{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Navier Plate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we model a so called Navier plate, simply supported on all edges, under the effect varius loads. We run run a linear analysis and do some plotting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a COM interface to a new instance of AxisVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axisvm.com.client import start_AxisVM\n",
    "import axisvm.com.tlb as axtlb\n",
    "axvm = start_AxisVM(visible=True, daemon=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "axisvm.com.axapp.IAxisVMApplication"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axvm.__class__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new model and set the working directory. An empty string means the directory where AxisVM.exe is located:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelId = axvm.Models.New()\n",
    "axm = axvm.Models.Item[modelId]\n",
    "wdir = \"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set Eurocode as the standard and \"C16/20\" concrete form the material library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "axm.Settings.NationalDesignCode = axtlb.ndcEuroCode\n",
    "matId = axm.Materials.AddFromCatalog(axtlb.ndcEuroCode, \"C16/20\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The definition of a geometry of a model follows a hierarchical workflow. This means, that we cannot directly define the domains. Instead, we first define the nodes that make up the lines, with which finally we define the domains."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the beam as two touching domains, which makes up for a total of 6 nodes. We also store the indices of the defined nodes as a reference for creating the lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from axisvm.com.tlb import dofPlateXY\n",
    "\n",
    "Lx, Ly = 8., 6.  # side lengths\n",
    "t = 0.2  # thickness\n",
    "\n",
    "# the plate is in the x-y plane\n",
    "# the origo is located at the left bottom corner\n",
    "coords = np.zeros((4, 3))  # we have four points in 3d space\n",
    "coords[0, :] = 0., 0., 0.\n",
    "coords[1, :] = Lx, 0., 0.\n",
    "coords[2, :] = Lx, Ly, 0.\n",
    "coords[3, :] = 0., Ly, 0.\n",
    "\n",
    "fnc = axm.Nodes.AddWithDOF\n",
    "nodeIDs = list(map(lambda c: fnc(*c, dofPlateXY), coords))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the lines that make up the domains and store their indices in a `list`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_of_lines = [[0, 1], [1, 2], [2, 3], [3, 0]]\n",
    "LineGeomType = axtlb.lgtStraightLine\n",
    "lineIDs = []\n",
    "for line in nodes_of_lines:\n",
    "    lineIDs.append(axm.Lines.Add(nodeIDs[line[0]], nodeIDs[line[1]],\n",
    "                   LineGeomType, axtlb.RLineGeomData())[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are in a position to create the domains by providing lineIDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axisvm.com.tlb import RSurfaceAttr, lnlTensionAndCompression, \\\n",
    "    RResistancesXYZ, schLinear, stPlate, RElasticFoundationXYZ, \\\n",
    "    RNonLinearityXYZ\n",
    "\n",
    "sattr = RSurfaceAttr(\n",
    "    Thickness=t,\n",
    "    SurfaceType=stPlate,\n",
    "    RefZId=0,\n",
    "    RefXId=0,\n",
    "    MaterialId=matId,\n",
    "    ElasticFoundation=RElasticFoundationXYZ(0, 0, 0),\n",
    "    NonLinearity=RNonLinearityXYZ(lnlTensionAndCompression,\n",
    "                                  lnlTensionAndCompression,\n",
    "                                  lnlTensionAndCompression),\n",
    "    Resistance=RResistancesXYZ(0, 0, 0),\n",
    "    Charactersitics=schLinear)\n",
    "\n",
    "_ = axm.Domains.Add(LineIds=lineIDs, SurfaceAttr=sattr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Case 1 - Constant distributed vertical load on the whole domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcid1 = 1\n",
    "axm.LoadCases.Name[1] = 'LC1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "qz = -1.0  # load intensity\n",
    "\n",
    "LoadDomainConstant = axtlb.RLoadDomainConstant(\n",
    "    LoadCaseId=lcid1,\n",
    "    DomainId=1,\n",
    "    qx=0,\n",
    "    qy=0,\n",
    "    qz=qz,\n",
    "    DistributionType=axtlb.sddtSurface,\n",
    "    SystemGLR=axtlb.sysGlobal\n",
    ")\n",
    "_ = axm.Loads.AddDomainConstant(LoadDomainConstant)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Case 2 - Constant distributed vertical load over a rectangular patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcid2 = axm.LoadCases.Add('LC2', axtlb.lctStandard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axisvm.com.tlb import RLoadDomainPolyArea, dtGlobal, \\\n",
    "    ldtConst\n",
    "\n",
    "xc, yc, w, h = 2*Lx/3, Ly/2, Lx/5, Ly/3\n",
    "qz = -2.0  # load intensity\n",
    "\n",
    "# origo is located at the left bottom corner\n",
    "patchlines = axvm.ObjectCreator.NewLines3d()\n",
    "patchline1 = axtlb.RLine3d(\n",
    "    LineType=axtlb.ltStraightLine3d,\n",
    "    P1=axtlb.RPoint3d(xc-w/2, yc-h/2, 0),\n",
    "    P2=axtlb.RPoint3d(xc+w/2, yc-h/2, 0)\n",
    ")\n",
    "patchline2 = axtlb.RLine3d(\n",
    "    LineType=axtlb.ltStraightLine3d,\n",
    "    P1=axtlb.RPoint3d(xc+w/2, yc-h/2, 0),\n",
    "    P2=axtlb.RPoint3d(xc+w/2, yc+h/2, 0)\n",
    ")\n",
    "patchline3 = axtlb.RLine3d(\n",
    "    LineType=axtlb.ltStraightLine3d,\n",
    "    P1=axtlb.RPoint3d(xc+w/2, yc+h/2, 0),\n",
    "    P2=axtlb.RPoint3d(xc-w/2, yc+h/2, 0)\n",
    ")\n",
    "patchline4 = axtlb.RLine3d(\n",
    "    LineType=axtlb.ltStraightLine3d,\n",
    "    P1=axtlb.RPoint3d(xc-w/2, yc+h/2, 0),\n",
    "    P2=axtlb.RPoint3d(xc-w/2, yc-h/2, 0)\n",
    ")\n",
    "patchlines.Add(patchline1)\n",
    "patchlines.Add(patchline2)\n",
    "patchlines.Add(patchline3)\n",
    "patchlines.Add(patchline4)\n",
    "\n",
    "RectPatch = RLoadDomainPolyArea(\n",
    "    LoadCaseId=lcid2,\n",
    "    DistributionType=dtGlobal,\n",
    "    LoadDistributionType=ldtConst,\n",
    "    Component=2,  # z direction\n",
    "    P1=qz,\n",
    "    WindowLoad=True\n",
    ")\n",
    "\n",
    "_ = axm.Loads.AddDomainPolyArea(patchlines, RectPatch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Case 3 - Vertical concentrated load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcid3 = axm.LoadCases.Add('LC3', axtlb.lctStandard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from axisvm.com.tlb import RLoadDomainConcentrated\n",
    "\n",
    "Fz = -10\n",
    "xF, yF = Lx/3, 2*Ly/3\n",
    "\n",
    "concload = RLoadDomainConcentrated(\n",
    "    LoadCaseId=lcid3,\n",
    "    DomainId=1,\n",
    "    Fz=Fz,\n",
    "    x=xF, y=yF,\n",
    "    ReferenceId=0  # global\n",
    ")\n",
    "\n",
    "_ = axm.Loads.AddDomainConcentrated(concload)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lcomb1 = axm.LoadCombinations.Add('ULS1', axtlb.ctULS1, \n",
    "                                  [1.5, 0, 0.9], [lcid1, lcid2, lcid3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add two hinged supports at the ends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Stiffnesses = axtlb.RStiffnesses(\n",
    "    x=1e12,\n",
    "    y=1e12,\n",
    "    z=1e12,\n",
    "    xx=0,\n",
    "    yy=0,\n",
    "    zz=0\n",
    ")\n",
    "NonLinearity = axtlb.RNonLinearity(\n",
    "    x=axtlb.lnlTensionAndCompression,\n",
    "    y=axtlb.lnlTensionAndCompression,\n",
    "    z=axtlb.lnlTensionAndCompression,\n",
    "    xx=axtlb.lnlTensionAndCompression,\n",
    "    yy=axtlb.lnlTensionAndCompression,\n",
    "    zz=axtlb.lnlTensionAndCompression\n",
    ")\n",
    "Resistances = axtlb.RResistances(\n",
    "    x=0,\n",
    "    y=0,\n",
    "    z=0,\n",
    "    xx=0,\n",
    "    yy=0,\n",
    "    zz=0\n",
    ")\n",
    "_ = axm.LineSupports.AddEdgeRelative(Stiffnesses, NonLinearity,\n",
    "                                     Resistances, 1, 0, 0, 1, 0)\n",
    "_ = axm.LineSupports.AddEdgeRelative(Stiffnesses, NonLinearity,\n",
    "                                     Resistances, 2, 0, 0, 1, 0)\n",
    "_ = axm.LineSupports.AddEdgeRelative(Stiffnesses, NonLinearity,\n",
    "                                     Resistances, 3, 0, 0, 1, 0)\n",
    "_ = axm.LineSupports.AddEdgeRelative(Stiffnesses, NonLinearity,\n",
    "                                     Resistances, 4, 0, 0, 1, 0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meshing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define a triangle mesh, fitted to both concentrated and distributed areaa loads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "MeshParams = axtlb.RDomainMeshParameters(\n",
    "    MeshSize=min([Lx/10, Ly/10]),\n",
    "    MeshType=axtlb.mtUniform,\n",
    "    MeshGeometryType=axtlb.mgtTriangle,\n",
    "    IsFitToPointLoad=True,\n",
    "    FitToPointLoad=0.,  # fit to all loads\n",
    "    IsFitToSurfaceLoad=True,\n",
    "    FitToSurfaceLoad=0.,  # fit to all loads\n",
    ")\n",
    "axm.Domains[:].GenerateMesh(MeshParams);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the use of the semicolon here. This simplifies carrying out the same operation over a range of domains. (the colon at the end simply suppresses the output)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We save the file and run a linear analysis, with all warnings suppressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpath = wdir + 'Navier.axs'\n",
    "axm.SaveToFile(fpath, False)\n",
    "axm.Calculation.LinearAnalysis(axtlb.cuiNoUserInteractionWithAutoCorrectNoShow)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with AxisVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot vertical displacements of the previously defined load combination. At the end we save the plot to a file in the working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we want the domain to fill up the screen entirely\n",
    "axm.View = axtlb.vTop\n",
    "axm.FitInView()\n",
    "\n",
    "# turn off the grid\n",
    "GridOptions = axtlb.RGridOptions(DisplayGrid=False)\n",
    "axm.Settings.SetGridOptions(GridOptions)\n",
    "\n",
    "WriteValuesTo = axtlb.RWriteValuesTo(\n",
    "    Nodes=False,\n",
    "    Lines=False,\n",
    "    Surfaces=True,\n",
    "    MinMaxOnly=True\n",
    ")\n",
    "\n",
    "BasicDispParams = axtlb.RBasicDisplayParameters_V153(\n",
    "    ResultComponent=axtlb.rc_d_eZ,\n",
    "    Scale=1.0,\n",
    "    DisplayMode=axtlb.dmIsosurfaces2D,\n",
    "    DisplayShape=axtlb.dsDeformed,\n",
    "    WriteValuesTo=WriteValuesTo\n",
    ")\n",
    "\n",
    "ExtDispParams = axtlb.RExtendedDisplayParameters_V153(\n",
    "    BasicDispParams=BasicDispParams,\n",
    "    DisplayAnalysisType=axtlb.datLinear,\n",
    "    ResultType=axtlb.rtLoadCombination\n",
    ")\n",
    "axm.Windows.SetStaticDisplayParameters_V153(1, ExtDispParams, lcomb1, [])\n",
    "\n",
    "axm.Windows.ReDraw()\n",
    "imgpath = wdir + 'Navier_ez.bmp'\n",
    "axm.Windows[1].SaveWindowToBitmap(axtlb.wcmColour, imgpath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AMmGmVxpRdOzyZqdPN1YMfB8IgiAIgiAIYqXOZnqlEWVU5IlXGrWfXdq4+eQhItq8fCy/9YlmvEw1e3b6/cvH/qy9lS/aYPP1Y9EzUfRMFD2Tvqx3GinJ8h6/XdfXAEEQBEEQFLB6D/Gomrr2TE8vx80T7T+9efnZhemn40MSPJQaa1ppRMcui3+c+OqlZ6cub5548lDbHQ2kzct/Njb7C6J7L/WxwTuNsbcf3/jGzUNEm68fG3t1JX70BBHRP15OPujv23V9DRAEQRAEQSXQAH6aswU3lbox5ALEze+//Mb008UW9MQj07XXNogOEdGhzz5OU9/ffHIgN735+rGxtx/f+IP8Fx968g/iJ9+/fOy77b60zQaj94ldHbrnY9RH/GHh3rq+BgiCIAiCoBJIuM8rV670+yXlVlczvVCLFuQ/cmXqjVtvTD9S7KVFzbqZ/OvQfR97I3XWnnV/c+P9Y9EzNSKiey9t/EH68v+mFv2N3Gh6+RudCvIQBEEQBEEV0wATWDgPbdm7d6+R/Qzb5pHTG7Nj0SwRiYYPI3700Cdvxp80sSOh9y9Pvfd0/I0TRLT5+rGp1z9785OHiAraPDZfPzb2ozeIhOc23aICQRAEQRAEBS/jOdOtnmmmWvnx7Mfuj8XHhz759Mee+ZOVTxa/XTBs4iEIgiAIgqCyachovLHDDy281ktOx+a7P3vo8Nhg32Tz9WNRP1kZK69G0attX9TY3ocW/kO6t3deW7h30JcFQRAEQRAEmdN2szYqVGtu97rB2tlRqbNredhmV+bU1Uwv1DpF4x367OM9uenN779Mj3/WeMF68/KfRdH87Bu/mB17Jjr2unDIK6/9zUOXPnWi3QaHPnlzeXR2TETgvUzLsn/jb2rdovEKv10hhCAIgiAIgvrRdnPm3OGXdnZ2dnZeOnxuJu+BCzfY3qTz6zs7Ozs7O+vnb50Sdnpt5VYKi3dlUlEcx/If7//qg3s+3G/jx0ojevZwl9aOlUb02iP99V4PrPcvH/suXUGLMwRBEARBkDFduXKlVqtRn+v2+liAuN2szdD8cmN/7h+9brB2dvTi2HqWEa2drW3O6JD6/UE6aPie6RPN5deisWPU1k+vNKIaLceusjHeu0Uf/SqcNARBEARBkAVxDOhYOzt66iodOZ9z0rTdvHj18Jk5m9/cxDjxE8047lCZPtGMHRWliYjo/maSzgFBEARBEARxVdcO6d41Prezs7MzTzOtrmkiIlo7e/T6yfW58eH23kXG0zwgCIIgCIKg0qr3oYZTU1Odytj7G8s7jdY/h+9r3n/85JHrm9s0njaCHL1+Ml+rNi6YaQiCIAiCIKgP/YMvfKGn7foaf7j/+Ek6Or/WmBsnWps/RyfXhQteOzt6il7amRsv3GC7WZs/tCxqz9s3rr95+Mx+5YuW7dakhXQz/f6vPnDwXSFN/a/7hCAIgiAIKpP2N+bP146OjhIRHTm/Ppd46ZWrR86vj7fbYP/xk7cSRHTk/PryOIlGaSI6NXo12fUTL+3Y6/WAh4MgCIIgCIIYSGv8ICLa3rx15OTM/vYbFKBCZk8mFiBCEARBEARBkHltbdDJ47a7noeTCTN948t793yqudXLpj84s+dDZ24Y+J4QBEEQBEFQ2TU+Z38F4ZDqbKZ/cGbPh/Yq/xX54B+cefTtZ3/648YBohtf3vuVHyR46/ma4rDXviK+9jMX7y7Ro19ey+9mcL3ffPhbHa18boOtm7U9z+3d89zePc+lP9HPz6Qky3v8dl1fAwRBEARBEFRCde2Z/tJ37v5ph47trfk/vvrFf/XeASIiOnT/x1+4vvb8Z8aJaPOdv6L/SCubjZlDRJubP/vt+2bFV3xm5sIf/+H85viMgTDorflvHX1qh2j02T42+PmZB26d/OnXlw8Qbd2sPfDa2t1HxomIPvqd5IP+vl3X1wBBEARBEFQC9R6KN8yXONNUX2Ej7TXkAsTN1X/7H5/42meSfx2onfzEdze3aPwArX3v7WdfvXj9e5tEh2hr+Tr93r85kG514vfoS8tbM1850G6vmrZu1h64dfKnv9/IfcGBmd9/b+b95sN/0e5L22yw95DY1YF77qc+pvgU7q3ra4AgCIIgCCqBhPu8cuVKv19SbnU109/+/J5vy3888eqvLx5XP7v57k++VGuRQxP/nP5wZbMxQ5s/++ih5++7/9Hra89/Zv/Kd+mfv9Bywgfuu/8n17eJejXThvWRiz99v7bnuc8TEY0++9PfT6vRb39+z9tyoyde/Xr2J4UgCIIgCKq0BhgkznH2eKq9e/ca2c+wbR6aDox99K++t0lb717/2Mll+gx98Y83t4h+/h/v/9wQTR0Hji3fPTb4l+t6v/ml987c/fo4EW3drH3p5sSPjh0gKmjz2LpZe+Df/xWR8Nz5ujgEQRAEQRBUcZnOmT5+8olHr699jugjNSLa/xG6uPL8/S98qfa84e8zuG785dMfuz95l3Tg2JmPPTd/41hxEdqwiYcgCIIgCILKpiGj8Q7d94lvL9/QyecfffvkiUMk2qP/7Xff+cT9mUyTrXd10llbN2t7+snKuPHa3j2vtQ0MObT34y/8ON3bz5dfGL3PwEpICIIgCIIgaDhtN2ujQrXmdh8btPDZxACunR2VOms0RS6vrmb625/vFI13aOKf//bV7/1AI0QfTVf43Xf/T/4j/fOa2iKxtfJdjQysrflv7d3TfPonO08/8Nzeh28Kh7z2vbc/fuF3xtttcODY8qt7n35AROB9l16V/Rtvf75bNF7htyuEEARBEARBUD/abs6cO/zSzs7Ozs5Lh8/N5O108QbbzdrR6yfXd3Z2dnbSoeHbm3Q+Ievnb52ya6ejOI7lP97/1Qf97+EHZ/b88X0iZ7rX7a/X+unD7lPvNx/+C/p2YC3O93wYc90hCIIgCOKrK1eu1Go16nPdXh8LELebtRmaT0a0ZP7RcYO1s6MrJ1ITXaC1s6MXx9aLJr84W4DYVZ+5+Or1vQ98inry0z84s2eSXv21NSdNRP/9XTo8E5SThiAIgiAICkW8Ajq2N2/R1aujV8W/jpxXbPPa2dFTV7PIikyMEz/+p+/d7bEy/ZmLd39tOXLuIxeTdA4IgiAIgiCIq7p2SPeoI2lHh9YdMj63s7OzM08zlrum0V0AQRAEQRAE9arehxpOTU11KmPvbyzvNFr/HMZPJxo/8cSplS0itRC9//jJI9c3t2ncWnUaZhqCIAiCIAjqQ5/8whd62q6v8Yf7j5+ko/NrjblxorX5c3RyXdjftbOjp+ilnbnx4g0USGsrV4+MzRBtN2vzh5ZFH/X2jetvHj5js89DN9NYCQdBEARBEAQ51/7G/Pna0dFRIqIj59fnEi+9cvXI+fXxthvsbyy/dHZUQHripZ2GcNi3ku1EH7XN1XqoTEMQBEEQBEEcpDV+EBFtb946cnJmf/sNSDRHz3XbkT2ZWIAIQRAEQRAEQea1tUEnj1sN4xhaJsz0SiOKjl3e7PtrGisGvjkEQRAEQRBUUo3PWQ62G16dzfRKI8qoyP6uNGo/u7Rx88lDPW4vdKIZL1NtWDu90ngmisR/f1bs5jdfP5bfoAC+04jkrp6Jomfav7L3Lx/LfK/urwGCIAiCIAgqqbr2TE8vx80T7T+9efnZhemn40O9bq/oxFcvPTt1efPEk4e6b1usd1772cMb8ScPEdHKq9HU65+9+cnsvt6/PPWjjy1/4+YJdYNCSET/eDl+tPMr37z8Z2OzvyC691IfrwGCIAiCIKgM6j0Ub5gvcaapvsJG2mvIBYib33/5jemnu5nnlUZUW0j/0XLbhz77OE19f/PJbm568/VjY28/vvEHue3ub95MPzxx//Sz7+e+8J2X6eEr4pud+NSlZ7/7/c1PPklFsDf/e+jJP4iffP/yse/28RogCIIgCIJKIeE+r1y50u+XlFtdzfRCLZI+OF923rj1xvQjJzpuP3b5WO1nlzbitA/ktda2h+772BuvbRANX8rdvPwfFj72vzaH2sff1KK/kf+YXv5GjwV2o68BgqDKK4q6bxPH9l8HBEGQrgEGifOaPZ7V3r17jexn2DaP7tuv3Hpj+umbw/jlQ5+8GX+y0wYrr469/NGNm/cP8T2ooM1j8/VjYz96g4jo3ksFdXGTr2HhSA+3TwiCKqBpooUHO27wFq4YEAR50N/5v7/o+yUwVfA505uX/2zs5Y9u2OhU7mrizb2G6bcG/UoIgkqnrhcEXDEgCHKvPno7KqYhzfTY4YcWXltpnuhQu1Y32Xz3Z0SPyE9tvvuzhw5/tes3adszTSuNZ2r0eKzVg1dejWq0HD964tD9j9P8n6x8snmCaOXHs/TRjUNEVAgHV/Fr6FOdC1EQBFVH02/1UJnGFQOCIOf6Ow6+x3azdvTcm0RibGFBJF7hBmtnR09dJSKiJ17aETPEFdZhb4Y0ZM/0oc8+/tBsxk3nt3/yyqVjY6IL8KGHHqKPyc9ufv9levzK4FZ28/VnF4joZfkNRaPzymt/89ClmRNERPc8eeXhY2PPRERE917aePRQW0i99EynaR5EY8/MPvTwxs1PHmrzGvrV1ltERN9cXPxavZ7/LDg4eHU4LS5OF/JUXyMipTLN7fWDg4OXlQ/ZTduDtpsz5w6/tLM8TrR2dnSmeVw3wEUbbDdrp+ilnZ1xIlo7O1prStvcctaWFcXKQpb3f/XBPR/ut1a90oiePZzkTPey8WuPJIZc/dig3r987Lt0pVuLMzN9LYp4njng4ODg4ODg4ER0/4sv1mo16nPdXh8LELebtRmaT6xw5h+dNtg6O7pyInXNrS3WVNxGzhYgdtWJ5vJr0dgx6tVPC600ohotx6adNBG9d4s++tWgnDQxOEPAwcHBwcHBwXvhvAI6DowdubqyNjeet81XT43qzR92ZGIB4olmzzFNJ5qJf+7ja/qUEvwcjvicIeDg4ODg4ODgHbgxaQ3Qg+1kf2P+fO3o6Gj67yPniYhofG5nZ05+l7NrNu108GkeZRW3MwccHBwcHBwcnPoZajg1NdWpjL2/sbyjWOjtHvfaYTdrZ0dXDmkLDfcfP3nk+uY2jTtbgNjXVBvIkridOeDg4ODg4ODgUlN//YUOn1W262f84f7jJ+no/Fpjbpxobf4cnVxvhXXQSztz4+02SLTdrJ26dX59TnzNWZoTxejtG9ffPHzGmpOmvJn+7O+dtvjdoDZ65wtfkB9zO3PAwcHBwcHBwe1L7dg4cn59LvHSK1ePnF8fb7+B7BZRAvAOjN2SrR9Hzq8vW031QJsHL3E7c8DBwcHBwcHBv7m4eOWDD/LcsLTGDyKi7c1bR07O7G+/Qc/Mmn7D1TeCuovhmQMODg4ODg4OXsidaGuDTh632aQxvCpqpte+8tzePc/t3fOt5paR/f38zJ7n9u55bu/DNwfeH7czBBwcHBwcHBzcq5MmovE5i7MLzaiHNo+t52sPnPmr1r+/9J27f+pkoMxA2rpZe+Df/1WWPfHq1y8e1zcshIPqIxfvfv3ijdf2fnPAr+d2hoCDg4ODg4OD+3bSYaibmb7x5b2Pvv3sT3+9fCABW/Of+sP5zfEZrmNRDhxbvntM/mtr/ltH/+3hGWOm2ZZYnSHg4ODg4ODg4HneeyjeMF/iTFN9hY20V2cz/YMzj7797E9/3DjQQgdmfpz8Vm58ee+j3xYfPvHqry8eJ6IfnNlznb747asvEBF9/MI7Z35+/+fTj5dnDuU3WO7FlG/drD1w6+RPf199Gcqnkjr0xy80lmfuyXz2xmtHn9r7nbvH8l+X1c/P7HmHvvh2+qoaZ37eTF+22GfXDcyLz5kDDg4ODg4ODk6p++wrRtmUYeWsjmZ66913PvF7M4VWdOv52qNvP/vTXzcOENEPzuz5VDPx3N+mz/36vedFc8j9y6/Kjy+uzYjmkHQD+sGZPV9snvhxgUXuVT8/88B7Z+5+fZyI6P3mw3/RPKEY7q2btUfpO3cf6a0j5W363Nffe1648+byq/Ljv1ybEXvouoFJsTpzwMG78gtERBQvLkZF24NXma8STXB6Pe34UznO5/wCB+fDBxgkzmv2eFZ79+41sp9Bo/E23/mrL/6rtPfjM7UvTi5vEh0goi/VRE/Fgfvuz3z8TvqVKaTP1L44eXFls9G1OJ3t3GjpxjtX6e2re96W4InkNbSK2T3b3I+mL/We+zMfv9fzBsbE7cwBD5dfULhVR7JK9OnFxR/W66u57cHBV/vc3gtv5/i1T8Gpg1eZQ+3U0UwfuO/+n/zx6tZXhiged9H9Y8P1Xn/x9957/iM5+vMz7dpC2IvbmQNujztwutI6uHEk+dcDDh4K7+D4hz+PXNbmn+JxfQMvJXeRMx2mOlemPzNz4Y+PPvApUtumb3y5tnFm+cT9H3/hj5uzn2kcINp6/uILv31ytt9v/YPlF377vlkiorWvfOjztPTe858p3rBdz/Tx+5949LtnPpeEcmzNf2t+7PcvHn+/+fB36dWvw0mDM3fGcLrg4BXhjmvz2nVGWPn8dUnUyzlcP8FD4fe/+GL+U4Ylhxmq0wwLNpqh+dZn186OnroqPky/qrWfRE+8tDNnLYquS5vHgZkfv3fi+doDH3q6xb70nbuHiL6y/FOS/IlXf92zef325/d8m/r+qkJ95OJPf7f2wHNpw8tHv3OX6MZfPv0Tokefu6psZzQFz5Z4njmV4h5rwPn9g4ODgw/GtetMuy6sCRO1cDjyKnML2m7OnDv80s7yONHa2dGZ5vGcnU5N8pHzLba2cuv8+k5jPylflRmAuHZ2dOWEzVDnKI5j+Y/3f/WBxW9FJNI8agxiqte+8tzy50w77Buv7f3m3vUfdQ0PKdA9r/05qzOkBLzfOwTDOyI4ODg4cz6wI0cfeYj8/hdfrNVq1Oe6vT4WIGYqznr5ud12Wa2drW3OZD/TYWvfCxDD19VHn7tKo8+aaa3++Zk9371KRJ/43cG+3vsZEhbvJTsCtWFwcHBw2zzueF0duI9cWm0+9x1wVWwDOrabF68ePjOXYWvz5w6f2bE7QtGxmf7MxbttGqPdavz5r7/3vMH9feTi3a9fNLg/fmeOYy5bkOPFxa/V6xeyGw+cHZH/vuDg4ODg7nnnPnLZ5/3D7PUfXSUcuElpHdJD7m3t7NHrJ9eXx7Ps1K3z63PtvsSQqluZ5ixuZ45xrpnjfI1ZXFXFFfbTRJ/Obs/hTgAODg4ObomvKlwz2R2eSfK835WMC/U+1HBqaqpTGTvT2Uy03eNeC7TdrB29flJfsrjdvHj1CdtlaYKZZihuZ44Nx9z72vb8/sHBwcHBq8k7dJVgxaQDrmrqH3yh8wZyw942IyKi/cdP0tH5tcbcONHa/Dk6uS5s8NrZ0VPUIY0j+fyy/vm1+XN0ft3BOj3dTN/zYdhrn+J25gzQlQHHDA4ODg7umPfY9ZevbaM/u3duP2d6f2P+fO3o6CgR0ZHz63OJl165eiT1xK3GkKOj50QO3nbz4lUiOjWa5rglKXhJh4f1sjTl0zxgpr3oa1FELM+cHlf+qV0Z+f2Ag4ODg4Pz5IU53HDYhVymeSwvL/damf50PPRqxQ5pHMMKaR5lE8MzZ4Cp0fn9gIODg4ODs+WF9zVMuumFu9LWBp2ccVFfHlww0yzE4QzRZvjBMYODg4ODV5P3PummsjVshxqfyzVDc1NnM73SiGoL08tx80Txv71r8/VjYz96g4jo3ksbf/DkId+vZ1BxqD1rM/zy24ODg2vQyAw575zP7xMcnDnPV5q0GrY2jIabAw7QSYehrpXphx762bOXN088eYho8/KzP3voIRevqke9f3nqRx9b/sbNE0Qrr0ZTr3/25ieDtdO6HGRr5N9zE78rF3iV+USOM3SiW/X67Rw/EA538Psx8ndkeHyCg1Ouhq0e7aVM6+s9FG+YL3Gmqal+wkbaq6uZ/tjTT1PtT1aebJ7Y/P7LH3v6aaq9RkREm5ePjc2+kWwkitWbl4+NzX4srVuvNKLazy5t3By+XLz5+rGxtx/PF54333mZHr4iiuQnPnXp2e9+f/OT4RanVdlYKZjP1sjvBxx8AC5vHl0d0mruTtN5e838MXSiW0WvPyzu4Pdj5O9o9Z0Vh/MIvBxce8Z7IXvde4qZMx6AC/d55cqV/KfayZRh5aweeqZPPDJde22lSa+9/PhXb9KfJPTQkzfjJ8WHm5ePjTVW4uaJJ29u0LGxY5c3bn72+8dqtBzf5NIOEpKM9D1jpSB473xIRyLcT48O6XYb3m57jedfD3hFuL13Vh2O89tEB4u4tvEqy/ManAMXt2DVYQfdb00DDRJnO3uc3KZ5nPjqpWenjv2MHr9yiDZaeKUR1RbSf0yL/zv05M3lW9FYNPvQpQ1jTvrQJ2/GnzS0L+4a7IhXPbSQ9ysIuHfeYw0YNVrwivPOx/nt3rbv/R0pnHfFeWG/NSvH7DVnOlT1lOZx6MmnPzb72iM3D5E00yuNqEbLcZx2dLwmNx47/BDRGwV7gbpp4BWEWvmZ5xUEfGCuPeDusWbcYw04vx9wcPB++WC1cLXy3fm8xjPGUnL5zkpUxNTFiwydtMiZzn8Kop6j8U4041ydefqRNOPjtQWiR5KPG2MvP74RX/n+sbHGfYZSP9r1TB+6/3Ga/5OVTzZPEK38eJY+uhFyw/QA/dA/xBU2WN57nzH1XBuDYwYHD4Kr52mPXVLoNik31xYvhlKrtqLWhEMx3rDdRpkxLmtnR09dlZ9Nv7BFn+gwityIBs2ZPtFcfi2KIiIimp4WTR7pAsQnDxE9eXO5EUXHjCxAbKt7nrzy8LGxZyIionsvbTwarpfu8QgWNhpXzCB45y6LvvqMNZ7/vuDg4OXmg3WbrObSG7ESnTOXs4Tl34tzrdqOtpsz5w6/tLM8TrR2dnSmeTxnp1OzfeR8lut+ebtZO0Uv7eyME9Ha2dFas60zNyGME2ehryXvSzLKp9phajdPXjgrS+uyyO8HHBwc3DFv11WC9TY8ubi58MkAkePE+1q318cCxEzFucMU8YLK9MqJrJnOoLa7wjjxkuubqCUw4+2myxLRbdSSwcHBQ+DtnokJk52/vqGC45fzrFUTy4COq6dGMy0dB8aOXF1Zmxt3MzsRZpqX0A/tl8Mxg4ODV5C3u74dRD43Dy79gPe+asPSOqQH3c343M7OnNzh2bWdufH9jfnztaOjo+kmeluIWcFMs1A+Hzq/DfiQvDATQ1vZA8cMDg4OLtU5PbPrJB10jxjn+Vo1Ocyrlup9qOHU1FSnMvb+xvKOYqG3e9xre+0/fvLI9c1tGt+v7nvt7OjKIXsd0zDTTIQrjllemJXRbgVPnuf3Dw4ODg6u8a6TdAq7R3C/M8K95FWrOdNTU1/Ib1OkfsYf7j9+ko7OrzXmxonW5s/RyXXhgNfOjp6i9okca2fP0pz45PaN628ePpPxzdvN2qlb59fn+ngdfUs30+//6oPC7SAH4nnGBsEHyMrI7wccHBwc3CAvfNaXd9hYFzQk1/KqhUS5OrScabU548j59bnES69cPXJ+Xa4lTBpDjo6eS0LwDozdkg0dR86vL2tbdsjYMyU9zcPqN4Pa6a/3fIjVmcmca6lzfO4c4ODg4OD98nzGCCaRDc9lKuJTue0HdtgyzWN5ebnnynQ89GrFDrEewwppHqUSqzOQG59A6hx4yPxgjvc4w9IGZ/j7AQfPP0tUzxqsdByMyyQDma8nxHQlYidtbdDJGbuV5WE1cGX6B2f2XK/d/VM3mSOl11/v+VAecjszUXsGZ86dOdR4lqJLvW4fz2Y/cWSR3iy6M9nnvhx8h2yc/Pbg4BpHv7URbqSv2lNl2qJMVaY7m+kfnNkzefWLS+89/xnx77WvfOjzL3zpO2w99NbN2gP//q+IiEaf/envNw602+7nZ/Z8l179+sXjg0HzyptpPmegM47aMzi1z7ulbtyXQwUfjKPWDj4AV68PSI8d7D47TNcHzHQ7dW3z+O2P/+yPm1ufaRwg2nr+4s9+++NGvq0Vvd/80r+//9WvLx8nuvHa3i/dnPjRsSI7/X7z4R+/8wm6f0DoQgzPQBtczdzYwkrBwHnnPNreeTybODC9oEtdeH7/4Jx5v3/fdnzILhrUyMPi6opG9U+PVNkeuej6GGbyS++heMN8iTNNTfUTNtJeXc30R898jT5/6QeN5z+ztfLd+7/2r+jR60SktHn84Mye6/TFb199gYjo4xfeWZ45ZOSVtbR1s/bArZOdKs1is59fp9/9N6KEfPx3nv3mX6xuHct/ydpXmu9+7etnvvfc8kDQgXiegaa42r+h9snltwfnwPtyKgYdUn7/4OCFvLCLpq/jrRfnjWdlDLmWaopskB756hBTyil1n1euXCn8bIcvKbd6WIB4/OQTj15fe56W/+3vzfyI5gu2+DZ97tfvPU+09XztgYtrM1ybQIi25r918SON5eO09r1BoANxONPMcm2mYGEuaX4/4LZ5j90UfTmV/H7AwfnzXpy3aJFHtwlnLm8uWjYIukHyXE5+GSBneoCeDbR5CH1m5sIf/+HDb9M/f+EAbRZt8KWaqAcfqJ38xHc3t2i8cwm5Xx04tnz3WAHXOqS77ufGa0d//qn3nr9nQGhffM60IblWftaudFI8r8jl4NpaeHRTgIMPwzufL6hts+JaNoj2eA3ZICq/kG35IP8506Gqp2i8AzP/6v6nrtd+dIiKzbQnaSZ7q8vmWxvv0Qtv730h/fcLz71zofFvqFe4PGPXW/M803rkWnodZgra5l27L1q3fDhjcHDL3EhtG9dPS7xdN4iQrNHyuZ+65FoLNeN0PO7qMWf6MxfvfsbuC+mgHnumD3zkJDXnbxy7eJzoxl8+TYfXxfY3Xtv7KH3n7iPjM7//3kyy7dpXnlv+nMjo6B1alPczajCOFYQOuPDNt7Mr/NB9AQ4eFu/9WVCH1EWsMxmea/ep/MrpqqXvrQ6ammdLrQmHHQYX5sa4rJ0dPXWViIieaE0d72lXZlSmoS33NL79u7UHnttLRDT67E8fEV567Xtvf/xCg20btxCHM6p3Lj00ruwGeX4GWPLP2fQDOGZw8Arw5BwvOt+jS8gkMczzlaAKpu+JLJS+UvOsabs5c+7wSzvL40RrZ0dnmsdzHjh1yEfOq+gUvbSzM05Ea2dHa8315cb+HnZlUKUfJ/5+8+G/oG93q2r7VhA503gKaYS3W/mn3kHz+wEHBwfvwOGwbXBZ46hIN0jX1DyZM93Xur0+FiBmKs4dpohnP7V2dnTlRFqPTj9FPe0K48R71H9/lw7P8HbSeXk/o1TJfmh0cfTF2+Uud33am98/ODg4eGfe48pIOOy+uJoQUoWVi2oLdeeaNK+AjgNjR66urM2Ne+xBKL2Z/sjFH33E92voTxzOKCHZR4V+6M58gNzl/Pbg4ODg7h02uvV64eI+KFscS9xXvZpW0Bx1d2htzYPtZH9j/nzt6Oho+m+lA8SVSm+mAxOHM0ptieZ2RfPO8ylLcMzg4OAB8V76sOGwC7kWZV3WWnXcvn/6ygcfiI97H2o4NTXVqYy9v7G8o1jo7R732mE3a2dHVw7tH3xXgwlmmpH8nlHaskJicOXyzvP9zZn7EMs7JTg4OHjvvLAikHfYuC+oup19Jlm+mYvqlEShfM70G1NfyH95XlPUz/jD/cdP0tH5tcbcONHa/Dk6uS67ok9RK6ajrbabtVO3zq/PddiVHWXM9D0ftuKtF45E02/RwoO09VbBZ/vNNfTCL5T3PahcWYjZhFu5/Gb0N4ODg1eQ5x12PicbK9G1QvUqy/v7MP3TlLZQO8z0UDs2jpxfn0u89MrVI+fX5QLDpDHk6Oi5NPJOQiUDr3hXluS5Ms3KMRfyC0RUUic9gX5oIuohvzm/H/AycRnrK9Vhska/21eHy3ee+e3BS8DzlQU1D5vP9dwLl10x5UvTE+8QnKbjaY0fRETbm7eOnJzZ3/bzbWAbakU+zTQfx9yOXyjde03KTVrJb8/nCmWPa9O2UXsOkWumdmBHGGt3v18s0r11HaacPkvxZ/vYvlIcjrxSXE3zVK+oVe63vl3SND1RVcx3UbvS1gZJL81U3sw0H8fcmfM8sgfgmLTSYdp2fntwx3wAZ5zxc0adYsHrBO+Be3HkPI/nqnGtIaTK3SClTNOTLR8v5jdyofG5ZeaT9zyZaW6OucR90pW9olF2piBqz754u5ltZNoZF7we8AC5ceeNd86OuXa9rWw3iJqmV44ViqtEf5vfDiIiL2aalWNux0vjpCvVD42Zgl54fmVSvEpR+igkXoQzBrfFezmuxKHYvdtnNvsJNudX6Lzi3SDix1QrO9x8Ql+c+gnFG+ZLnGlqqp+wkfZybaZZOeZ2PGgnXbVJK2p6HWrPNrjagFHc57pa4GCSj1k6MPBK8cLjU91ecPn2j9BVYo1XthtEXaFI2UI1cfIPXfmVDz4Q7vPKlSv5DdrJlGHlLKdmmpVj7sADddLiXlD6SStIrzPLu2ZTtCwIasng5eW9HOeobRvk1ewGUQvVQa5QfPHFAQaJ85o9ntXevXuN7MeFmZ5+M6Yomv4jXo65A+d4BHfk0kaT7yuFVY471sC8g2PuL8siL3DwyvDBatuY9NQLr1Q3iLTUceBpepCUCzO9cCSaJlr417wcczmctGqjic2VwhTHCsJ+ebtMDDhmcHDbvF1tW10/gIpAL7xdN0jJJovJbky1UE0MfEWPHFKFnOkMZ3Wk9uKkS9ln1vLQs0Tk/8rOk6tPRQfIxCjYPzg4uAWurh9Qa9hinS4cdmeuVlK09Rs8718DcLX3I5QuU4tqTThUphkqWjs7euqq+LC1Qe/QkpAz3eKsjtTOXCtIE48rwpAcT0U7cz2rJHeHzoiNkwAHB29Xw1azbvJdIngWp3E15VC9Hpbj2eztcFYo2tR2c+bc4Zd2lseJ1s6OzjSP6x54beXW+fWdxn5SN+gd2lIUx7G1nSdaOBJNv0ULD9LWWwlh6KQD6luaKNHTLmRx5HlhfzMHBwAODu6Sq/aakCXSnst0Tib3teH5Qa5dH3/74ou1Wo36XLfXxwLE7WZthuYT15v5R5HWztY2Z3Juu2dIFNYCRE1w0gPz0vSNZRo5jlS6HzrfrVHY31ywH3Bw8FLzfNdW8TttBtcxv1zeR+SdJfRa9W2uXR9SHAI6tpsXrx4+MzcoNCvkTIfkpEPPjdaboYnXFdkNR7cGODj4YLzwnTYcdn7lYglq1bLrg5U/ufLBB3k+lLQO6d6/cO3s0esn17Ux471D06p6zjS3I7Ud3wrcSVd5hY1We4ZjBgcHN8jhsNvVqkswEeZ2tuWDvPuTF18U/+x9qOHU1FSnMvb+xvKOYqG3e9rndrN29PpJbUlh79CGnJppOOnBOMMzvC9etX5o1J7BVd550ocDnu8W6Ov1g4fI8w5bvy7NEhHH66dBrhWqA51opmbnsfInz039g/yWefU3/HD/8ZN0dH6tMTdOtDZ/jk6uCxe8dnb0FL20MzcuP8xWmnuHluQzGo/41apZHakTIT+lkv1eVeiHTh4pziZOGo45UJ7PA9aUPFuIKYoKeIftdV1bpNP1+LQjLl9t8TT4mOjasA4e0+P5c+26pKbyUdkzlOQ7h0C7JeXkGj7+xJr2N+bP146OjhIRHTm/Ppd46ZWrR86vj5NofyaiU6NJ5h098dLO3IGeoTVf7SfNQ8ivk77AO30mXCed2OhZRldS4xxOgi3XAhCoL0coroXXFul00f7B2/MoGrB2jneeTLj6TpLb9dYsl1VqISb3zR75Qd++RaZ5LC8v91iZfpc+PfRqxa6xHoMr4DQPITjpDjxEJ13uSSvoe/bCB3DGenFgoJpuwesB78jjxQFr573X+Bken2XiakUgMxtlloi4XIeNcHX+S3D32ds8UvOca2uDTs7Y7noeTn7MNJx0Bx6ckz5IFM9SxsMwuGIOzzN3FPQ92+FwxhXnmT9x+79jl+MEaZLW+q3LOqNRXaEYVtqs9NOVcdJEND7npO95GCFnGk56cF6+K2x0CXdo87xtrRHOGLw33vk4UcvbqG2b5YUzGkvTxadWqWVfMrf7bLvUvMo46TBUuZzpC6xSZrIKzkmXZmWh2sWB2vNgfADHXLAfcPD+eau83dF5ay0lhMyTPnnrV/SL5PofXSrDykVpqQO6/27V6+KtjWM/c+WDD3oPxZMa4EucaWqqv7iRdqpWzjRbJy3OiiDO5IMlyuhoeWisIOzGtbX/qjpnVhTsHxzcB+/cNVSYeQKH3Y6L30z82fKsXBSvXyz+CSKXWrxI2ULtLGdauM8rV67kt2knU4aVs6qbM83KSQeRf6mm3eW353Zl7CWLA3fKQo6uDPAKcjXLpWuqoFqj5Xb++lq5mPn9zKYf8bv+d+Za4wef+287fjutcDnzLQNEc3CYPd5Owad5CLl00hcYrIEN0UlnYjp8X+mG4bIfGl0cKlcTM1BjBgdXQaHDptRkI29b5dqyRQq58hJWLnXsaQIdpMqnmUZNmrmTbiVGC7G50vXF0Q+tKt+t0WO/acH+wcGrysWkm/z5ks/brmYNO15NeOgpe6IvnNjnUrufOg5pqkrO9AXHfUU9cP5OOuiVJeiHpjY5zXDM4OCWeP780mrYVcsIKkHKXii51Lcd9k/b1XazdvTcm0RER86vFwxqWTs7eioZatjaQIH5L1RmkVtTtXKm4aR74XpBmlhc0Xrk1eyH1kxz55zmgv2Ag4Pb4VoNu9WEvZpmYlTmWVk+ZS+gWnUQudRu+qcta7s5c+7wSzvL40RrZ0dnmsd1O722cuv8+k5jP+kbtJkWvt2sXbx1hA5bft0VypmGk+6F6wVp4nVFa8cr1Q8t+xFb7xx6m3zBx2GAg1eT57uq8jMgmVxnrHJpqeOgUlb551Lb7p+WmrKU0LF94zqdnxeeeHzm/MWZG9uNrJtWBriMn3ji4mbn/a2dPbpxZufMyuiKjVerqBI50xfcrnXtzNk66eCevlFl+qH1wnObfk0iLo4BHBy8R55/J6zb61Wm16XhubxuZ8bBEJf7SzvOPJfaav/0lQ8+EB9zCOjYbl68evjMXPrPq6dGRaOHLFFvN2sXx9aXx2nNtpWuSM40nHRXHlBtgCrQD63VntGtAQ5eHZ4/30vfvSZ/LlGr5nnf0aTmUhOzFYoW+6dffDH/qaGkdUj3/oVrZ49eP7meVqnH53Z25uQOz67tzI2LovRcrufajiqRM+0lNQZO2jgXXRzRRDnvKJmIOtSewcHBU94u97p8z+JErbpVLplNP8HvfqTmUhNRdInX/d12/3TvQw2npqY6lbH3N5Z3FAu93dM+t5u1o9dPFq1OJNp//OSR65vbdGDzFl29OipXJV4dvVW4ntGQorggVNawFo5E02/RwoO09Zb+KThpIY+c5xVKSq6hZnXFN8K1lLr8096C/YCz4eqz+M6zIY3wxun6Qsdp2LY5jk+GPHMQlrEbJLi8am73d+P+529ffLFWqxHR8vLyF4731DUd//2+ekK2m7WjG2d25sQCxItjif9VAjmKsjnWzp6lOdnbke5A+fToyoniNI8yDG2pzkpEOOkBeGKjy3WH0CakoPbMhBc449xXaHw6NZfNa4uN0/Xp3N/RLFe/o8vvO92mJtru94Pj2SXX3uGUrxtEZp4k/5xVPsfjPqXxg8yyPuz1T1vT/sb8+drR0VEioiPn15MujbWVq0fOr4+TaJQmolOy5PzESztz4wfGbiVfI9pFbIbgFctbZdqZk77gNsm8kE8wy6c8yPu9fstGE68r+2C8w4SUgu3B++f5SRlSPXLNGee3B++Rq+Xz6Zia13r6/eO8MMvL2g3SCtRjcJ9qxxlmfRjsn7ZfmS7UdrM2Q/NWejTCrkxXpyZNcNL98IyNJi5X8AF40tj9C0znNszb1UTN1oDBB+Zq+bz3339msknR3xc17764NgW9NN0gssLCOXuKbdYH+5p0B21t0MkZRysJB1TJc6bVsjScdDKNhaWT1m00MbqC985bAU9YQTgEF44qjvXuCzjgsnJRw+7svPN5zNJ5o5+7My9fN4g6+YVY1qrF8Xk7x704adFlGqyTpky2NFeVPGf6qfQDOOmDLK84cqZU0FN2Mx5aiMEdlCHvpe9WOqoGui/Ae6t5Fx5XcNjteGE2CMPraleuPsNkmEkVzyYRH1J+uz4msi2vNFzONKTJac/09B+57u4QlemKO+nWeHCWTjrofj7kQBfyzFN7RcIPcXBm4KXnC5rDFh/DYbfhmTnnQvyut72ki3JL05Mt1Bz6pw8O/az+b5Wc6d57pq9cudLLll5kapKj52g8KunsQ1ZOmuFMKdFPxvOK3Jm3itCLHO+Ijnm7lX/yqX1+N+DgHjkcdleuvhMOsdIRTTCtHHHwA5T66YF9zt+++KJwn1euXOndTFdBPqPxCE7aJm8VpInRlSXQ3Gi9kYPNnc8lj4pyjtHHDB4QV5+NoEukkKvrPfRyte/rcC+8tfJ7Nrc9UvOIaGifM0A0B4fZ4+0UdpqHUCmniPNx0jyfdoW1lrzKtau8wyjMOc7vBhw8LN67w6aKrYuQP6x8BhVKrVrMyiVO90GthdqXk44NTXKBNHkz06WcfcjBSWcK0uT/CkKh5UbLmVtVS7XTVgQWOgwpPk4IHNwGb3f851P8qvBOW06YCqhWrSZ+MMmwEn6afPuE4Se5QHn9hpfvWtacab9nyMG0IM3NScerAVx5o4nkvzhO7xzlddJRlPmPhGOISf2PODkbcHAmfDpOu5vSM0U9j6J6QTeU9/PdII/j5L9ogsV1uyuPV5N3PsLFZuTj/hjPtp0k5bJWfZtITuNl56S3m7VRoVpzu+Dza2dHR/MbtL5q9Oxafsu2ezOl0uZMXzCRAtMv9+ukuSXYhzKBJSldlPrpbWulYJ2oKGUsvxtwcPBeeL6GreWj87kOGOTi50rq9OzzqjNV6lkiQmoe3U4nuPFy0rTdnDl3+KWd5XGitbOjM83j+tzDtZVb59d3GvtJ3WDt7NHrJ9d3lvcTbTdrR8+u7cyJYOonXko/sqyS50xLwUm75EFMYCl3P3QUZYaeYKUgOLgzruWjqyPWS7aCWfw4oeRVS0vNoetDtnx4TM0boH9a5kxPmYqU07R94zqdnxf2d3zm/MWZG9uNrJtWBriMn3ji4mbKDx8Sm+0/dJg2bLy0znJqpkvspH3NOuI2VZX/BBZxJS1rP7Rae24g1xkcnAFXHwRpz4jK8U5evkPgf/2n1PFHl/zfN2WN3Htigaou/ifNmeYQ0LHdvHj18Jk5IqLxufXN2ujoKSKiI+fXW/MSr54avUpE9kvUTnumyzpF3KOTznRIC/mewML2Spq0RJeuH1rt11Q7noW8OwlwcHCpDv3WTK4nw3DZTi2zRPlc/zUer1J0iVqN1L5T8w5msTMnrTZPk5f+6W4d0m0lOjsSg7zdnNk4s7Ozs7Ozs37y+kyyp/G5nUTr52+dkq3UVlTmnGmqgJPW5fGKcC/T1KRMRDTLO9AAXG3KlBNS0MUBDh4Wl8+OypS+J3s/mKfp8en6ELdy2UXtuCZ9e6BJLsvLywU/UZGmpqY6lbH3N5Z3Gq1/9uant5u1o9dPrst26rX5c4dP7KQ7PHN4dH6tkalD7z9+8sj1zW0a35/blyGVOWfayxRx8vd0BrMMNV6+lYVYQeiFL+TS0FQV8rF49d1rv+h9e+98+jQtRDpn8vuvAm+Xvhfn3iEzvC71kqbHdiV6zKZbUnRR+8oEG6Am/YW/11PXdH+t1fuPn6SjiRVemz9HJ9eF/V07O3qKRKdG8uGyYpYPjB25erE5My6WJa5cPTI2Q0RrZ8/SnPDU2zeuv3n4jDUnTR7Hidtz0heGm5bZF5/wNNOIyYpDtk5at9HE7k7TI9dWEOY35+kMQuELuRSzwtQq4YzvO31vfj8l5oXpXe1SvXB8WuL64kUe16UBuHyHwPB+kbzCCRapsh6fdfeyEvFvX3yxVqsR0fLyco9mOv5cnw3W283a0XNvEonuZ1F8Xjs7enFsfbmxX/lsoidaFvtqhmQ2be1Kk6kJiH7MdDmmiMNJM7wyJjUG7aBmc0fpyjWnghWEA/OFqJPzE3ws+wSaoaMNiG9Eau+lUpvPCc57YB56BhHzFYqJn+aRKut+VmIvk1xcmOkCbTdrMzRf7IaHVMDjxEs5RZyq5KTlDCcmV0DJA83oUJ/qTudeP8M7OjfernvhvtP3jp3Wqy3C+bXj+f2D98gL35kU/p47d5toYni8cegGyaTR8biO9cLjbA53vMrIScvX4309ktpCzS3Tw5O2NujkjM0mjeHlujLtIB3PjZPWytKVctLcroAyo6Nge5Z3lORlp3cUuYIwvzm45O1qnDydJfhgXCtva88WtPzm/H6qyYPOsea5QjHT8kE+a9WOnXTXlYhXPvjAR2XaooKsTDtw0v0mkAfkpPPc8RneGsXi+0rXekmiNzooJ616aCGed2gvfIAac34/4OFytbydr20vRBNUtJKy4t1Q+RxrcZ0JottNrlAk2f7B4P4Sryp+2ndqnuNu0h5zpiFNTs10KSe2uOxn8vheOSlIE4srHalJHSzvEIVcOACtkcP7ndg915zQWLy6EU0IhzSWK1JxcHjgTLhcCaq9s1pIjx95XFX2narsR59OB6Dyr1VL05+8E2BwfyHpp32n5mniMBOx91A8qQG+xJlMTXL0luYhZNZJX8i2z5MdJz3haWUAnHTyYtSkDgZ3gq5c5q1WsB86n7bGx5mBl5hvqDXsVNV02HIlbijrSfQoPWKwon02y53ffx3nT3dYifi3L74o3OeVK1d6b/OogpAzPTivQndHq7WD/DvpsHKjtVS7Kqysyucx55/a5/cDDm6cF9aw810iVchrl9ef1hWJdyVCznyRr9/jfac12GU25ZXPnx6gARo90xYFJ907z5Sl3TppPkn70UQYaVBaSzS3O6u9lU/U/ik8MXNa4NXk+eNzIV3vKB12ibuwprVuCpbXTynp+DmsUGy1UHvq7ojdzlq+TbRFROwyPVjLj5m24aTVHg97Tlr0eJTeSWcK0oQJLN15RWpdWu0ZOc3gQXNxAKsOW9jr6TjpjmBy3pnl4uda0ArVxPG6SmxmKCYr7yvTPz3RPmUYKlQJc6bLVJPOyKGT5tWvxnhNeun7oVF7Bq8UF/b63xHFi78o94pGtVBNvDORZOOH9yyppMw0q3BX92XR7OFsvVa7lYguVDQBUZUy61AOO5Rfqcx2aTcr0Y5+w9aO28iSk5ZlaatOWrxXc+mkW2XpSjrpOGbqpKMo+S/pRyyRk16IaCFKMjcWIhqLV9X/mDgecHAHXD3ytfNCE5/zdwA+HSfOSVzTMuJxvRWK41aFRZer+1S8SvFq8uSWyHVNOp6lg0TkdZJLwYs0rO3mzLnDL+3s7OzsvHT43ExzO7fBJp1f39nZ2dnZWT9/69TZteTLaqOjWe+8v7G8I/XSE/TECWtOmhyneUz/ka2a9IVuSeOmOJy0I87mCq7ysvZDq+YAjhkcvCtXh8uUrBtkgf1sxaRELeVxUpjz7g6XKxELMz3kOPG+1u31sQAxU1zuNkV87ezoxTGleN12+w47CnIBYtDdHZ92uwLAsZPWm6QJswwzXNjo0vRDy7Qs0fc8pvxcHJwKODhzLlcLvJubjh76O235+sXPxfDZYBwrXdSeKj5J2uBs7hMlWol4W/HTRjI9TEq0ehS3gRRsPX/u8Jkdu+PIfUbjkTkn3a5TPlAn3ZIrJ60vl8Ysw5SXZo1/ZuYwZgqCgxviHSbIBH3dEGmemUA9Bk669WIE95qal9y2ZlNexpWIZDvTQ+uQ7vGrxud2duZou1kbPXumWyP02tlTt86vzw37QruoJDnTbqaIa7J3BCdl6Wo4aZ6zDFv90CHnQ6s1M7WWxtCRgIOXg6vvVBfC7waZVrI+GNaq/U54yaRQl3ElosguK/Q/vQ81nJqa6lTG3t9Y3lEsdK5FupP2Hz955PrmNo13KDpvNy9efcJ2WZrKkTPtwEmrUw+F4KSN8FZ6NBsnLde2c7iTDcDV9DpM5wYH98i1d7AbytOhELtB1Fo1k+t2JuiDqpKaJ/20g5WIHTI9vvBve5qA2N+47v3HT9LR+bXG3DjR2vw5OrkubPDa2dFT9NLO3Ph2szZ/aFkUo7dvXH/z8JmOPnlt/hydX7e58jBReXKmVaEm3Qv366QZTgWXjdE872SdueziQHqdL76RmwCiKSDO4fdZPq6+uQ23G2Ra+lc2XXmii9rvrMTokuup495Seu1qf2P+fO3o6CgR0ZHz63OJl165ekR44v3HT95KPi06Q4RRbnWLHB09J3upkw4P62VpcpzmsfUWkQUnfaH9HHkyxNXKtNUj9SA5WiPs0UkznAquri9kcsfqhaspUejiMMt1Z5xTAb8TE1H8o2vRw7neoLB4Dz8vjjeDXB5swb2TX1Cr1EJ+17eQ5wqRy/5pZ+u41FgPmeaxvLzcY2U6/v8Mv1qxW6zHEAoyzYPCrEnDSRvk3KaCazEdPO9YGpRZHOjiGID3WqNVnfEd3XR24TkFxnv4eTeiiFDbNsTlidxMj89QatWySi3kt1Ydp10ovmYltpYk+kjNIycrEa988EHB67GurQ06OeOivjy4ypAzbXtiizTTcNJDclZOOqy0u3wWR357Js7AIxcVvk61ZIbOtZS8W21bfR/I5/jhwwOtVTOZWdua6ehx4thsjltw0m5WIh5U5nh4qkxbVJCV6eBq0lQ6J03+noLxcdIB1X7UTmjxAYc7vXe+ofSbZnQnLnnNOBTerba9kRYztb8jjnOhQGvVTFYoimyoqF7PPbpzmprnYOq4m5WIHTI9IKngc6bdzD60/fSk3DVpYaP5OOkgpn/LiSoqZ3Knd8zVaXNCSY0Zjjlc3qaLpkP3SGVnc2ope/xr1UxWKManSQ/Oc5ua1/LTgXd3aJkevYfiSQ3wJc401V/aSFt5WIAoZaQmfYHsTmyZIBfTO0s8LVwvSJPn3Oj85kzuQEKiH5rhHd0ZL1j5dydzADFyhOCOee76WU2HLX4P/GvVfLo+fM1KdDN13M1KRNHs8bcvvijc55UrV3pv86iCgs+ZhpPukVfWSQeRG72grODhdud2vSKwl5V/OYFXgueOh3evXdOOH/Ewh+FxbqlWzTmDCF0fbqaOa2NcyHKteoAGaPRM21IoE1vgpAfmHGoS/HOjtZZonndu4zxTe4ZjBh+SZ4+fjShSu0RKvNJR/Ggyr5rtrFZWXR/uK0rO+qelAs+ZDlUezHRA6XgO+pAcrPn14qQLuoecO2nONRtho0t8p1d5p9pzTuDgQ/HsOzRtpWP5JhnJ6eVqoZp8X98KX6f3XOpktos4BHx0fRAFvBLxNtFBohcLXgpEhJzpDtx2HxKctCXOfAJL6VcWqusFkxrhHf2AYOfAwMvKs11D0ltT6fqtxSXl31GSdcPtuqflUvvq+vAyK7FVnLZZkxb7t52U4CJnujXMUE4yzGjt7Oipq8nHT7y0Mzeuf5IkbG2a29C0fsPmznWFOPtQVXBOWlc1nPR0zM5JL0S0EFHz2uJ9p+8di1fL5KQ3oglRe96IJsR/dCcW/6U1QjhpcDY8PTjpTqwet+r2rM6vAbioVYtrjio+18PpmKLIW6aT6DbJRQTZvT/Gq45mIuaxWd9S8H0Na7s5c+7wSzs7Ozs7Lx0+N9Pczm2wSefXd3Z2dnZ21s/fOnV2Tf3a2sVbR5R/nSKxp52X6FQtvyuTQs50MVenHgpZ70Oyk+juMhXIr5PmOctQtnNwu+MOzDXngb5n8IB5UTcIz/OuXy47qgXnVquWXR++1tWIEjVRqbo+bK9EvE30ekqmpkxlymW1feM6nZ8XNeTxmfMXZ25sN7LF6f2NuUby4dbGm0fGDqSfWDt7dOPMzpmV0RX52SdOJNXo4l2ZVPA500IB1aS36vWDRHpZOnwnLae5ZuTQSXObZah2RXu/sw7PW33PSqWZqUMCB++Xq90gUaaiG/Q7YXn9kecvq+vkdOwz6yNWV0a6mVx2qTwrET0HdIj+DaUNZLtZuzi2vjxOayvpNgfGjlxdWZsbt9rdIeW0zUMTWyetlaVRk+7O/a3RJn5OWjxgle0cfO6s/XLxBPzda79IujXEf6kYOSFwcINcaQXRukHUxzJ8ztNeuLgcjcWrsv3D+3VSSHTlee76cDnJ5VLKra1EPEhEAWV6bDdro0L99GGMz+3s7OzM08zo2TVKitJ6e/X+xvz5W6eSnSdd2PYUfM50WDVpEpPDZ5VPhN8n7dFJa60dxGBlIQWe0ZHp4pDTuXMCB68QbzOjMcRsEHF14jalXKYZ+ur6cJaalyxGtJmK62AlIvUz1HBqaqpTGXt/Y3mn0fpnX33N+4+fPHJ9c5sObN6iq1dH01WJdHX01vn15cZ+Zd9rZ0dXDlnr8aCgc6Ztzz6kMGvSpJaly+6kmdwJhJMey40E4HMH7crRxQEO3hOX7zADzwbhNvkln5Nd1tS80GciSn3hC490+KxUf63V+4+fpKPza425caK1+XN0cl04YJnSsd2szR9aFskc2zeuv3n4zP6MIV87O7pyQkvu2G7WTt06vz7XzwvpVwHnTFty0rLHw4aTzpSl4aSH4HDSprI4kF4HDj4gV84aMYsxuFq1OvmFQ9dHgyhZlVjq1Dy9eZqCWYloX/sb8+drR0dHiYiOnF+fS7z0ytUj59fHiWj/8ZO3kk+L7LwOLdEyZK84Y8+oorggf8GwFo5E02/RwoO09VYAsw+FmbZUk26ZaTjpQblI5+HgpBeiUNf+6zMIc2LhVCrI92UmjBRsn+eYgMOKp3/BENdLbGTHvpDvCoWXbChnU8czYxGt+QHjlcErH3xQq9WIaHl5ucfKdBz/1tCrFbebtRmat+KHgxwnHsrElkC7O1oqtZOezl1hveRGEwXmpLVmaOLgPMrE96VTrEk/hPrgSzERxb+8Fv3WaVrSv0Ux7+v74u9um6e1ajnVPKBataxSM+n6iCIPsxI9TB0PK9PjxRcLvrV1bW3QyRm7leVh5boynReriS0TRLE1J52Upa2dOUlZGk7aJg8rN7rVxUGkNXKwcB7MeY+1Ycl/q2g/wgEz4ZNR8jonI337Hmvh6VHE8e/FkwdYq+YzQ7E1ftxt14eb/mnbKxEFN+tnXn/xRR+VaYsKsjKdF7eatD0nnQhOelBeuELF/ZU9iDui3sWBiSpdHHObmu5Sro+8c804v39uXDr+7I/WRy18XyvLQv+9IfulkGdr1RyuD125eJ0bDLKSpks9dTy2PwvZzUpEiPya6WGctFqWNpiOZ+/IO0gW34OW2EmLK5r3GkkQE1hajRx4mp/n+9rUYsvnmK3ypbjt70e8M1nSy97pCtfKv6MT72z/WzC1anWG4nTsuetjQZ3tImQ/f9pZ/3RG7Ls+eg/FG+ZLnMnUIEdvbR5D1qSlmQ7CSQtu7wyJLjlZg1xJJ63aaGJwh8vz1lN4pNoRaaa51aVQWIvN7wfcEs87bJ7Hj0ue9rsHkQXkvaJBMuiDSpU/bXslohrrMbyfef3FF4X7vHLlSu9tHr1sFroCzpmmoCa2hO6ko7rTaeFRusLPey2E7QSWqqfaKaY503WgmGbUmBnx/N+l8FmB9+PKJU+7sILIq95gkEY6LbsvXFV2HORPJ2NcZgOoSQsN0ACNnmkr4umktRHiZNRJ2+uLcuekHaYUiQxp77MMtXIRkztcRfuh85kVqTmDYw6V558VaH3Y1elWCiGveixe/Xfp9cfj9VlUWBYid7MSHeVPW3PSInPa1HowqJ1cm2nmE1ukgnDSRGVz0nI8OCaw5HlioyviMPLdGnDMpeedU/+q08Wk1Kq59VWr08g9rghPStTyiAi8f9r2SkSDyQqvf/BBwfeFiH7D5TczO7Elzxk6aeuZHuVy0tMxnHQB34gmNqIJuhOX2Unvi5IVbPsi2hfRUiz/CyN1Dtw4T95BpUeCODDU40TbntXxPHyt+k5Md+KNaML79aeQj8WrC1GypETI8XVb+GkiV/nT+fug0a6PeJWiS1luNNMjj/lmemw3a6NCteZ2wefXzo5KnV3Lf1UhLN6VQTk10xWc2JKZHy4EJ13EG2kKkq9Zhs1ri+IOoX3K78pCzUYTkzu9EZ56o8Qhac5Jbs/N4YH74uo7q6y9bjns/H68H+dD8sXF6OHT4jqgzl3i4LDH4lVpqb1ct6cd509/mlp/gaAyPeJZOpjFfJ00bTdnzh1+aWdnZ2fnpcPnZvIeeHuTzq/v7Ozs7Oysn791SjjntbNHr59c12DXXZmUUzOdFxMnLRumUZOW3JmTjiKSy2+8zDLkORU8WVmo2GhieKcfgKP2DG6Ka8dP1l4Tz+N/MK4UqrnVqsfi1XYTf9xczyP9EYV9h21vJeIlIgpjJeLU1FQc/1Yv/xW8sA7avnGdzs+MExHR+Mx5un5Dt8D7G3PpWPGtjTePjB1I+OFDgu4/dLjXXZlUqDnTqnjWpEmOPJQy+tTGav6lSyctV4hjliHJjI4yrSxMH8ej7xncOl/KvvNUVzSW49mOmIzDb4X02OlVmUgtVMr+6WQWo7VKVjIvwoKTFssQb3vN9DCptbOjp67SkfPry8JYj8+tb9ZGR08RER05v7487vwVeatMl95J64KTznFfTlpUo8UzSj5OWtScypAOJmvPSgUatWdwD1x5+lGqbhClSi16Pzhcx2TXB5W9f1ppt0ll9P5bwDnVpA1rsLbm8bmdnZ2deZoZlR0dG2eS3o+T1y13dBQqyJxp4xNbwp0insh2/1a5nDSrCSzq2EKmd+4eucxeaDdTML8fcHBnXJ3dqC5eDPe8S2vtrNL0xuJVL1kfLvunM+HTZGEl4oSVZ9qxocl0Qr0PNZyamupUxt7fWN5ptP7Zlwvef/zkkeub2zS+NX/u8ImddIdnDo/OrzXmDnT+YsNCzrTF2YfJ+WDUSSeTw1UZPZOjCRf5nXDSlI26Iz536L64NjkFXRzgQfB23SDhrk9I0/SYXN9E14c65MXZpC1n+dOJLFSyMn6aWf+0qi984X903kCov3Hd+4+fpKPza425caK1+XN0cl10Qq+dHT1FL+3MjW83a/OHlufGiYi2b1x/8/CZ/UQ0duTqxebMeGM/Ea2tXD0yNtN+V3YUas40/9mHB4ligpPuwqs5FVyz0eT9Ttwvlx5am5yS3x4cnD+X7wDV/n6G511nficmImGpOayoHotbXdTl658OfZKLEMuc6f2N+fO1o6OjRERHzq/PJV565eqR8+vjRLT/+Mlbyadb7dH7G8svbYweHT1HRERPvLTT2N92V5YUxQXtsYa1cCSafosWHqTpPzKWM8189mGy9ND0maCbadPviXUzbTNPmtw6aSZTwZN+uOwAcBZ34h65eP352XUMHRI4+MBcXs8DfcfLLC/ffUZT7KTrw/b6JRtOWixDpEF9zusvvlir1YhoeXm5x8p0HE8PvVpxu1mbofnlhgU7bGqceJA502YntmjnAts+aTjpMjjpbNQd8bwTa1kccuWWXNGlbs/QCYGDD8kLJ8VwOB975OnyRMn9PotbyKXXlaN/Oo5JX4xo9lnxpRwv8UrETtraoJPHrRaWh5bPaDzi0fWhVqaNHGEHieJFOOkC7thJa60d5NdJh5V2l23kYOp4wMFtc3n8B9dXzanrQ235IPRPD8xNOGnxzDwoJ01E43Mewu76E3KmWzL1Xs24k9YVYHYH+XDSHHJYA0u7k44BMwjBwVUeaF81m1xq8d0Xool4sVT9061kj3BWIhrM9ICkvJnpgZ30hWxjBjcnfcCCk86UpS2csZmyNJy0IS6esYZxx0WqHTh4iWvVd04T0UYUeV+BLa4zzlLzWn465EkuRLxWIvYeijfMlzjTVH9pI23ldAHi1lsJGaYmrZppI056gkzmTEf1eiYhUmjoFQPJNdBOTbplpuGkDfGNaCKA/FptNmF+ez4OJkQ+qTeKtpu67IGje8cgnwynVr2PRdfHhvPUPBeTXCKbWVsWKnSDdbe+/uKLwn1euXKl9wWIvWwWujyYaVYTW+Cky+SkhWPw66Rl7B2jO2ieCxsNRzUYn0wcSYFznYzjf5FmQUxmV2r+i2vRnxft3wtfiro7b6w07ZfL3yfzWjWb66Sz1DwH/dO6mTY7k3g2xz1lenhK87AoU2keVc+ZDmb2IZx0N+49DUpNj+Z4B0U+9OCOOXvICcf856dpUt9VF57/vr744mL31z+ZnciT/z3gmYbGQ+mrTrs+KLXU7p/die/bdJWa56B/OjMWkf1MxHiWokuD+B/7OdOhyqmZ5uak7c0+TDT0EZ+UpcNccShVFSfNeZahcEKoNeb5ZDSgY87vv2Q8rax3+j3AYRfyIPqq07gPv10fG8o0XKFw+6fDmuQyYCXxxRcLvgXk2EwbcdIx19mHyaAWKd41aZJlaWtXFlGWLr2TDmAqeJVXFqr9rO2cX5Ud85C83e9tqahfnMPx4JhrtWqG3V+idq6sTRRynJon/XTo+dOWnHSrOF2FTI/tZu3ouTeJxIDD9oNa5IRx9Sszs13Wzo6euio+7LwrA3I6tCUv7xNbNPGpSQvZe49bGid93+l7fTnpZCyCMoeF151SnbSibc/TeRjh9TpNRsp/MU3GqWOOtf8Snt8PT+caEM//nvW/S0Q8jx97teqlNKUuv73360Z2yIv71DwxE8BN/3SUmyBjeCXiRI4zq0kLHs/SwSxm4aRpuzlz7vBLOzs7OzsvHT4309xut1nt4q0jmX+PjiYePNXayq3z6ztdd2VGgeVMR/W67pt4OOlMWdrgER9md4dLJ53nzpw031mG6Zp9Lk7CKtdqz5O5dw7enSV4YQ37l9kukYr08S+dllXqhDO5bnjt+hiLV132TzeImvITgUxyie0kHEj163+mpkxlymW1feM6nZ8XxebxmfMXZ25sN/IV5bWzRzfO7JxZGV1Jyf7G8k5ju1mbUbZSRr2Mn3ji4qaFl9tSeDnTmljNPiQy/BQmV3I1c2ZGExQvwkkPyMUEBKZOOo3pYOokTHE1dW4ypg6OjaezBM//vfIrPss6OUj8XPIdL4frhuReuz42IiIiN6l59vqn7a1ELODD+Q2xDPH2QDVpjwEd283axbH15XFaW+m+cfolF68ePjNn81X5MdMcnLQqOOlEcNLdeFSvazaamDjp0qfdqSsFOafOgQ/G8ys+J0udgy7e8Yrjmds78zvxxn8j2hc5zvqQUxIdZEA56J827qT1WA9PNWnD0jqku24vitJz/bQ/r509ev3kuuWB5B7MNJz0gNzc0yI46cE4Uyet2Gji5hiG4dmhJ1gpWDmuPnNQVzSW6R3jknIKc0vVvBN76foQSxLdTB23lD8d0EzEfjM9pHofajg1NdWpjL2/sbyjWOhufc3bm7fo6tXRq+m/r47e6ri0cLtZO3r9pOXFh0TImTbipA8Kh2rOSWfmhwsZfI8LJz2Yk07zWVX5T7uj0j0Nlz+XUn5m5PDAvXD1WYT6jKIcaY9p4wfHro96fey0djcq29TxlszVqi09W06K00b9xkGl31Wox5zpL3zh+wX7zKm/3ur9x0/S0fm1xtw40dr8OTq5LlywjO5QvPfa2dGVE2qah67kiyzXpIWcpnlwc9KmJraYPbILuNFZSgUcTro934gmEiedk/+0O/Gf5EycQV9ciXeIf3ktk7kht/fu5MBZ8Uw2S4myQUQ3y76oNVxJcM8TXjJBH+Sq60NGfEjZuL+I8OlERrs+4pj0WA+Dz5Zt+g3fmR77G/Pnb50aHR0dHT116/x8UlFeW7l65PxMB1Ocpnm8ee7o6GituU2iUZroqtjX6Ojo6Nk1i6/bwzhxTX056QtkMmea5xRxvSxtdipp/q9t4goikupL6aR51YpE7YqVAxiw9pxKmwyS3x4cvEcuj6vQ32FS2uPEKpd6XzQWr7qfOu6gf3ohstL9mBkwbrR/Wh+ISEM5aTlanHrwP9lx4j1VpuP45aFXK+oB0gYV6jhxTR4ntpiafWg1rQZOWvKqO+nQ0+7UrAak14Hb4GVaubiU9k/X61y6y+7E7167Vsr+aUvriFqxHuXN9HClrQ06OWO763k4+RzagokteZ4pS4cwRbyUTlo81mThpMUz330RLcVBOgNleErr6by2PU9nBh4uL5wUI7fncF70zpdirfHDb616IzeSxE3/dJ4HMckljq3cx+NVii6lvCTdHZ01Pmd/BeGQqnTOtCoOTjoj40+FLKxZLquTZvF0NeiMDuld0MUBzqFWTcqzEYbnS2fOKpf6TrwRRZTG2Dnqnz6dGTlOdia5RGqyB/uViIl8Z3pAUsiZJhrCSWcGHxKcdCKDVzrHa8lZOOmsjSYmd/QeuchYQA40ODcuc6wDTdnjk0vtY1ai6Pdw0D9NZHol4uls8zSxc9L9Znr0Hoo3zJc4k6lBjhXNmeY5+zDp8UB3R8qjen0s96be3koXIgYrfvZF4aV9yZrfnyMHGpw9L0zZW8odt3zOL5XzyaV2nppnu386KU5bm8OQKPDp4tJ6XrlypWBvbWRn8jgvIWeai5NOZNpJ62VpQ1eEEjpp7xMTRM0pICet9HL4d0jg4ANw+Q5wMlOuZnF+deBMcqnvnN7IDh4PPX86caj5n7gaKxGlOvifwXI5PM4e7ypTaR6Vzpnm5qTjxQBq0oLDSRvm9boWGk187tx5nq4pTFZ6cXNI4OD9cpluPim7KXLbez/vNM4hl/pOLJckOsuf1ngwXR82potfUnglViLylVMzXTInnTRMG+xbMu2kM2Vpoysq8hhOenBe1wvSxPDOLRyGaqMF5+CEwMHN8uw4GOJ5Pgou3oSnltrT1PEJl/nT6jAXSysRWzL3LLd4YhqbmnQ8SwfhpIeQz2g88pQzPUHGZh8adNLGzzR7TnpaN35w0kNwkX6lce93aFViNmG9jtmE4BXirVp1xOt8LORLMS35W++xuOgyNU8OR7RUk275aaMrEQs4GyctxCXTY7tZS0YWikmG7bR2tmCoYe/QsALLmcYU8UE4nHQR9+yk90WJk85vz+QOLad8Ix8avMo8Of4zYdXE5zzV+D49MNltap6jqeNj8Wow3R0p1weMm7i/x6vWK3rOa9LbzZlzh1/a2dnZ2Xnp8LmZdnZ6u1m7eOvIwNC8QsqZjup13WeVZfahPkKchj3TWmVps1eE3GfgpAfhzKeCC9MwGRNDZxM0X0qzI3In0iCcz89VBS7eTPKfrShaPshT1sedWCxJdNP1sRGRVtxhvhKxJbOzjWdz3HL/NBFNTU1ZyejYvnGdzs+PExHR+Mz5izM3thv5eS1rZ49unNk5szK6MhC0IeRME/l20gWcpZOOrF25KuSkmU8FV2w0cXAw4fK2zvhzRLs5/r2BeK4G2bsjxzulwbj8vXHO3vGb9eFw6rgaPk2muz6mT2fHuJCB+2ks0/fMrY+KVymaMDPvQs4VF+pck/YY0LHdrF0cW18ep7WVQaAlVTRnWhW3TA+G3R2FHE66by6mgjO542o8a6OJlYNhxZeU2uQgjjm3/8H5bhFv932zvK8aOZx3nk8qbz55Tn6Rf0cvE16ykXlkt396YjoOJtPDemaX/Zq0YW03a0fPvUlEdOT8+nKj6/ai1Dy3f0BoTZXLmZ7Ijvnh4KQzPR5Dn1FJWdq0k9bK0oavXLnPlM1Jp0MN2d1xf4mZhW34UvoMQXecu9Tdueb2z473UyPv7LyZ/L288LT3g+mzJjnhJfXTpZw6PhavNq/9wt5KROP3U30gIgXjpA8sLspydO9DDaempjqVsfc3lncUC91pxSER0fbmLbp6dfRq+u+ro7fOr89Tr3A53zJiSE7NtHcnrYmDk87IyIoEC06ayKKTXojITQ3Dp5PmWbuSGR0aZ+JUXPICp7gbvmN247yVbpNq1rDFO4qlVpU64UzO97SL2nXXh8Op4/YmuUzbuZ9mNPR9P+n0MJSI0Nn//N2dnYJdGdT+4yfp6PxaY26caG3+HJ1cF+537ezoKXppZ25c8d5rZ0dXTuzMjRNR79CWnJppOOlCnjhJE046OaNMn/lqWRpOug++j+tT4FZGh/4pdk7FLF8q7DOGYzbUbZKvYVen/15docjtGdRSOonmTu58L8XU8bHTmeZpMt2FaHYlYmsgIpmcLm4qHa+vhAaxBlFvLk/VjnfU/sb8+drR0VEioiPn15MujbWVq0fOr9s0w8Mqivv/WfvVwpFo+i1aeJC23tI/1ZeTvkAGcqZFm8eQTjoZ10IGnHTS42FwNhKcNB8nnZapGN1ZJ9MaFU9HYpArvlmZmLCb2d6/Ey09HyH191+dbqI6y0lMk62gD3J5PdznYur4RuqnjfdPL5heiWh8urjZbDHNTKv+R1amRZvH1NRUL0566NWK283aDM1b6dEwNU7cWzQe+ZjYYsRJt8Spu0Nwq2k+tld4lMdJ78s88PV/BxVKlxhydB5WujV2E45uDZ882y2Tf4dT1mcjk6fV5YnE5DqQBn3QHbcTXtLIPLI/ddzGfarVPE1m7qdJN2b4KxGN1qQ7aGuDTs44Wkk4oCqXM83NSRtc2xvV6wWH73BnvixL23iv76ZW4cFJc+ubLHdudGal4K6+PS9nCb6b5buZxpuSvdNTGj94rVD0MivxTmy7f9reSsSWjE5Ay0VYDe6k9YA8V5keTpw0EY3PLXNu8SDyNQHR10pEjrMPrb43hZOuuJNOphjGJXTSS1HyH+2i7zlkviv/S7sjcn3tHI63gflk3JqhqG1ftVmJ9lci2qtVG1+JqA9EJC416XiWDhLRoOl4Fpx0GAomZ/oCkfYurgSzD82+N9XFtbsDThq50YPzltPaTTgLRwhuiu8S5bpBGB6HA/DJmFcutUj5cNw/bT9/esFaimsYMxEt16Q7J+K1c9K95+i5l6k5jpXLmdZUmtmHramHQiZWSDhIwoeTtsJFNVrl3p3EMDyTEbGb2Z6pIwQ32A1ST/rgS/DOkFsutfvUPKV5muxc/9WxiMxnIrZiPYyk4or9GOo+bed/DpxO/r5XrlzRPtuhJm1l8DgzIWfaAB94zayt5HauNWnKlqXL46TfYpMvK5xHOZz0UkSTceqkP4cujqry3eTfS2VZQcsql9p9al7qp+11dwg/HS8GsBKRiGOmR4dn+H/3+PGCL+m2EtHj7PGuCjLNA7MPO3FOTjqKyMaVqIROeh+PO6J00iWYwKI2ciz9MnXSue2ZOj9we3yXxHFS/1xrUky4OdZyeSKHro+l02rLB9m+ft6J3712zWqftI37V0bmposb7PaMV4m+n+WlyvRgLT8LEKWqWZMu4MM56VaPB2Mnrao8Tnop5uKk//x02E5arDxbipQVaaycHDgrvpv8txTRUsTxeO6RT8Y0Gfnvn6ZWywe5un5u6OvvDN8XFvQFlkxXIhbwIbo9M4KTdiifZrrKTjrT48GpJk3W3tPLsnSpnLTK/TrpPOfjGDpw4YSSVLtdtZeDgWMD5893k2cXyTsxZXtWx3kHzifrYymmfZG77rg7seqnjd8XxuJV1U+bXYmY5wPff3XHOYQfiFcpupTyIfyJDPQgOOme5c1Mu5/YIsRn9mEiZk6a7Kw4tO2kpSrkpCejpKbFyhn0zkVNEf3Q4Gb4rixUB1mrllkfgvt6Zy5W+jpLzUv9dBDpeJKLzumMhkyhle8pAsn0kIKTlvIztAUTWxKZOHOiCYoXTSbJT+eugcy7OygtS1fMSYe5EktpiWbgwMBLxndTXg9vJW7qpz1nfSzpn7J9XQ1xJWJGQ1eyklgPUxPcnDjpDgEdFXTSFFDOdJ77mtgy/JGa9HgYeg9q2EnnToPhrziiLA0nbYaHOxU8sdG7CWfkwMDLx3eJWgEgxOH474WL87peL3C0jq8zbxFNOpk6vrgY1etjp/VamcGViDYqRK1YD3Pdlcb8gM10PCnOAR1e5LrNw2M6XmGfk7fZh+bScKyuhICTznAOTnoyDsxJ6ysLifw7LfCK8F3R+5H25ee2Z3i+iJXEYnap5OWeOp5tnibTtWqteZpYrkQs7sMeyCe02qalBkrHy+MDi4sFG0NE5NhMD+yktfGHfmcf5vkA7/kM5koWcDZOWgh90ga4HAzu/U7fI8fKQnAufDGpVWcXKfI6XzQ+Gbe6Pjzn1ue4jeut4qcD6p82eP9lO11carDp4tWRUzONiS0JN1WTNroSMbIwhXUhonhx0d6VcSOacDe7S+OunXQchpOWK8CwshCcHd8NLE1vMvadPx1rixEtXlfvxBvRhCUnLYvTtmf6ek/H0zWok1YDPQhOugf5WYAoFZaTbkV5SA309MRUQrs+QpzY1aTL46R9TQufbDV9Erc7vcrVSStcnBM4eCHfTXnm5CIO51Eh9zsrUfhpN6mjNvunx+LV5rVfVMFJx6sUTVA8i5q0UyFnenDOKtODaKgzWS1LG3zvbnvGVfmddFqQJu939HYck1bAQ+W7au8H0/OLMi0f5NhJCznOn87KYK26sCtj+OniiYxkekz4r0lrgpPuUd4q03DSiRg4aVVm+8nsrdF+99ovKuGkJfd+R89zkT6WbeRg4JDAwfviu8Tz/NK4mJXoKzVPrBe6o3/KziSXaCx9emt4JeLp1YVowng3I1FpMz3a+Z//8Pp6wU6G0K/+j/9udoe9q0PGX18KI2eaycSWlgZ10pnBhzSUk870eAyZGJ+WpQ076Vw7C5x0rzwMJ72rc0YOCRy8X/5LopEE8VyfIGYlLnlaCb10WvZ7JNziJJfIagaUEM9MDyNdoFG9rrekkuF0vN97tOBUGkzfffV7phytR4WRM42JLZ04z5o0nPRgfJJ3HydWFoKXme8m/y+Ocz7nncpFy0d6aSpr/7TNlYgT07Hh+12c/wkGy/Q4TVFkoOLWapuWGmh9Vz4DzZI8plbv3bvXyH5cm+lwJ7Ykqw9L56RFWdrwGufcZwz3vTl42ujLSbOd2baUzmbz73hKxouvV73xXQavv5RcHOdc39l6nJUo+qdtr1exPMnF8HTx07kB2jxWIrZkrn/akuddXl62s+PuCrLNw+PElpLVpJMeD3416cbpupjSonLTTlof0ggnbZ0vRZj+PRAfoe7OuJnjH47qv+qN97L/dny3h9dfcb5L1BqjyOh8JK+zEt30T985rTZPE/uZiC2VxUnHsxRdIlmcFr7o7+7sFHzJ0EKbR3/yOPtw+Ikt7GYfmjhjo4jiRbu5m3DSPXG2TjqdAc7A2XDjI1le6Fyb/Thjlee/byEfeP/N9q9/N8u9/569812idJ3AZM5B+j1PJ9PGMMddH876p1MZ7vrInal8povHcXJfHtInJJ0ellcimhLaPIZVQJkew3f0R5cMnCFE6RliaO2wWSetlaVtXwHhpC3y1EYTL2fjkY+kT/9HiEj1qYacsWPe7vWPKBw1bJXv0hK/lcHi9UxGtORvwktoKxFl57QQt5p0KJkeWa00omcPb9x88lAKNi8fG5t9o7XB9HLcPNHmi9HmMZQCctJGuj5MniHm1g7be9pl9gq4EU1oZekyOGmRLsfNSSs2mvw7GI98JMsXo/qviP5cM808nLFB3szywho2z7+XA77Lt+vDsZNOFyParUnfid+9do1/pgeZvi/zyfTowUmvNKLaAhHRQ5e0zzx0SXXXHYQ2j8EVlpNmNfvQ1NrhqF6f1psmhrqyqGVpOOnuvK7baPLupLM2mrg4GGd8JNutka/d5vdTBa79HvLXgV1mf8eqdn04Ts1bsuykBbezElEUp80+m12IDM0kzn/GU/9053S8VCeacdzcvHxs7OVuW7YV2jwGVGVzpnUN5qRzDtjUGYuadMKr66R3M5yRg7HHRxS+GGC3hnue//18L+ewd4nL39ce59f14SU1L9s8TcavzzZXIvKcLp60TcufeFAnrQfkDeRbDhINHpD3xuxYJL59px4PQpvHYKpmzrQ+roV4rUSkoa8gsixtO2kfTto8F90mFXLS6vGsdTXktwfvhWsOe6Qa/db8uj5SP+2u60MJnyYb12fKjEU0Xakh7fHs8NPFk2pXWTI9BtehJ2/GT4oPVxpR7djhDi0faPPoW77S8UpQkzbrpKPsRYRnTZqItLI0nLR5Xn7HQ7L8jNqzKy5/zyMp2yVGx4NBvkt+z9/q9E/br9R4r0nrYuakB86ZPvHVSw+N3dogamem0ebRn8Kd2JLIt5POjBAnLjVpKTjp7pydk/5ceZ30iMKaxNFxVoHL9y2lr1Uzm/BSsv5pOysR1VgPVk46Sdlj5qTt5UyjzaM/BZozXb7Zh2pZ2sgVZCGieHER3R1dOB8n3ZoKntueqVPpkY8oKwjLnbkRHC99rXqXiGQXtf9atfv+6brlSS4hzEQU1x9TCQHDJxbEJlJ9aZCc6c3Lx/7kvpuiT3rz8tTsG9PLHXqm0eYxrELJ9CiTk1Zl8Api3EmrZWnra8bhpMX2/h3JYHwk5eji4M/LXaveTc4vDl0fjvunl07TW0QPKjyQlYhmZwAXZHGEnOkhKokdnbSMxqM3xqLZJA7v0H0fW6hFC8kmXdYfos1jOIXipIdPPk9WHw53JrR6PJg56ea1AJ30Puc1Gzhpi1kc6OIIkZe1Vr2YdH1wyI933z+dLkYMZSUi2+nipjI9dA2U6tsx0ONEM471AkY72kZo8xhcoThpI7MP41lGNWnR42HQSVt9+gYnbZKXykmPEBGyOErBS1mrbgV9kK/zXXKX/dNLMe2L4sXFkFYi5u5syPTowk0LbR4Dqi8nfYFomJzpCSUl0WemBw8nLWTYSecauwxc6ezVNlTuqGaTe3Pl/s66FFGrZpbb3r/z6IuPEBEmqpSRl6xWvUtELLo+HPdPW3XSFlYiMp8uXnonTWjzGExhTWxhMvswmiBT00oNJ9XnPjO8kxY9HracdFqWduSkJxk87V2KyjKjThxtWFlYeq7Vqrkdh33xxSTH3e91wHH/tO1JLtnmaeLXP80n0yMZ3TJ0t6pVoc2jb1VwYoupNblm3uNes9sfBied4XDShp00VhZWjYta9UgKdlPO8PjswHeJSB2X6K3ro0yTXBRxmy7OMNNjGCcdz1J0iW4LXzQ6SkT/Z6ItZfOuPuogkfpDaD5tJD3DO/i6wbLgDjxI02/RwoM0/WZ+cLRh/Ybtb6AKE1sSDTRFvIAP5qRzB9XATlpOPRRi3SetcjhpsX0AfIRoJF78HjI6qs2b6X8j6fHA5Pjsiydd1N6c9GLxfcT69dDWSsQJ8WHpp4vrMuUfDKXj9csHSzfO8359o205NdOY2ELEaCWiEJ+aNBFtRBMW++32OXzKCSdtIO1uhKiZ1ibz23t3eODueVOpVY+oc3nYHLed+a7nlYii2UPltlcisq9Jq3wh0nkJnLSR/mlTPmqwJImncpybk6Yq5EwPObElkVcnrQ8+JBZOWi1Lm0wpKoGTznM46V75SMqQdgfegbeaqimpufI8ngu513faymJErETszDlkemQC8pitRHTjpInoApHqpxk6aXJcmdYUSjpeaWrS6uBDYlaTFrwM3R1EWlkaTro3PiJK0el/HBwbOH/eTLsXRnLb8zzOKen3WEqqoB6uD5MxTUaOrodv5bjRmYh5PvR08YSXqSYdr1J0KeUD+ZnWl6dy5qQ18XTS5NFMh+Kkh+zcT8a1kH8nrWnIK4UsS5t00oW/fyNXXpF7CifNwknkeV2x0ZKzcmzgzLnsqJac4XGu8t1kVmIV+qf3tZonTM9EbHVOC3GoSWfEwEln5KkmbSqTja2TJl9mOhQnfcBUmky5nLSUYSd9JzfZBE66Xx6Skx5RVhYi7Q58eB7cCsU2jrZ8/dOCh9A/PRavmro/tvv7DpBm691Jx7N0MMWDTb7Lc9tO+puLi3loTx7MdEA500bWwJZvirgoSxueQZWTsSuvMyetcTjptnwkzQ/m4MDAy8SD6/rYlf0eCXffPy04ViKmvPC+P9h9M5+dNdh9PFt/JyLX6XhSvjI9TGXB2ZNrMz1YznSeh5KOZ+w9JbMp4madtMiWVrnJK68zJz3pNUc2DCc9gowOcPu8laOXcibHfyHfLX//dLsa/PDX+TvxRjRhu7IzTHpsRkjHI6KBMtmYO2lCzjSmiA/A48XFMGrSFXHSyWIm5k56RDZG+3Za4BXhiaXmcfx35r77p708uzP37NHCdPEWHz7TI1Hg/dMe0/GQM62rajnTxe8FPc0+NJhIH4yTdrbCxq+Tpl3eTnpEXV/IxmmBV4SLZyAjOe79vMi/zqLrlaP+aT1g2dZMRMmNdn1s5NoghrwfyfaM0qxEzAR6kDE/06OPuk0k/kIlzvTwGY1HTlYiBj+xxdCZacpJG08jUns8DDvppdyKxnI6aYbOIJ92Jzg3pwVeEd4k+nP2XR/++qezixGDqUlb658WlWk+TjqOKZoIPtOjxE6aSp8zPcx7qZZKMUU8jwdz0ur8cCGONWkvOdNw0hk+grQ7cGacf9eH1/5pwUNciWhhurjBrkhTz5aDdtKlT8dDznQXXpqJLZqYrESUZWnDV9h9kVaWtnWHSMvScNIKr6dOWuV8HBV4lTn/rg9P/dOhr0QkIrOzeA1VoBqn61FuSrmpihjS8Tpzl0LOdCduKk2mHE5anR8uVPWaNJx0Aa8jNxqcPWee9eGpf9rlJBeVG5suzjTTQ5en/ukKpuPloT0hZ7r8E1vYThEXZWnzTjpbloaTdsjrmo0mFs4JHDzPmWd9eM2fVnlAKxFNr+dRFbqTFjw/FbzETho50zr3lTM9ZC5jMkh8CCfdGtdCZevukEJNuj8OJw0ObpIvMp7w4qN/GisRU64G5HFw0skaRKGKpeMhZ1pX5XKmfdekNbFz0ouL5q+kSlkaKw4d8l/BSYOHyeWEF8n5nF8++qdTPx3eSkRlGSJxqkknadO+0/F0uU3Hk0LOtAFVyknzmX0oNOQVQTZMm5zaGmJNmpKytNM7XDqWhXjd6Sl9Ss7QIYGD98Wb6ZR7VucXeeufxkpEIuKX6YF0PCFuTppKnzMd+sSWEk8Rt3IlTcvSZXPStMvVSS/ycELl5o148cNRvU7UUP5j+DpD5822Dq9q/dPhrkQ0Ol3cYKaHkZTb0NPxSuykieg3fX1jcpUzfTvHQ0nHM/VelqmTvpObqIKadJ6nBWnyf0fXBCfdI2/keDvH1oFfJ3osixs97wcTc/riokS9q3AO590uLbldpzF5miadrOSWnOd08dwZNuD9NP+ZgdLxcmMegnHSpc+Z9mamQ0nHC332oakp4ravUAaupPsiWopL1SfN1EmPUOLYGDohVlwc5Y9l+ckiZ9yV5/df6LAL99NIt4fD7pG3/DSn826XlqJ4cbEkmR6UxnrYmDMQRZZSXAeuTIm26SETBeLTFEWU+cn6r9BF9Xo8m/uE/UyPqF7PvxEojZMmX2bajZOeINLK0s6cdBLlITXQ7MM4f63wN0Xcan5nADVpIlogb3cyRnf0EaImD8fDkMvui85ON78fq/yx3hw2ZlWqaqbPXpicd4K7vf6IlYgOKhSMMz1Uccj0yMhT/3RA6XgL/9qdw/ZgpkPJmS7BxJaoXp/OXVsGnCJ+Wn9XOfAVSk49FDJzxdwXxYuLFq/4k85rQhxXHI7ASee42sXxGHlzzMM7bPWdABy2/D3s5rjf8zFp9mhxq+/kJy076SULcwayGtJJj8WrC9GEwXVHlXXSt4kOEkkb4SZn+sCD+c/YEnKmSz6xRRO3/mmDfXLW155X2kmPwEmnamRXBD6m/MfNMffFHyN6TKmpi/8aaQ2bz+/fJW9qEdQMzsfMYkQXXWe2VyLaWImeZuShJt2F959R5iUdL4icaaeV6YBypo30FZXDSZudIq6WpU3WJKw66V96mj3m/84tNCIqlL6djUeeKT/3398cLhfvEGT/dwWnxDPsn04WI5ZlJaJ+62FSk27t39y6o6CdtMdMj6hefyrHWTlpQs507zw4J812iriQ7Smy1msn9u5kSxHRLps790iFnXQ9Lc22ys+cnK5LnilUE9O/l9X+aSbnY8oxXbwzN7pifiHS+WD30yi3nwESBcyk7objpENZiVjynOlAJ7a0BomXq7tDlqUNO+mlXMqeWSc9mds/nHSyfSm57OJo9W+k2/N0us54YqmVWrW2Pau/o0HOLX+6zesJdyai4JYrLOj6SBRaOt4q0QWFM3TS5NdM9/IbuTBExzomtggxcdJShlecWHXSXtLx4KT98EbaD80qi4Mhl7+fSk2Q4dY/7XySS1jTxbOjxQlOOpWpZIXBumfzvAQ1aSFvZtrNxJY8d+OkDSSrD3emyR4PblPEDV8x9+mPzQxf8Sfdrp0XObJw0k55I/1fdQVefntujpYDfyxbq+b59zXIW37at5PO8dBnIi4uss/0IArfSYvU3QJe0nQ8l/JjpjGxpTPnM/tQOHIjTnojmrB9xQy7Jg0n7ZTXlX7oH4afxeGXi3cgmUI1Mf27l6l/OilOu1qJmDR7tLjZ622+MsLDSUsZuZ9GkedMj3iVoktZXlIn/c3FxTy0pwBypqvmpI1MbDGVi7kQUbxobE10YE46W5Z2UvvhcIeWnI+DMcjlFG50cRjn8lcqJ8Lw+bsb5Kzyp3fLsxJR48PfF+7EG1FkaiUiMj2ETHXPuknHQ850S5jYkshTn7TBNdFW8kQlD7omLTgXJz1Sxjxp9EO75I9la9Xq9kyOhyE5q/7pousqViIKbvn+NdgEtDzv6/4exz7T8bzkTLfr2mXV9eHUTGNiy4A8/CniUb2uTj0UGvaKuS9JP4WTNsdHyuWkG2q2HfqhnfPH1HWKDI4Hg5xP/3RZViKmGXk8Mz1k27SUx/5ppOMRMydNyJku5EY69Ad20q1cPCFDZ5qp99DsViJaurKnPR7eZozBSQ/O67l8aA7OsrL8sWytWtue4fETXP90y08HvxIRmR6DcVdOOp6lgwqGk5Yqbc70BNHtCk9s0VTadLwy1KTrbPovWTmVgXkd+dCM+WPlStNjlj8dev90u99niVYiJmsQhSqWjtfu71sCJ038c6ZVVWRiS0tw0h34viheXLTlpCddTetl5KRHSrFiDP3Q/Plj5Zr8wqR/2uFKxGysRyj908Pfv0SnR/A16TDT8eQ4cZ5OmkqcM42JLUIDn/kyYVpomCuRHHxI5q6MFp204L5qPHDSg3MOThG8Ry5XgpYgTY9J/3TRfTCUmrTdTI8JY5UgQ5ke3tPxdPF20pROQGTrpKnEOdO+JraY6frw7aQ18apJU9uVJWau4JNRvLjowkkvRVpZGk56IN6Akw6ZtxYpMjuu+uIc+qcdrkScjGkysuKk02WIZPB+wSzTY/h0vGRV1RDdpJmo6UDS8ZjnTHsw08iZ7syHPNNMzT6UYuqk7U0Rh5NOtufjVDrwBjI6SsEfS/spw12hyKF/2u1KROY1acltrqQf7D4bRblPBJIz7Ssdb7Cc6Ty3J+RMG3bS0SXKPEMx1c+EmnRnbrBG4uVpqbeaFpw0OB8uq9SSszreunIm/dP+MogYOmmjmR5aQB7S8YSQM03ImeY2sSWq12O95OrBSYuG6SpOEa/X1amHCTd+Z8qWpb0+HWboSHrhDTjp8vLH0hmKfI633rn3/mlXKxHtTRevQKZHRqGl42lCOp4QcqYLuN+caV19nlGmVkgYXLERjJP2shIRTrpv3sAElrLzx0LO+vDeP+3j2Rr/lYhEBCedymM6XimdNJU4Z1qogjnTplZI8J0ing4+JONX8HRQS4vbuAMpZWk46T55I3XSrJwfuCX+WLDTyP32T7taiRjQdHGjmR6hO2mP6XjImbYiOOku3F+mR6VnH0qOmjQvLqeC83R+4JayPkoz4aWk/dMVy/Ro904pICdtpOvDfTqeEE8nTciZ7sorOLElqtfHckGUgzlpNWFaqOpOOi1Ls3sKzM55FDppJg4P3CV/LMzUvMxiROfne1KcDjjT405MBu8XzDI92jlyOOnOnK2TJuRMd+bBTWyRuXhCJcz02BfRUmzFSSs9HqWuSdeJmjnu3Xl04A046cpz0R8fbmpeSWvSyUrESOdGrs/7ItsZUMF3fXhy0siZLhRyptnlTGNiSxceaE2akrI0nHQ/vJHGpTF0eOCO+WNZzuH47Mz9rkRs07cdSteH5ZXroTvpIZ+Bx6vp3BbkTBsScqZ1fpAonvXjpKMJGn42klBpnbSlp4ppWRo1aTa8VZAmFk4OnAP/YWipeYteZyLmeChOWvC0bbrF4aQVns3OJqIyp+MhZzqjUHKmA519KMXEScuGaZNP6wKtSQvu884anJN+DE4avIgHl5o3kuPOnHQm1qPqmR7p6JZyOOkC7iodL56lgwpGzrQQcqZ1+Z3YUsDDdNJStleQBOOk/8U1XjUqps6D1II0sXNy4Ex4QKl5flcipjysmnSeoybdmQeSjoecaVvi5qSHnNjSmiUe7MQWg7MPyayTttc/PRnFi4t+pu86urOOaGVpBg6jkDfgpMF75iGm5rl30ru0FIWa6ZEG5CEdrwsPJ9MDOdNWxNBJh54zbeQMr9zsQ0dOejf3feGkVZ5p7SAujg2cMw8lNc/3SsRwMz0M3i+QjteZIx1vOCFnugsPy0lPxzr2OfvQrJNWBh+SwSv1L52kR+U5nHSGN3JZDXwcGzhn3iazgt1x7nUl4lLO6YbS9YFMj84c6XgduUshZ7oTr+bEljxnMUVc5QaddOHrNHunUeaHJxxOugvn5tjAw+LcjmfvKxGzPBQn3e76PPB9JF2DKBS6kzbmNwJJx0POtK4S50wPM7ElmqBYFF6RM92Zh5szLbi7O2goThp90uDD8MxiRAbHc4+8XE7aUqaHMlc84ahJp9zMhLhA0vGQM62Lf850IkxsISI+Tjrt8TB8pVamHibc+J0mW5ZGdkeOw0mDD88fCyF/uhXrUdrp4sj0GIh7W4kYiJNGzrQu5Ex34b6dtIjykJyLk5YcNek++IjCOTiJQg4nDW6KP8bgeO6Rl6sm3fq+djI9JOfkpMfi1dCdtKkUYORMCyFnWpevnOmSTWyR4uuks2VpK3capSzt3Ek3U87NSaBPGtweb+Q4q+O8WeZMj8L7KTMnbXKFvYnsrGGcdBxTqwM8nJzpsqbjIWda50aOsIpPbDE/+9B2zaNUNWmVs3ISGse0cPAKrkQsdBilyPRI26Zb3Mj1eSkucTpe1XKmS5zpgZzp7jyIdLwoouEntgixq0kHP0UcTroz5+bAwMPlj7Gf5NLUYj1K2/XBM9MjG+hBvvunI/0NDpx0F87TSRNyprvyIJw0UamdtI2nh2mPRxmd9AhR07dj6MqTsjQPBwZeJv5YIJNcBIeTHohz6p+mMNPx4lWKLqUcOdNDq7Q5016ctMecaSP9WyanrQYx+9DqHWUp8jepgZtjgJMGd8mZT3JJitPI9BiQw0mn3Fc6HnKm8ypnzvTAf+mDRPFs+gm3OdMt+cv0sLoyo1pOOrmje3HSizwcQ1fOzYGBl5tzO/7LWJOejK04aSVtGk66Cy9pOh5ypnWVOGc6qteVWLlU4Thpqysz2E0R97W23cUdlJVjyPMG0WP8nBZ4yfhjvCe5tKudI9OjI+fkpJGOJ4WcaeKfMx2Kkx6yfzoZf+g7Z1qTfyet8bCmiOe5CyddeMXk4yTgpMGd8cdCmOSi8lJkeuQ5Myfd7p1MiOl4GYWTjoec6WGFiS3teLxaHictc/GEmDrp7OBDKomTLuTcHAMTpwVeEc7t+Je8mZ2sVMauD4PXbcvrcAJNx2tFTbvN9Ihn6SARmUsf7urrVokuKJybkybkTGvyNbGFflESJ62Jo5N20z+NKeIFvBEvLjJzWuDl5sxXIkpeIietpE0zTcfLyWP/tJH5EkjH68zdCDnTGR7oxJYooukYTnpQXp6a9IgcfEgcHQOcNLh7/pg2FpHTedEkGkGmR09cWYOY8PCddON0fTomPWraYTpeRkjHG06lzZmu1MQWKnFNGrMPB+R8HIPkDJ0WeNU4t/PCU+ZPWE46z0vhpPMc6XidOVsnTWXNmcbEFqHgnTRmHw7IuTkGOGlwj5z5SsSRHA8808N2BWT4+8udeCOaKIGTHrLrI8kfK286Xh7aE3KmMbElEceJLYVnFJx0MR8RPR6+nUEhLzzemDgt8Crwx1ieF776p4vOR7PXw7RtusX5OGnBDd3vwnXSRBRNBOOk+afjcc+ZHuA37jFnuoBXL2ea+8SWyYgmY8w+dMjrYt5hlrNyWuBV4IXv6DicL61YD1fvwHe1gLxguj4MPvM0cb8z9UwYOdODcT5OmpjnTAc0scVM10f46XhRva7m4gkxctKCl3b2IQdnoHE4aXAmnPNKRMFLlOmh8vKm45l6JoycaQrcSRNypgteZSWd9EJEY9n5jez6pzH7sBMfwRRxcPB+OLfzAk66B54N9CCvTvq+0/eOxasL2VeEdLyu3Iiv4+akCTnTmnzlTJt6b1raTA/MPuzCeTrphlaW5ueowCvFH0s5Nyft/lnWLi1FFq97kzFNRsj0GIyX2EmXONMDOdMZHmjOtBCc9IDc7B0lO66FHNWcCt+hcXMMTBwVeJV5g995gUyPgTicNBERIWe6M3cm5Ex34aGk48FJ98QnI22QePg1aeGkmznu3TFkytLMHBV4ZTlmIkpu+Rld4f7hpDty5Ex35mydNCFnujMPxUmXNmcaE1sG5N6dAZw0eEDc+/micfRPd+Rw0p25fSeNnOm8kDONnOlE7Jw0JrYMyLk5A27OCRxc5dzOFzjpjpxZznTQ6XjxajBOGjnTupAz3YV7yvTAxBbD3N0dcUTt8eDnDLg5J3DwVkYev/MFTrojN5qOtxFNaNxXOp63mrSndDzkTA8r5Ex34ZjY0pnzd9JLEdEuatIp5+CcwMFRk+7IQ3HS7bKYAu/68Jgz7SXTAznTBlTinOnoEmVjmkNy0mF0ffB30oLDSVOD6DF+zgkcXOV8zhfB262MhJNW+FKsRU2H7qSRMy0UupMm5Exr8pIzHU0Ye2/q10lvRBPa+EN2TtpBGlTVnbTg3JwTOLjKeeay57nV68ZuYE46z8N30siZplI4aULOtMa95EyXw0nnOTsn7WBii+va0ghRk58DWGTmnMDB87yR49zOo8DT8ZAzPRBHznRnztNJE3Kmu3JMbBmMc3TStrs+6nVtXAu5uCNycwAfZumcwME7c27nUfj901ZXkLNx0mKiuN90vNxCSuRMexBypjtx5EwPxpk66RJObAnlaTU35wQOrnJu50v4ThrpeAFlegSSjoecaV1lzZn25aSprDVpTGzpzlk5AMHzT8+5OSdwcJXzcdJNopHyOOnJmCajFmeYjmczwyoUJ21qnRhyppEzjYktRAbfo2Nii+C++h0ZOQPBuTkncHCVcztfPK1gDqUmXcZ0vOkYOdMDcj5OmpAzjYktgmNii0le3XQ8TBEHD4U/xuB8yfPC+yCcdEcespOm6qXjIWfagEqcM13Aw3HSmNhikrtz0s0s5+YMWDknx3xjg16J0/+oXt+gu0RE9FZczAfY/1vTBfy/LSc731B4Cz7E5ffDhAfxbAdOuiMP30lXLR0POdNWxC3Tw0vONBEmtnTj/J2069mHI1nOzQEwdE6O+YEGPUaLixE9FtHvbNBPLhERPRjRY1HC/8km/b3v057+9/9WTH99qIDfvUR/SWL/9V9R/c80eIz+682H/xc2vx+enNt5ZH8Fs+3cfTjp8qbjIWdaFXKmMxw501RiJ239zlHZ7g6Nc3NIfnizxf/uu/Tf78tsf+0S/TXRJ2b73P80vRLTLy/TP9nM7F/o/7iPDrwmtl/88CbdmVYgEd28eGLzR/922trPGz7ndh4FnjNdmXQ8IY/peGa6TJEzPZyQM92FI2d6MM7OSdue2ILZhwnn5pD88EP0vzSU7ovP0t97V9n+IfrJk/RPppKy9Fuy60N8sNx+/wv0WEQPz+r7F7o7Rv9T6rD3bNAvD6fwZ2I/Z/5zCt39HsLhTM6jpnjo5Oqd+S4tRRkeStcHnHRnjpxp50LOdCceSjoeByetzhLn6KRdTGzJ8co5acw+JCKKL35sk37UbLVH//UGPTzb2v6tm/RbDRp7I9n8wbQbJPmv1tP+/3MO/++Hxj52TN/+fz9EdJPb74cLf0wkOfI7j8qSjqdxZk4aOdOJkDNtQsiZDt5JY2JLr7xUE1t2+TkAzD5M+ZmfiZ5p1R+n2/+3ZdpaoQcXetv/dMuR/7fs/nPbX3xocyPP/ycqcNimf96wObfzCOl4HTlypjtz5Ex35PaEnGnkTBMxdNI2Vq4oDzQDd9LEMme68PVwc06e+TT95Qn6nWzt+X9TIz5Em4fcfqHlyP9+l/2f+c8b9L9/LAF3x+i3bhHFixMrG29okMPvgQ1nOEPUxzOuUJx0GXOmSfgH5EwH3vVh30xH0fRbRETTbyFnuhv3lOnBcWKL1ZUrZXDShZxbjY2bc/LO32rSPzlGfz/LH1R7PCJ67MSA+/+779LWV+kuET1E//UQ7WsunozqP9PgAovfAx/O7R0p0vEcdn3ciTeiCcmRM52oRDnT0nxSFOW3NCsXlemFB9P/7fM3gpzpAbivnGmTT98CTccTHE464Qydk19+9xJtEf31TSVnWjZvDL7/i39ns/6/TRMR7Zml39mglZheuUn/My3+gViJqMJj9PcZ/B5Y8aRtWuHczqOQ0/EmY75OOqvgVyIG4qRd5kx/UzWf9vWbLr5Je3HL9KhmzvT0afp3WT5g39gd/SIFJ+2Kc3MAPJ2TMz42VsD3zNJjsxb2r6Td/f0aPZbbPoEmvm/pObfzyEm32GTuum12ZfZSbv9w0h05cqY7c1bdHVLImc7wEHOmp9NldQOfyQvZByDs+qeRM92Fc3MA3BxSufndw7RvoY/tweGkO/JQuj7gpFOOnOkO3JmQM92F88+ZFlaYQzqeKnZOGjnTjjg3h1R6vmc2bcLm8XoC5dzOIzjpjpxZOh5ypoUq66SJec70U+kHyJnuzOGkB+TImTbMuTkkcPBeOLfzyKWTbs1tqZyTNpSOh5xpIW45047FOmf6AhEhZ7obR870gDz4/mluqV6Y2AIeImd4HqEm7SRn2lDXB3KmiWXOdB5aFXKmkTNNxNBJ28iZVnnwTho50+DgRngQ55G968kITcbBOGnkTHfmyJnuyK3KtZlGznQnjpxpyQuPBzjpLpzb02qGzgkcvCvndh6hJt2RB+6kkTMtFLSTJr9pHkLImU6EnOnOnLmTnoyJRghOusW5OaSy8Th+WfxHp0/H8cveX09ZOLfzKOScabF/OGnkTCvcWc60Y3k209wyPaqZM53nA15Z9kUar4qTlhxOOuHcHFI5OV27FkWPR9HjTF5P+JzbeeSpW8z2ymw46Y4cOdOdOUMnTW7MtJzo+LUs5+akA82ZlkKmRxfuoAYDJ83RIZWTExGr1xMmf4WomXJu51H46Xj1Oi3FOoeT7siRM92Z9+4bv6aOE7cv+2Y6jgsnOjJ00oHmTAvBSXfnXmowcNLgdnge8nydgXBu51H4Tpp/Ot6+CDnTiUqaM90yn7H+ps64WOdMSyFnujOHkx6QB9/1wc0BcHNIJeettmkerydYzu08gpPuyJlNbEHOtBC3nGnH6Xisc6aFkDPdmSNnekAevJNmmI/LyiGx4r+TrBTc+JcPtd3+H13aeHl5WuMZmPK/vrTx8krjcdEwLf5j9vMGxLk5aeRMd+QGV7qbWHmPnGkhhjnTjvunkTONnGkihk4aOdPdeeFxyC0fl5tz8sL/Ubz7FeF9j33/EzeXf6dw+4cu/dGTh3SuQsk16O/nCp2/wu8dqfucaaKAnDRypjtz5Ex35Fbl1Ez3+5MjZ3ow7i1n2nLNAE66G+dWY2PlnHzyld/6q9cWiIjeePknmyd+ezq//UP/8spn/3ZlM8sV2OJZyPPnDYTzdNK7OY6JLR154E4aOdNCQTtpcmym++13Qc70ANxXznS7p10DXunuxDQZZTicdBcOJ82V/z/eH9v825+Jf7/x/92gf3D4IW37f3Tpyie+P/Xyu0Qn/q9K10cKlf1nINOfNxjO7dkOJrZU0UkjZ5rCd9KEnGlN1cyZno5pI5pQObuuDzjpLpyDA2gSvZJyhs7JH//YP8g2ZdCT/yqz/UOX/uiz3//Xs2/Qk3tp5f91XYOkOGwV+v+5Ssg5nEcqx8SWjhxOmoiEbwnESSNn2oq4OWnkTAuV1kkjZ9oR5+aQPPDp5XQ24fLv0M/+dlP51An68OU/VrZ/6F9e+exPpmb/S7z4u5ff0yGR4rAVSJkatv+ftxSc23mEiS0dOZx0ypEz3YE702/6+sYMnbTHnOk4f47DSQtuduXKpP4p03eOXZ3DSVeSN/9/16JI8t/ZOPTbHyN6gyj+3sOb9O6tN1pbPvT4Jw4d2nczfoSIniSi5ssbh786RS1IRCeKIB06vXHpr8dSb+335w2QvyLHtRDH86gc6Xj6LQZOujMf8Bl1/jNOcqZv53BlnTQhZ7ozDyIdL47hpAflwXd9cHMASMcr5P/13c2Pf/XSP4oXTx7b/D8dWvmPC8rn3pidTaeCf/XyJq00Hh+b/S9vzM5G0eN07VoeRtHjdO37ErL8eYPj3M6jcjjpHGfmpJnkTC9E6UQR5Ex35MiZ1oWc6S4c6XiCIx2vCy/+u/vOIshzbs7JPf8vs2PPH3ryT+j06Zuf/cmx2l8SET30Lzfir04PuP9/z+PnKgfn46QbRLvlcdJLkTpLHDnTnTlyprty5jnTrts8kDPdhRt6yuMtHc+UkxZXuqVcVwacdBfOxxkIzs05+eJ/L4oez6A3/t9/svLVZmb7/zI79nhuPy2ochV6/blC59zOl7I4aY2b7da7k7svDHjf0W+FA1aUhr7/eqtJiy7T2Rzn56T5d30gZ9rMU4+gc6ZNpeOZrBkg0yNIJ90K9CCOzokV/0fN//l/cHo9FeTezxeNY/bhQNxr//RGNDEdZ7jjiS1RRPEqEQWTjoecaQNCznQn7jUdL88950znOZx0F87NGXBzTtz4P4n+8fOcXk+lOGYfphxOmogwsUUKOdODCjnTGVUzZzrP2fVPG7nyLtmf9eXaSe+mnI+TbhK9wsw5gYNrnKGTdt/1MWI9LRROeiBeYidd4kwP5ExnuK+caSPvTb076bF4lfZFkvNy0pT22+W5yZxp97WlBj8HgEwPcOa88Dz1ex41tGBNTGwp4JMR3UmaKuCkhXxNbEHOtCZvZpqhkx6m6yOepWSGoKdMD+9OWhNHJ13Org+eNbZXcpyhowIHl5zbO1JMbOnI4aRT7mViS7t1YpV10oSc6c48lHS8IXMupeCkDXNfd0R2zoCbcwKvMn9FHddCHM8XZHp05EbuL/siUxlWnie25IWc6ZTnoT0hZ7oM6XjImR6El8FJF3IOzqAV6+HbOYGDd+YczheVw0l35OWa2NKSDycdTRBypk3JtZkua8506Ol4Bq8sVhP14aRzfJeooXA+zgArEcH5cz7ni+Bw0h05JrZ05uWd2MK/6wM50xafeoTipE3lTCMdjyZjopFq16RVjpWI4Nx4q8eD3/kCJ92N27y/+OqfHjJnuqWBJrYUcORMDyTkTOdUPSfNtOvjTkyTUYsHk47nafICO2eAlYjgDDlPJ93AxJYBeeBOGjnTQqE7aULOtCZfOdOm3puWx0lrPJR0vHZr2O3eEXeZOYMOnImjAq8ml3383M4L9056hCZtVhaWIlqK4aQH4yV20iXO9EDOdIZ7yZlu+7QFTlrwsDI9JmOikQyvdKYHViKCc+MczguNu38HHmBNWgmZJh5OWp0ljpxpoWo6aULOdFceSjoeEyc9Fq+KDyrqpPPcUR/krroMkTg6Bm6OCrxq/BWiJr/zwlf/dIATW1TOwEmrQs60UGWdNCFnujMPxUnzypneFyEdL339WIlIRE1Ojgq8ypzVeSG5+9mH37N73cPEloE4cqY7c+RM60LOdJ6n9Vwi8pMzPR2b6+5AOh4yPQo4ViKCe+SvcJ0VmuelmH24FOucmZPGxBYSxiMQJ42caV3ImS7kyShyQjpelmcDPcjIFXkppqWojJkeu0QNfs4AXR/g3jlnJ50pSyMdrws35aR/dA0TWxIeiJPm3/WBnGmLTz2QjifErn/adr+gn0wPYuwYCCsRwf1xzueFyuGkO/KSTmxBzrRQ0E6akDNd8CoDdNJxTAtpARdOuju3/ZTTZ/90I8e5OQZuTgu89JzV8a/yVlnaoZMeoUklEyMUJ/1LYzVpbv3T3mrSyJk2KuRMZ4ScaaEyO+nfOi06PTLc+B0lG5CH/mkiwkpEcB+cz/FfjZr0UiQbpjH7sCuv2sSWEmd6IGc6w5EzTdyc9B37yf8lqUnnOTfH0C5Pl6EDAy8HLzzemJwXSVm6VE5a5ZjYMhDv674fReQlZzq6RI5zpieIniIirk6akDPdlSMdr18+Fq+aTMcLN9NDcNd3yl3R6cHGMWi8qcV6sHRg4OXgdTk8XOHczgvH14ekx6PSTnpfJOchBO2kifw4afF9UZPWxDpn+ikikXIRRM50PDu4k45XKVL7DpCO15kH4aQnY9SkizhWIoLDSTeIdlGTHpDzqEnL8YeY2CLEzUkjZ1qXy5zp20TRpfQTbtPxWkI6Xmdu5Iq8FFu/oyDTowtn6MDAS8y5Hf9ldNJLkZVJW8ogcSZOWironGmiYHKmB5jYgpzpjJCONxgvVf+0kjaNTI/eOPNMD6xEBLfKX9HK0syO/4aPHHoiGsEUcSE4aSFTiQuY2ELMc6aRjtc7RzqeAV4SJ53nrJwEYSUiuE3O3EmTJydN3vL1mTlpU7MPMbFFCDnTQqxzpgfu+lglOphypOMNxsvvpLMBeVactBKQ59ZJ78riND8ngZWI4PY4fyf9YU9O+nt+nsVxc9KGZh8amdiyEBm774cysQU507bEMNMD6Xg0tJMWgR5ktJaAmvRAnKGTQP80uDPO7Thv90zGiZNWZrUk3Ox1T0mYTriR6/NkxHD2oZGuD49OGjnTxoWc6e4c6XiDcZPpeA+flm3TLQ4n3YUzdBIqT4rT/BwYeKA8U5Zmc5yrvC5HHiq8jCsRDV6fLWdDhTuxJYooXg3MSR8kWs3ycjhpKnHOtJd0PCJvThrpeANy6066+O/iJNOj8PuychhYiQhuivN30pXJ9DB4feY6+1Dm4gl5W4noyUkjZzov1jnTUi7T8TLq/wgb+MjORE0jHa8zN1jz8NJHiFp1ixeeR96dGXhYPAgn3dDK0g6vAyNqj0dIThqZHu25r4ktyJkuVDlzpn1leoSeMx1G1wcyPXrlu2pGnm8n0Y5jJSL4kDwIJ53n5apJpw3Thq/PSsJ0wuGkU+5rYgtypguFnOnypOOp8u6k5RpEIQNXwDsxTUZBOmlvmR4q5+YkVC78NPqnwYfnHI7nQp4pS5fNSUvOvCadDhIvgZOu1MSWIHKmf9PlNxssZ3o1x8vqpIdc2xvHFEU0Hft30ppYZ3osxTQZiWegJapJa9y7k+jKmyln5czA+fNWWZrT8dyZO74OjDi6vjF30qngpIUCmtgSRP90OXOmVYWSM12ydDwpk5kehX8vzjVpqcnYk5Pe5eckOvNXcpyDYwPnyUNx0q2ydDlr0pamiKucmZMePjtrIaI49jmxxUvOdFknthBypvN8mCMsnqVogogCTsebjmkjmuDopIPO9Gg3GwzTxXXeavkgLo4NnCcPxUmr3L2THqHJ2EVmkQ0nnTZMc5t9+O61XwRdk/aYjlfWiS2EnOleeKXS8cjcFcd8Ot6dWE2bDizTQ+mcTr8vuj4KeVNYah6ODZwhfyUoJ52UpctZk+5QKWBWkzY4scXUfbZqTrrc6XjIme7CXabjZRR+Ot59p+9V1yAKsatJlz/Tg0KYLp7n7XKyvTs5cO9Ougkn3RsfiRcXXTjppdxsRZZOWqw+VBXuxJZEPpx0dMmYvyqNkybkTOf5baLoUvoJt+l4rajpsmR6aOLrpJdiWlJq3pacdBr16uPOukvUYOMweuSZlg9i4eTA/fJPIwWvH27fSdtbiWh2injpViJGkbnKXUkntiBnWldAOdNIx+vA+Tppjdu904z4vLMW13q5OQ8tNQ9Tx8FlTVrlHI7PzrxBtOvvfP9ewE5aXK/gpNvzIZ10NEHxLBEFM7EFOdO6kDPdmQ+fjrcQEQ13hos1iOJjdk7a9prx0k4XF3xXW4zIyXl04Jg6XnH+Ciaz9M+LrjPGr2/prJaEG6x0wEl35JWa2BJEzrRTMz1YznSeIx2vM+dTkxajWwxneihrEBNu5Aq+ZH/NO/qnh+KvYFZiJXmmSZr8H4c98ka8uOjTSU/m+phDqUkLbm29DQcnneTiCWFiy0CclZMm5EwXciP5i+Gm4wkZzOMMIx3vl66cdDbWA5kePfNWywf5d3jgLp20yr0fhz1yj066iIflpBlPEd+IJgxORqvaxBbkTNsSQyeNdDwiMpjHWfy0kaGT/q3T6hrEhJeqJi21y8959M6RmlcRXg/ZSX/Yq5Me0crStq5jaY9HALMP90Vj8SqHmnRGFZvYgpxpW4KT7sK9rkSUbdNS7PqnA830oKQ47bWf8nNBTXLR+GLa9aFxho4QfGAn3YSTrmJNOs/Z1KST/Rt69lvBiS3lTsdDznQX7jhnunzpeKJtWnIDV8Y7MU1GgWd6cHgKvKv6aZaOpGvXxysKZ+gIwYdx0hr3frz1zv2e15mytMXr2FJES7GV67DS48Fw9iEmthCcdJGQM92JO86ZbqksTlpTAJkeSzEtRa5WIo7o3HXtalf4aZaOpBeeBFGzdITgg/FfwUmjJp1wcyvXDTrpaX1h5yD32aCdNHKmC4WcacPpePEs6f0RbtPxpErrpNtMhTWZb1r+TA+pXZaOpK/UPMxKLAF/JXXS+e1ZHW/MnXSrLO3i2VogTtrkTN+cfM0+9DWxBTnT7YScaV4508On44mo6TI7adv904XHoY07kxLrgf7p4XgTqXnBcvFsYZHlcdUXr5KT/hfWatKTEd2Jbd8vyrAScTgnPfDEFuRMtxNyptu85/PkpPmk403HjNPx7sRq2rTpTI9Yi/UoaU26A2foVLpypOaFxV+Ro1jgpA1zB07a6rNBs056XzQWr6q8BE56yG7S5OF5OE76h/X6UznOykkT/5zp/G+w9DnT2RN/kDPNyBnevGZsxcZ9p++1l8Af9krEyZiHk94NdpKLqibRp9MVbPntOThIcFLGGTaJmpyOn4G59/OXZFnakZNeyk2EqcAUcZEwLRR8Op4nJz1wr8GFLOfmpIl/zrT2G6xCOl6m5XqgM21BN65lTMezmumRFqe91Xg8OOxdogZLpzJw18crqFXz49JGC87z+OmLc3DSKfc1wxVTxHvmHGYf+prYcqC8E1uoxDnTq/6ctPd0PNE2LVXa/mnbK2Dc3JmyAxHJ45247Uo+hg6ml66PJia8sOF1oleIPh3gVPAgnPQITcaOrlfpoJYWt71eZZj7hdLjwcFJZxTmxJboEt0mIkxsyam0OdPDpOPdJooupZ+ocDredEyyOD3klUimTRvuh1M6pxNu8Gmjs5WIme/rt+tjV1uMyNLB9MXlhJdXkEvtnMuMjqAnsMBJO6lJ1+vaCHEyd7/AFPEuHDnTQ6u0OdNBZ3ogHW9AHmj/dFqc9u2k85yhgxmMtwrVqFXb56+UKKODuZMmcuCYJc+WpUPJmS5ZTTqK0oVVcNLdeB7a02+6/GZCcNJdeVSvx/lrQuhOWtR67+g/2FBXzDsx7Uuu71ami09GjtKmRG2JxR16l2ikLCvD8lz8XPI4f0zZnokTDZfLwn+T2P3dy+qkR9zl4gfnpPdFY/Eqtynioc8+9DWxZbCc6QMP5j9jS8iZLm06nqmpp1YT77nXpKu4EpHCn+TSlWcK1cTCiYbL61pAB/n/+1rllXTSGjd+vVVGiJO5+4Kp+5SpKeIcZh9GE2nINAUzsQU507pCyZkeMh2vNQTRk5NmW6uWndNC3J205JVbiViOSS69cJHOJh2htj1D58qHa/3QJVtZyN9Jk1MnrZSlA6hJC75oLN3V1BTx0GcfesyZznNWTpqCyJmeyPIqpONlVBYnrclkpkdhTTfElYiMZiJSuhixBPnTvfBMoZrYOVc+vAr90EE46e+VpCZNmbK0wXU1Juck5BTuxJZEcNKmxT1nWlMoTtrYe0c46cF4uLXqyZhohIGTFtotV/50V66m6RWe10wcrRfe6uWAk055NZz0krV1KRo3eF+w2Z3IZCViS4E46YG7dieI1Pl9DJ00lThnmohMdcq7T8dLoqaHO9Nk2vSQZ74MyDNyJRqLV807aavTxYm0AeP20/Ha5T0jf9oNX0xr1a8gTY+Isi3RDP9e1XPSzp6VqdySk07L0rYrLAPfv9TBhzTE/TQzroUGvL+3ojykwpnYgpxpK+KcM50R0vGIyP5KDtSkM5xd//Ru6fKne+Gy90NOKa9UX3Vds9HE5e/imPNz0pO5PGZ716WliJbikGrSFUjHa8ntxBapUNLxXAo508U8mdviNR2vgPe/UsFUmo/B/jNtGSIZuWJanS5OSXHa22QEFl0fu5Xpn9YkUwLVvupXytgNov1clVpZCCftvCYtuXEnrQw+JDjprrxnPyPGH4bipJEzrQvpeIkGWqkwnbvmDLYGeey09myJX/+07enijlOoJkVvyS4PJy20m6RxsXM8jvuqVd7M1ewXeThjoVfad+nkudrF0cxtz+T375IzOe8kd+6klyK719XJKF5c5FyTlj0ecNJCAzvpg0Sqh3DgpB3nTLs204Ol4+k+rre/xCrRQRpqjnwrjlGqn3S8eNVzpkd8mqKIhu/3mj5NC9GEjff6hmsS+zJjBch4TWUp0m5mlZnkIvliWrPUOEMn5LIbROP5bhA3Drvw+3Zwxvg7BuSki3jQTlocn8adtFKWRk26Cy/1xJY8tyqnZnqw9xZ5HkqmxzBHvFiDmKxXKF2mx1i8uhFZr0mUpH+aIqLdDPffPz1S+af/nXmziHd12K8ovMdassYLv+8Arx+ck2NO+IjTd/LiuLJ6/fxlhfqkFyIafh1UsvrQ68SW2znsZmLLUznOykmTYzNdKSeNdLzOPEgnrQwYd9c/nfZ7JJxFbWw3XR9Z1qnjXhx2L10WqDHDSRO5uf7YdtL1Ot3RP8XNSWOKeGeOnGkpDz3TqsqdjqdH2JAHJx3HFEXGrggGMz3uO30vRZkRsjT8lfROLDo9ylKTTsW0f1pwbk4oLI5aMh/eiBcX2ZxfKi+jk/6t00T6LcaAk94XjcWrBu9TRu6bYt1RnP8JMPtwIM7QSVO5c6aRjif3M/wVYTo27aTzr5NzTVpqKfaT6TEZ+76jt+NVmDoOXnoOJ63wwsqU2evqpOlKSirbKa6YfSjkbGKLJp5OmsqdM22k6yP0dLx2Z+Bg763zfJgrl5qRF8x0cbkSUeO+7nD+HfZulaaOg5ePN+CkM7xe15ZxUyhOel9kNsU1zwdz0vq4FvK2EhETW+wJOdOduEcnTffWkzmIqsraP43p4r1wdpNcpHYrNnUcvDS8QbQLJ93iDpx0npurpBh00trgQ/Kd6TH87MOqTWxxnDPtwUwHlDM9zJEn0vGIPKfjEbXmigsNfEWQc8WF+Dpp29PFKTNg3GnXh+Knfd/p83wx7fqo4KxE8OB4I3XS3M4jf07azcSWbFna5PXf9LNTVSVIxxsm7VcqICftuFbt2kwPljOd56Fkenh30prY1aTbdWXwr0n7mok4GRON+L7Td+C7auMHG+cEDq456V04aZ0vWc7pz3OjTnost+QfTroLR860OTk101XLmUY63mA8gOnigrd7J+Ag/5Vp/7TULtFuvPjhNq+TiaMCryBvpE6a1fmi8lI7aaUsbbu7b5j7kdrjMeR9c8H0vAiP6XhenHS7JAlWTpocm+lKOWmRjlfAQ3bSNjI91GWIZPAK62YlYp5XvX9a47vo+gBnwxvpkxPv50U7Xt7uDrLppJWph0KoSXfhhvwM0vGkfEbjkZOc6dDT8YxkerTbD4dMj8zrDG4lotI8TY7vfKz7p1W+i64PcN+8QfQvmE0+ynN/Tjpblg6pu8NOOp4oS/Nx0sPPPswIOdMWVPKcab+ZHq01iEKl6J/WliESZyftYCUitfw0+qfb8110fYB74q2+jpTzOS9UXnYnnZalzV/ns2VpI/ejhciYk2Yy+7A1SNyHkz5IVPqcaW8TEEvvpHWVwknnuZEr11i8+u610GrSqsTMRS9PY1vPHHaznKdj2JVzyFPO04GBl4AHdF6UurtD5exr0kS0EU2YmhncNHS/Dn32YemdNCFnujM3lXBeMicti9O2Z00FsxLR4yQX0T+dbaH27Qw6813Z+MHSgYGXg9dle7TCORz/ee7VSStlaYvXycmI7sRWnLRSljaYMWXKSWP2Ifmb2IKcaV2+cqbbHQGBOmmZNs2qJi25tgyRDNYwnK1E9Ng/TSTvx76dQY98N/27YBo5uEEu3qF9TrPRxO74h5NOt+dUkybTU8QX9ALLgE46F/oX0uxD5ExbUVg507eJokvZTzhPx0vmIPKoSbdeleWpreGtRNS4l6e0k7/l2xn0y3cxjRzcHGc7Fbwdr4CTFtzS9TwtS5t10trgQwp/ivjwsw+jS3Tbn5NGzrSuquVMC55drUdEA72nNNR3ZfDp1UbuB+PrpN2sRCR/k1wyfCSQ1DypXUwjBx+ON0Jz0vUKOenFxVBq0kjH68B9OWnkTBfIi5M2NbPH40pEI+l47fqu+HR9iE6P4GvSvia5SJ5toWbpJAq5cEKYRg7eF5dTwYNy0slJqvBSOmmxXiUcJ63OahEqh5NuRXlIBTL7MKCViCXPmW73niYgJ22wf1q2TUsNc6VQM/JMThe3dOV1uhIx1hYjIjWvZ76rNH6gVg3ejjeUzLtdBsdt77yu2Wgqq5O2XZPeF9nuNhzm/pjp8RjCSScJ00JVndjyVJYzdNJUhZzpfON+cE46aZuWYtY/bfgp275cJkZwKxEpsxjRW9dH8vOO5DhDh6HxXaxQBC/iddVD855lWMir5KQt16SNO2mtLF2OmnQBD3BiywWF83TS5NFMO0vH0xp7kY4nxHa6uKaAuz449E8HlpqX57tYoQiePqOoqx463Z7ncVvIK+akC++n5q7nbGvSGZXCSYvVh6qQM50Xcqa7cz3Qg/o7IuNZRk5adHqYulLYmC6uZuQFvBJR8MVFrysRU6V+mqXD6IXvYoViJXkju7JwN7c9k+OzF+7bSavcjZO+k/t5DV7PTd931LL08PfHpMcDU8RTjpxpKwooZ9pU18fwievxqrH3uLYT6bnXpFM/XfJJLpom4zRDILc9R+dRyLFCsTpc7YcOaGVhGy6eEWnc8XUgLUuXw0mP5YKXUZNux5PVh/6c9JBdHxNETyFnOq/BfiMTOR6Qky53poe6DJHMTRcPuLtD434nuWg82/JBPJ0HVihWlzeyLdEcjrdh+EjipPPbl9hJ286ZtuCkZVna4BwGTBHvwMs3sYWQMx2Kk2ab6aEqgOnigrtciUgtP+07f5qIKMzUvK4rFFGrDp3XtWWFxOt4G4CPcOnycuykJyOtLB3K7EOD3Y9matKGpoiHPvswiIktVJGc6XKk47HN9BDF6WCmiwuebZ4m23eapZiFkxYKNTWvkO+iVh0sl3+vz2nLCsn/cTUMH6mukyay66TTkYdSw993RFna9joiX1PEh599KISJLV31m16+q1QQKxFv07BOOp6laMLoSgI2TlrIRt7nWLy6ESVFjpJ0fSxFPic1qDy500clcjC7KZfHZ1PZno+DBJePEXY5HT+m+AhNsnnnXLKatAUnLWTQSTdO1yn/mcHu1/nP+FiJGF0iTGzpRT7NdBBOmtK1qPoAIQqsu0MFcUxRRKani5OlK53FlYj7IlqKnfZPT7b8tP877qTs594lXo5kGL6bgpGULzJwkOAtD51ynsfPwHyEiKrrpPM8kO4Os/fBzKwWocrPPjxItFp2J00ezXRATtpI14eRlQRVy/QYi1ffvWbzinwnjn/k9k6T+mkud1zh7JeiMmQmdK9VkyxX+3aWleLi97+b5XyOEyN8RJxKXM5r9046W5Y276SzZWmDXYVW74MD368zgw/J8+zD2zmMmnReYeRMIx1PclP9WHS6vqCHtvFy0i1ubyai4JO5/Venf1rwdisyuTiVIfmu1lrN0nGWjJe1HzrPR+CkrTvprGyvdPfupHUFnjPta2KL45xpD5XpAX4jUdFU8ICcNL1Zb7VNS7Hp+hAa8goyHdNCNDEWr4Y3EzHt90h41fqnk9S802rLB3FxKsa57NMdyfImJycaHM9fH3aZ/d2tOWk+XVtCpXTSSlnarJOe1uOwh7oPZno84KRTbsS/DZYFd+DB/GdsybWZRjpeouEyPYyfsUxr0kTkZiWir/5pIkY1rbTloxpOaDfH5fGMlYudeUPhi+mUb217739f23yEiOCkw3XS952+N7/Cj0lNutXjMZyTbjVMD+pbtEHiyJnuIKdm2peTHr6D/na6prUEOdPyX2IZosFUIHFnHTutvwsd/goo/HS8uFi2/mkimoy4pGgJiX5upg7GKt9NwUjKZfdLM7s9B0frmKvn+27Ktd8bZXl+P+XgI0SkPlbi4qSniaZL56QVmXXS6vxwoSHvg62yNGrSHXlZc6admmkvTlrkFA7fQV8yJy1lOxXI4JrrqF6nO/pF1uxsLVrK7d/2nUn8XJO57+u56yMpUSuvk6ezscF3Fb5LRGpPSLa/vJQ1bG0CzmIlHXM7PsKrO0tqKSqnk07L0ra7B5nUpFuCk055VK8/lePcnDQhZ7p3zicdz2ymR1SvT+euacNMFxed04LbfipnepLLabV5mpzdmZZOq5F5xOQOrQTn+XYw3vlulu+mfCTl7VZwCt7McS+OudHtdQrtKpzJ758DrxMRRyctTtJSpuBZc9JaWZqbkzY1RTz02YdhZXogZ3pwPkA6nsGZRnH+WmQoGZ5t/7TaPE2WruDKYkSndyYlgprFHVpIvp6K9sV25bsK322z/a622LHn3OuGsn2768nncispu27f4XUW8vx+Ksjrmo0mJufpkvOV00KTlq/DioKoSS9EFC8amyJupib9Czhpp6pEzvQq0UFK+uhLsxJRF5uuD1GctjETkRQ/bfEKfiemfVG8uOi6xrMUs+uflrwqWR+W+G6Oj+S2zzvgXWX73dz2cv+7RbzD9uC985HkfOTWhUWZgjSV0knviyxlQ6llaYPrhUw5aZMVt9kcdz77sCITWwg5071zsQYxkW8nLTI9WhrCSccxycBpztPFM/u3PV28XW3PTf/0Ui732vudm4gmY5qMRaouM8cTIt9V/1Nqxup/HF5nZflIy0nnt/d+PoqCNJz0cNzgeiFjNWlO/dOhTxF3nDPtwUwPljOd56F0d1SnJq3yjWhC44ZXIua54f7pWBvm4qp/OqalWPXT/u/cqibj9Pc/kute4OOEwMEH4yNyCAt3Jy15+Zw0EVnr7pBlaYP3O4OT1PTBh+Rtini7SXMBTWxxXKuuSs60qfdYpXTScUxkNtMjJ7NXxrHTmeZpsnRl99U/TfymjmtcPvXGCkXwkvARIn650RrPtnZQWZ30vsj2s02zTjrW2+mDr0mXY2JLnluV08q0Lyf96Ta1TPfpeGIOIrdMD2rz3nqYK850TLI4bWmSixwzbrd/ejLys0ae4dTxwhWKk3GyQiu/vX+HBA7elY8kgXf8nbTS2kFw0v1zUZY2nAabF5x0R17WnGmnZtqXkzaYjpfnA/QhmVqra/Dpkto5LWRkJeJGNBHedHGN++qfllPHNc7zTp+2U7c4U+cEDi55XbPR5P08KuRiHcVSNpWvlE6aKKCadON0PTM/XGgIJ53p8YCTTnnh/ZebkyZfCxClAnLSpro+ih3wQGeOvgyR2PVPW70yjsWr1q/sfvuniZL+6dRSc7zTY4UieGB8JHHSWRtNTM4jjdfrWkGaSuykf3TN3v1iI5qIFytRkx5+irgmpOP1Ip9mOjgnnQn0oAGP1KTTQxWn/mkKZyViwvflareW+qdTP+2hf3oppqWI450eKxTBQ+IjStody9xojQsnrXE46f55dZx0S8M56eiS4TThcjtpCiVn+ikGTlpX6VYixjFFEdm+4gTc9eErf1pw8X1FiZr5U+mCFYqUpr/ltmfktMDLykeS/2e7oreQe3fSKg/ZSRPZuq+1ejyGvv8mPR48nLTUkH5JhEzTEP5tgkidJc7WSVMoOdPDp+OtEh0so5NOOj3MTTE1fsUpyUpEwf32T1OrSi1MKkcHULxCUdaqs9uzcFrgJeYjra7osJy0SMnUuGMnnZalQ3fS7177hQ0n3VplxKkmHU0QpohLnof2FEbOdMnS8QxmehCRySmmp+sLetNEACsRhZ8uf/+01FIcRtaHypWOauGqGTgt8BLzkTAyOjSJbi4vtWeNP0hw0h14a+mhuUqWMT9gwkmXYGKL41q1azONdLzWfvhleuRxECsRqUPNuGT90xpX1iYmnJUzyHNZIxQrwFCrBjfP62FkdBQ6aV9dZFqf9KSrZ30/ulZ43WbupFsyOPvQqh/AxBb7qkrOtMH+6cwaRKESZXrYmC7uaCViTuXMn6aQuz5Ujlo1uEk+Ei9+TzmulO15Hv+a+DjpO7GzmnRUr4/pE/9M3heMz08QSsrS/Jw0t/5pI7Org3DS5HgBYgmcdJ4Pk+mRuYyUfSXidEwL0YS8dBp30mNxZiyio/7pJf1Tfro+BOfjDPqqVUuenF+7me15OTZwbnwk5Vynf3fl4p0wHycteCmc9HRuNiHDmnRUr+d+DT6niNObdRnlIYWJLV2FnOmhOJ+ViIkYr0RUZakmLRcjOu2fVlqo0fUxFNdr1SNsHBs4Kz7S+k+ucGV4PPfClyJaiuGkhYJw0gsRmV2nVMAH9QP6b5RHpodUiSe2kMdoPHLupFeJDhLdLqOTFp0exlciahcjIysRF6IJ6/3Tbpy0kLj97ItoKWbR9UGi5TEmVo6hL15QqxbaTThThwfuph+aguziyHPxvpfDdYPK5qTvO30v5e5IBtf/GLzPZqYeCg3hBzKVad9OWubiCZU+Z9pbZbrf34ipTnY+TppzpkceG7wSWV1x4nQlotQdHndEoYAmvPRYq8ZsxUrzkeD7oTUuniCJTB7v1w3xbK1cTnojmjBeCZKcZ01al28nrWlgJ/1UlrN10lSdnGkhPul48vWwzfSwtxIxz81eSZNmD2UyoqNa9WRuFqOvO6Wc8BJ014em1tP8EW28IifnB26Ej6iNHAF3cWhcsdHk8fqgOul0uSHBSffAzTppvSyNKeI5fkHhzHOmPbR5+E3Hu53jA6S9RPW63uZPA2Z6mFp5kHR6qBcRZjVpZysRSTRPE4n1iO66Pu7EiYNncqeUXR8U2uy3zlzWJuXKLaaOELxfnl43gku169VJKz+X9+uDUpCmsjhpIvPdiZJH9Xqc/8lQk+7I/eZMH3gw/xlbCiZn+ikiNVmtHCsRk04PVZxWIsaxrZq0HItoNR1vLF512j9NlJR5OKTmSckJL4XvDL07jGE4atXB8xGle6eutPSk27M63gbj9TotRTRNVXfSRdz49V8LYDXspHMrGoe5z2bK0pycdHTJ2DP86kxsoarlTK8SHVQ4n/7pRJyctOCWcjqFbOdMJ3xfrvui9FPHC7lIpFY5Q+cxcK1a76uuYyIMS15vm8UxqVsV/8fV8Lxel00dLV5NJ70v0srSNpy0Wpa23Z3IqiZtcIq4ESd9kAysc5sgeioQJ02OzTRypjtxQ5keJpPkT9fVzmmhUFYiqvnTHvqnOUwdz/OlmKYpyAkvPXLVmRWk7BUehwwdZ2n4SOb3r60oDTpzpjOXU8GXcu8Q/F4HsssNqUROWpNZJ72gtVDSsPfZVlnaYAKBER+C2YeDCjnTQ3GGmR5BTBcXXDZ7SJUkf1qIz9RxTWXt+sjz1vRyWfscQY61ZV7XCs+lWkHYlS9FXGYZ5nl2uSGVy0mrZWnmTrolU8+izU0RZzX7MKCJLRRWzjSfdLzbaV8Rt0wPG10fYiDitJ3+aXUxou3+6XevOe8LvBPTPq531qXTcm1iwvk4Enu8oB831wWU3Al2c5yhc/XORxS+mPQ9a9tz+Ls74KxyozVerxPVVRtNcNKD8uGddFKWNuekDfZP85l9KBIjnspxnk6aPJrpwdLxVnPcVzqe2acqpjI9iEiP9TC0EpGsXaHcTHJJeBS5vqMsLoo7Cru+ScrEfVSodqjxwtziydN6f3mr1rKb5RwcrW0+ovBszUl7Jzap/6r9/30d8CVOGT55XtdtNLlx0vU6kR7fEVZ3B4l5hxYyssw6aVNTxPM49EwPl/Jjpv2uRJwgA++9TKXjBbQScTp3zTTYP+3CSRONxasbkdsJBTxT81SJWpqoXZVgIoYpnq9hC6eomOw0lW+EiFSTzcMB98jT83GytT4VjrlXrtho4nZe+3bSDlLwhGRZOggnncwq5leTzuPQZx9+c3Fx4V+7c9jVypnm2T8t2qYNpuTYmC4en06aPaTMXrk2InJTw5B+2kNqHhHrrg+i1qLJMuVSm+W6sySimIjaOlG5nwzfVbjbWrLKNWesrgiEY+7MszaavJ+/hfzh03Qn93eEkx6Im6pMld5Ja6pOzrRrM10aJ520Tc+mn2BWkw5lurjLSS5SSf90ve7hTpM4mzrHrg/K1tjqdTiqPngvTlTy/px3/xy1ZHuc8/krNBkl3WX57eGke+YLprslE27aSWd6PIbzIbJhuhxOOs+tyqmZLo2T1sXMSSd9VKbXIFtdiUiKn3aTPz12OtPyQW7uNLJWxLbrQ61VV3CFohvel/Mekrv8uUrMZUbHUu73zOr8hZNOxdBJm10fpQs16Y7ctpyaaTjpdjyeNb+2N7iViETCqTuZ5EJE2RZqdH0UcKxQBAeXU+u9n49dOZx0Km7dHZacdKssDSfdkTuQz2g86vM38pShv9AqMyctuN33rEYnuSxkm6cp2P5pSv20tzsQ56wPKZn2tZQpVBMrxwMObpbLlaY80+40Lt/xwkkPzVtlaaP3Tcp/hllNms/sQ/l6QnHSFFbOtKl0PJFfeDvHPabjWZ2JGFz/tCbbXR9Jv6mXlTr8sz5UrhSqCbVq8LJy/hkdKhfno/sV1QoXXXMat3fdDtFJt0YeSjFz0nTEWGqwESf96aCcNHmcgFiO/unoUu4Tw2V6tMR4JaI6Y9zSJJd/RsnvwlH/dHbkOLl00kJiMtlkxOUO3YEvxbQUJ+9AlnITT7w7IXDwwbg4npcicYQnnM95l+dyNrhvJ53n9q7bYmhuQN0d9nKmowl2U8RNzT78dJv9sHXSVM2c6Txn1/XBeyUiUbIYMV60eEXbcDbJhYj89k9LHkTXh+Ramh5q1eDBcbUfmvnKQpWn1eiEV8lJ21sBT7IsbdpJ62Vpg5Uym8/GS9A/jZzpnvgwf6FVooNkOAVm+CM4CZzmPV289WoXrU9yiep1l08PPfdPB9f1IaSl6YnGQPRVg7Pl6uOUIPqhVZ610QQnnYqtk9ZlsFIGJ80pZ9p1mwcHJ61qyCNGpE0TmVwBYNxJi87pRJavFGavdNNx8lBPylH/dJ6j66MXLp6PL8Xp4/LIv3MCB6eiLg7RrcTzPCrk9bps6mhxL056X1Q+J53Iwv0xU5Y256QL71MD+xCZMC1UgtmHyJnuiTNx0i1VMtODqJU8LWSpf1oOcyl5/nSeh9X1oUqtqS+1piom2zN0WuCl5LKLo66PWSFW50tXXvc0FbzQSTvsvhNy4KQX7K/UN55+qws16Y7ctpxWplk5aSMpMGI/Bp+26MsQycyZFq/aqknLxYj2rnSiPu3GSYuPk/WI+yLy+hS11fUhVhoJzvBO3zUDRClXJzXC/PZ8HBh46Dx9MJKslBXPTLTtvZ8XvXDxbOrh03DSgTrpVlna7DoodeqhEJx0R+5AIeVME5lMXTGSjif4QbJ1ZPNfiUhkfZILkZ/+aSJ695pvJy0UxISXrryVV31aXbZIkzELBwYeNJf9+hROql0H7j03ujJOmigwJ13AMUW8I3ejiuZMc+76SJYhrto/A4Oa5DJ9utXvIeSo64N+uPHfPq1y/10fRGqljZED6J1rr184oclsNwtDxwbOjYsp36F3cTDLjdb5vsTZl9JJNwNacXhvndSph0IlrUmHNbGFPJpp7/3Tq0qgB7Fx0i1ZcNKZAeNBrUQUH7vvn07+sa+1gt7/HS6tUhMFmEXQjiNlD7x3vhTRZBxqql0HPsnpOiO1LxqLV51eb4kofCedlKV516SljPgfg7MPRe/AUznO1kmTr6Et3p20JoaZHsVrdVnWpJPXbHmSi5D7/mkiGotXRRc1ozucmNTwyzb9xxycwQBcZizIiTDZoTAsnBy4Fy6PBxHHMU2tfuj89kyO5965Mn6F4KQdOuni+yzXmjRpZWkTfiO6ZHL9mCknbarrIw/tqaI506rY1aRt1qrtrUQkcjHJhchD/7T6fYn0ZfWe73x39MYP/87ACFdrjZNKJgO6QSrCl5Sp9WXq4tA4p9zoCjppfRERmbk/ivtgEDVpU076gOUuXP45067NdFmdtPFZRK3OaSGj+ZRx/loayCQXz/3Tp1eJaCNi9jRWafwoT61OlbpykdANUlIu3ilJZdIV9S/hdXwOxhm+M5faF43Fq+TcSYupAqE7aSJbTrpVljaaxmsqiSHPK7USsdI502bfk+mBHsSrJi15pnNayGytOvcZe/3TROS+diJMPItZiarkiqV9qdcsWa1aSvu5ljLBIMTTKYJXtvZcyOtscqM1vi+iVpaRayctlrA7cNKN0xYrSnQtjJo0Zyc9QSQbpoNw0uTYTA//G3nKnJMWfIJIO5gGO5JE27SNNbbBrUR0MMlFKL3yun4KKcS064OUR8YlrlWrXDVhk1Hr70KYas6MV6323I6L7iyNe79uKDaaSu2krT6bpWu2Jq8lZWnL3aQcnLSqUJw0BZczXZF0PKF4NrB0vNYrT/20mxrDRkRj2atXpbs+pEStWjqY0NP0euFatsNktvapPTRBvrVVvpTtxqla7VnjDHOjs07afdec/KdrJ22tu8Oek05k1ElrI8RpOP+jDhKvoJOm4HKmOfdPt4rTIUwXt7oSkcjFJBfJ1cg8cn4nYNr1Ibmsge1TnCUTh2Gbd6195h02oQ+7Z65lrWjvWPRunCrVniWX/f2scqNVni1IE5z0cDzO/8Qm7tfRhPl1WZpQkx5S1c2Z1sSqJi25vgyRglmJKN6j216JKP8p/bT7tedCfLs+pGTtvFK16t4zQzI80nm+tj0ZJxNDeDpdU7yDY84fP9V0zHmueOiE87kOSF6vE9W9PNOT/yyZk26NPJQydL+umpMObmIL+TLTZXXSMVYiKjw+nWmeJstXzOnYW/90GF0f7WrVpZn8YpBnGxLaOsV/sdiH8+bgjFXJ2YGT+o8Axzw4Z/6OWvK6bqPJrZOWwR0EJ90LN+2k1R4Pbk7a1MSWby4uLvxrdw4bOdPGjiRMF89zdTGis/5p8vrUknvXhyqtVl3oIPP7Add4L85bWxmpbt+Bd8i+6Gs/7bh8ndNwzENwtR+a4cpCVfsiMRXcfU6/5qQdrFCX3IGTLuBmnyHP5jizmjTDiS3Ime6JG3yaYDUdhvlKxFZxOvBJLmr/NJGnqeOKAuj6ULlwAEqmHiOnUg7eeep1L/3cvWRf9MuH/7mqzPn3Q2tKnXR+ezjpIblelubtpGVZ2mAlkeHEljy3KqfjxI38Royn4+X5MEdYa7Q4GV6JWMBDWIlIqbNc0B+Gl23quMblEPLWyj9Wd9Y8F9OMyzelPFCeDldP/vP+esBlP7Q6/TsIJ70vgpNuKRwnbTXTw/Yz+YH9mBoyTYE4aXJspo38Rr65WOwsOTjpjCysRMzI6HvfAm62fzqmOCbVT9u+ksZtjhP3XR/CUjO6s/bC78Qt0yA4T2cDDu6M1+uqh044z/NXal8kYu+8O+mNaAJOul/eGnkoVXYnrSkUJ02OzXRe3Lo+jBxJt8l6srqNlYgZWcufFn7aTf/0dEz/jCY2lHchnrs+9uVWqjG54xZyaRomI47OBhzcAZ+MkuNfvMMMomtLKLXRHjOOVCc9HbuY5CV56H3S9mrS0SWTs58FV0OmqZJOmpAzrcps/5DxPierKxGtzkRU5TJ/Wsh7al5gWR95LlZW7YvoTiwDQIiV4wEHN8uD64fOOWlfuft5rhakqVxOOlOWNn1f1svShipxxp20pmo6afJYmS6xk5bTxTNiWZNWQVKftv3e/doinXbaP01E0zG9e+0X6PoYiouanFqrRl81eMl4iP3QGk8L0oL7ddJaawfBSffMLTlpOgInbUt+zHS5nXSeG3xPaW8lYqf9m74SOe6fll0fG9nGc49dH/edvlddm5hw73fiHnmSAqb0VYt0MJ4OCRy8A5fvDIPrh9a40tchuHcnrbZ2EJz0wNxykgE3J21qYss3Fxfz0J48mGluTrqdg+TmpB2sRHSWP03kun9afCz8tLDU3p9+ZuI+QqxVk9JXLTNAlDWLyfasnBM4OJH6JrD1zjCgfmiN1+uajSav17eoXtcK0lQuJ52Rhftvpixtef3V8D5HNEybndiS5/xr1VXPmbZXq76ddvoHtBKRiPSxiOXqn6bWzK1fRPW638kFQjIPO6Rc6nZcm61Iab5yfnsOjgq8UlycX0KB1p417UsmAfmdZajxqF6fzjnakjnpVlk6KCetTj0U4laTDrrrI7ycaWpfS+bjpOXrtOGkW8Vpm5NciMrZP038uj5CzaXuwEWtWtT8sq0gxNNpgZePy9qzcNLKU5Rkez7nS79cyY2Gky6Tk86IfU1ayOzsQy1kmgJx0uS4Mm2qDyag/umDZCUn0t5MxGT/q46cNFFm5Dg57/pYiCaIyHvWh/xnSNPIe+RqrX1fdqq2MoibiwMDD5ertWfKZuYwn/LdO98XEafrlcobp+vTuZ8ATrov3ipLm3bSWlnaoM9BTVrIZzQesXHSq0QHiW6HsxJRrCTIlSRMJ8ar/R5C1vqnRS3c2dRxKW5dH0LJCsWolc+VcCZ39IF50p+afkrtBqnXMfUavA+utOYnx88d3VKxO/6HyeggIqUrjI+T3ogm3F+3JS9NnzTZdNKaMEXchpAz3ZINJ510Ts8SkZWViE6nmNrvn47qfmoboqYiq9SCe79jyVci0qnLU6uW0mqHWrYJ+q3BNd6h77k0tWeNp6Voyb1fl6REm1yFnPQPbc15yMiCk1bL0ph9aEnezDQ3J207ydzGGZLx0yFPcpE8Pp1p+SDnXR9ELCa8aFC8npaTUCpwTB3AYDz/c2kzI5dijg4P3E3tucSOuZDX60T64kIm1yVho6djp9dnjTt20kmDhzUnnZSlw6lJiy5WrSxdWSdNvsw0NyctO99trHUNbrq4ChI/veiof5oo00Lt5Uo9HbPr+lBnKFJaqKZS1qo1ns820fpiMYuxZLxD7Tm/fSm5XFnI7PojPpY2mjxdnwlOelBetZr0NxcXF/61O4ftwUzzdNJ5zny6uJDtlYhEST9inL8XWLtyCT/t8ekhz64PKT1Nj8pbq85zrTaJFY3h8slcV0/Vas+S78vU4JlcZzQuoqP/mY+V4hovmZNOZM1Jix6PUJy0wYktX6vXDzyY/4wthZozbTaNJc8DWoko8iP1xYhGz/yClY6Wuz6EQ/LVPy0+Ztv1QVWuVWu884pGqtNSnJg21LA98snc34Wq9A6wHc8uKyTf15MO3P1U8HZcdAPqCtlJRxPW51HY8DNqj4fZiS1P5XgQXR9OzbTB6r2NNaSr1jI9MssQKcyujx9SNFGh/mkptl0fKql0rVpVYRqa+FVoNWwhOGyzfDJdKSt+z3fixC+WMqVuGM64H1rjal+HkJfr8ILbbkOhEjhpMTkuiJp06P3TUVzwvMSwFo5E02/RwoO09VbBZwf+TV3INr8b+ctNkK0jrxU4bcdJt4rTDs58h1c0UXL1u9JFcDFchn8NSUjOo6lcrbr//tTCCVDIEinmeo05qzsxr78vK74veafB4frQCxd/X/dTwavjpBNutbJ2xJaTNj5F3MbElgMPkjCf029aN7rImc7I3ns429PFk2SPxRL2Twvu10lTCF0fKinVlHJ7vHOf7j59OKde204rr5muEnV7Jg5Y42qz8lJMk0U1e7F9nqPG3C9Xejn4XB+6cl+zDDW+kM4fgJMejEf1+u0c5umk8zyUmrSQz8r0kL8pWZkOItNDcNtnjvUz/946kdLvIeSk60OWqKX8roBhNcW3xzX4RKgdWub7cpNE8tt753hnhX7ojtx75UJoIUrmD8BJD8YtOWkST9otOGm1Mm3ESVeiMs2tJh30dHHJba9EFB+6z5+mtETtNzVPcs5ZH4VceZ3XUKu2yHucJMKNd/25wHvn4fRDa9DvLEOVJwXpUjtpexlfHbgpP2M7ASKsmrQQcqY78bAyPYhIH4tYovzp5PsySM0TH8uuDwqqD1JmgMgAECL0VYODD81550N35t5nGWpO2nGlRqpMTlqdeihk0M8E4aQd50z/hrPvJMU8HW+V6CARBeikhYSfJnKUP12wvf3UPDpdX9DbWb1lfYjfw0Y00WqlICJOd8pCPhaviv+Sv+O+KPkvFS+nAg7OkMuzJnXS+e2ZnO/tuLhwiesYByfdJDhpA9yqky687zN00o5r1aHmTAt+wdo0S0tOupWRF2I6Xo5n+j2EHHZ9qF3U3rs+hGTvB5M7ZY9craWJinXisNEVAA6ucdElRUThdHnluXznz+H6KT4W9ZFm2Z2044ktQkFMbBFZaoE6aQo3Z9p2poe9zn0H08WTzA39YWN5+qeTb512UTOpqVDrzvSLqF6nOoXYNynfCYydXlVbQQjdIODV5NlJQGGtPM5zJrnRlHPSraKMcydNP3TnpJPJ4apM+wGtLG183ZelCmbQTpoct3kE4aTdTBfPc7NnVKvZQ8py/7SHK6D47m1+n55r1TFNx6T2fvC8s3bmshVE7wZRxMXxgIObrD23Wjjk8X/f6XuDdtKiG01cmqS8O+mFyLOTTioGcNIDcbOzD/M8FCdNyJnukZs9IvWBiBR810fitOqkBy06nJVISrnF+x1CSC5STGpaAa5MUjmml4OXlit54WPxalqrTMTzfOyFqysLp3O/Ce/XyQVPXYJC4jLmsrvDtpPWFJCTLkGmh08zzdlJr6ajxcnOEZnx0zZXIjq7UohbT+SpxiBbPogYdX0kry29k4WVpteBy4kw952+l9RWEHWiR07g4Ly48oxF7W7Studz3vXFZTuHd8dcyFvhd1LOnbSDmcEq18vSFu77alnahm9RezzgpDUhZ7oLDzTTQyiedb2iotVC7aPrQ3zfqM6xBkMhp+l14OqD741ImaWHHGtwnlwcn9lD1/t5ZIozXFmY53pBmpzeL1oFaSqVk1aFmrR7hZ0zLe7ceiUhECftYCWiy0kuUvGqt/5p5l0fQrJWtBERleuOrubs6jnWdWSDgPvg2ZmUZao9a5znykKNe+wGFB+2CtJUNicty9IhOmnjsw/Jec60BzONTA+V256JSORukovKI69P8Th3fUiuFqqDS9PryrVuFmSDgLvjae2ZStFV1ZULhzqddajer295HtXruo2m8jtpXcE6adHjEUpNWvADD+Y/Y0th50znObo+unBnKy1Wichbap6Q7PrgfKcpQZpej1yrwauOJ9GdmKMzA+fM9+nv0Epce9agnP7Ns6tNaiGdlRvn/5Ku7guZ1g5y6qQzZWl0d3Tk4U5sIeRMd+aryjJECnYlIsni9KLr9+K+po6rXFzB+Ux4acfFHXEhbXksX61a40WO5xocNnhbns17LnHfc2cuu6LZPnOTWkifDXq8/pNWkKYSOmlRlrbtpC3NnBZ6KnAnTY7NdFhOWpO9IzXx04vlmeSifV/R9eGxT46I44SXQq70eZe/Vp3nvTpsdImUlRc5ZiE1Q6YitWeVB7GyUCUL/laiS64XpAlOenCOmnRnBZ8z/VQ6hTJQJy1k20mrk1y8XFmSwS7ks+uD0js086eiVFSrzqTR5VRuXuic2jpswZElwpnDMffMxTtqYr+yUOWt2DvfTtrlynuNl6m744DlpAcxseWpHA/ISRNRFBesCDCshSPR9Fu08CBtvZXhBt+LWHXSn7Z/pLpZiSjUCvfwsQIj6Tbx+tSPQuj60KDoOxQzoipVqx6MbyjzP/UZmanP5uIsS8mFXb4T075I//1X+51hj3wjnfQ0nbs/s7ouaXyBQcUkWSdDlXDS4pk2nHQ7fuBBEuZz+k3rRtebmTbopB38pW1neoiPM37a/hnoZy3zL9IrndeuD6Eodagh3rGoAn3VNricCVc4vRbdI73y3Mo/9fdZhQyNivdDq1xekTxOBZfcSzejmoJHbu/jbip92m/UbFetmosnZMofujTTYedMl69/OpHlp0K++qc5zEpUJdeYc86lzvN8XzVVciXWYLzzrLsu3SOqyroycl9U/PN2W/mHrozBuPoUJaBnZcQjN1rj3p20y8ksbpx0ngeU6eEyZ9pDZdq4k76QfdtkKVHcaqaH1EFX/dPJx59VuPOuj+RNPIOuj+T18M6l7sxbxaGyzFbkz9vWtg3yO3HnGrANzu33XFYufv9sUzu7cu+50Tr39KzVi5OmI46c9EEn/kpWpo3nTJe2zcNGTfqC/Xnx0ky76fTXm6epnP3TgnO7IkuHEdCKH40vKO4L/ang4By41sfP8LrRI1/wnXaqsSR9tWJO2mX3qQN/Jcy0cX9Y2jaPcLs7VtOasYP3gpnkaaHS5U+rXA/6IP/TyIVk7werO1kvXH0bsJCuZKpayh44uHcuu7AonC6ydlx99gUnrfISO2lVAXV3IGd6QP6Uk3Q8X/1JDvqu6M16q0Qt5bBWnemi9uqkVSDXpDOfodiZqzPSFrK1MW7OAxy8BFxd4RrWyuZCri8uZHN9JgZOWl9uSGVz0rIsbc9JT5RiYguVIGdayLaTdpnpQepYRPtnpuB6BLWPro94NV1Bkl8rxaBWHVyaXp5rtbENdIOAgw/N1f4NUp5lhZJn344vaA8MiZGTlutb/DppN2m2XTlq0p25G/k00wGl4/2wXl/Njhan8GvSGm/5aX/907Lrg+EVXJ2hGNDklw4c3SDg4IPxDeV8CfSZVUAZHXkn7f0+5ctJi3mHquz5EFGWtu2k41I4afJops2+FxGdHrbXnKpyMBPR5UrE5JvOpisC/aULkTYukc0VPHltYabpdeXoBgEH78C18rN6vqjidl73zhdkrfc0u2eD8l+t8eCVdNJ5HHpN+tNlcdKEnOm+uCxOO8j0cLwSUeUep45LJd+dxxW8kMtCNQW+Nj/P1XcI6goqTOIArxoPcZp3v5zJ9O+uvDUe3Gv2FLmaVZznWlnaQUXPQffsUzkeaM60BzMdqJOWcuOk89zle9+Mn/ZbA2Dct0fKHWghW6gmNnfKIblae1tQZweiGwS8jFybQVim2rPGOa8s1HirIE2+U1xnc7ykTvqAfSftoH/6wIP5z9iSazNtz0k/VcZMD/crEaU4pOaRFvRBLK7shVwtVFPpatVC2soqrRskqtcxAw88OK4+e6HAVxj3yDmvLMzzVkGaquukNZXDSdubIu6l66NUOdMOnkqII8DZCgAS38vTGcshNS95JatpCpLvK3tnXo40vcGyQaZPZ+w1od8anBPXmp6pArXnPGe+slDlrcIEnHS2LO1mcpztRIc8D9pJUzlypiX3cgSUbCZinnNIzaM0BSnSHk0S0zuBmqZHYU5+GYDnu1w2In17OGxwB1zt1qjCO9sOPIiVhSpPHL/vCo7kVXPSSMcbTCXJmXaZ6aFm5JV7JaL8F5PUPFKqFJFyhyjYnscdQnP8Ze2r7sALHQwcNrhx3qFbo1K15xBXFqrce5aUzt+snJM+mDVRZMdHqT0eJXDSVJqc6dKvRMz4aR9dH0xS81qvZzVdWx3IU0uqRl91LzzvsDUnJIQ+bHCNtztOquyYNS4NNP91JlLqBBYm95eE++iu9Ngn7cxJqyqHk6bS5EznudUjYNXH05bETy/6PMM5pOapXC5PFAqiBqPe4RaUGm0VVj614+2cUGEftnRUapwIQ+cHPhiHY+6XL0St80JWFoJw0kxyo4s5Aycty9Lo7hiMu1QU62U981o4Ek2/RQsP0tZbCbH6G7xQ0pWIgjM5w72nFHXqb8sf0TzuHJ15a7Zi7vWzunPz4QtKjT/KnS9j8SpDpwhO0jHnVNlnNQNw9X048263PNcXF3K6jyQZVr7vs9ElIrfdpJSOPJSy56RFj4dtJy1ypoX5nH7TutEtYc60Gyed586ewnifvcQkNa+QZ6YnCvm+c/TItdmKQhVZuTgY79YX27bGqXFR4WboOAPihVkZyNAwy9XjNrjrm5rRwefZpsb4OGmsODTCXeZMu65MO/gNusz0mHD+3pGIDhIR+eyflpxDal47Lm7iwdVs8lzW8Cq1ctElbz0lz6myNe/89OzOv5/KZmXY5jKLI9xnboInswO53i+o8k5aLUuHPkVc8gMPkrPKtFMzPf1Hjn6DF1w9raB0Uoz7944HicuZTyyf1kmeuOpAJhR04Fi5yIf37ryH5GPxqp7y5uT74p2bR55p4QhhHUhXzmfleiFvVV4Y3E/hpA3y0ppp2TOtysZv8ILbI8PLUxjv+dOqkpWRPK6MhbxlqfndaQauVVNuckp+c3Bw8CC4PK9L8M6fshkdBdvzuC+QKEjPen7Gm7wSf90d0kw78EtRvf5UjlvqWXBppkuVM53nDo4MXysROeRPS8maFtuneMkLC2oGWDuuTl6IlKfA6EMFBw+Ll6aLI5iMjnyFZda/k1aXG1KpnXSJMz3KmTP9VNp94ebIUMe4kI/8aWJwRRCensOsxN7T9IJIj+rMZa1dW7lI6AYBB+fHF7STNC54h5yI03WmR55ZX8jy+i//lRSkiYWTVotxcNJmuTOVNme63JkeKknOQ99XBCER9EHsn+5JS12CFYqSa0+H1bX/6AYBB/fC1dpz6M/E2nE96o64XOcLeasgTf5X8BP5dNJSzpy0OviQSuSkyZeZLlmmB6XFaS8rESXPpHwI+ZqVeIT7ipPkpS4u0r31zMwXIZZ3rH55u24QqiN7ARzcIi9cQViO2rPGk3cIXLv7NJ6x0eTfSXvpDlWJKEuXtSYtcqbz3JJKmDPtq39aOJXbOe7sDBHf2u/UcclbJWr2tQr5ClsrZpjdscx2gxBlGkLQbw0OPgzXas9UlhWEXXkQFZNMptOswuGky+6kHedMuzbTzpy0aJt2menxw3pda56mqk4dFx/KLmpi3/WRvOBVol9U4o6o1eAL+63hsMHB8zw/CajctecOGR1BOOlkZfxslvtz0pFS+ZKCk3bDrcqpmS5rTVrlqp/21f8Us5mVSErXBzHO+lC5VqimktaqNZ5/56A67DhWpp3DYYNXgGsrBal0mRt98UxXNMvrdiHnkx4ruN9n11K+nLSzKeJe+qedmunSO2lVvpy02vWRCc7z3fVBlG384HflLU7Tq0atupCrP2/htHNCDRs8fL6Q63pqt1KwCrVnyTPTy9l366mSswzhpPk4aaGyOmlCzrQNvsrASQu1/DSbrA+SVzo2V96uvJq16na8cw072QYOG5wlz/c3U1HXky4G550zLp9BBdHF0cFJF2xfeSct5MtJx+V10lTWnGmh2Hmmh8p9TXLR4G1KszVmc1/gb8KLTAXKXK85XZELuVpTL2sGyGC8R4fNxFGBV4RXvL+5L56pFATSD61xPlPBNc7HSR/0lxrsePahe5U2Z9p7rdrjJJc85zMrUXK98YPNFblHns8Aieq4Q3dx2IV9qNwcGHhwvPC4gmPuyjOpPoF03xVyPrMM83wLTroCTppKnDPNoetD+mkOXR+32aTmqSB5Pb8IaYWixuQ7gXi1oBbL8A7qkRf2obZb6YWJM+CS52vMVPZJKJa47OIIKB+6HRcemnhMYCnk3u/7Un6dNHKmragiTlqIT/80cUrNy3O1UE1h1qop17XSoS+zYD9V5V1XekVFfa5SmEQTOi/sY5bqnKGB2nNXXvAOP8wuDsmj9L7G6llrnjO571NVnTRypk1ymTbt64j5tO9JLppYpebleetV/SLgFTCSq68f3SAD8659ru0m0cgMFrUfFM7bKl/IzdoszMpQ1ePfN8/zrwdc5ck1J6h1KV05q/U/hVxUrPg4aaqqk85zq4rigrf8hrVwJJp+ixYepOk/8vAbvMDgSJrwPcklzzN+mtOVSOVikSIF3s+X57IAT4qTKNge3BovcN45JZkkuQskH+fqjHf4/egQx7Mnrq0gLNje93VvYM52ZWEQTvqAb/9DacK0kEsnfeBBSsznm9aNrlMzvfVWwWcd/Ga9H0mk+GkOTlroIBEFdYUqQa26kLeeWlYsxzoIXpj6l9/cEpeVXUv7l2X7zttXc1IJf16aFYTtOOeVhXDSvXNpph3XpF2a6d+w/Q06y8FvtvAO4f7phliMyMdJE9Ht9ndQVlcoSh1/PEvRRKasy+SKPySPV0n8F0XJfzzv3NXkcaz8JzIiYtL/s8abZHf/1Nv+Gf5dqsnl9SGq16OI5KWjZE5aXOejCYpnw3DS0SWmTpqJ/xEqZXeHVJlzpgWXbdNSHhPLWfVPs03N68AzgXqle5pZmGNNsmLN5o4ODg7u5dmImh2ki811bGAeyspClYteRCaZXRr/ob85Gyop9xRxKeRMO+Va+DTxOANvp1eEIGoA6usMcfJLj1xbvNh5ZlvBfsDBwcPk2vleyutbnvNfWahxaaOJx31c4xy6O6RK76QJOdPuueqn+ZyBib9nNiuxr8kvVMZatVA+zSqfFod+a3DwEHlUOHGmpLXnDisLA7rvqDaaON3HJffuc1T5miKOnGnzXHR6eM/0kFpNcx+5nYGy64NCyPqQkon94U5+6Zfnn/bm098wyQIcnBsvOE8rcL3K87BWFkqu2Wjyfb8u5Ex8juS+nDRypq1wPk5avh6G/dPiY/GqEksdQtaH5Fqhmspbq+7cby0+1tLfODgJcPCqcS1XO3+eZsTmemKDZ67Ms0TE9D5SyEWlRrtlc7hfa5yVz/E1RdxL14dTM+3rNygyPTh04qucZ/+01G1lLTDPCS/tuFpTb61oqUbfocpbP3JuRWOyARw2OLhRns8vr5pj1lg00cqMCus+onK2lS+Nw0l34Lbl1Ex7rPbzyfTg3z+t8kyVepaIOF7putaqqQJ91X2taBS83fRm744EHJw5z3drJP/MvoOtrJPOXG+D6odWJdPu4KSxErGrfEbjkdvfrOqn+RxhnPun5T+lpQ6r6+P/394d+8ht5Qcc/01wrWPEcBm40Bo5wE4nXwxfWgtnq7/mINjdurM3RaAr8gdEVWR13u6aK6ImTaxz5DY2HFmlDRinVeG4CWA4CPwHTAquKC7J4XDfDslH8vOppK/3VnuD3Znfvnl8rIbqbwLljRVjoXc66Nl3vdK3rmG7C7q+wt680rew5t0au/qsd3E0z42O7F+Xqz2Teabaq3c9LCx4ko41nDNdLbldiVjIef907et8euvW+X0TTyr/IZtnwJ69+sUX70Jm+Mo0YW+9Iqq4p0yVCVtfWG++V7Psc50P1ee+i2Mu50abpBP6aFZ3znRuk3R1/3TkfdpO0esbP/J4Bkzu5Vp7fbk6v1esyXtz3/muCfvCGp47zugZ9F1rzIX1nEZ3kF47i2OmuzhqPfNzo5v9KNd5ptkXP0nH2s6ZLvqdi5unI4/vvOJLOprJ78TVc6ljEWsSF/4v/M+KTtm7Ym+dsKuad5wpOcVPP0jf+xtdWGM+RH++E/okIrJ43j5Iz//c6HlN0rVl6QnnPedMD9trFyNO/p1X9TCzn9i9fe5XKO7q9dvBWKtO7d1rfq2n+Fnb1mt91z7+XfcINDEffBdHzHx3X2ufxbnRJunk7pzp8frk33mt/cYczrNc2BWKrf35i0flFSUa1+y3fB79Mr37DIQ+a9vhHpDz7Ht3X3Tc2cTEPOgujpj5WRzdfS7nRs9rkq7Jbd4b1FrOmZ7FlYhlrx1BndVPckdfzBWKrb122WI4R3as3mdtO3afVlb9PB3dlZSD7rLY1ffuvvDzNU5f0lkce/ssrvif4yRdXZbOZ94bx2Zb3654eKdvbI4fx+n1ePq45b+6k8uufmMm+6db+/OROr9n0iH225WcDTLTXh0mopz8Gpp9u62snXd+/Mhr532+/uKLb/ZdH9/aV3hHpAX06opALOK6l729PDe6+eGZvG52d5P0Zfu161EMn8dfDz7oTjxMT/6I38nvTi61Pt/foYu+ubXMdwm73z0srfkca73j3fPkybV/t6arV3v918WTHJ8/B+qzOze61s8Xp/KbT3KepGPcYXpd50x39xy+I5s987uO7+1nlbXbxb972NxfWL2bbsmEvc7e504fQ/erfP36jHrzN7c1rD3XevnSM9/3eCPiKOLh1HNI/57bXDea1Z0zXevlyR65fUfWTs0rT6HO5ye8fy9/E1jeFYp7e+sVPM0J2xWNuq6n9fbf2E8iov03/LK3fP6l9DmedtfaZzFJl8vSmcx1k1jjOdO1eDub78iOXsxa+fyEp/XqFYpRXSmZ+pl35N6csGv7F88/zISt63ql79rVs86JudnLx2e+7+WWfy1eKE3SV+nOmZ6g53knl2af3al5zV5dqI5Y3Vp1a2+9l1hzwnall66vp184YWN9159c9srCuV9fVJukiyf7POeQ8q85T9LOmc6iZ/Kd2uy1G49n8pOf1stdK0eVuIZ91T17c8Ju2QfpHo26vojesvvr5Nmf1rpbo6NXj1HaPrs+pyar17uevVyQjgzmjZ49t/nNOdNT9uptEXP7Tq314ot8GvF2Bj/5B+nVJ8FyjSFWecVMd2/ugzxfvT55/ri58kzX8++bG7F9WF97NjF39+pvGgvYD13r1TE6spk3OnqxLJ3D/NanD23UYTqrR3Z2d3Jp7cVT8QL2h5Wqawy1tYfcntlz6OVLb/lK3Hpesglb16fqzd0aUfzk/vt6rxvp38vdgEvaxVHrtTE6pp4r+nSTdM2UR+NFfo/4Nvs7udR68aXO+qyPjl5de+h497P5edbcm/ee3DVhh10iun64vusccbs1Lturyyjl69oiJ+lN5XW8NPlcsbebpJucM72/5/MdvKuXZ30UP5lzv0Kxtde2gsS6z0+9bG+dsGPHmtnz4jxsXd+3xvy8nESEiTm9L+ksju5e3stwRit3NbnNaZOfjrf2c6abvbp5OvL7Dt57h5dY0BWKu3ptrcJukOTevWbWen7t+f/QGSP6snqvu6+fPPuTifnKfalXEHb3/O9luLcXu2HznN8m5Jzplj6LO7l09HKkXvY+s/KvdoMM1LuviOo4Y6T5eezb1ifp3WvJ1b73+7zWW/5dfV9f9hWEHb08sWq+c0VhRpO0c6Zz6Vl9Byf08l2k6kJ15PHMMly3G2T83ufd7Y5928+LXSX65Xv391XPtWQT80C9XH5eycpOs8/ujI7uPqNJ2jnTWfTiO+btRs/tO7tPr+792E79zDJmtxskq75r33ap566SWs9zwtMTem0sjn1ryRGxPdn/fVX2ln9XH6DXLh+MRV9BuLAzOvb2rOa0rHZ9OGe6qxfP7cv4nbJ4d2k9V3g0u90gmfeeu0qq/bKTt4l8oN5/N0VHb33vyFryLHr1/gCLvAL+Un2mZ3R09Hnt7phk//Rmu90O/W+cvrE5fhyn1+Pp45b/mtsj3ux35r/PqdnLA6ozfCYa/4qQs2fPgF6hV9ivOJEXt95IniB39eaphYf9/IftfiNdYd/cPb9dVKzsPc9dvTyjo/nxmbzup/X5TtLXrkcxfB5/PfigO/Ewnckj3qfn85192G4todqPLna7QfSpem1Xkt/09Gl7de25sJLrcPr0BZzRsavPd5KOcYdp50z37XciYnH7n9Zzml7P3tz9ctT4eBO2PkJ3Cps+Ya/+LldYz+l1/ftizuho7cXb1/OdpEfmnOm+/XZEVI6gzuc7/iC9epre5M9QWfXmmn3zXe/tSaaviLqu6929+R5IeK+y33uYy7ieqrXP6x6Hk0/S4Zzpy/bbz971yOQ7/rC9+P9VXkrkXbzW3lybibZ9tyZsXddz63t3a1h73ntlYSzxPepqWcYk7Zzp3HvxTDTfu4Du7eX/tRWeppfcTdi6rmfVd105amJO68t+3S8tZpJ2zvQMeu2W45HTT8IBe7kPrDz6o5ThM12G3YSt6/oIvbbFebV3SDl4r15ZaJKeex+Uc6YT++3KKdT5/CQM1KsXKYa16qv1nhN2uNJR1/WLvXV/c7StdJikr7gfurCG1/eSSfoqRh2ms3pkD7J/OnL6SRi6V6+8bNyqzIR9pd76ytdxvq+zHXR9qX3XxBz2Nw/Qq/cZKMri33Nu9uIF3SR9FVMejRf5PeLJp+Yt6V5HfXrz/29t8nPK3tX7rlfKsx3v6jY/j7VtXc+kN6/8i4izZ2comZhH67XTTh9GXFvNLo7WfuPZymCe89XV+zicM32AXttCnclPyMi99kx0tO9dyObn0fv3/u/qtp6T7d51un7A3rGWXO2tP6euCBy6l/uel3eX76t3k/ShOGf6MP125YTzHH5CJu+1Kzaq+61dGTNmbz0vthrLuwFb29b1Xb37vSBryVn15tpz64pP5Pe6aZI+bB+Tc6YP1ov9Ris5Peeyvbrf+uHFKzzOpn7mXXkvH//kte3zeDL9xKPrl+2tuy8KrvCbUT+y9nyZXm6SjszmqMN250zPuK/k1Lwr9urjc809t/Lue9e2z+0+k6R/N5Hrl+qtd72+7Pdhx2RsYs65V3/PX8/pdVfv5YJ0TD0vDd2dMz3vvqpT8w7Sa0fvhbNB5tkvO5GYyFfb+19B2917/qZnMl5Mr34/rPw6pbS+nkm62QflnOlB+tpOzTtIrz4zduxHbH4efUk9k4m8f9+etO87z/PKzp67Gobu/XdNmIz16hWE1p6Te3VrR+Q0L43Th+ac6WH7nYiw6+PyvfqI1a7FthtE39sPNZn16U/vtu8733tlZ8/eenpawucp+8iPT5/e/Dr1lffWO6eYpNN6bYyOqeei8fsInDM9bC++fctbnDjrI6HXnkntBtFn0fde2dm/H3YC7vn16/povbZiElagDtQ3lTmkNPlcNHIfh3Omx+jlNbOeKa7ea/vk3ItR13V9Rr35nom150P16spdcSJCTT5z0Th9NM6ZHrWX39nWqg/VW09BMmHruq5P3msHa9r3PFAvX/IWf9pdnpN0OGd6ql79jr9T6dXzmJufR+/Z+0/Y7oKu67qe1vfu12++B2uSPvgujlj3fuiO7pzpdfXqj8ENJ88P1lsfz+67oFdZ29Z1fYW9Y2LeNRmbmIfr5b2W7eLY250zvd5e/QkpV1KdVz1a7zj534St6/pS+67nt47J2MQ8/i6OeHav5azmlrn0QTlnOt9e/tL5tn1mGfRdj/+u3SPOUtB1Pbd+1Oj2MefcnWo3i0k6nDM9l3774kJ1TP0Tru/dPdI8J7W59nM29SurruuL6c1dag8ruwI6dg+apDPsTrU7YB+Bc6Zn06sngVhLyL/3ufKm491Vd6jRdb3stfP1++/KcOrcjLr90EP0cThnen69/EkrfvBq5y43P17PuXfvR+z5Ctp899aat67n2VtPi2t+fHONuWAyXmQvt3PkNm/MvY/GOdMz7tXrFJ1Xvcje/xW09p/2rnmXnAao61fs5XzcZzLuf1qciXnx3fnQy5ikwznTC+jl77J3LnZngKy893yF7j4NsNxt0r1GbiLXZ917/ua5q1evYzEZ6x39xsXvH1cWDtqdM62n9NoWq7crV5zcsBtE39H7nH71dN8aeZ+JPK2Xu8YznMD0MXva7og+PWECNhnrPfuNymWgYT/0uN050/phevET+3nE7X7XdDc/j6736YedSKr96NnnT56czi75Lnxan/Xa/KCPz6Ee/6vvjjAB6yP05trz53Zx5NEHtdlut0P/G6dvbI4fx+n1OP6HvB7ZNffqnhD7rXX9iv3GYJN683Szg3/+QlaPp67PpdduNbDN4PVdL/587XqcD59fDz7oOmd6pf32xX6n8fEmbF3v34dbm48ep5tZo9X1cXrzN1v7nvPvI3DOtH4rdpwMb8LWdV3XV9h3vddUXvFv3/Nc+jicM63v7CZsXdd1fdl9124oE/MC+micM61frpuwdV3X9Zn26hbnYu3ZxLzUPibnTOsH6M0J25VPuq7r+pi9z+tO7Xohk/SCu3Om9dn3Xc9Q1d5xRoE7zui6ruvNnrArw8S8zu6caX0VveNKjrcvXitdbiOxe0TXdX3xvXq+uDue6Afpgxp1mM7tkdVz7tVnzOqJ9637szvu7ND8/Lqu6/rkvWNXRvn8/7k7nujZT9LhnGl9dr31CsgyVtcw+t/Jwq4SXdf1tJ52x6KONWZrz/ph+wicM60vqlfXMPo/U3evkVyq5/MKp+u63t0P9bxnMtaz7eNwzrSup7wStPaOCbt2y1lr4bquD9Gbd7c+4ARsMtbn1UfjnGldH3wij8bulAOuCTmFUNdz7mm7IJJ78+7WJmB9nX1Mm+12O/S/cfrG5vhxnF6Pp4/PS26PuK4vuxdXbSa/Qj9su9NB8WdXfOrL6Dcavfg+f3iZtd6ONeBmz+f5QdcX2U//5VYxfB5/PfigO8EwneEjrut6Wq+eWjjmWrs+l97xm1hWX2frlc3Nj9d1fS792vVY7DCd5yOu67qu67quL6aPOUz/xdD/QNXkj6yu67qu67q+qj60UYfprB5ZXdd1Xdd1fdl9BKMO0025PeK6ruu6ruv6Mvo4phymc3vEdV3XdV3X9WX00Uw2TOf2iOu6ruu6ruvL6GOaZpjO7RHXdV3XdV3XF9ObcTgTDNMZPuK6ruu6ruv6gvtwxh6mc3tkdV3XdV3X9WX3QTlnWtd1Xdd1XV9sH9ovBvq8m835H6o3WPz9rVt/9eyv//vsA3J7xHVd13Vd1/Vl9BEMtTJdztDlVB0RJmld13Vd13V9nD6OoVamI2K7PZ+kH38S8UY8/iSuRYRJWtd1vbP/c+2/3bpVLb/P5uvUdV3PuY9ms63uwxjA048iIq7d2zz9cHvnvc31Dwb91wDm7fhxnF6/0gcAHMTTx3Ftts82d97bnn20Ofp4GxFnHw77bw0+TB/di4g4+2iz7wMBAOBgxhmmB9zmUXh4FhFx9PH24VlYmQboZmUayMTx19vTN+a6GHrnvWEXi6uGXZnebM5/G7hxFA/PzlepB14KB5izTY+XLk+jAJ2KmbMw420ezdPxWs/LAwCAmRrqaLzWubn1vDwAAJipwS9ABACApRr1duIAALAkhmkAAEhkmAYAgESGaQAASGSYBgCARIZpAABIZJgGAIBEhmkAAEhkmAYAgESGaQAASGSYBgCARIZpAABIZJgGAIBEhmkAAEhkmAYAgESGaQAASGSYBgCARIZpAABIZJgGAIBEhmkAAEhkmAYAgESGaQAASGSYBgCARIZpAABIZJgGAIBEhmkAAEj0fJh+8ug/Nvfub/747ZPn//WHD+7d39y7/8GTlv8lAACs3PNh+tVfvXX35Ygfv3n/0c9FefLo29OI+OVbn7w6zRcHAAA5q27zeOGjd17/dcQXX377ICJ++vb9L/8v4pVPf/PXU31xAACQs4t7pl967Q9vvRjx/c3Pfnjw6JsvIo5vvvnuRF8ZAABkbrPdbi+Wnz/+459OfoyIiJdf//PvXrPFAwAAWjVP8zjf7BHx4t13TNIAALBT29F4L/3l30ZEvPg3L438xQAAwJw4ZxoAABIZpgEAIJFhGgAAEjVP8wAAAHqxMg0AAIkM0wAAkMgwDQAAiQzTAACQaOcwvbl3f8yvAwAAZqd9mC4mafM0AAB0sM0DAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAAS1Yfpzb37tb/WyrCefLW5d39z76sH53//4YN79zf37v/9o5/H+xoAAKCfC8N0MTc3p+fx5ulX3/z0lxHx/c3PfoiIB599eRoRL7/+h1+9MNIXAAAAvV0Yprcf/rb1g3b1Ibz7m7eOI+K7Lz949NXN7yLixbvvvPbqaP88AAD0ttlutxf+PuamjooL8/qTrzaffl/88ddvvfOflqUBAMhSfc/0mIvQVReG+Fdfu/ty8adX/skkDQBArvZcgDiaC0P8T//9rz8Wf/r+355M8uUAAMB+v+jzQSMvVz949M0Xz/58+l/f/uOr9kwDAJCjltM8ImL74W+rA/TIp+Pd/C4iXvn0w3fuvhzx4zfvOxcPAIAstZzmUY7RzT8M74cPPv0+Io5vvvluvPDR370SEV98+eXHP4317wMAQG/10zym9eCz+ze/i3j59T//7nxrR7MAAEAm8hqmAQBgRuqneQAAAD0ZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAASGaYBACCRYRoAABIZpgEAIJFhGgAAEhmmAQAgkWEaAAAS/T+k/rqiAot6awAAAABJRU5ErkJggg==",
      "text/plain": [
       "<PIL.BmpImagePlugin.DibImageFile image mode=RGB size=964x898>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "axvm.BringToFront()\n",
    "axm.View = axtlb.vTop\n",
    "axvm.MainFormTab = axtlb.mftStatic\n",
    "axm.FitInView()\n",
    "axm.Windows[1].screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close AxisVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we created the interface with `daemon=True`, the application closes without any warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "axvm.Quit()"
   ]
  }
 ],
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