Simulating datasets MCMC noise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seaborn-dark', 'seaborn-darkgrid', 'seaborn-ticks', 'fivethirtyeight', 'seaborn-whitegrid', 'classic', '_classic_test', 'fast', 'seaborn-talk', 'seaborn-dark-palette', 'seaborn-bright', 'seaborn-pastel', 'grayscale', 'seaborn-notebook', 'ggplot', 'seaborn-colorblind', 'seaborn-muted', 'seaborn', 'Solarize_Light2', 'seaborn-paper', 'bmh', 'tableau-colorblind10', 'seaborn-white', 'dark_background', 'seaborn-poster', 'seaborn-deep', 'arviz-colors', 'arviz-white', 'arviz-whitegrid', 'arviz-darkgrid']\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import langevin\n",
    "import langevin_cached_model as lcm\n",
    "import lmfit as lm\n",
    "from lmfit.models import ExponentialModel\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.optimize import minimize_scalar\n",
    "import theano.tensor as tt\n",
    "\n",
    "\n",
    "SMALL_SIZE = 16\n",
    "MEDIUM_SIZE = 18\n",
    "BIGGER_SIZE = 20\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "\n",
    "SEED = 35010732 # from random.org\n",
    "np.random.seed(SEED)\n",
    "\n",
    "print(plt.style.available)\n",
    "plt.style.use('seaborn-white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to calculate A and B from the dataset\n",
    "def OUanalytic1(data):\n",
    "    N = data.size\n",
    "    data1sq = data[0]**2\n",
    "    dataNsq = data[-1]**2\n",
    "    datasq = np.sum(data[1:-1]**2)\n",
    "    datacorr = np.sum(data[0:-1]*data[1:])\n",
    "    coef = [(N-1)*datasq,\n",
    "       (2.0-N)*datacorr,\n",
    "       -data1sq-(N+1)*datasq-dataNsq,\n",
    "       N*datacorr]\n",
    "    B=np.roots(coef)[-1]\n",
    "    Q=(data1sq+dataNsq)/(1-B**2)\n",
    "    Q=Q+datasq*(1+B**2)/(1-B**2)\n",
    "    Q=Q-datacorr*2*B/(1-B**2)\n",
    "    A = Q/N\n",
    "    P2A = -N/2/A**2\n",
    "    Btmp = (N-1)*(1+B**2)/(1-B**2)**2\n",
    "    tmp = (2+6*B**2)*(data1sq+dataNsq) + (4+12*B**2)*datasq - (12*B+4*B**3)*datacorr\n",
    "    P2B = Btmp - tmp/A/2/(1-B**2)**3\n",
    "    PAB = (N-1)*B/A/(1-B**2)\n",
    "    dA = np.sqrt(-P2B/(P2A*P2B-PAB**2))\n",
    "    dB = np.sqrt(-P2A/(P2A*P2B-PAB**2))\n",
    "    return A,dA,B,dB\n",
    "\n",
    "def OUresult1(data,deltat):\n",
    "    A, dA, B ,dB = OUanalytic1(data)\n",
    "    tau = -deltat/np.log(B)\n",
    "    dtau = deltat*dB/B/np.log(B)**2\n",
    "    return A,dA,tau,dtau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to calculate A and B from the dataset\n",
    "def OUanalytic2(data):\n",
    "    N = data.size\n",
    "    data1sq = data[0]**2\n",
    "    dataNsq = data[-1]**2\n",
    "    datasq = np.sum(data[1:-1]**2)\n",
    "    datacorr = np.sum(data[0:-1]*data[1:])\n",
    "    coef = [(N-1)*datasq,\n",
    "       (2.0-N)*datacorr,\n",
    "       -data1sq-(N+1)*datasq-dataNsq,\n",
    "       N*datacorr]\n",
    "    B=np.roots(coef)[-1]\n",
    "    Q=(data1sq+dataNsq)/(1-B**2)\n",
    "    Q=Q+datasq*(1+B**2)/(1-B**2)\n",
    "    Q=Q-datacorr*2*B/(1-B**2)\n",
    "    A = Q/N\n",
    "    P2A = -N/A**2/2\n",
    "    Btmp = B**2*(1+2*N)\n",
    "    tmp = (1+Btmp)*(data1sq+dataNsq) + (2*Btmp + N + 1 -B**4*(N-1))*datasq - 2*B*(1+B**2+2*N)*datacorr\n",
    "    P2B = -tmp/((1-B**2)**2*(data1sq+dataNsq + (1+B**2)*datasq - 2*B*datacorr))\n",
    "    PAB = (N-1)*B/A/(1-B**2)\n",
    "    dA = np.sqrt(-P2B/(P2A*P2B-PAB**2))\n",
    "    dB = np.sqrt(-P2A/(P2A*P2B-PAB**2))\n",
    "    return A,dA,B,dB\n",
    "\n",
    "def OUresult2(data,deltat):\n",
    "    A, dA, B ,dB = OUanalytic2(data)\n",
    "    tau = -deltat/np.log(B)\n",
    "    dtau = deltat*dB/B/np.log(B)**2\n",
    "    return A,dA,tau,dtau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Ornstein_Uhlenbeck(pm.Continuous):\n",
    "    \"\"\"\n",
    "    Ornstein-Uhlenbeck Process\n",
    "    Parameters\n",
    "    ----------\n",
    "    B : tensor\n",
    "        B > 0, B = exp(-(D/A)*delta_t)\n",
    "    A : tensor\n",
    "        A > 0, amplitude of fluctuation <x**2>=A\n",
    "    delta_t: scalar\n",
    "        delta_t > 0, time step\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, A=None, B=None,\n",
    "                 *args, **kwargs):\n",
    "        super(Ornstein_Uhlenbeck, self).__init__(*args, **kwargs)\n",
    "        self.A = A\n",
    "        self.B = B\n",
    "        self.mean = 0.\n",
    "\n",
    "    def logp(self, x):\n",
    "        A = self.A\n",
    "        B = self.B\n",
    "\n",
    "        x_im1 = x[:-1]\n",
    "        x_i = x[1:]\n",
    "\n",
    "        ou_like = pm.Normal.dist(mu=x_im1*B, tau=1.0/A/(1-B**2)).logp(x_i)\n",
    "        return pm.Normal.dist(mu=0.0,tau=1.0/A).logp(x[0]) + tt.sum(ou_like)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "A,D = 1.0,1.0\n",
    "sN = 0.5\n",
    "delta_t = 0.01\n",
    "#M=1000 # number of data sets\n",
    "N=2000 # length of data set\n",
    "#print(np.exp(-delta_t*D/A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = langevin.time_series(A=A, D=D, delta_t=delta_t, N=N)\n",
    "dataN = data + np.random.normal(loc=0.0, scale=sN, size=N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9658c163c8>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dataN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f967c008a58>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 120000/120000 [02:53<00:00, 693.37draws/s]\n",
      "The acceptance probability does not match the target. It is 0.9567777405698861, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.9191283456010527, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.9483361616134554, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "a_bound=20\n",
    "# compile model for reuse\n",
    "with pm.Model() as model:\n",
    "    B = pm.Beta('B', alpha=5.0,beta=1.0)\n",
    "    A = pm.Uniform('A', lower=0, upper=a_bound)\n",
    "\n",
    "    path = Ornstein_Uhlenbeck('path',A=A, B=B, observed=data)\n",
    "    \n",
    "    trace = pm.sample(20000,tune=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd_3%</th>\n",
       "      <th>hpd_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8306.0</td>\n",
       "      <td>8295.0</td>\n",
       "      <td>8136.0</td>\n",
       "      <td>6767.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.34</td>\n",
       "      <td>1.274</td>\n",
       "      <td>0.478</td>\n",
       "      <td>2.607</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.013</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>8129.0</td>\n",
       "      <td>6714.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean     sd  hpd_3%  hpd_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "B  0.99  0.004   0.984    0.997      0.000    0.000    8306.0  8295.0   \n",
       "A  1.34  1.274   0.478    2.607      0.018    0.013    5147.0  5147.0   \n",
       "\n",
       "   ess_bulk  ess_tail  r_hat  \n",
       "B    8136.0    6767.0    1.0  \n",
       "A    8129.0    6714.0    1.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f967d08b7f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f966992d2e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f966995be10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f96699905f8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [02:33<00:00, 64.94draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "a_bound=20\n",
    "# compile model for reuse\n",
    "with pm.Model() as model:\n",
    "    B = pm.Beta('B', alpha=5.0,beta=1.0)\n",
    "    A = pm.Uniform('A', lower=0, upper=a_bound)\n",
    "    sigma = pm.Uniform('sigma',lower=0,upper=5)\n",
    "\n",
    "    path = Ornstein_Uhlenbeck('path',A=A, B=B,shape=len(dataN))\n",
    "    dataObs = pm.Normal('dataObs',mu=path,sigma=sigma,observed=dataN)\n",
    "    trace = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd_3%</th>\n",
       "      <th>hpd_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>path[0]</th>\n",
       "      <td>-1.537</td>\n",
       "      <td>0.238</td>\n",
       "      <td>-1.998</td>\n",
       "      <td>-1.102</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>10228.0</td>\n",
       "      <td>10009.0</td>\n",
       "      <td>10251.0</td>\n",
       "      <td>6759.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path[1]</th>\n",
       "      <td>-1.543</td>\n",
       "      <td>0.217</td>\n",
       "      <td>-1.961</td>\n",
       "      <td>-1.148</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>9809.0</td>\n",
       "      <td>9554.0</td>\n",
       "      <td>9821.0</td>\n",
       "      <td>6457.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path[2]</th>\n",
       "      <td>-1.542</td>\n",
       "      <td>0.204</td>\n",
       "      <td>-1.941</td>\n",
       "      <td>-1.178</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>8773.0</td>\n",
       "      <td>8624.0</td>\n",
       "      <td>8769.0</td>\n",
       "      <td>6372.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path[3]</th>\n",
       "      <td>-1.561</td>\n",
       "      <td>0.197</td>\n",
       "      <td>-1.915</td>\n",
       "      <td>-1.171</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>8220.0</td>\n",
       "      <td>8020.0</td>\n",
       "      <td>8207.0</td>\n",
       "      <td>6089.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path[4]</th>\n",
       "      <td>-1.582</td>\n",
       "      <td>0.190</td>\n",
       "      <td>-1.938</td>\n",
       "      <td>-1.225</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>8028.0</td>\n",
       "      <td>7980.0</td>\n",
       "      <td>8036.0</td>\n",
       "      <td>6789.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path[1998]</th>\n",
       "      <td>2.306</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.906</td>\n",
       "      <td>2.717</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>10783.0</td>\n",
       "      <td>10768.0</td>\n",
       "      <td>10789.0</td>\n",
       "      <td>6891.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path[1999]</th>\n",
       "      <td>2.276</td>\n",
       "      <td>0.243</td>\n",
       "      <td>1.832</td>\n",
       "      <td>2.755</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>10422.0</td>\n",
       "      <td>10422.0</td>\n",
       "      <td>10411.0</td>\n",
       "      <td>7295.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.992</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.542</td>\n",
       "      <td>1.615</td>\n",
       "      <td>0.476</td>\n",
       "      <td>3.368</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.023</td>\n",
       "      <td>2569.0</td>\n",
       "      <td>2569.0</td>\n",
       "      <td>6196.0</td>\n",
       "      <td>3043.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>0.507</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4029.0</td>\n",
       "      <td>4029.0</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>5760.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2003 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean     sd  hpd_3%  hpd_97%  mcse_mean  mcse_sd  ess_mean  \\\n",
       "path[0]    -1.537  0.238  -1.998   -1.102      0.002    0.002   10228.0   \n",
       "path[1]    -1.543  0.217  -1.961   -1.148      0.002    0.002    9809.0   \n",
       "path[2]    -1.542  0.204  -1.941   -1.178      0.002    0.002    8773.0   \n",
       "path[3]    -1.561  0.197  -1.915   -1.171      0.002    0.002    8220.0   \n",
       "path[4]    -1.582  0.190  -1.938   -1.225      0.002    0.002    8028.0   \n",
       "...           ...    ...     ...      ...        ...      ...       ...   \n",
       "path[1998]  2.306  0.218   1.906    2.717      0.002    0.001   10783.0   \n",
       "path[1999]  2.276  0.243   1.832    2.755      0.002    0.002   10422.0   \n",
       "B           0.992  0.004   0.985    0.999      0.000    0.000    1998.0   \n",
       "A           1.542  1.615   0.476    3.368      0.032    0.023    2569.0   \n",
       "sigma       0.507  0.009   0.490    0.524      0.000    0.000    4029.0   \n",
       "\n",
       "             ess_sd  ess_bulk  ess_tail  r_hat  \n",
       "path[0]     10009.0   10251.0    6759.0    1.0  \n",
       "path[1]      9554.0    9821.0    6457.0    1.0  \n",
       "path[2]      8624.0    8769.0    6372.0    1.0  \n",
       "path[3]      8020.0    8207.0    6089.0    1.0  \n",
       "path[4]      7980.0    8036.0    6789.0    1.0  \n",
       "...             ...       ...       ...    ...  \n",
       "path[1998]  10768.0   10789.0    6891.0    1.0  \n",
       "path[1999]  10422.0   10411.0    7295.0    1.0  \n",
       "B            1998.0    1992.0    3020.0    1.0  \n",
       "A            2569.0    6196.0    3043.0    1.0  \n",
       "sigma        4029.0    4020.0    5760.0    1.0  \n",
       "\n",
       "[2003 rows x 11 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f96498031d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9669db6a20>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f96497d2c88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f967d8c1e10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f96497bc5f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f967d882908>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f965a1d8c18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9649772978>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hx/Qn10NFAf8AFE6DPxvZ3dDiJBDPnFhXUmTkRIiX2qOcVGFnXtWfnGuqeqxTVm86cV46BkYbAO7uFowlpeTlv3deckVd7zwf3kUsE6PN28a+gSHOLD+P6gILOq2G4hwj7lCCXd1uTqnOZ3513h9CUekJ9DA1dyoG7Yn1y9i+/WgsFnLOOfv4vIzHQ3awFfOSs084NllZQafVkFWypOX0e44GhTqOEupspXLlKnRFRcfn+2Np1nQ+iUaT4eZTbgZAicdJtLRgWbTohLrv8OGjOH0xVioKklZL80QzGjUHNV3IrAoze0f3srJ6Jdo/O7eT3d1ED9YjWSyYZs5832P555pHgowG4nz2pHIMOg1qNs2ewc0M+WeQa8xFkqA2R0NmzIns92M/71yyShad5t2bNGo6TvNEM0NBF/v7vSx6j683kU2gRc+QN86M0hzijY2YFy5Eo3ufplI2zXh/C9GsFvyDx66DAJnEsc7wsnnIKngiKcymFP6kn3SiiIMDvr9YrPaOO+64490WhMNhVq9ezXXXXYfd/u6VsP/r0qNOBi7+HKRSKLHYsb9IBH1lJcbZs3GHU1iN2g92S8h7yMoKP1zfymdnF/DZYj9xfSHrmpx86ZQqcs1/X8VNbXqWQM8+TKt+hnToUR7JXsRdE8vYq8wjNdHH9NfWIQeDTH5xLcEXnsfsfgmTPc0reyqIGw2com3hZd0Udjr3QySLPqQya844pvwsvrARq1HGJCtMc9ZTa+kk6Q1gl10cTTiY+dYT2Nbew+iT9cS2vcz65Rl+c4aJSbo4Swvq2ZpYiV4bQVGPXSyLCNIQf4xJWzZjfu0tkrLK5Y838NvDHvYd7ULz9FNUR5w8cJGNy5tc+AtzsSWSRHVGjIpMoriaYm0L12s3o1Vl/NY89kon0WWdgsecz4i9jPub7uGqI1u4pGs3R8pqUbe2knjwf3Cu/S3J9TtId3WRGR5Gb0pTFG/BXhrmhthNlEghTko28+vAVaj6CLFQjBZpMWo6n3RqOtrhl/hq9nUysp6bKeapChvaze0sGlD57nqZzxsDGGxZkOCRnFzmZ8YISFpytVlaE9NpVyejJKaSjc3EULQLZ2Qqv+reR86wxM+8D/G90nWs0h5Cj0J42IR9agrzjKn4X36N+6ZcwYC5mjX6u1gvn8m6RidFNiNzUq1kn7oOjbuRI3c+xGrbKfy+egUnd+zjgqsv5ounVPN0ywZ6sqtxyB5cjk5m/+ZN8s5eiKbhd6RdXRxSZ5N3ZAcpdRq68kpG6vYxdPt/MmHKY7RYz9k9LdyQ2IfXF+RFZxn6k+cz/+h/kG8/goFhlLbn2FlUQgKVUmvp8d/l0fa3+MWjV3Ov4wnSksyp5af+Xb/zj8udB+9kKDzEryov4ivt25H8gzwxspU7xneyx7mHrUNbWTV11fGEmJEzaCTNO64PA6EBrth4BduHt+NL+lhZvfIT2JtP3j9DDvnffZxSVEO1RUKfiaM3po9Vet0doNGydXQ3sZAXXeM+9GNHCGzZj6QzckgZpPlwHUNDnaR140wHAiis63wbp9dCRZ4Vrc9DT9cO2vV+zvQ0UhAP0pKaYLOnCZ8Ey6uWAxzrqZYk0oODJI4cwWktxB+Ikqtm2NP4Kof8gxzoshIIOphapSHXmMuhvo284djOtrYIriEti2qKkP73NsEDD4GSZWJPBH/DPraWOGnxtrG8cjnarIoj6sSizeGt+kb0mRDzSi0w2gCZOEFLAVuHtvKrw79Co5qpHjrMprf34rXZGB3qxahTSGkVtnQMEIhqmFV27Lfh8MUZc/uxBz3o8vPJKtnjo4ShVAhVVdFr9SiKSjwt0+QIkFJCdEw0UN33NkP+BBu6Y5w0azaRbdvx+ZzkLF9+vNKWzMg4/HE0ugQm3bG8lDy8F2JupPAAUswD9grcb64lsfFxUtocHNYUD214luq6w2SDUYJlCfqsUeZV/qFhPNbMaweH0GoNlM+bhZrJsLWvCWNfA1pHHwf8hQyGJaIeNwWO10k59qJOZDEUl9I1tofcvEnH49vv2IMnOMbMktkARJuOkBhzYaooB8DV/jZtB7exw1eIK5KgqGgcu9GORWdh5/BbBIJ28jz7SI0fJVI6B4vpWGfS0/sH2druosI6zsG+tyhtchDMeukLHKHM0wdRD0ljIZLjAErXHqTKBQxHRvj5wbtoHOtirlqDGYWwlCKSjjAaHWX3yG7W9axjkgx262TaDraScTqZbPfjHWylcSSJfGAPrvwkilbD1NypbGodZ9AbO/Z9x3zQvelYA7HrDbAUweBuFK0ejHkgy7xw2EkgmmLRwGE0NhvanBzUoYMEu+p5tDXDeCjJspmFf2wctKwl42xEUzIHSfuHCvV4M4weAmshalZmzbO309Fex+LZS8GYg6zI7B5uwUAOOSYDvYFenu14Dgk9k3Pf+62s24e289CRR2nzdrCjsYj9R7x0Oo8QSNuZLYc5tG0/rzgVrCYdCyflQ+s6fDEXbUkPSTlJrjGX5zufp9xajk4y49vyBjoUzPMWkBo4iG54D/51G1EO/h6lopj9wQIq880M+2Js73CTlEZ5pfdF5M27yB30Yp4z53hs8UycTPtrHGmsI9HUSum8JegKCo4v39g6xsahtXjSDs7yl5BqPDbSFXztNZ5zZEnb85lUcCzH7XtoDYlRJ2VKAo3ZzOrRV3m7v58C3UyOel9nQ89m9NiZUTT12MvN0mliASej2zeiGx5FZ8kQtymEtBJbW4NkFIUy+5/9Q+Zs+ljDWnusPpxVsmzpOYyOHKaX5KDXaoj1bOPl7rU0ThiIRPPJMeqwNfyW6Po3SZos+E6pYp+rHvwpBjf+kMqCSsitPHbeDO/l8S030eIbIpXVkUnMo8BYwuQ/7COSRMozQNY9Qf36x9gST/PCgTDlW9ejPvcMaDSYZ80CzTtzvS+aYiyYpKBrLaPt+2hOV/PFBYXEzXmklTTG3m3Q+gpZbQm7Xn2RFw56idudhGUviaANpydEw/bX3jNH/tOOoKmqivPW76DG48d6J1SVwjkRIqMmAg/9jO8FCtjen+D8OaU8ds2id3wxh12H2T68nYtrLmZe8TySGRnTuzz31DIaIpLMsqL3XujczvIZ3wBWsr/fy+X51WSGh1HSaUwzZ6Io6vH71LPDbcgNL2E461qkkunAsWHpTn8nVm0ue7uyvLZFx+HUoyz+TQNPWEw8k17Fv3s2k7RpeaZ/Jee1N5ITSTL2lUvIqYih1at4u63Ma+sn26Wl44symUnPoWjgKetc1OKb+FnRrUxLG7ivJI97dqSJjZrInxHn5FNGUcsz/LhKS1yv4xdpmZ9stpGX1HKgWsvaWi0/8vmpNCn8Pj6T2V4HnUU1XODZwxLzAPfarmKW81RyNj9BNJvkgKuLgZorOd11lMV93VxRsIM1S4ysPGokrjMQ1lgw5mRoLprGisGjeFpGqFqpQaMqdKWqKBn28XLeT1nnPIN7Z16NJRUno+pBVhjMr+KClgZmXejEc9RO2GFBllRGKmWmjGoJ5lupmBQgFdNSqA9Tp8zhdm07c6QhqjIerjFs58nsKg6qs8mYnVylqUPJwP64kZZaLZfvVvhCvQrIJObIWKuT/EyzmHUlEl9NdpOnKNxSVsj1ExkOp2uRtFH0eQ2kfeehBE7hVPUFXOixnfomTn2SmS4tR1qqWXPGTD5zmYdZjsfIrHmCRHEFM4cdzDQ6CBUUcqbcxK7EKdT/4tdUOzfy30vOJ+qoYr5ex6vTzsKYTTNuLeKyGz6L2yRR+rVaomk7F3bG2Dwfrt3p5/Btd/BQ8SWUJacxMz5ITqMPzfbbUK0WdEqYgoSOnppysnO60YzBRe1HebG0C2/VD9kzmsM0QwytxciTs+14kOhu+DkAd55+J5fMuIRwXxfpq7/FzQmVK6ty+Lb6KAuLFx6vSH5auWIutg1tY3HJKUw58DD9eh3/VlGBU1L5rK6QS8/5b27aeRP3NtzLL8/6JavbV3N/0/1U51Tz6HmPUm4rP17WI82PYNKaOL36dF7peYVrZl9DTd579IoL/xCaB5opiDk42RHAMDYX7crvcuDtNdgL8hgosLKvtR7iQa4bNJMIaLBWFrEu2IjHNcTcsQISPTGClwRpRGEimYMm00dyYBhNVz323oPYZuoxaSOo6SQBvUREG2U0MgrudsKvryXWO4F59gwomIGk0/Gr1VtQQnHunJKltfNZ/NNy8PkKmGTqYKLfwDPtOjTJTQQSw/R5qkikHcyiiSUrP4dqsCBHPBjSKUaGmsnEgqywdHOocCo+Ry8v7z3AG2kPV+VOYv6RTWQmlcJJN5Ju3EpwyhKua/g9yewIC9MJhnoirBvLIzHkZvPbvaiTZFYZPCjnf5G3O4c5OVREu/kcZs+o5shIAMOz/4PR5WbSo7/nty0P0uk8wuKZZ9Mf7Gdp+VImWWezprGBZSVn8ELjMIptPwuqgywYa6TTX8Y+3VSkrbupPdqI15jFPX6QhU1hMmNjdK78FxxBJ/rcMUKBSVw2uQTP/feQzk5gNEXQnZ5PwZeeom58CxU9AxgjGwjaz6bVs59aRSY/FCK11Ukq5oKKRUzoy0ht/CXxOol6g50FF52J7/GHIThGuXkP+ckggbcC7Kn6DKGcETSaXfyLmmQiGia/VMv9XU4u9If4vFElWbmIw2sfJJoI4rrSz0z7fNo2tRLMarhm8nQGHaM4Wtro7vVxwOjEZtOQtO9ixXguxjPPZ82eAfocUb5QsgmtwU1lg8TnTv8hBZZCdneNUBLtZW93HYloiCmVX6Bjz5NYzypjoccJU5ezucVDpbOBRMcoUxLTuda9mpg8QWFax+PP/4rp5mGGlhSgrSrjzISGI9Fm3Mlx1jt+xWXz44zUOfBoYsRytZhDJXiO7MU4eJSOzBSK5s3GmD6JVmcQS8pH/ySZmpiX8aYWshyiIrAbnbkQd6CPtaM7WBpbiWbfYSaM0wjmlnDEsQHf809x7nXfpG9oHQZHiFOMpSjuBDcaknxn6Q+YbZmCsvtBdhkSmCw5DGZNaFUTV5sV1ICTsSO/5n5vCYGgkbM0LSS334XpwrvpDA3xZPNaao0nc92iszg67sDQdZR1jhpOXmzBXDEdrfkPI9vhAEo6yz0H32Kn9wVSrgoyeXGi6QFMKQeJeivdOTU8tucQo30j9FWeTp7NwHXLJuHe+wyeqIYXJ6lclVtDc+FUhjIBNje9gSU+F2nndmqNWUbmnMrBx5/hS5OcDNYHSFa5KK9/hf3aDBV55/PE3j6GvWkWy5244g4mbR6gX2mhYmk5FUWzSblDNO58FFvOAOZmB0d8M5g86iKvahKZoJPORJp1bQ30Kz1Uh6P0Nj9HuQOGy6uxj02wQw1yWDfIGdOLeOHoDkITPiRVQ/St1yEyQXR6lEAqgRQYQuN4jtntEp6e3TDrbEZ/egfy+Bivzqsl0B/g2t59mM11dAzlcGD+52gbOomSYTNJpZK5pZXYjH9ofnS8DjEPLLgCjHYGxlqoH+3ENTZCZW8c62Qz8YZtKGYHzW6VRqUBZ1hmVt96EsEU4Zb9OJt1vK3ksuVoD8VxHUWRn1N13dNoLXnsaHqOtrCbwokMdkOQPrmNNftVlobSaHMK0U07n42vP8HoxBglQyO0Zs1I49Uk2w7hngjS/sYujLKdJVMLsNfOoDWuxeGP84WFlezu8RBJZSlpf51SOcYy9QhdRzo5HJpPnifLCkc99mg3ezvB1ezFWBwmfihBYP50xnYGOOxL/sW88k87ghbbtx//738PgK66CottgoqlIeoy8/lR1XXYIl1MnTWPXV0ejKYwC6uL0UrHGmD9nm6+su16mj3NbBrchM89m68+2c7BAT8XzitDr9UwEUnS446wu8dD/aCPnyaeIuKdgTRez6s5q3jLsYu1HTdS/Is1aB9by5u7mnmldQ/9oSgax25KXroGQ+AAyoGnYM6/MCxH+Mb2b/BIyyO80PU8b3VMMKkLLg7t54280+hQJnFpfR1nd9Uza7CfrqLJ2GcnWFZ7lHhI4rBUwfRJPkb3FRLU25i+zM3ts3IxKeANnoMh/xCllla25ElsyLHy3b06Dhl07Fmq0qwzsrvAxIFihW6Dnu/4g5g6jN4A8wEAACAASURBVCzpklABQ9LGgooAF40lSWwuYFl7iG2TT0Wrytz19lNMGXCRm4jyZvmZbKg5i6nBcU7r62VGZi9fbm1mptuJEtUwu0ri6Nhixm2FnDLehy6TZaw4l8k+H/pElvw5Me5PruLUVCeSW8tEk50Fs4YI2O00a2ewqex04jYTFXovRd4Q7mQuWYeW/sla8hIycavE3i+lWDYexTo9gapqudh4iCVSDxpVoSozxs2mN5ms8XCxtp76Ah/+il28Zjcxvc5C+QET5zarzB2Gw3NVArlQ2i+hZuEoSW4Z8mNurMEajVNZEOfn1iX0R1cyvehFEoUNyJG5TEtqkQqb+HrOCOen4vxKV0jZTit5boUpQxF+WXkmv57uQ5UUVmTCVBz1kjcRZX3VMv5fbBevG5bzGcdefnnBJEaKtYQLnHRZF1IcTHGKr4ebjr5OQqtHUbO8cmaQ+V025g2aqJ+VoD9/Li/Yr+Yi80HMhf1smd/EjlPgzaUaNi2QWTCgR5dVmZZ0UdWmIWaE2jGVDYtV5lla8UUWMaoWotqbye/TY3DpmWJLUjrpNF7sWcfZ1Wfj+Pa3wR9E+saVmN9qxGDJ4TVrN5fOvPSETg41m8X5H7cw/pM70BUXY5o1613PU1mR0Ugf/qOyKTlFg6sBs86MRW/hha4XqHfVM9MzxJXhCKsnzeFtTQoLEvcNjtDhr6GoZi6vD6xjau5U7jxwJ0tKl9DrneCgY4gv1p6HRiORzCb5yYGfcHHNxXx3yXd5tvNZkOCM0tP+ODrxT+IfPYfAH/fx3BlaTtJ0YZoYY2AohE4XZH1zH+u8PXRqhwmO9pOIZYgnEmyxOTiU7aM/PUbf+NlE46Usq38b35QQjaPtdPkcVPv8TD38PIEtBwl4MpQbYphsaYwZ6MqG6NSDfczB0o5Oel/bw65UgMHuEC5/inZXC/UTYwRjCWxtLzFsj+DJSyFNTKMk2oXJe4gNnsmEkm/jSOSTiJZS5A/idjcxMd7KiO8QIUc7rypaeoMBysaD5E2JsLAziLLnIGu0/aQlJ4vfPkx+KITHnOTXDg3xjl20OvrYq48zLeUlP61Q5jAwMpKLLRtmWFeA3RjlzNZ6htrdnBSuo2C0mTf7A8hKggZ/EzM2vYDB4yLV2s/9ybfIxF3YkkF2hloZ8A7TN2agfeQA+u5G9vpLCWjqGQ6OcIZnjHDMRnRMZnr9ajQ+P3sq4uyVPfi3+jEM9DAeTjDYXc+BqI4DvRqs9QcJjG3BJcfplGCACNHmcUJHjlLem4CRccKuIToVP+lkLTWRcezxENGsmzxdOx2H6pAnhnG7MwSzYWZJvdC6h70eLcuN3aTSenblGinNBmjW5VJEO8mQgnvASNNQlKZoAWpfA3b/S2wd3cP+6BheUwZ/coKd+45QfdBJmbOVLWyk6ZADtzfJLF8n9coUMlKWL/qeYF5fK8OOQ6yLlpHvUokYmwnqQ8QcfeQ/tpE9lVma2tZQ5h+gtLefUKCE8TEXarQXX8rJyXKCaJ+TNxpTFHib0SQjtEUtvBXWk5SyRCI2UhEr+cFWVM8o5uQoc44coV4fxKWVSWfi9Ie8FO6LUptsQG8YpLU3zYFYCR36AtrSMn5PDwN1ncTbegjH++hMHqD60D4ONo3R6s0y1dfGke42NkhdDA84SY/4KepoJiuPcsRSQo67jVR4gpzuHnKSvahBF2k1jTHlZp9xAFd0lLL1LShH9iJbI9wbcnJ0/xgNAy5KvY0Yho6yy5XgTc0I2AcxJzUMujUkB4Y4+uKjHKIL70gvwTefo9mfZSSUIjTspfCFJ2k/9Cpa/wATT79M4+Ff41+/ieeTbUyEJyPF8sjIMhmrAyvjVCQHqfA6iXaNks84bqMFJZngwsBmBjsO4AmkCEVSJMaDDHh7OBhXKWrtZGywm9rBVmKpKFvaRnAb9BRFOxiOxcgvjhIOpohHw+zrcLBzAsYjQ2g8z2KfGGZyQ5qJVJodBUeZE0kQ39aEv+kQLmOCbFuURkMJWwNDdDQdxfr0o7zt3kUXDlKqi7nOOKc3dzPmiLMuU0pfUMavMeM15fDFxZP5r/VrqOxwY0rEyI/4cboDrCvpZjTqpnRMhz+yn8O6FPb+KIskLxvbXsadHaNPKqYi46EgMoTfmMSfkXl6JMtwxIkn1EDcdQhffxhD0zP4JloI9O4nz9VKNJqgr/kpPA1PsdmbQdcfpWbb6wyN1zHd4yQsJ9mXOJ2MJkuh+yDnDnSRntCRVhQGZ0zwVtBCfKKIAbmCWsduDg1u5uD+TeSM7aU/q1BOhvlSgoPJeUwEM5w3uJoW1y70vU0cGvSQF/ZS5hhlRPZS4u8nL+QgrQPJGuYVk52W7peZ1zlOW9ksfCEv/vbfEh8co6X1caSxCRy6XEZyx7AEYvR54nCkjvyJYaRMgFfcpYSkOG1qFWe0bsU37mZzshKXPIjc2yVG0P5c8JV1x0fOsiMjlH4uhDuUy39Vf43Jkpv9unl8zeihwRLgf3aOsrvnKzyz5Ec4gjXctvs/0BWl+B/3WfxgWiNPd/+GeVXfpnT4dY4+9giTV93CVc/3EosnMOQUsjzRwnidkR8svZwO21SmRoYhXciT8ji//VweheutnDlax6WFYbQtq9mbmo3GKPPyxAV8qWAbkYeu4rq5FhRV4TOlN/Nmz27+4+hmVh4+VuENpyWac+ewzNWOcVYG2SHxi6OPkkrocSqlVJ7u55KKTh5OX8gSqQvtyiTDZVm6jQbu8PjY4svlQIEerzXIv7YksHdYeO0kib3zAP53VPDYGxgX95o5OxDglF0qTdMkKjw6SsJRyl4x0qPNQasqeM25dBRO5fr2jcfeBAa0FM9AUhVkjYa7l1zNvHgdZ/X66S6Y4NxpLXQ0VXFX8BraFkzjjPEjSHnNpK0KNZ0BggYrry1PsG1KJTeG9lEykUSzOMHrUwxMOZrDot42jiycjiaZ5dWKs1i6txOXtYCKUR8ZjYbOZXkcmAhwzS6FX2lyiJ8T4+SMhXG7jnMDEuYmiHsMLD21H0wwsj+P0iVhbokc5nWDBe2onqpuLXUnSZzWrQIanjq5Bqvk5n/6IuzI5DKUq+H+2PUcWDyP72sf4pmSIRhZRC5hXorsYK3GzAXSz5guBQkPmmgcs2ObGeBbGxTkqI41Z+m5qDHBosRmjPEYdlVhf63EfuV8rjqwHeNwBoYz5J0a5u1lKUK5MhZbE3pZJWpyMqFcz5f7BrBkUzjy7SRzUigauLh1Am9mIUoKWqtj/DJyK1+IB0CGHfvy+e4ZOWQ1WvKiMiWeLD2lZuaOq8S0RnKSKSQkHnksy7aFPkrtz3Brbic5R9OMHbABCv2deqbdUsaXlSaufemLXGZSaP9MLofixdhuMRE1xJnS0U6r+SXmnXs5kiQR27+f4f/6Eb3qOFNtxbh+eifWU09FV1JMb9PjmHQmbLUXc+vu73Fk4gjXzLySW5bcikarRVVVXL5hWqJdLCtf9oGfCftT3oSXr275KoPhQXL0Oay5cA0b+3egk8u4MOhEMedz3qrf8eSWa7g4nODryR/T1TEZW38c/XQ99zc+gN2Yy8Wl/8melidoi3Vz3mO/ZNPXbuWQu55ENsG5k84lT2/na6PTqXnyObpcq7EsWUL1ww+hsf6dz9wIfxVZllm9ejUvvfQS4+PjVFRUcOWVV3LVVVe95+3r3/zmN6mrq3vH/KamJqzv8v2Vazoxy1l0UQWnM42nYCPm1ExCgVo0qRH01jTTdXEm52TZYTCgSY+R4zaSlu24dDo215zK8PgwJwd7me3R0moKsSjuQJtNkbSYCKh2BhWJmbosSQ1YFIWoonKktYt9JVM5bF/Eaa4OIkN7SVrjnJQtRyPJ1FWmMGglrCkVydLM4voOFEuIz9a+RDQdptRZRLGyH2NOPhtiqxjp8HDe6Ns8k76cjOLhosRuwmbYa7Nwll/C7IxyeirJizWfZ59ujCsiezGrWbyxN9hakmGaX6HMW8wUvZ8ic4IcfYqhkghOjUowbccc0eKTzaRCI9jNIfonR2kJO9FsfZFgUT+5mgQBkwZ/4w6MU0zoNRn8zh4SeQrOeDdlHVoSZRmaozlo5SiSLKOGivApbqamXGQcGuyRGJbcFPFcPXF/Cx26xRw22JnRuYVW80wc6TBuNUKHs5PL3Wkap2gpcklkEho8SjPxEjgyxcIkh5kefwfaXDPhnDgeq52EMUQ6L8XI4QbyPAbap87EbHBTplEYastQae2gstCPPq4yoJYybihhSJqOLi0R1+vJBFMkshL1TKMiHqIiEGLE4kWb8jNJb8QbNXL+y91oKzSkmIqUCRBuy1JgcVClK0QfTqHYM2QUP7XpNOZcmSE5RK6+nyK9RFiXIZnSsrw1g+RwsXPHY+RqNJRnTZgTMKyvxmkt5PzYALY+FwO6FFqvm6R9KkFdmpgmTXnPRs4umM42XRGfTYaQ87M0mwuoDQyijwSxpROccbCE2EwdhUqE9qIJanRZnHoTanSEg/KF6LQwYbaiBg3UDDZij49QErUxasujs9lIsK+OglgpsclltIWLGDBFaFZhsjuDxjmCYlWYlBhhpXyIjhIVdVIUv7eDy46miBbZqLJMoEhZMmSJ9NSztm05F0RKKB9zs7BPZopvkHSZi960ATnRSVtyBpayLKdOJAjLNbTLVl4IFTNJtRLXhHDlRwlIU9FljzA5G6QqVMs+mx1rbh/xrgmKxiWSwRTaCS8XZI08VzmLhNaCIsfQSBkUSUPIUMzcWBtbpiuMW/ScdrQVXbgNp3M/5fo0xTGJ9fKl7LMVMHnCg806TLhgjBmpCawhBUUDw3oLC/IG8WYT2OwJqqU08dQ4G0NnEEv7MBtMTJGOYBnL4+isMaaWZMkLGMj2hvFtfpDYoA1tKkRVeYTEJDuHjAuQGeKMzpfR96TIS5uInprHmZ0ydkOGziIZUzLDhNWGklFZ4mwmZ/ggR5J38ZXDEcJYkeR84p4gmlCcEq8XlXx2e1NkilTSWpXBvACe7Y8QsCaIRAoYyhZh1mTYOV3CmqdiChaQKVSpNdXjo4BGj5nx0R5W2NYjHY6zYWImCxTwJR9i72KJ2ngKQ9zMv7S3YlRkdK40g5Mz+LT5ZDRaNBkNy5t9jAZK0WpjoMg0JaMUjcaYkBSy2jCjaYXtaYVrU4OEDSpeLFRogowbtVjDEkFZxZsNMaBPsSvUSji1khxNISfZHZzTOUJudZxUrhmzopCXk6JwZA9jUyY4Muig63A7gbATezyOFI4gqRJd2pmEJT3FcpCDshmN04u2385Rk5WFU4awKZ2Mz0wwb0SPXoJKVz/mmgJUWbwk5B3kSITozl3HHiDVaLBMSWKwyjw/dDbPT7qb+ZpBRrNFXN/6Pa61refR+M0clUu5Z/t9vOj9FpraGJM9J1H6wi5Ov6yaHdM7udfwGNVtG1EmNEjuLezQJJCMsKl3MbE+Pb+YfzUdhVOZF+ijNX86KArWjIF7g17u+1KWXTYLv3ErVCVVzjO2UBc+ieeLFqEkklxq2UPJ2DLuueQefvbz7VTrTmPl4cPYpiRwyzou69hHUVWKhFaPqSpFkS2B63AeOoOM1qowVp+P6eI4/2rYxM9Pu4wfaNbzW10OWlllpt/OBbrfc5e/ho1FGbLjevbMNXFgXoJal53ukjBaRUXRgCpB0BzD/1oeOhSidg02hwYJyKa0GDj2drAD5ScBMMc7QGPpbPpyK9hTtZBVgwfYNuVUamJdOGb08sZUD6PSYn7Sq6FWHeeXex5hX+U0NixTeGTGsYZh5pQqNJ5T0E57mtyUyu/y8yjaaid6fgi1zUDVhEpWN8JtLb/j1mu1zA5X8sSilTyw9Rm0wES+jSn7IpT74fnac+n2XsBA6QY6owfwea8k293KCmczGpOCyZ4l5DAxPFFKc7iWzxXVU9BjI9mi4M7V8uDKqZiDUdZP/iJD8SokKUFDxZM8dqGbK96YxX0L52G2NtJa2IeaNROKn8SXtHU4DBI3hSJkVC2/li/h3+2vMtulZfPRMhb7JDorNGw4VUfzdJmlphh3+PzsN5tYbc8hd1YbQ22lnDXUzNby0zk/dJCdMxJcQg+3DQewqwqbrH5uz9/FBY56TOUZ5md8PDbFhFZWMce1vDV9JrUTbeQXOvlCPMBrciGdxTLPVZUh6WIkRq5gWehldEqWad4EWpNMXm6UrPvYPeGmuMTn90P5qYex5qRobJ6GLl+mutgLPTYSd7zK9C/q6a2UePp8LRBDIz9IVKNyRrvKzW8oaJ66g+FFGyi/+y4ab7+Zn30hhStfR0Uiwc8fz+B9+mkemVrH+tQYGlVlSvujOMlw/WgNZ9z3FG22V6i6+Ts4H30QzbgHx1yJez5fxiuXv0yhufADn/uqqnLLrlsYDA8CEMlEuGbDtwjLY2h8K1iRbGF8+r+wpWMPqiThCp9GlzqZnxnXcG/my1hDixmXD1AxcTL/6XwUQ+kmUDX4pG6++UoBp8yMotPoOLloAY6vfo1z6o+9WU4FYg0NuO/9b8rv/OmHch0TPpiHHnqIxx57jJtuuomFCxdy+PBh7r77bhKJBF//+tffdZvu7m6uvfZaLrroohPmm83v/q9CpqhBqtNZfD4bU80xurP5YPKzvEMmZziCab6GOaMxZJ2OxHyVLBKBdAkmUhg1Mn2TFApiWmY3S5RFtSSL3EhJLdm0Do+pioGIjbSvj6LcMBqtkahixJ6x0ZA2MWiuIqyzk8JCSSRD3bQssUyGuX4/4wUyWTNc7EpwNJOhruBUlkUamVAsLOmxM56UGLcUsSd3JlFMOKVCtE6VhL2CrFKIVtqNbJAoj5rxolCoVchLKiwKjLDc3IfbmCGqi1IhRcg1yuSZDYRykgxr9eRMyARkDQlrmkXSEFWyF60lS5kphrHHhMYM2VId4ZwEQwEjc+JBBnPBHAWDqjAjHkeWNdhjueisYTSKwpxeJ7ZYBpfVhpGDpDP9zOtTkfJUbFKcUp0HjcmA0RBjelbFogbpmNRMStWR8U7jkr63aJEr2FGwgjM1LbQWTaZbLmcZjYxbdLSX5zBekKQsHqPEZcFr0aBX0uRLQ/jzMthTUK6VKferKB7Iq/IjZVQmTFms6XZCZjiYY+HkIRPmdIbcAgM+eyWynCKhLcUedzBsVoga/aSzuSRzdIzpDWhRqFQV4pIOoyZDOl1CoFhPxVicQl8BprSPSluGtMnMwkwXzdoSXMo0gposVmmc05RRcrQKB5CJmDVUZFWklA5DJEmiUGWBJkImoyU/G2HYXsoUZxBfjp6fzjqNqQktBVEXTZNgtiOBOsPPfE2SaO9C5pQHMGom2BGrwmJ0UyhbiQZCPFd6DRZPhMuH12ObEad/QYJ56Tg9chkDRljs9JMq0CKhR7bE0CRyydc56ZBLkCP5vDYNrOk0EU2KWdE4/lyJDGbGKpJYwlamWhwoeokxfZiI0UVhMoOkUUjk6LHmJBkwlGFVw8wKZshmJtGSO5NyS4BSt59aXZZyJYzF5WM8z0LCmk8iaaHIX4R23I1i1aBNWlik6WLfpGrSKGgM4xQYh8gjzpxknJ0VESwxHXk2sPrjBK01THE70euzzB9KUmeHflsRVaTxqHF0ssqwsfL/s/ee0Zod1Z33r+qkJ6ebY9/Ore5WbLVytIQkkoUkgkkGLMBggxM2mBkMNmP7nRmDbWwchmEw2ESLYBEEApRDK7U6q/t29+2+OT05n1g1H26jAEKv1/vB7wf8X+vetZ5zTqVdu3btXbVrF9+1xnDEHjZU4qyv1EnrEvVhSCxB1BWkRchWmozWZjGpMtMbx8FlrF8RhYJi2mQyHEF2Zzk77LCKSaVHMV+x8IMYSXsKr7UREjM0jAjX1CTDiN0zyxgljYx8lJQ0gxArATuZ4wE9jFPuUDJzhN0cA36FSi6gmZRETZt84DMcr5Iqt5noXeZQdz2biqcYlVXmTIsWMJnpwVFdtjRcrj1e5VDmCKcGApqGQawdcThQ9E87TFR9DsclZT9GUmxkrs/lVM9WhD3DvK2IexXGFvKsr00TWoITOs6hfs3WYzW0DFg0DPo6kr56wAZzgWToETQEMU9TUBEYdWSUIhIBR9Pj1AqrxCKXsJ4g0xCc7m8johj7cjaJmkfKqbMa9rNlKsY6u87xjUl6VZWz5kvkIpeoN2J/Z4C0X0BqTRuDTkzT4wu07dFIWKBjDMarNLsGXdVlZeUoB5MDbHVh1F8l7ybxnBirRsSOVptT7RhnLXTphkkkDZ7IGswlmxSdDJ4dJ5dok/ZdNlrTlIMk9ZeYu34hDbTmPfegfR8NHOpfx0UXnuSgZXP9+NNMuCtMP14gNezygYl/5Qed3fyO+U1+ZLyaL3cC7MwzIBQnxCDv/J1jvGFhlnf8UKL2PsGB1HqqIsVWPU98V5LmcoENk4v40uTQRZuwQ58blh9nNjGI0IqvNrdx6/AB3llv8Ixj8cG+Xr45v8S0bfKZ0RIDx57mH3pv5g3iQX6p2E/jde/h/X6N1XURQko+eMmlFLOH+L0v93D54mH2jG7mHdYDeGdcMeWoZmRLldM/6GfmlEVmm8N1gwfZk7a4Ox/n1qcj7Ps1s0YPv3HxNN/r6eWz18fJF3Oc88A1XDV7gjeICndeKjmwUYLWzAwKtFK0Y4L1cwVSQQk/ZSKDCOlp9q2DR4Z2Mt5YJhV67BvYype3vgy0Znv5FNfNPcUjuxd44z+HZLrwjV2HiC+ZONqnMmZy+dwUd6Yl1z8NA+2If73iAH2dE1Q6Djf9YCfp+F7+9vJN5DnJnx6OmNks+MEmyXu+r/jgkRbX5h/HTuzleGqEdMvD8V22LgU8OnoWRwobOL/2NHH7CXY9sYv9fYprFvbzxW3Xk5jw+a/WV1mZyfGHV7wHIx3xah7nUf9sdq1O8tCOHbTm38HHL4iweu/nbSc+w4UnNF+4OsMVRzT3Dl9OX7fCjpEv8nDS4crZc7gLm8s5woPxBGcV28w9XuDq+l72OiP8xZs1VzwpcC3NJ26DhB+gLIP3Vxs8bKd470AeW2tcu8VDv+7yJ8tl3tRdZCHRZkkN8t/KZQ46Dm5Xck2nwysL9/H5dzi8Paqzww94pn+YgYaJcXE/u9L7+cqw5iuzHVaiLH8ofh8r+TmsyCbvhsTy3+Lme9t89C0Gk2OS808K/uCbAqNHYUc+fs3GiIdk1nVZ2J8l223ziXNezX8ZuhNxSqECwUwfoBV/eKxNtaD5XwMZrn0qxZserjPXB6WM4IK9e5l616/xP17p04zB7feb7JnocMfVNhsWvsw3h+HN8Qme9Escj1r8QXAxu7/0GMc3JHl4XYNb/uKPUQIOnu1w7SGPUKzyOz0fpJBI0QyavHz9y7lt820v6RL5pSP/xuTCPs5dFBxYD7lQsdoaxM4tcJnzCMm2x/tOFphufY1BK+IHrddyo7OPW4qPEF+ucaw6z9cuEaw7lmbhqrvXMhVru8T7uv+LztxmtmTO5Vsf+D3Of/xxVjNQT4CpYLQMtTvuoO+3fwuz599vVP4n/r9DKcU//dM/cfvtt/Pe974XgEsvvZRKpcLnPve5FzXQGo0GS0tLXHnllZx33nn/rnJSFSiKGMoCW0ZsCcpo4M6+HYzk9nJLvUG8KYkCTSwQRAKaVkAiNkNapcFqsyghl0uT1g0atstc1EPFETyVyzE9usLmNrQMi5Jp0pIWPZ5mU6MKrSZRbgUv4bIcSRb7fBzPw2p5+E5EVkTkYj6mmyAuIw4NrENVe1msphGmS0c6lOx+Un4bS4Z0DButNDoCFaaQXcUj7k52qRI5MUUybrPLniUjKzxQsFCZiNSqpseCTlzSNDIovcrGRodNzFLcME9dKWTSY9Ie5ax2ivNViTBUeFKT7vh4lklfNeLB9TZawVULPovxJE0zTlS/lpx3L3bQZKJe58jGBNV4imH7EPGVFqY/yIzbZbHXZmzQxihB24pj1XPkRYow38QUbWR6mVpG0WdXSFmLNNM1Cs00qTAi1HESzRhDsYCEp7CGfeycwOn0UcqXGF/0MYyIvO0TQ7LgDpNL1Nk6v0pCKJ62Lapphe0Z5GsGVddChiYlu0A6cukvV7i0U6Zv1eLI9iKqcJRU6ULGnSL10CKhfVaDAttOWcykoC+p8Ywq1VQ/DZlmw0mPZiJB3I44uzsPhibeilAZibZMOhhsL5VoeyGTO0JcmaWdEWSbIf29Ln1GiE+Ebzhkww6VWIacWyZMxDg6kuTttQOIRpKqHuY8fZBeFTLZ6RJXAVJrsBx6222YH6LR9BA9NhsqdXqaPn2tkFrewC5prjvqsjjcpK/qMtTu8ND6ASqmTWx1gKNWkr3GBmTQwRIJFrIT2CrO6Mgcg26MOdNgZzNgqNHgRDrDjONxSuW4cqnOsFVjxR+hnZ1F6yxlkSNn1DjXC1gJY5ySiolskaTl8pRxLgPGJGdVpjErBm0zy1xqmL5mxPl6muPljTRxIeHRE4asKJCRpiMjziu2aFUleqDDiF3CtQXHozg3NiuM5ptILTmU2InhJrAd+BXjSX5IlWZzlJRcwkm5bF/oMNSIWExlCZyIfCrE9Q2k0uxqnEC1QvbGBQsjPk5riOTKCtUeGGjZpFWF4aUmphFDtkyeNh06sZAsZdKdNkthipYwWL+imd/iIg2BoSyimIlOKOwwZDWwOaehaPV22GGe4AHLYdEYxMj1M5uLcV57gbYPGTtgcNnCLgYsErDTnWVCL1PvjWGETdIyIh4qTo3GWbTGScRdxqrTOGXBq+on+eZWRVMIRmWZ0SlJoEzaIovvQF/QomDCapBlKRViChvHz+AbTUb0ImdHVfYaiiOpGFWjyaFzIkZrOTaWuqwvh5xUSY4OWQx4HaxOnFouy2y6wWBthqsoM9FucireQ9uyUabPqh4lNraAFfSAFGxbCqilTeZzQ6wIk2QQ4ChBYVXyspNzVHQCmdDUYTGnqQAAIABJREFUTEk15ZHXpwnrWabcPNODJfoqBt5gyLyjuJyQUquPpXVTPOKmOCAHaUQx7k6bBKmdFHSVWnmIbStTJDEZS3dYtvvoIyJjNjlmpJk1baQy2FiqYviC6XySWqmXEa/xkgbaL9ahiDNo/eguQCOAYNThdKrB3bFeNjcWOfHDProrMYr7MvQ/MoeIFLcYD9F7QuG1t9Mbf5xcpDCSJ3Dn3sd5ix4v36vZs00gwy5PDW7j1y76Q742fwV6skW4xeXEdWM4YcCn7/9L+kp13rf/G0RC8uncW/lz5yJ6leKj03VO2xbPOBZvGh7kSK/FI5cfpeA/yFynh0vMwxxIDPKh146yblrASMTh8UkqKclKf5Z00OXUhGLPkMldrTyhhKGNVcJcRGU8JDVp87lED98aLPKBQi+qbXDbfRo35aDjgvbDWV4x6/KWx9J86mvLfPDxu7hq/hg75+C//qviur0m6a7gTfcpLAXTPVnGKmt3bCRkF9NXeKYmls9xtGeCi1aOEgtdNqsfkowa9LklfMPGz8zw2kdDuqbDYjxPLLOFbYsruDu7iCuKYGhueVLx9pLi94dXuHNukaWBDm/7QYzbJh/nugMhb9h/nF1HNAiwt/Tw0JYC1YTBWdUU/UqRc3zGBtbunaknA/7+5YJzluf4sz3/mx3qX3nX3QFf2XId73zmO4Sm5srYU1ybPojrmXzHuZRLlo7wyeI/UPLS7Kyt7bR8Z+Rmbj75IHbqMG+e/AG/8pBCIZgZbnL9AYcDfRspdJugBUmlSHd7MXXI8PdWuP1EhxM/7CeomPjS4p+vsjlaMLjkiMlyxqaZklzsdtllNimokE/lUyRCwWW1BNLo8LmVZa4JW/TbSzyQhg+0TlIyDD7b6OXBJ1/J63o2cGcmxY8HTN40PMgnM1mqMcH5hss1iYMc7jvK+x4P2EELvV9zVv8/kq/uJHSq3N5aor/S4I7LDE6MCIgM1Op1mJFi7KwK628sExvxyU90kRI6p+IEUtBJ5fmEfAO59V0SIVxed/ndYoM3xqp8J5Vk3arid/evkO3C514m+exNEi3gW6NLzPbBH3014sY9Ln/8FcUNe1z+5TzFbjfgD276Amk/R9xTbPjnPbTHepju1/zqvZBvw9+8RvLqc+cobG8yUok4UHycp1efpubV+Piej/ORRz7C3+37O95/z/u58+SdL7i/5e/vP85fPPwXfOAbEceHNVfVXL4+W0Sg0Mrmcq/JI06SJ9f9iE6sRtzNE2mLNz/9DU48lGLPhkUe3AzDZcmebRFCRAwHST478lp6fcX5pzuUZk5w+f+us/PeB3lyE/zWr5t85iaDatZAKkBrql/56n+wxPvFRbPZ5DWveQ033HDDC56vX7+eSqVCp9P5mTSTk5MAbN269d9dTrthEbZMkBqLkGzokhFlcpzidFpxJOxnf2yAki2JkMQiQTrqMGTM0G9PM65KbAkadLWgbWco+RtRjsHTvRNMrmuS9gSDho8vTfobLtIPSFVyZOuSZLJOrVDGdg1E1iXX0sioTLym2bAECaE5nDbJxRsMtqdo2xVWlIWU4OSX6TMXCBIVgtQKrlHB7EqkiHCcKu20Sz3dx5WlIh3boN1rMm2bRHMGWnkY+JihZkCG6FBTkzAR1RiqBXSaFnohIuwkaIsYi3YMJTQBGltEnLRjuB5sLjUZk0U2RWmO5oY5mbVxOwZ2O8TuwqXeKbb7oFWS5WQetKAai7PhBAz4EXnZJWp7LIc2M7aDsAQrMUnTihgqdugYPVSNHpItKBpDnLDTmPEp5mKKWORxRXQaWXCox+OMFLv01hy2nYhjGyERNgNBSK+Oka3EibXAbRfwtcVcrMBk1E+zk2TQg25kU1vJM7waMBvLMZlR+FoS9wLyXpOBSshkcivD5RjZpo0fQR8d3KCfJfoolEAKMJUibAgKJY/+pQ7nd+sMW12cToTdVmwoL3JD82lYVdQ7CVQtxbaZDoIOwy3F+lDRzTdpZlxEZFNWKSpGApEKKE7MsTQ6R80q4GoLrXwGazXa9S7NpEGqZZBYMIkVYcybpiE9ek432L10mr05h6geEJVhW2UWhyZLWQPdGSII+4k1FAYxXlvcR2/YJO1H9HWKjK1qzvZnySLQHVCYNByDsUWPZNAmvuIz6LXZFtbYiMt0IY5c0WSXDa7WKwwGHWQrTiGMWJgZRtcMOjrBAjaJruCaQ6dY5x6lJNpUSGJ26kjfpSMslkhwOhrEqfVx9mqNWMJmQ65IjjI1kWC85LPrZID2MvTis37aoFCX7F5aZtOiIt+wsNs5cm6NUEhOZySnh1vEndPs1ofoNcr0+SYgMGREaLk0rRijocuAVcKVBk0kZqCRCuzAx1Qefdrn0nLENjVPwwzpyDhtK81AJYnr9mBHgnlrjFVZYNqKU8yuouyIiDibGjNsbrQY6GgiYTGXWE+1L89pp5enhnqo9EOjalPrJjCaXdaLVUKZoJ6v00q5KCPLurLD8HScSCRYtkfp+JJQQttyWLHixOouTgC9Tosr+ia5Mnaci4PTjNckJjDsVcl2BTs6Hp32AC1tUcuFTA4nGTCqbF5eIlHqUkmXsEUTlEOLQZQ7RkOkaNKmaIHtB/S2A2Q1TS2EnC8RlRwDuogwwHOgGxcclzHsms0rVvYzolaJ2wEa0EIQiwmuNI9z/b42N84d4qz2aXqkT58KCHzBqt1FAEpZpGuCjhXSqzymxAhVnSHTirFZHeNq7zBd0SDd1iz1RxSbNrol8REUswE1P8ejqZ14hk0Kn47jg1R0IwNLnsKkybzq54ST49FzGnQSHSqOx6pp0JR5JnyXcV3CET49Ucjm9hKF1ksHCfmF20HTUUT74UfoGjbxKOCqgT38Wk+eO06XOPHQIL5QfOR2yXmnNW95UFJw7+H38pvo8eaBzTSTFVQUw527ndfbX0McjHFsyOJ/XnUJvRd2WWi9ioF2mbHqKlUnxTHb4nByJ3983/9htFUiEXn0lE+xu3iIh7cbfHvsek5mD5NOdDi76/GdMMvND2ruz9xKeePDTJ+1j+VHAy4Ym+Kj597Abz70LYQvuWtzAYwWH/rXkLq5tmM21H+AD/T18JlpxeKIYn1vyFsGBsldZPChryvuQZCqJHj7UY2MNBL4xCWv4K/05yjfm+d1DzgkF1YxkyFhZBIKeOf7EnzkGy7v/lGXoVTATXttTgzCYBiAgpWCJL5JkHlC0N7tYVZSRNJkpFVk4dwO/Uf7aG/P8hv7vsjFq/t54BzBBdOaQHp88erL+Y2F79KSCX7nqiT1VIoPbw24vt5l/PIqi2S5PxExUIVrJ8sc7NnAQk/A0gWnuel7JkGfye7UCW53YxzaEePd48s8GW4mSMwyVLVwTfjIr0r+2xcshFb8/dXDpIwliuY6dhVP0Ntp0HNFg7NHlwhdycn7+3hl7fFneaXUGyNNi27O4Ov5D/MjtZX9sYhb9mgUcNcuycYVxZSzFSUMTk6UWM1Y3FBT7FObObt2isGxGvsOD5JXEQ+tH+Yfdr+ccMMXeMWBPKmgxMObIBMJMGzeslLmKRHnWMJmaPJcDo4d5O8ebrBt1ONBo585PcCS6HK+V+HPevKc7F7IA7dOIu2Qd1drvLHR5FXDE3y+kAEh2Ox3+WTnHLqVFV7bLKMG4MkRyclYjCuD4zyqNUNhyOaTDl/9pYgLmx6Pl97KO6a+g3QivpI9m52qyQWXT6Eq0KpadLXkeGGEpwYNYq0BuuscxAnBh+716Lu4wQfkAItJg/fX6ght4WQD3qS6fCyZ56HtcOclkmueyDCxXGUqN0hmS47H4kdpWJJfXymw92O/z97z5vmt7yqyJXji/AY3POISmJqnJyS7UiH7ntzKyFlT/OWQQa4N1215OR+97MN8ev+n+czBtXvv+uP93D9/P6udVd51zrs4NF/nrx79Bm+YabDQK+g6gptqbX6p8DJkbAmtLDZ1NP+gCmxv+hzNSY66u7lx4R7yUy6/917Br90V8KMdBrue3Mz8rqfJRhF3zp8gNn+Ur59McLptM1sqcFbxGI9vEXzuujiREEwPety7UxFzYes8VL/5Tfre95v/8cLvFxDZbJaPfvSjP/P8vvvuY3BwkETiZ+8QmpycxLZt/vqv/5p7770X13W5+uqr+aM/+iP6+vpetBzfjTHt9CBlhAxNOtphU2me3nSFwA8pywjD7+NUwWdD0SXuQZSxkMYyyo+TMTy6SlO2NBlh0o61COyA7VGRqUSHy0+3SCmD+Z4cnXiL3pUk44saChZGaoHdYZVYf0SqpYkpGwKbmpFD+i0K9Qo9SxaHcwETyQabOxGD9dOEcR9LhqSUSyxxgh26SEb7nG7v5NXswYuaJItZ+uwujUaIZZu0LU06Ckl4ipnEGLrdxNFVeuYlhlKUxm2uWCiT60aYUlM3LYZOWywPxEm1I+qjCqEUncTaYlCqZNNf98i7IbVEjmEt6HYkbVOytQjNIMWm5iKrQwbxKMOjm018u0088Jkb1OyuRXTEmjdMdsEh1C6esKmbBnaoaZg+O9wmLdvGTPmkZk1GVjqUMzHMELQEoQX1uCIlW1ieRkiJQqKkRqJ4xbGQdc155tJxyr5BvqboGpK2qUA5LMRNgkDjNMp0vASWUHQMEAhk+hROu5ekbiKEwjHbdCybgVYLr7CfpurQkgW6GPRHHRwZYSro4CIM8M2IfLWDKaHPbLLoWNScCNeUhI5m3qgzVDJomwrfCIlhIrwsyXqDYjpBvJNmeLJDXbssi2EymSrr400GU21KZi+WU6Rh93KokGY5ZtATD6i6NvWgh3imyZwK2RBJ4slFNhc9sjEPnbJZ35zFT7ZoFyyykUtJxIijCB2BIbsMBmUis0vesYkbXcrZJPGwxWZ3HxuaAbWYTapRRYQRoivpdjy2ZwzCKEfKDZhP2LQNCzNyCYVGSokSPtKxaRkxBrtVFlOKvrJD2zCZiCbxqwHT1iiZ9hLYTap2RJ4uyDaXzR9nLDHHEz0x0lUfN11mvVEk1ZaUYxGqmiCXClhR4zjmAtkojjS6jM+YFF3wA4OcG1Af0GQ9xam+JudHZeaIgY4QQiO0wgw1nTDBMTuGCDSWCGkVHWZjmpmEha86mCIiIxTVyGTEDel0HAJho4F15iopr05UgGAhYnDOp16QrDNqFGIOq6HFOn+Z5USe9UvQNOIox2bGsgnjXSKpCO04D5FkdDFGTnboGZhmMNbmtJmg1w3x9dqVHB5xNNBKRhiyTSrWoGY5GL4GI8mxpKBhKvIrKZJhi+lciKmylHvi7CiXSLUhGUGPbhGkJcpSJIwuffm9+HELmaozEpq0rQ4rMoEdhgxXXfJ1g5RwiRZTNGJpVnqSnKfKRMKASDKThbizslY3mcZxQppmRE/RISYa1IwUtWSGuO9hyIiE9lnUFpudFjJSxCIf3zSRIWRWOtg5gS3AFw4aiYw3sJyQOTNC1tNcNVVnwOhgyZAeu430Jbpp4cokHgb3yAiRa5JXCVpGHal8ds62KY5EFO0U/eWQ4XITS0aYaIQbIgMDP9YCwAo0FzWK2FpBpHnGyTLebFOIuxSjF7/D7if4hdtBcx/6DspTLCd68E3JP66Lc2OrQ3jUwWwr/voWSSUtuM1v8I13e+wd9Hnvt2dYKK1nIjyIZ0O7eSFSgdM5SG8Tvj3+Sn69c4pu+2XkmOX//Oi/s7Myzb+dO8Knrs+wspRkY30JL6MovGqWj79V8PRZcOUzEZ/63t3sfWYjfSri1sU2L/umzWv2aD7+wHcI5l9PIOJ8fGcGLTQXeg9zdjUEoblzq4XZHGT9sqCn0mEpUeDBEZtd84p0F9aNdPkfhTynYhZvSLSI4hGBafPhr4fM9AuueEbTGk8Qa9S4I5ekZ3uD5HIIPZpu12R5UPHdi6CT8um/ooQ0NLfevXbe7Afb19G70gEEnz7vl8g8I8BQXDxU4XuptQv6dsRPcO4TPg9uOHNJYWyJ6QG47JhmekCwcH2cV/U8TXkuzpObBUlX4Mxdzv1bTAZ2Nul2LeygxY/8DK94EiJhMNZcIRcXTDkmwxX4576b+EBfL9d22mQ3dQgl/Lk5zp+mR+lWLB7bJrn4cJINRY/P3+Rx/2WrXHZU88DETl5/4l5iBR9rWHF0X46FPXm8psFfvsbkT95o8Fe3CAbOruOWLb59lsmbx/M8ODzMqw6tghaUYlma4yE3P6V4pm89hqzS0/8NQiGYKG/gOONcc3ovXgqWbagl4Us3L3HFwGeIq4jbH14G4NsXSl5RdTnlO2wTLv+SykFk08gmedvdIRc6HeqdBO9t/hmb5TzXuQ084Eciy9Lm/VhGnYyveE+twScLedpWxLtn1gTDM9Xd7M+v8IZHNNmJLi6CY2dBrrad5WSF8xclX5u6kqMTAUlPU5l6F3ZrjKGVEg9sMfjbc1b40LDEk4p4T0BtJoYTwf6di8TG/wW2/yO/vWUUKxOgCiZ3xZLkZtfWfG5sduiWbMxMyM26xSbf556LBYUmvPv+KvM9WT5w5W/w1/0X873dkku6XZ58eoJ7qo8BcKk3yp6dcNlel9a4jxUKHj0Hjq2+lg17a/w/6V4qacEHvhVx8MnvA3Bh/4UA9IUO77ffzvmFa/n0/k9ztHyUf3xgikzmIW5+POLHFwo2eT4f7xnE744inRXG3TbBj00+/LUIxdrBXaO4i/fs/zF//1rYdUzQSAi0FBzo34Y0u7yn1qCksvxG7X0cmM9wqlHgrGKZg2Nw7zmCX3m4y6uf6hLzNTMbFFoaGBqixUWCldX/AGn3n3gx3HHHHTz66KO8853vfNH3k5OT+L5PMpnk05/+NB/72MfYv38/b3vb2/B9/0XTnHQsprKKaUfSEZJ618KtOSRVwDkrEek2JMwOdjfNltMm6ytJxldcNp3y2LJYY3zGY6To4QtN3QZLhsgIRFhnS1sitSQUJrlqyKqVYdeipmYaFBMCy5P0NXwKnS4+grI5hsCikYZxXzNcSpH1HM5y5wjMEIFm3CwTT9QxhCIhAs5XS6SjLhDSbx7H8BYYXiyxUrAIjQhJSE508aUgFIJupk7oa4aKIelVB4WBL236SwEJN8IXNr6wmU3G0MDASkiypck1Ikw/Yqq3l45IEGua9Jtt0qFHZrFLT8vj2hWXtFS0w15inqIRa1GohOyoNthcq7NtFa6errKlFuFHSSQaxNpOeSQ0dTuia5gIpYnQJMOAbOSRtysEmSqmEXDNdBG7HqPmSDqWAKGRaBayGQpGBw0EUiHFmiwIpEmYCaj1KkKhcQ1NyYmoxzy6JswnQ1YDk6apX8AXfbrO+ugoY+48fgKCTJWC0WY0aJAVKywtOwyd7qK8EJREoHEMxVp8ZE3JCWmbCldaVG2LWixECQVa0zQjnE6IJzRCaTQahcH6E4KimSZpKjKmR4yQORHDkyHrll0uON6kFAsRuQbr/CIp8wRJZTNaD8nmqoTCJBQaT5oMrgS0LYUyIG9GNM0Y5WwWlVulN7GCkBJpeWyaq9M1ITIUUkT02Q1UvM15pTIZILB82vEmSV3HtJfJtzQZ4ZHuBngyRqxjkFty6C9GBCJO17DQCOLVLgCaNdrE0hGeJTHLTcIgouGlaUuHmFzrp3ilSzZZxTIlGStkMe5TUStEyRKlpEvdCtFRhGkERISERohFxES9RRglcawWhm0T0wEiMEEJ+uwGy4UYpWQBhMGA0WT3fJnRuTKiGlIoh1y4WOHihQrDSz6OK2kIRd2OUID2BLMJiSttAjOga6y5xOerIbIkSOOhgaalSMQX0KkKZi3ES3UJhER5cdKNiB53jg3+M4TZMiJbprcekTZ9OvkSphcSSE0EBEISCliIe5RiIQXfI7B9+owWceEztrgmA37CqaYMWdddIJIR0+kuFiVWgxhLcZOGFWPFCVhI+CitUEiyhsexrInXzdMmi0JSiVsU471krA6i69PNVnENRUJFDKsml8+VePnUEltWOvTLFqt2Gk/YJN2Is+YgQgKarBsSFxEZmmggFBLfgIElDyPSGKEiCDqoZIOe3BwF2QZgbCaibDoIrekph/hi7bJtoTWptiInu4TCRCExiPCNtTGUq0XkjS4KTU35dI0YqVhER8bxhYmOJEbNZrzic+1UlZdPzXL9iSKj7RYXzrQwCBgJugyYDQqyQ0Z2uag+zytPFHHO8OTWEy5DFZ+hlYCSE1I2YNWOYWrFmHopB8dfQAOt9Z0voQHfVswMKH6civO6cpviZJqnNiaYHYQ/ONimuDVij5fiT74Sko8sVnry/ErfPwEQ1C7ht6qfJzMjcC3Ju0bvZbunqege3pX8JjMDmlDCg5edpNs+hxum91NKWmSvqvCB8TwnBmLMDZxP780VHt8GO/ev4pUtxHSWtAvfv2SEjNvldVN76C7fxnSP5EvxNFcfKBEKiZGPqGRrvL4zx1cvuJjRZonVHjgdt7jpxNrWrxzucmc6yTUHFZeFdb51VZ7bHvVwQoiLCCeAfxu7kjfvO8hDIsFSM4vQUAlTKAl/cqvF9y+VbPF8skLxzIBF2DaYHIJffXyRrgULqRx9I/dBS5KccFlaLjCZ3chEfYlCYxXXhj2bNjHmL7G9WCTtQq4N1jkei5nNyM4cia7AijS/+ZiLm1umL74VJxtydzXNMz8eYNmRXHIUTuRHwYrYOnaYibk1tu1rV7m8HudfchkuMTr8cbKXk+tPcPHTGoHgwAa4Ya9kMQ8rI3leMd9l0xKYhmCwXSG/uU1edmkaNp0Vhy9cLzHEKH/0tZBLnTaPmQmkFpxOb6CpbfbGFFce0QRpg8Zuk+mY5OwpzZG+CfpSPyShArZ4PnYjwTWLj/LL3iN8MTbIjvmI7lmKDzaqPJS0efVjPURNk25aUcpI9nm7ee/RGq4heKDPgfoOtviPcfWCT6Iv4O+NX+Zlrac4FvO5wS3zUDxO2zTJdBSRAZfODxAIwd3JFDdUumhjLTJd/8llShm4zGtj2Jr7kjH2xh1e3q4xK0ze+XVNj9vi4IRk2+w4Y8tV3l79HiIQHF4nePVjilqixbdieRDwlXwegFV7mNc8EnH5ZMTpbIknt4EcCzh8Ks/xUcE6P2R5dgKAD6x/B00V54PFFsWC5g/vDHCMkOuuOMb7C5/kYB5aluSmqmTD3DyPbrPJrQ5QLs6xdQZqecVhawu+AelCnCHRwR/3Od0rOK/ss3lRc9G+MkdKR/mbfX9Dj0zw3bmTvGb/+3jZkycxdJI/f/RvuPvkXt5yeIHAgLmCxNKSrlTYnV6E0Lz7hw49Rcld15pM5kwsP8ENp47Qioc8OiG55VHFgQ2CeCDwep8B4PpWlzf7/4V9cozZjMmOxTV3hfEyfPjrimsOKX71XsWffCmihkF9S0D1TADA5j0//o8ReP+JF+Db3/42H/vYx7jxxht5y1ve8qLfvP3tb+cLX/gCH/nIR9i9eze33norf/u3f8vU1BR33XXXi6ZZdeJoNL5UzKZ8nsl3ecaBVDNEqjVVKHBcFIK63YsUEHPVs+l9qbD9te+W4wHKCAmkwheaTDNCieecXdbP+FhC0eeUaFkKDQgUoTCxhWRINplYsTEDRSoIiLchEDapBs8pZUaII33WXP0VvV2XTDsk1lWU0i062qVhh8RFSMcJUAJCoXBFDIWgbUZkVzpESGQ3Rkck6Io4jvecgdIRCbryhZfSxlyNGSoSsedFLxMSV0palsIiQvtJlCXJuQrnzGLJ8FJA0o2ISUVBtukzmjiRQv9EhXmeXVSKhWcUvjUkugozijDaFg4hUihAI9WakVWMRWfCW2kcEWKc+VVxQnLGmoEwl1gzTCMhWXXiLCWDtT4VmqX42pzb8JJ0nmegaTRnLTa4oH6MZHaJZtYiJzsYKAa9DgXPR0aaUIVMzPrEvDV+UD+lljVtRSgMqk6EOtNQ243O0FOtGWxnSCDQWOFzfJUSLj+JU9oxFXE/Qurnvh+fU5zdCCkELkNhm46pWYxlqTqgz5zlXY0HRAI6Mk3Nkqxk2mSttUXAaM08XqOFXDPslhIBxfgazwAEmM/y2XpVIx6GpFoRBhEGa3VZSJnIcM39VxvRs+aDGZ7J/Uxe88mQivQpWQHpkkABXVMTEwEazem0hyUUMUPRlQkSbUW22EXGW2foswbrTNlSKiSKfC1i4wkfw1rrb/ECO1vTsiBkbaFYoMgbXSpOgGi6DK4EDIgmFhGZRkQmDM7Ufu1/gyShkC/gyZ9APM8Fv2FFuHKtXyPxnHK+aVKRbkcoocjILo71E7dsQVwE9Fl1hpc9XjAIWONNgI2nfbwz8qMgf9alu2C0SaZKrMQDzFAjIpdyTDxbX5eQUGiMUD9Lw7KdwIjAcdeelWOQlmt1GFp5bhEr1pL0eV3OaZQZDRuMGFUEGoVGIbBFRFI85+YXCoeGHcOUwbMU9IWN46ln5WVvOaTmhFRiAbZYo5fjPcfzz0cxFtJffE7WCL1G15/wXa6xxocajdYaVzoIIYjO0MuINFag6C2F2L4iEYaktUdcBFiB5vxqiV73hW6KhojICBfzTN3QYPvP1U8oxYqQZxZhXhq/eAba44dpmjHWNVZp9UasDyMyJ2xECN+4wuMVixHXp+p8KpXjQ3coXBFn9coe/rv9We7JG2hlkmwlMPNHOH9KUxlIcZl1jE+qaxFGk43GUbIdOLgebv+xYufhDZxbPMncxoi/3Jhl2TS45OGdGKspPqnfTumKDo0knD5SoP+kZmpYMHnRDEYs4hVTT5LupAjb67m/mWGgBrolOTUUB6G5zqvyxuxBMkGHxdEGhSiiMGNAWvPloRSmErz5PsVdpHlkKM4VRzRio8u6GYEb13y392wsHXDbQxpjGvQgFOptvnWZZGIJqnHJm+ptfrv9MfYnrwQEU70Fcu2AWAAPb+rjV3/kA4KedR32rG5mNd7P7vIzJBct7r1QUAs2cGXjPgqdCM9a20nzJKXBAAAgAElEQVQ6N9cgT5MTSyl8AzYsCjZMGZCc4lfCEzwVxgmnY4QCkq4g141wLU0s3mJyWLBtTlN14rx6+lGKM+dwHItjjs23+xMMTG3j+r1rLp9Fq4dtxTr3nJ3m89VD9B636ZoGF8wdRzqKroDP6GHSy7Cahx9vGSC3uhmp4bagyfFagkDCa/a02HzgXLbVj5PtwMS2ItaWKc47CSKSTCd66fYdouwYvKztshg4/Na+bxKOBDi+Ryjh2tFlVk2TQh1esaeKljA1BsO+4riR4yLP5REngZKK82p1LjwSEFu3JuiONkb5w6WvsN/OMBhF3JFK4cZ9NhU1hobXB0X+KT5IIOHXHguZzQu2z5kU+8tcdlSTHvaIIoEnBYcYIZ04ySuf1Nw3fCGOPUfK1fzuD2e4f/h8tlSfxrVA5gWv2aO4/khE3HL5cSKO40Z0LINLqkV++XGNDGDbqVHu357libTil/Yqjo4Lrux2mFvqoxjPcri3nzvDy6g6mnOekQysSkpWipWaw/u8ZxjP30VvGBFb6CMfLjMzGHHrXC/HN2v667B3x3Y2lBY4Ng4LzZuIaY+HLo/wLYGoWXz9vKvpmd3Ok2+9ncPlw9y+ssxBtnBHdj1vlY8wtjrGE1MeZmovFx3zeGDn2hR9zDGxlncTJCrkagYbp9o8shOe6V+bNO3aEG888WM+ezPsmIVMFw5uMGi2LsBKnGY8CHjKv5B2FGcd32f3CcXBCfiXawTpLlQzmkM7FZ4BEyvw1nsVD+xQTA2vTTr1737v/w/x9wuNz3/+83zwgx/kmmuu4ROf+MTPDbG/ceNGLrnkkhc8O/fcc8lkMs+eT/tp/PRUOzLvUTNe6L5iEdFrtBG8uDLx89BbXlNWXBH7f/kSXKkYL3fol00KZvvn1q9pR4TP00LXT3uMzfvkGhHrpj1SrbU6ZmWXhhXRtKKfyaPivHSIaFjb0Xo+8rW1NMXYz6ZdjQcMrgYI1pSynOyQk91n3+fqERbhs8rVc+16aUUnX4tYN+fTt6qxRERKeC9470n1bD2zzyuvayocEeJj0jQSdGSClkyvGdA/p8wXU8JdubYj5ogXtnl4OXiBgv7s9z/VzxGahqXoGC/ONz+Tw8958NN98RPIn+LHih3RMtWzdNbP+/vJ7xdTLtsiyUo8RAF1KyKQP/uN8XN43xPOs7SzztBJohBnFjcUz41VXyoCoc5EEVgzbF7YhrU04bNXBEFcrM2lw0vBC8p9Pk1l9PP5SAELqef65ue14/nlPz83V7x49NefRrwT/VQua0h01sqLi+BF37/Y738vzjj0AmvueEnVJRQvLaMioemphPSWz+ww/xzeGlkKWD/jvaQx8vwxroHcixiRPw//HiPn+TBDjfM8Gv6EvwBCsbYL7YrYc+P4JbIXWjOy6D+7APfTyL9EO37eWPxp/EIZaKp4GresOTqYIRbAYj/8cqNF5XiSo4NpTg4JrvJiPNAf4+Z7BD1N+NSFN/OUtY1d1gGOWXGi7hivPP0Y00lJoQXnDM0zuTLEnLedl506xPeqOXqasJSHKw5r1jeWcVTI6qjFA4k4tz0WcuvBKeKZDt9UV7LeS3HPuYIn5TZ6KzA/Llht2Dgbu8RdxUXFR3CXbmPX0Th+TIESPDKWRmroPWqxWFmLBvfYBsWNlS59JYnbB3enEkTNs/ntKz7MZ7pv4KrJVSSaxQE4e1qzOJTkak5y/6ZBNtUlQgmWApNyGgIk0yMF+sKQxfJVjC6VuKC6H8+AayarnB5cW0VZHpkntywRhsbNm5y2FR0rztWVvUQC3IkkDWJcNnkEtGDdKhzYbHCIbayL7WfTlODpkSGO5jdhdASvn++wyZzli14PO2bhSxdt4sKTCiVgePdRxgY77I85bJsTHB+y0AKYg1uaAZ8o5Ci0JcOnzgGrSyMOZ8+sDYLePodjls2lz2hOruvnguVJkhs7/OaOAnemJFRMvnuhZMdpm6fMs0gOeEymTcaW4HRPhs3+IqmuwXlzbSIBmVGX7ztZrj6imO/pg9Qs60trE8CWhTS9i8ex0DzqZLj4RIA3ElILbH4YT/B73xJAhFDw9JBBvLmBq5b2ky74fCvRi45s0uEqFx3XBBts/sEa5x37vkJswGMsWJuQprRDX01T6Y+YWLHYbc1yZyrLuTWfvjBk0rZ51cOSh7ZLXnYkIjXqUl+wKZoG4/UBTsa6vPJJxUKvw+FtBu//Ycie9Nn0WyVyc3BiXHBn90N8+VrBm36o2er5/M9Cge3TBg0rzQO960h50LUEH3/iGLv8HpZOpSlmITAEg55mvLjCvr4tmBNf4PLyXr6dSHLrI5qZTIFcu0P5oQL/JjZTSlS4pdXCK1U5sk6gBVx9apaReUk1r7lLXk1/PaA5rng0uJgfhev59kicndOaYvGtXHRykouWj/HwuWtnN17ebrP37Nfxp/kQt3czf+kfJGxu55fbBygsC57aYRBTCo3mlsc0SWOOt39nECuE1W2KpybWDh6PLORJBG0OjBhcfUjzvQsF9YTG+r/svWewZMl15/fLvK7qljevnnft7UxPj8OgZ4ABMIQnAYJcAiC52FitFIwQl5JWIZIR+qDQB1GEKC4jxIgFFitGSCCXBiRFggYgHAdjMaZnps20e/369fO26pW312Tqw33T0+NAgByQjAVOxIvuqsqbeTLz5Lnnn3nOyUEWKX3u7g/4nP8xKmaMf/XCdWpJGKtGQO4/fliSawreS4fiRJdz++B95zVbXYfBeGSAdl9+Ga2+P0P9R/T3p9/6rd/i13/91/nYxz7Gb//2b2Pb9luW/cpXvsLZs2df853WGs/zyO2dIv9DqGh0/u5C/wBKdCK5ut0QeT0N5JvLXqoVvun3f1/j7/Uk1Fsbc8CtUyRP2G8Kdizx5vz9IOgVLrvy1XvvbgcK/5jUsv7x+v120SuQ/vsdsegk5vV1GW/47vUnjd+Nvl9D/s2o9xYA+b9E+rtG6/sZTSN4u7THK/SDXYOvuEf+c6AfKoDW/eaXQAu2hyJk+8SkxQOrIX7H5Bv3DAgapynaA57dzvDuS5oXx8Y5XfoKD4aX+L/TORAK1Z/kkeBJgt3ojqjUUJ+rSxP0TYciV5jaUoRS86mZMuM/UcUN+mjgD476nOh76FAw2q2ybKfZ19jgy82PcfWoxnYa+AYU410+8y1F+c4+oPnXL5xHei73LvTpFSMF8dSs4oDn8QwpdnaSKODmiODBKyFWCBvDBm0peXgzZKyzw2YG3nNRY0547AQmmS68NHScnzSe4cKx6Ng3yASMlkOePZJiI3yY3VSNj1c9fr7yTf6nF7/EoXKd5aJLcqAxYhYdy+LITg8EBCM+nymOcn1oGoDhVpmrk4J5Y4YRzjJbGfD0/ix2CL3ZPmu9Ii/2HIpNmM/O8p+O/jQAP1Pt8flshgcvKMpugo19Pvfc0MSLHu9JNsgfa7M4iDHUUlTzLV7aZ/LQ+gW2bMm2adKonuFTc4/iGQY3RiWn12rcHJb8kvky1zouhRb0SCGBz93jsFCU/I9fVyihefGgTXl0jTuql4gdCPiWnWD/Jlwdc9BH+mwOd7ljUWMVPCpxwRVpcmJJ8+LIQczEFYzQYtQPmPNCzsw3WTlu8Uw2x1ATZobbbIcmp5412L8V8LV7IwWzWBIstU9z5mYDMxvwfMLg7msOhm6TVIKXC11Sz4QMNRTV8ZDTfoOLlsV2SnL/YshSwuK9Oy02MdlMdPm5sz7m9AC/aSLMHpbS7PN8TEdxyXG4GIvx4XqXYN1hoNPc3X4Jpx1y6qrgm1P38GPBn5FrwdlDIEXI+X0WbVezYxgM+pLxWkCp2+C+1VV6psFdi5rnzQKTeos7LkouHInW2WB9hpTfY7E0jLn7ENZ4G3/NYbih+fqJDAKBpTRfUIfQAj7W6iArkmsTguFdxYK1y+w2XDowySOVv4zqTJcIMdhISXYsA3qneXhpnsnWNkbM49zBAe/u9lktTzL8H55ECoNv7ruPaW8LKRTvvRbFlWwNmzhKcOpKHp84/9VzVygOGiyVIFHoofaMx0eul3nsFNieZmZb8427IlWZSb6MLwQtHaOaeY67wy8yswMvzNoUG5qxCpw7rrlwGMqX0+QPt7nD76MFfOhF+PYJSShB+j6D69d/sArvRwTAF7/4Rb7whS/wmc98hs9+9rOY5nfPjfWHf/iH/Nqv/RrqNgD9+OOP0+/3ueeee942vv5pTP3vTpb/9zOq/8noLRl99Qf9tnbmzSvTb3Eai4jiVl99+h/CzPf/7D+PefzeudDRzWnfY9m3q9W378l/fq3c3s7rW/yn6ufb2658W+sTvJ6/H+Qo/V364IcKoLW/ESUTsEWH0NA0corYgo1nCl44HHLUr+NQ5sOPwWrG5dqhdSZvuhxMzPNH6QQITbFmsj7T4sAGhI7mKfcoX99/L0Irzp2+wf1zmtiIh2kL/kofZaq1QyULcUPzyUabx05EO0FWFU7vXOdp3kFSlDi0XuHFAzDkJjmyBkstF3/cJ9PV3FN9iXRfkVIKkQypZNu8t9LnhZIk0Ruwk5PMap+hSw4Ac1M9UqHi373wHJ9Y+TZ3tJ4j24HYhCbwoin/y9x7GLWv866WxOqErLkmPRvy4zk271ggphTvfipEK0BG+4a5rsflKUiXLS6Nx3josgYt+NIhh8lVg54/xlCnSrKhubIPLoj9fHLuCTSgdJaeDUP5HiNOld5qjFDAqdI5ym6WzVSGeMrniV6K4ytwYeQQCWOJyR34i8wZ/nf/p1m1TMY3o7l0Pc2jdykSQYevpm32DwIOz08y29qi1PK5UUhwcCtkY9LFAPqrUYrnyXKFwVDAX43HObahKexKrkwJRgYmOwV4+dRlEqU+c+0Edgg3Zmo8fRL6mV32b4EzHPBzo8McvamRSrAtCpxoXGJxVHGm12Or62CFULQgPYj89HcLCa4Ih594TrOQy1McRC+g7YLkruAq9zU9LsVs+obPJ56vs38pZOHkEN/ZzHD3DY0x0+eabXJi0ONLiTQA+3uRa8GBpMWfb44glebgsmB1CN7/kuKFA4JTi5AcHaA11EdCnnPiDLsr3DkPZ0sHCYYHfOh5TccxuVScZXxnNQL7JThTfpK+d4jHH1T8USbFnYt7Rq3QHFi3WBgd4fQNzTf2n2a30qbQgo0Jxbgf0FjeH/GmV3mkrXk0Feehy5pazOXZQzl8A3ypqec2GB1Iij3Jvg3F5WnBj11WdC2JEvCX6Y8wpLbp2XDFPMPJDMTH/zOmZ9FtneTHl54mc6BD75N1fEvxkU4Lc/qDHN+Y55e+6rL+1cs8s/suCu4FMut9XjgATQKahuAnn/PYSscZXytwoFLn6n44G0tzeEdhhJozK0s8fxT+/e8opirwW78TcuaioB/fJhWGPBkz+JcXz/K//vU8AA9djU5Qf+8RyaerPf6vDxq0HIutF7IcubPBpWk4cznkUtxmeTgayu7zz7/NGu5H9Hra2dnhN3/zNzl06BAf+chHuHDhAufPn7/1FwQBKysrnD9//tYzv/ALv8C1a9f45V/+ZZ5++ml+//d/n1/5lV/hAx/4AKdPn/6+2n8lRurNXsOvuGhFJ0Xf7UUtbgEALcDmzRKVRIaF3vt3uGe9eT1IEOJ1T30v9GrJV/733dy8AvON5d/Q6vfQ8FsVeeX7gS1vfXhTGCTAt15v5twG3t60hb+r1Tf2f6dkvulzobE3L0LgWfLW/PxD6Y2m9mt5k3tnfVoIEH83LLz9NKqTeGt5fEsgelvr+pY8R2VNLTD2+HlFIgz9qmzcqnvvue/Wt9u/fTPJuv3zG7caxK2IuQg0Rx4btwN4edsaek1d4vZaIjJeZz7fDsRfz8vtnwz96nMKiUbiaXPvd0Foyu9BRKLS/mtOFl/7kCbaHDDw9+Tw9jJvxesb+dVaEuo3hwrJwKA4ePU3H+M1Nb8CngRga+9NweLr4dCrOvPNB0EJcHUXV0fp800lsG7xJ261GJqv5fm7rfTQjPSR5rVnra/8buqojTdzTx98D8nwbx/Z18/Sd6MfLoA2t8V22mKyAvWC5ky3T2slxtkDBrFOnp8Wc8xfy5Brwe8deYS5SUG+r/hyxkYNoniCo9Ut1geSQ+satzDgcW+cbyXuZ7q9QmYQMtQEoytY67n8P+5JZlrL3BwW/A/bDb7Y+LfUM9BOak5sbXGndx0lDKzrB3D7irlD4CYGNGMC50qMrbv23ObqywBYDYOtYvTiPTUPS0MJxptlFkY0H2126XbiBFLylQmHu/t9pIKVqTQPLO4SmpraaBerLamnoJZr8UfJPPcurQFQqsLlSfhy/E7K6RU+XOkz342xsZWkmdN4wFArYHc2iFzU8l3i3UjUlscEs4tj1M0ij6xFRmdjwod2nocWynz7mMuRrSpXJgVhv0jGXeDEPFRTkvvmd7m3fImdfQW+OJTggSuaQEjOlL5DvBwt9pnKFru6wPm4w5FVjTI1Dx3a5cKs4Dt3CcqWwUSjxKeuP8Za1kBqCPf01gPFbXYxmF0UNIqa0WaVr50wmKwqTrY0Tk/x/CHJfz+3xUee16RKm6wmNSPbe377puJPciWO764hNfyFuw+lHM5c0XQSJkv5FId3GvimZrypOLXosVCMsyYHHF/zCZIhN/vjjD4TYzMnqccN7r2uqSfBYJhSb5NU3uOZeIy7bmjWJiX3zWku7vf4+ONQSUNOhiwZKQpacdF0uHdOcc2xsXzB/TdrXM5r3n09JD00YM60OXNZ88JBwXsWApyiR6tnoOIGQzuj7Mar3HlTMyiE7ORDTt/UXM3MMGaWKa4IVsY0H5wL+Tdnz7HTepDdyZCz8RgnF5J0HTALilRPM7z/OtmWIO8mSc45VDIwN2RwuKuYLJe5OWQztn2JnzCe5gmZ4NSCpjKZ507tMz8GO1lBO7vLI90O/o7EtzTrRbhrVTG1CmvjgpFahWJVUS+FnNNH+eTGU0gjJN2Y5Lcu/ScMU7G9L+RLagRDw75OjAObv48yJA9cqPPub+3wt8tjfHS5Qnpb8OKhSN2N7WqObjc40ZhjJV1CamCyz5WYINOGYzfjdKyQT30LUn0YPNxClHz+7d/4zG47mB58/ndD3ndec+N4yB8/KIkrjc7DjKF4ave/pmsm+Kt7LAZVm37NYiQ7INWH+65nmJ+JZKv+6KM/UH33I4KnnnoKz/O4fv06n/zkJ9/w12w2+dznPscnP/nJW8889NBDfP7zn2d5eZlf/MVf5POf/zw/9VM/xW/8xm+8ZTuvNfRfNcoVkVEbSvGal7QW4pYZohC0tY1+nSGtEdgqKqX2DCzt9nB0FLMhEZgogtBBEwX1KyHwtUEiuP3VLhB7/AykQXibqWDo1/P9WgMfAWIvqYYWGo0gqZu3YlZiIazmHVZE5jZQJvjGxDgIcctci4dg6ahOH4mWEJhyb4wkSkiaOo4t+xgoQimIhVDsG5FxRMhkx7nFaVx3UEA9a6AMi1AYxH2DV0Cqb0l2SiaeJQll9FnLCKyYWuJZBj0sAiQSxXjHJuu93o0u6ktFpWjr6L27NGXv9UfvgbRoLbddyW7B2Jvb6GkDKBdMbo7E8IWkKy0UFqGKEe6Z96833PQtgPDaOQEIEQgtMbQgHmosJSMZ0ETjLCIjU6IwNaR0j1dASJTv443gUBGNi36FaQEd12A3Y76mvL5tE+EVmTFQmPp1GxBKEiDwicZdIhnpm2TCxmsArSH0Xt6+V/gQ+KZgbfTVOQaohS5S742NfO2IbJQssr7BeCeak15c3qrrFf5kqHEUyL3VllA9BIKBLRg4go5jRjIoIzkcbkUy8YZTV/Eq2Fb6lTUibgGQbmqAFK8CC/d1wElqga37aAVt5dBUUUxaVcZZVVkqQeJW3+pZSWhE82aKkJiOklDEQrk3F6/Oi6/NW228jl0QUewcVsDAsrGVIOFZryt7SyO9RjokMHCiUdv1EjSDOKEZAb5OwohkRkSyl/VCHBWNRL8PQqtbNfUTEpQi53cwReT0qgXEdOJWO8oELfUewI3kJ0Tihv036dkrujMkpvpkwi4pXzLZsrCUxNcG6Cg+cXXEZmV/Ym+OxN6c7Um1eLVeTxqsjllUC5KeMG7Jj5YRWDN1pCfdQODIFq7uIABLR3q1tpcI6XbtffsatvSrsaeeLVGmvDXW5pvEoN5OPzQATbUqeA3BxWnNgS3N3Kjkx5YUyjN4+rhiaPMYj9Q0o5dtHj+S5MnRd7EVL2CODvjjVJLR7TwAn965xFJOMLELVgHuuXYTX1gcyH+Tu+ZBSU27YZO8BvjjjLWbNHKKB68quq0U0704F/cLTlfm6WY8nGDA3TeqtOKgEiG0fJ48UGR6SXIw3mMlV+RgY43trCbsGFwciWF7BrmrFulglFK3xfIwTOwM07STdDMGZduMDMt9Ma6OBNw7r5FjHn2pGSoL1gpJ7mhd4wXXwCn7NNIadwBSxYilruBLwQNnIdmXhOsOYSApp+P0bLDMJACWUuwMRdkq42I/8aF51hPD3N+8SDMOlbzBHeWbJDzNdyb3M95q8vKMQNRHuDIwGKtB0lPsbma4b/Akat+Av0gkuP+a5NpInpWDiiNrmlAIxvwVfjx4muedBEdWAVMxK3zGVMCjd8OYHxCfO8mR2grXxiLFN9Lq07fgeLrBY7sZpsvQsrJo4KvHTT79twp3JbrTqFYSZOdifPh8wHvrfR51XQ6tawYxg4MbmjEvzuEVRWDClbTFwbrJiWVNc8xiZdhHq+jl/WKY4NCGZnUsx9eHhji2rEmXBsyurZGvCb583zi/++EqphIslgRdP8fodgM5pHk8lubHnzNoZCWJnkFyrUm6B1sZiOV9UkpQkZLlpMHPPhNwbUIy0UpQuZDi4qzko+dCYmM+q9tZqmloJODgssIpBnzHdrjpSH5s22G56UTXMKibDC0HhAKu5ffxDuMbzG7BtWnJT7tVSp0+Q+0ARTSeJ5ZbXJ0UdCf7FPotlg2HS8V9HA5f5vAKXDhmUbEN7HqJ47uLXJ7x2RwOOGpcJ75qYikYHqnx0+HLXJkwuDYe7RJ+uNtgebPI3ES0TkNDMFKD+fERxsNzTJbBKoRs6iydta+i0fwvi1fxtk2Eq5i/nuE7eZu7+32WKnFWHk1jWVHszRffKyjfneTYTgeJYHlUI7TmvecFNdvlgZWbJHSdahLIh0xta1ayFu+4LtkYggNb0Hpnh/MHDVbeE2Kaip95rMv/9mWIlwWfvffTzAqT594R8qVHJEZV8MvPK5b1JN3aw/zlmQEr6TxLLw9x/0yVngX3LzQ5Ox29vLvnXvpRHNoPmD7xiU8wNzf3ln/5fJ7Pfvazb0j+8Z73vIc//dM/5fz58zz11FP86q/+KrHYWyfp2BExBNDImmhTkBFdhNZ0sDGdJgMrMrgSvoGSgtDZS8AgwdQhXhBjfijxGqNdoDFUBANAYNMHQ+HbkQFkKoGtNKvJBJeC+5gzJ5GeIFFJkEgOiNPnlZyEIwOTWCDo2iYDK8pEF2qJ1IJ+XN4CgiDohw4GUVITSaT3QlMQmIJtYvQVOKpPyhfs71aJ6S59SzMw98ARkporUaaJVAJLeaR8galgt58mkBLPFqioW6yrDHNWgfV4gnrBwCgEjDp1zHiLTM9kpG9SCgW2hNCUDLSJEgExv8dsu4iPhQ4SZHs+mD592yCUJuWSydJMBDT6yRw1y6FWcLCwMZVJTTr0tEVM98kEIaVuH0NHBlkyiMZfa4kX2nQDiyfzI5xPDTOwDUb6AlOHSB2ZYsqI5jIWdqlmTTxTIrTAMwTP5cb55qERNos2bRUj048R745ihHGkgNDWpH0DU++laxAC7zacIrSBpcAOFLm+ERmL4eCWDOSDBhlfIpG0bWOvD5qi0WFp2qGFTc80QUebARGIkwgtaeC8ctZGNrQJLThnjPJ0YRhLR54e/ZhEqygL4eKog0Rga4krfdJhsCc1irZ2SPVi+NqgiovScfIDh6SpySqFQCEFbOcdYtpgtCvJ+018KdjQcb48fph5o0RgGXtbCoJe6OBpGwPFwoE4IEj5Bo6SzNsliv0QHUSDtTphsZt36DkSvRdj6QtN24kMXS3Yy+AZgUwlBAGRLAamYNlI0zETKCnoGxIlBQOi9erbgsHe5ofEi8ZfwlhPgu2xW/KQaUkr59DJmChxG5hSEh2YuAOfVN/Cb42RaqUY6fmcG5lkMzZELW6T9WCkb9FxJYEp6FoGptDYSmDqaK4rJHh8aJxqwebmdJxNM0HbNPClRCuTdADGns2vdAQ8W1nFIEyQ7CTweykGmBhakPUNkApEBBqFMgj2ULDjmASOQSIIMAKJ8MCmS2AIkm6CKEepoJ2JcdWfJDeI5Heo16LQfTWLan3Mpus7JJSHvZeEpWVZVJSNS0hMCxIaRKxLIAWOTrLhD7Ht5Vn3UntjGIElUws0BlUdpzDQnGz4nKr2KPYF0tHElKCjbexggFQ+dcem4CfRGAwwyfZMhAYhNYYVaUaJoq0s2ilJa6TNC7M5BqaNLS3Wxm1SIRyve8TDkOGupoFNPz8gF9Y4s1MnhiY0NUKECC1RoUlHWbR0tGkmtKQ06GBFzmZ8df844bCBZUXAOhV+90RLPzQArfrnv4tQgvWhEMeDl8ckpUUT34ALI8OU8ndTv9zHCOGP7xnlU9e/ya4+ztXpHlVD0rEd8B2mlzyUEQ1bxchg7eXN9ksLnLmqaSc1LxwS9BZdnHYbqeFwokd9O8nDS+dYab6Ls9MSOwhYiOc4tTPP3VvXefK4YHYDjG2bhdkprBDCFYc/O30Hx7crBMOR0fnihGKoHbJimZzcjPhIZT2WFmdwlE+7EAXYxpcSJIb61Mw+hRYUiz0WHIuxKlzNzPCTL8+zqwLsXZ+yKwgkLJvHWc8u8c6Ox1rKYLqsEb4g0xBk/AHn9gsGm+OEAsarMDAEa8MwX/0wAydFw1gFy5AAACAASURBVEky3K2wOCwQa+Pct32Nrg0jjWiMquMhrgXeagwFxPvwcn4fw6LGdmmJ0YpguBmwfWLApbTk6AosZ3N84f0mp40FLuoEkxWN7Bt85eIQd9c9ruUNPtjqcuzGJo2YSdLzWctJDmz77I5ARWe40Y8WeroWsjgBSVNw14JmrAwNF07ueDwWO4KZVnxso8fX3ASHViVxy2c69Dipypxc1PgC7ihvM73aRWr42t1wqH+FKxNxxv0AqyaQGj6UmqfuBiQH0C04eA1JOQ0LI8Ps5EH2JCslqLtlpjd8BgVNrSc4sepT2gl4ZmqS+88pNkcFYzXwhjWjXpcn3TilGuR2Jcsl+NnzDVaTBuYApjYgURiQWzR48YBguAZxHWUtG78Z5zkrxiGjgbtusZgawYzbHFgQbA9JzpX2sb96DYBiW5Id8tjOCT5683m2LMnJsiLXi9xbn5uKwMVgLcG3999JaXMVACcTyZ3emMLUCi8u6O9TPJOwObmkqTtxZtqbvDvY5GppmCeOJrH9GEcHHr3NGEuHFGMVTSUTvWwfz54hmdnC0KDTGWx7nYsHAmKBZPZciGcZbOaLlBqKZnzAmU6f7NMabcaZ+Y//J0HJ4v3nNA+/9Chuo0YtARILJxDcOydYHy2wZZc4Xlnj8rRgRbh84JxiOxty30KbkSqsDMN/+0Caf19M8yuzSf76AcHxZSgsab468w5eKh3iX4//z3x6F/7sLslqyYCrit81f51CJ4bScb52Ko7VDAmaDmsTmuM3A67nos0eY+DjLS7+4JTej+gfjXbtOOViQD/lg1Jo7WPTJ91rM+9CN9MnTHqkfRM/blI50GJhn8FucYAT9lDKoGNkECNdwmKHXNgGNIGA9MChmh2lKHtMt/ukigOG5YBZ3+NA0KQ6ZPDkxB2s5UfQlkndlRwtrYDpR/GOIqTghWhLs5u2CKQEw+Qp9wRZYWNlXZ4sHYK91NfNIEbea2MS4McF6VgHZcLCrEM9Bg0kxc6Aw+1dup5BNtTYssP1rElDx1i300x7m9yYSaKEJNaT9Fo5tsnREzHWh2KsjNl0MgZ9S9KIG4jQwBcgVZp7WwOShiTWLLJt5ogNDLLxgCG9CwJ8BBMdRbGZxtUDhrppRLmAFWh8P8VAJzBjGRwd4NkGV/dL9t/YYBAbZ9K0kIkMS7FpKjGHNSeFG2iG/DqG53G61uOuWov+IB0ZhloilIUbdrmRz7KiipwzThCzDEa8NqGWuKFFNlbB0F0yNRMRarrKoOi7VOwEtTDJusrxWHqaRTOPrAYcsZYZ0iZxU2BMd0l5BlMtC6UFjdBhO0wgNRGAFg6jLZ/RTZ9ue4h4J8+QD5YymexZzPYD0r6ir7MExDCNgJg1oLPfI23G+PbECGeHTFoxh1ALVq0UoRaYyqAsE3hCEsoEvz99gBdzI6wWHTrCZVXnWCxm6SZBaE3DT7NojbDbz+OGMJJscpA2Y6JJWjXoBAaOMOmqBF0dI9FPkkQyKFpMWQEjPnRFAm0JikWfTNil4IUoETJfirERK3DVPMhuMnIDNLRAhhBv2zjCI+nsoE3NqV4F0xpms/tB5rvjVMMkW8M2fVuTSMbomhJtauK6RzumuT7h0MspfDdk/ajH2riNoQTKy6AxuDFtM7AVG65NrNhjvWTz1QNjnB9yMTs2SZqkwjqWbJPwYjjNOLNtxU7iEMZYnOq+Po4Mube8zYdau9zpe7gKfG0QIMj3BSW1y04ppK1TeMqhp206zTh112StaLNQcrEMMJ2QWtKhkjLZzVtkZAs7VEgtwJBULZeB9BCWSxAzcb0BA39AN0hgNQuMtgziKsQiRJmCZtKgUQrxpMlmOMSaLrClcmwEeaLoFU3PlPRMGOk5oG0sBGNJicj1yZgNmk6SdkxyzAnwhzPkpA1mBGy+UcpSziTxgxSZmkWqnsOp5RlppJhsx8ho2J4IyKoQR2lsqenIkL+YPYSfhqTWxPw4O2M+9YyBzmcZ2XaJdyVWx6ZlSwYmxMwQW/i0Q5cgSDLke7iWwvRhuLNDZazFcK+KK8rUZcCzIxmeiU/S8yfQSqB8i/iGQaKvMbQiFmpsBTYDdmwTLzCZXVd0Onli3SFGeza5YIyZdkiu36DeifMtcZL19jGm0h4zPYWyYEwEBJbCMywCYdKykrR9l55ySHgurk4xtmQzUbMxtYAgy0AO0yzFwTSImd893vKHBqCtfOPrwJ6vKXBzWOBvmlyeEjjlO/kXqTm8m3GePSLYLvZ5pP4NJso2z+Y8Ti0bVNMBJxctar5JoRHtBHRqNpcTs+Sp4TVDCs2orQvTCbatYSa9ywDcG2+jtgQPrVxit3Uv82MRD2HP5N6da1hK8cQJSX5TktxRpFyLtQI0VuKsGJO4A0127+6S+XEfZYc04hKnGbm6HLUGqHqd3KDNckkz3go5MhegbTiyG7kwaiHotiNXgBvuAU4vr/Oel2IIDa4HVddh+e4GTUNwqtPDmvZQwI2ZyBUh2VOsTmmyZcV6TlKsa0q7oPIhmVoHUR7FUCGpRshaSTN2bZK71ne5MCuZ2Fb0LYinfIadHaYWJPPjgNAIN+Ri8R18K2XyoXN7aVbHt3nJjLF/EzayFv4E6HifXMW/JbCib+GtOighuHPZ5b6tq3zlHpgtezx3IM5kBQpDHWorMcbLAZ24Jtvr8ORhyUev9KgkXKaqu1yZEtx9DiojgpQClQpp9g0KnRCtFfsKXXp9j5E6SG0wHnOZWoRaSvJXxX2c3llgY2TAjOdxx6KgnhD0uopSO4o/29AJzF2DJ04IRjsJjm6HGApqWYsj/ToHdhQ30yHvvqjwTMXRm5rKxIBcB7oZh0JXU0+HlHoB34q7fOI7ivmxyDnkxJWAs8diPHhFkyx5oDQnF3wuHBA8sBQSL/hUrqbwh00yywZhYYNDq3B9Mk8/02diF/oWtHMJUhshrTjc9XJAMzDoTAW8c/kGN2KKh65Ho+6b8CczMRBQqKRYLI0yPA83xzRhPNpAKK6ZhEJgemOMpXs85ricXIK5YonBho2FplewuDHhM9uN4TcNMp0uN8Ykd61F90bVk9DoDqH37hS6kDqOTs9z9pDJv7zSY1C18Q14PHeKlyci8D9zOYnbFyz//Aew3/Exyh//JVxcqBYZXgtZmBGs5hXFGow0fQQBc/lJcj2P8ljIJSvOWFUzuRvdWZJvw58+KPh4OQMr/wLdHeev7zLoxGA5n+XPDj7A//HkF+hbDt+s/hzTQcDfPKAIOwbFzTqf03+NWznCk/ds4xuSl+dnKIx3yXbhvrJkK8Jo9M6de1v024/on5bOT6TYch127RE2x4ap2JpO6GMOPFxD8W9iK3yks43jGxxoxvlvtmu8r66Jo3Ea0U697maJ2x0Koo30bUI0UoEbethG5OKmcmkKvqCQ6VN0m6RGuuwIjak1Ga3omQ5+0aaS8KmbDltmnELTwzQC0kmb5eQE6yNj1IaK4I1guHGKnsWmO0LFdOn6kmmvwqzRR4+3sd022g1ZyyS5YKbp2QZTfUFqx4COIN+S3MwUUKUOjXzInx3axxMTY2S7PjnRYrKj2V9VlMMSfncM33D5jniAzf5hbNdmc9hkraBJGQMS+MTTNVazKTpLU3TLRQbCQoWwEoxQMROEYQxPJ/CbI4gupK0mpvDwpGRsp8Ph7i714XGMRApLQVW7ZDshLx71mCidZ7+7gJP1WJMl3tddYn9Yxsp1ccbaLNozLNbH2WinqSRimCryDZRhHDOAfN3GbpXYTZQoZdY40fI4VW1xl1NG+XF0aLJmzLCsRwlCiRVIKlacuO9jejHMzhB2uwBBQCORZMspsD4ywrjyIGOznc9TzxsQa3NjKGDdcChbaf5m+CSXE0PYTZsdnWNRF2gWbDKeDYaPnw3w3ZA/GPoYa3KYcKJNIufTMyWJQLDh5rhSMnk5OMKfzB7kiZkkqZ4k6Qni3RjuwKKed7l3c4Ug32MsHDDevc7VSYe59Cj9pCLuGQzCGI0wRayXodixOeJ0KAzVSM00kEow3i2zOOqhQoNnRg5zNneY5miC0DHYLg+z2DlIst4na1YYpYGQMTRxBjZUCgYJ5ZMd2FQGfVzPoRGP4ZsercAmaQwIpWKcFjl7wP7UGvusOcq2jUlAJxOCUYUgwZWxPK2UJGf2WHMDdrM+EykPY6KGqxSttEHMN1n0ZnhmpoAyBTvTA+z+BB2d5VxmP4G2SNg7pMMKKq9oF8Ev9kjR5NJokuuTOZZ2x5jwtpkcDBithRT8ATm/TWAJusJFBTY1laRa0nQzFlMMGOQsLo4O0Q1TKG3wcN9nVJTRhiaWaOObFnExYHtM0U1JxGQPQkXXt1iLJ7laTBImaqREgmI/SaER0EwKVsNpil6ZIAHDXUkntKkbDlfjOSoSFDGG+y16MsnAjIGfojai6M3UkV6XVLfFTScOJFifGKIVs8hqh7F4wMxOlTPXGmT6guFBAmHC9IjHn0w/xJKRZb7oUs27tOwseuAy6FoEoU1LZLE7Bu9LrTIqQ3JWgI3maD1k3yDNfg3pnsWXM+/katbiXHKGxWqWl6YyZNsGzxbPsKjG2JVxvAlFRndJqIC1fTkyh3uIlKYRWiwHJtuNAdVYGtcOSMqQm8Uig/4ESgu0llR1jJrTh12X0opkpO3h6i4p5XNtKiDu9zlzPeTTT2xx2T/CudwBdvs5nHbIIJCUEz2qjst0vc9OPUHFzHCdHH2ZZsOV9FQCnzRYLjU7Th+TvjDx7STtYpJ4P2C8bdAeTNL1XHqyx8q0gTH0xuykt9MPDUDrrG1QdwUndqMsiwV8nIbkyozFsQ2HA9eewxwIHj2aISnrvDQp+NTSOc7HLE5dGUHFanzocoe1IZgua8K44hn7IAuZcR6Of5U75kEJTbopcboJLhUmmW0vERiauA91HAr9Jvv7dYJYjG5c887KBofrK9QzgtVhTcOSBDXBXcEVzh40WO9N88HmCwAk2pJ+UtNyBUEgqMYypJpVqikINGznIvebF4ZNig3NsRXN/ysPc8fKAC8V4m9bGNXoWFUWfTzb5J3zMQKpGanBajrNwtgqB/sBF7FJBXkuTwsaaU3Diuouj1pMNXfYyStujEPMhyCR5nj7HOVggunWJoYStAqa3XScoVbA4/smOVhd48aYYLgSQ/llZnbg2SOSXkmxZZfID82xYlnccT0S1sOqB1UTO9TYdDjVjnHBcTi8FsVBtJzI3WOpKBn3A9R8ES0EL80aDDc0vhPtSszkBvxBOMuhVYWXiEDx2rTi4ScMnhk9RqYTUh5TZHcNMuEWsX7IE9k4h9cjYDDoG4ynFN5WdKrTH4rRmRhwxxJcH0ti94YpNnr07QCjJ9m/pWgNhzyfiLN/S6NNRafTRWjB5Rmb/Vs7vH/vku2QBPevQCIRcDYW48HLmvKsJt+CfdtVOg44VRMn67Ml42REwEuWzTvmNI8dGOXIGhgtg0vTFg9eU5gziuvVNG4flkuC+1cDrFxAZd5lfbTLe27azLs+Bzc08fguyXq0m1DzJ3iH9RLTK5IbYw6bI5LyWpyZqQaP3llECcHMaojvCI6saHZcA5UPKMdHOTN4mlJNcHNaszYEyRBOb90EoOXPcszvs9JxyHU0L42O0S47hCGcNNfxHZ8Huy1qixHAupaNcWo5ZHxTsTJucqo2R7w6wHc0zzrHcJKX6FkhDz+raWUM1iZ91qwilw872EpR3Mvx8Owgav/0z34cK9+jOgIxT+Afy9I34dRitNExWavRdKINCzniM7nusVKQnFzSVNKwnhMcik3w1c7H0KKLjG3wC9c7PHscJhp13tX4BvuaWzysvs1T3MmPNUO+fVjiGYKbV1Kcljf47zqbDCzJiwfSZFbbHMlGGyr71qtcGxOEAirPf+ftU3I/on8yUjvvJbc8y7s22tzZu8LRIMBMS5azCYTvYAwkhViAECGO06Fo+JwWi+z4eRZySXbdIbQcJiZSHOsKJrNtpjpgdx16tuAFK4ZoSZb9oyyuHeFqYPHkAZ//zz7I5PxB/pX3BBMKRvsGvpPj6bhLxYSs6pKRPSaMbe4ZLDHa72MHGUqNAcnYFgMtWTLT1NMXeG7GoJmw6Y4nuJwYo9FP0Q3ieMoi36uRq0ucoMChIUV7v2BHGiybBt9yJ8hbKT442OWQWudIf45Pbm/zgF7FGmrS2TXYGk5xxF1h1mgw0pGMd0zu7tRpJjWrJY0Y2mCjkKAwEIS+ZsvNoR1NKBUD2ybjeFwaPcLfTB3j8QP7MAZNqrEkFgo93KQ4dg20ZsztMRxCUdTY11dIbVLpldiwCsw2u9BJ8BfGIW5kbI7hcYhdJpwBJELcjo/T6zHk7dJTJiEmBgkCP4fApdDtML2RxDdr1HJRRFWqN2DQDCEI2HA0W+4Y29kinnCom0lK9S7DTZ+xRpd4q8CZ9QUOtGrMrU3hNSwmalukezE6aWi6LuRMcnmLVrpN1THpB2PUuney5h8jZoApA7YclwvjsDZk0C0KbK2xAo2lalwZzrPpJtlyk7QMiYMgv3KUkzcNQkfhhn0Oiptk/QprJJlpeXTqOartFJl+kuuDO6n1Shzd9ji50MX3sjQSDkq7mL5m18jQVzbKAzeIvEpKfUmsKZkow/3mMjdmD9C0UiS6dRJGh35Hc7M7yka2yL4ujFWTbGtBIE3aboY1SzHTW+czNzc52elw71rAIJUnsGeY2TaZam+QH2nSkJLhsE88MyAfr3K0Ps+B3jwd28RtCrKVFHfKJQzfZKFgcz4f59K04pqlEK08d7Y8prwQCyiPFSiP5hnpdwi1ponDKbPKuipihT5tYVCJS7YOBFiuQIUKacLOWEAs3SeVbXKws0V3R/HwSp/7t0JQBmagUI7HnxeHWBhJsDVhMN9NY9oDpiyPZEazMuKgDY9hfH6m/zzvHFxj1rvBlmnxO8UP0g/jqEDQbxTYVWnqIcR7dWbnNvnwjcu8b73OKi43e1las0muZk/xwrGQfjEBk7tMWmVSNCgfaNDKdlky02RiTXJWHUSACibwLBcTgduRrKdDzp2CldkE38oPsxlf4aLMUenOopTLxYMhz+7PsRZPsS0yhBqG0aRDE0v6NFJr+PFtjFYPV3mknC5PWcd5PjhIS01zR+izlo9xKGxxV6fJ7HqA5UviQnMos0a6Ca3aBCUxz5HyZe4uz5ETZaoZi7arWBva5Vqsj6l8ju1W+XH5HMPWgF0DeoZGIdl1LNYDGFvJMHHTZNCcJOhNsividOMmlu6DOyAV95le67Jvvc/hrofhhsRFm4LfQLqwv7vBT13/Ww6vLZKxO4TdkE7H4mpxmIFMcHEiSQ2LNglKzZC1fo9crIwRahJOgqHNgPnhMR6fnuXq9DhtO04/FFScFB3hEPh5bhCj28lyuO6y6Tjf9b3yQwHQ2j0PpxkwPw7Ht3zWi4KHFqLflvLHmJBtxPV1mnG4kRtiwh/i2jhszPSim8y7QxhGg5OLHi8eKzC9o+nl4IXpBAvZCfLJ89x9Q7NUioDMxxd2eOxohqndAUFWseo5BIYkEJKT1Zs0eieYGxPsK2+xr77JxSOK0YHJSkkgOgaju5ucnRzhxdIRjrWu4plgNCRLJRO3Y/Pv/lyxnjOYqLbpFkJ+dzRB24ju57lekjgtkBqeFjmOr2jcYQ+/a2DuGtRdSbO0yvzkOKVuj4185Jb30nSJbSfkA4uCrmFgbWe5dFCTrkqCOKwMwW4wTX7QoWcLKnsnABcTp7gz/wKVWJGH69HdQYOMIOdXANhNjrCvscn8GMyUc1Q3I4G8PAnP7ovx55Pvopa5ycxOSLHjYcZCaqbkyB5Imqq12RcMOOu4HF4DP2uyPq7JdOHGuOCdnR7ZtRYN22WyEsWCjbQ8fFOzZRe5MR5dK0Ag2cnAXUIh+pJmPIqtCooBlSHNeLnDbinOo26co8sOvhTYAWhTMbUiKKck3nQfq13HDqBqFCh0LOqxqD8bymSkDhP5Do8Npzi4CU4uINGGbkxTSeW458oC+YokkGB2XbItj37JYa0eY6gZZQMaSMGR5QGL+zV5t0E4quj5DusJg+OLgrgHL08meP+FEG2Cb/aY3tLEh33mt4dYKkX3k01sCHzfxM4p5pTJjGWx0I3heoCI4W7DZh4W07Mc6L9Epgs9laad1oircQ67fa4e1CQ8xei64uaExb1rIVagWRrTXM9McmTrKoEE29csJE1Gd1NMtisYWpOyy1haM7URAeNz4yWWi4JGzWEkbAHw0cEW7U2HesGkZQck/BipLqwWihSMl5naUYhcwDXjEAVrkxPLYO2aXHn3LC9MmRzcXqM80uX98wFWFxSa9NWrAGSTYFQDpFVDAe17P0C6raMEKSmItX3GejtUkyDSgnde1ZyfSXFwXTHcNfjijx/kP49V8WZ/D8a/jtc6zpGnYhwc7WKFMNQrs52FRjfLz899jUbtIQwBc9Map27wtWaenxdnmWqM8e2TbQytmKvNslPQ7FsNODc+jKGh/fxzb7/C+xH9o9NDu08w3OgyEa5RTQhuTLks2GOUs1Msx0qs+w7K0ZjpLfr+Dtcaaf5D+zTX3Ak6CRc/O44wbDqDURrNHIMdh5qU9G1NI5Om0I4x1Vec6t4g22yRmlf8jZni2+ZJVlMT5GgwNfj/2XuvXknS/Mzv94aP9D5P5vGm6pTrct09093jyOFwl0unJcSVBFFaSLqRAAHCfgRBgi70BYRdQQYCuEtgIZIgl24ce6Znpqe7p6urusvXsXVseh8ZPl5d5EDYK95II0jUPJcJZCAQ8bonnv//ec5Q9Dn/IP8DzESSigTfGPQpzRPIxQgX5pdbxFGWku/y76qfE2UFqjVCs4fce91juVPG76Xo9DXUsaTl5xhEaTKBQ3OsENo6j/VVNCtHfDfmO3dUcqVHfHPQR7ze5M14wnuTAXuFNHoMZ77B4VKF3dwpq7S4qp3znrLHJOXwdKQzFBEZzeEo3+DcznLUyyMHGke1Jex1l6dqgydqnfeja3wvuMsNjrib7OGvByhLC5XQt/qY9SHcd/izjTx7ssD7yTXysQO+hu4p3DhO8di/ydRPc6b45LjAzsbs5h2WugrHQYan6SXcypy8lvC15DmxkTDFomcUMcQcJ2uQEWOuea95XzEYqiqBqpATMWfuGmO/QttMcRrc5NrQYzI3WT1UWJkO+LXhE/7T6LuUlDG2GnOjc8DV18cwh7mr4yghldRzvjbaJ2lXyJBwdL3Pvdkz1mYXdEp5JlWD/WULrzBEFT6JJjCsAbPViMeZOd+YPMTVYr6r1vmJlcJJNJJYsD7ts+n7/Kr4EV/jKcvCJ6tF3Pcv6abyTHWb5YnkijegPC6wMoy4dpHmnWOXtc6UKDF4tlIEtUhzYFH2p6STCQmC3EziTtMU5mlElGOeh38SfMDSUFLrz3H8CM3vcDv9nGbxHFmfcFAK+ZtiHlWNyAc+VqiQ6VncyX/CH3h/xvVezIrZQqY1gpsDwnRCzze47Xq4GYujmsbHbgrlQuMyvcI4ZXOWLDGiwA+9NUJHsH45ZqkD+USyNvNpxT6PCjqGGrE8K1Ceq6xGPm/NIyIpSfCpOJ8TiBm6OmU5OmN5LgmTHA/sKmduntwU7szGPCqOOTc9vtn9mOxLyWE7SzulcRnn8HSNcyq8riucrneI8l2ycYl6L4OLzlesT7FUn05OY2CoHE8Fb3k+v9Gf8Uk2zQwVa6gz9FPkBl2+N73GTFFodhP0WOKmVM4MG3V0hPQcSuMBu22VlY7FaVnhcJTnk+009rbPylRiJAN6QuHPKwY9a0a48wRVn5NX5qRih/LQIgtoUZ6mOOVqeMC6VBmQ5Xm/RnKgcMUPGerLPPB2+CC8SVdmOBQFrmovaSRHVKMz+j1JYexQUcaUUiOKwZixkcUQCZdxgaOKQF0KKPouqp4wT1TEMGI+NrmavOLNsx53zhyWhhFrbp9BxcGKVF7WynRLcBZYNNWInfqQ91OS17rGWNcIXA0tFCRFg0EzJE4c5rZgeewSySxS85gUc9jJhNNyhrN4iUhVmE91CCTHTZWZabPW1Rga4GtQ9afULwdksy/4+JrFSFfomGs8aOzi2CnW/Rir0efp9govbgd45YhZReOVVWMtd8FOOMJJC06Fy74q6FguP9rO8KC6RKAErPRD7oljNMPlc/F3O0D+/4Kgfe+v3yc7h4MlSPVU9hqC+qlCqwjPk1/n5pLP7FLn0ZZAiJh3l/4DjmppPl8Oefc85tlSgavnYPqCx/UcK13oV2KOti+Y5W36UchqD95f2+W0Ahk35LSesNaV5HIBj3WT0jxAkQl3TveZOjd5uawwj1IoSP7yDQXVa3C68KxA76Y5ytU5W65gD0LOlxJiR+NJExqzNDstaOcNGgOJmZE4mkpuBokq6RTgzgFclMB2J6R8yOoxgxWDWldwXC5wnjlEbE5ITz0mC3NKLnZbZOOEr3wYkj9XCScJzmpAswu5ucfTNYF23gQgiVNEUiCR/JvU11kK5rTsMjfmT/E1OLUzXOuf08nD9tBAlZJXy4J8opM51jmuwUYX/njtNvnMlB+lNX7v4UL1spZCPrUsdk+ha+dYGsH1cMpHRpYrF5LLUoVkKWSQAV8TfOMwIu37RMaM1X5CLwNbFwnzeszlaZHdQReA9DThiy3BtZbBXNW4ErzCNSCKIKlGbLQSpqvwmWWyfaYxsjWsUshLQ+HWa8njDYXz5TTamcbchGGuxf3+CY/X0liJJD1dTKWimvCimbDekXgFncJI4dmKQmGYYWUQYI0iziuQHTewBwm9ZYPmvkosoHop+dlmETuAYUOwvD5i3rBJBR4fpyx+5YuEvpXBy3e4vy95uV5m5yTByEXk9TmZi4QfvqGQdiXmWCHqKwyupODYJGn4aD2NiW6jpSyqlwrdmuRZeZ1ip08CXFOmrJ4mBBOdVpCiV5rwu69CzEDwYF2lxhf2aQAAIABJREFUWvO4cyR5f13n88o2K689Dlbgq6OAA11n+zDzf865axzymW2x2ZY4moFTecn+ssb8tcUndhElzLAZukQTje5qRMqTOKmFgvoytcOk2mWtC1pe58bmCoGi8U8ehEhNYJeqPFkRvNV+ylk64r2HGq4hGddh59ij43QJvvvP0UY69a5DuyZ5PL2k2ZdcuZBEJTjKNdkdnPFqWTCw6tzflxwuqaylPX70T2c8Wj7kfjjmn7U9sn4WPf+E//qbb9CcRJw1JPfOu7za1vjK0WO+7P4tP1RX+Q3H4U/fUVAl7A2KfGKZ/IvRpzxZ1whUUA8CguWQrUvI/9zly2wNiEejX/QS+Ev8grGkOLxqlvisliEUKh/IFeonJczeMpaW5lVZZeYbfKYZvNItHqo1Pihcx7MU2qsZprqgpC7Kvv554Rt8Hm3zyt5CyfjERkA1jslaMcbUo+ENsULIx2D7OYSmoXg+18cHLLcnlPyEvlB5VczzPM4wySokKvhSUDIGiGjAy8Sg5cKDcYF4rPBfvpzym08C7h0/xw4G5MKEJIIw2CITpOhnFSaFEenqPgOtwuf5Tf48s8bTdQ1bRiiGgzWY0O7VkJd1EqXOR+41nsZ1JkaKq/oFRjXhOFMDp8uy8Zzvb+eIYg8xWWJ//DXK/pj1mUMp7vGfOX/CG+4eTnrMcUaj2OlxrfWKX5nv8dvRPlklQsZdXocx3kUR41jnwbzKRKTpRDoPknXeV27jyICvnJ7iaiHf1m/xL83brI88dLfM01SJiWkwlDpW18SovCDYmKCmAS3k8VqJD0s7jPIahysJBScgZ77Gy55xtaXT01RiXdBbU3iZ3KNxscJ7k0/5deNPebRr8zi/wea8wzdOX2OPS7TmTeJYQxMRZhIhUfhxcJt/PXmTy06DYuiyl2TwBdyfxrzzOiIbOuyMzngjOcMsR/gZgW8JRkpAKI8w03NUIaiPBQiPRA9wzTliHjFIBMPAQOgCoRl8/TTg1458ym6CnVjsFzcZ5hR2oqfc7z1li3NuyZe8MRgS6glnNZP3Oh3so1U8S2Ot0OcPXnyIsPq0s5LTfobHoxwP21scKyVeV7P8hVXiYO7zW70/5eb8BdmgjWE7BEWbu+IE1U24P3L5Vssnn3eoaA7jQoVQbvEdkac91FClpDQb83byhMtwg8CpURoq5EOVaUbne1GRF4rJRnyJHU5xkoWnaErzyM/6/O7Ba+5eKjSihG/2fa7OIyZESL/AcGKRcWIs3ydPh+HIpOvnCMca2kRD8SVXgykbvRDNVRC2S6QoKH4FOVLpzw2Knsp3TJOzWswoLThc0/l81aRjltn3lyi1fdZ6M9LjgFLf41vTR0zQeBGYjBRIa2f4mkpRD3GUKhUkq+OEZjjlN/t/w9X+EVe6Fyz7AxrhOZEuGKfg8brF+tik2jWZ5zxmOZ+TlRSm5SMzCWupS84ags/TRWquwXv7DggFE4/d2SkFJaCmDNmJnrEbPWKuxGwFLu+4kJd5TjrXqU0TLqVN2Rlw03lMT9MIjIgg6/FKVnEilUmUpSUtQCXnT0h5MDUsDopNjtNVLq0ST+pVXpWW+RP5Dh8oDXpKhteRjbAlyYpK4uucjJYY99Osa8e85XrcOtbwhMnMEFzUE77hfEElHmAhKaoh+5s2Yy2FP7Z4YKSYI8jImNGSxDMjxuUQYSwMYO6eXaBoAx6LOnuyyMc3UjxZXeNv127iWDpxrNKbGDwJVxGzEjNHIF2FKC1BAcWSbE8k85JGe0Vlaz6hrp6ykXmCkQvo6yq5yYgrlzrL5zmGaYvInnOYqXNu1MmPruEnkBsNEJMCYTSnlTK4Nt8DoTLqm6Q6HXLjX/ag8fLDfwVAkJEovsJBDRoXMFqOccMUb2gzFF/hs22FYqbF777zO/TUBoeZmG8+FRzUTd7cT3A1nVQ8xYhhWklIzJD39CekThbKzaPadT7eFYQzC0O7pDSDQi5gsKjiYqJb3LncRzhr7DdBlQntYoajqsrI3cWzFtfJuYItf8Kt4jHZroJWXPT37DUkMrrCoCCxNBcFcA3JylDl2qTDqChJhwrvvoKPrma5MlqEhvmBwtGywkpvoRDutObEobtoQgaCdMJefczXZyH+zODtl/AsP8KyIOuCHsHpEqy0F2rILCVoDCDMSsxggnZpcJKtU5qOOanCo3STnd6Evaag3F38Z1RJSBsOK5eCj64Jbp4kvFbvc8/+DlNV4fpLlUgohKsGP7Msti4VDnNNhCJZl2OiscSMYD9To1zweLq+uG51L4Wja3x0G9Z7Pg93BGtdSFdDviPfYPPSYZKRGDHIRoBxEvLTzS1WRn3Oa7CyrxEXTawQXtfn6CFs9me8XlXJNHyOpjYZD06WYnRdpX6q8roKj1eK7PTPOV+KWfFDbp9KEuC1plGdRWgxKFpCagp7TcHOecggkyE/kHTLCuulMyodwWk94t0XknYJsjNIjIU1rqbCwbJGKuVhOQmfKTZ3j+D7a/fY6E9IuYI/vV7mS68SlPWI0FVoDHwO1iVvnsQY2Qi3axA0Qm48F1zUply5kBysFchqI1I+9NKgliXZc5XTmkpUVUm7Cv0CfHt0jaHp8M5ziWPCR9sh6YbP/T3JRys5VvUzSmNBu6kR1SJcReHKUcLUXljob0cTPrZMti4V5prNu609TitF5mcWT22ozvO4HROk4GVGcuVcImVIIsCbVejkBFYI59ka9zdV8CN29wXzSpbthx2O6+BtTlB92DpO+Pi6wGkabLXg2w/+mulf/jFf1MtsXCY4dTifvaAyVkkF0FXTHOSXaMwcTpbAa4VYAWy5fWZ3Av5FKcudSZrt3ga/M5/xw9bnXB2kObj2ir/xr1HddFgeJDysrvCN4z0+XxGk0p/z5jjD01XBJAVLA5f/wS7RlAHvjdLsLYPe91GrBkYMN09awKLn0vm3Mrh+if9vojzrsdV2OR7v8MQ0uSwKnJKOpwkuM3OemxavMXhetmlXBGktZGiZNLQzKuoFW+oBzeicl3KFS73J59dDKktT8qbPYcaimhrQX8ni1VVQY5CCOErxW50j/uHZR9zxXhELl8FazFjm6CUm7XyZD980OMvoGDFEkcbv9X5IIfUZh7WYQk+QGWrkXZXbfkCeBFTJWC2SJs2vP/UZ+yt8kL3Da9WgX5xSNPoU4hEv5ATXmhFJi2mU4o/UqxxmLIQz59P1Il/Eq3welql6Y0Z6Bkcz6ORMumGIOnPZvRjzn3zRY22k0IwEW6KNkorJGjFOSedokKY10Bg3ZpyWtzjNbnJjcMa257M1D5kERbJ9hUobMt0UeTPkqByAPuKq+l1CIh7Eu8zULH95t8oPVrbR5lmqYZqcLtBzDp+UJW1dkvR1Mn5EPpfQSqVwhEnoCpwkRy00+MZ8jztDBUPPc7rsMLQgdk2iArxai/E9DdeK+HC9hrERUdQn9JQaWtHDtD0UW/CjK01Epc3DjSpH5SyG4oNIuDnsUJvFdCjy/dQSU9+kORvztW7EO57PWJfcnD/grf5zCkxQ0lPs8gk9VWcU6PhCIdThcD2NnVf4+mBMfS7o5DQeqCuMhiFp2SZT8jE0ybri8+7naS7cGi/DTW7N+jzdVnFWXS6boBkK6+NzhPSwvJicO2VTvWQeFehrBa4kF5TdAS+Dq7RnFRjppOM5l1enzLdcOiY8L6jM7QRij1Y+4cerNpWyi+VJYmlTUEOyGej0daQRkBqV0X2N0rlGW+ToGTqf2Gv8T+ImH2R3mKpp9HONL9Qaz9UaL0yNs2JCWpF0cirDWoRpBGRNh0ISs9ZWyHgxshpgapK0FtNuqnxPbPB9ZYsn9hzfuaTR/xzNvaDmuby3LykPVY7SSzj+dbT5LpKQA03hWBaxyNPsKUQzldJMpxJbHDQ1Hq0Y/LS4THVe5M/Lb9ETeX4S3aZ2sUPVh+uHkDM98oWIop8i6qZJpZ7y8EaHy80ap8M1giMT35IYuTlN5YwbXsiVHtjTLIWJwmURejnBzFQJE9iaTxiVM5RyQz7SmyiDmKwTYaFg6QljaVNyBE0/5tdeh1hScF43mKYL9KSOt9RhYkn2NJtWyqCbz1NW+qwGr8mEM061NK8yKdbaUB7DWifPV+UjbgUvMZMZL6ZNPh2/SSEa0cvq+CJFNojR9BZDNceH9hV2x0fMDQOFEX9s3OCxUeb9TIpRTfBpfYe+mWdKBh+DqgjQUz4p3WPQMPnDr+XxFJ25JaiM0hTEwvV2bHkIQn7jJOZHepazuY0nFWZlyUwL6GHwV2/aPK6XmaYslsYOZ8EWZrvIxkmD/GiJtKvg5FNMUjanOZNnRpnq3EONEmxfR0TGwsWzZrAUumw5UzJRwA3niA3zKe/Sp5oIljsCI1a574xpChMnc85y6il24TXvKZ/y5uCAf3p0RGqWI+ck/MreBfdbz3nn8oC3vI/ZSQ2I6yHdTP7v3Ff+3hM0KSX500XZ02a4IDrCktgBqAWLrfkJheMTEgGPGxXuZrLsNvNkYpNYwMqhTr8Q8ea+5NGVHZbGi6/dmhYzDZa5Zj3g9gGMs5LKLOCTqwp7hRU2nEV2mWLHbGRcYgEn5TRWHLLrd5ibKfLBnNOtBdFoO9eZhqsMMmAEEVe6Nt90HmBEgsLP7UkPlwTnztt0dkPS/qKH6N9s6JxGq6xN2hxVBZlRmYwPnyyvsdX2CdIJD7MWUxGhSLjMGdw9ShA9m1iB2hguciqBAjv9HMfNZe4cSU4aHmrLYGAvgpF7NY26M2NmwSjnsdqVRFmDt+ffxR8syiSK/YRWFdKDGpVZzHFdJT+eMzcllhFjDMcAPNuBwihFNN/Bz++xOogpOJJQj6E6pxtqVGYRJ8UKajHmlaWxc7HoHfppQ2c747PXFKx5EfGlwfNlk7+9qbDdjvHSi2eV2Bm+f7XM1mVCu7IYC2/kfCov4cHSDrVejJdPuHohaZUWIeTPsgo3zxRUKTldCjGWArzLhcSoxpJlZ0BpulDuztOr1IYzRnmPwhS2W+A1Qj7KmmxdLu4h5S1y7A6a8PZJh0d30+RngsuKArUuqz1wAo/SDIL8z/vmBg6HDbj9wue5qaG6Ltgx9pmKmsDxWsK9A4kU0K8PuHIBsqFzebEocR0XJF8+jVGshX2uG8y4Oot5lpJcPYdOSSXjLFTFWZzlprlHsyU4LOU5TaWRQtJeEXwsmuixpH6s8mrZpFWB/aUy944kPWWHr3d/TAIMi1XOmoCU3L3ocbEMe6tQ0z0eaiYrvQSpBby1n9DLW1yYKp4Wcd9zmV6YJMDjmsnNM0jPIjpFuNM+RSaLefFFdotU/oi39hbB4Ef1DOkvDmlmV3j2hrr4XcLekqCfXsStJt//QwZHEw5WQI9BMxWqF30K88V7yU1ihunFe/VqEdUjh4sS/IfulP++WiA1q/EPWnV+kiT84+y3eGSY/M/Tl+iJ4I/upWhmQ0IVrrTaKFLwoqlwvR2QPcmwHkU82lLZPgbGCk/nNv/t9DnPlw2MCGZWgVAFLZSc53U8Hc5++r3/K0vcL/H/ArhBhp+WbmOS5kkmRWJ0MaYjOkaV1Tgg7ylkPEnaU9j1YorzKVgXIGNWEpeKSMgZAWXRZ2c25Op5lV1/hAh9fhbdIhY6r/RlXto1Xq7Dj3YtTvMZNtJddpULUnMFZzlLu1Djb88L7PkGl9E6T5XrDEolpJLQ72tQV9hVdNrGEo9zWZRQsBY4yHyEV0/whU5brjCdphGRRNdjPGlxXINuSue1mmNj0uKtzmtu+CMK/RKqU+RvSyscq+ucLNmE2RS98oz6PEDERQb5EodmmWMty4vsMrpb5kYxpJJXmac0IkXFkHmuTivcbg7JKgpDXfAyreFHKdp6nhCLcjLnB9Mmj9xlXrtVvFKEuRzxxZciZquStbIgEFmKUUAz6FIPDK7Hc/RBjcDbZJwRfKTcop22OFZLKMIlMCQi5aM252xZPpvColNIs2H5/HvBMZ5lY5Z9zmWTLx23qHsTXEOjESoEkca6lNSdkGm4SuQ3kZ08zekUu11m4Nns2SmO0xm2YpeZ7eKv9QlzNv2UgjRGXKayFMMRKc/nPBTszBJO8nl+7FwjnGroQsE1VfajEsnY5OZkwqFR5jS8z50jmLqCp6bFRbHEK9Hk7dmcpdYS1sUmaqTgY+ObEVhT4lDSH6aYTVTSU5e5TPE02WZah/ZSTC7ns60/BcsjbVhsTh20CXyo3uL3Tz7nZrRPrhbiGQkN9xRMH78AtUqf5aJOj2u0ZQrbXYShm6HgbGBxOF/mYbSFMoZUF+xuzCwWPJE5HozynGUKGK6HEQdMVDjO2byf0wn9gOLQ4jxTJogUVv2YmhqQGSfUpgnHorHIC8ucYBR6zJUsbzMgP3fwhiqflg1un0rqQUxKc0jLPN1KjEoWMhlSic7E1NAGRdJuHdvQ8Q2Lc0PixRZWCB3dQDLnavMLLm8kDNAo9xOWPMlOP8YVBkN9yt3whP9m8q/I98+ZiAyByJOOc9QTBc2KSaX7LHkRfVHEiVRUe8THswaBrzILTTLpEE8VzHcC+jdCvn29xg+XbkCcol2LOS7ksAODQiCpTAM2ZY9jIVif7eMZAhGWidwa7/Q0fiM8pGrMUAsxbwwV9MhmKAq8ymfQajE5K+JiLaFjw2M9gxQxyISg2meup9lxHX591kVXQrKGz2a6xxVlzu3wklV/TjdtEeT7VNUBlhKzfpEl60bEUmVo66TliFvjFutum7LfRUiFPSNDXzXxhM2TZB1VFaQTBxELklZCBoFtCtxUirwwyMYlOmmTjAipSY1I2PxMK9GZZNiwJLfPPXRHLjIOgV95JFgea+xnV3jSvIGtBNyJH3FP+SsQMZZbwpA6X+s8oxVVGAubvUKZncE5Ob/PQVnhQXQVPdQw9JiSPwIZkBqo5AY66+0J26lPuYJHa1LiLDGxowmfeBtcO+mTmicEyYgbwZwgKTNL8mwGPdaSM2J0VmaCdzJ93hr2qQQRZSWmLlz8dPvv3Ff+3hO0l60pzc6MVgHynSqJgJXh4qDWL25xzz3GP+pwWgUZZXijdhchBFkjYa2bcKkus+KesNqD761co9qPSYRk+0QivTqWfsS1U0mUkfxHD39KK5/jR1cqrPcXtuOJp/BO7HFSA0P6SAFXx6esvF6whtOtGak44WZvSkve4qguCGcQR1XEbEEG8xPBJA1BUmQYNrnclqx1JIEFn20IBqJJ2vV5VVOxB3UAWmmLnUuJWgg5Wq4ROYvnYScjVgPJUsfhoA6lieSkopOLY5xxkxeZDfQY1nqS5QOQlmBiQ8uusuJcclzWOGlENIaSs2yNb4knnGVXuOu9wvIFw7LkjeMFMXEKZXanJ7yuCpZHBtp5wlkZVDvih8t3yTHjZ1nB73+6ICfzWsRxIWSr9fMg32ZEe6vIZ9bCIGRiKfQ2zxjRYG9F8PWOj+bGzHIOItIoOJB2FEJNMpvb5I1Tig5ovliUFQYFrECQSQ0wI9BJKPUFn1RUZDHkY9vk5vFC7pxqEaIcsXKacFLRWRpViHsLk4fCDKI4i+EleFrMBIVmR5KuB3xcNrlxpqIYCeF0UQY6KJms9F3C9Hwx7soBiu/gpSyUi8UUTM01TsrQ6Pm8XobMuQ4jFZmY7GV17h/AxLBoaC+5dyhpV7Lcag9QpCSX9zht52nloWuobJ4JfAxmqxrzUxOxlPBENVntSvpqFbsb0MmDPW6yNXuCHoOjGVjtKXo5xFBjXtY0bh9JlEgQ/jxg9CdmkaYVkhpssH7Z42AZhn6R47LCageygY9bSPhiVXDeSCgNFfRE8nQzxd1DiZsZ8rMrC+L1raDN5NImUeCwbnHzTFLuSdoVjQovWe0tklY+Ll7n2HnIuy8TojR8se6ClNxRVrmshHz1+cIa3PbhtRngmZIbl6/wOwaFaEasQDsuc2cvZqsd49uS/NBHjxb5I0El4vax5PGdNH++ZOGj8V91Ev7MWsOY1nEzp/z0PI92ofFfjAeExWP+0L+Nuxby7iuXj7bSHDbgNx6csf6wz78zc/jptcUHoN2eyp9ZGYpKhF9qoEpon8/p1hMqU8nYzhBqMP7sZ/+3r3u/xP/DsF1yQR8zcUkMh3Iyw4pV1mZDRkmFu6FDrRJwUljDjHTijCSvn1HwQ3ZDj+v6M+7pn9Ehw7ra4siq82S0wWlYoT7zGE00LlSbz7QmZ0mWvfWQL7XblKMB5XgMsWAaFPmOKOGnJjQHIKI82vQa18cBH+ZMpldCWk2JIKBr53iyq7K3FnO9eEZV8VC3Az5Yvs5FpkEjaePrYGXGCC+FLiVxkud13GDgJ6wYHjfigH//I8l6zyeUGo+adfbSq9QnJn6cZpgqEpsmnq1hSygz4IozoZj0eZyT/GX+Gi3lOuP5bT5P1klHFloQMchL8lZEQYmpx3OcpIBIwPJdPk5f53/MfBMnLtNwAlpxhTg0maoaXwlcfnMU8KWBxx+8/jH/uf/HOGGTJ5UmvYKPmjtmPekyil1K8QETqWEmgnQ2wlMiUlFET6vwY3bpDQ1G0qApesghOOESr5UajuXQtdJsG21uSJ8tK6aQCtH9DMg0xizmIDC4NArMoxKet0o+8Ym0OWu+jfTXOXALPNo0OK4plMRTTGuf9egl9y5nJEcZzvw8ajAl3dKw54JEn6NYHUQ34ifB10ldfJXN6QpZkeAhSAeS7vAaEzfDJ+PrfPXhjPuDS1ZHEUOtwSt7i/35MsFVH3cz5Ge1bda3Wvxe+ANmeporfgqZjenYKsNYoW1CYerhahp67NFwZ2x5HW5pJ/hXYvpVSc13eF0K+Kt7WS6aKbpKitlUYjg6iWfz1lPBw6bNkzXJ8syhraX56+w9vvvGHV7qG3w7vM9BdpmzXIGq71EIOxihwM3M8A2Vtpki8iuUHZ/l2YCKC9uTiDvzCcWhwdVBwLhgMTYzuBmHdychX+6GZDQHQwlJuwq7sYmVEzSkRAQ52kkNN8iT+CmUTJkdLSY3LTKId3GMDH5JpSBhkJQwYoV3zyPmVo4w1aHImEE6xw+2rnCw7PP2yxGur6CIAFdL2IhclEhSimOGRh6hw9J4wHhmcjJK47aLpCKfv2kWCGOTYmCQ9l1WJ21GFYNxqHNoa4QCmhOFsjqkbu2TdT22T3SeN0t8vlPjZX2ThhLx1dmADe0SI0p4u3VIxujScyWp0Qh3pqHIhEFokDFjNN0l401wpykGGrzOWYRTm2JXkO56/LVzj7+Qb+Fr60zDa3zt0sRSFUQ1xrjvY5Ri/ihf4fF6QCN+wi2+j6eo3HdcvtH32G4FyKDG4/xXGBt53DUNsSS4WnzELfMnZHyVCn02oxlLwZgwtjiPy3yhbNCPU5iuZFdzMPIx+1qd5qsrvPPC4/d7P+P+8CXF9hWS/i7qrMRReZ0lbcxvX4Rc6cZ0kxIPlF0+y2+wY81ZSlyqrs6fLf02o9xVvtl7QjoRNObnfKpe47tLN/h46Rqf1Ld5VmhSGUfk53Ceh9hOSAsXoSRMZwaP01WepdZxly0ev5fwbjAjMFR6pkGSzJhGCQ13wAflNJEMkdMUQ8/iQC+xry9xKuqkqgonpTxUYopBhJ2eIGY2szzs5zW6mb97W/l7T9D+5PGn1EeSg4YgHthclGD1QjAoSp4bO7ydPsftKbxaFmjS59bObwHgGz1+51nIQb7JV85PiITCq1KOtQ4EaYnfLXHFg9FEYMSQ9RW2Ji2W54JH6xZrXQlGwrxnYrgKF0sKawOHSJNcaZ1w6zhhaGb4YjlmcxbztcsnDJNrHNdBGStUbI/B0GRuAGOVvSXBcjfN5uScF9ky2y2Jmza4OtGodxek4rQCKy0DT1OxE5fGEGwSGpZAG6jMLIU3z89p1TTKfZ+z6iJn5cVqzFdcj5/m08w8EyctyXcVrpxCKpyz1xSM5xtsTLoc5etMTQU1Ebxf3WR5GnKWqfKV+cc/f26S+91DEgGWVWBp0uekJrh1kSfTUnm8qfAr5yGfV69xO/1txqrKGy8Fl9kMmUrAp5bJzoUgAS5X+vg1m0+MFNdOJZOsJCfmfCLrTG3BvZ878o2ycPVcY/Bv9Z+9mta4NdkjUGCpD+5SxJlvESPY8F8BULxcBLWOfJeT6wljVWX9XMdXNe7MQ57rGlfPYX8NalaKoGXQycPKQHJvOuUyu1BhIiEoOGCnIp43BdcuY0QhwXd1WiWBHWfIzMGcL1jylhUyH6m0VgpUT1XaRSh3JPtLGnoMfgniRKE0yKHoIU9TBvcPJJ9Vr2LabTba8HF5l/XTBJmPKIsJdivkh3cACYWuSuJAZ8emdKjR2VERfR2kwLQssi11Qa60JqnBmARoqAO29uZY9YT1IwW3POCbz2MSTbJ7vlCeDyVkqx75YZFaVzKsJqxzwoGp8+bBojy36EUcLUuelnQ22gui/eyKhhVCZhbw2ZaFTFS+POyTOCpKAtOMhxGkKU6hl0/TqzisdyVKNuZZJuCzox9z61hyuW5y1FwYjLzbjjhWFG4eSwJNZfdC0s8L+suC9bmPCAWbLY9+PeFRpkxpBlfPJbIY4gmDtVmL8xLowmTnEuTWjB+kU6S7N/i81OVF8xOm2ZB//EmHX/2xwtGPK/zadxW22gn/+1aGzeUphTk8XjfJCgM99IgnKm+P4fnqwqHxZivm23WDYKbym3aHRIDmxEyXFFY7knQ4IeOCuX+GlPIXug7+Er9YaHOd616Lb2pPWI8iwsjGUXRaqSLVWYrn/g4feFfoaHlO86sEY4V0PEL1p1jzkJIyIq/6bIpT3gxfsmEfEzQueNios6q59IsjHoplTqfbTOoamqGT12E5dU42dPBzOkd2gY6Z5cW6CoaGESXcVA/oVAwOCjofFDI8EirvezmuXqQoBgbTooowwRAhBWOOZ6p0ciaZeEivqJObT7ndP2LdjamKNnl5wjw956yZ0BYpdnotMpGLUGLi+VVSvR2o+3PXAAAgAElEQVSy4QBNCrzaC/KlI47tEq9lAXdURJ6XuZRNPtBT/FhbpSdt+uqYaZJnNWoRxpI/MVeYzHUiBNeMY7aiU5r+Ba4ukKmXOFbAj9eu8t1lk5cTm55v4BuSLjpJekKjOmdbdAkO55wFJWIBVmofWx1jeiFfep7hHz1LmMs001BnNLCIHBUxCWnLgLvdFpWxyofeHXQ1ZlS1EKlLXF3hXC0iZcBeq4DjG7TSGc4KWbLqmKXgEnv9Nf/oCFadGWkilKjA0iBiY37MSMtxbpSQxoRCkKE+UChNcpQmOTLGhMTyaDpj7vWfQbqFUpqjah5hNiarTPlJtUElGJN1I+pyRI0EOxAUQ4X02KPmSDLegIwcY4Ud1kd9qukxga7w9cEFnmIT5LMoVRPNSMjaDmvKMXQjPGFghgZnqTxH6RKHNUHHtjnKlXhq5bjU0phaTGte5Cuez3phzoslwVwEzJQZTxDcCV6A5dKtuPRzAlV1saSKn4MvijqfFLYZKBkGVgzKHKIVGoHN3A4pewrtgortCuZKBs1pYCB50izz8eoKvquwNp5Qdkc05h6OrlIt7hMUT6jFIWthjx3/nFcnOboZQbcqOIlVHsUN5kd1ngzvk41jMtqY68o+yBCZ+BSsCan6AcOKxawasB3vsaT2UIOA9Fwl42TYHkDdkdwb+1yftzko2/yb6zlaQqWbS5CRwUToiBS4oY6QCZGp0pyFDKohx3aB134TTQiKwwoOm9yeJ7xhDEmyI0J1xqSkcGRZeKGG9GxuBDGDis1pVUcaMblRnoxbIjAz+AbUwoSKiFl1YjwtAeMFV8QeoRPyUFh86F/jf9G+yrlZpDI1EDLDehyTaxmITpEHBYNJJmGzlVAdxEzdLf7CuIKnW+y4J1xVz6nlx7yepnnZqaJ3qnRVjahxQC11xh8MP+RG0uKbA5fqNEL3UxTmIV/xP2MrnjLU83x59hBVOPzHyl/xz1qv2ZyoJCiMY5tECxGpKdnI5SxTITHAzIU8TW/iJnkepXbAAW0Qc3ZRxx/VmDs7DMcFnvcazFWBjUpggZBQ70lSnsVdeYye+hmeYnGWLHGnOORu9AHtRkQpbrHrtOmlSnyw/C6NqU1zKniyFTIsxKiNOb6tEaHxUW6DR60q2fiSSwGlqWCcaCihQkYKloOYZc9lK5qTRArVkcogbzNK6wxlCUTCmd9AaU15sbvEv776Jh3VoD9L80Br8BdWgZ7QKM/+7n3/7z1Be/D4b8i58LRRZ3k04Mk6bJ1BvBTyMl7hdmoKocKrpsDN97m2/nXm4Zyh1ufeKzjMNbh+4vKztQ0a9j7rHQmapBWVeDMaEbcXB1PDS0gUuJVMGORD1jpg50MOEgN3oGOWAlKhpG3luHV2yL3zDof1Bq8snayj8PbJc2RY5ryi4qoWN60z6OucVyTxTOVwCVZcjzfbrzhVJSs9SRxqrLVj3uwtDq0XZcHWcMCrQoOd6aLHRZmrrEQDah24KKa4eZ4wSVKoCXjWQs3YW4KbjkJFnpBPLrlcN9ClpDFLSM9CDpeg0iqRCUI6eY3qzz0NPl4D2ganVo212SEJUDgp0ZiNOa1AelrCDGNOqoLlvkBJBMOsoHms0VK3MTOPaUxi0jMY5gTkVD61LDbPTaaGzVn5nKI9pjU3KM9gUNOIE4tHxoKYLZ+qTC0dVxNsDHxeLMNaB2RJ4a8ab7PcG7O3DHYAtbLHbORxXKxQGQ3xdagNFsN/pRXweEMBKdnozJBC8kYYcDxIoSWwX49IlQKKFyovV0FL4KpzwbONFGlX8uWLaFEimdNQpKQyiHGzCXGkclgX5GYml9kixjDBsWAnMlF7Ohcrgu1zkEKiJjCxFvXISUbQzRisD2ISQnpjk6wL7UKKJF44dD7e0Ln+GpKdBG+gYwcx7a2Y5R5oSYJwBBnVpTIStGshV88lB800S8aUzFxwVhYMSnnMtuCsKlgrD7EDQa+S5qW2gTBbvHEAk6WYvA95V9LSfajCe85jFAmmTPjyvMsrQ+eNQ0GQkjRPVVKZiC8sgysXC5ez05UpkRCsXUaclXV0v0x8uXAvOqxaVKYxveriXbiGzcs12GxLwrxJnH5CY6+PFgvObxU4yCUE1TrbF0dkL1S0BDrZElcvJadVBW81hfAULkqw0ZYkdUkpHjDISGpjmKU1TrJ1rgzPOWgI9L6OIiWPMwl2DF75Gd/OKWQDl+76C16sCeJYMLMFwZHFf/e/xdztvuJH1U18Q7LbGbPeN3BMFdeAfV3wZuyx31CotGKyEtqXNu+oB5wVTaxQMqgXUCT08wIFsN2EyfH+L3op/CV+gSgaPdZo04jnXJ8J7GmBXDxDDX0upjr/Mr3J/yq+hu8v8RPvLvFA5a1RwreOsmz1BIUoxheC5WmbWtjivvOUQ11loGR5YjfoacswNrnq7TMzUxhzBTlXOdEUrB2PqW5zEqwSpCYMZJFfPfN50+mSSxySYMbhoMQssOhNBckkRzZQWDuxefM8Zh4bxK7KB14Jv9znRnSGrUtEbLMbveZt94iSIxGhJAk0gn6a4NLgwLNQE8F2K4KwRCp22HRe8bA6xp6pFPQpfnWGoe7xfWeXZ8LmJLfERaGEqsVsmJ/SCP4P9t4kZpYsPc97TswROc+Z/3znW3eoW9XsqupudbNFipPapDUtDBhayIAXhgCtDcPwyoAXNuCNIHhjQAsDhAxKFqwWrbbYTYrNbnY1q+pW1a268/DPf+af8xAZc5zjRZbtHTcmKIjub52JyDhx4jvfm+/3ve8p74VfclU8pD0+49GwySyyGJkGWWLS0AJ+Of0Rt5cntNOIRqzRig75h9G/Yrilc3x1yKi6phvmDAyDT0yblb0pppSnwJhT8p5S0WZ8axkT6Iqu0acdjPHXNb6v7RJOPdaJzdqQ/PWzUw7mF6xDC0tk/LG6w0dZj+vJK742eUEtXtKNIxaORhRLfs+o8lKz+BU+xan1OS2X+ULbwSnkmF5CSczI0CmUFIfunHCZcDDWmVkKhUl1HWMFJisEuSVZuNBcp5R9jVe2Q9+y6Bopog5+McVQElul3AiPKDRynFcNfpZ3mNcUX8ueI5oDTH3NlsyoFDN8zcSyZny/sc+/kO/y76IDMuEymxX5sXiXtdbAjAOKSw09EliLBueNFoMti3rnlHIqKYUe57MmpycNzrMWF65D3M74zjhhd52TyYwqxxT9Nd84CqktN6rHkS1oRNBKI5raKdvmx4xZ8JNalZky6OYn7EdnfP3lGFO63J2kXFlJLisSzVgjnBNsfcRRvc4f3tORTkhGQj2GSr/A/9LdZtGDoWZzMnG4mJkEds6ipLFoJfgiJG0scDG4eSZpOk+5kUq6iznb2WN+3pbsZj5b51Uuwga65vDc2OMBb6ioOfOWhuc38GSJlpGRFjM+Lu9xay458noEZYNhXbAf+nyxdJlEFnFuIcwEN/VpDgQ3tyPuRXNk6PN6VeSVVqVIimNOuFV9QnQVnot9Xp81cVce10eKUhhTkw4q63Lc3GJeU/z2qyc8mKYEZhHfgijQ6EU5WkHilARJJvmjlsEz2yRwc14vK2TYbC0v+O3PI66PWnSSKb/5XPErz2eYIiEspLw6EHzWvkcmS7iJg5ErfMq8FF0+a5v8vr5NOsqxjTlJZnDhWMyCGqtjgzg1KNoprh1SzAPuLl6zJVY0c5/92QXxUmCuXD62WsSTIsXAYGmaOEZITcx4N3hFy56SWCExGl+6VyinGSrLOdeqvI5usxVaPMgesW+9ZJ7s8rLh4XgRIrVIlca6FFKonNF2V5T8nL0FqCzBTqCXr6jGGtN9D6sa87XJEd+KHtHMZ1TVCmFc0A2gM1e0xyaHkYZ/L6V4b41e7bMfnnLZtXDKZZK0yFnm4ecpzXTAYdVipumcpDbmUrE9hZvjEpXU4lv+a96df4rUU/qlHJ8pa33Nq9wkaOvcm19Qeyr4sShwe/HnU2h/pQFalksap58A0Hfv0/ZjopLCS6DRjFgGOqV0U+y/artcLTjYus3DwWNac0V5rHPadLh5pvhw5xYt9YbmCvQUpo7Hbf2I7oXGvKQQKYxaEj/bRVkz9kcKioqf7RnElxY3ahvj4pOGQyteUUoj5js5qRB0A4NGuKIrzzgttnhW2+XrPKM+FqhKjlCC47bBSWvK7flLzGWEpgSVZUj91OGdxQWprpgXTFThjI9bd9ibLFAo4qXBmZmzN4K+08DK4Mq5jhRQ2Nw6F3XozIt8YxbRTIactLeRtmJulxDAaVtwcLn5cFScsTve+JFV9JeYQ53TYofK3GdQB2OxR3uVcdHUSKcbhmlcV5RmPoENkZBc+leRGDwrx/xHTzMEgu1kwbpp8tw0uHqZo0tFYaaTeiOunW7+ZXi53aE6cnlSW2PmEjGwmBckL7ZgeyQZ1Ww0QMPj+S5c70tm1c093ijFFI9Tjvbr1EaK8VdzaUvb4sG55FHd5IOBpJyEoAQ9IyPpuyQ6RJrGdjLBiQVf7AsSTacdjhluZdwcSq5caJi1lA+LNlcGAqEEk5LAjOG0JbgxEjy/V6AzhnlFUBcx3SGQrtAV1DOJBLrrnMACz5CAoiF9XhcMOl+1QRbdPt548+xKtUO6c9BbBpN+CSkg8xT3ziVp2QChsC5DMCVHxZwbF4onV4t04kMAQhPcWkR7oHHSdHGLX81CGhaPeh2u9AOcSDDcNzYKk77iohzzur7N3elTYgN2TjTaXswFOrcuIlJbIVaCr0cJj2ybg75OpgsGbsxHV/a5cyxZFmO6scN64KCKOT+61eNaX5FZG5auMVQct6A1V/TLTbTia+6cKJSm8L77HXxdY1p1ORF9HrxRpJogMXQqK8FI13BrGouZzeNtC12Bu9Pm7vmc0N6oJsZ5iYtSlUYYcdwF6zLn5EbOJ55NioEhdaJX/4jB8X+He/QrfLmv8U9+R+d3f00h1OZ9/If/OufDVRW5H/P+y5zyGMwIXmwJfrfp8nd9ny8PNix0JZR8qlw0AYtagd2J5GndJDFAWpu9lOrw7Ge//xed/n4Rf4nhuDl6kpOtBG+fw/derLm2GPDN6SdU9dfUxRF18YRiPsWrXDB5B65YGZ18jutG+Dm8ECVe1iXjsMAj+YDicIdGeoqj+Xjmgt+Z/Zzf4SG/PBlQWCuu9lPGsUXkQ7KCXx88pJz4pMKh4MLbpc/opi9on0VYsSCXsD/U8OKMgV6i6kmuuIdMIp1RYNFP65xs67QXZww1k9wL8CmiIiiODHxbEJUUcT0lQyNHJ9UUtwcxD8Ix71rfp1n4Ga04QDddksTjwvTYN6bsG0/x9IDDSo2goPjWMsW+vE/n9A6/qj/l286n/F67zf+8ew3dcrk1yLkxkUyNnLk7oq0GzG2PsBSxpfpcX424GkcoimzPXHYjm5Eo8kd2nR+u6/TtFjJwuD1/jSImjiu8urhPbTnDlQFkBteGOr9+saZaSrFzjdXKJswFqWaCgJJI0ZXOys143LZYmza/9anNBycKKxQM9QLX1yOa04T3Vw8pJE8wxiG6CKgkK7rxkIZKMGKPZ/ENbiSnvD/5nJlV5cEiIZNFEtOmls+prhURAlNBKdExbAdvZnPSKKB1JWYnpalM3pk+ZnfRpxYP+T1xh4f39tmPI/azc5LEphGlhN2Uq3PFWy8V/cJrLPc5puoyTrf4pfk5X4ufc6G3CKXDPC3g5QnSyFDKpbG0ufda8RuTMW5RctLxaEZjrCznZ/kVvlwf8GofHjcKaLMq9ZHOnVDxvXlMSSneP4epu00psCkHLk3fpKArUivH0ubsjodUVzl6UORlL+T7vQbn9jbHxQbnTYtBVyOpLRCVV1xsR1jFz/m1y99HOvDF2uFx4GLvJ5QagijbRiR1GiKnpWt4lZiTG5Iv7isKaYnF2qOlAvKdGFfNyaIGtXWdSaHNIrUJShZLChi5JFY6f2Tf4dSu8lo1Ge3HjDRQyxo3VxYFoWhkS95fPScyCvzt84iyZeIaAk1KtLXGRNcICzlFMaWWHOMqQWsh0FwoyClDTIx1kSi8RjixeaJX+ES7waKY08sTriVLakmOs8yx5i5R1MG3ihi+QEiBNZnxzfALRonFTFnooUbmF7nMC3T9mEQYXHQE3ZHBLw0uOIjPuJAOeggfpK94U3VokVKTgnFJo1/VeHED4kKGxKUejPnu8kP+wPolPCSlrECUVvlZ9xYrrcyV85y9I51ISgJPcjopchpVGNslWnLOdjImdwWevkA5GrtrxXbmMjm9xmBs80VW5oer+1iLmIqccNqOiV2YGgWGusVzrYsvXK4HQywyfMdlvNvjRnWMV4qZWxl/XX2BrlIKKiByDIalJid2DT8os4oKiLnF/izj9vIV11fHHAc1tAuNk8kNAkdHBJLA3FgRtf2cornixhAMDQa7BrGZsHZNwrzI0rGwLYeoYHIaHHA82iY8tQhHOk92DXaynF4kkUaGGUp8HEZRmyvBmLJcsh3OMBKT0E2oxE1Siqy1IgtXZ2dR5R/8XPDd6Z+v4PxXGqA97S+5d3FJIjRyf8NOlNJNsW+WC9ybHxGeLlk7sNLLvN24A8CP3nzCBy8UOYK39Cd4CXxZuUZntfH20iKNlWNTtF9z4wJGdQ2VashGymB+n05ygZOAQPH5NY1obHLfCvFtEFrC/01qrvamALw/2BSn3/Sf8dK9w8l2i2gdYeZg6xtgNCh1eNOWRM1zDr6a0dJRzNQWO6sBZw2BERY47qa8bhe5OpAEVUUsNIaJwJCQ5BWkgP1xyFEHSmvJrKDREDnuwKIjTO4Mppxp13ECjUt7g27mNSiHKbnQmFdDrg8Vlx2Dv396imlL1mWH8gSGLcXc9mj4iqwK9mIzh6dchTuK+eJA4MQeh8Yt6t7nLA2Nu881YlNRXsG4HFNdC+phjJdFvHPR5sOCzv0jRWjCQHd57/OUiRfw184lIoXUyMlrOdf6ilKYk+oK5gK99IqrA9AyCG04tDrsXwrC/ZD9Iaw8iRTwvLrH3kDxzNO5f7TxNLPyDOqK7imcNkycVRd9uRE4ebqr8bK9RXkeMqlEVAJFfQxiO+GjpsWDk40X3qGzWbt+A37ryQWn+2t2x3DZlCzsgNbUpDINiE2QwuC8CbuTgMMuGGlOc5VgOpIvPYvbp4qzco177nOungtO6yWuz4ab/VzMGPWLvOopJrbG3XNB6FrELQ39TGd0I+VT2+bmOQzMFu5qjgT25gY72UvsDHzdw1yUGdTAuIx5ctXmvZcKpSsCu8xRBw4uFLMS/BvtAbsDn9fbkKPRb2vcvAArV4hIMDyQ7McZA11nb5ywMB2yHB7falAOQOo576581pc21V7Ip/sFrvUV5WXCtAg3zya4kYZAcOy0KaYRd08UquNy89ZvATAur3hdjXlwqJgUTQ78zaDtzhAK6YRoapIbG0DmXn2fWycp5RBSQ1GZpSzdDesddFN2+op/9V2BphSZljG8+M+5PVhxb/0h305Mvjcu8PPbGu1qhGblODHMqorf+eiEXivBTcDyFZ0JvN4WvHAs7s3h5Z6GpuD+AH5etchjwVZN0FjBOJ5x2VV0JxmRYbJw4fLhLwyr/0MObSnYnk3IJwJbKD72blIl593JBV7UphqWCBzoyBN+OXuGp0dkeYF5q0wuBT9wq/zQLvPSciBXnLgeV3OfejzhhvWE78TPmZmKo3WV46BOMZOUVpCvTIaGBYkir+X0LnZpnu/jBhm74oywuuLzHY2W8Hl7sOazKzbraoomAwr6kPvZKaPqksc1k6+nAdeTGU4yQddykl7M7egl7WBBBiS5RXEFuZtxvKNYuAZ/cOM6p1dL/L30J3Q4wTITJgUDEdo8MWoMDIe6Psf1Rvi6QVJc8bRtE4Uaw7jDid0kMQXzrM6zyhUGpRJh0OZly8X2IiwNFp4iRxJ7PrdGEd84WSF9QTPNuJKccT05IQ1yzLiAmRg0JhWebQUkusm4FlNMEmTuoGU6O9Mpi8ThejLlhr7kZnWKXsmwKgnuWkNZCTLPSJRAMqMuRuxMEqorg0e3tgi7HZwA/tk32/i9lBSTS6PE4bpI2w/IrJSrlUtCZWMmio8r23zYuMZLY5c/q9aY6VWOvS5/XHmbQpLTSidE5YRpHUa74FmgzAxXN/iTr5s87kk+Mj2eiAJbccCD5Ijfin5KtLSw0wI35WOuySnfe73ADiWdrSHdaoiQkiQR7K5DXN+lNFzTSj/mkDYDUSOPNPJYUglWjB0DsdQwFoogtdgPRphRRieQLEoQuIo/vl/myT2DK4xpWTFGMWPVbbF0rlGeGmhri57MsGKNUrTFcbuIb5W5zoqbowwijYukQC9YU1jp+FaZQN9C1wv061U+3GsQ1hQvi1Veu10MWcRKitQXNlIzsJcWQa5zJkyaVkq6t8aJakwMwZFVwE8U68TASOBFyySue+RRlZXUsFXKWLSZpjvcWh3TSyJqjstuauJIl9+YfcSwHTGIO3RXI35Nfc7b1jF764T7y2e0VgNSBQsFtXmEE5e4iO/gO4K1EJw7da4RE9YVly2H29oTuslT8j2NULd40+iS6SaGkoyLa3pizNJp8ijq8dyxedgt8axdRmQuT8MS89jma/4pIi9iLa9zdykZOxYXZRc1BhQ8ckweFhy2VzOu+WP8ksB1Mt5ZJWypnHK6prEeYinFsOHysFtg0Ehwy2u8WkhxVaS5tGjpMdfVgpZccjABO8u4EZwwz4tkaUY7muCogGedBoGTMy1nGw8+U+PQMMiImVUqXBarvGxsMXIKPLM6+LlNVUs5EEuMtEhjnbC/WtBaLPje6iFvhRf01jZJaqPCNoksE0mDAgHHdoPTWgVdTvFtwcq0yfWMLf1jGmqO3zBY9CSzms3j7m1qsy5B7nE5b5BMJFsnBn+n/2/429mPyRsJZ26NUDq8MHb5uHSVkhxSjc+JKPBlc4eX2xqRnUGi+JfOVf5M9MAJcEiYqAK5ruHbOsW1zsDVeLEn6Uw7aEBRX2OYOeuG5M+KklwGLNqK5702dryiMc+4vZzwNy4+xhYpzVqCosXAsBk5Hp/F1//8c+Uv4/D69xX/9uUXHExzfnpwwN5640xdWWisXcUrd4/vxM/wBz6HHUEpgnv7vwrAw8GnfPup5LjT4Z3JC0LdYmBtU5xtgJSeC7SSySyN8GJ43tkUfB0txfc99qYbMQgCwbpmMQ1NXKU472h0VwFSKCZOiWljRimV3D6P8V34zcFnZPE+Ri/jeLExUjZjjYUHIuziScWTexswErs6vgNjYxd3FXLWFGz1K5y3HRbVFdcvFOua5E0X1Fd9rl1/xaBWoD6NeborKM8F/Tq8G8XUDkNKMqYW5YRzg90LMAxFbMLSrdGL+zxqdRi0cg5Gih/fKnB7KJk6Be4YL6ktBYuKwjUvAHDLOjdXp4zLcHBhY6/h0YGguLJ5Vjtgq/Jjmuuc3oXgZU9gSMULD25svs5FqcZebvNjx+X+0UapT6ghO+ElQsD7X81FzT24N1WYOVwZZCw6kj+1rnA1esrahuYCknrGc1XEScEQU6wMRCwY1zR8q8DcExuW7NwmMnRiG/qOYmcCmSFpqzqrmcHShcuK4vVWHW+mGNopS8OkNYKgk3Na03hwkaE5ksu8C0BSNimNEkJ9hZWB3U45XTu8ruzQHkjO2gI9VLzuQXuacNmCy9hk3LZx2hkvHZPbZ4rjQo+kkXHjXPFFbZ/KUJLbOZ1shj3NOLolObRNtvsGWpTQP/AoTXSCvZx+bFJZK2xRQM40+nVwZzt05ucA3OzPOF13GHYlleOc04bi3pGCakbrTcbZFnzwZLOHnsVdmlPFZQeOdxTnRcXbhwopwAsFftsmVhpbE7ByyWnb5ua5Im1POe5sWLpfPx+icoHXSZhUc+70FfVpzmVLcN7J2R1trjXNC5QibdOqeOsm19tvbzZHZ8x5sjEGP+6mGOEGbF+/UDTSAD3U6C0SZlXF5cwiNeDKIMdv5XiLBDtNyTQoljN2R4o/q5tIIWD8ATfO4bed/4l/PPld/qtn/yv/xUcjvvdM8sO6Q/U/nqBp8Nk3DVoLxaNxl6CguDVYI3UIygozU/Qzwb2KT2zA20eSw67AH9jcLJ0B4AUas57JzkgRWTGZDurZL1oc/0OOhQXSjQkNgeFGRE7Io70ib4p1spLgV9fHJFaCk4Z00hFT36WbLUkLikFSYSQUz03F/ZdQMxQlZfORcZNJOaNunfOWH1EVii8L2zxpl8kxSG3FYV1DG2uUk5S2lZBqNRZ6k0tPg0wwNw3UymZmGzw+8JAlwaKbUbWf0XE2M5Aj32VsWViJxoMsJtELqLIkb+dkByFac826mlOVMa7KWWo6h65NrjJ+eOuAf36jw5eYHLommQH7oxAzXFMP4Xa84q0gZpl4zKOU8tyilvVxJh6miKhlPr3XBcqBga1SdlZzHkQ+vutgOVAPJAqN2DYJkwqtsaC8EsSGTSHXaJFzoKUsUoN13mTnzX0eZQ+orBVVM6GTOnTzFfUoxdTW7KopNxYD8jwjcn1eeAVWVUWS6EwcwWtZJMZAKg13vaASTdnxz+gtAypOQFrLOSm12MvWnBZgEQum5pJcBgRegIw0PjdsfOkxsmskssRc2wK/jp4IPrmlOGw5fGf8EwxzxYtth2XV5KxhoxQIQ7J0FYNqhKEbiEQQ+AaHUZFP8+s8jnYJIxO5G9MsjhjaGsrMqURzYkPjB06d73tFTkwTpemU5rvsnnXoRHO2woxH1gGfpTeomQF312fsx32chUF66TCKN+tQC1JG/QKjuUUWG1xWPe5q5zzoD7llvsAiwRAJK1Hm0NrhUd2lZOgEa4dESSJjwUDdxnYLpGaBS6tNrHtcOhUe7zaI25fYYsw1ucSu+AxKHkvR5CKxSZZdOjMJ6zoyz7k9mHPlUkPTMgqxRltm1LKE7UyCGRCZGaeiws/MCueawXenMf/odE1Rrq4AACAASURBVIGumyTBdRpLhefrzG2PG7MTCjJAhAvWqUkpLtH2pgRSsiM+QYQL9ocTWtOQVb7mjWNT8jO8pSJLBQ0fnExw53KEvY4Zr3dZK4/y2kA4Jt1AcnWeYVVmDJqKzBKsPRt7HbDWNVI94f7sBKP4EF+zEPMKdh6jSUVb+dR9xdlWmcWySDizqMs55XAE0uCirXPmNOmXHKauwVRZtALFcVfHt23qC7grU8RWzmmrSrJroqRG0Rf4ssgL4bDQYuLSprvHSSWWkTKUJmYu8URGzZjzuObxz91v82XYYiZz2isNmZdJlcOgpqHcnHVRoZFhA6OyRaEx4qLa5rLc5OeNHX5q7HEqHdw4Yyg7JLLGuKszKpq09RmrpYk3DdlfLKkX15TdFfHSoqsmdOWUqpryS4sn+Htzvh1+iU3IJO7Rm2Z80jNJzYjzks4XN3V+KTnh2+oZNWKiAvx0z8R3LALNI6oY1BPBQPSIhYMjV9TTS+6M+3ww/5Kec8qitKQUw94Evvl8iTa6wlPVY9EWXNQEdnJGJTlhYTaY6200YXB3BL3ZBJoRXlHSyTy0tIjwznir9ZqbeYIXC5Ki4PVezroY4SQhxljnh/lb/KR2jefNHofePiNN/3PPlb/SAO2nZx/RWij+dPstrq2OGZegOYKgKXmudnmv8IZsrnHSAs1YcH/rAwDiwRdc6cOb+hY7lyv+5GCffa1PdSIINgQJbjtjutgwLh/e3MwgeYFB7CzY26iYEy8NunOXh/s68dwkaUj2xilSCN7Utji2FfurnGSpcdiFK/1zzKDGgTdhNbVYuuBNNd50Be8eCt5bJBw3BXfOJctqgeM22JaJud6oFL79wmFSKNA131AJINMVZz0dZwKRBTuccVzcQc/hcFvQnUK/rng7SrCGKVEYoQBhHrE9lxQzn5MmmH6Pg+UFn29XmBYElQV8tG9ijwxO9C63408B0BJBNd+wGdJrs7W65LgluHe8WadZXaeQZLyo7bAsXPJ3nqRoCn56R8OuZXzkONw6dZEIzupFtEpIPjVxE7AzsPQF444g0+DgAhJTsChCcSp42THYH0La0PjDa/vsDyMe78HeCMqVlIs0RKFwlpt5ve5YcFavUoxD3lz7CsAOMpaORnAFzsYbgFzNYuqOQB8ZnLRhfy0IWzpzvUxzDk0y3FjweW0D0q8MI6KGZH+YEBug4xFkBnK1UapslnKWM5t+q0pnBImrMBMYlFzMHKKqQPggLA3LiFlFBm4C0pbMhI6dQb9Qo9uH9EpOcGkhgGgvgRyakxRvnuNlm+st6xr7fcF5HXp2SmmscdyBvtajOI24rMLBJOds2SRvZ2ihgy0G7I0hbuZsHa2JW5Jb5yCU4uZ8AzI0oYi3U15YJm8fwqi+STSFWOfMNrj2Fcu7qGW890pBoc+jHQ+lNG6cr0EoPvZuYdgDhF+mOYVlVePlNuyOFWiK6mTC1jhDU4LV+w8oWSW6wkWvr9AuNuv9s7e+8uYrFblxIan4m/bNq4OcuJ2RfPGU59twcAmyJNAkbK2nnLbADAXHO4pI19Ayk/2XN/kfPv3HfPtHBtFDm/HjEtHnFv/gX0r+m38K/1teZvtXJ1zIFg+vQfs4QO3GPDiUfLpn0l3ldGeKf9b0+FuBz9NdQa2vEZYl/qVNo7ppZ6j7OZOtDcN6vOVQDaB3GjLwB//fk94v4t9LjNuwbhaQRYNP5AFW5vFHjR4/2y9zw3nDUjfpJWsyN+PfUiPr29giIMXFyjSaS0VBZKzba9JcckabCQ2cQCOIdKaaiVnSedLoUBYp43rO0z2NyNSQKx0RZdRExPuFj3iwesP1QUZ1Ktg50ukFGVdWGXE5ZpxVGcdbXJb2KEsLKhJ95PFWlrDaMWjlGrfnEywpuDR1XiYFNFNRSyUVmSItRc/ICC2NwFLgnTGzdB6VHVJN4+1zyc4so9B8xY4+w9Q0vrUY8puPLvjgKbzj/xnvnU9YWYp6cUhFzlkbsM4dvEDHUjHVKODTvTrTmxptHfbDDN+qITUdIpdYGciqQd03aQwsCmODlS34SG/SLyka+SmrhovdjLgbSyw7YVmAmVWhaU6pZzFGqrO0Wzw0aizbAquqKPuSO7OcwElJ9Jxrszl6MCMpzJE6uNYbVnlOKLs0JvtcKo9FQ6cz1nATjd7Qxpq6TPI6h50Ow3KNS7vK3rrPW+oNtVRRyEq8s/6MuJSTlFM8FeE3TMoipjIrMbELrHUduc6x5hmNlc5vPk/Zm7n8dP8ac9MjNQ2k4TLwahybHodlm8s9j9Nah0vN5bxsozSTFJNKBDkmx/UO9lRnb3ZCczVEhZKWMcVJAzSVI5A8sa5ixWuebZt8vlPh1DWR7owGD5lM6jBrIsYVmlnOXppynpUZpmViMhIrpGosWZZyhHnIFo9Y6hEP5R5j3ac3XHLjMuCt5RC/ZCPIaBorrmkRwjCQmsmqqDEvaOjmkp38IVcWA0Y1jS/3Nd7s2gyv66R1Rd8TDDSbSDmYqaKSrXlv6dO4NHhetKjrGYnh4UnJK22HN5rHb05/xI3oBT+uV5GRTnO0opouSH2FpeVUwhhDJQyLJXTfwJmazCydoddkXrDJlMaypDF3K1ilFTvLV+hRhe2RR2cRcL4jOGnDw1tF+sUy53VFVJRkCER9xbMrCY+vCNLCmFCTFIxDmmsfZ91ECw0+FreoGWXuz3MWhsJwUn5Z/0Pez36AJ1OcyKORLqhmK3JX4pmS6hKunQmsJCXXdVaxjTsxuf/GJIgsIq3CuSqRRFVakypOlDOdOQRJgUzzMFIXULhaRkHGWAWJ09ExbUlb98ldyUVnTWCb/NrZBQ8mMVcGiqVhoOyUs6Kkdq7TC9csYxd9mTExtjGSMqY7I2hBVsrpqqcESpGa8PlOg7o1p270ucBlLAtIP2Hqu7zxSxjrhA/GL+iu+9yUS1QroWH4zJ0lqV7nb0xD3lrFRKHGXEnyIORF3eR4q0VsNLkzVPzB/n0+37/OkaOxihW3/UMK+GjeirC5oqxN+Jo8x99eM6mtuShaaGZOyR5zN3vETnQG/S7epEyARjS2MEKXN95btENJ14kp+UX8dYl5btALQq6sYr7zRtIJc7bEiAPvObNqxp2ZoHJZ5SzzMHPJrv+CSUlj3nbRy2P6vfDPPVf+ygI0pRTnk88orwTPKje5Op1zeCDZGYNZT+knZRoNBy0TnDY10obkSuUKy3jJ/Vebdra0bNAYwMOtA67bn7M3UpzWXVId6sUlcmqQa4rDniCqKFhpLCv+RsHRy1FS4+BQ8Mk1QTQ22a6u0RWYEtZNm5eWSdkH5gbBbkq+0NmRE97V3+CNdCY1RXkqGDZ17h+N2R/npFlOcy44afaYtxRXtU3b5bAu+PrphCwz2fY37W/C17jYrdMbwqxmMOhFWOIrSfuiQTGGQU3QjIqEmiCfpcw82FZHpAWDiu9z0haUpmX2FnMWzSlbE8iFYFBfoc90zr029a8ESSpnW1gqITJhTo/KcmMvsD3PWDmb4l0aJutiwMxUPHixUbv76W3ImgnPLZPdvkloGhiaz7g+4u2j/1flxokEF40N2KteavgVhdhOsSYabzoFNAWe0jneSrh5rjhvgZNCq5QgpzGzZhFzCpEJlQieVq5xIM95tQNXFxmN1Ro3S4kPUtK+xcKDoCloWSsq003P9p2zjIIx5c1Vh3vHilvjHMPJ+ahoU12YmEvFRVfRW084a8L2hcmnX/PojEEK2LMl5liH0mJDkWeb+3PTr4ZFbZ3eCLr4fGya9PqbV3S7OCEdbUDJ6e6Eg0ugqxFc2vg2WLZidwSz8kaAwptHLF2YOYIbF4pP7jlsmZdUV4JZTTGrV6iMYFzX8CsSw5dYVcnryjb3RhsQFu6lCARBuYSTQsl3uRZsmOhuH9yyxmlucWWgOK95DCtQmsUcOhq3zo0N2CfkW08UQyvgy70SZlIi7lvotuT78mu0/SXPr9XRFRi65M2+4PpQkZYM2vkRb51t5s8e3rYAuFW5wsLV2b8Q+K7Fq7tVpKaQGtxdZGQzg6M2VAIw64ruizccdQS6goXlEmkG12ZDXvcEamrygw82AO/tz27y3//JP8XLM15/O+O//U9v8L9/AJmAf/KNX8FMLD741w6j0OTvqzd88o6NE0MpF5j5xirgG5+DFwn+j7aLG9qc7wjqM4GewuvURrcVy4LJ/kjyslciNkETOWYqKMXwyaMf/EWmwF/EX2KkocnnWpmjuEpxFHBr/Ypmeszl1ow3NYPLagkdyazg8aLa4U+uVPhMtqms5xi1hKrI2ckz3slMzhMXPVmi6RmNlYt3aYETU6qu2JEJFSPEsAUtobO1gkzq+AuHShBwP064VZwh6im2BqW1SWlq0PIzDKUhjQQzW5FmHj9Ivs5Z6nJ+KyJXGgEZ48zB0HO0skQJC80UhLGJpucEOhgyZ1JW6EKjqEkKpJDWuTHJePu1xiw2kRn4ix6FsImX6RyOm4DO2HPZ8ecUsyWHRY12+CXvrT7BmWhMdIHpCOZWl8fRdfbmGVVS2lrMlglJSWKZPiidRclglloMahZBZpHPTcxUEaQV5k6NJ9d0AkPHzTUq6RyZaRSykLpxwYtuh8w2kI7JF+qAOVXyLKOTxTQXAlMXJA6si+CsU+4Fn7JsLPnsuoEWOxwXikgxo7QMaZ/XkYZLA6jEOUraPLpusi7lXOZVRlmZSjZnUhLcjr/kNwY5b8sUXdhc7ipe7mg0jJTaKuavhT7XogIDt46pbYqEZ6UyvmkwkjXy4orECTlqVris6QRxhdvyNd+4iFGZhd8LsVQIpk3BSGmmkmaWcnvyim44IBQNtEzD820sXZBKE3cecm11TjmUpB5oa5OKeYnSBG6SMI8dnEiw1gus9AqpXqQftNgNM5wUPnHLNMURL6TLh1qd57eb5I0md98UqPpFbALKTp/YcViVbLZWayJNURm7lFKTQNqgNBJiqvmCuyNJLx4Qm4qzSpn9aYYpJRkN1qYkEZJ7YY58UeI08LDTmHI6ZU/NCXKXZc2iamkECK6qY3b8AV9qBc6dhGzXwutE3OqHFAwNIkkSxRgLjSx26RsF8ryInWaMGxl3ZcTb7pLD2g7VqUVqCs4ch6etm6ikgSgtuVU44c5IpxwaXOgOg8zDci4pLAJ0aeMtdSrZnP+z4XD/LOPdQ5vtiaSdpJS1lLNrIxy9j9RSXtbbvLK2Gcs6xVTj8arMZ6bLm7rNShfMrQZFcUnozHAKik4qubvM6QQZhSglLKW0pzmLtcUo91imDiiTkDIXXpe9vMDffGMxDnWWS49rM4N1wcIydMKlJA51+nGBU2nz3eXnXM2OuUnMlTjjbfUx190j3BwsDWq+wbih4Xk53tjGGhg8GL3ia/NXKC1AxjnFtU5WVKhcclzcJTM1NEPyVjxDNzWEFjPzNESioRmKV3aBpVYj1zPWqUMjDRkWLTLdoGBnXPEH1NUhuRbiGy5/WvPoOzozz2IrDnCLGZHhotBxFBzoIw4WE155DV72XBx3zr1FxiovcXTV4GcHZa74EVFmsSpmrMoGPSRJrpjnDnYecdEoUPVz/JLGvfBTvrn4GCMWrGc2T1suaxdmwiWRKZYVU84yPtGLDC2DbpjQmtkUfI+umrIuC053TY52TKJiyt4yYiILkP7/FKCdTkNuXD5h4lRYegnbE0lSkhgSmpUYJilSbViSsVfi/tY9NKHxJyef8f5zRVbJ6WkzrFRwXNymZz5hdwSnhTpHLfAKl3SHgmVZkeg6rzoO0cpkWVlydahY1yWyk/LeswWP9wXhxOKt6vr/+X1Zd0EmBGGmoWWCrqMhpOBOPqDGJd0p6E6OpgTTSpVr8wvslWD/fFPQf9i4Q1Iz2Iv7AAzLDrvrEaFy6Y3XZLqiMdTxyxtmCUfyp3s6u0Gf4xZsDzazbcMqEDU56YE51DFrOb1VyKvqDk4sOWkJSuMSTiZJnTU7Y8WLbfiN0wDbzRlVqljzjEUBIr2Ck8C4LjiZt9EkDOvgriNebQl6fY9xaQe78BQnVjTPNsqJuerw5TUNCVwdhURexjLv8aaw4uuvJcNSidhV7Pfh0hN4kcJY6MhuTKEVs38BnkzINSiOFXn5mDvnkszcFN/zUoGdgWB+y6A1FAwbm23/1L2BurniTUPjO0cb4FqKQC9qtM50DlsWL3plOsEZGnDYcrjzWNAIxlxu5WxNJL2hQG8lfFYxeefEAwQnjTKVdchpS/DgccKzm4r9IUgbNGvN7gAawYREB0tsgMfeIia0oL7aqA+WnJhPLI/bZ4qlZ3PfOqV4aTAoW3TMQwwJWtlhOvR4fqBYmIK3zhVhWSAF2APJ4wMYOBuD6uNynUqwAV5VPcctz6mtBJElOG8Krq3OcaTDw602ty4iclNR8hRvOqCFdaZFcJc92osJ4zLsDgRd3cbom2jAY/sGn18RNKKE55bF/iXkOuwMU0prEEODZTnm5lgjWZmEBjx2Ha71FZa2SVKtScpxW2NvpJiWC4xrGTfPFEFd46fTjdjPra338IXJzXPFulji/u57nLY1Stmaipcxm9lc1DfP15EOhSCjsLE8w4oE56U6pSThspdjTDWeN3S2Rxr/5b97hFNO+Rf/Scr/uPcd5q0B7x0pjmp14p1P+K+//Z+xKED/pzVuuQv2pzavuhBeWgSO4mAaUlxplMwCCMHpyuRmY2MmvzOAL2sm8VrHqAkOLhUX6ZBBT9Ibp0SGydqCo5//6C8s//0i/nLj+Q0NkeTE0iFFZ5yXuPvGoO800PQITcvxCym57jC3XEylEaYZH5d3iV2NrJazJXNcXUPqDvfSR/zO9CfcuaxC2sTWBDIXfCsYUppa/K1xztcji3eWOlIWiEyXvtng1Ojy2rqBWc5ISvCTdol6LtkzIhqhTZMVRW1GKjQ8GSMtxdrNeVizGBgu5xksa/C8ZFFfp8wSid4MWDmCRBec2Ca+71IAyigKyysbdTZHcVE3aC+gLCX1pI+l+niGwCxlxLrB62YBYpPEMBhuKa6XfLa0BF038SqHFOw+uaqQyQK1gYGT6ZSUIkkU2+YIa51gZz6RazIxWpxlFY6v2UwfZORuDlqOMsBMBG+P51TzFbNIgsxJ3Ihblz65XySuukjLQJk6jaxISdiMHJO0kDO2PUY1GNckzdCinBiQFdkfVThJ91nkbUb5XWLf4ajTpha5GC2BlumsXMWok2LpPneiYxrM+b/Ye5PnWa7rzu9zc66srHmu+s2/NwMP4ANAgiQ4CZTIULeklrodIXcobHU4HL3ohfcO/wte2eGwW9Ht8KKtUMhtyWyL6qbEEQRIAgQBPDy8+b3fPNQ8V86Z14tC9MrmwqHmQsGzvhn31s3Kc873DN8ztvLoSsxfdK7y2MpzXTvlBy+77B3A9ZGkFCosfUlvafIsK1HMiJOGTt2TvD7okgkS5kkRuZTcDH/I0/aSd2q3uLI8px0EqB4EisaB0kB6EU7kYSaCJw2VB3WLVSHifr3KIs1ynO/gpRZnThk3yqCGESvVYmQJvEiQ0y/ZXkh2oyl5GTFwJA83BdKYc2nr9JUC4yDHd3pFPlgUmOdH9Gyf9pmFcZnh2XKLe+EViksoLbuIeM4sE/OYqzw02zxrCbIyIZvCXDF5227zvlFgNwooxAqptcIrTunWdRJTwQhSOqMIQwvIRZLcQmOxEPi+4CDKU1cPee3I5LfGcw72PO7XUwpSpSQkehDiGz6roMWxuskHlSbnugNqyjTOEdUFUgUlk2IngtbQ4s7oPoV4wLOSwmVBI9Zhu/hTrM4Qoafko4SrsYcsZrn3ooWy2SN/8xy3HFAKdJKcxc9KWwzqOYQi+cvwdX7g36HqKbSnCvsDl9unPq8OBYEJ92sG/ZzGXM+COeQyLHBr3MPVDOb1AFld8blZwl4S0/RGVEOXkiXY1SXlocaBI0jiFLmwufkky7PYouvmEFKjllngJANW1po5PLR1Fk2VokgwIoEdG8xVlZmW8uX+j6mvTsjEl/TaHmXjOYtggeglKIMi5W4RZawzzSoc103USLLnJuTHKpXApRwtqUVzEj3G9G2Kc58XehOOj7L8tXuTx5Ucqp/S8FLcrEcumOBaK+5tKhznstRmI667C87yJaZ6BjPTRy+vyJspTwyV1UqlNHZJxYxgbqKmCfNsSuD4BJGLo/pMLRPbXbJMJVYaMwxyrHIpKDquLCFlkdp8QH2U8HPxKrPBDd7NZpiLPCoaGc/kIs2gJAHnBYUHjSqzbEgh1pmUBLVkRlXrotR9Os4KaY45CzOchhXsMGWuwt22wUflMveNLCcbGhsiwEhjMsKgb5voQxvjvERdWfIZcc4bgwNudye/1K78vQVobx085fXugvuVXXajD9FSMOO1w17Jpbx2+pzpICYFVqrJa9u/BcDbH/+IW6eSTCOi7a0zUT19k6I/wIxBhHkedHKs7BG7PYmaSZGJyd22ypHRxjZOaY4kx3VBv5WyMYqRlkIvNCibMZ4OCQK3vS4FnGtlALZnay/yxfiShwsbRUJsrcHYSW4bOw5YGDYvnEhCXXCSVFnk2+TdHqEGcVzGNVJIbLZ6KYuSwEjBWboYCdh+xLxQpjxZ8WBL8PLj9X5KLuYgabFoJJRHCmZD4CwEl3oJgFEF7JVCL6eycCJu9lLu7il843kMArSajzNS6FUlsebTnkhEKSI3WzvdwpJk5inP2tA5tRgqW+Sdu3zjSYySCvp5A3tc55OGSnMK+cBnUhL0qhUeqip755KLts6ibnDrVNJ1Al4+X4NLWRcoWspuDzaHPqNayg+zO+SUI0ozgeWtKewPc1l2e5J+O2CnD0szJRFQiF1mluQkq7J3qhN+Wg8sVpB3IVZTDowNxKdlkSfNDOVRjfx8ybTgYUQK+RFcbqeMMwovd9cgz9ca2J5kWIbrFzOOyiF7PUkuH/BQMcCv0R4uOWqBiAXDPDQnLocNaD2zaI1BdgSHlsatU8l5vo7WWrJzDg9rdXaG6x7HSrJCWQqC/ZAnhsHVcx0Fl+6Wje4qjHZSDjSd7a4kjoqk8zVpy/WJpJwerX9rJHlcU6gvJzCq8+Guyf6FRFZiWheSR/vQem7yrAPOuE5hFNOrga5rVHC5cgyhCs8bFs+2BLOGwkRR2BhEzB1ojQSPmhlefygJzBVfPVgHKe7VVfrFmJ1LQW0xZ2WCPVRZKBJnKejmLU7qgt0eTEoWg5NHpDLlWvUFvEDSmEJh6fHZxme5vwGqm2IWYtKRDkIhUiXBaF2P3B7BsCIpTmO6+XVQJl8LmVoCKSX/zbcFOgl/87UOf1p30PwdlnJFqwenlTqd3oI/7L7Pf//1W2Q8wdm9An8UnfC9OyrmXJC0Ql46iunnYTtjUvDhzFX4krXENeD2meTgUzDXLM5pjcGLY+ZNlfZIsrASJlkI7n9CKtO/C/X3a/kVSxBJ6mcmjfGYZ50ZlrgkP88wD27hxiWuhheEQhBHZRQRoThdbuhzWgfnJFGKlcbkAoknM2RnMVvTOTv+kB2/x76YowchC4ocWk0emq+SJgapB2oqKUmfCAMhU6bkIFboqxp/0spz7uT5vvkqywOHnDYg0BLOC1l6Whklo5A4eRaY3ErndM2UmZKSTyJenC+peQmBKgmGBoYHc1UhMgSlZUIjjPhMMuOfeT/klel7rCKNMJXouiSsJ5xuWXySvUKSrBnwnMDAXumc1mNmewu2lAVf4JxKucu0mKFs9MhYp3hGluNMk1WaYxqZHGgZLnIWB5rJk5rB86pFOs7yrnmTuSjyvrnFv8216I5yhJFCQQ6JQof+Kssg0LB8m/wox2Z8zsvOM3YZ4poa7zebaAJeMn6BpUUYio5pSISnECEQCgQlDb9gk10kvG9leC9fYiJMzMTh552b+Dlo0UWzFhxci3jUNLlwN3mY32U7f8p14yn9ss/d/Tqr6AaqsBgpNoU05lElpZem+JMC83yG94s6ZhBST+eEqoKwUhpehIdNc7KieLyLtsgRiDIf8SLTTB1daDxKruEmFXSZcMft8/XhiE7kkREujXjFYQsWmSwisPiL/dv8YnOTceqQ6iphpJCLXCqBpBZI/NqcajzjM+MVWU+juDCIdZdCsOTW7IAn5gZdu0zjxOHZ+DrRapeh0sHVDFTLpmzOSPM2Jw2VlS14r1NhMchheVnipEp+IQhVDb8woqPeQ9p9Pkmu8yBsE8Up9DSyszqdeMxMZFhkJRoJrw5Oybopx6WUKFFxVgpKmGGmtdBoYWcjpvWAqOixbffJxx5PDraxPJ2lM+OOSHnTvWBXTtjemtDYXlKPXapKwGLL4qxawzUkT5s6z6ptzFhhpSmIfMSwmHK3ZhCqEKQGh4sd9uMxT5Qaj8wcK73C03abS61MV1c4V7McpjUeNRQuqwUeqdfJRwUUX8NNBf/hBZWflHU+cExGdko/vU7XqmF5gq93z0jEgmbRpYbGKrB46tzktKgSpCbP7AJ35TUMueL97YAPWgoyVNBDSXMq2Azh5njBjrgk78358cspy8KCyNKI0hS7F3Fl4bOb7XPiXGciMxwZGu83CuS0pzxt2Dw0Kvy78m8xatZIHJvv7r/ISBSJT0vkuzYfOxlGdsi7Wo5P4hqmGaLYkkDqJGHEKpqQVKb0bEFBiemkp9TlY/pVFbnQ2Q9XdCKXDW9FUbpsT4ZIYpLVCqksuLF6TBjrjFH51+FvsdBz/Mi2OWsLTAHPAovFPIOf6viqRJtfBdGmlKTUxILD3YhRYcoV4wwtG7MszpjlbLxIZ+fiGCOJCM0lnx88QAlVatGAShDSMVckGZfzssbENhnaRaypzZPcFsHMIVS2kYsSTSXAySdcOxX8u71Xeadzncg2aa40rNDgb1df4ftc53PekmZpTreQ4SJf56BiUci6LNt9amZCKbcgcVKsVPulduXvLUD78enPqU/h49oOV5brBnzd1XBNOM00uL8/LwAAIABJREFUeV08Zdxf0i9CNvb4bOcLACg/eQdFArZGYT7hqOQgFQttvgY0jYXHvFHiWaJRWkExThAi4lEn5FGlQWM+Q00FHzZVfrFhIYCNpc5HFYUkECQ69LIlJs6cfAQH1lUSAc4owTUkRWXJeGiSAmm4JgJ5kNlgkZf0CyYvHEvOqzo3h2f01H3URcR5Gconu/z8hiTn6+x1wXd0VpqJM1s78/pSsNez0WI4aq+Z9wAqdsSHShG9GqJKWBga9TFo6RocpnnIxTN+dNNhWE64Nki5t6NSHgmCQMUpX9IewtKWpHaP4gqsvGBvdU6sQH3kIIDLusLuaMWR3ia0L/nq44TAlNxv59m71PnE0fnMyTqb1CuZDHYnXD8BVQqC/Ix+uUVlAaOi4LXTGAksczqLqcllzmDnUuJWBd+5VWH/MuWsCptDiPIpT82Y4gq8xMOIwQpgUNb4fP8JwzglUQSNC5XjigNIktEaaNXmMYpSIBhrXJZglYeBU8eapGTmAac1g9IQnrbWwP/F3hJhp+wt11ERv6ATqyldJaW0AL0ZcT63eFLeojZMGNQl5irmpALVSUyvDrV+SKyr2LmQRapSWMHcynBqqBRcuLQr67svxTiD9btt5iMemwadrkpxGiDt9XnK5YhgqnFRhY1EJx5r9IswWW1TmQ9JBRTnKadVgSolT8NtJsWQnT4UCgHRWENphlTPl0ybkheWkuJEMKtIRCXFM1bcPpK4FsjKEYuS5MGOSWMCmUhyUoO8J/nptSJ7XQki5aXjFUom4aPdMlF2hLnYoDrxuahBt6Sw8Wn/5lhLmRRVrAh6jkHRDTicHXJLsfFHa6VmeS6vqDt8si0gFSh2ihoJ6rOEeVmynBssDdi+lCzrCZlZgpAQaqBnUu5dEXzxIexeBni3df6Hdp1ECGIj5NopCAS9bZXrZ5Jv9n7GIrjNd14RuAcZnGzIebuEa0IxENghPGvp3Oq57PVTflYxOaPKaVuwc7a+s1XPJFd016WtLkStHAAHrQxClbTOA07mJ3+XavDX8iuS6jTF0kbosWBiw8I06OarqHGRF8MYNVWIWbHKXIBxSphJ+Yw+Y7/vcvhIJTmy6Hwi+Qu5zypReF4SnOQlcaySeD77qk9N9WgrYzaiFaNog0O7zEOryCoxKUcz9jlFM2IeiRZWIMgkBtLIo29DeMfD0BNiLWJhBCzjXd6KX+QorCCDLK+uYl4LT3lxtaLkK5i+jqUIvnCUsu+HFEhIUwU1svg3t0ze0dtkU3gtOOLWU4OVZpBoEXFmnXk/XXyRdLVLOTAIU4mSmBTHRZ5l9tHtDJ/3ApyZxlKItS4PslgyRMYamkyoLlTKQcjHRp5BmPLtTcl5JWFs5hmpea5Nn2JFl5TEgLK35K16gWbxW3xe/Q5vPBmzeWDjyQwH+xm2rSL/wptzI3uMNo+YzB16uSKfkedkA0gin0s1YjW3WZZNrnYFnx2GxDWFh0aRmeGxMXIRfp5UqgwyDgO9wOZAoS4cdCPFMhJW6Q6F86uM5m+gagErxWGaUVj4G0RehUyqsx9MyKt93tnR8YVAmYLrXiNK4erFgGxlzDSuct/Y5Ymps7kcsrWYc7bpcU2avNaPaI0lEy3P0Mjzv2+8yWNVsJ163JycsnnX5StHPtX6iuu1Ka/1JFtTA91QKKpLyD2hEp5wHlXwkiymr7A/kDQXcFV6ZFKf83LCScNFL3p0Jj5SC/jC+QRft/m/rn0F0zOZp3uE/iYaNSxljmNccGtxwNeVe2SqC/7mizoUPCIhsIchSzEm0iTPtDw/LxoYrQFvjjMMVy/TDp7jW338rIqx1Cjdu0nu7HXm+YiLhos2tAgjhT3fw7BjzFjDj2+grK7zzexTNGCgqpypJgWRolkQhymuotG1GmQVH0ebsWXOMKQkr0bIvORuwcIthai5gLNWxA/2N+ll9in5Ca1lShSZbIxDggAsmRJaBnvpCbeUHhX9nJUWYUY5znq/y9vqdR4bJguhMkirpEpKJ3tCp3iBbvtMmhK9EFEb5bkxrJANDfBaqItdzEmHWuhxsdPjX72apdEeciXn8XvveZSep2jzPIdOmyOnReKXOVsZpLFCpEFuAUoqOdzWeVVfcc1YUHm8YHBW4nG5wLQdks1dsqWfYcgUYQjOjTofKhV8MqRITjqSn+13cLN1KudbzPwOMtpDzRpols9fb30WTTroUUjbXLLr2WycZdleTAgWGsoqIVUVDCXlKvf5WuYuP/9ighip7C4PefPogJcPQ2puRP6pgeoLdJFy/SLF8DVmGhxWckghGWcLnJSyLCIDJ55STQRWLuRpUeO8oCPzglhJcRWFSBhclNu8koz4z5Qf83n9EVuzIp8dH/HZ8Qm3o4Ad0WOeGbHfP6a1dNFkgqbOia2IL0wWvDzP4IRFMoHC46uCpR1CUKG+yrCI2/Q0yY+bTaZOibeuVImyKUoUc2DpOEuTjemC80KTh+UrJPErFKIsd5YapVASJAqmrrEw2wh/g/R6l6Lj8cDdJFJjHm9UeNwu/1K78vcWoD2cfkRuJvik3mZvMiTWJIUhTGsp3ahAtuQjJwkndYFu+FwprukuX3h4wTwvOTNrKGPJT7Y3eVEcok5UEiHZm13g2NZ/JAiZxDk2PZ+jBtzbcNjpr4HN84rgrbYFqmT3QuG9HQ1vbGBG0C1W+Dijc3UAbrzNZRlWK5XeZkLVnCO6BhcVsOYKwxog5zxpKSy0iNYEfnYl5sXRc0hyGFOFaQFePVpysCvZX42xIlATnU+qW+THEaG2zoLduVg79ONSHgFMsrAlUy7NPlrhU3p4LaXgS2rxlLEDzVSh6XfpFmMGeUFpAocNCXOVONQpRmdoKThTi8hcZ81cp0pnec5FBa6erUFXYGtk5YyjqgCZ0D5S6ddTFkWNN6YDHpoGO+c2iYCfVksM6n1ee54ikZiLmOe5FvLTM1+9gGneRFoB0UDnccfBSEBXVU5bPjfP4aAJW31JphDRSwKEKklnnwKvKZyXCryRPGKiqmQCSXEc0S3EhEVgoNItguNqdERCtqdyWQE347LoGLhhmdqlpKZG6IngvmNgBirlUYBiSaz5GqAFVoG7L6U0hmvApLQEy1GGvp1HT2BZguwsoe846DG4eQXF9FE0SVeLKQ3Wz7XzA4afZoNCRaHTB7khGfUtpjaU1Ahi0HyLwkJScEMWFjQMj81Lwb09QUsNKQxVLurwjA6FYUS/DNkQzqrrfZ7HLZpeFy0FuxQz9XQ6BZ/masC8YrGTXqIA5xWVfMvjxDeoz8AM4QtPl7h+kdNqyu6nBCF3twQCCHMJg7yGkkrKXYlqJ5wVd3GMI/rKDtWhZFmSHDU/JQgBIjJkvU+zSXGIb8HHg4+pTC6xexrRp+RHnfOAyytFUiGRiWCYg62BRJYSLN/jwTZkAwhzGmoEFW/JYQMWvkbPgf/ixxI9H/Pf3Xod3T5FhiVKziNuH0l8HYabYz6zCokmKl+7/Cl//uo2viYYPnT4L0cTfnpDEPd1YjtFE7D/bEp+JrnbVHk3LqE3fNojmGdgOjSximv20foiYrF1hUgFocc4nmC3J7k3+PjvQv39Wn7FYmcjHr+ocpIRPM+U+dGVKn91u04l9x4/cFy+m9h4VkoxPafkxby6CrCESlA2EUuVVxKfRiHiqPqcUc7gFzsWx3YGp+5Rzi34yM6iiCmva59QNF0ea03eL2g8zk3J6D45w+UgKvORKJHaS45sm9ZSY9PPs2MPeWEWko9SpEgx0gA9jXhGh7+MbuKh0jMUjERBLdr4ZZWh0JBJwoOaYDHT2DyCehpT65rc6MVc8S54LUjRVzqdgU1mXOOTjkPU1viGMuaPMv8WpfQRljFkuzIkb63Ym3b52n2Pgb9HLZL0NBvzUiVb6NExltwOXHLBip3FCZoM+dnwGp1lTPk8xjdAJBodt8+4bDDXSmzNTLJBwotPPb68UPFCnXs4zLQWf3b9KzxPm7zNLvfFNnfpMKpLPtK2ea9R5+Ninb9NbmCoPu1ZjBGnsDfhXhF0PyabSDrOIZvuMbOcwpM9j8ZiTMGN2QqO8c2nfKvwKu8qTcpRyO/QYx58lQPzNRrGz4gClRJzNtUPaZsPEKpk1++h6nNMBEKFXl7FUzS21RVCNfjbHZ3/Jf8mTxo6Rm5M5EhWFFlaBrX0OfnFKX/04C2+efkTqpMJ4zRPIjTScEIol8wci8uWSlqMeFNbULMSjsIqUk2JFcEmI6ZlD12fkZErYmGSJgJ9pbAZhZQMj0UseDzZoVQOuB0uuD1ZsTtSGKg2nqqSDVc8q24yL2o4LKkz4VoC80kOzzZxxJhUrEjVJRnf42RrQTl9iGX0uL8FrqpTW3pUIgi1I8rmT3nhok9Rjuk4Ol+5nPLyqcfIbCISlZIriR0ouCbntsqilHD05QjLDqjEI5xkQDESlE9s3vzY4Gyp8SxSsA2Xi1wNzT5nx/qAvjtH+NAIQjKEnBTyPC9ByIS8cUhfKyNXO5hLm6pr8wPjyyQH23Q+MpiFFQKhsR2lfMV7TGm+JFDnbMQ+JbmkZh3RNy95omTJahfYYYCZBtQY0PSG9Od5TjsxD6oFzmsmH3ANZ5LHjDPsx11qaY8g02Sm1xmIDKkVs5nO2BhHbC0GpFODgdFgqnbYFX32Rch+GPPFiU/BVQBB0VbQVxGhq4KqMXByJMJC1hdk0MhrS+b7Hp9kyjwwt/nHz95ndzLkNdflc+OUdHaDW7Mc2VWOjnjCMyq8a9S5tAVLR3BYbNFRI/7xL3y+ejHiG70uW6uYsVAIpUAUFpy+oHLdUCiPJF85CDi4qZBTa2xfOtRnEZETgiGJU8H+meTH+zZ/uvcqqbBAxAi3wNJuUFw5tBbw6mLJJLWYmAqDbEpxXOCF9zSejxrM5AbOKk+5P6GyHFNSZxxsmXyQ/jaF4VWSbIDrqyykQts7wS+f0ysLhvkIqUT85TWDCTW+wgXFxORONGcz9vCzEcXkksvcmMX2AxayxES2+Lb5eay0gDbWcBcGqbuFpahQfcz3duFfm1/lw6hN0fyY0mhO2AvwNlPerTfZUC85r4+w4gzfmHoopbe4NAdUVmXuFlu/1K78vQRoUkpmwSfkpybdashOP2XeiOkMJKIcM1tpmHUDayYYFFVqV68hhKDXO+HF44jlToSnmWgzhU9qG9xWnlAcCoYliI2QDWWJPlp7iHPV5quDgEBTed5S2OmvSQ0yQnKaSbErIS8/dHm4JVj1dfQIwkrKqa7z0rMYyytzXBeECw2vo9LUztm8hAcdhcYI3JLOC71z7m0I8qO1czfbjihwRjUZkV+siTZuTE7pZrJszUYARGnAz/ebdHowLa+d3tJ4wUkNVu4WoQbdEuRXNrZ2iCc36RbBSxIm1Rz11YDjukCbV9hedBEoGJHkNKfQDiLsVNJziujz9QBu4VZZmuvMxmPzCoWZy0lN0Jh5XJZgo5fwrF0hyvW4dSJRI0GkwKI6I+NckghBq6fg2oIntRbnzpRXDyTLrML+keA4k+e8LokUSaWn0LfzFHSXcldBMUIAmv2IOH/Oq2cxx23WJZOFGDHS8as6+mCdQc35cJBv0qqPmWXh5tmaxj8jV3R3SxQvFI5aKuOMzXYyIOcKQg2meoRRXDLcyHD9DLanIUJL+Tins93NkbgqWpoSLjRWJiirEg9vGGz1JSDJ5j0KA7CtT+fppWW0RGCJNfhSFYWwmOBYPm8pDtfPITAFV60e8cBgnhE4+jkbI1CrKou+ycF+yiCjsNOD8acEKuY05GlHsFLWvVr39jWy5pjaFPxCwqiUpdEHN5fSLYGXWwMhkQquTdY9jX5GYWwLbsch57WEidkgH6xLfh9VFdyKhd9b75eJ4EuDGZHXoGsn3Dpf98F9tLcGfs35jI+2HPYvQEQKagojdZvGcsisaGDGgJlw0BBc66dITXC/3KIxTIk1aPc9zrMK905/zgcHP+LqueSktt47fPqMF/e+wFlHMnM1nrXXBCGJY1JZBiwyn+oEfX3H29MpF52UjzWDzz2FykTy9GqHJ3qdVFuRWe4hMifcOZQcVDNkvAGVio/iK7iVM4qzFt+9I5ifZnjT6/LeTQURC7R2wFYvonQm0VAZFGF+EbFVXActchPBs4JKhEKsw05f8jEBo1pKdRaS8QQFF54+effvRgn+Wn6lotg6YbnBk52bvD4osLAcRtUJKzXkxFAZWg7ZSCHnS144gv96ssRG4UN7C8NIue7GlNyUVdZl9FIWmQn5DfWAVnXAVnnKRIdPMgpx7LLtX9CM75K/Z3LzuYGSxqAI8lJwpoxZZMb82UYeMU75g+QdrmgPkYqkeKLjenV0t0RGmaLJmLZnsfcsy78ya/wkm+U0LPO+dZtCVxL5GmGaxUss1FDFSBW2pwtK85j9k5RFmuecGgsjw95JkatjcAoB7yUWT5QiR2qOt4wKZialePM5fm7AtBAzkzk0XzAmglBlf7DklWCCOlMxrGd0sw5WGjJeqvwf/jU8wyASGueVElo2JSmGfLv2Oh8rr6MMqhS9lEY0ozaTfO2DDLOsTkN9TsF12YgOeRYWOdNzPDc19iZDcgmUjVN+R3wPooTLXoZlrPDjhkPhxMT2VHwheIxOCY/rFxovHGvcmlwyVxWO8ynTvECYU34sX+AxeSpmilAjpObzB8/HNFbniOCEnfAYNxLsxl3KF5dsxR43iMhaGjf9kB05pH18TiVNGVYNnup76NNt2icLdk6yLOQWj7e3aKghGXdFHKoU8z4fant8WGiQTyZcWyxonRT5uHiV8qRK4VLneGlycZLhyjzhHyzfRlHmvBt9iZ7Y4rhlclGpcemUiFQNP7WYGzkS0+Hseo7yUgE/zzPH4OVRwnYUEVkhL6/e4Z/P/zdeNx/yxvKn7EzP+SfLR1zZOeM7L1j8eT3LLMhwr7RBq6/hk2JHMYkxpjTOMFPaTPMWW37Av7c3+UHdxai+y5sXT/kXTx/gJA5qoBGgU9WfcZ7LclgTfFIqc7fa4Em2SDev8GRS5ZvD7/J72v/NyNBRBkVSt8LLsxmhsZ49ls31KXkpN5YDPhEWx1WTf5lp8RO1gAwjtFShGzf5lpZjeL9J5/A6u6cm2/1TlkcBH6k7fMt8g29vv8HB/E30KOV9/yYXXplHFw4DqdP3W7wtryONMV8Lzvht/4wrJ7ep9VWabspKH6KoxzTOBswGLdxQpznzeN09oZysMNOEuj9CmdWxwgIvuik7PYunRpFJnOG4USbbcPGyOQwx5kbylOvVY7aaK/6hWKKbMM3pmKpP058zn+eIAx3VjPnhC4Kaf8rPNIuuecZy0UKuatwL9rkXbeJ4PtfHfa6FES/lTqjLEa+MH+NEHkfOjI+TJo97eyzSLIq0WWoqC0OlPYlxDnR0YiJF4WdXTEZJjrMcyCTBi2weOHVWJrzGnE1nxtXZjGosmcssblZnnFXwEp3fCD3CjSJ/c+1FPuMP+G/jv2Jl13j71k0uXpEUknN+FO3yMxw25in/7KiLYvrE1pLb4YAvz44xtYSplSOSKn3VIR8sGRSLHDgFfpRU6ffzfPPBnJdcF9sR7JkrqgyJwk3suSSaS740WuLqFp/zAramCbVwiFN4SD2aMk+vEI6+zCLJMl/q3Dstc3FSpJBLuFk84yc3NGbOJbvLY14aHdAL9xn1LPJPA0It5a8rV/kfzTto6hQ1yTBQWxixyfMoTy7pc12/98vtyq/GfP1q5UG3y+eGXS4yGwj7gJ0erCopegK5Ykj2PGRqt1CkwDUtvnTn9wH4xf/5b9ATKGx4ZGMfIQWX+R12zYds9WGaFRzXBabVZ7sPgSHRjJBXphEyzBGUJuz1JF4uZf9SEqqSuBpz7ShlkoPpZO1UrhrrAdXbR5IbkzWQ0ZcqUbZGfxlgxnBSWdOuL3I1Pnvg8aRjsX8GgQF7mYBHHWj7TwDIL6G5GnJobdEeufim5GkpYdFasduDUS0lBexxxL1tQfvEREhBrySoDCxCM+Ik3uK8AUQKHzS3KM08TuqwmjWQmodnQWcED7cEv3/mo6iSxYYFYxXPXDcqW7EkMCXPwzrZpWRYljjLNUHIK/djnpS20DOHfOVxilQk5YHCR9s+lzUXJU3ZHs7QRIqRSuxFQnEm6Ddjcj7YcsyzK1Cfgu4LLFciVMnVc2hOPAZlyS+yBYQ2Z6MLS2MNoGeOyVYPTq4bbPagV16DyIVwUJyUs6rg88cJKYKNCQQZg0wg6NYlI7NMfrWuBc2vwAokuXRArx2xMZAUx4JZJ+Yiq/LZ8zUYCQKBtYSzKhR6IWcVuNVLUZ2Ui4ykNsqw5V6ytKA5X7My1nwfX4d2fz12IG0IDhWdG6eScTlHvTineKnyqOlg2f11Ca6qYboKopPyyNC5cgGpvSLWQJvB07bgUlfZP5eEUqCGa1BYkzFKZUjOAzVdz6bbnqaM8xJDm7LbD4jMlMnSIlfz6QzhoCPwx3s4syXzDDypS5KMRuZCZ+yI9X8rjSgGBs9MhSuXMDdMLiqCWIWty5BHLZNXDhRAEroqE3Js96DJGhDanuSwpXKln5KWMpzthGtK35KKruZIWA9z/qR7j70uPOyoSFshePKEzzU/R/dKzGJs0Pu0YiBKiqgSyv46e5WfCwKhYCUJUSvkOK/wT99eZ8/eltvI1ECPJfZ8kzBcUpvCaa5GdhCh2usAx3nB5J8en/BXdxwSIVgcZNgyc4wdMFYKzYnkFI18qYyZCqoLj4nTIdDg5onkeUvg93T0Ysx2X7KYneJXYbOf0supLE2Y3X3vP4VK/LX8JxZLC3kj7dOvFQiUHczQQPMcRJAhFwUIs8iVsyJXL+D1J2uWvgfpFj8373BR0liFgkfSwJ7nyK/yJEbAf2hZKOo6KHUXm2e6ZJRN2MrfI5fp8lrvkErP4HGxxswxeL4o85J2D815zO2xxhU5YpX6KCufv2kZvFW0iSa7lC/2aMgunVWPqnufw/ac0XCf+2e/SXexQ/l0xKWh8Zas4SaSnlLCiFQcGaMlkm98AP/oYcJb8Ys8CDaoiBF5P+GLh1nGicMj02AqFdLY5h25RSo0RJJnYRQ4K3ZQCfmYHMqhwzixeLp0GD/LMRxpTOtnRIUBvqFihTbVaZssOqaoEPASAzZ4UzlGMxTeNu7whYcuxVVCw+3xxqng/naGs60Bu/FdpCrZ1KdkjAFp5pxDzeCV+VOunQz44vyY346OWYQafVXnebdOf7xFaSWZliQXS4vHns15TdJvBVyUUx5fm1Er/3vi/EOqC3gtPWQoND4wTZ5FRWqZB5TtX/Dursep6LB/f0qjqxGEbeapTVoTGKnkZS/AC+v0DJ1YT+mbMTcO4AuzOTes9+iWFjyt38BYGdiBTrqIcEzBuCrp2gpbRo/fr32fwHybTedPGW3CkZ1nWCjwUf4Gj1sOg1WWC9/hp9c7fGe7QGV6Tp2PscUI9BG9vM394g6PKk3utjqEZUkjXiJKGf7qM69zrlTJzHSWhmDP81EzMV8/v8871RmLuc7t8yWby2NUd8KF12JHWlSkwzJKKfsmNS9ld6Dx5YeCxlyQmBpZP0tSHnC6obLtxuipT4YA1XRp2xFxZMEK+naZILZxixPULEAbNdgj8Df5ZLnBKF/i4mpEfTVksSgycXPsdwXFSkBNiakEKWkm4Df9d3jFm/H1keD3xgmFkwbPJhucxTq/EBoPNEHgVZhpOW5M+vxO912+fvkhq1Rja+gyMnNcZLZITcH/lKvxjlnjQXaL+3aG7XlEo3/CD71rXKoORXL8o7mCljgs2OS/OnH5cj/mpceC2/2E/CzDWOyxc56hHT4jCBpU51epOAt2jSFTaRN6Fts9QV+3WNU0vnf1KnfNDt2Giu0r/KbyHMcOIFFIQ4MHMkuUCShZLj27wtmsiB8ZkFfJOWMCEzqjlD+4POBBr8y7l3ewhMvt5EO20ks2RrC/ipG6xz+/+DbV+ZwrbpdSkPDN/vf53YOP2PAL2PhMSw6nQQF1oGHJFDObMm2HKEUPW3X5zvYm//Pedb5Xz/GdbI2JqTIJNWRlSqRI4khDEXA/bfI8brIwsrygueyrp8T5hCPRpDxZsops5tSJEwNRHXM9nTOJG3QmBo7hc9lICKseX/FSrk5UrsUnfMg+b1HhE7Jk5XOOKPKO/Dw/0De5nOxzZwgrNccHmW12gojNVMEIi1gZD3Vs8mfuGyw9h6Um0BNBJ5C05ktycQyqR6xquKrJUWaLiZ4QSUmzMcTQQ67PlyhLk+NSh4G9QXMSMDKzRJ6OGkLG18jrT/jmaE5Wn7Bz3Gd3HNOPt5H+Bvml80vtyt9LgPbXT3/K3iDloNCmnTzGDsHT1s7wZsFjsz/mcLl21CMEX9n/hwCs3v4x8wzsOR4ld50ZWmhtHHFGdQGGDyd1ySQzZ6svERKK4YrNOSRJEcM6Z7cvmRQl5mAduj+rK1gRdHyFQbI+w3lnSi5OCYomN4bnXJTWL8lMbcZdi1gB89PmwZG1QUv6yGyGW8eSRUHlzkDyYWsDy1073a0RHNUhkFm2ugnzquS4oVCUl1gRDAqSYUGiJvB4S+GNkx52KFnkJfE0JU4F96gTVBIMV3Cq1VBT6FZATAocNiSzXMhL3YSHm4LPXEiSSGG5HVPtCbpVCO0lnXFCVExwvHXWQDETLE9y0BLsd+HUamJkDnn1qWRZVCi6AnS4X1e40xfYcYgeSTR9wp2DdVYnyCREKrxy+YijtsHVi7WzvK12OfVMtFBh5zJhWhf89e081TkQKmS89brjks5eV3LaSNgcgGsmpAJeCi8ZLXUOSyrXT2FkOzTHIII1IchlLWGlFlCnHqEKGwNoDyyc5RQhVgxzKs5YcLS/PudnLpcIPaWvqhSnKZMKoJ5wYMN2T0GrhjxITYbqNhujCYdN0AMfgPLM47ABxbGOvhSoHUmQqrQmMLPzpJmE+hQOiyVozPS8AAAgAElEQVSQAJLLeP3sdS3ggWGwe26Sc116GxkEgn4zpRsrRAaURmXU2Xr9zkhSjNegKDtW6LVg7xwO6oJRabXuFavEJEcq2xWPdKzi11KMmYM9hssaBIbAUD12TuFx0+aiAqtUIV8Yc6ppdPopvmGSkTAuCzYGkkFJcucwwSjG9LMaMzOmOGiztTwnUqHQU+mWJI0RpKWU3VbIbg882yRyEtpjENMe04shWgr3twO0oo/3+BGfa36OpBajj1Q0uVZps1X2P34b3abEGQWcffqdHbUF+z3WZD5Xm1jaJwglRE2hIgN2Tj9l/6xluTOJiVcqkQpKIqjMQxq9Km/fEkwOsvzn4z7v3BIEPQOzGNGt6LSbJbamJqEGP1B3GDRTXjiF0wZ4XZNCIWCrDxMhoWpgRfCwnWWYB/35BWES/v/Wfb+W/3f58z//c77xjW/w0ksv8Yd/+Id8+OGHv3T9kydP+OM//mPu3LnD1772Nf7kT/4EKeX/5/okFthyRU/L8FB0+I1RnxtDFyVzgabF6G6bQrbN734k2O3D98nyrfgWOT/kScng/sThqWKxNc4yT2xKPnR8mC8sKiONC5HnbrRN36syywr6GR0tUTDiiO/Ur/Ivd9/kW/GXIQs7yZzmqcLNYoxjhpw7Ooc1jWfbOotsQD09ISr+kC8NPsDPjzmoafzG8Zhdf8VLj5+zO58wqZW4395gKVQU3eX9HcFcKtwexXTGKYqrcOjVeFjZJpv3Gedt5kuTT5ZZLgyToWGCOcVMIp57LX6SucH/+tI/4Ieb+5zKPL8IXuZ98SrMTX5aLfP97SIHFY2FHhFtf5drxfep22dY1gLfvMHWooUWNJiGm1S8BS8tDmgvhiCzNMeCw4zKzWnEYvEbPC1uc17J85Pidb5nfgZj3sIm4OoqJSwqtP1T9jjG1hO+HK64hctP869xqt5hViww3IJ3XjDwEo26khA2Ew7qWSozi+xc4ZXxgP3o5+yIC1TniIvEZBA1mSYNlNTBcefkp4dkJ0XuPLX448F3eSP9LjkC3BBEnPBZcUwuiZmUJY+KFbrZ9UzPr2p3kbVz/NwlVVKuTw+Z1XosRgooKrol0MyEjFhyYNm8PPGYqjr12hHteMwLl4e8G23wpGzzUaGBYUg+Kphsh4/4bHdKJoCMW+TFi8f8P+y9V7AlWXae9+30mcf7613d8l1d3VNtx/U4WIqDAUANRRIhCYQCAUJ4ZYSkCAWe9aQREKFRIEKACAUEGZAECHCAmQEG02N6pl11d/mqe+v6e+493uZJn1sPp4kgoQHECOJpiPV0zj7pzs7Mtfe/11r///zwIaeFZfR8ypv1DA9Enl23SMaYonhVLkYzjhcl+56DkUDfgUiziAcm48Ci5PW5N67y5uwK9myDPGV2VyN+vLfDF96PuLpbYhpXKMSwoM3YjO6w7Uo+11R4ttNjbRZxYRrjCJUoFriTkJN8gbcWrhGmDa67MUNF8vXNPGfONjcCl7Gs8XeOB6wGBUwk476keOJzs+2S1wNsLeWwIPFyKm/YFaaaii4jxERlOW3Sy4+wchHZeMrzQZ+w4PLdK1mc0OWsXCYxUtbaBsu9PdbcI4piwHkpZU3zWNdaNJQxOZGybQVsuTmkMSXVOwwyTaw4RiYOkaZwz7E4MDc5DurYMx0jKNDNN/jGxjZidUa3BIVUsqWes9g/54lY4M+V64zyGc7CCg/1mIXZY7phjWMlBzLD0/E2VjhiFmkEU5tbx3kqMQhN0tXzvFm9xiy2UIOUinDZnCosDR0e51Nuud8hFzT5gxc9vnJxym++lOGwAb9dd/hDmUGvDri2+JRVrc3V+JRPnx1TybcYxRm8VONj0Tf4WP8cPQZTSYm3InZuJNiZlJ6l0ymCpYy4m6yjCo2vrmZwq5IgipgoKugRncYiYHFSXsCJfaYdhUNN5UrmiDBQiAcG20ETy4PFToGPd6BgKBz5n+eZJwvEaooVK1xMXXLBDD0oYIoZ/5P6Cv9D/hLfypd5a3WV04LKGxmVSDvn8bLCW5kSX1swaXS6WBOFjWGFXnyRJDTpm4tgTDjxltk+UXjlUUwiJFfdkOUwwrHfBSISIfBEg7oHM6kSJhZ9YZP0ttjqm6wrJ0ytDM83h1SnknycMI50nj3x+Nk3A0J/BSdUGHkhi+Mp7qRMx6/C7Jm/dhz6oQRo3zl9A3uislsuszWca3TFkYVngGHolB2XcXdKqEFW8ShaRaSUrDw5YWdNYA0VcqMZzaJGIQiYjeeT8JUeiLrOI1VncQBGJCgJH6Ovovp5st4YxxMYTkx9oiFS+PbinA1xcyAYqQquYXG7IrnRjUmUhO2zE3Zz6wDoWkrhSOOkIVkbzPANySDJUDP6NNyQpQE4QcrqccogX0WOE3wdcgGMCgo5fy7MrNkJuzWL6mheCzWSKjIjSYWkXc2wxJyIQMvF3Cu5rLQFh3pMWo2o9kEJPizwKUqUIKWbU+mXY662Y54sC9SeIHJVJqUJax0IjZRJbi7QbRYSlvxjAJJoDlJHFRUthbNslqXhlOxU0HbmKWfLA8nDrMa14/l3LVCYlvt8/GmCVCWlocbjNcHm4ZRmWefZk5REFUQbAcO+xb21Ik4Iia1y0FC4eaRwXJvXMqWG5G4lpjEALw7RUrB9Sbeo8kn5hObYYKgJ6i2BaymkCJyeR6sIaqigVBSUrsppDfI+LPXKaMOE/NBnb9mi0IfWh5Tua80ZsiJRpcAJwCtoHK2mTAXUuhJqCa2BzX5+kVo/plMH05vRyUGll9CqCQbOIrqnETs+men8uKYTcDCa9+PYccgOBWEpYW+qE6lwSbjz+r1zWOjEGCpIJHU9Ie4aPFoRrM4yMFDp5+DUX6U8HM1BkQtRQWXlWHBUF3TyAasd0PMxZkchYyf0ZwbViseS0aHaFYyzEjWVxO6UvAc9M8vBgkCbqBR0n+oYrEhikLI8UzirpRSnAlVMWD9P0IoR928sYVonxN469cGEZgU8RcH2JeZM4SQ3ZrE/jySnsYkk4EYzIWtFKO05eHqyCpNKQrS/z5q1yEqkgRRUhhI3J2EW0MtBvQdeLcEcJviahmvAe47FF96UKGbKHzvP8XChhG4d42saeecpNw4kE0fSX5lxS3iM+gb7DVidemi+y99/64Q/uaVAIlg6nfHgsopIBUoxxogkl4YBjY7Og4bK+4pB2ohYbcM4L5m1DaxihBOCGkNl+woAQTYl1WH9XPK4//hv3Cf+x2y///u/z6/+6q/y+c9/nl//9V8nl8vxC7/wCxwfH//A7Xu9Hj//8z+PEIIvfelLfPGLX+RLX/oSv/mbv/lXnmPkwmSWEKUmbYokqYYTS3R3k1tTn1hMeF0r8S8XX+b9ao3ubpn7s1WutfZYayWMS3CzGyFp8JHRAR+/XcQ93mD3LMvk3GCQVghkhsOcR5pIPFuwV85zUoHpRou9/Cqvr93C1xSmUuUk3OBxWOf39BVmapYLYUw9SnH8Al55Rmr59C8MOV4s4EwttMjHVN6m9/wxyQs2+kYBkXh08wqnVoUkk+IoCS/FExQBXdvg+9YyL2SfYC0G3LlQ5aXOU37kzQnJ3gZPdRNBRMMfEHoqbykV/OwBgcySTp+l5d2ipVcZGg4tdZNpdoXTJRMhYNGL2L8uyNYH3Eo6vKYfE/gb9ESOZl5yXFD58d03uNnZQ4/A8WEna6EJyVDNEaZZjhuCNxYuMxk+T3Xa4Ysjn4+ICZmMT6oFVNQYR0DRThEOxHED1SsysQ2uRwmGrPHSic7LrZiVQUrx8DpVb4nYLNMny6OllHX7KRvKCZoe8E5aJJI2Ms1ylsvyypMxehrjmjUihpSSc25Xy4x0jbuxxURJKJESGvCossAf3cqwZ2u8Y1iYYsYNo0lnW3BvcZGbxy4VLyWXD6itjNkon5BH5fqhSRjrGLFEP4faaEBHLeDNyjT31rk8CMkrAZHQyY9NVrsxF70ZSd6kXROkpRNG+ZDvlW9y5Fzi3XyGd+szbGtKmPUpOwFX/JC9FZ2pobDRS3nlcMbALDJKLTQ6VIcZMoZKQ/b4I63Bv8pXGfZzWGHKYn+CtNtUrDFLcpfnJm1WOmdMez6FOGLVkDgCDuIy02OLUnBKc6XLTmmZvmpzklE4LijkS9/nM9p73AxOqdHgn1deRaDRkGAmCntWFaH6HEuL6UywMoJPGB66f4nm0cuMWwV64yyD7YSVkY8bGnxBGVB3RohEsh6ckg1dbD8g64aMzSrL9pjPju+QajZLScrFWcSl2KVe6HGhPOHTvYh7SZkAh8iMaRHhjHxcYXORD3CPVvlftY/hVzRytkvZHzHKWtypXiCyJSvBKc8lj7mk7XNczZCEeYqxzrOexsn0GVrjEpW9hCI+eSPgbm2L33H+Lm/6N5gJjX8lLnJiXcDOhsyqgrXWOWU5xgk9Wr7BsieRqo5jF+jULD5YzdGrxoSqxUTJ0SnGfPsZhVNdp9BV+Xo1x21hs3Aw5uIDm2HF53xtRhQaGGFM+TxBL0pcTedxPoushTwTxERPc7gzi+12n2zkkrXfZ73ziDACUp18X+XpZyckV2NqszaL5hm3Mm9T3pwwkDr/bfQG/9A743ev3iKbd/nZ/jd49fwuK/qQxLKYxAWMoMZersQgKxjnE95qCL5tVPi6ucaeMaEk91CMI4RIkAq08jlujg9pFxNmlSa/e/MZxtEtrAT8E4vjo0XWhkOacZnhyMYtNnlbU2jv5FgfQ/08z7JRoeGoTOrnaNl7oEg8zeZ6bYybKfJ+coWBX+fYrqAIFTWEBj2wE17sPkbRE4Jug492b/PM9ID3wwbfH1xgqxRQzTRI0mvsFRoUrR88Bv0b+6EEaIeze+QHgserko12ihQSu5vSqkE3cLDLIdog4KwCi5UaAOHuLsVpjLscc+pVCAYKb6/VeV7ZxRvNI196CmVbpTfR52lmgGZK+l6e4sRh82wO5OppykYvoDSVvFmxUIyUa/shRig4L+ZoGho3DiQiSLncP+ZU28I1QYohS11BPyNY7kRMqhKz7bOsTVhszaNSlivZSwwuAlZfpV9WGGTBCiVXRwMUCUUv4dhZo9oNCHVJdiLI+gp7CwItKBLn5iv0ji0ZmHMhY1Pfp1+0WBwIlsPBnIEx41NOekRC0MsK1FiQEynaUEMgUIIJegJOv8DjxQxZH7rZBo1Zk6kF1YFJpIIqDEINWnnxF8LThu8zM6E2TfEVhYWmQ6TM09Em1pStkznhQ7EnaK1CYSw4y6RcPU3pZyxYitBaGkFuzq5ZHSbM8iNePol4ugTrHYlWjDhNBEJIlMGcIKQ0hmbZoVqbcixNLpyDmoCdzHU4Ch3J4YKg3ga9HFJpK/RzgCK5GBi4QZXymaRihKhScGqrLHYVxFTgFxOSD1lTg6xJUhYs9UFPJGpZJekYuJqJKmG/tkRmFHFatjFi8HIqT5x1RCLZQVDtSmJNUsoO6Hfn0Ril4rPelnzzowrVtkKnbjIuSIgEM80i40POjTgvC9bjgMq54MGGYEGFUlvhvAZP1GXKnZh+WaJLaMxslJS53l3aR5UQVBJAMAlMjnWNK7pP0TieA1xXcGVs0+3O03ULLnQqOvZMIPQZF87m9zc/m1EfqDyuC8Y2XG0GqKlAkdArNFhOd2nry1R7Mf2qZHdZYXUeEOa9qop9OF9YGRJgjiOeO03AVlk4EwzLDhNHcFwTkKSEBwesdbU5MO9I3JLK8mjAQW3u4GRWw5gJ8mHA6zdAmSk8u5fibRkYfZ+nhRUse58kXESxD3j2QPJoSUFR+1QrPmHf4OmioOpDse8R2BGuUuZpQ2Wwm+GmYtEuQOqprJwlrD0+w+lLjmqC4uiMtGKjAIYHI1QSe+52q67H4dILeJakOPPIfEgUcqf7t0Qhf1MmpeTXfu3X+OIXv8iv/Mqv8Nprr/HlL3+ZUqnEP/tn/+wH7vM7v/M7xHHMl7/8ZV577TV++Zd/mV/8xV/kN37jN4ii6Afus3KggRsSJzpemsM/3aJxnKU6MilHgrK3w/bwAe/UX+Ru8RaBYXAz/jNq7ohr+wYTB45KGv+k+y7POLvcXitzXJlxRoHxyODlcMCS2mTH1Cn6UAtdsvSJjRjfyxJKyEQu2dYNnMefZGHg0nxsMRsViYMKYZTnZTfiVnwPO+2hy5TiwlNeVI+RGcHZRki7JuepxJbCQy1HtzzjqF5kUhFcH8Usxwm5QoyeiakqKW6wSCuuslddox+uoShQShNqfpbCxKF+vsSNg4i3wst0RYrARBEBeXVEwRrjOSYnJYN313LcDm6Q+GWMWGFtKvhJH65MPJbCEXGgczda5UwpMsl7vBtreJ6FGYVUgyFIuC5DCvUZBX2CEyr0dIPUHrA620VVO3RxqJFSvjLkratQ9CWaBBD0hcOPnbzBp1rvIYUkXxuTo0IcldHNmOVQY2GSRfckL05NepUIRQ9I0phPxSfMTq+TPbWppRHXxQFn1RLfuqpyb6HMg3yJ/3Ppef6oVIBzhf/HKvDP6znyMmW8nHI/V4HcPp99f4w6UHFjBVVMCQ4MHhX6TBf2WI08bg1DPsmUvA2RoWDjc6M7ZNGTWNMs1k4O9Szm0vCEzckBm12PxVGfTfsuKQpSSVgb7tHPnjCLY5qbHd67qVEPzvhC70+xwwFmbDFyUnZUnQeaTWoIlqwY118hGwm0FNb3BW8tbPHu4jpfv6XzJxev8MiroYwfoMUhvr/C7foCoaKSmFOerEnUMkyLCtFyxGacUO0rzIIlmskSJ7qFrycQpGRmAltCqieE0uGJbRIrkJohnn7ClXBEwWyzmmvz3XSd07iOmuYprof4SyEfpBUC10YD0jREMyU9ZZHvTa7yunyORSLEks/BeYUghc+NZtiBhWnAv/5IBX0aY4aSQSaLXpkytMfU4iH/VfpVAuAF7TabhQcI28DQNQaY3Ow/pJEOUFV4MHT4IGPzhnOTdLyGN1vmt1de4fe2L/A/X/sp3susEDpDnEDyD+8O+fjofcpak6B8iqqEXFEO+Lz9PV4Rj+mcWuiBh2F2aERP0dOITOzzL4KrvN7KEiUtNrvHGElCz9AYZXt8JLjPVM3SVxbQ0HD8Cro0mbobvFW/SqX2GNMeMbZNSsqUXChZm8R87p0Uc5LjfqXKm/VLqKbLvmHy3UWTbrWDkmkxkSqHjsP7G3V6KwZjDG74kq6+RkQBO5lh9y6SDUzMVEHvvcDvZj+On8lyfazStQp8o1REP5JstgXmWsDaMuTikDQFobZpB1mGcQ51CJYbsJK2OBE6K8Zddhp5vnbF4HhZctEIURUTtzoknw54xv8eN89mNNwhAbBuvMFnD6akCmTCGT/28AwRF0kcnUhVeb7/Nn93/09RZlVcaXKxlZKfOlBKSDMpoWbyprjOgWwQqQmmeYKWRBjEJDkTpaJwR7mIP9PxgYkfc6CV6WsmzaxF07YISynHdY379SJn2QaeusXD7DLtUhbfsXgt85D/Xv42fz/560safugAmhd5GMkpK23olV02WnOtr+VOTFyNGU5tgrJGsScZFxRuPPcaAId/+icAFBc8enGBdKJxt7HAR5Qd9L7GxJqv3JuGpNydd1vKPHUyTDVyWsilE0mqSixXUBnFFGeSUzvELodcfpJSH8GwNgcUlx8pRIlFLppSnGXYqyuI/giAiHkkLCipXDk/IzINGucJvgFKAgcYlJUeS13oZ3L0K5LCVLL+IUFIkOjIoMbquWRcEmyfgz2Q3N0QXGpDasxRhKla5KYql08ltjgmDCw+2K6z6R5zWoGGn7AQnREq83SxlqXysudj+YCZEI/mk3GfHIE5//wws0JxOOO0ChdaIfsNuLET0KwqjDMBz+5LyCSU1Zj9BQV1nrNHo5VyUrF4+4LB4gD0QLC3qGF7AsoBqYCRElHrCFxVISgkrDeh7Hr0cxBMU9A8Lp7H7K7Aehsy+Ri1q+FVNLIdlYkjKM2gnXHQMjHdkuTK8fz8lXFKazVPxhW4OcF6G3SjhRmD7QGZhIYaExccNppQmwSA5MBWefHpvD/bZYVedQ7mZ5pJYmhcb86BoZENWG5Ksur8HvXsJYpDgczOWS41FMzYRyumPIktLp1KhjWVSmmEbBvsLmhk7HOqI/i9yxoXz1O8VZ1HhsHmuaRbn/8PfRpxUBdoOlxsSh4vK8jsXHsrySQcrlmstSCx5osJi8O5UPUkB+uT+bVNqnMq+rNxFreQcj0Iyfjz38wArk9t0nODsxLcPB1yVqgDMHR8rjQliQAtScn1s+w0FN5fLXLtWCKFJPFUxkGNC+0OfSdD1oMok3J7K8tWe35Nt5dV8mceoQ6Dok+mL1gbqoSKyaVTSXztGkuZJd5emD+XweNHhK2YB2uw1AfXzFN0I9wPCUKcD9OFF8cz3nhO8JPvSASwu7XIpw/v0FcXkMoYY7aAdGfkZ9DP6KwPRwhlHul6uihQR3WKg5ReVeFnvz/iqy9IwrHOFw47vHkZ/I5B2YgZno8pKz7HNXjpuM1O5hliBTbPYWdZEPvzKOdaN+XtzgP8cspiL8ZTBJUJ7O1879/f4f2t/bV2eHjI6ekpn/nMZ/6iTdd1PvWpT/Htb3/7B+7zxhtv8Oqrr2Lb9l+0fe5zn2M4HHL37g8u6t5s2qy4KT/beZ/6bMLKpM324JyXHg0ZnV7hxabPxzozrvh7KOaMyiigFsx4Wlpkt7zOegeeO5Sc9XSkqvGx/Nt0RY7vbdSYrjnkx8tc21vgMNykO8lw6UDBmup851KGPTtH4huojHgYLDIKi7TMGsQ2VmxRVVwiqXI2bPCSp1KK5/qf39TzvHo24b/cPcDqSH6iH/LZPZ8/VK7ygZonn54zDp/nnnodtZXjH/VmpKZAr6ToGXDCkN/VPsGfi2dIjJTc5Sn5+oT/pHOXX3hvh4L0qLhDJkMda2pRMp5SUqb8pPYd/vHsX/Nzza/x/HGPgnKCkn3AUFho0mZRGAxElmDL4Gpxl23zAab+CKl4rLgjnGGdB8VFXjt7m63pIULALSNG1yJ0mbIepKwqLZ7R77HudxgXQpqKhUhBWJCfKWRHc3p/kgSJztTWsSOX53fP6GkKQ8tmUFF5fF7jkdZghWN0+ZTCcMTGNGDt3KR5/BzbnuBW0uTS4CH/nfpb/BP5FRodk4Kn4VtjHlx2yTm7jNUita7BWGioiaQS6MjEJAoSDCVAV1N+bifkvx6N+S9eT1g6zvIqHsVyl5GmMcPEDA3axTLjscNxlKPaUqg1FXajF/jgxQxLoy7lcEI1ClmYDTizHe6qNZzJEvY0z7TgcpzJUBvOiBSNihFgByN6U4cb7w0pTLPktC6XtNfZtnfIVHwcK8bulbAaFs2rCn/8bINuNUGRKsWnH6XmnXB5cJun5ZRM5X3yvQpPjG3u1QrEukIlGzDSJbdVk9uWxR9e1vm/P6Gwv7iG6ZaYBEXcrKS/WeTsUoPLHUnd76KLCUpikHMVmmmG71gW5ijhH4dvMFAlHV3ytdJVfuviZzCSCQMZsRkHjKY6QhVICxaNHgV1n2vDp7x4voMSKjQNnbHwUAJBLATPaR4Xs33SYkxo6Uwsh6mu8JZp80Fdp0SLjpJnIdbY8BIYlthMQi7EM149dSmEAVkxLx/YyWuoUYZQtWllHUhMtCTDnRWNs3yRNCpTkrtc9mLilYgTNaLr11hJxmj6GTfVJ2DEJCceH1wJ+GBT4fXFLMVCwkdm7/Di+A1uTO9ztAieYbAtz0k1wXeKBV6/Jni4HXOYrfGoto5aDcgoPlfcAcp0jVm0TDXjUvdLpHKe8aFHUO0IdlYFcb/Me6PnqYwGnNYKvLOtMbOhUPsqfvUd7lbK3PfXCCsOtbrHQ81iR7W4mB5hy4hP37F47nDGy488NpoalVAwEnmOOzkqHUntYMJCv8t0poCdMIkU7qlVWqnBwRDyocled4njaY7D5SISMKKEVTlGJWJd6fOxb2Tp93LYCGx9xpldprkksWP46H2FQn8FggXi0GSCwYuPYbupknUzfFp7m3quCwrkUo+6NeRifMJifMp/erjHj7UOiLIB/mbAo49MGGhNtuQBVqJRVx9QCfvk4zFalLCUdPmp86/yqdY7rCYpB4UKa/0eG/KU+yseQ0fhrprlgbzIqbzEbecCZ4bJmbR4y6zhJha9IMeSHGEmf/349UMH0P5k97tsjCOUoIR0zrjQkoxrKUYMTilk3Clyu3GFggtSFzz7sTlBSOvP/4xmGS7rPmo4nyge5BtcUx9T60Ivq9EuCaamx3pbEqrQLIMbKYgkwrJ7XD6VeLYkHmsoEgoJRFpIXI1ZPBcYieR8waWYJNTaAiUV7F5NudryebpaQm9CpwBCgBHDJF/iwqjJG40FLh5Lmgvz2xXLHDpN8h74UZ6kHCOCIisdl3Fe8mdrSywkHhtt8ByD5Z5EkYL7a4KXD/v4voVrQmFgUOnCcleiaBPkOOXNpS0awwFHdYHXq4OYMMiCGUr2ygqfa/moqaC3rWK0NVxTIEVMyZ9T+N/PLFHuS2bFlFo/YHdJcP1JxG69gaWfcv1IIrIC1ZvrnbVzChtBxMJgyL3qMt+6ZvPs0bz/72wVASh3JTtLsNoBNRU0PJdTRWexJ1hsR3QW4Cs38iipJNNRaWcM7BAmOYP1Fjy6ZnHxVNKqzMHTgvQJ+gbHy3DjKGWYddAkCGsu5JyoOvWegT6dk7msdsHPSbLKiFnZpz4EZwTjesJBTuXmcYJipoylRipSxg5YnZiWmXCjKcBKObYTltomW7MjBhlY9GcYMWSiOUFI3g252TrCWzXpBxrrLZgUHWqmS6Ur2KkWSQOPr39EYE8FZijIl6c8MAwunIEp50Qu6lRwVBO4pqAyguxMQjpCARpeiii0sSLI+h8C42bAzrIgTUtstQJCJyUNLPYaMGvrVCs+7VEDe+Th6dAqwiU/oXimcLQsyUcBO5ltfFXl1NmTZ0sAACAASURBVEq41BQ0PyTqMNt5jmqCh0tZNs9BK8bMhjr7osDqucK2nOsTmomkWYcbZwkokrEGjXZKu6YRmAZWKFAtk+wkpTCDzU/9KJfKl3hrwQRFEj18k6irMqzO2Tj90AGgOpO0ipAbzP+ra0mO84LPfpBirMTcDzcJsmNIbPIzyU03YeFk/o6NjTK3Zh5ebw6gXUOhoDoIBEKTfOJRyDubDq4p0J9C54JApAIjF3Om6OSWKzjoLI4SPtBqNBck147hybIgauqQT1hvS4aDPZSSYK0Lb20WmFgwvnP7b9ot/kdrBwcHAKyvr/877aurqxwdHZEk/98R8uDg4Adu/28f7y+bFcWkjx2Wn+7xY7P/A90yMDWoTXxWZ13O8lnOnQwLap8tv8lIzZJmTc6KJVr5PI0oJtAlbm5KO8zz0DIZF1w6eZPXlZdwoxJ3tgTSDJkdm5yNLQoaCO0G5f5Vtvw+N6dn/Kz3HqtuFzWVHC9JLoyaeLEgSC2OrCqxncNMVAJd8swoQZcZPL/BxoHD9Mxk2HL4iNvnAgMm0iETRFRmIwr9hFQIOqMch1EBREJGemykHTw1oai5hKZCR5q0Iov9LY+Vwtv4mZib7UesRx9Q0075yezvcDdZohcXcKcGqSJJC4dknAcQrLPWv8wCkm9zmXeDC7TeL3Fyf5nrw4fUjTdYaTm8k3mBp8sC1w6ZZlW+/slbBIaFVolZS5usRm0Wzhd5aeAQX9jhaW6B76QXmSiCkaKyNIPTwGagqCSxQtEMef+VMRfGbRQpeTTN8ZL2TS5MdpmYCjNRxN54REnEHPmb/Mh3C3zyTpaRXaLjO6yNQ75+I09i9wnMmPNiRG6S47nzMc80S9xs2hj9Vf73lUtYfZVqR7I91VlTLHpZjUCxiB3YKvikKLgLMVMTzg2VyAKdFM1PCGJQWyEnusVxkiEQKlGQYzy4zhJZdDUmUSVmZcKsopG5OGLRn7HdF9g6GESsTCKKA4PVJyrpgwIXuiG5ccrCdMgfh1c5x2KvrBDkXaaWIO4IbvSaPDBWOS2tcZhfxt98j7jQ4mJ/SsXepbvR5rQhsUgo5R5hpUMSQ8ezG/xSb4puJ2g6jFXBgaZz6pj09DHHfoXP7ICrWrxuPs8f2D/BUC9Q86YoMsGZFkmFhZpk2H5aoDfJYigxLwQdqq2Yg6HDeVzif0tfZJBViLIBrZ7FU1Vn6kDFPOYyI5aSKctplz9Rc9zXHB5etPAUhcUwZkGEfK9WZDEZEioCO/VBNdkrxHywaXPPWOJ1+SynQQF3luO+U2CqpcSkaDJlZdKjpp+jCqBbQxAgzD7r4zblcMCqPONSMkMDzDjl450HPBA2D0sWYZTjXXmBrmry6mQfoc44IkNzY0izBLdXMwxMlW+Ky1zW3+WZybvcss/4tB2wlTj8C/NZnlgF9go6ui4xFmPuLW2ys1QiG2X5pe5Dng0P+VH1T9nOv07OSvmRoYI2XSbjGpgh5CaChQCe7R7z+Ud3KfsRflBiYxazFSdspT45c8JUKzIrntHLJygjm+I+dMcrPKxeopp2aRc03llbRgZlrFSjk89wsZ8ydZfZlw1UPSVyYhKh0GnbvEOG705f5ivaOo8zOj8x2GVqqlxUDvl70R9TcwYY+YTPxI/5pdPvcilJ8TeyDBydh6qOI0co5hG5YkScS7GVmLzwqZo7dKJn+LPiq/zobcHf+47gsFLjrOCjFwPiVOHesko/o7A+O0OPQzJqwJY8Y7eu8OaSzmmkc+MgpOolfGw/y2bT4O+cvs1nJ+/hKB7GcMbV/X1Kwwmf2DvHihKK1vvspz7qcJnD7CqpMKh24IL7hHq6zwV/ByuQDJOEyumQQ08SBoLw3+gF/RX2QwfQ/uWTr5GbKhzmFihwTHkCx7n5jHEx72NPI/a9eTSsmIaojSvINCX7+Cn31wSXgugvSC5m1gKGccZKBxQz5bxq0nQiNluQKrC/IFCGgsRXsdI2a23JYUkhmmgoZkIxnQOCu/UsCvNz3lkOedELOGkoVNyIVl3lcrONKNgsnKg0lwX+vByLo9waZhpy5AiW+7C7khJpEjdeQ3XHAFSGITKvsVNcZP08YVKRHBcN1rw2WgpBmkc3UxJFcl5xqPU8VD+lVYL8QLLcnk8as77AGUR0ZZ3sLOGkBuPeEpGS0i+GfOQ8YXdJUO9BHCrsbmtsn8BRTSN2hiz3UmI7RcXDCgEtRY/hvA4ZT2e/sMqz/T2MeO6QrbFCiMr9qsbLTYGRJjzNbLC3HPLRo5hEl7xf3WCYga2nCm9fEWx/SBBSLAScDy0eNUqUJxBkVd5fd9hqKcRSYAfzSddxUWOzBc1qwsIQZsa8/RXjlFHbZK+qcelE0srNJ/SWPyZSoUOdiV5H68dMrTktf6CCGg4xwgknJY1cT9DaTJjpcPE0gLpkey8iM00ZlCUXjkec6IKlcw0WIw6HDkfZRTb6PfYWoebNKevzo4ijOkylYGXQQS6FOJ6OAuiWQX+io6XQM4swEXzteYUfPZynWS1ZU961TC6cGlSHAYOVedrhSU3iTxXOKoK1loKYzFf4Km2Fkj9XgjaGKt08ZHoKs1pKHGbZOpco5ZjuYIO9JYFzJLlsBDyOF3F6gvOKwsGy4EpvhBkKzlfmQNqfNHh4scRTXWW1lbKzDLEmWRoJBlnYr2lcOJcopYT9ksLTPDiDJS66O/M+GAj6OZ+NNiDhYydzgDpYqmC58/dmUEjIdOf3r/rSy1yvXOfcDNHyMcmT72IPFNRo7s78aE7zv9gVnDZAHydMTPjGTcHHH4AVQuuSYDhRedpQEKqPKqGqtbmxL+kVJFFF8Izh0enmmZrMnXA8r+ks91JUCa88ELx+AyanFtekySgDcSyQvsryhRXKkxyBIdhVwF1IWD+HVkUya5tkChHrbUlHUSgvVjFiOKsotArgHI1wI/c/xA3+rX1o0+mc7CmTyfw77ZlMhjRN8T709X95nx+0/b99vL9ssZKgyxgzUkmdCaXoEF/qLLkDJl6e3bzk+NKUy0tvscEpx84S+/o1tpJHLIWPaCoOJTVBUSWDIM+lgyILZ3Xs0bNU4z1uDR6SMY4J1JhSABuGz6VynwtBQkyOU7PETLG4JM8oiSmpotLJLEASItwcqq/RGa3yFa7SqeYoy5Rc4nOazTMNKry1ZXFWTJFxyj9w3+an43e51NYpxl1mWZf9pZj7WYUPchtM7CKOFyIUWFE6aInJhbDH0MvwRCnxlReWeX29Ql5KFCeBzARpqywyZjsMidWY3tCkFLjMdB3PBlNN+bR7QivJczUd0FBOyFk99taKHEbLTLQyTiSJwgI/8uAxP/PoHjuLFv3VDHHOJkEjSG2W+z0KroeCTiUYEZkRSvaU+yLPB4rJI6HTzyp8V6zwB8YinamJkwT8cv+ENDZQY0nTs3gLg36oESkeLz3ep3v+Mna+yuXFE8z8mMcfkTS0Ade7Hstun4/uDJlogq4u0IOAFIktfD6j3eMz6imViUHszFgYxWz3JTGCbmpw7uQwZwEzNUOfCmMlRylvYKYmZQ9+fOxSdQISTeGeVWR0bDGQC0wyJZo1g0drJbbcDrfSJyiFeZbN5jDhyq1Dlis+nwyGLOpTvr+1xVlZoypcBtceUYhi9qsVuoWY2OliqCG2kkWJ8wyjIq+4Pk4kSUIV2x4x6gqUc5+6esJ5PubNlSzDSsLMSYk0FVVJ0Wch24Mu/yh+g8fZq+hFAy0xaSoKsSIxpaRCQhKZvJzu8E93vsVsWqc2UplmM9TkhFLq8d/s/i6V3B8DEmN4hUbP4ZXz+zyZaBxhctPf5YXWEZ8+OGZBHzIr2bx/yeHpQonZSypbps8dS2dvkmVL7KIWIkQemvkiThpjRyY7ms1MhQeiyoQcF/tNjCBmIDO0KnuouR66MyRvjplIk+JJwrf6Vxh14d2zAt+IGhyZiyh+ji/223zhicokXSOrjKhoHZamPgvuEOGnDEQRGx8jjbCk4P9q5Pk98yanbolG7xEXmx6f6b2N6akYqcG94HMEwRqhrOF5mwRhjYWOYM2T88i4UAjVDGFoEOQmBN4VkijDYzXLe/WLjFd3+V+KBe4cFfn+QQkMj5fjNje8EIOIkmvzifMxjWlKdwm+8bzDo0KJ7b5HRvMZ61kWpjbbU5XPDwJe6uXYr+qc5+DGMGClOyE+svlm0WR1mGBJcFwJkcK42GYsTZTQ5pOD+1yQB2TsEclIwwoVslFKHCosxlBNJ7itdUqPVMzSKXWjx2mgkRtCvyKRDniqQqJAGZ98MmOr5RAeG/Qjn1eCJv/5dMRPz8aEpZSVUosyIdf6+6ykD/F0lVHWJs0HvGUV+R831vF0i35WoZ1RGLsZDNfm0NE4Liu8VdW5k9PJ9OpkznU+ehLz0z2Xxt4Gn3Tf5T8zvomWEUSugROnnGlZ2kadeuciGpKtvscF9y4vHx9S66skY5X7ayYPtwTu+g6nTp2no0+jZFIs44RYUTH4q4mn4IcQoD0c3CEzgvuLRTZ686jOLLHmNOZGxJo4J+7PJ1oLQgXNJNzbwwpiOg0FZSywRwHdosKVJKI50zASKDgRSVnjjqmz1paYERzVVQxX4bS4zNKZi4Lg9gUBUiDqEdVRASUR/PnyPBKUCMHdhsrz/ZBpI2G9G6F5cK17RPXDiIplhegphLrkfmaVoxqY0zmz4HFRY39BEBp5wvF84roy6TC2GzxtmFQnoDkxgpDF4Tz6E8xsUgQ7ywLFL5DvxeSmMV4+ZRIarHXgzgVY6kkqA7Cn84hBWkpIXYtIFwxLETc6MaNSwqRrIEOFgROzNACkwv2VDKsdicynLM/mBCRDCgBEeYVUCJr6ElebQ1JFEs9UQOD4M8a6wsbxPJXIdXQCK2KjCX5GMJYVHq4K7JFKZyXl0qlkZhuEqyFxR6f7ocZffga9QsorxylHNVjrSiSSdxeTuWjxbF5zZwQJ3YJCNe/T6zjIEKxQIPDYK+coDebSAjI06C3bVFoKg6LgtCzoq3niSUy+H7Cz6VAcCfpllaU+WDMI8pLZVKPRl8zyGiL/YT1iN0FZkHhtg6Ncncow4qSeIefO01nLvZRWTWG3Bmqacmr7cx0vRVLUJ5wMbFLA1xVcVdDPC147CYlVwbIxY8cwqfckK22Jrs9f56KVknZ0DhqwMiyi9RUmDpzIJap9l0CXDCqSp4tQ7qYUVA1PUVjqQc6Kedu4gVtPEFOBHQrOTZNGRzB2NIZVk2xv/jzurAsiU+Vqu8vhtkFlKLBCOKvN690uDYbkY4HUPXIe+BXJvRWborXPRK6wOOpxVoJkYhOJhEJPIKVgIVAxElCvPc+qNweXkYzJt8A3YLCY5dnas/NnppyStI8JdEFxAKEB5WDMaRlKE5jVEsyhpJ3T+erzCj/1JljlkO6CTXV6xJ2VRTLWIRNLJTFbXD+W3FkXiNyYUjGkN7DZXRJkPI38YMJJBTZO5u/eCwcT/vyGCqngU0+GvHURvJ7BQi/hgrTJ9PI8rQsy6Slu1UZL59HCOFZQssp80UAT6M+8CoCpTPAclc2W5EHvwX+4M/xb+wvmRSHEv1f7/58pyg8eMnURkmRUMjr8nN/ho9EpN9o7lP0uR8kmd1YLzByFWlpgqincbiyRahb+UptuZcL3NotULns81xhz1i0THea50Trgs+33+Gz6iElRRU1sxqrB7S2Dk6LGWVyhEbVIE4+ZpmFOQ9pdydP1Cn+4/VH8WovvPx/QNy0W+xUmpQ7HWMRIVv0MirvFWjRmEGZZGEOtL3BCyXRfcN+JWXOHrKR3ePeZM96/Dg/COm+wiaP7WGHCP3j8dX5E+RafML7HjhXxIGMR5jU+an/A1ZNLfPTM4MGySveKSSe/SSPSeRJfJhR57q1USAwFVzMxUgU7lfzT/nfRsm9w4EjW5JihZnLFmhDYGbJTDS2NcNUsK5MJ5VnEyx2X5+tdPp5+gJSCdzOXUEoJqS5ZdlUuyT06wQrp+Cqmt40VKziJxsVzlT2e5bf8nyGqgl3yWRUJWgJO7LEkZpgTi3ZO5bgGp5kSe/oC+/ksXStkdanNx8ZHmGFCWpE8EQb1tsOqF5GRYLYu8vtXX+SDWwqqMcR3EqrxBMvu8GgTIlth2JkQuX2ebfWxhM+k7PL+bIuOV8NVZry7scD3MiXauo66EJJeS7m9d4lgmOW9AfymkePeqwn69j45zyeLj52NKRoJmqdRDFPs2OeeUFjjERNtzEExy0InpSAUro8n/Mz9fRDzxeZcNv1/2Xuzp1uys8zvt3LO3PO8v3k8Y9WpUVKpNAEGLDVYuJtujBtsBwGBOsyFgiv+Bu7gxuDATRCOcNBqorvD0GZoUKPSVKpSVZ2qU2eoM33zsOd575wzly/2QUQbtcKO5sJBaN3t3Lky9165pief930eMgRo4x32H5t8aEKiCy4VFS+OMfUTEtFhb7Zgd5JjbySQ+QFmOMXQfKQqcDMxpjCo6Ck9u877xWukqEgDrAhiL0M8LLA2Cegakn5DRbXH2OaQvPoBtficK3GXqrag1lpju1/l8w8X/KPDIUFG5+RmShzC2EuxujClQEfXGWNS831y9DnzPVQJN72E48wmcjVhllf4euUGL48TMmlInjEDrcwwcpD6hJvTHrfOIhpyhCN8dGOMtGIUJKvpFEef83Vth7tGnUXSwJ5kODra4cRqEOox8aXF6sTnVnpKLneP2LlAkyk3xif8LG8x1VMWWEhDZZrLYk9VYiPi3C5yKTS2LuZk/YTtQgdVS8n2UjZHFi+FXfbn55TVHvWCQV0JuGa2qA9Sfmp+m89dfsAk1PnnpwfULjd47sRBjSSlaRFfRPzBrU2++1wGPzb4H4cuV2KfcyWDpvUQuYRVLWLPdPnEfMyl+jomkozr82Z5g14zZncYkI/nNKI57dURj3Yk5dRHD2POVmKOcwonUQUlFlzpT3Byh7S3UrqlMnvC42GxyR9eW+MbtZsosWBlERNnVZxMyl6Qcl25x67ymMBMaJc0zl8o8yefvEbfUenGKn0UJobKQWRwgcc7XCMx4LNHgs8/kHwxWtDQYtZyEf/xlk7LiFGlwuv2Ea+UH3FRNQls+KnoQwJV0CtOcdIQPZVEnsYitXmU3+I7mybfKRv4rsY0UTnN5SlEXaZji1ZB55vXDdQ4ohkOGDsOJBp2GPOwWudf136CO8UtHpif4MLcotYvsjkdU5vP+YuN17jjfIbY32NijlloGo/Y4f+Ut0j0En+p53jX0n/wevP/aXX6/3mJ0xg/bbHZg8drKtud5fHCYEGrBq7vYJQTciMX15JkNtcAWDyTXLarAYupTTRSeLCm8Vx0weSZd5mlaygZhZGn4YQggG5uKdt9b7PCtSPwLHjryjLfRTgpi9Eaq33B7YpGqKn0CyaxJrhxCE7NW4ouDODK5IxKd0qoQWORUB7DvJISqzGPGxalfkCsQm4Ej1YFfnNBrS/wbIkiUqZJE9tcgs6in5D4BZp9F9eWrI0HpIHgwYbA9kCNBaWZJM4KRjTQE0iyKSVSIkVn81lYXzXvUxF9QqEwyILpw8thiNbXgb8V3chOMtzdMNnoQ6dYoDnpEKmQn6tMbbAFSAQDbRlm6JdTlGfswGIpcEmpbRFpQPaSjCexxirTomAjWvBkVWC4CvMcXL9MaRXKzFZjmi2BnbhEKqycRCTmjJcug2f5ZxIll3BkCrREkumlRCqUp9CuGOjZmAmC3YvlJq28WPDBxhq1bsqgCo35lN7ajLUBGHHCk6aFtLNMwzqlNhT05Vv3E0fh+eO/yf2K+fZNEzuE2LA43klpjMCMUtSCSq6tMrSXYhGXhVXMaUS7qi8ZxZKGbyw3ij1XZaWfMKpJ8oZL1DU4qyrITIdhTpCJJNWOwG3ahFqCDKBdsjESyPkRvg7XXJ9SS+HxpkJRV2l2YVgS3G3UWemkzEsQlFIOGwq1WcCKa1MKZiiAKSUfaHvYpRAQ9LwsMXOsCIhTMDaY9TXOqnCQ1aAgeKl9yqQSfU8g5Kxs8dGqoDEeUZ/qbE6WYLRdldyrr/Dc6AlPi+tUhwH9quTN3SarA1ATwTALpr/M0/zYyy+TZjW6RbjMZdm7gPOGxtOn73CreguB4EnVYpakHK5JtjqSYcGiOZ3TXb4TIS2oOBNoV1O2epL6OMXdk5zNn+fV9gFPq9uY5hFJWkMdu5gR9HOC6+EImQryk4Cnq2DNbcq9mJOmIOMJPB0mJZBOiaOGID1S6F1NELFCVY3hvacYE4OTuuBW55iL3M5yXA3htAbJsxdnJX/B7eotUkWyMoqYm5KdtuRB5wfLwP+w/L8ruVwOgMXiP2UkXddFURQcx/k7dbLZ7N85/28+Z7Pf37cmzAn+bG2f4xcyHDfgTHU4bCT87o9t0a6WUSKHsmcjialNXP6b4zfZO33IXb3KZAOi4hTLjqiJmG/t7PNRc536XOPa7BIvk6HVPKdHBFLwaMOmVTd4t7DHu8k6ZhwDHtvuAYtCwsdyf8VP5/6Q/zZ5hBmq/OXiJf5Uf4HEOeX58BtciyZkApvRbJVZD3b1Y0qOx0mhwTgjGKU6b2s5nLHK0FCY2QZGMWGSn3KRz/Lt7C0MO2ZT7/JX6T5/xBVqeofB5pTJbpcPwwxzXP44vcXjjMq/re3S8nZ4y/0Efxz/GB+JOh86a/zr/R/lvLBJdip5oRvieyp+tMnDMEeqwruiipcx2Kq3qJnv0dE3mdgqh4U6qSEZrvhYasSq6DPxDM6SAq/UPmBDnPKzi/fIJgEBAT55TMXjHJskyeAXFT41fpvXRh/wVNioGuhpQMUdUQ9nfFTUeGezSKeh0akJVtUJ9+x94riKLxSSJxn23hGsKnf47TWN9+saZDwqoSQTKXy4ss5xWWe3mHJs5AkHeW7N2nx+OuS5TsJnHyRcHwvGWZUg46GIhFML3jBf5B1u0V5kyF04lFornPgZvJ2Qg1THNs/IpUNspYdfOeMXL8e0XItQdwmkhl4O8HM6/swi9VTOPIs3yxa3qy2K1TskuYjTNRiImBVXo5zGTHKSRIHBNIeVu8v1I/jcURtznmcgFb67Z/K/fXoPJwnI+5KXT+aorVVuDC8YVVt8sJqhHMBcEfQtweudmI+6RRznkJv2IT1d4RNhxM4sJZPC//RmyK+/qePPd/hOvsnDao1//2oBX/F4VD9DLFLSmeC11oD/Op7zo52HbHQzdPPrXLdD7LnJ6XiNSBGkSkArKXGl/4DtwOclT3B3cpW5JphpCq2VGTV9BjWfG6W7/Iv2MT8z8ujEtziInkOJNNoYTOcRaRRh612qwYBQFURSZ5BmeUqBB/Euf9L8x9yvZfAbAw7yewh3hcD0ONwY0XcEXXSOzAKaNNlwDR5UN7jiHrE7vGR1EbOVXPAj4ltEVpGhk2VNOefacMC1wyzX+iFqz+ZbyjZvjPb5Qu8Onxx/xJ7Z5ouP3qc+tpkHCrGucmbmaRg+7VRFKgqvXiQ8Nz/hn773EU1lwW7+jNeTp7yw8MkVFLbtPBf+Fo/NImNFZzWI+NRHHp8LPPJWgjfTSc4Mbpf28DSdrPT4hP4hDyRYFwb2oUX9bsD6oktGxlyKHP/Lziu89bLC6/MLXpneoUuJSLP5ZHCAbWaZb/n0hy6V3j2+MHrCTXEfLZewF0uGVxPMtZSM4/JE6pyUF7x1VSVE48+V1wjMHO8XHL6l2XzbKvEo3qNqjfm0f87O/Iz9rMu9cgHKJqnUCaXKodKgG1dYCJdN/QnrxgCvpHKwFTKpDbmvj1mLHnFl4pMaksBICDSFmZnhbn2beLDBtZbkv7sf8D/cSehr+zyo1vmmavPbaxlW0id4kxyLqcGBus6kkmGmmaB4PM8dHKOPn/Vx1QKN2RwziWjn80ysDGqQ5+cu30FHoogFUyPDyNbRFwv+ysnxl2buB65f/6AA2psXb1IMYvY6klY9YKctEWbCRs/HqyZcejUeNJvUhgluEbIvvgrA+ZvfZG7BVXtBNygQuxq3V2u8rDwhHegEGpheilvx2er97f0Sddl8s+qYF48k42rK2KjgmaAGAl11eeEkZmzOkELQqaWUkwTrkcVKeckMqL7JcMunfhHxZF3Q01S2uhCXBZ8Vd7jX2Ge1JRnU4Pppnqcrglmjz05HEhclFxWIhgoriyGRCl3dIJE1tlspg4Ykl7gIKXiyKiiICf3sUo1JOjq+vwzbyS4UJjmD97a3uek+YuLAbhpRCkd0sssO1Nc1PuV6ZF3QsglmRyXQIFQ1EnuBGcPt5grVvk+nLtnuezxdEXjzJkYSM8777LckohDjVVUWlsbxusROU0rdgFZNElstbpwv21YYCR8PD2jVlvGeQaRQmQhGms1FSWHvEmrDgHYN7qyrCCWh2RY83hBsdyVWIUHvq5ytW2xcQqeuUp/C3NEJxjrHdcGNc4nvKFSnEFkGRgxhNuVjp32QYwAUJAflIkZZwdRN6j1B0Q1IgSNH4aWjZ6zlhUGUXfYHa27zeFXjU8+83Dw7YvcSMnK5yYudHMUh9BpLMJ81NUpzWFRNZjN9adBcU6lmXCpthY+aBWbFGYcrgpfHCcHIQCulPDYM9i8lYXYJaIxFyGkNyjLh6jkc1xUmJX8pnU/C4V7Cdhd0JcFz4LixbOvmNGV7tARRSSTIBD5rYcI4A72RgzVfhtMGRsKKb6P0dB6tCQaGglFa8FL7jJntcvVyKQTS1RwerihoSYo+rbHd90kVyZPUoptdYbM75TxXpjxZtvfBls5Oe9lWhyvgTBJcE9btDv29Oq2yQS8n2OjD3U3B2Zv/Fzkjx05hl9sNi2mk8/BmylYPunYZM5IEBiQCsqmKmgoebMJPf1ei2gn3d0Na4yJrQ+hRJuvPeGWmUrxQSQU4vuRG6DEbWCgSzqqC/Wke2xdMCgKBXPoENuAn2gF//aIgHBnc8jQ8UyIEXJ5f0kwCzquC51oBj5U9l2U4zAAAIABJREFUWmW42pY8WhNEvWcM+Njl4OA7iELKVgc+WjGpT+Dx46//F8+HPyx/m3v2/5TUPzs7Y2dn5/syaNvb25yfn/+d8wF2d3e/730eNMvMawlX7XPWQsmwYvJkXaMZDPlM9y6f7CXoocmppoNrsjb1+eS0Q5oNSGYqL9yJmc01WoHNPE75YDWhEU2pJmNa2TLftQ00VUNLLN6NPs6TWF2q0yYe4+qIq/5T9vQDHtg2XzUURrkR5+4qKycFbo4uyM+HXO+E3Dw0KEyb5L0qd409xkWdV7Q+XS3PQq3w/hX4blPj5WODoV7G9hVM6fK+laUjHfbM27z+6A6ep/An2x/nbngDI55S6QYUFimGjDgtqoxLgueDGFURzFUwRmU0zyCXLsODM4xR1QVq7ZI1V+O1OyoXlxl+6t4xrz0SfEPUWLh58u6M3aQFqoWp+aS5CYuKYFrS0FUVxYmIVfht64t8K73B/XmDqJ/jPKcSaYL9IEGJTJ4qFkeKQSsxOamFTEuSRysq/8pao69YDFODVsHCXUlYWBKl0KeWCl4ZhuzLGSvzFm6okXFV4plGkqishjBUdB6tK3x7z+Y90+YN3UFVYtSogBMnpOeb+J0iD5wyqSKoLCTqQtAZGuT6CqFQEBrUByqeBh+GKzyoq2z5HeyjClvHGWZPM7wvshzWDVKWdj9aIlBdaJ5skvU9SGOkrTDJZogSm7sfNPgTv8L53OLTTxK+4E35Z8Mxq15KbZqimjHDukpVkxTCAt/dKzGP2vzcwdfZHggeKVd4auR4M7NKcZJD1VMyCeDrfK51zLbs89IJFKc69pmJCAQ35jE3zT4fGwRsz4Y8FnnmQwvFE/xoN2F/bkAmJOd4HI/Wmar7uKUi0nQp+xlWY5XYU0hiQV4/4HXlPazUp+b2sXNP6BopFyLHm/pNvrb+PI83LIpRn7OVlB1XkpkbbF8U+Deywr8yCryoHzBTEi5si7WzhOY84J5t8634c4SqSRCqXHM1WisK39jTOdWazHWL7ETHnGqkwOZ5jD2BYuKhBnlKpS5+TeHR6ioN4z1yoo89V7i3m+LUFiSRhYPPbuaMFdFimBpUjRnXuGRiX9IPtyhPVTJeme0oxTUEQQUOKnn+Vz7Ht+p1RpbCdhDwYwOfpppy5f4JubM5nV6WPx9/jPdHGxzWVCYrIbNAYx4bnNchm5+wlZzRVH1eCm3M2VVa4VX2aPOn+ZRUCH5K+S7P8yF9YXBrlFILUqSVgObRTQr00xz7+pTrHYhnGtZC5xvXE3oFmCoWt80ybxU2+WzH4eeOx0wcCyONkKpCMDWZKxblVOeftG5jzBK6cxU3TAnz8FRsMbHr5DfHeCODm2cx367kGZYl9djlcbTLAJs1P6U5h7m7zllQxI3BywY0K2Ni06Zb3GVQsJEiIUEnTbO8em5ykeZpTcvcn9X49/4OceSwUvFoZn2eP4UvHEbkmkNWhjDIp5xmKhRCj+dbkshXGZQVhJ2hHhvcKb/OoKiDGhCZEU4S4JkaIow5VqqYBFwJ52xkOvyTySN+7uJDhNlFyUzxcwpP6w5SD3GUMUVvwJWhztVJh/35MZ+efBONOZmezerlD4Zg/6AA2r979GeEKqz1FbzcgCttSaeZw4rAKIecjWv8RX2TzR4YZkL1taWCo3vnAx6vCp4PQ6JguWl+Um7wqvKYUlcwLIDWjekXPfZaEgmMHdgdubSLUAi75D1wKzEZ1+SwIUimGuWow9VLiR3GmFHM05WYK0HESV5jPVI5r0AmgEfbGcpjQaup01Z1rAiCXI4b6X2UImy3IS6klBSFi6rDzBqw0Qfh2FzUINP22Ogt6NYlb+1kiJ2lse+4luDmlhvfs7qGoQQ8XV+GE+rCIOvNCXVJuS8IRMrT3Dpr0x4ndUGurSIin25RgJQc5RVuuSFmBCfXY3bO4bxmo4sZ5cUzgZB8k7UueHlJeRxxuArF8wqx0KjHl1gRWGpC4hjMDZvTCrw0DynMXO5sCbpVl9dPExASw5X8+MUpp/kNPB2K/eVGajXt0VmYtJwKqx3JtKTwzWsFHF9ijBSOygaNESxyyzymB1cNtjswKi6p5P10jNs1ubvrcP1MMistAWAlXjKHkZaS9cEe+aQCDm6kDJwCsjBHV1wUwBpDq57SshVunqXIumChCiqTiESRFNohj4sKLx0ozHIqnTBFTRR2vfOlHLs9Z30AahzhmvC86/PqkWRxRYWpiZ5AULCYixgrgoNKhdBIGeUEP9KJkYmgkp9xVzfZa0He83EzCUzgrCaIULFCMIOUULqoEmoD0M2lyXNhthS3mVaXfUOZhux1YsJcQjDW+fjsESsDgyergkXHxJoERCpMKgo3hyP0UHDYXI65i3qCQDCyPK5fSiJTUhnbHK4sn5fnV9ltLfuv0lKIjRxrLZ11cQSAI1MmtQUvtJd2GEcbUOpLLmoqp5dvYXziM8x1E/WZqfeHmwnt46WIxou1F7i3lqCMFAY5gR3CUFs+z8ZcclaD7Fjg6nBcV3nhRJLd9XEzJoHv0i6BEpkUXMl+MuX5Y0m7DLlUY08POBks1SljRVLTlqyp7acYCRgJXFYkjcmC7+xniVV49ZHPu/sCf64RziRbTfCzFjvtlCfYdJop25eCgxWIBzqJJdm/lLT7R+hljd2O5P6GwcyC2cMfeqH9fZTt7W1WVlb46le/+r1jURTxxhtv8Prrr3/fOp/85Cd58803cV33e8e++tWvUiwWuX79+vetE1ouKRGpLtEjwSg0WOsLXns6oxF0iNWIdy2bD9QKQ9XAnpV5XLpCNdJITIvTDZM/TUu0h0U+Ke/wwqKNWzMxt32ScJnPi9Sx+69iuwXqc4Pt/AGvZd7lRe8JL4qn3K3mkYssShJyEq5yGGVYmWsUXZ+VwYJPHLpcH7lYixyjaIVsEhPFgJIw3JlTjIa0q4InqxrNs5j/6rTHyixkP16QRAaRELSdKjIx8EcWupyxJsd8tnXAfnSIfW5gXypsLOBHvfvsDtsUXJW1+DFb3iU/0+1gSx0N2NMes5M9o1Qb8qk4ZL2l4I8NVFVnMNMwpy6vffQBw6HORHd4vuVSiy64LOm4toaeT7EaNqPUQUqVAQWOlTqK6RGsRmhhhkyg8uOLCTIuotnHPI5yhBMHnRgKPkX7HI0hPTUi7/rYiscg45BqkoYbkZtFWMBYCP77/l/zefcdXh+2SQ0FVEm9q3BlkiCk4HG1QXeR4wE62aTHc0GfReqzbZ8QFi9Q8+eYpGwGkoa9nNf1WcLzoxhnLljvKfzM8A0qlffRVI3N2QUf673PQxo8zWRJ/DxOEGFGkupMkokh9jW+ur/PR7V1pmpCv6RypKsEVsBCaDwu1bh+pNOcxzTGAeVpzD+6l/DFU4VFbFFYZNDSDH5jQeyck2opJTdFiyU/MXjK+90qYurwI+I2P7bw2SPhvFzh7UadohIztLdw5T4Lrch2kNBQEjQjIkoNdqZjcmqbe1aDO5FDeWTwek/Dl0WOwgpb3gHr2iGf9Ae8vghQ1k+JzJATO8c8tMloJo9Nh3EeYhkBCd+xLP5C26Dm96iIQ1LFYWse4bReRGtX0Icxtyan9CYOI2Gx40XkfJV9P2Q+0ZkMDRQh8NWIhQLdTIKpjJFmjJ9P2FHPIavw/CCh2VMoTB3W1D4bHFIUXdYWc/y4SMHoMdMl97Yl9zbg7ZzJvTBLo93ntdGCj1/G3FP2eLO5y4fXytTTBM+8TyXqUokf82lf8so04QlVunaZf7n2IvfqeVbEA3a9D8hrLT7hzKkKyZklWJg2/sIiF/u82H9EJ8jh9EyKaoJpwVjPcLZn8q5uM0qKrCZjypHBp5KHyE6faSfPfSVDIiVKkhI7kgcljZqUVPsqxkwlm454e3OFyx2VtjHkm7sF5s2IpOIRVHymRgYxb+KEC8xFFXO6RnEOGc/EjHyUdEFkFckN19gLVYoFj3ZZcFFS6ZXg7SuCy3qF9VmMFUsSNeV+dRdttI4ZCCZSp4CPMr7G12t17pQc/oOywVt2jYdbKRthTJJRKUqXiuYSiAwd6aCksC3OuVlsUxBjJtJkNkvxFJtBXjCzNEJNYSNK2B1LWjmNl55Kfvy24I39l3lUXCMvF7h5eFowmLkWRjhjWu3Sy+o0+zqBCn7JYE4GK4qpVi6Q6xGzvMMjfcBG7i7jXY+s8YRFZoSLTT2Q3PAP2FSf0GoW6ShblKwR/+Li/+BGOONHBiP0CpSS9AeuX/+gANo77dvYvqBlNjC0CxpDyXFlKRBSK4SoCziPIqwI6mmItvYiyWxG7rLHwapg248w/RgJWJZNqM7Z7EHspCxShQNHcuNcIVEkZ3V49WnAkxWN1faMVECmGFBdxBw1IJporE7GrPUlG89Yt0cNaIYJD9bBOIXTNViZhFizJSjslAziZ//F00pcuiZbixaqBD2NKdtTkKtE8xA1hYmWIykniERho5viliWpGVEPlx5SkQaKJhnmJUaUZ3UEXm4ZW5WdheyqF4yqKvWBRFmkLIImlVFAvyIZtiqEwLAccn2U0GqmLKYacaTwYF1nsweX2Sztosp6f3nNWtzHimCglRHApArPDfqc5Ju8MDoEwJwryEyMjCUnBYVXL1IU4Lyk0y8F3LxIiLKQ6Ss4bZNessF7VyR7l5JUwK7Sw+0bnOwamDFoEh43ctw4k0SqwI5iBHBe1tlvg29EaClEYqnQeDUzY9SzGBYF5Tn41hJw12YjZhZkBwqPVoo0OjDNC45WdYpuRCQnWP6cTsak2BdM1iV2IHB88Esqj66kVIYpw7LA0Uf0FIWds5TpfsS0b3CRqbI56vN0RaDIAVYEhVHMZV2w3nbZ7ya0mx7ZaNkXaiLh5JlB9UnNIXo2VD/eWwKFamHCbS3D7qXCSj/G3VRRAoXTmiCdaJzWYactUWdLQREZGtSmS1YwWahkOxZraszUgftagb2WRCknXEqN58ZPCdxVjlZgFhVx+gqDMmQqITujZdzwOAN5T/LeisZBucmpJlnvLuX5V3pZOiVJoko8VWO3LdHshM025BiDt8pz83vL37Uo4doT9tuSyJJk8irNnqBXMnm/d49rr32B3ASyg5hEAc8EV13mwN2q3aKaejhzBekt28dRwNNgtSs4a4I5ifnOTfjxDyNSVdIqJ0TuNarBY44aCgWjj5AglTFXLuFwFbyGIO/EtId5unmwQxUjmOHrUHs2lufmUh3yfjZiPzZ4+6ogPNYZ7EcQKTR7KVevrqMHNTxLoCotZjWNnLcckyBIKgZXLiVdTWBvr5F3YZF1OWwIcpcek2DyXzAb/rDAMsfsV3/1V/nKV77Cb/3Wb/H1r3+dX/u1X2M0GvFLv/RLAJyenvLBBx98r84v/MIvEEURX/rSl/ja177G7/7u7/J7v/d7fOlLX8IwjO97n4eFGtZol9NFlrfCCt1ojQQdJ5TYgcutscKmMuBQCqqLFJmodNQSlUClyZCg7HInY3GRa7Jv9anmfIzAoudaRDMXK0mpMqIkZ5T1QyaOzk1zxHNywadnj7k6TZjWJOQDPn87w8Y4S6KYvHY8IU0jrkzOsSKVfOiS+Aln0xKv9u9jtg1MK+ZF30NJHF5tSSojk5KvcGRkUEb76BerqKlLJfV4NfBJhIJUJLF+SqJ4rI8CFug0TnV2DgzeLtTomnmyQURj7FCd6USWzkGtRE+WIMoTt3foJQ0SoSIskKWIWTmmV46oJDHb7REHKxYYESWtxyKn8mNui88PT6lNA/q2wrFnEsYGPcNGlyEWY24X8hhWhLoQ6L4gN4Ca8ZhmesZK32CWxmgZhcucRnmhYXsq8ULFMFKOnk+5e0Wy1lFhZDCiQCNUcRcKXqzyyCtwe17lzRsZYl3SbxVoKQVCA9rpPn+mv8QEjaJxjjA7fN3IMA/nJInN0EzpODm8Ypl+zSC7gD0/Zs2XrPUUigsBVcEryZiOVsZXFJREcmdd5y/zuxT6ZVYvtgkVi4P1lMIsJZnqlMMTPta9S3ficBmbzITHrOwxaYYMS/BoNculoeNoCVomhplJ2FOQGYGjRayKBWuJRz6CepiiSYiEwuG4RBwpPHc+Zf9cMCtJFusSrzhnpxvxVsWhMj9nZzjiuxsNQlOhFsdk0gg1hiTMcHf4KkfRJkFFMjYE7aHDJB/SzeXQVYlMFHJ+wqUueEtZ5yjVOV5zONqokpYsVhdgZyyiTJbLgmDgaDxcueD2rQF3dlM2xhOq6YKxXeHpvMRJr0hsKMwsk8JIxekViV2Vp5pDN8rhLRxKcUTe/i4D64KJpVDB55+de3z2KMVvjjHXBpxdF7Qz6+x4ATdnA/5x/xs0gjahseAttc531TqRojN2TGwp6FUFgdQYaQrbvXdJFy5HhRKn2RrvWRpvZhTC4oBb44DN0CRjTbHVgA/Wi3x3T2N7cU7VG3I9OWA9iggy8Dij0/Z0RKqxP2thxVDAZ0/tgH5JraWSbVno2ZDHRo1WuMqNIOZTw/s8ckv8S/Ep7oQ1qnGX83IVNV0KZ0WG5GvbJR6QIR44eIqFM1NpBD4PqtvcvSnRhcoirvLGzQztfRAThSunRV49bFCd+NiewROjQWirZAoBoWnwtFbnXI/5KM1zIhUGjkDkUu5sFZmGNW6MYt6Lb/LUKOOlCqeqxbvKBva0wRcvXG4kLiXfI+NFnGSKPMxnmBseqXPJTBF4kUrOddmUl3xReYfnxSFPMREiwlUT/sissrbIM7CK3GuustMCO7B4zfOpkBAUFD70c/hHJVSpoApBrGmEuklBG5GRkg0t4KbeZ8P4c+rcYW/ocn3Y5qUTnWhVpxXnuTfcRtFmnGcFu4sjNrw2ykxlbbogT5fKWDC2LQr5CbbVwSPlDyufZKiUiDyHrBNTUWMaBY/nxha7yvdfT/6m/IMBaFJKplGX7a7kg40im8MIRQqiMFluGtWYfeWcwuSZip0N5Ffx795FAdwapGMNYxbSLSu8lsY8dpcmwoapwFbAXWMZiqWlgllZstZOOaw22DxNadcUmq7Czf6Qo4ZAJIJr84DEhq3zZSjhSV2wNoD3NhS8loHfiHFCSa0d0SpBTzPRU0GkSYLUYDY2aPQnpIA6VanoE1YWBqXe8rGNI4tCMaRVVjFi0I0YdWqwNegTaiAmKoan8NGaQvPSZOMyJuNHeCa0VMF2ecA0nyG0BUYI+amNnoBRjJGRRajDrBDwQjtizYgY9W0IFeTkWSjfQvD2bpGdjiTISfb6LQDC2CEVoJpQn4x5Wmqy3x3jOpJkpNMpSqb5BbEiWD1f5oDMHIlAUuoodIsKpi84z2VQUp3DK5L9S+gXHRZbEYW2imIsN+krrYhxVvLJ05STOmw8A4vvraXsdFLsyVIgRAklw7wgl49IRxrV8RKwyTDkQbNAY+Bx0YT9M8F7LypcuZRIXUFx4fXTPv4sJduPeHrFIuMJzusqN555qDlexNvPq2x2JJOywZObCVcuwYwkYT3GaOncbTYozWKeNksY7oRQlTT6MKjqxH2B6SqMoojyIqJdgaKywOuZ9HMCv9AjZSmN7wwUEkOQyYc8NCwsX2VlAMJeDnQ/D1ZX5eG6YG0gKAzAN+FBdYV6L8KzJVNbsDKVbC0kT1YEQ02hNgWRlRyuKDTGfd4xbhDUYwYrKmsdmJWgnlexRnPGDtTHUB9K/sOayeWuQbmvoMegRIKVsUouFfQrkJcT8h6YIqU+gt3JGSe5NdYnXcYZeMpVJnpIvSMIVIV6qCwZRF3nHRlxM9dgVnDYaEnGZcHznZRWSWHhjnil/gofu0gYFVIq/eUCtDmfcFYDOwC3lmKNJO9vOXzmviS/4/KtvEV7uMXuYMTtrVVKzgGqqZAOElQJ3SysmstQVHvscbAqcHyN3DDgYEWw3YVQhZMm/OTbCu/UNP7n2Rlfu6VBqPDiBCJVUk5SykkOZdzguC5YiY7pl6oAlCeSiQOqTFkdgmYucJ9bsjkb/YTDdZW9luR+74eG1X8f5Rd/8Rf5jd/4Df74j/+YL3/5y8xmM37/93//e9L5v/M7v8PP//zPf+/8er3OH/zBHxDHMV/+8pf5oz/6I37913+dX/mVX/nP3mM7drklTnBjh7FicLwf8J2rNqcVlYNmhu+oG3iLTbRQJVV8nGTBXnjB68EJ1zqwdylZG8XEdsB5ISKvTRnbC951asSjHJlYYuozniv+ObHaozhf4JIyxWZ3ZLGmj3lp0aGiLuhtSGqLDhsXAUM7j6W16TsmapQydHy01Ut+tvWn7AwGpIMMWcVnI1YJozJhpJDoMZOiZFiAID+hGE1o+BH/fDLjJ4KnpFIiE8F+r8OWfIc4n5KdVzisZTleAT0sYioaqhmz6sVUZgo9UWEa57gmDqn6fRwvS2uxwsPxJnGkMDZUjkoa56bKaJ7Dj3VOsmW+Ua8w0HIERagh0KcO2lxlfS4oKTNM18OVGgX9A1SlzzDOkI00ukkJr+tQcocUjYesJZdkcwGavs7m0yxXL2JWgymVkYH50KbrZRjWFQaOwcsTyU88NinPCtTPDESiMnZU7m6s8OFLgveuqSyKKdnsnJtzl0aUMtIrTNJV1lwBhFw4MLUc/L7OeFThuK5xOQ05DiExYmQpZVY2cTIaQUbh3hYc2R7nSoabwZQwD/fWChixQawl6FKiigULW2ctTNhPIo7qFSyrz557Tv+kxvnJFWqzmMRIOcprGHOT1FzQXZecT03erGgcrCTMnZR0TSEWGjLSKEaSvBmyHqd0swoX1ZT7t2JWpz5S6BRjiW9K7CTlo8Iu769vsT9yyXpzLKVHxg+w+haf6YaEC50L12E+kejzFD3xWaQqi8hmJnxsEfDq4oi549I15qikBNOr7B3VyA8rfJC7zpOqyb8rXqXlOHxjX+Hf3BCc6hpnehYr1OkXYk7LRa6FHZ6/uMtz/UdUZAddiVGZsOfNqLqScaSxkqaEqoIRgR5JKiLBki6xmLHdU5BnGT6QRR7pNqOMygfXJMcrklFOY1OJ+bj6kN3MmI+H9/nU5CEZ/ZxmMmBiaCRYaKmCpqTkIoW+meNe2WESSKrzCRvROTnP4HOzFld9D2XhUZ92cKcNzrwGvWaGBeuosc57tasoURZlrsJiBdfNEA50sq7AjFPMJETEKUNbMkoKLNSEh0WDrqoRZEZ4TkSoCU7KNqlpM1dsDpISxWjATjAmoy/oKCZD3aCjlpipNdyCg5UJ6Jfhs4M3+Kfdr7KVGjRjh7WFyif6VWaRjR5pfOFpj/3JMX+1doVANZklJnFsEtkah41V3q3uYUZjUgSh1LlbLLFxYVNu7dCMbM6zJlWljx+G3Pds7scGW+F99vX7lE1JXpP8Wu9f8ZOXb7DRMYjSDHV5Qda+4Go/4n0vQ9g2yIUKmic4CVWGRsxt3eZDPU/LaHCg5/GoceZsojkau22VSWTw0iwiSFS6JYVZQSVSBaEVoagBVW/MSVEnPzN5LRCEiQFqyjgrqEvBcyeCq5cufkbQLA94ef4RzTRlx/QolCeMowpZL2a/Lxlnsnx0RYF6QjfJ4qOTBfJkKEU1gvkV3s6V0Uo+DTkiHcS80dj6z64p8A8IoN3r30NPYl66THmwrrPdeSYAMZxwWYPIz+PXVJqjGRIJKzUQgsE7SyfvjdIMb6ATjVQerwpeiS5pjZcMRgaN+XZMZqrgBMvrxhmJlgiUnMpWG3o1i3iR4dOPfc7Ky3oVkTIuq2xf5HBNsWQR7ms8XRXMOiZ2ZcluNDsJxxuCkZ6jOpLMqym2nBONc1S6Md2KIDdQKNg+hj5jty1JFck88WhkUjL6khkpuymTaJPt9oJ2U1JvK+ie4Omq4Ep/wWpLUhwnjEqwMErM8w6+UuDedZtetsy18DEAOxkPYQf4qkovK8kEgo95PkZbI1Ul5QvBJKuQiWLON2P2W5JHaxm2L2dc1mBjOuO0BlFawfZj3l+rsdOOWdQSEk9hjuTJ9pLaLV9qhJpEj1M2eqDEgjdWtgEwzQXFdMCwKtlvSS6KJY42BPsXksLUZeqAvlDBGnHjPOXhrmSzK0FPuVsAESs0OimDkqDZh25ZI4kUXE2w1YoJTUlhmHC/uUJ9mDKpSUozwSzrkQmgMIvJ9GFHCZj7dYpdQUFdymy3TLEUCCnEyDNBIlTyHvQKOQ634MXDlBRBbBpsXArm5jM2rLiCPUk43VLQUmgX8/R9nZlhEY8UNroxw2aCZacU2ioPmzkycYeFJdidJ/hDHX/VJBaQBgndsokC5MNlX9okpHKp8NGejsiY7LQls5zGt28U2G1L4ozkuCkoeBElL+FgRZCLl6Fc3bxcfp7FvM8++ULIvOCSCWBaitCSOlFf5dG64NqFRE+ga6n0N3yunz8Dq4HgunKJ7Qqe1gWbz+wEhqZgapTY6vd4UlyjPvTpVyXvbZdZHYAeCVyZoTRe/o/q3OOBaWK173HvusX+JSRmyqttyUFR5eBr/zu7hV02OwanV1K2uzDMOdS7Q0bPdBzSkkp2CNu9CCMBuxwjqgnuAK5dwkflVZyoza6ikL1QCVUozOFq6jJzTapzl6crAkutkxvARU2QqEuWsFvR2elBmhMUxzaHmS0GOdh5DHd2IYkUWm/eRh3nOakLXrhoc2RdwTNgYwiP1gXJeCm0U3OnPDSXE/VOB04qcPVC8uD4P/49zIw/LAC//Mu/zBtvvMGdO3f4yle+wssvv/y9737zN3+TR4/+05DSW7du8ZWvfIW7d+/yta99jS996Us/8PreYJ97chtnJEilRmBkmeZj/vpFndaKjq77tJ0Ap/qEWE0xhMf18IyqGpBLUka6pLUR4zgd8kLDnJSp5+Yc6M9zmdsi1CU6KXMtRpgL8gvQjBhDClwnQ6CZ1JwFKyTcLVQZVH1OaxZzvcBH1xd88PEO//YzWS62IhrqiIm1jpZAw50RFRJs2+XeXpGoKLnM6ERJkZIfU5IDkpzPlTCk4Emc+B7FAAAgAElEQVTmkYqbVQlVhY5l4gRQD1KCTIZ39nd573nIBy4fTy+wN7vc7M75WGfE2rSNYXUZG2M20ke0TIfjfB5jJumpOrevZ7h9XTDKJVQij6FfZFKekqgOtUlIqhYoSZ8xDluLc+ZRjorQaQclpISfnTzi5vAxrw0TDsQao7KJZkUUcik/PXtMVfXJiVXe1m6Qpjovjnx0VLqVMoYhiFIVz4JGnPKNK0Ue1SEzcSn5Liu9kPVBj5/kHYpaB4MJ47xAZhU+dazymTONinrE5qRDIGJUIvJJimUGdPMmlrqgUzJYHyT83+y9x69s2ZWn9+3jT8QJ76/3z2cyLTOZTHaxmijTqpLUQqmg1kCC0GihRtLfIEDQTDMNNFG3WmgBpeqGisUqsRpksplJJsn05nlz3/U34ob3J47fGsSDoBEnqhIkgmsc5uwdO/Y+v7PW+n71YEq/EqDcSYjKWSYijUGEFsekVEE2UVCx+cE3K3y+U0MJdW6PXRwS8iEgdYS27N3NajPqkxonTpFHKxJ9MaQ41nHGGoFfxZjV8IHbY599PeSdRYKyGZAT8HW4x31jg16gcOELuo7CsSFQtYhRPsZe+FQSj9vdOX9Tu0Ycw5NQZZJVUI0xvbWEbk6CMcZVLCqnOaa6gmrH+JZJTu2xNTkj77qITIw6Nah0c2SviphjnbI34NgscN/fYnO8wFZThFznxtUUzY8ZWzHHKYdOTtAq6Cx0E8dXUAKFUuLSSIbIQYQRR1yfPqboqkSxzUIDWwvZm81Zi0eMFQc7dIhNgZEKsdSEJOWSUQVjU4VxnmNRZrSmcWnoSEwmcYweJDzyawxSgitbJaW2ySt9yp7G80QnMcbcvNAwI5UkEiQpi4KS4qAXowuVdBgyDLdIghL2MMObLUkjBSOhMxx7pHuXFI2nzHIhl+k1xHyNu0qNc0slHlo4Q3BNjcww4lkmz0wzCA3wc5KxleaXNYfyPELOdLa7M96ZPcMSgtw0JGM12Uy61GYtrGmBFd/jKikSaPBEz6KnJanFGhcZhXs4CBJqap+N4pBvzc64rpzQNxQOFzmyU5PiDJypx2oyZ9cYUkwW7A/GPA63+Gn0Mllvxu60xUx3SLk6H4a3uKtXaFoZCp0iZ46CojrkJi30eYf2wiDVg+v+Kbf8K6wYUlLi12dcrZuMqFMa2Vy5+0x7r3Mno7MbBRBAZ5LnZFRl4i8B9TU/wg7SlJMJJX9GEmSojBLuF/dom9toRISKwth2uLu2ytfreSa2wkKFpl5nktaIS2nSCLwB+KjkA5VXuwm1sg2qip4E9KYxXykljECijQVpHcZxnl55THtgcTxPMUolRKYKqsJAK/HuyRNuDZsUiFAiB0fERPMMma4CA5Vz8tRxf+258hsj0L5/+H1iFe5cRVzUIg6aEOoKmz0Xtxzz1N/gvdUVNrqSKJuQunUHgItf/ZyLErwsfSZuithTedRQeE0+I+7puAZYbsTzms7+Cx+uSIB4QWFrzIYoQKALJpHFVj9kqJcIVUiEQqRpbPQjzqrw2txn40gQ64JeVsMSFiN7iewfVTTscL70gipKCuqA2M6x2YSrsoERCWIrYZoZsd+SiASyzFnIDRrzIUMH7KHCLKix2pVMyglqtOwDOmwINryA/FRZYuDTKpkZTAMbL0jxvBFznF/lxuwhkQI7Cx8lDjjP5pFCMNJUXvJ9slO42I25eQJH9Tym9IjNMcUpfFFpsNFMuFhVWO3NeLIqOHbvkPYDOnlJYwhqOiZBoExjDrcUSlFEphvgZxN8XfD66XJSf1J7i3EKzFHI9cEzdE/FDmCiW7QUFT8pUGsn9CoKn+2pqNKj3BYc1lW226BnE+y+4Mv9NHuX0K/q1EcQ25JF1+Cj7TLXzyWTikJuDp6pokhY5CMSIDVZZtf6psHalSDalhR9DS0WOPOQQIOhAjfPJVpFEs01ioPlX2mgW5wVVN54DkPLIZ5LjAiq/jIr42UEaz3o55YGhU+yNUITzjcLpNtLg+15WcVQF+Rm0HQKqBK6edgJJN5QxyhHPDd0dtoJQg8BiT3zGTiwuQiojuCyBP1CwkYX9EClvz5irQ9ZP+F0YznPhZ7GswbUJi5SSD5c05jkl8LZWSzYmcUo4VKQLlZC/IWFPlV5tioIVIEeKkgJs/yE6xeSyQtkbMpuk+8UuF93WBvMSITkk5LBqVNh5SrgeaFMdSDxMpJFY8TBC5qmq+uYw5iZCTtnPrqI8c4/RhdTjBhK/YRqFHNpKRx+/LcIIch2BM92JVttyTxbR0kkqgBfg3Sk86t9wffuhizWQs7nBmlZoyDP0WIIohzWKGIniLh1DOdllcZQsqv6PO4tsyuTFNwKl6CRUJfY/hIZ3bRyBBoUQ8FgrnJzGPKz2wLZ0hhuRchQIT7q0ghjLkuw34LTeIWjBmy24cmqwHAhEVDpBvQf/QwllXCjBSdlk+IUnj/+6O94l/xt/H1FbTanOunzVy+n+cFGHj1+yp9+vuBPfwV/0u8jMpes65dk1QUzUyIkaErExiJAb2t8uGZwnFLZmurcHE/RF2MubJtRzmFcKLEVxNTDCEPMeedkyrWOj0mCmfWJcoKM7uH7JpNQkPUXnOoBUyvPOG2xoRRplhzmpTGLhiRSE8q1JoIYv+hjxZKpUNF9QS0bsjtPUGYxfuDgYZCJfRYmPLU0fmZf47JSRlOhOtLYGWWoKTEHi0tWg6ekJ5Ld7hVZOWNsqYzyDkwMvtO+izR9TH9KOA0YagaCFNZcJ3RVCtLnIPLJzVVyXkgtCXj5cZk/Gl2iZn2ep1expgo1v4M9VziZVnkvu8/j/CYCm3G5SL+YYje74B3tAU8cjbNqnV7FZgONP5rOyPhzIn1II92lpamITp7vRSGfZq/zLGtSmCsseiq34hGnjYT1RQ/pCZyyy2WqguenqbsSRREUzJBDN0eUibk9jSgvpvzJ1WPKM/A02JoqvDuySGyNZkkSKQkPyg5ut87tIGDFGVMzeuT7U/K9BDWWfJpcp5OsYMz2OPNvUZjlsKwzntQMDIZEqqCXCRFaQnoh2VcmhPMMPbtCmJrxuJbirFShc+Axs7OMbIdRxSPMxiQWWHpMJkl4sqrz88wNPsi/yqe5FOd1liCXhaAyl2ixhakWGVgFnmh7nKwk/MRI06ok/In3Ew68T/l50eG4IjhfiVDSLX7xUsiziYPbVShZU6ZmCtcWxE6DP5hIKizQ45BCz8Xq+4wyCkm6i149p6D3GBsC3xS8v7fHLzf2CKTknpJlp1mkdHmbzUXEnCyWo5BXVLIEHNYgMRT0JCRZCKQQVC2JricodZ9sXRIZAZ+QoVuJ0IuS3SAg279No7VLuhdio3Bwkua1E41BWuHxwsQZqvTUEq7lMC8lfLKmcOIIxtmEWEsw5jXWZn0OqwU6KcFMMUi3btBM1nj3fMEbvT6vjs7YDVtsm5dMUiv8UPldfsgrnJkaU8PnImPweSrNrPSETfOYQapClKhkR1DyBmhqjLcXcl7S+Wxf8vGOzYOaykf2Lj+r7hEqNnNspAQjlDRasO/OKAQxQStD2n/AeniBEmp8oB4goio/Ert0kgLf8vqs5n/Op7k5QjNRPMGhlsGaaGwFc/KKC+aInxhrfNh9mZtnCWs9SU8YlEN7ue8YMV+E+wxih8JowsZgzH7LIlYjUpGLsHqkdA/VF5zPbvE4E7AVPSUWPp9mDcZ6hpm9QiefZZRVUJWEKCfo1gbUwjbmeAPdyyDMAXeDVbyFiTH36MVZPrb2UNoZbh1lCBOb8ljhjSd19NE+qrLgpBaSZsq0EvIwStOZWGSbJt/szejVUswsnYWpkVnMGOoqPy6rFJMxC09D1SJ+Z+DzmrSwrIhpVqGfMrmvbnM8PSCOFMRMMI8FO+MRjiuZln1sdUZlIij2VQYUeXP4kPVoxqujK75hfshN5Rmb4yY3+xMcD9SFQmB6fKSt/dpz5TdGoP384pckilj6NeVHXG8lPNtukArAKoQ8dtf5eSbHZkdi2xG5N95BSon59JCnq4KXFj5ussxyDCs5KmJAtSMZZUDvJTxyNF46kXia4KIMlbZcIt7bc2YWZKceX+s7pDIRuanGaQX8uUIQaKxPBhxX4dYkQlsIthOLs5WY2pHOZX15oz4qpbjZWvYmybTFPAnwsx5WCJcvSIr3ZJWeMWO1BwJBEOe4vzhgvetyVZd0dR0n8lAkGHqM+sKgepwz0cw0z/dt8nPwNZ3SaIbWDJFhzECJmAUrrAyGdMuSTstBncd0Csvl0S6AkYDjCU6rglQAZ84Kth9RHC7FTC6OUCW0MxWMUHLRgO3WcoGtucueuGwSM8ipS5hHTePdyxA9TNBSMYcrCq+fRySGoKXe4smqwOoofO9oRPEF8W6HIZOhwYMbaepDSHT4+X6FnStQYsFhSWerI5lnXxhUNwRpH9wXdMVrcsywZ/L1ls3KEKLsst+rGiwBISYJpzXYu0jwbTgpFthrSRZFn0zoIgF7JHiyBrarYoUwKC9/m0o/JlYklt+ji8paWzJJachhQixgw21yWQRhjdhpS+x5zDQNF5qKlSh0byzQ/GUpbDqtcTZ74Y8R28Tqkkj20iCCRFDKTvhCcbh2ISlOfNxGRDBZ9p/ZM4VIASWI8USEHkN6ElBY9ACQc4Xe9jJzIy5KzGoxey1JmE/4cFVDVZZi6RvzI1b6CtYwItCgng9I95agjvO8w+GqYK0fUxnmmJoTbp5LHlSXUA03gUJznUfFFVa6EZMCPK6qXKyarLU18krzhfl2AzN9ya2LhERIHGeG3ROc1hTSnuC7pzpfn3xItbukSKqeQiejocaS89kpUkqynYSrtEppCuYLIF9jLDmqQ3qU0M+lyM+hseXys5RFb3yLnH9KJyfYEiNyLmiBy+oA2pUYrxGTsWLOh2USAYqEam9ZYpqaSYwYtATyPYevtwTVfsip9HnNesKHOysIKbgxWo6n2onYKMIkl2bnSjIkpFeDWlcwXNoEMioZbLYEgXuKWrXZ68S0SyHNHITPmv+P9sTfxv97sdu4gJWIZ0mdsdDp6jlaWp22vU5GC9nXTimIETJWGDkqcZSir+QZTB1iTyHSBIGh4BkBFD3mVo5LK8ft0UP+yfRHvDvwuOMF/LOrObf6HiNTMI9UUp5g0+5yNcphLgzyGZVB/pJYn/NM2eF/Xv8ed/MGI3NG1waLEuVpAQPBwDGZpHSGpkrJH/BfaD+lbwhs26UoOjSCkGZd8Hk1g/AUplKhLzP8ePUG85WIy1KeTlhHWjanwQ6zpEIoFPabAXe/vskPzHXaoxor3YRW1uLN5IxU5S4F7Sm9wpjSYkTdH7IpI36/PeKfHLr85x9MGKswsBY8yG/zF/m3uWfu0hsecFyuc1naYWbYxKrJMXdwDYXnfoOfqRUeFAwUAnB1Xr9a0MkVaSlVjihiGQk7uUvWeUBXsykpkn/sPOaOeshgZUElrXIxsUhdVDkaZbEjlZ5poKRjgpHD7nxMx6/SjcpUZwkzSxLaE+qDKSsMeDf5itRM8kpT42Yn4nflCZHRR+SmjHMakebRrHkEIs896zpaCOVkhj4PuXEG//BugpHtUg407sgmfrJLkyqJ73DmwExodPQSD95W6V+LKMmEtsgh4hLZmUlF9zlw5+SVc3qawhffuGSWafHmA5WuLoiyBh2vSqtv827+CsXU8dM6kS45MlOEAtysZJ4SOFOTr2dv0Z2uYbmCkj4lUASppsZVbLBwEook+CUbYSl0awmDrEHLaWAkgsIsQiQlFNsgUVTWwhmPCiYwJwECYCMMuSa7vK19wv9WWWNcWnBHPOaGdUXWGrPbD3h7FvKmd8V3w1/yxqlPYbDKdz2NUMCFVqBdFwjbJR0FxIFFN59Hu5nikZ3ia1LYC7A0m0atx7BgEVs6O4uYsbfHTKQwigGjSpdGeEbJjXjrQqPY2mC8WENi0FMs1HmZtTgmmmo8jyyuZIpsrOKlBIo9wEwiasOIXDCnwJiul8EJDbKpBRNP4BojnroruEqKtq0zFw6/2DG5v+eTX6i8e+jyzVYHUFD6B3zn2CeVzNEAK9RQlQWKCJllIsY7GepzSVFa1EKFsVXmXN8lM9eYmAqdyKYb5vhEbnE3b7IQWTLJnDfHd1G1Np+rGd6L93k9arKxiHnL9WlkLb4oX+PlSUDKT5jOLQbCIPJ0huksXjYgt9AQUmFrPOG94DaLQMNpz6mORxSCLiJRKCxcSpUzqlOPa67L6tUdZr1r1NwBV7k2Vzb8ajuiWJzxrXbI4VoBv+xRD2aoAnKBSqDo/Htxm6xooiQqc1Mjg8JfFvL8RcnBDXTSYoBI9xB5k8crVY5tB1VIXo6ekFr5FFfJQpxly7rH78YPiQyV76/p/H7+AW+qz1n3rphmXPpORC06opI8xROSQIAlAtS6j1K0MBYqn/rb/K+vVPjBq1nWOxG3zs5R1Wj5cIqQuG1y51iyfgR1P0KEJYapTfKlewjDY5IIuqZKQetAMme/2WJ1NgUSFpYk7c8oRv6vPVd+YwRa270is5AMozSK3qXek1wUloKr6vhkfI8uc+ojSJkxysarhKenpNyAfhWEq6BFMbEC27bJE2Gw3oPElnSygkeWyu1TwZNVuCqpbJwpnBdh7TzhbE2hfhVz19xHZBI2Bh4ndUE0UvHCAnYULsmIngII9mcan20orN4PsGJBOw+hMNDkCw+12CAex+gvsi7DjIZrwq+UDVIDUBMYpkAPIk7HFcpjyaIgmWUtdrxLYrE06XUCOKsKnFmKtKsyfpEdmSvQ6C7oRj5U+tiuQHWLrHcS4mKENtZwE8G8MGdnFKFkI86DNHGkIF2VSIEJEZ/tl9hZai+2e31cE2be8s5TOAmvDZ4zNB3uDM6IFMhMBP2USWkILVvy+unyeryUYJKWrLXAs1VWwwmnVYvURKGkmtSuwDUVVo0rjLZGkHnRf9YJeVYucOM8wdMhUSOsAC5KOntXEjVeLv6FWAqLFScg6usU4+W8CiUhErAyHtHJw+aRwmfXNW6eSfyczlhL43gSL3QxZi73VxwKPXh8TVAfgUTiK4JuTmO9ldCvKGxHU2rnCgrgOAucpsKD1Sz10ZhnDRXXGlAbSFZakk5NZWvaxZpK5vqITBTSy8Jm4DLtmcxNOOiNIV4Kqlvd5ThK6Smf6Hl2m7DWSVDWIxgvTVX1vsbzOqx3JOZ4+b7TQoFSb9mLF0tBRom4yoPSTaMrgoOmROQFmzIkPVVoFeDW6BnDaJNsV9ArKGwHkuJgSKCB5pucNQQ3zqHUqTLzYnJzuMhZdAqQzDQ2u5KOk2HnSjLLC86qAq3Qo2PWecm9B8CZco3A6nPjQjJKC0p2QL4vOC8vxekr55J/NbLZuogYFSARgsiD7a7kPKcSfPUBlgdystzG1MWIqxxUe4LTOujjiG8+XTAoJoipQrgSMbrKsH8Z8f61LNl0k4oGXmf5vxunQKsu54xRwEUZCjJHujvlpA6bHQgUaOfg1Yshn++m2X0q+DSnsWV7WP1NnjWg9Fzj2To4QcKdzSoDbY1QhbzSZFi2USXUpgmRAr5tsteUxIUp+sE+zgi0JOaj2xr1ZkRv/n/z9fht/H82cpkLdqOH7I6fkUta3OqMKCgjKlGfq5SgJiQnms7Xts04lxAqGv9S/D6fTA4oLyIKAZRHJv9LusJzcrjtOubpNkPzhKo2Ym2cUOqBKxTMtYAfv7o0AJ5mXZqKRTOtk+3luXmRYLgOrqGRUo/YGV7xdrtPyZ/jRNALtpkGKfppk4erGk/zMYFnUBQh69M+n1RiJsWIVSHZVXushT6hX2WlbXAtjvi2+T6i9gH/7Wvv8N7GPv9i67u8H1/j4Pwp9f4AcyFx04K7qeu87/5jlEDBsQJW6g9w8Vj1A768HnN7fMWtyVOqapdvaT0US+deOcPFNyT3t+AyI9kIDgkuZ/wPyTsQjqmFh6wZx6TpYSgekdTw1Av+Tfg6n5sp0F3mmsta6pRt+4RYhlwFZX5YNrmvGXyhwic7A4a+SaoQoAG/inf44+EZe96EW0mWvzx4mzhq8GY3QhomnxkN/rv1/4TvbD/gDxaPefP5kP/gUcC6KzGHGg+nFj9NbNozi2kqIYjSXLDOn6s1np0VqfQiKkkXhMd5weAnu6/yXC1yqhvobsQgDZ4uCNHojG9QFy2kiNmIO4z1ApuXDdTeKn+zdZN/u/0q48uXEH6al2KfmtnjaaqEt5rhex2X4mDMRQo+LNmMFEHd97h15iAPHd7zX+aku4p+uEZ+obFCj2rSJ+uGtHOSn66bRBoM0nBYzeCoUyZKBkWR5Ocebz9PeP1EUoojXpvHvOq6bJ9s8u1zgTIvIyKNDRGzQMFzJM18jrkVQxLzqFfluWnyi7VrRFLFFRpvpV3+eO4y1xPWtUsUEVH0TVpRgVuzYyL7gkLiY6cW7FtzPtnXyNtdDNciJiKK86wWI77YdLnKhzSrBZ6vr3KpFDAsE9cUnAZ5PhAHSGlj1bus1NpMdclb7j0y4xn/OnqLwTSPoxkcb+iYVsi1sM13RI/1YQ4tzPEX029ysdhkpa0TqgbloM/W5D4bgzN0b8g7Jwrf6QXYyRjRmDJxErrTDEfzIvdKKf4ytQHFrwi9iLPKBZ8eaJSUmIMe/DfPT1ifVZi4OlowJabEHAWpRPx1eI1/Ge4gEcw1i6ZdJ1QVirlL/unwR7yjnjNKzzgr2xwV8/ygYfPDTJGvHPh2/wGvXfjoeMQJXIkib/SHFLUj/mjxAfGVwWBc45PFHb6vvcxZrsSxcYOLXIWhrlHpqPzxsytKC5+CZjBLmfTzAp+E/egLXvM/YUW7Yj06543+XVKhh5H4fLGicD1q8x8Pfs5AK/Dlxi7PamuMNZfHhkViaiQ5wW7d5U44Q9WG9DMWEzVHOgxhlDCamaSCBaeFNJGxYJ5EyFSbw02PRzmFKy3mUE/zsfISW50e34jabGTOWeQVpDDYTP+U68bn6P1NbgUz5tMV9nHxULnQ0xS7C/ZbUBmr3Bqcsu89RFf7vP+OJLoGtqZhOyN8xeCn+gpduY0bbdNMNVBdjfm5ySBRaXsWrqKxPxFMTYvLtMXqyGKa0vnbayb3bYN/W77Bx/WXqY9cvjf4iGLUQsQRl1Obu3GKyjzgj2b3fu258hsh0HpuD5mEfPtc8sl2ibVBghoLxGJGqEJJClbtNmvT5RNpLRVD9SaDzz4DoFRcELZ19GnEZVnwZuRzf5xDS8BRVcLdBVNXozRJON4STC1Byhc0tyxyLgzKFrk5+JmYMK2wPfY5rgqUQCGKK8DSePqz9DLbsnfsc39NQc4V6p2Yo1Uoncyww4RAl2Q7HkRF8r2QUQZeKV7wvKrTLIn/qxzs2So05hMysyVVzxQxHTPP9qDHSQ2KxxrGTPBkRZCaJJTOp6jR8iZ9UfBIeZJhWeXJgUJMmkZyRsqHRtbHsiShBoNCxOuXERsyIujqjHTB1hl0GiqNRZeLvYjdlmSQhZ3LOU824Hp3yigNemyyPrzifq3BbndEtyIJhwaWJ3AtkAJqp0sT8M/KDrmZxJwqTHS4Mz3i6+IuANOqx15Tcll2aG4E7DbBmc2X1MyJip/2ee0k4cmOZKO7nJt7qxGbXUm5F+KmJNoY+jmBpktEItjszwg1STyPubtWYK3jMagm1E5ULisKxRksbJ3qZI6RjeiNbZyOxK0qGJFgbqrstuJlr+DZlK92bXZb0K8YRIWEbxxJ5rqOX/dYbQseVlZJuwn3VisYU5/LBpSnMMk6vHIxwBMmYpzQGHqcrIKpSVJtlSf1FLp6hR6DFktyXYXIBisd80SzCYVJaQqGraHEgnZR4LQVPr6pk3OXvlqhBj99Kc/BpSRISeYFHSsSHDUE5cWYbM8g5UOg2Wz5ClEMj9cEtd6QB4Usm23wDIX2dBd94HHYgLeez3CzKfZaCQVFp9pabiP5cEi7rFPsCmrzCfuLOTkXdEWhn4NcMOB5bpWNSQtPh8fmHm4YUhkIriogUwpaIuhUNGYW5AYhz+IcBxcQZ2LOqwq5tuSd84jDksKTf/M/4tYjir3l96enYy5LoMcwq0omIWx1JdxYMDqxsfMm5WmLxgg+2K+gKm1W0iHpS42xrZAfw5oICBOF1cGAw4agnr9Buruck8wCRo7goqHiOWM+a6zzylM4qiqUZileal3xy5sm9kBlsBojY4Xc0Rn9xQ0OVwRb/mMussuShsYgWWb5Zi6pAHQmTPe/iWAJIjmrwP6l5OHxj/4ed87fxt9VONOEMOPhJQ5KlCXJxTgrE/wtg9nIIXB1vMEKrqJwdz/ENzRSwkObR0SqgpQKoaLSTMX8QN/jmVKhpWZ5uaPTm9gMfZvSQ4uyZ7CSVviurzJa1DgWOl/rBu9V6oznNoezBq88KvN7j0Lenj7kZfUu8tka+0caSZimJctcWXnGdp8w38EPVjmZVmnNHUKpYgcO165UnptZFjONO82ImyOD1CTmaUrhY0dh5izIJCFZzyIvHuPOfIZOxMfXVJ6sa3Qtg55VJhVpFFQX1Uy40VFpHb+Odn+PP/0IDjpNtrL3UTcjxjLDJJtmHJbZTAKebqp0sgq/d3jGqnZFyjwlKl8ycxxKmadY+pxbs1P+UHzMNnO01BWxKkgWDZ7JFGeqRVMoJKMRw0nAWrfLZStN9kHCKyPBdtinf5SnOagykSn+KniDp0EDQ03YUb+mV4OK1Hm4dsD76W8zzWtIQ2ND9Em6BqeOwi8aKb6q63jY/GLNYqpp/Ou1Xb6/sYZrCzLWOb3qGk0nR3aiIqQkE/t8x/iENWWIrgg6GZNO4NAswfGK5Jvjf8fnCLKiAyKm7vbZmx/z759/RdWLWDEf4RiXTEQeB5WzdIqyfchm6oLcqqRZF5xuQMEX5KMF15NLlDs+H29nMPUuugx4VFjnlBJRonEe53EUn+veglIs0ScKMRDFDtZM/tIAACAASURBVLe7kkUhz8i2KZ6lseYqE11jOExzmWjsnJisHcI/bCb89+f3+b2zHqtBn6+tHI930uyXj5H5LHV9ymihY08C6nEHXZvhWxFpN8FDRfgm/0Bt8196vySOdA5mTdbCFNflgPcLY34sDQbdHM1kmz0/wJAu/9XpFf+i9x5W0ifRLNLS4Id3diE1Z1M8pXyVYb2fJvYExfCSLX9G9SqgOzRYzFQO0m1SZsR5OkO1D67qcljU8LsmN4ZDEjvmNS74R5MWh3mTp+NXyflZ3vxaIfB1WmKVFDPygcPztRoPdrL0ywGtbo2j9VVEMWEjaZJYOUpRRDfM0K22qEUxt6LnqMmQbz2f4swkszhDqyy4rvw1dxYfoJoqI1FCSw3w9SmupWNHEqmkaQUGR3OHotAZZxNaqzGT7Ixf7BWwJxk+77zOzpWFFdrklIiwGNKuhBi1CGOwzTvtgC/jHKNRldlig525RTpZsG4d4qR8vl/Y4WKSpz/W+I+iIX929hGbpz065NGchKyekNYv+UP/SxqZCW4xZJT3CcyQYXXBN8MR3yg+YFqeUNROWUma5PRnKFKwMvWouTBJCwInoa6NsJoGD5r7nI/XOVTTnBllPlQb2DKmGDex5Iyb40+puz1SJIRFqJ6apIJLat0zLgclzkWaUZIlzge8Gvlk4ynfmx1zrBT4P9zrDGTEuJ3i6SDLT1jBuTKZZ0DENjORZ62rcvNM5Wdyj0PVwEsiolDjY3+D3dl97P4NgqiKITp4us7ZqsnHL2nMfIfxVkJgwvNKnpvTgPM1jSdFlaeGyeG+QlrCwlPJXQo+DhwG+ZhJWmFeSHGxLZg1PBQv+LXnym+EQPvbk79FCnj7yuPeusnO1RKd3hiO6JQlzWSdi5rC5ossglp2QLd49P6PWRiwn1owa9n4fY0H6/BycMGoZ5EIsKRKbyPh9ov+KD8ncF+QMRPx4mm7vczUXQvv8VxtoKVVWvklqUALBZECFSfgR1UV1YjZe7D0YPJ1SHswrQm2Wx4r/Yh5KUFvRxxXsmw3YVhUSRsJlzmHYXnGjYuEUIfuakJhHrK3OFqK0CGcizqrHZ/WCjTzoMbwbFWgiwmF8QxnGDOzIS4va8GGep7jQsQ0leG6+wiAShTRziu07RyddELegwPfx27Cw23Y6MFVpUbZmzEovyhta6TIziXnqwb77S6P1gWnyQ0sL+aLRo3NdsSskrDwVdaHcw4bCnokySyhj3xV0bn5AjLxoCG43jnlgfMqng4D22OtB8/yVY7LAnOaodyNGBQEnRxYape9C3i8sQQsgOSjytLvYv9SMqmq7LagV1QJphpHmSpbLZ9+TZJvwpNqmeJM4pcSRKKQnSyNDqZOxK1OBxoxxjSHHgjycpm5k17MtUtJXEs4Nw0muWXZ2+GK4LQuePlYcp4p0dYVFAnpeFnG2qo4bLUlg8Jy/j1R5Ebb40l5lXxTIbuQTGsJqhZS6wvOswUeban4umBjlhAMdOYbGhLIDV0G5WXmJ+svr7loJuT7guMNm8TS2W5LvJTB5e6M6xcSI0k4r9RJIovndUHRnVPtLN8rx7DharTzguNVMH0J4RAzgiSOabpV1IHC01VBfeKheyU6eYHmLD87ViQ3rwZclooUZrCeO+V6f5n9yY8S1oc6+b7P49Iqtb7LVUXFb8zZO1vORXNNZaEur2U1M+LpiiAeCg56vaXvmKFzVk2x0hK83IcTR3L18DHH1yTbbcnE1En5MYul5ifOmaTnOnMTKlmVu3mN2fwlquEJiYBsbKJ3I2pqwLVTOC7b7FzBjvQ5mtTJ+gGdPOzNAtRY0MsLFMCMJD1H8nRNYLlpugWBzAhSbYukcMKn5TcIVahNl+tZ/PJzjEmRw4bg9smUc2WbZgEaQ8HTVUF6vByz2pVM2ycA3LxUaOZ1dq7g3rPfgkL+/xBj1yBxHVbGgkro4tgRFV3Hn+skicZGVyPf3MKcVrE0jSevFDleazBcVVGdEI80AhMhfB6qOS4zFU7WDXqmwZNxnpatkOyGpKyIehTwVhtUN0SbKyTamJvB1xQrcy4rJbTYIkpMhpmYjmpQnM7YPdO4OXM50ko0NYcBKXw1iyscPtJq/Ku1Tf75dpZPnRJ/na3Rs4v01yQi6/Gn3XuEfo6ZqjNXBBoeO4vH7Psf0uALmms9PtoqcvNEIRXDqGzQLGbwVcmwbCJKMQOpc5ZWWBMdFAGp0GUkVaQWcRrkuRcU6XZy9OYmb7fhP/xMoTwV5McJ2eSSzeQZGWPAvgaZWoC97hJoE/48s0su3SfjOWSnJh9E65wsGuiBzidJFd2aUJQ2paHGnXPJYpjh0je4ypb4lF2OBnt8LF/l55fX2VF7FAsPqKQesZVpsrV4iqKF/KPj95jNVBIloZsL+WrTYJiD/Y5gdxTyn34Sc3tg0948YrB2yO9Pn5IEEIQ+m4qPjGxykSAzNXCCiENd58/9BvfaDnNDx7dU7u3F6NqC1PyKL+UWOh0Mo0UlGHJr5PH7V7/ktcmvSJk+Q8NgIHTCJGA3/oKN6IQ4iBnUIwqJJNEUin6A6Sr8lfkqU6uMHI8JgznpwOdilqdMh2udU759HpDWI+rDBCcQ9IqCOFuhGAyZmhYnxQa3z5sgC7RrDudlwWRh8X51jbZZ4H9S/oD3Z+tk3AUEM8yUyz1ZwwoCXlvc583pfS5CiavbfDP7ACUbMc9IHrgO09hmY6TjawqxhN+ZfM5B0kGqEdt9lblr4PdsZhOdDbfBuhdTcscUkRApHLQUXmuGKLrGn/kfs5N0+EXaIh20KJ8V+XnmJZLRgpNwwVeOTXdqkIwNVmSLSfopd+IvmeTSpBKLb/Q9XgnHOGpMMEzzXXmP14JThFDQYpczpU4SF7j+LI1X6nO1EXEnalOJ59zNqPy4uMMjY5Pn2R2CNYtpweGbow+52e3wav+KPb3P3mLMy22BDFVSM0lnXMQ1BFNb40bL41vzNv21IqNcCT1lUVs4bI4cvv2lzrsPJ9w6mVHuC6aaiu7FKMkILfec14PnlCYGulek4I6w5iF/dNnk1csFci1L4tgcZ+oUR2nuRBU+KN7mzMvi9WMOJl2+3euxY52wmfgM5jpH/SyJrTHLZpj6Bg/WTaYbWbyMwrY/538vvcVjf42eLBKqefQyuGmFAFDSHg0rId1PGEcpGrMe787vk8VnrEtaiYang6f53E48hv4eX8ktupGgM16hMXFZiwJujAJ+p3XEd57F7A1jdnvQsKGbt9AWGQ7tawyVAiE2KjrbUcS+FzE1Ez6RRa4HH1NdOaQsmrRNgxOxSjy5zknF5MMDk9iURLkuz9c1ElXn7eaIZFpATiR/4elszH/FmuvyZ89/wIHyEz7aL/E3t96k+bpAlmPMqiSjxfzzb2zz/l4d04n5w9khiTZGxibXnzn8s6OP+Kfjv+Fk6JDvqTxZEcQG9OtFnGIRM9HoX2m/9lz5jRBoPzz6IQjBTjfhtJFwvQnNXI6trkdYivhQ3uaDUomtjgQjwbp5E4Dk4T2e1wU3gwAvMCAWdNZj6nJApZXQy4I5Djkt6Lz+LGFoG+QnAUokCTRwRhFjByoMGKdhq9viS/2AFFPGShZXMyi7Pc6qUI4lvh4jSxEr7YR0IuiWljdxi3yKia2z2YGkAEGsMMrMyc9Bj1V6isLUMOhmxly/lMSailVY+rVtDEYc1SF/pkGgoscwLcaMGsvywdOaQj8n6dV06m0Y5DVWZgrDNGSGIecpF3dRoT4cEmmS5tAiDCUX2TyJsnzikYsg21eYL4n4HDsbrE58Es8n7YGmLM2Bx+oaziLi/hb03QMyfszcXkIVpBMxt5YL7uGWwbtHMUoMsYDzashbZzFSwHs3TA6aZ/jBLk9XBQtPQwHuVSuMpgZf3Mqx0wLXUfli02K7PUeLBM+LOtdaEuGAM1W4u2VTHcOioFOcgWn49HsW792os9kBr6SQ8sE3lvOkpyNOK5Jr58tMU7O6hgpMtjRqs+Vr7Hm8LKHrL7M0k4qDmzZw3GXp4YOcx1Aq5OfQyWowXq6T/VmHWIBML9i9kiihwDMkUkjqbsLgRkBmkAUgzmsMl1ZnLESaxzsG/SzsLyTeWIeGz6WmctCKMJIQaccYk+gFYt5HQTBOR7SKgu22RKKhRUMcD6Sn8NH6BunTFN1qQrMEO83leO2Oz/ZwwbNVQZJajjc/WPpwHdckzsRFSQRuLWKcV3GusjxaU4jsIbfPJL4GRV/lQakBwNyMWR1MiAWYfclGp0qlDU8qeda6ksf1LVZSD7h9uiSSemWVeJYwteDb6pjDFUEwtdm/6hMrEv/gTXpGAS0BEUliIbA7AQ+3dLauJO3UMjtdCiUTGzIe3D4LObkWoT9S6K3HjJtVVjtTHtfrvBzMqPUkyVSSXcCoIGmVJFU75MlgdbkmQnBOj0gAI0hwFpBxl4Lz6arGQTPi4wNBYSGZzCPiasCNJxm+3BXUn2s0Kwn5ns9avCRn7rckl1GZo1VYaUO/qKAmgn5WJdPUSI0+RpjwciviqrgcR/vB/b/TvfK38fcTU8+keWnyndMpe3OXjfMcakfhgbbKU7XA04ykm7dp9CusDGPM6iNe1r9ga+sBcS3kjRPJ9QuLrIC8DJnZJooy4EHN5t+9liOoppiWTKaKxTPDxOsGKHOfi0GNBDgspPGkyqfKNRZOlrFVIO+rmOqYSAjahYSim6JonPKBkeFhWCdCZZp3+aJu8VjP0ZZZEm3KvOTyJFfkwx2Nz1c1zkODwPb5RDdpRTrGQsU3FSJTEGYXDC2LQcpCCS3CaZ22vUWiANaAUvExYaTAIqRof8m+2aLkhaBE9AsubjHm63ifX8jXGDspGumIvS5kXQ09keRUhf+sdcmdJCSOHBxCqmVITAM91thLzvkWX/JfL75iO2gytwKSBHBTuOEKWUPhljJj/1LStjUOkiE1mVDwFxRkSFEErM876J7kiV5BxDGzMGERKGQXLq+2nlBN5kwCjY5vclhJ85mp8wslQ85TmKsKIhToM5OxkSIWKYZpi8SGyBhwNo/JzyT1keQ7ZyMq8Qn78XMSZ8LXVUE+8NgbxbwzjPBzMFiZ0hYTMskJu/7XOFHMjDShsiTdGqrOE/I8SdLUZyE74ZjZdMJZ36J+qrFxKlh1PVYWsNZt8MaixcaVwI8bfPF/svdmPZJkV57f79puvu9bhMeSkRG5VdbKWkgWySbZQ7DVzZ7pntEIGgGS5hPoQc+CvoE0gABBgLqBETQaCJrpReqe3rg0q1isYrG2rKzcIjNj9wjfd3dz268evKA39RMHUks8jw5zmLvde4+dc889v381Tz+t8a5ao6JM+F3nfTQrJtIUtlxJMVIY52zGWsSjZB0vViGK8M2Y42wVZVlga6XDskSl77DptPmngx+Rlz2elDTGU4ODrs8bjOltuGQTbbLugqSrYShLegsDL6ng2AZ/cjPNz/Q00zjFJ0aKX+TTFDIOXspAGgmkrPPV53leOgU1MLlSdBamBcWIJ6qNHwpqkUdVF6RUk7o65RanHEZ5Htt5EpqP7sGn3ObQyeEYNlZaEgqVOOFxkdN4UM7xxbUE53sa6XBGMusRF1XeWM44ah3QCsvsd13+UfeQq9oml6kKcZRFddIMtE00M0fFVRk6OywXGySZkJpHPJK7zNQ8ITpvH/q8Es35neAJ/6A7R51rfK2rUM773Eidctd9yPlWjqG1zQeV2xyZNf5q92UIqrykDNHTPifVJlvujJIDKUdnmRbUtBDHFmgIDiYu6SWMzCLPShVUI2QysViNLMZyk7aRZG71uVdP8b52CzHz2VudkrQvse2nqFOL84XGLf1TquGcGWmOwwbFaElWRMy0BnPdIqs77PgBz6MSJ6sKx/U6vVyBcVInhWTHD4iFJDkLeWn4mFurZ0wsm99qr/gnFy4ZP17HPr7KRqhwLiqElkPfMum1UrzdesK3wjYbaY/f1j7kVdpsLCMiI2aQVJmvdFZxRN51sMM5XWOTMMgyIkOgR9TiMWq8Iucu1+wJMcMRGsWxRmWZ5B/3f4GWnXFSFXy6WSIwllzmFH5yvUgwtzkcWvyBVuOiCMd1ga9o5PMTMsk55dTH/Ied93jpeE7mKklgKQSGQlXOqc880EP2nDF5r40XZBkrORQlppvTGJUsYlul21R4fr3ASbjFruPynTikU/r/gQ7as/ExagypgcI4P+NmO+azm2VSLmTSISdelUMz4loXrHRI4rXfIF6tqHTGjKsxcqUSGOu+l1LZ5EjRud6WeCYsXMlzzeClY7i/bbBzpFKfwqfXYOdKMqmo6I5HtxGz1fa5Z15jw1uw0495d2ub/fEVRzWB6q+rbb0KxJ5Cw9VxdcEkCa6S4jSTwQrAVgXnFbCXa/xm/dLHnccsUjqhF5JeCkwzIp+QnGUK1AYB7YZgmZZsBuuAWhWShGPiWJJ5yqSfEzy+abE5hEholDo+rQ2JGoTMowTWKsVOJ8bPR+gTFcYKQWFBciUZFWJmMocaqxRG4KRiTk0Xf8egerWePs3xkvMylK7Wk21ZjLkzGAOw6azhFHlXEikggZO65BvPQqQC4xQM8hE3L2NWCRM71Mh5M+xA4XnNQJuqRAJEY0Lc15mV19j38tjj5ztNXjxZB/gnZZXrnZh5Tmenu9aaAXBS6+e+p7pEfR3X9lEkaIl15aa2nBEqUFlEPN0W3DmX+CWNI/2ApQnDfIC9XHGVEaSGgg/uKBTnGqGyRuRrC0G1v+6R2l/E2FfrefS8Jqh2JJ2aQtEZc1GGhT1jvxdT7UkGVYXb5inCF/j5DrqEuQU7scd4aBIqoMcuAztglhS8PnQhFqQLM36uFrjRkpTGPtGuz2hmcFWAYl/BV0B3PFwijFBwZUGts34GoRAc5nVuHLpY2YDHTcHNlmRRMhAC/J6gYAbklzBJSgrdiFFW8PGBQmm5lnKw0iFaPuDOs4B7jTJhMKM+gquCQI1jniaSOCY8Z4fyIGBQAKSgKQLKYxXT6GEFcJTYx7BOeelEIlMxL099xFDjuCbYdSK6RZUvite4dukxrML1b/8e6aFFqMAyVknPIgpDSd+SNMawNA1cHeojwfO6oHS8BqSkdny8cx2zrLLZalOfwuHNF7ESE/aUmHl3XXJTtCXBdoCqSWbjGMeATGhgdkecVmGrK4iUNXXxpVNoNSt85XTMRweCjU7IU1WSyQV8+/wBj66VSawEVxsSPYS3Ui7tQoFmH4SYMawoJFeCRLCurF7WLbZbgrQ9QDTy7PZCPDPkb2/raK0FUspfrcP8tf3K7a/zDRxTMI9s5GATfamwMDVKxgO6lSEDNSZhnBEnJogYWsksL0c9Fk6B4cLG0Q5YyNssbJ+UPKPpP2Nv4tI0hpCM+UDd5EKHlmLwx+YWn++kGUcRF0mTzzWdUuyxmNkEjoa3MHj7YcyLF/DS4pL29gxXy9GeXGdCgpUWct29pBA6dLdWtE2FjmLimyq+7jKXaZJ+gVk5xtENer6NDBQUF7ILherQZG7UKPhQm2a43pZ869mKh8VdxnqB/EpH0efk0o/4tKbR2jGY6SnyJ02uOkVMVyXlSbRkiK7AMlSJnQTSdIhUiXtdZZJIEBuCTX+GlYanRRVpDEjj4iZ1jjObbJpt/ulqwtZqxoZ5TDE+xFETRJjonoUvFI7tAOGZKFJDKHBjGZMpScZGjnOjzBvaPareEKEEjKRNIhKEUmE0VGhFZVZ5j4fbcKGYnJomfcuiPhbs9iJSq3UimV9I5FxZr9NIkBznmIkUxYXC7QFYaHz3mcXuVKLES54CDzIGy6zEMyWGJzB8nUR2vZEYeX0Ub0LSlai6ynF1l9FOGpnPo0RZvuV0eXk+wQwVbNvinW2Nf1u1cUwFy4XNK5W9XsDWcMLH/jUUJ8vXL9tsaguC/DGKPOVJvE2g22imQB9qTOwk323F5BYKeuGSV5dfMAsziHgtpbIZP+Zm4pyUEyNtD2e1QS9f40HY5IusjSUVRrrKaUEjs2pjCYeCDpqtUNIdXrhQWfiCU0vStTSCeczWzGERJugYOj3FIMGcm9OnDFyNv0q/gl6JKMUhe94VL+ufcyOYsCRJd5Lhcy/Dc1WnXYz5oNkkjgWKtqJvhqTjOXnhEWEQiQTtVINeKo3pSVKR5MIMiLWQ2FixVGOuz1e4rkbbSDAlg2kEvGk/ZHMxZHs2wV76qIbko/p1vJTHxYZOMvTJGUOKuoq23KfpLGksA3aiPneDI15YnqL7Eb/YyqNvSHRFoqQ88l2bs0SafNZFK0kEKbYWRfxsiZlSYlVKUtc6pENJwsszaObY8gc0U2O2nICEozL0VY4bJrsDKPXhXtLgNGuDGtC30lyVQzqWTaRITsdZfr44IDPKUJsn+R3nF3zv4jHlxQI3I/nKOCAz9ejICoeyxkTT8Q2FD8JbTEYGJ6kcXaWE9DUCDTrYGERkfZctMWTSkMxyaXJujBQx1+YRgSexFJemc8lvHF9gdjX6moKMJXNX51MnyU/CFFdLlRw9AtPhrKzh+Bk2vYiUGrMshswUhYWuMDcFgVS4F25xkskxFTm2hwOuzc5RQkiz4j2xyx/peaaGTWGmUZgNOVQ19roG9U6dTr6AYkvutD0OJh6BrtPPlmgnMyjTLSpLl9QyZt8NaY5iMoHPSVmwJSc0V5K653JzccpQizAmPrHr8DynUNCnvDgcgKfgWWsfkPfmfMXpE9gSbJVcZJDRTN4YSnThkchcUlJH5MQCy///OCTECRy8yOH6HLqeAfqU6kAytdYBekUNOeAKT+/R7MfodoSy/SpP339vTWMreMiuhuJHDDLwmpbk81kGI4SkCp1Nid5WMCK42HdpdsE1MjzdhsICfCtNOFGRRUl+AY69orzSeP1sxNPiJkk/ZFiRxAMNIS0+qdmAYL8PpTEcbkJPaBjqeqBKU5+rikKpH7BIwKwgoacxqpn/V/9ZMrki4Ro8r2TWwJCiCYWQ5qLHZQGaFwqVLhw2FFaqIB8bzBPrxATfJx6raBWfh3tpiq6NaQ251oVsKiSXCXADhWnB5/WTGMOKmfoaj+qSO2fg1RVqy1O6Bx63LiQzE+o9j/vXBLvjCZ0cVF3Bm/2HRELh1qjNJAnljiDpwjSxHpv95wrjRkQnDyKW5HoKU93guw8XTJIKd9wTjktVyh1Bu6RzW3tK80pguetjhvZc4bKQ5qUTyWUzJlZDUgvBeRX22jHFmU+oSSa+R6hA3ggRkeD6rE8kIBFJVjpsj6Z0y5LaqUYnL8gtYZU1iboqnabCaCGxhyHPt7MUxoKLqkZjHNGuSeRoweN6nv1LiZsR3HU9ds8ErbzNSXWN0x+UUmRHPve3dCa6g6NAcwCjUoaUNWEpbNSlT8lZcrgJlTDC6GocVUxE5hJztU6ubnx5FLFs+vzMLFPtqmz2JFYtwJvqXJQF+Y7KT15IkZ1Jtr7UAXzvNZtbF5JQF6xMAzc1R0/ElGTMaVNSmkG3lOOiBItWljtuhFgKvritsnMJ/YrOUTWNPZnSKkImkuzk5nz9rEUr0yDbXlfbHm0JDE/yau+Qy7og1NM0etBZt2CS94YcZTe4u1w3xT6yb5CYzSnNIN70qU8hPRRclDR6U4tEMeDTrQI7XcmsHlF65WVebXc4rQsyfckPHqTXu7+TtQsr+UuOq5AdwWlN0jyHozokVlmmpmCs3aQRnABwtJHGNaYU8jHpS43zgkFxKKh86TPKwynPGoJy+jrmQPJ4S1CaSy7KClclBSsERVPZmbcZ2yk2W/BZSWMrkKjWBfeT32CagMJMIIA328f0uM4gA0X9jG6psL7POKSfgVjxSbuwiF30vX3ssYIaSZ5vwdYVXFz+8t+H6/y1/QptmvYhFJSHsBKCdzYb/FnxNvqwQHkZoROR8OcIrYtwI8relPejF3DDLJoachUVGRTyJKXkIqOQqH3OS/kveKuz4K3ugHczS0ItpKTOiIVJUp+wLMSkpiqWmyMf22wrV1TmA3QZ4CYtvigUGFR0ri8MNiOfpAhIRxGlyKUmHSpeH1WJkLqHqUyIjTHJOGQpUhyLTRLLmFlQJDOvYskVliJJuzGJUJAK8mjigBu+ID8vM0pVyWQO+f7lCf/MecId/SElMSUdqJCKmStFVN8mvxAIX6E4FbxxLOmECbqmwx6XdKsmg0bMZnqGsFTmMkFYhIazYmEJJqpAUyKmaoKZlWQZ5FnFRRTDZBCV8JKCWInpeTbtUQHT13irJ9G8CiOliK9KFsuYyPZIRQ6vDZ8gViu2ZZuc6/JDdZNFVOJWPyYpVeqmj9QkoRJxZKR5p5hhmEhzq6tzfSDwhUIcrxO/anpEeqEQz5uIdkz2+S6DKMcyFWFkF1wzh/QbMZ/XVD7ImcwMhWVCEEiTMEhgE3NsKugqRLn1UWpflbSjAg/0Eq+IEZuxwb9Ov0o4z3K6sBmrBhVUkn6Wb/U8qkmHeuSTsjwGWZWP1F2UlUQJVTAV7mhj8jODlJPmF8ENjtnGNSSBrbJSUvy72leZFKu8EMz4atxma9bnqLzJRy/H2BkPrSFZFLfZnSX4hntCHKucJTZ4WC6yNBWEppJGJxX6HAQ+ptT4WWGbH+9mKLEkHUVkFgJFCblzGrNaZdiO+qBF7HqCKBnjNQMWqQHfj99hoaaJfUEkNOZSYRBm2QpdDq8l+XQ7zUKFtO+yoz/g3/I6f5C4haZoVHWBqgvK9hgdn5vKETdEm+HIoD8z2JJz8rFHLCOepgz+xvgKf1zL8oVM4CwNemcJdHfFItAZmFnOjRoDM8Ugm+C9+ja/6x3zA++c5irFBWl6lkIiVMjbI27NTnFKNk4pQTdlUVhI3LbFcmEQlEIevwi9fMAwIYgLIX7dZl+/4lurj7i0M/i6gZfM0pnmyUhomjOspWCcTPJpdodPKlv8MFXG62h8e6CT8RKcZC1O8wlCqeGRQngKgZBMUwHZcEo+8DAdPIdv/gAAIABJREFUm6fqBs8UjR9XX6FXALsf8GyQxUs7HG+sOA2riKWCFkouwgqdOMdsO6JvlHnq7uBk0lyUMkiZxNMMfqTeRXUqLPIZ4nJITsYoM4X+3CBKCvSlxYwiCGinFY7zOo6fpThRWC2vr2WTcgGR7nCah//l2jeJCypWVlLrxiQnBr5UsFzJypA4tsdHWzkGOUEiiCktHH4+LqNEId8Oz7iuLvGiHJtXJhoRSyQle8Ff7L7OZ9ZteuUEbiNgux/T6DtcZFw8BVBWTC1BrLooKlRWEdvziM1gwSS0eWUEO30TOZeYQUB/0yWR9hhGaezQw1UWzFSV5TiNRCEyQuKqwaqk0bLKdN0Ci9Disuzi5oZ8Q/uCRuAivIBmEPyd75W/9wnax92PAclvtH0+2smy0wU1FBRnAwJVsjTziPyMxnKCHgpUK4LaXT770Z8BUM+6rI5TRFPJ46bg9rLLuJsiFpAQKovrPnefwcpQIGuQ8OEstYkWf4nHV1IkhyrF9Loi89b8l3T0CoUgRITrilI+57ExKdCw9nlvc/3Zaw9CcgsY1iVP0hH1qcMyKVkNNbpFhb2WpFcx+embCWrnCuO8xwvn62NxyobAOTfIa2ts7VKkmWdzNAZznjQFG6eC1DTiWUOwUlbsBRGJ6br/rt+IKHRhIxtwshMSGw4b7jlGCFKJWZZjJrZBPxPyesenFkVoY49uao3XH5dqHLgTevkVN1uSVlFFjeG8ZnNt2OOLXcF0tUdl3uWTRoWdwZyrmiQe6HRyNodbCrcuJIaj8OBOzHlVsH+1hnfoqSUvnoVcFSNe7B4TZLNcu4KTchFTW5KZJMkPQjwDfEWwGXbZ6cHhBlzrrBOSj7diVAn7lzFuWZLpaPQLClGocJkssd+b0qtIlHbMx1tZtns+bjkiHugY/noMZwWb7UGXZNMlamcwXEFJWSfQmWXM5iBm3Ih44ZcO41JE0oOUJxF2zI1LyVm6yO3ZunKoG1kMX/DpdomdDsxy63l7kdrlzLd4XNrC7ikU5xHjeowwBPWu4LiQ46qkkvxygyXfUfDTChUZMVsKLpoZEh7krBBzrtDLKmT6gke3MigxXG9LYg2e7a64cxGjSEk7k0cPpoyyGdLBGiwCcJbP8rwhqA1Cdlcq/aRCv6KTdmGUyrHllDEGa3HlsVB4IbUk589pSLh5Dq4pOdtar4dvPxswKCWxs1OyzjpZihSJNV3xqLDD9rRL304SpxSqR4JYSPTrAYaQCAQrHbq9BK+wJK+3USR0tkMu2k+wVJfTDZtaT7AzkJzdSFPqKPhCpT6eM8yAABRdpT6O6dwMsB6GnG1JwvO1aPphNUlpdp9oFJMzHK614PjLfs/dyGfkJtkcOQwzUDR0lEhwUhUYPiBjLgopZknJtdMBJ40AfVliZatMSlAc2TzdFHz7tM3nNxS2zgTjNCQ/u8dsdcDhpuDlzlMuEk0WFpSWEU+agmp37aS7Q4tA8yAW7LcFU1tw40Ly80d/9Cv2mr+2X7WdFwxkLMi5K2SuRdlZ8pne5MNaDiWyucyrTLcHtFMhLw7gNpKGOKepDDBlzNvhI35ff49/NlugCh0rPadlCZ7kDQapCrEe8pFqouPxn0afcXe+oplw6dx1eWjX+PnqDhxqfOf8Pil3BUrAsArlK4nvJbntDfmPZ59Qczu8wEMG8mWm5uuEqzqhXyQ/yHPQjnmxG6J13wRFcpa0eKTXsCKV7CLmWU4l68b8o5MRD5UqiUSPXirLu9t3udeoUvd8Lq0Kn6j7pAOLh6tXeWkh2DBmdAtFHpT2cTUfbQWeKll1LT6XFa5FAeNSjlyc5gXPwwwDPEulygTlMuLnYR47CsnFIXYYgyp4wDYXfokTJeZc5DkJm8Q5WJo6DzigS4ahlUctLKgk+uskahFRdQJ+ltHYavVoPp+QOw7pLAvMVItU1CIYFihemvhCYEcB2VVAwQz4sNDgJKzSszOIRBUrn+CzZpnHhTLPqoLT8oq873Jt1qYczyibv6AUnzNRs/jSIh1G1EVI0g5ZGAq+gGrskVRdlNjjMhNhBhF54TJIptjqqiySkkFqA1XLs6HNyFoOURTxk1Kfe4kdRokyKUJell2ihk8675FLBMzzMa6iEiVPOM2nOKzYtMpl+mxxVMnyUbWKh+SvM3eJkdx1Q4rLbS6SWQzyfNsdoC9ibswvuS5b6GO4vKVzmcjjhUmK2T4Fd4q2CpnqBRJLwdiw+HTPZpFJ8GK8JL8KOCNJNeowtRX+55fu8s7r8OhGyEk15KIc00nlIZ3mtX6NyixHKpKUY8k3Iwcr7SKMDjPVxPNgoimUzQtCRaDFKqoMaKU02mqCq6jOg2ye+5ZBE59cwkFhwsi1kKbF1wYe+8sIkY1IZF1CRVARMZ6i4CkmLdmglVfJPlbIXS6IhMMzP8fHhS3OMxk+K+yTDJfEcZKiO6VqhEg2eB5scUSZ5EqnbVr8NJvDQ0GqKsucRWaZAKdAyvep6iveSZi00jPSashkpTOWCR6rdczlnOdKnRO9yPvRLk4coS0F3TDHF8YWh4UmIyNNQsYgLFqaCUoA+Rmj7B4dcZujisl5NstS5nCzATVnSW3hs8cDbqvv4tkdvvrsM7aez4kik91Omm9O+xjGjI2Cg5afYcdrGZ+lbrO57FCbXPLMyDE3DR5oe4zHGXKZSzLKhGfVTQhsOrpJnSGP8jCxBcuZRuCp/Df7b/E3Gzs4QuOa5VJKBjSloNXI0m8a3FmcsDUakpwUmUdVCmOTuZGmYE0pazNMCWnD5bIAgRTsdhSkHnK10aK/O6OXKDHWc3TDIul4ilQciuMahZNvc2TfYSkEpxmV+yWdfipFP5nnT8qv80mpRnqp0Jgu2W5rSKmTmlSpTGNsX+AVBY9uCj7ZVzjckUQlyC0svtKSiKVCyfJ5U13w1MsxdCSfLV9noRaQQiOvT8i4AYoQ/PfJ3+aHzSSPvH0S2Q7Z9CXfvhfzvdGKrIx4Rokv1DR8Saz+v7O/9wnanz7/UxCCN65W3Nux1oK5hs5Of8m8FPMT+ys8KWnstdcBvFFOgJWFx5/Rz4KXALFaC8f1NiVlf0jjfI0hN7yYYUXy2nPJ00qe0mQd0AahRXUQExiSIHaojkDL+QxykoNOl3vGLdp2nqbTwdegqYW8WXuNbzTf4rAcIIyYW6fryHtZNlCcDNfaEctyzMNdQWCt1dvn6QRt02KnLZgkprx8FoMEpxyT71nUp1POK6D10pyyh+HD2QZo0Xrn/qQOdy6zvL2cULqCfk5h2QzR4jXZ8jhtICZpmr31ccrRzODS1hhki3QSPoYlUb08yhWYEcSK5ItchVfVBWeBQWUKpmKxMkBbbmAEMQ+2Bc94mZQjuV+rUp7ELEsxQ1ujn5Hc27Z5+5FEqvCzDY1n1yVvnMVrqEt9QtqFj7bT3Lk45uaohxHBlV7BcRQ+u5ll70qyyKo8a6i8erpOgk4zGntfAkceF2KeVTR2uhAUdPbaknlGMBobvLOzzU4nZFmNSbUFR9UcCR+0XMTQEGx3JV4y5ipTpeGdcr04pTr9EsSxWrI0YO8iRJEwqVgkXEF5utboskaCeaSjR0Co0+y5uKak6K6Pnc7zktutGD0QeIbkVC1htU3iqku2t9Y/i/IKnrceH58kl+UUkQoVN8Ybmsjql/Cb9pIw5SHUGNtbV7AakUCRAjfnM8jp3LmMWWQ03NgjtxCIEEzfZ7/TZ2AWkdMkmx0IdDAUi+MapL2QvYXPs01B9ktYynSV4622RPcEo5LkeU4lhaRdEiS1PndPJd2ijqEkCRVozOEqmycTrQEhHbPCeDPCHCh8Wt+kPvD4cGefF80Pef0QRlnYSqxQVi6xgJS/1hfZcU1u99pMk/C8rPLpL/6c6f4NrrLr6lNhsuLwQOXgUnK0dYAqoSDX1cbdroKrg1nVSV9Jwqrg4Ok5W2OXX9Z3uR502OjCfKCjxTDKmVwVYUMP+HC8iQLYniR9eUHMWk7BDqA2ga6ZZ9iMeeXRisNNQW2g89MDhSgtKV8oPN8QfOfwEw63NtEiwWUjIjlzqU1sHm0JXjr26YQ1DjcFjT50Kxq1EXTygrirI1dHALx9FtHPagyz8PiLD36FHvPX9u/DpCKJdBglTUrjkP9k8hARpznWXieehCwdjTYZynOL9KrAsXuHrBJzWZzz764b5PJTbnDCThjz1lyw6FRQLgvM4jQJscLT4VFCp6uogGCUNyjpguuiSxhkSXWLSClJhh6N+ZCLzZDYiBFTnYNFh2tLj2TKYXv8mFvPXEJLI4zSrKIMiekmb3ay1GWKGxcazYlHU+9QUVfISGUZmBSmgpddn68tNTJmwNurMd+KO3xfe8A3cn/GzekD5vqMT2qCktIi4RfxnTsc2RpjTaCbAk9NsTQiWjlYmbCs+PzmrMV+HFFnyMuyT9IxuJplkGlB1JDEKZ2uovOOte6HEkBWzFDiJV7U5S8Sed5vf4MH069wNv4aG+MKmaVJIo6YZ20K+oqC4XGxX0G3VNqezT2tgFBiYiXmQrHZml6w0hW+4jyl3jjj862Yf/FqhnyjTT47Y2lrVL0+GdknI8f8RaHGgAQDK8ufvJXnfD+kUgkYp2vsXllsuyusSKDKmH1vgLNKc3+aJRFrRIrGSleYkOKtVYD9yojixpjGUuBECVaoXCz26Pqv8zsnK76Zv88/l39KnHD5kd4gDMvYkc/j4hVa4DLQA3aHLjNd4XlBpXstZG8U8XpvxdlGyBsXZ7xxNkaY4GJy+yJmc+5zw3iPVjHBOxUDQ8Q88m/Rcrf45fwO7ym70JPsLy550XmKFUqOrYh9WnyPT9hKLMmZc1KrGWbgkZyb2FpAwx1SzrY4b4KmxXwjOufF8RQzzPDAztO28pylNLILGCcFuqfRGheJjQ4TO2KpgBX5/Ni/yx/Fr/Mj7y3C0MW1VILxS1QDhafRFlJKpDXnqqbDJMt8tMlbbpd/PF1RiB2WCYXxbYewksJKaPxSzfGX6QozTaGVMFA8sEXESk1hRA63tQG2pvOLmxZHjTrXCzOqzpQXjw/ZW33Gu7kbfGzU2XKeEmx2ibUlHVXhT5wbvB8fEOshXy/+kGT5EaeizlBkeRjucLQ74Givxbt+hh+nUzzSMmTCmOZizmlUoeOW6Xgmj9yI/6PyKoo6oRD2WM7T5IMF96JrfBod8A3tPt8ufM4r3UN+u/UuN71D/tvraf6nQolBlGfpHWD6MZE06GaKVI2AT16I+JffgZ++7PN5dUVWP8FsOni3FiwtyUmxRrISsjsLCRKSg7FNcmHQS9o8yN3g0+ouAp/QPEfGCnM1w2Jmoak+t1NX3M9soscgRgX+zfK7LKoKqpBYUkIgGGUVfnRrm8/q+2g5qIqY3xMdTqMtHhg1qpdJNkYRG6vn6KyohSa/L/8VrSsNxxF4nsDxgQiSS4VGbsnpzS4rO6CVEDwqFHmS32aUKvJpSuep5fO5WWPPO2JZDHn/jZhVUqFoKziqwUTqXMVzhuELzGWV5nLOP3l8yO5plrmRZJkM+fCWzU/zeY5Mi0dJnQs1xTijYqV98uYMzY4oFDySAkYmXI9i7O4BvxS/x7vLBn+Y3+PeTsRZ0sZJ+pxmTK70IRuLgHlRkqmtyCsh+ljliXGAu6oTOem/873y9z5B+6T7KUJCpSs4qce8fC5574UKux2Jlo94X9zmoSnZvwKhxdivfIUwimleDplWI6KFgp9YB+FbWyUOI4NrXYlngJhErOYWKReOGiZW3yESYGkn3GhJnJLGttNHi6EXhvQ2Yq61Iu5ZTXr6NjemJ5xW4FYfSt/6h3x39w0QAq8Y4UlYmrAy8hRmOtUJ6KaktR+SWayrXdpqRW91wGGxQhA6lEYCgWDoxuRWIeW+z9NNQWnqk3TW35nkJL3iOuCflDTemGSIFhmuX8IkqWMUBJGAc9/EiUsYgc2tc0mYkuQW4M4UloWAa224rErcoMRAW1/jV0KeWnMaxQDly36z2njFg23B1oWGRLIqRuTnkqQnEdo6YLZFhBQxpcDhpKbytccxMikwJypP64KXzmPG6RTVxPo/+KpCc95hYzBcj8vCIxjoXOysaPYhu/R5v7nDi889ehXJ4YbBK5cRfkKlOFaYpwSqhEFJJ+FDTXGIegadvIoRQZxV0SKBEa3vV1Z8DnfhhVNJVJV8Jm4xLo3pWCrJVcAgDfYEfn4HGhMFX4NBpsiTQp1G18VJSow4Qo5UYgHn2wMOWjGrWoSxmrEywEnNePkyYrMDgwbUhh3KPQUld4EqNTwNqipMhusdle2+xzLhMU4J7s5DorlKJbGgrWjcuQhILV2UWsBiur5+cxYyNzQ8bUY3G1Lvw4OtmIOLL0E0GjSnY147c3iSTCKnMbcuJJ1CFtdLEXyp/xX1BatcRL4bMU3B3njAjd66/0xXY+pqyGyusyhqJN0BhQUMzDR7RybHNfA1hUOrTHa2WK+V8Q6LXUGtLXheN9juSh6U9jlY3qcyBTWAKBA4Y43zMmwMBY5p0HJucnDuctmMeeU8wWzyBaU3vonVz3CUT1HphUxzS/YvJUtr3UdWXKi0c3DzKOCXN8HorpO5dnqPjdVaYuMwv0uQXfGqF+C3TCYJyIVTepsRyVRAe7IW+i5PFJK9EecVaIxgbkPWAceQLOomN1uSw03BnZbDSRUqi5C+FxCWQ5ILj5Zyk/MyFCYKqpT8YHLKWc3m4FKyiFROG1AYC6RmIIDjDZPypYqlj5EZi5daAdO0z89u6sjT0a/70P5fbpo24Yt6gV/ubHJh5pmECXZnPkFYpjnQMBQTxdmk2SnyoXaXi6hJXnG5Z2R43jDZTI7ZUIc4MqTlF3kevEZj6PCbnSG6HGIqS1aKpI1CrECOkJ94XyflpjDDMqo0OE3VCfMKqdSYfibN22P44cY+75dKfLGv8kE5wd5coSwkReHxsSkoOTNeOgpZjiwiL8W2lOS8Dra44JmepCie8dX2fbJLydcnLje0CVEm4j+L/goHQUrMuOuFdDN3ea2vkjc7DNJTtsIrNsMRtZVPIpa8X2hwmkpyWgMtUuiXIk42oe5C309xQ5xTMDp8rtv8r3yPv/VfYnKpIY8Ubk/gwFnRdxSkFAxUi9MgyXKQ49Q9YGM45tIt4yy2WcqIO7NnqLmQA+WU+0pILAPOSkkO7+zwF3svcy3UUFWIhMLja/DzF2y6u/D1VsBXGNK5FfIgVeDoPEFmalB2r4PlEqER9d+kojjczxe432xQiircVTVWfpq0tqBT9LlozPnteyE/+CTi3jWdw4LGX79c5cO05FBsEMQl8CrszLPcDcCoQzUQXIR7fGZaJMIFj7difry3gzKIyHpTLmSG9/zXSQYe33zo8f0nPlf30/ypl2R6kkBvK9T8kEtT4W8LNowVDi5D0tqcu+NL/nz2On/A97mmK5QKc74+mXDN+nN+cSD5kzq8NHpMfTbGjDw2lBGHG2UWySW/1fkIywj5xEgxjpMsVZVfKFnifEykQjaY8+IxbLY9enaWcyWFb0k8C85NgZZdYaZ67I8GfK8/wkr5vH9H4bSQodBzSHYn/O/ZgKfhipkW4XmS7d4lfT9Jy7RRQpNx2EDrJGnPctyTJRLaipku6MgiOaPHrvE5deUprcScAMHtucvb/pgELln1kp9uvsajjMKn24LTYkxuEfDiRGBJj3lwm79R3mDh1Dgq7GBkIv7spo5Z8NiOpiyMBAkrpDJ3mJaKVPUQRw9o2vdJlB9Q0z4gn3rKd5Uu33VXHG9keBA2eahs82izyEUtx7/5SpV/bb/CVKRIupL6HJ42Yz4oz1FqH6LWoeGHlBcK3x8855/Hf8ZNccp3Jp/xg+hD5htJRlaCQGrogeTaSRLTtWheCLKdJfXZkkBJo8kIzXDQNIOPd1UuSwoXKYWLgsbTa5LVXJIYKXyj9Zj9VoejtkX20mT+NE3lZMHblyccbbzMUfYuoZrCTjv8551zbOmQ9WbUFiMOJg0O/Ru0jQpGfsFuqoUVRHSdGnNFIawF9DZD0koXaSz48dYtHhc3+LrjcSNw2bF+yVW4xZleJ+mpNHsKgRBsB1lecALcp2mirskf3rb5l3dMTFenk1FwpEJoaBBr5MMVo7RPO1kj5885lgbvJ1LMjTmveMc80XM8T2lcqhp/XDC5OXrCd1rv4msanjXArA5QS0v++usgNx6zZR/h7QU8quSx4xihxMxNSVIJ2IxmfK7WOe6UiBWVmQXzSOVHqev8j1tpUqGHoujsOCqrcoRUBUQG0azOdx/l+C9PT9kpjjEqHuFKZfAwxcXA4olM86Syy6F6/e98r/y9TtCiOGLkjmg64I5MxrkZB1cxJ3UNM4ScEZIJFkyMES+0JGYmxH77B/zNBz+iOJdohYjSucJcEYxS8Gouw+P+mqZXiOCiqJA/FQQayN0F6aGknzMYVoZsDmGZTpOarqtqnamFLIVYATTi55ymd9nrz5hUY5S2QuKNr/Ji+UWQgssqECk82RQsgjR7zgiA0jSkGkoKwxDHhnrHZRjc4o9fL3HzfB2ghQImQ4NRMoseQrciaM4mbI+uuMqD6QgsKbnKgy3BTq2YrtYkxfw4YHcZ8WwD3rXq1OQckZpyqyUx0hHJUoAY6rRz8LXHMYu8JBAxrVK87lGrRFz3zhhXA26fShYGJJcxn+0Jbgz6HNcE+8OY32zfA+DGsrMOdodr+l1tJCnPVyQ8gRYGbA4kI02h1hZ0jSyOb7GyJK+1JoRaSPUqoFVSuJ19TKmjoPhzFEBzFPqZPNc6cLIjuSiG636pskqzJ6lNQmJFcmWt6YplLUYNBFurHgBZdT1mO6M5swQ0+jDIKGRW4BeSLPoJ5GbI04WJNQp5sJUgNxA83FYpLCVPNgWJjsafvrzFwaUkqIREDZ9MW+W8ZHDVnFOZgiibaJOY+zuCsekhXUF1AsNKlq+enrDwE0QyoOA5PG9A3fURPY3LgsatYQfV9ejm4a32utqaKAT8WKuyc2VQGkTILZ/OzFxrhvUkf3njGr4bsDkENRa894LOi+cxkSp4uAuqhKxXYpxZMi04bPXhMHENb6RRS3ksTei1Sux7EXst6FRN6qM5mcmImQ35gc6LnsdRYDKpFsh31r+r3gsoDMacNyAzi9leLjEH0M1DbqawSkscJUdRPCflwuP0dWqtKaEC2aXgaVRG9DUONwSVsSQ0Yk67aWwfwkzEzU6IsFpkX3+d145H3Nuu0UlX8d0VmRUUpmecliE3gEkSzADGN0K00wX9giS4LDBMmwzSgnk2pJdwyZVjKmcqn25n2WvNyacihIBi36FVgoldQh3GPG4KUivJVQ0WtsAptenlm9geTHIJ3jzrghBUJ5KPchoNPaSXhe+5RxzeUGl0BXNT8NrZQzr6NTwDMtolk/JamiM/juhnYJGIsH2AFWKrSbmjAJKnG7B1KTnv/d2Clr+2/2fthYFgkL3OcLNCt+TzXx28xdROkYyXXN2psDfPsDveILnao5sqcO44PL8M+YefLfgHT31+ZtzmC7nBUyXFvrdguP0F/9uNJGag8Vbk0FiFVGcx3/McZCiZzy3+0PkO/4LfxBQPSaVnfNK8wX939zf4g9sv8YVyg2NVJR/6OIFP6yzNrUObx5U3mZg2jFx+S3lKvvR03d+RsHg33eDHiQrFbI9KYsr2XKBGJqouwZSUWoJqaUkpZ2J7kpe0c9ISfhTdxktLftfu819c9vju6YT91RUHoy6laZqcH2IYF1TFiHEqItBjpmWNJyWVmRC8m6rwTniXv4xf5p1Rk/HCZpZWOM+pPM+Uue/f4T/4oEL16Sa/6G/xy6sD9t0xet9EH99hoRQYJWFcGlPOPuCbjQ95RT9iT37ETyyb/8FMIgr3Kac6/L75nNfjGWGoMrVU1I0p013Yi8+RqYjUUie9yrAbjNHUiGfJJO95L3CZT/GkVuBKNIl6d+h5t8gz4s3o58z1BVurKTW/T/vakD862OSPXsjyt69u8LOtPH++v8O5mWRuZvhI2WW1PMBzivzXs/+IfzXO8ZNVhkNjk0WyQ6DGJNHZySxp2BEfy12eUKE1qzAJ80SaJF9R8LAo/5/svVmsrNl5nvesf/5rnoe9a897n6nP0H165tDNpkTRsmVZEhUZAhInkGMjCDIgCAIEyE1ukwvf5sJIIiRGTEmRZMkmqYikKLLJns/UZ95nz0NV7Zrn+ueVi+r4UkBgKYACfpd1UX/VWuv/1nrX973vG/W4OR5TUXxeugipuwYfk+CyErFSddkYzsnOO7QLFh8W1hkLjU4xx0oQ8ofhL+GefZVWTOPzskZC/xjFOEVNnlIKOoicID2zMJIRpbhLZ77Mf5d6nX9m53nYusTFNImXDxgnZqjGlF9/rPFbFwEbRx5ZP8AVCsck6Lkxzr2AKNvlK70Zvzh2yPkVPFFgpBf5abKC5UFDddjD4kE6w/9ee5mZriPEiDtbcH+piG92mL7w+enKIw6SI9RAxVY7VLIeO/aAz20P1xigzWN0ogpSSH7H+GPi1jkicQ8ZCdLJgDCpst3UUMcmq7MsxrTGxLX5Lx/W+YXHKnskMfUkf7yS5JNigZ8Vv8y18JBYZDIY5FgNhigRSCUkKzxs45Avqw9YYcBa1CU0LigqI4QMibW2uNlYpXxyBbrvIv0Mj4wU340v0yNBTE4oDAPe+9zjvxb/kq/Ovk+3onE1e0qucowZe8FRu8h31Rs0FYtpUmOlp1Bqp6iOY2Rmkpw7Iqk4LM8F8dDDDn3uHL2O3UmhBwqGH6c4i5Nt53mW0ZiWJCk5RgsCjKHAVjwaloK7NKZQuODd4FNi9pyS2kFbc1kfzXi7+RmvDj7iPKbzw7NNnkSbpJkTRfCf2d/lSnSKtpvlz6MkL8YpdiYhK+4eRfE5ty8e8+PhGm4oeeSUWJ9lcfw1jlJVPAMOMtfINy6TdF0qgWCsSSYazGwYxxRnHnseAAAgAElEQVSerbooRLQcm0vPyxSmPvmxx9rUw7Zm9BLL9HWFwSDOH7nf4n9c+XVml36I4cJq32f7KM1rk4+YlRrUzhJc9F7nLGnzg1qcia+wfuZS8ZsEwzgFt0unc5NoWMbwdN6YDZgrGqe2RTNZYK4KjIbOYG+ZUy2JoUBSfUp52qSc6JH34lgBJOmwPjrnh6m3iFXhohZw1Q1ITxW8sU4wVbmkfEBGDklZk79yX/lbDdCe9p6CjHh95HNoquRmAfZUkHAW7VVOPE7FbqFrF1Q7EZodIrbf4fMf/h4Ag5IkdhzH7EU8WRXU+gdEZxaDmMBSVVorPlf34XjJIi9jbDThOJvFNb7g7iRiJAaCmaEgOxqVDPgqvNZ9xETRMEKw8h7zM52mMyOmx7BEnoc5k/gcDmqSRzFJbdTHVyWypxMfSy6dSNrlhTx8XJ/RXAm4fiIJNcFpAYymjvqFOuc4HbHemZLrjHm8Jri6L8kMHV4sC147iJinL7BbPqEAx5JcPnR4sCl4mk0SaRdY3oCYC1MkSsllJgXdrMe7BwEzEoiohxYuqiv1YpEvBSPuGBY3jiT97GL5nBZSrPY6fL4hsLrLrAxOmGoqq90BRxUoHOg0MhadiuCNZyGBKZk7OoYPm40FZ9DwJSfTIk4p5ErDZVSNWG4Inq+kONmZEp9YlFtf+M4heKtzsnh2VlIZgOkIDtd9EHDtWEIhQDZhZgoURdCM5bjWbtAoSOJdn04Cdi6mdJdC/L6OGnwBtHMZNrp1rhUG2KcxjLmgoC3k0NMzQXYkOV6RvPlxGy83pDACK+VR3w5Zb8BBLk2xu/gukdNIdgUfXEqwXQdnUdjkML3D8nTMuJKAmUKpHzCqRoSWQaWh8KKY4vGqRmEQIYVgs75os7PyPvujFOdLRSodSGc9pn2L0wLEBoLjnSwyUBcKpBo8rAbcPlxIC3/2ekQEjNUU8+QUz11UjVtWjPXWGVszwbOaoNoM2WhqpGdwXkxSz5UwOiG7y4IwMrgx8vnUMmmWY1w+gVESRHzMtBjiZwy0EK4zoXShMIpDUe6jjEOe5Na5On3KaaJEyRhQOVA4qC28xezxwi+wkxa0ipAd+Cz1z/A00OeCeNflhRmwO2+zOr4gp85obBQRLYOGnaXWGXBakVhzSM/hLA+6mWP71KO3pPHukyeU+w6freX48uRz5j0Fw3OxPdgv5qldCDZ9jzYK6xc+ngZGzkIJBE9WBNkJzBUY7pSZVRUmwyp7y7De9IkMDxFqKKrgxbJCdajzrCb4hcYux0vL+Cq0S5LieZ3J+CqPVgXrkzqtVBFPhZjn8GJFsNRcvKPNto6muygzhfwIhBpS6Ul+fP//+JtKoz+Pv4a4KXukzQmPMm06RZcw3eFdeZ+12Cmt2QqpnkrWlbTjJmupx3QLRwTxNuszn5WB5PfEdWKO4JvemN82PsdmxDQzJ9IDsp7CL4+GfCXwcFSDy2Mf+1hyK/ZHZI1DbvaabLbvcXczxE8fMtSqpIbg71WpB9f4KPdNquMs95Wr3Jle5nfzr/BnVxose3u8eurzvGyzWjznlj8iLBq8Ez7iq60XTOMuZzGf81SMaSEikfeZCZWOMJkIhUQQMQx0+p7LN8THBLbO71cTDJZUPjQHpNQzJsLhMy1De16jIl0CW6FRUBipKbxA48eaiZs9wZER99UldlcLaNkh32j9hOw0oB9XaScspqMiH+mv8j+LX2UvvkU9k+Bgs4ivJXmW3mI9+ISb+iOSdoNyZsZ5XjKfuZTcCMMI6AmLuC6oaT4lw8fecNA2x1zWPH5FfcQ/mr5AxiB7LBmdVXnlpMQl5hh1l9fOv0NXKTIfv0o8nCHGI5ygiuevIbsZfhC7xHfUS3SGGW4eS4IozvdeNfksfwkjUshbd8l2bb42k6haH9U8R3Vq5GZdDjMK/+ZygpOhzkrfRw0VfjM44JfnL7iqnGDbPf758iqPHIu83yLnj/mu/QbZVpn8xOLdFxFDz0ZuBbzch+zcoBqo9BJwvGEwFVnuqy9jOSFFZ8jjcJ2hE+EYNvXYxoJ3HQpieoDwBZnCfT4pgV3o8fDVZZxqgjfCOanBKh0/xZUzA9UwmfZMrp8eoRkNWkWXT5NXWJ3MMHyJ5UX0I4UfuymetGwsqVI2eiQDj1fHPmH3OtlhgW8mP6W/sk9+Lrl5rHN8eJXncom1kUo8+Ygd5ae8/0rA01eeoNS6/Pe3/wO6aoEXQQ5jv8aJpuGbIU9twc+SCrGZjS9mlNsTZpFOhI7nm4ycqyj9t+n3KqydGDzOrFGPrfIfOU+47vSxlQFZNSRR9PEn8PHJV+loOj9d3yRnPGHJfMC70w+o8oSMG3EqE/xesEOhXeGlMwGBg0mEPoW//8k5S0dN0tofo8gm01GZb5ztcTs84rLrEPNNnOEV5GmNxizG1Tsa55FFu6fj20PuW1XaWoLH6xHv10z+TeoWu1GeZ06eUUEwN01GSpHCNEUj6yNiA9IrfYoixIwczOmc3lqDW3s6b+wl+I1uwD99FPDmi4gP1+P83nocofrU/C6xCNSYRNghn5BlMokzamWwnTmR7eF2V/ggXObBRprPro35zrUsjQ2DN+LPuCzqrAYNKuGY38z8hK+f7OEOkjwb3sLatbn5HNZGPTqJOJ9pRX6/d5MP55fY6ufJeVk+Xr3Gv/xKhe9fzaDoE36SvMGHk22OcyaPvCROaGCECvmhTmouifdD3m26/DcfTbg8iLg5uUvJqePObIaoTFkh1D10/Yi5EfGNY4ffOp+xkz9iS0a8NDVZG83ID0DRLe6uSTbakpd2TcadHYLxFrdP5qgiJNeq4Eyusi907Dn8yvAuV5PPmC0LJn2bzkjj0lmCzUGOL5l9Xvc/5/cTV/lB9ArrjYjCNCA0Q3TTI2c1aKDxB1aeQ9tiHilURYQf6USVfQKt91fuK3+rAdr/43/29caEe+sxLp9JujFBrTPHNyXfz75FoxSy2paooUBL65BaIvn8Hr4mOSlKoh7Yc8FsNYY5aLBzFHFY1FBdiUxDfgzHxTSD0CI3gWeFOBvNhVjHvYyHKgXDjMpaE6yUw8mSJN1WWJ89AyCT8DEHKh98/38CwHOT9BfndyYVlU7MZL3u0ClB29JoYZObQDu96E19Y/QhYbzN7cOIbtZmFIdyS5Aa9TnLQ87w6KXSaP5CSe+VXQ3Lidhfgld2F6aYtSM4XlJ5sgbucYzhasChXibWFWyfLzhM0UijntEQGZP4ZEZYCbiYX2cwmbPRBN+OeD+T4ZYx59EoS2oOyUilnofaSQlVwt4q3NdfxvR9PlwvUW0F9ErQSug08gF3inFefyFxK2BGkmlG8tr+AjgU5gOeu1XSBZflHvQKCloEnxSvcmzoPN5KcKkuceOCs4zBS8dd9pZgFum8dLL4Dw9WQ7oJ2GwCRY1Lp1DPK0yHCj9ZW2f7C+CnHip8vJWiOJJoJY/hRKPWkoxyEYd2lWV3j3XFo9xfoOCSMyUScONgARC7FR3DV9gYnQEwDlQGqoEeQiA1rh3DLBkhHQ81EjSqKm8chJiuYJSQPLYrqHZETvTQehaKhDBrgOcRd2Gat/j0curfCoSkmhqypGDpPnYzZFpy0KQkb3skeyA1BU/RSCb7DGyT26cR9y5DbgjxiUALAipOyEkJkpMxG26HUkPgq4JBZsC13gn6pMqzFUFlPCP1BZ/vpGThLenEhwonVdjbcFmpw48LJudxl+vHknpZ4+6agpO2MeVCASUR65BwQESwGq8TdjTuVNZZ6fW5W97hFyY/JT8UnCwJPEOS7y5ukYSUDCoKI1lm57zL4zVoSQ23rRF1Tc4+/w6j5XWqgx5OLaB2KnhaWMYIJKv2F1y8HnxwE8KLHEYA9XSFjDclFkTcqWzwanDM9YuQ9oWNq0EQ1+imJRUdfjAqY4SQmUDZXXALZ5bE9GCrDtatW8Tzq7idiM5ayEsHLk9qgtQwwYfLCeaFiI3ziOc1ASchoq/w8VVYboARhrx9MOHJquDt3Q5diuxXYbUF/aLGlTN4sSQIGyaqt7h8eOcwZGyZvFgWPHnys7/O1Pnz+GuOdXx+Z/I+v/LiiAQBFXlEVbb5srxPVs54pTXjRveAVfMDRqMuUnOpZxTmjuDzZZ9XxHc5bJfxpUnL19gaTLhWd/j+ukqouhiuTm+YZOhrTCKLe5UqlemMf+/JOTePp5jqhFvOKa/wnHfVn/Et5Tu8OT7lF7rvc312xNS3GMcT3JwcEJ/P2XTHFE5D5EmBrNogZZ6SU8/ZcRv8QewWz1IFlh0fO1D59to7jLahFETckzY5OjQ9aIUaP0yaWNEu03GJPxUvYXhJ7nib3KlkmOontI/L/HO+gh7OqLXPESpImeZEW2cmDXqWzW9xn38sv8d/En6P/7b3mN8OvkMj7/PhFYVBocW1/jEld4ASSDqGypp1wdcSf44bxkFCpAkqsz7/aNjjvzqfEmHwQO6wOUngKQZyvMSFk+YIjSWlgan1UdYn6CWfVSfiykgwHOiYZwZiINg6T5Bt2hy6MfL+mJlh4kw3ELMK7w4/5J+c/4jyqI86UzhJ5XlqVDF6gneeD1HHJq/ODkhO4nizDbrTNcxpgiBK8rvTr6PqLQzjgmQ4ZGu8y3JHcLU95nZnyi9/Llg/TbE/us53skv8oGDx3FoimG+RO09TcC/QrH1eFAR/vrpBI6FyEBmsjn3yoeSS7fJr0RAZg1zXYms/xaH+Mp+Vr7AyGfC104+x6338owJr4wZrs2PWmjYpJ+LekonEJNXcoNiMMY9mnFgF/sS4zUMlwd+xfsyrznOuTCTvDk+Y1MZYcsb2qM752ipqSeNPp2/ipha8x37D5O9cTJAbHorlkBk4fCbiaJ7kd07f51veBzSKEeflkMgIyZgjLuw0z0avsewOKWjH1NMaqh/jsuKxGgywfQP6V0h4EneWIzdR+TMvy0fBJpVulrWLDF/rxfjE2ET6CSwZ8VguofQ3ud0LiGsF/pf4Dp86eZ4Fa6zONd7T7rMzPKXgjPj69A7L4SmmeoCuWvy9xhNuNOsUjpvMZgE7yh38VozT41VKA5tqzEURKtddnWkQ52y+zXFmDVMGKJpNZT6jXhzgXt4jYZ6z0gxQPcEbzSN2Bi5t3aQdT9BcEVjGmLwxwg+PeDbKMWwnSDkGtzpHpEaC3XiMVknhpBbnw5UV+sLk+kVA3JvzlvaYvNYlEh4Fv4sUgrtLl+nqBdLdOdfDFo2ChuEW6MwvY0nwcw79gmSqCuLJgE5M57kQjEJB0j8nCHs8VnLsrbrUwja1YYWW/ibr9i7/qfIv+JL7IZrTJAIy1oTTgkroV0BJMFcMbjyP89aZxVsXTYbRBkNb57b3nG8OHrIcnmPoXZ5VFOzkI/TUOc+sGB8u38ByCzyVm7TcPKvdkP5KwJtHCgnpcyt3QK4QIRpJ+pZB30qyE+2z0nVZUXrU+h75+PtEKCQvdExH5WhqEk+OWQnq5Pwx7x1+hNLu4GhQ7mqM9By92ArPUzto8ib/MHjE/3D8E751sMcvPvVIOi5xV/I46zPImuy9bvOTN0r0tcskhpf4SuIhb2m79EsKK8oxb51IvnoeYms+yAGq77H+MMbj8Tb1YRwhFDxVp+es8q/Gr9FsbvyV+8rfaoD2/vn7AKw34O6GxY1TyQ9u5rh0LpHZiO8ab7NvTblUX7QH2jdu8KJ3yubZnHEpYuNU0i0t0NLm+hKPLxIYARhBhDYMsXo6gQKztE7XWQhpDHM9rpxK/KxKJ9chAizVZ6UNnxka3aWIc22Hm709mllQfBWBQP/0LzkbnyEGVZZ6EXMDnFiS2jDOehP6RcmzZQhmiykJBoJBQhK5GtZsSH4gGJsRngVqBKm2y+M1wVf8GVp6cThtFzT6VgEANyvI1yXFjk2tA8+XTJ7XBF5Pw45rFJUpBia3DiNCG1K+ZDxW6dhxbu6GvNiKUIMYddXm1qEkXnHp2R2yCR/jC/+zdNfn7qbg0mmEp0HSDFgejMmOBbOigh6C0AShEMSkj6eoC9NqezHm+yvw5n7ESa6MNOY0A5sle4FK4qOFF9Vu/GX8tsH+xkI1Uo1CPqmtUO6EPN0WPN9UeesoxNcVzhUV11BQJTRKBks9sM0AeaFzltcxfVByYE8UxvZCdbCc8Nktq1w+k1D1+DC6xSR/ymmgo08DzvJgDgM+uQTbrYiZCZ10ik+XNllpTvEMiWjq0BdEAgylw/Vjib7kMpl5SKCTmvHKUcRqA7rLMPOnhBJ0bYIaangqWIqB01+s07WUz/nKwicu7YWELZ3M8pgQ2D6bYoVDrIyPN1sYk5eciA+WL6Pah/QyLlfOIj66AW/vLwBllICN/UVL7Xr/gqJwuXYieZEv4xePMIOIk+kOzaWISEC+Ieln4erogoSx8LMb5UJ+ugWTVgwjGWAO+sRckHPJgw1BZm5ROdI5LyiYk4UwihbCUtwl1VK5t55kuy65s3SJK80ni/dKk8y3BP22wXkOaj2JnzHZzy6RHkc01iKerSiojsLrZzHU+VOef+nvoY5stFSd68cS1wqJgNxUx1UhUMBbF1i9OoEqcYeSXmLB8esrJS4Kkjd9F/XU4PN1lZvNFo1aRCzrM7gwCBQQkUW+u7AVSM4EzbxgvQVrX/oGVypvE5jHzKo623XJw3XBxkXEkS0phgGpZsCLZXD6OrdHB5y9pGIGMDYFf/fsGftLFtdPQppenMNlqLYgsiy0CF6sWuRaCkFqUU19+yCklw75yVWN6KRHJKO/9hz68/jriSExwia89tzjd3Zd/uHTgFR4wHhyRGb2lNJ5h6Venc3oDMPTmQXL9PQkxrLPG45HzjV5Wlnl/+x8id9VK/TjGjJUqTkRM0VlK/RJaHP+KGWy5+tMhIWru3hTk8NSAl8P+PUXh3yr3ufrTpOX5gHLyhlppcMrg12+ffMlHscLXNc+pTY7JjIkngC3bPI1ntMaz2hrEzaHAUuzCmd2GVcKZJjgML3Kv4iv8U/Ll/k4VuDM0IikoBnXsdWQrz4LeNBZ4qGTYCv0aVJgoNgovs3Rqs7VcZzl3pSRMmardY1sIkZFO6faNrneS7Dq+7QSPrfn52zNOpSUiMQ4iZKQeIU5twbHGHOX4uQCR80yETqe6aCGPVJuk9J8QORV6Z9oTJpl/mjwBsfGJZzMMt/sJik0I35175QNp8GRYnFMhkFcoZdQGUQKgathpCPmmDxNm3x5vMvl3CNmukY7B2eZODPDgMCiNqszi3S+PPiYV+Z3eKv3FClz9I08li/ZHju8EajcfLJKfCJYaRponkFgeFxoMVQnQlFdbvYfsT1pEKKSm4IWm/M6Q957GOOFsk1XLsDMrclTfm33KdgB25Mj1FSHpPUcLdnBS89QNcn0pkLaAiKJL3Ueiyx/WopxuuKzW17lrd7n/GrjO6y5B2zPdknKIanIZWfe5J3TKSkh8VWoTIeca8swsbmYmRzLEn+hX+ZxIsfIdvm6Uuc0v8puvIYWCYYZiSkg2a8SSo1MYsSRZbCXUDmrCbaKLl/W5izP42xNQ0JNY1+LUYx7lCYJMhONBBHXRgFvyzmvjtqkWhErk2OKk5DyEAyRQaoal9UXmDJAnZeRVkQQC3ll5GH1q5jt68TdDGuDKXp3jjmzmIkYvVmckbPC6szhStjgc1HlIh9yuN7iHeMRR1EFS9/nRXXMaVlFN2YcVX1Oqi65rsKT1Aq/eHLGtSMHPdWjJlyimEKsuYburtP20yjRGvlehOmFHE9q5KIJr86fMk9BvZRl29ylmJixPW+SP1PJnW4hnCRXuucUu1nuli/z59nLnExjnAsdT++S2w24VA9YmzoINNZGXXqTPCjwFzenFOOfonHK/Wqe3fh1Yi8mGI0JXxr/CQVnxCfVFdqVC8RcEoUF2pHGg4pJS02yNtCYlASdJcleRuN5SUHzJL/0dER1FLExbIFoo/gabIaIlODm+XM2enBjfM658Nk3Y6TSXaalAUeeRcMzaVkllNDkzcFd5prKvepl/GKZ98Z3+O0Xf4kROyJlzJifqfzn5/+a/9j73/jlcY/XZwe8rPwU15yA0efBmsbdm4K3Pk9yc+xxoxXjeM1Ei2vIULLv2Cx1FHpmGj8uuZp4iDJVWOm0+AetP2OnK0k6JleOdcZjg3lMEGgRh2oCz5ySiQZc9Q2Sc5sPLhl8einGhZEj0g0UTyGt6syWPNbNFk1p0JwaPM1qhNs+btFAiwT9sEZWG3M1dkQ8NuPjlRWUmMGSPyBleGiRwWujn/Jr3R8SDg3MUch2z0EnJBKwJ8s8sm6S6ZbJz0d/5b7ytxagSSk5HZ9RmQuCC5OjZYdbpyFP1zVWOmDHQtRwTlfr8PKpRDEiEr/xT/j2T7/NagsGFZWdfQ0HQTMLr2YTjI9jtFOwOQ4Z2ILqERzVBJcSYyrNLp4maJRHbDck7VwSUyocl0F1Q1QJzyY2qWTIvdI6l5tjZpWA9nzRSpbuDfio/ikvXwTcOobny4JEJ8sbF2cogOlGPKsJyo2IVl5w+6RHe0nwLJvn1sHi4B6bO5zkdmilkmgBPF9RWL6wSLsTmlmw3ThR3GdmgNAjDE9w6WQBLEcSdpcABL35Bgn9hGHG4eaxxI2DXXTxWwZ7OZu3nkmelk1Mo41OQGoOsXLEba9DXVO5frSQ8hUR3NsSXOvWeVoTvHIsee/0EWoEVX9xsK+eheQmPtfGHlvtGbOY5CLt0LcVmqZC9ULwpFpmbMFLrVNCV8VTI8rnkt1Vhf+QvyBVV4n8OXEHtLmgGixkyc/LEYfpkEtHgmFFoTAUbDYjpCJ5llqMWdIwUQPB0mwhOFI0F59XBy4zE5aHEQNr4XOn5w3UFiirHqeNGLF2xOMNk3hb4bMdQcxdtLw1lDQ/vFHgyqlkVITlKCTeUDktqoxSCx5iqeoTdFTubQpkGOJEgqQDnWKOa+095mmLdk6Sn7mcVMEIA6ILnXZKYdWZMzGmdDPw3omPEoKahU+iHOlJlmTXRdY8XkwWPKbkSPLj7Ss0pE9+JMFV+HRV5d39kLlu4F+eUDtQeLoqMIKIQXxRuXmW3CRVXAiAzFoWmazH0xW4dAqdikXxYo41HRAoUPBDHF3haVhmzUmwdO4QKbDVCNmrCa7uDomCFsNrSZyexswALxsxzoA6zTJLXbDeEBynKxROfJ6sgtYt4a/GCC8MHq0LXht5VAKFgrGYKyPvM60s5mu9G+LZdc4Vh6fJK1hOm9hc4/L4jOOyRGsGoMCnlwTSr3L5xKNV1nn3eZ1QwKM1hZdPRuzFBCk7Ij4W3K2VuHLQRzMB22H1zKeTgnsvv4XajXi8KrA9yekyIGD5zff4xuu/xVntgrlWJTuGw4rg9vGIQA3ZHPvcSRqo6RBHE1w1NZx4nCcrEKmC7fMjRtEWgQa60aVf0VEjiE0lrTT4xgJQO5GPKFepnQh8NWRqR5Tbkien7/+N5NKfx797jF2Y+RqeNNl4pJPuJbFDk9xIRZ9MmWGy1e4TG+oMEjp2JPnVzww6+2nGz5Pga6wpXXAVMDx8Q6Wdg4rtkSZiXQvQExpjU+UPV02K9pza2OC4FuDZI57VQsYznX4vzg/GN/nU2+Gj1CbduIVtBCSiKW1VUB+sYbkqChEPbkrKxWfYUZ1T0yTbF+SNKV9x72GqdXZjOhNDQRoBxZlgtTulLy32ZJHvr+jsmhpnqobpwT94eodXT56SO4S1Xh0hFX60vU4nr2OHkokZo55LUQx6hDOfvjql4My5Y11nKpO4oUFXi9MPVKZeigtzk6ThIgseRBIFlx9eWqJjxagrWbJOyNyWLI8HvNX7iHH1go9jKY66RQZugY4b47NYlWeTMpGQeH6OIy1L1tOpBZLO1CDtwnJLoRNYhFJBuAJ9ohNfvmB9JIknPQwZoUoX5hXUwCRwMpwZZWQU8uZsj53ZhOSsRDNVZmkoudwLsXWHS8MLfmP/B/zm3h7/xYM212ef8fb0+2x054jAxFJtSoHOkzWNpZhDfq3H4Y2Qu5d8Vq1jbk0dqoMqkWUTJDViKRe/OuFav4mpBBxXXR5WEryoqjxQDcaeyr6T5A+ibT7kCitjDTdK8pXmx/xS82N0MWNqKExNBTeo8HfPf8SV4SHr6ZCurTDRQQ8i2n4Sxx2xF+qkRJ+87zIwHIbCRIQuHZJ07AwJVSHUBHkZUFYfMByrvJu6x3vamFjdxI9sXtg2Ld1CGyYYo+EsS0RSEsUVTqIMD/04f7/uc6vrE7dCroxPuDI4YXM24dfuJak1l3Flla15nBv+nMCag6qQlFCa5aiEgq2+QtxUeKfnoYYWmgvl0TPais3TeQ11YLHlNrkSHbOs1FESdc6iIg3yEAoa0iWVesJHiTz7hsmJlmGuaUyNKZnwKcmpwthQeLhisJV3uaVE3FsqUTIaVMxzenaJM5nkh+4aJ34Bd67TU+Mw3qTiTMjJJjIS9KIV/q+VNzlOr6BIG9PVMM0xTW2TSOZQZpBtqgxSIWMigrFFZV+hNOxza7jHN7t1TL3EtZnCl86HKMKhl/SJ+SGEIb/UOeVGY0xMmeO5VbLjgOLMZ8WLKCoButYh7igU2iVqhotp+dSXVPqWoD6zqKcFrbTCaSbNg+UUj0pZDk2VvvBwdcFjtYqYm1w7l6ihge5JPF3wB0aa940kXlCh1rQ4VQqMUgFBrMVBMiKQFlnZom2rdNs28yCGlILrQ8l6e0RRmZNIjrHsc3aVAg/Lyxj6OVe7Dd47DrjiuuRMyXrk4vgq8VmaQFe4PjvitvhXXJqeE/d0Xj+JuDJt8s65QkxK9lclwtPZ7mp8MisQjVWc/Jh6TTIpxClPNAb6EglXsBYe8w33E7ap03LSdNIKxROV9Y7A1EJuGgHvTB3+RLzOP/O/RWnc50Z3l5JSp6Mo7KpJLCfHVifJynObl16EZLo6uVmAOjVQA0EvYfJRtcpRQfD5Sppe9HQAACAASURBVJIWMc5TFiNb+yv3lb+1AO14dEwY+bwy89mLDDTpkukKcrMvbvxTSXZiLYTZ4Op5iJEI0W78EqOP/zUKcH8tQD0xKLYlZ8uC9PEnFM9U7mzEUE2FZiWkMIDzmkXXLHO5HvKwmkONBHoouDBN8rOIkzJYw8UwWh2NSiagXZiQcCCX89isfJ2xBWLs8Af37/GV82eUeot2xMLzGBsXHVwdlvdV2qWQ7XO4qNqYImKSNmisznl1P2KcgmoXHiau4WcEEZJBLsaD7hJ6N+ThmiB/liDtjNhbEkQ9m6mhUzzzOS8I3tl1KYyhkVF44e8gJkPSgxDDF3gyIqh5ZGcSU5lQDEOe+7eZWSestCMkkjvlKq94Lj/2c+ycgxJX8TXJUMlRHs14uAlqc52YN0AiyXdnXGSh2FJ4umzSS2hcP4zor0VMsjqfXlW4cbDwrdrfiLCjiPXxiOfTGl4tIDUWHFTTFO1HEOpsny0O6hLB1YsLni+DMYPNhsB0BBc7Cynqlw8XXK2w6eOrYOFxYae52T6jmYd0x2dgw7X6nM5yiN9Z3FQHCjTTFW52XnArPaZ6rqNGglWpo4WC4ljDdOHRmqDrSUSqSbUPh7k8k5rKclNwULAwAggV6BY0ig2FH1+P8doLiZtY/P4H6dt87egMSw2Y+CpLnYhJBWxDUK4rvCglOekZVJsBx0XB7aOFBYGVDnh0kaO3kaRUV9CWPc6GMTwFcHTSmZA6KW4eCz7bEURA+VwllAof7MRQUhH9YoRqBThDBQXopXVKakQzA8v1cy47kr31haBMs1Dm06UKWifiqAyvPlus8ZNKiOMUeelQMi1FaBKqjslpwicqzcnlIN7QOMsLopLJSaTxoLjDtflDjpJr/Mr0YxIjhfMymGrAkjdCDQTHNUky77GzP2ejd87uEsQbKW6ZUxpZsAcO7YFC6fjPuFjJEzRVHuXXWWmP8Tc8zK7A9OHudcl8DqsdOCmksH0ojgM+W6lwy3qIeqEy6i/UNvvZNPkhbMxDfipirF8sqn7F2AThC56uQGoikYGkt1NCicXYKG/RXFEoHCXYXYG1liAWLipbmQjurqlcmYTsLQty44hYY8QHr0B6FqHKiJeexri3Kaj5HXrZBK4GxmjO4Sq89shjZMOwq6BO2mhzhdUWpPxFNfl797/9/1l+/Xn8v4s8IcNJBd8OGbs6nVSSO6k0R0WbeUIlMnxmmsrd9DU+vaIwNuBcTTG0YkSmSk2ZockQ2/QouhHJyMdUITESDC2FqQE/isfpRiaro4iMMeCkbJHumMwNyVg3+ayU43vLWc5iGvfDVV5ol5hMN9AISI/SbPdcVGXCyBZ0dZWxrnAhbEQrjq4MaQiVTwY53p8tY84T5OZgD1fJ+h0qPR/bs5Bn24SdFdShwlCqVIOI3ZpCLyvIRDZb+oSS1UG3OqjWGdNJiYqzz9dGP2JncsKN4SE7sw6JyMHNeYwTMx55Bf4wfIPPvS3qY5sn7hKyoPOgqBMqAjUCFYVqN0SLArTIRxeQ1hR0CZOUxzChsZ8q8+HKTS70IpMUPA8M+okXjOU6u8kiH06LpP0JxWiCmgXDjNAEpMSc02acKIrw8XkxjPPJaANlnkHxFF5tjIj7cRRFcilqkkhM6WRdnucBLcKQGr1Emu6axm4hz/tKhmQIacVjrJvEkNQGIdVwSGmoUh0p+GkNL2Xw3lMfN1QIMcnKgI5S4MCacSJX+H50kxM9w/1Ekb/MVDjQ0ySmKexJiKv5bFghN4chx5rFw6HJh7NN7qW3SY0k9ekqmZaBOYtoawLbU9jsDbAdFUVv4NsuOa1OM5AQCUJHpeC38dUJT1IGB6UisviXXBd/SKIe8dpYJ+XovDO4x/XwKVHR53rS4Upyxi9P67x0dMjRUKcnNNrDBDEJ6cjjWFicJS0+ypTYI8bEVRGNKfSaTMKQ+2GMD1YNupFAugqqOUOPeyhaQNUZsNRKEcxqFNSAsvKUvL6LFCb9ZJbISjGsWaimzlm6zPcyr/NYqTFSdI5Yoh4vUU/luG+tM/UEB1EGx8+CoTGSkjXvgtd7GkvTAie6yo+iAiMlie7bbByB52koXghSUGJAzNWJhzMG2T6O4aCLGWryHDHwuWNVOCsqpO0RMhGwLFqsqiOuRAobnsKZSFKPZ/D0gMNMksACbW6z7d9lR72HYrBQe7ZgpNuIUOdhTeArgtDViPlDZomQkVwmCAI2hiHVoU4jG8PSJLlEQGhrXOKIvKMTTVYwXJt+FGOqxSj3Q8peh1ziGUZ+jJtUeG8448okxJ6qXGpIzAjsMEBxSjArM59l8DUP6WcYWjYzVSceTXl8miF9EPFWY8KW5qFbMeaahRNW0X2FeTwkqcSph+v8ry+9zLdfLqMZEb0a7G1W+H7xNkeVFDMjRiI0uTSPsAYvofgZrvqHxE0HHYfpXGMsUiSCFVINg5YbIxFMic8F6YlgaVSg3Slx/URjPZpTVn0GVY2BanGR0mjlFPKDkIybwNpsUtDPKMkGBafP7ZbOV54b+IkLdHPMvG2wcVTnV7yfMR9lsYWG9KGvGTg9A2dkMZgJDK3Na+4eq2YdTYwQEZSiAcf+CnU3j+bCXGYplMY0V+KMYoLxqiDhavhajM/W00ilRcpxiIUGs+X/n1bQvnPwHRCCbzbGfL6qcfVU8vmazuUzn0iT/Gn1K4yLPvlxRGykoGQTDL0JpdMOoSLRfEk/FWJ5oK3n6Tx2UCM4SyYwxgHCVBYm0KkUe4rN+gXcWzO5dP5FZaY1JtvOslsRGKFgbqtcOZX8LFVg+wv+mW5nWP76P+a0LNA6CvbDY3ZOFgDyrCYQvs7qWcD5MihC8FJLQ5XgGWm0ZZehzCDjZ1w7kTwulbnIwOXJY7LTGUcVQTHUyPshwhc82BSYwzT5vs95RZIdOTy+lWX5XPBkQ0ULTK4fSx5sSiwtIDmHlw8ipID4QDBIKngpwWp9wnzDpe1t0AhDbh1IRD7gs5jCtu5yfpFBQZLohTxeVXjj8YIrJ0oBTT2HayocvGRSPYfzokYzHaOV9zhQU5gBkNEItBT9FY+3n0kGsRj31zuk0iG+CvedDSamQijgWXqDYyXi+bbN9eOIwBIcpvJk+i4fXlGYpRXe3g2RwGebkn4C1lowXzLYOhac5zSiXsAPt9bZqTsMawHOgcGn24LiSGJXXDquTq0b0q5G3NfXKIgFEdhyNRwdVodjAgEv7y+qdoc1hZ2mYKu34AntGhvsFxathp9vhFw5kzjlgF1fEHfhcAV+8WlIcqzQKEme+8usRi7ayMeZ6SgSJqkM8XBCag4nyTL3CjGWuhJXE6wca3TKOhlzRvIsICoPyEwlpZSL2tYZJQWfVq6yHbvPMBHw5cOID16CN05DFB/M0OO5niJTcti+kJzf9iifK/iqYJgKWL0QPFkV3OgeoU1WMdyFefR+PkEurZJtKZwVFVINg5Qfcb49wPVc1ltQKsw4q8LtFw4fb6nEEioFmlS70E1DIa4y6pjcKW+y2W1xt3SJrzY+JRQw1WDNH2B05kRAOyVQlkKOhhXSvYCPrgiqT3XedObc3RbQFnQb18n1e2TyMwpHGvvZPHoINQGRBu0U+PkU6dFCUn8kRkRf8D0/KW8QFjp8vecwPbV4vCq4ORowXvXR0gn2O3EUFvyz9YsXAHRSAlUKXt0V2G+/9W9zTzW2hOh3mdRCbh6G7JXAdFQ+yOWZVEJeagR8vibgzGX5DJLVgHYKOrE4v3BQ59G6wuv7PRpKgcdrgmtHEYOqxmoXnqxr2OcaMrGofL+1F+IYMR6vKTw9vPM3lE1/Hv+uYY51zMjBFzazJLgihuIWGVp5+qaKGiiovsJJuE48clgb9rAdlWjZIWMGWDNJXMzIiCGBJsi5EZVA5eOMwTSUuJ5CSpnRtBRcXeXxaIWB1Dgrmkg/xRt7Kopb4EWqwoAckSq5iGWRuo8IHRrxEm2zxkFinVY8Qz4I0SPJ3WCLu/kaUip0EwqPV1Se55Z4uTPh39/z0bwUiakOkUHCNVBmGmHP4utHEf3Aoi8sPFvnMBvnNK4xneuMvBgIELMyqU6K5WjAxXLIuDBn7Oj4Q4mphISqwNS7jMc7jPxNpkqWwAxRtTbJ3ogXCYuPTAsRCFLOhDhDzGBESfR5fS74DXbZztynHxcItUWoK0RThXeDB+h27/9m781+JUnPM7/fF/uS+3ZOnn2pU6f2ruqN7GaTPSRFiiNRHg9mdDHAyJYtwNe+kv4CX+ov8IUNg7YhjK2RBjMjaqHY6maz966qrv3U2dfc98zY4/PFacgYeEzYA+hCA71A3EUGMiMzny+eiPf9PUwsk9WhTlctEYTzvHYUo6UKY0WlGRuoI4VE6oiRiebAOOeQCTRWhzAXdmkqKVPFIBAqRhojjIQvbthcS074zrDNckchn024Gh9R5YKKM6FaPKGQZglii5Wwhe8LBmOBnqikqU6aZlmZRqRpgYZWZqUtufpMw/ah58P11pj58YD+NMu3mo9Ybs9YmpzRMsqMDJuRPSXKxjhiStcs87RY5Pa5TZyB0BiTiz1yzjGV4mMm5ZBMOkNRfYwkRQiBqbdRNJuZzBDogqNqwnKQ8I+/SJGpTWJEpGqKEav4cRGroXPlVDBsp+zoCk8th144QREpTpKQjnRaSYHzXBYv1bkfLPOz63Ue2RYXqoYclhl310glNLAZ2CZ6W5CNIkoD2BzEmFOFcVblRT7HnlNEhApj4dAVyyyoHbxU5bHp8ubsA141foExLaIo8I1pEyMZY096ZEOfU3MOY6ShRim7ssbfhLfwdZ80ErzsFphN8yxOBHXlkJwYossILYT58JipO+GzVZXH9RoiyHNlELOUXM7HJ0LBkRYNmed/m14ndCbsxzkOnRHj/BlP1lyE1mYx3kezhxTHY3579oiteMoLtYymQEqKHsW8OvyYX2s8QnUzvNWNGOQ1CqHBxIWn8xmiKIMSZ3C0EM+VnJUyCBImrsO5nOPEqPOkVudZtcjj5WW8jElkKkxCDXekMI1NtqLnzPIjzHRKo5fl49ktDLWOiBd4UM5w7qgcWyYvFYNuXbLUh3wk0GTK02IJZouIqIA3XKV6vsVAbBEaIY8ydf79cpb3rlQZWBrXBuAkCSLycLQG87MOidVlkJcYVXBDk2m8gBZl+CKt8VzTOI0qTIYuvoip5aaU5ZgZDpbew7XP6JgFumRQEh1FE/iRxYfeXdAi0jZ87qqERsCwArYyohdnUNOIi0BHXx5TcDqUkhGn5ZhWNqasBLxqHPHqNGBRDZnOp9hGwup0wHa/wfJ4zIL7FTW/Q2bq0/eyuLMsoV7CMVREv4g3i+m/sHn35IRq6lHVQwzVpx6NKfiS8iQlVFU8y+WkUuEk6/Anq5ucalVawzLjSY5cd55eXAcRkxoe9ex7OJmH5Dj9levK31uD9v7p+wBsnsAnWzZvHEj+/G6OW0cSUZD8mfENjowuN7/G0zvf/yf88bOfcu1EMqwqfOOZSqd4OWO2feUKgz2Xx6uCNw488BUKbeUStDGbsJ9ehge3aiNunqTErmCa9UjLZZp5efkntmNuHsG/Ebe51T5h5krGwuDx++8TrJcodhXeHnWoDFPGNkzzWfrrQ+p9OFtU2V2ArYOEkQPzDY/4TsQOa2ycjdESwQfFN3jvjuDm2SFOP+LRquD6xYhy6JMokrOiQm0mUCQoDrRzGooxQZVwvBJxtBaQncJ7axvMmTsMigpv7KdMchaWljLu6zzV6tzeTbh/QyOrdZEoXD2H8ryPa7RRVcnCkU9Qi5EzhU+2BTfaAxoFeK0ZcaVzSr0rmdQvkeGqr+MEKetRSGUS0suAGqQ80rK0c3DnIOUXc3fIBB327yUc1TTCFKyWxvMVsBKXsKXzcsXn1rFEIvGLl4+En67Dk3nBd55KpnmTHWn8bVj1iyX3EmySdTA6Kv0sly2MVYE9UPEMC4C1QsAFGqttSVqPuD++SWupz2nDRuumPF4DZZDwxRXY6iaMHRg7RQJ1hZXzgNCULPd8kvFlb3Ga9VlrwVx9RqerM7JhZPgsnsFyE5pLOtfPz9EKKXYxQhEakQpDioxHl+1tJ+Y1Ttd9rEhSmEiclkLn1pQwgeULcKcdzEJMAiy2UuwU/nz9FYbZQxI9YKUR83Bd4beeR6RCkCJwWhGFis+1F4K9LcmNE8nz0gKVYYf5J/BsDZwo4WS6wPqpZFgVmC2F7UEXNRXs1SSul3DjPOX9sk6xc9kW6cgEZdnn3Y8T7m8K1gNB6+s5OjOQ5NQB2YbKg3WDrfOUB7UNimdDHq0J5lNJJWfTajjs12HhAjLZENe6zKa7qIEdTrg6vDy2SAShvsl5eIUb8ikbp7AgTol1SaYh0WLBz+8oRGGBq3uCYU5hvRnTy6gc1iAzyfB0Hm6lEdZQ5eMtm3s7F3RKkNRSrFMdT4cv1raptDtcFC+x+kc1wdUzydUf/vbfas+rS2+zt9AjKrtcO5E82BRsn0meWhorUYzbg4frglnHYCuNUEXEn7+mUJtN2WqfMXKLvLIbcB6bHKxAeSix9EstaldtXE9wPmfgFXJ8aydl4ibs1EE9nzAMhn+HqvoP9Z9afiFgVg6QhkQvGFQnE77JAd89O2Xt3EWLVRwv5pXkExajDrEi2S0XcNQEbyGl5Rd4ManQQmUEmJrC+szn3n7K8hTURFAPIqxUIeOpvBYfsy1DVG0e00k5zC1zXF2mLW5QC5vYyZBEG7CzkOfzep2ObdG2MvSqAcVhipiqTEOd7x70uDY+QQsllSDhnkgYZhQauy6nFxZLvRlLowalIZixjz2LkIFPVxeMkwznIoellTh15/m0XuMnyyuMUpDN69iDRa72ezSSLIsdiYgs/upOnr0VnQtX4czRmNfG1ApnXDOfsjJr8axm85fzJR5YVxCJytSFmMtswjvjL7gbvE/TPkeJE6bCZk9W0KwTAj2iNBuyIDvY4z6zuIxMTQphjNAUOlqJ0LQZKQY9YXC9H5PvKpw3DJ43y8iBjZtPGJuClq1ghhGOD4O8wE5CCsoBmhiwLRrc15bw/Rz7/gaJLvhn8qf8KPmQmvS4KEseU+ZlvsKOm6UYNlC1BG8hYN30WepJVpqCbwQ9XOlTm0UYE4UMPm+PY+YGMcuyyz8L/wQjf0K7OuTG+JzKNMYJFNwkIJAKSVzipZbBT2KiYcCzkoFZGLClnrLjFrCyGgv2OaNSB89KmWoJzxcEH9/IoekDEpHiCkFop/iaxARiPaYSDBhXEjA6tIyQvnF54ytVMyTBKqXTkMqZwNEEzZLCeUXwQbHCs6UFHs3r9F2TYyfkgVmgH0k+CF362Rn5tE+aJpyqJdT5lP58yqCccluBN4eSI8fl3IyIY5sw1NHCGDcMyKDgKimGVGjloIvOyN9A01KCoc7m5ILt4Ql+a8xG2GZ+NODVZsRo6FEOTnDSGNM8YMUfUwldvnHiMQ2rRLGPIVJkbONEOnoSYCUSxZtjlF7haEXQ2YCubVAaa1jda3wZ3SYOLW62m2RHGh0nRFNDxjMbxd8k0RU0NyCxVPLjmHK/wINgi5+KVbLmBfPpM6Tmo6gKnUyeF4WrzHpvs+df52jNoJ93yI8t2kaNg7yJakK2fEE5d8GTgqCdZJDpkEZ1xtwgIhwXGXkVnnpFnsocoaKgxT5DFDKRxzA/wfFn9AKdqG9yJbjAck751/ki+5bLlQuTlVPIjaE8FkSqyUwUMWRCLp6QDUKm/jXmw5j8RGCZpyhmgJLpc6qamNMJhZ0M+0cLZHwPrfCYlxuS4bzGnH3O8nRIaTxjJdkn1sc0TYPb4hFzsz5fskEkJDkhqCcpa1GTsb/OkbJCMtOZ6Rr4UI173BD7zCoRhpoST+fQ/SK1SZ5GpsL6rI0rI6pBzE5scmYEXOmHjA3Jy3nIiJioq/GVOc9+xkXTDAQCJYixkwn9nMKa7LFduQAdKtaE6nSMxgRF0+jEdeKjDIWhwfJwyjCaZy6YUI6GKCooioDUR8gIaSYUg5hSOGJq9+jKIaGXoSgSPNdCTXX6Gei6CpHrs+B8hRH86rnyv7cGbbf/kkqo4Lds9pYCXjlKaFWnLHZBc1NyyoSePOFbBwlCTSn+N/89v3jwv7PegL1FQeVQpdhPOVmULJ9fYEwU/uqWy43+FE+DuY7kYh2EbbLWOCURcLjocfNE0irZnNWgW55xb19wUIckhkwgGSQWN08StLkQ7WzK0yf7rHz/n6AAuaCJFPBgQzBIc1RnDQA+XlzlcCll/Qier1vMXwxYsqfcX7N565lE6im71Ry/vJphwTcRqeDZiuDa04Bsd8bLRUE5TJhTmwDU+zEz06LcmDBw4ds9wfPtiFZBcGzfpTA6o6NLlpqCvq1i1gO0c42pK8jPJD/N3iXNPWWjGaNIaM3luevPeCYvAQcioyORPFhRuNrqs7MO9ac5Vls9TJGiDEJSAdfOPCI95vokYuMi4uRKSs9MCD0fvaeipYILp8Kdo5TzasJMU/jG2XMKAzhYtfiXys9wmwr6JMYOBLoPa/0eO0tQGaXUmoLsWBBc8wilyuu7EtWNea4mKEDGEAgp2B5c4OuwrsQALHVjunkwhzpN3QFAq2pcOzqhsjhjbl/DmgkmKyZmV+WwrkGs8GhVYDXnGGcjbh5LpuWUG+ExVkPhrKKwevb1j3MpxrnQeO+Wzr09SXtOokjYrV7hWyfPIVYZFCXlXszFvER1I9KWQc9VuE6DmTZkWIZ3XqQIoFjQ+LJdYrBRQbYVlKWAL6buJYTF0+lXS3yuFFhpwZdLKrEi2NhX6JlZVCVmpRHy+ZzGtW7MqaGy1oQTdxHXPuN+0SZZiEgFNKIeW+cwLOe4cTpE6cyIVHi2fYntv/tccpHOsXYSMs3ARWhyfW5CZSxwTEgzKuOGxdiGu0eSnUqEPqmT5vZZObe5qRxjjRUeX5WUHEGhOMVpqzxfU3hzR/LA0akNe+wuQGYMqaby4mQVtZowshWumXs8y29T6x7RtMtcazYY3fbxGwYp8OKawpw84/ahpLuYstUA14+5v2Hw4/1dulOdaKqQCjiZL1HvJ+hdnZPigHu7Et+A7p0Noo7K0xVBaZTSraQkukL+lVf/Vnu+dfM3ub8hqF5k0WNJL6ey2YkYmzNujUMeWRajSsLUVJhzdWYtk5NrMYEGDbfCzScZho6Cbg8YzV2GbJvdiOfLcO1oSgr04gg9SplvKIggYrWfYsSCD1786d+9uP5D/f+u0UCwcR6QxCFRT2XYc5igEKgppjUmSQWqJvkvuidsjxO2gjZb6iFPC2W+VDeYlRVW/VPsRodAKLR9m/tyAUWXtNDxfY1/1I4hNigNLfxOjc/0q5yrC2xFPuuTGfPqmJIyYVSZ4eTPqCY7KNYhndU+tjpB12LeSLuUfZ9cS2XzXGPbGuBaHp4FkyLcN002vS6RAYWpwliuECoWa50JYbyAL0yuyRZrfYEb+SyHEz42X2PXvs6rg2Ms6WOHEqGaOLGCFiZscEaclSS6wqlcgMQisiWaYTM3mGMxPWEx3cEQXebHDovdAhvKQxxfxfVUEhXQU87noBR0IJjQmumkg4CN4SEzXWJqHlfaA0qjAYtRA1cGuBOdq62QxekFxXDMgVrjf1B/zP8Yv8uk69A14OmcTdfIMSLmRCrcr97gyWaeKDV46Gyw2Ukp+XA9/gu+kfufWJgkKIHNSVjBVhKmTkohkqwnFp6mcq6rGE3Jar9LaTjhFzehWZAkhZiX9ZSTlZSzxQwfqEt8KTZJJASJipdajI2UjNJmIfKYyhhtYjLX0omKMa91dlgb9TjJL2KQEo+ukKCx5AesjAPakUoqY7KTDEG0wmedt/kwepOPV9Z57xUbIVJCQ+AZPbbNLhvBGKFErKYxagoHKzGj8imm+4i2YqDGIJKU/EBw9UTSAkZpQGVisDnyUHo2GSfls4rCIPRI1QDfK3HeWqOTNUAZsW9aOMkQLbtHLfa5dj5icdglDkArx1ilkLAU8Em5yAXQK8fY+j51u0XJ8uiKIgvxCa9ou2TFGE+AMtSYBSWexXVmXQVPKCwOzpgPp4TxDDMMMGLJf9f6kP82eh/dDmjYSzzfnJHP7fDFOszUkLFm04szDHD47mGPMBMyyCU4E5ecmJEzJWpHMDMVAs2gYZc4MueYZl1QDFqlMTkE1TilZ/oceHWWwj6dKEvHy9MtZJnYWRb9LDfjM94ST3mtcUo2SBFhyGb3CLfTRCYwjuaYqhlKXpWSt8TZSo6qPeIop7Ie7UNpjONPsKdfUvS+YrUzZayW+Jc7X/Lb3fcISwrWSGNGnrFSplVvozPlymDCVnhMOZ3SKqgYWpsrQ5U4gSBKSBSVpmmipyluEnJrOCJPi2X/hII/pRjMyNojDq0KYyOP4rukpGRbm7Tyy3x6RScZ2cwNQyJFQwFC16d0XiHrdPim8XN+aP8px2WH7FRQaunkRI/1WYOWn+XlbJlWoqElEe1JlUrQRyRTNhsRM0sy9XRUf8pvmJ+iViRKJSJM5gkUGyfv45oTvtjOYMmEZKZQ7EA4sik1HRLt8ibxc8VhV3fZ7S4TDAy8JIHcjFkV3l9Zoi3WcBs2Mp+Qf3VI3vZw8x6mnPEkm6WdOIwKEqRKNggZa5Kuq1EYC1Ivj6cJmrkQM5qiTBPONxU+2HJQNB9LjvCXAqrlPgu0qfptnmwm3F8TiPYaVyYWm1r0K9eVv5cG7WR8QpBGvDP1eGDpl0nmlsqNo8sP+3B+i+1FC2Gcc+1IorgKSc5BfbmHKqGvJfSdlHpX0ltymHy0y9CBI3eRSIfm3OVTgKjk8FH2Cm/tD/loo0hpDJYvmMYCEpVir8vNE8nOEuRHggTBon9EYQq1ok92L2FHLXP13f+KWIFMY4waCx5sCMKJzdqJR7sMrUqfopZcztDU66gSWv0qsnLA67sS3Uj5/uB94vEWhpESK5KRGaDZXwAAIABJREFUqzAnNJKBypcbCpsypTbscTgHV5+o3PQy1I8V9hcVVs4jPl1ROF1UqEYayVTyxs7luXS9KclSRImYucmAqJiyG12jbfS5cyBJjZQ/K1d5NQ74qFlHTyVOP2a/rnBr5xJlnqlGvDQW2VvUUTYC3FOV44pOz7F4uKGwLy7NhF8xmRSLXAl7vP4cRo5ByRvw2q5g19JYmPoEtctw6WFmjlZeMs1qvHJw+X30DBdnFPGL6woFRfLuo5RYVXhxc0Y7J7lzKDHrCaV9n6mpkPGHPCvXuHfa4XglRTRjmjm4fh4xXYwYDgRmJBi40HSWudN/zHUvRP+arHijK1Ck4MY5KJHg4brgzsGImrpPbQgPVgSNDZOlC8FJRWH1XDDNSH5WsLl5BJ/cNvnek0sy4tiG98zvcUfbI2wpnCYaS21o1W2umj3qZwrHJRtF9Fg4SdhdUnj3ecLEMLmtjukcZrDXZhTPVYz5kIPeZajaZ3PX+Y76FQ0T3n2u88sbgjutGGWoImSKV1G5cSz5q7zN/NqUfvcS62+pEa05j725KqtxxM4iFNJTVAknmRLzhRFaQ2d/XvBGEvHptuDujqA8cbh9CN58wvtZi3kt4rwMb51EFPM9Fo4UzucVcnMBjzybR6VttsKvOHCu8uOLD0kEPLwCV/0x6XAMwHgpZbMBDycF9GHKL68pZGTK3CCheVrg+8GEP765zerkCf6CiThWuV9fpjxIKJUlxCr3rwgsLYPsJGgpHLuX0mZHgq9WNlHqB3zvKKJ/bvN4VfCdWZfCikdizLHbN3EDyE/h7uRLlFDh+RIst+HWruC0piKU/1sqb1bv0KwIth/12V+Bu7sJ06+zETNofLClcnsc83wV1F7K9UcKt+KA924L5mddXt+d8nAdbk46DC2XRgFK54L2SsrWUcpxXSF3KknDKQDffCHJonBeFvzl03/zd6is/1D/qXUrlmRDEEaEl0SY8YDPNY2XGcFO0WJnrs5pVee0bDMpaKCq6DOFYlNS68zY7A4pyQ5pbGCGkA8D1oYTnicZZmOTi8Qh8m3sfpn7lSLjBYflGcwnPdQUXiwbbOtP+UH0EbVQMpSrlMIA05siW0WSIIMeCzbiczzFYqSaWMMiigrr/ZS1JigiZWMscSfw11ducL6sE1iCnpXHST2yxiHXJ08YT3w+VOcoSJjzfWSosBA3+E53wm8FF7gDk5v7IzKhw1VtD5lPUO2UWHiYqkZdTHnnuY7dspgb7qDvudRCgcOUrifQyhdMamMMPURTBI0ytKsCXdi0iypuGNKVGrP8gN05laFpIzUF2xlwq3VIbOjEsY6hzAgcSVCdEBtT8ulLVGWHA7nIL6nwTLrkJzrNReiXBRo68+mIh5lbvLgdc9vbw54pLHohtanBvW6EJQ1MLyQSGu1alZFaoJHJYcYzhGkQDfJ0syblpIGipPQzLj9/xaJtKkgF1nshWj+mH+TxSj5YIBWddjaL9ByWu5KDQDDydPSewzjj8Dijc1SfMMhN2Qx3+cEDA2dYw+rXqV8knFkF9osWU6mRFQrGVOXm4AVqoFLqOPzwMOHlquTpkoIkwTJTNCGRM4VgZHB7ElGeSdw4oWebrHQgMGL0WPLJVYsPbpgsjwI0bcJa3MCRPt2OSWtk8Uo34QcHI+6ETzDMARvqATcaGoVJSLWlUx4LliZjEk1SkCmTsaSXy3KkWbihZNhUuHfc4+ZMwYwE896QM0dhL5OQM/Y49QOU2ZgjqZMmBlc6sDnwMGTEzJTMzJS2q5PRJbNCxOFGll5NcIjBYaKRCT0qskU3I9niKamIcOOIlTBFNWNsp4lSi1DVFEsN2dCPKIhDBobNSl+hX0jZW15iIBz0ZMa7s2OuphoTM0snLHOkutwY+Hx/9h6b0xZn5UX+9Y0b9M2Ec2mRSTw2BnCjP0KIlKvdhEo8pDjqMzdtoCk+iaoR+fPkxgZXJzHZdIbQoDbTsfom9V4CImUu8Xh9P2VW1NmY6TwqXmPPWWC7O2I5nlEfxswnPq5fp6depxBqJKmkNm1gGMcM3ZjvXnS4eaqweqFyGpcZu4JYFUgUEl2y1T/B9TzUNCKN4Em0TIjKSM9COE+xWWKU1tl1KlRthWwyJRN7jDIWH18pU2tb+EIhRXLkKij5hG0Er57a3N01mBxmyXljMmEG4cPJeY2fJUUWMve553/JQtzmIu/wsp6jmTVxzARNxOxYGp+sKPxa4yErfhvbjInQ2XctXhY1Dhc0BlmNG1NBXiTECgxclVFoossEJduj5IfMdUEK6NVDKsqY2Fsg1gWjOCTyBc2BRWtgc+pnOa5kWAyOiEKLk0qWL+qbCCF5lsmRn00wjqa0JyYXSZHVpMG8fs61tEVtuMyge5Ol5xqV8w6O6vPD9FMWzJcYSYI6y7I6k7zTCij4v3pd+Xtp0P6PF/8KIeBHhzM+uaJx50jyJ29keONlinQk/+v8D+kXRiz2JM5YIb56l785fI8bhwmxBrfOBCcLl61y1cUNvFONn98RvLoncYYJeqRyVIUrE50HusPWOXyxqXHna6OQn0xJxRILJyFzQ8lH25cXZ5+vzXO3+XWAckVgBgob2jmP/+wvaVahePF1wPWSxkbssXEGQR7UVpbFU5WhA08zVQD+KH6T7cYZZgQDVXD79Byjt40z9Hi6ouBMdET28niPVhUWJsvUmzHNukC3UjanA6wIepYkaarklZSptciG+ZhZQfLO85RezqE8lExijXPTZuso4KtvuqAMEUJwb1+Smw8YOD3KekzuMCVcSYj7Gh9dE9y6mOLr8Govohh2qMxiHt0QbDRgpLuIVGdJmSJ8lW4Wih2Dl0YNL5twd1/y86VXebPznJuHKQ8Uk8G2QJspvFyElUZCd6Lw8JrCWy8TAkfFqxhIJJ9dFexVFN5+njLKOfzMstm6AD2GvcUi9/Zht1LAbEg+3KxQHqewkDDbt/jiCtghLNQ8zgOdKy2fs/WUT5Or9GtNgj0b0Rc82IBqa0w7A9fOLtH/z1Y1UGwWOi0A/u0NjV5mgpHAp5sJdw4l1qrH7shAkTC0PG7uSeZaCkcrAmOQoM+HWPmYcXqZsXaUWQZ/SGEKE0tyspBwb1fSU2H5REHLpxSjGUsXCbZ+QmUkyRUCnHONwIGfL79CMfspiTXh7onH41XBP39++X6NJGRiZKj34Sh0Ga/FLJwJIkXhzYvHjMICwbjK6hF8tK1Q7qUkmuS9BRDzM2otweMVwZtHkr9+RVCawDdPO9gh1JSIR6sKkzOL4XrEj5+G7I1Usj74GYf+dkDSNvib9VWunU54ML9J+azHozVBFKo0qlXOGzbNPOQHERk95trTS9P5ybZgwU1QJFQGTd4aB4yWy5yM67yhPqd4rJJ1e6hGgrqvYUSSv7wnWA19ru0IAhsy/ZSxLRhZUNz+Ec8WYr4Z+KgjlU+2Ve7t9DnPCgbrJvahQaDBV8vLbHcPAHi6LBhmBHf3odSNaI8af6s/hmpwO7PBi6UIf07hzZ2UF3WBGcJPc/Nk3Yj1TsqnmwJOJRt+TE8R/NVrAiNNaNtVMAxefxLwVJa5fxU2TyPUr4PfR0sOKy34xb0Mw4zN289SpGGyuyg4OX5Bkib8Q/1/ry+//JLf+Z3f4fXXX+edd97h93//9+l0Or/yNT/96U/Z3t7+f2w/+clP/qP7H0iLc9ukWzV4cl1hWPfxkhnVqYmnL/PVcp2zQo3xqIozdTkzqzyr1vjU3SCxYl7YdQ4mOZw4YLWroIUqWmXMQibGTxWcnkrSymKkJfKxyYU7I9Ji1JlLzXd5s+uxkxlzpujcGDQpJSPQYwpRzEb/HCfp4Umdnb5GMY6pt4usTAZ4RKihRq+U0tY1NGuEEs0oyBAn52BYM9Aktowo+FOEFpKoKv2izp3Q4xU/4Mb0M9bHx+wsuRQIGNZSlhWffy7+nOmiCrbBwjBlrTVjK/Ml1bkBmpfBVceYiYeRKsxijSDRKCkBVjIhO4DEzzNLXXY2JM+2wIuz5Mcm04rETwy+UCxsY0hgx+S8GGk6NGp59q0sYWLRsSq82MjyirrDu8NPqY4lfpqw7X8KscZJkmEvW2aQ1NktlLGnKbW4S019iWlFpLpH30lp6yrHwetsTR2S1CAbhshE40N7g4Opy0dygZFvYsUaqDqm0aHvmnRyCoEsEgYlBorGqWcQpjErvTHfHnzEXeMBejViZpt8mGxxaJSIY5teVWfiSoRIyE4ilvqSvJeSCT20UDAsKWhGG4wh/Q24v1HDjiRqAZpqCXWaJwlsjisORppyZTxGNSWNksXIcdgZLPG4sICfU8l2bFZmCZO5FMVIKU8iCHV+7VHKWg8wJO2MSd43mAtNZhlJUJC0haAQJxSnGr6tM4orvDZIGLk93jw75o1xypu9kPrMx4zANyFVJfWMz8SAwBc8lC4PHJcHBRsZJyz0BKZX4KOlModzkt0FnZHikEx1zg2DQU5BMVK+bX3Bb/Ie0zmVQEkxjYiynFKWEY21EV9sGoSRSjNyGfrrJKNtXr9IyM80bgx8avGM2GrQCizet+Zol0xeHwoqEx0zOWYp3uWlltBJBL4teFCuo+tdLPsrXLWDFpnY6ZCWqhLIGCcJqKQ6n9WrtHImeeucCI0V0cIwQ5SMhy9U6qmHJlIUkaIpKYU0oBw1EYrK9kwyszVGZo/b7XOOVRPrXONpLsORfknDPHcNPt82CA2DHiqW7+GLCJWUqJIg1JgX5Bl4K+iBS9ZXMROQUkCqMEkNzJmKaeepRRleZmtIReW0qNI1NGJfA0VhO/ySW+MHXORNFoJDrg2fshTtE1geOW2ApyjU1H1eYvK05pBUHBqFJTb9KfulMkfzFiHQcwVlLaI6hmoQ0yvBzDKY5RT+y+in3EpO2a1VaGsuRa3FXblDzm/iqmMiqgRqjiEKiR9xisKObmGkkiQRDGKNPSPAVQaUREpaiXhY13ms5ZhNNBQVzARcKXEkbIwbzGoJ/YxGdpqy1pIYScyKt4OXQjmAMNboBxaj2OKjlTIdW6AWIEp0ns8t8nR1ATXIk1UkQV7nMCrRTkzUwQLzsxGb4TOEGzHQC0yDOjMLElTGU5OJD/lYsNZ0WPQlHQpMLBMpf/Xa9ffSoP1s79+hpIKVA4VPt1W+fRixs+Zx61iiuinHaoHdyX2+8fKyv7P+X/8ef/zgf+HuvuRs3qB+BpmZpF2UbJ4MkcCfv+LyG89OSRHUWymNjYSleEzodVEknNenfGM/YZrXeLkKL1dK3NuVNBclqa7Qz8C/u5PllQOJyMd8Ml8iVOGV/kMe/c37BAVB0YeDeXD9HNvnbRRgsRHza8ca80eChxsG756ecVSFf1ub5xsvUlIj5WQVtk5TlpIR2ugSbT+wK1j9gFYexqKC37nMHcskMZ1qEbc5YWxB39VwBwq/daxxPnmLhcELDnIKV04lu5UqRi5memZwLudxffigvImoPGC+l1KYQncux5oy4CLSuH4UoZiXxvaTK3D3eEp7NWZ0UmapNWZNBvQ6OgAb7TGanHJv5rPUjNm9AtIfMx5HKAMVVcLn5WvkrTF6KtneF1zUQioDuNjQ+QHPSS40zp2YtXOBCBLmJkP2lwSlScrSocAKBdkrE05Ug+8+AkVLeb/ikpuBkslg+IJ5r0ciYK6g4YxVjFglUSCr24zGLnYIykKI8xx6V2IWzwXWTNDd0ElbGk/XFWwhOavAUKxwlquwdZwSWJILcwO9ewm5UFIdPQG2fKpHGg83BN98JhnXEjIevKwv8BuHnyFmKmY1RPcEQxfO1BLe19eK3ZLHpNgjn6S8/RQUCemKYOcoy+xalmbTwCzEPDMNtk8kUWKyX1vkZ65NtQ+PawqpIri+JxhYLsNshO9fXsxvN0M+LpncOpQcFAtkZhGrxxusDVq89Gy+uiq5dSSZVAyMhX0mwRgFOFzTKDYSGospe2WXq0cTfFMycSXaXMjwyGZxZYK7pNI9dpmZYM4C/nLbZPPYobnQ5/YBrGWaaGOFL6/Ad54CRkjtVKWVFyztWHhbKlsnU07moTSTXNNMTioQ6dA43OAfqSccpjd45+whMTpXm+dEt316nQKtAowWBAYjXtuVTNYDbh9BpF3Oh719cIgYqCSRTiqgNW+x6KeMTl1O6z1eeykJdOhdq9DtapwXQU/gyRpkfUl9AB//xf/8H2jQO1v/mL+4rlEdaWyfSk6qgutnKc/tkHujkFGs8NW6IPZV3JqGdWRwx/R4sCG41j/CPd2i0paEpkd7RUNNodJWOanAQudyDm+m+/iaYOsc4jSgMkgxvJSHrft/l/L6n1Xt7e3xu7/7u7iuyx/+4R/yB3/wB3z55Zf83u/9HlH0/95e8uLFC1ZXV/mjP/qj/2D70Y9+9B/dfxApNFwFL9E5LelY5FlpGaxOTe6OWsypEzJuyKyospyGrMQ+s1yPatAlSBMOzAopJhkCgmxEvR9zc+QROyGTjInlScxEUPZSyhcdSBJeCY55a/oBxaTDRnrC3aHPC7vEz2rzPC/r3GqE3G766KrJb/b/PT8e/CvsJEGkKiNjgcCEZKbiehIjlXgS9sMMNaXN3ck+ijfku9FDrocPOc06VGOBqiR4TshBXeXcttEjQcutc5pd4NNonYdKgWV3BnaEpksyuRG67mEnUPASrnoRw9DCii5b8e18wGLmnOHQ5CjIMk5USi2Tmj+jMk3RZUJqCgaWwvNanpelEj3F4dCuExoq47yOJ7JcaVs4kwTNT1lKJizTppp0mPgRX5BnlpRQjIC82uf6qEveV7EnC1z3xui6yqo/RsYRkecThxEOIY6SMFiOuFjQ2Uh3+dDYphGZKCJAFSEb0RGZjuQsWOez6S12Ixs9DMAOEdkpZGJeGXRYT8YsjzSqvs4sKzipC7aTNso05WXGYKQK3jOW+Ym5TGMtYbWZUvI0UhMKYUplpiLsAEcJKA0tekrKt/xf8E/5lJxj89boDFOGJOOUvH5ArrSPURhwb9DmxmiPaaLiqYJQNVGSDKM0wQwn+J6CkoYspQnziSAuCgojk8OKyUlFQY0gN9Io+zOGAk4dgekKpKEzsQTPE5cvCxaKTKkmLQQwygs+vmEztjUmiYuZCpYGMWmiMNI1jmSendjklWbCrdOYcqAx01V6gzxLQ4NM4BONI6aRwvUkIqtJPtfL3NctFEXwdMFmKTljOT1jIBRuJAobXkqQSakabVa0Cd8OB8S5lKNNg4Kzx2J0TFb1uDLxyfoppmrwWC+wP5qjMJJkBxGLngAsikkXXYZkhzaB1JEywfX7KEOJUCM+Kxtok1MiwyNSPDxFw7AiEnPC9vmEwShH0F5Ca0oUc8C30y94qupcOIKppjArmIQoRIlgIAzW/Qtu9F8QhoJIdon1CVM7ZXc14eWihrCHfLEscTyHDAYpCjLw6OkmvaLJzIaTskEzr/NkScPPesxN26z6LboFSTtrcFbUMHubvDUAsjFm36frC7biA9JAI8HC0+Asq9EzMjRzJioBbpAws2LaxhzzsyGb3g6Rm/Bu/AlF7xwz8YnnizyozPOD/gOWPMlVGVI1Wmz2Er7Vi7HGBrlIw3ZcyrOI2Mqiajr5VMPwIiKpEiQKb/QD8npKpCjkpwobYYPGimBcVXiSzTLQdJxAMDZV2lmX540qs4sy32mlxJEKgcLKaYoVjpnkI4xYYashMVKFdjaLhorbdnAjSThSkZ6C0xPovoVf1FFJGQqToWUg8zFmHLLWH3CaT1G1iF25QiMoE2oqn8sMvyxkOKloHMZ5PDXPdesYV/HBEyRKghsHLI5iUt/kSCvzRCvgdlL0meDOcMqy3+Mo0PD5zwyzHyYhR7M+172Ur0KHkRvRMlRe3UtRE0GjUub2VgWpPeXbLxOELrG+/U2OTx4xN4C2mTC2JZunKY1lnfj+KZ9sK4T+BtIMOVyQKIBbU/mseI3v7+7QdXRaxYC1M5jpglSD4uyEjA8vFrVLyuIKHK20uXEicecC7udSnqxC+XTGTlqk2JSIUPDxVYVS0+LmS49uWSJmGt/y9jFiweeLi/zGg3M+XJ9D5Pd4bVdi5yKmpomWwr9oX+YgnZVUfjC9wGvq/PK6YLFbY250QaTC6kuF3ppL9Ujl8bpKaXxp0a8dBxhxhqkf88oeCARCXuZpVYIUR4xoFmwexst4xoB7+5fn+0/nr/CtccAvL2poaorRiHm2BFfOTPIzqFUT7tvzfLXiwI0Zc3sqRyWLTs7i0ZrOrnDQEpgUs4xrGd7tHrD9QuGsYpI3mnw2F+CrOm8/k2gnXwcFGwscrWVQUrh1cGlUYttEG6W8d0NhKU744f2UkWPx7M6YsQ2v7aWYCzG5gx6hKqjRZqCbvHXY4mBVUh7O8HW4fprQXUg4H5oMDYdAA684x68ffsztfoSnqUxN2B5aqLFgfiKIfJUvNgXRbAGWznhlX+K7Ka83q9SOBe0yvL4XM3XgT6oub+xI3n/d4nsPU7qlSyLlz93v8SP5SyYNgwGCpTM4XDa4Z5xhnOmcVVQ+Xl8l2x6xfzXlu1+ldPIZ3MWQ6YsMpY0h4tRErgU86mfQU8mn1Ru8q3/FgR3zo4c6H9xSuDmISZsGU9VisBGy2p6S6vDjoccXWGw04cVKTKxK7s6a9Oc63F+v4aSXaPqeU+Y3JwbRmUHfFQyyBf50zuaHE4+fvFPj3q7kYlXwf97QeKMfcl83uUWAXhqwsSvYW4TKWo+/sCzG0XXWjI+xB4t8r/kFiYDQgNePLdodDyOBcUZy4yShU4wRA/jrmwpv9wNEp8ST6ynVccovx7dYnj3lpLyEcTrl85VF5psRZ5ll8sMZP31V4ebEYHZioKXwuGRjxlAYw7OFHC/7f8aPX0TMLjQerwp+fdamNOdzkm4juyNyHmQ8ya/nv0S2DJ6tXEI/rp1B75ZBrELvr/4v9t4kxrLsvPP7nTvf++YXL+LF8GLKiMjIubIG1swqskiRImWy3RK6zW5abYIGDBlow1stDK+80kJaCAINARK61VLbEtoS6SJFUgTJIlnzlHNmRGZEZMwvXrx5uvO9x4toS5BNyl61Tbs/4GwOzjl39537ne8/fP/v5aHn5l5iowbPfOSxOw3XHitoMYzMgGeHAW/MGqROSn1SoHgar7wHa92Y158VlIIx42SG7WmFin5CL2NxUoTMgcKNdSg/DulNwOpuRM/yAFjckSx7knZe8J07/9N/sHz7yx5/+qd/yuTkJL//+7/Pq6++ype+9CV+93d/l42NDd5+++1fuG9zc5PLly9z/fr1vzcqlcrPXX+UU7DSlEoQMNaLFKpjstKlp7joscus/5hNa5YkVagofcb2GFfqOHHEhfGQr0RvcsmtoysJxSEMDI1AFzhqRMkPsaOYxB2SVfbBtWhFs9TDKZrJFDdLBltulvowy1AaZ4T1JOHusiAtZDmpqIhKj2S2QZg1yY2glu7Ss3WCiYiBDroCGVeh1IbaacSxW2UgDS6HDyllUjyjSJWYwIppFFJynkvoxgw8ge9WEKHOF08eYrcE+wWLmzMpiUwJ/RwDv8iWrTIWBnfVDCJSOZYzaCOVdKgT90w6xRBNRmRS78x7RarkegpmFCJGOktNlam2TXbsUAwCPlYW8bMaI8XA7KxxozyDW4rIFE8RBZ/5sMN6s8vsAJbaPpWoRxyZPLZr3C1Oojs9MpljWii4gOUb9DIOg5IOScyOabKzIhlWYmqRwWI/ZXHwiKHi0zMy2LJPpXwbWfaoyTpLaZ1TIyDNR8xJn1apSnMxZTgboYqY80ONS3sZpgcwpSQc6RmOqgmRDpQ9puUGq8MjionOVC/HeTdkNueiWyF9XaGjTvDyUczCMKDUzJJaGqsDj9wwJhMnXD3WeL4fsqAe4zpt7mQrrPVOmZIt/EAwFCrEBiPy+PEEmTihkkgcxSAONdzUpptYhIbObOizXSoSqiZqbJOJUyJhMI4L9DWTQKqsjAKcIKGST5mKR/S0iFZBoa8UCHCww5RGKY9fDNFjlQwphi5RghxTbZ3jrEI2gdVexJN1A7sNQaDjhDqmI8iZEfujBd7NXGQzXSL1NJb7kuc6MZOpT1n3QIsh1pAFUDwD3z7AlZJu6lDNhqgapCKkKMYU+hpvW2WCnESxUmZDizWvzmzU5B1ngnsUqUUpQ0xGQkEGGXIDg+ogYSW4xzIbKFpMnyyhGZAZOxieTRqrRCODbmRiDi2MWAMJfV+yFWUxooRZN2ViJLlRPXtcIJtwmBfslG0apk3iSBJHkhpZ9MijbxYp+jo5AkqhzlJHY9YbcBzlSeOYUWoyPeox23JZGZ6wNQO7RUlkBWxOFjFlEyVzSJCPaE0L+pbBhf4xzw+GBBHctQskqaSghow1h3dWy3QqBqbU6GUtTgsO/UwJTcJEzyaURa6ONpg0NzFHKjdVm3zD4Pm2y2JwRC4SjGRA5GWZj2ymxhOcGmXSnkM3UNHShESVjIvgZRyCnEHXKJG6JiVvSM3v0B4pDIdZKm0HLyoRp5I0jnEHYPUk109jJkYmMm+R1fvUxn20ZAYx1nBUlemxgogELQfKvkYpNnliT2EhCZgeDZFFjchU2dZKbNsZfjRtINKEsucSkqKbkM357KdVOs1p8r7ATTIcmgpDQyAUSKWKLnq4psqpaSClxByr5OUAZTBGESp6IpgY+3Q0kzEKUaLR0StcTEeEGYkVZkEqXGk3mepFdD31H7y/fukKtO9s/AUoEV856vOjVZ3lU/jO0yafv3P20vatcy8xP5Pi+C6zR4LxzDnerr/NhcdnnYTV04TtxTMeTtmYQobw7WcVXrinUuzHIFVOylB1s/zImePFRwHfu1zlyp5ETQW5bkiqTrK+1yHQJQwkr32U8s1nTNaa/TOulS34KCPYPGeRHQqWjFMiNQ/Ah+uCNDRZqcPpos7RlCRpxZwUoV7UsGPJe+UXeProEWYE8WLE/FZCNyfU0ewqAAAgAElEQVSZbJ8pJtp+xGd3Q4QUfLyi8OzBmEo94mgG2iXJBLs4AZzmE4xxnkYBNgfTXNffpzENn7uV0srnubp3QqQJTgoG547GvPv8PEHuECEEzz9M0Usxp9kma0pAdkthsKKSDjT+5kmFK0c+kS4xBzoL3hFzuLxTNVitQ8coUh6EXEwGJCONThbK2zkeFhYYlxLONeAH88/yQnyXD66BtAWXdyXrDwWHiylBW+MoidhcF3zyfopvaFACieTd84KuorJ+BO1SgW/aGa4+lpgBbC5UeHEj4e50hfyRyw8vTFMZpqTLIY0Dg3fXYa4D2dmA9jBkvTnk3jnYjM6zU/OZvGtgnSq8fVmwethmYMGFto9Iz3iD6wcqBe+ATAB6z6SsHbByAo+nU57aSQmWfA66JjkPPBEy25UU64KtBcG5+hhnKsTOxxzKM87TvckamVyd5SOoV6GqZrm4BW1bZb4FB8UCEyc9CmqMMNqcOwRnxic6NMGSvDF/nXz+HaQ24sqRy9as4F/c8xESFGIuTY1QgNZElhXTQzk+625a6YBbSwrnTg7ZzRbQuiUW9s/gsnYrpBxIFvcEzZxganuZn66rfGpPYTrukAkgNGNen7e4uiF457xK3Df5uJ7HiuDO+ZTdBZ3pI4U3axdYOT7lYG4a52jE3UXBC8MQdUnB2DZpFgTjp84MryuPdaSQvHlZ8KLms3Ngk18ISIH8tMtOs8CT5iNEwyCXGUIF1G1BpEnevQxX0j7P3IOoFGOdSsYmpAL8tMpWMWDe0mGo8e4FwZXHCXXPYP9SgblNlbEt2b1eYXLQPvO6W4C+Izi/L6l99p8zmImp3T6h6Tb/Ng9dnLiIozuczEra0wpf+ChiZ0ogJHyQK0FW8mQ34u1LYG8HZLIpO9LAmQo5rMCLJ3fYNF/h+Y2EB5T48CLMH0WIyTOBG2PG4sIRfOu5DJ2cyat3UnTN5qQs+NnjnyD/r7AR/zEAWF1d5etf/zq6rv/t3Llz5wA4PPzFEsebm5usr6//3/5ObZhghypbRhGpdNAyQ7K6oOBLsvGI690OT3KbirlPzxTEcZHZnoU+1JjNuExpPvlIEMcKSLCihPI4JfBV5DBED2LKeCzSRSegq+Y5LE7wztw5upbFMdOcRjWmewFZ12d2OCA2IJvtYhkdmpZOVhO8OzPJncl5NrNTdHImHxZsDqZVXhq7qCmkBQ1F9xGJIOenzMdjakmTTKmD3RfoCVT6GrPtiFrs056Bi60jLpw2sJSEC7rHcZjhIF7hL+Wz/LjxLG81n6H8WGehrXBuKNFjQG3jxCHECkqkMR5U6BgmQleY0MaIjkpihqClHM5LOrMJQtFxXINnWy69rE7bnWIU2WRUF0ftczgVUQli9L5grX+DBW+bOE3YmC5z6uj4WR9VG3G7UuYnCyV28jqabpBVDE6LUwjPoBsU2M87ZIhwCinlECxzQCOTpdj3iV2N4wnJoBRwVLTYtlWm0hFhWWGBlEdyBrNtUvK6nO9LJl2TcSApuQlqohKmCoOiZFAN8dOYMJMgQoPp0YjtiTJ3rcv4psVAlmgmFYb5MrurOqLUQgkTMiOJJnwK3RC/ZVCQIyrhkOqpTj6WDDQN6OPpKm+XF1GDDJGmc65vMpHGGKFDqnepzzhECzHq2CRyBdkgZqwKcumA5cCFxEKNqsSGwsQQfFOg2R6WHFNgRGKBawg+UjJ8oK5yceDQ6wt2/DLOEHxfsDEboQx0bE9lRUSsmClHwiYsjHmll3K+n5C6gmzXY7TgU4hdytGQIiOCkuSg5DLQspAmXGlBOVKJciF3phy6uk55JGhaJo3+JEXfZagpnKITDspkhyrTXkQ5bnHB2uPjmsU7uRrtvEle7RE6AdlMQFUP2I3nGbaX+Thap6NMEScWg+yYWItIFUk7I6Hc4Wrcx9AjZMVHVbtUOzaVyCXwVbqpze3lPF3DYl7U8TWDumFjWBG9ssJwIuHZRpM463JUsHh3XScnetTcHpoPiYip6Qe09DK9MI/QBCEa/YxEi1KKfZgbtrHdM/snEThUAo8orrDgeahqyJtrOY5KKVJJqfptHNVjZpjQcXLYsU6rlyHEwJvucLgS8H6+SCJdnCjFc1QaEzr3Z3Q+UZc85W1xmssQCYOpcZdcqpAbKQwmIk6KNncrs+zi4IdlDr1Vpj2Ni70xO6LMpr7CKJmgHebZGS0StDqchAPmVMFYL9KzctwLJ7E9l+t7Ks8/hNqpiqcWaJQt4ozC/YlJzKBPdWBSUlSWQ8HUSZZOUiDxCuSCmE/Fb2OpEbk4RDUEg4kYWzcwpMK5YYDqJDgixC0GlIMRp6UUhMORXaSSRjxaCNkoLhI3aigaZFIVcxBjuimL3SHrbp18GNHPCUaOQ2QmLMR1PvkoYdw9z0jOc7eUp+A1EEFIECaYqY+b2LQch142JTETFsM66iDmrcUqj4t5GqpkiM68F3DmWPuL45euQPuzm/8GJFzdhDevKDzRjPD1mLU9UKyUu/ki7+28zsv3JAJB4T//TV6/8294Zidl5GgUh1AehJyUYG6ry9GMZKsywae3D+k7sHiSMlqKyHshp3EbJ4DHSx4vbaREhmBzWXC7tspTD1PqSxJbtZjuwdGEwwsbCkJNURsqZgAPz9mEKlxvfkwmCWiUJbGW5VLvgESR7JSy6Ms+2brGzy5b+JmIUBHUJ1M+eS8hziZUVj2qvZTOuorSEHy8qpDJCuzjiEYBpEhhsk+tCd0SxE8LunUNz4BL+/D6EwvsVg2+X/hVZvv36KoKCw14s3aejJ7gHhsceLOQaHxYqhKXNykPUs7V4eHcHLXMIZtDm4XTFD1MCDXYqwhe2JDocxHfSp6m0gm5mBnibjmEqqAcDwj1GKMUMX8k2V/WyFLHG6TEfY1EgQ8KT3HtaJtLeRellHB/EcojkPMGi5MHRAcqD0qCq4/B1TVyPZeH8wrTg5Sn7kGiQu1Sgy1T54u3zuCNPypnKLgQ5krYI8FENCDQYLJkkznSGNlnRUjoTKI0bXJ+Sn81Jnc3Iqok5JQENRH0akXius6DNQXLTPEN2C7N8fSwyfReSqLAO7PXKLbP/LaaWQUrgsPrEc/cF9y+DM/fSwmWQsp9wY35Ghf0t6CjYk2FBOGZx9pta4Wo76OlEEQCSz/mmf2EhW0INUH/YoH6/Ty9pw1uN3IYWsrdisozGzCUNntTVX6YF1x7rPP+ooqQkrVNQd/KYAUhc7rENaGVh9s1lWuPwTcUPvEQ3lsHc5wye7xORjviyW2J7yjMhC06gxFWBEU35cKjmLyicjy0uf5ogG9Japsqeqqy4Wh0FyLGWZXcfZO9isJcJeKbJZMXNnSmzn3EJzbgYn4fbajywTqMqzbbtQ4X9+AnV2AqzfP9p8FspGwuaYSqoJD1ud7dYz3J8d6azfXSI/ZGl/nc3gdEAhaPu3z78nPMHrX43lMKC4nJI1dw/hiC5ZAntyW9nOTukmDBavHEI4nunfnsBXMx82rM4dEE7uRjntiBRMDFlSYfNCZIBDQLsFkTdJ9c5sUv/EtycwmzHXjrjb/jH2mKxou1l/nOJYPCMOXqrsS3TFZOUn6Q13ml7yEjwRsXFJJARZsxeOFnGl8+9Pj2JxRW+ie05SpTj0vE5pj2ogESFvYcdqbBGJ/B72YGLo8rCUunkB8F5EcpWt/nYffhf/jk+0sYX/3qV/nqV7/69+Z+9KMfAX9XqP0fYzwec3R0xP379/n85z/P5cuX+dKXvsRPfvKTX/ido4KgJ02UVEONfWb6CevthHIvxfAkSjWm5SYkIqYsEwynS98q0FMEGgk5VWKnZ0p7kQYBGveHRT7K2GT1GNNIsYwU1U5oGw4iVgkjFVvuU6s79OLzPM5MI6XDM1sCzc9ROYwhCJirw8VhiWNjgmuDNoliIuIiy8MRK8MUAw3z0Oa/eMuia+uc5kymRQulPKKjCYzhiNpJQEUNyBPjaiYjkcPLK0xEcKG/x4r3GKcckxEhz+0lzLZttL5FKEziJMZTNRQREUaTHHXmsAOTjVUVD4P9YplRWqaXTOEObTLHCr4VkogEw1OIPZsxKoXsHWraTbqaQS+d5LBg4Gcg0kvYepeenXAsTMyeQ6kliS2XTjDL5bpNIUqIEwvf9sk7+wTTbQremOI4YNJq8HapwtgJ8M2QybgNiUohtliJFBI7RUnqHIUOuWFMaupURcRL4z5zvYS4q1Acd/GGJk46YD87yzCbZ69YJLBhuWHwsWqxa+d5mDdoIimrCZFUSQcC2w+JNIEUEkUqNKMcQ1nAUA12qtO04mnKXsx20WDk58k3JyiaEsvxSDoKfhozPe4j6hpDf4YIjUpwTM4fEQcRSQiXj8f8+n6DkryPEyf0YsG7pQLbToFeEfK49AohrVxKXdUodiZY7fuUtSMGVoouAzTHoxQlWEjkocJ26PAoZ7NrlkmkxqVuwHTPpZAMENmQWtRFExIlleTdlCj1efK0ThLa1Ls5xDjDZqnGnfx5TE9nv6qiaj5j1UGmKZ9tt1ken1KfcXk7O4ud2AhN5S2nwL5SxjzOs98ysMYjVNfFj+HC0GO616EQxFwbuLTUebZTh0bRpOhO4QRZJgKNG1qObpwh8gT/6HCXSc9EkQVmBgPQA7CGYAZooWCtE3GtHXEY16glKdf7EQhQyicohkbR0ymlknE0T8cIeBjOsFfJYScR/TCL1BISNWVdJGzLi7SMDI9mDW7Nwpy+xbKxha0GyOEq5waSjl1g9sAkCmyCOGR/OuTWYhkrtDDTBBCUwlOCxOThxCSTnYS10whV+mRHBUg0jrM5glChGatkPcHt1Qn+5lKRn9Yczo0G5EZjEqPNQbXCpO+THVsEsc1qu8tzw31mnYSKP8Cmy9ygy05a5FExiydNAmy2CzMMRyqNfoay2kZJVfYLs3SVPA/iKd4Ta9zJX+ZYTNKRNr6bhQD60uLb8QUO3BLuuEi1PWJWHVPSIzbUJVTFZip/xAvtPSb7Cg2vyLYyyaZuMRB5uvksrmHjaSqJaeEbPhRCrJkh44wko465uerQVWOGIiEODDacOcauxfqBwvlWnc+6dZ7tRKyclhjYU4SWjarAOLbQfcFM0qSdjRlGBlYrQ5M8Wjik6nb5+sNtVgcRKxxwZzHGdVxOqj59Q2c/q7FVMRhnLWbHCZNdhcJQRfUS7BG8dtCkHDbRdYUg46HHMfx/iYMWRgGbfo8LnuB9mcG1Uu5WNb58I0FIQW82y2dfeZZEucHnbidIFaq/8au817jFlceSU8egU1CZP5Z0Jgzoefz5cyqF+jnQ+txfFKgSFidT3imt82ubd3F1jUZ1wFPbkkBXSZ2YmfY2VgRuIaXa9NmelUh9wIubMU41JGgY/GcfS07yA26dE5R3A5x+wJsXFC5txnxiI6RXS7m33GF6eOaj9uHMOea6Pf76wioL6rtc3gecBEU9k+yvSIGaCPZmTH6lqxKcGPzkqsLKKEH/9ySmTA8uZXvMPFZ5PKWzVgctd8BUJ+JkJk8zSXntliRVILQitJkAq6BSSPu8vv4kJ8oIhZRPnPn08q/mrvPJQcLuVp5wIkXfF3zvSYULx5x16Ko5MuoBH13Ocfei5On7cGNmGlUN2Z0xuKU6ZzA2dZ7BnMHn+ndZ31R4MJ9nNd3kuzWTl49V7uRDQl0QGJKdyWUm8xFCkVzeEqgpTFbGJEOVH18RlBLJK3clx5UJ3l4cEalwZTchntV44vYpvYzKbLzHYS7D89tdds4n2G7AyBSs1CXDiYTbXoVQtRlZMCrP8urubS7ugRtqPJqBFw9DlESw2ktw+zofrAnmug7J2g4vPpCMLcn3ry5yYTOll5cs1QXdguR7hSzPPpR89ITKy/clJxmNRMAb5U8wNb3PoGESBwrVA8H9ZZ2XqCMPdFwTRuFFJuodemshzz6ABwtlnqnvoI8Fy1MdjC2LZNnnTitHTiS8O3WVV6036Nh9/sV7Pj+7InipGRM1TNpGhmLs0TjMEy6EPGfW+W7G4dpjeFxLyAeSk0lJpMLnm1sczAc88Thla8Gg8ayFv2fjmnB/Hl46vkNhMMW/Xpzm6S1JexEyoWB1u8IfXzf5laHP/Z5GtQtHlZRC02RX0TC9JYrqbeZbeVZP9vD1M0SK8FXaRzoK8NYlaKWCxYJP6it85wl46X7Kd50My9UR2705di4uEChH+JkK5uOQOxcLNMQsrzy+S6LCt55X+PWRz8xtg0iXfODkMRKY6go2VifZKnQ5P5glPoAP1wSvdD3MTMTHmSeZ2W2BhHwVlvUTOPz36o0twVxb8sQXfxPNyhA+8zkSRdL+5l/+vXz02sJrvHNO8ORmysY8fOGjADWCrpnwcjPgrSmddgk6JUhGJpM9OEhMlGWfgQ3/6PGP+Yvlf4JpDDmyDG4vw/LDkDcuC7THMUzGvHYr5c2LCrEKc49SVkYZRg58d/M/qjlGUcT29vYvHP3+/9kzrl6v8zu/8ztcuXKF559//uecetY9k1JyeHjIb//2b/ONb3yDubk5fuu3fot333335+6ZObCY6Q8xzEMySczEwMMII3QvYlyVhKqO5ZucpDonQmPK81kZtlESE9M8Q/Uls4KwEtIrS4aWZMcpYbZszJ4KicRQE0pRhs1qiYamELmSLx1tscYJY8vBVfMUvIT5MKAUHYFUuG1ZnEwrLKtDKoHB61OX2akN2Vk84K1Fg8elEh46TpRgyBSZeBxNhER4TNoJ3lhnX1rECJxqiJdJkarHz9ZK9EhIwpT97ARRxqFcCnByggfhIkli4siYJ083mQpOKScB2TDihu7xwXzKg6U+jSnJYnZAIeMxtgXrA7jY8YhslXwvS8l3ESS4uqSra2wXNBYaNjNxwnl5QCITBsJEIaXJJFFiYk1HFLMBb02vcbewRN22uKdOkW1nWa+HWGKEcPPMpZK5MKZAD4TKHCEVReI4fQJNxVcFrXSCD+wSe0GOburgKjm8Ykh3ZsytqYtU2zEZJ2CYGaO1VXJHKXUjy7YxxaPsAhUxoqXaNJI8jjdiZLXIBkPyQxU1mqSc0Vkq+ShawO5sn0xyxOygxcTYoy5yfEt/lh+PrrGnLPLAnKWd1SgO+1xstsjv6Bhpyn7FZlwTDCoJ+3lQhE1oqNxYMLgzXaYyDKn6HqkDRdVEDwwqw4DzhyOagc4w12JsSHJWhGYmdHIqtyemeFSd5K3FGrNhjIgFOxMFnEEFtZSi5mJKUcxT+yEv7PscFTOchEWyTcHF4y4zocul2KOtJrQtyVhRcUJJdSy5HI2oRacUgjF532MtOWLSDSiZEj1x6Ok5zrstsnGK3R7zZPtjFkd7VNwhb4dX6QeTBGpEzauz7rcQsY7T8TlQDE6FQ6PlkB/HKE5KSR2R04bcmMlz/kjwRNznhlrmHSvLUFVwdMmaSFjzmny58zazXZ09Zw3NN8ilEl1NGBRSLrdT+o0yN9Q5MkhqypBcErBrljBGOrNTA1YLB3wi2ORT2ztcOXC5nyzzoZzC1hMemDpvmRb7/RIisnmvcp4TzSDQFRItxZw5xUQ546MXJnBkxIFTQKQRbUvjcSXFSyHTLbJRU3mwqNN3TLKpTymCCT/moCAIrQjDLfDEUYNSr8OOmmOvYHM06bKbG6JmfNrZEHdo0tc0pNDQgzInuWXUoU++lzKwdI5zU4wcByXrc77dZadco2tUqE+MmZA+mfYKqn1CrKbMpHVGCwe8PxORKwyZUoeMZZaHwmDCPUJPe9jxkNAa4ng+jSTH9kSZh/M2HadMv5AwujBCzklMU2KN57k+1JBaRKDFdDWDW8YsaV9Bi1MW/CYV2ceWkvmuyyoBxzOSU1vy8n7MuWTEomyQ2DFoKW/lFrnnLDA9stC7VbK9IqO4gJoIgjAklAaZfkwSK7QSh6zn4kUaqoxRZEq74NOaGXDVf4iu1BFGijkOecdcoVscYrsq63UTteRQyaRMyYiMEtPTJ1C9HMXYY77SpD/j01RMljspZuLxw8oFulEWy/2HS7BfqgLtj37634Pm8vXHXf7qCZO5fsLOhODzH6cMLYu7s4Lj9iaVpk+1LWgsXOJv9r/P+l6CHgtqHZfD2tnP4cLIYpxL+WBN5QsfB0wOQ4ojGOQlQVLi9fw0r2yE/M3KEqv7CUYo0IKYTnWRFx40Oa1IWuMM6weS77+kcXFfYvsgNYlrCF6+CW0N7qzkyCQSIeH9dYW5QUh5BOOFLLMyJNixuLEiuNYZ8NUfj/ne2iWe3WohkWgtjT8nT/8pSdIMaOXgifqICwcjJII7CwqyWGD6MTRLCjNzDh93LfIelAdnZsZP7Hb56dRLfEL9ATtz8OnbKffml/nMg01GqsatXJVa3ePehRqD6YcIAa89SPDzBnblBnNhwOK2QmdCRySCt69p/MrNBIoxf52+wPPbLV5MmuwdZM+KVpGn1ogpF0fM7Cn0ilDd8dkqzXNShNIYXp/9DC/7N+msRSzcknxwWfDUliRejDiKPBrBmHuX4Fc/TmllMhjKWffjzqJCti1wQghnTb6r5vjkfYkWCm4uTPDkbsJbc+tMHfjcWshhRZBdjjk+lrxzEdaPIJ4x0bvHnGsP+HAddvsXeVhLmehJnIHCW0+YTO31eTgjWdHHiFDh3fMav3FnD98bUh7Bjak5JrM3uHAIJ8sJl/cljWsB1YcqhhNTOEjJ50Jy+wqb8zpPtrb41ElIcdqnrmlkfMG7tSW0yi7XtmBzVrB7JcuFDcGpreGEsFFZwX7g0Xgy5lEC5w8lw5WIhXsawpT8cPk6W1MbLDQFXgSnRcE/u+0jgBvLgs3zEN3XOT89RF0JaXRtiq7kjQsKzIS8clPw/nnBOG5SbSaYkeDB4ohubcDVR7Azn+GN6wpOHDJx+wKf2DtFS+Fn12OGFvyXP+zRyatcdyXmLYeDikJnPeLbKwaf2kiZvdLE2dJRrwi8fZP31gVPbackJZcXb0PfFrx6pPBntZAnHmhEpuSjNfjcrYSd4xzmakD3aMwzpsuDkyIvxfdII4WqG/D48hz5/TE/fEKQFQaDeMTzGxJ91WP5YUKzcCbyUV0/R7UumCi4iEDh7UuC5w4SHh3m2b2W8NxN6Eym1J7p8U1vmbm25MaKoF6CZlHhC8tfAGDhn/x3tOdTzt9ocTr8OzXHV2qvIDSdoyWN0ynBl99JOJpUyY8lPyhnqaUxV4YRb16B/OYYZS3k2nsqXzrw+fGT8MzJNr7IMfnwGZqpxdZFhfxYYitn/CNj1mKuA91iym5V48V7kgkE3Qx85/6/+/89zLHRaPDFL37xF46/+qu/+nvr6/U6X/va10jTlN/7vd9DCPFzz11dXeUP//AP+ZM/+RM+/elP88orr/AHf/AHrKys8I1vfOPn7nECn0RR6OQUBo6g7lkEvkJbM3h/psDtfIZeYtMKdf7ayfHQUjnOjeloZYaqSivO8CFTHCymzHRSqqOYl+o9nt8do8aQeip5O+E0mmPsXyeRFZrZPE0rJcm5qKakk9VozXUomG2qw5BSGGDGCjkpIBGIMIvq1ojUgKYtUQ2LA7tDP3QRydkDVSGSjEsq+6yx500xChxsxWC7MI9lp8z0JU6UsDRusjaAhRDaVonb5gyaoqFYEa+vXKNRzDF08mxNz1GOdESqMEoL5JoT7E0YHC5GCGLasxEzZp3V4SH9OGFMhqHlsRqfEhsxRiypjH1ykcv1TR17oFI40hjoEeXQRfMqOGmbwF3C9WqsJ0OyZY9PNDeZdbdY6fWQaULTtCmIlCc7EWYSkeAhpIofCK6oH/Ji6wFX9CaxbxKhIGMTUp+W6tHtlDFHZY7OR9RXJKExw63CFFEiWfZ8Lmf65OWYVE8JTA1pHDMnNplyoWPE7MxIiqcaLz8Y8+SuxEh8TDlgGJs8cmd4d/IcquLhZVIWBqcYaUqz7HJoTNB08niVTaJMzN2pKqNqTKDHOCOPaKxxKot0LRNtWhCuxpwTdZJ4AidxmXAFUkkZapDoKelAoTb0uV/TmA1D/tmNAeWGzU+TApGvsl43UEMdS7QIUsGRzDHsl3l2R1ANXMaaga5I1Eiyl9PYmJGECYz0HA8m8xi64Eozpdgz+FgxUcwYI1WYbptoNqS6Qm9skEl17KEgHmk0Jz0uZu8z1x5Tj6ucMEmtrfLUIOV2fpWDaYGqu8SGQqJGvJFepD6qoh1bqNmEpfIRm0sxrbkIxR6xO5tgZBIeRRr38pKvRrf4bxqPWewELAUNfqP/iF3vEt3xOrlcgJ/XGJgKUZIw2+0TWSb5FELPRo5UrhzDKLRIBxoNoZPRO+SkZH0XCqc201tZdCmwegK9f8S5zhBb8Vn09pBJiabu0FA13izZ3F8s8U5phX1lGanEDDISxUowADfOsCB7rKUdOgWbUEwy7QVU+gIl0DCtLmPbYznwmJAB1fSEQjTGSVwai5JJK2ZhbxIzSRnaKrGeYkUhpVDy2l6fgiuIA8k5N6ASmcz3HXKeTbU1pK7axJqL1Nrs5hdJcjoyipkKIg4yNU6NMrVxj+ppmda4zMAekxEd3l+w+Fl1nrwbUxlIBoHNTjyNhsulpI7IHjIl9shEkvvVCo/WVrgzO41nSea0Q4Jcn7CU4GkKkLIU72Dm9pgTY9Z8n8xY4cP5GvfVKSIjS6pBK5PHjmPUKGFHaNyuZplQU6b9BE8XHHgGl0YexQFk4oTBRIcoe4JbgDm1iTBTRkJHZgTdis2grDASNp0jwV4CWhCShAItVkhRmW+UqagFRoVpBuky/VAhrKpc0ero/SzzgcDpW3QTncNiiJ4mKD5c1DboZS1SKyVRFE4Sg3vmKgf2eeZck5kRNMIC+fHPv4P+9/ilKtD+1c576ImCva/zYDHFQvKZBwnGWKGQHfP+U/8JraO/5MvvJUghcf7rf8n/cu9P+MyDhEBTMVLJ/IHP/hRkTwZ86xmVdHCVUlTn3QtwdU9SOOcyipZ6BKIAACAASURBVC0qg3uYEexfGvPp2xLPVtk9B0dhlrm2pL0ak2oxI0vyzoLCr91WEWrKTmhgBZDvp7x2P+HhSkLsKDQL0DfLXDqIGZRSvnlF8rWPEnRf4btXS7y0cchOcR1vapfP3kwJKgqkCof1DLGdJX+ocXtZ49mRRm/P4Pay4Dc+iPGUIeePIRckrC7u4+84DGyYHkgOyhN88l7Kt86/hDXe5vpD0BPB95auUVVGpH2No2iSD6Yvc5zdBt2nOIDFQ/hfF55hxT7k5uM8AklxL+bH1wW2m7DcgOOlApp2wM2VMsOFkKduKryzMEM5/5iTvMa31zUuHMLjmUXM/DHnGkc4ewaDjGDfnqfcOOLLqctRy6TSOoMIDuZn+E1vj3DPpBlqzDchMxPgHmm8ty5YbcW8dlMyyOssrd1ht6jw5VsSxU4ZdkaEOuSzY0So8uzBKds1Sc4ukt01CKwzsZH3nOtUWwIjhtM1jU++t8PyKGFoKZwWYC4tobc1mBJ4UsUz4GHpHNXZhKV7Z9DEv7j8SZ6/v0esSsbhmQrPjXWdX/sg5dZnUl77WLJ/KaU8EPxs7gJf3b1Dv26AJRl6GiMTbufXGIyGOCGcWgtkcnd4qh8w9VDlYELjlaNN0kRgnVfYe1hAWikfWBZPNGNOgxKZ6VO2M2P+2596/OSawIpTqvcM6tkSrQtt/vBTCgQQGQY/ndR5/kFKCmiR5NaK4LXbkncvCb7/aoYXN85gm3LGZu9Uw4zB6OeYLIXUy5DmdvjMjZjBTMoL2TE/vC6Y77ksNWscDBUqPWjkVIyCxu18wlceSaLyDp/ZiFlP9lEjwdvrJhPLEVsDhWoPHFPy4ls2a/0Eeajyw6s6cy2Y9FX+0zcT3s+rPC8HXNbv4p+ucu7hMa0pyZ38Vb50/y0iC/7sFZXf6EmSmxmkArvTJkunkk4OtuYkh713mT29SO2oQd+BqbJHtRDw8eg6lzo3yfkwPR1iKwFHj8+6nUMLDisqVw9SMuMz3urUdI3BpQtUhvDOv/4f/jYf5Y08L869yL972mGmLjEieOJYZfVY8r0pk3986jJSBK9fV0lSBT8/idNT2PFtFpdHRBr8V4/+iJvGF2llY1plneMyfPpGyhtXINnzQU/5zK2EH14W5HyYaAwotCeQgxG3mrf+n0jD/6+JWq3G5ubmLxxf+9rX/nbtw4cP+cpXvsJoNOKP//iPWVhY+IXn5vN5Xn31VbLZ7N/OqarKiy++yMbGxs/dE2kS0zN4PGkRaBp9kee4lOW9lXk29HnuDAuIscJ1V7DoCoqJw6Tl8/5Kljcys/zb8hI3F1SajspAVVCEYH9+mtFsglmOsKoh5BXuWwWa9gSByJPIMs80JSf1HJlgRIZT9GyT/bWEdknwo8tlnHLAa6Mek0aDUZqQxBKphOT6OQ66F+jHGuZQASWlqnYJ82PyosvHlQs0lGWK013kokc3f6aAOteXrO6bzLUriL7NsSwhRYDGEDUFRYZktD0uxre4mNwirEq2zpXpTEp6jsWjiRlSu0ckIrqqhX6qI05Mppoam8VZduwScS4hLQoUVTK2BL7QKccKDy+GTEYh696Al8z3Sc0WVfU+52niqTE91eTOeI53lAVGpsG8NyIodLgq7nDR3UWosNqwmGnZvDKEopEwnoi53A8p4jNs6sydxhRd0ByXNG0zNBKi3IBWReOhVsQWKetRihnZ5LUhMuNz7IRsLaWM5iVTbYNy0mYntfhz9SloF+nmJI2SJEoEtxdMGGc4IMQzXMRRhOMPURRJbmihJpI0lXSZpK/nCdBRzAYdLcCiRb1o8fi8xu6CxmFg48ZDGqc6d/pLXOuZKGadGf0mFWJqyj1sPaAYSUJPoWWOUaTKQCyyU8qj5VPyOYcTMc9OmOOo9zQH+SwNxyJNdTLdRaxhjUwCB9YydcsgUUL6kUpswg+uZJFpyGwvYGXQo5t3OLk6xcBe4UoMl/oai52Igtpjr+Dg2Qp9QycNM1RGCkkCb05neTRhM8BGKm12JwrcKayS9E3yns/GQsrBJAzMhPnTmK/tfsCa16SpQZqAn1EwQpVsKOgpMOcliCQlyaUMLMFK2GFVOyGThsx0Gix5Ls5giZl+hdRUEVrEv52e40N7hsNskUgxYZRDRA7mcJIkNulkTNpmhprxPoVoTEZJcDSXp9p1xrUOSiWHOi1xdcH9iTkauUkmxmMsGnyYLrNj6Jgi5Xx+C6kkoB9hBoK2ZdLVdAISrlrv8/XRj3mivYsWW7j6Ip68wsrIQuoOdyoZHOUUL5sSE7Bjl/ESFdUeMzQFTcVCVXtktAPWmwMqw4TpUUhfkdzJrJHoOjWZ8E+bHtFcxP5SRCbfZsXsoutdfFWQKILCcY12nGNSdni1fZcrzW1WekeocUrNq+ObVbJ6HY0TtiehZWZ5ud3H0xT6o5SJYRfN2eFV9w7PjBIyClgpXN5X2POLRCmo0sMeH5EMBL3BMnrfQlgp50t7fHrvgIfjOd7P5bi7pnB3ZgktyWEpkt2LISfzOv3IxopCigXYUjLc1036qcDrafR9i/2TIkthjFVKCMZVLh1o9KZSOgtFdlciMvkOOT0g78bMjT3KWpu6rqEkIwou6KnPzaUSHX2ZkROza4TcUZfBUehNOGxE07yTXscaTfPYSpniEZe8JnVLZT9j80rxfT5tvkm3PKCTUehnFKSW0sgUqds1loyQKg12CxYfVif+wTvul6ZA++HHf4Sr9fj11oj/ecFBT1K28yr//K2UgWHjz4eUJmdJvQZPb8HQyFJ4aZZ7w32ubUHLyrG1blEagC1NQlPyvesKT90tsdo9IdQVUgHZGZX/cfI5/vHtY1qZHH62ybVdiRZI4pmEVzZ2aBYhVAQv3U/468+AkqY8/TAkqibcr6hIAa4l+ac/g/+NvPeMsuO47n1/HU/Oac6cyTljEAY5ECBIgCQYRJGiKAbLonWvZNqmg5Zk2ZKvbUlXln1ty77Wk2zZihRFBVISKVEiKJAECAbkGQAzmMEETE5nzsycOfl0eh/gJS8/e+m+T8/2enut/tBVu2p3d3X9q3fXDrqRQ0xbnOoUaJ81aViysDeXcOczlG7YmagA03JSMw8/2VZJ7/JVglnYsIlM+CNsm4CVZO6mn5KkUQqXcWRFhuokVpoNAuM3h9DbleFnqo1N4xZL1QZD1eAu5bBn3WxmlEU7HDtrkUp4aE5OUo5rPLf1VrYujPCdzp1shK+jWhb39esICJxu1ehLSTRclZmqV1BLAi/vsPHut0ywmzzjvoXbB8dp9CWZnnVj0+C6u43WGwXOdQvUjotYgsVCsYKpBhce7xrtN+B4Qyc7tDNc6hBouqbynX0CuwagFNa5YtVTDmQQK8ocPgdZu0zCs4ZQFnmjXcIQRJoWYbEyxj9ZAXw5qJo1makJcmCowJnGGC1zU7zeEiOyYSK1FVnS15mNW2y+DhshgWnTJFg2uF4Js7ZeotoMoZSAa03kxFaRncNzJN2w07ZOfsbO6Q6BY+MzjIc26BuxuFrlwSdMs2fQYrZZp3kSJpt09CmZWmeR1JpEjaPE6opKzgZGPo67qYxatDDzIpEpidOdDqpdyzQMi5RUGA5sITGSpVyrE08KnE60E59L8/M9JgFtjfYhgbUtBaIXbchuk1drN1GMH6dxWSc4JfFGh8gTV0qYeYnhiIdtFyxmQyITzRKSZfFNt5dd10CXBd593uLLPXZM1cJZEBmIFtg9DMOVIo26RduAzJJPoDm5SP1iPcd7RbzSDMEsvNxn0bDgZqjLwhLgyallnBedTMQkJis0BlQvO8YNattyXJ73E2+CuRtOln3g1orkaw16B0QydpHolhTRdJaPvGZgAS9st4itW7jWTRKrcG08RKolyquWQo+ZxsiLzCWi3KWepZxS+O4uEYel0rieZMcQlDoKZKZuPvPmWdA77RSXFaoSWYpzKie7BY6NaSysOjjTVc+mfo2liEVjxyp/p+9ny3iOS40CliiQcZocPm+S+tK/7JY0P/l51t0gv/AqmP/i2Ht/0/1cqixQkxa5Vifw0PECyaCIP2/hWRFI2QVKTovpOhPv1Sz5niI7TkuYazLXN+lsn1ql1nyb8vK7GRVsXNkMFSsGY1EX+rqMvV5gz5DJcI3Aik+g5xJUWyorbgf/eOFr/9+C8H9RGhgY4NFHH0WSJL71rW/R1tb2S/mHhob43ve+92/Ki8UigUDg321zuVrlRFeAxskorulORoUQMTlHs69Me87CnbMx765m0RYnJxTxl9fYMLNUmW7Cls6qPY+u2JlSXGRNmchigkJRQFBN7OEyjjoNyW1wr36BTvE6LtNgc+kKKa9FMJClRp+jRlhCy9hpW7MQPXayXp237Q5GBYVpO5x3RzAEgYbJCJHlAJF1idiyRXBDJhURWPWLiGUZzRAxFI0RowInKsliNVeqAsxqdtZNkbJTxxUqkcFGoSQwGq5gKNyEiYjLBM2WIykEWLQFqfG8TX3oHYZjCpM+GU1ScBpFbCUBl1YmM+8kmQwzUOFjJuLE8hvUmxa+XBmHdtPHuujUmLKpyEKYcHUaZ30R3XKSE/xUrYpkLJWKvAO17GG0VMXgRivTVTaUskSwrFGTFCnLCum2HNUFi3DKx5XMNk7Vx8gaPgqrMuuxMJbgwJN1UaXmcJY1VvMOCqpBMpTBa8zgK6YxCy5GjRhxc5GTUS8/j/qpKjjZUSqhlUX8rBBeL9G1sgbuFBmHB1tJoj5pYdihbt3F/eMFtpp5FHuRF7cFeLE7ThSJGjHLsurFJizSaFzALU8QlS9QNedEEIr4HAU8Pp1EXsSzZmMj68K9YqKJJTSzyDP5Pn5g9iBLEjsKJRpzTsqyHUUSeSfcymCdj5TPgU0TsKcVjBkV72yRJnEO+4pMfeEaqlWkJBt4pVHsSg6XYuB1FnmPPsxhc4yBfIAl7EzXuNAEG2pZp7Z0HVxpVLdF0JtClDZY1mUuiB7KboO0V+Cay+KS6mKuUkGM3DQfMyyJpGojnbNxLteMq6wjSzOccTtJZmM4LJOOVQtNsVi1S4RYZJOxxD2FLBRsLAoKhmrhL1goawp2SyQTMJFiJYJ5i/SiG93SKOsGC3YfUtFi3S1jiiobooO8JjFSclIKFZird6EZPij5WGtbo7+1xFzAwQ2nG3t5A7tngZ1WmYqSSH/JTdoDK7EiU0E/ZcGBKgoMtctcaRBQbGCzNGzKCv2ODGgivXmdNr1Et/4yTt8U1Svgzaq85arnW94okzYbumVjkRgN4hQ9xgSq4qXSWcJpKVSuOZiosqGty0gZiZKo4NUX6ZTP4zEszLKLdTGGYAiIqklVGtwSDDTI+MIDREsZHCsWhUUPJdnFIhVECxqR0gKV0hSt8yahtIhqWkxpYUKhDA2+KdxigXFfnJCWQRMcVIlJHEoauezBoZlEPFmy7gr8+TC7tVF2Js8TWnUgplTEnEbDClSuWYSKK4QW0jjzKv6cnbgSBFMlkVmiX5dRpTI2QSETdiIsy5TzPirWVOKaTMgQ6CsukRHyrLnmqMiu4DA1+islZAyWVRnHqkooL+FXLDZnLLqvybRPWihimqgi051JU2UuIEgpsvY865KEw7bGJukC2+1XkQIaUWeBvM1k3e5AKsaoWM/gIIvuSxOxDbE1fZVK4wYF7wYFp8y6UE+jXsJVEqlWyhzd0DlvdjDkrOCqUMXlhjBa2ENWUphLQMpmR7atMOMUmI+KvN1Rh/DvBwX+Bf2XUNAsy+KT554FwWDndZ03OwRiJZPbhg1cKRHTIfJs522sjj/DXadAl2D0tgf4/tWv0DtpoZYEgoU8cqFM1g6RZIkfbxMxix1U5pa51AS7r5mYiTI3zDjLtlHaZuBUW4K+fgNdgqUKg0tSA3VLGgtdGhtpBVOwONEs88BFAUEXyGpOzuz0sxQAtSwQWIOPngLBEjjXrHDL4CoFu4Vsd/MbA0WktMSPtro4NjCHIUjM+zMcuWigO02C8wbD8SrkZh/KiI35sMDWspPkDZmkFw4HsjzdJXP0kslIvUK4ocDYlB/ZBPuaxJVeg3CuyOc330976ad0ToFqWHyj7TbuGR3gFe8OrhTjLEj15KtOI6hlysLNnZWzlXXEo+dZnhCwLIvqSZ2XdgrYsxpdUxYjrWE6tItcbq/HchfouSzycmsdlbYRdFPiZ1t0bu83mYlH2bZ0GZtYZiVtwxLglcjt3DF2nr3hDXJDMpodqlIgNdi4W3mHwSUnV/0KW8YtCm0qK9N25oIgCRaHLlqU7QJdNdc4V6HwvncssASWNYWsE1aCcYJLIr2Ly8xEIeTyMj+r0t9l0bgIb1Rs55aZfvxZg3O9AvGrZZyWScFjkXaCyx7CMy2xsF2nYIpIusDpxioeEJKoAw4MCa7Zu/noq++w4bEYDch4CzC8Rea+MwalXTm2nBMZ363RMCZyrjpGR+kspXwZKSqy4pARTIEX6nvJu66wdcxi3FWFp26AfWMGKxsq63aBO8bHmA9CXYXJ0EgAybQYccr0pcsUkwpz25OMOjR+92WTN7pESgrsPw8bdgeD1dA7btCzqnFib5HLtQbRqZsKw/W4SHBNYPMozLSauEIqrdPgKMJUc4LxbImaJLywA0zFInbWwbVGP3ec08gH4HqtxAld4mFrnYEWCyG5ji8Hp1vsvLbZx1V/jj8czZJ157h1uEzUvYp9UebNLoGPJXO8oSv03rCQLJOv+5tJt4io0xIXmiR0h8pkjUnOBmm3xK53oFaZoTjupnp6iY0odMdWMfstUiF4YYfII6sW5jseMg7QKmS6JwyG6gRMxWI4lqNypYvtM0NYCNxoN+golfnp2i62504Q3oBwS4ElghSmlgjkbvrcDVXZcFoiGz6LtWe+jbZ406SxramBoZ5KGqYtrj39h7/Apv3V+wk5wpzeGkYwLCrWBdqzsPeyyd82efjgWoa6vM43d8nIKZNg1ElGFlHWJeSGIgWbxRODPyWnwqLYRbJSJOWBOy4XOdMGxpqBIMBdFzSO99hpXoDW5ArljS6ujL1KTsv9R8HyfwmanZ3lgx/8IKFQiG9/+9vU1dX9H9tcu3aNT3ziEwwNDf2irFgscurUKbZv3/7vtplOWJzptYjKBepLFte7U6z6HfhkE4dlx1J8lNzgCS2TcivMYWfJ70Ur+8ioflwlL8GSjQk1gCejcClcxUSFRDqtYJkCimRgMyGiaKz4ZthjP0G39wrjdolcyKJH6adtchq5KGIWHOhui6ayQbVRpmbFwlk0CCuL1IiX8fgusla5jt8xxYGiSdLn5nTEw41ggFhSonZGIqFP4JdnGJVcnPA04ZM09KKNCZ/ClYSPcXeQuLhOpVEkXh6nqjhM2ZIoGk7MjV6m6aIxVaAll2LZ8nAhEWckEmHdlcZYC5BHwpkBZSPMSXsvo94m4o5+rrWtMBboZd4VIJEGVYdAVmc6VuIdOUhUzCJlLV4R64kt+fAVy9hcEgIO9GKC8dpqJhxhztuiLFcWqSqW8edMkvYQA9USNmGNhL6GVy4gLQtkQlmWKnXsYp6020NrZo2aGzozuoJDg851jfCGSc6dIW+08Y6a4LJvA+wlTKVMW14jb1dYcIVZLNjQLJms22LQ3UitOkeNPIThXqAiY+LQLdYTZcSygGXYmVccFLzyzUAagobHkadHmKJDV+ial2jKXyFWXCVttILuIiOp6M4gHj2HIOWwawWCGwqusklAXGHRIbGa3EaxpGIoMqlsHVJUQXcKLHmc1JYL2J0FREnFXhYQJAs1LzLtN5l2uylbElkbaDgxnevsMC9R4R1m0W5RVLKkVI2RVgflsIiq+9lh2Ripd5P2JgkZKYLOVcreApOeFLolUJXzIBo2ZE1CKriIZmTqlSyVwiqaJaJLEm7dxJMXqS5fJxkw6W/2kYzPsuoosBh1oAUtopaBKElcCYWYD9tJ22vRnAolu86bQYGzjTAYdeAtG2y4RNYcIkZJRPaDy67jtgySMR/jHSI5dwm54MBhQVZ0MRQIk/OYLMlBTMXChoM7MkkOLOUpReZYaFlFb7IYTUgIG06W0y7GsyGGfX7KbgtVLnBNinDR46RQFIisi1StJ1kOO0jZQiQNP2XLhWDamTUi2ASDvGSnZdLN9sEwxWIVU3olJWyUigpjmQjV8iQtTGK3yqTlCHI+jEerpXLRYEOW0ESRhYBMrlLE6SjiR+SEmGA04uetpnq8G9CctMi6JVI+EAwXNcYsxrKTVEliQPazbtrQFImc6WZdEVh2eDEsO9sXh2iyDeNGw+PWWQ24ERWV9agHTbVRzq2hZjSWvG42xCjTgot8WaaQ8bOo1WNYCh1r1zGn7SxYMobTQpME8rKMULaRMGZo1JYpKBEmfTHWbX7I28lkZMSVMjFjiR8Furha3YxRaVE27OQVmdUqjZRdoF+yUVIMVu0Ch8sb1MhriGkJ3eVjpduDI6qiVkBRU1jUbCh5N/XFFcyCBNkCSlLkuqJSDhqsqwpiOYUh6GQcAiNOGVGw0AU3tUmBhSoB1bVGWVRweKfp0ccpFuyYeQ+LVpysw8SnSUS1EoOpSl7VgwwqLt6wWtFFk6S3TCaqkSjrLHqc+IQJ1iWLFdXDmNnKvuIcDXr+l65F/yUUtGdOf4qsusKxtTzfSHiwGwbzdolHXrdYsXuJVSYZqRFwJRfZMmGhI9P6oXfzw8mf8fAFjbysMl9vUjdtUXJJlBWLl7aKNF+tY/fcEEmfSDALVQ15vuzu4339Y2iyRKl2kn1XLZY9bpbaLW69NMdiAHSfzr5Bi9OHTbIy3HWujOkWsN/3BH2HHiavgGBB3m8QmDMYTkDdosmmGxZKZ55ioUhm1MVYHC5UR2iZlfhxaxfNhXN0zMCa3YFsQS6WZ8yXpGYZ3uqF1zoK1M0JbNhF/qFaoXcCAhvg6V3hBaeTWy8arLlg1axnW7nIaExlPWgnWy5yaMCi2CzjSKe51BvnCzUP8/DM9/l65x7ykTFk4P6RMkpW5Nn9Im1Jia2XRWaqVEwBXtqh8CuvGZh2eM6zj6YxnTb/NQpDLooqnAzdSndqii8fVdgxCI6SwDlnC8sNRVyuNNsuC7xTW0WHdpXJKo2mcZUv75U58jboboOT4Q6s0BqtVonud2Q2XBLtjnnElMJLW2wIMmwZt1isCfPSqoeCDfZe0clUONg8keRCp4++qQFeb4kTSxsUewuM+wvUahqNIxJlWeCGHKAiB7MhGI30cGT0Cv51Afu6xI93COwcWCLps7jDucridS/X4wJ3Jmd42yuyawjO1rs4OHcZ2bRYviVL54jAYtgkeM2irWed62M+Eok8l1ec2HSoThq0JDZIpsOouTLmpMqFBomyt8DmwTJY8M2eIySmJ4hW5QlNSoyEE/hzBb5xO1Sv67RclZjYZ9ByVgXV4nq1n+mKEY5cMwjOCbzcJ/CuQQ1hReGNhiixulEEoHVYok8w+ELAz7EzAogW1UmD1ajEY68ZnOjw8nZFgfvPQlkQoTZF+yWZtBPOdsPrW6B1cYzNY2kq1kExLVxze/hKj0R4sQKjWSd2VeF0u4NTm4vU6iLvPauTaM7x4nyCjtYsAxNBCiq0hXWWW4t0nlMoKgLOEqSXISoXsMoiz+0WCORF3p8r8uPtIr6swWxPnLOuHPtmJaySgHuXgPtiHgORz90vEM056R5Yp2oJsrtzLA3bydmgZsFiY4/Fxqyb3dUTrE44Od8scMd8kbSmMt5SQfelEjOVJp1V6/x19jb2D6cZrgLBslgKarTKfXztgIxp6iT/4s9+gUPVv/GXpJ2Q/PoPsLI3A/MoosKjHY/y/Z4ULbOwEILbTmishiWqiya91yw0Fa7WCKSiJtZVgeLOLN1vy5y03AidZbqnLO5NfY0NrYOLeoK5XpOGBYNLVRUUUgpKg8WhAZORKsg4oPtikQozQ6bQy9+c+ep/DCj/F6HPfOYzZLNZnnzySRYWFujv7//Fsby8DEA2m6W/v5/V1VUAjh49Sl1dHU899RQvvfQSJ06c4AMf+AD5fJ4Pf/jD/66ccsiHqUUIhiSi0RmsiMT1igQpI4gl2khUawwkdrAc30XMUtmIeGBjG2XJzfamTsKmzGOFYTxKiVd36IiRccKlJdI1IWZ1D6Ziw+W0aIzH6WuvJeQdJ2pP4REk4gU7Xl81Y82t5PweKvdU0OCBCbGStUIbS7KTRY+MyzmD0zVKwS/ijyoo0Qy66GdkSw/JhJewUSIXUZhsU6lOzFBTHuSC14vmXeQ+xzphv5OgICBETB7SrgMuxJJO3cYsbj3HiKueWUJs2Dx4nGGkxu28ldiHFmvnUM9DCOUwpuYl5bRhIeB0u9Ha9jAUbWHVuxelLUwNce5suouGOpVWxc9tYyqxvMq6zY49soLdISDqEtWhbu4pRcgH2xGrYpj2DG7/OK7WDgKxMNuyMpGCgd+S0XSJumyKW0QXniqNo+2TpGuPscQW3IV6yhEv2IqEAqs4BRPZa6L5TbSYSThgMFUBV2pk7tiYJi+0IJZrUX1TNJXKVBl5XJZCrhxBkzQ2bDoLMYG0X+RUtp13An4MmwW6gKSLLFbv4saB2/ha4lepUCM8lC3QKuTxFjWEskCwLkj1U3/E9t/5HAdjB9gh9aL4Aog2sCkFqswpJiM2llrbWAsFGWsOM7QvzFi9jBZZx+8tMuZz8B1HiMW+Y7zQ9d+Z6NrMx9sWqVdKtJecNLn8rMRqsFsyndua8YTfheDQWfC7MAw/oiDh9xcR/UEqizJhQ8CVtTNvVuIzHCTcGobXwbH7fx29tZdVhw3LZyI5JCqdEnp1gEaXSsEVZ7oyzne2xhhXDSYkk3jaRLOFKQo3bRSLpRriuShut53V2krU5m3IETcvd9Wy2HQLXtOOIAtoHi+OQBs/9zZz1d1Dl5WlSRYo+MOoosR0REXHyazgRcjJVDks9ssurjpFFmwiuttL9DVLrQAAIABJREFUMlKmLApUekSQZOpsRcKaHb3sIyNEWPHE2eqdJRuwMdPgY3zfIdy7juLfcwDR5ibvcDKUaOUHWw4wFmlkOOCgJEpcLW1mzaxmKuhh6+owmzcmaBUtHFgEU2Hk1W4srZlzVUfRut+D09vAO41VvF3ZjqTWI5phnIZC0J0jGzXZbZ9mt3yDhxdPkUgXUbNbOfjuh3EoCg1JWHdZrDjcjCfqiDpldNUkEywTjM3R1bDBaKVIyWFw14LE7zfcSybWieH10+ZYJ90Rx9KryNhkTkr1fDH4LiypB7+mExaKHL/rfeyM53A4wliyi1qnxW0dcfqPfIjuukZuqY5R1f0+tFIPot7EgZ5mBoptrHijLHraOe3to3HeYFmOk1dbGXE4yMgqZUmhWHZwWOpns+McH+nxsBKrpqBKSB4HIy430VKZ7k23YA/YmFWdZLJ2tmeneU/sEtP1TTgCOuF8nvPNToqNETpUgZ3TCreUBSor1+ja0c58+wMkK6sYb1R4vcfG4VCQHimBs2xHKztQyna2rJhIlsh0HSh1IrW6F3nCg2s1QkG1kbN7mHRUsNRUYLBGZtVQuaM8Qb1rDilrsW6vw6du4A8W0GN+CpaHyXANW45+il+p2USV4xpDcgKbLce5QIEZXxWdm3+dto0xWspvYKlu3h+S2aZNUWWt/tL165ensf5PQPPpKT4/+DqWQ6d7VODHOwRiRTg0oONcF5iNeHm7eyvB5Dne/XPI2wXObr0bLfldlLxO1YTMtNNLUUhRlCGUMnhut4iV3k1FcY4LrSbHzlmYIZ1rvhpGQiN8bMjibH01nf2T6DLong2m8gG2rqU5f5tGw2k7mYDJ860qj5wzEPMiZUWg54lfoaE4xSd2fol737CoTZjoVxRO7JY4dMUg64RotUhuxELIi3zrLh8l/xx/+24f0y6TJ0/olO0mQ5UaCdGDtyJH+JxCToGBVh/3vZympFhcaze47FD5v04KFP0mPfYSLy7G2Za9aS7W36LhuxDkn7bdwcHSM/QNWJSdJs803cnRt87xsX2/Tqd2imyhkZXuH+NDJy1IHHtD4aVeO2Z0nrbndVbdAk3TGl94l0DrDZ3GefjZ1nrumznByKEK6mfm6JoV+Pyu/WzNn2PVUrjYaPLlL5rMhKPsWrhGen+eqZSLLg1+mLiLjwx+lYqD6yy87iN3QKBp0aLQZ+e90lu8nPRhyNA3A4v7FSaueSh7Ie8WOXbaoGQXSGQX+PsdNu49byGVRFZxMVtbYMmVYNNykT5lnsm4RcLh4vhKmbVegce+aXG8biu3Jk/hzZo8f0CkbiKPC4O8RyAvQIspEp2zWH53hjPLHqo24MdbojxmG2H5NT/Lbon6BZlIMc1379dpXrTTsgrn9tq5K5WiYLdouSDw7KMSe74tcjXuQzJMkpLKdrFE0S5hLwj87z19CL5z3HPe5KW2nTRVvMXBV3TmfSqGLNM3N8uJTQK3SiWK523obgsWBGp9BTKzDt56Msmq7uCB4zJvdWisuAXe/TqknB6W22M8rI9wsUngrssaA6068pKT9jmDXIUN32KZH28yuLPfotKd5Upapm3S4KtHulgpDHLnlMVwJWwql3h6l4OpmJf3/myNVKVAaB4iY2UOREy+JyncfUaiqELSqiWSBzIjPK5lmLNk7kutUqo18N6QOLfZ4pg9w5+Vw3xwxOK1rjiJtQUevLJCLq9ysUWgcVahdypPaXMby51jbJxVqG2aZ3nIgXOphPuOEuqYTmrZw8/3mswGVf7X2TT+ATvnuix8WoDWmTKvbpK4faTM03GDzQuVNE72s1rycqnb4vev6XxBvYdbFo/jywvY92U5afbQNnaGYBZ+tENgKOHHb2b5y/k3uT0c47W+JQ795GV87zmLa8d2btu8iT/dGec9ry6w/JnHiX32JQAebnuYr179Kpe6LSoW16hdEkjoGm1vGnyl2cMfJFf5rD/IV2+R+Mh3DRp6Vd5uEXnkpMbv32/jf81qPP6qxehjX2VSu5crzZNUXLN44Owin7+9lo+9Po5pE3nkZIGXtgZ46PQae2dv8J3abTx/8Sc8ue1X8Nk9/5Ew/Z+SNE3j1KlTGIbB7/3e7/2b+o9+9KM88cQTDA4O8vjjj/PZz36W+++/H5fLxde+9jX+4i/+gk9/+tPk83m2bt3K008/TTwe/3dlVUUa2MgaqKVGrOB1HDaBA00VvDHlwFkTp15tYZfrDhKNnVy75qYpZ2c4tYVYRYDuBx+m4/pJiqOj3O6bZnxliXioRN4TwNe6H3PoNWR9A9VaR67dxQcOfpRnS9NkcbB9Xac+sYYvHMeptTFesPBU52hYmqUxGiArZJmwvQt7copdqzqnEjNI7nqeEg/zavYnNC4W+MhdB7lY0OgLLPIcOusBiW1770H+0jfod6+xOxLFG7gXV1UtyedewZibAu866YCfpD2Dek0mKKsY+/6Il67MYsyphCvjhHQbslRFZEsfRxp38dZzI/g2YLjSDapBqaKCHTvv5+pPB6juqqa1+U4qlDTOUANL0iaa10TCjgouJa8ge5fZFunB62xAN0U+d+RJxqf/HLvbjXfLNrT0lzhJJds6D3J7xxly10rMvXSd6modda5AGejZ/h70wecp1O7mo3c8ztqVSzim3sRwd2KZQP8MG4tZhmoNzkcXqCxYrJdFJisUckKIp9PHqGl00upV6WhswHz5L8GuoDc/gXpmAi9zBAWRJr+P1VSEwWiYTd11DA2fpCwVKFJLjbOO1WAN97Zs4h4hzuLoABljitWUTGcZolt3IR47hiAI3Lt1G8k336G2XeAfBieoEhNMF0fIZeLsazhA+L5u9IU1vuN8ganMDAdGw4xKG8zb/RQkmea2ZoaGdSb89/BYn05lMUtuYoXmVAT3xE8QY35in/ws71pa5GXFoCJVxuMZRzVVPlG3k7m6/TgdOnXTG/wIFwNCCJ88g0M16a7eSUv9LewuZPnxiIGmO1F27aQlFuWPPD2MTF5g5OICem0b+UQGy3mNlnkZc3aColyFKa9gN8vsajrC7sf7kMoiybUimySJ0yvjSHqWDt9mfp7zIIohdgd30hXeTn0oznrIRpXrNUKuCrZ07Oeq9nN8K2lmws3EVgu4EytUqArx3Xfz9ti3sdQCLtNJxJTIqgpVDZ1Eps4T9tZw+xt5QjMmzzS0UN0e5ZatfZwbXWBP6zH2Vh4lsrSGUhFj+Ad/TMieJ6ur7I88gOA5x8CqhWOhi051im3hGAO+vVRJX4cWO73N25lx1BEQ0ohNdWh46ettYnt9nM+9/iKTsoWkBYkGIthzDWxKrkBrjB2HfgXt+BQOeRl1VKVmPsvDu+vp2bqTfxjuY/Obk5xad7HgbOKBx57AJQ7y7ORPuDvaQaqQYm/lXs4PzLJ8cYnGzr3cdsuD5Mx3KPh+lXDHcXKRKMUzjYTFLO0HggxOu9BliXVnlFJDJS32LYzYPo2hlom+8w9s3vUQ/tvvg2sryEI772p1815HiL977Sqj68Nsrc+i+yYQwh4e/oPfYfTTn2E66eF6ooe+hw5y+uULsHSaupVFroUSHNzWS8wYwhNrIrCrnWsRiQP2AFLJj6VlqXzsszzwzCu8lR2gO9zBtmAcq6+Rn2Vv0FDlonZPHXcNu7CZRbzrX8a5WUG3N6A6C4gHPsJn4j38/MJWJgde5FBlnJqlamxNjSR/+lMMpmjNi6wpJWwdYfZ17eeupetYdSrtBQ+F1lqMT36Bq8F2Fpsa+e3IKK9moUYMc9QVYNqxxlTYxX0NtRzb/j6qgwqRsTMUVv6cnvfcjn/nnej1rVR+bZZ84zb+3pxmrORBr7uLh3f/Govf+jolr0FHYwP3NO1i9aVzXFzd+KVr2H9qBa1slPnNH74fzb7Cu5byfKXDQXVWJ29K3POGxXCghjbvFH9RvcoDrxUIb0BOtdHwW+/lk/2P8Vtvl5FMmct9Oe5+zSITFCgXLM72eOg65+fojdNcaBWIpi0qD2T4mPsOfv2t42CJEJ6n5wxcbAhT6Fnh1uMbjCegZs3Ak5N5+QEJzYSjb5usuzw4730QyefD/r3fwS/pOGI6SxkJ2QGhDYH2WQt1xwbGdRlrROGtdoGmiQhN8g2+H4qzb36Q7imYqIGf3GondPUoQb7DbRMif/y+MKTX2DpmcXmLybd3KjwyVCaclIgcXeNLHh/3vWQwGZbIewx2XZ3li9tvx18uUzmXpSoF6VtsOMfT/M8DD2MJBveMvsy3du3EaxsiJSv87uU8V0I2vnZbmaODJm2zkPQLXGywuFQj8vl/0sn6VayCg1cbb2VX/lmazzsYrHEw5NjOweSf86W7ZR5908CeFzhT2Upf8CSzPoGDr8AbNY3sK5xGr8kQvK7wibskPvyihenR0SIuzjhMdmdzrLwdYKZWwa+n8CRd/MOtYcKZFO2zkKxxsyhkmQ/Bn3zXoBix4c6s8OqeKHe/PkR/fYhbrqfIHCxwJgIHNkpcfzuIIQisKV4OzeUZqoaB6Bb++LVzqAUBwRJ49ajOLSdhZH+RalWjfNHDWEzlscwoNya8VOZhMqDQvJLmb+4Reci2gTwQZKNWZ9dUkVBflnM/jzN+b57ZOTfBrMn1iIqvNksh14bb/g4bQ3b+98FmCpUDfPikTlr080LLLh4d/Wvi1XlSA15SDpWCo8DJvSYfOKESXoXBrQZbxg1KukL/Yzl+6vbx0efsOEs5fngYPvSqhZoR+frBGI+Nv8GVxl7sbRdZtNx80afwpz8AQ7KQkgZaJRy+ZDF6p43nggZPPi/wTHcvFzqv8NGnLTTZ4vhWGc+8RGUkTN/lJWQTzlT2UK8OcGzsHH9e+x4eWvgekSWRb9wW4OFzQ9iyAkdPlwntzXFhKM7h9kWe66+iRdTZ37jBtGqy4w0oS3C1Ms9MmwvXxQLxDZOT29w0D7azbewsL4ZTPCrbGLi1Ek0Z4fAVG7Fb0ghpi/nBINNNJv+4V+HoqIeKk6sM1kCwp0DgBYHZkEDLtMHoYZ3W6xHurjnDyFsRBhsFblss0G+rYVt0gMbjAoPtFnf7S/ztcIijoyu80SHgyUtMVmZpnTO4aMuyqXSEr+w7wd5BjcWP/zYNP3sdQVVp/LWPMzT6W7T+eAL/LV/CduRDuBQXv7X5t/jq/Kf4yy/DQtTi9p8JvHhEZTtZIlcVtm0v8L0GDzO1Bk0XBFp2pyi8FOSOaYvXNzs4+FqZP/iuzh899iNO5g5wYOsrVL3soGl9kZfrG9imT9IyCiMNGySDAnedKnDp3lWuq308/twn+dEjn/+Phuv/dKQoCoODg/9Hvh07djAyMvKvyuLxOH/1V3/1/1rWrbWHMKwGdlhLnCg5qVDstCgJWqQ34bbfwww2sMe0yOlV7EvsIL1W4vvOJba0xMEVQtp8P67NcHS5n6n0FB1KNcVrw6TqYtQ0HMA5exoWrsCWx4k4IxiNB8jYg/Q++BhcexHq99OpOOmQFISNebotgw9qaa6vXcch2SDSgdMRYfXyl6gPVrHlwBO0XdxMOvNTwlsO0Cltgum36dMWKYkCR7of4NXf89Ez8kN2djzMNk8NhFs40lfme6WLnHAGud2fY3dFlkyXE83SedeevVxJDWJbW2RbTYDb86sUvIepbNuGz+ZjLtZEsbxIyO5CI80jmx6mqWEbT3V2EnSrLOTj+JwbuKsb6GkNISgeQhfH6PemccpZ6r31SPv+O5JlYFMcdPzmJ6kXdZbYwH7bPrShWWJ+Dz7NjvfwMYLhIIIEqf5nkSUZqW4v0uoYttY7QZWJb+0D7zoU09BwgFxoHEJXuetgG9LicToWU0ilLC57lu6O/4bR28bepigFzaQmaOfEhXdo8oXoOPoAWd9Fhl84xWqzg4/teIpXl3/GRrSZRr9OW0ecWvtLrNndFLY/QFull7YKL0oxQH3VVg5P/pyLVa0IazNIPffBP0cXdUbC1N53jNLqdap81WxJ7GM0OoIpSvTu/M1fvHvdEzZsSxe5MhNnwnDgty/T6KvhV/fWM2Bepcolw+b3osxd5IEqHTPURaa7BSUQAF8lR3yVHGrqRhEVEhe+S8LrZJczTj7URTHkRZqeoDh0A9PVzt01Y0SvQP3+fZRFmSp/kOaeQ4QEJ4ljdyEWVrB7KpBizRwffoZIvIptdSOkig08uufDaIXPMFPZjnj2J9gFmQ8fPIorkQAg0D+HKBbwOkN0xbbxSFsX4uVbyWt59ldt5s6GwwBkyhnExREUR4Anen6Vp2UbmfVJGiyL5q27iV87i1ObhLodlMxFiqOvIUgJks4+mpt3sLtjJ6dP2wkcfhJT+Tq7llNcq06we0crds8SHd3v45bqW/DZfOCPAPDQI59BBM5PrOMqWMRjMlX+MNv6DsLaGg2JGPWaE78/hU1fQN1yN301O7FME0EUWc+X8TkUBEHgk4ceZC1X4vzUGt0JH2cmwohiE4n6Btr8IUrjRxHHT2CqdsSsTG9vK07Vza/v+E0W7Wf5jRdPML13C02dzUAzH228FRGRk7Mn2VKxhbO9bRBoJ/jgxxCCcQ4cDmHKNgLO3Xizc7RPr2FZFr99Sxc/ubxwEyt7P0xftZ/DisREMk5XrR8jVoutvg4cdlRZpCrgB3cABfiNg+28MjVLheGhIp5Gjzbg97iIxONMV+fpqA5zcP8+Xp5SuL6RoduIsdK9n00PHcQuGqA4eHI5w7kWH6vWAI1KDf70Oti83P7QnVg3Qmz1VhD1VSK57FRMvUJEgNvrbkeqzSOU0vDyD5BFFfmRr9ycM/88b7Z2bqfsydJbuQe/6UR0OjHW1vGxg6HkVRyWQe/9v4lDdqDWrYHdR5Nip9606O85TZdm574Hj7E3cTfu8beJjCeRwhtUSC6U+RVadt1LqKri5uQL3YmzoRkhdDOnphxtgF3vRvTWE19+EwyN3ftu/hysdYZZVzaobD4IXQ8QvPIcLcUk/JJk1f9pFTTLsviTE79DuZjCrhjMZ32s+8uIosinvquhmyoFt8qfHAzTM77GkYsWa147Fw9/gPm5v0Yp6nT32zje6aN2cpWFmEliSeR7hwTUa3dw5/hxfrpT5NeOG9jqC7wW3oSpnKdvEK42BNl8McVElUixagnHsA13QWd1r0H0uI38/gLPV7n4+A80LF1GlgW6nvoQuXe+wLXpIR541cPqfp3oCzb+8ZCLR97MsV5h0hAxWbpuI2cX+Nr2Dr709BClWYH+e6Z54hUT02ESn1cZW3oPrfXf58CPRf7wfSGmKtf4+3/USHlE/v6QSEAzOPCqByORYzmqUx52EEubfO5BibzDwUe/k8UsObhz7kd0TcJSV5lnlXtYaHExQzX3yp/guq2bTMPbKKJIW6FMcjHI0/cVaUjJvOfVIv31DpoXCnzxPoUnjmu4cgI/auygP7SJbdVfoepHDkoq/M+O3+a9kz/k9W4JWxFuP2txprKNQzMXWL0vj++CBxBYdoQ4OnSGxgNZ/swK0jkpULlmUTjsxinP0j0hMjXtw2FZCF0ZpFdcDCVknL413vMji/mYg/h0hj/+sMQHXzGRyzAne3n+2Cq3jeaYsoXZO7bCtQ6DeMDi/vUczy6HOXLd4pnm/dy6+jqiASf3eLnvyiV8ZYuyLDJeY9B9WaLs04nV5znTH+JA3kLoKrK07KR2WuSNxiD7xlf5wU6Bu8MpBocC3OsqsKGJ1O1cY+BilOTmAn9X6eavXoQ1u4103OJV5918fPhZ0iWRdyqreXPrCo0rBgfOi/zhzvdyh/VPHEsWmF13U7KrBAoF/uR+ifvPCNQN61zcJtA7DLaYxmiLwVdiXg5fEdk6luWNB8oEFx3suWTyVk0Cfx78V2x4oitYwQr+unqDh05J1CUNvnlY4NHXDMatKFWRFK/U5KmftnHR18upQ4Pce9agZhlAYJNRJDlZx5GFebonYbpS4ODAEBcOxGifXOLY5At0DlqcbRdIxCSMgzn6XnLjOSTwxo0wB2uXeboQYft1HeFAjkYxx++mWvhvk1nKLnj87XWe3xcjMZHj1GaZ8KyN8o4z/KTQwt3vjPJ8aDN3+N9A/LmNql1rWCbceCvKagQ+fp9Ez5SH9z+/ykSFwNUjJdpec9OYNTnVLXLXfJ6rKwpHGuYY6Q+ilGGk2+LRKzJnWmPUnhkm6xDY2ZXkG6lD7Jq4StYBN6LwRnOQLePrvP+4yCd+LczfLL7AryqdfPHIFE89t0byT3+X6Kf/jkc2H+aeYw184usTTP/hX1PfvA+5oZMHWx/kpRsv8XrvRXYN6kgWdF3Ticgyr4VlnlpcZdKy8Wd3qnz+yyKJlJ2nj+gceV7iLx/X2LGzjO+0jU9/Q+dzD7zFl6s28ZHGQe4+A//jwTKR0wnW6pe44y2DU7fb2HOiwG+cGOR/3CUweqOZT736TT556LH/UMz+/zMdrj1MLF6JLApcGDFRtTw4asHhB4cPURRQRQFVDoAdYl74WHUl4v8jynJvtJfeaO/Nk7ot/1IRa4L5foh1ANAV7qLOW3fzo6Tjnl+wCQC+BCLQDXSGOzEsg7JRZmZjhmp/A62BVgAcvb0oiQRKLAZUQLSN9xfXMC2TkCNE2BNjrHITwXAzhDsB8B3YT19tB9HVFNvCTuydHdyytkQyl0QQBAIuG81RN4mgk7qjx/7VvfVVbeLqspOtFQ4coo891Tfz0FX4HQDUemvB+8/M7k0AeI804BpeI3RjiUp3JcjqL/pTQiEUwEuMJr+Fz+5iS3UAzLsRJBV7ehKAyFNPIYVCULcHMKF2z79clN1/U0FTXDi39GFrbGFXwM+mml5ciguySYTSBoSb/s2Y3/74FyC/Cg4vvr272Zp5L2I0T6BmN/KuZjryV6nzwe723aSrhsgVDFRFojvhv9mBKwyuMB3RVtpMA2FpCCIt/0aOz+bjttrbSHgSjKyP/Zv6o/VHuTXcyx9fOoWv6OZDWz5FzK/jtSvsa/BCYf0mY+Lm+yQCvoMH/1UfiqgA/N/s3Xd8VfX9+PHXOefue3Nv9t5AWElI2HuJgBNr62j1ax211dpa/bW1tlq1y6qtA62Luiu24qhSFwgqQwRFlsgKEEISsvfd43x+f1yJhCkSSKKf5+PBQ/O55577fp/1Oe8zuahoNmbNDJoZG2AdOhi/QSM5aRjFdW76nzWNSP5gjIUTMasqQxKGMOPc9Oh3AGKiO65OZwxjB+dQ2i+DClc27YF2bCmD4YYnaGkJ4H1hLQZPG2anszOGMwrTCOp+Qnsm4DQ7ibdZGZkykoHxA0mzf3nmOsYUA8UXgsmBy+xicuZkQunjqPXUkhubj81ggdBIyBlPsUmhKi6PGVkXkpSUgcWoRfM8J7oMJP34alAUbjcYUBSFZn8CW5q24DB++QRXAJvRBsD4AVYEENITiLXEMiA2DSUlHYAsgNN+AuUrIGsMAIoavYso1vblcqupCokxFmYXptHQEUDTFLTEAcTERpcLc8FMiAQw1oDSEcRSVASAZcgQsnNySMofjG34l9uGGFP06okz888E4Ddn34NBMaCp0VzjY7+cxtnObEblKgQiEWwmA7MLU9EUhQ1VraTHWjFqKilOS3TgwqGd3zuzKA3tgNeSGDVj9PciIc65MAVSozGm/fRaWLuH4akOYixGfnjaYFqTBaPGDaU0qGExfzkd+ifHEBsziI9r2klPG4PRGn2iod1h5/yi6V2m/7jMifjD/uhyFmcGdwRUA6hq9N8B4ixxzOw/B4P6ZXkTc/oMFEUhv76ATc2bsRlt0WU+JqXLfBlyxfcZajRiGZgMwMhh58IwIOjFZrLxHQ6lJA044A+FgqLRxNlMvN1kJsb65VNA2q48m47Gz7CnFn8xM8aitvqArYcZ6xcx3XHHHXcc7oP29naee+45fvjDH+I8YCU6FYQQ3LvmLvZufoPNNiitsLIuO0BOh87kTTB+k8LC/DGUl+6g2urlV69E8MeYqIrtR+KNI3hk9wL+8EaYNmD58CD9agQpzQp1CbAo7zucsWk92wfVceHKCMIhiB8b5NqEIdz/3zK8mHD6PIQMsLJfBo5EL2M+jFA9LET+BgVfsY9HSh0M2QLTP1WodCST//vfEXTU8elbN9K0xoV/UoCWdXZWDIjltK0dWMOCgWObqfnUgaHRyCNnOHAaguyLj+GdwXaueLeJRLfgwZlxLEy8nH7x20gv28X/RsVSntnOvW/4Saw08Ow5Bj5PUvjDQiNpDV4Sp7VxpzGRK94UtDlVVgzRqYhLxOjO4by9y0ivV9iTJVg+aAYN5ggfK5MZH/cQE1boLDi7mlQidGgqJRvjeWWSG5fPxk/e9LInNo6iyg5uucrE4Iow310Be+Njea3fDIr6v8jQpTqZjXDXuAvIaG2HzA9Y3V/lb/8JE9DtNBsd2CaUszjs4NzVsC0xk2HNZSSVtPKhz87abANXvwm2Uh9Kqw/nPgNNHWac+wysO1vFscaALSDYMcnIlMVhQiaVuJYQz50WvVfw/FUCT38bz450kxDOZuSqZmyql4gKSeN8pIc8bN0RS+knBpblppPFNvpX67w1TWN1agw/+agVb9iM0MK0OlWy6wTbvudlUWMcFy4X7Btgx97hJbbcyDtFcUzb1sL6PJW4sW1UtFn5QZ2HlqCBAaVtVO9zsN2q8cfJNq5aojCwUufFmfFsdsziZztepiU+wO7WXP5+UQSj4ubRl9w8nv8dLJll/OCjHbRgwNqsYQtHeGWiQqw5yPT3YdUwF0N2e8ka2EGdS+GRYTZoNnDDawL3LA/zUmz89kWosybwzPhMShs/J8ENm7VE5g9vomgbXPy+zt5EhUfPMhCnhhi2K8CS74VYKcwk7B7HqsmfMazGx4/eFESAjXkq1j15WLLbGL88QNkAwaOzYMomHbXFRCDeT05VmMYYaB5r53uePVStcFFb0g97ezXFDg+vJNvIet9Ezsh2ctLd3Kae/5Z6AAAgAElEQVSP5vxFjbhjDLw+fARG0cikje20OqHRYuC8TztYa87i7Wn72GeL5XR7C8ZP/AwtbiXoN7B7ZTKtMYLf/p9KcYWVX7/czrYchee/IyjYYmXi+ghLii1M3Brhs4J4ZiW1EGgRRNZZWDxCZdqWANsKB5CyaxvJVRpVp/lQyMD1YQ1Or+AfZ6kYfQmU53Xwx+cDJHh0zD6NB0tNzGxM5N2cFkqaFWzLdmLLcWAaWEpMfBoPKouYsl7gfuO/xMw6C4MrjkmZk5jrf5MJaz34nZC3V6GiIMw03cPurS7mpDbyntPG7gSV0g8Vxg9s5vFMF9e8GebNiYJckx/7XhNTN4Hb0MwHI5Mo3uVj2B43f/xOGkOqVRwBH8mVYcpPN5H3WYRxe5rYkhtgVUst7oiHCTnFp3RbfTQ92YecKgfmGBfriu4EuPIpTirGYE+EcAASC0A59HZvVVGO+C62QxjMEJfTOZ782HzirfHH/JqiKGiKhkkz0RpoZU/bHhJtiRQnFUc/c3TdEbUarJ07o5kxmcSaYylMLOyMU7VYSEuNZ3BeBpaUZBRFwWyLId4V3dEJhHW21XZwdnH6lzt6X/D4BVYtjnOGFpJgTWBwmgv1K+QfEREavA1MyJhAij3lsMMoikJ6rDUap2qIFq62eEjoj7V0FJb8fFA1iM+Lfr5fTCrEpIHVhaKqqNboOEyaCUVRUMz26HgO+6MqmOzR/9U0cgaNYkT6KMwGM4kOM0VpWSTYHaTaUwntq8YxZDADp4zGbNAOE7+KEpPSNbYv2Iw2IiLCwPiB2I128mLzyIzJ7JK7wRxDv1hoxMUFwweSvn/H05UFsdlgsBwy3sMxa+YuO7aKpmFMSaYgzcnwnHjsVgtaSn7n2Yo4S1yX4fdTDUYGZKXiSMkj05lFv9h+0Q80A7F2C3pqOo54FzGTJnYuW5qqEBFhqjqqMGgG+sX2Y0DcAOIt8Rg1Y9cfcCRHD34AKfYUEqwJtARaGBw/GINmgo5aSCkkJzaP3MTBpManYdAOXQcVTYv++yIGq8FKtjMb9TDr6/5prSoKBtVAgjXh0PXXaIGkgZ3T51jsZgN2k4F+SY4v1wVnOiQOwF/jBl0nZvqXxYpiMmHKzETRDl2G9jOohiPGDzAozUlhevSgkdmgYTSoZMXb0A4+WnQATT3CtkrV0FxpaF/EY9BUBmXEkRQfLRoz4mz0G5SD1W47ZHsA0WU735WP/Yv16EisBmtnIQpAJARbXgNbEgw++5DhD85fMRhQNI0YZyL9U4agKYeffsaUFAyJh3m04sHL31E4rUZMBpVmfzPFScXkx0bPrjWHWmkPuRmbNhan2QmREO31VTy3fNcR+8heV6CFIiH+/NGfqFs3nxUxGhktCnviQgyvjmB2q/zobZ3/jMrigyll1Fsj/GV+GKuiUG9JIunv1/H7DX9gzoYQ2TsV7rnAwJCqCFn1kNQu+N+YyQzaUo3BVcbM9ToBBwye1MTV1gv57fvLCfs1hEFgCcD7E9Nw5lYyaiEEYwQWYqjL8rC2xMa+FpVr3xTsdqbhmjiehvHw6pKb4XMLicVegj7Yl2Jk5PoACW5B0ogONtY4iK0w8coEDUvjQC5YXc6WHPj+smZS2wS1uTYezP4JqrONIc1vsDY/hupkNw++5SPtcxNvTNF5o0jl9sVh+n8ewTjbz/w4E2e/ZiBohOe/ZyS2ehRnbazjtOadGJs0auMVXhk1A5OzkqWh8+nnWsDsj8p55qwwWWqAnWYTpTvtLCty4whYOW+Vl13OHMZW1HLnD6zYfCFuelXHbzZx5+hLGJz7AsPf9zGwEv49ahxbrQPJSH6ONf3gby+GiGk0sDSrmBF5n/JUtpWr/6vQZrOiGrzExbrxWODxERau+68gJcePe7cJlxqhQ2gY95pZdWaE5PUKeU06O6caKfggjCoUwkJje7ZOZbLCj9/RMaeG+GexneokF79YWEFlhkpetaBpWpj8Di8tK+JwVRl5cbST2EgLo8si7Biu886AdO76sAp/jQ0rQfYmKcS2wYuXh1kRjOGXrwraEmzEKa1YKky8XWpl2ucemhN1Yk9rpsln5JJmN5X1NgYMbKfRa2KNx86dMy1csEYwc3WEp85OB204BZ5l7IrzYNuWyZ9/YEA1NvL0fzt4xTqTrcUdjKpdg7NJJb5ORQPWF2gsKdG5+A07b48fRElFBcOGNFNl1Hiu1MxOv5U7XlGxT2/kj9lObnxRwRg0ceuc/vw+9DkpmwR3TR/Dp2PKKNoV4Zo3BKotQmybwmdZRpYW2NEHh1kZMmOrnMzWkWsY1uTjNwt0CCusHqRRpZagZQUYu7KOqhRITvFiyQjycZqBSesCCKGiRgR7ZgrmtNaxc2UC/xlyJiWhFRQ53byabaVop8KIQS3YYkP8KXwuwz/cTmI7LCvMJFOtY/BWNyiCtya7WDY0keSWdmZ/1o4n0I+cBCejy7ZRMKyVDbVx+FY7qIkX3HuhwsxPLVzxrodlw+GpM2HMbiNnLQ1Tlmrmg9gziMns4Oz4XZgiQSpWxNHgBIMzhGVwHFp1FVlbTKwfrTPeGKF1mUKMT+eRs1QGl1t4faKfjGadujgo2a0zoFbHpxtYPLiB+I4CFg9p4owtgsC7K4gZ3o8hxTN5tXEdy5Nrmbw+SPt/X8E2dgKujDyG547nMffbTF3tpz4vQu4mAy1JgtQ0P8pWKyVWDy8OsOILqAzdppA3tJUXXE6+vypMZLgPj65iadIYVAWDt/tYP9iAq8nKyOo67p6ZiW4zMmKnm1CzTuXZOmk7dc76tI1UdwdrGzaxpGMDk/pP+PKIdg/6thVo+3M0asboTqvR2qWo6mlGzcg+9z6GJw8n2ZZ8zOE1RSPVnvrVi0ggO95GaXYcA1IOvSdSUxUsRo1pA5PJSbBjNh55J/NACdYEEq2JDEoYdMQdq8MyO6Lz4GgUFUy2rz7OY9h/1sJhMRBjMRFrjhYR4bp6bNlZxOQe+f17RwxRUUi2JWNQDWTEZHQpzg4Ul5DMpIIUjAcWIqr2lYuzozGoKjbTcV5wZbQecdl35edgHzOm8wzTfkIIdrftJt4ST4Yj4yv/lKZqZMVkRdc7W3z0TLOqoipq5wGH3splNXY9UKFqYIvHMngw1uJiVPvRi5fjpSoK6lGKsRN1PNsLOLSY+koMZvA0wbCLwZF0/N8/BfJcefSL69e5zUqzp5FoS6RfbL/oNIrNpt2WzXMvLDhiH6kIIcThRl5VVcVpp53G0qVLycw8/AahOwkh+LhqOQsX/ZqAt4kPrFZCBshrEGS0QyQI538U4Z+nW9mWE2bkTp1fLoxgEIKNWQXk3ngat+6ex7S1gsG7FO47XyO7Ncyc5QqFFTqLRg7H0lrN0Pp6ktqhpr/O2KImbtYvZdi2D6iND3LaRoGmw6dTnWxN6+DS/4Wxdii0DIijJa4Od57KtgozFy4XVNuT2JqTxhtnbqcjHOanawOMzG3FKnReqU9k5nsqfrPCxkHJuNqbKS4LsbxIYUNMKT/cuBafUSO1VdCcIFiRV8y/k75LkuVjzq54i9fHOkjo8PK7t4NYGw2U5UF5nMLoMp14n8A0NcDiGJWixWbsfqhKsfCvswL8eJVOihIhuM3EmqwMNo1I5yLzam72/pJ08SaDa1r4eFwjYaOgVSiktxipSArjCqpklTtRDYILVzbxyPlW4puD3PJyBHSVJeOGsTdnA2e+DamtgkUjh7EgJ4eU2DdJbYAfL41gcWu8lzuEsXnreKi/kR+9qODyqlTHW0miHXtOgKVDYPZaHbXORKjBCEO8hGrNGNtVPj8tSNynFtJ9YeqGKsSu02i3K7QYrWialyanwugygZoeYOHoLJakdvDABw2U6TYG7FCpnhhhYF0Ad5mN6jiFR84wc+lyP4OroL5E4ZP0/syp3Eyg2oCZCFvNJt4bDnsGCsKtBm5/QWCKgaApiKPawJvDjUzeFiIx00uw1MPuNhtjrG42l8UyKrONj90Otuhm1mQYOf9jncSAgf9OyiQuYubjxDrGbmojsTqXud8No5pqeOAtP68p57Fl3Coymlu59k0dewAMOmzsr/G/sRpn7UynKbuBgR+1M6p/E1viTLySaaOxxsItqwPUTfLylwQX1y9QyWhUuHP6GO6wL+dvtrHssJjxJq7mojVhzl0GQlHYON5JdmWQ15JLqR/xKZmf2VmeOoaOAUuZWRbkqi/OnH2Sa2JR/LnM8Kxn7Gfl7E4Dv0HQMCjM2XoH78XEMuhdDYtf0FgsyK4Psrs1iwcmTeLntf8hrsDDCMWHmeimZG0wn3uCZ/KDda8zYF+ERifEBKOP869JEjSlpjBkRx1/vdiI35hG0pZULmgtY3jqTlbHm/B94mJQhWBDvsr6oVbOWh4g0RPiidkq6wfDtM80zns3TGOMSkVyf4aJreQPdaO7dXatTEAoOm+PdXC6s4PGrYKCHSqfFwoy9BDOrSZa7fCPszWGbzfw8kSFv+xuZXeLkTdyc0l1V/Gz/4FRCD7ub+Gfs8J4zXEkupu5+7kwZoPAfdsfiZswjovfvIDBe0Pc9qqbSMBA8g3/j/jLf0ilr4Zn7rqU777awL7CILEVBlL7uREKWINQnabz5yInAzcYuLG5lbUjdd6qiedHO92klXRQviEWxw4TVVkaGZU6KtAUYyRCmDdLXXRY4rju3XK2ZinsOd1PaVWE1I/MaO3RjmD1II34u//MOQXnfr3Or5uc6j6kJ/S1HNsCbThNzuPeieouQoge++2epPv9KEbjUc98SFGNvkZcZlfnZZeS9E11rP6jxwo0IQRN/ibKWsr4uPZjFu58g+lVu1hjMlNBDrQOIt/fRChuC1M2+NibamT5EJ2CGsFPV4ZI3a2ixOi8P2o4ZkMZ4WoPheVQkSH4eJggb5dKYXl0B9hjNpPZFMCgQ0UapBS2Y4p18HttDAPq1jNid4SiCkFHQoTmkXG8khThhrdbUL0GmtNjsCqttAgFe40BlxfWJ+fwr+kGcO4hTtcRRoW2EOTWakz6PMywcsFHWbk0GOMY5tlCbl2AZcOg3hLPsL0tFNQIdqfAxrEGNoVH4EXjnN2fYg3peC0RBu3VSWoHHYEavauAoFnQVmykMbMDd5mFEZsV9iYqfFis8xNXMxYRoW63HdJceJxm7IZG3lUyWKYJGhM9GAwh2jQVo1+Q2CEI2AADNBsMgM7s9TrxRo1Xh+qc8bHgeyuiLyNePz5Ci8fE1I0RQmbB3efHktziZmC1zugyQYwX9sXEU5/tQIyoZGlIcOX/wOGDPek2EgwdhGNgZGILjTsc+KosVKfa2FYkCIUFmj1EcmWIIZ8pGJODdISMxDSp7E7WiPMoGNQQMR0KfgO4S72s75+MpjeQX6Hi3GIiOaTTMlxg/UzB0K7x0nioj9WYs9LCHeP/Dy2hkimRD7hggZuN+Qp7UuHzTCPeBBturZ0Jm2H0Vg1PjiDvszAKOr50neQwuC0RbOh0ZIbYbDSTuE/DoAP1BuqSVOKbdTypNnb0z2SLyALTLvrVVnL6pyqvDRrGh+N2klsfZszmPD7rJzhn514KGrxY90WIaKBG4JNzM7jQuoFqTbCwPp6todnExexiimUjO3wWxuyLoGeGeTnPzKaQld8ugNRmwT9Hj6F/xnZeNxfgTSxj8L4WLlsZJrlOpSnFyKrEAax3FiNyNlG6ZxuitZiPxreT1LqH764Nk7dXJaTBx8mFLBpUwk+2vkxGbfTdWx8XwpWvK2xNSkdPClL6eQM7Y1NpNiZSZ4tjQ0YSGaxnkGEv210hOtBI1zVqHHFU6BmM3l3DnA11GISgsl+EpBoDuxMUKvJhu/lMxoX2UrRsJzuTXMTFuylIqWNtLHi32Rm3GYIGheVDUiis8ZJZ105Dss6dc4yYrBG+s1hh7DZBeaqJ3dYCcvq1MsOxnu1lcWhbDbQ44M1pZkrqPPTbaMDuh01DdfrtVInxw3vFCm+Xmpi80cjKYQaSfQHO+sjLuIR23nXZeFn7HlbnDn6+uAynT0eosKJI51+T7KS0+7n1xQgK8G7hZF4bUYg37gny2gz87Y1m9EoL4ZRUtCuvQZ86gfdev5XRz6xBy/JRYTcwRfUS8msID4j0IAuHmvjQ7eC28na2DxU8F4jjt2VusnPbqdnuRFtvY9cwjT0xEbL2QH5t9L1Qi0vMvFkwlF++tYewo5kXzoZLRSsj3QHeaUhhhTGVz1KS8NobKczP5zsDZ3P+gPNPebHW14qXr+PbkKMkSZLU/XpFgRbWw2iKhhIJ8cC6B/jPtpfx6QH8rSNRmguYtWcdqmsz5RkCk1FgDKpMdXsw6LDbYqAqbGbkNp0x5TpxMUE8KRGqTCZaQiop1eBs1/ClhGlI1alUTIwuF8TXRQsbvxEqE8GbGaYjR0XstWHwR4htCZNXK7CEIGTVcQ3yszyjhBZDGWdv9OBNc+BJ8dFWoZD8WfSo+67EOHbGDSc+qZw3hpSTVK+QWyPIqVfIbNT5xHE6G5JzyFd3k9VcS2qjh+Y4Pz6Xh5JdHoQ/BaFGUBIDRCzt2NwRbDq0x5hp0xRsLYKMujC6Ao3xsK00zKpMIyluSK4VFG2DiAb/OEel3aIyZRMkd8CwgAdfrE6rXdBhsvFxTByNmkqtw4wpJBi7q4VMt5/3S+3U2L1MaA2RHg4TGxQoQqHRYqBBhfQ6MAbMxDVpZFdFsPoiJLl1MAh0I+DXUCLQYYH2GIEvzkBWtofyuBBlBhOi3UrudhsGr8Lkwr3sqjdTFrRgERomYUDoEerCWQQSFKzuPaQ1Q1q7QFMFRo9Cq92OmRA2TwjVqGMwh2lIgnCcToXJSJwapl8gRFBV2J5oZqfTRNbnYQbuhl3ZKu1OGNnhw4pOo9PF2pgYLF4/BZ4OLM0auqJgE2E0oWPSVZoTXQTimjE1KbgqNQgqJKT4SBzsZr2SybrdAxBeI+gq/e17cJhaMGqCPdZc1sSEMYZ19EAqGT43PuJotuk0OkLE+91MKK/BFgzh0CPE+HXaIw4azS7arHb8upnarDDeoGBYaA8JHRHCwTBmIrS5VCIBiB3gYbfdwFq7mRZdJRJOIa7NSOleN0YimBU/WjhIcodO/0gQFahx2flv3gRiRA35/j2keSOEfBp1hhAJ7RH6t0dQzAbaLHbqXRa8Qsfe1EH/qggdFlg8zkEIF3UOI9n1EYa0+BnZv4pgjcp9lu+yIjWVBPtmipu2069cIaPOzrYcP1jrSWqD+CY7O2JLSIprQE3fR0NSE2/bTWTuMZHWnsy6uDQsqk6mqZGBgWaS3S2EmhQS92r0r4GgprA1I57+LS3YO3S8NsHL41TeLVWY+ang+8sEHjN8MiSFdLsg37eXYL2GtcqIMQIrhygYwoKxO6LbnZ1pBpyeMMntsD1d4ZUxVrJqLeQ1+9menUzEE4NV97Al343H0cycLWESlRCr3LP4IGMUP9r2DplNLeS378McCVObqlObLxi8HcxNGrpLZfGIRF4Y2UpQiXDJlgDTVqo4WlS8FpWt+UnYZo+ievUHzNjdRNWoINmtGia/Ab0aYlM9+DMjvJJuJdBmoEjx80Cqi+/t8DM03kNajcLuLbGk1sOisy0szg/SHtLQLYISn056UMURVglGLLzZOoKgnoIh/VOaNTuK6ifNE4tHhaASwtiYg8WbjhKjMmSkmb+ffQlWwzEu/zpB34bi5duQoyRJktT9vnaBVlFRwcyZM5k/fz6pqalfO4Ddbbt5+JmfMWNtEIMOWgQMEUjxCOoSoU1RGVgtCBkgpEHQCAEjCODTAQplGSpxHYIr3u36KMpVgxVWDlH59SsRDrxgQgA1UxIZYdvKOouVZiWejmAOI8UePhRexi02IlSB1wm4DDQmprLeMYAR9pUsTQzgVRVCikKzptKhakQCiYTqZqMHk9GFhqKAxb4dLWkp6AbOWOtlVLkPSwjKUxSemJpKWNMprHJz3TuezrgeGn4uFfZMEvzt/C7h37y3vZBnB5zNLcb5nK6s5arQr6gSCQQUlTmh1Zxp+ARV0dEQODUPpmAYox5GVQXNBsECYwzn/E9DBRaXKiwc9+WlE3rEiqLoJLeGuHpxiEprLq/mzSSQ8wbXfVBNUYKbjGwvigJCQDWJ6ChY8WPHj4pARWAQEYKt0csM2iqsdFR1vZZdMQiyJjZzff949piMqLrggcUe0lJ9tO2w42/88ok9O/MEc2cbEIrCD9+NMKosutgFjFCdAFuzFHIawGeCJhec9VmEiD96xH9bpkKrI3ofoRlBvi9EsOXLyx+aLDbqbck4Qj5UIqTRgjkSIuRTcaQGcGb7UYw6lthwl3t3f5KaxLC1KgP2CRw+8Fii9/ZGVCtpoQgBr4kg0d9R9QiJtBHIDpCZ58YhdNw1Zrz1ZhQ1+mYGXY1ecm/QBYvTLLxUZELRVbSIQsASAQTqAWubJSwwhEBXQCjRadGvVnDtm18u6440P57CMPviIghdYfBegRpSEEBrDOz54uFWBqCgWcVljtCIE4GChsCoh4hVPViUEAD3t59PqmhivG0LmiIwKGFUVcdKkFcaEnhpmAVEhAS8mBCEFYhEDFTU/hg9FMvkqk1cueVNTI4wqikaZ6NRo86okh6JYNSj66AiQNGNGCI6hohOvUklGFaw+8Hup3OdFUCdK1oEp3YEEAqUpSt82l9hWxYUlQuK90CjU8Hu1xlYBZbQl9OwJQa254Ax18+QjhBVDXHUGW1kVflQtDA1CQbWpruwdcQzZk8j1pgASquX3Kaum73qBHhqaiL1iW0UVIcZUqHQyBC2JSbSr91DenuQ/LY6slrq0fQIjuQAIa9GoM1IuxXeK1HZ2E+hMQYKqgTjtwoGVQrsgei83ZwNrXEKEywdZFoDVCgpNHpcmGoCpMbXoiWF2B2nstFsZqdmplVVOb3FT5oWwFCn0dpgw9UENTkWNvRT2eMM02b8MocZKzM4b1MFEQUaXdFlKaR9sa1tAy2iYLZFQFVA0XENb8NpD6MBwmRHSxoMkQCEg2COgTkPdT4A4euqra3lkksuYfHixeTk5JzQuHqr7uonJUmSpG+XY/WRRyzQ1q5dyyWXXHLSA5QkSZK+uebPn8/IkSN7OoyTQvaTkiRJ0ok4Uh95xALN7/ezefNmkpKSOh+hKUmSJElfRSQSoaGhgcLCQiyWE3+SXG8k+0lJkiTp6zhWH3nEAk2SJEmSJEmSJEk6tXrHC1okSZIkSZIkSZKk3l2grVixgu9+97uUlJQwc+ZM/vWvf/FNOeG3bt06/u///o+RI0cyceJEbrrpJhobG3s6rG7ldruZNm0a77zzTk+H8rUtWLCAmTNnUlxczEUXXcT69et7OqRut3TpUkpLS3s6jG4TiUR4+umnOeOMMygpKeHMM8/k+eef/0ZsO4LBIPfffz/Tpk2jpKSEyy67jM8//7ynw/pW6u3bhmOtB5999hkDBw485N/dd9/dOY5gMMidd97JhAkTKC0t5frrr6euru6U5tHS0nLYOK+//nog+sqeRx99lKlTpzJs2DCuuOIKdu3a1WUcPZ3HmjVrDpvD/n/V1dV9Yn4crq/orunf1tbGzTffzJgxYxg1ahS33HILbrf7lOXh9/u5//77Of300yktLeW8887jrbfe6jLMO++8c9h59Pzzz/eaPLprOerJPF599dWjri/79bb50d2O89Xwp8769eu55pprOPfcc/nlL3/J559/zl133UUkEuHyyy/v6fBOyK5du7j88ssZP3489957L+3t7cydO5errrqKl19+GaOx77+g0e1289Of/pR9+/b1dChf22uvvcbtt9/OddddR1FREf/617+46qqreP3118nKyurp8LrFunXr+PWvf93TYXSrRx55hHnz5vHTn/6UkpIS1q5dy5133onP5+Pqq6/u6fBOyF//+ldef/11fvWrX5Gdnc2//vUvLrvsMhYuXEhGRkZPh/et0Re2DcdaD7Zv347NZuPpp5/u8r3k5OTO/7/99tt57733+M1vfoPNZuO+++7jxz/+Ma+++uopu+du27ZtADz55JM4HI7O9tjYWAAefvhh5s2bx69+9SsyMjJ49NFHufzyy3nrrbeIiYnpFXkMHTqUF198sUtbIBDg+uuvZ+jQoaSlpfHRRx/16vlxpL6iu6b/z3/+c6qqqrjjjjvw+/3cc889NDY28vjjj5+SPO644w6WLFnCDTfcQH5+Pu+99x433ngjAGeeeSYA27dvJycnh3vuuafLdw98RHpP59Fd63VP5jF16tRD1pfm5mZ+8YtfMGfOnC659pb5cVKIXur6668X5557rtB1vbPt5ptvFjNmzOjBqLrHHXfcIaZPny6CwWBn28aNG0VBQYH44IMPejCy7rFmzRoxe/ZsMWrUKFFQUCDefvvtng7puOm6LqZNmyZuu+22zrZgMCimT58u/vSnP/VgZN0jEAiIefPmiaFDh4pRo0aJkpKSng6pW0QiEVFaWiruv//+Lu133HGHGDt2bA9F1T3a29vF0KFDxVNPPdXZ5vP5RHFxsXj44Yd7MLJvl76wbfgq68Gf//xnccEFFxxxHBUVFWLQoEHizTff7GwrLy8XAwcOFIsWLTo5gR/G008/LcaPH3/Yzzo6OkRJSYl4/PHHO9taW1tFaWlp53rSW/I42J///GcxZswY0dTU1Pl3b5wfR+srumv6f/TRR6KgoEBs2LChc5hVq1aJgoICsXnz5pOeR1NTkygoKBALFizo8p2rr75afPe73+38+9prrxU33HDDEX+jp/MQonuWo96Qx8GuvfZaMWvWLOHz+bq09fT8OJl67SWON998M/fddx/KAS+uMhqNBIPBHoyqe/Tv358rr7yyy5my/Px8IPriur7uuuuuo6CggCeeeKKnQ/naKioqqK6uZvr06Z1tRqORqVOnsmLFih6MrHssX76cefPmcdNNN3HppZf2dDjdpqOjg6EmGcMAACAASURBVPPOO4+ZM2d2ac/Ly6O5uRmv19tDkZ04q9XKggULOP/88zvbDAYDiqJ8I7aLfUVf2DZ8lfVg+/btXS4XOtjq1auB6NHs/XJzcxkwYMApzfNocW7cuBGv18tpp53W2eZyuRg9enRnjL0ljwPt3LmT+fPnc8MNNxAfHw8cPU/ouTyO1ld01/T/6KOPSEhIYNiwYZ3DjBkzBofD0W25HS0Pj8fDxRdfzMSJE7u05+XlddknO9Y86uk8vkqMfWF+HGzFihUsXbqUW265pcvTDnvD/DiZem2BlpaWRr9+/QBob2/ntdde47XXXuPiiy/u4chO3CWXXHLIu3Pee+894MtCrS+bP38+c+fO7ex4+qI9e/YAHPLywKysLPbu3UskEumBqLpPUVERS5cu5bLLLutyEKSvc7lc3HbbbQwZMqRL+/vvv09qaio2m62HIjtxBoOBIUOG4HK50HWdyspKfve736EoCueee25Ph/et0Re2DV9lPdixYwc1NTXMmTOHwsJCTj/9dP773/92DlteXk5iYuIh60xmZmbnNDgVtm/fjs/n4+KLL6aoqIjJkyfzz3/+EyFEZxwHX1Z6YIy9JY8D3X///eTm5nLhhRd2tvXW+XG0vqK7pn95eTnZ2dldPldVlYyMjG7L7Wh5ZGVl8Yc//IG0tLTOtkgkwvLlyzv3yTweD9XV1WzZsoVZs2YxdOhQzjnnHJYtW9b5nZ7OA7pnOeoNeRzo3nvvZeLEiUyaNKmzrbfMj5OpR+5BC4VC7N2794ifJyYm4nK5ALocqSwsLOT73//+KYnx6zqe3ParqanhnnvuobCwkLFjx57sEL+2r5pbQUHBKYzq5Nh/E6ndbu/Sbrfb0XUdn8/X5X6IviYlJaWnQzhlXnrpJVatWsWtt97a06F0m0ceeYSHHnoIgOuvv/4bcWCnr+ir24YD14O6ujpaWlqoqKjg//2//4fL5eKNN97g5ptvRlEUzjvvPDwezyE5QjTP2traUxKzruvs2rULq9XKb37zG9LS0li2bBn33XcfgUAAo9GIyWTCZDIdEuP++dQb8jhQZWUl7733Hn/84x9R1egx8t48P47WV7jd7m6Z/kcbprse6HC8fd6DDz7I7t27efTRR4HogQIhBFVVVdx8881omsYLL7zANddcw9NPP83YsWN7PI/uWo56Oo8DrVmzhq1bt/LMM890ae8t8+Nk6pECra6urvOmy8P57W9/2/kgEIfDwbPPPktjYyNz587loosu4rXXXsNqtZ6iaI/P8eQG0eLs8ssvR9d17r///l59NuN4c+vLxBdPOjt4fhypXeqdFi5cyO23386sWbO+UZdyzpgxg9GjR7NmzRoeeeQRQqEQN9xwQ0+H9a3QF7cNB68Hfr+fJ554goEDB3Y+PGD8+PHU19fzj3/8g/POOw8hxGFzOVL7ySCE4LHHHiM9Pb3zjOXYsWPxer088cQTXHPNNUeMZX97b8jjQC+99BJOp7PLww6cTmefmB/H89vHM/2FEJ3F6sHDHK79ZJs3bx6PPfYYV155ZecJgv79+zNv3jxGjBjReQBmwoQJzJkzh0cffZSxY8f2eB7dtRz1dB4HWrBgAQUFBYwbN65Le1+YHyeqRwq0zMxMtm/f/pWGdblcnWeVBgwYwLnnnsuiRYs477zzTmaIX9vx5LZjxw6uvvpqwuEwTz311CGnYnub48mtr9v/9CmPx0NiYmJnu9frRVXVPn2p3LfFM888w1133cX06dP5+9//3it3nL+uQYMGATB69Gg8Hg9PPvkk11133TfiCbC9XV/bNhxuPbBarV0uF9pv0qRJrFixAo/Hg8PhwOPxHDKM1+vtnAYnm6Zph+yYQTTO//znP1itVoLBIKFQqMuy7/F4OmPsDXkcaMmSJcyYMaPLWae+Mj8OFhMT0y3T3+Fw0NDQcNhhTuXZaCEEd911F8888ww/+MEPuOmmmzo/czqdTJkypcvwmqYxfvx4Xn/9daDn8+iu5ain89gvFAqxbNkyrrzyykM+6wvz40T12hJyyZIlbNq0qUtbQUEBRqOR+vr6Hoqq+2zcuJFLL70UTdOYP39+5w6X1DvsP1pbWVnZpb2yspK8vLxv1M7+N9F9993HX//6V+bMmcODDz54yCU4fVFDQwOvvPLKIZdmDB48mGAwSGtraw9F9u3Sl7YNR1oPysvL+fe//33Iw2UCgQAWiwWbzUZubi6NjY34/f4uw1RVVZGXl3dK4q+rq+PFF1+kubn5kDghegB3/2VOR4qxN+Sx3759+9i1a9chD2/pK/PjYDk5Od0y/XNzcw9Zn3Rdp7q6+pTlpus6N910E8888wzXXHMNt99+e5d1ecuWLbz00kuHfM/v9xMXFwf0fB7dtRz1dB77bdiwgY6OjkPWF+gb8+NE9doCbd68eYe822D16tWEQqE+f49TVVUVV199NQkJCfz73/8mNze3p0OSDpKbm0taWhpLlizpbAuFQnzwwQeHPaIr9R7PPvssjz/+OJdddhl33XUXBkOvfd3jcWlvb+d3v/sdixYt6tL+4YcfkpCQQEJCQg9F9u3SV7YNR1sP6urquOOOO7rcUC+EYPHixYwcORJFURg3bhyRSKTzAVYQfShEWVnZKcszGAxy2223sXDhwi7tixYtIjc3l9NPPx2z2dxlXrS1tfHxxx93xtgb8thv/0Hn4uLiLu19ZX4crLS0tFum/7hx42hoaOhyUH7NmjW43e5Tlttdd93FwoULufnmmzvff3agrVu3cuutt7Jly5bONr/fz/Llyxk9ejTQ83l013LU03nst2nTJhwOR+cDAw/UF+bHieq1ey7XXHMN1157LbfddhtnnHEG5eXlPPjgg4wePfqQ05p9zV/+8hfcbje33XYbNTU11NTUdH6Wnp7e5YWCUs9QFIWrr76aP/3pT7hcLoYPH87zzz9PS0vLN+Y+u2+i+vp6/v73v1NQUMBZZ53Fxo0bu3xeWFjYZwu2fv36MWvWLO6++25CoRBZWVksXryY119/nTvvvLNPXFP/TdAXtg3HWg+GDx/OiBEjuP3222lrayMpKYkXX3yR7du388ILLwCQnZ3N7Nmz+f3vf4/b7cbpdHLfffcxcOBAZsyYcUryyMrK4uyzz2bu3LkoikK/fv145513WLx4MQ8//DB2u51LL72UuXPnoqoqubm5PPbYYzgcDi644IJek8d+ZWVlxMXFdR7h32/UqFF9Yn4crLum/9ixYxk2bBg/+9nPuOmmmwiHw9x9991MnTqVwsLCk57H559/znPPPceECRMoLS1lw4YNnZ+pqkpxcTGzZ89m3rx5/OIXv+DGG2/EbDbz5JNP4vV6ufbaa3tFHt21HPV0HvuVlZWRm5t72KsS+sL8OGEn/U1rJ2DJkiXi/PPPF8XFxWLChAnizjvvFF6vt6fDOiHBYFAMGTJEFBQUHPbfE0880dMhdpvKyso++6Lq/Z588kkxZcoUUVxcLC666CKxbt26ng6p2z344IPfmBdVv/LKK0dctwoKCjpfCttXeb1ecc8994hp06aJoUOHijlz5vTp9asv683bhq+yHrS0tIjf//73YtKkSaKoqEhcdNFF4pNPPukyHo/HI2699VYxatQoMWLECPHzn/9c1NbWntJcfD6fuPfee8W0adNEYWGhmDNnjli8eHHn56FQSPztb38T48ePFyUlJeKKK64QO3fu7HV5CCHE7bffLk4//fTDftYX5sfh+orumv6NjY3iF7/4hSgpKRGjR48Wv/3tb0VHR8cpyePBBx884rpy4HD79u0TN954oxg3bpwYNmyYuPLKK8X27dt7TR5CdN9y1NN5CCHEj370I3H55Zcf8Xu9bX50N0WILx49JUmSJEmSJEmSJPUoeU2MJEmSJEmSJElSLyELNEmSJEmSJEmSpF5CFmiSJEmSJEmSJEm9hCzQJEmSJEmSJEmSeglZoEmSJEmSJEmSJPUSskCTJEmSJEmSJEnqJWSBJkmSJEmSJEmS1EvIAk2SJEmSJEmSJKmXkAWaJEmSJEmSJElSLyELNEmSJEmSJEmSpF5CFmiSJEmSJEmSJEm9hKGnA5CkvmL69OlUV1d3/q1pGgkJCUybNo1f/epXOJ3OHoxOkiRJknqW7CclqXvIAk2SjsOsWbO48sorAQgGg1RUVDB37lyqq6t58sknezg6SZIkSepZsp+UpBMnCzRJOg6JiYmUlJR0/j169GgMBgM333wz1dXVZGRk9GB0kiRJktSzZD8pSSdO3oMmSScoJiamp0OQJEmSpF5L9pOSdHzkGTRJOg5CCMLhMACRSISKigoee+wxJk+eLI8KSpIkSd96sp+UpBOnCCFETwchSX3BwTc/7xcbG8u///1v8vPzeyAqSZIkSeodZD8pSd1DFmiS9BVNnz6d4uJirrrqKgDC4TD79u3j8ccfp76+ngULFpCdnd3DUUqSJElSz5D9pCR1D3kPmiQdh/j4eIqKiigqKqK0tJSzzjqLf/7zn7S3t/PMM8/0dHiSJEmS1KNkPylJJ04WaJJ0glJSUnC5XFRUVPR0KJIkSZLU68h+UpKOjyzQJOkEVVVV0dzcTE5OTk+HIkmSJEm9juwnJen4yKc4StJxaGxsZMOGDZ1/19fX8/DDD2M2m/nBD37Qg5FJkiRJUs+T/aQknTj5kBBJ+ooOfjqVoig4nU6GDx/OddddR1FRUQ9GJ0mSJEk9S/aTktQ9ZIEmSZIkSZIkSZLUS8h70CRJkiRJkiRJknoJWaBJkiRJkiRJkiT1ErJAkyRJkvq8pUuXUlpaeszhduzYwQ9/+ENKS0uZOnUq8+bN4+Ar/deuXcsFF1zAsGHDmDlzJi+//PLJCluSJEmSDiELNEmSJKlPW7duHb/+9a+POVxTUxNXXHEFiqLwwAMPcOGFF/LAAw/w1FNPdQ6za9cufvSjH5GZmclDDz3EtGnTuOWWW3jnnXdOZgqSJEmS1OmIj9n3+/1s3ryZpKQkNE07lTFJkiRJfVwkEqGhoYHCwkIsFstJ+Y1gMMizzz7L3LlzsdlshEKhow4/f/58wuEwjz76KFarlSlTphAMBpk3bx6XXXYZRqORefPmkZGRwX333YeiKEyePJnm5mYefvhhZs+e3WV8sp+UJEmSvo5j9ZFHLNA2b97MJZdcclKDkyRJkr7Z5s+fz8iRI0/KuJcvX868efO46aabaG1t5emnnz7q8KtWrWLcuHFYrdbOthkzZvDoo4/y2WefMXz4cFatWsW5556Loihdhlm4cCF1dXWkpKR0tst+UpIkSToRR+ojj1igJSUldX4xNTX15EUmSZIkfePU1tZyySWXdPYlJ0NRURFLly7F6XTy0EMPHXP4PXv2MGbMmC5tWVlZnZ8NGjSI+vp6cnJyjjjMgQWa7CclSZKkr+NYfeQRC7T9l2ukpqaSmZl5cqKTJEmSvtFO5qV/BxZLX4Xb7cZut3dp2/+32+3G7XZ3aTvcMAeS/aQkSZJ0Io7URx6xQJMkSTqVWr1BNle3s6/VR0cgjEFVSHGaGZASQ36ivcslZ5LU3VRV7Xya48HL2v52Ve2e52qFW1pQrVbUk3RvnhDia68vui4QgKYe+fvekJeQHsJldn3NCI+PL+yj3ltPjjPn2AP3kHZ/iFZPiOwEG5H2dnS3G2N6+pG/EPSCyXZ8P+JrBUUFi/OwH+u6oNETINFuRhE6qKrcbh6kLdCGoig4TYefhp38baBoYHacmsC6Q0cd2BNB7eP3w4Z80WlvMB3+4337iHR0YBk48BQHdmrJAk2SpB7T5gvx0tpK/rdxHxur2o44XKLDxOzCVC4elU1hxqnZKZS+eRwOBx6Pp0vb/r8dDgcOh6NL235erxeAmJiYE46hPdhOeOVyDGYrzpkzjzl8mzdEqy9IToL9mMMC+LduJVRTi2PKZJTDHJmNuN1ojiPvdK4ub6LdF2J2YdoRh9lQv4HWQCuz82ajKt34MGhfK7TsgbRhcEBh8XHNx3hCHtLt6Rg143GNMlRXh2/DRmJmnHbY6RGsrMS3cRPOWTNRjMcYd0cd+Fsh6dAdww/LGnHVrCQhJ5lwmRsQuLQWSBhw6I5moAN2LIKUoZA8uOtn/nb0MKiOgwqIgBt2LgFVQww8BxTlkHw272ujvNHD8Ow4nKvex5iSjG3UqKPntH/04QgGVT1qYX4iArt2YUxPRz3g/s/u4A9F+GRPM6Ny47EYj12YfLTvI3Shc2b+mUcfsOzd6H+LvnfQD7ZB/VbIHI07pOP2h0l1HceBlqZd0SLK0k39mLcZrHHgroc9K7osU5HWVsINDZgHDOie3zqQvw00ExiPPj+b/c04TU4M6nGUG9vejBaZQ78DQH2Hn201HQTCOqcPScG7bj0Aprw8VNPhi7ivwhcM82n1LsbnDvhyOxboAKMduulg3ImQBZokSadcZbOXJ1eWs2BtJd5ghOJMF788vYDhOXFkx9twWo2EIzo1bX42V7exoqyRl9ZW8fzqvUwpSOK3Zw5iUOoxjoBK0kFyc3Opqqrq0lZZWQlAfn4+drudpKSkzraDh8nNzT3hGFZWrSSpdTcFcQWgR6ByDaQWH/FI/Qc76gHISbATaWvD++mn2EaNQjuwWIyE2dngRigaybt2A6B7vV2HAQK7d+PfshXHxAlosbGH/b2GjgAAvmAEqym6wxsM6xhUBfWLnffWQCsAHcGO7j2LVrspuqNpckDIEy3UiJ5BA4ie2zs+gW3bEKEQuseD5oxuM4QQuN97D2NaGqG6OgD0QADtWAXanhXR/x6mQAuGQ5iCLWz7tIr+zoJoQVW/NVp05k7oOrD/i4NRngZ0fRBA57SNbFyIe1MlltMuwpyf/+V3vE3ROH1+Ot5ZBIDr7LO6jLauPTrv1u1tYSoQqqtHDwYJ7tyJOd2F4kw94tmVdzbXEmszMaUgicDu3YhgEMugQUefHl+R7vPh37qNUFUVjilTumWc++1t9tLsCbK7wcOQ9GP3CbrQv9bvuJctQ7FYsce3Redf0iCWbvfQFmwgMXEfU7OmYjMe44xoyA/71oM5Bgpmfa04DtRYV41321KyC0oQRhueSABHyPtlzCs/BDjhAs0TCGM3H1QuHK6A7aiNnv2KzyOkh2jyNbGubh0p9hRGpIyI5u9tBNdXuBxcjwDRAvyjXU2dzV3eWakf57yMhKj3RHBajViMGh/sLOeTuk8wmHyMzSiJDrNjEVhiYcCMrz7evaujxWrG8OOL5xh6vkSUJOlbo8Mf4q9vb+W0e5fx/OoKZg9N5Y2fT2Thzyby89MGMKF/IlnxNlxWIwkOM4UZLi4enc3Dlwznk1tncNPsgWysauXMuSv461tbCYa/XmcrfTuNHTuWVatWdZ4RA1iyZAmxsbEM+mJndNy4cbz//vtEIpEuwxQUFJCYmHjsH3E3wGcvR3dUDiB0HT0YBCHwhr44Q9dRC+37oGbjV4o/VFuL7vURrq3t0u7b+DLrVj/Ehur6o34/3NgIRIuRY+nwf/nKgrc317ByZ+MByQjQdT6s/hDCAdj2FvhavlIOX0nNBmgsg0g4Gu8XO9WHLdCC3mgRBHg//ZTA7vKunxv+P3vvHSbJVd77f6pzT57NUburCEhCCCSMbLIBCa7BYGOL5It/xhY2JtgYDPcanB8Ql+RrMCJcwAZs+AmwQVYABVZa5V1t3p3Nk3tmOufK55z7R1V3V4eZ3RXJV/T3ecTSXadPnTp1qub9nvd9v69nWCrXbRu/NEyvrWp9Z05MIEolyrffgVsonNeQh2ozXjedIYX+OlBC4KS9+6NbFVwlwSix784vccf+aQDKt9+BeSYFroE5cax3P45LL5iOQLdbxxqGrHXiBNbxIzh77/TIwQoo6bbX18QxrNNnerYR1SqVH96NNM2uY/maxVK5+/vpgsF8UUea/roTrveM5HufI3ARKx8PNAlOu5CCo/mjOLJH2Q3HRTPtjoGfgezJFc8jqjXcbBZ7fgFhWM0TZq05FIr75+7HESuX+UA0rv8s7c4R03vuIFM1cWs5MlaRXeWTLBROtbUx9CqFmoWQ57+5AXAmW+PeY2nKxjJjDpKk6YcgtReAvem97EvvA6Bkes8nMw95ZMa1O3vpIlsV08F0BFO59miG2w4ucGi+5K3VcyVotSwc+0/k0e+xb+I4j056hM/156Rslvn+gRQnlqpe+8Z4zxXleShMtn2162SWR4LvzCeBPkHro48+fupQSvGdvfO85BP384UHJnn1VZt48AMv4VM3PuucQxZHElHe8eKLuf99L+bGay/gC7sm+c1bHmEyWzv7j/v4hcTs7CwHDhxofn7Tm96E4zjcdNNN7Ny5k1tuuYUvfvGL3HTTTcT8UJm3ve1tTE1N8Z73vIcHHniAm2++mdtuu413vOMd53bSqQe8f7Mn2r7WH3+c8g9/2GZ0uqUa5mwaRA+DpRcaBknAGnWlyxk9R0aUyZgzrba9jNvz2HE2HNH2uajbPHI6h1KK5BPHGdzlz2v2BDg6nL6v2VZIxeTpY0inmwhmqibfP5DiTI/nNmtVuLNwmMlaGiUldBCyBlEzbEHFJ5C1if/APHkXynVxFpcwJyaa7ZVSpK08CtlObIJz4/fpLCxgTU5Rf+QR73NqYcX5EVKhlKKk29x29Aj7qrt5zDpJVdTbz+HfK3NiAn3PHtxymfvTuzlQm2ve96hTJlPxiI1bbvy+416lj3SNOYh8vX0NiTZPgwAhwaqi2y6Lc2egNLvi9S0He3oG5ThdmwQAD53O8fhUvuv7I6ky6YqJatQpbHh58qcBOF44zn1TP+xes0e+C2d2kjNyzFZ6j7dB2oMEbbY6y0x5hslSu9EslWRgzzEGHjvS9j0L+z3vbbCtabM0McM9Dz3athFoHD9D/cCppocHvKkFcOrZlee1eX3t15mumByYKyFdF2dh5XUHYE9PU779DvDXtOW61Fxv3ZT1LNQ9YmAaNRZP7ObgEw8yk68v299KSBW9jQFXtK85UTdxK3XIHu/5rikYrQ0OV/nPXmPTqnP91rJw9N+b4xZScjJd5dEz+Z4ht46QnMnWUJ2k06qRm30Yp5MATj0AroVSiphdom413gXe703Xu5ezT3KOeqGo22Rry2+EnSye9Da4VkCfoPXRRx8/VSyVTX7vn/fwvm8fZNvqQW5756/wyd++io2jTy4XYWwgxkd/40q+8DvPYa6o82ufeYi7Di/+hEfdx1MBn/vc57jxxhubn9etW8dXv/pVXNfl3e9+N7feeit/8id/wtve9rZmm6c97WnccsstzM3N8c53vpOdO3fy0Y9+lFe+8pXndM6HyqeZ0YvkrXbDws0XPKKgFDXbRSpFfe8BrNl0zx11kc8i0i1jbypXbxlJgfyIu6fv5rjhrX+NlfOHlG+I6Hue8L6oLHg5F+eIbM3CljahesA72EOE4sTMAplj9/OZH3yMh6ZbnqBguNKRlBfmVzUdcqkMxsGD3LVwhJrlkn54P/qRqS5DruEVuntiiZ3HPW/UrvJJflQ63pN8zlXnmK7NkqlnepIav9PGhXgfZW8juhO3H/LyZvfNFjmQOYDSvNDBotuYT//3/nlFuQKAXi+BUlQXMyj/fmpK+kZj4JylANnuHG8Pg7hldHpoeky0EPMlHct2KDt17jhyitTB+2But9edffbNASEFD6UeomC2jO7GvXCEg5BiuZ8ucw2iOTaAycIprNlHcHzvSxv0PLsXd3Mkd6T7WADBtd8gbZ3hjEIJNPvcvFf6iVmypxeIl1I4HeRECdlB0HwP7+TO5rz2hsKRwrt/C/sh7W0m7J8tMZOvUz4ygb5vf9PTvRyMI0cB0Oot76zmSAb3zKJKOkzeD7nTuLZHEGJWAdPpXv8n7n6Ae/7le+1fSul5g/z723w6NA2lFM7iIqKYIbf3KNn9E5CZgGKH17qa7rjq9mesbZ0bRaj6pLTmPdPCP1wxHUIridx03F+ZOcbuqbt5bPIuAMzjxxBLrbEpBSKcJORfS8acI+waRGcfJmFkiIa7z6WU8jeLloFof+7cbLaLzHZirjLH6eLp3h7eAPo5aH300cdPBUopvr13nr+7fQJXKP761c/gv1+3vZlr8ePi+ss38Mwto7zjX/fxjn/bx1+86um87fk7+qplv6B417vexbve9a62726++WZuvvnmtu+uvPJKvvWtb63Y1wte8AJe8IIXnPcYdKtGRRj8IH2ai5zLuXK1yfqRloCAUjBf1IlVLMrjNuMYuEIylS6y/ZKA8mItQ+3Wz3r//7q3A7B7JsWOfBov+6n3Glcoz2jRQmcNDzMOHyZuHIJwiNBz3th1vGDkSdRrrDcqbFh8lMy65yPD8TZjfPD+fVSvHMARNqtWrW5+77gOAgnC5qGZozzvgks8g6iewxEmCoiFvXnZeSLL4GMPcM3mEUp6ndXTKbRQxNud77gGheKxxcdY0C02DVzccawbTjZDJFfCHejILQv02wy7exKvjZl8nXgkTCI8CPi7/8Q84901gZiXr1Scafbv2CbhQpXkdAEzsgThBCiJ44r26+0VBkaDQEqgPZesEghB00wD6fdV0i3yNRuKBjOl4xwzSuzw29nzKYwDBxh6wfNXvM6p8hQVq8LR3FGu0Va1Hbtn5h6GY8O8YMtKz0vH3WkYvI18OOmHslbmgfMrah90VJZ0G90WzblWRsnb/PCFZVTHejJPnCC8fh2T+hIXJtcSJaCAqhQNJ26TJAQIgZIuB4s7/U/dUSCyXkeLx9EiLTO7alfZWTjKBjZyrWt551n/DCx/40RaNiECazKAdN0jPesHA6VF/OuRUqJV66CgMpsit3Y9axYPBH6tEYt0L/DUqVnCwWsGyB7zcidDERi7oH2+Dh3CnpuHhb0c1z0P6rqqTriToHf+DVYd31cWIDFG2QmTmLqbeMRfB8KG8jwy4BnL+14ozbZRoVAzZNm/8PbT+CGkVaeKsURArgAAIABJREFUEgLr8buxrSIj113RHIYMRQhpkCpPU7JzRN06RCBhplF0CyMZTzyBk8408z3PlM4wGB1kw+AGL6du6XCzrZvPU398N/bW7UArx7d85CjRcJiBp3th9IdzhzkX9AlaH3308RPHYtngf/z7Ye4/keW5O1bx8dc/85xV6M4HG0eTfPMPnsd7bz3A399xjNmCzl+9+vKfmhJZH30sB6UUFcML24tqYTQlSJWMdoKGxDAFMSBUTYGT5XS5yO7ME7yufANbxkZxyimWTt1BUO5Akw7F+W9h1WJUiptYu7rGVRfShZAwYX4PjG/vTdACj4V98CHsqud5G77CIhSPAxB262hKcSg/waId51V1nbAwCQsDrabjZFpEYDwxzrH9JwltgvF1m5vdS6kIoQEaQrncP3c/trCxbIul6YcxkhvYtPYGYLNnNGsarpQMLZWI1SwYaZgm3SGOBaNA1sixKdExAT2uV+093KOX3niyGzuaBhohFP70KgUL+3xj/pleo+oSyIZXR6A5wvP4CAFhCOk1L+fxnAQsOjxoSnXl/w3tfRS10VtBmusAElMoyqUiY3oOYt7c2dk0trAQ1SorMdSTRS8/y5YB0ji5C+K+F9Tu7YUt334HAMlChwHf4UFroBGWqYTwSgR09Hfn5J08b9PzWJVokUQpFSU7A2qYB05mAXjmDs0jyaknwHFgxwu9toH53TuVJ/b4QZIJnYNji6TKBr8yPIV5dILhV7wcNA2JAlTLyxj4vVVoebkad8M8NY+bq1Pa9EoSu+5laMM6Bq+7jvLtd5C47FKqYw7ZqkldpHnG8DhDHcIbmVNTbBob6PLKgJfTBfRUn1TCARXCFpL5YoVMaZLXrH1ma2QKGvdX1OqEYlG0gPqhcfgIySsuRzMKHjmDpldfKxaIVHXUxWs8cgZt46sdPM3o8ACsucQj3D28qU0PWuN++7mQp2Y8j+wzt4wRC4e8kNf8aX/ePSz5ob9Dex5ChSPUnvfCVr8BguZIh1zAw4uUXn5q2zg8RK0izqldRE2FoOER7b0hYqeXWKgtMCAcouEoJwpe6Pqrhi70wo4DwjCqkZep65BsEbS9D+xjOBHlWp+gISSEzx7A2CdoffTRx48PpUDYKCn49oEsf3fHcVyp+JvXXM7vPG8boZDm5YgsLOAsLiEKedx8IfBvAanrSNNEmSbSMlGG6SX2h0NooTCEQ4SHhgmvWkVk1Tjh1WuI7dhO4rLL+Mzrr2TL+ABf3DVJzXL5+Ouv6pO0Pn52KM9xPDzCodk0ds0GNDTfCHVSKdy8F9anpETzDYGQXQUNylJneMGmVi/C2CjHj32HY5UMl5XrbB8dwZE29cpRXLdCvC4Rag2pssFVPYYREQ0DYRmBC7OKNEqEkqNQbYUFK8uCeBzTEWxYuh9dWmS3dSutDRzZTy3fLinuuC5xPI+Xe+AAiauuwjRqTWNIKBfbz7WSvtEXs4qcqR1AWpcQyWUADaUgWWo3qIpmkUfndjY/W8JCKsXWvccYHE/gPi0g2hIw1tx83pPMl663K29WfCGRDd6YSj1EALp2/RW67XLPRJqrtoyxfc1g4JAiVCkjh0cCIVgKNAhLge1KYm0S+KoV6igEtu2SrphUTQGOQfLkQdS51tuSiqpVoSJNRgGRPkooc4xI7GogCW67cR+ZfYTh6hxybAOmIwgpgS69fJyDmQO4+QWu47kdJ1HULcEotIVhrU6shgYXc3TPoB7sVi4MKoAChKutEirWmTPE1/q/8cNChVIUaxbOuDf2yl0/ILJ6FYP+lETms6hEDLFmlKyebSNoS0aKmfpRtutx2jxZSnjEwGyduyk243uyh6sW1UKFTNiCsIkz64UUK6MVwhuSLqe+fxfbR+NtBM1RrXlp8GU3UyISinJgrsTIYpVnR1smtnniJOq52xBSEbIFqlqDeLuS6mLZZN1IDEc4LCceL6SgYObb5gBHR6ssYdouJKIwlUOOtAhHaCHNVPo+Nr3wKpyjRwkNDjD8kpc0wxatA7uIRExiI61nQFRqaIMmsUP7SNouU8+Ik6zOsWV46zIjg5brsn1LJF8zKdRtVjXCiH2RoQYOnclyWSnP4JUXEUrGl02V1YTb3reUML8XqovcE7YgfYiCbpO0G+0UAoVQkrDm17lUikThGO6wIF4pYcXiJI8tEnejmNs6nwNP4CSrZxGF41y59srWgUZYp6OjlCJVrDNcT3nXF/h9w2tbNR3In0GoBIMPHsB62rae4eFB9AlaH330cX4QricNPvWAF2+fPwOVFCiBBvyWgpfqq9DEBsQ/DzL/KbALNk6mAG7H7loohBodQo4OwcAAWiJOZHSAxNBGoskBL5xBSJQU4ApErYooFLHPnMHN5z3DEtAGBnjLNc/hsq1X8VePeUZen6T18TPD0hHyaj2acimbDgNazCsUDOj7W6FGsrrAkJhGU+3rcjBbg5OnyI5sxJYumarFRsvGVoKJ8sNE3JrnUWsaiO2/123vuRotebkpWBWUXScYZlM1Sxw5dTt2tc7Ipqtp8z/V85CIcHjBRAMWRAHY3HM3/PB8iaSW9Y4lxn1vSAT9wAlYo7Clw9yPboULh9Bcz1ASrk1YA6U0IoZDWEoYA333bpInplHhsG93edelSy9HL6O3q1LeN/0wJ5aqbAUco0z53ntbNrm/qy+US/VHdxKKD6NljoNV8OoazTyC2n4ZO+d2ctHBHGNah0e/s+6RUtwz4YWVTRw8xZYrNzUNJjebZfDwXswdlxDa4QUMxqo6hBQhx2KpBBcECB2BPJapyhS25RJFo2y7aCEFRJDlcrsHzSx5c9whi6+U4kz5DIRjKKX44ex9rBcukfDTgSSJaU94I1+zGVgVQVMtwtZ4HR5yZng+GzCF1dMIzFYtZgs6kYpJPG6hGRYqEqZiV3Dk2aXs755Y4teftbnnMfPYceJrfElz36OSqVgYpstS1WDEN2jdfAF8ghY/PYflSiaf9TQu6qgOYfmbEm6AMKUqWVLFGtuUglDEIyGnTiHWeouloQkKEHMqRAwHBhVCKvZn9rOhuJ7G9kTCSFMojLJ9dB2FmkUpV2PbqkGkDChmNv4NWubCgYX9qPp1za+kkthCMXpikcqiYOilV6M5pvdD31g/kjuMtVjhFRdeTFbPsmdpDy+94KXNPo7nj5FbOkA0NIA2PO6F7SlwJ7xSILGaRVxIjFPzoNYBEE7nCI+Mktuzn9GBGLKUg8PfYVV+P4Wxyz0Pb2o/jLRk4muPPIa2KkVIGJDfx9RsiXX1AluSa3veV6kkmlJUXINwh0d1MlfnQZHl1we8a7SmF7FSOdi8yfOUFsu4loOdLpDYvrEZntsTnQStQZT0AtJwKOkOS9lqkxg9UZ1j8pTJG7Ze1QyljFt5zhyfZHXNIJm0kKZgwTzKgF3qciQ3vH9z1bkWQRM2BBRTi7pD5tAU2aEkF64dalNyrZkOIWEiwwlY2I/Ml0G4RLIlWDe+/HXSJ2h99NHHWeBIh6X6EqmFPZSO3YY98zCWW8fWQjgjG1EbLqCobUM7WmF7ymDHokHcEEAJGSpRXiUpjCuyV2ssrIkxuypCakBQGJDUEqBCOqB3nXfH6A6uWX8NL9/2cp674bmEOw0VKXFSKcyjR9H3PEHtgQd4xq4H+VY0xt1Hr+bvczfy4T+84SeW89ZHHytCuh0hap7Kn5CSsG/8K7PMSKaAIcc8OyOwNKvzE5wYfwarTEm0ZiGRGE11R2/th/AJU2OL2THAqlAxfWMhaLxYNXJGjkNLM1wweBl3H9vFeLHChnCIilVpH/v0Q5AfpB59vmcTN3a4A0bouszDmJaOFRlAmZXm0LWGuasUGqDPTiPreSqWYP2JPOxYRV4/wrrhBGrV5aw+mUFpEbJbvfpYjXEHJfTzNYt5RyfuzSK5qs2a4RhVszWeJXMSpTa0LtcxKJpFZsqThIvTDCbGGFASW0hypsUlKFzpYroms+UZxsae0bxP3oW0vycyVbNJEJInjlDX5xjd7n0Wde99FdY91bd4qcKqUyns1RJNDeAq0EIhdNtlIBbxzuGHOFatCpqSKBS1uQxcsAFNqaZB6QrFbLHO2qE4ifIS8fEOoqMUui1IJvxQQCBtV9isFLG5aTRDb45/NBkhSEOXC+M0HANoecJ026FopSlWdTbEwww8fhQVi1L75St5In2K59Ca91OZGqYjeOU2QUivIwe8M8oesu6G4yKEYrQx574Xt/HUuFKCbXvz1WEppyueF6aztIryn7mwT/Z0t8JMappa1eA4WZ626mmI+Xmsk6cwJ1rrDQWudCg6OVafzBDZGuNQ6ihFK0+hcJq1AaGH4eppYB25qsGCmyVpKda3DYKuNRx1KhCxIT2BI21s6ZA1vLBITUgyVZNQxWT9/u8yvEdiXHp513xNV6YByJstZUyjuoRVzSGH1jWXrH857VAKzfB/5zeMNMLqjBIwDhrE7BIy4f09rT6yj+SmmNe+kkJVUgyVDGbdPKFaGoopTlYW2J7oLjfyg6kfQPGwt6mwFPKKZ3fBG4ed9kNypfQ2RsJh0GiK5qwUkxwydapOkZAWRpTLTRIzsOc0spKBi9agBYR0XKlwEMzkdS5eP4Tmdx6umUgUyYKOHY8gkFRzj2KvThILFNXW0BqPLnWnju4I8pOHcTZeRtSfz8lcjXBNx4y3b2gppTjx+F2Mlo9RHn06e2cMhk0TjBywmrOhT9D66KOPNpStMo8tPsaepT3sTe9lsjSJJPBHcSwBJNiYV1y3W+d5xyd5nr/RXRiCRy/WOLElxNTGEIUNg8QSAwyiMSYk41aNjbUczxCCcZFkfPBKVm19HgMX/Ap2KIQlLMpWmfnaPEdzR7l98na+ffLbbBnawpuf/mZef+nrSUS8ECstFCK2dSuxrVsZueEG1If+AvPIUUq33sorvvd9+Mwe7nr8Tq7/1F8TWX32l2Efffy40AIELewaVKb3c2C2yHO2e+tPoYjWbIwe8Uv1wgJKKaRUrDqTQ4VHOGN4OTWDmSIR5XjeZBSJqZPAL3nFYrMtlcQ2EQSl2L24mydmClw1vgHPAoJczWJdp32lvBC8xs+TcwUGlg7Dy3xPh2lDJMRgfRYjuYGGAb1QMhhVjY0T/8d6gbAwCItRNCVIlGtYI75aYWmm3fbyDUfDraA77RswrlQkFGSqFnMFvWEDtyG4074/sx+nMg0oXKUoGiUMTaOk24xoYWwhiTUk2YOe/MxxsCpoV1zR1rfT7TxkzipwuJ7iBQPtAaZRn2iGHAEhhUYIwxbMLVbYOj7A+mEvCkAqRSwUw9JNIg3zS3ieBym9e9AQjMjWLI6cyPLSjS72/DxDvuPqruwExarGkAgzV2kVVNfKZcIzp0jpJ1kT38JgZAQpnDaa0yv0CuDxxcdgoOWlKdhpKm6e7PRBNqx5NrgWmi+RLv3QXSUk0rQpG969re/fR3LfMYxnP4/EyaOIS67HdEWrthQwseBtDKxxBZqQXt5RAKmyTmzn1whPpll94dVk6nWsSLtgRmctPCElA/oCgzN5ymOvo+aWfGeoZLFictrIcOb0GPGFY4wmw/61e4Qka82i/OsJGzYD+gyuvsTppTLb83U/pxDCwmS+qFO3HGzlMu8UWN85s74Vb0oHoVwaZKTy4BMcVyYCiZHTGZvKNz2HqZLBmqEIqAjRzGJ7f+BHjljNwvAAc/kqruFwgizjY+ua91Kp9pnZU5lmiz4Q6A2OL1XYNJZko79hpAED+jzEB5G6iTQ0jNNZpNFDHr7xeAubiXqrFIBQkiW7jJNMUKzarB4IEe7BsBb008yHC2zx170n/qF5JK3BNBtqmP7v16YfIr/mGs/75GNw/25mbG+u3FyOuL8DoUlJ3XJIFHWGpqaQz/HygRuO8ZxT42I51LqQAIlrjLYmatxbmubZQxewoZ6HwdWUDJvpfJ0R/4WQr1oYtkNRt1k1FCPn+OtbtuZfATkrRUo/ydpamQFAunUetea5TB8mpvUuFt+JPkHro48+AM/A+frE172Cm9IhGUny7NhqXlqpscV12XzRDYw/442Ed02gf/cOwme8GjMn1+8g/9aXc8XrXs4FF+7gak1D0zQS4UTvHVu94NVMOvkDOH0PTNzjhSBd9kq44jfg4ldAxBMsMF2T++fu55vHv8nH9nyMrxz5Cn9+7Z9z/fbru/rWNI3klVeQvPIK1rzn3dz5gY+y45EfMvGK3Wz7m79k5L+9qq/w2MdPGZ6BMTyVYyi5D3nBBnRX0twt7RR3CGBW5ijWj+IEauc0isOOzaUZ0DOwfjxgXyg0YbeFDQdNNGvmIfS16xjMFhmY3k1itEbEXFnWueGNiBVqOOvHm+cJH5tsqqdpym1GZJUNm3FXghFuetCUX7BV00CGOtQTmwVgFavPzONuW4OrHLLGLIezaYJeE1PaDNUzuK53TXXLaXkAGr3Uc+Bv1JetUtMHVNZtLAmbhwKlPJQnsw4gpItUgpAWBt+baOr1djW7gOe9If4xZ3k7/3qjaLTmi4S4Ak3BiCMoRWpcGhnDERItW6SejIBSnE5XKZVqDG0ex8q3SEsolYHkJk9avCOkNG4VMA6nvMH7BM0LlQzhCMmBxTOElPL8q1LhSC/cr+4WGYyM4C4cgU3DjZvbtuaWI2vBo6HjJ+GaZ3uFeH3UbEFRtxjPp7Fm04QuuhAZjfDE8R+RMS3WTK4iXK9Rve8+clULV0hiVgEnOoLyPRMPnswyli9xzbZ2RUgNb82HywWoWey0TzA4CBsbd7bDK6eUIry4SEJPEx7cRliY3hyEWtdQMC1iCzOkygVMESMRCeP5uqQnu+/fb4XyPC+AJmmSBs11UVqYdMVsIx4qsGkp/d2DqjBZsMtMVQ6wrjVIhO+JThyZJl4x0UIeq9AWslRdAym3d7wb/H8W9kN9CW1oEyweQBu8gIGHT1Pxx5uTFdYwhlSdtBUkioxTJUl7buNCySCRcBglKHrv/9oodpUmsDrTEjpwRF+gVgszUU8yZLrEQiGMfBmnPsvQxosZHvSewaw1xyFtgc2DO8jZVcqGQKvphKa8vC1WDXZ5sWNOmaHaDJXRy3qeW7kCe6lAbMMqUArLlQzk6kihUX/sca+R32VJ1oPbzGjKmyV/Arw5y2RgS4iSa7Bhcidc+Xp002tTNR0M10AqiLg6FWGwqOdIWUUG5eo2USdbuaQML9TYVDYDtGrBZUSFLdrZBUKgT9D66OMXHpPlSW5+/GYeXXyU0fgoN152I9cPX8Tlu/430cVH4NJXYl/9ZxS//yMKf/t+3FqNqbEt7HrWa9n+m6/hd19zLYPx83iVDKyCZ/6W959wYeZhr0jlxG1w5DsQH4Gn/Ro8640ktj2fG3bcwA07bmDP0h4+9cSneP+u93PH1B186Jc+1C45HEB0zRp+7Uuf4C//8UVc8///E9H3vY/6gw+y4a/+ktBAd1J7H338xCAVsYqBppfggoa3yYMrVbOmUrdJBWUny0YaNoWGKySan9sBYJomQ1EwRZ07Ju/kZbU6g/tSJEcSGGsG22y8mmviSsWq6QXCA0OsXzjYI5C4c+ytAq75qsl6YaEacu6++ITqGrlEOzbJ4ugYm9cFrlZpyFCUiLDo1kdTDBTKHB48glIew8rVLILyI/Miz3juJEQ86evOQswA0tYpGw6RsMaQb2vptsAWXqmBYNUlheLI/N5mcd/pyjQXjl7kHVeK40/sJCbWsn1NknQ9jTbS6XVXhCwXwu3GOXi796BIGg7OaMQ7r5SE5tOeJXjhxei5eWJCUrV67Z4rXKkCBNaDF1q3qaOpN8OGI1hIV9jkZIhHwmQss6vXSr3GKoYDZ2nh4fJpGuxWAZbQm3e2KV1PN3mbKegMlatcjSIS1lg9OU/2su0gBbao+XPhkZZ01aJa2seIqzMSWUXZDyvVsplmFV7dqoGSxCom8fRJzIsuICJtbCFxhIMrI2DXAY2teydgYDWs8/rZ9+hh9IPHGUjWCK3T2LB0PweSYcaGW9c8ma2x0YqD5nlj48pmXaRGuFpEIZtrpKZMUJ5rO1FpFVEPTS+gtPaNBilVx7x4MYaLtidIojtlVA9VzGq5/QkMpfMcThSZlxab1TpkIuk9bQ3PTqNGYT0HrkWo0J6TieWQyk1xOqqx0W6JoTTOHGq+SdpR1G0StQJzIs/GUIAkd5AzW7TIWfJU69wl3caVijVD8eaaabzXCnUbpU+jKUXpzG5KI5uJ2sNEbAtW1XlcTmFaBVAxRiqtwtASQNM8ItTmvlaEhNnK4wpA5DMYC/NExoda1+mLgzTnwvL6WpJF7itPkGyov0qBaiiF+s1HZ3OwZR3RoIcrwKV2L+5Gc1yQinm7QKVuUVE2A+UaSMlodh9seD6O6M5PDPmkTGiivdMV0CdoffTxCwrd0fn8oc/z9Ymvkwwnef817+f1l76egcPfge/8ESTHsV/waXL3nqb09/8fEnhw05Xcec0Ledb1z+fPX3Ypa4fjP94gwhG48EXef6/6hCc8cuQ/4NhtcPDfYGwbPOtNcNUbuXbDtXzjVd/gG8e+wWf3f5bXfv+1/O2v/C0v3/by3l2HND78x/+N342v5tK7b+UNt92GOTHB1s/fQnRz7wT2Pvp4MjhYnyOx2kIBwwvljqOKicUyz9g4SrrqGdFxMw/DPcJuVSuXy//IZQ/8oLWzD0gcqk6BAbWVYs07V7xqYo3EcRdmmQ8PsiXuGd4hYRGSDoP1FK5TaztV3jCwcNgQH26ZMwHDImlmsWYPI4Yu7Rii74nRNEYqp4jE1pETFaqlImv1Hc12numjGKhkkNUhIkLCaLuBJQERVMPrsCSLdh0VWT4hxRVOk7gNr/EMxPYcJa9DE+8c5s5dDPpGaLW6ACM7/GvyygtUrDHS9QppPU3c3gy0K9aFLQEDIDpz+ILUR/M/NbwgQmBNzzFY8cQMqs4YlgINpzmPKIUtFXR6HMETYtp4VaB7jZB0kFqMNbUUbkRh2DbDK0RNmdJmwp4mHNjnLzgGIw2CpuB4xfc45DTCvjiLpkFe7y6aPKcfx6gv8MIN2wj7hZ9VcZLBer3pBUxXPPEL21oiB+REhYSzicHoGJptQwJydo3dT3yGRKlCZLFCyBniWOUMY6LA+uJJ1FqJEsorfKx5RF2bX2qOI1OsoVCEXNlU04xbJdTwcPOZkZonYNEo3B0yC8iYYGDvDyg7NZry86ol7r75+BNog60aYK1NB2/tlA0Hy2k9K+mKxUggbFlbRobQsIVffKIdWkDVsFC3qZXNtvNqNZNQ3QZ3CU1Gm8dii0Wqogobd9AJzfY9xD2ggJMPP4YhbaQme24WKaU4Y2ZRMgZoRMom+CIbdrAQc4fjT2tcjw9Zy7DhVIaEmYEXbWHRrDIkFK60UG6tKWM0W6izZc0YM6li01uvlCLjZNixcJpo+QTFVVe3jzG1r1VAW0qkAk0qwqI7UkDDz3EEnMgQBVGF0EjnJRDJVEnu2Mr0xATZA58G1S4mtOaJo5hOkaWROJYeoqocLpxcgJC/AGwdOT8Ha1W7F77NO66hAuHMy6FP0Pro4xcQJwoneN8D72O6Ms1rL34tf/LsP2F1dBh+8EF44ss4a57PzJlLsb7yKYSmccf2X+aHV76MX3vpVXz5l7exbjhx9pOcL8JRuPhl3n+v+jgcvx32fwPuvxnu/yhc+GLCz72Jtz79Lbx060v54IMf5L33v5ffu+L3eNfV7yIS6n6dJaJhPv/Wa/n1ms3sxov5wO6vMfWGN7D1ls+TvKI7KbuPPp4Mik4dacyQVKsYyNdBC65FhWELyrrdMhJ9UnKskKPstDwfEVFHt6oM0TLitLbQJ0XGnCOpJVDl+baIoJH5MvGqSW5YNQlaJHOYAT2FGmxX3stWLTIlL2zNjIa4vGbA8ECboRYWBu4jU9TWd+72qqYhpoCQdDBwQLi4mQmKrrcrHq2aRH1SOH7GJD4UJ/T0dbSZJBrU3RahDXWE+NUWluDCbV5/dQNnMNl2fL5YZ1gvU9sw7OWSEW0LFWt4EBzl4gpFqmiwdjhGxK1CLeup1+HzJOV5UwSeoScC3gTN32aPzBXhshFk5hhBUY0gIo5oywfSFJjHTzSPu0pRMxziyqWkdMYYoOKWiDQ9lD3gGCgVpy5t0CBhZrFjDoNWkloEkH646zIoSR06hNtTJYORZI/GiweJGDkIrwY0TuSO0auCpalsasJCa/g9fVW7Vbm9uCKBM3hJ128MY5bB6FjzHmXqOrHJFCN5nWosjHIg7PcTtqugJFqn2IgdJPTe2hzM1pBS8Zh1Ejnokeqw5c2HQjXz5rzPGnXLxTWOkADMxPrAscC5AsazDMebLRp4fKoA/vwVdZvk0enmsYhl9ywrp1UXur/0Ea553rKS4VD0N3JSJR0ZdZGP7iZZz2BethZNtEKgLdU7ZNmVipjlojVMfD9HzZEWsXAChUbMfzpcJaEHn6wIE11YVKXRfbAHksZS85IjhoMbj0DI20yAKBFXJ1+zKZsO6+oWmlLknBxDapiITyTnZ3OETQtrx0YYHSInq8xZZZJCshVP1KUuWu+Liukwmoyxf7ZIumwRAyKWiwh31t3zNkIUqikSoikI2xUknniNB41w3UbTNGZmp2D1RVTMOp2Ilw0kGoay/V+1UHaqnCkvsXXWxEnEYLtHQQfrs0Dc86xqcDJdY8pZORetT9D66OMXDPfN3scHd32Q4dgwX7n+K1y74VovL+xrr0FOPcrxpRfifmcWJWe5a/t1nHnZb3DDi67kPVdu8BXJfgaIDcAzf9v7rzQLB74J+74G33oTjG1j63Nv4qsv+Uc+dvBzfOXIVziaP8onXvgJxhJjXV2NDcT40n+/htf9k8WnXv0+3n//F5h961vZ+qUvMfDsq3ucvI8+zh9e7sby3p7T2RrRgHenalvNnCaAZFFnTeowq6bKPUICffjdJ8wsw/dN4w7o/U6rAAAgAElEQVS0JK8bRC6oPgigNI2CW24zz2um0WZ0G2dSaOvGMVwnWEmqK0/O+8onaFqDorV7H/KOZ9AMLVUC1+EfbV5/q9+a2wrrC4sOY1AqYuVJBkqjrJ6cJ3fRFoxVrREKKRnI1RjI1bC2WWQyMUaTDS+U8opB+yjWLUwhqZgOq0LCG4F0m0qbDSMuRIjpfJ34UJXBtZDMniFupEENEqpbhOo2yjcANcdBc1xkrdR0H0QrJioS8H5oPinXNKRSuK5sisnkRIVoOEzJLhEqH4LRjpDtWiskbtLMccJYanYcFlZz3kem8ozVdpEeakQGtDQ20QKzsMzyVI7B6GKZkfkimSu2tefqBLwloXKtTUK85OqgxYmYVjM6LiRdYnbvOnyDZg5GwJE2RVnjZD3L5mydiOkSd1xUOEHYLxlhux7RjZTb18TJTJXJA17eUjRwf7NOhWSuRi2pserIGUQ6jbXOobCmzLgMeCa1UE+FyS4E2oiIL7YRIHoay3s/Vk/OI5TnIddds2dEW0nWSTafSu9ctp9DqKFQ9TwFy8Q2XdbXWyqOtQBhqivf09ZxOXXLxbQFjhZmTThJUZRZ1I+hgPWJ7QwHSkErXwLMXipQdg3ioQiJUJQpM0fNcjnXcLyk4W12hBzB6pMZjPEBKhf4G0WuN+ZyRw6sJhUpUWBbxHuPlfUyRVmmUqhiJlexITwOhFgspxiVDllrrjlHfg8ASFe0ed+jkfYxa0IitLDvpFStUMiOnLeusFSzjPRfptFMkZQ7yqj0c21RuP6TEvzVYu44KjoGCpK1IjCGLV3mRQ4IRO50hGL2wrnNfB999PGUwDePf5M/3fmnXDJ+Cbe++lau3XAtqrKA/sXrKT56iD13XoL24GkOb72CPX/zed78jf/Nl977Sn7zOVt+duSsE2MXwIs/AO85CL/1LzCyGe7+C2L/+Gw+bMX422s+yL70Pt5855uZLE/27OLS9cN8+sZncY8xxNfe9CHCa9Yw9/u/j75v38/4Yvp4qsIL/eveitZURxsfab17Z3Z8smWIlWT38QYJE06x61xBT0PKKiGln1+jaVi0G0aJgGx3A0t+WJVbrzcND3/QAESbdcI8xUHH9a7GkHZT5VX02ooPQrX/qwVqCfVsLhWVSoWo4Y0talhse3w/Q7VpQsImbrWuQ8wWSRpLhKqp5jnKbsuY9cIO28dXdurszEwyV9TRpCKETzwVKL9A89rHvkesPodemfcIn5RN9choPot48HYS1XprZ17rMJj9UzZ+o6UPEQ3kC9WVRViYhFNHWDTac4zCp2b9iQpRmZ4HpdpsSqUkKI8UhpTDgF8oFzx1PjU80JrrFWxBLXuCTcf3A5Dae5K66dWvS+vzbQWFQ/XOPDev04hptda2f50hq0JItLe3kSwZk0yVDnPCWaBquggZDLFTngcaqFoumlQMTXeQveB1BOaiks0wkiozspQnWvWIbVT3tghyVoogVM9PnRPUPWF5O906tYKhpTxrj093tQsJyZLteahP9iheDB45nxM5v72DKx0wF4k6VVAK1ypjq47nQ0HI1Zv/v7HOKmXDJ1MtCD9UWipFUZSa57dknRPVU8xX6gilmBN5Kv5zMmXmOK63QkgtV2Irt2cIJIBuu9iubDt3g9BHdRvNFYzOFADV9HZC610Vr5pNT7UlHdJUcX2vqYMgpHme0FVncmRFBU22v8fm7Dw1YeEeP0XMbm30xEIehbKF6+2b+MPP1HqoUwagQg2VT0WhbmGWWsJF4cNTLJYM6n5OW7DkR6NNUdYx/TzSqFslYWbBtJgVWX9DqPEwakSdCmPlY6yEPkHro49fACil+NTeT/GRxz/Ci7a+iC9f/2VWJ1Zz/2N7WLj5JWRuL7D04ChqaBzxvz7Dm+78Br//W7/C1lX/hQQ1whG4/LXwe3fBH/wItv0y3P8RXnfbB/nK2pdQs6u85Y638OjCoz1//orLN/CeX72Eb5wxOPS+m4msW8fc2/8Q88TJn/GF9PFUhGZbxOfn27/Ll1hul9RSvY2Fxt52l3HW1kZSNV1m8y0PS5CgZZ0qeadGoWahQt07w+EOw1lIhVrIoCk4uXAEU7V8X428DYGgUetJtwWG5QKKlGiRpJP1bNd1BNEQVmiQmaklj6hEnRpDtemu9vVMlZCQbV6bxm581O0msEFoHee3p1Jofohgw7JdNJZwpcRRAi2dxxaSUt0m5pQYLuwnYlcwpM2Um+bkkr+Zo8BFUnNKuMrBcWrEA55Qr4mi2vAWdLg34lP5rrBV8O7pVHHR84AGoPuhi+FUicSpHJrw51A6hAwbOdWa/5C0/fnRiJvZZt/BnEaAZL5ORG/dY61HTYGGh8g0HZwO+fOOi2XtqbnWEX8diswxRjsMUFfZ2KLOUK6MJiSOkOTrdlMUorN3TXnfma7wRFQ6EJJ28xpFxgunTVaLaPnTxKyib5h3xxo+WS1fRwV9woLVM3MMloqEnOCzqjyCIf22jZDEZQiykJKElSVlnKJaPUbcypMwc0xO3kusYrYrVypFyF8PbaTIcNCX2exYFIW2/Cchu9vNGO3iNChFxVg+ZLaBmuVSMmxyyxCfoXSNRMloKz8Sq7Svb4WkJk3mRK75Dmt4wzQ0okYrVDXmtOd+VoTBlJklJdqfKakUe1OnmcrXkVKRzLfeFTG72NYuiLDv/ZfKE+2pmA5uQDhpoFxq1n203O7NqEwzXFuh+d7WUqBo99hMobn4os7K7y/oE7Q++njKQynFp/d+mq8e+So3XnYj//Dif2A+7/K+z32bC7/wBmp3gL6YZOzd7+GX7r2TK17zsv/6cvSbnwNv/Ca8/UHY8UKe9dj/4d8WllgfivFH9/4Rt564tefP3v2rl3Ddhav5i11LyI//I6FkkrmbbsJZWD43oI8+zgVDp6ZJpNp36kOzSyxnmVXdTjERD65aIXE80FVbon4PSBSG205uevepmCnUUQs5TzWwo/li2cRVgkknTV7WvFwWIXF6nD5ft6j4xMRty/vpHj9A1KmSMNLErW4hCgBVMhmdybUZX62deElWdIp19IzKBKBglv0Ctp7Br1AYjdCxwDWnygYRV8dVLmOZh5oEVDTIjlKcrqTI2wuk9FMYlWNEnTJh37Oh+SohzfvTQSwkEA7ck4pqefl0u91DYEmHk0aaRSODLizCJYPEnGdghpRLcjbHQCYg2S8tEma6bRLm7AI6vgEtJAOZKiPzJVafyoLrh9TZPQialBR0hxMLJYq+EEuhaqJC7QukQYSbYYPuyl7RqFMFFJqQjMy3PwNdt04pbFdQMQJ5Z0gvp0wJ1mYfJWl6Xi2t4M1/zBdwCcuzE4zOs867gXUYi+IqSUHUWm0C82qVJxjQUySNBcbmWl4n6BAJES41y216qDvRIJ7hQA5hwswxP7PI+FSe4cUKZcPGdETb+YPkItpD4dQfCYay28IxNc9P3NaqbjuYrosrJTXbpWq7mD0IyHlB05retLiV80iaUoxPtXvvFV6OKNAkNQP5uv/caG3vL+/ZkmhKEBYGsvFdB0zLJpWfxJQ2hi0YSlcJ+++lwfpc81ntRMg1vAEF5rbxPos6ZbYdeMSrGweEre513nhHKFrvKRkQXopXzMBGmkI7yxz3CVoffTzF8fmDn+erRz1y9oFr/gf/cO9p/vTjX+Md3/sL6o/GiG6/hIu+9x9sfMcfokV7qIj9V8bGZ8Ib/hXedg+bR7fz9RMH+GUH/u6xv+Njuz8W2P3yEA5p/MMbnsVALMw7711k/S2fR+o6s39wE6JUWuYkffRxdoQMo213tgnV+4O2EhFbBloH+7BxsVxB2BZdCogyEE5zDhk3gF+0tsPgEUrhKoFSUJQtY7UhqNEJs1eF5yY6vElWnkhn3lkHoqbDyGLLcFa+2RJ1qs0cnMY4V0JB1AFJyXDIV02yVavpJQgKgjSMs0ihRkV47wSpvMK6QinCFZP4nBd2F3HrCH8nPBQQbAhysrbZVwokaKHepNly2t9XLoKq6ZA2slg91kttGS9iyL8vFaFzopL1hCCAwYUyw4stUltJHffa9zA2myGbPtmomDaLFastVE3REvU4VzTCO0NCES933vv2e6gp1clvqbpFTlaf8Ax+DaJ2O0kPday/3quix/wrcNwA0bEdlkSRgqyCvkBItEJ5AUpOy1u8oVZHRSNt50uYGVyfoBiLJzCdlpep15i0oOCHUlR9r1TYdrFcL3cy1INIAwylqz2/732lqsujbiuXR3IzFOo2uuWyVDk3YRDwQnqDvQVDaUMdN2/Vmd4bMaEem0ghIQmhtYUxR4XFgL7IYH2OpOET8179KYeEmUVJI7CxEmjQw4sIHll2heRgLdV1rLkB4z9La46nWX3CC0nu9CwH/dYDk+3kPTmVRjdtIq5Osrhy4ZM+Qeujj6cw/vnIP/O5g5/j1y/6dd5++Z/xli/v5uB3v8Mnd34Gez7M2j94Mzu++z3il3Qrbv0/ha3Phd/7IUO/9TU+UxG8pVzhG8e+wbvufjs1u11efP1Igk/+9lWcSFe5+aTLln/6LM7sLHPv+GOk2XuXs48+zoZ4eZ5YILdIKYVUss1A7y4IfH6I6u075VlRxrCFV4C6s3B71fDyP5TCksuv68aITF+1rpPodXr0tKaQgbZsbspy0AI8qDPMsheEUt2S5U/Su+8G6sk10AhTSol8U62uWQtMuIiO67McQXih0gwnS5hZuqG1hXcWa5ZfOqFREJnl3XydBAXNC/87yzQrvFzAnPQMdU0JLOmw4OYxAq5Oy2w3TL0cMEW4iyi1xjJQ9wxMs9GP1t6icTuCl1SSNXRp9bzOIPELdXiBVQeZDbndoaDg1WyLuPWeayEU8EoM5GpsPHSKpLFE1OntsW48s72M5eB4EmaaoNxh8MwSWqw8MORUyWg2DrXlT/WYF9Xedy/vcGy2tGJ8ZmfYXUnW/Bws6XsulwlThSaJB9CXCb/uhfBKz2MHQVvO09fLC9Y8FrjvEWG1bYQAWCtEErTvjbW8oMV9e5b9Tc10mM7Wur7vNWsR37sWPJatmph6ay0ZqvuaayXvvTGQ6z5PEH2C1kcfT1H8YOoHfHLvJ7l++/W89dL38/rPPcJV93yd/7HrG4Q1xfZ/+ghr/uxDaJGniJirpsEzXkP4jx/nA5e/jQ/nyzy29Di/8++vZr4y19b0xZet4+0vvJB/e3yW+xNb2fTxj2Ps38/ih//yxzai+/gFxMEU0rW9Yrc+UqLApJsm+Of7p7GyGn12kjcWiiRKBmFHEHUqPUVHABZ9L1Hdcsm43V7kmjI9I8/3+rSKGAeo51nDKL1/QoFQtYSZWaZxO0Ku9Ixr34A8W8jmcnPsFf0Wy7ZKi1LbMY9ktbcr6l6+Td08dwMWpahZQaGFbq9QA5ryDGWjMzzPV5q0XNGmQNicC+WRzGaoGL7wRmeuW4/zurJVdjsku2XbNwRKBDTQ8GoU61bL7vU7N6RNTlRZEIUughtEUBCngWpgI0EBY9N57EFP4n6wPtsc/1B+iXWZh3v220n6wo4gLMxWnmDH+gktEwppS7ddxl4Ldc9nYLBSSHTbJVez0Pz70PTeABE3cG09/8YExFhs0fQuxitPftMweJbGPYtZxRUVKAHyspscgpcv53RsmPTyfkH3Rs9KWI6gxdLltlDCzla65ZLrEq4JjCEwA7G6TaxqouVKbZ73TtQd2TZxIemgKadnzpl3EoWKt8p7KMCtpHu3PU/0CVoffTwFcSh7iA89/CGuXnc1v3/Z/+Rt/7SLd/7wH3ndkZ0MX+Cw49++TPKlv/HzHuZPB7EB+NW/5Ld/515uUetI6xne9B+vZv/k3W3N3nf9ZTxr6xgf/O4hys99AWvf824q//mfFP7lX35OA+/j/1VYumfIlQMkqCG0EQqE09jL1C06L6gVPy77/XLtdOUpqZ1xl5h0ug2LRvhfwpeujjiVZrHdprfpHIZc0G3UZCvESeuheLkc4laRofosCTO/ohohtBtx1Q6PUcwuEXF1tGVCnGbqE5iyJRSw3BxqSjaN8JXO7/1IkfXD1RT4ghjL37W0KJISed/76kFISVG3KRsO5QDJ7fQIBs+poXXVEDubzTygB0O7uvuOWDYomt4Y3RbNfKDGUIKiMYuiO+RXal4YfVd9MwJ1vbSWdzZWt/yxq2Ze2bqpo+jSWlFIZ1mobnGIXjACRSJsXIq1E0TN1vMxmGl5PxSQq5hN4Y7BertYENDmwQuqpDo95iFZPHv42/lCoREWFnEzTdzqXQahhV6BkZCv2818xAa8uWyTFj3vsfW6j5qQxBbzbSG554sgER5eKDM+mSc0n27zFnYiUTKIBEjhgJ5isN4d8tgap6IWssnL1nrQfhLvefoErY8+nnJYrC3y7h+9mzXJNfzZMz/CBz59F39918e5Yukk66+ts/mLXyd86fN/3sP86WPtpTzvrffyrxf/DiOOzdt2vZf/vO+DzT8g0XCIz7zxahTwZ7ceZOwPbmL45S8n878+Tv3R3kqQffTRC0IpXII7vS2DJWmkGC/sxxI6RdE7zOrHgdsjxEdxfnZSG3Hs2BGXKEKhQASXcmkYZE1VxnM4mStkTyW+80HcyhJxVw4LCqLTENeUIGFmVsx7a6jvSWSXpyNI2TqLai8Lq2XQNkj7crMQcetYvrHqBOY5XTaW9br1woC+6BG0gO28PDlTXWvF9j0snXmSw0t5Ynq7lyuYD9QJs0d4V0g5Z82/NMZ7VdCGpLHQ9PIdrZ5m/syhFfv5cRCcr4wotXnHAWIB5ULTEdTPIxcvGEKo+2QgtMymQRBnEwZaERpEXP2cvPiJjqLmgFd6oQdcqRAd4xqIRc4rFLkoez/ToY5V23kPzoZeSrKlZc7VgKZUT+9uL+8yeBsNOVVf9hqWxTnciD5B66OPpxDqTp13/uidWMLiw9d8gls+8l3+8s5PsNkpsO0lBVZ96Ato2677eQ/zZ4dQiB0v/CD/+mvf5GoS/M/5O/jE11+EU/LqC21dNcBfv+Zydk8X+D8PTbHxox8lftGFpP70vdjz3bugffSxHIK7sr3yPNyf0K7quQdKnrtF3zBkliNaWvN/GhID3oez1j37CWMoXfVyj1bATzKM1O0IBTuf3JxeSAnPc3Eu5HlO5JveqLDZW1yhgc7u6rLeLIvQ3rDHie16FxE1lY2mFIP1ua7mg/lWGKzJk5uPYM2qXlgpjLXh5UuUzl3I4sfFWT3EUvRclxV57l6whldyJZR0G1c8+RUedcrn9HzUVPfcriTCs/7wAoOZquf1AsI+szh3eaJunM1Tfm7o7iQnzj7PvbCcx19Tqsfux9nJ6bnMTZ+g9dHHUwRCCj6w6wOcKZ3hb6+7mZ1/fSvvuu8LjKwKceHLFhj4/U/CZa/8eQ/z54LRDVfx+Tc/zI3jV/Ivqsjvfvt6Uvu+CsBvPnszN1y+gU/efYITFcGWz34WJSXz73wXUv/Jhpn08YsH2fjDrpbPdzkfrPSHPajOVjDO3Xg+L1vo/7J35vFyVGXe/57aq3pf7+27b8nNTXKzB7KSsAQQBGGUxUFeBQQRHRVQ3McVxQ1GcEBFXrdxVFAUF95RcRRxRATHBVEBWUNCgOy5e9/uev+o6u7q7uq+fbMQ0P59PoHb1aeqTp06Vf38zvM8v6eYV/PCLMWROwADtoDCEZ6dmikUzH+/A0Heh/TOFA7qd95d+bGKemvVbYRtM76zVvhW9Q5qdqSMiNQLFTsgNOB98QuRPKhdmEXbwOiTvoIxzx6Ax1wV/rnh++uFLs2FQzNuxs4x0n96mujfnMUEdWwKbd/+L2gcjPtbLwdyhrM33tS2Zy5l4rvfzE2aBK2JJv5OcPVvr+bOp+7kiuVXsO1fv8PL774FbV6K/vWPoJ76Hlj6qsPdxcMKVdV5z6n/ySeXvZVHVZUzfv9J7rj5DMTkPj7yT8NELY23fPN35Ns6aP/UJ5l88EGefs97mqIhTRwQHpt2wr8KhZWVGhLrjWPm+TgyMc3EVOPGc1EmwvZP7s/lS6fNYyOJ2mpwhxszye3PBoHNPmUT6iCLCgKemd596MiLH3yv2S5TzXQ3VWFq2i8QzB/65PYaypUHH/XyywQ2Sg3J+Zkg5xpddDs4CxDZfH5W4akFHKrn61A9tYpPqYYDQfxvzx1wXw/Vb7cql6iTPJVD3zd7IZdGCGiToDXRxN8BbnnoFr7y56/wqr6zSH/wDlbc+yOmjxhgYNHvkda9HtZderi7+ILBCcOv5ubTbqPbTHLp+F/5yJfXEnjmbj7+ikU89MwIn/zRgwTXryd12aXsvf3/sfOmmw53l5t4kcGu+Hs8P1WsHyUfIEGrm7/jHnoiOztjaayOZ29PfpRJSuGZI/aEozTok1/0YoeccxT4CmIUhq/8vAM/b2hh2z57nF35EXJ2nkezzzBZI3/lYMHPq2LjlCjQ9zpeDG1ksrb31bPZKczseMsOF/KKxM5QnQY2RZXDyjDU2rsIRyCmQeLsVzB+Kqg3tK8XlaIazwfycm3T/mATv+fDk67V8CbOhP291pn28l5xZeHtRlGQ6K+HJkFrookXOX799K/5yK8/wtHxIznuU3+g/Q9388zRK1nY+wvEolfA8Vfud+2gv1d0Rnv5ypl3cG7n8XzdgHPuuIjuR6/iNUe08YVfPsav/radxGtfS+glJ/Lsp65m5K67DneXm6iBm2++meOPP55FixZx1lln8bvf/a5u+x/84Ae89KUvZXh4mBNPPJFvfOMbVW2+8pWvsGnTJhYtWsRpp53Gj3/8Y58j1Ua2YvV/S24H2ydK+TxBXWHHnNSsjlmANbZ1v/arh31uzkk8oGLsqV4NnshnD93S+wsI5vjTKNON5TbNlDOkIDFmT5Inz64aJQ4OJQoEWnLr22kjkw2t2u90a6nJMxQQr8SBCsB4MRE1mYwFyrZlAyXhCi8BaXShQGCjzaheWMIe28fTNsuf0f0KfXPhRxAbPu/zRNAsUSKs0iGgE9tcFdD9PXbjvuHZodZt3T6YLv4dNQ+8fFGToDXRxIsYj+15jMt+fhlDSicX3/gc2oN/5t7jjmFD6w8QA8fAaTeA1HzM/aDKKlcc8yn+/ahP8qwR4OxtP2J4++vYEN/B5bf8gb0T07RdeSX64CBbLrucycceO9xdbqIC3/3ud3nf+97HqaeeynXXXUcoFOKCCy5g8+ZqcQOA73//+1x++eUMDAxw/fXXc8455/Dxj3+cz33uc8U2n/3sZ7nyyitZt24dN9xwA8cffzyXXXYZ3//+9w+or/pUaaXV0hTyqtzwvposEdSVmjWHKjGupxjfD0ELXZaQZ6FGd7jQp7QesnX7mRQGC/DNJ1SM0p+i/L2bt+2qPtfKMwLIqvVcSPuHWoWCD4bRPpHdf0JRiZyukK/wNCuJAMVNgv1adJRmIctvoFZtOxDCdbAxpUZrfmdXjJ0tZk9oZ8KA0kqbHCt+7lFShCWLViW6P0r7dTFtaWyf18J4zJrVfrUI2oQx0+JY/ftcaxp43+m60vj7vRaallsTTbxIsXtiN2/86RtJjEr8639MMf3Io3x3/Qmc2/ItRGYxnPlVUKrlcpsox1G9J/CtV/wXw9EB3q+Pkw5+iGPHv82/fvd+JMui4zOfQSgKT73hjeT27Z8CVBMHH7Ztc+2113LmmWfyxje+kQ0bNnDDDTcQi8X4co1adp///OdZunQp11xzDevXr+fcc8/liiuu4Prrr2fXrl3kcjm+8IUvcPLJJ/O+972PtWvXcskll3DeeefxsY99jFxu/43QQmhV4bd9VoVchUPqFLnBnYRgWjaqNu9tr23UAQQeqpVfVN/i8lKPeuFVBwuSEMji4Jyn0pitpzCYn8HomjZT5FXH8yM85tWIPe4o47mn0t2acl1ysva5pANfgZ8JBSXEgkGtPQ/3rlFUkiFFEiSD7pyebR2J/cDBmV+HhtBN6om639sV74kpLVaj5QFAiCqmkpYjRCSr6qqDovpdNBvkAjo5XWGkNXxAxykeT/Yv4zATUrJzfl2RsdTq59M7Zw/GoscL52lsookmGkY2l+WyOy8ju3ULV31dYeqpbXxu9Sm8o/sWRLQDzrkF9ODh7uaLBmkrzedfditvmn8+PwkY/LHzv1n78Bv50a9/j9bRTvun/42pJ59k61vfhn0ARnoTBw9PPPEEW7Zs4ZhjjiluU1WVjRs3cleNkNTHHnuMtWvXIjw/pMuXL2diYoJ7772XHTt2sG/fPtatK68TuHz5cp577jn++te/zrqfll7xQy6q/qjCtHJont3xeO0V6ICuII3750XkK8yNsKgwcDzjORHdP+PnUKCRcfSGzs2EicgMhqYkYavO9dvYZd4KP0W/wqjt6k2Qq/Co5iWNCX3/wmBni0KB5ReQgwi7Qmfd+6mRunsHdm7J37yuGKB6HlCYXTH22cCegTxWLjocKqLod46xUDcxJeD7HcBUqBQW2aOkaZfjMx7Z3N5APqTP5PW+tQ6G99MbwRAw6t/7RqMd6h7jgI/QRBNNPK+wbZsr77mSRx/8DZ+8xcLePconVp3BVb03oxoBOPc7EKi9MtuEP2RJ5sKVl3Lj8Tex2wxzdecepu88jV33fYvAEUfQ8q53MnLnnTx37XWHu6tNAI8//jgA3d3dZds7Ozt58sknfb1dmUyGrVvLc7iecuvdbdmyhUQigaZpddvMFgGt8od8xsrB+4/ZZLdXoLqfJVTW/6pndhqpSuPs4MIvH8UWMnmpmmhN6rG6IgZTQZ3d3TMbiAXsa4vU/d7rSXwmt7tYB2vH3DTPLshU7+Ax4nI+Bp8tHXiY1IsVlV4gLw6GBPtYovY8nVYC5KVqQZDCWafdRRed+kZ6zseL/XzAdtMa8rLEVFAnmNz/cNmwoRY9cDMRQ3A8vy2uJ62AUtSAxETEWcCwhI4iZEyfcQbIe8mvLJcfCIrPTkIK0aOkCVQuhFHbi0udFzcAACAASURBVDXj7KlBrgrqjYV3Sq3cOGHnURuNdqiDJkFrookXGW68/0Z+/ttv8alvBVBHs3x0zTlc1fk1gvIUvOpWiHYd7i6+qLEycwTfPO02MqE+3tkS4uZfXkru1ouJnX4y0TPOYMfnPsfe228/3N38h8fIiLOqGgiUG1qBQIB8Ps/4eLXIwamnnsr3vvc9brnlFvbu3csf//hHrr76aoQQjI2NIcsyJ598Ml/84he544472LdvH3fffTc3uUqeYw3WxZM9P/BV4T4+hkQ1yk0Iw/WuHHAIWg3Dw1AbOW6pTwVVx6waYdxsKTu81hKeMRSwEURMFcknRDsg/A26rFptcNtCJibV9qJNG6qvoEK7nECqQex29dULL6veZ+dAimlTxVbqj/FUYPYKgQcbLyQPWq5OX7SRA6ivVVwgqX+xE365WhW7yKJ6noeFSUSyXJJX3zs7YTiiEmPJ2XvM/RQ5Dd0hhDlFwlIVJsMGu/qTqNH998iHVI2cXHtuFp6v0uyWEELUWBgR2JIgJgVoU2osjBQ9yaX9R/rS/m0pET3F5/kaP8ihnZXBD31qS62mMD1FYhbeeT80CVoTTbyIcPODN/O1O6/lYzcbBMby3HDCRbwj8VXa2IF45TehZf7h7uLfBTLBDF8/7RYWhDfwmViUt277CZOfW0frqzdhLlvG1ne9m/H7/3S4u/kPjUKNG1FhaNXaDvC6172Ol7/85bz3ve9l5cqVXHjhhVx44YUAmKazsvuud72LtWvX8oY3vIEVK1bwjne8g0suuaSszUwQFSFGBVKmK1Ix9KUy5EatQ74KCedmHS+XTy9m0VIiptQOf6w8kuyaDpNarCyfoxB59txQteEyG9s/LxyBhqilMu0JlzKFRlr292Jl1Qh7W8vJk5Bk9qeqgS6UYr5J+QEha5aMLl2RiJUZYbUMUwd7umJsH0yzc3F71Xej6dkZ0aaob/zZHnGomNS4V7MRL8mhxEiL6+2pfK7r7KMrUjGvbybUGjdNKFhurpRpBpiyq8N9q57ZGmOlufO3MJZBYdKntPq2nQrp7Guv75mthkD3IX9ToU4ApLCBOpQuenynk7M9voOEFGKO6X2W7eL2AuJykAVGO0rh/SUEtlzeN++o2ZLAqDN39eJiUemO543q9t5cVxsZLRglUVEGYbxi0SZUXMxzelQVfl515tooXFPBUzgV1CsuNI9cIdBWmKOqLBFt4JlsErQmmniR4EeP/4jr7vgQV92sERmDH59zGefIX2CBeAxxxhehe/Xh7uLfFQzF4GunXUsXZ3GHZXFBEPZ8/TQ6zh5AicfZfPHFTD355OHu5j8sQiHHSBgdLZcwHxsbQ5IkLKuacGiaxgc/+EHuu+8+fvjDH3LXXXexfPlybNsmEnGMmHA4zLXXXss999zD7bffzk9/+tNiGGWhzWwhuSyhHleIWf5Gi9cm9O4/bVSrzM0Epc5PvhCCQI0Vf0mU1us12fEt5WTT0yG7coeyj61y/ZXsUEVO27jlrJgLSoprnXKSNjleRbxzqlwMJavMvYlq1URRFmJGL6akqtS8W57NiiyhzkIlN5cIkDNUNM3Hw+heV3nuijOuto+npp6RC5ANqDzjEkFLGNV5gzVQ6fWZ9hmrvR31xWZqITRD3s60EmDUFYLYbZd7wP08mpaqkA4ZREyNiDmzt6Ij0VNzLrbJcefpEAJh1A8JLKTHyTVCHJ2+CqbUwvvCLt7XyUjpPkzLFpNajNFA54x994b8TctW1SPnXSgRwHQqWPTaTif831uVYiN+4iOaLDMn1uWSMucaCqQkHTLIREzaoiatbn6mLSQmY3N9zxe1NHKaQkCqDv2cVgLouo7RGSfh8SZvn9eC5BPqOx4rXW8+k2Z87eqyyAU/5EJtZWNdL6y7EpXzRghBQFPoPGoJI8MZdvVWjN1U6XdpNFW++GJqcnGRqx6aBK2JJl4E+MVTv+CDP3k7H/62SmwENr/1/azYeS0rpYeQ/unzMO+kw93Fv0tIksTXzrgCa/d5/FFReFV3H8/88To6T5IgO8WTr72Q6R37V6iyiQNDgTRVSupv3ryZ3t5eXw/a3XffzT333EMwGGRgYABN03jwwQcBGBoaAuCnP/0p999/P9FolP7+fhRF4cEHH0QIweDgYN0+PePjGWkEtUJhNKEQ9iFitiSxY7A87GdP18zhPLW8O8L916rOTEB11RFQyKrhKq9F0iMAMNrSeN6L4kNA/Np47+m44Xgk9nbFGAm3sW1BPzkPmehWUoTU8jCqWEBjfbqXfjNV0xMHEDXUhgrwzjYTSlck2qJmTTIRNlSSPsWQ89LsybgXNrYvWW0EObXcmN7Vnywb59lAqwh99T6iaTlCzut58XxnqQpW9MDzudT2NuioPQ4FWXYBjMdLHo5iCHChw66r2FJl30hJHZVJPVZsP2KXaguOeJ8LAZNGkkk9waSeYEAtz1N8ZlEbAJN6kknDIQA52WAiGmSkx3n+C8Rv3GwhpJXP6XRQx1SkmmsNlcTfqxyak43itc1PtxGTg4yk4i6Jc74wFBlTlRGSDEUC6S+wEjIUGEiR8yxOTBqld5gVbyOSGUBIIHsWWnKq7Kt8W3j3CARdw73YgQYk+IXkXvPMC2aVYacKUtEjJgmYZ7bQEjYIh03ymgKSoFtJ+h532ix/fht16jcJWhNNvMDxsyd/xlt//Cbee6tC+tks8vs/jPXXj7FK/gv5l10Pw6843F38u0bU0rjxFecz9dRreSovcW7vIFtyf6Fj/Xamt21l88WvJz/6/Bei/UdHT08PmUyGO+64o7gtm83y85//nNWr/b3Jt99+Ox/+8IeLn23b5j//8z9pa2srkq+vfe1r/Pu//3uxzdTUFLfccgtLly4lFmuEBJVgVtY683xZMI6EEGWhMIrHOFEo3z+8fJBdvQm2D1ar+03ErLK8FD8jqZ70s0Cg1VClE6JkECnFRHloj5oOgXQP6/VyjLSG2d3TqABHdb+8wYJ+5QEKAhqToQBPL55L1jKYiJrFAuC1CFZi5XzCiklYqm3QSVL53lOae34h9kMNzrm2nCuGYCiSr1HflwwUcw2rsf/JYSPBHnaH5zAaGaRFru/5KoTQ7hxIFg3/vMd7sacrBhGD/KxVBB1UbvUS/MoZsCO9mN0dzr0MGgr5ZJDxBftHMsv6FnLu+6SPIqdXrbRwn71epYmIQdZU2Ztx+iV5Zun2eZ6+CX+v5zOL26uM9aLwiOJDQIvh0OXj/dzc7iJJDpu1yXvQUMhETfZn/jgLINX7ZdUQFPpT9nX5HfR+NWG2YNuQi1koqoydjAKCMctZ0LKFxK7FcxlfMVS2/5jVDpIgS7ng07RskgoZ7JiTYmqom3S4fv6mUjkfPR/79Or8NgFlYaddoTDtoSAxSyVsqKT1EHrFoklCCpFRHJKdCQSKc7UgQqQrEnokiiRUWtUolp8XvQJNgtZEEy9g/PSJn/K2n17Ku3+g0/34GKkPvZ+993+M1eJ+9h3/byhLX3m4u/gPgUUdUf5100vZ+9hF7MvLnNfVw1NzW2g/8hkm/nQ/T735zeSnDk4B0CYagxCCCy+8kG984xtcc8013HnnnVxyySXs2rWL17zmNQA8+eST/P73vy/uc+aZZ/LII49w5ZVXcvfdd/Pud7+bX/7yl7ztbW9DdpXCXvnKV/Kzn/2Mz372s9x999286U1v4tFHH+Xyyy+v2ZfRVLAUcuj58Tc02Vlu9eFFu+LDjAR7INYNrpciFtCIhoJMeVfCJUFUcaXbTY2psEFeU2ifpZx9tkYIZfE0CNQ6qoG6IpMKGcjJJGOLO1mS6Kc96BhYXmLoJR+FMbGxS0bdLCGA8WQAuyIPxUbGFgqjgY7itvlaJ9PudeaF4hAqt2thYTIUbEMy9eItytUwktQ5pWOOBHvIqjXqL/nx3Rohj5MVxXF3DiQ9ghWAVp2TcqBy8nFXwEFVwyBkQlL9OVMI11RCRpE87e4uKQJPhg1aIyYjwW7f/QuoCveaAY6oTIWsvlAYS5ePez6gl4WATptxSPTXPfbYvHJSNzzQwuiyXidnqAyuV6VizPOShu2S67wisXNumj3xOW5birUJC4QpamlIiLrhdmWiPMV25e3zkuRL8gqYdAUwNFkioCsYikSwRsierQWxhTJj/bQyCD96VmM+eut/+bxDinmkQhAwFGyl1M/d0YWMWR3kDZV8sHx+FjzHu/IVC6BCkE0FmLa0qveCHxS5+h1cCHXsic5cW21esIWIqSEJqc4iird7gnxAZ/SILia972lFpldpIakEMZsErYkmXrz43iPf420/v5y3/TTA3D/vpeUdlzPy0KdYkv0DD6+6isia1xzuLv5D4Z+P6OKM4ZVsf/i1ZPMK54dlNp/yT7Su2M3oL/+HLW+4CLtJ0p5XnHPOOVxxxRXcdtttvOlNb2Lfvn3cdNNNdHY6P77XX389Z511VrH98PAwn/70p7n77ru5+OKLeeCBB7jmmms46aRSiPCmTZv4wAc+wK233soll1zC7t27+cIXvsCKFStq9mM8GSS9cBlQMmq2tw8xumAhOWuG8DShMLFmPWgBVElCinVV5QEFgk5YVEothd14vQ8z5V4EhUHn/JW+HrSiZHQdAqW5RokAbNWksyVE/+JhUm6u2LiZIeuGRx6I0qQuCmNVup5dPR3kU0PsC/Ziux4+Ox5h+2A3D686uujd6rSGiGAgITDjbeyJzCs7jiQkLHccjfm92KEAE25JgEryqgatMm+gVz6/ljOikCOUryO24kVbV4zuhKdtsjpvp2CgdgYX1jyOUme856ptdAfmowifMNka97u1tY1WT55UvsKQVCRRRrZDhlI2F3fMSZEN6mUhgn4ICINJN4xsNB5uqLBvp17pkS3tM7YoU5bfVYAUMTEj5UI2i/Uep69zS96TfDJCWooUhUKm3ftY8CCWicEUTiskwobqk1tXIRJUUag55JIKXTJI6f75Z0+sXEq2qp5YCdNalGk37yygKbRFzVpcDySJ0UAHWTVU9DBXNmyX/chb5YEaIGgV+0wbKjkrgKZI2HqAqFn+jNuSiiR5cj4bWJOwhcB2F3+8vu7x+S0V11dbobZAfg+CGn7DqHV7auHQl6pvookmZgXbtrnx/hu57n+v5Yp7Wlh031aSrzsPnruBzNhj/HDoKk498eLD3c1/OAgh+PDpC9m6Z5xfP3QBbUNf5rWjf+T/vuVDtFz7UZ656x62vPYs2m+6GaEeWM5IE43j/PPP5/zzz/f97qqrruKqq64q27Zp0yY2bdpU95hnn302Z5999qz6oSBhCY3Cz++UaTHV3Qmb/waALUvsXJgh/Dc3Z9E1amRZkEtGybd0I42WclXGkkGsHY6SXGTxXDpy8zF0f4NN9gkpy2oRFLfUgCWHMGSDfLba+pFFKWQRHMW/XflRRtMhAs/uw9KV8mR6MwaZBTASB5yV7Zysu1Lc25yxCM5heuThsvPkkRE+FdTScoSs7RRKDgqDSY963uRQK+19nZiqTPbBvc655naDZTAZDJHzHE+TdCQ7i8BdeXYN64hkMWFniUlBjCHH86PGguQHOpGyD5PP2+yck0KeyJJ88FkA4gGNmKWxZV/h6F6C5o1TrbochHDyd+Sccy+ndcX1wLmNg60wsg1ZiAotlep7mJdU9OhKJvUU7HGIeM7j4bGEjmXBxFSuWGx6LBHA2lE/5HpSTyDlJ1GzpQLAY1Y7xCOORLqYKgrb+KFA7mwhY0sqEdPmuX0T2FD0YO7tjGLuLO/HdNyCkUlGeroY2JbncWOcaSCnWIiBBHvyExTmVGFMRjJhwq2h4gjpisR4trrGIZTfDluImh5IGQlNKEyZkEiHmNgzjgBUSSEtImwFJvQUowHbWRQQlInBFI4rcHLApIEUsnDUWk1NRg50Epf28ZxbDqCW57LV7GNaS9FiBGnR98DUI4C7YCApeCP7qgof27hzsXSNqkuUdU0BWYf8NNg5sGHLkkGwbaIjD/j2xZQ0Kie0VRCiUQwEgkkjiTHmtwgpIelhYARdVShUSpQEWIrE/NOPR9s6nwciU4S23e97/npol+Nsye0sXbr7bLdHTYbCrgdMCPJBndxoef8EEnv646h/fa7xE9bzXDfCrjz3Kh3S2bN7HFlIpOUw0z0xaDBvvelBa6KJFxCy+Swf/PUHue531/H2h+ex4mdbib38ZKzsl9H3PclnOz7KS8983eHu5j8sVFni+nOW0R/rZsfDF6BJJhc+/GV2fPgrtBwTZ99v/sqWfz4Oe3zfzAdr4u8GhXwVZzXX+XHf054uriYbksqu3ji2T3hMzBWMGF86yNiRC4rbx+Me74oiYQSryVkqaDiiEsXFZ0GHnGBPR6qsSK4tJGyh+C5OCwTZoEbeNUqCwiQvVEbTTtimIiqyuYQAo3ZYUFA2iMTXAxDyCJxMWK2+XptauWAGGvmISdjUHAKaz+NYykqVBDuAVAwHE6CZnu0SGSWGLCQkVXG3uav3dumSzLBR2BtJCCzdc68qTucnyFJuKjvY1Z/EVmS3VpxdaugTzuhn+GWMPoIeIQUvEZeF5ORAyVLZ9kJuVaUyZgFBYZJVQsSlaNFbZMuSc28K45rsx4y1VOU3FfPvirlRMrnAzHlhWxYuYNuSDqYGUjyzuJ2xVJzcojlsiPdzZLyLta3LCEYSjCXK55UNjKZD5OIBQCCEIOiZU1VDVngOhGDM6qj8FgBVFkhCsETtLV6KVzKieB8F2K7ncdqqDIe0Pf+FvKXSnQhgaQpCCDIxq+yeVOZDimhnMdwwr2m0mr0YsvMc5FSZnR4v0HPzW2mNGBiKZ357r11Ati9NOtaJmoqTDOqEQjq0LYHWhcUQ0LyqkNcq72f1+Hi9gXKBWEoKOxNLmVYcojylxcv7ACjpuSQHVtCbChUHxjAMIpl5yKZJ9/IT2NR7IuvCA2DbFZ42UaxHmA9qaBWhnYVaa5XzT1ckJ8/XQ6iEzztm2keEaTbqqwXoroiKMYNoj+71arvCN3O0DJbQmU45QjBaA2UhmgStiSZeINg+vp0Lf3wh33roW7x73waWf/sBwkctI2l+g/HRfbwv+lEuevUFdVc2mzj0CBkqXzxvJWElzc6/XYBA4bW/ehe7r/wy6dOXsu/+Z9ly5jryzzw888Ga+LvAnvAQNjBmtpVsBY8B0Ko5ho2lloyfhO4otBW5hqpgW+WhUIYmoUXbyrblZIPd0YXElADhWKxsZX1KjyOsDpLtFWqSQjjJ/R4obh5ISgqjSIKIUjDobVS3n1lLRa6KAXJDIoOO0RQOtzIeDbFtQT/jQ2lSarBokJY8GDZ5qbym2Uzos5KsicyhO+R4vXoH3FCw4riW96tA0AQgCiFTNV6VhXdol6u6FhYmx2YGPJcoqlRAR4I9xbC3iZjF9sF0BSErePNqCGTYbhCfkMj0b2JjZBAlEmTCSDEa6Crbr0CMNNlfuTAnaagBJw9qypW8n4iY7BxIFidUh5L03bdVjtAZGCSmRFDcczpS+p5+B1JMLJ1HYGF5rtmE0QLpBUwZbu0nrbGyE1OtvfQng1UheKZhEtF1OturRW+cduUmqoQgH9KxNIXt81rYsaI8/yzvITHe/K18f5JsQGOgI14U5Km8S890HA2AbejY1C574TRy++PDcGzbRu/rLds2PaeL0UUriyG6yBpZNcT4nCEme515VyAfnUqi7HPeXdSJVIZKu89WLqhz3HH/wtDpr8ZeOp/J+S3k3cUGFAMs/3lQC14yuSe6ADpWFj8nDOe9MuZXFkCSCYcixfEt+hjzHg+frBY99tUnFixrWcbarsUcufFYcmHnXTEa6PKEGxcWBvyeMWfbpFG9YJC3IS+X1ynriJuevRwUwiFjgX7mqe3EpRCdcmn8grLOoNlCWnHefcElc6rOpVT0bTrcVRSGGe1f5ZQTWDYXZWkf1qoe36EooEnQmmjiBYDfP/t7zvr+WTyw/QGuiV/Eks/fhTm3jdbW23l0OsGF+se54oJzGkosbeLQIxMx+ebrVhNRW9n1t/OZzsFrf3IRo++8ivT5L2Pfw1NsPvtkcn/4weHuahPPE55tP4mxQEdZwenCaq6qyHRELYKGApE2iHbQYQ3SHahdWN4WjndN6KGK7RJ5yTWtlXJjMycbjFntCMXJjSmtskvsiQwxYZSMjbjZxoCawZJ0NiQGsDwS59N6hDHLyVmpXGkukC6tt5fA6lUEN5zC9jldZC0DKRZjntmKKpd7byo9S1mvnL9VajvenWZSj9MWtQhqKtHEHObF57GpZxOda1cxPdQHioxAFPOqwobKsq5YWZ5VrZS8Al8sXJFJgQAJOvUYptCcekdCEPEjR56VemfF31NMt0auS7F9wTDN51CEUhzvaSXAlBpmptip6bCFLQkn5FTXmDLLDdG8KpMN6GDbZOrVnRPC42309K/icz4axI6VezcnjCToQXKazra+pUXCLYCArviqbRbO6b0+QzLQFRkjkyAw3IfS2hiJEAgm5qXZs6zdEeWomJuTHRHG4wG2Li6RbVOTUGImkSN7iLr1tYRwQtZXaCWCl4slyQ32kEtFsW1ojdRWBpxyxSxU2Z1zkiCpBpGEIKWGMPt7KIxot5YgEI5hBwOk5qzA7FxERHcLSKcztMWDKJ7rsITBoNJWNl6ycHI706EZSg0IQb5vMXP7jq/4ojGxmbwsMeWt2SXK8w3jWivdgfnFxQvhbec9m+d0Il8d1uzsW2oUDjjEL22lCco6kq7SvmKIkY1LmdTj5N1ncSTYS1aNkFN0z3E8XrXOIxkLVJc6ydkwYaTL1Bz9nrawoTE22Iu8ZA1RKUBcDlapNQYMC4RA72pBDlZ7qXvnZphePFx+fl1BWjHEVJfzvpd0DdnUq+pFVqJJ0Jpo4jAil8/xhfu/wHk/Og9VVvnqok/QdeXXUEMSHQt+y13yCs6XPsTHLziZdPjA68A0cfDQGbf45kWrSRqd7Hr0PMayk1zw4wuYuORfyLznUsa2yTz5+jcz/f0PuOFZTfy9oie4EF02WdIZJVyx0j3VHcMIBxEhlwDJhpMf4iIfruFVqqi5VEBbWGcgHazy8OyMLS6KSgggGdSKYTQ2gqwWIZrYyEiwh5FgT9lKudcLVzjbhJH0ZzpuDJgQAiVRLixgdByBGDoFRVIJCaOmWeiVbs8NdCBcU8SOh8iqYWRJgbalkFmCEAJVUhGyDKZRHJrewAJajV5MTUYSAsUVVhEIt9u1jVJFlgjO3UBPyjGwTNfz0q4knCK6wjnJYPeqmseocp4U4yUh5+bseXuQlxTmaK01j+eFX05hn9ri5DlpStHDVhY655knAcmgJaSzN1xdt6+q9IPb52qT1flsegtiu/NB9YjY5BWF6WJ4X/WhJ4xU2TzqilucMbiC4XYn302JBH3OXQHVgvSQ201BVAn6NsvLKk8tOBKlbYidPW3s6k1guffCT0jHW39PCMHe1kXsTDhiQN4QvELOU6cbdjxt6E7Nw+F+ugcHyQd0NKGwKNCBJWtIkszIfMeLFlFLRnw4YJFJJumN9NJpOZLyK3viLOuOUmmOm5pKOmyQCGhlZThiAY1EQEfghGp6h24oMURnapi+Dv8yIwDZ8FDN77YunsN4V4z2ln5ajZ6a7apRQdBc4prXI3XzubJJZ5GhNZjmnIWnlQkVJSydLneBoHCfkqk42waG2D6ndr1JIcmMtUXY7qkTmQjqGKqKLvsXiNdkiYj7TMWHlmBbtYV+lGiIwIJe9E7n+Jo7NyaiC4iaKsGhHuzu3qr9ssEMtqeUQj1RpmKbGVs00UQThwSb923mvB+dx6f/99Mc3Xk0X1/3edTLPggTe+hYvZkvBs7mUi7nxgs3MmcWRV+beP7QFjX55kWr6An189zDr2HX+Ajn/+h8Jk4/lY5rr2Fyr8ETH/wPpq4/HcZ2znzAJl6UMGU3hFFTkBCEDAVLVTA1lXxAp235cNVqfzygYR61gfFFA2XbJ4d62N7fWRbG4zXEg7pM1NQQQKGEa1wOgRCMxcJMxz3hYvF+8pLGuOkQg5TRSUhprDaZLUD0V9cIquQ9XqIoZLVYMqB6P5u4JxdEFoKwodIWtYhpYeJSiLQUdY8JcrzcsFdlyZPrB6qkE3fDRHUP4RVeb41rfPlh3txBVFXQGtbpiVe8X93zdLcM+OxZfu0LAm0EdAXbU+TX9pGGm1JjTEYHIFIKD5PDbt6RVp5Xp0pScbV/stcJo4qZFsNh12umOful5DDdQbcmV85ZBCoQqqil+Ra4jro5j5N6ycsWk4JFUjg5WAprFAgW6nOKx3vdES/jqI6jSOglA3m0t6umJ2BKi1WFtSqSIJzo8m3fbwyQT21kd7REIkxVdkpRaH5Ertr4n1ZM0hGT0VSMKe+ipmd8ywhwvJdcMINAYl94DhPt/Yy1JMj2tvHsYA/b5veRDTp5Q5W31VYUlDZ/9cOpeARZyOSLYaDlOVdSjefbm6sYt7QicShsNxSJ3mSAwYEW+mNxsulQkQTpss5warjK+PdypKweL6pu5iWNvFCL0vEtoYX06EkMoaHL5SQl7FecvBavFhJ7w/PIa/XtFttdGFnQFq6Wr1eN4rtFRmaVPhdNlhhpTdQtlL6sO0ZiQTs5N1dRlkBXJTJRw3cBAZy5rbt9UWWJoYxzz+yAfx6n0lPyIqbUEINaGxmto7SI7nnXF8Y6pJfuoxDlNSNroUnQmmjieYZt23zn4e/wiu+9god3PcxH1n2ETxx5JXsuPJ/s1q1kjp3m0th7uW76dL762tUMZWau09HE4UM6bPDt16/h5MEV7HzkNTwzsovz/ut8xtcuo+tLX2Y6H+TxG//K2AeOgv1QsGrixQGvoIIqScxvCyN5fqiDcimcDmD9nBSrh7tAKTdMplvijMfD2IVdbbtcgtzOu0cR9BpJMi2LCMUWA7BjoJPxoeGiMRqxNMasNnJlIYduja11rAAAIABJREFUdkidGkvYNiu64rTG/T0Vs0VRlbzCKDFUmUzYIL9ggMiSpUxY7ST1NubE5qC2ZcraypJg8fpTip/706Hi8QIe0Q1DqGVnaY+aRYGFSiU8S9KwNIXWijBSL+kMKP55VomARkB3CLlQDJ7pG3av0alhVQUBk5FekOTi/dS7M2xb0M+0aVAeAqjQFogztmAJcmcXkx3dpOZ3Of2PdYMRZSgTpiVoMhBtB8Vw88hA8RQbr8zVmfKElk6rYUaCXVi6gpROoqaqwxMlNxdvMmygyRKpYJSgFsS2baYsE10yyZs6wiXlmlDolJMMqR2+5y8i6i8tH1WixM0u8m7dsagh0xo2imF29apJ2IaXjPp4yzzmbp83jE/SsFWzeM/TkQChpf2gKkyGA2QDJnvm9pLNhMlbGhmfAtdVEIKFiSW0Gn1MmyUCd1THUSxNL625T/lHp7/HRodoMatDQNf1r2LZS1/GMX21y38AKFI5mRFl5FAwFmgvkoik0elbuBmgPxWgI+bjWQq2QKq8RERjAZUztMosQZEEKb0DSQjmpIOOIFIFRIWSpSJJSJnF9A2vJR3WMdO90FIjlDzUAimPl9kdlyrFTG+vbRtkd0zdxZaUEnZIcVF0qPRuNTWZzphJR8wsI8r1zlFsM2OLJppo4qBh58RO3vKzt/Cvv/pX5ifm8+1Tv81L44vZ9qqNjD+0hfCJrZwS/DC/FQv4xkWrWdjeWBJ2E4cXpibz6bOX8M5jNjHy+Gt4au+znPOD8xhb0EPPt25FTrbx5Pdz7H7XyfDHWw53d5s4yFjeHWPjXMeTkU0HmWqrXlRRXIMrF21QKKNmIlW+eDxZSLTE5zJtlOcc2XVqYxXIQdbyhNsJEKonLK8idKpbj5O3VGxdZrKvdnhRAX3JoGfFeIbrAWeZW5HZFV/Ekd3dhPUaK+96mK1tJ7C9ZS2qZqBIKgvjy1ieXg7AvtAAXYEFdIe7nZw020YIQWvYoCVkVCmnJbrWsjEySIdeO2crpvmrFIZNlwim5zMdamdva4tLtJzaTpUIqRWeywWnIxacRrYgDOMOT0oNMS/Sz9KuRawdSBK2NKa6+4v3B9fgtjSFkKkihyz2rN/A0wMrGLM6KmpulY/5aLCX6cyS4ue8bBCwNHrntJJKVY65YGnQ8XQ9MbyKsXUri54BG5vRVIxdS5aQjYYwhEKnHmdQaaNdiROpUOZMh3RIzfMdR+dM5d6l4n5BN2xSksvaBQzncyhZTvRU1zMmSYL+YOk6B81WVoX7ip/r1epb3Zcs5pH2JJxnNa9rTHVGQQjMSGk+ePQvykOOhUTSbEGR1KIwSE7WCWpBMsHyhQcAvxpwedfzqEsKGbOaNBmKgSSkusXlAY7tOrbi6NXPYcF75BBh/7GRJIn+VNDj0XNgHvVSUPw9TcUobW8PXNGRvFQSphG6D/FSHE+vKjnzOWJq9EcKoYO1yZ1AOM+IohPUFISsgR4ueuvKoAXBcBcmZB3izhxpCZvlRFy1nPIijSJfXgZC9ZlvNcVSPGgStCaaeJ5w5+Y7Of2207lry128dcVbuen4L9D2t5/z3EVHs/eBvUwct5yjjLehRzN855K1zPcx8pp44UIIwYVH9XHLea8ksvdinh59mpd9+/+wMxmi59vfwVy+jKd/FeDZ912Kffs7ITd9uLvcxEFCPKAVDbSpVRvIrtzgfOHzIzyxeA6jaxfNeMya3oeAQwQXWG0QKZGlgdBSWty8kSWD65kXH6I96HgyVE8eUSro/u1ZWTd62wgefTzSMWeTD/RAheJjTA2AJBhb3E4+FqowKKv7OdwRoS1qItkwrQRLnhu/a/IYLycsbC8Ziz65K0KALSlkPYQqqidQZRVJCPZGBtmTXk+sgrBKkiBQIyzKK46Sm9/v1FkDQivnEdp0HO3RauOz0ouyd5ETBrhrYR+7lg8xbqQcxUMPDMkqJyKSXCQeAGnLaR+UdWgdBkkhqHlzpHy77/Q7HGRas0jG19IabsOOhjD627ErTTwhwEMe26ImuY5eTDNQnL+LUs7cTBgxTEkFbHJaALulp7jfkk7ntylvumMjIKEGUH28sj2JgCNuoQchs8iRfq+Adz7lbY9xG+8DPeSoEVKaaZos05cMYBnlhn06bDDUEkeVBaZSmsP9ZsoZ1wNESDYgUSJ6uVq5xe59HRtaRHZgGLN3FUNL1lY184qDTLWW1Fq3tR0LSh0VyYrz1JscsmeO9QYXzZj7NNZ/Es+ljqz5vbf8hADUTDXhDJvOs6arPs9coOQNtDWdif55WCtXVrfzwWBsDnNC5R7Dmf1QDSDS7og3AZqsIUUytEedRQY11OI8j0Hn+RRCgOouspn+ojjpmDNHNkaqc0AL77VoAwXtmwStiSYOMcayY3zg7g/wxv9+I0kzyTde+g1enVmP9B//xO6rL2PH/Rp/W3wEpwfOZs1Ailtev5o2H6OgiRcHlnbF+PHrX8P60BXsyT7NS775Ku7YupOu//slome8gh1/CbHl6q+Tv+lUGJlF8cwmXvAYSgw5q61KuVcEoFV1F1wkyanlVQcD6SCt0RqhVG5Yjbb4bESrk6sTtVQCSpRWs5eNg2kGk90YikHKSrGkZWGZXHsi4JAlTTLpigeYu2oectBEimdILRwmmelhVcf8otE3Pt9DNJLloUwzwcYRGxm32qq+2z5vkNGl7SViq8Wq81BqoNzf4nzaNL+FBW0RDFWu6yEBSnlglaF2ugpu3omka0i6XiXGAm5elCcfbjrkGFu2opA3dEAwrRQESEpepwLn9POY1ELCzdtTZQm5r9oYJtxWrF2lSQZClsj3tjsqcXUM95ChEmpLMrl4MWLgmOJ2qYH6UJorAiG7yplFj+1MJWAUs2Tc1oAhe77Xw5CeT9HTMoM5LrfMY1FrPy/pOQnZ9VzpUv1nDffIxb/qjFmPkSi2Ha2Qmrdt2+kvgEtUc/Ekdv9cFixYRIvXcy6gPxNh42ApXzSb6WRST7I35JB9q1KxuUCsvNeTWew8k+GZvNrOfEtbaeKWScBQsHT/uojIalk+ZVkXVKWh+MWwoTEvE8aoUevLuY/OOGZb25CM6ned914X8xiFPCPBtNQS8ZlY2Md4hRS+LHyuTXHCaLvDPRyROQJUE9qX0ZcI0h7zscUCSeg/pux9OOGpV9gbn8um6PyyxR+K4kUOTEllY7S2VxmaBK2JJg4p/rT9T5zx/TP49kPf5rwF5/H1E7/C3Ad+CNevZuQ3v+fp+xI80DaPt/a+gnecNMSXXrOSsFEdHtPEiwuWpnDDK87mLYs+Qk55hkt/8QYu/c59GO94D+l3vJ19W0ye+NLDZK/eAFt+e7i728RBQtyoFOBwf5HNKH1mik3R6lyIdR3rWJxeXLYtampF9TlbqW1gmgucwtbJYMnAqQxB6ktEy2o6xfQ4HdZcdNlAkQRaRShiJmKi+IhcOCeMuVdVS+CgHLkKI9PbcnfnXNCcfj2d2cRzqdrKc36otBMNVWYgHeSEBa1FI9uWa4xd5xHO/xW9ZFTXQPqYDTW/WxCoJp61RiNv+3tbBtIhWkJGkbRV7j+QDnLsUAuWpiC3xhhfMURuyPXidKyErtUIBCu641SOyrhZInR5vdoI7pt7BG3RTjKBUruIFqE30stwwp2rPp7MnnAfbeYAMVeVMtcexehuxU6UQvIbSLHxgY0m1V6czLvX56fICLhCIiWs0Pr9vRg+5y3AO58LIbGGJpVdkPNsCua2+ovLIMkOYauB8Ikn0nXaS8u8uraiMBboZFqyWD/QSlvlAo1iQDBV5oFC0R2v5AyDbWkKYUNlVW+CJZ0JJhe0snj+gvJGmls70O96XOgDA2VhnYU2waPWV52zkGOVi1aHK8/rS9GTsMgHZs5xtbHZkVgG3Wsc4ZCKPlZ+9ub45ZJR8u75JQRL0kvrEryYEcMsLGapARRZIGk15qMVLxsfW1LJxfuLpE2tKkZvs6QzSksiRqhBG69J0Jpo4hCgIJ9/7u3nMpWf4qYTbuKy1g1oNx0Pd7yffdZqHrkrzqPBFm489kK+cck6Lt7Q3yxC/XeG1y5/CVdvvBrVfJof7/wwx/3bT/j1shPouP4GpiZCPPZdm/GPnwK/+4/D3dUmDhVah6HXMfL98kXCWpj2oP8KuD7UxfiKwbLQseHUMMMpV5BCayAMqgKaIiELhYipYA12ocZq5Hu5xseKQHmx4p5ID0vSpfyeWnaokGSQJTavXFBmdE/rCqMFQYqgs+q8Zm4rR/T5qeHV7VrV316EDLX2l7NgD+GWBH1Jf6+PKuSqY9Uyy0NqvGhKeo33ZMCkM24VDUdRQSqFEAQ9hnw+aBLpc0mHkGpfS7it7LvpWLV6p6boLEkvQZZkpuMOUVUiEYYSQ1iuB3Bea5jeiuuXJZmU0VkyjSXhyo6XzqcIgS1m8nlVo54HK9/ikApZSMVcpulklKxHSEv1hHAqQm4o16cWwobKUGuY5W0DnLTqCuhyFhHmZ8IM97Q6hedbPESnQMKFRDKoI0uC/lQ1CRGyjKjwVNpqoQyHTE+sxXfhY+zotYytWVy1fSZIQjC3JUTEUukOd5MP6CT6ujEUuVQbzGfcJ3rnEH7JiWX9tjccw+ii5WXt5LDfIofN6FFLmPDUpCvAaokRO3Ih0/Ha9e9K88DGljRnPlMSTymdxS57Cak++Z8VRy7+NbHQqYMX1n1y/UMtjocylKnaD8v/XTXdstghzABzNsHAcaxJL2O+G0FgqDJzlqxH6qodQupFI77fJppoYhbYNrqNd/3yXdy77V5O6DmB9y67lMgvr4N7PosdauVX/R/CvubrIDQeu/xD3PZPqxoO7WnixYdNPUdztfgUl915OUK9iTd8PcumoU7ef9NXGX/rm3jiDkFmz1uJnPE7OOGjjeUeNPGCxabuTeyc2Mlvn3E8o8NtR4I884rpQGwAWcg8uPNBwMlRmNfeS6pnLdFHflFs1xnyV8BrFLois6gjiipLqKn6baNaiOiUYNSzbX6i3AtY05zuWsW4vJleMYQu/7HYeMe8FsYs1zCLdkHPiSR8wurkeDWhKNlhol55JQDmtoSQLIv8g/XbFRHvRTyxq/HwQz0EwULIXilvyrv/tGJRCCSLamlgd9Vhjuo4itHsKEEtyGPphYQiPQ12eAbYNhsH0zzwG5mJbG7G5rl0jNF4GDniGqtmDGI9xJNzOW56tEwpswghnKhGn5txXFs/U3MGeUzdV9y2oXNDXY/rTAuUOTfnR5aUYsjf5MI+2OyEig8nh+kIdTBd6eYJZWDf03WOXB7iOBgfJG2l2T2xm/u33+/0WbMcQYmRzU5DLQh9G2ByV3E/Yj2w+wmwEhiSzEsXVXtYa3dBMD53Abmgs2CSCWZ4evtDxa87Qh0kezfyP1v+p/Fj+iBuxDkp7izwtEUt59J3lPpQJqQjBEKWsZYvw56acrbJCvlQhKCh+pJpY8F8Jh74s/PBL1zWDZ+Ukh2siyWZmK7wLLcvh8m9NeeJhMSEnoSgDiPPVj2thedPkiTydeqP2ppKLhmBZ236In34Lq0YYRiveGaNSDmZbR1G2fsAAKo38kDRQdGJ2nmiRqIkRiLJzntv829q9q14iBlbNNFEEw3jzs138q5fvotsPssH13yQ03IG4sbjYe8Wdi/4P7xtyyZe9pl/oyM7jnXDTbxx3fKZD9rEix7Hdh/Lx9ZfxdvvejtDS27hF386ixMfMXj3O69h1Zc+wdZf38fknm+Sevp+xFlfgVBjBW2beOFBlVVaAi2sbltNVI/W9Qh4MTfmhMa0B9vJ5rOYIyPIQiJt+cteeyFH/ZPVCyjkKJX6OAuvgtv/gKTTGWs8B01IKkgKwp7hXDVynhQfgubTrZnlqlULsmMNqQkMd0SYrjAYaxIK1YRYH4IKI9BbEQEJL3nz64ShGBhuzmImUZ6TIlm187UCa6pDQitNzIipYqoOQRPul3ImQzYfwN67o2r/spIPQkCHI8iQodxDEvbMp0RQg5Hq8/dbSZi3hs58ll885SwwaLJW7eFoX4YxOULEVBnoiDIRS/CHXf7XHNdjPA7ElSBbfb7vDBcWLypGovMI+PNt/gf1QX/U8axM5iYBCNWq56UFigQNcOZaaqhM/GU2mE6V8j2XpJawaHQERp4BIGmmiOgR1nWsY9dEjQGqA7OG0mIR7nNUEPjwwisEUvDmFt8plWGQvb0lguYHRXe8S1qQhN84xV2lRnehqnLBRJE0MtYA0QzsfvhHTi1Iz3FkIZMwE/RGerlv233F7QE3/HW8p5+9uV20Lqxc6JrpBVGqT1eG1CDd8TmY+yaJWj6Lq1bcyVebjQqkiyZBa6KJg4C8nedzf/gc1//heobiQ3xixdvp/uV18MB3yKfm8fWFn+cD95l84J4v0rdvG5033ECoSc7+oXBi74lk81ne8z/vYdkR3ya/7XzeccdmVq+5iPe3d7HjtluZ3Pcobc9uQH7VV6DBMIgmXpioVBL0bq8XhmMoBgal2k8zIXTsMQhVhT89U36c+UOQyzHx4EOkQg3UbnIhWRbsGanaPj/QRjBWHa6U8KlNVEQN78qBQgjoSwaYzObpT81QtqBlATx1r/N3/zEwNVqzqSZLVQIjBYKmKxIxOYyJBkyXGWo1U6JSfUzIo0xLjZVWUBJxpnc4Be2tZUuRg5795p4AY8/CrgexbbsGgbWr/i70LRXSiU4HkBIJ2LqvfsdngBCCDXNT7BrX+cueR/D1PmQWO1Ln6MiSTC5fw4sX70Ns+xNz0iGwNPCpM9YT6WEwNoicn+bo6DwkWeVPjXbWI+fuh0XpxWyd2sbjPt8lzSRr29cScUPgTPXQiHf5LQIIIZD1IIw8g7VsKdndE4ATEh3WZqfwvKFzA5pUJzIj1EJ23JlrlqawqjfBlrGdhH3K/PQkA0QtFekeBTtv798cMmZRPshnaqWNblZlMow++yiapDgh5Ft/CTjjdmSm+rezL9YP2TypthUYapZ1c3oZy47Bn++ralu3O3b19SqyVF/Yzaq/2FQLzRy0Jpo4QOyb2sebf/Zmrv/D9ZzadwpfaT2e7i+fDn/9IU8supRNox/i3fdaXPXUj1jy9F/IvP99hDYcdbi73cRhwCn9p/DRdR/lL7v+iNZ+Ex88rZ8Hnh3jVGUtm899AyNbdZ74gczUZ06Be286JMZtE4cXq9tWs6K1fnHZKgRqxyJKponwERLR+/rQ58zx2WOGU61dg3ZE4/2rZZgIIcButGhtY9AViYipsrQziiJLDHdEUGbyBnqJrhWvWSS5Ci3lIgrtUZM5ejd9SosjGR/2P45jr7pXLalEPPL0M8E64gjkiGt8V95TPeR4bOrg6EGPt9V9d3TGLVIhncF00EOmD/yuRC2NeLBO6K7n3VXwQEm1TM6CAVvDyyAJqSgXb0qqb92tNeH+qm1PZzbBvJNrkghVEuiyVte7FPHkJyXNJGva1jh9qswhO4B3tddLtLrfk9/Uugi6VqEOLMRaMct3hgcBNYBaJ8y6MzGfPbHSfNdVmY6YRUvYf2HH11P0PEMSklP2AJxQxBmguNcvSSphNUFIC9ES8K9xWITfLX0eZQKaHrQmmjgAPLrnUd78329m877NvGP+Bfzzn36MeOLfyXau4RPq6/n8b2R6kxq3hv+EedtPSVx0EbEzzzzc3W7iMOKkvpNQZZUr7ryCbP793PrGT/OB2x7noodtXn/O23nZd6/j8Z9IdIy8A2vr/8JJn3TCqZpo4lDCLQ4t6TpyIk4hhkwOmmiZBPpgdf0qqC3qUPIKlKycegW0i92YM0B+3z7f74QQbBycOeSzfn9qwIwDj5U+p4eK5yxgZ3wJwZEnID1VrFJcfdzyz3FLo6t3mF3V6WfVfZRlhLr/xm9Z6KrikDFFkuiKBzwK+IfAwnTJScRU2TU2VfX1ypaV7JnaU1aTqwzhNodI1XjPFcdY0aF9mUPotv4KIQS2baMK2beuVF7WnPxPn1ykxUccjaQajN//WJHkd8Rmrk0VNaIsSC4gYTQuatMoWsOGUzOuAEmGSMdBP08R7rAmzAQPi8a97H4IHrUee9pT21MrEUJNnv2crvVeSQX1hlUQy+AS6tV9CdRgYx7tctR63g8dmh60JprYT/z66V9zzg/PYe/kHm5sOZZz/usjiGf+xENHXMnapy/liw8qvOmYAb6Z2Yr51RsJn3oKqbe8+XB3u4kXADZ1b+Kao6/hoV0P8c6738g1rxzgbScM8rmRBO878a1MJ9t54ucpdn3rVrjxWHiuUaWDJpooh9bdhdZZbeQpSY+C2twTy8JwdLeob0KLIITAPOoUpHnHVB6iCj0egYuCgZW3bSY7epgKmIwu6S3mL9UqJ2IMDh6QtwDAHF6I1jVLMRUrDvNPq9skpwTYEy3V5aqEjY0AJkOOAZh3SyXE9dpqdVWoEzI2k3FYlq/TttS/0X6GNfofqpyE9yYDyJJwPC+e86iyStKcYQw85KwrMJ+uQHVJCsApXu22bdhr5XPNanoOcsyZIzFLY1lXjGVd9XM5C+gOdxP0SvpLMkIIlHgMtWX/FhAOJwJr1zDRN7sah5WQw+Fi2G3o2GNgjZPCEdbDdARnTzILc31Vf5yNc0tjumYgyXDHLEIkiwd0jhe3tP3zALpzTet4/vLDmwStiSb2A9975Hu8/ievp1WP8vXdWVbe/QVygydx9eDXOP4XvUQsndvesI7X5h5lx4c/THDjRtquvLJKWreJf1xs7NzIdcdcx2N7HuO8H72G01dafP3CVTxuJjhn6evYs2AZ2+6Nsu2O7dif3Qi/+1oz5PHFDG3mmj+HAubwMOZiR5rbUGXUViesp4y0VYTOWarFxs6NzC0aVqKmYS8QWJqCJESZwqTXgzbV3cez8/sRukIyqDMnHaTTrwDsQYLW3Y25aBHWsqXlRHQmyAoMHOvke7moJEXmDIq7QsCe9jT2yuPIGeX5eYbsXPNsc4hmgldIZjTQxZjVXqUcWiQzgpJT82CRtdZFEEghCcHSzhidDXii6iGmtRBQZmGEty2Bvo1Vm5XC722d65Tjjicskww1LOhThdbF0LGCwJo1WCtX7t8xDiOUWIyVfcn99k5XQjJNUJ351xZo269xLSwShXWTiFXDYzb3hGIJEz8sTHq8/oU+1KhHWGxm1Z67gUX9yOGD9B5vQDSkaS020cQsYNs2N/zhBt79y3ezXE/x5T/fR9u+7ew89Su8/LkLufaevZy7qpvv/8s6uh69ny1XvB1z+TLa/+0aJ5G/iSY8WNu+ls9t+hw7xndw7u3nEo/t5PY3rWd4Xgf/3Hsmv1/1Enb9WWbz/2TI3fwG+NZ5MLbzcHe7if3BwLEw+JLDdvqNc9NsHEz5K8z5GFCWajVsWM3PhFnWVW5wFItFe7w6hTA7XXE8Dt56aocCcjSKtXyZ26EGdzJjxXDPSpyyqI3jhty8leLYlB9YlgQD4WUkYvNoDZaHwYW0KGvb19Ib6W2wM43Be592x4bZFa8eVznoGJZa0DFADfUgmn96yCFJ4BRUjvc5/w4SZpyHqllewHkWMOYPEdy4wSEV+wtZrTlnXsjIW6WwxraoWVXkvh4ks4440EFAZ6iTxenFdIe7azfSQ07x7hroCncV/zbmOh5CySgP5TSXlJ6V4Lq1SLVqSxYXRw/SokbvBug7um6TZg5aE000iGw+ywd+9QFue+Q2Ts2bvP8v96DOfxn3LXwvF9/6OONT+7j+nGWcNJxh7N57eepf/gW9v5/OG26oeik00UQBy1uW86WXfImLf3Ixr/6vV/OZYz7Dl16zkhvufIT3/EThzPVJzv31N3hsbJDOqf+H/sSv4NTrylb5m3gRQFYbqod2INCV2kZ3YRV67CCfU5VUAmqginT45aBJHuMmoAZoC86iTtRhghACVdZoDbTOWKcLIGqptEXDdEWTPLTnSXZNTni+tctEJ54PGINzsfN5tIEB5EQCJR5nyTEmgc2PlcmnHzTIqpMndhCRCZT3s1DUO6gFGZmqVhyVXULX0YCXVkhSkbweCA5EJORwoe+40/4/e/cdHlWxPnD8u5vspieUUAIEQgstBEKvShGkww8FvMpFAQteuyLXK4heRUVFELyARLqoCCIIioI0iQRFpSpNakKAENJI383u/P4IWQkphGRr8n6eJw/k7CkzcyZ7zntmzgzGa6ll2ta7VUNMaVlo3IpuUW5UpREZxowyz9uo0Wio61u3TNveqHNQZ9KN6ej86xBQp/D3jb5eXbIOHiwmEUUmrNxpAvJa63Ul3xdKgCZEKaQb0nl+1/PsvbSXx69l8nhGKpqRi/kiuxMvr/qTBtXyuqc1reVHxr59xD42CV2dOtRf/DFufq73ZE3YV2jVUD4Z9AmTfpjEoz88ynt3vMcTvXvTMaQaT3/uwZ9uVZix/xPO7apL3b5u+H42Gtr8A/q9UeITRFF59GgSiI+HlS/pNZrlTdTqX/yNkpvWjTuDi+9mpFTe4AcxObripjyzLSvcULWq3qrwQs2NfQVvWIyGBoE+VPP0gGt5y/JaEe04/NsNbhzJM/8doVqBARBo29ZLa/F09yw0D5lOq6NTUCf89H5sP7/dModgPq1Ww6DWQbiXIqCuzBoENoEiGh4111uRihodNp9Wr0NbvfgHTh5uHrc/Wq0NVPeqTnWvMg7ooor6RQYJEcJpXM64zLhv7+fXS3t5IyGRfwW0hsejmX05nH9/9QfdmwSy4cnuecHZL9eDs7p1aLBi+e29/yAqtbq+dVkxcAVNqjTh2V3PsuroKjqGVGXzMz2p1rkDE7s8QbxvTWK/ySQxZxDq8Jfwv/aw72Mobo6hSmDNmjX079+f8PBwxowZw4EDB0pc/5tvvmHIkCG0bt2aAQMGsHr16kLr7Nixg5EjRxIREcGAAQNYtWqV/Z+Q12gONYsZKKEI1X098LzF+1G3zcMPmt4F7rf/Ur3WMrxXvdSlAAAgAElEQVS9ItDPg6a1fPMmr4ZiJ6d2FXcG30nv+n93T+pcNwx/Tx1BN83h1aZGG+r716d+lbx3e4obtvx23Tx5b34LU7ETKtuD3jdvSoNaRY/2eTt6N69J10YlXzsDvQLxcPNgUKNBRXaD07lpy/5OWRnY81jlVq3kLrYeTZrgFdYK9yJanCq00nwv2fE8u/a3pBA2dizxGPdvvIdLKWdYcCWFEXf8F8M/1jF5SyLzdpxidId6LHmwA/6eOjJ+/oXYxx5DX68uDZZLcCZuXzXPaiy9eym96vXinV/fYcbPM/Dz0rD0wY5MGNmVxzs/xr7gNlxZf5BLyWMw12wDmyfDojvg+OZKN4jIhg0bePXVVxk2bBgffvghfn5+TJw4kdjY2CLX37RpEy+88AJNmjRhwYIFPPDAA7z77rssWrTIss7+/ft54oknaNy4MfPnz2fYsGG8+eabfPrpp/bKVp7aYVCr9AFaablVyRuprqSX4a0hv4tjgWDCvw5UCSlxXjerUyqvBdCrdCP0lYaPzscyd5a+Xl2aVW9K++A6eLgXDJC9dd6EBYYR4KVncOugUg3jXhZ1fOswoOEAfHRlGT78b+Xqfql1g7CREFD+bmn+njqq+9pwri1tJe88VqcdtBpZ7McaNzf0ISElB53BnSHINVphS8vNv6TBe+x/ba3ktVSI4u2O3cXknc8RYMxmZY4PTR9cS5p/Ex5f8Rs/nbrKc3eF8nTfJmg0Gq5t2crFF19E36A+9Zcvx7269edIEZWDt86bOb3nMHf/XJb+sZTzaed5/873ebxXY3o2DWTymmoc/3E9477fQsbpUBo8PQv90fmw+h95o6nd+W9oNsjlWyluRSnFvHnzGD16NE8++SQA3bp1Y8CAAaxYsYJp06YV2iYyMpKIiAjmzJmDRqOhZ8+e6HQ63n77bUaPHk3VqlX5+uuvCQoK4p133kGr1dKtWzdOnz7N6tWrGTt2rL2zmceKT209GjXEvUZgXtfrC1bbbSFF3txpNOB3i8lhbSGgHrhbuXUR8L+7v2VC6RvzW9Rw+LecULsUSrph1mrKt/+7GtyFm8b6ZVSshnda5moryc2thWXWtD+c+iEvYG/S1zr7tJJqPnq0Gg1Nalp3pNcqnsU8lNAUPypr6XdetnfLbMVN64apjD1J/O7qe+vrpcrvpiwtaEI41BeHFvPUjqcIyc7ks4AuNJ24i8seDRn10V5+PpPIe/eG88xdTdFoNCSt+pS4Z5/Fs1UrGnzyiQRnoty0Gi3PtX+ON7q/we/xvzN281jOpZ4jrG4AXz/Vg2qPT+K1bg+TfP4CJ19cSHKjN2DEQshJgy8egHltYfd7cO2So7NiM+fPnycuLo4+ff6en0un09GrVy+ioqKK3Obs2bN07969wI1u+/btyc7O5tdffwXAYDDg7e2N9oYLdpUqVUhJKcVMwy7CHu/FFtmCdoPaPvabTyhP2W+sNPqi37XR6HR27dpW1bMq3jrvQu9cWYPeTV/8RNK3UKYgyrcGeFp3uoESefpDOVsYi5IfGJcnuPVwd2NomzpU97XeyIh3NbiLzrU7W21/zq53cO8S34UtidbTs/jRG28mXRyFcAyDycAbW//FjINz6ZmVw/KIF6lxz1JOJCv+b8EeLiRnsfShjozqEIxSiiuz5xA/Ywa+ffpQf9lSS/chIaxhRJMRfNzvY1JyUrjv2/vYem4rHu5uvHh3c2a8/SjLHnqdE941ufziFPYtPYRp/I9wzxKoUh92zIA5rWDFMIj+EK4cq1BdIM+dOwdAgwYF3z8JDg4mJiYGk6nw09SgoCAuXrxYYNmFC3nNSHFxcQCMGjWKc+fOsXLlStLS0oiOjmb9+vUMGTLEBrlwsMZ9oFYRA2BYQWjVUGp61aGqvuhAzBZBRpHc3UGTN5x6Wfn26oVvr17WS1MJPFs0xy3AH/eqhedJ0ml19ArudcuuiO4VvPW8zCxzYVnvezDIJ4jGVRrTrGozq+3TGsoTcLsivZu+3F18S5Q/jUKVEob9tzL5Kxbiuvj0S4xfezdrLkUxPseND4Z/iXfHh9l7Jol7P4rGZFZ88VgX7gitgSk9g7jnnicxMpIqo0dTb+4HMpS+sIkOtTuwZsgaGgc05oUfX+DdX9/FaDbStJYfC54bhPf8j/mhdT98tm5i711DiDrmi3ncJnhqP3R/BtKvwNZpsKALvN8MPhsDO9+CY5vg4sG8z80lT97pjNLT84bX9vEpeFH28fHBbDaTlZVVaJthw4axceNG1q5dy7Vr1zh8+DCzZ89Go9GQmZk3AH27du2YNGkSb775Jh06dGD8+PG0b9+eyZMn2z5T9uZdDWqWPXApic5NR5OAVsW2LNir5Umj0RAweDD6+vVvvXIxtHo9br6lv/nTu5X9/Sk3f398e/Ys87yZ3ZsE0qe5dSYcrnCC2uTNmaa33o28RqOhWbVm6Gw8hYZwMHdPaDnMrl205R00IYCoU98ybc9UssxG3vcOpf99K8HDj02HLvLCmkPUr+7NigmdqFvFi5wzZ7jw1NMYzp6l5ouTqTZhgmuN4CRcTpBvEMsHLOe9397jk6OfcCThCLPunEUtn1oMjAim3+oP2PL59/jMnUmt6c8RufoOAp5+luF3TMX7rlch9QKc2gbno+HSYfhrK6gbgjKtDjwD8lrexm3I+7+Tyx9V8ea/veKWAzz22GMkJCTwyiuvMG3aNKpUqcLUqVOZMmUKXtcnqp0zZw6RkZE8+uij9OjRg7Nnz/LBBx/wwgsvMHfuXBvnquLTaDSF5rWqCPK7dIYFhtn2Sf4tBFqxm5wjFfUeX7n51Ybmg62/XyFsQAI0UallGjOZvWsKX1z8kSYGI7NaPEzj7i+ARsPiqDPM+PYYnUKq8fG4DgR467i2dSuX/vMyGg8P6i9dik+XytPHWziWzk3Hy51fJqJmBK9Gv8qoTaP4b7f/0rt+b9zdtAweO4icEXfy+/S36bn5K1KeOcB/wgbgNng4Q9sH07PtONzbP5S3M0MmJByHa3F576mlXYTsVNB5g5tr3OD5XX+PKiMjg8AbRkzNzMxEq9XiXcQohXq9ntdff50pU6Zw+fJl6tevT0JCAkopAgICMBqNLFu2jPvuu48XXngBgM6dO1OnTh0eeeQR9u7dS9euXe2TwaJY8cm/o/Rr0A93ZxpFr1ojuHQQ3G89qXFp+Ovt+F6Vg+W3jnq6Se8RIazNib4lhbCvvXHRvPnji8QYUnnQ4MZTgz/Fo257zGbFjG+OsnTPWQa1rs3s0W3RZWVw8eXXSP3qKzzDw6k39wN0QRXvKbBwfgMbDqRZtWb8e/e/eXrn04wOHc3kjpPxcvfCw9eHbrNnkPngKM68/haTfl9L7F9RLGw5iMmN2jCgdRC9mtWka+Pq+NZtB3XbOTo7ZZb/7llsbGyB99BiY2Np2LBhkS1oe/fuRavV0rlzZ5o0aQLAiRMnAGjRogXJycnk5OTQpk2bAtu1b98egNOnTzs2QKsT4bhjV1SBTfJ+xG3z0fkQXiOcmt7SpVIIa5MATVQ6l9Iv8d7PM/ghbjfBRiOLfVvR6b5l4OlPek4uL6w5yJY/4xnfPYRXBrckc88eYl95hdwrV6j+2GMEPvGv0o/4I4QNNApoxKeDPuXDAx+y/M/l/Br/K+/0fIcW1fPeJ/Ju04ZWX64mbds2dLPe578/L+XKuRA+Pd2Df9UMA72O1nUDCKsbQFidABrW8KGWnyc1/W0w2bGNhISEEBQUxLZt2+jRowcARqORXbt20auYAR02b97MwYMH2bRpE5DXHfKzzz6jTp06NGvWDKUU/v7+7N+/nxEjRli2O3z4MAD16tWzbaYqmNDavuTkmqhfzZssTT1irsWUezh4Z3WrUSsrqnp+tvmbyB/goo5PJZssWTgtjc6+930SoIlK43LGZZb/uZwvT6xBYzLwdGo647q8hEenx0Cj4dzVDB795DdOJ2QwfUhLxjX34/LUqaSuX4++SWNCPlyNV+vWjs6GEEDeQAQvdHiBbnW6Me2nady/+X4eC3+Mia0notPmDf/t368ffr16kfr11+iXLOW5X1bxjJ8/58O6EG1szjcX6rDS/PdlQKOBhQ+0Z0CYvYdAv30ajYZHHnmEN954g4CAANq1a8eqVatITk7moYceAiAmJoakpCTats2bUHX06NGsW7eON998kz59+rBp0yZ++uknZs+ejZtb3g3hpEmTmDVrFn5+fvTs2ZPz588zb948wsPD6dmzp6Oy65I83N3oEFINgFbVW9GsarMKG6AJ69Jpdc7XHVZUWl5hrW5roCBrkJovKryL6RdZcmQJ60+tR5lzGZqWzuNmP4JGbYA6eTduO47H8+zqg2i1Glb+M4Lm0d9x+pkFmA0Gqj/yCIFPPoHWwzXezRGVS9c6XVk3bB1v/vIm8w/OZ9v5bbze/XVaVm8J5M3VVOXeewkYOZKM6L2kfrUOt527aJi1lbF6PbRpR1rTliTWDCbRrzqtq7jODfQDDzxATk4OK1euZPny5bRo0YIlS5YQHJw3ieqCBQtYv369pRtj69atmTt3LnPnzmXNmjWEhIQwZ84cBg4caNnnxIkT8fHxYcWKFaxYsYLatWszdOhQnnzySUsQJ26fRqOp0CPdtazekj8T/6xU76DZWkWuL8K1aP3t/3etUaroCSEuXLhA37592b59u3TrEC4p5loMi48sZtPpTWg0Gv7P5MGECyeo22IkDJ4Nnv7k5JqY+d1xlu05R4vafixocA3zovkYzp3D9847qfnSv/Fo2NDRWRGiVLbHbGfGzzNIzk5mfNh4JrWZhEcRg36YDQayfvuN9N1RpEdFYThzxjI3kMbbm6Y7tpd7Tr/KcA2xWx7P7s6bDiGkR95IdEIIIWwq/ac9mFJSAPDp1hX3atWsuv9bXT+kBU1UOGdSz/Dx4Y/ZfHYzOq2O0bU6M/7wVmobsmDwh9D2ftBoOBmfxrOrD3L0YiovVU+m349LyT52FH2jRgRHLsL3jjscnRUhbkvf+n3pUKsDs36bxeIji/nh/A+81OkletTtUWA9rV6PT7du+HTrRq2X/o05I4Oc06cxXroMGsc8LRRCCCFEHgnQRIVxMvkkkYcj2XpuK57unvwzdAwPXYkjcO8qqN0a7l0GgU3JyTUxf+dpPtp5kp6Jf/HW5Wg8TvyBqluXoLffJmDoEDTu8qchXFOARwBvdH+DgQ0H8vYvb/P4tsfpE9yHFzu+WOwL/VofH7zCw/EKD7dzaoUQQghxM7kLFS7vTOoZ5h+Yz9bzW/HR+TCx9UT+6d2Iat+9DKmx0OUJ6DsddJ5E/ZXAjA2HCT4QxbLYPVRLuIB7nSACX3uVKiNHopHRGUUF0a1ON9YNW8fKoyuJPBzJ0A1DuafpPTwa/qgMi+1ybDBprxBCCKclAZpwWXHpcSw8uJBNZzbh6ebJo+GPMq7hUAJ2vw/7p0P1JjDhe6jfhZPxacxa/ws+275l+pkoqmWm4BEaSvUX38F/4EA0OnkZWVQ8ejc9D7d+mKGNhvLxkY9Zd3IdG05tYESTEdzX7D6aVJX5n4QQQghnIwGacDkJmQlEHo7ky7++RIuWsS3GMrHVeKod+xYW9YbsVOj2FPSeytEEI6s/+Bq/LRuZFPs7Xrk5eHboQI1HHsbnjjuKnMxWiIqmlk8tpnWZxkOtHmLR4UWs/2s9X5z4go61OzK00VB61utJoFego5MphBBCCCRAEy7kmuEaS48s5dNjn5JrzmVk05E8Gv4ota6ehk9HQ9zv0KA7uQPeZeeVKvw+dTGN9nzPPxLPYHLX4XP3AGo/OFbesxGVVj2/erzR/Q2eb/8860+tZ82JNUyPng5Afb/6NK7SmB51ezC62WgHp1QIIYSovCRAE04vOzebz49/zuIji0kzpDGo0SCeaPsEwRmpsOFJ+Gsr+AVxtd88PotpRMbTS7jj5B6G56SRWb0W/s88R637RuFetaqjsyKEU6jqWZUJYRMY32o8x5OOE30xmiNXj3Au9Rw/8ZMEaM7GMlmvtPgLIYQ9uNesYRlmHwf0tpIATTitXHMum05vYv7B+cRnxtOjbg+ebfcszTLTYPN/UMe+waz355e6/2LHQV8abVhH34RTaIDs9p0JevhBAu7oiUYmlxWiSBqNhhbVW9CiegtHJ0WUpG478KoCvjK4ixBC2INH06bknPzLYceXAE04nVxzLlvObeHjwx9zOvU0rQNb83a3GXRMSyJ3/bMQG02mxo8fEvpy+YQ7nS59yz0mI1mBtfGe+Ah177sXfXCwo7MhhBDW4e4BNSWIFkIIe3H0GAUSoAmnkWHMYOPpjaz4cwVx6XE0DmjMu+1eotXxI1RdPhZz5lXiLlXn2IV2VL+USqjxKI28ffEZNozao0biFRHh8D8oIYQQQghRcWh09p+CSQI04VBKKQ4lHOKrv77i+3Pfk5WbRVO/5kzU9af3H7/RZOuzpMd7cDq+GrqkumjNZoL9MvEf2J8q/e/Cp2dPtB4ejs6GEEIIIYSogNx8fex+TAnQhN1l52Zz4MoBdsbuZEfMDuIz49Ghp2NiTQb9lUin+F8wJv5OxlU9MaZAlEaDb8tm+I/ojM8dd+Ddrh0ad6m6QgghhBCi4pG7XGFTJrOJ82nnOZl8kuOJx9kfv5/jlw9T82ou9RO0jLzsTvhFEzWupmHOyQQgReOLe0hdqj3QG+8uXfDu0AE3f38H50QIIYQQQgjbkwBNWEWmMZPz185bfmLSYjh39S8yzpyiVnwOwQmK4KswIUFDjRQzWgVgAo0Bt6o69G0b49+5J15d++PRvBlaLy9HZ0kIIYQQQgi7kwBNlIrRZORyxmUupF/gYvpF4tLjuJhxkYvpF7mUHIv7xQSCryrqXQ/E+l7VUDPJjJvK215pFHrfXDwCTKiGAeibNqNKlz549LgHjV+gYzMnhBBCCCGEk5AArRIzmo0kZyeTmJVIYnYiSdlJef+/+ffsRBIzr+KfqQhKgqAkRZ0kaJPixqCrZqolmXAz5+1TaRQ6XxOe/kb0LXIxVveHBiH4RXQhoMWdULc9ePg6NuNCCCGEEEKURIPDenRJgFaBmJWZNEMaqTmpJOckk5SVVCjQSspOIjHzKonZV0k1pP29sVL4ZEPgNQi6BvVTNTRJVdRIMVEtxUxAigZ3ww1D2GsVet8cdL4mDKE6Mqr6YQwKwq1RU7xCWlKjaTs8g1qA3tv+BSGEEEIIIUQ5+A8a5LBjS4DmIFm5WRxOOIxJmVBKFfjXrMyWH4PZQKYxk6zcLMtP/u8ZxgxSspNJyU4kJSeFVGM6JmW2HENjVnjngH8m1M6AOhmK1mlmAtNzqZoJfhkK7ywNHpka3DO0aHILziGmtGDw1pHp7UlMsB9XfKtwNaAWpuAQfBo1o05wAxrWr0ezoCr4eEhVEkIIIYQQFYMj59a1y1218coVVE5O3i8aDaD5+7/5/8n/pcC/+f/PX6S5xfq3WM+UC2YjaLSg1aLRuOXtXKvN+zz/35v2W+D0FHG8EpeZTCizGYxGVK4RDFkoQxaRByNZf+YbtGbyfhS4mfN+dLmgM4E+V+X9Pxf015d5GcEnFwKNivpGRUC2Gd+cvNYvLwPoc0Bn0OBm0KA1Flex3DF46Mj08CJR702yjy+XA6tzwSuQy17VSfKtir5OHarXq0VIoB8NA70JCfShTaAPdat4yWTQQgghhBBC2EixAZrJZALg8uXL5TpA9okTXHjiSVCqXPupaNpc/ykPpdVgdNdj0LmT7Kbjos6DDJ0XaV7epLr7kObmTabOg3QPH3K8/DD5+4N/AO7+Afj4eOLn6U41Hx01/DwJ8vOgjZ8Hgb4eVPXWFRGE5UBGDnEZ5Uy0EKJSyL925F9LKiJrXSeFEEJULre6RhYboCUkJADwwAMP2CBZwirMCgyGvB8hhHBCCQkJNGjQwNHJsAm5TgohhCiP4q6RGqWKbtrKzs7mjz/+oEaNGri5udk8gUIIISoOk8lEQkICYWFheHp6Ojo5NiHXSSGEEGVxq2tksQGaEEIIIYQQQgj70jo6AUIIIYQQQggh8jgsQFuzZg39+/cnPDycMWPGcODAgRLX/+abbxgyZAitW7dmwIABrF69usDnzZo1K/Zn/fr1ACilaNeuXaHPR44cabN83g5rlwnAjh07GDlyJBEREQwYMIBVq1Zxc6Pptm3bGDp0KOHh4QwbNoydO3daNV/l5YhyqYx1ZeXKlfTr14/w8HBGjBjB1q1bC61TGevKrcrF2evKjbZv305ERMQt1zt58iQPPvggERER9OrVi8jIyELfG7/99hujRo2iTZs29O/fny+//LLQfpy9vpTH7dY1ezOZTCxbtoyBAwfStm1bBg0aVOB77siRI0VeL9955x3LPgwGA2+99Rbdu3cnIiKCp59+mvj4eLvmIzk5uch0Pv3000De39/ChQvp1asXbdq0Yfz48Zw+fbrAPhydj19++aXEe5S4uDiXOB9FfX9Yq/xTU1N56aWX6Ny5Mx07dmTq1Kmkp6fbLR/Z2dnMmTOHfv36ERERwYgRI9i8eXOBdb7//vsiz9GqVaucJh/WqkeOzMdXX31V4t9LPmc7H1anHGD9+vWqefPm6sMPP1S7du1SEydOVBERESomJqbI9Tdu3KhCQ0PVM888o3bv3q1WrlypIiIi1EcffWRZ58CBA4V+/vGPf6gePXqoq1evKqWUiomJUaGhoWr9+vUF1jtx4oRd8l0SW5TJ77//rpo3b64mT56s9uzZo+bPn6+aN2+uPvnkE8s60dHRqkWLFur1119XP/74o5o8ebJq2bKlOnDggM3zXBqOKpfKVlcWLlyoQkND1WuvvaZ++uknNX/+fNWqVSu1ceNGyzqVsa6Uplycua7c6Pfff1cRERGqbdu2Ja539epV1a1bN/Xggw+qXbt2qfnz56sWLVqoxYsXW9Y5deqUatOmjXr22WfVjz/+qN566y0VGhqqvvvuO8s6zl5fyuN265ojzJs3T4WFhakFCxao6OhoNW/ePNWiRQsVGRmplFJq7dq1qm3btoWum3FxcZZ9vPTSS6pTp05q3bp16rvvvlP9+vVTw4YNU7m5uXbLR3R0tAoNDVVRUVEF0nn27FmllFIffvihat26tVqxYoXatm2buueee1SPHj3UtWvXnCYfaWlphcr5559/Vp06dVLjx49XJpPJ6c9Hcd8f1ir/f/7zn6p3795q8+bN6quvvlJdunRRjz76qN3y8e9//1u1b99effLJJ2rPnj3qjTfeUKGhoerbb7+1rPPBBx+ofv36FTpHCQkJTpMPa9UjR+YjMTGxUPq3b9+uwsLC1NSpUy3rOdP5sAW7B2hms1n17t1bTZ8+3bLMYDCoPn36qDfeeKPIbYYMGaLGjBmjzGazZdnnn3+uwsPDVVJSUpHb/PDDDyo0NFTt3bu3wLLmzZurzMxMK+XGOmxVJtOnT1e9e/dWJpPJss7zzz+vBg8ebPn9gQceUBMnTiyw7/vvv1899thjVslbeTiyXCpTXcnNzVXt27dXzz33XIHtZs2apbp372750q5sdaW05eKsdSVfTk6OioyMVK1atVIdO3a8ZYA2d+5c1alTpwL5mTNnjurUqZMyGAxKKaWmTJmiBg0aVKDsJk+erIYMGWL53ZnrS3mUpa7Zm8lkUhEREWrOnDkFlr/22muqS5cuSimlZsyYoUaNGlXsPs6fP6+aN29e4Ab17NmzqlmzZmrLli22SXgRli1bprp161bkZ2lpaapt27Zq0aJFlmUpKSkqIiJCLV26VCnlPPm42YwZM1Tnzp1VYmKi5XdnPB8lfX9Yq/z37t2rQkND1cGDBy3r5Afmf/zxh83zkZiYqEJDQ9WaNWsKbPPII4+oe+65x/L7448/rp599tlij+HofChlnXrkDPm42eOPP67uvvtulZWVVWCZo8+HLdm9i+P58+eJi4ujT58+lmU6nY5evXoRFRVV5DZnz56le/fuBebmat++PdnZ2fz666+F1s9vvh08eDBdunSxLD9+/Dj169fHy8vLijkqP1uVicFgwNvbG63279NcpUoVUlJSgLwm/QMHDhQ4LkDfvn3Zu3evw+cvclS5QOWqK4mJiaSlpdGjR48C27Vv356EhASOHz9eKetKacoFnLeu5Nu9ezeRkZFMmTKFsWPH3nL96OhounbtWiA/d911FykpKRw5csSyTq9evQqU3V133cXJkyeJj493+vpSHmWpa/aWlpbGiBEj6N+/f4HlDRs2JCkpiczMTE6cOFGgu9DNfv75ZwB69eplWRYSEkLTpk3tms+S0nno0CEyMzPp27evZVlAQACdOnWypNFZ8nGjU6dO8emnn/Lss89SrVo1oOR8guPyUdL3h7XKf+/evVSvXp02bf6eHbZz5874+vpaLW8l5SMjI4P77ruv0Hd9w4YNuXDhguX3W50jR+ejNGl0hfNxs6ioKLZv387UqVMLjHboDOfDluweoJ07dw6g0Jj/wcHBxMTEFHnhDgoK4uLFiwWW5f/RxMXFFVr/888/58qVK0yePLnA8pMnT6LX65kwYQJt2rShS5cuvPvuuxiNxvJkqdxsVSajRo3i3LlzrFy5krS0NKKjo1m/fj1DhgwBIDY2ltzc3CKPm52dzaVLl6ySv7JyVLlA5aor1atXR6/Xl7hOZawrpSkXcN66kq9169Zs376dcePGFTEBfWHnzp0rshzzP8vMzOTKlSslruPs9aU8ylLX7C0gIIDp06fTsmXLAst37txJ7dq18fb25uTJk1y6dInhw4cTFhZGv379LO9rQ94DjMDAQLy9vQvso169epYysIcTJ06QlZXFfffdR+vWrbnjjjv4+OOPUUpZ0pFf94pKo7Pk40Zz5swhJCSE0aNHW5Y56/ko6fvDWuV/9uxZ6tevX+BzrVZL3bp1rZa3kvIRHBzMf//7X4KCgizLTCYTu72eyO8AACAASURBVHfvplGjRkBeEBcXF8fRo0e5++67adWqFUOHDuXHH3+0bOPofIB16pEz5ONG77//Pj169KBnz56WZc5yPmzJ7gFa/st5Pj4+BZb7+PhgNpvJysoqtM2wYcPYuHEja9eu5dq1axw+fJjZs2ej0WjIzMwssK7ZbOaTTz5h4MCB1KlTp8BnJ06cICYmhj59+rB48WIefPBBVq1axfTp062cy9tjqzJp164dkyZN4s0336RDhw6MHz+e9u3bWwLXko574+eO4qhygcpVV9zc3Bg8eDDLli1j27ZtpKWlsXfvXpYsWQJAZmZmpawrpSkXcN66kq9WrVr4+/uXev309PQSz3Np6oKz15fyKEtdcwZr164lOjqahx9+mPj4eJKTkzl//jyTJk0iMjKSjh078tJLL7FhwwYg7wbo5jxCXj7tdf7MZjOnT5/m7NmzjBkzho8//pjBgwcze/Zs5s+fT3p6Onq9Hr1eX2wanSEfN4qNjWXHjh2MHz/e0ovDmc9HSd8f1ip/e+Ttdr8H582bx5kzZ3j44YeBvO95pRQXLlzgpZdeYuHChdStW5dJkyZZWqUcnQ9r1SNH5+NGv/zyC8eOHbOch3zOcj5syd3eB1TXR5C6OWIubjnAY489RkJCAq+88grTpk2jSpUqTJ06lSlTphTqVhQdHU1sbCxz5swptJ+33noLHx8fmjdvDkDHjh1xc3Nj9uzZPPnkk9StW9cqebxdtiqTOXPmEBkZyaOPPkqPHj04e/YsH3zwAS+88AJz58695XFv7ALoCI4qF6h8deXll18mMzOTJ554AoDatWvz5JNPMm3aNLy8vCptXblVuYDz1hVb0Gq1paoLzl5fyqMsdc3RNm7cyKuvvsrdd9/N2LFjyc7OZvHixTRr1oyaNWsC0K1bN65cucL//vc/RowYgVKqyLwUt9wWlFJ89NFH1KlTx9Ji2aVLFzIzM1m8eDGTJk0qNi35y50hHzdau3Yt/v7+DB8+3LLM39/fJc7H7Rz7dspfKVXkd0Jxy20tMjKSjz76iAkTJli6Mjdp0oTIyEjat2+Pr68vAN27d2f48OEsXLiQLl26ODwf1qpHjs7HjdasWUNoaChdu3YtsNwVzkd52T1A8/PzA/Ii28DAQMvyzMxMtFptoWZXAL1ez+uvv86UKVO4fPky9evXJyEhAaUUAQEBBdbdtm0b9evXp3Xr1oX20759+0LL7rjjDt5//31OnjzpsBspW5SJ0Whk2bJl3HfffbzwwgtAXt/bOnXq8Mgjj7B3717LsTIyMgrsO79lID9djuKocunatWulqiuQ98U+b948UlJSSExMpEGDBuzfvx/I6y5143FvVJHrCty6XMB5v1fKytfXt9B5zv/d19fXcjEsqS44e30pj7LUNUdavnw5M2fOpE+fPsyaNQuNRoOXl1eB7kL5evbsSVRUFBkZGUXWA8jLp73On5ubW6EbM8hL5+rVq/Hy8sJgMGA0GtHpdJbPMzIyLGl0hnzcaNu2bdx1110FWp1c5XzczM/Pzyrl7+vrS0JCQpHr5H/f2INSipkzZ7J8+XLuv/9+pkyZYvnM39+fO++8s8D6bm5udOvWja+//hpwfD6sVY8cnY98RqORH3/8kQkTJhT6zBXOR3nZPYTMfwoWGxtbYHlsbCwNGzYsMrLfu3cvv/zyC76+vjRp0gS9Xs+JEycAaNGiRYF1o6Ki6NevX6F9pKWlsXbtWmJiYgosz87OBqBq1aplz1Q52aJMkpOTycnJKfByJPx9M3n69GmCg4PRarVFHtfb29vyBMZRHFUula2uQN48JEeOHKFKlSo0btwYd3d3Tpw4gUajoVmzZpWyrsCty8WZ60pZhYSEFHgxHv4u10aNGuHj40ONGjWKLOv87Z29vpRHWeqao8yePZu3336b4cOHM2/ePEtQcPbsWT7//HMMBkOB9XNycvD09MTb25uQkBCuXr1qqcv5Lly4QMOGDe2S/vj4eL744guSkpIKpRPyHpLkd3MqLo3OkI98Fy9e5PTp04UGb3GV83GzBg0aWKX8Q0JCCv09mc1m4uLi7JY3s9nMlClTWL58OZMmTeLVV18t8Ld89OhR1q5dW2i77Oxsy/e8o/NhrXrk6HzkO3jwIGlpaYX+XsA1zkd52T1ACwkJISgoiG3btlmWGY1Gdu3aVeSTMoDNmzczY8YMy+9KKT777DPq1KlTYASXpKQkLly4QNu2bQvtQ6fT8frrr7Ny5coCy7ds2UJAQAChoaHlzVqZ2aJMqlWrhr+/v+Vpf77Dhw8DeS+Eenp6EhERUeC4kHdT2rlzZ9zc3KyVxTJxVLlUtroC8OmnnzJ//nzLOgaDgbVr1xIREUHVqlUrZV2BW5eLM9eVsurSpQvR0dEF3u/dtm0bVapUsXTj7Nq1Kzt37iwwIMa2bdsIDQ0lMDDQ6etLeZSlrjnCihUrWLRoEePGjWPmzJm4u//dYSY+Pp7XXnutwAv1Sim2bt1Khw4d0Gg0dO3aFZPJxI4dOyzrnDt3jr/++stu+TQYDEyfPp2NGzcWWL5lyxZCQkLo168fHh4eBc5Famoq+/bts6TRGfKRL/86Ex4eXmC5q5yPm0VERFil/Lt27UpCQoKlfCDv3aP09HS75W3mzJls3LiRl156ieeee67Q58eOHWPatGkcPXrUsiw7O5vdu3fTqVMnwPH5sFY9cnQ+8h0+fBhfX18aN25c6DNXOB/l5fbaa6+9Zs8DajQadDodCxYswGg0YjAYePvttzlz5gzvvPMOAQEBxMTEcPbsWWrXrg1AjRo1iIyMJCUlBZ1Ox8KFC9myZQuvv/56gQDt8OHDrF+/nieeeILq1asXOK67uzvZ2dmsWLECpRS5ubmsXr2aZcuW8eKLLxbZTclebFEmWq0WNzc3Fi9eTFZWFm5ubuzZs4c33niDJk2a8MILL6DVagkMDOR///sfV65cwc3Njfnz5xMVFcVbb71VYESjylQuer2+UtUVAG9vbxYsWIBOp8NoNDJz5kwOHTrEu+++axlsp7LVFbh1uTjz90pR9u3bx4EDB5g0aZJl2c3l0qhRIz755BP27t1L1apV+f7771m4cCFPPfUUHTt2BKB+/fpERkZy/PhxfHx8WL16NV988QWvvPIKTZs2BZy7vpRHaeqao125coVJkybRuHFjHnvsMeLj47l8+bLlJzw8nH379rFhwwYCAgJISEjg3XffZf/+/bz33nvUqlWLgIAATp06xYoVK6hatSqxsbG8/PLL1K5dm//85z92eYcjICCAc+fOWbozpqenExkZyaZNm3jrrbcsrdiRkZF4eHiQnJzM9OnTMRqNvPnmm3h4eDhFPvJ99913/PXXXzz11FMFlgcFBfHzzz87/fm4+ftDr9dbpfzr1atHVFQUa9asoUaNGhw9epTp06fTuXNnJk6caPN8/Pnnn0ydOpXu3bszYsSIAn8rV65coVatWjRo0IAtW7bw3XffERgYSExMDK+99hrx8fG8//77+Pn5OTwf1qpHjs5HvjVr1mA2mxkzZkyhbZzxfFidNSZTK4slS5aoO++8U4WHh6sxY8ao/fv3Wz7797//rUJDQwusv3XrVjV48GAVHh6uhg0bpjZv3lxon99++60KDQ1Vly9fLvKYubm5avHixap///4qLCxM3X333Wr16tXWzVg52KJMPv/8czVgwADVqlUr1bdvX/X222+rtLS0Auts2LDBUiZDhw5VO3futEn+ysoR5VJZ60q/fv1U27Zt1ZgxYwpM8p6vstaVksrF2evKjebNm1doQtCiyuXw4cNqzJgxKiwsTPXq1avARLT5du/erYYNG6bCwsJU//791bp16wqt4+z1pTxKqmuOtm7dOhUaGlrsT2JiokpOTlavvPKK6tmzp2rdurUaM2aM+vXXXwvsJyMjQ02bNk117NhRtW/fXj311FPFXl9tJSsrS73//vuqd+/eKiwsTA0fPlxt3brV8rnRaFTvvfee6tatm2rbtq0aP368OnXqlNPlQymlXn31VdWvX78iP3OF81HU94e1yv/q1avqmWeeUW3btlWdOnVS//nPfwrdq9gqH/PmzSv2b+XG9S5evKiee+451bVrV9WmTRs1YcIEdeLECafJh1LWq0eOzodSSj388MPqoYceKnY7Zzsf1qZR6vrQU0IIIYQQQgghHMr5x5kUQgghhBBCiEpCAjQhhBBCCCGEcBISoAkhhBBCCCGEk5AATQghhBBCCCGchARoQgghhBBCCOEkJEATQgghhBBCCCchAZoQQgghhBBCOAkJ0IQQQgghhBDCSUiAJoQQQgghhBBOQgI0IYQQQgghhHASEqAJIYQQQgghhJOQAE2IMho+fDjNmjXj8OHDjk6KEEII4XTkOilE2UiAJkQZnDhxghMnTtCkSRO+/PJLRydHCCGEcCpynRSi7CRAE6IMNmzYQPPmzRk1ahTffPMNmZmZjk6SEEII4TTkOilE2UmAJsRtMplMbNq0iZ49ezJw4ECysrL47rvvHJ0sIYQQwinIdVKI8pEATYjbtGfPHhISEhg6dCi1atWia9eurF271tHJEkIIIZyCXCeFKB8J0IS4TV9//TUtW7YkNDQUyHsJ+sCBA5w+fdrBKRNCCCEcT66TQpSPBGhC3Ib09HS2b99Ov379uHbtGteuXaNLly54eXnJ00EhhBCVnlwnhSg/jVJKOToRQriKL7/8kqlTpxb5WdWqVdm9ezd6vd7OqRJCCCGcg1wnhSg/d0cnQAhX8vXXXxMeHs7kyZMLLD916hSvv/4627dvZ+DAgQ5KnRBCCOFYcp0UovykBU2IUrp48SJ9+vRh2rRpjB07tsBnJpOJO++8k2bNmrFkyRIHpVAIIYRwHLlOCmEd8g6aEKW0YcMGNBoN/fv3L/SZm5sbAwcOJDo6mri4OAekTgghhHAsuU4KYR0SoAlRShs3bqRdu3bUrFmzyM+HDh2K2Wxm3bp1dk6ZEEII4XhynRTCOqSLoxBCCCGEEEI4CWlBE0IIUWmYTCaWLVvGwIEDadu2LYMGDWLVqlXkP6tUSrFw4UJ69epFmzZtGD9+vMzdJIQQwq4kQBNCCFFpLFiwgNmzZzNs2DAWLlzIwIEDeeutt1i8eDEA8+fPZ+HChUyYMIHZs2eTlpbGQw89RFpamoNTLoQQorKQLo5CCCEqBbPZTIcOHRg3bhzPPvusZfl///tfvv/+e3744Qd69uzJ448/zqOPPgpAamoqvXv35qmnnmL8+PGOSroQQohKpNh50LKzs/njjz+oUaMGbm5u9kyTEEIIF2cymUhISCAsLAxPT09HJweAtLQ0RowYUWiEuYYNG5KUlMTPP/9MZmYmffv2tXwWEBBAp06diIqKKhSgyXVSCCFEWdzqGllsgPbHH3/wwAMP2DRxQgghKrZPP/2UDh06ODoZQF6wNX369ELLd+7cSe3atYmPjwcgODi4wOf16tVjx44dhbaT66QQQojyKO4aWWyAVqNGDcuGtWvXtl3KhBBCVDiXL1/mgQcesFxLnNXatWuJjo5m2rRppKeno9fr0ev1Bdbx8fEhPT290LZynRRCCFEWt7pGFhug5XfXqF27NvXq1bNN6oQQQlRoztz1b+PGjbz66qvcfffdjB07lkWLFqHRaIpct6jlcp0UQghRHsVdI2UURyGEEJXO8uXLmTJlCr169WLWrFloNBr8/PwwGAwYjcYC62ZkZODn5+eglAohhKhsim1Bs6av/vqKb898iwYNrQJbMajhIJpVa2aPQwshhBAFzJ49m0WLFjFixAjefPNN3N3zLoUNGjRAKcWFCxdo2LChZf2bfxfiZpdSszgUm0q/lrVw0xbdCiuEEKVllxY0szKTa84lKzeLFX+u4N5N9/Jy1MtcM1yzx+GFEEIIAFasWMGiRYsYN24cM2fOtARnABEREXh4eLBt2zbLstTUVPbt20fXrl0dkdzbpkwmzNnZNj2GyWwi15xr02O4mv3nU8jJNZFtNDk6KUKICsAuLWj3ht7LvaH3ApCcncyqY6tYcmQJv8X/xsf9P6aBfwN7JEMIIUQlduXKFWbNmkVoaCiDBw/m0KFDBT4PCwtj7NixzJ07F61WS0hICB999BG+vr6MGjXKQam+PZm//kru1UQChgy22TF2xe4ix5TDoEaDbHYMV5NrNgOgLeYdRiGEdZhSUjBnZaELCnJ0UmzKLgFa/MS7SNobhwbwrF+V++8eQK+RH/Pkz5OZuGUiywYsI9gv+Jb7EUIIIcrqp59+wmAwcPLkScaMGVPo87179/L888+j1WpZunQpmZmZREREMHPmTJd5By33aqLNj5FjyrH5MVyVQjk6CUJUaOk/7QGw6UMoZ2CXAM2vewc0bqAM2WSciOfKos/xWrOeRS8/zcMZS3hk6yOsGboGf72/PZIjhBCiEho5ciQjR4685XqTJ09m8uTJdkiREEKIslAGA5qbpkSpSGz+DtrphHQGXL2bQfVf4h+tZ7B6ymf4PdoTjSkd9eK7LEjsS3xGPK9Fv4ZS8uRJCCGEEK7Jprcxcb9D7K82PIAQrqOiRww2D9Cqeeu5u1Ut+reqRYNq3qzcf5UeV4azceLL+NTLRffhGt6KjeCH8z+w5sQaWydHCCGEqPDkgWcFlHQWUs47OhVCCDuweRfHqj56pg5uafk905DLoh/P8NGPWn7vOZm3fppFyCd7uf+xlsz+fTa9gntRy6eWrZMlhBBCVFxKgQxY4RCmlBRMaWnog+XdeiFE2dh9ompvvTvP9Qvlm6d6cNajKW90mIBHFSMjVhynaqKBWb/NsneShBBCCLvJOXMG0zUbTzNTgVrQlFIYzcZbr+gEFHmDGGQdOuzopAghXJjdA7R8TWv5sf6JbsTW6s7m7neiNeXy3291bDnzHT9f+tlRyRJCCCFsKvvoMdJ3R9n2INeHfa8IjiUd44dzP8jca0KISsNhARpATT9Plo3vyKpqY8jq4If/+TRG/+HLB79/IP3nhRBCiLKywzXUXtfpi+kXATApmQRalE+mMZPsXNtO5C7spILHCQ4N0ACCArxYOr4LU+r+C68gA/+39RrxZ/5gR8wORydNCCGEcEkV+9ZFiLLZHbdb7i+FS3B4gAbQso4//xzSl41teuBmNvHobj3/O/g/TGZ5WiaEEELcNnu0oEkYWIj0/nFu5grU9VdUbE4RoAGM7VyfI+GPoGuuaPtHFuroX/wQ84OjkyWEEEK4HgkUhBDCZTlNgKbRaJhxbweWhP4fbh4mHt6lZdmRpfI0SgghhLhdFegdNCGEKKSCf/84TYAGUMvfk5ABD2NopafxeSO6X//k18u/OjpZQgghhGuxQ1cu6eJYmM1KJNdgqz0LIZyQUwVoAA91b8yXrf6Bu3cu90VrWPbHUkcnSQghhHApFfzhcuWTneroFAhhHye3QKw0zjhdgKZ313LHyAnkNtfT+IKJpL1RnEk54+hkCSGEEC5EBglxWsZs+OMryEwq/TZuOtulRwhnkpMGKecdnQqHc7oADaB389psbns/Wk8T9+5RrD6x2tFJEkIIISo8pZTTvlumQePoJFhHRgIoMyScKP02mtLl3Wx2znMnhLg9ThmgaTQauv3fI2ibaQg7rzgUtZ4MY4ajkyWEEEKUi92CnzIeZ/uxK3x75FIpj1GmQ9iVMhrJTUiw3/FKUyaWYOs2CrAUO8405LLp8EXOJ8r9kqgEnPRBkrU4ZYAG0LVpTX5pNxTlbqbv3gw2nd7k6CQJIYQQFVqGIRdTBWqFyTp4kIxf9mHOznZ0Um5wPUC7rRvMW6+blp0LwMUUZ8qrEOWXnpNLUkblGijHaQM0gCbDn8K3oZFuxxSbf13ltN0uhBBCiMrGFd5BM6WlA6ByTQ5OyQ3K0oJWCubr90jaCtITVIh824/FE/WX/VrCnYFTB2idm9XnUJsuaM2KZjvP8Gfin45OkhBCCCFwjQDNnrSm7LwBQG5Fc/3W63YeOpdi3Rxj3tQKWq2GrD//xBh/pfT7F0I4FacO0DQaDf5DnsOrXg53HVB8ffRLRydJCCGEcH4VsMdJmQJCO7YmBV3aju7kt6VYM7+Lo3Xnqjt0IcWyd8PZc2T+KkOVVxpKQfzR0j0gqCgq4HfcjZw6QAPo2akDyaG18M+ChO83kZ1biSqfEEKIiqUC3VTY67WDCtdSZ6NBQgrtXlQeGVfhylGI+93RKRFW4vQBmlarIaXPRMy+Jrr/nsX2mO2OTpIQQgghXIozBXllGSSk9CrQMwBRWspU8F/h8pw+QAPoMPABfBqaCDuv2BX9maOTI4QQQlR6Fa5lywpKVSJlGiTk73XNWVmYs7KKXbMCDcLpfNKvwNGvwWR0dEoKskTllaj5tII/iXCJAM3fx5uYzn0xaxWBPxwkLj3O0UkSQgghnFcFv3kpNWv29zNmQ3aqFXZUhha0G9ZN276dtO07SlhVzr3NXD6SF5xlX3N0Sm5y/ZxrXOK2XpSCy5zJesOfRV83h16HzWw69pWjkyOEEELcvgp082zvQMDhgcdfW+CvH6ywI3XTv9blJuPsVz75fxvyAmKF4TIBWqOmLclsVgf/LDi76QvMVh79SAghhBAVlDWCu1J0a7NdCHnrPdf08wSghp+HzVJRaZzZBbH7ivjAWQOhStjFsYJzmQANIHfQYxj9THT4OYl9l4v6wxFCCCGEcGJlCRZLsU1+y5mjGxorhIyrkBLj6FSUXn6jhdMFjrZT0eu5SwVo4f3uw6ehmbAYxa49MliIEEIIUSQ73L24wiAhmlvcsJoNBgyxsXZKjZVU9DvT22A2K07Gp2Gq7COjVMZBQio4lwrQdDo9Kd3zBgtx/2YXmcZMRydJCCGEEHZgi4Awa/9+sg4dxpSeYfV9F68sN9N52yhjrtVT48rOJmZw7NI1Tl1Jt88BnTYOdNaul6KsXCpAAwgZ8RSqrpHuR4xsP7PV0ckRQgghSq8CtX44fNCO21FMWlVOzvX/WOe9dpVbvnmozNnZpH7zLbkJCTftOC/9uWnFP5h2qfNhJebrLWfSgpbfxdHlbutFMVzuTFZvEIa+aRWqZMAfm1Y4OjlCCCGEcFHWDmpMSaUYfr2EY5pSUgAwnD9/80Z5/0gLiXNwtvMgXRytI/0K5OY4OhWACwZoAKYB48jxNlP3xxNczrjs6OQIIYQQTsUerSlW73KYkwZXjll3n3ZmtRK5+fxd/700t9/O1pakcnNJ/eZb13vXr0jOVro3qVTxWfnOhVKKpMy0ggvP7s4bwdMJuGSAFtrvIbwaGmh3SrH19zWOTo4QQghRKiZTLk5/k4eDusudjYL4P4t9gl2mNNm7paNUhyuhNczZWmaswJyddz5zTp2y+r4d/ZeUnZvNyeSTDk6FKIt9MTH875evOZV8U2t1TlrRG9iZSwZobl4BmCLa4KYg/qs1lbLftRBCCNez9fwPxKQ5//DdpbmsWr0FTV1/f0vmOS3ivubWXRyd/k7I6RN4O/LOw+GEw5xKPkVydnLpNpP7VacRk5IEQEJG3r/Odm5cMkADCBz6BBm1conYl8jRxD8dnRwhhBCiVJKykhydhFtyyK1K/gAHtrhRstPNV3Lm9cmslRlifgZjlnXSkr9NxWtgc2m5Km9UzVI/rPhjHVz43YYpqnhsNwBM/ryBzhWY5XPZAK1m6774N9JSNwmivl/i6OQIIYQQFUZpblqsf2NzPfpw4Ra05ExD3n8yEyH1Alw6XMRaJZWb5harlNCCpvL/dc4bzgrBGmWbfLb8+6hEvjl8segPynkunP1Zh8sGaGi1mHv/H0adQvPNDoxmo6NTJIQQwoVs376diIiIAsuUUixcuJBevXrRpk0bxo8fz+nTp613UBe5eXZoC9pNRy9XwGH3d9A0Nx23FMFYqdywHxepQ/kq4Gt1ZeNi580a4tLjMJgMjk5GMa63oHH9gZCTnR/XDdCARoMmYQwx0PGogehT2x2dHCGEEC5i//79vPjii4WWz58/n4ULFzJhwgRmz55NWloaDz30EGlpzvHieKkVcbOhlLIM416GzW0v/07eEQe3ehRRwv5Kyl9xm1WILo7OdQNcXicup3EwtnR/T4CdWob/riDKbMac47gh47Nyszh05RC/xztnl06Ntf6YDJlFd2UuJ5cO0PSBIfi1qIOXAY58sdjRyRFCCOHkDAYDH3/8MePGjcPd3b3AZ+np6SxZsoQnn3yScePG0bdvX5YsWUJGRgZffvlluY9dlpag2KRM9p21zjtrhlOnSP9pD7lJt96f2TERWt4/triRddTT8bIet8TtKlag41r+Lvvjl6+RZbiNicnN5ZvE/HZl/vYbaT9ss+sxb5T/fZdjco55xYpT7q+GE5vh+LdWScuNXDpAA/Ab+ghpVczU/fEoqTmpjk6OEEIIJ7Z7924iIyOZMmUKY8eOLfDZoUOHyMzMpG/fvpZlAQEBdOrUiaioKHsnFYD9MclcSrXO01nT9VZAlZ1tlf1Z/V2nUnULdHKWPJQUbJYlf9fnQStxFEdVaO/mnByyjhxBmV33vb4KI78u2KnPZ+6VBLscx1Xl/y39PcCLc33vuHyAFtRlNB6NjTS/oNj506eOTo4QQggn1rp1a7Zv3864ceMK3eyeO3cOgODg4ALL69WrZ/mssnDIMPu36OJYtuM5qE9gaW7Ci1in2ACsjMFw9p9HMZyPIffSpTJtbxUV8iW0sozEmR+gufytt3Ow0gMiq3+PWYnr1xKdF95demDSKC5/sdrRqRFCCOHEatWqhb+/f5Gfpaeno9fr0ev1BZb7+PiQnp5e7mPb+kagQItWUTcvt3FD45ibFtu9g+aIkQ2VUkW3oJUqLcXMg1ba496mc1czSM6w8WAONjwHdosBy5OHihSgZSSCIcPRqSgXjQ2/b6yhAtQSCB7yL1Lr5xL+ewLnk2T4UiGEELdPKVVsC0ZJXcsqIoe2oDnpE+1Sht/1gAAAIABJREFUsfRwLGNeLvyaN0R/WVw/VGrm7Y9qfehCCrv/ki5xxclNSCjVu5slsgTrVvwuMWQ6JsA4sxNOfGf3w1r3QctNXRydLFCrEAGaZ0hnAhp7UjUDotZ+6OjkCCGEcEF+fn4YDAaMxoI3uBkZGfj5+ZV7/640P5VjUurcT7RL5+Z30IrKSwn5S78CiacKb6cKv19WnAvJmYWP6MpF6gQyftlHRvTevxeUqUDzR+K0UoCWk5Y3QEXCCevsr5Jx9mduFSJAQ6Oh2qCxZHgrtJu3Y3bhSS6FEEI4RoMGDVBKceHChQLLL1y4QMOGDR2UKscozSiO9g44y3U8h0UoEhlVLE5UBw3XA/H0+FsctuLUwQJZKfdE1XkR2t/fdc5VThUjQAPq3DmenMYGWv9l4MDxXY5OjhBCCBcTERGBh4cH27b9PTR1amoq+/bto2vXrlY5hubGmwqzCS78Zr05dG71DloZd2V/Vjy4nR+TK8vhSmhBsywrIm23Sm6p+p7esI6TtxII4TjO/cdRYQI0/GoRGN4EdzMcXL7A0akRQgjhYnx8fBg7dixz585lyZIlbN++nYcffhhfX19GjRpV7v0XemfrWhwkn4NLh8q9b6tzxDtotlRcYHPTcmUykXXoEGaDFQbMUIoyB5vFdHEscZOyHcn2KlALTvlU8nIwZEDi6XLtwrpvoDn3O6/ut17FddQb9gS/bZ9Cg5+OkmXMwkvn5egkCSGEcCHPP/88Wq2WpUuXkpmZSUREBDNnzrTOO2iFbgSs985Vek5u6ee6KkWrkkODLwfe0BtjYzHEXgCtG16tw0q1zY1dyDSlSno53l9Spd1e4XQtBBUpUHOlvCjlHC9cnY0CQzoEBIO7/tbr28DV9Bz8PN3xcHezLHPWB00VpwUN8AkbhEcTE3UTFbu/X+bo5AghhHBiTz31FAcOHCiwzN3dncmTJ7Nnzx4OHDjA0qVLady4sW0SYMWbpu3H4tlx7LLV9lea+88Mo32H2S7TjZTlIfntblvem7YSti9FWsyZhQf6KN+xnfMm1OUUce4cdoOfc80xxy0rU871/5S9vG45lcgttt1z6io/nylmNE4nC7orVICGm456dwwkRwdXVn/u6NQIIYQQJbBhF5ty3myUZpCQP6/+Wa5jlJazPuHOV7CoypvW6wMXZOcUXHzbN6PlTIYVOVNayq+odwpLu6l1CyL3wgEyDblW3efNrqRl8/XBOK5ll2LqhrNRcGxTCSuU/4FUeUowv/ivZRmv78u5K2bFCtCAugOeIKGxkVZHrvL/7L15sCTZVeb5c4/17blnZWVlpWpfVJJKC2gkgaRGSAgY1D20mSS6h54BdY+BjZihMQOzaRiaHnrMmhmzsQZsBhuBaQQ0DKg1QoiSQFKVasusyszKfX37Fu/Fe7Fv7uG73/nDPSI8IjzixculspTln1lVxgt3v/f69RsR57vnnO8Uypm7PZwIESJEiBDBw6CUpDfLgt1Voeq7iTtPWPXr13Fqtf7jN+nVFEKQsYvYI6lI30xfOyvNdTkXdtHyXYGSB2f39druOt5Cin9zuQbXt0bwot3C98tmxRMwqqojPCslB7a3sWC5FmX9FuvGvYkoKQaG49y29kzbxbRvTVH+niNo0qEnmX7qMGMmvPDH/+FuDydChAgRIkQIx+0qzFxaYkJZu+XhBHFXvB5vUp6McF2M5RWUV0/ctjarZo5Np0zWCRilu5xDV9dHEzPZhZLjm1lgPd/QQ2uw9cHSYeUVyJy584O67QgLcbw7UvaadfsIxSC0POnyLtnCudw5LhcuA7d3bm6p0saAdwSCE4tFTi0Vb77xHvz91S3+/urWLbVxzxE0gCc/++vkDwj2fetlbOfOun8jRIgQIUKEUXCrIiGurqNdudovBpK9wJ7q1du6qX+viIS0CMquc1d26W1M6V7+X6cO6+5z0BrPP0/z+srI/Q4bUF8Pu7gfp17HrlR23e3rSyXOrYVd19O369tldzGH6qZJQ1gOmoAL+QshJw+69vaSZoEY/nxv4fPk+Ms5Jg8YsxtOEmtGLfT9u4lBz7z1tqLfecK7G9yTBG3mPT+D/TQcyzu88rd/fLeHEyFChAgRIvRjlx40/do1zLU17Hx+x3Nvedf6bkZw2RpU+j2Cd5Q03uJ8xWy/lt1AB5joDqkcMAbhDhqH6PpnJ9zK3SivvIp68rXwdk3T2yS4iXCwe6Ngcus5dN+L6dyGsgw3CVd4GwN5s3Hb27b9zaDYLj2xd+qzGmx3V+tJCERhDkRn3bbactre5tszxtuFe5KgIcd4/Kd/nsYYVL7y5bs9mggRIkSIEMFDl1ExukUgglLZPcZxwzI4ZczTMAujNTaSzH4Hz1/PcWOUXJfbhc3zsPEG7DICxqlWcZQBypLBGxpm2N2ElSaEwHGCxcb72zdu3EB59QSOolAzFSzXYVeelBGMUavlWe0qVH17rU59bh5zbQ1rY+MWWvkBJmqhKo6tQ3fnvoQQLCkZziqrFIxq2Ak33XZrScmDPGgDrxuSf7WL8RQVY+eTRkFlBSl3hen6Yt9HovXcdnmLdxz3JkEDjn3ylyk+bfPkvMLi5ZN3ezgRIkSIEOFtjr5dZWl3IY5SzKvd0+u9yGkKACXj1nIeggiqOKqmzXzu9u/O324oJ06ivPTS7WlsF0ZkzaixaWWHXu9UPcNZGAYnC+c53dhtKOPw8aiG3VanC+keigte7tctI9yDNAynV0pc2th9yORbE4Nl9pv2Tvl3d4bAuUJgut6zV2xth7N/MNDQLRq61UfQbpprug4IkES/57fltA4jaK5p4ijKTXZ6a7hnCRrj+3jgwx/AleGNP/hf7/ZoIkSIECFChAEY0eqQwwlaB7dvC3iwVsWb6SXoJPHfrra8l2FS6TffR1nvkA+pq6mwfCUvQazu3KQhPWCcqjHE22g0oLQIG29QUc031xsKlBo6thM27jvjshBCsF5fD+QDdt5vvw4+m7XXYePsqI23W+htR7pbhcEFJKQ4AJZ4k3UXRqnpR683bedr5nINvj+bZ5gjblcQ/d8ior3XMDg3UH3lFZSXXr5Ng9gd7l2CBjzzs7/J6mMOT5xZQSnn7vZwIkSIECHCvQ4hID8bGp7XT25G96AJAVLM/8nusVpCCcwdSkG7s+IhfbFHPX8O7rtPOKXd5B0wmnvGMVu+wahkwyFIrgVoISFpI/TZd3inawUgHF5ZKNyiN/QW5rNjEe/qsqbV3JXoxEZjg6vFq6zURvRS1jehsjrauWFlFEa9nTu0uSEEyGFiOHewX9ey/Y2iEQjaSKUnBl3b375m2buXsPfHIELWbzuMU+rvq68e4ZuIe5qgyUeeYfrZA4yZ8N0/+J27PZwIESJEiHCvo7oOuavefyPj9hhQt5OKDFY8e/PzbEbyTOy01T6qiuPN5KCF9TOAXHrGqkCWZK8W2OLzoNd8z9qQcZUW+/sK6bY1ojv3mN785/9S5iVObo6eqmL54X5vinDHIK/cHYZrdIiDiyCEW9xW9K6nxunrKBcXb09jQxBG0K5n61zZ7N/YEEJwYvME2+p2eLcQ+vlu9SDfLQ/oANzTBA3ghz7/O6weFRz69iu45t1T2YkQIUKECG8DtCTEQ3Id/AMDXg/HsDNHbmU30vEDTu0PVxqAW4hNMrdK2JUGg0IcG7pFqVc84HYzkltob0iEI44/Ly1j0HFdbKM5+IIW3IBH1h/bwJC9ndp6szFoKG812bwQuD2qmrUTl9GWs93ErMXJHXuHPL/wiajrFuulEerH+TBXV2l873nQ9K7+vR4Gi5jcbriacce8gi30Nl/TBhfMdoVL3ahzMX8RALtcRpgmlJYCG2bBNdf9OXqrLcd7nqCNP/lJzPek2F93efFL/9vdHk6ECBEiRLiHcbFwmaKlgNT/8zpwh/0WjJyiVqRkhyWxD26zefbcTfc3irXnmjqF03+JyM92vW85Ln97cZPV4gClRR/a0ibqtcHhad+fzXNisaeo7E5z2EVmhh/fEaHnjmbduV6sYVu2/EKmyvM3crds6PZevlNrN+8JvR1W7B0mjwOGKBDEynUSG/mR7j9X1/m7y1n0dkFo7xoz21l7Xa2svgqzz+16uC/O5rmQGV1ExS56/ct+3uGdnE0xYJPkVlsdFb2k6dRyKdBMeDuucBGui/ra66hvvAHZTo06gdTnke946d68TY3l2jKnt04PPeeeJ2hIEh/63P9E5hAk/uKvbqp2R4QIESJEiLAThBBkcxc501ih10oUto2r670XtF7cdJ9nts6waXrGnXQbt4DDQosgxFBrluHK18DokMSlrSJrpSbFjYWuUw0/b+TSRpVcfQRFwVFIhFIYnj804py4pony6qu4zd2Jd1iOi2bEu98UHgkL7aftgeweV9MY7Bnobzvkde9prReShOk4w/P33qr1ya5+HVZevW3NCSFIX14kuThaiYCWl9awQsoXBOtxtUiMvkM+4Q7z3OutG4ieNe01e7ee4Sj5s/45u1Sthd0Jr3R9N7VCiuv14Aldc3dnyOdomC3Nou2guHnvEzTg0A99ltp74xysOJz48n+828OJECFChAj3IBw/rFFC6jOi1Ndeo/nCi+H2zAj2QZcRPYh0vAl2Rp8x0yJHSkeIy7C9eVBsY6Dx37UTPqS34D+hWHkZNs7ufOs7EBs7m8Wp1TFXVvxTRpvMi5kq6yUtVHygu3//Hz/Esffshn5r6nvB0UqBfLam6XBjq0G+0QkJna7NIjYvcHO4iUXWCsk0e0noTnPmdq2rW8VuDHHhuIi67+kdOszb96GzdhkW3KUgOeA7wTUUXpvPh4YGOvU6dmVEz92d/G5xbM/Ltcu6h70II2gjXdfDH98qeFsQNGSZD/2zf8PmARD/z1cQ9pssQxohQoQIEe55dHIZ+n/pnXqYct4uPGjFhe48pDuMMPvGsEM8MSG74kJ4ct+nGrNcL18f2s9aSeW5y9lb9+LcJS+QZjo9huEOT7O1RnqsfsfxxEN2RNBb0jXnov80OmRZDXjophpLUF7qHdKbh54Ot9Xt0RUXb6XbXbAMfWkTFtaRtEBu2ECv8tDDO6L1faGvb1B77lu4zdHz0dojaJFgNRD6a2tomUtYW1dCSyvkn3+RpW+/cHODbnc9ggdtp/zI4ryXJ1Za8JvsHI/dbPXo0ILinVwzIQQ5LTN8XDfZz+3C24OgAQ/88OfZ+uEEB8s2J//o9+72cCJEiBAhwj2GlgdNRvIKo46KHX7kU3oBti9BYW7oeV2mzCiGg6UN3LUOu/ofrm4jEKRm10hfXBh6hS1c8g2dNzKrQ4dwMVPFcUW7WKxwAl6EltE5xHBa1UvkzBHqeo2q4jgSQq4PsyND+nSFG9r/rUiR93a107BuDbe/xfO589zIvAr2HZY07+LRw9eAo3ohuJJjBzwr/c9TBBQzdyaAw49bW16xc6cxvAyCtdWtUiiCoZG2Dg3f62hbOK4gZVZIJ2J97dzYbrBZHS2kd+DICzf6F5/ZTTBHnpf2nHYQj+1ivXVFoIYQtMBbm8omql0fdOpbAm8bgoYk8WP/9f/C0lFIfOUvcbV7o9p6hAgRIkR4a6BlZMtIkJra8XyrUERf2Qo95tTrbUNNahnvzvA8pd2YzkIImP0WLL84/MTqOgfzr3VdF98uEau2jEi/161L3WPx396udX5r3SGktbVrXre0vqT91rEwL9v1ZpZzytoQK2u3hGI0a+3l+QIblXD1xX4zM3BMhIc4eur7gWvUws6DGHTPov9Qnw9jJ9GU24odPCiOp7TnrJ64vX0uv9SVn7irXCMp9GUX1tRr6M5wwZuuEQnBil7EGvQ5uMkH0Vpvcl0HV4BjtI8UVQMEbVGangG1x7VjH4PIT2kJysvdJ9ezYQMc0Fg/gvmvQ0/t8xoL+j59A+IWnYDKbutc6WaY2k1cs1xb3vkk3k4EDTj6vn9C4cMzTKsuL//737jbw4kQIUKECPcQWgRNkqTBKo6BH/TmuYsoG9vMqht9RpLyyqsoL7/iXSfJnhVxy0p/neudahVhO6CHFwBun5s5Q9KsILktxbgBIY4917ZC+FrnF5oFns98F9UO6U8IXNvm5aVtvrFxhYxRbr+/i5vre8tyLZZqSziuPdjAbG/e78BoelBtmpxbq3TdfjIm7Xihl4Mm+sJgnV6BCC2QGzRAnCJM6r0PgX6ajoHhaL2t3BK+vfxtzm6f3d1FA0QjXDNMjXS0pvrgul5IcPZi59zd3rUAuWtTpPs5VM38rprLa0VuNLeYbXYTmPYGxE07JwVC0RibzcNah9grxmgh0bfytWJul+kr3i3vklr0Eq1dkLIuqCXInPEEiwac58TG/GaCn51bmICeciquujNhny3N7ngOvM0IGsBn/rv/m4uPwb6/fR59YzQlnwgRIkSIEGEnuG2RkNGR0cssN3OU9XL3AeF6noXuHoa2tRvFM/Xka6hXlrreG+ZVkV2z7xwA0xHUh9QmarVU1IoIAWb9GrbVHcKVWl2k8Z3vcLlyhfSVbVTn9oS6ZeoZis0iueYuDOmRvAlhHrOd2ml5K8KfoXszdeO6nlf4uIPReS+VrjBbP9X+WzgO5traSF3l1Bz2gBzI/G7mdwjc2+rL6yGB9K5vwWZVG65uCUw1AqG8gXODIamjjrr1/WAPqJHYcRz3t7ilbLGlhHvbBYDlP5tmWL1fMVQAo0uxVcl7qqy6F/4nNVVkpT5wfWlhiphSdzjlbmv0BY/mGyOovbauq/pruUX0lRysn+oZR7+cT7y8Qbx4k2s4EDngVKs0XnwJfWk0D9lOeNsRtP3H3oP1yScRwMn/8b+928OJECFChAj3CFoKfbYjKPcWUh6AVl2sLiMmexE2gvV7bmJrfQSi0cqzCcOWuslqbbVtwMquR8J6ja1LmQrz+UaXoeuKznnt6yUZbAOlcppM9v9lS+sYMYm8Z3hO5OvIdtB4DQth6qBLlnwowemEPvmD6jtD2oUEeL+3bcg1PTl+wnVBgBS4T0mI4RLrIR6/XvSqQPYOqWnaOD3NGHNzaFeuYmZ7QtJ6UDNqnMud42rx6tDzBmLE+RpU2uHW+uyXVQfIVnXOrpb55qUspu2O7G1qwQ15Jo7SxMyVwy8gjKj0YAiLupC/wIV8uPKmCHjXt8wai/PPeaGHreM7yBN2jarmC2f4IbaJN15n4lLHQ2ptbeFUdygnIMeHH9/hOQc/X2U1jHAOuK61gSDHvT4aPbl6Xa+9v5JmlT0nXmbvxedH7qcLWmcuWt6z0tbt2bB42xE0gH/+r/6MEx+UuP/aJmvP/e3dHk6ECBEiRLgHIHwP13q5yYX1fkNNCNAtB9W0O294F7axvfwC3174G6xgjoQke6fsZMDuZN8Gjzf6jfJg8wvVa1wvBRUYBXJTxal3hyg2dXNo1x3lNKmTf+Xa5PURPDe7yFUJhdWEVq2hEUVCOsbhcM9KGKTA/9vnGXWodO5VCIHUNEidW+8y5oeTvvAeBXgEsLTUEyIZQgWEoNDjjRCm/+x2qA97NucZ6IoVCEGsZUAthp5/KbODAd/J+ul5d/jzNEI8q4ajM1s7hW5rzG03eH3JL9/Q8nAN8KAZlsPUye+TXF/m8maV2e06Qghcs4cQCNFZZ11hcf0bCfqlZbQFz6NkLC3h1MLDhwHYPN+Xt+nkcn39jILeeZvXcl3FmQfCn5tRuhMCXMOgee48yokdcgXlHg/abu9n1NMDJ6qWymxjre9Yw9GpddUb625c9kmd7Fq3Tf4m3lti5cwZmmd3GQbM25SgxcameOLzv0zmAOR/5zeHf4giRIgQIUKEESDcbqMtzCuyWFDI1Y0ectF5ndn2DCvdDRqKEhW7Sd0ant+wq0LV1V6J6V6IrpFJQjBx4TTaqx3BkIMFE8cNMSOEQ4fmCC7kL7BYWRxKIG/GeRKc3jAj0P3uV4mtznpqlWb43PVJmo9irPZ464IGsislMO0eb16glpdwHWKqDkjY1Q7hcYXYjWXaebl9CbIXiDd76oWFtGUHXGi7mW7DV1d0gus7d90T4QjBasmba7tS2ZVk/E6E+4W1F8ip3feZ17IYrkZO22R2ux4IiRvuQWsJQqQ2Oka9Mb/A9rf+BkNtdJ3fURbtXG8r/flywdHrN2ZRXu0hMqK1WSF7whrFbiXUvjzEURG8LFRNdPjlo3oum6f8cMHaDt8dO7bnHXcdB7HwPVB8T5enfz881LW37ZVX4MrXOJc7R1YvopkOtivaHvUFLc/J+mKwW//lzl7p0RDije8pDVDf2ELLhoenDsPbkqABfPKTv8LZTx9gTHU49cV/cbeHEyFChAgRflDhOpQUgxML+YD9IJjd7pXLFu3f86bpMMwy6LXV/vPaVV4v9Erb7xIh+U/acrZNOLooYyBsisDRoDEXW1gjvrLuHR0/0H1x2+ngklU63jrJdpGtHo+NJOGEGnXd71mOyytLy2iO0jeWsBBHUfIN7dKSZ8gF76333AFGpWrYWIqKq3e8T6FnCtoeiflco99j1xKC6A23bJ0ycohjz2vbJ/I+eSroGWzX6tCTAGmXfcPxvOmFlyqWSr6X2A3BoBy0MLiuQD35Go3vv4hs9eQoDgpxHMFSrhrd3rmw2oOa6bBV84nhAA/aasFbG5ZjcMqYR3F1rK0sC5UFzm683nszrRbabzlmx5s3Kq8SQmDYLqslNZQUzeeGy+vv0DoCz0MfhmF7N+bcnPcdYBtQXhnQOjgNn5T2hA72egJ3ZDyLL8DGOb59YYXZlUwnTND1RYvCopVtK7x0ieKFEyqmgitgq66zVgpsCgRvXNykUiMh3w9m0wtF12sIIdiqaVg+ke899cZWnbnAb0E8VwZn55zTty1BA/jlX/ka3/ugxL435ln98y/f7eFEiBAhQoQfRCx8l2uZAophYvo/vJIQ7SLBQaT8uj6OUoCSt7Mb3DmXQy0pz/jSTIelQoN8PTx3TAwy5FuobJAvXOsKVTOzRdweb4AQ/v5yIIzrYOF1fyTdaBnf3e93cuoEwtvR9s85dH2bQ9c2AXBqNVLL84Bn0CdDBQ46WCs1eWn9Nebrb/TfYgj57K4LN7TpUAgheP5Gjstf/Tsaz78QeB/ixVzbcBcIJER7/h3HhWp4CGdBzbct+iCp8NbAoEF2v1/RqyzXlkAItvVKW1RFtatktUWyzQCRtw1P4EUIYnaTuK1iCo9oXc5fIqtkQ5aKQNH7hV/GE+MDxtePIAEZu3G5/dqLWOrtMEhed4eOn6wzl9ezdbarGrrthKupCqhr3lrTfbKfd2vt51c3O8a0JASL11c6F7b7HWxg15sGTmDDwMrl2qWdSoqBpjSpFW4ucstxHUpaqe9+BN5mQqVp0TR7iXRHVdWp1XCrObjyNSb88hTG/CzWRgZynRxDu1IdqkjoCHeg2EmY/L03t4FPZMWbU9W0vdBSzYDCLCw+HzqzU6dfZeL8qZAjwW69fg3bHrQHAOB9Rw/cZelvz//DG7ZeoWbUIHO6/f1d1y02qxrz242QVjy0iLNdKpG6sUpyeXPovcDbnKDtnTnMQ7/wa9x4AOq/97/TvHHtbg8pQoQIESL8ACJmaz0kYbixKavezq9q2KwWGmimA8I3pESvsd55XVQ1rm4OyPFxLC/3xOjfic/VNNzCPNniDRYrPZ64FtEQgunqDbS8nzAv3D4jpV/p0bvW2MhRe+5b7XaCWC02O+cH8nnUU6dJbm0g2Z5BGfeN5o7yoPCModYwe0y3bqGGgKqeZXXlVQ01/G2jo/rWg2rTIyk13Sb4DKzcNmNz10hurHad7whvHHsrF73xhOSz5QPhjkiAYTJ+6iQiP8ST1TOfa4116oansne+tsArtQXPyPUN5gMXOtLy8cxrTKhrvvfAYbq+0DukvueVaxjM5RrkG3pX3td0cnrwGHuHPOB95dUTnfvxH7QrBCXVxLoJJct2XmNX3TIJCYHjdJOCzqZB5/+yY5BQja62OvL/3j/1c+f7+nV7cv6Cz+jFG9ss5BVPCVEImm+cbedtuUKw50YG+1q4p8ofCACWY7FYWex6PrPlWTLVJRqBz4VA4F6abyuB9nv0JBby3neM8uoJlH/4BgApo+yV0Mieh7XXsPIlhO0gHBf17CUaL70UGFJ3o9eaWa6qg8Rlus89t1bhtaUOqXQ0AyOTZ0zzwv42VvM0zs1hlVqFozvfEUHIxnDhpe4xhnip/f9Lro27do49i6vgen7bmJ73vsOD8OfTER3C+3r2dU5unuwvqu4KjGIr/zHQt6Ux3tzseNAVL+9UMobXtIS3OUED+Ccf+5dc/OyzqCm4/os/h1Op7HxRhAgRIkSI0AvhYA8JXRGBEMfWb7hue8Ss2SZodKvK9e5GC2eg9TtTu+5J89e7pa9rTYs3FjYoDPK8zX8X1CKLBYUpZRmpuRbad9Ise0ZQfaNdAFhq3ceSH+ro1/ny4Bm6uq6B3d33wbnuhP6wfL2suuUZQ+3zuo93hzh2CFn9O99t15AL9gGQaWTQ7UAI1NZFyF0LCZ+SeGWh0LkLn5w2HRNhePci+4ISMV3n/vNLxBo6VTs8TE03bd9r45FvV7hYhSqS6tcl2+7JUdHDSXiLzNiOCISmeveWKBWZzha63gsjie3jPhFR7W6CqpveXDQNB93WSaxtEyvXu4r7trBV07pDygLjHAz/mB/alqsb1DSLfEPHcQXa3DxWNoviGMw1t4e0491H3FZJ5y+17zUe8444QnSHOPpESlgakr/e4k6TfYtFkoU6p5cKVPu8uJ37aBo2i+WaV2ha6pHZFwElVtfFUKpQuIHIeUI7wjARwsURXpjvIOt7S8l6+ZrAtdI15ivzFLWOGIvWLGEUV3CDxaBF9zhTW3XOlBfR/Jp3kumRgaKvLNt6jBIg+aTUrRRoXplDW9xAm1vvtDvgMQ59viHHigFV2+bVZfS1bWZKVwCo+6HIjtIcdPnj/6hxAAAgAElEQVTO/Qi3XTtQCsnnzJuN9q3M1G5QPXmKmc1V7r+4jOaarNo5mqU3utt3XZp2kyuFK2QbAY+XEF2bOnXNRt7IITa93LygaqaTOUtKzxO3Fa98wcoroFdHCrV82xM0gP/5C3/GX39mimTd4tJnfxJ3B5YeIUKECBEiBCH8OKN8w2gb9ACupmG3Nv7UAkfPfh9J2F27vTMbJfKZU5T1EhIS29sNiqqBJRwuVb7PxZpHUiQnnGDlGt5vVqxlaPtNZ+oZXt14FcN2sByNWWWAsWtpsPwSxUbHOG15x4JmxIS6Ti6/DZaB5LZy6ETwAhCCpJLtyrWaLl/x8lQC95xuqF0RT2HmitIjirKpeqTu8LUlxi+fwy50ivIGG7Atg1Khf7PVdm2uFK5wbj0kVCp3vfvv3khTIZjX8rxUm0O3zcBpEumaN+/pqoYkBHKIUsPljRoLeYXx+DgISMtJL++stU4kr8i14wrWSyo1zaJSaBnhnZv7h8I1GrpFUTW7csJEo8Hk/Bwzm55ntq75hcW7jNiu22njbO5c11h1u8maeh3N9ygkV7KkLy921/7yG9isahQUHRo5xgIFmIfzsx6RFf9v3XJ47nKWi6+cpXz6Df506QLX1eE5cgLBdH2BhFbmSPZ7yI5OxSgAguv1HKbrIByH2nPfovm9F6BZgtom8Z5afDHNRAhBuWmhG3Y7VDl4IxvVJstGnlk117WhIAQdt5UQSMJp1w0MyrBDIJy51bzrkl6cRfJz2nLNHBk/PLb1fIPEeHNlk5pm0VQD7ZqN9jATqkk6W0OZX2fbzy9MzC2R2N5EUvx7lmOs6iUW7S1/BkVbCMXVTaxyvX+eBd4mywCxnZ6zhx91ussa2K3PS1ee5qB2BA2rgmrXu9e2km+TRknqJmi67VKylfb5E+o61aqOIyDm6O1eanb3vQnXJVursVxUyTbynf6q613n5eoaUk0JfgV2hlVsCaoEvhPs0Wq7RQQNSMQS/Pqv/h1f+akEY5kaV//ZTyN6k1ojRIgQIUKEoQjUtvItMOXFF1FPesqHQi0hmw6ya3eF3Y2VmzTOfo9T2VM0XZP983lAULd1JASJpuHtutMigi1DIQOlhX5j2KhjaXUuFy9z8sINzr18ji19hbqthSvFtUOqOmIh7ShLAbEri8gbnrFXVZvtkSuGDUJgC4cNvYzAxVheJvvtv0R3TZKK0iXeIXpqgjkhBj/AZkP11fi6x1rSPUKWbOrEGjX0NwI73r4RO1ua5eylb7G9NovtCpqmzWa1iStEm2BIV+b656BHfEKSO+aRL0ZOySfAvXLvrRDNmYkksmORjMtYjsv59Qo1zcv/ahmPCeJ+WFXLUPcmubXrrlsOthCUVIMziznschljYTFwn57XUiBYqMx33re7bZZcXacRzCMbwaHVQt3ywrDKuu+5MRug18gqWVbrq6FNLM/+DVOls0zlr7H31eewq0NyrAYU6y4oOluV11gqX6bStNp5VdgOru2E5FaBrLSMaglZWIwr6yh2DUkIVNPhWiPLyqU5NNPCMU1wvXDVuFHHFhY1JzBOf1yzWzXWSt46H9O2kB0T3XYw/TwiXVgIeryJbXIBkuPQWjXWdsf7tays+yOFjZpGUdGxN9dI5LKkl4JrskX2XHAsTL9gvdxokr7gnSd0FalUQ55f87zmft2ytmfGcbsM/PTSHHPf/j7zOa9m4Wxzy88NCzwLIXAUP8yvsgrNMprmza9z/Zq3yZIbXAvPbq0j2/RyzAJe6YRZ7SIm+YaO1DSIXZjFUbUuFdPWV9SkshK6VsrmFkVjA4LlIVyXQs1r33Y6bRm2Q7aqka010R2rMz9+u3Fbbc/31IV5LqkbGK6NUytj53LtjY7N0lbnM9uz0SWvZsG2A3tVnQ/UcrFD+kqq4YdNj0a9IoLm4+jMYf7Fr/0lf/7jMolrm1z7/E9GJC1ChAgRIoyMIMmQhIvtirZRB90/3OEmqkALyOtnjDJ7Klc5fv40+5aKvhhA4MrMaVCKgas77dfVbRq6zeEbK6TWl73xjCUoqUa/e6Nt03kvUvk6B1671DGobBvJ90j5dAJJktisNJFwWLXzzFcL1I0GlVP/QNVRWG5usGepzqGlJbAd5LpOrHTDmxu/20uFS1iuiSOcrrwZV/jkz3VwXEGm0uwLqdKdZvdt+MZgrl6h7jZJGhWES3un3rLdNkGTDROK855h66PWtLrk8fuEWISL7BfNdXvCISWfzabzCuMxCSFEWxSg0lNot26omLZDrqZTbZrI7ZDEcKivvY4+HyBigfzEslZqP6Mwj1UvGQ/+1ejJgSnoGba1Fe9+fLIotaTBG/l2zbNNP9TL3CpRf+1K+/q5aoar+avsWTnDVHUO7eQJNMvm7OISU6VzJI2AR7Nv/XX8tXb5NCl1LZCHCBMnLlF/6RLXt+rtTQTwxDfS5y6RrjTb5+dcz/vTMsRnc3XmNko8v/wGc6V5BN5nTxIuBX0d2y/AbuEwqayQMio9oXMw1Vjk2mYNO0CC3Z7acZJPtIuWghEQ4dHm/ILsUr9HeLWgIK285P3RRTb8/revQPY8dbMOjolkdK8lqa7gKCqOT/h7l0CvH1dybBq6xUq5RswXT5GNwqAvI6z8HDO167iFV7h28RtsGTUc4VJSjL5Q0PlCjvNrFRq6Rf2lb+FsreOqHQ+3Vr9K2b//pv+5k8oeOZYaKl1Rif6/M7VZkvXA2gcm3+iEPIuNi/50CdJX15i6vo3kuNx/fhHLL74u/M/hjUaBlxsdEiy5om8dNpwmucw6GaOM8pf/Ee3yFWK+8mnitbM0rSaxch16ippLVc8zKbcjHCQKeoZLlRfRnNaaEVzMF1grhee7hiEiaAE8e+QZfuxX/4g/+4RM7Nom1/+rf4TbuBXZ0wgRIkSIcK9jw6j4Ag2eMZAuNIgVCmSrGpc3Op4ZQVAAozfMS5Db3sQKKB4qus1kzjM447pFix7Fy57h42oGE2c3SJe93A1NmJitsLeti+30G1e4JA0V4f/tuJ4C21q93vaCAbj+tWPZMrHGFubKFczt3oLbAiE69yFZNg2nieO6xCQZUfPy3yTHJe5oTJaz7FkuMzabR2p7QDqGkWJX2WjOMV9Z6ptXsXKafEMnV9f7CFNOX6VQ7+Q+lfQKC5X5NkletLMovsHUmtFC0583verlqzS22nO/VVFYyPu5MK6NFaLAKftGqRsUCBACEQg1lIRoc6jY7CpCN7Grdcara6T0ApqlYxs2Aig3TS9fydE91W29295oiSgEIflhWbiCraqOY6hYlkOx1J+zFlO3O/L9vUIjPXljWW2RXHOl+/q5hdAi1pfzN3hje4GmZbY9pKnZAnvWOvLhtiu4nq1TLr/Omp0n3lggV2uwUWmSK1TI1YNlC/wnJNw2OdmqBYinXsPxn3WQqDdKVRTDYqzcJOfU2HIqFK1Cu1WAnF0hsb6CKwRr5SZbNd1T4bQaOFaV1kpuCJ2EUSZh1ZioVNstSLbdCVf0xSJM28Wyu72TkmkhBGSMCouN84gAPSpqBRYrizhNk4l84BnrJqfWrpBtLiC5LiIep6HbVH3RklZ4pCitQuYcktohuWVHwRUuq3aOF+s32uQnCLnZ+9kF27VYUNewm7qXg2arHX7W7C48rjg6QgiqrkK8niFn1bmibrLZbHRCQIFts863F1/itDXPq+vzfPfKKZRLWa5tNchWm0zmStTnXuL1+jKXGpu8nFtFdfWOd12SushlzdEwhEXNVbmmnGe9chL0ArawULQO6bNXruE0mjjCJdbQiBs2U9k64FK8cdFvui9WGQDV0aE3p1KCmGoyJic7cxiIvnQsDen8HPUz61SaXgh6Ru/M8WTlOtgamqWR1Tyvd1N4z9K0m9ywNth0K8NrHgQQH+mstxF+/NGPIn7t/+RPEl/kF79T5PpP/SiP/6f/TPL4Y3d7aBEiRIgQ4S2IRS1HTFtFEuMIYDJbYzJ/luSkjRoM48NtewBMYXOusY7q7+CXVRM1t0QtnkDGMyOals3kdicfxHIE29oqx+QUua1L7NO8H/90TUPEJKZWy1yaSPJD08cBkPQGMVfHUpc49vomNdcLX1ovq9SbW1jC5lHzCIdaHTgdo9PQFMQbKtqhBACqqzMmpdpCCzFkUnoBOQFTSzkQIALERfYN6VRT8UzVqTSS3kPQDAshJBAum9UC6d6JzdcQDz3gz4+F3LOlrJY2WJ5WGUvEOLlxBrtcJuHbPpJwaQRCEU1b8FdvvMAj9+9lQjhsmVVm0tNsa0WOJfeB5IV4VvQyV/NLTBx5H+w9Tmp10X9uBvHVArz7gC8RLpPIZXFOF5nc6hiNMp4nqKZbHlnZKqDmVMaUDAhB3pkiruqoQqfo1FGFQcO0UZv7eUDtFneZvPw6HDgCk4dxHQfNz9ZJmhUs4VJvJLBMi/R8gb31F7HTe4k5JsQmvfVSWiV1ZB8IgSo6hGjffJ563IZ3PtF+L6ab3H9lAS35HAl1m9b+vTC7PSUCwWKhxGTToJRusGcjR/XBI+ArcGJ5z7i0vUHcirWvyThF6iuncKafovjqBSYOGRycSsHyKVKldQxXIJkGkm+0z5TOw2HZWylqEaQjjJXrCMukoShMTU5yarWCZjokFQPFNSjbDVytyVS5xv6FJvV3pLGnJ2hoq57X0/caqYbNwYsvMqbn0VMH/fXSWZcz2Ty4EnVHY3Jls31d3S61rz+zXARf1HKjqnFkbpFlPyRUNg0cf7NGd0yu5JapahbT65tM6hZIY6zbBaauK8QmLGw35nmAJYlCw6Ba9shWSTXQZRf37/+eCauJ8YgJjAGguAbN1maOEFgiGF7tr0VHB8ZxEWS1JSzX4Ej6YUAifSWLhsSGU2LKlEnZe3Fcr7xCMi4TiwtuKDkkJ0Evim53jpolbLRGmUlgTS9zwP9478ktkb6wTCw2zqRSpfSOGVZKGQ4BFnanFphlYzsJHMtGW9zgTDJH3rR4MHYAYjGk8hnq9Rza1BPork9EhYtRrGEVayR/+HEcx8uXTTY84rdUXeJJ189ucwXIUtceheEaIMdbi9qbN98z7routiOoNeuYfnF3Sa9S+/LvoTkyMdPmSiOLozjk7DrvcC1SsjdPemmJ1fw8sWOHidFRhZxUV5GcJE21yh4SqJrO+srwupYRQQvBJx/5OKl//SX+j8lf4lf+1mDhH3+GI7/9G+z72V+420OLECFChAhvQZiOTtKSOXxpEyTvpzXm6MRtB8d12a7pVH1PV8KqU9RiXrihD9lxOXxpE7mdvdYfsyYcFwlBsXKGsy9f4p3VifaxMd8jUlFN6uMaCcDdvkbKzNOUTKYCO/oTaoY2FRMCbT5D7D2PknILLGSvEveNeckXPjFdmy2nggRMiBkM20VyXZJWHc0xiflESMmeJ++rGI4Fci8E0DRtbMtp53MBZPJFxrUCkyExVovWFgeyDdwj3uZo0zf8Y47BpLJKc+x+wHtPsxzKy5eYrFSRkh2DsuVtUV2dStPg8PVFxAHPIM9bDarlOQzXRnctkOM4rmCtUqbYUEnUT2Mffxf23HdpqMucUA8wrpnoDZ1KLMuaWudA/CCpio3sdohtutDEZqJ9jxXFIOUblQJwajnM7W1koOq/H3M0yvmr3EhIPEmq3ZbUUCG1jYinuVDOoZybJwXoQMKqsWfhClI+QxJBVcjUGluMAZacQnItqprFhICKq7JuqWBNM52pktAsSuRI6Q8zAyRnL/JotkRz7H62Tr0KZgUS+6kbDb47/yJ7bYdk3CMpy0WVZFtCBsbLddy02xFoqHv3JFdXmHJcghIfNTOHqiU4rE0g7d3D9zerJK59m/2azZjtMC6rJI63jGaB5LgYhtX2sB1YynAl80dsP/Q4G/vfy7jVJN6Oz3WYWSujHLYYW6+QsCzGKgJ7PInd2GRMTtIcvx8A23XJ6p6YihRYewXHIx7jzU3U2F62Hc9j9XD8CAANu05rdVluk8naFrIlUz9wrN2GAA4sb5I3bUinmS3PUnP2I+QETiAn0BQ2JdHgIGkk4RJTFSaqV5k01qFWZj0zx+biGuZUmhlD8eojOhZasyNX3/LOCKf/u0LWLSCJ4ZqsmlWs2GHA81ibtksxIIY33yiyfLXKpJQmIcWZlNPkRRnFttsbHsEAymD9s5KlsFHLMVWvItsustPxiqWrTVKaivBJcN3foJFcQVE0KFYaPJo4ghCCa+UCoppn71gSph04MOl7v7x+LWETs2oQixNzNMa0HOdI8kBqD/vMJtt1nQQQsxzSCQNHCHKqigBm1isY02nG1ivID5dZF953lCvJxPBERAAk2wAmEI7N9/OL2OUtxPiD3nxaKiuOTVy3MYFNrYhpJWgIkwdb32dWiWK5hDl1nOPnL5Ayy4j3fYBlK4eLy6GrkJTicPAAxdw6TAyoI+cjImgD8NHjH+bgr36V39z33/BLX68j/Zvfo/LNr/LQ7/8n5Jn9d3t4ESJEiBDhLQLNcjh4+ntM1226Ssj6pOJixgtVahkvcbtJQcS62kj6Sowt3cSWsRhEK88lZlXRrsNKbAppHFI94X/LepFDNwxifiikGTcH5jM0hUml1iT/+hukHqphNdUuw6DpGu36YwIvDE01bGzfqxAc55VynrGEd1/pandNIcWwUYsqk3jEb1JZ9e93MKyGgXz2IvFHHufI1UW2nnmUo9e8osdpo0hZFDl8qVM7TQByQIhE98MUHVzUXJ6kWUNuxKmYKnHLYjIp0TAtTjUy7Nt/P8Rsig2TtF4kLhKI4kmEuuyPpYgtpSlVdMa2b0D8KM3aBWzJYdwVODHP/ydJIFzRDq3SbZ2cYvlhXILU7A10q/uuE5ZCoqKwnZji4QmZuttkXErhCIfv5xaZdHSkzQKO5bSLfgPEsxe91RKXKTqd0Lm4ukpKr5GVdexmAlUYIJIk1ysky53Qxq18gbFYgli5jOznBdV0C9W2Ie6yUS9SzkjImkcs1OVtTNshLVpeX4N43OTotYsYCW/VbNtl9sqTQKKvdhbAhLpBKbWfwmoG9fAUDxq2JxYjPAowmfPuoyE0ZDuNVNcpNAzsCRckiVS5zsKxLchtQUnlgD6BCYxt1bGrGpIjSPjjHas0cX1iKbsmcb9Q9yCojt7exgjql647OSaNEgm9QzfLtfMcW/Y+7c4zkzRNm6aiM5lOIAsHwzXINjw5/YnmJmZiJrRPw3KICYNMc5ZDpkcaEXDqxS8ztVWHrTplIC5LTDcMZF9Z07QdzJbQxZk1anG5nbpW0yzGbJei7edpxtLt3MW0uoolur2ijhA4WBj+c31Euo+67w0PCvn0FlMXeCGdzVMFpiZTjJcCcysEMb9cQ9oIqK36x1owhY0mDEpuA1woN0BqgLR3HDkuETOb7F3yPJOStgET72BMy/lz4FKMNbg+e5lEIMdRt1z0tes4sT04cUG6ppGuaWhAYvYcVnMVGUj0KHk6QqAWVU6l1pHwBEQMY5s4cdy4J/TTGvn0xSyFvWNwZLqt2ttwdExNJikXvfIFQlDSSsR6vuUctUwiJtPyhg5CRNCG4KkDT/LHv/xtfuX+/573P3eZnzy1wtyPfYR9//wzHPqVf4+USO7cSIQIESJEuKehrJa5T5MoBQyfiqNQuvAq+8cfI2FW0cbuo2yXaNEyy9l9fnO6pqOOd3LUik4DQpoR0BXyFLcVBlX4zDpl9IaFmdBY0mWme6QFThUzHI3tIyZJOEIQ1zyRCBkJq1fJDo+sDkLL+B4VqukwU1gnqVg4cpLxco1U00/Id02CFLBlOAXJj2Z1yNp8eZ0YYFe98VuAZnVaKJU2EVIexo4Qc3R0STC5cKlrelWhs2dVRwHiaRVZ2G3lupijAwkkCZbsbVJSAkNYWGaShG3yjvghNpwS9gAFQ/AMxnNVta8gd66mkTL6pblb92z11N6TENTcJtW0oNIokBImE4Xe+l4woWxQ5gAH/JZSRgXDjaMIjZhr4cpxHrzUUcqsZcvM5IqM+3L6eUNhwvBED1pjVoSO4ujIjkxCihGGpFlBMlzGKk2CmXAFp0bCt/GbronkJJlZ91bupLqGkdrr3Z/jImIysuNi6t7ztNe9TZAWOWudFwwRTvtesyBtTBkeyRordxO34DOwhUu1errreLrSWTsPXD+L4uep1TWLeKpJ0ixz1RDsm/D8jQmroxZZF51r67pFihKaHEfxPdepho603S3lbrsCUVAw7FZJgu5nbtjdfwc/hzFHJ2VWiNtNDOESH7ot4n0n9M5DQ7dpeaxbKDR8T7sE46XuY3HDJt4jpjGRa3Q9D4B1u4e8+dizWqZhFTiYipMM1FBLmZ1vMtWwcV2Bs6qFNUHVqdL7FVVXF4aKbxh1HaneqdGY8omfbko0A8SyadlM5Bso9015Ic3CW8mG7WLUSgh/rcauL3e1bwqbkgrxRIwDW0OUTokI2o7YP7afP//sX/DHT36F3/nW7/ML37XgS39H7avfZPqnP8ah/+F3kWcO7dxQhAgRIkS4JzFRUNAnOqFprfAlgFLTyzPQnDxjA+qYjQwhmOjJUwpDtWlSckev51l2G2B4hmGvoQqw6Xg7vo7jGf7gqd5Zol/2/HaiVfS7JZYxtrLl5VjdIUjCgqZnGDeFgZTbHHhuWu83LG1XtI2/ljci4QuLrNr5kcbQS84AUkYx5MzANSEKjorQSe5QssrzvG5Q8Tl5wqrRqtqU1vNIPSIK9y9210sLopcwuLgYA8ioNISkttAQGvsXug3vlrE8ueWHIpZChDFCxF1uBmEe7CDGKh1qKbvda7JF+sDLLR0FLa8QeLXMwtCrCLobJCzvfnaeeU9saDcYtbB0LzkbhhYpi/WQvCDRheEbQjcLV0ogi26V00GFuac3amSd/nWYNIeTr9gI445UHEdATI7xS89+gf/wxW/w17/+Ef7wZ2TW4hKVv3iF+Y/+KEtf+BTKS3/VVfMhQoQIESL84OKrX/0qn/rUp3j3u9/N5z73OS5cuHBL7QUNsDsNy3EHGhTDsGe1X/WtBS8kJ9DHHSZnYYhZd46cBWHMeKFHvcLlrXC5ew1hS6WXnL2Z0GeGh36Nl9RQcvZWgyu9PX0gvd8Vt4ppafh6GAWir+jA8LNH/ayHbWjBaJsQO+He/La5Q3h45mG+8l/+CT//r/+EL//GB/h3Pydz9lgM4+Q6mV/6d8x/6GkW/uWnKX39/0IopZ0bjBAhQoQIbzl84xvf4N/+23/LZz7zGf7wD/+QqakpvvCFL5DJZHa+eAeMJ+LsnUhSeeRA3zE7PoErJ9uhXC3E/HymtNQdVh80ORr3z7SLHdfvO9B+HcR+eSp0TAkpzjvih3hy+mDocbmTq89EKs6YlGQ84eVixwNhbOOpOIlnHwhtgwHhbsMgJAkn2W3kTg0w1uz4ePv1ZMq7Rp14sOscOWRO1EP9c/Lgo89iTKex0wm/bU+MpfT4IZxET+5gwBg1x4/09aEemqL24F5qx/Yi7x1DkmDvhPccH4x7863tG0f4KnCtZ20NyFlqYUwKT7GYkSc4GJvpG2fYeIOQJZgZ61fsC0PjyHT7ddg62wlh1+yVO4I39WN7sFPdzz392H3t5zoIrTxAJ5bCjntKlr1dHYx5Y+9tf588xYOxg37+HAg5/L6EFGv3cyTW+ZwG1+mM7K1FI93/GQ/CSO0DoPJQuK5B4/7wNaBOHCUR8/oIPs/W/LTWbWtMLXEUV0rgxDqfHzHCZ3L/RJI9491rLREgnq2+WkhOPMhj435UWc/kW4lJGkem24RrShrj0cSR0H7tVBw3LiNiMsn9Byg+eZjqO8Ln6bD/ORpLdD9TK9H5bLc+XzIy2tjh0HbAmyM7MUn5kQNYPfedkrw2gutQyBLqwUmKT93H4Wc/yKSURkgy4z3rS0bGTicQuyCvb096f4v40P0f4kOf+RBXP3yVry98g784/xzPzDb40KzEEydXyZ/4Q/K//fvEDiVxHz3O2Pt+iEMf/2nGnnj/yPUPIkSIECHCmw8hBH/wB3/AZz/7Wb74xS8C8OEPf5hPf/rT/Omf/im/9Vu/1XfNoZ/5Ih95+GHWbZnKX/82++ITnHv6g7zXUXhgMo2zqbExOYNIbtFcXyLWtPlHH/1XZOv/QPYdTzGd3ERJJyhZB/iwk+RkY4m9jSUer9lU3vs+ntFMDFHj/v1H+Juz29QmHN718H4eP/Isxe9+A+fxR1lPPIB5ROV4Y5HEj/0E0tVttuQVqjfyzCQfQn/kONbEBPvfOIejb/L0Aw9D/gZN0+boE8dIPvOj1G4so26tkz90mCvJJlOlLX5KaXDQTbJ9MMGmmebgBz7CQ4cew94sos3PkjxyP19fPUOstMqPfvgzjB8a5+LD4+SUOaTiPFyY5Yn972TZ1ZkylihqFrlHnsJJJjiervCpseO8kdvL5vYJxg5OoZcaTMXGePTjH8A+/AipZgJl9TWWMy7S8Q+yJy3zbFJlYX6W+cICew7sQd67l6NH3kWzVkW1bjC1YXBMOsDe409jLFyhWLjB/vQ4B37k06RTccyKzerCKvGPfYDLc8/xQ8emUGoxRFPBMmK870d+GuX4cV6PHWGj0EDcOMkhCx47/AEePLjJ+exl6ocf4V3zGxw6/jQ3tGXm4vfxrmKZxNRj7N/aYlokaRwYJ/5Qig888RlKiaM8nKpxKn+GDzz0aZpnvol9eQlL28Pmsx/nnc8cQvn6V3l47CDNYobS1JPsObifA0f3UTbSSNppsnqRSmyCRHyKuPMODuYvse+pAyRST/DV3BKP7vkwtqYw8/q3ePjwJI0H303s+jX2JdKMPfVOWDrFHrvOrBPDlcZ49JmHyTQN9j72XuZnXyaV2eRDz/5jste/zwOPPoW0eZkrDz7EduYy+1e22P+pz3HFbrBVN3lgbIsjtXESM3UOvOsZtFOvkpicZvu+9/PgQYfz67McmiuiWwqp/QeIJ26ClUMAACAASURBVBwefewA5/PbxN7xeR5d/iZ7D8+wvVGHB+5DXiwwMXYf+pjFtpxB+8RPkRAb7N1c5fiDz5DZl+TjH32aK2qWSinOon2BZ2fuZ/rSIuNaEeu9xzgt7aeWqXDfQ/tZXy1yeGovn3z302xoKlOzGxx5+nEmags0Eo9xPbOAYTpIlRXMWoWxg48h5Uo8/vEfIZWcINFc5PIblynUbN795AcoLp1k7okn0axxjL0zHMsWeWLC5ljc4VIyy/3je+BqjeOWzfhP/Czpo+8kM7vKAbdM4fSrmI89jZiOsSk1OXC1RCG3SvrH/ik/9P53U9MEr5+f4/Br/x/pD7wbzUgQe3CKnNPgYTVD2jzKxtJFrOIaE4++h9WZKdyJ9/JY/iXSEw4Jw0XeqDL9kZ/llRf+jo9+4CHO6cuoU0c5qjqUjzxA/ZU1Hj/6ELXDT/Psma+w1dDIH38XxsoLPDlxmJiboPb5XyS+9h30uQa2OMTDTzxEsr6OVWggGWXqz36E5dMnENZB9tYXeejAJPH/4gk2D76Dw85BXp+t8O4PvpvJYoblS8+hP3QQJzXBM9NPI9eK7D/yBPuMLWqXX6cwV2b7yONUgf12g8mP/DBHJsaZVjaxVmbZ986PUEzPcOnqOsLReUJdxzryfqTS37ApVJ7+qY8y08jyxNgUzn0/wouNi7jXXuQjD36SzbllxNghlsePsm96iTV1FUf+IE+N2WzGH2cit8qR2W9iuVXeNXGUdaVGNS4xs/9pHvnsz7FRraBffZ66tc4exWQyNU0yOUWsvsFrj4/zyAPv5oF8EfPo0+Sy17lorDIz9TM8IRYZ+/Q/xTzyKOe2TnL/lk752FNsrP4972k+yuS7DvNcKc/PH5ohZWp8bfYcPHdt4G+RJMJkdoCNjQ0+8YlP8MILL/DAAwN2xCIAYDkWr2y+wvdWX+DK/AmOLpR4OiN4OiM4FghTd9MCsS8O+2bgyFESjzzB1Lvez8Fnf5jEviMReYsQIcI9gx/U35DV1VV+4id+gi996Ut87GMfa7//u7/7u5w4cYLvfOc77ffC7lEYTZAkpGS4p0e3dSTHJZUaDz0O4LgOspJHkmNo6SnGYmnQazC2Z+A1m1WN6XScqZ4dbSE8RUHVsL36Ro6NUygQO3gQGcsr2DzTeT5N02YsEaNhNZhMTCILgWtaSKlUSNHX/n6C97ChbDATO8yeiTSWY1HSiqBUybt7efTgXhKyTCKWaF8PYLkWsiQTl0ffP+7tW5gmwraREgnscgWMJrE908jT+8IuBqsJyYn+Yz50WycpJz3vmKXhSDGkeALZV7I0HIOYFEO1VKYTUwjDwFxdJfX440h6BSZCPCm2CZJE05FQDJtDU2mEZeEoCvZmltTTTyEFC78pnjGhJMeYSEwgSRJCryMlxsCfw7WSynJB5dGZGA/ct887pyV/L0nevQpBX0E5QLVUknKy/Ty8G69Daoq61SAVS5GKpbquEaaJgsFEYgJZkhFCYLuiL8RNCIErXGJywGOjFiGegpTv5dCq7fXd+zx3hGOBa0Oi85mr6xYTyTixAZ4w23GRJImYLOGYBrYcJxUPjM+xQMljTR4ZGrLnCpfZ8iyP7XnMyyuyDeTJ4d5PANcVSFKnkPJSQeHBfeOhfQkhcC0TOZFEIHBcx3tOQnjzONnv+a4ZNdLxdN8z8xtE2CYICVfTwDGQZRNpj/8d5rpY2S2SDxztu9RpNJCSSYSaQ7Y0pMNP9J3Tmpf5yjwAT+x9ovt5mk2Ip9vr0LTddhmHvv5cXz1RQDIuk61qJGJwcGqsE5srSViOhe2YjIV8jh1X0NAt9own0S0HWZJIxmXcWgl5fAISfRUY27BLJZxancQDR5ETCUzXIhnr8V4LsXv7XQg2NjJ84sc/OfA3MvKg3QYkYgk+8eAn+MSDn0D8qGC1vsqZrTO8UrzKwtoV5OUVjuVs3pEXHCs4HFkoM3G1jPu9K9T4GlUE1hhY4xLORBx3Mo2YmULas4fEvkOkDt5H4tBR0vc9wPh9R5ma2Ut6fBIpOdn+Yo4QIUKECLeO1dVVAI4fP971/rFjx1hfX8dxHGKxwaFB0hDiBZCOp3f85Y3JMZj2Qn/aJucQcgZwdE84IWwZRhOtkJtYEvloy/BK9hGTcT8sajrph7FJIKd3DoXqNahjcozj0505TMQS3Dd5BCaPcN+Q6/uMnxHQ27eUTCIlvXYSh3cQ8ZKkoeQM/GfWQnKc3tloGcEzKc8wl8bGSD/1lHcwjJwBxL3xjcc6cy4lEsT37iW+d2//+b4RPhkcenq665Tj+yc4vr/7XrrmxmMEocOZSITMgd9+ey30QEommaLzvCRJIhHrb1+SJGK94XS98xJY37siZ+DZQT220HR6uG0UDxChWDLV90yJJWDmKDtZWLIk8/T+pzvX9JdbD7+uhzg+cnBywJn+/CW9NSYhIbfGLkmh5Aw6a3FAg0gJr71Ysv/zJslyKDkDiE35hDr1YOjxFmRJ5sl9T4YfTHZ/Rw4iZwAxWSIWCOa+P/g9F1gniViie3Ohp41WmGY6EPo7Ssms+P79xPd3zgv9froZ54oktQugD+x7961GGAZJknho5iEemnmIz/E5+BFvRzDTyLDZ2OT/Z+/M46Qozsf99OwJy30JInIpGAUEFASRiHhhSCJf45GIGqLBEw+iQf15YKLRaOKFIoIaNKJRvPBCQUAuucFdzr3v+5qde6Z7uuv3xxw7szt7sOwyu1DP5wOzU91d81ZXdVe9Ve/7VpG9iB9thRTmp6PlZtGpzEyPag897IKeNuhl99Kz0k5Ppx0oAY4APvN/F2A3CfISwZMIaoJASwBvHOixCnqcCT1OwYiLQY+LRcTFIuJjITYWU0wcxMYhYuNQYuMhLh4RF48pLhElLgFTfCJKfGdM8Z2Ije+EKa4TcQlJxMZ3Ii6uM7FxnYiP70x8fGfi4hKIj08kNi6emPgE4mPjiIuNITZGIS7GRHyMqd7LRyKRSDoCdrsvbHhSUviANSkpCcMwcLlcdOnS8GBKIpFIJJJjRSpox4E4UxzDug9jWPdhtYkTav8UQmDTbJjdZqrd1dS4a8h0WLAVF+EuLECrLMeoMWOy2oixO4l3qCS4NDq5DBJcBp2tggTV9y9RE5iEgW+Xl9ZBV0A3gWECTQGPCQzF9z3wKUK+i5BPoYSmKYhguuK/RgHF/92Ef1bBH3FHwR/GRvE5FfuPC/85KCb/OUrtzGDwWMh3k4JC4HqT/9LAtSb/JSYURfH/raBgCkbQUQKyhHwG1U//zEn9c0JnAZWQ5BD5qD0cyFPgk6FuXqH5hP5umCy1mYXHHqubRx3q5hC4A8G4AIoSEk1ICfusOzur1BY+JNe696129i/08lDHcRFWdt8Gr4GTa3eB8pvsEHqd3zRGhNwDUXtVWFKd5EBOIrRIIpBnyE8ZgTLVXqyEXB80uxCEnRNIV0SgHL58w0KoCeHLXtSmCyH80eQEiuE/FJKfN747ut8hPlCegFlTsOgiJCKdgPikvnDxNYjYWJ/VU8g1vZMSmHJG76OfxT5BCDMJa0a6RCKRSCStTYMKmq77QqyWlpYeN2FOdmKIoS996WvqC12BkedDZPPeBhFC4PW4UR1WPA4rmsOG6nagqm4MzYOuqRiqhvC40D1uvKobQ3WjezwYqgfDq2F4vRi6htB0hOFF6F6EroNhBD+D/3TfbukYBorh//TbuSsGvrSQvxUBihC+v/F/N3z/TOA/7tdhDMD/t8moPWbCl24KfDdqr5G0HyI5tx594O/6NFbPSgN/dySONrRuBA+DJtFN8OR+naIu9c2/4mIUVt0zpdkR3Roi0HcE+pKOQle/CY/D4aBPn1ozLKfTiclkonPnWvMc2U9KJBKJpCU01Uc2qKBVVPgcUmfPnt0GYkkkEokkahjAtmUNKnfXfNd6P1VRUVHPn6s9E5C1oKAgTO6CggKGDh0atoIm+0mJRCKRHAsN9ZENKmijRo3igw8+oG/fvo06REskEolEUhdd16moqGDUqFHRFuWoGDJkCAMGDGDdunVcdNFFAGiaxsaNG5k2bVrYubKflEgkEklLaKqPbDDMvkQikUgkJyMffPABTz/9NHfccQfjx49nxYoV7N27ly+//JJBgwZFWzyJRCKRnOAcrbtDGCtXruSKK65gzJgx3HDDDfz888+tJZekGZjNZkaOHFnv33333Rdt0U5o1q9fz7hx48LShBAsWbKEadOmce655/KnP/2JrKysKEl4YhPp/h84cCDis/D8889HScoTC13XWb58OVdddRVjx47lV7/6FStWrKgNRnKCtf/Zs2ezYMECvvzyS+677z5sNhvvvPNOmHLW3vu/puqsOc+Mqqo8++yzTJkyhXHjxnHfffdRVlZ2XMvRVD/XnLYX7XLs3LkzYhkC/4qKijpEfbS072uO3BaLhUceeYQLLriACRMm8NhjjwUjqh6Pcrjdbl5++WUuv/xyxo0bx6xZs1i9enXYOd9//33EOlqxYkW7KUdrtaNoluPzzz9v9HkJ0N7qo7VpcRTHVatWsXDhQu655x5Gjx7N+++/z2233SZnGI8jqampALzzzjthYZ979Gh8vxxJy9m3bx9//etf66UvXryYZcuW8dBDDzFw4ECWLFnCnDlzWL16dTDogOTYaej+p6Wl0blzZ5YvXx6W3q9fE3sgSZrFG2+8wbJly7j77rsZO3Yse/bs4dlnn8XlcjF37twTsv3feuut3HrrrRGPdYT+r6k6a84zs3DhQjZs2MDDDz9M586deemll7j99tv5/PPPj5tJZ1P9XHPaXrTLcc455/Dxxx+HpXk8Hu677z7OOeccBgwYwPbt29t1fRxL39ccue+9914KCwt56qmncLvdvPDCC1RWVrJ06dLjUo6nnnqKdevW8cADDzBs2DA2bNjA/PnzAfjVr34F+PqZwYMH88ILL4RdG7rJcLTL0VrPdTTLMW3atHrPS3V1Nffffz9XX311WFnbS320CaIFGIYhLrnkEvHkk08G01RVFdOnTxdPP/10S7KUtIDly5eLCy+8MNpinBR4PB6xbNkycc4554gJEyaIsWPHBo/ZbDYxduxYsXTp0mBaTU2NGDdunPjPf/4TDXFPOBq7/0II8cwzz4jrrrsuStKd2Oi6LsaNGydefvnlsPSnnnpKTJo06aRr/x2h/2uqzoRo+pnJy8sTZ511lvj222+DaTk5OWLkyJFizZo1bSN4BBrr55rT9tpLOeryzDPPiAsuuEBUVVUFv7fH+jjWvq85cm/fvl2MGDFCJCcnB8/Ztm2bGDFihDh48GCbl6OqqkqMGDFCrFy5MuyauXPnit/97nfB73fddZd44IEHGvyNaJdDiNZpR+2hHHW56667xJVXXilcLldYWrTroy1pkYljXl4eRUVFTJ8+PZgWFxfHtGnT2LJlS6spj5LGSUtLC1vulbQdmzdvZtmyZSxYsICbbrop7FhKSgpOp5NLL700mNa9e3cmTpwon4dWorH7D/JZaEtsNhuzZs3iiiuuCEsfOnQo1dXV7Nix46Rq/x2h/2uqzpxOZ5PPzI4dOwDCAqMMGTKEM88887iWszE5m/PubS/lCCUzM5MPPviABx54gF69egFNv8OiVY5j7fuaI/f27dvp3bs35557bvCcCy64gC5durRa2Rorh8Ph4Pe//30wKFCAoUOHUlhYGPzeVB1FuxzNkbEj1EddtmzZwvr163nsscdITEwMpreH+mhLWqSg5ebmAtQLCzlo0CDy8/M73L43HZW0tDRcLhe///3vGT16NL/85S956623gj4GktZj9OjRrF+/nltuuaXeRrWB56GuadNpp50WPCY5Nhq7/wDp6emUlJRw9dVXM2rUKC6//HK++OKLKEh64tG9e3eefPJJzj777LD0H3/8kf79+wd9F06W9t8R+r+m6qxz585NPjM5OTn06dMnbN83OP712lg/15x3b3spRygvv/wyQ4YM4frrrw+mtdf6ONa+rzly5+TkcPrpp4cdN5lMDBw4sNXK1lg5Bg0axN/+9jcGDBgQTNN1nc2bNzNs2DDAp8QVFRVx+PBhrrzySs455xx+85vfsGnTpuA10S4HtE47ag/lCOXFF1/koosuYurUqcG09lIfbUmLfNACDnZJSUlh6UlJSRiGgcvlCrMVl7Q+hmGQlZVFp06dePjhhxkwYACbNm3ipZdewuPxMG/evGiLeEJxyimnNHjMbrcTHx9PfHx8WHpSUlKHcUZt7zR2/8vKyjCbzeTl5fGXv/yF7t2788033/DII4+gKAqzZs06jpKeHHzyySds27aNxx9//KRr/x21/wuts+Y8Mw6Ho14ZwVfO47Uxd1P9XFxcXJNtrz2UI5SCggI2bNjA3//+d0wm3xx5e66PY+37miN3Y+e01juksXJEYtGiRWRnZ7NkyRLAN1EghKCwsJBHHnmEmJgYPvzwQ+68806WL1/OpEmTol6O1mpH0S5HKDt37uTIkSO8++67YentpT7akhYpaIEVmrpab0PpktZHCMGbb77JqaeeGpzJnTRpEk6nk7fffpu5c+eSkNDQNrSS1kQI0WCbl89C29OtWzfefvttRo4cGXSEvvDCCykvL+f111+XClor89VXX7Fw4UKuvPJKbrrpJpYuXXpStf+O2P/VrTO3293kM9PQe62x911r01Q/d+eddzbZ9tpDOUL55JNP6NatW1iwg+a8w9pbOZr67aO5/0KIoLJa95xI6W3NsmXLePPNN7n11luDpsxnnHEGy5Yt47zzzgtOwEyZMoWrr76aJUuWMGnSpKiXo7XaUbTLEcrKlSsZMWIEkydPDkvvCPVxrLRIwkBkHofDEZbudDoxmUz1lk4lrU9MTAyTJ0+uZ2YzdepUXC4XeXl5UZLs5KNr166oqoqmaWHpDoejw0aw60h06tSJqVOn1ovYOHXqVAoKCuq9pyQt591332XBggVMmzaNf//73yiKctK1/47W/0Wqs+Y8M126dIn47DidzuNWr031c506dWqy7bWHcoSybt06LrvssrBVp45SH3VpzrPfHLkbO+d4rkYLIXjuued48cUXufHGG1mwYEHwWLdu3bj44ovD5ImJieHCCy8MRhqNdjlaqx1FuxwBNE1j06ZNXHXVVfWOdYT6OFZapKAFXpYFBQVh6QUFBQwdOrRdziCeaJSVlfHxxx9TXV0dlu7xeADo2bNnNMQ6KRk8eHBwqT2UwsJChg4dGiWpTh5ycnL43//+h6qqYekej4fExMR2N2DuqLz00ks899xzXH311SxatCg4wDzZ2n9H6v8aqrPmPDNDhgyhsrISt9sdds7xrNem+rnu3bs32fbaQzkCFBcXk5WVVS94S0epj7o059lvjtxDhgyp9zwZhkFRUdFxK5thGCxYsIB3332XO++8k4ULF4Y9y4cPH+aTTz6pd53b7Q6Ot6JdjtZqR9EuR4Dk5GRsNlu95wU6Rn0cKy1S0IYMGcKAAQNYt25dME3TNDZu3FhvGVLSNqiqypNPPslXX30Vlr5mzRqGDBlC3759oyTZyce4ceNISEgIex4sFgu7du2Sz8NxoKysjKeeeirMOVgIwdq1azn//PPb1YC5o/Lee++xdOlSbrnlFv75z38SG1trHX+ytf+O0v81VmfNeWYmT56Mruts2LAheE5ubi4ZGRnHrZxN9XOXX355k22vPZQjwP79+wEYM2ZMWHpHqY+6NOfZb47ckydPpqKiInh/wOd7ZLfbj1vZ/vnPf/LVV1/xyCOPBPc/C+XIkSM8/vjjHD58OJjmdrvZvHkzEydOBKJfjtZqR9EuR4D9+/fTpUsXhg8fXu9YR6iPY6VFPmiKojB37lyefvppunfvzvjx41mxYgVms5k5c+a0soiSSAwaNIhf//rXvPrqqyiKwvDhw/n+++9Zu3YtixcvjrZ4JxVJSUncdNNNvPrqq5hMJoYMGcKbb75Jly5duO6666It3gnPhAkTOO+881i4cCEWi4W+ffvy8ccfk5aWxocffhht8To85eXl/Pvf/2bEiBHMnDmTlJSUsOOjRo06qdp/R+j/mqqz8ePHN/nMnH766cyYMYMnnngCu91Ot27deOmllxg5ciSXXXbZcSlHU/1cc9697aEcATIyMujZs2c9C5fmvMPaUzkCtNb9nzRpEueeey7z5s1jwYIFeL1enn/+eaZNm8aoUaPavByHDh3iv//9L1OmTGHcuHEkJycHj5lMJsaMGcOMGTNYtmwZ999/P/PnzychIYF33nkHp9PJXXfd1S7K0VrtKNrlCJCRkcGQIUMiTrJ2hPo4Zo5lE7V33nlHXHzxxWLMmDHihhtuEPv27TuW7CRHicvlEi+++KK45JJLxKhRo8TVV18t1q5dG22xTngWLVpUb1NFTdPEv/71L3HhhReKsWPHij/96U8iMzMzShKe2ES6/2azWTzxxBNi6tSpYvTo0eKGG24Qu3fvjpKEJxafffaZGDFiRIP/qqqqTsr23577v+bUWXOeGYfDIR5//HExYcIEcd5554l7771XlJaWHteyNNXPNafttYdyCCHEwoULxeWXXx7xWEeoj5b2fc2Ru7KyUtx///1i7NixYuLEieLRRx8VNpvtuJRj0aJFDT4roecVFxeL+fPni8mTJ4tzzz1X3HrrrSItLa3dlEOI1mtH0S6HEEL8+c9/FnPmzGnwuvZWH62NIoTcNEsikUgkEolEIpFI2gPtP86kRCKRSCQSiUQikZwkSAVNIpFIJBKJRCKRSNoJUkGTSCQSiUQikUgkknaCVNAkEolEIpFIJBKJpJ0gFTSJRCKRSCQSiUQiaSdIBU0ikUgkEolEIpFI2glSQZNIJBKJRCKRSCSSdoJU0CQSiUQikUgkEomknSAVNIlEIpFIJBKJRCJpJ0gFTSKRSCQSiUQikUjaCVJBk0gkEolEIpFIJJJ2glTQJBI/I0eO5J133om2GBKJRCKRtDtkHymRHD8UIYSIthASSXsgOTmZU089lX79+kVbFIlEIpFI2hWyj5RIjh9SQZNIJBKJRCKRSCSSdoI0cZScVKSkpDB79mzGjRvHxIkTue+++ygqKgLqm2/s2LGDa6+9ljFjxjBz5ky2bNnC2Wefzeeffw7Aa6+9xjXXXMOqVau4/PLLGTNmDHPmzKG8vJyPPvqIadOmcd555/HQQw/hcrmC+WZnZ3PfffcxadIkRo0axfTp01m8eDFyrkQikUgk0UT2kRJJ+yA22gJIJMcLl8vF7bffzpQpU5g3bx5Wq5V//etf/OUvf+Hjjz8OOzctLY25c+cyZcoU7r33XjIyMnjggQfQdT3svJycHN566y0WLFiAy+Vi4cKF3HzzzXTr1o2FCxeSkZHBSy+9xJlnnskdd9yBw+HglltuYdiwYTz//PPExsbyzTffsGjRIn7xi18wffr043lLJBKJRCIBZB8pkbQnpIImOWnIyMigpqaGm2++mXHjxgHQs2dPduzYgWEYYecuW7aM/v378/rrrxMbG8vFF1+MyWTi+eefDzvP6XTy7LPPcu655wKwceNGvv32WzZs2MDAgQO55JJL2LhxIykpKYCvszr99NN55ZVX6NWrFwCTJ09m3bp17N69W3Y+EolEIokKso+USNoPUkGTnDQMGzaMHj16cOeddzJz5kwuvvhiJk+ezMSJE+udu2vXLmbMmEFsbO0jMmPGjHqdj6IojBo1Kvi9d+/e9OrVi4EDBwbTevTogc1mA2DUqFF8+OGHaJpGZmYmubm5HD58GK/Xi6qqrV1kiUQikUiahewjJZL2g/RBk5w0dOnShRUrVjB58mS++OKLoCnHhx9+WO9cs9kcnL0L0KdPn3rnderUiZiYmHppjfHmm28yefJkZs6cybPPPkteXh6xsbHSvl4ikUgkUUP2kRJJ+0GuoElOKs4880xeeeUVVFVl7969vPfee/ztb3/jnHPOCTuvX79+VFdXh6XV/d4SVq1axSuvvMLChQv59a9/TdeuXQGfCYdEIpFIJNFE9pESSftArqBJTho2b97M5MmTqa6uJj4+nsmTJ/PEE08AUFxcHHbuhAkT2LRpU5jd/fr1649Zhp9//pn+/fvzhz/8IdjxHDp0iOrqajk7KJFIJJKoIftIiaT9IFfQJCcNY8aMQQjBvHnzmDt3LnFxcbz33nt069aNCy64IOzc22+/nauvvpp7772XG264gdzcXF599VUATKaWz2uMHj2ajz76iNdff52JEyeSlZXF4sWLURQFt9t9TOWTSCQSiaSlyD5SImk/yBU0yUlDjx49ePvtt0lISGDBggXMmzcPj8fD8uXL69nSDx8+nDfffJPCwkLuvvtuVq5cyaOPPgpA586dWyzDNddcw5///Gc++ugjbr/9dlasWMFtt93GtddeS3Jy8jGVTyKRSCSSliL7SImk/aAIuWYskdRj27ZtJCUlBUMDA2zdupXbbruNL7/8krPOOiuK0kkkEolEEj1kHymRtC3SxFEiiUBycjLvvPMODz/8MEOHDqWoqIhFixYxYcIE2fFIJO2IlStX8vbbb1NaWsovfvELHnnkkeAeTpG444472LhxY730ffv2kZSUBMCvf/1rMjIywo736NGDnTt3tqrsEklHRfaREknbIhU0iSQCt99+O6qqsmzZMsrKyujevTuXX345Dz74YLRFk0gkflatWsXChQu55557GD16NO+//35wBn/QoEERr0lLS+OWW25h5syZYemB0N+qqpKbm8uDDz4Ytv9T6H5PEsnJjuwjJZK2RZo4SiQSiaTDIYTg0ksvZerUqfztb38DQNM0ZsyYwSWXXMLjjz9e7xqr1cqECRN46623+OUvfxkx3yNHjjBr1ixWr17N8OHD27QMEolEIpFEQgYJkUgkEkmHIy8vj6KiIqZPnx5Mi4uLY9q0aWzZsiXiNWlpaQCMHDmywXzT0tJISEhg2TPu0wAAIABJREFUyJAhrSqvRCKRSCTNpUGbDbfbzcGDB+nbt2+9XeAlEolEImkMXdepqKhg1KhRJCYmtnr+ubm5AAwePDgsfdCgQeTn56Prer2+Ky0tjfj4eF555RU2bNiA2+3m4osv5oknnqBv377Bc3r06MH8+fPZunUriqIwY8YMHn30Ubp06RKWn+wnJRKJRNISmuojG1TQDh48yOzZs9tUOIlEIpGc2HzwwQecf/75rZ6v3W4HCAb2CJCUlIRhGLhcrnoKVVpaGqqqkpSUxOuvv05BQQGvvPIKf/zjH1m1ahXx8fGkpaVRWVnJyJEjueWWWzhy5AiLFi2isLCQ9957Lyw/2U9KJBKJ5FhoqI9sUEELzCZ+8MEH9O/fv+0kk0gkEskJR2lpKbNnzw72Ja1NwH1aUZRmpQPMmTOHmTNnMmnSJAAmTJjA8OHDuf7661m9ejWzZs3ioYceQlVVxo4dC8D5559P7969mT9/Pnv27AnrSGU/KZFIJJKW0FQf2aCCFjDX6N+/P6eddlrbSCeRSCSSE5q2Mv3r2rUrAA6Hgz59+gTTnU4nJpMp4ma5w4cPrxf449xzz6Vbt25B/7Szzz673nVTp04FIDU1NUxBk/2kRCKRSI6FhvpIGSREImkhQlUxPB6ErkdbFInkpCPge1ZQUBCWXlBQwNChQyOuoH377bfs3r07LE0Igaqq9OzZE6/Xy+eff87hw4fDznG73QD07NmzNYsgkUgkJyzCMDBcrmiL0WGRG7tIJM1As9kpX/8jzu07MdIOQ3ERhtUaPB7brx/xQ4fS+bzxJE2ZQqdx41BMcv5DImkrhgwZwoABA1i3bh0XXXQR4Auzv3HjRqZNmxbxmv/973/Y7XY+//xzTP7nc9OmTbjdbs4//3xiY2N57bXXOOuss1iyZEnwurVr1xIXFxc0e5RIJBJJ47hSUtCKiul21QwUGUTpqJEKmkTSAEdKrGz9eiOdv/2CURl7iDe82GMTyeg5iMI+o1DO7MWpfboxsmc8/d01aBkZVL65lMo3lhDbvz89rr2Wnjf+gdhevaJdFInkhENRFObOncvTTz9N9+7dGT9+PCtWrMBsNjNnzhwA8vPzqa6uDipWd9xxB3PnzuWvf/0r11xzDbm5ubz66qtceeWVjB8/HoA777yTJ598kmeeeYbp06dz4MABFi9ezM0338zAgQOjVVyJRCLpUHjLyn1/6DpIBe2okQqaRALgVaH8EFRnk1NYzJatmfTeeoCLSkvwxCdSMOky1KnTMc4ZgwmFWIubg0UW3s2uwuHWOb1XZ+5ccBf/N7I76pYtWL76isrXX6fqrbfoNWcOvefOJaZLUtNySCSSZjN79mw8Hg///e9/effdd/nFL37BO++8w6BBgwB44403+OKLL4L+ZVOnTmXJkiUsXryYe+65hy5duvC73/2O+++/P5jnDTfcQFxcHMuXL2flypX06dOHu+++m9tvvz0qZZRIJBLJyYciAiGv6lBYWMill17K+vXrpfOz5MTEMCD9e0j5H2T8AF4Xhg6Vh7pSdaQLplhB71/Y6TUmHtNZ02HM9XDGZWCqnQlyazrrj5SzbEs2KQU19OuawENXjuTa8aeh5WRTueRNrN98Q0zfPgxYuJCul10WxQJLJMePk6EPORnKKJFIJC3BumYtQtPodsXlKPHx0Ran3dFU/yFX0CQnH0JgO/gpWVv+SZW9CK1TD2pOm8qeQ925aksqp9ZU0eWqyxgw93fEeoogdyukfwcHP4Vew2DKA3Du7yE2gcS4GGaOGcCvRvdnW1YVL65NY8Gn+/lkTwFPzxrFWf/+F71uuZmShU9ROO9euv3mN/Rf+CQxdfZnkkgkEolE0gysJaA6oM8Z0ZZE0gwEUD9kk6QppIImOWmodFXy+f7/8H3qx2SgQhKQ5Nt/YnzGYR743kCNhffnnE7StP6c19nNlDN+S9dxs30mkGmrYevL8PV9sPGfMO1hGHczmGJQFIUpZ/Rh8rDefLq3kOe+O8KvF23loStHcvvU0Qxd+TGVy5ZRufgNXPtTOG3RaySOHBHdGyKRSCQSSUcj7yffp1TQJCcwUkGTnPBYPBaWpCzhk9SPUIXOeI/GvQOnMvDM63htfRVnbtnGnT+vRztjEIceuIwKPYN1WV/yUdpHxJpiuaD/BUw/fTrTh02nz9lXQ/aPsPF5+Pp+2P02zHgehkwBwGRSuH7CIC4/+xT+3xcH+Od3qfyUWcmL159Lv3vuIemCCyj6y4Pk3XgjAxe9SpcpU6J8dyQSiUQikUgk7QkZB1xyQrMubx2/XfVb/nfkA2ZaLXyjn8J716/lqsmv8tKXsfzyu83cvW8d3aZNY8zHq7j10gW8dcVbbPvDNt6/6n1u+sVN5NvyeXrH00xfOZ2bvruZt53ZZF2zGPG7/4DTDO/+Clb+EWryg7/bMymeN2aP57lrRrM7t5qrXtnCxrRyOp9/PkNWfkzcaadRcMed1Hz2eRTvjkQikUgkEkkbELBrjBzq4thxmSF/R9vlH2WkgiY5IfEaXl7Y/QLzN86nv9vJR0Ul/P3MGxn8x+/I8PTkusVb+P26//B/6RvpOXs2p73+GqbOnYPXx5piGdtvLA+e/yDf/t+3fPbbz7hr7F1ohsar+15l1lf/x8yMd3jhwtnsnnQr3vQ18PoE2PAPn208vjDgf5h4Ol/Pu4i+XROYs3w3L/+QTky/Uxj8wQqSJk6k5LHHqHh9MQ3E6pFIJBKJRCLpuLTV+CZ/B1gKQbW3Tf5RRpo4Sk44HJqDhzY9xNairdyod+KhgkziZr4I5/+J3EoHNy3bztxtK5iSt5e+8+fT+/a5KErDLqyKojCi5whG9BzBXefeRamjlM2Fm/mx4Ec+yviU9w2NrsOGMtmIZ8q+xVyYsoL+l/4dRl8LisKZp3Rl1T1TeHzVQV5dn0F6mY0Xrz+XQUvfpOTJhVS+/jpC0+j7wP2NyiGRSCQSH5puYHaq9OuaGG1RJBJJY0RS0IQAOd5pFKmgSU4orKqVO9bewZHqwzzpSeS6shy4/j34xW8oqnFx07Lt3LptBVNy9tB3/nz63HH0exv1T+rP9SOv5/qR1+PUnGwr3sbGgo1sK97G2r69AThj5+NM3fsil014gNHn3EBiXAz/unYMZ/XvyrOrj5C3xMlbfzyfU//xDEpsLFVLl6LExtL33nmtfUskEkl7RAgQRti2HZLIGIYgq8LO8L5dMJl8g7rdudVU2DzMGNWfhNgT8B66LRDbCWJleHJJOyL3J/DYYOSMludhr4CcTTBsGiT1aS3JTjikgiY5YbCpNu5Yewep1am87DBxSVUB/OEjOONSyq1ublq2jRu3vM8vc3bT9/77WqSc1aVzXGcuG3wZlw2+DCEEGTUZbCv8ia1pn/G+PZfle/9B/73Pc8Xpl3Lt+Hv489RhnNGvC/f+72d++9pW3rz5PM7/21MIXady8WKIMdH37rtb4W5IJJJ2Te5WsJf5VtqPAk92DrG9ehLTo0cbCdb+yKqwc7jEiqIonNHPt0WJ1eUFTlj3E9/enAndYMQV0ZZEIqnFVtL8cwMrZIYRnm4v83+WH6OCdmKvwEkfNMkJgaZrPPDjA6RWH+FlB1xiroCbv4AzLqXaoXLTOzu5cuunXJKziz733EOfu+5qdRkCppBzRv+Jt6/9ho2/W8s/+k3lLLebD/O+57erfssdX/wfJO7n87sn061THDe+tYOVewsZ8PTf6X711VQueo2q5e+2umwSiaSdERikHCXuw4exb/2plYU5OtbkruFAxYE2y18zNHRDD373Gj4tTDdOVG2sATzWFl9qV+1Y1ZZfL5G0GUHTxpPseT5KpIIm6fAIIXhy25PsKt3F391xTKsqgdmfwODJWN0at/xnJ2N2fMfV6RvpedNN9Jl3z3GRq3vXU/ntVW/w2pxd/DDkRuY5BZnVqdy7cT5Prvs1f7/KyqRhvXn4swP8fXUqfZ9+mq4zZlD+/PNYvv32uMgokUgkR4tu6BTYCtos/x9yf+Cn4qaUUDm4a4zNhZvZWrg12mKcMOiGYG+eGbemN31yU1Rm+FZITxLqr3IrDR2QhCAVNEmH57WfX+Ob7G+419uZ35Tlwh8+hMGTcXi8/Gn5bvrs2cqt+7+i6+WXc8qjjxz/QBwJXekz7f9xxx0pfD/lJZ6OOZVyVwV373mM3rH3sfCsPXz0Uyq3vr+PLgufptP551HyyKM4du46vnJKJJJ2Q5G9iNXZq3F5XdEWJSrYI0Rma+6ru9JVyZbCLRjCaPpkiaQZFJldFJqdHClphVXJkhSfj+EJTLW7Go/u8X+ro4gdywqarQyqso5FtA6DVNAkHZovMr7grQNv8Tu6MrcwA657F4ZPx63pzP3vHrR9e1iw9390HjeOU//1AkpMFJ3JTTHEnXUVs25awzezvuae7mPYJpwsFSv5e78H+U3eP1i47D20hf8k7vTTKZw3D3d6evTklUgkUaPQVgj4otJ2JOyqnXV563B73cfl9yIpbQcrD2JTbcdNhlYndGXBWR09OY6RUkcpWwq3tNk2MlkVdvbmmdskb4ByZznpZl8fHHSnaqgoBbvgwKdtJktHY0fxDg5VHvJ9qVf/x7CClrsFin8OfnVlFWHbtOXo8sjb7lP0jgMe3YMhDHIqHeRWOrC5NVRv8yaOpIIm6bAcqjrEMzueYVJMdx7POYTy29fgrJmoXoO7P9hHUfIhnt37PgmnD2LQG4sxJbafcMydep/BnbM+4ItrVnN2z7P4e++u7D/lCC+4/x+JH0xHuXEypsRECu64E29FRbTFlUgkxxlxnE340stsHCo+9ln9PFseqq5S6iitd8xbUYFaWBTxukq7hx3ZVREH8w1F6W6K430PW43QwmVtiJ4cx0hy7npsVelttpJ5sMhCodnZJnkD7CndQ6Y5s05qA22qJr/N5IjEocpDrM5efVx/s9VQAqpHhHtZdtgXJbIBHB5v2He1pArDcRSTWIYB1iKfonccWJ+3nj2le9hfWENKYQ0bUsv5KauyWddKBU3SIbF4LDy48UF6KbG8kH2I2Eseg3Gz8eoG8z9OJuXnDBb9/C7xSYkMWras3UY8G9htEG9f/Ql3jLmDL7skctPIieTEdWdYxgv0nVyOXlVB4bx5GB5P05lJJJIOi82tsSO7ql4gDOU4RSo7UmIls7zpDV8rnC2fMHLs3IUrOTnisd051ZRZ3aj6sQ/mj9c9azs6qGJZl8qM46K4FFS3gZJWuCcsSIvSztym8qx5rZKP2aHyZXIRLrUVfOsi4awGrc74paGbqbmh/DDk1FeeSi1usivsHCm1YnaobSNrC9B0A28T76xKV7hCVmFvnpmsVNAkHQ5DGDyy5RHKHCW8lJ9Nz7E3wS//imEIHv7sAD/uy2HpoRUkup2cvnQp8acNjLbIjWJSTMwbN49Xpr1Cvm7l8eGdua/XA2QlduHUCeW4UvZT+uDdbWYmIpFIjhNuq88MylK7iiSEIMOcwc/5lZRZ3VT7Bx+B5729bV6/u3Q3cOyrU9mWbFZnr65XTmGvgMp0IikpoXci0q9n1WSFmYS21Tuz0OzEqXobP0kIKE8FXTv6HwiRW9QNUR5l9pXtY3vx9lbLz2s2o5Udm7mZ1R1+j1dnr+anomOIdCoEmHOh/EgwKdg+W55rizEMQUpBTYNKlNA0rGvX4q0+enPY3Crf81Jha2QSuDrbdy+O1m/O8EJVhs/8M4yGfND830V4OTXdYGdOFQeKfL/vPdoVWV0Leee2bg2uPlDCdwfrWws0hEu3k2rdQbYlu8lzpYIm6XAs3b+UrUVbeaSyitGnXwwzX0YX8NdP97NqTx5Lsz8jqSSfga++QuLZZ0db3GZz6eBL+XDmhyTGJrCrz1qeHTyfv5xyD93GCizrtlH9/2a3rLOXSCTHBfu2bWSl7cRrNDB4d/n9Zay1ClqZs4wMcwYFjnBTqrY2z3NreotnzTXDYG+emayKplfcGiLDnAGAV/juVUAPNRVs992nhu5hHQK6jKZrpFWnsbNk5zErtarXwKl6qXaoEf1F9uaZ2ZLRhJmSpRDKDkJp49sR7MqpZm9e3YG1r1CGy4N128EGzUKjQamjFLO7eX5fzWnBjp+24dy9p1664Wp+cJxIerjF07pBOIIqRRQ0tEq7h9wqB8kFNRGP6xYLQtXwNMNn3Z2aiuWbo4wSXbQPyg61oPCBh9OJbrfXWgI1uBwZ+bmte5oorkQtLGx+1KCCXZC/HTz2sMy0svIWKbV1MY7ivqi6r1035xmSCpqkQ7G1aCtLkpfwG4eb65OGwnXv4sXEgyuT+WxvAW9WrqfPkZ/p/+STdJk6NdriHjXDewznvRnv0adTb/ITXsE6cjBXDH4azuxJ+Rf7sD91BTiqoi2mRCKJQGVxNoU//RAMLACA5vINbkIJ6dAD/jl6nVnjwNhGLyxGtzXsk9FS1hwqZe3h5s/8huLVBSbDS3HhsZtZFVgLcHvdIQPg5g12GhrbqUat+VNLldwf08r54XAZWzIq2JEd/r4NyNdkuPXALH8TimZlehbFRXWUPf9v6E5fkBNv6VFsDtzWeHWSNu7Dk50T+bjHVjsR4edo60ErK8O2fgNaWXmT5ypuF2zegG5vvh+Sc98+3EeONHxcdbC62q9Ye31KRa1O0bEtWTyZ4REQm1uan6yZrCn4scHjjQaEEWDfuAnbunX1DzRDmrp6mFZQjis5xX+JIKXyIAXWRrb9CESEFUbYbzh378axrfVWg5vD0bQeqaBJOgxF9iIe2bSAM7w6T6iJKDd+ije2M/NXprAquZjXlIOctm0tvefOpecN10db3BYzoMsA3p3xLoO6DSLT9CpjRjuZNfIh1AF9KVpVjOdfl/hMDiQSSbsgs9yO6tWDylbYClrhbp95kKOq+TO+IXgPHMa+aXNridoqmBToasukW+n2Fi8pBPzEUqtT2Ve2D1PAhCzi+C5C4JAGhjqG6gC3rd45ZreZXSW7GgxYEdjWQDO0MOXL7DxGf5cm7k9iZipJP+8E4MvkIg4UWmqVu0iXqg6fmay5mcqxEL6od2rz/LQaW3mqcaqkZJSh6Qbuw4cjrz6kr4HM9b6f9hfgaBU0vcYng26JvGIUSlxlOYrXi1bY/H35tOISPFkN96GVrhA/S8NntaKgoGhquMnpgU+hJAXd7sCZlt/m5qhtsaoeaJ5NvZosXlf9SaQQkiuSsam2iOcYqt/yJyB+UNsNOccQeHU9XKhmoCMQCI5UHcb63Xeo+RF8HgPPUz1Nr/m+i1oDfmZxqqVF70BVb/q9IhU0SYfAo3v4y4b70VUbL1c76TT7M9ROfbn/o2S+TinmxT7lnLHqPbr96ir6zn8g2uIeM3069WH5lcsZ3mM4mbzOBeO83Db6TrTEbhR860F/Y4Yv2pFEIok6OZUOUhvaHymorDWvE68du9QfOVndGt42GATW9eEJUFzjYmsDpnwxuttn2hNY7TH0Zq8urM1dG6bEqoba4iAM27OqfNEnA9eV7Ecp3e/PSwRlSqlIodJVicvrIrk8OXyVE5//GtB6oflbaGaZXWmn4X2jCPoBeary+DK5iKKaEFNAV01wxSeAsFdwKH8Lrjy/T1bedsjeFPG3cyw5/FT0E1WuKhAC9+5NYeZw+f5gHC6PBm4rhrXpYAex5TY8hyOvVmWW28mrOrptJFSv0cAqTespL2E1V3YIrCUoCnTZtRXTkYMAfPlzIUU1TqjMwL0/Ba2iBt0WpT0LW1p2zUWcswn/v4beNxECeUB4gB4DqLJ7cOXXXQGu74O2Mb2c7w8WR8yzbvEiPlm6gfBPHDScgRKema00/JyqLDDqK5iZ5XZWHyipv2ruqKJf+Va62BufMHd76+cpTRwlJwz/3Pkch81p/KOimsHXvoet61Bue2833x4o4fmRgnP++wqdxo9nwHPPoZhOjGbdPaE7Sy5bQr/O/cg0vcIZ4zvx13FzUF3xFP1oQrz7W6hIi7aYEokE3wxwAAUF7OW+qGQRhxMhg4QGTJYjzZb/mFpOemnrmzs2tC/P7txqqhyesLKF4hu7+Y6tyV3Dztx1vsh9IfkGBzWOquBMdiQfPUXxKVRFamDVpHkKjs3qYEf2AZze+rPhnp17sH5bPxR5sb04LHx6ubMcze/f29QqxVGPhS3NX9mp9yON/JjTH248vyqk3JnrfP9CqPJUk+epYr/NN4gUNQWIBvaACqyeubwuqEjFs2d92D5sxY58hAKxjhKoONKsm5GQa0ataw7ptiBUJ4eKLVTaGwlOEaJgA3h1g+8OllBgbmtFqE65rMXB1qiUl/mfB4MSS11lvm3MH63u5vljHg1CCMjeSJfSHbWJFWn193Lz1FfC99nzSS71+Q2qulpbR0L4Vnj9mF1eLG6Nmsbq2I/N7SVOa/jd1ulQMl1/anjLiUb9TgMrZYoJELg0b628+APxWAqg+GechfV9RgOTIPUUNH9Aoni1cX/Hg0Ut84c8MUaykhOaVZmr+DTjM26rsTD9kqcp6z2R65fuYFtWFa9c1JtxS/9B3IABnLb4dUwJCdEWt1Xp06kPy65YRqfYTpR2eo3E8wexaMw1OIoUynfHwHu/9TmjSySSdoPZqVKSvAZywlcqbBE2nRb+MOT1HOHrJBhOJwiBqynfp6PFq2KqycWk1x9ExfonuzT/LLrqNTA0L122JJMQGJyGyFld8JMvrLpfEfvuYAlrDhRj2Gsg+8eGTbNdHmKLi6hSizhgb55CEzTN2v4N7Pw+LGqfTffJpkcwwasbgt8QBntK9+Dxl7+hVUDr2rXYv/4AMtZGPG5xarXh3rWGV+EqXZWsyV0TVAgjcwwDfS2y8lKl+ga/9j2pWLcfbDQLRVFqB7Wu2r3p8mzpgBK2SpdSUMP2rMb9ojXdCDcRy/iBwu2fEp+X1fBFgCcjE8fW2nr1+icKAmHWE9wVYYsiLfEPE7oewTSx/mA/VHHXhUARhq8t1VUMDn7W6puLN7k/YUuD4qh13keRAtpEyLtUtVCs1qDpGuvy1vFT8U8YhgHVmYj07/1KnqgNnlFngmdHZQppzvr+r72r9vr+qGP+JxDE1tTeU9WrszevGptbizCh0vi9KLe5OFRspcLuJhiMx2oFXcPm1tickcPewvAJhUA56m3f0dh+bo2QYLUTn11MjqUBP04/UkGTtGtSq1N5ZtvfuMDlZt4Z15Mx6DqueWMb+VUOll9zJqNeWwjAoGVLie3ZM8rStg0Duwxk2eXL8AoNtc9b5Ey6kO/OmEr1QROWVBU+/H2jGztKJJK2J3SQd6CwhqIaF8JdO/ts9ljYUrabHHdkk0GXbsdraA2uZtk3N+6H9mVyUXCmdl/ZPvaUhkfGc2s6XyYXsSO7ik3ptT4229d/jiVjO6eU1c8/xuQbkHh13wDku4Ml5OZVoRiCpDIrDsNDZsku34BMcxEcHIUMlBOz0rCtXeO7Pw0oD/HJacRnpeJVnQgEuiHQQ+6nEBBjMYOjNmpkrGolxuvCa2iY9KNTWmPLqknauA/hDV+ZiMstwbBGfpcKVUMvTIu4ogA+E619+WZfOO/Ub3wrqBHIqslCN3QsqqW+QuH1EldaFMGmK5LC0DxKnOFyGKq3yYsVj8NnLgnhyoYCCWq48pFb5aDc5qY0ZDXJULWwsqUUmXlxy//CAjlUOTwkNCPIjG5pWDlJctT6GxVYC/gu5zs0o1bxVb1Gk5FKrd99j33jRt9ysL/NBe623e0NbqcQOjjXDZ+CZjIRMkj3I0TYKnJdDENgdfll1I9yZSwQFNHQwKsTl1PoU4wawG2vwahqXAnw5SvCm1iDim54eiACqzXwTLhCfAYNI3i6EuKXVlFlZVN2HofszdtaodRRSk0dc0Cn5itztbP5Ua1LVQvrslPYme1rvw6PHgwaUmAtQBc6bq/OIVcqO0v2oBs6h6oO+XzFAuWoU9VZ1nwchjvi/WpssqBfWh6x+aVk1+Q2KrNU0CTtFovHwgM/3EV3zcPznUayd+Rf+d2Sbai6wcc3j2HQ84/hLSnltDfeIH7w4GiL26YM7zGcl6e9TLGjkFPO+JiVF8witf+ZFO/sgutIOnx6W0TbaYlEcnwoqnFRHPQHMkjXivnOfBC7x0tqqRW7vQy8Hixe3zm6MKgIMelxeR1k25PZnesbQAhEWMcvIvgxBKh0VaIa7mDY+1JHKeV1BuYBP7Myi4uakMAXcZqVaoeGyajvtB4X4xu1qQ4nQvNdb1O9BOaTD2l5pJfs9g1GwhSX2kFjbGUZNtWG19AbnNxWNC+BIbBAkFft5EhJ7b0xhEHc/p2IHbWrKf3KttC/tGGTJwCn5sDwDw5DB0xxub7Ze8Ptrk03DOJzS/DsqLtnk5+6vmmGjqHWv2e6zX/fHZEVcQWFzj/tx71xa8jSjxfhqiExO53ErLTmhf6OoLR5DaNeyO8CR2S/nkgIBFkVdjJ3rgaXGbeh4RV6UHExed10dhSGXoDLa0MzPOzMqcIwBIbmxbbrCJ7U1OBpuqGh6nqz9n46GpSAiRqCMr8vVejK5IbU8gYilYowX07D6YJDn0PGGgBcqte3jZ3dQ6nVH8UxEPBEhChoKKCYUA2NUjWk/TeyonW4xEp6uQ2HxwulKS0q9w+5P5Bw8ADx+7bjzc+MqCBousGBTZ+yb/sP5FQ6wFoMNbUKsnPnLt87RfjuR5r5MHvtkRTmhssSaUN4gc8stdpqwxQw/QtZQavwK/LupvYR9LOvbB87SnaEpUXa8kBpYtJhnz2f/eVpBLUtv/+s2V1DatURMm3hwUWyLdnkWfIosBWErKAFLhXUOFUO1WRzQMtHwdcmQrF+uxrX/v310gOUWz1NroxKBU3SLjGEwaMbHqDMVcGLngT2nPNvbl6+j75dE/jstvPp+uxjuA8fZuArL9N5/Lhoi3tcmNB/AgsnL+Tnit1cMOknnpt4M+bE7hTsHox3/w9kjga9AAAgAElEQVSw5rFoiyiRnLToukGVw4MADMNLtWHHawhyKh3YPV4O/bwNS2kOAoHq1fkqN52t5mxsft+pvslpmEoC5o6RfJBEgzPbu4p2kO8Pgd3gHmxAD/MBuhR8gkdv2IcndCUkMM50b/wR65at9c41FBH0E4opzA2aNmpeHVfBPvqWb8MrNLKsuex3FBIYchgNmWoagk4Z5cQ6VdQQhTTLephiVyaa4W0wCmMkCm2F5FpzGzyeVmrD4tIAUTuIbGhFwh+cI1A3nffvxbb2h3qn5VbZ8RRVoJsjKGheD4qioGhehMsZrM/ulnQ8Gz4gwb+qYK4XbKJ5JmzJBTVkVzQUdKM2DyEEe/OqsXvC24oQArNTDdZNqrOUQ86S2jDlEUbB6bY9HLH4QpULQHV68BqCwzv3BAentcH7amXwNjBwbQxPyOqy128yqRheYlzVESMoehqY1Ci2uEgpqAk77rU5EQ4LwlrC9qwqymy1z4HQdbSff/aXRWBVbSRbNlFj2AGFnJpsSlULNd7aevN4dVYfKCFz307SUn7EsXMXWnGxTzEDVN0ImsJqusHm9IraYD261qhVTGxRBYrVt1ol8rahF2VATR5UpmPxWDCEgeo1UFQVDIP9hTWQtw2RX6voeCsr0SoDCoKgxFFAmbsG3eUJX/1pRNmscXp8MhuazyzR/2yW2TyYc1PQbFUYwggPvBPy/ArDwJOZGbHujNw9CLfvupia+vstmnQPenpucHXf5/9mR5Q0vl9bQKk3/P/nVVtJLjCj6ioKCl1Ka4h1uIIyxygxwey2ffkj6Zt28lVKMTuT91OSeyRYprrRXnMs2aQf3EKBOXKkSEczFFSpoEnaJcv2vc6W8j08bHWTMfxl7vwsizGndefT2y/A9OxCnNt3MOAfz9B1+vRoi3pcmXXGLG4bdRsbir/i8isrePy8W3DbNQr3n43YtgT2fxJtESWSk5buliPYzJVYVN9AOxD1DnyzzVUOFYHPh8ame7C4VDThRbHZSbSU0yu3hC62LEqsxb6gF4YB1Tng9dR14wgjPqeYXmlZeM2l/Lh1BZ1DfIxc+/cHNzpOcuRzRCsi0xK+90/oGGxnTmR/Is1WO0gKDLsDM+ieogqUlBwq/Kt/aTm5rE/bRrxq9vuICBwBvyXDS3aFg0PFlrDVHtXw4CzfRly1kx55ZkBgeGxQmYlpo2+l7FDlIbYW1VcU6+LSdNIKzQgBjoihtAWGgMwKOxvTy33bIBT5Vs6EYRBbVRHhmtqy9y3fRozdt2IihODL5NqNpFWXB3dOCfbkkCiRzmpfWPwjX6P4I8cJQXAgaTI8GA433ay+oE8B5TSSmZSnIhVPHSVc0zW+yVqN1XBS41IxVJW4/LIGB6oer0Gh2UVyfg3VDrXRPd2EEJDv2wZAwVRHR6tdwQKfgnhk2xEsThWl7CC2YHRQ0egKh93j5esNKZSm1g96JTB8EYs9NnblVIMQaIZBdqUdAXSvOUSCNScYDh9LQT0fprqYHb5zQ82JHSmZOA7nUpjsU7qdIaaRlsIyjMIsYrxOEFDtqkYRArOwg64GQ8vX1pdClV1F0w32bfmG0r0/sefQWjI3f4PJbzZcaffg9N/3SrsHs1MlNbBqnL3Jt11B2I3wneutriYho4AYq79dG17c278HWyn2vCNsT/2BvQe/Y13+D3gOpxKTmhvMQrfVPgslFhcHCn2mg4oQoCgk7SnEvjctGHVT1VVW560lzx35nbA5o4LMQjMxh37ybeUAIHzKc8CssUQ3k+UuDa5kh06AWHLzKFj7DuoPy+rlbfvuK2xffgBAQuiz5NFwe3USPFU4y6rJNzuDAXOozMBsK8Ky6rMw09gMVznoBiaPFr7q7rail6WGtc2uJWZ6JaeiV2YAAkVRgu+puMoyCg+mI4Sgd/U+NNX3TotXa6hwlmIIX4TRKmc1Fo+FGrc5rI2Z9KOLECsVNEm7Y2vBFt44+Ba/tjvRez/GX390cOXZ/Xn/tom4X3gO29q19HvkYXrMmhVtUaPCfePv47LTL+PrwjcZfVUS/xp7Ha6cakrTzkR8/QBUZjadiUQiaTUM4fNnUBB47bWDe9HAyodD9YIhSKjxddix634EVxVxuoXullR2p32GYRhYqsvJK8hHr871B0bwDRTyi0uwJ3+A5rEjhMDk9A0UzM4C4jMLUTy1A1Q1vwBXcjIIKPL6FKhu5nDTqsZiDMRW+1aClKK9dK85RCeLHXQviiFA10HzYrg13xYAbi9CCHoWZ9GpMmQlx14BKL6VmKK92Nw++UKDOxQ7s3x7CvlJchRQcnA99j1rMPwrDQ7VS565yn9vGx7xV9o82FVvmBmb8HpR1HCflbK8zSRmZ6A6a8P0CwTdUzb5AlA0QLxqJkYPyFRHudmdHMiolqwNvv3wAMVl9vseCbTSWj8craq27JpuoBsGHsNLsr2AGk+tb8/6wk2sr6kNW68oYPaY0XSdYr0ak+bFtvYH4rOLiM8qQrFUBv2rhL9vCBVt9/5DHF77H3Bbg/dUQcGphSiBQiBUNdhQIplSgj/anUfFpACqBnY1eE8DCn1WhZ09eeEmnBaniid5LWu/fg8txC8wx1zInuLdeMsOQu5PuDwqXbf9iFJSidvvhxRreIIyYuhQuBcqfOaVoXtyVTtUNHs1ZkdFyN5f4Q1ftzn9/mHhZTtUZCam8gjdrGkIfKt/iqidoLBV+4Kd7LPn4/QrhwH/zdDbVO4sD5pBW1wa+/LMZJbbMequ9LnD93+L8ToYWPQ9CbY83zPnkz5QiOB5mtBJ2LID5/efYjgrqFYt4FGJLS+pNZG2l2MIEZTDZ+FoEGupnYDxVvmeeafmBBSyQp6F0MmBGK+Lvul5JKZXRFx5Fgp4hBdFiNo98UJW0FIqj1DkKEXVat9XBWYnqX7TP+H1pReGRO2MOVwbWMaj6YBAc1lRUKjWbOR5qil3V6GV+EL727ZtI7s4h84HSuifUqvoBerFZ9odbrEQ5yrHa84Bj+8dFjo5luc4zP6ajWHltOk1HKhKId2czoaMHBZv/6beveiWv4dTU78kNkK02YaIbfaZEslxoNBWyMM/3s+ZHpUxyvU8eqAft0wezMLfnEPVyy9T88kn9L7zDnrPmRNtUaOGSTHxj4v+QfGaYrZbXmXQxQv4xHop1+1fT2JSJ3p+Mgf+/APEdYq2qBLJSUGRK50+9AF8w6akSjMJdhfunp2D5wQ3Zi61MKirky4FVejVNVgG2aiyu4mBoAmZ2eamdy9BfrWdoUC5XaXYVavwuKoK2VxjYMr6mm7da/1vq9VwfyPdGxhs+n67QK8EQ5BoC5/JjaSfudPTwZNIbEUZItaXVxd7Lt2LyqGriViPl74HS0ksdMPgM1GAbkU1VA2w0iMwAB/o+7A7XFSYaziXTv7fM6g7PyyEAUpMcCAXp1lJK62iS3VxmFmUpwHTIMXpRlE1QoeJHt0gxuRLSdi6g5iybBy/Gx40c4rNzyS+cwzZuBjW0yeb1WMlyZFHYuUuik6bCfgCOxgCKlQrpZYsOtO79h7rAl14MRETzLcuXosdb42d+P69EMKBWpVDpiWW7gOGB8+pcapY0fCYnKSV1FClWugf4xuYltUUcGqdPK0eFyg+d4Bie7G/lhUUjwYm38A2rrCc2Bqf35diqoLT6qyEGF76ZKzH6J6Ekb6Gkri+wUOHM2t9GLUKM67MH4hLcuIF3JrPnDepbh34PwOrRAkHi3ElxEICIHwKUXqpjUTDxSGtgD6unvRJPA0BOHULuhB8tb+AM+1WBgP7SwsxDBd51TZUBHT21b2psgZ9YN/A3gzYPV600EAx/oF9hTuP/p2G4dUNtqSX07fsc+zuSnStLwlKj9p2H6J0FHur0bzhJnXhgTh8AWzibQ68qanoA4eDy+w/IthRmU93Rx9OHxNQ0GrbRDBgiSGCN8tamEVsRhaxPU9H6TGMSMRpPnm6VKbAaafXOVqbv8PtpdRppW+CgVKVEfx97+GfqDnFQ+eBfcFRgZnu/ksFQkBccQHdijLDstTMNXyXWUiVZudMoRNbZiMheROZE88MnpboLiXOP9kSOsERrvcKFEMHzQPxsYhANE8FNK+LOGonW/SYRMqsbmKO5ECXuEZNFUOJ8VhAcwXNu72GF0wm3Gnp6IWZJOYFzI3DAw8BqEKrHzRHQLlmBXyROtOtezCMWMYQsk9l4B3iVIkpzsU0fAgur4vCGne994CiG7j3r6dSrSDm9LHNKhNIBU3SjnB73fzl+1sRXg/X28/h4fIpPDzjLO68eBjV//kPVW+9RY8bbqDv/fdHW9So0zmuM69Nf40/fPMHrPHL+H7y3YxwlMGuwyR0SqPz94/Ab16NtpgSyUnB/2fvzcPsuMpz39+qac9Dz6O6W7NkSZZHPIDnAchACA6QA0nIgRADIZBzEuByIeE5J4DBjMGAc7khwQHCeJOQE0gIBGOmMBg8YcuSLFlSq+e591y7qtb9o2rvXbWH7pbcshj2+zwG7d1Vq9Zatar2967v+94vW15mYOpeKvu8HScmiOUWWQbE8QdhR6+vALULLWdi4+6kVwiI7uX6lE+eZFrtdNPOAOlIXAbnQrdWKWd1onMPs7zbIeTJfgeolpTMPvT3ROdXoXtXVTY9PpMhNpsh1PkEPfmfgJSIjoQbApTP4URj2MvLjP/kPmIzRylG9yA7u5haqQlqCKsmsb+wtER+0PauLTk2OUHC64IjHSYKT5AsWw2epuTkHHNTs/Q8Y7erlOZhpVBGhlzT5JHyk+wycwhHB6mAUOj88eOUuk+yaGfpUGoUIfrDxyjbDqvbakbY7GoJVbhGmz3zJLbjcOg/vwCxWPU+mH4PDDCTnyFZnUMHhML3ji3A1Aq2vcKqnUSXNhPFE+yxugkhOTT9n6RDfeyxDHdu6iyr3CMuSZo7MsHRrSG0vElHOMshX3HzxbzJXHmKbNxgqLwT5YFDyO1pAJRF97jSE0+gZEssawoPFWahH+amv4FuAmq0Jvs++QCEDPdeVXJ0lEY7VF84hXJyEtmRZLIjxrh5ElQVUbYQJV++1+IKqAn0XIGCN09502L12BFC+RylZCwwh5V16Ei3vlU05pCcnkdcsIOiXWbWcj0yTmGccSeLFvLTT8HEcoFRj78L6eY0KXoEavsdSAklq0j49AyOlJxayNG7E/I/OUpIzlEahpniSVShc/RxE9BYNovEjo6zbCQJaSCKS5CreYcsafNkeZbs4vcIWU7TnQuJK/CTmphDLTscnZquPhf6bIbZzih6fgWlzpAHsL07oD5yFDQV40CI2JHjKFkNTUaZXB5wy1SshUP/ClYBSpW8wFoni5aNahexbaV6r+edDIulaR49skJ0pUS6JDlR9DZ7HNezqRZc4l3tZ2aVJ77yNcKawUpXGcUQGOPL7vG+8RzKPkCPXWiIxTNlrTWn0pPph2HkKjBNlLIFuu8EIRg6/WUcxSuTVCy5BC0zDeXellOhSJuKZJE2PR/MbyuZmId+gjN/hLLjoCsKpmW798OycI6Pgy5YcfJEC9NgD3oPiK90gmOBlOSsVcp2ndfY2xBITK2SyUvUTB68/Q1RF48eXsmyYmdQAXkGJRHaIY5t/Mzgnd98I4fyU7xmKcpfzL2M973wIl59/XYW/+4TzL7nvSR/5bn0/8Wfr12Q8JcIvdFe/urGv2KxuMDQ3i/yrkteyEKqj9M/HKL8rU+6L/I22mjjaYG/jlglpyE6lyVrmlCqhdZVf7q9H/ulbKlWBNcLyUqfWKT3u/f51RWCJ0uJ+cNTGCemCd3/PdSTR4jlTrmy45lpyC/B8ils6VR39xWP/KlevoYxcQrl2DjK8dOAQDl+msT993nNSyZXT3GyNEt07jg506oZWoDIBwUwSpZds2X9HgNp4tM+Y6mcI+/NU2piFnF6LliouG7nWSLIOyZqbpJofhLVLhKaO8LEd77MEWuSOScoeT++5FPS9Jqypc0jp8eZz5a8HEDpm0eYKZxw56dOMAOzzNDRfwJcSXg/lhy3JMJcYZbj8zkGHzlK9EffIf39L6OW3T45UnJkZjWQ35UrBSXuVwvNc6XEqhviZS/4xmfmKT5+mMhjMxTLNmVvrYiVozD7GNJzyji+n0dbQtZT3bTNPH9z/z+ynDd5ojjDbOEE2oRLBp7Mn+Yrkw+hL7lrJPLoCZQTk9hStlShs6Xk3ge/Su/hE9Xv4j9y8wPrc+eEbRKfnUcgeDLzMBnphfZaLlFYLM61DFmtejvNVQxfiYqcXeKR/CFWnSwL9irFwhLjS3kemxlH8wkzZCYfxrr/RySP/4gTlusVzFlurpd6/BtubT7VJbOmYyMl9ByeovfRKXp/OkXPITcMNWMXsKQrHV+Zk7LtMLNSJOMVktYWC2iZAsahwzgnPMVKpyaegeMQefRBNxzQLOPkllk9cpJVfzjtuE9FdO4IxeIq9dTaXjpdLUMhBZiOxbIXOiekjUSS8/pkS4dwaR4HiTO7wA+Xj5Mpe7L1Unrhe41zb9kSJZdFtQuUpY2svIJ83sRwcZZIISiX/0Rxju/mWpcZsB74MQMPP4kb8hwM91Oc2nN2Yj7H8fks6j9/OXCM6VjM2ato5ioL1gInPbKvH/eVp8gvYJ48CXbZze/LmZ7ypuSH5lGOzB3GydeiCMKFeVh6srGz80dh9tHqx9niCfdauQLqw0cJL+XdYUgLWTZZXs24b7taDC1YbhhmbY43TrvaBK2Nnwl88dFP8k8T9/J7yyU+vPAnvPe3L+e2S4dZ+LtPMHvnnSSe8xwG77wToarrN/ZLhP3d+/lfz/xfPL78EBc+88e86eLfpWSpjP9gCOefXu/lfrTRRhvnEnp5lRl7ybdzXMnlgfHikyAEwnboPjSDUjRRHRPbY3GKf1faZycJ20GpZu/UkFg9Wv1cdhwoO2RWlxCAXs64xWcLi7AyiXV6nrlyhkcXfooEQssFwitBhcCCY3LcmkZk86RWDqFPngbpFtuWZZuiLDE9MxfoXrmuVtuqXahySH/C/SPlU1XS2fvoNCdLixwpzCIkrsdPSoRlI7zd6GZbb1JKimUHRVqonufOLCwSm81w3Jxm1XLD9vK5TKDektsXi1juNP/xg//Px/1ENUcvUpwmnjvh/vvRurpMPz2KfWjtfN7HpzKcWsh5rTquAe8hX7JYLVpM1s23vup+dpDYsnkdp8lSndx5fhEOf8Ul37ihgjaNwh4LTpYfl49hWjYly2ExVyJfspjLFFnJm5jSZjFvUnDKLKz+hPKUO/YiFsknZkkfcwlb2Wt7IVtiIVdiurjIQ3MPIhyH+IxLbhwpXcGTyrw6DqJcZtnJMlFeCqht5pZ/SDx3muXSMgXbJYHCdim/4pQJLT+JsFopi7r5S/LBQ3R/9x+rZMekDEKQcfIsOTkihRlW5ieYKeQo27VF2HXMlZaPLRwlNu0SXkuWKTsl8j91SdRycYnJ0jIgUYtl9IJvs8V20E6e4mh+ngl7wc1BsyyiK14Yr5QBz0r8mEsCZcadp3B2whX6sU20lRPkxmsiN4+sHuWENcPp4hwF28t3KplYSxnmMkWWx3/ANx75BLOlSW8m4FRpkYWcyULOZKXgig09nJ3kcHa2unlt2dJXSkOiWVkEovp+ylnLVGTmPbUaFrMlZn2hzwKBtrrIlgfdeorFigepnrB7D+1y3mR2tcjDy1MsN6lPZuP26fjS6eq86Z4HuRk1r2wIZQrBtqadJVacHI6VYdmphX1b2VkmKzmsvhIXpXJFr9Edp5YzcbLZuotKb8VL9PKquzFkWm6u4UqtrETJKaBZBSIZN19Tz5lIBIoQ6KceZPbIv6E4ZYSUPLmQw3YkfT96FK1k1kpCBOrNra1I2yZobZx3PDD9Y95x/51clS/yo9k/5G3/7UZ+/eCgS87e/W4Sz3kOQ+99D0JrR+Q2w69t+zVevv/lPLD8FQauXuDtF/82xTmbqfsk8l/+eMNx3G200cbZIZY/zUlrnmUnh8BHUrxnz3Ys1OUsqmkRnlnFKk5T9IKKFq1FqoTOLbIEeJLi1R9w91c9/oNvoVnZqrchV7JYKZqeYEIdvXEkuZUVJkrLlKwyuVKZ8HKjETxhL7A6N1X9HH7yCCuFMquFMkrJYrI8S3z1MEt2FsfL4aiXZz9aqpGb+HTQq+UvJgyu5wUksdwp4rmTdH/r36rGUmQhmPsT2I0ODE0Sn1ql55CrnCakQ3buFLGca4xL3LnUyzmEtOiaeox6M1DaparQRxVm7fonrVlO2XMIx2Qif5SiU8JyHLS5LOEHjmKYS+hVlUoAp1FsxWrMl1O8wxesBU7lHm34O0DWqSlEAoiyZ4guu8StWAkX9daHKJTRp2v5ZUcWl5mZXwhMneVIFL9ao4TTBffeCEeiVnL7pGTeDt7DE4VZpJRET88SWnXnrGanS7b86FES//VNJDBvZ1i280zbQaELpEO2WMb21BYVj+Rr3tgihSmaQyKXVjlhzXKieJoOn8CNZmYD+T6V+2k7DvqTU+i5AqJSwcy2ic1ksBzHLYQubYqLGY7PZzm8fILZcgYJRBaalSlwDfayR3D0b34Xw1yuzpc3nUE4NpG8Lyd0eRwkFH3PSq3vkpLjepPGv/so8w8+wcnFPI9ML4JjkbFqc7nqW7Mly+G/lk6wmCuxUihTdGqiLFqxhYy7d81wcQZRtnCWM6hWCUortb9LN48sXJjByJWwM0WWi5Yrqb+Sp1AoB8gcwFK+TNa0mc82eoWlhIcyp1ktllgpu+RJz5Yajlurv9U5895zkiC5keXWBN89z90U6nxijvBygYJlV9uOT2dY/q9jgbOmV0tMLRdACNRytiruES7OkJ474tI9RYAABUH41DKxJ+bom/22K4oioZTPolh59IJ/rmoviYFH1t4AahO0Ns4rprJT/MnXXsVguUzv9M284iW/w3MPDLDwCY+cPfvZDL3nzjY5Wwevu/h1XDt8LU/Yn2LiohRfvOjXWD0ZYvFL34YHP32+u9dGG7/QUEyL+h1ZqBG1E0d+ghh3vR9ZimRlsRq2KOrUz9yQQxdqJTer6p1yGzxu+dT/yr7zfQRBSMlMaQWJq673+HQmSOF8hk/slOsNKEsLY/Uo95/6j2pjimOy4GRYcDLcbx5rSpiaFaxt1qeSZbOQLaHna8a4WihTSd6PNNQMkliB4UkvL8/zPlb/WOuTljcxVgp0Lj6E7oUbShG8PVKC8BXtrSJTm9fKDr4o5DCP/4Tp0mmW8iahE4ssOBlUu4hRmCGScyX2XaU6f89BPXKqqm63ahVcAQXfaEKZJmRAQjxb65sjXc+DP2yw7EhXZry86ObQ/HSK8KFjaIUyWt5k0l4kXJxtuFddR2rCH3quZjRWjPmCU6hO1KyPFIiCWx6i6Kv1VB+SaDq1HEUJWE08fOXJh+lcWqc4s2MRGj/h+0Iin3Tnw0FWPXNKtohmmUCNWFaet2zJwj56mv7HjiOEcD1w3nmLOXcMjrSY9shmzkdmInO5BuLuH6uUVOXxK4O1bIe5TJFiVY3RQSwepXPxgUA7tmyl6wr5qccQj9/PwkpNNt70Qmt1q1YTTanzyJZ9Xpg577hi2ane66wXTjpnr1D2ZZqpdgmxuIJ9ZJyOpYcJlSpCJy5EOeeJ+UDk0WkQAseRcGwG+b0nyZbq7u8a+8CrxTJLOdNThXQRP+J65cuOw2NTK0gpm5aVqIfizWCRIAEtlK1qFxwpmc8WOb1cqIajmlajt2rZ885pxTLF5Szfmj7hDcULYXUkIEjPPkb3xMPVccbmV3FwkKqCFALFi3VQMyX3XeiNIzt3EjU3TSw32fAsqnbRyxtea6xttHGeULAKvO7f/jslK89vTG7huhe9jWfv63fJ2bs8cvbe9yB0ff3GfsmhKirvvubdjCZHsXvu4VO7DnBo9+XMPpQk+zdvcWvwtNFGG+cEncfmic77vT/BH2PFspmzKzLqguPWjLv7CkTrvEZWpua9CBdmmXGWqoaLI+1AAr5pOQGDr+N4nUqflG6eU3HZDX/0YXL1J8zZFU+N+z8nrTmmCkcpzj0SGEHFKNZzparRthFE57I+EgVlq7J7H8xhs2wnGDnlc0WVmtToWsuM6zo6R+pJl3BWQx6FqJ6VM203/GiDkQXho4/ROT6OZuVcJXffeVlZoHPpQfRyxgvZrOtcycT58eNk7CLHi/McMaco+Q1Lp4y6esTNFazs5udOuPLkThkJZItlJnMrTB1tJJRPrnyfsrSxbIfy3Am6jsySPF0jVppndFegWE71XsZnfR6ugLiH++9V6Su6vJxlNlMK3Itxax6/72gm+zgdHiHJ1+fzVVr25RzFZrMo0sIoL1enTC+voBR94YUVsRG/+h6A46AfHaf78bqwVA+2lMxWPTRVtY7AMZV5sKSDbdsULZuTG1gXJctmOe/ro2WBV1etVHZzMSfzR5hfdPOwLKcWApkrWah2gZJT9u55pcMWQlosnPo+U/YyK8UyYmaB8GMnEbkSkcI0jpQcNacoTZxd2oKNw6y94nqSfG8N03EQi7USD7aUPDL7AMsz30c3a2ukmoN2Vld3MT31QAMpKVkOUsBj5XF+YDbmrgWOljaGcO3Bsu89mDctMkWLgucFXsqbnFzIs5w3q/czW7IIlYLvR3+x9IwskDnt2klqOU/fQxMkJlaQjkPX4zPVd2vljJPWXMCD5vbDJnRskoFjP6l6hKV0cy0VL5y5cn6kML2G19hFm6C1cV4gpeTP7/1TDudO89oZhZ5nf5Rb9vWzeM89Ljm79dY2OTtDxI04d914F4qAoT2f5c27f4X84DAT34pQ+ts/aFqnpI022tgcxOZcomXaDkqlaG4TY08Kl7hUxDrCK0GvUSXp3TsaSzpkvR392eJJTll1Blorg9Jx80VKlkOotEhq5VDg2FBxhhVfDkdDK022+jufmG8gVy0OBSAxuRL4HPHwZocAACAASURBVKit5cPCxDGU3GzVc6EVy4RWCk3HVjAt5kt14XPrmI163qweUTHcbV+eklaX57Jg+8isbREpTFf7tlqXX1OUZUKlhVrIWxNYTo3YZO0a8QkXF5ktPFHNbfJj3J6nULaqBmr2ZI2g9T4yiTG9RHLVNWgXc2bTudUa5olq+KJwHEq2lwfom+dmBaUzBdMVOPH10qkj6n4vTyssF8oIy0Ep20QW/WvPbdcwV91aZsCpuWUi+dMoTpCgSyQ4DlN20OPjdqrWpwopqknpB++b4xnMOVmkaDmsFsprrCLfX6Rd7SOAfmq26gUvWRXSZ3PCXOD7JbfulmUHWx633Xw21SO8wjaJ5U4jpE3e83gpk95znnMLcpcoM11ertZZq2DeJ2BTf0/OBoezs1j5RZbs2v3JNSHcfmTN4NpQSxZdj88ENmcATHMezVsnedN2xW4sVzClIhyznKkviu1fmxLVoy3+/LMKKgr+fmGetd4M9R67aF14a3Q+C9lirY26xqQiqqqMjpeLqK4WiM8todp+8aPaP0/mH6+NZ533VpugtXFe8DcP3c1XJ7/NHy7mEQfv4jev2MniPfcwc8e7XHL2vve2ydlZYCQ5wvuufx9L5QkG9n+F1x74XZxQlNOfexL73g+c7+610cYvBep3av2ILuRITK6g2OsbUxVj2cxnsKVNySk2HlNHj8qOKw5hO5KC37BaWt3w9nfs0HTgs/801W7M9VjMm4Hd6DNBtuR6D7S62lPpE4voxcbxSmBmtfH7tRBqcrw/bK3ryKwXFlXiybkVlpxaX8yVmbrzgni47O66C2kzbs+Try9c7aHsOJi2g+6F2GlWtjqXprSwmxjXj63MBkKzLFsymykiHIkxvdRwPKyfclzxjEWWcwjbrBrGrUe4dsOtyLnf0+tvtufQND2PBddXxaAW0ib62EMgbTLzE6iO6eVc1mDXeWAqnpToQo6Bh04ErqWVc8Qzx3Amv8uitVIdQrg4T3zlMV8raz+LfgXTjqWHSaweWeNo79rzOdJPuu+BQtlqyNkypdWQr1kfWrni5HGsWp2xki0p1Re19qFY3ihBExjmYtO/TOQyZFbmAzfWtBwQAsuRZItBoitxPaa9j06THHfXZGQhh1aymr7jQqUlQqUFHClZLZYp2w7q7DLCO3ZiNpgLVtk8sFoI6gRRmx/FLhLNn17z6FOF5psq/vV2ZLH1u7xCziSyquTZ7Dn2PyPR+dYbOfVoE7Q2nnbce+pe7nrwbp6bzZHu/r94yXOuY/Hv/94lZ7fc0iZnTxFXDlzJGy5/AwvyAazdD/Huq1+JmdGZfOdHkFM/Pd/da6ONXxpo6+w8AxsS8VktL3A6f7jp38qeYVMhHEs5lyyZpkXZRxaW58YJZVoTmxUZ9OQ186S0QqsCzU8VPYcmq55GcEPgAEQp6Jmrv74j5RpkVFIp0OvHfLaEIyWGGWx7rjyx4f5KJA6S4/NZFipiCZ4Rt+QRs/pd+gpysrbjXvHGVrqo2gVmy66yXwUlz2A9q6Iz0p1LvZxhJiDmIQnlVpue4s5Lk/xDaRMuzHjCCGuvA2E5DTWi6hFbOUnH0sNEih6JqyNk43ajB7cZpKTq1XyiMMeCVRuXZmVZcnLknZIr7HMm61cG41itOoO8iHtflgtmdWOg1CT/qXnbtX8WHJOcLLLoeXwrz/dKnbf3bGIObcqYXlHnZves4vHL+zaEKiGOk3lXTKXvoQm0QjlAwiKLFan/M+9TBa2ej4ncEcLF2YbcRz8ktRIIqZVDWNJm0ck2PbbgmIEIAj/8myLHcku+OQ5eO3l6uRqqXsG03bhxEvLlDXaeWDus0Y82QWvjacUTS0/wxm/+KXvNEtdZN/GS33klS5/8JDPvvMMlZ+9/X5ucbQJesucl3LbzNoqx/+D7g8t858bbyE6EmHvT74HVvPZOG2208fRDa+KRqqCSY5P3jHdR75WAlgRPsyWqz3haWp5telwF9cp9kUJNgW49e6timJ0LVNUFffDnsejlVcLFZnk5LTw+cu3QovocmXpj02kx34oTfK+a0uKJ8lQ1PHU9WLK1ZwRgxSy5ynMe8rIEjiR+orknxO1ri2s5DqrT+DsgpEXPRPNNPKO8jF5uNGij+Qk0u4BmZQOepkbIqkT/WgiVggTMzYrz1CrXmaO10IyETdqLrgfO+1MzYRMXtXOLdZ6ceoO8wXO4AVjVcEx3nCtOrkpECmUHiWTaWmx9Q1v2uvF4xTGx7CKn7Dls6XDMmm4gKpYjKZp2IA+xAv899ovOPGX4urrq1G8W2USKM0xmHg54t9dsBJi2l1uSsAl7IVAWY2NdC7YvpNxQJMTZok3Q2njasFxc5lVfvZ1oucTrFtPc/Kq7WP7UpzxydnObnG0ihBC85Yq3cEnvJUQGv8gd3cNkL93Pwg8LrH7wj85399poo40A1veDRAozxHKNITt6/Y66h+jRORT77LayS2a90bHJHrKzDIdshlBpsYEcwfq7+M1U3YAGj83ZouLhWrSznMyurHM0rDfHEtmQf2hkSxgNypc1mC2Mx/oyCRWESkvreHTX72MrCEfWCels7BqKYzJvB4ndRtT+Km1VCdMapyjeMTP1pQE8lJ1gTtPaRPTMURFVqeQtzdmrTNqe0A1eWOfDh+l7ZLLh3GalM9ZDpfRFJTx0qQmJqd+IOCOv2Bl61CvqkpVc0FlPUKnSv1huHNUurptjVyw73tpuFJc5V1BN+4xWQ6Qwvf5BHtoErY2nBZZj8fr//B8sFGe5YzbDnt/9B3Kf+wIz73ynS87e1yZnmw1d1Xn/9e+nJ9pFx7Z/4HdHn482lGDyE9+i+M0vnO/utdFGG2eAZrlfa+GpRB22EvPYLCQnNkJYXKwXEtcKShP1xxpkS/KyXuJ+PWKzdV4hb+I1VMDNSVlqWaOphmZGsh+Fst3gnQmvFDedO6+1VSCctfOA1vMCnglMx2rZXisiVZkMgSRSmK4SL1g7DLc+/7Ee0+VaHuKKk2PFOTce42b9SEyuEH/weMvnoFVIINQ2CVqhIrSxmfftbDBlL6GW7QaP3FSTcMGNwEEyay1v2EO2FnRfaYmQ2dgf/983jA0+s22C1sbTgnf94E5+Mn8/fzG/wPB170V84/vMvPOdxG++ySVnhnG+u/gLia5IFx+68UMoaoHw1s/ypqtfi2oonH7DX2DNNKkD1EYbbTztkA0Vjn+20Czx/akgtLJxsqmuSbRao/NY61yltULlRJ1qYNOwUh/iU8HQ0PoQuIJtYp2jMCi/EuLTgWZhkX6U2TxiP+m0Dt3MNgibeGhStLqCsyX6G772OUKzEN/NwpmMxQ6tX4vWUV1KcTY5aJsZLplxCqzKwqZ40PxKtPWlStaCsia92tgEtQlaG+ccnz/8eT535DP8zsoql/Q9n/jxMjPveAfxm29i+P3vb5Ozc4w9nXt45zXvxDZOcKjnG3zjeS/FykkmXvHbSOvc7pS30UYbG4DY/J9iKeWmEYOC3OS81fPIR4W0ieYbQ8Wqf68jGZFC81pbrZCRRc8j4RphJWuzA+KC2Oy2xVmGxQKYm+CJKckyx8szZ+XVWdv7d/bjWs/D9suAjcyeYjv0PTSxaRsH9Qq1hY7ohs57OkIb/WgW2hsVre3ajdaSbBO0Ns4pfjj1Q97x/XdwRd7kZaUeEvIKZt7+duI3tcnZ04lbRm/h1QdfjZb6Me+NZik/9wD5JxaZfXM7H62NNs436nf7NwMrhXLLML7NgK1Gzvrc+vpITyfWzQF5ioxnxckxbs0/bSbiZhO0tULm1kN5EwjalL101ga20qQMRRXnSGl0PdjG+p6nnwco56GOaj1BO58bO2eKhr77sFFPXJugtXHOcGr1FH/8n6+nz5S8e26FUOfLmHnnu4jfeCPDH2iTs6cbrzr4Km4auYlw31d4cdelJA4YLP6fb7H82U+e76610UYbP0eQQqEQ6Tvf3TgnONMctGawcZiwW9dP2lRsNvF4Cu09lRBHuQle5LXyzM42VPapwtbV83LdzYaRKa1/0GZCyiaeqZ9NhtYs12+tnq5V1N6PNkFr45xg1VzlD//jNdhmgY/PTCATL2P6PR8ldt21DH3wA21ydh6gCIU7rrmDvZ0XoA5+nj+6+DaifSbTf3kHhQcfPN/da6ONNn5u8PQYSprSeJ3lsc7zct2fVZxtgfBzgfVEKVrBViNYWmKTe/OzAan94pvZSWVjoYdnArfaXHBty5+fx3JT8Iu/ctp42mE5Fv/jG3/GZPYUH5mdIly8lrmP/R9iV1/F8Ic+hNImZ+cNES3CR2/+MH3RHo73/xv/ePMNaOEyp1/1B5RnN7GmSRtttHHekIsNb+i46DkMv1LPgfDJ0vZueqM9m95uPTT1l8wSbAKp/nKbh5vh0TtXKMcMsn3NCe3Klo5NuUYp1Ekx3IujrG+vxUX4jNrOd8fXPWZF5s+oplxYX/9+beSYpjhPIk4/uyuwjZ9b3PnD9/LDmf/if8zn2THRz/w/HSV6+eUMf+QjKKHQ+e7eLz26I9187Nl3Ew0J/qbvODO3DGNnskzc/gocs13Euo2fL3z+85/n1ltv5cILL+TFL34xDzzwwJrH33777ezevbvhv1yulntz//3388IXvpCDBw9y66238sUvfvFcD+Os0MwYy8WGkWJjxCt0tgbLBjxoIW2TQ7uEwIyHCCvub8jaKmlPDZE64ppSYmd0fil19vl5PyuY29u3YVGGp4pz4QO0NqA62AxScddVMbw5Ibz2OfCglZJhSskWpGgTuISlxSjrSSyt+f2vv8SZXtJW1r838/bqusdUry8gGW5NJMt6yj2urqcbfU9uNOy3O+6zb4VY1xNvK2sT2zZBa2NT8YXDX+Azhz/NDcsGLzhSYPYbNpGDB9ly90dRIj//P1q/KNiW2sZHb74LPbzEq0aiRJ/pUDj0BNN//pYzKALaRhvnF//8z//M2972Np73vOdx1113kUgkeMUrXsH4eOsSEocPH+b3fu/3+NznPhf4L+K9n44dO8Yf/MEfMDw8zF133cUNN9zAW97yFv793/990/qd7U9uSjuyiQGwYaNjE9Fsl105C+siGtJIRnz1MH0715W3kq5ojGo9bNV6z/wCG0T9rCbFmf12FVsZz02wUW9nKzji3NQPlarylLxoZ0TuzoGH4mzFOWb3929qP7L9QU/XZhA/KUTTOStEBilHNzdCqd4aGFF7GFa7675d+/7Vvx9sfePPh+IfZ4t1Yqy7Tt1RqHXvyzN59jSx9oZTKqIH+wokIms/m3IdotomaG1sGn40/SPe/v13MJBL85ePjjP53TThffvY8rH/ByV2ZjuQbZx7XNp3KXdc806IneLF+/fSsS/Lypf+lcW//dvz3bU22lgXUko+9KEP8aIXvYjXvva1XHfdddx99910dHRwzz33ND1ndXWVqakprrnmGi666KLAf4rHKD72sY8xNDTE+9//fq699lre/OY387znPY+PfOQjm9b3clQnKSJs1zbXGATY2rXxd+3ZGgAVo61qeDXZ1AmdoeegJxEmbmiIUI28KriGT1fMQPWMwA4jhi40xDkMO/K33Kemn1oD5wFPZWosvzrnU9issyI6+Q2vxY112FnDEF8dSp1Nk0264p7obMDLsxH4Sa6lxbC0yJrz4igG2fhY4LtSKOgpb5aLZWlxbNXAURVWh89izQbQmhRpQllTobApfOvI1lUKkcHq53CdiMrB+FDgcyKsEfW8obJF6HEyYmDtPMCRZ91EPjrY9BgIesZtxSChbSyfVRMqYUWlO9E6Aqw+YiAV0dEVhdianty1n682QWtjUzC+Os5r//NP0EoJPvnwUaa+10149x5G/t+PocbXjzdu4/zguVufy+svfj3Z1HFef/V+ElsKzL73vWS+/vXz3bU22lgTJ0+eZGJightvvLH6na7rXH/99Xz7299ues7hw4cB2L17d8t2v/e973H99dcHCMDNN9/MkSNHmJnZeE2sNU0YIejV0ggh1s0ZWTOUr8lFzsw4PzsrtmJ0Ooq7Qyzr3GWWFkNVFDpia+/mR3XXeFGFZ/IZMVRVJ+55rCSu4SOEYFTv5QpjJ5py9qGTGw17U33jcc3RM5unYjpCrkWOELhGbg1Pjc3VFzlPRwwi2lMgF5uYe+Vs6SPXs5Hf/zUM1Q14UABKyaCX86kUf8/GRpDreEw2DF8/imE3f7K8xnPh73fEez4qIXrN2gTIxscohj2vVt2wl7Z2nWmP3TZ7Ku8lQVTUiMkZkzPAf39Xh9OB/L5ybCBwpH4Wz7f77ohgqwYgqnPojyYIaUqg54XIIKLJWm/mLR9TexBCoGxg7MmIjq4qxD0yuGaY4zr7H22C1sZTxkpphVf+x6solMp8/JEZlr7TgbFtO1s+/jeoqdT6DbRxXvGKA6/gtp2/xWPdp/nEjX2EOspM/NmfUXj00fPdtTbaaIkTJ04AMDo6Gvh+y5YtnDp1CttulNU+fPgwhmHwwQ9+kCuuuIKDBw/yute9jrm5OQDy+Tyzs7NN2/RfcyMICSOg4FYhIxDcAS92rh0Kpq1pMAd//DeS0L/G6RtH1bBwG7C0oBF+Rf8IAPo6cY6VP1d3n/UImtDQPePYH26toCCEaOo5W9i+sbCxZZ+xahrppuGglTCluEfmNpLrZhp1HgshmoaxRrzitUnhelCeqvpdWBjYarANoaxt9200JLLHl0+zcU9YEGuNL6VESSsxFBQyA603KZwzVNSsEoinwHuLqXOnKLk6EAwPHFDrx17reNhQsNVgOKAj9DXJ5wF9BMN7fgqdMcwW4bb1IaD+fLNsb5Lh7c8FQAqVfrVj7SLg3v9rLTyc/tIVDf1Rg+uxPkzQ334xFaGUWCs80nsfRb2No+qZspHbCxoI1w59gE61Sfj5GZB9rSdOR9TYlKjdNkFr4ymhZJf4o6//MRPZCd7yiEr4XoE+NMTI330CrWNz1ITaOLcQQvDWK9/CdcPX8bnBAvffoqAoRU6/6nbKZ+AxaKONpxPZbBaAWF34dCwWw3EcCoVCwzmHDx/GNE1isRgf/vCHedvb3saDDz7Iy172MkzTXLNN/zU3grgSoitWUxyMhWsGkaU3N3j9yodmzBPDWMOglgJK0ZpBUYicWcjkU7Uh3B1rWNg6zPSB7VV1trAWxhxJY3VuMHcr4CRxjTkpBIamgqpxQWKkSsyEpmLvGkUma6SwlKzNp9NCnKTeC5HrSbVQ6nOvHzU0ehIhhtNR4uG1PVJlPVElaQu7arlx9Wp1Ec8TIZHs0Afo1vuoDD7axLtXDK2tWKmhtLiJzSmapcUpRtbL3XMbjBgb92S0Klpe3zU/0VMQdKtJerfsZHWwl1L8zJQA173YGliPdC9uHWr4zk8cmtU2c5qQ/XoiNX75PlaGg5sJETVIBoUT3FgyjQ56wyPVz/nYENnEtpZ9jyphInjrzLv8zIHGsL/6/FX/PSxHQqjeeCwtigBinlLjWtPcGTXQFFGd33x0kGJiO+u5ii6MN863HyIVZvqiYayIzvK2LmYOtjheEczuGWVprNFrqDTZLKr3oG3rjjeMr0I6y3brMVRy4Hr0BPYauZdlPVW3mdUOcWzjHMGRDm/59lt5aP4Bnv/TLVz0tUX0rk5GP/UZtK6zc6u3cX6gKRrvue49XNRzEe/YFmXuphzW0gLjt78KJ58/391ro40GVLwr9R6VVt8D/P7v/z733HMPb33rW7n88st5wQtewF133cWxY8f4yle+sm6bzX7kW6EjagTCxfwtqnpzkZDkYO37qOdtiYjmeQ+53gRmIsRqX42UrScNXkwHDenKMJsZnALIxbZgaU3IpHdiWUtSiPSTS3ez3LWTUqiLQmQQfc+llPuTlHacuSR+XIuSEBFsI81A33a6By5B7TsQPCgWAc8jme1PUgrXfm+cFnkqllYjAKpoXY668n23HscQKik9FNhpL4W6Al6NYrgHKVRMPU0p1IXlEwbIDKUodLrzFxdhXyvuVVaTO5HAwp5etCbrtZWK3nowk83XTDHUiaPoLYl8pQdr5Xo1XMtIYxrN13PM0AKemnyTcMeoEWVn32+ibEI9N1uN1HwmQjQNYVse7cRZh3w264niEZpmHp4RtadpgfPVYTfHaWFXbyCU2U/cSqHa2h1Wu0gqjfdORWNE6yHsvRMcRUGKmpc3ANGERCmCzKC7QVEpMN8sj61C0rr79lbfgZYWoxAZoE9NM6b1NXiToiIUCHvsiIUY9LyCITVKXO+iGO6v5jbWk9ZyKk5/orbOpax5misI6SrFcHDNnrzkkoC8f6XdUiLeUHuuI2IQ863DyntwI+Galu247Te5v/FUmK6YQcoTZukzEmsy2FKog2K4m3B8Fzv0gdYHemgTtDbOGh/6yYf46sl/58LH9vE7Xz2Cmggz8tl/ROs593Vq2th8RLQIH77pLralt/K6/T04165QPPw4p/70DUjHOd/da6ONABIJd+fZL48PbpiioihEo43G7fbt27nyyisD3x08eJBkMsnhw4eJe/myzdr0X3MjCOsKeEadWZdcfmXHgYbjHaFT2F3zboSVEFvULjqUOBGPQPmV8bIDSRACKWuG1HpolOZ2z83UCyywtpdBAFpIB4FHVgRSqKTSl4IWRenqga7t6/ZndW9vYDd8V3KMLT370NUIe3qvZPvA5QwnRqi3eha6LiXsGVyOqlAMr6/oKKRTJW9d8RCdcQOkRPd5PvyCDsOhDvbFhkAEzbgKOavcE0uLEdNSIFoJSwQNu/k9fWT2uupxUtHQVZWBlqqejdaeX3RA8eXblKNGNccuN9CiPY+w2Gq4jmS681cpu7C0rSt45TXitdzrN8/OGUxGKXS5azYR1ujrqMsT60hSGNgBWhRhN/+NqbSbWMeLKYUamP9yzKAY7qdYV/KglI5QiAbDDNMRg6hfZVoIBlLNPXpj8UYBDk0oCBn0fCVEpHo/rIiOMdh4T5JKFISoKi+GFYNepfm9M4TGjphH5rwNo4ihNZl3wY6uFLnYlsC3+Z54MKxRCEqhzsB7w9IiZPsSRPTgu7MQ6We58+JAuHWPF2oY1tz+VjzA/v64IYYSWzUCmyh+hLoGvHaUKuFsyHlTG+99PlVHcETDP6qIhvXAt1bY3URRhEqnlw+oCEFopDFUukKKK2+BsDCq7wx5yRZUpbb2NaGS1KOURj0yroerxN6PDm1jDow2QWvjrPCFI1/g4z/9OCNH9/Dn//4Qakhh9JP/gD6w/q5AGz+7SIVS/PXNf013rIc/vGIA49IMhXu/wck77jzfXWujjQAqeWL1kvrj4+Ns3bq1qQfty1/+Mj/60Y8C30kpMU2Tjo4OYrEYPT09TdsEGBsb23D/bCQIga2GKRuuEZTyvCuqUHG29OOMBN+XfjNXCklIMSiHOtiZdA1Ku8mOeWtf0PpRX0IPu163Jk24+UPNW+hPhslfvb9KDgxvxzpl9DAc3eWe10IFzy/UYUfqdspVAyMcp3fPVYz2dRC97FLUhOd18YstRPoxIhXD0u18lWg1y2FRFKb3jSF94Y/lVBRbCxNBRwjX4CykI00lu+vluQES4WahpzVxgnoDudI1O6QF7mNvMoyuKmT29gXCI33NVRHWlYDogC40BlM1A7tCMMOqwsLOHhZ29mAazVMN/KR2cdgljHrY3YCo9K+/Sb7PFp/Euqjk+vjm3B/WORrqZqnzIOAawGrXELFONzyv1JNi4eLnUBq4HADLq2PliOYevogvh7NeGS8dNVhOX0Blwmw9wdzgQeYvu4CVsUalvkIq6MlTogbRmPudaXQymAoT9q0V00hjegIfKTWC8D0v7jPd/DmR/ufZl0+51LeLZHwvqeg2Vq68mdzuXkJenmvM5812TxFIJLnYKGbSfedJpbb2JG5Ioh+7E310hbdgae79THu5gPN7esl1uwRTCoGlRT2SFqesxdzWhEBBxYnUSJqAhufK6nXnK2R0k44aVVGM6vFe3+rzVUFBESpSqFhajF0dF4IQJDv7iCbSmB07gpMogHAjKd7aXVv3bs5q5dmrHVMKe97uSBfEGx0HilDRFIWeRJiueAg1EQ3cyRG1h0HVXT8ZT1mzW0mgVqiTIoI5dIoK/Rdi9SXc/ihq0zxcGZyhlmgTtDbOGN8+/W3e/l9vZ+DUKHf+6yFU1WbkrjvQd+w7311rYxPQF+vjr2/5a9RQjFff1ENoZ57CJ+/hyN9+6nx3rY02qhgbG2NgYICv+xRHy+Uy3/zmN7nqqquanvOZz3yGd7zjHTg+j/B9991HsVjksssuA+Cqq67i3nvvDYiMfP3rX2fXrl10d9fX/3Fh+gzZimdCQXhGjWgIJ1JCOrI7jezyea7qwyqBbGwr+egAPXqcfFcMR1WQQgvIVA+no3TEDNJqAtUTB+iOu8buFq2HbZFan/2GtK2GINZNMdyDqJNTV4VgLNrBUGRn9bveRLjaRakoIJSqeuOBjmdyQerqoCx7i3psDSQEGuzbRFhHVRT0/n6iV1yB4pG9eKjmMajwFCFha/xCUornAakbSzTdx66ha5CGUm0DQOuKM7t1N2Z6O+nuQQTQlwiRjDQKrdRLgVeghKKB7puxmvqkP3StMsi1QqpGO4awIjqDiUFyw52sDqXoDY9UvR6aqpAMG4Ed+cFQir54X3VMjkeUk7rK4HAaK2pgGrU1lvKRyko4bFKJsjLYSzY2RqTL8+x5N1pVBXodYfWPQQJSUZFCqeYoBeZHCKRQyMbGcOID0DGGGU+5fhVDJx8d5qYDLjFZGutlcUcPmd6+oDCG735WQucq4WrFdAQzlECEEkihIFEAwXB4GztSVyM0d7z1Qhy5/mRgHYbV2j2XAvrTQQ/S7NZdGB5BdJe826fuZMxVGW0YOTi6SjlaI07+VVmMdbHjV3+Lkee/lGv3b+Hl2y5ncGAn9v4dxGIGoUinN/RKYJ3EDHWiImBY+QAAIABJREFUeMS14ins0CI4Qg+EpF482oGqqsS0NJaX31bjSP5kT/d/pFBdBUifd0wVKqgqyTU8PeZYJ4bQMGNjTb14tXHLKkmprOU9/Sns1Kj77hEChp8B0U6I9+GE0xSiNa+6EIAeqea7AkQ0ha5YiLAnijQ8dEXTa9tqmGK03/0q1OiZrGx2CGA01Ine4W1QeKGyhqKhVQiWb+6SSqQWFeFXnRy8BKE3vj9SER3TSDN++b5gF9cpY9EmaG2cEQ4tHOJ/fvNP6Zvq5t3/eBodk9E3vRDjquef7661sYnYmtrKR2/+KCuGzht+I43eb2K+5w7+67NfPt9da6MNwM0Te+UrX8lnP/tZPvCBD3Dffffxmte8hqWlJX7/938fgFOnTvHggw9Wz7n99tt5/PHHecMb3sB3v/tdPv3pT/PGN76RZz/72VxyySUAvOIVr+DJJ5/k9a9/Pffddx/vete7+Jd/+Rde85rXtOxLMRVm5uAQ83v6ILqFsp7E2/yuGkdWZ5RSd4y5vX0QDhqzlhalrLvGgSIEmiKwwhrlULpKdBTbQaqC7alebNUg6RESQ1PQFYXhZJLhtGuIJsM6IU3BQGUgkmhKMPSOEeKRLkYiOwNerQFjgBGji55EGFXR6VDTVUOmOx6iOxFCJjoDBqehG+i+3BlFKKC7n1O+nCxHU9hh1LyGkjpxhRYGi6rpjHUn2N2XqF63YuMUIgMk9S7SSrya1+Evdh1PdzO2ZStaqDZfAMOJTrYPpNnW343uhT5qal2oXnoUhELU5x3I+Ui1Eq0ZsNMXbGNuV6PXzC/8Uu5OeeP2E1kg0sHQ1l/hQn2U0N7dzI3uxw6lUFCJe+FelVb8O/ID6QjDXbuJGhqKcCXMyz1x7ES4qbx3VzxoPI5pfVXvSFfC/ZtppFD0WihvOmrgRT6SiuiNoYZSYihhVKE25ET5o862dR/kuuHrcYSbJFWIDAWMXqkqlGMGma39TB2oeVH8eZPhutwkK6Rhhjpw4i7ZktU8MRVDi1R74A9jTYY1pFCwdZVQZJR+tQPXc+SVjqhcQ7jevJmDQ6ijF9ARUemIGa6H1bt9rXJSbTXMyt4hHK22DsO6ypbo3urnkT07Gdw1RiykoSqK+xzoGsPXHaT/2c/z2nf77+BtKNWVHJBAIdofKHOhJPqhUgZDhFjsvAiJrHk+9RCG0JrmncYMlURY48pt7nx2hPoYjV2wds1B4XqRyr1xSDaKkYTUKGFdoT8RYqvai8QV/4l6eYBqk7b13gPVjQAnNQqhBKbRQTa+lYFUmN4u16O289YruPHSq0glBjG01jXKWmG0q0bEw55X0lAVpBCoiiCsqVWP+kCqdmwi2Q3bGp91hFIjuiO1jTEjajA0Msr2njgpvZuynsJI9GKG1iux0kYbG8T46jiv/vpr6JjR+d+fWybmFBj9nTGMF/7v8921Ns4B9nfv54M3fJDjhsIdL4ygp8qE//LNfO4fvhYI12ijjfOFl770pbzxjW/kS1/6Eq973evIZDJ8/OMfr8rif/SjH+XFL35x9fhrrrmGu+++m5MnT/JHf/RH3H333dx2223ceWcthHfPnj3cfffdjI+P89rXvpZ7772XO+64g+c+97lr9MQLrQppFC65jlLI3QGPdPZgxiNk+1OUdnRjbu3CMbSG8LhiuBcpFIRwSVBnLERmeIjcgUu91gWZgRTFVISo6hoimvfzXfkR74yE6IzWDFhNVcAzNLbpbm6FPwctrCv0RnsRmoEVNVja3o2tq0REmP5oLQTMxqkahgK3FpC9ZcwL+/PeA6K2y9yTCBEzYqDoMHw5oVht51oKgS4013vnwQx1snzhIOX+BKpQ0Afqwtsq3hwjhBCCipZEsXsvthrG1IL5c8KRhDWVaEgLhCtaRpKV1F5kxCVVl6R3sX/vVXRHurG6W5SDSfSDUivMu7Crl5JP0GBrupvhjgjpHcOoyW4crZKvUns/VoiSooA57BptEsjF3HC14p4x6N6Fkxwm6pFcNTqC0rkVRSgMig62af3sMQbYG+2HWJ0X15t7iee1Geuquhdz6QtxuoJ5NZpPFbRKR4WoGsqOopPrvJhbOi7gmcntbAt3kdI9z6CsE9CpeIgRZPcOsdJKXQ83xzmsRch1jyAQ5NMt0iFUgRWurY/efb9S9XbJUPC5EYBdCaWVkief8QwmDu5jddulyEg0EGabjhqELh6mOx4CJItdBzFi21FjWwmnUxBOY4Z6qiIa4JKf5fRehFAQWsglx/6cxIpKq6py8hkHq+dJoTBi9AS8yMm6Z16r9s1dK7YnYiOE4MqtW0j0Jbzr1Y7xo+yFL0qhUuioPWNi7Jmol93mtjlQKxcypHVyZWgXV19xG6sjndV3gT88MqZrpCI6caM52amIiKjemkt4n52eg9B3AeiNap4RNU53PERYd+sZVp7nVMQgHdEZri8zYoQoXnARnYr7DlJ8+WemkSKy7WrUwYuqbcS9NVFZl5JG4rm7s3ndS/8eRqW+YiVkVVMVBn2bA6qiuO8DoLR9Z40UR+ufR7fRxJba+s5fOEj+6v0ApI1eEIJicmzdWnttgtbGhjCTm+GVX3sl2lSOt366TIeTZ+RXJaHbPxl08bbxC4WrB6/m3de+m/vjKh99kUokbDL67jfzzo9/A9NqC4e0cf7x8pe/nG9+85s89NBDfPazn+Xiiy+u/u1d73pXtTh1BTfccANf/OIXefDBB/nOd77Dm970JsJ1Hq1rrrmGL33pSzzyyCN89atf5QUveMGafZDAcnofy+n92LEYUlGQRhRDD+Nc9d/o8OTFI7rKUDrCFp9REhY6SyP9mDFfHxSVg1f+Fk6yRhwcQ3UtirrNEeExlrgWYTg9SqcXHhU1NAZTEcK6SmfFCFMEKyMdFNMR9nZcSNJIYXoGvxkPUUqGGemKVgmkrqlkxvpJ+bwm5mASc+sAg56CGcCgJ/5Q2jLGjkv2oW/ZwnBi2CMP/qK0Bpdc/WsUIgPVeQOQmuKFmYnWtTN7XO9DJXxTjXVRiPQ35OBVwvziqV7SXcFwSlsNkd87jDmSJqzqbB28gL7n38azdlzT/JruDKPGIszu68eK6K6Mv3QYMFIkjQSGqiD6e+gNjyKFSkKEQfrejV73EoMdVaInpRuyBlDubdxFH4zuYDC2FV0Jsdq9j0J8BIQgFO2GcPP5cby2/fwpGRllOLGPUqiTQqSfi3ov5plbLvJICtQodqMXQ/NyE1NalGu7tyE7d2Aku0lFgsTXUQ1Cahyhqg3qeaLug6oICr3DZJ7761g9gwz4BDwGjEbC1qHEKXVfgRXRKe7sZuuLX8OedDcJH8FHuHI2A4kwUtPIdG2jtM01yP0GcLcRY0v/AAdiQ26NQs+TV4z0s+Wyl7N76GJkqNO3GVERHvGupRo43fXCFAoYbt6S5SmzdsQMLu8eJSR0TKNOQj/Abb0PoRR0jLHS8wx6h3bAyFVE9Sg3HbiKQS881dl5MfVYTW1Ddu9iMN5PtteXZ6eoKLEUmWfeSMeAe96Iz1MkFIWBnjEc3Q0HVSwLAF0x6Al3gBA+8hhEJSxWFQrXp3YzGnHXrqoaXD3sPkMh37nStyaFoiGFSj466Ab7CuiMGYTqN2R27AKgGN9eKzPRwoMXCBmuHFN3aNeuISJalIRRiVAApMSMhl2/pBBEFL1aO06//IJAXb6E5xU2NAFGjOWOA1gxz+sKDKTGIBSrkjfp5cxGVYOtz7yewr5+96JnoABcQZugtbEulovL3P6127En5njLJwU9dp7R6+cJv/LjkNhYgdA2fn5x69itvP1Zb+feLp1P3SZJyxxX/PXb+MMP/ydLOfN8d6+NNs4/BKh6CEfR3GLKjgMdW5kauhWphqpKYQAhTUGkhqu5PQdD28n2dTG3xwuZMWIc3PUbHOjbynP293NwS2OCvB9KRdFt91aiA8OMJEYrXSLshRFFDY2rLz7Ib48coNgRDRYeXiN66cLhNPsPjri7xx7Swxe5XgQhSEcMxrpi7OxLcvPePm7YP0Rk/z7XWK8wEy+JfmFnD1fd+utoSfc3Ixcbxu65qNqurKiptVLKNFxjfiAV4TcuGkK0MHiKntKl09mN09UYcnXZ3ls4sGM/NKtFJ6gZ4VUiJNBSsarASKEjwdTeA/TFB0CPkHvmhTidaUDiCA1D6Dg+EYdEWHMV4voS2J5iXjPJbiGEm0+khIiocXpibj5YKT1EaXTn+oVvvb83yMBLSVlPVvO6uuIhkuGa4l5FjXL1gKu6acbccgDVVnovQNv+LPqSIZRoJ8qWy4h2uIQg1zVAX3wfaaOfAbWTEaMW8mmrIayx6wirMZJaJwI3n+263d309qa4Zlc3z9jaKOBRwdT+Hcirf7NqeNsdUXp6u9m1Zw/bI72uSipQ9rxtA563oy88BsD1u3qrm8cyHGL38HYujo+wJdTJzVvdYyq1vlRFpfem5zJz0AtBFLWQ3gryV+6neKEbehlShetpESrbq7L1gnx0CL1zlJ5InHhIw1HcEMM1oSgwfBnPuWQ7IxffCCnPCyklvUaS0egenIER8vsvRsaTdHpE1jF0UBSeMbyNP3vmbwM0CNxEtATXbB/iwnjtOdjVnyStxFCj7lrsMjQvbFbQa3RxbXoXCMGuvgRdcYMDQ2n2eSqv+ehwtZ2oaqB4OVtuuq1gf/cBdvdeRG7HXrLxsaCkvoDlzoswjQ4ig8NsT+9gS3IENZUievllOJEwuWsvQgy4fQ1HBknEWtd7AxCRThRPXIZYo/cuEdJQD7htjCTdWnJhL5dwZt92dyMlPRIglZ3xMJqqVEMwExGdbV2xwDsQWXuCo3oM4v1gxNAHB8GoPfvq9stwYgbdulcfskUuayu0CVobayJjZnj1119NZvwE//c9Kj1WkbHrpgnf9ibYutauYxu/SPj17b/OW698K18aMvjS8yyGCgv85uffw2/91b0cncmc7+610cZ5RcUQ6QoFCcElW3sYSru5MFtCQWP04LBX2LjLFScx9TTEeiHWXa3fFNJU0tddS/yindSjauJ7HjTRRH0wedX+6r8Ht2+nOxwjHqozEoRA7XqW+0/p5Y/50VXLBworOheMXFs7VUp3R1oIYiGtIYzLPamDUqib/ParUbf4CJnQ2N7l5v0llDAXj+4jfvVl6H11m37rhVPX/X2bZ8BavWkKlzSGNiW699C/61eblgGIKgaE4m7uWfeu2jgRDKYj3r2ETG8/9O1HU3XQNcJapHIgq6k9yI7LqueGVIXuRBhVKCx0u2IG/h77w8WnBm9hauCm6lX3D6VIRzRCqQSdPvGSePdwILcPIOoZf0F+Vms7099E8KESrifA0TW2pbdhRmuhdu4AEqAFjV8nEmF+Tx+rI33owvDyJlX2xwfojOsUwz1k4mOE0r3sTj6DVHIU1VOKrBYc9+0M7BtMcfkF12CrYeKdA3RGo6z27uGmW54DwEpqLwxe7HoRt/QRGumrijfM79hCYd9WfuWC5/MXN/wOL7nkMp69r59UVKcjPMxqcjeTN/4h0b21cL89225if8e1VYIGoOg6dqiWfwb4iKxAhg1kvBs6xkgP7yXUvQ1UjYQ3N9uie11irhrVeR8Jb2d38hluY746fB3GmWxsewQ11QGxOAm9gz1dB9jV63mEFDdHcktHpEpSAa7e3s2ztvdyw8gNdPo2I8KaSlfM4ILQCJF9NxB5lvuOiKhx39oR7B1Isvf6KzE0papoaXt1+SpEUCaS/ulCUzSUjjEWxp7VVPlUFRrb4ge5aMezSBgJujzpfb2vj8IVF/i8TKLaJ0VRkIrg4HCafYNB77G+/Ub0a/87sWdejT3oetsGItvpTYToiBkBgq0rBoNGys35876T4IbBdiaI7tvqHqcqbOmM1cRBoMGD53qeJaTHGsYY2PHychANoXHL2C38xs61wuQbsXZhiTZ+qZExM7zqa69i5sRj/Pk9ITrLJttunCVy2XXwrP95vrvXxtOMF+1+ESW7xJ3cSerWAtf92zh/cO/HuK1g81cvvYwb9qxfi6iNNn4RkVDCrABpvZeiUynsLhnojOHEUzyyJJFILomPNiTFS1/+Fp1byV85QLSzFtKkplKIuM9A3r2FuaIg0jlIZ7qEqQCZ02i7LqV8fCbYMd+1Wif6CyzdJy9dF5cmVYPCzc9h16FFuuKD6KoOVn0LjagSDwHD/X109CW8jwKpCArpJF3hPg5ED3BAmyeuhiDmGoDxa69B6HX5RvXz1u0aZIW0z+OmqVyybyc/OLFMebh5PU4hRDUcqYpIBwdjw4Ti3Xw/n3PrLvnui8Ct0wRwoDeFpYTQ84P0GGNc3NtJb7SXYzOuGI0Z6mQ0uoeinUNdPs72cJyTaWDgQpJqD7oSY0TrptRsXJ5nUkoJwiXoBwaS6N0J8kdVMGL8/+2deXxU1fXAv++92dfs+waBBEhCEsK+CMgqoGgVpC21YlFRq6hVSn9asVWrdQe1KNZC1brvrYgLoGih7oiIhkVAlhAgAbJOMpl5vz8mM8ksCQEmJCH3y2dIcu97d+659y333HvPOdTVYtJpoc5f0babtOiar4wqej9F0Kt4ASRZkvjaUAk7dvuOcZsMmIcWcOT7BkyAaktGjqiDGpAN/jZJLqMOl95jm5gaZcKokYjsGY2FGuINFmoMCuBxXmM3ajk2cAS6XqE9oAL0irMAFtyGS9hxbBu9Inr52Q25FL1H8Wm20mHUadAoMlq9Fjm+aeDe3KFLfnw2NiXJM+kQnwNH90CvcSArKLKWhqhYNOWHmprfdyUHXtFNf7sNEVB/BBQNZq0Z0ICqYtEnEBkQCD5Kn0CdYiYz5VLOKerNiq++a7ENApEaV3qcCU3bKo25OdQ5ZZIykyh1BkyMpsaj7G+SJbZ57EVFh16jUNfg8l3POkmDITkHt/VbAOzaOKCi8cs98urS03HX1eGuqKBPRAzO/fuxYvbZblUUXQjvrglqrTF94tn8g4n9R5xBckUZYtDKISZyAoi3GbAadeysOECDoqBVZLwLUGatmSGJQ3yOQTSRkVDlybNqo7DoNTTkJeJM60eiJRHYD0CczkaE1sxBTRnQZIOmZiWgtXifT/jmNTR2C4aMBKq+2R5QO5UIyQwaHXrF33GN5Pe7BJY4qK9vk8yBCAVNEBKvclayczO3/VOPrd5Jr2kuTDGRcMGyk9pPK+j6/Krfr6htqOURHsFWVUvhx1tYaHqB3zhd/GFKDnNHhY4/JRCcyWRo45Gjz8bhdFFXV+tLl3xG/p53foLO39Vzr1gLOf1SObBjO2WN24VVk8GzVaYFbHFxOCoc5MVloep3ely6nzUFKSIZ8FfQQt2LPXVxHJOPNT+oWa4arGxJEm67FbkwA3YcRrZYMFebqXZWN61eHeeelyT/uuwt6uf7XSdpg05XbM3aqYUVNNVqb3JbDeydMI/E0tXYTDqcCTbP7HVjfAMlIoIUXRTOhtBDHm2PbOo+s6FLjMZd4kJ21KCNj8NZehBvB/Y1JfJ9TQlaWUKrVTA12jp6h+QjekWzZqeBhjoJWZIxaawkaxOQ1P0excJgxSbrSdN6trDFRpow6RQ2eVe+GsuJMukoq64jJcoMhzzyy5EJHgVDZ4aDh5vqHWXDCSTo7BySG3A0b8ekAtQDHls4reLviTLOFM+kzHFEHHyCyuqqpnaPjvK926W0YejSh6GUl/spyypQmxkP+3cDnph4qlvF3G8S/PAWeeYUqq3lmOIsIYP0tuZgSpFbD0WAooX0EfDTa0hAfGYhUSktbwHOSbKxu6zas5oT19fzaV4XTZODEZ8JU8DFmBFtJtmQRh3bvAZMAOgVA5kRmYBE5REtDVormsLe8O2OoGu2Xh8JmuYu94/vZEtjNWHO6UGdoWn13GIxMP6coThc1ZTuBVNuHnJZ43dkpVIfwosoAH2mkc0qaquOBmQ0OobRekIFSB4vMDRXMwxZnpVkE3Ds7behTMLQ0/N8cjXKpMiSX7sZNApKiG7MT43A1rfliVxDs1VGWW7ajtg8fmGoYz2SNDrjQabIks630j7qTQZ6RfTC3aigEdsXxVlFREQC2QlWtGWe56DcbIXRI4enfxSbCaXRKVCiOZFdx3Z6vNWqKkmaKPonjaR252bPsV57x8CHWWQPVMvJxQcWCpogiCbl7DsWPWPA4qinx8WxmN1fwYyVYG5bFHTBmckV/a/A0eDgbp7kDoeDws8/516LiZvelthaWsmdF+SiD/FAFQjOVOr10QRGgVJRQZaJaYxDlqYPtreJMOnAGkmC3cio9P5sPbI1ZPlSXF/0P35GnUvFpjWQHzmMNLuF3Y6dbatgbB/PVsWy7aRoozEFOHPQKya81qSS1Hh8TZnfMUpyEta0XGSzmaH2oVTUVbCFr5ud5I+7maOMWFMs1SGqFTRWDzV49yoMev9VnPRoE1pFxunyfI/bYKYyqi/0LiSnKocKtYEMewZS5EBM9hjit5dTVdcQVDyAbDBgG5YPdRXUZSXjrAVT0SDqdu7EUfINAD0MMXxfUwKoTEifEFRGqj2etCgTx8qTkSUJd6AsksRZWbE06HPBEg/7KnDu3+/LTrAZ6BljoXe8BYNWQXW7cVQfQZ+djWw0Ypk4DVfZYdyfb0M+7FmllRqVuwSdDYfJSQVglHXUuj296VY9sfxSI43UxVqgWZf2jrdSHZ2EpdazEjMgdmiQTJIkoYmOxnXsWGCG76cmMRHnvv1+k7ayLPvsbbytEGgbF0oR8yourU7yGSObLrc2OihrSelriIxCe7AESa/zfWdKhNFvGSTGomdcz96s/HEbTbMtKkaNAblxUO7UeGyMXNF2qscMwI6ZAzt2AKFljjC1bTVFE2lFrfcfpntWkqyclXIWZq25bROisoxOo0EXGOOv8dxD/TJJ3u7dIiu1OOGiz8yk7qdP0SVGg9ZIQ2OoSFnyd+4TWCeDxoBF6wn30dLYYEL6BCRJYsfBWlStFqJi0PVOgHXf0xDveXYOThzMZyWftWjDCZ5rKF5no7j2AEEW8gYb6EzImlKsem2TE9r0YXCguHlhRARs67XqrOTF9scd7WYLnpVBnUZPLdA/1uPBUxMVSZJVoQwwK3qczZwChcKubXlVGYSCJgjAq5wd3r6ZO543YKiuI2bOACIrX4XzHoHUwR1dRUEn4NrCa3G4HPxRfZq7nHXkfPkRj0dYmfflGHaVVbN0dpHf/m+B4EzmaHQu3k1zUqNpt4QMioJR0jAltghqyls8/5weHtuEI3VHOFRzKPiA+BwSsgbiPrAZq17D8KQYos06YmqL+LL0S6IbXceHUnAshVm4EkdAUjqUebfqSE1jMAni9OnsbcrxOMhodJIRZYxiT+UerDorssEz06xX9MSamm0hPIlV8+Zx01pD1usx9s9DExcHVXtA65nRNmgVcpPsbCtRibXqOVRZR6UtCwx20pt7OoxpwelICxTGF3CU0B5qB1kzUOKKPNs8AzBqjEzpOYX6NDeSBCu/LaFOHwOGWqivBMkTg02XOdxzQjyohQWw8x2PnLJEXkqzQa4sYyxostlTLGYUi5laVzbm9w4RozVj6JGEbO+HbCpB1R+DhoqmmFl4leRW+iayB1YlgbyIISRYogBH0/e3cJpBMaD3xoyS9Rjz8zH0a4yVFeL68w5OQ62mBWJqtHEyh3Lg4qtY23fvaBWZjGgzaYGu3PE4EfnBZuCgyYwp2bMiNLRnNJX1leBuwUOxLPnkURNjoSb0YVgTgR00aIxkJ1iJDXgX9ksMDprcEs0dbTRXNC06S9CxgatKfjQrp86WAdWeySCdosNl0OGMi0M65tXgQ/eVoU8fDM7+nj/6TMW1w7OaK8ugTU6Grz3bfE2KAZveTnSju/4+UX1Dltccv3tKkpAKi9BFa6geMwCtomVI/ED0jd47m0/+eCmKL2LLHjAorVw7XtECFv4DvVYOyzyHhv1veex7s8/BEj8YyRJD5Zq1AdexhLF/Hg2lpRj69UM2m0kFUiJzkSQJxRiNIiv0tId2dmLVth4HTShoAh9HHEe4+oOrqSzewl0v6HA5Gqibey7pR5fA4CtgwCUdXUVBJ0GSJG4eeDOKpHALy7nbWU/m6v/w/EVmLt07hOmP/penLh1In4S2v4gEgq6LhKvRWUeELg63NoZES8IJb/ctii8KOfiQJAlFkVEajfO99iVKCysIWlmL1/pDiU5ASU/3y6+2ZBBd4/keZ0x8s3o2bVUyDx+Gs6SEZEsy0YboNg/+vDSf5Q61gqFIHk+PDmMcOEtaLhvQpXk8sGHwd5Zybq+p1GW4cLpUVn9f2poq0mYSzQkk+rmy964WycRqrWBo3aumrtnq5OHYIWQarFDxKVZd8LPwVLaDp+ijkKzR6Hv2gOoIqPKshLkkBWI8DlBU3NAsLpRiteCqrPIrJ85m5Ow+CR5bsToHoZC0WpIsSTS4XciSzOjYAUSWNqBa8pBkOWh1szlena0N+hkp1hSMGmPThEPIyjQWFEJJDkVLXlDtJi2De0RRm2IP9q7XUr9IzYI65GZjjehH5XvvI4G/U4zYLMrSzLhqPStw0Y0KWrw5it3VkGpNCSy5FTzl9kmwEW9ruZ1HpozEoLRyjzajJqY/+2TPNTI8aThl5VtxV9UhHQPZqG/zhIs31E6gYyEJ6GnvSZmjFkdSKrgOB51rGliEu7Y2KD092kTJsVoyos24Gy01FUkh0hCJo8FzfYZakTJrzSQamxz/9DMlsVlrwqQxURV4cOP5UYYonO4GMiOzwCVBZQlkjkX744+4EnJRUzNAZ0aJa670+Wt4urS0pueT9wivDZ+iY1LGpKC6thVhSCQAYF/VPi555xIatvzAnc8pOJwyxXNmM6zyCcgYBZP+0tFVFHQyJEnixqIbuSL/Sv5vmo6d2W4iXnmRN+0baXC7ufBv63l/S+nxCxIIzgC8bpllSSZSF+fnIa6tyJLcYgyAyZFKAAAgAElEQVSi1ghUgLKissg0Nq5w9R7vl5ccYUSfmEOEUYsSEYEz0TNYtOoVKhOt6JLisI4fhyYqCmOOx8arReWsFRu05opmvCmeVGsqadamgYx3iFWvj8YZ2Ssgte3oNUqbBv/HJSHXYysWuDLhlS1tKMRmH1dBC8SqtZIVlU2aPf34B7cBv8GwEjxgd+utYIxAlmWSTVl+eZbRo0OWGegREvyVR9lkouekC0lqdNcuqSp6SYsm8BqXmrzweT2Run0KWsB2vxaUgFaVM/AoZvG5YGvZTrOtSJLkZ5vnJauFwMbgUXrrG22wvBIUpkZwTq6/nZHa2DfNdYlxfVL53fBZxJvb7sXR60QoO8HaqkJv09nQBTis8MOb53I2GoV6yjVpTUTrE3EmpmAo6o8mwtJMstbpEeNRXLzPviAkyfd8CUSbkIC+R4+gdINWYUx2nJ/S7J3skX1B2Y/znMgYSUzqcMakjgmaxJKQUL3eOpGIN8V7jonrA5ljffVGZwFt8Cqlt0bHpYUJBLfBhNsQvKIbCqGgCSguL+ZXK39FwnelLHpO5Yhq5MOfX8av6h9GikiFGf9s82yVoHshSRLXFl7Lb4uu4/+ma/kxE9SnnuZV86f0irNwxTNf8NQnbbSTEQi6KAoh4k+FG3ejwUdzZwMt2DZoZR2JRXkh8/QahYEZkciy7OeaP8ZiICPehrF3GrKhbTPxLntLAxh/FFlDXmxe0GApzur5nogQCsKJ4FVQW+uCgRlRpEWZMLc0mLQlQc75fnZNkiRBYzBejFGQkHfC2zllk2cWX9a1Mng+Ac5KOYse0ZlIGqWprr3Gozau+hllz/cMjB+ITm5bP3pRVTWkwgL42RjpknKpNSaSmB6gyPQcA5EZ9Inuy5BET0gBry2e9/7wTkC01atdYWokWfEB21RtSX4eHcOFVwGKNcWiajTUZaUFHRN17nScaQmeLb6Nx8uSFORWvk+C1RMmo5m9mVaRMetPrN7j+yYyvm8Y4s3aGxUljY6+AVssvX2ksTUqDm28xtOjzRSlRwU9+2SzGX2vTGr6erZDSrrwjB91io5UayqDEgaFzM9JspFgM3i8tMY2TU5oYj2TVabCAowDB+JMbd3jtE8RDtjNYBpYhK5HBpKqYtFraFGRTRsGvcaHzBr0i/MY9IvzSBw5FEfPrJDH+Ordaq7gjOfzA59z3ZrrGLsZZr9Zw0+WON6Ydgl/096L5NbD7NeEUxDBcbmi/xVE6CNYpN7Jolca6LX0WZ68rJJFOTO44z9b2H+0llum9G2THYJA0NVw6Q1+c6q1WTno4lpxYHC82F6h8K6WxATHRAs1s65YggO3+vA63tA0DQGa26S1FUdeJnJtXcjv9yqPiebQHsxMOoVYq57z8pOQDpRBNSfXLrRtPGk3ailMa93mIxAlJhbsyWgHF4H2xJQdL4acHDTx8X4Kzqlg0ppIvXAOlH7XNAg1RngG4BW76WVORJcwqNE5zb4TLr93vAUtrSveOoOJUePODc4wRkB0JoaKXegaFUXvdeDtox72HmhkDanWFjwOBpAWbWLzseABvl7RBzuBOEXyYvIoLi/GqrNSM7J/UL4kSdj1dsanj0en6FBdrhbLirbomZyb0GJ+WzGHii14MkRnehQXnTnIoZF3e3ZgCJBTwdCnD+pGz/VnGTGCyrUfnnAZoZ4rebGhJ54AesVZ6RVC9zIPGYyqqkiShOx2wa7jeJ01ehRV2ej/DNUmJKBNSCA3NQnT0W1gamFs7A02HiqrcTLKnpVBSlXwFs/mCAWtG/Purnf5v3V/4JLPTUx8v4zvErJ5evQsXo18GLmiAuashMjwbMsQnPnMzJ5JtCGa/5Nv4nev15P3jzf588xy4ofP56lPdnKgwsEDM/KD9/sLBF0cu9l/yNMQG48xv+WX9Emh0UPqkKaZcNrmrjtkUVFRGPpko01Lgy0BTkmMJ6DEaBTc1tDbdbx1MwU4fNBr9BSmRvoGWuEMy9Gqi/aTQLGYsU+bdkplSIoSHHy7GVHGqCYX3W0tU5Yh0X+g6vOWCP4OXAIw5uUi22wtKsNaWcauD9+OmcBdsLIkk247sXFFXmwexgCvetlR2Ujx6aw/CSW0Jex6O4MTQztCm5A+wTd54dtKGHDtjkge0bnDzOia7sVxfeN9K2cu7yqnLQEqd4Mp2OPsqeC1UdRlBG9pPF34AqT7/WzhHoiPwzR4kG/lLRCbLRJsp+4wLzWydYcmQkHrhqiqyuObHufJLx5jwYcR5H9exudZw3gybwLvxSxBe+RH+MWLkBg8gyQQtMa49HEsnfIU8zXXcPnrFQx66WOuLt9PyoX3cee7P3Koso4nfzUQexvdDAsEXYGCtAh2N/pXGJYZjbmFbWJ+aE1NdiEnSUtbHA39+kK0ntZsJfS9eoXOSMg/pTp58SpL2tHDseibZpq1spZze031Pzi2DzhrPLP8ZwiFqZFtChc6NDHYvf1J4bX/CrwmlEZ39427F3SNDmMaylv2Knoq6DMzcVdWokvxTFAkRhjZXVaN5hRipwautslmM7KkoLPawBU+Ba01QnnuDFTQ7PrwrJICnlAMVe1nw21pttVSanR8oYlIgYikNocvCESx29HENN3rOUl2qusakDQa7NOmtnJmaMI94XIiaONa3wYZDgza1u8JoaB1M2obavnjf//I55tWsfhtGzG7ylg9+FyeTilkTexijEd/hJ8/12QsKRCcIAPiB/CPaU9zle5Kal4rZfQHO5h68BJSrnuca98pZcYT61kxZzBJEa1swRIIuhAaWSLGouNoTT0WvaZFOx4/+kw55e+16jy2OSmWxlU170y40Qj2EFsLNXqPk4AWSNTZT3pwFojXsYhTJ6NYjuP6WqPzOOE4SZqF5eo0pEW3zRFAuFC0nuepHLDtypHRiyHmOLSZoV19hxvZYMA8tKkv81Ps9E20BtlonQrapCTMBgNE2GDX92ErtzlmbWMw9lZo19WyjJHgDh23L9yM7B1DaYWj0QTh5O9/y6iRfn/3imubjaogNMJJSDdib+Vefv3Or9n98SqWPKsn9mAdL553Lc+kFPBB7ENYqnbCL15o0bhRIGgrvSN789y5L/DxJfm8Olzi6KYq+t7+S14f56DkqIML/vZfvi+p6OhqCgRho1+ijfF940MrZ1GNg2PdcRSVE8SkNTGl5xQSLaHtvILoMw36nR8ya0pUHoWWtLBpOX2i+pBiTSHBfOp2OMejI2faOwv9EgfTO/sC4lKH+9LMOg16g46IvnmY23DtyY2rXPoQ3iFPFkmSWgxOfCpooqJOyuNpWxmePPz4BzUiH28C4mSQpNPmnM1u1AY7YjlD8T4rIg0nZo/aEQgFrZuwbu86Zv17JnmrtvOn51VMEbEsm3Ur6zUKa+1/xlazF37+AmSe3dFVFZwhxJniWD55BQ1zZ/LEOTJV+yT0ty7g/eyPkFWVmY9vYP324PgoAkFXRJKklj20RWZA3kXNPDCeuO2YXW8nL6ZlA/k2IUm0ad9dGNApOvrH9m/XQXQg3VlN0ypaeicUeOzTGhnfL57JuW1U3vG4as+NySU/NjzbXLsybfUyaR42FPOwYe1cm27OyZnahkSSJEYkj6Aovgjr2WMxD++8fScUtDOcBncDj379KLe+cTULn6vnotW1GMeO48/n3ETp0a38x3QHJsXtcQgitjUKwoxO0XH78NsZcdXtPDBTT1WllrL7XuWdhrvJt1Xw6+Wf8ebG02NDIBB0ZUYkjyDV1jbPdyeFtf1XutqLk3WWIggmzZYW2t5KEBJNdDRyK4G6BZ0Pu96OVtYim0xoosLrECWcCBu0M5j9VftZ+PFCNJ98yeJ3NRhdLsy33sa8w0kML3uZP+ifR47sDb98GSLa8cUv6PbMyJpB//n9uT9pPrNW7IYX9vNg0QJeHzCb+S+42X/UwbzRPTu3ByyBoLPjtR87wVUyvUb2xO5xhdtx+enBu23JdIIxpgQnhnnoENSG02MXJeik9BoHLQWuDzPdfTwgnmZnKKt2rmLxe7fzi1U1DPzejb5fJlU33cb8D3exoO5PjFO+hKwpcMHjYAij5yGBoAWyo7JZfNlrPJR5J6mL32DQFwZGHPoX74z8mGve/SU/HKjgnp/1x9hSIFmBoMvTvgMOQ24OstmE5gQ8kI3JikOvlT3Kndw1HffoNDKDMqKItoQnGLQgNJqYmI6ugqCjCQjDoc/sibumpoMqc2YjFLQzjHJHOXdvuAvnm6v4y4cSBrdM7I03siZvPCtXvsw/lWXEKUdh4j0wZF7ncnslOOMxaU3cMv4vfJw1ibfuW8CU1RXUvnGA5wYu4rXN45hV8iseuWTkafeAJhCcFuL6wa6PQW9rl+JlnQ5DdvYJnXOmhLwQXmEFgtOPoW/fjq7CGYtQ0M4g3t/9Pi8/dxsXrDpGRqmKYdBADL+/lXu/3MuQlb9lhfIJrsieyBe9CMlFHV1dQTdmVNpoBjy4hn++tojMR9/G+JGdgqz1TM/9mKeWXEjB9Gs5b0BGR1dTIAgv1niPs5AzhJEpI3G5XR1dDYFAIDjjEAraGcCeij0se/NWsl78nBu2q5AQR8J9N/NedDbbX/kbf1BfxqapxT3iJpTRN4P29OwfFghaw6w1c/XF9/PdWbNZf9eNDF1dQskemFzwMjGv/ZtnPv8N582+DrtZzIwLBJ0Rm659VgIFAoHAS3d1AiQUtC5MbUMtL71zP+o/XmTW9y5Uo46oG67i64GTeOWD5/hV9Y1cLJdSmzIMZfpDECeWogWdj5zEAvo9spoP166g8r7F9N4AP8VA1oAlVN77JHvzL6ff1GuQ9CLopUAgEIQTJTKio6sgEAhCIBS0LojT5WTVvxdT8fSzDN5Sh1OvoL/0l3w3aCrvf/ka096cymR5HxURvXFPfQRj1kRhaybo1EiSxNiz51A/+pd8+Mw9mB5/Cdt7NjYnSmiPPEbCt48gFc0lavS8Lu0OXCAQCAQCgeB4CAWtC+FwVPHxc/fjev51eu2px2FQKJsxjW/7jUXa+gYXrDuf6dJRjkZk4zr7CWz9ZzS5XRYIugA6RcfES2+jbuaNfPLkn7G+8A5R75n5Ik6i4sDTDPjiMZJ6jsU49DfQa7y4vgUCgaAbk25Lp8xR1tHVEAjCjlDQugAHt27i66fuw776K1Kq3ByO1vHp+RPZb49hYM1aLv/h72gkN0eSRqGOu4GIzLPFipmgS6M3WRg3/17cV93JV/9ajHnFC6StVSk36FnX91uiiucyMMpMcuGvIG8GxGZ1dJUFAoGgyyBpPMM/2dS1PebmxOR0dBUE7YQseeI5dldbV6GgdVLqyg7xzctPUPn2SpK2HSFZgi297WzPyCTbVs6FyotY62qpNUbhLLgGzdDLiIzq2dHVFgjCiqzTMXDOzaiX3sS+j9+j5B9/Y+BnW9F8beG7GPj35qexpj5B34RE8nJnoM2bAZHpHV1tgUAg6NQoNhumgUUitpmg06KVtQxNGopVZ+3oqnQIQkHrRNSW7GPzv5+m4v33if+uBKsbKqNk3h0STUyizHT9VqzS99RpbbiyL4DCGRgzzgJFdKPgzEaSJFLOmkTKWZNwHj5M8StPI7/1FqM2lCJvMFMSWcHSHk9RnfIE9lQrBRnDyOl7AfYeY8T9IRAIBCHQJgh7XkHnJsoQ1dFV6DDEyKUDaWhwUrxhJfvfewvt/zYRv6cKC1Bjl/hvgYHIFCfnG/cwTtqLwxCHkn0h5JyHvudY0Og6uvoCQYegjYkhd96N5M67EWfpQb5/5WWcH7zD6E070X2loV5Ty5bUtfynx4eUJrkh3ka6PZPMxCL6ZI4kNaIHMcYY3/YJgUAgEAgEgs6EUNBOE6qqcqBiP9s+f58jn36C/M0PxG8rx1qrkiTBzkSZPUOhZ/wxhhirGSHpqI4tQMm9DLImYojPEXZlAkEA2vg4+l9zDf2vuQa3w0H5+k/Z9vbb9PjsvxSsKQegTlvBDylfsyX1G95MXc6PCeDWykRKNqINCSRE9CItMolUWzwxxhiijdGejyEao8aIJO47gUAgEAgEpxGhoLUDTpeTXeU7+Gnzeso3f0198VYMP5aQsddJbD3EAocj4FC6C0ecg+zYKtINZo7FFGHLGomh9yhIKsCu0Xe0KAJBl0E2GIg5ezQxZ48GwHnwILVffsnh9Z+S++l68tftAUCVoCLSSUnCQbYmHWJT0nd8Eqfg1AYrYoqkYNJYsOrMWHVWzFrPT72iR6to0cr+n4EJAxmTOuZ0ii0QCASCdmBI4hA0shgmCzoGceWdJC63i0NlP1GyewvlO7ZQ+WMxjj27kA+UYTtcR/JhlSQ3JAENClREuanu1YA2xkFkpAY1KhN7bA72HoXE9BuOEpeNRczUCwRhQxsXh/acc7Cdcw4ADUeO4Ni0idpN32LZtInIbzbSZ0sl5wFILlSrhDvCSV2kk6NRKocjVcrtcMBQRZlioEKjo0rRcEij0KDIuGUJtwwuVJy4aHC7OFhTKhQ0gUAgOAOINkZ3dBUE3ZhupaCpqorqdKLW16PW1Xl+Nn6ctTXUHtiGo3gdjppq6mtqqWv8VFXVUFftQKmtR1vTgKHGjaVaRd8ABjxKGIBTUamyQYPVRUWOC8mmxWWPpia+B+6onthS+xGdM4yklHSxbUogOM1oIiOxjB6NZbRnhU1VVZx79uD47jvqtm2nbts26rZvR/l2Nya323dfo1PAqiCZnOj0VZj0tegMDWgMbjR6N7LOjaxRkbUqFG+jdu3TOBUjTlmPUzbilA00KAYaZCMNigGXxkhaXCQ2iwUUHWj0oOg9dqWK3vO3Ru+fp2hAUkCSPbHfJLnZ37Lnpz0VTN3XoFogEAgEgjOFFhU0l8sFwIEDB07pC0qqS/jrZ3+lxlkDgIrqy1NV1S9NbfznzfP+Pu6jY2T8VIek0uyjIqlAs9/98vD8rrhUNK6mnyeKWwKnDup1UKcDhwEaoiTciQroFSS9AbclEtWeBLFp6O1x6KNSsMelkRFrI8Lk78xDBfbt23fiFREIBOFHliEvz/MB9ICuvp76vXtpKCnBWVKC80ApDSUlNJQeoKG0HFfFcZyLSBJurYyqSKA0gFSFJFciy24kWUWWVKo0KoqsIkkuJNx4/JWo4J23Cfgp+f5T/dM8KZ7/dDbU3pNBVcHtApcbVDeqWwW3G9Xt/dsNbhVcLs8z1nucyw2qimK3Ef/73yMZDKfUtN53h/ddciYSrvekQCAQCLoXx3tHtqigHTp0CIBf/vKX7VCtE+OfHfnlKlDX+PFLdANOwAEcBXYC/z3dtRMIBJ0NVYV693EOkho/4fQk6YTif4enqHXrwlMOnndJevqZGZuuM70nBQKBQND1aOkdKaneZawAHA4HmzdvJjY2FkVR2r2CAoFAIDhzcLlcHDp0iNzcXAynuBrXWRHvSYFAIBCcDMd7R7aooAkEAoFAIBAIBAKB4PQiIrUKBAKBQCAQCAQCQSeh2ytoL730EhMnTqR///5cfPHFfP31120+95FHHiE7Ozsofc2aNfzsZz+jsLCQyZMn8+yzzyIWKps40Ta/8soryc7ODvpUV1f7jvniiy+YMWMG+fn5TJw4kVdeeaW9xehytEe7r127lhkzZlBYWMjZZ5/NnXfeSVVVVXuL0mVojzZvzsKFCzn77LPbo+qC43Aq747TgcvlYvny5ZxzzjkUFBQwZcoUv3fRt99+G/Ja++tf/+oro76+nr/85S+MGDGCwsJCrrvuOkpLS0+rHEeOHAlZz+uuuw7wOBRbunQpY8aMIT8/nzlz5rBjxw6/Mjpajk8//TSkDN7Pvn37ukR/rF69msLCQr+0cLX/sWPHWLhwIUOGDGHQoEHccsst7fYuCSWHw+HgoYceYsKECRQWFnL++eezcuVKv2NWrVoVso+effbZTiNHuK6jjpTjtddea/V+8dLZ+iPcdCs3+4G88cYbLFq0iGuuuYa8vDyeeeYZfvOb3/Dmm2+Smpra6rlbt27liSeeCEr/6quvuOaaa5g2bRo33XQTGzdu5K677gJg9uzZ7SJHV+Jk2ry4uJhLLrmEqVOn+qUbjUYAduzYwdy5cxk7dizXXnst//3vf7nllluwWCxMnjy53WXqCrRHu//vf//jqquu4oILLmD+/Pns37+fhx56iN27d/Pkk0+2u0ydnfZo8+Z88sknvP766yQnJ7dL/QUtcyrvjtPF3/72N5YtW8bVV19NQUEBX3zxBX/5y1+ora3l8ssvp7i4GJPJxPLly/3Oi4uL8/2+aNEi1qxZw+9//3tMJhMPPvggV1xxBa+99tpps7n74YcfAHjqqaewWCy+9IiICAAee+wxli1bxk033URycjJLly7l0ksvZeXKlVit1k4hR05ODi+++KJfWl1dHddddx05OTkkJiayYcOGTt0fX331FTfffHNQerja/9prr2Xv3r3cfvvtOBwO7r33Xg4fPhxynNUectx+++188MEHXH/99fTs2ZM1a9Zwww03ADBlyhTA83xOT0/n3nvv9Ts3JSXF93tHyxGu+7oj5RgzZkzQ/VJeXs78+fOZPn26n6ydpT/aBbWb4na71bFjx6q33XabL62+vl49++yz1TvuuKPVcxsaGtSLLrpIHTVqlJqVleWXd9ttt6ljx45VXS6XL+3GG29Up06dGl4BuiAn0+bHjh1Ts7Ky1I8++qjFchcsWKBOmTJFdbvdvrSbbrpJnTZtWvgq34Vpr3a//PLL1Z///Od+aStXrlSzsrLUbdu2hafyXZT2anMvVVVV6tixY9VRo0apY8eODVu9BcfnVN4dpwuXy6UWFhaqDz30kF/67bffrg4dOlRVVVW988471RkzZrRYxu7du9U+ffqob7/9ti9t586danZ2tvruu++2T8VDsHz5cnX48OEh8yorK9WCggL1iSee8KUdPXpULSwsVP/xj3+oqtp55AjkzjvvVIcMGaKWlZX5/u6M/VFXV6cuW7ZMzcnJUQcNGqQWFBT48sLV/hs2bFCzsrLUjRs3+o5Zv369mpWVpW7evLnd5SgrK1OzsrLUl156ye+cyy+/XL3wwgt9f1911VXq9ddf3+J3dLQcqhqe66gzyBHIVVddpU6aNEmtra31S+vo/mhPuu0Wx927d7Nv3z6/7UFarZYxY8bw8ccft3ruihUrqKqqCrkiVl9fj8lkQpabmjYiIoKjR4+Gr/JdlJNp8+LiYoCQW0m9rF+/njFjxvgF/x4/fjxbt2497dtxOiPt1e75+flB7sV79OgBwN69e0+12l2a9mpzLw888AApKSlMmjQpPBUWtJlTeXecLiorKzn//POZOHGiX3qPHj0oLy+npqaG4uLiVq+1//3vf4BnNttLRkYGvXv3Pq1ytlbPb775hpqaGsaNG+dLs9vtDB482FfHziJHc7Zv386//vUvrr/+eqKiPMHlO2t/rFu3jmXLlrFgwYKgMU+42n/Dhg1ER0eTn5/vO2bIkCFYLJawydaaHNXV1cyaNYuRI0f6pffo0cPvXXa8PupoOdpSx67QH4F8/PHHrF69mltuucXP22Fn6I/2pNsqaLt27QIIij2QmprKTz/91GLguN27d/Poo49yxx13oNPpgvJnzJjBrl27ePrpp6msrGT9+vW8/vrrTJs2LewydDVOps2Li4vR6XQ8/PDDDBkyhPz8fK677jpf/KGamhoOHjwYsszm39mdaY92B7jmmmuCtuKtXbsWgJ49e4ZZiq5Fe7U5eOwtX3vtNe644452q7+gZU723XE6sdvt3HbbbfTr188vfe3atSQkJGAymdi6dSslJSVMnz6d3NxcJkyYwOuvv+47dufOncTExGAymfzKSElJOa3P1eLiYmpra5k1axZ5eXmcddZZPPnkk6iq6qtH4LbS5nXsLHI056GHHiIjI4OZM2f60jprf+Tl5bF69WouueQSv0lQIGztv3PnTtLS0vzyZVkmOTk5bLK1Jkdqaip/+tOfSExM9KW5XC7WrVvne5dVV1ezb98+tmzZwqRJk8jJyeHcc8/lo48+8p3T0XJAeK6jziBHcx544AFGjhzJqFGjfGmdpT/ak26roHmNBM1ms1+62WzG7XZTW1sbdI6qqtx6662cd955DBw4MGS5AwYMYN68edx1110MHDiQOXPmUFRUxE033RR+IboYJ9PmxcXF1NfXYzabefTRR1m0aBEbN27k17/+NfX19a2W2fw7uzPt0e6h+OGHH1i2bBkTJ04Meih2N9qrzevq6rjlllu4+uqrz9jgz52dk+nbzsDLL7/M+vXrmTt3LqWlpRw5coTdu3czb948li1bxqBBg1i4cCFvvPEG4BkABcoIHjlP13PV7XazY8cOdu7cycUXX8yTTz7J1KlTefDBB3nssceoqqpCp9MFTZY2r2NnkKM5e/bsYc2aNcyZM8e306Yz90d8fDw2my1kXrja/3TI1pocoViyZAk//vgjc+fOBTzPZ1VV2bt3LwsXLmTp0qUkJyczb94836pUR8sRruuoo+Vozqeffsr333/v6wcvnaU/2pNu6yREbfRkFai5t5QO8MILL7B7926WLl3aYrkPPfQQy5Yt44orrmDkyJHs3LmThx9+mN/97ncsXrw4jBJ0PU6mzS+99FKmTp3K0KFDARg0aBCZmZnMnDmTlStXMmzYsFbLbL7VtLvSHu1+/vnn+x3/ww8/cNlllxEXF8ef//zn9hCjS9Febf7II49gNBq57LLL2lkCQUucTN92NG+99RaLFi1i0qRJzJ49G4fDwd///neys7N9zgOGDx/OwYMHefTRRzn//PNRVTWkLC2ltweqqvL444+TlJTkm5AYOnQoNTU1/P3vf2fevHkt1sWb3hnkaM7LL7+MzWbzc3Zgs9m6RH+cyHefSPurqhryXd1SenuzbNkyHn/8cS677DLfVuZevXqxbNkyioqKfM5qRowYwfTp0zXfcYYAAAhOSURBVFm6dClDhw7tcDnCdR11tBzNeemll8jKyvKN9bx0hf44VTp/DdsJr3ehQPfVNTU1yLIctPxbUlLCfffd59sD29DQ4HshNzQ04Ha7cTqdLF++nFmzZvG73/2OIUOGMGvWLO69915WrVrFhg0bTo9wnZQTbXOAzMxM34DVS35+PjabjeLiYt+NGarM5t/ZnWmPdm/Op59+yuzZs7FaraxYsYLIyMgwS9D1aI8237x5MytWrOBPf/oTQNAzyPu7oH05mb7tSFasWMGCBQsYM2YM999/P5IkYTQaGTVqlJ9nN4BRo0axZ88eqqursVgsIcM71NTUnLbnqqIoDBs2LGi1eNSoUdTW1mI0Gqmvr8fpdPrlV1dX++rYGeRozgcffMD48eP9Vp26Sn8EYrVaw9L+rR3T3HNne6OqKnfffTcPPPAAv/jFL1iwYIEvz2azMXr0aL/6KIrC8OHDfZ5GO1qOcF1HHS2HF6fTyUcffcQ555wTlNcV+uNU6bYKmveBv2fPHr/0PXv20KNHj6AZhg0bNlBdXe1zi5uTk8M999wDeFzoPvbYYxw5coS6ujo/g0SAoqIigKDYIN2NE21zgLfffpvPP//cL01VVerr64mMjMRsNhMbGxuyTPAYv3Z32qPdvaxevZq5c+eSkpLCc88957eHvzvTHm2+du1anE4nM2fO9D2DnnnmGfbt20dOTo6fnYGg/TiZvu0oHnzwQe6++26mT5/OkiVLfErBzp07ef7554O2K9fV1WEwGDCZTGRkZHD48GEcDoffMXv37vU5A2pvSktLefHFFykvLw+qJ3hs7bzbnFqqY2eQw8v+/fvZsWNHkPOWrtIfgaSnp4el/TMyMoLuJ7fbzb59+06bbG63mwULFrBixQrmzZvHokWL/O7lLVu28PLLLwed53A4fO/EjpYjXNdRR8vhZePGjVRWVgbdL9A1+uNU6bYKWkZGBomJiXzwwQe+NKfTyYcffhi0lAowduxYXnnlFb/PnDlzAHjllVeYOXMmUVFR2Gw2vvrqK79zN23aBPjHZuiOnGibAzz//PPcdddduN1uX9pHH32Ew+Hw2QEOGzaMtWvX+hnnf/DBB2RlZRETE9NO0nQd2qvdN23axPXXX09eXh7PPvss0dHR7StIF6I92nzmzJlBz6Bp06YRGxvLK6+8wtixY9tdLsHJ9W1H8M9//pMnnniCSy65hHvuuQeNpsmiobS0lNtvv93PoF5VVd577z0GDhyIJEkMGzYMl8vFmjVrfMfs2rWLbdu2nTY56+vrue2223jrrbf80t99910yMjKYMGECer3ery+OHTvGZ5995qtjZ5DDi3cs0L9/f7/0rtIfgRQWFoal/YcNG8ahQ4d87QOenRlVVVWnTbZ77rmHt956i4ULF/rinzXn+++/59Zbb2XLli2+NIfDwbp16xg8eDDQ8XKE6zrqaDm8bNq0CYvFQmZmZlBeV+iPU6Xb2qBJksTll1/OHXfcgd1uZ8CAATz77LMcOXKESy+9FICffvqJ8vJyCgoKiIyMDNq69eWXXwIebzRe5s2bx/3334/VamXUqFHs3r2bJUuW0L9/fz8PNN2RE21zgCuvvJLLL7+cm2++mZ/97Gfs2rWLxYsXM2nSJAYMGADAb37zGy666CLmz5/PjBkz2LBhA2+99RYPP/xwR4naqWivdr/11lvRaDRceeWVbN++3e87MzIyfIFkuyPt1ebx8fF+3xMVFYVOp/N7Bgnal7b0bUdz8OBB7r//frKyspg6dSrffPONX/6AAQMoKipi0aJFHDt2jNjYWF588UWKi4t57rnnAEhLS2Py5Mn88Y9/pKqqCpvNxoMPPkh2djbjx48/LXKkpqYybdo0Fi9ejCRJZGZmsmrVKt577z0ee+wxzGYzs2fPZvHixciyTEZGBo8//jgWi4UZM2Z0Gjm8bNu2LeRYYtCgQV2iPwIJV/sPHTqU/Px8fvvb37JgwQIaGhr461//ypgxY8jNzW13Ob777juefvppRowYQWFhIRs3bvTlybJM//79mTx5MsuWLWP+/PnccMMN6PV6nnrqKWpqarjqqqs6hRzhuo46Wg4v27ZtIyMjI+SuhK7QH6dMO8RW61I89dRT6ujRo9X+/furF198sfrVV1/58n7/+98HBaJuzvLly0PmP//88+rkyZPVnJwcddy4cerdd9+tVlZWtkv9uyIn2uZr1qxRL7zwQjU/P18dMWKEes899/gFK1RVVV23bp163nnnqbm5uerEiRPVV1999bTI0pUIZ7vv2bNHzcrKavHzzjvvnFbZOivtca0358477xSBqjuI1vq2o3n11VdbvT/LysrUI0eOqH/84x/VUaNGqXl5eerFF1+sfv75537lVFdXq7feeqs6aNAgtaioSL322mvVAwcOnFZZamtr1QceeEAdO3asmpubq06fPl197733fPlOp1O977771OHDh6sFBQXqnDlz1O3bt3c6OVRVVRctWqROmDAhZF5X6I8lS5YEBRQOV/sfPnxYnT9/vlpQUKAOHjxY/cMf/tBu46ZAOZYsWdLivdL8uP3796s33HCDOmzYMDU/P1+97LLL1OLi4k4jh6qG7zrqaDlUVVXnzp2rXnrppS2e19n6I9xIqiosywUCgUAgEAgEAoGgM9BtbdAEAoFAIBAIBAKBoLMhFDSBQCAQCAQCgUAg6CQIBU0gEAgEAoFAIBAIOglCQRMIBAKBQCAQCASCToJQ0AQCgUAgEAgEAoGgkyAUNIFAIBAIBAKBQCDoJAgFTSAQCAQCgUAgEAg6CUJBEwgEAoFAIBAIBIJOglDQBAKBQCAQCAQCgaCT8P+Il/4KCSr7oQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000,)\n"
     ]
    }
   ],
   "source": [
    "avgpath = np.mean(trace['path'],axis=0)\n",
    "stdpath = np.std(trace['path'],axis=0)\n",
    "print(avgpath.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1849213373266804\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data-avgpath)\n",
    "print(np.std(data-avgpath))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5006603960093267\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dataN-data)\n",
    "print(np.std(dataN-data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18400117818442982\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stdpath)\n",
    "print(stdpath.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total noise:  1.118033988749895\n"
     ]
    }
   ],
   "source": [
    "AA,DD = 1.0,1.0\n",
    "sN = 0.5\n",
    "total_noise = np.sqrt(AA+sN**2)\n",
    "print(\"total noise: \",total_noise)\n",
    "delta_t_list=np.linspace(0.01,4,50)\n",
    "#M=1000 # number of data sets\n",
    "N=2000 # length of data set\n",
    "#print(np.exp(-delta_t*D/A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [02:17<00:00, 72.90draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09142857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:46<00:00, 213.15draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17285714285714288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:49<00:00, 202.24draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2542857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:38<00:00, 260.87draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33571428571428574\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:45<00:00, 218.85draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.41714285714285715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:42<00:00, 235.95draws/s]\n",
      "The number of effective samples is smaller than 10% for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4985714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:37<00:00, 268.94draws/s]\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5800000000000001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:35<00:00, 282.96draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6640191185143672, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6614285714285715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:33<00:00, 300.38draws/s]\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7428571428571429\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 5 divergences: 100%|██████████| 10000/10000 [00:33<00:00, 297.60draws/s]\n",
      "There were 5 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6980552172292815, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8242857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:51<00:00, 194.99draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6525859556269771, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9057142857142858\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:38<00:00, 256.78draws/s]\n",
      "The acceptance probability does not match the target. It is 0.7192832433128111, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9871428571428572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 28 divergences: 100%|██████████| 10000/10000 [00:42<00:00, 234.77draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6948161377217118, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 28 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.47882319921566263, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0685714285714287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:40<00:00, 244.26draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6581521457478888, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.6956812626745382, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1500000000000001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 2 divergences: 100%|██████████| 10000/10000 [00:42<00:00, 233.24draws/s]\n",
      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2314285714285715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:47<00:00, 212.31draws/s]\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.312857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:45<00:00, 219.01draws/s]\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3942857142857144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:46<00:00, 216.44draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6710803520943539, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4757142857142858\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 5 divergences: 100%|██████████| 10000/10000 [00:48<00:00, 205.37draws/s]\n",
      "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6934024381530582, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6852725810103365, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.7030110052004772, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5571428571428572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:40<00:00, 248.11draws/s]\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6385714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 4 divergences: 100%|██████████| 10000/10000 [00:55<00:00, 181.31draws/s]\n",
      "The acceptance probability does not match the target. It is 0.5910260697272723, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.7055257383039877, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.7198369420994941, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7200000000000002\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:39<00:00, 250.98draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6940316934946712, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.7100344570373643, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8014285714285716\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 3 divergences: 100%|██████████| 10000/10000 [00:45<00:00, 222.11draws/s]\n",
      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6700089549053334, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.882857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:48<00:00, 206.63draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6917931000331292, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.6601446414640424, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9642857142857144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 23 divergences: 100%|██████████| 10000/10000 [00:44<00:00, 223.74draws/s]\n",
      "There were 23 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6177972952590962, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.6244682077634804, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.8894925163024149, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.7181985073825966, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.045714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 22 divergences: 100%|██████████| 10000/10000 [00:42<00:00, 237.41draws/s]\n",
      "There were 7 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6968121314937902, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 15 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5503725878640895, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1271428571428572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 0 divergences: 100%|██████████| 10000/10000 [00:44<00:00, 222.78draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6299917481209413, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2085714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 106 divergences: 100%|██████████| 10000/10000 [00:32<00:00, 307.42draws/s]\n",
      "There were 106 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5314943570005327, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.5429243774527994, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 88 divergences: 100%|██████████| 10000/10000 [00:46<00:00, 214.61draws/s]\n",
      "There were 17 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5164815255072401, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.5346101254247814, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 71 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.29786322940585036, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3714285714285714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 23 divergences: 100%|██████████| 10000/10000 [00:37<00:00, 266.76draws/s]\n",
      "There were 22 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.7038729658942227, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.452857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 1,738 divergences: 100%|██████████| 10000/10000 [00:51<00:00, 193.10draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6306746037774664, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.44150831572606436, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 1686 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.10302195322980978, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 49 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6529038157294412, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5342857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 38 divergences: 100%|██████████| 10000/10000 [00:37<00:00, 267.42draws/s]\n",
      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5830478573942026, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 13 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5620552843215928, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 14 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.4241942106193511, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.6419455949989588, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6157142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 237 divergences: 100%|██████████| 10000/10000 [00:44<00:00, 223.53draws/s]\n",
      "The acceptance probability does not match the target. It is 0.47618837454540125, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.7029787495466048, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 220 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.43195374347393506, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 16 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5883389161479401, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.697142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 1,481 divergences: 100%|██████████| 10000/10000 [00:38<00:00, 257.29draws/s]\n",
      "There were 155 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.34152034477121845, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 338 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.313112241088505, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 344 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.463426786396113, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 644 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.24752037125586, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7785714285714285\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 867 divergences: 100%|██████████| 10000/10000 [00:50<00:00, 197.59draws/s]\n",
      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.39871195732223447, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 456 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.32426207034632787, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 201 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.34297983869429044, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 199 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5846778224815644, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.86\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 2 divergences: 100%|██████████| 10000/10000 [00:38<00:00, 256.94draws/s]\n",
      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6695904735761614, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.6653602001399579, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9414285714285713\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 803 divergences: 100%|██████████| 10000/10000 [00:46<00:00, 214.91draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6299921675679738, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 97 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.4206003931422535, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 40 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.4524163814280895, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 666 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.42992946084914146, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0228571428571427\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 167 divergences: 100%|██████████| 10000/10000 [00:52<00:00, 190.18draws/s]\n",
      "The acceptance probability does not match the target. It is 0.6945309379395636, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 14 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5894783396278976, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 87 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.39294670523795794, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 66 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6749821509794907, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.104285714285714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 1,346 divergences: 100%|██████████| 10000/10000 [00:48<00:00, 207.45draws/s]\n",
      "There were 18 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.29253547444313227, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 142 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.27246164389569927, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 647 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.361987712292889, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 538 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.31017333066546954, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1857142857142855\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 196 divergences: 100%|██████████| 10000/10000 [00:41<00:00, 240.08draws/s]\n",
      "There were 49 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5263792730425672, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 64 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.39312826236825354, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 68 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6604457008421161, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 15 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.267142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 1,102 divergences: 100%|██████████| 10000/10000 [00:51<00:00, 195.00draws/s]\n",
      "There were 47 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5416383656760005, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 456 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.22399650043660474, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.6305958694350025, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 599 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.2460177098300321, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3485714285714288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 1,379 divergences: 100%|██████████| 10000/10000 [00:40<00:00, 248.51draws/s]\n",
      "There were 24 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5609226706247241, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 131 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.3843392028321246, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 701 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.2504730237427591, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 523 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.18831943437364865, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 697 divergences: 100%|██████████| 10000/10000 [00:36<00:00, 272.31draws/s]\n",
      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.45271547980415267, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 51 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.40549402501919474, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 35 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6142202627839791, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 610 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.1626384354761168, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5114285714285716\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 474 divergences: 100%|██████████| 10000/10000 [00:39<00:00, 252.50draws/s]\n",
      "There were 41 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.48840938967826214, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 16 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.45205181888649665, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 392 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.3459353859283875, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5895656196042378, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.592857142857143\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 471 divergences: 100%|██████████| 10000/10000 [00:49<00:00, 200.97draws/s]\n",
      "There were 158 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.31420439197333994, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 124 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.30474269859285064, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 33 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5423283776262252, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 156 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.31049200497981405, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6742857142857144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 274 divergences: 100%|██████████| 10000/10000 [00:36<00:00, 271.51draws/s]\n",
      "There were 6 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.6308139948780492, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The acceptance probability does not match the target. It is 0.4192264664431626, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 237 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.47174771665635035, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 31 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.35604900459942923, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.755714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 920 divergences: 100%|██████████| 10000/10000 [00:45<00:00, 217.44draws/s]\n",
      "There were 88 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.3485509628603022, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 173 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5567535406895027, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 562 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.35977175804593525, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 97 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.2465050605713298, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.837142857142857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 1,110 divergences: 100%|██████████| 10000/10000 [00:42<00:00, 235.68draws/s]\n",
      "There were 76 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.37180195398419763, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5449386238869827, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 59 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.3436098771149524, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 973 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.29218337900004054, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9185714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 189 divergences: 100%|██████████| 10000/10000 [00:34<00:00, 287.41draws/s]\n",
      "There were 23 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.48692516151519827, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 100 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5147053191432638, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 38 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5527403276099869, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 28 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5215931623691359, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.2 for some parameters.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [path, sigma, A, B]\n",
      "Sampling 4 chains, 56 divergences: 100%|██████████| 10000/10000 [00:37<00:00, 267.91draws/s]\n",
      "There were 18 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.4371630298141488, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 20 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5511946067498134, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "There were 18 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The acceptance probability does not match the target. It is 0.5873166349625069, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "The rhat statistic is larger than 1.4 for some parameters. The sampler did not converge.\n",
      "The estimated number of effective samples is smaller than 200 for some parameters.\n"
     ]
    }
   ],
   "source": [
    "result_array = None\n",
    "for delta_t in delta_t_list:\n",
    "    print(delta_t)\n",
    "    data = langevin.time_series(A=AA, D=DD, delta_t=delta_t, N=N)\n",
    "    dataN = data + np.random.normal(loc=0.0, scale=sN, size=N)\n",
    "    with pm.Model() as model:\n",
    "        B = pm.Beta('B', alpha=5.0,beta=1.0)\n",
    "        A = pm.Uniform('A', lower=0, upper=a_bound)\n",
    "        sigma = pm.Uniform('sigma',lower=0,upper=5)\n",
    "\n",
    "        path = Ornstein_Uhlenbeck('path',A=A, B=B,shape=len(dataN))\n",
    "        dataObs = pm.Normal('dataObs',mu=path,sigma=sigma,observed=dataN)\n",
    "        trace = pm.sample(2000)\n",
    "        \n",
    "    a_mean = trace['A'].mean()\n",
    "    b_mean = trace['B'].mean()\n",
    "    a_std = trace['A'].std()\n",
    "    b_std = trace['B'].std()\n",
    "    sigma_mean = trace['sigma'].mean()\n",
    "    sigma_std = trace['sigma'].std()\n",
    "    avgpath = np.mean(trace['path'],axis=0)\n",
    "    stddiff = np.std(data-avgpath)\n",
    "    stdpath = np.std(trace['path'],axis=0).mean()\n",
    "    \n",
    "    results = [a_mean,a_std,b_mean,b_std,sigma_mean,sigma_std,stddiff,stdpath]\n",
    "    if result_array is None:\n",
    "        result_array = results\n",
    "    else:\n",
    "        result_array = np.vstack((result_array, results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "tau = -delta_t_list/np.log(result_array.T[2])\n",
    "dtau = delta_t_list*result_array.T[3]/result_array.T[2]/np.log(result_array.T[2])**2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\sigma_{GT-model}$')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(delta_t_list,result_array.T[6],\"o\")\n",
    "plt.xlabel(r'$\\Delta t/\\tau$')\n",
    "plt.ylabel(r'$\\sigma_{GT-model}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f95d86274e0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(delta_t_list,result_array.T[0],yerr=result_array.T[1],fmt=\"o\",label=\"A\")\n",
    "plt.errorbar(delta_t_list,tau,dtau,fmt=\"o\",label=r'$\\tau$')\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\sigma_{noise}$')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(delta_t_list,result_array.T[4],yerr=result_array.T[5],fmt=\"o\")\n",
    "plt.xlabel(r'$\\Delta t/\\tau$')\n",
    "plt.ylabel(r'$\\sigma_{noise}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}