diff --git a/.gitignore b/.gitignore
index 36179ed..8cfb2d5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,3 +5,8 @@ package-lock.json
 _dist
 _cache
 /_src/localStyles
+
+#local
+environment
+.ipynb_checkpoints/
+borrador.xlsx
\ No newline at end of file
diff --git a/model/model.ipynb b/model/model.ipynb
new file mode 100644
index 0000000..2ac2455
--- /dev/null
+++ b/model/model.ipynb
@@ -0,0 +1,30900 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# $$\\text{Análisis y Selección de Variables para nuestro Modelo}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.express as px\n",
+    "from plotly.subplots import make_subplots\n",
+    "import plotly.graph_objects as go\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>instant</th>\n",
+       "      <th>dteday</th>\n",
+       "      <th>season</th>\n",
+       "      <th>yr</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>weathersit</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>casual</th>\n",
+       "      <th>registered</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2879</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2011-01-01</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.2727</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>32</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   instant     dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
+       "0        1 2011-01-01       1   0     1   0        0        6           0   \n",
+       "1        2 2011-01-01       1   0     1   1        0        6           0   \n",
+       "\n",
+       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
+       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
+       "1           1  0.22  0.2727  0.80        0.0       8          32   40  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bike_train = pd.read_excel('../bike_train.xlsx')\n",
+    "bike_train.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Lo que podemos suponer a primera vista, no usaremos la columna dteday ya que no queremos que el modelo dependa de una fecha que nunca es igual al anterior o al del dia siguiente. Lo que si podemos decir es que esta fecha ha sido dividida en columnas más independientes como la estación del año (season), el mes del año en el que ocurre el evento (mnth), la hora del registro (hr), la semana en la que ocurre (weekday). Que son eventos que se repiten siempre.\n",
+    "\n",
+    "- También podemos prescindir de la columna instant, ya que es solo un identificador para llevar en orden los datos que se registran.\n",
+    "\n",
+    "- La columna \"yr\" nos indica el año en que la persona se hizo cliente, para este problema no requerimos ese dato, ya que buscamos la cantidad total de consumidores (independiente de si son clientes o consumidores casuales). Para un problema en el que nos pidan monitorizar a los clientes registrados quizás pueda ser un variable a considerar.\n",
+    "\n",
+    "- Finalmente, tampoco usaremos las columnas \"casual\" y \"registered\", ya que queremos calcular la cantidad total de registrados, independientemente si son clientes vips de la empresa o solo toman el servicio cuando lo necesitan."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>weathersit</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0.2879</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.2727</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   season  mnth  hr  holiday  weekday  workingday  weathersit  temp   atemp  \\\n",
+       "0       1     1   0        0        6           0           1  0.24  0.2879   \n",
+       "1       1     1   1        0        6           0           1  0.22  0.2727   \n",
+       "\n",
+       "    hum  windspeed  cnt  \n",
+       "0  0.81        0.0   16  \n",
+       "1  0.80        0.0   40  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Nuestra variable de predicción es la cantidad total de regsitrados (cnt), y veremos si existen alguna relación directa\n",
+    "# o inderecta entre las columnas = [season,yr,mnth,hr,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed]\n",
+    "\n",
+    "new_bike_train = bike_train.drop(['instant','yr','dteday','casual','registered'], axis=1)\n",
+    "new_bike_train.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>weathersit</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>season</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.865268</td>\n",
+       "      <td>-0.007610</td>\n",
+       "      <td>-0.008928</td>\n",
+       "      <td>-0.003688</td>\n",
+       "      <td>0.012630</td>\n",
+       "      <td>-0.009029</td>\n",
+       "      <td>0.410790</td>\n",
+       "      <td>0.413196</td>\n",
+       "      <td>0.198937</td>\n",
+       "      <td>-0.142924</td>\n",
+       "      <td>0.111369</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mnth</th>\n",
+       "      <td>0.865268</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.007060</td>\n",
+       "      <td>0.009372</td>\n",
+       "      <td>0.014500</td>\n",
+       "      <td>0.006441</td>\n",
+       "      <td>-0.012239</td>\n",
+       "      <td>0.353330</td>\n",
+       "      <td>0.358541</td>\n",
+       "      <td>0.201669</td>\n",
+       "      <td>-0.147042</td>\n",
+       "      <td>0.073431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hr</th>\n",
+       "      <td>-0.007610</td>\n",
+       "      <td>-0.007060</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000414</td>\n",
+       "      <td>-0.004037</td>\n",
+       "      <td>0.003454</td>\n",
+       "      <td>-0.024003</td>\n",
+       "      <td>0.139251</td>\n",
+       "      <td>0.134633</td>\n",
+       "      <td>-0.257202</td>\n",
+       "      <td>0.130441</td>\n",
+       "      <td>0.396698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>holiday</th>\n",
+       "      <td>-0.008928</td>\n",
+       "      <td>0.009372</td>\n",
+       "      <td>0.000414</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.102503</td>\n",
+       "      <td>-0.248601</td>\n",
+       "      <td>-0.014707</td>\n",
+       "      <td>-0.028519</td>\n",
+       "      <td>-0.038005</td>\n",
+       "      <td>-0.036672</td>\n",
+       "      <td>0.022681</td>\n",
+       "      <td>-0.030304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weekday</th>\n",
+       "      <td>-0.003688</td>\n",
+       "      <td>0.014500</td>\n",
+       "      <td>-0.004037</td>\n",
+       "      <td>-0.102503</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.031057</td>\n",
+       "      <td>0.017259</td>\n",
+       "      <td>-0.010002</td>\n",
+       "      <td>-0.009841</td>\n",
+       "      <td>-0.025932</td>\n",
+       "      <td>0.008447</td>\n",
+       "      <td>0.022618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>workingday</th>\n",
+       "      <td>0.012630</td>\n",
+       "      <td>0.006441</td>\n",
+       "      <td>0.003454</td>\n",
+       "      <td>-0.248601</td>\n",
+       "      <td>0.031057</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.042202</td>\n",
+       "      <td>0.062273</td>\n",
+       "      <td>0.066305</td>\n",
+       "      <td>0.022805</td>\n",
+       "      <td>-0.002493</td>\n",
+       "      <td>0.025132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weathersit</th>\n",
+       "      <td>-0.009029</td>\n",
+       "      <td>-0.012239</td>\n",
+       "      <td>-0.024003</td>\n",
+       "      <td>-0.014707</td>\n",
+       "      <td>0.017259</td>\n",
+       "      <td>0.042202</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.080149</td>\n",
+       "      <td>-0.081386</td>\n",
+       "      <td>0.430378</td>\n",
+       "      <td>0.009195</td>\n",
+       "      <td>-0.149848</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>0.410790</td>\n",
+       "      <td>0.353330</td>\n",
+       "      <td>0.139251</td>\n",
+       "      <td>-0.028519</td>\n",
+       "      <td>-0.010002</td>\n",
+       "      <td>0.062273</td>\n",
+       "      <td>-0.080149</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.991785</td>\n",
+       "      <td>-0.019289</td>\n",
+       "      <td>-0.013984</td>\n",
+       "      <td>0.395743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>atemp</th>\n",
+       "      <td>0.413196</td>\n",
+       "      <td>0.358541</td>\n",
+       "      <td>0.134633</td>\n",
+       "      <td>-0.038005</td>\n",
+       "      <td>-0.009841</td>\n",
+       "      <td>0.066305</td>\n",
+       "      <td>-0.081386</td>\n",
+       "      <td>0.991785</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.003669</td>\n",
+       "      <td>-0.057619</td>\n",
+       "      <td>0.395234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hum</th>\n",
+       "      <td>0.198937</td>\n",
+       "      <td>0.201669</td>\n",
+       "      <td>-0.257202</td>\n",
+       "      <td>-0.036672</td>\n",
+       "      <td>-0.025932</td>\n",
+       "      <td>0.022805</td>\n",
+       "      <td>0.430378</td>\n",
+       "      <td>-0.019289</td>\n",
+       "      <td>-0.003669</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.295765</td>\n",
+       "      <td>-0.303672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>windspeed</th>\n",
+       "      <td>-0.142924</td>\n",
+       "      <td>-0.147042</td>\n",
+       "      <td>0.130441</td>\n",
+       "      <td>0.022681</td>\n",
+       "      <td>0.008447</td>\n",
+       "      <td>-0.002493</td>\n",
+       "      <td>0.009195</td>\n",
+       "      <td>-0.013984</td>\n",
+       "      <td>-0.057619</td>\n",
+       "      <td>-0.295765</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.094791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cnt</th>\n",
+       "      <td>0.111369</td>\n",
+       "      <td>0.073431</td>\n",
+       "      <td>0.396698</td>\n",
+       "      <td>-0.030304</td>\n",
+       "      <td>0.022618</td>\n",
+       "      <td>0.025132</td>\n",
+       "      <td>-0.149848</td>\n",
+       "      <td>0.395743</td>\n",
+       "      <td>0.395234</td>\n",
+       "      <td>-0.303672</td>\n",
+       "      <td>0.094791</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              season      mnth        hr   holiday   weekday  workingday  \\\n",
+       "season      1.000000  0.865268 -0.007610 -0.008928 -0.003688    0.012630   \n",
+       "mnth        0.865268  1.000000 -0.007060  0.009372  0.014500    0.006441   \n",
+       "hr         -0.007610 -0.007060  1.000000  0.000414 -0.004037    0.003454   \n",
+       "holiday    -0.008928  0.009372  0.000414  1.000000 -0.102503   -0.248601   \n",
+       "weekday    -0.003688  0.014500 -0.004037 -0.102503  1.000000    0.031057   \n",
+       "workingday  0.012630  0.006441  0.003454 -0.248601  0.031057    1.000000   \n",
+       "weathersit -0.009029 -0.012239 -0.024003 -0.014707  0.017259    0.042202   \n",
+       "temp        0.410790  0.353330  0.139251 -0.028519 -0.010002    0.062273   \n",
+       "atemp       0.413196  0.358541  0.134633 -0.038005 -0.009841    0.066305   \n",
+       "hum         0.198937  0.201669 -0.257202 -0.036672 -0.025932    0.022805   \n",
+       "windspeed  -0.142924 -0.147042  0.130441  0.022681  0.008447   -0.002493   \n",
+       "cnt         0.111369  0.073431  0.396698 -0.030304  0.022618    0.025132   \n",
+       "\n",
+       "            weathersit      temp     atemp       hum  windspeed       cnt  \n",
+       "season       -0.009029  0.410790  0.413196  0.198937  -0.142924  0.111369  \n",
+       "mnth         -0.012239  0.353330  0.358541  0.201669  -0.147042  0.073431  \n",
+       "hr           -0.024003  0.139251  0.134633 -0.257202   0.130441  0.396698  \n",
+       "holiday      -0.014707 -0.028519 -0.038005 -0.036672   0.022681 -0.030304  \n",
+       "weekday       0.017259 -0.010002 -0.009841 -0.025932   0.008447  0.022618  \n",
+       "workingday    0.042202  0.062273  0.066305  0.022805  -0.002493  0.025132  \n",
+       "weathersit    1.000000 -0.080149 -0.081386  0.430378   0.009195 -0.149848  \n",
+       "temp         -0.080149  1.000000  0.991785 -0.019289  -0.013984  0.395743  \n",
+       "atemp        -0.081386  0.991785  1.000000 -0.003669  -0.057619  0.395234  \n",
+       "hum           0.430378 -0.019289 -0.003669  1.000000  -0.295765 -0.303672  \n",
+       "windspeed     0.009195 -0.013984 -0.057619 -0.295765   1.000000  0.094791  \n",
+       "cnt          -0.149848  0.395743  0.395234 -0.303672   0.094791  1.000000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Correlación de Pearson\n",
+    "new_bike_train.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos decir que:\n",
+    "- Existe una correlación fuerte entre season-mnth, esto es bien sabido ya que las estaciones del año cambian en el trancurso de los meses.\n",
+    "- Existe una correlación fuerte entre temp-atemp, donde podemos prescindir de uno de las dos variables. Por lo que podriamos usar atemp (sensación térmica) como una de las variables importantes para nuestro entrenamiento.\n",
+    "- Existe una correlación media entre season-temp y season-atemp, donde la estación del año influye a la temperatura del lugar.\n",
+    "- Existe una correlación media entre weathersit-hum.\n",
+    "- Existe una correlación media-baja entre temp-cnt y atemp-cnt, y es una de la correlaciones más importantes a mi parecer, ya que nos indica el clima influye al comportamiento de alquilar una bicicleta.\n",
+    "- Otra correlación media-baja importante es hr-cnt, que nos indica que el comportamiento de los alquileres influye respecto al horario.\n",
+    "- Finalmente existe una correlación baja entre cnt-hum, donde el comportamiento de los alquileres influye respecto al clima en el día."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KjI2Npar/OrNnz0ZOTg7y8vIwZMgQDBo0CCUlJVLvR54UOabOnTuHPn36YPz48VBRUYGpqSlWrlwJoHE+T/WRla+KUhKhhXapxPIW2qVIz63+Jy1XE5rg+HU9rB2W2hhNVCiOj/TqM09VtmnTJiQnJ1dJBr+p6jMv1ZW3tzdevnyJ7777rkH7URRZjBfG3jRvVEJFLBZjwIAB6NKlCzw8PLBnzx5kZWUhLy8PDx8+hJeXF3R0dITyxRdf4OHDh8L2P//8M9555x0YGxtDR0cHn3/+ucRfJhcuXIgZM2Zg4MCB2Lhxo8S2MTExeOeddyTa88477+D+/fvClxkAEjeIFIlEMDY2xtOnT2vsU1FREbKzsyVKUYOixBqViwvw2WflN7Lt3h0YNQoYOhT4/PPq63t5AWfPAjX8RrSxTZw4ETk5OUKpuCJL0Xr37g07Ozvs27dPYrmKigqICJMmTcKff/6Js2fPYuHChfD09JTLVSrKFJ8mTZpg3759CAsLQ+/evfHOO+/gzp07OHPmjND3PXv2YPv27fj9999RXFyMiIgIHD16FED5PUZkTZniU2HBggXo3r07hg8fDisrK2zZsqXaep6ennj+/Dn8/f0bt4FKxM/PD926dUPfvn1x7949HDt27LW//28MyjSmAgMDsWTJEuzatQtFRUW4d+8e/vj76kN5fJ4UIbdIhKUnTLB2WBqaa70dfZIljk/DeXt7Y/z48Rg1ahSKit7MM9zGmpe6d++OefPmYerUqXLZP2NMPt6ohIqqqioCAwNx9uxZ2NnZYdu2bbC1tRWe0LBnzx7cuHFDKHfu3EFERAQAIDw8HJMmTcL777+P33//HdevX8d///tfFBcXC/tfvXo1/vrrLwwdOhT/+9//YGdnhxMnTkjVxsqTrEgkeu2J14YNG6Cvry9Rari2gclbejrw8iVgZCS53MgISK3hL1Nr1wI//QTs2wfcuQP4+5cnWJYvB0QiybpmZuU3pFXgX8QrO3XqFOzt7YWSnp4OADCqFAMjIyOk1hCD9PR0vHz58rXbpKamQlNTE/r6+nXa74wZM3D9+nVcu3ZNYnlKSgqSkpKQnf3PZdcxMTFQUVFBmzZt6tjrulOm+EycOBHt2rXDtGnTcPXqVURGRmLixImwsLCQuNv9smXLoKOjA3NzcxgbG+PKlSsAgEePHjUgEtVTpvhUSEtLQ2xsLE6fPo2PP/4Yc+bMqfavYtOnT8dPP/2k0Csy6tL3ylJTU6Wq/zrZ2dl48OABLl68CHd3d3To0AGjRo2Sej+ypGxjauvWrWjWrBnMzMxgaGiIkydPApDP50kWDLRKoSqiKjdYzchThaFOaZX6iVkaSHqujtlHWsNujTXs1ljD/6Ye/herDbs11kjIVHySVJY4PtKrzzxVYdGiRVi2bBnee+893L59W57NlCtZzEt14ezsjFatWiEhIQElJSUoKSlBu3bt8PXXXyMuLq5BfWgsDRkvjL2p3qiEClCeoHjnnXfg4+OD69evQ0NDA2FhYTA1NcWjR4/Qvn17iWJhYQEAuHz5MszNzfHf//4XPXr0gLW1NeLj46vs38bGBgsWLMD58+cxevRo+Pr6AgA6duyIsLAwibphYWGwsbFp0CMUly9fjhcvXkiU5fXeG2uQkhIgKgoYMOCfZSJR+evw8Oq30dICKifMKq5YqpxQmTYNePoUOHNGdm1uoNzcXDx8+FAo0dHRSElJwYBXYqCrqwtHR0eE1xCDkpISREVFSWwjEokwYMAAYZuoqCgUFxdL1LGxsYG5uXmV/Wpra2Ps2LFVrk4BIHzWtbW1JfZTWlqKJ0+e1C8Ir6FM8dHS0kJZWRmISKhT8bryk1nKysqQnJyMkpISTJgwAZcvXxZOAGVJmeJTnYq4VL7qwsXFBdbW1tWOscZUl75XFh4eLlEfAAYNGvTaONSFSCSCSCRS+BUqyjqmUlJSUFhYiAkTJiAhIaFKsldZaKgCnUwLEf5IS1hWRkD4Iy10a1NQpb6lYTFOz34M/1nxQulvmwdHiwL4z4qHsb5y/QSsoTg+0qvPPAUAS5YswYoVK+Dm5oaoqKjGaKrcyGJeqouffvoJXbt2lUjeJCUlYfPmzRg8eLAsuiJ39R0vjL3J1BTdAGlERkYiODgY7733Hlq1aoXIyEg8e/YMHTt2hI+PD/7zn/9AX18fbm5uKCoqwtWrV5GVlYWFCxfC2toaCQkJOHr0KHr27IkzZ85IXH1SUFCAJUuWwN3dHRYWFnjy5An+/PNPjBkzBkB5lr1nz55Yu3Ytxo0bh/DwcGzfvr3B927Q1NRU+AlstbS1gfbt/3ltYQGIxUBmJqBkNy2UqS1bgAMHgKtXgStXgPnzy2Pxd2INBw4ASUnlV6EAwOnTwMKFwPXrQGRkeczWri1f/mqiRSQqT6gcOPBPwkVJffPNN/j8889x//59xMXFYe3atUhOTpb4aURQUBBOnDiBHTt2ACi/x9CBAwdw9epVXLlyBfPnz4e2traQkMzOzsa+ffuwZcsWZGZmIjs7G9u2bcPly5cRGRkp8f7jxo2DmpoaDh06VKVthw8fxooVK+Dr64tVq1bB0NAQmzdvxo8//ljl5pHyoqj4BAYGYvPmzdixYwe2bdsGFRUVLFu2DC9fvhTu09SiRQu4u7sjNDQUTZo0wbRp04RHwjYWRcVnyJAhMDIywp9//onc3Fx06tQJmzdvxqVLl6okz728vBAREYG//vqrcYLyGrX1/cCBA0hKSsJnf8853377LS5cuICFCxfizJkzGD9+PHr06IGPPvpI2KeBgQHMzMyER5Ta2toCKL8qIy0tDRYWFhg3bhzOnz+PZ8+eoU2bNli2bBkKCgqEn7QoE0XOSYsXL0ZAQADKysowevRoLFu2DGPHjlXqn/xM650Fb39jdDYtQtfWhTgQ0QwFJSoYbV9+Zd/SE8Yw0n2JRQPToalGsGlVLLG9XpPyY9Sry58XqCDlhTqe5pSfNsall1+ZYajzEi2rubJDmXF8pCftPLV06VKsWbMGEydOxOPHj4WrFXJzc5GXl1fj+7xJ6jMvaWtro/0r59YWFhYQi8XIzMxEYmIiMjMzq9y4t6SkBKmpqbh3716j9EsWpB0v6urqsLOzAwBoaGigdevWEIvFQiILqD12jCmUgp4uVC/R0dE0ePBgatmyJWlqapKNjQ1t27ZNWO/n50f29vakoaFBBgYG1LdvX/rtt9+E9UuWLKEWLVqQjo4OjRs3jrZu3Ur6+vpERFRUVETjx4+ntm3bkoaGBpmamtInn3xCBQUFwvYVj01WV1cnMzMz2rx5s0T7zM3NaevWrRLLxGIxrVq1SrqOyvNxVnUtLi7Vt83XV/Ftk3eZO5fo8WOiwsLyxyj36vXPupAQyRioqhKtXFn+GOT8fKL4eKLt24n09SX3OWhQefysreXefsig+Pj4UEpKChUUFFBgYCBZW1tLrI+Li6NVq1ZJLJs7dy49fvyYCgsLKSIignr16iWxXlNTk7Zv304ZGRmUm5tLv/76KxkZGVV577CwMDp06FCNbbO1taXz589TXl4eJSQk0FdffUVNmjSRSb+VPT4DBw6kixcvUlZWFmVkZFBQUJDEY4NbtGhBly9fppycHMrNzaXAwMAq7/O2xqdfv34UFhZGWVlZlJ+fT7GxsbRhwwbS19eX2I+enh7l5eXRjBkzGj0uNZXX9T0kJIR8fX0l6ru7u9Pdu3epsLCQbt++TUOGDJFY7+npWe30XRFzExMTOnPmDKWmplJRURElJCTQoUOHyMbGRuGxUKYxBYCCg4OFMRUeHk5ubm4y6Y+8H6n707xm1K+nBXXq2J7cXdvSjU1NhHWTB7Uhbw+jGret7rHAvy7RIxsbmyrlu5ktFP4Y5H9rfJR5noqLi3vtHPS2FGnnJZcazq0rz/GV9/GmPTZZ2vFibm5ebVxCQkIaFLuGlrcW5Pcd5N9KREQEplwq/1SEMSnw6GGMMeVGfopuAXvTiSYpugWMvd3e2q/I8vye+bbGrBZv3D1UGGOMMcYYY4wxxhSNEyqMMcYYY4wxxhhjUuKECmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJiRMqjDHGGGOMMcYYY1LihApjjDHGGGOMMcaYlDihwhhjjDHGGGOMMSYlTqgwxhhjjDHGGGOMSYkTKowxxhhjjDHGGGNS4oQKY4wxxhhjjDHGmJQ4ocIYY4wxxhhjjDEmJU6oMMYYY4wxxhhjjEmJEyqMMcYYY4wxxhhjUuKECmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJiRMqjDHGGGOMMcYYY1LihApjjDHGGGOMMcaYlDihwhhjjDHGGGOMMSYlTqgwxhhjjDHGGGOMSYkTKowxxhhjjDHGGGNS4oQKY4wxxhhjjDHGmJQ4ocIYY4wxxhhjjDEmJU6oMMYYY4wxxhhjjEmJEyqMMcYYY4wxxhhjUuKEigyJRCL4+/sruhmMMcYYY4wxxhiTM06o1MPq1athb2+v6GbUz5w5QFwcUFAAREQAPXu+vv68ecDdu0B+PpCQAGzZAmhq/rM+Lg4gqlq2b5dvPxTN2Rk4dQpISirv74gRim6RTPn4+CA5ORn5+fkIDAxE+/bta91mzpw5iIuLQ0FBASIiItCz0tjS1NTE9u3bkZ6ejpycHBw/fhytWrUS1nt6eoKIqi0tW7YEABgbG8PPzw+xsbEoLS3F1q1bZdvxOqitn5W5u7sjJiYGBQUFuHXrFoYMGVKlTm3x7tatG86fP4+srCykp6dj9+7d0NbWrvb9mjdvjsTERBAR9PX1699RGZF1vEaNGoVz584hPT0dRASxWPza/f3xxx8gIoxQks+oIsYPALz//vuIiIhAfn4+MjMzceLEiWrfT9nGTwV5zEkzZ85ESEgIXrx4UW1/zc3NsXfvXjx69Aj5+fl48OABVq9eDXV1dZn2TR78ruij/zcW6PJFe3jsbYtbSU1qrHs+RgejfzBDj41WsF/fHiN2mcH/pq5EnWX+RrD1sZEoXoday7sbciPr+ADAw2camHXEFA5/1xuzxwzJL9Tk2Y1GJc3cNWPGDPzf//0fMjMzkZmZicDAwFrnujeRtPOSs7MzTp06haSkpBqPS76+vlXOgc6ePSuvLsiMrI9tNZ0PLl68WKhz8uRJxMfHo6CgAMnJyTh48CBMTEzk0j/GXocTKv8mY8eWJ0R8fIDu3YGbN4Fz54C/v6xWMWECsHFjef2OHQEvL2DcOGD9+n/q9OwJGBv/UwYOLF/+yy/y748iaWuXx2/uXEW3ROaWLl2K//znP5g1axYcHR2Rl5eHc+fOQfPVRFolY8eOxZYtW+Dj44Pu3bvj5s2bOHfunJAIAYCtW7di2LBh8PDwgIuLC0xNTfHbb78J63/++WcYGxtLlICAAISGhuLZs2cAypMyz549wxdffIGbN2/KLwgN6OernJyccOTIEezbtw/dunWDv78//P390alTJ6FObfE2MTFBUFAQHjx4AEdHR7i5uaFTp07Yv39/te+5b98+3Lp1S+Z9rw95xEtbWxuXLl2Ct7d3re8/f/58EJHM+tNQihg/ADB69Gj89NNP8PX1hVgsxjvvvIPDhw9X+57KNH4qyGtO0tLSQkBAANa/ekx7RYcOHaCiooKPP/4YnTp1woIFCzBr1qwa6yuLP+7oYMP5lpjrkoETHyegg1ERvA61RkaearX19ZuWYrZzJn72SsSpWfEYbZ+Nz04a4+IDLYl6zu3zcGnRQ6FsGZPSGN2ROXnEJyFTHRN928LSsBg/eT7BqVnxmNM3A5pqyjP/NIS0c1e/fv1w5MgRuLq6wsnJCYmJiTh//jxMTU0bueXyU595SVtbGzdv3sTcWs4dz549K3EuNGHCBFk3X6bkcWyrfD44bdo0lJWV4ddffxXqhISEYOzYsbC1tcWYMWNgZWWF48ePy72/jFVBbzkXFxf65JNPaN68edSsWTNq1aoV/fDDD5Sbm0tTp04lHR0dsrKyoj/++IOIiEJCQggABQUFkYODAzVt2pScnJzo7t27RETk6+tLACSKr68vEREBoD179tDIkSOpadOm1L59ezp58qT0ja7+mo+Gl4gIom3b/nktEhE9eULk7V19/W3biIKCJJd99RXRxYs1v8fWrUT378uvD8pYiIhGjFB8O/4ulcentCU5OZkWLVokvNbT06OCggIaN25cjdtERETQtm3bhNcikYiePHlC3t7ewj6KiopozJgxQh1bW1siInJ0dKx2n4aGhlRUVESTJ0+udn1ISAht3bq1wf2VptTWz8rl6NGjdPr0aYll4eHh9P3339c53jNnzqTU1FQSiURCnc6dOxMRkZWVlcS+Z82aRSEhIeTq6kpERPr6+o0an8aIV0UxNzcnIiKxWFztvsRiMSUmJpKRkdHfH9ERCo2FosaPqqoqJSYm0vTp02ttn7KNn7r2saGxdnFxqXN/Fy9eTA8fPmxwn8hPfsXdtS35TGolvC49BHrXwZJ2zzao8z5G9jWjrV4thNfeHkY0e5ipXNvdWEUe8Zk/ypgWuxs3aj+Uee6qXFRUVOjFixf04YcfKnw+UeS8JDEH1HBc8vX1pRMnTii8f/IcH9Ic6yvKiRMnKCgo6LXtGDZsGJWWlpKamppM+vXWwuu/RzSo1MP27dvJ3NycNDU1qVevXhQZGVljXRcXl2r/rd5//32hjqenZ5X1gwcPrlfb6upfcYXKgQMHYGhoiCtXruDTTz/F7Nmz4eHhgT59+uDatWt477338OGHHyI/P1/Y5r///S++/vprXL16FWpqapg+fToAYNy4cVi0aBE6deqElJQUpKSkYNy4ccJ2Pj4+GDt2LG7duoX3338fkyZNQmZmZqP3uQp1dcDBAQgK+mcZUflrJ6fqt7l8uXybisv2LCyA998H/vij5veYPBn48UfZtp01GgsLC+GKiArZ2dmIjIyEUw3jRF1dHQ4ODhLbEBGCgoKEbRwcHKChoSFRJzY2FvHx8TXud8qUKcjPz1eavzbUpZ+VOTk5SdQHgHPnzgn16xJvTU1NFBcXg4iEOgUFBQCAd999V1jWsWNHrFy5ElOmTEFZWVkDe9tw8ohXXTVt2hSHDx/G3LlzkZaWJn3j5UBR46d79+5o06YNysrKcO3aNSQnJ+OPP/6Q+EsgoHzjp4K85qT60tfXV45jeg2KS4G/kpugj2WesExFBPSxzMP1J01r3Z4ICH/UFHEZGuhpXiCx7srjpnDabInB29th1e+tkJX/5p1CyiM+ZQSE3tdBu+bF8DrUGk6bLeGxty2C7lb/s8w3jSw+T1paWlBXV1fqz4406jMvSaNfv35IS0vD3bt3sXPnTjRv3rzB+5SXxjjWt2rVCkOHDsW+fftqbIeBgQEmTZqEy5cv4+XLl/XoCVOEn3/+GQsXLsSqVatw7do1iMViDB48GE+fPq22/m+//SZ8/05JScGdO3egqqoKDw8PiXpubm4S9Y4cOSLXfrx5R8N6EIvF+Pzzz2FtbY3ly5ejSZMmMDQ0xMyZM2FtbY2VK1ciIyND4jLndevWwcXFBXZ2dli2bBkuX76MwsJCNG3aFDo6OlBTUxMuQ2va9J+D8NSpUzFhwgS0b98e69evR25uLq5cuaKIbksyNATU1IDKXy7S0sp/qlOdI0eAlSuBS5eA4mLg0SMgNBTYsKH6+iNHAs2aATX8FIEpP+O/x0LlL6FpaWnCusoMDQ2hpqb22m2MjY1RVFSEFy9e1Hm/Xl5eOHz4MAoLC+vVF1mrSz8rMzY2rjUuFctqqvO///0PxsbGWLx4MdTV1dGsWTNs3LgRAITfCmtoaODIkSNYsmQJEhMTG9hT2ZBHvOpq69atuHz5Mk6dOiVdo+VIUePH0tISQPm9v7744gt88MEHyMrKQmhoKAwMDAAo5/ipIK85qT6srKzw6aefYvfu3fXeh7xl5auilERooV0qsbyFdinSc6v/SQsA5BSqoNv69uj8hTU+Otwanw95ines/vkjk3P7fGwalYr9U55gycBn+DO+KWb6tUGp8uTe6kQe8cnIU0V+sQr2hDWHs1UefvwwCYM65OKTn01x5XHtSRplJ4vP06ZNm5CcnFzlS/Sbqj7zUl0FBARgypQpGDBgALy9veHi4oKzZ89CRUU5v7I1xrHe09MTOTk5Ej8Tr7Bx40bk5uYiMzMTZmZmSnO/NFY3W7ZswcyZMzFt2jTY2dlh165d0NLSwo81/HG+efPmEj8FCwwMhJaWVpWEiqampkS9ivMdeVHOT6eMde3aVfh/VVVVtGjRAl26dBGWGRkZAYBENuzVbSq+tNSULavpvbS1taGnp/fa7YqKipCdnS1RiurQp0bh4gJ89ln5jWy7dwdGjQKGDgU+/7z6+l5ewNmzQMqb+bvqf6OJEyciJydHKMpys8XevXvDzs7utX+N+LeIjo6Gp6cnFi1ahPz8fKSmpiIuLg6pqanClQQbNmxATEwM/Pz8FNxaxRs2bBj69++P+fPnK7opSqHiJHzdunX47bffcO3aNUybNg1EJJyAKNP4UdY5ydTUFAEBAfjll1+wd+9eRTdH5rQ1y+A/Kx7HZyZgQf8MbDzXEpGvJAOGds7BANs82BoVY2CHPOyemIzbyU3eioRBXbwuPmV/Xzw4wDYXU52eo6NxET56Nwv9bPJwNEpfga1WDt7e3hg/fjxGjRqFoiKlOcOVSmPOSz///DNOnz6NO3fu4OTJk/jggw/Qq1cv9OvXT27vqeymT58OPz+/asfP5s2b0a1bNwwaNAilpaU4ePCgAlrIKlT7vbaGz31xcTGioqIwsOL+myg/Zxk4cCDCw8Pr9H779u3D+PHjqzyoITQ0FK1atYKtrS1mz56NjIyM+neqDv4VCZXKE59IJJJYJhKJAEDiMufa1kvzXq/bbsOGDdDX15coNVz/0TDp6cDLl8DfySOBkRGQmlr9NmvXAj/9BOzbB9y5A/j7lydYli8H/o6JwMys/Ia0b+GJ5tvs1KlTsLe3F0p6ejqAf5KMFYyMjJBawzhJT0/Hy5cvX7tNamoqNDU1qzxFo6b9zpgxA9evX8e1a9fq3TdZq0s/K0tNTa01LhXLXrfPI0eOwMTEBK1bt0aLFi2wevVqtGzZEo8ePQIA9O/fHx4eHigpKUFJSQmCg4OFNq9evbr+nW4AecSrLvr37w8rKys8f/5ciAcA/PrrrwgJCZGyF7KjqPGT8neCOzo6WlhfXFyMR48ewczMDIByjZ/GmpOkYWJigpCQEFy+fBkfffSR1Ns3JgOtUqiKqMoNVjPyVGGoU1rDVuU/ezFvXoKOxkWY3icLg+1y8cOlmn9m0NagBAZaLxGfqSGztjcGecTHQKsUaioEq5bFEttYGRYj+YVyJAQboiGfp0WLFmHZsmV47733cPv2bXk2U65kMS/VV1xcHJ49e1anJ5spgryP9e+++y46dOhQYyI7IyMD9+/fR1BQEMaPH4+hQ4eid+/e9ewNa6hqv9fW8MuG9PR0lJaW1vtzdOXKFdy5cwczZsyQWO7m5oaDBw8iODgYmzZtwoULFzBkyBCUltY8xzfUvyKhImsaGhoy+0dZvnw5Xrx4IVGWy2TPlZSUAFFRwIAB/ywTicpf15QF1NICKieDKvpdOaEybRrw9Clw5ozs2szkLjc3Fw8fPhRKdHQ0UlJSMOCVcaKrqwtHR8cas8UlJSWIioqS2EYkEmHAgAHCNlFRUSguLpaoY2NjA3Nz8yr71dbWxtixY5Xu6pS69LOy8PBwifoAMGjQIKF+XFycVPF++vQp8vLyMG7cOBQWFiIwMBAAMGbMGIjFYuFkr+Lg4uzsjB07djSs4/Ukj3jVxcaNG9G1a1eJk18AWLBgAaZNmyZ9R2REUeMnKioKhYWFsLW1FeqoqamhXbt2iI+PB6Bc46ex5qS6MjU1RWhoKKKiooQre5SZhirQybQQ4Y/+eQJNGQHhj7TQrU3Ba7aUVEZA8UtRjetTs9XwPF8VLXXfrHsVyCM+GqpAF9NCxGVIJpceZ2qgtX6JbBquQPX9PC1ZsgQrVqyAm5sboqKiGqOpciOLeam+Kv6QkqKkV3/L+1jv5eWFq1ev1unpcxVXZL7uSUtMvqr9XrtcLt9ssW/fPnTp0gW9evWSWD5+/HgMHz4cXbp0wciRI/H777/jzz//RGhoqFzaAQBqctvzW6xdu3aIi4vDjRs30KZNG+jq6tb7w6upqdl4H/wtW4ADB4CrV4ErV4D588sf/+vrW77+wAEgKan8KhQAOH0aWLgQuH4diIwE2rcvv2rl9GnJRItIVJ5QOXDgn4TL205buzweFSwsALEYyMwElOweBNL65ptv8Pnnn+P+/fuIi4vD2rVrkZycDH9/f6FOUFAQTpw4IXzZ2rJlCw4cOICrV6/iypUrmD9/PrS1teH799jKzs7Gvn37sGXLFmRmZiI7Oxvbtm3D5cuXERkZKfH+48aNg5qaGg4dOlRt+8RiMQBAR0cHLVu2hFgsRnFxMWJiYuQQDUm19fPAgQNISkrCZ39/hr799ltcuHABCxcuxJkzZzB+/Hj06NFD4q/cdYn33LlzcfnyZeTm5mLQoEHYvHkzli1bJtyTpuJKlQqGhoYAgJiYmCr3rWlM8oiXgYEBzMzMhMdvViQKUlNTkZaWJpTKEhIS8PjxYzn3+PUUMX5ycnKwa9cu+Pj4IDExEfHx8ViyZAkA4Je/H2+vrOOngjzmJKD8r2DGxsbCX367dOmCnJwcJCQkICsrS0imxMfHY/HixRKPAFWWmx1XZ1rvLHj7G6OzaRG6ti7EgYhmKChRwWj7bADA0hPGMNJ9iUUDy//KvvuiATqbFsGseQmKX4pw4b42Tt3Sw+qh5X3MKxZhe2gLDLbLhaHOSyRmqmNzUEuYNy+B8yv3WXlTyDo+AODVJwsLjpugp1kBHC3ycfGBNkJitXFw6pt9PlBB2rlr6dKlWLNmDSZOnIjHjx8Lf4HOzc1FXl5eje/zJqnPvKStrS1xpYmFhQXEYjEyMzORmJgIbW1trFq1Cr/++itSU1NhZWWFL7/8Eg8ePMC5c+cau4t1Jo9jG1CepPLw8MCiRYuqvGevXr3Qs2dPXLp0CVlZWbCyssLatWvx4MEDmSe1WN1J873W0NAQqqqq9boXUV5eHo4ePYo1a9bU+j6WlpYwNDTEgwcPqiTyZIUTKvUwZswY/Pbbb3B1dcXz58/h6+uLqVOnKrpZtTt2DGjZElizpvxGtDduAG5u5VeWAOU/23k1UfLFF+W3tP/iC6B1a+DZs/Jkyn//K7nfgQMBc/N/19N9evQov0Fvha1by/+7f395cukN9uWXX0JbWxs//PADmjVrhkuXLsHNzU3iN5BWVlbCly4AOHbsGFq2bIk1a9bA2NgYN27cgJubm8T9gxYsWICysjL8+uuv0NTUxLlz5zBnzpwq7+/l5YXffvutxi9yN27cEP6/R48emDRpEh4/fgwLCwsZ9P71auunmZmZxE/8wsPDMXHiRHzxxRdYv3497t+/j5EjR+Kvv/4S6tQl3r169YKPjw90dHRw9+5dfPzxxzUmnJSJPOI1fPhw7H/lxtc///wzgPKbrvr4+DROx+pJUeNnyZIlePnyJX766Sc0bdoUkZGR6N+/P54/f95ofW8Iec1Js2bNkvhJ08WLFwGU31z+wIEDGDRoEKytrWFtbY2kpCSJNokqX6WpRN7vnIvM/HR8F9oCz3JV0dG4CHsnJQk/aUl5oQYV0T9X2uSXqMDnj1ZIzVZDEzWCpWExNo9KwfudcwEAqiLg3lNN+N/UQ06hKlrpvsQ7VnmY55oBDTXlvmKnOrKODwAM6piL1R+k4YdLzfFFQEtYtCjGd2OT0cNMOW6q3lDSzl2zZ8+GpqYmfv31V4n9vAnzdF3VZ17q0aOHxF/Jt/597rh//35MmzYNpaWl6Nq1Kzw9PdGsWTMkJyfj/PnzWLFiBYqLJX9SpkzkcWwDyq80EIlE1T6hJT8/H6NHj4aPjw+0tbWRkpKCgIAAfPHFF0odK/YPDQ0NODg4IDg4GCNHjgRQfnuN4OBgfPLJJ6/d9pdffkFRUREmT55c6/s8efIEGRkZwj1R5UFEyn796r+REp+oMeXHo4cxxpQbKf7+v+wNJ5qk6BYw9nZ7a78iy/N7ppQx+/nnn+Hp6Yndu3ejV69e+Oabb3Ds2DHcvXsXRkZGmDJlClq3bl3lPizOzs5o3bo1jh49KrE8NzcXPj4+GDNmDIyNjfHw4UMsXboUOTk5uH37ttx+FcJXqDDGGGOMMcYYY6zRjBs3Ds+ePcPKlSuRmpoKe3t7BAQECD8TTEhIqPLI8NjYWFy6dAnnz5+vsj9VVVXcunULBw4cwPPnz2Fqaor33nsPa9eulestNvgKFWXEV6iwBuDRwxhjyo2vUGENxVeoMCZfb+1XZCW6QuVtwU/5YYwxxhhjjDHGGJMSJ1QYY4wxxhhjjDHGpMQJFcYYY4wxxhhjjDEpcUKFMcYYY4wxxhhjTEqcUGGMMcYYY4wxxhiTEidUGGOMMcYYY4wxxqTECRXGGGOMMcYYY4wxKXFChTHGGGOMMcYYY0xKnFBhjDHGGGOMMcYYkxInVBhjjDHGGGOMMcakxAkVxhhjjDHGGGOMMSlxQoUxxhhjjDHGGGNMSmqKbgCrSqToBrA3Gim6AW8A/owxxhRJNEnRLWCM/ZuRn6JbwBSG/+1ljq9QYYwxxhhjjDHGGJMSJ1QYY4wxxhhjjDHGpMQJFcYYY4wxxhhjjDEpcUKFMcYYY4wxxhhjTEqcUGGMMcYYY4wxxhiTEidUGGOMMcYYY4wxxqTECRXGGGOMMcYYY4wxKXFChTHGGGOMMcYYY0xKnFBhjDHGGGOMMcYYkxInVBhjjDHGGGOMMcakxAkVxhhjjDHGGGOMMSlxQoUxxhhjjDHGGGNMSpxQqad+/fph/vz5im4GY4wxxhhjjDHGFIATKm8hHx8fJCcnIz8/H4GBgWjfvn2t28yZMwdxcXEoKChAREQEevbsKbFeU1MT27dvR3p6OnJycnD8+HG0atVKWO/p6Qkiqra0bNlSqKehoYEvvvgCjx8/RmFhIeLi4jBt2jTZdb4OlDU+xsbG8PPzQ2xsLEpLS7F161bZdryxOTsDp04BSUkAETBihKJbJLXa/t0rc3d3R0xMDAoKCnDr1i0MGTKkSp3axp+BgQEOHTqEFy9eICsrC3v37oW2tna172dlZYXs7GxkZWVJLLezs8Px48cRFxcHIsK8efOk7LnsKeJz96rmzZsjMTERRAR9fX2Z9Kk+FDGmTp48ifj4eBQUFCA5ORkHDx6EiYmJsN7c3LzaucnR0VE2nZYBWY8fAwMDfPfdd7h79y7y8/MRHx+Pb7/9Fnp6etXuS1nGT11JM86knS+8vb1BRG/0MUrW8Zk1axZu3ryJFy9e4MWLF7h8+TLc3Nzk2YVGIe3nbtmyZbhy5Qqys7ORlpaGEydOwMbGpkq93r17Izg4GLm5uXjx4gUuXLiAJk2ayKsbcsFjSDp+V/TR/xsLdPmiPTz2tsWtpLr9e5+5owtbHxvMOWoq5xYyVn+cUGkkJSUljfI+S5cuxX/+8x/MmjULjo6OyMvLw7lz56CpqVnjNmPHjsWWLVvg4+OD7t274+bNmzh37pxEImTr1q0YNmwYPDw84OLiAlNTU/z222/C+p9//hnGxsYSJSAgAKGhoXj27JlQ79ixYxgwYAC8vLxga2uLCRMmIDY2Vj7BqIYyx0dTUxPPnj3DF198gZs3b8ovCI1FWxu4eROYO1fRLamXuvy7v8rJyQlHjhzBvn370K1bN/j7+8Pf3x+dOnUS6tRl/Pn5+aFTp04YNGgQPvjgA/Tt2xc//PBDlfdTU1PDkSNHcPHixSrrtLS08OjRIyxbtgwpKSkyiEbDKOpz96p9+/bh1q1bMu+bNBQ1pkJCQjB27FjY2tpizJgxsLKywvHjx6u834ABAyTmqKioKNkHoR7kMX5MTU1hamqKxYsXo3Pnzpg6dSrc3Nywb9++avenDOOnrqQdZ9LMFz169MDHH3/8Rh+j5BGfJ0+eYNmyZXBwcECPHj3wv//9DydPnoSdnZ08uyJX9fncubi4YMeOHejduzcGDRoEdXV1nD9/HlpaWkKd3r17IyAgAOfPn0evXr3Qs2dPbN++HWVlZY3RLZngMSSdP+7oYMP5lpjrkoETHyegg1ERvA61Rkae6mu3e/JcDZvOG6KHWX4jtZSxeiJWLy4uLvTpp5/SkiVLyMDAgIyMjGjVqlXCegC0c+dOGjZsGGlpaUmsqw2Aepfk5GRatGiR8FpPT48KCgpo3LhxNW4TERFB27ZtE16LRCJ68uQJeXt7C/soKiqiMWPGCHVsbW2JiMjR0bHafRoaGlJRURFNnjxZWDZ48GDKysoiAwODBvXxbY3PqyUkJIS2bt1arz6SMhYiohEjFN+Ov0td4ljbv3vlcvToUTp9+rTEsvDwcPr+++/rPP46dOhAREQODg5CncGDB1NpaSmZmJhI7Hvjxo108OBB8vT0pKysrBr7ERcXR/PmzavXWHpbPnezZs2ikJAQcnV1JSIifX19hcRBEWOqujJs2DAqLS0lNTU1AkDm5uZERCQWixU6Thpz/FRX3N3dqbCwkFRVVZVy/MhrnL1aXjdfaGtrU2xsLA0YMKBBxyhFF3nFp3LJyMig6dOnK7y/9S31+dxVLoaGhkRE5OzsLCwLDw+nNWvWKLx///YxRH6NV9xd25LPpFbC69JDoHcdLGn3bIMat3n5E2jcgLZ0bJEeeXsY0exhpo3aZvJDfb4evhk4ZjLHV6g0wIEDB6CtrY3IyEh8+eWXWLNmDQIDA4X1q1evxqhRo3D79m1Mnz5d7u2xsLCAiYkJgoKChGXZ2dmIjIyEk5NTtduoq6vDwcFBYhsiQlBQkLCNg4MDNDQ0JOrExsYiPj6+xv1OmTIF+fn5En8FHT58OK5evYqlS5fiyZMniI2NxebNmxvtMk9ljw9THnX5d6/MyclJoj4AnDt3Tqhfl/Hn5OSErKwsiSsDgoKCUFZWJvHzC1dXV3h4eGDuG3D1j6I/dx07dsTKlSsxZcoUhf4FVFFjqjIDAwNMmjQJly9fxsuXLyXWnTp1Cmlpabh48SKGDRtWr37KmrzGT3X09fWRnZ2N0tJSYZmyjJ+6qm/f62LHjh04c+YMgoODG9pMhZFnfCqoqKhg3Lhx0NbWRnh4uEz22djq87mrTsXP4zIzMwEALVu2RO/evfH06VOEhYUhNTUVoaGheOedd2TbATniMSSd4lLgr+Qm6GOZJyxTEQF9LPNw/UnTGrfbcaEFWmiXwqN7dmM0k7EG4YRKA3Tt2hWrVq2CtbU1pkyZgh49ekicaEycOBHTpk2DpaUlzMzM5N4eY2NjAEBaWprE8rS0NGFdZYaGhlBTU3vtNsbGxigqKsKLFy/qvF8vLy8cPnwYhYWFwjJLS0u8++676Ny5M0aNGoX58+fD3d0dO3fulK6j9aTs8WHKoy7/7pUZGxvXOk4qlr2uztOnTyXWl5aWIjMzU6jTvHlz7N+/H1OnTkVOTk49e9h4FPm509DQwJEjR7BkyRIkJibKpD/1pagxVWHjxo3Izc1FZmYmzMzMMOKVexrl5uZi4cKF8PDwwNChQ3Hp0iX4+/srRVJFXuOnshYtWmDFihUSP69TpvFTV/Xpe12MGzcO3bt3x/LlyxvaRIWSV3wAoHPnzsjJyUFRURF27dqFUaNGISYmpkH7VJT6fO4qE4lE+Oabb3Dp0iX89ddfAMrPA4HyPzju2bMHbm5uuHbtGoKDg+t0XyRlwGNIOln5qiglEVpol0osb6FdivTc6n/yczWhCY5f18PaYamN0UTGGowTKg3QtWtXidcmJiYSX4Z69OhR6z6KioqQnZ0tUepq4sSJyMnJEYq6unrdGy9HvXv3hp2dXZXfoquoqICIMGnSJPz55584e/YsFi5cCE9PT7lcpfKmxYexutizZw8OHz5c7b1TlIEyfe42bNiAmJgY+Pn5KawNymLz5s3o1q0bBg0ahNLSUhw8eFBYl5GRga1bt+LKlSu4evUqli9fjkOHDmHJkiWN3k5FjB9dXV2cOXMG0dHRWL16tbCcx0+5Nm3a4Ntvv8WkSZNQVFSk6OYordjYWNjb28PR0RHff/89Dhw4gI4dOyq6WXUij8/djh070LlzZ4wfP15YpqJS/rVj9+7d2L9/P27cuIGFCxciNja2Ua7kVnZv8hiSldwiEZaeMMHaYWlorqX8VwUyBgBqim7Am6zyAUckEklcElzTkzletWHDBvj4+NTr/U+dOoXIyEjhdcWNwoyMjJCa+k9W18jICDdu3Kh2H+np6Xj58iWMjIwklr+6j9TUVGhqakJfX1/ir8GV36fCjBkzcP36dVy7dk1ieUpKCpKSkiSSRjExMVBRUUGbNm3w4MGDOva8bt60+DDlUZd/98pSU1NrHSfV7ePV8ZeamlrlKTWqqqpo3ry5sE3//v0xfPhwLF68GED5vKOqqoqSkhJ89NFH8PX1rWevZUOZPnf9+/dHly5d4O7uDqA8VhX7X7duncSXZ3lT1JiqkJGRgYyMDNy/fx8xMTF48uQJevfujYiIiGrfOzIyEoMGDZKqj7LQWOOngo6ODgICApCTk4NRo0ZJ/AxKmcZPXdVnnNXGwcEBRkZGEscsNTU19O3bF5988gk0NTXfiJ9DAfKJT4WSkhI8fPgQAHDt2jX07NkT8+bNw6xZsxq038Ygi8/dq7Zt2ybcVD0pKUlYXnFD1ujoaIn6MTExjXIltyzwGJKOgVYpVEVU5Qa0GXmqMNQprVI/MUsDSc/VMftIa2FZGZX/126NNQI+eQyz5o3zoA/G6oqvUFGw5cuXC49Iqyh1lZubi4cPHwolOjoaKSkpGDBggFBHV1cXjo6ONf4Gs6SkBFFRURLbiEQiDBgwQNgmKioKxcXFEnVsbGxgbm5eZb/a2toYO3ZstVdfhIWFwdTUVCLRZGNjg9LSUjx58qTO/a6rNy0+THnU5d+9svDwcIn6ADBo0CChflxcXK3jLzw8HAYGBujevbtQp3///lBRURFOdp2cnGBvby+UlStXIjs7G/b29jhx4oRsAtAAyvS5GzNmDMRisRCrGTNmAACcnZ2xY8cOmff9dRQ1pqpT8Vfi1z2tw97eXiFPiGqs8VOxn/Pnz6O4uBjDhw+vcvWFMo2fuqrPOKtNcHAwOnfuLDHv/Pnnn/Dz84O9vf0bk0wB5BOfmqioqLz2M6ZMZPG5q7Bt2zaMGjUK/fv3x+PHjyXWPX78GElJSbC1tZVYbmNjg/j4eJn1R554DElHQxXoZFqI8Ef/POmpjIDwR1ro1qagSn1Lw2Kcnv0Y/rPihdLfNg+OFgXwnxUPY31OpjAlpKi74b7pXFxcaN68eRLLRowYQZ6enkRU/qSeEydO1GvfaMBdu5cuXUqZmZk0bNgw6ty5M504cYIePnxImpqaQp2goCCaO3eu8Hrs2LFUUFBAU6ZMoQ4dOtCuXbsoMzOTWrVqJdTZuXMnPX78mPr160fdu3ensLAwCgsLq/L+06dPp/z8/GqfgqCtrU0JCQl07Ngx6tixIzk7O1NsbCz98MMPjXZndmWODwASi8UkFovpzz//pEOHDpFYLKaOHTtKd+d2ZSna2kRicXkhIpo/v/z/27ZVeNvqEsfa/t0PHDhA69evF+o7OTlRcXExLVy4kGxtbWnVqlVUVFREnTp1kmr8/fHHHxQVFUU9e/akPn36UGxsLPn5+dXYzuqe8qOuri6MpaSkJPryyy9JLBaTlZWVVGPpbfncVRQXFxciUtxTWhQxpnr16kVz584lsVhMZmZm5OrqSpcuXaL79++ThoYGAaApU6bQ+PHjydbWlmxtbWn58uX08uVLmjp1qkLi1BjjR1dXl8LDw+nmzZtkaWlJRkZGQlFRUVHK8SOvcVaf+eJNfsqPPOKzfv16cnZ2JnNzc+rcuTOtX7+eSktLaeDAgQrvb31LfT53O3bsoKysLOrbt6/EZ6pJkyZCnXnz5tHz589pzJgxZGVlRWvWrKH8/HyytLRUeJ//TWOoMZ+Wc2a5DnXu2J5+W6JHD77RoBXjW1GPrlb0bLcqkR9oibsxfTXNsMbt+Sk/MsYxk7l/b88bSFkTKgDIx8eHUlJSqKCggAIDA8na2lpifVxcHK1atUpi2dy5c+nx48dUWFhIERER1KtXL4n1mpqatH37dsrIyKDc3Fz69ddfycjIqMp7h4WF0aFDh2psm62tLZ0/f57y8vIoISGBvvrqK4kDbWMUZY5PdeLi4qQ7SCpL+fvLRxW+vgpvW11j+bp/95CQEPL19ZWo7+7uTnfv3qXCwkK6ffs2DRkyROrxZ2BgQH5+fpSdnU3Pnz+nffv2kba2do1trC6hUvEY3MpCQkKkGktvy+euoijDF+LGHlOdO3em4OBgSk9Pp4KCAnr06BHt3LmTTE1NhTpTpkyhv/76i3Jzc+n58+cUEREh8ThqZSiyHj8uNc1PRGRubq6040ce46w+88WbnFCRR3z27t1LcXFxVFhYSGlpaRQYGPhGJ1MqirSfu5p4enpKbOft7U0JCQmUm5tLYWFh9M477yi8r/+2MdTYyYmf5jWjfj0tqFPH9uTu2pZubGoirJs8qA15exhxQqWxcMxkTkREBKZUKn6nzVh98Ae6dvwJY4wxxti/Ff2777VdNxPf0jPqw3I8C35bY1YLvocKY4wxxhhjjDHGmJQ4ocIYY4wxxhhjjDEmJU6oMMYYY4wxxhhjjEmJEyqMMcYYY4wxxhhjUuKECmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJiRMqjDHGGGOMMcYYY1LihApjjDHGGGOMMcaYlDihwhhjjDHGGGOMsUa1Y8cOtGvXDk2aNIGjoyOuXLlSY939+/dDJBJJlCZNmkjUISKsXLkSJiYmaNq0KQYOHIj79+/LtQ+cUGGMMcYYY4wxxlij+fnnn7Fw4UKsWrUK165dg1gsxuDBg/H06dMat9HT00NKSopQ4uPjJdZ/+eWX+O6777Br1y5ERkZCW1sbgwcPRmFhodz6wQkVxhhjjDHGGGOMNZotW7Zg5syZmDZtGuzs7LBr1y5oaWnhxx9/rHEbkUgEY2NjoRgZGQnriAjffPMNPv/8c4wYMQJdu3bFwYMHkZycDH9/f7n1gxMqjDHGGGOMMcYYaxTFxcWIiorCwIEDhWUqKioYOHAgwsPDa9wuNzcX5ubmaNu2LUaMGIG//vpLWBcXF4fU1FSJferr68PR0fG1+2woTqgwxhhjjDHGGGOs3oqKipCdnS1RioqKqq2bnp6O0tJSiStMAMDIyAipqanVbmNra4sff/wRJ0+exKFDh1BWVoY+ffrgyZMnACBsJ80+ZUFNbntmjCmESNENeAOQohug5HgMMcYYY28v0SRFt0D50URFt+DNs2HDBvj4+EgsW7VqFVavXi2T/Ts5OcHJyUl43adPH3Ts2BG7d+/G2rVrZfIe9cEJFcYYY4wxxhhjjNXb8uXLsXDhQollmpqa1dY1NDSEqqoq0tLSJJanpaXB2Ni4Tu+nrq6Obt264cGDBwAgbJeWlgYTExOJfdrb29e1G1Ljn/wwxhhjjDHGGGOs3jQ1NaGnpydRakqoaGhowMHBAcHBwcKysrIyBAcHS1yF8jqlpaW4ffu2kDyxsLCAsbGxxD6zs7MRGRlZ533WB1+hwhhjjDHGGGOMsUazcOFCeHp6okePHujVqxe++eYb5OXlYdq0aQCAKVOmoHXr1tiwYQMAYM2aNejduzfat2+P58+fY/PmzYiPj8eMGTMAlD8BaP78+fjiiy9gbW0NCwsLrFixAqamphg5cqTc+sEJFcYYY4wxxhhjjDWacePG4dmzZ1i5ciVSU1Nhb2+PgIAA4aayCQkJUFH55wc1WVlZmDlzJlJTU2FgYAAHBwdcvnwZdnZ2Qp2lS5ciLy8PH330EZ4/f453330XAQEBaNKkidz6ISIivj+jkhGJ+JaQjMkTT3qvxzMQY4wxxv7N3tqvyIfleJY38S2NWS34HiqMMcYYY4wxxhhjUuKECmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJiRMqjDHGGGOMMcYYY1LihApjjDHGGGOMMcaYlN6YhEq/fv0wf/78em+/evVq2NvbC6+nTp2KkSNHyvU9GWOMMcYYY4wx9nZ6YxIqsvbtt99i//79im6GXPj4+CA5ORn5+fkIDAxE+/bta91mzpw5iIuLQ0FBASIiItCzZ0+J9Zqamti+fTvS09ORk5OD48ePo1WrVhJ1evTogaCgIGRlZSEzMxMBAQHo2rWrRB0PDw9cv34deXl5ePz4MRYvXtzwDkuhtn5W5u7ujpiYGBQUFODWrVsYMmRIlTq1xbtbt244f/48srKykJ6ejt27d0NbW7va92vevDkSExNBRNDX169/R+tIEfEwMDDAoUOH8OLFC2RlZWHv3r01xsPKygrZ2dnIysqSWG5nZ4fjx48jLi4ORIR58+ZJ2XMl4OwMnDoFJCUBRMCIEYpukUJJO285Ozvj1KlTSEpKAhFhxFsWP2njsWzZMly5cgXZ2dlIS0vDiRMnYGNjI1EnJCQERCRRvv/+e3l2Q2bkcVybOXMmQkJC8OLFixrn3Io55tXi7e0ts37JkqxjZGBggO+++w53795Ffn4+4uPj8e2330JPT09iH/3790dYWBiys7ORkpKCjRs3QlVVVeb9ayh5jKFdu3bhwYMHyM/Px9OnT+Hv7w9bW1uJOnU5N1IGioiPp6dnlc9XRWnZsqXM+1hfsjxXUlNTw8aNG3Hr1i3k5uYiKSkJBw4cgImJicQ+Tp48ifj4eBQUFCA5ORkHDx6sUkfZKeozx1ijozeEi4sLzZs3r97br1q1isRicaO+Z30BqHdZunQpZWVl0fDhw6lLly7k7+9PDx8+JE1NzRq3GTt2LBUWFtLUqVOpY8eOtHv3bsrMzKSWLVsKdXbu3Enx8fHk6upK3bt3p8uXL9OlS5eE9dra2pSenk4//vgj2djYkJ2dHf3yyy+UkpJCampqBIDc3NyouLiYPv74Y7KwsKD333+fkpKSaO7cuQ3qc11LXfr5anFycqKSkhJavHgxdejQgdasWUNFRUXUqVOnOsfbxMSEMjIyaOfOnWRjY0M9evSgS5cu0S+//FLte544cYLOnDlDRET6+vpvXTwA0B9//EHXr1+nXr160TvvvEP37t0jPz+/Ku+npqZGV65coTNnzlBWVpbEuh49etCXX35J48aNo+TkZJo3b55UfSdlKG5uRGvXEo0cWf7BHzFC8W36uzTG57Gh85abmxutXbuWRv4dvxEjRjR6u5UpHmfPniVPT0+ys7Ojrl270u+//06PHz8mLS0toU5ISAjt3r2bjIyMhKKrq6vw/sojHnWZ3+bNm0fe3t7k7e1d45wbFxdHn3/+uUTMXo2pshR5xKhTp050/Phx+uCDD8jS0pJcXV0pNjZW4vjVtWtXKiwspBUrVpCVlRX17duXoqOjafPmzQqPSWOMoZkzZ5KzszOZm5tTt27d6OTJkxQfH08qKioE1O3cSBmKouLTpEkTic+WkZERnT17lkJCQhQeE2n6+Wqp7VxJT0+Pzp8/Tx4eHmRjY0OOjo4UERFBf/75p8R+5s+fT46OjmRmZkZOTk4UFhZGYWFhCo+Hso8pWZa3lh/kV/6l3pieu7i40KeffkpLliwhAwMDMjIyolWrVgnr4+Pjafjw4aStrU26urrk4eFBqampwvrKCRVPT08aMWKE8Do3N5c+/PBD0tbWJmNjY/rqq6+qJFQOHjxIDg4OpKOjQ0ZGRjRhwgRKS0sjIqKysjKysrKizZs3S7T7+vXrBIDu379f57425MOfnJxMixYtEl7r6elRQUEBjRs3rsZtIiIiaNu2bcJrkUhET548IW9vb2EfRUVFNGbMGKGOra0tERE5OjoSAHJwcCAiojZt2gh1OnfuTEREVlZWBID8/Pzo2LFjEu/9ySefUEJCQqNM7rX1s3I5evQonT59WmJZeHg4ff/993WO98yZMyk1NZVEIlGNcakos2bNopCQEHJ1dSUi+SdUFBGPDh06EBGRg4ODUGfw4MFUWlpKJiYmEvveuHEjHTx4kDw9PaskVF4tcXFxb2ZC5dVC9K9OqNRn3pL496S3K6HS0HgAIENDQyIicnZ2FpaFhITQ1q1bFd6/xoiHNPObi4tLjXNufeaXtzFGFcXd3Z0KCwtJVVWVANC6devoypUrEnU++OADys/PJx0dHYXHpbHj06VLFyIisrS0JKBu50bKUBQVn8rF0NCQioqKaPLkyQqPSX37WZdzpcqlR48eRETUtm3bGusMGzaMSktLlSoR9yaMqYaUtxYnVGTujfrJz4EDB6CtrY3IyEh8+eWXWLNmDQIDA1FWVoYRI0YgMzMTFy5cQGBgIB49eoRx48bVed9LlizBhQsXcPLkSZw/fx6hoaG4du2aRJ2SkhKsXbsWN2/ehL+/Px4/foypU6cCAEQiEaZPnw5fX1+JbXx9fdG3b986XebWUBYWFjAxMUFQUJCwLDs7G5GRkXBycqp2G3V1dTg4OEhsQ0QICgoStnFwcICGhoZEndjYWMTHxwt1YmNjkZ6eDi8vL6irq6NJkybw8vJCdHQ0Hj9+DKD8Z0OFhYUS719QUIC2bdvC3NxcJjGoSV36WZmTk5NEfQA4d+6cUL8u8dbU1ERxcTGISKhTUFAAAHj33XeFZR07dsTKlSsxZcoUlJWVNbC3tVNUPJycnJCVlYWoqCihTlBQEMrKyuDo6Cgsc3V1hYeHB+bOndvwzjKlVp95620mq3hU/HwlMzNTYvmkSZPw7Nkz3L59G+vXr0fTpk1l03A5kddxTRrLli1Deno6rl27hsWLFyvdz1kaM0b6+vrIzs5GaWkpgJqP602bNoWDg0NDuiUzjRUfLS0tTJs2DY8ePUJiYiKAup0bKZoi41PZlClTkJ+fj+PHjzegR7Ijj3Ol6ujr66OsrAzPnz+vdr2BgQEmTZqEy5cv4+XLl9J3pJEp05hirDG8UQmVrl27YtWqVbC2tsaUKVPQo0cPBAcHIzg4GLdv38bhw4fh4OAAR0dHHDx4EBcuXMCff/5Z635zc3Oxb98+fPXVVxgwYAC6dOmCAwcOVJm0pk+fjiFDhsDS0hK9e/fGd999h7NnzyI3NxdA+Y1uY2NjceXKFQDlCZjDhw9j+vTpsg9GNYyNjQEAaWlpEsvT0tKEdZUZGhpCTU3ttdsYGxujqKgIL168qLFObm4u+vXrh8mTJ6OgoAC5ublwc3PDkCFDhBOvc+fOYfTo0ejfvz9EIhGsra2xaNEiAJD770Lr0s/KjI2Na41LxbKa6vzvf/+DsbExFi9eDHV1dTRr1gwbN24E8E+fNTQ0cOTIESxZsqTRDgiKioexsTGePn0qsb60tBSZmZlCnebNm2P//v2YOnUqcnJy6tlD9qaoz7z1NpNFPEQiEb755htcunQJf/31l7D88OHDmDx5MlxdXbFhwwZ8+OGHOHTokOwaLwfyOq7V1XfffYfx48fD1dUVu3fvxmeffYYvv/xSqn3IW2PFqEWLFlixYgV++OEHYdm5c+fQp08fjB8/HioqKjA1NcXKlSsByP+4Xlfyjs/s2bORk5ODvLw8DBkyBIMGDUJJSQmAup0bKZoi41OZl5cXDh8+XCVJpyjyOFeqTFNTE5s2bcKRI0eqnPNs3LgRubm5yMzMhJmZ2RtzrzBlGlOMNYY3LqHyKhMTEzx9+hQxMTFo27Yt2rZtK6yzs7NDs2bNEBMTU+t+Hz58iOLiYom/kDdv3rzKTY6ioqIwbNgwmJmZQVdXFy4uLgCAhIQEAICpqSmGDh2KH3/8EQBw+vRpFBUVwcPDo8b3LioqQnZ2tkSpq4kTJyInJ0co6urqdd5W1po0aYJ9+/YhLCwMvXv3xjvvvIM7d+7gzJkzaNKkCQBgz5492L59O37//XcUFxcjIiICR48eBYBGuSpDEaKjo+Hp6YlFixYhPz8fqampiIuLQ2pqqtDnDRs2ICYmBn5+fgpurXLYs2cPDh8+jIsXLyq6KUwOlGneUgbyiMeOHTvQuXNnjB8/XmL5nj17cP78edy5cweHDx/GlClTMHr0aFhaWjb4PWVF2cbH1q1bceHCBdy+fRu7d+/GokWL8Omnn0JDQ0NhbVJEjHR1dXHmzBlER0dj9erVwvLAwEAsWbIEu3btQlFREe7du4c//vgDgOKO640dHz8/P3Tr1g19+/bFvXv3cOzYMWhqagKo27lRY1Om+Lyqd+/esLOzw759++TaHmWipqaGY8eOQSQSYfbs2VXWb968Gd26dcOgQYNQWlqKgwcPKqCVtVPWMcVYY3mjEiqVP6AikajRDth5eXkYPHgw9PT04Ofnhz///BMnTpwAABQXFwv1ZsyYgaNHj6KgoAC+vr4YN24ctLS0atzvhg0boK+vL1Hq6tSpU7C3txdKeno6AMDIyEiinpGREVJTU6vdR3p6Ol6+fPnabVJTU6GpqVmlba/WmThxItq1a4dp06bh6tWriIyMxMSJE2FhYSGRUV+2bBl0dHRgbm4OY2Nj4WqeR48e1bnf9VGXflaWmppaa1wqlr1un0eOHIGJiQlat26NFi1aYPXq1WjZsqXQ5/79+8PDwwMlJSUoKSlBcHCw0OZXT1xlSVHxSE1NrfJ0KFVVVTRv3lyo079/fyxevFiIx759+9CsWTOUlJRg2rRp9ewxUxaymLfeJrKOx7Zt2/DBBx/A1dUVSUlJr60bGRkJAI3yk9S6aqzjWn1FRkZCXV0d7dq1a9B+GqKxY6Sjo4OAgADk5ORg1KhRVa7e3bp1K5o1awYzMzMYGhri5MmTAOR/XK9JY8cnOzsbDx48wMWLF+Hu7o4OHTpg1KhRAOp+btSYlCk+r5oxYwauX79e5ef2iiSPc6UKFckUc3NzDBo0qNorcjMyMnD//n0EBQVh/PjxGDp0KHr37t3AXsmeso4pxhrLG5VQqUnHjh2RmJgo8XOJ6OhoPH/+HHZ2drVub2VlBXV1deHkEgCysrJw79494fXdu3eRkZGBjRs3wtnZGR06dKjy0wUAeP/996GtrY3vv/8eAQEBtf7cZ/ny5Xjx4oVEqavc3Fw8fPhQKNHR0UhJScGAAQOEOrq6unB0dER4eHi1+ygpKUFUVJTENiKRCAMGDBC2iYqKQnFxsUQdGxsbmJubC3W0tLRQVlYmca+QitcqKpLDrKysDMnJySgpKcGECRNw+fJlYfKVl7r0s7Lw8HCJ+gAwaNAgoX5cXJxU8X769Cny8vIwbtw4FBYWIjAwEAAwZswYiMVi4UA0Y8YMAOWPhd2xY0fDOl4DRcUjPDwcBgYG6N69u1Cnf//+UFFRET5/Tk5OEgfmlStXIjs7G/b29kISk725ZDFvvU1kGY9t27Zh1KhR6N+/f53uz2Bvbw8ASElJaUgXZKqxjmv1ZW9vj9LS0mqP/42lMWOkq6uL8+fPo7i4GMOHD0dRUVGN7UpJSUFhYSEmTJiAhIQEhX0xVuQYEolEEIlEwl/LpTk3aizKFJ8K2traGDt2rNJdnSKPcyXgn2SKtbU1Bg4cWOVeV9WpGC/KeCWGMo4pxhqVIu6EWx/VPcJ4xIgR5OnpSWVlZWRvb0/Ozs4UFRVFkZGR5ODgINy5n6j2p/zMmjWLzM3NKTg4mG7fvk3Dhw8nHR0d4T2fPn1KGhoatGTJEnr48CGdPHmSbGxsCABdv35dol2fffYZaWhoUMeOHevVVzTgjtRLly6lzMxMGjZsGHXu3JlOnDhR5TFlQUFBEo8qHjt2LBUUFNCUKVOoQ4cOtGvXLsrMzKRWrVoJdXbu3EmPHz+mfv36Uffu3as8vs3W1pYKCgpox44d1KFDB7Kzs6ODBw9SVlYWGRsbEwBq0aIFffzxx2Rra0tisZi++eYbys/Pp549ezbKHcdr6+eBAwdo/fr1Qn0nJycqLi6mhQsXkq2tLa1ataraxwTXFu+5c+dSt27dyNramubMmUN5eXn06aef1tjO1z1x4m2Ixx9//EFRUVHUs2dP6tOnD8XGxlb72OSKUt1TftTV1UksFpNYLKakpCT68ssvSSwW1/mpCaQMRVubSCwuL0RE8+eX/3/btgpvW2N8Hhs6b2lrawtjoDx880ksFr/2KQlvSqlPPHbs2EFZWVnUt29fiUeQNmnShACQpaUlff7559S9e3cyNzenYcOG0YMHDyg0NFTh/ZVHPOpyXDMyMiKxWExeXl5ERPTuu++SWCwmAwMDAkC9e/emefPmUdeuXcnCwoImTpxIaWlptH//foXHpDFipKurS+Hh4XTz5k2ytLSUGFevPqJ08eLF1LlzZ7Kzs6PPP/+cioqKlO6pW/KIj4WFBS1btoy6d+9Obdu2JScnJzp58iSlp6cLj3mty7mRMhRFxaeiTJ8+nfLz8+V+3lOfIutzJTU1NfL396eEhATq2rWrxOdKXV2dAFCvXr1o7ty5JBaLyczMjFxdXenSpUt0//590tDQUHhM3oQxJYvy1uKn/MjcG9Pz1yVUiBr+2OScnByaPHkyaWlpkZGREX355ZdV3vPw4cPUrl070tTUJCcnJzp16hQBVRMqDx8+JAD05Zdf1quvDZ0AfHx8KCUlhQoKCigwMJCsra0l1sfFxdGqVaskls2dO5ceP35MhYWFFBERQb169ZJYr6mpSdu3b6eMjAzKzc2lX3/9lYyMjCTqDBw4kC5evEhZWVmUkZFBQUFBwmOVgfKEyuXLlyknJ4dyc3MpMDCwyvvIu7yunyEhIeTr6ytR393dne7evUuFhYV0+/ZtGjJkiNTxPnDgAKWnp1NhYSHduHGj1scBNlZCRVHxMDAwID8/P8rOzqbnz5/Tvn37SFtbu8Y2VpdQMTc3r/azExISUqd+kzKUVxK+Enx9Fd62xvxM1nXcVJ63XGqIX+Ux+6YWaeNRE09PTwJAbdq0odDQUEpPT6eCggK6d+8ebdq0iXR1dRXeV3nEA6j9uLZq1arXxqxbt24UHh5OWVlZlJ+fT3/99RctW7ZMab/QyDpGNX3GiIjMzc2FesHBwUKMwsPDyc3NTeGxaIz4mJiY0JkzZyg1NZWKioooISGBDh06JPyxraLUdm6kLEVR8QFAYWFhdOjQIYXHoKYiy3Olms5fiMq/6wDlj9YODg4W5utHjx7Rzp07ydTUVOGxeFPGlCzKW4sTKjInIiICk6mLFy9iwIABSExMrPJbwLoQiURyaBVjrAJPeq/HMxBjjDHG/s3e2q/Ih+V4ljfxLY1ZLdQU3YC3SVFREZ49e4bVq1fDw8OjXskUxhhjjDHGGGOMKb+34qa0yuLIkSMwNzfH8+fP8eWXXyq6OYwxxhhjjDHGGJMT/smPEuKf/DAmXzzpvR7PQIwxxhj7N3trvyLzT35kjq9QYYwxxhhjjDHGGJMSJ1QYY4wxxhhjjDHGpMQJFcYYY4wxxhhjjDEpcUKFMcYYY4wxxhhjTEqcUGGMMcYYY4wxxhiTEidUGGOMMcYYY4wxxqTECRXGGGOMMcYYY4wxKXFChTHGGGOMMcYYY0xKnFBhjDHGGGOMMcYYkxInVBhjjDHGGGOMMcakxAkVxhhjjDHGGGOMMSmJiIgU3QgmSSQSKboJjLF/MT4ovB7P0Iwxxtjb7W39iizP75lva8xqw1eoMMYYY4wxxhhjjEmJEyqMMcYYY4wxxhhjUuKECmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJiRMqjDHGGGOMMcYYY1LihApjjDHGGGOMMcaYlDihwhhjjDHGGGOMMSYlTqgwxhhjjDHGGGOMSYkTKowxxhhjjDHGGGNS4oQKY4wxxhhjjDHGmJQ4ocIYY4wxxhhjjDEmpX9FQmX//v1o1qyZVNuEhoZCJBLh+fPncmkTY4wxxhhjjDHG3lz/ioTKv42Pjw+Sk5ORn5+PwMBAtG/fvtZt5syZg7i4OBQUFCAiIgI9e/aUWK+pqYnt27cjPT0dOTk5OH78OFq1aiWsb968Oc6ePYukpCQUFhYiISEB27Ztg66urlDH19cXRFSl3LlzR3adl1Jt/a7M3d0dMTExKCgowK1btzBkyBCJ9aNGjcK5c+eQnp4OIoJYLH7t/v744w8QEUaMGNHgvsiTIsbUq5o3b47ExEQQEfT19WXSp8YmbQydnZ1x6tQpJCUlvRFjROacnYFTp4CkJIAIeIv7L+t5aNWqVYiJiUFubi4yMzMRGBiIXr16SdT57LPPEBYWhry8PGRlZcm8T7LU2PO0ubl5tccqIoK7u7vM+ycv8pi3Z86ciZCQELx48ULp52NFHbf69++PsLAwZGdnIyUlBRs3boSqqqpEnffeew/h4eHIzs7G06dPcfz4cZibmze80w0g63gZGBjgu+++w927d5Gfn4/4+Hh8++230NPTq7IfT09P3Lx5EwUFBUhLS8P27dtl2jdpyXrOAWqPr7W1Nfz9/fHs2TO8ePECFy9eRL9+/ap9P2U8J5JlzNTU1LBx40bcunULubm5SEpKwoEDB2BiYiKxj5MnTyI+Ph4FBQVITk7GwYMHq9RhrFHQv4Cvry/p6+tLtU1ISAgBoKysLLm06XUA1LssXbqUsrKyaPjw4dSlSxfy9/enhw8fkqamZo3bjB07lgoLC2nq1KnUsWNH2r17N2VmZlLLli2FOjt37qT4+HhydXWl7t270+XLl+nSpUvC+mbNmtGsWbPIwcGBzMzMqH///hQTE0N+fn5CHT09PTIyMhJK69atKT09nVatWtWgPte31KXfrxYnJycqKSmhxYsXU4cOHWjNmjVUVFREnTp1EupMnjyZVqxYQV5eXkREJBaLa3z/+fPn05kzZ4iIaMSIEQqJgTKPqVfLiRMnhFjp6+srPCaNEUM3Nzdau3YtjRw5stHHCClDcXMjWruW6O/+04gRim/T30WWsZbHPDRhwgQaMGAAWVhYkJ2dHe3Zs4eeP39OhoaGQp3Vq1fT/Pnz6auvvqKsrKxGG1vKEJ/a5mkVFRWJY5WRkRGtWLGCsrOzSVtbW+ExqUuR17w9b9488vb2Jm9vb6WejxV13OratSsVFhbSihUryMrKivr27UvR0dG0efNmoU67du2ooKCA1q1bR5aWltStWzcKDQ2lqKiotypenTp1ouPHj9MHH3xAlpaW5OrqSrGxsfTLL79I7GfBggX05MkTmjBhAllaWlKXLl1o2LBhCouFPOacusQ3NjaWfv/9d+rSpQu1b9+etm/fTrm5uWRkZFTlPZXtnEjWMdPT06Pz58+Th4cH2djYkKOjI0VERNCff/4psZ/58+eTo6MjmZmZkZOTE4WFhVFYWJjM+vW2kudYqI/t27eTubk5aWpqUq9evSgyMrLGuj/88AO9++671KxZM2rWrBkNGDCgSn1PT88q7Ro8eHC92lZXChktp0+fJn19fXr58iUREV2/fp0ACAdoIiIvLy+aNGkSERFdvHiR3n33XWrSpAm1adOGPv30U8rNzRXqFhYW0qJFi8jU1JS0tLSoV69eFBISIqyvnFB5+vQpOTg40MiRI6mwsJCIiM6cOUPW1tbUpEkT6tevH/n6+hLwT0IlPT2dxo8fT6amptS0aVPq3LkzHT58WNjngQMHqHnz5sL+KowYMYImT54sVXwaMpCTk5Np0aJFwms9PT0qKCigcePG1bhNREQEbdu2TXgtEonoyZMn5O3tLeyjqKiIxowZI9SxtbUlIiJHR8ca9/vpp59SQkJCjetHjBhBpaWlZGZmppADQG39rlyOHj1Kp0+fllgWHh5O33//fZW65ubmRFRzQkUsFlNiYiIZGRkJ40QRMXgTxtSsWbMoJCSEXF1diUg5Th4aI4avlsYeI6RspTwAim8HZJ9Qkec8VFF0dXWJiKh///5V1nl6eip1QkWR8/Sr5dq1a7R3716Fx6OuRR7z9qvFxcVFqedjRR231q1bR1euXJHY7wcffED5+fmko6NDAGjMmDFUXFxMIpFIok5paSmpqam9NfGqrri7u1NhYSGpqqoSUP7HuLy8vGrnJkUVecw5tcW3RYsWRET07rvvCnV0dHSIiGjAgAES+1bGc6LGOI716NGDiIjatm1bY51hw4bJ9HP0tpLnWJDW0aNHSUNDg3788Uf666+/aObMmdSsWTNKS0urtv7EiRNpx44ddP36dYqJiaGpU6eSvr4+PXnyRKjj6elJbm5ulJKSIpTMzMx6x6suFPKTH2dnZ+Tk5OD69esAgAsXLsDQ0BChoaFCnQsXLqBfv354+PAh3NzcMGbMGNy6dQs///wzLl26hE8++USo+8knnyA8PBxHjx7FrVu34OHhATc3N9y/f7/KeycmJsLZ2RmdO3fG8ePHoampicTERIwePRrDhg3DjRs3MGPGDCxbtkxiu8LCQjg4OODMmTO4c+cOPvroI3z44Ye4cuUKAMDDwwOlpaU4deqUsM3Tp09x5swZTJ8+XZbhq5GFhQVMTEwQFBQkLMvOzkZkZCScnJyq3UZdXR0ODg4S2xARgoKChG0cHBygoaEhUSc2Nhbx8fE17tfExASjR4/GhQsXamyvl5cXgoKCkJCQIFU/ZaEu/a7MyclJoj4AnDt3rsb6NWnatCkOHz6MuXPnIi0tTfrGNyJFj6mOHTti5cqVmDJlCsrKymTdvUZRnxiyf4fGmIfU1dXx0Ucf4fnz57h586bsGt8IFDlPv6p79+7o1q0b9u3bV+99NCZ5zdtvCkUetzQ1NVFYWCix74KCAjRt2hQODg4AgKioKJSVlWHatGlQUVGBnp4ePvzwQwQFBeHly5eyCYIUGnO86OvrIzs7G6WlpQCAQYMGQUVFBa1bt0Z0dDQSExPx888/o02bNjLqnXTkMefUJb4ZGRm4e/cupkyZAi0tLaiqquLjjz9GWloaoqKihO2U8ZyoseZpfX19lJWV1XhvSwMDA0yaNAmXL19WyOeI1c+WLVswc+ZMTJs2DXZ2dti1axe0tLTw448/Vlvfz88Pc+bMgb29PTp06IC9e/eirKwMwcHBEvU0NTVhbGwsFAMDA7n2QyEJFX19fdjb2wsJlNDQUCxYsADXr18Xfiv34MEDuLi4YMOGDZg0aRLmz58Pa2tr9OnTB9999x0OHjwo3KvD19cXv/zyC5ydnWFlZYXFixfj3Xffha+vr8T7xsbG4p133sHgwYPh6+sr/Kb1+++/h5WVFb7++mvY2tpi0qRJmDp1qsS2rVu3xuLFi2Fvbw9LS0t8+umncHNzw7FjxwCUf0meOHGixHseOnQIZmZmNf4GUtaMjY0BoMqX9LS0NGFdZYaGhlBTU3vtNsbGxigqKsKLFy9q3e/hw4eRl5eH5ORkZGdnY8aMGdW+r4mJCYYMGYK9e/fWvYMyVJd+V2ZsbCxV/Zps3boVly9flki+KStFjikNDQ0cOXIES5YsQWJiokz6owj1iSH7d5DnPDR06FDk5OSgsLAQCxYswKBBg5CRkSHbDsiZIufpV3l5eSE6Ohrh4eH13kdjkte8/aZQ5HHr3Llz6NOnD8aPHw8VFRWYmppi5cqVACDc2+Hx48d47733sH79emF/bdq0wdixYxvY8/pprPHSokULrFixAj/88IOwzNLSEioqKvjss88wf/58uLu7o3nz5ggMDIS6unpDulUv8phz6hrfgQMHolu3bsK8vXDhQri5uQkJBGU9J2qMeVpTUxObNm3CkSNHkJOTI7Fu48aNwv3CzMzM/n33m3uDFRcXIyoqCgMHDhSWqaioYODAgXU+3ubn56OkpATNmzeXWB4aGopWrVrB1tYWs2fPlvv5j8JuSuvi4oLQ0FAQES5evIjRo0ejY8eOuHTpEi5cuABTU1NYW1vj5s2b2L9/P3R0dIQyePBglJWVIS4uDrdv30ZpaSlsbGwk6ly4cAEPHz4U3q+goADOzs4YPXo0vv32W4hEImFdTEwMHB0dJdpXOUtaWlqKtWvXokuXLmjevDl0dHRw7tw5iasrZs6cifPnzyMpKQlA+dOFpk6dKvFelRUVFSE7O1ui1NXEiRORk5MjFEUcfCpbsGABunfvjuHDh8PKygpbtmyptp6npyeeP38Of3//xm2ggg0bNgz9+/fH/PnzFd2UainTmNqwYQNiYmLg5+ensDbUhzLFkP17hYSEwN7eHn369EFAQACOHTuGli1bKrpZb5wmTZpg4sSJSn11yr99zlGm/gcGBmLJkiXYtWsXioqKcO/ePfzxxx8AIFxRYGRkhD179uDAgQPo2bMn+vbti+LiYhw/frxR2qiIeOnq6uLMmTOIjo7G6tWrheUqKirQ0NDAf/7zH5w/fx6RkZGYMGECrK2t4erqKvd2KZMdO3bg6dOncHZ2Rq9eveDv74/Tp08LSYY39ZyoodTU1HDs2DGIRCLMnj27yvrNmzejW7duGDRoEEpLS3Hw4EEFtJJVqO57bVFRUbV109PTUVpaCiMjI4nlRkZGSE1NrdP7eXt7w9TUVCIp4+bmhoMHDyI4OBibNm3ChQsXMGTIEOHKOHlQk9uea9GvXz/8+OOPuHnzJtTV1dGhQwf069cPoaGhyMrKgouLCwAgNzcXH3/8Mf7zn/9U2YeZmRlu3boFVVVVREVFVbmLuo6OjvD/mpqaGDhwIH7//XcsWbIErVu3lqq9mzdvxrfffotvvvkGXbp0gba2NubPn4/i4mKhTrdu3SAWi3Hw4EG89957+Ouvv3DmzJnX7nfDhg3w8fGRqi0VTp06hcjISOG1pqYmgKoD0cjICDdu3Kh2H+np6Xj58uVrB3Nqaio0NTWhr68v8ZeZ6gZ8Wloa0tLSEBsbi8zMTFy6dAlr166tUm/69On46aefUFJSIn3HZaAu/a4sNTW1QR96oPzu/1ZWVlUuWfz1119x8eJFhZ9AKNOY6t+/P7p06SI8VaMiMZmeno5169ZJnJQpE1nEkP07yHMeys/Px8OHD/Hw4UNERkbi3r178PLywsaNG2XbCTlS1Dz9Knd3d2hpaSn1SXpjzdvKSpmOW0D5Vahbt26FiYkJsrKy0K5dO2zcuBGPHj0CAMydOxcvXryAt7e3sM3kyZPx5MkTODo6SvRFHhp7vOjo6CAgIAA5OTkYNWqUxM8xUlJSAADR0dES+05PT4eZmVn9OtgA8phzKv77uvj2798fH3zwAQwMDIQrMObOnYtBgwbB09MTmzZtUtpzInnO0xXJFHNzc/Tv37/K1SlA+c+lMjIycP/+fcTExODJkyfo3bs3IiIiGtgzVh/Vfa9dtWqVXMbnxo0bcfToUYSGhqJJkybC8vHjxwv/36VLF3Tt2hVWVlYIDQ3FgAEDZN4OQIFXqFTcR2Xr1q1C8qQioRIaGir8TKZ79+6Ijo5G+/btqxQNDQ1069YNpaWlePr0aZX1r146pqKigp9++gkODg5wdXVFcnKysK5jx47CvVAqVP4ghoWFYcSIEZg8eTLEYjEsLS1x7969Kv2aMWMG9u/fD19fXwwcOBBt27Z9bRyWL1+OFy9eSJS6ys3NFU6YHz58iOjoaKSkpEgMFl1dXTg6OtZ46VRJSQmioqIkthGJRBgwYICwTVRUFIqLiyXq2NjYwNzc/LWXZKmolA+vioN1BRcXF1hbWyv0L3516Xdl4eHhVT6IgwYNkuoy8I0bN6Jr166wt7cXClB+Zc+0adOk74iMKdOYGjNmDMRisRCnip+POTs7Y8eOHTLvu6zIIobs36Ex5yEVFZUqc7GyU9Q8/SovLy+cOnUK6enp9dq+MTTWvK2slOm49aqUlBQUFhZiwoQJSEhIwLVr1wAAWlpaVe5/UfGX04rzJnlqzPGiq6uL8+fPo7i4GMOHD6/yl+qwsDAAgK2trbDMwMAAhoaGiI+Pl0l/pSGPOScuLq7W+GppaQFAlXFRVlYmjAllPSeS1zxdkUyxtrbGwIEDkZmZWWtbavrewRpPdd9rly9fXm1dQ0NDqKqq1uunpl999RU2btyI8+fPo2vXrq+ta2lpCUNDQzx48EC6zkhDrre8rYW9vT2pqqrS999/T0REGRkZpK6uTgDo7t27RER08+ZNatq0Kc2dO5euX79O9+7dI39/f5o7d66wn0mTJlG7du3o119/pUePHlFkZCStX7+efv/9dyKSfMpPSUkJubu7k62tLaWkpBARUXx8PGloaNDixYvp7t275OfnR8bGxgT885SfBQsWUNu2bSksLIyio6NpxowZpKenRyNGjJDo0/Pnz0lLS4s0NDTo6NGj9YoLGnB35aVLl1JmZiYNGzaMOnfuTCdOnKjyaLagoCCaO3eu8Hrs2LFUUFBAU6ZMoQ4dOtCuXbsoMzOTWrVqJdTZuXMnPX78mPr160fdu3ev8miyIUOG0NSpU6lTp05kbm5O77//Pv3111908eLFKm08ePAghYeHK/SO5HXp94EDB2j9+vVCfScnJyouLqaFCxeSra0trVq1qsqj8QwMDEgsFtOQIUOIiGjs2LEkFourfexdRSFS7qf8KGpMVS7K/lQJWcdQW1ubxGIxicViIiKaP38+icXi197hXlaFlKFoaxOJxeWlPADl/9+2rcLbJstYy3oe0tLSonXr1gmPkuzevTvt27ePCgoKyM7OTthP27ZtSSwWC48DrhhryvZYYEXO01ZWVlRaWkqDBw9WeBykLfKat42MjEgsFguPnH733XdJLBaTgYGBwvvcGP2vy3Fr8eLF1LlzZ7Kzs6PPP/+cioqKJI7xrq6uVFpaSitWrKD27dtTt27d6OzZsxQXF0dNmjR5a+Klq6tL4eHhdPPmTbK0tJR4DLmKioqwnxMnTtDt27fJycmJOnXqRKdOnaI7d+4o7IlH8phzaotvixYt6NmzZ3T8+HHq2rUrWVtb05dffklFRUXUtWvXatupTOdEso6Zmpoa+fv7U0JCAnXt2lVi7FR8T+zVqxfNnTuXxGIxmZmZkaurK126dInu379PGhoaMunX20qeY0FavXr1ok8++UR4XVpaSq1bt6YNGzbUuM2mTZtIT0+PwsPD6/QeiYmJJBKJ6OTJk1K3r64UOlrmzZtHACgmJkZYJhaLydjYWKLelStXaNCgQaSjo0Pa2trUtWtXWrdunbC+uLiYVq5cSe3atSN1dXUyMTGhUaNG0a1bt4io6mOTS0pKaPTo0dSxY0fhsUynT5+m9u3bk6amJjk7O9OPP/5IwD8JlYyMDBoxYgTp6OhQq1at6PPPP6cpU6ZUSagQEX344YfVPkK5rho6mH18fCglJYUKCgooMDCQrK2tJdbHxcXRqlWrJJbNnTuXHj9+TIWFhRQREUG9evWSWK+pqUnbt2+njIwMys3NpV9//VXi5LNfv34UFhZGWVlZlJ+fT7GxsbRhw4YqE72enh7l5eXRjBkzFH4AqK3fISEhwuOzK4q7uzvdvXuXCgsL6fbt2zRkyBCJ9Z6entX+m1aOd+XJR5kTKooaU5WLMp08NEYMK/pbWeUxKY9CylBq6D/5+iq8bbKOtyznIU1NTfr111/pyZMnVFhYSElJSeTv7089evSQ2Ievr2+14XVxcZH7+FJkfIC6z9Pr1q2j+Ph4icfbvklFHvP2qlWrqo2dp6enwvvbGP2vy3ErODhYOBcKDw8nNze3Km0bN24cRUVFUU5ODqWlpZG/vz/Z2tq+VfGq6RhGRGRubi7U09XVpb1791JmZialp6fTr7/+Sm3atFFoLGQ959Qlvg4ODhQQEEDp6en04sULunz5crVjp3J8leWcSJYxq3ik/euOUZ07d6bg4GBKT0+ngoICevToEe3cuZNMTU1l1qe3lTzHgbSOHj1KmpqatH//foqOjqaPPvqImjVrRqmpqURU/p162bJlQv2NGzeShoYGHT9+XOKxyDk5OURElJOTQ4sXL6bw8HCKi4ujoKAg6t69O1lbW9f7e3ldvL2jRYH69+9Pn376ab23V/SkyIULl393IS6NmlDhwoULFy5cuChXeVspW8y2bdtGZmZmpKGhQb169aKIiAhhnYuLi8QfQV5Nxr5aKpL9+fn59N5771HLli1JXV2dzM3NaebMmUKCRl5ERERgMpGVlYXQ0FC4u7sjOjpa4jeh0njdU4EYY0ze+KDwejxDM8YYY2+3t/Ursjy/Z76tMauNwp7y8zbq1q0bsrKysGnTpnonUxhjjDHGGGOMMab8+AoVJcRXqDDGFIkPCq/HMzRjjDH2dntbvyLzFSqyp7DHJjPGGGOMMcYYY4y9qTihwhhjjDHGGGOMMSYlTqgwxhhjjDHGGGOMSYkTKowxxhhjjDHGGGNS4oQKY4wxxhhjjDHGmJQ4ocIYY4wxxhhjjDEmJU6oMMYYY4wxxhhjjEmJEyqMMcYYY4wxxhhjUuKECmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJSU3RDWCMMaZcRIpugJIjRTfgDcBjiDHGGGP/BnyFCmOMMcYYY4wxxpiUOKHCGGOMMcYYY4wxJiVOqDDGGGOMMcYYY4xJiRMqjDHGGGOMMcYYY1LihApjjDHGGGOMMcaYlDihwhhjjDHGGGOMMSYlTqgwxhhjjDHGGGOMSYkTKowxxhhjjDHGGGNS4oQKY4wxxhhjjDHGmJQ4ocIYY4wxxhhjjDEmJU6oMMYYY4wxxhhjjElJ6RIqjx8/hkgkwo0bN2qsIxKJ4O/vL/e2rF69Gvb29nJ/H8YYY4wxxhhjjL1ZlC6hUhcpKSkYMmSIopvxRpgzZw7i4uJQUFCAiIgI9OzZ87X13d3dERMTg4KCAty6datKnEeNGoVz584hPT0dRASxWFxlH7t27cKDBw+Qn5+Pp0+fwt/fH7a2tjLtV33JOh4A4OPjg+TkZOTn5yMwMBDt27evUuf9999HREQE8vPzkZmZiRMnTlT7fs2bN0diYiKICPr6+vXrZAMoIj4nT55EfHw8CgoKkJycjIMHD8LExERYb25uDiKqUhwdHWXTaTmpy7h41bJly3DlyhVkZ2cjLS0NJ06cgI2NjUSdkJCQKnH4/vvv5dmNBpP1mFq1ahViYmKQm5uLzMxMBAYGolevXhJ1PvvsM4SFhSEvLw9ZWVky75PScHYGTp0CkpIAImDECEW3SC7kMS9V+P7770FEmDdvnrDM3Nwce/fuxaNHj5Cfn48HDx5g9erVUFdXl1mfZEke8enQoQNOnjyJ58+fIzc3F1euXEHbtm0BAAYGBvjuu+9w9+5d5OfnIz4+Ht9++y309PTk0j9ZaOwYVejduzeCg4ORm5uLFy9e4MKFC2jSpIlM+yYLsoyPmpoaNm7ciFu3biE3NxdJSUk4cOCAxHEdqP3Yr2jSHsOB2uOoqamJ7du3Iz09HTk5OTh+/DhatWolrO/atSsOHz6MhIQE5OfnIzo6Gv/5z38k9uHi4lLtOZGRkZFsOl5PihhDcXFxVeLg7e0tl/4x9lqkRIqKiiguLo4A0PXr1xXdHFq1ahWJxeJGf18AMiljx46lwsJCmjp1KnXs2JF2795NmZmZ1LJly2rrOzk5UUlJCS1evJg6dOhAa9asoaKiIurUqZNQZ/LkybRixQry8vIiIiKxWFxlPzNnziRnZ2cyNzenbt260cmTJyk+Pp5UVFRk1jdlicfSpUspKyuLhg8fTl26dCF/f396+PAhaWpqCnVGjx5NGRkZ9PHHH5O1tTV17NiRPDw8qn3PEydO0JkzZ4iISF9f/18Rn/nz55OjoyOZmZmRk5MThYWFUVhYmLDe3NyciIj69+9PRkZGQlFTU1PoeHpdqUu/K5ezZ8+Sp6cn2dnZUdeuXen333+nx48fk5aWllAnJCSEdu/eLREHXV1dhfe3McfUhAkTaMCAAWRhYUF2dna0Z88eev78ORkaGgp1Vq9eTfPnz6evvvqKsrKyZN4vUpbi5ka0di3RyJHlB48RIxTfpr+LMo+hijJy5Ei6fv06PXnyhObNmycsHzx4MP344480aNAgsrCwoGHDhlFqaipt3rxZ5mNJGeNjaWlJ6enptGnTJrK3tydLS0saNmyYsM9OnTrR8ePH6YMPPiBLS0tydXWl2NhY+uWXXxQeD2WJEQDq3bs3PX/+nLy9vcnOzo5sbGzIw8ODNDQ0FB4TecZHT0+Pzp8/Tx4eHmRjY0OOjo4UERFBf/75p8R+ajv2K7LU5xhelzju3LmT4uPjydXVlbp3706XL1+mS5cuCeunTZtG33zzDfXt25csLCxo0qRJlJeXR3PnzhXquLi4EBGRtbW1xLmASCT6142huLg4+vzzzyXi8Oo5U0PL20qeY+HfSqqenz59mvT19enly5dERHT9+nUCQN7e3kIdLy8vmjRpEhERHT9+nOzs7EhDQ4PMzc3pq6++ktifubk5rVmzhj788EPS1dUlT0/PKgmVly9f0rRp08jW1pbi4+PLG43yL55EJNT/9ddfqV+/ftS0aVPq2rUrXb58WeK9fvjhB2rTpg01bdqURo4cSV9//TXp6+tL1NmwYQO1atWKdHR0aPr06eTt7S2RULly5QoNHDiQWrRoQXp6etS3b1+KiooS1k+bNo2GDh0qsc/i4mJq2bIl7d27t85xltWgjoiIoG3btgmvRSIRPXnyhLy9vautf/ToUTp9+rTEsvDwcPr++++r1K34kltdQqVy6dKlCxERWVpaKmyyl1c8kpOTadGiRcJrPT09KigooHHjxhEAUlVVpcTERJo+fXqt7Zs1axaFhISQq6srETV+QkUR8amuDBs2jEpLS4WEiTRjTVlKffpduRgaGhIRkbOzs7AsJCSEtm7dqvD+KXJMVS66urpEVJ5wq7zO09Pz7U6ovFqI3sqEirzGkKmpKSUmJpKdnR3FxcVJJFSqK4sXL6aHDx/KdBwpa3yOHDlCBw8elKod7u7uVFhYSKqqqgqPibLEKDw8nNasWaPw/isiPpVLjx49iIiobdu2NdapfOxXZKnPMby2OOrp6VFRURGNGTNGqGNra0tERI6OjjXud/v27RQcHCy8rkioNPY5ojKOobrM3Q0pbyt5joV/K6l+8uPs7IycnBxcv34dAHDhwgUYGhoiNDRUqHPhwgX069cPUVFRGDt2LMaPH4/bt29j9erVWLFiBfbv3y+xz6+++gpisRjXr1/HihUrJNYVFRXBw8MDN27cwMWLF2FmZlZj2/773/9i8eLFuHHjBmxsbDBhwgS8fPkSABAWFoZZs2Zh3rx5uHHjBgYNGoR169ZJbH/s2DGsXr0a69evx9WrV2FiYoKdO3dK1MnJyYGnpycuXbqEiIgIWFtb4/3330dOTg4AYMaMGQgICEBKSoqwze+//478/HyMGzeubkGWEXV1dTg4OCAoKEhYRkQICgqCk5NTtds4OTlJ1AeAc+fO1Vi/LrS0tDBt2jQ8evQIiYmJ9d5PQ8kjHhYWFjAxMZGok52djcjISKFO9+7d0aZNG5SVleHatWtITk7GH3/8gU6dOknst2PHjli5ciWmTJmCsrIymfRZGoqKT2UGBgaYNGkSLl++LHx+K5w6dQppaWm4ePEihg0bVq9+Nob69Ls6FT/5yszMlFg+adIkPHv2DLdv38b69evRtGlT2TRcxhpjDlJXV8dHH32E58+f4+bNm7JrPFMK8hpDIpEIP/30EzZv3ozo6Og6tUVfX7/KZ1HR5BEfkUiEoUOH4t69ewgICEBaWhoiIiIwopafk+nr6yM7OxulpaUN7JVsKSpGLVu2RO/evfH06VOEhYUhNTUVoaGheOedd+TQy/prrHNFfX19lJWV4fnz59Wuf92xv7HV5xhelzg6ODhAQ0NDok5sbCzi4+NrjV11c8+NGzeQnJyM8+fPo0+fPlL3U1YUPYaWLVuG9PR0XLt2DYsXL4aqqmr9O8NYPUmVUNHX14e9vb2QQAkNDcWCBQtw/fp14TduDx48gIuLC7Zs2YIBAwZgxYoVsLGxwdSpU/HJJ59g8+bNEvvs378/Fi1aBCsrK1hZWQnLc3NzMXToUDx79gwhISFo2bLla9u2ePFiDB06FDY2NvDx8UF8fDwePHgAANi2bRuGDBmCxYsXw8bGBnPmzKnye9hvvvkGXl5e8PLygq2tLb744gvY2dlVaevkyZPRoUMHdOzYET/88APy8/Nx4cIFAECfPn1ga2uLn376SdjG19cXHh4e0NHRkSbUDWZoaAg1NTWkpaVJLE9LS4OxsXG12xgbG0tV/3Vmz56NnJwc5OXlYciQIRg0aBBKSkqk3o+syCMeFf99XR1LS0sA5Tc4/uKLL/DBBx8gKysLoaGhMDAwAABoaGjgyJEjWLJkicKSToqKT4WNGzcK98QwMzOTODHNzc3FwoUL4eHhgaFDh+LSpUvw9/dX2qSKNP2uiUgkwjfffINLly7hr7/+EpYfPnwYkydPhqurKzZs2IAPP/wQhw4dkl3jZUiec9DQoUORk5ODwsJCLFiwAIMGDUJGRoZsO8AUTl5jyNvbGy9fvsR3331Xp3ZYWVnh008/xe7du6XsgXzJIz6tWrWCrq4uli1bhoCAALz33ns4ceIEfvvtN/Tt27fafbZo0QIrVqzADz/8IINeyZaiYvTqsX/Pnj1wc3PDtWvXEBwcXKd7cTSWxjhX1NTUxKZNm3DkyBHhD5AVXnfsV5T6HMPrEkdjY2MUFRXhxYsXdd6vk5MTxo0bJ/HZSklJwccff4wxY8ZgzJgxSExMRGhoKLp16yZdR2VEkWPou+++w/jx4+Hq6ordu3fjs88+w5dfftnAHjEmPalvSuvi4oLQ0FAQES5evIjRo0ejY8eOuHTpEi5cuABTU1NYW1sjJiamSib+nXfewf379yX+gtGjR49q32fChAnIy8vD+fPn63Rzzq5duwr/X3HToqdPnwIozwBXvmlh5dcxMTFVbnJZOVOalpaGmTNnwtraGvr6+tDT00Nubi4SEhKEOjNmzICvr69Q/+zZs5g+fXqN7S4qKkJ2drZEeRv4+fmhW7du6Nu3L+7du4djx45BU1NT0c1qdCoq5R+xdevW4bfffsO1a9cwbdo0EBE8PDwAABs2bEBMTAz8/PwU2VSF2rx5M7p164ZBgwahtLQUBw8eFNZlZGRg69atuHLlCq5evYrly5fj0KFDWLJkiQJb/I+JEyciJydHKLK4ceWOHTvQuXNnjB8/XmL5nj17cP78edy5cweHDx/GlClTMHr0aOHk/d8iJCQE9vb26NOnDwICAnDs2LFak+6MAeVXDc6bNw9Tp06tU31TU1MEBATgl19+wd69e+XbOCVQccw6efIkvvnmG9y8eRObNm3C77//jlmzZlWpr6urizNnziA6OhqrV69u5NYqRl1iVFFn9+7d2L9/P27cuIGFCxciNjb2teeEbxs1NTUcO3YMIpEIs2fPrrL+dcf+xiKPY7gsdOrUCSdPnoSPjw8CAwOF5ffu3cMPP/yAa9euITw8HF5eXrh8+TIWLFigwNbKz+vG0NatW3HhwgXcvn0bu3fvxqJFi/Dpp59CQ0NDQa1l/1ZSJ1T69euHS5cu4ebNm1BXV0eHDh3Qr18/hIaG4sKFC3BxcZFqf9ra2tUuf//993Hr1i2Eh4fXaT+vToAikQgAZP7TCU9PT9y4cQPffvstLl++jBs3bqBFixYoLi4W6kyZMgWPHj1CeHg4Dh06BAsLCzg7O9e4zw0bNkBfX1+iyEJ6ejpevnxZ5a7fRkZGSE1NrXab1NRUqeq/TnZ2Nh48eICLFy/C3d0dHTp0wKhRo6Tej6zIIx4V/31dnYqff716WXlxcTEePXok/IStf//+8PDwQElJCUpKShAcHCy0ubFOUBUVnwoZGRm4f/8+goKCMH78eAwdOhS9e/eusb2RkZFK81e+U6dOwd7eXijp6ekA6tbv6mzbtg0ffPABXF1dkZSU9Nq6kZGRAKA0sXiVPOeg/Px8PHz4EJGRkZgxYwZevnwJLy8v2XaAKZw8xpCzszNatWqFhIQEYc5t164dvv76a8TFxUlsZ2JigpCQEFy+fBkfffSRDHsmG/KIT3p6OkpKSqr8FComJqbKz651dHQQEBCAnJwcjBo1SuE/1aiOomJU3bG/ch1lIM95uuKLsLm5OQYNGlTl6hRA+mO/PMjiGF6XOKampkJTU7PKeX51++3YsSOCg4Pxww8/VLlFQXWuXLmisPMARY+hV0VGRkJdXR3t2rWTviOMNcD/s3ffYVFcbR+Af0uXjiLNgiKICtJEkUQCikZMYjeSaGwhJiqx97w27OWNWGOPYmKNBTUqogb8LGBBRQ2IDUQRUIr0zvP9wcvEFRAWdtnVPPd1zRWZOXP2nCdnzs6cPTMj8YBK+XNU/P39hcGT8gGV0NBQeHh4ACjrDC5fviy27+XLl9G6desa3d82duxYLF++HH369BFuqakta2trXL9+XWzd23+3bdtWuDgpFx4eLvb35cuXMWHCBHz22WewsbGBurq60PGWa9SoEfr164edO3di165dGDVq1DvLNnv2bGRkZIgt0lBUVISIiAh4enoK60QiETw9PascpAoLCxNLDwA9evSo8aBWVUQiEUQikVxnqMgiHrGxsUhMTBRLo6OjAxcXFyFNREQE8vPzxV4braKighYtWuDp06cAgIEDB8Le3l74Mv/uu+8AlB1rGzdulELtqyev+FSm/Je9d7UXBwcHsWcVyVN2djYeP34sLFFRUbWqN1A2mNK/f39069YNcXFx1X62g4MDAChMLN5Un32QkpLSv3IG3IdOFm3ot99+g52dndgFVEJCAlatWoWePXsK+5iZmSE0NBQRERHCrEJFI4v4FBUV4fr162LfWQDQunVr4TsLKOvTgoODUVhYiD59+qCgoEBa1ZIqecUoLi4OCQkJ1cZR3mTVT5dfCFtZWaF79+41ev5QTb77ZUEa3+E1iWNERAQKCwvF0rRu3Rrm5uZi+bZr1w4hISEICAjAnDlzalQHeZ4TKVIbcnBwQElJiXCHAmP1pjZPsnVwcCBlZWXatGkTERGlpqaSqqoqAaD79+8TEVFERAQpKSnRwoULKSYmhnbt2kUNGjSgnTt3CvmYm5uTv7+/WN5vv+XH39+ftLW16eLFi0IaoOJbft58zXL5Wx1CQkKIiOjSpUukpKREP//8Mz148IA2b95MjRo1In19fWGf/fv3k4aGBv36668UExND8+bNIx0dHbG3/Dg6OlKPHj0oKiqKwsPDyc3NjRo0aFChDsHBwaSmpkbKysqUkJAgcXwhpSctDx48mPLy8mj48OHUpk0b2rx5M6WlpZGRkREBoICAAFq6dKmQ3tXVlQoLC2nKlClkbW1N8+fPr/CqQAMDA7K3t6devXoREdHgwYPJ3t6ejI2NCQC1bNmSZs2aRU5OTtSsWTNydXWlY8eOUUpKSpWvT6uvRRbxmDFjBqWlpVHv3r3J1taWjh49WuHVev7+/vTs2TPq0aMHtW7dmrZt20ZJSUmkr69faTnl9QR3ecSnU6dO5OvrS/b29tS8eXPq2rUrXbp0iR4+fCi8WnL48OH01VdfkbW1NVlbW9Ps2bOpuLiYRo4cKdf29K6lJu3i3LlzYq9C3LhxI6Wnp9Mnn3wi9gpADQ0NAspe0zlnzhxycnIic3Nz6t27Nz169IhCQ0PlXt/6alOampq0ZMkS4VWbTk5OtGPHDsrLy6N27doJ+TRr1ozs7e1p7ty5lJmZSfb29mRvb09aWlpSqRcpyqKlRWRvX7YQEU2aVPbvZs3kXjZFbUOVLW+/KcLMzIwePHhAZ8+eJTMzM7HjUd7HVH3Ep1+/flRQUEDfffcdtWrVinx9famoqIg+/vhjAsrerBUWFkaRkZFkYWEhFh8lJSW5x0QRYgSAJk6cSK9fv6aBAwdSq1ataOHChZSbmyv3Nx7KOj4qKioUGBhI8fHxZGdnJ9Y+yq8VavLdL8+lNt/h1cURKHttclxcHHl4eJCTk1OFV0Xb2NhQcnIy7d69WyxuhoaGYu2qT58+1KpVK7KxsSF/f38qLi6u9E13H3Ib6ty5M02cOJHs7OyoZcuWNGTIEEpOTqZdu3ZJrV4fKlm2hX+rWtW8/MQjOjpaWGdvb08mJiZi6cpfm6yqqkrNmzenVatWiW2vyYAKEdHPP/9MOjo6dPny5bJCQ7IBFaKy1yY3adJEeG3y4sWLK5R3yZIlZGhoSNra2jRixAiaMWOG2IDKzZs3ydnZmTQ0NMjKyor++OOPSutQWlpK5ubm9Nlnn70jilWTZsP29fWluLg4ys/Pp/DwcOrUqZOwLSQkhHbu3CmWftCgQXT//n3Kz8+nu3fvUq9evcS2jxgxotIyz58/nwCQqakpnTx5kpKSkqigoIDi4+Pp999/p9atW8uto5dlPACQn58fJSYmUl5eHp09e5asrKzEtquoqNCqVasoKSmJMjIyKDg4WOzi7+1Fnq/Eq+/42Nra0vnz5yklJYXy8vLoyZMn9Msvv5CZmZmQZvjw4fT3339TdnY2vX79msLDw8VeO6ioS3XtIjY2VjhugKq74hEjRhAAatq0KYWGhgqxevDgAa1YsYJ0dHTkXtf6alPq6up0+PBhev78OeXn51NCQgIFBgaSs7OzWB5vDty/yd3dXSp1IkVZ/tdXVLBzp9zLpqhtqLLl7QGVqr7npP39rMjxGTVqFD148IByc3Pp1q1b1KdPH2Gbe1XtjsrO6+QdD0WIUfkyc+ZMio+Pp+zsbLp8+bLYgIsiLdKMj7m5eZXto7wPrsl3v7wXSb/Dq4sjUPYdtmHDBkpNTaXs7Gw6fPiw2EDt/PnzK41b+bUOAJo+fTo9fPiQcnNzKSUlhf766y/y8PCQe7zquw05OjpSWFgYpaenU25uLv399980a9YsqQ7Ifahk2Q7+rURERPgXGj16NO7fv4+LFy9KPe/s7Gw0adIEO3fuxIABAyTev/wZMIwxxhTPv/JLU0L8LcYYY+x99qFeIsvyOvNDjVl1VORdgPry3//+Fz169ICWlhZOnz6NgIAA/PLLL1L9jNLSUqSkpODnn3+Gvr4++vTpI9X8GWOMMcYYY4wxphj+NQMq165dw8qVK5GVlQULCwusW7dOePintMTHx6Nly5Zo2rQpdu3aBRWVf014GWOMMcYYY4yxf5V/7S0/ioxv+WGMMcXFX5rV428xxhhj77MP9RKZb/mRPolfm8wYY4wxxhhjjDH2b8cDKowxxhhjjDHGGGMS4gEVxhhjjDHGGGOMMQnxgApjjDHGGGOMMcaYhHhAhTHGGGOMMcYYY0xCPKDCGGOMMcYYY4wxJiEeUGGMMcYYY4wxxhiTEA+oMMYYY4wxxhhjrF5t3LgRLVq0gIaGBlxcXHDt2rV3pv/jjz/Qpk0baGhooH379jh16pTYdiLCvHnzYGpqigYNGqB79+54+PChLKvAAyqMMcYYY4wxxhirPwcOHMCUKVMwf/583Lx5E/b29ujZsydevnxZaforV67g66+/ho+PD27duoV+/fqhX79+uHfvnpBm5cqVWLduHTZv3oyrV69CS0sLPXv2RH5+vszqISIiklnurFZEIpG8i8AYY6wK/KVZPf4WY4wx9j77UC+RZXmdKWnMXFxc0LFjR2zYsAEAUFpaimbNmmH8+PGYNWtWhfTe3t7IycnBn3/+Kazr3LkzHBwcsHnzZhARzMzMMHXqVEybNg0AkJGRAWNjY+zatQtfffVVHWpXNZ6hwhhjjDHGGGOMsVorKChAZmam2FJQUFBp2sLCQkRERKB79+7COiUlJXTv3h1hYWGV7hMWFiaWHgB69uwppI+NjUVSUpJYGj09Pbi4uFSZpzSoyCxnxhhj7APEsy+q92H+ric93IZYXdEeeZdAsYmGyrsEjP37LFu2DH5+fmLr5s+fjwULFlRIm5KSgpKSEhgbG4utNzY2xv379yvNPykpqdL0SUlJwvbydVWlkQUeUGGMMcYYY4wxxlitzZ49G1OmTBFbp66uLqfS1B8eUGGMMcYYY4wxxlitqaur13gAxdDQEMrKykhOThZbn5ycDBMTk0r3MTExeWf68v8mJyfD1NRULI2Dg0NNqyExfoYKY4wxxhhjjDHG6oWamho6dOiA8+fPC+tKS0tx/vx5uLq6VrqPq6urWHoAOHv2rJC+ZcuWMDExEUuTmZmJq1evVpmnNPAMFcYYY4wxxhhjjNWbKVOmYMSIEXB2dkanTp2wZs0a5OTkYNSoUQCA4cOHo0mTJli2bBkAYOLEiXB3d8fPP/+Mzz//HPv378eNGzewdetWAGVvMJo0aRIWL14MKysrtGzZEnPnzoWZmRn69esns3rwgApjjDHGGGOMMcbqjbe3N169eoV58+YhKSkJDg4OCAoKEh4qGx8fDyWlf26o+eijj7B3717MmTMHP/30E6ysrBAYGAhbW1shzYwZM5CTk4Pvv/8er1+/RpcuXRAUFAQNDQ2Z1UNEH+pLtt9jsnw/OGOMMSZrfGLxbvwtz+qK3/LzbvyWH1ZXH+olsiyvMz/UmFWHn6HCGGOMMcYYY4wxJiEeUGGMMcYYY4wxxhiTEA+oMMYYY4wxxhhjjEmIB1QYY4wxxhhjjDHGJMQDKowxxhhjjDHGGGMS4gEVxhhjjDHGGGOMMQl9cAMqHh4emDRpUr1/7siRI9GvX796/1zGGGOMMcYYY4zVv/d2QCU0NBQikQivX7+Wd1EAAGvXrsWuXbuEv+U1sFMZPz8/vHjxArm5uTh79iwsLS2r3WfcuHGIjY1FXl4ewsPD0bFjR7Ht6urq2LBhA1JSUpCVlYVDhw7ByMhILE23bt1w+fJlZGZmIjExEcuXL4eysrJU6yYtsojR6NGjERISgoyMDBAR9PT0xLabm5tj+/btePLkCXJzc/Ho0SMsWLAAqqqqUq2bNEg7PgYGBli3bh3u37+P3NxcPH36FGvXroWurm6leTVs2BDPnj2rNI6KQB7tBwBiY2NBRGLLzJkzpVYvaaiunm8bNGgQoqOjkZeXhzt37qBXr15i2/v3748zZ84gJSUFRAR7e3ux7ebm5hViUr4MGjRI6vWTBmnH6E2bNm0CEWHixInCuvep76kTNzfg+HEgIQEgAvr2lXeJ6pUs+iVjY2Ps3r0biYmJyM7ORkREBAYMGCCrKkiVvOLx008/4fLly8jJyUF6erpU6yQre67podualmi/2BJfbm+GOwkaNdrv5D0dWPu1xrj9ZmLr14c2gteGFnBYaomOK1ph5O4miHxeszzlQRZ9cnXtz9HREcHBwUhPT0dKSgq2bNkCLS0tYbudnR327t2L+Ph45ObmIioqChMmTJBOhWVE0mPOzc0Nx48fR0JCAogIfSvps3fu3Fnhu/306dOyqgJjNfLeDqjUp8LCwmrT6OnpQV9fX/aFkdCMGTMwYcIEjBkzBi4uLsjJycGZM2egrq5e5T6DBw/G6tWr4efnBycnJ0RGRuLMmTNo3LixkMbf3x+9e/fGl19+CXd3d5iZmeHIkSPCdjs7O5w6dQpBQUFwdHSEt7c3+vTpg+XLl8u0vrUhqxhpamoiKCgIS5curTSPNm3aQElJCT/88ANsbGwwefJkjBkzpsr08iKL+JiZmcHMzAzTpk2Dra0tRo4cCS8vL+zYsaPS/Hbs2IE7d+7IpH51Ja/2U27u3LkwMTERlvXr10utbnVVk3q+ydXVFfv27cOOHTvg6OiIwMBABAYGwsbGRkijpaWFS5cuVTlw9OzZM7F4mJiYYN68ecjKylLIky5ZxKhcv3790LlzZyQkJIitf1/6njrT0gIiIwFfX3mXpN7Jql/avXs3rK2t0adPH7Rv3x5HjhzBwYMH4eDgUA+1qj15xkNNTQ1//PEHNm3aJMsqSs2pe9pYFtwYvu6pOPpDPNoYF8Dn9yZIzXn3D2LPX6tgRbAhnJvnVtjWolEh5n32EifGPsXeUc/QRL8Y3/7eBGnV5CkPsuiTq2t/pqamOHfuHB49egQXFxd4eXnBxsZG7IfaDh064OXLl/jmm29gY2ODJUuWYNmyZfBV0P6tNseclpYWIiMjq63T6dOnxb7jv/76a2kXnzHJkJScOHGC9PT0qLi4mIiIbt26RQBo5syZQhofHx8aOnQoERFdvHiRunTpQhoaGtS0aVMaP348ZWdnC2l3795NHTp0IG1tbTI2Nqavv/6akpOTiYgoNjaWAIgtI0aMICIid3d3Gj9+PE2fPp0MDAzI2NiY5s+fL1bW9PR08vHxIUNDQ9LR0aGuXbvS7du3he3z588ne3t72rZtG7Vo0YJEIhEREf3xxx9ka2tLGhoa1LBhQ/L09BTKPGLECOrbt6/w77fLFxsbW+NYvr1vXZYXL17Q1KlThb91dXUpLy+PvL29q9wnPDyc1q9fL/wtEono+fPnNHPmTCGPgoICGjhwoJDG2tqaiIhcXFwIAC1ZsoSuXbsmlu8XX3xBubm5pK2tLdU6KmKM3lzc3d2JiEhPT6/askybNo0eP34s95jUZ3zKl0GDBlF+fj4pKyuLrR8zZgyFhIRQ165daxzHDyk+72o/sbGxNHHiRLnHQFrtYP/+/XTixAmxdWFhYbRp06YKac3NzYmIyN7evtpy3Lx5k7Zv3y73eNRnjMzMzOjZs2fUrl27GrUTafc9pGgLEVHfvvIvx/8WWbcrWfVLWVlZ9M0334jtl5KSQj4+PjKv0/sejxEjRlB6errU6kR7ZLMM6tqM/IYaCX+X/A7q0sGCtow1qHKf4t9A3p7N6OBUXZr5pTGN7W32zs/I+lWJWrduTVcWNZBZPWobV1n0ydW1v9GjR1NSUhKJRCIhja2tLRERtWrVqsqybtiwgc6fPy/340tax5xY+yaivn37Vli/c+dOOnr0aL3U4UPFMZM+qc1QcXNzQ1ZWFm7dugUAuHDhAgwNDREaGiqkuXDhAjw8PPD48WN4eXlh4MCBuHPnDg4cOIBLly7hxx9/FNIWFRVh0aJFiIyMRGBgIOLi4jBy5EgAQLNmzXD48GEAQExMDBITE7F27Vph34CAAGhpaeHq1atYuXIlFi5ciLNnzwrbv/zyS7x8+RKnT59GREQEnJyc4OnpibS0NCHNo0ePcPjwYRw5cgS3b99GYmIivv76a3z77beIjo5GaGgoBgwYACKqEIu1a9fC1dUVo0ePRmJiIhITE9GsWTOpxFkSLVu2FEa9y2VmZuLq1atwdXWtdB9VVVV06NBBbB8iwrlz54R9OnToADU1NbE0MTExePr0qZBGXV0d+fn5Ynnn5eWhQYMG6NChg9TqWFeyilFt6enpibVDeavP+Ojp6SEzMxMlJSXCurZt22LevHkYPnw4SktLpVAj6VKE9jNr1iykpKTg5s2bmDZtmsLcVleberq6uoqlB4AzZ87U6bhycnKCo6NjlbOf5ElWMRKJRPjtt9+watUqREVF1agsitb3sNqTZb905coVeHt7w8DAACKRCN7e3tDQ0BA711M0HI+aKywB/n6hgY8scoR1SiLgI4sc3HreoMr9Nl5ohEZaJfjSKbNGn3EgQg866iWwNimQSrmlRRZ9ck3an7q6OgoLC8WuKfLy8gAAXbp0qbK8itpv1+aYk4SHhweSk5Nx//59/PLLL2jYsGGd82SsLqQ2oKKnpwcHBwfhSyQ0NBSTJ0/GrVu3kJ2djYSEBDx69Aju7u5YtmwZhg4dikmTJsHKygofffQR1q1bh927dwsX4d9++y169eoFCwsLdO7cGevWrcPp06eRnZ0NZWVl4eAxMjKCiYmJ2PMF7OzsMH/+fFhZWWH48OFwdnbG+fPnAQCXLl3CtWvX8Mcff8DZ2RlWVlb473//C319fRw6dEjIo7CwELt374ajoyPs7OyQmJiI4uJiDBgwAC1atED79u0xbtw4aGtrVxoLNTU1aGpqCtPR5HGRY2JiAgBITk4WW5+cnCxse5uhoSFUVFTeuY+JiQkKCgqQkZFRZZozZ87go48+wldffQUlJSWYmZlh3rx5AMqmNioKWcWoNlq1aoXx48djy5Yttc5D2uorPo0aNcLcuXOxdetWYZ2amhr27duH6dOn49mzZ3WphszIu/2sW7cOX331Fbp27YotW7bgp59+wsqVKyXKQ1ZqU08TExOpH1c+Pj6IiopCWFhYrfOQFVnFaObMmSguLsa6detqVA5F7HtY7cmyXxo8eDBUVVWRlpaGgoICbNmyBf3798fjx4+lXAvp4XjUXHquMkpIhEZaJWLrG2mVICW78vPYG/EaOHRLF4t6J70z75AHWnBcagm7xVbYFW6AX4c9R0NNxfqhRBZ9ck3a319//QUTExNMmzYNqqqq0NfXF26Rr+qc2dXVFd7e3mLnTYqiNsdcTQUFBWH48OHw9PTEzJkz4e7ujtOnT0NJiZ9iweRHqq3P3d0doaGhICJcvHgRAwYMQNu2bXHp0iVcuHABZmZmsLKyQmRkJHbt2gVtbW1h6dmzJ0pLSxEbGwsAiIiIQO/evdG8eXPo6OjA3d0dABAfH19tOezs7MT+NjU1xcuXLwEAkZGRyM7ORqNGjcQ+PzY2VuwL0NzcXOx+SXt7e3h6eqJ9+/b48ssvsW3bNqk8XKygoACZmZliS20NGTIEWVlZwiLPBwyePXsW06dPx+bNm1FQUIAHDx7g1KlTACDXmQaKFKM3mZmZISgoCH/88Qe2b98ut3LIIz46Ojo4efIkoqKisGDBAmH9smXLEB0djT179si8DDWlaO3H398fFy5cwN27d7FlyxZMnToV48ePh5qamlzLpSg0NDQwZMgQhZydIitOTk6YOHGiMKOzOorS97Daq89+adGiRdDX14enpyecnZ2xevVqHDx4ELa2tjL7TElxPOpPdoEIM46aYlHv5GoHR1xa5CJwzFPs93kGt1Y5mHTIrNrnsvxbREVFYcSIEZg6dSpyc3ORlJSE2NhYJCUlVXrObGNjg2PHjsHPz09sBr681Ocxd+DAAZw4cQL37t3DsWPH8MUXX6BTp07w8PCQ2WcyVh0VaWbm4eGBX3/9FZGRkVBVVUWbNm3g4eGB0NBQpKenC4Mi2dnZ+OGHHyp9OnXz5s2Rk5ODnj17omfPntizZw8aN26M+Ph49OzZs0YPiH37QBaJREKHlJ2dDVNT00qnY775UNk3n6wNAMrKyjh79iyuXLmC4OBgrF+/Hv/5z39w9epVtGzZstoyVWXZsmXw8/Or9f5vOn78OK5evSr8Xf7gJ2NjYyQl/fPLgbGxMW7fvl1pHikpKSguLoaxsbHY+jfzSEpKgrq6OvT09MRmqbz9Of7+/vD394epqSnS09PRokULLF++HE+ePKlzXWurvmIkCVNTU4SEhODKlSv4/vvvJd5fmuo7Ptra2ggKCkJWVhb69++P4uJiYVu3bt3Qvn174c0sIpFIyH/JkiVigy/1RRHbz5uuXr0KVVVVtGjRAg8ePKhTXnVVm3omJSVJNS6DBg2CpqYmdu/eXav9ZU0WMXJzc4ORkZHYjw8qKir4+eefMWnSJLHvK0Xqe1jt1Ve/ZGFhgfHjx8PGxka4lezOnTtwc3ODr68vxo4dK81q1RrHo/YMNEugLKIKAx2pOcow1C6pkP5ZuhoSXqti7L4mwrrS/9210m6hFYJ+jEPzhkUAAE01gnnDIpg3LIJD03x8ur4FDt3UxQ9udf9xUlpk0SeX/7e69rdv3z7s27cPRkZGyMnJARFhypQpFc6Z27Zti/Pnz2Pr1q1YsmRJresqTdI45morNjYWr169gqWlJf766y+p5s1YTUl1hkr5c1T8/f2FwZPyAZXQ0FBh9NDJyQlRUVGwtLSssKipqeH+/ftITU3F8uXL4ebmhjZt2ggzTMqV/wL75vMWasLJyQlJSUlQUVGp8NmGhobv3FckEuHjjz+Gn58fbt26BTU1NRw9erTStGpqajUq2+zZs5GRkSG21FZ2djYeP34sLFFRUUhMTISnp6eQRkdHBy4uLlVOfy8qKkJERITYPiKRCJ6ensI+ERERKCwsFEvTunVrmJubV5pvYmIi8vPz8fXXXyM+Ph43b96sdR3rqr5iVFNmZmYIDQ1FREQERo0aVekzeepTfcZHR0cHwcHBKCwsRJ8+fVBQIH4v9cCBA2Fvbw8HBwc4ODjgu+++A1DWz2zcuFGa1a4xRWs/b3NwcEBJSUmF/lIealPPsLAwsfQA0KNHj1rHxcfHB8ePH0dKSkqt9pc1WcTot99+g52dnXDcODg4ICEhAatWrULPnj2FfRSt72G1V1/9kqamJoCKs0xLSkoUaro9x6P21JQBG7N8hD3RFNaVEhD2RBOOTfMqpLcwLMSJsXEIHPNUWLpZ58ClZR4CxzyFiV5RlZ9VSkBhiWLFSRZ9cmxsrETt7+XLl8jJyYG3tzfy8/PFZqC0a9cOISEhCAgIwJw5c+pUV2mSxjFXW02aNEGjRo2QmJgo1XwZk4i0n3Lr4OBAysrKtGnTJiIiSk1NJVVVVQJA9+/fJyKiyMhIatCgAfn6+tKtW7fowYMHFBgYSL6+vkRE9PLlS1JTU6Pp06fT48eP6dixY9S6dWsCQLdu3SIioufPn5NIJKJdu3bRy5cvKSsri4jK3vIzceJEsTL17dtXeAtQaWkpdenShezt7enMmTMUGxtLly9fpp9++omuX79ORP+85edN4eHhtGTJErp+/To9ffqUDh48SGpqanTq1CkiEn/LDxHR6NGjqWPHjhQbG0uvXr2ikpKSGscQUnza8owZMygtLY169+5Ntra2dPToUXr8+DGpq6sLac6dO0e+vr7C34MHD6a8vDwaPnw4tWnThjZv3kxpaWlkZGQkpPnll18oLi6OPDw8yMnJiS5fvkyXL18W++xp06aRra0ttWvXjubMmUMFBQWVPrFb3ousYmRsbEz29vbk4+NDRCS0OwMDAwLK3sLx4MEDOnv2LJmZmZGxsbGwyDsmso6Pjo4OhYWFUWRkJFlYWIjVXUlJqdJySPK2pPc9PjVpP507d6aJEyeSnZ0dtWzZkoYMGULJycm0a9cuucekpvUMCAigpUuXCuldXV2psLCQpkyZQtbW1jR//nwqKCggGxsbIY2BgQHZ29tTr169iIho8ODBZG9vX+G4adWqFZWUlFDPnj3lHof6jtHby9tv+amPvocUYdHSIrK3L1uIiCZNKvt3s2ZyL5us25Us+iUVFRV68OABXbhwgTp27EgWFhY0ZcoUKikpoV69esm8Tu9rPJo1a0b29vY0d+5cyszMJHt7e7K3tyctLa061UlWb8c5OVubbNta0pHpuvRojRrN/cqInO1a0astykR7QNMHmdB/RxlWuf/bb/nJ2Smin79tRLdWaNDzjSp0d5U6zRpsTLZtLemBv5rCveVHFn1yTdqfr68vOTo6kpWVFY0bN45ycnJo/PjxwnYbGxtKTk6m3bt3i/XZhoaGcj++pHXMaWlpCcdHWZc9iezt7alZs2bC9pUrV5KLiwuZm5tTt27d6MaNGxQTE0NqampSr8OHSpb/3/+tpF7z8pO26OhoYZ29vT2ZmJiIpbt27Rr16NGDtLW1SUtLi+zs7GjJkiXC9r1791KLFi1IXV2dXF1d6fjx4wT8M6BCRLRw4UIyMTEhkUgk9trkdw2oEBFlZmbS+PHjyczMjFRVValZs2Y0dOhQio+PJ6LKB1SioqKoZ8+e1LhxY1JXV6fWrVvT+vXrhe1vD6jExMRQ586dqUGDBgTI77XJAMjPz48SExMpLy+Pzp49S1ZWVmLbY2Njaf78+WLrfH19KS4ujvLz8yk8PJw6deoktl1dXZ02bNhAqamplJ2dTYcPH65wMn7+/HlKT0+n3NxcCgsLIy8vL7l38PUZo7df1/1mWwEg1iZl3QYULT7lgyOVMTc3r7QMijqgIq/24+joSGFhYcIx9vfff9OsWbNkclJRl+Vd9QwJCaGdO3eKpR80aBDdv3+f8vPz6e7duxUu1Ko6bt6O75IlS+jp06dir6FU1EXaMXp7eXtApT76HlKEpap+ZudOuZetPtqVLPolS0tLOnToECUlJVF2djbdvn27wmuDFXWRVzx27txZaTN0d3evU31kNRBBe0C/TdQnj44tyaatJQ3q2oxur9AQtn3ToynN/NK4xgMq+QEi8u1jSl06WJBNW0v62MmCxvQ2o8iV6jKtQ11iK4s+ubr2FxAQQCkpKZSfn19pO6rqnCA2Nlbux5a0jrmqzg3L462hoUFBQUGUnJxMBQUFFBsbS1u2bBH7MUqay4dKlv/P/61ERERgCqX8WRGMMcbY+4hPLN6Nv+VZXZHiPK9dIYmGyrsE7H33oV4iy/I680ONWXUU6+ZFxhhjjDHGGGOMsfcAD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEVORdAFYR7ZF3Cdj7TDRU3iVgjP3bieRdAAVH8i4Ae+/xdz2rC77WYEx6eIYKY4wxxhhjjDHGmIR4QIUxxhhjjDHGGGNMQjygwhhjjDHGGGOMMSYhHlBhjDHGGGOMMcYYkxAPqDDGGGOMMcYYY4xJiAdUGGOMMcYYY4wxxiTEAyqMMcYYY4wxxhhjEuIBFcYYY4wxxhhjjDEJ8YAKY4wxxhhjjDHGmIR4QIUxxhhjjDHGGGNMQjygwhhjjDHGGGOMMSYhHlBhjDHGGGOMMcYYk9AHP6Di4eGBSZMmybsYCmPPNT10W9MS7Rdb4svtzXAnQaNG+528pwNrv9YYt99MbH1wtDa+/a0JXFa2grVfa0Qnqcui2PVKkhgFR2tjwNbmcF7eCg5LLdF3c3MERuqIpZkVaAxrv9Zii8/vTWRdDZkZN24cYmNjkZeXh/DwcHTs2LHKtO3atcOhQ4cQGxsLIsLEiRPfmffMmTNBRPD395d2sWXKz88PL168QG5uLs6ePQtLS8tq93lXHA0MDLBu3Trcv38fubm5ePr0KdauXQtdXV2xPLp164bLly8jMzMTiYmJWL58OZSVlaVeP1mQdswAYPTo0QgJCUFGRgaICHp6erIqvlRJckwBwKBBgxAdHY28vDzcuXMHvXr1qpCmTZs2OHbsGF6/fo3s7Gxcu3YNzZo1A1Dz9qXoZNGGjI2NsXv3biQmJiI7OxsREREYMGCArKogX25uwPHjQEICQAT07SvvEtWfceOA2FggLw8IDwfedcypqABz5wKPHpWlv30b6NlTPI22NuDvD8TFAbm5wOXLgLOzLGsgdfLqkx0dHREcHIz09HSkpKRgy5Yt0NLSklq9pEne31tqamq4desWiAj29vZ1rk99kvb1B2OK5IMfUGH/OHVPG8uCG8PXPRVHf4hHG+MC+PzeBKk5774Ae/5aBSuCDeHcPLfCttxCEZya52Fa91eyKna9kjRGeg1KMNYtDQd8nuH4mKcY4JCJn46Z4OIjTbF0bpY5uDT1sbCsHphYH9WRusGDB2P16tXw8/ODk5MTIiMjcebMGTRu3LjS9Jqamnjy5AlmzZqFxMR319nZ2Rk//PADIiMjZVF0mZkxYwYmTJiAMWPGwMXFBTk5OThz5gzU1aseXKwujmZmZjAzM8O0adNga2uLkSNHwsvLCzt27BDysLOzw6lTpxAUFARHR0d4e3ujT58+WL58uczrXFeyiBlQ1t6CgoKwdOnS+qiGVEh6TLm6umLfvn3YsWMHHB0dERgYiMDAQNjY2AhpLCwscOnSJdy/fx8eHh6ws7PDokWLkJ+fD6Bm7UvRyaoN7d69G9bW1ujTpw/at2+PI0eO4ODBg3BwcKiHWtUzLS0gMhLw9ZV3SerX4MHA6tWAnx/g5FQWgzNngCqOOSxeDPzwAzB+PNCuHbB5M3D0KPBmm9i+HejRAxg2DGjfHggOBs6dA8zej4tAefXJpqamOHfuHB49egQXFxd4eXnBxsYGu3btknYV60wRvrdWrlyJFy9eSKU+9UkW1x+MKRIREZG8CyErI0eOREBAgNi62NhYZGdnY/r06bh48SK0tLTw6aefwt/fH4aGhgDKZrW0b98eysrKCAgIgJqaGhYvXowhQ4bgxx9/xKFDh2BsbIz169cLvwyGhoaia9eu+PPPPzF79mw8ePAADg4O2L59O2xtbSUr+F6RVOr/ti+3N0N7swLM++wlAKCUAHd/CwzrlI7vu6RXuk9JKTB0VzMMdMhARHwDZOYr45evKnbmz1+rwHOtBQJ/eIq2JgUyKX99qE2M3tZ/S3O4W+VgUrdUAGUzVKqKmyyIhsou7/DwcFy/fh3jx48v+yyRCM+ePcP69euxYsWKd+4bGxuLNWvWYO3atRW2aWlp4ebNmxg3bhzmzJmD27dvY/LkyTKpg7S9ePECP//8M37++WcAgK6uLpKTkzFy5EgcOHCg0n1qE8dBgwbh999/h5aWFkpKSrBkyRL06NEDnTp1EtJ88cUXOHjwIIyMjJCdnS3lmkqPrGPm7u6O0NBQ6OvrIyMjQ7aVqSNJ28L+/fuhpaWF3r17C+vCwsJw+/ZtjB07FgCwb98+FBUVYfjw4TUux9vtS9HJqg1lZWVh7Nix+P3334X9UlJSMHPmTKkOOCnciRcR0K8fcOyYvEsie+HhwPXrZQMkACASAc+eAevXA5X1vwkJwJIlwC+//LPu0KGy2SrDhgEaGkBWVtkMn1On/klz4wZw+nTZ7BYZkOaZorz65NGjR2PRokUwNTVF+eWIra0t7t69C0tLSzx+/FiKtawbeX9veXl5YfXq1Rg4cCCioqLg4OBQpx+gaE+td5WYLK8/ZGqIwvXUUiESyeY6EwBkNayQlpaG8ePH48SJE1BSUsLAgQOxdu1aaGtrV5l+/vz5CA4ORnx8PBo3box+/fph0aJFYrPAKovFvn378NVXX0lUvg96hsratWvh6uqK0aNHIzExEYmJidDR0UG3bt3g6OiIGzduICgoCMnJyRg8eLDYvgEBATA0NMS1a9cwfvx4jB07Fl9++SU++ugj3Lx5E59++imGDRuG3FzxUdPp06fj559/xvXr19G4cWP07t0bRUVF9VntShWWAH+/0MBHFjnCOiUR8JFFDm49b1DlfhsvNEIjrRJ86ZRZH8WUq9rGqBwREPakAWJT1dDRPE9s27W4BnBdZYGeG1pg/p9GSM99/w49VVVVdOjQAefOnRPWERHOnTsHV1fXOuW9ceNGnDx5EufPn69rMetVy5YthV/YymVmZuLq1atVxqS2cdTT00NmZqZwsauuri7MOCiXl5eHBg0aoEOHDnWplkzVZ8wUXW3q5erqKpYeAM6cOSOkF4lE+Pzzz/HgwQPh+y08PBx9q7md4+32pchk2YauXLkCb29vGBgYQCQSwdvbGxoaGggNDZVZfVg9UlUFOnQomz1Sjqjs76r6EnV14K2+Fnl5QJcuZf9WUSlb3pVGgcmzT1ZXV0dhYaHYRVheXtn5UxcFip28v7eMjIywbdu2Sq87FB1ffzBpGDp0KP7++2+cPXsWf/75J/7v//4P33//fZXpX7x4gRcvXuC///0v7t27h127diEoKAg+Pj4V0u7cuVMYJ0hMTES/fv0kLt/7d1UnAT09PaipqUFTUxMmJiYwMTHBpk2b4OjoiKVLl6JNmzZwdHTEr7/+ipCQEDx48EDY197eHnPmzIGVlRVmz54NDQ0NGBoaYvTo0bCyssK8efOQmpqKO3fuiH3m/Pnz0aNHD7Rv3x4BAQFITk7G0aNH67vqFaTnKqOERGikJX6y3EirBCnZlU+5uxGvgUO3dLGod1J9FFHuahMjAMjKV4LjUkvYLrbC93ubYE6vl/i41T9feG6WuVjRPwm7hj/H9O6vcP1pA4ze0xQlpTKrikwYGhpCRUUFycnJYuuTk5NhYmJS63y9vb3h5OSE2bNn17WI9a683pLEpDZxbNSoEebOnYutW7cK686cOYOPPvoIX331FZSUlGBmZoZ58+YBKJtGrajqK2bvg9rUy8TE5J3pjYyMoKOjg1mzZiEoKAiffvopjh49iiNHjuCTTz6pNM/K2pcik2UbGjx4MFRVVZGWloaCggJs2bIF/fv3V6hfylkdGBqWDX681Q6QnAxU1ZecOQNMmQJYWpbNZuneHRgwACjvZ7OzgStXymaimJoCSkrA0KFlAzQK3BeXk2ef/Ndff8HExATTpk2Dqqoq9PX1hdtWFel7TN7fW7t27cLmzZsREREh0X6KgK8/WF1FR0cjKCgI27dvh4uLC7p06YL169dj//79Vd4CZ2tri8OHD6N3795o1aoVunXrhiVLluDEiRMoLi4WS6uvry+ME5iYmEBDo2bP93nTBz2gUpnIyEiEhIRAW1tbWNq0aQMAYidMdnZ2wr+VlZXRqFEjtG/fXlhnbGwMAHj58qVY/m+OOjds2BDW1taIjo6usjwFBQXIzMwUWwrkP6EF2QUizDhqikW9k9FQ8z278q9nWuqlCBzzFIdGx2Nyt1QsP9MYV+P+GXX/3DYLntY5sDYuRPc2Odgy5AXuvtDAtbiqR+b/LZo2bYq1a9di6NChKChQ/FvFhgwZgqysLGFRVVWV+Wfq6Ojg5MmTiIqKwoIFC4T1Z8+exfTp07F582YUFBTgwYMHOPW/6ealpYpzzMojZv9mSkplX+vHjh3DmjVrEBkZiRUrVuDPP//EmDFjKqSvqn0pkvpsQ4sWLYK+vj48PT3h7OyM1atX4+DBg5Lfuss+HBMnAg8fAvfvA4WFwIYNwM6dwJv97LBhZYMtL14ABQXAhAnAvn3iaRSEIvXJUVFRGDFiBKZOnYrc3FwkJSUhNjYWSUlJcv0eU6QYjR8/Hjo6Oli2bJncylCf+Prj/VbpdW0dz+/DwsKgr68P5zce9N29e3coKSnh6tWrNc4nIyMDurq6UFFREVvv6+sLQ0NDdOrUCb/++mutbltSqT7JhyU7Oxu9e/eu9N70N0fD3+48RSKR2Lrye67q2uEvW7YMfn5+YuvmDwAWDKxTthUYaJZAWUQVHgCVmqMMQ+2KU7yfpash4bUqxu775200pf9rX+0WWiHoxzg0b6gAIz9SJGmMyimJAPP/xaKtSQEep6hh66WGcGmRUGn6ZgZFMNAsxtM0Nbha5FWaRhGlpKSguLhYGEwsZ2xsjKSk2v2K0KFDBxgbG+PmzZvCOhUVFXzyySf48ccfoa6urlCDA8ePHxfrvMsfRvd2DIyNjXH79u1K85Akjtra2ggKCkJWVhb69+9fYVTd398f/v7+MDU1RXp6Olq0aIHly5fjyZMndammVNV3zN4ntalXUlLSO9OnpKSgqKgIUVFRYmmio6MrTKGvrn0pivpqQxYWFhg/fjxsbGyE+N25cwdubm7w9fUVnlHD3mMpKUBxMfBWO4CxMVBVX5KSAvTvX3brT6NGZYMmy5cDb/azT54AHh6Apiagq1uW1/794mkUhKL1yfv27cO+fftgZGSEnJwcEBGmTJki1+8xRYpRt27d4OrqWuGi9MaNG9izZw9GjhxZ47zkga8//l0qva6dP79OP9gkJSXByMhIbJ2KigoaNmxY42MpJSUFixYtqnCb0MKFC9GtWzdoamoiODgY48aNQ3Z2NiZMmCBRGT/4GSpqampi94Q7OTnh77//RosWLWBpaSm2SOM1beHh4cK/09PT8eDBA7Rt27bK9LNnz0ZGRobYMrtPnYtRgZoyYGOWj7An/7x9ppSAsCeacGxa8aLewrAQJ8bGIXDMU2HpZp0Dl5Z5CBzzFCZ6H15nJmmMqlJKQGFx1Q98SspUwetcZTTWUcyLl6oUFRUhIiICnp6ewjqRSARPT0+EhYXVKs/z58/D1tYWDg4OwnL9+nXs2bMHDg4OCjWYApQNyD5+/FhYoqKikJiYKBYTHR0duLi4VBmTmsZRR0cHwcHBKCwsRJ8+fd45wp+YmIj8/Hx8/fXXiI+PFxugkrf6jNn7pjb1CgsLE0sPAD169BDSFxUV4fr167C2thZL07p1azx9+lT4W5L2JW/11YY0Ncv6/rf7nZKSEmHmD3vPFRUBERHAm8eQSFT2d3V9SUFB2WCKigowcGDlD/DNzS0bTNHXL3u1sgI+5FdR++SXL18iJycH3t7eyM/Px9mzZ2uVjzQoUowmTJgAe3t74Rzps88+A1B2u/R//vOfWtaw/vD1x79Lpde1VdzSP2vWLIhEoncu9+/fr3OZMjMz8fnnn6Ndu3YVBnbmzp2Ljz/+GI6Ojpg5cyZmzJiBVatWSfwZH/wMlRYtWuDq1auIi4uDtrY2fH19sW3bNnz99deYMWMGGjZsiEePHmH//v3Yvn07lJXf/Qqv6ixcuBCNGjWCsbEx/vOf/8DQ0PCdD7dRV1ev+Mo1Gc0sHNU5HTMDTWBrVgC7JvkICNdHXpESBjiUPfBpxlETGOsUY2r3FKirEFobFYrtr6tRNjD15vrXeUpIzFDFy6yyphSbUlZ4Q+1iNH7HrA5FJUmMAGDLRQPYmhWgecMiFBaLcOGhFo7f0cWCz8vumc0pFGFDaCP0bJcNQ+1iPEtTxapzjWHesAhurd6vB4sBwOrVqxEQEIAbN27g2rVrmDRpErS0tLBz504AZQ9zTkhIwE8//QSgbKZXu3btAJQNbjZp0gT29vbCyUp2djb+/vtvsc/IyclBampqhfWKas2aNZgzZw4ePnyI2NhYLFq0CC9evEBgYKCQ5ty5czh69Cg2btwIoPo4ll/sampq4ptvvoGuri50dXUBAK9evRIu+KZNm4agoCCUlpZiwIABmDVrFgYPHqxwA1Fvk0XMgLJf/kxMTGBpaQkAaN++PbKyshAfH4/09Jq9pau+SXpMrV27FhcuXMCUKVNw8uRJfPXVV3B2dhb71WXVqlU4cOAA/u///g8hISHw8vJC79694eHhAaDm7UuRyaIN3b9/Hw8fPsSWLVswbdo0pKamol+/fujRowe++OILeVRTtrS0yp4LUq5lS8DeHkhLK3vrzYdq9WogIKDsLTzXrgGTJpXForwvCQgoe7PP/445dOoENGkC3L5d9t8FC8qek7Jy5T95fvpp2cBMTExZTFetKrtF6I3+SZHJs0/29fXFlStXkJ2djR49emDVqlWYNWuWwr2hTV4xevbWsVj+Br/Hjx8jIaHymdCKRhbXH0wxVXpdW4WpU6dWO8PKwsICJiYmFR6xUVxcjLS0tGqfR5SVlQUvLy/o6Ojg6NGj1d6+5+LigkWLFqGgoKDG9QD+BQMq06ZNw4gRI9CuXTvk5eUhNjYWly9fxsyZM/Hpp5+ioKAA5ubm8PLyksovUMuXL8fEiRPx8OFDODg44MSJE1BTU5NCTeruM9tspOWmYF1oI7zKVkZbkwJsH5ogTLlLzFCBkkiy+8b+itHG7GP/NObJh80AAD+6p2K8R6r0Cl9PJI1RbpES/E4ZISlTBRoqBAvDQqzqn4jPbMu+8JRFwIOX6giM1EVWvjKMdIrxcascTOyaCjWV9+91bAcPHkTjxo2xcOFCmJiY4Pbt2/Dy8hI6uubNm4tdjJmZmYlNh50+fTqmT58uvGb8Q7By5UpoaWlh69at0NfXx6VLl+Dl5SX2i3+rVq2E17ID1cfRyckJnTt3BoAKD8Ns0aKFMNOgV69e+M9//gN1dXVERkaib9++CAoKknWV60wWMQOAMWPGiP36cPHiRQDAyJEjERAQIPuK1YKkx1RYWBiGDBmCxYsXY+nSpXj48CH69esnNgAZGBiIMWPGYPbs2Vi3bh1iYmIwcOBAXL58GUDN25cik0UbKi4uxmeffYbly5fjxIkT0NbWxqNHjzBixAicPn263usoc87OwJtvL/L3L/vvrl3AqFHyKFH9OHgQaNwYWLiw7EG0t28DXl5AeV/SvLn4s080NIDFiwELi7IH0J46VfbMlDcv+PX0gGXLgKZNywakDh8G/vOfstuL3gPy7JM7deoEPz8/aGtr4/79+/jhhx/EXluuKPh7q/Zkcf3B3n+NGzdG48aNq03n6uqK169fIyIiQniL5V9//YXS0lK4uLhUuV9mZiZ69uwJdXV1HD9+vEYPm719+zYMDAwkGkwBABHJ6oXR/zLlF4jp6enQ19evW2Z7Zfd+cPbhEw2VdwkYY4y9C594sbriM0VWF7RH3iV4Dwz5MHvq8ueAyoKshhV69eqF5ORkbN68GUVFRRg1ahScnZ2xd+9eAEBCQgI8PT2xe/dudOrUCZmZmfj000+Rm5uLo0ePij3Wo3HjxlBWVsaJEyeQnJyMzp07Q0NDA2fPnsW0adMwbdq0Cs+Bqc4HP0OFMcYYY4wxxhhj7589e/bgxx9/hKenJ5SUlDBw4ECsW7dO2F5UVISYmBjk5pY9TuHmzZvCg6Ut37y1FUBsbCxatGgBVVVVbNy4EZMnTwYRwdLSEqtXr8bo0aMlLh/PUJESnqHCFAXPUGGMMcXGJ16srvhMkdUFz1CpAZ6hIrF/67ACz1CREg8Pj39tI2KMMcYYY4wxxv5t+D2AjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEVORdAMYYY4wxxhhjjMkW7ZF3CT48PEOFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYk9EEPqMTFxUEkEuH27dvyLorC2HNND93WtET7xZb4cnsz3EnQqNF+J+/pwNqvNcbtNxNbHxytjW9/awKXla1g7dca0Unqsih2vZIkRsHR2hiwtTmcl7eCw1JL9N3cHIGROhXSPX6lhjH7zNDhf+kGbmuOFxkqsqyGzIwbNw6xsbHIy8tDeHg4OnbsWGXadu3a4dChQ4iNjQURYeLEiRXSjBkzBpGRkcjIyEBGRgauXLkCLy8vWVZB6vz8/PDixQvk5ubi7NmzsLS0rHaf6uK4efNmPHr0CLm5uXj58iUCAwNhbW0tlsbZ2Rnnzp1Deno60tLSEBQUBDs7O6nWrTZkEQ91dXVs2LABKSkpyMrKwqFDh2BkZCSWplu3brh8+TIyMzORmJiI5cuXQ1lZWSzNp59+irCwMGRmZuLly5c4dOgQzM3N617pOpDkmAKAQYMGITo6Gnl5ebhz5w569epVIU2bNm1w7NgxvH79GtnZ2bh27RqaNWsmlqZz5844f/48srOzkZGRgQsXLkBDo2bfCfVNFm3K2NgYu3fvRmJiIrKzsxEREYEBAwaIpfnpp59w+fJl5OTkID09Xap1kqlx44DYWCAvDwgPB97VplRUgLlzgUePytLfvg307CmeRlsb8PcH4uKA3Fzg8mXA2VmWNVAMbm7A8eNAQgJABPTtK+8SSY08+umGDRvi9OnTSEhIQH5+PuLj47F+/Xro6Pxz3rRz504QUYXl3r170qt8LckiZiEhIRXqumnTJrE0ivpdLwlpX38wpkg+6AEVJu7UPW0sC24MX/dUHP0hHm2MC+DzexOk5ii/c7/nr1WwItgQzs1zK2zLLRTBqXkepnV/Jati1ytJY6TXoARj3dJwwOcZjo95igEOmfjpmAkuPtIU0sSnqWLIzmawMCzEbyOe4/iYpxj3SSrUVai+qiU1gwcPxurVq+Hn5wcnJydERkbizJkzaNy4caXpNTU18eTJE8yaNQuJiYmVpnn+/DlmzZqFDh06wNnZGX/99ReOHTuGdu3aybIqUjNjxgxMmDABY8aMgYuLC3JycnDmzBmoq1c9uFiTOEZERGDUqFFo27YtevbsCZFIhODgYCgplXXbWlpaCAoKQnx8PFxcXNClSxdkZWXhzJkzUFGR32CdrOLh7++P3r1748svv4S7uzvMzMxw5MgRYbudnR1OnTqFoKAgODo6wtvbG3369MHy5cuFNC1atMCxY8fw119/wcHBAT179oShoaFYPvVN0mPK1dUV+/btw44dO+Do6IjAwEAEBgbCxsZGSGNhYYFLly7h/v378PDwgJ2dHRYtWoT8/HwhTefOnREUFITg4GB06tQJHTt2xIYNG1BaWirzOktKVm1q9+7dsLa2Rp8+fdC+fXscOXIEBw8ehIODg5BGTU0Nf/zxR4ULHIU2eDCwejXg5wc4OQGRkcCZM0AVbQqLFwM//ACMHw+0awds3gwcPQq8EQds3w706AEMGwa0bw8EBwPnzgFmH/hFjpZWWfx8feVdEqmSVz9dWlqKY8eOoU+fPmjdujVGjhyJ7t27Y/PmzUKaiRMnwsTERFiaNm2K1NRU/PHHH7IJRg3JKmYAsHXrVrE6z5gxQ9imqN/1kpDF9QdjikRERO/fVd0bgoKCsHjxYty7dw/KyspwdXXF2rVr0apVK4hEIrG07u7uCA0NBQBs374dP//8M2JjY9GiRQtMmDAB48aNA1A2s6Vly5Y4cOAA1q9fjxs3bsDW1hZ79uxBRkYGxo4di/v378PNzQ27d+8WOsaRI0fi9evXcHR0xIYNG1BQUIAhQ4Zg3bp1UFNTq3ml9oqqT1MLX25vhvZmBZj32UsAQCkB7v4WGNYpHd93qfyXt5JSYOiuZhjokIGI+AbIzFfGL1+9qJDu+WsVeK61QOAPT9HWpEAm5a8PtYnR2/pvaQ53qxxM6pYKAJh8yAQqysCq/kkyK/ebRENll3d4eDiuX7+O8ePHl32WSIRnz55h/fr1WLFixTv3jY2NxZo1a7B27dpqPyc1NRXTp0/Hr7/+KpVyy9KLFy/w888/4+effwYA6OrqIjk5GSNHjsSBAwcq3ac2cWzfvj3u3LmDVq1a4cmTJ+jQoQNu3LiBZs2a4fnz5wAAW1tb3L17F5aWlnj8+LEMals9WcRDV1cXr169wpAhQ3D48GEAgLW1Ne7fv4/OnTvj6tWrWLJkCXr06IFOnToJ+X7xxRc4ePAgjIyMkJ2djYEDB2Lfvn1QV1dH+VffF198gWPHjkFdXR3FxcWyDE2t6v62/fv3Q0tLC7179xbWhYWF4fbt2xg7diwAYN++fSgqKsLw4cOr/NywsDCcPXsW8+bNk3KNpE9Wx1hWVhbGjh2L33//XdgvJSUFM2fOxI4dO8TyGzFiBNasWQMDAwOp1EmmJ17h4cD162UDJAAgEgHPngHr1wOV9S8JCcCSJcAvv/yz7tChstkqw4YBGhpAVlbZ7IxTp/5Jc+MGcPp02eyWfwMioF8/4NgxeZcEAFCXM0V59dOVGT9+PKZPn47mzZtXur1v3744cuQIWrZsifj4+DrUum5k1Q+FhITg9u3bmDx5cqV5yOq7nvbUardakeX1h0wNea8vkasmo+tMAB9uzKrx3s9QycnJwZQpU3Djxg2cP38eSkpK6N+/P0pLS3Ht2jUAwLlz55CYmCiMku/Zswfz5s3DkiVLEB0djaVLl2Lu3LkICAgQy3v+/PmYM2cObt68CRUVFQwZMgQzZszA2rVrcfHiRTx69KjCyej58+cRHR2N0NBQ7Nu3D0eOHIGfn1/9BOMdCkuAv19o4COLHGGdkgj4yCIHt543qHK/jRcaoZFWCb50yqyPYspVbWNUjggIe9IAsalq6GieB6DsSyP0oTZaNCyEz+9N4LrKAl9ub4Zz97VkVg9ZUVVVRYcOHXDu3DlhHRHh3LlzcHV1lcpnKCkpwdvbG1paWggLC5NKnrLUsmVLmJqaisUkMzMTV69erTImtYmjpqYmRo0ahSdPnuDZs2cAgJiYGKSkpMDHxweqqqrQ0NCAj48PoqKiEBcXJ71KSkBW8ejQoQPU1NTE0sTExODp06dCGnV1dbEZGACQl5eHBg0aoEOHDgDKZv2UlpZi1KhRUFJSgq6uLoYNG4Zz587JZTClNm3B1dVVLD0AnDlzRkgvEonw+eef48GDBwgKCkJycjLCw8PR941bFRo3bozOnTvj5cuXuHz5MpKSkhAaGoqPP/5YBrWsG1keY1euXIG3tzcMDAwgEong7e0NDQ0N4YeX95KqKtChQ9nskXJEZX9X1U+rqwNvHTvIywO6dCn7t4pK2fKuNOy9Ic9++m2mpqYYMGAALly4UGV5fXx8cO7cObkOpsj6u37o0KF49eoV7t69i6VLl6JBg3/OORXxu14SfP3B/g3e+wGVgQMHYsCAAbC0tISDgwN+/fVX3L17F1FRUcLMkUaNGsHExAQNGzYEUDZQ8vPPP2PAgAFo2bIlBgwYgMmTJ2PLli1ieU+bNg09e/ZE27ZtMXHiRERERGDu3Ln4+OOP4ejoCB8fH4SEhIjto6amhl9//RU2Njb4/PPPsXDhQqxbt07u06jTc5VRQiI00ioRW99IqwQp2ZVPubsRr4FDt3SxqHf9zKyQt9rECACy8pXguNQStout8P3eJpjT6yU+blU2PTE1Rxm5hUrYdrkh3Frl4NdhCejRJhs/HjDDtbjqB2kUiaGhIVRUVJCcnCy2Pjk5GSYmJnXK29bWFllZWSgoKMDmzZvRv39/REdH1ynP+lBeb0liIkkcx44di6ysLOTk5KBXr17o0aMHioqKAADZ2dnw8PDAN998g7y8PGRnZ8PLywu9evVCSYl4G64vsoqHiYkJCgoKkJGRUWWaM2fO4KOPPsJXX30FJSUlmJmZCQPepqamAMpmH3766adYunSpkF/Tpk0xePDgOta8dmpzTJmYmLwzvZGREXR0dDBr1iwEBQXh008/xdGjR3HkyBF88sknAMpuCQKABQsWYNu2bfDy8sLNmzdx/vz5Gj0ToD7J8hgbPHgwVFVVkZaWhoKCAmzZsgX9+/eX2+wuqTA0LBv8eKvuSE4Gquqnz5wBpkwBLC3LZrN07w4MGAD877hBdjZw5UrZTBRTU0BJCRg6tGyApjwNe2/Is58ut3fvXuTk5ODFixfIzMzEd999V+nnmpqaolevXti+fXvNKygDsuyH9u7di2+++QZdu3bFsmXLMGzYMLFZc4r4XS8Jvv5g/wbv/YDKw4cP8fXXX8PCwgK6urpo0aIFAFQ5kp2Tk4PHjx/Dx8cH2trawrJ48eIKJ1FvPvDJ2NgYQNm0+zfXvXz5Umwfe3t7aGr+8/wMV1dXZGdnC78qv62goACZmZliS0FRzesvK9kFIsw4aopFvZPRUFPx7qlXJFrqpQgc8xSHRsdjcrdULD/TGFf/N1hS+r+Zb57W2Rjp+hptTQrwfZd0eLTOwf4IPTmWWrHExMTAwcEBLi4u2LRpEwICAtC2bVt5F6uCIUOGICsrS1hUVVVl+nl79uyBo6MjPvnkEzx48AAHDx4U7tfW0NDAjh07cPnyZXTu3Bkff/wx7t27h5MnT9bbg0XrOx7vcvbsWUyfPh2bN29GQUEBHjx4gFP/uz2hfEDb2NgY27ZtQ0BAADp27IhPPvkEhYWFOHTokNzKLW3lz9g5duwY1qxZg8jISKxYsQJ//vknxowZI5Zmy5Yt2LVrF27fvo0pU6YgJiYG3377rdzKDtRvm1q0aBH09fXh6ekJZ2dnrF69GgcPHoStra3MPlMhTZwIPHwI3L8PFBYCGzYAO3cCb/4QNGxY2WDLixdAQQEwYQKwb594GqaQFKmfLjd58mQ4OTmhT58+aNWqFVavXl1puhEjRuD169cIDAys1/LVZ8y2bduG4OBg3Lt3D3v37sXw4cMxYMAAYeBbEb7r6xNff7D30fvxNKN36N27N8zNzbFt2zaYmZmhtLQUtra2KCwsrDR9dnY2gLIOzMXFRWzb22+DeLMDLX8ey9vr6jrzZNmyZRVuCZo/AFgwsE7ZVmCgWQJlEVV4AFRqjjIMtSuOcD9LV0PCa1WM3ddEWFc+ONBuoRWCfoxD84YKMPIjRZLGqJySCDD/XyzamhTgcYoatl5qCJcWCTDQLIGKEqFVY/H22MqwEBHP3q8ZKikpKSguLhYGF8sZGxsjKaluvyIUFRUJA5o3b95Ex44dMXHiROECUFEcP35c7D7w8sGNt2NgbGxc5dvFJIlj+SDro0ePEB4ejvT0dPTv3x/79+/HkCFD0KJFC7i6ugrPAxkyZAjS09PRt2/fKu/plqb6ikdSUhLU1dWhp6cn9uvn25/j7+8Pf39/mJqaIj09HS1atMDy5cvx5MkTAICvry8yMjIwc+ZMYZ9vvvkGz58/h4uLS5X3+MtKbY6ppKSkd6ZPSUlBUVERoqKixNJER0ejy/9uzyh/QHRlaap6jkF9qa82ZWFhgfHjx8PGxkaIw507d+Dm5gZfX1/heTTvnZQUoLgYeKvuMDYGquqnU1KA/v3Lbv1p1Khs0GT5cuB/xw2Asn97eACamoCublle+/eLp2EKSdH6aaBslkZycjJiYmKQlpaGS5cuYdGiRRXSffvtt/jtt9+EmZn1pb6/699U/rmWlpZ48uSJQnzX1wVff7B/g/d6hkpqaipiYmIwZ84ceHp6om3btmKvNSx/EOybU+KMjY1hZmaGJ0+ewNLSUmxp2bJlncsUGRmJvLw84e/w8HBoa2tXeF1ludmzZwuviy1fZvepczEqUFMGbMzyEfbkn9kzpQSEPdGEY9O8CuktDAtxYmwcAsc8FZZu1jlwaZmHwDFPYaL34XVmksaoKqUEFBaLhDzbm+UjNlX8ocRxaWpo8p7FsKioCBEREfD09BTWiUQieHp6Sv15J0pKSu98cr68ZGdn4/Hjx8ISFRWFxMREsZjo6OjAxcWlypjUNo4ikQgikUiIi6amJkpLS/Hmc8XL/y6fgSBr9RWPiIgIFBYWiqVp3bo1zM3NK803MTER+fn5+PrrrxEfH4+bN28C+Cdmbyr/fqivmL2pNm0hLCxMLD0A9OjRQ0hfVFSE69evV3jFduvWrfH06VMAZbc+JSQkvDONvNRXmyqfSVpZe5BHW5CaoiIgIgJ4s42IRGV/V9dPFxSUDaaoqAADB1b+8NXc3LLBFH39slcrK8gDWlnVFLWfLld+vL39ne/u7g4rK6sKD4iuD/L8ri9/y1j5wLcifNfXBV9/sH+D93qGioGBARo1aoStW7fC1NQU8fHxmDVrlrDdyMgIDRo0QFBQEJo2bQoNDQ3o6enBz88PEyZMgJ6eHry8vFBQUIAbN24gPT0dU6ZMqVOZCgsL4ePjgzlz5iAuLg7z58/Hjz/+WGWnp66uXvHCUUYzC0d1TsfMQBPYmhXArkk+AsL1kVekhAEOZQ98mnHUBMY6xZjaPQXqKoTWRuKzKnQ1yi483lz/Ok8JiRmqeJlV1pRiU8oKb6hdjMbvmNWhqCSJEQBsuWgAW7MCNG9YhMJiES481MLxO7pY8Pk/98z6fJSOyYdM0bF5Hlxa5uLiIy2ExGhh98jKbwNTZKtXr0ZAQABu3LiBa9euYdKkSdDS0sLOnTsBAAEBAUhISMBPP/0EoGxGV/nrj9XU1NCkSRPY29sLJysAsHTpUpw+fRrx8fHQ0dHBkCFD4OHhgZ49e8qnkhJas2YN5syZg4cPHyI2NhaLFi3CixcvxKYonzt3DkePHsXGjRsBVB/Hli1bwtvbG8HBwXj16hWaNm2KWbNmIS8vT7iN5ezZs1i1ahU2btyI9evXQ0lJCbNmzUJxcXGFZzvVJ1nEIzMzEzt27MDq1auRlpaGzMxMrF+/HleuXBH7FXHatGkICgpCaWkpBgwYgFmzZmHw4MHCRfPJkycxefJkzJ07F/v27YOOjg6WLl2KuLg43Lp1q/6C9AZJj6m1a9fiwoULmDJlCk6ePImvvvoKzs7O+P7774U8V61ahQMHDuD//u//EBISAi8vL/Tu3RseHh5iafz8/BAZGYnbt29jxIgRaNOmDQYNGlSv9a8JWbSp+/fv4+HDh9iyZQumTZuG1NRU9OvXDz169MAXX3wh5NusWTM0bNgQzZs3h7KyMuzt7QEAjx49Qk7OPw9ZVCirVwMBAWVv4bl2DZg0qez1v/+rOwICyt7s8782hU6dgCZNgNu3y/67YEHZc1JWrvwnz08/LRuYiYkpe9bKqlVltwiV5/mh0tIqq2+5li0Be3sgLa3szUnvKXn107169YKxsTGuX7+O7Oxs2NjYYNWqVbh06VKFwVwfHx+Eh4fj77//rp+gVEMWMbOwsMCQIUNw6tQppKamws7ODv7+/rhw4QLu3r0LQHG/6yUhi+sPxhTJez2goqSkhP3792PChAmwtbWFtbU11q1bJ5w0qqioYN26dVi4cCHmzZsHNzc3hIaG4rvvvoOmpiZWrVqF6dOnQ0tLC+3bt8ekSZPqXCZPT09YWVnhk08+QUFBAb7++mssWLCgzvlKw2e22UjLTcG60EZ4la2MtiYF2D40QZhyl5ihAiWRZK+7+itGG7OP/fNwrcmHzQAAP7qnYrxHqvQKX08kjVFukRL8ThkhKVMFGioEC8NCrOqfiM9ss4U0PdpmY8EXydh6qSEWBzVGy0aFWDf4BZyb51f4fEV38OBBNG7cGAsXLoSJiQlu374NLy8v4VlCzZs3F/vF18zMTGw67PTp0zF9+nSEhoaia9euAMoGPnfv3g1TU1NkZGTgzp076NmzZ4U3mSiqlStXQktLC1u3boW+vj4uXbokDNSWa9WqFQwNDYW/q4tjfn4+3NzcMGnSJBgYGCA5ORn/93//h48++givXr0CUPbcmd69e2P+/PkICwtDaWkpbt26BS8vrzrfglUXsogHUHbPfWlpKQ4fPgx1dXWcOXNGeNV9uV69euE///kP1NXVERkZib59+yIoKEjYHhISIrytbcaMGcjNzUVYWBi8vLwqvCGovkh6TIWFhWHIkCFYvHgxli5diocPH6Jfv35iFx2BgYEYM2YMZs+ejXXr1iEmJgYDBw7E5cuXhTRr166FhoYG/P390bBhQ0RGRqJHjx7C7VGKRBZtqri4GJ999hmWL1+OEydOQFtbG48ePcKIESNw+vRpIZ+FCxdi5MiRwt/l/ZmHh8c730wiVwcPAo0bAwsXlj2I9vZtwMsLKD+emjcXf/aJhgaweDFgYVH2ANpTp8qemfLmw0X19IBly4CmTcsGEw4fBv7zn7Lbiz5kzs7Am2998vcv+++uXcCoUfIokVTIq5/Oy8vD6NGj4e/vD3V1dTx79gxHjhzB8uXLxcqnq6uLgQMHYuLEiTKMgmRkEbPCwkJ0795dGGh59uwZDh8+jMWLFwt5KOp3vSRkcf3BmCIR0ZtzyFidjBw5UjoPz5Ll+8HZB080VN4lYIwx9i584sXqis8UWV3QHnmX4D0w5APtqWV5nfmhxqwain/zHWOMMcYYY4wxxpiC4QEVxhhjjDHGGGOMMQm9189QUTS7du2SdxEYY4wxxhhjjDFWD3iGCmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYUzhpaWkYOnQodHV1oa+vDx8fH2RnZ79zHw8PD4hEIrFlzJgxYmni4+Px+eefQ1NTE0ZGRpg+fTqKi4slLp+KxHswxhhjjDHGGGOMydjQoUORmJiIs2fPoqioCKNGjcL333+PvXv3vnO/0aNHY+HChcLfmpqawr9LSkrw+eefw8TEBFeuXEFiYiKGDx8OVVVVLF26VKLy8YAKY4wxxhhjjDHGFEp0dDSCgoJw/fp1ODs7AwDWr1+Pzz77DP/9739hZmZW5b6ampowMTGpdFtwcDCioqJw7tw5GBsbw8HBAYsWLcLMmTOxYMECqKmp1biMfMsPY4wxxhhjjDHGFEpYWBj09fWFwRQA6N69O5SUlHD16tV37rtnzx4YGhrC1tYWs2fPRm5urli+7du3h7GxsbCuZ8+eyMzMxN9//y1RGXmGCmOMMcYYY4wxxmqtoKAABQUFYuvU1dWhrq5e6zyTkpJgZGQktk5FRQUNGzZEUlJSlfsNGTIE5ubmMDMzw507dzBz5kzExMTgyJEjQr5vDqYAEP5+V76V4QEVBSQaKu8SMMYYY7VHe+RdAsXG3/OsrkjeBVBwInkXQMFxH1Q9GiLvErx/li1bBj8/P7F18+fPx4IFCyqknTVrFlasWPHO/KKjo2tdlu+//174d/v27WFqagpPT088fvwYrVq1qnW+leEBFcYYY4wxxhhjjNXa7NmzMWXKFLF1Vc1OmTp1KkaOHPnO/CwsLGBiYoKXL1+KrS8uLkZaWlqVz0epjIuLCwDg0aNHaNWqFUxMTHDt2jWxNMnJyQAgUb4AD6gwxhhjjDHGGGOsDiS5vadx48Zo3LhxtelcXV3x+vVrREREoEOHDgCAv/76C6WlpcIgSU3cvn0bAGBqairku2TJErx8+VK4pejs2bPQ1dVFu3btapwvwA+lZYwxxhhjjDHGmIJp27YtvLy8MHr0aFy7dg2XL1/Gjz/+iK+++kp4w09CQgLatGkjzDh5/PgxFi1ahIiICMTFxeH48eMYPnw4PvnkE9jZ2QEAPv30U7Rr1w7Dhg1DZGQkzpw5gzlz5sDX11fiZ77wgApjjDHGGGOMMcYUzp49e9CmTRt4enris88+Q5cuXbB161Zhe1FREWJiYoS3+KipqeHcuXP49NNP0aZNG0ydOhUDBw7EiRMnhH2UlZXx559/QllZGa6urvjmm28wfPhwLFy4UOLyiYiIn2ulYEQifpQWY4yx9xc/lPbd+IGQrK745P3d+Eya1dUHe4m8V4ZHx5APNGbV4BkqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJPQv3JAxcPDA5MmTZJ3MRhjjDHGGGOMMfae+lcOqDBx48aNQ2xsLPLy8hAeHo6OHTtWmfa7777D//3f/yEtLQ1paWk4e/bsO9N/CDg+Ffn5+eHFixfIzc3F2bNnYWlp+c70s2bNwrVr15CZmYnk5GQcPXoUrVu3rpCuc+fOOH/+PLKzs5GRkYELFy5AQ0NDVtWQGknjAVTfrjZv3oxHjx4hNzcXL1++RGBgIKytrYXtI0aMABFVujRu3FjqdawracfIwMAA69atw/3795Gbm4unT59i7dq10NXVrZDPiBEjEBkZiby8PCQnJ2PDhg1SrVtdSdLHAMCgQYMQHR2NvLw83LlzB7169RK2qaioYPny5bhz5w6ys7ORkJCAgIAAmJqaiuVx7NgxPH36FHl5eXjx4gV2795dIY2i2nNND93WtET7xZb4cnsz3EmoWR9x8p4OrP1aY9x+M7H160MbwWtDCzgstUTHFa0wcncTRD5X/H7nbbLoh0aPHo2QkBBkZGSAiKCnp1chD0dHRwQHByM9PR0pKSnYsmULtLS0pFav2pJFPNTV1bFhwwakpKQgKysLhw4dgpGRkbC9YcOGOH36NBISEpCfn4/4+HisX78eOjo6QpqdO3dW2m/fu3dPepWvL25uwPHjQEICQAT07SvvEsmVpG1uzJgxiIyMREZGBjIyMnDlyhV4eXnVU2nlg2PEPkQ8oPIvN3jwYKxevRp+fn5wcnJCZGQkzpw5U+UFmYeHB/bt24euXbvC1dUVz549Q3BwMMzMzCpN/77j+FQ0Y8YMTJgwAWPGjIGLiwtycnJw5swZqKurV7mPu7s7Nm7ciM6dO6NHjx5QVVVFcHAwNDU1hTSdO3dGUFAQgoOD0alTJ3Ts2BEbNmxAaWlpfVSr1moTj5q0q4iICIwaNQpt27ZFz549IRKJEBwcDCWlsm77wIEDMDExEVuCgoIQGhqKV69eybzekpBFjMzMzGBmZoZp06bB1tYWI0eOhJeXF3bs2CGWz+TJk7FkyRIsX74cNjY26N69O86cOSPT+kpC0j7G1dUV+/btw44dO+Do6IjAwEAEBgbCxsYGAKCpqQknJycsWrQITk5OGDBgAKytrXH8+HGxfEJCQjB48GBYW1tj4MCBaNWqFQ4dOiTz+tbVqXvaWBbcGL7uqTj6QzzaGBfA5/cmSM1Rfud+z1+rYEWwIZyb51bY1qJRIeZ99hInxj7F3lHP0ES/GN/+3gRp1eSpSGTVD2lqaiIoKAhLly6tNA9TU1OcO3cOjx49gouLC7y8vGBjY4Ndu3ZJu4oSkVU8/P390bt3b3z55Zdwd3eHmZkZjhw5ImwvLS3FsWPH0KdPH7Ru3RojR45E9+7dsXnzZiHNxIkTxfrtpk2bIjU1FX/88YdsgiFLWlpAZCTg6yvvkshdbdrc8+fPMWvWLHTo0AHOzs7466+/cOzYMbRr164eS15/OEbsg0X/Qu7u7jR+/HiaPn06GRgYkLGxMc2fP5+IiGJjYwkA3bp1S0ifnp5OACgkJISIiEJCQggABQUFkYODA2loaFDXrl0pOTmZTp06RW3atCEdHR36+uuvKScnR+LyAai3JTw8nNavXy/8LRKJ6Pnz5zRz5swa7a+kpEQZGRk0bNiwei03x0d+y4sXL2jq1KnC37q6upSXl0fe3t41zsPQ0JCIiNzc3IR1YWFhtHDhQrnXrz7iUZt21b59eyIisrCwqDKmBQUF9M0338g9JvKK0aBBgyg/P5+UlZUJAOnr61NOTg5169ZN7jGQVj33799PJ06cEFsXFhZGmzZtqvIznJ2diYioWbNmVabp3bs3lZSUkIqKilTqRXtkswzq2oz8hhoJf5f8DurSwYK2jDWocp/i30Dens3o4FRdmvmlMY3tbfbOz8j6VYlat25NVxY1kFk93rdjzN3dnYiI9PT0xNaPHj2akpKSSCQSCetsbW2JiKhVq1ZyO65kEQ9dXV0qKCiggQMHCmmsra2JiMjFxaXKfMePH0/x8fFVbu/bty+VlJRQ8+bNJTvGFG0hIurbV/7l+N/yPrS5ypbU1FT69ttv6738HKNKjrEPlYy+12jPBxyzavxrZ6gEBARAS0sLV69excqVK7Fw4UKcPXtWojwWLFiADRs24MqVK3j27BkGDx6MNWvWYO/evTh58iSCg4Oxfv16GdWg7lRVVdGhQwecO3dOWEdEOHfuHFxdXWuUh6amJlRVVZGWliarYsoNx6eili1bCr9IlsvMzMTVq1drHBMAwrTx8rg0btwYnTt3xsuXL3H58mUkJSUhNDQUH3/8sXQrIGW1iUdt2pWmpiZGjRqFJ0+e4NmzZ5WmGT58OHJzcxVulkF9xQgoa1eZmZkoKSkBAPTo0QNKSkpo0qQJoqKi8OzZMxw4cABNmzaVUu3qpjb1dHV1FUsPAGfOnKk2LqWlpXj9+nWl2w0MDDB06FBcuXIFxcXFkleknhSWAH+/0MBHFjnCOiUR8JFFDm49b1DlfhsvNEIjrRJ86ZRZo884EKEHHfUSWJsUSKXcslafx9jb1NXVUVhYCCIS1uXl5QEAunTpImlVpEJW8ejQoQPU1NTE0sTExODp06dV5mtqaooBAwbgwoULVZbXx8cH586dQ3x8vET1ZIpDGudGSkpK8Pb2hpaWFsLCwmRVVLnhGLEP2b92QMXOzg7z58+HlZUVhg8fDmdnZ5w/f16iPBYvXoyPP/4Yjo6O8PHxwYULF7Bp0yY4OjrCzc0NgwYNQkhIiIxqUHeGhoZQUVFBcnKy2Prk5GSYmJjUKI8VK1bgxYsXFU7wPwQcn4rK612XmIhEIqxZswaXLl3C33//DQCwsLAAUDZIuW3bNnh5eeHmzZs4f/58je57l5faxEOSdjV27FhkZWUhJycHvXr1Qo8ePVBUVFRpvj4+Pti7dy/y8/NrWx2ZkHWMyjVq1Ahz587F1q1bhXUWFhZQUlLCTz/9hEmTJmHQoEFo2LAhzp49C1VV1bpUSypqU08TExOJ0qurq2PFihXYt28fsrKyxLYtX74c2dnZSEtLQ/PmzdFXwZ9/kJ6rjBISoZFWidj6RlolSMmu/PacG/EaOHRLF4t6J70z75AHWnBcagm7xVbYFW6AX4c9R0NNxb7dsFx9HWOV+euvv2BiYoJp06ZBVVUV+vr6WL58OQDI7Zk8soqHiYkJCgoKkJGRUW2+e/fuRU5ODl68eIHMzEx89913lX6uqakpevXqhe3bt9e8gkzh1OXcyNbWFllZWSgoKMDmzZvRv39/REdHy6ys8sIxYh+yf/WAyptMTU3x8uXLWudhbGwMTU1N4cKwfF11eRYUFCAzM1NseV/MnDkTX331Ffr374+Cgvfjl7z69CHEZ8iQIcjKyhIWaVyEbty4Eba2tvjqq6+EdeXPBdmyZQt27dqF27dvY8qUKYiJicG3335b58+UFlnE41327NkDR0dHfPLJJ3jw4AEOHjxY6b3GnTt3Rrt27So8P0Qe6jtGAKCjo4OTJ08iKioKCxYsENYrKSlBTU0NEyZMQHBwMK5evYqvv/4aVlZW6Nq1q8zLJW8qKio4ePAgRCIRxo4dW2H7qlWr4OjoiB49eqCkpAS7d++WQyllJ7tAhBlHTbGod3K1gyMuLXIROOYp9vs8g1urHEw6ZFbtc1nkRR7HWFWioqIwYsQITJ06Fbm5uUhKSkJsbCySkpLq7flXihSPcpMnT4aTkxP69OmDVq1aYfXq1ZWmGzFiBF6/fo3AwMD6LSCrE2m2uZiYGDg4OMDFxQWbNm1CQEAA2rZtK8XSygfHiP2bqMi7APLy9oEtEolQWloqXNi9OX21ql+E38xDJBJVmee7LFu2DH5+fhKVXVpSUlJQXFwMY2NjsfXGxsZISnr3r3lTp07FrFmz0L17d9y9e1eWxZQbjg9w/PhxXL16Vfi7/GL+7RgYGxvj9u3b1ea3fv16fPHFF/jkk0+QkJAgrE9MTARQdnL+pujoaDRv3rwuVZAqacRDknZVPsj66NEjhIeHIz09Hf3798f+/fvF0n333Xe4desWbt68WZfqSUV9x0hbWxtBQUHIyspC//79xW5ZqaxdpaSkICUlRSHaVW36mKSkpBqlLx9MMTc3R7du3SrMTgGA1NRUpKam4uHDh4iOjsbz58/RuXNnhIeH17FmsmGgWQJlEVUY6EjNUYahdkmF9M/S1ZDwWhVj9zUR1pX+76u93UIrBP0Yh+YNy77fNdUI5g2LYN6wCA5N8/Hp+hY4dFMXP7ily65CtVTfx1h19u3bh3379sHIyAg5OTkgIkyZMgVPnjyRKJ/aqq94JCUlQV1dHXp6emKzVCqLWXJyMpKTkxETE4O0tDRcunQJixYtqpDu22+/xW+//VbleSZTTNI8NyoqKsLjx48BADdv3kTHjh0xceJEjBkzRvoFr0ccI/Zv8q+doVKV8qe5l5+IA6jRhWJtzZ49W3gVWPlSX4qKihAREQFPT09hnUgkgqen5zvvTZw+fTrmzp0LLy8vRERE1EdR5YLjA2RnZ+Px48fCEhUVhcTERLGY6OjowMXFpdr7WdevX4/+/fujW7duiIuLE9sWFxeHhIQEsdcCA0Dr1q3x9OlTqdWnrqQRj9q2K5FIBJFIVGGGipaWFgYPHqwQs1OA+o2Rjo4OgoODUVhYiD59+lSYCXb58mUAEGtXBgYGMDQ0VIh2VZu2EBYWJpYeKHtWzJvpywdTrKys0L179xo9w6n8x4R3vW1B3tSUARuzfIQ9+eftYKUEhD3RhGPTvArpLQwLcWJsHALHPBWWbtY5cGmZh8AxT2GiV/VFbCkBhSWKeYokz37oXV6+fImcnBx4e3sjPz9f4ufS1VZ9xSMiIgKFhYViaVq3bg1zc/N3xqyqY8vd3R1WVlYK03ezmpPmudHblJSUFLofrimOEftXkdfTcOXJ3d2dJk6cKLaub9++NGLECCIi6ty5M7m5uVFUVBSFhoZSp06dCKj4lp/09HRh/507d5Kenp5YnvPnzyd7e3uJy4d6fOL24MGDKS8vj4YPH05t2rShzZs3U1paGhkZGREACggIoKVLlwrpZ8yYQfn5+TRgwAAyNjYWFi0tLbk/PZzjUz/LjBkzKC0tjXr37k22trZ09OhRevz4Mamrqwtpzp07R76+vsLfGzdupPT0dPrkk0/E4qKhoSGkmThxIr1+/ZoGDhxIrVq1ooULF1Jubm6Vb7VRlKU28aiuXbVs2ZJmzZpFTk5O1KxZM3J1daVjx45RSkoKNW7cWOzzv/32W8rNza3w9g1FWmQRIx0dHQoLC6PIyEiysLAQa1dKSkpCPkePHqW7d++Sq6sr2djY0PHjx+nevXtSe5tNXRdJ+xhXV1cqLCykKVOmkLW1Nc2fP58KCgrIxsaGAJCKigoFBgZSfHw82dnZicVFVVWVAFCnTp3I19eX7O3tqXnz5tS1a1e6dOkSPXz4kNTU1KRSL1m9QeDkbG2ybWtJR6br0qM1ajT3KyNytmtFr7YoE+0BTR9kQv8dZVjl/m+/5Sdnp4h+/rYR3VqhQc83qtDdVeo0a7Ax2ba1pAf+au/NW35kcYwBIGNjY7K3tycfHx8iIurSpQvZ29uTgYGBkMbX15ccHR3JysqKxo0bRzk5OTR+/PgPrs8BQL/88gvFxcWRh4cHOTk50eXLl+ny5cvC9l69etHIkSPJxsaGzM3N6bPPPqO///6bLl68WKGMu3fvprCwsNofY4qwaGkR2duXLUREkyaV/btZM7mX7X1oc0uXLiU3NzcyNzcnW1tbWrp0KZWUlFD37t3rvfwco0qOsQ8Vv+VH6v61t/y8y6+//gofHx906NAB1tbWWLlyJT799FN5F0smDh48iMaNG2PhwoUwMTHB7du34eXlJTz7pXnz5mK3LY0dOxbq6uo4fPiwWD4LFiyQ261LssTxqWjlypXQ0tLC1q1boa+vj0uXLsHLy0tsdkCrVq1gaGgo/D1u3DgAqPCmg5EjRyIgIAAAsHbtWmhoaMDf3x8NGzZEZGQkevToUW/TxmurNvGorl3l5+fDzc0NkyZNgoGBAZKTk/F///d/+Oijj/Dq1Suxz/fx8cGRI0fqdXabpGQRIycnJ3Tu3BkAhKnA5Vq0aCHMQBk+fDj8/f1x8uRJlJaW4sKFC/Dy8lKYt9lI2seEhYVhyJAhWLx4MZYuXYqHDx+iX79+wgOemzRpIjxcNjIyUuyzPDw8cOHCBeTm5mLAgAHw8/ODlpYWEhMTERQUhMWLF6OwsLCeal47n9lmIy03BetCG+FVtjLamhRg+9AE4ZafxAwVKImoxvkpKwFPUtRwNFIP6blK0G9QivZN8rFn1DNYGSl2LN4ki2MMAMaMGSP2XKKLFy8CEO+7O3XqBD8/P2hra+P+/fv44Ycf8Pvvv8u4xu8mq3hMnjwZpaWlOHz4MNTV1XHmzBnh+w0oe8PR6NGj4e/vD3V1dTx79gxHjhwRHtRbTldXFwMHDsTEiRNlGIV64OwMhIb+87e/f9l/d+0CRo2SR4nkpjZtzsjICLt374apqSkyMjJw584d9OzZ84N5kcHbOEbsQyUiopqfebB6IRKJ5F0ExhhjrNZoj7xLoNhEQ+VdAva+45P3d+MzaVZXH+wl8l4ZHh1DPtCYVUMxbxBmjDHGGGOMMcYYU2A8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxISERHJuxBMnEgkkncRGGOMMcYYey/xxc278ZVG9T7YS2RZXmd+qDGrBs9QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxKq9wGVXbt2QV9fv875eHh4YNKkSXXOR9ZatGiBNWvWyLsYjDHGGGOMMcYYk6J6H1Dx9vbGgwcP6vtj2Vv8/Pzw4sUL5Obm4uzZs7C0tHxnejc3Nxw/fhwJCQkgIvTt2/ed6Tdt2gQiwsSJE6VZ7Hoji/js3LkTRCS2nD59WlZVkJlx48YhNjYWeXl5CA8PR8eOHatM265dOxw6dAixsbFVtocxY8YgMjISGRkZyMjIwJUrV+Dl5SXLKkiVJPEAgEGDBiE6Ohp5eXm4c+cOevXqJWxTUVHB8uXLcefOHWRnZyMhIQEBAQEwNTUVy+PYsWN4+vQp8vLy8OLFC+zevbtCGnmRZjzKVXc8WllZITAwEK9evUJGRgYuXrwIDw+PSj+vYcOGePbsGYgIenp6ta5nXUnaxwDVx1ZdXR0bNmxASkoKsrKycOjQIRgZGQnb7ezssHfvXsTHxyM3NxdRUVGYMGGCWB7u7u4V+ikigrGxsXQqXgPyaEOOjo4IDg5Geno6UlJSsGXLFmhpaQnbaxI7eZNFmxo9ejRCQkKQkZFR7TGjpqaGW7dugYhgb29f5/pImyziExISUuFY2bRpk1gaZ2dnnDt3Dunp6UhLS0NQUBDs7OykWrf6ImkM3/fv9zpzcwOOHwcSEgAioJpz5/eZtPttIyMj7Ny5EwkJCcjJycHp06crtLeaHH+M1Qt6T7m7u9PEiRPlXYxqmZubk7+/v0T7AJDpMmPGDEpPT6c+ffpQ+/btKTAwkB4/fkzq6upV7uPl5UWLFi2ifv36ERFR3759q0zbr18/unXrFj1//pwmTpwo8/q8L/HZuXMnnTp1ioyNjYVFX19f7vWVZBk8eDDl5+fTyJEjqW3btrRlyxZKS0ujxo0bV5re2dmZVq5cSd7e3vTixYtK28MXX3xBvXr1IktLS7KysqLFixdTQUEBtWvXTu71lXY8XF1dqaioiKZNm0Zt2rShhQsXUkFBAdnY2BAA0tXVpeDgYPryyy+pdevW5OLiQuHh4XT9+nWxfCZNmkQuLi7UvHlzcnV1pcuXL9Ply5c/uHgANTseY2Ji6M8//6T27duTpaUlbdiwgbKzs8nY2LjCZx49epROnjxJRER6enpyiVNt+piaxPaXX36hp0+fUteuXcnJyYmuXLlCly5dEraPGjWK1qxZQ5988gm1bNmShg4dSjk5OeTr6yukcXd3JyIiKysrsb5KJBJ9sG3I1NSUUlNT6ZdffqHWrVuTs7MzXbp0if744w+JYifPRVZtauLEiTRz5kyaOXNmtcfMmjVrhGPL3t5e7jGpj/iEhITQli1bxI4VHR0dYbuWlhalpKTQr7/+Sq1bt6Z27drRH3/8QYmJiaSioiL3uMg6hvL8fidFWLy8iBYtIvrfuSH17Sv/Mv1vkWasZdFvX7lyhS5cuEDOzs7UunVr2rx5M8XFxZGmpqaQprrjr85t6EMly7YlI6mpqTRkyBDS0dEhPT09+vbbbykrK6vK9LGxsVX+fz148OAboai4fd++fRKXTyo1P3HiBOnp6VFxcTEREd26dYsACF/AREQ+Pj40dOhQ2rlzJ+np6Qnr58+fT/b29rR7924yNzcnXV1d8vb2pszMTCFNdnY2DRs2jLS0tMjExIT++9//VhhQ2bhxI1laWpK6ujoZGRnRwIEDhW3u7u7k6+tLvr6+pKurS40aNaI5c+ZQaWmpkCY/P5+mTp1KZmZmpKmpSZ06daKQkBCxel68eJG6dOlCGhoa1LRpUxo/fjxlZ2cL25OTk+mLL74gDQ0NatGiBf3+++8KOaDy4sULmjp1qvC3rq4u5eXlkbe3d407mKoGVMzMzOjZs2fUrl07io2NfS8HVGQVn507d9LRo0flXr+6LOHh4bR+/Xrhb5FIRM+fP6eZM2dWu68k7SE1NZW+/fZbuddX2vHYv38/nThxQmxdWFgYbdq0qcrPcHZ2JiKiZs2aVZmmd+/eVFJSIvcTdFnEo7rjsVGjRkRE1KVLFyGNtrY2ERF5enqK5T1mzBgKCQmhrl27EpH8BlRq08dUF1tdXV0qKCiggQMHCmmsra2JiMjFxaXKfDds2EDnz58X/i4fUJFXbOTRhkaPHk1JSUlig0a2trZERNSqVasax06eiyza1JtLde3Cy8uLoqKiqG3btkSkeAMqsopPSEgI+fv7V5lHhw4diIioadOmErUtRVzqem5UvtTX9zsp2lJ2cij/ckD6AyrSqGhJHQAAWVdJREFU7retrKyIiMQG3kQiESUnJ5OPj4+wrrrjr85t6EMl63YuA15eXmRvb0/h4eF08eJFsrS0pK+//rrK9MXFxZSYmCi2+Pn5kba2tthADFB2ffZmury8PInLJ5Vbftzc3JCVlYVbt24BAC5cuABDQ0OEhoYKaS5cuFDlFOzHjx8jMDAQf/75J/78809cuHABy5cvF7ZPnz4dFy5cwLFjxxAcHIzQ0FDcvHlT2H7jxg1MmDABCxcuRExMDIKCgvDJJ5+IfUZAQABUVFRw7do1rF27FqtXr8b27duF7T/++CPCwsKwf/9+3LlzB19++SW8vLzw8OFDoYxeXl4YOHAg7ty5gwMHDuDSpUv48ccfhTxGjhyJZ8+eISQkBIcOHcIvv/yCly9f1jqustCyZUuYmpri3LlzwrrMzExcvXoVrq6udcpbJBLht99+w6pVqxAVFVXXosqFLOMDlD37Jzk5Gffv38cvv/yChg0b1jnP+qKqqooOHTqIxYaIcO7cOanEBgCUlJTg7e0NLS0thIWFSSVPWalNPFxdXcXSA8CZM2feGT89PT2Ulpbi9evXlW43MDDA0KFDceXKFRQXF0teESmRRTxqcjympqbi/v37GD58ODQ1NaGsrIwffvgBycnJiIiIEPZr27Yt5s2bh+HDh6O0tFRq9ZZUbfqYmsS2Q4cOUFNTE0sTExODp0+fVtu+0tLSKqy/ffs2Xrx4geDgYHz00UcS17M25NWG1NXVUVhYiLJzqzJ5eXkAgC5dulRZ3qpiV99k1aZqysjICNu2bcOwYcOQm5tbu0rIkKzjM3ToULx69Qp3797F0qVL0aBBA2FbTEwMUlJS4OPjA1VVVWhoaMDHxwdRUVGIi4uTbkVlSBrnRu/T9zurOVn02+rq6gCA/Px8sTwLCgoq9MnvOv7YhyE6OhpBQUHYvn07XFxc0KVLF6xfvx779+/HixcvKt1HWVkZJiYmYsvRo0cxePBgaGtri6XV19cXS6ehoSFxGaUyoKKnpwcHBwdhACU0NBSTJ0/GrVu3hOcAPHr0CO7u7pXuX1pail27dsHW1hZubm4YNmwYzp8/DwDIzs7Gjh078N///heenp5o3749AgICxC4c4uPjoaWlhS+++ALm5uZwdHSscG9zs2bN4O/vD2trawwdOhTjx4+Hv7+/sP/OnTvxxx9/wM3NDa1atcK0adPQpUsX7Ny5EwCwbNkyDB06FJMmTYKVlRU++ugjrFu3Drt370Z+fj4ePHiA06dPY9u2bejcuTM6dOiAHTt2CCdlisLExAQAkJycLLY+OTlZ2FZbM2fORHFxMdatW1enfORJlvEJCgrC8OHD4enpiZkzZ8Ld3R2nT5+GktL78bItQ0NDqKioyCQ2tra2yMrKQkFBATZv3oz+/fsjOjq6TnnKWm3iYWJiIlF6dXV1rFixAvv27UNWVpbYtuXLlyM7OxtpaWlo3rx5tc81kjVZxKOmx2P37t3h6OiIrKws5OfnY8qUKfDy8hIGodTU1LBv3z5Mnz4dz549q1M966o2fUxNYmtiYoKCggJkZGTUOF9XV1d4e3tj69atwrrExET88MMPGDhwIAYOHIhnz54hNDQUjo6OklW0FuTVhv766y+YmJhg2rRpUFVVhb6+vvCjTlXPJqosdvIiqzZVU7t27cLmzZvFBjAViSzjs3fvXnzzzTfo2rUrli1bhmHDhuH3338XtmdnZ8PDwwPffPMN8vLykJ2dDS8vL/Tq1QslJSXSqqLM1eXc6H38fmc1J4t++/79+3j69CmWLVsGfX19qKqqYsaMGWjWrJlYn1zd8cc+DGFhYdDX14ezs7Owrnv37lBSUsLVq1drlEdERARu374NHx+fCtt8fX1haGiITp064ddffxX7caWmpHYl5+7ujtDQUBARLl68iAEDBqBt27a4dOkSLly4ADMzM1hZWVW6b4sWLaCjoyP8bWpqKszsePz4MQoLC+Hi4iJsb9iwIaytrYW/e/ToAXNzc1hYWGDYsGHYs2dPhV9JOnfuDJFIJPzt6uqKhw8foqSkBHfv3kVJSQlat24NbW1tYblw4QIeP34MAIiMjMSuXbvEtvfs2ROlpaWIjY1FdHQ0VFRU0KFDB+Ez2rRpU+0bjQoKCpCZmSm2SNOQIUOQlZUlLKqqqlLNv5yTkxMmTpyIkSNHyiR/Wamv+ADAgQMHcOLECdy7dw/Hjh3DF198gU6dOlU5c+vfJCYmBg4ODnBxccGmTZsQEBCAtm3byrtYcqWiooKDBw9CJBJh7NixFbavWrUKjo6O6NGjB0pKSrB79245lFIxbNy4ES9fvoSbmxs6deqEwMBAnDhxQjg5W7ZsGaKjo7Fnz556L1t99jGSsLGxwbFjx+Dn54ezZ88K6x88eICtW7fi5s2bCAsLg4+PD65cuYLJkyfLsbSyFRUVhREjRmDq1KnIzc1FUlISYmNjkZSUVOlspqpiV18UqU2NHz8eOjo6WLZsmdzK8Lb6jM+2bdsQHByMe/fuYe/evRg+fDgGDBgACwsLAICGhgZ27NiBy5cvo3Pnzvj4449x7949nDx5sla/gtYXacaQv9+ZpIqLizFgwAC0bt0a6enpyM3NRdeuXXHq1CmxPrm644/Vv8quawsKCuqUZ1JSktgD9oGyc+SGDRsiKSmpRnns2LEDbdu2rTDjduHChTh48CDOnj2LgQMHYty4cVi/fr3EZVSReI8qeHh44Ndff0VkZCRUVVXRpk0beHh4IDQ0FOnp6VXOTgFQoaMWiUQSTcnW0dHBzZs3ERoaiuDgYMybNw8LFizA9evXa/SK5uzsbCgrKyMiIgLKyspi28qnBWVnZ+OHH36o9Kn+zZs3r/Wbi5YtWwY/P79a7VsTx48fFxu9K59GZ2xsLNYIjY2Ncfv27Vp/jpubG4yMjBAfHy+sU1FRwc8//4xJkyahZcuWtc5bluorPpWJjY3Fq1evYGlpib/++kuqectCSkoKiouLK7zt4+1Y1UZRUZEweHnz5k107NgREydOxJgxY+qUryzVJh5JSUk1Sl8+mGJubo5u3bpVmJ0ClN3qkpqaiocPHyI6OhrPnz9H586dER4eXsea1Y4s4lH+33cdj926dcMXX3wBAwMDIU6+vr7o0aMHRowYgRUrVqBbt25o3749Bg0aBADC4HpKSgqWLFmCBQsW1K3y7yCNPqYmsU1KSoK6ujr09PTEZqlUFv+2bdvi/Pnz2Lp1K5YsWVJtHa5du/bOW1+kRV5tCAD27duHffv2wcjICDk5OSAiTJkyBU+ePBHLW9LYyUJ9tama6NatG1xdXSucMN+4cQN79uyRy48s8oxP+edaWlriyZMnGDJkCFq0aAFXV1fhV88hQ4YgPT0dffv2xYEDB2pVR1mT5rnR+/j9zmpOVudCN2/ehKOjI3R1daGmpoaUlBSEh4fjxo0bVZbl7eOP1b/Krmvnz59f6XnWrFmzsGLFinfmJ43ZbHl5edi7dy/mzp1bYdub6xwdHZGTk4NVq1ZJ/BY/qc1QKX+Oir+/vzB4Uj6gEhoaWutf4Vu1agVVVVWxjj09Pb3CAIaKigq6d++OlStX4s6dO4iLixO7SH17SlB4eDisrKygrKwMR0dHlJSU4OXLl7C0tBRbyn/hdHJyQlRUVIXtlpaWUFNTQ5s2bVBcXCw25TUmJqbK5x6Umz17tvA6ufJFmrKzs/H48WNhiYqKQmJiIjw9PYU0Ojo6cHFxqdM9rb/99hvs7Ozg4OAgLAkJCVi1ahV69uwpjarIRH3FpzJNmjRBo0aNkJiYKNV8ZaWoqAgRERFisRGJRPD09JR6bJSUlIQTOEVVm3iEhYWJpQfKZti9mb58MMXKygrdu3ev0TMaym8bk2fMZBGP2NjYao9HTU1NAKgwCF9aWirEZeDAgbC3txf6pu+++w5A2ffWxo0b61Ltakmjj6lJbCMiIlBYWCiWpnXr1jA3NxfLt127dggJCUFAQADmzJlTozo4ODjUSz8lrzb0ppcvXyInJwfe3t7Iz88Xm4FSm9jJQn21qZqYMGGC2LH12WefAQC8vb3xn//8p5Y1rBt5xsfBwQEAhONFU1MTpaWlYlPIy/9W5Nt9ZXlu9D58v7Oak9W5ULnMzEykpKTA0tISzs7OOHbsWJVlefv4Y/Wvsuva2bNnV5p26tSpiI6OfudiYWEBExOTCs8kLS4uRlpaWo1uUT106BByc3MxfPjwatO6uLjg+fPnks+qkfgxtu/g4OBAysrKtGnTJiIqe8WRqqoqAaD79+8TEVX5lp83+fv7k7m5ufD3mDFjyNzcnM6fP093796lPn36kLa2tvCWnxMnTtDatWvp1q1bFBcXR7/88gspKSnRvXv3iKjsLT/a2to0efJkun//Pu3du5e0tLRo8+bNwmcMHTqUWrRoQYcPH6YnT57Q1atXaenSpfTnn38SEVFkZCQ1aNCAfH196datW/TgwQMKDAwkX19fIQ8vLy9ydHSk8PBwunHjBnXp0oUaNGigcG/5mTFjBqWlpVHv3r3J1taWjh49WuHVd+fOnRN7HaSWlhbZ29sL/68mTZpE9vb273zzyPv6lh9ZxEdLS4tWrlxJLi4uZG5uTt26daMbN25QTEwMqampyb3ONV0GDx5MeXl5NHz4cGrTpg1t3ryZ0tLSyMjIiABQQEAALV26VEivqqoqxCUhIYFWrlxJ9vb2Ym83WLp0Kbm5uZG5uTnZ2trS0qVLqaSkhLp37y73+ko7Hq6urlRYWEhTpkwha2trmj9/vtirAlVUVCgwMJDi4+PJzs5O7FWA5X1pp06dyNfXl+zt7al58+bUtWtXunTpEj18+FDubUna8QCqPx4bNWpEr169okOHDpGdnR1ZWVnRypUrqaCggOzs7Cotp7zfZFObPqa62AJlr02Oi4sjDw8PcnJyqvA6bRsbG0pOTqbdu3eLtS1DQ0MhzcSJE6lPnz7UqlUrsrGxIX9/fyouLqZu3bp9sG0IAPn6+pKjoyNZWVnRuHHjKCcnh8aPHy9R7OS5yKpNGRsbk729Pfn4+BBR2du07O3tycDAoNJylJ+7KdpbfmQRHwsLC5ozZw45OTmRubk59e7dmx49ekShoaFCHtbW1pSXl0cbN26kNm3aULt27Wj37t2Unp5OJiYmco+LrGMoz+93UoRFS4vI3r5sKTs5LPt3s2ZyL5s0Yy2LfnvQoEHk7u5OLVu2pD59+lBsbCwdOnRI2F6T46/ObehDJcu2JQNRUVEEgG7cuCGsO3PmDIlEIkpISKh2f3d3d7G3/77L4sWLycDAQOIySrXm5RfP0dHRwjp7e3syMTER/q7NgEpWVhZ98803pKmpScbGxrRy5Uqx1yZfvHiR3N3dycDAgBo0aEB2dnZ04MABYX93d3caN24cjRkzhnR1dcnAwIB++uknsdcmFxYW0rx586hFixakqqpKpqam1L9/f7pz546Q5tq1a9SjRw/S1tYmLS0tsrOzoyVLlgjbExMT6fPPPyd1dXVq3ry58CpoRRtQAUB+fn7Cq6HOnj1LVlZWYttjY2Np/vz5wt/lFyBv27lzZ5Wf8b4OqMgiPhoaGhQUFETJyclUUFBAsbGxtGXLFrET1/dl8fX1pbi4OMrPz6fw8HDq1KmTsC0kJESsTbx5HL8pJCRESLN9+3aKjY2l/Px8Sk5OprNnz74Xgym1iQdQdpJw//59ys/Pp7t371KvXr2qjRdRWT8GlL1y8/z585SSkkJ5eXn05MkT+uWXX8jMzEzusZB2PMqX6o7HDh06UFBQEKWkpFBGRgZduXKFvLy8qiyjvAdUalKnt/uY6mILgNTV1WnDhg2UmppK2dnZdPjwYTI2Nha2z58/v9K2FRsbK6SZPn06PXz4kHJzcyklJYX++usv8vDw+ODbUEBAAKWkpFB+fj7dvn2bvvnmG7HtNYmdvBdZtKmq6j1ixIhKy6CoAyqyiE/Tpk0pNDRU6IsfPHhAK1asIB0dHbE8unfvThcvXqT09HRKTU2lc+fOvfNV5oq8SBpDeX6/kyIsVZwb0s6dci+btOMt7X57/PjxFB8fTwUFBRQXF0cLFy4UfliS5PirUxv6UMmybclI+aSFq1ev0qVLl8jKykrstcnPnz8na2trunr1qth+Dx8+JJFIRKdPn66Q5/Hjx2nbtm109+5devjwIf3yyy+kqalJ8+bNk7h8IiIifOA8PDzg4OCANWvWyLsoNfLmw3MZY4wxxhhjNffBX9zUEV9pVO+DvUSW5XWmjGKWlpaGH3/8ESdOnICSkhIGDhyIdevWCc86jYuLQ8uWLRESEiL2mJGffvoJv//+O+Li4ircZhkUFITZs2fj0aNHICJYWlpi7NixGD16tMS3ZPKAigLiARXGGGOMMcZq54O/uKkjvtKo3gd7ifweDqgoOsV9IhZjjDHGGGOMMcaYgvpXzFB53/AMFcYYY4wxxmqHL27eja80qvfBXiLzDBWp4xkqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEeECFMcYYY4wxxhhjTEI8oMIYY4wxxhhjjDEmIR5QYYwxxhhjjDHGGJMQD6gwxhhjjDHGGGOMSYgHVBhjjDHGGGOMMcYkxAMqjDHGGGOMMcYYYxLiARXGGGOMMcYYY4wxCfGACmOMMcYYY4wxxpiEVORdAMaYdNEeeZdA8YmGyrsE/9/enYfVmPd/AH+fVkoptDGkUhRFaexLyDaGIUuWkX2GMdmXxoyULWN+yvLMMMZSZqzDWOZBjCiPJEObpZHUyJJEkkpSnd8fns7jzEmK6nvqvF/X1XXpPnf1Pl/nvs+5P/d3ISJVxvM0vS++j5VOIjqAkpOKDkBUg7CHChERERERERFRObGgQkRERERERERUTiyoEBERERERERGVEwsqRERERERERETlxIIKEREREREREVE5saBCRERERERERFROLKgQEREREREREZUTCypEREREREREROXEggoRERERERERUTmxoEJEREREREREVE4sqBARERERERERlRMLKkRERERERERE5cSCSiUJDAyEgYGB6BgKvvjiCyQnJ+P58+e4cOECPvzwwzfua2dnh/379yM5ORlSqRQzZ85U2Kdr1644cuQI7t27B6lUik8++aQy41e48rQHAAwbNgzx8fF4/vw54uLi0L9/f7nHpVJpiV/z5s2T7XP48GHcvn0bz58/x/3797Fjxw6YmZlVyvOrCjsv1kXPtRawX94Mw7c0Rty9WmX6uaNX9dDc1wZf7GlYyQnF8PX1xf3795Gbm4s//vgDzZo1e+vPvO31uGnTJiQmJiI3NxcPHz7EoUOH0Lx588p6ChWiIo8xDQ0NrFq1CnFxccjOzsa9e/cQFBSkcPxUt2NMRBsVn9df/1q4cGGlPL/KUt5jrCzvV9u3b1dol+PHj1fWU6gyPE+Xju1TNuU95qZOnYrY2Fg8ffoUT58+xfnz59GvX78qSltxKvqzorGxMbZv34579+4hJycHx48fV2jLM2fOKJyLNm7cWOHPTbiuXYEjR4B79wCpFKhm1xFEAAsqKmXEiBHw9/eHr68vnJycEBsbixMnTsDIyKjE/XV0dJCUlAQvLy+kpqaWuI+uri5iY2Mxffr0yoxeKcrbHh07dsTu3buxdetWODo64tChQzh06BBatmwp28fU1FTua8KECSgqKsKBAwdk+5w5cwYjRoxA8+bNMXToUFhZWWH//v2V/nwrw7GrdeB30gjTuz/Gwc9T0MLkBSb90giPc9RL/bm7mRr49mQDODfJraKkVWvBggWYMWMGpk6divbt2yMnJwcnTpyAtrb2G3+mLK/Hy5cvY8KECbC1tUXfvn0hkUhw8uRJqKkp56m8oo8xHR0dODk5YdmyZXBycoKbmxuaN2+OI0eOyP2e6nSMiWojAFi8eLHc+WrDhg2V+lwr0rscY2V9vzp+/Lhcu4waNaqi41cpnqdLx/Ypm3c55u7evQsvLy+0bdsWzs7OOH36NA4fPgw7O7sqTP5+KuOz4qFDh2BpaYlPPvkEjo6OuH37Nk6dOgUdHR2537V582a5c9GCBQsq9bkKoasLxMYC1fA6gqiYRCqVSkWHUFZFRUX4v//7P2zevBl37tyBiYkJPv/8c4wZMwYWFhY4cOAANmzYgMjISFhbW2PTpk3o2LEjQkND0aNHD7nftWTJEvj4+JTp70okkkp4NsCFCxfw559/wtPTU/Z37ty5gw0bNuDbb78t9WeTk5Oxdu1arFu37o37SKVSDB48GIcPH67Q3JWlvO2xZ88e6OrqYuDAgbJtERERiImJwbRp00r8GwcPHoSenh5cXV3fmGPgwIE4dOgQtLW1UVBQ8J7PCpDufO9fUWbDtzSGfcMX8P7oIQCgSAp0D7DE2HZP8FmXJyX+TGERMCawMYa2eYrLKbWRlaeOH0ber7rQACRjKvf3379/H2vWrMGaNWsAAPr6+khLS8P48eOxd+/eEn/mXY5Pe3t7xMXFwcrKCklJSZXzZN5DVRxjzs7O+PPPP9GkSRPcuXOnxH0q+hirSKLaqCzndGX2LsfY6970frV9+3YYGBhgyJAhlZJb9vd5nlYa1bV9Kvt97J/e95gr9vjxY8yfPx/btm2rrKgVqqLP0dbW1khISEDLli1x/fp12e988OABFi1ahK1btwJ4dWMgJiYGs2fPrpTnpZQXf1IpMHgwoCzXETX1ErmSrjMB1Nw2ewvlvK2pJL766iusWrUKixcvxvXr17Fr1y6YmJjIHv/6668xb948xMTEwMbGBqNGjUJBQQE6deqEtWvXQl9fH6mpqUhNTZUb8iGCpqYm2rZti1OnTsm2SaVSnDp1Ch07dhSYTIx3aY+OHTvK7Q8AJ06ceOP+xsbGGDBggOzNsSSGhoYYM2YMzp8/r3QXem+TXwhcu18LnSxzZNvUJEAnyxxE3639xp/7Pqw+6usWYrhTVlXErHIWFhYwMzOTe61kZWUhMjLyja+Vd3k96ujoYMKECUhKSnpjIUGkqjjGAKBu3booKipCZmZmiY8r8zEmuo28vLzw6NEjREVFYd68eVBXL/2OvLJ4l2OsPFxcXJCWloa//voLP/zwA+rVq/fev1MUnqdLx/Ypm4o45tTU1ODu7g5dXV1ERERUVtQKVRnn6OIePXl5eXK/88WLF+jSpYvcz40ZMwbp6em4cuUKVq5cidq13/yaJCJxWFB5g2fPnmHdunVYvXo1xo0bBysrK3Tp0gWTJ0+W7TNv3jwMGDAANjY28PX1xe3bt5GYmAgtLS3UrVsXEolE1k2vTp06Ap8N0KBBA2hoaCAtLU1ue1paGkxNTQWlEudd2sPU1LRc+48bNw7Pnj3Db7/9pvDYqlWrkJ2djYyMDDRp0qTazT0DAE9y1VEolaC+bqHc9vq6hXiUXfKF2aWUWtgfrY9lAx9URUQhil8P5XmtlOf1OG3aNDx79gw5OTno378/evfujZcvX1bgM6gYVXGMaWtr49tvv8Xu3bvx7NkzuceqwzEmso3Wr1+PkSNHokePHvjxxx+xaNEirF69+j2fUdV4l2OsrIKDg+Hh4YFevXph4cKF6N69O44fP660w+rehufp0rF9yuZ9jrlWrVrh2bNnePHiBTZt2oQhQ4YgPj6+0rJWpMo4R//111+4ffs2/Pz8YGBgAE1NTSxYsACNGzeWm+tq165d+PTTT9GjRw/4+flh7Nix+OWXXyr4GRJRRaienxCqQHx8PF68eIFevXq9cR8HBwfZv4tPgg8fPizX33nx4gWysrLkvqhmmDhxInbu3IkXL14oPPbdd9/B0dERvXv3RmFhIXbs2CEgYdXKfiHBgoNmWDYwDfV0ikTHqTCjR4/Gs2fPZF+ampqV+vd27twJR0dHdOvWDQkJCdi3b1+pY9hrKg0NDezbtw8SiaTEoS6qeIz9U2ltFBAQgLCwMFy5cgU//vgj5s6dC09PT2hpaQlK+2ZVeYzt3bsXv//+O65evYrDhw/j448/Rrt27eDi4lJpf1OZ1NTzdEVRlfapyGPuxo0baNOmDdq3b4+NGzciKCgItra2FZi2eikoKICbmxtsbGzw5MkT5ObmokePHjh27BiKiv73mvrpp59w8uRJXL16Fbt27YKHhwfc3NxgaWkpMD0RlURDdABlVZZuda+/wRTPe/L6ybAs/Pz84OvrW75w7+DRo0coKCiQG7IEACYmJnjwQHXushR7l/Z48OBBmffv0qULWrRoAXd39xJ/1+PHj/H48WPcvHkT8fHxuHv3Ljp06IALFy684zOqeoY6hVCXSBUm7nuco44GdQoV9r/zRAv3MjUxbXcj2bai/w61tFtqjeAv/0aTesrX0+Jtjhw5gsjISNn3xcWNf742TExMEBMTU+LvKM/rsbjwmpiYiAsXLuDJkycYMmQI9uzZU0HPqGJU5jFWXCgwNzdHz549FXqnANXjGBPdRq+LjIyEpqYmmjZtioSEhHd4NpWnIo6xd5WcnIz09HQ0a9YMp0+frtDfXRV4ni4d26dkFXnMvXz5Erdu3QIAREVF4cMPP8TMmTMxderUig9ewSrrHB0VFQVHR0fo6+tDS0sLjx49woULF3Dp0qU3Zin+/2jWrJlSzplGpMrYQ+UNrK2tUbt2bYSEhLzTz2tpaaGwUPHN+J+++uor2XJyxV+V4eXLl7h8+bJcjxuJRIJevXpVm7GsFeld2iMiIkKhx1Lv3r1L3H/SpEm4dOkS4uLi3pqluCt5detloKUOtGyYh4ik/81KXyQFIpJ04PjBc4X9LRvk4/dpf+PQ1Nuyr57Nc9De4jkOTb0N07rV80NodnY2bt26Jfu6fv06UlNT5V4renp6aN++/RtfW+96fEokEkgkEqV87VTWMVZcKLC2toarqysyMjLemkVZjzFlaqM2bdqgsLCw3L0sq0JFHGPvqlGjRqhfv/4bV7pTdjxPl47tU7LKPObU1NSU7lz8JpX9WTErKwuPHj1Cs2bN4OzsXOqiDm3atAGAansuIqrJ2EPlDWrVqoWFCxdiwYIF0NLSQufOnZGeno5r166VOgyoWNOmTZGdnY2QkBC0bt0aOjo6CsuhAa8+4FfVG4u/vz+CgoJw6dIlXLx4EbNmzYKuri62b98OAAgKCsK9e/ewaNEiAK964BQvbaelpYVGjRqhdevWsjda4NUylM2aNZP9DQsLC7Ru3RoZGRlKOVHm68rbHuvWrUNYWBjmzJmDo0ePYuTIkXB2dsZnn30m93v19PQwfPhwzJ07V+FvtmvXDh9++CHOnTuHJ0+ewMrKCsuWLUNiYmK1LGxN6PAECw+ZolXDF3BolIegCwZ4/lINbm1eDV1bcNAUJnoFmOv6CNoaUtgY58v9vH6tV0XHf26v7tauXYtvvvkGN2/eRHJyMpYtW4b79+/j0KFDsn1OnTqFgwcP4vvvvwfw9tejhYUF3N3dcfLkSaSnp+ODDz6Al5cXnj9/jmPHjol4mm9V0ceYhoYG9u/fDycnJ3z88cdQV1eX3QnMyMjAy5cvq90xJqKNOnTogPbt2+PMmTN49uwZOnbsiICAAPzyyy9vnNxX2bzLMfa29ytdXV0sWbIEBw4cwIMHD2BlZYXVq1cjMTERJ06cqOqnWGF4ni4d26ds3uWYW7lyJY4fP46UlBTo6elh9OjRcHFxQd++fQU9i/KrjM+Kw4YNQ3p6OlJSUmBvb49169bh0KFD+OOPPwAAlpaWGD16NI4dO4bHjx/DwcFBbphmjaKrC7x2XoaFBdC6NZCRASj5dQRRMRZUSrF48WJoaGjA29sb9+/fh5mZWZm7KHbq1AlTp06Fu7s7Hj9+XK5lkyvLvn37YGRkhKVLl8LU1BQxMTHo16+f7I5kkyZN5IYsNWzYUK4r5/z58zF//ny5ZaGdnZ0RGhoq2ycgIAAAEBgYiAkTJlT+k3oP5W2PiIgIjB49GsuXL8fKlStx8+ZNDB48GNeuXZP7vSNHjoREIsHu3bsV/mZubi7c3Nzg6+sLXV1dpKamIjg4GMuXL0d+fvX7MPZRq2xk5D7C+tD6SM9Wh63pC2wZc0/WVTr1qQbUJKq3hNrq1auhq6uLzZs3w8DAAOfOnUO/fv3k5tOxsrJCgwYNZN+/7fWYl5eHrl27YtasWTA0NERaWhrOnj2LTp06IT09vcqfY1lU9DHWqFEj2eSysbGxcn/LxcUFYWFh1e4YE9FGL168wMiRI+Hj4wNtbW0kJycjICAA/v7+VfSs39+7HGNve78qLCyEg4MDxo0bBwMDA9y/fx8nT57E4sWLlfK1U1Y8T5eO7VM273LMGRsbY8eOHTAzM8PTp08RFxeHvn37KqyCo8wq47OimZkZ/P39YWJigtTUVOzYsQPLli2TPZ6fnw9XV1dZ8ebOnTs4cOAAli9fXnVPvKo4OwOvnZfx3/MyAgMBJb+OIComkUpVdMFoJSapzPXBqcaT7hSdQPlJxohOQESqjOdpel98H6P3wYu/Mqipl8iVeZ1ZU9vsLTiHChERERERERFRObGgQkRERERERERUTiyoEBERERERERGVEwsqRERERERERETlxIIKEREREREREVE5saBCRERERERERFROLKgQEREREREREZUTCypEREREREREROXEggoRERERERERUTmxoEJEREREREREVE4sqBARERERERGR0lmxYgU6deoEHR0dGBgYlOlnpFIpvL29YWZmhtq1a8PV1RU3b96U2ycjIwNjxoyBvr4+DAwMMGnSJGRnZ5c7HwsqRERERERERKR08vPzMXz4cEybNq3MP7N69WqsX78emzZtQmRkJHR1ddG3b1/k5eXJ9hkzZgyuXbuGP/74A//+979x9uxZfPbZZ+XOJ5FKpdJy/xRVKolEIjoCVWPSnaITKD/JGNEJiEiV8TxN74vvY/Q+ePFXBjX1ErkyrzMruc0CAwMxa9YsZGZmviWGFA0bNsTcuXMxb948AMDTp09hYmKCwMBAjBw5EvHx8bCzs8Off/4JZ2dnAEBwcDA++ugj3L17Fw0bNixzLvZQISIiIiIiIqJ39uLFC2RlZcl9vXjxospzJCcn48GDB3B1dZVtq1u3Ltq3b4+IiAgAQEREBAwMDGTFFABwdXWFmpoaIiMjy/X3NComNlUkZeo09OLFC/j5+eGrr76Ctra26DhKh+3zdsrYRtLRohP8jzK2jzJh+7wd26h0bJ/SsX3eThnbiO9j1Qfb5+3YRlWoEq8z/Xx84OvrK7dtyZIl8PHxqbS/WZIHDx4AAExMTOS2m5iYyB578OABjI2N5R7X0NBAvXr1ZPuUFYf8UKmysrJQt25dPH36FPr6+qLjKB22z9uxjUrH9ikd2+ft2EalY/uUju3zdmyj0rF9Ssf2eTu2Uc3w4sULhR4p2traJRbJvLy88O2335b6++Lj49GiRQvZ92Ud8nP+/Hl07twZ9+/fh5mZmWz7iBEjIJFIsHfvXqxcuRJBQUG4ceOG3M8aGxvD19e3XPO1sIcKEREREREREb2zNxVPSjJ37lyMHz++1H0sLS3fKYepqSkAIC0tTa6gkpaWhjZt2sj2efjwodzPFRQUICMjQ/bzZcWCChERERERERFVCSMjIxgZGVXK77awsICpqSlCQkJkBZSsrCxERkbKep507NgRmZmZuHz5Mtq2bQsAOH36NIqKitC+ffty/T1OSktERERERERESiclJQUxMTFISUlBYWEhYmJiEBMTg+zsbNk+LVq0wMGDBwG8WjF31qxZWL58OY4cOYIrV67Aw8MDDRs2xODBgwEAtra26NevH6ZMmYKLFy8iPDwcX375JUaOHFmuFX4A9lCht9DW1saSJUs4QdQbsH3ejm1UOrZP6dg+b8c2Kh3bp3Rsn7djG5WO7VM6ts/bsY2oNN7e3ggKCpJ97+joCAA4c+YMXFxcAAA3btzA06dPZfssWLAAOTk5+Oyzz5CZmYkuXbogODgYtWrVku2zc+dOfPnll+jVqxfU1NQwdOhQrF+/vtz5OCktEREREREREVE5ccgPEREREREREVE5saBCRERERERERFROLKgQEREREREREZUTCypEREREREREROXEggoRERERERERUTmxoEJEREpl3LhxOHv2rOgYSmv79u3Izc0VHUNppaSkoKQFDKVSKVJSUgQkourszp07uHPnjugYRDXK0qVLS3wfe/78OZYuXSogEdG747LJVKKbN2/izJkzePjwIYqKiuQe8/b2FpSKqoOCggKsXLkSEydOxAcffCA6jlJKSkqCpaWl6BhKa/DgwTh27BjMzc0xYcIEjBs3Do0aNRIdS2mYmJjg+fPnGD58OCZNmoROnTqJjqRU1NXVkZqaCmNjY7ntjx8/hrGxMQoLCwUlUy6FhYU4ePAg4uPjAQC2trYYPHgwNDQ0BCcTr6CgAL6+vli/fj2ys7MBAHXq1IGnpyeWLFkCTU1NwQlJGa1fv77M+86YMaMSkyg/nqepJmFBhRT89NNPmDZtGho0aABTU1NIJBLZYxKJBFFRUQLTKYfMzExcvHixxIKTh4eHoFTKQ09PD1euXEHTpk1FR1FKampq6N69OyZNmoRhw4ahVq1aoiMpnfT0dPz8888ICgrC9evX4erqikmTJuGTTz5R+YuZgoIC/P777wgMDMTx48dhaWkpKzyZmpqKjiecmpoa0tLSYGRkJLf99u3bsLOzQ05OjqBkyuPatWsYNGgQHjx4gObNmwMAEhISYGRkhN9//x2tWrUSnFCsadOm4bfffsPSpUvRsWNHAEBERAR8fHwwePBgbNy4UXBC8fLy8rBhw4Y33nxTxc+KFhYWct+np6cjNzcXBgYGAF59dtTR0YGxsTGSkpIEJFQebzpPnz59Gu7u7khPTxeUjKj8WFAhBebm5vjiiy+wcOFC0VGU0u+//44xY8YgOzsb+vr6CgWnjIwMgemUwyeffAI3NzeMGzdOdBSlFBMTg+3bt2P37t3Iz8+Hu7s7Jk2ahHbt2omOppSioqKwfft2bNmyBXXq1MGnn36KL774AtbW1qKjCZeWloZffvkFQUFB+Ouvv9CvXz9MmjQJAwcOhJqaao3qnTNnDgBg3bp1mDJlCnR0dGSPFRYWIjIyEurq6ggPDxcVUWl07NgRRkZGCAoKgqGhIQDgyZMnGD9+PNLT03H+/HnBCcWqW7cu9uzZg/79+8ttP3bsGEaNGoWnT58KSqY8xowZg5MnT2LYsGEwMTGR+ywEAEuWLBGUTDns2rULP/zwA7Zu3SorWt64cQNTpkzB559/jjFjxghOKIahoSEkEgmePn2q8Bm6sLAQ2dnZmDp1Kr7//nuBKYnKhwUVUqCvr4+YmBgOSXgDGxsbfPTRR1i5cqXcB3b6n02bNsHX1xdjxoxB27ZtoaurK/f4oEGDBCVTLgUFBThy5AgCAwMRHBwMGxsbTJw4EWPHjlW4a6OqUlNTsWPHDmzfvh13797F0KFDce/ePYSFhWH16tWYPXu26IjCRUZGYtu2bQgKCoKZmRmePHkCQ0NDbN++HS4uLqLjVZkePXoAAMLCwtCxY0doaWnJHtPS0kLTpk0xb948FuIA1K5dG5cuXULLli3ltl+9ehUffvghnj9/LiiZcjA2NkZYWBhsbW3ltsfHx6Nbt268e45XRadjx46hc+fOoqMoJSsrK+zfvx+Ojo5y2y9fvoxhw4YhOTlZUDKxgoKCIJVKMXHiRKxduxZ169aVPVZ8ni7uFUZUbUiJ/mHixInSjRs3io6htHR0dKS3bt0SHUOpSSSSN36pqamJjqd08vLypP7+/lJtbW2pRCKRamtrS8eOHSu9f/++6GhC5OfnS/fv3y8dMGCAVFNTU9q2bVvpxo0bpU+fPpXt89tvv0kNDAwEphTrwYMH0u+++05qZ2cnrVWrlnTkyJHSP/74QyqVSqXZ2dnSBQsWSJs0aSI4pRjjx4+Xe62QIgcHB2lISIjC9pCQEGmrVq0EJFIuvr6+0lGjRknz8vJk2/Ly8qRjxoyR+vj4CEymPGxtbaWxsbGiYyit2rVrSy9evKiwPTIyUlq7dm0BiZRLaGioND8/X3QMogrBHiqkwM/PD/7+/hgwYADs7e0V5itQ9Ym03NzcMHLkSIwYMUJ0FKrmLl26hG3btmHPnj3Q1dXFuHHjMGnSJNy9exe+vr7IysrCxYsXRcescg0aNEBRURFGjRqFKVOmoE2bNgr7ZGZmwtHRUSXv8g0cOBAnTpyAjY0NJk+eDA8PD9SrV09un4cPH8LU1FRhXgMi4NXQlQULFsDHxwcdOnQAAFy4cAFLly7FqlWr0KVLF9m++vr6omIKM2TIEISEhEBbWxutW7cGAMTGxiI/Px+9evWS2/e3334TEVG448ePY/369di0aRPMzc1Fx1E6AwcOxL1797BlyxY4OTkBeNU75bPPPkOjRo1w5MgRwQnFKyoqQmJiYolz8HTr1k1QKqLyY0GFFPxzUq3XSSQSlZxI6/U3vvT0dCxduhQTJkwoseDE4SyvhISEICQkROGNUiKRYOvWrQKTiefv74/t27fjxo0b+OijjzB58mR89NFHcnNe3L17F02bNkVBQYHApGL8/PPPGD58OCfrfYNJkyZh8uTJpXaLlv53iWBVudBxc3NDYGAg9PX14ebmVuq+qnoB/LrXzzXFcxgUfxx8/XuJRKKSq21MmDChzPtu3769EpMor/T0dIwYMQJnz56Fjo6OwmchVZ9PLj09HePGjUNwcLCsbQoKCtC3b18EBgYqrG6jai5cuIDRo0fj9u3bCsvcq+p5h6ovFlSIyqCskzvyTeAVX19fLF26FM7OzjAzM1OYrO7gwYOCkikHa2trTJw4EePHj4eZmVmJ++Tn52P37t2c2JeoDCZMmID169dDT0/vrRfDqnoB/LqwsLAy79u9e/dKTELVlaurK1JSUjBp0qQSJ6Xle9crCQkJ+OuvvwAALVq0gI2NjeBEyqFNmzawsbGBr69viZ8TX59bhUjZsaBCpfrnHSuisjAzM8Pq1asxduxY0VGomrp06RL27duHlJQU5Ofnyz3GHgZATk4OwsLCSmwfVR+WSUSVT0dHBxEREbIhUVSy/Px8JCcnw8rKChoaGqLjKA1dXV3ExsaiWbNmoqMQvTce2VSiHTt24LvvvsPNmzcBvFrZZv78+bxAxqu2cXd3h7a2ttz2/Px87NmzBx4eHoKSKY/8/Hx06tRJdAyll5ubW+IFsYODg6BEyqH4OOrbty9OnjyJPn36ICEhAWlpaRgyZIjoeMJFR0fjo48+Qm5uLnJyclCvXj08evQIOjo6MDY2VvmCyvPnzyGVSmWrsN2+fRsHDx6EnZ0d+vTpIzid8sjLy0NcXFyJ8xeo+tDVx48fw9vbG2fOnCmxfVR9OAvwqreFqq8GVZrc3Fx4enoiKCgIwKueKpaWlvD09ESjRo3g5eUlOKFY7du3R2JiIgsqVCOwhwop8Pf3x+LFi/Hll1/KlsM7d+4cvv/+eyxfvlzllylVV1dHamqqwvjXx48fw9jYmEN+ACxcuBB16tTB4sWLRUdRSunp6Rg/fjyCg4NLfFzVX0MODg74/PPPMX36dOjp6SE2NhYWFhb4/PPPYWZmBl9fX9ERhXJxcYGNjQ02bdqEunXrIjY2Fpqamvj0008xc+bMt84hUtP16dMHbm5umDp1KjIzM9G8eXNoaWnh0aNH8Pf3x7Rp00RHFC44OBgeHh549OiRwmMcugp89NFHSExM5HCWUpw8eRK+vr5YsWJFifPJqeJkxq+bOXMmwsPDsXbtWvTr1w9xcXGwtLTE4cOH4ePjg+joaNERhTp48CC++eYbzJ8/v8TXj6rfWKLqhQUVUmBhYQFfX1+FnhZBQUHw8fFRyVU1Xqempoa0tDQYGRnJbY+NjUWPHj1U9s7VnDlzZP8uKipCUFAQHBwc4ODgoPBG6e/vX9XxlMqYMWNw+/ZtrF27Fi4uLjh48CDS0tKwfPlyrFmzBgMGDBAdUShdXV1cu3YNTZs2Rf369REaGgp7e3vEx8ejZ8+eSE1NFR1RKAMDA0RGRqJ58+YwMDBAREQEbG1tERkZiXHjxsnG66uqBg0aICwsDC1btsSWLVuwYcMGREdH48CBA/D29kZ8fLzoiMJZW1ujT58+8Pb2homJieg4SkdPTw/nzp3jcJZSFM8t989ikypPZvw6c3Nz7N27Fx06dJDdGLC0tERiYiKcnJyQlZUlOqJQJc1NKJFI+PqhaolDfkhBampqicM1OnXqpNIXMo6OjpBIJJBIJOjVq5fcWNjCwkIkJyejX79+AhOK9c+7LcVL3V69elVuO+fjAU6fPo3Dhw/D2dkZampqMDc3R+/evaGvrw8/Pz+VL6gYGhri2bNnAIBGjRrh6tWrsLe3R2ZmJnJzcwWnE09TU1P2YdTY2BgpKSmwtbVF3bp1cefOHcHpxMvNzYWenh6AV3fR3dzcoKamhg4dOuD27duC0ymHtLQ0zJkzh8WUN+Bwlrc7c+aM6AhKLT09vcSVfHJycvg5CFD5m7NUs7CgQgqaNWuGffv2YdGiRXLb9+7dC2tra0GpxBs8eDAAICYmBn379kWdOnVkj2lpaaFp06YYOnSooHTi8cNV2eXk5Mg+aBkaGiI9PR02Njawt7dHVFSU4HTidevWDX/88Qfs7e0xfPhwzJw5E6dPn8Yff/yBXr16iY4nnKOjI/78809YW1uje/fu8Pb2xqNHj/Dzzz+jVatWouMJ16xZMxw6dAhDhgzBiRMnZMNUHz58qPLDEIoNGzYMoaGhsLKyEh1FKf3www/w8vKCt7c3WrVqxeEsJeDqT6VzdnbG0aNH4enpCeB/N5O2bNlS6pL3qmLXrl0wMTHBxIkT5bZv27YN6enpWLhwoaBkROXHggop8PX1hbu7O86ePSubQyU8PBwhISHYt2+f4HTiLFmyBADQtGlTuLu7o1atWoITUXXVvHlz3LhxA02bNkXr1q3x448/omnTpti0adMbl1FWJf/617+Ql5cHAPj666+hqamJ8+fPY+jQofjmm28EpxNv5cqVsh48K1asgIeHB6ZNmwZra2ts27ZNcDrxvL29MXr0aMyePRu9evWSXbycPHkSjo6OgtMph3/9618YPnw4/vOf/5Q4f4GqT2xsYGCArKws9OzZU247hyP8z9mzZ0t9vFu3blWURDmtXLkS/fv3x/Xr11FQUIB169bh+vXrOH/+fLmWLa+pfvzxR+zatUthe8uWLTFy5EgWVKha4RwqVKLLly8jICBANtbc1tYWc+fO5YfR1+Tn55c4+3+TJk0EJaLq4pdffkFBQQHGjx+Py5cvo1+/fsjIyICWlhYCAwPh7u4uOiJRtfbgwQOkpqaidevWsuFRFy9ehL6+Plq0aCE4nXhbt27F1KlTUatWLdSvX19uCIJEIkFSUpLAdOK1a9cOGhoamDlzZomT0rJ3xpvnwCjGohNw69YtrFq1CrGxscjOzoaTkxMWLlwIe3t70dGEq1WrFuLj42FhYSG3PSkpCXZ2drKbKkTVAQsqROV08+ZNTJw4EefPn5fbzjtX9K5yc3Px119/oUmTJmjQoIHoOEKUZ4I+drenN3n58iVq166NmJgYDn8qhampKWbMmAEvL68SL4xVnY6ODqKjo9G8eXPRUZTW06dP5b5/+fIloqOjsXjxYqxYsYLDM6lU1tbWWLJkCT799FO57T///DOWLFmi8kVdql445IcUREVFQVNTU1ZBP3z4MLZv3w47Ozv4+PhAS0tLcEKxxo8fDw0NDfz73/+GmZkZJxej96ajowMnJyfRMYQyMDAo87GkikXL4kmxy0KV5+HR1NREkyZNVPI1Uh75+flwd3dnMeUNnJ2dcefOHRZUSlG3bl2Fbb1794aWlhbmzJmDy5cvC0ilXG7duoXt27cjKSkJa9euhbGxMY4fP44mTZqgZcuWouMJNWXKFMyaNQsvX76UDa0LCQnBggULMHfuXMHpiMqHBRVS8Pnnn8PLywv29vZISkqCu7s73Nzc8OuvvyI3Nxdr164VHVGomJgYXL58md3GqVxeX1b6bVRxWenXJzX++++/4eXlhfHjx8vmv4iIiEBQUBD8/PxERRSqeFJsAMjLy8MPP/wAOzs7WftcuHAB165dwxdffCEoofL4+uuvsWjRIvz888+oV6+e6DhKady4cdi7d6/C5PP0iqenJ2bOnIn58+eXOMeMg4ODoGTKz8TEBDdu3BAdQ7iwsDD0798fnTt3xtmzZ7F8+XIYGxsjNjYWW7duxf79+0VHFGr+/Pl4/PgxvvjiC+Tn5wN4NQxo4cKF+OqrrwSnIyofDvkhBXXr1kVUVBSsrKzw7bff4vTp0zhx4gTCw8MxcuRIlV+W88MPP0RAQAC6dOkiOgpVIz169JD7PioqCgUFBbI7oAkJCVBXV0fbtm1x+vRpERGVRq9evTB58mSMGjVKbvuuXbuwefNmhIaGigmmJCZPngwzMzMsW7ZMbvuSJUtw584dlZ+Y1tHREYmJiXj58iXMzc2hq6sr97gq9+ApNmPGDOzYsQOtW7eGg4ODQsFAFYu6r3vT/CAc2vs/cXFxct9LpVKkpqZi1apVKCgowLlz5wQlUw4dO3bE8OHDMWfOHOjp6SE2NhaWlpa4ePEi3NzccPfuXdERlUJ2djbi4+NRu3ZtWFtbQ1tbW3QkonJjDxVSIJVKZROtnjp1Ch9//DEAoHHjxnj06JHIaErh22+/xYIFC7By5coS71xxfgcqyes9MPz9/aGnp4egoCAYGhoCAJ48eYIJEyaga9euoiIqjYiICGzatElhu7OzMyZPniwgkXL59ddfcenSJYXtn376KZydnVW+oPJ6bx4q2ZUrV2STzF+9elXuMQ5jBZKTk0VHUHpt2rSRFZle16FDB5U/BwGvjrGSVrExNjbmZ+nX1KlTBx9++KHoGETvhQUVUuDs7Izly5fD1dUVYWFh2LhxI4BXHzBMTEwEpxPP1dUVANCzZ0+5D568c0VltWbNGpw8eVJWTAEAQ0NDLF++HH369FH58cONGzfGTz/9hNWrV8tt37JlCxo3biwolfKoXbs2wsPDYW1tLbc9PDycy7njf0vc05u9XuAlRebm5qIjKL1/Fp3U1NRgZGTEc9B/GRgYIDU1VWEVm+joaDRq1EhQKiKqDCyokIK1a9dizJgxOHToEL7++ms0a9YMALB//3506tRJcDrx+EGU3ldWVhbS09MVtqenp+PZs2cCEimXgIAADB06FMePH0f79u0BvFry9ubNmzhw4IDgdOLNmjUL06ZNQ1RUFNq1awcAiIyMxLZt27B48WLB6ZRDZmYm9u/fj1u3bmH+/PmoV68eoqKiYGJiwouZ1yQmJuLWrVvo1q0bateuLbsxQK9WG9m0aROSk5MREREBc3NzrF27FhYWFvjkk09ExxPO3NwcISEhCAkJwcOHD2U9m4upei+VkSNHYuHChfj1118hkUhQVFSE8PBwzJs3Dx4eHqLjEVEF4hwqVGZ5eXlQV1dXGOKiivLy8hAXF1fih4hBgwYJSkXVhYeHB/7zn/9gzZo1chfE8+fPR9euXREUFCQ4oXh3797Fxo0bER8fDwCwtbXF1KlT2UPlv/bt24d169bJtc/MmTMxYsQIwcnEi4uLg6urK+rWrYu///4bN27cgKWlJb755hukpKRgx44doiMK9/jxY4wYMQJnzpyBRCLBzZs3YWlpiYkTJ8LQ0BBr1qwRHVGojRs3wtvbG7NmzcKKFStw9epVWFpaIjAwEEFBQbyxAsDX1xdLly6Fs7NziSseHjx4UFAy5ZCfn4/p06cjMDAQhYWF0NDQQGFhIUaPHo3AwECoq6uLjkhEFYQFFaJyCg4OhoeHR4ljYDnkh8oiNzcX8+bNw7Zt2/Dy5UsAgIaGBiZNmoTvvvtOYRJNVfPy5cs3Fm4fPXqEBg0aVHEiqk5cXV3h5OSE1atXy00Gef78eYwePRp///236IjCeXh44OHDh9iyZQtsbW1lbXTixAnMmTMH165dEx1RKDs7O6xcuRKDBw+Wew1dvXoVLi4unAMDgJmZGVavXo2xY8eKjqLUUlJScPXqVWRnZ8PR0VFhqCYRVX8sqJCCwsJCBAQEYN++fUhJSZEtZ1YsIyNDUDLlYG1tjT59+sDb25tzytB7ycnJwa1btwAAVlZWKl9IKTZ06FDs379f4Y5nWloaevXqpTCJJtHrXl+p7vWL4du3b6N58+bIy8sTHVE4U1NTnDhxAq1bt5Zro6SkJDg4OCA7O1t0RKFq166Nv/76C+bm5nLtc/PmTTg4OOD58+eiIwpXv359XLx4EVZWVqKjKL3iSy0OpyOqmRTXhSOV5+vrC39/f7i7u+Pp06eYM2cO3NzcoKamBh8fH9HxhEtLS8OcOXNYTKH3pqurCwcHBzg4OLCY8pqUlBSF1XxSU1Ph4uKCFi1aCEqlPAwNDVGvXj2Fr/r166NRo0bo3r07tm/fLjqmMNra2sjKylLYnpCQACMjIwGJlE9OTg50dHQUtmdkZHDZUgAWFhaIiYlR2B4cHAxbW9uqD6SEJk+eXOIqNvQ/W7duRatWrVCrVi3UqlULrVq1wpYtW0THIqIKxklpScHOnTvx008/YcCAAfDx8cGoUaNgZWUFBwcHXLhwATNmzBAdUahhw4YhNDSUd2WoXNzc3BAYGAh9fX24ubmVuu9vv/1WRamU07Fjx9CtWzfMmTMH/v7+uH//Pnr06IHWrVtjz549ouMJ5+3tjRUrVqB///6yOXguXryI4OBgTJ8+HcnJyZg2bRoKCgowZcoUwWmr3qBBg7B06VLs27cPwKu7wikpKVi4cCGGDh0qOJ1y6Nq1K3bs2IFly5YBgGzSzNWrV6NHjx6C04k3Z84cTJ8+HXl5eZBKpbh48SJ2794NPz8/lb4gnjNnjuzfRUVF2Lx5M06dOgUHBweFYZr+/v5VHU+peHt7w9/fH56enujYsSMAICIiArNnz0ZKSgqWLl0qOCERVRQO+SEFurq6iI+PR5MmTWBmZoajR4/CyckJSUlJcHR0xNOnT0VHFCo3NxfDhw+HkZER7O3tFT5EqHrBiUo2YcIErF+/Hnp6epgwYUKp+6py74Jid+7cQZcuXTB06FD8+9//hpOTE3bu3MmJ/PBqSFTv3r0xdepUue0//vgjTp48iQMHDmDDhg3YvHkzrly5IiilOE+fPsWwYcNw6dIlPHv2DA0bNsSDBw/QsWNHHDt2jL3BAFy9ehW9evWCk5MTTp8+jUGDBuHatWvIyMhAeHg4bxjg1c0lHx8f2bDMhg0bwtfXF5MmTRKcTJyyFtskEglOnz5dyWmUm5GREdavX49Ro0bJbd+9ezc8PT05Dw9RDcKCCilo3rw5duzYgfbt26NLly74+OOP4eXlhb1798LT0xMPHz4UHVGorVu3YurUqahVqxbq168vNyZWIpEgKSlJYDqimiMhIQFdu3ZF79698fPPP3P8+X/VqVMHMTExsiXtiyUmJqJNmzbIzs7GrVu34ODggJycHEEpxTt37hzi4uKQnZ0NJycnuLq6io6kNFJSUlCnTh1s3LgRsbGxsjaaPn06Xr58iSZNmoiOqDRyc3ORnZ0NY2Nj0VGoGjEwMMCff/6pMAltQkIC2rVrh8zMTDHBiKjCsaBCCry8vKCvr49FixZh7969+PTTT9G0aVOkpKRg9uzZWLVqleiIQpmammLGjBnw8vKCmhqnISKqCIaGhiUWTHJzc6GtrS3XM0XVJ8Zu0qQJZs+ejdmzZ8ttDwgIQEBAAFJSUhAXF4c+ffrgwYMHglKSMlNXV0dqaqpCkeDx48cwNjZW+dXqevbsid9++w0GBgZy27OysjB48GCV731Bb+fp6QlNTU2FoU/z5s3D8+fP8f333wtKRkQVjXOokILXCybu7u5o0qQJIiIiYG1tjYEDBwpMphzy8/Ph7u7OYgqVi6OjY5l7WERFRVVyGuWzdu1a0RGqjcWLF2PatGk4c+aMbA6VP//8E8eOHcOmTZsAAH/88Qe6d+8uMqZQISEhCAkJwcOHD1FUVCT32LZt2wSlUh5vupeWnZ2NWrVqVXEa5RMaGqqwwiEA5OXl4T//+Y+ARFQdbd26FSdPnkSHDh0AAJGRkUhJSYGHh4fcfDSqPt8MUXXHHipE5TR79mwYGRlh0aJFoqNQNeLr61vmfZcsWVKJSagmCA8Px7/+9S/cuHEDwKuhmp6enujUqZPgZOL5+vpi6dKlcHZ2hpmZmUIh8+DBg4KSiVd8Ebdu3TpMmTJFbqWfwsJCREZGQl1dHeHh4aIiChUXFwcAaNOmDU6fPo169erJHissLERwcDB+/PFH/P3334ISUnXB+WaIVAcLKlSin3/+GZs2bUJycjIiIiJgbm6OtWvXwsLCAp988onoeELNmDEDO3bsQOvWrTmzPVEluXXrFrZv345bt25h3bp1MDY2xvHjx9GkSRO0bNlSdDxSYmZmZli9ejXGjh0rOorSKb7ICwsLQ8eOHaGlpSV7TEtLC02bNsW8efMU5n1QFWpqarICXEkfj2vXro0NGzZg4sSJVR2NiIiUFIf8kIKNGzfC29sbs2bNwooVK2RjqQ0MDLB27VqVL6hcuXIFjo6OAF6tlPA6TppJ5XH58mXEx8cDAFq2bCl7Xam6sLAw9O/fH507d8bZs2exYsUKGBsbIzY2Flu3bsX+/ftFRxSuqKgIiYmJJQ5p6datm6BUyiE/P589dd7gzJkzAF6tOrZu3Tro6+sLTqRckpOTIZVKYWlpiYsXL8LIyEj2mJaWFoyNjbnSGL2TrKwsnD59Gi1atECLFi1ExyGiCsQeKqTAzs4OK1euxODBg6Gnp4fY2FhYWlri6tWrcHFx4VJvRO/p4cOHGDlyJEJDQ2WTHmZmZqJHjx7Ys2eP3Id4VdSxY0cMHz4cc+bMkTsHXbx4EW5ubrh7967oiEJduHABo0ePxu3btxXuokskEpWfUHThwoWoU6cOFi9eLDoKVXPXr19HSkqKwnwqgwYNEpSIqosRI0agW7du+PLLL/H8+XO0bt0af//9N6RSKfbs2YOhQ4eKjkhEFYQ9VEhBcnJyiXfKtbW1VXoJTqKK4unpiWfPnuHatWuwtbUF8OqD+7hx4zBjxgzs3r1bcEKxrly5gl27dilsNzY2ZkEXwNSpU+Hs7IyjR4+WOEeIKnp9gseioiJs3rwZp06d4rBMeifJyckYMmQI4uLiIJFIZIXL4mNN1YuW9HZnz57F119/DeDVvE1SqRSZmZkICgrC8uXLWVAhqkFYUCEFFhYWiImJgbm5udz24OBg2cUfEb274OBgnDp1Su54srOzw/fff48+ffoITKYcDAwMkJqaCgsLC7nt0dHRaNSokaBUyuPmzZvYv38/mjVrJjqK0oiOjpb7vk2bNgAUh2USlcWMGTPQtGlTnDp1ChYWFoiMjERGRgbmzp2L//u//xMdj6qBp0+fyiY1Dg4OxtChQ6Gjo4MBAwZg/vz5gtMRUUViQYUUzJkzB9OnT0deXh6kUikuXryI3bt3w8/PD1u2bBEdj6jaKyoqUrhrDgCampoK82GoopEjR2LhwoX49ddfIZFIUFRUhPDwcMybNw8eHh6i4wnXvn17JCYmsqDymuK5QYgqQkREBE6fPo0GDRpATU0N6urq6NKlC/z8/DBjxgyFAh7RPzVu3BgRERGoV68egoODsWfPHgDAkydPuDQ5UQ3DggopmDx5MmrXro1vvvkGubm5GD16NBo1aoR169Zh5MiRouMRVXs9e/bEzJkzsXv3bjRs2BAAcO/ePcyePRu9evUSnE68lStXYvr06WjcuDEKCwthZ2eHwsJCjB49Gt98843oeMJ5enpi7ty5ePDgAezt7RWKcw4ODoKSKYeJEydi3bp10NPTk9uek5MDT09PbNu2TVAyqi4KCwtlr58GDRrg/v37aN68OczNzWVLlROVZtasWRgzZgzq1KkDc3NzuLi4AHg1FMje3l5sOCKqUJyUlhQ8f/4cUqkUOjo6yM3NxdWrVxEeHg47Ozv07dtXdDyiau/OnTsYNGgQrl27hsaNGwMAUlJSYG9vjyNHjuCDDz4QnFA5pKSk4OrVq8jOzoajo6PKLuX6T2pqagrbiud54KS0gLq6OlJTU2FsbCy3/dGjRzA1NUVBQYGgZFRddO3aFXPnzsXgwYMxevRoPHnyBN988w02b96My5cvcygZlcmlS5dw584d9O7dG3Xq1AEAHD16FAYGBujcubPgdERUUVhQIQV9+vSBm5sbpk6diszMTLRo0QKampp49OgR/P39MW3aNNERiao9qVSKkJAQ2bLJtra2cHV1FZxKueTn5yM5ORlWVlbQ0GCHymK3b98u9fF/zn+lKrKysiCVSmFoaIibN2/KrZZVWFiI33//HV5eXrh//77AlFQdnDhxAjk5OXBzc0NiYiI+/vhjJCQkoH79+ti7dy969uwpOiIRESkJFlRIQYMGDRAWFoaWLVtiy5Yt2LBhA6Kjo3HgwAF4e3vLLgCJ6N2FhIQgJCQEDx8+VJg3RdWHJOTm5sLT0xNBQUEAgISEBFhaWsLT0xONGjWCl5eX4ISkjNTU1Epd8UgikcDX11e28gZReWRkZMDQ0JCratEbvb7a2NtwtTGimoO3/EhBbm6ubOzwyZMn4ebmBjU1NXTo0OGtd0aJ6O18fX2xdOlSODs7c9nbEnz11VeIjY1FaGgo+vXrJ9vu6uoKHx8flSyoHDlyBP3794empiaOHDlS6r6DBg2qolTK5cyZM5BKpejZsycOHDggW2EDALS0tGBubi6bs4iovF5/PRGV5J+TFUdFRaGgoADNmzcH8OrmgLq6Otq2bSsiHhFVEhZUSEGzZs1w6NAhDBkyBCdOnMDs2bMBAA8fPoS+vr7gdETV36ZNmxAYGIixY8eKjqKUDh06hL1796JDhw5yxaaWLVvi1q1bApOJM3jwYDx48ADGxsYYPHjwG/dT5TlUunfvDgBITk5G48aNS5xrhoiosry+2pi/vz/09PQQFBQEQ0NDAK9W+JkwYQK6du0qKiIRVQIWVEiBt7c3Ro8eLVtxpGPHjgBe9VZxdHQUnI6o+svPz0enTp1Ex1Ba6enpChOKAq9WaVHV3jyvDwvj0tqlK55DJjc3FykpKcjPz5d7XNVXQSKiyrdmzRqcPHlSVkwBAENDQyxfvhx9+vTB3LlzBaYjoorE2zekYNiwYUhJScGlS5cQHBws296rVy8EBAQITEZUM0yePBm7du0SHUNpOTs74+jRo7Lvi4soW7ZskRV4Vdndu3ff+NiFCxeqMIlySk9Px8cffww9PT20bNkSjo6Ocl9ERJUtKysL6enpCtvT09Px7NkzAYmIqLKwhwqVyNTUFKampnLb2rVrJygNUfX3+mR1RUVF2Lx5M06dOgUHBwdoamrK7avqk9WtXLkS/fv3x/Xr11FQUIB169bh+vXrOH/+PMLCwkTHE65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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Mapa de Calor de la Matriz de Correlaciones\n",
+    "colormap = sns.color_palette(['red','orange','black','black','orange','red'])\n",
+    "plt.figure(figsize=(14,6))\n",
+    "sns.heatmap(new_bike_train.corr(), annot=True,cmap=colormap, vmin=-1,vmax=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Para nuestro mapa de calor, hemos coloreado a las variables con mayor correlación de rojo, y los de correlación media y baja-media de naranja. Con lo cual podemos confirmar lo dicho anteriormente. \n",
+    "Para nuestra varible \"cnt\" que queremos predecir tendría una correlación media-baja con los valores de temperatura, sensación termica y hora del día. Y casi nula relación con el resto de variables.\n",
+    "Lo que nos hace creer que un posible modelo de regresión logística no sería el adecuado para entrenar estos datos."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### $\\text{Dias festivos (holiday)}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "bingroup": "x",
+         "histfunc": "sum",
+         "hovertemplate": "holiday=%{x}<br>sum of cnt=%{y}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": "rgb(103,0,31)",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "",
+         "offsetgroup": "",
+         "orientation": "v",
+         "showlegend": false,
+         "type": "histogram",
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+           "bgcolor": "#C8D4E3",
+           "bordercolor": "rgb(17,17,17)",
+           "borderwidth": 1,
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+          },
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+           "aaxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
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+           "baxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
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+           "caxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "updatemenudefaults": {
+           "bgcolor": "#506784",
+           "borderwidth": 0
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "#283442",
+           "linecolor": "#506784",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
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+           "zerolinewidth": 2
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+        },
+        "width": 500,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "holiday"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "sum of cnt"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numero de veces en el que el dia festivo cae un feriado:  333\n",
+      "Numero de veces en las que hay un dia no festivo:  11666\n",
+      "cantidad de registrados  dia festivo no es feriado:  43770\n",
+      "cantidad de registrados donde no es dia festivo:  1859211\n"
+     ]
+    }
+   ],
+   "source": [
+    "fig = px.histogram(new_bike_train, x=\"holiday\", y=\"cnt\",template='plotly_dark',color_discrete_sequence=px.colors.sequential.RdBu)\n",
+    "fig.update_layout(height=250, width=500)\n",
+    "fig.show()\n",
+    "print(\"Numero de veces en el que el dia festivo cae un feriado: \",new_bike_train.holiday[new_bike_train['holiday'] == 1].count())\n",
+    "print(\"Numero de veces en las que hay un dia no festivo: \",new_bike_train.holiday[new_bike_train['holiday'] == 0].count())\n",
+    "print(\"cantidad de registrados  dia festivo no es feriado: \",new_bike_train.cnt[new_bike_train['holiday'] == 1].sum())\n",
+    "print(\"cantidad de registrados donde no es dia festivo: \",new_bike_train.cnt[new_bike_train['holiday'] == 0].sum())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 5, 4])"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#semanas en el que cae un dia festivo que es feriado\n",
+    "new_bike_train[new_bike_train['holiday'] == 1].weekday.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>weathersit</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>holiday</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.212326</td>\n",
+       "      <td>5.524859</td>\n",
+       "      <td>11.556146</td>\n",
+       "      <td>3.044060</td>\n",
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+       "      <td>0.627013</td>\n",
+       "      <td>0.195952</td>\n",
+       "      <td>159.370050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.153153</td>\n",
+       "      <td>5.720721</td>\n",
+       "      <td>11.573574</td>\n",
+       "      <td>1.792793</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.378378</td>\n",
+       "      <td>0.433874</td>\n",
+       "      <td>0.410731</td>\n",
+       "      <td>0.582222</td>\n",
+       "      <td>0.213266</td>\n",
+       "      <td>131.441441</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           season      mnth         hr   weekday  workingday  weathersit  \\\n",
+       "holiday                                                                    \n",
+       "0        2.212326  5.524859  11.556146  3.044060    0.703583    1.436654   \n",
+       "1        2.153153  5.720721  11.573574  1.792793    0.000000    1.378378   \n",
+       "\n",
+       "             temp     atemp       hum  windspeed         cnt  \n",
+       "holiday                                                       \n",
+       "0        0.466612  0.449977  0.627013   0.195952  159.370050  \n",
+       "1        0.433874  0.410731  0.582222   0.213266  131.441441  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# promedio de registrados en un dia festivo que cae feriado contra un dia normal (sea feriado o no)\n",
+    "new_bike_train.groupby('holiday').mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podriamos pensar que es en los días no festivos es donde más registrados hay. Pero, si vemos el número de veces que hay de días festivos, es también muy inferior a los dias no festivos.\n",
+    "\n",
+    "También podemos ver que el promedio de registrados en un día no festivo con feriado es superior a los dias festivos con feriado. Lo que nos hace suponer que no sea una variable que sea de importancia para el modelo.\n",
+    "\n",
+    "Otro dato a tomar en cuenta es que esta variable puede afectar al balanceo de carga. Descartandola como una variable para nuestro modelo de entrenamiento!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### $\\text{Estaciones del año, meses del año, dias de la semana, horas del día y dias laborales}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>weathersit</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>workingday</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",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.190451</td>\n",
+       "      <td>5.497758</td>\n",
+       "      <td>11.521498</td>\n",
+       "      <td>0.08784</td>\n",
+       "      <td>2.917700</td>\n",
+       "      <td>1.394619</td>\n",
+       "      <td>0.448425</td>\n",
+       "      <td>0.432339</td>\n",
+       "      <td>0.619037</td>\n",
+       "      <td>0.196893</td>\n",
+       "      <td>152.996571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.220029</td>\n",
+       "      <td>5.545322</td>\n",
+       "      <td>11.572856</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>3.051657</td>\n",
+       "      <td>1.453704</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.456532</td>\n",
+       "      <td>0.628879</td>\n",
+       "      <td>0.196220</td>\n",
+       "      <td>161.180677</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              season      mnth         hr  holiday   weekday  weathersit  \\\n",
+       "workingday                                                                 \n",
+       "0           2.190451  5.497758  11.521498  0.08784  2.917700    1.394619   \n",
+       "1           2.220029  5.545322  11.572856  0.00000  3.051657    1.453704   \n",
+       "\n",
+       "                temp     atemp       hum  windspeed         cnt  \n",
+       "workingday                                                       \n",
+       "0           0.448425  0.432339  0.619037   0.196893  152.996571  \n",
+       "1           0.473684  0.456532  0.628879   0.196220  161.180677  "
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# promedio de dias laborales y no laborales\n",
+    "new_bike_train.groupby('workingday').mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es en los días laborales donde hay un aumento significativo de registrados."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+   "source": [
+    "# cantidad de registrados en el dia de la semana\n",
+    "dias_semana = new_bike_train.groupby('weekday').sum()\n",
+    "fig = px.pie(values=dias_semana.cnt, names=dias_semana.index, hole=.3,color_discrete_sequence=px.colors.sequential.RdBu, template='plotly_dark')\n",
+    "fig.update_layout(height=350, width=600, margin=dict(l=20, r=20, t=20, b=20))\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Existe un ligero aumento de registrados para los dias: Martes,Jueves, Viernes y Sábado"
+   ]
+  },
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+           "bgcolor": "#506784",
+           "borderwidth": 0
+          },
+          "xaxis": {
+           "automargin": true,
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+           "linecolor": "#506784",
+           "ticks": "",
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+           },
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+           "zerolinewidth": 2
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+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "#283442",
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+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
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+           "zerolinewidth": 2
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+        "width": 700,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
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+         "title": {
+          "text": "Horas del día"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Cantidad de registrados"
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+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# promedio de registrados en las horas durante el día\n",
+    "dias_semana = new_bike_train.groupby('hr').mean()\n",
+    "fig = px.bar(y=dias_semana.cnt, x=dias_semana.index,color_discrete_sequence=px.colors.sequential.RdBu, template='plotly_dark')\n",
+    "fig.update_layout(height=350, width=700, margin=dict(l=20, r=20, t=20, b=20))\n",
+    "fig.update_xaxes(title_text=\"Horas del día\")\n",
+    "fig.update_yaxes(title_text=\"Cantidad de registrados\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos ver que el aumento de registros durante el día es entre las 7 am y 8 pm, siendo los horarios de 8 am, 5 pm y 6 pm los de mayor registrados en el día.\n",
+    "Podemos asegurar entonces que esta variable será de gran importancia para nuestro modelo de entrenamiento."
+   ]
+  },
+  {
+   "cell_type": "code",
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+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# cantidad de registrados durante los meses del año, dividido por estaciones\n",
+    "fig = px.sunburst(new_bike_train, path=['season', 'mnth'], values='cnt',color_discrete_sequence=px.colors.sequential.RdBu, template='plotly_dark')\n",
+    "fig.update_layout(height=350, width=700, margin=dict(l=20, r=20, t=20, b=20))\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podemos ver que que para las estaciones del año (pie chart interno), es en verano donde se registran mayor cantidad de alquileres de biciletas. Dentro de este mes, son los meses 4 y 5, abril y mayo respectivamente, donde hay mayor registro de alquileres.\n",
+    "\n",
+    "Podemos asegurar que estas dos variables son de gran imporancia para nuestro modelo de predicción!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### $\\text{Tipo de clima, nivel de humedad y velocidad del viento}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season</th>\n",
+       "      <th>mnth</th>\n",
+       "      <th>hr</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>weekday</th>\n",
+       "      <th>workingday</th>\n",
+       "      <th>temp</th>\n",
+       "      <th>atemp</th>\n",
+       "      <th>hum</th>\n",
+       "      <th>windspeed</th>\n",
+       "      <th>cnt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weathersit</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>1</th>\n",
+       "      <td>17475</td>\n",
+       "      <td>43848</td>\n",
+       "      <td>92109</td>\n",
+       "      <td>223</td>\n",
+       "      <td>23498</td>\n",
+       "      <td>5269</td>\n",
+       "      <td>3732.66</td>\n",
+       "      <td>3591.1903</td>\n",
+       "      <td>4453.30</td>\n",
+       "      <td>1555.3200</td>\n",
+       "      <td>1350224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6642</td>\n",
+       "      <td>16483</td>\n",
+       "      <td>34123</td>\n",
+       "      <td>94</td>\n",
+       "      <td>9253</td>\n",
+       "      <td>2156</td>\n",
+       "      <td>1398.26</td>\n",
+       "      <td>1355.2130</td>\n",
+       "      <td>2172.41</td>\n",
+       "      <td>569.5837</td>\n",
+       "      <td>455036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2406</td>\n",
+       "      <td>6024</td>\n",
+       "      <td>12401</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3348</td>\n",
+       "      <td>781</td>\n",
+       "      <td>456.50</td>\n",
+       "      <td>439.2429</td>\n",
+       "      <td>880.25</td>\n",
+       "      <td>231.4818</td>\n",
+       "      <td>97498</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>0.5607</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>0.6120</td>\n",
+       "      <td>223</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            season   mnth     hr  holiday  weekday  workingday     temp  \\\n",
+       "weathersit                                                                \n",
+       "1            17475  43848  92109      223    23498        5269  3732.66   \n",
+       "2             6642  16483  34123       94     9253        2156  1398.26   \n",
+       "3             2406   6024  12401       16     3348         781   456.50   \n",
+       "4                3      3     35        0       10           2     0.56   \n",
+       "\n",
+       "                atemp      hum  windspeed      cnt  \n",
+       "weathersit                                          \n",
+       "1           3591.1903  4453.30  1555.3200  1350224  \n",
+       "2           1355.2130  2172.41   569.5837   455036  \n",
+       "3            439.2429   880.25   231.4818    97498  \n",
+       "4              0.5607     2.65     0.6120      223  "
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_bike_train.groupby('weathersit').sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hay más registrados cuando el dia es despejado, seguido de un dia con neblina y nubes. Y cae a medida que el clima emperoa.\n",
+    "Es una variable importante para nuestro modelo de entrenamiento!"
+   ]
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+           "borderwidth": 1,
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+          "ternary": {
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+           "baxis": {
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+            "linecolor": "#506784",
+            "ticks": ""
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+           "bgcolor": "rgb(17,17,17)",
+           "caxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "updatemenudefaults": {
+           "bgcolor": "#506784",
+           "borderwidth": 0
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "#283442",
+           "linecolor": "#506784",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "#283442",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "#283442",
+           "linecolor": "#506784",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "#283442",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "width": 900,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Nivel de humedad"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Cantidad de registrados"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "humedad = new_bike_train.groupby('hum').sum()\n",
+    "fig = px.bar(y=humedad.cnt, x=humedad.index,color_discrete_sequence=px.colors.sequential.RdBu, template='plotly_dark')\n",
+    "fig.update_layout(height=350, width=900, margin=dict(l=20, r=20, t=20, b=20))\n",
+    "fig.update_xaxes(title_text=\"Nivel de humedad\")\n",
+    "fig.update_yaxes(title_text=\"Cantidad de registrados\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No vemos un patrón en la distribución de los datos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
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+          "hoverlabel": {
+           "align": "left"
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+          "hovermode": "closest",
+          "mapbox": {
+           "style": "dark"
+          },
+          "paper_bgcolor": "rgb(17,17,17)",
+          "plot_bgcolor": "rgb(17,17,17)",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           },
+           "bgcolor": "rgb(17,17,17)",
+           "radialaxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "rgb(17,17,17)",
+            "gridcolor": "#506784",
+            "gridwidth": 2,
+            "linecolor": "#506784",
+            "showbackground": true,
+            "ticks": "",
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+           "yaxis": {
+            "backgroundcolor": "rgb(17,17,17)",
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+            "gridwidth": 2,
+            "linecolor": "#506784",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "#C8D4E3"
+           },
+           "zaxis": {
+            "backgroundcolor": "rgb(17,17,17)",
+            "gridcolor": "#506784",
+            "gridwidth": 2,
+            "linecolor": "#506784",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "#C8D4E3"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#f2f5fa"
+           }
+          },
+          "sliderdefaults": {
+           "bgcolor": "#C8D4E3",
+           "bordercolor": "rgb(17,17,17)",
+           "borderwidth": 1,
+           "tickwidth": 0
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           },
+           "bgcolor": "rgb(17,17,17)",
+           "caxis": {
+            "gridcolor": "#506784",
+            "linecolor": "#506784",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "updatemenudefaults": {
+           "bgcolor": "#506784",
+           "borderwidth": 0
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "#283442",
+           "linecolor": "#506784",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "#283442",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "#283442",
+           "linecolor": "#506784",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "#283442",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "width": 700,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Velocidad del viento"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Cantidad de registrados"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "velocidad = new_bike_train.groupby('windspeed').sum()\n",
+    "fig = px.bar(y=velocidad.cnt, x=velocidad.index,color_discrete_sequence=px.colors.sequential.RdBu, template='plotly_dark')\n",
+    "fig.update_layout(height=300, width=700, margin=dict(l=20, r=20, t=20, b=20))\n",
+    "fig.update_xaxes(title_text=\"Velocidad del viento\")\n",
+    "fig.update_yaxes(title_text=\"Cantidad de registrados\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vemos que mientras la velocidad del viento aumenta, disminuye la cantidad de registros de alquiler de bicicletas.\n",
+    "Es una variable a condirar para nuestro modelo."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusión:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Las variables a considerar para nuestro modelo serán:\n",
+    "\n",
+    "- season\t\n",
+    "- mnth\t\n",
+    "- hr\t\t\n",
+    "- weekday\t\n",
+    "- workingday\t\n",
+    "- weathersit\n",
+    "- atemp\t\n",
+    "- windspeed\t\n",
+    "\n",
+    "En cuanto a un modelo, descartamos la de regresión logística, ya que las correlaciones entre las variables con el target son bajas o nulas.\n",
+    "Lo que si podemos usar es un modelo de bosques aleatorio o de redes neurales."
+   ]
+  }
+ ],
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+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('environment': venv)",
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