{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean variance optimization solution\n",
    "\n",
    "For assignment and data, see https://www.fransderuiter.com/JADS/\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pyomo.environ import *\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>SMALL LoINV</th>\n",
       "      <th>ME1 INV2</th>\n",
       "      <th>ME1 INV3</th>\n",
       "      <th>ME1 INV4</th>\n",
       "      <th>ME1 INV5</th>\n",
       "      <th>ME1 INV6</th>\n",
       "      <th>ME1 INV7</th>\n",
       "      <th>ME1 INV8</th>\n",
       "      <th>...</th>\n",
       "      <th>BIG LoINV</th>\n",
       "      <th>ME10 INV2</th>\n",
       "      <th>ME10 INV3</th>\n",
       "      <th>ME10 INV4</th>\n",
       "      <th>ME10 INV5</th>\n",
       "      <th>ME10 INV6</th>\n",
       "      <th>ME10 INV7</th>\n",
       "      <th>ME10 INV8</th>\n",
       "      <th>ME10 INV9</th>\n",
       "      <th>BIG HiINV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SMALL LoINV</th>\n",
       "      <td>0.003369</td>\n",
       "      <td>2.301398</td>\n",
       "      <td>2.301398</td>\n",
       "      <td>2.075644</td>\n",
       "      <td>1.904954</td>\n",
       "      <td>1.873764</td>\n",
       "      <td>1.804488</td>\n",
       "      <td>1.760517</td>\n",
       "      <td>1.838048</td>\n",
       "      <td>1.898293</td>\n",
       "      <td>...</td>\n",
       "      <td>1.610037</td>\n",
       "      <td>1.971464</td>\n",
       "      <td>1.388215</td>\n",
       "      <td>1.478249</td>\n",
       "      <td>1.711632</td>\n",
       "      <td>1.394308</td>\n",
       "      <td>1.504389</td>\n",
       "      <td>1.754384</td>\n",
       "      <td>1.621627</td>\n",
       "      <td>1.613875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV2</th>\n",
       "      <td>0.041349</td>\n",
       "      <td>2.154002</td>\n",
       "      <td>2.075644</td>\n",
       "      <td>2.154002</td>\n",
       "      <td>1.863827</td>\n",
       "      <td>1.842441</td>\n",
       "      <td>1.769529</td>\n",
       "      <td>1.726058</td>\n",
       "      <td>1.800831</td>\n",
       "      <td>1.846832</td>\n",
       "      <td>...</td>\n",
       "      <td>1.582968</td>\n",
       "      <td>1.925417</td>\n",
       "      <td>1.365061</td>\n",
       "      <td>1.471519</td>\n",
       "      <td>1.681836</td>\n",
       "      <td>1.376549</td>\n",
       "      <td>1.467429</td>\n",
       "      <td>1.737659</td>\n",
       "      <td>1.578213</td>\n",
       "      <td>1.559424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV3</th>\n",
       "      <td>0.039155</td>\n",
       "      <td>1.892804</td>\n",
       "      <td>1.904954</td>\n",
       "      <td>1.863827</td>\n",
       "      <td>1.892804</td>\n",
       "      <td>1.724930</td>\n",
       "      <td>1.654159</td>\n",
       "      <td>1.611129</td>\n",
       "      <td>1.689768</td>\n",
       "      <td>1.724745</td>\n",
       "      <td>...</td>\n",
       "      <td>1.458070</td>\n",
       "      <td>1.782231</td>\n",
       "      <td>1.268083</td>\n",
       "      <td>1.349650</td>\n",
       "      <td>1.552639</td>\n",
       "      <td>1.277392</td>\n",
       "      <td>1.371330</td>\n",
       "      <td>1.610320</td>\n",
       "      <td>1.454294</td>\n",
       "      <td>1.418375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV4</th>\n",
       "      <td>0.037095</td>\n",
       "      <td>1.879435</td>\n",
       "      <td>1.873764</td>\n",
       "      <td>1.842441</td>\n",
       "      <td>1.724930</td>\n",
       "      <td>1.879435</td>\n",
       "      <td>1.679332</td>\n",
       "      <td>1.610732</td>\n",
       "      <td>1.710631</td>\n",
       "      <td>1.742087</td>\n",
       "      <td>...</td>\n",
       "      <td>1.466828</td>\n",
       "      <td>1.853272</td>\n",
       "      <td>1.290937</td>\n",
       "      <td>1.367081</td>\n",
       "      <td>1.585465</td>\n",
       "      <td>1.301183</td>\n",
       "      <td>1.411083</td>\n",
       "      <td>1.672035</td>\n",
       "      <td>1.469236</td>\n",
       "      <td>1.461106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV5</th>\n",
       "      <td>0.049750</td>\n",
       "      <td>1.760228</td>\n",
       "      <td>1.804488</td>\n",
       "      <td>1.769529</td>\n",
       "      <td>1.654159</td>\n",
       "      <td>1.679332</td>\n",
       "      <td>1.760228</td>\n",
       "      <td>1.558254</td>\n",
       "      <td>1.637907</td>\n",
       "      <td>1.668230</td>\n",
       "      <td>...</td>\n",
       "      <td>1.389965</td>\n",
       "      <td>1.712064</td>\n",
       "      <td>1.239707</td>\n",
       "      <td>1.312110</td>\n",
       "      <td>1.496432</td>\n",
       "      <td>1.228574</td>\n",
       "      <td>1.330135</td>\n",
       "      <td>1.573344</td>\n",
       "      <td>1.405970</td>\n",
       "      <td>1.383310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Unnamed: 1  Unnamed: 2  SMALL LoINV  ME1 INV2  ME1 INV3  \\\n",
       "SMALL LoINV    0.003369    2.301398     2.301398  2.075644  1.904954   \n",
       "ME1 INV2       0.041349    2.154002     2.075644  2.154002  1.863827   \n",
       "ME1 INV3       0.039155    1.892804     1.904954  1.863827  1.892804   \n",
       "ME1 INV4       0.037095    1.879435     1.873764  1.842441  1.724930   \n",
       "ME1 INV5       0.049750    1.760228     1.804488  1.769529  1.654159   \n",
       "\n",
       "             ME1 INV4  ME1 INV5  ME1 INV6  ME1 INV7  ME1 INV8  ...  BIG LoINV  \\\n",
       "SMALL LoINV  1.873764  1.804488  1.760517  1.838048  1.898293  ...   1.610037   \n",
       "ME1 INV2     1.842441  1.769529  1.726058  1.800831  1.846832  ...   1.582968   \n",
       "ME1 INV3     1.724930  1.654159  1.611129  1.689768  1.724745  ...   1.458070   \n",
       "ME1 INV4     1.879435  1.679332  1.610732  1.710631  1.742087  ...   1.466828   \n",
       "ME1 INV5     1.679332  1.760228  1.558254  1.637907  1.668230  ...   1.389965   \n",
       "\n",
       "             ME10 INV2  ME10 INV3  ME10 INV4  ME10 INV5  ME10 INV6  ME10 INV7  \\\n",
       "SMALL LoINV   1.971464   1.388215   1.478249   1.711632   1.394308   1.504389   \n",
       "ME1 INV2      1.925417   1.365061   1.471519   1.681836   1.376549   1.467429   \n",
       "ME1 INV3      1.782231   1.268083   1.349650   1.552639   1.277392   1.371330   \n",
       "ME1 INV4      1.853272   1.290937   1.367081   1.585465   1.301183   1.411083   \n",
       "ME1 INV5      1.712064   1.239707   1.312110   1.496432   1.228574   1.330135   \n",
       "\n",
       "             ME10 INV8  ME10 INV9  BIG HiINV  \n",
       "SMALL LoINV   1.754384   1.621627   1.613875  \n",
       "ME1 INV2      1.737659   1.578213   1.559424  \n",
       "ME1 INV3      1.610320   1.454294   1.418375  \n",
       "ME1 INV4      1.672035   1.469236   1.461106  \n",
       "ME1 INV5      1.573344   1.405970   1.383310  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"https://www.fransderuiter.com/JADS/Meanvariance/MeanVarPortfolio.xlsx\"\n",
    "# Create pandas table\n",
    "df_input = pd.read_excel(data_path, sheet_name=0, header=1, skiprows=4, index_col=0)\n",
    "\n",
    "# Show table\n",
    "df_input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean</th>\n",
       "      <th>Variance</th>\n",
       "      <th>SMALL LoINV</th>\n",
       "      <th>ME1 INV2</th>\n",
       "      <th>ME1 INV3</th>\n",
       "      <th>ME1 INV4</th>\n",
       "      <th>ME1 INV5</th>\n",
       "      <th>ME1 INV6</th>\n",
       "      <th>ME1 INV7</th>\n",
       "      <th>ME1 INV8</th>\n",
       "      <th>...</th>\n",
       "      <th>BIG LoINV</th>\n",
       "      <th>ME10 INV2</th>\n",
       "      <th>ME10 INV3</th>\n",
       "      <th>ME10 INV4</th>\n",
       "      <th>ME10 INV5</th>\n",
       "      <th>ME10 INV6</th>\n",
       "      <th>ME10 INV7</th>\n",
       "      <th>ME10 INV8</th>\n",
       "      <th>ME10 INV9</th>\n",
       "      <th>BIG HiINV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SMALL LoINV</th>\n",
       "      <td>0.003369</td>\n",
       "      <td>2.301398</td>\n",
       "      <td>2.301398</td>\n",
       "      <td>2.075644</td>\n",
       "      <td>1.904954</td>\n",
       "      <td>1.873764</td>\n",
       "      <td>1.804488</td>\n",
       "      <td>1.760517</td>\n",
       "      <td>1.838048</td>\n",
       "      <td>1.898293</td>\n",
       "      <td>...</td>\n",
       "      <td>1.610037</td>\n",
       "      <td>1.971464</td>\n",
       "      <td>1.388215</td>\n",
       "      <td>1.478249</td>\n",
       "      <td>1.711632</td>\n",
       "      <td>1.394308</td>\n",
       "      <td>1.504389</td>\n",
       "      <td>1.754384</td>\n",
       "      <td>1.621627</td>\n",
       "      <td>1.613875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV2</th>\n",
       "      <td>0.041349</td>\n",
       "      <td>2.154002</td>\n",
       "      <td>2.075644</td>\n",
       "      <td>2.154002</td>\n",
       "      <td>1.863827</td>\n",
       "      <td>1.842441</td>\n",
       "      <td>1.769529</td>\n",
       "      <td>1.726058</td>\n",
       "      <td>1.800831</td>\n",
       "      <td>1.846832</td>\n",
       "      <td>...</td>\n",
       "      <td>1.582968</td>\n",
       "      <td>1.925417</td>\n",
       "      <td>1.365061</td>\n",
       "      <td>1.471519</td>\n",
       "      <td>1.681836</td>\n",
       "      <td>1.376549</td>\n",
       "      <td>1.467429</td>\n",
       "      <td>1.737659</td>\n",
       "      <td>1.578213</td>\n",
       "      <td>1.559424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV3</th>\n",
       "      <td>0.039155</td>\n",
       "      <td>1.892804</td>\n",
       "      <td>1.904954</td>\n",
       "      <td>1.863827</td>\n",
       "      <td>1.892804</td>\n",
       "      <td>1.724930</td>\n",
       "      <td>1.654159</td>\n",
       "      <td>1.611129</td>\n",
       "      <td>1.689768</td>\n",
       "      <td>1.724745</td>\n",
       "      <td>...</td>\n",
       "      <td>1.458070</td>\n",
       "      <td>1.782231</td>\n",
       "      <td>1.268083</td>\n",
       "      <td>1.349650</td>\n",
       "      <td>1.552639</td>\n",
       "      <td>1.277392</td>\n",
       "      <td>1.371330</td>\n",
       "      <td>1.610320</td>\n",
       "      <td>1.454294</td>\n",
       "      <td>1.418375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV4</th>\n",
       "      <td>0.037095</td>\n",
       "      <td>1.879435</td>\n",
       "      <td>1.873764</td>\n",
       "      <td>1.842441</td>\n",
       "      <td>1.724930</td>\n",
       "      <td>1.879435</td>\n",
       "      <td>1.679332</td>\n",
       "      <td>1.610732</td>\n",
       "      <td>1.710631</td>\n",
       "      <td>1.742087</td>\n",
       "      <td>...</td>\n",
       "      <td>1.466828</td>\n",
       "      <td>1.853272</td>\n",
       "      <td>1.290937</td>\n",
       "      <td>1.367081</td>\n",
       "      <td>1.585465</td>\n",
       "      <td>1.301183</td>\n",
       "      <td>1.411083</td>\n",
       "      <td>1.672035</td>\n",
       "      <td>1.469236</td>\n",
       "      <td>1.461106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ME1 INV5</th>\n",
       "      <td>0.049750</td>\n",
       "      <td>1.760228</td>\n",
       "      <td>1.804488</td>\n",
       "      <td>1.769529</td>\n",
       "      <td>1.654159</td>\n",
       "      <td>1.679332</td>\n",
       "      <td>1.760228</td>\n",
       "      <td>1.558254</td>\n",
       "      <td>1.637907</td>\n",
       "      <td>1.668230</td>\n",
       "      <td>...</td>\n",
       "      <td>1.389965</td>\n",
       "      <td>1.712064</td>\n",
       "      <td>1.239707</td>\n",
       "      <td>1.312110</td>\n",
       "      <td>1.496432</td>\n",
       "      <td>1.228574</td>\n",
       "      <td>1.330135</td>\n",
       "      <td>1.573344</td>\n",
       "      <td>1.405970</td>\n",
       "      <td>1.383310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Mean  Variance  SMALL LoINV  ME1 INV2  ME1 INV3  ME1 INV4  \\\n",
       "SMALL LoINV  0.003369  2.301398     2.301398  2.075644  1.904954  1.873764   \n",
       "ME1 INV2     0.041349  2.154002     2.075644  2.154002  1.863827  1.842441   \n",
       "ME1 INV3     0.039155  1.892804     1.904954  1.863827  1.892804  1.724930   \n",
       "ME1 INV4     0.037095  1.879435     1.873764  1.842441  1.724930  1.879435   \n",
       "ME1 INV5     0.049750  1.760228     1.804488  1.769529  1.654159  1.679332   \n",
       "\n",
       "             ME1 INV5  ME1 INV6  ME1 INV7  ME1 INV8  ...  BIG LoINV  \\\n",
       "SMALL LoINV  1.804488  1.760517  1.838048  1.898293  ...   1.610037   \n",
       "ME1 INV2     1.769529  1.726058  1.800831  1.846832  ...   1.582968   \n",
       "ME1 INV3     1.654159  1.611129  1.689768  1.724745  ...   1.458070   \n",
       "ME1 INV4     1.679332  1.610732  1.710631  1.742087  ...   1.466828   \n",
       "ME1 INV5     1.760228  1.558254  1.637907  1.668230  ...   1.389965   \n",
       "\n",
       "             ME10 INV2  ME10 INV3  ME10 INV4  ME10 INV5  ME10 INV6  ME10 INV7  \\\n",
       "SMALL LoINV   1.971464   1.388215   1.478249   1.711632   1.394308   1.504389   \n",
       "ME1 INV2      1.925417   1.365061   1.471519   1.681836   1.376549   1.467429   \n",
       "ME1 INV3      1.782231   1.268083   1.349650   1.552639   1.277392   1.371330   \n",
       "ME1 INV4      1.853272   1.290937   1.367081   1.585465   1.301183   1.411083   \n",
       "ME1 INV5      1.712064   1.239707   1.312110   1.496432   1.228574   1.330135   \n",
       "\n",
       "             ME10 INV8  ME10 INV9  BIG HiINV  \n",
       "SMALL LoINV   1.754384   1.621627   1.613875  \n",
       "ME1 INV2      1.737659   1.578213   1.559424  \n",
       "ME1 INV3      1.610320   1.454294   1.418375  \n",
       "ME1 INV4      1.672035   1.469236   1.461106  \n",
       "ME1 INV5      1.573344   1.405970   1.383310  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Rename the first two columns\n",
    "\n",
    "newcolumn_values = df_input.columns.values\n",
    "newcolumn_values[0] = 'Mean'\n",
    "newcolumn_values[1] = 'Variance'\n",
    "\n",
    "df_input.columns = newcolumn_values\n",
    "\n",
    "# Show table with removed column\n",
    "df_input.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set with assets\n",
    "assets = df_input.columns[2:] # first two columns are only mean and variance\n",
    "\n",
    "# risk parameter\n",
    "alpha = 0.012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MeanVarModelConstruction(alpha,df_input):\n",
    "    # Create model\n",
    "    m = ConcreteModel()\n",
    "\n",
    "    # Variables\n",
    "    m.amount = Var(assets, within = NonNegativeReals) # note that short sellings is not allowed, so we take the nonnegative reals as range\n",
    "\n",
    "    # Objective\n",
    "    m.value = Objective(\n",
    "        expr=sum(m.amount[i]*df_input.loc[i,'Mean'] for i in assets)\n",
    "        - alpha * sum(df_input.loc[i,j]*m.amount[i]*m.amount[j] for i in assets for j in assets)\n",
    "        , sense = maximize\n",
    "                 )\n",
    "    # Constraints on budget\n",
    "    m.budget = Constraint(expr=sum(m.amount[i] for i in assets) == 1)\n",
    "        \n",
    "    return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solve the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def OptimizeMeanVarModel(m,printResults):\n",
    "    solver = SolverFactory('ipopt') # Take the ipopt solver for nonlinear problems\n",
    "    status = solver.solve(m,tee=False,)\n",
    "\n",
    "    if printResults:\n",
    "        print(\"Risk aversion parameter = %s \\n\" % alpha)\n",
    "        print(\"Status = %s \\n\" % status.solver.termination_condition)\n",
    "    \n",
    "    obj_opt = value(m.value)\n",
    "    mean_opt = sum([value(m.amount[i]*df_input.loc[i,'Mean']) for i in assets])\n",
    "    variance_opt = sum(df_input.loc[i,j]*value(m.amount[i])*value(m.amount[j]) for i in assets for j in assets)\n",
    "    \n",
    "    return (obj_opt, mean_opt, variance_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solve and show the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Risk aversion parameter = 0.012 \n",
      "\n",
      "Status = optimal \n",
      "\n",
      "Objective value model \t= 0.034\n",
      "\t Mean return \t= 0.058\n",
      "\t Variance \t= 2.005\n",
      "\n",
      "Assets invested in (and amount):\n",
      "Portfolio composition:\n",
      "\tME1 INV5 : \t0.018519064522454592\n",
      "\tME3 INV6 : \t0.18522268857591723\n",
      "\tME5 INV2 : \t0.3111380937936984\n",
      "\tME9 INV5 : \t0.48508440190071744\n"
     ]
    }
   ],
   "source": [
    "# Construct model with chosen alpha\n",
    "m = MeanVarModelConstruction(alpha,df_input)\n",
    "# Optimize\n",
    "obj_opt, mean_opt, variance_opt = OptimizeMeanVarModel(m,True)\n",
    "\n",
    "print(\"Objective value model \\t= %.3f\" % obj_opt)\n",
    "print(\"\\t Mean return \\t= %.3f\" % mean_opt)\n",
    "print(\"\\t Variance \\t= %.3f\\n\" % variance_opt)\n",
    "\n",
    "print(\"Assets invested in (and amount):\")\n",
    "print(\"Portfolio composition:\")\n",
    "for i in assets:\n",
    "    if value(m.amount[i] > 0.0001):\n",
    "        print(\"\\t\"+ i + \" : \\t\" + str(value(m.amount[i]))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize plot\n",
    "fig=plt.figure(figsize=(16,16))\n",
    "\n",
    "# Plot mean returns and variances of individual assets\n",
    "plt.scatter([df_input.loc[i,'Variance'] for i in assets], \n",
    "            [100*df_input.loc[i,'Mean'] for i in assets], \n",
    "            s = 200*np.ones(assets.size),\n",
    "            alpha=0.9)\n",
    "# Plot mean returns and variances of new optimal portfolio\n",
    "plt.scatter(variance_opt, \n",
    "            100*mean_opt, \n",
    "            s = 200*np.ones(assets.size),\n",
    "            c = 'red',\n",
    "            alpha=0.9)\n",
    "\n",
    "plt.title(\"Mean-Variance portfolios\")\n",
    "plt.xlabel(\"Variance\")\n",
    "plt.ylabel(\"Mean return (\\%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show efficient frontier (not part of assignment)\n",
    "The efficient frontier consists of all the mean-variance optimal portfolios. They are formed by varying the risk-aversion parameter alpha as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6L/N55IYT6V0U7uRKO46CqYiISL455xx4662P/8x1g1VwY7GWA2rT/NNw6k1JaWnrxzY/JxYLOqyPPQaDBrW/5lAIzjwz+GrGAH1++u/tv10RkR7o50+9R9L3KQjtf7RMQchhe22MB+et40unjOuE6rJDQ3lFRETyzRe+EATKvRctKilpO2gWFu4ZtptIBAsZXXZZcE4yGYTQRCJYJKlpG5lJk2DOHDj22Ow9HhERSdvyLTWs3l5HxE0/qjkGHnhr/UdzULsiBVMREZF8U1oK110XBMfmix6Fw9C0JUvznzd1S4uK9ux36jjB6r533gnvvw+/+AUccwyMGgUTJsCFF8Izz8Abb8Ahh3Tu4xMRkVY9tnAjnmcx6UzfSAm5DrGEz/w1lVmsLLs0lFdERCQf/fjH8O678NprwTDepk5oQUEwB7WmJuiCNm0rU1S0ZyXeww8PFiWaPDm43KtXMA/0mmty81hERCRt63fWgdnPSuwt8Kxla20G+1bnGQVTERGRfBQOBwsJfec7cP/9e/Yxdd09QdR1g2B6zDEwcCCMGQMzZmS1A2qt5d2NNTy8YANrd9SR9C1De0c595gRnDC2P66T/if8IiLSsfa3s1g+UzAVERHJV+Ew/P738P3vw333wYMPQlVVsAjShAnwla/AqadmtsXLAVi4vor/+PtSNlY1kPT8j4aZLdlYzcvvb6dXNMx3zpzImYcN7ZR6RES6oxF9i7B2Z8bnucYwsLTrbsFlbB7F6vLycltRUZHrMkRERGQvLy3fxjcffoek7xNxnX3mPllrSfoWA8w8bTxXTR2Tm0JFRLq4dzdWc8Xd83AMac8zTfqWaNjhpW99kkgof5cRMsYssNaWt3Rd/lYtIiIieWHl1lq+9cg7WGspCLktvlEyxhB2HRxj+MPzK3nhva05qFREpOs7dFgvRvYrJO6l30D0fcvFx43K61C6P123chEREekUt728mnjSJ5TG1gWOY7AWfv/cSvJpVJaISFdhjOEHnz4E1zEk/f1v/xJP+vQpCnPJ5FGdUF32KJiKiIhIqyrr4ry4YntGn8KHXcOmXQ28u7Emi5WJiHRfx4/px3997jAMhsaE1+IHfb5vaUx49CoMc/eVx9G/pOvOLwUtfiQiIl2N78OWLVBbC8XFMHRosDqtZMWrK7djACeD/fSMMcSTHk+/u5nDR/TOXnEiIt3Yp48YytA+UW56biVLNlbjeV6wvTUQCTkYYPqRw/jaaeMZ1Cua63IPmIKpiIh0Dbt2wezZ8Mc/wo4de7ZKKSmBG26Ayy+HwYNzXWW3s6s+QcLzcZ3Mwr8BttbGslOUiEgPcfSovtxz9fGs31nPs8u2sLWmkbDrUDagmDMPG0KvaDjXJXYYBVMREcl/r70GF18MsViwSVsoFOzl6bpQUwO//jX87ndwxx0wfXquq+1WXMdk1C1trisvwiEikk9G9S/i2pPH5rqMrNIrhoiI5Le33oLzz4eGhiCQhsNBKG0SDgdfngfXXAP/+Efuau2GRvcvIuRmHkxDjmH8oJIsVCQiIt2RgqmIiOSveDzolCYSEIm0fWw4HHRTr7sOqqo6p74eYMrY/hRGXJLe/leGbOJbC8Yw/chhWaxMRES6EwVTERHJX089BfX1UJDmSoNNndMHH8xuXT1IyHWYMXk0PqS9/Us86TNtwgAGdPEVIkVEpPMomIqISP66+eaga5oJa+GWW4I/pUPMOGE0Zf2LiXv+fsNpLOnRuzDMd8+c1EnViYhId6BgKiIi+WvJkv0P4d1bKATbtweLIkmHKC4IcdcV5YwdWELStyRaCKhJzyee9OlbFGHWVccztHdhjqoVEZGuSKvyiohIfvL9YG5pOMOl8I0BxwmGAPfWHpodpX9JAQ9eewJ/f2cjd7++lp27Y8FmegAmCK+XnjCaC8tH0rc4ww8TRESkx1MwFRGR/OQ4EI0GQ3Iz2a7EWkgmobQ0e7X1UIURlwuPG8UF5SN5d2MNm6sb8K2lf0kBR4/sQ8jVQCwREWkfBVMREclfp5wCzz4LhRkMC43H4dBDoURblWSLMYbDR/Tm8BHqSIuISMdQMBURkfw1cya89FJmXdNwGL72tayWJenzfcu8tZU8/vZGNu9qJOQYxgwo5gvlI5k4RF1tEREJKJiKiEj+mjoVyspg1ar0toyJxaB/f5g+Peulyf49u3QLv35mBVX1cRJJHyf14ULF2ioeW7SRgwYW8+PPHKrOq4iIaFVeERHJY8bAo49C377Q2Nj6FjDWBqG0qAieeCL9fU8la+55Yw3fe3QxO3fHcIBo2CUScoiEHArCDq6B97fUctVf5vHG6h25LldERHJMwVRERPLbiBHw8svBvFHfh4YG8Lzge88LLvs+jBwJzz0HBx+c64p7vBeXb+Xm51ZijCHsOpgWhmEbYygIu3i+5WsPvc0H23fnoFIREckXCqYiIpL/hg+HV16Bf/4TzjknWAzJ2uDP00+Hhx+GhQth4sRcV9rjWWv57bPv41twnf3PCw67DvGkzx2vftAJ1YmISL7SHFMREekajIHycpg1K9eVSBsWf1jNlupGwm76W/yEXcOzS7fyvbMS9C7McN9aERHpFtQxFRERkQ7zzyWbSXh+i8N3W+MYgzHw2krNNRUR6akUTEVERKTDbKtpbNd5Sc9SWR/v4GpERKSrUDAVERGRDhN22/fWwhiIZDD8V0REuhcFUxEREekw4weXpLXo0d5CjmFE36IsVCQiIl2BgqmIiIh0mHOOGg4Eq/OmK+n5FEZCTB7TL1tliYhInlMwFRERkQ4zqFeUEw8aQDzpp3W8tRYLXDZlNKF2DgMWEZGuT68AIiIi0qG+d9YkSqIh4kmvzeOstcQ9n9H9i7nk+FGdVJ2IiOQjBVMRERHpUCP7FXH3lcdTGg0T93yS/seH9VprSaR+PnZACXdeUU5xgbZWFxHpyfQqICIiIh1u4pBSHrtxKrPnrWf2vPUkPYvFYgAL9C8p4KqpZXz+6OEURfR2RESkpzOZLE6QbeXl5baioiLXZYiIiEgHiid9KtZWsrMujusYhvaOcuSIPjjtWL1XRES6LmPMAmtteUvX6SNKERERyapIyOHEcQNyXYaIiOQxzTEVERERERGRnFIwFRERERERkZxSMBUREREREZGcUjAVERERERGRnFIwFRERERERkZxSMBUREREREZGcUjAVERERERGRnFIwFRERERERkZxSMBUREREREZGcUjAVERERERGRnFIwFRFpSzIJTz8NZ58NI0bAgAFQVgbXXw+LFoG1ua5QREREpMsL5boAEZG8tXAhXHQR1NRALAaRCIRC0NAADz8MTzwBhx0Gs2fDoEG5rlZERESky1LHVESkJfPmBV3SnTvBcaCwEFw3+N51g8uOA2+/DaecAtu25bpiERERkS5LwVREZG/V1XD++RCPQ0FB68cZE1y/dSvMmNF59YmIiIh0MwqmIiJ7e+ghaGxsO5Q2F4kEndPFi7Nbl4iIiEg3pWAqItKc78Mf/hD8mS5jgkWSbr01e3WJiIiIdGMKpiIizW3aBNu3Qzic2XmhEDz7bHZqEhEREenmtCqviEhztbVByMyU40B9fcfXIyIiItIDqGMqItJcYWFmw3ibWAvRaMfXIyIiItIDKJiKiDQ3fHiw6FEymdl58Tgce2x2ahIRERHp5rIaTI0xfYwxjxhjlhtj3jPGTMnm/YmIHLBwGK67LrOuqbVBmJ05M3t1iYiIiHRj2e6Y3gw8ba2dBBwJvJfl+xMROXBXXQWum37XNBaDgQPh5JOzW5eIiIhIN5W1YGqM6QVMA+4CsNbGrbW7snV/IiIdZvhwuOmmoBO6v3Da2BjMS33ooWABJBERERHJWDbfRY0FtgOzjDGLjDF3GmOKs3h/IiId5+KL4Y9/DPYojcc/HlCtDbqkngd9+sBTT8Ghh+auVhEREZEuLpvBNAQcA9xqrT0aqAO+t/dBxpjrjTEVxpiK7du3Z7EcEZEMXXQRvPMOfPvbUFwchFNrIZGAsjL47W9hyRI48shcVyoiIiLSpRlrbXZu2JghwJvW2rLU5ZOB71lrz27tnPLycltRUZGVekREDkgyCdu3B3uVlpYGc0qNyXVVHc7zLWt27Ka6IUk07DCqXxGl0XCuyxIREZFuwBizwFpb3tJ17dhFPj3W2i3GmA3GmInW2hXAacCybN2fiEhWhUIwdGiuq8iayro4jy38kPveXEddzMN1guaw51vOOHQwl08p4+ChvXJdpoiIiHRTWQumKTOBB4wxEeAD4Kos35+IiGTo7Q27uPH+BTQkPAwQch2aBtMYA08t2cIzS7dyzdQx3PjJgzDdsFMsIiIiuZXVYGqtfRtosVUrIiK5t3RTNdfdW0Ei6RMJ7bvsgGMMBSGD71vueu0DjAM3njIuB5WKiIhId6a9DUREeihrLd/86zvEk16LobQ5xzE4xnDnq2tYsaW2kyoUERGRnkLBVESkh5q/toodu2NE3PReChzH4PmW+99cl+XKREREpKdRMBUR6aHunbuWeNLPaM5o2DE8tWQzu2PJ/R8sIiIikiYFUxGRHuq9zTWE0uyWNnEcg+sYNlY1ZKkqERER6YkUTEVEeqi459Oe9XUNEEt6HV2OiIiI9GAKpiIiPVRpQRi/aV+YNFlr8aylNBrOUlUiIiLSEymYioj0UP926GAyi6WQ9C29C8OM7leUlZpERESkZ1IwFRHpoS44biSGoAuaLmMMV544BsdpzyBgERERkZYpmIqI9FBDexdy2sGDSfo2rXAaT/oURVw+e9SwTqhOREREepJQrgsQEZHc+dnnDmNDZT3vb60l5NDi1jHWWuKeTzTsctuMY+ldqPmlIiLdkbWWBeuq+NuijWza1YjrQFn/Yr5QPpKJQ0pzXZ50cyaTIVzZVl5ebisqKnJdhohIj9IQ9/jJ399lzrKtWIJVdx1jsNaS9C1h12Fonyi/v+Aoxg/WGxMRke7opeXb+MW/lrOzLkY86QevAwAWXNcwdmAxP/7MIRwxok+uS5UuzBizwFpb3uJ1CqYi0qaaGqishFAIBg6EgoJcVyRZsrWmkYcrNvD00i3sbkwSDbscMrQXl00ZzVEj+7TYTRURka5v9rz1/Prp5VgLYdfs8/veWks86RMOOfz+wqM4efzAHFUqXZ2CqYhkJpmE55+Hm2+Gt96CcBisBWPg4ovhi1+ESZNyXaWIiIgcoNdW7uCrsxdhDLj7Wdgu4fmEHIfZ109m3CCNoJHMtRVMtfiRiHzctm3wiU/AFVfA3LkQiYDjgOsG4fSee2DaNPjhD8H3c12tiIiIHIDfzVmBb+1+QylA2HWIJT3+/PIHnVCZ9DQKpiKyx65dcMYZsHx50B0tLAz+bOK6EI0GQfX22+E73wnCqoiIiHQ5SzdVs35nPWE3/akakZDD88u3UVkXz2Jl0hMpmIrIHt/8JmzYEMwjbWs+YVMH9d57Yc6czqtPREREOsyzS7eS8PyM1hBwjMEAr67cnr3CpEdSMBWRwI4d8OSTwXzSdDgOJBJw003ZrUtERESyYkt1Y7vOS3i+OqbS4RRMRSRw//3BsFwng18L0SjMnw9r1mSvLhEREcmKUAZDeJtzjCHkKkZIx9K/KBEJvPoqeF5m5xgTbCPzzjvZqUlERESyZtygkrQWPdpbyDWM6FuYhYqkJ1MwFZFAXV3b80pb4/tQX9/x9YiIiEhWTT9yGAbwM1jIMOn7FIRcTho3IHuFSY+kYCoigb5927f9i+NASUnH1yMiIiJZNaCkgGkTBpLw0gum1lp8Hy6ZPIqwhvJKB9O/KBEJfPrTwZ6lmfB9SCZhypTs1CQiIiJZ9e0zJ1EaDRFLtj2dx1pL3LMM71vI5VNGd1J10pMomIpI4Nxzgy1gMplnGosFgXbgwOzVJSIiIlkzvE8hs648jl6FYeKeT9L/ePfUWkvC80n4lpH9gmNLo2mu4C+SgVCuCxCRPFFcDNdcA7fdFgzP3d98U88LOqxf/Wrn1CciIpJHrLW8vWEX985dx/y1lTQmPKJhl+PK+nH5lNEcNbJPRvuD5tL4waX87capPPjWembPW0/Ss/jWfvRWoG9RhMunjOb88pGUFCg+SHYYm8Fk52wrLy+3FRUVuS/d7egAACAASURBVC5DpOdKJILO6ZtvBqvttvaCmkwGW8v88pdBmBUREelBNlTWM3P2IjZU1ZNI+oRdB2OCl8aEF1we2a+IP158NCP7FeW63IzEkz7z11aysy6OAYb2jnLMqL447Vi9V2RvxpgF1tryFq9TMBWRj4nHYeZMeOyxYA6p6+7Z2zSRCAKr48BNN8GFF+a2VhERkU62obKei+94k92NiVQg3TewNQ1/LYmGmX3dCV0unIpkS1vBVHNMReTjIhH4859h/nz48pehd++gQ2oMjBsHv/kNrFqlUCoiIj2O51uuu7eC3Y1JIiG31aG6xhgiIZfdjUmuu7eCpNeOVe9FehgNEheRlpWVwX/+Z/AlIiIivL5qBzt2x4iE0uvtREIOO3bHeGP1TqZN0EKBIm1Rx1REREREJA2zXl9DPJlZ9zOe9Jn1+posVSTSfSiYioiIiIjsh+9bFqyroiDNbmmTgpDDgnVV+H7+rOsiko8UTEVERERE9qMunsQxJuMtYIwxOMZQn8hgn3CRHkjBVERERERkP6JhF89aMt3RwlqLZy3RDDutIj2N/oeIiIiIiOxH2HUY1a+IhJdZME14llH9igi5etst0hatyisiIiIikoarppbxi6eWZ3SO6xiumlqWnYKkx3hvcw3/WLyJTbsacQyMGVDM544a3q32yFUwFRERERFJw1mHDeU3z6wgnvQJp9EBTXg+kZDDWYcN7YTqpDt664Od/Prp5azZWf+x/XAdY7jrtTUcNbIvP/j0JMYPLs1hlR1DYwpERERERNJQXBDi9xcchWMMCa/tbWMSno9jDP9zwVEUF6gXJJl74u2NfOmBhazavhvXBPOcm74iIYeQY1iwrpJL73yLBesqc13uAVMwFRERERFJ04njBnDTRUcRdh0Snv+xLhZA0vNJeEFH9aaLjmLquAE5qlS6snlrKvnpk8sAKAi5La4GbYwhGnaJJ31ufGAh63fWd3aZHUrBVEREREQkAyePH8jTX5/GV04dR2lhGC+1R6nnW0oLw3zl1HH862snc/L4gTmuVLqq3zyzHM/3CTn7354oEnJojPvc/urqTqgsezSuQEREREQkQ/2KI1xz0liuPHEM22obqYslKYqEGNwriptGmBBpzftba1m9vY5IBis5h13Dv5Zs4TtnTqJXNJzF6rJHHVMRERERkXZyHcPQ3oWMG1TKsD6FCqVywP65eDNJz29x+G5rHMeAgVfe357FyrJLwVRERERERCRPbK5uaNd5Cc+nsi7ewdV0HgVTERERERGRPNHerrsh2Eamq1IwFRERERERyRNjBhQHQ3MzFHZdhvWJZqGizqHFj0REJOd21cd5fNFGZs/bwPbdMay19CuOcP6xIzj/2JEMLC3IdYndypoddazatptY0qNXNMwxo/tSon0WRUTywmePHM6tL63GWpv2PNOkbykIOZw0ruuuBK1XIRERyRlrLXe8+gG3vfzBR5ddx2CMoaouzu2vfMAdr67hkuNH8Y1PTWjXJ8gS8H3L88u3cfdra1ixtRbXMVhrcYzBt5bpRwzj8hPLGDOgONelioj0aEN6R5k8pj9vrN5BNOymdY7nWy4sH0kk1HUHxHbdykVEpMv77bMruO2l1Rgg5BjCroNjgmAacp3UZXjwrfX88PElWGtzXXKXlPB8vv3IO3z30cUs21yNa/aai2Th0YUf8oXb3uDFFdtyWquIiMB3z5pEUSREPOnv99h40mNQaQFXTi3LfmFZpGAqIiI5MWfZVmbP24DjmDYXenCMwXXgmXe38teKDzuxwu7BWssP/7aEF5ZvwzVQEHL3GRrmOIZo2MX3Ld/66zvMW1OZo2pFRASCeaa3X34shRGXxqSH5+/7wWzSt8SSHgNLo8y66jj6FEVyUGnHUTAVEZGcuO3l1Xi+TWsFQWMMFsvtr6zGb+HFWVo3b00lz7+3jVBqiHRbQq6D51u+9+hiPc8iIjl2xIg+PPKlE/nCMSMxqWkX8aRP3POxQGHY4dqTxvLIDScyom9Rrss9YJpjKiIinW75lhrW7Kgj4qY/ZzTkGHY1JHhrTSVTDuqfxeq6l3vmriXh+WnPUwq7hprGBG9+sJMTxw3IbnEiItKm4X0K+dH0Q/jGGRN4bdUOduyO4RjD0N5Rpo4bQNjtPn1GBVMREel089dU4vk+oVB6YQmCrmksnuTND3YqmKZpx+4Yb66upCCDxTCMMcQSHve9uU7BVEQkTxQXhPi3Q4fkuoysUjAVEZFOV9uYbHG+zP4YY6iqj2ehou5p064GwiFDpmtGhRzDmh112SlKRESkBQqm0rPV1cHy5VBfD8XFcPDBUFiY66pEur2iyL4L8KTDWiiN6qUrXQnPh/ZMFTUmOFdERKST6NVdeqaVK+HPf4YHHgBjgi9rgz8vuwyuvx7Gjs11lSLd1qHDe1MQcjLaPBwgGnY4ckSfLFbWvfQpiuBZi4GMnmfft11+dUcREelaFEyl57nvPvjWtyCRgHAYXJePxrklk3DHHTBrFvzxj3DBBbmtVQ7Y7liSp5ds5p0Pq6mLJ+lXHOGUCYOYclD/NrcoaeL7lljSpyDk4KRxvKTn2FF96VsUYcfuGOE0F0BK+sECPp+cNCjL1XUfYwcU07+kgG01jWk/zxBsH3PuMcOzWJmIiMjHKZhKz/LQQ/CNbwSd0Wh03+tdN/hKJuErX4GCAvjc5zq/TjlgtY0Jbpqzkife2Yi1EPd8DMFnEI8v2khpNMz108Zy0XEj9+kkJTyfF5dv4+7X17BsU02wVYm1HDa8N1dNHcMpEwd2q1XwcsFxDFefNIZfP708ra6ptRZr4dLJo/XcZ8AYw9VTy/jV0yvSPsf3gw7r9COHZa8wERGRvRib6YoIWVReXm4rKipyXYZ0V5WVwRxSayGUxmcyiURw3IoV0KtX9uuTDlNZF+eKu+exvrKesGNa7HQmUkH17COG8dPPHvrRMcs21fClBxZQF0uS8HwirvNRMI17PmHXoTQa4tYZxzJpiP5dHAjPt8ycvZC5q3e2ucemtZakbzl0WG/uvvI4IhmsMCvBqIHP3vIaO3fH97s6r7UWz7fMOGE03zhjYidVKCIiPYUxZoG1tryl6/TqLj3HAw+A76cXSiEY5ut58PDD2a1LOlTC8/nifRVsqKon4rYcSgHCroPrGP6xeBN/emkVEITSK2fNY1d9HMcYCkJ7FugxqcuOMVSlgu/yLTWd9ri6I9cx3HTh0Zx28GB8C7GEh9/sw1LfWhoTHj5w/Jh+3H75sQql7VBSEOLuK46jVzRELOnT2gfSnm/xfDhl4iC+dvqETq5SRER6OnVMpWewNuiW7twZBM50xeMwYgS8/Xb2apMO9dyyrXz3scU4pLfYi+9bLPDM16dx7q1vUN2QSGvPx3jSo09RhDnf+ISGlh4gay1LN9Vw35vrmLNsa2qhniAoTZswkMunlHHMqD7tWsVX9thc3cBPnljK/LWVAPgWDMEHAJFQ8EHNlSeO4YvTxmo+tYiIZEVbHVMFU+kZGhpg2LBgXmkmb26tDc6trARH4aMruPSON3l3UzUFITftc5K+zxmHDOG597biZLJyqbX8/POHc0Y33/C6MzUmPKrq4/gW+haFKYpoKYSO9mFVPY9UfMiSTdU0xoMPWM46fAinHzyYaDj9/zciIiKZaiuY6hVfeoZYLFjUKNOOS9NWMvF4y4slSV7ZXhtj2eYaIhl2MK2Fv7+zibDrUBDa/7+RuOfTEPeIJX1uuH8BfYoiHDGiN1eeWMZJ4wYQUge13aJhl6G9tZdwNo3oW8TXP6WhuiIikl8UTKVnKCkJ5os27VWaLt8POqUFBdmrTTrMzrpYu4bVOsbQEPcoLmm7W+T5luqGBH5qhdimVX4NloXrqnh3YzW9C8PcOuNYJgwubeejEBEREel59LG+9AyhEHziE9DYmNl5sRiceWbmnVbJCbedf09Nn1e0NYfR8y1V9XE8P5j+0NRMD743RMPBwkg7dse47K63tDCSiIiISAYUTKXnmDkz6HymO6/aWohE4Mtfzm5d0mEGlUZJev7HVnZNh29tqvvZ+nm7GhL7NNybDm8eZwtCLrGExxfvW0As6WVUh4iIiEhPpWAqPccpp8Do0cF80XTE4zBpEkyZktWypOP0Lgoz5aABxJN+RueFXMPIfkUkvJaDaTy1xUZLDdWQu+/+m5GQS10syQvvbcuoDhEREZGeSsFUeg7Xhccfh4EDgyG6rXXHrA2uHzo02MNUw3i7lCunlhF2nTa7n80lfR/XMXz1tPG4bst/1/Vxr+V/LgYKW1nFNOFZ7n59TbplSzdmrWVDZT1LN1Wzalst9fFkrksSERHJO1r8SHqWoUPh5Zfh6qth/nxIJves1mvtnsWOpk2DO++E/v1zXbFkqHx0X04aP4BX3t9OyNn/vFGA7591MGceNoTfz3mf2sYkkb32MU14/j6fTzQN621tz9OIa1ixpZZ40t/n9qRn2B1L8s/Fm5j1+lq218ZwHYMlCKpnHz6UGSeMZrwWyRIREQHUMZWeaPBg+Oc/4fXX4dprg+G9/ftDWRlcfz3Mmwd/+5tCaRdljOFX5x3B5LH98XxLwvP36Z5aa2lMeFjg66dN4JyjhxMNu/zvpccSdp2PDQVuqVHaFEp7F4ZbDb7GGFzHUBdTd6wnWrm1lk/f/Cq/fnoFW2sacZoWygKw8PiiTVx4+5vc8sKqtLv7IiIi3ZnJpxfE8vJyW1FRkesyRKQbSHo+jy3cyKzX17B9dwzPt3i+DfYYtZajR/flhk8cxHFl/T523uIPd3HjAwtpTHgkPUvYNezY3WxesgnCRe/CcJtb01hriSV95v3wdKKtDPeV7mntjjouvuNN6uNJCkKt/937vsUHrjqxjJmnje+8AkVERHLEGLPAWlve4nUKpiLSnVlrWbh+Fyu31tKY8CiJhpk8ph8j+xW1ek5jwmPOsq3c/foaVm3bTUPcI+lbQo6hKOJSEHLaHCIMwYJJw/sW8s+vntzRD0ny3Pm3vsHKbbtbHebdnO9bLHDP1cdz2PDe2S9OREQkh9oKpppjKiLdmjGGY0f35djRfdM+Jxp2mX7kMKYfOYyE5/P3tzfx86eW4Trpz35wHcNVU8vaUbF0ZUs3VbNmRx2RVhbS2pvjGGIJj3vnruXX5x+Z3eJERETymOaYioi0Iew6TD9yGEWRUNrb0CQ8n3DI4azDhma5Osk397+5jqRv99tRby4ccnj+vW1U1yeyWJmIiEh+UzAVEdmPSMjhT5ces8/CSC1JeD6OMdx84VEUF2hQSk+z5MPqjxY6SpeTWihr7c667BQlIiLSBSiYioik4YgRfbjzinKKC0J4viWe3LPar7XB6r+ebykIufzvpccweaxWde6JGpP7bi2U9rkJr2OLkS6puj7Bmh11rN9Zr1W9RaRH0cf5IiJpOnJkH+Z8Yxr/WrKFWa+vYUNVAyEn2A91cK8oV00t4+wjhlIaDee6VMmRXtEwO3fHMj7PAiVRvST3VJ5veWP1Dma9vpZF66sIuQ7WWnwLn5w4kMunlHHEiN4ZDREXEelq9CooIpKBokiI844dwbnHDGd3LEldzKOowKW0IKQ3jcKnDhnM7a/szuicpO9TGA4xYXBplqqSfFZdn+CGBxbw/pZaEp4frPoNqd8nlufe28pLK7Zz2sGD+O/PH97mNlUiIl2ZfruJiLSDMYbSaJghvaP0ioYVSgWA848dAYCfwVZs1sIlk0cpcPRAdbEkV8yax3uba3BMsCJ4898ljjEUhFwcA3OWbeVbD7+D7+fPNn8iIh1Jr4IiIiIdZGBpAWceNgTPt6SzT3jC84mEHL5QPqITqpN8c9Nz77N2Zx1hx7T54ZYxhpBjeHXldp54e2MnVigi0nkUTEVERDrQT6YfysQhpST3E07jSR/XMfzhoqMZVBrtxAolH9TFkvxt0SZc03YobWKMwffhrtfWpPWhh4hIV6NgKiIi0oGiYZdZVx7PqZMG4flBAE2mVm32fEtjwsO3ln7FEe684jit4NxDPf3uFqy1uBnsLxR2DZurG1mysTqLlYmI5IYWPxIREelghRGX311wFBt3NfDw/A38690t1MYSRFyHCYNLuXxKGVMO6p9RKJHuZeH6KuKeT6Hjpn1O0DX1Wb65liNG9MlidSIinU/BVDrOhg1w993w8MOwaxeEwzBhAnz5y3DWWcFlEZEeZHifQr7+qQl8/VMTcl2K5Jn6uEd7PpbwfEuD9rwVkW5IwVQOXCwGM2fC3/4WLC9pDDgOJBIwfz5cfz0UFcGdd8Kpp+a6WhHJkm01jTy84EMeX7SR6oYErmMY2beIy6aM5lOHDCYaTr8zJNLd9S0K056poiHXobhAb99EpPvRHFM5MPE4fO5z8NhjEApBJBJ0Rl03+IpGg5BaUwMXXwz/+EeuKxaRDpbwfH7yxFLOvOlV7nj1A3bujuFbSyLp8/7WWn765FJO+c1LPLN0S65LFckb0yYMpCCc2duwpkWPJo/pl42SRERySsFUDsz3vgcLFgRhtK1VBSMR8H249lpYvbrz6hORrEp6PjMfXMTf39mI40DEdQi5Do4xOI4hEgq+jyc9vv/YEh5b+GGuSxbJCyeNG0BRxCXp+WmfE0v6HDmiNyP7FWWxMhGR3FAwlfbbtQseeCDojKax1D3hcNBhve227NcmIp3iTy+uYt6aSkKOwWnj90DIdTDAfz/1Hss21XRegSJ5KuQ6XHvyWCyktf2L71tCrsMXP3FQ9osTEckBBVNpv9mzgzmlTgb/jMJhuP9+qKvLXl0i0ikaEx4PvLUeY0hrH0bXMSQ9n1mvr+mE6gJVdXFWbatl1bZaqusTnXa/IumYMXk0px88mKRv8dsIp55v8Sx8+ZMHcYK2FxKRbkqz56X9Hn8cPC+YW5ouN7X4yaJFcNJJ2alLRDrFM0u34PmZ7sPo8PzybVTWxelXHMlKXZ5vmbt6J395Yw0L1lURdoMPzxKez+Qx/bnixDImj+mHo61aJMccx/DL847gf+asYPa8DXh+MKzXdQxYSKb+fznG8L2zJvKF8pE5rlhEJHsUTKX9du3KrFvaXLU2Bxfp6p5/bxsJz8fNYB9GxxiMgYXrqjj9kMEdXlNtY4KvPLiIdzdWk/B8CkJ7fkeFHMMbH+xg/rpKjh3Vl5suOoqiiF4GJbdcx/Dtf5vEVVPH8NjCD3l0wUaq6uMYB0b0K2LG5FGcdfhQrcQrIt2efstJ+xUV0a617o2BwsKOr0dkL/XxJP9asoXZ89eztaYRgEGlUS4+bmSXe6NnbbB3Ydh1PuoA5tqu+nib80pb4/uWuniyw+tpTHhce08FK7bUEnbNPtvTGGOIhlystcxfW8kN9y/griuOy5vnU3q2ASUFXD/tIK6fpjmkItIzdZ13ZZJ/pkyBxYszO8f3g+G/hxySnZpEUmbPW8//zHkf37cfG266umE3v/jXcn719ApmnjaOy04Yndb8yFyw1vLOh9Xc+8ZaXlixDWvBt5ZBpQVcdsJozjl6OH2KsjMcNh1FERdL5h9OOYas7Gl6xysffBRK2/o7NcYQcmDJh9XcN3cdV580psNrERERkczoY2Jpv2uvDYbyZtI1jcXg1FNhyJDs1SU93i0vrOQ3zyzHTwXSSMjBdczHvvet5fdz3ufm51bmutwWVdcnuPzueVzzl/k8v3wrIcdQEHKIhhwq6+L84YVVnPa7l3lkwYac1XjM6L4Zd0yttXg+TBxc2qG1xJM+s+evx3HaDqVNjDEY4J65a/H8doz8EBERkQ6lYCrtN3YsHH98EDbT4fvBqrwzZ2a3LunRXly+jbtfX4tjTJuL8jQF1XvmrmPOsq2dWOH+1TYmuPSuN1mysRrHQEHI/SgAGmMIuw6h1GP7+VPLefCtdTmp8/NHj8BAm6uJ7i2e9DlkeC/KBhR3aC0vrdhGPOl/9LykI+Q61MWSvLF6R4fW0p1U1cWZs2wrjy38kKeWbGbVttpclyQiIt2UhvLKgbn9dpg2LVgIqaCg9eOahvBee+1Hq/FW1yfY1RAn7Dr0L4lQEOr4oX3S89zy4io83xIK7f9zN8cYktbnlhdWcvrBg/JmSO9//H0pH1Y1EN5P9891DJ5v+d2c9zlqZF8OGdarE6uEgaUFnDJxEC8s30YktP/nzrfBPozXTO34obNLN9XQmPApimT2eySW9Fm+uYaTxw/s8Jq6svc213D3a2t4fvk2XAOeDYZg+xbGDyrh6pPG8KmDB2tlYxER6TAKpnJghg+H556Dz3wGduyAZBIikWCBIwjCaDwedEq/9CUSP/oxLy3byt2vreG9LTWEHQeLxRjD544axqWTRzOmgzsp0nO8t7mGNTvqiLiZbF9i+LCqgaWbajhseO8sVpee7bUxXli+jVCaQ1JdxxBLeNw7dy2/PO+I7Be4lx995hCWbKxmW22MSBtzO30bzPWdfsQwTpnY8SGwLpakPRHJWqiLex1eT1f2xNsb+c8nl5HwfCIhJ5iTm3pyDZblW2r4/mNLmDNpK78493AtHiUiIh1CryZy4MaMgQUL4A9/gAkTgnAKe+aeXnopvPACW775A865dS7ff2wxyzZXp7pBQdfK9y0PV2zgvFvf4JYXVmHbs9qv9HhzV+8k6fkZdT6NMcSTPq+vyo/hnI8uDOaMZjJ3MxxymLNsK9X1iWyV1aq+xREeuHYyo/sVYYFY0vvY/1/ft8SSPr6Fc48ZwU8+e2hWOtN9isLtOs9xoE9h+87tjl54bys/fXIZlmCBqr3/HRpjKAi5uAaef28rP/n7Uv2+FhGRDqGOqXSMaBQuvDD42rQJqqqCzumwYVBczPbaGJf8eS6VdXEiIWefYbvBfD8X37fc/foaYkmPb54xMUcPRrqqqvo47V3HZmddvGOLaadXV+7IeBcmxxgwsGxzDVMO6p+dwtowqFeUR288kZdWbN8zGsI1YMECZx8xlBmTR2d1qPHUcQOY9fparLVpB19rLSHH4YQcPGf5KOH5/OiJIGjurwvatLLx0+9u4fxjR3DMqL6dVKWIiHRXCqbS8YYNC76a+dHj77KzLrbfeaSOY8BaHnxrPdMmDOS4sn7ZrFS6meJICNqxfYnFUpIne5rWxTza01C0BMNZcyXsOnzqkMF86pDB7Nwdo6o+QcR1GFAaoSiS/ef2qJF9GNwryqZdDWnNdwWIe5bxg0uYNKRz5+bmqxeXbyOW9NIemmuMIZn0uG/uOgVTERE5YBrKK1m3aVcDb63ZSSTNNzuOMSQ8n7+8vja7hUm3M3FIabv2x4yGXSYN6djtS9qruMDNuGMKYICigvxYQKx/SQHjBpUwqn9Rp4RSCELSjZ88KLVAz/6fQN9aXAM3nnJQJ1TXNdw7dx0Jz8/onEjI4eX3t1Pd0PnDyEVEpHtRMJWs++v8DVhLRvPKIiGHN1bvYFttYxYrk+7mpPEDiIZdkhm8uU76wQIvp0wclMXK0nfSuAEZd0x9a0n6lkOG9uzO39mHD+WC40biW9rcm9TzLb61XDm1jFMnDe7ECvPbhsp6Qk5mbwscYwg5hm01+l0tItLZPti+m38s3sTDFRv415LNXf53cX6MXZPs8zx48UVYvhwaGqBPHzj99GDhoiyrWFeZ8TmOMTiu4f0tuxlUGs1CVdIdhV2HGSeM4raXP0hrrqG1Ft/CxceNIpLG9jKd4bxjR3DHK2vwrU17AaRE0uffDhtCn6JIlqvLb8YYvnvmJPqXFHDbS6tJ+hZrLa4xWIJA6qYWXfv6aROZccKoXJecV5LtnaANxDPstIqISPtYa3lh+Tbuem0Ny7fUfrSVl2OC17kTDxrANSeP6ZJTLBRMu7tYDG67Df70J6ipCS77frB9yw9+AMcdB9//Ppx8ctZKqI+3f85cQ0LbOEhmrpo6hlfe38GyTTWE3dY79dZaEr5l4uBSrps2tpOrbN2g0iifmDiQF1dsS2vbG88P9ga9fEpZ9ovrAowxXHfyWC46biRPvrOZh+atZ0ddDIDBpVEuPn4Unz5iaN7MKc4nfYvCbNyVxM1g4x1rLZ6FfsU9+0MREZHO4PmWnz25jL8v3oTn+0TcYEuvj7aUduDVlduZ+8FOvn7aeC4/sSyX5WZMr8zdWU0NnHMOLFwYbN0SiUBBQbA/AgQ/mzsXzjsPfvYz+OIXs1JGr8Jwu1ZKNQTz7UQyURBy+fNlx/LV2YtY/GE1iaRHQWovRgjeSMeSPmHX4fDhvfnTpccQDbt4vv3/7N13fBzltfDx3zOzRb3Ylmy594I7Fm6YTsCU0ELvJSQBknuBN+XNTXhvkpueQBrkht5bCC0khBRaMLYBuWBsjI275SbZKlbb3Zl5nvePkVxlaVdotSvpfD8ffVx2Rzpqs3PmOc85vLtuNwvX76am0SEnHGDGsEJOnlDcbtOuzvb9cyey9r46ttU0NY9Vaj1R8LTBGPjPU8ekxQzWdJKbEeTyWUO5fJasisbr/KMHcfcb6xI6xvEMo4tzKMnPTFJUQgghWvz8tU/484fbsCxFoJVrE0upfdc0v/7XWrLDNl+YMSQFkXaMSqf5Y6WlpaasrCzVYfQMGzbA9Ol+cnqocBhyc/1VU/DLfLX2V1UvvrjTQ3l80SZ+9a9PCVjx34XX2oCCN79+IrkZMmNQJM7ThoXrd/PQgo0s31qzr9Oo42mmDi7g2mOHM290PyyleOK9zTz4zkYaYi4RR9PcHJpw0CJgWVw6cwg3nTiqSxPU6oYYtzy1lLW76oi5/j5YSyl/hUobDP4L0DdOH8elM9Mj+dpR28Q/Vu1kR20U24KhfbI4faKUGHcXVQ0xTr3rbSwV/xxdbQzfP2cSZ00pSXJ0QgjRu62rqOPiexfHfY52tcFS8NY3TkqrKiGl1BJjTGmrj0li2gOdfTb89a/tPy87G/Kam6U4DmRlwbp1+xPWTlLb6HDynW+hErjYiTqa86YP5PvnTurUrD/kbwAAIABJREFUWETvVFkXZXe9X87ZLydMUW4YANfTfPNPK3hrTQUAgVY6R7c0yjmqJI/7ryntsi6z4K/uLt1SzWOLNvPWmkoUfiJQmB3iqtnDOH/6IPrmhLssniNZvWMvv/7nWt7fVIVpbjxkwO/EreCUCcX85yljGFyYlepQRTt+9tonPPP+FgJtrNS3iLmaQYWZvHDz3C6vKhBCiN7m+39exfNLyxOaPuBpwzfnj+OSY9LjBja0nZimT/osOse0afDhh/E9t6HB/zMvz09Go1F49VU499xODSk/K8g5Uwfy0vJtKKv97ryu1gQDiqtkz5zoJEW5+5PRA/3stTW8uaaizYtw21JYBj7esZdbn1nOvVfNSKjD9GehlGLGsD7MGNYHrQ0NMZdQwEqrJOCdTyu57dnlRF1NxgEl0y20Mfx95U4WrtvNA9ccw4Re3jk43f2fz41lR00T/15biVL+z/+htDE4nqFfTogHrilNq59HIYToiaKuxysrtsc9erGFpw2PL96cVolpW9KjDaXoHLfdFn9S2qKhwS/lBYjF/EZJSfCtM8ZzVEnevi6ZR+KP+VB87/MTGV2ck5RYhAC/7PRPS7Y2d2ltO9FUzSMxlmypZtnWmi6K8GCWpcjNCKZVErByWy23PbscTxsyg3arX8eW/S71UZcbHy1jR21TCiIV8QrYFndePI0bjx9FMGDhGUPE8Yi5mqjr4XgarQ0njS/ij1+eI3tLhRCiC1Q3OBjjXwskImArdtR0nxEysmLak/z61x07rq7OHx8TCMCWLZ0bU7OMoM2D1x7Df73wEW+tqfBHYVj7S3tdzxCwFaGAzY/On8QpE2S2oEiu58rK9+3TjIdSCsfxeGzRpm7Zgj0ZfvH3NcRcHVdZUThgszfi8MA7G7nj7KO6IDrRUbaluOnEUVw/bzhvrK7gHx/voqYxRlYowLQhBZx/9CD6pUEJuRBC9Bau1h2acKEAN422bbZHEtOe4qtf7fixTU2Q39zRUydvFl1G0OauS6axZU8jT3+whVdX7KAu6hKwFKOLs7lm7nA+d1T/hGrnheio55eWJ1wyEgpYvL2mkojj9fqf0y17GvloWy3hBOa/Bm2Ll5dv5/bPjSU7jRoxiNaFAzZnTC7hjMnS2EgIIVKpICuE65k2x+C1xjOG/G7URFSuDHqKe+75bMdr7Zf0liT/AmRo3yy+NX8835o/PukfS4gjqWl04poTeiBLKYyC2ian1yemf1mx3Z+hmkBialsKbQxvr63kTEl2hBBCiLjkhAOUDi/k/Y1VCV1/aAPnThuYxMg6lySmPcF9933292GMX8r7xS9+9vclRDfwWdoXdVHvo7S2tbqxzf3iRxJzNZV10SREJIQQXc8Yw+oddWypasDxDAVZQY4Z3qfX37wUne/auSNYvrUGY0xcq6bGGCzg4mO6zxxTSUx7gttu++zvQ2sIheC88z77+0pzjqd545MKXl2xgz0NUcIBm6MG5nFx6RCG9JFxFr1FUW6Y3fVRggmsmmrtJ2IFmTKXs8PSIKmvizi8+tEO/lhWzu76KJZSDCzI4MpZwzh5QnFaNZgSQqQnx9P85cPtPPTuJrbXNO27YamUQgEXHD2Iq+YMZ1CBNAgTnWPOqL6MKsphzc66dkt6jTG42nDGpJJuNapNEtPubt06aGz87O9HKbjjDn+WaQ9ljOGRhZu4/50NxFxNzNV+aSawZHM1T763mamDC/nBuRMlQe0FLps5lLvfXJfQMY42nDN1IKEEyld7qqF9sju0chyyLfrnpaZxjjGGBxZs5N631++bt9oyDqWqIcodL6/kB3+x+M6ZEzh7avcpfRJCdK36qMtXn1zKim01aA1B++Du7p42PPP+Vl5atp17rpjOjGF9Uhit6ClsS/GHK2dw1UPvUV7VRMBuvYFjy/z10uF9+P65E1MQacfJ1VV391maHrUIBOArX/HfeihjDHe8vJLfvbGOiOPtG2Hhz4S0CAUsbKVYuqWKS+9bxKe76lIdskiy86cPAvavgrbHGD+JuXL2sGSG1W2cPaXEv7GTQDmvp/3yoxPGFicxstYZY/jxq6v53zfXYYz/Ah8KWNiWwrYU4YCNpRRRx+OOl1fy1HubuzxGIUT6czzNV59cyvLyGmzln0cOXblqOb9EHI+vPLGUT3buTVG0oqcpzA7x9I2zOWtKCcb43XqjjkfU1UQcz+/9YCuunTuc319xNMEE556mWveKVhxuwYLP/j4efhj+53969Ma5+/69gVdX7MBWELBa/7FXyr84rY+63PBoGbWNThdHKbpSYXaIm04chTb+ncW2tJTEnDl5AOMG5HZRhOltSJ8spg0pJOrG38nb0Zrzpg0kM9T1pbIvLdvG80u2YTUnokcSsC0spfjlP9bywaaqLoxQCNEd/G3lTlZsqyUYxwzsUMDyb3a9tLKLohO9QW5GkB+dP5k3vn4it506jhPHFXPM8EJOPao/3ztnIm9/4yT+89Sx3S4pBSnl7d527YLIZxya+4UvwJVXdk48aaop5vHggo3+vo84ku9wwKYu4vLCsnKuO3ZEF0QoUuWL80awt8nhyfe24HgeIfvgO9/GGBzPn7l74rhi/vvz3askJtm+OX8cVz/4Po6n230BjLoe+RlBbjxuZBdFt5/WhnveWo/BYKn2X6htSxFxPP7w9nqOGS4leEKI/R5asAGdQEfycMBifWUDa3fVMba/3NgUnSc/M8hVc4Zx1ZyeU8nV/VJpsV9VFdifYeXBsuBPf+q8eNLUayt3HLSXLD6GxxZtxouzzFN0T0op/s9p47jzoqmMH5CHZ/yusY0xj5ir0QYGFWZyx1lHcedFU7vl3cdkmlCSx28vm07A9kvWWlt51toQcTxyM4I8dO0xFOdldHmcH2yqoqYxRiCBc0A4YLFsczXl1Z2wh18I0SOs3VXH1qqmhJrmKaXwPMOz729NYmRC9AyyYtqdhcN+J91YrGPHP/5458aTpl5ctg23ncTUABGnJRnx98HF3AiL1u9m3piirgtWpMRJ44s5aXwxn+6qo2xzNQ1Rl4ygzaRB+UwdnJ/QMOveZs6ovjx94yx++/o63vm0Eg+D4/nlvS3dbc+dNohbThrNgPyuT0oB3lxTQdTVZCYwvkEphcGwcP0eLi6VZmhCCNiypxHLUgk3F1cK1lZI7woh2pPUxFQptQmoAzzANcaUJvPj9TolJZCZCfX1iR9rWXD55Z0fUxqqaojR1s3N+qhLxPEw4Geo+H9xgJueXMop4/0SzsJsGRHS043pn8sYKbVK2OjiXH572XQq66K8vnoXu/ZGsC3FoMJMTp3Qn9yMYErjq2qIdWhKjacNdRH3sP+vizj8ZcUOnnpvC7v2RjAG+uSEuGjGYM6fPoi+OanpOiyESC5XG3/uewduVrbcsBNCHFlXrJieZIzZ3QUfp/fJzITLLoM//CHxVdPbb09OTGkoYFu0VpBrgNpGB0drFM3jFQ95rbEUvLWmklXbF/HUjbMpypULTiGOpCg3zKUzh6Y6jMNkhQKtngPa43fv3l++bYzhqfe28Kt/rd03bibQfNerYm+E37+5jt+/tZ4rZw/j1lPGYCW0fUAIke4Ks4IdmsWsjaE4NzUVI0J0J7Jhqrv7ylcgLy+xvaY5OfCjHyUvpjQzuiin1b2idZH9SemhjPET14Dlj5KpqIty42NluHLHU4huZ9KgPEIJ7g82xmDb6qBmJf/71nru/Ofag8bNWEphKUXQtpo7+sITizbx3ZdXJjRKRwiR/qYPLSRgWf7KaQJCAYtzp8lsZCHak+zE1AD/UEotUUp9Kckfq3eaMAFuvhny8/15pO2Vl2RlwfPP+3tTe4nLZg0lFLAOukj0tCHqtp6UtggeMFYiZCvKqxt5Z50s/gvR3cyfVIJS8c+sBb9krzArxIyhhQC882klDyzYiAVt7le3lH/eeG3lDp79QJqdCNGThAIWl5QOaXfE2IFcT5MRsDl+rPSrEKI9yU5MjzXGHA2cAdyilDr+0Ccopb6klCpTSpVVVlYmOZwe6r//2185LSyE7Gw/6VTq4LeMDOjXD556Ck47LdURd6mjhxZQlBsm5u1/IWlyPI5U22cMoDho1qJSCsfVPLxgU3KDFUJ0upxwgHOnDsLRJq5VzJbnXDt3+L5y3P99az2e1nGV5yqlMAbuf2dDQsmwECL9XT57KNkhm1gcM5y1MRjglpNHS1d3IeKQ1N8SY8z25j8rgBeBma085z5jTKkxprSoSO4mdYhl+aW5L7wAZ57pl/b27w9FRVBc7L/dfDMsXAjnnpvqaLucUoqfXDCFoK32NR+IOrrVfSIt16wh2zqs9C8UsFixrYa9ESfZIQshOtmtnxvD4MJMHK/t5NQYg6sN04cWcskxQwBYX1nPJzvrEioHDliK2iaHxRv3fObYhRDpozg3gz9cWUo4aBF19BHPJ67nd/m/bOZQLm0+lwgh2pa0xFQpla2Uym35O3AasDJZH08A8+bBs8/CihXwxBPwwAP+v9etgzvvhDFjUh1hykwbUsBvLvXnLUZdD8+Yg/JSYw5ISgMWeRmBw0aEKKUIWoraRklMhehucjOCPHr9TEYWZaOBqOsddEFpjD9vVQOzRvbl7sun71vheG9DFVqbhMYGKaWIOJoFn0r5vxA9zeTB+Tx142ymDMnH0/75JOpqYq7eN9M5OxzgW/PH843Tx8nIMSHilMyuvP2BF5t/GQPAU8aY15L48XoXx4G//Q0eegjKy/1V0xEj4ItfhJNP9kfJiIMcO7ofL9w0l0cXbuLBdzfuGw+jmlvyBpQiM2QTDlhHfBHRhn1dOIUQ3Uu/nDDPfGkOb3xSwcPvbuTTXXX7fp9dbZg1si/XzBnO3FF9DyrZbYi6uFoTSvBerqWgurGDc6aFEGltVFEOj98wi427G3iubCtrdtURczVFuWE+P3Ugx43uR0DKd4VISNISU2PMBmBqst5/r/bkk/Cd70Ak4o+JaenI++mn8Pbbfinvb34DZ5yR2jjT0JA+WXz37KN4f2MV6yvrsZsHZduWavcFxNMGpaCPzDMVotsKBSzmTxrA/EkD2FHbxJ76GEpB/7wM+h1h/mhG0MK2Er/ANMbf3yqE6LlG9Mvmm/PHpzoMIXoEuZXT3fzyl3DrrdDQ4K+SZmRAMOi/ZWb6/1dVBVdf7ZfzilZdP284mSGbjKBNOGjHdVfT1Zrzpg0iHEhgNI8QIm2V5GcyaVA+EwfmHzEpBZhQkkfQVgmPfwkHLaYOKfisYQohhBC9giSm3ckrr8DPfuYnn8HgkZ/X0pX39tvhvfe6Lr5u5PSJJdiWinsuqTYG27K4bNbQJEcmhEg3M4YV0ic7lNDsQk8bbEtx6oT+SYxMCCGE6DkkMe0ujIEf/AC03l+625ZAwN+H+tOfJj+2bigzZPODcyaCUnjtXGxqY9DGcOWsoYwqyumiCIUQ6UIpxQ3zRgDEPW7GM4aLS4eQEZQKCyGEECIekph2F0uXwpYt/mpovDIyYMEC2CpD3ltz+qQSvnOmvy8k2txF70D7unQauGjGEG49dWwqwhRCpIELZwxh9si+uO3MQjXG4GjDmOIcbjlpdBdGKIQQQnRvkph2F3/5C0SjzS1k46SUv9L6j38kL65u7gszhvDkjbOYP7nEHxkD+xLUli6dd182nW+fOeGgLp1CiN7FthS/vnQaJ44rRhv2jYRoceC4mSmD83no2mNktVQIIYRIgLQL7C527uzYcY4D1dWdG0sPM35AHj/7whRqzoixfGsNDVGPzJDFuAF5DCrITHV4Qog0EQ7Y3HXxVD4sr+WxhZt4a21l8zxkg6sNc0b25ZpjhzN7RF+5kSWEEEIkSBLT7iIrq2PHKQXhI3ebFPsVZIU4cVxxqsMQQqQxpRTThhQw7ZJp1EddqupjaGPokxMiL6ONpnRCCCGEaJMkpt3F5MmJ7S9tkZEBY2VvpBBCdLaccEDmlAohhBCdRF5Ru4sLLoBvfQs8L76uvOCX8ebmwimnJDc2IURaKK9u5KVl21hXUY+jDSV5GZw1pYRpQwpQiexPF0L0WGt21vHJzr1EHU1ORoDS4YUU52a0eYynDYvW7+GxRZv4eMdeYq5/7Cnji7l81jBG9MvumuCFED2aJKbdRV4eXHIJPPlkfImpMf7bTTf5o2OEED3Whsp6fvzqapZurkYDWpt9vc9eXLaN/nkZfOP0sZw0XmZqCtEbaW34+6qdPLBgIxt3N2Ap//8sS6ENHDuqLzceP5IpgwsOO3bltlpufWYZNU0OMVcTtC2UgprGGM+VlfP80m3MHN6HX1w0hVwpZxdCfAYqnplsXaW0tNSUlZWlOoz0VVUFxx8P27f7JbpHYgzEYjBhgt+Rt6P7U4UQae+j8lpufKyMxphLOGAdtjJqjMHxDJYFt506jqvmDEtRpEKIVHA8zbdf+Ig3PqnA04aQrQ46T2hj9iWc35g/jkuPGbrvsaVbqvnyY0uIeZpwoPVBDi0jkoYWZvHkjbMkORVCtEkptcQYU9raYzIupjvp0wf+/ncYPhxc108+D7yxYIw/UsbzYMoUeOUVSUqF6MF21Dbx5cfLiDgeGUG71XJdpRShgIVC8at/ruH11btSEKkQIhWMMfz3n1fx+upd2IpWb15ZSu0bbfTzv33CX1dsB6C6IcYtTy7FaSMpBf8cE7QUW6ob+dbzK5L3yQghejxJTLsLY2DhQnjpJX+/6fz50Lev//9K+W+eB+PHw913+wlsYWGqoxZCJNGjCzfREPMItXHR2MJuLtn7xd/XkE6VMkKI5PloWy1/X7mTgKXa3WduN484+sFfPibieLy0fBtRV8d1fmlJThdv2MPWqsZOiV0I0fvI5sN0pzU8+ij86lewa5e/Sup5EAz6e00HD4bzz4d582DQIOnAK0Qv0RTzeGHpNgIJzMsM2ord9VGWbqlhxjC5cSVET/f4os04nt63ItqegG3hev5+1McWbW6+iRXfOUYphfYMT7+/hW/OH/8ZohZC9FayYprOHAeuvhq++U1/X6llQWYm5OT4s0ltGzZvht/+1t9LOmZMqiMWQnSRxRv3YNi/yhEPpRQxV/PK8u3JC0wIkRbqIg6vf1IR14rngTxteHDBRuoiDkE7sWMtBW98UpHQMUII0UIS03T2ta/Ba6/5XXVDIb9c90BK7U9QH3gAfvnL1MQphOhyVfUxPJ14Sa5SsGNvUxIiEkKkk8q6KLalsBIcFWVbih01kYRuerVQSlEfdRM+TgghQBLT9LViBbzwgl+y296LimX5b7/4Beze3TXxCSFSyrZUnAV2rR8rhOjZtDEdOkeo5mM7shXdQJuNkoQQoi1y9khXv/+933k33judtu03Qnr88eTGJYRIC4MKM+M+PRxMMaoop7PDEUKkmYKsEI6nE2525hlDYbZ/rONqdALHu55m0qD8REMVQghAmh+lp1gMXnzRL99N1H33wW23dX5MQoi0cvTQQnIzgtQ0xuLeB2aMwVaKLxw9OMnRiZ7E04ZF6/fw5Hub2bSnAa0NfXPCfOHowZwxeQBZIbmUSEf9csJMKMlj1fZawoH4mh85nqY+6tLkeCgg4mhQELQsssI2oTbONcYYQgGLq+cM75xPQAjR68iKaTqqqvL/tBL89gQCUCFNB4ToDWxLcc2cYQBxr4hEPc3EQXkM75edzNBED7Jw3W5Ovettbn12Ge+u282u2giVdVE+3r6XH7+6mhN+/hb3v7NBRhClqeuOHUHAstr9/higtsmhutHB9QxZQZvMkO3X9Ro/Ya1tcqhpdI5Y4hvzNP3zMjh6aEGnfx5CiN5BbnOmI63p0OYO8I9rmW0qhOjRLiodwovLtrFpTwMh22pzTmHM1WQEbO44+6gujFB0Z39ftZP/euEjPO2vhLU2csTThv99az3bq5v4f58/qt1ZmZ1h1fZanli8mQ82VtPkeGSFbI4d3Y8rZw9ldHFu0j9+d3LiuCKG9sli454GQvaRvze1TQ4xV6OAjKBFoHllNDtk0xD1AD9HdbSmpilGQVbooP2rUdcfSfObS6d1yc+AEKJnUul0l7O0tNSUlZWlOozUi0RgyBB/32giq6aOAwUF8OmnyYtNCJFWKuui3PDoB2ytakTBvgvKFlobYp4mKxTgniumM2NYn9QEKrqVtbvquPz+xWhtDvuZOpQxBs/A108by+WzhiUtpm01Tdz2zDLWVzbgaoNtgUL5H1+DbSsmDcrjzoumUZQbTloc3U1lXZSrHnyPnbURAvbhXXqbYh51URcFhAIWeRmBfcmlMYYmx6Mh5vnLqs2yQjZZIRtXGyylyArZ3HtVKUcNzOvCz0wI0R0ppZYYY0pbe0xKedNRRgaccQZEo4kdpzVcc01yYhJCpKWi3DBP3zibr548mtzMIMaAq01z0xNAwYUzhvDcV+ZIUiridv87G4i5ut2kFPwRIRjDH97egOvppMSztaqRS+9dxNpd9VjK7/wasCxsSxGwLcJBC1vBivJaLrlvERV1kaTE0R0V5YZ59stzOGPyAP/84BkijkfU9Yg4Ho0xD0v5yeaBSSn439usUIA+WSEygta+Yqwmx8PVhv55GXxz/jj+duvxkpQKIT4zWTFNV++9B+ec46+YxlMWozV4HixfDoMGJT8+IUTa8bTh/Y1VbKtpwtOawqwQc0f3IycsuzZE/KobYpxy19tYioRmYGpjuPPiaZwwtqhT43E8zed/t4CdtRFCcYwiibma0cU5PPeVOVJWeoiaxhgvL9/Gsi01NMY8Yp5myeZqwrbCiqNCyxiDNuBqzddPG8eVs4fJ11gIkZC2VkzlaiVdzZwJ8+bB22+3P8vUGD8pve46SUp7AE8bFm/Yw8ptteyNOORnBikd3ofpQwrkAkC0ybYUc0b1TXUYopt7b2MVtlIJtyqIuprXVu7o9MT0nU8r2V0fjSspBQjaik17Gli6pVqqBA5RkBXimrkjuGau/+9H3t3I0s3VcSWl4K+g2gpiLpRXN8lrkhCiU0limq48D66+Gj74AHbt8hPTcBgyM/fvOzXGn3UKcPbZ8JOfpC5e8Zm5nuap97bwyMJN7I04RBwPfweVv++nf16YLx8/inOmDZSLASFE0uyNOHg6vjLeA1kKqhpinR7Pw+9uwnE14VaaL7VGKUXU8Xhk4SZJTNvR5HgJzSndR+HvOxVCiE4kiWm6MQbuvRd++Uuor/cbIVmWv980FoO6On8sTEYGZGVBnz5w++1w442Jj5cRaSPmav7jmWW8v6EKgyFoWwfNBjTGsL0mwvdfWcWSzdV875yJWJYkp0KIzhcOtN3h+YgMZMQ5LzNeUdfjw601hONcLW0RCli8u253p8bSE+VmBBO+AQH+a1Lf7A7MWhdCiDZIYppOtIabb4YXXvAT1GDQTz6zsvzHIxE/OXVdf+X0d7+DCy7wu/eKbu2/XlzBexv2ELAUSh1+kaCUIhTwu0/+ZcV28rOC/J/TxqUgUiFETzeyXw7gJx+JJKiWpZg0OL9TY6mLuATaGYXUGgU4nt8ELNiBxKu3mD2yL5ZSCX2vjTFkBG3mjemX5OiEEL2NnK3TyQ9/CM8/7yeaweDhj2dkQF6ev0rqunDXXX4yK7q1VdtreeOTyuaktO0LA6UUlqV48r0t0nUSfwzCexv28PbaSpZuqW4ufxZCfBaTBuUxID8Dx4u/xLOlHPS8aZ3b5yAjaOPpjjVptJQiIJUlbRpdnMO4/rnEEuim7GpDn+wQpcMKkxiZEKI3khXTdFFdDXffHX8X3lAINmyAV1+Fc89NfnwiaZ5YvBlPGwJxlqpZSuFpzfNLyrnpxNFJji49lW2q4uF3N7GoeZUZ/BF7loKLS4dw2cyhDCzITG2QQnRTSilumDeCH/5lddwraY6nOWlccafPD80O2fTNCVHdEEto5dPxDMP7Zcl+/DjcfNIo/vOZ5Wht2t0ioo1BAV85YZR8bYUQnU5WTNPFk0/6f8ZblqsUOA78+tfJi0kkXczV/H3VLoJ24mVqz36wNTlBpTGtDT/722q+/PgS3vm0kgOvoRT+fL7HF2/mvHvelf1lQnwG50wdSOnwQhxtaG+sXMz1RxN956yjOj0OpRRXd2AkiW0prj92RKfH0xMdN6aIG+eNQBvaXJ3W2h8Vc970wZw/XSYACCE6nySm6eLRRxMvyw2HYeVK2LEjOTGJpKttcoDEZgWCf9FV1RBr94Kxp7nzn2t45oOtKOWX+B36dbMtRci2cDzNfzy9jCWbq1IUqRDdW8C2+O1l05k9si/aQMTxDjrfGOPv33S1oSg3zOM3zOr01dIW500fhKX8zuXxaNlXevrEAUmJpye66aTRfGP+uOaKHEPM1X4iqlu+zxoDfHHeCO44e4KslgohkkIS03Sxe3fiTYyU8veiVlQkJyYh0siq7bU88/5WbEu1m8gHbQtPG77+3IoO708TorfLCNrcc/nR3HXJNI4eVohn9u800cCAvAy+NX8cL91yLEP6ZCUtjoKsED86bxIAbjs3cB1PYynFnRdPJSPO8TLCd9nMobz1jRP59hnjGdEvm1DQIhiwGFSQyW2njuWNr5/IzSeNlqRUCJE0ssc0XXyWUS8B+TZ2V/mZfpMrbUxCq6aeNhRmh3rVBcITizfjahP32IhQwKIu4rBw/W6OG1OU5OiE6JlsS3HC2CJOGFvEjtomttdE8LShT3aQUUU5XXYOOn1SCY5n+N4rq/Yln/YBtfyu1hjjr/T+4sIpHDtaOsZ2RHY4wIWlQ7iwdEiqQxFC9EKS0aSLYcPgww8TWzXVGjwPSkqSF5dIqlDA4rSj+vO3j3bEPTwe/EY/F/eiC4e6iMM/OrAXN+ZqHlu0WRJTITpBSX4mJfmpayp29tSBTB9WyLMfbOGPZeV42mAp/0ZdVjDA5bOHcuGMwRTnZqQsRiGEEB0niWm6uOkm+NrXEjsmGoXTT/fHx4hu68rZw/jnx7vi7n5mpIILAAAgAElEQVTpd0VUXDRjcBdElx521EYOWh2JV9C2WFdRn4SIhDgyx9NxjX8SiRtUkMntnxvHLSeNZmtVE00xj6ywzdA+WYd17TXGULa5mg82VVHdECMvM8jkQfnMG92PgMw2FUKItCOJabo45xy4/XaIRFqfYXooY/yRMV/9avJjE0k1cWAex48t4q01FQQs2ryYNcbvinjF7KEU5/WeVYGYq1H4K8WJchKYzydER3jasHD9bh5ZuIklm6pxtcG2FBNKcrn+2BGcNL44oVEnon3hgM3o4pxWHzPG8OKybdz79gb2NESJOv45wGDICNpkhWyumTucq+cMl++LEEKkETkjp4twGH71K788NxqFWAxc109AD2WMPyrm9NPh2GO7PlbRqZRS/PQLkzlmeB9cbVrtPNnSAdPThvmTBnDbqWNTEGnq5GcG4xpbcShtDHkZcdzoEaKDNu1u4KzfvsNtzy7ng41VBGxFZtAiZCtW79jLf734Eafc+TYfldemOtReQWvDHS+v5Id/XU1FXQQFZIZsMkM2WaEAllLUR1zufmMdNz2xhIjjpTpkIYQQzSQxTRdLl8Jrr/kJZ3U1VFXBnj1+t96GBj9hNWZ/wnryyXD//ftbJAqMMZRXN/JReS2rd+ylttFJdUhxCwdsfn/F0fznKWPIywyijaEx5tIU82iKeWgDxXkZfPeso/jReZPaHYLe0wwuzGRAXhjHS3zN9OypsgdbJMfmPQ1c8cB77KyNYCm1b4SRan4LB/x/721yuP7RD1hRXpPqkHu8X/1rLa+u2IGt/FL+1ipQArZFwFIs2VzNN/+0oteN3RJCiHSl0umEXFpaasrKylIdRtfSGr7zHXjwQT/hDIX8hkb19f7KaQuloKAAhg+HW2+Fyy9PfLxMDxVxPP6+aicPLdjI1uomgrbCGHC14bgx/bh6znCOHlrQbfZ7tZQFriivYW+TS0FWiGOGFzJjWGG3+RyS4bmyrfz0b5/EvddUG4PW8Nqtx/WqsmfRNbQ2nPW7d9hREyEUR6fomKvJyQjwj9uOJysku2iSoaIuwvxfv4Ol4psN3bI14pHrZjJ5cH4XRCiEEEIptcQYU9raY/LqmGrf/jY89JCfZGY0XzxbFhQW7i/rbSndDYXguedg5MjUxpxGyqsb+eKjZVTWRfG02ZeUAlgK3vykgnc+3c3pE/vzg3MndYv9RLalOG5MUco7ydZFHD7evpf6qEtmyGZc/1z65oRTFs+Zk0v4/VvrqW6ItZsIGGPwtOHMySWSlIqkWLxhD5V10bg7RYcCFk0xj9dW7uSCo3tP47Ku9PyScowxWHGOX1NK4bgejy/exM8vnJrk6IQQQrRHEtNUWrAAHn7YT0pbeyG1LMg8oDV/JAJf/CK88UbXxZjGKuuiXPXg+1Q3RAkF7MNW0lpK64wxvLZyJ65n+PmFU3r1qmM81lXU8diizfx1xY5D5gQajh/bj2vnjmDakIIujys7HOCBa0q5+sH3aYg6RyzT081J6ZTBBfz35yd2eZyid3h04SZiriYjgTFPnjY8tGAj508fJOehJPhjWTmJflVDAYt/ra4g6nqEA1KFJIQQqZT+y0c92e9+56+Exnl3l3AYVq6E1auTG1c38b0/r6KqOSlti1KKgKV4/ZMK/rW6ooui655eXr6NS+9bzMvLtx92gWcpeGN1BTc88gG/f2tdSvZljSrK4Zkvzeaogfl4BqKuh+NpXE8TczWuNhgDF0wfzP1Xl8ZVYilER5RtqU745ytoK8qrm6iPukmKqneraoglPFbKUgoF3aongRBC9FSyYpoqu3b5K5/hBEojlfL3od53n9/Btxer2Bth4frdhOIszVVK4XkeDy3YyOeO6p/k6Lqnv6/ayfdf+RiAcCsX3C0r0FobHvj3RsIBixvmdX1Z+ZA+WTx142zWVdTzzPtbWF5eQyTmkZcZZP7EAZw7bRD5WdKJVySPMYaoo8kMJpaYKqWwLWiIeuRKt2ghhBDiIJKYpsqaNf6e0URXnSwLeluDqFY8v7QcaHvm56FCtsWaXXWsr6xnVFHr8+96q8aYyx0vrQRj2h08b1kKow33vLme+ZNKGFSQ2ebzk2V0cQ7fPfuolHxs0bsppQgFLAwkVDpqjMEzhqywlIwmQ7+cENUNMQJx7vsFv/TfgNzMEkKINCB1bqnS1JR4Ugr+qmljY+fH080s21KDTvDrp5TCVrCuoj5JUXVff/toJ55uPyltYVsKYwx//GBrkiMTIj1NHpRPzD185nBbXG0ozg2TG5Z7wslwUekQEn1VdVzNaUf1l/2lQgiRBiQxTZX8Dram19rv2NvLNTkeKuE2F6ANMlC9FY8u2oSnE7uks5Tij2Vbcb3ELs6F6AmuPXZ4wntMLaW47tgR0vgoSb5w9GAUxH3T0jRXiFw5e1hyAxNCCBEXSUxTZfp0vxuvm2ATDMuCCy5ITkzdSJ+sUMIrpuB/+XIzZLXiUFurGuMee9HCthRRV1PbJE1DRO9z3Oh+5GUGibnx3ehyPE3Atjh7SkmSI+u9inLDXHvscH+GcTuvD8YYXG04YVwREwfmdVGEQggh2iKJaaqEw3Dddf4KaLy09jOryy9PXlzdxGkTByS8WqGNQWuYMaxPkqLqnlou0DpCAY7X9d15hUi1gG3xv1fMIBy0ibaTnDqexlKK31w6TZoeJdlXTxrDedMGozXEXH1Y93BjDI6n8TTMGtmXn1wwWVawhRAiTUhimkpf+pKfoMZi7T/XGPA8uPJKKOj6GZLp5tSjirEtlVD5qeMZTpvYn/xMuTA8kFKKnHCARHPTloQ2L1NWoEXvNG5ALo9cN5P8zBCeNgclQsb4//a0IRywueeKo5k9sm+KI+75LEtxx9kT+P65ExlcmLlv+0ZjzH8zQGFWiNs+N4a7L5sue0uFECKNyBVlKg0eDE88AZdeCtGo36W3tTu3WvtJ6axZ8NOfdn2caSgcsLl2znDue2cDlmm/O6+rNUFbcf2xI7oowu7l9IkDeHHptoRmAEZdzYxhhWSF5DQieq8JJXn847bjeX11BQ8u2MC6inoCtoWnDQMLMrhu7gjOnFJCjjQ86jJKKT4/dSBnTylhRXktH2yqYm+TQ05GgEkD85k9si9WgvNOhRBCJJ+8UqbSli3w9tv+qumePf6qqFKQmem/tZT5KgWXXAJ33QVBWe1r8aUTRrFmVx1vr63EtvzGIq1xPI1Siv85dxJj+ud2cZTdwxWzhvLnD7djjImrrM0YQyhgca0k+kKQEbQ5a0oJZ00pIeJ41EddskI2mUFbykRTSCnF1CEFTB0iVUZCCNEdSClvKjgO3HQTzJgBd98NkQj06+d36rVtf5RMVRXk5sI3vwkrV/rPC4VSHXlasS3FnRdP47KZQ9HGH8Xg7x0yeNoQdTy0MeSEA/zm0mmcMVmajhzJmP65zBzRB1ebw/ZktcbxDIMKMzl2lJQmCnGgjKBNv5wwWaGAJKVCCCFEAmTFtKs5Dlx0Ebz7LgQCB5fuZmT4b+B3662vhz59oH//1MTaDdiW4pvzx3PjcSN5efk2Xli2jerGGEHLYljfLK6aM4zjxhQRjHM+Z29250VTueah91lfWX/EFWhjDI429MsNc//VpXHPPRVCCCGEEKItKp7Vka5SWlpqysrKUh1Gcn3ve3DPPYcnpa3xmjs9vvqqv7oqRJI1xTz+5y8f89qqnRhj9lWXGwMoP1mdNaIPPzp/Mn2yZQVfCCGEEELETym1xBhT2upjkph2ocZGGD3aTzjtODsBNjXB2WfD448nNzYhDlDVEOPFZeW8+Unlvv1ys0b04aLSIQwsyEx1eEIIIYQQohtqKzGVUt6u9PLLiSWl4DdGeu012LVLSnpFl+mTHeKGeSO5Yd7IVIcihBBCCCF6Adkg1pVeeSW+maUHsiy/7Hfx4uTEJIQQQgghhBApJiumXammxk80E+V5UFfX+fEIIYQQacD1NO+u38OqbbXURVwKs4PMHNGXqYPzpbuxEEL0EpKYdqWcnOYuMgmyLMjK6vx4hBBCiBRyPM3jizbzyMJNNMZcIo6Hwk9EQ4ENDMjP4CsnjOLsKSWSoAohRA8niWlXOu44ePPNxI4xxl8xnTIlOTEJkUJrdtbx/qYq6iMOWaEARw3Mo3RYoVyACtELRByPm59cyrIt1SggYFtkhfZflhhj2FbdxH+/vIoPt9bwnbMmyLlBCCF6MElMu9Lll8P//A9oHX9JbzQKxxzjd/MVood4ffUu/vft9WyobEBrg6s1lqUI2VZz46URXDhjCLYlF6FC9ETGGL7+3Ics21JNwFKtJpxKKUIBhTaGF5Zto092iJtPktdCIYToqSQx7Up9+8I558Dzz/uroK7rr4jaNmRmQjB48PO19hsf3XprauIVopMZY7jrn2t58r0teNoQshWBgEWouQ+bMYbKuig//dsn/PvT3fzq4mmEAl3Xo83ThnfX7eaxRZtYu6sex9PkhAOcMWkAFx8zhMGFUlIvRGdYvrWGRev3HDEpPZDVPEz5gQUbuXTmUJmhLIQQPZQkpl1p3TqorPQbGXkeHPhi3NTkJ6i5uf6IGK3951xxBcyfn7qYhehEDy7YyJOLN2NZfkJ6KKUUQVthjGHhut1858WP+PmFUxIu3zPGsHxrDU+/v2VfgtknK8S50wZyxuQSssOHn/qWbK7m6899SF3EIeZqgraFAvY4UR5bvJknFm/mhHHF/Pj8yWSGEhj5JIQ4zKOLNuF4moxgfL9LlqXwPM2Ly8pljJUQQvRQynSkGU+SlJaWmrKyslSHkRxLlsB550F9PYRCUF3tJ57G+Anqgd+HzEzIzobrroMf/zixuadCpKmaxhin3Pk2QFwlusYYtIFHr5/JpEH5cX+cj8pr+faLK9hRE/FLhJXfSsUz7FuduW7ucG46cRRWcxyL1u/ha08vxfXMEVdojTG42jC2fy6PXj8z7gtqIcTBGmMux/70DWxL+auhcXI8TVFumH/cdkISoxNCCJFMSqklxpjS1h6TFdOuUF4OF1wAjY1+0gnQr5+/f7ShwZ9t2vLibIy/WvqTn8ANN6QuZiE62UvLtmEMBOz4LkSVUriux+OLN/OzL8TX/Ov9jVXc/OQSHE8Tsi3Cgf3JY8vJztOGB9/dwOaqBn56wRR210f5z2eW+aXFbZQNK6UIWLB2Vx3ff2UVP7lAGpL1ZhV7I7y4bBtrdtYR8zTFuRmcMXmANO+KQ1VDLOGkFPwbWnvqE5wFLoQQotuQxLQr/Pa3fvluRsbB/x8O+28tySj4TZFiMfjDH+D66w8u9xWiG3v6g60YDBD/z3QwYPHPj3fx/XO8dlcot9U08bWnl+Jpc1BCeijbUhgD//x4FyP7bcDxNLHmRLY9SilsC15buZPbPzeOotxw3J+L6Bm21TTx47+uZuH63YB/o0MBGvjzh36DnltPGcuZU0pSGqcQQgjR3XRdV5HeqrERnnji8MZGB1LKL9e1bf/voRBs2QJLl3ZdnEIkWWVdNOEuuy1luNWN7a+SPPXeZqKOvze0PUr5qzUPvbuBp97fQiJhtazyPL+0PP6DRI+wrqKOS+5dxIJ1u7EtRdC2yAjahIM2mUEbhf9z/p2XPuKeN9elOty01Sc7hKcNOsGtRJ429M2RxkdCCNFTSWKabK+/vr/zbryU8ldNn346eXEJ0cWMMQmsle6nFOh2rl8jjseflpQnlPjaliLmahqiLoF4xzcd4B+rdiV8jOi+qhtiXP9IGXURh3DAarUM1W/eZWErxQPvbOClZXLzojVZoQAnjCsi5uqEj73kmCFJiEgIIUQ6kMQ02Sor/bEwiVLK35sqRA/RskqSCG0MnjYUZrVRcQB8uLUGbeJrqnQgxzNEO3BxrBTURZyEjxPd17NlW5uT0vZvMlqWv9J/5z/X4nqJ/3z1BtfMGU7Qtoi3AaPWBqUU508fnOTIhBBCpIokpslmWR3fJyrdeEUPcuGMwSTaAzzmao4bU0RWqO3t8LVNDgm/c8BS/gVvooxBuvL2Io6neXLx5oRW/AO2RVPMY8G63UmLqzubNqSAOaP64mrTbnKqjUEDX5w3QmaYCiFEDyaJabINHdrxBHPMmM6NRYgUunDGEJRSce8rM8YQtC2umTus3eeGAlaH7v8opTCQ8F43bWDK4PhH2IjubUV5DRFXE4hj//KBYq7mxaXbkhRV96aU4pcXTWX60EJcbVpdWTbGEHM1WsMF0wdx04mjUhCpEEKIriKJabIdf7w/IsZJoOyvZU/qVVclLy4hulhRbpjLZw5Fx7FC0jIzdMawQo4eWtju+x7RLzuulZdDKQWjirNxEijnNcZgW4qr5rSfMIueoaqhoyvyip17I50fUA+REbS596oZfO3kMeRkBNHG0BhzaYp5NMU8tIFBhZl8/9yJfOesCTKGRwghejgZF5NsgQDcdBP89KfxHxOJwKxZMEruDoue5fbPjWVPQ5S/r9qF0ZqApQ672HSbu3VOLMnj15dOi+tidFjfbMYPyGXV9tq49gBCS4JpceupY/nuiyvR2mDFsUc15hnG9s9h/IC8uD6O6P4sRSJTjg6S6L7n3iZoW1w/bwRXzxnGwvV7WLm9lvqIS0FWkJkj+jJ1cL4kpEII0UtIYtoVvvxlf2TMhg3+Eo0x/t7TUOjwMTKxmD/v9Je/TE2sQiSRZSl+fP5kpg4u4P53NlDb5NAUc/0LTwPhoEU4YHHFrKHcePzIuJNMgOuOHcG3nl/hd/+N40I25homDc7jjEklrK9o4KEFG0DTZnIadT2yQwHuvGha3HGJ7m9gQea+1fhEkiRtDCP6ZScrrB4lYFscP7aI48cWpToUIYQQKSKJabI5Djz6KNTV+Suhnrf/sZb5pTk5foIai0FWFjz7LEyalLqYhUgipRSXzhzKxaVDWLxhD4s27KG60SE3HGDqkAJOHl9MKJD4LoNTxhdzwph+vLW2koDVdgIRdT0ywwF+dN5kAG45aRSWBQ+8s5GY4xGyrX0JqjEGx/PLdwuzQjxwTSlD+2Z17JMX3dL4AbkMyM+gvKqJUCC+xLRlj/SlxwxNcnRCCCFEz6AS3ZOVTKWlpaasrCzVYXSepia4+GJYtMhPQgMBaGiAxkZ/1VQ372tTyt+HevXV8PWvSwmvEB0UczXfen4F/15bieNpwgHroATV9TQGyA4HuP/qUiaUHFyOu66ijqfe28KfP9yOUv7ID1cbSvIzuO7Y4ZwxqYTssNzP641eWFrOD/+yGrudmx4toq7HsL7ZvHzLsVKKKoQQQjRTSi0xxpS2+pgkpkliDFx6Kbz+ur8aeuiFSTR68OppIAD/7//Bf/xH18YpRA+jtWHBut08/O5GPtxaQzBgYYzfuyYzaHP1nGF84ejBFLYxdqIp5rGjtomoq8nLDDIwP0OSi14u6npc89D7fLKjjqB9+N7oAzmexrIUD1xdyvQ4mncJIYQQvYUkpqmwcCGcf378c0xbktS1ayFfxlCItkUcj4Xrd1NZF0UpRf+8DOaO6kswwXEWPd32mibKq5uIuZqCrCATSvI63Iymsi7Kn5Zs5S8rdlDb5BCyLUYV53D1nGHMHdVPmtz0ArWNDl96vIy1u+oADmvepZvLvkO2xV2XTOW4MbJfUuxnjGHV9r3sau7UXJybwcSBeXE1XRNCiJ6ircRUatKS5Z57/D2jmZnxPd+2wXXhmWf8ZklCtKKiLsKjCzfxpyXleNrgaYMCbMsiGFBcdsxQrpw9rM3VwN5kYEEmAwvi/B08gpir+eFfP+aVD7fv+z9LKZrweG/DHpZtqSY3I8gvLpxC6fA+nzVkkcbys4I8ev1Mnv1gC48t2kxtk4PX3EU6YCmMgTMnD+CGeSMZXZyT6nBFmmiMufx5+XYefncTexqiWM03M7QxFGSFuHbucM6bPogc2SYghOjlZMU0GerqYORIP9m0EljBisVg6FBYujR5sYlua+2uOm545AP2RhwClnXYCp2r/UH0fXNCPHztTGnQ0wkcT/OVx5ewdEs1tqX2XVAeKuZqbEvx60unySpZL6G1oWxzNZv3NOBqQ35mkGNH9SM/K9j+waLXqKiLcMMjZZRXNwHmoFX2lnnNCkX//DAPXXsMJfmf7UaaEEKkOynl7WobNsDcuYklpeA3Q7Is2L69/eeKXmVbTROX/GERdVGn3REqUVfTNzvEc1+ZQ9+ccBdF2DP9+NXVPFe2tdV5q4dyPE3Atnjx5rkMLpSbAkL0dnURh0vuXcy2miZC7exLjrmaotwwz31lDgVZUvEihOi52kpMZUNaMhgT375SIeL063+uZW+k/aQUIByw2NMQ5b5/b+iCyHquvRGH55eUY6v2k1KAoG3huJqn39/SBdEJIdLdff/ewLaaxsO6g7cmFLCoqIty9xvruig6IYRIP7KhIRmKivz5pYFAYqumngcDBiQvLtEtVTXEeP2TioRmewYsixeXbePWU8eSGWo7md1a1cizH2zl359W0hB1yQzazBrRl8tmDU35Prmaxhhlm6r3rRSP7Z/D6OLcLvnYr3y4HQMJNTWyLcWflpTztZPHkBFs/yaCEKJnijgefyzbSiCBa4CApXh5+XZuP20sWSG5PBNC9D5y5kuGvDw46ST417/ib34E/irrDTckLy7RLf1t5Q6AI+5vbI1tKbQxvLmmgjMnl7T6nNpGh//7wgoWb9izb5FfKagy8KelW3lx+TYmDszjzoumUpyX0SmfS7zW7KzjoXc38M+PK7AthacNlgJtYFRRNjfMG8lpR/VPajfL1z7aidYGEkxMjYGV22qlEZIQvdhbayrwtEn4xpanDf/8eBfnThuUxOiEECI9SSlvsnztaxAK+WW98WgZF3PFFcmLSXRLm3Y34Lg64eOirmZbdWOrj1U3xLj0/kUs3rCHgKUIBSyCtkXA8v8MB2xsBSvKa7no3kVsq2n6rJ9G3P66YjuX37+Y1z7aiaVA4a8kWEphK1i7q57/evEjvv6nD3G8xL8u8aqNOB2uyK+Lup0bjBCiW9m0u5GI47X/xENEXc36ivokRCSEEOlPEtNkmTcPZs3yS3rbS0619p9z223QR1ZZxME8bfzsLEF+x8fW//9rTy9jR22EoH3kvU9KKcIBy5/d+FgZbhKTwBb/XlvJHS+vwmAIB+3DVolbYrIVvPlJBd99aSXJauCWEbAO+9WNuprGmEdjzCPieOgjfOxwAmXXQoiex09KEz9xK6CpAwmtEEL0BHL1lCyWBXfc4ZfyVlTAzp2waxfs3g1NTX4iaow/Isbz/JXS//t/Ux21SEPFeRkJlfG2CNk2fXMO7+64ctteVu/YSzDOErNQwGLX3gjvrNudcAyJcDzNf734EcaYdvdlKaUIWIp/fbyLJZurkxLP1CEFGAzGQEPUZXd9lLqIQ0PUpSHqUhd12dMQo7bJwdV+gmqMwfEMo4tkhqUQvVnfnBCBDmw1UAqKcqWbuhCid5LENBn27oXzzoMzz4T6ej85VcpPRB0Hamv9JDUSgUGD4De/gbvukk6+olVnTBqArVRCK4Pa+KusJ48vPuyxxxZvwvF0XJ1mWziu5pF3N8X9/I7499pKIo5H0I7vtKSUwvE0jy5KTlyXzhyKUoqqhiiNMY+Wr37LXlyF/xZzNTWNMaKuJupqZo/s0+V7coUQ6eXEccVYFgmdt40xBGyLk8f3T2JkQgiRviQx7Wx798Kpp8KCBX5X3sxMvxlS//5QUAA5OZCVtb8p0k9+ApdfLkmpOKJhfbOZOCiPaAL7TGOu5vix/ejXyhzTt9ZUEooz+WsRClgs31rToT1T8Xpi8eaE99KGAhbvrttDdUOs0+MZkJeB62k8sz8RbY1SYIDaJgeAa+cO7/RYhBDdy5A+WUwdXJDYedvTjOufm/Ju6EIIkSqSmHa2W26BDRsgGDw82czIgNxcP1HNz/cT1xtugC0y91C07fbPjSVoW7i6/Yscx9OEAza3nDj6sMe0NjTFvITvg7SUzu6NOIkdmIDNexoJJJgwW81x7dwb6fR4Xli6DUspLBVHD7Pmx0O2xTHDCzs9FiFE9/PVk8cQtC2/T0A7PG2wleI/ThnTBZEJIUR6ksS0M5WXw2uvtZ6UtiYY9PeYPvBA8mMT3dr0oYX88LxJKBRR12u1PMwYQ8TxsC3Fry6Zxpj+h8/7VCqxuZwH0sYQDiRvNqcbx8Vba5QC1+vcBkhaGx5ZuBHbUhRkBbGU2rct/MAv/b5/K8gMWmhjWLW9rlNjEUJ0TzOGFfLdsyZg8KtYjnTedjyNAb4xfzxzRvXt8jiFECJdSGLamR5+mH0DIeNl2/5x0Wjy4hI9whmTS/jDVTMYU5yLZ/yuj9GWN9fDMzB5UD6PXDeTeWP6tfo+lFIM75uFk2Ai53qanHCA3HDyRh8XZgeP2OX2SFqaDRVkBTs1lg/La9jb5BKwFAHLojA7SE7YPjypV34H3oLMILkZQVxt+OOSrZ0aixCi+zr/6MH8+pJp9MsNo/HP2zFX43iaJsffu16YFeLnX5jMZTOHpjpcIYRIqeRdZfZGr76a+DGBgN+Vd+VKmDGj82MSPcoxw/vwp5vm8snOvby8fDvbqhtRKIb1zeK86YMYGUc32GvnDudHr65O6OMa4MrZw7A6uNoaj/OnDeK3r69L6BjHMwzrm8XgwsxOjWVXc2lwS4MoSykyQwEyggZt/NVjBVjN81VbWEqxZU/rs2OFEL3T8WOL+Metx/PBpir+WLaV8uomDDCwIJOLZwxm9si+ST23CiFEdyGJaWfau9cfE5MopfzuvULEafyAPMbPz+vQsfMnlfDTv31CzNNxdcD1tMFSiguOHtyhjxev86YP4rdvrEMbE9d4HAO4WjO8Xzb3vr2B4rwwp4zvT34nrJ5qAwaDOqTlkVIKW4HdxnzCRFd9hRA9n2UpZo3sy6yRUqorhBBHIqW8nSkrK44uKW0cK0QXyAzZ/OzCKSilcLFw1O0AACAASURBVL22myl52mCAb585Pumz9QqyQlw+c6ifFLbxe6SNoS7isrsuSlPM4/WPd3H3m5/yo7+u5uQ73+Jbz69g856Gwz6PvRGHiNP6/txDFeWGD0tK46GNYVBB567eCiGEEEL0BrJi2pnmzoX16/2mRvHymsdvjBuXnJj+P3v3HSdXXS/+//U550zZvmmbbHojISFAQjaEhGgA6SJdpVcFwYJy9f702i5e/epV71WviqDSq4hSBClSAoEESCWVFNI2dTfZ3mbOOZ/P74+zm0I2uzPLlC3v5+Mxj8DMnN33lpk97/N5f95vIdpxysQSfnbxsXz3qVXEfY2t1CH7J31t8LTGsSy+ddZELp0+IiNxfeOMCeysbWbeugqUObxRk6cN1Y1xDGCpIJk9+DnaGF5YuYs31lVy55Un4BvDAwu2MH/DXpQKmhoV54a5etYoLp42jAHtjNMBmDaimGjYorHFS7hTsDGGsGOlfWVZCCGEEKI3UskMf063srIys3jx4myH0XVr18LcuUFDo0QbILW0wGWXwZ13pjc2IdpRXtXEXxaV89cl5Ycs9msDF0wdyuUnjsz4TD2tDX9+axP3vb2FuK9xPY2lFL4xNLR4QDC/tCDiHHFfVszzaYr75EUctA4Sxrby4P2rxErx72dN5LIjNBy5+40PueuNDxMqd4ZgTM/A/Agvff2Tsl9MCCGEEKIdSqklxpiydh+TxDTFTj8dliwJmhq57oEuveFwcN/BfD94/LXXYMqU7MQrBEGnyI0VDTTFfXJCNmMH5ZGXxg68iYh7mtc+2MOLq3ZT0+yyubKR3bXN5EdDHY68MQaqm+J42hCxFUW54Xaf52uDNoZvnDGBa2aNPuzxmqY4F9+5gH2NcSJOx8lp28f6n88ez2mTBif1dQohhBBC9BUdJaZSyptKWsNFF8H8+UFS+lGhEOTnQyQSJKVaw/e/L0mpyLpoyGbKsKJsh3GIsGNx9pRSzp5SSkPM45RfvE5BNNTpamR9i4vf2jU37ptgcH07x9iWAg2/eWUD00f145ihh379xblh7r1uBtfe9x7VTXHCtnVYUyZjDHHfYCvFv599tCSlQgghhBBdJM2PUsV14frr4Y47gkZGlnWgnLftX9eF6mqoqQmWdX70I/ja17IXsxA9xOsfVGAMnSal2hhivkZx4GUXc/0jPt+2ggZQDy7c0u7jowfm8debZ3HulFKMAc83NLs+za5PixvMjj1qcD6/vWKazCAUQgghhPgYZMU0Vb72tWCOqeMEt5ycYP9oY2OQkLadJbeV9t5+O9xyS3ZjFqKH2F3bTMzT5IbtDp/X3E4S6neyXSHkWPxrTQXfOSdOcTtlvyWFUX52yXF85xyX51fu5MOKRlo8n8GFUc46ZggThxQk98UIIYQQQojDSGKaCsuXw9/+FiSkB5f6RaPBzZigbFepYCXV9+FXv4Jbb4XCrs2iFKIvMZDQ8Ja4pxN/citLKSwFa3fVM2vckWcMFuWGuGLmqMQ/sBBCCCGESJiU8qbCH/4AnnfkTrxKBZ16rdZvt20Hyerjj2cuRiF6sJKCKOFOGhBB6xjhj7wMP7ov9Eia4l4XIhNCCCGEEKkgienH1dwMTz8ddN1NhtZw113piUmIXubUiSVAsIe0I0oRrJhyIEmNhjou/22T7S7EQgghhBB9mSSmH9fevQdKdJPhOLBzZ3piEqKXKcoNceYxg3G9jhPTiGMfsmIaslSHo2UgSHZ9bZhUKmX1QgghhBDZIonpx+UfueNnp7ROXRxC9HK3zB1PTtgO9pEeQTQUvKW1rZYmsgrq+oYzjxlMUU4oVaEKIYQQQogkSWL6cQ0YEOwv7aTE8DC+D/37pycmIXqhkQNyuevq6URCFi2u325ZrwKc1hXSwohDyO74Lc7XBsdSXDt7dBoiFkIIIYQQiZLE9OMqKIC5c4PRMMkwBq65Jj0xCdFLTR1RzOM3zWLuxEH42uD6muZ4MFc07mk8bZgzfiDjSvKxLIXp4IKRrw0GwzfPmsjRQ6SMVwghhBAim6TbRyp89aswfz7EYtDUFMwtbZtXGolAbi6EDioTbBsdc/312YtZiB5qzMA8fnv5CVTUt/DCyt2UVzWhjWFocQ7nHFvKsOIcaptcvvLYUtbsrMPzfUK2haWCRNXTBqUUCvj+pydz0QnDs/0lCSGEEEL0eZKYpsKgQcGKaUPDofcbE3TtbWkJmh0VFx+YY3rttVBamp14hegFSgqiRyzBLcoN8eANJ7Jiey0PvbOVV9fuIa41BigpiHDNrFFcOHU4Rbmyr1QIIYQQojuQxPTjWrUKzj47mE1q20HS+dG5icYEq6j79kFREXz60/Czn2UnXiH6CKUUx48o5vgRxRhjiHkax1I4new7FUIIIYQQmSeJ6cfR3AwXXhj8m5MD0WiwatrUFDx+8P42pYL/HzAA7rsvSGKFEBmhlEp4nqkQQgghhMg8SUw/jqeegvr6YB8pBMlnQQHk5wflu/F4kIxaVvCccBhqa2HFCpg2LbuxCyGEEEIIIUQ3ITVtXWUM/OpXwaiYj1IqWEEtKgr2lRYWBompUkFJ7x/+kPl4hRBCCCGEEKKbksS0q6qqYNOmYBU0GaEQvPhiemISQgghhBBCiB5IEtOuqqsLksyPNjrqjGVBY2N6YhJCCCGEEEKIHijtialSylZKLVNKPZfuz5VROTnBPNKPMubQpkftPd62J1UIIYQQQgghREaaH90GrAUKM/C5MmfgQMjLOzC7tKkpaHjUlpRaVvB4Tk7w323icZgxI/PxCiGE6JIW1+edTfuoaoxjW4rh/XKZNqIYy0qyYkYIIYQQR5TWxFQpNRz4NPAT4PZ0fq6Mcxy4/nr48Y+D2aUfXSXVOujY29AQdOrNzQ3uD4fhttsyH68QQoik7Kpt5oEFW/j70h0YwNeGtly0ODfMdbNHc+n04TKKSAghhEiBdJfy/hr4d6CdmteAUuompdRipdTiysrKNIeTQs3NQRMjzwuSUqXavxkT7EdtbIRYLBglc+aZ2Y5eCCFEB94vr+HiOxfw2Hvl+NqgAMdSWEqhgH0NMf7n5XVc+ed3qWqMZztcIYQQosdLW2KqlDoPqDDGLOnoecaYPxpjyowxZYMGDUpXOKn3ne/A+vXBKJiOGiC1PVZfH/z7+OPBaqsQQohuaWNFAzc9tJjmuE/EsbA/UrKrlCJkWziW4sOKBr74YPBcIYQQQnRdOldMTwbOV0ptAR4HTlNKPZzGz5c5tbXw2GNBgpmbGySncOSmR23JaVlZcBNCCNFt/fi5NTTHfcJOx38igwRVsamygSeXlGcoOiGEEKJ3StvSnTHmO8B3AJRSpwDfNMZcla7Pl1GPPx4koXbrvqKcnGDvaHPz4aNg2pogRSKwdClUVEBJSeZjFj2aMYZVO+pYu6uOFs8nL+xw4pj+jOifm+3QhOhVtu1rYsWOWiKdJKVtlFKA4f4FW7hy5ihpiCSEEEJ0kdSUdkXb3tKDS3JtO9g/mp9/oBmSUgeSVwiS1MWL4dxzMx+z6JG0NjyzfAf3vL2ZXTUtaGPwjcFp7fQ8dUQRV580igH5EZrjPvlRhwmDCwjZMqJYiK54cmk5WhucBBNTCPae1ja7LCuvZvqo/mmMrnepqGvh6eU7WLe7nrivKSmIcvaUIZSN6tea8AshhOhLMpKYGmPmAfMy8bkyoq7u0BEwH2UfoUOjMQf2mgrRibin+daT7/Pm+kqMgZCtsC2LUOvjruczf8NeXv+gkryIQ17ERrc+7/IZI/ncjBEMLoxm9WsQoqfZWNGAoYNZ1O1QSqG1YXt1M9NHpSmwXmRnTTM/eX4tCz/ch8Hsby6lgWeW72BAXoSvn34U5xxbmu1QhRBCZJCsmHZFfn4wDgbAdYMZpvH4gVXSSCTYe/rRJkdKBccK0QljDN99aiVvrq/EsdQhqwfGGJriPk0HNVtpjHtYCqIhmxbX5563N/PQO1v51eencvL4gdn4Etrl+po311fy+KJydtU0A1BanMPny0Ywd+IgWek9gpjn88qaCuZvqKSuxaUgEuLEMf0559gh5IblbTyVXD+5pLSNweB18di+ZGNFA9fd9x71LR4hW2Epi4On7RhjqKhv4btPrWTL3kZuOXV89oIVQgiRUXJG0xWnnQbz58O+fQfGxRysqSm4RSJQVBSsrmodlPhOnZqdmEWPsmJ7La99UHFYUgrQHPdpcoOktO0hAzTEPCIhe3+Zr+trbnt8GXddNZ2y0dkvL/znil38vxfW0uL6xD29v9Pptqomlm2rJhqy+fbZR3Pe8UOzHGn34fqau+Z9yMPvbsPXmrin90+hemXtHn72wgdcPH0Y3zh9gszSTJEhhVF0F/JLW1n0zwunPqBepLoxzo0PLKK+xSXitP/72tZQSmvDH+dvorQ4yoXThmc4UiGEENkgyxNdMWdO0JnXdY88wxSCuaX79gVJaSwGc+fCsGHZjV30CA8u3IKn9WFJqacNja4P5tApRUH7FYi5B1ZRQ7aF5xtuf+J9XP+Io4Qz4qGFW/ne0ytpjHlYShEN2YRsi5BtEQ3ZWErRFPP4wTOreWDBlqzG2l3EPJ8vPbSEe9/ejOfr/d+3iGMTDdnYlkIbwxOLyrn23vdoiHnZDrlX+MzxQwk7FuZIXdbb4bdmsjPHZv8CUHf2xOJy6pqPnJQezLKCebH/+6/1eFl+/xJCCJEZkpgmy3Xhuusg1LrT70gNGtoSVN+H6urg+V//esbCFD1XQ8zj9XWV7Za1Nse9w5LS/QyHlPcChB2LprjHWxv2pinazi3YuJdf/Wvd/tmPR+LYFkrBb17dkNV4u4vvPb2KpduqcSx12BzNNralcCzF+j31fP3xZUklU6J9ZaP60T83jJfEsqmnDRdOGypl1R3wfM3D72wlmZZGjm3RFPd5a6O8HwghRF8giWmy/vlPqKyEfv2CPaSJnAi6Llx6abDSKkQn9jXEsBRYH8k+jTHEvCOvHCgFup3fR9fT3J/FVcjfvrYR35gjJlcHsy2FrzW/fW1DBiLLjIaYx9PLdvDLl9bxk+fX8Mc3P2RjRUOHx2ze28gra9ov5f4opYLkdNm2Gt7fXpvK0Psky1L825kTgKArdmfiniYnbHPt7NFpjqxne397DS2uxklyH3nc0zy1dEeaohJCCNGdyOXdZP3610Gjo2gU+vcPVkPb9pl+9ATy4GZInpTZicT42rSbjGgTlOsmO0XBsS02VmSnG/TGigbW7aknnMTJaNi22FDRwMaKesaXFKQxuvSqbozzu9c28sz7OzAGYp5GEfz87npjExMHF3Db6Udx0tgBhx376Ltb0UajVGL7RpVSeL7Pgwu2MPXzso/94zp7Sinbq5v5/esb8X3T7gUCYwwxXxN1bO688gSG95OZwh2panRJarm0laUUu+taUh+QEEKIbkdWTJNRVwcrVgSJJgRNjQYMCFZPI5EDq6dt/+bnw8CBUFAQrLQKkYD+eWE8Xx9WlmmM6fC8znD4KisEiVDcy06J5/wNlXj+4XtlOxIkWZp56yrTGFl67ahp5nN3L+TJpeUYE6wE54ZtcsKt+0MVrNlVy62PLOUvi7Yddvw/3t+1v4lVokK2xWvrKrK+n7i3+MInxvLTi49lQF4YDTS7PjFPE/OCf30Nxw8v5pEvzOSEkf2yHW63Z6ku5aUACVVbCCGE6PlkxTQZtbVB+e7BJ9ltq6U5OZCXF8wwtaxDn2MMNDZmPl7RIxXnhpk6opglW6sP6bSqlAqmKx5pjym025lVG0NRTqidZ6ffvoZYlzqcGgN7G+KpDygD6ltcbrx/EZX1sQ47j0YcG18b/vuFDxiUH+G0SYOBYMW8Me4RdZJLTC2lUBjqWzzpDpsiZ08p5czJQ1i0pYpn3t9JRV0LjqUYX1LAJdOHM2ZgXrZD7DGGFufgaxMkqElcqNLGyPdZCCH6CElMkxGNBmfMxgRddhsbg/2jh7RHVcEM05ycIEmF4Pmh7CQGome6/uQxrNheG6yStv5+WQpspfA72NccDR2ezPgazpw8OG2xdiRIlJPPTA2G3HDPHH/y96Xb2VPXQjiBxNK2FK5v+Mk/13LKxJL9nUi7yiCrS6lmWYqZYwcws52Sa5G4o4cUUFocZXtVM2Ensd9RYwwh2+KyGSPTHJ0QQojuQEp5k9G/f5B0VldDTU2QlH6U1tDQAHv3HnjcdWHChMzGKnq0k8cP5KjB+bja7C/pVSooB20vczFATuvYlUPub206dMXM7JzYjS8p6NJ8zWjIZnxJfhoiSi+tDQ8u3JrUMY6lqG12eXdzFRAkQoPyI/tHkCTK14awbZEfkeuNovtRSnHDyWOwlEq4e3Tc1wzrl8OUYYVpjk4IIUR3IIlpMpSCoqKg+VHb/3+0JKntPmOgqioo9Q2F4LbbMh+v6LFsS3HXVdMZ1T8XV5v9SUrEsbBQB7Yzt94izuEJiTEGTxtmjxvAqAHZKYU79ehBOJaVVJLl6yCZPu3okjRGlh4rd9RS1+zhJLFqqZQi7mn+uqR8/31duZDga8Ml04fLiqnotj59XCkThxTg+qbT5NT1gw6+d5x/TFKlv5nUEPNYu6uOZduq2VhRL/NWhRDiY5JL68l47jnY0dq2vrM/lG3JaXU1lJbC+eenPz7RqxTnhnn0iyfxq3+t55nlO/G1Ie5roqFgtp8xweJpXsQh5yNlr21J6dhB+fz3Jcdl5wsAIo7N58qG8+DCrQntLWuL+/ITR3ZppTXbKupjoCDZglxLKXbWNO///4umDePOeR/uT9I7o43BshSXS8mj6MYijs1dV03npocWs35PPXB4x2NtDK6nCTs2//v545nWDRtLbdhTz8PvbOX5lbv2V6loY8gLO1w9axQXnzBc9nkLIUQXSGKajN/8JijVLSyE+gTHb/g+3HHHgU6+QiQhL+LwvfMm840zJvDCql0s21ZDYyxYkVuxo5a9DXF8bXB9vf9aSJtPTRrMjy44htxwdl/mN80dx+vrKimvaiJkHzk5NSZIvEf2z+PmuWMzHGXqdHVt5+DjBuRH+Mpp4/ndqxuDpLODhF6bYEX9ypmjGDlARpaI7q0oN8QDN5zIXxZt48GFW6ltdvG1QZsgSTUGzj2ulBvnjO125fzGGO55azN/mPchnm8I2QdaDVtKUd/icufrG/nz/M38/sppTB/VP7sBi4/FGMOK7bWs2llLc9wnL+IwY3S/Hj3GTIjuThLTRG3aBCtXBglm20lifX3780vhwP25ucFxQnwMeRGHS6eP4NLpIw65/4PddTzyzjaWl1fT7GoKIg6nTx7MZ6cPp6QwmqVoD5Ufcbj/+hnc9OASNu9rRPuakH1glcQYg+sHK35jB+Xzp2vKKIj2zGZhQwqj6NaxPsl2Hh3R/9Ck8vrZo2ls8bhvwRbink/EsQ75mG2JvG1ZXDRtOLefIfvYxaFcX7OrpoUm1yMv7DC0OKdblHpHQzbXzh7D1SeNZvHWarbua8T1DcW5IU4eN5Ci3O75+r/37c3cOW8jFopIO43mnNZ5zS2uz5ceWsq9183g2OFFmQ5TfExaG559fwd/mr+Z3bUt+NrgmwOr+0eV5POlueM4tQduNxGiu1OJNiHIhLKyMrN48eJsh9G+l16C668/9D7Pg6YmaG4+/Pm5ucHN9+H44+HVVzMTpxDdVMzzeWn1Hu59axPlVc3B6gjB3sjh/XK4Yc4YzjpmSI8s4W2jteGsX7/J3oYYITuxLfzGGAzwh6umM2P04SssCzbu5U/zN/H+9hqUUkF5b+vooAmD8/nCnLF8alJJt92HJzJvd20Lf11czmOLtuF6BqWC11lB1OGaWaO5cNowKTVN0qbKBi69ayGKxDpfxzzNoPwwL39jLlY3uBggEuP6mm//bSWvr6tAa3PIRVQ49ILglTNHcvsZE+S9V4gkKaWWGGPK2ntMVkwTFYsdvjrqOEFZb0HBgXmmlhXc38b3oaUl8/EK0c1EHJvzjx/K+ccP5cPKBirqYgCUFEYYOzCvV/xxtyzF9SeP4RcvfXDIqJ+OuL6hpDBC2aj299LNHj+Q2eMHsm1fE8vKq2mIBatfU4YVSkmZOMw/V+zi+8+uap0ZqvY34rJbuz//7vWN3P3mh/zmsmmcJCNwEvbou9vwtT7ibOKPCtuKmmaXdzbtY/b4gWmOTqTKj/6xhtc+2INjKZx2Rn61zaDWxvDIO1spzAnxxU/03K0nQnQ3kpgmqn8He0WUOvKcUq1h0KD0xCREDzVuUD7jBnWv/WOpcuG0oTz23ja2VTUR6WReo6eDEuYfnNd559GRA3JlD2kS9tS1UNPkYluKIUXRPjFG58VVu/je0ytBQbidFfu2Vfy4p/nKo0u566rplLWzSi8O1eL6PLN8J46V+CADpRQx1+f+BVskMe0h1u+p5/mVuw5ryNUeSwX7i/8w70MukWZXQqRM7/9LnSozZgQrobHYoSuinQmF4PLL0xeXEKJbyQ073HNtGdfe9x47a5pxLOuw0j9jDDFPE7ItfvCZycw5Sk5cU6HF9Xll7R7ufWszW/Y14lgWhqDE+ozJg7nqpFFMGdY79/xVNcb53tOrADpNoEK2RdzT3Pb4cl7/5imE21kZSrV9DTH2NcaxFAzKj3bbfaTt2VnTDArsJKs6QrbFuj0JNkoUWffwO1vxfYPTzv7h9liWwvc1Ty/bwQ1zxqQ5OiH6BklMExWJBHtMf//7xBPTthmmF1yQ3tiEEN1KSWGUv9w8iz++sYm/LinH14YW1wdU69V4mDG6P185bXy3HIfRE22vbuILDyymsj6G37o3DGhtRAUvrNzFy2v2cPEJw/jOOZO6RROgVHp62Y7Wrzuxk+qwYxHzfF77oIKzpwxJS0yer3lr417uX7CF98tr9sfm+pqTxg7g2tmjOXF0/26/BzPm6S5121YEq9Oi+3N9zfMrd+1/30jGo+9tlcRUiBSRxDQZN98M994b7BkNd1K2YUxwu+02iHaP7qhCiMwpjIb45lkT+cpp45m3roKt+5pocX3650c4ZcKgw7rwiq6rrI9x1Z/fpabJJewcvkJtKUUkFOwL+9uS7Xie4YfnT+4V+5ohWBF+cOGWpJMn19fc+/bmtCSmtc0utz68hA921+P6mshBq7KOpXj7w728t7mKGWP686vPTT1sFnN3UhB1WvfsJt9tuy+UkPcGNU0uxiTW2OpgjqWorI8n3FNACNGxhN8xlVL9gKFAM7DFGNP3LgMOGwZ//StcfHHQiTcabX9UjO8He0svvBC++c3MxymE6DaiIZuzp5RmO4xe7ftPr6K6Kd5pYxpLKbDgHyt28qnJJXziqN6x/7+uxaW22U14tbRNyLbYkIZS0+a4zw33L+LDygZCljqs07ZSiqhjY4zh3U37+PKjS7j76rKk48+UYcU5DMyPUFHfktSKmjZwxuTBaYxMpIppHfPV1WOFEKnRYWKqlCoCvgxcDoSBSiAKDFZKvQPcaYx5Pe1RdiezZgWjY266KZht6rpg20GC6vtB6a5tw+23w7e+FXTpFUL0GnvqglEcz63YRV2LS8iyGFeSxzWzRjNn/MD9swxFZuysaebdzfvabfbTHkspWjyfe9/a3GsS0xZXB0l3khRBV+hUr/bcOW/j/qS0o4+rlMKxYNm2Gh57dxvXzB6dshhSSSnF9SeP5hcvrUv4GGMMlgWXnzgyjZGJVCnMCeEbgzIk9VryjaE4NyyrpUKkSGcrpk8CDwKfMMbUHPyAUmo6cLVSaqwx5p50BdgtHXccLFwIy5fD3XfD6tUQj8PAgXDllcFKaa6U6QnRm8Q8nzueXcMLq3ZhCE7qLaWIoVm8pZoV22vJizj88rPHtzuPVKTHk4u3Bz+PJE4MI47F8vIayquaekVJdX7UCTo8J1lqagzkhOyUnlS3uD5PLC7HVp13NoW2eA33L9zCVSeN6rb7TT99XCm/f30jdS1upyvzxhg8bTh5/MBe8fvVF0RDNqdMGMRrH1QkNUtbG7jkhGFpjEyIvqXDxNQYc0YHjy0BlqQ8op5CKZg2De66K9uRiF7I9TXzN1SyZmcdDTGf/rkhZo0byJRhhXJlNgvinuamB5ewYnsNtqUOu6JuW8GJTG2Ty5ceWsKvL5vaa1bjurul5dWQZCWdUgrHVmysaOgViUN+xGHC4HzW72nodETRweK+5rRJJSmN5ZW1e/C1SWqvnmMp6ppd3t1cxaxx3XO2akE0xB+vKeP6+96j2fUJ21a778VtSenogXn87JLjshCp6KprZo9m/oa9CVcQaGOwUHxuxogMRCdE35DUrnyl1CDgNiAH+IMxZmNaohKij4q1lhg+8u42WlyfFtdHte58ufvNTQzrl8Otp4xPWxdN0b7/fvEDVmyv6XS+XdixcH3Nvz3xPn+7ZXavSHq6u+a436XjjGnrlNw73HDyGL73zKqEn29M0MH3mlmjUxrHyu21xFydVDOjtpmf63bXddvEFGBSaSEPf+EkvvGX5eysacbz/f0rw9oYDGChmDthED+56FjypPFRjzJtRDHTR/Vj0ZYqHKvj6gNjDL42XHLCcEqLcjIYpRC9W7Kbof4HeBN4EXgs9eEI0Xc1xDxuuG8Rf3xzE00xD0spcsMOOWGbnLCNpWDbvib+46mV/PzFD6ThQobUNrk8vWwHdgJD1+HAjMjH3tuWgehEUU4Ik+ySKcFJZ3609yQOp00qoSgnlPB4Etc3jBmUx7QRxSmNoynu05UuMtoYmnvAhYLxJfk8+5WTuee6GZw+aTADCyLkRxyGFudw3azRPPe1Ofz6smmSlPZASil+fdlUJpcW4ukg8WyP52s8bThlYgnfPufoDEcpRO/WWfOjF4GfGGPmt94VBrYQFE5F0huaEH2H1oavP76MNbvqjrgqp5Qi7ARX5h9/bxsD88PcMGdsFqLtW55ZvgODwVaJX8ezLcXflmzna586Kqn9SiJ5Zx4zFZw5ogAAIABJREFUmCVbq5M6RusglZ2a4qQsmyKOzZ+uKePqP79LU9wnZLf/PmKMwdWG/vlh7rzyhJRvDeifF+7SRTPHtiiIhlIaS7oopZg6opipI6ZmOxSRYrlhh3uvn8Fd8z7ksUXleL4m5mqUCvZkR0IW+dEQN84Zw9XdeE+0ED1VZ5f0Pg98Xyl1C/D91tsPCUp5b01zbEL0Ge9s3seybZ2XikLQcMco+P3rH/LZshE95mSup3p5zR60Jqn6EttSGGDVjlrKpBFSWp0zpZSfvbAuqX2NrjZcMHVor3vtjBuUz6NfPImvPraMnbXNuK5PyLZQgIb936PJpYX85rJpDCpI/fXlOUcN5JF3tybV6dcYg6VUty7jFX1HxLG57fQJfOmUcby6toKlW6tpiHkU5oSYPW6AdF8XIo06a35UC3xTKTUW+AmwA/hy6/1CiBR54O0tuL5OeHXNthSeNjy3YpeMI0izuha33XHFiaiPeakNRhwmL+Jw2YwRPPruVozpvCut52tCtuKaWaMyFGFmjR6Yx7NfOZll5TU8uGALy8praHF9CsIOs8cN4KqTRjGptDBtn79sVD8G5CU38zPua44ZWsS4Qflpi0uIZEUcm3OPLeXcY2UOtRCZ0lkp71jgFsAF/g0YBzyhlHqOYIZp998QIkQ3V9vk8t6WKiJOcldgtTY89t42SUzTLJrkz+XQY6WMNxNuO/0o1u2pZ/GWKmzryHMIXV+jlOLHF05hfElBhqPMHKUUJ4zsxwkj+2Xlc996yjju+MeaoGtpJxcKtDbYlsUtp4zLUIRCCCG6q87OuB4jaHT0DvCQMWa+MeYsoA54Od3BCdEXVDbEcI4weqAjjqWorI+lKSrR5vgRxegk98wZY3B9zbiSvDRFJQ4Wsi3uvPIEzj9+GNoECajra3RrA5OY56ONITfs8OvPT+XsKbICkk7nTx3KJdOHoc2RG8hAUFrsG8PNnxwr45WEEEJ0usc0CmwG8oD9cw+MMQ8opZ5IZ2BCCNEdXH7iSP62dEdSe+ZinuYTRw2ipCCa5uhEm5BtcccFx/Dl08bxtyXbeWb5TmqbXRxLMWpAIdfOHs0pEwcRkr1haaeU4j/OncTA/Ah/fHMTnjb795HCgb2ullJ86+yJXDZDqj6EEEJ0npjeAvwCiANfOvgBY0xzuoISoi8ZlB/B83VCjY8O5mnD0DQ0LxGHGjson2OGFrJyey1hJ7Gh6yHb4rqTR6c/OHGYkoIot5wynltOGZ/tUPo0pRQ3zx3H5TNH8o/3d/KXReXsbYihUAwpinLlzJGcPWUIuWEZqyKEECLQWfOjBcCCj96vlCoAzgDOMsbcnKbYhOgTinJDnDimPws/3JfUaBHbUrK/NEN+evGxXPbHd6hv8TrcC9xWuvj5GSOYId14u2x7dRNPLCrfv+ppWYrSoijXzBrFuceW9rpuuplW3RinqilOyLIYWBBOa3JYGA1x5cxRXDmzdzabEkIIkToq0XljSqnjgXOAMwkS2leBF40x76YqmLKyMrN48eJUfTgheowFH+7lq48uw1KddxUF9u/bmvetU+QkPUM2723kxgcWUdvk4mtzyJxIrYPZkLaluOLEkdx+xgSZb9cFrq/5z2dX88LKXWjAUmCrYPSOr4NSUKXg2+cczaXTR2Q73B7F9TVvrKvkvgWbWbOzDse2MMagDZx1zGCunDmKKcOKsh2mEEKIXk4ptcQYU9beY5115f0cQTI6DXifoBHSZ40x+1IepRB92EljBjBtZDFLtlbjWB0np9oYDIavnHaUJKUZNGZgHs99dQ4vrtrNvW9tZmdtC22VvQY477hSrpw5islD0zeKozdzfc2XH1nKoi1VOJbCOeg1oADLPrA/8SfPr6Ux5nPt7NHZCbaHqayPcdODiymvbsL1NeHW2abB+4zhnyt389LqPZx//FC+9+lJMqNRCCFEVnS4YqqU+i7BquiSTAQjK6aiL2uMedz80BJW76xFwWEnh0Gn12DV6IqZI/m3Myck3clXpIYxhp21LdQ2uYQdxZCiHPIjslfu4/i/Vzdw39ubE9pr3VYx8Kdry7IyEqUnqW1y+dwfF7KnroVQB9/btjL0s6eU8v8umiLvLUIIIdKioxXTzi6LbgWWdfCBxyml5nyc4IQQgbyIw73XzeBLc8eRF3HQxtAU92iO+7S4PtrAqAG5/OySY/nmWRPlxDGLlFIMK85h8tBCxpcUSFL6MbW4Po+8u7W1VLfz32vbUri+5s/zN2Ugup7tJ/9cw57almCVtIPvraUUjqV4afVuXllbkcEIhRBCiEBnZ1MDgGVKqSXAEqCSYITMeGAusBf4dlojFKIPCTsWN88dxw1zxvDWxr2s3VlHQ8yjX26IWeMGcszQQklIRa/zyto9+0eIJCriWLzzYRV76loYXChjedpT1RjnlbUVOHZi31elFL7vc89bmzhj8uA0RyeEEEIcqrOuvL9RSv0OOA04GTgOaAbWAlcbY7alP0Qh+p6QbXHqxBJOnViS7VCESLt5H1QS93RSXamVUliWYcnWas49tjSN0fVczyzfAbB/fmgiwrbF+j0NbKxoYHxJfrpCE0IIIQ7Taf2ZMcYH/tV6E0IIIVKqpjnepUoAraEh5qUhot5hydZqfF/jWEkm/BjW76mXxFQIIURGSes9IYQQWZUXcUh0dNnBLAtyklhl7Wua4n7XEn5jaI77aYhICCGEODLp2CGEECKrThzTn/kb9iZ1TNsMThnPc2RFOSF0FxJ+W1nkR+X0oCeorI/x5JJy5m/YS2PMIy/iMHvcAD5bNkL2Xgshehz5yyOEECKrPnP8UP735fVobbASbIAU9zXHDC1i3CApNz2S0ycNZv6GyqSO0cagtWH6KBnD053Vt7j88JnVzFtfiTEGY0ApMAbW7Krjnrc284mjBvFfF06hKEfmXQsheoaEElOlVAS4BBh98DHGmB+lJywhhBB9RWE0xLnHlvKPFTsJGTotP9XG4FgWN84Zk6EIe6bTJ5fwo+cUnp94x+O4pznt6BIG5kfSHJ3oqtomlyvveYft1c04lsKyDt+VZYxh/oZKPn/3Qh794kn0zwtnIdLOGWNYtaOOBxduYeGmfTS7PlHHZuqIYq47eTRlo/pJJ3oh+pBE95g+A1wAeEDjQTchhBDiY/v3sycyol8urjYd7jfVxuBrw3nHlXLa0dK1uiMRx+bqk0ahTcff0za+NoRsixvnjM1AdKIrjDF89bGl7KhuJmSpI3ZcVkoRsi1217Vwy8NLurSHO91217bwubsXct197/HS6t00x30UwVzj+RsqueXhJZz327fYvFdON4XoKxJNTIcbYz5vjPm5MeZ/2m5pjUwIIUSfURAN8dCNJzJpSCGG4OT04JNprQ0xz0cb+FzZCH7wmWNkJSUBX5o7jlnjBuBp0+F+U8/XGAP/39kTOXZ4UQYjFMlYsb2W1TvrcCyV0O9/yFJ8WNnA4q3VGYgucbtqm/n8HxeyYU8DloJoyMZuTbRtSxEN2Shge3UzV/zpHTZWNGQ7ZCFEBiSamC5QSh2b1kiEEEL0acW5YR75wkz+77JpnDR2AG5rMuVrAwouOWEET9x8Et85d1LCpal9nWNb/OayaVw0bRhag+trPF/v30sa9zS+MYQci/+68Bg+N2NktkMWHXhw4RY8rRO+KKOUIu5p7n97S1rjSoYxhlseXkptk0vYsY74tSiliDgWTXGPmx5aTNzTGY5UCJFpiTY/mgNcp5TaDMQABRhjzHFpi0wIIUSfY1mK2eMHMnv8QBpjHjXNLo6l6JcbJuzIhLOuCNkWP/jMMXxp7jieXLqdp5buoLbZxbYVo4tzuHb2aM6YPJiojN7p1owxvL6ugpCd3Osg7Fi8tXEvnq9xkjw2HRZvrWZ7dRMhO7HkOuLY1DW7vPZBBWdPGZLm6IQQ2ZRoYnpOWqMQQgghPiIv4pAXkebxqVJSGOXWU8Zz6ynjsx2K6IKYp3F9Q04oueTSUgqlDI1xn6Kc7Cem97+9hbink7oQ4vqa+97eLImpEL1cp3/xlVIW8LwxZkoG4hFCCCGEEB/RtlJqjEl6f7XWhnA3WC0FWLhpX9LVD2HbYu2uOprjPjlhWdkXorfq9J3BGKOB95VSsvFECCGEECILbEsxpDCKp5PrsOv5muLcMNEkV1rTwW/d15zsDvG2LsMNMS8tcQkhuodE36VKgdVKqVeVUs+23dIZmBBCCCGEOODqk0YlvVpqgKtnJX9cOtiWwulC4zLT2gStOyTXQoj0SXTzzh1pjUIIIYQQQnTogqnD+M2rG/C0SSjB83VQ9nvRtGEZiC4xEwbns25PAxEn8QTV04YB+WHyZc+5EL1aQpeejDFvtHdLd3BCCCGEECJQlBvi++dNBoKk86O0NjTFPKoa4+xtiFHdFKc4N8R7m6uIeX6mw23X9SePSbgjbxul4LrZY7rFqq8QIn0SSkyVUvVKqbrWW4tSyldK1aU7OCGEED2L52vqW1w8X2YOCpEOF04bxrfOmggEnXq1NhhjaIh57GuM0xj38bRBG8gJ2exriPGDZ1Zxyi/m8dLq3VmOHk6bVEI0ZCc8l9TzNbayOH/q0DRHJoTItoRqIowxBQf/v1LqQuDEtEQkhBCiR2lxfV5Zu4d739rMhooGbEvha8OkIQXcMGcMpx09WGaQCpFCl584khNG9uOBhVt4ceUuGmM+8baLQQqijkVu2Dmk3Dfm+nzn7yupb3G5dPqI7AROMJf0N5dN4+aHFuP6usO5rJ6vQSl+fulxFOWEMhilECIblDHJdXfbf6BS7xhjTkplMGVlZWbx4sWp/JBCCCHSaMX2Gm59ZCktro/nG0K2QimFMYZ460lnXsThrqumM6m0MNvhCtHr/O61Ddz95iYUB5oLHankta389/7rT+TY4UUZjPJwi7ZU8dVHlxH3NcaYQ+L2fI0BHMvipxdP4fTJMr9UiN5CKbXEGFPW3mOJlvJefNDtUqXUzwgavQkhhOijVmyv4cb7F9PQ4mEpRdix9p9YKqWIODaWUtQ0uVx733us3SU7QIRIJdfXPPbeNkK2RTRkE7KtDvdh2pbC9TX3vLUpg1G2b8bo/rz09U/y9U8dRf+8ML4JTix9bciLOnxp7jj+edsnJCkVog9JtL3ZZw76bw/YAlyQ8miEEEL0CC2uz62PLMX1dadluhHHIub63PLwEv51+9wOS/eEEImbt66SmKeTGsESdizeWF9JZX2MQQWRNEbXuaLcENfMHs1VJ42ioj5GQ8wjL2IzKD+CI+8TQvQ5iSamfzbGvH3wHUqpk4GK1IckhBCiu/vXmj00u37Ce0cjjk1DzGPeukrOmDw4zdEJ0Te8snYPcU8TDdkJH2MpBRYs3lLFOceWpjG6xFmWYkhRNNthCCGyLNHLUb9N8D4hhBB9wD1vbcb3k9vR4fqa+97enKaIhOh7qhrjdGWCiq8N9TEv9QEJIcTH0OGKqVJqFjAbGKSUuv2ghwqBxC/PCSGE6DVins+mygYiSXbaDdsWq3bUYoyReYRCpEBu2KYrPSxtSyW1yiqEEJnQ2VlFGMgnSGALDrrVAZemNzQhhBDdUXPcx+6g8+eRtD0/luD8QiFEx6aNKE56L6YxwYzTiYMLOn+yEEJkUIcrpsaYN4A3lFL3G2O2KqXyjDGNGYpNCCFEN5QbdvC1wbGSW/lsG0+W7EqrEKJ9F0wdxv+9thFtTLB3NAGubxhXks/EIZKYCiG6l0TPDoYqpdYAawGUUscrpe5MX1hCCCG6q7BjMa4kHzfJPaZxX3Ps8CIp4xUiRfrlhTl9UgmeTuy1aIzBshQ3zhmT5siEECJ5iXbl/TVwFvAsgDHmfaXUJ9MWlRBCiG7thpPHcMc/Vid1TMi2uOHk1J8Qb69u4olF5Ty9fCe1zS6WgqHFOVw1cxTnHV9KQTSU8s8pRHfxH+dOYnl5DXvqYoTtI5fYG2PwtOGUiSWcfYzMBhVCdD8J11MZY8o/cpef4liEEEL0EGdMHkxOyCae4H7RuOdTEA3xyQmDUhaD62u++9RKzv/tWzz4zlYaYi5hW2Fbip01zfzy5XWc+st5/GXRtpR9TiG6m+LcMA9/YSajBuRiCJqTmYM6ImljaHF9tIGzjhnCzy89DiuJuadCCJEpiSam5Uqp2YBRSoWVUt+ktaxXCCFE3xMN2dx55XRCjtVpchr3fCIhm7uumk4oyUYtR+L5mq88upR/rtyFbSnCtoVjWSilsJQiZFvYlsIY+NkL67j3rU0p+bxCdEclBVGe/NJsfnLRsRw9pPCQ0l5t4FOTBvPna8v46cXHpuw1KIQQqaZMAn3GlVIDgd8ApwMKeBm4zRizL5XBlJWVmcWLF6fyQwohRLtcX/Pm+koefmcrW/c14WlDv7wQF08bxoVTh1OUK+WfiVi5vZZbH1lCk+vj+WZ/KaExhrivCdkW+RGHu68uS2mzld++toF739qMk0B3YF8bDHD31dOZMbp/ymIQoruqqGuhqimOYylKCqMUSjm7EKKbUEotMcaUtftYZ4mpUsoGvmaM+VU6gjuYJKZCiEx4Y30l331qJS2uj+vp/eMWtDEoFErB5SeO5BtnTMCWkrdOxT3Nax/s4Z63NvPB7nospdDGMGVoETfMGcMpEweldJWmxfWZ+4t5eL5O+OfT4vrMGjuAu69p92+hEEIIITKgo8S00+ZHxhhfKXUBkPbEVAgh0u35FTv5/jOrMcYQsi0iBw2ZtwmSHG0Mj767lV21zfzi0uNlP1Ynwo7F2VNKOXtKKb4O9rPlhOy0fd9eXVuBrxNPSiEYUfPelip21jQztDgnLXEJIYQQousSvYT9tlLqd0qpTyilTmi7pTUyIYRIsXW76/nhs6sB0+EKnqWCBjqvr6vgz7I3MSm2pciLOGlN5t9YX5Fw06U2qvVnunRbdZqiEkIIIcTHkei4mNmt//7ooPsMcFpqwxFCiPS5561NuJ4+ZJX0SJRSYOC+t7dw3ewxhB1pGNJd1Da7dGUUqq8NTTFpKN9d1be4rNxeS0PMIydsM3FwASWF0WyHJYQQIkMSSkyNMaemOxAhusIYw/o9DexriGFZimHFOYzon5vtsEQ3VNMU55W1FYSSSDAdS+H6mtc+qODsKTL3r7vIDTsk0LfvMJaliIY7vyghMmtjRT0PLtzK8yuCDsuGoMui62tmjRvA9SePkaZVQgjRByS6YipEt9IQ8/jH+zu5/+0t7GuMBXvNDLjaMGFwPjecPIZTjy6Rtvhiv0VbqrGVSnqlLe5pXlq9WxLTbuTEMf15c31lUscYYzAGJpcWpikq0RXPvb+T//zHalzfEGot/257iTqW4q0Ne3l3UxVXzBzJ7WdM6LQDsxBCiJ5LztpFj1Ne1cRFv3+bX7z0ARX1LSjAmKC23Fawdlcd3/77Sm56cDENMS/b4Ypuor7Fxe/CMpulFDVN8TREJLrqvONKAdA68Z9n3NdMGJzP+JL8dIUlkvTKmt384NnVaGOIONZh+5KVUkRDNkrBw+9s5XevbcxSpEIIITJBElPRo1TWx7jm3veorI/hWBYh2zrkCrpSiohjYytYVl7DLQ8vSbpJiuidoiGbrvTjMRhypPyzWymIhvjM8UPxtCGRWdzaGBzL4sY5YzMQnUhEi+vzvadXQ+vPpiOWUthKcd+CLWze25ihCIUQQmRawompUmq2UuoKpdQ1bbd0BiZEe372wlqqGmOdNqJRShGyFKt21PLkku0Zik50ZxMG56MNCSUyB7OU4vjhxWmKSnTVt86ayKiBuZ0mp9oYfG349HGlnD6pJIMRio78a80ePH1ghnBnLEvha82j725Nc2RCCCGyJaG/CEqph4BfAnOAGa03mVIuMqqqMc7r6yoT3jfatpJ639ubk05GRO8zvqSAcYPyiPuJ/y7o1t+bi08Ynq6wRBflRRwevH4mk0sLMQQrcAe/zrU2xDwfbeCzZSP44WeOkf2J3cgDC7bgJfFaBHAsi6eX7yTmSWdlIYTojRJtflQGTDZydi+y6NnlOzDGYHVS9nUwx1JUN8VZsrWaMunq2OfdOGcM331qFcaYhJIU19ecMrGEQQWRDEQnklWUG+KhG2fy7uYqHliwmXc2V+FYwZ5zpeDSE0Zw+cyRsq+0G9pW1YRjJ3ehwLYUvjZUNcYpLcpJU2RCCCGyJdHEdBUwBNiVxliE6NCaXXX42pDACMr9lFL4vmHT3kZJTAVnTh7CS6v3MG9dBY5Fh8lp3NMMyIvw/fMmZzBCkSzLUswaN4BZ4wbQFPeoaXJxLEVxblhmz3Zjrq+DbupJspTC9eQauRBC9EaJ/tUeCKxRSr2klHq27ZbOwIT4qGbXpyuFeNoYaYAkgCCJ+e9LjuP0yYPRJij/1AcVgpjW3xVfG4YURXnoCzMZmC+rpT1FbthhaHEOJYVRSUq7IO5pGmJeRrY+FEZDh7z2EmGMwdWaopxQmqISQgiRTYmumP5nOoMQIhGD8qN0Jb20bSUnMmK/sGPx80uOY/HWah5csIW3N+7FsoP5pp6GUQNyufETYzlz8mCiySzPC9ED1TTFeWrZDh5auJXKhhhWaxXB3AmDuHb2KE4Y2S8te3PPOXYIj79XntRsgLivOWZoEUW58n7eFcYYVu2oY3l5NY0xn7yIzdQR/ZgyrFD2XwshuoWEElNjzBvpDkSIzpwxeTDPvr8j4f2BEKyWGgOzxg1Ic3SiJ1FKMWN0f2aM7k91Y5zddS14vqE4N8Twfjlykib6hKeWbufHz6/FECQtUScYv6WNYd66Ct7euJcJgwu488oT6JcXTunnvuLEUTyxeHvC7+fGGEK2xQ0nj0lpHH2BMYbnVuzkj29uZmdNM54OqkJsS+FYFkOLc7jpk2M477ih8t4nhMiqRLvynqSUWqSUalBKxZVSvlKqLt3BCXGwmWP6U5QTwtOJl3/FPc3cCYOkHFMcUb+8MJNKCzl2eBEj+ufKiZnoEx57bxv/9fwaIGgSd/BMaEup/XN/1+6u44o/v0Nts5vSzz9yQC5zJwxKeBat6wfl9XMnDkppHL2drw3fe3oVP3xmNeVVTVgKIo5Nbtgh4gQ/4/KqJn74zGq+9/Qq/CT+vgohRKolWkTzO+ByYAOQA3yh9T4hMsayFF857SiUIqG9SZ7WhGyLL35ibAaiE0KInmH9nnp++dI6FKrDBkRKKcK2xa6aFr7/9KqUx/HTi49lcmkhnjZHfE9v2/fdPz/MPdfOSHhcmAj88qUP+OfKXdiWIuxYh114Uyq437YU/1y5i/95eV2WIhVCiCR2dxhjNgK2McY3xtwHnJK2qIQ4goumDeOyGSPRmiNe2TX7mx0pfnzhFCYPLcxskEII0Y09sGALXmspZyJCtmL+hr3sqWtJaRzRkM2918/gomnDMCa4mBjzNK6viXuauK/xtGHWuAE8cfMsBhdGU/r5e7vyqiYeX1SObalOK0GUCi5SPP7eNsqrmjIUoRBCHCrR5kdNSqkwsFwp9XOCsTF56QtLiCP71lkTGVqcw29f24jnazytsZXCECSrYcdiQH6YH10whZPHD8x2uEII0W3Utbi8uGo3oSRGtSilMGieWFTOVz91VErjiTg2P/jMMdx2+gSeXb6TVz/YQ32LR27YpmxUPz5bNoKhxTKztCseX7QNY9jf0KozllJ4GP66uJzbz5yY5uiEEOJwKpG9HUqpUcAeIAx8AygC7mxdRU2ZsrIys3jx4lR+SNGLtbg+r66t4Mkl5VTUx7Atxaj+uVw+cyQnjRmA1YUZeUII0Zst2lLFlx9ZmvRxcU8zcUgBf7l5VhqiEqlmjGHWT19Lel6srw0hx+Kd73wqjdEJIfoypdQSY0xZe48l2pV3q1IqByg1xtyR0uiE6KJoyObTx5Xy6eNKsx2KEEL0CI0xr0vHKdX1Y0XmxTxNU9xLeuSVbSmaYh4tri/jsoQQGZdoV97PAMuBF1v/f6pS6tl0BiaEEEKI1MqLJLqD51DGdP1YkXk6ibFq7UmgmE4IIVIu0eZH/wmcCNQAGGOWA6PTE5IQQggh0mHikAJ8bdDJjgVR8ImjZM9+T5ETsgnZKunxL219GqIh6X4shMi8RN95PGNMbVojEUIIIURaFUZDnD1lCG4SCYsxBgvFZ8tGpDEykUpKKc4/fhie1kkd52nNhVOHyTxnIURWJJqYrlJKXQHYSqmjlFK/BRakMS4hhBBCpMG1s0fjWImvprnaMOeogTKupYe5YuZIbMsikSaXEFyAsC2Ly2eOTHNkQgjRvkQT068CxwAx4DGgDvh6uoISQoieaOu+Ru58fSPf+fsK/uPvK7nrjQ/ZXi0zAUX3MmFwAd88ayIG02FyaozB9TVDi6L814VTMhihSIXxJfmcMXkwnjadJqfGGDxtOGPyYMYNys9QhEIIcaiExsVkioyLEUL0RCu31/LLl9excnsNvmH/SaClFJaCaaP68e9nHc3EIQVZjlSIA55aup0fP78WY8BgcCyFUgptDHFPE3Ysjiop4M4rT6BfXjjb4YouiHuarz2+jHc37UMBjn34eoTnawwwc+wA/u+yaYQd2V8qhEifjsbFdJiYdtZ51xhz/seM7RCSmAohepp56yr45l/fJ+5pIo512N4sYwwtniYasvnd5dOYOXZAliIV4nA1TXGeXraDh97ZSkV9DKv19/fUiYO4etZoThhZLPsNezjP19y/YAsPLNhCs+vT4mqUCjrvRkMWOSGb604ew7WzRrWbuAohRCp9nMS0EignKN99Fzjkr5Mx5o0UximJqRCiR1m9s5Zr730PrU2nJ3SurwnZFo9+8STGl0ipnOh+XF8T9zS5YVuS0V7I8zXzN+xl0ZYqaptdinJCzBjTn0+MHygJqRAiYzpKTDsbSjYEOAO4HLgCeB54zBizOrUhCiFEz/PrVzYQb10N7UzItmhxfe58fSP/+/mpGYhOiOSEbIuQJCi9lmNbnHoqU7UFAAAgAElEQVR0CaceXZLtUIQQol0d/gUyxvjGmBeNMdcCJwEbgXlKqa9mJDohhOimdtQ0s3hLFZEk9mOFHYt56yvZ1xBLY2RCCCGEED1Pp2dUSqmIUupi4GHgy8D/AX9Pd2BCCNGdvbxqN8aQVMmjpRQYePWDijRGJoQQQgjR83RYyquUegCYArwA3GGMWZWRqIQQopvbWduc8BzIg8V9n4q6ljREJIQQQgjRc3W2x/RqoBGYAHztoJUBBRhjTGEaYxNCiG7L+hjNYWxLGssIIYQQQhysw8TUGCNdEIQQoh2jBuTi2MknmBHHZmhxThoiSq3aZpdnl+/k5TW7qW12iYZspo4o5vITRzJmYF62wxNCCCFEL9PZiqkQQoh2nD2llF++vB5tTMKrp1oblIJPTRqc5ui6Lu5p/vvFD3h62Q4MBq3ZP/Nw/e56nly8nSnDC/npxccxrAck2EIIIYToGf5/9u48Pq7yvvf493fOzEiyLcubvK9gMMYGYyMbAyEEwpYQQpKbhSWsaSFpmjRNenubm7Rp0tukva9b0ps2zc3GlpUQAqE00JA0gWAWI5vNYHbb2LKNd9nWNjPn/O4fkgk4sq0Rnnmk0ef9eulleWYkfS0dHc93nuc8DyOiANAPY4bndMacRuWLaZ8/Jp+ketfxkzWiZmC+JthVTHTNzc362coNMkmZKFIu072FSC7T/RZH0pPrW/Whbz6ktdvaQkcGAABVgmIKAP30qbOO1oiajLr6UE67ioka6rL62NuOrECy/vninc/oiQ27lIlM0QGugzUz5TKR9nYW9ZGbHlVHPqlwSgAAUI0opgDQT9PGDNO3r2jSiJpYnYVEaS+r9Capq6uYqqEupxuuWqwJI2sDJD20V3d36u5VmxRH1qctcHKZSLvaC7pn1aYKpAMAANWOYgoAb8K8yQ269aOn6P0nTpVMSt3VWUjUWUyUuiuOTBctnqaffuxkzR5fHzruAd3avF6u0lYbTlLX9cvWyr30bXMAAABeb2Be6AQAg8jkUXX6mwvm6dPnzNH9z2/V1j1dkqQJI2t1+tGNqsvFgRMe2l1PblKpawxnY1PLrg5tbO1kISQAAPCmUEwB4DAZUZPRO4+bFDpGv+zuLJS8N6uZKWNSa3uBYgoAAN4UpvICAJSN+v/fQS5T+n6uAAAAr0cxBQDoyPHDVUj6vvWN1L0va5K6JjYwWgoAAN4ciikAQJedPFO5TGn/JRRS17nzJw7YfVkBAMDgQTEFAOi02eM0LJdRvg97skp6bcXhD580o8zJAADAUFC2YmpmtWa23MyeMLOnzeyL5fpaAIA3JxNH+j8fWKA4skNO6U29ewrvxUum6djJIyuUEAAAVLNyjph2STrT3RdIOkHSeWa2tIxfDwDwJiyZNUbXfegEZaJI+WKqJH3j/qTes0dr6tIHm6bpM2fPCZQUAABUm7JdGOTdO67v7flrtueNXdgBYAA7/ehG/exPTtEPH1mn21a2vHa7qfua0tOOatSVp87U4pljwoUEDqKYpPrdi9t047K1Wr1pt/LFVMNysU47ulGXLZ2heZNHykrcGqlarNvepp+u2KBnN+9RIUk1vr5GFyyYrFOOHKc4GprfEwADh3X3xzJ9crNY0gpJsyV93d3/x8Ee39TU5M3NzWXLAwDou85CoqdaWrW3s6iabKTZ40dofH1t6FjAAT21oVWf+PFK7e0sqlBMlc1EMkmpdxfWTBzp6An1+pdLFmrciJrQcSvm5a179cV/f0ZPtbQqdZe7y2RK3FWTiTSiJqNPvv0ovW/R1NBRAVQ5M1vh7k293lfOYvq6AKMk3S7pE+6+ar/7rpF0jSRNnz79xHXr1pU9DwAAqC6PvbJT19y8QoUkPeAK0+6uQuoaN6JGP75m6ZAop6taWvVHNzWrI58ol7FeR4sLSSoz04eXztCnzz46QEoAQ8XBimlFVuV1912SfivpvF7u+5a7N7l7U2NjYyXiAACAKrK7s6CP/3DlQUupJJmZcnGkbXu69Oc/fryCCcPYsqdT136vWZ2FRDXZ6IBTmLNxpMik7z+8Tj9dsb7CKQGgWzlX5W3sGSmVmdVJOkvSs+X6egAAYGi68/GN6iwcvJS+XjY2Pb1pt57dvLvMycL64cOvqK0r6dP3JTKT3PV/f/2CiodYmRsAyqGcI6aTJP3GzJ6U9Kike939rjJ+PQAAMMSkqevGB9eqlEuTzExJ6vr+w9V7+VC+mOqW5vUlLWqUiSN15BP97sVtZUwGAL0r56q8T0paWK7PDwAA8OqeTu1syytT4qqymch0//PVW8BWrNupYuolr7abL6b6+WMtOmPO+DIlA4DeVeQaUwAAgHJo6yoqjnpf1OdgzKSOfFKmVOHtbM+XNIq8T2SmrXu6ypAIAA6OYgoAAAatmkystB8FzF2qyVbv06BMZDL1b2/STFy93xcAAxdnHgAAMGhNbKhVLhOpmJa2YE8hSXX81IYypQpv6uhhr+1ZWorUXUeNH1GmVABwYBRTAAjM3dVZSPo17Q4Y6rJxpIsXT1dawq+PuyubiXTFyTPLliu0uZPqNXlUnQpJ378x7q5MHOlDi6eVMRkA9K5six8BAA6skKT67XNbdcOyNVrV0vra7cdNbdBVp8zS6XMalWU6HdAnH2iaphsfXKtimioTHfr3Jp+kmtRQp8Uzx1QgXRhmpqtPnam/+4/Vcvc+XYPbVUx1zMSROmpCfQUSAsAb8awHACps9abdOuer9+uzP3tST29sVU0mUm02Vk0m0qqWVv3Vz57UuV+9X8+/uid0VGBQmNhQq7+9YJ4kO+SU3q5iotpsrH+9ZKGiElesHWzOP36yjp00UoXk0FN688VUNdlYX7xwXoXSAcAbUUwBoIJWb9qtK29Yrh1tXYrMVJOJXxvJsJ6/R2ba0daly777iJ7bTDkF+uKCEybrS++eJ5OpkKQqJr8vqO6ufDFVkroa6nK66eolmj2++kcFc5lI3/jwIs2f0qBi2v092L+gJqmrq5iqJhvpG5cu0txJIwOlBTDU2UC6pqmpqcmbm5tDxwAGlZ1tebXs6lAxdY2qy2rG2GElb5uAysgXU5391fu0qz2vmkzch8cnGj28Rr/887cyrRfooy17OnXbig36wSOvqCOfKDJTIU01bfQwXX3qTJ03f5Lqcof+/asm+WKqOx9v0fXL1urVPZ0yda9KHJnJTHrvwim67OQZmjp6WOioAKqcma1w96Ze76OYAoOPu2vFup266aG1WvbidmUjk6v7le8JI2t01amz9M7jJml4DZeRDyT3rNqsz9/xlKISXjhI3fWV9x2vs4+dUMZkQPUpJql2thfUWUg0vCaj0cOyQ/5FO3fXqpbdemVHu4ppqlHDcloyc8yQK+oAwqGYAlWkkKT6wp1P6z9XbVYhSZXLRK8VHXdXIXHFkWnUsKy+e8VizRw3PHBi7PPBbz6k5zbv7tNo6T5dxUTHTmrQj65ZWsZkAAAA5XewYsrcMGAQcXd9/o5VuvupTYpMqs3Gbxh9MzPlMpHiyLR9b16XXf+INrV2BEyMfYpJqtWbditX4pTcXBxp1cZWpaXshQEAADDIUEyBQeQ3z23Rr555VZnIDjklLZeJtLujqL/5+dMVSoeD6Sgkivvwc9ufmSkyU3shKVMyAACA8LgADVXlhVf3aP3OdhUT1+jhOZ0wbVRVLRpzw7Luffr6OhU0F5tWrNuhDTvbWdQisNpsrCR1edS3/QT3cXel7qrLcg0YAACoXhRTDHqFJNU9qzbruw+s0fod7Yp7FgKSpJo40iVLp+uDTdM0bkRN0Jxv1rrtbXp6Y2lTQc1Maer6SfN6ffrsOWVMh0PJxpGOGj9CL29tUy7T92KaT1zHTKxXXOX7LQIAgKGteoaSMCTt6SzoIzc9qi/8/Gmt3damfc/dreetvVDUt+5/We/5+jI9u3l3yKhv2otb9vZpCu8fcOmpDa3lCYWSXP2WWcrEpf38srHpI2+ZVaZEAAAAAwPFFINWIUn1Jz9YqSfXtyqOuq+p3L+0ZaJIuTjSns6CrrrhUb2yvT1Q2jevq5eN0fuqk+sTB4Sz5k5QXTZWvpj26fH5Yqq6bKwzj2GrGAAAUN0ophi0fv5Yi1a1tCobH3oUsSYTqz1f1Bf/ffAuBFRfm+nXHnzuroZhuTIkQqlqs7G+fukiZePokOU0X0yVzUT6t0tPVC7DqRoAAFQ3nu1gUHJ3Xb9sjdzV57KWiyM99spOrd8xOEdNF04frdS95G1DMnGkd8yfWKZUKNXxU0fpO1c0aURtRqm78q8bCfeev6fuqq/N6PorFuu4qQ2BEwMAAJQfxRSD0qqW3Xp1T5eyJVyvZ2ZKJd22YkP5gpXRiJqMLjh+sgpp36aBSlKSuuLIdO48iulAsmDaKP3q06frCxfM06xxw9VVTFVIUnUVU81qHK6/ffc83fvp0ymlAABgyGBVXgxKa7e3dS9wVOLUVnfXc6/uKU+oCrj8lJm668lNKiapModYndfd5ZIuO3mGatlqZMCpzca6YMFkXbBgsrqKidq7Eg2vyTBtFwAADEk8A8KgVExTlTijVZJksj4vPDMQzRo3XP/7/cfJ7OD/jtRdSep6y+xx+pO3za5gQvRHTSbW6OE5SikAABiyeBaEQWlUXa5f+zom7mqsH9z7mZ5xzAT926WLNHpYVqm7OguJimmqJHUVklTFxOUuXbRkuq774AL2vwQAAMCAx1ReDEqLZ42Ruyt1lVS8ajKRzj9+UhmTVcZJR4zVrz7zNj340jZ976F1enlbm4pJqtHDc7pwwWS9Z+EUjWIlXgAAAAwSFFMMSiNqMnr3gim6beV6xVHfrp8sJKka6rI65chxZU5XGXFkOu2oRp12VGPoKAAAAMCbQjENrKuY6P7nt2nd9jblk1RjhuV0+pxGTWqoCx1twLvq1Jm668mNKiSpsodYCCh1l5n0iTNnM7UVAAAAGGAopoHs6SzoO79bo1ua16vYs02EuysbR/rHe57VSbPG6ONnHMV2EQcxbcwwfe3ihfrTH65UVyFRLhP1ukpvMUklM12yZIb+24nTAiQFAAAAcDAsfhTAlj2d+tA3H9aND65VoZgqMlNdNtawXEbZOFImMj340nZdecNy3f3UptBxB7SlR4zVDVcu0RGNI5S61FVIlO/ZE7KzkCh117BcRn953hx95pyjQ8cFAAAA0AtGTCusPV/UR25sVsuuDtUcYGsIM1NtNlYxTfW5O1Zp1LCcTj5ybIWTDh7HTW3Q7R8/VU9vbNUtj67Xi1v2qpi6xtfX6D0nTNHpcxoPOdUXAAAAQDgU0wq7/bEWbdjZrlx86OscM1GkfDHV3931tP7jk6f1Ok0VvzdvcoO+dCFTnwEAAIDBhmGkCkpT1w3L1kpSn0tmNja9urtLj63fVcZkAAAAABAOxbSCVm1s1a72vDIlrAprZiokqW5tXl/GZAAAAAAQDlN5K2hza6dMplJn5EZmWr+jvTyhAAAAACAwRkwrKHVJ8jfxsQAAAABQfSimFdRYXyNX6QsYpe6aPKquDIkAAAAAIDyKaQWdMG2URtTEKiZpnz/G3ZXLRHr/iVPLmAwAAAAAwqGYVlAcmS4/eWZJo6bF1NVQl9WSmWPKmAwAAAAAwqGYVtj7m6Zq3IicuoqHHjVNUpeZ6X++c66iElbyBQAAAIDBhGJaYSNrs7r+ysUaOzynzmKitJdVjdxdXcVELum/nztHb587ofJBAQAAAKBCKKYBTBszTD/56Mm6cMEUubqn63bkE3UUEnUWEiUuHTNxpP71koW6eMn00HEBAAAAoKzMfeDsQ9LU1OTNzc2hY1TUns6C7n5qk17c2qZ8MdX4kTU6a+4EHT2hPnQ0AAAAADhszGyFuzf1dl+m0mHwRvW1WX1wMaOiAAAAAIYupvICAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIJiH1MAAABIkgpJqt8+t1X3P79VrR15Dctl1DRztN4xf5KG1/C0EUD5cIYBAAAY4pLU9d0HXtZND65TVzFRVzFVJMkl3fvMq/qHu5/TexdO1p+ffbSG5Xj6CODw48wCAAAwhBWSVJ/+yeNa9sI2yaRMFKkuG7/hMUnq+umKDVqxbqduvHqJRtZmA6UFBpe129r0w+Wv6IEXtqmjkKguG+vkI8bq4pOma/b4EaHjDSjm7qEzvKapqcmbm5tDxwAAABgyvvTvT+v2x1qUiUxmdsDHubsKqeu4KQ266aoliqIDPxYY6rbv7dJf/vRJPfbKLqXuMpMiM7m7EnfFUaT5U0bq/3xggcbX14aOWzFmtsLdm3q7j8WPAAAAhqhNrR26/bEWxYcopZJkZspGpmc27dbytTsqlBAYfLbu6dJF33pYK9btVBxJuUykbBwpjkyZOFJNJlZs0hPrW/XBbz6kza2doSMPCBRTAACAIerW5g1ydY/k9IWZqZCkuunBtWXNBQxW7q6P/WCFtu7tUi4THfAFHzNTTSbSrraC/vjmZiXpwJnFGgrFFAAAYIi6/bGWkp8M1sSRHnp5u9q6imXJBAxmK9bt1Nptbcr2cap7NjZtau3QQy9tL3OygY9iCgAAMETtas8rLvFaUTNTJjLtbM+XKRUweN300Frli+khp8bvY2bKF1Pd+OCa8gYbBCimAAAAQ1Rkpv5MIHTvXr0XwO+5u373/DblMqX9btRkIi1fs0OFJC1TssGB7WKGoB1tee1szysbRRpXnxuy+5G5u57ZtFvb9uZlkiaMrNXRE0b0+RUuAAAGu4kNtdq4q0NR3Pf/+9Kea+FGDWPLGOD1uoqpEnflrLRiamaKI1N7V6KGYUP3BZ+h2UiGoEKS6r7ntuqGZWv0zKbdysSR5FLqrnPnT9RlS2do7qSRoWNWRFtXUXc+sVE3LFujHW351xZ8SFLXxIZaXX3qLJ1//CTV7reHGyprzbY2PfDCVu3qKKguG2v2+BF6y+xx3ccuAOCwuOSk6brul8+X9DGF1PXuBZP5fxLYT67nOYq7lzTQ4e5KUldNdmg/x6GYDgFb93Tpj29u1vod7SqmqXJxJJMkk8yl/3hyk+5ZtVnvWzRFn33H3JKvNRlMNrV26CM3PqrNrZ1ySZnX/Vsjk1p2dujvf7Fa339knb59eZPGjagJF3aIevClbfr6b17U6k17lKauYpq+tnJdXTbWh5fO0BWnzOQJEQAcBhcsmKzrfvm8ktT79P9/6q5MZPrw0hkVSAcMLlFkmjZ6mDa1dihbwiyEYuoaV1+jmhKnAFebof2vHwJ2ted16Xce1pptbYpMqsnEb3gFJ4q6n/BHJt22YoO+cOcquVfnctU72/K6/LvLtXFXpzJx935Sr/9emJlymUixSWu2tunqGx7VXlYcrKgbH1yjP/3hY1rV0qrYuvf9GpbLqC4bKzJTW1dR37jvJV1x/XLt6SyEjgsAg97I2qz+4tw5cv/9FN0D2Teqc8GCyZozsb5CCYHB5YpTZvR5+6V9TKbLl84Y8peTUUyr3P/6j9XasrtLNQfZR0nqXvwgE5nufmqzfvPclgomrJx/+a8XtWVP1yEvSDczZWPTKzvadf0DrJBWKXc+vlFf+/ULMv3hCyj7ZOJI2cj03Kt79PEfrFRxiC8SAACHw8VLpuva049QKqmzkPzBC9TurnwxVeLS2cdO0OfPnxsmKDAInH/8ZEWR9fk5SjFNFUXSexZOKXOygY9iWsW27+3Sfz27RZk+TiUwMxXTVNc/sLa8wQLY21XUnU+09HlPKTNTFJl+/OgrQ36FtErIF1N95e7VkuuQU8nMTNnI9PTG3brv+a0VSggA1e3a04/Uv126SE0zR6uYuoppdxktpt2FdFbjcP2v98zXP/6347nWHziIETUZfeW98yU7dDktpi7J9LcXzNOoYbnKBBzAuMa0it3xeIvkrqiE5dxzcaRnNu3Wmm1tmjVueBnTVda9z2yW96H0vF4mMuWLrvue26qzjp1QxnT4r2dfVSFJ+/xkx8xUTBLdsGyt3j6Xnw0AHA5LjxirpUeM1fod7Wpet0N7uxLVZWMdO2mkjp08NBZIBA6Hs46dqK+krs/dsUr5JFXcs+ruPknqStwVm+mv33Ws3rVgcsC0AwfFtIo1r92pxL2kH7KZKZLruc17qqqYPrd5r/LFVHW50hbM6SomWru9rUypsM8PH+kema7J9P3ns+9FlE2tHZrUUFfGdAAwtEwbM0zTxgwLHQMY1M6dP0nzpjTox8vX69YV65V690Kb+wZKPrBoqi5ZMkPTx/K7tg/FtIq15xOZSr+I2r37GpNq0lVI1I9vhdy96r4XA9HG1s6SV4M2M2VM2rK7i2IKAAAGnKmjh+kvzp2jPz1zttZsa1NbV1HDchkd0Tic3QV6QTGtYqPqskr7scJuZNLwmuo6NBpH1vSnlyoTRxo9nDn/5da90Eb/VqLrzzEOAABQKbXZWHMnMR3+ULh6vYq9/dgJypa4H1LqrsSlRdNHlSlVGGceM16ZOCppKxx3VyTp9KMbyxcMkqQJI2uVHGKbgv25dy/O0VjPXrMAAACDHcW0ip1z7ARlzEp6wl9IUp0xp1FjR1TXk/1jJo7UrHHDlU/6/r3oKqZaOGO0po5m7n+5XbRkep9Xj94nn7hmNw7n5wMAAFAFKKZVrDYb65KTpit179NIYZK6MlGkq06dVYF0lfeps45S3MeinqSubBzp42fMrkAynHPsBGWivr+I4u7KRKar31KdxyoAAMBQQzGtch8/Y7ZOOmKsiqkf9Fq8YpLKXfrsO4/R/CkNFUxYOacd1ag/O2u2UvcD7k3qPfe5uz5//lwtmj66wimHptpsrE+ddbTcD36cSj0/o9Q1a9xwnXkMW8UAAABUA4pplcvEkf7l4oW68IQpSlMpn6QqJqlSd6U9m2cnqSubifSV983X+0+cFjpyWV1xyiz9/XuOU31t98JQnYVEhSRVIUnVWUjkksYMz+mrFy3UexdNDR13SLl4yXRddeospd59XPY2yp+krkLimj56mL51eZNyJV5DDQAAgIHJSlkMptyampq8ubk5dIyq9eruTt3avF53PL5Rre0FZWLTlFF1uuKUmTr72AlDatnqYpJq2UvbdWvzem3c1SEz09TRdbpo8XSdNGuMohK3LsHhc+8zr+prv35BG3d1KN8zsu0u1WQjZcz0vhOn6uNnzNaIKls5GgAAoNqZ2Qp3b+r1PoopgIHG3bWqZbfue36LdrTlVZeLdczEkUPuBRQAAIBqcrBiypADgAHHzHTc1AYdN7U6r3cGAADAG3GBFgAAAAAgKIopAAAAACAoiikAAAAAICiKKQAAAAAgKIopAAAAACAoiikAAAAAICiKKQAAAAAgKPYxBQBgiNm6p0stuzpUSFKNqsvqyMYRiiILHQsAMIRRTAEAGALcXcvX7NBND63VQy9tVzaOZCYlqauhLqsrT5mpCxdO0cjabOioAIAhiGIKAECVKySp/ubnT+uXT29WIUlVk+m+ksddMkk72vL66q+e17d/t0bXX9mk2ePrwwYGAAw5XGMKAEAVc3d9/o5VumfVJkUm1WZjmf1+2q6ZKRtHykSRWjvyuuL65Vq/oz1gYgDAUEQxBQCgit33/Fb96plXlYnsDYW0NzWZWHu7ivrc7U9VKB0AAN0opgAAVLEblq1VIUkPWUr3ycWRVrXs1pptbWVOBgDA71FMAQCoUut3tOupltbXrintCzNT4qluefSVMiYDAOCNWPwIAIAq9fK2NmX6sQ2MyfRUS2sZEgEA0DtGTAEAqFL5Yirvx8eZSZ2F9LDnAQDgQCimAABUqfrajEofL5VSd40Zxn6mAIDKoZgCAFClTpg2SiYpSUsbN81Ekd553KTyhAIAoBdcYwocJq9sb9ezm3ers5hqRE2sRdNHa9SwXOhYAIaw2mys9y2aoh8tX6+4j9eaJqkrikznzaeYAgAqh2IKvAnurvue36rrH1ijVRt3KzaTy7tXtUxdZ80dr6tOnaW5k0aGjgpgiLp06QzdtrJFhSRVNj74RCl3l0u6bOkM1eXiygQEAEBM5QX6LUldf3vn0/r0T57QE+t3KbbuBUMiM5mkyKT/XLVZH/7uI7rjsZbQcQEMUVNHD9M/X3SC4si6F0Py3qf1pqkrSaXTjhqnj58xu8IpAQBDHcUU6Kd/uHu17nxioyKTarLxH2xeH5mpJhtLLn3p35/Wvc9sDpQUwFB3ypHj9O3LmzR+ZI1cUmchUTFJVUxT5Yupimn3SOklJ03XdR88oc/TfgEAOFyYygv0w+pNu3XbyhbFkSmygz+BiyNTMXH99R1P67SjGlWbZXocgMpbOH20/vNTb1Xzup363kPr9MKWPSokrtHDsnrPwim6YMFkjaxlJV4AQBgUU6Afvv/wOiWpK5Pp26SDTBypmKb69eotOv94FhQBEIaZafHMMVo8c0zoKAAAvAFTeYESteeLumfVZmVLnOpWSFw3Pbi2PKEAAACAQYwRU6BEW/d0dS9wVOIlWNnItGFne3lCAQAAAIMYI6ZAiQqJS/1ZF8SkYomb3AMAAABDAcUUKNHoYVkVkgNvuXAgaeoaWcfCIgAAAMD+KKZAicaOqNExE+uVT9KSPi6V9J4TJpcnFAAAADCIUUyBfrj61FnKxlGfR01Td0UyfaBpWpmTAQAAAIMPxRTohzOOGa9JDXXKJ4cupu6uJJXetWCSJoysrUA6AAAAYHChmAL9kI0jffeKJo2vr1FXMVV6gJHTJHUVU9fimaP11+86tsIpAQAAgMGBYgr00/iRtbrl2pN1xpxGJamrkKTqKibKF1N1FRIlqSuOTJedPFNfv3SRsjG/bgAAAEBv2McUeBPGDM/pny9aqK17unTbyg1auW6n2vNFNdTldPax43XuvEmqy8WhYwIAAAADGsUUOAwa62v00dOPDB0DAOG7f68AABjrSURBVAAAGJSYWwgAAAAACIpiCgAAAAAIimIKAAAAAAiKYgoAAAAACIpiCgAAAAAIimIKAAAAAAiKYgoAAAAACKpsxdTMppnZb8xstZk9bWZ/Vq6vBQAAAAAYvDJl/NxFSZ9x95VmVi9phZnd6+7PlPFrAgAAAAAGmbKNmLr7Jndf2fP+HkmrJU0p19cDAAAAAAxOFbnG1MxmSloo6ZFKfD0AAAAAwOBR9mJqZiMk3SbpU+6+u5f7rzGzZjNr3rp1a7njAAAAAAAGmLIWUzPLqruU/sDdf9bbY9z9W+7e5O5NjY2N5YwDAAAAABiAyrkqr0n6rqTV7n5dub4OAAAAAGBwK+eI6amSLpN0ppk93vP2zjJ+PQAAAADAIFS27WLc/QFJVq7PDwAAAACoDhVZlRcAAAAAgAOhmAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgqKYAgAAAACCopgCAAAAAIKimAIAAAAAgsqEDgAAAIDK6Cwkcpdqs5HMLHScIFa1tOoXT23SptZOZWPTrHEj9J6FkzWpoS50NGBIo5gCAABUsfU72vWT5vX66YoNausqSpJqsrEuXDBZF580XUc2jgicsDIefHGb/vd/Pqf1O9pVSNLXbo8i07fuf0knzRqjv3rHXM0cNzxgSmDoMncPneE1TU1N3tzcHDoGAADAoJekrn/65XP68fJXlEqKzRRH9tp9xTRVHEU6d94EfenC+crG1XuF109XrNeXf/Gs0tSVje0PRovdXV3FVMNqMvrO5U2aP6UhUFKgupnZCndv6u2+6j0DAQAADFHuri/cuUo/Wv6KosiUi6PXSqkkxZGpJhMrNumeVZv1yR89piQdOIMVh9MDL2zTl3/xrExSLtP7FGYzU202Vmc+0TXfa9am1o7KBwWGOIopAABAlbnryU36xZOblIlM0UGuJTUzZSLTwy9v180Pra1Yvkpxd/3jPd0jpa8v5geSy0Rq60p047K15Q8H4A0opgAAAFXE3fWt+19W6urTAkdmJpN0w7I1Kr7u2stq8FRLqzbu6lA27vtCT5nIdPtjLerIJ2VMBmB/FFMAAIAqsqpld+llLI7Unk/0uxe3lTFZ5d35xEYVkrSkFYjjyOQuLauy7wUw0FFMAQAAqsjj63eqmJZWxiSps5Cqee2OMqUKY9OuTvVnV5wkTbWjLX/4AwE4IIopAABAFdnbVezXQkZm0q72QhkShRNHkvqzppOZoj5ckwrg8KGYAgAAVJERNRnFUelP8dylhrpsGRKFM2vciJJHjiUpMmlyQ20ZEgE4EIopAABAFVkwbZQykanUveprs5EWzxxTplRhvG/RFEWmkr4XxSRVXS7WklnV9b0ABjqKKQAAQBU5bkqDJo2qVSEpvYyddtS4MiarvBljh+u4qaPUVcJqw6mky5bOUCbmaTJQSfzGAQAAVBEz07VvPUJRH0dN3V0u6YqTZ1ZlGfvc+XNVk4mVLx66nHYVU01uqNXFS6ZXIBmA16u+sw8AAMAQ967jJ+u8+RNVTF3pQcqpu6uYuk46YqyuPGVm5QJW0NET6vWNSxepJhupq5Aq7WVhqGKaKp+kmjyqVjdctUT1tdV1rS0wGFBMAQAAqoyZ6e8unK8PLZ6m1KV8MX3DSr1J6uoqJkpcOufYifraRQurcrR0n6aZY3Trtafo3SdMlktK3ZUvdpdRd9ewbEbXvvUI/eTakzVhJIseASFYqRfGl1NTU5M3NzeHjgEAAFA11u9o148ffUU/XbFBHflEZqZsbHr3gim65KRpmj2+PnTEitrTWdDvXtim7W15ZSLTlFF1OvnIscpWcTEHBgozW+HuTb3eRzEFAACofu6urmKq1F112bhf26gAwJtxsGKaqXQYAAAAVJ6ZqTYbh44BAL1izgIAAAAAICiKKQAAAAAgKIopAAAAACAoiikAAAAAICiKKQAAAAAgKIopAAAAACAoiikAAAAAICiKKQAAAAAgKIopAAAAACAoiikAAAAAICiKKQAAAAAgqEzoAAAAoNuu9rzufmqzXtq6V4XENbGhRufMm6gjG0eEjgYAQFlRTAEACGxza6euu/c5/Wr1FsmlQpJKJpmkb/9ujeZMqNenzzlai2eOCR0VAICyYCovAAABvbhlrz7w/x7UPas2KzIpE5vqcrHqsrFqs7Fik57Z1Kprv7dCP3+sJXRcAADKgmIKAEAg2/Z26SM3PardnQXVZmNFZn/wGDNTTSaWSfriXc/owRe3VT4oAABlRjEFACCQHzy8Tq3tBdVk4kM+No5Maer6yt3Pyt0rkA4AgMqhmAIAEEC+mOqWR9criv5wlPRAsrFp464OrWrZXcZkAABUHsUUAIAAHlmzXYXElSmhmJqZCkmq2x/bUMZkAABUHsUUAIAAtu3tUtKPKblm0oadHWVIBABAOBRTAAACiMzU97HS/T/2sEYBACA4iikAAAGMr6/tdRXeQ3GXpo8dXoZEAACEQzEFACCAxTNHqzYXqZikff4Yd1cmjvT+E6eWMRkAAJVHMQUAIIBMHOmypTOUlnCZaT5JdWTjcB09ob58wQAACIBiCgBAIBctma4JI2vVVUwO+dhikioTR/rc+cdWIBkAAJVFMQUAIJCRtVldf9Vija+vVVcxVdLL8Gnqrs5Coigy/dMHFuiEaaMCJAUAoLwopgAABDRlVJ1u/ejJ+vDS6crEptRdHflE7fmiiokrSV1vmzNe3/+jk/S2OeNDxwUAoCzM+7GHWrk0NTV5c3Nz6BgAAATRWUj02+e2qmVXh4pJqtHDczpjzng11teEjgYAwJtmZivcvam3+zKVDgMAAHpXm4113vyJoWMAAFBxTOUFAAAAAARFMQUAAAAABEUxBQAAAAAEVbZiambXm9kWM1tVrq8BAAAAABj8yjlieqOk88r4+QEAAAAAVaBsxdTd75e0o1yfHwAAAABQHbjGFAAAAAAQVPBiambXmFmzmTVv3bo1dBwAAAAAQIUFL6bu/i13b3L3psbGxtBxAAAAAAAVFryYAgAAAACGtnJuF/MjSQ9JmmNmG8zsI+X6WgAAAACAwStTrk/s7heX63MDAAAAAKoHU3kBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAFgECkkqVrbC+osJHL30HEAAAAOi0zoAACAg0tS17IXt+mGZWu0Yt1ORZEpTV3jR9bqipNn6t0nTFZDXTZ0TAAAgH6zgfSKe1NTkzc3N4eOAQADxoad7br2eyv06u5O5YupajKRzEzurmLqMpMiM335vcfpnHkTQ8cFAAA4IDNb4e5Nvd3HiCkADFCbWzt1ybcf0e6OgnKZSLXZ+LX7zEzZ2CRJxSTVX/3sSRUT1zuPnxQqLgAAQL9xjSkADFCfuuUxtfaU0oPJxJHk0t/cuUotuzoqlA4AAODwoZgCwAC0etNuPf/qXuV6RkUPJRNHKiauWx59pczJAAAADj+KKQAMQN9/eJ2S1GXWt2IqSXFk+knzBuWLaRmTAQAAHH4UUwAYgJav2aE+Dpa+Jo5MSep6ZUdbeUIBAACUCcUUAAagjkJS0mjpPpFJbV1JGRIBAACUD8UUAAagYblY/dnOK3VpeE186AcCAAAMIBRTABiATjlynJISLxUtpq5sbJoxdnh5QgEAAJQJxRQABqBLT5quOLaSRk1Td128eLqyMad2AAAwuPDsBQAGoKMm1Gve5JEqpH0rpsUkVSaK9IGmaWVOBgAAcPhRTAFggLrugydozLCcuorpQUdOC0kqM9M/vG++JjbUVjAhAADA4UExBYABqrG+Rj++ZqmOGDdcLqmzkCjtKajurnwxVZK6snGk6z50gs46dmLYwAAAAP2UCR0AAHBg40fW6raPnaLmdTt104Nr9cCL2+SeyiVNHzNMV506U++YP0nDazidAwCAwYtnMgAwwEWRacmsMVoya4zcXV3FVLk4UhSVvs8pAADAQEQxBYBBxMxUm2WfUgAAUF24xhQAAAAAEBTFFAAAAAAQFMUUAAAAABAUxRQAAAAAEBTFFAAAAAAQFMUUAAAAABAU28UAwADi7nphy169urtTkjS+vlZHTxghM/YsBQAA1YtiCgADQGch0V1PbtQNy9bq1d2dinqKaOquCSNrddUpM3X+8ZNVl2MPUwAAUH0opgAQ2NY9Xfqjmx/V+u3tSl3Kxr8fHTVJLTs79OW7V+vmh9bpO1c2aXx9bbiwAAAAZcA1pgAQ0J7Ogq68YbnWbmtTHJlymegN03bNum+LzbRuR7uuvH65dncWAiYGAAA4/CimABDQN+97WRt2digXRwe9jtTMVJOJ1LKrQ9/47UsVTAgAAFB+FFMACKSzkOjWFeuViazPixtlokg/W7lBnYWkzOkAAAAqh2IKAIH85tktSlJXHPV9xd04MhUT169XbyljMgAAgMqimAJAIC9t3duvkc+uYqKXtu4tQyIAAIAwKKYAEEhnIVX3urulMZk6mMoLAACqCMUUAAIZOyKnEmbx/p5JjSNyhz0PAABAKBRTAAjk9KMblYkjuXufP8bdlY0jvfXo8WVMBgAAUFkUUwAI5IjGEZo7qV75Ytrnj8knqeZMrNfs8SPKmAwAAKCyKKYAENAnzjxKcWRK0kOPmnav4BvpE2fOrkAyAACAyqGYAkBAS48Yq/9+3jFydxWSA4+cFpJU7q6/OOdonXLkuAomBAAAKL9M6AAAMNRdvGS6xg7P6e9/sVptXUXli+lre5smqSuXiVRfm9Xnz5+rc+ZNDJwWAADg8KOYAsAAcM68iXr73Al68KVtuuXR9WrZ2SFJmjyqVh9aPF2nzh73WlkFAACoNhRTABgg4sh02lGNOu2oxtBRAAAAKoprTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBUUwBAAAAAEFRTAEAAAAAQVFMAQAAAABBmbuHzvAaM9sqaV3oHFVknKRtoUOgKnFsoRw4rlAOHFcoB44rlMNQOK5muHtjb3cMqGKKw8vMmt29KXQOVB+OLZQDxxXKgeMK5cBxhXIY6scVU3kBAAAAAEFRTAEAAAAAQVFMq9u3QgdA1eLYQjlwXKEcOK5QDhxXKIchfVxxjSkAAAAAIChGTAEAAAAAQVFMq4CZXW9mW8xs1QHuf5uZtZrZ4z1vf1PpjBhczGyamf3GzFab2dNm9me9PMbM7Gtm9qKZPWlmi0JkxeDRx+OK8xVKYma1ZrbczJ7oOa6+2MtjOF+hJH08rjhfoV/MLDazx8zsrl7uG7Lnq0zoADgsbpT0r5JuPshjfufu76pMHFSBoqTPuPtKM6uXtMLM7nX3Z173mHdIOqrn7SRJ3+j5EziQvhxXEucrlKZL0pnuvtfMspIeMLO73f3h1z2G8xVK1ZfjSuJ8hf75M0mrJY3s5b4he75ixLQKuPv9knaEzoHq4e6b3H1lz/t71H3ynLLfwy6UdLN3e1jSKDObVOGoGET6eFwBJek5B+3t+Wu2523/BTQ4X6EkfTyugJKZ2VRJ50v6zgEeMmTPVxTToePknukod5vZvNBhMHiY2UxJCyU9st9dUyStf93fN4iSgT46yHElcb5CiXqmxT0uaYuke92d8xXetD4cVxLnK5TunyX9paT0APcP2fMVxXRoWClphrsvkPQvku4InAeDhJmNkHSbpE+5++797+7lQ3g1GYd0iOOK8xVK5u6Ju58gaaqkJWY2f7+HcL5CyfpwXHG+QknM7F2Strj7ioM9rJfbhsT5imI6BLj77n3TUdz9F5KyZjYucCwMcD3X1Nwm6Qfu/rNeHrJB0rTX/X2qpI2VyIbB61DHFecrvBnuvkvSbyWdt99dnK/Qbwc6rjhfoR9OlfRuM1sr6ceSzjSz7+/3mCF7vqKYDgFmNtHMrOf9Jer+uW8PmwoDWc/x8l1Jq939ugM87E5Jl/esHrdUUqu7b6pYSAw6fTmuOF+hVGbWaGajet6vk3SWpGf3exjnK5SkL8cV5yuUyt0/6+5T3X2mpIsk/Ze7f3i/hw3Z8xWr8lYBM/uRpLdJGmdmGyR9Qd0X6cvd/5+k90v6mJkVJXVIusjdh8SUAPTbqZIuk/RUz/U1kvQ/JU2XXjuufiHpnZJelNQu6aoAOTG49OW44nyFUk2SdJOZxeouBj9x97vM7KMS5yv0W1+OK85XOCw4X3Uzfn8AAAAAACExlRcAAAAAEBTFFAAAAAAQFMUUAAAAABAUxRQAAAAAEBTFFAAAAAAQFMUUAIBDMLPfmtm5+932KTP7txI+xy/27YsIAADeiGIKAMCh/Ujdm6G/3kU9tx9Uzybpkbu/0913lSUdAACDHMUUAIBD+6mkd5lZjSSZ2UxJkyU9bma/NrOVZvaUmV24734zW90zorpS0jQzW2tm43ruv8PMVpjZ02Z2zb4vYmZ7zezvzewJM3vYzCb03D7BzG7vuf0JMzul5/YPm9lyM3vczL5pZnEFvycAABw2FFMAAA7B3bdLWi7pvJ6bLpJ0i6QOSe9190WSzpD0T2ZmPY+ZI+lmd1/o7uv2+5RXu/uJkpokfdLMxvbcPlzSw+6+QNL9kv645/avSbqv5/ZFkp42s7mSPiTpVHc/QVIi6dLD+g8HAKBCMqEDAAAwSOybzvvznj+vlmSSvmxmb5WUSpoiaULP49e5+8MH+FyfNLP39rw/TdJRkrZLyku6q+f2FZLO7nn/TEmXS5K7J5JazewySSdKerSnC9dJ2vLm/5kAAFQexRQAgL65Q9J1ZrZIUp27rzSzKyU1SjrR3QtmtlZSbc/j23r7JGb2NklnSTrZ3dvN7Lev+5iCu3vP+4kO/v+0SbrJ3T/b/38SAAADA1N5AQDoA3ffK+m3kq7X7xc9apC0paeUniFpRh8+VYP+f/t2bFJBEEZh9F40NjAQ7EAwMDEwtQebMLIBM2sRLUEUOzBSELuwhDV4A5oIa+K84JxsBnaWDT/2n+RzROlRkrMVzzwnuUyStjtt98beRduDsb/fds37AWDrCFMAWO8uyUmS+7G+TXLa9iWb+50fK854SLLb9jXJTZLfxn1/ukpy3vYtmxHf42VZ3pNcJ3kcZz0lOfzLxwDAtuj3xBAAAAD8P39MAQAAmEqYAgAAMJUwBQAAYCphCgAAwFTCFAAAgKmEKQAAAFMJUwAAAKYSpgAAAEz1BceadjXNCvZWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize plot\n",
    "fig=plt.figure(figsize=(16,16))\n",
    "\n",
    "# Plot mean returns and variances of individual assets\n",
    "plt.scatter([df_input.loc[i,'Variance'] for i in assets], \n",
    "            [100*df_input.loc[i,'Mean'] for i in assets], \n",
    "            s = 200*np.ones(assets.size),\n",
    "            alpha=0.9)\n",
    "\n",
    "for alphaFrontier in [(i/100)**2 for i in range(0,40)]:\n",
    "    # Construct model with chosen alpha\n",
    "    m = MeanVarModelConstruction(alphaFrontier,df_input)\n",
    "    # Optimize\n",
    "    obj_opt, mean_opt, variance_opt = OptimizeMeanVarModel(m,False)\n",
    "\n",
    "    # Plot mean returns and variances of new optimal portfolio\n",
    "    plt.scatter(variance_opt, \n",
    "                100*mean_opt, \n",
    "                s = 200*np.ones(assets.size),\n",
    "                c = 'red',\n",
    "                alpha=0.9)\n",
    "\n",
    "plt.title(\"Mean-Variance portfolios\")\n",
    "plt.xlabel(\"Variance\")\n",
    "plt.ylabel(\"Mean return (\\%)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus extension question: Short-selling allowed\n",
    "We are going to introduce two extra variables $x_i^{long} \\geq 0$ and $x_i^{short} \\geq 0$ that indicate the long and short position in asset $i$. To comply wth the fictitious regulations set out in this question (total short position cannot be more tan $10\\%$ of total long position), we introduce the constraint: $$\\sum_{i=1}^Nx_i^{short} \\leq 0.1\\sum_{i=1}^Nx_i^{long}.$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that creates model with short selling allowed\n",
    "def MeanVarModelWithShortSellingConstruction(alpha,df_input):\n",
    "    # Create model\n",
    "    m = ConcreteModel()\n",
    "\n",
    "    # Variables\n",
    "    m.amount = Var(assets)\n",
    "\n",
    "    # Add variables for long and short positions\n",
    "    m.long = Var(assets)\n",
    "    m.short = Var(assets)\n",
    "    \n",
    "    # Objective\n",
    "    m.value = Objective(\n",
    "        expr=sum(m.amount[i]*df_input.loc[i,'Mean'] for i in assets)\n",
    "        - alpha * sum(df_input.loc[i,j]*m.amount[i]*m.amount[j] for i in assets for j in assets)\n",
    "        , sense = maximize\n",
    "                 )\n",
    "    # Constraints on budget\n",
    "    m.budget = Constraint(expr=sum(m.amount[i] for i in assets) == 1)\n",
    "\n",
    "    # Short selling constraint (1)\n",
    "    m.short_selling = ConstraintList()\n",
    "    for i in assets:\n",
    "        m.short_selling.add(m.amount[i] == m.long[i] - m.short[i]) # actual position is long - short\n",
    "        m.short_selling.add(m.long[i] >= 0)\n",
    "        m.short_selling.add(m.short[i] >= 0)\n",
    "    \n",
    "    # Short selling constraint (2)\n",
    "    m.short_selling_total = ConstraintList()\n",
    "    m.short_selling.add(sum(m.short[i] for i in assets) <= 0.1/0.9)         # Short position cannot be more than 10% of the long position\n",
    "\n",
    "    return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Solve model with short selling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Take the same alpha\n",
    "m = MeanVarModelWithShortSellingConstruction(alpha,df_input)\n",
    "\n",
    "# Optimize\n",
    "obj_opt, mean_opt, variance_opt = OptimizeMeanVarModel(m,True)\n",
    "\n",
    "print(\"Objective value model \\t= %.3f\" % obj_opt)\n",
    "print(\"\\t Mean return \\t= %.3f\" % mean_opt)\n",
    "print(\"\\t Variance \\t= %.3f\\n\" % variance_opt)\n",
    "\n",
    "print(\"Assets invested in (and amount):\")\n",
    "print(\"Portfolio composition:\")\n",
    "for i in assets:\n",
    "    if (abs(value(m.amount[i])) > 0.001):\n",
    "        print(\"\\t\"+ i + \" : \\t\" + str(value(m.amount[i]))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show portfolio in plot\n",
    "Note that *with* short selling we can achieve a higher return.\n",
    "\n",
    "However, with short-selling there is a potentially unlimited loss, so it could be more risky. These are things that the Mean-Variance Optimization does not take into account. With some extra constraints (such as on the total short selling position), one can try to somewhat limit this risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'assets' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-abc6d702b578>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Plot mean returns and variances of individual assets\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m plt.scatter([df_input.loc[i,'Variance'] for i in assets], \n\u001b[0m\u001b[0;32m      6\u001b[0m             \u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mdf_input\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Mean'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0massets\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m200\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mones\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0massets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'assets' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x1152 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize plot\n",
    "fig=plt.figure(figsize=(16,16))\n",
    "\n",
    "# Plot mean returns and variances of individual assets\n",
    "plt.scatter([df_input.loc[i,'Variance'] for i in assets], \n",
    "            [100*df_input.loc[i,'Mean'] for i in assets], \n",
    "            s = 200*np.ones(assets.size),\n",
    "            alpha=0.9)\n",
    "# Plot mean returns and variances of new optimal portfolio\n",
    "plt.scatter(variance_opt, \n",
    "            100*mean_opt, \n",
    "            s = 200*np.ones(assets.size),\n",
    "            c = 'red',\n",
    "            alpha=0.9)\n",
    "\n",
    "plt.title(\"Mean-Variance portfolios\")\n",
    "plt.xlabel(\"Variance\")\n",
    "plt.ylabel(\"Mean return (\\%)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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