example_optimize_complex1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# More detailed optimization example (finding the solution by iteration)\n",
    "\n",
    "There are costs associated with waiting times. In addition, the operation of the system costs money depending on the time. Each successfully served client brings in a fixed profit. The number of orders accepted per day can be controlled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Processing results arrays\n",
    "import numpy as np\n",
    "\n",
    "# Simulator\n",
    "from queuesim import Simulator\n",
    "from queuesim.tools import SimProcess, run_parallel\n",
    "\n",
    "# Station types\n",
    "from queuesim.stations import Source, Process, Dispose\n",
    "from queuesim.models import mmc_model\n",
    "\n",
    "# Plot model\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Defining general plot style\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrivals to be simulated\n",
    "count = 100_000\n",
    "\n",
    "# Service process\n",
    "mean_S = 80\n",
    "c = 1\n",
    "\n",
    "# Costs\n",
    "profit_per_client = 100  # Revenue per customer served\n",
    "cost_waiting = 0.02  # Costs per waiting second\n",
    "operation_cost_per_second = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definition of the $x$ range (=Zwischenankunftszeiten)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_I_range = range(81, 141)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallel simulation of the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60 parallel processes started.\n",
      "All processes terminated, runtime: 20.9 seconds.\n",
      "Results have been collected.\n"
     ]
    }
   ],
   "source": [
    "models, simulators = run_parallel([SimProcess(mmc_model(mean_I, mean_S, c, count)) for mean_I in mean_I_range])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilization of the operators\n",
    "rho = np.array([model['meanS'] / model['meanI'] / model['c'] for model in models])\n",
    "\n",
    "# Mean waiting times\n",
    "waiting_times = np.array([model['Dispose'].statistic_client_waiting.mean for model in models])\n",
    "\n",
    "# Served client per day\n",
    "clients_per_day = np.array([model['Source'].count / model['Process'].statistic_wip.time * 86400 for model in models])\n",
    "\n",
    "# Revenue per client\n",
    "yield_per_client = profit_per_client - waiting_times * cost_waiting\n",
    "\n",
    "# Revenue per day\n",
    "yield_per_day = clients_per_day * yield_per_client - operation_cost_per_second * 86400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output of results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optional: Interactive diagram mode\n",
    "# Run \"pip install ipympl\" first\n",
    "# %matplotlib ipympl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Revenue as a function of the utilization of the operators'),\n",
       " Text(0.5, 0, 'Utilization $\\\\rho$'),\n",
       " Text(0, 0.5, 'Revenue')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABTgAAAMTCAYAAACFUng0AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAxyhJREFUeJzs3Xd4VGXexvF7SnrvgYQAKYTeQ5GOCoiIC9g79hXXXctr2XXt6KqgK3ZsrL0Aig2xUqSH3glJSIH03tvM+0dgJAICMXBSvp/r4oKcOeeZ3wwzTzJ3nmKy2+12AQAAAAAAAEALZDa6AAAAAAAAAABoLAJOAAAAAAAAAC0WAScAAAAAAACAFouAEwAAAAAAAECLRcAJAAAAAAAAoMUi4AQAAAAAAADQYhFwAgAAAAAAAGixCDgBAAAAAAAAtFgEnAAAAG2c3W43uoQm0RwfR3OoqTnU0BRay+NoajwvAAAQcAIA0CpdffXVio2NbfCna9eu6t+/v6ZOnapFixYZXSIOKS0t1a233qo+ffooLi5O+/fvP2P3XVxcrHvvvVfx8fGOY1dffbWuvvrqM1bDidTW1ur+++9Xv3791L9/f61Zs+aoc5rj4zhRTenp6YqNjdXChQslSQsXLlRsbKzS09ObtI7PPvtMTz/9tOPr03U/f8Y333yjMWPGqGfPnnrooYeOeU5LeBxGSEhI0OWXX250GQAAGM5qdAEAAOD06N69ux5++GHH13V1dcrMzNS8efN07733ytfXV6NGjTKwQkjSF198oV9++UUPPfSQYmJiFB4efsbue9euXVq0aJGmTZvmOHbka6Y5WLFihT7//HPddtttOuuss9S9e/ejzjnW4zDaqT63o0eP1ieffKLg4OAmrePVV1/VoEGDTvv9/BmPPfaYOnXqpP/85z8KCQk55jm/fxyo991332nTpk1GlwEAgOEIOAEAaKU8PT3Vt2/fo46PHDlSQ4cO1cKFCwk4m4HCwkJJ0hVXXCGTyWRsMZKio6ONLqGBw8/P1KlT1aFDB2OL+ZP+6Ln19/eXv7//aa/hTN3PqSgsLNSwYcM0ePBgo0sBAAAtFFPUAQBoY1xcXOTs7NwgTLPZbJo7d67OPfdc9ezZU+PHj9d7773nuP21115Tz549VVRU1KCtefPmqUePHsrLy5MkHTx4UHfddZcGDRqkPn366Nprr9XOnTsd5x+elrt48WLdcccd6tevnwYNGqQHH3xQ5eXljvNiY2P14osvNrivF198UbGxsQ2OxcfH66qrrlKfPn00aNAg3XfffcrPz//Dx19XV6e5c+dq0qRJ6t27t/r27avLLruswdTnyspKPfLIIxo5cqR69uypCRMm6K233jrRU6vPPvtMU6dOVd++fdW7d29deOGFWrx48XHPv/rqqx2Ps2vXrrr//vu1du1axcbGau3atUede+SU67Fjx2rOnDl6+umnddZZZ6l379664YYbjprivmzZMl122WXq27evhg8froceekjFxcVau3atrrnmGknSNddc42j79/dTVVWll19+WRMmTFCvXr00btw4zZ07VzabrUFt//rXvzR37lyNHj1avXr10mWXXaatW7f+4fNVV1enDz74QBdccIF69+6t0aNHa9asWaqqqpIk3X///br//vslSeecc84xp5wf73FI9WsTvvHGGxo9erR69+6tSy+99Kia9u7dq1tuuUX9+/dX//79NWPGDKWlpf1h3cea/n7k/9vJPrdH+v2U698vMXHkn8N2796t22+/XUOGDFGPHj00YsQIPfHEE6qsrJRU/xo5cOCAPv/8c0fbx5ravXLlSl1xxRUaMGCABg8erLvvvlsZGRkNauvevbu2bNmiSy+9VL169dKYMWNO6j2xbds23XDDDRo8eLD69++vW2+9VQkJCQ2eM0l6+eWXjzvl/FiP47AtW7bosssuU69evTR69Gi9+eabDa6tqqrSM888o1GjRqlnz5664IIL9O23356w7uzsbD3wwAMaNWqUevfurYsuukg//fRTg3NiY2P1/vvv67777lO/fv101llnaebMmY7X72E//vijpk6dql69emnYsGF64oknGvR3L774os4991y99NJLGjRokIYPH66ioiJVVlZq9uzZGjdunHr27Kn+/ftr+vTp2rVrl+O6l156yVHL4b7kZN+z99xzj+644w717dtX06dPlyR9/fXXmjx5snr37q0hQ4bonnvuUVZW1gmfLwAAjEbACQBAK2W321VbW+v4U1VVpaSkJD3wwAMqKyvThRde6Dj3kUce0Zw5czR58mS99tprmjBhgp588km9/PLLkqQLLrhAtbW1+v777xvcxzfffKPhw4crICBA+fn5uuyyy7Rjxw79+9//1uzZs2Wz2XTllVcqMTGxwXUPP/ywwsLC9Morr+iGG27Q/Pnz9eqrr57S41u/fr2uu+46ubq66r///a/++c9/at26dbrmmmscAc+xzJo1S6+88oouvfRSvfnmm3r88cdVWFiov//976qoqJAkPfnkk1q+fLnuu+8+vfXWWzr77LP1zDPPaMGCBcdt94MPPtBDDz2kc845R6+//rpmzZolZ2dn3XPPPcrMzDzmNQ8//LAuuugiSdInn3yi22677ZSeg3fffVdJSUl66qmn9MQTT2j79u267777HLf/8ssvuuWWWxQQEKD//ve/uueee/Tjjz/qzjvvVI8ePRzrHT700EPHnD5tt9t166236s0339TFF1/seG3897//Per8JUuW6KefftKDDz6o5557Trm5ufrb3/6murq649b/0EMP6amnntI555yjV199VVdeeaXef/993XbbbbLb7brtttv017/+VZL00ksvHbPGP3ocGzZs0A8//KB///vfevbZZ5Wdna2//vWvqq2tlSQlJyfrsssuU15enp5++mnNnDlTaWlpuvzyyx2hfWOczHN7Ip988kmDPzNnzpTZbHa8XrKzs3XllVeqoqJC//nPf/TGG2/o/PPP13vvvad3331XUv1zFhQUpFGjRh13WvoXX3yh66+/Xu3atdNzzz2nBx54QJs2bdKll17a4Dmw2Wz6xz/+oYkTJ2ru3Lnq37+/nnnmGa1YseK4j2HNmjWO9SGffPJJPfHEE8rIyNBll12mxMRE9ejRQ5988okk6aKLLjpujX/0OB555BGdf/75mjt3rvr166dnn31Wv/zyi6T61++MGTP08ccfa/r06Xr11VfVr18/3Xnnnfriiy+OW3dubq4uuugixcfH684779SLL76osLAwzZgxQ19++WWDc1944QXl5eXpv//9r2688UZ98sknDd6DX331lWbMmKHIyEi9/PLLuv322/Xll186XuOHHTx4UMuWLdPzzz+vBx54QD4+Prr33nu1YMEC3XzzzXr77bf1wAMPKCEhQXfffbfsdrsuvvjiBv3HxRdffErv2cWLF8vDw0OvvvqqbrzxRm3YsEH33nuvxo0bpzfeeEMPPPCA1qxZo7vvvvu4zxUAAM0FU9QBAGil1q9frx49ejQ4ZjKZ1KVLF73wwgsaM2aMpPqQ59NPP9Vdd92lm2++WZI0fPhwmUwmvf7667riiisUFhamuLg4ff3117r44oslSampqdq6dauef/55SdL//vc/FRYW6qOPPlJYWJik+unwEydO1AsvvKA5c+Y46hg1apQjBBg6dKhWrlyppUuXntIH6dmzZ6tz5856/fXXZbFYJEl9+vTR+eefrwULFujKK6885nXZ2dm68847G4ykc3Fx0d/+9jft2bNHffv21bp16zRs2DCdf/75kqTBgwfL3d1dAQEBx60nLS1NN9xwQ4OQMiwsTFOnTtWGDRscbR0pOjpaoaGhkuRYTuDIkXMn4u3trVdeecXx+FNTU/Xiiy+qoKBAfn5+evHFF9WtWze99NJLjhG7zs7OeuGFF1RZWemYMh0dHX3M6dPLly/XqlWr9NxzzznqHzZsmFxdXfXCCy/ommuuUUxMjKT6zYDeeusteXp6SpLKysp03333adeuXerZs+dRbe/bt0/z58/X3Xff7XjdDRs2TMHBwbr33nu1fPlyjRo1ShEREZKkbt26HXN9Uk9Pz+M+DmdnZ82dO1e+vr6S6jf+efDBB7Vv3z517dpVL730ktzc3DRv3jxH3UOHDtU555yjN998s0FQdSr+qKaTdeTyEoWFhfq///s/9e7d2xFS7d27V926ddMLL7zgqP2ss87SypUrtXbtWt18883q3r27nJ2d5e/vf8zlKmw2m2bNmqXhw4dr9uzZjuP9+/fXxIkT9dZbb+nee++VJEfgfPj9P2DAAP3www9aunSpRowYcczHMHv2bHXs2FFz5851vEaHDx+uc889V3PmzNELL7zgqCs0NPSYNUr6w8dx1113OULUvn376ocfftCaNWs0ZswYrVq1SitWrNDzzz+viRMnSpJGjBihiooKzZo1S5MmTZLVevTHoXfeeUf5+flasmSJoy8bNWqUrrvuOj3zzDOaNGmSzOb6cSL+/v567bXXZLVaNWrUKJnNZj311FP629/+psjISM2aNUsjRozQrFmzHO136tRJ1113nZYtW6bRo0dLqn//3HfffRo4cKAkqbq6WmVlZXrwwQcdtQ8aNEilpaX6z3/+o9zcXIWGhh7Vfyxbtuyk37NOTk569NFH5ezsLEmaO3euXF1ddfPNNzuO+fr6atu2bbLb7c1iCQ0AAI6HEZwAALRSPXr00Pz58zV//ny98sor6tKlizp16qT//ve/mjBhguO8NWvWyG63a+zYsQ1GfI4dO1ZVVVXasGGDJGny5Mlav369cnJyJNWP3vT09NTYsWMlSatXr1a3bt0UEhLiaMNsNmvkyJFatWpVg9p+H1KEhoY2mLJ5IhUVFdqyZYtGjRrVYKRqhw4dFBUVpZUrVx732tmzZ+vaa69Vfn6+4uPjtWDBAseorOrqakn1geann36qm266Se+//77S0tI0Y8YMRxhxLPfff7/uueceFRcXa/PmzVq0aJE++OCDBu02tV69ejmCI0mOsKOiokKVlZXauXOnzjnnnAbBxMSJE7VkyRIFBgaesP1169bJarU2eL1I9a+Fw7cfFh0d7QjaJDk2izk8KvZYbUs6Kvg9//zzZbFYjpqi3xjR0dGOcFOSIyAtKSmRVP/aHzRokFxdXR2vIU9PTw0cOPCo16xRamtrdccdd6iiokJz5sxxBE/Dhw/X+++/LxcXF+3bt08//fSTXn31VeXn55/06y05OVk5OTmaNGlSg+MRERHq169fg/9fSerXr5/j34cDx+O9b8vLy7Vt2zadd955DV6j3t7eGjNmzFFtN9bhQFCS3NzcFBgYqOLiYkn1fZLJZNKoUaOO6ttycnIcU+V/b926derXr58j3Dxs8uTJysnJUVJSkuPYBRdc0CAkHT9+vKT6XzAlJSUpMzPzqL41Li5Onp6eR/VT3bp1c/zb2dlZb731liZOnKisrCytWbNGH3/8sWN06vH+j0/lPRsZGel4PUlSXFycKioqNGnSJM2ePVvx8fEaPny4br/9dsJNAECzxwhOAABaKQ8PD/Xq1cvxdZ8+fTR58mRdf/31WrhwoWOjkcObuBxrhKEkx/prEyZM0OOPP67Fixfrmmuu0TfffKPx48fL1dXV0U5KSspRo0YPOzLocnNza3Cb2WxuMF3zRIqLi2Wz2fTGG2/ojTfeOOp2FxeX4167bds2Pfroo9q2bZvc3NwUHR2t9u3bS5Kjhn/9618KDQ3Vl19+qccff1yPP/64+vXrp0ceeURdu3Y9Zrupqal66KGHtHr1ajk5OSkyMtJx7qk8tlNxrOdRqh+ZV1RUJLvd/oejTk+kqKhIfn5+DQIqSQoKCpL0W1B4olqO1/aRbR1mtVrl5+fXoO3Gcnd3/8OaCgsL9e233x5zTcbmshHP448/ro0bN+rdd99tsMO4zWbTc889pw8++EDl5eVq166devfu/Yev/d87/N4/VtgdGBjYYP1cSY73+mF/9L4tKSmR3W4/bttN8f8r/XFfUlhYKLvdrv79+x/z2uzs7Aah4mFFRUXH3NDq8GM5HKBKOmrX98Pvt6KiIsfz++ijj+rRRx895v0fycPDo8HXK1as0JNPPqmkpCR5eHioa9eujtf08Z73U3nP/v7++vXrp7lz52revHl65513NHfuXAUGBurWW2897tqxAAA0FwScAAC0EYGBgXrooYf097//XTNnznRMSfX29pZUP8X89x94JTnCPy8vL40dO1aLFy/WkCFDlJCQoH//+9+O87y8vDRo0CDHlNbfO3Kk0Mn4/dqNR44U8/DwkMlk0nXXXXfMYPb3ocdhpaWluvHGGxUbG6tvvvlGkZGRMpvNWrZsmZYsWdKg1r/+9a/661//qoMHD+qXX37RK6+8orvvvlvffPPNUe3abDbdfPPNcnJy0vz589WtWzdZrVbt27dPixYtOqXHfXik1O+DwbKysmP+/xyPp6enTCbTUZsuVVVVac2aNerTp88J2/Dx8VFBQYHq6uoaBCaHgxk/P7+TrudYbUtSTk5Og5FyNTU1jin2p5uXl5fOOussxwYrRzrW1OUj/dHrs6m8//77+vjjj/XYY48dFdIdDqIeffRRjRs3Tl5eXpLkWJPxZBwe3Zqbm3vUbTk5OX/q/8DLy0smk+m4bR85svZ08fLykru7u2NN0t/r2LHjMY/7+Pg4Rqof6fCxI5+XgoKCBuccfrz+/v6OvvXee+/VoEGDjnk/x5OamqoZM2Y41vTt0KGDTCaTPvjggz9c9/TPvmdHjBjhmMa/Zs0avfvuu3riiSfUp08f9e7d+w+vBQDASExRBwCgDZkwYYJGjBihr7/+2jFV8fAUz4KCAvXq1cvxJz8/Xy+88IJjFJIkXXjhhdq8ebM++ugjtW/fvsGH9kGDBik5OVmdO3du0M6iRYs0f/78o0YU/RFPT8+jdu7duHFjg9u7d++upKSkBvcVExOjF1988bjTm5OSklRYWKhrrrlG0dHRjhF9y5cvl1QfKlZWVmr8+PF6++23JdUHvFdeeaXOP/98HTx48JjtFhQUKDk5WRdddJF69erlCMeObPdUHrukBhsTFRUVHbVR04l4eHioW7dujimthy1fvlw333yzsrOzT/h/MmjQINXW1uq7775rcPzwlP4BAwacUk2/b1vSUYHxN998o7q6ulNq+1ReW7+vYd++ferWrZvjNdSzZ0/NmzdPP/zww3Gv8/T0PGrjqMNLOfzZmg5btWqVnnrqKV166aW69NJLj7p9w4YNio6O1rRp0xzhZlZWlvbu3dvg9Xb4NX4snTt3VlBQkL7++usGx9PS0rR58+bjjnw8Ge7u7urZs6cWL17cIAwuKSnR0qVLT/m180eP43gGDRqk8vJy2e32Bv3E3r179fLLLzs2m/q9uLg4bdq0SQcOHGhw/Msvv1RQUFCDYPTnn39ucM6SJUtkMpk0ZMgQRUZGKiAgQOnp6Q3uPyQkRLNnzz5qhOyRtm/frqqqKt18882KiIhw/OLjcLh5eATn75+XP/OeffrppzVt2jTZ7Xa5ublpzJgxjnVoj9f3AQDQXDCCEwCANuaf//ynJk+erCeeeEKff/65YmNjNXnyZP373//WgQMH1LNnTyUnJ+v5559XeHi4OnXq5Lh2xIgR8vX11SeffKIbb7yxwbps1113nRYtWqTrrrtO119/vfz8/PTtt9/q008/1QMPPHBKNY4ePVrffPON+vTpo44dO2rhwoVKSUlpcM7hTZHuvvtuTZ48WXV1dXr77be1ZcuW4+5G3rlzZ3l6ejo2BbFarVqyZInmz58vqX4avaurq3r06KGXXnpJTk5Oio2NVXJysj7//HPH+nq/FxAQoLCwMH3wwQcKDQ2Vt7e3VqxY4Rg5drx1KI8lNjZW7dq108svv+wYhfn6668fd1TqH7njjjv017/+VXfddZf+8pe/KDc3V88995zOOeccdenSRXv27JEkLV26VD4+PkdNvx85cqQGDx6sBx98UFlZWeratavWrVunN954Q1OmTGnU5jmHRUdHa8qUKZozZ44qKioUFxenXbt26aWXXtLgwYOPu3HNsRwO+I73OI7ntttu02WXXaZbbrlFl19+uVxcXPTJJ5/oxx9/bLAp1u+NGTNGP//8s5566imNHTtW8fHxR+3K3diapPrRe//4xz/UsWNHXXLJJdqyZUuDKcnR0dHq3bu3XnnlFc2dO1d9+/ZVSkqKXn/9dVVXVzd4vXl7e2vnzp1at27dUSPwzGaz7rrrLj3wwAOO91FBQYFeeukl+fj4HHNk66m4++67dcMNN+jmm2/WFVdcoZqaGs2dO1fV1dWaMWPGKbX1R4/jeEaNGqW4uDjddtttuu222xQVFaWtW7dqzpw5GjFixHGXIZg+fbq+/PJLXXfddbr99tvl6+urL774QmvWrNGTTz7ZIFTcvHmz7rnnHl144YXavXu3XnzxRV1yySWOKe533nmnHnroIVksFo0ZM0bFxcV65ZVXlJWVddzlPKT6NZStVqueffZZXX/99aqurtbChQu1dOlSSb+NGD48SvTrr79Wnz59/tR7dsiQIXrnnXd0//33a/LkyaqpqdGbb74pX19fDRky5KSecwAAjELACQBAGxMZGamrr75ab7/9tj766CNdddVVeuqpp/T666/r448/VmZmpgICAjRx4kT94x//aDASzWq16vzzz9d7773n2LTisJCQEH388ceaPXu2HnnkEVVVValTp06aOXPmKU2blaQHHnhAtbW1evrpp2W1WjVx4kTdfffdevDBBx3nDB8+XG+99ZZeeukl3XHHHXJyclKPHj30zjvvHHc3Zi8vL73yyit65pln9Pe//90xyvH999/XTTfdpPj4eI0dO1aPPfaY/vvf/+rtt99WTk6OAgICdNFFF+nvf//7cWt+5ZVXNHPmTN1///1ydnZWdHS0Xn31VT355JOKj48/6TXsLBaL5syZoyeffFJ33XWXAgMDde211yopKUnJycmn9DyOGTNGr732ml566SXNmDFD/v7+uuCCC/S3v/1NkhQTE6NJkyY5pr3+fiTf4XB1zpw5mjdvnvLz8xUeHq677rrrT4dfkjRz5kx17NhRCxYs0BtvvKHg4GBdc801uu22205pxN6JHsfxdO3aVR988IGef/553XvvvbLb7erSpYtefvllnX322ce9btq0aUpNTdXnn3+ujz/+WHFxcZozZ45jN+8/U5MkxcfHq6ioSEVFRZo2bdpRt7/77ru65ZZbVFBQoHfffVcvv/yy2rVrpwsvvNDxf1ZcXCxvb29df/31evLJJ3XDDTfonXfeOaqtqVOnysPDQ6+//rpmzJghT09PjRgxQnfddddR66OeqqFDh+qdd97RnDlzdNddd8nZ2VkDBw7U008/7djJ+2Sd6HEci9ls1ty5c/XCCy/o9ddfV15enkJCQjR9+vQ/DFiDgoL00Ucfafbs2XriiSdUU1Ojrl276pVXXjnqdXHttdcqKytLt99+u/z8/HTrrbfqlltucdx+8cUXy8PDQ2+++aY++eQTubu7q3///po1a9Yx1/k8rGPHjpo9e7Zeeukl/fWvf5WPj4/69u2r9957T1dffbXi4+MVGxurcePGadGiRbr//vt10UUX6ZFHHmn0e3bUqFGaNWuW3n77bcfGQgMGDNC77757RpYUAADgzzDZT9eq9wAAAADQSsXGxur22293/MIAAAAYhzU4AQAAAAAAALRYBJwAAAAAAAAAWiymqAMAAAAAAABosRjBCQAAAAAAAKDFIuAEAAAAAAAA0GIRcAIAAAAAAABosQg4AQAAAAAAALRYVqMLaK3sdrtsNvZvQkNms4nXBYBTRt8BoDHoOwCcKvoNAI1xuvoOs9kkk8l0UucScJ4mNptd+fllRpeBZsRqNcvPz0PFxeWqrbUZXQ6AFoK+A0Bj0HcAOFX0GwAa43T2Hf7+HrJYTi7gZIo6AAAAAAAAgBaLgBMAAAAAAABAi0XACQAAAAAAAKDFIuAEAAAAAAAA0GIRcAIAAAAAAABosQg4AQAAAAAAALRYBJwAAAAAAAAAWiwCTgAAAAAAAAAtFgEnAAAAAAAAgBaLgBMAAAAAAABAi0XACQAAAAAAAKDFIuAEAAAAAAAA0GIRcAIAAAAAAABosQg4AQAAAAAAALRYBJwAAAAAAAAAWiwCTgAAAAAAAAAtFgEnAAAAAAAAgBaLgBMAAAAAAABAi0XACQAAAAAAAKDFIuAEAAAAAAAA0GIRcAIAAAAAAABosQg4AQAAAAAAALRYBJwAAAAAAAAAWiwCTgAAAAAAAAAtFgEnAAAAAAAAgBaLgBMAAAAAAABAi0XACQAAAAAAAKDFIuAEAAAAAAAA0GIRcAIAAAAAAABosQg4AQAAAAAAALRYVqMLAAAAAACgMVIyS/TjhjSZTCZdeW4XuThZjC4JAGAAAk4AAAAAQItht9u1LSlfS9alaldKgeN4fnGl/n5RbzlZCTkBoK0h4AQAAAAANHs1tTat2ZGpJevTdDC3TJJkNpnUr0ugtifla+f+Ar20cLtun9pLTlZWYwOAtoSAEwAAAADQbJVW1OiXTQf004Z0FZdVS5JcnS0a2ae9zh3YQQE+rtqTWqDnP92ibUl5em3Rdv31Lz1ltRByAkBbQcAJAAAAAGh2sgvK9cP6dK3YdlDVNTZJkp+Xi84d2EEj+7SXu+tvH2djI/x0x0W99cL8rdqUkKu5X+7QLRf2kMVMyAkAbQEBJwAAAACg2Ug8UKTv1qVq454c2Q8diwj21PjBEYrrGnzckZndO/nr9qm99OKCrYrfkyPL17t006TuMptNZ654AIAhCDgBAAAAAIay2ezalJCrJetSte9AkeN4z0h/TRgUoW4d/WQynTio7BUZoNv+0ksvf75Na3dmyWoxafrEbjKfxLUAgJaLgBMAAAAAYIiqmjqt3Jah79enKbugQpJktZg0pHuoxg3qoPAgz1Nus29MoG6Z3EOvLdqhldsyZTGbdc2EWEJOAGjFCDgBAAAAAGdUUVm1ft6Qrl82HVBpRY0kycPVqtH9wnT2gHD5err8qfYHdg3WTTa75n61Q8u3HJTVYtKV53Y5qVGgAICWh4ATAAAAAHBGHMwt0/frU7Vqe5Zq6+o3Dgr0cdW4uA4a3rudXJ2b7iPq4O4hqq2z6e1vdunnjQdktZh16dhoQk4AaIUIOAEAAAAAp43dbtee1EJ9ty5VWxPzHMcj23trwqAI9e8SdNo2AhrWq53qbHbNW7xb369Pk9Vi1rRRkYScANDKEHACAAAAAJqc3W7X+t3ZWrw2VSmZJZIkk+rXyJwwOELRYT5nJGgc2ae9autsev/7vfp2TYqsFpP+MiLytN8vAODMIeAEAAAAADSpvKJKvbN4l3buL5AkOVnNGt6rncbFdVCIv/sZr2ds/3DV1tn18U8J+nLlfjlZzTp/aKczXseJ2O12bU/O157UQp0b10E+Hs5GlwQALQIBJwAAAACgSdjtdv26NUMf/5ygiqo6OVnNOm9whMYOCJe3u7Fh3bi4Dqqrs+mzpYlasCxJFrNZEwZHGFrTkfZnFuuzXxK1K6U+FN6xP1/3X9FfLs4WgysDgOaPgBMAAAAA8KcVlFTpf9/tdqyzGRXmrRvO765QA0ZsHs95Qzqqps6mL1Yk69Nf9slqMemcgR0MrSmnsEILlydp7c4sSZLVYpKT1aKUzBK98fVO3Talp8ysGQoAf4iAEwAAAADQaHa7Xat3ZOrDHxJUXlUrq8WsKSM7a3xcxGnbPOjPmDyss2rrbPp6VYo+/DFBVotZo/uFnfE6Ssqr9dWq/fpl4wHV2ewySRrSI0RTRkSqoLRKz360SRv35mjB0kRdPCb6jNcHAC0JAScAAAAAoFGKyqr17ne7tSkhV5LUKdRLN0zqrrBAD4Mr+2NTRkSqts6u79am6t0le2SxmDSid/szct9VNXX6MT5N365JUUVVnSSpRyc/XTQ6Wh1DvSRJgb5umj6xm974aqcWr01ViL+7RvY5M/UBQEtEwAkAAAAAOGXrdmXp/e/3qrSiRhazSZOHd9bEIRGymM1Gl3ZCJpNJF4+OUm2dTT/Gp2vet7tltZg1tEfoabtPm82uldsy9MWvySooqZIkRQR76uIx0erR2f+o84f2CFVWfrm+XLlf7y3ZoyAfV3XrdPR5AAACTgAAAADAKSgpr9Z73+9V/O5sSfUh3Q2TuqtDsKfBlZ0ak8mky8+OUW2dXUs3HdCbX++U1WJWXNfgJr0fu92uLYl5WrA0UQdyyyRJAd6umjoyUoN7hPzh+poXDu+srIIKrd2ZpZc/365/XTNA7QKa9+hYADACAScAAAAA4KRs2JOj95bsVnF5/ajN84d21KSzOslqaf6jNo/FZDLpqnFdVFtn069bM/T6oh2ymE3q3yWoSdpPOlisT3/Zp71phZIkD1erJp3VSWP7h8nJeuLd0U0mk66f2FV5RZXad6BI//1six68ZqC8DN6RHgCaGwJOAAAAAMAfKq2o0Yc/7tWaHfU7fYcFeujGSd0da0a2ZGaTSddN6Kq6uvrNkl79Yrtun9pLfaIDG91mVkG5FixLcoxytVrMOndguCYO7SgPV6dTasvJatHt03rpif/FK6ewUi8u3Kb/u6yfnKwtM1QGgNOBgBMAAAAAcFxb9uVq3ne7VVRaLZNJmjikoyYP69yqAjaz2aTrz++q2jqb1u/O1sufb9cdF/VSz84Bp9ROcVm1vlq5X0s3/7Yz+lk9Q/WXEZEK8HFtdH3e7s76x8V9NPO9DdqXXqR3Fu/STZO6y/QH09sBoC1pVt+RkpOT1a9fPy1cuNBxbNeuXbrqqqvUt29fjR07Vu+++26Da2w2m+bMmaMRI0aob9++uummm5SWltbgnKZoAwAAAADakvLKWr39zS69MH+rikqrFervrn9ePUDTRkW1qnDzMIvZrJsu6K7+XYJUW2fTiwu2aVdKwUldW1Vdp69WJuv+11frp43pqrPZ1TPSX49cP0g3TOr+p8LNw9oHemjGlJ6ymE1asyNLX63c/6fbBIDWotl8V6qpqdE999yj8vJyx7GCggJNnz5dERERWrBggWbMmKFZs2ZpwYIFjnNeeeUVffjhh3r88cf18ccfy2az6cYbb1R1dXWTtQEAAAAAbcn25Dz9+621+nVbhkySxg/qoEemxymqvY/RpZ1WVotZt17YQ32iAlRTa9ML87c41s88ljqbTUs3H9D9c1fr8xXJqqyuU8dQL/3fZX111yV9m3zjpe6d/HXVuC6SpC9+TdaaHZlN2j4AtFTNZor6iy++KE/Php3/p59+KicnJz322GOyWq2KiopSSkqK5s6dq2nTpqm6ulpvv/227rnnHo0ePVqS9Pzzz2vEiBH6/vvvNWnSpCZpAwAAAADagoqqWn32yz4t3XxQkhTs66brz++mLh18jS3sDLJazLptSk/NWbBNO5Lz9d/PtujuS/sqKuy3cNdut2tzQq7mL0tURl79IJ1AH1dNHRWpQd3+eGf0P2tU3zBl5Vfou3WpevvbXQrwcVVMuO9puz8AaAmaxQjO9evX65NPPtF//vOfBsfj4+M1aNAgWa2/5bBDhgzR/v37lZubq927d6usrExDhw513O7t7a3u3btr/fr1TdYGAAAAALR2u1IK9PDb6xzh5tkDwvXo9YPaVLh5mJPVor9N7aVuHf1UWV2n5z7dov2ZxZKkxANF+s8HG/Xiwm3KyCuXp5uTLj87RjNvGqIh3UNPa7h52EWjo9QvJlC1dXa9uGCbsgsrTvt9AkBzZvgIzuLiYt1777168MEH1a5duwa3ZWZmqkuXLg2OBQcHS5IyMjKUmVk/HP/31wUHBztua4o2GsvaCtelQeNZLOYGfwPAyaDvANAY9B04FVXVdfr0l336YX39PgSBPq666YLu6tbJ3+DKjGW1mnXXpX0166NN2pNWqNkfb1ZshJ827s2RJDlZzZowKELnn9VJ7q5n/qP1bVN6aea78dqfWaIXPtuih6bHnfIO7Uei3wDQGM2l7zA84HzkkUfUr18/XXDBBUfdVllZKWdn5wbHXFxcJElVVVWqqKj/LdWxzikqKmqyNhrDbDbJz8+j0dej9fL2djO6BAAtEH0HgMag78CJ7EjK0wufbFJGbpkkacLQTpo+qbvc/0RQ1to8dutZemjuau1JKdDGvTkym6Sz4yJ0xfiuCvQ19j32yM1DdfcLy5WRV67XFu3QIzcNlfVPhgz0GwAaw+i+w9CA84svvlB8fLy++uqrY97u6up61EY/VVVVkiR3d3e5utbvRFddXe349+Fz3NzcmqyNxrDZ7CouLj/xiWgzLBazvL3dVFxcobo6m9HlAGgh6DsANAZ9ByTJZrerorJWJRU1KimvVmlFjUrLa1RSXqOSimrlFlZq3c4s2SX5e7nohknd1SsqQFUV1aqqYMPVI915cR+98dUOmc0mTRkRqfBgT8luU0FBmaF1mSX94+I+euJ/8dqSkKsXPtqo6RO7ytSIafL0GwAa43T2Hd7ebic9MtTQgHPBggXKy8tzbO5z2MMPP6xvv/1WoaGhys7ObnDb4a9DQkJUW1vrOBYREdHgnNjYWElqkjYaq7aWbwo4Wl2djdcGgFNG3wGgMeg7Wg+73a6KqlqVVtSo5FBQWVpRH1aWVdb/XR9gVqukokZlFTUqraiVzW4/YdvDe7fTZWNj5O5q5fVyHM5Ws2ZM6eX4ujk9T2GBHrrlwh56ccFWLd10QMG+bpowOOLEFx4H/QaAxjC67zA04Jw1a5YqKysbHBs3bpzuuOMOTZ48WYsWLdLHH3+suro6WSwWSdKaNWvUuXNnBQQEyMvLS56enlq7dq0jnCwuLtbOnTt11VVXSZLi4uL+dBsAAAAAcKZU19Qpfk+21uzMUkFxlSOwrLOdOKw8FldnizzdnOTl7iQPNyd5uTnJ081Znu5O6hLuo9gIvyZ+BDjT+kYH6rKxMfropwR99ss+Bfu5qX+XIKPLAoAzxtCAMyQk5JjHAwICFBISomnTpunNN9/Uv/71L914443aunWr5s2bp0cffVRS/bqZV111lWbNmiV/f3+FhYXp2WefVWhoqMaNGydJTdIGAAAAAJxumfnlWrrpgFZuy1BZZe0xz3FxtsjL7Yig0t2pPrx0q//b093Z8bXHoWNObH7aJpwzMFyZ+eX6ZdMBzf1qh+6/sr86hXobXRYAnBGGbzL0RwICAvTmm29q5syZmjJlioKCgnTvvfdqypQpjnPuuOMO1dbW6sEHH1RlZaXi4uL01ltvycnJqcnaAAAAAIDTobbOpk0JuVq66YB2pRQ4jgd4u2hkn/aKDvOpDzPdneXpZpWT1WJgtWjOTCaTrjg3RtmFFdqRnK8X5m/Vv68ZKH9v1xNfDAAtnMluP4lFWXDK6upsys83dsFpNC9Wq1l+fh4qKChjTRsAJ42+A0Bj0Hc0f7mFFVq25aBWbM1QcVn9hj4mSb2jAjS6X5h6RQbIbD71jWKA8spaPfX+Bh3ILVOHYE89cFV/uTqfeGwT/QaAxjidfYe/v0fL2GQIAAAAANoKm82urYl5Wrr5gLYl5unwSBMfD2eN6NNeI/u0U6CPm6E1ouVzd7Xq7xf11hPvxistu1SvL9qhv03rTWAOoFUj4AQAAACA06iwtEorthzUsi0HlV9c5TjevZOfRvcNU9+YQFlPcoQKcDICfd30t2m99cxHm7QlMU+f/LxPl58TY3RZAHDaEHACAAAAQBOz2e3alVKgpZsOaNPeXNkOrQzm6eak4b3aaVTf9grxdze4SrRmUWE+uuH8bnpt0Q79EJ+mEH83je0fbnRZAHBaEHACAAAAaJXsdrsO5pbJbDbJx8NZbi5WmUynd5puSXm1Vm7L1NLNB5RdUOE4Hh3uozF9wzSwaxAbBeGMGdQtRNkFFVq4PEkf/pCgIF839YoMMLosAGhyBJwAAAAAWpWCkiqt2p6hX7dmKOuIkNFqMcnbw1k+Hs7ydneWt4fzb1//7u9TCUPtdrsS0ou0dPMBxe/OVm1d/WhNV2eLzuoZqtF9wxQe7HlaHitwIucP7ais/HKt3J6pV7/Yrn9ePUDhQbweAbQuBJwAAAAAWryaWpu27MvViq0Z2p6cp0MzwuVsNctiMamiqk61dXblF1c1WAfzeKwWs7w9nBqEoT6eR/zbw1mebk7anVqopZsO6EBumePajiFeGtM/TIO6BZ/U7tXA6WQymXTteV2VU1SpvWmFeuGzrXrw2oHy8XA2ujQAaDJ8twUAAADQYqVmlejXrRlavSNTZZW1juNdwn00rHc7xXWtDxmra+pUXFatovJqFZfV/ykq++3fjq/Lqw+FobaTDkOl+iB1cPcQje4Xps7tvE/XwwUaxWox6/apvfTEu/HKLqjQiwu26t7L+8nZieUSALQOBJwAAAAAWpTSihqt2ZGpX7dmKDW71HHcz8tFZ/UM1fBe7Y7awMfZyaJAXzcF+rqdsP1TCUMDvF01sk97ndUzVO6uTk3+WIGm4unmpH9c3Ecz341X0sFivfnNLt16YQ+ZT/O6tABwJhBwAgAAAGj2bDa7tifn69dtGdqckONY59JqMalfTJCG926nHp38ZTb/+bDmVMJQoCUJ9XfX7VN7adbHmxW/O1tf+Ltp6sgoo8sCgD+NgBMAAABAs5WVX65ft2Vo1fZMFZT8Nl08IsRTI3q31+DuIfJ0Y+QkcLJiI/x07YSuevvbXfp6VYpC/Nw1rFc7o8sCgD+FgBMAAABAs1JZXav1u7P169YMJaQXOY57ujlpSPcQDe/dThEhXgZWCLRsw3u3U1ZBub5ZnaJ5i3eruqZOYweGG10WADQaAScAAAAAw9ntdiWkF2nF1oOK352jqpo6SZLJJPWKDNDwXu3UJzpQTlazwZUCrcOUkZHKLqjQ+t3Zeu/7vfp50wHd/Jfe6hziYXRpAHDKCDgBAAAAGCYzv1zrd2Vp5fZMZRdUOI6H+LtreK9QndWznfy8XAysEGidzCaTbp7cXV06+OqLFUk6kFOmh99Yrd5RAbp4TLTCAgk6AbQcBJwAAAAAzqiMvDKt352t+N05Ss/5bRd0F2eLBnUN1vDe7RQd5iMTuzsDp5XFbNbZA8I1pEeIvlmdoh/j07Q1MU/bk/I1qm97XTiis7zdnY0uEwBOyGS32+1GF9Ea1dXZlJ9fZnQZaEasVrP8/DxUUFCm2lqb0eUAaCHoOwA0RnPsOw7klil+d7bi92TrQM5vPydbzCZ16+Snwd1CNDA2WC7OFgOrBNouq9Wsilq75n6+VRv25EiS3FwsmnRWJ50zoAPLQwA4ptP5M4e/v4cslpPrexjBCQAAAKDJ2e32I0LNHB3MbRhq9ujsrwGxQeoXE8Qu6EAz0T7IU3+/uI92JOXp45/2KSWrRJ/9kqhfNh7QJWOiNSA2iJHVAJolAk4AAAAATcJutys957eRmhl55Y7brBaTenTy18CuweobEygPV0JNoLmKjfDTv68bqNXbM7VgWaJyiyr1yhfbFRPuo8vOjlHndt5GlwgADRBwAgAAAGg0u92utOxSxe/J1vrdOcrKbxhq9uwcoIFdg9Q3Okjurnz8AFoKs8mkYb3aaWBssBavTdF3a1OVkF6kx/8Xr6E9QjRtVJT8vV2NLhMAJBFwAgAAADhFdrtdqVmHQ83sBrufWy1m9Yo8NFIzOlBuLnzkAFoyF2eL/jIiUqP6hmnhskSt3J6p1TuytGFPjsYPitB5QyLk6sz7HICx6IUAAAAAnJDdbtf+zBLF78lW/O5s5RRWOm5zsprVK7J+pGafKEJNoDXy83LRDZO66+yB4fr4p33am1aor1bt1/ItBzV1ZKSG9Wons5n1OQEYg588AAAAABxTdU2ddqUUaMu+XG1JzFNBSZXjNmerWb2iAhTXNVi9owIYwQW0EZ1CvXXfFf20cW+uPvtln7ILK/TO4t36aUO6Lj07Rt06+hldIoA2iJ9CAAAAADgUllZpa2KeNifkamdKvqprbI7bnJ3M6h0VWB9qRgbIxdliYKUAjGIymTQgNki9owL088Z0fblyv1KzS/XsR5vUNzpQl4yNVqi/u9FlAmhDCDgBAACANuzwJkGb9+Vqy75cJWeUNLjdz8tFfaMD1Sc6UN06+srJSqgJoJ6T1azxgyJ0Vs9Qffnrfv2y6YA278vVtqQ8jekfpsnDOsvTzcnoMgG0AQScAAAAQBtTU1unXSmFjlDzyKnnktS5nZf6RAeqb3SgOgR7ymRiXT0Ax+fl7qwrx3XR2AFh+uTnfdqamKcf49O1enumJg/rrDH9w2S1mI0uE0ArRsAJAAAAtAFFpVXakpinLftytWP/76aeW83q3slffWMC1TsqQL6eLgZWCqClahfgoX9c3Ec7kvP1yc8JSs8p00c/JejnTQd0yZgo9Y0O5BcmAE4LAk4AAACgFbLb7UrNKtGG3dnavC9PyRnFDW7383JRn6iAQ1PP/eTsxNRzAE2jR2d/PTJ9kFZsPajPlycpK79cLy7Ypq4Rvpo8rLPaBbjLy8NZZsJOAE2EgBMAAABoRfKKKvV9fJo278tVTkFFg9s6hXo51tOMCGHqOYDTx2w2aVTfMA3qFqJv16Roybo07U4t1O7UTZIki9kkPy8X+Xm5yN/btf5vLxf5ebnK37v+mJe7EyEogJNCwAkAAAC0AlXVdfp2TYq+W5eqmtr66eeHp573iQ5Q76hA+Xkx9RzAmeXmYtW0UVEa1be9Pl+erN2pBSosrVKdza7cokrlFlVKKjrmtVaLSb6e9cGnIwR1/O0ify9XeRKCAhABJwAAANCi2ex2rd2RpfnLEh2bBcVG+OqSc2IVEeQuCx/8ATQDgT5uuumC7pKk2jqbisuqlV9cpfySSuUXV6mgpP7fBSVVyi+uVFFptWrrTi4E9Tti5Gf9SFBXRyga6OsqD1d2cgdaOwJOAAAAoIVKPFikj35MUNLB+vU1A31cdcmYaA3uESJ/f08VFJSpttZ2glYA4MyyWszy93aVv7erJJ9jnlNbZ1NRafURoeehALS4SvmHwtDiQyFoTmGlcgorj9mOySTNmNJL/bsEncZHBMBoBJwAAABAC5NfXKkFyxK1ekeWJMnFyaJJZ3XUuLgOcrJaWFsTQItntZgV4OOqAB/X455TW2dTYWmVIwA9PPrz8GjQ3KJKlZTX6JOfE9Q7KkBWi/kMPgIAZxIBJwAAANBCVNXUacnaVH27NkXVNTaZJA3r1U5TR0XK15P1NQG0LVaLWYE+bgr0cTvm7VXVdbr/9dXKKazUss0HdfaA8DNcIYAzhYATAAAAaObsdrvW7srS/KWJyi+uX2czOtxHV5wTo06h3gZXBwDNk4uzRZOHddJ73+/VVyuTNaxXqFydiUGA1oh3NgAAANCMJWcU66MfE7TvQP0GGwHeLrp4TLTiugYzFR0ATmBEn/Zasj5N2QUV+n59miYP62x0SQBOAwJOAAAAoBkqKKnSgmWJWrU9U5Lk7GTWxCEdNWFQhJydLAZXBwAtg9Vi1tSRkXpt0Q4tXpuq0f3C5O3ubHRZAJoYAScAAADQjFTX1GnJ+jR9uzpFVTV1kqShPUJ10ego+XmxziYAnKqBXYPVcW2qUjJL9PWq/brinC5GlwSgiRFwAgAAAM2A3W5X/J4cffrzPuUVV0qSosK8dfnZXRTZnnU2AaCxzCaTLhodpdkfb9bSTQc0bmAHBfoee2MiAC0TAScAAABgsJTMEn30417tTa9fZ9PPy0UXj47S4O4hrLMJAE2gRyd/de/kp537C/T5imTddEF3o0sC0IQIOAEAAACDFJVWacHyJK3cmiG7JGerWRMGR+i8wR3l4sw6mwDQlC4aHaXH5sVrzY5MTRgcoQ7BnkaXBKCJEHACAAAAZ1h5Za2+W5ei79enqbrGJkka0j1EF42Okr+3q8HVAUDr1CnUW4O6BWvdrmwtWJaof1zcx+iSADQRAk4AAADgDKmuqdNPG9P17eoUlVXWSpIi23vrsrNjFB3mY3B1AND6TRkZqQ17crQ1MU97UgsUG+FndEkAmgABJwAAAHCa1dls+nVrhr5cuV8FJVWSpHYB7po2Kkr9YgJZZxMAzpAQP3eN7NNev2w6oPlLE/XPqwfQBwOtAAEnAAAAcJrY7HZt2JOjhcuTlJVfLkkK8HbRhcMjdVbPUJnNfKgGgDNt8rBOWrk9Q4kHi7UpIVf9uwQZXRKAP4mAEwAAAGhidrtdO/cXaP6yRKVklkiSPN2cdMFZnTS6X5icrGaDKwSAtsvH00Xj4jro61UpWrAsUX2iA2Qx0y8DLRkBJwAAANCEkg4Wa8GyRO1KKZAkuThbNGFQhMbFdZCbCz9+A0BzMGFQRy3ddFAZeeVatS1TI/q0N7okAH8CP2EBAAAATeBgbpkWLk/Sxr05kiSrxaQx/cJ1/lkd5e3ubHB1AIAjubtadf7Qjvrk53364tdkDe4eImcni9FlAWgkAk4AAADgT8grqtSiX5O1cnuG7HbJZJLO6hmqC4d3VqCPm9HlAQCOY2z/MP0Yn6a84ir9tDFd5w3uaHRJABqJgBMAAABohOLyan27OkU/b0xXbZ1dktS/S5CmjIxUWKCHwdUBAE7EyWrRX0ZE6q1vdunb1Ska1ae93F2djC4LQCMQcAIAAACnoKKqVj+sT9N361JVWV0nSeoa4atpo6IUFeZjcHUAgFMxtEeovluXqgM5Zfp2TaouGh1ldEkAGoGAEwAAADgJNbU2Ld18QF+v2q+S8hpJUscQL00bHakenfxlMpkMrhAAcKrMZpOmjYzSnAVb9WN8ms4eEC4/LxejywJwigg4AQAAgD9QVlmjpZsO6KcN6SosrZYkhfi5acrISA3sGiwzwSYAtGh9ogMUHe6jfelF+nJlsq6d0NXokgCcIgJOAAAA4BhyCiv0w/o0rdiaoaqa+qnofl4uumBYJw3v1U5Wi9ngCgEATcFkMuni0VF66v2NWrElQ+PiOqhdAGspAy0JAScAAABwhMQDRVqyLlUb9ubIXr93kMKDPDR+UIQGdw8h2ASAVigm3Fd9owO1eV+uFi5P0owpvYwuCcApIOAEAABAm2ez2bUpIVdL1qdqX3qR43jPzv4aPyhC3Tv5scYmALRy00ZFaktirjbsyVHSwWJFtvc2uiQAJ4mAEwAAAG1WVXWdft2WoR/i05RdUCFJsphNGtIjROPjIhQe7GlwhQCAMyUsyFNn9QzVym2Zmr90n/7v8n78cgtoIQg4AQAA0OYUlVbpp43p+mXjAZVV1kqSPFytGt0vTGcPCJevJzvoAkBb9JfhkVq7M1u7Uwu1IzlfPSMDjC4JwEkg4AQAAECbcSCnVEvWp2nNjkzV1tUvsBnk66pxcREa3qudXJwtBlcIADBSgI+rxvYP0/fr0/TZ0kR17+wvM6M4gWaPgBMAAACtmt1u186UAi1Zl6rtSfmO41Fh3powKEL9YoJkNvPhFQBQb9JZnbRi60GlZZdq3c4sDekRanRJAE6AgBMAAACtUm2dTet2ZWnJujSlZZdKkkwmqX+XII0fFKHoMB+DKwQANEeebk46b3BHLVyepM9XJGlg12BZLWajywLwBwg4AQAA0OJVVNUqI69cGXllyswvV0ZeuRIPFKmorFqS5Oxk1ohe7XVuXLiC/dwNrhYA0NydO7CDftqQrpzCSi3bfFBnDwg3uiQAf4CAEwAAAC2C3W5XQUmVMvLLlZlXroN5Zco8FGoWllYf8xofD2edPSBco/uFydPN6QxXDABoqVycLZo8vLPeW7JHX61M1rBeoXJ1JkIBmivenQAAAGhWautsyiqoUGZemQ7mlSszr6x+dGZ+uaqq6457nY+Hs9oFuCs0wEPtAtzVPsBDXTr4ysnKtEIAwKkb0budvl+XqqyCCn2/Lk2Th3c2uiQAx0HACQAAAMNt2Zer5VsO6mBeuXIKKmSz2495ntlkUrCf26Eg013t/OvDzHYB7nJ3ZYQmAKDpWC1mTRkZqdcW7dDidaka3S9M3h7ORpcF4BgIOAEAAGCYyupaffzTPi3fcrDBcVdnS32I6e+h9oH1f7cLcFewnxsbPQAAzpiBXYPVcW2qUjJL9PWq/bri3C5GlwTgGAg4AQAAYIjEg0V646udyi6okEnS2QPD1S86UKEBHvL1dJbJZDK6RABAG2c2mXTR6CjN/nizftl0QOfGdVCQr5vRZQH4HQJOAAAAnFF1Npu+WZWiL1ful81ul5+Xi26c1F3dOvoZXRoAAEfp0clfPTr5acf+An2xIkk3XdDD6JIA/A7zewAAAHDGZBeU6z/vb9QXvybLZrdrULdgPXbDIMJNAECzdtHoaEnSmh1ZSssuNbgaAL9neMCZl5en//u//9OQIUPUr18/3XzzzUpMTHTc/uCDDyo2NrbBn7Fjxzput9lsmjNnjkaMGKG+ffvqpptuUlpaWoP72LVrl6666ir17dtXY8eO1bvvvtvg9pNpAwAAAI1nt9u1YstBPfzOeiUeLJabi0U3X9Bdt17YUx5sDgQAaOY6hnppULdg2SUtWJZ4wvMBnFmGB5wzZsxQSkqK5s6dq/nz58vV1VXXXXedKioqJEl79uzRrbfeql9//dXxZ/78+Y7rX3nlFX344Yd6/PHH9fHHH8tms+nGG29UdXW1JKmgoEDTp09XRESEFixYoBkzZmjWrFlasGDBSbcBAACAxispr9Yrn2/XO4t3q6q6TrEdfPXo9YM0pEeo0aUBAHDSpoyMlMVs0tbEPO1JLTC6HABHMDTgLCoqUlhYmJ544gn17t1bUVFRuu2225Sdna2EhATZ7Xbt27dPPXv2VFBQkOOPv7+/JKm6ulpvv/227rjjDo0ePVpdu3bV888/r8zMTH3//feSpE8//VROTk567LHHFBUVpWnTpum6667T3LlzT7oNAAAANM72pDw99PY6bdibI4vZpItHR+n/Lu+nQB82aAAAtCwhfu4a2ae9JOmzpYmy2+0GVwTgMEMDTh8fH82ePVtdunSRJOXn52vevHkKDQ1VdHS0UlNTVV5ersjIyGNev3v3bpWVlWno0KGOY97e3urevbvWr18vSYqPj9egQYNktf62n9KQIUO0f/9+5ebmnlQbAAAAODXVNXX68Ie9eu7TLSoqrVa7AHc9eM1AnTeko8xmdkcHALRMk4d1krOTWUkHi7Vxb67R5QA4pNnsov7vf/9bn376qZydnfXqq6/K3d1de/fulSS99957Wr58ucxms0aOHKk777xTXl5eyszMlCS1a9euQVvBwcGO2zIzMx0B6pG3S1JGRsZJtQEAAICTl5pVorlf7dTB3DJJ0tkDwnXx6Cg5O1kMrgwAgD/Hx9NF4+Ii9PWq/Vq4PFF9YwJkMRu++h/Q5jWbgPPaa6/VpZdeqg8++EAzZszQhx9+qL1798psNis4OFivvfaaUlNT9cwzzyghIUH/+9//HOt0Ojs7N2jLxcVFRUVFkqTKyspj3i5JVVVVJ9VGY1mtdHL4jcVibvA3AJwM+g60JDabXYvXpmj+L4mqs9nl4+msmy7ort5RgUaX1ubQdwA4VfQbJ++CYZ20dNMBZeSVa82OLI3qF2Z0SYBhmkvf0WwCzujoaEnSzJkztWXLFr3//vuaOXOmrrjiCvn5+UmSunTpoqCgIF1yySXatm2bXF1dJdWvo3n431J9cOnmVr+uk6ur61GbBVVVVUmS3N3dT6qNxjCbTfLz82j09Wi9vL1ZcwzAqaPvQHOXXVCu5z/ZpO2JeZKkIT1DdfvFfeXj6WJwZW0bfQeAU0W/cWJ+ki49N1ZvfbldX/yarPNGRMmFWQpo44zuOwwNOPPz87V69WqNHz/esUam2WxWdHS0srOzZTabHeHmYTExMZLqp54fnlaenZ2tiIgIxznZ2dmKjY2VJIWGhio7O7tBG4e/DgkJUW1t7QnbaAybza7i4vJGX4/Wx2Ixy9vbTcXFFaqrsxldDoAWgr4DLcGq7Zl6d/FulVfVysXJoqvGd9HIPu1lq6lVQUGt0eW1SfQdAE4V/capGdo9SF8sdVVeUaU++363zj+rk9ElAYY4nX2Ht7fbSY8MNTTgzM3N1V133aU333xTI0aMkCTV1NRo586dGjt2rO69915lZ2dr3rx5jmu2bdsmqX7EZ4cOHeTp6am1a9c6wsni4mLt3LlTV111lSQpLi5OH3/8serq6mSx1P9GZc2aNercubMCAgLk5eV1wjYaq7aWbwo4Wl2djdcGgFNG34HmqLyyRu99v1drd2ZJkqLae+umC7or2M9ddXV2SewuazT6DgCnin7j5Jhl0l9GdNZb3+zSVyv3a3jvdvJwdTK6LMAwRvcdhk6Q79Kli0aOHKknnnhC69ev1969e3X//feruLhY1113ncaPH6/Vq1frpZdeUmpqqpYtW6Z//vOfmjRpkqKiouTs7KyrrrpKs2bN0k8//aTdu3frzjvvVGhoqMaNGydJmjZtmkpLS/Wvf/1L+/bt08KFCzVv3jzdcsstknRSbQAAAOA3drtdG/Zk66G312ntziyZTSb9ZXhn3X9VfwX7uRtdHgAAZ8TQHqEKC/JQeVWtvl2TYnQ5QJtmstvthv5qvaSkRLNnz9aPP/6okpISDRw4UPfff79jKvrixYs1d+5cJSUlycvLSxdccIH+8Y9/ODYKqqur03PPPaeFCxeqsrJScXFxeuihhxQeHu64j61bt2rmzJnauXOngoKCdP311zcYnXkybZyqujqb8vPLGn09Wh+r1Sw/Pw8VFJTxG1EAJ42+A82J3W7XjuR8LViepJTMEklSsJ+bbrqgu6La+xhcHY5E3wHgVNFvNM7mfbmaM3+rnKxm/eeWofLzYu1ptC2ns+/w9/c46SnqhgecrRUBJ36PHxgANAZ9B5qLvWmFWrgsUXvTiyRJLk4WnRvXQROHRMjVudnsW4lD6DsAnCr6jcax2+36zwcblZBepJF92uu687oaXRJwRjWXgJOfRgEAAHBcKZklWrg8SduS6ndHt1rMGts/TBOHdJS3h7PB1QEAYCyTyaSLR0fryfc3aMXWgxo/qIPaBXgYXRbQ5hBwAgAA4CgHc8v0xYokxe/JkSRZzCYN791OF5zVSf7ergZXBwBA8xEd7qO+0YHavC9XC5clacbUXkaXBLQ5BJwAAABwyCms0Je/JmvVjkzZ7ZJJ0uAeIbpweGeFsIEQAADHNG1UpLYk5mrD3hwlHixibWrgDCPgBAAAgApKqvT16v1avvmg6mz1S7T3iwnUlJGRCg/yNLg6AACat7AgT53VM1Qrt2VqwdJE/d/l/WQymYwuC2gzCDgBAADasNKKGn27JkU/bUhXzaGF4Xt08tOUkVGKbO9tcHUAALQcfxkeqbU7s7U7tVDbk/PVKzLA6JKANoOAEwAAoA2qqKrV9+vTtGRdqiqr6yRJ0WE+mjoyUl07+hlcHQAALU+Aj6vOHhCmJevSNH9ponp09peZUZzAGUHACQAA0IZU19Tp540H9O2aFJVW1EiSIoI9NXVUpHpFBjCdDgCAP+H8oZ20fMtBpWWXat3OLA3pEWp0SUCbQMAJAADQitntduUVVyo9p0ypWSVauumACkurJUmh/u6aMjJSA2KDGGECAEAT8HRz0oTBHfX58iR9viJJA7sGy2oxG10W0OoRcAIAALQSpRU1OpBTqvScMqXnlCo9p1QHcsocU9APC/B21YXDO2tozxBZzHzoAgCgKY0b2EE/b0hXTmGllm0+qLMHhBtdEtDqEXACAAC0MNU1dcrIK3eEmIcDzaJDIzN/z2I2qV2Au8KDPNUlwlfDeraTk5VgEwCA08HF2aLJwzrpve/36quVyRrWK1SuzsQvwOnEOwwAAKAZKyipUtLBoiNGZZYpu6Bcdvuxzw/0cVV4kKfCgjwUHuSp8CAPhfi7Mz0OAIAzaESf9lqyLk3ZhRX6fn2aJg/rbHRJQKtGwAkAANAMlZRX68uV+7V00wHV2Y5OMz3dnBQe5KGwQyFmeJCn2gd6yM2FH+8AADCa1WLWlJGRev3LHfpubarG9AuTl7uz0WUBrRY/AQMAADQjNbV1+nFDur5elaKKqlpJ9bucdwjxPDQisz7Q9PZwZsdzAACasbhuwVq8NkWpWaX6ZnWKLjs7xuiSgFaLgBMAAKAZsNvtWrcrWwuWJSq3qFKS1CHYU5eOjVb3Tv4GVwcAAE6V2WTSRaOj9NwnW/TzxnSdMzBcgT5uRpcFtEoEnAAAAAbbm1aoT37ep+SMYkmSn5eLpo6M1NAeoTKbGaUJAEBL1aOTv7pG+Gp3aqEWrUjWDZO6G10S0CoRcAIAABgkq6Bc839J1Ia9OZIkFyeLJg6J0LhBEXJxshhcHQAA+LNMJpMuGh2tJ96N16rtmRo/OELhQZ5GlwW0OgScAAAAZ1hpRY2+XJmsXzbWbyBkMkkj+7TXX4Z3lo+ni9HlAQCAJhTZ3lsDYoO0YU+OFi5L0h0X9Ta6JKDVIeAEAAA4Q2pqbfppQ7q+XrVf5Yc2EOoVGaBLxkQpjNEcAAC0WlNHRmrj3hxt3perhPRCxYT7Gl0S0KoQcAIAAJxmdrtd63dna/7S3zYQCg+q30CoR2c2EAIAoLVrF+ChEb3bafmWDM1fmqj7r+wvk4l1toGmQsAJAABwGu1LL9InPyco8WD9BkK+ns6aMjJSw3q2YwMhAADakAuHR2r1jiwlpBdpa2Ke+kQHGl0S0GoQcAIAAJwG2QXlmr80UfF7fttA6LzBERo/KEIuzmwgBABAW+Pn5aJzBoRr8dpULViWqF6RAfyyE2giBJwAAABNqLbOpkW/Juu7tamODYRG9G6vv4zoLF82EAIAoE07b0hHLdt8UOk5ZVqzM1Nn9WxndElAq0DACQAA0EQKSqr02qLtSkgvkiT1jPTXJaOjFR7MBkIAAEDydHPSeUMitGBZkr5Ykay4riFyspqNLgto8Qg4AQAAmsDO/fma++UOFZfXyNXZoukTuymua7DRZQEAgGbmnIEd9NOGdOUWVWrp5gM6d2AHo0sCWjx+TQAAAPAn2Ox2fbUyWbM/2azi8hqFB3nq4eviCDcBAMAxuThZNHl4Z0nS16v2q6Kq1uCKgJaPgBMAAKCRSitq9MJnW/X5imTZ7dLw3u304DUDFOLvbnRpAACgGRveq51C/NxUUl6jJetSjS4HaPEIOAEAABoh8WCRHnlnnbYl5cnJatb0iV11/cRucnZih3QAAPDHrBazpo6KkiQtWZ+m4rJqgysCWjYCTgAAgFNgt9v1Y3ya/vP+RuUXVynEz00PXjNQI3q3N7o0AADQggyMDVKnUC9VVdfp61X7jS4HaNEIOAEAAE5SRVWtXl20Qx/+mKA6m10DY4P00HVx6sAu6QAA4BSZTCZdNLp+FOcvmw4op7DC4IqAlouAEwAA4CSkZ5fqsf/FK353tixmky4/J0Z//UtPublYjS4NAAC0UN07+at7Jz/V2ez6YkWS0eUALRYBJwAAwAms3JahJ96NV1Z+ufy8XHTflf117sAOMplMRpcGAABauMOjONfsyFJadqnB1QAtEwEnAADAcVTX1Gne4l1665tdqq61qWdnfz0yPU7RYT5GlwYAAFqJTqHeiusaLLukBcsSjS4HaJGYUwUAAHAM2QXleuXz7UrNLpVJ0oUjOmvSWZ1kZtQmAABoYlNGRmrDnhxtTczT3rRCdenga3RJQIvCCE4AAIDf2bAnR4/OW6/U7FJ5uTvprsv6avKwzoSbAADgtAj1d9fIPu0kSfOXJsputxtcEdCyEHACAAAcUltn08c/Jejlz7epoqpO0eE+emT6IPXo5G90aQAAoJW7YFhnOVvN2negSJv35RpdDtCiMEUdAAC0aeWVtUrJLFZSRrE27MnR/swSSdL4QR00bVSUrBZ+HwwAAE4/Py8XnRvXQd+sTtHCZUnqExUos5nZI8DJIOAEAABtRm2dTWnZpUrOKFbywfpQMzOvXEdOAnNzsej6id01IDbIsDoBAEDbdN7gCC3ddEAHcsu0anumhvduZ3RJQItAwAkAAFolu92urIIKR5CZnFGs1KwS1dYdvaZVoI+rOrfzVud29buYBvi4GlAxAABo69xdnTRxaEd99kuiFv2apMHdg+VktRhdFtDsEXACAIBWoaisukGYmXywWOVVtUed5+FqdYSZndvX/+3j4WxAxQAAAEc7u3+4foxPV15xlX7ZeEDjBkUYXRLQ7BFwAgCAFm3Dnhx9tnSfsgsqjrrNajGrY6inOrfzVuShQDPY100mdkMHAADNlLOTRRcO76x5i3fr69UpGt67vdxdiW+AP8I7BAAAtEjF5dX64Pu9Wr87W5JkktQu0EOd23kpsr2PItt5KyzIg02CAABAizOsV6iWrEtVRl65vluXqqkjI40uCWjWCDgBAECLYrfbtX53tt7/fq9KK2pkNpl03pAInTe4I6MbAABAq2AxmzV1ZKRe/ny7vl+fqrMHhLOkDvAH+BQAAABajKLSKr33/V5t3JsjSQoP8tD153dTp1BvgysDAABoWv27BKlzO28lZxTrq5XJumpcrNElAc0Wc7YAAECzZ7fbtWp7hh58c6027s2RxWzS5GGd9NB1cYSbAACgVTKZTLpodJQkadnmg8ouKDe4IqD5YgQnAABo1gpKqvTud7u1JTFPkhQR4qnrJ3ZTRIiXwZUBAACcXt06+qlnZ39tT87XFyuSdfPkHkaXBDRLBJwAAKBZstvt+nVrhj7+eZ8qqmpltZg0eVhnTRgcwcZBAACgzZg2Kkrbk/O1ZmeWJgyO4Je8wDHw6QAAADQ7eUWVeu7TLXpn8W5VVNWqcztvPXxdnCad1YlwEwAAtCkdQ700uHuIJGn+skSDqwGaJ0ZwAgCAZsNmt2vZ5oP69Jd9qqquk5PVrCkjIjUuroPMZpPR5QEAABjiLyM6K353trYn5Wt3SoG6dvQzuiSgWWEIBAAAaBayCys066NNem/JHlVV1yk63EePXj9IEwZHEG4CAIA2LcTPXSP7tpdUP4rTbrcbXBHQvDCCEwAAGMpmt+unDelasCxR1TU2OTuZNW1UlM7uH06wCQAAcMjkszpp5bYMJR0s1sa9uRoQG2R0SUCzQcAJAAAMk5lfrne+3aWE9CJJUtcIX113XlcF+7kbXBkAAEDz4uPponFxEfp61X4tXJ6ovjEBspiZmAtIBJwAAMAAFVW1+mZ1ir5fn6raOrtcnC26ZHSURvULk9nEqE0AAIBjmTAoQks3HVBGXrlWbsvUyD7tjS4JaBYIOAEAwBljs9u1alumFixLVFFZtSSpZ2d/XTMhVoE+bgZXBwAA0Ly5u1p1/tCO+uTnfVr0a7KGdA+Rs5PF6LIAwxFwAgCAMyIhvVAf/Zig/ZklkqRgPzddOjZafaMDZWLUJgAAwEkZ2z9MP8SnKb+4Sj9vPKAJgyOMLgkwHAEnAAA4rfKKKvXZ0n1atytbkuTqbNEFwzrpnAEd5GRl3SgAAIBT4WS16C/DI/X2t7v0zer9GtmnndxdnYwuCzAUAScAADgtqmrqtHhNir5bm6rqWptMkkb0aacpI6Pk4+FsdHkAAAAt1lk9Q/XdulQdzC3T4rWpmjYqyuiSAEMRcAIAgCZlt9u1dleWPvslUQUlVZKkLuE+uvycLuoY6mVwdQAAAC2f2WzStJGRenHhNv2wPk1nDwiXr6eL0WUBhiHgBAAATSY5o1gf/ZigfQeKJEkB3q66ZGy0BsYGsc4mAABAE+obE6ioMG8lHijWlyv365rxsUaXBBiGgBMAAPxphaVVWrAsUSu3ZUqSnJ3MOn9IR40fFMHOngAAAKeByWTSRaOi9PSHm7R880GNj+ugEH93o8sCDEHACQBAG1NTa1NadqmcrWa5u1rl5mKVq7OlUSMsa2rr9P36NH29OkVV1XWSpKE9QnXR6Cj5eTFNCgAA4HSKjfBT76gAbU3M0+crknTrhT2NLgkwBAEnAABtRG2dTb9uzdBXq/Y71sY8zGwyyc3FIndXq9xdneTuYq3/d4O/64+7Hfq6oKR+1GZuUaUkKbK9ty4/O0ZRYT5GPDwAAIA2adqoKG1NzNO6Xdm64txqebuzmSPaHgJOAABauTqbTau2Z+qrlfsdYaS7i1UWi0nllbWqs9lls9tVVlmrsspaSZWn1L6vp7MuHh2twT1CZGadTQAAgDOqQ7CnwgI9dCC3TPvSi9S/S5DRJQFnHAEnAACtlM1m19qdWVq0MlnZBRWSJG8PZ50/tKNG920vJ6tFdrtd1bU2lVfWqryqVhWVtSqvqlH5obDz98fKq2odf9fV2TS0Z6gmDukoV2d+pAAAADBKTAdfHcgtU0J6IQEn2iQ+jQAA0MrY7HbF787Wol+TlZFXLknydHPSxCEdNaZ/mFyO2PTHZDLJxckiFycLa2YCAAC0UDHhPlq66YD2phUZXQpgCAJOAABaCbvdro17c7Xo1ySl55RJkjxcrZowOEJnDwhnlCUAAEArFRNevwZ6alaJqqrr5OJsOcEVQOvCJx0AAFo4u92urYl5+mJFslKySiRJbi4WjYuL0LkDO8jdlW/3AAAArVmAt6v8vFxUUFKlpIxidevoZ3RJwBnFJx4AAFoou92unfsL9PmKJCUdLJYkuThbdO7AcI0fFCEPVyeDKwQAAMCZYDKZFBPuo3W7spWQXkjAiTbHbHQBeXl5+r//+z8NGTJE/fr1080336zExETH7bt27dJVV12lvn37auzYsXr33XcbXG+z2TRnzhyNGDFCffv21U033aS0tLQG5zRFGwAANCe7Uwr09AcbNfuTzUo6WCxnq1kTBkfomVuHaurIKMJNAACANiYm3FeSlJDOOpxoewwPOGfMmKGUlBTNnTtX8+fPl6urq6677jpVVFSooKBA06dPV0REhBYsWKAZM2Zo1qxZWrBggeP6V155RR9++KEef/xxffzxx7LZbLrxxhtVXV0tSU3SBgAAzUVuYYWe/WiTnvlok/amF8lqMevcgR309K1DdcmYaHm5OxtdIgAAAAxweB3OfQeKVGezGVwNcGYZOkW9qKhIYWFhuuWWW9SlSxdJ0m233aYLL7xQCQkJWr16tZycnPTYY4/JarUqKirKEYZOmzZN1dXVevvtt3XPPfdo9OjRkqTnn39eI0aM0Pfff69Jkybp008//dNtAADQHGTml+vZjzapoKRKFrNJI/u216Shndj9HAAAAAoP8pSbi1UVVbVKzy5Tx1Avo0sCzhhDR3D6+Pho9uzZjnAzPz9f8+bNU2hoqKKjoxUfH69BgwbJav0thx0yZIj279+v3Nxc7d69W2VlZRo6dKjjdm9vb3Xv3l3r16+XpCZpAwAAo6XnlOrpDzaqoKRK7QLc9eTNQ3T1uFjCTQAAAEiSzGaTosPqR3EmpBcaWwxwhhk+Rf2wf//73xo6dKi++eYbzZw5U+7u7srMzFRoaGiD84KDgyVJGRkZyszMlCS1a9fuqHMO39YUbQAAYKTE9EI99d4GFZVVq0Owp+67sr+CfN2MLgsAAADNzOFp6ntZhxNtTLPZRf3aa6/VpZdeqg8++EAzZszQhx9+qMrKSjk7N1xLzMWlfqRKVVWVKioqJOmY5xQV1b+Zm6KNxrJam01+jGbAYjE3+BsATkZyRrGe+XCTyipqFNneW/dc3k+ebmwgBOCP8XMHgFNFv9E6dD20e/q+9EJZLCaZTCaDK0Jr11z6jmYTcEZHR0uSZs6cqS1btuj999+Xq6vrURv9VFVVSZLc3d3l6uoqSaqurnb8+/A5bm71I1uaoo3GMJtN8vPzaPT1aL28vRl1BeDk7EjK03/e36iKqlp16+SvR24aInd2RwdwCvi5A8Cpot9o2fp7uspqMamwtFrVdpNC/cklcGYY3XcYGnDm5+dr9erVGj9+vGONTLPZrOjoaGVnZys0NFTZ2dkNrjn8dUhIiGprax3HIiIiGpwTGxsrSU3SRmPYbHYVF5c3+nq0PhaLWd7ebiourlBdHTvaAfhjO5Lz9fynm1VdY1Pv6ED9/eLeqqqoVlVF9YkvBtDm8XMHgFNFv9F6dGrnrX3pRVq//aCG925vdDlo5U5n3+Ht7XbSI0MNDThzc3N111136c0339SIESMkSTU1Ndq5c6fGjh2rwMBAffzxx6qrq5PFYpEkrVmzRp07d1ZAQIC8vLzk6emptWvXOsLJ4uJi7dy5U1dddZUkKS4u7k+30Vi1tXxTwNHq6my8NgD8oa2JuXpp4XbV1tnUOypAD904ROWllfQdAE4ZP3cAOFX0Gy1fdJiP9qUXaXdKoYZ0Dz3xBUATMLrvMHSCfJcuXTRy5Eg98cQTWr9+vfbu3av7779fxcXFuu666zRt2jSVlpbqX//6l/bt26eFCxdq3rx5uuWWWyTVr5t51VVXadasWfrpp5+0e/du3XnnnQoNDdW4ceMkqUnaAADgTNmwJ0cvLtim2jqb+sUE6u8X95GLk8XosgAAANBCHN5oiJ3U0ZYYvgbnc889p9mzZ+vOO+9USUmJBg4cqA8++EDt29cPo37zzTc1c+ZMTZkyRUFBQbr33ns1ZcoUx/V33HGHamtr9eCDD6qyslJxcXF666235ORUv0ZZQEDAn24DAIAzYc3OTL351S7Z7HYN6hasGyd1lxMb1gEAAOAUxIT7SpIy8spVUl4tL3fnP74AaAVMdrvdbnQRrVFdnU35+WVGl4FmxGo1y8/PQwUFZUz5AHCUFVsOat7i3bJLGtYzVNMndpPZbKLvANAo9B0AThX9Ruvy4JtrdTC3TH+b2kv9ugQZXQ5asdPZd/j7e5z0GpwMCwEAwGA/b0zXO4fCzdH9wjT9/PpwEwAAAGiM36apFxlcCXBmEHACAGCgJetS9f73eyVJ5w7soKvHdZHZRLgJAACAxmMdTrQ1hq/BCQBAW/XVymR9viJZknT+0I6aOjJSJsJNAAAA/EmH1+Hcn1miqpo6Nq1Eq8cITgAAzjC73a4FyxId4eaUEZ01bVQU4SYAAACaRKCPq3w9nVVns2t/RrHR5QCnHQEnAABnkN1u1yc/79M3q1MkSZeMidYFwzobXBUAAABaE5PJ5BjFuZd1ONEGEHACAHCG1NTa9L/v9uj79WmSpCvP7aIJgyMMrgoAAACtEetwoi1hDU4AAM6AzPxyvbZou1KzSmWSdO15XTWyT3ujywIAAEAr1aWDryQp8UCRbDa7zGaWQ0LrRcAJAMBpZLfbtXJbpj74Ya+qaurk6eakG87vpj7RgUaXBgAAgFYsPMhTrs4WVVTVKT2nVBEhXkaXBJw2BJwAAJwmFVW1em/JHq3ZmSVJ6hrhq5su6CE/LxeDKwMAAEBrZzabFB3mo+3J+UpILyLgRKtGwAkAwGmQnFGs1xZtV05hpcwmk/4yorMmDunI1CAAAACcMTHhhwPOQp09INzocoDThoATAIAmZLPbtWRdqhYuS1Kdza4Ab1fdcmEPRYf5GF0aAAAA2hjHTupphbLb7TKZ+GU7WicCTgAAmkhRaZXe/GaXdiTnS5IGdg3WdRNi5e7qZHBlAAAAaIs6t/eWxWxSYWm18ooqFejrZnRJwGlBwAkAQBPYnpSnN7/eqeLyGjlbzbr8nBiN7NOe35IDAADAMC5OFnUM9VLSwWIlpBcRcKLVIuAEAOBPqK2zaeGyJH23LlWSFB7koVsu7KmwQA+DKwMAAADq1+GsDzgLNbRnqNHlAKcFAScAAI2UXVCu17/coeSMEknSmP5hunRMtJydLAZXBgAAANTrEu6rJevStDe9yOhSgNOGgBMAgEZYvSNT7y3Zo8rqOnm4WjV9Yjf17xJkdFkAAABAA1Hh9ZtdHswtU2lFjTzdWB8erQ8BJwAAp6CyulYffL9XK7dnSpK6dPDVzRd0l7+3q8GVAQAAAEfzdndWuwB3ZeSVa196kfrGBBpdEtDkCDgBADhJe1ILNG/xbmUVVMhkkiYP66wLzuoks5mNhAAAANB8xYT7KCOvXAnphQScaJUIOAEAOIHSihp9+vM+/botQ5Lk5+WiWyb3UJcOvsYWBgAAAJyEmHBfLd+SoQTW4UQrRcAJAMBx2O12rdqeqU9+3qfSihpJ0uh+YbpoVKTcXVm7CAAAAC1DzKF1OJMzilVdU8emmGh1CDgBADiGjLwyvbdkj3anFkqSwoM8dM2ErooO8zG2MAAAAOAUBfm6ycfDWUVl1dqfWcJMJLQ6BJwAAByhptamb9ek6JvV+1VbZ5ez1awLh3fWuXEdZLWYjS4PAAAAOGUmk0kx4T6K35OjvWmFBJxodQg4AQA4ZFdKgd5dskdZ+eWSpJ6R/rp6XKyCfN0MrgwAAAD4c2I6+Cp+Tw7rcKJVIuAEALR5JeXV+vTnfVq5PVOS5OPhrMvPiVFc12CZTOyQDgAAgJavS7ivJGnfgSLZbHaZzfyci9aDgBMA0GbZ7Xb9ui1Dn/68T2WVtTJJGt0/TNNGsokQAAAAWpfwYA+5OFtUUVWrA7ll6hDsaXRJQJMh4AQAtEkZeWV697s92pNWKEkKD/LUtRNiFcUmQgAAAGiFLGazott7a8f+AiWkFxJwolUh4AQAtCk1tXX6elWKvl2TojrboU2ERnTWuQPZRAgAAACtW0y476GAs0hj+4cbXQ7QZAg4AQCtnt1u14GcMq3bna3V2zOVV1wpSeodFaCrzu2iQDYRAgAAQBsQE14/WykhvdDYQoAmRsAJAGi1DuaWad2uLK3fna2MvHLHcR9PZ115ThcNiA1iEyEAAAC0GZHtfWQ2mZRfXKW8okoF+LgaXRLQJAg4AQCtSmZ+udbvytK63dk6kFPmOG61mNSzc4DiugWrX0ygXJ35FggAAIC2xcXZoo6hnkrOKNHe9EIN9Qk1uiSgSfDpDgDQ4mUXlGv97myt35Wt1OxSx3GL2aQenf0V1zVY/WKC5O7Ktz0AAAC0bTHhvkrOKFFCepGG9iDgROvAJz0AQIuUW1ih9XuytW5XtlIySxzHzSaTunfyU1zXYPWPDZKHq5OBVQIAAADNS0y4r75fn8Y6nGhVCDgBAC1GbZ1NK7dlaMXWDCUdLHYcN5mkrhF+GtQtWP27BMnL3dnAKgEAAIDm6/BGQwdyylRWWcOAALQKBJwAgGavts6mX7dl6JtV+5VXXCVJMkmKjfBVXNdgDYgNlrcHoSYAAABwIt4ezgrxd1dWfrn2pRepT3Sg0SUBfxoBJwCg2To8YvPrVSnKK66UJPl4OGv8oAgN6REiX08XgysEAAAAWp6YcB9l5ZcrgYATrQQBJwCg2amts2nV9kx9vWq/cot+CzYnDumoUX3by9nJYnCFAAAAQMsVE+6jX7dmsA4nWg0CTgBAs3GsYNP7ULA5mmATAAAAaBJdwn0lSckZxaqprZOTlZ+z0bIRcAIADFdbZ9Pq7Zn66vfB5uAIjeoXJheCTQAAAKDJBPu5ydvDWcVl1UrOKFGXDr5GlwT8KQScAADD1NbZtHpH/YjNnMJDwaa7k84b0lGjCTYBAACA08JkMikm3Ecb9uQoIb2QgBMtHgEnAOCMq7PZtHp7lr5etV/ZhRWSJC93J503uKPG9AuTizPBJgAAAHA6xYT7Hgo4i4wuBfjTCDgBAGfUln25+uinBGUXEGwCAAAARokJ95Ek7Usvks1ul9lkMrgioPEIOAEAZ0ReUaU+/HGvNiXkSpI83Zx03pAIje0XTrAJAAAAnGERIZ5ycbKovKpWB3PLFB7kaXRJQKMRcAIATqvaOpu+X5+mL1cmq7rGJovZpHPjOuiCszrJzYVvQwAAAIARLGazItt7a1dKgRLSiwg40aLxyRIAcNrsTinQe9/vUUZeuSSpS7iPrhofyw9PAAAAQDMQE+5zKOAs1Jh+YUaXAzQaAScAoMkVlVXr058TtHpHlqT6dTYvGROts3qGysTaPgAAAECzEHNo9/SEtEJD6wD+LAJOAECTsdnsWrr5gBYsS1JFVa1Mkkb1C9O0UZHycHUyujwAAAAAR4hq7y2zyaS84irlFVUqwMfV6JKARiHgBAA0ieSMYr27ZI9SMkskSR1DvHT1+FhFtvc2uDIAAAAAx+LqbFVEiKf2Z5Yo4UChAnxCjS4JaBQCTgDAn1JWWaOFy5K0dNMB2SW5uVg1dWSkxvQLk9nMdHQAAACgOYsJ960PONOLNKQ7ASdaJgJOAECj2O12rd6RqU9/3qfi8hpJ0tAeIbpkTLR8PF0Mrg4AAADAyYgJ99EP8WlKSCsyuhSg0Qg4AQCnLCOvTP/7bo/2HlqMvF2Au64eF6uuHf2MLQwAAADAKYkJ95EkHcgpVXlljdxZOx8tEAEnAOCk1dbZtGRdqhb9ul+1dTY5W826YFgnjR8UIavFbHR5AAAAAE6Rj6eLgv3clF1QoX0HitU7KsDokoBTRsAJADgpKZklemfxLqVmlUqSekb665pxsQr0dTO4MgAAAAB/Rky4j7ILKpSQXkjAiRaJgBMA8Idqauv05cr9WrwmVTa7XR6uVl1+ToyG9giVycQmQgAAAEBLFxPuq5XbMpVwaAkqoKUh4AQAHNe+9CK9s3iXMvLKJUkDY4N05bhY+Xg4G1wZAAAAgKbSpYOvJCkpo0Q1tTY5WVl+Ci0LAScA4CiV1bVauCxJP21Il12Sj4ezrhrXRQNig40uDQAAAEATC/Fzk5e7k0rKa5SSWaLoQxsPAS0FAScAoIEd+/P1v8W7lVtUKUka1itUl50dIw92UwQAAABaJZPJpJhwX23cm6OE9EICTrQ4BJwAAElSeWWNPvl5n1ZszZAkBXi76NoJXdUzkkXGAQAAgNYuJtznUMBZpPOMLgY4RQScAABtSsjRe0v2qLC0WpJ0dv9wTR0VKTcXvk0AAAAAbUFMuK8kKSG9UDa7XWY2FEULwidXAGjDisur9eEPe7VuV7YkKcTfXdPP6+pYZBwAAABA2xAR4ilnq1lllbXKyCtXWKCH0SUBJ42AEwDaoOLyai3bdEA/xKertKJGZpNJEwZHaPKwTnJ2shhdHgAAAIAzzGoxK7K9t3anFiohrZCAEy0KAScAtCHpOaX6MT5Nq3dkqabWJknqEOyp6RO7qlOot8HVAQAAADBSTLhvfcCZXqjR/cKMLgc4aQScANDK2ex2bU/K1w/rU7Vjf4HjeKdQL42L66CBXYNltZgNrBAAAABAc3B4qaqE9CJjCwFOEQEnALRSVdV1WrUjUz/Gpykjr1ySZDJJ/bsEaVxcB0WH+cjEwuEAAAAADols7y2TScotqlR+caX8vV2NLgk4KQScANDK5BdX6ueNB7Rs8wGVVdZKktxcLBrRu73OGRCuQF83gysEAAAA0By5uVgVEeyllKwS7TtQpEEEnGghCDgBoJVIOlisH+LTFL87W3U2uyQpyNdV5wzsoOG92snNhS4fAAAAwB+LCfdRSlaJEtKKNKhbiNHlACeFT7sA0ILZ7XZt3perxWtSte/Ab+vkxHbw1bi4DuoTHSizmWnoAAAAAE5OTAdf/bghXQnphUaXApw0Ak4AaKEKSqr0/vd7tCkhV5JkMZs0uHuIzh3YQR1DvQyuDgAAAEBLFB3mI0lKyy5VeWWt3F2JjtD88SoFgBbGbrdrxdYMffLzPlVU1cpiNmncoA46d2AH+Xq6GF0eAAAAgBbMz8tFQb6uyimsVOLBIvWKDDC6JOCEzEYXUFhYqIceekgjR45U//79dfnllys+Pt5x+/Tp0xUbG9vgz9VXX+24vaqqSo8++qiGDh2qfv366e6771Z+fn6D+1i9erWmTp2qPn36aMKECfrmm28a3H4ybQBAc5BdUK5nP9qkeYt3q6KqVp3beevh6XG6eHQ04SYAAACAJhET7itJTFNHi2H4CM677rpLOTk5eu655xQQEKD33ntPN9xwgz7//HNFRkZqz549euSRR3TOOec4rnFycnL8+5FHHlF8fLxefPFFOTs76+GHH9Ydd9yh999/X5KUmJioW265RdOnT9ezzz6rpUuX6t5775W/v7+GDh16Um0AgNFsNrt+iE/T58uTVF1rk7PVrKkjI3XOwA6ssQkAAACgSXXp4KtV2zOVkFZ04pOBZsDQgDMlJUUrV67Uhx9+qAEDBkiS/v3vf2vFihX66quvdNVVVykvL099+vRRUFDQUddnZWXpiy++0GuvvaaBAwdKkp577jlNmDBBmzZtUr9+/fS///1PsbGxuvPOOyVJUVFR2rlzp958800NHTr0pNoAACOl55TqnW93KzmjWJLUraOfrp0Qq2A/d4MrAwAAANAaxYTXr8OZlFGs2jqbrBbDJwADf8jQV6ifn5/mzp2rXr16OY6ZTCaZTCYVFxdrz549MplM6ty58zGv37BhgyRpyJAhjmOdO3dWSEiI1q9fL0mKj493jNQ8bMiQIdqwYYPsdvtJtQEARqits+mLFUl69J31Ss4olpuLRded11X3XNaXcBMAAADAaRPq7y5PNyfV1NqUkllidDnACRk6gtPb21ujRo1qcGzJkiVKSUnRP//5T+3du1deXl567LHHtHLlSrm7u2vChAm67bbb5OzsrKysLPn5+cnFpeG6c8HBwcrMzJQkZWZmKjQ09KjbKyoqVFBQcFJtNJbVym848BvLod94WfjNF05C4oEivfn1Th3IKZMk9esSqGsndJW/t6vBleFMo+8A0Bj0HQBOFf0Gfq9LB19t3JujxIPFiu3oZ3Q5aKaaS99h+BqcR9q4caMeeOABjRs3TqNHj9Y///lPVVVVqXfv3po+fbp27dqlZ555RgcPHtQzzzyjiooKOTs7H9WOi4uLqqqqJEmVlZVHnXP46+rq6pNqozHMZpP8/DwafT1aL29vN6NLQDNWWVWr97/brS9XJMpul3w8nXXLlN4a3qe9TCbW2mzL6DsANAZ9B4BTRb+Bw/p0CdbGvTlKziwh38AJGd13NJuA88cff9Q999yj/v37a9asWZKkxx57TPfdd598fOrXfujSpYucnJx055136t5775Wrq6uqq6uPaquqqkpubvVPrIuLy1HnHP7azc3tpNpoDJvNruLi8kZfj9bHYjHL29tNxcUVqquzGV0OmqEdyfl6+5tdyimskCQN6xWqK87tIi93ZxUW0p+0VfQdABqDvgPAqaLfwO91CKxfFmtHUp7y8ktlZsAFjuF09h3e3m4nPTK0WQSc77//vmbOnKkJEybo6aefdoyotFqtjnDzsJiYGEm/TT0vLCxUdXV1g1GY2dnZCgkJkSS1a9dO2dnZDdrIzs6Wu7u7vLy8TqqNxqqt5ZsCjlZXZ+O1AUmS3W7Xwbxybdybo017c7T/0No2/t4uumZ8V/WOCpBEX4J69B0AGoO+A8Cpot/AYeFBHnKymlVaUaP0rFK1D2QUJ47P6L7D8IDzww8/1OOPP66rr75a//rXvxpMwbz66qsVHh6up556ynFs27ZtcnJyUqdOnRQUFCSbzaYNGzY4NhJKTk5WVlaW4uLiJEkDBw7UunXrGtznmjVr1L9/f5nNZg0YMOCEbQBAU7HZ7Uo6WKxNe3O0cW+OsgoqHLeZTNLofmG6aFSU3FwM754BAAAAtGFWi1mR7by1J61QCemFBJxo1gz9BJ2cnKwnn3xS5557rm655Rbl5uY6bnN1ddX48eP15JNPqnfv3ho+fLi2bdumZ555RjfccIM8PT3l6emp888/Xw8++KCefPJJubm56eGHH9agQYPUt29fSfUh6ZQpUzRr1ixNmTJFy5Yt03fffac333xTkhQSEnLCNgDgz6its2l3SoE2JuRqU0KOikp/WxbDajGpeyd/9e8SpL7RgfL2OHpNYAAAAAAwQkwH30MBZ5FG9Q0zuhzguEx2u91u1J2/9tprev75549525QpU/Sf//xHH3zwgT744AOlpaUpKChIl1xyiW6++WaZzfVz8MvLy/Xkk09qyZIlkqSRI0fqwQcflJ/fbzt8LV++XM8++6z279+v8PBw/e1vf9PEiRMdt59MG6eqrs6m/PyyRl+P1sdqNcvPz0MFBWVM+WgDKqpqtT05Xxv35mhrYq4qquoct7m5WNQ7KlD9uwSpZ2d/RmviD9F3AGgM+g4Ap4p+A8eyPSlPz326RUG+rnr61rOMLgfN0OnsO/z9PU56DU5DA87WjIATv8cPDG3DntQCLV6bqp37C1R7xALLPh7O6hdTH2p27egn60l20gB9B4DGoO8AcKroN3AsFVW1uv2/y2W3S7NnDJOfl4vRJaGZaS4BJ8OGAKAJ2Gx2fbkyWV+t3K/DvzUK9nNT/y5B6t8lSJHtvdl1EAAAAECL4uZiVYcgT6Vml2rfgSLFdQ02uiTgmAg4AeBPKiqt0utf7tDu1EJJ0rBeoZowKELtAz0abJwGAAAAAC1NTLivUrNLtTetkIATzRYBJwD8CTv25+uNL3eouLxGLk4WXTM+VkN7hhpdFgAAAAA0iZgOPvppY7oS0guNLgU4LgJOAGiE309JDw/y0F//0lPtAjyMLg0AAAAAmkxMuK8kKS27VBVVtWySimaJVyUAnKLC0irNPWJK+sg+7XXFOTFydrIYWxgAAAAANDE/LxcF+rgqt6hSiQeL1LNzgNElAUch4ASAU7AjOV9vfHXElPQJsRragynpAAAAAFqvmHBf5RZlKiGNgBPNEwEnAJyEOptNi37dr29WMSUdAAAAQNsS08FHq3dksg4nmi0CTgA4gYKS+inpe9IKJUmj+rbX5WczJR0AAABA23B4Hc6kg8WqrbPJajEbWxDwOwScAPAHtifn6Y2vdqqkvEYuzhZdOyFWQ7ozJR0AAABA29EuwF0erlaVVdYqJatEUe19jC4JaICAEwCOoX5KerK+WZUiu6QOwZ766196KtTf3ejSAAAAAOCMMptMign31eZ9uUpIKyLgRLPDmGIA+J384ko9++EmfX0o3Bzdt73+dfUAwk0AAAAAbVZMeH2oyTqcaI4YwQkAR9i4N0fvfLtLZZW1cnG26LoJXTW4e4jRZQEAAACAoQ6vw5mQXiS73S6TyWRsQcARCDgBQFJ1TZ0++WWfftl4QJLUKdRLt17YQ8F+jNoEAAAAgI6hXnKymlVaUaPM/HK1C/AwuiTAodEBZ3V1tebPn69Vq1YpJydHTz75pNatW6cePXqod+/eTVkjAJxWB3LL9Pqi7UrPKZMkTRgcoakjI9kZEAAAAAAOcbKa1bmdt/amFSohvYiAE81Koz695+fna9q0aZo5c6ZSUlK0detWVVZWaunSpbr66qu1adOmpq4TAJqc3W7X8i0H9fi89UrPKZO3u5PuuqSPLhkTTbgJAAAAAL/DOpxorhr1Cf6ZZ55RWVmZvv32W33++eey2+2SpDlz5qhXr16aM2dOkxYJAE2tvLJGry3aoXmLd6u61qYenfz06PWD1DMywOjSAAAAAKBZOnIdTqA5aVTA+csvv+jvf/+7Onbs2GBR2f9v787joyrP/o9/Z8kymSSYACGg7EtYFMKSCGoQaR+0iAvS9mkrPJVWxKXlEbV046e4VitWRUrRiuJGeVxoXXChWIuigsStAgFBCIQlCUtIyGSyzMz5/ZHMaAQlGSaZmXM+79eLl3DOmcOlxhv8ct33lZSUpJ/97GfatGlTxAoEgEjbvrdS8x7foA1byuWw2/SDcX01+79z1SE1KdqlAQAAAEDM6ndqumySyiu8qqyui3Y5QEhYZ3DW1dXplFNOOe49h8OhhoaGk6kJANpEwDD02rpd+vvbOxUwDHXqkKyZlwxR324dol0aAAAAAMS8lOQEndo5VXsOVGvbnkqNGpgV7ZIASWF2cJ5xxhlatmzZce+9/PLLOv3000+qKACItCPVdbpv+Sd6Yc0OBQxD+YOyNG96PuEmAAAAALRC/+6N/w/1OedwIoaE1cH5v//7v7riiit0ySWX6Nxzz5XNZtMrr7yihx56SGvXrtWjjz4a6ToBIGz/+eKQlqzcrKM1DUpMsOvy7w7QOUO7NjtiAwAAAABwYv1P66C3PtrLOZyIKWF1cI4aNUqPP/64XC6XHn30URmGoaVLl+rAgQN6+OGHNXr06EjXCQCt5q3z6ck3tuqB5z7V0ZoGdc9K1S1X5KlgWDfCTQAAAAAIw4CmQUO7y47KW+eLbjFAk7A6OCUpLy9Py5cvV21trSorK5Wamiq32x3J2gAgbJ9sO6inVm1VxdHGg6+/M/I0/fC8vkpwOqJcGQAAAADEr8z0ZHVMT9ahqlrt2F+lIb0yo10SEH7AGZScnKzk5ORI1AIAJ63KU69lqz/XB0XlkqSsDJeuuGCgBvbMiHJlAAAAAGAO/bt30KFNtdpWcoSAEzEhrIBz4MCBJ9zeWVRUFFZBABAOwzD0/qZS/W31NnlqfbLZpAvye+iSc3orMYGuTQAAAACIlP6nnaJ1m8o4hxMxI6yA87rrrjsm4PR4PProo4+0e/du3XTTTREpDgBa4mClV0++sVUbdxyWJHXPStX0iQPVKzs9ypUBAAAAgPn0P61xkvoX+yrl8wfkdIQ14gWImLACzl/+8pffeG/OnDnauHGjpkyZEnZRANASAcPQWx/t1fP//kJ1DX45HXZdck4vnZ/fg19gAQAAAKCNdOvkVkqSUzV1PpWUV6t3V5pLEF0RTwAmT56sV199NdKvBYBm9h306O6nP9Iz//xcdQ1+9T+tg279WZ4uHNOLcBMAAAAA2pDdZlO/pi7ObSVHolsMoAgMGfq63bt3y+fzRfq1ACBJ8vkDem3dLr38XrF8fkNJiQ79YFxfjRt+quwnOBsYAAAAABAZ/U/roP98cUjb9lRqQn60q4HVhRVwLly48JhrgUBApaWlevXVV3XeeeeddGEA8HU79lVp6WtbtOdAtSRpaN+OmjYhRx07JEe5MgAAAACwlgHdT5EkbdtzRIZhnHAYNdCWIhZwSlJqaqq++93v6re//e1JFQUAX7W77KheXLtTH287KElKdSXoJ9/trzMHd+EXUQAAAACIgl7Z6XI67KqqaVB5hVddMlOiXRIsLKyAc8uWLZGuAwCOsfdAtV5cu1OFWw9IkmySzjo9Wz8Y30/pKYnRLQ4AAAAALCzBaVfvrmnatqdSb328Vz/6Tv9olwQLi/gZnABwsvYf8ujFtTu1oahchhqDzbxBWbr47N7q1skd7fIAAAAAAJIm5PXQtj2fadWGEvXumq4zB3eJdkmwqLACztraWv3lL3/RW2+9Ja/Xq0Ag0Oy+zWbT6tWrI1IgAOsoq6jRS2uLtW5zqQyj8dqonM66+JzeOq1zanSLAwAAAAA0MzKns743uodeW7dbj79apK4dU9SjS1q0y4IFhRVw3nnnnXr++eeVn5+vQYMGyW63R7ouABZSfsSrV94t1nsbSxVoSjaH9++kS87pzS+OAAAAABDDpoztq5Kyam3ceVgLV3ymm6/IU6orIdplwWLCCjhXrVql2bNn66qrrop0PQAs5GClV6+8t0vvfrZf/kBjsDm0b0ddWtBbvbLTo1wdAAAAAOBE7Habrrp4iG5/YoMOHKnV4hc3avYPh8lBMxzaUVgBZ0NDg4YOHRrpWgBYxIEjXr2+frfe/nRfKNg8vXemLinorb7dOkS5OgAAAABAa6S6EvTLy4bqjqcKtbm4Qi+s2aEfntcv2mXBQsIKOM855xy9/fbbGj16dKTrAWBixaVVen39bm3YUh46Y3NQzwxdWtBb/U87Jaq1AQAAAADCd1pWqn42cZAWv7hJr6/frZ5d0hg6hHYTVsA5ceJE3XLLLTp8+LCGDRsml8t1zDOXXnrpydYGwAQMw9BnOw7r9fW7tGX3kdD1Ib0zNWlMT+X0yIhecQAAAACAiMkf1EW7yo4ydAjtzmYYwT6qlhs4cOC3v9RmU1FRUdhFmYHfH9Dhw55ol4EY4nTalZHhVkWFRz5fINrltDmfP6D1m8v0+ge7tfdA438LDrtN+YOydH5+D36RA1rIamsHgMhg7QDQWqwbiJRAwNADz32qjTsPq1OHZIYOmVxbrh2ZmW45HC07yzWsDs4333wznI8BsICaWp/WfLpXqwv3qOJonSQpKdGhc4d104S87spMT45yhQAAAACAtsLQIURDWAHnqaee2uzHdXV1SkxMlM1mi0hRAOLP4aparS7co39/sle19X5JUofURP3XqO4al9tNKcn8iR0AAAAAWAFDh9Dewgo4JWnHjh1asGCB3nvvPVVXV+u5557T888/rz59+mjatGmRrBFADNt/yKOV7+/S+s1loYno3Tq5dX5+d40enK0EJ39KBwAAAABWw9AhtKewAs6ioiJdfvnl6tixoy666CItW7ZMkuRwOHTXXXcpNTVVkydPjmihAGLLvoMevfxesT7YXKbgQb453U/RBWf20Bl9O8pORzcAAAAAWBpDh9Bewgo477nnHp1++ul67LHHJEnPPPOMJGnu3Lmqq6vTk08+ScAJmNTeA9V6+b1ibSgqDwWbw/t30oVjeqlPt/So1gYAAAAAiC1TxvZVSVm1Nu48rIUrPmPoENpEWAHnJ598oj/96U9yOp3y+/3N7k2cOFGvvPJKRIoDEDv2HKjWy+8Wq3DLl8HmiAGddfHZvfgTOAAAAADAcX196NDDL27U9QwdQoSFFXAmJSWptrb2uPeOHDmixMTEkyoKQOzYU16tl95rDDaDRg7orIsINgEAAAAALfDVoUObGDqENhBWwHn22WdrwYIFGjFihDp37ixJstls8ng8euyxx3TWWWdFtEgA7a+kvFovvbtTH249ELo2KqezLjq7t7pnpUaxMgAAAABAvGHoENpSWAHnr371K/33f/+3LrjgAg0cOFA2m0133323du7cKcMw9Kc//SnSdQJoJ7vLjurld4v14eeNwaZN0siBWbr4rF46jWATAAAAABAmhg6hrYQVcHbt2lUvvviili5dqnXr1qlHjx6qqanRpEmTNH36dGVlZUW6TgBtyDAMbdx5WKs2lGjTzsOSGoPNvEFZuuisXjq1M8EmAAAAAODkTRnbV7vLqrWJoUOIIJthGMaJH2vu8OHDyszMbIt6TMPvD+jwYU+0y0AMcTrtyshwq6LCI58vEO1yJEn1DX69t6lU/9xQov2HaiRJNpuUNzBLF53dW6d2cke5QgCxuHYAiH2sHQBai3UD7ana2xAaOjSkVwZDh+JYW64dmZluORwt+7oIq4Nz7NixKigo0CWXXKLx48czVAiIM0eq6/Svj/bo3x/vU7W3QZKUnOhQwdBu+u6o09T5FFeUKwQAAAAAmBVDhxBpYQWcN910k1599VVdf/31SktL0/nnn69LL71Uo0aNinR9ACJoV+lRrdpQog+KyuQPNDZvd+qQrO+OPE0Fw7rJlRTWkgAAAAAAQKswdAiRFNYW9aCSkhK98sorevXVV7Vt2zZ169ZNF198sS666CL17ds3knXGHbao4+uiteUjEDD06faDWrWhRFtLjoSu9zutgyaM6q4RAzrLbre1Wz0AWoftYgDCwdoBoLVYNxAtz/17u15bt1uJTrt+N20kQ4fiTKxsUT+pgPOrtm3bpr/97W/6v//7PwUCARUVFUXitXGLgBNf196/YWjw+fX2p/v1zw0lKj/ilSQ57DaNGpilCXnd1btrepvXAODk8T8bAMLB2gGgtVg3EC2BgKH7n/tUm3YeVqcOyQwdijOxEnCe9H7UQ4cO6bXXXtNrr72mjz/+WKeccoomTpx4sq8FEKYGX0Br/7NPr7y/SxVH6yRJ7mSnxuZ203dGnKbM9OQoVwgAAAAAQCO73aaZFw8JDR16+MWNDB1Cq4UVcB49elRvvPGGVq5cqQ0bNsjhcGj8+PFatGiRCgoK5HA4Il0ngBPw+QN697P9euW9Yh2qagw2M9KSNHF0T51zRlclJfLfJQAAAAAg9jB0CCcrrIBzzJgxCgQCGjlypObNm6cLLrhAqampka4NQAv4AwG9t7FUL79brIOVtZKkDqmJmjSml8YO66YEJ3/qBQAAAACIbV8fOtQrO035gxg6hJYJK+D85S9/qYsuukjdunWLdD0AWigQMLRuc6leerdY5RWNZ2ymuxN14eieOje3mxIT6NgEAAAAAMSP/EFdtKv0qF5bv1uPvVqk7MwUhg6hRcIKOGfOnClJqqysVGFhocrLy3X++efryJEj6t27t2w2JjIDbSUQMPTBljK9tLZYpYdrJDW2808c3VPnjThVSQSbAAAAAIA4NeXcvtpdXq1NOw9r4YrPGDqEFgl7yNBf/vIXPfzww6qtrZXNZtPQoUP1wAMPqKKiQo899pjS05nQDERSwDD04dYDenHtTu076JHUODzoe6N7avyIU5WceNIzwwAAAAAAiCqGDiEcYX11PP3003rooYc0ffp0PfvsszIMQ5I0depUlZSU6MEHH4xokYDV+QMBPfLSJv3lHxu176BHKUlOTR7bR3+85ixNHN2TcBMAAAAAYBqprgT94rKhSkywh4YOAd8mrIDzqaee0lVXXaX//d//1ZAhQ0LXzz33XF1//fX617/+FbECAasLBAw9+kqRPigql8Nu08Vn99Ifrxmji87qJVcSwSYAAAAAwHy6Nw0dkqTX1+/WB0VlUa4IsSysgHPfvn3Kz88/7r0+ffro4MGDJ1UUgEaBgKElK4u0fnOZHHabrp18ui4t6KOUZM4fAQAAAACYW/6gLvremT0kSY+9WqTdZUejXBFiVVgBZ9euXfXxxx8f997GjRvVtWvXkyoKQOOZm0tf26L3N5XKbrPp6kuGaHj/ztEuCwAAAACAdjPl3L4a0jtT9Q0BLVzxmaq9DdEuCTEorIDz+9//vhYvXqwlS5aouLhYklRTU6M33nhDDz/8sCZPnhzJGgHLCRiGnnpjq9Z+tl82m3TVxYM1Micr2mUBAAAAANCugkOHOnVI1sHKxqFD/kAg2mUhxtiM4ISgVjAMQ7fccouee+650I9tNpsMw9DFF1+su+++W3aLT7fy+wM6fNgT7TIQQ5xOuzIy3Kqo8Mjn++bF2DAMPf3Pz/XWR3tls0kzJg3W6CHZ7VgpgFjS0rUDAL6KtQNAa7FuINaVlFfrzqcKVd8Q0PfO7KEfnNcv2iVBbbt2ZGa65XC0LF8MK+AMKi4u1rp163TkyBGlpaUpLy9P/fr109/+9jddfvnl4b7WFAg48XUt+Y/eMAz97c1tWl24RzZJP7twkM4+gyMfACvjfzYAhIO1A0BrsW4gHnxQVKbFL26SJF19yRDlD+oS5YoQKwFnq9os3377bc2ePVs33HCD1qxZo169eulHP/qRrr76al1++eWqqqrSZZddpjvuuKPF7zxy5IhuvvlmjR07ViNGjNCPf/xjFRYWhu6///77uuyyyzRs2DBdcMEFWrlyZbPP19XV6dZbb9WYMWM0fPhw3XjjjTp8+HCzZyLxDqCtGYahZ9/artWFeyRJV3xvIOEmAAAAAABNGDqEb9LigPOll17SVVddpdWrV2vNmjW6+uqr9c9//lNSY0h50003adq0adq+fbumT5/e4gJuuOEGffzxx/rTn/6kF154QYMGDdLPf/5z7dixQ1988YVmzpypgoICrVixQj/4wQ80Z84cvf/++6HPz5s3T2vXrtVDDz2kJ554Qjt27NCsWbNC9yPxDqCtGYahF9bs0BsflEiS/ueCHBUM6xblqgAAAAAAiC0MHcLxtHiL+pQpU+R0OrVkyRIlJibqt7/9rb744gs98MADmj59uvbv36+CggL97ne/U+/evVv0k+/atUsTJkzQsmXLNHLkSEmNQc+ECRM0adIkHTp0SEVFRaGzPiXpxhtv1JEjR7RkyRKVlZVp3LhxWrx4sc4991xJ0s6dO3XBBRdo+fLlGj58uG6++eaTfkc42KKOr/u2tu2/v71DL79XLEm6/L8G6DsjT4tChQBiEdvFAISDtQNAa7FuIJ5Uext029INOlhZqx99p78m5HWPdkmWFXdb1IuLi/XTn/5UqampSkxM1C9+8Qtt3bpV1157rerr6/Xggw/qr3/9a4vDTUnKyMjQI488ojPOOCN0zWazyWazqaqqSoWFhRozZkyzz4wePVoffvihDMPQhx9+GLoW1Lt3b3Xp0kUbNmyQpIi8A2hLL727MxRu/vg7/Qk3AQAAAAD4FqmuBI0Y0FmSdKS6LsrVIBY4W/pgTU2Nunb98jzAU089VYZhyOl06qWXXlLHjh1b/ZOnp6eHuiaD3njjDe3atUu/+93v9Pe//13Z2c2nR2dlZcnr9aqiokJlZWXKyMhQUlLSMc+UlpZKkkpLS0/6HeFyOq09SR7NBf/U4at/+vDyu8X6xzs7JUk//m5/fW90z6jUBiB2HW/tAIATYe0A0FqsG4g36e5ESZK3zkf+EkWxsna0OOA0DEMOhyP04+D3Z8+eHVa4eTwfffSRfvvb32rChAkaN26camtrlZiY2OyZ4I/r6+vl9XqPuS9JSUlJqqtrTPAj8Y5w2O02ZWS4w/48zCs93aUGn1/Pv7lNz721XZL0PxMH6QffGRDlygDEsvR0V7RLABCHWDsAtBbrBuJF546NmUu93yB/iQHRXjtaHHB+k6ysrEjUodWrV+umm27SiBEjNH/+fEmNIWN9fX2z54I/drlcSk5OPua+1DgV3eVyRewd4QgEDFVV1YT9eZiPw2GX252kV9fu0Io1X+hgZa0k6bJz++i7I05VRQVntgI4lsNhV3q6S1VVXvn9nIcFoGVYOwC0FusG4o0t0Ph1eqSqlv+fjqK2XDvS010t7gw96YDTZrOd7Cv09NNP684779QFF1yge+65J9RR2bVrV5WXlzd7try8XCkpKUpLS1N2draOHDmi+vr6Zl2Y5eXl6tKlS8TeES4OZkZQwDD00aZSvbi2WHsPVEuSOqQm6rKCPioY1o2vFQAn5PcHWCsAtBprB4DWYt1AvEhObNxZXO1t4Gs2BkR77WhVwDlv3jylpqZKatyyLkn/7//9P7ndzVuBbTabnnjiiRa9c9myZbr99ts1bdo0/f73v28WmI4aNUoffPBBs+fXrVunESNGyG63a+TIkQoEAvrwww9Dg4R27typsrIy5eXlRewdQLgMw9Cn2w/p7+/sUEl5Y7CZ6krQxNE9NX7EqUpMcJzgDQAAAAAA4OvcyQmSJE9tQ5QrQSxoccAZDPuCweY3XTvej7/Jzp07ddddd+m//uu/NHPmTB08eDB0Lzk5WdOmTdPkyZM1f/58TZ48WWvWrNHrr7+uRx99VJLUpUsXXXjhhZo7d67uuusuuVwu3XLLLcrPz1dubq4kReQdQDiKig9rxds79MW+KkmSK8mhyeP669yh2Urg4G4AAAAAAMLmTm6MtDxeX5QrQSywGS1NI9vA4sWLdf/99x/33uTJk3X33Xfr7bff1r333qvi4mKddtpp+uUvf6mJEyeGnqupqdFdd92lN954Q5I0duxYzZ07VxkZGaFnIvGO1vL7Azp8mDMgrOiLvZVa8fYOFe2qkCQlOu36zqjTNOmsXupxaoYqKjy0zwNoMafTrowMN2sHgFZh7QDQWqwbiDee2gb98oF3JEmP/GqcnDQSRUVbrh2Zme4Wn8EZ1YDTzAg4rSVgGNq5v0qvvFusT784JElyOmw6N/dUTRrTUx1Sk/gNA4CwsHYACAdrB4DWYt1AvAkYhmbc85YMSff/8hx1cCee8DOIvFgJOE96yBBgVQePeLV5V4U2Fx/W5uIKVXsbz/2w22w664xsXXx2L3Xq4IpylQAAAAAAmI/dZlNKslOeWp883gYCTosj4ARaqNrboC1fCTTLj3ib3U9KcGj4gE66+Ozeys5MiVKVAAAAAABYgzs5QZ5an2pqOYfT6gg4gW9Q3+DXtr2VoUBzd+lRffU8B7vNpj6npmtwzwwN7pWpPt3SOfMDAAAAAIB2ktI0aKiaSeqWR8AJfM2eA9X698d79f6mUnnr/M3undrJrUG9GgPNnO6nyJXEf0IAAAAAAESD25UgSaoh4LQ80hlAUoMvoA+3luutj/dq257K0PVTUhM1pFemBvfK1KBeGTolNSmKVQIAAAAAgCB3Uwenx8sWdasj4ISllVfUaM0n+/TOf/Y3GxI0fEAnjRt+qgb1zJDdZotylQAAAAAA4OvcyY0dnB46OC2PgBOW4w8E9J/th/TWx3u1cefh0PWMtCSdO6ybCoZ1U0YanZoAAAAAAMQyt6upg5MhQ5ZHwAnLOFRZq3c/2681n+5TxdG60PXTe2fqvOGnami/jnLYGRIEAAAAAEA8SEmigxONCDhhWoZhqKS8Wp9sO6iPtx3UrrKjoXuprgQVDO2qc3O7KSsjJYpVAgAAAACAcIQ6ODmD0/IIOGEq/kBAn5dU6uPPD+iT7Qd1sLI2dM8mqX/3UzQut5tG5mQpwUm3JgAAAAAA8So1mSnqaETAibhXW+/Txh2H9fG2A/rPF4eanb2R4LRrSK9MDe/fScP6dVK6OzGKlQIAAAAAgEhJaZqiXs0ZnJZHwIm4VV5Ro7+t3qZNxRXy+QOh66muBA3r11Ej+nfW4N6ZSkpwRLFKAAAAAADQFtyupjM4vXRwWh0BJ+KSzx/Qn/++USXl1ZKkrAyXhvfvpOH9O6vfqR1kt9uiXCEAAAAAAGhL7tAWdZ8Mw5DNRhZgVQSciEuvvFeskvJqpboSdNOPctU9K5WFDAAAAAAAC3E3bVEPGIZq6/1yJRFzWRVTVhB3dpUe1cr3d0mSpk4YoB5d0gg3AQAAAACwmMQER2iAsIdBQ5ZGwIm44vMHtGRlkfwBQyNzOitvYFa0SwIAAAAAAFESHDTk8TJoyMoIOBFXXn63WHsONG5NnzYhh85NAAAAAAAsLLXpHE46OK2NgBNx4+tb09PdiVGuCAAAAAAARFPwHM6aWjo4rYyAE3GhcWv6ZgUMQ6MGZil/UJdolwQAAAAAAKIspamDs5oOTksj4ERceOndYu054FFaSoKmThgQ7XIAAAAAAEAMcLuCZ3AScFoZASdi3s79VXq1aWv6tAk5Sk9hazoAAAAAAJDcTR2cbFG3NgJOxLQGX0CPrSxSwDCUNzBLo5iaDgAAAAAAmgTP4GTIkLURcCKmvfTuTu092Lg1/XK2pgMAAAAAgK9wu5qmqHvp4LQyAk7ErJ37q/Taut2S2JoOAAAAAACOFdyiTgentRFwIiY1+AJa0rQ1PX8QW9MBAAAAAMCxvtyiTgenlRFwIia9uHan9h30KD0lQZf/F1vTAQAAAADAsYJb1Gvo4LQ0Ak7EnB37qvTa+qap6ecPVBpb0wEAAAAAwHGkNHVwVtPBaWkEnIgpDT6/lqzcLMOQzhzcRSNzOke7JAAAAAAAEKOCZ3DW1fvl8weiXA2ixRntAmBtPn9AR47W6VBVrQ4frdOn2w9q/6EapbsT2ZoOAAAAAAC+VUqSUzZJhqSaWp/S3ewCtSICTrQpwzC096BHZYdrdKiqToerahu/HW38fmV1vYzjfO6n5+cotekcDQAAAAAAgOOx221yJTlVU+eTp7aBgNOiCDjRJqpq6rVuY6ne+Wy/9h7wfOuzToddmelJykxLUsf0ZJ3Rt6OGD2BrOgAAAAAAODG3qyng9HIOp1URcCJi/IGANu44rLX/2a9Pth+UP9DYm5ngtKtHVqoy05Obgszk0Pc7picrLSVBNpstytUDAAAAAIB45E5O0AHVysMkdcsi4MRJKztco7Wf7de7n+3Xker60PXeXdN0ztBuOnNQllKS2W4OAAAAAAAiz900SZ2A07oIOBGW2nqfCrcc0Nr/7NPneypD11NdCRozJFsFQ7vqtKzUKFYIAAAAAACswN00w4Mt6tZFwIlW23OgWnc//ZFq6hoXDptNOqNPR51zRlfl9u8kp8Me5QoBAAAAAIBVuJt2jdLBaV0EnGg1b51P3nqfsjJcKhjaVWed3lUZaUnRLgsAAAAAAFhQSmiLOh2cVkXAiVbrf9opWnTDuUp02hkOBAAAAAAAoirYwVlDB6dlEXAiLEkJjmiXAAAAAAAA8JUhQ3RwWhWHJQIAAAAAACBufTlkiA5OqyLgBAAAAAAAQNyigxMEnAAAAAAAAIhbTFEHAScAAAAAAADi1pdb1H0yDCPK1SAaCDgBAAAAAAAQt4Jb1AOGodp6f5SrQTQQcAIAAAAAACBuJSY45HQ0RlxsU7cmAk4AAAAAAADENberadCQl0FDVkTACQAAAAAAgLgWHDRUQwenJRFwAgAAAAAAIK4Fz+H01NLBaUUEnAAAAAAAAIhrwQ5OzuC0JgJOAAAAAAAAxDU6OK2NgBMAAAAAAABxze1q6uD00sFpRQScAAAAAAAAiGt0cFobAScAAAAAAADiWgpncFoaAScAAAAAAADimtvV1MHJFnVLIuAEAAAAAABAXEtt6uCsYYu6JRFwAgAAAAAAIK6xRd3aCDgBAAAAAAAQ14Jb1Kvp4LQkAk4AAAAAAADENXdTB2ddvV8+fyDK1aC9EXACAAAAAAAgrqUkOUPf5xxO6yHgBAAAAAAAQFyz222hkJNzOK2HgBMAAAAAAABxLyU5GHDSwWk1BJwAAAAAAACIe25X0yR1Lx2cVkPACQAAAAAAgLiX2tTByRmc1kPACQAAAAAAgLiX0jRJvZozOC2HgBMAAAAAAABxjy3q1kXACQAAAAAAgLjnZou6ZRFwAgAAAAAAIO65m7aoe9iibjkEnAAAAAAAAIh7wQ5ODx2clhNTAefDDz+sadOmNbs2d+5c5eTkNPs2fvz40P1AIKAFCxaooKBAubm5mjFjhkpKSpq9o6ioSFOnTlVubq7Gjx+vJ598stn9lrwDAAAAAAAAsSuFDk7LipmA85lnntEDDzxwzPWtW7fq6quv1tq1a0Pfnn/++dD9RYsWadmyZbr99tu1fPlyBQIBXXnllaqvr5ckVVRUaPr06erRo4deeOEFXXfddZo/f75eeOGFFr8DAAAAAAAAsS3V1dTB6aWD02qiHnCWlZXp6quv1vz589WrV69m9wzD0Pbt23X66aerc+fOoW+ZmZmSpPr6ej322GOaNWuWxo0bp4EDB+r+++9XaWmpVq1aJUl69tlnlZCQoNtuu019+/bVlClTdMUVV+iRRx5p8TsAAAAAAAAQ24JncNbQwWk5UQ84N23apISEBL300ksaNmxYs3u7d+9WTU2N+vTpc9zPbtmyRR6PR2PGjAldS09P1+DBg7VhwwZJUmFhofLz8+V0OkPPjB49WsXFxTp48GCL3gEAAAAAAIDYlvKVMzgNw4hyNWhPzhM/0rbGjx/f7EzNr/r8888lSU899ZTefvtt2e12jR07VrNnz1ZaWppKS0slSV27dm32uaysrNC90tJSDRgw4Jj7krR///4WvQMAAAAAAACxze1q7OD0BwzV1vvlSop67IV2EtP/pj///HPZ7XZlZWVp8eLF2r17t/74xz9q27ZteuKJJ+T1eiVJiYmJzT6XlJSkyspKSVJtbe1x70tSXV1di94RLqcz6g2yiCEOh73ZXwGgJVg7AISDtQNAa7FuwAwcDpsSHHY1+AOq8wWU5ubrua3FytoR0wHnNddco5/85CfKyMiQJA0YMECdO3fWD3/4Q3322WdKTk6W1HiOZvD7UmNw6XK5JEnJycnHDAuqq6uTJKWkpLToHeGw223KyHCH/XmYV3p6+F9XAKyLtQNAOFg7ALQW6wbiXWpKgiqO1smR4CSXaUfRXjtiOuC02+2hcDOof//+khq3nge3lZeXl6tHjx6hZ8rLy5WTkyNJys7OVnl5ebN3BH/cpUsX+Xy+E74jHIGAoaqqmrA/D/NxOOxKT3epqsorvz8Q7XIAxAnWDgDhYO0A0FqsGzCLlCSnKo7WaX9ZlTJSYjr2MoW2XDvS010t7gyN6X/Tc+bMUXl5uZYuXRq69tlnn0mS+vXrp+7duys1NVXr168PhZNVVVXavHmzpk6dKknKy8vT8uXL5ff75XA4JEnr1q1T79691bFjR6WlpZ3wHeHy+fhFAcfy+wN8bQBoNdYOAOFg7QDQWqwbiHeupkFDVZ56vpbbUbTXjpg+jOD888/X+++/r4ULF2r37t1as2aNfve732nSpEnq27evEhMTNXXqVM2fP19vvvmmtmzZotmzZys7O1sTJkyQJE2ZMkXV1dX6/e9/r+3bt2vFihVaunSpZs6cKUktegcAAAAAAABiX2py46AhT21DlCtBe4rpDs7vfOc7euCBB/TII4/or3/9q9LS0nTRRRfp+uuvDz0za9Ys+Xw+zZ07V7W1tcrLy9OSJUuUkND4Bd2xY0c9+uijuvPOOzV58mR17txZc+bM0eTJk1v8DgAAAAAAAMQ+d1MHZ02tL8qVoD3ZDMMwol2EGfn9AR0+7Il2GYghTqddGRluVVR4aJMH0GKsHQDCwdoBoLVYN2AWf1u9Tf8sLNH3RvfQD8b1i3Y5pteWa0dmprvFZ3DG9BZ1AAAAAAAAoKXcrsYOTo+XDk4rIeAEAAAAAACAKbibzuCs4QxOSyHgBAAAAAAAgCkEz+D0cAanpRBwAgAAAAAAwBTcrqYp6l46OK2EgBMAAAAAAACmENyiTgentRBwAgAAAAAAwBS+3KJOB6eVEHACAAAAAADAFIJb1Gvr/fL5A1GuBu2FgBMAAAAAAACmkJLkDH2/po5t6lZBwAkAAAAAAABTsNttcjWFnAwasg4CTgAAAAAAAJhG8BzOGgYNWQYBJwAAAAAAAEzjy0nqdHBaBQEnAAAAAAAATMPtCm5Rp4PTKgg4AQAAAAAAYBp0cFoPAScAAAAAAABMI3gGp4czOC2DgBMAAAAAAACm4XY1dXAyRd0yCDgBAAAAAABgGil0cFoOAScAAAAAAABMgzM4rYeAEwAAAAAAAKZBwGk9BJwAAAAAAAAwjeCQoRq2qFsGAScAAAAAAABMgyFD1kPACQAAAAAAANNwf2XIkGEYUa4G7YGAEwAAAAAAAKYRPIPTHzBU1+CPcjVoDwScAAAAAAAAMI3EBLucDpskyePlHE4rIOAEAAAAAACAadhsNiapWwwBJwAAAAAAAEwl5SvncML8CDgBAAAAAABgKkxStxYCTgAAAAAAAJiKO6mxg7Omjg5OKyDgBAAAAAAAgKnQwWktBJwAAAAAAAAwleCQoWqGDFkCAScAAAAAAABMxd00ZKiGIUOWQMAJAAAAAAAAU2GLurUQcAIAAAAAAMBUgh2cHjo4LYGAEwAAAAAAAKaS0nQGp4czOC2BgBMAAAAAAACm4nY1dXB66eC0AgJOAAAAAAAAmEpwinpNHR2cVkDACQAAAAAAAFMJnsHprfPL5w9EuRq0NQJOAAAAAAAAmEpKU8ApSTV1bFM3OwJOAAAAAAAAmIrDbpcrySFJqmGSuukRcAIAAAAAAMB0gudwerycw2l2BJwAAAAAAAAwnVDAWUvAaXYEnAAAAAAAADCd4DmcHraomx4BJwAAAAAAAEzH7WKLulUQcAIAAAAAAMB0Ups6OBkyZH4EnAAAAAAAADCdlKYzOKs5g9P0CDgBAAAAAABgOm5X0xmcXjo4zY6AEwAAAAAAAKYTnKJeQwen6RFwAgAAAAAAwHTcTFG3DAJOAAAAAAAAmE6wg9NDB6fpEXACAAAAAADAdFLo4LQMAk4AAAAAAACYTqqrqYPT2yDDMKJcDdoSAScAAAAAAABMJ7hF3R8wVNfgj3I1aEsEnAAAAAAAADCdxAS7HHabJKmGbeqmRsAJAAAAAAAA07HZbHI3bVOv9jJoyMwIOAEAAAAAAGBK7qZBQ3RwmhsBJwAAAAAAAEwpeA6np5YOTjMj4AQAAAAAAIApBTs4PXRwmhoBJwAAAAAAAEwphQ5OSyDgBAAAAAAAgCm5XU0dnF46OM2MgBMAAAAAAACmlEoHpyUQcAIAAAAAAMCUUjiD0xIIOAEAAAAAAGBKbldTB6eXDk4zI+AEAAAAAACAKbnZom4JBJwAAAAAAAAwJXfTFvUatqibGgEnAAAAAAAATCm0RZ0OTlMj4AQAAAAAAIApBYcMeev88gcCUa4GbYWAEwAAAAAAAKYU3KIusU3dzAg4AQAAAAAAYEoOu12uJIckyUPAaVoxFXA+/PDDmjZtWrNrRUVFmjp1qnJzczV+/Hg9+eSTze4HAgEtWLBABQUFys3N1YwZM1RSUhLxdwAAAAAAACD+pCRxDqfZxUzA+cwzz+iBBx5odq2iokLTp09Xjx499MILL+i6667T/Pnz9cILL4SeWbRokZYtW6bbb79dy5cvVyAQ0JVXXqn6+vqIvQMAAAAAAADxye1q3Kbu8dLBaVbOEz/StsrKynTLLbdo/fr16tWrV7N7zz77rBISEnTbbbfJ6XSqb9++2rVrlx555BFNmTJF9fX1euyxx3TTTTdp3LhxkqT7779fBQUFWrVqlSZNmhSRdwAAAAAAACA+uZPp4DS7qHdwbtq0SQkJCXrppZc0bNiwZvcKCwuVn58vp/PLHHb06NEqLi7WwYMHtWXLFnk8Ho0ZMyZ0Pz09XYMHD9aGDRsi9g4AAAAAAADEp+CgIYYMmVfUOzjHjx+v8ePHH/deaWmpBgwY0OxaVlaWJGn//v0qLS2VJHXt2vWYZ4L3IvEOAAAAAAAAxCe3q6mD00sHp1lFPeD8NrW1tUpMTGx2LSkpSZJUV1cnr9crScd9prKyMmLvCJfTGfUGWcQQh8Pe7K8A0BKsHQDCwdoBoLVYN2BmaSmNmU9NvY+sJsJiZe2I6YAzOTn5mEE/dXV1kqSUlBQlJydLkurr60PfDz7jcrki9o5w2O02ZWS4w/48zCs9PfyvKwDWxdoBIBysHQBai3UDZtQpI0WS5AuIrKaNRHvtiOmAMzs7W+Xl5c2uBX/cpUsX+Xy+0LUePXo0eyYnJydi7whHIGCoqqom7M/DfBwOu9LTXaqq8srvD0S7HABxgrUDQDhYOwC0FusGzMwuQ5J0uNKrigpPlKsxl7ZcO9LTXS3uDI3pgDMvL0/Lly+X3++Xw+GQJK1bt069e/dWx44dlZaWptTUVK1fvz4UTlZVVWnz5s2aOnVqxN4RLp+PXxRwLL8/wNcGgFZj7QAQDtYOAK3FugEzSk5ozIOqvQ18fbeRaK8dMX3wwJQpU1RdXa3f//732r59u1asWKGlS5dq5syZkhrPzZw6darmz5+vN998U1u2bNHs2bOVnZ2tCRMmROwdAAAAAAAAiE8MGTK/mO7g7Nixox599FHdeeedmjx5sjp37qw5c+Zo8uTJoWdmzZoln8+nuXPnqra2Vnl5eVqyZIkSEhIi9g4AAAAAAADEJ3dyY/zlqfVFuRK0FZthGEa0izAjvz+gw4c51wFfcjrtyshwq6LCQ0s8gBZj7QAQDtYOAK3FugEzO1RZq1/95T05HTY9fNM42Wy2aJdkGm25dmRmult8BmdMb1EHAAAAAAAATobb1djB6fMbqm8gwDcjAk4AAAAAAACYVlKCQw57Y9emp5ZzOM2IgBMAAAAAAACmZbPZOIfT5Ag4AQAAAAAAYGpMUjc3Ak4AAAAAAACYWgodnKZGwAkAAAAAAABTcyc3dXByBqcpEXACAAAAAADA1Ag4zY2AEwAAAAAAAKYWHDJUwxZ1UyLgBAAAAAAAgKkxZMjcCDgBAAAAAABgasEOzmo6OE2JgBMAAAAAAACmFjyDs4YzOE2JgBMAAAAAAACm5nY1dnB6vHRwmhEBJwAAAAAAAEyNKermRsAJAAAAAAAAU0tpOoPTwxmcpkTACQAAAAAAAFMLTlH31vkUCBhRrgaRRsAJAAAAAAAAU0tJcoa+X1NHF6fZEHACAAAAAADA1JwOu5ITHZIkj5dzOM2GgBMAAAAAAACmFxw0VM2gIdMh4AQAAAAAAIDpuZsGDdUwaMh0CDgBAAAAAABgesFBQ2xRNx8CTgAAAAAAAJhesIPTQwen6RBwAgAAAAAAwPRSms7g9HAGp+kQcAIAAAAAAMD03K6mDk4vHZxmQ8AJAAAAAAAA00ulg9O0CDgBAAAAAABgeilMUTctAk4AAAAAAACYnrupg7OaDk7TIeAEAAAAAACA6bnp4DQtAk4AAAAAAACYntvVdAanlw5OsyHgBAAAAAAAgOm5vzJkyDCMKFeDSCLgBAAAAAAAgOkFhwz5/IbqfYEoV4NIIuAEAAAAAACA6SUnOuSw2ySxTd1sCDgBAAAAAABgejabLTRoyMOgIVMh4AQAAAAAAIAlpDSdw1lTSwenmRBwAgAAAAAAwBLcrsYOzmovHZxmQsAJAAAAAAAAS/jqJHWYBwEnAAAAAAAALCF4BmcNZ3CaCgEnAAAAAAAALIEOTnMi4AQAAAAAAIAlpDBF3ZQIOAEAAAAAAGAJbldTB6eXDk4zIeAEAAAAAACAJaSyRd2UCDgBAAAAAABgCWxRNycCTgAAAAAAAFgCW9TNiYATAAAAAAAAluCmg9OUCDgBAAAAAABgCe6mMzi9dT4FAkaUq0GkEHACAAAAAADAEoJncEpSTR1dnGZBwAkAAAAAAABLcDrsSkp0SOIcTjMh4AQAAAAAAIBlpHIOp+kQcAIAAAAAAMAygudwemrp4DQLAk4AAAAAAABYRkqog5OA0ywIOAEAAAAAAGAZbldTB6eXLepmQcAJAAAAAAAAy2CLuvkQcAIAAAAAAMAy3E1b1GsYMmQaBJwAAAAAAACwjC+3qNPBaRYEnAAAAAAAALCML4cM0cFpFgScAAAAAAAAsIxUzuA0HQJOAAAAAAAAWIabDk7TIeAEAAAAAACAZaQkcwan2RBwAgAAAAAAwDLcri87OA3DiHI1iAQCTgAAAAAAAFiGu6mD0+cPqN4XiHI1iAQCTgAAAAAAAFhGcqJDdptNklTDOZymQMAJAAAAAAAAy7DZbF9uU+ccTlMg4AQAAAAAAIClBLepe2oJOM2AgBMAAAAAAACW4k7+ctAQ4h8BJwAAAAAAACzF7Wrq4GSLuikQcAIAAAAAAMBSUujgNBUCTgAAAAAAAFgKZ3CaCwEnAAAAAAAALIUzOM2FgBMAAAAAAACWEurg5AxOU4iLgLOsrEw5OTnHfFuxYoUkqaioSFOnTlVubq7Gjx+vJ598stnnA4GAFixYoIKCAuXm5mrGjBkqKSlp9syJ3gEAAAAAAABzcLsaOzhr2KJuCs5oF9ASW7ZsUVJSklavXi2bzRa6npaWpoqKCk2fPl3jx4/Xrbfeqk8++US33nqr3G63pkyZIklatGiRli1bprvvvlvZ2dm69957deWVV+rll19WYmJii94BAAAAAAAAcwh2cFazRd0U4iLg/Pzzz9WrVy9lZWUdc++JJ55QQkKCbrvtNjmdTvXt21e7du3SI488oilTpqi+vl6PPfaYbrrpJo0bN06SdP/996ugoECrVq3SpEmT9Oyzz37rOwAAAAAAAGAewYCTDk5ziIst6lu3blXfvn2Pe6+wsFD5+flyOr/MakePHq3i4mIdPHhQW7Zskcfj0ZgxY0L309PTNXjwYG3YsKFF7wAAAAAAAIB5BLeoe7x0cJpB3HRwZmRk6PLLL9fOnTvVs2dPXXPNNRo7dqxKS0s1YMCAZs8HOz3379+v0tJSSVLXrl2PeSZ470Tv6NSpU1h1O51xkR+jnTgc9mZ/BYCWYO0AEA7WDgCtxboBq+mQmiRJqqnzyW63yW63neATOJ5YWTtiPuD0+XzasWOH+vXrp9/85jdKTU3VypUrddVVV+nxxx9XbW2tEhMTm30mKanxi7Surk5er1eSjvtMZWWlJJ3wHeGw223KyHCH9VmYW3q6K9olAIhDrB0AwsHaAaC1WDdgFWlf+VpPdCUqLSXxW57GiUR77Yj5gNPpdGr9+vVyOBxKTk6WJJ1++unatm2blixZouTkZNXX1zf7TDCUTElJCX2mvr4+9P3gMy5X4z/8E70jHIGAoaqqmrA+C3NyOOxKT3epqsorvz8Q7XIAxAnWDgDhYO0A0FqsG7Ci5ESHauv92ru/Ul0yw8t/rK4t1470dFeLO0NjPuCUJLf72E7I/v37a+3atcrOzlZ5eXmze8Efd+nSRT6fL3StR48ezZ7JycmRpBO+I1w+H78o4Fh+f4CvDQCtxtoBIBysHQBai3UDVpKS7FRtvV+V1fXqmJ584g/gG0V77Yj5wzW2bdumESNGaP369c2ub9y4Uf369VNeXp4+/PBD+f3+0L1169apd+/e6tixowYOHKjU1NRmn6+qqtLmzZuVl5cnSSd8BwAAAAAAAMyFSermEfMBZ9++fdWnTx/ddtttKiws1BdffKE//OEP+uSTT3TNNddoypQpqq6u1u9//3tt375dK1as0NKlSzVz5kxJjWdvTp06VfPnz9ebb76pLVu2aPbs2crOztaECRMk6YTvAAAAAAAAgLm4kxs3NlcTcMa9mN+ibrfbtXjxYt133326/vrrVVVVpcGDB+vxxx8PTT5/9NFHdeedd2ry5Mnq3Lmz5syZo8mTJ4feMWvWLPl8Ps2dO1e1tbXKy8vTkiVLlJDQmNR37NjxhO8AAAAAAACAeXzZwemLciU4WTbDMIxoF2FGfn9Ahw97ol0GYojTaVdGhlsVFR7OtAHQYqwdAMLB2gGgtVg3YEVLXyvS25/u1+SC3rro7N7RLicuteXakZnpbvGQoZjfog4AAAAAAABEWrCD00MHZ9wj4AQAAAAAAIDlpDSdwenhDM64R8AJAAAAAAAAy3G7mjo4vXRwxjsCTgAAAAAAAFjOl1vU6eCMdwScAAAAAAAAsBx30xZ1pqjHPwJOAAAAAAAAWE6wg7OaDs64R8AJAAAAAAAAywl2cHIGZ/wj4AQAAAAAAIDlBIcM+fwB1Tf4o1wNTgYBJwAAAAAAACwnOdEhu80mSfJwDmdcI+AEAAAAAACA5dhsNqUEt6lzDmdcI+AEAAAAAACAJQW3qXu8BJzxjIATAAAAAAAAlpQa6uBki3o8I+AEAAAAAACAJaUkN3VwskU9rhFwAgAAAAAAwJLcrqYOTi8dnPGMgBMAAAAAAACW5E6ig9MMCDgBAAAAAABgScEOzhrO4IxrBJwAAAAAAACwJDdncJoCAScAAAAAAAAsKSU4Rd1LwBnPCDgBAAAAAABgSW5XsIOTLerxjIATAAAAAAAAlpTKFnVTIOAEAAAAAACAJQW3qDNkKL4RcAIAAAAAAMCSglvUa2p9CgSMKFeDcBFwAgAAAAAAwJLcTR2chqSaOro44xUBJwAAAAAAACzJ6bArKcEhSarhHM64RcAJAAAAAAAAy3K7Grs4maQevwg4AQAAAAAAYFkpSU2T1L10cMYrAk4AAAAAAABYViodnHGPgBMAAAAAAACW5U5u6uDkDM64RcAJAAAAAAAAy0ppmqTOFvX4RcAJAAAAAAAAy3K7gh2cbFGPVwScAAAAAAAAsCx3sIOTLepxi4ATAAAAAAAAlhU8g7OGDs64RcAJAAAAAAAAywptUecMzrhFwAkAAAAAAADLCg0ZooMzbhFwAgAAAAAAwLJSk4NDhujgjFcEnAAAAAAAALAsNx2ccY+AEwAAAAAAAJaV0tTB2eALqL7BH+VqEA4CTgAAAAAAAFiWK8khu80miS7OeEXACQAAAAAAAMuy2WxfGTTEOZzxiIATAAAAAAAAlhY6h9NLwBmPCDgBAAAAAABgaW5X4zmcNWxRj0sEnAAAAAAAALA0d9OgoWq2qMclAk4AAAAAAABYWnCLOh2c8YmAEwAAAAAAAJYW7OBkyFB8IuAEAAAAAACApYWmqHvp4IxHBJwAAAAAAACwtOCQITo44xMBJwAAAAAAACwteAanhzM44xIBJwAAAAAAACwtdAanlw7OeETACQAAAAAAAEtzu5iiHs8IOAEAAAAAAGBpTFGPbwScAAAAAAAAsLTgGZw1tT4FDCPK1aC1CDgBAAAAAABgaSlNHZyGJG8d29TjDQEnAAAAAAAALC3BaVdiQmNMxqCh+EPACQAAAAAAAMv78hxOOjjjDQEnAAAAAAAALI9BQ/GLgBMAAAAAAACWFxw05PHSwRlvCDgBAAAAAABgeW5XYwdnDR2ccYeAEwAAAAAAAJYX7OCs5gzOuEPACQAAAAAAAMsLncHJFPW4Q8AJAAAAAAAAy3O7Gjs4a+jgjDsEnAAAAAAAALA8pqjHLwJOAAAAAAAAWF5KaIo6AWe8IeAEAAAAAACA5QWnqHvq2KIebwg4AQAAAAAAYHluOjjjFgEnAAAAAAAALC94BidDhuIPAScAAAAAAAAsLxhw1vsCqm/wR7katAYBJwAAAAAAACwvOckhm63x+x66OOMKAWeTQCCgBQsWqKCgQLm5uZoxY4ZKSkqiXRYAAAAAAADagd1m+8o2dc7hjCcEnE0WLVqkZcuW6fbbb9fy5csVCAR05ZVXqr6+PtqlAQAAAAAAoB2EBg3RwRlXCDgl1dfX67HHHtOsWbM0btw4DRw4UPfff79KS0u1atWqaJcHAAAAAACAdpDS1MHJJPX4QsApacuWLfJ4PBozZkzoWnp6ugYPHqwNGzZEsTIAAAAAAAC0F7eLDs545Ix2AbGgtLRUktS1a9dm17OyskL3Wstutykz033StcE8ggcVd+jgkmFEtxYA8YO1A0A4WDsAtBbrBtBozv/kqa7er1RXglxJxGYn0pZrh91ua/Gz/JuS5PV6JUmJiYnNriclJamysjKsd9psNjkcLf8XAeuw22mcBtB6rB0AwsHaAaC1WDdgdRlpydEuIS5Fe+1g5ZKUnNz4xfv1gUJ1dXVyuVzRKAkAAAAAAABACxBw6sut6eXl5c2ul5eXq0uXLtEoCQAAAAAAAEALEHBKGjhwoFJTU7V+/frQtaqqKm3evFl5eXlRrAwAAAAAAADAt+EMTjWevTl16lTNnz9fmZmZOvXUU3XvvfcqOztbEyZMiHZ5AAAAAAAAAL4BAWeTWbNmyefzae7cuaqtrVVeXp6WLFmihISEaJcGAAAAAAAA4BvYDCPSQ9wBAAAAAAAAoH1wBicAAAAAAACAuEXACQAAAAAAACBuEXACAAAAAAAAiFsEnAAAAAAAAADiFgEnAAAAAAAAgLhFwAkAAAAAAAAgbhFwAgAAAAAAAIhbBJxAhAQCAS1YsEAFBQXKzc3VjBkzVFJS8o3PNzQ06L777gs9P3XqVBUVFbVjxQBiQWvWjoceekg5OTnH/fbb3/62nSsHEE2t/X3HoUOHdOONN2r06NE688wzNXv2bJWVlbVjxQBiQWvXjuLiYl111VUaNWqUxo4dqwULFsjn87VjxQBiycMPP6xp06Z96zMVFRW68cYblZeXp/z8fN16663yer1tXhsBJxAhixYt0rJly3T77bdr+fLlCgQCuvLKK1VfX3/c5+fNm6cVK1borrvu0gsvvKDMzEzNmDFDR48ebefKAURTa9aOn/3sZ1q7dm2zbz//+c+VkpKiK664ov2LBxA1rf19x/XXX699+/bp8ccf1+OPP659+/bpuuuua+eqAURba9aOyspKXX755fJ6vXriiSf0pz/9Sa+99ppuvvnmKFQOINqeeeYZPfDAAyd8btasWdq1a5eWLl2qBx98UGvWrNG8efPavD4ZAE5aXV2dMXz4cOOZZ54JXausrDSGDh1qvPzyy8c8v3v3biMnJ8d46623mj1/3nnnGe+99157lAwgBrR27fi6TZs2GUOGDDFWrFjRlmUCiDGtXTsqKyuNAQMGGG+++Wbo2urVq40BAwYYFRUV7VEygBjQ2rXj8ccfN3Jzc41Dhw6FrhUWFhoDBgwwSkpK2qVmANFXWlpqzJw508jNzTUuuOACY+rUqd/47EcffWQMGDDA2L59e+jaO++8Y+Tk5BilpaVtWicdnEAEbNmyRR6PR2PGjAldS09P1+DBg7Vhw4Zjnn/33XeVlpamsWPHNnv+X//6V7N3ADC31q4dX3fbbbdp1KhRmjx5cluWCSDGtHbtSE5Oltvt1j/+8Q9VV1erurpaL774onr37q309PT2LB1AFLV27di1a5f69OmjzMzM0LXBgwdLkgoLC9u+YAAxYdOmTUpISNBLL72kYcOGfeuzhYWF6ty5s/r27Ru6lp+fL5vNpg8//LBN63S26dsBiygtLZUkde3atdn1rKys0L2v2rlzp7p3765Vq1bpkUceUVlZmQYPHqzf/OY3zRYCAObW2rXjq9566y19/PHH+sc//tFW5QGIUa1dOxITE3X33Xfr5ptv1qhRo2Sz2ZSVlaWnn35adjv9DoBVtHbtyMrKUnl5ufx+vxwOhyRp7969khrP9QVgDePHj9f48eNb9GxZWdkxa0xiYqJOOeUU7d+/vy3KC+F3NEAEBA/MTUxMbHY9KSlJdXV1xzxfXV2tXbt2adGiRbrhhhv0l7/8RU6nUz/5yU/4zQJgIa1dO77q8ccf13nnnadBgwa1WX0AYlNr1w7DMFRUVKThw4frmWee0RNPPKFu3brp2muvVXV1dbvUDCD6Wrt2fO9739ORI0f0hz/8QTU1NTp48KDuuOMOOZ1ONTQ0tEvNAOKL1+s9Zo2RWvb/NyeLgBOIgOTkZEk65nDuuro6uVyuY553Op2qrq7W/fffr3POOUdDhw7V/fffL0n6+9//3vYFA4gJrV07gvbt26f169frxz/+cZvWByA2tXbteO211/T000/r3nvv1ciRI5Wfn6/Fixdr7969ev7559ulZgDR19q1o1evXnrwwQf1+uuva+TIkTr//PM1btw4ZWRkKC0trV1qBhBfkpOTjzu0rK6uTikpKW36cxNwAhEQbMEuLy9vdr28vFxdunQ55vns7Gw5nc5m29GTk5PVvXt37dmzp22LBRAzWrt2BK1evVqZmZk6++yz27Q+ALGptWtHYWGhevfurdTU1NC1Dh06qHfv3tq1a1fbFgsgZoTz+47x48dr7dq1WrNmjd5//3398Ic/1MGDB9W9e/c2rxdA/MnOzj5mjamvr9eRI0eUlZXVpj83AScQAQMHDlRqaqrWr18fulZVVaXNmzcrLy/vmOfz8vLk8/n02Wefha7V1taqpKREPXv2bJeaAURfa9eOoMLCQuXn58vp5ChtwIpau3ZkZ2dr165dzbaG1dTUaM+ePerVq1d7lAwgBrR27SgsLNS0adPk8/mUlZWlxMRErVq1Si6XSyNGjGjP0gHEiby8PJWWljb7A9QPPvhAkjRy5Mg2/bkJOIEISExM1NSpUzV//ny9+eab2rJli2bPnq3s7GxNmDBBfr9fBw4cUG1trSRp1KhROuuss/TrX/9ahYWF2r59u+bMmSOHw6FLLrkkyn83ANpLa9eOoM2bN2vgwIFRqhpAtLV27bj00kslSddff722bNmiLVu26IYbblBSUpIuu+yyKP6dAGhPrV07+vTpo61bt+qee+5RSUmJVq9erTvuuEMzZ85s1hEOwLq+vm4MGzZMI0aM0OzZs/Wf//xH69at080336xLL730W3eoRQIBJxAhs2bN0ve//33NnTtXP/7xj+VwOLRkyRIlJCRo//79Ouecc/Tqq6+Gnn/ooYeUn5+vX/ziF/r+97+v6upqPfnkk8rMzIzi3wWA9tbatUOSDhw4oFNOOSU6BQOICa1ZO7KysrRs2TIZhqGf/vSnmj59uhISErRs2TLO0QMspjVrR2ZmphYvXqxPP/1UkyZN0t13361f/OIXuvrqq6P8dwEgVnx93bDZbFq4cKFOO+00/fSnP9X111+vsWPHat68eW1ei80wDKPNfxYAAAAAAAAAaAN0cAIAAAAAAACIWwScAAAAAAAAAOIWAScAAAAAAACAuEXACQAAAAAAACBuEXACAAAAAAAAiFsEnAAAAAAAAADiFgEnAAAAAAAAgLhFwAkAAAAAAAAgbhFwAgAAAAAAAIhbBJwAAACIOsMwol1CTNQAAACA1iPgBAAAQERNmzZN06ZN+8b748eP129+85vQj9988039+te//sb7OTk5euihh77xfiScqAYAAADELme0CwAAAIC1LV26tNmPFy5cqNTU1G98/kT326MGAAAAxA4CTgAAAMSUwYMHn9T99qihraxbt05//OMftWPHDknSuHHjdPvttystLS0q9QAAAMQDtqgDAAAgaqZNm6YPPvhAH3zwgXJycrR+/foTbg8P3l+/fr1ycnKO++2rW+Rra2t13333acKECTr99NM1YsQITZ8+XUVFRS2uwe/365lnntFFF12koUOHaty4cZo/f77q6uqa1bVgwQLdc889OuusszR06FD9/Oc/V3FxcYv+WTz33HOaPn26+vTpo1/96lc699xz9dprr+muu+5q5T9VAAAAa6GDEwAAAFFzyy236Fe/+lXo+/369WvxZ4cMGaL/+7//a3btlVde0VNPPaXvf//7oWtz5sxRYWGhbrjhBvXo0UO7du3Sgw8+qBtvvFErV65sUQ0333yzXnzxRc2YMUOjRo3S5s2b9ec//1lFRUV69NFHZbPZJElPPvmkRo4cqT/84Q+qrKzUnXfeqV//+tfH1Pl1X3zxhebNm6ff/e53oXD28ssv19SpU7Vy5Urdfvvtcjr5rTsAAMDx8LskAAAARE2/fv1CZ13m5ua26rOpqanNPvPpp5/q2Wef1RVXXKFLLrlEklRfXy+Px6O5c+dq4sSJkqT8/HxVV1fr7rvv1sGDB09Yw/bt2/X888/rxhtv1FVXXSVJOvvss5WVlaU5c+bo7bff1rnnnitJSk9P16JFi+RwOCRJu3fv1kMPPaSKigplZGR849/LwoULlZOTo6lTpza7PmrUKG3YsEGVlZXq2LFjq/75AAAAWAVb1AEAANDugh2PkVJaWqrrrrtOw4cP15w5c0LXExMTtWTJEk2cOFFlZWVat26dli9frrfeektSYwB6Ih988IEk6cILL2x2/cILL5TD4dD69etD184444xQuClJ2dnZkiSv1/uN7/f5fFqzZo3OP//8Y/65eL1e2Ww2Bh4BAAB8Czo4AQAAEFEpKSk6cuTIN96vr6+Xy+WK2M/n9Xp17bXXKjExUQ888ECzgFGS3nnnHd11113asWOH3G63Bg4cqJSUFEmSYRgnfH9lZaUkqXPnzs2uO51OZWRk6OjRo6FrX//7stsb+wkCgcA3vn/z5s3yeDwaOHDgMfeKioo0cOBAJSUlnbBOAAAAq6KDEwAAABHVqVMnlZeXH/defX29Dh8+rE6dOkXk5zIMQ7/5zW+0Y8cO/fnPfz5mG/ju3bt13XXXadCgQfrnP/+pDz/8UMuWLdN5553X4p+jQ4cOkqQDBw40u97Q0HDCrectERx2FAxdg8rLy/XRRx/pu9/97km9HwAAwOwIOAEAABBR+fn52rdvnz755JNj7q1evVp+v1+jR48OXQt2OYZj4cKFev3113XHHXdo0KBBx9zfuHGj6urqdNVVV6lHjx6hLeDvvPOOpC87OL+thvz8fEnSypUrm11fuXKl/H6/Ro4cGXb9krR161ZJX26Flxq3rc+bN0+pqan60Y9+dFLvBwAAMDu2qAMAACCiJk6cqCeeeEIzZ87UzJkzNWTIEAUCAX300Ud69NFHNWnSJI0YMSL0fHp6uj7++GO9//77Gjx4cIt/nlWrVunPf/6zJk2apD59+ujTTz9ttuU8NzdXQ4YMkdPp1L333quf/exnqq+v14oVK/Tvf/9bklRTU3PCGvr166fJkydrwYIF8nq9ysvLU1FRkRYuXKgzzzxTBQUFJ/FPS/r888/Vr18/LV68WF6vV507d9arr76qzz77TAsWLIhYtysAAIBZEXACAAAgohISEvT0009r8eLFeu6557RgwQLZ7Xb17NlTs2fPPmZS+OWXX66NGzdqxowZ+sMf/tDin+df//qXDMPQK6+8oldeeeWY+1u3blXPnj113333aeHChbrmmmvUoUMH5ebm6qmnntK0adNUWFionJycE9Zw5513qmfPnnrhhRf017/+VVlZWfqf//kfXXvttSfVgSo1Bpw/+clP1K1bNy1atEiHDh3SkCFDtGTJEo0ZM+ak3g0AAGAFNqMlJ6sDAAAAiLj9+/dr3Lhxuu+++zRp0qRolwMAABCXOIMTAAAAiJLPP/9cUuM2eAAAAISHgBMAAACIkq1bt8rhcKhPnz7RLgUAACBuEXACAAAAUbJ161Z1795diYmJ0S4FAAAgbnEGJwAAAAAAAIC4RQcnAAAAAAAAgLhFwAkAAAAAAAAgbhFwAgAAAAAAAIhbBJwAAAAAAAAA4hYBJwAAAAAAAIC4RcAJAAAAAAAAIG4RcAIAAAAAAACIWwScAAAAAAAAAOIWAScAAAAAAACAuEXACQAAAAAAACBuEXACAAAAAAAAiFv/H9HVw6vl5y9kAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.plot(rho, yield_per_day)\n",
    "ax.set_ylim(0, max(yield_per_day) * 1.05)\n",
    "ax.set(title=\"Revenue as a function of the utilization of the operators\", xlabel=\"Utilization $\\\\rho$\", ylabel=\"Revenue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost-optimal utilization of the operators: ρ= 87.0 %\n",
      "Analyzed range: 57.1 % ... 98.8 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Cost-optimal utilization of the operators: \\N{greek small letter rho}=\", round(rho[np.argmax(yield_per_day)] * 100, 1), \"%\")\n",
    "print(\"Analyzed range:\", round(min(rho) * 100, 1), \"% ...\", round(max(rho) * 100, 1), \"%\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}