{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Markov switching dynamic regression models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an example of the use of Markov switching models in statsmodels to estimate dynamic regression models with changes in regime. It follows the examples in the Stata Markov switching documentation, which can be found at http://www.stata.com/manuals14/tsmswitch.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# NBER recessions\n",
    "from pandas_datareader.data import DataReader\n",
    "from datetime import datetime\n",
    "usrec = DataReader('USREC', 'fred', start=datetime(1947, 1, 1), end=datetime(2013, 4, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept\n",
    "\n",
    "The first example models the federal funds rate as noise around a constant intercept, but where the intercept changes during different regimes. The model is simply:\n",
    "\n",
    "$$r_t = \\mu_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\sigma^2$.\n",
    "\n",
    "The data used in this example can be found at https://www.stata-press.com/data/r14/usmacro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import fedfunds\n",
    "dta_fedfunds = pd.Series(fedfunds, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "\n",
    "# Plot the data\n",
    "dta_fedfunds.plot(title='Federal funds rate', figsize=(12,3))\n",
    "\n",
    "# Fit the model\n",
    "# (a switching mean is the default of the MarkovRegession model)\n",
    "mod_fedfunds = sm.tsa.MarkovRegression(dta_fedfunds, k_regimes=2)\n",
    "res_fedfunds = mod_fedfunds.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>226</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-508.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 17 Dec 2019</td> <th>  AIC                </th> <td>1027.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:40:27</td>     <th>  BIC                </th> <td>1044.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1954</td>    <th>  HQIC               </th> <td>1034.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    3.7088</td> <td>    0.177</td> <td>   20.988</td> <td> 0.000</td> <td>    3.362</td> <td>    4.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    9.5568</td> <td>    0.300</td> <td>   31.857</td> <td> 0.000</td> <td>    8.969</td> <td>   10.145</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    4.4418</td> <td>    0.425</td> <td>   10.447</td> <td> 0.000</td> <td>    3.608</td> <td>    5.275</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.9821</td> <td>    0.010</td> <td>   94.443</td> <td> 0.000</td> <td>    0.962</td> <td>    1.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.0504</td> <td>    0.027</td> <td>    1.876</td> <td> 0.061</td> <td>   -0.002</td> <td>    0.103</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  226\n",
       "Model:               MarkovRegression   Log Likelihood                -508.636\n",
       "Date:                Tue, 17 Dec 2019   AIC                           1027.272\n",
       "Time:                        23:40:27   BIC                           1044.375\n",
       "Sample:                    07-01-1954   HQIC                          1034.174\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          3.7088      0.177     20.988      0.000       3.362       4.055\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          9.5568      0.300     31.857      0.000       8.969      10.145\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         4.4418      0.425     10.447      0.000       3.608       5.275\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.9821      0.010     94.443      0.000       0.962       1.002\n",
       "p[1->0]        0.0504      0.027      1.876      0.061      -0.002       0.103\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the summary output, the mean federal funds rate in the first regime (the \"low regime\") is estimated to be $3.7$ whereas in the \"high regime\" it is $9.6$. Below we plot the smoothed probabilities of being in the high regime. The model suggests that the 1980's was a time-period in which a high federal funds rate existed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds.smoothed_marginal_probabilities[1].plot(\n",
    "    title='Probability of being in the high regime', figsize=(12,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the estimated transition matrix we can calculate the expected duration of a low regime versus a high regime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55.85400626 19.85506546]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A low regime is expected to persist for about fourteen years, whereas the high regime is expected to persist for only about five years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federal funds rate with switching intercept and lagged dependent variable\n",
    "\n",
    "The second example augments the previous model to include the lagged value of the federal funds rate.\n",
    "\n",
    "$$r_t = \\mu_{S_t} + r_{t-1} \\beta_{S_t} + \\varepsilon_t \\qquad \\varepsilon_t \\sim N(0, \\sigma^2)$$\n",
    "\n",
    "where $S_t \\in \\{0, 1\\}$, and the regime transitions according to\n",
    "\n",
    "$$ P(S_t = s_t | S_{t-1} = s_{t-1}) =\n",
    "\\begin{bmatrix}\n",
    "p_{00} & p_{10} \\\\\n",
    "1 - p_{00} & 1 - p_{10}\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "We will estimate the parameters of this model by maximum likelihood: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma^2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fit the model\n",
    "mod_fedfunds2 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[1:], k_regimes=2, exog=dta_fedfunds.iloc[:-1])\n",
    "res_fedfunds2 = mod_fedfunds2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>225</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-264.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 17 Dec 2019</td> <th>  AIC                </th>  <td>543.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:40:28</td>     <th>  BIC                </th>  <td>567.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-1954</td>    <th>  HQIC               </th>  <td>553.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7245</td> <td>    0.289</td> <td>    2.510</td> <td> 0.012</td> <td>    0.159</td> <td>    1.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.7631</td> <td>    0.034</td> <td>   22.629</td> <td> 0.000</td> <td>    0.697</td> <td>    0.829</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0989</td> <td>    0.118</td> <td>   -0.835</td> <td> 0.404</td> <td>   -0.331</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    1.0612</td> <td>    0.019</td> <td>   57.351</td> <td> 0.000</td> <td>    1.025</td> <td>    1.097</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.4783</td> <td>    0.050</td> <td>    9.642</td> <td> 0.000</td> <td>    0.381</td> <td>    0.576</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.6378</td> <td>    0.120</td> <td>    5.304</td> <td> 0.000</td> <td>    0.402</td> <td>    0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.1306</td> <td>    0.050</td> <td>    2.634</td> <td> 0.008</td> <td>    0.033</td> <td>    0.228</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  225\n",
       "Model:               MarkovRegression   Log Likelihood                -264.711\n",
       "Date:                Tue, 17 Dec 2019   AIC                            543.421\n",
       "Time:                        23:40:28   BIC                            567.334\n",
       "Sample:                    10-01-1954   HQIC                           553.073\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7245      0.289      2.510      0.012       0.159       1.290\n",
       "x1             0.7631      0.034     22.629      0.000       0.697       0.829\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0989      0.118     -0.835      0.404      -0.331       0.133\n",
       "x1             1.0612      0.019     57.351      0.000       1.025       1.097\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.4783      0.050      9.642      0.000       0.381       0.576\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.6378      0.120      5.304      0.000       0.402       0.874\n",
       "p[1->0]        0.1306      0.050      2.634      0.008       0.033       0.228\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several things to notice from the summary output:\n",
    "\n",
    "1. The information criteria have decreased substantially, indicating that this model has a better fit than the previous model.\n",
    "2. The interpretation of the regimes, in terms of the intercept, have switched. Now the first regime has the higher intercept and the second regime has a lower intercept.\n",
    "\n",
    "Examining the smoothed probabilities of the high regime state, we now see quite a bit more variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAADSCAYAAABAW6ZrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd5xcd3nun3f6zPYmrVa7qpbkKluWW8DYJjbgAjY4DtiBUOILgRAI5YbgkECAQC7kAqEnYHMhBLABg3GhuGDjgptcJFuWVVerXe1K2l5mdmdO+d0/zvnNzM6cc+bM7vR5v5+PP9ZOOec3/T3Ped7nJSEEGIZhGIZhGKbe8JR7AQzDMAzDMAxTDrgQZhiGYRiGYeoSLoQZhmEYhmGYuoQLYYZhGIZhGKYu4UKYYRiGYRiGqUu4EGYYhmEYhmHqEi6EGYbJCyL6FyL6nyXe951E9KjD9b8hondY3ZaI5ohow1L2m+caw0R0FxFNE9HPLK5fzuP/RyK6efmrtNx2wZ6fXK9TnttyfL6IaDcRXeJyW4eJ6LJCrCtfiOitRHRvOfbNMEzx8JV7AQzDFB8iOgxgJQANQBTArwF8QAgxV851ZSKEuMLhukb5byL6PoAhIcQ/FWEZ18F4rjqEEGohNyyE+Hwht5ex7cbct8qGiNYB6AfgL/TjdYMQ4rRS73MpCCF+BOBH5V4HwzCFhRVhhqkf3mAWS2cDOBdAVhFJBvX+vbAWwL5yFIVM8SAiFn4Yhsmi3n/wGKbuEEIcBfAbAKcDABE9RESfI6LHAMQAbCCiHiK6k4gmiOgAEb07YzMhIrqNiGaJ6FkiOlNeQUQfJ6KD5nUvEdGbMu5LRPR103rwMhFdmnbFQ0T0v6zWTUSCiE4iovcAeCuAj5l2gLuI6O+J6PaM23+diP7DZlunmPuaMk/NX21e/mkAnwTwFnPbN9o8jU6Pv4eIbieiUSLqJ6IPpl2XtAkQ0TrzMb2DiI4Q0RgRfSLttmEi+gERTRLRHiL6GBEN2awn+fyY//4+EX2TiO4x1/gkEW20uevD5v+nzMf8J2nb/L/m/vuJ6Iq0y1uI6BYiGiGio0T0r0TktVsbgAAR/be5lt1EdE7atpJ2B5eP+Swi2mW+f24jopDN8/FOInqMiL5CRBMA/sW8/K/MbU8S0e+IaG3afV5LRHvNbX+LiP4g34+UbdURRPQ3RLTffFyfJaKNRPQ4Ec0Q0U+JKJB2+9cT0fPme+6PRLTV4fliGKZEcCHMMHUGEfUBuBLAc2kX/yWA9wBoAjAA4CcAhgD0wLAKfD69YAVwDYCfAWgH8GMAdxCR37zuIIBXAWgB8GkA/0NEq9Luez6AQwA6AXwKwC+IqN3t+oUQ34FxivqLQohGIcQbAPwPgMuJqNV8jD4AbwHwQ4vH7wdwF4B7AawA8AEAPyKiLUKITwH4PIDbzG3fYrMMy8dPhpp+F4CdAFYDuBTAh4jodQ4P6UIAW8zbfpKITjEv/xSAdQA2AHgNgLflfHIWcwOM578NwAEAn7O53UXm/1vNx/y4+ff5APbCeJ2+COAWIiLzuh8AUAGcBGAbgNcCsDyAMbkawK0AWgHcCeAbNrdz85jfDOByAOsBbAXwTof9yvfaCgCfI6I3AvhHANcC6ALwCIz3OoioE8DPAdwEoAPGY3+Fw7ZhrmM7gAsAfAzAd2AcpPXBONC8wdz22QC+B+CvzW3/F4A7iSiYY/sMwxQZLoQZpn64g4imADwK4A8wCj7J94UQu007QDeM4uwfhBALQojnAdwMo1iWPCOE+LkQQgHwZQAhGMUAhBA/E0IMCyF0IcRtAPYDOC/tvicA/IcQQjGv3wvgquU8MCHECAxl88/Niy4HMCaEeMbi5hcAaATwf4QQCSHE7wHcDbNocYnd4z8XQJcQ4jPmtg8B+C6A6x229WkhxLwQYieMAlqqy28G8HkhxKQQYgjA1/JYHwD8QgjxlPma/gjAWXnef0AI8V0hhAaj8F0FYCURrQRwBYAPCSGiQogTAL4C58f4qBDi1+a2fojUY8zEzWP+mvn+moBx0OH0uIaFEF8XQqhCiHkYhei/CSH2mM/L52EozGthHBzuFkL8wrzuawCOOWwbAL4ghJgRQuwG8CKAe4UQh4QQ0zDOumwzb/duAP8lhHhSCKEJIX4AIA7zM8MwTPlgzxTD1A9vFELcb3PdYNq/ewBMCCFm0y4bAHCO1e2FELp5+roHAIjo7QA+AkPZA4yiszPtvkeFECJj2z15PA47fgDgfTAKz7fBQg026QEwKITQM9awOo992T1+AaDHPOCQeGEoj3akF1sxGM9Xcp1W+3SJ3Xbzvr8QImaKwY0wVHA/gJGUQAxPjvVlriVERD4LH7abx5y5Laf3Tub91wL4KhF9Ke0ygvHaL9q3EEI4WVFMjqf9e97i7+60/b6DiD6Qdn0gx9oZhikBXAgzDAMYBZxkGEA7ETWlFcNrABxNu02f/IdpB+gFMGwqa9+FcZr/cSGERkTPwyg2JKuJiNKK4TUwTpcvdb2SOwB8m4hOB/B6GKeqrRgG0EdEnrRieA2AfXns3/Lxw7AL9AshNuWxLTtGzO2+lLnPAmP1XDoxCEPN7CxCQ2GhH3PmYxsE8DkzAWIRRLTJ3Lf8m9L/XiZyv3b2FIZhygRbIxiGWYQQYhDAHwH8GxGFzKaeG7E4Omo7EV1renE/BKMwegJAA4ziYxQAiOhdMJvy0lgB4IOmp/bPAZwCI84tH47D8JGmr3sBhsfzxwCeEkIcsbnvkzAi5D5mruESAG+A4WF1i93jfwrADBH9g9n45SWi04no3HwenMlPAdxERG1EtBrA3y5hG24YBaAj4/m0w7Sh3AvgS0TUTEQes0ns4gKspdiP+T/N7Z8GJJv+pJ3mHgBnENEbzdf1/UgpusvluwDeS0Tnk0EDEV1FRE0F2j7DMEuEC2GGYay4AYa1YRjALwF8SghxX9r1v4LRjDYJwzt8ren5fQnAlwA8DqNYPQPAYxnbfhLAJgBjMBq4rhNCjOe5vlsAnGp24N+RdvkPzH3a2SIghEjAaN66wlzDtwC8XQjxch77t3v8Goyi+iwY2bxjMPzVLXlsW/IZGA2L/QDuh1Hkx5ewHUeEEDEYr8Nj5vPpxrf6dhin9l+C8Rz8HIaHeLkU9TELIX4J4AsAbiWiGRi+3ivM68ZgeMy/CGAcwKkAdhRi/0KIHTB8wt+A8XwdgHOTH8MwJYIWW/UYhmGqFyJaA+BlAN1CiJlyr6eQENH7AFwvhCiE8loVlPMxm5aXIQBvFUI8WOr9MwxTGlgRZhimJjALl48AuLUWimAiWkVErzStB1sAfBSGOl+zlPsxE9HriKjVjDX7Rxje9idKtX+GYUoPN8sxDFP1EFEDDCvGAIzotFogACNvdj2AKRge5m+VdUXFp9yP+U9geMyl7eONZuwawzA1ClsjGIZhGIZhmLqErREMwzAMwzBMXcKFMMMwDMMwDFOXlM0j3NnZKdatW1eu3TMMwzAMwzB1wjPPPDMmhOjKvLxshfC6deuwY8eOcu2eYRiGYRiGqROIaMDqcrZGMAzDMAzDMHUJF8IMwzAMwzBMXZKzECai7xHRCSJ60eZ6IqKvEdEBItpFRGcXfpkMwzAMwzAMU1jcKMLfh3NA/RUANpn/vQfAt5e/LIZhGIZhGIYpLjmb5YQQDxPROoebXAPgv4UxmeMJczzlKiHESIHWyDBMhbBnZAbff+wwdHMQz8mrmnHjhevLvKrS87vdx/DAnuOQ84iu3LoKr96yIu/t/PbFEXQ2BnHOunbb2/zi2SG8fGwWfe0R9LWFcf76DoQD3qUu3ZHdw9PobY2gJeIvyvYZhmEqjUKkRqwGMJj295B5WVYhTETvgaEaY82aNQXYNcMwpeSO547ith2D6GkJYTau4vZnh/D2P1kLv7e+2g3+8w8HsXt4Bh0NAYzNxXFsZmFJhfC//24vNq9sciyEv3zfPgxNpqb8/uUFa/HZN56+pHXn4i+++ySuP7cPN115SlG2zzAMU2kU4teLLC6znNsshPiOEOIcIcQ5XV1ZUW4Mw1Q4CU1HU9CHP950Kf7pqlOgC+DY9EK5l1VyFE3HKzd24PGbLsW2NW1IqPoStyOgaM73VTQdbzmnD0/cdCnWdkQwNhdf0r7cMLugoH8sWrTtMwzDVBqFKISHAPSl/d0LYLgA22UYpsJQNQGf1zj2Xd0aAYBFamW9YDwPxten30tQdctjfxfb0aFozvdVNQG/j9DdEkJrJIBoQlvSvnKh6wK6AAbr8PVkGKZ+KUQhfCeAt5vpERcAmGZ/MMNYs6BoePHodLmXsWRUXU/aIHrbwgCAoclYOZdUFhRNRyBZCHug5lB1bbejC6h6bkXY5zH21Rj0IhpXl7Sv3Gsx1lGPryfDMPVLTo8wEf0EwCUAOoloCMCnAPgBQAjxnwB+DeBKAAcAxAC8q1iLZZhq5tH9Y/inO17A4fEYvv3Ws3HFGavKvaS8SagiWQivag0BAI5O1Z+CqOopZdzn8SCRQ9W1Q9F0KKrzfRVNwG/uKxLwYXyuOIWqaj6G2QUV0/MKWsLcMMcwTO3jJjXihhzXCwDvL9iKGKbGWFA0fPz2Xbjj+WGs64hg04pG/POvXsT5GzrQ3hAo9/LyQtX1ZAEY9HmxoimIo3V4Kl1RUyqt30tLVoRVTSSVWNvb6HrShtEQ8CJWJGuEmlbMD03G0BJuKcp+GIZhKon6avVmmDLw2xeP4Y7nh/Heizfitx+6CF+7YRum5xV8+q7d5V5a3qiagM+T6o9d3RauS0VY0VMqrc/rWbJHWNH0RQVoJkIIUxE2C+Ggr2jWiERaMV+Pvm+GYeoTLoQZpsiMzhpd/u9/9UaE/F6csqoZ73/1SfjV88O4d/exMq8uPxRNXxSVtrq1PgthVUsp434P5Ux+sN2O7pwaoZkFtt88+GgI+hBNFKcQTvcqcyHMMEy9wIUwwxSZ8WgCfi+hMZhyIv3NJSfh5O4mfOKOF7GgFOdUdzFI98YChiI8MrUAfYmKaLViKOPG16fPS46qrh1CCGi6cFST5XXSGhEJeLGg6MkCuZBkWiMYhmHqAS6EGabITEYTaG8IgChVQAZ8Hrz7VRswOhuvKkU1UxHubQ0joekYLWK2bSWi6DoCvrTUiBw+X8ttmIWnk79YqsXShiEPpmJFUIUVtkYwDFOHcCHMMEVmPJpAWyS7Ka4xZBQ180VqfioGiqbD70mzRiQj1OqrcEr3Svu9npxZwJbbMItnp/vK6+S+IgHjPRONF/49k65M19vryTBM/cKFMMMUmclYAh2N2YVw2O8FAMxXkzVCW2yN6G0zhmpUk6q9XIQQpkXEtEYs0SMsY9Oc7ivV4mRqRNB4zxTDJyzX0d0cYmsEwzB1AxfCDFNkJmwU4UjALISrSRFOKwABo1kOqC9PqVRpZQObz+tZkkdYxqY5eYQV87pA0iNsWiOKoAjLx7W+s8HIEo4pBd8HwzBMpcGFMMMUmfG5ODos8oLDZiFcrFzYYqBqerIABIwUg9aIv66yhKWlIX3Ecq4sYMvtaPkowjI1wnjPzBUhQk3ua11nAwBgsI4ObvJlaDKGc/71Puw9NlvupTAMs0y4EGaYIqJoOmYWVLRZFcJJa0Rx4rCKQaY1Aqi/CLWkIpw2WU4I5J3kIAtgJzU56RFODtQoZrOcsa8NZiHMPmF7Do/FMDaXwMP7Rsu9FIZhlgkXwgxTRCZjCQCwVITlae75xNIyaMuBoi9OjQDMQriOiiY1meSQik8DnJVdy+2YhbNT4oS8LpUjLD3CxWiWW6wI15PdJV+kR3vn0FSZV8IwzHLhQphhishk1PBZOinCxVD3ikVmfBqQmi5nTFuvfVLZvkZxKv27+U6XkwW1ognb50421PnSJssBKMp0OalMdzYG0Bj0sSLsgPT17xqaLvNKGIZZLlwIM0wRGY8a+brtDh7hqhqokTFiGTAU4VhCw1SdNFclVKnSZijCan6KcPpIY7siWtEXe4RT8WnFS43wez3obQtzIeyAVISPTMQwGU2UeTUMwywHLoQZpohIRdiqEA74PPB5qKqa5RRtcWoEAPSaWcL14hPOVITl85Fvw1y6N9jOJ6wmEypSk+WA4jRYpvzIZBbCbI2wIz3pZddRVoVrmS/89mW8+793lHsZTBHhQphhisiEgyIMGPaIaiqEVV1PNolJZJZwvSiImdm+0r+bb4RaujfYrohWMybL+b0eBHyeouQIJ/3IXg962yI4Olk/dpd8kQNNiIBdg+wTrmUe2juKP+wbLcpYc6Yy4EKYYYrIhPQIW+QIA4Y9ovqsEdnNckD9KMJWOcJA/oWw4kIRVvTFHmHAGLNcHGtESn3ubQtjNq5iZr56/OulJKaoCPg82NDZgJ3sE65ZVE3HwdE5JFQdh8ej5V4OUyS4EGaYIjIRjaM55MtqMJOEA9WlCCuaDr9vsSLcGvEjEvDWzan0dOXU+L/pEV6WNcKdIgwY9ohiDNRIzyyWdhfOErYmFtfQEPDizN5W7ByaYuW8RhmYiCV7AvZxZnTNwoUwwxSRiZhia4sADGtENY1YVjQ96VeVEFFdRaile2mBVEGctyK8yBphowjL4jTtOW8I+IozYllP9whLuwsXwlbEEhoiAR+29rZgdDaOYzML5V4SUwT2H08Vvy9zIVyzcCHMMEVkIhp3LoQD3qoZsazrArpA1kANIBWhVg8omTnCnqXlCKenTNglTmQO7wCMLOFoMUYsp6VhSEW4Xnzf+RJLqIgEvDizrxUAsHOQ7RG1yN5jcyACVrWEsO84F8K1ChfCDFNEJqLOinAkUD2KsJJhCUhndWsYw3VSCEvlVxbA8vlY6kAN49821oiMcc6AkSVczGY5n5fQEvYj5Pfg2DQrnVYYirAXp6xqhs9D2MWDNWqSfSdmsaY9gq29LTxOu4bhQphhikhORdjvqxqPcGYBmE5L2I/ZBbUuvJJKRnEqFfJ8B2qkF86KXbOchSJcLI9wal8eEFHyNWWyMRRhH0J+L05e1cSDNWqUfcdmsWlFE7Z0N+PweLSqGpsZ97gqhInociLaS0QHiOjjFtevIaIHieg5ItpFRFcWfqkMU10IITAZVSynykkMa0R1FBvJQthCEW4I+qDqYtGQiFpFzShOpX83b0U4nxzhDEV4roiT5eS+mkN+zCzUx5CUfJGKMABs7W3FLm6YqzkSqo7+sSg2r2zElpVN0AVw4MRcuZfFFIGchTAReQF8E8AVAE4FcAMRnZpxs38C8FMhxDYA1wP4VqEXyjDVxlxcRULT0eFkjaiiZjmphAYsPMJyXHS1+J2Xg6pZp0YUJUdYqs9pKnxDwFeUsdyqroMI8Jr7ag5zIWxHLKEhYo67PrO3BTMLKg6Pc2NhLdE/FoWqC2zpbsKW7iYAYHtEjeJGET4PwAEhxCEhRALArQCuybiNANBs/rsFwHDhlsgw1clkjgxhoLri0xQt268qKebEs0pDpiv4M1Mj8oxPc5MjLKOb0p/zSNCLaJEmy6UngjSHfJie50LYilhCRcSfUoQBYCcP1qgpZHPcphVNWNcRQcDnwV5umKtJ3BTCqwEMpv09ZF6Wzr8AeBsRDQH4NYAPFGR1DFPFjJtT5ToanQvhavGdOXmEw8lCuDpsHstBzYg0kx5hO59vru0Y97VThC1SIwI+JFQ9bytGLhRNX5QI0hz280ANG2JxDZGg8Z7ftKIRIb8HO7lhrqbYf3wWHgI2dDXA5/XgpK5GVoRrFDeFcPavnqEAp3MDgO8LIXoBXAngh0SUtW0ieg8R7SCiHaOjo/mvlmGqiMlYAkAORdjvhaKJghc1xSAzNiydhoBxmrguFOG0wRPA0lMj0gtn20LYKkfYPCVf6IY5VdMXHeSwR9gaIQRiSsoj7PN6cHpPCzfM1Rh7j89iXWcDQqbyv6W7iQvhGsVNITwEoC/t715kWx9uBPBTABBCPA4gBKAzc0NCiO8IIc4RQpzT1dW1tBUzTJUwPmcUwh0NQdvbVJOlQNUXD5JIp5oex3JRMprKZPG4nIEatiOWrXKEzee60BFqii4Q8KV+ElrCfszMK9wElkFc1aHpAhHz4A8Azuxrxe7hadsJgUz1sf/4HDavaEr+vaW7CcdmFjAd44PDWsNNIfw0gE1EtJ6IAjCa4e7MuM0RAJcCABGdAqMQZsmXqWuSinCD3/Y20lJQDfYIqylnkvq0RiwzR1hzlyPs8xCI0uLTpCJc4OfaUITTPMJhH3SBoviRqxnZECoP/gBga28LFhQd+46nUgUmognc9ItdmDK/B5jqYUHRcHg8is3diwthAOwTrkFyFsJCCBXA3wL4HYA9MNIhdhPRZ4joavNmHwXwbiLaCeAnAN4pWEZg6pzxaAIBrweNQZ/tbWTaQjUoqbJwC/iyFeHk6foqeBzLJenb9S0vR1h1kSOsaiJLgW80valzBbdGLN5Xc8g4gJvhhrlFSCW+IV0RNhvm0gdr/OLZIfzkqUH88rmjpV0gs2wOjs5BF8DmlY3Jy7aslMkRM+VaFlMk7H+h0xBC/BpGE1z6ZZ9M+/dLAF5Z2KUxTHUzGU2grcG/SM3LRKpK1RA75qgIV1FBv1ySdgXZLGf+P9/T4oqLyXIJTV+U5AAgeUo+VuAsYUUXi/zfzWGzEF5Q0INwQfdVzcjPajhNEV7bEUFL2I+dQ9O4/jzjst/tPgYAuHvXCN71yvUlXyezdGRixOaVKUV4VUsITSEfK8I1CE+WY5giMRFNoN3BHwwg2Ygxr1S+pUDRcnuEq6GgXy5qRrNcIGmNKI0iLJXIQlsWFDW7WQ4AJ0dkIJ/3hmCqECYibO1tSUaojc7GsWNgEp2NQTwzMFk348drhX3H5+D3EtZ1NCQvIyJsWckNc7UIF8IMUyTGowm0O/iDgZS6N5+o/CYbqVpapkYEZXFW+0WTkuERTsWnFSE1QtezcptlARYtsCKcua/msPGasjViMdKbHfYvPqG6tbcFe4/PYkHR8MCe4xAC+Ow1pwEAfv3CSMnXySydIxMx9LVFFjWPAsDGrkb0j/HglFqDC2GGKRKTLhThSBU1mTnlCAd9HhDVhyKs6GJRA9tSPcKKZkxyA5xTIwJZhXBxDjqMfVkowhyhtggZW5euCAPGYA1NF9g9PIPf7T6GvvYwLj+9G6f1NOMeLoSrimhcRWMo2zm6piOCsbl4XXzP1RNcCDNMkRiPJtAecVaEU9aIyv9idcoRJiJE/NUzJW85qBmDJ6SHdympEdJb7ZQjnGmNSB48FbpZLksR5mY5K2JKdmoEkGqYe/zgGB47MI7XntoNIsJVW1fhuSNTGJpkJbFaiCW05Gcznb72CABgkF/LmoILYYYpAoqmY3ZBda0IV4PCkJpyZv21EQn6qkLZXi6KtripzOMheGhpOcLyx9ZOTZbqczqRQPEU4fR9NZmK2MxC7b+m+SCbFNNzhAGguyWElc1B3PJoPxKajted1g0AeP0ZPQCA37xwrLQLZZbMfELLOtABgDVmIXxknAvhWoILYYYpApNRIzs0l0e4mtIWMieqZRIJ1IkirOtZBwM+r2fRgAxX29FE8oyAXeKEombvy+shhP3ewnuEtcX78ns9iAS8rAhnELPIEZZs7W3FZExBR0MA29e2ATBOp2/tbcHduzLnUDGVSiyhZh3oAGmF8AQXwrUEF8IMUwQmYrIQdlaEZQRTdVgjFseGZRKuG2tEtkob8HryVoRVXU++/rapEXp2agRg+FMLnhphkVDREvZjmgvhRcizHlaF0pm9LQCA15y6Et6098hVZ6zCzqFpDHIBVRXMJ7RF8XiStogfDQEvF8I1BhfCDFMEJuZyT5UDqqvJLDM2LJOGOrFGJDQrRZjy9ggnVMNi4fPY31fJmPYmiQR8hc8RtnhczSE/N8tlEEto8HkoK1EAAM5d1w4AuPKMVYsuv2BDBwBgzwgPY6gGYoq1NYKI0Nce4QOaGsPVQA2GYfJj0pxH394QcLxdNTWZyQEQTtaIuQIXZ5WIVbavz+PJP0dY1+H3EnxesvUIqxapEYBx0FHwyXK6gD/jcTWHfZwjnEHMxj8KAOdv6MB9H74Im9IGMQDpTVacJ1wNxGwUYcCwR/SPRUu8IqaYsCLMMEVA+jedxitLwgFfVVgjpCLsZI2oBmV7uai6nmWN8Hsp78ly0mLh93hy5AhbWCMC3oKr76qF+syKcDZ2/lFJZhEMpE6ps5JY+Wi6QELVEfFbv8Zr2iM4MhGDEPkd+DKVCxfCDFMEZEd/g8MPpiQc8GC+CiwF0gPrtzglDBgqZX0M1BCW1oil5Aj7vB7jvg45wpkDNQAjoaMUHuHmMBfCmUQTGiJBa7XQDnlKnSPUKp+UB9xGEe6IIK7qGJ2Nl3JZTBHhQphhikCys9zFD2bEXx2KcCJjolom4UCdKMIWXlonVdd2O6YVwef1JKf2ZaJoOvwWz3dDoAipEbqepfY3h9gakYldtFYuetsiGJxga0SlI7/D7KwRnCVce3AhzDBFIBpX4fWQpb8zk1CVxI4lFWG7HOEq8TovF6skB/9SUiPMgjrgtfcXW/mRAbMxseDNctaK8OyCAj1PtbuWicadrRF29LWHMTjJp9QrHad4PIAj1GoRLoQZpgjIhho5hteJiN+LhSpQhFXdGAnstVGEZY5wrRdNhkpbgNQITcDn8TjeV8mY9iZpCBQjPs06NUIXhR/eUc3M2yQK5KKvLYJYQsOEmTHOVCa5CuHVrWEQAUfGWd2vFbgQZpgiEEuorvzBgHEKrhqUVEUTto1ygOFbBYAFtfIfy3KwUk6NgRpLUYQJPo+9R9guNaIYU/xUzTo1AuDpculE4+4/2+lwckR1MK8Y7/WwzWsc8nvR3RxiRbiG4EKYYYpANA8fYbV4a1XNOsFAIh9vNRT1y0HVslVav2cJqRG60Qjn9zqkRmjZCRWAkUaiaALxAh50qBbqc3PIyMHm6XIp7IYt5KKvPQwAnBxR4eRShAFwlnCNwYUwwxSB+Tw6yyN+b1U0yxnNXfZfGclx0QXOt600FE0gkKUI26u69tsxGuGcEicU3SY1IlDY51oIYSr+2R5hgAvhdKIJDQ1LtEYA3GRV6chCWH6fWSEj1KcZsGEAACAASURBVBh3DE7EKnrYEhfCDFME8mmoqRZrRMI8lW9Hg2mNiCmV+4VXCIwc4QxF2OuBYpP8YLsd02Lhc0icUGyec3lqvlDeXS05LGXx42qRhTBbI5IYinD+1oiGoA/tDQFOjqhw5l0owmvaIzg2s1AVvR3lJqHquPJrj+DbDx0s91Js4UKYYYpALA/VKByoEkXYZtyvJFw31giryXL5K8LSiuB3UJNVs6EuE3nQES2QIixTK7JSI9gasQhF05HQ9CUpwgDQ1xbmLOEKJ2WNsD/YkckRQ+z3zsnu4WnMLqgVPY2PC2GGKQK5pk+lE/Z7kVD1pCpXqdhFeUki5qnEavA7LwdFt8gRdvD52m7HbITz58oRtnjOpe2mUIqwVLMzG/Nks9x0HoWwEAKfu+clvHh0uiBrqyRiOTJmc9HL3tKKR57Cd3qNk42P/Frm5JmBSQDA8ZmFMq/EHi6EGaYIxPJolpO3q3RVWMnhEU6plLV9Gl1RhcWI5aUUwkYjnM/rQcJOEbZ5zqU1olAeYalIZz4uOSI8n+lyc3EV332kH//18KGCrK2SkEVSg4vR6Vb0tUVwdGq+4g966xm31giA/d5ukIXwyHSVF8JEdDkR7SWiA0T0cZvbvJmIXiKi3UT048Iuk2Gqi2hcdf1jKf2GldxMANgnGEjCVVLQLxerdIWljFhWzfHJdokTQghoFsM7gNSP9FyBDjrk/rMflweNwfymy0m7xh/2nsj74KDScZMo4ERfexiKJipaHat3YooGv5ccD/o7GwMI+704Ms6FsBNCCOwwC+ETM/GKHSaTsxAmIi+AbwK4AsCpAG4golMzbrMJwE0AXimEOA3Ah4qwVoapGmJ5RCyFq8RSoGjOinC9xKdZpkZ48p8sZ1gsyDZxQnGY5CeV2kIdPMkMZCsbRnPIl6cibNx2ZkFNqkG1glTgnRIFnEgmR/Ap9YplPqHlfH2JiJMjXDA0OY/R2Tg2djUgoekVO0zGjSJ8HoADQohDQogEgFsBXJNxm3cD+KYQYhIAhBAnCrtMhqkeEqoOVReuG2qqxhqRIzVCeqJr3RphmSOc52Q5TRcQAuZkOevECbk9KxU+5REulDVC7iv7J6E57M+rWW4uza7xwJ7jy19cBbFsawQP1ah43PZ39LaF+XXMwY6BCQDAVVt7AFSuPcJNIbwawGDa30PmZelsBrCZiB4joieI6HKrDRHRe4hoBxHtGB0dXdqKGabCkT+W+TTLGfer7ELYyhKQTrKgr/DHsVwUC7tCvtaIZJHrJdMakX3fpG/X0SNcIEXYXI/fZ1EIh/z5KcJm1FpTyIcHXq4tTWS5zXI9rSEQsSJcybjt7+hqCmJ8Ll6CFVUvzwxMoinow8WbuwBUbsOcm0LYSgLK/Nb2AdgE4BIANwC4mYhas+4kxHeEEOcIIc7p6urKd60MUxVIla7B5UAN+aO6UOEFpKJlN4ml4zejwGIVrmwvF1XTs0ZN59ssJ4vmZGqExX2lSmylwof9XhAVTn1P2jAsXt/mcH4eYelbvvL0VTg0Gq3o2KR8kYXwUkYsA0DQZ4zn5SarysXt5MDOxiDGowno3Phoy47DkzhrTSt6WkMAgGNVXAgPAehL+7sXwLDFbX4lhFCEEP0A9sIojBmm7phPOM+qz8StIvzAnuO46muPlC3EXdWyY8MyiQR8BVMpKxFNF9BFdt5uvoWwoqYUYbvUCNXBI+zxECJ+bwGtEfbqc96KsPn6X3OWcTq0luwR0eTZnqUpwoDhEx7ioRoVi1tFuKMxAE0XeUUL1hMzCwr2Hp/F9rVt6GoMwkPAsSq2RjwNYBMRrSeiAIDrAdyZcZs7ALwaAIioE4ZVovaycxjGBbJrPl+PsJOSqmg6Pnv3S9g9PFO200uqTYJBOpEqmZK3VJIWgsx0hTwHaki1NzlQI0+PMABEgr7CKcJ6qjDPJG+PsFk0b+luwpaVTXhgT+3YI9xEa+Witz3MinAFE1PcTQ7sbAwCAMbYHmHJ80emIARwztp2+LwedDUFq7cQFkKoAP4WwO8A7AHwUyHEbiL6DBFdbd7sdwDGieglAA8C+HshxHixFs0wlUw0X4+wC2vE7c8M4bAZ1VMuBSJXagRgjouuYWuEapOu4PN6oOrCdTyQmmZFsEucSO3L+jmPFHAiYWo91s1ys3HV9SlgqVI3hnz401NW4OnDEzWjmuX72bair80YzxtXa/dzUs3MJ9TkcCAnOhoDAICxucpMQig3OwYm4SHgrDWGS7a7OVTV1ggIIX4thNgshNgohPicedknhRB3mv8WQoiPCCFOFUKcIYS4tZiLZphKRkYsuVWNUtYIa3Uvrmr42gP70RTKf8pXIcmVGgGYinANWyPs0hWkt9Ztw1y6FcEucSK9oc6KkM9bMJuM6rCv5pAPQgBzLqPaZhdUBLweBH1eXHbKCqi6wMP7aqM5ej6hgQgI+Zc+i6qvPQIhgKOcOFCRuLVGSEV4PMqKsBXPDkzi5O7mZNRjd0uoqpvlGIbJA6mIum2Wk+rSvGLtMf3xk0cwPL2AD1+2GUD5CmFjoIYLj3BNWyPsFWEAru0R6Y1wdokTikOkGQCEAl7b90y+JGwsH4ChCANwbY8whskY7/0zew016ODoXCGWWXaicQ0RvxdEzgeETmxa0QgA2DMyW6hlMQUkn2Y5ABib5UI4EyEEdg5O4ey1qcyE7uZQVcenMQyTB1IRdXv6NGhGVs1bKG6xhIpvPngAF2xox5VnrAJQXmuEG49w+un6E7ML2Dk4VeyllQxVt57AJgtjqzxgy+2kNcL5vR6jCS+jGFZtim5J2O8pWNKI076aQ0Yh7PZ9NxdX0WievfB5PWgIeDG7UBtnCeYVFZElZghLTu1pRtjvxVP97B6sRNwqwq1hPzwEjFfokIhyMjOvYjauYl1HQ/KylS0hzC6oFTlBlQthhikw0TwjljweQthv7ff89QvHMDaXwEdeswUt4fwKkkKj6tmxYZlEAt5FDVxff+AArv/OE2VLuig0impaGjzZqRHG9e4K4fRGuOR9M4pou6JbEvZ7sVAgn2lyX5YeYeN97DZCbS6uLnrv59tsV8lE4+6KJCf8Xg+2r23DU4dra+peLaDrAvMum+U8HkJ7Q5Cb5SwYnjZsP6tawsnLupvNCLUKVIW5EGaYApOKT3P/gxm2SVsYnjK+UM7qa0XI70HA6ymjNcKNIuxbNFDj8HgU84qGZ2tk1G7K0pCRGuHNzyOcnj4hi+pMW4WdDUMS8nsLNrzEaV9SEXYboTa3oCb97IAxWKNWFGFDLVyeIgwA565rx8vHZmqmibBWkAeWbg92OhsD3CxnwYgshM38YMDwCANcCDNMXRBNaPB7CQGLKV12hG2KmvG5OJpCPgR8HhBRWdU1xVWO8OLUCFnIP3pgrKhrKxV22b5SKXebJSwLZpkjnL7tXPuS2J1FWApO6nNLvh7hhLpoBHG+OcSVjDF+d3mKMACct74dQgDPmCNomcoglmc8XmcjT5ez4uiUUeyubrVQhCuwYY4LYYYpMLG4u1n16dhFYY1HE8mmDABoCfvKHJ/mrAiHA95kaoYQAkdrrBC2S3JIKsJum+XSGuHs/MW5coRDgcKlRthZPoB0RdilNWJBTXaKA7WoCC+/EN62phV+L+HJfi6EKwkpRoRdxKcBrAjbMTI1D5+HFv12JRVhLoQZpjhMRhP4zF0vYbICGheW8mNpZ40Yn0ugvSGQ/Lsl7C+rR9jOryppCPiQ0HSomo7JmIIFRUdnYwAvHJ3GVKz8r81yccoRNq7Pt1mOkr7cbGuEfZIDYHqEC5QaIYtwq7MYjXnG9s3GFxfCzWFWhDMJ+b04Y3ULnuZCuKJIKcLuhIwOVoQtGZlewMrmELxpB9aRgA/NIR9bIximGGi6wAdvfQ7fe6wfj1SA8rikQtjmNPdENIGOjEJ4Klb6okIIYSjCNuqkJH1KnsxJ/bOzeyEE8PjB6u+St8sRDkhV16UirKZ5jZOKsJbZLJeyT1gR8nswr2iuh3g4rkezV4S9HkJDRhOkE9F4bSvCbptgc3He+g68cHS6YD5vZvnE8hyh3dEYQDSh8WuYwdGp+UW2CEl3S4gLYYYpBl+5bx8e2W8UwCNT5Q+pz/RIuiEcsPEIR+PJCUYA0BoJlEUR1vTUAAgnZIPgfEJL2iKuOGMVGoO+ijhIWS6KZl2c2qm6brbjT6rJ1oqwXY5w2O+FpgvXxbfzepwTKhpDPsy5KGY1XSCW0JIqMmB6hOeVghTs5SbmMmPWDeetb4OiCTw3WBuNpLVA0hqRh0cY4DHLmYxMzy9qlJOsbK7MoRpcCDNVzb27j+EbDx7A9ef2oTnkSzZnlZPYEiKWIgFvVr6irgtTEU73CJfHGpFLnZRItSwaV5OF8Nr2CC7Y0I7HaqIQdk6NSLhslluUGmGnCJsFbsCmOA2ZPsZCNMzZWT4kDUGfq8lycgTxYkXYD1UXBbNxlJPYEg5y7di+th1EwNP9XAhXCvk3yxkiBWcJp9B1gWPTC4ui0ySrWipzzDIXwkzVMj4Xx0d/uhNnrG7Bv1x9Gnpaw8lu1XISU/Jvlgv7fVmFwtS8Al1gkUe4OezH7IKaVGhLhSzS7IoyiVRSYgnDGhH2e9Ea8ePCkzoxMB7D4ESs6GstJqpNfFpS1XWbGpFmRfDZJE6kkhzs49MAIF6IQjiH+twUdKcIy9ss9gibOcRV7hPWzGLebSNVLlrCfpzc3YynDle/ZahWkIk3+aRGADxdLp2xuTgUTaDHQhHubg5hdDbu+nuyVHAhzFQt+47PYTau4u9ftwUhvxerW8NVqwiHA54sRXjCnGGfbo2QUVazJS4qFAcPaTrycc8rGoan5rG6LQwiwoWbOgFUf3qE3fOQzAJeQo6w3yZxImFjw5CEC6gIJ3JkFjcEfa48wvI2DRmKMFD692yhmc9zdLobzl/fjmcHplzH7jHFJZUB775ZDjAsbIzBsOkB7rFQhFe2hKALYLTCrCRcCDNVi0whkEflPa3h5ESbchJNqHk31EQCvqyCRsbyZFojgNJPl1NzeEglkQxrhGyY2NjViJXNwaovhO2yfeXzsqwc4czJcrJYtvMIBwpojdB0+DwEIutCuDHow5yLQnjWvM1ij7BMnajuhjk5Ot1tkeSGc9e1Y17RsHt4pmDbZJZO0hrhUvWXjcwcoZZC9ulYeYQrdbocF8JM1TJhFsJtDUZx2NMaxlRMcd3dXixiCQ2RPFWjkBmFpacpihOm78xKES51Iazk8JBKImnNcsNT8+gxC2Eiwis2dlZ9XJSdXcFO1bXdTnqOsMc6cUJ1qQgXwnur6s5TA90WwvKzl+kRBqpfEY4lR6cXThHevrYNAPDcEfYJVwKxPJvlQn4vmoI+bpZLQ/aGWCnClTpdjgthpmqRMWJtEaNQlJ6kkTKqwkKIJcWnydvLEZ8AkvmUmfFpQBkVYRt1UiIfx3g0gfFoAr1tqS/DvvYIRucqzx+WD8lRxB4bj7DLHGElrRHO77PJEbbxI0uCfuPyQkQ3KZpuqzwDhsLr5gDTyiPckvQIV7ciHM0zWssN3S0hrGoJ4bkjUwXbJrN05hMaPAQE85gK2tEYwDgrwklGpheSvSGZVOp0OS6EmaplMppA2O9NNg3J0/DlbJiLqzo0XSyhWS7VZCaRnchtFVAI28WGZSKVlAMn5gBgUcNEV2MAQqSU/GrEbrKcP5kakedkOS8l/cV2qRFOAzUAFGS6nKo5K8IN+VojLBThco0GLxT5Dltwy7Y1rRyhViEYIobP1iJkRWdjkBXhNGR0mtVz2N4QQMDr4UKYYQrFZExBW9pRpzwNX86GuaWePpWd9enDMsbnEmgJ+xcVQmVThHV3qRHSG31w1CiEV7dGktd1NRle59Eq7rBW7UYse/JMjdCzc4SzC2EdRFg0nSmdgnqEc0wNbAz6oGgCcdV5X1bWiOakNaK6FWFZyDeHs5Wu5bCtrw2DE/NV/bmoFeYVNe+caFaEF3N0asHSFgEYFrkVzUGcmKms9zoXwkzVMhVLoDWSUktXNAXh9VCZC2F5+jQ/1aivzSgY0+PFJqKJRf5gAMnTTaWeLqeoLgdqmCrl/uNmIZxmjUiFz1fvj4Zik+3ry9MjrKQ1wiXvq2enRjjZFQqpCCdU4XiQIwvbXBFq8vr01IiQ3wOfh6o+Pk0W8k2hwivCAPuEK4Gl2No6WBFexMjUPFa1ZDfKSSpxqAYXwkzVMhlLJBvlAKNI624OJc365SB5+jTPZrk1HUYhPDAeTV42Nhdf5A8GjOaMgM9T8tPMSo5MW4nHQwj5jVNfXg9hZVMq8UIWwtWsfKWa5aw9wopLj7CqCXjIeL5sc4Q13fH5LuxADed9ycI2Gnfe11xCRcDnQSDNY0lEZv51tRfCpiIcKqwifPrqFvi9hOcG2SdcbmIJLe+c6M7GICZiiZJnu1ciCVXH6Fw8eXbWipXNQS6EGaZQTMaURYowYHhSy6kIJ3NU81SEuxqDiAS8GMhUhNOi0yTlmC6n2jSJWSEfe3dzaFHBKK0R1aye5MwRdqsIp1kR7BInVF045jYnC+ECNMupmvO+pCI8G3d+380tqGiymLzWFPJhpsrj02aKpAiH/F6cuqqZFeEKYH4JinCn2fswWcW9D4Xi+MwChIDlMA3JiqYQWyMYplBMxhKLPMKAmSVcxma5+TxHdEqICGvaIzgyniqEx6MJtGdYI4ByFcLuFGEg5V3N/DJsCPoQ9nurWxG2aWCTyQ+uc4S1lBXBLnFC0XTbRjmgsNaIXPtqdKkIR+PWI4ibQ9WvCM8sKAh4PckDkEKybU0bdg1NV3WiSi0QS+Q/FTRl+are77VCIUUoq/HKkpXNIczG1bLHnKbjqhAmosuJaC8RHSCijzvc7joiEkR0TuGWyDDZaLrA9LyC9ixFOIyR6flFebylJLqMzvK1HZGkIqzpApOxBDobKqMQdpsjDKQOAlZbnB7raqpuP51i08DmT9ob3HuE5UGFzyZxQtWEY3Hq9xK8HipNjnAoNSjFibm4uqhRTtIU8lV9fNrsgppsai0029a0IpbQsM/01jPlIZbQ8m+WM7+juWHOiE4DkNMaAQAnKkgQyVkIE5EXwDcBXAHgVAA3ENGpFrdrAvBBAE8WepEMk8nMvAIhYGGNCEPRRNmKrWSz3BLGsK7taMCRiRh0swgWwoibyaScirBTYSaRBwFWX4adjYGqVoQV3TpvN9Us5z5HWHqD/TaJE0oO3y4RIeTzFMQjrGi6Y0Z0o/l+nl1iIVwTivC8koyCKzTb+ozBGs+yPaKszCtLa5YDWBEG0oZpOFgjVppZwpXkE3ajCJ8H4IAQ4pAQIgHgVgDXWNzuswC+CKByHl2ReP+Pn8VX7ttX7mXUNZMZU+Ukq80PYLka5lLxafkrR2vaI0ioOo7NLKRNlasMj7DicqAGkKYIt9WeImyotNnFaTIL2OWZCFXTk9uxS5zIpQgDhg2lUIWwc2qE8TlzpQhbeGhrwSM8u6AW3B8s6WsPo6MhwIM1ysxSUiO6aiANp1CMTM+jJex3PCMqFeFqK4RXAxhM+3vIvCwJEW0D0CeEuNtpQ0T0HiLaQUQ7RkdH815sJXBidgH37BrBLY/2J9U/pvRMmvFhVoowgLL5hGWhkO/pNcCwRgDAwHgsWSxmxqcB5SqEC2ONMMLnq/cHw0hyyP7aJDIGY+STIywLYLvECUOldX6+Q34vFgrVLOeYGmG8pm7i0ywV4RpIjZhZUAqeGCEhIh6sUQHMJzSE/fkd7DSHffB5KDkJtJ4ZmVpwtEUAwApTEa6khjk3hbDVt2NSuiAiD4CvAPhorg0JIb4jhDhHCHFOV1eX+1VWEA/tNQr4ubiKe3aNlHk19cuUVIRtC+HyKsJLGcO6tr0BAHBkIppShC1SI4yiQi1pXI9dbJgVUg2w8whPRBOum8oqDUW3VoQBQ9nNzAK23U7aSGO7xAlFE65ymxdyDLlwtR7deV/yDEeu6XJzcc2yWa4p5EM0oVV1M1gxFWHAaJg7NBpNfrcxpUUIYTbL5d/o3NEYqOozXYVieHoBPQ4ZwgDQZDZNV5siPASgL+3vXgDDaX83ATgdwENEdBjABQDurNWGuYf2nsDK5iA2dDbgtqcHc9+BKQqyUMxslmsO+dEU9JXNGhE1c1TdeGkz6WkNwechDIzH0qwR2Ypwa1hO6iqdwmYXG2ZFJJkaYa0IA6nXr9pQHby0fq8nr9QI+R7xeghE2R5hVddzKvAhv7dA8Wk6/A6vrcdDaAh4XRTCimWxKJVUN2OaK5XZIirCAHDe+nYAwMP7x4q2D8aeuKpDF0s7m9fZGKz7ZjkhBI5OxrDKwR8MGAcOK5uDOF5BvSJufq2fBrCJiNYTUQDA9QDulFcKIaaFEJ1CiHVCiHUAngBwtRBiR1FWXEYUTccj+8bw6i0r8JZz+7BjYBIHTsyWe1l1iZys1tqQ/cNkRKiVpxCeT2h5j1eW+LwerG4LY2AihrG5BIiyFW+gPGOW7WLDrFjX2YANXQ2WymC1j1l2shD4vZ68JsvJ7RAR/B5P3qkRgKEIF2SgRg5rBGDE3zl5hFVNx4KiW/rjZXFczT7hmfniKsJnr2nDiqYg7tk1nPvGTMFZavQlYBTCx2crR+EsBxPRBGYWVKzvbMx520qbLpfzV00IoQL4WwC/A7AHwE+FELuJ6DNEdHWxF1hJ7Dg8idm4iku2rMC1Z/fC56GCqMJxVcOfffuPuPmRQwVYZX0wGUvA5yHL8P6e1hCGp8ukCMe1JUWnSWSW8EQ0jrZIICumC0gVwqUcsyytEW48wu951Qb87kMXWV6XnC5XpacREw55uz4PuVaEM60IPm+2v9iVRzjgLUh8WvqADzsaQz7H1AiZMWzVLNdsvmerdcyyoumYV7SipUYAxpmBK89YhQf3jla9n7oYLCgaThSx2IwpSy+Et3Q3Yd+xOSTU6rX+LJeDo8ZU1I1dDTlvW3WFMAAIIX4thNgshNgohPicedknhRB3Wtz2klpUgwHDFuH3Ei7c1ImupiAuO2Ulbn/26LLf/Dc/0o9nBiZx8yP9PKbRJcZUOT+IsguFcg7VWIrHLJ21HREMjEcxPpewjE4DgJZI6RVh+R534xH2eMi2WFxRA4qw3cGAYY3IIzUircj1ebL9xaruRhH2FGyghlNqBGAM1XBShOfM5uFGi+jApCJcpQWebBIsVo6w5PVbVyGh6nhgz4mi7qca+doD+3HlVx8pms98PiEbnfN/jc/qa0VC0/HysZlCL6tqODhqZGBv7HKjCAfNKXSVUe/wZLk8+P3LJ3De+vZkV/RbzuvDRDSB+/ccX/I2R6bn8Y3fH8CqlhCOzSzgjwfZH+aGqVgiKzFC0tMaxkQ0URDvZL7EEhoiFiq1W9a2N2BmQcWh0WgyqD2Tslgj8hio4US1T2FSdXuPsNEs594jnG5FsPIXp9sn7AgV0hqRQ33OWQgvyEI4WzWV3trZKh2qIQv4YirCgGGP6G4O4W62R2Tx4vAMxuYSePlYceyI8oxGZAmTA8/sawUA7Bys3/i7Q6NzCPo8lk3SmaxsDmFB0StmyA4Xwi4ZnIhh/4k5vHrLiuRlF23qQk9LCD98fGDJ2/3cPXugC4Ef3ng+WsJ+/PyZoUIst+axGq8skR/EctgjYgl1SV+kkjVmhNq+E7OWjXJAuTzC7nOEnQgHvGgM+qpWEVYcFGEjPs2lR1hfbLEwrBEWqRE5nu9wgZrl3CRUNAR9joWsbIRrsFCEZSE8U+LYv0IhH3dzET3CgHE25aqtq/DwvrGSRyRWOv1jhuL49OGJomx/OYk/PS0hdDYG8fzgdKGXVTUcHI1ifWcDPC4aqlMRapVhj+BC2CUP7TVOVb365FQh7PUQ3v6KdXj80Dh2D+f/AXj84Dju3jWC9168ESetaMTVZ/bgty8eq9rTh6VkMqpYNpIBqbSCocnSF8LRuGZZCLhFZgkLYR2dBpSnEM4nRzgXnY2Bqs0SVh28tPmmRvgWWSM8WTnC6UM37Aj5vQWxRrhJqGgK+hB1yE6XhbBlaoRpKahaRXi+NIowAFy1dRUSmo77X1r6mcZaI65qye/zHYeLk7U8ryw9A56IcFZfC3YOlU8R3nF4oqyTCQ+OzmHjity2CABY2SSHalSGIMKFsEse3DuKtR0RbOhcbAS/4bw1iAS8uOWR/ry2J4TAp+/ajd62MN53yUYAwHXbexFXdc4ndoGhCFsXwmvajWLyyHi0lEsCID3Cy2uWk9h5hEN+LwI+T0nVNVXXzZiv5RfCXU1BjFZph7XiYCHwez355QinFdQBX3bihBuPsFEIL98zqbpQnxuCPseBGk7WCGknq9aDfHkKt5ipEZJtfa1Y3Rpme0QaR8ZjEAJoCHjx1OGJonhLU4rw0l7jM3tbcXB0rizv8bG5ON7xvadw7bf+iJt+savkZxMWFA2DEzFX/mCg8sYscyHsAk0XeKp/Ahee1JlVCLSE/XjzOX24c+cwjk27f1H/sG8ULx+bxYcv24yQeSp9a28LNq1oZHtEDoQQmIopltFpgGHED/u96B+LlXhlxpfpchThSMCXjBjrtLFGAKWfLufUJJYv1TxdLrOATcfnzSM1ImM7VokTbjzCYb8XCU1fdgORounw+9zEp9mrz1EHa4TP60FDwFu1irBMcZBnY4oJkWGPeGT/WNXmbReaQ2OGqPGGM3swOhvHwHjhv9uXY40ADJ+wEMCLQ6W3R3zj9wewoOq44bw1uO3pQVz25T/gjwdK1280MB6DLtwlRgDACjlmuUIEkYothG95tB83fv9pfOnevfjtiyOYLlFU1Ncf2I87nju66LK9x2YxF1dx7rp2y/vceOF66ELgB48fdr2fWx7tx4qmIN5wZk/yMiLCddt78czAJA6ZHZi5EELggz95DnftrB/1IJbQkNB0W0WYiJLpC6UmtoQRnZmsNVXhQTlGdAAAIABJREFUdhtrBGAM1ShpakTaJLTlYijClXFKLF8cUyM8eVgj9MXNcj6LxAlVEzmf83DAuH5hmck1iovXtynkQ0LTEbeZZCej1ZosFGHj/v6q9QiXUhEGgD/f3gsB4P/eu7ck+6t0DpnRXG8+15jtVQyfsPTaL8UaARhCFgA8V+KGuYHxKH705ADecm4f/u3aM3Dn316IpqAPf//zXSWb4JlPYgRgCD5NIV/FjFmuyEL4xaPT+Nw9L2Hn0BS++eABvPd/nsXlX30YI0Vufrpr5zC+dN8+fO33+xdd/syA8aHbvrbN8n597RFcfno3fvTEgGNXteTlYzN4ZP8Y3vGKdQj4Fr8Eb9q2Gh4Cbn/WnSr8/OAU7tw5jL//+U7sO14fwz0mk+OV7dWZdR0N6C9xISyEQDShLksRBlINc3bNckB5FOFc6qRbOhuDmJ5XbAuqSsZQaR1SI1zHpy22IvgtEifcKsIAluUT1nUBXSD3QA2zQLBThZ0UYcDwCVe7Ity4jESYfNi0sgnvfMU6/OSpI2X1fVYK/WNz6GoK4qzeVrRG/EUphJerCLdGAljf2VDy5Igv3bsPPo8HH7p0EwDg9NUt+MRVp+Do1DzufL40ApkU7ja4VISBysoSrrhCWNV0/MPtu9DRGMQDH70EL33mcvz3X52H2QUV7/p/TxfNfzM0GcM//vIFBHweHBqNLppM9szAJFY0BdHbZh8LcuOFGzCzoOJnO3IP2Pjeo/0I+7146/lrsq5b0RzCxZu78Itnj7rKFL571wgCXg8agz68/0fPliUyrNQkp8rZKMKAMd1scCJW0lzmuKpDiKV7zCRr240vE7v4NKAMhbCLgQtukdaPahxJavh2bVIjvB4oeXmEM3KEs1Ij7G0YkqBZCC/nc68kh6XkGqhhjkm2KWbn4ipCfo/t+6Qp5K9aj/DsgoqGgLdgnwE3fPg1m7GyKYRP/PLFomXnVgv9Y6lEgnPWtuPpIjTMyRzhkG/pQsaZvaVtmHvx6DTu3DmMv7pwXTKJAQD+9OQVOLm7Cd/+w0HoJfgNPDgaRU9LKK/fPpklXAlUXCH8vcf6sXt4Bp+++jS0hP0I+b24aHMX/vNt23HgxBze+8NnCj69RdMFPnLbTggBfP2GbQCAR9P8NTsGJrF9bZtjo9D2tW04Z20bvvPwIUela3Q2jjueG8Z123ttC7nrtvdhZDp3prCuC9yzawQXbe7CV95yFvafmMOn79rteJ9aQPrm7JrJAGBdRwSKJko6alkqYssZqAEArz65C6/Y2IG+tMa5TEpdCCuaWDQAYjlUc5awqtnnCPs92dPhbLdjaY3Qs2+T4zkvhCIsC/DcOcLGvuZsznrNxVVHxbQ5VL2K8My8UpLEiHQagz588g2nYs/IDH6wjIjOWqB/LJpsVD93XRv6x6IFt1cZtjavq/gvO87sa8XxmXhe/ULL4T/u34fWiB9/ffHGRZcTEd53yUYcODGH+5Yx58At+SRGSFY2hTg1worBiRi+fN8+XHbKSlxxevei6y7c1Il///Ot+OPBcdz4g6ext0Ch2kII/Mf9+/DU4Ql85prT8NpTV6KzMYhH9xtF6PGZBQxNztvaItL54KWbMDy94Njs9sMnBqDoOt71ynW2t7n0lBWuMoV3DEzi2MwC3nDmKrxqUxfed8lG3Pr0IL5y376CRCpVKq6sEeaX5uES2iOWe2pNsrW3FT9+9wXJJkormsP+kvnmAbMALLAiXI0+YcXBIuL3Zic/2G8nIzXCInFC1QT8vtw5wgCWlRyRLIRzTpYzPm92EWpzC86FcLUrwsWeKmfFFad34+LNXfjyvXvL0vNQCUzPKxibSyRPu5+73ujV2VFge0RM0Zb93S0HazxfAntEXNXwyP4xXLutN5nTnc5VZ6zCmvYIvvXQwaJOcBNC4OCJOdf+YMmK5hBOzFbGdLmKKISFEHhgz3H85S1PwkuEz1xzmqX6+qZtvfjcm07H80emcPlXH8bf3focDpxYekGcUHX84y9fwNd/fwDXnr0ab9q2GkSEC0/qwGMHxqDrAs8OGKdg3BTCr9rUibPXtOJbDx60VK3n4ip++PhhXHryCmxweNOE/F5cfWYPfrfbOVP4rp3DCPk9uOyUlQCAj7xmM646YxW++sB+XPqlP+BXzx8tyWmRUuPKGtFhFsJjpS+EG0rgI2wJ+zEbV0tm/VAcLAH5ItMwqlERdmoqW1ZqhMV9FV3PqcLLxp7lTJeT1ohALo+wVIRtVN1oXHV871ezR3hmofSKMGAoe//6xtPh93nwru8/XdKD30qh3/wOX99p/Gae3tOCkN+DpwpcCM8ntCU3yklOXdUMv5dKYo94YWgacVXH+Rusm/h9Xg/ec9EG7BycwuMHx4u2juMzcUQTmuvECMnK5iAUTWCyAt7TZS+ED49F8fbvPYUbf7ADHg/h5necmxyIYMVbz1+Lhz/2arz34o24d/dxXPblh/Hm/3wctz8z5KpRTXJsegF/8d0n8JOnBvH+V2/Ev193ZrL4fuVJnRiPGqMcdwxMIujz4LSelpzbJCL83WWbcXRq3lLNvfmRQ5iMKfigaWp34rrtvVhQdPzaJlNY1XT85sUR/OnJK5I/Pn6vB99869n48buNKXV/d+vzuODfHsBNv9iFB/YcrxmVWCrCrQ5RRiubgwj5PThchJgdO6RStlxVwQ1SDZfPRbFR1Nx+VbekrBHZa1c1vaLfp6oubGPG/N7soRi227EaqJGmJmu6gBC5VdqQ37h+WYWwnBqYY18yMcHOGjGbwxohUyMqQQHKl9kFtWSJEZn0tUfwX2/bjsGJGN77P4W3BlY6cqLcevMsX8DnwVl9rQUfrDERTTj2Zbgh5PfilFXNeGag+A2O8kDALs0KMOqIrqYgbnk0vzkH+ZBvYoSkkrKEy/PJNhFC4G9+9CwGJ2P41BtOxdsuWOvqx7atIYB/uPxk3Hjhevz8mSHc9vQgPvqznfjfP9+J9Z0NOHVVM1a3huH3euD3eqALAUXTkVB1DE7GsHt4BkOT8wj5Pfj6DdsWRZgBwKs2dQEAHj0wimcGJnFmb2tWuoMdF23qxFl9rfjmgwdw3fbe5P0mognc/Eg/rji9G1t7W3NuJz1T+Przspvqnjg0gbG5BN6wtSfrulds7MTdH7gQv35xBL954Rju2jmCnzw1iJDfgwtP6sJlp6zAOevasb6zAd4C+T5LyVRMQVPI5/jDTURY19FQWkU4vrxA9nyQ/uGB8ViysCwmmZ7W5RDye9EUsh6z/M+/ehFP9U/g/o9cXJDhHYVGcfAIux2xLIQwn8+M1Ig0RThVnOaeLAcsr1nOrUdYHnDbeoQXVPS0hiyvA4wxy6ouMK9oJfmMFJLZBSVZiJWD8zd04At/thUf+elOfOKXL+CL122tyM9HMegfjcJDi4cNnbeuHd948EBOX3o+HJ9ZQG+bfV+GWy7a1IVvPXQAo7PxpA2sGDzVP4HNKxsde2VCfi+u296L7zx8qGjrkYkReXuE0wrhU1Y1F3xd+VDWb6PHD43jpZEZfOHPzsBbzs0u9nLR2RjEey/eiL++aAOe6p/AE4cm8NLINJ4fnML9e45D0UTy1LHPQwj4PFjZHMKZfa34i/PX4LWnduMkixevuyWEk1Y04v49J7B7eBr/61UbXK+JiPChyzbhnf/vaXzvsX681zSxf+vBA4glVHz0tZtdb+e67b34t9+8nOyYTefuXcNoCHgXjXxOx+MhvH5rD16/tQcJVceT/eO4/6XjuH/PCdxvmudDfg+2rGzCKauak/+d1ee+6C8XTlPl0lnX0YD9y7DO5EspFWH5fjg8FnVl21kuTgXgUuhqDGI0wxoxEU3g9meOIqHp2DU0nfTbVRJOOcJWWcCW2zC/k9JtD74Mj7AshHPmCJuF8HKi6JL7yiFCyELY7sybER3opAinxixXWyE8U0ZFWHLt2b04PBbF135/AO0NAXz8ipProhg+NBZFX3tk0e/Suevbof8eeHZgEhdt7irIfk7MxgvyXXrNWT34xoMHcM+uYbzzlesLsLJsNF1gx+FJvHFbthCWyZ+d3YtvP3QQv3r+aF61jFsOjkbRGPRhRZ5F9kpzqEYlZAmX9ZN9yyP96GgI4JqzVi9rO0SE8zd04PwNHVnXaboAAXl3gl54Uie+/8fDAIDta/L7cFy8uQuXbOnC//nNy3h5ZAbvu+Qk/PcTA7j27F6ctKLJ9XbetG01vvDbl/GV+/bhP95yVvIx7D8+i3t2jeCyU1c6NlRJAj4PXrWpC6/a1IV/uVpg3/E5vHB0GntGZrBnZAa/3X0Mtz5txL61Rfy4+sweXHt2L05Z1bzoy2d6XsHxmQU0h/zoagqWTU2ejCloc3EKa21nBL9/+QQ0XZRkrdK/V4rpU33tEXg9lPTPFZtCTpYDgE6LoRo/2zGIhKbDQ8BvXjxWmYWwQ4ycVRaw5TYsmtP8GZPlUrdx6RFejiKsu9tXQyBVyFqRq1mu2fxczMwrSTWoGhBCYLZMHuFMPvyazZiMKfivhw8h4PPgo6/dUu4lFZ1Do6nECMm2NW3wkNEwV4hCOK5qmIgmCvK+3LSyCSd3N+HOncUrhPeMzDgO+UrnpBWNOLOvFT9/Zgg3Xri+4AdPB0fnsLGrIe/tSnX6WD1bI+KqjgdePoG/u3STq2JuqSy1AEovhM/O8yiRiHDz28/Btx46iK8+sB937RqB11SK82FFcwgfumwzvnzfPjSGfPjcG0/HwHgMb735SYQCXnzkNe7U5cy1beluwpbuVEEuhMDxmTh2DhnDOX7y9GAyrqcp5DOiumJKcnIUYCjsq1pD2L6mDRdvMYrsUpyiB4DJaMJx2IRkfUcDEpqO4al5xyiyQiEVzmKeDpP4vR70tYVLNjRE1QuvCL80MpP8W9cFfvzUEZy7rg0hvxe/fXEE/3D5lopSvIQQjjFyblMjUrm9qe1k3lfeJqdH2FeAZjlpw8jx+no9hEjAa6sIu4lPA1JT2qqFuKpD0URZUiMyISJ8+urToGg6vv77Awh4PfiAi56T5fLS8Axue/oI/un1pxasV8ANQgj0j0VxQYbI1Rj04bSeloI1zMmDcqlSLperz+rBF3+7F4MTsaL89jzZbzzu89bnLoQB4LqzV+Off7Ubu4dncPrq3P1ObhFCYP/xOfzJxmwRMhdBnxddTUEMTZauj8eOsn2yx+biaPJ58LYL1pZrCY6cv6EdXo8xqtfJg2OHz+vBBy/dhIs2d+GmX7yA1566ckn+ow/86UmYVzR8+6GDUFQdfzw4DlUXuO09F2BtR2E8a0SE7pYQulu68brTujE9r+D+l47j6NQ8JqIJTMUSaI0EsLo1jBXNQcwuqBiemsfARAyP7B/DHc8Pgwj48GWb8YE/PanoxctkLGFpaclEPj+Hx6MlKYTH5uJoDPqKemCXzrrOBvSPlqYQVjSRHOdbCLatacU9L4zg1qeO4Prz1uCRA2MYGI/hI6/ZjLm4ik/88kXsPT6Lk7vL6x1LJ2mzcpgsl3CRGqGo2VaEzNQIWRTnSnIoRGpEcl82TYDpNAZ9lh7hBUVDXNUd7QNSUa22CDU5FroSFGHAOLv5+TedgYSm40v37cMZvS24ZIu1Ra5QfOuhA7h71wg2dzfhreeX7jf7+Ewc84qG9RaJBOeua8ePnhxAQtWXbec7YRbCKwp0puINW41C+M6dw3j/q08qyDbTeap/HGvaI1jVYh8ssGg9Z/bgs3fvwe3PDhW0EN5/Yg7HZhaWbClZ2x7BQAkb2u0oWyE8GUvg7WetLol6thSaQn5cf27fsguos/pa8Zu/e9WS709E+NjrtkBRddz8/9s77/g4qnN/P2d31Xvv1UVucscV2xQDBgIGA6HDhQAhgSQEbnJzSSA3IQmk/pIQEkqA0EsMwYbYENNsA+5dcrfkIkuWZEtWtaQt5/fHzq5X0q62a1f2eT7Wx6vZmZ3ZPZqdd97zfb/vF9UkRht4/e4ZjMjyXGLhLUkxEVwzJd+jdS0WSWVtK8+truIPK/ZystPITy4f7ZcpuTtOdhpJHsBD2IZdR3uikznBT5pwvN2zTHWgKE6LY311E1LKoN98WG3DArePO2aXsHJvI48uqaQsO4FX1x4iLS6SBeOyaT1l4ifvVbB8x7GwCoRt+l9XGbEInc6jhhrOpAgRfRpqnC5gc9NZTgsA/PER9jQjDK4DYZvOb6BAIilmYGlFuGLLYCeGWCPsiE4neHxROVsPn+Rn7+9k5rA0ovzoiDYQbV1GVuy01pX86eN9XD0pb9A03lWaY0RfaQRYG2u88GU1FbUtTPZSvtiXBm163ludqysKUmOZUpTC+0EIhKWUrK9u4kLNNtUTkmMjmT8mkyVba/nfS0cHrA7I9ndx0RjPj8WRwrTYoFq7eUrIqqKkhG/MCY5+JlD88upye7FbKBFC8OPLR/P4onLevGdmQO/o/EWnE5TnJ/HH6ydyx+xiXviymh++sz1oLUF7TBbau00eFctlJmgWaoOkoz3e1j1o8hCw9nXv7DEPSmMK0wCNJHxBrxM8eeMkspKiuOeVTXyyq57rphbYp8vOKUrlw4pjAdtfIHAmaXDEoBdYJG69u50Vwhl0olexXI+HrhFCCGIi9H5Zzhk91CMDxEcbnEoj6lqsHRxzklwHwvaM8CB2RAwEtgy2s6YFoSTKoOfRK8ZQfbyDF744GLT9fFhxjG6ThR9fNpqGtm5e/DJ4++pLVaPNQ7h/IDxV08duqPZfHmHrcBZI7fqVE3LZfawtYM2/bOxvaKe50+ixLMLGNZPzaero4fM9DQE7lv/srGdCfpLPn1txWhx1LV0ht8wMWSCcEGVgZBCzmmcaQghunFbImNzwyZA5otMJHv3aGB6YP4LFm2p4ZElFUPxCT55y31XO8ZiK0+IGrSPT8fZue7OIwcDWNKRqEAJ94wBFYr6SHBvJM7dMpa3LiARunn7aOWbBuGz21LfZrXnCAXc2Y7ZMsTsvYWeFcIY+GmGTxTMnB7C6v/hXLOf5vuIinWeEbQUvAwXCSTER6MTp7NtQwZbBDrVrhDPOK8tk/ugsnvx0X9Da+r639ShFabHcNaeE+aMzefrzAzR3DI5/+Y6aFuIi9WQ7CbQyEqIoTY9jQwB0wvWtXRh0glQPEiyecll5DjoBS7cdDdhrwml98HQvA+G5I611PO9sHrhjrafUt3ax7chJn7PBAEVp1hn3I02hlUd4dGUTQiwQQuwRQuwXQvzIyfMPCiF2CiG2CyE+EUK4FREVh9CTUREcrNZxI7nv/GG8sf4Iz6yqCvg+mjvcd5VzpCgtdtCcFU509AxqRtjRQi3YmAYoEvOHMbmJPH/7OTy2cFwvGdICrcX6h5XhkxU2uWk8YQuQ3RXMmZwUwkXoRa8A2lNvX8DvjLA3+4qPNtDe3X9ftiAsewDNYnSEnpL0OHYFOEMWbNpsGeFBcIPxhUe/NgaTRfKrZbsC/trHWrr46sAJrppo7br6g0tG0d5j4q+f7w/4vvrS1mXk/e211oDSxd/m1OIUNh5q9ruDan1rN5kJUQGV9GUkRHHuiAze3Xw0oDOk66ubyEqM6uWr7AkReh1XTczl090NNAXgRsZmw3rRmGyfX6PQwQ8/lLgNhIUQeuAp4FJgDHCjEGJMn9W2AFOllOOBxcBvAn2giqHDQxeVccWEXJ5YvptlO5x3xvOV/Q29uwy5ozg9jiNNp4LeithkttDcObiBcG5yDJF63aAE+iZz4DPCNmYPT+9XNJubHMOEgmQ+qqwPyj59wWjz/3UhIbBlVN0FwkYnhXAReh1Sni7I89TbFyA6Uh8Q1whP9mXVCPeXNtS1dBEfZXDb3GB0TiI7a1sHXCfcaD0VvhlhsOos751bytJtteyqC+xnu3TbUaSEqyZZLU7LshNYNCmfl9YcoqEtuJn997bW0tlj5uYBCurPKU7lZKeR/X7OHDW0dQWsUM6Rm6cXUtfSZQ8a/eV4ezcrdtYzd0SGT3Uh10zJx2iWLN3qf5b64531FKbGMjLLu0YajtgK2g8NgYzwNGC/lLJKStkDvAksdFxBSvmZlNL2TtYCnlVaKc5IdDrBb68dz5SiFL7/1lbueHE933p1Ew++tZXHl+3ipa8O8tmeBp+C08raFgw6wQgPT75iBwu1YNLU0YOUVn/cwUKvExQOUsbbaJGDapsEMKM0lV21rb2KyEKJyU1RmS1A9lgaoevtGgGng1J70w1PAmFDYDTCHkkjovR0uMgIZw8gi7AxJjeRoydP0TKEdMK2jHC4uEY4447ZJUTqdbyl+cEHin9tqWViQXKvxMP9FwzHaLbwqmaxGQyklLy29hDj8hKZkO+6Jsamk/VXHtGgZYQDzfzRWeQlx/DSV4H5rJ5dVUW3ycy95/lWuzQ6J5ExOYm8s9m/QLij28SXB05w0Zgsvwq1U2IjSIgycHiQ5Iuu8OTKlgc4nl012jJXfANY7s9BKYY+0RF6nr11CueXZXKio4f9De2sq27ixS8P8tOlldzx4gYeXVLh9etW1rYyPDPe4wpp2xd4sHW0dg/hQdQIgzXQPzgIXyJGsyWgDTU8oSwrgR6zZdA03u6wZ05dVFzbMubuAndnhXC2wjnbtp62WAarhZpf9mkWz/cVHxVBuxPXh7rWrgH1wTbGaK1UA525DCZtXSZ0AuIGoWOkr6Rojivvbq4JWOHR7mPWhktXT+p9uS9Jj2P+6CxeWXsoaEVOmw83s/tYGzdPLxow0CpMjSUjIcrvgrn6tq6gNHnR6wS3zChiTdUJ9tb7JwlqaOvi5TUHuWpSHsMyfM/CXjslnx1HW/wq4lu1t5Eek8UvfTBY5ZSFabFDIiPs7K/QaSpPCHELMBX4rYvn7xFCbBRCbGxsbPT8KBVDkrT4KJ6+dQpL7z+XFQ/O48sfXcDuxxaw4cfzuXN2Ca+tO8y7Xgr3K2tbGZvruWvGaM1+q7K2xav9eMvxdqvmajClEWB1jjh4otNvjZw7TGYZ0IYanmArpt1bHx4Fc/bMqQsdoccaYSdSBFsQatv2dJbWU42wP/ZptvfliTRCT4/Z0q+lc31Ll9OCpr7Yin2HkjyiVesqF07NXZxxw7QCWrtMAXNbefKT/UQadHxtfE6/5+46t4TmTiPv+plZdMVraw8TH2XgygkDtxAWQjCtONVuI+kLXUYzJzuNAWum0Zfrzykg0qDj5TUH/Xqdpz+vwmiWfPcC/7xAF07MxaATfhXNrdhZT3JsBFMD0JK6KC2Ww+GuEcaaAS5w+D0fqO27khBiPvBj4EoppVM/Jynls1LKqVLKqRkZgekPrhha6HSCjIQoHr5sFNNLUnn4XzvYfcyzi2JDWxfH27sZ64VzRlJsBAWpMVQeDe6F97hmYTbYgXBxWhw9Jgu1LcGVfoQiIzw8Mx6dIOD2Q77irDWyIxEeZoTtPsK63q4RcFpW4U6G4Uh0hN4/1wgvss82DbCjPMJkttDQ5llGODMhmvT4yCGXEQ6HrnLumFGSRlFaLG+sP+z3a328s55/76jjuxcMJ83Jd9q0klTK85L4+xdVAb8Jb+7o4YMddSyanEecG805wIxhadS2dPlccNUY4GYafUmNi+TKCbm8u/moz81k6lu7eHXdIRZNyvPbaCAtPorzyjL51xbfivhaOq2+0heOygpI3UhhahxHmjuDXsczEJ68iw3ACCFEiRAiErgBWOq4ghBiEvAM1iA4cCZ1ijMWg17HkzdNIiE6gm+9utmuwxuISi2L5K2F3LjcJCqCnhHWAuFBbhBTnG6tug22TthkCayPsCdER+gpSovze0oxUBjdSAjsWV0PfYR7uUbonGeEPfnMoyN0/mmEnTT4cEWcPRA+LY9obO/GIiHLg0AYtIK5IRUIG0mICl99sA2dTnD9OQWsq27yy3awrcvII0sqKMtK4J65zrWoQgjumlNCVWMHn+8N7CX/pTUH6TFZuMnBTnEg5gxPB2D1/uM+7c9W9BcMjbCN/5pVTGePmcUbfcvC/umTfVgsku/4mQ22ce2UfBrbun36zJ5dfYC2bhPfODcwfSCK0mIxmqXdizwUuA2EpZQm4H7gI2AX8LaUslII8XMhxJXaar8F4oF/CiG2CiGWung5hcJOZkI0T900mYMnOnhmpXurtZ2+BsJ5SRw60RnUAp3j7d1ER+gGXUdYmm7VigXTQk1Kidky+NIIgJFZ8WETCJvcSAgMOg8zwk5kD4Y+jhPeePvGRPipEdaON9KDfdmcExy9hOta3HsIOzImN5F99e1hUwTpjtZTprB1jOjLtZPz0esEb230vWjudx/t4VhrF09cUz5gB7LLynPISYrm6ZVV9JgCM5brqk7w5Kf7ubw8x+OukkVpseQlx/DlPt8C4WA00+jLuLwkphal8P9W7GWjl4V9L3xRzevrDnPbzGIK0/zrdGvjglGZpMRG8MY672YPGrWGKldMyA1YT4MizUItlPIIj65sUsplUsqRUsphUspfasselVIu1R7Pl1JmSSknaj9XDvyKCoWVaSWpXDoum5e+Oug2UK2sbaEwNdbrDk+2TnzB1Akfb+8hLS5q0HWEWYlRxETog1oMaLf7ClBbTm8YmZXAwROdIe88BO4lBJEG73yEI/r4CIOjNMJzJ4eYSH9dIwb2R3bElhF2DITtHsKJrj2EHRmTk0iP2cKBEDRLaek0ctmfVnPHi+t5dtUBj74TWruMYesh3JfMxGguHJXJO5tqfLrR2HbkJC+vPcTtM4uZ5KZtcYRexzfnlrK+uokLfv85izfV+DW93dDaxf1vbKEoNZYnrin3eDshBOcOT+erA8d92n+91uAlmIEwwJ9vnERGQhS3Pr+eLzwM2hdvquHnH+xkwdhsHr5sVMCOJdKg46bphazYVe/V7MFfP99Pt8nC9+cHJjMN2IP7UBbMhayznEJh477zh9PWbeKlrw4OuJ61UM77u9Bx2jbB1AmvUuDkAAAgAElEQVQfb+8edFkEWC8CxelxQc0I2wOlIDTUcMfIrATMFmlvtRpKetz47XqaEe5xkhHuqy/25jP3NyNs9KahhpNA2OuMcE7oCuYqa1vYWddKZW0rv1q2m8v//AX/3j6w13lb19DJCIO1OOt4ew+f7fZesrB0Wy0Reh0PXTzSo/Vvn1XMP+44h+TYCP77n9u4/M+rfeoSZjJbuP+NLbR3mfjbLVO8tqqbPSKd1i4TO456n+yob+0mQi886lbqD7nJMbz1zZkUpcVy5z828JdP9/HelqN8vqfBLq1zZPmOOv7nne2cOzydP904MeA+7rfPKiZCr+O51dUerX/05CleW3uYaybnUeqHa0VfcpKsfvihbKqhAmFFyBmbm8T80Zm88GW10/atYNWtHTrRab+IekNafBS5SdE+fUl6SmNb96Bbp9koSY/lYBC/RNwViQUTm3PEvobQyyOcSRocOe0F7JlrRC8fYRcaYU8ywlGaa4SvRUveZJ/tgbCDhVp9axdRBh3JHgYSJelxRBl0IQmEa5qtOsR3vjWL9Q9fyPDMeJ76bP+AjgOtXUavZ6FCyTytle7iTd7rUddXNzGpINnjQFQIwXllmbx//7n89ebJ1J48xbVPf+VxATTAlsPN3Pr8etZXN/H4onLKshO8Pu7Zw9IA+NIHzWtDaxeZCdGDMpuXkRDFm/fMoDw/id/9Zy8PvLWV/3pxA+f/9nP+ufEIUkpMZgu//nA333ptM+Pzk3jm1ike24V6Q2ZCNNdMzuedzTX2gsGBePKTfQB898LAZYPBajGXnxoTUptMFQgrwoL7LxjByU4jr651bjy+q84aCI3N802XNDYvuAVzx9sHt6ucI8VpcRxu6gyYTq8vRvtU/uBnhEvS4zDoRFg4R9j9dl021ND1Ws/l6zgphOubEfbG2zcmwnqR7PZx/E0WC0JYL0jucFYsV9didYzwNJAw6HWMyk5glxfBUqA40tyJTkB2UjSZidHcM6eUnXWtfHXghNP1LRZJe7eJxCGUETbodSyanMenuxs44STT6Iq2LiOVtS1M15pUeIMQgsvKc/jnvbMA+PrTa9xqYasa27nrpQ1c/dev2Fvfxi+uGmfvYOctafFRjMlJ9Fhy4EhDWzeZQbJOc0ZybCSL753Jlkcu4tOH5vHG3TMYnZvIDxZv5+6XN3LDs2v52+cHuHFaAW/cPcMj5wxfuXtOCUazxe1s7NGTp1i8qYYbpxWQnxIYnbIjRamxKiOsUEwsSGbOiHT+vrrKqRWUTcvnjYewI+Nyk6g+3uEy4+wPFoukqaM7ZIFweV4SZotke81Jv16npdPIuqr+AYGzTmiDRaRBR2lGeDhHuPP29dRH2OhEI9zXccIbb9+YCOs6vuqEjWbp0X4A4p0Uyx1rOeW1vnJMrrXVsq/er75S03yKnKQY+2e/cFIu6fFRPLvKebFue48JKcO7q5wzrpmcj8kiWbK1n9OpSzYdasYiYVpJms/7LctOYPG9s0iPt2phnbnZSCl5Zc1BLvvzatZVN/HfF49k1Q/P79dm3VvOHZHOpkPNXlsJ1rd2kZUQXH1wX4QQpMRFUpoRz8xhabx59wwe+doYVu87zq66Vv50w0QeXzSe6IjgFl+XZsRzyZhsXl5zsNfNbV+eX12NBO6eWxqU4yjSkjnB/D4YyF9bBcKKsOG7F47geHsPD/9rR79p3sraVtLjI322uCnPT0TK4OgSmzt7sEhID5E0YtbwdPQ6wcq9/jWp+dn7ldz893X9LiTedDkLBiOyEsKiqcbpLK2fPsJONLl99cXeePvGaE4lvuqETV54RMdFGhDC2lLchi0j7A2jcxJp7jRyTCtUGixqmjvJTzld1Bdl0HPH7GJW7m10Op3fpklAhoKPsCNl2QmMz0/ySh6xvroJg04wuSjZr30XpMby2t3TidALHnx7ay+v2oa2Lm5/cQOPLKlkekkanzw4j/svGBGQrOe5w9PpMVtY76UrQ31rV9CaaXiKTif4xrklfPLQPD5+aB4LJ/qWGfeFe+aV0tpl4rV1zmdjT3b28OaGw1w5ITco2WCwdghs7zb1+l4JJO9uruHeVze5fF4Fwoqw4ZziVH5wSRn/2nKUR5ZU9Lo73FnbyuicRJ91XOO0THJFEHTC9q5yISiWA0iKiWBSQbJfgXBDWxfvb6/FZJH9WjbbspSe2GsFg7KsBA43ddLZE/hsvje4Kyo7LY3wzEfYsVVzX8cJ22t4otu1ZY18DYSNZovH+m+9TjA+L4m12syBxSKpb+0iO8kzxwgboSqYO9J0qt/F/ObphcRE6HluVf+iIZu/+VDLCIPVK9ZaGOjZd9766ibG5SURG+l/UJqTFMNjV41jy+GTPKNl23cfa+Wqv3zJ+uoTPLZwLP+445yANrE4pziVSL3OK53wqR4zrV2moDXT8Jb8lFhyvDyX/GVyYQrnlWXw+//sdfq38sqaQ3T2mPnmvOBkg8FqgQfBcY5Yva+RHy7ezsxS1zMdKhBWhBXfPm8Y984bxmvrDvPLf+/iQGM7FUdb2NfQ5rMsAqy2QhkJUUHRCdubaYRIGgHWApntNS1Oq4894Y11R+yBXt/pzFBnhEdmWSuU94U4K2z7HFzZyJ0ulhs4I+xM9tA3I2wPlj1qqGENhH2WRlikV/rv88oy2XLkJE0dPTR19mA0S68zwqNyEtEJ2FYT3EY3jnSbzNS3dVGQ2jvQSI6N5PpzCli67ajdCs7GtiNWuVFxmn/dvELBFeNzidTreGeT+zbIXUYz22t80we7YuHEPL42Pof/t2Ivz39RzbV/W4PJIll87yxunVkc8OK0mEg9U4pS+Gx3g8eFo7ZmGsG2Tgt3fnfdBFJiI/nWq5tp6TxtY9plNPOPrw5yflmGx77OvmALhAPtJVxxtIV7X9nE8Mx4nrltisv1VCCsCCuEEPzPgjJum1nE37+o5sLfr+RrT36B0SyZWOB7IAxWG7VgWKiFRSBcZm1Z7kuxSI/JwqvrDjFNuwi6DIRDoBGG084RodYJO5M0OBJhD2Y9dI3o1VCjt+OE0WxBrxMeBQsxfgbCJrPFq7G9YFQmUlozLXYPYS8D4fgoA2NyE1lf7bxILRjUnexCSpxO794xuxijWfLO5t5Sgv9U1pOfEsPoHO+dDEJNSlwkF47O5L2tR90W0m49cpIes8X+HRAoHls4jtS4SB77YCf5KTG8d99su697MLh2Sj77Gtp5zcM20w229sohms0LF9Ljo3hKc/148O2t9huJf248womOHu6d57zDYKDIT4lFJ2B7AG6M61pO8fbGIzzw5hZufG4tybGRvHTntAGdX4aW8ElxViCE4P+uGMucERl09piIMuhJjDYwfYCpDU8oz0ti5d5GTvWY7brKQGCznskIYSA8LjeJ1LhIVu5t9LryenlFHY1t3fz22vEcPtHZz7PXnW1YsClKiyPSoAt5IOyu8YS94M1dRtjSP6Du6zhhMkuPfZvtGuEeH10jzN61zy7PSyItLpJPdzfYp9GzfcioTS9J49W1h+g2mYNiD9WXI83WbJOjRthGUVocU4pSWLL1KN8+bxhCCDq6Tazef5xbphcNeqOcQPH1cwpYXnGMf++o5epJ+S7XW1/dhBAwtSiwgXBKXCR/u2UyH2yv48GLRgZdYrJoch7vbT3KE8t2cX5ZhltN62A10xgKTClK4SeXj+b/3t/JzCc+oaPbTHu3iUmFyQG/QepLdISer43P5fX1h7hnbqnXN9ZgTeA8qXkz22p2zi/L5PsXjXQ7vioQVoQlOp3gojFZAX3NsXlJWCRU1LZwTnHgTuzj7T1E6nUhLajR6QRzR6Szam8jFotE50Xzixe/PEhpehxzR2RQkh5H9fHeEgR3RWLBRq8TDM+ID3nB3GndrhsfYTfTstYMbO9srzMfYU/0wQDRBj81whbP9wXWv7V5ZRl8uruBSQXWwipvpREA00tSef6LarbXBPZ8dIXNQ7gg1XlwdNWkPB55r4JddW2MyU1k5d5GekwWLh4b2O+hwWTeiAyGZcTx3KpqrpqY5zKgX1/dxKjsRJKC0FRiSlEqUwIcYLtCCMGvri7nkj+u4uF/VfDSHecMeBNzur3y2Z0RtnH7rGJOGS3srW8jJTaS1LgIFk3OH5QbwR9cUsaHFcf4w4o9/ObaCR5tI6Vk06FmXlt3WGsGI7hzdgnXTs2nLCvBc0tHfw5coRhKzChJw6ATfLyrPsCBcDdp8ZEhzxrNK8vgva21VNa2Up7v2fTjlsPNbD1ykp9dORadTlCSEceyHb07bZ3WtIbu/Y3JTeTjXfX0mCwhafUMzhthOGKTRrjLCJss/TOwznyEPc3SxkT6Z5/mjWuEjfPLMnl381E+rDyGQSdI82E2xHYOrqs6MUiBcCcGnSDLxTT45eU5/GxpJUu2HmVMbiL/qTxGSmwEU4sGbjUczuh0grvmlPK/7+5gzYETzBqe3m8do9nCpkPNXH9OQQiOMPAUpMbyo0tH8eiSShZvquG6qa7fV0NrF5EGHUlDpIV2sBFC8K3zgiuDcEVBaiy3zSzihS+rufPcErsmuaa5k8MnOmnuNNLc2UOX0YzRLGnrMvJhxTGqjncQG6nnv2YV8815pWT6YIWnAmHFWUNSbASzhqezfMcxfrRgVMACV1sgHGrmjLDqhFfubfA4EP5gex1RBh3XTLFOm5amx3Gy00hzRw8pcdb3ZJdGhCgABbisPJvFm2pYtbeR+QGeKfAUdz7Cts/HrY+w2dIvA9vXccKrjHAgXCO81H/PHZGBXidYW9VEXnKMR804+pISF8mo7ATWVTdxv9dbe8+RplPkJEe7nNlIjYtk3sgMlmyt5fsXjeST3Q0sGJsdspmQQHH1pDx+99Eenltd5TQQrjjawimjOejT34PJLdOL+GBbHY99sJN5IzNcukLsrW8j14tmMIrgcv8Fw3l74xF+vXw3P7tyHH9YsYcl22pxZS88rTiVe88bxuXlOX5Z8A3tM1yh8JLLxmVzuKmTnXWni+ZaOo08vmwX72056pOP4fH20DXTcCQ9Psqug/aUTYeamZCfbG+dW5phrY6vciiYO10sF7qLxZwRGaTFRfKvLe4r4IOFydJf0uCI7fMxetBZrm+Q29dxwmS2eJyB97dYzhp0eze2SbERTCm0Zkp90fPZmF6SyqZDzW6dNpo6erjthfVsOdzs875qmjspcKMZXTgpj2OtXfz5k320dZm4eGy2z/sLF6Ij9Nw6s4jP9jSy30mr8rc31qDXiTMqENbpBE9cU063ycKP36tw2qjh6MlTrNzbyGXlOSE4QoUzkmMjue/84Xy2p5ELfv85H1Ye45tzh/HG3TP46IG5rHv4Qrb/38XsfmwBB351GW/fO5OvTy3w24daBcKKs4qLxmShE7B8x+kuM3/5bB/PrKrigbe2MuUXK7j+mTVeBcTH20LXXrkv80ZmsPnwSU52uj/+LqOZytoWphSfnvotSbdalVU7CYS90ZEGmgi9jism5LJiVz2tXUb3GwQBo5uiMru8weQ+I9z3pqKv44Q33r72jLCXHbVsWGUY3o/teaOsMxB+BcKlaXT2mAf095ZS8sPF21m1t5FX1jg3/feEmuZTTgvlHLlodBZxkXqeWVVFTISeOSP6Z1CHIrfOKCLKoOP5L3p7JW+vOcmbGw7zX7OKw+Y7LFCUZsTz0MUjWbGzng+21/V7/nWtgcRN0wsH+9AUA3D7rGLmjEjnhmkFrPrB+fzo0lHMHJZGWXYCWYnRJEZHEB2h92kWyhUqEFacVaTFRzGjNI3lFdYvxvrWLl5ec4hFk/JYct9svnPBCNZVN9m/JN0hpeRECNsr9+XisVmYLZIVO+vdrru9pgWjWdoze2CtqDfoRK+COdt0fah8hG1cNSmPHpOFD3e4bpUZTIxmy4CtiK12Z6eLC12/juuMsE1fbHSiI3bFaR9h31wjjF44VDhyflkm4JtjhA27TrjadTew19cf5uNd9WQlRrFiZz3dJu8D/i6jmYa2brcuAjGRei4Zm43ZIpk7Mj3oLW4Hi7T4KBZNzuedzUftbdQtFskj71WQHh/FA/NHhPgIg8Ods0uYkJ/ET5dWcsLBY73bZOatDUe4YFRW0LqlKXwjOkLPK9+Yzi+uKh+0RicqEFacdVw6LpsDjR3sq2/jqc/2Y7ZIHpg/kgkFyTx40UhmDUvjzQ1HPDJlbzllxGiWIWuv3JfyvCTykmNYPkBfdRsbD1mDj8kOxUAReh2FqbFOM8Kh8hG2MSE/iZL0ON7d4nnb2EDiic1YhE7n3kfYSSGcPRDW/uZMboJuR/Q6QaRB51eLZV8KEEdlJ3DH7GIuH+/71HJGQhTDMuLswVlf9je08dgHO5kzIp0nrhlPW7eJ1Xu998o+etLmGOG+a9eiyVa9/KXjzqwp8wfmj6AwNZZbn1/Pkq1HeWvjEbbVtPDjy0YPyc55nmDQ6/jNtRNo6zLy8L922G80P6w4xvH2Hm6bWRTiI1SEAyoQVpx1XDI2GyHgudVVvLH+MF8/p4DCtNNZgRumFVLTfIovPGjVaWuvnBEmhuxCCC4rz2b1vka3EoLNh5opzYgjNa53EF+SHtfLSzjUPsI2hBBcPSmPtVVN9sBmMPFEQmDQC/euEU4ysBF9Ost56+0bbdD57hph8S0jLITgp1eMZXKhf64K00vT2HiwGXOfG8+jJ0/xnTe2Ehtp4PfXTeDc4ekkx0bw7x39p7ndYbNO8yT7d+6IdN751kyunJDr9X7CmazEaN65dxaTi5L53ptb+dn7lUwrSWXhxDPrffalLDuB/1kwio8q67nnlU109ph4Zc0hitNiOddJ8aDi7EMFwoqzjszEaKYWpfD2xhqEEHznguG9nr9kbBYpsRG8ucF9d6Jw6CrXl0vLczCaJZ/sci2PsPkvOrOGsnoJd9gz4rap/lBqhG1cNdHaLGTp1tpB37fRLN0WsBl0wp7Vdf06/V0jdDqBTjj4CHvp7ZsWH8U+J4VQntBj8k0jHCiml6TS1m3iqwPHMZktdJvMPPXZfi78/edUH2/n91+fQGZiNBF6HZeMyebjnfVeB/1Hmlw303DGlKJUr7y4hwpJsRG8dOc0FmlNdx5bOO6scEy4a04pv7hqHJ/vaeDKv3zJxkPN3DKj6IwcY4X3hP7KplCEgAXatOfN0wvJSep9cYwy6Llmcj4rdtbbA11XrNWmdN1Vow8mE/OTyUmKZtkAWtoDjR00dxqZ4iQQLs2Ip9tkoU7rutRjDg+NMEBhWixTilJ4ec1BapoD25feHSazxa2FXKRB59YBweQiyI3Q6+yOE956+147JZ8v95/wqfue9XhCN7YzStPQCbj1+fWM/Mlypjz2Mb/9aA/nl2XyyUPn2bXIAJePz7HKI7xsJV7TfIoIvSDLB4/RM40og54/XD+RLY9cTFn20Gsd7Su3zCji2VunUtPcSXSEjuumnBm+yQr/UYGw4qzkmsl53DqjiO9c4LxI5IZpBRjNknc2udajNnf08PfV1VwyNquXtCLU6HSCBeOyWbm3kfZuk9N1Nh+y2lA56/hUkm61UKvW5BG2qX5PNavB5qdXjKG928T1z6zl8InBC4Y9KSoz6NwHwlZHiP6vE6HX2R0nvPX2vWlaIdEROl7o4wrgCSYffIQDSVZiNO9+ezZPLCrnvvOHc8WEHF6+cxp/u2UKecm9b1JnDkuzyiO2ezcjUNPcSV5yjMoAOhDINvNDhfljslhy37n8445pQemipxiahMeVTaEYZJJjI3nsqnH99LE2hmcmcE5xCm9uOOLUgxLg6ZUH6Ogx8d8XlwXzUH3i8vIcekwWl/KIjYeaSI6NoFQLeh2xeQnbnCNMYZQRBhifn8wbd8+go8fE9c+uYdmOOn730R6+/vQa7nppAwcdCv0CiTNJQ1+sGmEPpBFOAk+DXthlKO6s2vqSEhfJosn5vLvlaK/qeE/wdl/BYGJBMjdMK+Shi8t4fNF45o7McLpehF7HgrHZfLyrwSt5xJHmU8odQAFYNcMzStNCfRiKMMKjQFgIsUAIsUcIsV8I8SMnz0cJId7Snl8nhCgO9IEqFIPNTdMLqT7ewW8/2tMvGK5v7eIfXx3k6kl5jMgKv+nFyYUpZCZE9fJLdmTToWamFKY4zZBlJkQRG6m3N9UwhpFG2Ma4vCRev2sG3SYL335tM39beYAuk5l1VU1c8sdVPLvqgNuiNW9x1hq5L1Z5gxvXCBeBp8HBccJkcR909+XO2cX0mCy8ts69tr3X8VgsRIbR2LrjsvIc2rtNPPnpvn4Fdq442tzpkWOEQqE4+3DbjkMIoQeeAi4CaoANQoilUsqdDqt9A2iWUg4XQtwA/Bq4PhgHrFAMFldOyGN9dRN//fwAzZ1GfnHVOLuJ95Of7sMiJd+fPzLER+kcnU5w6bhsXl9/mMeX7eKO2SX2xgfNHT0caOyw20T1RQhhL5gDh4xwmE0rj8lNZPn35rC/oZ0JBdbueMdauvjJexX8atlu3tpwhJumF3H1pDyXmX9v8ESuYNC5d40wWiSxTjXCp7d15izhjuGZCcwbmcHLaw7xzXml7D3WzkeVx0iINnBeWSYjs+KdFkZ561ARamYPT+eKCbk89dkBvjpwgt9fN4HSjHiX67d1GTne3qMywgqFwime9KWbBuyXUlYBCCHeBBYCjoHwQuD/tMeLgb8IIYR0NaesUAwB9DrBr64uJzUukqc+O0BdyylGZiXQ1NHDe1uOcuO0QgpSw/fi+p0LR9DUaeS51VW88GU1F4/JpiA1ljbNVs2ZY4SNkvQ41lc38faGI2w7chIhCGgnn0CRlRhNloPpenZSNM/dNoUPK47xzKoqHvtgJ79evptpJakUpsVSmBpLQrSBLqOFLqOZllNGGlq7aGjrRq8TFKRa10mJjUAg0P4hhKCupYsUN7pCg17H4abOAbXlx9u6SXcSmBv0ggON7by98QhNHT0My3Qd3LniG+eWcNsL65n1+Kec6OhBrxOYLZLHl+8mJymacXlJFKTEkp8SYy/s6+gxhdwj2hv0OsGfb5jI/NGZPLqkkgV/XE1afCQmi0RKSV5KLMMz4slLiWFHzUnWVln9skucyIAUCoXCk0A4Dzji8HsNMN3VOlJKkxCiBUgDepX2CiHuAe4BKCxUbQ0V4Y8Qgh9cMoqU2Eh+/eFuvjpwgrS4SKaVpPKdC4e7f4EQkh4fxZM3TuKHl5Tx3OoqPt5Zz392HsNoliREGxifn+xy2/H5SXywvY4fvrMdgLzkmCFjsySE4NLyHC4tz2H3sVbe2nCETYeaWb6jjubO3t7KkQYdmQlRZCZEYbZIp+s44q55RFZiFJ/vaeShf24bcD1nGtjsxGg2HGxm8+GT9t+9Zc6IdOaPzqTbZOGK8blcMjabTqOJlXsaWbWvkf0N7Xyx73i/5htDLUgUQrBwYh4zStN4euUB2rtMGPQCKeFwUyer9zXS0NZNaXocX5+az9yRGb3cJxQKhcKGcJe0FUJcB1wipbxL+/1WYJqU8jsO61Rq69Rovx/Q1nHeLgiYOnWq3LhxYwDegkIxOJjMFq2N7tAICJ0hpaT1lAmhg8QBuklJKalpPmXPBCfHRJ4RVeatXUZO9ZiJNuiJjtQRqdf1G8/WLiNtXSaklNi+HqUEiSQnKWbALmxdRjMNre6L1XKTo/t593YZzTS2WbfV6QQ5idFBcTmQUtLcabRrgyMNOmIjPcmJDC26jOYzpkWyQqHwHyHEJinl1L7LPfn2qwEcDffygb7eNbZ1aoQQBiAJcN08XqEYgoSy6UCgEEJ4ZBskhAhr2YevJEZHDHgD4Ok6roiO0PtspRcdoR+Uz1wIERDNdLijgmCFQuEJnlzZNwAjhBAlQohI4AZgaZ91lgK3a4+vBT5V+mCFQqFQKBQKRTjjNiOsaX7vBz4C9MALUspKIcTPgY1SyqXA88ArQoj9WDPBNwTzoBUKhUKhUCgUCn/xSBgmpVwGLOuz7FGHx13AdYE9NIVCoVAoFAqFIngMfdGjQqFQKBQKhULhAyoQVigUCoVCoVCclbi1TwvajoVoBA6FZOeBJ50+nslnOElAS6gPYpBQY3tmo8b3zOZsGl81tmc2anz9p0hK2c/EPWSB8JmEEGKjM2+6MxUhxLNSyntCfRyDgRrbMxs1vmc2Z9P4qrE9s1HjGzyUNELhC++H+gAUQUON7ZmNGt8zFzW2ZzZqfIOECoQVXiOlVCfkGYoa2zMbNb5nLmpsz2zU+AYPFQgHhmdDfQCKoKHG9sxGje+ZjRrfMxc1tmc2gza+SiOsUCgUCoVCoTgrURlhhUKhUCgUCsVZiQqEnSCEeEEI0SCEqHBYNkEIsUYIsUMI8b4QIlFbXiyEOCWE2Kr9PO2wzfVCiO1CiEohxG9C8V4U/fFmfLXnxmvPVWrPR2vL1fiGGV6euzc7nLdbhRAWIcRE7Tk1tmGIl+MbIYR4SVu+Swjxvw7bfE8IUaGN7wOheC+K/ng5vpFCiBe15duEEOc5bKPO3zBDCFEghPhMOxcrhRDf05anCiFWCCH2af+naMuFEOLPQoj92lhOdnitX2vnb4UQ4nq/D05KqX76/ABzgclAhcOyDcA87fGdwGPa42LH9RzWTwMOAxna7y8BF4b6vakfr8fXAGwHJjiMq16Nb3j+eDO2fbYrB6ocxliNbRj+eHnu3gS8qT2OBQ5q39fjgAptmQH4GBgR6vemfrwe3/uAF7XHmcAmrMk9df6G4Q+QA0zWHicAe4ExwG+AH2nLfwT8Wnt8GbAcEMAMYJ22/HJghXbuxgEbgUR/jk1lhJ0gpVwFNPVZXAas0h6vAK5x8zKlwF4pZaP2+8cebKMYBLwc34uB7VLKbdq2J6SUZtT4hiV+nLs3Am9oj9XYhilejq8E4oQQBiAG6AFagdHAWillp5TSBKwErg72sSvc4+X4jgE+0bZrAE4CU1Hnb1gipRKpcnMAAAP7SURBVKyTUm7WHrcBu4A8YCHWmxW0/6/SHi8EXpZW1gLJQogcrOO+UkppklJ2ANuABf4cmwqEPacCuFJ7fB1Q4PBciRBiixBipRBijrZsPzBKk04YsA6u4zaK8MLV+I4EpBDiIyHEZiHED7XlanyHDgOduzau53QgrMZ2aOFqfBcDHUAd1gzh76SUTdr6c4UQaUKIWKyZJzW+4Yur8d0GLBRCGIQQJcAU7Tl1/oY5QohiYBKwDsiSUtaBNVjGmt0Ha5B8xGGzGm3ZNuBSIUSsECIdOB8/x1cFwp5zJ3CfEGIT1rR+j7a8DiiUUk4CHgReF0IkSimbgW8BbwGrsU7LmQb9qBWe4mp8DcC5wM3a/1cLIS5U4zukcDW2AAghpgOdUsoKADW2Qw5X4zsNMAO5QAnwkBCiVEq5C/g11uzih1gvrGp8wxdX4/sC1uBoI/BH4CvApM7f8EYIEQ+8AzwgpWwdaFUny6SU8j/AMqzj/QawBj/H1+DPxmcTUsrdWKfJEUKMxKpTQUrZDXRrjzcJIQ5gzSJulFYD7Pe1be7B+qWsCENcjS/WL9qVUsrj2nPLsGrYPlHjOzQYYGxt3MDpbLBtGzW2Q4QBxvcm4EMppRFoEEJ8iXXqvEpK+TzwvLbNr7Ce54owZIBrrwn4vm09IcRXwD7tOXX+hiFCiAisQfBrUsp3tcX1QogcKWWdJn1o0JbX0DvTmw/UAkgpfwn8UnvN19HG3VdURthDhBCZ2v864CfA09rvGUIIvfa4FBgBVPXZJgX4NvD3wT9yhSe4Gl/gI2C8Ng1jAOYBO/tso8Y3jBlgbG3LrgPedLGNGtswZ4DxPQxcoFWfx2EtuNndZ5tCYBF9boQU4cMA195YbVwRQlyENRusvpvDFCGEwHrzuUtK+QeHp5YCt2uPbweWOCy/TTt/ZwAtWrCsF0Kkaa85HhgP/MefY1MZYScIId4AzgPShRA1wE+BeCHEfdoq7wIvao/nAj8XQpiw3nXeq+nQAP4khJigPf65lHLvoLwBxYB4M75SymYhxB+wVi5LYJmU8t/aemp8wwwvz12wnr81UsqqPi+lxjYM8XJ8n9IeV2CdZn1RSrlde+4d7WJqBO7TptMVIcbL8c0EPhJCWICjwK0OL6XO3/BjNtYx2iGE2Kotexh4AnhbCPENrDev12nPLcOq398PdAJ3aMsjgNXWuJpW4BZtdsBnVGc5hUKhUCgUCsVZiZJGKBQKhUKhUCjOSlQgrFAoFAqFQqE4K1GBsEKhUCgUCoXirEQFwgqFQqFQKBSKsxIVCCsUCoVCoVAozkpUIKxQKBQKhUKhOCtRgbBCoVAoFAqF4qxEBcIKhUKhUCgUirOS/w9+fdbQBMoDpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_fedfunds2.smoothed_marginal_probabilities[0].plot(\n",
    "    title='Probability of being in the high regime', figsize=(12,3));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the expected durations of each regime have decreased quite a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.76105188 7.65529154]\n"
     ]
    }
   ],
   "source": [
    "print(res_fedfunds2.expected_durations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taylor rule with 2 or 3 regimes\n",
    "\n",
    "We now include two additional exogenous variables - a measure of the output gap and a measure of inflation - to estimate a switching Taylor-type rule with both 2 and 3 regimes to see which fits the data better.\n",
    "\n",
    "Because the models can be often difficult to estimate, for the 3-regime model we employ a search over starting parameters to improve results, specifying 20 random search repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get the additional data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import ogap, inf\n",
    "dta_ogap = pd.Series(ogap, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "dta_inf = pd.Series(inf, index=pd.date_range('1954-07-01', '2010-10-01', freq='QS'))\n",
    "\n",
    "exog = pd.concat((dta_fedfunds.shift(), dta_ogap, dta_inf), axis=1).iloc[4:]\n",
    "\n",
    "# Fit the 2-regime model\n",
    "mod_fedfunds3 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[4:], k_regimes=2, exog=exog)\n",
    "res_fedfunds3 = mod_fedfunds3.fit()\n",
    "\n",
    "# Fit the 3-regime model\n",
    "np.random.seed(12345)\n",
    "mod_fedfunds4 = sm.tsa.MarkovRegression(\n",
    "    dta_fedfunds.iloc[4:], k_regimes=3, exog=exog)\n",
    "res_fedfunds4 = mod_fedfunds4.fit(search_reps=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-229.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 17 Dec 2019</td> <th>  AIC                </th>  <td>480.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:40:34</td>     <th>  BIC                </th>  <td>517.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>495.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.6555</td> <td>    0.137</td> <td>    4.771</td> <td> 0.000</td> <td>    0.386</td> <td>    0.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8314</td> <td>    0.033</td> <td>   24.951</td> <td> 0.000</td> <td>    0.766</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1355</td> <td>    0.029</td> <td>    4.609</td> <td> 0.000</td> <td>    0.078</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0274</td> <td>    0.041</td> <td>   -0.671</td> <td> 0.502</td> <td>   -0.107</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0945</td> <td>    0.128</td> <td>   -0.739</td> <td> 0.460</td> <td>   -0.345</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9293</td> <td>    0.027</td> <td>   34.309</td> <td> 0.000</td> <td>    0.876</td> <td>    0.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0343</td> <td>    0.024</td> <td>    1.429</td> <td> 0.153</td> <td>   -0.013</td> <td>    0.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.2125</td> <td>    0.030</td> <td>    7.147</td> <td> 0.000</td> <td>    0.154</td> <td>    0.271</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3323</td> <td>    0.035</td> <td>    9.526</td> <td> 0.000</td> <td>    0.264</td> <td>    0.401</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7279</td> <td>    0.093</td> <td>    7.828</td> <td> 0.000</td> <td>    0.546</td> <td>    0.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.2115</td> <td>    0.064</td> <td>    3.298</td> <td> 0.001</td> <td>    0.086</td> <td>    0.337</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -229.256\n",
       "Date:                Tue, 17 Dec 2019   AIC                            480.512\n",
       "Time:                        23:40:34   BIC                            517.942\n",
       "Sample:                    07-01-1955   HQIC                           495.624\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.6555      0.137      4.771      0.000       0.386       0.925\n",
       "x1             0.8314      0.033     24.951      0.000       0.766       0.897\n",
       "x2             0.1355      0.029      4.609      0.000       0.078       0.193\n",
       "x3            -0.0274      0.041     -0.671      0.502      -0.107       0.053\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0945      0.128     -0.739      0.460      -0.345       0.156\n",
       "x1             0.9293      0.027     34.309      0.000       0.876       0.982\n",
       "x2             0.0343      0.024      1.429      0.153      -0.013       0.081\n",
       "x3             0.2125      0.030      7.147      0.000       0.154       0.271\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.3323      0.035      9.526      0.000       0.264       0.401\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7279      0.093      7.828      0.000       0.546       0.910\n",
       "p[1->0]        0.2115      0.064      3.298      0.001       0.086       0.337\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/travis/build/statsmodels/statsmodels/statsmodels/base/model.py:1354: RuntimeWarning: invalid value encountered in sqrt\n",
      "  bse_ = np.sqrt(np.diag(self.cov_params()))\n",
      "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:901: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "/home/travis/miniconda/envs/statsmodels-test/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1892: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>222</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-180.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 17 Dec 2019</td> <th>  AIC                </th>  <td>399.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:40:35</td>     <th>  BIC                </th>  <td>464.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>07-01-1955</td>    <th>  HQIC               </th>  <td>425.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 10-01-2010</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -1.0250</td> <td>    0.290</td> <td>   -3.531</td> <td> 0.000</td> <td>   -1.594</td> <td>   -0.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.3277</td> <td>    0.086</td> <td>    3.812</td> <td> 0.000</td> <td>    0.159</td> <td>    0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.2036</td> <td>    0.049</td> <td>    4.152</td> <td> 0.000</td> <td>    0.107</td> <td>    0.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    1.1381</td> <td>    0.081</td> <td>   13.977</td> <td> 0.000</td> <td>    0.978</td> <td>    1.298</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0259</td> <td>    0.087</td> <td>   -0.298</td> <td> 0.765</td> <td>   -0.196</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9737</td> <td>    0.019</td> <td>   50.265</td> <td> 0.000</td> <td>    0.936</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.0341</td> <td>    0.017</td> <td>    2.030</td> <td> 0.042</td> <td>    0.001</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    0.1215</td> <td>    0.022</td> <td>    5.606</td> <td> 0.000</td> <td>    0.079</td> <td>    0.164</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 2 parameters</caption>\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.7346</td> <td>    0.130</td> <td>    5.632</td> <td> 0.000</td> <td>    0.479</td> <td>    0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.8436</td> <td>    0.024</td> <td>   35.198</td> <td> 0.000</td> <td>    0.797</td> <td>    0.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    0.1633</td> <td>    0.025</td> <td>    6.515</td> <td> 0.000</td> <td>    0.114</td> <td>    0.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>   -0.0499</td> <td>    0.027</td> <td>   -1.835</td> <td> 0.067</td> <td>   -0.103</td> <td>    0.003</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Non-switching parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1660</td> <td>    0.018</td> <td>    9.240</td> <td> 0.000</td> <td>    0.131</td> <td>    0.201</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7214</td> <td>    0.117</td> <td>    6.177</td> <td> 0.000</td> <td>    0.493</td> <td>    0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td> 4.001e-08</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->0]</th> <td>    0.0783</td> <td>    0.038</td> <td>    2.079</td> <td> 0.038</td> <td>    0.004</td> <td>    0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->1]</th> <td>    0.1044</td> <td>    0.095</td> <td>    1.103</td> <td> 0.270</td> <td>   -0.081</td> <td>    0.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->1]</th> <td>    0.8259</td> <td>    0.054</td> <td>   15.208</td> <td> 0.000</td> <td>    0.719</td> <td>    0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[2->1]</th> <td>    0.2288</td> <td>    0.073</td> <td>    3.150</td> <td> 0.002</td> <td>    0.086</td> <td>    0.371</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  222\n",
       "Model:               MarkovRegression   Log Likelihood                -180.806\n",
       "Date:                Tue, 17 Dec 2019   AIC                            399.611\n",
       "Time:                        23:40:35   BIC                            464.262\n",
       "Sample:                    07-01-1955   HQIC                           425.713\n",
       "                         - 10-01-2010                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.0250      0.290     -3.531      0.000      -1.594      -0.456\n",
       "x1             0.3277      0.086      3.812      0.000       0.159       0.496\n",
       "x2             0.2036      0.049      4.152      0.000       0.107       0.300\n",
       "x3             1.1381      0.081     13.977      0.000       0.978       1.298\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0259      0.087     -0.298      0.765      -0.196       0.144\n",
       "x1             0.9737      0.019     50.265      0.000       0.936       1.012\n",
       "x2             0.0341      0.017      2.030      0.042       0.001       0.067\n",
       "x3             0.1215      0.022      5.606      0.000       0.079       0.164\n",
       "                             Regime 2 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7346      0.130      5.632      0.000       0.479       0.990\n",
       "x1             0.8436      0.024     35.198      0.000       0.797       0.891\n",
       "x2             0.1633      0.025      6.515      0.000       0.114       0.212\n",
       "x3            -0.0499      0.027     -1.835      0.067      -0.103       0.003\n",
       "                           Non-switching parameters                           \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "sigma2         0.1660      0.018      9.240      0.000       0.131       0.201\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7214      0.117      6.177      0.000       0.493       0.950\n",
       "p[1->0]     4.001e-08        nan        nan        nan         nan         nan\n",
       "p[2->0]        0.0783      0.038      2.079      0.038       0.004       0.152\n",
       "p[0->1]        0.1044      0.095      1.103      0.270      -0.081       0.290\n",
       "p[1->1]        0.8259      0.054     15.208      0.000       0.719       0.932\n",
       "p[2->1]        0.2288      0.073      3.150      0.002       0.086       0.371\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_fedfunds4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to lower information criteria, we might prefer the 3-state model, with an interpretation of low-, medium-, and high-interest rate regimes. The smoothed probabilities of each regime are plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, figsize=(10,7))\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[0])\n",
    "ax.set(title='Smoothed probability of a low-interest rate regime')\n",
    "\n",
    "ax = axes[1]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[1])\n",
    "ax.set(title='Smoothed probability of a medium-interest rate regime')\n",
    "\n",
    "ax = axes[2]\n",
    "ax.plot(res_fedfunds4.smoothed_marginal_probabilities[2])\n",
    "ax.set(title='Smoothed probability of a high-interest rate regime')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Switching variances\n",
    "\n",
    "We can also accommodate switching variances. In particular, we consider the model\n",
    "\n",
    "$$\n",
    "y_t = \\mu_{S_t} + y_{t-1} \\beta_{S_t} + \\varepsilon_t \\quad \\varepsilon_t \\sim N(0, \\sigma_{S_t}^2)\n",
    "$$\n",
    "\n",
    "We use maximum likelihood to estimate the parameters of this model: $p_{00}, p_{10}, \\mu_0, \\mu_1, \\beta_0, \\beta_1, \\sigma_0^2, \\sigma_1^2$.\n",
    "\n",
    "The application is to absolute returns on stocks, where the data can be found at https://www.stata-press.com/data/r14/snp500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the federal funds rate data\n",
    "from statsmodels.tsa.regime_switching.tests.test_markov_regression import areturns\n",
    "dta_areturns = pd.Series(areturns, index=pd.date_range('2004-05-04', '2014-5-03', freq='W'))\n",
    "\n",
    "# Plot the data\n",
    "dta_areturns.plot(title='Absolute returns, S&P500', figsize=(12,3))\n",
    "\n",
    "# Fit the model\n",
    "mod_areturns = sm.tsa.MarkovRegression(\n",
    "    dta_areturns.iloc[1:], k_regimes=2, exog=dta_areturns.iloc[:-1], switching_variance=True)\n",
    "res_areturns = mod_areturns.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Markov Switching Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>520</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>MarkovRegression</td> <th>  Log Likelihood     </th> <td>-745.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 17 Dec 2019</td> <th>  AIC                </th> <td>1507.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>23:40:36</td>     <th>  BIC                </th> <td>1541.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>05-16-2004</td>    <th>  HQIC               </th> <td>1520.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 04-27-2014</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>approx</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 0 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    0.7641</td> <td>    0.078</td> <td>    9.761</td> <td> 0.000</td> <td>    0.611</td> <td>    0.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.0791</td> <td>    0.030</td> <td>    2.620</td> <td> 0.009</td> <td>    0.020</td> <td>    0.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.3476</td> <td>    0.061</td> <td>    5.694</td> <td> 0.000</td> <td>    0.228</td> <td>    0.467</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime 1 parameters</caption>\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>  <td>    1.9728</td> <td>    0.278</td> <td>    7.086</td> <td> 0.000</td> <td>    1.427</td> <td>    2.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>     <td>    0.5280</td> <td>    0.086</td> <td>    6.155</td> <td> 0.000</td> <td>    0.360</td> <td>    0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    2.5771</td> <td>    0.405</td> <td>    6.357</td> <td> 0.000</td> <td>    1.783</td> <td>    3.372</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Regime transition parameters</caption>\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[0->0]</th> <td>    0.7531</td> <td>    0.063</td> <td>   11.871</td> <td> 0.000</td> <td>    0.629</td> <td>    0.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>p[1->0]</th> <td>    0.6825</td> <td>    0.066</td> <td>   10.301</td> <td> 0.000</td> <td>    0.553</td> <td>    0.812</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                        Markov Switching Model Results                        \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                  520\n",
       "Model:               MarkovRegression   Log Likelihood                -745.798\n",
       "Date:                Tue, 17 Dec 2019   AIC                           1507.595\n",
       "Time:                        23:40:36   BIC                           1541.626\n",
       "Sample:                    05-16-2004   HQIC                          1520.926\n",
       "                         - 04-27-2014                                         \n",
       "Covariance Type:               approx                                         \n",
       "                             Regime 0 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.7641      0.078      9.761      0.000       0.611       0.918\n",
       "x1             0.0791      0.030      2.620      0.009       0.020       0.138\n",
       "sigma2         0.3476      0.061      5.694      0.000       0.228       0.467\n",
       "                             Regime 1 parameters                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          1.9728      0.278      7.086      0.000       1.427       2.518\n",
       "x1             0.5280      0.086      6.155      0.000       0.360       0.696\n",
       "sigma2         2.5771      0.405      6.357      0.000       1.783       3.372\n",
       "                         Regime transition parameters                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "p[0->0]        0.7531      0.063     11.871      0.000       0.629       0.877\n",
       "p[1->0]        0.6825      0.066     10.301      0.000       0.553       0.812\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_areturns.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first regime is a low-variance regime and the second regime is a high-variance regime. Below we plot the probabilities of being in the low-variance regime. Between 2008 and 2012 there does not appear to be a clear indication of one regime guiding the economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAADSCAYAAABAW6ZrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOx9Z5gcxbn1qZ6wOa+00ipLKKIEiJyMTc7RBBuDr21wDtzvYmODwQYLg23AIMBkEAYRZaIIEsoZ5RxWu9qc4+zk6a7vR4Wu7unZXQVYjPo8jx7tzPR0V1dXOO95T9UQSilcuHDhwoULFy5cuDjSoPV3AVy4cOHChQsXLly46A+4RNiFCxcuXLhw4cLFEQmXCLtw4cKFCxcuXLg4IuESYRcuXLhw4cKFCxdHJFwi7MKFCxcuXLhw4eKIhEuEXbhw4cKFCxcuXByRcImwCxdHAAgh9xBC/n2Q372ZELK8h88/IoTc5HQsIaSbEDL6YK57gGXMIIS8TwjpJIS86fD5odz/7wkhzx56KR3P/WXVz2JCyA+/6OscLhBCTieE7O7vcnyR+LKevQsXLnqGt78L4MKFC2cQQvYDKAGgAwgCmAfgF5TS7v4slx2U0gt6+Cxb/E0IeRFADaX0zi+gGFeD1VURpTRxOE9MKZ15OM9nO3d270cdeaCULgMwvr/L8UXCffYuXHw14CrCLlx8tXEJnzCPBXA8gCQSSRiO9L48AsCew02CXXz5IIR85QWa/4YyunDhom840idPFy7+K0AprQXwEYDJgEx1/4UQsgJACMBoQkgpIeQ9QkgbIaSMEPIj22nSCSGvE0IChJANhJBp4gNCyO8IIfv4ZzsIIVfYvksIIY9x68EuQsi3lA9Spt0JIZQQchQh5BYA3wFwO08Jv08I+T9CyNu24x8jhDyS4lwT+bU6CCHbCSGX8vf/BOCPAK7l5/5Bimrs6f5LCSFvE0KaCSEVhJBfKp9JWwUhZCS/p5sIIVWEkBZCyB+UYzMIIS8RQtoJITsJIbcTQmpSlEfWD//7RULI44SQD3kZ1xBCxvTw3TcJIQ38mSwlhByd6ljb9zRCyJ2EkEpCSBMhZDYhJI9/9hIh5H/530N4+X7KXx/F2xZxOOfvCCFv2d77JyHkUf7393l9BAgh5YSQW5XjvkEIqSGE/JYQ0gDgBfGe7fyO7ZNwOw4h5O+83isIIRconxcSQl4ghNTxz99RPruYELKJt6mVhJCpPdQbJYT8jBCyF8Be/t4EQsh8Xi+7CSHfVo4v4u28ixDyOSHkPmK1Ddmf/ROE2Yy6CSErCCGDCCGP8DLvIoQco3w3ZXt14cLFgcElwi5c/BeAEDIMwIUANipv3wjgFgA5ACoBzAFQA6AUzCowkyiEFcBlAN4EUAjgVQDvEEJ8/LN9AE4HkAfgTwD+TQgZrHz3RADlAIoB3A1gLiGksK/lp5Q+DeAVAA9SSrMppZcA+DeA8wkh+fwevQCuBfCyw/37ALwP4FMAAwH8AsArhJDxlNK7AcwE8Do/93MpiuF4/4Sp6e8D2AxgCIBvAfg1IeS8Hm7pNLDU/bcA/JEQMpG/fzeAkQBGAzgHwHd7rRwrrger/wIAZQD+0sOxHwEYC1YfG8Dqty+4mf87i5czG8As/tkSAN/gf58J9szP5K/PALCMUkodzjkHwIWEkFwAIIR4AHwbrJ4BoAnAxQByAXwfwMOEkGOV7w8Cey4jwNq0HX1pn7vB2ueDAJ5TCPvLADIBHA1WVw/zMh4L4HkAtwIoAvAUgPcIIWkO1xe4nF9rEiEkC8B8fo8DwZ7dE0pA8jiYpWkQgJv4v57wbbCMTzGAKIBVYM+1GMBbAB7i5T6Y9urChYsUcImwCxdfbbxDCOkAsByMpKh+1Rcppdu5HWAQGDn7LaU0QindBOBZMLIssJ5S+halNA42qaYDOAkAKKVvUkrrKKUGpfR1MMXrBOW7TQAeoZTG+ee7AVx0KDdGKa0HsBTANfyt8wG0UErXOxx+Ehhh+yulNEYpXQjgAzDy0Vekuv/jAQyglP6Zn7scwDMAruvhXH+ilIYppZvBCIlQl78NYCaltJ1SWgPg0QMoHwDMpZSu5c/0FQDTUx1IKX2eUhqglEYB3ANgmlB2e8F3ADxEKS3nfvM7AFzHA5ElAE7nZOsMMFJ5Kv/emfxzp7JUgpG2y/lb3wQQopSu5p9/SCndRxmWgAU0pyunMADcTSmNUkrDDufvrX1WUkqfoZTqAF4CMBhACSfLFwD4MX8mcX59APgRgKcopWsopTql9CUwAnpSD3V3P6W0jZfxYgD7KaUvUEoTlNINAN4GcDUPBK7i9xSilO7g5eoJ/6GUrqeURgD8B0CEUjqb39PrAIQifDDt1YULFyngEmEXLr7auJxSmk8pHUEp/amNJFQrf5cCaKOUBpT3KsEUo6TjKaUGTPUYhJDvKSniDjALRrHy3VqbElgpvnuIeAmmavpdOKjBHKUAqnm51TIMSXG8E1Ld/wgApeLe+f3/HmzxXSo0KH+HwEi6LKfTNfuIVOe1gBDiIYT8ldsFugDs5x8VOx1vQylY3QlUgi2cLqGU7gPQDUbATwcLNuoIIeOhEGElhd9NCPkOP8+rMAOTG2CqwSCEXEAIWc0tBB1g2Q21rM2cADqiD+1T1hulNMT/zAYwDKxftDucdgSA/7U992HouV2rz3MEgBNt3/8OWFA6AKxOD6QtNCp/hx1ei7ZwMO3VhQsXKeAa/l24+O+FSkzrABQSQnIUMjwcQK1yzDDxB1f8hoKRnBFgitK3AKyilOqEkE0AVC/oEEIIUcjwcADvHUJ5Bd4B8CQhZDKYwnZ7iu/WARhGCNEUMjwcwJ4DuL7j/QNIAKiglI49gHOlQj0/7w77NQ8zbgCzepwNRoLzALTD+sxSoQ6MTAkMB6sDQbyWgFlr/JTSWkLIEgDfA7NrbAJS7hTyJoB/EEKGArgCwMkAwK0Gb/NzvEspjXOfrlpWp7YB/v2+tM9UqAbrF/mU0g6Hz/5CKe3JfmKHWs5qAEsopec4lNkDVqdDYbbRw9UWqnH42qsLF0c8XEXYhYuvASil1QBWArifEJLOF/38AFbf6HGEkCt5CvzXYGng1QCywCb4ZoAtbAJflKdgIIBfck/tNQAmgm3ndiBoBPOkquWOgPkfXwWwllJaleK7a8D8lrfzMnwDwCUAXjuA66e6/7UAughbrJXB1dbJhJDjD+TmON4AcAchpIAQMgTAzw/iHH1BDlj5W8H8rweyxdscAL8hhIwihGTD9FeLHTeWgJV7KX+9GMyTvZyn6R1BKW3mx74ARtR28o/8ANLA2leCsIVs5x5AefvSPlOVqR7MS/0EfyY+QsgZ/ONnAPyYEHIiYcgihFxECMnpY7k+ADCOEHIjP6+PEHI8IWQir6e5AO4hhGQSQiaABQKHA4ezvbpwccTDJcIuXHx9cD3YQq06MI/h3ZTS+crn74ItRmsH8w5fyT2TOwD8A2xxTiOAKQBW2M69BmxhVgvYAq6rKaWtB1i+58AWGXUQZeU+mD1iClLbIkApjQG4FMzv2QLgCQDfo5TuOoDrp7p/HYxUTwdQwc//LJjKeqD4M5jlogLAAjCSHz2I8/SG2WCWhlow9Xn1AXz3ebC6XgpWzggY0RVYAka0BRFeDka2l6J3vAqmUktbBM9Q/BIsSGgHU7P7nE3oY/vsCTcCiAPYBeZ1/zU/7zown/AsXq4ysEWEfS1XAIzQXwfW5xoAPABG+gEWTOTx918GC0AOuS0c5vbqwsURD+K8ANiFCxcuvhwQQoaDkZRBlNKu/i7P4QQh5CcArqOUntnrwS6+1iCEPADWxnvbPcKFCxdfIlxF2IULF/0G7tW9DcBrXwcSTAgZTAg5lbC9escD+F8wdd7FEQbC9hieym0XJ4BZldy24MLFVwzuYjkXLlz0C/g+rI1gKf7z+7k4hwt+sP1oRwHoAPMwP9GvJXLRX8gBs0OUglky/gFmz3HhwsVXCK41woULFy5cuHDhwsURCdca4cKFCxcuXLhw4eKIhEuEXbhw4cKFCxcuXByR6DePcHFxMR05cmR/Xd6FCxcuXLhw4cLFEYL169e3UEoH2N/vNyI8cuRIrFu3rr8u78KFCxcuXLhw4eIIASGk0ul91xrhwoULFy5cuHDh4oiES4RduHDhwoULFy5cHJHolQgTQp4nhDQRQral+JwQQh4lhJQRQrYQQo49/MV04cKFCxcuXLhw4eLwoi+K8IvoebP7CwCM5f9uAfDkoRfLhQsXLly4cOHChYsvFr0SYUrpUgBtPRxyGYDZlGE1gHxCyODDVUAXLly46A+sr2zHvK31/V0MFy5cuHDxBeJweISHAKhWXtfw95JACLmFELKOELKuubn5MFzahQsXLr4YzF61Hw9+vKu/i+HCxZeKmvYQInG9v4vhwsWXhsNBhInDe46/20wpfZpSOoNSOmPAgKSt3Fy4cOHiK4OEQaEfhp+gb+yKYGNV+2EokQsXXzwuenQ5Xl7luMuUCxdfSxwOIlwDYJjyeiiAusNwXhcuXLjoN1BKYRiHfp5LZy3HFU+sPPQTuThk1HaEccJfFqCqNdTfRflKglKKznAcHeEYAKAzFMfI332ID7e4FiEXX18cDiL8HoDv8d0jTgLQSSl1e40LF/2I3Q0BtAVj/V2M/2oYBiMGh4rGrigAIBCJH/K5vi6o7wwf9nMGInHc/e42hGOp0/rVbSE0BaKobAse9uv/N6G2I4znllcktW/x0uD/N3RFAAAPzd99UNd5ZU0l/vCfrQddThcuvgz0Zfu0OQBWARhPCKkhhPyAEPJjQsiP+SHzAJQDKAPwDICffmGl/RrAMCi6o4n+LoaLrzluen4tnlqyr7+L8V8Ng1JJCHpDT57KTL8HANDQGTnkMgUicdz2xiY0B6KHfK7+QmVrECffvxDrKw+vXWRDVQdeWlWJrbWdKY8x+APt63P9uuK3b23BvR/swN6mbsv7BqWW/9N9jCIcbHtbW9GGpXvd9UAuvtroy64R11NKB1NKfZTSoZTS5yil/6KU/ot/TimlP6OUjqGUTqGUur+b3AP+tXQfJt/9Cdpdte6AkNCNw6LO9RcopfjVaxux7EuaFIKxxH9VwBVLfDHPt64jfNDnNWjfPMLzttZjwl0fY1dDl+PnJbnpAID6PhLhu97ZhhVlLY6fra1ow9wNtbjtjU3yvVAsgZG/+xAvr9rfp/PbkdANhGJfXlsRmYrW7sNL5gV503tguQY1jw3FEugIHZnjsN/Lpv7yZqsyLtq7PWDoihxc+zAoUtqLttR0fGXG9K9KOVz0D/rtl+XUwaq6LYTGrgNTS8IxHXHdQCxhYP6OxpQNORLX0XKYB9y+glKKaMKqFK0pZzvRrdiXPNF9vK0BTQdYD19X3PPedry7qRYAEE3oOOoPH+Gh+Xv6uVQHj5hu4N1NdbjxubWH9byp2r1h0B4JwVcJ0YSOE2YuwLytDZb3T5y5AP/vzc0Hfd6mrghO+etCPPjJwaV1Ddq3CXI5J62fVzjvMlmSmwag73aAOWursGyvMxHOSvMCgOVzEfD0tX8s3dOMq59ciViCMZTfvLEZk/74yZdGBlQyejjw6poqLNnTLMsv2n1zIIpnl5Vb7stQiN5ls1Zg+p/n963Mtr70306cRhRlAgD2t1qJsN0acajPyDCoPMf2uk45H26oasels1bgicXOWau4bnwh9hknLN/bglF3zEOZTR3vDbpB8ciCPQfELwyDYtGupqT2JLC+sg33fbDjgMrxRWHO2irc/9HO/i5GSuys78K6/T3t7Nt39BsRrmgxO+DpDy7CiTM/O6DvX/XkSjy2sAwvrdyPH81eh4+3NaA7mkjy4c1aWIYrv6CFKo1dEcyctzNlWvTBT3bj3IeXWpS5acPyAQCr9rVajtUNih//ez0uemz5YSnb+so2SSRVhGIJ3PbGppRp2m21nfjju9t6JFFx3UBNu3WxCaUUuxsCh1ZoBS+u3I9fvcZUryifsB9bWAaADSbPLitH8CAUz85wPOUg9EWir/PJQ/P3YHN1R5+OXba3GcfeOz/pWQBsIksYFI1dEdz5ztakgOyLQE17CKfc/xlq2kMob+7GrS+vw6l/XYiuSByvrKnE+5ud19CGYzo6QnFU2+6jsSuKt9bXgFKKytYD93QGuVf0ST7Z1nWEccUTK/q8UKqv1oi8DB8A1racMCDnwBRhdt0UAQ61CggAQPjGPe2hvnmQ11e2Y11lu1SwxXOpaT/8xINSmhTci3tIHKZ++NTSfXhrfQ10rjwmuAQ5f0cj7vtwJ5qUtL6Z+oe0BcT1nldErq1ow7Q/fWqpr3F3foS6jp7rSzcoHp6/57AvzDMMipVlLYcU6BZns+Bsf4u1X9mtEYdK+EVbDsd0XPToclzwyDIAQDTO6vyznY2O33ty8T6cfP9C2cZ7wwMf78LTS1NbwRbsaEyZIVvLydS/Vx/YThm7GrrwyIK9+O6za3o99v3NdVhT3oole5rx/Rc/xyMLnIPWn72yEc8ur+izjWp1eWuf60jg1TVVSc/dCXfM3YqnlpQn1VtCN/DqmirLnGIYFHfM3YLtdcm2pHc31eL3Np94JK73GHi0dkd7FUf/8eke/OzVDYclKO03IhyO60m+I3t6bldDF657epXjg27siqCuI4wYH8TWV7bj569uwE9f2WA5rjkQRVVbSKoffUGCK8294cMt9Xh6ablcUdsejGG5otTsbexGZWsI//jUVKR8Gpu0VpVbibAYjPvixeoMx1MuhNrTGAClFLMWluHXr2/C57aIaWNVB+ZuqMXPXrXW06JdTWgPxrBoVxNmr6qUKpcTHpq/B6c9sMjSUFeVt+K8R5ZiWwp/XlMgkkT++wo7cd3TFMB9H+7E0j2pbQaCLKv1qRsUZ/19sUUhpJTiltnrHKPwZXubceLMBWjqiiChG9ANlk598ONd6OyBdLQHY7jwn8uws95MlauTllAQttV2Wu6ttTuKRz/bi5dXVzICEeh5INhQ2YH2UBzPL9+ffP+Uyon436ur8OGWejyztLzHyeJQUdUaQl1nBFVtITw0fw8+2d6I2o4wypq68eTiffhXCs+yqIJECkLyyfYGnPm3xShrOrBASyWNsYSBTdUd2FjVgedXVCQdu7O+yxKci3L1RRETRDhV+tjDN5is7+h9cqOcfKvtIhRLyMFeTTOLH/voaSLYWd+FBTusZEMEkJtrWF+dPCQXAFPpDjcW7GzCKX9daAnWxL3ZidzsVfvlcV2ROG54ZrVlPO0Mx3H54yvw8TbrWmxBtuzWCJ1XlnodJ/uEOr/sbwliztoqy/m31HQgEE3gvg92glKKpXuaEdcpfvv2FkvdU8rIwFqeGahsDeKfn+3FGX9bhCcWl+Gh+XtQ0x5KSWCX7W3Gba9v6jXAf2VNJW54dg3eWMe279/TGMDVT65ERUsQd8zdgnve2245vqEzgvc211mECvEM7IqwKJq4LbVLHgzxFsGkmE/LW4JYsqcZGdw3X93OrEv2NizKNW9rPebvaOxVHHhy8T7MnOe853drdxQ/nL0OH6QIxIcVZAAAPttl9pP2YAyXzVqOlWUtqGwN4o3Pq+VzFYjrrMy7GgK9WoseXrAHs1dVgvCx4LFFZY7HDedK/eaaDmysakd5c88q9U/+vR5/O4CMV31nGL//z1Y8u7y812P9HkYP7TatZWUt+P1/tuLP75tzZlMgijlrq/Hw/L1J5/nVa5vw6poq7FPu5d+rK3HeI0sd76+xK4ILH12Ga59a1aNoFYjE0dgVxc76AK5/ejXWVrQhFEtgT+OBC3L9RoQBRr5UrLE1tFX7WrG6vA0/mr0uyU+mU4q4bmAAj2x3NQSwurwV6/a3WzqsIJhO6YtoQsePZq+zpBQppfjh7HU46++LUdESRFlTIOlhbKxqR3MgivIW9hDFwPnbt7fgphfWyoGsNciu+eLK/VLREh6s8uagheioZe5pYnty8T6cNPMznPvw0qQA4bOdjTj34aVYU9GGqrYQKAV+/domPL6oTJYp3ccGIHWhSnc0gf956XO8tb5Gqq9vrqtGKmypYYPSjroulDd3oykQwfZaRvrUMgmVWDco/rW4HN99bs0Be6MFMRDQDYoEH4BiPSg5+5q7cd+HO3HaAwvl86tqC6EtGMPsVfulN3DBziZ8uqMRzy6vwNYaK4l/c10NGruieHtDLa57ejV+OWcj3ttUhycW75OTkBPKmruxo74L72w0FXmVUG2obEdFSxAXP7YcM+eZqac9jaw9ba7uwOufV+O0BxahtTuKH7+8PmlyAyAV1Nc+r0oi5pQrwvmZfgBMDX1lTSUe/Hi3o7pqGBSPLyqzDFYHCl0ha7GEAR7zoao1hPrOCPY0BiwqwqfbG/DUkn2ybsTEYsdsvqfp5mr2fKrbQn1aCKj22/WV7VKZfHt9TdLE9bu5W5N+PKOv9pLcdK4IpwiOxCnq+2B7kgSE/9EejGHSHz/Bv5aU83OZ5XmTq+U9FfGFFRW429Z2hMKzhZOLcQNzALAgGWA7Cvz1o1247Y1NKYMTFfuau1PWU1VbCAmDYkOVSWSc0u61HWH88d3t+NmrGwGwsWXlvlb8z0ufywB65oc7sam6Ax9ts1poxO4eYtwUSrPTdUQgQSmVqqjqk527oQZ3zN1qGYOFUr68rAXLy1okiVu2twWfbDfLEk0YmLO2Gt9+alXSdR/8eDdmLdyL0x5YhDG/n4fnlycHY3M31GLuxlr88KV1liwjpVSS6KZABA9+zIjPSyv3g1KKDVzhP+vvizFnbTVeXLnfUq4nFpfhl3M24rxHlmJHXZelbiptarVuC1LUezgYT7du8H6knOeFFRXyvM2BKB6evwdXPWnN2o4ozAIAvLy6Ere+vA7XPb06KQ3uNE86zS9iTkuVuRVlqW4Lyz5c0RrE5ppO3PDsGlw6awVuf3sLvv3UKksZ1PO9vSE5+2q5hkGRMAzZvykFLnlsOX4027qkasIg1hdXlrXg2qdX4+yHluCOuVsd1exIXEd7KI5tDipsKggyv7m6E1trOvGr1zamFP2mDcsDkMzT0r2s/b+1vka+JzLxC3c1JllaTh5dBID1LYG9jWzMeNbWDyil+NkrG9DYFcX+1lCPglyQj+H3frADq8pb8cd3t+GW2etx/iNL8Z+NNZZjuyJxnPHgopTn6jci7PNoMgLLTWe+t+U2X1xdRxgejWBPYwAnzvzMoqwaBkUsYUgytLysBZG4gXBct0QZ4vMmB6X1rne2Yf6ORsxauBfzdzTi0c/24tHPyrB4dzOaAhGc9ffFOPuhpbhj7la0BWOoaAmCUoobn1uLh+bvlgPousp2PLusHJ/uaIRuUFRxMtgWjGHasHxQCizezdRLdb5YvKsZ932wA9c/vVqSO8DcskZADPKGQfHgJ7twdGku4rqByx9fgfMfWYpP+aD34sr9AIDtdV2obg/jxFGF8HoI/vbJbkn21cFDpPa6wnFQyiZI0bk/3d6YciHJyCI2SFW0BPG959fi7ne3Yy9X61qCMSza3YR7P9iBX7++Cec9shRvb6jBnkZGiBfvacKKshZ8vr8NexoD+GR7Q4+pyZbumGWS3d8alK9V4mQYFPd+sEP+cIFQ56IJA88sYyRCpGJCMR2/mLMRv5yzEfd+sAMjijJRlOXHX+btkPUT1w0s2s0GgFkL92JdZTvmbauXauK8bfUwuEJshxiMlyiKtarkra9ql4HJy0o6TtRhWXM33t5Qg1jCwJaaTizc3YRX11QlZQGq20IoyvIjFNPx8XarSqZTCt0wMDiPpeUrW0Oobg8jYVA8ssCM2v+9uhI3Pb8WL67cj799sjslwWwPxnDH3C09bk2lEg+DUgwrZOrG+koWnMZ1ij0NZt/8YEs9XllTJQlrqnawkhOh3TzSn7e1Hvd/xFT5Z5aWY+Eu5/SqOvku3tOERj4GBKIJvLvJqg6FlLZv3g/tk6VFLDwSe6/aISbahj54Hs2UNHstJkDRhsXn158wDGVN3VhX2d7jgr6ETqVVQCAgiLAI/HjAspET47vf3Y5/LdmHuRtqJQnsisQd1ZuKliDOeWgJXv/cDAzDMR3VbUz57ORjiJopMr285nlE5kaouCI1TAC8uLICW2o68Pq6avg9WpI6KLIfov0lkTnDeiz7HxhVzNqnEDQANjaIzwVq2sMYVczGvI1VHegKxzEkPwMDc9IsBMhu9RAv/3rlFKz9/bew9Paz8IcLJ2LykFw8sXhfUntL4+1oVXkr3lhXLcf8lu4YHv1sLxbsaMSb62oQiCZw6xmjsashgHWV7fBo5m9afWvCQEwcnIu73tkmzx+O6fLcYrGuqIf6zoilT8vMg+1/IHlecsLH2+pxwzOrZd1Tm1oPMFuEGqR+tqspyVYn2mxNexgFmX4MykuXVjmArSG59eX1Sddf57ATiShLKiuO2g4/2dEgyyiQl+HD2z85BZl+j4X8RRUCed8HO3DXO9twwT+XOdoDDMquo5Zhe10n5u9otNggvBp7TnM+r0YsYeCCyYPx+udVOO2BhTj5/s8sc4roMxUtQQSjataI4tPtDY7BlhAbd9Z34ell5Xh3U12vP/azaHcT4jwjyu6Fyvvv4gRYzLcGBV5baxWJCrOZGDN3Q608h9i+8K31NZasbVynWFfZjlvOGI2iLH+PdpVQlLXbVeWt8Hs07GoIYHlZC0rzM/C/b2zGn97fLsfPSFyXvMwJ/UaEc9K9WLa3BdGEDsLzBZ9sb8B7m+tkhFLXEcGIwkzM+9XpOGdSCWYtKsMmPggalE2cTpOnun1OKstBQ2cEb6yrwXXHD0Nehk8qww8v2IOpQ/Pw/i9Ow49OH4WrjxuK19dV48SZC3Dxo8vQFIiiO5rAxqoO7GvuxlnjB6Awy4/7PtwpUwkiym7rjuGYYfkYVpghF7dQSkEIMHpAFl5ZW4Vnl1dgVXkr4spovbaiDQuUBYBPLy3HOQ8vRSCaAKXA+ZMH4fmbj8f0YfmI6QZ+Pmcjnl1WLq+xal8rYgkDF08rxZL/OwvnHz0I72ystTRmwEytBngjjiR0RBI6NGIu7nLCgBympKypaEVNexif72+XamZLIIpnlpbjuchQS0kAACAASURBVOUVeG9zHXwego1VHZKEPr98P258bg2u+dcqnPvwUtz68npc/eTKlIsEK1uDlkF0d0NADiYJ3cCb66rxvefXYvGeJjy3vAJ3vrMNlJpb1A3MScP9H+3Cy6srZRlOH1uMVftasb6SKft3XDARvz57LFaXt+HtDbW4dNZyfP+FzxGIJPCN8QMQjOkYlJsOAqbaDshJw8aqDlz2+Apc+M9liCUMlDd3y3bbwb2iuxoC0j6i3sOWanPRSFTZLUFMBpQCn+9ng9NH2+plwGdXoWvaw5gxsgAA0Ba0qpEGZcq5l+fl1+5vg25QDMhJw7ubamW/eGdjLZbsacafuTVkUG46ttR04LbXN1kmq7/M24k5a6uTCLflmspAaVCmlOZl+LCmwrTEqOqFLidJ9to+UXk1649W7uL1I8oejut4bnmFpZ1+99k1MjBUCdCOui40dUUxOC8d40tykjIecd1IUlZ78uo63Xcqj7A4RV+sEbokidbriiBIlOeSaaXITvNijhJIOJbNQTHu5v19b1OAT6Ds/R11nYjEdYtqL7IOzy4tx7VPr046//wdDTCoufh3fWUbjrtvPk5/cBEeW7hX9gWRRbLeo/mA2nj2rChLLCxkdXXCqEKUNXVLJevGk0dgf2vIEqSLe7QTHicyJ60RlMqFh6oiHI7rSd+paQ9hdHEWsvwedIbjCEQSyM/04dJppVi8u0kGvqp6rmYT8jJ8GJibjqEFmfjRGaNxxwUT0dIdxdwNtYjEdawuZ+N1MKZjdHEWjh2ej2eXVeCSWcvxw9nrlDIzW4vPQ/Crs8ci3afho60N8n6fvvE4zLrhWPz2/PFoCkSlkmdQ5gkeOzAbK3hQqQoiqiXIDGbFfciP+uRbXba3BSv3tco5WDwbtT51W5vc1RBAMKYjljDwzsZaNHRGpICV7tNw18WTcOUxQ1DbEZbkfndDAAt3NUlBoSiLkS2nxVMJW7uwQ7TH4mw/Hlu4F1E+DwLAI9dOx/s/Pw3HjSjABZMH48Mt9bIM4v+Xf3ACRhVn4eXVlahqDeKW2evR0h1FJK7LNL1uMGFCtPmnbjwOc396KgDg0x2mei/qKZYwUJqXjlk3HIM3bj0ZZ40fiO5IwrLuR2SUKQWeX16BSX/8BL99awsufHQZbnl5Pf78wY6kLNWa8lZk+j1IGBQfbmHjZqodamJcaGrsiuLsh5Zg+p8+xT3vbbeMTaKNCUU4O81rsZgAgK6bQdcLXEiqbgtj+rB8xHUDs1ftl8eKAKgg04+rZwzFgp2NKe2iQUWE+v5pI3HKmCJcMq0Un/7mDFx3wnC8uHI/nubCjhrYOKHfiHCmz4NQTEdTVxSxhIHibD8aOiP45ZyNuPLJFahpD6GuM4zS/AxMGJSLB6+eiuLsNJki1g2KaMLq5R2Yk4YMn8dChMXndr+lGPBOHF2I+6+cgounDsanvzkDf7t6Kh6/4VhMGJSLP1w0CQ9eNRU/OG0UJg3ORTCmS8/tnsYAGruimDGyEIv+9xt4+NppeO7mGQAYeYsmdASiCRRn+3HaUQOwurxVElEPIfj2jGEWZUNVhG9/awt+OHsdfv+frdANirUVbShr6paNOjfDh+NGFOC5m4/HWz8+BcMLM3Hfhzvh8xCMKMrESj4pDeeK3DUzhqI1GMPCXU2WAeiBj3dh/o5GdEfZeSMxHZG4gUG56Ti6NBdvrndO/4uOsGgXi05buqNS8WnpjqKhM4JzJpVg/Z3nYMaIQqytaEVDVwQ+D8HW2k7kpPvwt6unYuYVU/DQt6dhe10XXuBqNmBNae9vDVkG0V31XWYq3aB4cvE+LN3TjNve2AyPRrC9rguf7miUk/3zNx+PM8cNwL0f7MCWmg4MzEnDS98/Adv/fB5W/O6b2Hnv+Th/8iBcd8JwjB6Qhf/35mZsr+vC8rIWpPs0PHDVVIwsysQfL5mEb04oAQDcf8UUACzg2t8awp3vbMW3HlqCn/x7PVfBzMFH+JjVewhE4wjHzHYrFu3saQxg7MBsS11/xHdSGJSbjtcU76JYVT12YA40YvXXU65k6spkLIKzaUPz2UI6nVmLttZ2YuzAbBRksvS+TilW7WvF3I21MkACIKNpkU52gko8DEqhEWBoQYYMkgixBqlC8TKtEdbBSpB4AJg0OBe7+WIlMUCH4zpCsYTsO5RSLC9rkWl4cd40r8Z/SCGCgbnpuOq4IdjAA1mzPpNJb189wuKYznACL6yoSNrGTDyDgMNiXjvE5ZxIHGASk5w0H04fW4yN1R0W1dpehzpN9th3RxPwagQGZUqtmmERNoch+cw3aSrCCUfrx4KdbCL8vIIFWne+sx35GT4UZvlR2RpCB//O9tqupC251KK2dDMyWcTVo4bOMHLTvZg6NA/7W0PYXteF4mw/vjlhIABFzYa5u4dchMdPbK9L9dqqpUQlwpF4sq+4tiOMoQUZyMvwoTMcR1ckjpx0Ly4/ZgjiOsWHXFBQM1TV7ea4pdkCulPGFGHKkDz8/j9bMf3Pn+K6p1dj3tZ6hKIJZKZ5cMsZY1DVFsK22i4+ZpuBg25QeDSCTL8X2Wk+hOO6JHjHDC9Aht+D044qtqjVBqXQNHbdzyvaEEsYlixCmdIP7IvkDlQRFmTZHPf4zhHKs6bUajkSf9d1hPHr1zdh7sYaxBMU2WlebLn7PFx+zBAM4pktISyI+xbzsRgr7GtiRL0BqdcgiHZ518WTUN0WxsurKhHl/GBcSQ7y+Nh41bFDEIgmMJ977gURHpyXjrd/cgqW/N83MOeWk9DSHcVls1Zgwl0f49yHlyIQiYNSyn6ynRdhVHEWpg/Lx+gBWRYbi1ovF0wZDEIIZowsxMPXTsfp44qxel+rfDbih3oAcyH5WxtqENcNXDKtFACkYguwuXlfcxDXHj9MPhuvRmRwZEdCN3D2xIF45nszkJ/pR8KgLNOjtAlhpxJzxUmji7CrPmDJduiUYsKgHJw7qQQPfLwLm6o7UNcZxpnjBuCciSWYvaoSczfU4LnlFbIP+TwEVxwzBAaFpX5UBKO63KP98ulD8MoPT8Rj1x+DTL8XM6+YgtK8DDl+9WSjBPqRCBM+OEQTOmK6geuOH44dfz4fT3znWOxrCmLWwjLUdYRlajcn3Yfrjx+GTdUdXL1h1gh14J8xsgBHl+ZavJ6iYpu6omjqikhiLAcpQnD+5MGYdcOxGFeSg2tmDJMpXYANYnddPAm/u2AiADNNK9rrmAFZyMv04YpjhuL0sQNQkOlDJfeiAkBhVhpOH1uM7mgCm6s7YFB2ziuPHSJTWn6vZklfRhMGjh9ZgDlrq/HBljo5UNV0MDIirCTs/H7M++XpePdnp2LuT07FjBGFMr0niPCZ4wagODsN72+uk/f9zPdmYMyAbNz7wQ6Z1ojEDUTiOtJ9Hlxz3FBsq+2SnjIVcQePrhiMW7qjqO+MYHhhJgqz/JhUmot9fKK54pghAIBffmssrpkxDDecOBxXHjsUxw4vsNhi1I62vyVoGRx2cc8xwDrqdL4LR0cojp99YwyGF2bi5VWVktwXZPnx4zPHIJYw8OmORowtyYamEaRxn5OAz6PhzosmwqsR/OXyyZh1wzG47/IpKMlNx+L/OwsXThmMP1w0EfdfOQVnTyrBzaeMxF+vnIKjS3Pxxroa5KR58dmuJjy+qAztoRi8GkF+pk8uQFLvKRzTLQPFirIWUEqxp7Ebx48qxIiiTPg9Go4fWYAAV4AuO6aU+77NScOg7Bln+b3ojiawaHcTfvrKeovCavfdHsWJtk6ZfzuaMPCLb43F+jvP4e2QymepZilq+YAish4qypu70dgVsahIukFBCJGEihDguOEF2K4QYZHONomYdbAS5xuYk4bLppeisSuK9mBMTmhhHriJ74nziKBAvB5VnIWa9jDqOyMYmJOGy6cPgUasnjUxpqjozX9rL2dXOI4/vb8Dd7273fa5QiZ6UdXsBNi+dkC8Twgj+LrNf2n3SDqp2t2RhBwbmrujls9FQDA4Lx1ejcjFawaliNn28m4PxrBufxsG56WjKRDFzHk7sbO+C3+4aBKGFWSgpTsqFeFANIHKNvNcgLVPCNVHKHv1nREMzsvA2IE50A2Kz3Y2YvygHEwZynyLqsIs1FeqtHv1/E6KsMGDMMBqjRD9UnxFKMBDCjKQm+FDR4i9zk334ejSXBRn+2VZ1DF8Z32XJH8asRJhQgieu2kG7rhgAi6bxsbElu4ogrEEMv1enDOpBBdOGQQAGFmUaVG6hZACMLKQ0A3ZH0QGxevRcPkxQ7B4dxPagjEelBKcPKYY4biOzTUdss36vRq22uoScPYI90URrk8iwsnWCPaskjuWCEyjcQMJw4DXQ6TtaHBehuX8ws4hFp2L8u6sT14oJcezFGsQxHfPHDcAR5fmYuGuJml7SPOZ491xPPsmRAF5jNeDrDQvRhRlYerQfLx+68lQH3kkbsigWrQR0SbOO3oQVpe3Sb4g2uywwgxcM2OopZwnjy6Si5EByCyq36shphu4+dSR2HrPufj0N2fi4qmDAVizVGLNymXTh8j+fcOJw7GpusMxQI/rBryahnMmleDdn52KcSXZTM1XxiRRB4IInz62GAmDYodtobjPo+HBq6fC59Hw9092g/K569Yzx6AzHMdtb2zGvR/ssLTl8SU5GF2chY+2JWchKaUIxhK48aQRePsnp2Di4FzpLBAozPKjnWeOvrKKsLhwMKpDNyj8Xg1+r4YLpwzGjJEF2FjVgaZAFKV8IgWANL7QS+cqkthHGGDp+m9OKMHkIXnYXtclG7cga9XtIZww8zO5cEQ8TPsglQpi30X7zgejB2TbjstCZWsQrd2CCPtx4qhCACx6olwpG5iTjplXTMaMEQWWBWBFWX6cPXEg5vzoJGT5PVi1z9weRUQ3YnGOgN+rYdqwfEwZmofRA5iXjRBIEuL1aBhWmIHOcFzWS0GmD6cdVYyW7qhUTyMJRiz8Xg2XTR8Cv0fD7W9vxgMf78IPX1onJ0U1pTlxcK70n6V5NVS0BBGOMyuB+FzgljPG4OUfnICbTxlpKf8Z44qxra5TLsawe4JVNUHd/iyhU8T532dPLMHNp47C5CG5aOiKyI6ZnebFjJEFyE33QjcojrI9LxXfnFCCTXefi+tOGI6Lp5bi6uOsA9Go4ixcf8JwAMA9lx6N604Yjv937ngUZ/sx+wcn4oSRhfhsZyM6wnHkZ/pQkOmX5VDH/UjckKk3APjXkn1YXd6GznAc40tycM1xQ3HDicNx7HA28I4ZkI3cdB8MqrTnNtYWhhZmICvNi2A0gZVlLZi3tcFCotRnlZPulbYW3aDSE3rMsHxoGoFPI1IpBqzEtI77W+1+VMOg+OY/luCKx1dYJk9KAY9GMISvyC7JSce0YfnS3sCOY+eTBEZPPveNJ43A3J+eggm8He1SrDHd0QRiij1KNBvT58neGFGUiYRBUd7cjYE5aRiYm44TRxVJ3z7AJkv7xv+6QXu0Hcjj+HVSeerVU/T2wwR2X6tKINpDcUsAr2kkiWQIZVXAicx3RxPI5TtdqARSvDYMFhgOzk+XY47TgsYV+1pgUOA3Z48DADy3vAKnHVWMC6cMQlF2GtqCMXSEYhjI25xQ6yTZUtqXUPq8PNCq74xgcH66DNy6IgmML8lFbroPowdkYVO1qghbAyo7iVPvX1Wjxect3TGpdkds1ggx5g0tyER+pg9d4Ti6wnHkpPtACAuo5bZtSt3srA/IczjEjhiYm45bzxyDmVey7FIwqiMUYwqXRyN44jvHSUVMtAVdZ0GqRxJeYsn6qBmU844eJBVTQZ7FPLShsh0GpUjzaji6NFfaDdX6sf8PWBXh7XWdju29oSsCQpjfvDMcd7RGsNfJdSKIsOjTPqXiBuWxNqQqwgCwep+VCEcSehLJFs8l1YJOVbkvzPIjHDeFCrG4HAB83L8rzhd1OAYApg/Lx4LbzsSdF02U59cpm+PFtUTQctn0UugGlUG5YVAMzEnDstu/iQmDci3nPXlMMQBTjGsKROHVCI7nBP3aGcOQ6ffCoxHJEcT80xSI4J8L9uLMcQMwbWgeLp46GJdNH4LzJw+CztuJXalP6BQ+r/kMNM3a3tQ6EET61KNYGbeo2W6DQtMI8jMZFxIL4EYUZeK4EQW48aQRAIAMn0eO7T6vBkIILpwyGKvL25IWakbibOFhfqYfx40ogBMKsvxo4/26t+1D+08R5gRUSPdpSoUfXZqH3Y0BUAqU5qfL98UAINK+Md1ATGck+vM/nI2rjh2CKUPyLAvmxCS5dA+r/A3cTC+epUfrGxEuyU2H38OIXk66F0PyM6ARkyALjCjKRGVrCK08wivO9iObK7gxxRoBANcePxynHlXMFxKxct596dF49qbj4fVomDwkDx9ta5BlFZNSjo0IqxjDiXBpXoaMpgHAQ4gMIADwhulDKKbLaFT4A9N9HhRk+fG3a6aitTuGp5bsw7K9zbj5hc/RGYpbJsPjRxZgyhCm0hw3okD6cEUqaxInMD4PwciiTJw+dkBSnZ8+dgAoNX+YQPVy7bd5hA1qVSzjCQPjSrLx7E0zUJjlR16GHx2huPQIZ6d54fNo+MZ4llI9amBqIiyOPxCcNWEg1v7+bEwflo8RRZlo7IqiIxRDXoYPGT6PVC7EPfg9GsJxXb7/zPdmIJowcP0zzH85YVAOfv7Nsbjn0qMxnq8gnjQ4Fxl8sI3EzMAOAIYVZCIzzYNgVJeDnqkgGZa6HFWcJQdg3aDYVNWBoiw/hhaYAZM60KkTu3gEdiFnPVe86zojlnSqbrCATwRjwwozkJPuRTRhLpSRi0AdSJY4T36mD0MLMuVq6j2NARn8inZr94QKRVi8Fos7DWr+yltBls9iq3JShO0TeCqI+w6mWEioTsy9DcgmAUkmcVVtIfla0xgZNmzqmtipRsCJzAcicUmExViqllWk0ofmZ8oxR5A9NTgStoczxg1AfqYPmX4P7r9yCgghKMryo7U7ho5QHCeMKsS4kmz87ZPdXKHkZVOK1dRlDYKZIpyOMQOypbom2sD4khzLtl/iOUlrhAwmHBRhw9pG5b3whY6CYIngRmRChuSb1ohAJIHcDDZOaJoaJFgVYXEOu1KlwqMRZPg86I7GEYwmkOU3xx9CYBmzhSIsggWfpiGuZH3EQivAHMcSOgt0CGGBMADZBzVCMH1YPrbWdkolridrhLqt23efXZO0GCsSZ3PJSaOKoBsUW2s6YRhCfTePE8RQYDRfiCgsKvGEgbhOLdmnQTZFWAS7W2s7EYollMV5yV5gmXFK8RN34nMPYc+CZUa5R9lGBNkzEbtQJKvGAuk+j3wGhjLOiTFVzIETBuXi2OH5eHVtlSSiqTjJmAFZGJCThtXlJhEuzk7DzaeMwq++NRYjeT0CkO1T8KsXVuxHOK7j7ksmgRCCP1w0Cf/49jQMK2D8pS0Yx/Mr9uO8R5bKc8R0Q273KuonaeGjoghrhJVxYE6a3JoRYH1OzDuCKANmxvreyyfjljNGg8LkQSLoOGdSCXSDJm03K+b3rDRrEKKiINMnM2S9bYfbf4owr9+uMLshv4UIm5GQqggLAinM9vEEs0ek8Q5DCFFSZ+xB2LdPE+cWjb+virBHIxhayMoyJD8DJ44qxLiSnKQU+4jCTNR1hNHIO2xhll9eg/khrdf08ShedCp1gdDUoXmW1EYNV4ZFI3fCGK54Di+0EnTN1og1QuT+p0L1iMQNROOG/H35y6YPwbLbz8KWe87DS/9zAsqauvHa51UyvePVCE4aXYSzJgzE6OIsjCvJkZOcsLQcNTCbk+AsOYDbMXlIHvIzfXKxn67MkO3BuGXQTCipYLEiXp0A2GQVQyCSQBZXVwDWoQBIZfFwQngAS3LT0dwdRWt3DAWZfmT6PebEyismM42R4wjvmNOG5eGtH5+COy6YgFk3HIPjRxbK8wpFYOLgXLllUyjO+kt1WwhejWBwXjqy07z8x2QSso7ENdW6HFmUJcuqGxRbajowbVi+nKi9GuELUNl3Yg7KsF1VER7mk0cXWVQkgzJrxFA+0A4tyJQKj5iQpDXCgUgIJVOUTaTMO8Nx2f4EEY7bJnExSYrTqROEUCcJIZZ2dSjWiN62WFOv09uALHdUcLBGqAtHPYRAI+ZqdAH7ziJ2Ms9Sirrs+yrRUl9rhGBoQYbMRtEUzwhgY+M9lxyNR687RtrKirLT0BqMoj0UQ3F2Gh659hh0huL42ye7TZKv3JtYw6EbbExv6Y5iUG4GMvweGUyJwHBQXrolTS985uI2dFuGwGkfYcNBCQdMIkz5bYpAQHiE20IxBKIJKUaIcRWwErDm7qhZP73MMdnpXnQrirCAIB5qW1AVYY9ms0YoirCYRsSY79GI/J7odx6NEeFI3JA7stitOeozUttZMKonBX5CrRViQ1w3lAWx1mcgXt9zyST89aqpABQizBVh9X6y07zISfPK584sfBoMykiRRaW09TE5HqayRijtOMPvsSwYTbOpvV6NyPOJY9K9zmRMzPNCXEgofU0luzecOALlzUGsrWiDzvueEwghmDQ4V9ZTUyCKgblpOGdSCX5zzjjLsUIR7uL8obErgpLc9KQMtriUYVDUtIcsP/6iLrYGTEVYqrYeIusgEIkjO80LQgimDs3H5hpVETbk/Z42lhHhdJ8ms5MAr1fdDBTEdQv49p8Rm7VBiB1q4GhHQaZijehl3D0w+eswQkxwAakIm40pFRGWk7gyUcd1wyLfjxmQLRfMXXXcUMQT1sY/gP/cqUkI+17mEYWZKG8OYmhBBv58+WTHfQlHFGXBoJANoSgrTQ6EJkEwjxfkUKTKVSI8ZWi+5dyprBEqhhdlQiNMgVOhaSzFJoJiDyHI441MnDcSZ6tlCznpEOXL9mg4aXQRNMIGnYTO0jfv/+I0SSx+cuYYPKlsvSWUN79Xw4wRhRhZbCXmKjwaI8piizvdPjGrE5nyOsFJm0/prPmZPsR1iqZAVCrxAHDRlMEoyvZjRoo0yuFASW4adINiX3MQ04flIZowkqwRWX4vOkJhRGJmWm1gTrqjUj1xcA7uvXwyLpk6WG6bI5TksqZulOZnwOvRkOX3IhRLmBMyn7EShmkdARghVBXh9lAcM0aaGReRapXf5/1M/QUtO6kSCxmy0jwWTyblE61Qm4cWZMjnlNAp0ryCxKn7v6oki/0v+o6HqzFx3ZDlEjsNxG2pT7Gtjng9tCADHj6Ii3apEWIhQixtaa1/8ZpyUp8K9u+pZEZ87vMQxHXa64BsXhOWewDYfswjioX1iREb3UYy7NYIEXwLROIsK5XHg2n791lgAk6EM9EUYKvf1dXsAlJJ0wgu5/5/gaIsP+I6RVxPIC/Dh0mluZhUmoua9lAS2QfM7S11g0pCJYLpsQOzUdsRxrgSRoRL8zLkr4jmpPuk4igDZJsSrD5nNVjTbfct6kctW21HGOk+DYVZfuRn+qWXWazTEJk2wBokqPXem9giAtlgNCF3shD1qnqZxZaIqhc4rpvERJ07xFxpZmeIbMNCnSQEco3FpuoOHF2aZ9owlHoy68iZzAoItVbM2aJv24MxsbcwwBb4jeJZTOHVjnFC5LMJJyU8AKKUbVtZmJWGSDwqffJpXs1cQK+s6XUKVFSoNsl0r0euPQCsijAAOY4ArK1oBJb5R4V4BpSaY11C6TMCZ3ByuKepW46bqVCany63Z2vqisjx1Q5JhPn8E4wmHLOdHqWdGAbLshsGszLY7SmirYv7F+o5wBRhERxOGpyDBTsbpbqtG1SKVeNLclCc7Udhlt8ypvo8Gl/TIoI6jdch+9ye1QryMb4nRbgwi1kTVQttKvQbEZaKMCfCqiI8sohtVROM6SjNy0j6jlCqYrzRqykUj0ZwdGmu3MXAvlrQni7rqzUCYCQXaMaQ/Axkp3kdG5YgfGsr2uDViEW9FZOOek0xeAmCoza8aVzdFh1cpMNVgmdHmteDv1wxBdNsJNrDG7aZrgPypSLMiXCCL5ZLEeGKgTfOBylBKsT5irNNAq1+9tL/nNBrwOHRiElwFUJk32onoXTEOO84ap2pKrf6fDSN4JQxZlrmi8BAfs8t3VHkZfjRHY0npXxFxxWp2FR1DTCyI/xTwocWjuvoDMWxeE8zruOrf7PSvKjtCCctChEeYY9GcNdFE3Hu0YPkokT7pAqw1Ko6sYpBSfiRAeuEVt8ZQW2HSJ2bhFZMTD6NYFRxFobkZ2DGyELs47YZQWTFZGqm3ZXJ0haoEkLg4+1P9GmxZVzCpgAK1VyUx+/RUJqfjuq2sFQhPMR8JuaKbutgq3pOvSkmO/U6AoLAqZ+n+zyI64leB+RUPlcAqGwLScVVI6xOqI2Q2L109jR0gC8iFROlXfUWdhWx4wfAAiHxjNTx1CR6yfdRpIwF+XzVvUcj3IphvVf2M8xmPxGEajC3xV08tRRF2WkyKyJsVw2dEU6ErcTM9Fkn16FlsZzD+3aP8JqKVowfxBbiiLFFrT9CTKKtWokE8UlVPyqyucc/HLcqwoQQ7mU260tVhH0eAt0wpOKmEgtTfLFmIUXwRPl7wwuZ93mbst2Z+r/atlUiqXr7BYRaK9YFqP5gewAtTuXRCHLSmK9VBHFxnW0Xad8+cXBeOuq7IszaQZnVo6U7ioTO+m52mjdpJynAfC72/bTlvQhxiCvCwiPs1UhSFtOraRZFOM3rSRkk21V5VdRROYAko4ZpKUuFwXkZaOmOIZpgv8x7zHBnYUdwBKEIB6O6I2mUqrXyTGK6gXTNk0yENaYAi2eZleaVinBXJCGtNxlcpY3rBjyaB7pBke5j1yGE4LZzxifdowgmREZGWDJkFsPW2MTWaZk9KsKsj7aHYr0KEP3vEebWiDSbF2dSaS4KMn1y8APMShEN2lSErbWqLpizdwrR6PsarasQk9CQFFEYwPzN6T4Ne5u6gurkrwAAIABJREFUZdRDeBpT+u9skRBgNgC1gwwvzJRbCAFsIUKm35MUKdtx/QnDManUagHQeDSnpjPFBCWsEdE4i4SdPE8AIxRx3VzRa4fYA7Q4O80S2Pi9WkpbhICqrMjFH5wcqwTF6mE1ktI3gtzXtod79FJ/EVDJf0Em8wgLUiYGD9Fx20NxeDSSUk2wQ3iEwzEd721he21/ewYjwtlpHgQt1ghhaWATp1cjuPnUUSjNz7BkVex+NK8ysbLvs//VjcjVuFLtW6rqQ/nfGiHISvNixe++iTPHDZD3KqwRQjl28lhKEqGUT7a/FIqwaNtSEabmOYRVSFWE7d5kO6F18uk6wU6g7f2TTQTs+fU2INu9mRYfayhmCeBZ6tyqeLbaFWFqvS+xMNa0RlivoVOzXQgiXMN/BhewP/PkZyRQpGyzJ8YZzeZ5FdftDMclwU4YVP4ylQgorjpuKP5+zTR5PvG+IMyp9qNWCaQss0KOLZkmByK8pzGAbbVduIxvRZWrEmEucKgKoeg3fu6176l+VGSledAWjCGuU6s1QrN6mQXh8yokQezyYieNZlqe90VNvG+SdBFM5Wf4pMJmJ8AW1VwJXChNbvdiMd0QHsAIAqgGP6xM5j0RXgY1yIhzu4ffpsaW5KajsTMin5EgXqKOxNhq9+E7rXmwfK4E3ek+QYQNCycRsCvC6SnmSnY+xYpimPYIwGqXUS0rOqU9thfR9qvbwmgNmgtRncqZk+aVc0K3LdtgL6OaIRE7LCQMa7aVBVHms8z0exSPcFwGh+I7ok/b55kbThyO6/iicwExZgpbm1SElYAuljDkotag9Aj3QIR5ZrsjFP/qLpbrSREGgO+dPBI/OG2U7TtmahVgHSaqG0lbOokFc/uauxHXDct2Y/ZBuLdBSsUIQYTzU6f5030eqTyqFgNBRA1qXTzhlR5h3fIaYMfNvHIKbj9/Anwepqb0ZIvoCRpXj0QjVj3C7bxxiUg4tSJMJBGxD7wAUMw7pVjhe0Dl05IHLJ9Hs6gJPrFKWiEwccOmCPNJtykQlQPll4WSXOvkn+H3Kovl2PtCpe4IxZDOV8b2BWKCDMd1vLWuGhMG5UgLkdg1QtiMzFXSBnTbs5LWCJpMhD0aQVxV3Pl5RCaC3UeyaiveN4PMZC88YA5u6nZnlsVADovz1PL5RPsTHmHebpO3T7NaI4Ty5dGI7JOakoGw7zphL0NvC+bs30tedEdlINObImzYrqmemwUbkPckiKVKSOyL5eyLwsQiE7lrRJIyCjlGqRkOtc/Z79NJTChSxr78DL88zkm5VX/10zBMdXhgrlVZF1AVYcBUGKntvKnqUFxHfd/uETYM9ktYHo3g0uml/D7MsVf1CNvrxu/VpF0jVf2oyE7zScuFqnDZFzjrfPGrVIQ1LeV4rKaUDWou0FafgTiP2H3EWj/W+mLeWPZmqn7BFHqvrBuDIinLx4i4VZABrHUr1ik4KcJNAeuOQOx49iyFaJakCCvCgBMMJSjI8HkQSxgIxxNJu0HY60FsNZoKpj3FDD6knchGMNlxjDD35CkXthOh4A/MTT3X5mb4JL9KZY2QqrUikgnSyHzaVkVYtSZm+r2StwQURVhwuXjCJNS9+eS9NkFQ8CB1fdXTS/fhklnLAZhjfM+L5di40xaMfXWtEaYi7EyExYbQKkSDUa0R8YSRpMCILbv2NjIifM6kQRhemIlnlpUnDZIH4hE+eUwRbjp5hDR8p8I3xg/Awl1NlvQgG2zYA1WLK1ZHigZlv5eLp7J6yMvwoaU7dtDkzu4n9GjmBCXAFgmkjnJ90hqRXOeAOfkNyk2tmKeCV9MQ1q0Exu+1Kis+jyYJHGAuqnCyRgAHvgPEoaI4O02mSvMz/WgPxZN2jRCEtj0Y73EQtUMcG4rp2FbXhR+dPlr2IeExFP0jrqhr6sQJKD57Pqmqk41P06Dr5ip0cR51j0nr4hkrKVIJjphcVIhrmdYIWAiDRRF26J8+rgjHuO9fKMJ2BVCkzVQyfdMpIzF9WL45+RPz+FTbKzl5TJ1gJwTJ54FChHtWJqTP2mH7NDW1KpQ0e9rZrjirSilgKsJWawSVi4DEhOghxOInF+ewqvbsf6dJTv3hlbxMkzQmDEMhXZwIKz8MkDBM60tGiv4xMCcdhJhb+ol2ZCqzVjVTVcRlG6XWuhV/qv31o231OH1ssbyXPAsR9sp7shPwNK81gO8lGYbsNI9cLKhO7PbnmzAoD2zZCb0ewn4cw6BJGTdVSdMN0+MudhpRBRk1O2LWj/W110MU/7D1M4H6Trbvv+izarsxF0FpSQEdYLYRAIglWBuwzzGD8tJhUDNDZRJhVjAxttr7gJlpTGGNUIKCDD+7ZmfYeXxWFeFowlk1FnCyRqjZTvWcopx2ccIOEQSu5VsRDkoRLAKsjZrWiISjjcB+bXFflJoWSPN+rEJUht8jiXYgGse4dLbOxS8FD7Nf9GZBlZsGCIuoJhRh9rlB2foHkXWQu0b0slgOYKLTV9YaYSrCydaI3r6jKsIxPbkxikEqFEsgrrMtmMRPUpoTDR+kDsAakZXmxZ8um2wZEJ3wjXFsqy5hFRBlFx1CVQhEAxGm81QNRig4ub1cOxU06TczVZycdG/Sxt89Rbk+TShyzp5J4b+0eyT7VD5LilFVVsyBzOfRLL9EJlaZqoNKfqZJ7r9sIuzzaPKZi+2kQnHd4uMUqZz2UOyAiHCGJNAxsMVOZjvI9DNvnF0JZak4a1RvkhtYVCFApFrNVeim0mqWw0lFE39TSTySzy3qRz2veJZO1ggntdHn0RBLmJvStwetirD4TjimW/bE1AjbgePa4810nAgMATOwTiK0DulhJxjKdcT9q6CUIj3FJJ3qXE67RljJFTEVGuV6yXsxm2UA2A9bALDuGmGYCoxcXKVZF1wZyiSplgcAnIbQgiyzfQq1zyPFAF5Wfs42288lyy2mUozNfq+G4uw0qQiLAEHUg25TLu11KOpDtRmIc4j70w2KjlBcZgHVOgPMQEIlRqIdynFL1k8vinC6V5IGiyJss4bZPcJeZYGRXT1VvZWUmq/NZ2AGqh5CkgIvadFRxl47AbbzyoZOtjOBSsJVEg+wOUT1rYsy2BXhhBMR5qRP/CS0UJ6F2peRwn5kbrdp7RvsVynNreQAU3BoD8YdOYnY3QDoXRH22OpB/bEidVyz+nRT7xoBQK6ZEj8jL3bJckJuuqIIx3RkO6inxFJG9l40oVuel7wfm1Uny++RNgp1sZx4bjGlL/W0xkL9jmmNMNurOIfO2zqlFKE+WCNE9q8tGP/qbp9mV4T7RoStildcp4jGkzuMOJcwzovPRTQM9H2QOhgML8rERVMGW5RjjxKJqw3dbo3wac71IAbhg1WEhUdZTRdrGrFYLcJcEbZvGSPg82p8pwbrlmUC6T4PvnvScFwwedABl89Dkm0rpteOX58P/DIQMpJ3DclzSF9+mRD2iIJMPzL8HlAq9u1knwvVoiMU79FfZof4nkihqgsm7ekhManaPYWA1bdmV4vFzgb2RXcWFdimUAqwgEX8nWwBYufnG9IrWRmh5qnvq9e0bzWoeoRbpUfYSmSFqmhOtsl9XCw0Y8c7E+FUKWA7RLE/+MXpOO/oEkeFWKw+7337NPF/ct2rk5WwRqj1J65lubatTwWlNUL4K8EXNmryOINSua5BHOMUrNjT2yrSvB45VgllRgS7hu3exKb8QpXWDbYavzevZL1l72ooz9N6z2qVqB5hSq0BgEqgRDCnliE/00kRTs4spHk1S6q5N7FFnczVvmzP4om6EWUWaXonokGUZ6cuwCLEfMaiXIQ4ZSKs//s95iIxuzIs0ByIYmCOnQiLujF3A1CDZnGsKmAIa4R9/YSof+Ehz1H25wdURdjZI2zfPu2Sx5bjqaXlFsVSENuOcNxxHvR4FI9wD3MlYI5/Yg5TMzqOirDDug07Mvwe5Gey7PDQggwMzEktOuVmeNEVTrAtE1N4hO22DIAJYtKeqP4WgcgaUTNoi/IfMFGtEeI7qke4N3uQ3DTAlhm37H7C+2zCoHLrPvsOPSryLYvlvqIeYQJWsV0O26elgj31CwChuJ5kqxDnisTZzzf7lehCNGLRhw9k14gDwePfOVYuZgJML5lhmAsXgOTFcqkiJ0FYD9Yj7NGIhVRKb5YlJcXqNVVQIqwRCYdBSuC+y6fglKMOfHcGjxJpy0UnXs0yEfg9JEkRjuvUErVm+T2yU/W0u8YXBaFa5GX4kKnYGeyKcNuBKsL8WLEfdo4yqNmVb1F/upGsmEt7gmEkbdUj2oh8DmIgs6SQUxBh5bnIdm63Rnis/Vekp02SZSXWokwC0hqhC48et4A4WBuEKmw/h7xXZXGmatVQIdWxnrmrrJ+Jg3OQ6fcm//oeZW1ZI70rwnbiqipnalqScKKo/uSpUPRVqEQRMFOKImAUhM0khOyfhxBz33bl2Vq3TwMvm/NYICwFIoslgnF7wCvqJMPvgWGIxV89T02DctlWWiZho0mEx65uqvWgG9YAwKDUsh2mIduweW9OQbbqr00at/roEVb7sqoIa3wxpFlmmyKsETkG2utLqpGGVWVkO3dYA1V1xx67RcJqjXD+DGB13BKMoThb3Tdf9QhzYuURO4fYibDdI5wscIn6b+jkYgCvN9EmRd0le4Stz0egriOCxq4Is0bwcohxtiMUcxQqLLtGxPUeBTz7D4BZFsup466iCOu093VL4uemU+0YIZCb7kMgGkc0wbK4zovl2P9qsBtNmGOsXURRMxRsz2UD4Tj7dWDRJ/xJ47zzmiIVkgfJ3bOIvCYrnznOxRIGgtwK2FP9p/s8yPR70M49wj11w34jwgDbo8/pBzVSwdw1wuyAwWgiWRHmDVishBXnVhc2SNP6l1QDmkYsq+kF7JFQqgaTJ60RB6kI2yZNUQSRklIbSSqC5tWI+et4hzmA8CgTihiv/NwKIa0RXo2rIuxzkcZXU//qCuScL9kaAZgLfAqy/OaPYCh7/GYpg3UqD6QTxDMRC4uyLSqSnQirChJ1XJghJgsLSZa7gvABR7EwCOgGRVVrCHsbA0mL5VTS5WyN4P1X2aWBTRLsc8ddI4j6fU2mTVWIcqr8MxTTTQ+rQ1tVlbxU1ginCd8JYrsxpqKaaWb1c49G4PdqSds52pG0aIn/L7yVqpIm/KlmxoTAJnolrYkI2HeNoNZfKzPtW+o+qNTxGfW2zqIoy4/cdK+ZlhdigCB2ysQLsL4hFM5eeDBXhMMW24BUH21ih72dqvepBgBhhQjrXIFS246Y6NN9mnVOMayBXJrXY0n/95Z0zLIQYXNM0AiSrBG6YZI20R9UlVjAovQpc46YAylVd5Jw8AjLOhQE1iSAdnshwCw3sYSB4uw0eV67mg0wIukoyCjrVWIp1qGIgKqhiynC8hdbeftJtTOLsMr8f/bePtaWJbsLW1Xde59zz733fb/5eO/Ne/M8Hx6PZzC2H+NPOcbYwnaCbRlk2RIBAsIQCRDmQzKJZVkmClKkKFIiKxJySCRCsByJkFE0wvkHS8EKlg0EgsexNAxhPDHgN54Zv7nv3nv23t2dP6pX1W+tWtVdvb/OucJLujrn7tO7urq7umrVb/3Wb2kdYZ7rWDeXKDnCX3i4mVeN2PXTyXLjI8H7xu8WRssi6tlzslyxSSIiemmkHn7Nq89MHvfUnRW99WgXo0B2shxQD8bbc73r4hyLflk7+hCCGrFLFU0jIqwocLtuoGbmhc4AwfH4tBlPY3Mz0gDvrsvSdWzPXq3p8yNHeJLPPdnKaM6573TO/Zpz7lPOuR81/v6qc+7vO+f+iXPunznnvrum3YtVY5ZYnugHEaVsRKLgCOeIcPj/g1Ezk2+ytfM9BTXCMkZ0+iHnPRIlyZKSzFiiRhyiGpGumycgLqrxHISmSiH7NVMjejtZ7hAzZYharRrhR+QnUWM2hvA6J17cBCL87qcvo0Yz6yk+2nRp8oDQ5xJE+GJEFC1qRIYIg2qERtf4ufNigehDy4hw38t2hkGEWv/zT/wq/ejf+b9hM8myOmmBDN+R7xb3Q6s8pPMZThYiwq0PYvtqQdspBJAobD6shDs2VI2I31ftWglrlklHw3Kow/t30TaRBlAyLZ8WOZpeZmwzYotIzqrxWUIQIqZEARFeNz6OPQ5fr7Tj5J1YJK3Kcv04Lkpz6Iv3L4Ryjhs3CTkinMKcXU9ViPA7n76ktx6nRR7nNnQc8R7gfeB7yfNt1w/CEU6oafpu4x09ddmKqFzjbES476epI2j3CohwpEZoRzhyhMP7uu1zxE2H5VOSqIt0mDhmfdpA8b2KmwgFShClCAne18+N89IL9xMijOg/v9u6SEgEZEBrejsimDrqyGsgy+bxfbuOiLCtGhGBE4w4jegsz1UpWY6jybaTi6oR19suK7iBhonJ4WdOR8N2LSUfy1hfew4Rvn/Z0pceb6EccdkRxmd1vUvVRfE91NSmO+uW+iFVs9SqEcgRnnMXeCOXqBE8NsPfe9iMb7q+SPXQ9uzdUGb5eperi4nzzzXknGuI6KeI6DuI6LNE9EvOuY8Pw/BJOOzHiOhnh2H4b51zHyaiTxDRe+favmx9vIlViDA7wvAG8sSu+kzr1scBgI5wpxaGJclyhxhPAN0gQ8ZxAGwqEeF9qREqtBqzdcd2X7x/Qb/19nSRh1CCNw+3H8MstH7VyOQaRiVSSDtIaZUmzHPLpxER/eGvf40+8vJTdPeijdSIR9suLhq40C3hCLO0z5sPKhBhoRrRi4l1DhF+e9OJZFSi8DxColpILHm47UbENT0nRCaZf5nLp3HITDoo6Xw5cofv57oJi2RJHgmpEQ83nckzZpM6wowIy2OQ6jFlXZ/OgZGN9Pfwztcgwp1yvmOWeZQSTP1Pm+vw4brxWbJcRo14vKN7l62QJQrIqI/H9eP1pJBtjt5yH6fmz7/wHR+M0ozh3kjeaHSEt0CNqHQGIu0IdH91GN5C9BFpFxuAXlMjyJSyevpqJdYbrthJhIiwFwt3TWU5NtwoM3iRKuYFtJwjnk2cj/OcDR2W5y4wytwP6d1iyop1z8QmS70P+F7w2vH83QtIcEr3m+9NKMsrkz6JkiP84r2LQI3Y9RkodNE2dGfV0L8dHeEyR9hGhHETh7rVHMkhknOytQ5mqhGTiLCc75gjbEaofEpmnxsvX/2eZ+nnf+1N+vC7n5o87qmx2AxHEe8afFrxrOL72MV7JXSEIzUi/J/vN9P1ntLJckCNmEOE+Z16VNIRhnlus+vp7c1ukh/M9uyo3vSup6efVY2n8DEi+tQwDJ8mInLO/QwRfS8RoSM8EBE/laeJ6Dcq2hUdq+MIh5+IHAVqRD5wLlsfFSlWMYxFsLum8bPzOMIshTMMmhpRxxE+CjWilwspUaJGBD5fqDdfVI0YQ3FaX/AYJhHhcWFnPcIoVM/yLRSPs0px8jWdWzWCKGSqftuH3klEaaJ4uOmiw7kvIkwUHAWWmkInX08I2z5NvHrTohFhnKACIgwFNWJ4NExUm10fEapuXJSJRtk1mKgYpdRDOSXLScqFLuDBfSeyVCN6I8Q5CEecKNCi9GKLhqoROtmOLXFMs68LwzAzVwPT7TjnaN346PSVLEM1hSMCkSwvUb94jLo3OtP/wagnKp2VNEYSpzScg69POzT83an58wNjSWS2qGGrHFVGa1gFYdfnKgjaIk0OFtvkYMuNjVU4gx1u3ACgI8yggR47T99ZZZJSu0FGUC6U7OOsfNqljQhH3Xx8n/uBrsbBtvK+mCzH3e7UmoM6wvzokC9fcojbxtFmI98T5F5HRHiUkOTj7A0dZevQN73/Bfqhj71KX3h7Q//Pv3mLtn0+rxOF+88SWvcVNaKsIyz7QKSUa1xCLHFOtgpLcUIn0agaUSGfhjkMJU3d8AyCtOBctPUPfu0r9Ae/9pXJY4iSr8AI+jRHWCLCO5hTYh8jai03HhylzKkR6bprEeGHo/RlLBoD/OnoCHc9vX3dVa3vz1yt6dc//5Cud90kIlzjzbxMRL8O///s+BnaTxDRH3bOfZYCGvxnK9oVdIgaRDhSI0R4zv7uxaqJ3BUrWS6FXmt6erixRE3f68U9/P5YcWO0HYMagYtQthOHCjXTyXJ22OpQw2cTVSPGzRFTYXihx0IImy7nx90kIozGE7OgRghEeLkjzBumuxPJcsiJ04uk1uLGW8fJNxGhNeRv2IHgf0Rho8nOL1HiNWonKTrCisqQkiQDKvfpNx9E51NQI2Ajpg2zmYmIHm13pjPN5lyOVmXUiIgeTnvCMhkpr1A3jOjbxcrT9VKOcIa6j++vc+AM8nNwGXod6R1jO196HBxhXAC7PpdP48p1RJS9c+m6hln+K5oOrXK/NyN/r3Fp7MwhwjwuUlVAGE+9fJ74WAd4ppgk2A1DTL7kax6GnPbx/V/9Cn3f707LH26oSpSuOfpdiSPM6zYWJuj6tGlpG0yW045wenbo0DeMPPaSGpEc1vH+9PJ+tbDJQqoG2+dGRPiF++tMNgzvDdOvdGTyhXsX9Ne+/6N0/7IN9QG6PNJHJBMW76rkuDnVCIwkb/CeDnmyHNE8IlwrnxapX305v4afS43CQq0xQvuvvxg41ZYjHCrfSurB9TbNsbh28PubEOHQXkzgjslyoyPM97igMoXGf3+4kaoRSMnj+x44wrYusragsd+FOWaqCuBsS0HgQZteGX6IiP6HYRheIaLvJqK/6ZzL2nbO/bBz7pedc7/85ptvKkR4ATVChf+sHdTlytODx5IjjOH3qbDpKcy7lIAheJmKJF5yMKOO8L4FNUZHWCchPR0RYeQIlxDhcijuUOOwCxEgwkpuiwtsxGSwsS9aco6leO5dnF8+DY1f1KAawZ8hIrzsHuIkPUWN2GoEAnleTlIjGtQYblLJViKgHABKwZw6XMxWTZIGJErUiJwjLDeynXJctrue/tYvfob+/f/6H0RHPU+WG7LwP1EYC+iwIiJsZWE3DuTTCtQITSsoGSKjSLlg60fEuAoRVo4rn5v5oIikpWQcjpjkiLB2rAO3rhEL4DAkTi6fw4GjjZscqRqxLGk25SmM348IVEcXKz9m5PfC2ZtqC6+dr4P7Ja8dUez0WT+QUI3gsCwep9G7P/7Nr9Mf/cb3xv+7kXNLpJPl6ul3nNS7brxEm3mjoxDhWFkOqGKlghrDMIzgC/c38UFFcRlFydFILkfjwt9I/CQKiLBzIddEhLMVP5fzPFLSp7wXMQ/AiPQRpfVq1bi4TnGRmjsl1QjImWCLc+SgkuVm5ufWJ/rRXAIWz38pB6H8znBuQY1qRK09VeBU5+d24plc75AaoSOGad5P1AjJEV61cp7vh3me/LpVgODoB7GMo6ZGPLjuJqvKsd27aOjt6934rCZoLLMtBQT4PfD/VyinPvwJIvpZIqJhGP5PIrokokxDaxiGvz4MwxvDMLzx4osvxkHkXJkbi5ZUI+RANxHhFhDhFjjC6mU+lXyaNt51c4Y5G1/39UxBja949316+Zk7Wbhxyfkx85JfNuQIs01XlhvLX54CER7khLVWL1REhCFE0g/5Ruip24IIR/m0nUic4ftb4mLPtbdqpGxMOVkuDzPz7yZH2PvIuyaSSOkKnaJxQe7U4obvVlhoZf+1jjD7J1EXvO/p829f06Ntlxx16N+6dUVEmNVM2B7B5sNyRES2f58WRTSNgJWsh3ca243tjNSIi1WzQDVC9mnlZXEZ53JUdGVwhLVDs+uVrvr43HgDPgwUK8tZIW4djVsCJDQuOR5EwEkcFynm2+56qXRityWdDFygp9QNEPnsABHue5kshyWB564pJV0mRLgDJ3DuHvFG9kot7Fo3P+iCp/c5SE6yrrs8h6a+IIedaSRCPq1AiZCJmOX7+rkH1/Ts1ZraxqckMdgMRES4kRJc+t6sQSLRcoR5br+zarLoVqmgRsopyRHhrlPJcoIaUUaEh2GYR4SVv7KLJbINBxuincdaWnn9+42ICNt9bUY6F+YBIKebjec2lsbkdUxTIxjASjzsftbP0hTRlUrwRrWKTRcQ4ZpkubsXLT3advTYkNlFq3GEf4mIPuCce905tyaiHySij6tjPkNEv4+IyDn3FRQc4TfnGuZBdNH6KvWGOPHv5GRvcT8ujGQ53tEQEYRmZk97FEMNPqughhaS1vba83fpF3702+jlZ5aXLybCRBU5AX3d68/T7/3yF+krX0oVauY4wrs+R2EPNawsh5nKRHKhxxfi0cbmVX/ZC3fp/kUbhfxvyiI1Ytul8eZdnGzvVJD9rfbuXbTifblceTGOcaN4ve0LHOFw79BJZP5bliw3DJFnzw4HOh7r8bkgv9AK8WU6wmqB2nUpPM3H4HVOUiO6XnB5394ANcIYqh6pEeOiqCkNiHBPWQ9RHkY90SI1ovGzJZaTAzz+v4fNRj+IDRWGv/mYHBHmPiSHMTpFLm2Oo3oCR62c0qLlsKlKllsyfyI/Fa+RkTWJCE/PL145QQJpBroE3gN9P4ahzBHmOWcOnbNyG2KyXKRGTDYRF3RdLlZHQDUi3DaOthER1tQIvt5cPq0fSAAyGCnV94z/LyrLKcSdiOi3HmxiVJHbFaoRPEa94gir+8tFc+aoEVfrxHNnp3bdemq8m9ARBkcY5rauT9SIS0GNsBzWoBqx7cJmYiqqx5eGycFdb/Pf+RksjbJMGet4f3qsxFdChDlKgEmAvKnTHOF+oLhx4I3C5x6EaACPX6uy3KwjHDnCox/UpuNTqXGK7b593VVTI4iCssVB8mnDMOyI6M8Q0c8R0a9SUIf4FefcTzrnvmc87C8S0Z90zv1TIvrbRPTHBr2qGMYdmyIxi87ywKpAhC9XDT2IHOERAYGdr65sc2qLpS0HLVkV+vZ4Rkf4UPNqMPGL/+rzV/Tf/0cfEzJHZUQ4OUrHRoTFJgVYpDBaAAAgAElEQVS4dkSItAQng///cGPTSb7nq16iX/gr37bY0Ty2XQFHGEPaPNku5giPx2tZOOec2B0jdeh615scYXYmcYJizmGqLAcc4YgID3FDhw4YOiJMk8gqy3m5sdFo0Q5Qudi/zBEuUCN6SY3AIiamagRfT5/k4jTwq2kFJUP1BKZA6b97F5JvZkssq/kp8bClBi+qOmBpX51IqCkWOP94UHHQzxcz6TFRRcunLQnjBsk6cFhBhmrd+ijjVZKYEm05biONpYwjbFAj0InrINLR9VI1IpZ5nukHL9JEqsSy2rRMGS/WOuk1UiMAWcN3ceVZzjLfOEjup6ZB6M8cPBN5z0SyZva3dL7PPbiO5eUbuKfcLt/PtpGScBk1ovH0eNvRMNigEDvCd6BwUoweuRApyznCiavKhnMbjmN0lqYQYT7HZLjdiGBvdjY62viwgcNNy6H2rqcuad16+vSbD4iIio5jQrnD/6/HQmREMmcpFuUaN9N8r9780nXIOxivKya5wwZuzq/JVCPwvE5KR3JBDUsFQxuvi1+YcYSrYsfDMHyCQhIcfvbj8PsnieibatpC445NyVqg6XAYm/XCXLSeHmyUfJpDZ6tukjqWOZcWFMl7lIjwqfrDYY0SSnZZoeDRNp62O7vqz7H6R4RViPiFkrvTzfj8HxdQdF06+qYsUSM6MfHfgUjIovYiIpxf2911G6lAOOFf7zpqfNrkZPJpwknmEtYyCx4zmRnpkslyjh7vJDViMKgRfK7IyTWQGtaFjYiceFeC/JjeCPP1IBr6EOR1StQIvp5NJ+cEtoSOzSHCdpg5/T0sHKy8MdcW9oUdkdZ76oeUdOldQhqxapd1bmwnOO00tpEqNSIHvB9GxBk2C1phg/u4RH6SeZCarpEQ4ZA531XIM+oNHSb0oW4r3gNxP0aHvI2UEEmN2FZTI0BHuAtze8vIWSU1ohmjRFcKsUvAT3I8sThBO8pLbnc2ysh9G4Y0VhAQcbB5m6sstzI4wkI14sE1ffSVZ8T19kNqFzfeIukzQ4R9bN8CW54GagSPz2toe93m75iJCANaieOYZSofFarGcdSMI1fTiLBE9IkCEl2sdHlkRNh7R68+d0Wf+s0HtG58kRoQtaUFIhx+XwMyq1Fr9hM+92Aj1ltct1lxZrbEciMBQUnJoIhEc/8ebbsq+TR2hH/r7Q19+NCCGqcydr6qEWE1+bHZHGEPYR1GQPKX+WyIsEsTgKRGpAHQeneyAh9cTKSEhOMLXcquDI7IULXD26t/ygnQO0sdckmSczc6jIvmfeADBx3hdN8v96VGrMJLbVXMQ/4XTvjXuxI1ghd6RFxT2I9I1ornZ8ELPOo5M1o0gNPVDWXViK16B3Fjy1EcfselagSHTYe4SPGfd4A+EckERTNZLvIYk+OvnchqjnCfNrdcNEL+nUaOcAUiXHA2OCGRlRoQscWNo666hyg9X5Ou9Iah9X7QnGf+LPx/I6gRywoSIaIU+hI+v951gSM8UjXqEGHpZGAfNQddOsLps75PGwCtGrHr0vs62Q8PFJu+jxxZRNFrWGT3LpPuOFt09qPTJhHhmF+y60ynke8nJsbFghbwjJHeEe9ZTJ4LPwVHWP0k0tQIiC5oakQjK8tlHGFUkjIR4TD3mYiwZ0RYvQPsCMM8gxuorpdzBM/LVsSO7xU7bFMgngXclRBhLvCjk+kPtdeeuyKiMj+YKN+gXu9ANUIgwqBC4lKuy+ffvhb5OOxvITgxX2I5/P3hpqNG+UFa+pUpr5cV6+e9i8QbPzRZ7mSWEOG6biQUYB4RxkEck+Vg9x4RujMhwolorsK94/kfb3MZsOOen/lQ4f8aycHErVLIfs2OUp9nKR9qjU8Ve9Ju1EaE+f+JWH+eZ7iPXa3bscRy+H/jXZpolybLrcP1WxXzvuF9z9PveiXwvHGBerztxMTLz91Klmu8C8kjauHo+3QcOzJT1IhhSOEztJVChC2U8UvX0hHW1AieXJ9ScoK7rpfUiOuuGH4lSgjZMEhNUbRF1IjoaOQONScOLkGEtbPYArcSE5+IgDpk6QgrBwc5wrz4DkNa8CK/Gxwn5H+Loif9vD4oGissoDNKxIUJABFeoCPM6C+ijNpRw40J8tj7ASk/ssQy39NZR9jJCoUr71IodwH97v5FmzkrVrIcJhImEMVW8fF+5H7CptSBc4yflZBgvl9tg86yPObxtqMvXe8iJ5UfHSrJoEPEmy3r3iASaI0Brhp6tYZkuegI0wwiLCkK3C+d2MvcYLuyXIiasbM9rRrB58cIXYkakSgjxyzy9drzd4nIlk5jY8poeh+7DHwKfUwJfR4Q4X6QiemxslyX9Ijnkl+xxLKV+MkbVyKi334U1MBq1k/k3R+aLHcy4xu5mCNciQizicpyMcxUXiRPYR4mABznrRgAp3scfP5SAodMEpimRuyMLOVDLSDW4Xd+RhfK8V03cuJLiP/tRISJQggPq7A5oEbszRE2JrX/7Ps+Sn/hOz5IRPL9uN7JpJO8oIZ0NLdYUAOT5UTo3EiWg/9zODZPlmPOt1xQEcFOiHCOpq0aH50VnnhZNH7T9cKRfQgJiqVQJFEYa7rSHVstNULLp9kc4bHEcmWyXMbRhCINiNYSSWkqzREeVDvotMeQY59UI5grqOXgMGyqr6vWGIjQKOH1dtQR9lziu54jrGWwsF19D/n6+G9BNSKNa5Es15fHjuwHUiNGRNjJhbvGsfmR7/gg/fFvej1rG68nbD772B7PwY+2U4gwy6eljRpLqnE7CBBZGyeilBBLhGMpHPPW6JhgmWQ+TvOntZa4vr04l6+Mdf1pSzUCigOFd6ygGgHvxkYgwtL5ZKTRcnI1IlylGqERYWM8xGjJcFx65GvPB0R4qvgEU0b59qCOsK4sR5QiNnh/sL5BygVJiPDcOxDH8qbLqY5qDuLxVhNRRdDooGS5UxpD67Uc4TgxaEfYmAQQBmdHWdZUr5+kjmHMw8qpEWkBOiUizCgFZwvrkCaGmktaxszR7IdyBbxD+qflj3SFGl26ke2U9+1Qu7Nu6NGmE5SURI1YyhEOL7WFCHPbRIoase2FXE9WUAMm3VhQo5foX9cjRzhlWidEWKkBjFI8ej6POsKKw4ko5gOFCOO7sm6SIgNz0p6KiPAgHJ6H17tJRA7Dt3OV5eYcYSwswaoRg3C+Utn3eURYIXN8j0duJUuxEQFVbLyfKy7k0A/0P/7Df0XXu0QPQUdHo72oGjEMOvlPakQLjvBCR1iHYJN8WqBGsDNQpRqh0FILfcQIBVu6H+EfbgAeW4hwBTIdIxvjhiKBDrKvU/YHvuol+sb3S8VR9gcwjC9UI2I00QZReE6Vm6f0WRyzXt4XotzZFclyTJtQmyMGUFx8t3JaQswT6PP3m88Tfzeu6ek7gX5xZ53LpzUjD187wjvVByK1gVJ0hCmgoh0jl7yhnXKEtdY197XIEe5ttZ1DjB3heURYcYR7uXEhSuNxs+vFOkYkEWHvHbU+0NimwAg03vTsDD+IKT7c1ltjfYgaHX7cAEwhwjcqtMrO6kUlohepEQr1sFUjDETYAQcvInTncaISN0uhcKLE7QkRYeADlrLo160f9UNLjrAzQyZH6Z9P+owRaYwvR1roiXLB9NqIwk3Y1VgNjofsQdSIceKxOMJE9kbx8U6GmixeHf7Nkk8LjhKgPP0QM9iJUCQ/tKMF+9nixmYnF1l0rqYcYRxzWit610v5NExQLHHyiMKirivdsaXwcPb17DjMwOfvJPrFqBph8BfztuTPGFJumKJAwkklItBzDs/vk//6Lfqxv/vP6aVnLg3Jq/R9ByH8SH2JofRwfqxERqQry9XxX9l0ZTmdLDeM59/1w2y7Gm1jB57IUo1I30MnHAuJ9IMsqFFCLLNrAk44Fxri7zBtY99pPaNG9DI/Q+eXaHMONjYwPvthlPTDzyISLO+dpkYgBQVD6USS4sg5KXzv+Zm0ES0c76/q91ogwvk1Jfm0JqN5ec/KLHZlOYsawfcUAbHkCBcQ4W6Iuv+TKKOKWhAxMDGFCC+jG81ZFTXCpfeBSFIjdGU5ojAeGy+ft9bsZ4UfnptmOcITfhBHMbh/bz0aOcI11IiLJwoRrqVGjA9CLSamagTsVnjXf5MFNZxzUWBcUiNyJ+UU5n1AqqbI+Jetn9xl6Sozx7QGUIQu4wTPIcK31xGO1IghLax3FkZCUlsjR7jkCEcEKU28uOCFY+TigZMO30deSHaA3DYuVPjhjQoiCJzpLaSs+nyTGZIgoBqYgdREasQud2JXbT7xMiK82aUF+u66oYeb3WTWfuQxDkNEVNFh4mxnvv4p6wakEvBniEIOxYx2baXs/Xa8x5iYF+XTIHw9DInrutmhtnO6FkyW4805csCDE8/OskRxs8pyixDhUmW5wBH2LnHUlyLCnXheEuHH56e1rrG0NCbL8ZiYuz5GuYkoykpq/d99ET7t7Mcw/tjnVZPmTCsqlhD2XCFCl13WmwYevvz5GiJCiXISjnkcncI0n2n0nzfnbbym3txkoPM7JZ92uWriGMG5zOLh69wTIhlJwCgJ0XSyHFfffFyDCMM7xVZChDH8f0xE+OVn7pB3NCk1FqvOjs/qMVAjcGMSNx5dH6Xq2O4rlSae6yJHeMZfwPGrI9Ja8SoiwlXJck8ERzicvp4jzKEGhQhbyXJG9inyuVCG6BzWODuJSKB1J6ZGEAX0qDS531k3ky+2cNqP7HwiRQRLKhOlSeuigAjfZmrElaJGsDwPkaxiVNdWLTVCbxTT/eFnz84u+hs6GXWzS++K905wDsPfE49MIn3MH837uPLe0BGWCwVRckRwqApEmKkR48KICPXFqhG6wlY/kMeYEGq5gWCbo0aI0DMjzSoc70dqBKptlNoiyqW/1mNBDZn4FL6jxe8ZrUJenaBGRL7oGHLsoaCGWrjYSbISGxdzhBn16hUivA3UiHakGVRxhL1cC5CTmxD+cKygqYDD1w8EJZbDBsLHe1rnxCK4EqgRqbJabRvFa1QOtdYRFpvYAsrYj0gavzqoGoF0npz/m95lojS20CFBBJFIIm5IwQh9lwhj6RlLsKXsCF+tm7ghSDSv8O5PcYS5z4gIIxUoXEcNR3ikg1TIp2GEblOSugNE+JgJ/OvW00dffpre+8Ld4jGOn9XYzetdB1QWg1a3C31cNz6OIQsR3kACs1VNDw3vicURDpGq8P+3FiTLce5B+H3Ct5lt6YTGiNhy1Qi10FvJcpZqhECE7dDMqSw6EQqhc87Ffp0y6Qv5ZqVLvlw1k5O2CFsd2flEzmZM0FCOsA7rW/26bRZUIx6KpIHLdTn0NmX8vVKYq1HOgf6cKGXvWoiwfqY7QNYa5wwuWdJxRmoEL7T2Qucy1QjN+SdKUR9coDAX4PmxAAz/3HY9OUpIGSaWWVQfB+ONr3MYmO/rMkd2ygZwCOM4hksKBSrSRLzZ9cVED43IJdWIpAmLVezCMTJiwlxXTIDBeY9fF+cSjUUreiDqjM8WnQzsS41pdFlTI653/eiYV6hGKCdDIJV8rSr6h793yjFjjvDddUtfut6Z8n32NSXZR04i1hvSvakRar1LHGEv+h5+z0/C4zhXjZDarkjv0IVc+J6ijri+hxZNQKP/mnO663vzvcQ1cG1QI9atp7/6fR+hb3zf87CpTzQqExEGJJijAClZrqeu93QB55pEhKNqxHxBDR2xIRr5tQWqVjcsj7LU2P/8p79x8n1qPD+r9D7GNbdQjIklzi5aT4+3fY4INy4k1kdKzPx7xNTLjCPs09xMlFQjapLlnHN0d93QW493t58aUevI8GG6oIbFMbZUI1jfkegGkuVgJ67HBNaOP5U5eCnL1IhmcrDM7dYPMW4aEWHuCyN2Wj4t9eX2IsKXqyarLHeoakSZIxx+6vdDV+khkihK/Jt6prgAN95lITQuRLEaK2kJasRgO6AtKBukRTL3NBkF06oWbB945z366T/yBn3nR94Vj0eHkJHX0sauAYd1I1BO+ZMoL72sTaKs/H10pIeICBPlGzndFv6MjoihGqERfG6fHQN0OpNjrRDhEUmN3E2OlHl0nFI/dEGNJa8eUjG4f8Mw5AU1+gq1BkUDspPl5L0M90FeB4+pYRjoMWxQUkh35po83puArDeqb3tTI5RDrSUPZxHh0cHFKGSgN+kSyzkVh8cLcoRDX5AjHI6JUmIwnyHCSSTl04jGwiDGfVlXrDH/4de/Ru978V6mGtF6P8kR5v4TKR3hQUaemIJmIY5LEGEdsSEKc431rNLYP15BDTau2lgy70g8q6AaMc4pBiK87ZLyBW8EntKIcOtDslxlqXKi9C7qJElMJCSiWDiqFkhiesQtpkY04uecRWduISKMleU0SnCughqBmxUGXFZ6duzfKR061BwtXfPlyk/yViUCcRpEGEPH/BJymBz1CWW/bi8ivG5DEZIUgdhfPo15Xnr3zVZSVRGIsFo8mglEGGXFAjUi6QhjG2vmr6owqEmNGItiEAFH2KgUl6gRtiO8ajx9+4ffGekiO7jHgkZQeKeQI6wRI/5cf1Yyvj+h3RFhVIgyyg1NSajpZKXo3AMan1EjItoWPhCIsLoe3VdGB3XVP3TscZMjdIQXUiN0Batdl6TrLlaoGjGvqa5RV8FHLWwmiBJSH0O/wOPcdX1W1r1GRxiTsVaNy97DfcEWHqN8j7Tk4dx87F3iTuvNj+CKI0d4vD/a2eW5WIwpFRlCEMW5xDfHa2gjmFGgTql3fMp0hND7AIrp9QE32m892tK/ePOB1BFWzuediUh1O2rpR/m0KURYbVS5r1OqEfiszmUM0mGkT0dhiSQvX5ekLlEjUmXM+WtqjXFt9W+JjjBRiqDeWmpERIQrS81aoQYiG1GWiLCcBIiAI3wmH6rxISQ8DPnEyA/+pNQI4JuVdpzvevoyhpctk5PUcV/WKGwPiLDWnOTn/CRRIxgxSsUdHH37h99JX3i4pWevlpWBfuO9z9F/8t0foo+9/pz5d194P6zKcqWCGmgCEXYwISlu4KpxMlmJ0VwLEfY+kyvTCDbRfLIc95vHYQjXpfnk4aYz3zU2TGTRvFf8GX43m0h/7xFdC78MSI0Y+ZgasTXbArQUf7Yj/QQdWb3QRo6wKBYQ2pMlliU62I39C0Vt5CaG8yr4dsjKcgsdYZ+UB/j76ERJHeHpd1pfO27EEoeVxE+8D+xARx3h0UFfg6PG1z95TUCj2Y2loXUuy75+jaZGXKt3VhafyO+XFYWMGscDJkS6SMHJOcISEe76ITvGKi6BNEQi5LGnDYy1ScVrmltjYuIvSEFerHykarDhpvS/+wf/kv7WL36G/tS3fFn8m+YIX06o+rTjdfE1T6KMRs5GubJcAsrOvZzhmCDiEst9nBPYklxd4qkzmJNTIzxtdkNMXK1BuSMgWNAR5vHGykK1lVk5p+bWy6dVO8KAaqKZqhFjm6smyYEhFwodk3MYJg/oOYsnsdNSI8LP7QQi/F/+wO+ebGNu4j3EhAg7h+K9nEg4o/hJSpZrfV5S9H0v3qMf/a4PLW5r3Xr64W95X/Hv8R5qaoSZLJcjXjokhdnqnCyH6GB0phsfE0GJEpprLXRtE7SKUZVBV4oMn0muKpHkCMcwGtBl1m36rOt3k6F7lDnTKhtEy6gR6BAi0qz/flHhCGv9Vr6nLTiR2umOleVayRHGTQtyPxEJ5DZZFUQneKHsIpFOlls2Z0XqDiC26EQhWjmHIKVrB2dLRRiSuoF8FuE6ePPg4ueBK+1FG3PXxzJhoU0uqEHx/97tL9FplejFPjUz1Ah8X7VUGo4jpHekcULx/87lqiJEoPrBHGGIcHnnhAOo1QN2nb2JWgkAa36Nab0Tm/q1gQjjfPhv33pMD6530ZlilRJLR9hChJuRI7ytcYQNKicrLuTthkjZsZPlaiyh0Wljs+kGWnkvxi7K1fGzKyLCIzWiVjWCCKkR8lhd8IOtFhG+FxHhW+sIe/FzzvQOmc3WEQ43SQtCp3DZ+NkZHeE+oi/ynKuICJ+uL3ICso+Zqj5DdFpEOOm6pqxxvk0bxRHWE91trizn1SRzyghE3OwoqgEumH68r3wPrWQINkRuG+fGECoJaoR3+UTKE7/1aoXqdYNA6UxqhBGalol9MmEIuYurMXypFzh5TyheG26sNdoVfjebEH+PdAPY0Im/u7LqCZpOVmKZK5Y/lE63E8fmiHDu2Ei+qNQWR+clHuOlRq+WT1uyZjeqv92AjnCiRuwmolZsyakGZwsiGNw+ka0GEhFh0BHe9UPkh9brCEuu++XKzzp7tZZ40HZOBDoMFj0sbCxY0zpt1Bj9ltrCcl1E2T3vXJxDMETN97KkGoEbzJ2KWpSStgVHuGKNacAR9s4F1QiFCIvKlaMD/MWHIbxuIcLf+7tfpqcuV8WCGsMQ9Nlxg2BZSeWqJJ/Gc+i5fBI2B2OCKKi47Lo+W+ORI6w3C2ayHFaWq6FGxNLh8ljuX6/u42VlQSous3xrHWEeaLWIsOZMsU1RI9Y6XKNQgnONOe9o3ImXqRGnLKiB6Oq+yLOuO35Mw/KNnDWuVUKiI5zpSN9eRJg3X0nK6zybHTQ9WSOKIgpqZBxhmSznHcWCC0Sc+OHj4hrD8Dz5WYjPqBqBjqJJjTBC04IaoehEOOkGRJhG2Sj7fiOX16RGwKSrSyZr06FnbId/D7qbYb6b4ghrSkTXU0Rrg9OKaG34TtIRDh9EjjCg7oiO8mscMvsT3QKdF9QaxvG7UfdqCdqZgAxAhLepGAMmxcyqRqi2uD0ilFQLnwtqRERvR6Q9hvzDM9fa5bPUCFhTdl1P7UUrKv4d5Ag7Of+xNUYI2UaEU1TAlk+DDVEvxx2OF+8gUR3GAqp+EGlHWCepSVpHjXxaDeWt8UkBIuoIa0QYNkucaMU8Ux5v2JfXX7hLr3/z68XzEYWCPUE+rMIRnsjZwM+svpzDOKqRNjb9WOHNZ8cRhfHIvFtGZe2CGmlOruEI6yif7h/Ow87VUyLv3v5kOUaE6yDupjAx1CLCuoLOIWGrpYYTkB4THJI+ZYifX8rNASiFoEYcu8QyhN54ktRZ0+sCInybk+V4oTwHFUcjhGx6YvUOHOEC4kqE/MtEjUBKw/W2J++Rb9uP3ysnGrWjjjA6ilOIsFSNyH9Pde3TRL5ufcjAnkBX+L0fhkFwqpnbi7cQ+2qZ5N1yZINi+4zI1alGjG32eO9RxixRq/RCqxFhreQR2pcllpETHKgRkpKSeKbh/3rTsIwaId9nixrRD3WbdcvJiA52l+Z4Pg/2Gb+H1IhuABm5Qglgqx9JiWJQyXLzFfKmrCiH6HLkzE6WS5UDtXzaMEj6S6QEqU0EfzeNa6uyXEL1se84VpKOcNq4WmvveiE1QiDCPowjTnzU5yYienszIsKPNkSUePS1dAQsaz3niJVymqyxzdSnfjjtGmEZRwRwI/l42+V6vmO/N7ueeLhdrIKW8L21kSwH8ml1qhEMCOZINHKEiYIDXuu73buYF2W4YUd4ISKsEK913EHkN8Qq1qGT5c454HgCss7bFgbAMQ0Xjn0n57k68IcYTw78QrZjOJgol097khDhNu5mw/9PGfYqIUj6/rTeZRnoRHIxXTdehJqZGoFZ49uICMsJf2ryY9qCoEZMcIRxLpY61ooaAejDOsqnlTe6KUwvK1Xa4fQZRxhQWi2fxmsgcoT1Rk60pb7HVAbnoDJkPJdERbWOMDoAqAaAyXJ871knmp+hyKuARUgky/Xz1AG0SGcAyTNMOuLruS4UHUCznESNCFuqEZHfGpEqH/vS93k1y1mOMKCpuz68D7iBPwYinOVE+Hy9mCrSgE5v45Biw8fllMEEGIXvCgqS2qxdbwNNAOcZ75zkbytOdkk+bSnYoqNblrIQboS4ciVTI1KJ5dlTxXMQjYjwjN/iIkfYfn5oSEs6NyLMGsb4njy43uXUCFhfsEjFvXWbzfVRLWkBItxGQFDe11iVF25jbaIc0ROACD99Z0WrxtEL99ZVx6cdVrgjU6oT7GTrlxNf+HOS0nkCGozzlgbAUc8PxP19J2dZjvfUiHBaGLd9yGAtceaO7ZQf0xrnREUjd8KuJt6kRiDynX1KdLMX08uVjzrBiRqReO5EYbEJYVOJzlmJbvEcKmRGlCMmoQ3pkBHZOtZIjUgcYZ+SwAr3m/vG3FA2UzWi7LcSUVJdwP5qJ8w7UI3YlhuMKCZSGRwkjnaI7o3nj8mkEhHeKfSW+xW5oYDaMeK/U0i81vAUVIRh2VyiqU7dANSIkSPM/Z+jXlmbPqTyhGsm8TP8Lq+D14dQ0a4HpZpywqfsR2h/GIZYYplvyXaiimeNzXFMrfLoaM4h4s/PPNGb4obIu2zcI0UoRAvSuLZ0hC9aSRPwXkZ6khzXeH/7eY7wUmqEkCiEd8ziCHN1MqvE8pTxHPlwkyOmWd8KETrrXAFBT3PFOc27VFiHLTjCmqIQfl5Dstzlyme0CKLw7ESyXMV7sDLAy/BdGd0ikpWD5+zubU+We/pqRf/7j/x79J5n71Qdrye/y1WoGGINSHaSVwoRxpDjOUnpPAFZ1IhzIsKlrNUaW4s68MftK060XRccGAwxNs4l0f8JVYTbZroU7DmoEXMFRxrv6OEmOCCCGgHvyp11KlXK4TrvZdb49ZYrDIX/o8oE9kf3RVMjuilqRMER5rGY5NMgWW4MjzKSbVlRPg2QsPjZDCI8DIlPx+eLoWZ2hH3iCE8hwpitz/1hDedwncmB0Kgoc4SvLUR4SEodGBJHGkuQeJSLsYuOXvg/opNho1G/IDnl2FnUCD7H3DuNG3u2TDWiH8RPPicek5LAwr1fNfp9nb4mHkfDEDbsq8aLjeEhr3tJJSnx4+cQ4TyyEhH+Pkf9ifLNA292MKlQq3EER1gidDpZTnNFt53tfIrNbqUjHB/cdvcAACAASURBVH93jtbGO4YOFHOEv/goIcJLfIEGoi5ziHCaj9V6ZSHCguJx3vUMVRmYY/329S7X8zUQ4T/wu16ij778TNYm68UvSZbj5E/rvKiAQ5Qk7mrs1qtGEAVieq1pOZKYbGcly5mqEVBZrl+W8XyoIQ8no0bEAXA6uBD5efu+aLKS0XH7GuV5RoSuRUR4FPAu3Z7brBqhS8GecvOFqLr1ORvz0fTfcIK+s2piGJG1LdFxIiK6HrV7G+XgRGeqsNA93OwEyjotn5bawI0Yjz/nHLU+hBX5srnEsqXQwibl0xA5pXjN6bNpR7jrh1iilV8LXaDAA0e4JlnOUo3gz7VqhEbbHo/ta81fpGkQhbH4GNB77/INW0xU6VMUgMtQ43XXGK9vtiPciHejhptLJBVSMEnO4rLy34iAEuJTIjPKp1kbMfuaYN4ydIQPCXPzVzed/T6LZLkCR3inIiumfJpLZaJ1ARaOYOLGg48doiPcZU5G46R8Gkd9EsXCpo1I+bT5e4fPpwYR5vHGQACvybXPCRHhWUeY71mm4mNsWhwg22cE6IikbxIigT196fEu86taAB34Gr7ro+8222SOsNahnrKSaIDWHyeql04jInr305fUeEfPXJWZB7fXgzBMh9YuV15wSdEs1YgwCYTfp6pOncJS4oJBjSjo5x3TauTT5mw1M/EeYrjQcxJOWgg4BG8P11vtCDeMxPOCdLpzaWSWTSMQJUky4Qiv27h4cbJcoxzhIA6fJ0FFHWHjWkNlJpUsZyCkKWSfPiuNv7aRKPNF60cuY3mB46bYgWGbcp5K1g/p3qMaBbaD1Iga+TRE5rxLiaNbwxFmGbMmIsIjNQIW4L5PfYnooJdcWEs+zUpUQYdzyXhOOR6YLAeqEYVNmdkWRItiv5QMnkY5iaDEMlQuREdAF/GZS8iJso/DEEsso8znYfJpBUQ4UiPs9zh+3+UbXk5I5A1WOC6n8uBPpEZgUlWS2+ozzV3n7GfTwv21HrFMiK1AhJX8Y+IIp81mUAOxnwPPRbW+AN+zRxXUiBIiXEqWsxKEz2FczKMfhlip88EEIlyTXxVLLC+IhCbVCHls42QyH1F9eWUiom//infSz/+lb6UX718Uj7m9HoRhemBdrprirsxCixtPChE+34DjCQh34mxY+e5UdozJWVIjjpwsxxNtTyMiLLOvG3AGiNJGJzjI5504lphWOjllX5FTjaYjDaJspsfjEBGWFeAa57LFbbPrQrKcl+/l1OQXZHVkmEtzILEtIZ9W4A+uxmp1jGZFVG/CEXawsG+NMOpSagRqsvJn+F1MlqupLEfECVzSEdnuUkQn0SU4fO1E+5rPG58L9JUdFHYI430fby8nqgxDGjebHYyLBeNZO6/9AMUYWukIzyapRVoIIH8qFJ82E/lGRycJxkIeSjWiVr0izFtBexULYRyiSqQjLfFzQ2WolCynN6XOpXsTUWKfxqymRrBqhKXXLTnCOTXCSmRc4TpkRYwMrfApww0AIsKPFSJcUgwYhhG5r3xOfJ8fVVAjdDRQf47mvYtJuzelGtH3A12NlIMH17tsc7Xk/Vwr1YiaCLJOgI7980xdSuNpSbKc947e89zV9DE1DTnnvtM592vOuU855360cMwPOOc+6Zz7Fefc/1TdywWmJXMuV03xZYmV5cB5wwzffjivA8UTkCXpdI5kOb1o7mMl9PAYhgtbN8on5dSIdE7e6Nxm6TSinOd3Do7wLCJcQJLw9zvrJqgU9Cl0iIkpRGPShIckvU46YFM6wuicTFEjpHyazR9ctV6EbKMjvLMTcrBvfT8UKsvVI8KYNJZQE/ld72vl06TTFhMVBTUitUmU6E58TawagQ4+JsPwcSif1riRGqHGaUxUGYb4zuEGacl41nSGXd9nBTXYZhFhtfni9tLv4LChQzakv4d2UiJz1+clluemF+5mokZAZbn+sHK5yJlGS4jwNJ/Wu5xiFK7VrlAoE+F4U0sxkbJ0jEmN8I6ktB0nPCeH2ho73ocNnasEOPAQpB9JjnA/iSBuC2WPLUuqETtR6dIyvrycqpb3pXG5hve5jCmj/UB0NUqNPTCoEUuoS+t2BDwWcIStcc3nwtwUomXUiBqbfU2dcw0R/RQRfRcRfZiIfsg592F1zAeI6K8Q0TcNw/CVRPTnj9rL0eLEUIEIt2NoXegIcxhrnNjPOd54Aur7G6osB5uIfV+0peUvlxjKp3FlKZSH4mQetrjRucVoMFEexThld0vJchZH2Pob/n4HnB5Gj7QkkpZPSxxhRhnzPrYRvU2fdX1exciqTleqOsVhRSyoQcSUGvuGp1CvvF8or8imKxppm5RP69Nzr0OEFYo7sI5r+AyLNODGByMjzBGWlA/KFiWRLDc625nKgEvUCHYm2DHD4h41FukoDEb0siqZGItzyXIG2iYQyG6aGoEOP1MDuiFHhOcQXZy3tl0Yx0hpOGTjG6lOBY7pnNQYvq/4PNnp0TJ8qHc+DCQcZpEsx5s8pkaMqhGy76qy3Oj4IlpeQmFXja9eXzQ4E4vWVCLCRNPzROl8j2rk08YoWk6NyI/1ADKcO1nOuUSXeffTQbhgB9ER7CPb3OPZK1mO13Sjot1wQLJcjdUky32MiD41DMOniYiccz9DRN9LRJ+EY/4kEf3UMAxfICIahuE3j9rL0byaGN547Vl6+s7KPNY5lyHGmNgwLEQzDjWegIYhXzz45ToLNaI/gCPspyfeQywlelEMUSL3FB1jolTacTUzGd20pQSDgIicsoBLej/qEeES4sroHy8oTQydS47wvYscnZtUjWhy1QiWBGt96ruVOILRHa0pjG2u4Z6X3ikPTpktn0bZZyVDJZiyfJpLBWEmOcLyd+Y5Wxx/fN4YvuZnpikfmLjH398KhzBHpdg5HgbKVC8smteU8b2J42SAZLnFHOF07Ww7hQ6jdjKbpkb4kRrBoeFMR3jmfY0Um3EciXnrwBLL+MzR+N7M3S98X9PzTJsi/oy7OAx63CfUHxPVO/WOXG9zakTjk/47f887mUBWujWrxlEzLENo+feSjrDmMKNtu3rkPnKEKwpqEOVJg6ENAxGGj86dLMdRn74f6BVQ8NKbkRKAYtlq1HJfQglMqhEGIqw5wkdGhGsc4ZeJ6Nfh/58loq9Tx3yQiMg59wtE1BDRTwzD8Pd0Q865HyaiHyYievXVVxd3lndYPDH80W94Lz19ZTvCRAFlEMlyEF48d0GNMNnKEqdsulzsKQy5qvvuOC0d12MZht6YsxUR4V1Pd9YydMoT7ynLUh/DEKU99XhbohqR+gefC46wLAfceOYcpravx5BiDA+rZDlr8lt5P9JfAFkeUU7fONr1MqwvF3ubmsPoA+oIEwWHcw4RHoaBNrs+CMDv+ogYDsJ5MpuAv0ORi9iu/K73bkTVpx3rTjltTOFyMI6YH4e0m/C+jPzIHd9DRbOIyXLJkUKHEMPZ6GxzO+xM4IZnyeZdo7hdP0Snfd144QDM6gj7dD/Y0OGwQvj8OREmdI7JcuO8w85NLZWpiWOfCwH5OL9v+/2LF4W2Q+Nabi8hwtPzMVJfkAbBChl8aah3qzcNjPpHKtGQsveTfFpHz96VGfneOdr2KWGNVSJE3kdh7KzHZNcaw4Q/51A1As7dDXR3Pe3qLNURDlUE5x+ujqKFPufHybFf1ZWjGUZ97l209NzdNX3+7U2GzEoAZbqT68jVDs9hUYllDdw4R10v3+MlyXI1VtOadQV6lLZE9AEi+lYi+iEi+mnnXCYuNwzDXx+G4Y1hGN548cUXl/aViEjIjMxNMperRtxUDGOdmyPseddlINGRJH5SRDj8nApJzRmir6fiCAtOauQUyrAaEepEn3f3vNQkveO050JnCS0rlTke14KDxf9nY2eLk05YyxarsG12Sj6tk2ibSY1owsKAviCjReiIW1n7+KyFg9x4KZ8WUaFyiVsMB+/6FNrV3N7w+xwiLBE3bpcoOdTc3dZPL/LaaeNiHeis5PJp8hhLNYI5sKGPjAQiYpjyGPhv4fPkHDMKg8lyizjCEQ1M1/ho21HrHbWNn03+Qmvg2tlwc8UVw7ifbHwIyqehw4hJYdjnYj+8dFZXTXqnDplriUi0g2bpzlsROgSNMEGyH/ISy0RSI5j/zyW98b5orWuLGqEdQB63NcDAEmoEbuqIEv1oCSJMVI/CImWnpiKu93nyso0IA2B3ZkSY5cmYAvfyMwEVzgpqoLM+00XeUD7aMpAyf01RPStbr2QiJ1ECao5lNaPts0T0Hvj/K0T0G8Yx/+swDNthGP4lEf0aBcf46OYFajF9c3/k2z9IP/ixhDyLnW9/WB34pZZkiPLweNSFPKUjfARkcm7iPcTQMWFkhbvJIW6chFLlwNuNCDdHuO/V5yqEUjNEuGGnTX8OyXIaEXbMIZXcO5RPS0lQZWrEiivLDdJp8d6JZ2khws45WjWBYqAdeEmNSIhwsaDG+HHgCCcOodbxJarhCCd0DZ0KouQQR6fD54g9GvrcPVMjXELdd72d5ITJpFayHKJ9MVnOJQfQjRvNiOaDk5RUerxotx+W8RktDvujzQ4UYOyNzlRb6FRrp5j/hPvCyBGG6/bgMGr5tLl+8DjkzUfbeIF8H4UaoRwpze0lmlCN0Mly3gmwgQhzZ+SmYUBqBFBA9Dtiq0ZI/jYDAemaypJlq8ZX537wdXP/ShzhuVB6LSiG97mGGmEhwqXiJ0v7cizzsAFunKOXnrkkopyigP2ae+9TufcF1Aje4Bnn5THLx1zegCP8S0T0Aefc6865NRH9IBF9XB3zd4no9xIROedeoECV+PQxO8qG4t9zN/cHfs976Ou/7Pn4f/3Cn50j3LO2qfxbaQAc0+QEtF8b6KicLFmulyoFRKnevSWfdpuryhEhInx6RxidJbSsshwgwqXjeKLBicwO8yX5tIQIT1AjCjrCfnRy2TYqRJ++77Nnvm6VfFqbHLbShI0L+7ZLiNZ+OsI5Sqv1gBERnnKEtQOuVSNwHCHfsnHAETbk01CHk19dpEYwBzwhiNzvtPm5VJsjq0rmlFmqJg83XSx+tEw1gsa2UD5NOj81NBe+7oTojo5wXwe2aES4hXlrCxukfcxCvcM5Qh+5mAx+pr+vS9w6lxzcRI0IP3uFuvFmIvD3PXymOcIF1YheveNeVqEsPeJV46pzP7yXc1lJNcJChPHZ1M7NOEZrEGF8BlYb8fzoZJ4ZEdYUmpefCVJjGTVCIMJ1jvCjTSjKtIgaYVAyeO7iDc0S+bQam32SwzDsiOjPENHPEdGvEtHPDsPwK865n3TOfc942M8R0W855z5JRH+fiP7yMAy/ddSecofhHi0dL6myUU/dcF5SundO7LDRknza6foj0KM9rxv5oEenRkD/dn0osSp3oHICiQVTbjsifEZqBCYXWn2Ix6lwIhs+U9aTZKeHNyIZX9Gld3KrqBHW9baNdFr5+MaHxTbJV9mOyKrxpgOP8mmcLDeNCI+O8KhSwgsl3zqJzC6gRsRxPP5NoeMNTOp2W5ojnGfb29SI9L4kR7hAjQCnKHJlfRgXWr8ZneVXRy3Of/Gbb8f+LZlDcTPO9nCTnKiSgslUW1vl/MbfIepgcYTZ/BjRQEc2tFuOaojvj39mBHLVeHD29lfoISqvb5IW5MRP+f2ECCen16BGQDQu4wiPDjPSWnq1WXu8swpqKEdY0dtQ/USb9Y6XLCLCihqBHOGugAjjZ/WIcLrOGkc40FOm52Mi6YvchGoEqscwIrzy+ebG+t0ydmaZGlFzTRblh4j55uNmfFyXpsol72NVJZaHYfgEEX1Cffbj8PtARH9h/HdSE4TthQ6dlok558bLO4qyNZoaURoAxz1//SCeslXjY5LUMQ0n2oiCqR0onjPpCJ930lhqAsk78QRnIW5E5YIauSMM1Ih1jgiHST1Hp3QId5IaMTpbmhpx4cLkeXXR0OZhn8k+xe83nnSKAjvXWj5tCoXHjWFAhCU1QoaIpx3hDqkR4y3EErV4voC+TMmnQbuQy4DoL6K1RGGhvWh9Nh8KjiY4OdgX7J93ORLqwVl+/zvu0TNXK/pH/+oL9AO/5z3U98tUUKyN2kOgRmD/595ra6xrx4sdNosage0gD5o3+9tCRKLUD94wto29adnHSnMs3puV9/SYcvnB8H3K3iMuHoJovuAIo5M7jMViYO4NzjHBMcOICCvVCOeyTYp3aT4cJoCoGgczXaOcy2pUI9oRAb2zbhZxWPVxVZXvvBFFM65bOJnnRoSdTJpl5YiVKp++hBqRkuXkBnPKVhEQ1NzkNI6ZnnUT1IhbZUt2Jdo0NeKcXBwMP+iBnpLlTk+NIDos9BISGdyiBbDGUKeSK8vpPluI8G1XjTgnRzghsxIFKpVYzpBVWEx1GJw5qDoRz3uQDGNEeCKsjCVW2YKUUuAIc3b3BhwTtHXjsme+Hh1hdnJadIQLwwMd1qAzOoZ+96BGsLOA1xydMHY+gY7Qlf1g4YgwiuudRH/RSSVirrbL7hU6Axj2js6Dk++Xdy4+Q0SdcZH82lefpX/0mS/ENpcEZGJ/NTVidKJkudzphjUvnUiqWKAA/xQ1wjup9Zoc4zqOMPeDEwhXWGlxAvWssdK5xRoYq5Lm9wtpLRix6AcShZ3SmM01l1kjm+cKpJwQJQm8LFnOq42YAW6Ubs2q8dXOsM6vSYiw1hFO7bHCBSZc1T4nwRGuSZYDNFP3WR+X/l7VlaMZboAbT/TSmCw3WVlu5n7tkyxXpEbApipSI/6dd4TFi7RskuFBzAlZ5+TiOJdLE+l+nVIBAc95iP+/MhyRYxiK7fOz0eEi6QjnJbRvo2EC26k3XugEEKWJpZQspz/H/9/RHGFnO8JS3UNyU61hknisKXS5HbPr28ZHSkYJkVu1PkMq2pF33A1MseBzTOgIu3QMUVpAdWlkounktnCsRFn5M2wHNx/dJCIsz9sPg6jshQlYgvfvXIai4rNivjFRAgScWHwDDxmpEvw5FoP5mteepU/95gP64sPN4jnUojM82nYRrZNZ6dPtlhJDeQ7l8UBk6whjOziu23GTPxXVsPpxDd9HtPqQqbJ0apG0zCia5VzBs4sRCxfGwjCgMkj4m1aN4PHnHDrLUs9124WNpEbo0AlP1+PE+1x6N9eLCmpIZ75tQmW/DBEGxPr50RFGCa6lOsJEdYiwtxBh47qlpOV5EWFNiWLVCO3oN2q+mLLVHo5wiozrTVXqH0cqj40IV1EjbpM5tQgsMZn5et4B13ipKYrWRofldE5dqYjCUmOh7GOb4Gz2QZjeYSatcox58bzt1AiZLHfac0W5pfH5XIz13rMKQfwOFSITRLaOsK4WFT5PJWX1hG+9X7xx0dXVvAto77ptY1vO5ZvdVeMzFYdUUCO0ExOYJnSENZ92HZPlSPwMv89QI4YB6ArpMyKUTwOEdZIjLM/b9TI0XVKNcC5/r3VlubzEcjo2oM5GKF2hil/72rNERPSPP/MFk+Y1Zdhftrevd/TsVXBMlgj2p+hHPhYeb0OZ9qTckf6uNzXOyTA+b8BjVGOuHzDWiJIjRkRRD31fK31XOmPsAM6hjGnd1E4+FoHJVEuGxN/nY/AWPtqkyoD63PrZeDVGS+/mf/yt75vdfOo2NEjC7/UwsKMOiPA43jDhqhoRFspFNYhwnrw8lyx3zkg1EUd90vh/7u6aXnv+KuYEpD7K70wZJzs+3tQ7wrw26LHcQP/YAb6zvgGO8G2yRi04y76bXvh+OK98Gg6cjPfoy5PZsewQJB3tVI4wIjxBWcOrPqskkdEBO6XSxjGMu7fp8tLapzlfSsYqcc8jRzhzkNPvmY6wc4GrpcN8Lk3ielyY1AjlgLI55+g9z13RuvX0T3/9i5Eeo23VeOqc1klOBTVEQs4kR5hEPxgxSuV/JUVhyrBKJW7owk8Sn7eNm5RjE+hln6TR0LnSSU5EEpln2ypqhFaN0HOSUBnw6RysHe2co6965Rlyjuifffa3l1MjjHHyaNPRu5722fXMcQpjcSU1Hi9aT18inkfStbPpZ9koakTkCO+kjFzxmtQ4WqnI1SHvfJEjjIVlCigaEQmtV+30hvb5uPAZJlSG/yfACCsx4v1kqT5LNSLvj6RGlDYZ3/LB+hoDVnSLi+MQpfcPEcTnDGpEfbJcvgmZMmv+Me+Nn78vp7Igm5iiPs45+vm/9K1Fmdfw+3SbOlmuJopcKiwWtJiZIzwiwjdQWe5WmeY1LfouUCOWZjwfanLRkX9LQtIndIQX8HumLDgdx+9nRIsKHGHNgWx8uG/rW44IY0j7HOPNO6KOwsaBx1WJ66U/Z53ebTdERzghwrIEb2rLTzibhiPMiPBWOdTe0X/zQ19Nw0D0d/7x/1f8/rpxtHOy3633tBuVKLxLetOo5lDqGy+YUTVCObD4WcmsIhex+hYsMHydk4iwSFYKCB1yK9kxxjb5vPp5ah1hrRqhJZusOQoRrcY7urNu6KL19GjbLS+oYRz6EBKtBCJc8V5jkg8bL6LosEmOsNqsKWoEO2vXlclyjRpHAREuz/VLDJ8Pv5dEOSAQfk6jjFYUyMVxEP6fq0YkiTydaM72kBHhjBqRX48rjLFDjKOoOn+E5y3eKKGj/lykRix3hDFqW6sjnLdhbxKs389hWJuB+2Zt4JYoW/D9jqoRFZdULqiR3s+PvPQUPd529OXvuj/f4AJ74hzhkvRT1XcVz+mcO69JRHgi4eFYhqc85DRt42nVHB8R5t2g0E5V90ws/N7Ryp+Gr3xM4/6dgxpBxGNr5NwW3pWUOJN/nx21C5X1yxzS/PjyJGc9Gh7ryBHmvjDK59yoHW1+3xO5MjUCOcKh3WlHmPtxMU6+ForYzTjCTMnAdjvVjgNHZFo+Lf3ejfJVq+xdyK/Nudx53Al0D6gRPvWFDZUpdH91md6w8VieZ2Et8A+vk3zaEkSYjy9VUUSt6q6fcISdDOPzON9t6qgRTo0j5Ahze/sathPGeF6uNuoIz1AjcGOj/46brGz89VKaD7nXRMnRsagRWX/8svB6jfFl49hat0HZ6Kf/j0/Tr/2bLxFRuH/ehffreMly86ikmSdx66gRZdom2hIwLekId9T4uuT6dYHmg+o27376kn72T33DbFtL7clzhI+ECKOO4jlM8PEKjkltNZ39zj/vHNTYqvG08RNp73ta1BEeRh1hr3WEXZyQuxEx1mVZb6PxRLg9EzUC34+msEiWEGGikPXe+SFOZKgjXHKc54pWoPE5NxlHWE6yuwLaeO+yNROkWD5NU2hKrxTyiIksRLieGsEJbUTpeWv5NORo1uoID5DUa72/+p7pBRbLYSM1wkKUncvbC5+7DEnma1g6h1rjfwPFTJZwhLmPWteaud5Yclui+7INRsJRpQQdg9qkvagjDHKCRAdSI5Rzx+grbnhKKJr+PlJd2PSGahikIgQn1SHtplfO8sOxYEJeWc7YNDuVAH0MR7iICPf0C5/6HP1fv/7F+PfWe9p0PT13tSIiyRGuR4Rxc1I3RqfaSMel38+dLFfrG+gI7ZRx0vOXHu8W0E7GsayT5RYg0fvaE+cIT6FZc4Y7X05COZc5l09AbDGEfcrKckdCKVZNnp1+DIui7gIRTn+PO/9xEW5cKrd7mw35qsfOdDXPF0NbZb1ga/GIf2sctX3K2kZE2Oa2LaVGMIKmZdjUZFd4P3/yez6SoXqsGsFc3ZqxzocwosjXa1Ij5lQjDGpE0rBVaGpT7wh3IPOY3R+S84imNhAlXh2RDHtHepla2KwFsTEQvKDD2o/vafFSMis9C3aisP81kR7v8uS3pCEtNxRsOSIckPAtIMBT0TurD0RQWa7R89b+cyXeAnR0LZ6qnYCF/TQ2T2p+6AapCMFRBO/S8+DqqGxLOMLeucl1cB+z5rh129Bm19OjbUdffLSNx7WNo01nI8K1z2kf+TRt9rzrJ/9+SrM2wOZxC8C0q1EG80uPt/X3toAI1/bvEHviHOG4i91jsPAA63uKFXPOZVO7rpgsd8IXwC2Y3Kcs1IE/gXwabFJYF5XldlDhAxfxF+5d0Av3L47el2Mav9Tbro+75FMaP1pEEbMKQbCp0NaOiHBKakNE2EYyytnt+We8oOeIMPbB0Ybsd/zV56+yz5gjHOXTYCItzRMaEc4dYcmVnDLkIsc5JqKRCk11c6oR0mnjTZ8V0pbIYz5/YEWrkAhF4nva4RNzhJfH6s9iwvGCuaQ0vUX5tIWIsPVssaogW9cP9E8+8wW6f9na8mnOieQ4b1zzXB9SZbll359sGxFheJksZ9YMtxtIGr6TkSMcN285Nz7Ip5WT5RJHWOcb5NejozXHcPgsqiQjwo82XYrINAGpX7dJqxyBiVp/QjjclfJpbMzzNlHiI28QlpimWJVMFLyZ6STf4wfXu/j7nK1jdEM7wvD77yDCwSx+W/13w89uDAGdU3Fg6mEmRPh0b8DxqBGnQYQ53Nf1A3WgueudE6HnFp7/3/6TX38WlPUQS8ly59GtRoeMUZxMHaKAFBONOreNyyoDac52Op8vTtxWWDhRLqapEeEzu12rz7se5dNsp8E630bLp8USy2UUUVsPm2ruc6eQZf57491ke4KjydfkdUiZ25TvdKAPJToGoqKYLId9Ed83nG1rA71q/FjSelmeheYAR5k/gxpRAwpY4xerCrL1A9F/+r/8c3r9hbukJZydC9e4UaoRU+cQfeBx1KXs+CWIck3bRBJ9FIjwSMWw3rU5XrlXY7Yf8mIZfU+ivD3z1tmSfJqqLGdGj+wxdojZiLCnza6LTjoft2o8OUphe0mNqD0fJMtVyqfhd7ddV7g38J2zI8LYj/K5lyhb8L3l+avGri6CO3qlHOdj+S5TdrvjyoZZGc9Lv4tFG85lU9myUQvylJXljrTjvLtuq3d4SwwR4W5IiGR02tRzbxpHz95dl4VQ7gAAIABJREFUi8nsNlpEHs9QYpkIHZgyWtQWPicKm7G28Rki3PjS4lZ+F6epETpZzkKv6u5XO0r6MTVCcE0LTURHuECNQOppjSNcUo3gn8jL1hxn0ZYOTfchNC1kr4yNgvWstXxaRo1Qc4LF3zQ/i4jwsrlEJ3+xRWrEUkTYGF/snOhrf7zr6PGodIHGlJAtcISX8Fhj0uU2USOOxYPVaCL2ma1tcm64dRxyvvXfUxRDor0Dqyv5FF3SWsMlaoSZLKeiSseYD63oBiPC6Agzh/7OuqH3v/Mefehd9+lDoDxQ+5wQVFhKjZiksZwh/F+yU3CE161PusCVz/mb3/8C/Y0/9oZ4LkR6XqhqarE9cYiwNxaBWpMv/Hl3XlMoQZTAOWVlORhAh4Skfuw/+LAIuR7LEHHYjTrCROgAy+POPVnsa/ysz6cakc47V0HOGv+td9R5FwXRr0FH2LrlUxxh6xkxTSPnCOf9q000an2Q/+HNbc1iyx9vdhKVtKkR86oRWbnaTD4tjeNqjvCQquVZCVhWUmA4T2hDo6JaNUI70pbGbMnZ3u2RZ9Eop2Ckb9qI8J4arRb1ph8G2nUDbVVYn9vwjsARXkYj43F7DRQbyc2dvYzp9l14dux06eeEm9b8u8aYgUM1NSKAEOnvST7NSaDClE+bd4Sdpp0cYT60NoAXrafPv91HRQs+rh3ntXfcv6S/9+e/hf7pmEiH7cyZ5Gcvc4TxGZauY0lfjmWuYr4kku9vzXt/ddHQ5mG5umfWvnf0bR96Z/b5saMIlj1xjvBB1Ah4mTkJ4Fxm7c7ZSoUPjmnWpLiPvf7C3WN0JzNMMmJVCKJ8x69RjNtuURbuTColeJ8QhUSb4hW2jR8lu6RqhEbKUlvlXbp1ua3B4cR+i2uoRWm8o36gyC1HJ2quOhcjwuuICIe/S2rE9Pm7Ps0lCTmT3+VutN7To66jkmUczejcp8+l08vtp2d6bbXV56oR2rk2+aOG44II6pK5BA8ViPDoRMlkufl2JznoWFWvZweuz54lb5x2kRqxzJHVFJvWH08+jb/fd0Mcn7o9Vs+x+ybbIbKfJ3+Gm6Xw/yFWD0REGI9J8mnzOsKNX+5MzZmOHHJfmCMcjxsjXViIYWpNLpmmYMyZlfA45wifXzUi/T41XK2E3Sm7u27piw+3h78DR4qwTNkTSI0Yf+5xc+ML399EQY30uz7tV770FH3s9efoy164d7Lzyx3nyU6zt/GEFpEmhVrpDdC5J4t97RwvMRpGTFrvzNK7qcSu4Qh7RyvvYxgvcoRdKVmurBFpczjDZ1PUiKXVI7nNbWfJp9mNcJ+3u5TkRASV5ZRDWjJ2mLk9Pp0u5oAblOqCGn1ChK2M7SkFgKzdGWqE5oDHMVKgRrDjt2SR0yF9tlhQQ4T/5ycpa/6+KFAjtl0ftKZ7jQiH64ryaa5OdUT/ncfzqjmefFron4vtEtk0p5KMVzliIH/3MGYFRziuk1wMI1eNYEdYO4Wl8L9cBw+fDy1gbN16erztTET4siCZVp0sh+epWEhlFOR2IsJLaBnpfs+3y1zsQ6/HovMc2544RDhJBy2/IYLw3x/nRay1KemRV569OolItDj/DXKQaozvDyIrRGVE+LbrB7MdK4O81pBWwJN/fkx5Qg7yXi5b4L239YJ1KWw0kyNc1BGGNg00csqa2GaXcYRnqRGMCLec3CEpDUTT8mlRb1aN01451OiodjpjS7QnkWjesFt0DwslLqGD/axqhD1HWZ8FGaq6ymtoZY6wpEER1SHCdjKmnEeIkvqGRjOJZOSEKLyjyLOfWyM017xtJIXo0LmW2y+F1bXjjWaFvKc2O30/iGTCJJ8GY3eQOsJJP1n2oZS8dzJqhNhYeXqLeTejsXzaJTjs+yDCXO4c6SpT5sSYH/tqlsNGZ6+qK0ezJdHixjnqaKhySDn57dBI9+8kyxk2hWbNGXKhhmGZBuahdsxw2X7nT7+fcwOwxATSpPit2hF+EhHhc2xA+BQhOcReJHncm9QI76ltPDkXNJqljnB+vsaXn4U1zGtUI5ZudtH5yZDNwjseqRFKNYL9pFr5NKuEMn6nG/K/T1HsM9WIccNuJbvO8YZlu3mJZa0RLJ8BiWPxs8b7eN+WRObwfGuDGrEUobMjDrZqxK4fQhluixqhHCK+5Jrxx1+NyXJqY3jo5pev0eJRE3GynH0SSwNa0l/457guKkWIfkjjj/vCycxsWIikdO7Uh+Ovg9Z6sG49vfV4p47z1HpfLKu8pC98v6sQ4QhMpO+Z9+YMzl7JljyTOC/UOMKrPAl2HzsHWv7EIsL7jBVMlsPw+zlM1ng/22nT+W8w9FJrWClKZzlHxGsmBHzb7Jhh0iXn847DgQb6MIUIe0c9CJvP6ggrTiWaNQFGasS2XFBj6TPm45gaIXSEZ9DqTYEagcDhtNyZ5N3y6ZJ8mqROtDOIMDoZQ5ynVIl09W4Q2VQG0U9AQ61kxJAQlZ/DG5+tfOIIL1m0ZZJX+p2pEY0xBqbMGh+8ocmT5XpRdjn1SV5j2ySEuGbjqrnmuQbqYe98mvf4nZUv23d95N30gXfYlDpLqchKBIzrYp9z4zGXhsuD4zExgqe8u5JqhFwHj+AIGxtArWBBFMbTn/2299P9y5X4LPZt0YbOEXUUE4qnDO9d4ubPIcLnXdtqOcJEPA77qnfj7sVIeTrweuTcd1BTRXvyHGEYWEstJctxQY3zDThrITunHTskdQqzuId6otNyarfdLJ7eKQ2dJF2mOvUp7xtb2zjqhtHhaXxEbjXSim3NOZuyfZmEl46F343Q/5TxRLvZhQm6JpSmQ9pTqhFTohH8N53gp+XTkGowxRHWjoilGqHHVFDAyf+GhlXDrOevn692kvRnj7nc74L3UFIfDGrEws26deqULAeO8FikZ2dwhBufy50hNWLO+Ngkn+YFreZgRxg2ha3PqU7f+ZF3EdG7JvuGv5tll8ePODmOjXnlktYj6RN6I2mdG6/FCXUYs9uLTEcOiWzKQuMdfddH313s45JxzM+gqqBG3FCmZzdXdvnc/kGtagRRGitViPAos1rD95+yZkH/9rUnzhGeSwqZMqyOMwzLBv+hdtOIbI2k1E0bOsIZR3j8+SQjwufoc5qowvmsRJqmgC4REX33R98tFrfrimS5JdSI6LR2ZWrE0oTI6Fx3/bjg2DxAq2+lynLozEzJnWnqA9KviFKBDr6m1rtpzjHclg5UI2RkIR3T+KAqgPxdy5jviX3UdAurspyVqNI26T1dMoUKjnCLjvCeiLBxcltHeKRGFFQj9DXO0Uz094nSxk73+9BXHvnyekM0+13DucJXPlJAxuOwDDcRRxHkBiijRuwSJQTNRoT1mDt8PrScS61ggceJzxo5/muNne8ljrBzclOTtbknOn0MW8KVXhItYUT40A3PPlzupfbEOcJWWLDWNDXinOPNClOd087NVd3HvAvODBHF2uuaq2ghVbfZliQiHPN8jIyaky44Zdr+yDe8N/6+anzMvPZKJs254Fw13hcnOmuhixxhRY2wNgyLqRG7PjoMqQ/T30nJclo+LR1bRY0AhwXbYaeBb0UzgwhnHM3Bkk/TY2qYfS96gQinxTm26SUH3HKW0zX4WLVuiTODvtIaqRGGfFrNs7c5wuNzVSWWS8lyYZNh/79mnrR0hPuhg78f9s5jdIQTvqq/qzZM4bPcEcUohkgSHWTVxJQsl3OE9Vxic4TtqMMhZo37EiKcfbYnOBQR4QpqBM5lUyDOOZy9kpXmFsuWzM3HQoTPoRrx5CXLxQexx3cBrVlaHvRQw3PdRLKaNxa522YmIhwn63QM0e1FtbUJHtoZuowLXpkjTPGYKWsREVah86RrWh7PZsEOdlQUImxxF5cU1OA2vatDV6J8WqYjbBXUKJ+b0V3NqS1RI5hnWbJBIXKcy1ByEnWVudIiioUQSrQHU1HAmDdav598GrYvStUa0mA1VTZLqiTOyfLS/Iy3neUIq341y6gRscTyro/Sfcd09pD7rWkcc2ZqBht9S1GMfAPIEQmi0RFW9JLrXRfuYYaE287esdchy7k0OcJmZGw/B4uPrVONCD8xqmOdy0pKPZctObfe8E/Z3fVxOMKyCMxBTRWt6pY7577TOfdrzrlPOed+dOK4P+ScG5xzbxyvi9L0y7vou4AI4wt+DhO78xtwRM/BsznUMBtdO7w6rHRbUW1tS8vGHmqoZ/vCvQt67u46O4Z36HMT1Krx9HiXuKB4LckRnpJPM9r0NiJsoVe1m12mRmx2TI2oQ1dw48Whei17RiSdU218nHY0tB4x0gqmK8uhFnEqxCJR4HS8Pm8ZEc4T+7RDbWWul0ry7iOfJqgPBiK81DGxFm1+/ogIc1+5LLQuV6xVI5ZEnfi717s+ju19uadm+/x8/XJqhEWHk/eY/xZ+6hLLzBnGOVfLp212vSnZZ21iXYYIV19K0ZomHzvooD5ztRrPNe0IL/EFWu+zDXfJcFM1pXh1s4hw/X3gubXGuWX5tIPfgTPcm1lqhHOuIaKfIqLvIKLPEtEvOec+PgzDJ9Vx94nozxHRL56io2yHOMISET7vgDumpM4+dtPUjBprfM6103zRQzjiN2HnToLARKi//Pu/nB5v8ypmaQGccYS9T8lgXobn160nug5tla7LLLHchs80Imxt1Ko5wowIG9SIqXHinYUI0/hziG3XUCP0uNTtRMTWTzvCXR+q+m3GktGsGlEaR3qjWOYIJwdGK7KE3+W84GAcpfOGn+hoLtlU4+MU8mkjp5OLNvTD/hxh7zjJM417LpoSOMIDrZuGtmN1v8w5g3FeM/4SR7g3S5ofOtcjNaIpyCGWzOLjimes5lZWFuFn0PeKGtG4WOSFbbPrC1rlxrU42adjzOG2akTiCL/z/iV98eG2EBnbry+Nd1VoMLbb+ERrmbtf517bltyHJXPz3SMV1FhKmdrrHBXHfIyIPjUMw6eHYdgQ0c8Q0fcax/1VIvoviOjxEfuX2VTG+/x3kyPcDcNZHVK56JzfiTvHrupQa1wectU8tifOET5imLTGcKN496Kl5+9d5H2qRYRbOWZ09SZuq/QeWeO8jYhwVzy2XTDZhj4k51ovtlPvmncucjvXBdWI4LiWz80IcqmyXKf+3riZynKQpT8MPE85EwXma8DPSnw8QY0wOLA67K4pF3iutvF7yadZVbaIZCi7Ga+1xsG2jvE+IMLXBiK87UKSdOv1dcs+Lgn/8jVd7zoYt9CfQ6kR6Ej5ZRxhp65L90eDC+z88jsaKs3JOXin5NOuC46wBbygRnPo3+HzoZUsh07qO54K898xUdhQza/OecBN1VSk4SaT2eV4nT52H47wocWv8Fyn8p1qnubLRPTr8P/Pjp9Fc859NRG9ZxiG/22qIefcDzvnftk598tvvvnm4s4SoYO0/Ls3SY24ydCH7sNtpUZ47yK3L6dGhGMs7uJttprErWNacsjKJ9N865Jp9QUcN7FcqCsv9lbzqcSy3vBg/5ZtdmKJ5VE+DR3HqXfNw8aLEcqk9jCMbfsqaoSm7PD3k7waH+cnVSOGIS0c3VjyVqtGWMU1krNC4nqwn3mJ5fR3rZ5gRd5MjvCCuUxzcdm0I1zLKbSVCYKTgtx2HmtbqP6Wjs8RpyUSjXzIZtfHd8K5ZUU5pgyfQ6BG1E980oHI+6NLmXMiHN+fqLcPGwOU4SOS1231myjNI24cY+ldqL6UollrGo6nd9y/DH2Y4QgvAcUa70wesmX4/KaS5cRce2b/YEkxjyXrL6tGHP4OwPlvEBG2zhzfBOecJ6L/ioj+4lxDwzD89WEY3hiG4Y0XX3yxvpfYGWOCrjXUEUbk5Rx2TJTg0D7c1mQ5XGDTpBH+ltCo/Z//TdjZqRHxGU8cM1HzHg2dKaccBiz5KrifM2E2Lv1ckskjSotS7evJzgHLp8l2p77nMtWIVFluvJ5mmhpRlE8zkGVub1I1oh9Ayo2iI5IrRaRrwPPygooLtXNSEcByHrC0MP8fj8XfG5+uYcmQtjjmREQXWO3L1fNgeT5fKccWqRE4p1jFH7xTHGGP1IiKPvi0sUNn65AKqGg4rkIVufr2rGcn5dPk2BnYEY4RibGgxvgdlv4TJZbHao55v/NNj35HjjEfxvcKxxaM/RfvTyDCe4bcm0WIcLr2Kfk0fC7njnYu4QgvoUZERPjgdyB/r45tNU/zs0T0Hvj/K0T0G/D/+0T0ESL6eefc/0tEX09EHz9VwtxSjVG0qCMc5dPO6AifGRk0+3DL0VTvUyWzqHWrFhS94N92ExugMzz4mkSfKfk0tFYlFWGbEREes/TxOLYSKo2ha27H4oHVvp+r8fjAEZZtTCHjwUGU16Md2MARLp9bUyMy+bRMVWIuWS45IlHH1ZeVCPR5+bo5AY0ocL0RybMQz4A4kvh/fq7wc26zUzI8dIoaUftuJwdNfb9J46uFzQ4771LJxWUO0RJqhIV84ueHLjG4+ViaLFeL8GOCZz+kjQL/Hx3mXS9VIzadjQjjRyVN+GM6wpIjHE5+Z9XQU3fKztje1IhmOUdYIMLGuc4NmIhzL9gQLNngXZ2AI3yqZb+m2V8iog845153zq2J6AeJ6OP8x2EYfnsYhheGYXjvMAzvJaJ/SETfMwzDL5+iw4dwRDFsidync9hNcoDYljoY57bGOXq8leFLPWlGHt6T4QeLUOA5Hnsq9Vs+WW1RElzgNL9vHROD5EQqvlNof9X4pD/a5A7DUsTIWkRSSdqJDYGBIpkc4QlEmP+k6QpJPi18hk7kXHt8DzmUr1FSkVgDiBNRup9r9RxMaoRqx3SSjHu776ItIgeAEmac3VpE2EADnXO0brxAf/Xt1hs8Lem1ZJ7E7wrKhYpk7Ws6tL4IETYcCAtdw9wZjJT2kZoTjm8bJ0p1E7FqRN4nWU5bbnY1MnyIWfkE7AhfrZtYUrkUnYp9WYQI+6piGuEc/B2QT7NoGnui08cwfAxzj0RTwKYs6Qgfdj3n2CTMqkYMw7Bzzv0ZIvo5ImqI6G8Mw/ArzrmfJKJfHobh49MtHNfcHgOXLUuWO+N4O3fSlGXHDEmdwhrv6EuPt0REdHctpVf0wnxbE/4sa0cVgPMky8mf5jHVjrDcvBWT5Yww6FQfsF3LKV+62ZXnlIvtHEeYjRe2jBrhpznCkRqhQptJPi3X7u3GhCNrs9IBR3MXqQzyXkqkT14L/0S6ATswnPRnOXqaImCNI8vxXDIP4/nY2b9oG0X18OSnalob7ekx1zaO3r7uxvPk/VsJ5FajussqyzXqu7pvx0LDvAv3Zl8E3t7EhJ98CUyFiBGJYSzxzd8dEeGB0jgOSYKWRGP+Pken8IiRSUsthVUj7qwb+v1f+U76wtsbevmZO+b3eW4+VbIcXvuUE3mTEWPrWc0dW6UjfHEkRHjPCNQSq6osNwzDJ4joE+qzHy8c+62Hd6tsVqJHrYlkuRvlCJ/ttGYfbur8c9Z4R29vwgKmifa62tO5d82HmPdE1J0nElCzY98XEbY5wvK6LGcgOz+0y8ithVTVrk0iJB0XRi/+b5l0zNImmSg5sKvGibLH2rSjG/qd6BRZMh1sxi0kDeelXcdljG2NX7w+HTFBukGodjaYTnlqRzpGqHKRzsvX4rPPasy630jhCG0T9UNdo8kRlpSEwBHeju3lbeF9t+TTanj2ug9E+fsS2q+4kAnDcXO1bujOKi8fXOwbOhBGf7TDH0p6p/szjEnlSL9BylDXD7TtBpPKgvdupcZ+oo0cPh9aiPAaEOF33L+kP/f7PlD8Ps/NSznCtdQIfNemNvj4fM6tKrUPR7iOGnEsjjD+foOO8G2yJdB89t3xO4HndN4BJxNTbsaJO4RWcg7DfjEirEN6TXO7r8GyQ3jtS60G9Y/O4sw9FA6GLxfUwGYsnqS2FXzBokYsHacWypUW3fL3+G8tLFK9ojQ0fjpZTleO43ajfJpRYpk/tybffkiIJSqoWNn+eF593Zp3a5VY1gsgIn+6ffxdFKRYMKaFcxQRYeUIO1flgBKVEepWJMtZ/FVJm9GI2JLImTfGMn5+zIIaf+37P1odkg/fzZ+dubFxaez3wxDHX6fk09j5JeKExL64oZNRIkWNMMbfvpYcs/RZ5Aiv592bMD76RWvyh951P9KW5ixGsPH9shzhI42XfWyZakTdcUQAZh24Vp+DNvLEOcI1HMiS5Vyoo3Zt0pbsuk5lS8IaN2F4X+5eSH6RXlieJEc49f0M56pAohpXNw50BS7sPy9umr9aQ41AB3sF7eC59GdTZmXr16DeiNbwfMKJQLzgt95Pq0b0si3uQ5JPk45yC3OQZQNQIwRHuOicys8iRxgRYR+0kFPintFnuAelfAYLSV4yD8txkqgR4pjGZZzeksWNjBpz6waTMfP+ab1fsQlzy6gR5U0g/zzQEYa+fvCd9xd9V2yYGFBQTj8RibEvOcIkKIScLOedvOdWZTlNNwnn4b/l/dvX0nueGlvHZLn5E+jNQI395Pd+pPpYRHpjwRXjXDe5NkvEdfrYJSDFZduE6z7wmiwq2LHtCUk5SnbIS+Thhe+Gc+sIYz/Odlphh0jPncMEIqypEcqRuK3XYNk5+6yRQcssJM0y5FJiCV7vkoOhK8vVUSPy4y3nq9bJQgdEj/Gpe67vQ0Byw98wWW5SNSIelz7zLiXE9b1NRyhJqAWEzYtjdEENZ9wrjbKhg9mOWshcJSzdI2wz53Lq67LGzZINKfabky0tRHipjvBKRSFao1Q7mpZgzAqLLEh0w/b1xpHoeGjYPkjhlEIEUU6N4JLe/H8eM/G5c7JcL9+5OUUG7QAeM2nb2jTz2L+qQYQjhergrpiGY8y7crGYm0SElyCu1jtTPNY7ulo1B4NW50iWe+Ic4UMQQVyEzq0aYUnZnNsOmVTPYbgQMxdOh7iXcJRui2nk5RznqnEA58T5sbIc6sxi+FjLp2k6hdmukrvi9uNnse3J7qVzCseM+zF/H/hPiHIk1YjUzjQiLOXTiMJ166Q7PW+VimoEakQ4JhWtUOF7uCRNjbA4wnwNPSQ+YV+4f9YmSjpO/Pf9OMLYdkSEM47wctUIfR0rxY/WtlK89FxHOB+TJZObQGtDNt9GTfv7ONQa8df9SUhv+MmR0qRjXZBPGwYz4VWem+Dv8n4fE5CJAAn0JyLC63k+9RLHbh/DDeqU6sexkiv3sSXUiKTmVNf217z2LH35wkiGtnOUn/53ihrB93DXl9GCU9ltUI2wEJ/bZHyP7q7bLFlH81oPDbec03TC3ylNl/q1LOlZTrelqx0hqoQFK6zM+anHww62dzZStRQxshy3xs/fcz4GK4IxksuyUd5NI8Kpchz2IZdhc+ygzyDCSI3geSrTEVaOH56fndRcRzjQOEq5Ct4VqBHG7xYXtta8I+qI1RkMaoR35MwaTkZbyqkmCuPe4qCjRYTSikbAmKxCvZx9rmPlBRwiwyYpLPlnGrXmksqxsmFPQj4tcs0Hea1mZTnjGWh62zGmcGvDG+XTKhILaxOH9zVuNkRcyqofNTkNp7LS3DJ1bO39+pt/4uv279ho5wARnzhH+BBUk6ta7TrJlTuHWQv9uc2aDG+Tcb+YFkGUh3x1KPhJsJugRtQkyzUznvlaJVxF5Q6fSr2W5NOmzo8av6XELeznnOFCrLm4k/dBO80OkdxAnfKOJuXTLGpE46fk08ZEpClqxHhMmqdkAhkuDPqd5uvWOsKDgQjrBdBGDfN565BQZTg+KBE0Li9V23hf6Qbnz5o/k1rWVrJcOJ67jsgoKnTUgC2SGpEj5ccKC+/TjvUcp+gSnFCZ5NPGEsswtrpRRQKjOnPJcjpqcax7E9pI6jWxP+Mm66oCET41Nxef34dfeoq+5tVnZ487t1m65OVj83fu1GbpYR/bnjhH+NDQeONSpaFzOlNStP1spxV2TG7WKYyfByfKEeWT5lR1nttqh0QxllrNhFqLqmOSiwPEMCTIUfxdyqfNy5ZxWLVUbGQJRzMcnyMaWhnAMv6T4AjHZDl2QKcrwSVVCHROc/m0NI7H7xWpEZgsl3jKfK/6Qd8r5dSN/0ekddWEa0CnhihH5rxqA/uL52iNNmotoa3hPLkjvOC5Q3+dC+h8451EK43nn6oiynclhfBJfD59Pel3S1/50Fe+hus+910iG6GOm6joCEv5vsGgRoSKrIPJ80fDJDG97hxzPkyUI0lLeenpO/TKs1fz3z/xeoLP7w997Sv0h772lel+3IBzsCQZ7ZwKSPGcB8w3tfbkOcIVod/J73uJtJzLrCSFc1utWsBNGU+odyHJIZtEF4ZmboPVhOmPZTXcxNrQr1Z0wPHTRIeX2xz5rc38+SMijOF44Xzx3ye7B/3MJ/IaZzreB0CxkRrhHI3UiD3k0/ZEhIch3R9UjeA2SqiuRkeRGtGOOsJdL4sIaYdvCjXEfkgE1ryMoiEC17hcj1XL8U0ZJm82ztFufGaIhpvUCHW9aQyOfVgQdQwbRFkRMFzHcRwsjabu893Qz/EzQ3qP/9aNuTOx3HhURAk/23FD1XipHWxJ1KGTXUqSO26ynPz8537kW+iyQuv31A5obXTrJhxMfW6i+X7eRI4O3pKTPaeTtHpC04UVFn/fuWyBOYdZ3Kxz222vysaTgKBGqIXgJnfO+9o5J7lGLe6WaY5kyVaa2wc/E6os0TWrQEbp/N7ZUYroxFY+YwsxaCqQ6azPXlIjGh/K706rRsjz8u+RI6xUI+Y4wh0kIiFHWPw0EBz9XpRKLFvo+ZTShvV700j0bYnx4c6FMZRxhF192FVn5PNnEq3Ml7iSaoR+d2ovzeROH+mdP2S+Q6qLVSBFt83yaWmMjuNPIcJdr5LlrJLBMFbTM9fnXXxJmZXmsnsXrSnrNtXPU5gV8TKPA9rZua30vlt2E6pNOGZPBSI+eY7wgROM9y6GHM9cswyyAAAgAElEQVTpD1pIzLmt9qW8KeN5y0KEM2rEbb0Iw85JSYmOUQUSai1gaBoRdjB+9GLmVJtT5+d2QxKY7Dd+t/Z+Wdn6NRQafax3WFkuUSMmSywrbV5uN8qnKWoEO59T1Ai+t1uVy8A/8ZJ0CN5EhD2XWFbUCHWPLI6w5TjvK5+mz2lRI67WLV1WVk/DcZLGppOVC01EWFMgxu+q669Wr4jjKOcIHxp9O4gaYcw7U+W5+4HGMcIRCVmApfEuFtmQ1SHzviHarK8BN0OHmo60LLVT69LXzv03iQhbij3lY8+//t6aEsu3yaxQ6hJrPCDC53yYC3Zdp+7DbaVGpGQ5cIQ1WnUDO9JD7ayOcEQPy8fU7urbApfSOywXKp1lLZ4/d35rTC6NXIhsfYWsTA31/NiE5IakoISClSxSH9Rkjcgyt000X1Cj79NGYafmKWvRLlGGGGnlTQvLp1losv5pqQ2U/r50KkFe8vN31/SOpy7E33/iez5MtFA1wqmNmaBGVCDC2eZp4fvKh61MRLiqiaIdAl5YzrjesOFn3cgJTmOUS3yHv3MSaKciC2ZBDbiXev44pownqtfsYzXzxCFmIfGT/bgBaFJslGbOfxMFrSx61rHtiXOEDw2rNM5lIZ9zmM7Qvgm77cly7FTZqhHh/08iInysRXHJuabQltrM37Xi+6KzpRHX+HyU82wZ67w23i5nm7jIk93Ljhd9rNj0aWTc+5TkNoxOo3Mz1AijspxGlrEffF94Dsrag9D0VtEqJu+VclIZaQ3X4CLaZ2bze/XTCJ/j51ZSWK0hDeNn//Q3iOgPEdH731GvOYobGRz32D9rjOs5RP9cCrbE98kqqHHgXHsQNcKYdywaESdiDsOg5PsUv33cFGodYat6H84ZCQFOnxEdx+l75mpFF62ndzx1udf3w9g5YchdXXvJ+BncSEENY0zMHXtTyXK/oxox2qEPwntHm12O4pzaDkFRjmXJkbmZ888Zz6eCGpGFbZ88R/icvOaaENtrz1/R13/Zc/SRl5+ebEs7teiIZSjx+HPVzJ9/FdtLE5vFXax9x5ligRnuNRQNHVb1DpPcxv65aWpEQnxlf5AjLBwRDkPbfnCkRjhHtFXV0SwlgojWKSeVk9BCUhpLYalFz3iuun3LKbac41pLVBqiF+5dTB88Y9hvdLJWqpiINh7X6Tu6vbGvlZdmUSMOjVyyHSIXaaGRU/xvXVBDy4xyslzfzyfLIZ0q28gd0Zl65mpNv/Rj3073L/ZzZRCxPoUtmftDTsL51zUxd808k5tYf5f0b1974hzhQ5MQbipZrrS4nNPOKeO1j3H/rlA+TfH3nkREWCNNp7TkWJaPuX+5op/54W+YbUvLTCHqaNElwv9TcYqSxbKmzmXOR7iG5e9424SyupruMDVO+E/smIskt9Eh5USzklnUiOA8p78LZ1IlwuXtMTrkMkROo774O9/vq4uW1o0X1AimfAzDYKoGWM9XHxPOEX7qQitL7JgLKTruGClEyTS7spw9do9JjUiqJVVNFO2Q9c66DhynYk3yDjjC4xjt7GQ5VIfB86Ah8p82FnKcHWsdfOpytfd3MZpwCltSRc87dyOIsBwf08ceKlawj/0ONcKwQ1+ixiM14li9qjsv2005wo2akG6bcb/umaoR42I1volPUmW5U/PQ0A7dKKJpdBedS01R4v+v1Odz7VpUjn02O613tKHcqZm6DVZIvANHmMOVVRxhNVnrpDt9bWWO8EBcrW/byXnKdGzU8/6h3/MqvfHas/QvP/d27Itzjro+cEBt6TW7LTwnt0UkUdalw+yY4xOfH46j1UwiV6OUTUpI5XJqRL5BOHSuPWTOjs+1EI3Un8fKcuwIa2qED+9HpxBhixphzRV6HN8GMOPkiLARZbmpvpSsFCWwTMtmnsPOQSt94hzhQznC3hNtb4AaYSWpnNtuO62AF9grSzVi7Pv3ffVL9PzddXVm+W2wcyYY1HCEay1DhGFBS86qzMBfUlnOu+lw+5JLKIX5p+65plF4n0om933on3PTOsK90lrlfiOybGVlT6lGcKh/p7L2LfSc7xHPh09freiN9z5Hn/n8w/idxgf+Z9cPKimR4jGl9q2w5CGqEXzOY+zF0YHH3+dUI7TWdSm6UXtpfDwi0UuQwMm2KzZ0c9/F6/j/2/v6YEuK677f6bn3fe17+/btN7vLsiAWkLAQoAVJyEbiy0KKApFlW8RRDCmlqEoiR4mCYxRXqRRXOSnkKpedisplJZbtfFn5sOwQFxhRMkpSiZDABgQCyUKyCy1CbCy0fGhZ9r17O3/M9PTpnp65M3d6Pu57/at6de+bO9Nz+kzPzOnTv3OOneaPf0/Tp6Ullk1qhAqW4zxiICdYjvQnWc+kMrnO2wLPc9wEXKssRbJ04SSrxhHOHtM02nAk9ZQtmo+qs3UbERHWuw6W68gj66vaUVNQelk2KsuZMp+1uoifvuLs1mWrA/3yb17xdSeKHHZ2CW5c2pkdUj5ilTzCIq+ynNl2GQwj87zlOMLqfDrzhWHAkgoiyj+vyyMsyOYIZw3HfENYGa/MI2xdB3K0Z4+tiN3rSh67GIfNJ5+0lO5KnVSZI+xxUsgNPR7QZeS4LZE1Im/pvqyMKW3LUVDDV/q0OsFy/Ni8KmIiWQ0ZGwVdrPRpxKkRE9KnsXGZmXBQVpauMIia5eVWoXnxMudtgovWx8IfbRjfM2gIq8/plCIET5/mS6oS560w2JqCz7Q1TUANdCN9Wo+W0aaFnbe0SVDN+4ODZ3cAzIdg6hG2DM4h867mYY4ZrbaXyDhfBYVlqRqTucrpkjZ7WdmUhpgjXJEaIcwSywY1worIz7Q3VgY4pVkjbIOmqLKcvV3RBlwllm3D1jV2XN5RgyNc8RnqlRrB2uJyDid4hO0iMLYeqnLU7dUQvq3uPV9n4lA0cbK3q2IyI0dBDbXfcCCwPhob+wDuyQafmNgePf5b1xDUrPFZZXVhILoJlqvice2mslzzxvfMGcLTeIuM44k6KbHch6wRvrwUTUFdj21zmvawGQzhNiv6eaVG2AYD8+zY+YNTY6CCRzgS2fKr0/ZBZ6IwjdviYDnLEBY6yE0Flk2kRqjKctb9rQ1qu6xx/M8op021v6Bs1ojUUHUGvJntGLmalZEzdhu5xK6r0oOCywOUZ0yVgWu5flpwD7ZqrwxHWPPHzb7l0WsmyqEMRaOghp+Xd50AZ1dGFtfKi9quaQ9J1giLI7wwiLA+ktgYjSfST/hzIUOJSMdZ5S55x0A0S0coU+pdQWV4aRtVVh26cKa1QS3swVCshroP0kgQzozap0aQ4wXUNnx6C5uAekFxj7AStQ/LaNNCPdzamID4pEbYXEruDRhYLzO9PDzZABik3lp2vPGCNj/LQHmms8ZN/jE2jUKQ9vCOpPbMFmWNcFWWM9KnSZOXq/o+GhVQIxLP0IajoAHvIz9vxiPMjFrVL7vEcl5qQt4XcoxdI19uVUPY4zNIG1bme2ESfzVdxbAMxQxVouT9ao99o62a93ydKH0n5zvHIaNWPsZSn3NkpU9bSKoVnjozmpirmU9MshMNU74uEdMRGmy/woSIe8/bRBXHQyQoeW63aAhXXKGZBqWGABHdSETfIKKniehOx+8fJaInieirRPQFIjrHv6gxyiTKL4LogUe4K+9m372rmhrBPMI9WkabFrZHr0n4nOwU5RG++oI9uO2qI9i9bd74bWC97FwYMjqF60UxjTdNL3er/ye3oQ0e7cXOllhGKWqEPdHNTZ+WLjsXB8vxlasMl9fRXoYjzIwgxf8sGyznelblexKrGsL+XmouakQkyKgsN3QMxKG1apHxiFvbJ0FPqBwc4ZrdLDOhy4PLw8/1TtZ3ziMXBB1LkxyvApRf2xgnlJv42KLKckJww9eSqwfP9IFoNliOHPdUHnj2kzaRXpcS5942P8gUwWkaZI2fJjCxR0QUAfgUgBsAHAfwEBHdLaV8ku32CIBjUspTRPT3AHwSwAeaELguv4gHoXTFEe7q/ndVGuoT1MPTzRHuRCQvqEvnqXQuj4bGnBWAxoMWzt29DZ+46WJ93tQrVoYa4cojnPUwVTOELWOuhGfaxREeMwOWSG3LN4SlgxqRTZ+WPWdem4oaQaSDeos8afp56O5b6tUeJ7mR2X62x5B78ex2+Ng1KADTcoQ93M982ZnLPskjPLBWDzJZIyrer+o4bnT7okPVoTq5xksuTSKZeEmex9pyGCmPsNqm7g13ZTnd/6zHvbxx2DTU6ktT0IGBZWTpZnJQZdJ269uO4LrX721YIhNtOPDKPI6uBPC0lPLbUsozAD4L4Ga+g5TyASnlqeTfBwEc8iumRp1KO+q49Q6oEa6XV9tog3ReB+qBydOn+eS8doU2PSBVvVlF4FzeuM38B6Y6XxkjlnNybSOMn6+KvuxjynCEydpXCG2gytQzRrlV4AA3NYLIbKeaR1gbdnaax7LUBfsYQSx9mkMW20AxltJdxlQNj3CeB3sa8PHIqRETg+XUSgeZMrm48OXkMNvlbdTtZ51JdNF44W2r72os8wBLfjxPWcl17qwsxyYp9nvH5zOqLlbmB0aWIt+oEnjZNTWijE21ujTExQeKK5L6RhsTpzIj4CCA77D/jwN4S8H+HwJwbx2hilCFfO48nnRamK4M4a6W+evqrmkszUeYi4Q7WK6nMpeBL75gqXN51JedR7jI22wXWyjqq0515p7Yai5yeVltbnIZYzojM/P+jsY6fVrVrBGCNL/XNj51QY28ynIyTaNkV8B0BrPlGJaaQpYYOQ5qhDrENgSdWSnYcQZHuOKYrrIMOwlcH/y6m+nT4u8LQ4HT62Njmz3Jtsd3WSPWzpjCt9V9eVc1yg25CsZL3LY5FtaZ4UuEjMNIVStU7aimXBxhHSCXXXHwOQbq4p/8+IV4+fR6Y+1XMeKihr3TefBZ8roJtOEMK2MIu87ufDMQ0QcBHAPwjpzfbwdwOwAcPny4pIgmXLPcKjCpEe1d+MjxAmobfecI/60rz8Hbztvl5Nr1VeYy8MUXLIP0ZePhZEWV5bLnTYyBEqniuIHtokZMEwdgp6jjhmAeBKN6KBl42rP4JV6OGmEbG2dGuh2Xt3vksINlUtlLJOe1J+yuFZ08qpiwPHJjGcvqCpbjRkte+64+8GPKwudL1+QII/3OPZTq+8IwyhrC9gTPGg9lJ2J6FUIf4KtoRK2CGgXjxW4z9gjrdGmRYCW+2WRCH0uZiWTeue3JVJ+yF+1ZmceelfnG2q/i/Y6z6DQmSi7qTLbaQBvOsDK3+nEAvILBIQDftXciousB/CKAm6SUr7kaklJ+Wkp5TEp5bM+ePdPIW9tNzrlPbRqkeqmsu2X+PlX0cWF1aYjLDq8Z2/q0jDYt2qRGVCnpOQm2R9j2IHLYL7mi+5NzNF1e5mmMpYxxU6qghmnACIrz+AKa0hDTJfLPO5JuagRPn+bi3Lo8wuo86rwbaUENdaz6PdsHW908yFT1a2RnjbAMPm2gILOPy6ttby+Duo4Moy02weTPCU6NUJO5BebN5CsS/JNnMwHKv3hdWSN8rb7VyiPs9PBn2463mxOveFXDHNuLQ+4RZvePKzMHkzuvIExf30M+UWUcDIRw6rJp9D52qAVDvYxH+CEAR4noXADPArgFwM/wHYjoMgC/CeBGKeUJ71Iy1DUqePq0NmdARHHakS6Xg+pm3OgCfZ+tlkGbevdJjbCrsxXde6prQysQyQWdNYJ7LvXv02QKyeMIF+nc5enWac+0x1qWoEbYmRxkyhE2x64uVpBtM20rOa+rshdQLn0an7yofmWpEabOnB7ECcvrVceZz3uBPxvyqBEqI8j8MH+Vye6jS89FULbL0EEZ8VZZbor7mTtfFNR7SFqFXgTpiVck1GTOpEaYHGFt4LqpEfp89hjtojpZV7BpN0W4890XYXmh3YwMQP9jh9qoiDtR61LKDSL6MID7AEQAPiOl/BoR/RKAh6WUdwP4FQDLAP5rotRnpJQ3NSGwD2qE3VZb4MtEXWAW+bZ9SrUzLdqlRvjTV5o1wvJquCYl2awR+e3yoht2kn3eVpUuaO+1amNy9gotszZ8TI5wfHxeOWRAe5Dt9Gkjq530t+SfsaPN0VivVPFS8Nky1tlnmN1N7qFXlA87a0ResJzZvvkJmBSAquOM0vbqj0/Oj+Z9cQXLDQSlAWE25UcbaKZsZSff7VAjqjeU501W45M3aXqAzYlYriGcHO8yhPkzT+tT9Um3sdlRhqKlcPUF062S10Xf37F9CZaDlPIeAPdY2z7Ovl/vWa5c1OWMDqPpH+J1ESWz8a7g00hqC7Mos402gxFchsu0sFOhFV0LOwND0WRryALqiiqXVbnH7Yd5Ga+y+onnPlaMBUVpEFRMjdB0Br1NEBntuGgFLo+wZNQI5bUDTKM2c64cXXG9EiXUiLG7oIY9Zgwqh0OPRuGIiiu5PieF3CnC+8DlU2NtGAkMEkPYrn5oU0PI0kdZOVyFRuo6HepQI/I8fRERxpSl7GjKoJ40xMfH+9jp05RMQ8dyPjde7IDONgybvmAWjP6+r7q2UUtg5rKzpg+8KS/a6uIw01ZbIOp2sGnddSZCZWyGYLkyxqEv+AxEsVOQ6Qdmdt/UO1ShspxtwChM46GwvXw6e0X+MdrI0FzoMaM0CCpTYtmkL6i+8HbI0TeXl3nM+MYur6+LBjGZGhFPNlJqBNuPyNzXVbXTLo8L+OEI+4iT4JMeLicvqDFg1zYdz1bFxEn5hCfLEX+aThaz7WlRdM9NQl4/1IqDuU1nOlH35cYonxoR0yeyEwD+u2rXXgXYDHEfZTELjpw+lbx2wa5e2gTaJ6TURF3v2o4lbQi3bVxxLlsX6PsSiAt1S2r3AW0++H3yvYaR7TnLn5SkEeRWwJGz3YE2ToqW+6s8+LLG+uQJVIZPTJrSoDy5MUc4/7yjHGoEp1i4AtRchrAOvDOfE3kFCXh7tr65tzMu+YxsieU8T90E73ze9zLI82BPA25scdqXcKRPGwhKVjhGaWYTmyqWt6owWQ5zHAH++umDGmGLEHPY7W0wOOmxYayyRsT78IBDHujq6qOR4znH8z7Lzo2y8Bkc2hT6bqy3ocOZM4TrusnXlubS720Hjallyq7g8yXUFmYxwM9GmxMQl4d1WsQTt3wuqXneeFsaCFcgwJC1Zxtj/Ngq93jqZbaMkiKd2+myVJoxgOfzLc4jLB3UiEhY7Tj65qRGjFVbpiGcKYXMPDd5y8z8mhHBXWLZepa6vJiu8WRyYTPdKITP8cnHI/eMi9RAZuMhoUbE390TgCLPexEoHftZ2l3dftaJ6yA1vq1j4zEhM9s2WPq02CNscoTtgEOXJzz9PefaxO2b/29mTDOpbxttUA/qoI3gyp46w/NR17u2tsSpEW0bwj2hRvR0wLsQ5TzMZwllqpz5gsuwrINhJDIPc1c/tFFZtcRycrxhSGa3TYJNySjj5cgY74Q028N4rL1ZhcFyDmoEkQ6GG0ubVhB3bhI1ghu7A8uDaVIj3P3kaZsiirNYqKp1+tjksyB/rmsFzkvWCA/jk8vLcyEPjfGlxywPnDNkyaSPq2a8uPLpVs08kQd7haMK8nTteg8J4iWV42M3xnZBDWHwp+24ALs9JYPt1e57ui6f6HsRK4Bn+OhWjjxQjXugLGbPELYeWlWxg3mE2zaueAWkLjCLD6C+p3YpgzbSvyi4qAZ1MIxEZjLium9sbm5RXweMo+nOdVrdi2IbiUf3LePc3duwUpCOSBtJ2nDi+X9jz9kEaoSjspzJNbayRiTfi6gRkSBDHzu3xc8slyctz8NqBpLF/RqNzawRyvNXNHlwrcjU4Qj7vJ+5vHyZPs0gQpSO3WGkC22kEzF7XGdWPsrJoakRzCPsixpRw/HjWkFQsrk45bySnEGNYEb9/EDfK65+63MglTu78jD7z/Sy8P08bgJ9p6qo51QwhBnqlq7k1Ii270OV3L4rzDY1omNBasBXudUq5/J1qmHEU1PF21xerjSC3PJeOttk+7iCp6ahkmive/z/jx3dgwfueKcR4JMnM885bFMaBJWlRpjGYZo+zeLl2hH5HOo8RJo7vXt5DnMD2yOsj8mLmeA6VP2yqRHp7wUGissoFIIFQFUcaHYKrTrgnkZOH+GTMW2IiIxH2E7dZ0/0yjpK1G48j7AvCkgdIyUvY4Ere5EgTdeJSBV0yRaLUUU1+GrO5Mpy+hyxXFvHEPZZ4KgpzEJeZx5P0gRmzryou/TbZbAcUT88wl1VtpsGfZ+tlkE36dP8nGvgpEZk97M9H8XUCGbAOAwG1xJ9GTm5HGVgL7kJMikNMUe4OGuEnWIq/s7Tp2VligRNTJ+mZNq/uqCPKzRUswYuoLx/ShbpKMXMJjqO6Ow8Y2ooxFRGnk9voJ5gmgb7nIMaMRTZdF+2PjPp08pSI9SEylEavm4/63CE8/jjrvcQp0YoL25KjWDHq4llJDTlYejMGqHPbb936tA9Zg2zkD7NtdLUN8TBmc21P3PBclUqtbiwto0Fy7V84SMBELobbDPpEa7xIugLeJ7apuHb6J6LRDaa3tER9ZAaRpP7aixdO9qcxkMxTYo6tS9PvTY2skboADpplUpWGDuoEYLAKsvJjC4ichvXyqiODbt42/7ti+nvLlqB9rJZ52BjTu2/PpKZa8dXqVyrCfkcZIKc4lnmc3zySR83/AcOIywSlFmtmOSpLCsjN7btbXX76UpfVxZ5jo84ANRhCDNOcEQ8j3DWEObjykWNUIeYXnn3hGMzw6aF9BEpRarHbtGIOQeawMwZwq7lwSpYMzzCPiQqD0FdmsHuZc6+4+oL9uAfX38BXrdnuWtRpka7WSP8XuP3XXYQR/fFui8yUDVHeHKw3JDl+HUZd9NRI0zeZxnoiaHOOGEawtpItPMBK6QFNYQpv52GzZRVe9/MtjQ1Qh2zf3WetavOBbYt31BV29X39dHYQY3Qx7qcDHm0noEgjGT1QeZzfBrUCGYURwl1I/ZsxtuHbEJnl9+2t1ddkbDHPt9W9+Vdh1alKR/WdiIIK2tExMakSEosrzuoESlHWBSnTzOzRtgTDz+6mQXMyjuX8+z7iKapEbNnCJP5EKuKxaGZC7FNxEEIrZ7SwCxEsNpYXRziI9cf7VqMWmgzOCRyLG/XwR3vujD9XhSoWiVtGTeWXd6habyGZQp5ZGSmrMwpR5hljQASg9YxjR2Psx5fnoYtDlCzjNSIMFLcCQZOjTiTBC6dtao9wq5xlBd8xo0N9dPGaOykRtgvayN9Wo7jIYoI0XgaL6UpXx1wLi/vAxEl1A0zT7WagE3KMEIVZVT33KARjnDyOaW++BjW27JGRWz4ao9wTJXIp0bwd1lRZTlBXBdufW9muGIg+gjRsKFZF0TNetV77Ax344L9y7jtqiN4y7k7pzreVT60LQjR7c2/lWbifYLPAKFJaPLBW/QCE9ZLsWweYRdfkHM/y2IaL7J97ohgeIS5pzEvg5rL4yvITp9myUraY2y3FcsDnHjpNQDA/u2aI+xMZZZzTXgsRUqNGGepEXwykrfapoxLjoGYLvDXK0eY0z9Yf4HYKI0EGd7eshzh6akRWY5w3ftwz8o85gaiMPtJETiXV0GI7LuPB3CmhrCTGsEzcpgTC/McWqd2OjrbMN7MqJvlqi0I6reM/F5uAjPnEZ4fRPjETRd7aavtC9/10kObS/QBGmVy6/pCk5y0osDFKvy/ATOWXR5NlbhfZUsog+EUYzuzVE4mNYLnQM0LmFNBdXa7PH1aZM2A8rJGcEPk/70cG8JnsWA5zYMF24bMNoB7S/X39dHYaTDbHtAMj9jxEnIZWGXgs5xrKrfITqji/NdaPwOWPi0bHGgeWzVAVxftoMy2uu+Yay7ciwc/dp0R21IFfDKXyuagRggirHND2DCM9X7KI0yUXVEx2+PnNw3guvTGWcKsUCO6zmg1CYKme96UxcwZwj7Rtj0oiNAlSfi9lxzA8vyg1zO/zQify8GT0OSDl6wXmfmbMkAmG6Q8vZXLcL5w3wp+/ZZL8aPn7yktW5QaOaUPYd5DnXGCZ3vgKzj5hnCWGiGI5yPOGn35HGEtl6JG7DOyRuj203PlTAB4tLr6ScqsfrhHOK+cr8uYGggBQVl6xyT49AYaKeKscT+MyKJG6PRpKn2YPWnMC6KbBJdB6IsaQURpHulpwGkjfNuGgy6RUiGSCYTTIzzIZo0YOG46M1hTn0P1yW53s8K+t/oK4RgnfYJwPIN8Yksbwm0bhF3fDOfvXcb5e2c36GxW0W6wnPpswCNc8ALT1bXK5xE2qBGsTSLCzZcerCSbXTa3DCLLgOHllEtTIxwcYEG6CMdo7KBOCDc1QmWa4O0Z1AiH/vOW8fnSPL8WRcFy2mNnyuUyphT1oCp83gs8y4U9IRhGIjH89QSNGyX8xZ+teFbNeIkoHkMu2l3XxgXXjQJRdnImyAz8dI0xAFicc+QRdnqE9eQim3VGn2ezY1ZoIF0X+5qEyEHn8YmZ4wj7RNsXns+OA7YOfC4Hlz+X/4Fmv8jM3+JPXawgv50h88xVNTryME36tOxSrfbkSml6SfI8wqM8jrCRPs0yIvOoETK7FL1tXvsq3FX4kDlG9SXum9twVojYC7CoJK/7uEwXJkIXsah+bG5bhkdYTcjIeN7GwXJqAmZyXG2v3TQcYZsnaxfr6Ap5VeRcHOH0GMv7xq+V4gjzrBFFwXK8OIxtFG6Fd+Gs9FVQvznCgRrRALYvDPDS6Y1uqBEBWw5lMin4QpMPXiEIS3MRlueH2d8s72pRXzlH05e8Nu+zDOxMFzzbQ8wR1vLJHBaAK62amT4t27e8ghqKlkFEWFsa4gen1s3jlHHhSp9mncSkDWTbUPg7bz+Co/tW0vOqYziEoIxebQ9oWfj0lJoeXiTtxp+xR1hzojy4/pkAABIJSURBVHn6NEUXIeuYgTWGyk6qhCAjUI7L1oe4EHf6tOw11r9bmUPY9/lBNo+w697lRm/6PeMZ3vzvQ5/BoU1iWs5/WxDUbHq3LWkIn7t7Gx47/qKTp9ck+n4zBDSDNh/8TafI+9zfvwoHdyxmttt8waKHFhGlHE5fS4d22dwy0N5DzS9W9ARFaVDyuagMQGIwZ5aeLYPaYaSOXYZwmjWC8MWfvwZnNkzr21WlKs945WkmzUpx5n63X/06Qy5nW06vopiy2lm2D9NCj/WsgT0UAhskjXuP89eHkchM2jJZI0pTI7Ie4TxPfdtwGr3kKrFsjhHTQ6z3SyvLUTw5GkbuCZGroEve52bGrASou+IA+gQhml1R3ZKG8L+59Rj++Inv4YDjhd4ktsIMOCCLNqOkm16SvWj/duf2PK9aHgZJnldfL4oyBriNNEcrp0ZITY0gthyXS41wcYBJp08bjbMV6SIhDI/wvY8/l6bJUsevLuZ73U0PLzLbANP7xuUr0k8RzcJF75hmiLnoHdOCG1S6Alv823BAeG1Dj8O4xLIuuvIvf+KNuPhAPJbz0qaV7d+OpWEmq0Ne4GHbcC0pC+HIDELWMdb/CooaobJBuKrKqd/jdln6NHZtiOrf87MAe9Whr+BVPvsIV5yCT2xJQ3jvygJ+9m1HWj9v32+GgGbgK6doqXN1xEmzc4pO6uvywgBLc5G3CPJp6CcuOofO/6tLLKv/XRhLVwlbnj4tey04R1hKiY/9weO4aP8K7nz36wv74AyWK+i34vEahnPBBMUuhcvP6y19moPeMS2451b1M80EIoRV/UwYKfb++psOZNqxV1PKOi4+fO35uO3tR4xtdSrC+YQryMhlVJg88nxqROoRTrzGrkA5fW7KVP0DgPdffgiHdy5VSo84q6iaiq8r2Ne8b/i5a4/irB0Lk3ecElvSEO4KQuho8oCtgzb5gl0tO3IDBJjc19++7QrsX13A/U8+D6D+sldarW6K9GncI2hTGlQ/8u7bOE9wtt2xjH9zFtxghvAzL5zCyVPreOLZl9Jqc3kGpst7XjSRUF4el+Fc1L79QrSNaUB5hKuPMa9ZI5in0aZvzEVmZTme5SJ7PVR75hgqaxisLAyxsmB68PsSJPULN16UKU/PA9gUTI5wgUeYrVqQgxJitEkmNULpf9/2Bbz3kgO5x20m8KDBPiOmw3QtRT7e/+ZDjbYfDOEWIYg6zSMc0A3aTKUkGG+yTejcrOXO/yMHV439uvAIq3e4LrmrPb+jscmbc2V5AICTp9Yzni1uPI+kzBif3CP82PEXAQCvvLaBp0+8ksrhgmuZVXOBs/sLYRY0AICdS/k5afOunYsjPG36NJ9LxTY1gss4iNQkIPlfECvtbbbDi4/wdutMXM/fu4xzd2/D0ly3r9ifuDxrQLgyftiGr5lyT/+WllgWcRCeK4cwb4cb1T23BRuB6nv/PcL9Tp/WNErNAYjoRiL6BhE9TUR3On6fJ6L/nPz+ZSI64lvQzYC+V28JaAa7V+ZBFHMJm0ZXHmFByhhWEfnlzu+LGjHNEqTOGqFzH5tpz7RcLmrEK69t4It/fgJXHzULf6TGs5RpGjZb1o3E+/vYd06m2x955mRyfI5H2LHcnr5o8zzCVpDJNRftdbbNz+uiRmQ5wmIqw8bnpJBnd7D7OYyEUbBFLeO7xubObXP4+XddiB+/eH+6b/w5vWzXXLQXD9zxzl4u/wvKXmN7TJlUCeYRHvI8wsXUCMVP3kpZImz0ZWVgEvqeNaJpTJyuElEE4FMAbgBwHMBDRHS3lPJJttuHAPxASnk+Ed0C4C4AH2hC4FnG9sUhgMCN2Go4ds4aHvzYddi3vTmOk0JXUcrGC8/BKc2DNu7qnX8YVX/h2Bxhs7KcBI+ud1Ej7nviezi9Psb7LjOLf/AAO1fluYi0R/irx0/iTWfvwDeffxmPJkZx3qVzBWAVBWXZhSNW5geFYzBvGdcVse3KRVsGPidqaTo5yhrru5bncGbDTJ82iNxeLyLCP7jmfCaj3r4ZwSkjCvaYmsgRTozcImqE5qh380zqA2aFGvG+yw7i8M6lrsXoDGXWba4E8LSU8tsAQESfBXAzAG4I3wzgE8n3/wbgXxMRSRkYsRx3vf+NwQzegiCiVoxggC1vt+yI4kajzTEsPK4iHzMPit85Tfo07gE8MxrjN774LZxeHxsv8XufeA67l+cBAD98bQPPvHAKX/j6CZy9cxFvPmfN2e4fPvIsXj694aQVPPfiq/jcnx3HE8++hA9ccTbmBwIP/eULsRw5fXDptcjjpIKVvv/KGQDAda/P9war/QUBc5Zx46wsNyVH2M4gUAd2ZgwuzyduuhgbI4lTZzYAaI9wmfPaleY2G1zjyC7SYlIl9Pe0oEayj50/mSOdoHRE1+oDdi3PYd/2eZy3e1vXohTiH153tGsROkUZQ/gggO+w/48DeEvePlLKDSJ6EcAuAH/FdyKi2wHcDgCHDx+eUuTZxa7kRRoQ0BT2rixgIAj7t7ebGvDstUUcWovPeWDHAs5aLXf+t563C7dccXZtb8Tr9mzDwR1ahirH7ExSX930poP4yl+8gLv++OsAgN3L89izEt+z/+KerxvHzg8EVheH+Mj1RzPGtzrmF37/8aQdk5e7Z3ke//ubf4WP/pfHAMQ6WF0c4it/8QL2rszjUI4uXHo9sGMBC0OBNQf398iubTi8cwnn7olfwrdedaRQH5Eg/MYH34xLz95hbH//5YdwjvUiP3/fMhaScrtVcHDHAvauzHtZJt+9bR7bFwY4vGsJ3//hazhrVU82tyfBa8MoLgJzcG0RG6NxqXG5b/sChhG1nl6zLRxcW8Sr6yNjmxqzALBz23yqy4WhMKobHlpbwkAQ9q0u4MCORZw6Y7ZjnGfHIs7asYADq4uYH7jH6GbHysIQX/5n13ctRsAE0CSnLRH9FIB3SSn/bvL/3wZwpZTy59g+X0v2OZ78/61kn+/ntXvs2DH58MMPe+hCQEAAx+n1UbqE2RbUc4SIcGZjHHvfZtQF9L0XT+PMxhiH1hYhROy95cUt5gcR9q7M5/ZPSolnT76a0h8OrS0Zht/6aIzvnnwVQLxkf2DHIsZjiZOvrmNtaZjr1R6PJdbH47S6lzrXmZG5zYVXz4ywOIXh6hvjscTGWHrnzha1+9rGCHORgJQofe6N0bgwEGyWocYlH5PjscTxH7yKxbkIe1bmMRpLPP/SaWybG2DVim1Qz5fROKb+uEosA8CZjXGaZq2LZ1JAgA0i+lMp5TF7exmP8HEAZ7P/DwH4bs4+x4loAGAVwAtTyhoQEFADXbxwuPHWxwChKti/atJYynq3FYgIh9byPdzDSOCcXaaHVQhKPdN5EIIwL8xrS0QTjWAAvTCCgbgPcw1MkIraVfohQulzb1YjGHAHrQlBOLxrydgnzyNu5BIuSIPEnwPBCA7oM8rc7Q8BOEpE5xLRHIBbANxt7XM3gFuT7z8J4E8CPzggICAgICAgIKDPmOgRTji/HwZwH4AIwGeklF8jol8C8LCU8m4AvwXg3xPR04g9wbc0KXRAQEBAQEBAQEBAXZTK9i2lvAfAPda2j7PvpwH8lF/RAgICAgICAgICAprD5iVCBQQEBAQEBAQEBBQgGMIBAQEBAQEBAQFbEhPTpzV2YqKXAXxjwm6rAF6seSofbeyGlRO5Axn60kZdXfiQoy+6ALofG776sVnGRl/aCLrQCLrQCLow0fXz01cbvtrpw/joi06b0MWFUsqVzF5Syk7+EAfaTdrn0x7O46ONibLOSD8614UPOfqiiz6MDY/92BRjoy9tBF0EXQRdNK+PvvSjL/rwIUdfdNqELvLa7Ds14n/0pI266Es/+qALoL4cQRf+jvfdTl305dr2QR996UfQhd826qIv/Qi6aKaduujDO2WmdNElNeJh6ajw0UfMkqxNI+jCRNCHRtCFRtCFRtCFRtCFiaAPE0EfGk3oIq/NLj3Cn+7w3FUxS7I2jaALE0EfGkEXGkEXGkEXGkEXJoI+TAR9aDShC2ebnXmEAwICAgICAgICArpE3znCAQEBAQEBAQEBAY1gSxrCRHQ2ET1ARE8R0deI6CPJ9p1EdD8RfTP5XEu2ExH9KyJ6moi+SkSXs7ZGRPRo8nd3V32aFp51cZiIPp+09SQRHemmV9PDlz6I6Bo2Lh4lotNE9De67FtVeB4bn0zaeCrZh7rq1zTwrIu7iOiJ5O8DXfWpDqbQx0VE9CUieo2I7rDaupGIvpHo6s4u+lMHnnXxGSI6QURPdNGXuvCli7x2Zg0e9bFARF8hoseSdv55V32aFj7vk+T3iIgeIaI/qi1c3fQUs/gH4CwAlyffVwD8OYA3APgkgDuT7XcCuCv5/h4A9wIgAG8F8GXW1itd96dHuvgigBuS78sAlrruX5f6YG3uBPDCrOnDly4AXAXg/wCIkr8vAXhn1/3rSBd/DcD9iMvbbwPwMIDtXfevBX3sBXAFgF8GcAdrJwLwLQDnAZgD8BiAN3Tdvy50kfx2NYDLATzRdb86HhfOdrruX4f6IADLyfchgC8DeGvX/etCF6y9jwL4TwD+qK5sW9IjLKV8Tkr5Z8n3lwE8BeAggJsB/G6y2+8CUB68mwH8OxnjQQA7iOislsVuBL50QURvADCQUt6ftPWKlPJUm33xgYbGxk8CuHfW9OFRFxLAAmJDZx7xg/z51jriAR518QYA/1NKuSGl/CFiw+/GFrviBVX1IaU8IaV8CMC61dSVAJ6WUn5bSnkGwGeTNmYGHnUBKeX/Qjxpnkn40kVBOzMFj/qQUspXkn+Hyd9MBXj5vE+I6BBip8K/9SHbljSEOShevr8M8Qxrn5TyOSC+aIhnJEB8sb7DDjsOfVMuENHDRPQgzdjSt42aurgAwEki+lyyXPErRBS1JXsT8DA2FG4B8HtNyto06uhCSvklAA8AeC75u09K+VQ7kvtHzXHxGIB3E9ESEe0GcA2As9uRvBmU1Eceytw/M4OauthU8KULq52ZRV19JFSARwGcAHC/lHJm9eFhbPwagH8KYOxDni1tCBPRMoDfB/CPpJQvFe3q2KZmY4dlnJfuZwD8GhG9zrOYrcCDLgYAfgzAHYiXM84DcJtnMVuDp7GBxAv4RgD3+ZWwPdTVBRGdD+D1AA4hNnKuJaKr/UvaPOrqQkr5eQD3APi/iCdHXwKw4V3QllBBH7lNOLbNlKdLwYMuNg186WKz6NRHP6SUIynlpYifo1cS0Y/4lLEt1NUFEb0XwAkp5Z/6kmnLGsJENER8Mf6jlPJzyebn1bJ28nki2X4cptfmEIDvAoCUUn1+GzFH9rLGhfcMT7o4DuCRZIlzA8AfIua6zRx8jY0EPw3gD6SUmeWdWYAnXbwPwIMJXeYVxNzZt7Yhv094fGb8spTyUinlDYgNwW+2Ib9vVNRHHibdPzMBT7rYFPCli5x2Zg6+x4aU8iRiW2PmKFWedPF2ADcR0V8iplJdS0T/oY5cW9IQJiIC8FsAnpJS/ir76W4AtybfbwXw39n2n6UYbwXwopTyOSJaI6L5pM3diC/Qk610whN86QLAQwDWiGhPst+1mDFdAF71ofA3MaO0CI+6eAbAO4hokDwI34GYHzYz8PjMiIhoV9LmJQAuAfD5VjrhEVPoIw8PAThKROcS0RxiGtFMZd/xqIuZhy9dFLQzU/Cojz1EtCP5vgjgegBf9y9xc/ClCynlx6SUh6SURxA/L/5ESvnBWsLJHkQTtv0H4EcRL799FcCjyd97AOwC8AXEHpovANiZ7E8APoU4uvlxAMeS7Vcl/z+WfH6o6751pYvktxuSdh4H8DsA5rruX8f6OALgWQCi6351qQvEmQF+E7Hx+ySAX+26bx3qYiHRwZMAHgRwadd9a0kf+xF7f18CcDL5vj357T2II8i/BeAXu+5bx7r4PcQ8+vVk+0y9U3zpIq+drvvXoT4uAfBI0s4TAD7edd+60oXV5jvhIWtEqCwXEBAQEBAQEBCwJbElqREBAQEBAQEBAQEBwRAOCAgICAgICAjYkgiGcEBAQEBAQEBAwJZEMIQDAgICAgICAgK2JIIhHBAQEBAQEBAQsCURDOGAgICAgICAgIAtiWAIBwQEBAQEBAQEbEkEQzggICAgICAgIGBL4v8DnBPH1d7YzcsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_areturns.smoothed_marginal_probabilities[0].plot(\n",
    "    title='Probability of being in a low-variance regime', figsize=(12,3));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}