOHCEREaxSWzVciXj7eqAYc6P4whWzjf/4vhZSXl1uCb41yDIX2qwG3LY7zh+/8e/qq85zs3s62hTGKafXqF0qSbgppPeX8+fNEC+Xr/ShrIpPJtFRkt+ZBYDabJZvNXva9jfyP5hDChv79tDH4OdV64OdUNzo/o1oPMnMH6Zp8AaARBHasJm+lF4/2UEshlxkgiovEcQ6An6SzLJJSIuFMB0LAzeVJ/vDtf8+myixnilvYtDRNd32p7X0lSRtLCAH8d9oKax4ESpIkSbp+wtIkvecfByBdo3MBAeaWYl492QPAQPeDFJbPBhwLVQ6FRQCy0PbpfZ85/zb/+PCTlGqLnC9soqu2SKm22OaukiTdHJoOAsvlMsPDwxdfj4yMcOLECXp6eti8eXNHH06SJElSG0Kd+oH/h7g+TyBesxCwnsIzB3qppZDPbIYoTxw3uoJeT2cB2BnlONtGNWCpMs8/PvwknxndDcCp7m10VRcZqM61/wtIknSTaDoIPHr0KH/4h3948fVXvvIVAH75l3+ZL37xi517MkmSJElt6Zp4HqYPLLcDr80k3XMzGb61u59zM43gb6Dnw+Rz2xrvhQqnQ4UImAytn9/3ibF9/N7BbzBQnaMWxezedD8fHT9AZo1anCVJ2qiaDgIffPBBvv71r6/Fs0iSJEnqkPzsbronfgiszbmAtRReONLDj4/2kIaIOMoxWHqUKMoTRxlCCPy03qgGvCXKcbqFasBircw/OPId/trwTwE4V9xMNYr5+Pj+jv4ukiTdLDwjUJIkSdpgMosn6R15ovEiyhCHzrYEn57K8uTuPs7PNaoAu/K7GCx9ilo602gNBobCEiNUSYDRFqoBPzx1jC/tf5ytS5OkRLw9eD8PThwmv0YTjyVJuhkYBEqSJEkbSFIZo//cV4hCjUBC1MEQsFqHHx4u8dKxbgIRcZRnc++nKOR2UK1NkM9uJ4oiQgi8njbO7tsZ5Rhqohowk9b49RN/zhdOvUBMYDTfz0y2m4+PH+jY7yFJ0s3KIFCSJEnaIKL6PH3nvkycLhCIiehc9dzoXMLjbw0wPNuoAuwu3MFg6VGqtSkgpZDbcXHtsVBmghpZIs41EQLeMj/CP9v/Ve6eOwPAOwP3ct/0SbYsTXXs95Ak6WZmEChJkiRtBGmVvnN/SqY6vhwCdm44yNtnijy1t5dKPV6uAvw0hdxWqvVpCrntKx8jBN5YrgbcFmc5na4iCAyBv3H2Vf7B0e+ST6vM///s3XeQndd55/nvOW+4OXRCN6KYMwUGJZoUqUBL8o5sS7YVHMZ55Zkdu9az41A7s7MOVbO1VTtb5Z2yXbYsibICRUVSEsUk5iASIIlIAERGo4HO3Tfn9z1n/7i3Gw2gu9ENNAiAfD5VXY2+/Ybz3u4/mj8+z3ncKIPJNWzM7V+xZxBCCCGEBIFCCCGEEEJc+qwhPf59/PoRLApWKARsBoqHd6fZciwOQNQboC9zF62wACiiXv9p5+y1NYqERFEMLyEETDfL/Ie93+W9nQEg+9Pr6a3luCF/aEWeQQghhBAnSBAohBBCCCHERS6Wf4nE9FOEXjdBZA1BZDUtv/0Z7ZOYfpJoefvsXGC1AvccK7l8e2uW8c5AkGxiI+nYtTTDHLE5bcBzBdaytVMN2KNcjp9hSEh/bYr/a+s/0tUs0VIOb3RfzcapN9ErsH4hhBBCnE6CQCGEEEIIIS5isfxLpCYfBkA3juM1js9+z6IIvW7c1lTnawd9jvsCWguvH4vxk10ZWkbh6Bh9mQ/i6AjGthYMAQF22ypVDHE0w2cIAb2wxZ/v+jpdzRIjsR6MVdwqA0GEEEKI80qCQCGEEEIIIS5SsfzLsyGgxZkd/mE7NX8KOycEdNGc/YRgY2HveIRnDyQ5XvDb9/fX0Je5k2YwjeekUcpZ8PymNWzvVANmlEPVLt6e/AcHfsQV5WFKbpxI2KS7WTrrtQshhBBiaSQIFEIIIYQQ4iIULWwiNfkjoB3yqTkhn5ptAmbOa2cXAhoLu0ajPHcgOTsRWOGQTW4kGb2KVlgg5q9d9Bq2MyCkgSWNw8gZqgHvHt3Cx0Y2YVAMx/u4tjh4VmsXQgghxPJIECiEEEIIIcRFJlp8lfTEQ8DpIeBKCQ3sGI7x3MEkk5X2fxZo5ZKKXUcmcT1BWAPCeQeCzDVpW7wUFpmgHf5FURQXOX5dZYx/t+/7AGztvY7bJ/esxOMIIYQQYgkkCBRCCCGEEOIiEi2+Tmr8QWCmHXjlQ8BiXfOVTT1zAkCfdPx60vHrMKZBKywRcftQauGxI01reN2U2W2rWMBFsVp7DC0yKTgSNvnzXV8nalrsT6/nFgkBhRBCiLeUBIFCCCGEEEJcJCKlraTGv4/CYlZg8Md8QgMPbO1isuKiVYRM4kbSsWsITLUTAPbgqfSC51trOWTrbDIlqrT3AVyjfKZta9EQEGv5o30/YH11nLyXpK82xcI7DgohhBDifJAgUAghhBBCiIuAVz1Ieuy7KOw5D/5YzONvpjia81HKY033L2AIaAYlIl73ohWAAEUb8KIpMmzbgV8Khwhq9uvF3DuymQ+NbSFEMxnNclXp2Io8jxBCCCGWToJAIYQQQgghLjTTIjXxg04IeH7agQHeGInysyNJAPrSdxKYKjF/4IznWWvZa2u8YkoEWBxgjfY5ZposZdbvZaVh/nD/DwHY3nstt0lLsBBCCHFBSBAohBBCCCHEBZbIPY3bmsaiUOehHRhgvOzy/R1ZADLxG9FKLykErNqQF02Ro7YBQJ9yqVizeBvwHH7Y4s92fx3fBuzJXCb7AgohhBAXkASBQgghhBBCXEBOY5R47vnzeo9GoLj/9S5aoSLq9ROPrCfi9Z7xvEFT5wVTpI5BA2t1hCHTWNa9PzX0LGtqU+T8FOvKY+izfAYhhBBCnDsJAoUQQgghhLhQrCE18RAK02kJXvlqQGvhoZ0ZJisujo7Rm34vSsVQauFIrmkNr5gS+2wNgAwOFpYdAq6qTfPpo88AMBLv5Yb84bN+DiGEEEKcOwkChRBCCCGEuECixVfx64PntSX4lcE4O0digGJV+oMYY4n4sXmPDTsTgbeYMqXOetYrn2O2iT2Le//ewR8TMQH7U+u5XkJAIYQQ4oKTIFAIIYQQQogLQAclklOPAWDR5yUIPJrzeHRPBoDu1HswKiA+z76AdWvYY6vsNlVqGADiaOJohpYwEXg+t07t5f2TuwiUJtWqsPg8YiGEEEK8FSQIFEIIIYQQ4gJITj6MNnUsGn0eQsB8TXP/lm6MhUTkXfhOlqh3cgiYswG7TIX9tja7ghiaLuUwbltUO6Hgcrkm4A8OtKcE7+y+mlun9p7LowghhBBihUgQKIQQQgghxFKZJn7tMH7tAH71ACqskl/zu4SR1cu6jF/ZS7S8o9Nue3Zh22KqTcVXN/dQbmg8J0s2cROuk0apdl1e0xqeMwUG7Yk9/7pw8YBxAmr23Nb0i8deYE1tkoKX4LqctAQLIYQQFwsJAoUQQgghhFiItbiNY/jVA/jV/Xj1o6e18KbHvkNu/Z/AIsM3TmKapCZ+2Pli5QeENAPF11/r7gwHidOfvQeLQmsPAGstz88JAQeUR9UacgQrcv+eep5fO/IUAEPJfm7KHVqR6wohhBDi3EkQKIQQQgghxDx0UCQ1/n0i1X0nvW5RMGdPP685SrT4OvXMe5d03cT0UzhB7rwMCAkNPLA1y1DeRyuf/uxHCW2DqLdq9pjttsIR20ADWRxGbWtF1/A7B39CzDQ5lFzDjRICCiGEEBcVCQKFEEIIIYQ4RaS0g9TEQ2hT67TvOkCIAhQWTgnwkpMP00jehHXmn8Y7w6sdIZ5/8bys2Vr44RsZ9k1EUTj0Zz+CsTVi/prZY4ZMg9dMGYB12ueoObtBIAu5KXeAuya2Y1BEg4YMCBFCCCEuMkvsXxBCCCGEEOLtT4VV0qMPkBn7VicE1J3wL1w01NK2SXLy0UWvrYMi6dH7URgMTidQXJoDkz5ff62LZw8kGSu52HlO/em+FFuOxQHFquzdWJonhYAFG/CMyQOwXq18COiYkD/c32553tF9DWvqUyt6fSGEEEKcO6kIFEIIIYQQAvAr+0iNfx8nLM5WAS6ndTdaepVa5v0E0bWnf9MGZEa/iROWlj0leOuxGA/uzGCsYu94lCf3peiJB1zfX+eGgTrrsi02DcZ5/mASgN70HYAi6p1YR9MangzzNLH04HLMrmwI6IctPjP4JBuqY5TcOFcXjqzo9YUQQgixMiQIFEIIIYQQ71w2wKsfI1raQqz4avslNGCWvX+fAtLj32N6nsEhyYmH8epHO/sCLm0ir7Xw/KEEP92bBiDur8NiqTdHmKq6vHg4yYuHkyT8kErTAaAreSuukyDqDcxOCJ4ZDpIjIIamSriMWsTF9dem+Pjwy3x05DVSQRWAI+k13Dx9YIXuIIQQQoiVJEGgEEIIIYR457AhbmMYv3YQr3oIv34ENWdYhsVFncP0XHeewSHR4mvEi5s64ZuCJcRwxsIju9O8MpgAIBO/gVTsehwnAtZSax6n0hii1jhGpVPcl45dS8RbRdTrnQ0B4eThIDEU00sMIheireG2qTf5xPDL3Da9d/b1qUiGY4l+bp7et8jZQgghhLiQJAgUQgghhBBve279KPH8i/jVfWjTOOl7FoXFQRGcUwg4Y+7gELc+RGr8ofZ9lIu2Z75+K4Tvbc+ya7Q9eKQ7+R6ifj9aO2jlYrFEvX5i/hpU+ueoN8cITQPPTeM5aZRyZq81dzjIWu0zdA77Arom4OeHN/HLQ8+zqpEDwAD70+/CD5tcVhmhp1E46+sLIYQQ4vyTIFAIIYQQQrw9WYtf20889xx+7dCJl1G0Z+admAK8EgHgjJnBIeWej5EZ/SaKEIuzpBCw1lJ88/UujkxHAE1f5k5cHcdzM2jV/tNdKYUzZzqx7/UQmgqOjqO1N/v6pG3NDgdZp84+BHRMyEdGX+XXBp+mr9G+XtmNcSi1jvXlEa4tDp7VdYUQQgjx1pMgUAghhBBCvL1YQ6Syi3juWbzGcPsl2m2/mqAzrXd5+/8tV7T0Km7jGE5Q6EwePvP9yg3NVzZ1M172UMqjP/thICTi9aFO2XNwLkdHcHTkpNfyNuCxMEcTS69yOX4Ww0G0CblnbCufGXySgfo0ADk/xfHEKq7LHeLduf3LvqYQQgghLiwJAoUQQgghxNuGX95FcupR3NYUwEnTf1ey6u9MFOA1R5Y8HMRa+MGOLONlD0fHGMjeS2irRL01J+33txRlG/JoOE0dQxcOBRssaziIHzb5wOQbfObIk6ytTQJQ8BIcTa7hutxBbmoeXNZ6hBBCCHHxkCBQCCGEEEK8LURK20iPfQeFnW3/bVfind/qv8W0g8Azx3CvDsXZNxFBoenPfoTQVon5a5d9v2onBKxgSONQw9A682n01XPcPrWH26fe5Kb8ASKmHZqW3RgH0+u4PneIm6UCUAghhLjkSRAohBBCCCEueX5lD+mx73ZCQOeCB4Az9BKqASfKDo/szgDQlbwNY1unhYANaxiyDQaUT3LOMJBTj3kszFEgJIGmhaG+SAh5RekYd47v4PapPWyojp30vWk/xVByNdflDrFxWgJAIYQQ4u1CgkAhhBBCCHFJ86oHyYzej8JgcNAXQQC4VKGB72zrJjAQ9Qfw3Awxf+CkYwo24PEwR7HzXAN4XKVjXKaiRDt7B7as4fEwxzQBURQKqC0SAn5wbCv/cc+3Zr82KAaTq2k4Hqsrk3Q3S3RPl1b+gYUQQghxQUkQKIQQQgghLllu/SiZka+hbNCezLvCIaC1MFlxmK66FGoO+ZpDvt7+XKg5gOV33ptjVers9h985kCSkaKLVj7dyVvwnK6Tvj9imzwZ5mhg8VC0sIzSYtS0+BlF1qkIV6oo+2yNcVr4KCJoCou8Dzfn9vPHb34HgL3pdxEJG6ytjHN5efisnkEIIYQQlw4JAoUQQgghxCXJaYyQHf4q2jbntAOvnOMFl0f3pDkyHVn0uId3Z/n9908u+/pHcx7PHUgB0Jt+P+CitTf7/X2mxoumgAG6cakS0gIcwENRx3LUNjhqG9B5PYVmapH34bLyMH/5xtfwbMgb2Su5Pn+Q+RuNhRBCCPF2JEGgEEIIIYS45DjNSbLDX0GbGnZ2KMjKKNQ0P92XYtvxOAAKjedmcJ0Erk7gOkkcJ4FWLmP5Zzk05XFgwueqvuaS79EIFN/e1o0FktEr0Non4nUDYK3ldVNmm60AsEb5jNrm7G6D7d0P222/HidGovQqh1G7cGViXz3H/7Hjy8TDBgdS67imcERCQCGEEOIdRoJAIYQQQghxSdGtHNnhL+OE5U4IeOaBHEvRCBQvHErw0qEkLaMASESvoDt5K46OY2yd0DSwNgAUWnmk49dSrO7hJ3u6+ZPeUbRa2r1+sitNoaZxdYJ07Cp8bxUAgbU8ZwoctnUA3qUiDHYq/ubTngjcDgUXCwGTrSr/dceX6W6WOB7vY6A6iW8vnb0UhRBCCLEyJAgUQgghhBAXhg2IFrcQ+n20Ypcv4XhLpLyN1MSP51QCnlsIGBqYqrgcnvZ55kCScqNdIxfxVtGTeg9aeQRhHddJ4KgYjo6ddH42cTOl2gEmyi22D8e4dW3tjPfcNRJlS6fasC/9AbROopSenfo7QQtNuxJwsRBwqfywxf++86usq44z7aeJBXWSYf2cryuEEEKIS48EgUIIIYQQ4i2ngxLp0W/i1wcBaMSvpdzzCcLIwILHpyYeIlLZDXBWIWCxrjmW9xgre4yXXMZLLpMVl9CeKONznRTdyduIeH0EYQHX7cZz9YLXdHSUbPwmcpWtPP5mlpsGaniL9NtOVRwe3JkFIBO/CasUnpvEWsvzpsBEZ+BHBodjdumtxgvR1vAf99zP9cUjVJwoNcdjbW3qnK8rhBBCiEuTBIFCCCGEEOIt5daHyIx8AycsYlGAJVLdi1/dSz11O5XuezFeOyxrVwFuJzXxo04VIFjlohdpg53LWhjMefzscII9Y9HO/U6mlIvvZklELiMZu5JmMInWHlFn9ZLukU5cT7G2l3KjystHEtx9ZWXe40p1zX2be6gHCt/tIR4ZIOK177HH1hi0DTQQRzPB2U0hPom1/OH+h3j/5C5aymU43svVpWPnfl0hhBBCXLIkCBRCCCGEEG+ZaPF1UhMPoWxwWlWfAmKl14mWtlHN3kk9/R4SU48TrewCTlQBqiWEgEEIO0divHwkwXDxxCRe3+3Cd7vw3Cy+m8Vzs7g6gSWk2ZrEWkPMX7usZ9LKpStxC5Oln/HMgQzvWV8l7tuTjqm1FF/d3EO+5uA6KVZlfg6toyilmLYtNpkiAOu0zwXHozIAACAASURBVFGzMpWAX9j3Az42shmD4s2uy7h5+sA5X1cIIYQQlzYJAoUQQgghxPlnQ5KTjxAv/Kz9Jc6Ck34VIYn88yTyz3eOXXoVYKWp2DSYYPNgnHLT6VzPIRm7gnT8OjwnTRCWMbYJ6M6cDYNWLlH/9LZkay1TBFRtSB1DDUPdmtl/Oyju0mmSsSso1vbQDHI8vT/NJ28szF6jFcLXX+tmrOzi6Bj92Q8RWoOnYwTW8kxYIAT6lbciIaBjQv7kzW9z9/g2DIodPddwy9Tec76uEEIIIS59EgQKIYQQQojzSoVlMiP349cPA2Bw0WdofbXQaRrWgFlSCDhddfjSKz0U6+0A0NFx0vFrScWuJjRVjA0Ajedmzngtay1DtsFrpsz0GdYaNWU+6GToStzGWOEpNh2N83OXl+mOh4QGHtjaxdGcj1Ye/dkPE9oWsc6U4E2mSI6AKIrCIs8YDercmD/EruwV1N3ogse5JuA/7fom75/aRaA0u7uulBBQCCGEELMkCBRCCCGEEOeNbuXoOv4vOEEOi8KizhgCArM7+S11IEixrrlvUzsEdJ0UXclbiPvraQaTGNPCd7uXvOZh0w4Ax2kB7T+YEzi4SrVjSQshlgBLGcM+W+NmmyAbXUustppac4TH3szw67dO89DODHvHoygc+jMfxtgmMX8NAEdMnT22PWU4jcP4Au/LlcUh/rfd97O6PkXFifLY2jt4eN1dFPzUScdFwiZ/+ca/cktuPy3lsj+znndP71/ycwshhBDi7U+CQCGEEEKIi5wOCkQqe6gnb8Y6iQu9nCVrh4BfxAnyJ/b3w575xGWqNDRf2dRDrrP/Xn/2wxhTwxLO2+67kHHb5DVTZrgzrdcBViufMdukQMhCS7fAa6bEvU4X2cRt1Jo/YfdohAe2Ztk1GgMUq7J3Y1VAzF8HQNmGvGDa7cPrlc/QPBOClTV88tiL/NahR/FsSKg0ibDOrx59hl8ceoFnB27nh+vvZiTeRzyo8Z933scNhSPUtcfR5GpuyB9eztsohBBCiHcACQKFEEIIIS5iXvUgmdFvoU2F+PQzFAd+nVbssgu9rDM6UQmYP20oyEqqtRT3vdrNZMXF0XH6s/dgLUT9hSf+WmupYygSUrQhRRswQYtjnTBOA2uUz4Q98dqZHLENxm2TVX4PyegVlOuHOiEg9KbvANRsCGis5dkwTwNLF+6898g0y/zxm9/m9ul2W++u7BW8qzRMIqxTcWMkgxofG9nEvSOb2dR7E331aa4qH6fqRBiJ9XJN8egy3kUhhBBCvFNIECiEEEIIcTGylljhJZKTj6IwWMAJi2SPf5FK989T7boHlL7Qq5yXbuXntAOfvxCwGSi+9mo3o0UPraMMZD+MsZaolz3pOGstE7R409SYsi2KhLTmKe9TtAPA6WUEgHNtDkv8G6ebbOIWKvVBLCHdydtxnRhRb83scdtthVFauChanZ/tXDfn9vOnex6gq1miqV12Z69k4/Te2XbpZNBuJ644URJhnTsmdwJQcuPkvBRXlo8ve+1CCCGEeGeQIFAIIYQQ4mJjWqQnfkC0tA0Ai4vq7B+nsCSnn8CvHaTY/zmMm1rsSm+5tyoEbIXwjde7GMr7aOUzkPkwFkPU6ztxjDUcsHXeNFWm5tl/L44mpjQuiqY1NLAcP4sAcMZop6pwvZtkoOteQlPH0T4Rrx+l2jHeYVNniykD7SnBc++nreFzh5/gV48+g8YyEuvBALdMzz/sIxHWAag6EfKRNFjDhtrYWa9fCCGEEG9/EgQKIYQQQlxEdCtPZvTreI1hLGBxThuuYQG/dpDuo39HYeDztOJXX5C1nkoHhU4IOH1eQ8CZSbyHpiIo5dKfnQkB+wGYti32mBoHbG228s+hHbyVbEgFgwGqGKp2Zde42ZRYq3yifj/NoIDnJFGdys0h0+AZk8cC65R/WtXhbx56lE8PPQfA9u5ruC53iMgSpiXHwwbx6sSKPocQQggh3p4kCBRCCCGEuBhYg1c7TGbsW+iwgu00gmrC0w6daRHVpkp2+CtUuz5EpfvnL2irsA4KZI9/cdkhYLmhiXoGd4lLz9UcHtyRaYeAOPRnPoTFEvMHCK3lWVPgsK3PHp9Ek1IO47bFsG0t/7lMyFWlYySDKtGwSSxstD8HDaJhg7oT4aEN9xDo9p/VOQIO2jpXqxi+m5m9zrBp8KTJYWi3H58aAt4xvmM2BNzRdTUbp/cte61CCCGEEGciQaAQQgghxFtEhTWSk4/gNkdRtoUyTZRtdj6fCKmWE6QpIJF7Ft0qUOr/tbc8DFSmTiz/EvH8i2hTX/LarYUXDyV4Ym+KVNTwkatL3Lq2hrPA8q2F14ZiPLonTTPUKBxWZe8BBTF/AGMtT5s8g7aBol39V7OGAiHls6j6c0zIPWNb+JWjT7OmNrX4syj4/rs+Ovv166bE5SqK22kHHrdNfmryhMCA8hg5JQRcXxnlj9/8DgBbeq7jtqk3l71eIYQQQoilkCBQCCGEEOItoIMi2eH7cJujCx5jAZSLWkI76KnnxcpbUYQU+z8LyjmntS5FOwD8WScArHXWsbQQsBnCQzuy7BhpT9Ut1h0e2pnlpcMJfv6aEtf3N1DqxPH5muaHOzPsn4wCEPH66EvfSWBKxPw1GGt5xhQYtA00sEq5jJ5F9R+AawI+Mvoanz76DP31HNAeylHwkwTaxWiNQYGFWFhndW2KTx19jsfW3EHFiwNQxrDHVrlZJZiyLR4Lc7Sw9CmPcXvymJJ4UOMv3vgaMdNkf2o9G6fm3w9QCCGEEGIlSBAohBBCCHGe6dY0Xce/3GmbVTDb+HsyBbDMEHDmPAtEyzsAS7H/c+ctDGwHgC8Tz79wUgDYfqYzh4D5muabr3czUvQARXfqPWAt+coOJspw/5Zu1mebfPy6Iu/qarHlWIxH9qRpBO0qwK7krcQjGwhtnZi/FmMtz3XagU+EgMt/D/2wxb0jm/n00WfpaRYAKHgJBlNruDZ3iDW1yXnPs7SHdvzy0PPcf8UnZl/fZsr0K48nwjxNLD245GzrpHdIWcOf7PkOa2uTTPtpVtWmcOaZZiyEEEIIsVIkCBRCCCGEOI+cxijZ4a/ghKXzOkDjRBi4E6yhOPDrKx4GRouvkZx85KwCQIDD0z7fer2LakujVZRV2buBkIjXTzJ6BYXaHoqV3Qzlfb70Si898YCpavvP1YjXS2/6TkJTwdERPJ3EWsuLpshBW0cBA8pneJGpv2srY6yvjtPVKNLVLNHV7HxuFFlVz81O4c35KY4n+rk2d4h3T+9f9JlmAt1PHnuRn6y7i4KfBKCB5cfhNBbowqFIcNrc4l85+gzvn9pFS7kU3RiXVWXirxBCCCHOLwkChRBCCCHOE7c2SHbkq8vaO+9czIaBlV0w+k2KA78BagX+3DMtUpM/JlZ8FVh+AGgtbD4a5ye70xir8N1uVmU+SGgqRP217YMcl2z8JtKxa8lXdlCq7Weq6qLQZJO3kIhcRmhqxPw1nWtafmaK7LM1FLBGeRyfJwTMNkrcNb6VD41t4Yry8KLrnIpkGI33cG3uMDc1S0t+eywQNU1+5ejT3HfVL530egaHKoZTG5VvndrLrx9+AoBd3Vdwy5QMBxFCCCHE+SdBoBBCCCHEeeBX95EZ+QbKtt6SEHDGiTBwD2rkGxRW/9Y5hYG6lSMz+g28xjAWsMpFL6P11lj40RtpXhtKAJCIXEZXciPGGqKdUG/2XtpD49GdvJ1M/AbK9SMkohsITbVdBei2q+2stbxiSuyx7crEtadM4fXDFu+d3MWHxl7nlun9OJ33PlCa4fgqWo4H1uKGLTwTEA0bJIM6PY0CPY3Cst+jmarAjx9/hR+tu5upaBaANA5NDI1T2n37a1P86Z770Vi2dV/LLbIvoBBCCCHeIhIECiGEEEIsxlp0WMZpTeA0J3FbEzjNCdzWJMGhKt0qgnHiWB3HOO0PcIgVXkERYnFQhG/pkmfCwEh1L5nhf6W4+jexOrrs6/iVvaTHvo02tdldDZcbAv5gR4Ztx9tDNLqStxH1+tHax9MxrLWM0SKFQ2JOG7PWHlp7pGJXEZjqbBUgQMMaXjEl9ndCwPXaZ8h0QkBr+dyRn/KLx14gHjZmzzmSWE3D8bmsdJwNlYWHtZwr3wZ8ZvAp/unaXwWgOM/PPdGq8hdvfI1UUONIYjU3nqH1WAghhBBiJUkQKIQQQoh3Nmvxa/txmhPosIIOK6iwgg7Lna9LaNNY8HSHGk6Qn/d7Bgd9jiFgaOCpfSm2HI9x1+UV7ry8ctJE3YXMhoG1A3QN/QOF1b9N6Pct7abWEM89TWL6aRT2rCoajYWHZkNAxarMB9HKJeL1oJQmsJYXTIGDto4D3KQSbNQJfKVnr+E6cVwnPvv1oKnzkilS7axlg/Y5OicE/L2DP+YXj70IwGQkw2i8l3XlUS6rjCxr7efiI6Ov8uCGDzEW6znte12NIv91x5e5rDJC0UuQbpXx3qJKUSGEEEIIkCBQCCGEEO9wianHSeSfW/SYdmOnhtmJv4uHN+3j1TmHgOWG5ttbsxyejgDw2JtpDk/5/OrGPHH/zNNlZ8JAtzVJ19DfUxz4PM3E9Yueo1s5UhMPEam296wzuOjTxlwszlj44c4MWzohYF/mLpR2ifnrACjagCfDPNOd64bAdlthb1jjdp3kWhVDz0k7qzbkZVPisG0P80jh4MNJIeBvHXp0NgTc1n0tt0zvpfcs2nzPlWsNnzvyU/7H9Z8/6fX+2hR/tf1fGKhPk/eSlN0o6xaYRCyEEEIIcb7oMx8ihBBCCPH2FC2+NhsCWnTn43QKUBgU4ZIq49rHnzmoW8yxvMc/vNjH4ekISrmk49ej0OydiPL3L/QxOO0t6TozcZq2TbIjXyM+/RTY05/BbYyQHn2AnsH/TqS6rxN3OssOAa2FH7+R5vVjnRAwfSdae8Q7IeBRU+ehcIppAqIoejv/X9oF6hheMkV+EE4xZBpYa9lvanw/nORwZzLweuVTIWRqTsj6uSM/5VeGngVgS8913DJ9Yffcu3tsK+sqJyYAbyiP8t+2/iMD9WkmIlka2pMQUAghhBAXhFQECiGEEOIdyaseIjX+IMAF2cdvIdbCa0MxHt6VIbQKz0m3K+qUQzJyBRPFFyg2inzplV7uvabIB6+soJfQKjwjOf0kXv04xYHPYlUEr3aYeP652QpAaL8fEC67otFaeHh3mlc7g0H60nfiOD4xfx3WWraYMlttBYBuXKqETHaCxpm40QHyBDxuciTRlDvBaxYHCwydMhn41448xecGnwRgS8/13Da1Z1lrPh80ll8//AT/z03/lmsKg/yXnV8hFdQ4HusjFtToa5Uv9BKFEEII8Q4lQaAQQgghLi02wGlOEvr9LGmzvHk4zQkyo99AYVZkH7+V0grhx7sybDnW3hcvHtlAV+LdgIPvZgAY6PoE0+VXqdQP89N9aQ5PR/i1jXmSkaXtNdceIrKHrqF/wOooXuPY7OszE4HPJhS1Fh7Zk2bTYDsE7E3fiaPbIWDdGp41+dnJvus7U37nq5kMabesWKCMQQNr5w4EmeNTR5/lN448DsDrvddz++SFDwGhvfY7Jnfyy0ef43NHniBqWhxOrqG3liMV1i708oQQQgjxDiZBoBBCCCHOH2vwq/vQQRGrPazysdrHKh+0h9FRjNu19EDPtOg6/kW8xjEq2Q9S6fmFZYeBKqyQGfnXziRc/ZaGgMbCzuEoExWX0ChCA6FRBLb97+GCz3jZBRRdyVuJuL04ThKtPKo2JK4cXCdKT+oOYv4AU8XNHJiM8Pcv9vG7751iIH3mNt65+wbCzH6GLooAtYyJwHMV65on9qZmpwP3pu/AcSLE/HWUbcgj4TRFQhxgjfJPq+o77X3qfPZQ+Kh5Q8BPDr3Abx96BIDXem/gPZO7z2rt58PMb+TvHPoJAHvTG9hQHiU2z3MIIYQQQryVJAgUQgghxIpTYY1o8VXihZcXnKg7oxG7msLqfwv6DHveWUt64gezFWyJ/AuAQ6XnY0sPA21AZuSbuK2ps5qEey5CAz/YkWH7cHzR47SKsCpzFyhL1OunjuEZk2PYNrlKRblTp/G0SzJ6Fb7bw0ThBcqNAl/a1MsfvH+S1UsMA6E99qS9l+HZBYCluub5Q0k2H40TmvZVe1IfwHVixPx1lDohYImQBBoPdXIIaC1Xl4aYiHSRj6ROu34LS+uUukHXBPzq4NOz7cCv997A7RdRCDjD0n6fd2av5Lr8YZkOLIQQQoiLggSBQgghhFgxTnOMeP5nREtbUbYFtMOmE82eM+zs50htP5nRb1BY/dugnAWvHSu8RLS0bfZMBSTyz2KVQ7Xn3jMvzlpS4w/i1w931vTWBTONQPGtLV0cmIwAimT0SrT2UWiU0ig0KI1WLlGvH2NbRP1VjNomT4d5qp21HrB1JsMWH3W66FIuEa+b/q6fZzz/DPXWFF9+pZfff/8kazJLC/bOdqBJuaF54VCCTYMJgk4AGPFW0ZW8FWtbnRAw4CdhjjIhSTQGS37Oe+6HLf7n/Q/y0dHXqGuPH66/h4c2fIiG4y9432sKg/z7fd/nXZVRoN0OfNvkbs6uQfz8UsBwtIcb8oc6uxsKIYQQQlx4EgQKIYQQ7zC6lScx/SRBZDW1zB2g9Dlf06sdIjH9DH7twOxrthP+tcOmhdtv23vW7SM99m2K/Z+fdz1e9QDJyUc6x5+8p18y9xQoh2r3hxdeoDXEc88QK23pRDLqrEOw5So3NF97tYfhoovCZVX2gzg6isJtVzJaTqpo1MrH01l2mgqbTQkLpHEIsNQx5An5YTjFnTrN1TqG58QZyH6UscLT1FuTfHlTL7/3vinWZVsr/iy1luL5g0leGUzQCmcCwD66EhtxdJKQOjFvbScEnKaMIYlDiKE25/1eVZvmL3Z9jSvKwwBETYvPDT7JvSObuf/yT/DswG3YOb8HsaDObx56jE8Mv4zGUnLjDKZWc9vknosyBJyxpj51oZcghBBCCHESCQKFEEKId5BIeSep8R+gTR1KECnvotj/WYyXPbsLmhbJqceJF14CZur82hN4l9p2O7NnXbS8E6NjlPs+dXIw1pomM3o/CntaCDgjOf1EOwzsuvuU9TWJlbYQy/8MtzXRWeNbNxxkuupw36YecjUHrSL0Z+8BpYh4fQue0+wM1jhiGwCsUR7jtjXbvKuBAMtzpsCobXKHTuM6UfozH2Gs8AyN1gRf2dTD771vivVdKxcGDhdc7t/STb7WrtqMuL1kkxtxnRTG1vDcFL5KnxQCpnAITgkBb516kz/d8y1SQY2iF2c01sPVxSEa2qenWeRP9n6Hf3P8Rb565Sd5o+sq3ju5iy/se4ieZgGAHV1Xc3nxGDflDq7YswkhhBBCvFNIECiEEEK8AyjTIDnxMLHSa8BMtZ7Brx+m++jfUVr1KRqpW5Z1TbcxQnrs27jNMYA503eXH7LNhIHx4masjlHp/cTsurMjX58d7LHYNNvk1KNYpall70IHBWKFV4gVNqFNrfPMCqsc9FkOxDiVte19/9wFupmHCy5f3dxLtaVwdZL+7F1Y2u28C5myLZ4K8xQJ0cA67XP0lAETc+PVvbbGRNjio06WTCcMHC88S701xn2be/jd902xYQXCwC3HYvzojQyBUbhOkp7U+/CcDKGp4jkplEoDUOyEgJVOCNjCUJ+pwbSGzww+xWePPInGciSxmlSrwjXFIQCipkmIItQOV5SH+dvtX+RovJ8N1fbv13iki6Kf4N25/ef8PEIIIYQQ71QSBAohhBBvc279OOmxB3BbkydNiJ2hbYPM2LepV/ZQ6vsU1oktfkFriOVfJDn1BIqws98e51xlN1MDmMg/h9VRql33kBr7Hm5ztDPU4swVhqnJnxCp7MWrHZo93qKxgMYsaSqusdAKFY1AUQ809ZYiX3PI1RzyVYd83SFfdcnVHAKjiLqGVMSQiobtz5GQiGt5/lCSVqjw3S760h9AqSg1J8bjwRQFAizM+bBYToR8cTRR1Gkh4Kk0ME3AQ+EUH3e6GHAirMp+mPH8s9Rbo9y3uZffee8kl3WfXRgYGHhkd5rNRxMAxPy19KTeQ2BquE4Czz0x4KNgAx6ZEwIGc0LAZKvK/7rnAW6ffhOArd3XcmPuAL49+XfGweKYgKZycK1hQ3WMEM2Onmu4YXo/qxq5s3oOIYQQQgjRJkGgEEII8XZ1WmA3s2ff6WFYuzV3B17tCMX+z9KKXznvJXUrT3r8u/i1Q53znEWr9M5WcvpxvNohIrX9nShp6fv5zexTaHGg06K82D5yE2WHH76RYari0gwUjXB5eybWA0090ExUTv+zKuoP0J3YiNZJprTDE+EUjTM8S7/yyNkW00u4t6EdBrawPBbm+ITTxYD2WZX9EOOF56g3R7hvcy+fvjnPLWtry3quYl1z/5YujuXbwzuyiY3E/LUo5RPzM7PHVWzIdlNhr60S0t7PsDWnHbivNs1fb/8XVtenaGqXPdkruHV676L3ngkIS26Mihvj1qk3l7V2IYQQQggxPwkChRBCiLcba/CrbxLPvYBfP9J+6QyB3UxrrhMW6Rr+EkZHscoD5WG1h1XtD7c5gjb1zggQfV732ovU9s+ufTn3sZ1BIEsJKMsNzX2b+yjW54sKFVp5aO3j6gSuk8B1kic+6yRa+4SmTmiqhGGN0NQITK3TMpsm5g/guV0MYng2nCYEunGpEC4YCI7ZU6r3rOUTwy9zz9gWHlp/D5v6bj7p24b2zy84NQzM3MNE4UVqzWN8b3uW4wWPT1xXxFlCznl4yueBrV1UmhqtfPoyd6FQRLwuVGeyc8kGbDcV9tnabCVjDy5VwtkQsL82xd9u+2f6GnkmIxlq2mfj9L4zL6AjFdRIBcsLMIUQQgghxMIkCBRCCCHeJlRYIVZ8nVjhFZyg3UK5nMBubhSmTR2oz3tce68+c14qAU9lcNHzVDAuZqnTgJuB4quv9lKsK1wnRV/mLhwVQWsPcMBaLGH7wzaxtt1grJWL1hG08lFK4egI1qYxbgtrA6wNsRiMqRPxetll67xiSgAMKI8J21ryOxcL6vwve7/HnRM7APjLXV/n4bV38bUr/ycCfeLPOMtCYeDdFKo7yVd28vKRBCNFj8/fmiMZmb/Nerzs8uKhBFuPxbDMtDXfRWhrxPw1QLsFeLupsN+eGAHSi0uIZWrOz2pNdZy/2fZFeppFRqPd+KbF+trEEp9cCCGEEEKcDxIECiGEEJc4tzFMrPAy0dK22T3w2vv26c703pUN7JY6DXglzBcChgbyNYeexNk/l7HwwNZuRovtab596ffgORkc7Z/V9ZRSOMoHTpxvbZpNpsQbtgrAeuUzZBff82+uy0rD/Nnur7OmNkWgNCPxPtZXxvjk8Re5pjjI/3vjbzER7TpxP+YPA7OJW/DdHiaKL3FkGv7xxV5+4/Yc67InKg+Hch7PH0qyZyw6+1oyegWZxI1gFTF/DcZaXu08z0wA2Kc8mtYwecrPaV1ljL/Z9s90tcqMxHqJBnW6WuUlP7sQQgghhDg/JAgUQgghLiEqKOM1hnEbw7jNzufW1Oz3T+wDaDmb6b0rqREodgxH2XIsTqWpiXmGuG+IeZa4Z4h5hoRvuGGgTjq6tHCxFcKXN/VyLO/xkatKfOSaswuXfrI7w74JH4WmP/MBHKd7wRDQWksDS4WQqjVUCKnYkAqGFoY4DgnlkECTUA5JHCIonjdFDtt2VeWGeab/Lsha7h3ZzB/u/yG+DZjy0xS9OJdXRtvvgXK4pjTEf3/t7/gf132O13tvOHEq84SByicR3YDrpJgoPEexUeRfXunhl24skIoaXjiY4PB0ZPYa8ch6MvGbAIuj4zg6QsWGPBXmGacdHvYrj5o1TJzaxgy8qzzCX2//IplWhWPxPlLNKpmgsrRnF0IIIYQQ55UEgUIIIcTFzAZESzuIlHfiNoZxwuLphwBWuWgbvKXVegsZLrhsPhpn+3CcVrjYmI62Zw6k+aOfm6A7vnhwaS08uLObY3kPgKcPpPAcywevXF7I9LPDcTYNxgHoy3yApk6znYBSOE1g283AQecjxNLCnjlSXaAbWQOr1dJDwGjQ4Av7H+RDY1sAeDNzGWvLY1zeHJ09xrMhLeWQCmr8lze+yoPrP8T9l3+cUDuzS5kvDIx4Xazu+jiTpZepNo7x4M7sSStNRi8nk7gRa0OwCt/rQinFMdPgWZOnjsVD0atcRuYJAAGuKB3jr7Z/iVRQ5Wiin2yjRDqoLunZhRBCCCHE+SdBoBBCCHERUkGJWHETscImnPBE1Vs7b9KdgRghinboM9MSvBRvjER5ZE+GtZkmv3xTYcH94pajGSh2jETZPJhguOjNvu45aVKxq4l4vRjbJDRNjGkQ2gbGNKg1R6g0S3z5lT7+3Z3jpBZZywuHEuwYjgCKZPRyyvVDPL43jedYPnDZ0sKm3aMRHtnTnnjblbwV66R4SoVM2cYZz42giKLxlGoHbbYdFlrA6dRgtrDUMVjAR5HF4ficduDV1Qn+/d7v01+fpqVdAuUQaGf23331PKsaOUI023uu5ZapPcw328OzISEKB8unh57l2uIR/u+bfoeylwBODgMfDae5R2e4QsdwnBh96bspVHeTr2xDKZdU7BrSsesIbQVQRLweAIy1bDVltpj2718WhwC7YAh4XeEI/3nnfSSDGkcSq+mp50iF8+8zKYQQQgghLgwJAoUQQoiLiFs/TqzwEtHSjtm9/eYO/GjX1xnOXGd3unpL8eNdGbYPxwAo1qMMTkf47C3TXNW39L3rTrV/wucHO7ooNWYiK00isoFk7GqU0mjl4bvt6jJrDcYGnaEahnTsOsYKT1Ool/nKpj6+cMc4Me/08rq94xGe2JsGoCd1G/HI5ThOgkJlJw/vzuA5ltvXLz5ddijvJQwpPwAAIABJREFU8e1t3QCkYtfguVle1D5TtjEb8jWxmNkRIScX+jWwNAgXrP47VRPL+Jy98zZO7+M/7f4myTNMwc35KaYjaW6b2rPocU5nIYFyuKFwhL/d9s/8zcYvUPCTwIkwMASeNgWmbMB7dBKtXbKJm0lGL0cpl1aYBwVRt3/22jVreNbkZ0PMdcpn2DbnrTe9qjjEZwaf5L2d9R5OrmFVbYpEeOZwVQghhBBCvLUkCBRCCCEuMBVWiJR3ES1txa8fmX3d4tAO/ew5D/w4NOXz3W3dlBrtGsJ0/DpqjRGqrTxffbWHuy4vc++1Jdz5ys8WUG8pHnszzWtD7TZbRydIx68lHlmDMQ08twtHR8nbgBwtYlYTReMrDzVnP77+zEcZzT/ORLnO117t4/ffP47nnLjPeMnlgS09gCUVuwacLCPKYU18I9YGFKt7eHBnFtexbFxzegXadNVh89E4mweThAZi/lrikQFed9MM2RoOkEAzfb72VLSWXzj+M37/wI9xMBxOrqGnnice1Am1Q4gm1BqjNKHSRIImVzaPL/nyrg0JlOayyih/u+2f+OuNXyAXaYemczPL7bbCtAn4sM7gK43nprA2xHVWzx4TWstBW+N1U6aCwQEGlM+xeYacXFc4wmeOPMmtuX0AGGBX11VcWThKfKn7IQohhBBCiLeUBIFCCCHESrKWaGkrTnOM0F9FEFlN4PWB9k46TIV1IpVdRMo78KsHZvf2s7QDQL1C035bITy5L8VLh9tVYq6TpDf9PjQe2cS7yZW3Uqrt48XDSQ5ORfn8rdNLmsZ7YLJdBVist5PDdOw6ErENKHw8p4uWY9ln6+wNJk+bKKuAGO1QMKY0NzrxThj4BEN5uH9LD791+xSOhmpT8a+v9tEylqjXTySymkcdn6LJcbWK8cHE7VgbUqrt43vbunB1jhsH6hgL+ycibBqMs28i0rkrRLxeMomreNPpZq+toGgPvhheoN31XDkm5A/3P8THRzYBsKPraq7LH8K37ffYNZ0au3PsznatIVCa9dVx/nbbP/FXt/wR05HMSccoYMg2+GE4xcecLjLKRal24lq3hj22ym5TpdZZTAoHBSe1NmMtN+UP8pnBJ7k5fwiAEMXuritYXZng5tyBc3sQIYQQQghxXilr7RIbXFbWxMQErdb5+aP7QlJKsXr1akZGRrhAb60QZyS/p+JScEn+ntqQ1MSPiBU3n/wymtDvJfAHCPxVeI3j+JV9JwV9Fo0F9AoO+xguuHx3WzcTlXbYk4pdTSK6nkB3sVOF9CqXq4hSbQ4xVXwZY5t4DvzSjXk2rq2h5+k/bgSKx/akeHWovRed6yTpTb0PlCbiDjCuAvaaKodsffbpNMy23Qbz9NUq4OM6S08rz1j+KSwhG9fU+fS7c9y3qY/BnNu5z2087aQY4cTfDzeoOB9QSaZKL1OuH0QruOOyMrtGY+RrJ8oKY/5qUrHrcHSEISfOc7a9792ypvmewjUBvmlRdaKgTn+zkq0Kf77r69ycP4RBsbX3em6b3H1Wbd1LFSiNaw0j0R7+6pYvMBntOun7mnbm6KP4sM6SVg5vmAr7bG325xVHk9UOY6Z1UhQdD2r8hze/xx2TO2fvtTt7Jesqo3Q3S+fxqYQQQgghzkKmG/1X/x8qlTnzsZcgz/Po6+tb9nkSBK6wS/I/XMU7jvyeikvBJfd7alpkxh4gUtk9O9Bjpr5voeDnfIR/QQi7RqNsPppgMNduv9U6Sl/6fSjlcdzp4mVbotFZ5WUqwt06A2GZqeLL1Ftjs9eKOJaoZ4h5hphviLmGY4XIbBVgKnYtydgGtEpySDvsNBUKc6KjNA4JpZmwLeYbZaI7HwHgAJ90eog3RhgvPAdYeuIhU1UHpVwGsj/H6zrDHhq4KDzUbOXaRpXgdhVnsvgSlcaRE9dXPsnYlaSiV2FsA63jTOgIj5kcFtigfI52qt3WVsZYVc/j2gDXhLg2xDMBrg3xwxbZZomuZomuZpGuZolss0Sm1Z5WXHN8xqPdTESyTES7mIh2kfdTfPbITxmoT1NzfA6n1nJD/vAK/ITPbCYMHI908X/e8keMx7pP+v5MGHiqLA4+6qR9DWdcUTrGn+36BgP1aQLlsDt7BRvKw2Rby5vY/P+3d+9Bcp3nnd+/73vO6etcMbiDBEiQIinRJCVzbdmR7Uix13YcO7Ycp3ZzceLyriq7ztrOJrXljSvrVa3jpLxJJZVs7KyTbHxblxPbsbORrDibjS+i7pRIkQIJECBxnRnMffre5/q++aN7GjPAABgAA4IAfp+q1unpPn0uja6S6qfneR8RERGRd42CwG2pNVhEROQOmaLH5OXfohRfGEZ/9rrh3iB+M8N1/25v6Md2ljsBX71U42uzdeJs46iGevkYk7UnyOwEXzYF53wLGLR9dig47xNWihU+FkxxYOq7afbepNn9Bp6CpDAkRUAzDracK7R19k58K5iAJNjLS67Fghv8n3shsN+UaPuCFgUtf/02Y8eVQKoA/u9ijR8uH2bv+Lez0v4Cq73BefdPfCvn7BQnGaz/t9cELGyakvya7xIZw/MTH8G0Q7KiyXj1ScrRfpzrEQZ1rJ1mzWf8i2INDxw20SgE/OGLf8G/f/aPb/u7rxYpx7oLHOsuXPPecnmKxAbvWggIV9qE9yfr/Odf/x/5hRf+AxZqe0fvX/3LPGAiYu9obNeK7j3fO/8l/trb/xeRL1gpT9IJqjy/fubu3oSIiIiI3BUKAkVERO6AzRpMzf86YbY0qv27UYXfYI87r3DspYaFdsRCK+TNxSrn164M3whsjfHq+6iVj+B8xkI4xedcm74fBI+PbmqHtUAHx6eLNf6SHeP5+nOMV9+H8ykMJ/w6n+JcMngNSykax5gxzljLy26VAggxHLQRcy5lfpvBEjuR4vl0scbHq4+zx2c0uq8xNfYc63aKLwxDwKO2zEV37TTar7oOoTU8O/HtZEUT7wuiYAITTtLxBW8VbU76HhmevYQsDNcE/M7FV0ch4OXqDM4EODOMac2gYhMziGyjIiNyGdUipZ73iTaFnDmWblghCcrkNsDiWStPcqSzwL6kd1vfx50IvSPHsjdp8kuv/ir/9bM/zsmpx6/ZLwIWr7M+YiWP+Zun/w++c+k1AE5OPsYjnQX2Js27eekiIiIichcpCBQREblNQbLA1PyvExQt/LDh8m6s/9bPDKeXyqPg73K7RCe5eryvoVo6wkT1SYwtY00EwSQv+w6n3SC4mRgOf9i8Jp5jEE564GXXYd6k/Mt2kpqpXnMd3hc4n9PC8pJrsTSsAtxnQrreMXuHk2IN0B+Gkj9ce4ojlcdpFev8f7bAA4+Y0igEDF1Obrf+z5gvuTaRNTwdTuG856JPOOX6zPpkFL1OEtAixwHPrZ/hb536PQBemXmGD62euu1/vxDHZN6D/Erod7C/eptH2x0hg8rA6azDP/j6r/GbT/xrfPqR79iynuH1Fmk52rnM33njtznSX6HA8vWZp/nQ6kluYai0iIiIiLwHKQgUERG5Vb6g3HmD8eU/wroYjx1N/d1Ni+2QL56v8fW5Ovk2hw+DMUrhNOVoL9XSQZzPKYXTtE3IW67HGbdKPLyuo6bErE+3vcqNkGxjQuwfFqs8akpUsFSMpTyc8FsxlkUyXnEdCiDCcMBEzF6nAnAybfPRha/xsYWvMZF1aUc12lGNTlgbPW9HdT63/4MsV6bxw2toUfCZYp3vCab5Fz4kpWCGkHmfYrzjp0/9Ht++/A1+9ekf46UDH9pyzpdciyWfcckn9Dbd7V4Tgmc0wfix9jw/d+K3iHzBiakneOEOQsD3so024dA7fvKdT/FU6yK/+vSPEYflbfffkzT5oUsv8f3zX6DsctZL46yXxnlx9eS7fOUiIiIicjcoCBQREdmhIF2i0voq1far2GIwddYTbJn+e6cKB6eWKnzxXJ3z61fafaNgikrpAKVwmiicxJqQQT2fxdoSmDoXSHjLdVncVOdVx1LBjNbD23Csc5mV8iTdqDZ6bTC4BGIcZ3x85cVt7DcRbZ9fEwJa7/jg2lt8z+WX+UurbxL6K2HcVNbZ9lj/+qXP8vMf+iku1/aNwsBVcv6gWKYY3kOXAgf8W+f+OR9dfAWAnzn5v5PYiK/s+6Ytx3vL9wEoY9hrQlZ9zsqmNQX39df4e9/4J9SKhLfHH+Gp5nm2roL4YAm9G42t+Y7l1zjaXeAfftOPM1/bP9rnSHeRH7n0F3zX4qujlue3Jo5xqLfM8c78PbpyEREREdltCgJFRERuwLiEcud1Kq2vUYovjF4frAdody0EbPQtr81X+fL5MVqjtl9DrXyUieqTgCUIqgS2RmBLeO/pULDucy76hHfcMtloFMkgqMu8Z42czXNda1mfv/72P+Oji6/QjOr898/8FV6deWb0/karcA2LxRAw6CR1HvLhXU+akLmrAsA9SZPvnf8S/8rCV7esIXehfpA4KHO8dQkDxGGJ1ERkNsQZy1jeYzLr8guv/y/8/Id+ivXy5CgM3Fh7MMDQxfFdC6/wb178UwDWS+NMp23+kzd/h//yuZ/g63ueHp1zygSE3rBCztxV69+NZV3+3uv/hOm0zXx1Lwd7K5RuMNDkQWEAgyczAUd7i/zDr/0j/tEzf4VGaYyPX/xzvnX1zdG+Z8ePgPM81brwQFZJioiIiDzMjPf+zlcsvw3Ly8tk2fVWprl/GWM4dOgQly9f5h59tSI3pd+p3A/u9e/UZmvUGi9Rab2CHYZeg6sIgfyGAcl8M2SpE3FgPGPfWE54nYXV1vsBb1yu8I3LVeaa0ZVzmzLjtaeolw7j8dhwD0sMQr11n9PwOevk5FeV69WxTNqAFZexXbPuC2un+Vunfp+ZdOuwh3/2yHfxO8e//5o193bCeMf3zX+JHz/7GarF4KydsMq58SMc7i4xk7ZueowCQ4DnfP0g/9kH/ya9aLA+YQXD1HBC8FPNC/zi1/8xkS94Zeb9fPPqyWE9JCQ24hef/2u8OXX8hucpFRmffO1/4pnWBdZK4wDsSdu3fM/3u8wEWwadwCAAfmvycSbTFofv8dqGIiIiIrticg/27/93mPHJe30ld0UURezbt++WP6eKQBERkU3CeJZa47OUOycww6DNY4dtsw7Ib/j5L5yr85mTE6O/rYF9YzmHJ1IOTWTsH8tZaEe8Pl9jvrX1v4bL0X7Gq08SBXWMKRMH45zyfU67dZJtenQtMEZAzVh6vqCFo+uuXQWwkif8e2c/w/fPfxGAxco0fVvi0d4SAZ4fnv0szzbP8t+8/99mobZ3x9/Vgf4qP/XW7/Nc4ywA5+uHKIzlsc4cz62/vePjBHgKDI91F/hPT/wGv/j8XycNImI8Cz5nX7zO3z3xm0S+4M2px3lhuF7dYDwLlF3Gz3/j1/nkC5/g7Ymj257jaGeBHz/7xzzTukA3qNAPShx5SAOvyBejEDUzAaemjnOou8j7m+fu9aWJiIiIyF2misBddq8rWER2Qr9TuR+8q79T7yn13qLWeIlS/+yVlwkY1KvdnPPw/5ya5PPnBmvulcJp8qKD8zf+77pKdIB65RjlaM+g+s+MsWADTvo+l3wy2q+KZdwEODyJd/RwO2pKfqZxjp8+9Xscigeh16szT/P+tXeobFozb6NCrBeU+bWnfvSaARxXs97xA7Of59859yeUXUZiI05OHef5tbfuaKrsRmXgl2ee5b969t/F2YBKHvNfvPqrPNZd4FJtP3vjBtWrphM7DBZPO6zyCx/8G1wYOwRAJY/5jqXX+J7LL/NU++LwXkPOjx3kfe3ZO7jSB0eOIbzeQpAiIiIi9zNVBG5LFYEiIvJw8Y4gWyPIlgjTZYJ0kSieJcyWB28DnhBLvuP1/3IHf/DaHk5cHkxinR57gUp0GGvLeJ+SFz3SfH30CG2deuXoKPwL7TixLXPWx5x0Pdruynn3m4h8uNZf3+98MvHh3hI/MPcFvn/ui1g8a6UJVisTfGj1rWv2jXxBagJqRcLfPvm7vLB+hj8/8M10ohrtcDDZNw0GrctHukv8h2/9Ps+0Buslvj3+CFNJiw+uXXvcWxXgcRg+vPoGf+P0H/KPn/43+Nsnf5fHugs0ozrVPL4mBASww4rC8bzP33/tf+bXnvo4L66e4iNLr432z43lzMQxxrKOQsBNFAKKiIiIPFxUEbjLVGkl9wP9TuV+sJu/0yBdodb4LFF8kSBd2TbgG4zBCDA3af29WpwZfvur+7iwHgCGfRPfwlq4l9Wgwh5C9pqI2rBOzvkU51IKFxMGY3RsxHkfc97FrGw6b4Rhv4lY9RnxLQQ1pSLlX1r+Bt99+Ss8u6nN8/Xp9/FE8yJ1l9zg04M2W2Dbqr7EhnTCOuNZl5LP6QclTk89xvOrp3d9oMTGsJBz9UM83r1MZkJma/t4vHv5hp/bqCjcbLEyzUplD4+1Zm96/yIiIiLyAFFF4LZUESgiIg8sm7eorf0p1dbLGK5U0w2iIsvGbNqNiao3W//vaq3Y8r9+eT8rXYMxIfsnvpWTwTSvmgxcZ7RfBcOMidhLxB4bsm4qnPddGsXW880QEhpY8vk1U3lv5Hh7lu+5/DLfufgq9SIGBu2yZyaPUspSnl8/s6PjbASA7bBKEpQpFwm1PCbAU3Y55eGQkdMTR5mJG7ywenrH13grNoLFjeDv5PTjPL9283sI8ORYnLWcmTzGdNzgcH+VA/H6XblOEREREZH7jYJAERF54JiiT63xWWqNz2OGa/R5glEl4CBo2nmb7XZmGxH/9Gt76SQQ2Ar7Jr6FrwSTnBnO650xIfFwLb8Yz5xPmSNlc8GaBfaaCOc96+SsknOjAsBKHvNob4lHu4sc7S6MHpsn366UJ7lc28fjrVmebl64rXsbz/uM5/1rXvcM1th7X+virlcBbic3Aa/ufYZvWX5jx58JceAcz66/cxevTERERETk/qQgUEREHhwupdb8IrX1v8C6QZB1ZeDHztb7u5Fuanh9vspXL46x2AkAiIIJZiY+yJ/aMS6TYoFDJmLuqiEhEYYSBgtUTUDuHesULN1kmMjB3grfN/8lPrxygoPx2rb7ZCbgzOQxJpI2R/rL7E2ad3yv2zFAyd9a1eSdCH1xSyGgiIiIiIjcmIJAERG5P3lHkC4TJbOE8aXBNlkYBX4ey2DlvzsLAAsHZ5bLfG22xltLFdyoYs9SLx+lVjvKH9s6DXJKGCaw14SAABmebFju177J0A/rHS+unuT7577Ih9a3tt82ozqrlSlyE1LJ+0ymHSbyHh9onL3O0URERERERAYUBIqIyHuf99i8SZRcIoxnh+HfLHabdfQ8Fo/H3mHrbyexfOlCja9cGKe3KdcrhTOMV58gCifoEvF/WkdMQR2LxbByB8HjZNrmey6/zPfOf4l9SQMYNDCfmTg2bAteZDLrMpl17+jeRERERETk4aQgUERE3ntcRhSfx134MhMrJ4niWWzRuWa3QX1dwKDybxD8GdwdrV+33An43Lkxvj5boxhW/1lTYax6nHr5MBiLMxOctZ6vuDYFME1ITEF3eA2hy5lMO6yWJ8Hc+GqmkxbfsvIGH155g+cabxMOqwU7YZVzE4/waPsyT7dub60/ERERERGRzRQEiojIe4IpepS7pyh136TcO43xGQ4oD9/fPOl369CPO1/7z3s4t1bipbPjnFkujV4vh3uZqD1NYKvYoMaCrfC273PBt3DDgsMDJmLVZ6N5w880z/NzJ36TyaxLO6xyduwIZ8ePjLYL1Rn2x+t82/IJPrxygqdaF7GbJoRcqB8ksSWOty/x3A4m5YqIiIiIiOyUgkAREblnbN6k3HmDcvdNov65UVUfDGr8jAnwPsewO5N+AZyH9V7ASjdkpROy3A25sF5meTj8A6BWfpSJ6pN4LN1wmrfJecfHxC4e7TNJQN1Y5jetB/hty9/gP3rzd0cDNcbzPi803uaFxtujfRIbUXZb1xC8WD9IN6xwpLvEse7CHd+jiIiIiIjIdhQEiojIuy7sX6DWeIly903Mpmq4wYCPjfZeD8MQ8E6tdAM++84YF9crrPfsqOV3M0PAWPUJxiqP4IiYD8Y54fss+9ZonwqGGROx7nOaFDT9lWrEH7z0Ej/xzqexeE5OPsZj7TmqLqMXlOmGFbyxTKVtyi6jwHBu/AgOyyPdRY4q/BMRERERkXeBgkAREXl3eEepe5Ja4yVK8ZU17zwBHofdtM7fbmn0LX96ZoJXZ6tszv4MljCcoBRMEoWTRMEEQVCmMFXesiXe9D26wwDQMmj/7XtHg4K5qwaUGO/4iXc+zQ/Nfg6AV/c8w/NrbxEMz1grEmpFsuUzFs+T7dldvVcREREREZGbURAoIiJ3l8uotF+h1niJMFsFBuv9eQIsBYZiV6r+Nusmlj9/Z4wvX6jjhglgtXSE8eqTWBthiDDGYEyENWU6NuR11+O075MPg74KhhkbseRSLvts2/OUioyfPfm/8e0r3wDga3vfzzevnLzp/ez2/YqIiIiIiOyEgkAREdld3hOki5T671DqvUPUP4v1g4o4jwHsMPy78yEfV+ulhs+fG+Pz58bIh8WFlegAU/VnKEyZVjhFG0fTF7TIafqMlo/JN/UKTxJQMYZFnzPn0uucCSbSDn/3xG/yTOsCmQk4OX2cF1dO7vo9iYiIiIiI7BYFgSIicme8x+brlPpnKfXeJuqfJSjaW3fB4DFYHLsx5RcgyQ3zrYj5RsRsM+JSo0Kjf6XWrhTOMF1/P7mp8FpY56TvU7jGtscywH4TkQzbf5vbrCEIELiCD66f5qMLX+NbVt6k5HO6QYX52j6e14RfERERERF5j1MQKCIit8YXhOkiUf88UXyeqH+BoGht3QWAAIZDPzYetystYKEVMdeMmG2UuNSosNbbvsG2FE4zVfsAzlZ4MxzjhI/JfR+AGpaqsQQYMu9I8fRxOGDxOu2/eM/xzhwfXXiF71x6lcmsO3prrrYPvOd97Uu3fW8iIiIiIiLvFgWBIiJyXcYlBOkyYbpIkC4RJfOE8UXsVQMzrgR/fjjxF+6k8m+1G/D2SpnZRsSlRpXV7vYxYmBrlKO9lKMZSuEkhghnLWdshdd8TDYMAKcJMcAaOT2/s4EklTzhYwtf5fvmv8TR3uLo9VZU48LYYQ72ljnSW77texQREREREXm3KQgUEREAjIuJ+ueI+ucJ08VB+Jdv30q7sdbflWm/cCfBn/Mw14w4uVDhxEJ922q/wFYphzOUhqGftSUMIdaW6Jsql41j2Wec8X2SYQA4RUCIYYV8x9eyL17nX537PH95/ivUixiAzAScmTzKWNrl0d4Sz62/fdv3KiIiIiIicq8oCBQReVi5jCi+SKn/NqXeO4TJHIZrq+U2Qr+Naj9g2OZ7Z9N+nYe3l8u8sVDl5GKV3pbOXEMlOkCltP9K6GciMCV6psycgVWfseIz1kjIfMzmksEJAioYlnYaAHrP060L/NDsS3x4+QTB8GCLlT2sViZ5onmRDzTO3cHdioiIiIiI3HsKAkVEHgKm6BKmS6MW3zBZIEouYfzWoMxjAbNlou9G6LdbktzwymyVl85O0oo3XaOJqJUOUysfJrBVsBV6ts4l41nxGas+Z42Y4qrQDwZNyZMmJMKQes86OVtXLbwiKjIOxqsc6q1wuL/Cof4KT7RnOd6ZH+1zZvxRQpfzWPcyB+K1Xbt3ERERERGRe0lBoIjIe533mKKLLdoERRubd7BFBzt8blwCxgwr9wyY4RaweZswW8IW3e0PjcFjMcPqvu0qAndLK7Z88fwYX74wRloMkjxrStQrj1Mr7cPYCqmpcd6GXCRlwac437wm9AsxTJpBy2/sHT0KMmDNX7/673h7lh+9+Gc82Zplb9LAbrPiYGYCTk09zoHeioZ/iIiIiIjIA0lBoIjIe4kvhsM5LhMllwmTecLkMtb17vzQ12nxNbtY7Xc152G+GfGFc+OcWCjj/OBKwmCcydrTBMEYnWCKbxi46BOabK32K2GYMAF2GPp1KcjxrN4g9NvsWOcyf/XcP+fDq29seb0flFgtT9EPKwSuoFIkTCctrf0nIiIiIiIPNAWBIiL3mCm6VNqvUmm/RpguXNOuCxvZmBk9PAyn8243S/c659nlFt/tOA+L7ZBzqyXOrNS4sBaRbjplOdrPZO0JclPnTFDnTWISeqPwzwAzJsQC6z4nxbOyw9BvsyPdRf7q+f+Xjyy/PrguDG9MP8Gh7jIzaZNqkfJIb+nOb1hEREREROQ+oiBQRORe8J4oPke1+RXKnRNbqvIGmVgAMHrdjN7xm/6+93IHC62Ii+sR76xWOb9WIrkqtxus/XeE8eojdO0YL9uId3yCZzDZt4RhjwmJvadJflvBHzBY068zzw/MfYHvXHx1NPDjjakn2N9fUbWfiIiIiIg89BQEiojsJpdSbb+CzdbxQRVna/ighrNVXFADE1HqnqTaepkwWx59zI9adv0w5Lu7lXu3q51YLq1HXFwvcXatxmLLUlxVlGhMSCXaT6V0kFJQB1th1Vb4Eo4lMvAJAHtNiPewSs6Cz7Y52/WVi5Rjncsc78xxvD3H8c4cR7sLhP7KGocnJx9nT9Lk2cY7d3zfIiIiIiIiDwIFgSIiu8AUfarNL1FrfB7rth/McbVBfV+ApbirQzpulfPQ6Acsd0JWOiFLnYjFdpmVXkCcXduKbE2JcrSPSmk/pbCOMVViU+GyjZgn47LPiBmEfxY4aCJavthx5V8t7/NYZ34Y+A22R3pLo4q/zbphhUv1g0wkbd7fPHdH34OIiIiIiMiDRkGgiMgdMEWHWuPzVJtfxLpB2LVR3TfcY9PeG629m6v/7n3lX17AbDPi7GqZM8t15luWYttccnBPUThFJdpHOdxDEJTBVOnZCnMmYI6MRZ+SkIwq/wDKGPaaiGWfMn+T6r89SZNvXj3FC+tneLzob5xTAAAcAElEQVQzx+H+6rb7NaM6y9U9OAxjaZe9SYN6HvNM8/xtfhMiIiIiIiIPNgWBIiK3yjuCdJFq62tUW1/BDIMtf9VE3uHO2xzg3lX/eQ/d1LLUCTm/VuLt5TpzzW3ae7GE4QSlYJIonCQMagQ2BFMlNyXWTYkzxrPkM1bJcPS33GoITJsQA3S8o4djzqfbXpP1jidbl3hx9SQvrp3ieGf+mn3WShOsVqYw3jGedphJmkxmXSaznVVfioiIiIiIiIJAEZGbMnmHKLlEFF8kii8Rxpewm0Kt7QPAu8N7SHJDN7X0Uks3tWSFwRqw1mMNBMZjLVjj6SYBy92ApXaZxU6ZtZ4huzr1AwJboRIdpFLaR2grGFsnNRFdG7GCYY2cFgVrPicmA7ItwV8Jw5QJ8XjaviDGs3yT1t+nmhf43vkv8eLaqS2BngMu1Q/SCyvMxA1mkgZ70hZ70tYufYsiIiIiIiIPJwWBIvLw8gXGJRgXY/M2QdHG5q3Bo2gR5C2CbI0gX7/2o8Bgsu/ur++3sUbfYjvkcitirllluRPRzyDJPW67IsMdG3w4DMYohzOj4C8146wHZU4ax6LPaFPgr2rv3WCBSQLKxhJ7R5uClEF14M0EruDbVr7BD136HE+1L45e7wVlLo4dopwnHO4tcay7cCc3KSIiIiIiIttQECgiDw7vRiGezdsEm57bvIV1vWHwl2BdPGrp3dGhsQzW+yswsKuTfZ2Hc6slXpsfY65ZYbUL+TVp39a/jQkJTBlrK9hhJR44vN+6taZEFE4QBROEQRlrShhbJTFlVk3ACVOw4DN6FEDvmvbe+jDwc96T4klwJHjWKcDv/P7rWY+/fPkr/MDc59mbNAHITMCpqcfZ11/jQLymtf1ERERERETuMgWBInL/8jlRPEvUP0cUnyPqX9jSsrvjwwCDaM8OX3GYq4O3u9D2u9YL+OrFMb42O0Y33Tj+xnktpXCSUjhNKZwktBHelDCmhDNlnIlIjSE1hgyLxRAAwTCytH7QGpx4z5oxrOJoUND0OV0cDKf4bpzOMljTL8TQ9QUdHDnQvMXAb7OZuMGzzbM8t/42H1l6jYobBK+tqM758cM80bzEc+tv39axRURERERE5NYpCBSR+4YpOoPgL75I1D9PlFzCXLUO3ZVQb+Nx87X7zOiTd17hF2eGV2ZrrPUixsoZY2VHvTR41CJHOfKcXirz5YtTzDc3PuWwJqJeeYxKNIk3FdJggo6JWDCw4HOaFMS44Z0UV671Rm3CnuvuE2GYNAF2GPx1cazeZE2/G/KeA/EazzbO8oHGWT7QPMfBeG3LLnO1fbTDGk+2LvL82pnbP5eIiIiIiIjcFgWBIvKeZFxCmMwTxpeIklmiePY6a/VtVPJtbtn13Dgh232dxPLS2Um+cqFG5jaCx+pNP1cpHWKscphWMMPrtsRlcvo4IB88trkNC5SwlDCEZuOO/eiuvQeHxwEhhsqwtTfD08eRMni+cgfBX7lIeaI9y1Oti8PHBfak7S37FBjm6/vpRDUm4xZHesu3fT4RERERERG5cwoCReTe8Q6bNwjTFYJs+EhXCLMVbN64pj0Xtlurb3cq+W7XWi/gz85M89p8CTdcly8KJqiWj+BcSuFinEsofEzhYrzPCYNxxiuP4aJp3g4meYNkeAdX2prrWGrGYrwhxtGnYGNFQwfEOGLYUd7Zus3WXgDjHXuTJod6Kxzqr3Csu8BTrQsc6ywQXFVpmRvLbP0AcVBhKm6yL1nj0e7ibZ9bREREREREdpeCQBF5d7mUcu805c7rlLpv3XBNvyvVflfW7Lsba/Xdqm5qmGuW+PKFKd5a2lhX0FMO9zJWf4yzdj+zFsZNyDgBdQxVoIondI5lCj5nMrp4NtbqmyCgZizrPifB08XR9e/uvdazHh9onuOp1kWO9JY53FvmQLxK2W1fOdiIxliqzWBdwZ6kyXTa5rHO5Xf1mkVERERERGTnFASKyN23Kfwrd09tmdY7iPc2qvy2Dum419V+AElumGtGXGqUOL82xnwz2DTYY6BaOkyl+igngr2cMhmDtt7B2n7XMFeeljHMmIimz2lR3FHl3u2YTNt8oHGOZ5tn+UDjHEe7C9htSgxzY1krT9KOxnDGUM167IvXmco6TDU77+o1i4iIiIiIyO1TECgiu897gnSJUv8sUf8spd7pLZV/G+29Ztjey12u8vN+EOjFuSErDGlhSHNLWgz+TnJDJwloxGVacUQ7Ceimhn7qSYurr23wdxRMUI72YSsHeCWY5gIZkGGBQ6bEus/xeCxmMNHXDMeReM+YCYj9YIrv/G1MOd7MeEfkCnIb4Izd9v2ZpMmh/gqHeisc7g9afI/0ljjcX71m/8XKHtbKk9SyHvU8ZjJtE3nH/nid/fG1azSKiIiIiIjI/UNBoIjcOV8QpMvD4O8cpf45rOtu3WVL+Le7wZ/3sNoNuLBeYrFTpdWPaCch3dTQyyDO3HD9vp3aen2BrVOOZihHU1hbph9Ms2YrvEzOKjmQEWE4YCKWfMrclnDPb9kAdHbY8juWdTnaXeRod4GD/VUmsi7jWY/xrMdYPtjW8/6oiq/AkNuA3ISj7VjevW5rL8B8dS/N8jjjaYcDvVUOxGscuGrar4iIiIiIiDwYFASKyLV8TpA1sHmDIF8fPl8nyNsYl2B8Oti6dPB8mxbYQTQV4HFY/K6Gf2kBc80S51arvLMyxuUW163c28piTYgx4aZthDEhgS0T2AqBDTEmABNRmAqZrbJmy5zBMUtGNkr0YgCqWKZtyIJLmb2D6r7JtMOLqyc51r3M0e4ij3YXrpnCezMBnsDllMm3dFQXxrJanqQV1fHGUs5jxrI+k1mHw/1BlaCIiIiIiIg8+BQEijxMXEaYLhKlC7jMUG8sYYoe1vUwRX+47RIUt77u27XBHzBq/b19aW5YaIdcapS5sD7G5VZEo5dvKrAbBH4GSynaSymcIrAh1oZ4E+FMmcxUSW2F3Iak3pAbT44h854USPG0KWhS0MddtUqeZyP0G9whTJqQCEMybO/tu9sLAMeyHt+2/A0+svQa39R4h2Cb9flWy5OslybAeMp5SuRyIpdTKRIqRbrtZ66++kCtvSIiIiIiIoKCQJH7j/fYvEGYLhAmC9iiiwuqeFu9srVVfFDF5m3CZJ4wvUyUzBOky6PKPAfUbnQa4MoQD89gkMf13Unw5z10UstaN2CtF7LUqbDQrrLYDmjFV1cbDv4ObJVytJ9yNE4ejLMW7OEdY5gnI9k2HMtgY0jJDrqEIwxlDCUsJWOIvaNDQQ6sbTcEZBPrCqpFsv1xXcEH10/zkaXXeGH9NOGmNuGL9QP0wwq1rM9k2mY87zOTNJlJmje/4Ou40yBWREREREREHhwKAkXey7wjTC4TxRcHwV+6QJAsYv32IdOODokBLMaA91un9G52N4d4dBLLGws1TixMsta1tJNimzX8PJtDv1K4h3I0gQmqdO00i7bGOyajObrGbMunDVDGUsIQGYMFnAeHxw+PPnh43PBOU9yoozbDD9uA3Y6Cw8m0zYurp3hx9RQvrJ+mdp0g8GpztX00ozEe6S5wtLu4o8+IiIiIiIiI3A4FgSLvJRvBX//sYPBGfB7r4mt3A65U621EV/aqPfzwfcugmm/wqcG2AP/uVout9QJenx/n9flxljobFXUbEdzgykJbIwzGCYM6YVjB2QqdYJplW2OWnGU2V+INgjYLTBJSNoa+d7QpcGw09LpBU++tzAnZIesdx9tzvLh6khfXTvFke3bHn12o7GG1MsWR7iJHesscYXn3L1BERERERETkKgoCRe4BU8QE+To2Wx8O41gnyJaJ4gtYt7WSzG8b5sG11XrbVe8NQ797oJcaLq2XOLMywamlCo3+Rog32JbCGWrl/RRBncRO0LY1Fq1hxTuWyK66myth6BiWugko8HS9o49jnXz3wz7vKbuM8azLwf4qh/srHOqtcLi/zKH+Kgf6q0R+63d7qbafTqnO/t4qM2nruoc+GK9xUJN5RURERERE5F2mIFBkt/h8GOitYYvOaAiHLXqbBnL0CPIm1vWvf5irgj9zD8O8m3EenIPCGxr9gHOrFd5enWCuEdBONlfv5YChEh2gUtnLerCfV4MqC1vaedMtYZ4FJggoG0vqPTEFfTwdHB1/begZupwjvWX2JC0qRUJ1OExjsB38Hbmc0BWDrS8IXU403FaKdMvnKkWKvUm62A9KXBg7TFSkHOku8WhvCXp38o2KiIiIiIiI3D0KAkVuxnuMS4bhXhc7ejRHwV+Qr2Pz1nXX29v2sBgYPbZp3b2LktyQ5IasuPJIC0PuDHFuafbLNOMS7SSikwT0UuhnkBYO5z3e+23W9LtyZxtVf1EwQTnai43GWQwP8LK1tDat6RdhGCcgNINP5N6T4EiG6/Y1KOCqqrvQ5Rzor/Fob5Gj3QWOdhd4tLvI4d4KwV1Y07DAsFaepFUawxlLOY+p532m0g7VIuWZ5vldP6eIiIiIiIjI3aAgUAQwLiZIVwmyqx9r2KKL2WEwd+2k3cFju7X4zOj9ncsdrPcCljsRc61xFtsV0twwXi6YrKbM1PpMVVPGK47xckGSG5Y7IXPNMS63q6x0Qhp9R5LfeOrtFZvX8LsxQ0g5mqFcmiIPxlgLZnjblrhEuukIjhqWSRPQ8QVtHGub2nqNd0xkPQ6mbfYkLfYl6+yLB4/98Tr74gbTaeu6lXq9oEyzNE5uAwoT4Mzgm7feYV2B9YMKy8HWYb3H4jHeE7iCks+Iipxo0xUHePYlDfYljR1+ZyIiIiIiIiLvTQoC5cEzquBrYfMWQd7CFu1he26McX1MEWNdH+Ni7LBt96aHBdhSxQeDerErdnPSbj8znF+rcHp5irlmmUbf00vTq/baHOiFwPgNjjiYi7v5ao0JsSbAmBBDiDEB1oRYWyGwIdZGeBtRmIjMVEipgAkG9YvG4sygedlhaWM4ZzLaW+5/cL4pAirG0vA5PRzleJ0XGmd5pnWBffE602mb6aTNZNYm3Kbt92qJjViq7KEXVSjlKWN5j+mkRa1IqPVvf6KyiIiIiIiIyINMQaDcV4xLsHkLm7evCfo2XgvyFsZnNz/YVTZadT0Gc1XABxsh361X8e1EkhvWegHvrEzwzuo4l1vQSTaHdlfCLUNIFI4TBePYsII3IbgM71IKl1AUfQrXx/kUMETBBFE4QRhUKYIqPTtGw47TsxGZgdRDhiPFk+DJGLTn3vwutw/sShimTAjDYR5dHGG8wtONszzbOMsHmmc53F+94ZHbYY1OVKMXVnDGUMpTKi5lLOtRKxLKLuPR3uIOvlkRERERERER2aAgUO4Nn2OKPtYlGJ9iXDJ8pBifDCr2ivbWkC9vYf3Oq722rsG30aJ7/XjL3KCN95ZuzUNaGOJssN5ekht6aUg3LdFNIxr9Eo04ohVbuomnn+XkbnPr8ZUAMAzGKUd7cOE4fTvOWjDOgglpmMG03KuFGGpYJgiYBDJgkYIWxVV37oDkupmmAcoYSlgiYwgYrvzn/TVRqB/+Z6nIGO8vc7C3xOH+Cgf7qxzsr3Kov3LNBF2HYb62j3ZUZzztUCkS6nlMxaUYYDzvMZ5r6oaIiIiIiIjIblIQKLvDu8FE3GGV3pVKvdagJbfoYVx/0I5b9LH+6hbXWzgVsHUdPrdra/Dd8LwessLQywxxZmnGEUudOqvdMuv9YbCXOuIsw92wvdWztUV3eL0mohzuIYwm6YWTzAbTXLIBnWvWJ8wxwBgBVTNYvbDwVyr5WsPgb3bTJyIMY8YSYQizPmPxOlHRJ3QZUZFSKgYVd5UipZInjOU9Ki6n5DJKRTbYbjyKjNLGe274vMgo+euvO1hgmKvvpxvVmIpb7I/XeKS3tPMvX0RERERERETumIJAAe8GFXo+xbgM47PRltHzFOPS4bTczmhy7uYpujsdqDE67ejZ5jX32PTcbVvBt5vr8G3IC2j0A5Y6FRY7Y6x0y6z3QtoJpLkjLRx5keO3DRaH1XXbXKk1JayNsCYaPg8xNsLbMrkpk9gqHVtj3ZRp2IA1si3foqVgBsNE1qeaNJjqr7G/v8Qj3UX2JQ1mkiaRy2lGdZqlMRqlcVZKUyxVp1ktT1EvEo625zjWXRhV6d0osLtT7ajGemmCJCwRFDmVImEi7VIvYo521corIiIiIiIici8pCLzfeT8M6eLhIxkOxEgGlXdFF+t6o6o8W3QHwzJcOviczzG7FAxdO0zj+tV6G3te+eTuVO5lBXRTSysOWe9XafYrNJOIThwS55a0MGQFZIUnKyB3jqxwZMXVawpmw8d2LNaWCEyFMKhigzLOVkhthZ6t0TRVmkGJ2JvBGnzD9feyG97jYDrvuCs4lLQ40pnjfetn+NDqSQ7sYFrtrUy0LYxlrTRBGpQoNg38ADNYIdE7rC8InCPAEThH6AtClxP5fPT6dv+u41mP8UwtvSIiIiIiIiLvRQoC38u8B59hiy5Btk6QrxFkawTZOjZfJ8jWsEXnhuve3fIpgSsR3XZVejdqxd14f/eup3DQzyztJGStVxkEe3GJdhLSSQN6qSHOPHFekOY5zm9XlVgMHzdmCAmDMcKwhrFl8qBKYqvElIltiR4RnSAg9obEQDIM+K5z5cMs1FPCU3YFE76g5HLG8pjJtMVUvM6+/iqHess80ltkb9JkLO9ve7RmVKdVGiMNSnggylPKLqOWx9TzHpkNiYMyqY0ojKWwIQZPEpQoTEA5j6nnMZPpYCrvrQSHIiIiIiIiIvJgUBB4F3jvwaXYon9lAIaLB0MwXDIYkLHNw7r+qLLPFsMKvx22214b4N08uNvOlTBv8/bWeA9xboZVeTU6SYl+HhJnAf0sIMktSW5Jc0PuIHeGwnlyNwj+nPfkzpHmBbnbrirvZsGeJbAVAlvB2hLGlvA2wpkQR0hhInITkJuQ1ISkhKwHJZoEdIwnv1nlnoFykTFWJEynHWaSBvv6qxzsLbMvabA3aTCRdannMeUixe7we3QYVspTNMrjeKCaxUymHcaKPpNZl8mse93PBi6n4u5ey6+IiIiIiIiI3P8UBO4im7fYc/G/pTiTsG/Xq/RuPBzjVgI878ENH4Uzo23mLIWz5Ju2ubMU3lA4S5oHdNOIbhrRG4Z6cW5IMkOce+LMkeQFeZFeZy29nVXmbcea8iDYC8pYG+FNicKUyGyZ2JTp2TIdG9EhomcssfEkt/hvUC5iDsbr7IvX2Zc02B+vMRM3Gc+7jGV9xvI+9eEjvMEwkF5QJg7KtKI6eXmCwgS4YQsuDP51oiIjcjmVIqWex5R8jsWzdxgkioiIiIiIiIjsNgWBu8ibCOviK3+Pnm2u0DNXvXvzar3NwzHyAtZ6VZa6Y6x2q3SSkNRZ8sKQFZbMQV4MKu2urIXnyZwjLwoKl+PueE3AG62ft+m6TURgy1hTwtgAY0K8CfDDrSPEmWAQlBEMWlqH24yAro3omoiuCegbdhTsGe8Yy7vsSztMZF0msg6TaZeJrEstj6m4lHIxnI5bJKPne9IWE1l39G/RCat0wypJUMJZi8cQh2WSoEQjGsPiMd4TuYzKsEU3GrYl14qEWrHd8BARERERERERkXtHQeAu8rbMyqP/MWNzv03aa5IWAWkRDbZ5MAjqioC0sGTOkheWzAVkhSF3hiy3ozAvG4Z5eQFpAb2sIMlT8i0B084CuZ2zGCwYg8FijBm8ZgbViMZYjIkwNsSbCG9CchORm4jURPRshb4ZhHcdG9I35par8q65Iu8Yz1rsz7rDQK/DZNZhYhjuTWbDwG/43njWI8DTC8r0wgpJUCKzEc7YwSAMVxB4h/EegyfwjsAXOCAzAaVhmDeW96+7Xp+IiIiIiIiIyP1IQeAuWlvr8k9/53cZfK0zN9jTsVHhd3vssE22CraEJ8AbiyfAmQCHpTABmQnITERmQlITkJqQxAQkJiDHUhiDAwosHo8b/u3xw+3tqeV9JvsNDgxDunreH1XgVYqU6qZqvJLLCF1B5HMilxO5wXTaksuZyLpYXxCHFfpBmTQYrO3njSVwGVGRE7qcssuoFgmVIiHYuAZV5YmIiIiIiIiIbKEgcBcVZrsmX4sxwaCajsEWY4GNbTD625lgGOZZnAkpTEBOQGHC4Rp4JZq2RMsE1znX9QWuoFokVPMulSIlcjmBd1jvtmw3nm/+O/CDqDB0BbU8plb0B9vhJNpaEVPP+tTzHoF3ZEGJ1EbkNsQDocuxvsD6KxV4UZFT8oMgL/DFKMDbTl2BnoiIiIiIiIjIHVMQuItsFvOV6Y+RYcmNIcfALQZ211MqUmaSFofTy3xT0mJP0mJP2mQi6xK4gsAXhO5KcDd4DEO3YbsrZjC/1niP3dQeC36wNt6wBNBsqgW03m96zWO9H1bu5ZRcRuSLHU8kFhERERERERGRe0dB4C6qlQJeWH6D0A9bXH0+aHsdVt+FbjAZdlB1Nwzj8Fj8qAIvdDmhc4PP+oJwWD1Xccmgim7YBlvL+kR31F4sIiIiIiIiIiIPk9sKAv/kT/6ET33qUzQaDY4dO8ZP/uRP8uSTT+72td13KlNT/J2f/lH8P/hZ3MLcvb4cERERERERERGREXurH/jCF77Ab/3Wb/FjP/Zj/PIv/zLHjh3jl37pl2g2m3fj+u4rxhhMqTyctisiIiIiIiIiIvLecctB4Kc//Wm++7u/m4997GM88sgjfOITn6BUKvFnf/Znd+P6REREREREREREZBfcUmtwnuecPXuWH/mRHxm9Zq3lueee4/Tp09t+Jssysiwb/W2MoVqtEoYP5vKExhj8oUewtfq9vhQRERERERERkYdTpUpQqUAU3esruStuN1e7pU+1Wi2cc0xNTW15fWpqivn5+W0/80d/9Ef8wR/8wejvj3zkI/zsz/4s09PTt3G594lf/B/u9RWIiIiIiIiIiIhsccutwbfq4x//OL/xG78xenziE5/YUiH4oOn3+/zcz/0c/X7/Xl+KyHXpdyr3A/1O5b1Ov1G5H+h3KvcD/U7lfqDfqTwobqkicGJiAmstjUZjy+uNRuOaKsENURQRPaBlmNvx3nPu3Dm89/f6UkSuS79TuR/odyrvdfqNyv1Av1O5H+h3KvcD/U7lQXFLFYFhGHL8+HFOnDgxes05x4kTJ3jqqad2/eJERERERERERERkd9zyyoI/+IM/yK/8yq9w/PhxnnzyST7zmc+QJAkf/ehH78LliYiIiIiIiIiIyG4IPvnJT37yVj7w6KOPUq/X+cM//EM+9alPAfAzP/MzHDly5G5c333JWsuzzz5LEAT3+lJErku/U7kf6Hcq73X6jcr9QL9TuR/odyr3A/1O5UFgvBrcRUREREREREREHnh3fWqwiIiIiIiIiIiI3HsKAkVERERERERERB4CCgJFREREREREREQeAgoCRUREREREREREHgIKAkVERERERERERB4CCgJFREREREREREQeAgoCRUREREREREREHgIKAkVERERERERERB4CCgJFREREREREREQeAgoCRUREREREREREHgIKAkVERERERERERB4C/z/NZaIhhb600QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emissions.plot.area(stacked=True)\n",
    "plt.title(\"PRIMAP-hist Global Emissions GgCO2-eq\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}