Integer_Optimizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e30a631d",
   "metadata": {},
   "source": [
    "# Loads packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3dcb71eb-b0a7-46b0-b843-dabb47086fd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "from scipy.spatial import ConvexHull\n",
    "from matplotlib.patches import Polygon\n",
    "import sys,os\n",
    "sys.path.append(os.path.realpath('./src/'))\n",
    "import pandas as pd\n",
    "from materialEncoder import MaterialEncoder\n",
    "import time\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facd1d4f",
   "metadata": {},
   "source": [
    "## Loads the Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6443eef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocessData():\n",
    "  df = pd.read_excel('./data/MaterialDatabase_FULL.xlsx')# # \n",
    "  dataIdentifier = {'name': df[df.columns[0]], 'className':df[df.columns[1]], 'classID':df[df.columns[2]]} # name of the material and type\n",
    "  trainInfo = torch.tensor((df[df.columns[3:]].to_numpy())).float()\n",
    "\n",
    "  return dataIdentifier, trainInfo, df\n",
    "dataIdentifier, trainInfo, df = preprocessData()\n",
    "\n",
    "props = df.columns[3:].tolist()\n",
    "nmat, nprop = trainInfo.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02018f95",
   "metadata": {},
   "source": [
    "## Loads the truss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59649404-c5ec-4dc5-8de0-1061ddab8047",
   "metadata": {},
   "outputs": [],
   "source": [
    "from examples import getExample\n",
    "from trussFE import TrussFE\n",
    "\n",
    "#Loads the example truss\n",
    "exampleName, nodeXY, connectivity, bc = getExample(4)\n",
    "#Changes the force\n",
    "bc['forces'] = {'nodes':np.array([2]), 'fx':1.E3*torch.tensor([0.]), 'fy':1.E2*torch.tensor([125.])}\n",
    "\n",
    "#Loads the truss in truss\n",
    "truss = TrussFE(nodeXY, connectivity, bc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4219c821",
   "metadata": {},
   "source": [
    "## SELECT CONSTRAINTS AND OBJECTIVES HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef40e2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defines constraints and objectives\n",
    "constraints = {'massCons': {'isOn':False, 'maxMass':5}, \\\n",
    "               'costCons': {'isOn':False, 'maxCost':5},\\\n",
    "               'tensionCons': {'isOn':True, 'FOS':4.},\\\n",
    "               'complianceCons': {'isOn':False, 'maxJ':35},\\\n",
    "               'compressionCons': {'isOn':True, 'FOS':4.},\\\n",
    "               'co2Cons': {'isOn':False, 'maxco2':30},\\\n",
    "               'energyCons': {'isOn':False, 'maxenergy' : 1e10},\\\n",
    "               'waterCons': {'isOn':False, 'maxwater':4e3}}\n",
    "\n",
    "\n",
    "objectives = {'cost': {'isOn':True},\\\n",
    "              'mass': {'isOn':True}, \\\n",
    "              'co2': {'isOn':False},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\n",
    "              'compliance': {'isOn': False}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 3:\n",
    "    print('Error: Three or more entries have isOn=True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63a6d91",
   "metadata": {},
   "source": [
    "## ---------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154d475d",
   "metadata": {},
   "source": [
    "## Defines problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac0f696e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pymoo.core.problem import ElementwiseProblem\n",
    "from pymoo.core.variable import Real, Integer, Choice, Binary\n",
    "from pymoo.termination.default import DefaultMultiObjectiveTermination\n",
    "\n",
    "class MixedVarsZDT1(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, trainInfo , constraints, objectives, nc, no, nmat, **kwargs):\n",
    "\n",
    "        variables = dict()\n",
    "\n",
    "        variables[f\"x01\"] = Integer(bounds=(0, nmat-1))\n",
    "\n",
    "        for k in range(2, 8):\n",
    "            variables[f\"x{k:02}\"] = Real(bounds=(2e-6, 2e-2))\n",
    "\n",
    "        super().__init__(vars=variables, n_obj=no, n_ieq_constr=nc, **kwargs)\n",
    "        \n",
    "        self.truss=truss\n",
    "        self.trainInfo=trainInfo\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives\n",
    "        \n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        x = np.array([x[f\"x{k:02}\"] for k in range(1, 8)])\n",
    "        \n",
    "        matprop=trainInfo[int(x[0]),:]  \n",
    "        \n",
    "        x_tensor = torch.from_numpy(x[1:])\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _, internalForce = truss.solveFE(E = torch.tensor([matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = matprop[2]*volume\n",
    "        cost = mass*matprop[1]\n",
    "        co2 = mass*matprop[4]\n",
    "        energy = mass*matprop[5]        \n",
    "        water = mass*matprop[6] \n",
    "        \n",
    "        \n",
    "        \n",
    "        con_values = []\n",
    "        \n",
    "        if(constraints['massCons']['isOn']):\n",
    "            constraints['massCons']['value'] = (mass/constraints['massCons']['maxMass']) - 1.\n",
    "            con_values.append(constraints['massCons']['value'])\n",
    "\n",
    "        if(constraints['costCons']['isOn']):\n",
    "            constraints['costCons']['value'] = (cost/constraints['costCons']['maxCost']) - 1.\n",
    "            con_values.append(constraints['costCons']['value'])\n",
    "\n",
    "        if(constraints['tensionCons']['isOn']):\n",
    "            tensileForce = 0.001+torch.relu(internalForce)\n",
    "            tensileStress = tensileForce/area\n",
    "            maxTensileStressExperienced = torch.pow(torch.sum(tensileStress**6), 1./6)*constraints['tensionCons']['FOS']\n",
    "            constraints['tensionCons']['value'] = (maxTensileStressExperienced/matprop[3]) - 1.\n",
    "            con_values.append(constraints['tensionCons']['value'])\n",
    "\n",
    "        if(constraints['complianceCons']['isOn']):\n",
    "            constraints['complianceCons']['value'] = (compliance/constraints['complianceCons']['maxJ']) - 1.\n",
    "            con_values.append(constraints['complianceCons']['value'])\n",
    "\n",
    "        if(constraints['compressionCons']['isOn']):\n",
    "            compressiveForce = 0.001+torch.relu(-internalForce)\n",
    "            k = 4. # used in geom factor... see buckling theory\n",
    "            geomFactor = k*(self.truss.barLength/(np.pi*area))**2\n",
    "            compressiveStress = compressiveForce*geomFactor\n",
    "            maxCompressiveStressExperienced = torch.pow(torch.sum(compressiveStress**6), 1./6)*constraints['compressionCons']['FOS']\n",
    "            constraints['compressionCons']['value'] = (maxCompressiveStressExperienced/matprop[0]) - 1.\n",
    "            con_values.append(constraints['compressionCons']['value'])\n",
    "\n",
    "        if(constraints['co2Cons']['isOn']):\n",
    "            constraints['co2Cons']['value'] = (co2/constraints['co2Cons']['maxco2']) - 1.\n",
    "            con_values.append(constraints['co2Cons']['value'])\n",
    "\n",
    "        if(constraints['energyCons']['isOn']):\n",
    "            constraints['energyCons']['value'] = (energy/constraints['energyCons']['maxenergy']) - 1.\n",
    "            con_values.append(constraints['energyCons']['value'])\n",
    "\n",
    "        if(constraints['waterCons']['isOn']):\n",
    "            constraints['waterCons']['value'] = (water/constraints['waterCons']['maxwater']) - 1.\n",
    "            con_values.append(constraints['waterCons']['value'])\n",
    "            \n",
    "        \n",
    "        \n",
    "        obj_values = []\n",
    "        \n",
    "        if(objectives['cost']['isOn']):\n",
    "            obj_values.append(cost)\n",
    "            \n",
    "        if(objectives['mass']['isOn']):\n",
    "            obj_values.append(mass)\n",
    "            \n",
    "        if(objectives['co2']['isOn']):\n",
    "            obj_values.append(co2)\n",
    "            \n",
    "        if(objectives['water']['isOn']):\n",
    "            obj_values.append(water)\n",
    "            \n",
    "        if(objectives['energy']['isOn']):\n",
    "            obj_values.append(energy)\n",
    "            \n",
    "        if(objectives['compliance']['isOn']):\n",
    "            obj_values.append(compliance)\n",
    "            \n",
    "  \n",
    "\n",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)\n",
    "        \n",
    "  \n",
    "problem = MixedVarsZDT1(truss,trainInfo , constraints, objectives, nc, no, nmat)\n",
    "\n",
    "    \n",
    "from pymoo.visualization.scatter import Scatter\n",
    "from pymoo.algorithms.moo.nsga2 import NSGA2, RankAndCrowdingSurvival\n",
    "from pymoo.core.mixed import MixedVariableMating, MixedVariableGA, MixedVariableSampling, MixedVariableDuplicateElimination\n",
    "from pymoo.optimize import minimize\n",
    "\n",
    "algorithm = NSGA2(pop_size=100,\n",
    "                  sampling=MixedVariableSampling(),\n",
    "                  mating=MixedVariableMating(eliminate_duplicates=MixedVariableDuplicateElimination()),\n",
    "                  eliminate_duplicates=MixedVariableDuplicateElimination(),\n",
    "                  )\n",
    "\n",
    "termination=('n_gen', 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05ba216",
   "metadata": {},
   "source": [
    "## Runs the solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8c922d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luisy\\Desktop\\SMSUDL-paper\\src\\trussFE.py:74: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\cb\\pytorch_1000000000000\\work\\torch\\csrc\\utils\\tensor_new.cpp:233.)\n",
      "  Kasm = torch.sparse_coo_tensor(self.nodeIdx, sK,\\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |      100 |      2 |  0.000000E+00 |  1.6198471472 |             - |             -\n",
      "     2 |      200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.7432963730 |         ideal\n",
      "     3 |      300 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "     4 |      400 |      2 |  0.000000E+00 |  0.000000E+00 |  0.3242015115 |         ideal\n",
      "     5 |      500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0266008698 |         ideal\n",
      "     6 |      600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.4699373109 |         ideal\n",
      "     7 |      700 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0401397448 |         ideal\n",
      "     8 |      800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.8928965453 |         ideal\n",
      "     9 |      900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1602701228 |         ideal\n",
      "    10 |     1000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0418964939 |         ideal\n",
      "    11 |     1100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0153473840 |         ideal\n",
      "    12 |     1200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1001344531 |         ideal\n",
      "    13 |     1300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0201788800 |         ideal\n",
      "    14 |     1400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0066863460 |         ideal\n",
      "    15 |     1500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0183295778 |         ideal\n",
      "    16 |     1600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0063790423 |             f\n",
      "    17 |     1700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1044386379 |         ideal\n",
      "    18 |     1800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0667915687 |         ideal\n",
      "    19 |     1900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0165775843 |             f\n",
      "    20 |     2000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0642214204 |             f\n",
      "    21 |     2100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0032807453 |         ideal\n",
      "    22 |     2200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0234748989 |         ideal\n",
      "    23 |     2300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0175332172 |         ideal\n",
      "    24 |     2400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0280660508 |         ideal\n",
      "    25 |     2500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0186698875 |             f\n",
      "    26 |     2600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0047870312 |         nadir\n",
      "    27 |     2700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0193650414 |         ideal\n",
      "    28 |     2800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0154569042 |         ideal\n",
      "    29 |     2900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0111154350 |         nadir\n",
      "    30 |     3000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0196664724 |         ideal\n",
      "    31 |     3100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0077342161 |         ideal\n",
      "    32 |     3200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0128061091 |         ideal\n",
      "    33 |     3300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    34 |     3400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0624101565 |         ideal\n",
      "    35 |     3500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0048413619 |         ideal\n",
      "    36 |     3600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    37 |     3700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0523329599 |         ideal\n",
      "    38 |     3800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0053639411 |             f\n",
      "    39 |     3900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    40 |     4000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0123518616 |             f\n",
      "    41 |     4100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0021642485 |             f\n",
      "    42 |     4200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0132112206 |         nadir\n",
      "    43 |     4300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0003330929 |             f\n",
      "    44 |     4400 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0339366663 |             f\n",
      "    45 |     4500 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0012586454 |             f\n",
      "    46 |     4600 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0046832328 |         ideal\n",
      "    47 |     4700 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0017563898 |             f\n",
      "    48 |     4800 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0029563616 |         ideal\n",
      "    49 |     4900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0026346880 |             f\n",
      "    50 |     5000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0102786296 |         nadir\n",
      "    51 |     5100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0604615114 |         nadir\n",
      "    52 |     5200 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0030469368 |         ideal\n",
      "    53 |     5300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0208029084 |             f\n",
      "    54 |     5400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0191403260 |         ideal\n",
      "    55 |     5500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0046065398 |         ideal\n",
      "    56 |     5600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0119255919 |             f\n",
      "    57 |     5700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    58 |     5800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0069414587 |             f\n",
      "    59 |     5900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0003131639 |             f\n",
      "    60 |     6000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0082268214 |             f\n",
      "    61 |     6100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    62 |     6200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0089284427 |         ideal\n",
      "    63 |     6300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0021344480 |             f\n",
      "    64 |     6400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0067919853 |             f\n",
      "    65 |     6500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0585293262 |         nadir\n",
      "    66 |     6600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0219515148 |         ideal\n",
      "    67 |     6700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    68 |     6800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    69 |     6900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0033619267 |             f\n",
      "    70 |     7000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0054152007 |             f\n",
      "    71 |     7100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |     7200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0014372271 |             f\n",
      "    73 |     7300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0125485858 |             f\n",
      "    74 |     7400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0094141624 |             f\n",
      "    75 |     7500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0052872006 |             f\n",
      "    76 |     7600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    77 |     7700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    78 |     7800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    79 |     7900 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    80 |     8000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    81 |     8100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    82 |     8200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0076311916 |         ideal\n",
      "    83 |     8300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0016176842 |             f\n",
      "    84 |     8400 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0047430470 |             f\n",
      "    85 |     8500 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0029237457 |             f\n",
      "    86 |     8600 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0029722014 |         ideal\n",
      "    87 |     8700 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    88 |     8800 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0087325285 |         nadir\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    89 |     8900 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0052410127 |         nadir\n",
      "    90 |     9000 |      6 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    91 |     9100 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0039104202 |             f\n",
      "    92 |     9200 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0001696598 |             f\n",
      "    93 |     9300 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0001696598 |             f\n",
      "    94 |     9400 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0069094929 |             f\n",
      "    95 |     9500 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0002069085 |             f\n",
      "    96 |     9600 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0002069085 |             f\n",
      "    97 |     9700 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0251715452 |         nadir\n",
      "    98 |     9800 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0004753563 |             f\n",
      "    99 |     9900 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0022011770 |             f\n",
      "   100 |    10000 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0056951122 |             f\n",
      "   101 |    10100 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0155493374 |         ideal\n",
      "   102 |    10200 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0004686406 |             f\n",
      "   103 |    10300 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0004686406 |             f\n",
      "   104 |    10400 |      7 |  0.000000E+00 |  0.000000E+00 |  0.0004686406 |             f\n",
      "   105 |    10500 |      8 |  0.000000E+00 |  0.000000E+00 |  0.0036571993 |             f\n",
      "   106 |    10600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0036168991 |             f\n",
      "   107 |    10700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0050395570 |         nadir\n",
      "   108 |    10800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0038346153 |         nadir\n",
      "   109 |    10900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   110 |    11000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   111 |    11100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   112 |    11200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   113 |    11300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   114 |    11400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   115 |    11500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   116 |    11600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   117 |    11700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   118 |    11800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   119 |    11900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0000398449 |             f\n",
      "   120 |    12000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0044885252 |         nadir\n",
      "   121 |    12100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0028106567 |             f\n",
      "   122 |    12200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   123 |    12300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   124 |    12400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   125 |    12500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   126 |    12600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   127 |    12700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   128 |    12800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   129 |    12900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   130 |    13000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   131 |    13100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0022514165 |             f\n",
      "   132 |    13200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0024602234 |             f\n",
      "   133 |    13300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0026493733 |             f\n",
      "   134 |    13400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   135 |    13500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0004983412 |             f\n",
      "   136 |    13600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0064074622 |             f\n",
      "   137 |    13700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0015943609 |             f\n",
      "   138 |    13800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0017770413 |             f\n",
      "   139 |    13900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0017770413 |             f\n",
      "   140 |    14000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0017770413 |             f\n",
      "   141 |    14100 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0020575769 |             f\n",
      "   142 |    14200 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0023856081 |             f\n",
      "   143 |    14300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0070785315 |         nadir\n",
      "   144 |    14400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |    14500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0004099304 |             f\n",
      "   146 |    14600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0004099304 |             f\n",
      "   147 |    14700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012761341 |             f\n",
      "   148 |    14800 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012761341 |             f\n",
      "   149 |    14900 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012761341 |             f\n",
      "   150 |    15000 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0012761341 |             f\n",
      "Solver time : 33.92\n"
     ]
    }
   ],
   "source": [
    "start = time.perf_counter()\n",
    "res = minimize(problem,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "print('Solver time : {:.2F}'.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcaefa0",
   "metadata": {},
   "source": [
    "## Plots the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aaae91ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'x01': 9, 'x02': 0.00016573093462398208, 'x03': 0.00012036287675561346, 'x04': 0.0004276128445439798, 'x05': 0.0004444864000682588, 'x06': 5.85337974327048e-05, 'x07': 0.0003333861934574846}\n",
      " {'x01': 47, 'x02': 0.00024253507397993762, 'x03': 0.00023641553272951406, 'x04': 0.00071466324103375, 'x05': 0.0007117554507808361, 'x06': 8.029759278731706e-05, 'x07': 0.0005416458877239553}\n",
      " {'x01': 12, 'x02': 0.0001322851526847699, 'x03': 0.00010969803884065733, 'x04': 0.0004322938034985269, 'x05': 0.00044595463337639585, 'x06': 4.5905079984675704e-05, 'x07': 0.00033312756119900557}\n",
      " {'x01': 5, 'x02': 0.00011527842826595673, 'x03': 0.00010121220938221489, 'x04': 0.0005786636495105379, 'x05': 0.0006130169696425168, 'x06': 7.013249312893402e-05, 'x07': 0.00046901946612206715}\n",
      " {'x01': 14, 'x02': 7.870454761845295e-05, 'x03': 0.00010105391471320717, 'x04': 0.00041611890658470855, 'x05': 0.0004745896270614256, 'x06': 3.4724891705019063e-05, 'x07': 0.00032834657801187315}]\n"
     ]
    }
   ],
   "source": [
    "X = res.X\n",
    "F = res.F\n",
    "G = res.G\n",
    "Z_res = [[d['x01']] for d in X]\n",
    "\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "legend_dict = {}\n",
    "\n",
    "for xData in Z_res:\n",
    "    matname = dataIdentifier['name'][int(xData[0])]\n",
    "    mattype = dataIdentifier['className'][int(xData[0])]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "    \n",
    "unique_labels = list(set(types))\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "\n",
    "for i, mattype in enumerate(types):\n",
    "    if mattype not in mat_colors:\n",
    "        mat_colors[mattype] = plt.cm.get_cmap('jet')(i / len(set(types)))\n",
    "        \n",
    "\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y) in enumerate(F):\n",
    "    #if i == 3:  # Exclude the third value (index 2)\n",
    "        #continue\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax.scatter(x, y, color=color, label=label)\n",
    "\n",
    "\n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(label)\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "    \n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Integer/Integer_mass_cost.png',bbox_inches='tight', dpi = 200)\n",
    "plt.show()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "559e39c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "objectives2 = {'cost': {'isOn':True},\\\n",
    "              'mass': {'isOn':False}, \\\n",
    "              'co2': {'isOn':True},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\n",
    "              'compliance': {'isOn': False}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 3:\n",
    "    print('Error: Three or more entries have isOn=True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c312728",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MixedVarsZDT1(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, trainInfo , constraints, objectives2, nc, no, nmat, **kwargs):\n",
    "\n",
    "        variables = dict()\n",
    "\n",
    "        variables[f\"x01\"] = Integer(bounds=(0, nmat-1))\n",
    "\n",
    "        for k in range(2, 8):\n",
    "            variables[f\"x{k:02}\"] = Real(bounds=(2e-6, 2e-2))\n",
    "\n",
    "        super().__init__(vars=variables, n_obj=no, n_ieq_constr=nc, **kwargs)\n",
    "        \n",
    "        self.truss=truss\n",
    "        self.trainInfo=trainInfo\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives2\n",
    "        \n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        x = np.array([x[f\"x{k:02}\"] for k in range(1, 8)])\n",
    "        \n",
    "        matprop=trainInfo[int(x[0]),:]  \n",
    "        \n",
    "        x_tensor = torch.from_numpy(x[1:])\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _, internalForce = truss.solveFE(E = torch.tensor([matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = matprop[2]*volume\n",
    "        cost = mass*matprop[1]\n",
    "        co2 = mass*matprop[4]\n",
    "        energy = mass*matprop[5]        \n",
    "        water = mass*matprop[6] \n",
    "        \n",
    "        \n",
    "        \n",
    "        con_values = []\n",
    "        \n",
    "        if(constraints['massCons']['isOn']):\n",
    "            constraints['massCons']['value'] = (mass/constraints['massCons']['maxMass']) - 1.\n",
    "            con_values.append(constraints['massCons']['value'])\n",
    "\n",
    "        if(constraints['costCons']['isOn']):\n",
    "            constraints['costCons']['value'] = (cost/constraints['costCons']['maxCost']) - 1.\n",
    "            con_values.append(constraints['costCons']['value'])\n",
    "\n",
    "        if(constraints['tensionCons']['isOn']):\n",
    "            tensileForce = 0.001+torch.relu(internalForce)\n",
    "            tensileStress = tensileForce/area\n",
    "            maxTensileStressExperienced = torch.pow(torch.sum(tensileStress**6), 1./6)*constraints['tensionCons']['FOS']\n",
    "            constraints['tensionCons']['value'] = (maxTensileStressExperienced/matprop[3]) - 1.\n",
    "            con_values.append(constraints['tensionCons']['value'])\n",
    "\n",
    "        if(constraints['complianceCons']['isOn']):\n",
    "            constraints['complianceCons']['value'] = (compliance/constraints['complianceCons']['maxJ']) - 1.\n",
    "            con_values.append(constraints['complianceCons']['value'])\n",
    "\n",
    "        if(constraints['compressionCons']['isOn']):\n",
    "            compressiveForce = 0.001+torch.relu(-internalForce)\n",
    "            k = 4. # used in geom factor... see buckling theory\n",
    "            geomFactor = k*(self.truss.barLength/(np.pi*area))**2\n",
    "            compressiveStress = compressiveForce*geomFactor\n",
    "            maxCompressiveStressExperienced = torch.pow(torch.sum(compressiveStress**6), 1./6)*constraints['compressionCons']['FOS']\n",
    "            constraints['compressionCons']['value'] = (maxCompressiveStressExperienced/matprop[0]) - 1.\n",
    "            con_values.append(constraints['compressionCons']['value'])\n",
    "\n",
    "        if(constraints['co2Cons']['isOn']):\n",
    "            constraints['co2Cons']['value'] = (co2/constraints['co2Cons']['maxco2']) - 1.\n",
    "            con_values.append(constraints['co2Cons']['value'])\n",
    "\n",
    "        if(constraints['energyCons']['isOn']):\n",
    "            constraints['energyCons']['value'] = (energy/constraints['energyCons']['maxenergy']) - 1.\n",
    "            con_values.append(constraints['energyCons']['value'])\n",
    "\n",
    "        if(constraints['waterCons']['isOn']):\n",
    "            constraints['waterCons']['value'] = (water/constraints['waterCons']['maxwater']) - 1.\n",
    "            con_values.append(constraints['waterCons']['value'])\n",
    "            \n",
    "        \n",
    "        \n",
    "        obj_values = []\n",
    "        \n",
    "        if(objectives2['cost']['isOn']):\n",
    "            obj_values.append(cost)\n",
    "            \n",
    "        if(objectives2['mass']['isOn']):\n",
    "            obj_values.append(mass)\n",
    "            \n",
    "        if(objectives2['co2']['isOn']):\n",
    "            obj_values.append(co2)\n",
    "            \n",
    "        if(objectives2['water']['isOn']):\n",
    "            obj_values.append(water)\n",
    "            \n",
    "        if(objectives2['energy']['isOn']):\n",
    "            obj_values.append(energy)\n",
    "            \n",
    "        if(objectives2['compliance']['isOn']):\n",
    "            obj_values.append(compliance)\n",
    "            \n",
    "  \n",
    "\n",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)\n",
    "        \n",
    "  \n",
    "problem2 = MixedVarsZDT1(truss,trainInfo , constraints, objectives2, nc, no, nmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "529e7496",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |      100 |      2 |  0.000000E+00 |  1.6198471472 |             - |             -\n",
      "     2 |      200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.7432963730 |         ideal\n",
      "     3 |      300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.3110161606 |             f\n",
      "     4 |      400 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2532731763 |         ideal\n",
      "     5 |      500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0872656933 |         ideal\n",
      "     6 |      600 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1543149935 |         ideal\n",
      "     7 |      700 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0844190749 |             f\n",
      "     8 |      800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.1064820616 |         ideal\n",
      "     9 |      900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.2335086437 |         ideal\n",
      "    10 |     1000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1874477331 |         ideal\n",
      "    11 |     1100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.1919073888 |         ideal\n",
      "    12 |     1200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1562593365 |         ideal\n",
      "    13 |     1300 |      5 |  0.000000E+00 |  0.000000E+00 |  0.0326116011 |         ideal\n",
      "    14 |     1400 |      5 |  0.000000E+00 |  0.000000E+00 |  0.1559271048 |         ideal\n",
      "    15 |     1500 |      5 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    16 |     1600 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0513478705 |         ideal\n",
      "    17 |     1700 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0118755980 |         ideal\n",
      "    18 |     1800 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0136929203 |         ideal\n",
      "    19 |     1900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.4451472161 |         ideal\n",
      "    20 |     2000 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0421826782 |         ideal\n",
      "    21 |     2100 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    22 |     2200 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    23 |     2300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0951205731 |             f\n",
      "    24 |     2400 |      6 |  0.000000E+00 |  0.000000E+00 |  0.0929048100 |         ideal\n",
      "    25 |     2500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.4698824212 |         ideal\n",
      "    26 |     2600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.3387503522 |         ideal\n",
      "    27 |     2700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.7496103259 |         ideal\n",
      "    28 |     2800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    29 |     2900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    30 |     3000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0408106369 |         ideal\n",
      "    31 |     3100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    32 |     3200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    33 |     3300 |      4 |  0.000000E+00 |  0.000000E+00 |  0.1340632252 |         ideal\n",
      "    34 |     3400 |      4 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    35 |     3500 |      4 |  0.000000E+00 |  0.000000E+00 |  0.0348930840 |         ideal\n",
      "    36 |     3600 |      1 |  0.000000E+00 |  0.000000E+00 |  3.0335377880 |         ideal\n",
      "    37 |     3700 |      1 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    38 |     3800 |      2 |  0.000000E+00 |  0.000000E+00 |  1.0000000000 |         ideal\n",
      "    39 |     3900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1392389405 |         ideal\n",
      "    40 |     4000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0598736367 |         ideal\n",
      "    41 |     4100 |      2 |  0.000000E+00 |  0.000000E+00 |  0.2369153701 |         ideal\n",
      "    42 |     4200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1146928254 |         ideal\n",
      "    43 |     4300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.5906795978 |         ideal\n",
      "    44 |     4400 |      2 |  0.000000E+00 |  0.000000E+00 |  0.3262949914 |         ideal\n",
      "    45 |     4500 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    46 |     4600 |      2 |  0.000000E+00 |  0.000000E+00 |  0.6060056740 |         ideal\n",
      "    47 |     4700 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0719524389 |         ideal\n",
      "    48 |     4800 |      2 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    49 |     4900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1905128151 |         ideal\n",
      "    50 |     5000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0034702749 |             f\n",
      "    51 |     5100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    52 |     5200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0714160905 |         ideal\n",
      "    53 |     5300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0392983744 |             f\n",
      "    54 |     5400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1837799733 |         ideal\n",
      "    55 |     5500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.2098431652 |         ideal\n",
      "    56 |     5600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0564244844 |         ideal\n",
      "    57 |     5700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    58 |     5800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0474753444 |         ideal\n",
      "    59 |     5900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    60 |     6000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0119747882 |         ideal\n",
      "    61 |     6100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    62 |     6200 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0146326522 |         ideal\n",
      "    63 |     6300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0467161549 |         ideal\n",
      "    64 |     6400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0032245430 |         ideal\n",
      "    65 |     6500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1183071842 |         ideal\n",
      "    66 |     6600 |      2 |  0.000000E+00 |  0.000000E+00 |  0.0081772333 |         ideal\n",
      "    67 |     6700 |      2 |  0.000000E+00 |  0.000000E+00 |  0.1718582330 |         ideal\n",
      "    68 |     6800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0842989745 |         ideal\n",
      "    69 |     6900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    70 |     7000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    71 |     7100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    72 |     7200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0338102492 |         ideal\n",
      "    73 |     7300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0055366526 |             f\n",
      "    74 |     7400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0982988445 |         ideal\n",
      "    75 |     7500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    76 |     7600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0247778033 |         ideal\n",
      "    77 |     7700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0079771541 |         ideal\n",
      "    78 |     7800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0054050899 |             f\n",
      "    79 |     7900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0086519965 |         nadir\n",
      "    80 |     8000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0021310172 |             f\n",
      "    81 |     8100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0021310172 |             f\n",
      "    82 |     8200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.1328564780 |         ideal\n",
      "    83 |     8300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0161562462 |             f\n",
      "    84 |     8400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    85 |     8500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    86 |     8600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0078240063 |         ideal\n",
      "    87 |     8700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0026161972 |             f\n",
      "    88 |     8800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0024822541 |             f\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    89 |     8900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0024822541 |             f\n",
      "    90 |     9000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0024822541 |             f\n",
      "    91 |     9100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0024822541 |             f\n",
      "    92 |     9200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0025544370 |             f\n",
      "    93 |     9300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0030359011 |         ideal\n",
      "    94 |     9400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    95 |     9500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "    96 |     9600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000150580 |             f\n",
      "    97 |     9700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000150580 |             f\n",
      "    98 |     9800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0000150580 |             f\n",
      "    99 |     9900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0028386334 |         ideal\n",
      "   100 |    10000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   101 |    10100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   102 |    10200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0027876807 |             f\n",
      "   103 |    10300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0027652612 |         nadir\n",
      "   104 |    10400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0086493713 |             f\n",
      "   105 |    10500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   106 |    10600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   107 |    10700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   108 |    10800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0074572557 |         ideal\n",
      "   109 |    10900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0063195060 |             f\n",
      "   110 |    11000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0004105301 |             f\n",
      "   111 |    11100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0004105301 |             f\n",
      "   112 |    11200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0225683622 |         ideal\n",
      "   113 |    11300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   114 |    11400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   115 |    11500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   116 |    11600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   117 |    11700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   118 |    11800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   119 |    11900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   120 |    12000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   121 |    12100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   122 |    12200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   123 |    12300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   124 |    12400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   125 |    12500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0074476928 |         ideal\n",
      "   126 |    12600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   127 |    12700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   128 |    12800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0007747824 |             f\n",
      "   129 |    12900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0007747824 |             f\n",
      "   130 |    13000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0046266674 |         nadir\n",
      "   131 |    13100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   132 |    13200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   133 |    13300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0049922075 |             f\n",
      "   134 |    13400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   135 |    13500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0039823089 |         ideal\n",
      "   136 |    13600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   137 |    13700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   138 |    13800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0032831602 |         ideal\n",
      "   139 |    13900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0041830831 |         ideal\n",
      "   140 |    14000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   141 |    14100 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   142 |    14200 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   143 |    14300 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   144 |    14400 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   145 |    14500 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   146 |    14600 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0034841804 |             f\n",
      "   147 |    14700 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "   148 |    14800 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0026536577 |         nadir\n",
      "   149 |    14900 |      3 |  0.000000E+00 |  0.000000E+00 |  0.0033801758 |             f\n",
      "   150 |    15000 |      3 |  0.000000E+00 |  0.000000E+00 |  0.000000E+00 |             f\n",
      "Solver time : 39.85\n"
     ]
    }
   ],
   "source": [
    "start = time.perf_counter()\n",
    "res2 = minimize(problem2,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "print('Solver time : {:.2F}'.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae44ca79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'x01': 9, 'x02': 0.00016573093462398208, 'x03': 0.00012036287675561346, 'x04': 0.0004276128445439798, 'x05': 0.0004444864000682588, 'x06': 5.85337974327048e-05, 'x07': 0.0003333861934574846}\n",
      " {'x01': 47, 'x02': 0.00024253507397993762, 'x03': 0.00023641553272951406, 'x04': 0.00071466324103375, 'x05': 0.0007117554507808361, 'x06': 8.029759278731706e-05, 'x07': 0.0005416458877239553}\n",
      " {'x01': 12, 'x02': 0.0001322851526847699, 'x03': 0.00010969803884065733, 'x04': 0.0004322938034985269, 'x05': 0.00044595463337639585, 'x06': 4.5905079984675704e-05, 'x07': 0.00033312756119900557}\n",
      " {'x01': 5, 'x02': 0.00011527842826595673, 'x03': 0.00010121220938221489, 'x04': 0.0005786636495105379, 'x05': 0.0006130169696425168, 'x06': 7.013249312893402e-05, 'x07': 0.00046901946612206715}\n",
      " {'x01': 14, 'x02': 7.870454761845295e-05, 'x03': 0.00010105391471320717, 'x04': 0.00041611890658470855, 'x05': 0.0004745896270614256, 'x06': 3.4724891705019063e-05, 'x07': 0.00032834657801187315}]\n"
     ]
    }
   ],
   "source": [
    "X2 = res2.X\n",
    "F2 = res2.F\n",
    "G2 = res2.G\n",
    "Z_res2 = [[d['x01']] for d in X2]\n",
    "\n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "legend_dict = {}\n",
    "\n",
    "for xData in Z_res2:\n",
    "    matname = dataIdentifier['name'][int(xData[0])]\n",
    "    mattype = dataIdentifier['className'][int(xData[0])]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "    \n",
    "unique_labels = list(set(types))\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "\n",
    "for i, mattype in enumerate(types):\n",
    "    if mattype not in mat_colors:\n",
    "        mat_colors[mattype] = plt.cm.get_cmap('jet')(i / len(set(types)))\n",
    "        \n",
    "\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y) in enumerate(F2):\n",
    "    #if i == 3:  # Exclude the third value (index 2)\n",
    "        #continue\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = mattype\n",
    "    ax.scatter(x, y, color=color, label=label)\n",
    "    ax.text(x + 0.1, y + 0.1, material)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax.scatter(x, y, color=color, label=label)\n",
    "\n",
    "\n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(label)\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "    \n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Integer/Integer_cost_co2.png',bbox_inches='tight', dpi = 200)\n",
    "plt.show()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "580824a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "objectives3 = {'cost': {'isOn':False},\\\n",
    "              'mass': {'isOn':True}, \\\n",
    "              'co2': {'isOn':False},\\\n",
    "              'water': {'isOn':False},\\\n",
    "              'energy': {'isOn':False},\\\n",
    "              'compliance': {'isOn': True}}\n",
    "               \n",
    "# count the number of entries with 'isOn' set to True\n",
    "no = sum(1 for v in objectives.values() if v['isOn'])\n",
    "nc = sum(1 for v in constraints.values() if v['isOn'])\n",
    "\n",
    "# print an error message in red if there are 3 or more entries with 'isOn' set to True\n",
    "if no >= 3:\n",
    "    print('Error: Three or more entries have isOn=True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26b2dd9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MixedVarsZDT1(ElementwiseProblem):\n",
    "\n",
    "    def __init__(self, truss, trainInfo , constraints, objectives3, nc, no, nmat, **kwargs):\n",
    "\n",
    "        variables = dict()\n",
    "\n",
    "        variables[f\"x01\"] = Integer(bounds=(0, nmat-1))\n",
    "\n",
    "        for k in range(2, 8):\n",
    "            variables[f\"x{k:02}\"] = Real(bounds=(2e-6, 2e-2))\n",
    "\n",
    "        super().__init__(vars=variables, n_obj=no, n_ieq_constr=nc, **kwargs)\n",
    "        \n",
    "        self.truss=truss\n",
    "        self.trainInfo=trainInfo\n",
    "        self.constraints=constraints\n",
    "        self.objectives=objectives3\n",
    "        \n",
    "    def _evaluate(self, x, out, *args, **kwargs):\n",
    "        x = np.array([x[f\"x{k:02}\"] for k in range(1, 8)])\n",
    "        \n",
    "        matprop=trainInfo[int(x[0]),:]  \n",
    "        \n",
    "        x_tensor = torch.from_numpy(x[1:])\n",
    "        area = x_tensor * torch.ones((connectivity.shape[0]))   \n",
    "        u, _, _, _, internalForce = truss.solveFE(E = torch.tensor([matprop[0]]).float(), A=area)\n",
    "        compliance = truss.computeCompliance(u)\n",
    "        volume = truss.getVolume(area)\n",
    "        mass = matprop[2]*volume\n",
    "        cost = mass*matprop[1]\n",
    "        co2 = mass*matprop[4]\n",
    "        energy = mass*matprop[5]        \n",
    "        water = mass*matprop[6] \n",
    "        \n",
    "        \n",
    "        \n",
    "        con_values = []\n",
    "        \n",
    "        if(constraints['massCons']['isOn']):\n",
    "            constraints['massCons']['value'] = (mass/constraints['massCons']['maxMass']) - 1.\n",
    "            con_values.append(constraints['massCons']['value'])\n",
    "\n",
    "        if(constraints['costCons']['isOn']):\n",
    "            constraints['costCons']['value'] = (cost/constraints['costCons']['maxCost']) - 1.\n",
    "            con_values.append(constraints['costCons']['value'])\n",
    "\n",
    "        if(constraints['tensionCons']['isOn']):\n",
    "            tensileForce = 0.001+torch.relu(internalForce)\n",
    "            tensileStress = tensileForce/area\n",
    "            maxTensileStressExperienced = torch.pow(torch.sum(tensileStress**6), 1./6)*constraints['tensionCons']['FOS']\n",
    "            constraints['tensionCons']['value'] = (maxTensileStressExperienced/matprop[3]) - 1.\n",
    "            con_values.append(constraints['tensionCons']['value'])\n",
    "\n",
    "        if(constraints['complianceCons']['isOn']):\n",
    "            constraints['complianceCons']['value'] = (compliance/constraints['complianceCons']['maxJ']) - 1.\n",
    "            con_values.append(constraints['complianceCons']['value'])\n",
    "\n",
    "        if(constraints['compressionCons']['isOn']):\n",
    "            compressiveForce = 0.001+torch.relu(-internalForce)\n",
    "            k = 4. # used in geom factor... see buckling theory\n",
    "            geomFactor = k*(self.truss.barLength/(np.pi*area))**2\n",
    "            compressiveStress = compressiveForce*geomFactor\n",
    "            maxCompressiveStressExperienced = torch.pow(torch.sum(compressiveStress**6), 1./6)*constraints['compressionCons']['FOS']\n",
    "            constraints['compressionCons']['value'] = (maxCompressiveStressExperienced/matprop[0]) - 1.\n",
    "            con_values.append(constraints['compressionCons']['value'])\n",
    "\n",
    "        if(constraints['co2Cons']['isOn']):\n",
    "            constraints['co2Cons']['value'] = (co2/constraints['co2Cons']['maxco2']) - 1.\n",
    "            con_values.append(constraints['co2Cons']['value'])\n",
    "\n",
    "        if(constraints['energyCons']['isOn']):\n",
    "            constraints['energyCons']['value'] = (energy/constraints['energyCons']['maxenergy']) - 1.\n",
    "            con_values.append(constraints['energyCons']['value'])\n",
    "\n",
    "        if(constraints['waterCons']['isOn']):\n",
    "            constraints['waterCons']['value'] = (water/constraints['waterCons']['maxwater']) - 1.\n",
    "            con_values.append(constraints['waterCons']['value'])\n",
    "            \n",
    "        \n",
    "        \n",
    "        obj_values = []\n",
    "        \n",
    "        if(objectives3['cost']['isOn']):\n",
    "            obj_values.append(cost)\n",
    "            \n",
    "        if(objectives3['mass']['isOn']):\n",
    "            obj_values.append(mass)\n",
    "            \n",
    "        if(objectives3['co2']['isOn']):\n",
    "            obj_values.append(co2)\n",
    "            \n",
    "        if(objectives3['water']['isOn']):\n",
    "            obj_values.append(water)\n",
    "            \n",
    "        if(objectives3['energy']['isOn']):\n",
    "            obj_values.append(energy)\n",
    "            \n",
    "        if(objectives3['compliance']['isOn']):\n",
    "            obj_values.append(compliance)\n",
    "            \n",
    "  \n",
    "\n",
    "        out[\"F\"] = np.array(obj_values)\n",
    "        out[\"G\"] = np.array(con_values)\n",
    "        \n",
    "  \n",
    "problem3 = MixedVarsZDT1(truss,trainInfo , constraints, objectives3, nc, no, nmat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "816fa962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==========================================================================================\n",
      "n_gen  |  n_eval  | n_nds  |     cv_min    |     cv_avg    |      eps      |   indicator  \n",
      "==========================================================================================\n",
      "     1 |      100 |     20 |  0.000000E+00 |  1.6198471472 |             - |             -\n",
      "     2 |      200 |     13 |  0.000000E+00 |  0.000000E+00 |  0.0108765058 |         ideal\n",
      "     3 |      300 |     17 |  0.000000E+00 |  0.000000E+00 |  0.6729730265 |         nadir\n",
      "     4 |      400 |     24 |  0.000000E+00 |  0.000000E+00 |  0.0119880464 |         ideal\n",
      "     5 |      500 |     31 |  0.000000E+00 |  0.000000E+00 |  0.0146102755 |         ideal\n",
      "     6 |      600 |     32 |  0.000000E+00 |  0.000000E+00 |  3.009770E+01 |         nadir\n",
      "     7 |      700 |     33 |  0.000000E+00 |  0.000000E+00 |  0.0070063200 |         nadir\n",
      "     8 |      800 |     40 |  0.000000E+00 |  0.000000E+00 |  0.0070753083 |             f\n",
      "     9 |      900 |     51 |  0.000000E+00 |  0.000000E+00 |  0.0058071514 |             f\n",
      "    10 |     1000 |     55 |  0.000000E+00 |  0.000000E+00 |  0.0024905797 |             f\n",
      "    11 |     1100 |     59 |  0.000000E+00 |  0.000000E+00 |  0.0046773961 |             f\n",
      "    12 |     1200 |     70 |  0.000000E+00 |  0.000000E+00 |  0.0042462936 |             f\n",
      "    13 |     1300 |     74 |  0.000000E+00 |  0.000000E+00 |  0.6099514324 |         nadir\n",
      "    14 |     1400 |     81 |  0.000000E+00 |  0.000000E+00 |  0.0088958033 |         ideal\n",
      "    15 |     1500 |     89 |  0.000000E+00 |  0.000000E+00 |  0.0440266247 |         nadir\n",
      "    16 |     1600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0191688649 |         nadir\n",
      "    17 |     1700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0010289547 |             f\n",
      "    18 |     1800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0024470615 |             f\n",
      "    19 |     1900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0267300087 |         nadir\n",
      "    20 |     2000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0183101483 |         nadir\n",
      "    21 |     2100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0007210189 |             f\n",
      "    22 |     2200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022865079 |             f\n",
      "    23 |     2300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0036690258 |             f\n",
      "    24 |     2400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0005107208 |             f\n",
      "    25 |     2500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0018057076 |             f\n",
      "    26 |     2600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022442743 |             f\n",
      "    27 |     2700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0024554598 |             f\n",
      "    28 |     2800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0028777317 |             f\n",
      "    29 |     2900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0005625119 |             f\n",
      "    30 |     3000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0019961568 |             f\n",
      "    31 |     3100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0023968396 |             f\n",
      "    32 |     3200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0031013057 |             f\n",
      "    33 |     3300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.3660633404 |         nadir\n",
      "    34 |     3400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0211089920 |         nadir\n",
      "    35 |     3500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0005917844 |             f\n",
      "    36 |     3600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0011611866 |             f\n",
      "    37 |     3700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0013455020 |             f\n",
      "    38 |     3800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.3403568677 |         nadir\n",
      "    39 |     3900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0168414287 |         nadir\n",
      "    40 |     4000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0001566990 |             f\n",
      "    41 |     4100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0005625754 |             f\n",
      "    42 |     4200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009823317 |             f\n",
      "    43 |     4300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0011602533 |             f\n",
      "    44 |     4400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0015781931 |             f\n",
      "    45 |     4500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0017319457 |             f\n",
      "    46 |     4600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0018876153 |             f\n",
      "    47 |     4700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0018581666 |             f\n",
      "    48 |     4800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022053626 |             f\n",
      "    49 |     4900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022141368 |             f\n",
      "    50 |     5000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0026498671 |             f\n",
      "    51 |     5100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0003444592 |             f\n",
      "    52 |     5200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0006670427 |             f\n",
      "    53 |     5300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0011283618 |             f\n",
      "    54 |     5400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0012157270 |             f\n",
      "    55 |     5500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0013313342 |             f\n",
      "    56 |     5600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0016744540 |             f\n",
      "    57 |     5700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0017080477 |             f\n",
      "    58 |     5800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0019199381 |             f\n",
      "    59 |     5900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0019846231 |             f\n",
      "    60 |     6000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022520816 |             f\n",
      "    61 |     6100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022060234 |             f\n",
      "    62 |     6200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022654032 |             f\n",
      "    63 |     6300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0021753723 |             f\n",
      "    64 |     6400 |    100 |  0.000000E+00 |  0.000000E+00 |  2.3175688847 |         nadir\n",
      "    65 |     6500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0003891796 |             f\n",
      "    66 |     6600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0015517779 |             f\n",
      "    67 |     6700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0037269032 |         nadir\n",
      "    68 |     6800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009466960 |             f\n",
      "    69 |     6900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0020157139 |             f\n",
      "    70 |     7000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0043709957 |         nadir\n",
      "    71 |     7100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0042458235 |         nadir\n",
      "    72 |     7200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.2833452542 |         nadir\n",
      "    73 |     7300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0006224749 |             f\n",
      "    74 |     7400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.4382878695 |         nadir\n",
      "    75 |     7500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0006484835 |             f\n",
      "    76 |     7600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0014856868 |             f\n",
      "    77 |     7700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022887909 |             f\n",
      "    78 |     7800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0037843357 |         nadir\n",
      "    79 |     7900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0292730957 |         nadir\n",
      "    80 |     8000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0006088776 |             f\n",
      "    81 |     8100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0010858786 |             f\n",
      "    82 |     8200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0016320787 |             f\n",
      "    83 |     8300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0024488230 |             f\n",
      "    84 |     8400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0028095785 |             f\n",
      "    85 |     8500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0003415536 |             f\n",
      "    86 |     8600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0007915295 |             f\n",
      "    87 |     8700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0011984902 |             f\n",
      "    88 |     8800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0015922695 |             f\n",
      "    89 |     8900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0076733848 |         ideal\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    90 |     9000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0006548575 |             f\n",
      "    91 |     9100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0011664876 |             f\n",
      "    92 |     9200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0017238501 |             f\n",
      "    93 |     9300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0089394392 |         nadir\n",
      "    94 |     9400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009534025 |             f\n",
      "    95 |     9500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0030675153 |         nadir\n",
      "    96 |     9600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0010428982 |             f\n",
      "    97 |     9700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0014563497 |             f\n",
      "    98 |     9800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0021813568 |             f\n",
      "    99 |     9900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0029242535 |             f\n",
      "   100 |    10000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0004569455 |             f\n",
      "   101 |    10100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0008298858 |             f\n",
      "   102 |    10200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0012574165 |             f\n",
      "   103 |    10300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0015968232 |             f\n",
      "   104 |    10400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0021108939 |             f\n",
      "   105 |    10500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0023990349 |             f\n",
      "   106 |    10600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.1671512343 |         nadir\n",
      "   107 |    10700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.1476850177 |         nadir\n",
      "   108 |    10800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0008218375 |             f\n",
      "   109 |    10900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0013306394 |             f\n",
      "   110 |    11000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0275939434 |         nadir\n",
      "   111 |    11100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0007246137 |             f\n",
      "   112 |    11200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0012900605 |             f\n",
      "   113 |    11300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0017307730 |             f\n",
      "   114 |    11400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0019583133 |             f\n",
      "   115 |    11500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0618784577 |         nadir\n",
      "   116 |    11600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0008212458 |             f\n",
      "   117 |    11700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0010865217 |             f\n",
      "   118 |    11800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0019845978 |             f\n",
      "   119 |    11900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0024229043 |             f\n",
      "   120 |    12000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0028164269 |             f\n",
      "   121 |    12100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0937817552 |         nadir\n",
      "   122 |    12200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0058554477 |         nadir\n",
      "   123 |    12300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0003580627 |             f\n",
      "   124 |    12400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0007637822 |             f\n",
      "   125 |    12500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009348235 |             f\n",
      "   126 |    12600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0021801390 |             f\n",
      "   127 |    12700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0258395821 |         nadir\n",
      "   128 |    12800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0004339179 |             f\n",
      "   129 |    12900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009774289 |             f\n",
      "   130 |    13000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0013222547 |             f\n",
      "   131 |    13100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0021549168 |             f\n",
      "   132 |    13200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0024478604 |             f\n",
      "   133 |    13300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0026248704 |             f\n",
      "   134 |    13400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0007808944 |             f\n",
      "   135 |    13500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0028820861 |         ideal\n",
      "   136 |    13600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009688667 |             f\n",
      "   137 |    13700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0015874180 |             f\n",
      "   138 |    13800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0019275774 |             f\n",
      "   139 |    13900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0023810122 |             f\n",
      "   140 |    14000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0027873831 |             f\n",
      "   141 |    14100 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0009489483 |             f\n",
      "   142 |    14200 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0014842311 |             f\n",
      "   143 |    14300 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0018469892 |             f\n",
      "   144 |    14400 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022488571 |             f\n",
      "   145 |    14500 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0025771303 |             f\n",
      "   146 |    14600 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0004475635 |             f\n",
      "   147 |    14700 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0008051565 |             f\n",
      "   148 |    14800 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0016637252 |             f\n",
      "   149 |    14900 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0018598600 |             f\n",
      "   150 |    15000 |    100 |  0.000000E+00 |  0.000000E+00 |  0.0022246268 |             f\n",
      "Solver time : 38.49\n"
     ]
    }
   ],
   "source": [
    "start = time.perf_counter()\n",
    "res3 = minimize(problem3,\n",
    "               algorithm,\n",
    "               termination,\n",
    "               seed=1,\n",
    "               save_history=True,\n",
    "               verbose=True)\n",
    "print('Solver time : {:.2F}'.format(time.perf_counter() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "50a09536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution limited by the areas\n"
     ]
    },
    {
     "data": {
      "image/png": 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fhl3I2AWMxmYvIkfoRIos8kU1p9BEgU215NrEVlxm4VW9n1ol4GUThvZ/9QvNqIDMAjPjFu4lzy7ibOLObK26MRUEcNdr8LQds7uhWMAV/yx+ztOgwV2vdaznYVvuolWXEbIFJgtnkvM4nZzL6eRcTiXncSY5l8Tsogr3x99dh1atcgjcitBrVNTyMhDq54qfux6DRkW+yeoQvbm2sbJvp+TNSWVGr6J1yluuEooFmcEmxgxadYllKsdzLqUel17molWjLyHmSj+v18hjE59VSFxGofwzs4C4zELiMguJTsolI99Uzl46467XOIRdHR9nkVfHxwVvV+1VWCXJWEWJn/df5MNNp8g1WlCrBB7u1YD6/q68tPqYY73KiOjnVkVg0KoY3rb2Ve2DgsLNwGt/HOPnA7FOyzSqq/s+VTeKAFQA4ExSzmWfLzRbaffWP9W0N5VHlEC0SpiR0KoFfNx01PFxIdTXjVA/VzxdtGjVAnlGK2l5RlJzjaTlGR2/ZxaYESU5Z6MyeRtqlUCgh94pmmfQqtFpVKgFAUEQEEUJk2ilyGQlu9Di2HZWoRmTRZ5Gzcg3OV20gwzylIHRYuXA+YwK398uvhxizaCVlzkea4rFXYn1PGwizsOgwVVXVrhdLUaLlegkm8hLyuW0TfRdyiwo64doI9hTTz1fNww6Fel5JuIyC8kuNJOWV7548XfX0bm+L/2bBdK1gS/1fF0rvd+SJFFkFh2RUUfktMTj+J3HkUxmPPRaXhvcwkmAWawi3+yMIcIWwRrTPoTZQ8JwN2hxsUXcrnUMS5JdaCY+s5BLGbKwKy3yKvPZ9DRoHMIupJTIq+PtiqdL2cjztXAyMYeXVkdx9FIWAG3revPe6NZkFZqY/P0Bp3XtHwm9RgWXCUbOWH4EvUbN4JZBVbafCgrXmzfXHufHvRcB+RoR4K6nIP/mzwNUBKACAPd+veeaXi8gf/A1agG9Ro2rTo2nQYOvm45ATz21vVxw02sQgJxCE6dT8jkWn0Nq3n/3RypttGu2SqTkGknJNXI4NstpXYNGRZCXgcYB7nQM9aFRgDuhfq6EeLsgAam5RlLzjKQ5fprKCMbMAjNWsfg9roRaJeDnpiPAQ0/zWp74u+vxddPiqtM4Ijv2G8RLO08gGs14GbQsGNO+OAKnVzuibS4aNZIgR11EW/6g1WHcKzmWW6WShr7y8kKTSJ6xiPgs2cDXKtpfi2Mbon0bkuTIVTRbrCTnGknMKiIxu5CknCKSsotIzzdVKPRctCr83GWRrFOrKDJbScszkZZnIimn4nFz1anxd9Ph76HH06BFrRIoNFtZG5HAH0fjHfmHolj6uCl1TCWOzW5ubH/etg2rKDFSbcFVkHMz3fQaujf0o66vq2N/7mxdi3mbT/P5v2dZfSSeixkFfPlgB7xctFf825dEkiSyC80OMWcXdrLIkx9fLvJpx9tV6xBzpUVeiI8Lnoar26//SoHJwrwtZ/h253msooS7XsPzQ5vyYNdQYlLzeGzpIcxWCZWAU77o/+5qTvaBU+Rd4Tv/yNJwfpzaib5NFRGocPPzzroTfL/7AiCf8/99ri+/LY6+sTtVSRQBqABAVtGVc4TsdAr15plBYTSv5Ymf+7X5G2XmmziZlMPJxFyi4rI4cimLuIxCrJdxJ7JfWCpaQ6OydyooXlZkEbmYXsDF9AK2RKc4re+qU1PP15WwIA/q+7tR38+Vbg39qO/nhk+JaTGTRSQjv1gYppaIJKblmYrFY56RrKsUi+MMZtwEyCkyM3tFRAkxdsWX3nQUmkWH2LkaCkxWYk2FxF7l6/4rki09p9Bs5flVkQCEeLvQraEf3Rr60q2hH7OGNKVtXW+e+fUohy5mctfnu1jwQHu6NfQr3o4kkVlgJi6zgPgSIq9Y4BWSV4lkcD83nU3UycIuxLvE7z4uuOtv/Ol666kUXv39mONve2erYF4b0ZJgLwOpuUamLj5IbpEFF62aQnPxOWVS91Ae7t2QTw9W7n0m/xDOL492pVtD/+txGAoKVcK760/yza7zgCz+tj/XjzolbiJvdm78GUXhlsGexxN+MYuTiTn0ahJwzdv0cdPRo5E/PRoVn+jNVpGY1HyOxWez+2waEXFZxGUWYrTIuXfliSKtWkCSwCJK5eYgalSgVqkwW8Uyry8wydOZ0Ulli2A89Boa+LsR6u9GAz9XQv3cqO/vRps6Xvi66SqcUjNZRNLz5Uhial6R7WfZKei0PJPT9J4EjuOsLCpbuzKVLRdSrRKKcyNVclszVYnlErZcSauI2SphtFgxmsUKBbVKALcS08yetupnrVogo8BMck4RablG8k0V30To1AJBngZ5LP3c8HfXoVGrbPuFY/+c97fscvsxOH53Oubi4y69XN6GbMxqX/7L99EU5Jtx12voFOTD0UtZxGcV8tvhOH47HAfYBaEvU3rUZ/XhOOKzirj/m310qOeDh15NQnYRcZmFFFzm2O34u+tLCDxb9M4m8kJsRVY3Kyk5Rby57gTrIhMBeVzeGNmSQS3kKF2hycrDPx4kLrMQD4PGKaLZr2kAb45qddXvOX7RftY80YP2oT5VcxAKClXI+xtPsmhnDGATf3P6Ucfn1hF/oAhABWDxzrMVPqfXqByCpKRAeGdDNH7uesZ0qPqqPa1aRdNgD5oGe3BPx+Ltp+YWseNMGttPpRAZl01CdhEm276VVyShV6tQqwUKTVYsNhuW0rjp5DBQodlarrDMNVqIjM8mMr5sNaOHQUN9PznX0C5sGvjLItHPTUctLxdqebkAXpc9XqPFyvx50eTnmQlw17NzZv8yokctCAgqyogae1/V8kjPM3IqOZfTSbmcTsnjdFIup5JzK5xu1KlVNAp0p2mQO2HBHoQFyn+DEG8XVCqBhKxC9pxL4+/jyRyJzSS1gtw9gGBPA53r+zCoeRBdGvraxuHmQm2bf3fXa/hyQgfOpuSx60waR2KzOJOSS3qeySYI451eJ0lw6GLZlnGBHnqbmCtZYFEczXPR3dwVgeUhihI/H4jlg03R5BZZUAnwUK8GPDMoDDdbRNIqSjz9yxEi4rJx1zuLv6ZB7nw/ufN/fv8xX+1h7VO9aBVy+e+QgkJ18v6Gk3y9wy7+YPucvrec+ANFACoAb64/Ve5ytUrAaBGp4+NS7nTenJUR+Ljp6N808HrvIgABHgbu6VCHe0qIzuScQjZEJbHzTBonErJJzjU6pn6NVrFMwrlWJeCiU2OyShSZreVGrXRqFW56NVZRIrfIUmFkLLfIQlR8NlHliUO9hlCbGGxgE4ny9LIc/Sop2vQaNSrbY7VKcMpDqwzZhWbOJBcXYpxKyuVMSm6FxRVqlUADfzeaBnnQJMidpkEehAV7EOrrikYte8OLosS51Dw2HUti88lkjifkVDiNqRYEGvi70qOxPwObBdIh1AePaspHKw9RlHPuMgtMZBaYyXL6Wfy7b74JLZCcU0SXd7b8p/dSC9CtkR93ta5F7yYBV/23u5k5lZTLS6sjHfm0bep48e7o1mXE2DvrT/L3iWR0apXTTVaAh57fp/dEVU4lZGVLUSRgxIJdbHq6N02DPf/jkSgoVB3O4u/WjPzZUQSgAhVNOPq46jCYVcRlFjKoeRCbTyY7PW+V4Imlh/j50W50qHdjpmmCPF2Y2rMBU3s2AORoWlRcNttOp7LrTBqnknKdcpHMooS5VARMQI7mCYJAXpEFk1XEVFB2VDwNGvRaNSaLeMWqzFyjhWPxORyLL1td7aZT26KFxcLQZL3ytG+BycLZlDxb1e2VLVYEAer5utIk0IOmwe6EBckRvQb+bug1ztEok0UkMi6Lf06ksONMKmeS8yrcJ4NWTfNgD/o3C6RvWAAtanuiVV+fpkJFZitZBXYxZ3L8nlVgJjNfFnOln8suNFdYoFKScQYRrW1KXCXIUUvnKtriIotaXi5YRJFDFzPZF5PO5pMpnErKxSrB7rPp7D6bDkAdH3sOoZxHeCteGApNVj7/9wzf7IjBIkq46dQ8d0dTJnWv74ia2lm8+zzf75ZzoII89Vyy3Si6aNVseKoXLhVMa+s0lf+8SBLcOW8nm2f1pWGA+388KgWFa+ed9Sf4Zqct508Q+HdWn1vyO25HEYA1HLO54uR0jUrgvTGtefbXCDafTGZCt7r8tO+S0zpFFpFpiw+y6vHuNA70uN67e0X0GjWd6vvSqb4vzw1piiRJnEvNJ/xCBvvOp7M/JqOMYJKAnHKmRe1ebEaLSIHJKq9Taj2dWsDbVYdaJVBgsl5WGNo7ceSbrJxIzOFEYrE4HGcw4SbIlb1Gi5WY1Pyrslip7WWgiU3ghQV5EBbkTuNA9wrzyvKMFg6eT2fT8WT2xaRzKaOgwoITH1ctbet4M7hlED0b+RPqV3k7FjuSJJFTZHFE42TBZiIzv1jAOaJ0+cVRu5Li/Wpx12vwdtXi46pz/PRx1eJt+5mw6wTmInna/dSrd15RxOpQ0btJAL2bBDDnjmZcSM/jsSWHOZUs546qBIjLLGTVoThWHZJzCG81Qbj9dCqv/n6M2IwCAO5oGcTrI1uWO4X/z4lk3lx3AoDO9X04eEGeFlerBNZM70GAp6HC97nafEdRgsFzd7BtTl/q+rhd1WsVFKqCt9ed4NtdxeJv86w+hPrf2jckigCs4UxdfKDC54wWkdHt63DoYiY/7YtlbUQS/cL82XY6zWm9rAIzE787wG9P9KC2982V6yUIAo0DZTE0vks9AFJyizh0IZPwi5kcvJDB8ficcquOC83WMgJErRLw0GsQBFk0mmzWM6XxdpE99yTkKdrSxsSO/UOOqNnlVGqukeavbqpQjPm762wCz8MW0XOnSZDHFS1AUnON7DqbyqZjcv5eRZXJAlDb20Dn+r7c0SqYrg388LWZf9sxWUSyCo0lonAlRV1xZC6rRGQuq9D8n7vGqFUC3rZOKrKYkwWcj5uujKjzcZPFnreL7opRpk/3qzEXydv/LxHM+n7urJ/Ziw82RfPNzvOIErSo5UHXBn5ExGURGZd9ywjC1Fwjb607wZ8RCQDU8jLwxsiWDGkZXO76kXFZzFx+BFGCvmH+bC9xTvh2ciealTNda7aKFNm+T3az9cuZxpc2j7aKEv0/2s6uF/oTfBPmlCrcvry59rjD6kUlwMane9PgNohGKwKwhrPrXNlkdjvZhXKF56vDWxAVn0PEpSzS8k34u2lJK2VymZhdxMTv9rPq8R74lBIMNxuBHgbubF2LO1vXAuSp1aOXsgi/IAvCI7FZZfLdBOQpVasokVVOlM9Nr8agUWO0WMkzWsmyGT+XRKuWTaQNtmnk9HwTBSZZZNotSSTkaIeHXk2zWp5yVC+oOKpXGdsdSZK4kF7AlpPJbD6ZwomE7HIjnCDnRNbxdaF1iDcta3vi66Yj32ghs8DMvnPpbIxKKjP1WhlLk4pw0aqLBZub/NO3pIhzs4u54mUeek25eWQ3Axq1ilfuakG7uj7MWRXBicRcMgvMfPlgB8KCPBxTxvti0m9KQSiKEr+GX+K9DSfJsRV5TO5Rn9lDmlZoOxOXWcC0xeEUmq10a+DLjhLi782RLZxygs1Wkd1n0/jzaALrohK5WyPbHVlFiabBHhxPqNiAXkL+fJbs7GMRJXp/uJW9Lw7A36PiCKOCQlXx2p/H+HGPbPKsEmDdU70IC77xs11VgSIAFSpElCRmrTjK0mld+erBDtz1+U6OxedwT/sQ1hyNLxOlOpeaz9TFB/n5ka43taVFaVx1GicrGqsoEZ2UQ7g9Sng+g6ScojLTr662qs4Ck5V8o7VMv1WNSsDToAFBIKdQbucWn1U2X8/XVYsGwSnc0cQm+gQBzqbkcTYlj43H7EK0WAwJgpwjlZZbRExaAQlZheQUmSuMIApgqxwGqyjnRJ5PK+B8WoEj+lNZ7L1oXbRqXHQlfurUuNp+d9XJJtauenmZ1pZ7aBfUxb8LCAKOjh32oiP7eoItRir/XvK1gqOiwLGdEmNTcr2Ss9aCIFBka+lnN4gund92NdzVphZhQe489tMhYlLzuW/hPl4b2YIHutSjT5hsl5RvtFxRENb1daFbA5sgbORHyHWMqJ9JzuXlNVGOqdtWIZ68O7o1bep4V/ia7EIz0xYfJC3PSNMgD44nZjs+thO7hTKpRwOH6FsfmcjfJ5Kd0yJsp4XcIgtjuoZcVgCC/PlUq+TPqmOZVaLH+1vZ//IAfNyuzYdUQeFyvLImimX75fZuggBrnuhBi9q3T0X6rXOVVqhyLqSV9b0riYCc4P7trhge7dOIz+9vz6TvD/DbkXgmdQtlyb6LZV5z9FIWTy47zDeTOl23woDrjVol0LK2Fy1rezG5R30kSSI+q5BDtinj8AuZnErOLeP95qpT4+OqQ5Ik0vJMmKwiGQVlo4UeBg0GjZoii5XcIgsZBWYsBgl9Cf1xODarTDeTqkBCLt6pVHPcK1BkFikyi2Ry87c8Kg973mVqnjztXs9m52P/V9/PjYYBbgR66CuV89gkyIM/pvdkzspINh1P4pU1xzgSm8Xbd7fCoFXjptfQJyzASRCGlxKElzIKuZQRx8rrKAiLzFYW/HuWhTvOYbZKuOrUzB7SlMndQx1V4OVhsog88dMhTifnEeSpxyJaybUZyHcK9WFAswCeXxXBX8eTy82F9XbRohVUIMnFWg90rcdb60+WWc+eK2vHU68mq9Dq9JE1WUW6v/cvB14ZhOdVdmZRUKgML66K4Jdw+XsoACse7U7bG1TseL1QBGANZuwXl2//Zo+IfPTXKXo08qd3kwBmDw7j479P80v4JXo19mfX2bQyr9t2KpXnVkYwd1y7m3bq7moQBMFWDerKqHYhgBwJORybSbhNEB69lEWByUqBqdguR6dRUdfHFW8XLRZRJDmniKQcI7lFFnIpO8VcVWhUclTTw6DBRafBoFGh16jQadUYNCp0GjV6jYBeq0anVjk1LJfAEemUkLup2C+88vLiDivyc1KJ351fi1Rym5LTdop/ty0v/Z6lHjvvm1ThdqRS7+k4rtL7KoEuWQWiPPYmq+iItJbGVaemvp+bszj0d6Ohv1uZdAcPg5avJnRg0Y4YPtgUzapDcZxMzOHrCR3LWMS46TX0DQugbzUKwl1n0njl9ygupstFHoOaB/LGqFZX3I4kSby8Joo959Jx06kJC/Rgp+27b9CoOJOSy9TF4Y71vVy0iJLk8AS8p0Md/m94CxZ9GUVBvhEJ2HY6tVyLqdK5opmFVno28mX3Oece2UUWkW7vbmH/KwNvqO2Qwu3HcyuOssrm/ykAS6Z1oXMD3xu7U9cBRQDWYNIKL5/LZRElQn1duZhRwMxfjrDuqV482a8xRy9lsflkChfS8/Fx1ZJZTpTrj6MJuOs1vH13qyptQH+z4OWipX/TQEe+k8kiciwhm0O2PMLwi5lk5Js4l+osKBoHulPfzxUPgwaTrT3d6ZS8/xSQ06gEQnxc6Fzfh7ta16JbQ78KbTcUyvLpp3vIzTUS5Glg58z+XEjP53xa8b8LaflcsnX5KF21bcfLRVuuMHywWyitQ7x4avkRjifkMHz+Lj4b3+6ynpmlBWFeOVPG/1UQpuUZeWf9SdYckS9qQZ563hjZijtaBlXq+7ng37OsOhSHWiXQpo6XQ/yBLMSKLCL+7joGNQ8iI9/E3ydky6hgTwPvjWlN/2bycZecZv9y6zkGNQ9i8Z4LZd6vlpfBqVp/97kMRratxZ8RiU7rFZitdH9vCwdeHoTrTdAqT+HWZ+byw06fs4UTOtI77Nq7Xt2MKN8YhctyMaMAT4OGmNR83lp3kvfGtOaTe9sxYsEuYjMK6NXYj91n050EjFqQe/Eu2x+LVq3i9ZEtb9j+Vxc6jYoO9XzoUM+HR/o0RJIkzqflOwpLDl3MJCYtv0yUKdjTQP+mAegvqsFqvmwk0MOgoXmwJwObBzKkRRD1/d1uS3F9I6jr60pdX1d6l2pvaLKIxGUWOAlDuzhMyC4iu9DM0UtZHL2UVWabAbbOIBKQkW9i2g8Hmdg9lBeHNquUWHH/D4Kwnq+ro49xt4Z+1PIysDI8jnc3niSrwIwgwOTu9Zk9JKzSUbNVhy7xyT+nAVABe2OKI3FeBg0j2tXmrta1MVtFXl4T5Yjoje9cl5fval5hhfqxhGw+G9+uXAGYlF2Er6vWKYXi0MUMejX2Y5fNc9FOntFKt/e2cOCVQRi0t163FYWbhyd+CmfjsWK/28/Ht2NIq/Ir4W8HFAFYQ1m+L+aK67jrNVAE+SYLArD8QCx9wwIY2iqYryZ0YMyXe9h1Np1hrYLZcCzJ8TqrJOGh15BrtLB4zwUKzVY+uKfNdTyamw9BEGgY4E7DAHfGda4LyFGY8AuZHLqYwcELmUTFZZGUU0TS8SLGGaxoStSBCIC3q5asAjMSML1/I+bc0exGHU6NRadROf6OpSk0WbmYkc/51HzOp8s/7VHEtDwTqblyv2c7ErBk70WW7L1ILU8DjQLdnaKG9f3dqOPjUmHubHmCMPxCBvtiMtgXk05UfDaxGQXEZhSwwpa7VLKVY5NAdz66ty3t6npf8bjNVpE959JZvPs8W0+lFi8vMT07oUtd3ri7NQUmC+9uiGb5ATlZPsTbhffvaV1GTJdGkuDPowl4u2jLVMxLQM8m/qwtEYmJzzIyoFkQFzMKuJThPG2cU2Sh+3tb2P/SQHSKCFT4Dzzy40H+OZniePzemFaMtKX83K4oArCG8r/fyyZf29Gq5dwod72Gu1rXYn1UIgatiiKzyIurI2lX15uWtb14++5WzFkVycbjSbSv68WRS8Ut0bQaFSEuLsRnFfLrwUuk5Rr5emLHW7YwpCrwd9czsHkgoiRxPCGHku2L7XE8F62aH+/vTNeGfhi0an7eH8vLa6L4cts5Oob6MKBZ0A3Zd4WyuOjUNAv2LNfzLqfIzIVSUcPDFzMdnTISc4pIzCkqk0OrUQnU85W7w5QWh7U8DU45te56Df2aBtLPNq1sF4S7z6bxZ0QCyTlGh/gDOJOSx8zlR5wihCV9O+2ib0NkIn+dSCKrRPRNa/Pss39kBzQN4O0xbdh+OpWXfoskwTZdO6FbPV68s3mFFjLF25PPAyvCL9G9kR8bS9xA2tlzNo2hLYPZdLz4uaX7Ylk4sQPP/HKUQrNzp5rMAjM9PviXvS8ORHsVnUYUFKb8cIBtJW50/ndXM+7vEnoD96h6UARgDeVy/RX0GhWSrY3sx/e25WJGPsfic9BrVGQVmJm9UraGubdTXQ7HZrH8QCzn0+SpYrvfXEa+if5NA/AwaIhOymVLdAojF+xi6UNd8a+El93tRkJWIcsPxPLLwUuOqJBKgAHNgpjQrR6H158iN9eMl4uWviXyxB7oWo/jCdks2x/L08uP8vuMnjS6DQxIb3c8DVra1PEuY6lyLD6Lx5YeJj6rEI1KoG9YADqNSp5WTs+nyCwSk5ZPTFp+mW3qNSpHMYpdGDYIKO4v7a7XoFOr2HwyheQc+TPWto4XrUK8OJ6QU26EsK6vCw383DBaRE4m5jj5RaoE2ZOyvp8rqblFmE2y/Atw1/PePW14flWE03Y+uKeNw0qpIux1OW56DRTI/qEvDwsuVwCm55u5u11t9sWkO0UIP9gYzaKJHZn0/cEyubNpeSZ6ffgve14YgLoG32wqVJ4Hv9nH7nPFaQXPDmrCw70b3cA9qj4UAVgDuVz7N5DbqRXZBKCLTs03kzoxcsFuUnONqATZGuabnTE81rcRr41owbH4bKLis2lRy9MpUX7rqVReH9GC1YfjiYzP5mRiLkM/28H3Uzpf1mvsdkEUJXacSeWnfbH8G53s8OYL8NAzvnNdxnep50jaP7y+4u28NqIlp5NzOXghk0eWhPP79J5X7PyhcHPSKsSb9TN78cyvR9l2KpUt0SlM7BbKvPHt0agEknOLykwpx6TlcymjAKNF5FRyrqP1XEncdGq0apVDKHkaNDzetxEPdK2Ht6tcqWyPEO45l86Wk8nEpObbcgiLp1NVAoT6uVJgspKcYyTE24V8o4V8k+h4/qkBjRm5YBfJOUZHTuHzQ5tW6P0Zn1XIX8eS2HQ8ifq5RbgJoNeqHNPTfx1PdpqqLsmCrWd5Y1RLnv7lqGNZTFoB+85n8PrIlrz25/Eyr0nOMdL3o61sn9NfEYEKFSKKIuO/2c+B88U5rY/2acjMgU1u4F5VL4oArIE8vjS8wud8XTXkGy2owSFYanm5sHBiR8Yv2ofJdpL++O9T9GzsT6sQL76a0IHh83dxIjGHbg192VciSfy9jdGsfLw7L62O4nhCDml5JsZ+tZd3Rrfi3k51r+dh3jDS84ysPBTHz/tjHT1VAbo39GNCt1CGtAy6qqlwnUbFlw92ZOSCXcSk5vPsL0f5ZlKn28Jipybi7arj+8mdmbflDPO2nGHpvoscS8jmqwc7UsvLhVpeLvRo7BxJs1hF4rMKyxSjxKTmEZ9VRL7JSsm4fk6RhQ//OsWHf53C101HfT9X3PQasgrMnEvNc/KwdNGqcNVpyCwwIUpwPq34M5ucU+TUrq11iBf/ZxNdDfzd+HBsGzrXL2uPEZOax8ZjSfx1PInIuOLUkPq25h1FZit9wvz550QK/0an0DrEi/CLZbsSHUvIoUcjXwa3COKfE8XJ+V9tO8cf03sypkNtVh8ua2Ael1XEgE+3sXV2P1QqRQQqOCOKImO/3uvktXp/l3q8dGezGlVYp3wzaiBbTqdX+Fy7er4UWeSLQ3qekZO2iF6Hej68P6a1Yz2zVWLm8iMUmCzU8XHls/vaIQiwLyaDpkHFU5RGi8jzqyL5YUpnGgXITdxNVpE5qyJ57Y9jmK1l7/pvRSRJ4uCFDJ7+5Qjd3/uX9zdGE2uroJ7WswGbZ/Vl+aPduKtNrf+UBxngoWfhxI7oNSq2RKfwqa0qU+HWRKUSeHZwGN9P6YSnQcOR2CyGz9/J3nPlfzc1ahWhfm70axrI1J4NeHNUK14f2dLJW7COjwuTuoVyf5e6dG3gS5CHnGqRkW/icGwWO8+kERWf7ST+fF21tK3rzZCWwcwaFEZ7W4GI/RJYuldvhE3M9WsawPeTOzvEnyRJHE/I5tO/TzFk7nYGfLKdj/46RWRcNoIAXRr48n/DW+BmixIWmKw8N6SZ4/fWIWXzKO28vT6ad+5uhVcJw2dRgudXRfLmqFa0qFV+W66L6YUMnrsDUbw9zjEKVYMoitz95R4n8TeqXe3b1rLscigRQAUnfFyLT7JWSeKer/Yw97523NEymDEd6nA6OY+vt58DICYtn7fWneC9MW3o1zSQpwc24bPNZ7iQXoCbTm2LSkB0Ui4Ld8Sw7OFujPlytyNh/Me9FzmZmMsXD3YgwOPWzAvMLTLz+5F4ftoX6zQ117aOFw92C2VEm9q46KqmKrFNHW/ev6c1z/4awYKtZ2lR25Nhtn7GCrcmA5oFse6p3jz20yFOJuYw4bv9vDC0KY/0bljhxchosfLVtnN8ufUcJquIQavi2UFhTOvVAIC959LZEJXI6VJTxS5aNcFeBtQqgfQ8I5kFZjIKzLYqYmeT5Sv5Um47lcq2U9sI9tTj5aIlNc9IRon+4BqVQI/G/gxtGczgFkGO7/fH+9Xkm+WCE0EAf3cdaXkmDl3MdOQclmZ9RAJzx7XltREtmLUiwrH8ZFIuP+65yOKpXRjy2Q6nohU751LzGfb5LjY+3bvGXdwVyiKKom22qvi7Mah5EB/f2/aaWkHeqigCsIYRV2JKsjQCcrTAbt6gU6soKLLy2NJDzB4cxowBjZlzR1POJMtFHQDLD1yyWcPUYuaAJhy9lMW2U6n4e7uQX6Irxne7ztM3LIAlD3Xl3q/3kFlgRq0SOHAhgxHzd/H1xI6Vsqe4WbAXZvx+JN4RUTFoVYxqG8KEbqG0rnN9+kWObl+H4/E5fLvrPM+tjKBhgFu5VagKtw71/FxZ/UQPXlkTxeoj8by7IZqjl7L4cGzbMtW0+2LSeXlNFDGpcpFI37AAXh/RgrisQl79/Rh/HU9yMmb3c9MxtFUwd7WuRZcGvk6t3rIKTI6p5K3RKayNlC1XBC4vAOV+0rLXZ1KOkaScYqsbF52aNiFejGxXmwHNAqnl5WxMrSohwpYfiOWeDnVYuCOGYwk5hPq5Ok0/27FI8vnj4d4NWR+Z6Dj3AHz2z2nuaBnED1M6M/arvVhLN+xGvgEdtWA3f8zoqYjAGozFIjJs/k5OJxf7sPZs7MeCB9rXWHeKmnnUNZgxX+6q8LkO9bydwuK+bjomd5dL4T/55zRPLT+CySLy2fh2hJWY5n3htygSswtRqQQ+u68dId6y/UuzYOepmdkrI/Bx1fLD1C646tRYRQl3vYaknCLGfb2XFQcvVe3BVjFFZiu/HYpj9Je7uevzXfy8P5YCk5XGge68NqIF+18exAdj21w38WfnxTub0auxPwUmK48sCScz33Rd30/h+uOiU/PJuLa8dXcrtGqBDVFJjFqwy2EanplvYs7KCMYv2kdMaj7+7jqm929ELS89Y77aw8TvDvDLwUtkFpjxc9PxQNd6/PxwV/a/PJB3RremR2P/Mn1+vV11tK/nQ1iQh0NUta3jVUb86dUCTYPc8TTIYlTuJ12+RCw0Wdl/PoNX1hyj+3v/0untf3j4x4Ms23eRxGxn7741R+J5tHcDBOTIX5CnocLx+WbneQRB4J3RrfEwFItisygxZ2UEbep48+6YVhW+PjI+m7Ff763weYXbG4tFZMhnO5zEX8dQH76Z1KlGm4crArCGkZJXdprETsvanmQXOnejeGNUK94d3RqNSmBdZCLjFu4lz2jh20md8XaRT8TZhWZm/RqBVZTwdtXx9YSO6DQqopNyqedTHAFIzTXy/KpI2tbx4usJHdGqBfKMFur6umCyijz/WyT/+z3KUWhys3A+LZ931p+g23tbmL0ygiOxWWjVAsPb1OKXR7vxz7N9mNqzgVOO0vVEo1Yx//721PV14VJGIU8tP4LlNsmlrMkIgsDEbqH8+lh3gj0NnEvNZ9SCXbz25zEGfrrd0e2jcaAbZqvIF1vP8cvBOCfRt8wm+t6tQPSVJj6rkGmLD1JgshIW5O7I8bNj0KpQq1WcSs5zWMR4u2jpFxbAxG71mNQ9lH5NA6jv50p5M2hpeSY2n0zhld9lQZicI6d/qAWBrAIzu86l09R2o3ihHOsbOym5RsIvZBDsZeD/hrdweu7IpWx+3HOB+zrXY1K3ir3bDl3M5IFv9l12PBRuP0wWkYFztztZK7Wo5cn3UzpXWLleU6jZR6/ghN52J6TTqJyMAh/oWo+GAW488dMhouKzGblgNwsnduSrCZ2Y8O0+rBLsjUln0Y4YnujXiNZ1vHhzZEteXB1FXFahw0QaYEt0Cj/tj2Vit1A+vrctT/9ylEsZhfRo5Meec+n8tC+W6MRcvpzQgUCPiiMC1xuLVWTzyWSW7Y9l55lis94Qbxce6FqPcZ3q3tC8RR83Hd9M6sToL/aw62wa72+M5n+lLowKtyYd6vmwbmYvHv4xnKOXsvhxz0VAvlsXgbMp8oXMt8T0btdS07uVIafIzLQfDpKSa6SWl8EpOmLH/r2t5WXgjpbBDG0VTOf6vuXmS5Vum3c6KZeohGxi0wsc+cD2uKF9ym35gVge69OIZ1ccJSnHiK+bjowKItpvrT/BH9N7MbZjHdZHJToZ9364KZpBzYN4dUQLTiRmE34xy+m19mntPefSmfL9fhZP63pVY6Vwa1JksjBo7g5He0KARgFuLH2oS7XdsN/MKAKwBrH6UGyFzwW467iQLuff6NRqxFJO0d0a+vHnDPmidCo5l/GL9vH+mNa8dXdrXl4TBcBHf0XTs7Efbep4M75LPQ7HZrIiPE72FSzh2v/2uhN0a+DLqHYhZOSbeGPtCfacS2dS91DWHI4n/GImI+bv4qsJHelQz6fqB+IyJGUX2QybYx1muoIA/cICmNAtlH5NA2+aZOFmwZ58Mq4tTy47zLe7ztOitidjOtS50bulcI3kG828tyGaiFL9hUXk9oDDWtf6z6LPjtkqMn3ZYU4l56JTq0i0FWaVJNBDzz0d6zC0ZTBt6nhdMX/uSm3zTiRms3JxpPz+oojK5hrw7ujW6DQqTBYRN52ajAoCgRGXssnIM+HrruO9Ma0Z/OkO8oxyVLLIIncpWvZwVxZO7MSd83aSUqoNn10EbjudxqM/hrNocqdKjZXCrUmBycKgT7Y7ig5BrpT/6eGu+NXAZgTloUwB1yCeXxlV4XN9wwIchpi6Ctoo1fV15bcnezC4RRAmi8isFRFcTM9nQlfZz0+U4PGfDpNvOym/OaqVY1o5pETLKaNFZOYvRzFarEzt2YDp/WXX9Z/2XeS5O5rSONCd5Bwj4xfu45cDFYvWqkIUJXaeSeWxpeH0/OBf5m05Q3KOEX93HU/2a8SOOf35YWoXBjYPumnEn51hrWsxo39jAF5cHUVkXNaN3SGF/4TFKrLrTBoP/XiQ1q//zW+H4xzRMledGo3tc6fXqBjbsQ49KzG9WxEX0/IZ+9UeR2TbVE76wLBWwex/eSAvDG1G27re11w84aJT0zHUV+4AAlhFie4N/QD4NfwSvWy+h6l5ZYVoSd5efwKQvUlfHd7c6bk959L55eAl/Nz1fD+lM1q18z7bRSDA3yeTmb7s0DUdk8LNS16Rhf4fbXMSf4EeepY93LVMYVJNRhGANYjL9f9oEuRBdqEZd72mzImzJO56DQsndHSIjoU7YojLLKJTqDcgtzyzRwQNWjVfPdgRT4OG+KxCpynTk4k5fLjpFADPDWnK+M51ESV4Z8NJXhnWnDtaBmGyiry4OoqX11yfvMDMfBPf7IhhwCfbmPjdAf46noxVlOjSwJfP72/PnhcH8vzQZk5eazcjswaHMbBZICaLyGNLDzlazSnc3NhF30uro+j0zmYmfLefLSdTECVZqHRv5MfSaV2IfG0Im57pTaMAN5JzjNy3cC9L911EqqAQozSSJHEqKZd5m89w57yd9P14W5lcP5ANoUGeIvt4XNvrUjHrVsISyWrzfPntUBxPD5TPJ0VmCf1l+viuj0p0+PqN61SX3k2cDbPfWXeCxOxCWoV48fG9bcu8vuSIrY9KYtavR/7roSjcpOQWmen38TaSS5wHfVy1/PRwV0L93G7gnt18KFPANQSrteLuvypBNnYG6FzfB8oa8juvrxJ47o6mhAV7MGdlBNtOp9LQ383h6fXH0QQGNg9kZNsQ6vm58tn4dkxbHE5qrhGdWsBkey+7NUyfsADevrsVGfkm/j6RzMzlR1j+aDdah3jxyT+n+Xl/LKeScvnqwQ4EXqZSsDJIksTh2CyW7b/IushEh7D00GsY0yGEB7uFEhZUvrHszYpKJTB3fDvu/mI3Man5PLnsEMse7lZhJFfhxmGxiuyLyWB9VCJ/HU8qN9+tf9MAPr63rdM0VeNAD/6Y0YvnV0WwISqJV38/xpHYTN65u3W5PpOSJBEZl82m40n8dSyp3N7CJXHTq8k3WtGpVcy/v0O1JMdHxGUR4KEnNddIQlYRfm460vNN6CpoCwfy7MGPey8ytWcDBEHg/XvaMGTudvKN8vktz2Tlf2uO8e3kToxqF8KJhBwW7ogB5FQOu2a2TwevPpKATqvm/TFtrvvxKlx/sgtM9P9ku9P3ykOvYcm0rrfceb06UK4QNYQZyypu/9apvi9HbflG3Rv5VXqbI9vWZuXjcsViTFo+RrOIXXPMXhFBfKacUzigWRAzB8h3+KWjCrNWHCU9z4hGreLz+9vTpb4vuUYLU344yMi2IXw/uTMeBg2HLmYyfP4uDl10NqytLPlGC8v2X2TY57u456s9rD4cj8ki0rK2J++Nac3+VwbyxqhWt+xJwtOg5ZtJnfDQazh4IZM315XtkapwY7BYRXaflSN9Xd7dwoTv9rP8QCwZ+SbH1C5A40B3Vj7enR+mdik3R8ldr+GLBzrw8rBmqARYfTieMV/tIdaWu2sVJfbFpPP6n8fp+f6/jPpiN19tO0dMWj46jYq6vuVPfZXsw/vysGa0qF09vpKFZpG2Nsuk5QcvcXf7EAByiy7fq3yhzYge5KKsV4Y5Fz9tiU7hzwi5PdzzQ5vRq7F8TpMkHONdMhL4y4FL/N8fx67pWBRuPBl5Jvp+vM1J/Bm0Kr6f2vm6W3PdqigCsIaw8URahc/1CfNn/3m5BVW3hpUXgCB3p/hzRk/a1vUm12jBnk5ktko88M1+xzTP04PC6N3EH6NFdOo2kpZn4oXfIpEkCYNWzTeTO9Es2IO0PCMTv99PqxAv/pzRiyaB7qTkGhm/aB8/7698XuCppFxe/f0YXd/dwitrjnEyMceRR/X79J6se6oX93epd1vYATQKcGfe/XJLvp/2xbK8GvInFcqntOh78Nti0eftoqFNHS+0agGLKKHTqHhuSBgbZvYut69uSQRB4NE+jeREdjcdJxNzGDpvB5O+20+XdzYzftE+Fu+5QEJ2Ea46NXe1qcVrI1oQFuTOpQy5ErJkiodKkKuJLVaJgc0Cmdyj/vUcFgdq243gGZvP4c4zqYztWMeRo3e5VNukHCNHYounKe7vUteRQ2jntT+OkZZnRK0S+OKBjtSx2VFZRAldOSkuS/Ze5J11J67hiBRuJGl5RfT7eKtTNxidWsWiiZ2u+J2qySgCUIFQXzdyiyx46DW0qHX1d/+BngZ+fbQbo9uHON1ZX8wo4IXf5Ko/tUpg3vj2hHi7kFlgdpjKAmw+KVvDAHi5aFkyrQt1fV24mF7AlB8O4O+uY830ntzZKhizVeLlNVG8tDoSo6X8aW2jxcrvR+K59+s93PHZDpbuu0ie0UJDfzf+d1dz9r88kI/vbUu7Kkhuv9kY0CyI54Y0BeD//jhG+IX/FjFVuHrsou/lNVF0LSX6fFy13N+lLq8Ob06Ah4HIuGzMVomejf34+5k+zBjQpNJT9gUmCzmFZjqF+qAS5F66O86kkZ5vwtOgYWzHOnw7qROHXhlEx3o+fLAxmmPxck9vd73ake4BsuVMYnYRgR56Phzbptq+D662YpCL6QV0ru+DJMHGqESa2AzmVVcotnqrhFgTBIH3xjhPhWcVWnj9TzkK7uWq5fspnTHYchxN1vLzDL/ZdZ4PN0Vf24EpVDvJOUX0/2i7w6cS5OvN5/e3p09YwGVeqXDrhz0UrkhSTsXt34I89CTZHPpLt4q6GgxaNZ+Oa0vTYA8+2BTtyLVZdSiOfk0DGN6mNr5uOr58sAP3fr2XnCILGpXgaDb/5p/H6dbAlyZBHgR6GlgyrStjv9rD8YQcHlt6iB+mdubLBzvw5bZzfPz3KZYfuER0Ui5fT+jo6CAQm17AsgMXWRke55gGUKsEhrQIYkK3UHo08rvtBF95PNmvEccTstkQlcTjPx1m7VM9lcq364TFKrL/vC2n71gS6SWmn3xctQxtFcyw1rVoUcuTj/8+zVvrTgJy1O3V4c25u11IpT6T2YVm/o1OZtOxJLafTnWyVXLRqii0PW5fz4f/3dWcjHwTE78/QPjF4kiZh0HjNL06qHkgm0+mIAgw97521WqN4WarbLaUaP67IjyOZwY14cXVUVisly9wORybRXaBCS9XHSA7FLw8rDmv/l48lbsuMpGRbZMY0jKYsCAPPruvPY//JFf+Gi2i09S3nS+3nUOvEXh6UNOqOlSF60hiViGDSuSA2vlobBuGtgq+QXt166AIwBrA3fMrbv/Wv1kg+232L1c7/VsaQRB4vG8jwoLcmfHzYQpM8sn16V+O0qGeN7W9XWlb15v/G9GC//1+DLFEFaNZlHhq+RH+mNETvUZNA383Fk/twvhFe9lzLp1nfz3K/Ps7ML1/Y1rW9mTm8iMcic1i+PxdTOtZn30xGew4k+oQnrW8DNzfpR73da572RZTtyOCIPDR2LbEpOYTnZTL40sP8etj3Wt0y6OqpLKir1tDPzQqgbWRidzx2U7S8uSqxHGd6vDSnc3xcdNd9n1Sc438cyKZTceT2HM2zUks1fV1YajNmLl9XR/WHInn5TVRbD+dSr+PtpFvsmC2SqgFsErgolU7ib9+TQPYHyOnfTzetxE9S02hVgd9wvz5NzqVqPhsfFy1JOUU4eeuQ6sWnKKUFfHOhpN8OLa40vfBLvXYEJnIXttxAbyyJoquDf3wcpH/LjMHNuHzLWcAWQTq1CpMVtGp//HczWfRqlU82b9JVR6uQhVzKbOAO+bucPRit/PWqJaKH2olUQRgDSApt+L2b32a+PPCatm25VoFoJ0BzYL4Y3ovJn9/gITsIqyixJ3zdhH+v0Fo1Soe7CqbRK8+HI+rVk2BWf4CRyfl8tGmU46OFq3reLFoUiem/nCQDVFJ+Lod461RrejXNJAfpnTm0Z9ky5MPbHYyAH3CApjQtR4DmgX+52jm7YCbXsOiiZ0Y+cUuIuKyeWXNMT6+t/qm+G43LFaRA+czWFeB6LujZTB3tZFFn73LxaWMAl75/Rg7TssdKxoFuPHO6NaX/Z7FZxWy6ZhcuXvwYgYlnV7CgtwZ2jKYO1oF06KWp9Pf8p6OdXDVqXn6l6NkFZod+5VZYEatEjCaiy+S7ep6k1toJtdopW1db2YNDquSMbpaXh7WnH+j5Whmtwa+bDudxq8H4+jZyJ9tp1OdRFl5/BmRwPtjWqNSyeOtUgl8cE8bhny23REhTc0z8c76Ew6h+MzAJpxIyGbzSbn3sSCARi1gsUqoBNnLFODDv06jVat4pE+j63X4CtfAxfR87vhsh1MkHOCFoc2Y2L3+jdmpWxBFANZg1AL4uuvl/D+Dpkqr/5oEebB+Zm/Gfr2Hc6n5ZBeaGfzpdrY+109u6n53a04k5BCdlOs0hfXtrvP0sVnDAPRs7M/c+9oxY/lhftoXS6HJSqHZyt/Hk50iIgB3tQ7mk3HtlEiXjXp+riy4vwOTvt/Pb4fjaFnbk2m9Gtzo3bplsIu+9VGJbKqk6AO5y8a3O88zb8tpisxylGnGgMY81rchek3Zz+a51DxZ9B1PIrKUP1/bOl7c0SqYO1oG06icDhv2/Vy4I4Z5m89gsoqoVQJWUSLTlhCvQsJ+C1jXx4UuDXxYtOM87noN88e3d9r36qRxoAe1vV1IyCokJk1OU9l6KoXvJndk2+nUy4o/kNvU/bw/lgklLvj1/Fx56c7mvPZncRX8ivA4RrStTe8mAbJl0n3tGLVgFzFpBRgtIn5uOrIKTFglHGMH8M6GaPRaNZMUQXFTcS41j2Hzdjqm7+03CtP7N+KJfjeHYC99bbpZUQTgbc66iIorQTs38CXKdsHp2qD8/p7Xgo+bjk3P9GHgJ9uIzSjkQnoBd87byZone+KiU/P1hI6MWLCL3CKLY6oKYObyw2yZ3c+Rk9SrsT9DWwaz8VgSvx2Od2y/U6gPD3atR3xWIZ/+c5r1UUnEZe3j6wkdlJw3G72a+PPysOa8vf4k72w4SbNgD3rcgOm+W4WSou+v40mk5ZUVfcNa16J7I79yhdOhi5m8siaK6KRcALo39OOd0a2c2qNJksTxhBz+Op7EpmNJjkpYkCNSnev7OiJ9JTvolEd0Ug5zVkYSFS9/j/s3DWBYm1o8vzLSIaDsQRIvFy2zBjdl9sqjALwzuhX1/G6syfm0nvV5e/1JYjMKaFvHi4i4bI4n5Dqil1fiy23nnAQgwMRuoayPSnR0NgJ4YVUk/8zqi5teg4dBy7eTOzNy/i7yTFbS8000DnTjXEo+VlFymoL+vz+Oo1OrGN+lXpUet8J/43RyLsPn73L4t9qjtlN61HcUv91oVh+OIz3PiKtw8wtBRQDe5jzzS8Xt3/o3DXTky1TV9G9ptGoVW2b3o+Nb/5BTZCE6KZc75+3gl0e7U9/fjU/HteORJeGUTPnJKrQwe8VRnh4UxrL9sayNSCiTrP3ync14tG/x3V6bOt48tfwIEZeyGDF/F18+2JEuDZTyf4CHejXgREIOq4/EM/3nw/w5o9dN392kutlzNq1c0eftqmXoFUQfyEUaH/0VzbL9sUiSLBZfuasF93SQizxEUeJwbCabjiWx6XiSU3N6rVqgRyN/hrYKZlDzIKeOORVhtop8ufUcC7aewWyV8DRoeG1ES5oGuzNu4T4kKFPk8EjvBnz0VzSiBPd0qMOodiH/fcCqiGk96/PBpmjMVsnh0ffLwVhGtq3Nj3svXvH1CdlFHIvPplVIsc+bSiXw4T1tuOOzHY7jT8gu4sNN0bwxqhUADQPcmf9AB6YuPgjA2ZR8ujTw5cD5DMy2KmH7a19cHYVGLTC2Y90qPXaFq+NEQjajvtiN2SohIEdrLaLE2I51+L/hLW54ekuR2cqb607w8/5YxtnSzlU3ecqNIgBvcyyXuQHp3SSABf+eBa6fAARZBP4xvScDP92OKMGF9AKGfb6T7yZ3YnCLIJ7s14gvt51zqgrcdjqNbaeLvQubBXswoVs9IuOyWREex8d/n6ZViJcjmtUnLIC1M3rx6NJwopNyeeCbffzfiBZM7BZ6w08MNxpBEHh3TGvOpuYRGZfNI0vCWf1kj9vC+/BaMNtMK1NyjTzw7X7Hcm9XLXe0kKd3Lyf6QI7mrY9K5I21Jxwt+MZ2rMPLw5rjYdCw+2w6m44n8tfxZKcWfQatin5hgQxtFUz/ZoF4uWgreosyHIvPZs6qSE4mytYug5oH8e7oVlglibu/2E2ByYqbTk1+qeT4j/8+DUB9P1feGNWy0u93PVGpVPRp4s+W6FROJObgYdBwKaOQ7o38KiUAAd5ce5wVj/dwWlbf340XhjbjzRJ2MT/uvcjwtrUdvnD9mwUy546mfPSXnEN84HwGQ1sGsel4MkaL6JSa8tzKSLRq1U0hmmsikXFZ3PPVHof4s3eLGdY62JYHemPP8ZcyCnhy2WGi4rMRBNm0XTKZL+tneTNQs68AtzmXa/9Wy1OP2Wol12jB06Ch+X/w/7saGgS489bdrXhljWzTkJFv4r6F+/hgbGtmD2nK3nPpHLF1IynJoOaBPNGvER3q+SAIcn5OntHChqgkHlkSzq+PdXfc/dfzc2X1kz14flUk6yIT+b8/jhMVl81bd7eq8XmBBq085T5ywS6ik3KZsyqSBfe3r5HiOD3PyLsbolHnm3ATQJSkqxJ9di5lFPB/fxxj6ym5yKOhvxv/N6IFZqvEO+tPsvlkMtmFxdOYHgYNg5oHcUfLYPqGBZTbwu1yGC1WFvx7lq+2ncMiyvv8xsiWjGxbmzyjhXu/3ktyjhEXrbP4e2ZgE3acSeVwbBYAtbxcKt1HuDp4aVhztkSnUmgW6RXqy66zaayNTKRxgDtnU/Ou+PoDFzLJLTLjYXAW0VN61GdDVKKTFc7zKyPY+Ewfx/ngyX6NOJGQw/qoRAA2HU/mng4h/HY4nkKz6CSkn/7lKFq1wLDWtavq0BUqwaGLmdy3cC8WUUIF6LVqCs1W+oYF8Nl97W94sd/mE8nMWnGUnCILPq5aPhvfnkPrTpFbtsvjTUfNLZOsAcxaUXGj8/7Ngxz2L10a+FV5/l95PNClHgObBToem6wiz/4aQa8P/i1X/AGcS8mjVYiXQ6iobUnc3Rv6kW+yMuWHA1wo0efUVadh/v3tHe2yVh6KY9zCvSRkFZa7/ZpEbW8XvprQEa1aYH1kIl+VaKlVExBFieUHYhnwyXZ+OxznWO7jquPgK4P4YGwb+oQFXFH8ma0ii3acY8jcHWw9lYpWLXBnK9lr7sllh3lkSTi/HY4ju9CMn5uO+7vUZfHUzhz632Dm3teOoa2Cr1r82VMb5v97FosocWerYP55ti+j2oVgESWm/3yE6KRc9BoVhSUqfsd1qsOw1sEcT5BzBFUC7I1JZ9QXuzmTnHtV+3C9kItB5Dmzi+nyd/nv40mM61z5Kdf3Npwss0ylEvhwbBsn0+fz6QV8tvmM47EgCHx0bxuaBRe3gNwQlcS4TrKNSL7JikcJ0/onlx3hnxPJld4vhWvjwPl0xtnEn1oAd4OGQrOVLg18+XpCxxva79xiFflgUzQPLwknp8hCu7rerJ/Zm763kPm0IgBvY/6IqPhE1S8sgL3n7Pl/1ZMrJwgCn45r59QKDiAxuwiAjqE+ZYTo+fQC3t/o7M6v16hZNKkjLWt7kpZnYuL3+0nJKXJ6n0f7NOLHaV3wdtUSGZfNyAW7HL5nNZnO9X15faQ8/ffRX6fYGp1yg/eoejiZmMPYr/fw0uoosgvNNK/liZ/Nh0+vUVW6EvZIbCYjF+zm3Q3RFJqteLtoQYKNtty+ApOV2l4Gpvasz6+PduPAK4N4b0wb+jUN/E8XqyKzlfc3RjP6y92cTs7Dz2am/tWEjgR46JEkif/7Q7aa0agEp5y/Xo39eXV4C2b+chSjRaJ3E39WPd6DWl4GYlLzGfXFbtZHJl71Pl0PpvWUq9MvZRbSLNhDLsKQJKe2dSUpvXTNkYRy12sY4M6cO5yLAxbtOOcofgP5pvGbSZ3wcpGFXqHZyo7TaYwp0ZvYu8Q569El4Ww7VTO+NzeSPWfTuH/RPqw28eflqiOnyEKbOl58N7nTVd9EVSUpuUVM+G4/X22Tb6Kn9KjPise6U/sKRVs3G4oArIGoBbnq9+AFeWrkeub/2bGKEv9GJ/PsiqNlqvvss5DZhWZm9G8sLyvx/A+7Lzi81Ox4GLQsntqFUD9XLmUUMvmHg07TbSDnOK6d0YvmtWSh+OC3+1m8+/xNNf11I3iwayj3d6mHJMHMX44QU4lptluVPKOFt9edYPj8XRyOzcJNp+bV4S1YO6PnVdmf5BSZeW5FBGO+3OPIvQPIKjRjFiUa+rvxZL9G/DmjJ7tfHMBrI1rSteG1RdYPXczkrs938vX2c4gSjGxbm39m9WVY61qOdb7eHsPyA5cQcK44DAty58sJHfj4r1NEJ+Xi767jk3Ft6RDqw9qnetG9oR8FJivTfz7MO+tPYLGK5exB9TGtZ32H2LNPz/4SfokuFfRxLf0NLjRb+fVg+Y4HU3s2oEM9b8djUYLnVh51VJKC3Enkywc7Os47STlFnEvN446WQQBkFZjxtd0wSMDUHw6y64zzOUmh6th+KoUJ3+3HKoFGBf4eejLyTYQFufPj1C5lpvurk/0x6Qz/fBf7YjJw06lZ8EB7Xh/Z0ukGz3qLXGMUAXibkpZbVOFz3Rr5cSG9gDyjBS8X7X/q/1tZUnONfLH1LH0+3Mq0xeH8a4s42Zuzg/wh9HXTcTYljx92n6dHIz8kZKFq5/GfwknPMzptO8BDz9JpXfF313MyMYdHloRTZHbOe6zr68rqJ3owsm1tLKLE62tPMHtlRJn1ahpvjGxJp1AfcossPLr0ELlFV7bcuJWQJIlNxxIZ/Ol2vt11HqsoMax1MJtn9+WhXg0qnTd0IS2PZ389Qse3/mHV4Tgn4dGiliezBofx97N92DK7L88PbUabOtfeX7rQZOXtdSccHpoBHnoWTezI5/e3d4gQgLURCXxg611bcr8CPPT8MLULB2IyHIUUH9/blkAPeZrV313P0oe68FjfhgB8s/M8D36736lIpbqxF4OAbG3jqlMTk5rvJHavxHxbQVtp1CqBD8e2RVfib34qOY+vS6VA9Gzszyt3NXc8jojLRqdW0d02Q5KRb8K/hAic+N0BZVbhOrDlZDJTFx9ElECrEqjt7UJyjpH6fq789FDXK3bQuV5IksTC7ed44Nv9pOQaCQty548ZvRjepjgn9FJGAS+tjnJ8l25yFxhFAN6ujPriMu3fmgayz3bi6tLAt8orqCRJYl9MOjN+PkyP97fw0V+niM8qxMtFy8O9GvDv7L5sfa4fbevIxRtWSc7PalHLk5wiC/ti0gn00DtZwxSYRJ5cdqhM9K6enys/TuuMh17DgfMZzFx+pEw0w0WnZt74dvzvruaoBFh9OJ57v95LfA3OC9RpVHw5oQPBngbOpuTx7K9HEW/2s1UluZRRwEM/hvP4T4dJzC6irq+LrZd0xyv6Q0qSxKmkXOZtPsOgT7fT7+PtrDmS4PCFaxLozivDmrNjTn82PN2bmQObEBbkUWXFNAfOZ3DnvB18u+s8kgRjOoTwz7N9GNLSua9p+IUMZq+MKPN6V52aH6Z0Ri0IzFklP/9wrwb0axrotJ5GreKlO5vz1YMdcNdr2H8+g+Hzd3LoYkaZbVYXLw2TxVeRWaSNrbDr0MVMeZq9HHSlRHxcZiEnErPLXbdxoDuzhzh3PPl8y2lOl8qDfKhXA0a3L670XRuZSLeGfrQKkW+S0/JN+LsXi8D7v9l3Q8fsduOvY0k8/GM4ogQ6tUCovxuxGYXU8jLw08NdCbxBbT2zC808uvQQ722MxipK3N2uNr9P70njQNnfMza9gBdWRdL/420sP1Acib7ZZ5sUAXibEp9V8d18v6YB18X/L6fIzOLd5xkydwfjF+1jXWQiZqtE+3refHxvW/a/PJD/DW9BwwB3tGoV88a3x9WWx5FVaEZCYnibWoiSbM2hKSVM95/PZEk51hAta8st43QaFX+fSOZ/vx8r88UTBIGHezdk6UNd8XHVEhWfzYj5uxx5kDWRQA8DCyfKidSbT6bw2ebTN3qXrgmTReSLrWcZPHc7/0anoFULPDWgMf8825f+pQRQaY5eyuL9jdEM+GQ7d3y2g7mbT3PWZtAsIH9ndjzfj39m9eWRPg2r3EC5wGTh9T+Pc9+ivVxILyDIU8/3Uzrx6bh2eLs6RzzOp+XzyJJwTBbRyWZCJcD8+9vTvJYnz/4qp1q0CvFkztCKDXLvbF3LcSFLzjEyftE+luy9cEMuXCWLQeJsN2froxIZbJuGLY2pnGnrN9eeKGdNmYd7N3TcdAJYRJizMsLR+QPk88R7Y1rTuoSv4NzNZ3i0T0Pq+7sBkJZnIsAmAkUJxn29l4gKitgUKs+fEfE8/tMhm4elQFiQB2dT8vB317Hs4a7U8bkx3qXHbNeKf04ko1OreGd0K+be1w5XnYbzafnMXhFB/0+28Wv4JSyiRK/G/o5IfXUUV14LigCsYQR76gn1deWgrQK4KgpAjsVn8+JvkXR9Zwuvrz3BmZQ8XHVq7u9Sj3VP9WLNkz0Z27FOGSuW+v5uvDGy2I/sZGIuoijxwtCmCBW4qL/+53HOppStXuzeyI/Px7dDJcAvBy/xyd/li5mejf35c0YvWtTyJCPfxITv9vP9rpqbF9i2rjfvjW4NwOf/nmXTsZujKOBq2XsunWGf7+Sjv07JvWUb+rLx6T7MHtK0Qgsguw9gaq6Ru7/Yzdfbz3E+Ld8p/7R9XW/+frYPi6d2oZ6v23XZ9z3n0rjjsx0s3nMBSYL7OtXl72f7MqBZWeGTkW9i6g8HHD1+S35FXh/ZkoHNg/h6+zn2xqTjqlPz+fj25bafK0njQHf+mN6Tu1rXwmyV+L8/jjNrRQSFpupPk7AXg8RlFtI4wA2jRaSBf8XjXvoCtv98BnlFlnLXVasEPr63rVNhSURcNj/sPu+0nkGrZuHEjo5IH8DzqyL5eGxrgjxlk+7UvOJIoFWCe77azYmE8qOPCldmzeF4nl5+FAkwaFS0CfHmWEIOngYNS6Z1deqkU11IksSvB2MZ89UeYjMKqOPjwm9P9ODBrqGcS83n2V+PMvCTbfx2OA6rKNEnLIDfnujOTw93LROdvlm5NfZS4ar453hchc8NahHE8cRc8k1WvFy0NA/+b/l/hSYrK8IvMWrBLobP38UvBy9RaLYSFuTOm6Nasu/lgbw3prWTQ395jO1Yh7vaFOf5bDiWhMki8e2kTrjri+0X7Kds+7SL0VL24jS0VS3esYmZBVvPsrjUid1OXV9XfnuiB3e3q41VlHhz3Qlmrai5eYH3dKzjuPDOWhHBqaSbwx6kMqTlGZm14ij3f7PPES2Ye19blj/SzTE9U5qouGwmf3/A0dvXKkm4alXU93NFEOTPmKdBw/tjWvPbEz1oEuRR7naulTyjhVfWRPHAN/u5lFFIiLcLS6Z14YOxbco1hi4yW3lkSTgX0gscPWvt34uHezVgUvf6HI7N5NN/5JufN0a2rPSF002vYcED7fnfXc1RqwTWHIln9Je7HbYs1cW0nvUdkX9X2/f/z6MJ1K8g4mooVQkqSfDhX9Hlrgtyj/JnBztPBX/0V7STlRTIlklfPtjRkYdcZBaZ/vMRfnqoi2NKOi3P5Ij0WEQYuWAXp2+h787Nwq8HY3l2hSz+XLUqujTw5eDFTFx1ahZP61KlPeorS6HJypxVkbzwWxQmi8iAZoGse6oXeq2Kp5YfYfDc7aw5Eo8owYBmgax5sgdLpnWhY+it1X1KEYC3IU8uK5sbZKd/00DHtGfX/5D/dy41jzfXnqDru5t5flUkEXHZaNUCI9vWZsVj3fnrmT5M6l4fz0pWaQmCwLt3t6a2V3Fux9zNpzFZRFY/2YO6tmKRkvG51FwTL/8WWe727u9Sj9m2E/wb607wZ0T59hAuOjVz72vHq8NbOC5493y1h7jMgkrt9+3Gy8Oa0aORXBn6yJJwsgpubhdTUZT4eX8sAz/ZzurD8QgCPNi1Hltm9WN0+zrl5uSdSsrlsaXhjFiwi+0lqsrd9Ro8XLRcSC9AkuDudrXZMrsf47vUu24dBnacTuWOuTtYtl/OF3qwaz02PdObPhV4iImixOyVERy6mIlKkKvqVTaxOrRlMC8Pa05OkZmZy49gFSVGtq3N2I51rmqf7GkSyx7uir+7jugkue/qlpPV53unUqnoEyYXg5xOln0No5NyGVLBNHBBOVHKVYcqvgEGeLR3Q6cpXqNF4sXVkWVyYLs08OV1W+s4gOQcI7NXRvL79J6O1JWMfJPD1soiwl3zd3KunBkKhfJZtv8iL/wmtyt106npExbIjjNp6DQqvp3UiQ71fKp9n86n5TP6y92sOhSHSoA5dzRl9uAwXllzjDs+28HaiAQkCQa3CGLtjF58P6Uz7W/AflYFigC8DTFX4OigQp4q3XeF/L88o/MUitkqsiEqkQe+2cfAT7bz/e7z5BRZqOPjwvNDm7L3pYF8fn97ujTw/U/J8F429/SS19pZKyIwWUT+nNGLjuV8uX47ksC2CjzsZgxozOTuoUgSzF5xtIyFjB1BEHioVwOWPtQFXzcdxxNyGDF/F3vOppW7/u2MRq1iwQMdqOPjQmxGAU+VU0xzs3AiIYd7vt7Dy2tkT78WtTxZ/UQP3hndGi/Xsjce59PyefqXIwydt4O/jicjCDCmfYjDBzDPaCE5x0ionytLH+rCZ+PbV6of738hp8jMC6simfT9AeKzCqnr68LPD3flndGtL2tt8dHfp1gfmYiAnHemEuSf7ep6M/e+dggCvLLmGHGZ8jbfHt3qPxemdGvox7qnetOhnje5RRYe+jGcT/8+5ZQrdz15uUQxSGtb8UVWgbnCi5WL1vmZApOV3w5dqnD7GrWKj+9t65RjvC8mg+Xl2MhM6FqP+7vIhtQCEBmXzUd/n+KP6T0c03xZhWa8bR6CZqvEnfN2ElvNkdNbkR92n3d0hvLQqxnaKphNx5PQqAS+fKCDo81ndbIxKpER83c5rJPevrsVkXFZ3DV/F+ujEpEk+YZr/cxefDOpE63rXH6G62ZHEYC3GZfLZesQ6oNWrSL8gj3/r6wADL+Q4RCAqXlG3ll/kh7v/8uTyw6z51w6KkFuz/bD1M7smNOfJ/s1xt/92i+WXRr4Mt3mAahWCRSarTy6JByzKPLLY90cflwleXhJOBn5ZSNVgiDw2oiWDG8j5zQ9/tMhjl4mSbtHI3/WPtWLViGeZBaYmfDdfr7dGVPj8gJ93XQsmtgJF62anWfS+NDWI/VmIc9o4a11JxixYBdHYrNw12v4v+Et+HNGz3LvwOMy5cq8QZ9u54+j8l37sNbB/P1MH+5qU4uM/GLrm+n9G/HXM33o3eT6ufhvjU5hyKc7+DVcFidTetRn09N9rnih+3l/rMNwVqJY/NX1deFbmyHuqkNxrI1IQK0SmDe+faUj8BUR7GXgl0e7M7l7KCDnh05bfLBaIsMli0HsJvHrIhPp3KD86bXyDLbnbTlTzprFNA324JlBTZyWvbv+ZJmOQYIg8PrIlnSo5+2YhVgfmcj6qCRWPt4NtUpAkiDXaMFDL0cFTVaJIXO3E5dRM2cTKsM3O87xhq1gx8ugYXSHOvxmi+R/el87BrUoP+J7vTBbRd5ad4Inlh0mz2ihRS1Pmtfy5OU1xxw3jXe1qcWmZ3rz9cSOtKx9aws/O4oAvM14YeXhCp8b2iqYY/HZ5JuseLtqndof2SnZdcMqSnyzM4bUXCP+7npm9G/MzhcG8O3kzvRvGljl02MzBzahXV1vrKKEQaMiIbuIx5ceQpQkFk7sxLDWzlYYFlFi/MK95Qo1lUrgk3Ft6dXYnwKTlak/HODcZQyPQ7xdWPV4D8a0D0GU4O31J3nm16M3JBH+RtKiticf3dsGgEU7Yvj9SPwN3iP5pmZjVCKDPtnOdzZPv7ta12LzrL5MK8fTLzmniP/74xj9P5Yr86yixEBbDs/8+zuwNiKBh34MR7Jd0v3d9cy5o9l16xedXWBm1oqjTF18kKScIur7ubLise68PrIlbvrLt2PfdiqFV/845nhsjwB6GjT8MKUL/u56YlLzeO3P4wDMGhxWZdNmOo2KN0a1Yu59bTFoVWw/ncrw+bs4Fv/fih0kSXLY6VyJh2w5qfFZRYT6ulJgstKurne562YXli36iM0oLGPxUprH+jZy8kDNN1l5eU1UmfOJXiP30bYXgAB8tvkMlzIL+WFKZwQBrCIUWeTewQBFFonBc7eTmF1zraYq4outZ3lng3yd8XHVMrF7qMPd4b3RrRnZtnp7LSdlFzF+0T6+2yXnjNfzdeFEYg47z6ShEmBUu9r8/UwfvnigA80qmTNfXdHya0URgLcZKw4nVfjcgGaBDvuXivL/IuKyyn1tj4Z+PNK7ISHXsdWNbA3TDjedmiKLiF6j4nBsFi+tlk/KXzzQge6lopanU/IqvNvXa9R8PbEjbep4kVlgZtJ3B0jKrtgg26BV88m4trw2Qs4L/ONoAvd8tYdLNexOfnib2jzZrxEAL/wW+Z8v+FVBbHoBUxcf5Illh0nKKaKeryuLp3bmiwc7EOzl7AmWkW/i3Q0n6fPhVpbsvYjZKtGzsR+/PdGD76Z0po6PC9MWH+Rzm2Gwq04WX6XthqqSv48nMWhucZ7iw70asPHpPnSpIJpVkhMJOcz4+YjTxUQCtGqBRZM60TjQHaPFylPLj1BgstKjkR+P921U5ccwun0d1jzZk1A/V+IyCxnz1R5WhFc8xVoeB85nMH7RPtLzK2eQO7VEMYi9F+++mPQy0712SoozO2+uPX7Z99CqVXwyrq2TVce2U6msKeemJ9DTIPeeLXGzMXtFBN6uWj4f3x6Qp3+tooTBto+FZpFBn2wn5TKm/DWNzzaf5iPbzIK/u47H+jRkwVY5uv3q8BaM71KvWvdn99k07vp8J4cuZqK2pUzEZhSisqWJ/DOrL/PGt7+qQrDjCdnFBWY3uRBUBGANwdtFQwN/N/bFVDz9G5OS63SHriqRQ/RnZAIDP9nG5uvcCD3Uz403bYnXZqvoMG7+ZmcMgiDw7eROhJaqCPxs8xmiE3LK2xzueg0/TOlMA3834rMKmfz9AbILKu56IQgCU3s2YNnDXfFz03EiMYcRC3ax60zNygucPaQp/ZsGYLSIPLoknLS86u0SYbRYWfDvGQbP3c62U6lo1QIzBzTm72f7lDE1zi4088nfp+j9wb8s2hGD0SLSMdSHnx/pyrKHu9Ex1Idj8dkMny8Xfxi0Kube1xZPw+Wjb9dCRr6JmcuP8OjSQ6TmGmkU4Maqx3vwv+EtKtXDNCm7iGmLD5bJxwX4aGxbx/f3o02nOJ6Qg4+rlrn3tbtuvmPNa3ny54xeDGwWiMki8vyqSF5aHVVuNX5JDsdmMvG7/YxbuJf954sNk69Uca9SqehrK4g5m5KHVi0QEZft6BZSmvIii3vOpVNgKt8SpuRxzRzgPBX8xtrj5XZFaV/Ph7dHFxeFGC0ijywJp3N9X94aJdtZFVlE1IKA3jYtnW+yMuDjbWW6GNVEPvrrFJ9tlm/WAz30PD0wjPc3yWLw2UFhPNSrQbXtiyhKzN9yhge/3e/kBqBWCdzbsQ7/zu7Hp/e1o9FV2s/sOJ3KuK/3ItqiyFXkD3/dUATgbUTmZU4yvZsEYBElR/5f90ZlBeAHpXK+/N11BHgUe2Gl5Zt4eEk4z/569LrmAo3pEMKItrURJRx2GO9tjGZrdApueg3fTe7sqMKzM3zBzgpP9n7uepZM60Kgh55Tybk89OPBK07tdmvox9qnetGmjhdZBWYmfb+fb3bUnLxAtUrgs/HtaejvRkJ2EU8uO+zwzbve7DmXxrB5O/n479MYLSI9Gvmx6Zk+zCrl6ZdvtPDF1rP0/uBf5v97lnyTlVYhnvwwtTOrHu9Oj0ayWFgZfslW4V1IPV9XVj/Rk9Htr65C9mrYEJXIkLnb+TMiAZUAj/dtxPqZvekYWrmp2TyjxTFdbMd+HZk9OIy7bZ0qtp1K4VvbtNVHY9sSdJ27JHi5aPlmUidmDw5DEGD5gVjGfb23TN4cyFY7U384wJgv97DzTBoalcADXes5rJ0qk1rx0rBmgCyq7FO1ni7ltwHLyDdROjgoSvBxJfJYn+zfiKYl0mGyCy289uexctcd16kuU3rUB+SLe3KOkUeWhDO2Y11HTmG+yYpBq3JEC/OMVvp/vI2scvKVawrvbTjBF1vlyHstLwMv3tnMMcaP9G7AzIGNq21fMvKMjP5yN5/8U+wVq1EJjO9cl62z+/HRvW0dpt9Xw6pDcUxbfJB8k9Xxt1fd5ApQEYC3EXdfpv3b3R1CiIzLpsBkxcdVS1hg2ZD2zlJRLpUg8Ouj3ct4kq05Es+gT3fw1/GKp5uvBUEQePvuVoR4u5BZYKa+nyuSBE8tP8KZ5FwaB7rz0di2Tq+xiNDjvS0V9rSt6+vKkoe64GnQEH4xkxk/H75ilWttbxdWPNadezrUQZTgnQ0nmfnL0StGFW4XvFy0LJrUEXdbm7231lXcZaEqSM018uyvR3ngm/2cS83H313HvPHtWPZwV6c78SKzlW93xtDnw6189NcpcooshAW58/WEDqyd0Yv+TQMRBAGjxcora6KYsyoSo0VkYLNA1s7odd18xdLyjDy57BBPLjtMWp7cuH7Nkz158c7K5xdarCIzfj7MycTiiLZaJSAhe2bOGCBfKFNyi3jO1gpuSo/61ZY0r1IJPDWwCT9M6YyXi5aIODmyuttWOX8yMYdHl8hWO1tPpaJWCYzrVIetz/Xj3dGtHdPuZlEkOqn8qL2dksUg9ojcpuNJeLmUH7lt4F82WrMi/PKWMCBPBX86rq1jChBgQ1RShabor9zVnG4NfZFs1dhR8dk8tzKCmQOaMNFWNJNdaMHTReOYxs4pstDv463kFN5ePbcrw+t/HmfhDvlGpY6PC6+PaMHzqyIRJbi/S11eHta8ylopXg5Jkli85wLd3vuXiDg5rUUtCDzYtR7b5vTj/Xva/KcOP5IkRxOfWxmBxWbB5HuD+hVfLYoAvI24mFlxrknPRv4O+5euDfzK5P9dyiwo11OrYYA7i6d2xlCi0k5Avtg9tvQQM5cfKbcS91rxctHyma2zx4X0AhoFuJFnlC0pMvNN3NWmVpkpg6xCCwM/2V6heW2zYE++m9IZvUbFlugUXlxdNuG7NAatmo/vbcObo1qiUQmsjUhgzJc1Jy+wcaAHn9lsRpbsvciv5VhlXCuiKLFs/0UGfrKNNUfkXLmJ3ULZMrsfo9qFOC4OJovI0n0X6fvRVt5ef5L0fBP1/VyZN74dG5/uw9BWtRzrJmYXct/CfSzbH4sgyFNM30zqVK5NzLUiSRJ/HI1n8Kfb2RCVhFolt6Bb+1Qv2lZQuFDRdl778zjbThXbFunUKqyinMv47ujWCIIgewKuiCAtz0SzYA9evLNZlR/TlejXVC6qaRUid9SZ+N1+7pi7gzvn7eTvE3LV5Oj2IWye1ZcPx7alrq98YS152vntCn59UFwMkpBdRG1vA7lFFnpVUDWdaRNXJS9qeUYLfx69ciFTy9peTB/gHIV6Zc2xcmc6tGoVXzzQgRBvF0RJPh+uj0pk3pYzvDlSdh8A2Sja313vEJZZhRb6f7y1wpvU25H/rYli8Z4LgFxc8d7o1sz85ahDKL19d+vrLv4kSWJrdDJ9PtzK638ed7QQHNGmFjtf6M87o1v/5zZzFqvIy2uOOaKJj/VtyGf3tauqXb/uKAKwBtDQ3w2DVl3C/69sAvqCcgopsgrNWEWJ9vV8+HJCB8fJ2y6ZBODPiASGzN3OhqiqbyHWub4vM2zWMMk5RoI99cRmFDimI1+8sxmd6ztPq6XkGi/r5de5vi8LHuiAWiWw6lAc72+quGuAHUEQmNS9Pj8/0s1hkDtiwS52ninfX/B2Y1CLIGYNks21//f7MQ5dzKyybR9PyGbMV3t4Zc0xcoostKztyZone/LW3a0ckWeLVWRl+CUGfLKNV38/RnKOkRBvFz64pzX/zOrLqHYhTrlve86lMfzzXRy9lIWXi5bvp3Tm6UFNroupc0pOEY8uPcTTv8i9d5vX8uSP6T2ZPaTpFVuwlWbRjhiHMTSAQavCZBUJC3Lnywc7OuxOvt0Vw84zaRi0Kubf3/66VS9fibq+rnw8ti31fGUhdMpWdTukZRD/PNuHufe1u2wbtzVHEq4YhS9ZDGLvwJFSTn4eyFHCWp56Sm/x038q1+N6Rv/GNCnRPSY938Rb606Wu66fu56FEzti0Koc58N5W86wNjKReePb06uxnGKTlFNEiI/BkQuWnm9mwMfbasQswgu/RfKT7fPcwN+NuePa8fhPhzBaRAY1DyxTgFPVSJLE5hPJDJ+/i6mLw7mUKacqhPq6snlWH+Y/0IHa11DUWGCy8OjSQyw/IN9kvjGyJS/d2fy6mcdfDxQBeJuw61TFAqx/0wDMVpHwC/KFu3ujsnfQm8qZzi0yW+VQvSgxoFkQ79/Txul5CfmknJZn4sllh5m+7HCVFwvMHNiE9vW8yTNa8HPX46pVsTcmnTfWHnfciZf2IcwpsjDhu/0s3Xuh3G0ObhHk6H+7cHsM3+6MqdS+dGngK0d1bHmBk78/wNfbz9WIvMDp/RsztGWww1fxctXUlSHPaOHNtScYMV8Wau56Da+NaMEf03s67D5EUbLdYOxgzqpI4jILCfDQ8+aolvz7XF/u61wPbYmqTEmSWLj9HBNsid0tank6poSrGkmS+O1QHIPn7uCfE8lo1QLPDgrjj+k9r9j+sDzWRybyXgkLJhetmiKzSICHnu9t060AkXFZjirK10a0vG5t6q7EpYwC5qyM4K75u4jNkC+sdpFzNjnvilW+KkEgLc9YJu2kzHolikFiUvNRCXDwQia+ruVPsYXYOgeVFBYX0gsuawFlR6dR8em4ds5RysNxbDtVvuF8qxAvPih1TpyzMoKo+Gy+m9KZNjaT4NiMQhr6uzryOFPzTAz4eBtFt7EInPXrUX49KFeKNwl0Z8H49kz7MZx8k5Wejf1Y8EAHp+9uVSKKEn8dT2L4/F08vCSc47YCQQF4ZmATts3pR+NyUqCuhtRcI+MX7ePf6BT0GhVfPdiRybbc0FsJRQDeJkz9sWL/v/Fd6hEZl0Wh2Yqvm87pLhdk37TyvLRAPgG+9udxJEliXKe6jjZrdrIKzTTwd0MlyNMgQ+baW+VUjSjSqFXMu6897noNxxNyGNwyGEGAn/bFsnTvBQI9DXzxQPsyd5KiBK/+cZxX1kSVW7wwrnNdnh/aFJA9/1YfvvJ0FEAtLxd+faw793aU8wLf3xjNjOVHbvs7eruvYtMgD1JzjTz206H/1DtZkiQ2RCUy8JNtfL/7PKIEw9vUYsvsvkztKXv6SZLE38eTGPb5TmYuP0JMWj4+rlpeHtaMHXP6M6l7/TLRtTyjhSeXHea9jdGIEtzToQ6rn+zxn3J6rkRidiHTFh9k9soIsgvNtA7xYu1TvXh6UJNyTYmvxKGLGTy74qjjsUGrotBsxUWr5vvJnR3TU3lGCzOXH5G7TbQKZnznulV1SJUmIauQl1ZH0f/jbaw8FOfksbjmyZ7U9jIQk5bP3V/sZm0FbRhBFrhw5bZt4FwM0txWDNK2bvl5nHG2KE9p+403rmAJY6d1HS+e7Oc8Ffzy6qhyq7EBRrUL4bE+DQF5etteNZ+Rb+LnR7rRMECOgJ5LLXAqNEnKMTJo7g5Mlpuz28618NTyw6y2Wek0D/bg6wkdmLz4INmFZjrU82bRxE7XJWotivK5ZdjnO3ls6SGH8AMIcNex4vHuPDM47JqnnGNS87jnqz1ExmXj46rl50e6MbRV8JVfeBOiCMDbhIrav+nUAk2CPBz2L+X5/31p6zJgx/6st4sWQYCl+y7y/sZoJElixoDGPNhV9mrSqAS0aoHzafk0CnCncYAbGfkmnlp+hCd+OlyulcJ/oZ6fK2/abBbWRSbyYBc50fr1tSfYczaNrg39eHFo+XlQy/bHMvG7/eXmKT7RtxHTbDlGz6+KZGsFd/qlMWjVfDi2DW/d3QqNSmB9ZCJjvtxTYe7h7YKbXsOiSR3l5P9LWbz6+7GrEvoX0/OZ8sNBnlx22NF6bcm0Lix4oANBngYkSWL76VRGfbGbR5ceIjopFw+9hlmDw9j5wgAe7dOoXAuVsym5jFqwi43HktCqBd66uxUf39umyi8ykiTx68FYhny6g62nUtGpVcy5oylrnuxRaYPY0lxIy+eRHw85hIBWJVBklu2P5t/f3qnV1P/9cYwL6QXU9jLw/pg21ZI4b8durt3vo20sPxCLRZTo3cSfNU/KHoutQrxoV9ebtU/1omdjuaf0U8uP8Na6E+XegNkF4D8nki9rywTOxSD273FEXPkFJEk5RjrW8waKz2MAu86kVTri9tTAxg7hBnL+4QcbK04VeX5oM3o38UeU5HNiSq5cGawSYOVj3all86uMTsqlVYkCpLjMQgZ/uh3zbSQCH18aztoIeTaqVW1PFk3qyKTvD5KWZ6RFLU9+mNrliubnV4tVlFgbkcDQeTt4ctlhopNy0aqL//o9G/ux8Zk+dK5/Ze/NK3E4NpN7vtpDbEYBdX1d+O2JHpWu7r8ZUQTgbY79jvly/X9L5+/pbSdng1bNu/ap0h0xzNtyBkEQeHNUK4a0CMIiSmjVKjwNGs6k5FFkFpnYLRSNSmDT8SQGz93OH0fjqyQaOLp9CCPb1sYqSmw/ncLwNrWwihJPLDvMhbR8Hu7dgDtL3YUJgKtWxb6YDEZ9sYtTSc6dAQRB4H93NefudrWxiBJP/nSYw7GVy28TBIGJ3UJZ/mg3/N31RCflMnLBbrZX0Hf4diHUz40FD8h9m1ceiuNHW4L35TBarMzfcoYhc3ew/bQsnGYObMJfz/Shj216b39MOvct3Mfk7w8QGZeNi1bNk/0asfOF/swc2MRhH1KajVGJjFqwm3Op+QR7Gvj1se5M7BZa5eIoPquQSd8f4IXfosg1WmhX15v1M3sxvX/jMp1IKktmvokpPxwgw1ZooALMtsjVayNaOlX2/n4kntWH41EJMO/+9telmKU80vKMvLXuhMNc22QV6dbQl5WPd2fpQ13LtOCTLZe6OozEv9t1nge/2V/GDFmjFmgW7IHJKrI2suJIoR17MUhidhEBHjoy8k2OnMDSeNiW60tEY0UJ5m6uXC6gXqNm7rh2Th5uS/ddZL/tHFoatUpg/v3tCfVzxSJKaFQCx+JzeG5lBD6uOlY90QMf29/rWEKOU0eTixkF3PHZDqw3ad/tq+GhxQfZdFz2iW1fz5vvJndi0vcHic8qpGGAG0se6lLGUeJasFhFfj8Sz5C523lq+RFOJ+fhrtcQ5KF3+EI+NaAxS6Z1rZJ2pX8fT+L+RfvILJCj/quf6EnDq/QJvNlQBOBtwP9WH63wuTtaBmOylMz/cxaAaXlFZSJ1JSMn93epx/8NbwHIhsuLdpxDrRL4/P72dAr1ocDmeVTHx4W4rEL+OBrPO6Nb0aKWJ1kFZp7+5SiPLj1ESs615YwJgsDbo2VrmEuZhagFgbZ1vckuNPPwknByjRY+HNvG6c5dAtwNWur5unIpo5AxX+7mn1JG1iqVwIdj29I3LIBCs5Vpiw9yNuXyLaRK0rm+L+ue6kU7275M+eEAX247e1vnBfZuEsBLdzYH4K31J9l7rvwLI8Ces2ncOW8nn/wje/r1bOzHpmd6M2twGAatmqOXspj43X7uW7SPAxcy0GlUPNSrATtf6M/zQ5vhXUGul8Uq8t6Gkzyx7DD5JivdGvqybmavKmuDZkcUJX7ad5Ehn25n55k09BoVLw9rxm9P9Lim/Lsis5VHl4ZzIb24mtwemX+oVwOnfKLY9AL+97vsmTZzYJMqiWRcicx8E+9vjKb3B1v5btd5jBaRTjZz7V8e7X7ZfVCrBJ4f2oyvJ9gshC5kMPzzXQ4PUjv3dJC9GH+rRPrF1J71HVEde/5fed0/QI60ues1FJWKrP18oPLdS9rW9eZx29SunRd+i6ww7cHbVe6j7apTYxElVIJsJfPZ5tOEeLuw8vHujjZxRy9l0THU2/HamLR87vx8J6J464rAid/tZ0u0PIPSpb4P303qyOQfDnI+LZ8QbxeWPVw1Igzk7/4qW/7tM78e5VxqPp4GDXe3q41KgORcI96uWn6Y2pnZQ5pWSaHJkr0XHAUs/ZsG8Muj3QjwqJrjuZEoAvA24KcDFdsc3Ne57mXz/77e7lwA4apVOd05A0zr1YA5d8j5cu9uiGbpvosYtGq+nSy3o0rLN6FVq2hbx4ucIguv/n6ch3s3YNbgMLRqgX9OJDN47g5WH467JmHkadAyz2YN80dEAmM7hBDsaeBsSh4zlx/BVafh6wkdHdNLIFcM9mnsR7eGvuSb5IvuF1udBZpOo+KrCR1oV9ebrAIzE787UK65bUUEexn49bFujO9cF0mCDzedYvrPh8mvIG/oduDh3g0Y3T4Eqygx/efDxGU62+Kk5hp55pcjPPDtfmJS8/F31zNvfDt+eqgrDQPcOZGQw8M/hnP3F7sdRsEPdq3Hjjn9eXV4i8teLNLyjEz87gALd8if3Uf7NOSnh6ruAmMnNr2AB7/dz/9+P0a+yUqnUB82Pt2bR/s0uqaLiihKzFkVycELxdFmF60sHO5oGcTLw5o7lputIk/9coQ8o4UuJarirxf2riq9PviXr7efo9BspW0dL36c1oWVJcy1K8PQVsH8OaMnTQLdSck1ct/CvY6uCwCj2tdGrRI4Ept1xSINlUpFnyZytNgumk8ll/+apOwiBjaT1y15LsgtsrAhsvJuBU8PCqN+iRzSC+kFzL1MRXHTYA8+HSf7k9pTED//9yx/HI2ncaAHyx7p5jAIPnQxy8nB4HRyHsPn774lReD9i/Y5inl6NPLj+6ldmPqjnMIR6KHn50e6Usvr2luImq0iKw5eYsAn23luZQTn0/LxdtUye0gY93epx+9HE8gpstC2jhfrnqqa4i9RlHh/YzT/98dxh2/hN5M6Vfk09o1CEYC3Me56NX7ueif7l9JTY6VPiINaBFPepW16/8aOaZ1Xfz/GqkNxeLvq+HFaF4I9DZxPy0erUjGkRRAmq8isFRGoVQJ/zuhJqxBPsgvNzFoRwcM/hl9TBWmn+r7MsLVu+nDTKd6+uxUGrYptp1J5f+NJwoI8eP+e1k6v+enAJV4c2owJ3eohSXJLomd+Pep0N++qk1vGNQpwIzG7iInf7SfzKvwN9Ro179/ThndGt0KrFtgQlcToL3dzIe32zAsUBIH3xrR2+MA9uuQQhSYrVlFi6b6LDPhkG78fTUAQYFL3ULbMlu1azqXmM+Pnwwz7fCebTyajEuRI0Nbn+vHO6NZl+vuW5khsJiPm72JvTDpuOjVfPtiBl4c1/8/TsOUhihKLd5/njs92sDcmHYNWxf8Nb8Gvj3WvkimfT/455VQg4WnQyEKrrjef3edc0PTpP6eJuJSFp0HD3PHtqvQ4S5JbZObzLWfoVaKrSotannw7qRO/T+9J37CA/zSt3jDAnTVP9qB9XW+sEo58QLNVJNDD4KjwrUwRlr0YxGiRrXFAPseVh9V2g2exOkfsPv7nyp1B7Bi0aj69r53T+XDRjhgiLmVV+JqhrWox0+YnaP8zzlkVyZHYTNrV9ea7KZ0cyw9eyKRzifyxE4k53P3FnltGBIqiyL1f73H0l+8T5s/3Uzrz8I8HibiUhberlp8e7kqo39V31SiJySLy8/5Y+n+8jed/iyQ2owBfNx0vDG3GH9N7si8m3XEzOKl7KCse7/6fff1KYrRYeXbFUb7eLufIzx4cxrujW1+37+CN4PY5khpK7mVasrWp4w3gKADpXir/L6vAREIpMXaXzcS0PObc0dTRBun5VRGsi0wgxNuFxdM642HQEB6biQAOg+aP/jrFkr0XWflYd+bc0RSdWjZgHjx3OyvDL/3naODMAY3pUM+bXKOFr7efc1gxfLPzPCvDLzGqXYhjP+3c+/Ve3hjRwlG48cfRBO5buNdJjPq46VjyUFdqeRk4l5rP1MUHr7q698GuoY7pgdPJeYxcsKvSxSW3GgatmoUTOzl6Jj+2NJzRX+7m1d+PkVtkoVWIJ78/2ZM3R7Uip9DMcysjGDJ3O+tsNx3D29Ti72f78sm4YqPgipAk2Sz6voX7SMwuomGAG3/M6Mmw1hV/Xv8L59PyGb9oH6+vPUGh2UrXBr789UwfpvVqUCVTSb8ejOWLrcVFV75uOnKKLNTxceHbSZ2cilx2n01zXHw+uKcNIdfgWVYRBSYLX207R+8Pt/LpP6fJLbLQNEiu3Fz3VC8GtQi6pnzK1Fwjz62M5Egp0ZRbJH+v7NPAaw7HI17BO6ZxoAchtmIQR0eNCvYt/EImDQPcMIs4fARBtpK5mmKtDvV8eKR38VSwhGz1crnq3WcGhTGoeSCiJOchmiwijy49REJWIb2bBPD5/e0d6x68mOkUCYyMz+behftu+hQSURQZ89VeRxR7UPNAvp3UmSeXHWZfTAbueg1LpnUh7BrSJIwWK0v3XqDfR1t5eU0UcZmF+LvreGVYc3a90J/O9X0Yt3Avu8+m46JVM298O94c1eqq/TfLI7vQzJTvD/LH0QQ0KoGPxrbhqYFNqrXwqjpQBOAtzqjLtH8b3qYWRouV8IuyACxdALJoh/P0r0FT7LlVHoIg8NqIFozvXBdRgmd+OcqWk8k0C/Zk0cRO6NQq/jqRjMki8vqIFqgEWH7gEo//dJjJPeqzbqbsoZdbZGHOqkimLj54VVOtdjRqFfPGy9Yw4RczuZBW4LjrfmXNMcIvZPDysOZ0sFUDgpxcP+zzXUzsFsqSh7rg7Sq3sRq5QPahsxPi7cKSaXKy8tFLWf+pB27HUDkvsEM9b3KKLExbfLDMtPPtQoi3C5+Ma4sgwI4zaUTGZeOh1/D6iBb8Mb0XgZ56Xlkj24asOhSHKMGg5kFsfLo3Cx7oQOPAK0fUisxW5qyK5JU1xzBZRYa2DOaP6T2v2curJFZR4tudMQz9bAcHLmTgqlPz1qiWLH+k2zVHMOzsOJ3KS6ujHI8DPPRk5JvwNGhYPLWzU05Rep7cFk+S4IGu9bizioWuvZ1e7w+28sGmaLIKzDQKcGP+/e3Z+HRvhraqdU2GtpIkezgO/nR7uR6jJqtIvtHMwOaBeBo0JGQXOSJJl8NetZ+UY8THVUteUfk3aEk5RkdRmG+pgpk3/qycJYydWUPCqOtTLL5Pp+Q5+tqWh0olMPe+djQKcMNoEXHRqknNNfLwj+EUmCwMb1Obt+9u6Vj/4IVMOpWIBB66mMn93+y7qn2sTkRRZMSC3Y7z5p2tglk4sRPPrjjKv9EpGLQqvp/S2RGAuFqKzFYW7z5P3w+38eofx0nILiLQQ8//DW/BzucH8HDvBvy8P5bxi/aRnGOkUYAbf87oyah2IVVyfAlZhYz7eq9jluG7KZ25t1P1Wy5VB4oAvMWJSa9YQN3ZqhaRcdkUmUX83HRlLralfbqGtAy6onWGIAi8M7o1o2yVs08sO8yuM2l0b+THXFvLsKX7LpJvsrJwYicMWhXbT6cy7uu9eLlo+e2JHrx4ZzN0Gnna9o65O/j1YOxVi6O6vq68ZTuJfv7vGfqEBTC0ZTAmq8jjPx0iNc/Ilw92xK9ET8ZTyXl8ufUsPRr58+f0XoQFyblJ4xbu5fcjxXmUTYI8+H5KZ8fUst0M+2oI8jSw/NFuPNC1eNr5iZ8OV+gndisiSRLrIhN4flUkJf98b9/diuFta/PuhpP0/Wgby/YX24b8Pr0n307u5KhOvxKXMgq456s9rDoUh0qAF+9sxlcTOuBhqMJqQlFi7Nd7eHv9SYwWkV6N/fnrmT5M7F6/ylz9o5NyePynQ47csAB3Ham5RrRqgYUTOzmJWUmScwRTco00CXTn1btaVMk+gBxV+XHPBfp8WNxOL9TPlU/HteXvZ/syom3taz7m1Fwjj/8kt4nMKtH7tvRWP/37NAatmhFtawOVaw03pUdxMYi/u/zdLp2zXHI/VAKk5DnPkmw/nXZV/nsGrZrPxrdzWrbg3zOX7WXsYdCyaFInPPTy9L5eo+JEYg6zfo1AFCUmdKvPrBKeqocuZtK+xA3rvpgMJn63v9L7WF2Iosiwz3c5PPZGtq3NFw904KXVkayPTHR8nrs0uPpCpUKT7abkw628vvYESTlFBHsaeGNkS3Y8359pvRpgFkWe+Okwb68/iUWUGNG2Nn/O6FVlhujRSTmM+XIPp5JzCfDQ8+tj3S8bFLnVUQTgbYqHQYOPm45954rtX0qGr3OLzI7WOHZGtK3cHZRaJfDxvW3lfD+LyCNLwjl4IYO72tTiNVvF8Ed/nSKrwMQvj3bH312eIhz9hWzX8XjfRmyY2Yv2tmncF36LYtL3B4i/ymjg6PZ1GNVOtoZ5dsVR3hzVkua1PEnLM/Hwj+F4GDTMt1mW2Pnwr1OcT8ujnp8rvz3Rg0HNAzFZRJ759Sjvb4x2GMh2DPXhywfllnFrjsTz7oaTVy1S9RrZRue9Ma3RqmVrnNFf7Ob8bZAXeDE9n8k/HGTGz0dIyTUS6utCP9uJcs6qCHp98C/f7TqPySLSub4Pvz7ajaUPdXWywLgS20+nMny+fLHxddPx00NdebxvoyqfhknPM3IkVu5G8t6Y1ix9qMsVp6SvhuScIiZ/f8DRa9vPVUuqTZR8OLZNmcr8xXsu8G90CjqNis/vb1+u9+HVYrKILNt/kX4fbeO1P4+TklvcTm/zrL6M6VDnmqe4JUn2Y+v/8Tb+Ol5cbV/by4BOLbdM06qKLzn2NmH3dJSngTceS7riDZJaXVwMYu9AYqxAzG07leq4eAeWiK5aJYnPKmkJY6djqK9T73GrJE8FX66VXaMAd+bdL98UGy0iaps9lr013VMDGjOle31AnlqOjMuibQnfx51n0pi2+OBV7ef1xGIRGfLZTqJtdlr3dAhh3vh2vLX+BCvC4xzelVcrmPKNFhbtOEfvD//l7fUnSbV9Nt++uxXbn+/H5B71MWjVnEjIYeT8XWw6bvP7HNWSz8e3q7KCjD1n07j3q70k5RTROFDOXf0vXX1uJRQBeAtz8GzZqRU7dpuGfedtArDURea7XeedHuvUAr2bVL7CT6tWMf+B9g77lKk/HCQyLospPRvweF+5WOTF1VFkFphY82RPGga4kZBdxNiv9rD7bBqNAz1Y9XgPXhnWHL1Gxc4zadwxdwfL9l+8KqH11t2tqOPjwqWMQt7bGM23kzvh767jZGIOs1dE0K2BH3PucDaJvvMz2XLBw6Bl4cROPGErbvl6+zkeXRLuaNY+oFkQH9ryC7/ddd6RaHy13N+lHr882p1ADz1nUuS8wH+jk6/8wpsQo8XK51vOMHjuDnbYPP2eHtiE357oSZu6XqgFMFkliswirUI8WTKtCyse607XcvwnK0IUJeZvOcOUHw6QXWimbV1v1j3Vix6NK//5vBLhFzJIt4kwCegbFsDfz/bh/i71qlRg5hstTP3hIMk5stWSp0FNpi0q9uygMEa3r+O0/omEHN7bIJsO/++u5pWOlFaExSqywtZH+ZU1x0jMlqMqb9/diq3P9SvTTu+/kpZnZPL3B3hq+RGHiHM3aJjevxF5Rgsmq0jvJv74uRdH5I0WkdWH42hf15uG/m4Umq1srERP8ZLFII0CKp6eT8k1Mqi57KVotjgXg5TsuVxZnhvS1GFIDRAVn1PmPFqaAc2CeG6I7KBgD5Mv2CpXBguCwP+NaMFIWwTUKsLJxBwns+h/o1N4bOmhq97XqsZkERn82XbOpsiV1/d1rssn49ox95/T/LD7AgAfjW3L0FaVT1XIM1r4cttZen+4lXc3RJOWZ/p/9s47LKpze9v3zNB7BwFBELBXFFGx11hijV2j0ajRJCYxMb2c9ERjL9FYY+wtxti7oigCCirSRKr03pmyvz/2sAUpYju/5Hw+1+WVwMwwM3v2vHu9az0FZ0tDfhzZirPv92SSr6vE59sTlCiK6rKKtbY6XZjcudEz+64evJHMq5sCKdCq7fc+IyHJPx0vCsB/MSZtqn1heKmlg8j/q/D/c6/akq888gTo2cTusZMT9HUU/DrJm05uVhSWqZiyMZCI1Hw+HNiEkVqLkDl/hJBdVM7+N7rg42ZFQZmKVzcGsjc4CYVcxuvd3TkyrxverpYUlqn49MAtJm24SmJ28aNfAKI1zNKxojXMgevJBMVls3ayN3oKOcdup7L0dDSze7jTv5KpbqlKwytrAwCxm/nhwKYsHdsWPR1RpDJqzWUStFYTo7yd+VRry/Hj0Qh2B9XfS6wyvF0t+fstPzq4WlJQqmL6liBWnI5+7NHy/yUuxWTy0tKLLD4ZRbl2VPrn3C4Y6Snot+Q8y0/HoBYekO4dzAzw87B5rEU6r0TJ678H8cvJKIn/tnuW71OFtldGcm4Jb+24zuhfA1Bq1ZbmhrpsntbxmT1HBVRqDW9uDyE8RRyXGejIUaqRoure7lPV0qW4XMVbO0IoV2vo28yeyb6uT/zcao3AgetJ9F18ngXaHGUbE32+HNqccx+IF9cnia2rCbuvJdL1xzNc0FqByBDVmLte92VvcBL5pSrauVjw6yTvao/99nA4MplM6gLWxxOwcjJIgbbYrO0Mu3U/DwsjXXJKVBhXUgznlSg5FV77BromGOopWDq2XZXf/XIikthHWNjM6dmYQa0cUAsPbGk+2BtGSEKOFLHY3Uvc3JSrBe5mFNKsUmzc8dupvLW99qjP541ylYa+v5zjXqa4Jk72deWnUa1Ze/4uy8+IXMivh7WQPsNHIb9UycozouL852ORZGtpCD+Pbs3Z93syzsdFOjdLlWo+2hfGB3vDKFNp6NnEVvJdfRYQBIE15+4yb+cNlGqBQa0ctBzxmv1H/9fwogD8F6OsjijWrh42hCbmUabSYGOiR+NK9hXF5Sris6oWWGOekORqqCXJVnjoTVofyL3MIn4a3ZpunjaSuXJOsZKt0314uY3IHXx/TyhLT0UhCAKNbU3YPasznw9pjoGunEsxWQxYeoGtAXH1KpA6NLLiLa01zGcHbmFnasB3I1oCsPx0NH+HpbBoTBtcKo31guNz2XolTvp5eDsn9swSu3RRaYW8vMqfy3fFC9rr3d2lvM+P99/kVPiTde/szAzY/rqvZEfzy8koZv8RLHUc/6lILyhl3s7rTFx/ldjMImxN9fllTBv6NrPj1U3X+OFoBDnFStxtjFk+vh27Zol+Z6fupLPsdHS9n+dOSj4vr/TntHb8+fPo1nw/otUzUfUVl6tYfDKKPr+c41CoaE9jpL0YG+oqnvlYWRAEvjp0m7ORYjKMQiZ+V0qUaro0tuaHka2qPefXh8K5m1GEvZk+P49+sqg3jUbkZQ5YeoF3d4USlyVaZnw6qBkXF/RiWle3ZxaRl5ZXwuDlF1mwL0waw3ZsZMnJ93rwVm9P5m4XI/+87E3YNLVjjaO67CIlV+5mMqKdEzKZyH2rz+ZvhnYcm55fhqmBgtpWiVPhaQzXigNcHxrr/3Ss/pYwFfBxs+LVzg8K83K1wEf7bta5TslkMhaObkNTB1NKlGrMDHREZfDvwSTnlqCrkLN2UgfaaYuaEqWGpNySKp6th8JSeK9SXvR/C6XlKnotOkeCli403c+Nb4a3ZNvVeH7QxuMtGNiEKdpRdl3IK1ay9FQUfj+eYdGJKHK1a8biMW04/V4PxnRoWKUbHZ9VxMjVl9l5LRGZTLRh2fhqRyyNn01xptYIfHHwNj8di5De28rx7Z9LTvE/FS8KwP9BmOgrcLQwlPz/Oj3E/9t0Ka7Kgqkrl9HN68nHayb6OmyZ5kPzBmZkFpYxcf1VUvNKWTPJm1ZO5mQVlTNl41XyS1QsHdtW8hNceiqaD/aGUa7lx0z3c+PYvO74NLKiuFzN5wdvM2H9FakbVxfe6u2Bt6slBWUq3tl1gxHtnHi9m3iReH9PKPGZxWJnsFLX4/M/b3O/koFxG22WaRtnc3KLlUzZEMjWK/GAKD4Y1d5ZMj6+9lCqQX2hpyPn2+Gt+GlUK/QUck6EpzF81aVq4fX/BKg1AlsD4ujzy3kOaj39JndyYW6vxiw+EcVXh8LJKCjD2dKQhaNbc+Ld7rzcxhFvVyupAF92OprjNahAH8bBG8mMWH2JeO2IZ/8bXZ54U1IZgiDw5/Vkei86z/LT0ZQqNfi4WXHoTT/MnmEs1cPY4H+PP648GDM6mBuSU6zE086ENZO8q3XfDoelSBe6JWPbYvWYFzlBEDh2K5VByy/y5vbrxKQXYm6oywcDmnBxQS9e7+7+TLiEFVh4LIIuP56RxABWRrpseLUDe2Z3wc5Mn6mbAqVx3e+vdarWUTHWe1AMfnzgJo4WhnTR0lT2h9RubF+BVzs/EIPYmtTuHZlRWE6PJuLaFp1WWIUPHJ1eSGJO/SYNlfHhS01xMHvwnIFx2Wy7Gl/nY4z1dVg3uQMWRrrkl6owN9Qls1BUBheVqTDUU7B5mo8k1CsoVZFVWIabzYOidX9IMh/uC3vs1/ukKC5X0XPROYmbPau7O58Pac6f15OlZJo5PRszp2fd5uQ5ReWSufjSU9Hkl6rwsDNh2bi2nNTyTx/21jt+O5UhK/wJT8nH2liPra914q0+ns9MlFVSrmb2H8FsvRKPTCbSLT4f0vyZ/f1/C14UgP9SfPtX7QtBVy1XqiKi62H/v4dNV70bWT51l8XcSJet08UFLCWvlAnrr1BYqmLj1I5SFNu0zYEUK9UsGNiU70e0QiGXsTc4iWmbA8nXdsEa2Rizc6YvXw1tjqGugiux2QxYeoHNl+7VucvWUchZOrYtpvo6BMfnsPJsDB+91IyeTWwp0wpVrI31+Okhk+h+Sy9UMV611+bJVqicP//zFp/9eROVRuDHUa3o3dSOMpWG6Zuv1akCfBTGdnRh1yxfHMxEz8HMwvqbTv83cCs5j5GrL/H5wduip5+jGe/29eRiTCZf/RVOcm4J9mb6fDO8JWfm9+SVDg2rLOKvdGgoeTG+t+sGUWk1x+uVqzR89ddt5u28QalS5In9/ZbfMyFf30jMZeSay7yz6wap+aU4WxqyemJ7ds30fa7k7qM3U/ju8B3pZw9bE5JzS7A11WfTtI7V8lCTcor5aL/4fZ7Ts/FjpW0IgsCZiDSGrvRn9h9i+oKpvg7v9vXi4oe9mNvL45mmFlyLy6bjd6dYde4uakHsbL7Rw53AT/vSp5k9pUo1M7YEcfu+eOH+Y0Ynydw7Oq1A+p5XHsfeyyzmekKO5Am4//qjE4Mqi0EeJR47cSuN5g3MUGoEmjhUdUL4+jEtYUA0jX9YFfzDkTvV0nAehou1ESvHt0cuE0fQxnoK7qTk8+6uG2g0AuZGumyb0Ukab2cXKykqU9PQ6gE1Yde1RD7782ZtT/HMUFiqoufCc6Rquatv9vLg40HNOHE7lfl7QhEEeLWzq5QQVROyi8r5+ViEZC5eUCZ6TK6c0I7j73RnWFunasKjiojHWVuDKShV4e1qyeG3u+H3GPz0RyG7qJwJ669wMjwNPR05K8e3Z0Ylr8f/n/CiAPyXYv3l2rloPbzsKFWqCUkQ+X+V/f9Ky1XEZlRVoU56Cq5RZVib6LNtRiep4Ju4/goyGWx5zQdrYz1uJefzxh/BlKs0TOjkwvpXxezMSzFZvLImQPIElMtlTO3qxvF3uuPrbkWJUs1Xh8IZt+5KnckaojXMg9HvjcQclo9vh4edCan5pby+NZiXWjZgkq+L9JiiMjVTN1VV2hnoKlg6ti0LBjZBJoM/riQwZUMghaUqVk1oj7erJfmlIpfxUYt+XWjnYslfb3WlYyNLBG1PNqOgjFVnY0gveLrs5CdFfqmSr/66zcsr/QlNysNET8G4jg0pU2lYfDKauKxirI31+GxwM85/0IvJdXDJPh3cjM7u1mIE3+9B5BVXHXWn5Zcy4bcrbL4cB4hd3M3TfJ56xJOaV8p7u24wfNUlrifkYqSn4IMBTTj1Xg8GtWrwXM1cQxJyeHvndanD3szBhJiMQgx1FWx4tUM1YrlKrWHezhsUaHly7/T1qv5Ha4AgCFyMzmDE6su8tjmIW8n5GOspeLOXB/4f9mZeX0/MnqFVTm5xOVM3BvLKrwFSdni7hhZc/aQvH74kJrFUcB4D72Vjqq/Dltd8cLMxJiwpl1lbg+i35IKkhFapBfwqiXq+/fs2A1s6YKynID6rmKD4nBpfR2V8Mljk5papNLha107YPxGexisdxOLyYdHumcgMlI9hCVMBX3drJnZ6sI4UKzV8cuDWIwtXP08bKeqvRKlGRy7jRHgai06I42h7MwO2z/DFylj87NILyhAEoYr45I8rCXz99+MXrvVFQamSngvPkq79nN/t58n7A5rgH53Jm9uvo9YIjGrvzJdDW9T4XcooKOOHI3fw++kMq8/dpahcTVMHU9ZMbM/Red0Y0tqxRsV5en4pE367KontZvi5sXOm7yPTgR4H8VlFjFpzmesJuZgb6vLH9E51hh/8r+NFAfg/CB83K0ITcylTabA11a+ilNt6Nb7K+Fcug36VBBJPC3szA3EXq03TmLwhEEsjXTZO7YihroKL0Zl8tC8MQRDo1cSO3VreXWRaAcNXXeJWcp70t1ysjdg+w5dvhrXASE9BYFw2A5ddYP3F2FpHpsPbOTGinRMaAebtvAHA+ikdMDfUJTQxl4/2hfHZ4GZVlHYXojPZH1y1oJbJZMzp6cFvkztgrKcgIDaLl1f5k5hTzIZXO+Blb0JafhlTNgSSVVj2xMfLztSAbTN8MdF2adSCwMLjkXT54QxztgXjH535XxGKVFh49P3lPJsvx6ERoJObFc5WRuy8lkh0eiFmBjp8MKAJFxb0YkY390dyZXQVclZNbI+ThSFxWcW8tfO69LkF3stmyAp/guJzMNXX4bcpHZ46uL1UqWbF6Wh6LTrHfq3IqSJmbm4vj+fO7UnIKmbqxkCUavE9tnQ05U5qITIZLB/frkZj3OVnYgjWHoPl49rVS5EbcDeLsWuvMHlDIDcSczHQlTOrhzsXP+zN+wOaYG707Ao/pVrDqjPRdPj2FOeiRD6jib4Oaye158Dcrtho7VU0GoEP993k1J109HXk/DbFm8IyFZM3XOXllZckWxiFtmAoLFPx1csP/A2DE/KISS+UDK/r4wnY2NZESkepKCxrQlZROd6ulugqZESlFdLArJIljEZgxdn681Qr45NBzarYy1yIymBfPcbX0/3cpDWqYvO0+txdDlwX33MjG2N+f62T1CVNyilFXyGv8lwb/eP4UcvBe5bILS6n+89nydTGYC4Y2IR5fbwIisvm9d+DKFdreKmlAz+NalVtXJqeX8o3f4fT7eczrL0QS3G5mpZOZqyb7M2Rt7vxUqvazcUv381k0HJ/AuPEFJE1E9vz2ZDmz0ShXoHQxFxGrr7MvcwinCwM2fdG5yfyK/xfwosC8F+I4pLaRQOGunIa2xpL8W8P+//tCaq6sDZ1MHsmJPvKaGhlxB8zOmFjos+dlHxe3XQNd1tjVk8SffX2X0+WCNgtncw5MLerZMo8dm1Aleg0uVzG5M6NOP5Od7o0tqZUqeHbw3cYszag1gD5r4e1oKGVIUk5JXz+5y0a2RizRuvp9+eN+2y6FM+6KR2kogtg/p4w0vKrd936NrfnwNyuUldzxKpLBMXlsOU1HxzNDYjNLOK1zdcoegqDZz0dufRazA11ae9igUojcORmKpM2XKX3L+dYe/7uUxWadSEus4gpWguP9IIyHMwM8LQz4eq9bCJSCzDWU/BWbw8uftj7sUeKVsZ6rJvijYGunAtRGfx07A4b/e8x4bcrZBSU0cTelL/e8nuqTUiFIXWfX87zy8koSpRqvF0tOTi3K7+MaYO92bPrINSG3OJyJq6/Qr42maKZgym37otj7y+HNK/x/V2JzWLlGbH4+G5kq0d6DwbFZTPhtyuM/+0KgXHZ6OnIea2rGxcX9Objl5o9Nm/wUTgbmY7fT2dYeCIKlbZwH97WiZDP+zGgkt2HIAh8d+QO+0JEL7iZ3d1ZeCKKceuucDE6E4Vcxsj2Tpx8t7tkA1Ou1pBXosS5UsLGouNRjNYqSQ+HpVTJ6q4N0/0aAWLXyUi39svZH1fipc+guWPV8f/vl+vm79UGY30dlo5tW+V3//nrFuk1rCOVUZGj3crJnOJytWRW/+HemwRrO58tnczZ8GpHied4L6sYCyPdKp/xr+fv8suJxxey1IbswnJ6LjxHjrZT/+ngpszp6cGt5DymbbpGiVJNDy9blj6USZ2aV8pXf92m289n2eB/j1KlhjbO5myc2oFDb/rRv4VDrYWfRiOw6mwMk9ZfJbOwjKYOphx6y++ZJ9+ciUhj3LorZBWV08LRjANzujzTJKF/K14UgP9CjFhde/xbl8ai7UZArKhg9a1k/6JSaSQfpwq8Uk/p/uPC3daEbTM6YWkkdt6mbw7C182aH0eKHLxfz99l8yXRQ8vJwpC9b3Shq4c4LpyxJYgdgVV9uhpaGbFtRie+H9EKEy3Pb9Cyi/x2oXo30NRAl6Vj26HQZv4euJ5EFw8bvhoqdhx+Ph5B+P38KrYUAjBgyfkag9i97E35c25XfN2tKCpX8/rWIA5cT2bLaz7i+0vKY7Z2tP20MNRVsH9OV47O68ZkX1dM9HWIyyrmh6MRdP7hDG/vuM7V2KxnEitXplKz7FQ0/Zde4GJ0JjoKGQ2tDEnNLyU6vRB9HTkzu7tzYUEv5vdvUo27Vl+0cDRn4eg2AKy7cI+v/w5HpRF4uY0jB+Z2wc3myaPWbiXnMXbtFd7cfp3k3BIamBuwbFxb9s7uTJtnZBXxKJSpRBpBhbG6m7URd7VUhde6ujG1q1u1x+QWl4vcL0H8DlZ4wdWEG4m5TNkYyOhfA7h8NwtdhYzJvq5c+KAXXwxtXiVC7lkgOq2A8euuVPEvtDfT5885XVg6rm21kf/qc3clPzwHMwNWaLuaejpyJvm6cO79niwe0xZPe1PklTajq8/e5TPtGBfgQnQGxnoKnC0NKShT1Us8VFkMYldHoX8yPI1XvEVRUXB8NgaV3kNuiZJzT5jX3cXDhrEdH4iVCsrUfH7w0aNgMUdbTCrKKirHwcyAcrWGWVuDJFqJr7s1qyd6S8KVqLRCnCz0q3wPV5yJYcWZJ+tgVkZ6fik9F52Vklv+83ILXu/WmJj0AqZsfOCP9+skb6lhkJwrbrC7/3yWzZfjKFNpaOdiweZpHflzbld6N607RzqvWLR8Wng8UrJGOjCn61OtBzVh+9UEZmwJokSppruXLbtmda7zXPn/CS8KwH8hIjNq5511bmyt5f/lAlX5f9uvJfDwNHG8jwvPC00cTPn9tU6Y6usQGJfNzK1BvNzWUSIO/+fvcA6HicavZga6bJrqIyltP95/k4XHI6ospDKZjAmdXDj+bne6edpQptLw3ZE7jP71MjHpVUUG3q6WvK21hvn8z9skZBUzuXMjyYJl3s7r2JrqM7//A85VbomKOdtq9la0MtZj6/ROTNRGu/18LJKVZ2P4dbI3RnriaHv+ntBnNq5t1sCMb4a35OonffhxZCtaO5tTrtbwV+h9xq67Qr8lF9jof68ar66+8I/OZODSiyw5JXr6WRnpolILJGaXoKuQMaWzKxcW9OKTQc2wNnn6AqOlk3mV7sXM7u4sG9cWI70nEyikF5SyYG8oQ1eKYyMDXTnv9PXkzPyeDGvr9F8LbRcEgXd33pByUe3N9MkuVlKu0tC/uT2fVipwKj/mw31hpOSV4m5jzFcvt6h2H4Db9/OYseUaw1dd4kJUBjpyGeN9GnLug158M7zlM+VGgajW/PLgLQYsvSDl8soQ1Z8XF/SmrYtltcdsCbjHwuMPulD380ox1lMwq7s7/gt68e3w2jubpyPSaWhlhGGlzt3y0zGMbF/hCfjocWplMcj9vNo7bznFShpaGmJnqk9uiaqaqOCHpxinfj6keRWD6+O30zhy89HFq6OFIWsmeaMjl5GaL+bdVqQYVUwU+jW35yetGT3AzeQCGtsZV5le/HIiil/P333i15+WX0rvX85L3evvR7Ti1S6NSMwuZuL6q2QXldPa2ZwNUztgqKcgMbuYj/ffpOfCs2y9Ek+5Wkz7+WN6J/a/0YWeTewe+f0LS8pl8IqLkuXTT6NaseiV1s9UqS4IAr+ciOSTAzfRCDDa25kNr1ad/Pz/jhcF4P8YfNysuJGYS7mW/+deaTe186GuWgNzAwye4ReuJrRyNmfTtAf8vze3X+f1bm5M6eyKIMC7u25IamU9HTmLXmnNvD5i4bbq7F3e2XWDsodc/EVrCR9+GtUKU30drifkMmi5P2vO3a0SzTS3V2M6aA2m39l1HZVaw5dDW0jChOlbrjGuY8MqZPRjt9M5HFbzhUdXIee7Ea34ZnhLqbv4/eE7/DCyFTpyGYdC7/P13+HPpDtXAWN9Hcb5uPDXm34cetOP8T4NMdJTEJNeyNd/h+Pz/Snm7w4lOD6nXs+bXlDK2zuuM2nDVe5lFkk5qtnFShRyGWM6OHNmfk++HtbymY1OT4an8fIKf7KLytHTjo4Oh6WQXfT4yucylZo15+7Se9F5dgclIQgwrK0jZ+b35J2+Xs/0AlIfLDweyZFb4sXe3EAHXYVcTC9xNmfZuHY1chq3XU3g+O00dBUylo9vV22kHplawOytwQxe7s+pO+nIZWJ35Mz8nvwwsrXEe3tWKFdp2OB/j24/n2FLQLy0SXS3MebQW358PKhZta5fSbma9/eE8uXBcOl3Fka6vNvXi0sf9ebjQc3q7LJU8DHXnLtbRYR28k4abRuKI1r/6IwaaRkPo0IMUq7SVOH3PYzV5+9KxeXD3frI1AJSHjOKsgIm+jose8gg+ouDN8mpx/nt42bFl9oNQHpBGWaGOkSkFvCOVhkMoqL+k0EP0oxC4nNp5WQmmUqDaFL/qFSSmpCcW0yvReek9JafR7dmQicXUrVODhU+jlum+ZBTpOTDvWH0WnSOHYEJKNUCvu5W7Hjdl92zOuPn+WjTd0EQ2HY1ntFrAkjKKcHFyoj9b3RhbMdnm8CjVGt4f08YK7RG1W/38WTh6NbPlFP4v4AXR+NfhhtxGbXepquQ0byBmeT/17kS/0+l0kgZjhUY9Ix5FrWhQyMr1r/aAT0dOSfD05i/J4zPBjfnpZYOlKs1zNwaJFmqyGQy3u3nxcLRrdHRFlmTNwSSW1x1MZXJZIzt6MKJ97rTs4kt5SoNPx2LYNSay5LliI5CzhKtNUxIQi7Lz8Sgq5CzemJ7XKyMSMopYe7261rftQdjlbnbb9TJ45ns68rW6T5YaMe/3x+5w9vaonXz5ThWnY151ocQEIvpH0a25uonffhmeEuaOphSptKwLySJUWsu89Kyi2wNiJOsNipDrRH4PSCOPovO81fofen3ZSoNMplYRJ18tzs/j27zzHJw1RqBRccjef33IArKVHRsZMmRed1wszEmObeEOdtCUNaRpVoZFT53/RZf4KdjERSWqWjjbM6+N7qwbFy7Z57iUR/supbA6nNi50VfR4aTpXhOOVsasv7VjjUWo5GpBXzzt1g0fTiwaRU7mrsZhby14zoDl13g2O1U6XM59V4PfhnTBpc6lK5PAkEQOH0njYFLL/DN3+EUap3l5TJRkX3sne7V7HLySpSsOhuDz/en2KsVahjqKvh0UFMuadXH9UlRqOjCHL6Zwsj2zlWSPPYGJ9GxkSUaQUz3eRQqi0FUdXTgz9xJl9TA/jGZtHKqGrP3n7/Da3pYveDnacOo9g+y1LOKlHxdz783qZML47RjZJVaQFch42R4Ggsr8ftmdm8sRWwCBMRm493IUtpQAXzzdzhbA+Lq/ZoTs4rp+4uozJYBS8e2ZUyHhmQXlWvTmEpwtTbi+xGt+O7IHXr9co5dQYmoNKKCe9dMX3bO7Eznxtb1Kt6Ky1XM3x3KpwduUa7W0K+5PYeekeVTZRSUKnlt8zX2hYhpUz+ObMV7/bz+a1OBfxNeFID/Moz7LbDW2zq4WqGjkEsdtcrj3z0hSdXGvxU+bf8NdPWw4ddJ7aVO2Wd/3mTxK23waWRFgdZSpbKf1ysdGrJ5mo84Pr6Xzag1l2tMCGhgbsimqR1ZOLo1pgY6hCblMWS5P6vOxqBSa2hoZcS3WlPilWeiuRaXjaWxnjQKCLyXzeKTkWx9rROV14dByy/U+X66NLbh4NyueNqJauCVZ2MY0U68ACw6EVWNw/gsYWqgy2RfV47O68b+OV0Y7e2Mvo6ciNQCPj94m07fneajfWGEJeUCcDMpjxGrL/HFwdtSdFYFBrSw5+i8biwb1w53W5Manu3JkFNUztRNgazUFsNTuzRi++u+eNiZsG6yN8Z6Cq7ey67il1cb7qTkM+G3q8z+I5iE7GLsTPX55ZU2HJjTFW/X6mPJ/wYuRmfw0X7Rj00uEy19wlPyMTPQYfO0jjXy8kqVat7ecZ0ylYYeXra8puUGxmcV8d7uG/RbfJ5DofcRBBjUyoHj73R/5p9LBSJTRW7X9C1BxFayVvKyN+HgXD/m929SpeuXWVjGwuMR+P14hoXHIynQjgtbOpkR/FlfXu/e+JHioKIyldRp0pHL6NvMDkGATZfu0bHRg8/xyM1UqSu/L/jRnoBQSQxSWE5tQvLcEiWCRsDbVSwuvR8aaZ8KT0P1FDzer15ugWUlBfaB68mcjXg0t1Amk/GfYS1o72JBcblaokqsOXe3ihr6w4FNGFvJHN0/OhM/DxspehHg84O367X23MsopN/S85QoxeJvxfh2DG/nRH6pkikbrxKTXoitiT5N7E0ZszaAvcFJqDUCPbxs2fdGZ/6Y0emx8r3vZhQyfNUl9l9PRiGX8fFLTVk32fuJecW1IS2/lLFrRfGRoa6C9VM6MO450pz+7XhRAP7LUFqHMK6Llv93XctHqiwA2a51qq9YKoz0FM+s01Nf9G5qz7Jx7ZDLYHdQEj8dj2TdFG/JUuXVjVU7fX6eNux5ozMNtJYyI1ZfIlT73ipDJpPxSoeGnHy3B32a2lGu1rDweCTDV1/iTko+w9o6MVJru/DOzhvklSjxtDdlxfh2yGSwIzCRa3HZ/DjigUl0ZqGSeTvrzt90tTZm/5wu4nOqNBy4nkw7FwsAPj1wk2O3Hi9r9HEhk8lo72LJolfaEPhJX74c2hwPOxNKlGp2Xkvk5ZWX8P7mJENX+hOWlFflsT28bPnrza6sndyBpg5mtTzDk+FmUh5DVvhLi/CycW356uUW0vjF096UJVr15ObLcbXmK2cVlvHJgZsMXn6RgNgs9HTkvNnLg7Pv92SUt/P/mWt/ZGoB0zcHUVGX9Glqx5XYbHQVMn6d7F2ruvC7w3eITCvAxkSfRa+04X5eCR/uDaP3L+fZH5KMRhA5X0fe7sbqid542T97lWJWYRmf/XmTl5aJwp8KKGQy3urtwaG3/Gjl/KAjcz+3hK/+uo3fT2dYdfYuBWUqqcDy87Bm/xtdMaoHpyokIYcBSy9IBWC5SsOcXmKCxP6Q5CrdLYCI1AL0deREpxdyMzmv2t97GJXFIPZ1jIFXno2RhG8XYjKrdP5VGoHVT8GlMzXQZem4qqPgj/eH1SvqsSJX3c5Un7T8MkkI8fH+mwTHi44OMpmM70a0ZECLB4ryM5Hp9G5qJ9nrVDxmX3DtPrExaQUMXHaRUqXY/f91kjdD2jhSXK7itU3XuJWcj56OnIzCMk6Ep6ERoHdTO/6c25Utr/ng7fp41il/h93n5RX+RKUVYmuqz/YZnZjVo/Ez78hFpxUwcvVlwlPysTHRY9csX3o1tXumz/G/hhcF4P8QOrpZcT1B5P/ZmepLi4hGoyE8RRyLVuylK2KX/tsY3LqBpAjdfDmOtRdi2TS1Iw5mBsSkFzJjS1AV+4emDmYcmNOVZg3MyCwsZ9w60cG9JjiYG7D+1Q4sGdsGc0NdbiWL2bLLT0fz2ZBmuFgZSco1QRDo1dSOj18SuTVf/x2Oo6UhL7V0kP7ewRspnHiEEtHUQJd1UzpIF7DrCbk4mhugEeDtndelcfzzhrmRLtO6unHy3e7smukrdcayHuIhtXQ0Y8/szmx5zadGX7qnxe5riYz69TLJueL46MDcLgxr61Ttfv1bOPCu1vT4swO3JNNyEIuD9Rdj6bnoHNuvisKlwa0acPq9Hrw/oMkzTbZ4XKTnlzJ2bQDl2tH14FYNOHlH7PL8NKp1rSkex2+nSrGCnw1uxrLTUfRaJI7U1BqBnk3Egvy3KR1o7vhsC3Koekz/uFJVDNZEq3Kf37+JpPCMzShkwd5QeiwUFZ6lSg3NHEwxN9RBI0DbhhasndyhVhPwCqjUGpaejGL0mssk5Tzo8BeUqmjvYkmXxtaoNAIXojOxqSSkOHorVVqj6uMJWFkMklVUe8F1NjKdwa0bYKArJzajiNHtq8YNbrr0+Dy6yujhZVtF0Z2aX1ZvgYmdmYEYV6mQcy+zCA9bE5Ei83uwpAzWUchZNq5dlXSnE+FpDGhpX2WMPn9PGH/dqD4+D0/JY9Byf8pUGuQy2PBqRwa0dKBMpWbi+quSAXcFR7Jfc3sOvenHRm3e++OgIuXnze3XKSpX4+tuxeG3/R6rc1hfXI3NYtQacd1xszFm/xtdn8v69r+GFwXgvwiLjt6q9Ta5TFyUK9R7lXkZB2+kVLNKmfiM0j+eBKO8nflWm9ix5txd9oeIlipmBjoExefw9o7rVV6vg7kBe2Z3poeXLSVKNbO2BvF7LVwXmUzGiHbOnHy3O/2a26NUCyw+GcWk9YHM6+OBQi7jr9D7Erfo9W7ujGrvjEaAudtCmNfHs4rh6qytwWQW1k1EV8hlfPRSU5aOFS0y7ueVYqyvoFyl4fUtQYTff/LIuMdFXFYxS05GSX5iD+PW/Xy+P3KHvcFJ9fJZqy/KVGo+3n+TBfvEbOe+zez4602/OruLb/X2oH9ze8rVGmZvDSY1r0TipH17+A4FpSpaOJqxa6Yvqya2/693rB9GcbmKV34NkKwyBraw55h2g/BOX09JYPAwUvJKpAzXlo5mLNgXxh9XRBK9n4cN+97owuZpz6cgFwSBE7dT6b/kvHRMK4QpCrmMN3t58NdbXaWuX/j9fN7cHkLfxaLIRqkW6OxuzcoJ7SguV5FXosLTzoRNUzs+shCPzypi2KpLLD0dXY1+otRoCLibxVxtF3BHYAITHhrVlWmLkIOh96sJwWpCZTGInqLm7lJeiYrknBKJ/5xTXF5FqJNTrMQ/unaedX3w7YiWVUab268mSLScR6Gdi6VEWYnJKKShpSFZRaIyuKJ7aqCrYN0Ub1pW4jAeuZlaLdFi3s4bHL2VIv2sUgsMW3mJcrVY/G15zYfeTe0ITcyl209nua51jgAY2MKBw2/78duUDlU6wvXF/dwSxq4LkFJ+5vRszB/TO2Fn+uztVw6HpTB5QyD5pSrau1iw740uz5wv+7+KFwXgvwgrz9duWNrC0QwDXYXUcarM//v9ShyANLqRyarnA/+3McnXVfIA++VkFBejM/htithROBGexhcPeWmZ6Ouw/tUOjPdpiEaALw7e5rvD4bXartiZGbBusjfLxrXFwkiX8JR8Ptx3U+IafXFQtIaRyWR8P7Il7V0syC9VMWdbCFte85GOlQAMWV6772JlDG/nJCWbFJWJMU8FZSqmbAwkIevJI+Pqg1KlmoXHI+i7+DxX7mVLv2/qYMpvU7zZNsOHwa0aoCOXcT0hl/f3hOLz3Sm++us20bXk9NYX93NLGPNrADsCE5DJ4P3+Xqyb3OGR/B65XMbisW0lE/B+Sy5InDQbEzG3+a83n0/H4HGh1ghM2RBIvJaH6udhzcXoTNQagZHtnSTlek2Pm7sthNxiJTKZWICXqzT4uFmxc6Yvf8zo9Nx4jHdS8pm4/ioztwYTl1UsdevUGgEvexMOzOnC+wPErl9wfDavbb7GoOUX+TssBY0AfZvZse+NLqyd4s2ac3eJzy7BycKQrdM71RnXJwgCOwMT6Lv4PLe1mx+FXMaCgU2wqiQQWXoqii6NrWnT0IIylYaScnUV/l7A3SysTfTILVbWi0tXWQxSV+qLOAYWO39Hb6Uy8CGT7u+PPF3ChpmBLkvGtKnyuw/3hVJSR1pJZYzp0JBXO4sb9KyiciyNdEVlcKUkHVMDXTZP86nimfd3WAovt6lk0A3M+SNEKqQzi8pQqgUUMhl/TO+EuaEu0zcHMmzVJSn2rZObFcfe6cavk71p4fj4hR/A+agMBi+/yPWEXMwMdFg/pQMLBjatYh79rLD+Yixzt4dQrtYwoIU921/3feaG6P/LeFEA/o/Az9OWUqWaGw/5/2k0Gm5q+V+V7R2edyxWfTCjm7s0Bvz28B3uZhSxfFxbZDLRKmPlmapqWl2FnO9HtJJ8BH+7eI83d4TU2smSyWQMa+vEyXd7MLCFAyqNwJXYbAx1FRSWqZi36zpKtUbk30z2lpI9fjgawcrxD7g8qfllLNhzo17vqW1DC/5604/WzuaSIjGzsIzJG69KGarPGqfvpNHlh9OsOntXukC4WBmxakJ7jrzdjX7NHejqYcuqie25/HFvPhjQBGdLQ/JLVWy+HEe/JRcY82sAB28k16vTUhmXYjIZskLMDbYwEi9Kb/b2rDc/T6XW0KKB2MkoKFVpkyTcOPt+T8Z2dHmqaLhnBUEQeG/3DWk81trJjOj0QorK1XR2t+bHka1r5DPlFpcz4bcrkienIEA7Fwv+mN6JXTN9q2zSniUyC8v4eL/Inbx8NwsduQxDXbEjrZDLmNurscj1czLnQlQGY9cGMGpNAGciRMuZoW0cOTqvG+tf7UgLRzNmbAni9v18rI31+GNGpzr9BysUpB/tvylF4rVxNufEu92Z09Ojysj46r1srsXl8Ka2C7jzWiJ+2jEuiAVMxVh4b/Cj1cAgxqwBkqddTTgflUEnNysaWhlSWKbCu1FVTlt4Sv4j0zwehd7N7BnU6gGdJCG75LFSOz4b0pxOblYUl6sx0FWgp5Bz6k46Px9/UJzamOizdbpPFc7jX6EpDK00ghYQu5wVUMhlfD2sOb9djOXllZc4HSF2O2XA1y+3YNeszk/MCVZrBJacjGLqpkByipW0dDLj8Nvd6PsMo0YrP9d/Dt3mW62I7NXOrqye6P2PuK79m/CiAPyXoPQRRGKfRlaEJORQrtbgYGZAI20L/MjN1GrWCKMfkf5Rl5XCs8bbfTyY1cMdgE//vEmJUs1/tL5Yv5yMYve16hm9c3t5sGxcW/QUco7cTGXCb1fq9JSzNdVnzaT2rJzQDitjPUq0BeP1hFyWnIwCxEzedVM6YKir4EJUBkHxuYxo92Ah3R2czOk7NXMPH4aDuQG7Z3WuwgWKzyrm1Y1X60UIry/u5xTz8kp/pm8JIltrCG1trMeiV1pz9v2eDG5dPXvTztSAub08uPBBLzZP60j/5vYo5DIC47KZt/MGvt+f5vsjd7hXSRlaEwRBYM25u0zeIBrFtnA049CbfvTwsq3zcRVQqjVsvnSPHgvPceDGA1sajQDOlkaYGjxbdeDTYOmpaA5qX6OrlRFlKoG0/DI87Uz4dbJ3NR5cfqmSJSej6PLjGa5qu7ENLQ3ZNK0j+9/oUi+/tCdBmUrN2vN36bVQ9GnTCOBoboBKI1CiVEtdv/n9mnA2IoNhqy4xZWMgV++JApZxHRtyen5PVoxvR7MGZqjUGt7cfp3Ae9mY6uuw5TWfOlMaTt9Jw++nM1yK0fp6KuR8PawFB+Z0pXEtSublp6Pp09SOJvamFJSpcHyouIxKFZOLzkWm1ysK8dXOrpIYRLeWMXBBqYo7KfkS/+9keBruNlVHhvW1cKkLP4xojWmlMfkG/3tcT6iZmvEwKuyqnCwMSckrpbGdeNzXno9lTyXRlLOlEVu13bwKHAm7z5DWNVt8tXIy59M/b3M28sGYWwYsHdeWKU/hCpGtVf0vOx2NIMDETi7snd3ludA2SpVq3twewqZLcQB8/FJTvnq5xT9is/hvw4sC8F+C0Wsu1Xl7e1dLrkj2L1bSBaaCK1fZKqBvs9p3ZBkFZdJCm1OslPz5nhdkMhkfDWzKq1pj6Pm7Q7E10WdOT1FU8fGBm5yJqF54DWvrxO/TRd5gSEIuI1dfIq6OokUmkzGkteh1V5krs/rcXXYEiqP1lk7m/KId3Wy8dA8fNyscKu2uX/89iOzC+pkXG2jVrx8MaCKRs8NTCnht87XH7rI9jHKVhvd23aDrT2clda+RnoIvhjTnyid9GO3d8JGLoVwuo2cTO9ZN6cClD3vzbl8vGpgbkFOsZN2FWHotOsfE9Vc4HJZSzTS3oFTJ7D+C+elYhBRltu+N+i/256MyeGnZRb46FE5eiZKmDqZsn9HpgSDnUPh/TTzzKOwPSWLZaTFqy9pYDydLQ0nJu3FqxyoX3sIyFSvPROP34xmWnY6mWDvy6+RmyfkPetKrHgkJT4LKHok/HI2goEyFi5UR5oa63M8rlbp+++d0ISa9kP5LLzD7j2DCkvIw0BXzhC8s6MWPo1pXEo4JfLjvJqfupKGvI2f9qx1q9WsrVaqZuy2Y6VuCpPfs08iS8wt6MqVzozq7wf4xmVxPzGFOL/H7fvx2KraVxCACYGWsi0ojSEV4XVAo5HTXbkLq2seuOhvDKG8nZDIIiM1i1kMq5GO3UmuMhXwcmBvpsrjSKFgAPtgTWu/vv7WJPmsniznad1IKJKrAJwduci3uAc3Dy95UMtsHUAtw7GYKgyoJ2ipwIzEXhVxGy0pCo+9HtqpRqFVfhCTkMHj5RS5GZ2KgK2fxmDZ8N6LVc+nG5RaXM3nDVY7eSkVXIWPZuLbPRVH8/wteFID/EtxKq724cbMxxtxQlyux4qJQebRUYQlT0dUz1lPgYVe7r9if15MlpXCZSs1Lyy4yf3eopEJ7HpDJZHw5tAWveDtL6tkOrpZSLNzcbdelqK3K8HW3Zv+cLjhbGhKXVcyI1ZdqFT9UwNpEn1UT2rNmYnspBePj/bf4+tBtSpVqBrVqII2lvzh4m2+Ht5J4SRoBhq6o2x/w4fc1t5eH1FkEuBaXw4wt16qJcuoDjUZg9bkYWn11nP3az0lHLuP1bm6EfN6P1/zcnsjp3sHcgHl9Pbm4oBfrp3SgVxNbZDK4FJPF3O0hdPnxDD8fiyAxu5jotAKGrbrE8dtp6GlH8j+Pbl2vxT42o5DXNl/j1Y2BxKQXYmWsx7fDW/L3W3508bBhZnd3Xm7jiErLm0t+wmSGZ4XAe1m8vycUAENdOV09rLl8NwsDXTkbXu0gFbwl5WLnrfvPZ1l0Ior8UpVkdNxQawotlz+fpfb2/TzG/3ZF8ki0MdGjXUMLErKLRbsjOxN2zvTFwdyQgUsv8t7uUGLSCzE10OHNXh5c+rA3XwxtTgPzB0bagiDw3ZE7kpHuygnta+VhBsVn4/v9aQ5ro88MdOQseqUNu2Z1rvI3H4Z+pa7pstMxDG7VAFdrI+3osGqhmaNV9e4LebQaGOCTQSK3uK7v2IXoTJwtjeiqVW0n5ZRUSdZQaQTWXoit1/PVhX4tHOhXaQQak1HEqjP1N4pv6WQuRcEFx+fQtqEFSrXArK3BVXxR27tYskbrswqgEsSO7ICHxq/jOjZkbk8Pbmm5mZ8NbvbEcaCCILDp0j3G/BogxRoenOtXqxjqaZGYXcyoNZe5FpeDqYHYkX6awvUFXhSA/xPo7mlDSblaKpIqCsBT4akSD6cCPbxsa90tCYLAnkr+UQY6CgRBXHh7LzrPt3+H1yve6Ekgl8v4cVRrhrRugFIt8Ma2EIa1ayApf1/bfI3YjMJqj/OwM2X/nC60djYnp1jJhN+ucPRmSg3PUBUvtWrAqfd6YKRNa9h4KU5LXM7h7T4eDG4lvo4F+8L4XqtYBkjOK+NTrQFwfdGvuT1/zu0qmQNfjM5i+pZr9Y6MEwSBv0Lv4/3tSX4+FimRuvs1t+f6F/34dHDzZ7Lb1lHI6dvcnk3TfLi4oBdv9vLA1lSfzMIyVp8TC5wBSy8Qm1GEg5k+u2d3ZkKnR0c45ZUo+ebvcPovucCZiHR05DKm+4k8v0m+rhI5XCaT8dOo1rRwNCOrqJyZvwfVmzj/rHEvo5BJ6wPRCCJvapyPC3+FpiCTwfJx7WjT0IJSpZqN/vfo9vNZfjgaQXZROW42xozv2JDCMhU6chkrJrR/LuPs9IJSPtoXxpAV/lyJzUZfR86gVg4Igrjpk8tgRjc3RrRzZM62ED7/8xZJOSVYG+uxYGATLn3Um/cHNKkx53n1ubtSrNjPo1pXKWAqoNYIfLQvjNFrHqiiu7hbE/BxH0Z7Oz/ynDCslAF9ISqDm8l5kpXSreT8KpYmAqKA7fb9/HpNJCqLQWprPhaWqQhNzJWSQfaHJDOtS1VnhPUXn84SpgILR7fGWP/B93PV2ZjHcgYY1taJWd1FmkxESj6NbY3JfkgZDNCziR2/jGkjGdqXqQUuxWRKa5ytiT7tXS1ZfkbsaL/T15MZ3dyf6D0Vlql4c8d1/nMoHJVGYHDrBvz1lh9NHJ69dyXAreQ8Rq65zN2MIhqYG7B3dpdaLZdeoP54UQD+C3AroW5bAh83a4n/18DcAFct/2+DfxwAupVWwUG1cEMAbibnEZVWKC2+Fka6/Dm3K77uVpSrNaz3v0f3n8+y6mwMxeW1k6yfFAq5jCVj29K3mT1lKg2zt4bwenc3Wjubk11UzpSNgaQXVCdn25kasHOmL32b2VGm0jBnewjrL8Y+ssBqaGXEthmdpIvE3YwiRq25zI9HI/h2eEtaOpmRXVTO5oB4hrR+ME7ZFpjAuchHqxIro4mDKcff6S51X89FZjBpw9U6X6MgCFyMzqDnonO8veM6OVqeX1MHU87O78lvUzo8N66cs6UR7w9owuWPerNyfDucLAwQeDBW0whwJiKd+3V06dQagT+uxNNr0Tk2+N9DpRHo3dSO4+925/MhzWtUCRvqKVg72RsrYz1u38/no/1hzzRbuT7ILS5n2CrRLkMGzOrmLvGNvhjSnB5NbNkaEEePhWf5+u9wMgvLaGhlyKJX2vDrJG/+1I4q5/dv8tjeaY9CqVLN6nMx9Fp4jp3XEhEEGNDCge5ethy5mUpWUTnuNsa80qEhe4KS+Pl4FBkFZTiaG/DV0Ob4f9ibOT09MKvlvNl2NZ6Fx0WxwmeDmzGqBr7wnZQ8On1/ip1afq6Brpzl49uyfaZvnergyjDQkWNd6b4rzsQwsr0TDmYGZBSWVZtSVJx39fEEhAdikEeNgQe0cMDUQIfk3BLauVpWKTyzisolWs3TwMJIj0WjH4yC1QJ8sPdGldzyR2HBwKZ087ShVKWhqFyNjYkekWkFzHvIMmtYWyeJQw1QWK6mVCk+j1ItFu0AM/zcalWuPwqRqQW8vNKfw2Ep6MhlfDm0OSvHt5O63s8a57VCpYyCMpo6iBv+51Vo/v+GFwXgvwBj1l2r8/aObpZV7F8qdt8V5rqVRR0VEUs1YU+QuLjq6TzYrbZtaMGO133ZPK0jzRqYUVCmYuHxSHouFE16H2cRqw90FXJWTmhHN08bisvVvLE1hAUDmtDIWsxZnbbpWo1CCiM9HdZO7sAULZfw28N3+Oqv248ctbZzsZRGvjpyGRoB1l6IZdSay7zV2xNbU30iUgsoUwlV/AGnb75WLZ/4UbAy1uPI293ooLWiuRSTxfBVl2pUMQfFZTN81SUmbwgkXmshY26oy9pJ7Tn2TnfcbGsn4z9L5BYr2XolnuRcsfBu09AcK2M90gvKWH46Gr+fzjBjSxBnI9KrHOvLMZkMXn6Rz/68RXZROR52Jmx5zYeNUzvWKgiogLOlEasntpeyoH+7+PSjuPqiXKVhyHJ/SUU6s7s767XmwK92ccVAV0HvRef5/OBt0vLFwuqHka04M78nQ9s04N1dNyhRqvHzsJG6Ns8CgiBw5GYKfRef5+djkRSVq2njbM6CgU0Ijs/hZHgacpmouE3JK2HXtUTySpS42xjz8+jWnPugF1O7utWYT1yBw2EpfPan6DX6Zi+Pat0hQRD4z1+3GbTMn0wtF7ZLY2uufdqXl9s8/iiuIjoRxM1EZGoBr2uPWU4t360D1+/Xa82pLAapDZdiMjHQVUhirb/DUuj4kCL4uyNPLwYBceLQq8kDgdTt+wWse4zzWiGXsWJ8O1ysjEjNK6WBuQH6OnJOR6Tz07GqtjVTOjfinb4PijuNdgOVW1KORhDHwJ8ObvZEvLn9IUkMW+VPrLYTt2tWZ6Z1dXtuHLzdQYm8tvkaReVqujS2ZvfsuqkFL/B4eFEA/gtQrKq9iLE308fO1KBSASguYOcj06VRYcWjG1kb1RrUXqpUc1DrHF+uJSkXlanRaARkMlEwcPgtP5aObYuzpSHpBWJMV/8lFzh6M+WZdmkMdMUuUMdGlhSUqXhrx3W+erkFNiZiV+iNP0KqCRNAXCT/83ILPtVygLYExDNra/Aju5Vzenng08gKlUbA3cYYWxM9YjOLmP1HML7uVlI4+5DWDlK3UC3AyysuPvZ709ORs3d2F3pqLwahSXkMWHpBWqSVaoHJG64y+tcAQrUCD7kMZnd3J/DTPgxoWXsH91kjJCGHoSv8uXovG2M9BWsmtufgXD8CPu7N8vHt8HW3QiPAqTtpTNt8je4/n+Xbv8N5dcNVJqy/SkRqAeaGunw1tDlH53Wrt0IYxI3MF0ObA/Dj0QjORz2dOW99IAgCY9YGkKTtak7s5MLuoETKVRpaOJpx5k46H++/SXJuCfZm+nwzrAVnP+jJeB8XdBVyfjoaSXhKPlbGeiwe0+aZRdXdSs5j7NorzNkWQlJOCQ5mBnw7vAWu1sb8fCySzMIyzAx0kMtkhCblUaLU0LyBGasmtOfkez0Y06HhIxM7LkZn8M6u6wgCTOjkwvz+XlVuj0kvoPMPZ9h0OQ4BkcO3ckJbtr/uW68udKlSze37eVU2O2M6iirciqO0/HQM430aYmWsR2ZhuTS6rIzMwrIqEXa1obIYpDYUlasJic/mFW2+7rFbqczvX7UrdjM5/5FG8PXF4jFtq/AMl5yM4m4NtJbaYGGkx29TOmCkp+Bmcj6dtUkp6y7EVotTnNfHU/ISrAw9HTkedibStaG+KFWq+eTATd7bHUqpUkM3TxsOv93tuXlYCoLAslPRLNgbhlojMLytI5un+dTauX6BJ8OLAvBfjm6etjXy/9ZrOTyVd8EDa1CFVeBkeBr5pSosjHSlgrGgTMmE9VekMZ9cLmN4OydOz+/Bl0ObY2UsFkpvbAth+OrL9Xa7rw+M9HTYOLUjbbTcvg/2hvHt8JYY6Snwj8lkwd7QGk2gZTIZr3d3Z/XE9ujpyDl1J43x667U6cGnkMtYMq4tpgY6xGYWMbydM6O9nREEOBSaIo0qN16Kr7KoJuSU8sXB2tNZ6sKmqR3prc2pjM8qljoqWUVVL3Cd3Kw4+35PPhrUTIrpet4QBIGtAXGMXRtAan4pjW2NOfimHy9p0xP0dcSuyc6ZnTn1Xg+m+7lhph2jrfe/x3nt6+/XzI4z83swteuTiVMm+7oypoMoDHpre0idKu9ngbd2PBAbDWzpgH9MJjnFSvR15Ny+n09iTgk2Jnp8PqQ55z/oxeTOjaTP5GxEOhu1ncJFr7TGzuzpEw/S80v5YE8oQ1f6ExiXjYGunHl9PPl0cDOWnorhr1Bx1CxD9L1TaQS8XS3ZNLUjh9/2Y3DrBvWyxriekMOsrcEo1SKX65thLaWOjiAIfHc4nH6LL5Cq9cbr5GZFyOd9GdK6etdPpdZwN6OQozdTWHoqijnbgun9yzmaf3GMwcv9Jb6gaEZtStuGFgja93DqThqxGUW81rURUFUoUhl7H1MMUhdWnbtLG2dzPLVFUUxGETYPjbG/+ftOvZ7vUbA01uPn0a2ln5VqgQ/3htVqZl8TmjiYSsric5EZEj/z0wM3CaxkAF8hrKvsCShD7HB/e/gOPReeY9vV+Bo30g8jMbuYV34NYPtV0ej9nb6ebJ7m89wMl5VqDR/tu8mSU6JF15yejVmiTVl6gWeLF0f0H44VJ+pefDq5WREcn4NSLeBoboCLVplYYRNQeSxXl/3LXi23pvLiJwOuxGYzcOkF/g57YMGgr6NgWlc3zn/Qk7d7e2CkpyA0MZfxv11h6qbAZxZ9Zmqgy5bXfGjqYEpGQRlfHwrnm2Et0JHL+PPG/Wqjj8oY1KoB22d0wtJIl9CkPEasvkRMeu27bScLQ74f0QqADf6xjOnQUMoozqxk/fLH1YQqKSq/B8Rz8Qmio2QyGb9N6UB3T3Ekr3mog2ptosfqie3ZOdMXV+v/zrgXREXr/D2hfH7wNkq1wKBWDhx8069W5bibjTFe9iY1Fngn76Qzcs1lfj1/t14ebg9DJpPxzfCWtNOmtMzcWpX0/iyx8HgEf4eJ4iFvFwui0gqk0XuZSoOlkS4fv9SUCwt6Md3PrYroJj2/VFILT+vaiN5Nn874tlSpZtXZGHouOsee4CQEAYa3deTPOV2JzyrirR3Xyax0PAWgu5ctu2b6snd2Z3o1rb/dTFRaAdM2X6O4XE03TxuWjGkrFY1RqQV0/fEMv128h4Do67dkrKjwNdITC/6zEen8ev4u7+26weDlF2n+5XH6/HKeN7aFsPRUNEduphKbUYRGQOpSAhRpP8ex2i5ghUhixZloJnduhIm+jsR5fRgnbqeSV8ttlVFZDFIbLt/NQiaTSWKQPUFJzB9Qtft55GbKU1vCVGBoG8cqNJyg+JxaYy1rw8CWDXi7t2iefS4yna4e1ijVArP/qKoMlstl/PJKG4y0ghs7U5Gu0MDcgNT8Uj49cIs+i8+xNzip1rH6qfA0Bi+/yM3kPCy1Ru/v9PV6bp57RWUqXv89iF1Bichl8M3wliwY2PSFzctzgkz4bzOs64mAgAACAgIoLCxEEMQxpImJCZ07d6Zz587/1y/vv4ZGHx2u8/bzH/RkT1ASK8/GMLKdE4vHtuXK3SzG/Xalyv30dOTc/s+AGi/UqXmldPnxNM0UqbTUScMAJXKZeAEuk+lxo8yW2yoHRrV35quXm1cb+aQXlLLidAw7AhNQaQRkMhje1on3+nk9EyPQjIIyxq4LIDajCFdrI6Z2acR/DoncnM+HNJcI3zXhXmYRUzeJPDpzQ13WTfauM1ps/u5Q9oUk4WRhyJF53ZDJ4Lu/77Cr0ojF3EAHZGKuKICODII/74d5LeP1unDB/xInzl5EoS5DLhNJ63I9Q3r4daFnd7/H/ntPg4SsYmb9EcydlHzkMvjopaa83s291sU38F42/zl0W4r7crMx5rPBzXC0MGBHYCIHQpIp0F7odRUyBrZswMROLnRys3qsBT0tv5ShK/xJLyhjQAt71kz0fqrx6sNrC8goEnS4rbIn3cCVUpWaAi0H0FhPwRs9GzO1q1uNJHeNRmDKxkD8YzJp1sCMP+d2eeJOrSAI/B2Wwo9HIyQLnLYNLfhiaHMyCspYsDeMvJKqhc/AFg7M7eXxRHmtidnFjP71Mmn5ZbRtaMG2GZ0w1tdBpdbw09EI1vvfk6YBjW2NecW7IfHZxUSlFRCVWiB9tg/DQFeOl70pXvamNLE3pYmDKaXJd7h1PYiCwkIQBDQCmJiY0NGnE2+cLKREqUaGWMwendeNv0Lvs+bcXQx1FZJxe2V8N6IlEzs9Os98o/+9R5o675rpi7utCb4/nEatETjyth8vr7xUhTv96eCmvN6tcR1/pf7IKiyj609nJHGGoa6cE+/2eKy1UqMRmLk1iFN30nEw08fSSI87qQV42Zuw740umBro1noN7eDTiViZIyvP3pU2Eu62xrzb14vBrUTzeJVawy8no1hz7i4gJtismtAex0cU1E+DjIIyXtt8jZvJoj/l8nHt6N+i9qnVPxH/trrlH9sBLCsro6CgQOKWCYJAQUEBZWXPJ07r3whTAx1crIyq5f+uvSB+aSuPf71dLGsdw+0LSUIjgIOxAiOZ8kEOriCgpymjq5s5cpl4v0HLL1bz2rMzNeCb4S059V4PhrRugCDAgevJ9PnlPF8fCq8zpaM+sDXVZ9uMTjS0MiQ+q5jtVxN4S7sD/ubvcA6F1m4Q62ZjzP43utDOxYK8EiWTNwRKXMea8J9hLXC1NiI5t4RPD9zEVF+Hn0a3ZstrPjhox3p5pSoQHnCXVAIMW1m/vOCHoVEp0dWUScdcLgOUJaB5Pp2u2nA2Mp2hK/25k/Ig8mtm95oNVhOzi5m7LYQxawO4fT8fUwMdPhvcjOPvdKdPM3uaNTDn62EtufppH34e1Zo2zuYo1QKHQu8zbt0V+i4+z/qLsfUW0dibGYiJGwo5x2+nseIxfNRqwsNrCwgYy5ToyzRkFJZJxd+Idk5c/rgPb/b2rFXhuO5iLP4xmRjqKlgxvt0TF3+hibm88msAb+24TnJuCQ3MDVg2ri0bX/Xm52MRzNoaLBV/chmMbO/EyXe78+tk7ycq/jIKypiyMVBKNNk0tSMCsC84Ee9vT/JbpeIPRIX8j8ci2BGYQHB8DgVamxsvexOGtG7A/H5erJ3szfkPenL7PwP5600/Fr3Shte7u9Pdy5aiklIKCgrEPDzteyguKgSNikFaaoGzpVhcrDgTzXQ/N/R15DUWfwA7AxPq9T7rIwb59fxdbE316dVEpGT8eeN+lRQfgHXnn40lDIhepD+MeDAKLlFq+Hj/zcfiUcu1jgmNbY1JzS9DT0eOnak+UWmFvK1VBtd2DdWolEzt6sbFBb34+KWmWBjpEpshdpYHLb/I3uAkJm24KhV/07o2YtfMzs+1+LubUcjINZe4mZyHlbEe21/3/dcVf/Dvq1v+sQWgvr4+pqam0gVIJpNhamqKvn5136r/VZSX1z3m8POwoUSpJjQpF0AiBVfET1XmlgxuVbN4QBAEafyrq6dPkfCAA1hxzHs1d2LXrM44WRiSmF3CmLUBLDkZVW1s0MjGmJUT2nPoTT+6elhTrtaw8ZJoHbPidPRTWcc0MDdk+wxfHMwMiE4v5PSdNMnAdP7uUC7frZ0Ybm2iz47XfXmppQPlag3zdt5g9bmYGhdcE30dlo4Vx2B/h6WwL0QsFnt42XLyve4MbS1eGPJKVVSup+OyS/j60OPzAf+vz3ONRmDpqShe23yNvBIlbRta8PfbfjV6bBWVqVh0PJI+i89z+GYKcpkoGDj3fk9mdHOvxtEx0tNhTMeGHHzTj7/f8mO8jwtGegruZhTx7eE7+Hx/mvd23SA4PvuRF7/2LpZ8q/VjXHIqipPh9YvlqwkVx7zCME0jQJGgS5nw4PV/M6wFS8a2rdGqpgKhibks0lqmfDm0eZ0G67UhNa+U93bfYNiqSwTF52Coq+C9fl6cfLcHt5Lz6fj9GcngXS6DCT4unP+gF4vHtMXT/smsMDILSxm3LoB7mUWY6utgZ6rPkBX+tPzyOPP3hEmd7cpwsTKibzN75vZqzLJxbTn+TnfCvx7IiXd7sHJCe97q48mAFg64WhtXGQ9eiMqg209nWH85kSJBt4qNULGgS7kgl8bAFTzdIzdTySosl77flcu3ij99Mzm/XgIKhUL+SPFRgHYDXdkTsLKKFiCjsIygSukbT4sR7Z3wcXugOPaPyZRcGOoLUwNd1k3pgKm+DqFJebR3tUBfR87ZyAx+OHLnkWuLoZ6CWT0ac3FBL97t64Wpvg4RqQW8vyeUK7HZGOjIWTG+HV8ObfFc+XfB8dmMWnOZxOwSXK2N2PdGF9q7PB9xyfPG//V6/rj4x46AK7B48WIKCgowNTXlvffe+79+Of9VDFtxntDk2he5r4e1wM3GmMkbAnGyMMT/w16EJOQyas3lave9uKBXjSMG8csXgJGeAgMdOdnFSmZb3aGspKjaMc8vVfLlwdscuC4WRe1cLFg6tm2tHLWL0Rn8eDRCGhHamuozr48nYzs2fCJRAIg7xbFrA8gsLKdtQ3PsTA04EZ6Gqb4Ou2Z1pnmliKOHodEIfH/kjiSQGe/jInIKa3gtK89Es+hEFMZ6Cg6/3Y1GlTJQ112I5fsjNXMzt8/oRJc6rHZqw//FeZ5XrOTd3Tc4EyF6Gk7ydeHzIc2rdbE0GoE/byTz07EI0vLFi3RnrUq3WYPHC44vKFVy8MZ9tl1N4E7KA65oE3tTJvq6MLydU51Kvy8P3mJLQDwm+jr8ObcLHnb1L4IEQeDorRR2BCZyMzmP/pogjGVKigRd9pW1QSOII8h5fTx5t59XnX+roFTJkBX+xGcVM7hVA1ZOaPdYY+2ScjW/XYxlzbm7UpdrZHsn3unrxek7qSw6HkWR1gRbBgxr68gng5thZ1p/cYlaIxCfVURUWgGRqYVEpRVwJzWf2IxHi2m8XS0Y06EhTR3M8LAzwfgxPN5KlWq2BsSz8mx0tWJyjEGodMx3l7ZhapdGfDm0Ob1/Oc+9zCJaOZlzMzmPIa0b8PGgZvT4+Wyt2eSvdW3EF0Nb1HhbZcRmFNL7l/N13mfbjE74uFnh+/1psorKWT+lAz8djSC6UpHZxtmcg28+O1pGRkEZfj+dkRS5JvoKTs/vif1jCojORKQxfUsQggDjfRqyI1Ckq/w0qhVjO7rUa20RBIFlp6NZdiq6SufXp5EV8/t71UmbeRocu5XCvJ03KFNpaONszoapHbGpwZz834Z/S93yj+0AvgB1Fn8AHRtZSePfTtr837XnxbZ95exfGxO9WvklFbvOjo2syC5WVul4PLzsmhnosmRsW5ZpFbPXE3IZtOwie4ISa+zgdPO05dCbfizX+ldlFJTx2Z+36L/kAofDnsw6prGtiRR+fiMxj9zicskuZuqmwDoj6+RyGZ8Nac5XQ5sjk8GOwARm/F6zsOCNnh74uFlRVK5m3q4bKCt1O2d2d+fLIc1rfI4pG6/W6FP4T0P4/XyGrvTnTEQ6+tr4rm+Ht6pW/IUk5DBizWXe2x1KWn4ZLlZG/DrJm+2vd3rs4g/ErsUkX1eOvO3HgTldeMXbGQNdOZFpBXxx8DadvjvNh3vDCE3MrfH8+GxIczq5WVFYpuL134OrceIqI7uojIXHIui/+DxNPjuK28dHmLPtOhejM8mtJCLQU8gx0FUgACPbOVXr/tSELw7eJj6rWBQPjWxV7+JPEAQO3kimzy/nWHwyihKlGm9XS3bN8qWloznDVvrzn0N3pOKvg6slVz7pzdJx7Wot/gRB4H5uCWcj01l7/i7v7b7BkBUXaf7FMXr/cp7Zf4Sw5FQUh2+mVCn+mjqY4PGQn2RDS0NOvtudfW90ZWxHF9o0tKh38ReXWcQHe0Jp9eVxvjtyp0rxZ2Oih5dd9Y3ijsAEMgrKpO6bSiu2OHwzhZJyFSPbV1caV0x0dwcl1ktB625rgpNl3ePLtefvoquQS96Eu4MS+XRIVRVxaFLeY3t/1gVbU9FKqAKFZWo++/PWY6+LvZva837/JoAo5hurPZaf/XmrXpnaeSVKZm4NZqm2+HuppQNTOruipyMnMC6bseuuMHnDVa4n1B2z+bjYfOkeb2wLoUyloU9TO3bM9P2fKP7+TXhRAP5Loa8jp4m9abX8X2kUWmkR6anltjyM4nKVpHysUOF1crMiW7vIpeeX8dVft4lMLajyuGFtnTg6r5tUIH2wN4w3t1+vcXGUy2W83MaRU+/14D8vt8DaWI97mUXM3R7C8FWXuBzzaE+vh9GsgRm/v+aDib4OgXE56OnI8bI3Ib2gjFc3Bj4yrm5qVzfWThJD1s9Fii7zaflVvb4qUknMDHQITcxl2anoKrdP83Or0WdLpYEhyx7fH/C/iQPXkxi55hIJ2cU4Wxqy740ujH4o8SElr4R5O68zcvVlQhNzMdZT8OHAppx4tzsDWzo8tSpPJpPRzsWSha+04eonfflqaHM87UwoUarZFZTIsFWXGLrSn+1XEyTFKIhG4asntsfJwpB7mUXM2/kgCeHqvSxmbw2m43encP/4MO2/OcWqc3eJSi+s4nsmk4GVsa5kM6LWCBSVq+nsbs2Po1o/8r3tD0niwPVk5DJYNq7uMXFlXE/IYdSay8zbeYP7eaU4WRjy06jW9PSyYdbWYL7+O1xSvlob67F9Rif2vtEFe7MHxUtOUTkBd7PYcjmOTw7cZPSay7T+zwm6/HiGaZuu8cPRCPaHJHMrOZ8ylQZ9HTmtnMwZ2c6JVk5iwa6vI2fBAC8yCsqJqVQQTuvaiNPzez7WaFml1nDsViojV1+SVMvKGoqyzMJyotKrdh4VMhllKg3rLsQyur0zCrmMOykFdG1sjSCI6SCzezTm4U+jIt2ysEzNqTv1owJM71q7UAwe0GYqPAHPRKTT3NGsmhfhN48QlDwuXunQkPYuFtLPJ8PTpDX5cTCnZ2MGtXJAqRY4HZFOn2Z2YqTmH8F1muHfSs5j6Ap/ToY/yPZePbE9Xw9ryYUPejHJ1wVdhYyL0ZmMWH2ZGVuucft+3pO8VQkajcAPR+7w1aFwyXdy7WRvSa38Av89vBgB/0MRdT+H/surj3Ir4Odhw9rJ3rT5zwlUGoGLC3qRVyKOpR7G+ikd6FtDnueB60m8uysUFysjNIKGpJxSOrlZ4ZpytsqYBsDb1ZLxPi4MbtVAShNQawR+PX9X5ANqBBzMDFg8pk2dI9DCMhXrL8by24VYqcvR3cuWDwc2oYXj45HZA+9lM2XjVUqVGnp62RKZVkBKXintXSzYNsO3ztQDgBuJuczYco3MwnIczQ3YNM2nWsTQ4bAU5m4PQSaDna/7VhmFqNQapm66hn9MJgrZgwsTQP/mtqyb4lPv9/LfOM9FD7Bwfg+IB0Re47JxbauYg5eUq1l74S6/nr9LqVKDTAaveDvz/oAmjzV+fBIIgkBQfA7brsRz5Faq5FFmoq/DsLaOTOjkIp0jwfHZjFkbgFojdrtrGxMC6ClkOFka0dPLlte7u+FoIXbDf1m8mMKCAooEXULMurJvdhfMjeou5uIyixi8/CJF5Wre6+fF2/WI07qfW8LPxyKkiDgjPQWvdmmEWq1hR2BiFTWtDHjNz425PRuTkFNCZGq+NL6NTCuo1c9SIZfhbmOMl4OovPXSqm9drIyQy8RknA3+91DIZLRzsSCokpDLxkSPlRPaS5vI+iA1r5TNl+6xLTBBEs3UBJlM7LBWFOAPj4ABDHUVXPywFx/uDeN0RDqj2juxL0QssE++14MlJ6OqFUUKmQy1INDUwZRj73R/5OtVqzU0+fxYnefJlmkd6dHEjpdX+hOWlMdng5uRX6JkeSXRkZ5CTsQ3A5DLn13vJL2glK4/npFy262MdDk1v+dj++wVlakYteYyEakFtHIyR0DgVnI+E4zC0BfKq6wtgiCwOyiRzw/eplylwdnSkDUTaxYUJWYXs/x0tCQWBJFT/m4/z8eiYACUqdS8vydMEu59MKAJc3rWLDb7N+PfUre8KAD/oWjx+RGKlLV/NB+91JTmDcyYslHk/136qDdztwVz+GZqlQuiXAY3vxpQ4xhnwm9XuHw3i0m+rvxxJR49hQyZTMYwnevVFukKmBnoMLK9M+N9XKRiKSwpl3d23iA2swiZDGZ2c+e9/l51KiIzC8tYeSaGbVfjpYVvWFtH5vdrgot1/e0QLkZnMH1zEOVqDb2b2hIcn0teiZK+zez5dVL7Gvl9lZGQVczUzYHEZoiE+F8ne9P1oQL2gz2h7AlOwtHcgKPzulcpEnKLyxm+6hJxWcXoKWSUV6oCe3rZsmx8u3p1iJ73eZ6aV8qcbcGEJOQC8HYfT+b18ZQI+4Ig8FfofX46GsH9PLEb2rGRJV8MafFEKtOnRU5ROftCkth+NYHYSgbQugoZao1QZ8arqYEOzRuYMaq9MyPaOqKr9ewTBIHE7BJu3c/j9v08cq/+iZ5QRgl6zH1r3iNtOMpVGkb/epmwpDx83KzY8bpvnX5oxeUq1p6PZe2FB8X0oJYNMNZXcPDG/WppDBaGunjam5CaX0pidu0Zyw2tDKsUeV72prjbGtf6fVt1NkbK9zXQlUv2IwBDWzfg2xGtHnmOajQC0ekF7A5K4titVMmm5mHIZdDC0QwrY33CknKljqapgQ6TfF3RhP1NeUmRtLYY6sopUWqY3aMx7VwsmLU1GBsTPVo7W3AmIp2R7ZyY0c2dQctr76pf/qh3vRSqM7Zc49Sd2jO8uza2ZtvrvmwNiOPzg7dpYm/K7lm+tP36ZBU6zFdDmzP1ER3Fx8W2K/F8+ucDEdmwto4sG9fusf9OQlYxL6/yJ7dYyeBWDQiKz6ZHeSDGMqW0tpSUq/n84C1J/NenqR2/jGlTa0pUBe5mFLLsVDSHwu4jCOJnPbytE/P6etbLqzSvRMnM34O4ei8bHbmMn0e3ZmT76lnT/wv4t9QtL3qu/1DUVfyByNk7rR1/VOzcKxIkKtf0XvamNRZ/idnFWhNUsUMC4Glnyu2UfOmssDLWw8fBikCt+q0icWDz5Tg2X46jvYsF431cGNLakb/f9uObv++wIzCBtRdiuRidyfLxbWvdIdqY6PPVyy2Y1rURv5yI4q/Q+xy8cZ8jN1OY2MmVN3t71IsP0s3TllUT2/PGH8GcicigbzM7LkZncupOGp8fvM33I1rWubt0sTZi/xtdmPl7MIFx2by6MZAfR7WuMhL96uUWXIvLJi6rmE8O3KxC+Lcw0mP9qx0ZsfoSBaUqycsM4FxUBv0Wn+fHUa2e2hz4aXAlNos3t4vmwaYGosq5TyVT8LCkXP5zKFyy93GyMOTjQU0Z3KrB/8nOXKVSc/pOOifDU8koqDqaV6pr/l7IZbBiXDsGa+07xESKIg7dTOH2/XxuJecRnpJfpVs1xkCDngwsjWrnyFbGLycjCUvKw9xQV1KK1wSNRuBgaDI/HY2U0jNaOZlhaqDL0VsptRavuSVKrsU96MzZmupXKvRMJG+9xxFkbLsaLxV/gFT8Gesp+H5kK4a1rTnDt6hMRWhSLiHxOVyJzSIoLofSOlIjrIz1mNalEQWlSq3IRhT4OJgZMN3PjXE+DTE10GVx+BEqEzRKtK/n94A4XvPriY2JGAPn627FmYh0/ryRzNt9POnd1E4SK1Wg4rv21V+3WTelwyOPxSeDmtVZAF7Tnv8vt3Him8N3iEwrID67mK4e1vjHPODSrT5395kXgBM6ubDzmihOAjh44z5DWzvWOLmpCy7WRqwc354pG69y+GYKM7u5kaeNks8rURKbUcicbSFEpBYgl8H7A5owu3vjevlqNrY1Yfn4dszp1ZjFJ6I4EZ7G/uvJ/BV6n1c6NOSt3h61FuL3c0uYuimQqLRCTPR1WDOpPd086x8N+QLPBy8KwH8hdOQyWjmZ891hkY/i625FREq+FGBf+Rr5Ui3xb/u0cUpdGlsToOURZmhNQSvc+vUUcnbN8uVCdCYLj0dwS7uo68hlaASBkIRcQhJy+frvcEa0c+LVLq70amLLh/vCCE/JZ/Byfz4b3IxJvq61FhKu1sYsH9+Omd3d+elYBBejM9l8OY49QYm83t2dGd3ca/Vgq0C/5vYsGduWeTuvc+pOOr2b2nEuMp0dgQk4mBkw7xGkfgsjPX6f7sMHe8XRxPt7QknKKWZeH09kMhnG+josG9eOUWsuc/hmCj2CbRmj5QoBeNiZsGJ8O17bfK3axT29oIzXNgcxsr0TXw5p8cgR47OEIAhs8L/HD0cjUGvEcdmvk7wlRXN6fik/H4+UOgGGuqLp8czu7lWSLp43UnJL+O1iLGcj00nOKaW8llQCeOBtWbkQNDPQIb9UxScHbnImMp2YjCIiUvJrzDvVU8jxcjChpaM5JtHhqMuUj/SJA7HTvPZ8LAA/jWpd64UuOC6bzw/eJlyrcDbQlSOXyaSCqCYY6Spo7mhWZXzrZW+C9VMS4v8Ovc9nB6pbE3Vys2Lx2LZSSoYgCCTnlhAcn0NIfA7BCTmE38+vs8taATtTfV7t0oj4rCJWnImRPjsPOxNmdndneFunR1qIWBnpkV1cztaAeEa2d2bdhVgC72XTq4ktZyMzWHU2hrm9PKQCUE8hp1ytkTZapyPSKShVPjKT2N3WBHszfUnJ/jDKVRpO30mjTzN7BrRw4FDoffYEJfHxoGYMXv6AWpNeUMb1hBzaPUOrEjEZyBu/nx6onj85EEZHt5715phWwM/Thk8GNRPH/pfieNVUB6FcSYlSzYClF1CqBWxM9Fkxvp1kHfY4aOpgxropHQhLyuWXE1Gcj8pgR2AC+4KTmNDJhTm9Glehi9xJyWfqJtFz0t5Mn01Tfep0a3iB/x5eFID/QPx2LrLO21s4mqFUawhLEneLvu7WLDohPkYhl1Uh/fapIf5NoxGkArB3Ezu+OXwHGeLCBiJHSdBu02UyGT28bOnuacOxW6n8cjJKilQz1lOgpyMnp1jJ7wHx/B4QTzsXC97s5cGZyAwuxWTy+cHbnI3M4KdRrbE1rf2C1tLJnK3TO+EfnclPxyK4mZzH0lPR/HElnrf7eDKuo0udF5KhbRwpVYqClDMR6fTwsuV8VAZLTkVhb6bPOK2nWG0w0FWwbGxbkQtz7i5LT0WTlFPC9yNaoacjp01DC97t58XC45F89ddtOjaywq2SNUzPJnbSolsT9ock4x+dyXcjWkn5nc8TRWUqPtwXJnGnhrd15IeRrTHUU1CqVLPB/x6rzsZQrOVhjmznxIKBTXEwf/48v3OR6WwNiOeGVlVZW6Ehl4mdpfYulrzapRFdPWwoKFVyKzmPQ6EpnItM535eqbTxyStVSb6NIJ6fzR3NaOFoTnNHM1o6muNhZyKdR4sXn6KOiGgJmYVlvLdbjHqb5OsiZWrnFpcTmSpy80Lic7gQnUF2UVVVcuVxqwxRFFXx/RzY0oEPBzShkY3xM++07rqWwEf7blYZXerIZSwY2IQpvo2ISCvg2K1UQuJzCIrPrrUoqg0WhrqMaO9EYnYxi05ESpqzDq6WzOrRmD5N7ersKlXO+c3XquY3X4rj9+k+rLsQy9nIDH6b7M3ZyAz2X0/mrd6edGxkybW4nGobBLVGYMWZmHpl/87u0VhKEaoJv12MpU8ze17xduZQ6H0O3kjm08HNsDPVl9ZHEMUg++d0feTzPQ4czA35dHAz6fWlF5Tzw5E7/Diq9SMeWR3T/dy4fT+fA9eTKS5XU7FdUaoFGmqFX0+bV93a2YItr/lwLS6bRccjuXovm82X49h5LYFXuzRidvfGhKfkM2trMIVlKjztTNj8ms8j4/le4L+HFwXgPxDfHas76aBHEzuC4nNQaQScLQ1paGXE+ShtHm2lFd9IT0HzGqw6rt7LJjG7BFN9HUmxZ60dvchlYKynQ+FDQlqZTMZLrRrQv4UDf15PZsmpKJJySigqV2NjokcDc0PupORzPSGX6wm5mOjr0LGRJdcTczkTkc5Lyy6wcHQbejWtWZFcAT9PG7o07sqRWyksPB5JfFYxXxy8zQb/e8zv34Qh2qiimvBKh4aUKNV8cfA256My8HW34kpsNp/+eQtbU/0ai+HKkMtlfDiwKc6Whnz+p8iRSc0rZfWk9pgZ6DK7R2MuRGVw9V427+y8zt43ulTxM5zu50ZUmsiTehgNzPVJySvj9d+DGN7WkS+HtsDyOYWpx2YUMvuPYKLSCtGRy/h8SHOmaBXLR26m8P2ROyTliByudi4WfDGk+TPtZlRGfnE5my7Hcex2KnGZxbUmO4BYFLhYGdK3uQPTuzYCmUwa326/msCnB24Sl1WzzU/l0TuI7+u1Lm4MaOnwVCa2Go3Au7tukFEgdi8UchmTN1wlMrWgSkFQG2RA24bmGOgqCIjNRq0RcLcxZuErbfB2ffbHXKnW8OXBW2wPTKzy+wbmBnT1sOFUeDq/nIiq1h1VyMROeH6pUuquPnxMQVxT+jazIzm3lE2X4qTf921mz+we7nRoZEV9YG6oC1qXK5VGwMnCgOTcUs5HZeDtaklwfA6RaYV097LlQlQGq8/F8HYfTyZvCJReR3H5g/i43wPimNfH85Hj8Sm+rnx9KLza+6pASLxoP9TVwwZHcwPu55VyIjyN+f29+HDfzQf3S8glr7j8ieIf68LULo3YdS2RCK3zws5riQxt41iNl/woyGQyfhjZiuj0AjRZQhU37cScEi5EZ1ZT/j8pOjayYudMXy7fzWLRiUiuJ+Sy9nwsmy/FUa4SO7Wd3KxYN7nDf3UC8gKPxosC8F8In0ZWXNLavfi6W3Mvo1DyNVNX4v/5uFnVWCztCRYvDkPaNJDGKhWP7+RmRUGmyGWrSTGnkMsY5e3M0DaO7LqWwIozMaQXlJFZWI6rtSFtncWiLyG7WOIzGejKySwsZ9rma0zp7Mong5rVOWKUy2UMae3IgBYO7AxMYNnpGOKzinl7x3XWXbjLRwOb4edZ84I4pXMjisvV/Hg0giux2bRtaMGNxFzmbg9h++u+9XKYn9jJFUdzQ+ZuD8E/JpNX1gSwaVpHHC0MWTK2LS8tu0hoUh5LTkaxYGBT6XEymYxvhrfkXmZRFS4XaEfBXRux+XIcf964j39MFt8Obyl1k54Vjt9OZf7uUArLVNiZ6rN6Yns6NLLi9v08vj4ULtldOJgZ8NFLTXm5jeNT5eo+jNDEHNb73+PavWwyCsqohbKHDDA30qWloxnjOjSkTUMLbqcUEH4/j9v38xm8wr/WrpSjuQHNHc1p4WhGSyfxv3am+iw5FcWqs6IP5vWEXN5KuI6NiR6vdGjI+I4ujxQXlas03MssIjKtQFLfBsdnS0KGtPwytlyOr/KYivxmEAvYisJKVyFjVHtnWjtbsOx0FGn5ZchkMMPPjfn9mzyXEXtYUi5v7wghLqu6QCMlr1Qa9QNYGunSrqEFRvo6xKQXEpFaQJbWPklHIUOlFqoUSboKGZ3drUnOLeGv0BTpd8PbOjGzu/tjp5LIZTKGtG4gdagrrH42+t9jfn8vguNz2BOUyM+jW3MhKoO9wUnM6dkYD1tjYjKKKNVuJCpeY6lSw9Yr8czuUXder0Ihp01Dc24k1mxlUq7WcOJ2KgNaNmCUtzMrzsSwJyiR9a924NMDt6qsid8ducPPo9vU+HeeFDKZjE1TO+L381mpU/zhvlBOvNvjsW1SDHQVrJ3cgZXLxPG1pZEeM33cWXchlg/3hWFppPvITfHjvO6uHjZ0aWzNmYh0Pt5/U9og6SpkdPWwRqceVIsX+O/iRQH4D4NKVXdcmgyxu1Ex8u3sbs1qbWZj5YsRwJDW1ePfCstUHL2ZCkD/5vZM3xIkPq/2gX2b2xN5SoWxTBx9fbAnlHf7eVXjPOnpyJncuRGjvRvye0Aca87fJT6rhPisEpo3MOX9/l7cvp/PyfC0KmOw3wPiORuRzq+TvR9p+6KrEJ9jZHtnNvjfY92FWG4l5zNpw1X8PGz4cGDTGhWqs3s0prhczfLT0dxIzKWpgykRqQVM33yNvW90obHtoyO7ejW1Y/eszry2+RqRaQWMWH2JjVM70sLRnB9GtmLOthDWnL9LN0/bKjwafR0FayZ5M2zlpSpKSbUGTkeksX9OVz7YE0p0utilG9K6Af95+dFpBo+CWiOw6ESklN/p08iKlRPbIZfJ+Hh/GDuvJSIIYpEyq7s7s3s2fmrfrfJyNTuDEzl4PZnItMIaDbUroKuQ0cDcED8Pawa0dCC3WCl19z49eLtGQ2eZDNysjcXxrbbQa+FoXqs9xgcDmqIjl7PsdDRymdhlyiwsZ8050damm6ctEzu50KepHToKuWTunVuipP+S88RmFNVqE2Ksr6BtQwu87E3RV8g5eSeNuxlFaIQHNjRlKg0GunIm+LgytqMz6y7c45MDYtdI7Pq1xtu1fh2y+qKwTEVgbDarzkUTHJ9b6/087UzwdrWkvasljhYGXIrOZHdQklT0yWVos3c1qCpV7XKZSM9IzSvlglZkZqynYKKvK9O6NqKB+ZOP874b0UoqAHNLVLhaGRKfXUJWkRIjPQWxmUUIiJZX/jGZ/HohVhQt/BGCRgADHXkVYcrqszFM6ez6yPP6l1fa0GfxhVpvX+9/jwEtGzBaWwD6x2SSVVjOyPaO7A56QDH488Z9fhzZ6plawgA0sDDkgwFN+PFoBABJOaUsPB7Jl/VIPXkYThaG2JjqU1hQjr6OnHcGNiWzsIz9IcnM2RbCHzM60bGeXdv6QK0ROBORLhV/lka65BQrWXwymi2X43mjZ2Mm+br+VznGL1A7XhSA/zCMW1e79x+Au60xAkhqsU7uVnynjSWTPTS06V6DyupIWAolSjXutsak55ehEcRirlylwdpYj6uxWVhUuv+e4CQOht5napdGzOnZuJpVQEWe5PhOLqy/eI8NF2MJTykgPKWAjo0sWTWhHbGZxewITCAhWxzfJeaUMGS5Py+1cuCnka0xfQTJ2Vhfh7f7eDKxkwsrz8bwx5V4/GMy8V/pz9A2jrzf36uaDcG7fT0pKVfx28V7RKQW4GplRHx2MVM2BHJgTv34Ly2dzDkwtyvTtOq1Mb8GsGpiewa1asCYDs7sDkrivd03ODqvW5XjYmOiz29TOjBqzSVJ5QgQn1XCoRvJ/P22H8tPR/Pr+Vj+Dksh4G4Wo/RqFz48CtlF5by94zr+WlPt6X5uvNfPi21X41lxOkbymRvSugEfvdQUZ8v62+xURlxWIesuxOIfnUlKXmmtilwQY6087EzxdbOioaUhkemF3ErO48D1+9XGkyAWUZ72prR0NBMLPSdzmjUwe6QA6GHM6+NJeIq48dBVyPhplFhkXIzO5EJUBheiMrAy1sNYX0Hn4nKMZWJ0WVRaofZ16+Blb0JjWxPORKSTVVRODy9bNk/rSFJOCT8ei+Cwtmip+LapNAKmBjpM7dKIqV0aEZqUy5SNgc+86ycIAkk5JYQk5BAUl0NwfA53UvOpycjLwkiX0e2d6eppQ/uGlpgY6HAuMp0/rsRzLipDeoypgQ6CIBaSlc9VEIvW9IIyiWtsY6LPtK6NmOTr+tjChJpgbqjL0NYOHAoTN6QVlk1bA+IY0MKeA9fvs+taIm/38dRm5SYyp2djSSms91ABmF+qYvvVBGZ0c6/zeRvbmaKrkNV6/t5IzEWj0eBqbYyPmxWB97LZH5LE2328qhSA5SoNO64lMrFTdUP4p8Ws7u7sDUoiRhtFt+lSHENaN3iiDUSVLGW5jJ9GtSa3WMmZiHSmb77G7tmdaerw9KKM4nIVb+8QhXgyGXwxpDlTOjfiUOh9lpyKIj6rmG8P32H9xXu82duDMR0aPteM4f82istVXInN4kJUJuWFZfwbMk1e+AD+w9Doo8N13j6jmxt+HjZM3XSNhlaGbH/dl24/nZVur+gCOpobcPnjPtUe/8qvl7kWl8OHA5sSHJ9dxRZhhp8bmy7HMUrvBsYyJQZGxgSbdSVQOzY0NdDhjZ6NmdbFrVaT5azCMn49f5ctAfGSkW93L1vm9/OioFTFlstxnLqTJpWpCpmMYW0dmd7Nrd5G0InZxSw+GcWfN5IRtB2YCZ1ceKu3ZxWhiSAIfPbnLbZdTUAuEy9g6QVlNGtgxu5Zvo9UDVYgr0TJG38Ec/luFgq5jG+Ht+TlNo4MWeHPvcwiBrVyYNWE9tWI/MdupTL7j+Bqf2/PLF86ullzMymP9/eEEplWIBnkGpmY8MH8+fV6XQChibnM2RZCcm4JhroKfhrVCkM9Hb47HC7x5Vo5mfPF0OaPtdNXq9Ucu53GjsAEbiXnk1eirJU3pZDLsDbWw9POhOaOZlJ3Lzq9oMaLrIGunGYNRFFGRVfPy8GkTt/Ix0FBqZIRqy8Tk15IexcLdsz0Fbmc5+5yICRZEhFUHPMymR6pLn3xsDOhsa0xdmYGbL8az8nwdOxN9dn3Rhd2XEvgt4v3pHO6AtbGekzv5sYkX1cEQRQHVIxan7brV6ZSc/t+vqjM1f6rD+/wo5eaMqu7OzKZjPSCUnZfS2RHYGKVjnQjayOyisolW5zKW0d7U32yi8ulz87NxpjXu7kzsr3TUxexD6/npUo1TT8/VuV1xWUVM9nXha1XEjDUVRD4aR9m/h5MQGwWk31d8bQz4Yu/bgOgIwNVpVPMxkQP/w97P/J1zvkjmCO3Umu9fdWE9gxu3YC9wUm8vycUV2sjzr3fk4FLLxCZ9iCi08FMnyuf9H3Co1E3krKL6b7wrDTVcbcx4si87o/9GdR0DS0pVzNpw1WC43Ow057j9bFCqg2ZhWVM3xJEaGIu+jpylo1ry8CWDyZQSrWGfcFJLD8dLfmMOlsaMq+PJyPaOT3Sr/WfCEEQuJNSwIVocWMZVEmgVLG2GBqbsOD9+q/n/228KAD/YXhUAbhusjchCbn8ev4ur3g7o6uQsT0wsdr4d7KvK98Mb1nlsfcyi+i16BxyGZx+rwcDll2ULmhyGbze3Z2152MZaxCKkUyJUq6PU7fRGOgq2BmYIC189mb6vNvXi9HezrV+cVPzSllxJppd1xKlsdrAFg7M7++FuaEuX/51m2O3UqsUFW2czRnv48LQNo718jq7fT+Pn49FSgIYIz0FM7q5M7P7A+sYjUbg/b2h7A9JRkcORvo65Jeo6OphzaapPvXegZarNHy0P4z9WpXp3F6N6dfMntG/BqDSCPw8qjVjOjas9riVZ6JZdCKqyu905BD21QCM9HQoU6lZdSaG1Mv7MJYpKRZ0adj9FWZ0e7QVy87ABL44eJtytQY3G2M+GtiUrdruKIg+cgsGNGFUe+dH8vwyC0pZ73+P03fSScgurtFCpQIGOnLszPRxtTbCWF+HqLRC7mUW1diJMjPQqTK+belkhpuNSZ0Gys8C9zKLGLbSn/xSFS+3aYCejpz9IcnSd8TWRI9eqmu1Gp5XQIY4in54MmxuoMOg1g0Y0c6ZhlaG3ErO47M/b0ldv+ld3Xh/wON1/TILyyQblpD4HEKT8qoVnDpyGc6WhqTll1UT1OjIZWx5rSNdGttwJTabP67Gc/xWqvT9MzfUxcPOhKi0Aqnwq7xumBnoUFCqkr6TbZzNmd2jMf1bODyzz6um9Xzy+itc1Prs+bpZceVeNtbGepga6hCXWcyPI1vham3M+N+uoKeQc+b9HvRdfJ5SpaZGW5cvhzZn2iN8+opLlTT/6kStt3u7WrDvja4Ul6vo+O0pisrV7JrpS3G5immbg6rc9++3/Gjp9HzM0pefjmbxyQfrx9xejflgQNM6HlEdtV1D84qVjFkbQGRaAW42xuyZ3fmJsnjjMot4dVMg8VnFWBjpsn5Kh1rFQGUqNTsDE1l5NkZKtXG3Meadfl51Cvz+KcgqLMM/JpPzURlcjM6slszjbGlIdy9bTKKPoywp+sfXLS8KwH8Q7qXl0mvJpTrvc/3zfkzdfI3QxFwWj2nD90ciyCwsq1YAbpvRqZpybOHxCFadvUvPJraM7dCQN7aFSDt/XzcrYjOLSC8oqzGuydJIF1drI+Iyi8nV8rUa2xqzYGBT+je3r9XGIj6riKWnoqVunVwGw9s58W5fL8pUamb+Hlwl6QFEjtGwdk5M8HGp18J6+W4mPx2NIFQ7qrI21uOt3h5M6CQGmqvUGubtvMHhmynoKmQo5DJKlRpebuPI0rFt673oCILAklPRLD8t5gIPa+uIh60Jv5yMwkhPweG3u1Wxhql4zNs7b0jRRxVwsTLkwoLe0s8/L/qFkqJC6Zi7Whvx5dDmNRpIlyrVfHnwNruCxFFqTy9b7Mz02RssRjXpKeTM6ObGnF4etY5QL8Vk8vvlOEIScsgqqt2KRYY4FrUx0cPcSI+MgrJaUyDszfRpUamr18LRDGdLw/+zmKe/biTz9s4bVX7Xv7k97w9ogpe9KQsX/UJxUSEKfSMM2r1MekEp8VnFXIvLrpcHXk3QU8hp7miGp50Jdmb62JkaYGeqj52Z+F9bU30MdBWotckaFZ29kPicGhXOFTY43q6WuNsasS84mRPhogF85e+8no6cTVM7EJVWyLarCZJVE4gdYHszfa7czaJQa/tT2S7q4XSQnk1smdW9Mb7uVs/8s6tpPS8sVdJSW4zJZOJFNDG7hD5N7TgdkU473du8bwABAABJREFUFwsOzOnKmLUBBN7LZmqXRijVGqmz//BnZW+mz/kPej2y+G76+dEq77sydOQyor4diFwuZ8HeUHYHJTHa25mfR7Wm1VfHpRhLEK1v9r7R5SmOSu0QBIFei85J54YMOPSYBWdd19DUvFJGrblMcm4JrZzM2THT97FoF9cTcpi+JYjsonKcLQ3ZPM0HD7tHc6xLytVsvRLHmnN3JZFVUwdT3u3nVef15L8NpVpDcHyOSB+JzpC8cCtgqKugc2Nrunva0N3LFjetpdO/pW55wQH8B2Ho6oA6b3cwM0BHIeOWlv/nZmNMpta8WSOAvo6MMpWAjlxWzWJCrRGk7tUr3g05flscf1SsnV09bbhyL7vKTt/MQJe+bnYE3M0ip1hJTvED5ZxcBnczipi1NZjWzmZ8NrgFPm7Vd32u1sYsGduW2T0as/hkJMdvp7E/JJlDofcZ27EhW6f7sD0wQVJv6shlFJWr2X41ge1XE2hdqStY28LUpbENf87tytFbqSw6HklsZhFfHQpnw6V7vN+/CUNbO7JkbFtKlGrORKSjkIlF4F+h97E30+fTwc3rPO4VkMlkvNfPC2dLQz7Zf5ODN+7j08iSDq6WBMXnMG/ndfbO7lKlqyiTyVg4ujX3Mgq5df/B4pGQXcIXB2/x9bCW0vsG0V/NXk+f+KxiXtscRO+mdnwxpLlk3pyUU8ycbSGEJeUhl4n+g9fuZVFQJl6QBrZw4JNBzaooXovLVGy9Es/fYSnczSiUvP9qgo5chrG+AhN9HQrLVOSVqCgoE/9RqUBxtTaqUui1cDSv0+fxv4n8UiXrzsey8dI96Xcy4NvhLZno+4CvVXGuG+kpeG9AE1RqDcNW+ddY/DlbGNDNy07yg8soKCUmvZCE7OIq9y9Xa7iRmMuNxNxaX1+FkXqNz2NpSNuGFnRubE2XxjY00n6Oe4KS+GBPGPmV0mYqHq8jF2PMZmwJlrqCRnoKBrQQL6THbqVKnOGKuEK1RpDEK6VKDQq5jJfbODKzuzvNarCOep4wMdDFw8aImMxiBAGaNzAlMbuEG4m5yGWiojs6rYB5fTyZuP4q2wMTODGvG9sDE9AISBYyFUjLL2NPUCKTOzeq83lfbuvI7mvVLZtA5HX+FZrC8HZOvNKhIbuDkjhyM4WvXm7B693cWardBAIEx+fUy4j6SSCTydgyzYdev5xDI4if+wd7QvnrLb8qFlRPCgdzA36f7sMrvwZwMzmPWVuD2Di1Y73oGCfD03hrRwilSg0tnczYOLVjvfPCDfUUzOzemAmdXNnkf491F2OJSC3QXk/Mmd+/Cd09bf5PCsH4rCIuRGVwPiqTgLuZVYp9gGYNzOjuZUMPT1u8G1k+M+rK/wVeFID/IBSW1S0E6NnElqD4HNQaARcrIw5cFws6mQwEQVSagmgU/fDu91KMSNw3N9Slh5cNH+0Pk26zNtbjklbhp9YIEmnYSE/B+lc7olRrCE3M5WJ0Jv4xmdxIzK1iNh2WlM+YtQE0sjZibi8PhtXg/t/EwZS1kzsQmpjLohORXIzO5I8rCewJSmJql0ZseLUDXxy8TXJuCXKZGEsXm1lIWFIeYUk3+fbv8Dq7gjKZjEGtGtCvuT27gxJZeiqaxOwS5u28wdrzsXz4UlNWTWjH9C1BXL6bJWaQagR+u3gPezODRxLHK2NMh4Y4mhsy+49gAuNycLUyxNRAh7CkPJaciuLDgVVHNAa64nF8adkFabcLoiJ6UEsHfBvbVLnv6Xd6suJMNBv973EmIh3/6ExmdnenbUNzPtgbRk6xEhN9HUwNdCQbn2YNzPhiSHM6N7YmIjWP+btvEBCbRXp+Wa3KVhA7Vno6MsrVGspVAiqNQF6JWPiBWCR52JpIwowWjmY0dzTD7Dlc7J4WJeVqtgSIXYUKVXFrZ3OM9HS4EpvFklNR9GpqV2OKR0GpkrFrAwhPKajy+zbO5rzTz4ueXrbSxSivRMm3fz/gWDayNuKzIc1xNDckvaCUtPxSotMKiUjNJz67mMyC8irj2ro+j6ScEpJySvg7LAVjPQWWxnrklygls+vKOd8P/h6cjRRpEF72Jgxt7UhKXin7QpKkUX5F5m65WpDWC5VGwFBXwTifhkz3c3ticdCzwIoJ7XlJm7Zx+k4GThaGJOeW0MTBlMjUAnYHJfLJoGaSR+DvVxLwaWTF1XvZZD5sWooY1zamY8M6L87v92tSawEIsPlyHMPbOdHB1RI3G2PuZRZxJCyFaV3dWHY6Wto8C8CPRyP4bkSrpzkEtcLVxpg5PT1YeVb0h72TWsC6C7HM7eXxTP5+Y1sTNk/ryPh1V7gUk8V7u0JZPr5dnWP/P67E88XBW2gE6OFly+qJ7R8rorACJvo6vNXHkymdG/HbRXHTFpaUx6sbA+nYyJL5/ZtIUafPC4VlKgLuZkldvviHOvFWxnp087Shu6ct3bxs6l3k/hvwogD8F6FzY2uuxIpcmc7u1hzXkphlwgM1IsDgGuxf9miJ6cPaOnI9MbdKJurg1g78HpAg/WygqwDVg0JFVyGnQyMrOjSy4t1+XuSXKrkam41/dAbnIjOI16p747KK+WBvGB/tv0nHRlb0bWZHN09bvOxNpItnm4YWbJ3eiQCtaWhwfA5rL8Sy7WoCk3xdSMgu5sjNVCLTCmjtbE7XxjYcu53KvcwiqSvYyknsCr7ctnpXUFchZ2InV0a0c2LTpTh+PXeX8JR8Xt0YSJfG1rzdx5MyldjWN9RVUKJU8+3hO9ia6teai1oT/Dxt2PtGZ6ZtukZ8dgmmBuLrEO1GbOjSuOr43cHcgE3TfBi15nKV4nnihkBufdmvyn1N9HX4+KVmvOLdkP8cus3F6Exp8a+4vbBMRWGZCgsDBb2bOZCYU8zMrUEUVuJwPQwZosebWvOg+1Su1lCxwdXTkdPMwVQq9Fo4mtPUwfQfb9mgVGvYdS2R5aejJZGEh50J7/f3YkALB0qVGkatuUx4Sj4ztwaxd3aXKu+psExFu69PVimsvF0tWTCgCZ0euvicjUzn4303Sc0vRSaD17q68VZvD+5mFOEfk6Ed6eZKnfnKcLIwoLWzBR62xtibG6KvIyerqJz0/DLSC0q1ncUy0vNLKSpXa/9VHbfXVjwa6SmwNzOguFzNklNR0udbUfhVVvgKgnhRm9qlEZN9XZ+bIfnjoJmjuRTpp9IItHMxJzm3hDRtlvL+kGQ+GNCUeX08mbIxkG1X41kzqT1X72VTptJgb6JPmhRl+cD3sC6Frp2ZgXR8asLN5Dw0Gg1yuZzR3s4sPB7JnuBExnRsSDdPG8kWB2BvcNJzKwAB5vf34s8byZKB+5ITkQxoYV9r1vrjorWzBWsnd2Da5kAO30zByliPr4e1qNaBEwSBhccjJeuxMR2c+W5Eq6fuRpob6fL+gCZM69qIX8/f5feAeK7F5TBu3RX8PGx4r79XvTxc6wONRiA8JZ/zWleAkIScKmI1HbmM9q6W2gQsW1o4mv3juYlPihcF4D8EWy7ffeR9OjayYqO/ONZq4WQqccA0gKm+QhoD9n4obSOvWCmNfF/xbsjuoAc2HHIZqLQFQMVoyUhPh9I67AjNDHTp19xeijS7n1vCgevJbLsSz/28UtQagSuxWdpiVSyu/DxsxH+eNtibGdC5sTV7Z3fmbGQ6C49HcScln1/Px2JppMuwNo6cjkgnLCmP2IwivhraHCdLQ7YHJnJcO866eeAm3x4OZ1hbRyb4uFbzAzTS02FuLw/G+7iw6mwMWwPiuXw3i8t3s+jf3B4vexOi0gqlIvD9PaHYmOg/luN+UwczDszpyrTN17iTko9CJkMtCLy3K5Rj73SrZpnTtqEFi15pzbu7QqXfqTUCfZdcYFQN12APOxNWTmjHlA2BEr8RkPz25DLILVWz/3py9QdTPclB4EGGrqm+jhSTVmGo3NjW+F+lxtNoBA6F3WfxyShp1+5kYci7/bwY0c5J6mAY6ilYO9mbYasucSs5nw/2hvJG98bSJqiwTCUVVtbGemya2pHWDS2qPFdeiZLvDodLKS+2pvp0drfmRmIuPt+drhZPpquQ0dLJHG8tf6+9qyX29YzeikjN5/3doRJloLYipfLnW1yu5t5DXFqg2uMUchkOZgY0cTAlNb+ULQFx2JkaYF/BVzTTx9pY7//kPHi/vxdf/CXGoJ24nYaDmQGp+aWY6OuQVVTOmYg0BrRwkMzdr8RmSyKQEtWDDmtF8bv67F1e8a7baqRPUzv+1vqi6sjFbmoF1BqBA9fvM8rbmVHtnfnlRCTX4nK4l1nEgoFNuRD9IB+4TKVh17UExnasO3LySSGTydg8rSP9llwQu7cCLNgbxp7ZXZ6ZQMfP04YlY9vy1o7rbL0Sj7WJHu/09ZJuL1dp+GhfmLTevNPXU8pLf1awNhEpOdP93Fl1Noad1xJEy6+YTPo0tePdfl5PJLjJKCjjolat6x+TWa1r7GJlRHcvscvXubH1cxnn/xPxogD8h+DLvyLqvN3CUBdTAx2JyxN+XxxVVVwE9HQUUKbGzECnmtHxobD7lKs0NHUwpXkDU47fTpFu6+BqyaEwUaAgIBYpumUySqk/HC0MmdvLg7m9PAiOz+bLg7er8N0yCso4cD1ZGll72png52lDN08bfNysOfyWH0dupbD4RBSxmUUcDL2PjYke1iZGxGcV8/7eMAa3bsD3w1vx1dDm7A9JZkdgArGZRewIFC0uWjqZMd7HhWFtnap0Ba2M9fh8SHOmdW3E4pNRHLgukuh15DIsDHXJLVFKReCsrcHsmuVbbzsaEDt7e2Z3Zs62EC5o1cip+aV8vP8mqydWt4YZ0c6ZG4m5VRIlknNLyTdV8vAyGpVWwOT1V0mrxfrjUUKFipttTPSqFHotHM1oaGn0r93VCoLA6TvpLDoRKUVm2Zjo8WYvD8Z3ckFfR0FBqZL4rGLis4qJyyoiPqsIO1N9sovKORSawqHQFMYYiIbnFXCyMODke1UTF9Qage1X41l4PFIaw4J4Tv9VSdhjbaxHe1ex2OvgaklLJ/PH7pyWqdSsPBPDmnN3UWkE9HVkqDXViziAIa0deKOnByvOxHCskp1JRRetNqg1Asm5JbUKeUCklFgb62vFK+J/7SURi4H0O1tT/WfKfxrl3ZAv/xJj2srVAm2czUkNf7AS7Q5KYmDLBszr68m0TdfYGhDPrB7uLD0VTX6pClMDBQWlYiGoryMjObeEA9eT6izKZnRvLBWANQnftwTEMcrbGQdzA7p5ivnie4MT+WBA02oK5KWnop9bAQjgYWfKjG5u/HZBbAKEJOSy+XIc0/3qVjw/Doa0diSnqJzPD95m6alorE30mezrSn6paIV1KUa0wvphRKsaXQ+eFRzMDfhmeEtmdndnxZlo9oUkczoindMR6Qxq5cC7fb3qTJ8pV2kIis/mQpTo/xmeUlW8YaynFW9ou3yNHhLv/f+CFwXgvwRdPWwIistBI4gE/IpiowI5xeXS/R4uOirGv6O9nbl5P5/0gge7n/YulgTG5UiqwFe7uHLv7NUnfp3erlYcesuPc1EZ/HQ0QrpAG+kpsDTS435eCdHphUSnF7LpUpzYbnexxM/Thh9HteJuehErzoheUZmF5Zgb6lJQquRwWAoh8TksHtOW17u7M6ObG1fvZbMjMIGjN1O5lZzPpwdu8d3hOwxr68h4HxdaOZlLx8LZ0kh8bDd3Fh6P5ExEOrklYtFVolSjryOjsEzF1E3X2P+Ynlgm+jpseLUDn/95i53XxO7q0Vup7LyWyHif6heEL4e04EZCbpWuXrFSjbFM7EYNXHqBuxmFdRot1wZnS8MqlistHM2xM9X/x6jqnhYBd7NYeDyCkIRcQDyvejURF/CwpDz+Cr1CfFaxlHBRX+gp5Kyd3AGVRuBClDjKDbyXxbW4nGpjV5kMmtibigWftsPnam30VMc4OD6bD/fdlNS7D6dcmOjrUFyuQiPAgBb2lCo1DF7+oAPlamVEcm5JteLPz8OG2T0a4+NmWWncrB0551f+r/j/mYXlqDUCmYVlZBaWEZ5CnbAw0pUKRFvTyqrnqv9fn9QZY30dBrZ04Ki2oD0bmS6ZPgOci0wnNa+Unl62tHY2Jywpj7wSpbR2GeooKEAsACsasivPxjCyvXOtI8q2DS0kYUxNuJ2cj1qtQaGQM6ZDQ85HZbAvOJn3+jVhfv8mLNj7gEudkldKeEoezRs8H0sYgI8HNuPv0BRStF56Px+9Q79m9o+MOXwcTO7ciMzCcpadjuaLg7eQA1uvxBORWoCRnoLVE9vTs0ndme7PCg2tjPh5dBtm92jMstPR/BV6nyM3Uzl6K5XhbZ14p68nrtbGCIJAXFaxZPYeEJtVTezW0smM7p62dPeypb2L5f+UCfWT4kUB+A/Ao+LfQGzPB2j5f+1dLDhw/UHXztpYT7rgPcz/i04rIDQxFx25jOHtnNjg/0AZaWWsx4VosZBUawSsjfUY1KoBq87yVJDJZPRqYkcPT1sOhiaz6HgUybklFJeX4GptRP/mDhSWKfGPySQxu4TAuGwC47Rm0/o6+Lhb0bahBQGxWZJoQlchIyWvlAnrrzCre2Pe6+eFr7s1vu7WfDm0nP0hSfw/9s47Pqo67eLfOz2990J6QkILhN4FrNhQEAsqit21r7r7btHVXde19wr2gigqikrvLYSShPTee08mmXrfP+7MzQwJCAiIyvl8IJk7NXdm7j2/5znPOZ+mV1La1F8VTAm1VwVD5ZL+0BBpWm13aQv//TFfntY0mEWUCoGmLgM3vJfOV7dPOi5tlFqp4Kl5w4nwdeWZNVJM39++PsSoCO8BU5UKhcAnt0xg8n83DohA6zaYye9wHkQYdB8j6fnsJDE+0J1/XZrCxNhjb2Gf6RBFkZYeIxUtPWwvaubL/dVUtTpXrvRGC6uzBzf09XZRo1EpaHMwNYb+74sjXRse5iWbcg9mjBXqpePS1DAmxPiRGul90oZgug1mnvkpnw92SRVhu62JnfyFeuu4Ki2CN7eUYBUlM/M1OQ3y/ROC3KVKZ2u/cF1AOg7cPj3WqV0W4uXys9FtFqtIa4+xX5PYaaChs6+fNHYZaOyUtIpGi5V2vYl2vUlOUjkSPLQqAjy1TNIbOdqeu2NGrEwAjRaR5BAPtha1yDnLX+2v5q6ZcdxzTjxLPsxg+d4qpif4szG/icZuo0zmzFYRd42SqtZevj1Yy5Vjwo/4nKOH+LC7VDr+qBUCJgfCbxFFVuyrZuG4SGYnB+Ltqqa+s49tRU1cMjKUv67MdlogPL4ql+W3TTzqvvglUCgE3ls8lgte3IYIGCwij67M4pMl40/qIu++2fE0dxv4ZE8l//fNIUD67L2/eOwp8zw8GmIC3HlpYSp3zojjhXWF/JRTz9cHavj2YA0x/m70GM3UdTh3SvzdtbI9y5R4/xPyOPy94ywBPAOwaOnun73N2ChfPkuXBjU6DyMN/u79J7Spcc7xb/ZUgplJgfi7a/khq79tNWdoIMszquWpwKt+ZmrueKFQCFyeGs6Fw0P4eHclr24soqJFzzvbSqU84ytHEuLlwrbiJrYXNbOzpIWOXhMbHNJJPHUqek0W+QQuitKgxfaiJl5cmEpcoDu+bhqWTI3h5inRpJe18qmtKphT28nfvpGqgpeMDOWa8ZGMCJeqghNi/Pj6zkmsyWngPz/kUtnaKw9nlDb1sPj9dD67ZeIRE08GgyAI3DUzjhAvHQ9+kYlFFLnijZ1semjGAP2Xu1bFitsmct6LW484tGGHWimQGOxBSohU1UsO9WJoiAcqhYJlO8p4eUMRRY3dXPPuHq4ZF8lD5yaeEcL+Y4HVKtLYZZDbtM5tW/1R84VB8nwb4udGlJ8rQ/zcCPLUUtrUw5bCJnIcZAhBnlquHBPOZaPCqGjV88hXWYgOD72vsk3+3U2rpMempw310vH8glFMiD35k4gb8xt4+MssJz2SnUu4aZT869JhDAvz5Io3dsltYLvn57BQL4qbup2Il0ohsHBsBLdOiz3hipBSIRBga+8eLXlWFEU6ek0yITycJDY5DLbojRbJSqjJTJrOilpAzmE+HMPDvAjx0skVrl2lrfi4qGmzHfPscXCzhgaSEupJTm2nk+TD311DrY0IeLqo6TZaeG1TMZeNCj2irvGacZEyATQNoqv4eHcFC8dJ0oJLR4bywa4KVuyrZkZiIFeOCZer/gDpZa1095lx1526U2tSsCfXTxwiLxp2lrQcsdtwohAEgQuHhchG/oIAT10+7Fchf3ZYrSIGs4VhYZ5UterJrevEKkJxk6R9VQiQGuHD7OQgpiX4MzT49zu8cbJwlgCeAdhV1nHU613USgI8NLL/X16ds/7PfgIZ4ueKl2v/+tpsscqC3fljwilu7KbCVkFRCP3aIrtBs6NH2smEVqXk5inRzE8L552tpby7rYwDle1c9fZuzkkK5OHzE7l2/BAsVpFDNR2S6LeomX0VbUfUMx2q7eSCl7byz4tTuHZ8JIIgIAgC42P8GB/jx2MXG/lqfzWfpVdS0tTD8owqlmdUkRziydXjpaqgp07N+cOCmT00kNc2FfPihiK5+nOwqoOFb+/iy9snoj5OUjxvdDgapYK7PzuA3mhhzvNbWHX3lAE6k4RgD56aN5xHV2YPeAxfVw03TYliZlIg8YEeR2xX3D49lstGhfGfH/JYlVnLJ3sqWZ1dx5/PS2Th2MhTnrhxLLBYReo6ep2IXXmzjey19hzRjPdwRPm5cv6wYEZF+BDl70qkryuuGhWiKLK7tJUvMqRJYLv1iUohMHtoEBePDMFoEdmQ18C813fK2cjYOLlaqeCWqdHo1EqW762isUtK87hxUhQPn5d0XIuAY0Flq54/fbrfSQLgiItHhvDkZcNYe6iBua9slxc/SkGSbOQ3dJFV039fF7WSJVOiuHFyNH6nqcohCALerhq8XTUkHEWLBVKVs7Gzj4ZOAz9+dggsJroNg3tRCoLAbdNieOw7aRjEZBGJC3Jnb3kbApLTwJ6yVibESBP9t320jw15jbI/Y22HQT4u1tpsr8qae/g+S/L0GwwXjQjl3s8Pyguxw82lc+v628Dz0yL4YFcF63IaaNcbuWtmnBMBFIH//ZTPvw5LYTrZ+OfFKfxwqF5OonjyuxxmJAb8bIX3WLEqs5aHvsiUc667+sw8ujKb2EB3YgJ+3uj5ZKGxs4+tthzv7cXNtB4m7Qjx0iGKIvW2XPucug7SonwI8XI5S/6OAWcJ4BkKrQrs56mxUZL3lVWESB8dlW3S6lgEeVIOYE5ysNNjbClsoqnLgJ+bhplJgby1tX/SeHSkNz/l9LfOZg8NImwQf7STCU+dmgfPTWTRhCG8tKGIz/dWsTG/kU0FjcxLDef+OfGMjPBmZIQ3d82MQ280k17Wyo7iZrYVNct6QjtMFinr980tJTw1bziTY/3lL72PQ1Vwb3kbn6VLxCi3rpO/f3OI/9iqglePj2RkuBf3zk5gSrw/17yzRyYQmdUdTHp6E29dN4bRhxlr/xzmjgylqdvA49/l0tln5uJXt/P+4nEDDLoXjouUouyK+yeD/3P5MK45joD5YC8dL1+dyjXjI3lsVQ759V3839eH+Cy9kscvGTbgOU8FTBYrte29lLfoqWjpobzZ9rOlh6rW3gFTso5Q2uLN7BW8ipYe9le0y6212UODeOi8hAGB9fUdfSzbXsaKfdVO3l3xge5cMDwYnVrJrpIW7lt+0KkF7O+uZU5yIJ7FeRh7Tfi6amjsNfPONkkeMcTPlWeuHDmosfkvQU5tB/9encfOkhZ5m0IAAWl63FOn4olLh6FVK1jw5i6nzNmxUT4UNnSzt6K/UunloubumXFcMz7yhDzYThfctSrcAyTisE2nordHGngpbuweNDVi3phwnvg+F/tbdqCiDS+dig7bYvCLjComxPhxbnIQQ0M8yavrJCXUU7YACrCRQZBa5HvL23h5YxEXjwwddEGkVAjEB7pT2Dh4G9sqwqfpVSyaOISUUE/5Ob89WMsNk6JIDvFw8o/8Yl/VKSeACoXAshvTuPgVKTmqx2Tlb18f4t0b0n5RK1gURd7ZVsp/fpCGEi8YFswTlw3jxvfSOVTTyaKl6ay8c9IxT7QfLwxmC3vL2uR83cOP+e5aFZNswxvTEwJkrfbO4maeXVvA/sp23tpayse7K7h5SjQ3T43By+WPMdF7Ijhzjxp/ENS2Da73cux8TU+U0jgAPFw00NY/GZcY5C4TwPOHOceGrbBZVlyWGoZaqWCFw0p1aIgnGRXt8mr3+p9xzT+ZCPTU8e/Lh3PzlGieW1vI6uw6vtpfzXeZtVw/cQh3zYzDx02Dq0bFjMRAWXDc1GVgZ0kza3Lq2ZTfJJvrVrf1smhpujQQkBQoW85E+ErC/HHRvoyL9uUfc5NZeUCaIC5u7JargkNDPLlmXASXpobx+a0TuPad3ehtVammLgPz3tjJBcOCeei8xAET1kfD4snRHKxq59uDtXT1mbn67V28tDCVC4Y76zRfu2Y0//zPOrBAkIfuuMifIybE+PH9n6bw8e4KnltXyKGaTq54YydXjA7nkQsSf7GBqcFsoaq110bs9E4/q9t6nfwND4dGqSDc14UoPzeG+Lk6/QzzcaHXZOHdraUs3V4mO+9PiPHlz+clORFYo9nKxvwGlu+tYkthk1PLdFpCAF4uag7VdPDyhmKn548NcGNOcjBzkoNIjfBGoRB4/vn1GJHaqnZLpRsnRfHw+YnHNLRwLOgzWfghu45lO8qcYqTUSoEQLxcqW/WAyIQYP85PCeK1zcVObV2NUoGLRsne8n7iF+Cu4cHzErhidMRJSYM4nVA5ELB3tpby9JUjBtxGqsyHsDpbmkAxixDu60qHraW/OquWxy5JwVOn5p5z4rjjk/1O7f5Gh6n5vLpOvFzUlDb1sDq7jktGhg76ui4ZFSpndg/2Mf40vZJFE4cgCALzx4Tzr+9zWbGvihsmRfHn85NY/N5e+bZ9Jisr91czb/SRdYcnA8PDvFk4NkKuQG7Ib2RVZu1x+Zk6wmIVeeL7XN7fWQ7A4slR/O2iZJQKgfcXj+PKN3ZS3qLn+qXpfHHbRKdu04lCFEVKmnpkE+bdpS1OHQFBkGQB9uGN1EjvQT/zk+L8+SrWj80FTTy7toCc2k5e3ljM+zvLuW16LDdOijqjF0m/Fs7ukV8Z57+0/WdvMy7Kl79+LbUJ7cao9jZHR5+kjdGoFIwM95bv09pjZEO+JBafnxZOfUef3P71dlFLVSekg11MgBuT406t2/pgiAlw57VrR3NLVTv//TGP3aWtvLu9jOV7q7h9Riw3TY52ar/ZzZovHRWGKErTmk98n0dxk3TC1BstrM6qY3WWdOKI8nNlcpxkNzMx1h8fNw03T4nmpslRZFS08dmeSr7PriOvrpO/f5vDf37IZ+6IEP5y0VCe+C7XaTLwx0P1rM1tYEFaBPfNjj/mFfBT84aTWdVOeYseo0Xkjk/287eLhnLzlGh5pa5QCPi4auiytR5/CVRKBTdOjmbuyFCe+amA5RlVfLW/mrU59dw7O54bJkUdlTT0Gi1UtvZbpzhW9Go7egcdkLBDq1L0Ezt/Z6IX4uUyaPWlz2ThvR1lvL65hHbbwM/wMC/+fF4iUx2ioIoauli+t4qvD9Q4TfgmBnkQ6KGltKVbHh4A6cQxOtKHOclBzB4aiJ+bVrY/eX9nOTXtvRj1RlRIQv8hfq7874oRA4yfTxTlzT18ml7Jiowqp/QXQYBJMX7k13dS2apHrRQ4NyWYnJoOue2pQPL2FLAZdffaBkK8dPzloqHMHR7yu5jq/vpADQ+cmzDod2nx5CiZAALk1nbirlXSbbBgMIt8l1nLteOHcF5KMIlBHhQ0dBHh6yIPCXm5qOjoNdNtsDBvdDAr99fwyoYi5g4PGbQ1eP3EKJkAwsA2cEF9JyazFbVKwWWpYTz1Yx6HajrJq+tkenyA/NrseG5twSkngAD/vnw4a3Prae2RPmP/+CabyXHHP/DQZ7Jw3+cH5a7Q3y4a6pSO5O+u5aObx3PFGzspaOji5g/28tHN409IHtHRa2JncbOtytc8wJIo0EPL1PgApiX4MzU+AN9j1DMLgsDMpEBmJAawJqee59YWUtTYzTNrCli2vYw7ZsRy3YQhZ7yx/enEWQL4K6Ozb2BrzO5LB9KKOdRHR06tpPmx6/1EJN+ygnqJ/IwK93YSOX97sAaTRWR4mBdJwZ686pBdOSMxgG8O1vZX/yYM+VVPKKMivPnslglsKWzi6Z8KyKvr5Jk1BXyws5z7ZiewIC18gIBbEASmJwYyPTGQA5Vt/OnT/VQ75IHa9ULlLZVyaPyIcG/ZjHp0pA9jo3z5h4OvYFFjNyv2VbNiXzURPlIUleNJwGIV+Sy9kq8PVHPzlGhumx77s9OgrhoVr1w9mnlv7JDbkE+uzqO6rZe/z00+ZRo9f3ctT185goXjIvjnqhyyqjt4cnUey/dW8cj5iQR7uTh55NmHL+zV5CPBTaOUhi78XZ2GL6L83Aj00B6z7sZkscqaPbuXWmyAGw+dm8j5w4IRBIGuPhPfZ9XxRUYVB2y2LyANBgXbBgUKGrooaJCq6CqlQGKgB+G+LrJ58Ff7qnl5Q9Gg+ccLdFZUgvQe/Xjv1F9c9TNbrGzIb+Tj3RVsc0iJsCMlxIOYQHe+y5SITaAtO9m+YPHUKdGolE7fcYAIHxceuySFWUODBjzmbxUapQJjn5X3dpTz6AVJA64fM8THKeNXROocdNsE/8v3VnHt+CEoFAJ/mhXH3Z8eoNmh8mePMgTIre3AQ6eiqLGbn3LquXD4wKQkTxe1rCOEgVVAqwif7KngxsnR+LppmJUUxE859azIqOYfFydz6/QYnl/bf4ytae+jsKHrZ/WRvxRKhcC7149l3hs7Aejos/DYqhxevWb0MT9GW4+RJR9msK+iDY1SwXMLRnLxIJXSCF9XPrx5HAve3EVGRRt3f7qfNxeN+dkqtMUqklXdLnnyFTUNiBLVKBWMjfaRq3xJwR6/6HwkCALnDwthTnIw32fV8sK6Qspb9Dy5Oo93tpVy9znxXJV2dIPwPwrOEsAzEG4aAfug77AwLw5UtGMVwcdV7VRNmBDjx1f7pSGPuSOdD2r29q/d/uCjPf3Gw3brEaso+ajNO4pFwumCIAjMSAxkWnwAqzJreXZtAdVtvfz162ze3V7Kw+clcl5K8KAHhtRIH7Y/Ooul20v574/5mCwiItIqPiXUk26DlJJwsKqdg1XtvLqpGBe1kvExvkyJk1aZN04awv7Kdj5Nr2R1Vh1VbQONctVKgZgAdwrqu3htUwmf7Knk7plxP7uqHB7uxUPnJvLUj/mobdYt7+8sp7qtl5evHnXS2o2O6NCbKG/pobJVz6ykQFQKgazqDooau1ny4b6j3tdTpyLa340htuqdI9Hzd9f8ooOzPb3DflAGKb3j3tnxzLOld+wtb+OLjCpWZ9XJCyEB8NBJEXidfWY6+6SFj8I2wS4CZotITl0nOYeZvtrh764lzFtHmI8LYd4uiNm5WAwmPHWqX/QeNHT28Xl6FZ/vrZSnV6E/o9tFrWDRhCGsz2uUyZ+rRimTDT83DSPDvdha1ExnXz9RjfR15cnLUpiWcHo8104n3LQq6IFPdldw18zYAckLgiBw46Qo/v1Dv0F+aVOPnIqSVd1BQX0XicEeXDAshLjAIoobu2W7GJC6Ikazlfz6bm6dGsPb20p5eUMR56cED7pQOWdoIJ+nVw3Ybsdn6VXcOFkyXV4wNpyfcur55mANj16QxKLxUbywtshpov+xb3P49NYJv2AvHRtGD/HhslGhfHNQcnj4PquOS0bWc25K8M/cE6pa9dywLJ3S5h48dSreuT7tqFXwpGBPlt44luve3cOG/EYe/SqbZ+ePGHBMqO/oY2thE1uKmthR3CxX9+2ICXBjWnwA0xMDmBDtd9IHrUAix5eOCuOi4SGs3F/DSxuKqGnv5e/fHOLNzSXyMee3lH50snGWAP6KWLGndNDtbfr+1es5SYFy/q9dFG9v/zquvGY6GHPm1HaQW9eJRqng0lGhtHYb5SrL0BAPuf0LMG902EnzNDsZUNj8Ci8YHswnuyt5ZWMRpU093P7xfkZFePPoBUlHDAe/eUoMl40K45YPM9hfKZHm7JpO3LRKbp0WTaSvG3vLpaGS5m4jmwukLOPD4+punx7DjuIWPkuvdNJjmSwiFc09/OWCJFbsq6a4sZsnV+fx3o7yAfFjh+OWqTFsLWpiR3ELkb6u1HX0sj6vgavf3s27N4w97v0kipJf2+FaPPvPww+4g+5rAZJCPJmVFEhcoLtM9A6PsDsZEEXRKfYPJOJz18w4Lh4ZQkF9F498lcXmgqZBTZxFGHQi3F5IUCkEgr10hHm7yATP8fdQb5cBJP35gp84QsjKMf09O0ta+Hh3BWtzG+SKhqdOhVopZfyKIkyN9yc5xJOl28tkOw1RlOQKQZ5ahoZ4kl7WysaC/u+kv7uGFxaMYmpCwJGe/jcJq1WU95NWpSA2wI2Sph4+S6/k1mmxA25/xZgInvohH8ceia+tlQ/weXol/7wkBaVC4E/nxEmTvA4MzOxgpN3YJUXK5dd3sS5PipQ7HLdMjT4qASxs7MJgsqBVK5kWHyBXDDfmN3D+sBBmJAawyeF9lMyIzadkgXc4np0/kk0FTfLi/q8rsxgf43fUAYjs6g4Wv59Oc7eRMG8X3l889qjpGnaMjfLltWtGc9vH+/hqfzX+7hrun5NAelmrrOU73BfSQ6dicqzkyTctwZ9wn5NnXP1zUCkVLBgbwaWpoXyxt4pXNhZT097Lw19m8cbmEu6bHc/FI0L/kFPDZwngr4g/f503YJsggKMp/fhoX55YLWmD7NmlIhDh68J+m3eZn5vGKbnC7v03JzkIb1cN//j2kHxdcoinbCMDp3f443igVSm5ycE65p1tZRysamfh27uZmRjAw+cnDTBYBilL8qs7JvHpnkoe/y4Xo8VKj8HC21vLZMLx1OXDqWjVs72omW3FzaSXtQweVxfnzxWjw/nxUB0Hq6QWfJ/ZylM/5nN+SjDnJgexcn8NNe29PLQik3e2lvLIBYnMTAwcsCJWKASemz+K81/aSmWrnktHhbK1sInM6g7mvbGDixUDxXWiaPPIs1mmyBYqx+iRF+ihHVST12u08L81+ewtbyO3thO9wcw/Lk5m1GH5tycDVqvIutwGXlhfKE/0qRUCEb6uKBUC/1uTz7++z/3ZxxEE8HHVEOPvRkKQO2E+rk4EL8hTd1osb9r1Rr7cV82ne6QoQjvGRPrg5apmc0EjVlFKyLh7Zhyrs+p4a2v/Qk8UJXPnGH930stabQuQftw8JZq/z00+5X/HqUafyUJBfRe5dZ3k1naSWyfp5eYqDLjZSPBt02J5+Ksslm4v48ZJ0QNacr5uGs5zSAYBqGnvlc2eV+yr4i8XDkWjUjB3RCgvbZAWiyB9XqxifxV2dXYdS6ZE88YWqQp4bnLQgO9obIAHbhqlPIR0OEQRPtxdwS1TY1ApFVw+Ooy3tpSywhZRd/+cBCcCKALPringHxcfzVHx5EClVPDWojEsfFvylG3ukbKr/3flyEFvv6mgkbs+2Y/eaGFoiCfvLx57XJO9s4YGcv/seJ5dW8hbW0t5d3uZU1tXEGBkuLdtWtefkYdJlH4NaFVKFk2M4soxEXy8u4I3tpRQ1tzDvZ8f5PVNJdw/J4HzUgZ+Lo4EURTpNVlo15vo6DXJPzt7TbT3mvgtKA3PEsAzDGHeOqptU74KAaID3Jwm3Ow4NzmY93ZIthXTHCoFRrOVb22tgCvTpNbuSlubWKdWOJ1wxkf7nnKNyi+Fh07NA+cmct3EIby8oYjP0qvYVNDE5sImLk8N44E5CQNWk4IgcO2EIUyI9ePezw/I05ctPUb+9X0u72wr5d5Z8SyeHMUt02IwmC3sq2hje1EzO4qbyarpkOPqQKouhfu4UO3QFraLpeMD3RkeFsjuslYKGrq46f0MxkX58sgFSQPsV4K9dDx9xQhu+2gfqzJreebKkbxiM8dudTHigkTyb/soQ9bk2Vugg0EQINTLZUCb1tEj70j44raJrMqs5d+r8yhv0XPT+xnMSgrkHxcnM8Tv2HMxjWYr9R19VLfrqWmThixq2nqp7eiltKmH+s6+AYMjJqvoRJ4Gg0KAxGAPZiQEcMmoUJKCPX81naooihysaufj3ZV8n1UrtxjdtSouTw1jWJgXb2wuls2k544IITnEk6d/yneynwnz1hHq7cK+ijZq2wdqLf/vwiRuGaQSdqajpdvgRPRyazspaeoePKvaxjH0RguXpoby7NoCGjoNfHuwhvlpA7Nlr5swxIkAgqTXa+420m2wsC63notGhMpVwPuXZzoNcCgAC1L1PtBDi6tGSU5tJxvyGpmdPFBTOS7a14nEHY4v9lZxi204Yv6YCN7aUsrmwiYaO/sYEe7tZGINUtv4dBBAkCRBFzgQ5i8yqgfV8i3fW8lfvz6ExSoyJc6fN64bPaAFPxg69FJ6k73K5/h3WqwiXjo15w0LkpI34vxPSSfhZMBFo+SWaTFcPT6S93eU8eaWEgoaurj9433E+Ltx8ahQInxc6Ow109Frkv+1641Olzt6TUeM61ygk6I9DYMFTJ9BOEsAfyVYrYN/MFwc2lQx/u5kVnUgisjaMXv7N8xbJx/k5jrEv23Mb6C1xyhNUsX5U9HcLVeKUiN92FXSIq+Kz9Tq32AI9NDx5GXDuWlyv3XMyv01fJ9ZxyKbdczh02KxAe6svGMyL64v5I0tJYiipAup6+jj0ZXZvLW1lPvnJDB3eAiTYv2ZZItRa9cb2VnSwjYbIaxs1TuRPzsEkImiRimQEOROeXMP6eWtXPHGTs5LCeLP5yU5eZ2dlxLM1eMi+Sy9kmfW5PPpkvE89GUW1kYRBOgxmp2ivhSClGN8uHVKlL8r4T6uJzzRJgiSPmbW0CBe2VDE0u1lbMhvZFtRM7dOi+HOmbG4aiTNXU1bL7XtvVTbyJ1E8vTUtPfS2GU46mSwHS4aJf5uGroNZicdqyM8tCpmJAUyJzmIGYkBv7o0QW808+3BWj7eXeG0CBsa4sl1EyKZmRjIKxuLeOQrKQ822FPHXTNjWLajgu+z+idYQ710+LtryKrplIcaANlgF+CumbFnPPmzWkUqbQkMjmTvSINDvm4aUkI9SQ7xJDnUk+YuAwUbJL/LHqMZAYGbpkTz3x/zeXtrKVeMDh/QhpsY40eol45aB7LR3G2USd6bW0q5aIREci4eEcpL64tkbanjMB3Aa5tLWDRhCG9tLeXljUXMGjqwUn/DpKijEsDixm76TBZ0aiVxge6MjvRmf2U7Kw/UcPv0WO6fHc/DX/Ubu/eaLKzKrOGSkSdmzXK8eHnhKFKfWC8f8x9ekcmVDoW959cV8rJtIHDe6DD+O2/EEYchzBYrmdUdMuHLrGp3IvValYKxUT6YLSK7y1rpNpo5LyX4VxlWslhFufLmSNg6HSpzHQ7XO253/IyUNvfw0vqiozzTQKiVAl4uajxd1LTrTU5m1ZozXF94lgD+Slj83uDxb46VgdlD+/V/chQaklGtvaqlEHDSxNmHP+aNliZn//ZtjnydwfZBF0UpGuvclN/eVKHdOubWqnb++2M+u0pbWLq9jC9s1jGLJ0c5Vb40KgUPn5/EtIQAHlh+kNqOPgQBdColZc093PPZAV7fVMxD5ybKJwRvVykT2T4tWNmil9JJipvYkNcor+rsx0KlIOWW2nUvnrYT+5qcBtbJ1jEJBHtJR+K/zx3KnrIWSpt6ePqnAj5dMp7/PbsDTCbcNCoem5PMEH9psjbM2+WUTauJokifycKFw0MI8tTx0e5yypr1vLqpmDe3lKBRKQadnj0cWpWCMG8X/Nw1tHQbKWvpkUnh2CgfXDVKdpW0DjpYE+Sh5dwUyZ9vQozfGTGZV9jQxSe7K1i5v0ZODdGoFMwdHsK1E4YwOtKbdbkNXP76Dllbe1lqGO09Rv7+bX8729tFhb+7luKmHicC46lTcemoUDna8ZrxUnzfmYQ+k4XChi4nopdX13nE9mi0v5tM9Ow/Az20MsH6eHcFT/6Qx3ybO4lVFPk+q5Zrxkfy6sZiihq72VTQOIA8KBRSNd+er22Hm0ZFl8FMdk0HNW16wnxcUSkV3H1OPA+tkEim/cRut9Vp7jaSFuXDh7uUZFV3sLmwyUk7DTA9IQClQjiip6UIvL+jjNtnxAEwPy2C/ZXtrMio4rZpMVwyKoz/+/qQU6TcMz8VnDYCqFYpef3a0Vy/LB2Auk4DXZgRkIb/PrCRvz+dE8cDcxIGEODa9l6Z8G0vah6gu40PdLfp+AIYH+2LTq3EahV56MtMVu6v4c5P9vPJkvGkRR2/ibooinQbzANaqR2HkbiOXuOAbV1HSIw6VgiCtAAFqQtjf/eCPbVMivMnKdgDbxcNni5qvFzUeLtKP71c1LhqlHQZzNzx8T5Km3pQKgS8dGrMBtMvtvU61ThLAH8lbClqG7DN20VNu0PO7/hYP5497MAHcNHwEL6wGdfGB7rLBpeNXX1stg14XDkmHKtVlFMH/Nw07Hew0rhm3JDfnImsI0ZGePPpLePZWtTMf3/M/1nrmAkxfvx47zT+75tsvrdNl4Z66+jQm8iv72LJhxmkRnrz5/MS5UqgHZF+rlzjF8k14yMxW6zcv/wg3zlUeA7vAtgPmgJSleJzm3/d4snR3DE9Fi9XNS8vTOXy13ewNreBrw/UStUgk1QVsk8a/lKYLVbqO/ucWrN2L7yadqmqd6QYNrNVxGw72XtoVUT4uhLq7UK4j/NwRZiPC4gib2wp5aPdFRht5Djcx4XOXpOTgbEdsQFuXDg8hDnJQQwP8zojPO2MZis/5dTz8e4K0sta5e1Rfq5cO34IV44Jx8dNQ1OXgbs/PSB71IX7uBDr78a3B2rkk4ZKIeDrpqGxy0C7gx2Jq0bJHdNjGRPlw5IPMjBbpe/yE5cO+1X3QWuPkbzDqnrFTd2DkiCNSkFSsIcT2UsK8XTK43WEKIq8srGY59dJHntalRIs0jHu5Q1FXJ4axrXjI3lraylvbSkdtHo0f0w4z64pcJqw7XLQv764vohn5ktat0tHhfLyhiKbwfbAY+rrm0q4bkIk72wr46X1RcxICHDa94IgkBLiQVbN4JPkACv2VcsEcO6IEB7/LoeSph4OVLUzOtKH+WnhfOowTFLV1ktpU/dpi1CblhDAOUmBbMyXMtX1RjNugkSIFQI8cdkwrrWZzfcaLewua5FIX2ETJU3O0gwvFzVT4vxlT77QQdKiFAqBp68YQbvexMb8Rm56fy9vXDcGf3ftoG3TwytynfZqXZ/5qGbyxwJ3rUquxnkfRtY8HX73dtHIBM7LVY2HViVXnxs6+3h9UzGfpldS32lg5f4azkkK5IE5CYNmIde097L4vXQKG7px1Sh57drRHFidf8IDZqcTZwngGYTEEA/2lPaffOID3MkdxNIiLcqH1zdLsW7nD+tv/35zoAaLVSQ10pu4QHe+y6yVv1Ax/q7ydKVKIXD1uIF6m98aBEFgekIAU+P8B1rHbCvlz+f1e8oBeLmqeeXqVM5JCuQf3+ZQ296Hm0bJrKRAthc3caCynWve2cPkOD8eOjeR1MiBEWoqpYKXr07FXafiM4eD/PhoXzp6TU7RRY6HMoPZyptbSvh0TwV3nxPH9ROj+PN5ifznh3z+9X0ON3kd/4Gv12ihpl1PTbud5Dnr8Oo7+wbXYTntQ2lYRCJz0mBFgIeWzKp2fjhUh9kiCZ0nxfpx7+x4J61QV5+Jd7eV8e62UrkyZLfhOLxlPjTYg3mjwzgvJYRIv9M3AXgs6OozM+m/G2T/PaVCYPbQQK6bMESOFxRFkRUZVTy5Oo+OXhMKAeIC3Sls6Hb6W+1/v2MahUap4IZJQ7hjRhwt3Qbmv7ULvdHC1Hh/nr9q5GnLa7ZaRara9E5EL7eu00nL5QgfVzUpoV5OVb0Yf7djFvNbrSL/ckiWuOecODhUQFeX9HzlLXp2FLeweHI0y3aUkV7eyr6KtgHa2UBPHbOHBrIuTyI0dhmMfRhkVWYtT18xAoVCQK1UcNfMWB6xtWFNh0ltDlS1839zh/LhrgoOVrWzvbiZqfHO09ZXjIkgqyaHI6GkqQe9wYyrVoWHTs0Fw0L4+kANKzKqGR3pw23TY50IIMA/vs3h4yXjj2m/nQy8ce1oUp9Y51TBF4C3F40h3NeVt7eWsLWwmfTyVnnRBlJHaVSEN5Pj/EmN9CbCx1WyXeo1saeshQ69iY5eM+22KlyHA5lr0xsRkBbA176754Rfu1alGFBl87IRNqdtMpnrJ3gno6gR5Knj8UuHccu0GF7dWMyKfdVszG9kY34jFwwL5v45CbJ2/lBNBze9v5fGLgOBHlqW3ThWsm5b/YtfxmnBWQL4K6Cle/ADrtrhPBDqpSO3rnOAvirKz5VaB+f0OTYhs3SCktq/88dI5O6ZNf0eWrkOGqYLhocQeIqyHH8NOFrHfLqnklc2FlPa3MMdn+xnZIQ3j56fxMRYqU0uCALzRoczNsqX+5cfJKOijQ35jZybHISfm4Yv91ezo7iFHcU7mZMcxIPnDsygFQSBJy8bTq/RIntv7Slr5cWrRjE5zp+dJc1sK2piU/5AS5POPjP/+SGfl9YXcfc5sUyO82NHsXRgdVQwiqJIu95ETXsv1W39FTvHKt7hweiDQaNUEOKtG2CLYv8Z4nXkFvNDLYk8sTqXdbkNvLu9jG8O1vKXC5K4YFgwn+yp5LVNxU7VFegXPQuCRPquGT+Ei4aH4HMEN39RFDFbRYxmKyaLFaPZisFsxWjpv2z/Z7BYMdmus28zWfpv77jNaNtmMFsxWUSMZou8zWQWMVispHQb0SLp0Zr7jAR5alk4NpKrx0XK7XqQvNL++nW2bO7sqVPR2WceYHXh+PeDdDJdkBbBPbPiCfV2obpNz6Kl6bTrTYyK8ObN68ZIFbFTAIPZQlFD94AWbtcRJseH+LlKJC/Ek5QwT5JDvAjy1J5wZdJotvLQikxWZUrfj39enMziydE8f8j5zPj0T/l896cpXDYqjBX7qnl7awlvLUob8HjXjB8iE0D7IdGe1GMwW3lvZxk3T5GGM+aNDuel9UXUdvTRY7Dg56Zx+h5+truSa8ZH8t6Ocl5aX8SUOH+nv3NBWjj/XCURQAHnhZwdy3aUcfc58YCUtPT1gRq+y6zlH3OlIaphoZ4ccjjm7ihpps9oRncaLGEAtGolL141ils/6vf8VCkVPLgi08kkG0CnUuDpokajUmC2iBTUdzl1i04UCgEifFzxcdM4ETdvuRrXX4VzvP5MSeoI93Hlv1eM4Lbpsby0vpBvM2v58VA9P+XUc+nIUMbH+PHE97nojRYSgzxYtngsYYNUSM9knCWAvwJmPbNpwDalAMWN/eX3mUmB7HaoBtpx0YgQvreZybqolSTbrFAybSa/OrWCuSNDaOjopdIWixTkqaWh0yAfzK6feGJZs2c6tColiydHc+WYcN6xVaYyq9q5+p3dzEgM4BEH65gIX1c+v3UCr28u4aUNRazNbSDM24Xn5o9kW1EzX+2vZl1uA+vzGrhkZCj3z04gyr9/OlapEHh2/kh6TRZ5aOPBFZl8sHicU1xdSVMP6/Ma+OZADQX1XfLJpMdo4emfClEgVWRNVisaAdr0RuY8v4Wa9t5j0t95aFWE+Ug+d4cTvHBvF/zd+9M5zBY7GbJisEiEqKa9t59kWSwOhEnaduHwYCJ8XPj2YC3N3QYeXJHJgzaN1WBw1yoJtT2v2SrKlikmizNJs5Mxo8V6TEMkpwKJOitaQSLJb143mllDg5wqCBarZNj97JoCuX1mFQf3IzwcFw4P5oE5ifIAUHO3gUVL06nv7CM+0J33bhx70rJJ2/XGAVW94sZu2TfUERqlgkTHFm6oJ0nBHsc0BXqs0BvN3P7xfrYWNqFSCDy3YOSAfFoXtRL6ILumg5zaDm6dFsOKfdWszW2gpKl7QOb2tIQAgj201NsqqyqlgNki9g+DbC6RCaDapgW0x2f6uzsTwG8O1vLDvVP4ZE8lGRVt7CptcZJ9uGhURPi4UNXWOyj5A8lqy04AJ0T7yS4BP+XUcXlqOA+cm8BN72fItxdFaQDjrxedPoufc1OCmRLnD1JdAJPFSscgn90+s5W+I/QrPXUqvFwHtkwdydzh2wxmK4uW7qG2vQ8vVzUfLxl/RInAbwHR/m68uDCVO2fG8cK6Qn48VM83B2vlxX/aEB+WLR77qw+tnQh+u+/KbxjthoG6q+Hh3hysapcvT4r157VNxQNud35KCO9uk+xfxkb7yif3L/dV2a4PxlOn5j+r+z0GlbbVrQgkBXuQNmRga/P3BA+dmgfmJHDdhEhe2VDMZ+mVbC5oYkthE5ePCuP+OQlE+Eqi8XtmxTM13p/7lh+kokXPvcsPcvv0WFbfM5VXNxWzOquObw/W8n1WHQvSwvnTOfGyDsbeDr7lgwy2FjVjsYos+XAvX94+iWE2bVtcoDtxge7cPj0Wo9nCV/tr+HBXOfl1Ehm0IrXK7DCYrbL9DEi6MXetCleNEheNEq1KiUalQK0QUCkFrKJUbWnTG2ns6mNPWYsDwbJVvmzE6xfKa44J3QYLhQ3dg1bHjgWCjZBpVAq0KoX8u9r2U+OwTeOwTe2wTTvItsNvr1EqyPg+D0OvCV83jZOUAqRBkIe/zHL6Ttr3n32K3g5H25Gp8f78+bxERjjkcnf1mbhhWTplzT2Eebvw0c3jj1gRPRpEUaS6rZccR7JX2+E0YOIIb1e10xRucogXMQFup1T729ZjZPH7ezlY1Y6LWskb141mRuLAJBMPnRpVj4DZKvLYtzmsuGMSs4cGsj6vkXe3lfLUvBFOt1cqBK4aF8lLtiEGs636Z9/vTd1GthY2yZZYV44J54V1BTR1Gyls7HaqAlpEkdVZdVw9NoIPdlXw0vqiAbrf84eF8M42yb/x8FxgkNrX3QYT7lo1CoXAlWPCeXF9ESsyqrk8NZzpCYG4a1VOXp0f76k8rQQQ4O3rx/CvpzYCoBAEkoI9CPdxwdfN3lJ1GGw4rBrnoVOfsDzho5vHM//NXWRVd3DbRxksu3HsKat2ny4kBHnw2jWj+fOXWXy1v1renlndzrNrCrhrZtxxeSmeCThLAM8QJAS5O51sEoMH6v+i/d0wW61ym+lim/1Ln8nCKrv335gIrFaRbw5K3n8COJ0gbpgUdUaI7k8HAj10PHHZMG6aEs2zawtYnVXHygM1fJ9Vx3UThnD3OZJ1TGqkD6vvmcq/vsvhi4xq3thcwvaiZl5cOIo7psfy3NoCNhU08Vl6FV/tr2HRhCHcOSMWP3ctWpWSt69P4/ple0gva6PPJK1+V909xcmcG0CjUnL1OKnF2NVn4oOd5by1tfSoE2x6o+WYKoEnAo1KgfYIBMtottBlMNPSbeBoVlYapcDUhADGRPqgVSvRKAWHx1GidrisHYyUHUbQTqdZbNZPCg6vexjMFl7fVMyrm0oGCNJ1agV9pv6KpX1a1CrCyHAvHj4/iclxzkSiz2RhyQcZ5NR24uem4aObxzm1l48EuYXraKRce+QWbqSv64Ap3BAv3Wn9rte293L9snSKG7vxdlWz7MaxjB5ERwsSqbp6XCQf7a5gb0Ubla093DY9lvV5jXy1r4b75yQQ6OG8nxaMjZAJIDDAtPmJ1bmsS5gOSJ/te2Yn8PdvDiGKUhyjowfq0u1lrL53Cp+lV7GnrJXdpS1Obgo3TY6SCaBGpRh0WOrdbWXcNzsBgCtGSwRwZ0kLVa16InxduX1aDM/ahl9A+i7/kF03aBbxqYKrRkWgh5auLska7Kf7pp2W540NkKrcV7+zmx3FLTywPJOXr049bXrXU4E+k4U/f5nFdzZZw1VpEVS36dlR0sKHuypYvreKGyZFcdu0mF/5lR47zhLA04xV+yoHv8LhXOPjqh4wjQXSxODanH5TVPvKem1uA519ZsK8XZgU68dPOfXyAcvHVU2rzXfNw2Y/8UdDtL+bFF00TbKO2VnSwrIdZZJ1w/QYbpoSjbtWxf+uHMnMxEAeXZlNdk0Hc1/ezt/nJrPsxrHsq2jjf2sKSC9rZen2Mj5Pr+SmKdEsmRqDl4uaZTeO45p3dpNV3UGb3sTCt3ez6u7J+LlrB31NHjo1d58Tz10z49hU0Mj6L7IGFxsdAwTAXafC27ai93XT4Oemwd9DS4CHliAPLUGeOkK8dPi6adCqlagUghM56DVa2FrUJLW9c+udplftUAhSNOFNk6NBgMdX5VLQ0MWGvEYaOvt4/JJhAwT8vyXsr2jl7k8POC2YlAK4aiVbH/t3SqWQKlcWq0hsgBt/PkJOtdli5e5PD7CnrBV3rYoPbho36CRoh94kEb26TinGsfboLdyEYHdZr5cc6kVSiMev3n4qbuzm+qV7qO3oI9hTx0c3j/vZWLH/u2goy/dWYbRYeeTLbD69Zbzsq/f+jnIePj/J6fZh3i5MTwiQoyyNFmdSVtTQTWZVOyNtiTYL0sJ55qd8OvvMHKrpkLWbIEkwdpW0Mj8tnE/2SJGTjgQwxNsFLxc1Hb2mI07Kf7mvWiaAEb6usp73y33V3D8ngWsmDOG59YVO1eL//ph/Wgngr4mREd68tWgMN72/l9XZdfi6afjXpSm/yQJEW4+RWz/KYG95Gyrb1PMVY6SghZ0lzTy3tpB9FW28vbWUT3ZXcJXLL7OlOV04SwBPM+5ZkT1gm7+7hszqdvnylHh/2f/PEReNCOGuT/YDEOKlI8BDIhf26LcrRoehUAi84rBK7nAQ6c8fE3FacinPVIwI9+aTJePZZrOOya3r5Nm1hXywq4L7ZsezIC2CC4aHkBrpw4MrDrKjuIW/fp3NpoJG/jtvOMtvncC2omaeWVNAdk0Hr2ws5sNdFdw+PZYbJg3ho5vHc+UbOylq7KamvZfrlu7hqzsmHXWfC4LAOUlBHHSXVunBnjqK/34BG/MbWZFRxdaiZqfBAju5UwgCrXojLd0GrKI0ydrVZx7Ua88RGpWCAHeJGHq5qOkzWWjs6qOqtXdQwgGShdAt02K4emwkXq79RGP1PVP4eHcFz60r5FBNJ1e8sZMrRofzyAWJA6o3ZzJEERa/l+5kAKwQpPZ7t8EiV2jtrV+zVSTUS8d9cxKOGCZvtYo88lU26/Ma0KgUvHtDGimhnlQNYqRc0z74e+bloh5Q1YsLdD8j7Jv6TBY6+0x09prZX9HGv77Podtgwd9dw7zRYXx9oEa+vqvPJE+SjukyYJfJ69RKrhkfyfs7y9lV2kJtey+3T4/l1o/28dHuCu6cGTdAO3b1uAiZAJosIgHuGpq6+/V9//khj+W3TQQkTfBdM+N46sd8mruNzB8Tzop9/a27VzYUsfy2CSzfW8WO4hYyylud/OumxvvLht6H28kAVLf10tVrwsOWtzt/TIRMAO+dFY+vm4ZZiYGst9mxAFS2Slndx5O281vG1PgAnl8wins+P8BHuyvwd9dy7+z4X/tlHRcqW/Tc+F46pc09eOhUvHXdGCY5VPonxfoz8XY/thQ28dzaQrJrOuhWSNY7R0oKOVPwx2UDZxBmJQWxYl+/bcC0+ADe3VbqdJuYADfCvHWU2SK07AamdR29bCuSDohXjAmnsbOPPAcrEsfP36Lf6fDH8UAQBDmq6LssyTqmqrWX//v6EO9uK+PP5yVywbBgPrppPEu3l/HMmgLW5TZwsKqdZ64cwYzEQKbG+7Mmp57n1hZS1NjN0z/lS1OBM+P48KZxLHx7NxWtevLquljyQQYf3jTuuFqbKqWCc1OCOTclGL3RzNqcBlYeqGF7UROtPUZae4yolQIzEgO5bGQoo4Z406E309RtoKnL4V+3gaauPpq6DDR2GejqM8uDH0ciHY5QCJIlwhA/V3JqO3lpQxEBtqpigIeWAHctF40I5YLhITy3toAvMqr5an81a3PquXd2PDdMijojyMrPoaGrj01N0ndIIUhDBAazlW6Dc+tdFKWK+l0z47huwpAjTiuKosgTq3P5an81CgGmxvnzwrpCcus6j9juj/B1sVX1vOThjNBT1MIVRRG9sZ/AdfaZJJJm+73TZqzreH1nn5muXpP8u/EIuoDmbqNsUTUYUnVS4k2fzaT5/y4cyqd7KjFarDy4IpNPl0wgJsCN0qYePk+vZMlU53barKFBh+n5nB9/T1krh2o6ZL+2GyZF8cK6QvrMVoqbJJ82u6SitqOP4qYerhwTzud7q3h5YzEf3jROfqybJkfLBNBLN5AAAry9rYQHz5UqleelBOOhVVHT3svu0hYmxflz1zlxTgQQ4B/fHuKDm06fJcyvjYtHhtLaY+Sfq3J4YX0hvu4aFk34bZyLDlS2seSDDFp6jIR5u/De4rGDxqcKgnQ8np4QwJqcBraulDo6auWZXe08SwBPI8QjjDtGB7g5iYyTgj0oOExEP3d4CLtKWuQuoT3+beX+GkRRyrAc4ucmT76BVO2xH6inJQQQ7f/HWHUeCxQKKQrtgmEhfLqnglc2FlPW3MOdDtYxt0yLYXKcP/d+foCixm5ufG8vN06K4tELkjh/WAhzkoP59mANL6wvpKq1l3+uyuHtraXcPCWKt7aU0NAlRco98EUmLy0cdUInc1eNistSw7gsNYzGzj5WZdaycn8NuXWdrMuVkkY8dSouGhHC5anhzEsNc4rTslpFMqvbWZvbwJqcekoHkRYcDvu0uFWEuo6+I/rEyftSAD93LdH+bjR3GegymHlydR5vbinh6rGRTI73J9BGGt21ql+lBdTRayTdpvXKqe2kokXPVEMfbg4vxa7pGyy/002jZMnUGJZMjR4wMdvRa3IyUt5a2CT7AFpF2OBAANRKgfhAD7mqlxIqGSl7uRx7C9dqFekySBW1wapsEqE77HqDI9n75Ya74GyR4qZRkhzqKQ0V6NR4uqjw1Knx0KnwdFHL29YtzwGTiW6DGatVRK1SyFXA3aWtkhZwWgyPfJXN0u1lXD8xysmmSK1UMD8tgje3SCSztcdIjL8rpc16+TYvbyji7eslKxmdWsmCtAg+3F3Bwcp2bpg0hPd3Vsi3fWFdIa9ePZoV+6rZWtjEgco22f8zNdJb0sNarFS26eXWvyO+2FstE0AXjZK5I6WElxX7qpkU58+oCG/CvHVOEYDbipoxmq1nROrN6cINk6Jo6THy8oYi/vHtIXxdNVw04sxuhf90qJ77lh+gz2RlWJgny24Y+7P2aYIgcP6wYHLXSh2dMx1nCeBpxG0fDDTHVCoEeTUMUttpsNzZC0eE8OoGaSpYpRAYE+Ujm9OC5JZvtYp8e6BGvo/jKv3638iK63RDo1Jw4+RorkyL4J2tpbxzmHXMw+cl8d2fpvDUD3l8sKtCaleVtPDiwlEMDfFk3uhw5o4IZXlGFa9sKKKmvZd/fZ/HEF9X3DQWeowWVmXWEuyp/cUTgIGeOhsJiaGgvouvD9Tw7cEa6jr6+Cy9is/Sqwj3cWHuiBAifV3JrulkQ16DkymxQpAsOAaL9JoY48vf5iaTHOJJZ59ZriQ22qqIh1cYm7sNtPQYsYrI2xzR3G3klU3FvOIwza5TK+TqYX8lUedcWfTQ4u+uOe6pQVEUqe/sY0dxC/sr2siv76SqrZd2vXHwVsxhx3KLVcRVrUTv8H1UKwWumyBlTfu5aajt6GN3aaus1cut6xz0+wqSoW1qpHd/VS9EauEKAk4ELbu6w0bgnEmaI4Fz3HakQZDjhUoh2MhZP0nz0KmOQuD6L/+QVcu/f5B8Ri8aHsLzV408pvdrl1ZFt0lqo6/NbeD8YcE8cn4in+yuwGQVeWhFFp/cMp5n1xZS19HHd5m1stbKjoVj+wkgMMBOZ21uA/n1nbJ/56MXJPHxngqsIrT2mJyIXFZ1B10GE/NSJR/CVzYWs+zGsYB0Mh8V4UV6eRsikBLmSWZVh9NzNXQZaNcb8XaVproXpIXzWXolP2TX8filKXjq1PzpnDgeXXlIvo9VhBfXFw7QOP7ecf/seFq6DXyyp5L7lx/E21U9YGjqTMGy7WU8sToXUYSZiQG8es3ok2bbdCbh9/cXncFYmz9Q1zc60pvdJf3bx0b5squk2ek2sQFuJAZ5sN22fWiIJ1qVkozyVspb9LhqlFw4PIS1ufWDntjDfVyYmTTQiuEs+uGuVXH/nASumzCEVzYW8emefuuYy0aF8cCcBGYkBvLnLzMpaOji0ld38PD5idw0ORqNSsGiCUO4cnQ4H+0u5/XNJVTYoqjsFhJvbysjyEsne5X9UiQGe/DoBUk8fF4iu8taWL63ip8O1VPd1subW5zlA1qVAj9bNJnZKg74jIwd4sPDFyQx1kH/ZPf0svvYHQlmi5XWHiONhxHEmrZedpY0U96id7p9n8lKVWsvVa0/34L2clEPIIuBHlo8dCpMFpGadklPVdGsp77TQGef6WetbhSCZIdxeCUnPtCN8ha9TP4EYNbQQMZH+1Hf2cefPj1Abl2nk6bWET6uUiaovdKTEOROUrAHnX1mDla1sbWoSW6zOobP/xLo1Iojk7QjkTiH33VqxXFXY0VR5KUNRby4XtIZXzchkscvGXbM052OT/fapmLOSwnCRaOS49MyKtooqO/ipsnRPP1TPm9tLWHe6DCn1xnl78bEGD922XTS2TWdpEZ4c8DBReHVjcW8es1oQBrimRrvz5bCZtbk1HPF6DCWZzhrAR+9YChf7ZcSH7Kq22Ubn6vHR5JuizMM9Bh8oOvNLcU8eoG0uBsVIaUwFTd2831mHdeMj+TSUeH8/dscp0XIBzsHDrn83iEIAv+6dBhteiM/ZNdz64cZfH7rRIaHD4xX+7VgsYo8uTqX93aUA3Dt+EgevyTltLoTnE6cJYC/Ms5PCebpn/oTO2YNDeStwzQ0F40IpbJVT7ttmtdeOrcnf1w0PAQ3rYqXNwz0DQS4bsKQ3/T4/elEgIeWf106jJsmR/PcukK+y6zl6wM1rLZZx3x+60Se+iGPDfmNPLk6jy2FTTw7fyRBnjpcNEpunRbL1eMiWbq9jHe3lTn5gD3xfR4BHtqTFgxf3aaX28B7ylqP2NYzmK3yZKtS6NdNJYd48ufzEwfkoR4PVEoFgZ66I7ZGcms7+eeqQ3ImcJi3jhsmRRHp63qYVtFZu2iyiHLEVHHj8XsKKgVQKgUQ+xMjQCLjVpsUQ2H7mxWCQJGDCbvK1gpen9fI+jxn/daR0KY30abvJ4fH4oXorlXhqZPixOxkbXACN/B6D53qtPuqWa0ij32Xw4e7pBbqvbPiuW92/Al9dgQkE+gthU3MSAzkwXMT+XxvFVYR/rIym0+XjOe1TcUUNnSzuaBpwAJ24bgImQCCNIXrSABXZ9Vx3+wu4gIlvdbf5yaz5fmtGMxWfN21Tu3rtbkN/POSFC4bFcbKAzW8vKGYd2+QWsgXjwjlgeWZiMDO4hYCPTQ0Htba+zy9WiaAgiAwf0w4T/2Yz4p9VVwzPhIXjZKr0iL4eE+/A0SP0cL63HpmJwcf9777LUOpEHjhqlG06/eys6SFG99LZ8XtE09bTvLR0Gu0cN/yA7Kx/6MXJHHbtJjf5NTyseIsATxN6DhCbFeUv5vTCSohyIOawzRXc0eEsMHhRDQnOQi90cz3WXbvP2n4Y7DcYI1KwYK0337u7+lGlL8br1ydyq1TY/jvT3nsKJasY77IqOLWqdFMjvPjf2sK2FbUzPkvbuWpeSM4f5h0MPfQqblvdgLXT4zizS0lvLe9DJONnN3z2UE69Saumxh13K9JFEVyavu1f4e/33GBbsT6u9PQZSCzqn1QVxmLKBGPxZOjuPeceFSnWIeUHOrJF7dNZFVmLf9enUdNex//+SGfWUmB/OWCJASFQHlzD2XNPRQ1dGMVO+k2mDFZflmb0yKCxTxwDwi2/0Q7ERT6CaEdR5qGdoRCQCZmKoWCipYerKJUbZ89NEjOJj2c4HnZCJy7VvWbqioYzVYe+OIg32fVIQjw2MUp3DAp6oQfz1Wjgj6pUjc9IQA/dy0Xjwzl24O15NR2sr+qnWvGR/L21lLe2FIygACelxKMl4tKjjVbk1PPzIQANtkmhEWkx35xYSoAcYEeRPu7Udbcw6d7Kpgc58f2YolAWkV4e2spd50Tx9cHa1if1yAPkqiUCmID3Clu6qbHaOGa8ZG8YzPit6O910RrjwFfN6lCePnoMP63poADle0UN0okdMnUGCcCCPDk6rw/HAEEaTr7rUVjuPqd3Ryq6WTR0nRW3jnpVzVRbu42sOSDDA5WtaNRKXh+wUjmjvj9W6adJYCnCTOf3ThgW6CH1inXV60UaO1x1lHFBbqTEOTBo19lAZKXX4y/Gyv319BjtDDEz5Vx0b787ZtDDIZLRobiewKpA2chYXi4F58smcC2oib++2M+ObWdPL9emoa9bVqsTMRu/3gfC8dG8Pe5ybJWxNdNw18vHMrNU6L52zfZrMuVSPzfvs3hh0P1PH5Jys96pZksVtLLWmXS5zi9qxAgLcqXkeFetPYY2ZDfSHFjg3x9QpA77T0mGrsN8u2tInQbzLyysZgv91Vz6agw5o0OG3Sy7WTAYhWpbe/Fx1XDLVOj5RP8hvxGp+GIn4OHVoW3rc1qtoo0dBqcqquOOFJ+K/btg1ypFKT3K9BTR4iXC1F+roT7uODlqsZDayNzDi1VN40KhUKgqKGL+W/twipKtiHv3pD2m088OBw9BjO3f7yPbUXNqJUCzy0YxSUjf9nJ0U2rlNJYKtrYU9bKhBg/7p0Vz7c2Q/vHV+Xy8ZJxvLejjPSyVvZXtjmZSuvUSq4YHcGyHRIZM5itjIjwYnNhk/z2rsqs5d7ZCfLw250zYvnzl1l09JqZEOsrE0CAT/dU8uC5iVw8IpRVmbW8urGYNxeNAeDiUSG8sE5qeasVwoAkGICX1hfx+KXDAMmAfmZiAOvzGlmRUc1fLhxKlL8bI8K8yKrp1xCWt+ipbtMT7uNsGP9HgIdOzfuLx3HlGzspb9Fz/dJ0vrhtopPN1OlCSVM3i9/bS2WrHm9XNe9cn+Ykh/k94ywBPE1o7R2o+zk3OYidxf16v2FhXqzcX+N0m4uGh2CyWMm2HTgmxvghCILs/Xfl6HBEEb454Hw/O36vub+nG1PjA5gc68/32XU8u6aAylY9L20oIsrPlTlDg1if38Dne6vYXdrCSwtTZTNakKxU3rl+LMvTK3lkpTSlvbOkhXNf2Mrlo8O432Yma0e3wcyWgibW5dazMb/RKXtWp1YwLV6ysek1Wfg+q86pIhHipSMtyof8ui65BWm3LrlmXCSHajv5+kA132fVUdfRx5tbSnhzSwkpoZ5cnhrGJaNCj9vDTxRFGrsMlDb1UN4iVfPs/ypaeo7LC0unEvBy0aBTKzBZrHQZLHT3mekymI95+OFYnk0p2KqBQIC7lpLHLjrm12hHdZueRUvTadebGBnhzZvXjfndkb9WW7RbZlU7rholb143Ro5b+yVQCIJswvzapmImxPgRE+DOzMQANhU0Ud7Sw86SFi4dFcaX+6p5e0upTMjsWDiunwACLN9bzUUjgvk+SzLLt4qSzvDZ+SMByY7kb98cwmC28vHuKmID3GTDfYPZymd7Krj7nDi+y6rlp5x6eZDk+olRMgFclVVL2hAfWdJgx4p91TIBBCmRaX1eIysP1PDn8xLl2MklH2Y43e8f3xxi2eJx/BHh767lo5vHM++NnRQ0dHHzB3v56ObxuGhO33covayVWz/KoF1vItLXlfcXjz0j2tGnC2cJ4K+IWclB3PvZAfnyuclBvLLRWcd30YgQMqva5ZPopaNCqWrVs6u0BUGAeWPCWXeE4Y+REd5OmaRn8cugUAhcMjKU81OC+Sy9kpc3FFHeoqe8RU+svxttvSbKW/Rc8cZO7psdzx0z4py0l1eNi8RVo+JPn0vvuYhk4/NdZi3XupkQgDa9kdH/WueUcuDnpmHW0EBmDQ1Cq1TwzcEa/v1DnmxXolYKzEkOYnSkN2tyGvguU/IuG8y6ZFy0L+OiffnnxSlszG9k5f4aNhc0klPbSU5tJ//5IY8p8QHMSw3j3JQg2cRaFEXa9CbKmrspa9bLbduyZon0nay4uj6zeMRg+iNBpRBQK6VkE6PZOqCFq1YIJId6Mi7al6nxAYyL9kGnVvH88wV0dZlOSB/b3G1g0dJ06jv7iA905/0bx/7upgRr2nu5fukeSpp68HZV896NY2WLlBNBa4+Rzj6TTLxvnx7L53ur2FbUzMGqdkZFeHPnzDjZkPvZNQW8e0MaX+6rZk1uPaVN3U4n54QgDzk5BKC+s4/ZQ4NYnVUvLwJW7q/mnnPiifRzRadWMm90GJ+lV1Hf0cfNU6KcEpfe2FLK4snRXDgshNXZdbyysZjXrhmNj6sGf3cNzd1Gqtv6eH7+KBa8vdvpb9MbLTR3GfC3DYqckxSIr5uGpi4DWwqbmDU0iJlJgXjoVE5ekJsLmzCbradcinGmIsLXlQ9vGseCt3aRUdHG3Z/u561FY06LPOK7zFoe/CITo8XKqAhvlt6QdsTkpt8rfl9HrDMUaw4OrM6pFAJRvq5O1Z2kYA+nE2lCkNT+ffy7HHnblLgAedU7OdafMG8XbvnAeVVpxw1nq3+nBBqVghsmRXHFmHDZOqbEZtAd4K6lqdvAs2sL2VrYzPNXjXRq8Vw8KpSOXiN/+1Z6T+0+Y3qjBTdBqkQYLVai/d2YkxzEnOQgQrx0fHOghn+vzqOytX+qNjHIgwVjI0gO9uDd7WU8uTpffn2OecWDQaeWJscvHB5Ca4+R1Vm1rDxQw4HKdrYWNrG1sAmVQsDHVY0gCHTojRh+JVd7V41SjrKL9HWVE0xq2ns5WNVOc7fRifRpVArGRPowIcaPCTG+jIzwPqJp84mgq8/EDcvSKWvuIczbhY9uHo/P70xmUdzYxaKl6dR19BHiJUW72QcqTgTbi5p54IuDzDD1f84jfF25bFQYX+2v5tWN0uBF2hAfRoR7kVXdQWOXgW1FzcxKCmRDfiPvbCvjqXnDnR534bhImQACfL63ivOHBfHjIUkKYRXhjS3FPDVvBADXjBvCZ+mSddbG/CaneLjWHiM/HqrnT7PiWJ1dxw/ZdRQ1dBEf5MHsoUF8vle6X1Fjt5wL7Ygnvs/lpaslzaFGpeCyUWGybnjW0CCUCoHbpsXw7Nr+fGCrCC9vLOKBcxNPeN/+1jE0xJOlN4xl0dI9bMhv5NGV2Txz5YhTNnwhiiJvbimVhy/PSwnixatST1rlsddooanLcLjL1BmJswTwNOCOzw8O2DZmiA/Ztf0ifoUA+yuc2woXDZd0NuvzpIPZEF9XPHQquf07Py2cpq7Bhz983TQnnDl5BL/qszgMjtYxr24s4pM9lTTZ9HYqhUB6eSsXvLiNJy4bxmWp/ZO/102Moq3XxHNrCzFarIR46dBaFGCRtG7r75xGhI8rG/MbeW1TMVsLm2R7E3etiotHhnLV2Ai8dCpeWF/Ekza/KqVCmkC8Z1Y8od4umC1Wmrsln7J225Rqm95Ic7eBihY9NW29NHX10aaXPOYMZucqntkqOsVsHStUCmkaUhQlDeDPfZzsAxWiKMonY0+ditumx3LDhCE0dhvYVdrC7tJWNuQ3DvAbPNWEzxF9JgtLPsggp7YTPzcNH908jmCv38Kh/thxoLKNxe/vpV1vIjbAjY9uHk+ot8vP33EQGM1WnltbwFtbJWsiwbaremzt/DtnxrLyQDXr8xrIq+tkaIgnt0+P5U5b5OXrm4t56apRbMhv5Kv91dw/J95JojB3RAhPfJcrywN2l7by5e0TZQIIsCKjirtmxhHu48qwME8SgtwpbOimrLmHWUMDnQbsXlpfyLoHpnN+SjA/5dTzysZiXr46lZumRMkEcOX+ahakhfPhLuehjh8P1fESqfLlBWPDWbajjA15jbR0G/Bz17JwXCTPrXPOB162o+wPTQBB6ky8es1obv94H1/uq8bPXcNfLhh60p/HbLHyj1U5fGobyLlpcjT/d9HQk+aS8UN2LX/69ABXaKUBs84jJP+cKThLAE8DBgtNumBYMHsc/P5iA9xZeZiO76IRwXToTbJn2pzkIHaXtVDT3ouHVsV5KcE8uTp30Oe8amzECZ0Euw1SpJgL0NhlYNHSPXIOaUqoF9H+bmctZQ5DgIeWxy8dxk1Tonl2rWQdY7aKCECXwcx9yw+yKb+Rh85LoF1vorqtF1eNkmFhnhyq6aSuow+jzopKkAjTKxuK2FbcQqvD5PiYIT6cmxzEqAhvatt7+d9P+ewubZGJYaiXjhBvHbl1nVz19i7aekxHHJI4GdCqFOhUCsxWKVbMkeRJnemBtE+rUuDvrmGInxspoZ6Mi/ZjZLgX/u5aOb1kc34jf/v2ENVtvTyzpoAX1xXKE9R2aFQKRkd62wifH6NOIeFzhNli5e5PD7CnrBV3rYoPbhr3u9MLbSls4vaP9tFrsjAywpv3bhx7wkNkpU3d3PP5AQ7VSAvUa8ZH4l2ST2+PCaPFKk/aXjg8hNVZdby2SfLuOy8lmHAfHdVtffQYLGwraiY10psDle18sLOcP5/X75/nqlFxyahQPnGYsP36QA2TYnzZWdoKSJ/HN7eU8ORlwxEEgYVjI/nX99Jxs6JZ7zQ0VNzUw97yNv40K46fcur5LquWe2bFkxDkiYtaQa/JyoGqdj5YPHYAATRaROrb9QR7SxX/pGBPhod5kV3TwTcHa7l5SjT+7lpmDw1iXW4/Qe02WNhc0MiMxD+2V+uc5CCemjech7/M4q0tpfi5abh1WuxJe/xug5m7P93P5oImBAH+MTeZxZOjT8pjW61WFi1NZ0eJs9evp+7Mplhn9qv7HWNGYiBvb+037D03OcgpQzMxyIO4QA++z6yVt10wPIRPdkseXHNHhqJVKfjaRhod/d0UgmRgeSL4LrPWyR5jW1Ez24r6iaqLWsnQEA+ZEKaEepIQ5HFaTsBnCgwmC9Xteqpae6lt76OuQ6qkNXcbadMbifBxoaHT4KTj+zazlm8d3svDYT8B6U0WvrVp+KB/onVfRRv7DqsQO6K2o0/2+jsWKIT+6LMjuZ4oFQIapQKz1TpgkMNgtg4amWZHgIeG2AB3RoZLRG30EJ9B485EUaS8Rc+ukhZ2l0r/HJNL7OQv0EPL5alhzEwKPG2EzxFWq8gjX2WzPq8BjUrBuzekyXmzvxesyqzlwS8OYrKITI33583rxpyQrlEURb7IqOKxVbn0mix4u6p5+ooRnJcSzPPPr5Nv99bWUl65OpW7Z8axOquO1dl13N/UTWyAO7dMjeWfqySZxCd7KvnnxckcqGzno10V3DEjDneH13X1uEgnArhiXzXf3DmJC1/eLm9bnl7F3TPjCfbScVlqGP/5IQ+zVaS4qZv4QHeKHLwmX9pQyCdLJjB7aBDr8xp4fVMxz181ivHRvmwubMYqwp7yNnxd1bTqnY3BH/wyi0+WTJAvz08LJ7umgxUZVdw0OQpBELh9eqwTAQT413e5f3gCCLAgLYLWHiP//TGf//yQj5+bdkASzImgobOPm97fS05tJzq1gpcWpnJeysmx4Mmv6+Ty13fQa5MEKATwdlFj6hvcNP5MwlkCeIpx18d7B2wL9tTi665xOmG7qJVONRO72fO3mRLB0ygVxPi78sMhiRzMTwtnfW4DPbbAesfz86yhQSdsLfBpeiX2wrtCEPB312CxihgtVnqNFnpNFvZXtjvpbgQBgjy0hPu4Eu3vRnyQB0NDPAjw0OKmUeGqUeKqUZ1Q8sCphNUq0thtoKqlh+r2Pmrbe2ns6qO5y0Cr3kSH3khnnxm90UKfyTLogMGJQq0U0KgUuKiVR63U/ZJnUysEPHQqlAoFeqPZaVDIKoL1ZzR9FqtIr/XYhjsUAqSEeXHpyFCuSovA4wjZtnbCZyd7u0tbaOg8rKWrlCLUhoV6kVffyc4SiRQuz6gi0s/1tFs0iKLIf37I46v91SgVAq9dM5oJMX6n9TWcanyws5zHvstBFKW26vMLRp1QVm2H3sRfvs7ih2xpEndSrB/PLxg1aJt8dVYtD5+XyNAQT2YPDWR9XiNvbC7h2fkjmZ8WzvPrCunoNWG2iuwqbSHG343S5h4+T69kydT+RJ1hYV5yNR2ktvPOkhbig9wpsk3Cm6wib24p4bFLUvB10zAnOYgfD0mvUW90/v7tKG6hrLmHe2bFsT6vgW8O1nDPrHiunxjF5kJpMbwqs5b7z03g79/kON1392EVoEtGhvLk6jzy67s4VNPJ8HAvRkcOzAcube6hrr2XkBNstf+ecNu0GJq7DLy7vYyHv8rCx03NOUlBJ/x4BfVdLH4vndqOPvzdNbx7w1hGObg0/BL889tDfLCrP1s63MeFn+6dxtuvF2A69vX4r4azBPAUY/WhgV5nc5KDOOBAoAB+yql3unzh8BBEUWS3rY0xIsKLNTkN9JmsxAa4kRrhzdxXpBWuWik4VWhO1Polr66T7OoOhtqO1VZRpPkYNGCiCPWdBuo7DWQcpUolIAn6XbX9pFD6Kf1z06hw0Shx06pwUStx0ypx0ahwk29z2P20tt/VSnqMZipb9VS16Knt7KOho4/GLgOtPUba9UY6es30GM30Gi0YzRZMVvGkaB3tVTKdWoGrRkpo8HJR4+Oqxt8WYdbYZWBNTh3N3f0rwlAvF5bdmEZsoAeiKPLYf7LAbEKrUjA5zo+qVj1NXQZ5VXk0CPQTSqsVOc7MZBUHVCiOBoUAKoVkvzLYrnHTKJ0qv35uWrJq2ll1sJbS5h6yqzvIru7gzS0lXDwylHmp4aSEelDR2ntMhM/e0k2NdK7w7S5t4Z/f5lDQ0MX/fX2Iz9IrefySYYwZcuITqceDN7aU8O52afDq6StGMCf5xE9GZxpEUeSF9UW8vEGyObl+4hAeuzhFbskfD3aXtnD/8oPUdfShUgg8dF4it06NGfSxtCoF1j5Yur2Mxy5J4a6ZcazPa+TrAzXcOyueCF9XrpsQyWubpK7ID9n13DEjljc2l7B0exk3TIpC7TApunBsJH+r6fdCfXdbGY9dnMztNi0hwKd7KrhzRiyBnjoWpEXIBLCmvQ9XjdJpAO/1zcU8c+VI2ZbmtU3FPH3FCLnTsi63gefnjxxAAC0iFDd0EWfz1fR21XBuchDfZ9WxYl8Vw8O9EASBP50Tz6M2Syg7/v7tId69Yexx7/ffGwRB4K8XDqW1x8jKAzXc+cl+Pr55PGknsPDbUdzM7R/to8tgJibAjfdvHEek3y/3XWzrMXD+S9ucjmV3TI/hkVOgWzyVOEsAfwXMSQ5mT2l/WzXMW0d+Q5d8OSnYg7hAd0qaumXLgItHhDgMf0TQ0m0gp3bg8EeMvxuTY08sYNveXrZDo1Tw9qIxqFUK9AYLeqNUDZP+Sb/3GMy09hhp6DTQqjfQ2Wumx2AetFImIkUgDWZZ82tDEKTBDY1SgYtGibtWhbeLBh+3fiIX7KUj1EtHhK8rET6uuB5He+yfF6fw+d5KnllTQFefmYpWPbOf32rzCxRJcJiO3FHcMuD+CkHy8xNFkW6DxSk9RkTSHxktP79fPXUq3HUqBAR6DGbaHbJtrSJy29rHVS0RvTCJ8A0L9STKz23AyfycoYHcOyuezOoOvjlQw6rMWpq7jby3o5z3dpTLbWZHaJQKRtkI38RBCN/hmBDjx+p7pvDx7gqeW1fIoZpOrnhjJ1eMDueRCxKP27fwePDpnkr+91MBAH+7aChXnoR21JkCi1Xkn6sO8fFuqX16/+wE7pkVd9xVepPFyovrC3l9cwmiCNH+bry0cNRRLajcbPZCy/dWce+seFIjfZgS58/24mbe3FLCvy8fzg0To3hna5n8mdxb2oq/u4a6jj6+y6xl3uj+9+LSUaH8e3WenLNc39mHQiHgoVXSZeuSGC0ib28t5W9zk5ka70+gbXEGkr+mIwH8en8Nf7lgKPfMimdTQRMrD0hVwKQQT3JqO9EbLeTVdxHh40JVm3Ou9Z8+P8CP906TL89Pi+D7rDq+PVjLXy8cik6t5JJRofz920NOC/eN+Y1/aEsYRygUAk9fOYI2vZFNBU3c9P5eVtw+icTgY59E/3JfNY9+lYXZKjIu2pe3F43B2/WXT+t/vLuCv39zSF4ku2uVfHf3FKJ/g3rgswTwFKJrkOqLWiEwLtqXp37oH94YGe7ND4f6K4AX2aZ31zpUBROCPclYlYtCgMtTw3jZ5hcogNNBZNHEISe0eu8zWVixr9ppm9Fi5S8rs3lq3nC5JX2saO3uY3dpK7tLW8ip7aSspYfWnjNXEyGK0n40WSSCKlU+9WhUCufqpEZp+6ly/qk9vKLZX6100ypxUSuJCXBj8aQoVu6vorrdgAgctOWXJtg4jEohyGJzkN5fwZbg0XIS9l9nn3nAZFqwp45hYZ4k24heSpgXoV66YyICoihS0aInv66Tdr0R9WGfPUfyF+btwoXDg7l1WgwBx0naVEoFN06OZu7IUP73Uz5fZFTz1f5q1ubUc+/s+AEVoZOBH7Lr+L9vpCrNXTNjndqOv3UYzBYeWJ7J6mwp2u1flw5j0YTj7xxUtPRwz+cHybR9jhekhfPPi1N+VjuoUSlICZXI1Ee7K7hnVjx3nxPH9uJmVmRUc8+seII8dVyWGsoXGdUoBMiobOPy1DC+PlDDW1tKuTw1TP6MeujUzB0R4nQMW7q9jEUTo5y01R/vruD2GbH4u0vasjc2l6AQGHBsMltFPthZzv1zEpiWEMDWwiZe31zM/DHh5NRKx+6fsuu4f3Y8D6zIcrpvfl2X0+Upcf6EeOmo6+hjfV4Dc0eE4qpRsXBsJB85LLqtIry+pYR7ZsUf497/fUOtVPD6tWO4buke9lW0cf2yPXx1x6SflTeJosiL64t4yVbVvmRkKM/MH/GLTdqNZitXvLGD7Jr+wsvMxACW3Tj2jJI2HQ/OEsBTiJnPbhiwLS3KB4UgOAXFV7c7ryAvtJGtH7IlvZ+fm4ZttozL6QkBBHpo5cQQdwdjUVeN8oQFs18fqB4g6lcpFLT0GLn1o33MHhrI3BGhtOuN1HcaaOrqo7VHGnro7DXTbZDaqwazFbPVesTBguOFUsBGwlT4uKrxcdPg66rBz11LgIeGAHctvm5afN3VuGtVGC3ikauVthZwj8FCr8ks/XTc7nAfO3Exmq0YzVbaj6OV+ktgtopObV+RgbY8LmolwV46YgPcSAjywEOrwmC20tJjpKypi8yaTiez2aNBrRRQKaWDl5eLipRQT8KOokMSRZHKVruGTyL4dYcNn6iVAqkRPqRGetlIbgfpZa3UtPfyzrYyPthVwZyhQVyeGsb0xIDjIm7+7lr+d+VIrh4XyT9X5ZBV3cGTq/NYvreKxy9JYVLciVW/D8e2oibu/fwAoigNGTz0O7Lp6DaYuf2jfWwvlqLdXrhq1HHnnoqiyMr9Nfzj20P0GC146lQ8NW/EcS0Ub50Ww72fH+SDneXcOi2G8dG+jI2SUjbe3lrK3+cms2RqDF9kVMvHkwOVbbhplBQ0dLG5sImZDoMTC8dFOhHAPWWt3DUzzokA9pmtvLutjEcvSGK+jQDav186lYI+h2Pg0u1l3DEjlntnxbG1sIkVGdX8dN9U+fqvDtSw9eGZAwigCGwpbGR6gvTalAqBK0aH8+qmYr7IqJb39eLJUU4EEOCdbaVnCaADXDRKlt6QxoK3dlHY0M31S9NZcfvEI/qbGs1S0eKr/dLn4M4ZsTx0buIJFUUcsau4mevfS5eLLUoB3rhuDOeepEGSXwtnCeApRLN+YEvuwuEh5NR2OA1tFNb3rxiHhngSG+CO0Wwlz7aSnBTrKxO++WkRrM9rkAcH9A4DBJelhuGpO7EsRbvWxhFma//BcH1eI+vzjj271RF2bZlW1d9e9dBJOjlfNw0qpYDZItJtMNPQaaC8uZsOG4GxiNBrstJrMtLSYyTCYsXPTUuIl05uTwZ6aE/qCkwUpaEXvcGC3mRBbziMQBot9BptBNIktcH1RgudfZLFS117L83dRrkddSJwUSvwd9cS6etKUogHqRE+RPm7YrKIlDb1kFPbyaHaDj7aVTFoRJpSIRAX4E5KmCfJwR7oNEpKm3rIrumgtKmHNr0Rq63qWd3WS3VbL+vzGnni+zxUCgF/dy0JQe6Mi/ZlRLgXdR19P0v4JsT42jR8PgNMVWvae/n2YA1f76+hqLGb1dnS1Kevm4aLR4RwWWoYoyK8j/l9TI304Zs7J/NFRhX/W1NAUWM317y7h4uGh/B/Fw09Yd86kEjGbR/tw2QRuWh4CE9eNuw3u8I/HC3dBha/v5es6g5cNUreXpTGlPjjI82dfSb+9vUhVtmm2sdF+/LCVaOOunAYDBcND+F/PxVQ097LV/uruXb8EO6aGceN7+3l0z2V3DkjloQgD1mHp1UpKG/Ry63it7aUOBHA0ZHessefHT8eqmNirC+7Slrlbe/vLOO2aTHEBLiTNsSHjIo2VArBifyBRJS/OVDDwnGRTI7zY0dxC+/tKCfUW2eb/pcWwYdPEQM8+mU2u/46S7585RiJAG4raqKuo5cQLxdiAtwZFe7Nwep2+XZdfWa2FzUxJf6Xx+39XuDtquGDm8Zx5Ru7KG3uYfH7e/n0lglOk+AAHb0m7vh4HztLWlAqBJ64dBjXnKAbhh1Wq5U/fXaQ1dn9zgwx/m58d/dk3E7wXHsm4SwBPM2YkRjITw7tXi8XFR29/SfwubYVdEZ5q6yjiwv04Luserxd1cwaGsgVr+8EGCBcPtHhj8yqNqoP07H8HAQknYZSAKVCgUopoFEKaFRKdGolrhoF7lo1rmoFKpVSqjTZbqdSCKiUCtS2n/I2hYIR4QJKRRB6m5t6Y1cf9R0G6jp6abN5Ila19joNzXi5qIjycyMu0IO4QDcSgz0Y4uuGRmV/bIWt0qWwPY+AUiEc8aQuCAJalRKtSsnRxgwsVpGs6nZ2FDezr6KN/ZVtTu14QYCUUE+mxAUwNd6fMUN8UCoEp6qk3mChTW9k3RfZYJEMvEv+cSEmi5WC+i6Z6L2zvYz8us5BrVc0KgVJwR7ycMawMC+Sgo9uzSOKInl1nazNbSC9rJXixm5aeoxYrCJmq0h9Zx/1nX1sdbAAskMhSDrVc5KCmBQ7OOE7HGHeLtw5I447pseSU9vJyv12vaCBD3ZV8MGuCmL83bgsNYzLRoUdk1BboRBYOC6SC4aF8Py6Aj7aXcHq7Do25jdy9zlxLJkafdxtn6KGLha/vxe90cLUeH+ev2rk78b3srpNz/XL0ilt6sHHVc37i8c5ZVYfCzLKW7n384PUtPeiVAjcP0jk4bFCpVSwZGo0j3+XyztbS1k4NpLpCQGyd96yHWX8+bwkbpkaw6aCJrkqn1vXiUohsLu0VY6QA+l7e9XYSJ74Ple2T/pqXw2PX5rsRAD7TFaW7SjjwXMTWZAWQUZFG25aFR29A6v8L28sYkFaBPecE8+O4ha+yKjiitFhfL5XqjCtz2tg8eQo/vr1Iaf71XX2IYqifIyJ8ndjXJQv6eWtrNxfw10z4wC4Y2Yst320z+m+j63KYf2DM457f/6eEeLlwoc3j2P+m7vIqu7g9o/2sfTGNPn7XdPey+L30ils6MZNo+TVa0c7LQ5OBGVN3Vz2+g6n8/N9s+K5b07CUe7128JZAniKsCW/fsC2YE8tEb6ubMzr94Dy0DoTQHt6x/dZ0upaIUC+rUJ46chQuvvMHLINf2iVAvZgsHHRviQFe57Qa33su8HNpI8GEYkAWQAsFpCPnadf59fRayazuoPM6o7jup9MShVSG9SJlNq32cij0rbdbLHS1ScNT7T1GAcMu7hplIR5uxDu68oQX1fcdSpUCgUHq9rJqe1wIMHST+mxFbioVZgsBnqNFi56eRtFjd0Dhifsj58S6kWyjeilhHoSF+h+3Po3QRBIDvViaIgnVQ5TutuLmmnsPnoWr1WE3Lou8uq7+HRPBdEB7oyJ9GF2ciBpQ3xQKI78WgRBsNl2ePHXC5PYXtzM1wdqWJNTT2lzD8+vK+T5dYWkDfHh8tFhzB0eipfr0VfaXq5qHr90GFeNjeSfqw6xt7yNZ9YUsCKjin9cnHzMFhLVbXoWLU2nXW9iZIQ3b1435hfrhs4UFDZ0cb0tuzjUS8eHN48nLvDYRetmi5VXNxXz8oYirCJE+Lrw0sJURh9jNrDJYqWl2zjAT3JBWgQvrpcytdfl1nP+sBDumhnH7R/v48OdFdw6LZaJsX6yXtDLRU1rj5GhIZ7k1XXy9tYSXr92jPx481LDePrHfHlwxGix0tpjchoGAVi6rYwlU2K4cEQIj32XQ0evaYCbAkBtex9bCpuYmRTI+Ghf9pS1Ot1mRUYVn986cQABBFi2vZSbp/YbGV+ZFk56eSsrMqq4c0YsgiAwKynQKY4OJDPqxs4+Aj1/XwkzvxSxAe68d+NYrn5nN9uLm3nwi0xeWphKXl0ni9/fS1OXgSBPLctuHEtK6C/z6HxhXaGsIQRpeG7V3VOI8nf7pX/GGYWzBPAUYfH7+wZsOz8lGFEU2W8TTAO0OKQ9JAZ5EG37gNkD0aP83NiQL7Ve56dFyB9KQYA2B+J4otW/uvbeAZY0dqgUAjsemYlaqcBkFTFbrGwvaua5tYW06o0oFQJXjg5j7shQBARMVitmi3Q7s1WUDYTNFul3+0+nbbbHNdkuW6yi7Xqr/JzyNtt9LQ73kXJ0zfSZLPSZrMfl1Wcf+jiZ6DFaKGzspvCwltDPYYHOiJsAvSaLTPh93TSkONiuDAvzYoiv6y/Ss4ii1PLdZSN8e0olbZ4j1EpBNnCeGOtHqJeOLYVN7CxpIb++i/qOPowWK6IIrXoTrTaT6re3lSIAni4qhvi5MSrCm3OSApkc6496kMlGlVLBjMRAZiQG0m0ws+ZQPV8fqGFHSTMZFW1kVLTx+KpcZiYFcHlqODOTAo5KyJJDPfnitomsyqzl36vzKG/Rc9P7GcxKCuQfFyczxO/IB+/mboNMkOIC3Xn/xrEnZIJ8JmJfRRs3vb+Xjl4TcYHufHTzOEK8jr1dW9Wq5/7lB2WLp3mpYTx+aQruWhXteiNNXQaaugw0dPZS3tJLcWMX1W29NHcb6Ogz02e0yN/JBToDbjaTeQA3rYpFE4bw6qZi3txSynkpwZybHCS3cj/cWc6fZsXLekGr7XHKmqTv14+H6ilv7pFPzD5uGs4fFiy3pwE+3FXOpalh8rQzSFZJy3aUcv+cRC4cLjksRPm5ya1cx3SQlzYUMTNJmna/5t09fJdVh5tWSY/BQpZt0Rkf6EZRY4/TfnthfZETAbxoeAiPrcqhvEXP3vI2xkX7ylXQ59cVOd33798c4q3r0475PfqjYGSEN28tGsNN7+/l+6w69EYLu0qa6TVZSQzy4L3FY3+R/KNdb+Sy13ZQ3tKfuX5+SjCvXTv6d9MJcMTv4wh3BmIw97bZycGUNvc4tfEcBf+XjJLEwS3dBllnFeHrSmlzD0nBHiSHeMhawAD3fgsDPzfNCbua/+2bgStXO8xWkYte2c43d00iyk+qFgzxc+PC4SH87ZtDrM6uY3lGNfn1XTx/1Shiz5AxeLPFSmlzD7m1neTUdnCopoOc2s4j5jKGeOmIDXAnJsCNCG8XDBYrRQ1dZNV0UtbsfFBXCtI+iAt0J9rfjSBPLVaRAURWutxPZC0WsZ8gOxBZk0XapmlQgFXK+n1nQRopoZ6EHOMk7tFwLIRPpRAYFdHvwzd6iDeuGudDQ3SAOzc6xCa19hhYl9vA9uJmcmo6qe3opc8k+Qd29JrJqu4gq7qDD20mqe5aFZG+rowI92JGYgDTEwJwcXgOd62KK8aEc8WYcOo7+liVWcPK/TXk13exJqeBNTkNeLlIk56Xp4YxZojPoPtGEAQuHRXGrKFBvLKhiKXby9iQ38i2omZunRbDnTNjB/xtogg3vpdOaXMPYd4ufHTzOHxOMP7sTMPmgkbu+Hg/vSYLqZHeLLth7M/+bX0mSX7R1G1gdWYNH+2uxGgRJSsiNw0b8hv5PqvWyYroeNHmMHV7w6Qo3t5WysGqdpkY3TUzjns/P8iyHWXcNCWaC4eH8PSP+dR29MnWK3Yt3jvbSvn35cPlx1s4LoJVmbUokI7DDZ0GIn0HSgre2VrKkqkxLEiL4Mt91dS096JSSNFxjn+ZvXo/MdZP1gyGernSY9BjFWFnSTML0iL59w95To/fbZD0wfaFhJtWxUXDpUnlFRlVjIuWfO2uHjeEF9YVOT3n+rwGrFbrUavpf1RMjQ/g+QWjuOezA2y0FUemxPnz+nWjT1gDD9Ig5ENfZGGxLU5UCoG3F43hnKG/H9/Pw3GWAJ4mqJUCY6N9+MYh71elEJyqVXb7l80F/cMWdR3SyfrKMeFsKmiShz86evsrh4smDjkhC4z6jj42Fhx9sKNdb2LO81t55/o0OarIx03Dq9ekcm5mEH//5hCZ1R1c+NI2/nJBEtdPjPrFE1e/FCqlgoQgDxKCPLgsNQyQiFBtRx85NjKYU9tJbm0HtR2SmLuuo4/txQP1biARxMlx/lwwLJgJMX6npDL0/PM76eoy4K5V/WKj4SrblO6uoxC+kRHeTIjxZWKM/6CE7+fg66blqrGRXDW2X2TdbTCxIa+RrYVNZNd0UN3WK2tUuw1mcus6ya3r5PO9VYCkYQ3zdmFYmBfT4v2ZNTQITxc1wV46bp0Wy63TYsmr6+TrAzV8e7CGhk4Dn+yp5JM9lUT6unJZahiXp4bJVXNHuGtV/OXCocxPi+Dx73LYVtTMq5uKWbm/mr/NTeaCYf0Lpja9kUNNnfi5aY67OnYm49uDNTz4RSZmqxTt9p/Lh1Hf2UdmVRtFDT0UNHRS2SbFGHboTeiNFgy2yu5gsIrQcgzG8CB1KNRKBS5qBZ46yUsz1FuHsliamDVZrZQ2dRETICUGXTE6nM/SK3l7awnjon25aHgIL6wrpLxFz6d7KrllWgw3TYnmydV58kRwvW2RvGJfNffNTiDAQ5oMnRjjR5Sfq1MVZ31uI8khnuTW9Vt46E1WPthZzl0z4+Tb24c9QJLf2J/r1Y3FvHHdGO6dHc+ipenUOGimV+6v4b/zhg8ggACPrTrEM/NHyZfnp0WwYl81q7PreOwSyS4nwEPLnORA1ub2H4stIry5pZQ7bVrBs+iH1SpyqLbDiTDPSQ46YfJnNFu5ftkeOXQBID7QnS/vmDRofOXvCWcJ4CnAA18MbP+mDfFFq1KyxWbnAlKbwY5IXxe5jWHPjNWqFBQ2dKNSCFyeGsaN70mxco7DHwoBrhl3YpNO//0p76hpGF46NUKf1Cq98b29PHRuAnfNlIxi7ZWWcdG+PPxlFtuKmnnsu1zW5TXwzJUjf1EZ/lRAEATCvF0I83ZheLgX24qacdUo2V7cTGvP0U9qdR19fLmvmh+z6xga4im3ZZNDPYkPcv/VdWJVh9myHI3wTYjxY8wQn+MmfMcCd62aS0eFcemoMHlbn8nC1sImNhc0kVnVTmWbXrao0RstFDV2U9TYLWdaa1UKQr10JId6MSXOj3NTgvnrhUN55PwkdpW0sPJANT8dqqeyVc/LG6QEi1ER3swbHcbcEaH4Hlbdigt058ObxrEmp4Envs+lpr2XOz/Zz+Q4P0barX4sVty1Kj64aRwxZ0gV+1jRYzDL7dfc2i4KG7qoaNVT2NAlp/gIwLaiZqb+b/Mvei6FIC2uZFLnoSXUS0e4jyuRfq5E2yrjR9OuPfvcFnpsr+uGZXvZ9sg5ANwyNZrP91ayPq+R4sYu4gI9uGNGLI98lc3b20pZNHEIV42N4KX1RdS09zIqwpuDVe14uajp6DXx4a5yHrRZ9diHQZ7+KR+1QsBkFUkvb+WO6bHk1nWiVQkYzNJ7/8bmEhZPjmZ+WgTPrCmgx2AesDAHWJNTT11HL1Pi/EmN9OZAZbtMEDfkNeCmTR1AOgG+PlDrRADHRvnIt/shu475aREA3Dot1okAAry5peQsATwMfSYLD63I5PssaSrXrst87LscAjy0sob+WJFR3sqipXucOnGO57nfO84SwFOAlfsHDoBcNEKqOOx0qDKZHA4y9pOmKIpklEtaG393LTXtvZyTFIgAHKqR9CbuWpVMACfH+Z+QWLiqVc+qg7WDXqdVKcAieTC9f9NYbn4/A7NV5Nm1hWTXdPDiVany1GeIlwsf3jSOj3dX8O8f8thR3MJ5L27l8UtSnIxaf0109ZnYXdrK9qImthU3U9rk3NZ11SgZH+3LlHhpWjfc24WChi6nSmF+fRc9RousTbNDrRSIC/SwkUJPkkM8SQ71xOMUWgRUt+nZXdrKrpKWIxK+EeFesobvVBG+Y4FOreTclGAnvyyz2crushY25DVyoKqd8uYeOnpNiEhJKGUtespa9KzOruMvXx9Co1QQ5KklKcSTCTF+8ol85f4athU1cbCqnYNV7fzru1xmJEp6wVlDA+UpaEEQOH9YMNMTAnhjSwlvbilhR3ELYTY9mgC8e0Maw8J+mXD8ZMFskTwd69p7OVTTSUFDJxUteho6+2jTm+gxmG1+m8fWgj3arRxJnYdOjcUqUt/Rh4g0Xf/AnAQuHhGGr/vJaYk7Ngeq2nrJKG8lLcqXmAB3zk0OYk1OA29vLeV/V47k8tRwXlpfRG1HHysyqlg0MYqrx0fy9tZSRFFqSdsndz/cVcHt02Pl6vyVY8J5bm2B0zG2rqMXjUrhJMHpMVr4YGc5V4yWbn+wqoNLRoawKrMOq9hfBbSK8M7WMv5xcTL3zIpn8Xt75f3aa7KSU9vJZalhvLjeWctntooyoQXps2gnmysyqmUCOGaID+E+Lk5uDJ19ZnaXtDAh9veVO32iaOsxcutHGewtb0OtFHj6ihFcnhrG/31ziE/3VHLf5wfxclEz+Ri8QK1WyS9weUa/b6S3i4oVt08kPujEhil/izhLAE8TZiYF0djVR3vv4Do0e8RUUWO3TO7sB7f5aRG8vLEYEelkZdf+Adx9givEF9cXHtGs2UOnprdHeo7pCYF8c9ckrnprNz1GC2tyGrj4le28t3gsETZdjSAILJoYxeQ4fx74IpODVe088EUma3Ma+Pflw45o2nmqYLJYyaxqZ1tRM9uLmzlY1e40UasQYES4N1Pj/W0rep8BwfepkT6kOkw42nWFObUd5NTYiGFdJx29JvLqOsmr6+RLh8LvED/X/kqhrWp4olN9dsJnn9Q93LLHkfDZK3xn8gCDSqVgSnyAk9eZ1WrlQGU76/Ia2FfR5uRVaLRYqWrrpaqtl3W50gS9SiEQ4KFlXLQvLholVa16iht7ZL9KD52kt7osNYxxUb4oFAIuGiUPzEngytHhPLE6F0ql5/Z21TAh5tSeZEVRpLPPTG1bL1nV7eQ3dFLeoqeho4/WHhPdBjMGkzQscZI81NGqFAS4a/H30BDq5UKUvytR/u7E+EuVOsdYrNr2Xu5ffpA9ZVIbbO6IEP59+fBT3gK79cN97P/HHOn3abGsyWngmwO1PHRuIoGeOm6bHss/V+Xw5pZSFo6L5MZJUSzbXkZmdQfnpQSzJqcerUpBR6+J5XuruGmKpFMN8NAye2gQP+XUyyTuh+x6ZiQFsDangQB3DU22SuRrm4pZPDmaaQkBbC5owttVI9/HQ9fv0vDpngoeODeBGQkBjAj3kgdAAFZn13LrtNgBBBDg4S+zWHnnZPnyvNFhPLe2gPTyVnmARRAE7pwRx1+/HpgPvO6B6Sd3p/8GUdHSw+L39lLa3IOHTsVb142RTd+fuHQYbT1GfjxUz60fZvD5rRMZHn7kxVxFSw9XvrFTfv8BLhwWzAsLR/3q3ZzTjTP3LPEbhX4QL6lQbx1h3i786GAm6Qg/N408ofiDw226DWb83TVMT/DngS8OSrd118itHR9XtSwkPh4UN3az0kGL6IgQLx2qwzR8w8K8WX3PVBa8tYvGLgPFTd1c+PI23rh2jJOJbEyAO1/ePpE3t5Tw4voifsqpJ6Oijf/OG87sX6hrOxpEUaSkqYftRU1sL25md2mrrJW0I8rPlSnx/kyJC2BijN/PWoscDkdd4eWp/c9b097rVCnMre2ktqOPihY9FS16fsjurwb7u2v7K4U2cjjYVG9Ney+7S1rkwY3fOuE7FigUCsZE+TLmsMD33NoO1uY2sLeslaLDvArt2k35MQTQqZSYrSJdfWY+31vF53urCPN24bLUUC5PDSMu0INIP1feuT6Np5/ZTJ/eJFW8TxAGs4WaNj0HK9vJreuiokVPXUcvrT1Gug3SdLrJKh5VanEssFfqdCqp/errLpG66ABX4gLcifBx5Z1tpazLa0QQ4MnLhnHt+GNzBvghu46/rMymo9eEq0bJvy4dxhWjT2313kWthD5o1RtZdbCGS0ZJgz32IYv3dpbzyPlJXDU2glc2FlPT3svXB2pYkBbBxSND+fpADaIo4qJWyobrUvRbvx564bgIfsqpRyn051x72whtt4N/ao/Rwns7yliQFsHmgibW5jRwychQvjlY62TR1We28nl6JUumxnDPOfEs+TBDvm7l/hoeOX8oYd4uAyry+w9zWQjxcmFKvBQv9+W+ah46T2pdXzoqlMdW5cgWNiAVBJq7+/B3/+NawhyobGPJBxm09BgJ83bhvcVjSQjqzwRWKgReXDiK9mV72VXawo3vpfPlHZMG1Qa/samY/60pkBdZaqXAa9eM/s0nepwofttnjTMQM5/dOGDb+bYP146SwYcMZiT2V0J+tJlEa22tistGhbGjuFnWTbU76NXmjQ4/oYP0i+sLj3hCumNGLM27Mgdsj/J34/s/TeHqd3ZT0tRDV5+ZRUv38JcLJaNW++tQKRXcfU48MxIDeeCLgxQ2dLPkwwyuSovgb3OHnrTWaHO3gR3FzWwramZHcfOAdAofVzWT4vyZGufP5Dh/uVp5MiEIAuE+roT7uDpNYbf2GOUJZHulsLSpm+ZuA1sKm5x0oO5aFUNDPEjpMyMATV0GJv/X+TOkPIzwpf0OCN+xIjnUi+TDPL3KmrtZc6iB3aUtFDZ00dhlwGwVsYqSvcfhqGnv5bVNJby2qYQYfzeuHR/BJaPCUSsV9A24tSQyr+3Qs7+ig7y6Dsqae6jt6KW1x0RXn4k+kxWT5ZfHHdoTcnRqBR46Fb62hJvoADeSgjyIDXQnxt8dd93R3+tug5lbP8xgZ0kLGqWCFxeOOiYtlN5o5vFVuSzPkIZyRoZ78dLC1NPideblokbVI2ntHvkqm0tsEpjbpseS8WEGH++u4K6ZcbhrVdwyNZqnfsznjc0lXDE6nCVTo/n6QA0b8hu5fsIQ3ttZjkKQ3ufVWXXy4NfU+IABhGxzQROhXjpqO/qcUkNe3VTM7r/MwttVTX1nH1Pi/Pn2YC0iOLVm39hcwo2Topg1NNBpqKSh00BjZx8XDg/mnW1lA/7eVQdrZZcHgPljwtla2MRX+6u5f04CSoWAm1bFVWMjBsTD/ePbHCevwz8SfjpUz33LD9BnsjIszJNlN4wdtJOiVSl5+/oxLHx7Nzm1nSxaKuUGB9lu26E3ctVbu8lv6E/dSghy55MlE+ThoT8i/hhnkdOIhp6BLV579WtT/uATtzfb2hZ9JgtFtg+ofRU4Py2CP6+QCJnjahfg3lnH3/7Nre2UBbSHQ6UQuG58JC/uGvy+gZ46Vt4xmcXvp7O/sh0R+M8P+WRVd/C/K0c46cyGhXmx6u4pPL+ukHe2lbI8o4odJc08N38k40+g3dZrtJBe3mqr8rWQ5zDNB1IaxtgoH6bEBTAlzp+UUM9fbRrZ100jVRsdqqN6o5l8W7KHvVKYV99Ft8HM3vI2onVm3ASwiOIAwjdmiM+A2KM/MqL93bl9hju3z+j3WKtt17M2p8HmVdhJfYfBqZJiR2lzD0+szueJ1flcpTPgKkiSisS//XhSSZ1WJnUaQjx1RAW4MTTYk6RgD2L83dCdJE1mc7eBxe/tJbumAzeNkneuTzumPORDNR3c89kBSpt7EAS4Y3os989JOCE3gRPFPbPieH5dEb0mC69uLOTucxKYlRRIbIAbJU09crXt2glDeGNLCWXNPazOruOSkaFyHJzZKuLv0BV5c0sJl44KRRAk8/b5aeG8uL4IN42CHqOVxi4DFw4Ppja7Hq2yv92nN1r4eHcFl40K4/2d5WwqaGJirB87S1po6OxfJrTYWo0Xjwzlnlnx3P5xv+5jfV4Dt0yNGZQA/ueHXCcCOCc5CC8Xtew+MD1BKgLcMGlgPvCanD+mJczS7WU8uToXUYSZiQG8es3ooy58PXRSus38N3dS3qLnhmXpLL9tItsKm7hv+UEnzex9s+K5Z1b8r+5Y8Wvj7FnlFEOjVJA2xJceg5ma9oH1Bhe1Qq5wpJe1yicgUYQR4V4EemjItg1/6NQKmQDGBbjj6XL8wuzn1xUe8bpzU4J+9iDj5armkyUTuOuTfWy0mVV/n1VHcWM3by9Kc4rw0qmV/PXCocxKCuTBFZlUt/Wy8J3dLJkSzYPnJh41qsxiFcmp7ZArfBnlbQNO6MkhnpKOL96fsVG+R328XxuuGhWjI32ckhPMFislTZKuMPunHDCZ8HHVkPmXc88SvuNEqLcrN06OdvYq7DayNree7cXN5NZ2UtPe6zQAYFfbWUVx0Ig9OyRSJ6BVK/HQqvBx1RDspSPK35WkYA+GhXoRG+gxQEd6qlHVKkW7lTX34Oem4f3F446qfQKpuvnu9lKeWVOAySIS7Knj+atGMin2+PKATwb+dE48r28uoc9k5cX1xdwxPQ6lUsEtU2N4dGU2S7eXccOkKNy1Km6aHM3z6wp5bWMxc4eHsGRqNNuLm1m5X7KAsVuw5Nd3sbWon1AtSIvg5Q1F9Bj739+qtl4EAbJrO5gc68eOEsn25dVNxXx2y3je31nOutwGPrtlPDtLdmGyiMQFulNsM4l+eUMRc0eEcG5ykJMB9Kd7Krlm/BACPbROOm2A+k4DvUaz7H2pUyu5dFQoH+6qYEVGlfx64wLd5SljOyxWkXe3l3HrtFj+CLBYRZ74Ppf3d5YDcO34SB6/JAXVMSxOAjy0fHTzeOa9sZP8+i6mPr3Ryf/Vx0XNR0vGnzEDX782zp5lTiJ2FTUM2DY2Show2FveOsg9YLjDB/Gbg866vCvHhPPShv7hjzZ9v77w+knHn/xxsKqd9XkDX6Mdf78o+Zgex0Wj5K3r03jkqyzZmDq/vouLX93Oy1enygczO8bH+PHjvVN58vs8lmdU8c62MrYUNvH8glFOX8SqVr1M+HaUNNOud9ZThnrpmBIvtXQnx/njf5qHS042VEoFicEeJAZ7UL5ZRZdJav2fJX8nB77uGhaOi2Shg01Sd5+J9XmN/JBdh6JMWv0rBYGhIR6EeOkY4udGYpAHIyO8iA/wQHWaSd2xoqC+i+uX7aGh0yCbV/+chU1DZx8PfpEp+12enxLMU/OG/2qm14Ig8K+LU3h4ZbbUCl6ZzbPzR3JZahjPrSukrqOP7zJrmTc6nBsmRvH21lIKGrpYn9fAnOQgEoM8KGjowmSxEuPvRqnNtP2tLSXyMSjU24XpCQFsKmiS7V+yqztIjfDmQFU7kX6uMgHUGy1sK26WY+eyazrkx23r6Sd0RY3dsmH1/XMSufOT/QDk1HbSZ7IwJzmIT/ZUcjj++2M+j186TL48f0wEH+6qYG1uAx16k6xLvm1aDLd/vN/pvq9tKv5DEMBeo4V7Pz/AWtuw16MXJHHbtJjjkjpF+LrylwuSeOCLTCfyd15KIM8vSP3DyGeOBWfm0e03iuuWZgzYNneEpMXZXTq4/m/e6HD5920O2jCNUsHFI0L4ar80pu7tMLSgEKSDx/HiubUFR7wu2t+NkOPw7lMrFTx75UhumdpfcenoNXHjsnTe2FyCeJjI0EOn5ukrR/Du9Wn4u2sobOjmstd2cM9n+/nLyiymP7OJqf/bxF+/zmZ1dh3tehMeNlPkf12awoYHp7Pj0XP435UjuXRU2G+e/J3FrwN3nZrLUsN4+/o0Am3anwAPLT/eO41lN47jnxensHBcJENDvM5Y8revopX5b+6kodNAQpA7X90x6WfJ37rcBs5/cSvbi5txUSt5at5w3rhu9K+eeLJgXCReNo3jV/uq6ew1olMruXFSFIBs+eLlqpbjLl/dVAzAEtux58Nd0nSuHTtLWsiqbpcv2xcACgcSYY8m3FzQxEXD+/W7r28skY/JX2RU86DtcVt6TMQ7ZCe/slGa9j0/JRgP2+sXkWy+bpseM+jfunyvMykcFiZJAoxmK6sy+xf/s4cGyfvEjo5eMxlHKCL8XtDcbWDhO7tZm9uARqXg1WtSuX167HGRP6vVyt++zuaBL5x17OOivHnzurSz5O8wnJlHuN8oBkuVnZEkpWeszx1c/3eBTazd2NnnNJY+JyWIg1Ud/cMfDtWwEeFesg/fsWJ3aQvbigYnoQD3z4k/rscDUCgE2aTXDhF4+qd87v70AD2HTeIazBbctCouGRmKl4sas1VkVWYdn6VXUdGiR6UQGBvlw/2zE/jqjokc+Mcc3rk+jesnRhEb4H5GeAqexVn8mtiU38i17+6hs8/MmCE+fHHbRIK9jjwh2mu08Ldvsrnlwwza9CZSQj357k9TuHpc5BnzfXruqpGAdOywV76uGz8EV42S/PoueWjq5inR6NQKsqolacglo0IJ9NBS39mHwWRhzJB+ecVbW0vl389JCiTAQ+tk9ru/og1PFxV1HX3OWl2TheauPjRKBbl1nQzxc8VdKx1rjQ4ygW1FzZQ2daNQCNzloEX9ZE8Fkb5u+AziMtBnFilxyAi3ewKCRDbtUCkV3Dx1IIk83CLm94SSpm4uf30HmVXteLuq+WTJeOaOCP35OzqgqkXPpP9u5GOH6muIlxaFAOnl7fz3p/yT/bJ/8zhLAE8hwrx1hHi5YLZYKajvGnB9sKdO9tnakO/cmp0/JpwX1kt6PZ1a4eQNds0x2jvYIYriUat/OrWCS0aGHfH6o0EQBO6YEcvTVwyXTV4FYHV2HfNe38HG/Abe3VbKje+lM+rxdVz9zm6W7Sj/f/bOOzyKsu3iv9mS3kjvlRoSAoTeQZAmiijYEBV7xV4+62t/LdgRxAoqiIAK0qT3GiBACgnpvfdks22+P2Z2spuEankBc66Li+3ZbGafOc99n/scxePQ8hw7tYpnJnRn2b2DmTO2C/Fhnuek+ehAB/4tWHk4n7sWHUJnMDO6mw/f3znQxsuvNVKKarn60118v086Id49PIKVDwyhs+/FlXYytoc/fnI1dk9GBdnlDbg7ablJrtx9IZM5Lxd7bh4gVwG3nMJeo+b2oeEALNyZxXMTWzai644XkVMhtYS1ahXTZZ9VL7niaTSLRHlLn8PO9HLFhxXg2z05jI2WNu7LEyTrGYCcykZCOrV0SeZvzwDg7hFRSqrT9rRyRFFktByb2RrPrDhmc31q70A0KoHjBTWkFrcMtt3YP4TW9DytpJ7qxnOL4ruUcCCrkmnz9pBX2USopxMr7x9C//DzszdbuCODke9tpbi2pVX/wMhIdj49hren9QJgwfZMFlptDDrQQQD/Mjy74kib28b3lKp7qcV1tCcxt7Z/+c0qlcPPzZ7YIHeOy0aj1oNKggDjzjOcekd6OQezqzjdwNPU3hdG/qxxQ/9QPp8Zj51apWgWT5bUM/vbQ7y+JoVtJ8toMpjwdrFnau9A3r2+F3ufG8OuZ8YwtLMXepOZN9amMOvrA0r+cQc60AEJX+7M5PFliZjMItfKLezTdQFEUeTrXVlc8+lu0kvrZWH8AJ6fHH3RGt1+cnMf5bLFX2/2sAjUKoE9GRXKWnjPiEjs1CoOZFeyP7OCWwa0VAqbDCYl49kswsKdLSf7G/pLJK7CykYrWyaIG5NLuH1IOGp5fWwymFDL1dFfjhQwc3DLhtvNqjW78nABlQ161CqBrn4SmTSaRRJyqk7bBk6wShECidRe0UMiiz9bVQF93Ry4sh3v1Bd/PdHu616qWJVYyMwv91PTZKBPqAe/PHB2OYM1ahr1TP54J2+sTVUGKN0cNfx0zyCentgDjVrFjP4hSpfqjbUprEjIP8Mr/rvQQQD/Iiw92DZWbXxP6Qt8IKui3eeMk7/gZrPI0bxq5fZpfYP5bGvL8Eej1QRbfGin89LtWFf/rA2e1VZk8FmrnfP5or7ZyJbUEv6zOon3NpxUJnVbu2mM6urD2keGcfD5K/jwxj5M7xdCgLsjgR6OLJ49kFemRGOvUbEzvZzxH+zgt6MFbXSEHejAvw2iKPLO+lReXyNNuc4eGsH70+NOa9dSVtfM7d8c5NXfk9GbzIzt4cv6OcMZ3sWn3cdfLBgQ4UWk7D94qrSe3enlBHk4MkXWUC/YIVXb/N0duL6fVK37dOsp3J20SoVu4c4snhrfTdno/nQwj/J6qSIU5uXM0M6S/ZSHo0TiqhoNhHRyxGAS2Z9VyU0DW4aFNiQV4+/uQE2TgeTCWvqEeACQVFSHl7PUtTGaRRbJk6qPjeumPPed9Sfp5u+maAOt990isOxgns3vbtFz/3qkwKbNbEk1scbaE8WYzaefWL9UIIoi87ad4pElR9CbzEzo6c+SuwedV2rU2mNF9H9jE0mFLZXTMd192PHU6DZWY/eNjOQu+fN8esUxtqSefhjy34QOAvg3wU6toq+sSWlP/ycA/eUUj5TiWnRW+pTr+gaxXN6ltDaBtQyVnCv+SC7hWH4NdmoBvamFUFkuRge4nrGN1BpGk5nDuVV8vDmdGfP30vs/fzD720N8szub9NJ6BAG6+LpITv+g6Ge2pZXx8eZTNOjbKiVVKoHbh0aw5pHhxAW7U6szMmfpUR768QhVDZdfy6MDHTgXGE1SXum8bRL5eWp8N168qsdpvcu2ppYy8aMdbE8rw16j4rVrerJwVr9/PIrxQvGBrAUEmPOT1FGxTL6uPV5EXmUjIHkWqlUCO9PLScyr5s5hEagE2JFWRrPRzC0ykTOYRL7bna285o39pdutvR4tF5cdzOPBUZ3RymdEvUnE30363H4+lM/MQS1VwCCrNvCXu7KUyV/LX+VgdiVNehPDZD/G1lLLuRtt5Tijuvng42pPRYOeLVZesQMiPG1aziBZpFjsUS5VGE1m/u+XE7yzXvoc7hwWwWe39D1nGy+j0cyd3x7kgR8PK+c0jUrgjWtj+Oq2/u2ezwRB0qtP6xOEySzywA+HSci5vIdqzgUdBPAvgF7fNv5tQGQntGoVoihyOK+qzf3d/F1xk1MxrI2Z44LdKahqUsbX63S2gxTjziOyxmQWmfuHpCO09iizrgQ+N6lHm+dZvAbNorRTyypvYPHebO5ZdIg+r21k2rw9zN2YxoHsSoxmkVBPJ24aEMq8W/py5MVxbHx8JOvmDCfE05H6ZhOu9ho0KlifVMy1n+0mS7ZraI3OvtJE42Nju6JRCaw5XsSVH+7o2K114F8HncHEgz8eZunBPFQCvD0tlgdHd253cENnMPGf1Unc8e1Byuv1dPd3ZfXDw7h1cPhFM+hxLogL6aTYYpXX6/l+XzbRgZLXp1mUjIFBsvm4RjZV/nTrKUI8nZRhui93ZjFnbFcl3u+r3VnKMNqVPf3o5KSlVmfETm6B5Fc1oVULnJQTZW4b0lJ1SyqQKks70svoHequbGqP5dfiKm9s65uN/HKkALVKUKaERWD+jgzuHSG1gVubixfXNtOoazlnaNQqpsnpJcsTWqqDgiBwz8i21i8fbz517h/qRYb6ZiN3fneIJQdyEQR4ZUo0L14VjfocDZmP5FbR9/WNbLYiyuHeTqybM5xbBoad8XhXqQT+e30vRnfzQWcwc8c3B9vV5v+b0EEA/wKMfG9rm9umxEoLVF5lk011z4IRVl55G060EMAbB4QqZs2tM0pjg9wJOg+rlt+PFXKypA5nOzX1zS2VN4sjuqu9pk1r6Fh+tTKgUVqnI+blDYx+bxsv/pbEH8kl1OmMuDtqmRjjzxvXxrDjqdHseHo0b02LZVJsgLL7Cvd2ZsV9Q+ju70pdsxE7jRoPRy3ppfVc/emu05I6jVrFnLFdFLF6WV0zs789xHMrj7eZKu5ABy5H1OkM3P7NATYklWCnVjHvlr42XobWSCupY+pnu/lGrnTdPiScXx8capOVeinhrWmxyuU31qRgMpm5V64C/nQwT+kIPDCqM4Ig6fdSi2u5R56aXZVYgNEk8oA8mduoN/HjASlZw16j5jrZ4sXHKk7MEhf206E87hsVhYNGIhEGs4ifqz2iCOuOFzPZqvtibXj/yZZ0zGZR0RkCfLkjk27+ropO01Fru5a/+FuSzfXpclt768kySutaAgOu7ROEVm1LaqqbDBzNbVtUuNhRUqtjxvy9bE8rw0GrYsHMeBvj9rPhpd9OcO28PTbefrMGhbJ+zgi6nOPxrlWr+OyWvvQN9aBWZ2TW1/vJr2o879/lckEHAfwLUFTbtgJosX85nQH0oEip/duoN5JZLh2AapXAiC7eHJMFz2IrrUd7ouDTwWgy8+Emyauq9QJiwc0D23oJztuaYXPd0rL1dLbj1kFh/PbgUA6/OI7PZ8Zzy8Awm4WwNXzdHPjpnsH0C+tEo95Ek8FEZx9n6nTSLvDjzdLC2R56BXvw+8PDlJi8JQdymfjRztN+nh3owOWAsrpmbvxiH/syK3Gx1/Dt7P5MiGkr+xBFkcX7cpjyyS5Si+vwcrbjm9v788rVPS/qRJyzISbIncGyfqvJYObNtakM7exFz0A3mgwmJSats68Lk+TP5bOtGcSFeDAgwhODSWqR3j0iElfZ8+3TLRkYZG3yjQOkNa/IKh/YkiO++mghznYaGwuWMllD+HNCvlKlA0gqrMNeJoqF1Tq2p5Uprw3Surn0YB6DZJlP62VuzQnbOM7Ovq70DvHAZBb59UiLJ6CLvYYb+rVdp59deWlZwqQW1zL1s90kF9Xi7WLH0nsGc+U5drPyqhoZ+vZmFu1tichzsdfw1W39eHVq7Hkf7052Gr6+vT9dfF0oqW1m1lcHqKhvPvsTL0N0EMC/AcEejsqucms7lS6VgDLmviejZUBkWGdvvt6drQx/6FsVDsfHnHv7d+XhArLKG3Bz1FDd1LJjsrZqmTO2q81zCqub2JBUrFx30KiVlkxlg57F+3L47/pUdp0qP+cBDXcnLYvvHMiY7r40G81kVTQyOMoTUZRi6e79PoE6XVsCDVJc0otXRfPj3QMJ8nAkt7KRGQv28ta6FJqN7bkudqADly7yKhuZPn8PSYW1eDnbsfSeQe1GtFU26Ll7UQIv/nqCZqOZkV19WPfocEZ3b9965FLDS1NaEom+3ZNFvc7IPXI79bs92ehkicqDo6Us9DXHCsksq+dumbj9sD8HswhPT5AGM2qaDIqmurOvK/3COmEWIVjW15nMIm4OGuqajaw7UcQ9w6NwtpNOjWZRkszkVDSiEgSbDkyYl7Ny+aPN6TjbaxVzcYDPt2Vw66BwAJqNZqzndnQGM8fybat4M6w8Aa3X19tkU2xrpBbXUXOJWMLsSi9n+ud7KarREenjzC8PDKW3PFRzNizcmcHId7baxKgOjvRk8xMjueI83TCs4eFkx6I7BxDo7kBmeQN3fHuQ+n9hh6mDAP5JHM4ta3PbhNiWA3N3RtsJ4Nggd1xl/d8Kq9H/O4eF8/MhSQPS2uIh3MvJxon+TGg2mvhos1T9U7fSRFh2ovHhnXBqFUj/3/WpNtO7Hk5aVj88jLWPDOfaPkGKJcNtXx9g4kc7+fVIgbKzPhMc7dQsuDWeaX0lAe7ejEomx/pjp1axMbmEqZ/tVnI228OQKG/WPTqc6+ODEUXJz+maT3eTbDX91YEOXMpIKapl2udSiH1wJ0eW3z+k3bzSXenlTPhwB5tSpPbwi1dF883t/fF1Pb0Z9KWGHgFuXCGTWZMIjy87yuTYAII8HKlo0CtkLjpQepxZlMjWFd19ifSWOgzLDuZx04BQxffvvQ0nFVJlaac36VtO+EZ5mOCng3m4O2m5b2Rn5T6TvGj+nJDPdX1bqoDpJfWKm8LRvGpOFNQwObalWlta10x+VaMi5fFr9Td6doWtpctVcQE4aFWcKq23cYXo4udKn1CPNp/TS79d/JYwPx/K4/ZvDlDXbGRAhCcr7x9CiOfpu0YW1OkMTPlkJ2+sabF3UQnwzITu/HDXIKXA8mcQ4O7IojsH0slJy7H8Gu5bnPCvKyx0EMA/ienzDrS5bYLs/1fZoLfJ77VgkNWI+o50iUDaqQXMoqjoGxpbTcuO7+l/zoLunw7mUVDdhIej1ubnWwttX5xsO/xR06hndaJkZdNaexgd6MYHN/Rm+1OjmD00QvHdevSno4x6dxtf78o6qz6vdXTcmuPFXBUXgJ+rPRllDUz9bDcbk08/7OHmoOW96XEsuDUeL2c7UovruOazXczbdkpZoDvQgUsRB7MrmbFgL2V1zXTzc2XF/UOI8Ha2eYzeaObNtSnM/Go/pXXNdPZ14dcHh0oTsOcooL+U8LRVutDGlFIKqpuU6Lcvd2Yq3/kHx0hE7ZcjBRTWNHGXXAW0DIxYqokVDXp+kbPWJ8cG4OqgoaLBgLtsCdMoVxX3Z1WSXd7AHcMilPssq8va40VMsOrCiGBDZj7beoo7h9tq2j7fnqFUuxr1tmtkSlGtTaXPzUHLBLkt+nMrr7q720kGWXP84rWEEUWRuRvTeGr5MYxmkWt6B7L4zgHn5Dix7kQR/V7fxPGClg1+UCcHVj4wlPtHRf2lx3tnXxe+uWMATnZqdp0q5wnZa/Pfgg4C+CfRer9gr1EpX/jWpp8WWAhgYXWTorEbFOXFh5uk6S5rzZ5lYvfKnudW7m7Sm/hkS/tTYpYD29NZS1xIJ5v7nl5xDLMo7bLcHdv/kgZ3cuKlKdHseXYMT17ZFW8XOwqqm3j192SGvL2F9/84qfhutYfW0XErDxcwMNKL/mGdqG82cveiQ3y4Ke20ukCQiPCGx0YwLtoPg0nknfUnmbFgL9mnmSzuQAcuZmxKLmHml/up0xnpJ0e7ta5uWGKyLIkYMweFsvqhYUQHuv0v3vI/gm7+rjYZvQ/+eIQZ/UJwd9SSXdHIxmRJqtI3tBNDO3thNIss2J7JtL5BeDlL69K6E8VcHReo2Lm8tVaKAnO0Uyvm975Wn7WLrBn8OSEPF3sND422jcdsMpg4XlBDP6vIueyKRsX+Zf2JYtQqAWer7k1RjU5JXqluMuLt0rK2isAXO2w115ZouNVHC2myKgKMi/ZTCKkFRrNoE3t2sUBvNPPEz4l8LHehHhwdxQczep/VhNxoNHPXdwe5//vDNFv5IU7rE8SGR0eec9v4fNE7xIMFt8ajVQv8fqyI/6xO+td40HYQwL8YAyJaIsx2pbdtD6tVAv3CpQXE0u4FmD00nES57G+y8uszmkW8Xezp04qwnQ6L92VTVteMp5OW6qb2tXV3DrPdTRZUNfFHklR9uzou8LSJIRZ4ONnx0Jgu7HpmDG9cG0O4lxM1TQY+2XKKIW9v4flfjp+WkLWOjluVWIi7kx0zZe+uDzelc8/iQ9SeRhcI4O1izxe3xvPe9Dhc7DUk5FQx8aOdfL8v51/zxe3ApY/lCfnc+30CzUYzY7r7svjOgbhbZciKosjSA7lc9fEukgpr6eSk5Ytb43l9aux5Z4FfirA2Vz5RUENifjW3yn5887dnKt91C1H76VAetU0GZg0OB1oi5F69JgaQBmx+k6uAloGNrLJ6Zb2zaMCWJ+RjNJmZOSgMr1aZvssO5XOdHBtnIXoBchaziFSdHNrZVre57WSZspGP9LKt7M7fbhtNNjjSiyAPR+qajTZ6bK1axR3tTMxaHCMuFtQ0SRPsKw9L1jhvTYvlqfHdz1q1S8yrIv6NjWxKabF3cdKq+PCG3sy9obdCzv8uDO/iw/szeiMIsGhvzmmLKJcbOgjgn8DLvx1rc9vVcS0B1huTi9rcH2Ol/1sqO8JrVAK70iuUVoNl72Nxkh8X7XdOZe86nYHPZdPY5lbaPEv3WCXAva3aCY8sPYKIRE5fnxpz1p9jgYNWzS0Dw9j8xCjmz+xLXIgHeqOZH/bnMvr9bTzwQ4KNlsUaSnScRsWmlBLSS+t59Zqe8vVSpn66m1Olp/doEgSB6+ODWf/ocAZHetFkMPHCrye4/ZuDlNTqTvu8DnTgYsDCHZk8+bPUbprWN4gFt8bbkLrqRj33f3+YZ1cep8lgYmhnL9Y/OuKcJycvB3T2dVH8/gCeWJbIbUPCsdOoOJpXzcFsqcMyKNKT+LBO6I1mFu7MZOagUOw1Ko4X1LA/q5Ire/oT5CGRNEuiSs9Ad3oFu2M0Q3f/FgsRrVqgpLaZnenlONqpmTPOdlAuIaeK6EA37DUqpXtjmSIGiTzcZDUN7KBVUVDdRKD883MqG22MoasaDZTXtXRNVCpBySX+OcE2MeSmAaFt8oGrGw0cz68+62f5TyC/Shpi2pNRgbOdmq9u66fkOZ8JL69K4prP9lBjNazYK8iNdY+OYGqfPx9Teq64Oi6Ql6+SJANzN6bx/b6cszzj0kcHAfwT+G5vXpvbRskh4DqDicKatlNaFvsXo8msLBw9A92UaqCdVfvXssMdf47t3292Z1PVaMDL2Y6GZtvmtKUwNqKrDxorjd/O9DKlVX1d3yBcHGx3vOcCtUpgQkwAvz4whJ/uGcSY7r6IIqw9XszUz3Zzw4K9bE0tbVOdG9/Tn+/uGICLvYb9WZUsPZDHwlnxBMiTWdd8uttmF9wegjs58cNdA3nxqmjsNCq2p5Vx5Qc7FD1jBzpwMUEURd5al8IbayUicvfwCN673jbabW9GBRM+3Mn6pGI0KoHnJnZn8eyBf4nw/VLDnCu6KKSnqEbH5pQSxcvP0j4VBIGHZC3g9/tylc0hSBU5gNeukfwFy+qaWSETK0sySLWVTtp6GASkDGH/Vp/7uuPFChEP9HBEBLzleDiDSeRUab1S8VPJbM/iw1pS10z/MNtuzjMrbQsJlve+J6PCxqPOz82hXSnQMyvaFiL+aZwoqOHaeXtIK6nHz82eZfcNVs6Fp0NeVSPD/ruF71olmzwwKooVDwy1mbL+p3D70AgekY+lF387wdrjbYs4lxM6COBfiJBOjvjINgCJrSpfFmJn0f/9ZJUHOaKrNzXy8Icl2sZeo6K+2YSLvYbBUba5hu2hulHPQrnl0Xqc3bp4+OJVLRYLzUYTzyw/pjzmiSu78WcgCAIDI734+vb+bHh0BNf1DUajEtifVckd3x5kwoc7WZGQb5N3OTjKi6X3DMLbxY7kolpe+i2Jz2f2ZWCEJw16E/cuTuD9P06eUReoUgncOSyCNQ8PIzbInZomAw8vOcLDS45QfYlYJXTg8ofRZOaZFcdYILf9np3Ynf+b1BLtZjCZeXdDKjd/uY/iWh0R3pJlxr0j/1rh+6WESB8XpsmED+C1NcnMHhqGIMCmlFLSS6QuwaiuPsQESV6B3+zO4s5hEcpjTpXWM6aHr2Lh8vqaFMxmkat7B+Jkp6awRkeoPMxhWWU2pZRQXt+MvUbN462qgD8dzGWqXJm0WFhVNrSQyE+3ZtBDrio26k10ctJS2aBXKn9RvrbEZsfJMpvNcYinE4MjvRBFWJFQYPPY262SSixHRHJR3WmttP4JbEktUYaYuvu78ssDQ+kZ2HaC3Rpf7cxk5Dtbya9q8WP0cbHjx7sG8vSE7qfNuv4n8Ni4rtw0IBRRhEeXHmXPqfL/2Xv5u9FBAC8QBkPbL9xEqwmxvads9X96k4haJSj+f9Z5jjtOSgeY1mqRj/SRFonR3X3PKp4FSe9S12zE09nORkALLdYvAe4ORPm0WMl8sT2TQrkKOSk24C+tMHTzd+X9GXHseHo0dw+PwNlOzcmSOp74OZGR727ly52ZClGNCXJn+X1DCPF0JKeikbsXJfDCVT24Y2g4AJ9sOcWd3x1UEkpOhy5+rqx8YAhzruiCWiWwOrGQKz/YwbaTbbOYO9CBfxI6g4n7fzjMskP5qAR457pe3DcySpnszy5v4Pr5e/lsawaiCDf0C+H3h4cRG3zmE+m/AY9c0Vk5UTU0m1h2KF8xxV8oV/gEQeAh2Rfw2z3ZeLvaM072iftql/SYl+WJ4KpGA9/vy8HFXsOUXhKR83RuGc5QqwSMVobM0/oG2WTyVjUaEEXwdbWnTmeku78rZsDNQVqna5oM9LIaWIiXK35alfRb7EyvsBkUMZhFNqXYOiDM6N/SBrbe/A6K9CTEU3ov1lvil1fZJov8U1i8L4e7vjtEo5x9vOy+wQSeIa2qTmfgmk938dqaFBtz7LE9fNnw2EiGdG7re/lPQxAkKdSEnv7oTWbuXnSI43I4w+WGDgJ4gRjz/rY2t423cuxfeii/zf2xQe642Gto1BsV37sAd3uOFUgHl8HqG1Er6yHOJf2jrK5ZiYKqPQNJsrRJQDKd/WRLunL9vnYyJ/8KBHo48vzkaPY8dwVPT+iGt4s9RTU6Xl+TwuC3NvPO+lRK63Q20XFldc3cvHA/k2IDmDsjDnuNiq0ny7jm012klZw5u1GrVvHYuK6svH8IkT7OlNY1c/s3B3n+l44ouQ78b1CrMzDr6wNsTC7BTqPi85nxzJBjw0RRZHlCPpM/3kliXjVuDhrm3dKX/17fC+e/Wfh+qSDMy5nr+7VUAb/Zna0Mg/x6pFDR/F4Z7U8XXxfqdEYW781RzKNXHC6grK6ZcdF+Sjv3nQ2p6AwmZRgkpahGIWUWt4SfDuYhiiIatYonx9t2RxbsyOBaWZ/mImu1rWU3O60GADPLGujkpEUv67Lzq5qYNTjM5vXekLWJFkzoGYCrvYb8qib2ZbV4yQqCYGMJYykZrDr6z0pezGZJyvDirycwi1Lb+ps7+iv59u1hw4li+r2+iUQrMqVVC7x6TU8WzupnQ8L/11CrBD68sTeDI71o0Ju4/ZsDp82wv5TRQQAvEHnVtq1FB22L/YvJLFJc2yLs9ZDH9y3t31+OFCi7Nx8Xe1o3N2OD3CiobsJOrWJUNx/Ohs+3ZdBkMOHppFVyfltDqxa4yeqk8/KqJKXdPDjSq13T2b8S7o5aHhjVmV3PjObtabGKYeu8bRkMe3srz608Rl2zUYmOq9MZmfnlftwctKy4fwhBHo5kVzQy9bPdrDsHXUZciAdrHh7O7bKL/g/7c5n08U4Scjqi5Drwz6G0TscNC/ZxIKsSV3sNi2YPYLysH6tpMvDI0qM8+XMiDXoTAyI8Wf/oCCbFto1++7fj4TFdFNNlo1mKwesX1gm9yaxsflUqQUkH+XJnJj0CpHg1vdHM4r3ZCILAsxMlC6r6ZhMLd2TSO8SDbn6uNBtFm2qrAKSX1nNElvJM6RVIpJU348HsKsZ0l9bmo7nV9AvrhElsyfzNrWzCR7Z8yapoUNYhCxxbxZdlVzTabFAd7dRcFScdB8tbFROu7ROkSIosq73RLPLDPzS0oDOYeGTpEUXK8Pi4rrx7fa/Ttm2NRjP3LDqkTLxb0NXPhdUPD2PW4PBz9rj9J+GgVfPFrHh6BrpR0aDn1q/2X3YDhh0E8C/CgHBPxWg5tci2XGz5klq0fN/KCxag5ABrrI7/cFn8OqSzlzIxfDoU1TTx/X7pi3862xeACT39UcktiI3JJWxJbWmL3tXKvPTvhINWzY0DQtn0+EgW3BpP31AP9CYzSw7kMXbudp5ekcgTV3blCjk67t7vE0gtrmPVQ0MZEuVFo15qpb2zPvWshp2OdmpeubonP9w1kAB3B3IqGpk+fy//XZ/6r3N878A/j5yKBq7/fC8pRbV4u9iz9N5ByibwYHYlkz7ayerEQtQqgafGd2PJ3YPO2D77NyPE04kZ/VsmSv9IKmGanMrxw/4cRU5yVa8AQj2dqGo0sORAnlIFXLwvhya9iSlxgUo6yKdbT1HdaFCqgKVW07iWlcUynKdSCTbm1ADLDhXIk8QiPWVPRmt9s8UOTBSlCDp3x5a1/NfEQoJb/a3n/nHS5vr18dL7WnuiyMYWy9VBy/T4YFrj/VbP/ztQJROh348VoVULzJ0RxyNXdDktgTuWX02/NzbxRyuT/5mDQln10DC6+1/cXpauDlq+vWMAYV5O5Fc1cdvXB84qRbqU0EEALwAnCtpWka7p02JXYJk8s6CmySj5/4V1IreikXS5/atVCcrCZZRXHB8Xe7IrpFLz+HOwfPhkyyn0RjMeTlrMIjY2Ada68Rfk4Y9GvZH/rE5Wbo/0dmb0Waa1/g6oVALje/qz8oGh/HzfYMb2kCaHNySVcNPC/dQ0GRgc6YXJLPLkz4msPFzAotkDuGuYRFbnbctg9rcHqWknaaU1hnb2Zv2jI5jWN0iJjbrm092kFHVEyXXg70FyYS3Xfb6X3MpGQjwdWXH/YHoGumM0mflgYxo3LNhLQXUToZ5OLL9vMA+O7myT1NOBtnhoTGcsBgYisPRAHlE+Uidh6QHJEFmjVvHAKEnO8sWOTEZ19SHE05GqRgPLD+ejVgnMuULyDWw2mvlkS7pUUdOoyCxraGM2vDqxSEnwGN/Tj65+LRrq344WcI1s+5WQW8WACE9MYouRv7U9zJIDudw5rGWjnVXWwFvX2lpuLT1o6yrRN9SDKB9ndAYza47Zdj1mtTMMUtloILnw79Oq5VQ0cN3neziYXYWrg4bv7hhgM6DTGq+uTuLqT3fbFCbcHTQskL0sHbSXhpelj6s9i2cPxMfVntTiOu767qCSR32po4MAXgCmfrK3zW2juraQqI1WZpaW9m+vYHec7TUsP9xSzreXvwDWC//VvQM4XlCLIMDYs4Rd51Y0skxeNCxEyPpLZSmQRXg7KwMen245RUF1k/Iz77gIoqT6h3vy5W392fjYCGb0C0arFjiUU8XezAo85F3zG2tTeO+PNJ6f3IOPbuyNg1ayfJny6S5Si89O5Nwdtcyd0Zv5M/viaYmS+3Q387dn/Kuifzrw92N/ZgU3LNhLeb00FbniviGEeTmTV9nIDV/s46PN6ZhFabhgzSPD6BN6bibv/3YEeTgqOb4AxwpqGBttGfTIUnLJp/UNJsDdgdK6ZlYeKeAu2fj+KzlCbkb/EMVj9bs9OdTpjEySB/gst1tQ32xUyJcgSElGFhjNIvlVTWjVAicKapkmawKthzYsdjAHsiq5bXAYLvbS+iwC6VYm1AANepONp58gCEoyiHVoAEhDdn1ksmq9ej21/O+xhDmcW8W0eXvILG8gyMORFfcPOe3ARkF1IyP+u4WvrTpdAAMjPFn/2IhzKmxcbAj1cmLR7AG4Omg4mF3FQz8extjKa/dSRAcBvAC0HiUI9XTCy0WyfzGbzYrnE6CEtA+K9MJsFhXCBi12LRYColYJdJKzEuNDOymWMqfDh5vTMJpF3Bw0ipFzUzs7k6dke5dTpXXK1JzJLOLhpLUJN/9fo4ufK+9cH8fOp8dw78hIXO01NrvH+dszeHxZIpNjAxRdYG5lI9Pm7WmzQz4dJsQEsP7R4Yzt4YveZObtdancsGAvORWXn8C3A/88NiaXMOvrA9Q1GxkQ7slP9w7G182B344WMOmjnSTkVOFqr+GjG3szd0bvs0o8OmCLB0d3xs5Ka7bqaCE+rtJgmcX7006j4l659fv5tgym9gm0ipArwUGrVjoJJlHk3Q2pCrFMyK5Ukj0s+NlKgzeyqw8xVhF8Px7IZWRXSQuYUVavaAFVVnpFAL3JTGFNE7OtUpiWHcq3iZUDeGW17TTvtD5BqFUCh3OrlcFBC2ZbVRQtPy+psJZ63V877Lb+RDE3fbGPigY9MUFu/PLAELr6ubb72G92ZzHiv9vItbJ3UQnw5JVd+fHuQQS4X7oShx4Bbnw5q58SVvDcyuOXfPJUBwH8C2Bt/7J4n202o0W7MTjSi32ZFRSfQUQ6oac/ezOlia+zZf+eKq1TbApq5S98cKe2Xy4HrYpJvQIQRZEXf03CYBKVqtotA0Nxsrv4Jg393R14bmIPdj83hucmdsfXigj/cqSAsXO34+qgYfXDwxjaWdIFPvjjYd5ed3ZdIEikfOGsfrxzXS+c7dQckqPkftyfe8l/oTvwv8OyQ3ncJwvdx/bwZdGdA1AJ8PhPR5mz9Ch1zUbiwzqxds5wrul98Wy8LiUEuDty88CWKmBRjY74UA9Aavlavr83DghVsso3JpcqU8OWDfCsweHYy/3kVYlFONmpifR2ptFgJq6V9c6B7EoyyyTyJQiCIqcBqY3sJHddfj1aqAyhtLeMLN6bw+yh4UqL+GRJHe/PiLN5zOHcapv2oq+bg0IwWyeDjO/pr+gKrZe9l1efaPvDLxBf7cri/h9a4gp/umewTX6yBfU6I1M/281/Vidjsvrlgzs58vN9Q3hoTJfLQuIwMNKLT2/qg0qAnxPy+e/6v193+XeigwCeJ95cfbzNbROtpvasXc21aoHi2mY0KoH4sE78nNCykxRa/Q9SEse+TElfeGX0mcvkH2yU2kiWloKDVkVeRWObx1mE0qsSC9mbWYFWLVDdZECrFpTMzIsVbg5a7h0Zxc5nRvPO9b3wl3fm2RWNjHxnG6+uTuLFydHKbn/+9gxu/+bAOZk/C4LAjP4hrH90BAMiPGnUm/i/X44z+9uDlF5mk14d+PuxYHsGTy8/hskscn18MPNnxpNSVMvkj3ex8kgBKkFKtfjpnkGEyKbDHbgw3D8qSiFvADvTy3HSqkgtrmN7mmS/4qBVc5dslzJv6yluGRSKnVpFQk4VCTlVdHK2s4kpe2ttKjNkq5n8qiYbT1aQqnUWDIr0orcVSdyQVIyHk5ayumZApHeIRxtnB4C1J4rxcLJTyKgowp5T5dhbTQCKIizZbzvNa3lfKw8X2LQd7TQqbrOyk7G85b/CEsZkFnllVRKv/Z6MKEpDG1/cGt+uNdGm5GL6vb6xTezn1XGBrJ0zXPFBvFxwZU9/3p7WC5DOOZYAhksRHQTwPPHFbtsKn6NWTS/ZQsVsNpNl1Uq0mIf2CnbHJIqsO9HSphRb/d/Vz4WqRj0ms0g3P1fCvU8fg3OioIY1shWKpd08KNJLyRC2XrqeGd+dWp1BycC0GEFPiQu8ZKKl7DVqZvQLYc8zY3jyyq6oBOlz+/VoIRM+2klGWQOPju2Cg1bFzvRypny6i+TCcxvwCPF0Yundg3h+Ug/s1JLf4JUf7jjnlnIH/t0QRZE316bw1rpUAO4dEcnb02KZvz2D6+dLQyBBHo78dO9gHhvXVZkM7cCFw8/NgVsGthCfBr2JKLkl+YXVyXjmoDDcHbVkljeQkFPFVHlQzzKkd+ewCNTy9OrezAoCPBwlPV9hLSNb2W8tT8izIV8vX91TudxsEpWUkRWHC5gzVhoyaS+3t7y+mYfHdFFum78jk6us8uMBPpPz3C0Y090PT2c7yuqa2ZFuGzBw08CWfGBLFdBgEvnpoO156nzQpDdx3/cJSljBcxO789o1MW2OXaPRzH2LD3HXogR0VtPPTnZq3psex0c39j6jL+CljBn9Q3hGngp/Y20KKxLa+v5eCuhYjf4kBkR4KkMUm1JKbUr/PrL+b3CUF2uOFaEz2IpGrReIWYPD2ZgsDY+cLfv3g41pAErrwd1RS7aVSaXlLcQEuuHuZMfcP9Ioq2smpJOjMoFsPZF2qUClEnhoTBdWPTTMxlJhU0oJH25KJ8LbBW8XO/Iqm5j2+W5WnWMesEolcPeISFY/PIyegW5UNxp48MfDzFl65JymjDvw74TRZObJn48ppOO5id25bUg4N3+5n/f+SMNkFpkiV0EsCUD/ZlTUN1P1F0Uz3jcqEgdty+krubAWlSDl51pSG1zsNUqa0KdbTilr3vqkYnIqGgjxdFK89gDmbc1gbA9pmK91+lJ5vZ5tJ1vIV5/QTjat4lTZUeCP5BL6hnjQK9i93SrgL0fy6eRspxQNssobeXKcrcl0eb3eRu9np1FxjRw993MrT8AAd0fGRbd1cXhnfWo7P/3sKKtr5sYv9iqm5Z/e3Id7rRJrLDhRUEO/NzexPsnW3iU2yJ01jwzn+vjgi9Lb76/EfSMjlWPq6RXH2JJacpZnXHy4aAng3r17mTt3LvX10hehvr6euXPnsndv2wncfwrtxb9NtbJ/+aJVKTi3UmrJDor0Ynk7OwTLAuFsr2ZijL/SvrjyDFNSh3Or2JxaigA0ylqRmweEkN1O+/e5Sd05UVDDor3ZAMQGu2MyiwyO9Go3q/Fi/MzbQ0yQO789OFSJRHLQqtCoBFKKaimv1+OoVaMzmHlkyRHeXJtyztNa3eQcy4fHdEYlwG9HCxn/4Q52pJWd/ckXiEvlM7+c8Fd85jqDVCVZIVuLvHN9L0I8nZjw4Q4OZFXibKfm/elxfHxjb5vNyr8VG7ft5P25c1EZJa+9uro/d5z7ujrYSFhMZlFJ+Viwo6WCdvuQcFzsNaQW15FX2cTobj6IoqRtAxSfQJA0eYEeUnt+Z3oZMUG2HnXLWk3ivjkttuXni+DuoEVvNLPmeLFiNdMaP+6TXuPt61qeu3hfTpvp4w822mrLpsuegJtSSqhssCXR1vnAljZwRYOBVX9sPa/j/FRpPdM+301ifg2dnLT8eNdAruoV2OZxr69JZsonu6hutTm+d0QkK+4fQsQZuleXEwRB4PlJPbi2TxAms8gDPxzm57WbL6n1/KIlgM3NzdTV1SmiXlEUqauro7m5+SzP/Psw/oMdbW6ztn+x1kA42akpqtGhUQl4OtuRkFN12tedHh/C4dxqmgwmgjwcFVPR9mAx+7STNTAB7g5Knq81XB00DI705sXfpKie8T392JkuZQ6fzvj5YvzMTwfr6DidwYyDVs21vQNxddDYTEJ/sSOTW77c12bRPB3sNCqeuLIby+WFrLhWx6yvD/DirycUP7C/EpfSZ3654M9+5jVNBm79aj+bUkqx16j48IbeJGRX8cAPh6nVGYkLlqog1/0LqiDngtI6HUv2ZmAv6q1sT/78cX7viEicrDJ1Levg2uNF5Mmbbw8nO2bKmrtPtp5S1r5lh/KoatDTM9Cd4V1a7Ex+P1ZIgLsDtTojsa3SkTanlMg6Pwk9A91tfAFr5IG/5Ql5jOnu2+46nl3RQLPRRHSgu5Ia8t3ebGa0MnbekFRiY1YfHehGTJAbBlNLRrEFg6O8CJV1pdbDIL8fyTvn43x/ZgXXfb6HvMomwrycWPnAUPq1qloXVTcx8p2tfLkzy6a66eNqz+I7B/DcpB7KeenfApW8+RvVzQedwcyKg9mX1Hp+0f617O3tcXV1VRZQQRBwdXXF3v7M1ih/JzIrbYlWmJcTnWRX+W0nS21i2CyxQXEhHvx+Fj3ZrYPD2JBUDMC4aL/TnjT2ZJSz+1QFKgElUueFyT1Y20402i0DQ1l2KI8judU426npGehOnc54RuPni/EzPxN83RyU6Lj6ZiNrTxTz1rRYXpjcw8bKYX9WFSPe2cq2k6VneDVb9A3txJpHhimZnYv35TD5410czj09kb8QXGqf+eWAP/OZl9bquGHBXsUM9z9X92TuxjR+OpSHIMCDo6NYfv+QM2p4/00ordVx0xf7KGkwocNO+czNIpg1Dn/qOPdysVeqgJYV08Veg9mqwgfShtdBqyIxrxqTnNqhM5j5Xo5Os+SgC0BJbTORPtLfLr2knk5OLdVbkwgrD9t2cua2muIFaZI3q7yBR9qpAorAdnkdsnR6GppN9G3lBWk0i6w/Xmxzm6UK+HOrbpIgCMyWW93Q4itbUGfA5RyO89+OFnDrV1LCRZ9QD1a2U8X7bk82w97ZSk6lbadpdDcf1s0ZzvAuZ48svVyhVauYd0tf+oR6UGfA5ji/2Nfzi5YADh48mMcffxwXF2mH5eLiwuOPP87gwYP/x++sBdb2L9YLDqBMSw2M8GyzaFhjWGdvwjyd2Jwi6QdOZ/8iiiLv/yFp/1TywRXl40JpXTMGk63aRABmDQnjbVkHMmdsF6UFPfsMxs+XwmfeGu5OWhbfOVCJjpuz9CgeTnZsf2o070+PI8xL2hnXNxu5/ZuDzJi/l7SSunN6bSc7Da9eE8Oi2QPwd3Mgq7yB6z/fw3sbTtpEPv0ZXIqf+aWOC/3Ms8sbuG7+HlKL6/B2seP6+GBe/O0EWeUNBLg78ONdg3hqfPfTZqL+21BSq+PGL/aRUdZAlXM49z30iPKZN6FlUX0MrqHRZ3mVM+OeEZE426mVipTFW/Wng1KFD8DbxZ4b5Ri5z7ZmKG3f7/bmoDOYGBLlRUyQm/IaR3KrEYBDOVVt5DhLD9paRcUEebTxDQRpYvfKaD96BLStAn61KxuAx8Z2VW77YHM6vq52No/7cHO6zfVregdip1aRUlTLiQLbxI9p8cHYydPEFiusJKM/JaFXnPY4F0WRz7aeYs7So+hNZibG+LPk7kGKpy1I9i7XztvNy6uSbCy27NQqXroqmq9v74+3y8VJbv5JONlp+Ob2/ug9O7OkqRc6pI3Dxb6ed6xU54iTxW3j3ybHtugjDmbZ3p8jD2U422soka1g2sOswWEczK6iqtGAh5OWAacRi29LKyMhpwq1qsVcdO6MOJYeyGvz2P7hnny8Scq57O7vSrBsmCwZP58+uudShaOdmvm3xjOtb5ASHffdnmyuiw9m25Oj+PSmPooO60B2JVd+sIM7vjnA/syKc/L9G9HVhw2PjmBq70DMopQhOvWz3ZwsPjci2YFLHycKarh+/l7yKpsI8nAgzNOZb3ZnYzCJTIzxZ92c4UrWdweguEYif5bkiKX3DCbMq6Wq5KBVI4rw6urkP+W96elsxx1DpbauZYnVqiVD/MX7WuxU7h0ZiVYtsD+rEl9XewLdHSivb+a3owUIgsC9I6QqoFol0Kg3ESQ7OLRetbPKG9t0AV6+qi2JXXk4H1GER8Z0bnPfoexKRFEk3NsZN1n7d6q0nkERtskaWeUN5Fppuz2c7BgnFwhaJ4O4OWiV7GDL7wGSb2p7MJrM/N8vx3l3gyQpumtYBJ/d3NcmSWpzSgn93tjIkdxqm+dG+Tjzy4NDmD0s4rKVOJjMIrU6A4XVTaSX1HE4t4qd6WWsO17EskN5fLM7i082p/PWuhSe/+U4c5Ye4cmfE3G216BVC8o5+mIPmbr4XIAvUkz+yFbE6ahVKRqPPafK24zBF9c1o1ULHG+1U7NGgLsDV/Tw4/U1UjbvFd392rWJkKp/J+XL0m29QzwwiyIn26lmTe8XrEQCvTY1RpkIu2VgKI52l0b+4vlCq1bx3vVxeDnbsXBnFm+sTaGiQc8zE7pxVVwgE2MDeGb5MSWKb+vJMraeLKN3iAf3jYxkXLT/GY1K3Z20fHhjH67s6c/zvxwnuaiWKZ/s4snxXblzWORlYXLagfaxL7OCu787RF2zkZBOjtTqjCTkVuGoVfPK1dHM6Bdy2Z4ILwSF1U3ctHAfORWNMvlr633o5qDBQafiQHYla48XM7lXwGle7ey4a3gE3+3Jpk6u/lk6It/tyeaeEZE4aNUEuDtyfXwwSw7kMX97JrOHRfD6mhQW7sxienwIE2P8CfF0JK9SSrAoqpbkPhuTSxjdzYetVhPASw/kEh/WslEfH+OPs72aBqsEqMIaHfsyKxjf059ufq4267RJhNTiOnoEuDG6uy+/yb59hTUt6RkWfL07i1esLGemxwez5lgRvyUW8n+Te9hMK986KIwf90v2L5Y4OoNJbJMOVd9s5MEfDrM9rQyVAC9P6cltQ8KV+81mMw8tOcLaVi1ogJsGhPDiVdEXZYCAKIo06k3UNxup0xlpaDZSb/mns7osX29oNlLXbPU4q8c06v9k1q+sHDBc5HFxF99f8SKFsRWTHxjhpbRSv9hpO/0b3MmRtJJ6ega4sUXOBTa2sxW4dXAYKgH+kEfpT2f/siGpmBMFtWhULTuLT27sw6dbT7V5rKezVslgnB4fjJ1axcHsqkvC+PnPQqWSsjo9ne357/pU5m/PoLKhmTevjUWjVvHejDjGRvvy2E9HaZIteY7mVXPf94eJ8Hbm7uGRTOsbdMaQ8kmxAfQL68SzK4+zJbWUN9emsimllPenx3UY/F6G2JBUzMNLjqA3mvF1tSdPjrjqGejGxzf1UXw1OyChoLqJm77YR25lIyGejiy5exDBndp+L1Ry1e2jzem8uTaFK3r4nvF7dyZ4ONkxe1gEH21OV9ZIAaho0LM8IV8ZArl/ZGeWHcpne1oZ98tRk6dK69mWVsqY7n7cPTySl35LwkGrQmcwY69RUdGgJzrA3YYArkos4pWrYxSZj6TBi+CTLbbr8c8JeQzp7M3DV3TmoR+P2Nw3b2s6n9wcz/0joxQCeCinCh9XO8rqWgbWlh7M5YXJPZTCwPAuPvi7OVBcq2NTcqkNce4R4EafUA+O5FYjIlUvRaBOZ8SSEVVco2P2twdJLqrFQavik5v6Mi665bxzoqCGW7/aT1WrCV83Bw1vX9eLSbEXTtTbgyiK6Azm0xA1A/XNJoWstUvsrMhcvd7YbgLLn4FWLeBir8HFQYOznQZXBw0u9hqc7aXLznbSfS72GuVxLvYadv+SjLHZYGNYfjGigwBeIK7t0xLldKBV+9dVXhjcnbToTWZ8XOwoq7edQtWqBW7oF0JSYS0F1U04aFXtCmlN5hbtnyViZ2RXHzq52LEqsW15v29oJzallOLuqOXZid35z2qpungpGT//GQiCwP2jovB01vLcyuMsO5RPdaOBj2/qg4NWzYSYAKJ8XLhncQJZ5Q2oBbDTqMkqb+D/fjnO3I1p3DE0nJkDw3B3at++w9fNga9u68dPB/N47fdkDmRVMuHDHbw0paMadDnhp4O5PLfyOGYRnO3UlMoToPeMiOSJK7u28Yr7tyOvspGbFu4jv6qJUE8nltwzSDFIbg/3jYxi2aE8CqqbWLgjk4dPY51yLpg9LIKvd2dRJ8diWnjAlzszuWlAKGqVQKiXE9fEBbLySAHf7Mnm5oGhLNiRyRc7MhnT3Y/p8SF8sDFNIT+WQbvE/CrCvZwUq61mo5k1x4qY0b+l5TprcDifbjllMx275lgRr0+NZVJMAF180xUPVoD1J6RNf/cAN1zsNYp20V5te0zpDGa2nixTSJpaJTCtbxDztmXwc0Jem8rp7UPCOZJ7VH4sGM1gFkUQpCLEtfN2U1Sjw9vFjq9u609ciIfy3LfWJvPFjqw2/oUDwj354MbeNn/LZqPJlqzpjDToJYJWb6mq6YwSgWs2yI8zUa8z0NBsqdIZaNCbzim+83ygVgk426lxddDKZE2Ni4MWF3u1TNTkyw7SZWd7tQ2Zc5Vvc3HQXPB3/LCdmrqLc/DXBh0E8Bzw/rqkNreNkidpD2VXtikXW6oElnaCoZ0DfEqvQLxc7JXouJFdfdptz65OLCS9tF7Z2aoE+GBGb1YnFipVLAtUgtSOBnh6QjdpoZInhC9F4+c/gxv6h+LhZMfDS47wR3IJt319gIW39cPNQUsXP1d+fXAoj/10lC2ppTQZTAwI9yS/upHCah3vbjjJZ1tPcdOAUGYPi2j3JCYIAjcOCGVIlDdP/HyUg9lVPLPiOH8klfDWdbH4ul7+ZPtyhSiKzN+eyX9l6YRKkNImfF3teX9G3L964vF0yKts5MYv9lFQ3US4l0T+AtxPT/5A0u4+O7E7c5YeZd62DKb3C1HiHs8X7o5a7h4eydyNadhrVAp5y65oZGNyMRNiJKL0wOgofjlawIakEr6/awBf7cpiX2Ylx/NriA1257Yh4Xy4KR0PJ63ic7frVAUPje5s03H5bm+2DQH0cbVnRFcfxcsVQG8SWZ1YyI0DQnloTGfmLD2q3GcwixTXNOLv7sSQKC/+SJYIYX512zbw/G0ZNlW66f1CmLctgx1pZRTX6Gw+s4kxAbzkcIIanZHWc2oV9c0U6XSEejryn6tjMJjMbD1ZSn5VIx9uTKeiHauscC8nzKKZO789SIO+pULXevDwz0IQwMVOqqy5OMgVNnvbapvlskTc1DKRa6m8OdurcbXX4qBV/c824c1GE3szKqjVGdroRy9GdBDAc8An27Ntrod7OSnVoQXbbWN77DUqSuua0aggs7wBjUpoY5gJUvsXUL747WX/GkxmPtgkVf8srd+r44LwdLFj6YG2UT/eLvaU1jUTF+LBjf1DeWd9KiazyJCo9o2fL3eM7+nPd3cM4O5Fh9ifVcmNC/bx3ewB+Lja4+6o5ctZ/fhwUxofbznFgexK+oV34v6RUfywP5fU4jq+2pXFd3uyuToukHtGRtLdv+1EX6iXE0vvGcyXOzN5/480NqeWMv6DHbx5baxNRnQHLg2YzVK025dWU/1mEcb28OW/1/WymZDsgITcCqnyV1DdRIS3M0vuHnTORO7quEAW7c0hIaeKd9anMveG3hf8Pu4YGs5Xu7KoabJdb+dvz2R8T38EQaCzrysTY/xZe7yYnw/lMyUukF+OFLBwZyYf39SHWYPDmb89g+pGKS/dQnSajSYctWpFT5dUWMup0no6+7ZIAGYOCrMhgNLPzuDGAaFc1SuQjzank1nWktj06NJElt47mHtHRirnAUBpQVuQkFtFUU2TQqgjvJ3pH96Jg9lVrDicz4OjWwZN7DQqbh0crpBVrdoqZ1j+P7eyiTu+PXhOn2l2RWO7IQMWONmpW8iaTRXNmqy1/FNap/J1y2Unrfq07hQXOyrqm9l6soxNySXsSC+jUW9ihoMJZ6F96dfFhIu7QX2RwloHsSezwuY+PzfpBOHtIi2AvYLbEq+YIDd6h3iQU9FAanEdapXAFT3aevOtSMgnp6JRmSDWqATenBZDcmEtiflth0tK65oRBHj9mhiaDCZ+lEniv636Z43BUV4svWcQ3i52JBfVcv38PcpknUol8PiV3Vhwazwu9hoOZVcxb1sGb0+L5bvZAxgS5YXRLLLySAETPtzJ7d8cYG9G28lhtUrg3pFRrHp4KD0C3KhqNHD/D4d5/KejbU5GHbh4YTCZeXJ5og35s9eoeG1qDAtn9esgf+0gu7yBG77YS0F1E5E+ziy95/Tkr9loUlqdFgiCwEvyFO3KIwUc+RM+m64OWsXixdmqm3I0r5qD2S2v+8AoiTCtTixkUqy08V5zvIj8qkY8ne24oZ9U2fO3ksz8drSAq1tl9v5gNWUMMKqbD57OtlYu2RWNpBXXolYJPNxqInh/ljQNHB/mqcTaCdAmMhRg2UHbqV+LJ+DyhPw269HNA0OVy391pc4aWrWARiVIecqC1Go2iSIGk5lmo5kmg0katNAZqWkyUNWop7JBT3l9M6W1OoprdBRUN5Ff1UhBdRPl9c006U1/air8n4AoipwqrZfyvj/fQ783NvHkz4msTyqmUe4UWGJaL/bhwI4K4FlgNLZNf7DoLo7lV9tMfgGy3UiTcuIvrm2b0nHb4HAEQVCGPwZGeOLhZLtwNBtNfCz7QFl2EbMGh+Fkp2k36FstCJhEkVsHhREb7M43sh7mTMbP/xbEBLmz/L4h3Pr1fnIqGrlu/h4WzR6geHSN7+lP1IPO3LM4gcyyBmZ8sY83psbw492DOJZfzYIdmaw7XsS2k2VsO1lGXLA7946MYnxP28nh7v5u/PbgUD7anMbn2zJYeaSAvZkVvHt9HMO6eJ/u7XXgIkCTXop2s67gdPd35eOb+tDVz/V/+M4uXmSVN3DTF/sortUR5SNV/nxPozMuq2vmvu8TiGo24ixgM5kaF+LBdX2DWXE4n1d/T2bl/UMuuIV325BwvtyZSVWjAbUgTdwCfLEjgwER0uRuTJA7Y7r7siW1lE3JpQzr7M2uU+V8szubF6+K5q7hkXy/P5e8qiZcHTTU6YyU1unp7m97HCw7lM//Te6h+D5q1Squjw9uEwn68qokltwzmCm9AvloU7pSURORpnzvHBbJgHBPdqSXo1Wr0LczOfrN7mweHtNFqZJN6hXAy6uSyCpvICGnyia1I9DDkXE9fNkoDyBqVSoQoZOTHd/d3J9D2VXM356hkEPLsIhGJdA7xANfN3ua9CYalX9G5XKT3qS8P4NJxGAyUqv7axOSBAEctWqc7DQ42amVf872Gvl2NU5y1dDJXnqMs50aR5vHa2yfZ6fGSatu12XjXGA0mTmUU8Wm5BI2pZS0qYpGB7gxNtqPsT18iQl058MPj1FXp7vo28AdBPAsmPChbfybk1ZFtEwc5rdq/0KLfUCTwYS3ix2F1bYE0M1BwxR5J2lJ/xjfTvbvkv25FMpRckaziKNWzf9N7IHOYGrXWNokini72PHEld0wmUW+3i1VMc5k/PxvgiU6btbXB0gtrmPGgr18fXt/+ssLZ2dfSRf4+E+JbEop4anlxzheUMMLk6P57Oa+5FQ08OXOLJYdyiMxv4YHfjhMmJcTdw2PZHp8sDLBaKdR8dT47ozp7scTy46SXdHIzK/2c/uQcJ6Z0P2yteG5lCGKcP3ne0gqqlVuu2Oo9Pe60MnUyx0ZZfXc9MU+Suua6eLrwo93D8LHtf0K6YmCGu5ZdIjCGh1RMj+sbTLa6NeentCNdSeKOJJbzW9HC5lqNWR3PnCx13DvyCjeXpeKs71GISebUkpJL6mji0zmHxzdmS2ppaw4nM871/di16lylh7I5ZEruhDi6cTk2ABWJRYS7uXE8QLpuNieXkq/sE4ckmM9G/RGNqeUKPpCkJwXWhPAfVmVlNXp8HF14KExXXjy50Tlvg82pnPnsEhmD4tgR3o5epNZIWTWqG4ysPNUOSO7+ii/5+ReASxPyGfZobw2sW2zhoQrBNBgNmMnSNXsZQfzWNPK3kVEIjAf39THpqV9OhhMZhti2KSXKn2NBhONza0Jo5GGVkSySW+iQW+0/b/ZpGwKRBHl+X817DQqmTDKpLAdwmi5rFIJFFU3kVFWz8nieptNi0YFfcM8GdPNhytj/Inwcr4kh/86COBZcKrcVpQ7MNJL+UNbsnUtUKsEyhv0yhe4b2gnG20HwE0DQ3HQqimrayZBbndYC3wBGvVGPt0qkUtL9e/hKzqj0ahYfSSfuub2vxjPT+6Bu6OW9SeKyatsumyNny8Ului4O787yKGcKmZ+uZ/Pbu7LWPnzd3PQ8sWt8Xyy5RQfbEpj0d4cUopq+eyWvoR5OfPa1BgeHduF7/bmsGhvNjkVjbz46wk+3JjGbUPCuXVQmBINGB/WibVzhvPm2hS+35fLt3uy2ZFextwZveltNXnXgf89yuqbSWqSTvLujlo+vLH3v75qfiacKq3npoX7KKtrpqufRP5Olwax9ngRTyxLpMlgItLHGW/RnqYGAyIi//fLcb66rR+CIODn5sCDozvz7oaTvL0ulSt7+l2w19yswWEs3JFJRYMeB41K8WhduDOTd66XotviwzoxJMqLPRkVHM2rVrz6lhzI5b6RUdwzIpJViYUkF9Xh62pHaZ2e7SfLefWangoBBKkIYE0Au/i50jvEwyYXXhSlKuC8W+KZ2juQDzeeJF8uDNQ3G9mQVMSV0f7YqQX0JpEwq4ljayzak60QQJDI5vKEfNYcK+KVq3vafF5Do7wJ9XQkt7Ll/FVa18yaUon8WVdH7xwWwdMTup3zxKtWrcLdUaWY6/9VMJtFdEaZDMrk8LTEsdlEo8GaPBptnmepYFpewzJprDea0RvN7eryzwdGs+T+cSCrkrfXn0QlYEMqhzbpsaPDCPqywzSZUKUW1SqWAxZ4OdspVhFAGxNoAZg5UBr+2JRSgihKGsHAVlOmi/bmUF7fjFolYDKLuDtqFd3KknaSP0BqI0/tLe2av9ol7UBnDgzrqDi1giU67qEfD7M5tZR7v0/gv9f14no5jF2lEpgztgs9A9147CdpunfKJ7uYPzOePqGd8HKx5/FxXblvZCTLDuaxcGcWBdVNzN0otX1v6B/CncMiCPF0wslOw+tTYxkX7c/TyxPJLGvgus/38ODozjw8pnNHZNj/EHqjWZkUNcuao/7hnZh3S/xpK1kdgPSSOm5auJ/y+ma6+7vyw10D29VGms0iH29J58NNkoxlRFcfPrmpD199fhiQ1sItqaX8erSAa/tI3707h0Ww5EAu+VVNzN+eyePjurZ53XOBk52G+0dF8fqaFBzt1AoBXHE4nyeu7KbYYT00ujN7MipYejCPZyZ047XfU/h2dzazh0YQE+TO8C7e7EwvJzrAndK6MkSkgRdvF3vK66V1/mheDaW1OpvW94x+ITYEEGD9iWJyKxoJ9XLikSu68vSKY8p9r/+ewvieAfQO9eBAVtVpdcNbTpZSVtesHJ8DIjwJ83Iip6KRtceLlTUMpHXstiERvPZ7snKb5ThXyeTPy9mO92bEXTSbHZVKkKtvfy0tEUURvcls1daWSGFDs1S5TCmq5UheNScKamzO3wDO9mq8ne1xddSgFgRZ1yhVKxv1RkWvaRahTjaWBhjgIFVdL/aaYAcBPANyStoKkkd2k3Zg87a1bf92ctZSWteMCMQGuSmtAwtGd/dVzIL/kNu/V7aq/tXqDEpr2bJreXFyDwAyy+rbeA6CNMnz2tQYBEFQBM+S8XPYefy2/x5YouOeWXGMlYcLePLnRKoa9NwtC8gBxkb78etDQ7ln0SEyyhq4YcE+XpvakxvkTFEnOw23D41g5qAw1p4oZsH2DJIKa/l2TzaL9+VwVa8A7hkRSc9Ad0bKUXIv/ZbEqsRCPt6czpbUEj6Y0VtpSXXgzBBFkWajmYZmaadf12zxE5PMYhusTGStjWKtDWSrGvXU6ozo9CZEYIaDHmd5hX5odBRPXNntkmzj/FM4WVzHLV/uo7xeT48AN364a2CboQeQOhhP/pyoJEncOSyC5yZ2R6NWKbozF3sN6OCVVckM7eyNr6sDDlo1/zepBw/8cJgF26XN1Jl8BM+EWwaGMX97JuX1zYqli8kMX+/K4rlJ0no6OMqLvqEeHM6tprBah6+rPcW1On4/Vsi0vsHcOyKKnenl7M+qJMjDkYLqJn48kCuZPltZwnyxI5MXrOLgrooL4NXfk2yGOcwivLshlU9u7su1fYN4ZfUJGvXS/XlVTSTkVHLb4AgOZEmxoFdG+7XpHomiRGLvGynF1gmCwPV9g3l/Yxo/H8qzIYAA18cH8/a6lDafjVmE4V28eX9G3L/CrkoQBOw1auw1ajycJL3vrlPlbE4pYVNKqULmQSLH/cI9GdfDjyt6+BJ5FqN3k1lKWmmU00Ms1cdNy1JobjJwsS8nHQTwDBj74R6b6xHezrg5SGXvHa3G/QFKrQY+OrVjImyJ26nTGdh9Spoebq3/+3pXFtVWAmZ/N3uul6fSfjrYfvXv7pGRilD9K3mCcUpc4GkF2R04c3SchQRE+bjw64NDeWJZIn8kl/DMiuMcy6/h5Sk9sZMd3jVqFVfHBTKlVwC7T1WwYEcGO9PL+e1oIb8dLWR4F2/uGxnFkCgvPr6pD+Oi/Xjh1xOcKKhl8ie7eHp8N2YPvTyntFu7/De0Mo1tL5JJIneWy7Lzv166/HdONK49UUx8uOdFUw252JBaXMstC/dT0aCnZ6Ab3985UJE7WKOwuom7Fx0iqbAWrVrgjamxzOgfgsks8t/1qVQ0NOMsSFqsnoFuJBXW8uKvJ5g/Mx5BEJgY48+ACE+ptbYulU9u6nNB79fRTs0Do6J49fdkaUpVxje7s3hoTGdcHbQIgsBDYzoz+9tDLDmQy53DpDSPL3Zkcm2fIIZ29lLe46BIT1YcLqBBb8LFQaN0ZwCWHcrj+ck9lHXDzUHLxJgAfjlSYKPn+/1YEfePqiU60I1HrujK2+tSlff10q9JrH54mKL5drFv/9T83Z5s7h0Rqfys6+KDmbspjf1ZleRUNNjkLbs7ShIg8/Gjym1atcDT47tz579MG15aq2NzaimbU0rYmV6uVP9B2oyM7OrD2GhfRnX1bfe4Ph3UKkGxuLHGDo2KS8AHuoMAngmGVuebiTESWcsoq6e6VZleAKoapfKvnUbVxs4gpJMjwztLk6Db08rQm8xEeDvbiG6rGvR8uVMicJZz3dvX9QKkltXyhLYE0MNRwyNjJAf9guom1v5LjZ8vBGeLjgPJWmL+zHjmbTvF+xvTFI/Az2/pa0OwBUFgWBdvhnXx5kRBDV/syOT3Y4XsTC9nZ3o5MUFu3DsiSjnBPbPiGNtOlvH6mhQ2pZQQf5GIRSx5mjY5mZbKmr7F3b+9KptNLFOz8W9x+QfJe8zaV8xeo8Io78RrmwxUNOjRt3bBtYK7o5YhkV54Fkm7dJUgkFnWwB3fHGRUNx9emBx9TmL4fwuSC2uZ+dV+Khv0xARJ5K+1awFAQk4V9y5OoLy+GS9nO+bfGk//cE8qG/Q8suQIu06VM0P+ytQ0GXhtRgwzFuxlQ1IJvx8rYkpcoGILM+XTXaxOLOS2wWFtBhzOFVLSRwYltc0EeThQUK1DbxJZvCebB+Q1c3Q3X6ID3EguqqXZYMbJTk1qcR27TpUzvIsP946M4pElR9h6skx5ja93ZzG+p59S4azVGdmeVqaEA4CUx/7LkQI0Vl6CIvDOhlS+vWMAs4dG2BDApKJasisb6BnoRmJ+DXszKxjf048NSbZVwKIaHfsyKxkc5QVIE7/DOkut6uUJUovbGrcODuO749JljUpg5f1DiW3HmuxygyiKpBbXSVO7qaUktmrJB3k4Mi5aqvINjPBSNvT/NnQQwPOAZXp3XjsZvB5OWiVCqF9YJ/Zk2PoD3jYkXNlxWexfruzpZ9NyWrAjk/pmIypBKtNHejsri8rmlBIqGtpqQ96+rpeSSbloT/ZfYvzcIE9f1TQZePLnRMnrSSV7PqlUaNTSdbVgdbva6n6rx6uU5wloVKp2Hm+5nTbPVe6TH6sS2n+uSuCCW3dni44DiSg+NKYLPQPdeWTpERJyqrjqk118PjOe+LBObV4zJsidj2/qw1Pju/HVriyWHszlREEtDy85QoinI3cPj2TezX355WgBr/+ewr7MSsIc9Dhd4IbcbBaVilqDErnUDjlrtwrXEs/0d+VpCrJA2tlebevcb2ft4m9rFttyWa2YxTrbSzqclKJajuZVczSvmsT8aiVx50yI8HZmau9AJsUGKG33uXO30wz4uNhzb/9Ivt6dxbaTZexK38Gtg8N49Iqup40D/LcgqbCGW77cT3WjgV7B7iyePbDdz2R5Qj7/t/I4epOZHgFuLJwVT3AnJ47lV3P/94cpqG7CUavG3UGLsdmA0Szyy5ECHhzdmY82p/PyqiSGRHnh5WJPTJA7N/QLYenBPP6zOpnfHhx6QdUqB62aB0d35qXfkjBYbQg+3nKKu0dGoVWrlCrgAz8cZunBXKb2DuLHA7l8sSOT4V18mBTjzzudHMmvauLmAaH8eCCXktpmogPcFAIIMHdjmg0BHBThRYinI3mVTcp6DrDtZBn7MisYFOlFdIAryUV1ynOeX3mcWwaGkph/nKIaHR/f2LsNAQRYciBHIYAgJYPsTC9nRUI+j47tamNN1TPQHVcHLeZmA14u9pc1+dMbzezPqpCtWkopaJWqEhfiwbgevlzRw4/u/q4dcg86COBp8emmVJvrjhpB8YHaklra5vHuDhqFAFprCkAav7cYd+qNZrbKz7dO/yit0/HtHqn6Z1ksPrghTrl/STvt30B3e6WFXN9sVIyf7xp+4dW/HWll1OkMilfX8oS2ljMXI9onnVaEUm1LYtsjnN393UgpruWP5BIGv7WZ/uGeOGjVNq8xupsv29PKKK1rZvr8PQzv4kOvYHdbgmz1+C5+Ljw9vjv7MivYnVFOXmUTL/2WxH/XpXJFD1+eGt+NJQdyEWulP3plg57v9+VgMJnbtkbl6pp1JFO9rD35q6ESaEPKLHmZLW7+LSkAFoLWOgHgz7j8m8wiGWX1HM6tJlEmfKnFde1WFR21anQGk419Rnd/VybFBjApNuCMFT1BgOcm9eCmAaG8sTaFjcklfLM7m1+OFPD4uK7cPCD0gv3DLmWcKJDIX02TgbgQDxbNHtBm8tNkFnl7XQoL5c7FhJ7+vD8jDmd7DcsO5vHCbyfQG82Eezkx/9Z41v6YrGSkLt6Xw5ez+tHd35XU4jpeWpXEZzf3BeCJK7vx+7EijhfUsOJwPtP7hXAhuKF/CJ9vy6CoRkcPf1dSiuvQGc0s3pvN7GGRynvu7OvCqdJ6XBw0qATJ4SGlqJYeAW7cPTySl1clsetUOZ3kjf6P+3Po6udCWomU73s8v4bqRr1SGVWpBK7vG8IHm9JkbWHLOeHtdan88sAQXr82hmnz9iq3782s5L3pvRTCuCOtnG5+Lpwsqccaa48X8+o1LT/rymg/3Bw0FNbo2JNR3iaq0FnOpb0c6U51o56tJyU/x+1pZTZG4/YaFcO7eDO2hx9juvt2SKLaQQcBPA3e22Q75DE4yhtBEMiraFSInjVK66UcxU5OWtJbfWGv7ROk7Jr3ZlZQ12zEx9WePlZ2IPO2ZqAztHhAxQW7ExciVZfyqxrb1RwunNVf2cX8fChPMn72cWZU1wvTMZnMIm+tSyVWvu7qoOXZ0d0xmUWMJhGT2YzRLGKS/xlt/jfbXjdJ/5tFq/tNZ35em9c1mds8/nQwyvf/VbqLqkZDGxF2a5hFqZ3fOv7pXNCgN7EqsYhViVLLvo+8NulNZl749cR5vx5IJLh19cwSgm6JaGpbYdO0ydN0kQ1X/+kdckmtjiO5UlXvaG41xwtq2iRHAHg62+Hnak+TwURuZSNmscVYuLu/K5NjA5jUK4Coswi4WyPc25mFs/qxK72cV39PIq2knpd+S+L7fTm8dFXPf5WZ9/H8Gm75ch+1OiN9Qj34bvYARf9sQa3OwBy5PQrwyBVdePSKLhjMZp5beZwl8oZ0bA9f3p/RG3dHLWvl5zrZSUMg//fLcT68oTe3fn2ANceKmNKriAkxAfi42vPwmM68tS6VdzacZGJswGl1cWeCvUaqAr7w6wnK6psVcvXOhpPcMTQCQZA2jA+OjuKxnxJZnpDPuJ5+bDhRwpc7s3h/RhzT+wXz4aY0cisbubZ3IL8cLaSwppkbu/ooBFAE5v6RxqtTY5SffV18EB9uTqO4thkVYKlBHs2rZkNSCeN7+tlUBwHeWJtKNz+JqK46VshHN/Rh6rzdNr+T0Syy8nABs2WZj4NWzTW9g1i8L4dlh/Iv+6zqrPIGNiWXsDGlhIScKpsNobeLPWN7+DK2hx9DO3t3uGCcBR0E8BxxnVzBm7etbfsXUKowYV5OVDXa2r/MGhyuXLaYP4+L9lOqIgXVTfy4X1osLYfyx1bi52WH2lbhfFzs6RkklfOtjZ//jLj3lyMFpBTV0stBer6znVqZOLsYIIoiZhGMZjNms/R/G0JpEtvcbvsYcxuSarndLEpEN7eyka93Z9HQbKKTk5Yb+4fi6qixebzJLDnDWyKmvF3sGNrZG61aZUNu25Ja6WeV1+sprtHZmItaQy0IxId1IjbYXSZpUvi5dRu1da6mveZ/F4J+vqhvNnIsv5rEvBqlutdeao6jVk1ssDvd/FwxmUXSSus4kltNpVVwfXSAG5N7BTAxxv+sU3vngmFdvFn7yHCWHMhl7sY00krqmfnVfsb28OP5yT2I8HY++4tcwkjMq2bmV/up0xmJD+vEt3f0x7UV+csub+CuRYc4VVqPg1bFe9PjuKpXIIXVTdz/w2ES86oRBHh8bFceHN25zZrk6qAhyt6ZjLIGftify70jIpi3LZMXfj3BwAgvOjnbcfvQcJYcyCW7opHPtp7imQndL+j3mdFPqgIWVDcxILwTB7Kr0BnMSgoHwJRegXywMZ3cykaCPSSnhlWJBTw1vhv+7g7MGhzOR5vTSSutRyOAUYQtqWU426uVNKiVRwpsCGBwJydFn9dDHiax4N0NqYzt4UvPQHcbu7D1J4p5Ylw3UopPklvRSFc/F5ufYcEP+3O4Y2i48n2f3i+Yxfty2JBUTE2j4bKSLpjMIodzqxTSZ52nDNKmb2wPP8ZG+9EryP1fNdzyZ9FBANtBe1mEI7pKu/+NKW2rQo52Kprkkf6sclsDzz4h7kQHSskhZrPIRrmqZD39++mWdJv4n+FdvJVpLpNZZFk70W9zb+ilXN6Y3GL8PK3PhRk/6wwm3v/jJCBNRZn1+rM845+HIAioBVCrLLu6v293d13fYG79ej95lU0sP5xvEx1njW0nS3lkyRHK6/Xsyajg81v6nrNoXRRF9mZUMH9HJsh/YpUgEBPkxomCWg5kVxLu7cSTV3a7pHeyRpOZkyV1kmZPJnvppfVttIYqAbrKRrpxIR6EezlxqrSe9UnF/Hgg12an3zPQTWnv/h2ETKNWcevgcK6OC+Kjzeks2pvNppQStqeVcsfQCB4a07lNRexywJHcKmZ9dYC6ZiP9wjrx7ewBbSpve06Vc/8Ph6lpMuDv5sDCWf2IDXZnz6lyHl5yhIoGPe6OWj66sbeNLs4aAvDBDb2ZNm8Pa44X8e71veji60J6aT3/WZ3Ehzf2wV6j5vnJ0dy96BBf7czipv6hhHo5nffvZKdR8fCYzjy78jiZ5Q1o5cGMd9afVAigRq3i/lFRPLfyOL8fK6RfeCcOZVfx7Z5snp3YnVmDw1iwQ7J6GtHVmx1p5ZTWNTMwwpP9sjVXfbORjUnFjLNa26+PD2ZnejlVVhsWQYCMsgbJ0mVUJA/+cES5zyxCXlWj0glatDebmweEKi12CzLKGjicW61okGOD3BUz61XHCrl10KVtAVbfbGRnWhkbU0rYmlpq03XTqgUGRngxVtbzWazVOnD++PcJW84BEz7YZnM90tsZVwctRdVNlNe3JUYalfQxervYtTHxvGNYi7fckbxqyuqacbXXMDhSEvFmlzfYVPhUAnx0Q0v1b0damY1+BKSqyPAuLQurZXL4zxg/f7Uri6IaHUEejjjZX7pk46+CJTquu78rZXXNzFiwl4PZbT0YR3XzZfXDw+jmJz3uxi/2sXhfzjkFmguCwJDO3iyaPQAv2XrALIqcKKilX1gnBEGq/k79bDenSuvP8moXB0RRJK+ykdWJhbz+ezLT5+8h5pUNTP54F8//coJlh/JJK5HIX6C7A5Ni/XluYnd+umcQx18Zz+I7BxIT5M7qxEJu+XI/L/6WxO5TFZjMIjFBbjw9oRvbnhzFmkeG8+Dozn97Nc7dSctLU6JZ/+gIRnXzwWAS+WJHJqPf3caSVqT0UkdCThW3yuRvQLhnG/IniiKL9mZz69cHqGky0DvEg1UPDSUmyI0F2zOY+ZVkExMd4MbvDw87LfmzoFewB49cIU3jvvp7Mk9P6I5KgF+PFrJJ3iiP7eHLsM7e6E1m3lzb1tPuXHFdfDAhno6U1+sVN4Zmo5lPtqQrj5nWNwh/NwdKapvp4S9t9n7Yn0N9sxEvF3tmyDrEeqsAgJQiW6/X9zem2Vwf39Nf0ef1CfUAUDY+H2xMZ2QXnzbavBWH8wmTie6KwwU8Pb79yufSAy2FAUEQmN5P2vwvP9S+XdjFjsLqJhbvzWbW1wfo++pG7v/hMCsPF1DVaMDdUcu1fYL49OY+JLw4ju/vGsjtQyM6yN+fREcFsB2cLLWt4lnsX6zNnx01Ak1G6ZvcIOuUNK1Kz45aNROsdoN/JEvt39HdfZWx8482p9ucRK7qFYinS4vFwtJ2qn+zh4Yrl4/mVXMo588ZP1fUN/O5/Ls9Ob4rWVuPXtDrXG44W3ScBWFezqx8YAhPrzjGmmNFvPjrCY7nV/PqNTHnnCWrVavQAU5aNejgUE4VYV5O1DYZOFlSx9Wf7uKtabFc0/vCMlL/LtQ0GiTNnlzdS8yvbneT5GqvIS7Eg7gQd3qHdCIu2F0RZZfW6dhwopgPNqVxIKvSRhPVK9hdqvTFBFxQ9eevQmdfF769YwBbT5by+u/JZJQ18NzK4yzam8NLV0XbTGVeijiUXcltXx+gQW9iYIQnX9/eX3EXACn/9eVVSYpUZVqfIN6cFovRLPLgj4eVidjr+gbzxrXnftw/MCqKLamlHM2r5pvdWdw1LIIvdmbxf78cp3+4J+5OWl68KpqJH+1gfVIxezMqLuiz1qpVPDymC08vP0Zifg0OWhU6g5mPNqXz0OjOilnwvSMj+c/qZLaeLCXC24ms8kZ+OpjHncMiuGtYJN/vy+FwbjVhno7kVDZRqzMSLE8JA6QW19kkg1jr8wLdHbDU+jQqgeJaHT/szyXIw0GJhpM+axE/N3uyKxo5VVaPiEh4O/FwqxILeWlKtNKen9oniLfXpZKYX8PJ4jq6+V/cJvNms8iJwho2pZSyKbmE5FZkOtzLSbZq8aNfWKd/5SDW340OAngOuEYOJrfo9wCajS1nKcsJq3WlbuagMIXoiaJoY/8CkFZSx69HC5THa1QCb18Xq1wvrdMpO2ELBGDO2JaIJIvx89VxQRc85fTJllPUNxuJCXLjmrggPtx6QS9zWeJs0XEWONtr+PSmPsQGufPO+lSWHcrnZHEd82+NJ8D93NMM3By1LJgaz7MrjpFT0Yi9RlBORHOWHmVfZiUvT4k+5xPsX4lmo4mUojqO5laRmF/D0bxqssob2jxOoxKIDnQjLlhq5fYO8SDS29lGm1Nap2PR3mzWHCviQHalTTs4zkL6YgMuuh3+6G5SRWrx3hw+3JRGSlEtNy3cx8QYf/5vUo+L7v2eCw5kVXL7Nwdo1JsYHOnFV7f3s4njqmzQc//3CezPqkQQ4NkJ3blnRCQZZQ3c930Cp0rr0aoFXprSk5kDQ89Lh6pRq/jght5M+mgnezIqGNnVh0hvZzLLG3htTTLvTY+jm78rtwwMY/G+HF79PZnfHx5mY3VyrpjWJ4jPtp4ip6KRiT39WJdUgtEs8p/VSbxytaTdu7F/KJ9uOUV+VRPXxweTVd7I17uyuG1wGKFeTkyKDeD3Y0W4O9mBbD9U0kq7+va6VObe0Fu5PqNfCIv35bAxpVSZRLYMtM3blsHMQSF8tjXT5jWO5Uu6QFGUAgDuGh7ZZjis2Wjmt6OFzJTbvd4u9ozp7ssfySX8fCjPJp3kYoHOYGJPRjmbUiRT5pJa2xSO+LBOjO0hkb4oH+dLRtN8qaKDALZCQXm1zXUnjUAXXxdKa3VKTqB1kLZlistRq24j6Lc2Yz5VWk9WeQN2apXSGvlgY5rNiW/moDCbhXd5Qj6tww/6h3sqpPKvMH7OKm/g+305APzfxB4dAtp2cC7RcSC1Ye4bGUV0gBsPLzlCYn4NUz7ZxWc392Vg5LlXLcb39Kd3iAdP/pzIzvRyssobCZczP5ccyOVoXjXzbun7t7Y/zWaR7IoGZSL3aH4NKYW1NlpVC8K8nCTdXrAHvUM9iA5wa5egltbqWHeimDXHizjYmvSFeDA51p+JMRcf6WsNrVrF7GERTO0TxAcb0/hhfw7rThSzObWUu4ZF8MDozoRowf4AAQAASURBVBc0sfq/wP7MCu749iCNehNDO3vx5az+NjKStJI67vzuIHmVTbjYa/joxt5c0cOP9SeKePLnY9Q3G/Fzs2feLe17Yp4LIrydeX5yD1749QTvb0zjnetieWyZNJE7uVcAo7v58ti4rvx2VBpS++lgHjcPDD3vn6NRq5hzRRceX5bIvqxKZbhi0d4cnpnQHUc7DY52au4aHsl/16dyOKcKTyctBdVNrDtRzJS4QO4dEcXvx4pIKqhFqwKDWarYOdmplUHANceLeOf6XkrFKibITbG6Gd3dl5RiyfvPQaOipsmg6Met0WQw4+GoobrJyE8H8/j1gaG89NsJWisOlh7IVQggSJ6AfySX8OvRAp6Z2P2iyBsvq2tma2opm+QUDuvzpLOdmhFdfRjbw4/R3X3bjRa81GAwmf8Wa66/A5fGKvUPYtT7tiP3gzv7IAgCC3a07NCivJ1IK5PK8ZaTWGstUIC7A/7uLRU5S/VwaGcvXOw1nCioYd2Jloqig1bFC3JGpfS6oo3Gw4JXrmnZ1X1nZfxsGTQ5X7y7IRWjWWR0Nx+GdP732FycL84lOs6CEV19WP3QMO5ZfIjU4jpJy3ZVNLMGh53zjtbPzYHv7hjAt3uyeXt9KtkVjbg5aBCRdEdTPtnF29fFclWvwL/k9yuvb1YGNCzt3FpdWwuWTk5aZUjDQvrOFJ1UXKNj3Yki1h4v4lBOlQ3p6x3iweTYACbG+hPc6eImfe3B09mO16bGMHNQGK/9nsyuU+XM25bBzwn5PD2+G9f1Db6oN1R7MyqY/e1BmgwmhnfxZuGsfjbEfVNyCXOWHqFBbyLU04kvb+tHpLczb69LVfLKB0Z48unNffFxtf9T7+WWgaFsSilh28kyFu7MYtagcL7bm83/rTzOhsdG4Olsx6Nju/Lq78m8/8dJrooLuKAhnKvjAvl0yykyyxuYFOPH2hMlmEV4fFkin8+MB2DmoFA+3yY9ZnJsAGuOF/HFjkyu6hVAbLA7Qzt7sftUBRHezpySJ1KbrE74zUYzPyfkc9MAiaRK+rwQ5RjxcrajokGPTjan/vFALq4Oaup0tqTBQgxTiuoQVAI9AmwniQFOFNZyoqCGGNkRYlQ3H7xd7Civ17M1tZQrW0WN/hMQRZH00no2JpewOaWEI3nVNt/7AHcHZWp3UKQn9prLR3O+Pa2M135Ppq/spWtoZ8N8MaGDALZC6/g3S6vPUmkDMFnJdi0Pb10ZsdbpAYqnnGX61zJxa8HDY7qgsYqj2ZtZQW6rhAMvZzuiA6Qven2zkSX7/5zxc0JOFWuPF6MS4NmJPc7+hH85LNFxXi72ykmwdXScBaFeTqx8YAjPrjjOqsRCXl6VxLH8mvPSR6lUArOHRTCksxePLj1Kqlw58HW1p7SumYd+PML+zEpeuKrHeS2iTXoTJwol+5UjMtmzaJisYa9RERPkLrdy3ekT0okQT8ezktiimibWHS9WSJ81+oZ6MCk2gImxAQR5nHtr/GJGN39XFt85gE0ppbyxJpnsikaeWn6MxfskfeCFRpn9ndh9qpw7vzuIzmBmRFcfvrg1XjkuRVFk/vZM3tmQiijCoEhPPr8lHrMocts3B5Qc87uGRfxlVSZBEHjnul5c+eEOkgprGdbZmzC56v3W2hTemtaLWweH8cP+HDLKGvhkczrPTz7/FqdGrWLO2C7MWXqUXacqcLVXU9dsYt2JYopqmghwd8TVQcsdQyP4aHM66aV12KkFjhfUsD+rkkGRXtw7IordpyrIk78zlo6QILQUBOZtPaUQQICpvQN5e10Kx/JruHtYOAt3ZQNSBaxBb8LfzYk6na3Gr1k+p5hEkVWJBcwcFMZzK4+3+Z2WHMjljWsl6ZBWrWJa32C+2JHJskP5/xgBNJjMHMyqZGNKCZtTSsmttP1dYoPc5dauLz0D3S671m5mWT2vr0lRQiL6OUq/38VQgT0TOgjgWTCiqw+V9XqKaiSdh1oFhdWNZ3mWrfdfYXUTx/JrEAS4oocfCTmVinkqgJuDhvtH2rYT26v+3Wv1mGUH86hrvnDjZ1EUlam66fEhF71g+GKBpc3r6WTHsyuPtRsdZ4GTndQy6xXszptrU1hxOJ+0kjrevV6K77NO9NAZTr9T7O7vxq8PDuXdDSf5alcWpXXNSiLB4n05HMmr4rOb+9oEwVtgMoucKq23IXsnS9qmaQgCRPm4KNW9PiEedPN3PecFrFBuk609XkRCK9IXH9ZJIn0x/gReJqSvNQRBYFy0HyO6evPdnmw+2XyKY/k1XD9/L1PiAnl2YveLhvDuSpfIX7PRzOhuPnw+s4X86Qwmnl1xjF+PFgJSNezlKT1JLqzl/u8TKKzR4WSn5r/X9VKiMf8q+Lo58Oa1sTzww2EW7szk5Sk9eXlVEksO5DE5NpBhXbx54apo7vjmIN/uyebmgWEXJIO4qlcgn2w5xanSesb18GVjinTSfvCHw6x8YCgAdwwN58udmaSV1DOyqw/b08pYuCOTQZFeDO/iTY8AN1KKavF00lLZaGhj6JxX1URSYY0SyenlYs8V3f1Yn1RMo8GsPN4Su5lrNeBhsYCBFonR93tz+Pm+wbzwywlMrRwGfjtayPOTeyjyoenxEgHcerKUsrq/yhq/LWqaDGw7WcqmlFK2nSylzqpjYKdRMTTKi7HRflzR3c+mG3Y5oabJwCeb0/l2TzZGs4hGJXD7kHAcT56kvp1huIsNHQTQCgu32o7wR3k742yv4ePNLfYDQyK92HmqovVTbeDrao+9FRmweP/Fh3bCx9WeR5YcsXn8C1dFo1K1nGirG/X8fqzI5jFqQdpxg3RS/2bPnzN+3pBUTEJOFQ5aFY9f2TJUIoqisihdPgYXfw4ms2iTpVunM+Lv7sBdwyP5elcWfySXMHbudsb28EMvR7hZR7XVNxtxsddQqzNyvKCGCR/tbPMzZjjocRakYPlGvdFGCwrSNOGLV0UzqpsPTyxLpLSuGbUK7NRqThTUctXHu3h3ei/iQjxsyN7x/Brl72kNH1d7estt3N4hHsQGu593S62guol1x4tYc7yII7nVNvf1s5C+WP/zGoK51GGvUXPPiCim9Q3m/T9OsvRgHqsTC/kjqZh7R0Zx38jINn/bfxI70sq4e9Ehmo1mxnT35fOZfZXqcWmtjrsXJ5CYV41aJfDK1T25dVAYSw/k8tJvSehNZiK8nVlwazxd/f6eDeOk2ACm9Qli5ZECvt6dxU0DQlhyII9nVhxjw2MjGN3Nl1HdfNh2sow31iTz5W39z/tnqFUCj47twkM/HmFvZoVShTucW83R3Cp6h3bCw8mOmYPDWLA9k9I6afO/ObWUU6X1dPZ14b6RkcxZepRmuY3byUlq61rj3Q0n+faOAcr1Gf2DWZ9UzLoTxYzs5sPWVKkI4OqgoU7XkgHvoFXRJG8ILaTyWEENgiDQM9CNY1bG0SB1g9YcK1Li8rr4uSrrwK9HCvgrkVPRoEztHsyutEln8nK2Y0x3X8ZG+zG8i/f/9Dj/u2Eyi/x0MI/3/jipmNKP6e7L85N7EOXjwty09f/jd3huuHz/QheANzak21yfGBsASKJeCzr7upyVAE7ra2vVYdH/je/pz+5T5ezNbHm+n2uLv5QFi/fmtBH7jurmq5BEi/Fzpws0fjaYzPx3vdSCvnt4JH5u1lrFEiULuLyumU3JJW1sTy4FmM0iDXopQ7e+2SDn6EqX65tN1OsMMjmTbrPO2rUmbvU642nTOqyRX9XEt3uyz+s9Otup8XKxw9VBi7ZGBWZo1BsZ/+EO/ntdL4ZEtdVkDu/iw/pHR/DcymNsSCqhyWzCTqOirtnIfd8fbvfnONmpiQ1yp3eoB73lQQ1/N4cLasPkVzWy7rg0yHE0r1q5XRCgf5gnk2L9mRATcNHv+HUGk5KuUKsz8tbaFBy0ahzt1Dhq5X92Lf87aNU4tXP9dBVSbxd73prWi5mDwnh1dTL7syr5eHM6yw7m8ezE7lzTO/Afb4NtO1nKPYsT0BvNjO3hy2e3tJC/Y/nV3LMogeJaHR5OWubd0pe+oZ14dsUxlso55OOi/Xh/RtzfboD9yjU92ZdZQU5FIwPCPQnycKSguol31qfy6jUxvDA5ml3pO9iUUsrO9LILij6bFBNAN79TnCypY0QXb3aklwMwZ+lRtj89GoC7hkXy7e5sUorqiA/1ICG3mq92ZfLWtF5Mig3gnfUnKahuQiVARYMeB41K0fWBRLYrG/TKYMOILj6KfKO7n6tCAOt0RgRayF6TVSSoBQaTyKbkUmYNCePJn4+1+X2WHsyzyUueHh9MYl41yw7lMfG8P50WmMwiR/Oq2ZRSwqbkEtJb+ZF29XPhih5+jO3hR+8Qjwuazr7UsDejgld/T1Y8IKN8nOXN+YVFsP4v0UEAz4CpfYKo0xkUfZRaBQXtaKVa4warL2J1o15xih8X7ctjyxJtHvvmtFib66Io8sUO2xxigFem9FQuK8bPgy7M+HnJgVyyyhvwdrHjXquoN53BxOtrkhkiXzeJInctOsSEnv68cnXPv/2kLooijXqTVGWTyZflsoWYtSFpOqPSSlUepzO2W/X6s7BTq6zycrW4ynm7JjPsz6qg2WjG3VHLzEGhhHo6tcnZdbHXoFYJvLU2lVWJhTToTYwP8+TNabHM++QgdXXNqAWBvMombl64n1sGhvLcpB642GswmMycLK5ThjQyrBZivdG2feygVTEu2p9hnb2IC/Ggi6/rn1qY8yobWXtcGuRIzG+pPgiCNJU+OTaACTH+NhuJixlbU0t5adUJBjdLG51GvZFvd2Se/YntQKMScNSqcbBrIYgOMoF0spNuD/F0xF7jzeFcKe7u0Z+O8t4fJ5naO4hu/q4XTDLPB1tSS7hv8WH0JjNXRvvx6c19FTeB1YmFPPlzIs1GM519Xfjqtn5o1CpmLNirSFeevLIb94+M+keGWtwctLw3I46bF+7n54R8nhjXlfc3prFobw6TYgMYFOnFrYPD+GZ3Nq/9nszaR4aft0ecSq4C3v/DYakTIpO3nMpGViUWcnVcID6u9tw0IJRv92Qrlb4Vhwt4fFw3fFztuWt4BP9ZnYyDVpoA7h7gytG8lu+HWYRv92Tx+LhugKQ/nNY3mPnbM0gtrlNkHABujhpqmlpaqHEh7javBfDNniy+v3MAz6w43kbCkZBTRVpJnVKZnRIXyGu/J5NeWs9Yr/Pr5TTqjexML2dTcglbUkttKptqlcDACE+Z9Pm2Kzu5XJFX2ciba1OU4U03Bw2PjevKzEFhF73W73ToIICngaNWIMrHmQ+snN3HdPPhYCt9U2s4adVEWOWRbk4pxWQW6e7vSmZ5g02rLMLbmSt62FbXFu/Loa5V7mOAuwMhsgnukdwqxfj5QuJ+6nQGPtokVTrnjO1qY1cxf3sG+VVNqB0tWcAa1HqB9UnF7DpVzlPjuzFzUJgNmRBFEZ3BbEPIbNqlVkSuNWmr07U8znLbXx2soFEJEmmzs83NdXHQ4GKnkQmdBld728utyZuzvfqMgxbZ5Q1KdNyyQ6ePjgP46MbexIV48ObaFFYeKeBkSR2j5F/c28Wem2ND+PFAHj/sz+XXowUEujuSW9monISs4edqT7PRTLWcQKNVC+gMZrafLGVKrwC6+1/YdHheZSNrZNJ3rBXpGxDuyeReAUzo6X/B3pP/CxTVNPGfVcmslyvyw+Tj3MVewz0DImnUG2nSm9EZTDQZTNJ1gxmdXrreZDDRpJdutxynRrNInXycnw/yq5r4dGv7ueKtYU0yFWJpXaU8TdXSctup0nq+2p2JySxN7T40pjOF1U3Ya1R8szuLL+QN5Zjuvnx0Y2+O5dfw8JIjVDbo8XDS8tGNfRjZ9fyrbH8GQ6K8uXNYBF/tyuK7vTlc2yeQX44U8syKY6yfM4JHr+jKr0cKSCup58cDuTaa63PF+J7+ipavX1gnZWDpxV9PMKVXAIIgcM+ISH7Yn8OJwlq6+LmQXlLP4r3ZPH5lN27oH8JHm9OplkncqdJ6ZQLXgu/2ZPPwmC4KQZjeTyKA29PKuD4+WEmBqmkyolEJSku1vtnYpgqYkFOFIAjEBrlxNK/Gxo4MYOmBPF6aIg3GuDtqmRDjz29HC2kyGNukjbRGcY2OzalSlW93RoXNptLVQcPobr5c0cOXUV19L6uc4XNBQ7ORedtOsXBnFnqjpN+8ZWAYj43resnb1nQQQBmTPtpmc32obP/ymyyGBhjfM4CNKWWcCfHhtl5YlvSPcT18eW+DrcZw7ow4m+t6o5n3NthOBwM8Me6vM36evz2DigY9kT7O3Ni/pVKZV9mopIHYaVRglE72V/UKYFd6ORUNel5elcR/16fi7WKHySyRyQa96S+Pw1IJtCJf0mVXmchZqmnWlTWXdkibi70Ge43qH2m1WaLjZn19gNTiOmYs2MtXt/VnQETbCVBBELhzWAQ9Alx56McjJBXWEuuoxxGobtKzPqnF/Luh2aS0XVztNVIbV7ZfiQvxwMfVHoPJzCeb0/l06ykMJhE7tUCtzsg9ixPOa1Izt6KF9FkH1KsEGBjhxaReAYzv6Yev66VD+kDKIv52TzZzN6bRqDehVkmfv33qSerr9LjYa3h80rlPwYuiiMEkKoSw3f8NJoU4Nsr/6+T7Kxv0JOa3TF4LgqSfctSq0RklwtloaPleXSjJbA/7syq5+tPdbW5XCZIn4KA3NyvVc0etmggvZ77bk82yQ3k2JNOmWmmpgFrdZyGq1tftNOdXJXlqfDd2pJWRXlpPbZMRfzd7cioaeXfDSV6aEs3j47ry4m9JzN2YxtVxgXg4nd/JWKUSeGxsF+5ZnEByYQ0aFRjNkrD/483pzBnblUAPR67rG8zSg3nYyd+hRftyuH9UZ5zsNMwaFMbHW05hpxaobzYxKTbAJtqzpsnIhqRixa4pysdFIZutNXIu9hplI5dV1sjobj5ssRoWbDaa2ZdZwR1DI5iz9Ggbj9iVh/N5ekI3ZaBnenyIRAD1ZpxaLYGiKJJUWMvmFMmf73grXWGop5Ns1eJL/3DPS7bC9WdgNousPFLAO+tTFQ/goZ29ePGq6AveWF9s6CCAMpKLbBMNro8Ppl5nJEceZ1erwNP57F5X1/Zp0f816U1sT5O+wM4OWpuom17B7vQJtSWL87dntPFe06oFrpdbyhZDUrgw4+eimialffzMBFtS8PqaZJqNZrr4utBUa8JZkHah1gQYoFFvamNPA9JJzMVOJmHWVTWZmLlatU4lciZddrZX4yr/7+KgwdVei4P2nyFtfzVaR8fd+lX70XEWDInyZvXDw7h38SHMFSII0iJfqdOjVQt083fFaBIV+xcnOzWzh0Ywurut1kSrVvH4ld0Y0dWHR386amPp8uWuLBJyq/j05r7tTqFmlzcopM/aY0wlwKBILybFBjC+p/+f9nn7XyEhp5LnfzmhfIbxYZ14fWoMPQLcmJu67oJeUxAE7DQCdhoV7o4XXg05ll/Nf1Ynk5BTRXm9niAPR16eEs1kWXtsIZk6C4lsRSQbrUimhWjqrEhoZlkDR3KrEAEPJy1+rg40GUzUNxupbtTbVNutJ1ItaDKYOGKl8/yz0KgErrNrxkk4twEzB62aD27ozbXzdism21/uyuKbPVlMivXnpgGhfL8vl5MldXy4KZ1Xru559hdthXHRfsQEuXGioJboADdljf5sawb3jozEQavh/lFRLDuUR1JhLf5uDhTX6liekMetg8OZNSScBTsylep8Zll9m9i2z7dl2Ph1Tu8XzKGcKnakl9HV14U0eYNX3WRAI4BRlOQ3Nw8MtSGAAAt3ZvHlrHieWJaI0SwqkXaW529IKlbiIodEeRHk4Yioa/m0t50sZbOcwlFY05JgIgjQJ8SDsdF+jOvhR2dfl0tyDf6rkJBTxaurkxTJS5iXE89P6sG4aL/L6nPpIICnwYiuPnwtV9sArujmy7aTpWd93jirk/3O9DJ0BjOB7g4sT8i3edxHVlFBIJE762ByCywnA2gxfh7a+cKMn+f+kUaz0Uz/8E5cafU+d6SVsSGpBLVKIK+ykT5WR4WAlNIwvqcfDho1v1vZfPi62vPMhO5MiPHHUau+qE1v/ymca3ScBUEejiy/bwjvvLcXUW/AzUHLL7OHEB3oprScD2RV8vTyRLIrGrnj24NM6xvEy1f1bNOK6Rfuydo5w3nltyRWytN/KgGO5FYz+eOdzJ0Rx5jufmSVN7D2eBFrjhXZbEpUAgyOaiF93i6XJukDqGrQ89/1qcoAg4eTlucmdmd6fMhFc5z2CvZg+X2DWX2siLfXplBQ3cRDPx5hUXgOL02JJibI/YJJ5rrjRfx65Agikvnx3BlxaNQqDmRVct/3CZhFSW7w2S19sFOreOyno2RXNKKRvSdHdPFGZzC3qWqertJpU+W0kNF2KpmiTP3q2jEZbw8xQe48OrYr726QJqonxviz7kQxTy8/xto5w3lpSjS3fLmfxftyuGVgKF3OczpZEAQeG9uVO787REaZla7WZOa5lSf44IbehHk5c3VcIL8eLaSTs5biWh1f7cri5oFheLvYM71fMN/vk2y7DuVU8/rUnrzwa5LyWkmFtRzPryE2WLKEmdwrkFdWJZNZ1sCswaGkldYr7V4XB61SBfxubw79wz04mF2tvNaejHJUKhW9gt05nFuNvUZtYyG19ECeQgBVKoHr4oMplgu+JbU6bv/moPJYR62a4V28GRvtx5juvpf09/2vQlFNE/9dl6rYILnYa3hoTGfuGBp+Tl6rJrNIQk7VOR/f/2t0EECgtLLO5nqUjzNOdhpWHm4ZoZ/SO4i316W0fqoNAt0dlGBukCZqAaJ8XdgpT5kBDO/ibaMTBHhtdTKG1jV94IWrpNaUjfHzsMg2jzsbUopqWX5YIqH/N6mHsovRG828slparMK9nMgoa1COCpUgIIKS+frY2C78dM8gdqaX88KvJyiobuKJnxPZlFLCy1P+/iGRSwXnGh1ngYNWjYu9hjq9VOVrXRkeEOHJujkjeP+Pk3y1O4uVhwvYmV7OG1Nj2hi9ujlomXtDb0Z39+X5X45TK9tLVDcamP3tISWFwAK1SmCwUunzw+sSPwmIosjyhHzeWpeq2DNMjw/muUk9Lkq9jiAIXB0XyLgefnyxI5PPt5/iQHYlUz7dxfT4YJ4c3+28W+6/HyuUWoRmkam9A3lvukT+fjqYywu/nsBgEokJcuOLW/uRmFfNkz8nymbEDsybKU3//pWwRGPpDCa++jwFg85Ao97IxuQSmw3z6XDfyCi2pJaSkFNFWV0zPi52ZJY38MHGNJ6TqzIbk0t4bU0K393R/7wrNGO6+xIX7E5ifo1N9U6KU+uGv5sjD47uzK9HC0kpqsPFXkN2RSMbk0uYEOPP3cMj+XF/rlJRzSproJu/CyeLWwjlV7uy+PDG3oBEKib3CmB5Qj41TUYbD0EL+QPYl1nB+kdHcMX725XbdAYziXlV3DksksM/HqamyYBWLSjnjr2ZFWSXNxAu+yNOjw/mE5kAioCfmz1X9JCqfIOjvP4nmeIXI5r0Jr7Ykcn87Rk0GUwIAuf8/TOYpNb8+hPFbEgqoby+mRkORpwFbGxyLkb8+xr77WDYeztsrk+KCUCnNypB92oVjOzqQ0G1rr2nKxhhJZQ2msxsTpUI4MniFoIpCPDBDbbav62ppYow3RqR3s54u0gHn8X4OcrH+YIE2W+vk1z9J8cG2BCMb3ZnkVnWgIeTViJ/VjCLIgLg4ailpsnAK6uTmfTxTrRqFRsfH8G9IyNRqwTWnShm7NztSoWyAy3RcXfLKS1vrE2R/wYX9vk42ql54apolt83hCgfZ8rqmrlncQKPyGL91pgSF8j8W+MJ7uRo0+qzkL8B4Z14e1osB58fy/d3DeTmgaGXPPlLK6njhgX7eGr5MSob9HT1c+Hn+wbz7vS4i5L8WcPRTs2csV3Y+uQopvYORBRh2aF8xry3nc+3ZdBsPLep9lWJLeRvWt8g3p/RG4D/rE7imRXHMZhEJvcKYMldg/hubzb3/3CYBr2JgRGerH542F9O/kD6Lrg7avFzc8BB23LKeWp5IsU1Z15TQdqkzJ0Rh5OdmkM5VYrdxsKdmRzJreL5ST3QqgV2pJWx9Ry6NK0hCAKPyTrrwuoW+YQowiM/Sp6tXfxcmSBvtgI9HJSfDxDm5axYhgGsPFLAq9fE2PyM348V2pgyT5c7ApuSSxgqR3BaCtMWYweDSUQtQI9WJv1z/0hjYowfWrX0hLhgD5v7LVVvgBBPJzwcpWPfy9mefc9dwZvXxjK6u28H+UPaMK5OLGTs3O18sCmNJoOJ/uGdWPXgMN65Pu605K/ZaGJzSglP/pxI/zc2cetXB/hhfy7l9c24OmhwlD/bi90Wp4MAAq2zuKfFB/Pd3hxFpzKmu2+7UVmtMb1fS5vvQHYl1Y0GnOzUioAUJAJmIXUgWa+8vCqJ9vD85O6AVFb+erfUjp59AcbPu9LL2Z5WhlYt8PSEbsrtJbU6Pt4stZ3VVrtmyyUHjRoRaVeqVQs4aFWkldQz86v9zFl6lJsHhLL6oWH0DvGgvtnIy6uSmPb5HpIKbQXF/1ZYouOenSj9Hedvz+CZFccw/ol8yPiwTqx5ZDj3jYxCJUgn/Cs/2K5EFZ4qreOjTemM/2AHNy/cf9rj9lRZA/7uDhc9MToXNOqNvLUuhUkf7eRAdiWOWjXPTezOmkeG0/8ijGE7EwLcHfnwxj6sfGAIcfL36r/rUxk3dwfrTxSfcQPx29ECHl16BJNZ5Pr4YN69Po56nZE7vj3IN7uzAXh8XFf+MyWae79PYMF2icDcNSyCH+4a+I/qPLUqFdWNBubI7/dsCPOSvNZASr4Y3c0HswhPLz9GgIcDs4dKG63Xf09pY4t0LhjZ1Ye+oR7oTSJ+bi2fw4HsKo7JOsiHxnQGpGlfrUogIaeKhBzJ4uteq+p+VaOB0tpmoq1cAIxmkSVW6U4DIjwJ93KiQW8izFNyeLCQBmsp5pylR3njWlsyuVtuA/cO8QAgu8J24748Id8mg9ZCurVq4bLSr/1ZHM+vYcaCvTy85AgF1U0EeTjyyU19WHbvYKVdb41GvZG1x4t4eMkR4l/bxJ3fHWJ5gpQE5eVsx00DQvj2jv4kvDBOkW1c7J92BwFsBUetighvZ1ZYafau6R3ElpS2FTpraNUCvUNads9/yO1f6/VaoxJ45/peNs+bty2jTW4iSF/aK3r4y69VTH7VhRk/m80tkW8zB4XZ+Da9vS6VBr2JkE6ONm1BF7mN7eGkZfl9g+kd4oHBJNm9OGrVqAQp3WTc3B38fqyQRbMH8NrUGFztNSTmVXP1p7t5c20KjfpLQwfxd8ISHffOdb1QCVJV54EfDqM7B3Pp08FBq+bZid355YGhdPVzobxezwM/HCbm5Q2MnbuDDzalcbKkDo1KYGRXH965rhdL7h5IpE/L376yQc/t3xzk3Q2pf4qQ/q9hOQ4XbM/EaBa5MtqPTU+M5N6RUZf05GLf0E78cv8Q5s6Iw8/NntzKRu77PoGbF+5XDGit8cuRfB776ShmEWb0C+ad63qRXdHAtfN2szO9HEetms9v6cvIrj5c/elu9mRU4GSn5pOb+vDCVdHn7aP3Z+HhpMXZTs3+rErmnaMdzo39Q7iiuy96k5mC6ia8nO1IL63n483pPDSmM95ya3jR3uzzfj/WVcCKVhFec5ZKVcCYIHdGycQzWCZtC3dIG/NewR4MifJSnvPTwTzeuc52rV+0N1shp4IgKMbNJ0vqcHXQ0KA32WzEAakt7e1CSKeWAS6jGfacKlekQOX1evqHeSj3l9c3szmlhA60j9I6HU8vT+Tqz3ZxMFtOwxrXlc1PjGRKnK1Be63OwC9H8rl38SH6vraRB344zOrEQuqbjfi52XPb4DCW3D2I/f93BW9N68Wobr7YaVSXTIrWpbtC/kVYtNt28GJYZx/0RrNivaFWSRXAn6zK6tbQyNW4bv5uSrlXFEUl/s06ReLmgaE2o/9Z5Q3M397W9BlaWgTQYv1yIcbPvxwpILmoFlcHDY+M6aLcfjC7kl+OFCAIUFzb0obpEeCGs9XP6BfuyS8PDOHTm/sQ4ulIk8GEWZS0anqTmXnbMhg7dzvOdmr+eGwEk2MDMJlFvtiRybi5O9iS2rEQAczoH8LnM+Ox06j4I7mE274+QK3OcPYntgNRFDlZXMfm1FKb6km9bBMSHeDGu9f34tALY/lu9gBm9A9hcJQ3ax4ezsxBoTav9dnWDG75cj8ltWdvxV1MyK9q5K7vDnH3okPK7v3LWf34Yla/iyZz989CpRKY1jeYLU+M4uExnbHXqNibWcHkj3fyf78cp6Je6iwsT8jn8WWJmEW4aUAIb0/rxa5T5Uz9bDeZ5Q3SoNH9g6luMjB9/l4Ka3REeDvz64ND//I833OFWiXw2lSpsvXh5nQOZVee9TmCIPD2db3wdLYjraSefrLl1vztmWSXN/LklVJ346PN6cpncz4Y1tmb/uGdMJpF3B2t1umKRtYkSkMBD8tVwDx5074huZgcuQJnbaq/61Q5bo5auvi2aL3L6/WsO9GSKjWtbxAqAQ5mVymynlDZ79UaH29K4wW5+mnBS7+d4Mqefoo1TaSvbZt4yYH2z1f/ZjQbTczfnsGY97az7FA+oghTewey9clRPHJFF6UlXtmg56eDudz+zQHiX9vIYz8lsiGpBJ3BTIinI/eMiGTlA0PY++wV/OeaGAZHeaESBI7lV/PZ1lPcsGCvsp5e7ETwX08AX1pt6803vV8wP+xvaf+O7eGHo1ZN/mn0fxaR5+TYFjF+UmEtBdW2rTcHjYqXJrd8iUVR5OVVSeiNZlwd2pK6ZyZIwx8W42c7tYpbB5+f8bPOYOL9PyRfwQdGdaaT3O4zmUVe+k1qOwe6O9oMn7zZqt0A0sJ7Va9ANj0+khcm98DNQUOj3Kdw0KoorWvm8WWJ3P/DYe4eEcnXt/dT4ptmf3uIB35IuOQIxt+B8T39+e6OAbjYa9ifVcmNC/adc1i7KIqkFtcy94+TjJ27nfEf7uDjzelklDWgVQv0D+9EgDyEk1xUyx/JJW1aYY52al6fGstXt/XDy6r1uz+rkkkf7WSX1aDSxQqDyczn2zIYN3cHm1JK0KgE7h8VxcbHR1ySkYXnAmd7DU9c2Y3NT4xkcq8AzCL8uD+XUe9t46EfD/Pkz4mIItwyMJTXr4nh2z3Z3P7NAep0RvqFdWLZfYNYtCeH51YeR28yMy7aj98eGvq35fmeK6b1DebaPkGYzCJzlh6lpvHsGyIfV3vevFZKT/ojuYTBUV6YzCJPLU/kmt5BRAe4UaczMndj2lleqS2sq4D1Oml9s6htnv/tBKIoEh/myaBIT4xmUbJYEVs26CO6eNPdSq/306Fc/tPKmsbSigep3W+JsbNMmBbVNLXRjS3al0N0gBseVtPgp8oaqGjQ0yfUA5C0hNakdXtaGflVbTtL/0aIosiGpGKu/GAHb69Lpb7ZSFywOyvuH8KHN/YhwN2Rklodi/Zmc9MX++j3+kaeWXGcbSfLMJhEOvu68PCYzvz+8DB2PDWa/5vUgz4hHpwqk6rPNyzYS8wrG7j60928u+GkkvwFXPSa+H89AWyNEV19bKp9V8cFcbKkrl0mb6du+aJea9Wa3dDOQMcDozujsTJCXXeimB2yLq9OZ9sOjAl0w8VB+jIrxs+9A897GvCb3dkU1ugIdHfgjqHhyu0/7s8hpagWF3uNDVG9eWBomwlUa9hr1Nw1PJIdT4/mzmERSuqEgFQJPZpXzdTPdrPmWDE/3jWQe0dIQyL/z95Zh0dxdlH8N2txd0+IEgGCu7sUCqUtLXV396/uQmmh7i0VaIu1QCnuThJixN3ds1n9/pjdSZYECC1tqZzn4SG72Z3szu7MnPfee87ZlFLB5CW7+ergfyKREaFurLx5OO72KtLLm7jkgwMU1fZ8ojYajZwsb2LJlkwmvbmb6W/tZdmOHHKrW1HJZUyK8mTJwv4c+98Ufrh1JLsfmsB9kyNQygWxNbp0D6uPl3SbG5vU14vN945lQmSnmKi2VcNVnx5m6dasC/YzOpxXy8y39/Lq5gzatXqGhoi2N49Mj/pHB8+b4e9iy7tXDOT7W0YQ6ycSnQ3JYkVpQqQHT87qy+NrU3luQzoGo9hFeH1hP277OoFVx4oRBNFc+cPFg/7wPN/e4vl5sQS72VLa0M6ja5J7JZKaHuvNJYP8MRpF83IXWyUZFc18sDuXp01JGN8dKeqxVX42jAx1Z3gfV/RG0WNPUue2aXnX1Kq+y9RJqWoWF7XfHyumvlUjjXuY8f3RYoaGuEoLMxAdFRKLOtOkzDnwB3JrCHazRa0Vbbq6wmCEt7Zl88DUCIv7396WxU1jxDZwbauGp0+pEnY1pP63IrOimcWfHuaWFccprG3D08GKJQv7s/b2UXg6WPHxnjwWvH+AYS9t56n1aRzMq8VgFDOOrxgawNNzorlyWCAavYEPducw4+099HvmV/o8tompS/fw5tYsDufXSQWRU3GBa0D+I4BdEeJmg0IQyKwUVbsKmcCEKA8+2t1zTqiHiZA52SgtLFB+SbEkgI7WCu6c0HliaOnQ8dzP6QDE+XUfNjUbmpbUt/1m4+e6Vo00W/PgtEiL8vYbW8TVcdexHxdbJY9Mi+rVtp1tVTw5O5pt949jZpw3RsRKqHl+ZXVCCTOX7cXZVsWa20bQP8CZ5g4dT60XRSLpZed+Yv4nIdbPiR9vHUmAqw2FtW0s+OCAhV1AWlkjr/+awaQlu5nx9l6W78ghz0T6Jvf1Yull/Tn25GQ+vXYICwb5SwPHKoWMeyaH8/Ndo4nzc6KxXcsDP5zg+i+OUt5oWZH2cLDis2uH8PzcGGkhY0Rsn1316WHp4nYhoLalgwd/OMFlHx0iu6oFVzsVbyzsz6qbh//lVay/AkNDXLl8iGUrf2dmNUNf2s6qY8XIBPjfrL5cNMCX+e8dILmkEWdbJV9eN5Q7JoRdMD6IIFqiLFsUj8LkJtDb1uXTc6KlLoM5leHdnTk4WCuZGeeNwQjPb0j/Tar7+yaLRMtcQbcyLdyX78hBrdUxMtRNmot2t1eh1hr4+lAhALP6+eBruhZUt2jYkVHF/VMsidsXBwqknydHe+Jsq6S8Uc2gIBfp7576Ea1OKGFgkAvWXYoI3x4pYkiIq5Swcii/zkJ0sOpo0QW7mPujUdeq4cl1qcx4ew/7c2pRKWTcMSGUd66IJ62skYlLdjHmtZ28uOmk5GtrqxLtuGQCpojBYp79OZ1nf07nw915bEiuIKOihSa1TioIyQUBNzsV/QOcuWSgH3dPDOOli+NwMaXSyC5w0c2/mgDOXbbb4vac/n58f7xEEm5MjPLEVqWQ4txORUObOCzcdcVWUNNKThdDUYDHZvZFJuvc1cu2Z1PRpMbfxYa8Ux7rYKVgsEm52NX4+XS5sqfD8h3ZNHfoiPZxZN6AznSS13/NpLFdi6eDlUX4+FNzos854zHIzY73rhzE6ttGEB/ojN604xQygVaNnlc3Z3D3yiRuHxfKcxfFSCKROe/s+9eLRMzRcVHeDlQ3d0iD59UtHcxato93d+aSV9OKSiFjSrQXb102gONPTuaTawZzcbz/GSs4Ud6OrL19JA9Ni0Qll7Ezs5qpb+5h1dEiiwuiIAhcNSKYjXePsVAsHsgVK20Hcv/alrDBYGTlkSImvblbMlJfNDSQHQ+M45JB/v9aReOKQ4X8b10qILZ9FwwSj29zitD4SA+a2rVc89kR6tu0xPo58vOdoy1sqi4k9PN3ltwJnv05jazK5rM8Axyslbx5aX8EQfS+iw9wQmdqBT80NRKVQsaB3Fq2pJ/7DPKwPm6MCnPDYBTPZeaUjw6dgSfWpiEIgjQLaDb8/fJgAWqtHqVcxo1jOhXB3x0p4qIBvpLCF2BDcrk0EmOlkEvn5/o2LYIACUUNzOxiK2PG0q1ZFl6iegN8faiQISbiuDW9krER7tLvK5s62J117rY4f0cYDEaqmtUkFtXzyI/JjHx5OysOFWIwigUaG6Wc93flcumHh/hsf4FFUosZbRoxJccghjL1WL1ztlUyIdKTZ+ZEc/DRieS+PJPjT05h/R2jeOPSAdw/NZIrhgVKi4YLHX+PV/kH4USZJflaMMifbw8XSrcvjvcjp6qFlo7u5d2BgU5SdFLXlId1SZZld08HKxYN7VytZ1Y0S23duf19aWi3JEHXmlq1LR06VppWw+dq/FxY2yqtSB+f2Vda8aeUNLLyqGhF0HVIekQfNwuSeK4YFOTKmttG8u4VAwlwtZGqWQqZQGFtG7d8fZytJyv56OpB3UQiOzP+HSeonmCOjhsc5CIlJOgNRlQKGVOjvXj78gEc/99kPr56MPPi/SxMxs8GhVzGHRPC2Hi3aNPT3KHjkdUpXP3ZkW7zqeFeDqy9Y6SFlUVNi4bFnxxm2fZsDH9BFeGkqT3+6JoUGtq09PVxZPVtI3l5ftw5Z77+k/DVwQKeNJG/G0eHMCbcXeo42JrEWzsyqlm2IweDERYM9DNVm7uLCy4k3Di6D2MjPOjQGbjr28ReqeSH9XHjZhPZKqhtw9FGQVpZExtTyiX/zZc2ney1h2JXmKuA5gqai2lxvDaxhMomNROjPOnr40iHzoC9lZyaFg3rTOk7lw0JwME0wrMrs5q6Vo3FCI7eYOSbw52WMGb7sH3ZNQw2kTlvR+tuBGTbySoGBblYVPk+2p3D1SPFbde3aXl0WqTFc8wJJX9nqLV6CmpaOZRXy/qkUj7cncuzP6dx29fHufi9/Yx8eTsR//uFoS9u5+L3DrDqWDHqLvPPje1aGtu1UjtfLhPwcLBiaLAr8wb4cVF/H0aHuVkorY2IrXcbpZyJUZ48PUfseCU+OYXPrxvCtaNC8PkHiM3++YMzvYS1QiDAxUaKxlLIBMZHevLa5oweHx/oakdCUSOCgGRMCt3VV11FFUajkSfXpaI32VUknxLALdA5X/J7jJ9f25yJVm9kXIQHo8PFFaHBYOTpn1IxGsHDXkW1qeIkF8Q5nN9bTREEgVn9fJgc7cmKg4Us35FDo8nVXhBgb3YNB3JruWp4EMsuj+fVzRmUNrRz3RdHmRXnw1NzovFy/PcliZij4159fS/otDjbKEm4dwr2Vufn0Az3cmD1bSP5bF8+b2zJZG92DVPf3M1jM/tyxdBAaXFgpZDz2My+jIv04L5VSVQ2dWAwwptbsziSX8dblw/4U6KiWjp0vLU1i89N1W87lZz7pkRw7cjgP92q5ELDF/vzecY0OnLTmBAcrJXc+nUCICpYH5gSwe3fJlDexVw5qbiBw/l1v8k8/s+ETCawZGF/Zry9l8zKZl7ceFJSCZ8J90+NYHdWNRkVzcT6OpLa3sSy7Tn8cOsIfjhWQmFtG5/vL7CYzesNBge7MjbCgz1Z1QiCSK7MiR33fJfIyltGcOeEMO74NkFa8H6yL59LBwdgZ6Xg2pHBLN+RgxFYdaSYxcODeH9XrtQ6XHGwgDsmhGKlkBPj6yTlEPu72HK0oJ7tGVXMj/fjR1MalTkq7p0dOcyP92F1ojj72ajWU1rfjpVCRofOwCf7C3Cwlktz5Tsyqoj2uDDbwEajkfo2LRWNaiqa2qlo7KCiSU1lo1r8v0n8v6EX4qAzQSET6OfvxJRoLy7q70ubRs+e7Br2ZlezOa3cIkoPINbPkTHhHowJd2dQkEuvIuD+rvjXEsC6RkvzzNHhHqxJKJVWCROjPFHKBSlTtStc7VRkmIhioIutNF93sqyJyqbOylqwmy2TozvVwWsSSiWj2pvH9uGSDw5abHdIiAsqhczC+PmG0X3OaWYnoaiejSnlCAI8NrNzpm9NYikJRQ1YK2US+QNRnBLmad/Tpn4TzEKRSwb5s3xHDl8dLJBUxnqDkS8OFLA+qZS7JoZT1tjO5/sL2JhSzp6sah6eHskVw4IuePf08w0blRxHGyXNzWopFu58Qi4TuGlsHyb19eThH5M5VljP/9alsjG5nFcX9LOwnhgZ6s6We8fx+NoUNprMpffl1DD9rT28e8VAhvVxO92f+V0wGo1sTq3g2Z/TJVuimXHePDk7Gh+nv/9K+/fi0335PL9BJH83jA6holHNx3vFc8S1I4MZFOTM4k8PS5Fu8+J9+f5YCbnVrVzz2REmRnnyxKy+hHqcv2P9fMPDwYo3L+3P1Z8dYcWhQkaHuzPtlKjDU2GlkLP0sgHMfWc/qWVNRHk7kFHRzFPrU3lgaiSPrE7mnR05LBjof85G1/dNDmdPVrXk5RHoKsbEHcqvI6W0kemx3oR62EmirJyqFnZlVTExyotrRgbz/q5cdAYjXx8u5K5J4YyP9JSSSurbtGw4Uc4CU/fo0sH+PPNzOhkVTdiq5OTXtPLg1AiJABoBpQyOFdZzyeA4iQACfLg7l8HBLuzPqWV7RhUPT43iyS7hAqcTKPyR0OgMVHYhcRWN4s/ljZ33VTZ19Nq021opw9vRGm8na7wdrfFyssbBSsHWk5WcKO4ePGCnkjMhypMZsT4MCBBzk/dmV3PJBwctFkggdunGhHswNsKdUWHu/6pM5H8tARz52i6L25cNCeCdHZ2GpPMH+rE3u0aqYnXFDaOCeXOb6B84Iaqz+vec6QRthjmGCaCxTSsZMt89KZw9PVhuvDDPZG/Q1fh5YO9bs0ajkZdNf+OSgf7ScHSTWssrv4iVzK5DqQEuYsblHwGzUOTqEUG8tjlTIhMC4snvuQ3pRHk78OK8WL47WsyJ4gaeXJ/G6oRSXro4jmjfc5t5/A9nRx8Pe1bdMoIvDxTw2q8ZHMyrZdpbe3hkeiRXjwiWFhpOtkreuSKeCQmePLk2hXadgZoWDYs+PsQDUyO5bVzoeRUSFNW28fRPqezMrAYgwNWG5+bGMqFLZf3fjI/35PGi6bi+ZmQQR/JrSSltQiETeOaiGIrr2rjruyQAhvdx5Z0rBuJub8Vt48NYtj2bLw8UsCOjij1Z1VwzMpi7J4VLwqHfA6PRSJtGT22rRrzYN6qpalZT06Khrk1DQ6vYemvu0NLaoaNNo2eCtoMz0fmxER7cMrYPH+7J4+Efk4nzc8L3LK22vj6O3D81gld+yaCorg07lZwTJY3MiNVIGb9v/JrJq6eY8J8N8YEuTIj0kL6XRXVtWCtlqLUG7vkukR0Pjuf28WE88MMJZDJADx/tyWNilBfu9lZcMsiflUeLqWnRsDe7mpvGhFhE1b2/K4f5A/0QBIG5A/x4aVMGJ8ubmRTlyfaMKvbl1DIr1puNJiGgo42Y4/3ZvnzCPe3IrhKLGJXNHczp78P+nFoa2rSMCLVcpLVp9Gfc5+cCo9FIk1rXSeZM1bpTK3c1Ld3jKU8HNzsVXiZy5+VojU8XkuftKP5ztFEgCAJqrZ49mdW8vzuXpOIGC3cOO5Wc6bFiF8reSsHhvDo+2pNLcmmjRSCDlULG0BBXxoZ7MCbCnUgvh3/tPPG/lgCe4rzCqFA3bl0htlPM7d8Hvj/R7XkCoumzeTbELOMvqW/jYF6t9LhYX0dJ1QXwxpZMals1hHnac+3IYMa/vtNiu252KknR+IlpRvCq4UHnlNe4Jb1ScjZ/YGrnLMjb27KpaenA2UZpETb+/LzYPzwPMsjNjnevHMj1hXW8uPEkCUUNgLgfMyqaeXRNCjNjvZkYFc7He/JJMolEbhwdwj2Tw/8V9h5/JuQygetHh0jVwMP5dTzzczqbUip49ZJ+hJhC5AVB4JJB/gwNduWu7xI4UdKIwSiKiA7m1rJsUfzvjpHr0On5eE8ey3fk0KEzoJSLNhp3TAj7L6fUhA9250qLt0sH+bMppYLq5g5c7VS8Mj+Oz/cXSOedm8f24eFpkVKr3MlGyZOzo7liWCAvbjzJjowqPt2Xz9rEUu6ZFM6sfj60a/Q0tmmpalFT1dxBdXMHdS0a6ts0NLRraWoXyVurRk+7Rk+HzoBGZ0BnMPBbRkMN1uKEfU/51WY8MDWSQ3m1nChp5N6VSXx38/CzdgVuGtOHHSerOFJQJ0as1bbx5rYs3rikP3evTOT748VcNSKI2B5cF86E+6ZESATQYBQFK0fy68iraWVTShkXDfDlre1ZFNe1IxPgUF4dySUN9PN35tZxoVIu78d78vj6xmH4OllTZqpA5VS3crywnsHBrrjYqZgS7cXGlHIUJlX+huQyVt08XCKAta0aHKwVZFW2cOf4PmRXdbpTbEmvlMjp8p05xPg6kFbWbHrdxl5lkun0BqpbOqRqXUWjmnILYif+rr2XKUYquQwvJyuRzJmInJnkmSt5no5WZ22xtnbo2JRSwS+p5WxLr7SY75ML4vjDrH6+tGp07M+p5cHvT0jz+WZEeTswJtydMeEeDA1x/e/8YsJ/V1cg2M2GbRlVkop1YqQHaq2+R/VvtK+jlLtqrZDR10ckbW9usTQeXb4oXvo5uaSBr03ikufnxnIor5bKUwyA7zDZxCQU1XPcZPy8+ByMn7V6A6+aLhQ3ju4j2dJkVTZLtgNdkydmxflYzC7+0RgU5Mrq20byS2qFtFI3Y1NqBdszqrhyWCAlDe1sSavkwz15bEwp5/m5sRZV1v9wfhDkZsd3Nw3nm8OFvPxLBkcK6pj+1h4emhbJdaNCpAtuoJstq28byTs7cnh7RzZGo9gSnvLmbj68apCkWD9XHMit4cl1qeRWi1WMEX3ceH5e7HkdR/i7471dOby2WTRynxbjxboTZWh0BiK87LlhdAhPrE2luqUDK4WMRUMD8XCw4o0tWdS1dtDQZqq+qXVi9U2rx0YpR63VU9eq4emf0k6bQf5bIQAKuYBSLkOlkGGjlGOrkmNnpcDRWomTjRJVXgoYQKM38MavmTx4imgBRDujZYvimbVsH0cK6li+I5t7J0d0/4NdIJcJLLm0P9Pf2kNBbRsh7nbk17Ty5cECLurvw08nynnu53RW3TL8nKo9/fydmdzXi22maLWUkkbc7VXUtGh4Ym0qM2J9uHVcKE+sTUWlEAnYx3vzWb4onmB3O8aEu0vzzzUtGm4a24dnf+7sFL26OYMfbh0JiGKQjSnlHM6rk4hibnUr3o5WVJhGiyK97DlW2MDapHJUcgGNabymqK6dvj4OnCxvZldmFatvHcnkpXss3ovOYGR/To1p5q6T5JmreTUtHb0m9U42SqlK59O1WudkJZE9VzvVb66sNbZr2X6yUvLL7TilVayUCYwOd8fT0ZoDuTU8vDrZ4vdudipGmwjfmHD3f+V8eW/wrySA3x2wzJ68qL8fX3ZxaF8wOID1SWUWCRlm3DA6hFdNwpAYPycEQSC/ppW1XWYFR/ZxI8Q0a6M3GPnfulQpdmZEqBs3fXnUYptyQeBak5Lrtxo/rzxaTF5NK252Km4ZJyrjjEYjz/yUht5gtKj+2apkUrD6nwlBEJgZ58Okvp58faiIZduzpRZ7h87AZ/sL8HGy5uaxfdiQXEZJfadI5Ok50Xj+dxCfV8hkog3M+EhPHluTwr6cGl7YeJJNKeW8dkl/iYwp5DLunRLB2EgP7vhGFBnUtmpY+MFBHp4eya3jQnt9oq9u7uClTSel48XdXsX/ZkUzd4DvP74NozcYaVHraFJraenQ0azW0Wz6ubFNa2qddlDfqiWltFFaJCllAr+mddqZZFW28MjqFOl2h85g4S33W2EmcCqFDGuFHBuVHDuVHAdrJQ7WCpxtlbjaqXC1s8LT3gpPRyuxouNgjYONslezu2++uYtm0+L3nZ05DApyZkJU9wSXIDc7XpgXy72rkli2PZuRoe4MDTnzYiPA1ZanL4rh4R+TKa5rw0Yp53hhPaPDwrFWyjhSUMfGlHJm9zu3+Lt7J4dLBLBdq2dGrDdrEkupb9Py/q5cbhgTwrLt2dL896aUch6ZHom/iy0PTo1kb3YNRuDz/fncNj6UlzadlK4tRwvqKalvw9/FljHhHng7WlPRpGZEqBtljRWsTijhubkx3GzqTiUUNuDpYEVpQztxfk6kdBEStpgsaRrbdegMRguCCGJG8JWfHD7je1XIBDwdrCRCZ67W+Th1VvG8HK3POZK0N6ht6WBrukj6DuTW9Hj9FQB3eyvqWjVSZRZAKRcYHOTKmAh3xoZ7EO3jeEH5XV6o+FcSwMd+yrS4PT/el3d3iZm8CgHGRXiw8BSBBohq1rERHtxvag3P6Sd6Nb21LctiFuGtywdIP397pIjkkkYcrBQ8PqsvVc1qtp9ifTIu0h2ZTEZJfRubf4Pxc0uHjre3iRXIeyaHS3Yh4oFUi1IuWLR+H5oWZWFc/WfDSiHnhtEhXDLQn+U7svniQD7mBV55o5qP9uQRH+DMsBA3fjpR1ikSmRHFlV2Uq//h/CDA1ZYVNwxl1dFiXjC16Wcu28t9kyO4aUyI1FIcGOjC1vvH8eS6FNYmlmEEXt2cyZ6sGt5fPPCM9ix6g5FvjxTx+uYMmtQ6BAEWDwviwWmR52Ue7Y+E0WikQ2eQCJv4v46WDi1N5p/VWurbtNS1amho03RW3zTi7Jtaq+/xgtYbaM+h16o0EThz9c3eSoGDtRInWyXONkrc7FS4O1hhq1KwK6OKrScrMRhFknn9mBDunBB2TnZDvxXmI/iWFQlsvncMfXoQp8yLF+ewVyeUcO/KRDbdM+asFkALB/mzNb2SremVuNkraNfq+XBPLpcPCeCLA4W8vCmDyX29zqkFGOvnxLQYL4mE786qJtLLgczKZt7ens31o4O5eWwoz29Ix1ohQ60z8Pn+Ap6cHU3/AGf6eNiRV93K14cLeWhaJLPifFmX1FkweGpdGp9dNwS5TGDBID/e3Zkrtcj3ZFXz6oJ+kgrYAIR52lPV3EFRnaWQsbi+HZVChkZn4J2dOSwcHGBhN9MTvBytifZxYHCwKxMiPSwy7f8MVDSq+TVNbO8eya+zqEC626loUmstSKwR0SsVINTDThJvDAtxw+48i+f+DfjX7zGVXCCzslWa6ZvQ15OC2laLlZUZUd4ObOtiLDp3gB+ZFc2sTyqT7hsZ6iZVqmpaOnjdVC18YGoEng7WvLcrp1uZ3Wx3YDZ+Hh3mfk7Gzx/uzqWmRUMfdzvJc7Bdo+cFkyhFFH6IfzTG15Grhp9bpvAfBSdbJf+bHc3VI4J59dcMNiZ3KtsSixtIKmlgal8vihvaSS9r4sl1qaxJKOGli+PO2Rj7P5wZgiBw+dBAxkZ48NiaFHZnVfPq5gw2p4rVwEhTxqm9lYKll8Uzpa8X9/9wArXWwMG8Wsa/vovPrh3CwC5zr2akljbyxLpUThQ3AKLNwovz4ugf4PwnvkNLdOj0UluppUPH0+tTaWwXSVxju1as0qlF8tau0UvjIX80Oo9UJNsRAfHcU9mkpq5Ni4DYIbhscABOtmJr1dFGib1KcU6Lo0VDA8mqbOb5Densza7hw915rD5eykPTIrhkUMAfSgScbVWgFlvBl390iK33jevRiP7ZuTEkFNWTX9PKI6uT+WDxoDNWigVB4OX5cSQW1VPZ1CG1UlNLm/BxFCtnH+/J465J4ef0eu+dHCERwNpWDVcODyKzspkOnYEn16Xx7NwY3tuZQ62JuK08UiSJbR6eFsmtXyfQ1K5j+8kqbp8QakEAd2ZVUd+qwcVOxcJBAby7M5ejBXVShW9dYilhXUQfR/JqCXKzpbC2U5RihrWJAO7Oqubw45MsCKCHvRWvze7H0fw6jhbUUVDbJil1d2ZW8/qvmQS62jIk2JWhIS4MCXYlxN3uvFfmi+va+CW1nF9SK0g0zYSbEehqg7VCTn5tKzWnzIk62SgZHebOmHB3Roe74+9yYXtb/h3wryeA4yI8JMsVgIWDAvjhNBmKs/t1rtzc7VW42Kl4dI3l7MEHVw6Sfn55k1jtiPF1ZPHwIIxGI9+esiLzcbLGz9mWZrVWMn6+YUzvq3+iHYQ4DPzw9CiUpmrNe7tyKGtUY2clp7WLkfWLF8ddcH5qgW5ixun1o+p5qUs0j9EIv6ZXYqeSMy3Gi305NSQWNTB7+T5uHBPCPZP+E4mcb/g62/DFdUP48XgJz29I50RJI7OX7+XuieHcOj5U+n7N7OfLwCBXbvn6GCeKG2lo17Lg/QPcPyWCOyeGIQgCzWotS7Zk8dXBAgxGMeXmgakRXDUi+C+x+jEajSQUNfDj8WJ+SipjlkyDnSASwO8PFp59A72AIIgXYVuVAntrBQ5WCpxtVTjbKnC1tcLNQWyhOtkocbRWSOTN0VrB5/sLeM/UibBRymnX6vF0sOKOCWG8tS2L+jYtLrZKli8aKPl7/l5EeDnw1fVD2ZFRxQsbT5qIVgpfHSzk6TkxZ227/lZYKWRcOyKILw4WUtXcwfVfHmXlzcOl75cZ9lYKll0ez/z39/NrWiXfHC5i8VkWsO72Vrwyvx83fnWM8kY1VgqZaJ8yyI8fj5fy3q5cFg4OOKcuSF8fR2bF+UhuBj8llTIu0oPdmdWsTijhoemRXD86hNd/zUQll9Gq0fPdkSJuHRfKtBhvnGyUNLZrWbI1k1/uGUuYpz05VWIQgdEIj61J4YOrBhHsbsfQEFeO5Nfh62xNSmkjqxNKeGBKBLd+kwiAzggBLiIB1J1SVTanwTSrdeRXt+LtaA0mHlXT0kFuRiUzYn24e1K4tF+OmAjhyfImiuraKKprY3VCibQvhwS7mEihK319fluFMKeqmc2pFfySWkHaKVGgvs7WKGQyyhraKaqzNKn3d7Fh/kA/JkR60s/f+V9nEfZH41939Vz43l6L25cPDeTWr48D4vzDiFA3HluT0tNTGRXmLgWCDwtxI7mkwWI2J8LLHkfTKvZIfh2rE0oQBHhhXiwKuYwDuTWU1Ft+wR82DUF/f6yk0/g5vPemrUu3ZqHWGhgc5MK0GHGWprC2lQ/3iKSwrQv5u2p4EAP+wqrL2TAoyIUfbx3B5tQKXtmcQaEprqdVo+fXtEoCXW3o623NscJ6Ptydx8bkcp6f959dyPmGIAgsHBzA2AgPnlibwraTVSzZmsUvqRW8vrAfMb6iktLbyZq1t43ivV05LNkijkEs2ZrFzswqLhsSwJItWVSZ5r3m9PflyVl9/5I5zqLaNtYklLDyaLHkMQjAaV6KSi5gZ6XA3kokaM62SlzsVLjaqnC2VUmEzUzeuv5vp5Kfc8XEaDTyxpZMifzJBHHWrJ+fI6PCPXj25zQMRjE3/P3FA8975UMQBCb19WJMuAdfHSzg7e3ZpJU1cemHB5kV58OjM6L+kCSRJ2ZHcyC3lqyqFo4X1vPU+lReujiu2/6L83fikelRvLDxJM9vSGdIsKtUkT4dJkd7cfmQAFYeLcZaKadDZ2BTSoVoFl3WxGubM3jzsgHn9HrvmRwuEcCC2jbumBDGvuwa9AYj96xM4qOrBvHh7lyJhH2+P5/rR4WgUsi4flQwS7dlc7K8mcqmdu6cEMa9q5KkbW9Jr6CmWY27gzULB/lzJL+Ok+XNqOQCWZUteDvZWlSHD+TUEO3jQHp599g8c+X4nZ05/G9mX/asE+f+jMDm1Eo2p4rXLFuVnAEBzoyN8ODpOTGEuNuSVtbE0YI6jubXk1TSQE1LB7+YiBuIhHxgkAtDglwYEuLKgADnHtvpRqOR9PImifSZyS6IFW1XOxXtWj1tGj1lDd1zx0eFiqKwnkYD/sP5w7+OAB4tslx96AwGaTZnQqQHB3NrqW3VWBxsIB4stiqZZKppvsB1xWMzRONlrd4gxTVdPiSA+ECxLfbdKdU/pVzg4oH+6PQGPv8Nxs+ZFc38cFysGj42s6904nx+QzoancGi+udmp+xRcXehQRAEZsT5MKmvFysOFfL2tizphFpUJ64Q4/wcqWzqEEUinx9lVj8fnp79n0jkfMPL0ZqPrx7MTyfKePqnNNLLm5j7zn5unxDGnRPCUClkyGQCd04MZ0KkJ9d+cYTqZg0JRQ2S3U+wmy3Pz4tlzDksas4HGtu0bEgp49vDRd0qDiBmetopFKDV4mZnxc47x+NorcDRRtmtCvVHwmg08urmTD7YnSvdZzCKJtganYH3TaTwssEBPDs35g+1r1ApxBzbi+P9WLI1i5VHitiYUs7Wk5XcPKYPt40PPa9zVkq5jI+uHsy0t/bQoTPw3ZFiwj0duL6H+efrR4WwL6eGXZnV3PVdAuvvGH1WIcL/ZkezP7eG4rp2SblrPreuSSzlqhFB0rm5N4jwcmBOf19+PiGO/HxzuIgrhgaw4lARB3NrKapt49qRwSzbkYNCJlDZ1MHPJ8pYMMif28aHsnxHDjqDkec3nGTpZQN4fG2KdD0xGOGJdal8eNVgZsb58MxPaRTVtTEy1I0DubWsSyrF19maUhNZMgBudlZAdwJoHjHam13D+4sHcfBnmfiEU9Cm0XMgt5YDuaKNkCiwUDE42JUrhwXwyoI4alo6OFpQz7GCOo4V1tOs1rEnq1o0yEa0eonzd2JIsCuDg1ywUsrYm13D5tQKC6cHmQDWSjltGj1GkFrlVgoZAkjWLkOCXXh6Tsw52/X8h9+GC6sX+CcjwMWGrw50tn4uHRLID6bQ+VMnfoaFuLLmuNj+lQsiedud1alCUslljAoTL3Jf7C8gs7IZF1slD08TSWF9q0ZaRZkxyxT4vSW98jcZP7/8y0npYmH2HNyZUcW2k1XIBCxav0/Nibngh+27QqWQccPoEPY+PJGbxoSg7EKKU0qbqG3pIM7PEZkAG5PLmbRktxj+/Rfk1v6TYTap3XrfOKbHeKMzGFm2PZuL3tlHSknnnGyopz2XDQ7sZjc2ua8Xo8POT7vybNDoDGxNr+Taz48w8PmtPLE21YL8WStlzI/349ubhnH8f1OkvFalXCDE3Q43e6s/nfy9/EuGBfkTBLh+dDAZFc1sO1mFSi7j5flxvHpJvz/Nu8zN3oqXLo5j491jGNHHTRIVTFyyizUJJef1GAt2t+Oli+Ok2y9sTLcwSzZDJhN4Y2F/PBysyKps4YWN6d0ecyrsrRQsvXQAMkHMtlbKBZJLGokPdAZE437jOc523jMpXPqOJxU3MCPOBxvT53L3ykSuGxWCrUouxcN9vDcPo9GISiFnSrTYqfg1rQIZYtZ8V2xJqyS/phU7K4WkVDa3PNcnlbLQ5Dlrxt6cGoYEn57AtnToSC5uwM3k1+lhb8XNY0Pwduw56UIUWIjXqXtWnWDikt1c8fFhVh4pws1OxfuL4ll3+0ievSiGWf188HSwQqM3cLywng9253LjV8e46tMjfLQnj6K6NgTE7zOIpLRNo0cmwIAAZy4bEkCklwMdOgNqnQE/ZxuWL4rn+1tG/Ef+/kT8qwhgY5Olamr+QD+OFtQBYvs32seBnRndTz4Aw/u4se2k+LsQdzuWmpJAzJgW44lKIaO8sZ2lJkXuYzP64mI6+NYmlkonBTOemh0DdFq/nIvx837TalghEySS2aHT8+zPoreXvEsbZVSYGxf1PzfrgwsFTrZKnpgVzfYHxjPbpLoG0BtFImhvpcDfxYbmDh1PrktlwQcHyKjoXvH5D78PHg5WvL94IO9cIRpAZ1Q0M++9/by2OYPtJyuZ8fZe3tkpZp/29XZAaTKz/WRfPjPe3ktDW++TAc4FRqORpOIGHlmdzIDntnDTV8fYlVktiTbkgsD4CA8+WDyQpKem8uZlAxgZ6v6XzxIZjUZe3HiSj/Z0mvnaqeTcPKYPK48Uk1fdio+TNd/fOkISdv3Z6OvjyLc3DeODxYMIdLWlsqmD+78/wcXvHyChqP6ct9faoZOq+V0xf6Cf5KhgMMKd3ySQWdG9suVub8XSSwcgCGL17ZeU8m6PORWDg125xZQDrJCJl7vMimaslTISixosBHy9QZinPfO6ELdP9+bzkKmzklvdypGCOklkJxNEs/t9OWLq01NzxPO9Vm/kw7153HOKEMUIPGc6fy8cLEbEHS+sx91eRX2blj4mk/ausFKc+RL+zs5OyzO5TODxmdEcenwyOx4Yx7wBvhYLNhuljFM3pzMYKa5v58eEUhZ/fpR57x3gpY3pJBTW4WB15thKI+J8o1wmEOxmyxVDA/nkmsGEedjx/bFiMiubsVHKuX9KBNsfGMec/v98K6gLDf8qAjj8lPi3EHc7SWI+IdKDX1IrupE0MwYHu5BXI84xRHk7ciS/zuL302NFgvXChpO0afQMCnLhElPOY0/ijxB3O1ztVb/J+NlgMEqxcouHBxFsOjF8ui+fgto2rBUyyTpCIRPNp//uB1agmy3vXDGQNbePZHAXpWmTWkdJfTs+jtbYKOWiSGTZPl75JYP2vyAD858MQRCY3c+XrfeNZXY/H/QGI+/tyuWGL4+RX9OKp4MV71wRz6Z7xnDw0YlEeInzOxkVzQx/eTu7eqjs/FaU1LexdGsWw17azrx397PqaLFF5ml/fydenR9HwlNT+OL6oUyP9blg3P+NRiPPbUiXEn8A/JytmR7nzYd78mjT6BkZ6saGu0b/5TO7giAwPdabrfeP5ZHpUWLMWnED8987wD0rEylvbD/7RhCVnwveP0CbRiSALR2dRFAQBF6cH4efszjC0arRc8OXR6lp6ei2ndHh7twyViR0j6xOprTh7H//vskR9PVxpF2rx9FaQZspLxnglV8ypNfUW9w1MUwiTtszqhgd5oaHKT/2sTUpXD86GJVCJrVizSTfx8mGSHPa0958PBysiPUT3QzMC5KdmdUkFNUzKMiFPu52tGn0xJhiMTckl+NobUm49uXUMi7i9OMV+3O6R46CGAv51uXxbL1/rLSwbtca0BthZqw3j8+M4uJ4XwJdbVDJLa8dHXoj5Y0d5Na0WXyOZijlAv4uNvg4WSEgWkAV1Lbx7ZEirv/iGD8mlGI0imlZ714Zz+3jQy+YY/Pfhn8VAWzv8l1VymFdF/Pmy4acXv1rb6WgoU0rHdA51S0Wv1cpZKIiLKuajSnlyASRdJnnTRKLG7o956nZfYHO6t/cczB+Xn+ilLSyJhysFNxtWkWWN7azfLu42usalXPHhPB/1CDtwEAXfrh1BB8sHkiQa2fCZXmTGFHk42SNzmDkg925TFm6+7ySjv8gwtlWxeAgF6yVlqePGbHeTO7rhSAIuDtY8+u9Y7ltXKg446M1cO3nR3l0dfI5t93MaFJr+eZQIdOW7mH0qzt5e3u2JDIBCHS15bHpURx6bBLr7xzNZUMDL7ixB7M5++ddjOfjA53xcbJhtWnE5JZxffjq+qG4XUCh9FYKObeND2XnQ+O5dLA/ggDrk8qY8MYu3tqWdcbF1oHcGi56Zx8ZFc0IJurU0qHjQG4nOXG0VrJsUbxErErq27l1xXE6dN23+8DUCPoHONOk1nHPd4no9D0MuHWBSiHjrcsGoJLLaFLrkMsECmrbcLFVUtGk5oPdeWd8/qno42HP/IH+0u2P9+ZLOcN1rRrWJJSyaEhnu3Zvdg0ny8WuxEPTIqTH7cmq5oEpYvVQ36XwYLbvMrd8a025ujszqxjVwziFVmc4bdJbq0aP9gz7J8zTgXeuGMjme8cwPcYbo1FMZnrllwz0Brh2ZAiTo726HetnglZvpKS+ncZ2UdQYH+CEnVV3gpda1sT1Xxyj/7NbuOrTwyzbns3B3FrUvYya+w+/H/8qAtgVY8LcOZjX2f51sVORWdm97QAwNMSVNQniydlKISPjlPbE6DB3FDKBp9eLwo9rR4YQ7dvpU7fylOqfjVLGhCgv0Q/J1MborfWLWqvnjV/FFvNtE0KlPNaXNmXQrtVbHKiBrjbcNj60V9v9O0GsSviw9f7xPDU7GkebzlVxeaMahUzAwVpBSX07135+lDu/TaCqubvS7D+cO04UNzD33X0883M6aq2BWF9HJkSKFYgvDxYy8+29HDONVQiCwCMzolh7+0hp3m7l0WLGvraTyqbefR5avYFt6RVc+fEh4p/byhPrUi2OU1c7JbeM7cO2+8ey5+EJ3DI+9C81OT8TDAYjT6xN5csuljNTor0ob1BzrLAeO5Wc968cyGMz+l5wVk1meDpY89ol/fnpjtEMCXZBrTXw1rZsJi3ZxfqkUgtybzQa+fJAAVd9eoT6Ni39/J3wcOg0cr5nZRLVXQj8oCBXi8i3Y4X1PLY6pduCQSmXsfzyeOytFBwrrGfZDstkp54Q6e3Aw9NFsmXu/pv98z7cndurSmJX3D0pTNrO2sRSYnwdpereW9uyuWZkMIouYwZmq65Jfb2kKt7Lv2QwPtJDum2utCUUNbA1vZL5A/2QCSJRivC0R6s3EuHVvQ18IK+WcRGnn7Nt6Tg7oYryduSDqwax8qbhRPs4YjDCTyfKeG6DmBPe1WsQwNPBigUD/bhzQigzYrwJcLVBdcp3tk2jJ6e6lcTiRmkeXSmDQBcbBge5EB/gjKO1glaNnr3ZNby5NYtFHx8i7plfmf/efl7+5STbT1bS2KblP/wx+NeogNcczbe4PbSPGzsyxRXouAj3M86CDO/jKtmqdB0dMkftTI324sPdeRTUtuHpYMV9UzpnO1o6dKw/YbntS00ruy8PiP5oo8PcifLunbHxlwcKKG1ox8fJmutHiaTxUF4tP58okyotZrwwL+4fXVpXKWRcPzqEBQP9eXdXDp/ty0dnMKIzGGlW67BViV5qG5LL2Z1VzSPTo7jivySR34TGdi1v/JrJ14cLMRrBwVrBI9OjWDQ0ELlMYPvJSh5fm0JeTSsLPzzItSODeWhaJLYqBQMCXTj2xGSu/fwoB/NqKa5vZ9QrO3jtkn4WlRQzjEYjKaWNfL6/gM2pFd3C522UcmbGeXPFsCAGBjr/LcYbDAYjD/5wgjWmroMAzOrnw5a0SjR6A3087PjoqkGEeZ7Z3uRCQZy/E9/fMoKNKeW8vCmD0oZ27lmZZPIPjCbS24Gn1qWx6pjoUnBxvB8vz4/jveVHAHHRXd3cwX2rkvjy+qFSC/SOCaHsy6nmaIE4Y7gmsZRQT3vumBBm8fcD3Wx58eJY7lmZxDs7shkZ6sbwPm5nfM3Xjwph28lKDuXVYasSFanmiMxXfsmwyG8/G4Lc7Fg4KIBVx4rRGYx8vr+A5YvimWpSNH+0J48FA/2l9//ziTIeNiUwLR4exHu7csmoaCanqoUFg/z5fH+BRVLMy7+cZOt94xgf6cmOjCo8Ha3IqmphR0aNdN3pGvXWeoYKrEanR3GGQ6SmpYNNKeX8eLyElJLGbgJIM7ydrFk40J9Z/X2I9HLo8bgzGo1sTinnlc2ZFHZRAZuhNUBRfTtFXezQrBQCTjZKrJVymtp1NLRrJSeBD03V2UgvB4aEdPoR+jjZdNv2fzh3XJjLzD8A96+2VI0dye8cYr7UlP17OsT4Okpl+PYuBEujNyIIokv/u7vEVej/ZkdbRCn9lFTWLcj6kRlRNKu1rDp6bsbP9a0aaaj3gamRWCvl6PQGnjGFunflNXP6+zL2DLMh/yQ42Sp5fGZfdj5oKRRp0+gxGkULn2a1jv+tS+WS/0Qi5wSj0ci6xFJJZW00ihfzHQ+MZ/HwIOnCPamvF1vuG8elg/0xGuHz/QVMf2svB00WE1ZKOd/dPJznLopBLgjoDEbu//4EN355FK2pzVfa0M5rmzMY8uI2LnpnP2sTSyXyJ5cJjI9w57NrBpP8zFSWXDqAQUEufxvyd9s3xyXyZ62QMSbcgw3J5Wj0BqbFeLH+jlF/G/JnhnkmdPsD43hgSoSUvXvRO/sZ+fIOVh0rRibA4zOjePPS/haLUWdbFTZKOftyanivi1BBIZex9LIBUsUY4PVfM9mc2l3wMXeAHwsH+WMwwr0rk6hvPbPQyKwkdrBSSIpUc0TmzyfKpMp1b3HnxM4q4JcHC/BxtmasqRL3/bFiLhviL/1eqzdKec03jukjtWxf3ZzB/VMiEARRNGGuGubXtLHqWDGXmsQgmZUtKGSQUtpIuGm2tmtE2tGCegJceiZFPRG6soY2lvyawbjXdzL4hW08tT6N5FPIX7C7LXP6+0gziBWNaj7ck8d3h4ssRi/M0OrFCLxH1qRI5G9GrDe/3juG966IZ0q0J54OVpy6/u7QGalq1lBU1y59HuYujllkklnZzNeHirhnZRIjXt7B6Fd3cP+qJL47UkROVctvHiv5t+NfUwHsCh8nK2n+RCETUOsMNLb3XGZ2sFKQ0YPZpreTFRWNHQwKcuHt7dlodAZGhblJajYzvjlsmTAQ4+uIrUrBp/vyae7QEeZp32vj5+U7cmhW6+jr4yhZCHx9qJCMimaUckFaQdqqZDw5q2+vtvlPQoCrKBS5YXQ9z/2cTqIpeswsDlDIBBJMIpGbxvbh7onhf0io+T8FudUtPLkuVfIJ6+NhxwtzYxl5GlsXJxslr13Sn5lxPjy+JoWiujYWfXyIq4YH8ciMKOytFFw9MpixkR5c+sFBqpo72Hayiv7PbcXNTkXxKSbpAhDn58i1o0KYHuv9t0x9MRiMLP7kMAfyxH3oZqfC1V7FnuxqZIKYy33ruD5/CyJ7Olgr5dw1KZyFgwN4bE0yOzOrJZ+3i+P9uHpEcLf3p5AJPDc3hod+TGbptiyGhLhKFTx/F1tenh/Hnd8mSo+/b9UJ/F1su1mEPHNRDMeL6smrbuXh1cl8dNWZo+L8XWx55qIYHvjhhHSfSi5Dozfw7M/prL9jVK87BAGutlw6WDSbbtfoWXmkmLcujWfwi1sxGEVT9Dn9faXiwjeHC7lzYhiudipGhrmxP6eWHRlVtHbo6e/vTFJxA3KZIAkRl27NZut9Y3G1U1Hd3EF8gDOJxQ0Eu9mRViZek4LdbCioFY8b2x7m7LpCpzdy/6okdmd1fj5dYaOUMzLUjRlxPowJd8eri6/q4bxa3tyaxeH8Or48WMjKo8UsHh7EreNC8XCwYldmFc9vSCe3WnTa6OvjyFOzoxkRKn6mkd6OzDRZ2xiNRvJrWjmYW8um1HJSS5u6XX/NXZyukAni90ZjmjEsqS+VFlWuduJc8tAQV4YEuxLj63jBjlFcSPhX7qFxER5Sq3RsuAdru4hBTsXQEFc2pVR0u9/LJNgIdrVjZ2Y1SrnAc6eobdPLmrqZ0L4wL/YU4+eQXp1wCmtbWXGoABBX1HKZQE1LB0u2ivOAXdsHj0z/axIXLhTEB7qw5vaRfLB4IP7OnftBZzAiF8T/39+Vy9S3/hOJ9AS1Vs+SLZnMeGsvB3JrsVLIeHBqBL/cM+a05K8rxkd68ut9Y7limGhfssIk3NiXLS66/J1teG5eDK52YqW8TaO3IH+BrjY8PjOKY/+bzE93jWH+QP+/JfnT6gzMWr5XIn8BrjboDEayK1twsVXy1fXDuG186N+a/HXF4fxOU2GzPcnqhFImv7mbTSnltGt0FuephYMDWDBQrODd/V2ihep3dj9fFppcFKwUMtq1ojL41NlRO1NUnEouY2t6JSsOnT3Sb/5AP2bEemMwitvW6A0oZIIUu3YuuGtSOGaR7Pu7c7G3Vki2PftzapluSmcCMZ7N3PW5fbzY0jYY4cM9uVIiVNduUU1LB18eLGDeAHGxb/6aHM6vk342Gju/O5kVLZL9Uk+oae1gTWKpRP4EIMjNllvG9WHDXaNJe3Yan147hEsG+VuQP4BhfdxYefNwvr1xGIOCXOjQGfh0Xz6jX93BmFd3cO3nR8mtbsXVTsVLF8ex4a7REvk7FYIg0MfDniuHB/HNjcM58fRUUp6Zytc3DOPeSeH083NC1YO9jcFoWfXsirpWDVvSK3lh40nmvruffs9uYfEnh3l7WzYHcmv+c4Q4Df5+Z9XfgMs+sIx/K+8SPTO7nw8P/nji1KdIGNbHtVvix+AgF6m6tCdbJBA3j+1D6Clq25VHLE9GDtYK4gNd2JRSTkl9O652qm5moKfD679motUbGRPuLqUqvL45k2a1zqL6F+PrcNaszH8DzEKRiVFefHO4kCVbMmnp0GM+f8hlAsV1okhkTn9fnpzdt9cq7H8ydmZW8fT6NMnFf0KkB89eFEug27lFgTlYK3np4jhmxfnwyOpkSurbWfzpYQJcbKhq7ug2FmFGpLc9q24egbOtqsff/13QrNYy7a09UsxViLstBbVtGI3Qz9+J9xcPws/5nzHHpDcYee3XDGlea2KUJ28s7MePx0t4d2cuJfXt3P5NAgKw0LoDOwHpgvz8vBhOlDSQU9UizgNeN1RaED9zUQzHCuvJr2nF3kpOZVMHN355jO9vGWFRuY/1c+LRGVE8tyGdFzaeZEiwmFl7OgiCwIsXx3G0oJ6alg4EkKpur/2ayYw4nzP623WFn7MNlw0J5NsjRdS1avjpRClPz4lhdUIJaq2BJVuzmRbjJUWGfrYvn2tGBDGij2gdU93SwbeHi7h3cgTOtkoa2rQ4Wiskv8SP9uTxyTWD+Wx/PimljTjbKqlp0eDpYEVVcwelDe3083ci2WTKbq2Qo9Wf3tbGWiljQIALlw72Z1qM9zkluwiCwMgwd0aEurE5rYKn1qVR3dIhLd76+zux/IqBBP6G2EAHayWjw90ZHe7OvVMiMBiM5NW0klBUT0JhPQdza3ucKTwd2jR69uXUSB6MSrlArJ8TQ4NdGRzsypBgl7/9OeZ84F9RATxc0FmFkwtwtFCc/5PLBEob2jGcYXzAx8nG4mIlADP7eaM3GHGzU1HVrMHP2YY7J1iaeqq1en5MsKwsXjcyGIBPTIqwxcMCeyXSSCpuYENyOYIAj8/sK91nHjA2kz8BeGV+/7/c5PZCgkoh47pRIex/dBI3jQmRDLK72i78fKKMSUt2883hf2+SSHljO7d9fZzrPj9KUV0bPk7WfLB4IJ9dO+ScyV9XhLjbMj7SQ6oIFde3S8eTjUrOvAG+fHndYIJMfyOzooURL+9gZ0blabd5oaO4ro3Rr+6QyJ+/iw35NSL5u3xIAN/fMuIfQ/4a27Xc8OVRifwNDnJBo9Mz/vVdvLQpw6K11/XIalRreWJNCkq5jHevGIi1KULs/S6pKHZWCt6+fAAKmUBLhx5blZyU0kYe+CGp23F63ahgJkZ5otEZuOu7xLNWfFztVLxusm4xb0kmQHVzh5T33lvcNSlMOue+vS0bpVzgwaliRS+nqsXCt7S0oZ1NqRXIZALXjBQX6h06A98eLpIqnmbyJxNEIrMxuZw4PydRBWyaE3W3F8mLzmBkfpciQnMPvnxdIRME4gOdmRDp+Zti/fQGI98eKeKJtalUmyq2DqbtnChpZNbbe1m6Neu0I1W9hUwmEOZpz6WDA3hlQT92PzyB5Gem8tX1Q7l3cjhjIzx6tJY5HbR6I4lFDXy4J4+bvjrGgOe2MuXN3TyxNoX1SaWUnaMK/J+CfwUB7Ir4ICdpJmxsmBtrk07f/nWwVnAw19JIc3qsN4dyxWHhOlMp/dmLYrrNkv2SWm5hTCsIcPfEcHFFU9TQa+Nno9HISxtF0+cFA/3p6+OIwWCULGe6do+uHhFEnP9/MTo9wclGTBTZ9dB4psd4d/t9s1rHE2tTWfjhwR5TCP6p0OkNfLI3j8lLdvNLagVymcCNo0PYev84psf6/Kb2ZEuHjo/35DL2tZ2MfGUnXx8q6rHiNzPWm2cvimVcpBc7HxjPlaaWcbtWz3VfHOOhH0/06AN3IeNIXi2T3txNo8l01MVWSUl9uxTp9sqCPy/S7Y+CWqvneGEdr27OYOQrO9iV2RmJeaywnn05tTSpdVgpZAwKcuGG0SE8e1EME6M8LbbzzZEiRr68g5yqZp42pSIt2ZJpYbLfz99ZyjDX6Y0oZAKbUiqktCUzBEHg9Uv64elgRU5VC89tSDvr+5gQ5SmNKShkglQI+HRvPkW1va82+TjZSGKN4vp2dmdWc8PoECmC7YPdeYwN7xyd+HiPGA936eAA6fz98Z48bh8fJt12tFZIr2fl0WLGm6yWzHZW2VWdvrL7cmosKp6nO2IFREL5/q5cxry2kze3ZJ4TUTuQW8OsZXt5Ym0qda0awjzt+fL6oSQ/M5UPrxpElLcDzR063t6ezZhXd7B8ezbN6vNn4eJorWRshAf3To7gq+uHkvL0NLbeN5ZXF8Rx2eAAwjzPze82u6qFbw6LwpKRr+wg/rkt3PFNAt8cLiS7svlfISz5x7eAW1os499kXQ6PUeEe7DSRq54wLMTVIr9XLhN4fm4so1/bAYgrx8l9vZgc7dXtuSsOWrZ/hwS7olDIztn4edvJKo4U1GGlkPHAVNEj68fjJZwoaUQuE6RKlqudkgdMJ8r/cHoEuNrywVWDSCpu4H9rU0g9ZUbzeGE9s5bt/VeIRBKK6nlibapkUjsw0JkX5sVZeFj2FnqDkV9TK/hgTy6ppY0WVXUBiPZx5IYxwUyM9GL5zhw+25/P6oRS9mbX8NLFcUyO9uLFi+OY3NeT275JQK018MOxEvZn1/Dl9UMJ97rwFbKrjhbx2JoU6b2r5DLq27T4Olnz3uJBf3mqx2+BuRWXVNzAieIGkoobOFne1GNiUqiHHQMCXBgQ6Ex8gDOR3g4W2crXjAzm1df3oG7TSobQ1S0d3PFtIo7WCoLdxDb5Xd8m8Mu9YyWP05vH9GFvdjX7c2rxdbamrEHN8h05hHpYxrK52Vvx1mUDuPLTw3x3pJjRYR7MOkWUdyqemNmXAzk1FNSK2bVGQKM38NKmk3xw1aBe76d7J0fw/dFi9EZ49dcMxkd58uqCftz41TFqWzViFd2UHppS2sjh/DqG93FjUpQn205WUdemYUt6BfEBziQUNaAxLZjsVHJaNXpSShtRKWQU1LYR5GpLYV2bZGVzMLeWJZf259avE4CeVb9d71fKBFo6dCzbkcMn+/JNYo4+uNr1bDxeVNvGS5tOsjlNvBY6Wiu4b0oEi4cHSZ/vtBhvpvT1YnNaBUu3ZpFd1cKSrVl8uj+fW8aGcvWIoN9UcTwTZDKBcC8Hwr0cuGyISOQb27QkFotFlsSiepKKGs5aFTWjvk3LxpRyNpq8ec0Z4VOivZgS7U2Mr+OfmhX+Z+AfTwCHvLrL4rZZPaWQCWRXtvTwjE7083eS8n9BbN8cL6qXBCRWCoGn50R3e15udQsJRQ0W9700L/acjZ91egOv/CIS1BtGh+DjZENju5ZXN2cAlm3MZy+KxdH6wko9uJAxIMCZn+8azebUCp7+KZWq5k5VnFkksjG5nOfnxZ4xaunviIY2Da9uzmTl0SKMRrE6+uiMKC4bHHDOHomJRfUs35HDvpwa6aJlRoCLDVcMC+SKYUEWiRxPzo5mZpw3D/2YTF51Kzd+dYy5A3x5Zk4ME6K82PPwBK765AiZlc2UNaqZ9tYeHp/ZlxtGh1yQggm9wciLG9P5rEu6B4hEYmSoG8sXxV9QqR5nQk1LB0lFItE7USL+f6oasyv8nG14fGZfxkS49+r8o5QLqAEvRytenx3Hsz+fpMWUEWxufVY2dzB7+V6WXNKfoX3ckMsE3rx0ANNNM5XmmbeHVycT4GrLoC4t1pFh7tw+PpR3d+by6Jpk+vk7EXCGmTQ7KwVvXjaAS94/YLFo2ZxWwcHc2tMKGU6Fl6M1l5h8AU+WN5Nc0sDkaC8iPO3Jqmrhx+MlDAl2kfwNP96Tx/A+blw5LEi6xnywO4/n58aw+NMjqHUGrJUyWk12NbsyqxkZ6saB3FrcHVQU1rVho5TRptHTqtEzPMSVSC+H04YZdIW2yxtt0+j5aE8eH+3Jw1opw8fJBg97K9zsVThYK8mtbiGpqAG90YgAzOnvw72TIwh2s+t2rpDJBGbG+TAtxpsNyWW8vS2bvJpWXt2cwSd787h1XCiLhwf9oYtqJ1sl4yM9GR8pVpv1BiM5VS1S5GqCSTHeG2j1RrIqW8iqbOHdneJogpONkihvB+YO8GVevN/fUpzWFX/vV98LdK1wu9goqDe1ZkaEurLpLGHixws7vQJVcoFn58Rw3w9J0n13T4ro8eSy6ohl8oernYowLwde2JCOwQhjwntn/LzyaLGkrLrVlOixdGsWta0ai+rf6DA3C/+7/9A7CILAjDgfJvX14qsD+SzZmmXh81hU18Y1nx3hov6+/O8fIBIxGo2sTijl5U0nJSXgJYP8eWxG1DkRlNL6NpbtyGFzSjmNp5ADF1slc/v7ccu4PvicYc5tUJArm+4ew9JtWXy8J4/1SWXsz6nhhXmxTI/1YdM9Y3h500k+2ZePwQgvbDzJlvQK3rli4AX1OTSptdz5TQJ7srtnrt4yrg8PTY28YO0o1Fo9qaWNJBU3kGiq8JXUd5+FslbKiPNzItbXkZTSJo6ZzouLhgby7EUxPSo2e4OFgwOZGu3Dnd8lsNe0/8yWLGUNahZ9chhPBytmxvkwq58Pry7ox80rjpNc0sjAQLFSdsuKY6y7YxT+Lp3n4XsnR3Agt5bEogbuWZnI97eMOONnMDDQhTsmhLF8Rw4yAYkIPrchnQ13je71TPWD0yL54Xix9H39/pYRLFsUz/S396LWGiyEJdszqsipamZshAfejlZUNHWQX9NqYU5tp1Kg1mrwdhKrnmYFdFZlCwJQ29p5cXtnZy53Tgzjru8SpUpmT7BSyDAYjRZqbDPUWgP5Na3k1/RMkIzATyfK+emEGHfqaqfCzU4ki652KtztrXCzU+Fmb4WrnYqX58eRVNzA14cKKa5v58VNJ/lobx63jw9l0dDezb//XshlApHeDkR6O0jq7IY2DYlFDaZxLLFK2JORdtfvghmN7VoO59dxOL+Ox9emIpcJuNmp6OfvxMJB/kyJ9kImuzCP957wjyeAXeHpaE19u1j1i/Z1Ym927Wkf62AlZ29W50n9tvGhIMCvqeJwuo+TNTf2UMXT6AysNEn9zbhrYhjNaq10/w2jz179a+nQ8ZZpzuWeSeE4WivJqGiSbA7M5E8hExM/LsTKyN8FKoWMG8eGsnBIIK//msG3h4ssDvyfTpSxK7OKR2f05fIh514luxCQXdnME+tSpfmqCC97XpgXx9AQ1149v7VDy0d78vnheLEkbjDDRiljQqQnd00Ko69P72dQrZVyHpvRlxmxPjz0wwmyq1q49esEZvXz4bmLYvjf7GjGhLtz2zcJtGn0HMmvZ+Ibu3j78ngm9e0+dvFnI7+mlRu+PNqtomCnkvPGwv7MiLtwFmViK7eFRFN1L6m4gcyK5m6tXEGAMA97BgQ4MyDQmf7+Yiu3urmDW1YcJ6W0UYy9nBPN4uFBv/u842Sr5IvrhvLa5gw+3JOHRm/A39maEtN3rKq5gy8OFPDFgQK8Ha2J8nYgo6KZwtpWIrzsyaps4YYvjrH69pESwVLKZSy7PJ6Zb+8loaiBt7dn88DUM4/H3D0pnJ2ZVaSWdo6EnCxvYtXRYmlO8GzwcLDi4ng/VieUciS/jsLaVqJ8HBkd5s6+nBp2Z1UT4+sgdaE+2ZvPKwv6cdmQQN7eLvaHP9yTx6VDAvhoTx61rRoEoKxBjbVSJhYDbFXUtWmI9nEgvbwZuQz0BvEc9djMvry1LUvy4hP3heXnczr1/dkgIFb4jEYjBqNIjGpaNNS0aKAXei1RfGekurmDZ39O59XNGQwMdGFEqBteDta42YvEUSSQqj+0suZsq2JClCcTojqrhFmVzVKVMLGogfya1m7kTy6IRYOux4zeYJT8TM2VXKVc4BJVB9aA/gKfI/xHE8BNiZaVuOJ6cbBXIRNIMcnmzTiV7bvbW9PcIR5IckEkYd8dKUZjCtZ+dUE/rBTdVzDbT1ZKrQzxuQLXjAjis/0FtJiNn3vRUvxoTx41LRqC3WxZNDQQo9HI0+vTLNq+AHdNjCDYvXs+5H84dzjZKHlhXhy3jA3l8bUpUlUCRGXe42tTWJ1QwksXxxHpfeHPpIFot7FsRzYf78lDZzBio5Rzz+RwbhgdctZ5Fr3ByJqEEj7dl09mRbNFVUEhExgc7MKt4/owLsLzdxGBAQHObLh7NMu35/D+brH1fjC3lmcvimF2Px92PjieG788RkppIy0dem748hiXDwng6TndxVd/FvZl13DHtwndhuhDPez48AKIdKtqVnOiuJGk4nqSihtILm7scRbKw8FKJHsB4txenL+TRZIRwLGCOm79OoGalg5c7VS8e8XAXrdGewO5TOCxmX3p6+MoWgY1qLG3ktPSocfFVsmoMHd2Z1ZT0aSmwlQFq23VolLIcLFVklnZzN3fJfLx1YOlal2Aqy0vzY/jru8SeWdnDiNC3RgZenoPS6VcxtJLBzB7+T4LkvT6rxnM7u/T6/Gax2f2ZW1iKQYjPPtTOp9dN4S3LhvA0Je2YTBa+vatTijhgamRXDokQCKAxwvruWtCmCgUAXydbShtaCfKy5Gkkgb0RvG1mS8DckFAj1FK5rhzYhj3req0NdPqjdDlpY8KdWNshAcBrra0qHWUNrSzO6uK5JLGM7phGKHbtedccCoRUmsNHMjt9I08FdZKGW52Vrg7mEihqbLobqo2diWLrnaqHq/FvYVcJtDXx5G+Po5cOUwUZta1akg0VQgTCsVxiDaNHk55Hz1VVLV6o/h+BdEL9ELGP5oA3r4qxeJ2m0b8MAYFOXMwz/KLZ6eS09wlNLuwtnMVtWCQPyBIyrMAF5vTxqydakQ6PtIdgykaC+jVHFNlk5qPTdnDj0yPQqWQ8dOJMg7n11kQ1UBXG24d3+eM2/oP544AV1tW3DCMhKJ6Hvz+BHldWiLHC+uZuWwvt4ztw92Twi9oRee29Eqe/ilNCrqf3NeLZy6KtmiX9YSDOTUs25HDscI6ixObAER4O3D9yGDmD/I/rwPRVgo5D06LZHqsNw/+cIKMimbu+i6RDcllPD8vlrW3j+SNLZl8YLIbWXm0mP25Nbx3xaA/VfluNBr56mAhz21I73ZBnBHrzesL+/faQ+58od0kEkgqrjeRvgbpM+8KG6WcOD8nBgQ6S6TPx8n6jOejlUeKeHJ9Klq9kShvBz6+evAZZ+p+D+bF+xHqYc/NK45R3qhGEMTB/DaNniNPTGJfTi0bk8v4NbWCdp2B8sZO4+gdGVU88H0SSy8bIL2fOf192Zddw6pjxdy3Kolf7ukUlvSEcC8HHpku+gma26j1bVqWb8/miVndZ717gpu9FbP6+fDziXJ2ZlZR29KBu4MVlw0O4LujxaSXN0liF63eyIqDBdw/NZLxkR6Smvrrw0UMMKV+mM2xs6ubcbVTSc4TmZXN2FnJae1yzVqTUMzF8f68vS2bgi4qZiuFDEwP259by/7cWvxdbLhscAAXx/shCHCyvLlbdbCrv6yXoxWXDg5gSLALrR16Gtu1Fv8a2rU0nXJfU7v2jKTyTFBrDZQ2tPf4Pe4JNio5zjZKXO1UeDhY4eVojaeDlUQW3bu0pl1slWcdy3C1UzGpr5fUadDpDWRUNJtIodg+Lqxt67bPVHIZii5V199DTP8M/KMJYFd42iupahFX634uthi7ZAEDtJ0SON/1Y31ubizfHSkSy93APZMtPf/MKK5r67aieWFeHFvSKylt6L3x89KtWbRr9QwKcmF6rDetHTrJCqbrAfXy/J6rkP/h/GBgoAvbHxjHTyfKeGp9mlTt0RuMvLcrl59OlPHSxXEXXOZyaUM7z/6UxpZ0sTfj52zDMxfFMKUHtboZ+dWiam9nRlW3eRhfJ2sWDg7gpjEh2P/BQqNYPyd+unM07+3K4Z0dOfyaVsmhvDqenhPNI9OjGN7HjTu/TaSlQ0dxXTtz393Hg9MiuWVs6B/uf6nRGXj6pzS+O2XGVxDEhdotY//4SDe9wSgN5pvn9jIrm7uRUUGACE8H+gc4icrcAGcivOx7PY+o1Rt4YUM6X5rcDGbGefPGwv6/uTXX2qEjtbSxx1mrrojzFz//2785LgkmdmRU8dXBQm4ZF8qUaC/UWj1Pr0+TfFDNWJdUxp7sGi4fEsCsfj5E+zjy9EXRHCusI7e6lYd/PMHHVw8+42d07chgtmdUsj+n8zz+2f58rhgWREgvOy3PzolhY3I5BiM881May68YyDNzY1iTWEqHziCJCEHMEL5tfBiXDwmQCOC2k5W8MDeWxOIGOnQGvB2tqWhSMz7Ck40p5ShMcXF93O1JKW2UyOrHe/NZODiQOyaE8dCPydLf0OiMKARwt7fimvgg1iaWUlLfzpKtWVKSFMDgIGfunRzB3uwavjpYKOVwK2QClU0dLN+RQ4CrDXdPDOeSQf5n/S4ZDEaaO3TdiGFju5aGNi21LR0cL6onraxJEpDJBVDIBTp058Yc2zV62jV6yhvVZ38w4mLI3lqBk7UCF9P8opejNT5O1vg52+DpaG2aa1ThaC0Sxlg/J2L9nLhqhLiNmpaOzlnCwnqSSxpp1+rR6AHTmLLhvxbwhYGWDnOoPBw9JfTbPFfSE9zsVLR26Hj5F1F5q5SLAeg94ftTTkg+Ttb4ONtwx7eiPH/x8KCzVoyyKpul7Tw+MwpBEHh3Zw4VTWqL6t9F/XwY1YtYrv/w+yAIAnMH+DEzzod3duTw3q4caVVcUt/O1SaRyJOzo/Fw+GuVnlq9gc/25fPWtmzatXoUMoEbxoRwz6TwHi/cDW0a3tmRw/qkMsnU1QwnGyUzYr25e1I4vn+yabFKIePeyRFMi/HmoR9PkFraxP3fn2BjcjkvXhzH1vvHctvXCSQVN2AwwmubM9mZUcVbl8f/YQbLda0abv36uIVHHYheoR8sHvSHHYtVTWoSTTN7SUUNpjZ491aul6PYyu1vquz183f+zZXIulYNd3yTIHVJ7p8SwV0Tw3pNbnV6A9lVLRbWMVmVzRiMcKm1FjsBC4/UU+HhYMU3Nw7nmZ/T+PawSLZf+SWDOD8nRoa5Y62U88qCOKqa1ezMrMbHyRo7lYKc6hbqWjW8tyuX93bl0sfdjln9fHhgagT3rjzBtpNVfHmggGtHnX4GWyYTeP2S/kx7a4+kftYb4NmfUvni+mG9ev+u9lZM7uvFlvRKNqVW8GqHDlsr0TrllV8yqGhS4+VgRWVzB43tOn48XszlQwNxt7eSKn4nShpwsFbQrNZJJCK1rJEQd1vya8TqntkT0Azz7N+8eD/e3p4tCXqMmOfFBZ6dG8vs/j48/EMy+ad4HZbUqzmcX8fi4UHcPLYPH+/N56uDBRZZ6sV17Tz0YzLv7crlnknhzOnve9qFl0wm4GSjxMlGScAZ9lebRseKg4V8sDuX+jYtep2RUA87bh4byrAQV5rU3QlkY7uWxjYtDW0a6lo11LVpaGzT0tyhsyDYp0O7Vk+7Vk91cwecRRUsIJ6TrJVy7K0UONoocLEVW8+eDlZ4O1lz9YggfJyt6dAYyKxsJn9XKui0F3wog2C8wN0O33zzTZqbm3FwcOD+++/v9fOu/HAv+/Obut0f5ycq2bri6hGBfHWwqNtjQUzr0OgNfH9MzIicFuPFh1cN7vY4vcHI0Be3WYRsv3VZfwJc7Vjw/gFUchn7H514VpJw/RdH2ZFRxfQYbz64ahD5Na1MXbrbohVno5Kx+6EJf5ga8rfu838DGtu1PLE2hQ3JlgpyG6Wcp+ZE/yYrFfj9+/xoQR3/W5sq2UAMDXbl+Xmx3WYVdToDXxws4JvDhdKFxAwrhYzRYe7cOznigjEU1+oNfLQnj7e3ZaPRG3CwVvDk7GjmDfBlyZYsPjSNSoA4xvHygn5c1L/nBdqp6O0+z6ho4sYvj1FS344gdI4B9fGwY8UNw84b6WzT6EgpaZREGknFDT1WNGxVXVq5/qJYw8fp/LyGru/VTiVn6WUDmNqDcboZRqORska1RPSSihtILW3skeD5OFkzSXcUpaGDVqOSKZfecMaqNMCKgwU8tT4NI+Lie/0do4j2Fb+bNS0dTH9rLzUtHVw1PJDaVg2bUiok4UPXc6a7vYqaFo1pG6PP6nW5PqmUe1YmWb6WG4ZKMZxnQ02LmiEvbMeIaB/2yoJ+GI1GBr2wjbpWjTTjCGJG9K4HJ/D6r5l8YEpCUcoFFsT7s9JUDDC3e++cGMY7OzqTSszVQTPW3T6SAYEufHdE9KQEuNT6BHaCFjt7eyoDJ/NjQglGo3jOunSwP4IgsD6plPo2scMhCDAm3INFQwIYGOTCFwcK+PJAJxHs6kAR5mnPvZPDmRnr87vFcS0dOr48UMBHe/KkbkuklwP3Tg5nWox3r7evNxh7rjq2i4SxqqmDqmY1dS0asXWt1tHWoaNdq+/R3/K3wLzPbezsefjBB87LNv8I/GMrgF3Jn0IG5lb9qcO8TjbKboKQrugf4MRDP3bOEk6P7flkuCer2oL8KeUC8+L9ueMbsfo3L973rOTvQG4NOzKqUMgEHpkRhdFo5Nmf07pJ9h+fGf2HkT+d3iCtfHuyCvi3w8lGyTtXDOTBaS3c9W0SKaXid6ddq+exNSmsOFjIW5cPIOJPMi6ua9Xwyi8npQWKq52Kx2ZEcckgf4uKzebUcj7YnUdKSQNdP1aZAP39nbl1XChTY7wuODW5Ui7jjglhTIn24qEfkzlR3MDDPyazIbmcl+eLKub7ViXRpNbRqtFz93eJ7Myo4tm5MefFF3NreiX3rkyU/NjM14eJUZ68d+XA3zwDqjcYya5qlohTYlFnlawrZAJEeDlIM3v9A5wJ9+x9K/dcsDm1nPu/P0GbRk+Qmy0fXz242/e4Sa0lubiREyXiaz5R0iBWUU6BvZWCfv5OUkVyQIAzXo7WvPnmCZpNj7/ruwS+vWk4AwNduj3fjKtGBOPvYsMNXx5Dqzcy9939fHX9UEaEuuNub8WSS/tzzWdHWHGoiPeuGEhZg5qk4gaC3Wy5ZVwfdmRUszuzWhrf0eqNzHt3P7eM6yPNHPaEi/r7siW9ko1dFnqPrU5m10MTerXv3e2tGRvhwe6salYnlPDc3FhUChkvXxzHLV8fp6VDL2X+Fte1szW9ksuHBEgEUKs3Iu8yS+brZEN2VQsnihsYHOQiWfG42qksCOCSrVmsuGEYCwb6s3x7NmVdFhBVzR38cFw8T8wb4MsjM6KkhcNjM6PYklbJyqNF7M+pZU9WNXuyqnG3V7FgkD/f3jiMLemVfHmgQGrjywQx6u7ObxOJ8s4xVe1/+znE3krBHRPCuGpEEJ/vK+CTfXlkVjZz2zcJRPs4ct+UCCb3PbvgTC4TcLFT4XKGec/TQac30KTWmdrUGmpaOihraKeiUU1Vcwc1LRoa2jQ0tmtp7dDRptWj0RnQ6Y09Wu9c4AXAf24FMPjRjdLPKjlo9OJ8gZVSbrE6vWdSGB/uzkPdg1pHpZAR4mZLpskwWiETOP6/KTjZdr+wXPf5EXZ2iUSaN8CXB6ZGMu71nRiMsPneMWf0/jMYjFz07j5SS5u4ZkQQz86NZVt6JTd+dczC1ynW15H1d/bem+pcYDQaeXJ9Kpqkn7ETtLQalWR6jOPakUHMjPP5b96wB+zNrub+VUlUt3SSfwFR7PPgtMheE4Rz/Z4bDEZ+OF7My79k0GBauV8+JIBHpkdJJ76UkgaWbstmf05Nt2HlPu52LB4exNUjgi5Yn7pTodMb+HSf6Neo0Ym+ao/NjGJchAd3fptIUnGD9FhfJ2veXhTPkODT29ycaZ8bjeKc5xtbMjEaLV0Cbh3Xh0dn9D2n117RqDYpckWxRkpJz/Nw3o7WkgXLgABn4vycznuCwqkwGIy8vT1bUqKODnPnnSvisVUpyKxotnjduT20y+QygShvB4mgxgc408fDvsdzlHmf6+RWrGiJw8VWyerbRtLnNETMjJ0ZVVz/xVGMiJ/FsxfFSDY0L2xI55N9+bjYKvnmxmHc+OUxyhrVjApz44vrhtKu1bMtvZK1CaXszbH0a+zr48jsfj7MivPp5qbQ0KZh6tI9ksIW4IlZUdw0JrRX+7W8sZ2RL+/ACFw/OoSnZotCkklLdpFb3WohsogPdGbt7aNY9NEhqfXuYKXA38WGkxXNKGRI9itvXz5Aqk6afRPNsFbKyHh+BiBWT59cnyZVo1qNSr5X90cpExgd7s5VI4IYH+HZrbJWWNvKqqPF/HC8xILcD+/jykX9fSmsbeXrQ0UWRNB8bMT4OnL/lAgmRv0+ZwAQUz0+3ZcnOWiAGM5w35QIxkd4XHCLVY3OQGWTmqK6Nnat/hyduu2C76L9Iwlga2srMc/v6nZ/qIedxQlMJsC+RyYw8pWdPW7Hz9maUpMHk1prYHSYO1/f2H0OpKpZzbCXtlsoxE88NYVlO3L4dF8+Y8LdWXHDmedHzC0HeysFux8aj52VgqlL91BUZ9mm23DXaGL9/pj23Cd783hh48luJwwQWyiLhgZy5bAgvJ0uHCPeCwFGo5HP9xfw6uaTFsPLDtYK3lkUz7hIzzM8W8S5fM8zKpp4Ym2qZFQe5e3AixfHMijIlfKGdt7als3mtIpuFiUeDirmDfDjzglhONme++r4QkFudQsP/5gsvf+RoW68MC+Wbw8X8YkpahFEIn7HhDDumRzeo2L5dPtcrdXzyOpk1ieVWTxeAF6eH8flQ8/sC9faoSPZ1Mo1V/i6VmnMsFPJ6efvbFEl+7OPrZYOHQ98n8SvaaJgaFyEB8HutqSUNJLaZTi/KwJcbejv3/maY3ydem3HY97n9g4O7FYNJbmkkQBXG9bcNuqsHZJP9+Xz/IZ06faioQE8e1EsRoxc/O4B0subGB3mzmMzolj44UHaNHquHBbIC/NiJbKwNb2Sm786JhHJrhXXWD9HZsX5MivOR4xuA3ZnVXPNZ0ekx6gUMo48PgnnXh4/l314kMP5YpRn2rPTUMhlpJU1MmvZPgDp2gKw+rYRlNS3c8/KJEnoMXeAr/Q97OvtwMmKZq4aHkRNi5pfTJ60p17Xjj4+EQ9HG9RaPWNf28F47dFu53MzFDKBYDc7xkd6MCbCgxhfR9xNpvBavYHtJ6tYebSI3VnV0vXN2VbJrDgfjEbRf9BMzrruz/4Bztw/JYKx4e6/m6jVt2r4aG8eX+wvkIQp8YHi9keH/f7t/xH4u4xR/SNbwINf2d3j/fJTvijjIjzIqOg+J2iGeeXn5WBNYV0b02J6nlf54VixBfkLcbdFJhNY1UvjZ7VWz2ubMwHRcNrN3orl27MpqmuzqP5dNyr4DyN/v6ZV8OImUWnsYK3E0KHF08Ga+8dE8M3hQkkF9v6uXKbFenPtyGAGB7lckAffnw1BELh+dAiLhwfy1Po0Vh0txgg0q3Vc8/lRBga68NHVg6QT629Fq8kc/LP9BegNRmxVcu6fEsHCgf58ciCfu75NtGj5gDg7NDnKk3unRPZaxXihI9TDnu9vGcEXBwp4/dcMDuTWMnv5Ph6ZHsWHiwfx4I8naFbrMALv7Mxhb3Y1b10e36v3X9mk5uavjnGipNHi2JMLAp9cM1gyjzXDbCJrFmmcKDl9KzfS29FEmkRlbphnz1WyPwN1rRq2nqzgtV8yLUZXdmdVs7tTGIqTjVIkqP7izGE/f+ff/T0GkUx/du0QFrx/gMLaNq774ggrbx5xRuHK9aOCOVZQJ+Wzf3ekmKzKFt5fPJBli+KZvXwv+3Jq2J9bw9uXx3PzimN8c7iIME97rjMJP6ZEe3HnRDH1w1al4I4JoZIfXWppE6mlTby6OYP+/k7M6ufDzDgfrh4RxFcmNbRGZ+DR1Sm9zgl+dUEc49/YTYfOwNvbsnlgWiQxvk5SrFtHF8HCh7vzWLYoHmdbpVTVP5hbi7VCQK0zSqKP1QklrLp5OL+mVWIw0k0U9Nb2HF68OA5rpZzbxodxcutRQGyx3jkijAO5NZwsb5Zm3nKqW8ipbpEWTx4OVsT6OhLj60SMryPPXRSLTAY/HCvh+2PFlDeq+cYkzunv74SHgxUHc2uliqAgwIniBq757AiDg1y4f0oEI0LdfvO1wsVOxSPTo7hhdAgf7s7lq4OFJBY1cNWnRxga7Mp9pu3/h3PHP7IC2LX9K0O0dOkp1mXDXaNYujWb7RlVnA4xvo6klzdhNMKhxyZ1W6EbDEZGvrKdiqbOUvlX1w8hq7KFFzaeJNzTni33jT3jl/+jPbm8tCkDb0drdj44nro2DZOW7LJQM7nYKtnz8IRuJq3nA8klDVz64UHUWgOLhwfimrfVYp9r9QZ+TavgywMFkjUDQLSPI9eODOaiAb4XtB/en42qZjW3rkggoahzX8kEsRp13+SIHoeZz9aO/DWtkmd/TpNEAdNivMS2UUIZWZWWJs1KucCQYFfumhTGiD7/bKV4QU0rD69OltS5Q0NcuW9SOK9szuBEl9lea4WMZy6K4bIhAdKxeOo+P1HcwM0rjlHZ1GEx6K6SC3xz03AGB7lQ0aSWsnITzyB48HWyltq4/f1Fg+W/KjdUrdWTVtYkVSNPlDRQeIoCFMR2YrSvo8W8YbCb7Xld5J26zwtqWpn//gHqWjWMjfDg02sGn9FfskmtZfayfRTVtUlVMm9Haz66ehBpZU08tiYFpVxgzW2jOJBbw8u/ZCAT4NNrhzDBVInX6Q1c9tEhjhfWEx/ozPe3jKCpXcuvaZVsTCnjYG6txbWiv78TxfXtkgcfwM93jiLO37lX73nuO/s4UdKIjVJOyjNTUchlVDWpGf7ydgxGS1HFrgfH89XBQj7bn4+VQkaHzsCwEFcOm77fAS42FNe3879ZfcmqaOZ700xf10qiq52KhCenAOJn/+zLr2Ft1NBqVKKLnsljM/ri4WBFaX0b3x4pYnNqBXnVraeNjwOxHd3X15FoH0fkMoH0skYO59dJ+8neSkGIux25VS2SpVrXBdSwEFcemBrZ6+ShM6GqSc37u3P55nCRVKEe0ceNB6ZGMPgMIx9/Jv4uFcB/PAE0w8/JmtIu1RF/Fxv2PTKRuKc3WxhAO9koaGzvLGnfPj6Md3bm0D/AmfV3jOq23QO5NVzx8WHptrVSRuoz0xj3+i5KG9p55Swto4Y2DWNf20mTWsfrl/Rj4eAA7vgmgY2n5BS/e8VAZv0Beb+lDe3Me3c/1c0djDOdgJe9/dZp93laWSNfHShkXVKpNFfmbKvksiEBXDU86Kwmw/8mJBbVc8uK4xYzRA7WCj6/ZjCDQyxXrKf7nhfXtfH0T2nsMC1SPOytcLVTklvdaqFYExDnma4fHcz8eP+/ZVzdb4XBYOTrw4W88ksGbRo91koZ90+JpLS+nS8PFlg8dmq0F68s6Ierncpin4dOvIyHf0ymQ2ewJH8KGZcN9qeyqYOk4gaLz9IMs+BhQJdWrqfjXzMmIUa+tVq0n0+WN51W3ehiq+T6USGMjfAgysfhD5/z7el7nlTcwKKPDtGu1XPJIH9ev6TfGUlnSkkjC94/gEZvwM1ORW2rBiuFjJfnx7IlrYrNaRWEuNvx852jePbndH44XoK9lYI1t4+URC0l9W3MeHsvzWodt48P5eHpUdL2q5s72JxWwcZk0Xy/pyukt6M1Bx+b2Cty3LXl++C0SO6cEAbAwz8md7MOWzwskGtGBjNl6R6paGEmfdDZBg5wteHHW0cy8pUd6A1GKU/YjKwXZkgZzUuWvElLS7PUAna0VvDQ9CiuGBooVaBrWjr4+UQZPx4vIa2ssytmPo309PVRygVc7VS0mARYZng6WNHYrpWuD12J4Ogwd+6bEsGgoNMLf3qLikY17+7MYeXRImmWcky4O/dPiSD+DMKiPwN/FwL4j2sBb0ku7vH+U42e75gQJipeOyzv13UZqL16RLCk8px6GruCFQctkz8uGxzAr2mdxs/zzmL8/M6OHJrUOqK8HZg/0J/9OTXdyN+oMDdmxp3eiuG3olmt5frPj1Ld3EGUtwPvXBF/VkFAjK8Tr17Sj0dnRLHqWDErDhZS2tDOh7vz+HhPHpP7enHtyODfVfL/pyA+0IUjT0zm28OFPPNTGhq9kWa1jks+PESsnyPf3TT8tBVdjc7Ax3vzWL4jG7XWgCCIbcjqlg4Lzz4/ZxsuG+LPTWP6YPMXVZj+ashkAlePCGZCpCePrE7mQG4tL206yaAgF56eE82SLVlSm2xLeiVJxXt4Y2HnLFRLh87C8qOrqbJGZ2DFoU6LKLlMINLLQarunUnw8Gega+TbCZM6t1nd3SfQzU5FPz8nqls6SDVd4OfH+/HS/Li/vHo/IMCZd66I56avjvHj8RJ8nKzPmN0b5+/E4zOjeObndBrbtZIq9v7vk7l6RBDejtbk17Ty/IaTvHhxHIV1bRzJr+OGL4+y7vZRuNlb4e9iyyvz+3HHtwm8vzuXUWHukpejh4MVVw0P4qrhQVQ1qfkltYKNyeUc6eIfW9GkZuzrO7lpTB+mx3qf0ZUhxtdJyi1+f1cOt4ztg1Iu47m5Maw3LaTN9kKrjhXzwNRIBgW5cLywHiuFjOL6dnycrClvVHOyohlHawXFde0klzQyp58P65LKqDxlYfLd4UKuMbW9zadhNzsrYt0cSS1t4sl1qfxwrJgX5sVKbf3rRoVw3agQcqqaWZNQyrrEUouREhdbJUFuthiNkFfTSrNaR2VT9wWReZEkE8S/3eWSyr6cGvbl1DA+0oP7JkfQP8D5tPvtbPB2sub5ebHcOj6Ud3bk8MOxYvZm17A3u4aJUZ7cdwHZWV2o+MdVAHuq/p3a/rVSyEh+ZioZ5U3MffeAdH/XVZSDtYJf7x3LuNd3otUb2Xb/2G75nvWtGga/sM0i5zDjuWks+uQwiUUN3D0pnPunRJz2tRbXtTFpyW40egNfXj+UkaFuzHx7L9lVLdJj5DLY8cB4gtzO7/yWVm/g+i+Osje7Bk8HK9bdMUoy/D2Xfa43GNl2spKvDhZYuOeHe9pz9chg5sf7/eEqxr8D9HoDj6xJ4UdTywbElfHVI4J55qJoli5dKu3zEXOv4bE1yRZxTl3hZKNkZpxo0ny+/N/+KTAajXx3pJiXNp2kpUOHSiHjupHB7MupsahsACy2S5E86U4djjfDz9mms7IX6EzsOQgezje6+gSeKGngRHFjj1FZ1koZcX5OolAjUGxBWylk3Pr1cRKKGpAJYmZtb2IpzzfOdG7p6l334sWxUi5rTzAajdz2dQKb0yrwd7FheowXn+wrAESv19TSJowg5RbPe3c/RXVtDA5y4ZubhkmVzsfWJPPdkWI8Haz45Z4xuJ1hvrGkro1LPzzYbc5WJsCwEDdm9fNheqx3jzOSB3NrWGTqFD0xsy83jRUjPN/dmc3rv2ZZPPb+yeF4O9vw8I/JklVMkJut1LYfGOhMQlEDI0Pd+GDxIAY8twWDUfSHNI8jhLjbsfPB8d32+T333sc3hwt5fXMmzR06BAEWDwviwamR3dwtDAYjh/JrWZtQyi+pFRazhv39nZgY5Ymfsw2FdW2klTWRXtbUo9jpTJjc14v7poQT4/v7iVpRbRvLd2SzJrFUWsRNifbivskRZ/V9PN/4rwJ4AcHZVmUxvzFvgB9WCjlvb7U88PxdbCQC+MycGI4V1qPVG+njYddjuPuahBIL8hft40BaeTOJRQ2o5DKuGn76ExjAa79motEbGBPuzrgIDz7Zm2dB/gDumxxx3smf0Wjk6Z/S2Jtdg41SzqfXDPnNaQ9ymcC0GG+mxXiTXdnMlwcLWJNQSnZVC0+uS+W1zRksHBTA1SOCutks/Jsgl8t4Y2F/HpsRyZWfHCajogUjYhTUqmNFXOMgLpNrWzQs+vhQt+dbKWSMDnfn7onhv2vV/E+AWqunvk1Dfavo1VXfpqW+TWNKBRDvi/NzJLWsiWa1zsIsuis0egPKHviPSi7wyPS+zBng84f5bZ4NZnFJV4PlnsQlgiAutrqmgER4OVjM0SWXNHDzV8epaFLjaK1g+RUDGXeBxRcCLBoaSEWjmre3Z/PkulQ8HaxPaxQtCAKvXtKPtPJGiuvaKalXs3zRAB7+MYWU0iZplOexNcn8cu9YPrt2MBe/d4BjhfU8tiaFJQv7IwgCT82O4WhBPTlVLTz4wwk+u3bIaUmxv6stK24cxsy390rtTXsrBS0dOg7m1XIwr5an1qcyMtSdWf18mBbjLWUPjwh1l1q5y7Znc83IYFQKGbeNC+OTvfmSCTPAJ/vy2f3QeBysRPKnkAkU1rYhF0BvhJzKFuQygQO5tZQ3qhkV5s7e7BrUXbpchbWtGI3Gbu9FbqqWT4/15uVNGaxNLGXFoUJ+SS3nsRl9mT/QT3qOTCYwMtSdkaHuPDc3li3pFaxNLGVPVjUnSho5UdKIUi4wPtKThYP8mbjYk2a1jvSyJtLKGtmbXUNySc/JNWZsO1nJtpOVxPk5ctv4MCb39ZJa1+eKQDdbXl/Yn9snhLFsezbrk0rZml7J1vRKZsZ5c+/kiD/Nn/Xvgn8FAWxs01jcvmdyOEajkd1Zlp5Q5gqBtVLG/IF+3PVdIgBTo7u3X41GY7f5opfn9+Mj08XmbMbPJ4ob+PlEGYIAj86IoqpZzdvbsi0e4+9sI60Uzyc+3pvHt4eLEARYtij+vJXJw70ceGFeHA9Ni+LH4yWsOFhAQW0bn+3P5/MD+YyP8ODqkcGMC/f4V82odYWbvTWb7x3HobwabvrqOM1qMbqovk2DnQBaQ2e/REC0U7h1XB+mRvfeCf/vAqNRzAqtb7UkcWZiV2cidw2nkL127eljxM4GhWm+79S2h1wQ0BuNJi+54X9qxcBoNFLeqJbm9s4kLvF2tJbyffsHOJ018m1tYgmPrk6hQ2cgzNOej68efEGrwe+dHE5Fo5pVx4rPahTtZKPknUUDueSDA2xOq2BEqBs/3jaCm786TmlDOzIBmtQ67l2ZyMqbR/DuFQO57oujrEkoJczTntvHh2GjkvPOFfFc9M5+dmZW8/n+Aq4/g2tDqIc9T8zqy1Pr0wBxfOCNS/pR26phY0o5ySWNUpvzf+tSGRXmzuw4H6bGePHYjL7c/m0CzR06vj5UyPWjQ5DJBF64OJY7vkmU/kaTWsfm1AouGuDLN4eL8HW2oaiuDQ8HsUPV1KFjQIAzScUNfHGggMdmRLE3e5/F4sBgFKuOI8N6JvqeDtYsvWwACwf789T6NHKqWnjghxOsMrWFTyVKNio5cwf4MXeAH1XNan5KKmNtYilpZU0SyXKyUTKrnw8LBvpx67hQbhsvzjqWN7Tz8d48NqaU99gyBkgpbeL2bxKQCdDHw56BgaK9ULSvI319HM8p1jDE3Y6llw3gjgmhvL09hw3JZWxKqeCX1Apm9/PlnknhhHme2Xfy34J/FAG86uN93e4TwCL5IM7PEV9nG3ZlVtH19GqrktGmES++s+N80OgNUjh3T/YvicUNFNV1tl8crBS42qn4JVWc37th9OmJm9Fo5CWT5crF8X7E+DrxwPcnaD5lpfTawn7nfSh7c2q5lGv85Kzos0Yx/RY42Si5YXQI140MZnd2NV8eKGBXZjU7Tf9C3O24angQlwz2Py9pDX9HDO/jTsoz01i6LYvl2y2Jv4+jFTeM6cOVw4L+snbjuUKnN9DQru1O5kw/m+9v6ELqGtq0vzl6SSETcLZV4WKrxMVWhbP5fzvxf1fzfXYq9HoD7+7KZW92DTqDkXAvO/R6wFRsF88RRtzsVHxz07AzGrafDzSptd0i33pK0/g9PoF6g5FXN2dIC9JJUZ68dfmAP8RF4HxCEERCZM75veGLo2c0iu4f4MxjM/ry3IZ0Xtx4ktW3jeSnO0dxx7cJHMoTZ/aOFtTzzo5s7pkcwdNzonlqfRqvbc6kj7sd02N9iPJ25MlZfXlyfRqv/JLB0BDXM9ptXTU8iK3plezNFgsIz21I5+j/JnPLuFCKatvYkFLGxuRy0sqapESNx9cKjA5zkyqGb23L4srhgVgp5MyK82WJexZ5NZ1efst35PDBVYP45nAR5Q3tCGBBntpM14q1iSU8Mj2SPu52Fs8HeGtbzmkJoBkjQ93ZdPcYPt2Xz7Lt2RzJr2Pm23u5YXQId08K73F8x9PBmhvH9OHGMX3IrGhmTWIJ6xPLqGhS8+3hIr49XESQmy3zBvgxf6AfQW52PDUnhidnR5NU3MDKI8WsTypBret+7BuMYrpITlULII7LCAIEu9kRbVIhx5gsas7mGxnm6cDyRfHcOSGMt7Zl8UtqBT+fKGNjchnzBvhx96Twf3VXCv5hM4A9zf91nYsA+OK6IYyL8GDMazulsGwApUxAa7oY7XhgHIV1bVz3+VE8Haw49NikbtWX+1Ylsjax0yj2nknhtHToemX8bE74sFLI2PngeMob1Sx4/4DFY+b29+HtRQPP+H7PFUnFDVz+kWj3cvWIIJ69KKZbi6CxXcsbS95EoVejFlQkOo7CSinHRimGYdso5Vib/ok/yzrvU8mxVsiwUcmxVsjF/03Pq2npYGNyOZtSOmdJbFVyFgz055qRQT222P8taNfoePm1Jcj1amzt7HnoL86ObNeILda6VpGknY3M1bdpehQe9BY2SrlI5OxUFmTOxVaJsyl0vfM+keQ5WCnOaX7NaDSyLqmUZ34ShQNyARbZds4A7lAM4dubhp/3FpFWbyCjvJmkkk6fwNzqlm7KUnOaRleyF/obxSWN7Vru/i6R3VniAvaOCaHcPyXyggim7+35vLVDx6KPD0lG0atvG3nadrzRaOSWFcfZkl5JoKstG+4ejY1Szgsb0vnSJNITELN8R4d78NT6VL46WIiNUs4Pt44g1s8Jo9HIzSuOszW9kj7udvx81+gzzi5XNKqZuGSXdG0x5/12RX5NKxuTy9iQXE5GRXO3bcyP9+XZubE4WCtJKW1gzvL9Fr//7NrBvLk1i9TSJiK97MmsbLG4nplJ36MzonCwUvDEulSL56vkMrJenNHrfV5S38ZzP6ezJV00mPZxsubpOdFMi/E+67GmNxg5mFvLmsQSNqdWWFxzBwW5cHG8H7P7+UgG2s1qLT+dKOObQ0Wkl5/ei9dGKT9txd/TwYpo305CGO3jSKCr7Wk7JWlljSzdms22k+L7k8sEFgz0466J4QS4nl/3iv9mAC8QdP0iOlkrGBfhwZb0SgvyB0jkz0Ypp4+HPR/vFVfOU2O8un2hmtVaNpywVOpeMzKIsa/tAs5s/KzTG3j5F7H6d/3oELwcrbl5xTGLx1grZDw5J+Yc3uXZUVzXxo1fHkWtNTAh0oOnZkd3O6hbO3Rc9/kRwnR6FIJ4UPd04jpfaNPoWXGokBWHCrFWynCzU+Fub4WtSmFBHnskmz3cb9X1dhcSaqWQXdCKZBuVAmdbJc3N6vN6kTYYRNVxvamdam6l1rd1Erv6Hu47NTautxAEMWv7TGTOxVaFi10XMmer/FNUqIIgcHG8P6PC3HlyXSq/plVKM4AyQeC7m4YT/jvJn9FopKiuTarqnShuOG2ahr+LjYVtzLmkaZwJOVUt3PzVMfJqWrFWynj9kv7M6e/7u7f7Z8POSmFhFH39F0dPaxQtCAKvX9KftGV7Kapr47HVKbxzRTzPzo2lr48jj61NwWiE6744yvo7RvPU7Gjya1rZm13DDV8e5ac7R+PlaM1rC/oxs3QveTWtPP1TmoVS/FR4O1nz6oJ+0pjQyqPFXDks0MIbMMTdjjsnhnPnxHByqlrYmFzOz8ml5FSJlbo1iWX8nFzOhEhPZvXzscj4BViyJYtFQwP5X2kqLabrWFfjaA8HFXk1raw4WMiGu0TLm66xcBq9gZK67tF9p4O/iy0fXT2Y7ScrefqnNErq27n16wTGR3rw7EUxZ5xFl5vi5UaHu/PCPB2/plWwJqGU/Tk1HC+s53hhPc/9nM7EKE/mD/RjfKQnVw4L4sphQaSWNrLiUCFrE0u7HSvtWj1yAcZEeBDt40hxfTtpZY3k17RS1dxBVWa11KkDcSYz2sdRrBaayGG4pwMqhYwYXyc+uWYwySUNLN2axc7Mar4/VsKahFIWDg7gzolh+P3GWfi/K/4xBFCr1Xa7r6v/EMB1o0IwGuHp9andHmtGnL8TeoORraZVUE/zfz8llUmEEWBIsAtrEkpp6dAR7ml/xgHr74+VkFvdioutktvGh7LqaDGppZYroCfnRJ8Xt30zGtu1XP/FUWpaNPT1cWT5FQO72b2otXpu/PIYCUUNRNiIJMTNTsXXVw5DrdXTrtWjlv4ZpNvtpttqi9t62rUGOrre1oiP63qCsvz7Bkob1JQ2nJuKrLew7rGC2f2+riTTTB57qmh2fZ6ZdFor/ziyqdEZaGgXSZpYmevaajW3Xi3va2jT9Ojf1Rso5Z0tVmdTS9XFTmlxn4utCle7zp+dbJQXRJXpTPB0sOaDxYPYkFzO/nXJgGic+1vIX32rhqSSTr+9E8UNFsP8ZjhaK6SM3P6mf+fz+DZjR0Yl93yXRHOHDl8naz66evAflhz0Z8Dd3oovrxvK/PcPkGqaETudUbSTrZJ3rohn4QcH2ZhSzvBDrlw1IpjLhwbi62zNtZ8fRas3MvfdfXx+7RDeuWIg89/bT251Kzd+eYzvbxmBi52KpZcN4IqPD/Hj8RLGhLszd8Dpbbzm9Pflp6QytpoqSrd8fZxdD07oUcQQ5mnPPZPDuWdyOE+vT5Uqk1q9kS3plWxJr0Qltzx20sqaCPOww0Ypp7S+nShvB4sFeVJRAy62Skob2jmcX8esfj6sTSy12MabW7Px7/0uB2BSXy9Ghrrz3q4cPtidy67MaqYs3cMd48O4ZVyfsy7YbFUKLo735+J4fyqb1KxPKmVNQikZFc1sTqtgc1oFLrZKZvfzZf5APwYEOPPqgn48PSeaNQmlvL8rx+I6oDfCrsxq9ufUsGhooMk+S8HJ8mbSyxpFBXJ5ExkVzbR06DhSUGdh2aOUC0R4OXS2j/2cWH7FQDIrmnlrWxZ7s2v47kgRq4+XcPnQAG4fH/aviTv9xxDAmKe2dLtPJuv0IJIJcMOYEH5OLrMwzDwV8wb4klhUT02LBgdrBcP7dI+Y+eJAvsXtly6O49rPxbidM1krtHboeNOkPL57UjgGg5HXf82weExfbwcWDTlz1ui5QKs3cMc3CWRXteDlaMVn1w7utorW6Azc9vVxDubVYqeS42KrQt2mQSkXlafnE3qD8RSyaKCwtpWfTpSx/WQlLSZfRqVckMx1Ha1VqHUiiezQdZLJnkhp1/u0XYY/RZJqoIHuF+jzCUGgkywqZKa2eCd57FapNJFI80xPk1rHPSsTRSJnInUNbdozKunOBjuVXCRpXSpvnSROrNZ1nadzsVNhp5Jf0FXT3wNBEJjT35fs7VY0N2tQ9IK0qrV60subpDZuUvHp0zT6+jpK0Wn9/Z0Jcbf7Q/el0Wjk/d25vP5rJkajuCB9f/Hvjx68EBDsbsdn1w5h0UeH2JNVzWNrUk5rFB0f6MKjM6J4YeNJnt9wkvhAF2L9nBgb4ckHiwdx84rjaPVGrvrsCE/OiubTa0RlcEppIw/8kMQ7iwYyvI8bd04MZ9n2bJ5Ym0p8gIuUC9wT3ljYnxGvbKdNo6esQc3yHdln9DAEuH9qJN8dLUajM2BvpWDxsEA2p1X0aPv07IZ0psd6sTaxDBdT+9RsVN6hNzLY15H9ObV8tr+AeyeFdyOA205Wcu1vGGm1Ucl5YGok8+L9eHp9Gvtyali6LYu1iSU8NzeWsb1UkXs5WnPz2FBuHhtKelkTaxNLWJdURnVzh9T96eNux8XxfsyL92Px8CAWDw/iRHEDz21IJ6GwXiriaPVGvjpYyLeHi1g8PIjbJ4RaGEpr9QZyq1tIKxUJYVpZI+llTTSpdaSVNZFW1sQPxztfW7CbLTG+TiwaGkBiUQMZFc18dbBQqubeNj70L3MB+LPwjyGAmh4qHV2LTWPDPbBRynnmp7Qzbuei/r4s25EDwMQoz26rufSyJrKrOsvqrrZKsipbKG1ox+0sxs8f7cmjpqWDYDdbrhwWxPMb0i0qBgKw5NIB503taTQa+d/aVPbl1GCrEu1eTvWN0+kN3LsqkZ2Z1VgrZXx27RD2r0vnj6nDiScvOyuFxXxNmKc9k/p60a7R89OJUr44UMjJ8iaOFzZwvLCBocGuXDMymKkxXmeMiToVOr0Btc4gVSBF8miQyKSZhHb0sqKp7lrNNG3LXOU0ixmMRiQiei641FqHnSB6vZnD30+FIICzzSmih1OqcBKxs1PiaqvCyVb5h6c7/NNgTtPoGp12srzJYkFhRoi7nSnuzYkBgS70/RPSNLqiXaPnkdXJ/HRC/M5cMSyQZ+bE/GYrjQsRAwKceffKeG766vhZjaJvGB3Cobxatp2s4s5vE/j5rtE4WCuZGuPNPZPCeXt7NkajKNxIL/dn2aJ4rv/iKJtSKljqkcUDUyO5e2IYB3NrOFpQz10rE/nx1hGnPe842SpZdnk8N34ljvG8syOHmXE+9PU5PetyslFy5bBAPt9fQEuHDg9TBGhaWRPrk0r5eG9ngeFkeTN51eL15mhBLSHutuTXdBLFgppWFDKBI/l1ONgo8HexsRhvalLrMP4OTVOohz0rbhjKhuRynt+QTkFtG1d/doRZcT48OTv6nCplYls2mkemR7E/t5a1CSVsTqsgr6aVJVuzWLI1i6HBrswf6MeMOB9W3zaSupYOnv45jV9SKqRzrM5g5IsDBXx9qJCFg/x5cFokbvZWKOUyorwdifJ2ZIHpbxqNRkrq200+hY0SEaxoUlNQ29aNdCvkAhqdgc/3i9ufH+/Pg1Mj8PiLkn3+aPxjCODZ8OiMKFYfL+6xReNso6ShXYungxV2Vgp+TRPDxqfFdG//rjhUYHH77knhfLJPnBdcPDzotOXxqia1NFf48PQosqua+eawZYrIdaOCz6v9xAe781h1rBiZAMsXxXdrBxkMRh5encymlApUchkfXjWYYX3c2H+a7f3RsFHJuWxIIJcODuBoQT1fHihgc1qFVNL3drRm8fBALh8a2KvqhkIuw14uOycLgd8Krd4gVSJPrXBaVirNBNIgEUm1Rg+pqaDTYqdS8L9JfU9ptYpkz/Fv0GL9O8F8QVFr9by7M4f86lZyqlvIqmzu0YLFzU4liTT6m0ifeaj9r0BZQzs3rzhGamkTCpnAMxfFsPgs3qN/V0yM8uLFebE8uiaF5Tty8Hay7tEoWhAE3ljYn1nL9lFQ28Zja1JYvigeQRC4e1I4B0zEDuDH4yXkVLXw6Iwont9wkuU7cgj1sGdevB9vXR7PjLf2cKK4gSVbsnh0RlS3v2XG5GgvRoe5sS+nFiNw93eJbLpnzBkXqzeN6cOXBwowGGH59iwWDQ0g1s+JWD8n7K0ULO1iCWaeydUZoNjkPGEebyptUEtZwSsOFrJwUABLt1n627b9ju4BdFbMx0d6sHRrNl8cyGdjSjm7Mqu4b0oE14wMPqeFuUIuY1yEB+MiPGjpEC1v1iaWcCC3VjrXP/VTGlP6ejF/oB9vXjqAV+YbWLZd/Nsduk4i+N3RYlYdK2ZytBcvzI3tFsEoCAIBrrYEuNoyPbbzel7b0mGqEjZJvoV5Na3ouizytHojq46J2/dxsmZchAcDg1ws5gp7gtFo/M3uBn82/hEEcG9GxRl/7+tkTYiHHZd8cLDH35sNNEeGupFV2UJhbRsqhazbLJ9aq2dNQmeJXQbE+jryzM/pqBSyM558l27Lpk2jJz7QmekxXlz20SGL+SwnawX3n6V1cC7YlFLOq5vF9vJTs6OZ1NfS7sVoNPLUT6msSShFLhNYfkU84yI8qGvRUNPSgRViJuast/fi5WRFgKsdfdztiPRyoK+PA05/4IVPEASGhrgyNMSVikY13xwu5LsjRVQ0qXljSxbLtucwu58P14wMvmBMkZVyGUq5jN/aMXgzdwvNzWocrBXcOOb8ez/+W2EwGCitV5Ne3kR2VTP5Na2kljVRWNvKRfIO7ARoaNfy0a+ZZ91WXauGPVniLJJCJqCUy1DIBdNnL/6vUshQyWUoFTKsFDKsFHLT/6bZUfNYgNKsmFdga7qtUojPt5LLsFLKUMm73Gf637z95JIGbv8mgZoWDa52Kt67cmCP4yr/JFw+NJDyXhhFO9uqWLYonss+PMiG5HJGhLpx5bAg5DJBInZNah1WChlJxQ2UNbQzL96XdYllPLw6mQBXWwYFufDaJf249esEPtidy6gwN8aEn77t+dFVgxnw/FY0OgPZVS18uDuXOyeGn/bxvs42zOnvy/qkMhraRV/Am8eGAnDnxHA+219AY7tYrBAQlbTHCus7Ow1dtlVrioZcl1TGmttG8tb2LAuVeZtGz/moXzlYK3lqTjQLBvnx5LpUEooaeGHjSX48XsLz82IZEux6ztu0t1JwySB/LhnkT3ljO+sSy1iTUEJ2VQsbU8rZmFKOm52KOf3FecG7J4Xx1cFC3t2ZI40LGYywJa2SLWmVDAx05uk5MWe9LrjZWzEm3MPiM23T6MS5wvIm0kobOJRXR2FtG0agvFHNyqPFrDwqRs0q5QLhng7EmMQmAS621LZ2cLSgnr3Z1YzTiOeWC50G/iMI4FVfHD/j7++eHM7XBwt7nKOyU8mlIOvLhwZK1b8xYe7dbAA2pZRbKCTHRXrw2YECQJwdPJ0vUXZlM6uOinmiT8zsy08nyqVVqBmvXtLvvFWqEorquW9VEgDXjgzm/+yddZgcZfb9P9Vu4+6aibt7CAkJToIlBHeHxXZZYJdVYNHgLkGCBwsECSGeTFxnJjPJTMbd2rV+f1R3TfdYJiFA4Pc9zzNPW3V1dU31+5733nPPvXxyaFWyKIo89E0h72ySzKCfuGA4cwYnUt/uYNYTq5mLiFaQvNH21bSzrwagIWQfAqBRKTBqVUQZNMSHaUmO1JEVa6RfvCS4TYnSoVD8vFRUoj/dc/PMXJbvruGtDWXsqmzj0x1VfLqjihFpkVw+KZPThib9odJe/4cjw+7yUFhrprDWzKEGCxXNNmraHDRZXbTZ3dicHrrJ2nbgKDO1IlLUweMTkabcYzekPp4w291c/kY+SkFApRRQKhSoFIL0F0ROA0RV6yeRMrlUK9Gq/IVMKiVatQKDbO0k6VQNGhUGtRK9VoVJo0SvVaJXSdZPGqXiiD3Ejxf6ahQ9OiOKe+b2579fF/KPL/czMi2KQcnhpETqefjcYdz47nZcHh+pUToqWxws31XD4ORw9lW3c93bW/nspsnMHZLEovHpvLu5nDs+3MU3t03tMfNg0Kr49zlDuOdjqbDoie8PcMrgxF5tha6fniPLPZ5fdZBF4zMwalUoFQL/PmeIXGEsAkNSIthV0YrbJzIoKTzEOqWkwYpCkLTcb24oY2J2DBsOdrTl9IqiNGAfJwxOjuDj6yfx0bYKHvqmkMJaM+e/uJHzR6fyl1MH9NpOrzckRei5YUYO10/PZl91O59ur+KLXVU0Wly8uaGMNzeUkRNnZP6oVD6/aQqriup59sdiWu0d8/r28lbOfm49qVF6bjoph3kjU/vsMmDQqBidEeXXFUrBHJfHy7uby3ll7SGqgwpT3F6R/TWS1pDu6IefcXtP8EjgH4IA9ga1UuD0IUmM+c8P3b6eEK7jkF9HMS4zmn8v3w9I9i+d8dq60OKPW07O5bwXpKhib8bPD39TiE+UDKUHJIVz47vbQ16fmB3dbbr5WFDRbOOat7bi9PiYNTCeB84Y1GWbxSuLZYPYh+cP5ewRKdS1Ozj58dUSSfZfvDqVkuGpETRaXLQ73Nhd3pAVqNPjw+mR/OIONli6fA5Ihr0GjZIIvZoYk5akCB3p0QZy4k0MSAxjQGJ4n4ibVqVk/qhU5o9KZWdFK29tKOOr3dXsrGjl9g928u/lBVw0Pp1F49NJ+IPqNf5/Qk2rXYra1VkobbJS3Wqnrt1Bi9WN2eHG6fH9YqtrhSBFkSL1asJ0kl7V4Cc6INlrOD0+3F4frsCtV8Tt8eHxibi90q3XK+LxSfd9oojXJ/0d7znB7RNDXAl+SwiCxDUUguD/k1qKKQUBpUJgLk70HHtk5GiMoq+eks2mQ838WFjPTX49oEmr4rShSVw4Jo0Ptlbg9oqc1D+OVUUN7KtuJ8qgptHi4qo3t/LJjZN44IxBbClr5kCdhTs/3MUbl4/tUaN9wZg0Xl93iMJaCz4Rblu6gy9vmdIjOR6YFM7UflIbt1a7myUbD3PDDCkKeObwZB7/rkjWqL2ff5g5gxP5ak8Ng5LDKW+2hQQ0Av/+j7dVYlBLn6dSCL9YKlKhELhwbDqzByXyvxWFvL+lgo+2VfLd/jr+PHcAC8amHbOWXRAEOR3+19MGsLa4kU93VPHdvloONlh59NsiHvuuiAlZMdw9ZwCtNhevrSulOUjeVdli595P9/LgF/s5b3Qql0zMOGqDd1EUUSoUXDQ+nXNHprBk02FeX18W0la2Dzs5qs/8tfGHJ4DzRqTw+oZSOXKXFqmjIojJtzukiyYn3kR1m529Ve0oBKlJdTAONlhCmsknhmtZvrsWnwhT+8XSP7H7ld7Gg02sLKxHqRC4Z+4AnllZTH2Q679SgP+dN/y4VAm22aRIQJPVxeDkcBYvGNlFM/bymoM85deX/O2MQVw4Np2qVhunPLFGjoRG6NR4nG4iDWo+v3lKyPt9Ph+Hm2wU1Jg5UG/mcJON6lY79WYnrTYXNpcXV9Dk7PGJtDs8tDs8VLTY2VnR9bgVAujUSrmbSny4jrQoPVmxRvISwxicFEG0qSPlPCItkhEXjuCvpw1kaX45724+TF27k6dXFvP8qhLmDknk8kmZjM6I+sNWsv5e4fL4OFBnprCmnYMNVsqbrdS0OWi0OGm1ubG5PByjDeERoVMr6J8QxtjMaFKj9FSvK8DtcGPSqji9XxIHGyyUNVlxuH00W13dDvThOhXZcSay4yRJROB+ZozxqPwMvX6iKP0d+X6z1cUzK4spqpMWWjMHxDEuMxqnR6pud3r8GlOPRErlvyCSGry/QCQzQFIDxNTrJ6vSnzQJimLfCVtgW58o0t27fDopGtVgdtJocRBrOvrFmlqp4NmLRslG0Ze9kd+tUbRCIfD4+cM57em1lDZauW/ZHp66cITUA/hMidgdarQyIk3BrTNzefrHElpsbtRKgaI6M7cu3cErl47hmYWjOOvZdaw+0MDr60t7lWi8fOkYpv/vJ0SgoNbMa+sOcd303B63v356jtxR5IXVJVw8IV3u1rJ4wUjOfk5SZDs8IpFG6fkVe2u5YEwqr68vk/dj1CjwipLbgc0d0AyGnn/fcSQjXp903WhVCu49bSBzBifyn68LKKm38Ndle1iysYxbZuaSFWuSrnX/Neb2+vzvFfF4Rbw+6boMvGZzejA7vVicbqxODzZXR9Hd8NQI6tqdNFic2Fxeuf8yIEsnnJ1sxpweH+9uLufdzVIGrvNs8EvTs19oKDtu+N0TwEtf3dDr6zfMyGH2k2sA6Z/f2WcuMMifMihB9v4bkxHdJYz9zsbQgo07Tsnjn19Khs49DQg+nyibPl80Lh1RhNfXh0YR/zT7+LiQuzw+bnh3GwcbrCRF6Hj98rFdUthvbzrMf7+WdIF3z+nPlVOyqGixMefJNdhcXgTg3tMGUPbTbrRIfS6rWu0h5pgKhYKsOBNZcSZOI6nH42m2uNhf00ZRraS7qmy1U9fmoNnqwuz04HB75VWrT5R0KjaXlzqzk4JuzKcFQK1SyJYmcWFakiP0ZMYa+OupA2m0OvlmTy1bD7fw1e4avtotpXQum5TJWcOTfxWz4f/f0WRxsq+6jaI6M2WNNipb7NS3O2iyOP3/8+MbtTNpVSRG6EgM1xEfriXOpKXN7mbb4RaK6zsi0uMyo7lqahazBiaELIie2KzA7ZD287dFUtcdn0+kpt3BoQYLpY1WDjVYOdhg4VCDleo2O+0Oj2z0HAxBgOQIPdlxRnLiTGTFGiWSGGciKVzXJRqiVAgoFco+XZf7q9u55+PdVLbYMWqUPLVg5C/SwvFI8AZFN90eH3aXB5vbK0/SNpcXq8vTUfjkCi1+cnp9CHv3gteNTxSZ+shPfHnzZHKPwYOxr0bRUUYNzywcyYUvb+LzndVMzI5hwbh0jFoVixeMZP4L6/l2Xx0z+ktWMXd8uFMeC38srOehrwu4/4xBPHDGIO7/bC+PrChkfFZMj/3T06ONXDopg7c2SPPF/749wOxBiT22spuUEyOnntvtHt7aUCZrB4enRTI2M0qWC325q5qMaD2Hm+0kRehDInxWl4/ZA+P5vqCevHgTPvC3U+tAvdlJ9r3L0amVmDQqTDrpXAUWBx6fTyJlotixEPBJxFEUwYfE7vvyGy6sNXPTezuOvOFxQndm693hV4/HndgBwN8/AVxT0tLjawOTwnhnU7n8I8nsVEIfplPJLawWjk3jjo92AV3Tvy6Pjw+3doSuVApot3tk4+dpPXjlfbm7mt2VbZi0Km49OZc7PtwVYiWRFKHj2mk9rw77ClEUuW/ZHjYclHz8XrtsbJc06MfbKnnA3yroxhk53HRSLoebrMx9ai12tzTg/Xluf15dW8oMn6QBtDg9THnkR2bkxbFwXDozB8T3WesTbdIwpV8cU3oRTgeiQftr2jlYb6GiRdJwNZqdtNnd2IO8/ET/9i6Pjxabm9LG7h3ulf7ODm6fyD7/xPnAZ3sZnxXDRePTmZYXi0Hzu7/sf1V4PD4ONVkpqGmnpF4qkqppk8hdq92Nzek9rilIg0ZJQriO5EgdCWE64sN1xIdpSQjXkRCuJT5MInwB8mRzefh4WyVvrC+TrwuVQuD0YUlcNSWLYUHdGY4EhUIgJVJPSqS+i+jf4fZS1iSRwtLGDmJ4qMFCu0NaLFW12uWITgA6tYLMGIkYZscZ/eRQun+kXtjf7Knhjg93YXd7yYgx8MqlY457u7q+IkBaAdBK5Opo8cSh7zGbpUW43e1l7uK1LLlyHJNyj95vNGAUfe4RjKLHZEZz1yn9eWRFIX//Yh/D0yIZmBTO0NQI7p4T0Anu46tbpvLpjZO4dsk2ypuleeLVdaXkxptYND6dtcUNfLuvjluWbuerW6f2qNn+y9yBLNteRbvDg9cncst7O/jilindVvALgsB103O41a/3e2n1QS6dlClfF08vGMnEh38EoM3uYXpePIeb7Xy9t4b5o1L4cGulvK/KVjtqpcCBeguPnz+MOz/aTeePDF5s11t69sP9vSDw9Y519FErBfRqJSKST2/nYSwuTEu/eBNDksMZkR5BrFFLhN+ZIUynQa0U+HRHFU+vLJbtdxLCtYQr1Hid7hNel/6Hnglvm9mPG9+T9HZKgRDyB1IBiNnhIUynQq9RkV8quYd31uP9UFAnp0cBThuSxBv+8PvVU7s3fnZ6vDzqryy8fno228tbu0wMT1044rhcIM//dJCPtlWiEODZi0Z1sZL5ek8N93wskdvLJ2Vy95z+lDZYOHXxWhweHwJw99z+vLTmEC02NyqDAkTJ1FYUYVVRA6uKGogP03LBmDQuHJt2XKKWGpVC1nr0BJ/PR1Wbg4Lqdg7USSnnqlY7de1Sytnq9ITowbyiX/QcBKfHx5riBtYUS4UsAqBVKQjTS23LEsJ1pEbqyYw10i9Bquz6/0VH2GZzUVBjprC2ndJGKxUtNmpbHTTZXLTb3TjdvuOWxtCpFcSZtKRG6UmM0BMfrvUTPD+560TsjoTaNgdvbSzjvc3lcsVkuE7FReMzuGxSRhfPy59//ErZZywYoijSbHVxqFEig4carPL98mYbDrdPLlbpjFiThuxYf0o5zkiW/35qpJ7nVpXInqRT+8XyzMKRv6ntzPFElEGD0iVFsBa9upnHzh/GuaPTjno/mbFGXuuDUfR107LZXNrET0UNkh7wZqnX79VTsllzoJF1JY3cunQHy26axBc3T+bm93awrkQar/+6bA9pUQYeOXcYeyrXUtZk42+f7+WJC0Z0e0x6jZJ/nj2Y2z+Qxtx9Ne28vq6Ua6Z1nyk6bUgi//P795mdXt5YV8Zts6QoYFKknnNGJPHZTqn16MZDUgX6jvJWbpyRG0IAC2rMTM+LY/WBBtaVNNI/IYyiup/XylMgSNep8Gs6/dpOpULSeUoFRwpUKn+hkUJArRJwun1Uttpx+FPSMUYNYzOjSIzQSdXvaqWssTVpVYTpVUToJN1tlFGDSaNEqex75sbt9bG6qIFlO6r4vqAuJCqoVoC7m4FMkkV0aCmjjRpmDpCqgyfnxvbJbuyCMWmcMyKFj7dV8uyPxVS3OWjXuTEKXdPwJxp+1wSwu/ZvARg0SlYW1cmMPtKgpskaun0g+jciLZKVhfX4REmY25ncBPz7ApjSL5Yvdkvl6T21Cnp742EqW+wkhGtZND6DM59dF/L66cMSGX8cbBu+3FUtE81/nDWYkwbEh7z+Y2Edty7dgU+EC8ek8bczBnGw3sLpz6zD6fEhCHDHrDyeX3UQi9PD8LRIYhwaLBYn0UYNq26Ywftbyvl4ayX1ZifPrirhuZ9KmNovjovGpXHywKMzZz5aKBQK0qIMpEUZOKWXQhmzw83+aqkd0KFGK5XNNmrbHDRZnbTY3CHV2yJIBtFmJw1mJwfqui9gUSsFjBoVEQY1cSYtSZE6MqKN5PoLWHLjTKhO0BVesFazuMHM4UY/cW5z0OKvkD1eUTutSkG0UUNypI60KAMJETriwzqidYHb49HrFmBPZRuvrTvEV7trOqL7MQaumJzFeaNTu0gffmkIgkCMSUuMSdvFCsPj9VHZYudQo8WfTrZS6r9fb3bSaHHRaAltXQWhbSyHpIQzZ3AihbVmsuOMxJm0v3ttq1al4MubJzP/+Q04PD7u/Gg3la12bjs576j31Rej6IAe8PSn13Gowcr9n+3liQuGo1AIPHHBcOYuXsv+mnYeXVHE/WcM4s0rxvLfrwt4fb3k1XfZG/l8dtMknlowkgUvb+TT7VVM7RfLvJHdN1o7e0QKr68rY3dVGwAPryjglMEJ3fbTVSkVXD0liwe/lAoQX1l7kMsnZRJhkKKA/50/jC931eIVRRrMLsZnRrG5rIX1JY3MGhjPDwX18r4Cl8Xy3bXcdFIORXVm1AppQR9j1PDt9VOJ0KuJ0GuoaLHx8dYK1pY0UtpolYlaMARBqs4dnRHFWcOTmDkg/qicHZweL6+sOcQzP5bQZHWxsrCeq6dmc8vM3OOeiVErFcwalMCsQQm02d18vaeGj7ZWsL28tVvyF0Dwb63Z6mJ3ZRuDkiL61CEoAI3KXywyOoUPtlSw/zupGvxE9239XRPA7tq/BXDRuDReW1cGSOmgJqsbpSK0O0ggqjd/ZApf75XsX07ppK2paLaxo7xVfpwZY+C9fCkd3JPxc5vNzTP+lfuds/vz1sayEHd2jUrBP88a0vcv2gO2HW7mTn/a+qopWVwyMTPk9Q0ljVz/znY8PpGzhifz3/lDOVBn5qxn1+Py+lAIcMvMXJ5dVYLT42NCdrQkel4s6Sodbi9mh5sbp+dy5+z+fL+/jve3lLO2uJE1BxpYc6CBWJOWC8aksmBseq8tk35phOnUjM+O6ZVU769q4/mfDvJd0OpQKSAPRA5PaPs4t1ek1e6m1e6W2n4d7rpPpSCg0ygI16mJNmpIDNeRFm0gO07yTByUHC6Luo8XLA4PhbXtFNVJXQIqmm1UtdhpsDjlCtnjwe3USoFIvYbECB0ZMQZSIvXdpmOPF7HrDV6fyMqCOl5dVypH6gHGZUVz9ZQsTu6k7ztRoFIqyIw1khlrZGYnL2Gzw01Zo41DjRYO+lPJRbVmShosIcWDe6vaub+qo395mFbVJZWcFWskO9b0q/wvjhcGJUfw090nMfepNbTa3Tz5vZRGe/S84Ue9r74YRceYtDy9cCQLX9nEsh1VTMyO4YKxacSH6/jfucO4eslWXl1XypR+sczoH8/fzhxMXkIY9366B49PZN7zG/jkhkncenI/nvqhmPv9reIyY7uSOkEQ+Nc5Q+QiDq8PbnhnG1/dMrXb6tgLxqbx1A/FtNrdWJxeXlt3SPaFNWhU3Dwzh8UrpTnlkF/msGxHFS8sGhVCADeUNDE0JYI9VW3YXF5UCgG3z4dGkAhS/6DodV5CGH89vcMloqLFxifbKvmpqIHiOjNWl6TTDkgbvthVjYCUFh2RFslpQ5OOaL+lVSm5eWY/zh6RwoNf7GNlYT0v/HSQL3ZW8/czB/W6qD8W+HySPcu6kkbWFjewt6r9iO8J/NRMWhVOj5cDdRb++dV+Hl5RyKlDElkwNp0J2dF9WnRpVUounZjJ4xu1WMyu4+m+84tAEMUTs05548aNbNy4EYvFgiiKCIKAyWRi4sSJTJw4EYDMvyzv9r0CUhVvoEm3Ring8oohnn8GtQKbW0p/7nhgNhMeXonD7ePrW6eGpFAf/qaQF1cflB8/cPpA/rW8AI1Kwfo/z+zW++8/y/fzytpS+ieE8fKloznlyTUhEaiH5g9l4bif1+/3cJOVec9voNnqYvagBF68eHTIBLjtcDOXvJaPzeVl9qAEnl80iuI6M2c/tx63V0QhwHXTs3llTSken8jMAfFcluPi+5/WovQ6UQiSXsSOmn2eBAq8iRi1KuLDJCsXr/+H1hbkwTS1XywLxqYze1DCCa19aLO7+XhbJUs2lsn9XAUBTuofz6UTM+ifGCZVOdeaQyxImq1SZZrD4z2q6v5AytmkVRFplDwTU/wpZ2PrIRoO7sFus8rXuVpnQJ3Unzp9OhVNNqrbHLTaXCGayJ8DlUIgTKciLkxLRrSBzFijn9B1kLv4cO0JoZW0OgP6vlLZEkOlEDhjWBJXTcnuUYzfG/oytvwWWFfcyE3vbafN7ibGqOHWk3NRKBRyWrm00Upli61Xcp8coSO7UxFKdqyR5Ej9b0qQezvnQ0eO5dTFa6jwL5In5cTwzlXjjslD9MnvD7B4ZTEKAV66ZEy3xTLPrSrh0W+L0KkVfHHzFFlT+bfP97Jk42FiTRq+uW2aPLavKqznqre24BOliM6LF4/ilbXSQmRYquSJ19N4d+eHu/hke0ea9v7TB3D11Jxut338uyI5cGDUKFn/l5lyyt/rExn0txXyPBJn0tBgcbF4wQheXVvKHn+kEeCMYUl8tbuGccZG+gu1KDwOFAJHfZ03W1x8sr2ClYX1FHQa64MRZVAzJCWCOYMSOHtkSq8L3u/31/HgF/uoapX+1ycPiOfBswb/LElRbZuDtcUNrC1uZH1JI02dqveTI3RM6RfL1H6xhOnUrCyo58td1bTau88gxpm0aFUKKls7gjZZsUYWjE3j3NGpvaaFT9SxpSecsATwp59+YvXq1V2enz59OjNmzAB6JoCj0iPZ7o/aBSqlhqdGsLuyTWb7kQY1rTY3KZE6HjhjMNe/s43UKD1r7zlJZvpen8iof30nX/halcDJAxP4ek8tF45J45HzhnX57IpmGyc/vhqX18cbV4zl/fxyvt1XJ7+eF29ixe3Tfla/31abi/kvbOBQg5WhKRF8cN2EkMl6b1UbC1/ZhNnhYWq/WF69bAxFtWbOfWEDbq+IUoArJmfx+vpSfKLkOfXQvCHc8sS7ZLq7hrl2uJPY6em5x3FnaFUCE7NjuGJyNpNyY37RFPHPgc8nsvpAA29tLOOnog6j6+xYI5dMzOC80ak9DmYB1/jC2nY5ClfTZqfJ6sZsd2Nze/tkAjpCVcVIdU2X54/2nEOgz7KSaIOGlEg9ufEm0qINxIfrSAjTytG7XztFeiyoabPz1obDvLf5MO1+qUa4TsWiCRlcNjHzqHqQdkZfxpZfE6Io8sb6Mv7zdQFen8jwtEhevmR0tzpUh9tLebNNIoX+KuXA/dZu2lwGoFEpyIoxdokcZscafxVd4ZHOuc/n49wXN8rZlpw4E8tvmYzuKBchoijyl0/28MHWCnRqRbdG0T6fyGVv5LO2uJHceBNf3DwZg0aFw+3l7GfXU1RnZkb/ON64fKw8F6wrbuDS1/Nl8n3TjGze2VxBm93NddOyufe0gd0eT127gxmPrsLuz0GqFAKr7prRLeFptDiZ9PCPcnbippNyuHtOR9j4k20V3PmRlFqM1KtotXuYkB3NlZOzuPbtDjfi5AgdXlEkyXao27HFFdef+LzRJEfqSInSkxppIDFCd8RFu8Xh4Ytd1Xy7r5a9VW1diFYA4ToVA5LCOXlAPOeOTuli9WNzeXj2xxJeWXsIt1dEq1Jwy8xcrpmW3ac+2jaXh82HmllbLEX5ijtVOxs0SiZmxzC1XyxT+sWRE2fsEr1zeXysKqrnk22VrCyo76IbB0iPNpARY2BbWbNsraNWCswelMCCselMyY3tMo+faGPLkXDCEsAjMek1+6u5dEn3ZeYjUiPZWdkqPxYEePniUVzzdocBc4AYXjgmDbfPx6fbq7hqSlaIcfKPhXVc+eZW+fH8kcl8trNaaj3zp2ndVuPd9v4OPt9ZzeTcGK6dms1lb2wJef2b26b22ij8SHB5fFz6+mY2HWomOULHZzdNDul/eKDOzIUvbaTF5mZcZjRvXTmOwtp2zn9xIx6fiFIQWDQhnSV+W5uF49K4e84AZj2xmiRnOYNVdehx+6vHBBQaHe3hWexxJ1DX7sDi9Bx1FErhT7PGmjRkxBgYlhrJ6IwoRqZHEqE/MUTtpY1Wlmws4+OtlZj9BqsGtYI5QxKZMziJaKMGq1Oq/LY4PfJ96daLNfg5lwerU0qfWxweHL1YFAxW1Yac8+Co6z6PlB5RCJKwPFKvISFcS3aciX7xJhIjdCGRu98DsTsSdle28tq6UpZ30vddOSWLc0cdH33fibRKd3q83L9sLx9tk6JE80el8N95Q4/JtqjF6pLTyaVBBSmHm2y4vD1fg9FGjd/TsKMIJSfOSHq08bhF8vt6zm96dxvL90hynFiThu9unx7iAdoXuL0+rl2ylVVFDUQZ1N0aRTdanJy2eC31Zifnjkrl8QuktHNRrZkzn12Hy+Pj72cO4oqgLkpf76kJMfEflxlFvt+i5a0rx3VpHRpAIOIYwKCkcJbfOqXblOJfl+3hPb9nnUGjZN2fZxLtr7YWRZGhD34rt0ALYOWd07nolU3UtXdU9Z49PJmSfdsZoW1A7XMi0P3YEoAg4O/mJFXAp0Tp5Wr4lCg9yZH6LhXrLo+PFftq+Hp3DTsqWqlvd3ZbjWvQKOkXb2J6Xhznj04jzS8VKqk388Bn+2Qvv+xYI/88ewhTOrlq+PyODmuKG1hX3Mi2wy0h17MgwLCUCH9rt1hGpkcd1XXbanPx2c4qXl9XSnmzvcvr6dEGTuofx46KVnZXdkRaU6P0XDgmjfPHpMkL0hNpbOkLTlgCGMATTzyB2WwmLCyMO+64Q36+p+hfpF4th3aVgoBXFLlkQgbhehXPrTrYZftPbpjIlW9upc3u5oNrJ4RoyBa8tJFNQXqjSydmsGTjYablxbHkynFd9rW7spWznl2PIMCnN0zizo92caihw67ksokZ/OPsY9f+iaLIXR/t5pPtlZi0Kj6+YWJIRWJZo5XzX9pIg9nJ8NQI3rl6PAU17Sx8ZTNen4hSIXDuyBQ+9E82107L5pLxacx+aq0sAE6O0HGudheWbs558HHUmx3srGhld0UbuyvbKG+20WSVDDqPVn+mUSoI00mebv3iTQxPjWRkeiQDksLQqfs24Xt9okzIOsiZt1vCFkLaXEGvOzoI3i9RvKVSCHLFm1GrlO+rlAIpFT9KA7VGz/BTF3UUT4TrjluLwBMVXp/IDwV1vLa2NKQYYkJ2NFdNyebkAfE/K2LeE3oaW34t1Lc7uO6dbewob0UhwF9PG8hVU7p3Ffg58PpEqlrsHGwMpJID9jVWatsdPb5PISDpWf0Rw0BaOSfORHzYsRWi9OWcP/R1AS/5OxUZNEq+vGUKOT346PUEm8vDwpc3sauyjbRofbdG0ZsONXHRK1JP9sfOH855o6WCjiUby/jb5/vQKBV8fvPkkAX7i6sP8vA3hfLjaKOGZqurS9o4GA63l9lPrqYiiFzcd/pArunGP7a00crMx3+SJSbXTc/m3lM7ooubDjay4JXNgFRV73D7uG56NulRBu77rEMnOj4rmh3lrbi8Pm6IKcBhs6I1GMmduYDqVjtVLZKur9qv73P2wUcvTKfqQgqDyWKMQc2akia+2FnN1sPN1LTau23FqFUpyI41MrlfLOeOTOFAvYV/fVVAo9+W5szhyVwzNYvCGjNriqXe2y2dotspkXqm5cUyJTeOSTkxx2RJ1B0O1Jp5eEUhPxXVd5kD4sO0LBibRqvNxbKd1XIRqUKQNKgLx6UxPS8OlVLxm48tfcUfbmYxapUyAfSKIrEmDXed0p8znlkjb6NVCTg9IhqlAofLS5vdTbRRw5igCr76dkcI+eufYOITP3G6akpob12QSNF/v5ZMn+eNSJFc5oPIX5hOxT1zB3R539Hg2R9L+GR7JUqFwHOLRoWQv6pWO4te3UyD2cmAxDDeunIc+6raWfTqZryiiEohcOqQRJn83Tk7j6n9Ypjx2Bo5/D0sJUKqdHtqV6/HIQgCCeF65gzWM2dwVzNop8dLRbONVUV1fL6jhsI6M55eooYur48mq4smq4t91e185u+PGYBKIaBWKdCrlBi0CjQqJQJSkYbNT+C6q2A7HggsIgLQqhSkRxvIjTdJVgVaFUaNROgkYqcKuQ0me1qVosdJ84kn1mE2OzFpVT1Wlv/RYHV6+GhrBW9s6NBiqhQCZw5P5qopWb3aA/3esauilWvf3kpdu5MIvZpnLxrZxXfweEGpEEiPMZAeY+Ck0OJYrE6PFC30RwxLg9LKVpeXw002DjfZWFUU2gvcqFGSFWeULWyyYjsMsH9ulPbe0waSGqXnb5/vw+byMufJNbxz1Xgm5PTdNcGgUfHaEYyiJ2TH8KdZeTz+/QEe+Gwvw1Mj6JcQxiUTMlhd1MDKwnpuXbqDL26eIhfXXDctm4P1Fjli22x1oVQINFpc3PHhTt66YlyXxYpOreSvpw7khqDo4cPfFHLakERSokJTwVmxRuYMSmSFvyf9kg2HuWZqtqw7m5ATS5RBTYvNLY95n2yr5Ke7ZvCfrwuw+TXum0ubmTM4gW/31WFzeVEgLbQDJDcYoijSaHHJZLC61U5lJ4LYanNjdnh6tDPCv/+kSB3JEXomZMeQHKnH7fVRVNNOcb2FmjY7Hp9ky1VQa6ag1syra0tRKwWSInQkR+iobnPw5a5qvtwVOgeYtComZMcwLS+Wqf3iyIwx/CKV8HmJYbx++Vicbi9P/HCAJRvK5PR9vdnJ0z+WoFcrOWdkMv0Twli+p4YtZS38UFDHDwV1JIbruGBM6gnfAziAPxwBDHT6CJR2//W0gUQY1CGNnJUKBeClf2IY3/m7f8waGB8ikn4vvzxkv5NzY3l9fVmPxs+riurZdKgZjUrBpRMzWPTq5pDXHz9/+M8aGD/fWcXj3x8A4J9nDw5JN9S3O1j0yiaqWu1kxxp5+6rx7K1qkzUraoXA9AHxfLlb0oP8/cxBJIbrmPf8Rjlkf+qQBF64eEyvx+Dx+qSomqtTCtQRFF1zeUMibilReiL0amraHNS1O0L8FPsKj0/E4+820GzrfVsFoNMoCNOpCdepCder/WRM6SdqwQRNImahZK2DtBk0UmP2imYb72w6zPtbJM1Psd+0et7IFOaNTPlZKf3/H1Hdapf9+wKr6Ai9mkXj07n0Z+r7fg9YtqOSP3+yB5fHR268iVcvHdNtJemvAaNW1a0XpxTld/o9DTsMryWvSDtWl5e9Ve3dVlkmhGuDvA1Ncno5NarvQv9LJmaSHKnn2iXb8PhEFr66iScvGME5I/u+OOqLUfSNJ+WyubSZdSVSAc7nN0lk73/nDWPu4rUU11v4z9f7+fc5QwFp8fufeUM53Gwjv7QZtVKQJTFrixt5dd0hrp3Wtchj7pBExmdFs9kfVPD6RK58cysrbp/ahchcOz2bFftqEZDMsl9afZD7gqp137tmPKculmzFFAI0WlysLW7konHpvBrUrz6QBrW7vRh74UqCIBAXpiUuTMvwtMhut7E6PRIxDJDCYILYYqe23YHL65MXDD0hyqBGpVDgcEtdY3yitJDvLvUKkp7w7lPyWDA+41fVkmvVSu49dSB3zu7PB1vKefz7A7LG1u72stTvBDIyLZK/njaQ6lY7n++sorbdwdM/lnCBzolR4IQngr9LAnjFq+u6fT64yldEsoiYNzKFdoc7JBRt929z6pAE3t4kEb1g82efT5Q1ctJ+FXIhR3fGzx6vj4f8LdaumJzJWxsPhxCdsZlRP6vcfUtZM3f7xb/XTssOsTdotrq4+LXNlDXZSI3S8+4149lX3caVb0pVa2qlwISsaH7YX4dCgEfOHUaLzRWyGr1+WjZ/8YuYJesXaVJutLiY9r9VMpnrS5rgWKFWCkQapApZtVKBzydicbrlVK7T4+uTqaYPsLl82FxO6tqdKBUC4ToVieE68hJM5MYbyYkLk8TPUfo+WbSkRRu497SB3D4rjy92VfHmhsMU1LSzNL+CpfkVjMuK5vJJmZwyKKHPnVL+f8SuCr++b0+NPDBmxRr9+r6UE6Lq+JeE1yfyyIpCXvanN2cNjOfJC0ccd5ug4wEpyi/pSyd2iry5PD7Km60hhteBKuUmq4u6dum3F9B2BaBRKjhX40RH3zo3nDwwgc9vnsy5L2zA6fFx+wc7qWyxya3S+oIjGUUrFQJPXjiC055ey4E6C3//Yi//O284MSYtT1wwnEtey+edTeVM6xcnj+EalYIXLx7NOc+tp7zZJkfkAB76ppBR6VEh2aTA+fzbmYM445l1cnq3qM7My2sOcd30UMI4Kj0qpAXcko1SFDCg9R6YFCFXAQeGxKVbKnj2opFyYR/AT4UNjEqPBL9LjIhkPRS4D1Lv5sAD0X9HFOm0jfRKlFFDpEHDkOQIedvAjdvro97spLbNQU2bZNQfWPTXtjuobXPg9HdxOhq0Ozw88MV+/rl8P4OTI5g7JJFp/eJRK4Wu38P/HeT7PXyv3l7r/J1Bsit6+ZLRrCqq5/38ipDvsKOilR0VrWiVCsZnRxNr0krm2/7k4Ylu1/m7HHFXlbR1+3ww6VIpBP519hAEQeDzHZUh23UYrEZQ0+bAoFEyOagV0abSppBG8NPy4vlmb22Pxs8fbaukuN5CpEHNhOwYrggq/FAI8OSFI47hW0oobbRy7ZKtuLw+5g5O5C9BaeR2h5tLX9/MgToLCeFa3rt6Avur27lmyVZ8omR/Myw1krUlTaiVAk8vGMm6kka5MTbAQ/OGsNBPKGvbHFz79lYGuDx+F3Of3BIpGBqlQkp56qT0Z+fIWXCUzahVESbf70iT6tVKdla08un2SlYVNeD2ijSYnTjdXuaPSmXhuHT6J4YW2QR3XNhd0cKuijYO1FuobXdgcXi6JYhen0iLzU2LzU1BrZnPd4VWxakUApEGNalRBvonmOiXEEZatOR5lxZlIFyvkicLvUbJhWPTuWBMGlvKWnhrQxkr9tWSX9pMfmkzSRE6Lp6QwYKxaV16Sf//Cq9P5Pv9dby27pA8oQFMzI7hqilZfmPZE3yUPA5os7m5eel2uRvQzSflcsfsvN/ld9eoFOTGh5Eb37UIrs3m5mCjhVJ/5LCo1sz+6nY5QuQVRRCgweykrNFCZmzv2r4hKRH8eNd0Tlu8lja7h8e+O0BVi52Hzu3qwNATjmQUHRemZfGCEVz86mY+3FrJhOwY5o9KZWq/OK6dls3Law5xzye7GZYaKUeno40aXr98DPOe30CLzc3AxDAKas2IIix6dTPf/mkamZ1MnwcnR7BgbBpL8yvkyOH/VhRy5vBkkiNDu9ZcNy2HLWVbUQhSyvSF1Qf5+5mD5dffvXoCpzzVIWtaW9xAm83NlNxY1hQ3IgBmp4e8xDB8fgJY1+5g6IM9e+eeyHB7YWdFGzsr2nj4m6Ijv+FXhtPrY01Qp6/B/iSGcII7Af4uCWB3UAqERPmumpIlE4j3Nnf08Q20hIk0qMkvlSakGf3jQqruXlod2vmjqkUiQZdM7Gr8bHN5eMKfmr3ppFz+tyL04vzTrLyjSn0Eo8Xq4so3t9BiczM8NYInLxwhTxhWp4cr3tjC3qp2Yowa3r16AgU17Vz/7jZEURqk+yeY2Hq4BZ1awfOLRvHm+sMh7dDeumIs0/pLnUN2lLdw3dvbqDc7GaSXPiPKoOGTKyZK5C2I6B2vysDkSD2nDU2irt3BR1ulaFpVq503N5Tx5oYyRmdEsXBcOqcPTUKvUfbacQE6tId7q9rZU9VGYW07ZU02mq0uHC5vt1EHj0/0d2NwsbOitcvrKoVArElLVqyk+0uPNsrRw3+dM4T7Tx/Ie/nlvLe5nJo2B49+W8TilcWcOSyZyyZlHFUf2j8SLAF93/oyeRGhVgqcOSyZK//g+r7OKKk3c82SbZQ2WtGrlTx6/jDOGJb8Wx/WL4JwvbS4qzM72FLWwrbDLd2mwXyiyKwn1rB44QhOH9r7uUiJNLD+zyczd/EaKlvsLN1SQWWrnbeuGNtnr8AjGUVPyontMHj+bC/DUiPJjTdx1yn92XCwkb1V7dzx4U7euWq8PAbnxofx3EWjuOLNLRTUmjljaBLL99Tg9Pg49am1fHT9xC7X+Z2n9OerXTWYnR4EpDnrstfz+e5P00IySzMHxJMTZ+SgX0f+7uZyrpuWIxPQvMQwEsK1cuWvKMJHWyv425mDmPXEGnms23G4hTH+TiDHC4HWcNJ96Z6AtEBHkI5FFLv/SEGQdNUKQUCpkN4v+PcjiqL/fSI+UdqfT5QWkUdz+AL+NnUKAaX/c6Q+1gIqQYEi6HODv0PI9xMC+xLk5wL7DsDi9NBic/ea5nX3Un1/IuAPQwCDyV9ShI5bT+5IExxsCPIJEiR14JiMKL7dF+j+0ZGebbG6WHOgQ/A8IMHE7qp2NCoFF08IdZYHeGVNKQ1mJ+nRBlQKgYKaDk1MXJiG62d0b/p5JDg9Xq57R5o0UiL1vHLZGFmI7HB7uWbJVrYdbiFcp+Ltq8ZTWNPOLUt3ICIVKmTEGNhT1U6YVsVLl4zmwS/3yS3PVAqB5bdOkV3hP9lWyb3LJE1S/4QwYrwabFYXWpWC0RldidbxRkK4jptn9uOGGbmsK2lk6eZyvi+oY9thaQL5x5f7mDcyhYXj0nvV22lVSjky0VkrJIoiTVYXBTXt7KpolVrGNVipbbfTbu8+eggSQaz1pzE2Hmru8rpKIZAQrmVAgglREChttFLT5uCT7ZV8sr2SkWmRXD45k1OH9O6Y/0dBVaudtzaUsTS/Q98XaejQ9/3/0mM5gJUFddz2/k5JDxup5+VLRzM4+Y9Ffi1OD+tLGvmpqJ5VhQ29VhYHw+MTuendHWyc0Mw/zxrcazTUpFPx050zOPfFDeyqbGNtcSNzF6/lq1um9vl3tWBcOjVtDhavLOaBz/YSH6YLMYq+ZWY/8kub2XCwiZve3c5nN01Gr1GyeMFIznh6HRsONvHy2kNcH5SynZYXx9/PHMTfPt/HV3tquHJyJq+vL8Pu9jLv+fU8fsEIzhreQXBjTVpuOTmX/35diF6jxObyUlxv4YWfDnLjSbnydgqFwLXTsvnzJ3tQKwVcHh8v/FQS4iLx0qLRnPPCBvnxB1squPXkfmTGGGTT9KI6CyfFqHDanCSE6yi6fy7QM7EJJUYdz/WEimabbM+yvqRR9uwMICPGwNR+UuHGxJyYLlYyfYXb6+ONdaU88X0RDo80Vges3DpDxN+Dt4cxXa9WEhemJdakIcqgIVyvxuTXfGvVSpSA3ePD5rfzsrk82FzekMdWvybddwQTFcUJngP+3RHAvB7sX4LxtzMGyQUXLo8PV6f2XgDT+sXxt4J9qBQCJ/Xv6J/7wZaKkNVGfLiWwjoL80akdHEArzc7eGmNZC1z44wcHgqyBwB4ftHoYxKuBsxM80ubCdOqeOOKsbKFgcvj48Z3t7PhYBNGjZK3rhxHcb2Z29/fiQjoVAoSInQcqLMQbdTw4sWjuOndHTT4S+yNGiWr75lBrEnXRZM0e1ACT144gpef29rTof2iUCoEpufFMT0vjvp2Bx9tq+T9LeVUNNtZsvEwSzYeZkRaJBeNS+eM4UlHpRkTBCmSJ3lFda22dLi9lDZa2VHewr7qdg7WW6hstdNkceFwdx89BGmgqWp1yMVHnbGjopUd7+/kLuUu+ieEMTUvjgGJYaRG6UmJNBAfpv1dpgE7Y2dFK6+uPcQ3e2vlFXG2rO9L/V21KDseEEWR5386yGPfFSGKkh75hUWj/hDSAFEUOdhg4cfCer7dV8fOitY+i927mxDf2XSYbYebef/aCb36gqpUCj6/eQrXvb2Vb/fVcaDOwpRHfuS7P03rs5n17bP6Udvm4IOtFdyydHuIUbRSIfDUghGctngdRXVm/vHlPh4+dxg5cSYePGsQf/5kD499W8SknJiQyP6lEzMpqbewZONhluZXcMGYVD7cWonbK3Lr0h0U1LRz1yn95SLDyydl8d7mcsqabLJt2WPfFXH2iBRSojpSweeMTOGx7w7QYJbG7qX5FVw3PUdOF4/IiJK1gAB1ZierDzRw68n9uOPDDicHm8uLkkBHop/3O2x3uNl4sIm1ftJX1qngI1ynYnJurNR5IzfumNqD+nwiNrcXm1MiXlY/ActLDOOBMwfzwZYKdle24fGJ6NUKcuJMtDvc1Lc7e/VcDcDuN1PvTt50vPF/EcDjjO69xzswPS+OuUM6Inprixu6bKMQpFUrwMScGLnptiiKvL6+I/0brlWyrkQSMl81tav1y1M/FGNzeRmeFsnuylbaglrLzBmc0G2asi9YvLKYZTuqUCoEnr94lGw47fWJ/OmDnfxYWI9WpeC1y8dyqMEiu8Pr1AqiDRoON9lICNey+MIRXP7GFtkaIDFcy093zUCnUdFmd3Pr0h2s9kc7b5mZy59mnTiapPhwHTedlMsN03PYcLCJpfnlfLuvlp0VreysaOWfX+3nnJHJLByXflwiKjq1koFJ4d1GGAMVkTsqWthT2caBWjPlzXbqzQ7MPWgPO8PtFdlb3c7e6tCqSZUASZE6prtcqJEG6y1lzeQlhBGhP/GKA4Ih6ftqeXVtKVsPd+j7JuVI+r6T+v//oe/rDLvLy90f7+Irf9X9ovHp/P3Mwb/rCLDN5eGH/fV8vrOKLWXNXSI9vWFgYhjzR6Vw+rBk3n+9CHM3PVILasxMfvhH3r16PMPTorrdTwAvXTKGf3+1n1fXlVJvdjL5kR/56uYpZPXBK1AQBP49bwj1Zgerihq46s0tIUbR8WE6SQ/42mbe31LBxJwYzh6RwgVj0lh9oIGv99Ry69IdLL91aoirw9/OGERpo5W1xY2sLmpgZFokO/ySkhd+OkhBTTuLF4wkQq9Go1Jw3+mDuGbJViwOtyxfuuS1zfx41wx5n1qVkismZ/K/FUXo1Ursbi/PrSrhP/OGyts8PH8oVy3p6ALyXn45r146hvs/2yuP+44jVAH3Bo/Xx67KVn/XjcYuZF+lEBiSEsHojCiGpkSQGqXH6fFhdXrYXt7C2pIGbE5vRwTN5cHm7CB1VmcgutYRYbO7++4UYXf7uoypfYWAxAUUgoCIZBv3c1yRDRolUXoVKqUSwc8tteoT+zf/uyOAvUGtFPjHWYNDQtavrusgdIEfWlq0gR/8fYJPCUoB7Khopd7cQTEHJUewqbSZaXlxXbp+lNSb+WCLpC1cODaNez/dE3IcD83vu0g5GMt2VPLUD8UA/PucIXK0yucTuefj3SzfU4NaKfDSJaM53GTlz59In6tXKzFqlVS3OUiPNvDgmQO5+LV8mZwMSQ7ni5sny71Fr16ylUMNVnRqBY+eN5wzh5+YmiSFQmBKP2lF2WB28sn2Spbml3O4ycY7m8p5Z1M5w1IjWDgunTOHJ/8ipsmBisi5g5OY243vod3lYX9NO9vLWymsaZfTwC021xH9CT0iVLQ4cOl8qAVphX3+ixsByTIhO87EwKQwRqZFMiQlkuw442/eWs/i9PDhlgre2FAqm9uqlQJnDU/hyimZf7gU59GgqtXOtUu2sq+6HZVC4MGzBncrHTnR4XB7+aGgjq92VbO9vJUGc/ddHkDKKtjdoQbwA5PCOWNYEmcOS+42ChQfpmNglNRzOwCL08s5z2/gvtMGcnU3JsnBuP+MQaRE6vnHV/uxOr2c8tQa3r16POOyjuwVqFYqeG7RKNko+rI38kOMoifnxnLLSbk8/WMJf/10D0NSIsiJM/HQvGHsLG+lrMnGg1/s49Hzh8v7VCkVPHvRKOY/v56DDVaijBoi9Gra7BLB+6mogXnPreflS8eQG29i1sB4puTGsq6kkf4JYRTVmTnUaGXxD8XcNqtDvrRofAbP/VgiFzh+uLWCG2bkyLrymQMT5LZwAD8W1NNgdjJvZEpIsR9IurzaNodMwgKpzc6krLrVzsEGCxXNNuranV0WuEqFgMJvceLxifKi/HhDEMCoUWHQSIWFBo3S/yfdVykEShqkQiOfJD8kK9ZAcmSAhHqxON00W1xYurEfk0gf3baBC3xPo0Ypj7dur6/XfuwBIgsw/neidPldEcD1hV37Ggbjxhm5Xfy0dlV0VAwH/m0TsqL5YKtUGTw7SP/34k8l8n0BqbMHwNXdGD8//E0RXp/IrIHxfLg1NG38z7MHy+17jgabDzVxz8dSNO/66TksHJcuHbco8vcv9skm0M8sHEVVq537lknO7waNErVSMiPNSzBx3bRsrnprm3xMcwYl8NKlksff6gMN3PzedswOD8kROl6+dMzvRpAfF6bl+uk5XDs1m02HmnjPHxXcXdnG7so9/Pur/Zw1IoWLxqUzNPXX+056jYrRGdHd6iVFUaS82cbWwy3sq2pjR3krxfUWOQLdG1psblkH+c6mjsHcoFESY9SQHKknO87IoKQI+iWYfvGWcJUtNt7aUMb7+RVyu7wog5pF4zO4dGJGSEvC/x+RX9rMDe9so8nqIsao4YWLRzMu65fX0P5ceH0ihxosbClr5oeCOnZV9Nzn1ahRkhypR6mQ9K5Oj08mJzlxRs4cnswZw5LJje89GicI8PWtU3n02yKe/6mjQ5Mowr+XF0h6u0tG92qrdMWULFKi9Fz/zjbcXpEFL2/iqQUjOGv4kb0Cj2QUfdusPPLLmtl0qFnWA0YY1Dx54QgWvLKJj7ZVMi0vLmThHKFX8/rlYznnufUU1poZkxHF1sMteEXpd3Ko0cq859azeOEIZg5I4IEzBnHq4jUU1ZnJiDZwuNnG4pUHOGdkMhn+CuIIvZqFfn+/CL2KNruH51aVyAEGQRD489wB3OufC0SkTia3zsztQgDrzA4mPLTyiOfmSPD6RDrTKa1KIZM0o0aFQdtB1owaJQat/9Zvmq/XhD42aFToVApcPh8OPyk12z1yg4Ami5NGi3Rb3WqhyerssrgWgUONNg41dp/aFQSpsDFMq8Lt9WHxE9/uMjiCIM03o9IjOWNosmTx5Y/glzVa+GxHFWv9KfB2uxt3LxryExm/KwK46M3tPb6WHqXnhk4FF6IoyowcOjShgUqq4WkdZf1mh5sf9tfL22b7K7DyEkxM7WT8vPlQEz8U1KFUCIzKiAqp/M2KNXLhmPSj/m6HGixc+7Y0kJ02NJF75vSXv8PD3xTy9qbDCIJkKF3X7uDvX+wDJDIgAG12D8NSI5gzOFFOCQNcMzWT+04fjCiKvLaulP9+XYBPhNEZUbx48ehuWxed6FAoBCblxjIpN5Ymi5NPt1exNL+cQ41WluaXszS/nCEp4Swcl85Zw5N/U581QRDIiDGSEWPk3FEdLvwtVhfvb6lgyYZSaoJ6eIK08vT1UvkmrTTtVLTY/cayFSGva1QKmSAmRkikMD7Mfxsu3Y8L0xJlUPfJTX9HeQuvritlRbC+L87IVVOymD/y/z99X3d4b3M5f/9iL26vyKCkcF6+dPQxV///khBFkZo2B7sqWtlZ2Ur+oWb217T36PEZZVAzODmcnDgTTRYXqw80UFzfUVSXFqVn7pBEThmcSFaMQTL29YkcbrLi9op4fSJurw+nx0eDXzIRgCAI3DN3ADMHxHPZ6/khNl4/FtYz+ZEfWXbjJJIjez6PpwxO5LMbJ3P+SxtxenzcunQn1S2OPhXf9WYUrVQILF4wktMWr6Ww1sw/v9rPf+cNZXx2DDeflMszP5bw12V7GJkeGfJ/zogx8uLFo7n4tc1sPdzCsNQIuX/s8NQIdlW2cdVbW7nrlP7cOCOHReMzeHvTYTQqhVzUcPFr+ay95yR5n1dOyeLNDWW0+aN8H22t5IbpuXJk9YKx6fzzq/1yx4rX1pVyx+z+IZ8dgFIhoFcr0CiV+BBxun1dUq6CAEnhOrLjTAxIDCM33iRbfgVH44xaFXqNEoNa2S1RF0WpPWeTxUWT1el3W3DSZHFR1WKmsROxO1qPQJBkT7EmLdFGDaIoUlJvlb/P0JRwrpqcTf+kMGJMGqINmm6Ps77dwcfbK1lVWE9hrRmzwyNHS7/eU8vX/t7Ux4ITXQDzu+oF/HR9/x63e+OKsSHFHAB7Kls589n1Ic/p1ArGZcWw5kAD98ztz40zpMqrN9eX8eCX++Tt4sO11Lc7eeTcoVw4toPQiaLIOc+tZ1dlGxeMSWVlQX3Iavnb26d18a87EpqtLuY/v56yJhsj0iJ5/9oJst3M4h+KefIHyWbmoflDsTk9/Gu51HLOqFHi8flwekTGZUXTL97Iu0GWN/85ZzCLJmTi9Hi5b9lePva3L7pgTCr/OmdIj4Lg30sfw2CIosjm0maW5pfzzZ5auVm4QaPkzGHJLByfzvDUiF+kfdDPQaAP7rplb6LyOrGKaj50DO+ynUoBaoUCp9d33PoUq5UCcSYtceEBkugniuFaov0Ri6/31LKnqmMSmZwbw9VTspmeF/e71/cdj+vc7fXxjy/3yRHa04cl8eh5w0IKlERRlNNlbq8Pj1fE7fNJz3n9z/nve3w+3F4RT+A5n3Tf7X9NIlT+5/yvdX7O6/N/hlfE6vLQYHZQb5Ym2marC1cvQnmFIOlhDRolCkHA4vRgd3u7aKMCnZaOFhfodmEU3DjQcMcdtxMXJhU0WB0eFr22uUsqUa0UePaiUSFG/d2hssXGaYvXytrERePTQ7RyvWFnRSsLX96E3e3lvNGpIUbRaw40cNkb+YgiPL1wJGcNT8bt9XHBSxvZUd7KmIwo3r92Qhdi8eHWCjmbkxypo7rVwaScGDJiDHIXiTOGJXHvqQM41X/c54xIlttg3jAjhz8Heb7e8cFOPt1RRZxJS4PFyfmjU0NS0ItXHuDJ74vlx/87byjhOjXXv7NdPudKrYHDySex8WBTlwxETpzRXyAXy/jsmB6lNG6vj2ZrB5FrsjppNLtotPofW5z+iJ20zdE2DxAEiDZoiDVpiTFpJNsvo4ZY//3A87FG6dbgtwcLwOr08PSPxby2thSPT0SnVnDryf24ekp2txrcZquLTYca2XyomR0VrVQ026TGEX08bIUAYTo1Q1LCmNk/gdOHJZEYof/dzKF/CAI4d3AiL14yusvzt7y7nS/3hKaNR6RFsK+6HbdX5Ic7psupiskPr5QrOcN1KtodHmKMGtb/ZWaI99+Xu6q5ZekOjBolZ49ICWkZd9G4NP57lNo/h9vLxa9Kq8XUKD2f3TRZrjZ+Zc0h/uPvL/zAGYNwe3w8vEKqNDZqlTjdXjw+mJEXC4LAT0UdHn9vXDGGGf0TqDc7uP7tbWz3N5x/4IxBXD4ps1ci9Hu5eHtCs9XFp36t4MGgfswDk8K5aFwaZ49MOWY7gl8KgXNuMJoYduoiDtSaKawzc6BW0gb1VGWpVytRKqTCg17aLf9saFUKyRw7WqpcjgsQxnBdCHHs7JN5oqKyxcabLz2H12kDtR5xyOm4g8mUTKL8z/nJVoCkeXwidpeXyhabHHkJ83tkekURr5/kSduf0EPsr4oAGbGKaj5yDOfmmbkhpsxP/XCAxT8UdyGXl07M4J9BFijdwexwM+epNXLbz+l5cbxx+Zg+eQX+WFjHNUu24fWJ3NLpmB77tohnV5Vg1Cj56tapZMUaKW+ycdrTa7E4Pdw+qx+3z8rrss+Hvi7gpTWHUCsFBEGycvnLqQMwaVU8+MU+PD4pWjxrUDxPryyRo/Z7qtoQBFh5x3S5OKWgpp1TF69FIUiZLKVC4Ic7ppPllzzZXB6G/+M7WZ8Wrlex7f7ZjPzn95wmbJPPeWBxGWVQMzk3lqn9YhmZHoVKIXRKtfpJnrUjQtdkdcnt0I4GBo1SInNGicBJZE56HGPSEOf3do3x27Ioj8PC8kCdmfs/20u+v/VeapSe6XlxtNpcFNSYqWt3YOvBG7YzBKSmCiqFgEcUcXq6f5dJqyIvwcSM/vG4dn6B1WI54efQ3w0BdCu0vGPtuqJTK2H13TO7OKkDjPjHd7TaQy/Y+SNT+HRHFTlxRlbeOQOAfdVtnP50R3u59Gg95c32Lj9sp8fLrCdWU9Fs57KJGbyzuVyemA0aJVvvn3VU1iSiKHLb+zv5Ylc1YToVn94wiX7+YpN3Nh3m/s8kXcddp0jH8Nh3UiTQqFVic0oX76lDEyltsMoNuoM9/nZXtnLtkm3UtjsI16l4btGoIzac33CwkS/ffQWd6MIpaKjPmC319IyVGr5nx5mINWlOuEhadxBFka2HW1i6uZyv9tTIkQ+dWiFHBUemRZ4Q36U30u30eDlYb+VAndSIvai2nQN1Fqpau++fKSD1eBUEsDm9PYqcOyMwuQTvB44u0hOmU8mEsIMkhqag48J0hOtUv+p59/rF6j8W1rGyQEr1BJOR7qKuvzTUSgGVQoHKP7molArU/lv5OYVC2k6p8G/jf49CwOnxYXa4aXO4abO5abW7+1TFaNQq6RdvIjPGRHyYhuo2B0W1Zsqbbcfc7tGgURIfJi0A2h1uatsc8rUkAGOzorhgdBqHV3+ExWIOOefxYVo+vXGSnErdXdHKotc2h6SLAQYkhvHxDRMxaXtevHk8Pua9sJ49/h7FAxLD+OLmKX2qwH4/v5y/+Iv5/jNviGwU7fH6uOiVzeSXNTMoKZxPb5yETq3ksx1V3P7BThQCfHjdxC7t37w+keve3sYPBXWYtEosTi8qhcBH10/E7RVlvWiUQY1eo6S61cGlE9N5P78Sl9dHUoSODX+ZKf9OLn09nzUHGkiJ1FHV6mD+yBSeCOoydd+yPSG6v+cuGsn28hbMWz+Xo667o6agVSlwerw0W900WVxytqSvUAgQbQwlcnJkLojYBZ77tVo8Nluk9oPbDrdQUNPOoQYrjVbXUVgUSdG81Cg9I9MjmTUwgZHpUV3cGDweHysL6/lqTzXbDrdS22bvkpUJjC1avZG/3HPX8fqKxx2/GwLY0yB976kDuvRSDCCzG8/AUwbF893++pAQ+y3vbedLv2VDoFJYo1Kw4S8zQ7z/Xl17iH8vLyDOpCEn3sSmIGPgly8exSlDulaI9vrdvivi6R9LUCkE3rpynNyO7tPtldz50S5EUUoFaJUCT62UClSMWhVWf/j+vFEprC1upM7vE2XQKFnj9/j7Ylc1d3+0C6fHR06ckVcvGyuvFruD0+Plie8O8PLaQ5yv7X1iDNOqyJJJodT0PctPEH+pAoSfi1abi2U7qnhvc3mIhql/QhgLx6Uxb2SqbAf0W+BYoq7tDjfFflJ4oNZPDuvMPa7SVQoBo1aFTxSxOj19TiXHmTRkxBhJi9IT7rexcLp9NFic1Jud1Jsd1LcfXbpHq1J0EER/9FAmjv77cWFaYozaY44IWJwe1hU38ENBPasKQ6UaCgEuMuxB7XOCWo9h9FldyVaAlIUQNQW7Klt5c0MZLo+PhHAtf547gMxYI2qFpB0L3kdAT9b5OYXQu8FuMAK6vd2VreysaGNXRSt7qtq6LSRSKyQS3zkaHGvSEK5TIwjQanP3WOTRHWJNGhLCdSSG60iI0JHkv00M15EYrqXO7OTbvbUs31MTouMa4Ld/OXtEimwAHrjOtQYjb7QNDrlmrpqSyf2nD0IQBBwuD5e+nk9+UPtAkGQv710zgeFpkb0e8zVvbeH7AknTnRCm5ds+egU++f0BFq8sRiFIdjMBo+jaNgenPb2WZquLSyZk8K9zpGjknz7YybIdVaRE6vn6tqldyILV6eG8FzdSUNNOmE6F2eEhLVrP8lunYnZ4uO7treytapcXX2qlwN2n9Oe/fk/Zyydl8OBZ0mdtKGnkolc3o1EpcHl8KAT4/o7p5PijhPVmB+P/u1JeBASCCq+88MwRFzomrcpP5gKpVj+B8z8OELpYk5ZIvfo3k394fSL7q9vYeLCJ3VVtlNRbqG61YzmK8QzAoFYyICmMCdkxTMqJZUBSWBef377C5/Ox6VAzy3ZWkV/aTFWLnfmanRgFN0ZTGHfd+X8RwGNGbwQwKULLmntmdmuLUdNmY+JDq0KeizVpsLu8WF1ePrtpMiPSInG4vQz++wo5558YrqW23cmCsWk8HNRvss3mZtqjq2izu1k0Pj1kpTUiLYLPbppyVN/r422V3PWRZNb5v3OHccHYNAC+2VPDTe9txyfC5ZMyMWmVPLtKqpIzapSyUPqicWl8vrNafhwfpmXN3TPQqJQ8/n0Rz/nfc1L/OBYvHNlryrO4zsxt7+9kv7+LSWD14hQ01GXMIlynpsnq4lCDFHXq7YpJDNdJZNBPECVyaCItSt9rRd+vBVEU2V7ewnubK/hqd7U8AWlVCk4flsRF49IZnRH1q0cFj1faXRSlnsqFteagiKGZ4nrzES1plArw+foW8VMrBdKjDQxKjmB0RiQDEsJIjTLg8PioNztoMDupb/eTQ7NTemx2Ut/uOCoPOaVCIMaoCY0ihkm6xTiTViaOcWFatCollS02VhbU80NBHZsPNYdEN8K0Kqb1j2PWwHhm5MXzxkvPHtU59/lEnvzhAM/8KC3GpvaL5dmFo477wqHN5mZ3VatUqFHRxq7KVtkMOBhKQUCvUeD2isccuQOJDCdG6BicFM6ojCjSog0S2QuXiHl3WuGKZhvLdlSxbEcVpY0dMou4MC3njEhm3shUBiWHemo2W1w8/fSTCG4HYWFh3H777Vz51lZZugJSavKD6yaQlyC996XVB3l4RWHImCMA9542kGun9W4V8+AX+3hzQxkgEZzlt06Rq2t7QsCE/4OtFejUihCj6J+K6rnc3+f92YtGcsawZMwON6c/vY7yZhtnDEvimYUju4wdVa12zn52PY0WJzqVAofHx1nDk1m8YAQOt48/f7KbL3ZVy9ufNCAOq9NDfmkLAvDVrVMYnByBKIqc+ew69la1kx1r5FCjlbNHJLN4wUj5vdct2cq3++vkx/8+ZzBlP30kSx3iJs3vSMP6I3UxRu0JV8jVbHGywR/NK6wxU95spdHiOqrrXCFAjFFLv3gTE3NiMOlUfLilggJ/tmxISjj/OnsII9N79508Fjzy6OM4bP+XAv7Z6I0Avn/tBCZkd+/79J+v9vPKutKQ58ZmRrGlrIWEcC0b/3IyCoUQEvaHDnHzd3+aFuL9F9Bz5MYbMTs8cg9GAVj3l5mkdJOC7gkbDzZx6eubcXtFbjoph7vnSJHIVYX1XPv2VtxekQvGpBJl0PCSv0tHMPm7ZEI6S/MrZG3RoKQwvrplClaXlz99sJMf/Cvf66fncPec/j1GUERR5K0NZTyZa1ZtAAC5N0lEQVT0TSFOj0+uQusuNdYv3sTcIYmc1D8eo1ZJaaOVQ41Wf9N3K6WNVpp7iSqoFALpMQaygyKG2X6iGGfS/iZp2Da7m893SlHBQAodpO+6cFw680el9LnDwM/FL6279PpECmraeHN9Gd/ur+uSXusOYTqVlC5yS5YJfRkpwnQqsmIMDE2NZGxmFAOSwsmONYWk4Bxur58QOvwk0dnxWCaOkv7oaEanzilskAjF8NRIqTNBvzhSovSywP1ozrnZ4eZPH+yS/UOvmZrFn+cO+NmLGofbS0FNO1tKm9lS1sLeqjZq+thKrTeE61QkRuiI0KtxuH1Ut9pDon5qpcDM/vGcOSKZmQPi+5Sma7O7+XpPDZ9ur2RLUGROr1YyZ3AC80alMjknBpVSgdvro8UmFZ18t7eGd/MrqGt3ymOLHQ2LrrqBoamRbDzYyOVvbAmZ3BeOS+OfZw9BrVSwv6aNi17e3EXOM61fHK9fPqbX/8Fraw/JRXNqpcD7105kdEbvE77H6+OaJVtZVdRAlEEdYhT9yIpCXvjpYAih3FHewnkvbsTrE3ns/OGcNzq1yz53lLew4OVNOD0+eY559LxhnD8mDVEUeXnNoZBOUo+eN5T7P9uH0+Mj1qRh819noVQIfLGrmluX7pB16oIA390+TZYOFdeZmf3kGnk/OrWCa6MKTkhNt8frY191O5sPSdG8g/UWatodmO2ePstWQCociQ/TMigpnNEZUfRPDKd/gtRtqXOk0usTeS+/nP+tKMTsP38Lx6Vzz5z+x2WsF0WRimY777z6PG6H9YQ7553xuyWA0/NieevK8T2+b9J/V1LdaSCdnhfH6gMNISH8kx5dRam/nU1ApzEtL44lV46T31fZYmPm46txeXycOSxJThcDXQTDR0JJvYX5z6+n3eHhjGFJPL1gJAqFwIaDjVzhHwTPGJZEfJiW19eXAVJqN2Bnc/G4dN7NL5ejNLMGxPPq5WM53GTlmiVbOVBnQaNS8L9zh3XphxuM+nYHd3+8W+4EohQEvKKIXq1kkXEPXqcNldbAgfjpbDjYGGJ+mRIpWT/MHZLIqPQomWC22lxBpNAikcQGq+wX1hMCKWWJFJqC0su/TkpZFCV92NL8cr7cVSPbCGhUCk4bksjCcemMy4r+RUnqL0kAK5ptvLmhjA+2VMhpw2ijhosnZHD+mBTabB4O1EmRwiL/bU1b9yREIUjeZCqFArtbcvI/0gAiCJLlRm6ckRFpUYzNimJgUjiJ4bpez6nH66PJ6qK+3UmDpYMsVrXaKaxtp6LZTqvNddRV0QG92hTHZtQ+J6JKh3702UTq1UQa1EQaNP77GqIMalptbm56bzvF9dJv66F5Qzm3m0m+O4iiSItN0sTVtNnZU9nG3uo2Shut1Lc7ZT/FY4FKIZAapWdwcoTkApBgIjFch0+Uihq+2l0TYgOiVgpM7RfHmcOTmDUwoU/2SC6Pj9UHGvhoWzmrChtCxoHMGAPZcSYiDWra7R5abC4azE4aLc4Q+61gdF5cLhqfzgNnDEIlCFy9ZCs/BfVhD9epePOKsYzKiMbp9nLVW1vkzkwBxJo0LLtxEmnRPUf2Vuyt5cZ3t+ETpev3mYWjOH1Y73Idm8sjG0WnRetlo2iP18eClzex9XALQ1LC+eSGSWhVSp5bVcKj3xZh0Cj5+tapXfxooaOAMACDRsmXt0yRU7iriuq5dokUAFApBG4/uR+PfS/pvi8Yk8r/zhuOx+tjxmM/UdliZ1BSOPtr2jl9WBLPXTRK3u95L2wI6cxzRfg+cNt/MzLSZHWysaSJbYebKay1UN5s69bH70gQgIRwLUNTIhiUHEH/xDDyEsLIjDEc9UKswezkoW8K+HR7FSCNh/eeOoBzR6X2Ob0tiiKVLXb2VLVJf5XSbZvdLV/nJlMYd/5fCvjY0R0BFID8+2b16mGXc+/yEB2MUiEQqVfRZHXz9lXjmNovjoMNFk5+fLW8jUYp4PKKLLlyHNPyOoolbn9/B5/trGZUeiR7qtrkQTDKoCb/vll97szQZHEy7/kNlDfbGJ0RxbtXj0enVrLtcAuXvLYZm8vLrIEJJEZoZVsJg1qJze1FIcA5I6QClgCumpzJA2cOZn1JIze9t51Wm5uEcC0vXzKmV43Md/tq+cune2i2ukKiJqMzonj8/OF8uuSlEDLSZnezqrCeFXtr+elAfcgPN9ak5ZTBCcwdnMiE7JhuxdY+n0hNu0MmhofkqKGFypbeU8oJ4doQUvhLp5TbHW4+31nN0s3lckocJN+7i8alM39U6jGZfB8JvwQB3Ha4hdfWHWLF3lr5f5wbb+KqKVnMG5nSa8Vum90tp5AP+NPIhbXtPaZv1UpBWkGLYHNLLZ2OBLVSIClCT16CidEZ0YzLimJAYni3pL+31K5Jo2Ribiwj0yPJijFid3uDIolSOjqQgg7WzR1tEYhCgPRoI0kROqKMasK0atQqAYUgyNXDNpcHi9NDq9Xlj2i6+lwFrFIIRBn9RrU+Hw3mrpPkwMQwZg6MZ0b/eEamRcq/gXqzg6931/Dl7hq2BU3+CkHqbHHGsCTmDE4k0qDB5xNpd0g6wOZu/posTg432zjcZDsmgt3b9ztXsxNDp3MebdTw7EUjmZQTy7oDDVy1ZGvIgvGMYYk8cu5wjFoVb6wv5Z9f7g9ZdKgUAk9eMJwzR/S84N1R3sKFL22Sr5v7ThvANdN69wpstDhlo+ghKeGyUXRNm53TFq+lxebmsokZ/OPsIXh9Ihe9sonNpc0MS43g4+sndTsWPvXDAZ76oViOAg5OlopKAmn2XRUtzHt+g1zpmx1rlDXLH103gbFZMby5vpQHv9xPUoSOmjYHggDf3DaVAYlS2nzDwUYuemWz/JmB6/yXJIBer4891W1sOtjMnqpWDjZYqT2GaF4AiRFaBidH0D8hjP6JYfSLDyM7znjcXQY2HWrigc/2yud4bGYU/zpniHwuAwiQvb0Bsuf/605vrVYKXKDdjUZ0YjSZuOvOO4/rMR9P/C4J4O0n53L77J6jblanh8F//zbkuZRIPVWtdsJ0KrY/MBu1UsFdH+2SvfECxR95CSa+vX2aHJnYW9XGGc9IFcKBFHIAgR9kX+Bwe7nolU1sL28lPdrAshsnEWPSsreqjYWvbMLs8DC1XywpkXre97eYC/R/VCsFpufFyaldgH+cPZhLJ2SwZONh/vnVfrw+keFpkbx8yWhZcN3defnXV/vl/SsV0sSlUSr40+w8rp2WjVIh9EpG7C4vqw808O2+Wn4oCE0lhutUzBqYwCmDE5meF9cnXYnD35g7ECk81GCR08t9SylLlclZcpXy8Ukpi6LInqo2luaX8/nOajmioVEqmDskkQXj0piYHXPcooLHiwB6vD5W7KvltXWl7ChvlZ+f2i+Wq6ZkMa3fsfv3iaJIXbuTwtr2DnJYZ6a4ztJjhNeoVRKmVeMVRWxOT4jZb08wapWkROpJDNciIHC42dal6XxGjIGTByQwa2A8Y7Oi+7wIs7k8ciTxhw9fx+2wImj0MOQMWu2SzUWrTeo+UNfuOOooRV+gUyuI1GtIjtSRGy9F0JosLgpq2imoMYeQG5NWxdR+sczoH8f0vHjZuB4ku6Nv9tbwxc5qvyF4BzJiDGTFGok1abG7vDRZnTLBa7G5+1wZGQyFILVRE0X6XDmqUUpjTGAx3hvpPnt4Mn8/azAmrYqr39rCmuJG+TWDRskzC0Zw8qBEDtZbuOCljV0KWS4YncojQR5+nVHRLFm3BMasvljLlDVaOfeFDTRZXUzLi5ONolcV1nPFm5Ie8IVFozh1aBLVrXZOXbyWNru7i49fAKIocuv7O/lyV7VMAq+cnMXfzhwkb/PK2kP8x5+2BmRpToRezea/noxPFJn08I+02tyMSo9ke3krpw5J5IWLR8ufMfOxn+TMVuCca/RGpp97OXq10m/grEKnUWDQqPx2UkceF5osTjYcbGRrWStFte1UtNiPKZoXQHyYhgFJEeTFm8hLDKN/gmQ8/WsWE7q9Pl5fV8pTPxRj9wdbzh+dyoScWIrrzOypamNvVVu3RtVqpUD/xDCGpkRKkcmkcNw+Lyvefx2f0/Z/KeCfi4ceeRSXwyYPGFqlwP5/ndrrxfrGulL+8dX+kOeGp0Swq6qNc0Yk89SCkbg8Pob8fQUu/8gUiLQFF2SIosiiVzez4WATE7Oj2RhU9TsjL443g9LEvcHnE7n1/R18tbuGcJ2KT2+cTG68ieI6Mxe+vIlmq4uxmVGkRxv4xB+S1qkVONw+NEqBYamRckhfAF6/fAyTc+P4+xd7ZVPR+SNT+O/8oT2ukHZWtHL7+zu6TKQDEsN48sIRDEzqWPH0lYy4PD42HWpixb5avttXS6OlY0DWqRXMyIuXdIMD4rtUx/UFrTZXSBq5tNHKwQYLZU3WXgec4JRywLrm56SULU4PX+ysZml+eYgpclaskQVj0zh3dOoxV5AF8HMJYLvDzQf5Fby5oUy2h9EoFZwzMpkrp2R1WdEeT3h9ImVN1o5KZD8xLGuy9hg9ijKo0auVuL2ibDZ8JKgUAnFhWsakRzFnSCLjs6OJCzu29nM2l4enn3oKp90Kaj1tuadQ1+6gps1BbZujxxRmdzhWU+TeEKjYDter0KmkvqeCIODx+WizuWmzu3H8jMIPkH4nEQY1AtBud9MW0qVDGhOdHl+PEUytUsAHPfZGDYZGqSAz1sB46yZUXkePUVeDRsk/zx7CuaNSWFXUwA3vbAtZXEzvF8vjF44gQqfiune282Nhfcj7s2ONfHLjJKJ60HO12VzMXbxWljjMHBDHq5f27hUYbBR97qhUHjtfIpkPfVPAS6sPEaZTsfyWqaTHGPhmTw03vLsdQYB3rxrPpNzYLvtzuL0seHlTiOn165ePYeYAqeLY4/Vx2tNrOVBn6fLe04cm8tyi0bKDRP/EMIr8+uXl/mIRgM93VnHb+zuBvke6FYKARuWvXBcEfKKIVxRlL8tjWTQEEGVQk5dgYkBiOHn+1G1efNhv6rwA0hxf3eZgT2UbGw428vWempB5LBgqhUT2hqVGMCQlgmEpkWTGGiioMZNf2sTm0ma2HW7B5vLK5/z/IoA/E/c8+N+Qi/etK8YyvVPHj86Y/cRPFNdbQ55LjtBR3eaQV2sBD6dgxJo0rPtzh/FzYJWn9k881f5BQ6mAbffP7rNo9NFvC3lu1UHUSoElV45nYk4MZY1WLnhpI/VmJ8NSI8iINsjaQsmnyYdRoyQxQiebGSv9Hn+xJi03vLONLWUtCIJkhXPN1OxuV74er4/nfzrI4pXFeH2inPJVCJLFzG0n53VJVRwLGfH6pOraFXtrWbG3NsSjTq0UmJQTy9whicwelPCzyVJwSrm00cLBIIJY2WLrNWWVEK7tQgqz40ykRun7FEXaU9nG0i3lfL6jSo5mqZUCpwxO5KJx6UzMjjmmCNuxEsCKZhtvrC/jgy3l8vHE+PV9F0/I+E1b/TncXkrqLZ0qktvlAqrjAZVCINYk/U+HpkYwNTeWsVnRXRZCrTYXW8pa2FLWzObSZvZWtXGu36qht4kx1qQhPdqAUiHgcPtotjqpbXd02ykgI8bA8NRIhqdFMjQlnPRoI3aXh8oWG1sPt7KlrJniejONZlcXwij4WeTPHYwFf5ROp5IiOyadiki9mmijZOOREKElUqemqM7MlrIWDjZYjpjiVSoEDBoliNJiqLfNI/RqBiaFkRNnkn5jcUZyYk2kROlDsgs+lY63zFL0rbvinbGZUTxy7jCSI3Rc9dZW1h/s0P5plAL/PmcI549J4/0tFdz36R6C/x06tYI3Lx/HhJzuszMej4+znlsvSzwGJYXxxU1T5F6v3aE7o2i3Xw+4zd/y7aPrJ6JVKbn3090sza8gIVzLitumEdWNZKTe7OCcZ9fLc0qUQc2K26fJ2Zv1JY0senWzHHUNJsFvXD6WoakRTH74R5weH5NzYlh/sInZgxJ4xd/z3e31Mf4/P9Bsc//mfpfBUCkENCoFGqUCrVqBTqVEp1ai90ciDRolJq0Ko1ZFmE5NmE56LhC11KulvsF6jcLfsUYV8lrAdLs7BOyUgvV6e6raes00AeTGGblrTn9OGhCP1yeyo7yVzYckwrejorVLZ50IvZqzlDtQeR3/ZwPzcxFMAFeIo9j9j7lHfE+/+74OWZUGUqkalYIdD8zGqFUx+8nVFPtXWFqlgNMr8qdZedw2qx8gEZpTF6/hQJ2FcZnR5Jd1RP/+efZgLp2Y2afjD24JFKgQq2q1c8GLG6lqtdPfL2INlO4HPJ4i9SrUSgUN/tWIXqNk9V0zaLA4uXbJNimdrVXx9MKRnDSge0Jc0Wzj9g92hmiCQBJvP37BiB6r4X5uNEoURfZVt7Niby3f7qsN8d0TBBibEc2cIYnMGZxw3PulOj1eyptsHJIjhxY5gtib95lKIdmayBXK/rRydqyRuLCuKWWr08NXu6t5L7+CXUEr+YwYAwvGpnPe6NSjIl9Hc84DVjavri3l230d+r5+fn3fOUfQ9/2W8PpE1hY38NmOKjYeauoTGdSpFUQbNQiAw+3D4vQc0Q5CqxIwadXo1EqcHm+3q/qF+t3ocCGq9cRNnI/T7eXNDWW0OzxolAI5cSYqWuzd+u1F6FRkxZlIjtARZZRaUtndXr+GTuqiUG92YnEcuUimO6iVAjqVAo9PxOH2hexDq1IQrpciqF6fiNnhxuzsW5X2kXC05t8apcC4rBhunpnD4OSIIxaWBF/n0+Zfzt8+3xeitQ2GQoDbTs7j+hnZrCps4Nb3d4RMtqPSI3nywhEICJz34gbqO1nl3DIzlztm53VLCERR5Oq3trLSH0FMitCx4rapRPSyqO/OKLqqVdIDttndXDE5k7+fORiby8MZz6zjUIOVUwYl8NIlo7s9hv3V7Zz7wnq5k8yknBjevmq8nN26ZslWvt9fx+j0KKpabdT6fytalYIt98/i4W8KeW9zOROyo8kvbcYnwpc3T2FoqhQFfGXNQf7zdaFMAN0KLavU42ixubpt7/dHQGABpPH7bgqAKILH58Ph7j6arRAgPkxHerSBrDgjefEm0qMNfF9Qz6fbKvCI0u8iLlxLk9nZrcfm+KwYxmVFMz47mrz4MJ566skTsvK6M35XBPD2228jObJ3HyeP10fufd+EPJcWpaeixc6sgfG8etlYKpptTP1fqEdgZ+PnD7aU8+dP9hCmU+F0+2TNS0qkjnV/ntkn7deGkkYufT0fj0/k1pm53HFKf+rNDi58aROljVayY41kxhr4sVCqfFMrBdxekRijGrvbJ6eh4kxa1t4zg1VFDdzx4S7sbi9ZsUZeuXSM3MouGKIo8sn2Kh78Yh8Wp1TqHvgvXzoxg7+cOqBX24fjXZBQUm/h230SGezcnHxoSgRzhyQyZ3Bit9/leKLN5parkwOp5UAxSm8pZZNWFaIxzIo1khNnIjPWiEmrYl91G+/nV/DZjiq5qlOlEJg9KIGF49KZkht7xKhgX865x+vjm721vLquNIR0Tu0Xy9VTs5nWL/aE6GrSGRanh7UHGlhZ2L0h85iMaGYOiGNYWiROt5eiOotUkVxrpqTe0qPeLFynIkKvxuuT0shWp+eI7fBUCoEYk4bByeFk167G67QhqnSsVI+lornnzioalQKfKPYp3dkTAo3r06MNpETqifYb70YbtUQbpWhHSZ2VdSUNrCqsxxZ0TWbGGDhzeDJnDEvu0mtcFEXKm23sLG9ld1UbB+rMHPKL8H9O2q4vCNOppMppvSakgjrKoCYicN+oJsL/+rIlL2O1dFznXp/I0vxyHvuuqEcD89QoPY+fP5yhqRFc8caWEL2jUgF3zxnAFZMy+dMHO/l6b23Ie0dnRLLkyvE9Sj8e+Gwvb286LH+Xr2+ZSlpMz4vS7oyif9hfx9VLtgLw0iWjmTM4kb1Vbcx7fj1urxjSVaQzvt9fxzX+9wLcPac/N50k9acva7Qy+8nVuL0iT104gudXlXDAv5jOiDHw+mVjmfXkakQRTh4Qz8rCek4eEM9rl48FJOui0f/6gXPUO445Aqj2L4TyEsLIS+i4jQvT4fL4sLu90p9LurW5pPuOwH23F6vTjdnhweLwYnG5sTokLbDN5cHhlvbh9HhxuX04/X2vf+nr9udCKYBapUCvVmLUqqQoprojiplWvQqlx/F/VcA/B8MeWM5cZUf4+n8P/vWI7/l2Xy3Xvb0t5LlAtVRA33ffp7t516+dC6Qfgo2fbS4PMx79iXqzk0FJYeyv6fCI++GOaeTGhw7A3aGk3sy85zdgdng4e0QyT104glabmwUvb6KozkxKpJ7sWCNrSyShs1oh4PaJJIZrabR0VA4OSAzjq5sn88wqKY0LvRvQttpc3LdsL8s79UBODNfx6PnDjtgKDn5ZS5LKFhvf7atjxb5atpY1h6R+cuNNzB0s2csMTg7/1ciMzydS2+6Qi1AOBRHEI6WU48O0IZXJ9WYnm0ubKAi6ZtKi9SwYm875o1OJ76FAp7dz3mZ388GWct7acLhD36dSMG9ECldOyepCCE4E9GrIrFMxPS+OkwfEMzk3Fr1G0po5PT6cbm/IfZvLQ0WLnbJGGxXNVqpa7dS0OboVZHfGkaJZPzc1Fszpu7tGlAKyji9Cr0GlVKAUQKVQoFBIqVWFINBud8teiMERCr1aSXq0nowYKQqtVEj9ZNvtkgaw2eaixeqi2eo+6nZeAWiUAvHhOmKMUh9Wk04FosjuqjYqW+xdWgPq1EoUAn0q5ukOgXOu0hn48113ylXMLVYXj31XxHv55T1Gps4dlcIDZwzip6IG7v54VwgZz4038tSFIymuN3PXh7tCFgLhOhUfXDuBgX59XGcEImXS+VDwwXUTejQH7sko+j/L9/PK2lLCdSqW3zqVtGiD3DlKp1bw5c1TZK++znhp9UHZA1AhwMc3TJLNpwP+s9mxRr68ZQpznlxDpX8MGJYSQXy4lh8K6pk7JJHv99fh9YlykwOA/yzfT9uWz454nSsEyIwxSgTPX4yRlyAtcvtaYHU84fWJOHoglzVtdor8PdIPN1mparH3eD1qVQqp844CEJGr9d1e33HX7QYjcJ0bjCbuvuv/NIDHhMy/LA/y0zFxZx/ElOc+v55tQdWPASgE2HLfLCINGgY98A3OTiv5YOPnp1cW88T3B4gL09Bg7ohWzB+ZzBMXjuRIaDA7mff8eipb7IzJiOKdq8fj8vpY9Mpm9lS1yTq0QCs5pQK8Pim6WN3mkAfAmf3jeOaiUdz54S5W7JNWtldPyeIvp3ZvQLu+pJE7P9xFbSf/w3kjU3jwzMF9Ftz+0qbEATRanPywXyKD60u6eg3O8ZPB0RlRx6VB+LEgOKUcUqXc0HtKWakQZPPuwGpWIcC0frFcOimT6XnxId+pu3Ne3mTjjQ2lfLilIkTfd8lESd/3c7WUoih2kC2PF6c76L7HJ0e+Q0hZD9s53NLAXNFip7bN0SVtqlYKaP3FDD5RlPbr8f2maagTSRv1/wuCz/m2sEncPac/cwYnhrgu/P2LfV1kKwEYNUr+PW8IJw+I5/I3trC901h/zdQsFoxN5+LXNlHT1pESFoB/nD2ISydmdbvf5bslj76APvq5RaM4tYfWnt0ZRadFGzj/xY3srGhleFokH103EZVC4LI38llb3MjApHCW+XsId4Yoitz90S4+9hcAJoZr+fZP04nQqzE73Jz02E80Wlzcf/pALhibxph/f4/LI/1wUiL0VLXZJR3yoESW76lhel4cb/kLFGva7Dz15JMh1jupUXqJ4CV2RPVy4kwnpGykrt0RotfbU9XWfVcchUC/eJNUnOEv0hiUFN7lO1W22Nh8qJn80mY2lzZ1KYoEae7pF28iI8ZAqt9uzOby0Gxxsb6kkUK/dEwhQEK4DpNWhcvrw+7y4vJIY+YZiu3/1wrueOBoycjAB76RNRWA7Jg+LiuaD6+byIq9NVz/zvaQ9wT/YBrMTmY8ugqry0t8mFbWlehUCnb87ZQjWpsEV3hlxBhYduNkdGoFl76Wz9bDLUQbNWTFGGSSGohABhp8B3DFpAyunJLNNUu2UlhrRqNU8O95Q7hgTFqXz3R6vDz2bRGvrA3tfBJlUPPfeUM5dehR9ij+lQhgMNodQV6DRQ0hVaGxJg2zB0lkcGIPXoO/BdpsbkqbQq1rArrDI9kiqJUC2XEmpuTGMjQlnILv38dlt6IzGMk66UI+2VZJfmmzvEpNDNcypV8sg5Mj8PpCiZtLjpgFkbLO0bRuiFtn8fJvDY1Kgdb/B8jfszu9klIhEGfSkhypIy3KQGq0HqNWhdcn+qNiLurMTuraHVS32LutmA2QEZuoZtr8Kzh9WBKiKPptS6Rbu8vLlrJmVh9oZH1JA+Wd0sRxJg1js6IZkxHNkJRwNCpJlxf484n+CkqvjwP1FvJLm9ha1hLip6hXK0iJMhCmVUkehu0OmvsQ4ez1XCoVxJo0JEXq5R7AwccV+H4Ol5fqNjutdneXc6xXKzDpVAgIHd/DJ+LzSfd9YuA79v24uiPdw9Mi+fPc/kzKkSpmRVFk2Y4q/rO8oMdF1uj0SB6/YAT5Zc3ct2xPyOIxKVzLw+cOY9nOKj7bUR3yvtmDEnhm4chuyc62w80seHmTvK8HTh/IVVO7bzfXnVG0y+PjtMVraXd4uHpKFvefMYh6s4NTn1pLk9XVxe4lGC6PjwWvbGT74VaAEO1gQHsYplPx010z2H64hWuCslyBOWThuDQ+3FqJ1yfyyQ2TZI33I48+hsMmjS033Xq73AnnREN9u6OLqXJnXSdI37dffFgXstd5bhZFkdJGq5/sSaQvuDgRJM3goKRwSb+XFcPYzChijrCwLqhp54HP9srOHHkJJv519hDGB3Ul+y3m0GPBH4oAiqJI1r1fhzwXa9LQaHHxwBmDuGpKFqc+tZqCWonBB+wbAsbQAPd/tod3NpWTHKmjOoiQPX/RSE4bltzr5/t8Ijcv3c7Xe2qJ0KtZduMkkiP1XP3WVtaVNBKuU5EZY2S330ok8MNNCteFtH/6+5mDGJQUzg3vbqfZ6iLWpOWlS0YxOiO6y2cW1Zq57f0dIa3MAGYNjOeh+cOOqQr0t7547S4va4ob+Hav5DUYPFmG6VScPECyl5mWF9enFla/Nnw+kTqzg4P1FvbXmCmoaeNgg5XDjTbaHN1P7L91NEoQ8JMvpXSrDrqvUvjJmRKfKEodH9qdNFicIZO/RqUgN97E4KRwhqZGEGXQ+PfVsR+tSunft3Rfo1LgdHvZVdnK1rIW8sua2VPZ1kWsHWVQMzYzmnFZ0t+gpPA+G4GLokhVqz3E2Lqw1syw1nUYBTd6o4l7gtI0lS02fipq4KeietaXNIUsRlQKgTGZUZzUP56TBsTTL97Ua9Xhvup2vtxVzZe7quWKz8B+DJ0ixH2BACRH6tGqFTRZXLQFtUeLNqg5e2QK80emMiSldwnFd/tqefL7A3Jf1ABSo/RcOy2bi8en92qN0vl7BkilzycJ7n0+JAuRoPter8j7b7yA3WrBJqr5oNN1Pi0vjnvm9GdIipSqNTvcPL2ymNfWlXZLMhUC3H5yPxaOT+eqt7Z20RfPH5nChOxo/rpsD8H8PzFcy/vXTiAztqvm+HCTldOfXovFb2R++aRMHjxrcLffuzuj6A0ljVzrJ2evXCppBH8srOPKNyWd3xtXjOWkHlwsmq0uTl28Ri6MCmgHvT6Rs55dx77qdhaNT+c/84Zy9VtbQnxhQfr9njY0kWU7qpnaL5a3r5I6Zf3W43l3qDc72FvVxu7KNtlcubuCMIUgSYMknz1pXBmUFNFtIMbnEzlQbw4hfJ2jhSqFwNDUCD/hi2Z0RvQxWZT5fCKfbK/koW8K5Sri+aNS+OtpA4k1aU/Ic94d/lAEcHdlM2c9u7Hb19becxJalYJx/10Z8nz/hDBW3D4VQRAoqbcw56k1kjmyvxpX2sbEt3+afsRjffibQl5cLdm9vHPVeEZlRHHDO9v4oaAevUogM84ka8MC5DM4yigAr142mrp2F3/7fC8en8iQlHBevmQMyZ16Dft8Im9uKOPhFYUhkRyTVsXfzhzE+aNTj1lDdyJdvG6v32twby3f7quj0dLxg9apFUzPi2PukERmDkg4ph/ysUAUpaKDunYn9e0O6vw9bevapTZlFc026s0OWm3uPneBON4EUECKkqkUAmpVhy2IUasiXKciyqAm2qQlLkxLUoSOlAg9SVF6kiJ0IaTa65Pa5K0sqGNlQT1FdaGEITPGwMkDEzh5YDxjM/tmyFzf7iC/rJkt/oG6qM7cJfqUFKFjXFa0TPpy40zHbGDdE4Kv8wlnXyqTvs7+a/FhWj/hi2NybuwRK123H27h3c2HWVVUT7P12CJ5YToVOXFGcuKkDgjJEToqW+2sK24kv6xZPl9alYLZgxKYPyqFqf3iej3/ZoebR78tYtn2qpAWdCqFwNR+sdx32kBye9Cp/VyYHW42HWpmw2dvgduO3mjCnDuHpfmH6RycPWNYEned0l9up1ZSb+beT/awpYe0cFKEjqcXjqSopp0Hv9wf8puL1Ku445T+vLT6YEiGRaUQePLCEZw5vOuivtXmYs5THURs9sB4XrlsbLef3Z1R9ENfF/L6+lIi9GqW3zqF1CgDD36xjzc3lBFr0vDNbdN6XJiX1Js57el1uDw+lAqBFbdNpV9CGPmlzVzw0kYUAiy/dSpJETomP/IjVqc3xIeyf0IYBxsseHwiH143kXFZ0b/5eN5gdspkL2Cq3FmmBBLZy4kzMTQ1gqEpEf6Wb+E9LvI9Xh8FNWY2+z34tpQ1dykk0qgUjEiLZEJWNOOyYhiVEXlcgwatNhePrCji/S2SdjVcp+LuuQNo2PDJCTOH9oY/FAG8dskWvtvfsSoKdLoYlBTO17dN5Z9f7eP1dWUh7wk2fr52yVa+218nF40EsPHemSRFhBKwzgi2CHjywuGcNTyFW9/fwfLdNagVkBFroqQ+dGKJ1KvlBudKhcCn10/kkx1VLNkoVaWdOTyZ/507rMtqp67dwV0f7WJtkFM+wITsaB47f/jPslY52GDhjRefRe1z4lZoUQw7gyHJ4YxIiyIj1iC3LPotIHkw+b0G99VS2dIRzlcpBCblxjJncAKzByUQf4wGwRanRyJ1/jZide1+cudPJwZe64txcXfQqhREGtQkhku2A9FGNdWtDmLKfkCPC9R6Jp59KTaXlwazg0ZLoHuDi3a7B7PDjdXpweb2yho9j9d3xOrXo4WAFBXsjr9G6FWkRxsYkBhOTryRhDA9CeESkUyK0KELGmADzdE3lzaxpUxalXenu8mONYYQvtQo/S9SBCSKIvVmJyX1FlZ9/AZepw2bqOEDxzB5G4UgtUWc0T+ek/rHMzAprNtjcXq8HKy3UljTzqqievLLpIhDXwN6CkHSG+UlmMiJDyM71khOvORPGW3USJY5JY0s217Fd/trQ2QF47OimT8qhVOHJhF+BEK6oaSRR74tZHdFW4jwPT5My+WTMrl2anavHnjHAo/Xx67KVtYWN7KuuJGdFa14fGJI2l0/6mzOHJ7E0vwKPtlWGXJsSoXAgrFp3HpyPxLCdYiiyPLdNfzl0z3d2vIAzBuRzK0n9+PGd7d3iWxOz4vDpFWyfE9olfCCMWn8a96QLsTZ5fFx1rPr5MzKkORwPrtxcrfnqbNR9H/nDeGClzayq7KNkemRfHjdRLw+kbOfXU9RnZnpeXG8cfnYHhc0PxXVc/kbUpeR+DAta+45CZ1ayU3vbmf5nhom5cTw7tXj+XZfrSxnCtOp5A4n0QY1zTY3E7NjWHrthF+VADZanKE+e5Xdkz1BgNw4E0NTIuRUbm9kD6T/yZ6qVjaXNrP5kGS63PlaMGiUjM6IYlxmNOOzYxiWGvGraBu3l7fwwGd72VctWRotMu5B43P+HwH8uTiai3fYg9+GpAtNWhUWp4fbZ/Xj1pn9GPS3FSFaoGDj5+AVVvAAfs3ULO47vXvdRgBrixu4/I0teH2i/Fn3fLKbj7dVohQgLcZAWWPopBcoEACp2u+zGyfx4Jf72XioCUGAu07pz40zcrpMPCv21nLvp7tDqiA1SoG/nDqQyydlHnOUpK7dwW3v72DToeZeo1FqpUC4Xk1CmI70aD39EsIYlBROWrQkmI3Qq3+V6t1Aei1gLxMctREEGJMRxZzBkr1MWrQBmys4Yue/bXdQLxM76fZYqxuDYdIqSYk0kBlrpH+CiX4JYWTGGMmINfQ4WR+PQdrh8lBndlLT6qC23eEnkE6arW4/gXTT7pDsUuxuL44gAnm8XRcCBFLswdw4Qq8iLcpAXkIYI9IiyEsIJylSR1KE/rhoPH0+qXdnSYPUqq6k3kJJg3QbmCiDr/MfVWOZnhfPjP5xTOsXF1IwFdjXjooW8kub2VfdRnmz/YgGsgEYNUoyYgwMTAqnX0LAJNlIerShC/EIXNefbq/ii13VIRHvnDgj80elcvaI5CMu8hwuD4tXlrB0S3lIVEQhwLisaP562kCGpUb26fj7goDeal1JI2uLG9l0sCkkygiSdclJ7nwEd0cnEI1SwXljUjltSBJL88u7uBdoVQqunJLF9dNziNCrsbu83P3xLr7aHbpdAHq1kofnD6HB7OShFUUh6XWtUuDskSl8sr0yxMg7J9bI21eP7ybL4uPKt7byU5Fk05UcoePbP03rNgLc2Sj6gjFpctu5a6dl89fTBlJUa+asZ9fh9PhkSVJPeG5VMY9+ewCAk/rH8cYV46hotnHyE6txeXyy3UxwKjjQQCAYS6+ZwMbP3/pFCGCA7O0NKtIIDpwEIAQie8FkL6n7nt/BcLi9bC+XfnP5pc1sL2/poq0O06kYFyQNGZIS8ZtULYO06Hln02Ee/+4ApwrbMApudAYjf777rt/kePqCPxQBzPzL8pDHgerab26bSl2bg8v9vRsDCBg/i6LIvOc3sLOilQi9WtbVhOlU7Hhgdq9aowN1Zs59fgNmp4d5I1N4/PxhPPjlfpZsPIwApETpQyJVIBG2QAu6WJOG1y4bw81Ld1DRbMeoUfLUgpHMHpQQ8h6r08M/vtzHh1srQ54flhrBExeMOGYPvTabi7s/3s33++vkiTp4YvzITwD7epEEGtonRejIijXSL17qApAcoSclSk9CuO64/kDtLi/1Zgfby1tYVVjP1rKWEK0VdN9poDf0pbVXtEFNZqyRzFgjWTFGMuTbnkleb/i10zRHSu3GmjRkxRqJD9OiVSlos3tod0h+XlanR/L68vxyBFIpgErZ0THA4PfbCtOpCNeriTJoiPZblygEabJotbupb3dwqNHGwYae+xMrBMiIMTLFsRmV14HOYOLuO+9AoRCoa3ewvqSRLWXNFNS0U9Fsp8Xm6tP3UysFUiL1TMqNYWRaFNlxJnLijH3qGFTTZuezHdUs21EZspiJNmo4a3gy80amMCw14oiLq92VrfxneQFbOlksRRnULByXzq0zc0MitD8HzVapKnJdcSPrShq7COwjDWom58QypV8sU3JjSYs2yNe5Vm9kR8Rk2dNPpRCYNzKF2YMSeH9LRZcWb+E6FTeelMvlkzLRqZVsPtTENUu2hiz4gzE8NYIHzxrM3R/v7pJ5GZQU7u/o0kGuNSqBly8Zw4xu9Hn3LdvDu5vL5eNYfttU0roh4J2NomOMWq5/R9IDvnbZGE4emMDbG8t44PN9aJQKlt00SW7d1h2ufHOLfB4eOGMgV03J5rFvi3h2VQkZMQa++9M0rE4v0/73I5agVHCwfCkr1siZiu0/e2xpCpC9oCKNzuMsSGQvO9YYRPYiGZQc3qfCE4vTw7bDLWw+1ER+aTO7Klu7+G5GGzX+6J5E+AYkhv9mDhE9ob7dwXPPLEbhcZzwEcATT0F/jKhp7Wri6vVJHmwDEsP480e7Ql7TqBRcPCEdgOV7athZ0YpGKYSIql+5ZHSv5K/e7OCKN7ZgdnoYlxnNQ/OH8Mi3RXIKNzFC24X8KQVk8peXYOLO2Xlc9MpmrC4v6dEGXr1sjGxHE8D28hb+9MFODgelzpQC3HpyHjeelHNMhMrh8vDAF/v4dFsV3l7WAF1aViEN1gqFgCiK8ncJwOMTafB7mnUWZQfeH2PUkBKlJz3aQGq0geRIPamRepIj9SRH6gjTqXG4vTT4o3Md6dgOzV3gfk8TQDBCvMwEyXNRpOcepoFno40aMmMMZMYYZbKXGWMgI8Z43PSGDreX7/fX0Wpzo0RKc4ii+ItEUQOGzD8U1PNTUaghs1IhMDojilkD4zl5YALZscZuj8Hh9rKjvFVO524vb8HV6V+gUQpkxBhICNMRoVejUikwO9z+FLYHq0sikFIFs79QoNO/wiuC11/B3E0hYJ8RaKWlUyswadVEG9XEh+mINmlQHRDAK/XBHfOfH2izuXv9LXSH/gkmTh+WxIJx6UctO7A4PazYW8uyHZVsONgk6/o0KgWzB0q6vml5vev6QGpt9uKagyzZeDikalIARqRF8ue5A3psi3Y0cLi9bC1rYW1JA+uKG+V0VwAapYLRGVFM6RfLVH/Fek+Ts0al4IPrJpJf2swzPxaztriRj7ZV8sn2Ss4ansziBcN5b3M5m0sl3V+7w8PD3xTy2rpS7pidx/mjU9l6/2xuem873/u7KAVjV2Ub576wgdtO7odCEHjyhwPyNba/ph0BGJwcxr5qaeHj8ohc/sYWrp+Wzd1zB4Qc93/mDSUlSs//VhTR7vBw8uOr+ej6iQzvFEFdMC6dmjYHi1cW88Bne3npkjFcPimTNzeUcedHu/j61qlcPCGD1Qca+aGgjluX7uCrW6b26Czx8iWjmf7oKqpaHfx7eQHjsqK5YUYOH26t4HCT1ALy+uk5PHLuMG56bwci0v/c5fExJDmcvdXtlDZaaTVJY0tf0Wx1yWRvd2Ure6vau5D7ALLjjLJeb2hKBINTIvpcZRxozxjoo7uvur1LQVRCuFbusjEhO5qcuJ6Lrn5LiKJIs9VFcb2Fg/UWFCfgMXaHP0wE8L5P9/Bufrn8ONBV4+opWdx0Ui4j//V9yPYLx6Xx0PxhuDw+Zj2xmvJmm/wegHGZ0Xx4/cQeP8/u8rLgZUnnkRVr5NMbJknh3++lsH2cSUuDJXTmCo4sTesXy7isaB77Ttp+YnYMzy8aFdI30uP18eyqEp5ZWRIyMfWLN/HEBSPklj9HA5/PxyMrCnl9fVmPBOhYCxIEQKEABRI59PxKV5ZOrSDKoMGoUaFSSpYVdpeXNn/KszdoVQrSow0MTokgK0bqzJIVazyuJK8zRFFk6+EWPt1eyVe7azA7PCHnfEf4ZC6fnMm8kSk/W7Bc0WyTonyF9d0aMs/oH8+sgfFMz4vrNlLV7nCzzV+dm1/azO5uVuUR+kCFbhTjsmIYnBx+TIsSm8vDgVozOypa2F/dTlmTjZo2B00W1zFrLntDb9e5UaPEqFXi9dHFimR0RhRnDkvitKFJPRp79wSP18e6kkaW7aji232hur5xWdHMHynp+vpy7ZXUmfn31wWsK24MKXwI06mYPyqVu07JO2LBSm/w+UQKatvlCF9+aXOXqOqAxDCm5EpRvnFZ0Ue8Xnsaz7eXt/DMymJW+dOtggCnDUliSm4sS7eUd1lMZsYYuHvOAE4bmshXu2u466NdPUZ848O0/OucwTz0dWEX/Wl8mOQSEcw7hqdE8OrlY7sUanyxU+ofH/AKfPHi0ZwyODFkm85G0W9dOY5/f1XAnqo2RmdE8f61EzA7PMx9ag31ZicXjU/nv/OG9ni+GsxOJj/yIy6PD6NWyeZ7Z/Htvlru/GgXJq2KH++aTnyYrktV8MTsGOxuLzsrWuXrPKAvHpURJXuItvjJXrBur0eyF2sMsV4ZnBx+VNdXg9npT+c29Vj8lRat72irlhVNerThNyN8Lo+PZquL+nYHh5sDveYln9MGi5NWmxuLQ5LTdC72C5xzo8nEXX3wL/6t8IchgOP+8z31QabNSkGKInx43UR+LKjjxTWHQrb//k/T6JcQxuvrSvnnV/vRqRXyYCwAO/92So/GyT6fyI3vbmfFvlqiDGqW3TiZHwrq+PfyAgCijepeq/8Wjk3F7PTKOpbLJmZw/xmDQibNw01Wbv9gJzu6MTq985T+xyRsfWm11E3EdgSd24W6XRj8vSO3h0+ivt1Ju8PdJdr3W0EAtGoFCkHA6/PhPALTjDKoyYgxkhqlx+sTqW6zU1htxhlEhmKMGk4ZnMApgxOZlBPzixS7lDfZ+HRHJZ9ur6K8uWMiSonUM0fciuiyh9hjhOtULBiXziUTMkiL7lthz8+t2m0wO+XoXn5pMwW17V0G6YRwLeOyYhiXKRG+fvFHV6ErilKUuLjer83z/xXXW0I0b50RbdSQG2+S/uJM9EuQ7ieG6xAEAYvDQ22bnZp2B3VtDuotTpr8RTStNpecwra5pK4Cs71bMAhunIIWc+4pDEoORxRFtpS1sK6TMfmw1AjOGJbE6cOSSYnsvSCsu++7v6adZdur+HxXdYg1RXaskXkjUzhnZEqf/sc+n4+3Nh7m1bWlXSbqgUlh3Dm7P7M6yUeOBjVtdrlwY31JYxfymxCuZUpuHFP7xTIpN+aoo55HGs/3VLbxzI/FfBcU1TtlUALjsqL5YEtFSF9xgMHJ4dx76kDSovVc+/Y2ijoVfwTjjGFJpETpeGVNV1uZYE124PEbV4xjXFao9VZ+aROLXt0sXxt/P3MQV0wO1fJ1Nop+ZuFIbnhnO2anh+un5/CXUwewvqSRi1/bjChKRHLukFAiGYx1xQ1c/Fo+IGWNvr5lKue+tJFdFa1cMCaV/503nEaL5F8bsK8BePOKsVyzZCvz1Du7LHQM/qhjT3NBVoDs+VO5g1PCj1raUt1qlw2XN5c2c6jB2mWbnDgj47JimJAtFYB11mEeT4iiSLvdQ6NVGheaLE4qW2yUN9upabNTb3bSbJW00jZXV1LXVygVAudpdmLoxmLqRMMfhgBm37u8y486xqhh819PZuiD34aYQweMn9vsbqY/uqpL6fhfTx3AtdNzevysQHsejVLBu9eMp7jOwl+XSdqPYA0hdNWT3Tozhx+LGthb1Y5KIfDPs4dw0fh0+XVRFPloWyUPfrEv5MeZGqXnsfOHMyH76FM5H2+t4F/LC0KOqzvo1Qp8IpytCu0dmRKpIzc+jMRwLTq1EpvbS22bVERR22bH4vByYlkKS4UYCeE6MmIMZMaYSI7UkRoVSDPrMWqUrC1uZMW+WlYW1IecmzCtipkD45k7OJHp/X+e12C7w83Xu2v4dHsV+WUdPUyNGiWnDU1i/qhUxmdFy83DTaYwosbP462NZXLKXyFIBraXT8piQnZ0lxXx0aR2c+I6tKKiKBU35PstFPJLmznU2HWQzowxyBW647NiSIvuW4Wuzyf57xXXm0NIXnAhRndIjtCR4yd6/eLDZNIXbTyylu5oEBhb1Doj5ckn8WNRfYil0oDEMM4cnszpQ5NkS5KjQW2bg893VvHp9qoQIh5lUEu6vlGpDO+Drg+gosXGf5cXsLKgPiSKa1ArOX1YEveeOpBo09Gfn4A9y7riBtaVNHKw0yRt0CiZkB3DlFwprZvbi+9hX9DX8bygpp1nV5Xw9Z4aeQEyo38cI9Mi+WR7ZRdD7kk5Mfxpdh5f7KyWe/t2B61KwT1z+/PGulIqW0M1bDqVooth+D1z+3P9tJyQBU5pg4Uznl2H1U+2rp6axf2dCgU7G0XfOCOHez/dC8Abl4/lpAHxPPRNAS+tPkSkQc03t03t1WnifysKef6ngwCcPzqFBeMyOPeFDQgCfHnzFIakRPD5zipue3+n/J5TBieQFmXAvPXIreBUCoH0aAOjMqKYMziBSTmxRyzSCIYoihxusskefJtLm7rInwQBBiSGM95fsDE2M/qYfGqD4fR4aba6aLK4aLB0ELs6s4PqFge17XaaLC5a7ZJ7ws/RKquVAga1ijC96v+1d97xbdXn/v+cpb3lve04zk6cOJNM9t4bCrS0dHAptNDS0vGjvbe0F2gpG3pLaRllj7ITSMgmhCwncRLHjve2bGtv6ZzfH0eSJVuSZcfxiL/v1yuxLB0dHz8++p7nPOPzwKiUIF0tRY5OjkKjIlTvrkauTg6apsZdeidVTgsH0OX1Y/aDnw96/vrF+bhiYS5u/PvXMc+HhZ/DH8Do7qk0lQR7fn1OwkXu37ub8Ov3xQ/yEzeUgxcE3PvWQQiC6DhEd75FNx9QAH5+QRle3NGEHocXBqUEz3+rIuYO0+z04YH3DkfGvoW5YUk+fnPJ7GEruG861oVfvXcYXUMUUclYGoGQuj8A3CA/BDl8w04BcwwFo0oKpYSBIABOXwC9du+YpYKHg5SlkasTG1OyNDLwghgZPNpuj3EGpWy/1uDZMzNTGqcXTvO9u78Nnx/pjJxbFIVIx1qmRoYOqxuNvS409Toxp3cHFJQPLoHDh8FFYGjxxsEX4GMiURKGhkHJQS3j4PIFYQ9FtaJNzNIUMtRSZOtkyNEqoJQyEU1AuzcAk92LTpsHHZbBI9soAJlaGUrSlKIDlqmGUSkBS1NgGQosTYce02BoChxDgRcAk82DVosbrX0uNJtdaOxxobnPmTA6SwEoMCrExhmjAvkGBXJ1cmRqZWBDM2/D00qiJ5cM/OoLBqNG1olfo98T2S70nMfPw+kNwBMI4jLmwKALo1rGYkamGouL9JiRpYZBKYVRKTacGJSSISPvzkhdXxt21vX01/UxNM6ZnYErF+ZhbVl6yp3O7+5rwdOb69AwwDEvSVfix2dOx5WLclPaT5hE8ixhaAqYn6fD6lDjxsIC/ahO3hnuhfFEtx3PbK7DB5VtkbX0jGlG0eE50DZobbtwbhYWFerx+Bc1cPqCCRu6ZudoUJ6nxet7WoYcR7iixIDnvlURUyJhcflw3l+3RWouL5iTiedvWRzzvoFC0fNytXj9mxboFRw+vWc1jEoprn7uKxxus2JFiRGvfm9ZwprJ6EZFAPjTVXPxdX0fPqhsx5IiPd76gViu9N2X9sY00Lx/5xl448XnoaDE9Xy3cgVmZmmglbPwBXi0md2oarcNKrGgKWBWtgYVhfrIv1xd/42fIAg40e3A1w19kbTuQCFnhqYwN0eDZSVGLC0yYHGRfsiGqOgoXY/di16n6ND1OHzocXjR4/Ci09qvbjAapSEcTUEV0kZNU0mRHapJL0pTojhdiWytHOkq6bA+B8QBHCVSMeRzX9bi4VAtHdDveP3jtsV4alMtKqPqR8LCz+1WD87885ZB47A23LMaM7I1cX/O1hoTbv+XKPdy77llKMtU4b9eO4AgL0AhYWIidtHOH0NRuOvMaXhuaz18QR4zs9R44bbFMVIO22tNuO+tgzFF3EalBI9eOx9nzRxeSmdfkxn3vVUZV28tGilLwx/VwRnWkhpYG8VQ4tQCUGLtY7zQeKZaikWFOiwqMGB2thoqGYcOqwcNPQ5UtdlwvMuOdot7yPQzIDoHQOqdx6ki1ihSKU9eGNg9TFNiVOjMmRm4cmHuoILk6k4b3t0npnijo3BKKQOFhIXd4084Im68J4FMRUZic4WEgUEpiXIKpdArOdjdAdR023GkzRYToVtcqMPVFfm4KMW6PgDocXjwx0+q8VlVZ8wFTmwOycCvLp6FXF1qJQGpyrOEI3wrStJSnhk+EkZ6YWzsceLZLSfw3v62yPqzpEiPmVlqfHSwHRZ37I33BXOzcLzTPiiiGQ1FATcuycemY92DHMmBjqNOzuKl25dhQb4u8pwvwOOSp7ZHurYX5Gnx7g/PiNEKjBaKXlWaBrPLhyPtNiwp0uP1O5ajuc+FS57aAZcviPsvmIE715UmPF67x4+V//slbJ4AaAp49uZF+MmblfD4eTx900JcMj8H3TYPzvzLlkh0cs30NMwz74DL6YBKpcZ9cebSBoI8qjvt2NvYh33NFuxvMsetAzQoOWRqZOAFselyYH21hKGxIF8bGau2qFAPlZSFNxAMReZ8kfRrj8OL3lDErifk5HXbvTA7fSNOvQ6EoSlo5SyMSikyNFJRmssoR65egQy1DFlaGTLU0mFFOlOFOICjRCqGXPfo5kHOjkLCYNvP12HxQ7GTPx65Zj6uW5yPe9+sxHsH2iK1ggBw/pxM/G3AXVyY6k4brnluFxzeAK5alItL5mfjB6/sgz8oxNQPAgANRFKiMpbG5eU5eDMk33Lh3Cz8+doFkZPO4w/ikfXH8eLOhpifd9G8LPzhinnDSnud6LLjnjcqcaTDlnS7aBkaILb+haaAb6mqwAQ8kCtVuP2Hd8GgkERSIDwvoKFXnK+4o9aEg61WtJndw3LWNDIWmRoZZJwoV9Dr9KHHMVhTTcLQmJ2jwbwcNVRyDmaXH40mJ1rNLljcfrj9/LDGaA1FeHpGkBdS+n1oCpBxDFiagtsfTNhUM/BnZOnEyRtZWiky1DKkqaQw734fAa8LrFSBrJVXwxPgYff4caLbgfoe8Xf2JYimqaQM9AoJGJqC0xeMdNimcjxTmYEOIEdTUMs5yDkGLCOe74GgOIvY5g6M6MIkRmxFZ9GokkTka4xKCQyqsCMphUEpwcEWC/5vWx2OD5hEkqeX44drSnBTiuPZRiLPMlac7IWxpc+F57fW4e29rRFHuzxfh2npSnx2uAOuqHWYYyhMS1dFxJzDurAD0Ss4rCgx4LOqrqSfewrA7y6bjVtXFEVu/Hiex23/3BMR5c/Ty7H+njVQyfqdimih6PNnZ2FnXQ8c3gDuXDcN918wE2/tbcH97xwCS1N450dnoDzKyRzI/iYzrn7uKwgQP/fXLc7HizsbkauTY9N9ayHjGLy7rxX3Rale3JlWDbfTMSybt/Q58Z/Kdmw9bsLxLnvCkg2FhEG2VibqnBoV8Ab4fgfPKX5NVu4xEiiI53A4BZurkyFLI2YPMjUyZGqkyNLIxkyTNh6TxQE8LWRgWsyDI13rZqTj1a+bY55LU0lxeXkOqtqseL+yDUC/88fSwBM3LIy7/26bB7f/cw8c3gCWlxhwZXkuvvfyXviDAqRsrPNHod/50ys4zMrWRJy/sEh02Jk61mHDPW8ciNH9UksZ/OHKebhsQU7KJ2+HxY2fvFkZ0dRKRLjLOez8hWtenL4gFBJxMfnuqmK8/a/jsNs9YGgKvQ4f9jWZ0djjRGOvE409LjT2OuMKfg6FRsaiPF+HJUV6VBQaMD9fF0lrm50+HGy1oLKl/5/F5Y88DqNXcFiQr0N51D+dQoLGHge2h1JadSYHOqwe2Nx+eAOpa9QJwLAu8ryQuIg62c/osIjp12iuk/mhpACrx4+/h5qJUsXhDcLhjd+5l4xwWpihKQgCwAtiGUAyp1rCUNCFnRiVNLLYpqmkkHJh3T4GEoaGhAmliikKDEOJX2kq0inO0BQoSozEBHnR9oEgH/oqysOIX8XHQV6APyjA7PShqVfsyGuzuNFudUciHgPRyFgYlBJo5Rw0cg7q0IU5EBQgbWaAoD8i2eDnhaTiziwNGJVSUJRY2+ny8VGvUUhXS6GQMPD4xe5Btz8IX5BHp80TdxrCUOjkLObn6VBgVKDb4cMrXzdDHxV9NCol0IcmhoyWPMtEJ9+gwENXzsNdZ5Xib1vr8fo3zZE1Yk6OBoUGBTYe64IvKJ4r1Z12SFgaPC+Ob9TIWFAUYI2KGJpdfnxa1YXlJQbUdjkGNb6EEQA8+OFRbK/tweM3LIRKyoKmabzy3WX4xbuH8OaeFrSa3Vj58Jf47J7VkYaG8nwdnrl5Ie54eR82HO3EBXMysf5IF57dUodlJUZcW5GHrTUmfHKoA/e8cQCf3L06YbnPokI9fnpuGR77ogYObxCbqruRpZGizeLG37fV48dnT8dVi3Lxn8pWbK/tFX8/pw9DtepYXT7sONGDr+p6caDZgtpu+6AbyHgZFJcviDqTM2mkdThoZCyytDJkaeXIVEvFKJ1GhqyQY5cZWmsm6/k70Zj0DmCQF2JU3cOh+/PnZOGB9w7FbHvL8kJIGBp/+uzYoLqPh69ZELe+x+UL4Hsv70W71YOSdCV+tG4afvDqPngDPDiGGiQ9EN6t2L4OfFXXCznH4LHrFuDCedkAxCjaizsb8PD66pgP2ZqydDxy9XxkaVPrrLO6fPjZ2wex8Vh30jtXlgYCvKh7F45yBXgBngCPNJUU31lZhJuXFUAj47CzrieiSddl8+D8x7cl3K9axkYkU4qN4uSLQqOolef0BlDZasWBZjP2N5lxpN0KmyeAbbU92BY1wq7QqEBphgrFRnEyQmm6CgUGBc6bnYlOqweNvS609LnQbnHD5PDC7PKHZraaIvuQsTQUUgZSVozGhZFyDAAK3kBw1IWKxwqGpqBTcCjQK5BvkMPj59Ft94pdazZv3L87S1OYnaPBVYtyUZGvh80TQLvFjRazC819LjT2OFFncsDuDSZ09rK1MpRmqDAt3G2bLjZiGFUnV7Q9HARBQJdNFKA92uGIiNAOHPAODJ42EJ4jmqxu9rHHtsFu9yBDLUX1ry5Ah9WDNrMbbRYX2sxusabR7Ear2YVOqwcBHgnraQO8gE6bB5lqGXL1clQU6pGhkUIn56CUspCyYte6wxtAr9OHo+1WHGi2JJUpsrjFzwtqh287g1KC6RkqzM/TYnGRHjlaRSTieDpcPLO1cvzusjm488xp+Pu2erz6dTOOtNtwpN2G6Rkq5Onl2FZrQpBHpMyHpkQ9QQpARYEe+5rNMfv8ur4PHENhebEeXzeY4/xUkY3HurHukc149Y5lmJkllgs9fPV85Ovl+PPnNbC6/Tjzz1vw7o/OwNxcUarrrJmZeOiKufjle4ex/kgXlhYb8E1DH376ZiU+vXs1/njFPFQ2W9DU68KDHxzBX65LXJJw15ml2F5rwp5GM5p6XZiVrUanzYtnt9Th2sX5yNLK8Mg1C7D2kc3wBQVRQowSnbXHvqhBj0PUUG3uc6HbJkboUtG/FIARZ1wYCkgPTY/K1slDkbp+py5LI0O6WjomY9sI/Ux6B/DTw+0x3wsQL4AGpSTmDj0s/Ly1xoSdJ3pjajwKDHJcvShv0L6DvIB73qjEoVYrDEoJfnXhTPz4tQNw+YJgaSphim1OtgbNfS7YvQHk6uT4+62LMTtHXCg6rR7c91Yldtb1RraXsTR+c8ls3LysIKWon8cXwG8/OIL39rcmnQEbrmEL8OJjAeJorgAvoDRDhe+vLsHlC3Pg9gXxzr5WvPp1Exp7XbhOFoQydBgSRpxdq5FzUIVq2WQcAy6UIvMGePTYvWgzu7CpujtUeC8W5ocf+5JE4Zp6XTEC1yPBE+BD3XvJu5zDhE08sYsfRIK8EKmfORAVCQ2TqZFhxTQD5uZoUdNlx+dHOmFxB3CoVRy+nmwKCk2JNyqlGSpMi+q4nZauPCn9uJEgCKIDdbg1atpAmy2uLAw90NlLcbRUMmQcg+I0JYpD3b5BXsBXdeIc3qo2a4wNi40KTMtUQSvj0G33os3iRpvZDW+gP9q3rym+AyFjafjDF+UQFAXMy9Xi5+dNR1mmBn0uv9jZ6PShzyFKU7Sa3ajtdqDd4oYlBcHqPqcv1I3Zh79vjy0vkXNMJCVtiIomhhte9MrYNLVayk5I8V0AyFDL8OuLZ+OHa6fhHzsa8PKuJtSGus2L05TIVEuxu6EPAvo/BwKAfc1mVBTqQIHC3qi/lT8o4OsGM/J0cti9gYTKCT1OHy56Yjsevno+rl0szpK/66zpyNMr8NO3KuEN8Lj86Z34v1srcPYssYY7Wih6T0Mf8vVytJjduPuNA3jte8vw1+vLccP/7cK7+1uxpiwNl5fHb/ChaQrP3LwI5z62DVa3H8c67MjUSNFl8+KR9dV47PpyZGvlePDSOfj1f6oi77N5/Hhh0wjuJpLA0hQyNTJkaKQwKCQABTg9AXTbvWgxuyLXyKAAcTylw4vZfh56hQQZaikWFehPqfQLITmT3gH8v631g55bMc2Iv35RE/Pc1YtyoVNI8KdPqwHEFvi+ccfyuPv+06fH8MXRLkhYGg9eOgv3v3sYNk8gEkGLR3m+FgdbrRAEsePz2W8tiohufnq4A79891DMXf+iAh0eu648JZkJnufxp8+q8a+vEos4A/01iLzQX9MWPtxlxQb8YG0J1pVl4HCbFb9+vwofHWzv71QdsC9fUIw4dZ/MOIYBSFgaUpYGx9CgIF5sfaFaq3jXNbWMRYZaihytHHkGOXJ1csglYlQlyAto6XOittuBxh4XOqzuIfUKo3+GnGNCUjHiGLc8vRxShkZVuxVf1fXiRHdsakMjYwFBPF5fUBj3yGKXzYP/HGjHfw60x309fHxSlsbMLDVWTk/DrCyNGHVNU47LHbcgCOiwemJGS1W1WePWgdIUMD1DHYrqaTA3d+ih8SdDdaeo1/efyraYrsZCowJXLszFlQtzUWgc/FkVBAE9Dl/EGQxHEdssbtR22dFidoMXMEhmRHwvcKjVilte3AuVlBW7oTVS0DQFuyeAVrNrUIelQsJgWbEBFYV6zMzSQCVjYXGFncbQ19A/8bHoSPqDAtz+oHicCQR/ByJhaOiV3KCO6FjnMexQilHPkc4kHylGlRT3XzAT319Tgn/ubMSLOxvQ0COK9+bp5UhTSVDZEismva/JAoOCw0/PmY7XdzehM0pHttXiBgVgeroStQnSm7wA/PydQ9hWY8Kj14oZpCsW5iJbK8O3XtgNPy/gey/txe8vn4NbVxQBEMuAOq0evLm3BV12D2QcjW8a+vDEplrcd94M3HXWdDy5qRa/eb8Kiwr0CeszM9QyPHFDOb79T3G8afj8eO9AG25ZUYiFBXrctKwAL+yoBxxxd5EUihLLHcIRuoHRuozQ4+j68IH4gzyOddiwt9GMfc1m7Gs0izd5oc/8v75qBCBmGxYV6rE41G08K3tkQvKE4TPpHcCa7sFn95kzMvDfHx+Nee72lcV4d1/rIGHcm5bmIyfOXMdXdjXihR3infMDF87EQ59Uo8/pA00lDoPPydFEFpkblxbg95fNgSQ0Buv3Hx3FO/v65/hyNIX7zp+BO1aXpJSSeX6LKOKcrO09HNXk0e/4BXkBNAVcNC8bd6wuQVmm2DV3+TM7cbht8Kg2Af1OoJSlce7sTHCMKPfB0mKNF8eKUiAcQ4vPh17jaBosK9Z6RSRDGAosFfrKUBB/VSoUjRTA8+FjFhAMCmizuNDQI0qjNPa4YAoVEds9gUidCU0BcgkDhqLgD/IxGo/DxeMP4ninPVIoPhSpjJ6bKDChJhWXT5REOdhqRYvZjW8tL8SyYsOYOH+CIKDdGhvZq2qzxq2zYmgK0zNUkRTu3FwxspdoVNZowQsC/r6tHu8daMOxqAYqrZzDpQuyceXCPCwq0CWNglGUWAOYrpaiPF+HQIDHs1vr8GlVZ0zKmoIosrt6ehokLB1xGFvNLvQ6/XB4AzjeZR+0ToWhKXGyRUm6ChlqsSPT6vZDJWMxJ0eLbK0s4fhKQRDr4Pqji7EOYthpNDv7HUiXT6xj7LJ5BzmhiaApROpEDQNqFodbMztcdAoJfnpuGb67uhgvf9WIf+xoCKXx3cjUSKGXc6iOqrnuc/nx1421uGlpPvQKCf62tS4iWyUAqDU5RQUEQYg0mAzsEP7oUAf2NfXh33esQHGaEstKjFj/0zW4NNTd+/8+OII2sxsPXDQLFEXhoSvnotvuwebjJigl4t/q6c0nsLTYgLvPKsWOWhP2N1vwkzcr8eb3lyf8e66bkYE7VhcPivD+98dH8d6PzgBFifON//7crpjXZRyNLI0MeXoFMkI1vNEOXmYoHXuyThjH0Jifp8P8PB1uhyiW3W5xY2+TWBa0r8mMox02dFg9+ORQBz4JDUaQcTQW5OmwuEh0CBcVDC0fQxgZk74LuOiXnwx67kfrSvDclv7I4LoZ6Xju5gqs+/PmmEVMxlKo+v0Fgz5gm6u78d2X9oAXgB+sKcHHhzrQZnEnTacVGhVo6nWBoSn87tLZ+NbyQlAUhX1Nfbj79cqYu+0ZmSo8fsNCzEogNxPNW3tb8NAnR2OKlpPBUFQkPSTnGFy/RGzs8AV5/PvrZryzryWhE6MJycBcSyRJxpzxkIHJUEuwclo6LpyXhSVFBugUJ9c1Jwii8HN0CreqzRq3sSLs7M0LpXDn5moxK+vUOHsevzgW0Or2w+LyRx4f++J18F5XjM05hsJZM0W9vjNnpg97IkxNlx0PfXIMO070xNwoqmUsrl6Uh/tC49lSkWfRyFkYwt3dXjGtNlTEmaaALI1Yh5irkyNPr4g8Dn8djuPv8QejooreSFQxNroYeuzwDnmDFD7PfZQUyy+/FRfOzUo6b/1kcXoDePXrJvx9e30kwpymkkAl5dDYGxvZMyol+J/L5+C9A20xY9XCpCkl6EnSJEQBePqmhbh4fg4AoM/hw3mPb4uUMVw0LwvP3lwBIFYoWill4PQGYVRK8Nk9q+EN8Ljoie2wewO4++zpuPfcsoQ/0xfgcfVzO3G4Lbbx5/Hry3HFQjGF/Mif/wK30wGFUoX/uvueUxY9HwkuXwCVLZaIQ7ivyRz3HCrNUKGiIKRJWKRPOKt8ojBZuoAntQN4oNmMK5/9Kua58nwdajrtcEVFyl797jJUtpgjc3fDvHT7Yqwti9XYO9puw7XPfwWnL4hL52fjcJsVjb2uhIKiNACtQpQo0Sk4PHvTIpxRmgZ/kMdTm2rx1OYTkZQjBeDOM6fhnrPLhhSV3Hi0E796vyrl1Gu0cxpu7LhucR72Nprx6u4m7DzRG/d90pAIdPTFimjSjT0TweY0JToq6WoZiowKzM7WYFqooD5Xp0C6ur/7LjxFpN/Zs+JIuy2us8fSFKZnqjEvJIY7N1eLWdmaYTsiNne/8xbPoYt+3RL1eKDWZ5hom5/IWIurFuXhkvnZw4428DyPf33VhBd2NKB9QFp1drYa9503A2fPyhxSnkUr57Cy1BgZtTYw/ecP8ui0eqKihqFUc+j7dosnRoMwEWkqSYxDmKdX9H+vlw975NfAYzRHRRVb+9w41mkTpYz63Fji+AqKqPM83yDHHatLcG1F/imN9Lp9Qbz+TTP+tq0uEgTQyTnIOBqdAyKba8rScNn8HDy5qQbN5tjubYYCWIaKCJzHuy5cV5GHP1w5DxKWhscXwCVP78SJUKZqYYEO7/5wBWiajhGKDisyhAWhPz7UjnveqARNAW98f8WgkXTRNPQ4cfGT22OiqxlqKbb8fB0UEnbSOCOA2CBZZ3JEnMF9Tea404n0Cg6LCvSR1PH8PN0pzxQMh8li80ntAN76j90xHaUA8J2VRfjnzsbI9zOz1Hjlu0tx5p+3xmhAzclW45N71sS8t8vmwRXP7ESH1YOlRQZYXD7UdDsSOn8sQ4ESRPmIskwVXrh1CQqMCjT2OPHj1w/EpFgLDQo8dn05Kgr1SX/fvY19uO/tgyNqjJiWrsT315RgeYkR7+1vwxt7mhOmbQZqAYbhGArXSg9CKvjgo6XYKlkCq8sfGng97EOaslAQ62gEIbmgNUWJkdpL6f0xF0ZAjJKlqcTpEya7J6apaTygKHH0GENT8PiDcc8flqZQlqkWHb08MZU7M0sNGcfAF+AHOHA+8avLD6s7EHLcfHEdvUQC2qkiisJyETkYrZxDUcdmMAEPFEoVfj6CeZ0tZhce+vgoNlV3x9TkKiQMLpmfjZ+cU4aGHqc4deOECUfaY+cqj7Y8C88L6HF40RqpQ+xPL4cfO1NIwaplbMgxFB3EHJ0M6WoZNDIOEpZCl82DOpMTLX1udNk86HP5YHcH4PaL+pPRNccDSXSjo1dwuHVFEW5dUXhKO809/iDe3teK57fURRxwtYwFPUAahqaAm5YVQCvn8MK2engHnOsDtV8HkqeT4Y0frECeXgGe53HLP76JNP4V6OX4NKQVGC0UHb6Jv+fs6fjpuWW4961KvLe/DTlaGT67Z01Sge739rfi3rcOxjz3X2eW4ufnz5g0zkgi+pw+MUIYqiM82GoZpL7B0hTm5GhQUWiITC5JVU3jVDBZbD6pHcA5/2/9oAVtdo4aR9v762ceuWY+qtqseHlX7HzIg//vXGij7vSd3gCu+9suHGm3oSRNCbmEGaSnFU30+LhzZmXi8RvKoZQweHNPC3734ZGYYu9bVxTilxfOTBp6r+my4ydvHMDRjtRq0aJZVmzAHauLIWEZvLa7GZ8f7Yy7ALMJmlcoAHqFBFa3D0FhYkSjxhspSyNbI4NeyYFjaJhdfnRY3HCMoIaJCc3ZLApJ5YS7TYuMSuTo5GCiZkfSEjk+RcUgncUCgwLfWl6AigIdqtpteP2bFtR02ce9CWUgMo6GnGPAMaLsCS8I8AX4kHNwck4cTSHivCX7F+5aj35OFaeTdSSLNM/zeG9/G57efGKQ+Py0dCWuXJgLCUtje20PvmnoG/Q7z8xSY1WpKMK8tNgwpuk4nufRZfeiusOOOpMDTb0utPY50WL2oMfphcMzMrHr4TDU2sIxFK5fnI871pTEbbYZLXwBHu/tb8WzW+rQ3Cf+HRUcDW+Qj5EVk7A0vrWsAO0WN9Yf6Rq0n+hBAgMDBTQF/N8tFThndhYA4OfvHMTbIU1YnZzD+p+sRpZWHiMUDYg3Wq9+V5w6ctET29Hc58LF87Px9I0Lk6Y9w8MNon/+tvvPxFv/fH5SOCOp4gvwONphw97GPuxvNmNvozlupixXJ48ZZTczS31Kyw2imSwO4MQpBhgBA52/aenKGOfPqOQwP0+LB96N1QO868zSGOdPlHs5gCPtNhiUEqikDA61JXb+5BwdaTy468xS3HtuGSxuP37yRiU2HutfJNJVUjx2/QKsnp6ecF8dFjfuebMS3wwh4jwQmgIunJeNm5YUoLrLjj9+Wh03VB7elhcGixzLORp+P48AgD5X4tqW0ULO0Sgy9s+YnZGlRrZGBgkndgQHgv0q8l02D1pCaa52ixudNg8srtRkXkYDb4BHY58LjcP7s0SQMjQKjQqUF+iwbmYGVhQboU9hqotSymLXvWfjRJcdD6+vxtaaHviCPJr7XPjjp9XgGAqrStPwxA3lmJGlgcXpxW8/OIIvjnbF7TCNB0WJUxEoQYB7wKzhk8Xj54eM1tGUeGFVcCzUMhZ6hQRGtQRZGhlytHIYVJLBjp2Cg0rCjnl3aZhumwd/+qwan1V1xPx+HENhVrYGGWopKlssg8pMMtTSSIRvZWkaMtSjE5Vw+4KwuH2wuPwwu3ywusS0d4/dK+pmWt0w2cUIq9MXgMcfRCCY2oSbUwHHUJiTo4XaxoL3Jf4c+4MCXt3djFd3N6MsU4UHL52NlaWJ18+RImFp3LC0ANdU5OGDynY8s/lEZP2Mdup8AR4v7myESsri1hWF+KrWhBM9/Y5/UOhfXwfalheA7728D9dV5OGPV83Do9csQJ5ejr9+UQuL2491j27Be3eeESMUHeQFCAJwzxuV+PSeVXjyxoW45rmv8MmhDqwtS8d1IcmZePz3FXOxv9kcuTHhBeCHr+zDWaNqufFHwtKRIQBAf+1xdNr4WIct0un+4UFRIUEhYVCerxMbS0LNJamOZzxdmbQRwA6zCyse3hyz7YoSPXbV92s63XtuGY60W7Eh6s5NI2Nx8MHzYu6kfv/REfxzZ2NEKuNg1OzggYRD/zKOxqPXLMClC3KwtcaEn7xxAOYoB+XK8hz87vK5CU8wq8uH+94+iE1DiDgPJNzYcUapERuPduGDyrZIPUo0iRpWGBqhqQ/x98/SYgpYBt+wIoAsTSFNLcXsbA3OmZmJsiwV6kwOHGoV68OOddjiOhoShoKUZeDnh3Ycwr8XkPj4E6GWMigwKJChkUElZSEAsLr8aLO40ePwwulLLIo8WtCUuAhp5BKkqyXI1sgj0arAwY8g+NyQyJW49OY7kKUVJRYAAW/sacHft9WjYUDEKV0lxcXzs3DJ/By4fEE09zrxxt4WHO+wwz/RQoMpwtCU2MQQSj3mhuoPc3Qy5OnlyNHJRy1qlspd+mdVHXj8i9pBXbkqKQMZxwySrgnLs6yaLtbxTc9QJY3aePxBWFxi6tvsFNPiolMnPtfr8KHT6hbnpLr8cHpFZ+5U/HlpSvwcS1kGCikLrYyFUSVBjk6OfL0C/mAQxzrsONphR7fdM6JjCEcAXQKH6rQ16LB6hqxz5hgKS4sM+OWFMzEvTzeyX24IgryATw534Okva2MmMw3EoJRg7fR0fFrVhoFT5RKVCgHirPSPfrwKGRoZ3t3Xgp+9cwhCSKbrhVsX48yZGXjjm2b88r3DkfesLDXi5duX4W/b6vDI+uNQSBh8/ONVKElXJTy+Q60WXP3cVzFr7Xd1R8F7XRM+GjWaOLwBHGyxRBzC/c3muCPpyjJVkU7jxUUGFBkVo9JcQiKAp5iBd9oAcKC533HjaGB2tgaPDdADfPV7y2L+wP/a2RCpGSzLTO78cQwFj59HtlaG/7tlMaZnqvDgB0fw0q7GyDZqGYtHrp4fmfoxEI8vgF//pwrvH2gb1gKappLiW8sKoFNyeH9/W0RDKRED9x1enOLViDOU6BibQ/NOw8rx4fdJORoKjgFNAb1Of9xFLsAL6LR60Gn14MtqsYOOoRGZzsEkEM72BQX4grEfTJpCRIJgYAotVZtlqqVYkK/FrGwtaEoUIW3oceJoh21Ychbxfp6EoZGrF7Xa9AoJlBIWAZ6HzROA3SM2I/Q6fLB7AzHHzwv9Y9vaLW4cRP+5dp0sACUFmF0+XPLUjsjPl3MMJCwNmqagl3Nw+AIRO5ocXvzrqyb866vY8obRJvrCRlNio9VZMzOhVXBo7XWGGqWc6HX64AucXJQpyAtDatTpFRxy9XLkaPsbGcJNDDk6OYxKyUkt4laXD49sOI4PKtvgiBoxF96jgPDfMQiaAubn6bCixID5eTrkGxRweAOwuPzY32TGl9XdooPn6o/Wic6c75SnXGlKPFdlHAOVlIVeKUGmWoLsUONHvl6BojSxLGGgU+0NBLG52oSPD7Xjq7pedFjbEvyUkSEAMWutPHRjHc8a/qCAnXW9uPTpneAYCgsLdLhxaQEunpczZDNdqjA0hcsW5OCSedn4/Ggnntx0AkfjzFTvc/rwfmUbsrUyTEtXYkdUc12yv2SX3Yulf9yEZ24sx9UV+cjWynHri98gwAu4/V978NCVc3HTssKIUDQA7DzRi6e/PIG7zirF9poe7KrvxT1vVOLdH52R8Peen6fD/efPxEOfHot8bu0ef0TYf6qgkrJYWSpG3QGxPra2O7q5pA+NvS7UdDlQ0+XA69+0ABAdfNEZFNPG83K1KTWr+QI8PqvqwGeHO1DZYsEanwdKKvk5MRGYtBHAhf/9eUzEbWCL/g1L8nG8y44DzZbIcytKDHj9+ysi339Z3YXvvbQXvCDW5qSiBbeoQIfnb6lAj92HO/+9L6YO6MwZGXjkmvlIVw8uYuZ5Hn/8rBr/2tk4rEU/XFdkcnjx/v62YenQJbsjpSlAylJw+wdvcaprACUMBbWMg14pAUtT6AldEIeTiVRJxaaC6EYElVSc++oL8Oiye5JO+ohnmwy1FGvL0nHWzAycMS0NWgWHPqcPB1ssONBsxoEWCyqbLYPkOgBxCPzCAj0W5uuwsECH2TkaSFkGwdAM0jaLC/sbLTjUZsGxDjvqexwx82snUt0lTQEqGQuOpmF2+RI63RxDYVq6CiumGTErS4OyLDWmZ6hAU2KjQKfVjZpuB75p6EFVqw1ddm/Ci/xoIuPo0JD4cBNDrAxKllYGjqEHrS3ba014+LNqsVkjyf4VEgZqGQuWpuEPio0tJ1vfmAoMDUgYBkoJA42cg0HJIU0lareFo3SJHLpk8DyPQ61WvLW3BTvrekUx9ThZhdSPk4JCwkAr55CukiJXL0ehUYmyTBWOb3wDbqcDbkjwhnv+iH8GIH6GS9KVuHBuNm5ZXojMUSz6FwQBX1Z344lNtTiUJChQaFCAogQ09qY+i3tVqREvfWcpGnqcuOyZnZHu3R+tnYb7L5iBX757GG/uFR0SmhKDFsVpSlz4xHZYXH78YG0JHrhwVsL987yAb/9rD7bViOMyo9eWDwILIWFFPVdJSIw/8pWhBj8X3pahwbEUJAwDjqUgZRJsF/N+Ku7PkQ7chhU1ZMe6vKPH4Y2RnznUZh2kGBAuXVgcVUuYoZHB6vLhvQNt2HSsC0fabTG+CNBvc4lciQfu/9lY/lrDYtI6gMW//CRmkU5XSWCKSsf8v0tm4b8/Phb5nqaAo/99QcSbP9JuxbXP74LLF0RxmhINCernormmIg//c/kcvLyrCY9sOB5JGcpYGv9zxVxcU5E3KPLA8zye21qPp76sHVYn49JiAxYX6nGwxRIzNu5koACwFBDH54sgY2lcJTkIqeCFGxK85y+HPzDyizYd8rRGenmMrscZLiqpmMKiKQrmUAQmGrWUxYppRqyenobV09NRmEL4PyxTsL/ZjAPNFhxotqCm2z7I2ZQwNObkarAwX4+FBaJTmKuTR/YvCAJa+tyobBWdSm/lh5DwXvhpKToLzoFaxoYaKRBqpBDg9gfQY/ehy+6B2eWLcSCHQsJSyNHKoZQy6LB4YHbFj+TGw6DkMD1DjRydDE29LhzrsCcVJM83yDEjU42yqH/TMpQRTT1fgMeJbjv2NVlwsNWCI23iTFNXshNzFKEpcYTeWYE9kPDief6me/6Y362zoQi5UsJAo5BArxAdugyNDDlaGfINIYfOODyHLhqe59FiduNwqxU1XXY09rrQZhFnG/c6fPCO4LPN0hTUMhbZWlnE6ZyeocbsXA3K0tVgk0Tlotfzy2/5Ph7+rBpbjptSkq8ZCoOCw6rp6bhleSEqCvWj4lAIgoDttT3406fHcCxJgKDIqECn1ZNyHa6co/H6HcuRZ5Dj/L9ujwijX7ogB3+9bgHueHkvNofmnRuUEmz4yRrsazLjh6/uAyA2iayanpZw/ya7Fxc+sR09Du+EurlMBkNT4EIDBthoZzQ0fEASch7Dk6TEr0yUAzrYgZXGdWj7t4t2XgEBjb0uVHfYcaTNioMJNEyTBVb0Cg5zc7WYYdqG4CRIu09KB9Dl9WP2g58nfM+asjQ09jjR3Nd/V/b7S2fjtpWiGnmH1Y0rntmJLpsX2VrZoI7LgdAU8OuLZ+OCOZm4+43KmDmfFYU6PHHDQuTFmSby5p5mPPTJsZSjdjQFnDUzA+kqKb6s7k44eH60UUsZFBmVYBkah9qsuFpSGXfBoCkxFR3WDvT4g/CEujtHO5NFoX+ayXB2rVdw0MpZ+IPi3zn6uMLpy9Wh+qwF+bpRGTlk9/hxqNUqRgmbLTjQYom7cKSrpaEIoegUzs/TRi7sA8/zIC+gocchauy12lDVbsXRdluMlFEYCUMjzyCHlKXRbnGnLBo+EqQsjfl5WqyZnoYTJgc2Hu1OSVqEoSkUGRWYkSU6hDMy1SjLUqPQoIh05vU6vDjeacexTjuqO2yo7rSjpst+yqJro31hZGlAzrFihFDOwaDgYAxF6HJ1MuQZFCg0KlBsVEI+SnWMXTYPDrdaUN1pR73JiVaLG92hhilnVLnASGEooCRdhdXT03DNolzMytGeVHo93nrO8zze2d+Gv22tR50ptv6Oo8WpQcNNlUsY8Ty9tiIPFy/Igeok5kQDoiP4xdEu3P/OIVgSzAcGgFydFG2W1Nftm5bm44ELZ+LyZ76KNKFUFOrw0neW4Ib/242qkBLFsmIDXr9jOX7zQRVe292MDLUU63+yBoYkjWXbaky49cVvJo0DOJmhQvWzNEWBooDLmQNQUP4RS0yNFZPSAfzzhmo8vbkusg03IKp16/JCvPx1f11UmkqCvb85F4BYHHrd87twtMMGfUjAORkaGYunb1oEq9uP+985FIl8sDSFBy6cie+sLB50p/n5kU78+v3DMRHJZMg5BmvK0uANBLG9pmfEEa/hoFdwyNbK0Gn1oG+ADW5SHIJU8IGSyDH7vJsimmBZWhlYmkK33YtDrRbsquvFoVYr6nucMDt9YxJBkTA0CgwKLCzQYnaOFh0WN3bW9aLe5EwYldLKOczJ0WDdjHQsLTZGdOlOFYIgoLnPFYoQiqnjo+22QRcxhqYwI1ONhQU6KGrWI+BxAZwcVYZVONpui+tYyTgas7NFQeU5oZFp0zNUMfIGXn8Qz2ypw+vfNMeMIQPENPfiQh3S1VKYHD50Wj3osnnRbnGP6O+nkDAwKCVw+wLodfafR0VGBQqMCri8QdR02RPeBElYGtPSVZiRqUJZljoSOczVyUHTFAJBXrwr77ShusOO6k4bjnXYU55jm4xUL4zhxV3GMdDIWOjkEmRopMjVKTAtXYkZmSpk6eQwKCXQyEZvDm6fw4fDbaJzV2dyoNUc0t1zitHf0YiaxYtmFBoVWFeWjrUz0rG8xDiqUjVDFcf3ODx4dEMNPj3UMajUwqiSwOEJDPuGgAKQb1DggjlZuHFpPoqTNFEMBc8L+O0HVfj37ubEPy9UU53qjZhRKcHfb1mERz6vwdf1ouxAoUGBf39vKa7929eRAMWPzyrFnetKcclT21FncuKcWZn4+60VSR3yP316DOZv3icO4BgTXls4mQK/+sXPx/twEjIpHcAz/rQJ7VFRu+g0YZFRDnNIWDbM1p+vRaFRhUCQj4TWFRJmyLmUJelKPHF9OZ7fWo9PDndEnp+RqcIzN1egNCN2IfmmoRc/e/tgTOQxGUalBPPztKgzOVJ+z0ihKfHnySQMWgb8LJWUxRnTjFhTlo61Zel4+1+ibpRKrca6q7+D7bUmHGg240S3E912z5BRBSZKr03K0rC6A+hxeE+5xhggRqjy9HJwDI1uuzfhZIoZWWrMz9NiXq4YiSvLVI9aQXk8PP4gqtqsoQihGCmMjjzHc0bkHIPZOf3TM+blajEtXTksLauj7VY8vP44dp7oibG/hKVx5ox0/PKCWShOV4oiwk4vOixufFDZjg8r29DjPHnZHQlDozhNEamJ9PiDsLj96LR60NTrSui0S1kaapmo3ecP8PAFgvAHeZykHnQMpyIywtAU9AoO+qhZuPH+SVkaJrsXbWY3mvpcaO5zRZy7sJMznE8Lx4gzuHkgackGx1CD5GCkLI0V04whpy8DxWmnTn9vON2RW4934/GNtTjYaomJ5Ms5GhoZh267d0Q3LRqZWPpx49ICnDEtbUSf+28a+vDDV/fFXV/C0JT4L1V/9eZlBbB7/PjwoHit0Ss4vHDrYtz64jeRm8FXv7cUeoUEVz7zFXxBHv9zxVzcsrww4T59AR5/+N9HwQQ9cEOCrZKlIZF6MVJFUQAFcUY7RVERAfvw43BEC1GPKSr0OHo/0a9jwL7p6Of6fwad5OfFbhueId+PIIg32qDCjwFAlNDhxYcQBCEyc15AWP1CQIAX0G3zwOTwwe7xJ72ehdPFDC1+brwBPqXrWHhtkSmU+MXPSQ3giIm3YJQ88EnClOOZM9IjtRMAcOG8TDx382IIgoAHPzyCl3c1JRREHrif284ows/ePhiReqAA/Pis6fjx2aUxqcOaLjvufv1ASk0kACISFwdbLHGnKYwWHCNOP3B6g4MutHNzNVhblo4109OxqFAPlqZwuM2K9w+0wXvgI0gF75AXRpoSdevkHAOKAhyeQNJ0IEUBmWoZpBwNm9ufMPpKQewC5gV+xNNHwrI0C3K1WDbNAIvTj0NtVhxqjV/XIWFozMpWY16eNjTAXIvSdNUpFQ7tsLpRGUoZu/Z9AI73QuDkmHf+TZiXp8W0dNVJTYaIJhjk8dLXTfjnzoZBNwA5WhluXVGE760ujvl9AwEe/94tjjhrMQ++QaEg2nmiSM5QAOhQHRHL0GAoICAICWslw4u0l5LggGYleEGA28/D4fXD7jn59OlowdAU5BwDnYJFplqGfIMcco5Ft92NOpMLrWZ3wvVMIWHA88Kg2rSSNCXWzkjHuhkZWFZsOKUR8WhGIo/h8QXw9OY6vLmnBSZHbERbJ2fh9vMjLhNgaApzsjW4qiIPF8/LjtvAl4gehxc/fbMS2wdMoxpIshnyA0lXSXBGaRo+qBS162Qcjf+9cj7ue/sggoIAGUdjx/1n4oODHfifj49CytL46MerUJapTrjPySJJcqroc/jw7oEWfHnMhGMdtoQpfDlHw6iSQivnEAgKaOx1JjyvDEoJZmSqUZqhglrGwhvg0Wnz4EhodGx4bVGp1Ljvvolr80nnAPK8gJJffRp3WzlLISAgsnCzNHD8DxeBoSm8uKMB//3x0aQFnGHuWF0MXhDw4o7GyLZ5Ojme+1YF5uVpI9u1WVz4yeuV2BNVE5iMkjQlgoIwojFvqRIujh2oeWRUSrCmLB1rytKwqjQdBqUEX53owQcH27GnoQ+tFnekqWVgZCQs3MvRdEp6fdlaGYqMShSlKVGSpgxNv1Ag36AAR9PY3dCHd/e34pNDHQkjQDk6Ua+vw+oZ9LswodtGfpj1gYDYSamWielvo1IKrz+I4wlSlDKOxpwcMfI2P0/8V5w2ek5ZNGO5SLf0OfHwZ8ex8ViseDRDU1hWbMAvLpiBBfn6mPd4fAG8uLMRL+9qHDQ7FeiX7vGdRMPQyZCplqJsQH3h9AwVlFIWTm8ANV12VIdqC8M1hhdg32mXGpNx4ufU4Q3E/B3kHIMzphmxbkY61pZloMA4uGb5VFLdYcMbe1rgq/wwUl5y9z0/Gfbc5aPtVjyy/jh21vXEOOgMDchYJqV61GRkqKW4cG4Wrq7Iw9wc7ZDpfJ4X8MzmE/jrxhrwgigDFk9vDkjePDCQ8nwtDrZYIUD8XP5oTQme3iKWPRUYFPjy3rX47st7sbXGhJlZavznv1YmdOKnmgPY2OPEO/tasa3WhBPdjoSZPhlLQ8ox8Af5xNtwdP+akqmCQSmF0xdAvcmJylBpT7xSjHAZlVKlws/uIzWAI2bgyfvmN034xXtVcbedlaWO6dR69qaFuGh+Dr442oXvv7I3qSwIIDo5951bhjf3tqDe1N8VfNuKQjxw0azIB8zi8uHeNyvxZVSkMREUBRQZlei0uiPTQ0YbGSvKUUQHLFiawqJCPdaG0rolRgU2Vnfj08OdqGwxo8vujWsPmgKukQ6dGktTSUPOXWi8WcjhKzIq4w7lrjc58P6BNry3vy2mfqvAoMAl87OhkjLYfNyEw63WQdEKmoIogzA3G9cuzhs0Iqre5MDnRzqxv9mC4512dNk8w06hURBnO8tYMZrp9gfjRoCUEgZzcrWYn6vF/Hwd5udqU+oeHorxWKQFQcAHle14dsuJQeK3RqUEly3IxsxsNQ622FDTZUen1QOr2wePnx+1qB8FUY5IJmGg4FjIOAZSlkYwGESPK5A0xZYKBqUEhUYFZmapUZ6vw7xcHaZlKCFhaPzlscfgdDggVSix4vLb4PL60dznRkOPA61mD0wOLyyhbmtvYHwnaZwsFACOpSFlaEg5URtQIRH1AVVSFmo5B52cE9PXSgl0Ck78e0gYyFgGstB75JwogB3+XsrScc/9um4HXv+mGdtqTWjscUYyHQNvLtNUEqwsTcPtK4uxIDTZIRV4nseru5vxjx0Ng26opaH18GRPUY6hMD9Ph9tWFOKsWZlJG0m+quvB3a9XosfhhYyjMS9Xi/3NlpMSl1dKGLhDot8UxAbBTSGN1XNnZ+KPV87DhU9sQ4/Dh2+fUYTfXTYn7n5Odwdwf7MZ7+9vw676XjT3uhLWxiZTlKApoChNiZlZaszI1GBGlqh60Ovw4VCrFZUtZhxMkD3SK7jQZBI9ygt0WJCnxYvPPz0pbD7phKBf2NGQ8LVo5y9fJ8NF83NwuNWKu18/MKTzl6GW4vLyHDy64XgknWJQcnj25gosLzECEKMgv3q/KmbeYiI4hoJBKUGXzZuSxMxwoCiAofrT2GGHKU8vF9O6ZemYk6PBpmPd2Hi0C//Y3pBw1BsdrqEIfR/bNSuKroadu/AM20KjAmrZ0CN0rC4/PjrUjvf2t2J/lB6jSspieYkBBqUU9SY7/ratPmahZCgKeQY5AKC1z4WgANSZnHh68wl8dKgdF8zJwgVzs7AgTweaplCSrsIP15XGPQaH24d3DrTigwMdqDM54PAG4l4YBIiRY38w+d270xfENw19MaP7NDIW86LqCeflapGnl5+0U3iqsDg9+LLahG8a+1Db7YTTG4BSQsPl63eae50+/HOUBKYVElGuweb2D1qABQCegIAsrQTfX1OCqxblxUQyPP4gum1edNo86LR50GX1oMPqEXUGbZ5QE4s7YalAn9OHPqcPB5otEbFXQIyIXcH6IANgdvrx3X/tGVaNalikWy3noJKKUhRBXoDF5Y872YKmgBydHOkqKeQSBrwAOD1+mBxemBzeuALt8aBC+xIwvGk4AsR6MF+Ax2iLC0hZKlSyAfiDfEJHOVwrFk2Pw4cPKtvxQWU7pKzoOF1TkYerF+WBS1KbR9M0bl1RhFtXFKHD4sYjG45jw5FOuHyxM6eTlfsMTM2ydGy9nj8oRDTiAPF6cOaMDNy2ogjzBzirZ0xLw6f3rMI9r1diV30v9jSacen8HMg4Gv+pbBtROUE4mhk+zk3V3Sg2KtDQ68IXR7tQnq/Do9cswHf+tQf/+qoRa8rScNbMzGH/nMlEMMjjy+Pd+OhgB/Y3m9FudqcsMRb+E2RqpJiRpQk5e+JI0gKDAo29YlSvstmCDyrb4o5WlTA0ZudoUB7Sey3P16HAMDrTQ8aDSRcBLP3VJ3EX+zSVJGYs077fnANvgMcVz+wcctTQnBwNOIZGZYsl8txlC7Lxx6vmQyVlEQjw+OP6Y3hxR+OQxytjKVAUnVQnbSTEqyORcTRWlIjNG7Oy1fimoQ9bj/fgeJc9rlwIIC7C8f7iegUXETJ27vsAXrdzRHcv/iCPbTUmvLu/FRuPdkfuxmgKKM1QQc4xqDc5YB9Ql1VkVETkWZZPM0ITcjCtbj82HevC+qpObK0xxSzu2VoZzg85g0uKDMNKzXbZPPjkYBvWH+lCdYcNtmFo6qWCRsaivECP+blazMvTYkGeDpkaacKFYjTu0nscHmyu7sbeRjNqux3otHlgc/vh9YuFy6PxQacoMTKhU0iQo5WhLFONZSUGnDkjE3aPH09sqsUncTo4AfF8XVpkgJxjsK/ZPGiMWpgiowLfWlaA8+dmI98wdKqS5wX0On0h8emQo2jzoLHXicYeFzqtbljcg4u9EzWBiCLpYsevUSVBllaGAoMS0zNVmJ2twawsNWRJOmPtHj8Ot1pR2WrBwRYLKlsscafPhAvvE0WJws7eUL4DBSBdLUFJugq5OjkyNTKkq6UwqiTQyTh4Ajx6HF70OnzoC+lhWt0+2D0B2D0BOL0BuHz9kk6nsllroM2lrOg4D/yZ4c7dc2dn4PZVJcjVyVPa/4YjnXhyUy2ODhDzThb90crFtG34ECgKMCokcPkCcCXI2tCU2C197qxMXLUoD2WZatAh2aonNtbgqc0nIAjiNKoHL5uNjw924M09LaPSua2U9Ke6//ntJdhWa8I/dzbCqJTgs3tWI0MTK4g9mSOATm8AHx1qx+dVnTjYao3oJaaCWsqKygJZ6hhnTyvn0Gp2R4T9K1vMqGq3DRKBBsS1KDx3uLxAj1nZ6oieaTImi80nnQNY9MtPhnzPTUvz8cBFs3Dt87uGbMxYXmLAwRZLJD2rkDB48sZynDMrCzzP45ktdXj88xoM5R4kG2U0UuLVjMzIVGN1WRpK0pSo7bZjd30f6k3OlAVIAbGmZGaWKD+yqECPhQX6mBmIwz15BUHAkXYb3tvfhg8PtsVc2HUKDhQwqOFDIxNH9YSdvlQu9E5vAFuOm/BZVQc2V8fqzxmVEpw3JxMXzM3GihLjiDr7ehwebDjShR21PTjabkOb2YWTGIgwCJamkKmRYXGhDhfNy8aiQkOk6DyZzXscHmw62o29TWac6Hagy+aBzeOHx88PWycxHuH0t5wTpzfk6ETHrjxPh6PtNvznYPughbcsU4X/OrMUly3IievUNpgceHxjLb441hW3vkYhYXDOrExkaiTYdLQL9QkmKRiVEpwzKxOXledgcZE+pcU3EU5vAB1WN6o77DjcZoVr/wdgg14InAyFa6/D7GwN5uRooZKNXmKE5wVUd9qxvqoDn1Z1orHHmdTBYiggSyuDUSWF3RMYlD2QsjSyNDLIJQzcvgA6bN64F65o9AoOBUYlCgwKFBjkKDQokW8QZXqyNLJBN07eQBBmpz8SPe1z+VBvcmBnbQ9qux2wefwjTq8m67ymADChLuWBaOQslhYZcOvyIqwuSxsy4uLwBPD4phq8u6910NqTqBZPKxPH5UVPdxLnw6vg9AXR0uuCN4EXydIUSjNUOGtmBtaUpcPhCeAX7x5Cr9MHlZTFw1fPR0WhHn/bVofXdjeftLZlOCDA0hQ+vGsl7n3rIKo77Vg9PQ0vfWdpTO3iZHFGAMBk9+CNPc1YX9WFum5HSte1sO1nRDt7WRrkaGWgKApWtx+HWsPOnig+H+/mUyvnopw9HcrzdNAn0VlMxmSx+aRyAM+4/Bbc8PdvBm0THbqXsjSqfncevvfyPmytSV6jNztbEzPvcXVpGp64cSEMSgle/6YZv/vgMIYKDEkY6pR28mrlHFZOMyLfoECH1Y3DbTa09LmGdZeeppJExIcXFehjBIjjkerJ223z4D+VbXh3XxuOd/U72mwcAWeWprCoQI9V09Owenoa5ufpTqqZwuMPYkdtDz6r6sTGY12wRnV2aWQszpmViQvmZmFNWfpJdTj2OXzYcLQTO0/04Ei7Fe0Wz6gLE3MMhaslByGHDy6Bw7u+8tFx7ChRSFfGMdApOOTo5CjLVGFFcRpWl6UPy9E50GzGoxuOY3dDX0zESsbROGdWJn554cy4YuiAWLj/+MbaQRHcMCopi8vKs5GhkuKDynY0JGiS4hgKC/J0uLw8B2fPykROilGhRJyKRdoX4HG4zYodtSZ8Wd2NY532hA6ajKORrZVBLWXRafOg2x4/uiFh6VCEPA0Xz8vG3Fxd5AaH5wV0271o6nWiuc+Flj5XRFampc+VMMoa2Tcjyiblh0Sq8/Vy6BQS2Dw+7DzRi8OtNvQ6vSk5fFJW/H3SQp2UMo4By4jNWk5fEL1OH2b3bIMMvpQab6iQpzbwR4dlnC5bkINvLSuAcohylETnbiJkHI2lxQY097nQ2NN/LmrlHNZMTwNDU9jd0Jd0gAAFMULo8gUjGajbVhTiVxfPgs0dwAvb6/HK101DSpGlglbO4m+3VOC2F/fAG+Dxm4tn4XurSyKvT1RnJMgL2F7bjdd2t+BAsxm9zsRjJ8MYFBIsyNdido4mksYtTlNGVDn8QR7HO+0x0b060+BULsdQmJ2t6Xf28mODICfLRLX5QCaVA7hDugz7o9K08Xjte8vwaVUHXv06sVCnjBNHv4Q7PyUMhYeunIdrKvLw+ZEu/OztA7B7k1/kh9PRNRzE4fJa5OrksLj8qO9xoMuW2gIMiIvjnBxNjMM33Hq0ZCevxx/EhiOdeG9/G7bXmpIeV0maMuTwpWN5iSGlusGR4A/y+Lq+F59VdeLzI50xFz2FhMGZMzJwwdwsnDkz46QnAgCA1eXDF0e7sK1WdArbLO6kndHh2ichzsUszHA06cKOnTw0bzVXJ0dZlgorS9OxpiQNslGMYA0kEOTx9x31eGVXE9otsRfAfIMc31lZjNuWF4JJIJ+zt7EPT26qxa763rh1URoZiyvKc+DnBXx6uB1Wd+ILZLZWhvNmZ+HCeVmoKNQPe6rLaCzSLl8AB5ot2F5rwpbjJtR2OxI6GWopi/ICHc6emQGdQoKaLju2HDfF3IQC4vpkUErg8fPxJYtYGnNyNFiQp4tELBI1Ijm8AbSEHMLm3tDXPhcae5xos7hGJLOklDCYnqnC4kIDStJVKE5ToiRdiQx14hKHMGGbyxQq5K+5Fkc7rDjSbsPRdtuIb6yyNDKsnZGO21cWYUaWJuF2gQCPf+xswMu7mlISEqcp4JxZGchQy/D50a6YUqIcrQznzclCukqCnSd6sKfRnFJjlFbO4bYzCnHurCxkaKR4ZVcT/vVVY8KSnVQpSVPg5uVF+J+Pj4JjKLx/50rMzRUVK8bbGREEASa7F8c6rNh4rBs7TvSgzeJJGrmmKbGBa0GeDmfOyBDHDGaqY9ZvQRDQZnFH6vYqWyyoarfGXYsLDIqY6N7sbM2oSR8Fgjy6QpqebRYX2sxu9Ox6D1TAQxzAkyX65H2+d2bSaFtZpgrXLc7HHz45lnAbjYyNkfyYm6vFC7cuRlOvE//1773ocZ66MVqJyFBLUWBQwO0LoN3mgXkYAryZGikWFehDqVwd5uZqT/rEjie9s6exD2/sacFnVR0JnR2tnMOq0jSsmp6GVaWppXVHmyAvFm5/VtWBDVWdMYLhEpbGmunpuGBuFs6dlQmtYvQcUrvHj03HurG1xoTDbVa0md1J60ClLNU/GzfI4zLmAJSUHy6BwwaqQqyx08kxI1OFVaVpWHmKHbuRUG9y4OH11dh83BSzmLM0hZWlafjlhTMxKzvxBXnL8W48u7kO+5r64tZn6RUczphmRIfVE9NEFA8JS2NJkR6XL8jBuhkZg+qg4jGSC6PV5ceexj5srzVhW20PGnudCRvMdAoOiwr0OG92JqZnqlDT5cC2GhN21PYMqpGcn6eNTN9YkKeLGY93qNWKAy1iPeHBVsugmdaA+NlbEL7A5Ys1pyoZi+ZeF+p7nGjocaLB5ERDr/h44ISYkSBlaTGVPOBfoVGUfIq3DiWyeXj04ZF2G6rarNhxogcnuh3Dbp5QSBiU5+tww5J8XDwvO+GNSHOvC/+7/hi+PNadUppxYb4WtywvxNcNffjscGfM368sU4VLF+SgQK/AV3U9+OJod8Kmu2jkHIMF+VrMzdGgx+HDpuruhBIyA4lXE768xACVlMXGY90oSVfi4x+vgkLCjqkD6PAGcLzTLo507LDh6/peNPY6k/4dKYjyOdMzVbh4Xg4uWZCNdNXgm4nwyM3KFnEGe2WLBT2OweexWsaKTRohZ29Bng5GVer6jgPxBoJot3hiHLxWixvNvWK0vSeOIHn4hp6MgjtJok/eJ7tnJN32qRsW4O43DiaMssg5OlLrR1PALy6YiTXT0/C9V/aizZx8HvBowjEUcnVyBAUBfQ5fytpVEobG3FxNpG5vYYHupNNg8QjbXKFUobvwHGw40hlXJ4+hgIoiA1aXiinFebnaU6KRN1IEQcChVis+q+rE+qqOmNoelqawYpoRF8zNwnmzs4YlAJsqLl8AG491YXtNDw62WtBqdidM+YQXDA8kaMo5E9Mz1ViYr8OKaUZkpuDMjCeCIOCtvS3429b6QZ1z6WopblqajzvXlUKa4MaE53msr+rC37bV4VCbNa5DpVNwKDQoUNNlj3yGk0XhCwwKXDg3C+fOzkR5vi6uoHcqF8YumwffNPRhW60JO0/0DIp6RmNUSrC02IBzZmdgcaEBbRY3th43YWuNaVAtsl7BYU1ZOtbNSMfq6elIS/ECJYR0RA+2WiKRjyMJtMiGC02J0bSV09Nww5J8lGao+6OHA6KIbVG6oYkwKiVIU0mglLLgWBoUgNKureB4LziZAj/56U+TlqIIgoB2qwf7Gs14Z18LDrZaYHMHUs68hOWjLpibje+sLESaKv7n6IPKNjy7uS6mjCURGWop/nTVPPiDPP5zoB1fVnfH2H5xoR6Xl+dgVrYGX9X34KPKDtR2O5LsMRajUgKb2z9imaWL5mZiX7PYdHTj0nz86ar5p6zUoaHHiepOW4zD154kLR6GYyjk6ORYNc2I76wqRmnGYBHrQJDH8S57THTvhMkxaG1gaQozs9X9Miz5OpSkKYc1jtHhDcQ4d409ThzrsKO+x4E+l3/IGtt4kFFwo0S0M/JMz8yE211bkYsPD3YkTCNEXywKDQo8fPU8/PbDw6jtOnWizNHo5BwYmoLdG0j5hMrVyaMaNXSRcVqnghazC/ubzDjSZoX7wIdgg/EngYhpt0wxrTvNOCop1bFAEAQc77Ljs8OdWF/VGbPYUxSwpMgQkZc5FU51GI8vgM01YrrwUIsFzWYXnN5g0hQwTQFKCYu0UKR4ZpYaiwr0WFZiGLaQ7qmmy+bBI+ursb6qM+bGhqaARQV6/Oz8MiwvSUv4fp7n8c7+Nry4owHHO+1xL/YqKQuWpmIU/bVyDg7PYJkZoF8E+eL52VhT1u9sDbwwhmc4727ow/YaE3bV9yatoctQS7FimhFnzczA8hIj/EEeW0N/269O9MT8/hQFlOfrsLZMnL4xnJulcAqtvseJxlA0LxzVS6Z7NhQ0Ja4xK6YZcU1FHpYUGQZFXQJBHla3Hxa3H11WD1rMLrRbPOiwutFqdqPb7oHF5YfLF4QvMHRT0sDznKEoaOUsStJVWFOWhkvm56DAoEg6gaey2Yznt9Xhm3ozzK7UZ5ArJQwWFepw57pSrJg2+By0uHz4y+c1+PBg25BzfKUMhbvOKsUtK4rw+ZEu/KdS1KELX01ZmsKasnRcXp6DRQV67KoTRfd31/fGTbuPZknRebMz8MWxbggC8NzNi3Dsi9dH7AAKgoBWszsipB529up7Uo/OqqQsZmdrcO7sTFy1KHdQNE4QBHRYPaKzF3L4DrdZ42ZQcnVylBeEonv5Q2e8BEGA2eVHm9mNEyY79jeacbDNgnaLBzZ3YFRunKKhKYClaVwlqYQcPsgVKtz/cxIBHDHhRTrAyPCKY27cbWQsBaWUG7JFnAJwy4oCHG6x4ECrLem2JwtLU5CydETIcyhkHI35uTosLNRhYb4eiwp0KaWxUsXq8mFfswWH2yyo7XKgudeFLrsHVrd/UEo3epF+z1eOublaXLUwF2fNykhY6D/ZqDc5sP6I6AwearXGvLYgX4cL5mThwrlZKDqFc1HD+AI8/vLYY/C5nQgwUmyTLkOvwwunLzikfiVDUVDLWGRopCgyKjErW42KQj0WFxmSRlfGgg1VnXjyy8GSHFo5hyvKc3HfeWXQyBOn4QMBHi9/3YRXv26Kq8kFiGnI6Ju+HK0M2ToZ6k3OhKMGp6UrcdG8bAQOfgyPywFGqkBD9jrsaeiLG+mO3vfK0jScOTMDS4sNUMvYkPSSCVtqxKkD0aSpJJH52mumpw/ZUWh1+8VUbY8jlK51RR4nyxJIWBr5ehm4UF1zn9M35LSeMBxNQafgIJewouQML8DtD0b09E5GyDhM9IzXqySVKdW6ShgKaSop5uRosLhIj6I0FQqNYoo5+rzmeR7banvw+jfN+Lq+d0jHLfqY9EoJVpQYcPOyAizI10MZdTP7dV0v/vLFcexrMiddvykAa2ek4bmbKmD1BPDRwXZ8cLANVW391xeFhMF5szNx+cJcLC7UY2+TGS9sq8fOut6E+5VzDLyB1K4d8ShJU6K+xwmtnMMtqsNwOhxDOoBmpy/k5NlwvMuB45021HQ5hl2fmKGWRtbQi+ZlQT5gHXJ4A2JXblR0L55Um1rKYn6+Nia6NzBTw/MCTpgc2HK8G7vqelFvcqDP6YN7lBQSwtAQhdQVEgZ6pQT5Ojnm5GiwpNiAOTnaSSu9M2kcQJfA4c0EC0auXoo2c/KaFqOSQ4FRjgPNp87x4xgq5buiQoMCiwr7GzVmZKmHXcQejS/A41CbBQdbrKjutKGpx4kOqwd9Lh/cvtQWEgqAXMLgcuYAZPBBIlfi/p/dN6HSuqeCNosb66s6saGqE3ua+mKcrplZalwwNwsXzs1GWabqlAl+Jlow2iwufF3Xi8oWK2q77Wg1u9Hr8KWkM8nSFDRyDlkaGUrSleKCVWRAeZ4O7AhkckaK3ePHX7+owXv722KidhSAWdlq3H32dJw/JyupbV2+AF7c0YA39rSgNc5c4vD+wn86tZTBBXOzYVBKsK3WhOqOwdHEoRpv8vVyrCkTU7RLiw0wKCVo7nVhS003th434au63pi/QzjKGR63NidHMygV5fEH0dgr1uNFavNCkb1kN7A0BeTpFSgyKpCrl0Mn59DU60JNlx1tFk/C8yFs0tFa5cMTRWQcDaWEhUbGQqeQwKiSIEMlRZZWhly9HPkGUXZGr+Bi/q7h81yuVEG56DJsrTGhzuSExe1P2dnUyzmUZqpQYBBF6cOOYaFRCSlL4Z19bfjkUHsoipR6hEcjY7G4UI/FoYv6nBwNNDIOz209gdd2N8fVcowmTy/DC7csxswcLU502yMC1819/Vkmo1KCi+dn4/LyXKQpOXzv5X1DpojloYkriWbYJkLC0PAFeXxLeRgc742sLR5/ELVdjv70bZcY1RtKLzceDC2WMy0u0uPS+dlYW5YOmu5fW4K8gJoBqdzabvug6xFDU5iRqQ515IoRPoOKw87aXmw+3oXqTrEZ0uH1IxAUTnrKSxiaEu2kkrEwKiUoMCgwN1eLZcUGzMnVjrhxkTiAo0TYkIkW6WhRzETk6CRot5zcSKl4pBq2l3MMFhaIyuEL80Wnb7hFqTzPo97kxL5ms1ifYHKgzSI6Aw5vIGVZGClLQy3jkKmWIt8gR2mGGvNytVgYFXGcLCfvqaDb7sHnR0Th6V31vTEXpZI0JS6YK6aJ5+VqR9UZHK7Nw+fD1/W9ONRmxYluB9otbphd/pS6KSUMDZ2CQ7ZOhtJ0VWTRm5mljlnAR5vd9b348+c12NfUF7OIKyQMzp+ThV9cMBNZ2uSRb6vLh79tq8d7+9vQaUtec0QBOG9OJq6pyIfHH8AnhzuxrcYEly827f62ZwGK05VYOz0NK0vTsaTIAK2Cg8cfxNf1vdhy3IRtNaZBkcgMtTSS1l1VmgatgoM/yKM1VEtUH47ohZowhqqRUstYaGQclFIGNEWBFwR4/Tyc3gBsHv+oS06FU1Y0DfA8EqbECkOCuIsL9SjPF29YR6K1mVRhwBfApuPd+PRgBypbrei2e4bdBKKWsiiIOIVKqKUsjnZYUdVmi5l3nioZagnm5uowJ0cDg1KCjUe7sKexL+nfQc7RuP/8mfjOqmIIgoADLRZ8cKANHx/qiHHyw2Mw2y1u/KeyHYBo53SVBAdarHGPlQ4pCgzHLOHzPMjIsEO2DI29zoQOlJxjxHMuwRoiDUkSrZhmxFWL8jA/TxfzeqfVg8oWc0SG5XCbNW7ds07OQS1jEeSFkAh5AL5RduxYhoZawiBTK0NJmgoL8rVYMc2AskztiM7d4TBZrqGT3gFMBgMMKeB8Kig0KLCk2BCJ7pVlqlOKonXbPNjXbEZVmw21XXa0mF0w2b2weVKvG2RpKnQ3I0WeXo5p6UrMztZgYaEexUZFShf3yXLynmosIbmX9VWd2F7bE3NxzNXJcf6ckARJgX5YRcfxGE2bBwI8jnRY8U1jH6rabKg3OdBhFdP9qVxQZRwNg0KCXL0c0zNUmJenwxnTjINmMJ8MXn8Qz2+tw2vfDI6sFKcpccfqYtywpGBIu/Y4PHjqyzp8HEeseiDT0pW4Y3UJLpmfg+Nddnzy2t8h+NyQyJW46+57oJZxEAQBjb0ubDnejS3HTfi6vnfQaLHwjO15uRrQAKq7HKjtsqO+x4mWPrEu7hQO00gKR1NQSBloZFx/ZE4diszpFMhQS2FQSmBQiq8NLBNw+QI40m7DwRZLpPM4XsRVwtKYm6OJ6jyOPxIrEODRZHahweRAQ68TbdvfBeUXbX7nXXdDO0QNq93jj0wBOtxmRbvFPaKxagDAUkCaWoogL6YhRzqtSS1lkK6WotfpS5pyping7JkZeOLGhVBIWASCPHac6MEHle2RsXVh8vRydNtFUW+DUoL/vWoeAHGyyRdHu5KWJgxFvEi3nKMj9cNWtz9hc5pKymB6hhprZqTj2oq8mBIgly8gTrwJdeXua+qDaQjdyZMlLFqvkDBIU0kxPUONM0qNWFVqRJFReUpvXofDZLmGntYO4Fgg42gsKhBrrhaFwteJCvMdngAqW8w41GZFTacDTX1OdFo9sLr8cPuDKUUTaQpQSFgYFByydHIUGRWYFRK0nJMzOnc2k+XkHUvsHj82HzdhfVUHNlebYi4e6WopzpudiQvnZmNZiWFEqfyxsrnHF0Bli+gcHuuwoaHHGZosEhgyOhIuEUhTSpBvUIidygU6rCgxnlS9anWHDQ+vr8aOEz0xF3cJQ2HNjAz84oIZmB6nU3AgLWYXntpUiw1HOpNemGUcjWsW5UNXvwFOhwMqtRrlF96Mjce6sLXGNKjTVyERHSqGBrwBHk6vODZtPBZOKUsjWydDRYEey4sNyNDKYQw5dAalZNS0zaLpcXhFCZoWCypbrahsNidQBaDAMqIDGOSFuDVY8ZpA1HIWWRoZitP6yxQWFugTrmXdNg8+OdyBHbU9ONZhQ7fdmzADEp6bPpr1YCOhwCDHszctwtxQxExUCOjGBwfasLXGFHcU3vdWl+CXF84EBaCy1YKNRzvx6eHOGDWDVBiOxqiUpVGSpsQVC3Nw/ZICaOUcarvs+LK6GzvrelDX7USfywdvgB+1soIwDE1BxtLQKzkUGpRYUiRKKM3KGd1sy1gwWa6hxAEcJjlaGVaVpqGiSNTem5auikQpAgEe1V12HGg2hy6uLrRb3ehz+uDyBlIK21MAZBwDrZxFpkaGAqMC0zPUmJ+nxcJ83ZB3zKPBZDl5xwu3L4httSasD00hidbu0ik4nBuaQrJqelrKXdsTweZ2jx97Gs3Y12RGdYcNjb0umOweOLyBISNaNAUopSzSVVIUGBWYmaXBogIdlhUbUj5ng0Eer+5uxj92NMTUTQHieLRblxfijtXF4FKw6YkuO5788gQ2VXfBmWCcz3AujKOFUsJAr5BAJ2chQOxkFovW4x9jmkqCRQV6XLogBxfMyUzpdx8uPM+jzepBfbcDjb0utJpd6LB6YLJ7YXb5YHX54QzNCj6ZOcE0gGtlB6FI0eYDyxTm54nnU1mmalCkp8HkwCeHO7Crrhc1XY6k00s4hoJKykIpYUFRQJ8zdSmumN+HElOm/qCQcjepnKNx99nT8YM1JZHfoc/pw6eHO/BBZRv2NJpjttcrOPzigpm4YmFuxLlv6XNh47EubKjqxDeNfUN+NsfjPI8mHLVTyzhka6WYm6vFymlpKM1QI1cvhzZJI9hkZSKs56lAHMAkSBgas3M0OHNGOioKDZifr4XV6ce+ZjOOtFtR1+1Aq8UNk90LhzeQcmqCC30Y0lViHd60dBXm5GpQUahHrm78u2wny8k7EfAFeHxV14P1VZ34/GhXzOQGlZTFWTPFKSTrZqSPyvi98aLH4cHXdX2obLXgeKcdzX0u9Di8cHmHjoQN7FSenaPB4kI9Kgr1gzoEw7SZ3Xh4fTU+P9oZ09HK0BSWFhnw8/NnYFGhPqVjP9hixtNfnsCOEz0xTQGjcWHUyTkYVBKkqaSRSFzka/g5lQRqGYN9jRZ8crgD+5stccVjAbFJoDxfh0sWZOPiedmQjNDh8/gCqO9xot7kRFOfC+0WN7psHpgcXlhcftg9Abh9wZPqNAVEJyjc4QuIkb9k+xto83CtFgWxozMopFYHJucYGFUS5OnlKMtUY2G+HstLDMiOknCqarPi08Md+KahDydMDlhd/oTnqkrKIEMtQ45ejky1FDwv4Gi7DY19rpQnlEgYGho5C4WEQWcKM5oLDXL85JzpWFqSFplb29LnwkeH2vHKV03oiKpvlXM0LpmfgysW5mJ5iTFSUmTz+LGtxoQvjojrT7yml/F2ADmGAsfQYENfOZoCxzDgGAoSVnxewjCQsBQ4moYkNKmLY2hIWApShgbH0pCwDCSh5ySs2BgjYWhIWRpSTnxNyoW/pyENbSNlGDCMeI7SFAUKAEVRYj1l+CsoUHRsxzodOqmjvxffi5Qn3kzU9TwMcQCjMColWFQo1rIEggJMdg9azG502bywuf0pKcYD4kVKKREXqBytHMVpSszO0aK8QIuZmae2yH40mCwn70QjEOSxp9GM9VUdWH+kM6a2TcbRWFsmTiE5e1YmNAO6yyazzVvMLuyu6w11+DnQanYnjWpFwzEUNDIOWdpwp7IWS4oMWJCrBcvSEAQBHx9qxzOb6waJKRsUElxTkYd7zpkeI+GRjK/revHMlhPYXd+LK7jBkiQSRuycNigkyNTKkK+XI0MjCzl10kjtnFEpgU4hSVjbGwjy2HSsGx9UtmFfsxndtvgOn0HJYUGeDhfPz8Yl83IgkyR2+Ex2D+q6xWkeLX1ilK7b5kGf0weL2w+nNwCPn4c/yJ9UunO0NOkYClCENBt5QSz2vzKOzUcbsdFNzKCUpCkxP0+LZSVGZKqlONZhw+bjJuxrMqOp1zVoIksYCoBWwaE0XYWF+TrMytFgf7MZ22t60GZ1IzCMOkSGGrppQymhUV6gj3Qfz8nRoLbLgV//p2rQOMAMtRSXLsjBFeW5mJuriTgj4voTHrXYF9l+vB3A0xEq9F+MMxl6TFHA5cwBKCg/lCo1fnbfxF3PJ7wD+Ps/PgL43aN+8obFdZVSBgFegMsnLp4pyaWEQv96hQRZWikKDUrMyFJjQZ4O8/O1466/drJMZmdkosDzAipbLVhf1YnPqjrQ0tdfSM8x4qi0C+aIkyqMKulpaXOe53Ei1Kl8uE2MmLdZ3LCk2qnM0tDJOeTo5CjNUGJaugpVbVZsr+2JqUGjAMzL1eIn50zHWbMyUz6+Pz3yZ/jcTsgUSnz/zruhlXMjbubheQGbj3fj/QNt2NtoRpfNE9eJ0ik4zM/T4uK52bhobha6HT7UmcR64FazB11WN0wOL8xOP2wef6hD8uSidCcDTYm6jVo5B61C0v9YzkIr56CTi89pQs/rFFxkG4WEiYmUCIKAPz/2GFwOByRyJaaffT2aQpNF2kOZFLPLB7tn6JKDk0XCUFBIWejlHDiWhtMbgN0TgMuXOM1NUeJNR1mmCvPytHB5A/im0YwT3Y5TcrxyjkFZpgoObwB1JrH7nKGpWGWCdCWuKM/F5eU5MU1aexv78P1X9qHP6SMO4DgwWUbBTXhPxeULQHEK6j95AbB7AwnvAKUsDY2ci8zpnZ4hSmUsKtQlHClEIIShaSoyo/mBC2fiaIct5Ax24kS3A1uOixMjfvX+YSwrNmJOqAYpwAs40e2ISZtIGBosQ4vP0fRJdxyPFTRNoyxTjbLMOKOewp3KDX2oarehrtuBTltsp7IvwKPb7kW33YvKFkvM+8VyKFF3UwBwqM2K21/aC5WUxcXzs/Hz88uG/JxKWRo+ABxDDynSPBCeF7C91oT3DrRhT0MfOmyeuEXxHENBKWEh48RopsvHY1ddL7bV9OAX7x0e1s8cKRQFaGT9jplO0e+wRZ6LeqyJcuRUUnbUCvApigIT2peUpXFNRX7c7XheQLvVjTqTEye6HagziV3WdSYH+pLMSZcwVChlLiDACwgE4zeiAIAvKMDn8sedq5wIQQB6nT7squ+LibBxDAUFS4OmKHj9QXiHCPfRAFLJJbn9QRwcIFIf5AXQFGBQSmB2+VFvcuKxL2rw2Bc1KM/X4YryHFw8PweLiwxYf89q3Pnv/UDX4J8v5WhQFBDgxcjheN1gTAao8L/QAxoARYtlCzEIAniMnubmWDDhHcBTZcuwXEqaSopcnRylGSrMzhZHbBWmKJdCIKQCRVGh1I4W9503Aye67RFn8Ei7Dbvqe5Ev80NJiR2X5zy2Nen+GJoCS4cdw1BdTdRjlg7V1tD9r3EMBZahB7wnvD09uE4n8j0NCUOJ27Dh+p3BPzdmvzQNjhXfM/DnhZ0JlqWxIF+PBfmD6/g8vgD2N1uwt8mMo+1WNPa60GnzwB7VqSxmlwevDg5vAG/uacGbe1og52gsLtLjmoo8nDEtDenq4d+4hRskarvs2FzdjZ0netBm8aRcF+YPCqKAb3zt6mGhkrIxEbbwY02MIyeJcey0Ib21yXLTAIg3T3l6BfL0CqwtS495zeISI6aiY9jvILb0uUSnLjj4hl7CUMjWySHnaASCgNPnh8MThMuXYmMeBVCCeLbF29wfFOAPpt5EcrLDx3gBcUcUhkep/ffHR7GkyIDrFufh77cuxtNP7gTv80Mj4/DxHasSDh1w+4L4ur4Hu+v7cKzTjlazC70OH5y+1OvbE8FQYjQZAIL88GwQfeaOh28V+buHHgQBUTQzBU6maWosmNAO4G1/24LUEzqxhFO8eiWHbK0cRWnK0AxVHeZka8d0EgKBEE1phhp3naXGXWdNR0ufC+urOtG09TDAi4XGGhkbiWDE6y4MS2yk6oRMJBiaikQyuRgndYDDGfVcjk6OAoMCHCNG0SxuPywuH6xuPxzeINy+YNy6N7efx/baXmyv7R+5JecY6BQcMjVSLHD7QUPswrzg8W2whurovAEevsDJ1dElgqUpyCUMNDIWBqUUmRopjEoptAOcOd0AJ04TmiU+1dEpJKgoNKCi0BDzfHjCyoluB+q6nThhcqCu24H6Hgc8fh5NCaRTcrQypKmlYGgxemfzBGBz++HwBCJOiiAkdjw4GqAoGrzAx53xOx7wArC7oQ+7G8Qo5fUyPxSUGFF8alMt8g1i40x5vg7T0pVgGLHuVC5hcObMTJw5M/FVt97kwI5aEw60WNFgcqLT5obVLZZ0JPN1gkLyOsjwmR1vk4HPiROOWORoRRm0GVkazM3VQC3n0GP3wGTzweTwwOzyw+oOwOb2we4NwOUNwu0PwhMIwufn4QvyoXWWR5A/Nc7laIxSPJVMaAdwa4MT1yW5aecYCkalWKxdaFCKtRm5Wiwq1I94hAuBMJbkGxS4Y00JHtsrgd3uRYZaikP3nh95XRBEZ88fFODnefgD4qLlC331B8Wi/0Aw/Fj8GuB5+AICAjwf+3zUdoHQe/280L/fII9AaDtf9PaRbXj4ot4biByXEPqZ0cc1ePELO68e8MDwJ0+dNG5/EG5rEB1WD8pkQSgpcfrFwAaToaAgRpaUMjHCpldIkKmRIl+vQJ5eDr1SIqZSBzhxJzPukZAYGcdgZpYGM7M0Mc/zvIA2izsmalgXihr2On1ot3riTmfRyjkUGhVQSVkEeB4OTxAWtw8Wlw9uX//Ngdh0G9/zG61mmpMlcqxBHhuOdiXdloIYgQ1nGOQSBiopC4OSQ5ZWjkKDAjOy1bjrrFIUGeQR5zHMwChiS58TPQ5f0trK6GNMhQAvoM/pR5/Tj6p2Gz4+3Bk5doWEgVEpQZZWhhydHPl6OVRZanTaveiwuNFt86DXJcDtC8Lj508qQhdOC9ORjmKxmzj8GiA2/01kJrQDWKilYy4SF8/NxIXzclBRqI9p9ycQTleokLguywByjL4G3KlEEPprsZI5r2FnNOxwpuK8+kOOajzn1R/s37fV7ceJbgd6Hb4RXYwlDIUCowJnzsjANRV5KE5TnfIxUoTRg6Yp5BsUyDcosG5GRsxrZmd0OrnfQWwxu2B1+3FoQP0dIEq9lGWqka2TgaEouP0BWFyilqPFFasUcaqdP46hIOcY2DwBMDSFqxfl4oxpRpjsPjT0OFDb5UBTnwsYxghhAbEZBrs3gG67F/U9AGBJ+l4KUeUpLA2FhIFaxmFOtgZZOhmK0pSYnq6CnGPQ2OvAoTY76rod6LC6I9OuTsZmAgCnLwinz41msxuAeai3xEXC0FBKGWjlHIwqKTLVUuTo5Sg0KFGcrkShQYEMjTSpxutjjx2D3e6PSMlMVCa0A7j1gQvxl78chcPhR5ZGhnu/tXi8D4lAIKQIRYXSvRPAeRUEAe/sa8XzW+siHZXxUEgYzMhU4+xZGbhucf5JTTghTGz0SgkWKw1YXDQ4ndzQ4xzkGNabHPAGeBzvsuN41+CIcW6oW10r5+DnBdhcfvQ4vJFxnqNdDybe6Ig1j0FewFt7W9Hn9OMv1y6AVtGfAXvssUOw28VraOPvLhbf6w+grseJA81m1HY70dzninRhhyfd+IK82ESTJAUejQAxOhfgBXgCPGyeADptXtSm+PtQoQaLsc6aKiQ0sjQyTM9QYUmRAetmZmBaumrSTR8ZCRPaAQSGFlwkEAiEoaAoCtcuzse1i/Nhsnvx6IZqUFWHAIhdwD89ZzquXZyHnAkgxE4YX2Qcg1nZGszKjp9ODtcXRjuHfU4f2ixutFkGd/voFBwW5OtQZFSApSm4fEH0Or3osnkjQwRGy+nZeKwLZ/5lM1789lKU5+sSbsdxLGZmazEzWzus/QuCAIvLixPdTlS12VDTZUdjr0scJ+n2w+UPwh/gUxb0jt33+KTMXT4e9T0u1Pe4sOFoN/7waTUAQMIAWWopSrO0mJujwbISAxYXGiA9BeMWx4sJ7wASCATCaJKuluKRaxbgseZNsNt9MColuOecsvE+LMIEJzqdfOaAdHJfdDq52yE6iSZRFN3i8mNfkzhiMRpJaO7utAwVsjUy+HkeFqcPXTYv2i1udNs98ASG7xL1Of244pmdKDLIsfHeNcN6byDIwxxKafc6vOgNfe1z+tDj9KHP4UOvU3w+nPY+XfEFgWaLF82WbnxZ3Q182f+ahKGQrpJiYaEO58zKwqrSNKSppeN3sCOEOIAEAoFAIJwEBqUEBqUBSwakk92+UDrZFOUYdjtQ3+OELyA2Hw1sQKIoMZ28fFoaStKU0Co4ODwBdNk8OGFyoKnXBZcvOKTeXGOfG6W/2YDrZB4oKaDT5kHpA5/0h9lCyTVBENtYxir6xlDibGCWFrv+WVrs7GVoOvRVrHumQEGAAF4QxDGBvABfQIAnEIw0pvFBAcFxiBz6ggLarB60HerEx4c6B70etrknhWlI4wlxAAkEAoFAOAXIJQxm52gwOyc2nRzkBbSZ3ThhsqOu2xmJHp4wOWBx+dFqdqPV7MaW46aY9+kVHGZlaVCaoUKRUQmaplDTaceexl609LmH1NeLCSiOU4tyUACCAQFeBBFS1TttcSQYNDFRIA4ggUAgEAhjCEOL3eUFRgXOmhn7Wq/DGyNyHf7aanbD7PJjb5MZewekk6UsjbIsNQoMCtjcflS2WuDxTxBhwimMZILLPhEHkEAgEAiECYJRJYVRJcXS4sHp5PqewXqG9T1OeBOkk4ciWr9O/AqAokAJQmj0mahsF27QEIT+0Xric+IL0c8BE0P/cCIw0SWjiANIIBAIBMIERy5hIiMlownyAlrNrkGyNSe6HbC6kzdp8DFzzsJMgDzxacJEVzEhDiCBQCAQCJMUhqZQaFSi0KjE2bP6x7gJgoBepw8vPHcMXpcfeoUEf7u2IvTawL3EPhGvwWTgUwO3EUa0j6EdzOH+nJH83CHMgQDPw+Pj4QmIGokev/jY6+fhDQbhC/DwBnj4A0H4ggK4LhrgMeEn/xAHkEAgEAiE0wyKopCmkkLC0PBCrBM8f07WeB/WlOCxx3bBbvdioo/vntjuKYFAIBAIBAJh1CEOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgxJSGcY3DuzatQu7du2Cw+GAIAigKAoqlQorVqzAihUrxvvwTkuIzcceYvOxh9h87CE2H3uIzceeyWbzCTsL2Ov1wm63R74XBAF2ux1er3ccj+r0hth87CE2H3uIzcceYvOxh9h87JlsNp+wKWCpVAq1Wg2KEqcpUxQFtVoNqVQ6zkd2+kJsPvYQm489xOZjD7H52ENsPvZMNptP2BRwmMceewx2ux1qtRr33nvveB/OlIDYfOwhNh97iM3HHmLzsYfYfOyZLDafsBFAAoFAIBAIBMKpgTiABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUg01lI0EQYLfbT/WxxMXj8cDj8YDjONhstnE5hqkGsfnYQ2w+9hCbjz3E5mMPsfnYMxFsrlarQVFU0m0oQRCEoXZks9mg1WpH7cAIBAKBQCAQCKcGq9UKjUaTdJuUHMDxjADabDbk5+ejpaVlyF+GMDoQm489xOZjD7H52ENsPvYQm489E8HmqUQAU0oBUxQ17ieORqMZ92OYahCbjz3E5mMPsfnYQ2w+9hCbjz0T3eakCYRAIBAIBAJhikEcQAKBQCAQCIQpxoR3AKVSKR588EFIpdLxPpQpA7H52ENsPvYQm489xOZjD7H52DNZbJ5SEwiBQCAQCAQC4fRhwkcACQQCgUAgEAijC3EACQQCgUAgEKYYxAEkEAgEAoFAmGJMOAfw/fffx+LFi7F69WqsXbsWR44cSbr9jh07sHz5cqxduxbLly/H9u3bT3qfUw1i87GH2HzsITYfe4jNxx5i87Fn0tpcmEDs3r1bUKlUQnV1tSAIgvDSSy8Jubm5gs1mi7t9Y2OjoNFohM2bNwuCIAhbtmwRNBqN0NjYOOJ9TjWIzcceYvOxh9h87CE2H3uIzceeyWzzCeUAXnXVVcJ1110X+T4YDAqZmZnCU089FXf7e++9V1i6dGnMc0uWLBHuu+++Ee9zqkFsPvYQm489xOZjD7H52ENsPvZMZptPqBTwpk2bsGTJksj3NE2joqICGzdujLv9xo0bY7YHgCVLlsRsP9x9TjWIzcceYvOxh9h87CE2H3uIzceeyWzzCeMA9vb2wmq1IisrK+b5rKws1NfXx31PfX190u1Hss+pBLH52ENsPvYQm489xOZjD7H52DPZbT5hHECXywUAg5SzpVJp5LV470m2/Uj2OZUgNh97iM3HHmLzsYfYfOwhNh97JrvNJ4wDqFAoAABerzfmea/XG3kt3nuSbT+SfU4liM3HHmLzsYfYfOwhNh97iM3Hnslu8wnjABqNRmi1WnR2dsY839nZiZKSkrjvKSkpSbr9SPY5lSA2H3uIzcceYvOxh9h87CE2H3smu80njAMIAGeddRb27t0b+V4QBOzfvx/nnHNO3O3PPvvsmO0BYO/evTHbD3efUw1i87GH2HzsITYfe4jNxx5i87FnUtt8VHuKT5Ldu3cLarVaOH78uCAIgvDKK6/EaN98+9vfFr71rW9Ftg/r6WzdulUQBEHYtm2boFarB+npJNvnVIfYfOwhNh97iM3HHmLzsYfYfOyZzDZnR9edPDmWLl2Kl156CTfddBPkcjlomsaGDRugVqsBAB6PB36/P7J9YWEhPv74Y/z85z+HRCKB1+vFJ598gsLCwpT3OdUhNh97iM3HHmLzsYfYfOwhNh97JrPNKUEQhFHdI4FAIBAIBAJhQjOhagAJBAKBQCAQCKce4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDEmjQP4+OOPo7KyMvJ9c3Mz1q1bB5lMhqKiIvzgBz8AANx5550oKiqCTCbDunXrsGbNGixcuBDf/va3ceTIkXE6+onNmjVrcM4558R9zefz4YEHHgDLsmhsbBzbAzuNSWTzt956C+eddx7OPvtsLFmyBFdffTXq6+vH4QhPPxLZ/IMPPsAll1yCc889F6tWrUJFRQXeeuutcTjC049ka0uYp556ChRFYcuWLWNzUFOARHb/3e9+h/Lycqxbty7y7/LLLx+HIzz9SHauNzU14frrr8dZZ52F+fPno6KiAps3bx7jI4yDMEkoLCwU/vnPf8Z9/sEHH4x57sEHHxQKCwsj3wcCAeHZZ58V1Gq18P7775/S45xsNDc3CyzLCjRNC+3t7TGvNTQ0CMuXLxduvfVWAYDQ0NAwPgd5mpHM5hzHCRs2bBAEQRCCwaBw2223CdOnTxfcbvd4HOppQzKbn3/++cJLL70U+f7DDz8UaJoWDh06NNaHeVqRzOZh2trahIKCAgGAsHnz5rE9wNOUZHZ/8MEHiZ1PAclsbjKZhOLiYmHjxo2CIAgCz/PCddddJzz11FPjcagxTJoI4MnAMAx+9KMf4de//jVuueUWdHd3j/chTRhef/113H///RAEAW+88UbMaw6HA6+88gq+853vjNPRnZ4ks/nll1+O8847DwBA0zTuuusu1NbWYv/+/eNxqKcNyWz+0EMP4aabbop8v27dOvA8jxMnToz1YZ5WJLN5mB//+Md44IEHxvjITm9SsTthdElm80ceeQRLly7F2WefDQCgKAqPPvooLrnkkvE41BhG3QF85513UF5eDoqi8NFHH+HSSy9FcXExHnroIVitVnz3u9/FokWLcP7558NsNgMAKisrcdFFF2H16tVYtWoVrrzySrS2tkb2ed5556GzsxP/+7//i3Xr1uHBBx8c0bHdeeed8Hq9eOmll0bldz0deOedd3DfffdhxYoVeO2112Jemzt3LkpLS8fpyE5fktn87bffjvleJpMBEFPxhJGTzOYVFRVgWRYA4Pf78eijj2L27Nk499xzx+NQTxuS2RwAPvroI3AchwsuuGAcju70ZSi7E0afZDZ/9913sXbt2pjnCgoKUFRUNIZHGJ9RdwCvueYaPP744wCA2tpafPTRR9iwYQN++9vf4te//jWeeuop7N27Fw6HA08++SQAYMeOHZg5cya2b9+OHTt2YNGiRbj11lsj+/z888+RlZWFX/7yl9iyZQt+//vfj+jY1Go1Zs6ciT179pz073k6cOzYMWRlZcFgMODGG2/E3r17UVtbO96HdVozXJvv2rULOTk5WLly5Rge5elFqjb/r//6L6Snp2PTpk3YsGEDVCrVOBzt6cFQNnc6nfj1r3+Nv/71r+N4lKcfqZzrL774ItatW4eVK1fitttuQ11d3Tgd7elBMps7nU7U19eD53ncfPPNWLlyJc4991y8884743zUIqc0BXzdddcBAMrKypCWloasrCwoFArQNI0zzjgDBw4cAADccMMN+N3vfhfzvi1btsDtdo/6MWk0GlgsllHf72Tk3//+N2688UYAos1ZliV3jKeY4djc6/Xi0UcfxZNPPgmO48byME8rUrX5M888g97eXpx99tlYuXIlOjo6xvpQTxuGsvlvf/tb/PCHP0R2dvZ4HeJpyVB2LygowMKFC7Fx40Zs374dxcXFqKioQFtb23gd8qQnmc3DvsZvfvMb3Hfffdi5cyceeugh3HLLLXjzzTfH65AjnFIHMPrDrVAoYr5XKpWwWq0AxJz4gw8+iJUrV2Lt2rW47bbbIAjCKanVs1qt0Ov1o77fyciHH36Iyy67DACQkZGBs88+mziAp5jh2PwHP/gBrrnmGlx99dVjeYinHcOxOcMw+N3vfgdBEPDYY4+N5WGeViSz+YEDB7B792788Ic/HM9DPC0Z6ly//fbb8dOf/hQsy4Kmafz2t7+FTCbDs88+O16HPOlJZnOaFl2sSy65BIsWLQIALF26FFdeeeWEiH6zp3LnDMMk/V4QBADArbfeir6+Pnz++edQq9VobGxEcXFx5PXRwm634/jx4/j2t789qvudjOzatQvd3d24+OKLI891dXWhpqYGe/fuxeLFi8fx6E5PhmPzX/7yl2BZFg899NB4HOppQyo29/l8kEgkkddpmsb06dNx9OjR8TjkSc9QNv/ss8/gdrtx1llnAQA8Hg8A4Cc/+Ql0Oh1eeOEFUns8AkaypjMMg6KiIpIGHiFD2by8vBxSqRR5eXkx7yssLMSmTZvG+nAHcUodwFTZunUrfvGLX0CtVgOIX/Ae9qQB0ZELbzscnn32WchkMtx2220jP9jThNdeew0vv/xypOMUEO2amZmJ1157jTiAp4BUbf7www+jsbERr732GiiKwr59+wCIzQqE4ZGKzRctWoSqqqqY93V0dJC6yxEylM0fe+wx/Pa3v428Fr7hf/zxx7Fu3bpxOOLTg1TO9XvuuQdPPPFEzPva29uxevXqsT7c04JUbL5ixYpB5SRdXV0oKCgY68MdxISQgZkzZw62bNmCQCAAQOyaGUh6ejrMZjMCgQDKy8uHtf9gMIjnn38ef/zjH/Hvf/8baWlpo3HYk5ZgMIht27ZF2tLDqNVqXHbZZXjzzTfB8/w4Hd3pSao2f/755/HKK6/gnnvuwf79+7F371589NFHOHz48Dgd+eQlVZsfPXoUn3zySeT1V199FcePHyc3iiOArC3jQ6p2//DDD/Hhhx9GXn/hhRfQ3d2N22+/fawPedKTqs1/8Ytf4D//+Q8aGhoAiKLQ77//Pu6+++7xOOxYRltY8LPPPhMWLFggABDWrl0r9Pb2Cueee64glUqFGTNmCP/+97+Fv/zlL0JhYaGg1WqF66+/XqiqqhJWrVolzJgxQ7jsssuE+++/XwAgLFu2LCLG+vbbbwtlZWXCsmXLhKeeekpoamoS1q5dK0ilUqGwsFD4/ve/LwiCIPzoRz8SCgsLBalUKqxdu1ZYs2aNsGDBAuHb3/62cOTIkdH+dScdFotFWLp0qWA0GoW77ror5rUXXnhBKC0tjdi+urpaWLt2beTvuWzZMuGaa64ZpyOfvKRq80WLFgk0TQsABv2LJ4JOSMxwzvMnn3xSWLFihbBq1SphxYoVwhlnnCF8/PHH43Tkk5fh2Ly+vl4QBEG45557hGXLlgkAhAULFgjXX3/9eBz6pGY4dv/DH/4gnHnmmcK6deuEFStWCGvXrhW2bds2Tkc+eRnuuf7KK68ICxcuFFauXCksW7ZM+Mc//jFORx4LJQijXGhHIBAIBAKBQJjQTIgUMIFAIBAIBAJh7CAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDGIA0ggEAgEAoEwxSAOIIFAIBAIBMIUgziABAKBQCAQCFMM4gASCAQCgUAgTDH+PypKxTUPmSlTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'x01': 9, 'x02': 0.00016573093462398208, 'x03': 0.00012036287675561346, 'x04': 0.0004276128445439798, 'x05': 0.0004444864000682588, 'x06': 5.85337974327048e-05, 'x07': 0.0003333861934574846}\n",
      " {'x01': 47, 'x02': 0.00024253507397993762, 'x03': 0.00023641553272951406, 'x04': 0.00071466324103375, 'x05': 0.0007117554507808361, 'x06': 8.029759278731706e-05, 'x07': 0.0005416458877239553}\n",
      " {'x01': 12, 'x02': 0.0001322851526847699, 'x03': 0.00010969803884065733, 'x04': 0.0004322938034985269, 'x05': 0.00044595463337639585, 'x06': 4.5905079984675704e-05, 'x07': 0.00033312756119900557}\n",
      " {'x01': 5, 'x02': 0.00011527842826595673, 'x03': 0.00010121220938221489, 'x04': 0.0005786636495105379, 'x05': 0.0006130169696425168, 'x06': 7.013249312893402e-05, 'x07': 0.00046901946612206715}\n",
      " {'x01': 14, 'x02': 7.870454761845295e-05, 'x03': 0.00010105391471320717, 'x04': 0.00041611890658470855, 'x05': 0.0004745896270614256, 'x06': 3.4724891705019063e-05, 'x07': 0.00032834657801187315}]\n"
     ]
    }
   ],
   "source": [
    "from pymoo.visualization.pcp import PCP\n",
    "X3 = res3.X\n",
    "F3 = res3.F\n",
    "G3 = res3.G\n",
    "Z_res3 = [[d['x01']] for d in X3]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "labels = list(res3.opt[0].X.keys())\n",
    "labels_x = ['matID', 'A1', 'A2', 'A3', 'A4', 'A5', 'A6']\n",
    "bounds = np.array([problem.vars[name].bounds for name in labels]).T\n",
    "\n",
    "\n",
    "X3 = np.array([[sol.X[name] for name in labels] for sol in res3.opt])\n",
    "\n",
    "plot1 = PCP(labels=labels_x, bounds=bounds)\n",
    "plot1.set_axis_style(color=\"grey\", alpha=1)\n",
    "plot1.add(X3)\n",
    "\n",
    "plot1.show()\n",
    "plt.savefig('./figures_Integer/PCP.png',bbox_inches='tight', dpi = 200)\n",
    "\n",
    "\n",
    "# Maybe the solution is limited by the limits in areas. Example, you want to minimize the compliance but\n",
    "# you have a maximum area, so you can't reduce the compliance further\n",
    "\n",
    "upper_bounds = bounds[1, 1:]*0.999\n",
    "lower_bounds = bounds[0, 1:]*1.001\n",
    "\n",
    "\n",
    "\n",
    "# Check if each row of X is within the limits\n",
    "is_within_limits = np.logical_and(np.all(X3[:,1:] >= lower_bounds, axis=1), np.all(X3[:,1:] <= upper_bounds, axis=1))\n",
    "\n",
    "# Print the results\n",
    "if np.all(is_within_limits):\n",
    "    print(\"Solution not limited by the areas\")\n",
    "else:\n",
    "    print(\"Solution limited by the areas\")\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "names = []\n",
    "types = []\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "legend_dict = {}\n",
    "material_name_dict = {}  # Dictionary to map material types to material names\n",
    "\n",
    "for xData in Z_res3:\n",
    "    matname = dataIdentifier['name'][int(xData[0])]\n",
    "    mattype = dataIdentifier['className'][int(xData[0])]\n",
    "    names.append(matname)\n",
    "    types.append(mattype)\n",
    "    material_name_dict[mattype] = matname  # Populate the material name dictionary\n",
    "\n",
    "unique_labels = list(set(types))\n",
    "\n",
    "# Define specific colors for each material type\n",
    "mat_colors = {\n",
    "    'Steel': 'slategrey',\n",
    "    'Aluminium Alloys': 'violet',\n",
    "    'Plastics': 'lawngreen',\n",
    "    'Titanium Alloys': 'indigo',\n",
    "    'Copper Alloys': 'chocolate',\n",
    "    'Composites': 'gold',\n",
    "    # Add more material types and corresponding colors as needed\n",
    "}\n",
    "\n",
    "for i, mattype in enumerate(types):\n",
    "    if mattype not in mat_colors:\n",
    "        mat_colors[mattype] = plt.cm.get_cmap('jet')(i / len(set(types)))\n",
    "\n",
    "# add scatter plot for each unique label\n",
    "for i, (x, y) in enumerate(F3):\n",
    "    material = names[i]\n",
    "    mattype = types[i]\n",
    "    color = mat_colors[mattype]\n",
    "    label = material_name_dict[mattype]  # Use material name as the label\n",
    "    ax.scatter(x, y, color=color, label=label)\n",
    "    if mattype not in legend_dict:\n",
    "        legend_dict[mattype] = ax.scatter(x, y, color=color, label=label)\n",
    "\n",
    "# set legend colors and remove duplicates\n",
    "handles, labels = [], []\n",
    "for label in legend_dict:\n",
    "    handles.append(legend_dict[label])\n",
    "    labels.append(material_name_dict[label])  # Use material name as the label\n",
    "    legend_dict[label].set_color(mat_colors[label])\n",
    "\n",
    "ax.legend(handles, labels, loc=\"upper right\")\n",
    "\n",
    "# show plot\n",
    "plt.title(\"Objective Space\")\n",
    "entry_order = ['cost', 'mass', 'co2', 'water', 'energy', 'compliance']\n",
    "enabled_entries = [entry for entry in entry_order if objectives[entry]['isOn']]\n",
    "ax.set_xlabel(enabled_entries[0])\n",
    "ax.set_ylabel(enabled_entries[1])\n",
    "plt.savefig('./figures_Integer/Integer_compliance_mass.png', bbox_inches='tight', dpi=200)\n",
    "plt.show()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18e911a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}