Julia Space Charge

JuliaSpaceCharge.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "ef03f20e-4432-4454-aa25-b09160dfaadc",
   "metadata": {},
   "source": [
    "# Space Charge Example in Julia\n",
    "\n",
    "\n",
    "WORK IN PROGRESS\n",
    "\n",
    "Reproduce plots in:\n",
    "\n",
    "C. E. Mayes, R. D. Ryne, D. C. Sagan, 3D Space Charge in Bmad, IPAC2018, Vancouver, BC, Canada https://accelconf.web.cern.ch/ipac2018/papers/thpak085.pdf\n",
    "\n",
    "Also see: https://arxiv.org/pdf/1111.4971.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5f520c9-766a-43d3-a101-c642abdb3fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Plots\n",
    "using CUDA\n",
    "using DSP\n",
    "using BenchmarkTools\n",
    "\n",
    "default(html_output_format = :png, dpi=144) \n",
    "\n",
    "Threads.nthreads()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "809665a8-4091-4a63-b2c5-7e093c227aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(NaN, 2.7755575615628914e-17)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Indefinite integral for the x component of the electric field \n",
    "\n",
    "∫ x/r^3 dx dy dz\n",
    "    = x*atan((y*z)/(r*x)) -z*log(r+y) + y*log((r-z)/(r+z))/2\n",
    "\n",
    "Integrals for Ey, Ez can be evaluated by calling:\n",
    "    Ey: xlafun(y, z, x)   \n",
    "    Ez: xlafun(z, x, y)  \n",
    "    \n",
    "Should not be evaluated exactly on the coordinate axes. \n",
    "\n",
    "\"\"\"\n",
    "function xlafun(x, y, z)\n",
    "    r=hypot(x, y, z)\n",
    "    x*atan((y*z)/(r*x)) -z*log(r+y) + y*log((r-z)/(r+z))/2\n",
    "end\n",
    "\n",
    "\"\"\"\n",
    "Integrated xlafun over a 3D rectangle\n",
    "\"\"\"\n",
    "function ixlafun(x, y, z, δx, δy, δz)\n",
    "   x1 = x - δx/2\n",
    "   x2 = x + δx/2\n",
    "   y1 = y - δy/2\n",
    "   y2 = y + δy/2\n",
    "   z1 = z - δz/2\n",
    "   z2 = z + δz/2\n",
    "   (\n",
    "    +xlafun(x2,y2,z2)\n",
    "    -xlafun(x1,y2,z2)\n",
    "    -xlafun(x2,y1,z2)\n",
    "    -xlafun(x2,y2,z1)\n",
    "    -xlafun(x1,y1,z1)\n",
    "    +xlafun(x1,y1,z2)\n",
    "    +xlafun(x1,y2,z1)\n",
    "    +xlafun(x2,y1,z1)\n",
    "        )    \n",
    "end\n",
    "\n",
    "# Notice NaN at the origin\n",
    "xlafun(0.0, 0.0, 0.0), ixlafun(0.0, 0.0, 0.0, .1, .1, .1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ccac8a5c-64d9-4119-8cb0-3ce9f4c613cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BenchmarkTools.Trial: 10000 samples with 995 evaluations.\n",
       " Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m):  \u001b[39m\u001b[36m\u001b[1m28.827 ns\u001b[22m\u001b[39m … \u001b[35m59.890 ns\u001b[39m  \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 0.00%\n",
       " Time  \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m):     \u001b[39m\u001b[34m\u001b[1m28.949 ns              \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m):    \u001b[39m0.00%\n",
       " Time  \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m):   \u001b[39m\u001b[32m\u001b[1m29.029 ns\u001b[22m\u001b[39m ± \u001b[32m 0.782 ns\u001b[39m  \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m):  \u001b[39m0.00% ± 0.00%\n",
       "\n",
       "  \u001b[39m \u001b[34m█\u001b[39m\u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
       "  \u001b[39m▅\u001b[34m█\u001b[39m\u001b[32m▄\u001b[39m\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m▂\u001b[39m \u001b[39m▂\n",
       "  28.8 ns\u001b[90m         Histogram: frequency by time\u001b[39m        33.9 ns \u001b[0m\u001b[1m<\u001b[22m\n",
       "\n",
       " Memory estimate\u001b[90m: \u001b[39m\u001b[33m0 bytes\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m0\u001b[39m."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@benchmark xlafun(1.0, 0.0, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6b8753f-6626-470d-8e56-178132ac1c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BenchmarkTools.Trial: 10000 samples with 292 evaluations.\n",
       " Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m):  \u001b[39m\u001b[36m\u001b[1m279.253 ns\u001b[22m\u001b[39m … \u001b[35m400.579 ns\u001b[39m  \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 0.00%\n",
       " Time  \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m):     \u001b[39m\u001b[34m\u001b[1m281.243 ns               \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m):    \u001b[39m0.00%\n",
       " Time  \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m):   \u001b[39m\u001b[32m\u001b[1m282.566 ns\u001b[22m\u001b[39m ± \u001b[32m  9.034 ns\u001b[39m  \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m):  \u001b[39m0.00% ± 0.00%\n",
       "\n",
       "  \u001b[39m▅\u001b[39m▅\u001b[34m█\u001b[39m\u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\n",
       "  \u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m▅\u001b[32m▅\u001b[39m\u001b[39m█\u001b[39m▃\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▅\u001b[39m▆\u001b[39m▇\u001b[39m▇\u001b[39m▇\u001b[39m▇\u001b[39m▇\u001b[39m▇\u001b[39m▆\u001b[39m▄\u001b[39m▆\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▇\u001b[39m█\u001b[39m \u001b[39m█\n",
       "  279 ns\u001b[90m        \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m        336 ns \u001b[0m\u001b[1m<\u001b[22m\n",
       "\n",
       " Memory estimate\u001b[90m: \u001b[39m\u001b[33m0 bytes\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m0\u001b[39m."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is about 10x slower because of the many function calls\n",
    "@benchmark ixlafun(0.0, 0.0, 0.0, .1, .1, .1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32594f8d-5ae1-4c48-a02f-902037f77f39",
   "metadata": {},
   "source": [
    "# Charge mesh\n",
    "\n",
    "Set up test parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "165fe7b4-d989-4fcf-8284-501e9c91155c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.987551792311329e9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RATIO = 1\n",
    "\n",
    "const sigma_x = 0.001 # meters \n",
    "const sigma_y = 0.001 # meters\n",
    "const sigma_z = 0.001 * RATIO \n",
    "const charge  = 1e-9 # Coulombs\n",
    "\n",
    "# Mesh sizes. \n",
    "Nx=256\n",
    "Ny=256\n",
    "Nz=256\n",
    "\n",
    "BINS = (Nx, Ny, Nz)\n",
    "MINS = (-5*sigma_x, -5*sigma_y, -5*sigma_z)\n",
    "MAXS = ( 5*sigma_x,  5*sigma_y,  5*sigma_z)\n",
    "\n",
    "DELTAS = @. (MAXS - MINS)/(BINS -1)\n",
    "const dx, dy, dz = DELTAS\n",
    "\n",
    "const fpei=299792458^2*1.00000000055e-7  # this is 1/(4 pi eps0) after the 2019 SI changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4707db5-c344-4669-988f-dc513c60d147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007.845064777145"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3D Gaussian distribution\n",
    "λgauss(x, σ) = 1/√(2pi*σ)*exp(-x^2/2σ^2)\n",
    "λ(x, y, z) = λgauss(x, sigma_x)*λgauss(y, sigma_y)*λgauss(z, sigma_z)\n",
    "λ(0,0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68407475-f73d-4ad1-acca-657fba4cb741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 128, 128)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Indices of the center\n",
    "ixcenter, iycenter, izcenter =  map(n->ceil(Int, n/2), BINS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "256f6428-00fa-4cd3-8b50-fdc25c57377e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.9607843137254936e-5, -1.9607843137254936e-5, -1.9607843137254936e-5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coordinate vectors \n",
    "xvec = LinRange(MINS[1], MAXS[1], BINS[1])\n",
    "yvec = LinRange(MINS[2], MAXS[2], BINS[2])\n",
    "zvec = LinRange(MINS[3], MAXS[3], BINS[3])\n",
    "\n",
    "# Check centers\n",
    "xvec[ixcenter], yvec[iycenter], zvec[izcenter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56220f79-0f5f-41b9-aa8b-b5162dd926f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Charge mesh\n",
    "R = [λ(x, y, z) for x in xvec, y in yvec, z in zvec];\n",
    "R = charge*R/sum(R); # normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc6dc9c1-8923-4311-b7f4-1233d03e9b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Charge density\n",
    "heatmap(zvec, xvec, R[:,iycenter,:], xlabel=\"z (m)\", ylabel=\"x (m)\", title=\"Charge mesh\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69346d52-dec2-4c2c-b09c-e1ac1a26b43e",
   "metadata": {},
   "source": [
    "# Green function and convolution (CPU)\n",
    "\n",
    "Note that uses theading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0ac4662-b214-40ab-b0d4-f9ffedc88612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fillgreen!"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Fill Green function in double-sized array A.\n",
    "size(A) == (2nx, 2ny, 2nz)\n",
    "The center is at A[nx, ny, nz]\n",
    "\"\"\"\n",
    "function fillgreen!(A, f, Δ)    \n",
    "    nx, ny, nz = size(A) .÷2 \n",
    "    Threads.@threads for i in eachindex(A)\n",
    "        ijk = @inbounds CartesianIndices(A)[i]\n",
    "        # Symmetric vec\n",
    "        x = Δ[1]*(ijk[1]-nx) \n",
    "        y = Δ[2]*(ijk[2]-ny)\n",
    "        z = Δ[3]*(ijk[3]-nz)\n",
    "        @inbounds A[i] = f(x, y, z)\n",
    "    end\n",
    "    return nothing\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ac191c2-e07e-4f7d-8dd6-11fbd87435a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make empty double-sized Green function array\n",
    "G0 = Array{Float64}(undef, 2Nx, 2Ny, 2Nz);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9bacc63-3582-49f2-95fb-683d30a92a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that this identity transform works\n",
    "f1(x, y, z) = (x==0 && y ==0 && z ==0) ? 1.0 : 0.0\n",
    "fillgreen!(G0, f1, DELTAS)\n",
    "# Convolve and extract the result\n",
    "res = DSP.conv(R, G0)[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1]; \n",
    "# These should be the same\n",
    "res ≈ R "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92311e24-2bb1-4f98-a512-bc519616251d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1.602722 seconds (133.06 k allocations: 7.300 MiB, 1.00% gc time, 3.94% compilation time)\n"
     ]
    }
   ],
   "source": [
    "# Define the filling function and fill\n",
    "f(x,y,z) = ixlafun(x, y, z, dx, dy, dz)\n",
    "@time fillgreen!(G0, f, DELTAS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b54ded8-61fb-4081-a220-7b8a4b45b69a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 28.553607 seconds (14.28 k allocations: 23.773 GiB, 5.46% gc time, 0.12% compilation time)\n"
     ]
    }
   ],
   "source": [
    "# Convolution\n",
    "@time res = DSP.conv(R, G0)[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1];\n",
    "\n",
    "# Factor for E-field\n",
    "factor = fpei/(dx*dy*dz) \n",
    "Ex = factor * res;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "518c2ae8-07db-4e08-8a94-bc780aab48a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fc8k1hVzGV7JAzDp9yk8s/MfvOORZ8+USTvWqaClMp1qrBWIBzzOVQqH/igVeIFY4RJC/b3zK8/wWPYdQ3AIxJaO/uU4s1eG5QPRdRzLCGVNUORaIyjm11IiudZt9ROZQ+6xxtZzvXGy/P8+zTIbmhGq3QFTwXv5ePH77299u3rxZFogCL6QWZIXrz/kXo2hLrPrwkqLApaGx2q52aWjnF/2Ws4GHWKD/91m9+Z3N2i0f6bdqWMaK1jIB+b8InbVH1s/cuRzJWnEgRZZnzayVmWL6ZvzdjX+5eG6WgYhwxhIwunS1MLdOIj/zZP2hLGJhY/gLwgrT2iMxrFOLpZ4Lkc6AlcHCgfL6/YS/98xlYjyiXigyx9ZqfDXImCqR1U9JtfobE+RZsPpDAZTV5qK1tjKID6JAIBAIBAKBAEEYxFaAyjaXvTTnYXfcvFouc7yJQz+x6L8a7Cnn4jt9t4mbh1/cenecrd4XhcG+C5m7n93txJMatDGLiZKMvpSszcURZuwp+3CEibCTI0QKNZFnURg3anbUKWbh5HjiDOvMYOz0LcJjxzmhXZ1ynhxhyniFx/BRpasDf/P8DfJubDIopRqb2YTmhjCIAoFAIBAIBAIEYRCbFN6kReXPTrm9D710usu5VHiY5yds51osYlHZx+kwJyfqRQp68Yte3paex6dmlGF1/GIlfQ0t+h8Hmpp68HYydBmu0H/W9khRFfyirW8RHGFOQhH7/LFiAWbwbKSjD0eYMGScSyIjRok1J0eYzyUx6WEXw0SmS53R7CvM2JPBlVaH8DxW6omIfUN5/Ui0ACjxQRQGUSAQCAQCgUCAIQxiM6F6B6hqXA+Zugqeu/xotlxufJGMb4Acawk/kK/TocekUuMWQQp6+hF6UKqFuiRiJihDOKXZt4s5hOVgVsELFB09mQPU6lyHmrFbMd+Ycs6gZp72s2QzqYZQBEu2FyTm45hoEzPmrRyqKN3oDIVO6nw4Ql+XxBxsn2cgsNuB0pQxqlNHwAuIz6uKuiuE96uVx6JSwiA2twECgUAgEAgEgpYFYRBbFHK6viHhPA9Qfqrtbk657fScS75U2LZIQVouzunQkw31cEkshDLMqUp/9fFczKvTtxcXmV7tpWs/1M0dB5liBU04STe+PcUMpk6AF7PophWRJ5ohGjiEPZ0OyU+DZwHx9WxlAT3EErox+g/JUF9D8GPdPDlCL5dEfSdhiVUKn/yInpRcyh6G0fRl9/KwgLnSIhbEd1YOVd7aGURZIG7lqNlf0Ko2JbNU+iyJ8iWysbRyqtzrQpdA2Fr1mq/YIBWvFSHd/80l7GVG1omo6EJtxs1lE5blUpYDA17GUZ18Nb8EdPYi+9HuXWwqXN160StIxbmnTMQqC2GhK0W8IU6VWMo+O8526xWAfxrtWGXGbyLHLnZFaOrlWpNDtphli1kgEAgEAoFAgCEMYhPBuddUmS62lNW1qs3lXDvLXnQaEc6wxzMVts8uJGbXqtks9hQriBQsYHfYRyxfBvVcG82V8Yup7ll1eXTWGpZfXa4t5tQFnIcjtPViOcJcwvkIRS+xrD3lyulGgxlE+79pAYdYim7025YN9+tTe6VYRm9iu3ecvai7mNF0CVv3cH0yhKe1RacvS47V6TlGpka9t1+FJohtEQZRGESBQCAQCAQCAYIwiC0Eucg5V9+adfFGHus9AyzcOt1clKWrk+DM89I8uxL9f21JwUz2sRAlsZz+pybCHtVenvAtDDxxZCPKuJ5plTaOEP2X4hNZ78OKhCshFLWYFwtYPd1oe02f2yXRU8xN0aUYMUR/ZvBlfnSaT8xKXp0VoTJnRHRA3HAfzJpBSZCKMIgCgUAgEAgEAgRhELcKVOd6CF6cnKdXn0uFQ4p58k1Z68fCuv0FfQzL5Bcj5qwa4rAQh0WXWH4ldmHqVpiPKaysl1OqZbgephG4fctCILrRfo1ZVGEaELN+0Tebw2IsnO16aEi5CUVXq7cSbQnnRZfhdBi3uvPX+MYam7ZblFgdFsMyGsjqsBjdQj2Yf6cW3Mh4TBIxStFVlPXGO8i6xUMBiA9icxsgEAgEAoFAIGhZEAaxtijSSyIgpcoe0Gr+WOfFwblZq+SbxamOln380jwJKje/6GqthvbLnHKeIdyeiFYBHyUpt0JOmF6oTmFOgmssgF9sAcDXjwcVYxdRwF4sbhdGhQ1ghYl3Y4ZboZsLJK1eSjKimC3JAt0xzgEmwZSN58sftpyy0pOMRAPx/oE4qNk6YMIDu0asoQtigtyugdR4T5bSS3P1miQPojCIAoFAIBAIBAIMYRCbCh6PVbXgQZoi8aGfq0wOezwPhEWsIt9Ep1thltNhEcRe8a2eroE5KUMfH0SLcMb58hJ2akgpya+qYDgZ8KzwUzfdGFg0KFOYnhniiceGQpMLAnssenOBrLOjl5KMKObkx+hhgO2itsT/RjRbIWHLXhwhsdoCPzG/NIT6ynA6ZTqFPcnIXGkRfeBLChZIlm71UcyyQGxetHY/3gQZf+w89zedMu71R0Xrwoxd4+r2lKvYca5ZqyFQ0YqQ3z72XAhWsgSsrFdFyPVjrOzBCw3kbNQl2+rbtXxU9DkM/cUnW8zsNnQiVuF6kd/LriCWpbpWPSfmYNsCXHx3nAuISkFrTfsCCK+J0zMxxZw74MVt4bYE8Cv1IvWXN9VGc6uBbDELBAKBQCAQCBCEQWwNoLRNBY9/fl0sjIRl4zWH8ppGpbifHyvZNXa2Zib3ro5ZbOGUYTafl5fzq44jJDxWk6EACkaxV5n7gPvxi0GaogpLHLMYuGWKIxQNAUL75WhNcYHu1tgaZ05pN5WYseOcsR/No5L96FiskJgVN92YMiCbm6SaEzVeCM2m13Mz85xK0twIgygQCAQCgUAgQBAGsSYIcvIghTMgFcamVDZW9WN42uH7Xr7K3Qpr6XRYCb1XSStDKjEC1bSmSoptpdZSDzg3CKtUBdznpTrdWWSKS73PKxANEo6eGGCJ+ZCKcjOLWCexMUjFjCAr8xGKqRlSUpAQjdmt7hCWjLzZzlYtUBOXRK41NTxPRqbEtFKXlJuEq56h87UicQOtYjBuaE99lf/AlQSpCIMoEAgEAoFAIMAQBrHGsD6/NJl7RV7vQ3ed2eCl2c/70M/10N2akQq7ZTkdVuZWWCHdWCRlmMcH0ZPG9iSGkU7n2awGmRpzOUx6TM2QCBj9zuuEYcKis8l7LtJ0xAX4IOZpjQVosLPTc7GyKGYfp8OmdEmMhVCrm4Szt+qjGtj6Qtqrj4f+qXLXns0AXwZPYTW1AzeQJ8PpVisMYnMbIBAIBAKBQCBoWRAGsQXD8kRUtGITOZ62CnA9tIhx3kPKJZDR6jTH2VqhWyFnldEr/J+lBml9RquT/KusVy7K0M8HMac3odtJlO9Cx8qQqgYeWrJ+Reiw0RNh0R+TWpi1slxjNs/FCnwQiyEU3b6JGZQhuX5srU6HRcWQbVW7JBLjXa1U1h22zLWm7fdi8pxEWk6WTUtZDoiXEheoqgJVVwaJYm5uAwQCgUAgEAgELQvCILYi+Pn81c7lw4+niWU9uRwvx0EfVEaL8nY6j2EGcQi82QVGMVdJDWaRgi4eyycS2dOn0Nf1kD0CXl3SaCIiIsiiAbUc02iqSctaKUPCLPJaIzHig4hlCWVIUiq6CcWklaP3jGo30egiBe2tyJ2RkoLODIIZVKKFx8py8mMc4/IxYtlBzeHpc91BCkmLWAjyvQmwKTwXM6GCrd4HURaIzYla/ghy/fWl8m7Dqtlczth+zaW4ZnEnttVtpXvKOXaNC18vGv/z65LmXy/iSVnOJin7/YV1aawp8KJKV2f8NLi1l3URiZePvgtHLBaZhy4UuiIkrc4N6KSVvou5shUhWvOFvT1DWPgwFH1P4Hacbb3ca6wCI1oyl4/8lnLWpJydfRrTrblWk7Va8zXF/nO5sXa6WwVki1kgEAgEAoFAgCAM4taF6h7iPGmbqp/oUnSIjTt0dLfOtJKUNDXfU87aNc5BDXpsMTO9DCknZVjckU+dU1J2k7W2MTyuvZoSinTzkh3QtgGtkALuAAVYOHUudaum6jDhh1vZq0vZWtkN6IS4C0zFoXnGD8SyxcwRjQYzhVhSC2Xo2o+27h1XtOPsTo5DIkuiOm26O1G2T8yKo7sH8mW9qehH4qYhWx8UyBazMIgCgUAgEAgEAgRhEGuDwPYMltc1MCxl9yokNsXO/VTTGTeyZJuH+mp7VeSSmHFUa+Z0WCCh6KiPS5VThnwrNt7OFOY65i7nqawroZIfXYUqs3hRszP+Lz0X0jekr/jGgJzkgOiOqSncGsk4/RS9CUWFFFt9EF1Eo8LpmuMrM5fDYiUuiTZmjo2rqMzpkNdvmu4Nay8nLZpvSI4MLMTnz/PNe5Tv9CInrb+NSqHEB1EYRIFAIBAIBAIBgjCIWyWyWJwUnL5lkcoinE/yJMS29eU5QlsnDw7Mq0s2DenFEVZFKNqIZ/aJOtcsKgsYZ+fCfGPEUvSnRcxlFg9Dtoor1meqWUPEfJ5NNXZJRGxaqpum11hmUaErwdAZQJoydLdaCUVMDWJC0UY01oAyrMQl0Z7mBh90D1bSRrN58YuZQc1uR0YPZERkJyPylyaGPm388dkSID6IzW2AQCAQCAQCgaBloTUxiO++++6MGTPK5fKQIUMGDhyYKb906dJp06YtX768b9++3/zmN9u0aUMEPvroo+nTp3/55ZcHHnjgUUcdFVi8nz7++ON58+btuOOOBx98cAHTqDkKc/IrJN4zg6+qaCCWWayUI2Qb83KERTsdOt0HKQeWy9cwXZ+LMsTGe7W6mUKwTIrq5OGTiLFJ4GRYM3pyrFUMw6sP/RdxPCm3KzaZokE5BVgnJindrRmEIqYG8Um1BTtH9flinGvmkmglBV2sm40zzHpNH+ICvVzsUloCfAmgWshKi5hnoFxa7J18WMkWhECpYKv3QWw1C8Rf/OIX119//Q477FBXV7d8+fIxY8bcddddtiUdAEybNu2MM87YsGHDLrvssnDhwgEDBjz77LNdunTRApMmTbr44ou32WabDh06fPbZZ8cdd9xjjz3Wrl07omfjxo3f+ta33n333aOOOmrGjBm1mp5AIBAIBAJBi0Hr2GJ+/PHHr7vuurFjxy5dunTp0qVXXnnlxIkTJ06caJNfunTpGWec0adPn4ULF37yySczZsx47733Ro8erQXefvvtiy666Nhjj12yZMnixYv/+7//e/r06VdccUVa1YQJExoaGjp27FjsjIIW9iTltiejNVC+r02zQsWfZDj86K3CJ+Icxy1Q0YdtBBV+aD03l7CSq48+bD2dVEDqLcJBxALFM00qIYDYZiIczyWaMpD6yPhEqYK40jpZLRt9U/EpCBXGZuuDHLZGH9yaWBvK6EOhzI+hByCRoWKpXviCQGa0uA+9IKOZunvpg4lPhFUs+5inzlrciq4ieilm/TTwhRpdRfrKRPbT+thaKhxV4ktRC9OfXlKJ+XhW2P37df00aKXlTmJekJ6vDDVmirqk7nvoxlIZ3Dftav4cND1qaI/aXNindaJ1MIg333xz+/btb7rppnCbePz48X/84x9vueWWCy+8kJW/5557Vq9efcMNN+y8884AcNRRR5199tmTJk16//33e/bsCQC33XZbY2Pjbbfdtv322wPA2Weffd999911111XX331tttuq/W8/fbbN91009NPP/3d7363KeaJ4XnR50twgzdzslCE17E7rw3dBs3um25lD4DtqFi2g217x+5K5zYxrSeV/EYqm8ImIwyFrUzXe+6PY2vxxib4TM2YC261nBKvbWJ3lAwdOsd1W+Wflly/kHj/19WJbOlaJx5tUdINvfRqB5KYAxp0EbXiEBYjYw63PY2N11uoqV3iwLQmSPaDmXpb8u04hAVNzXjdszLnEluSa8cZCWP5ZFK5Il1qF7OS9MJaAnRuU92riGhhPBjcYjjkyA2fVzPbjrwIy3MAACAASURBVF5ToRm2mDds2DBr1qw33njjrbfeWr9+/QUXXHDMMce4u4wcOVLh4zh06NDvf//7hdjTChjE9evX//3vfz/66KP10i0IghNOOOHf//73J598wnZ54YUXOnbseOSRR+qaESNGKKWef/55LdC/f/8ePXpogRNPPHHt2rUzZ87UNZs3bz7//PPPPvvs4cOHFz8rgUAgEAgEghj/+Mc/vv71r//kJz954oknpkyZsmDBgswuU6ZMef3111ca+PLLL4uypxUwiAsWLGhsbAyZP41evXoBwL///e9u3bqlu8yfP79bt25mVEooP2/ePABYv379J598ctBBB7EKv/GNb4Q1119//aJFi5577rmC5+MAyxL4wraXWoglnIhTxntc380X/yFqzxEye8124XS9kyM0KpN6JydnUDx4IBt1x8b0EKYqHxvKC/vwnVYDaKtlCNLId64hAZF1nZN2ZS0kHVy8Cd6fTl02sYQ9fzX6T2ESLh+hiGNWlFGZ1CcUmB4Y0VoxSYrsIyEsBmVoaEj4xViYEnjZlCHhF231zcYvQiYUVsPATcJ5pq32N8ijkycryQk3PaPY5Axinz59nnrqqYMPPnju3LnDhg3z7HX66af/6le/qoU9rWCBuGrVKgDo3LmzWRkWv/jiC7bLF198YbKDWj5UtXr1aqWUW+F777133XXX3X///TvuuGNRExEIBAKBQNDEaGho2GabbZrbimzsvvvuu+++OwDMnTu3uW0BaBULxPipDj01lMtlMJ7t0l2IvDJSJIT/hhpYheVy+YILLhg2bNhpp51W3DyayY/CiQy/r4zefv4jbDXllohQ1rFyZmbJCbaXJxfors8mDiFFAtG+PhwhOZi+vobFUYYsU5iq58c1uvEG8NKWc+2+rpruN4i4DtuoKfMYziXFLfEsSjazGIlhRz1sSD5CEU8twB6T1AeR+ibGBvB8p4VfxPRe9DY2wi9SAi8vNejiHS18cJ5NGpvTodtf0J1AO0BSqd0NIk3g4vMy2Ee34uYEc14cCU8caC1pbj744IM77rhj8+bNe++999ChQ9MZ/SpGK1gg7rDDDgCwYsUKszIsdurUie3SqVMnVj5U1bFjxyAIHArvvvvu2bNnP/vss++//37Y2tjYuH79+vfff79Tp0477bRTQTMTCAQCgUBQWxS4ZmqBmDp16l/+8peNGzcCQM+ePR977LEDDjigEM2tIEilV69e9fX18+fPNyvD4t5778126du378cff9zQ0EDk+/btCwDt2rXr3r27Q+EHH3ywcePGI488sleML7744h//+EevXr1+85vfFDy9pkEQf3zzI3iKWYeKNdiUuFuRKsSfGfluMoR1fb7MNWz2B2RtKmGNrd6iMwh5GJTUg/YNkBhJlpHKEmIbCKVESQ4QyRmELgydKASlL7HnSUFz4QdyJ2Rh8rZgA5yZdLzSymBrU5VYcynrw2t2DeGZyMY538zDwh8QbJUt+4wlS1F06lF9cs1Hp15fRcxA6SQvGb8Fct2yWW9w3h/f3wLpi3/XtnpyF6j+TmK9R6FjqNzCKXjdaf1UuYfwE/O2u6VDKSg3FvapGSZNmrR48eINGzasWrXqzjvv/PTTT48//vh169YVorwVMIjt2rU74ogjnn/++S+//HK77bYDgHK5/NRTT/Xr12+PPfZguwwfPvy5557761//euyxx4Y1Tz75ZBAERx99tBaYNGnSf/7zn969e2uBjh07Dh48GABGjx5NYstPOeWUHj16/PrXv+7evXttZikQCAQCgWDLxO233z558uR0ffv27V966SXbdmgmzj///PBLx44dv//9769cuXLcuHGPP/74mWeeWbmtMVrBAhEAfvaznx1//PGXXHLJHXfcUV9ff/nlly9cuPDqq6/WAmeddda0adPef//9cBP5/PPPv+GGGy699NJ+/fp169btqaeeeuCBB0477bSvfOUrofwPf/jD++67b+zYsY888kinTp3uuuuul19+edy4cW3btgWAPn369OnTxzSgTZs2O+64Y4vKd1ORT0Wm0loN7dujMj9C3iXRIsvV22Zk8edLswVMPdVJ/LGI/xd1OuTd+Ig9vj6IJGwZW5uaO3FEq8LN0RjdkMGaqVW65Go1QKzNgwovNs4cHDzrowEsoaO2qaScEf3EsM9fLBPESth6t4cimqnhwhZuG8Ree4ExumGAIu/rwz6IkVuhQiPb0yIGYBx4PuSZHmqXSyKadCSG7QFWJ6qn59R2XbidDm3w6JXnQjR6scYXotpj3OyhmwnF+iDus88+RxxxRLq+ffv2YT7mQhDGPpMN0orROhaI3/rWt6699trx48c/9NBDpVJp/fr1P/7xj8877zwtsHr16hUrVui4k86dO0+dOvX000/v0aNH+/btV61addhhh911111avl+/fvfee++YMWN23XXXtm3brl279pRTTvnlL3/Z1BMTCAQCgUCwpWPYsGEmq1UjLFu2DADat29fiLbWsUAEgHHjxp1xxhkvv/xyuVw+9NBDiffhrbfeeuWVV5ovxBs6dOiCBQuee+65lStX9u3b9/DDDy+VkMPlqFGjjj766BdeeGH9+vUHHHDAwIEDHaO/8MIL5htWtnhkPdg6H/fcxAzlwPxGJxxYpnyagkrAGuAk/5J6LzLMEolMirivm1+k4xLuglA+lnq38RYDUGW6ng/NJuPaBuLrU/DgCG2MpldtldwFpug4ZbYB3C9WIUfccj3rI87TfoTng8jpFBOHGckLkWZCuhkBv/jkqpDeS3SZQ9jsSfGLbo4wvCcEhqydbvSLYvajDDGFmQgqPSNjeroPqTcU4UrQxw2/QMUmn1T6hEJb2UfGeLe1WwVwqpOWgPfee2/WrFlHHXXUXnvtBQArVqwwo2Y3btx44403AsDQoUMLGa7VLBABoEePHj169LA1pSt32GGHU0891aFw1113/d73vuczdFExQZ7IufHA/HIz/p7mUeWJXKu3ioTSvXJsdFpfPMovWL3Wi3YxbsllG8K9fPRYGhrCjCo9lt28bHvcBhitWBXWYE+U7VwCsmeTfnM/k7gbq/rDRzsHTLN1fPyHl/75d/5Ztq0timjFS1K8XqRJZ2z70YBWb1pLqNxYAeKVEFmfhZe3JesNv+OcTIpbTfrlr/FdEXJLrlyJsq1Zb9ywLRg5KesaNMd4GpbD5dRpb3UtSVklTb5CbYZX7SmlRo4cCQBLly4FgHvuuWfGjBkAcMstt4QRF9OnT//JT37y6KOPhgvE3/zmN0888cSwYcN233335cuXT5069YMPPrjwwgvJe0AqRmtaIAoEAoFAIBBsqVi5ciUAtGnTJox5CIuNjdFStVu3bkceeeTOO+8cFo888sg5c+b8z//8z7Jly9q1a7fffvuNHz/+nHPOKcoYWSA2KbyfEwt/WCpAYdY2X47WlKzfJmmeARMxTs6Tr/JKeZ0WIyQcHZHjF500W2prm6fx6I4UDUPhixVwlkarU1WeABer74FHBJKVBcy4RIr4UbiJEB8WEPFlVqSoIF614jaRU8QeqldgOfKGQkjNNFKVkErhHi7Z0kV0I4DeJo6rufTalITTl3dE2BFVLo4w9V4+Yp62jtB+vJiFMjTHp/Xu2AsbuxYZkd6gj4QRk0eVVErogX0uOcETjdUhm26sjhy1jNnkDGIQBO63+5566qnmvugxxxxDMq4Ui1aQB1EgEAgEAoFA0JQQBrFloMBnrco0e4nxz2bUiS1P3wqRy8uQH9pG7+URS3F1tBr1svCLeXz+snwQSRGZlxif368xi33kjeedCzP5V9Z4pp4OlG61nFWbtVXAYJmYxhQpx4tiTjCDX7QQVJahXK12QhGRkTzLhYmbJGSE5MSxpL9x+yCmXBLDvogU9IxKSdGtIcFIb10p2o8hrmweitlHl6EqdQPDTVYKN4GnL6sAUufUl/xz8uK5xSpA7TQjqKZnEFsahEEUCAQCgUAgECAIgyhIUCClYlOVYk0UU5lXCStj8wrzClu2EYekr1PMUizS6dCD3qtsRNsQufhO3q2QyeONdVLNnLXgarXCI8VSZVDEWuKmRs3QsohkyqAMyYisZtwK+vQp0qp92hSuZ3QaDniBaZYl5XUszAwb1iOSkhSB80FMuSRqexRkuSSCp4ci4UGJmM1DEZNwubJqE7CHC9nE1nPZcDyVVIACVbUWBC0vzU0TQxhEgUAgEAgEAgGCMIitD9W8ZM/e1eMJt8Ih8z9w+gW6JtUeL8TLGbacT1UFXoY2pzo3dxgViX+es5iMmBH7zBfjcWkv1jy/GGeb35zl+Lg5QpaBpuU8V2Dmpc4pY3lxpoUweZjzcY/sKZx6tR6m6Li+1jhlRS4nc/w0XxUYGiDOd4id+1K+ifzr+/K5JJKJxwbk8lCkPB9/+qiHoldQs00VQzda0yJWmC6RMc+7bzicrZnnrzM6uQckx7YlQDVDFHNLgywQtzQUuHHmq7OSJWAljb4bidWsF51itj3cXJvIlhWYpchv6fL7v1GRiQKpepUZ4L6JLrw+416sYjPefUBsxmcs/tjFpbVvRfBRosi3eObsIi+1XjOblU2Y/PW3LBz5v+TO1tSKRp+28FzzRX5tl9qWjLZlndlw6CpTFzOS4ARg7nHn34A27DHNKSZmhV8RYqROZka9X7NtMNfysZqlnnXAGuisMVQLfJNKE0O2mAUCgUAgEAgECMIgtl405cMYM1Yuni9Vqfj6PJvL9rw2RIyt9mEKYzE3UxjXGEQaUzS0e3CHFvYxg+2L+LwUm5SRDSfPiBVQhs7N66SrlSnk+NeMrVa+NYsIdzfzI/CNlJRD/FmKskOcEyEF8e4rHZ+5xjAxFNBeiMdx040sj0X5Mbz5SfqSc2iIxb25HedUVm1NnnIkJZlqxgY0KcakX67QFl/K0JThkTvrDR+VggzwPH3E4pSZbuq5QPBHr4VAglSEQRQIBAKBQCAQIAiDuMXD/QxL/i8OFTgmOpT5yPglys5wK2S7ZBlDaS1+XL8iS8JZvAwznB1zJcfxTazDUYZ+4S+8BqBztBwBDZZQpEPzYFVVhtTALisIk4aZIYMyDNlZPDcaZhE3Wly6WOaHEooJB8YQigalgypSL/ELwKCvaP4akhMnuRYUsocbkbokkrgTp0uiLSUNGYpmqIm/WDhUi5JKHBYtlCGG5dxWhMoCXNwqiT6roU1GQ9YMW32QijCIAoFAIBAIBAIEYRC3UBTy2OZWUtkQTo7HycsY1R70pGeccj5+kS/GNTYmLxeVyLsAutg1NxlZmZJ8ibJtlKGTDbV7PbI60VwMMVSZEiZg+xYB4mtmu/giAhE7jdm8sIhPHWW3QiqOkteEWUxlvTEb0wNijhBTYpx1mdybPiE8DUmy4VBHRmVOKWYftU5nEpzIYTFnFDOhAXNyhCxlSEdgYcl6gxVnMosV5LVxs4DZHGHVQ7RkKJAoZmEQBQKBQCAQCAQIwiAKrMh6FPV6JGSVeLqOMUmVWTHeUDdvZzOI4xczqUGWk/Pk+UiRUGLOEGNLBDSm03SNW0kuFrAiytCWptEnMpoqScCxjzbNPsiUdnMtWAgxY6CPj5bCPFZUhwgrg19E/BClBjFPY+UIWUKRkJR4DixxqOtTzoWYMsREZtLLki7RErash8Tso9sHkXKT2cW0AeRdiPHFFvAllczImCN/KrxinDHPajSgw2pj6DLoRtqJue5bL/dXECQPojCIAoFAIBAIBAIMYRC3CthpEffDoY3Yc1YW6NrlMzCA24/Qq4vbEzHLCuL6luU2xxWxsJF3EJvnJiMD3NdK3ZEhmF52RhOroua5qEp2aolyZ68MD8WkHllLwfF8ua9W3EH51BKij5YwIZTwNgxfZXdJ1P+HBxnxWoQhSw4TSygi62h96k0qpFUjMKdm7eVD+yXChHDljogljSRNkmjhWdmDSjlCSuy5SDgNnxjn2lJ2+Fz70Y3uqVnI0VRz60Ww1UcxywJRUDi4O4Pz73Dqz7xtYep5z3Gu9pxFusLwKtIR+VcSW7Zl3Uln8u0pU1WuZSXNXGNZn5EhWM02a90ZuVO7xvxay/26Z7p+5TTYNBcCtHxLDZhaOIbLNdRq20QGsqSI/sDruQSQ2o9OtKE1Z849ZbIASw6qgtSiwCiiCrrSSswkC8ewNR6C33FGQ9DX9GlV0av2FBbW9rmWj9Y4FL6YR9a9IsSock9Za7E0ZxsgoJAt5uY2QCAQCAQCgUDQsiAMoqBpYNsTZB6DPR9yM+JOUkP7FOles3P30xqVwpqXfMO8Gtl4jfbgXOSfe085neYmKwwFK+FoP88Ymnwv8fML02E32Y1uLF1tudhobYGbYJhJxEMYLBJigOgmaVSHaKSAUIgKSVlfxGfjCCmhiBk7zApSFpDboyRHOMUvIvbRMFufY3TBZOw4A+6L84qH3GGgAiysxw2QGX5BKjSxDj5cth1pSzG0R8+UMc8GKyeIadH4mJOTIKgUSoJUhEEUCAQCgUAgEGAIg7hVospnS4/uxT69+mTGrsb1MKMIlqLbbw9wMcCdMnwQ3aqIsCbMLGb4EI3EJdFvxAzKkDjEcWwo04sQjbTIKeEdE7GGVGuBl6gitBsezJiKi9BTOE90wCVzpuEmKaowosqoqyKRYsfXToeG6bmdDi3UoNWRMQDj6AXmcYjPstE3bNWXIu4b+SBqzcqlih5M8wCkOEBK+1VeTB1OHnze7IpgZR9Zucoodd8xWiGEQWxuAwQCgUAgEAgELQvCIG7VsD6iZjy7YpKpQAO8mMJKEtlUWOT84ZgnZdYe6oNIiDQ0VirpDEfjJa1Y2EbRZcQpW2hI3q3QOYQnZUgPJs+hUkYTC2NVkPLg5IQhT2tFMMyxeeUxrTEZhklBLEyT4NiimBOqDA1LMrNkEIp4Liy/SOBNN2p54vwXtlp4R+qSiIVDH0R89QTYg5PE/yaghBk6PvZQZJ9ZZh4DBj55swHi6VCnw0qQ6ktOfoY0oHO65UIYxOY2QCAQCAQCgUDQsiAM4haCAh2qbNoyhshFzFgzHXqCEGxQqyI2yxrFHPW1EKt8/j8LgcfqzOV0mPYm5Gg/NylISMqMVIvOgVJzgVQrOyLwrZxmG79IE2fm4cWzYNOl0pqSuFfMEcUhtzzLRaJ0ATsdEv4sGZEE3gakV2g6orWSAqHP3PwiKRLCLnK+ROSfls/IdGhzSQROmNBuOK47V1CzYQ+ira3Hp7hiBoGnDTBEjVoLS8qrcAnbSMFqqErPIVoolDCIAoFAIBAIBAKBAWEQBTUGTfuXp6svK6nyFD36UrZPGwRs0Sts2RKnDNjvKoPPAx/yD7Ua9hDaT0s5WylHyLB9ubIkpsjIWN7PJdHS11bE4C4nKpt5pSruPyqDtCTnC1FjmkFCpKBhLEM6JTG8vDujdlVE1hIOjHooJkXMWeIJ8h52NGUgEUbknxYwaKRA901abVkSAU0tIhq1AcYU9Cw8g5qtk8RDkPOVRftlUK7OvomRptk+MDhdRB4L8qPYPIit8kTIAnFLRZUUfkvcAfCLTckEs4aw5rUJV2DOrWSjAYvhaAz3Is++ejNszlq98a2JAZ6LPGdrBQEulogW2yZ7xsY0uwT0jWhB41pa8yF1LZAlGBkZbaEC3mImk4g2i/Hb4ZJ1YfjXH+d80b2MPWWyXkQLo3h4tKgiy1dj+egsItP5hSDohVpcz64jkyUxu8gDMHtnLC4T8wJsLl7BO7ee6Ryty8XsNZ8b6Tf/tRhUMzVytlsPVLFBKnXFqWo6yBazQCAQCAQCgQBBGMQmRat9mMoBn6ml9vU8H5kJtwSVF22ayf4mlqa8lV9UinuLme7hRo028g+P62zNxSCmWEA3R5iDjKSEIj3U/CxsW8z8BnRap2kA4EY3oZgbWB3eaY0rCQuI++JiarMY3zM0YRa1UtIp2vNNzoCLUIz2lCkriSZG+MUq72Bs8ArdRCatFhYwxT4mM0qsJbS9M2YlUUKI0ORoolMQUZhYtgnSaBMelBpr6+Qh06pRk6kJg9jcBggEAoFAIBAIWhaEQRR4o2a0Z6WKqYdXJUWWWcRuT0Yz/5jqdhyMOTCkhPo1kiEogUcGInwearUnyvYiBTMy15RYjpDXnPJf5MbV9ttYSeJ0yGmzx6wYlUw9Bkse2+QSKUaecoSWIBVFXpdHvQyVQzgaAhOKST3hxvDpopEuCaEIABDEpAmh91gYlFfILxJ6D8yi0cvTJRFMncjmxDxNnXJD+cWsmNqxLgVgzXqjSUtqfuVFL3hShpWghqpbJ8pb+4EQBlEgEAgEAoFAgCAM4taJ5nsw8nA39M5u4zegjzMioaBws9v10ABh3VC9LWmzhd7TYgxjl+F0mO2DyLWWbMI52Efem9DJTVrric74ePCh0BbXQ5+Tmx/c2ad1iAU02EbEG6XClk2ZFDNGst6EAdFJq6Ue2xc7+aGZU1KrpM0LkD34LIUphKkHnrMIqUOe5ZKIOhFCkdCN8YWAVRGXVcr2xQUaxRyrpI574Rg8oZiVCrty+Oa7wfRnE6ISHrQVQBWb5qZVQhhEgUAgEAgEAgGCMIhNhi0gjAw7AzHfmOZCHipTmajBv5gCR0GR4XK5HsbajGGVqTkjbJnQjZiEc/OLWQQeUYJ7lWxDcEUrv0iOg0sVL5z0ch0f1vUwOSB+Ucy8b2KVFyhhAaNKfLmSsNikMWT7EPln8IsuyhBoADLh+WxEozkCFaaR0WVkLHWaKyHrgkjY5VwIqR+b2+kwRTeGleSU84Ri7B9Ibllx3yAxEtLMHA0TRvwrtciaFxGrdPKLqaJirMqJ6Ag4D31ADuIWwgUWZ7z4IDa3AQKBQCAQCASClgVhEFsGXA+fORRUp6P6wTMrazSiR5F1PUyKFgqKiNmoRHDxfO6w5bhescWUUQxH6OuDqB8G3X6NXJwypf0y2EfXQISMhORw6SmzSlDR6oPoZJq9KMPM3w9/CbB8VlxHPNEIC4gdFg0uUDmFWfYRgrLLcZC8nSUg/CJ2uFIl1Ncd40yFOQ9FSB1dqiQ6P26OkKhCwragZspNRkwuPdkZ1F3EuiFCkc6RTCafl2Ytbt6ex76JUMxfhSbj9bZ6H0RZINYGqrl+gBq2rYVmRuqWXCvzcmbGxiWy3Uk10yVOCsyKMGtfGIllZLEGXHTHiOiloW0dWWIXeV6tWVlvWGE8NaYX2xr3wou81OISsDDXahOrBnQEtG4LaDVqNRZ5yKz4VLtWhCTHNUD8Bt4yvwQkb2omaz4gu8bhKipujdZYJWdrrCkoBbiVzBz30iMGrAxZAuoZ44eBqBIdkNS4/FaysahDAuSVzSQqhVrPrRdr+/Y8HJVCTkGLQXPvVldzRGSLubkNEAgEAoFAIBC0LAiDKGhJqOJp0ys2JTVEQCudlCGpT74xLKB3VAqYreymbVbCGj0QHteZucZoLaNW9xYzSxladpwj88iecop/ZTemfbeYM7hDVGnjdAuAIiWya2wTVpBmATG9lwjTqBQFKWowEdDV4VavLWYlJBpLej86SCr1FmpGawkNhKeovRqUZjS5I5GkcbHL6HrjWHI7vJaYFTK6wkeecpbaHrrXHF57OQhFgmoJxZZKEm6ZUFGc1tYMYRAFAoFAIBAIBAjCILZEkPyxNUBL8VBMMT7Yr4iwfc5iCk7KMC1mNNuOPOW6LKrY7gEVI8KWiVMykhQZTi77VXulMtNqizspOYtc3Ik7KiXtIglUJzIe2KJGYJhhafUtVgNyEdGoFF44fg8eEY6PA5/mBhUT90Hyzr3ELw0VacwK6prK/BwWbT6IUV6bqDl2OiwhYa2KukpinZqTs8uYU3QKW0hBULiSvymwr+AzAl+IjyRiPKk9+QlFd9GYRUtzOqy5l2E6nKiJUBPX0dYEYRAFAoFAIBAIBAjCIDYPWG8bQQpW7y2PYgSeMqQMAu+dZqES0xQdHonSWgxDZgtbzhmnjIo8F6jFLEyemyPkXRKx/6LVB5ENW85Mc8MX8fGxMKwthEH0LAbEQzEsYS9D0hoQ4SQgWbsVoiEygprDnQqF/Qi14yD5oYQ6ccgzkKw38cmlLomYdyQqM/hFfbiwHyN7C2VDm8HijJgIU95PFxQYBGcqMjrAYzgJRToS5Vg9igIeNWT5xAexuQ0QCAQCgUAgEFSOL7/8snCdwiDWBMrKERbg/Bd7qLQUP0Iv2HLR1WIoetBZyjD9jdUV/svTEmQIq+ci7ZWHIwSOI7QlGrT5IEakYNnsRQlFQuwlpCDHEVqKbMizbx7EJI+3oRlSh89dBK7VIISRbA0YRIWLMdFHxVCRcoSKbaVBzWUFMSOIxDjKMBmQ0JBRZXxhAOIXw4BlFccpA+EXcRGoS2JJy4B51+N+hiqgYshaP37RnEKigXYh54dcH/o3iak7260rQLLKqDQsi4fg3E1rghSp3LKBvGMLUUVQleImZxBXrlz52GOPzZ49e/bs2Z9//vnVV1995plnuruUy+Ubb7zxtttuW7JkSceOHc8777zrrrtuu+22K8QeWSAKBAKBQCAQNDPmzJlzwQUX7LDDDnvuuef777+/evXqzC5XXXXV1VdfPXr06BEjRsyaNev6669ftGjRlClTCrFHFog1Ri43ktbsc9KMJmeFMwMw5lHmIf6feeA0OCdERnonPuR6uRlNm7CPDyIppsXcgck4xjmi/TLoRsRNGoQiMMKJZjTlgNR7UoZREfE0Cdyv06H8ESeTBr1AEEcYUBmGOEpKnJehrRgxi9rnj1BS8Ssf2DyIycktEx/EktEYb3qQon7bGIlTLhn6NZWYuCQitji5seHDq3AlJfY4/s7G2KV8/lC/uJXxREz3suZH5FwSyV3bc1un6QhFMm78pclHLgK5jC5ihqrJGcT9999/3rx5vXv3njFjxrBhwzLlP/vssxtvvHHEiBETJ04EgBEjRmzevPn666//+9//f2x1XgAAIABJREFUPmTIkOrtkQWiYMsDWpBRuNcDZLHCd6VbhuxQdJ8Th7ZYQ1hIMEIoTP502laiJKDEvRFMi2WXErJ89N2eDouAW7HxYFkR4uWj0RrE/yRF956yfZmYc2logl0mpuvJ+5Sj9YcuoWVCvImMVWnhMjKSXy9CvFJMrhcFkMrbkoxQNs2LtpjxEpBMJYiHDMXIjnNycssKgIlNUXgdGW8xE/PiA0LOj+IqtXnov8y95rCSnsXoCJDKQOtGK1a6JE0tUbF9CpcFLR6qGbaYd9xxxx133NFf/i9/+cvGjRtHjRqla0aNGnX99ddPnTq1kAWiBKkIBAKBQCAQtDK89dZbAHDooYfqmn333XeHHXb45z//WYh+YRCbE2hzQuANPgyFyNAKZWl2bkVwSVW4k+UiBWNW0jKCcxM56817kCmc1ukmBSndWOJexGejDLmolIT0wkXKEVLKMKa1KL+IjybhCCll6OQXgQhXBLpNiAg8ymvRPWXEifFZbzSHEeBWQiiW9RkhvcJWYFvJTmrMccUcoUInJsqMrUlBKINBN4ZSCVMYMd+6iEdMSPWQD0bH0BqzgjtHhB35WVO6HjVTOi8dpMLeWKzRHoobEt3U7Vu6iFBsrh3nVo2mOFotPs3NZ599BgBdunQxKzt37hzWVw9hEAUCgUAgEAhaGTZu3Fgqldq1a2dWdujQYcOGDYXoFwaxtrBzhKzHCumrH3DzPyzhgakZzU5d5hy3KpbHx+nQmaGGsjqJC6DfENitkDoO0kQ2nHk2h0VKcHJEJtC4E5rmhnU61HEnbESLdlikqgwN2nhcTJ5JMyjDAIkF+KBbGUSuFfyKXIkBvVIwsecuRhyhm1BErSSZlZVQ1GLhqdZuc2G+G31yo/zVxKlOH4gSUhmNgFJjE2/C5IeDvBmjSy5xZyQ/pCTntTJ7pYSxzx++eRGnPno/5TjCpBUzy0YvwjhyLonaABxE4+WS6Id8hKKdpWwiKEepKqu837DHiBVzMMqeBnhhwoQJEyZMSNdvv/32H330UefOnSvQud1225XL5TVr1nTs2FFXfvHFF+3bt6/cUAOyQBQIBAKBQCCoIY4//vgTTzwxXd+hQ4eddtqpMp177bUXACxdulQvEBsbG5ctW7bvvvtWbKcJWSA2FfDzbgsZp9mZxErBPB/6OCZa650uiXZ3RmURQPaw9GcqXzSi/VKUoe6F2UdWOPNVezhvNn0DXuh06IxxtjsdAupL3AdLxr/GIcigDEuEFMxVxKqoDykpElAXOF6KeJ7Rog+D6CzitC4ZhKK2Wp/bKG82P4eAOCxCGWLaDCAVpww4UDlygtQXRgmMQ6YIkYnzaRtxwYbNmnTDk3T6+BnW4rnRfDqkNdKUPqcejoO2zN38rReZm3T16tuikZeiazp+s7iRVKHJxg866KAxY8YUqBAAvva1rwHACy+80Lt377Bm5syZX3755SGHHFKIfvFBFAgEAoFAIGjpeO2116644or33nsvLH7rW9/q3LnzpEmTGhoawpo77rijvr7+e9/7XiHDCYPYIuHxPGm8w97t7VKFL0weFEhGVhlXSrWh/4ArpYYmfIxNVYbzHyIaba2E88IykcNTRthyiajCTGGKd6S9MpwOceJDEsUc9QXcN54FoQxDfpFQg5BFGZZKqEiOl5tfJAHkTe+DSF6Qx3KEZWdr4vTnJBRx+DCk6kOEp54kTbQRZiROOcqMrV+mRxwWw8SH+DwofY0lTGfA1GOnwwDP0UgiiUi4qC65D6J5U4qOdzqkJ5s4dhKX7fhVhGnFSIhXVQXsvGNuNB2NB5D1W2IOphVNa3cyaJNHMZfL5ZAUXLNmDQDccMMNf/jDHwBgypQpPXr0AICZM2dec801AwYM2GeffQCgffv2v/3tb0eNGjV48ODjjjtu1qxZzz333Pjx4/v06VOIPbJAbBFoeRsM7LKyQHdoy13WU74AuBQa5uA/t+49SdI9vY4MSwGp5FZvpG+uqJR0mhu6qiune6X2o517yngTORWkAqhItpKTdSFeTST1eEUYLQFLvHCuHefwbxLNm13dz46ENfisCElRH70yaS0j88h6sYzXi3rGZXN4AG69SINU8NqGXSaC3nHW5kSXTVwML6r43Ec3tHjtq8gVC4DqbTErJIF2uLrVZUX6Aphng5xk53KN2pMMEVqL14kUliGrgvMPAr0NF2iA9cG5as1FoumWi4UGqfggCIKDDz44/H7UUUfpeh2n3L9//wsuuCBcLIb43ve+17Vr19/97ndPPfVU165dH3zwwTPOOKMoe2SBKBAIBAKBQNDMCILg7rvvdggMHz58+PDhpHLo0KFDhw6thT2yQGwiRI+E9GHPi5OLdl5q8uDEG8A/wBb5qFwAGWmNSrHazbT6HVWFv5DdpJTmgGjGZA/eNWY3l1O5r4HtSylDEjLiTqCdkII4UTblCAn7GLYCFo6twwxiUCLUIFfUh6BUQvWBu6j7IrpRkVa6xUxaDRmAfFc1JbLiAssRYkIxoHvKmKxNWkNrLYRiRBXSc0354IhQ5C/yiLHDP0b7jjMi8KINaJJGG1/VKk1wlpQ5Y+IyEWeuiW+ObAJtnZQH83r0VXb4d0NpY9KazS2ybfjoUQIWG8QxnAXtHRfHcmVxpLUalxnRRzlzTsFqbUVmlLf2II2tff4CgUAgEAgEAgJhELd0OL1ZqullPAuzzkvNBtZqK9uXX5XRjDk5ShnGUsRz0UkZZrUSLhDSrZTtA54FtBfdhCJgYQAAqAPc6qQMS9ibUNe4GcSkF0sZYp1ul8SMIJWsK8P9bj3gGERc1CEaIZMWaJYicknUXoaEQcTF0M4yzlUEMZVnoVBpCEskon3Z3C6JnJch9oMjxTTXrsKwGOxlSIhGIxO1+V+KbgSuFTF05mlBZy2grfxJ97qv2TjLClQ1KQLwt6cyu1vSbCtC0PQ+iC0NwiAKBAKBQCAQCBCEQawRggIeGvHTsFUqfHLF2WVtXSvjEzNQE6VNBULCsSKYj8kMLrRwmJpNQSwg6cLmtXFThhm+hp5i1pDnkNaKNYVFTBlqjpCPUy5ZuEDyhUQxY8pQEd6RFK0eiiFNYvNBdFK77C8nXaaUGaIMA5ZQTLwMy2ASiiEdGCeVDkIukBKKSQpsNJcyScihzUX1qVsSprp9XBL11LCXIet0mLB9yRlQZjEmyHUxPF+IJU1lvQnMYnIZKzRQ6v19pmzqZFrOPOsaaFCYhIdsVaiB2azKVGXM/fsYUIyRlf5lUlZqeeuBMIgCgUAgEAgEAgRhEJsUCbFHWTf06JxFUVXwYGXpSxyc8uutEITh9ONK84Gq8jtobBpCm0rLiJji4MlCgzRRWBh1odQm/+Y91JrKyM1zhGDLdEjfvAcAjkyHARj8XUTg1TkpQyuDiIqKpkUsWYqIQVSEUMxIiwi46OWVRU8uYUIwg5gKasavMQyjeTWhGJ6RmAuMjkDsBRVEzpeaQSRBzWSOWiz8FxGKNqfDmE/kWyOKrkSOkGYoS+ZMWX4xqcFxygkpiGnAFN0YpIfQzQpPhWQ+jHJcZ91BKcPK3kMyWCV8L6vmnp05QuGac41vHbeam7irrzFgkG6oxWFQ4oPY3AYIBAKBQCAQCFoWhEGsNQp7hKwFy9Z0BihMEjQf3MZTfggwvRfJpKhB/t16WGcyMObzwkbMAhLhVFpENG7uN6mQXtTp0PgXUpkOQ+9D4nRYR9wHcbHOxhTWsfWKJRoDwi/afBCZ9/IpQijaophT54mF4sOWSVGzbjhbYRjFrP0FFQ4YD+sD3BoXVRC+/8bChhLOm04C83yxTtbpUIO2Un4xnGDciCPo49a4aCG54zepYFLQkhYx7oud2IhfoeYm8RaNIkQvIZETHSkvTdTJxTG7ejYlqnCba3bjCzWgiL+TW30eRFkgNg/w7Sur1S3tP55LCXt/q8UGSasG3uG1SQVM0VhWklayquOELb7/luiTWHPJKUZaSdabZPlo/Bt/SfaU67hNZNuKsI5fIPIrQrx8NFaE7PJR7zOyG9DJEQG0oYn+y05wYyJZcOCtzKg+XtVxUSnJawyjVrQETF6KTdaLQSM2PgC0XgSzni4Y0+9sNoo0Nw1ZSulFHvlbGVaXkBjNjJ1sppNdY/56jj0EdGtgGsDmxEmlucGqyH3Psko0tp6rv9VuSbAdB+fxKeRvhXKUajIgr1m2mJvbAIFAIBAIBAJBy4IwiDWB8mT9KLHnydgxj8PGYzZqZc3w3CzGj+4ZBqQ6V76nbB+3EGTb40sNkmqWGtQjYq7HmhmbCHNhKLYtOZoo20YZ4jfyBWzR9+15eE+5zoMyjPlFRbeYLZRhWE9DWCyEYpgTxxqkgulGNs0NKSbAW5m2wA7y9jyFjny446x0a5mjDAmhGDTGUwnN4wnFwGY2f8XqK7NkWhuYU0jJRjvOuoiCUvQc9UzDKeCLKrUvHFBSUJmToSEshN4jbLoK4n8MYUIS4nsScyNwE41peYRasY9VvYjPd8eZEfMc1yaVqg+cOn3sxDLVmefXWRhEgUAgEAgEAoHAgDCITYaqnZixP0+ljzYWM3I9/bZ0R53CnFJ4atAiZ0g5DSDhKSRmxcvpEKmyZ9VWFrGQaMT2JEVARZ+35xGnwzqeMgyLirQC5QgV24uEoRC6MeAJxYysN9g/T7m5N8oq6V+jAkgSVKXerVcG0zEuSoUdM3Ykr01IFgakNTavMQxSacRzaYz7asPcPCiZTBl1Djsk3Cjm2bDTYThHfWGEvCOJPjGK2K2Q1NMwFJdLIs2MTVlB229VgT44Wsr2M80kGitBy75p5ppkVSygU2Uxx7qgg6wCVWSQSu1cJWsIYRAFAoFAIBAIBAitjEFcsWLFe++91759+/79+5dK2avbhoaGOXPmbNq0qX///u3bt08LrFu3bs6cOfX19QcccMA222xDWjds2LBgwYLVq1f36tVrl112qcBgt7df0kofLwMwaIkme+r09E1keyVP59HDOmnli97Ixb9iYW4wXwPcckkrdsDiSRyLZxNxk6Nz5DRbCEXiTUgzY1uLRt+4SFJhZ1CG1Omwji2qOuxNWIcYRGXrRX0QCaHI8YuYMqT8Ik0ujXzaKI+YAnHFA5zXBrAPYux0qI8togzjRDaxl2FofDkmBcskr00jAKhG7Z/XaNpuWBNw1Skp/H+MMlOpkLDhbRmg1hK4itgZUWEH5QBzhAkdi+8WMdeOWUDqIYipSnxHSyW70d6NwMPjrmP1UKR9c93zKrnlszeOVJG9GVWivwlgjMh4H7rtKcbaIqOYhUGsJZRS48aN22233Q4//PADDzywd+/er732mrvLk08+ueeeew4aNGjIkCFdu3a94447iMCdd97ZtWvXIUOGfO1rX9tjjz0ef/xx3TR//vyTTjppxx133G+//YYMGbLrrrsOHjx47ty5xU9MIBAIBAKBoIWh1SwQf/e7311//fUXXXTRxx9//Nprr7Vr1+6EE05YsmSJTf6dd945/fTT+/btO2fOnP/85z8jRoz4wQ9+8OSTT2qBp59++uKLLz722GPnz5//zjvv9OvXb+TIkW+99VbYOm/evDfffHPcuHHTp09//fXXb7zxxjlz5gwfPnzVqlW+FitbmFxQCCeoIAg/tF5F3kHmQEqbw1uVaR6q5HWoTOWtFfZzhudM5OJiACoImR3zQ49XXAy7BCr8JCfZ7BXXBwEEAUAixn8isZIKP5HmuEgNK6FP3DfAnxL+1MWfEpRKUBd/SnVQqlN10QeiT334UcnHbK1TqEkLt4k/9aquXtW3iT6hTH2b8BPJ6Nb6bfAHV9Zto+q2IUWoz/iwvaxDoMrIPMNaZi56pqmjFB4NfZTCDzpKEAskxzw8L8kZ4c5X8kFnOT71+EOvFv6iyndl6hrjJwBxvfFbyP7hWH90/C+Zv2cVdo9ugbDcpVN1jr8C9gOjIvoXa4vPpWJCmG1/xSqCxbwq/iqpclDUp8q5NRdaxxbz5s2br7nmmgMOOOCWW24JgqBbt2733nvvoYce+tvf/vbaa69lu1x33XWbNm166KGH9txzTwC47777XnzxxauuuurEE08MBa666qqddtrp/vvv33bbbQHg4Ycf7tat2/XXX//II48AwOGHH/7++++3adMmFB40aFBjY+O4ceOmTZt2xhlnNMWcBQKBQCAQCJoJrWOB+Prrry9btuwHP/iB9sg55JBDunXr9tRTT7ELRKXUM888M3jw4HB1CABt27YdMWLEpEmTFi1atMceeyxZsuSNN94466yzwtUhAHTt2vXwww+fNm1auVwulUo77LAD0XnwwQcDwOrVqyubgtv3LmqlPoC8V18R8XBIs1nG7fGAyPmHKqmGNvSdSjVT5vv6ORbxrZ7zRWIGi6Fw2VXknQ6JJbZo5ZJNDBlEEx9GI8bNuRIfRv6CxOkQ+xpi10OAyBlR2YpEJy2GboUkqDkuBshaBYyHIg1q9gVxOtTF8N0kcWByGbsklhoBjFftlUoAcfrD2FcySAKTkXlRykBARYBYmMwBOxnSW4eylBVKixh6+Rl5BtF1Er06Bb/IhPq/6lcyJi84Ya5Y41rkXBLpTyNgiwHOkogPAGScXJxMMdUaf+F/97abpmvADPj1LWK3hj8sfLZCZSsVQY/xyvlD2hTehyHzSF8fVA0as0VaHlrHAvFf//oXAPTv31/XBEHQv3//559/fvPmzfX1dBYLFy5ctWqVKQ8A+++/PwC8++67e+yxx7vvvquUCmtMgRdffPHDDz/s2bNn2oYZM2ZAvEzMAesNKsDN1YBRVWWuaa/1mN+9l5oXJcCwFPPYU+N82gy8x0lt16QqU6sScgRU6guAnilOwR3QVaMWJgvHWIBbESYGsVEpdIHoereesrTS9WKdXkfWm/VETJVwK7tA1DqDOmQAefMeSaMdr4UtRQsUWVShIk2FXUJ5baAcGh//qQiTaCS5rxlrk1cPh1EXQIop66KZoCtEB2cExHggq1vAxSAta/zqwgnqVgUAgdLmoStT4aOa1ANaL8aLYDIbcqPRz7RIJ108kFft+d3SCrwp+8CWNdrXALKcpatbj9tV7Vei1WXG9gSnqvqzqIoNUmmVaB0+iMuXLweAzp07m5WdO3duaGhYs2aNTb5Lly5m5U477aSbwn/DGlOhbiJ44403br755hEjRgwcOLC6qQgEAoFAIGg6NDa2SgKv2dE6GMTw7JI0NG3btgWATZs22eQJs2jK51L40UcfnXLKKbvtttvEiRMrn0P4rI43ZDgRgNTGC23F5ay82eipWJn6Xd2yn6X55/r4yT7ZvPZ6dI91hrta2Xu41T/qV7Y/roCdis+jO+UMeUIR6AZ0+K9ytsYlfv8O2C1muhONSbdUmpuSpYgTU4dkYR2/OxznzcbEYVyfUST71CVLa4BT5AR4AxrnzY6pRLydn4AS1riESdyYI4y4w4SEa0St4faxqosHbEwqNX9G9pR1EfAWM7aJXnGEFExsDS9gTH/SHD0ltjXAsjH3hzlCa753zREqc1KUvQZyoQbp1oD+fizC5GfNHq/URmZ818rFGxVCMnkpUbne/Kb0P8kQtn2KLDjHte78unqhP0iZ9tC5OKzg5TZt2lSn70veaL3BJUWhdSwQwxSGK1asMCvDYocOHWzyK1euTMt37NjRJhByh6GAxqJFi4YNG7Zp06aXXnqpa9euBUxGIBAIBAJBU6Fdu3bNbUKrROtYIPbq1QsAFi1aZFZ+8sknu+++u44yMfGVr3ylrq6OyC9cuBAAQv/CUGFYYwqUSqUePXromiVLlgwfPnzNmjUzZszo06dPYfNJ4Mlj1c43Rj+OY26MpTDj59cMhs+H/sxpXiVzdw5ZqT15QI+mpZWLSqFdiJtcQn1wlGFAiRGSEJrSNgEamIZu0CL2k6NOh0wq7CQqhY0+0WRhHXY6jBlExVOGqGhQhpizDLiYFRKkYj1MNjBMmsJBKkai7EYAwyUxaNSVkBCcJGgGFQN8QZDUzsnPQ/N8USsqGiRPHRIOv5Rt1w859XhIGlyFWiMPQCLMDIEYTUUpQ2R8TKkSA+gUUa/MVNhVI0NxVeNWcnNyk3AZfW0MJc/z1ZRd81Fea3ovgCKDVFolWsf8DzvssPr6+meeeUbXLF269M033zzqqKNY+Xbt2g0aNOj5559vaGjQldOmTevcufO+++4LAH379u3ataupsLGx8bnnnhs4cOD2228f1ixbtmz48OGfffbZM888E/YSCAQCgUAg2BrQOhaIO+200ymnnPLUU0+9/vrrAKCUuvLKKxsbG8eMGaNlTjvttEsuuUQXL7zwwlWrVt18881h8Yknnpg9e/bo0aNDx8RSqXT++efPmTNn6tSpocCtt976+eefa4VffPHFscce+8knnzz33HMHHXRQYTNR0Uc5n+iiVgWsHK2LFFaRN9tiirPRlAv49BCOHn6aQ+OZ9KpcpZ5jluagqEfPgOrynVamTJhAOMkhrDUHYH4iGWxQ0jfpRbITG2mHg1Sv8ENzYlsSZQclCEqpfMtRHmZVKilUrFMlM1E2SqCtSvXRJ0z+nBTDrNHRhy1G2aqZT1tV1xbqtmE/YWshn6whbOZ5TM1yWGhCbH1Uw4NsOQX0TNHTx57luMheJ8n1Y+Z1V+S6pb3wdU5Ukes2SF/nlf6syC80ZU6VcCmLR+XHdNzl7FHASHNuptLjpp2hGf8tw86OAWRmxsZ9+IGK+HNZDSRRduvYYgaAW2+99c033zziiCOOOOKIxYsXz50799JLLz3yyCO1wLPPPtu9e3ddPPvss6dPn37ZZZdNnTp1++23f/XVVwcPHnz55ZdrgXHjxr344osjR4487LDD1q9f/8Ybb5x22mnnnXde2Pr444+/+eabADBo0CDTjHHjxtlScwsEAoFAIBBsGWg1C8SuXbvOnj37/vvvf+utt/r37/+73/1u6NChpsCtt95qBqyUSqWHHnpo5MiRf/3rXxsaGs4999wzzzzTDFvebrvtZsyYMXny5JkzZ9bX11922WUnn3yy9vgZMmTI3XffnTZjwIABngaHZF7g+0Tj42VYhUOenwEp1yWvEak7FC7GtVqlShe9PQL9jgBvQfPByxLesSegldiFixSjL6RoczrERRK2TH0QSdhyTEWXsl0SUwHIuqi9DJHTYViv3MUSLgZYZ4BHTLz66pC1EHDFrLNF8yCWwfjhxD6IcWByWCzHKTbCsOXy5qgUuUjqYuhjh8657acRXeN62OScKwDqZWgUSwBx/kUAVVKJkQCRxxV1SYxHDk+6HqmMzeKvMXyhJjotFzB2HKT3ROSNCalDguFzQ21KeFmS606hy4GraOnld2CQKmWpLxSemnMYUPl7/JREMbeeBSIAdOjQ4eKLL7a1avJPIwiCk08++eSTT7Z1adOmzbnnnnvuueemm7761a9+9atfrdRSgUAgEAgEglaM1rRAbI0w8pYp0gCQTouIGLLkSZlSYhzbp7mEgGEujZeOoCfrjIdxS7NXtkLyTgWs0s0v2g6a/dUpAZg0CT8ZfZyQPRbjSacMVPSYaaH3yHUS4EYyEst5BZYvtiJPGWLeSL9JJeDepELeXGJrxW89sTGL8YtV6s2iKrWJhRkGEUiMcxAXCWWIiwGfBzHzZIY/XUR6JXHKmEGM6nWmQ7XZNCCmEm0nBhUU/p9lCg0DdC7DEiqW8RkhiQ9D7rCEXrWXvEyCXhhGl7TtbFGX4iL77o9kyqnbBOqci1BkxvGmF/3k+C0AIpOiQy2tRHMVHKGfdzgfv+yxT4MR3odt8kG6m5WVVOwQFkaTG7Fy4hBpEQZR0IxIVnUA9ltLU2yZRmPwqyiv7XKyiZxrWLaWabAowX84UuDW04XC6wS5lgE2lfqLNp7bkrPs3xnvkFNgLuixAWg/T3ezbjEHgDZtw+Wj3rLEebO51ygzaW5o5hq0pxwtDZP1IirGW8x4vYi3mHUxiL6QF/ElK2VAIGeIXNV6FYV3aSF81V68pxx+0Vu60VIM7SknPzr89jwyXrSmwUmtkzzV5GXQuD7J1B2+/TlZEZbBOJtBuDQs6+NTNs0jmagje/BVbbxbz5hg/JQIANQLAl/PQfRgbLm82d+M7WeVe3FTDDz3YTNDT2xKPardqvzWi/xSLCDl2sE9AqU3amdGuZQttEVja5+/QCAQCAQCgYBAGMQmQgExK/RB2rJnGm1eu3Zpg4A+aGdwYFwz2Qgm1tmf+tn2DDMsm9puuiDPnrINzs39tB2FwbZr7BRLSTG71ckXWsTUULKn7N6AZoJUPNNo02gVsvVsIRRj4TaxcEgZEgYxKgaYQQTMIBazxRzgLWYICTzEICrQRbSnHF3VlK0hVDfPBQLdYtb1daY9JColOvi0qPN4c+daXwllwK3KtNLCi/P3LOdGq/fFXzVU6kv6Hle1crf1ls1iv01knyPF7zsnRYt5+fhXfT2zwjzvyB8f67g5LoJiNpdDVVv9FrMwiAKBQCAQCAQCBGEQmxQ5Y1bSIgCQjllxgyMj7fY5LKCNVmE8ogq91jTVoeuSIvVL9/NizIhZATSiFc7D6GImXS21945hh2WpQbBY7HY6JLyaLUglwBwhV0xiMnCQSoYPIkmCYw1SaQNgUoaYUAw4H0RrkEoeH8SEMlSoGGW9SZIGAUCQBIWEV2aAi3o8huyH5DJGlCH1fdRehqEPYpk/I2E9KSZnMyrGOsvclUC5ZMul7r4Um42XyRiYc5DMgs1hUhENHL2X9z5BM9rgIWjRg4m17FFZRs+0jyDPaaaHyzm+m6osDK04wXVREAZRIBAIBAKBQIAgDGJtoDLc+niXRES6mc5JKi1lKMaEGa8Kj5Nwb2gIg8xgdRLeDxlgDEnty4SNi9QuIMT70O2SGDkdOtlQZuywxHr1UYfJ1HHwmXIhrCJ3NnP1Nb5Y+GonoajcLomYMsygG93pbyyRyIoLLFrlAAAgAElEQVTktQnqASDA/KJBGZJiCRWBRDEzHoFpKBx4G3v1aiavMbQyLpbA9GTCTocR61fSVGF43caaQyYvCUAOvRv5geL4aAspSM6I5fTFYdROylCDXkXYJTEPyP5BVfD8lVFfPJcnHCUUcxGMivyfw+nQ2xPRDz6O03hEQn/mcj2E9OHidFqtI0M4hTK4w4pvvMrw1t1asbXPXyAQCAQCgUBAIAxiTZAQiJ48VgY4P0I+QJAP2nX7PjLmRdbzOqnfoDJlU61hkeTNrujNe9ZZGGZAitpIwfXkmvLvrIL3I0xDDR0ULW5Qnj6I7mIGZYiLUc5t7XSIiiTGWb8fT3HprFMxzogypE6HhFDEPoiBzQcRM4gRc5b1qr3IF5Aec+2SGCqPE2VjVjK6gPVEQ9qevNquhDQb8cgo53Z80OLWuJ7EKYenIHFJDM+ILfc1XyTzdxZJPfM7Ip56xYGSggWOgwhgRc5mxs8M2eN2OrQxYbST5d16ii1alBBLLMIB3+yozJ6Fi6w1UDWZTLnbinRIFHNzGyAQCAQCgUAgaFkQBrE5UZsX8dExHK3E6ZA4e1jpTz7WGFNkfi9W4a2z9bXYk/XqP0IZKmsbMxDmbikXaLOTPT7AyKDBCkLmQy8hhqgzGSHSsLRTWBESLnQ6pA5wuAg6XWKdWSTJCxXJZRgQQpFwhHGxVA8AAVh8ECM7NaHoxSCmaFr8aruIO0THR/sN0jeF4DffhKpUQgqWTWuh1AgASsXG43e0kKOqSKQ2PkEBdSvENxhyZTiF0z8FHrUjYjw5Hv5VcqlK1lEvg/YjX7wIxXyvtqvGGdEnltk2hJWEC7xGd0soq2ZGAaeqBmHLRD9IFLMsEGsLz2WcX8yKawijNfz1KjDrE1XcQAC2BNoOnTRRtr5B4psqXmFFm8tG1hu0lrIdJHYJaAthwSPHZvM7HFl/uvBWO65NaaGb3GQM65ZMc8N5eZEVIRZWdNHALSlwDAR9TR/QhaNi15F4gRj4FcOlYWALUol+C3ptl2OBqH9Bij+5eG8uubxx3+gViHEYCt41jnaT8WEJLOEm5KjSJaAz5MiYMbdeJKDCzXf5ZozsXoG5L3d8+jzNwJvItnsB6mpb+XmuCHGaJFrkuqR2li0LMue5L+Rtyx6rR8ct0/kj5ZeelUC2mGWLWSAQCAQCgUCAIAxijRCQJ+yMTdtIBnFyZgOY+5nsszvVjPizFANmGYiKxSpZZs86F2yex34r4RcTxqWyNDf48ZNSYLaxHWKUDtVnwrL1THWxY1gftJkirk8d+CAtk42MZ+OAL7FH07YLSTk5QijaqDsfQpHnFwMalUJYtzqzyAepWLcI0b6wkYY9NL5sCgW0V51ZNLaYy46pUcqQS8pjbCU7j7nVfwDPlBwA0srCk2Fht27Tjeyl6/nTyCI8nTrz/IJSm8oOMSOvOX9wcWtWJccR2g2wnOWUkbZ6z4EKjEqpak/ZuSFeGZSkuWluAwQCgUAgEAgELQvCINYaiEaqyiXRVJQQHvk0W738wsbEj5CjPy2sJLG2kqw3trlQ83hCMXr6jK23eJH5ecBYSElsvRYmVKKes0rqdC+FWq2cCmUaAtQ3A3melSt00gnwOBzzYaMbWYdFsDky8sxZgGk2/nV5Fk4uIhSB7xtQ+pMAX8LJT4UhdVNJcHSxFE7YNIAElCgLk2pxH0zxnVwoif0dntlnMwO5ryKPIegFZPtpYAPorwy1xr859HvW95NEBz6ZKrqxxGJcmhsFFmvZmSWHy48yxOaRvvENl86CL3p5H/Kq4hssa53L0HRrxlysXbO5wwKJQ62hGX0QGxsb58+fXy6X+/Tp06ZNm+wOtYEwiAKBQCAQCAQtAs8880z37t332Weffv367bHHHg8//LBD+KWXXgpS+NOf/lSIJcIgNhk4Ts7pkkjh5/OXogjDx1zskJf4B2qShzj5AaSpuMQANGLKRTLUgNi+jIhj6q9ImDkbpaqf7BVjLX3OVkybtYwNI8HdzIkI0BABOubkBFE2R+GDyRqQlAI0LN8JU5XZqJBL9AB3WJk6jjSl8jaeJiTSUKqaIOnDFOm79bArJPnhGMCXYuImWzZ1BiwBTIray9AtzF+j7qOURu0oEI/LxkmYpXVQoiiDVmIYO0r+JS2Byx4rne82AMko4nToJBQVuRlQxcQ8citzGkVel2d7ex47ZdtNyO0RSE8i1mkbkJ17rltRDZwOif5mSXPzzjvvfOc73xkwYMCf//znbbbZ5oorrhg1atRuu+125JFHOnqde+65e++9ty4OGDCgEGNkgSgQCAQCgUDQ/Ljmmms2bdo0ZcqUPfbYAwAeeeSRvfba65e//OWLL77o6HXqqacef/zxhRsjC8RaweLsh7got+NgzjTaWASRWYkUDWrGVKKNomOtpQb4ZcamjKZHWkRIHR+FWTfCHdJWMhUWtnlTOywNPNGImbyEacB0IxmCOK3h1oA4WuHn/oj3TR9+Ths9trSXk0ywExFeSlhQasxJwuVTiilDiyr6e6KnGv0YCU1CX6ZXnLXUntyMRh6/MJ5ydl2o1gvBUlT0YgsvYMRFkVSCCve1t2IOjP5SjOHS5pFjbjtm/C/FJZyKYsYitlZybJN6TIvSoGaPi8NJKHpScWy+Q6OCUJhOc5hvqYEs3bytrRxN74PY2Ng4bdq0ww47LFwdAkCHDh2OO+64Rx55ZMWKFTvttFMT2yM+iAKBQCAQCATNjA8//HDt2rUHHnigWTlgwIByuTx37lxHx9tvv/2YY445/vjjr7rqqsWLFxdljywQawsVf1IICJGn7NIKAuZZKhaOewREZySl0t53gc2qcKAkTE/p7py18YhEFbFWKRQLRuyxmKEPSBB9DFXcU10AENhbExmIJ5jxUfiT1AP+aAF8+gybzaHZE5TMMeoVAAR8ZVIP+EPE0sfRo5hcT+FHV+CipTM60PHx4i9r+8WHPqkyltsiwc4U/84zjx4pK+NXbT1slhPEXgnus5lxjUHq8uauZHq1u38aBJbfGh4unpD+jePfdeZ9wP6x/sxDK5J7FHuvxneweI7uc250x7dWcuMlt2XnPTy+oyKd5K8DUWVo4+0xNScjWubm9yeP6LRZVQXKQWEfP3z++ecA0LlzZ7MyJA7DJhbbbrvt2rVrO3TosHjx4iuvvHK//fabNWtWFdNOIFvMNUN4bQZsSQNtSuqrubod58AYEBuT6Axog8W+qDH89VtjNcKdDmUaT2NWiPFkP5pkvWGCVBjrjNufiq0wm4Okzfjf65dKVeGGZFitOzAGjOvxFrOR9SZIt3JjGUV6n0M7YvSyMf/AIHuITmR8aghlEVZmMVAKIEkwlNpBVExfIEW9aUvtw4ZYWgEbgE9cwAqDwq3oOkl+XHTxQe1gdfLmkTblEvacePw7Iq30BKV0MqcvYxPXdiehFwbbmr4UicbA+NcyRLreMQSdSoCKwAuDtZ4d0VVMbXnbVorIPHYco4xvZXQ/Gm/pkh1e/I0eecuNJSO8zXo7YluRmHKK1WRP2T0XJ5QqkkFbvHjx7Nmz0/WdOnXq1atX+H3z5s0A0LZtW1Ng2223BYCGhgZW7YABA5YuXdq+ffuw+Mwzz5x00klnn3323LlzA0vKN3/IAlEgEAgEAoGghrjnnnvuueeedH2pVFqyZEmXLl0AYPvttweAlStXmgIrVqwAgA4dOrBqO3bsaBaPO+64c889d9KkSXPnzu3Xr1+VNssCscZI6J10yQSi/SIywyLN53zBxIdnGm06QqKEGYIEvth0Ijt0s/U4YPaROw6gOUX8Cj72/XsJdOALEFiesB21AX+ok+czQtFFBwidEkX5PMQCJjEQlF9kigrzMYGTRNECRpFMx00j5ShmEIoRk1qOi/Hr6cIvpD4xsoyFkZI4w5FCfZMX35WQASHTjHNcq1hVEBHhtmduTAoSii55vaPS/xpfyJT1ZazSU0uOA+DjwB0lfRyMo1oNZZi/qEEoMXzJGQcPXc+USMtz8aeyWLOqtH24iO8NVhqUHRFQkdD5BDZVVIwjBblbE6nnxEjZ5nLDcXI24pCNSmFknTwfvaezQ3AKHTpd9lRBHCY6Cg1SOfPMM88999x0/Q477BCuDgGge/fuAPDpp5+aAosWLQKAHj16eA7Ut29fAFi6dKksEAUCgUAgEAhaNHr27Dl8+HC3TKdOnfr16/fss8+Wy+VSKdrgnj59+q677tqnTx/PgebMmQMAu+++ezXWhpAgldohwF7S6JlGWR8QA/ORKxLjpG0uw6Fw0pc4DpsKVTIiHSFSYvFKpu7PoU7kUk3stPYljszEeOKmbQlDcUSuQNrTHDukg/sTDcCHjFCvdjIEOXrWUBKknPeLx5WGhcRnH8AInbGHAgAogHL0iY8DPi7l+MNF5Rg15cxPUI4/qhyocmbfWKwx/ASqMYi/g2oECD/a+kb0gTJAWSn9aVSqkRb13MJKUvT8WJVwI8ZFh80A5WhqlonHh8X20SeoHBjH3OcE2U+u60qILhttO/8Lole147o1L13nxW8LUgkAaH0qvIxo1vefgHY3lTtvEakDhq21xKw47mNG0FsqSIX8EakgKsV2l2bt0Xcw/o+XPmjovk3OSEF/xRKFzMHk/ngRM6h+Dyil70YFfPzHHT169JIlS+69996w+PTTT7/99tv/9V//VVdXF9b8+te/Hjly5Lp168LitGnTvvjiC9198uTJkydPPuCAA0IesUoIgygQCAQCgUDQ/Bg7duzTTz994YUXPvzww23atHnhhRcGDhw4btw4LfDKK688+eSTd955Z+iweO21186ePbt3795dunT54IMPPv744z333HPy5MmFGCMLxJpAP8MQX8DkyaZFuSQC0EhkqoQbwqaTuBWqRINWQt+8Z4lizkigTZRoaxQ6erZX8OEpWkBnjE5iclTJGVLIB1Fh44lnVewWBmxrlB8hwANZnLQi10zSmrIn8ojTmwdhvX7GLSsAgJK2lnUQLCPhsi6WzWJQLgOAKuG+5caoWKozewVBVK9KjQAAKhYL5QNUVBAVg1IJAJJgQ1UCdLUDxD+Q6CvgFgDjesDbKan067gn8vnTRaUaARK/xmgW8Vzi1kazVZXxTJWlqBoBIEiEyYnQYqg+wGcEaFEhJW5n0Ohc4wOijwryrkxdxvgStXsZIuHomrS5FSpD4f9n782j9Sqq/O9d50mCBMIQkAgIRBxaREEBYwPiANK2q5kUDP0GGgRsXSIoCI34A8VgI4i2SoOCLtB2QhEQcSmNA2GSZlAGtQHNwgGbZmoJIAkQcp9T7x91ati7dtWp8wz35ib7u7Jy767aNZzheW6dT+2qY3/xmdSZCfIjoZC+fVqn55Gx2RKD6GrmotmC6EaSgYMLiRuuitRM3oCH8Vv8dceTrbagQ5SbqlMnsmk/uTZK+hmdtGw3BlFm07QxaubMmVddddXXv/71a6+9Vmv9uc997qijjnre857nHI444ojXv/71s2fPNuY555zzs5/9bOnSpcuXL3/Ri170t3/7t4sWLUqtaOkqGSCKRCKRSCQSrRaaMWPGkUceeeSRR7K5BxxwQGjusssuu+yyy7h6MqZ6RUaEq6UekSznI8piP44jQgIlugeh9DJh88M9dWnUAk8BbVmF69Rhoq2KEk10jC1v3rPlg95ioIif81pWN0fxKe3y1wE/rPt0HecGjBB7YzNiKrgqlrg42qcQX2zOeRVhm1pBeEUq0ysCFD3rALC4CFoYIVRoLa0hZ8rmasqr+gCgVBWaAABqAgB0VWE3S92UIWcT1qz8MUTnRxM4SvFBL0w0a5yV9TYUkFLhSPZedfeeaRKdgYARToSmrle5RP8LNt2RRsgQAUXj5nKpG75e7oqQtc+W6Wb5Yo1vDHLbEEJWo1suJojGQWM3b5qPv06ZEOeWLnlmq4Koe/QjjI6RNaNF3rh7WMlcggxxZ3TSjbBD5K2xT7RCWcWJOnHzk6oiNonrjIrxTUQZXIkU4EzWz3RjCNVTQRBXK8kAcZJkB3UKJyCL3I2K/TUacg0/4+y75+vE4zZaCWqCjAjpZ51uo03Gds4l6Abtta2JTD23TDEDm8t+MyaFR/VUGl2DoB3UPU3HfMwQMLVVTduMM/dHkQwf44EjmSs0dVhTNVPMbpRg/vC70Vt2CNg308HOuQIAVSNT+1w7ElIVAKg+Mr1bcxToWDS+Jf31xiev2e/GXZjGdDXjsWbLNjdOaFyg6S48uQFic8h6VZNpxov1Ktu7Vd7HpVuzGRG6s9QMBLFpR4rReNGa/ezwsbmaGpk4nIBswgPEzA4EvUNqVMfPKfNm2/AR94d7Kovnf/l9bZItIjMxxVw2XgwP0HU29qfFic0NH1MTr+zQkP+jQatKDePy47ayOeXsiDBpc90YhXS9tq/iXduPXyQSiUQikUhEJARxUkWeyRTJwVAwgQghmpZlnDrOODcZwRyu+eGeyTRqoakENUFmnKPu4YYw/MubrsfJNSscMkxNlxg7WIGQJUbso6s/FEx2A3KFymIoqDSTSzf49XjPmO6CVaglAhQNivPAzKZXCOc0F8ghQ+PvuREAAPQxdasJULRrIPp9CO8EA/88zaoAQPcRsfNTzD59IqwE+uajgc8tNoObSUNws5lbQrkJXzXDHwIAqB4EbFIZE/DZYxqxTaEfdZgeIENCEM28sJ0fxwTRMEJlCSL0MVDsr3L/B7kEKEZTzCaFpPsrUqNcDihS090JNXOfeDSIkSGNhfAosQrdmilmj9lwLibilNh1m1POlnWi3BHRa/sLjwzZKWb64rvCKWb6xdLBBPy9R/7ijHROOSJ2Q80pc4S1xR49MqQNrvVTzEIQRSKRSCQSiURIQhCnUgEjVCgBQ0EOEULw/ISexRT6EaMX5ByhxNGHJEaLVIizggB6sabrSj4GkTx/K3xG0s+CnZ4/2bAeVxNPgAkyjDZNxVjCcCyNiZ2ja41ZoaIUKNpVFxVK90GiDZXEd4zfmMUQVsKNHKAyTbj1JRgU9auwoeZaYzQYgGd8AgDZmt7Itnv4VXL05X4VBniVWe/Ss/3pATQcEcCdKHTmi2MQCUHM7WvjIwUNSvQUECFDwwgVJojQd+aET2T44gRycy32EWi0MYg4QtHn4n2IMDI0NwYNOvQmuhMIGgxiWAn2q3K52KR8kQ1zbAGKgE3K8yI3KDATkLJJxS0CzszHIEZfWS0okY8UdN2DjDMuYkvRqlBuChyOchlK0mZO5jgkBFEIokgkEolEIpEISQjieGRfghTt0pxyB0BxV8zTVtsa5xT8Iw1huoZR4qSFJAbkEiB4UEvFIJK9psluOBYZOgu3gatCFXYV5rxUrtuA3OwOINatws/uZBWzCe3yWAsjOtZMAEW/fLhhKyg2UTn00jBBDFwtN1KGKrmzZdhhX4VmQItV6ExWHCucS3BFdD00/gVzG7ytjPIxfzMAbOihNx0y7AECima/H/eQjFFuUqQ/ZBUzs7u1R4aYIEKzVQ2igGmCiJHhxIQrEqY3y5wnXHo/dOOBYh/vtu3Mfg3BnWCDU91xc8jQBx0iNBhFGVZsKT7okAQy5qMMcdlOEYpg79XCQEYeGVpFC6J9N3yu90Zmp6BDQvImL+gwyh5m7+s2ZMh1oEylG1ZwBTu9Im+NlBBEkUgkEolEIhGSEMQxSdkHOPT8kgeKaaaCH+hUbEH0QIaIXfsaZ/LIqDCTI2GOCj3vRnGNCFImQhIxvsqGJLqUlpBEd1AYorKP+OzmiJB63MRglxPuD3E2j6H+1Xbo+jU01O/8XHsfVxbHyQUdI5ja/BI/9ZmwQgwUXRgh1GGDDUok58cvBDZ+vjD+Ycgu6k9Adk1SjBQ4RuhfVGjOLdqaG3CUIVTN95iuZgCAsibUMwBAVyjoUNMYRH7DxbRwJBreB9HGILo33aFVzBYZJsw+BxQpQXTOqwBATVCCaEtZtwY04mXLE/yS54YdeoKo/f/QXHPPVExEqVuKTpAhzg1+wZyvIYgOKObXOJNPShbvcWUDgAdMrgd7edBYhgz5dcoJoMgVaY9BHEHQIWmRNNiCDNlKwgxciHEriTVMt5xoNlFJd03Nq/ZWKwlBFIlEIpFIJBIhCUEct8gjmkapZUBxmNf0RfCPNBGhxKCJ5BpnzK00zrURgKjmtpBE4ozAoUtpCUkkj9DZNc6RbFUs421/gGXCfUIei8yGl9ioNUAmTwFdp2r8Orgmii7ui/nFcYoKwFM/6+VyFYBfl9zgV1e2oY+unOsnJ3wrkrfS0TsBE0T/0WgYKjLd3orQM1F99ourNwPAE8TmlwotW1YVikEkb1LRwxFERQkiBpzmvSbEJASxz5uNG4kyJKuVJxB99G44XXlkiOpsUKIjiBNo48Nm2bIPp1QAPvy04XzO7KM9LxsoSEISgYJGwG4W+xGgmAhYpLixggxQJPGCXIQik07DUtHHnG8CkDRlhC4DOXcMOvTfvLhF5lso7xw0gfudXadMaybpZQAvTyVpB/IqQJWDSQiiDBAnWUXjNiIyd6BIMh7MYR+Ivgg0SqKDPJdMZo1NYvCnP/xLj4+hbV8b48ufB6DOeJBXOOMcJLre+65zS1gC8VPPbWKmb4JVKeavoJsk9b0CP7RwDXHjRT+ArlA7bO98oma9yHOGavqO+hE8nJjhNR4P5eUHT+g+gWbu2l1c22LLG4H7AHZcyJgTAOEAsef/BzsixFPMfhsgsnE3PtVtR+fOIvd2Y406Hw0QUeeDPa7RjLPqkwFigQluzIfT/XiRm2KewKtSJvCc8oQ9Ic2ssTPxiND84mac2fGiG8b1+SEgsFPMpZtsY2d2z+3WRSq8G1gz69z84MeLdI4btwsFJthv6eREcHZOmXxbR4MqXBUugyqMlWqC90rZXUaEpoKRTSVnmljbp1jX9uMXiUQikUgkEhEJQRyLtH0YSj/UMGDP53UDiszDYDA7zLSr44c+7MfuhhNgPjLlgZAh3VVb+wrBwz/Un2DumPQGU8BuM86uBYQMG0KG+WJabZcxOIoAGeJMk14pZFon3ay6wItUyEvi/WN/zTWQEv9ITxghcTFNkAvgmW5zrdE20SEn8f/bdIoMdfOdo9wOzGbenBJEjAzdy+KqCQDQfVtJrwfgl6GY2WS3DKUxKzSnDP5Ve3hnbDLFnOCu9pARJY2mmN0iFXQsDUr0L74jr8WbQLmUEeY3rEmkY6BoTYcMDUFMzCkbdugnkY1p79sJwwItneWQoQeHGDSS9GgSOVEJXqRikSEum12kkgeKXHo+F3/PcECRTGp3mlOO50wpI+S+u5Kb4OSBIld/UAnbUJzGuXFdydec0mDIsBOVJKplm5up7oBIJBKJRCKRaPWSEMTxqjiibRigqHB5JnwwaiaKbuFiE/nARM/5XDpDJYMoQ1JW4bIkuNB3xtUMEAUOsq/ay4ckYnlCxvI0qiAkjxcOHGQxn4OCBhl63woAKoIfK/KqvQpXnGKVKSF4kPNxp9YHCKKi9NV2DSN00IkQRETXLE5D4BAAVN0D8EGHugnUs19NhnL1LAMzsLDnck2UYRXmqgptha0wQdR4kYqimwQ5i0B1cvoSx0hjEHGEIqGhhC/2CUHEr8VjkSHZvyZKj4IO0UY2Tbgh2cjGRRlOKGT2ETIkq1IigthDJiGLFDQqe7Y4oOihIOtMGCHBjSmgyKxoCUoBXwllhJjJ5UMScUO0Xc6ZmgBQsAyFSW/aAiyCP3Nlic/wsYZMMlvVpCPDqANCEEUikUgkEolEokBCECdVwwBFxWfGNWOgiJ/C8oudIwpImrCQiUBKk4gDB8kaZ41zu61xdmGF9FV7YZVx3ByGqMmgw8IowxL5vbABomXLTjVhhOa/yvpi/Eiet03F/tzWAFAVPubhuM/kITdQ0FGT2jUE7gJhRuiJb437p/F5wMuTG3AIAIY2eXJmGGFj6l7PJ4IjiKtC0yNDAwt9DCKHDF0wqLnnyYsK2+8EdPNbOmjtmgOK7gxoA0cTQLHPBF9SNEj4IiGF7hey9zVZp4yRYXIjmwm0MJkPH8zHIMarmHElJsYrGblId71RYMFhkIv5Yn5Rc+kqZsi4dVu2jM1o0xliOm+M98axTrlLRGCe5I0QGRZCypYGRyUtBFEIokgkEolEIpEISwji2ESi21qwX8rF5wTPlxraAxNR2VagSH82vhql+kdT5Ea3u+v0mj6DoEj7QV3OL+6uXx6L11xH3Qvbj6oKE+N0TsGm3+gXbzbbV1soaKiSwkDRhyQizGZQojs0Gz6XCjpUAFDjeEHPx/CtlzJteJQt11PIxPu3AY4jNL8E65HRsTTpPdt584s33d7XPYBmPTJAAwW1zW0iCHEuCTp0ZoMM/caHCiDY6bABioggKnIVSwkipqQedWNoSjb9bpAhv16bhCQ2FNCdTBqSyJFCn45elxcgQxN0aHtHkCFZp4y3NqRQsAAZthJEfrNrbNYkrDC/0yHOrTuuYiZBfvllyy1bG3ZatswGFyb2QRwg1tAXKok1tG4xwoxcmDq7IUNXB/5T0+LczWlACqi5S7C2SQiiSCQSiUQikQhJCOK4hSJCRgkU8RPckECRPL+Rnxo7twDFBgo6GwNFHE9pcY4DMGEiXfvsX+5H11ETvpg1hxB5x2BSNepdjYGi4WpuE8cm6NC/zwPhxgYo2jyFIzgrzcYZYfjnfullTc8IFQBom6twbhP96RmheQ+ebRjHIFqgaMv2CVC0h2xgocN+PRMpiEzAUYZ+YXKFchvTI0OFTEWQYQXovlDuP8C/mZbxT3THBvsgEr6IgGv0zhgGKNIIxX6ZSZChQ7wYGdJ1ys3LUez5yTJCyG9h2LLBIQ4cxGSxTkFBuk6ZQYaUL3ZBhqkYRKehCCKpufmRNfOxhtaPgr1E3J4gw6Gl6lH81ZjWkgHiuMTe0wp/1Mc4XswWi78IyDdT23gRzxpjJ36Tbc+/mtMAACAASURBVF+F8jXYXLonDp479pWTqWe6DMXmsaMmIj9EzfuZZu0hqk7fgVZ4vNj84v4o11VYs1kO4mZFK/LNbc6AK4uvtTm3Cs84A0BFhoAVY/pjrEwlaLxI3pbr13k0uW7GGb3MV/XMiNBeTTOZ7oaebqTYTCL3kemGgD1kKpLbzCmTZShoREgGiCQygO763jb8V9FEoP+fMdEUc3N+6L4/qXcMoi1paG4fzWVrPwQ06c40uXj078d8zHCtZVWKC40oGBHWeKjnfml5nzKZJh5wI5vOZpzO59J09N1ZMpXsnFvcoi+bUe5cw39JqsiFqYo2lFdi/FpWqNAp0e2hJVPMMsUsEolEIpFIJEJqJ4j9fv+uu+769a9//eijjz7++OOzZ8/edNNNt9122913333OnDmT0MU1SREUVHxGA9byZVMiz5GoXIqP8DQwCRTJExsDFAmJUbQq1BDZGDx+1R479ZzaXjvBF7EJ2Dkh3eoRVOLX9Cg0iUzNSgP4hR0mvSJs0pq1mZH3O7FoCPCegYXOrJoZTEwKAepejUshRmiRoWNymFT1zCQyhl52j57mLXmOchna18OUy5NCAIcVwb9cECqzFbbrTx12vvnFzUeTWeNmxtndmhUqmyWIze2EYXsxMcAIvJQgap/oTLKixc8OM+uB6NyxI4V+Ix2F0vuIydnNaDDYS6wsoTPO9O15mOexO9TEi1TIvDB5tx6ZF+Z3rrHrtoaYUyazLiPdyMbZWZNdlZKfSp4myHAsu1uTv0Cdyg4hIYjJAWJd1z/+8Y8vuuiia6655oknnmBKzpix0047LVq06LDDDtt4443H2UmRSCQSiUQi0eSJGSBOTEx8+ctf/vSnP/2nP/3JpMyePXvbbbedO3fu3LlzV6xY8dhjjz3wwAOPPvrobbfddtttt33kIx855JBDFi9evMUWW0xq36e/Uo8+isagtAPFoGwuofAlfqsNUFSAM1C654yac+YOrEx5uuhzcWxi++IVIy4k0SEeQ/IMZfQ1uwthkKHL1QocOLRMRdkFLhQZulIGGVrMZiL23NqIJtfxPBMiSZChNRt26KBgv4YguFBXtasfwKJE2w3/Sw/C7jWhip4vmt72Q5MywryZ2I5IsatSWi8ivnMjMxuSWBeYfokPhGZz23jaqACC1Sc4SDSAgub2wjvXZCkgBYqu5qIdahJoMLHQpCaMkNsZe4RRhi2kECznG2LnmhEiQ24v62mPDEu/lTlkOD5SmOyFn+ZYS0UHiD/96U+PO+64e+65Rym1++67L1y48PWvf/0OO+wwYwb1/POf/3zLLbf88Ic/vOKKKy688MJvf/vbJ5100sknnzxr1qzJ6rxIJBKJRCKRaPSiw77TTz/9T3/604knnvi+971v2223zZTceuutt95664ULF65YseLSSy8988wzTzvttEMOOeTFL37xODs8PaTt485gIQzsY1+0gpZgPr5sG1BE5cYDFJHzAEARPFMcDCiaR+kivpi/ak0u2UAndmtIHqaAnUzApo9uNHdCHeb6PWgavOfAoS1VswTRrQvGpShQ1M4HLDLUPZzbr8OqtGvIxC9WyJmYADaikZBFvy7ZlAKU665Q1ZwI64zMxi1vkqtdShCxHWClVtOFIDa53gRkNiueVWhSUhghOsBgD2hYIYMMaYRitxXHOWRIWGCcXpe8Lm+k65TzufZriCeIw2923REZxoATV96CDJlbeaTIsOiPW0udPntcsHCYqtwK+rVWdIB4xBFHXHbZZfPmzSuvYr311nvXu971T//0T9/85jfXX3/9kXZPJBKJRCKRSDTZogPEI488crCKer3e4YcfPnR/1hgp+0zGP8AM8GCSiibhySJ6Ui1pnTzConKKeVKl1UQUkNROntG7AEXbg0GBImBpCB6+8UbdVGRPR0VzUfskJJFWUrjGmSJDA/BILmpIe6CowO2eDVFYYSFQVBgZkjhCY7qdDiscE1mhNc6EPmrOdCmBqcNKdNMfezYbguhwo+8zOPRD3yoIvAkFZixdYOois8FLniAa0wGzIBFiNIiIHWCwBwnsByWRgslcLl4wiwxJ6CFMJjLEYdxs0CEJLozT2Uqcxo4Moy+lTsiQ/bOQrIpT1/XIZbBw8knhgBRQa1nFLPsgikQikUgkEomw5E0q45FOkjSbzzzwDPa0wpLFfMBi6mFLsb/6R8k8ViQUEDVNQk1SQJGvGuJnahV6DQcUcb0KFVW4Yhpc6MsrsKwLLOEjUDBZyQBAkWypSIGic7bpNcGQaGGyNTEUtMguYoSYL2LcCCQXs0BCQwloBIU6ABiaOr4IpBKTq3CuR4bozDvSC6GGZASUIOIPmA4S3X1FwBUGZvSdh8R0yBBv+0exX4I78nyR8DwSL0g3KUwtMWbXI1OCyEPBLi/EGybKMPhOQmZLJT6BYD+OIA6GDMvwXh7pJbij4pO71Eyds1VNXVihymcP0tBaTxCLBojPPPPMzTff/Pvf/55uaGx15JFHxsucRYwSH54mUzF+uQIl7eDSww0c2VFjU05h79TEh30PHj8ipEM7PMqmr0HrMgRMCY8Acz6xG3kFnxuoNbOirhQeEQI3Tdw2XgQ+V3POfgtu2wS3NXdqThnIeFHhASKZ8m5Gk2j4mB8RUmd3QnwHmCEgMQEfMplTJqc6vBIQCpuluxQBQPwHlfwh0fhvFRl/aORGJzoT48XIrJhcV1vheLFlzGdMPAFdOiIkI7/E+/FaTOjgHJ7DaGynybkNEwN12qqGfSFeywAx8ONLpcsyRblRXeHoDX1gWnxbs0c/EJy0IWBW8i7mggHiZz/72dNPP/3JJ5/M+BxyyCEyQBSJRCKRSCRaM9QyqvvsZz97wgknAMDGG2+83XbbzZs3jx0IyugwVgma4sskyqX44hjJYvaBTeFfo+daHs+wT67+NXi4XXpwtA1MNhO55rwFC19U2B/A09PkqlnT5erQVIDr9LzTkAZM3dz1oxQQ8UWCxFSYWIobI4JY43QOKIKfU+bqTEBB0mIzxaxqtiqomG6Y/jHpKUZYIaTKA0VAuYp8cJLmQEIXipp0TrlJTCBD41yjXLpLMyGF8T4yZKULS/JSU7olK0gS3SuZLE6lt5j+xOVy2xhhDv5pcr3yM78JCJd3Jg2RKthvReZbl3Qvdgh9Ob6YdM5WNb71Jam/R6OcJh6mrBDETJ7W+uyzzwaAE0888fTTT1933XUnq1cikUgkEolEoilTboD4+OOPP/LII1tuueXZZ5+tOkXoiKwKH1+KTm4+5iPBFwvr55/kkoEqGigWicsTasI3qOhjNt9uKmYRV+Z8FCkcC7t2NF33cBOe2GkdZpNIQeBWutAIRdcixwghmxs72zfM2bJ1eymKG1OAk+WL5FhSuDGLDAkjjMIK0aHlCWIAmllkmLzLc0p8GG30GAYhWYLoPxoUKDJ8MQUUaexdKnKR5lZMnUOsIIkaooecr6TFmT3SqBTORRciIoWJsl0WmtBrHaenq8KZ8TdVV1K4ejHCRPGRMcLRQcZME2v7sCe3zc2cOXN6vd5WW20lo0ORSCQSiUSitUc5gjhz5swFCxbcc889/X6/1+tNWp/WQpU8DLUM0rPPkfn10a2Vj48vpshiVDUfu4PsxHbeTX9c4CChglzzNOjQsz8FDkSFdZomPFk050eHpsKHyrJA90tw3DmgyJcldM0eVqeF0hHPy1HAlg5ky3ItQuwWmFxZcirI3d5CEIkSt3UZp+EJIiFSLSGJOD3B5PgV0DGT40heG/bLV8UTzUTQIf2Yd9ubhhwaLpI8jY0zoX1F8K8lyhCn0kqabgBWgsnhG6OYETJ1MpXnskfGCNNlR8AIJ4EOtkoDyCrmlo2yTzvttOXLl3/2s5+dnN6IRCKRSCQSiaZcLauP3/rWt37lK185+uij77rrroULF77kJS9hl6q86EUvkmnoSEkEMZgKy7c9XWbzs6AxdYHzfJEtq1XqaFTonNh209ZO+CJ+7le+Bwo5UwDAdQ93m/QiPkkKY8nm4EhIokeYhr0goun5okKdVxgxlPBFsgLal6JkMc8dcwTRMzmSC7ncCDeSo8BnIBGUyS5MpouayQkhStx7nb6/0jcmx+9JIlm2nFjyTPkZ4YtA4B9uK0kBg7Kp3GSAYA5G5nMDctaBEdI6uaXHI33THW2CVoIPhkXNESNMVELa5fAe49aSPW46OL3Q4AgGJLKKuX17mp122unVr371xRdffPHFF6d8li9fvt566420YyKRSCQSiUSiqVHLAPHmm2/ee++9V6xYMTm9WWOkNWBsBOSBpuzBZJDHrU5laDeyoDEdCaP55EzZxJMZJXZZ0JjBOKgjuNI8MgScq3FhhRKDYESCDFGqjWh03eGDERFQpEg3teQ5a3YCjW3LqF1dJbiRdyZoMIkM88eICSvwpwt1gNwILXC7q7hPRYSRchwrGQCXj7FjgWKczsUC5rFfypklmp3QYOFB0Tp9/tDIMPDjSzV1olK0UOJ7kP82yuLGRHfi7PGhwekCBds/pS1/DQaSxCC2DBCPP/74FStWLFiw4BOf+MSrX/3qzTbbbHK6teYocXcW3bSJWfsRDi4H++xE34gF319lo8nCcWQkNA4gfwzYQSYdESqUkRwv0pEnmXsiwxT8SIBfDBjt1E16wo94FDl7dBTFlS0be6VGk3lzkJqjOyEaZSKTjgiRD5BLXzIEDKwhRoqFo4Quw8e2nZbJ8IgxfUpy0jZn8qO60unggpqZbnOlus0LZ89eYMdV4UzaUaYSrJJ54bZRS3ZwWaYRjvnGPNSbmkHeMJIp5twA8dlnn/3FL34xZ86c//zP/5w7d+6k9UkkEolEIpFo7dRjjz32X//1X1rr1772tZtvvvlUdSM3QHzuuefqun7lK1+5Wo0OV61aNXPmzBH6D+8wLg1DH9PYJLI692CQxznPgwaaLml50laZzAQOdB1isETENXnAGBFezPtopSQXdZAgzKDGdkJGp3Sjw0jgNx5SRpVgs6khCw5TZbmqCpvIp8dUMlG23WdosZSL8Um5paBFhNnMD+pcRAFTdWaboJOkBXiP20cGVcLiQMLzUhiPBXgpFkhr5qtCzoxbS2quibx4CjhQXeO5uYv+YrTh8+kmPWVTzBdccMHxxx//7LPPAsCMGTMWL178//7f/5uSnuS2udlggw222mqrJ598ctJ6k9HDDz/8j//4j7Nnz541a9YrX/nKyy67LO+/atWq0047bd68ebNmzZo3b97HPvaxVatWhQ5PPfXU0UcfvdFGG82aNWv+/PnnnXfekC2KRCKRSCQSDazrr7/+/e9//z/8wz/85S9/efLJJ4888shTTjllqoYfLTGIxxxzzIc//OEbbrjhDW94w+R0iNWzzz77lre85YEHHjjzzDO33HLLCy64YOHChd///vf322+/VJEPfehDX/jCF4499tg3velNN9xww7/+678uW7YsHAUuXLhwyZIlp5xyyqte9apLL7302GOPXbVq1fHHHz9wi6u16JN9ocqen7rgSfrSvEIVhPu0yLMMFEhYEqFIagmiBylVwa5MFFRcvd1Vm+0snxG1lmKBrlC+Eo7/pYAizs2zSVpTIkQy8kuAxqaJ9vsn1eJUCF3ckn4kCWJYYVQXja5LtljE+TItpiLeNPoR1YwrbYkFTEUZ0roARgr/km5ZjSQEcKQ3aJeYyOkO+cYrNSUxiGedddbMmTMvuuiiDTfcEADOO++8H/zgB5/85CcPOuigye9MywDxxBNPvPvuu9/xjnece+65Bx100NTMtAJceOGFd99993e+852DDz4YAPbdd99XvOIVJ5xwwr777svuv7h06dIvfvGLhx9++DnnnAMAb3/725cvX37++ecfe+yxf/M3fwMAV1999dVXX/2JT3zi1FNPNQ4PPfTQaaeddtRRR22wwQYDtCgSiUQikUg0sJ577rlrr712zz33NKNDAJg5c+Y+++xz4YUXPvzwwy94wQsmuT8tA8R3vetd99xzz1//+tdFixYddthh8+fPd/0OdeONN7IbaI9Kl19++Zw5cw444ABjrrPOOgsXLjzrrLPuvPPOnXbaKfb/3ve+V9f1oYce6lIOPfTQCy+88Hvf+95HPvIRADDAljhcd911P/7xj9/5zncO0GKs1eTZbDIGs93w5Ch6hKFbW42EWnQOgoxCEqMcPs4xAdKSZDHTdB5VtgqHOWbFHxINgmTqZ/rD97bbDZLCpQVFJ+fuL9AQH5CuyIeP2MsCxWQLXcL4aCVlyH81j+obxXf42s7zpu8x6TTOH5/+8Ic/rFy58uUvf3mYuN122wHAvffeu9oNEH/729/efvvt5veJiYn77ruPdavresT9wjLDsnXWWcel7LbbbgBw1113scO1O++8EwB23XVXl7Lrrrsqpe644w5j3nXXXVtsscX8+fNJhXfccYcZIHZtUSQSiUQikWhgPf744wBAlgUb02RNsloGiGeeeeayZctaawkHUiPXM8888+STT2666aZhojEffvhhtsjDDz88e/bs2bNnu5RZs2bNmTPH+T/00EOZCtkWN9lkk0yLkdRqgi9G9AC3mj0HdkEyNBZw2NbKC4wgYpJak8UchjplhWuKh2lChFVKJYe4X8b5FVAWOLiafQmVaBp2OdTa/hmtR3oG7rnnnksvvTRO32ijjfbee2/zu4lr7/V6ocOMGTMAoN/vj7AzhWoZIO61116T04+MVq5cCQDhaA8A1l9/fQAw68BjPffcc/Gr/+bMmeP8Y4ewwgFaXNM1+LYLRlP4Rbm6fkdn4+JH2+luC3FK1GXibCQqq3R1vdZIpX9zxvDXuSgYY4Q1j3+AkVzOtdZrRM94a4iefvpp8ge9RKOdYr788ssvv/zyOH3GjBmOWM2ZMwciWPjYY48BABvdN261v4t5ymWiG8luO0888QREY7iwSLw7zxNPPPHiF7845RBWyLZozAFuMpFIJBKJRFOl1eEP9zHHHOO2SQk1Z84cN1257bbbVlV1//33hw7GfMlLXjIJnSSaBgPEddZZZ968ef/3f/8XJj7yyCMAsNVWW7FFttpqq+eee+6JJ57YaKONTMry5ctXrFix9dZbO4elS5fGFRqHAVok0sz+C6JyjX97kkTVHVvssqdGticlqwdK6/crEgba3rZJLboEidzUNsiFlZRsfTJAr/j6R63RzapntzTqVGF+I/F8JcGSo85lA7fcKqvi2vL7MbX3Je3R4fZKVjK222owSjrUl6f88QKAUb+LeeONN952223zPuutt95rX/van/zkJ+HrOX70ox/Nnz+/tew4RDfK/o//+I9HH310gIr6/f43vvENM4oauV772tfefvvtIdJbsmQJALzuda9j/RcsWOB8WP8FCxb85S9/+fWvf51y6NqiSCQSiUQi0TA69thjn3jiicWLF5t4xC984QtLly495phjpqQzlCBedNFFxxxzzDHHHPO+971vm222Kani6aefvvTSS88888zf/e53991337x580bey0MPPfSHP/zhhRdeeMIJJwDAo48+etlll+26664Oui5ZsuSGG2444ogjTJ8PPPDAf/mXf/nSl750wAEHVFVV1/UFF1yw7rrruq0mDz300HPOOef8888///zzAeDZZ5/9yle+suWWW+65556FLa4NKkRQo22yS3JKQ0fI5Xch7lpz6+7BmUp0bqePkm2QOWeM6PLbI+d8IjeczVcVd6zLdiqJhQuDIOcRUpJOnxMKhFq29SHpmKJF3ixxJJQtRSVJelS5LvThmiD+RRzUMkK0n1WiQefBb8/E15xMSBxFPhKbuZgJpzK5qujHbdK/ldu3plpDNSXTgIsWLbrxxhvPOOOMb3zjGzNnzvz9739/4IEHfvCDH5yCrsQDxFNPPfW444771Kc+9elPf3qPPfY4+OCDd9999+23354sqwGABx988LbbbvvBD35w+eWX//Wvf509e/bHPvaxwhnYrnrnO9/5ta997aSTTvrVr361zTbbfPOb33z66afPPfdc57BkyZIzzjhjzz33NAPEF7zgBWeeeeZxxx335je/2bxJ5brrrvvc5z7nthHaeeed3//+95933nkPPfTQjjvueOWVV/72t7+9/PLLZ82aVdiiSCQSiUQi0QillLrgggsOOeSQJUuWaK132223vffee6pez0EHiG9961t//etfX3DBBZ/+9Kevv/7666+/HgDWX3/9F7/4xXPnzp07d+6KFSuWLVv2wAMPPPjgg6bI8573vKOOOmrx4sVbbrnlmHpZVdX3v//9z3/+81deeeUdd9zx+te//uSTT95+++2dw84773zEEUeE20h+8IMfnD9//vnnn3/55Ze/8IUvvOKKK9yu10b//u//vuOOO37rW9+6/PLLX/ayl5177rl77LFHeYurgcYfqJfXaGgf0cii+iJrauBfqvJC5wF4XvzUSyqJOB9D7IJKcrnkhERuRblt54c5qDa+iHDbFNKPKE6OgXDU2Zn49LCllHfOMbmWXNcEzzJzuSS4kMDRuM/5g+KZJb6LUjGRKspHLtkzQDsZNs/YuUo0/jVikKP4Mz8E1xosgHJ8U0mr8yJ0PeoYxE7aY489wgHJVEnpxN3W7/d/9KMfXXjhhdddd91TTz0VO1RVteOOOy5atOiII44wewSKjH7+859f9P+9b9EW2425HRkgJiQDxGwlMkCcNMkAkXZ4gAEi7a37rX3M18k5yB1snpqIHSC2lCnznewB4vg0aQPEvW/9btciTz/46HX7fWBUHTjsTz87+uijTz/99FFVODlKrmLu9Xr77bfffvvtNzExcccdd/zmN795+OGHn3jiidmzZz//+c9/0YtetPvuu7s1wqKumoLwPlaTOMgbpGZNfo6iiWbQMEgNqW2x2O6Vxu1lh7NRLh6u0QEQyk0N8uwAEf2tDkoxo7qW3OzoNjVMTKaTSrL9IWXyA9ZEobLs7Oe1kEhZk39mUaxzYmSTcmPTU2MsPhdXVZqLq0oNLm1p3FucG4zPVFiWDrk0yeVq9hWpMJf18ekkI/GIGd0gGjsxT1bcQvBMlWFNObs0D0AP8Y0/jsFl6ltxAI0jUHJ1+TM9dWrf5mbGjBkLFiww64JFIpFIJBKJRGu8psE+iNNTarIfPqYJC2xLHnwiuPBhlJ3bbZuybHFuw344F6eyuUn4pzlnn5tjdZ0IInHWCWfo4sx3w/6mEwdFLm7ZqU6YudlavkisoolFfq42YSYIGaVruCzhVaRUivMplvN1ce5EEL0zmbMlleCL7eLxyTU26XQWG9FGCN6rSXCjr4HJxd1OsWM+3UNRBdGREjmARy4urZI0lwLmXB06lTFAKq41//Wb0ji4Y6IXI69RTWEM4moiGSCulmr5lhlGRXf8MIO/TmO+luZozYP8ac+/Lik/4IhqVqxP26gFDyvpiAeP6rjR0mCDOdoBzbuxddbZ/qTLYmfuSEk3CusE4txpgBi1mCjS6Y9BarCJBzGK8UkNEBU3aglyUVl+qGf9Fe4ebaK0TuRcBT7tZU034h2MjJu91tGgE4+xmjrdSIwMvhU6Uo06pHAuoEze9JXjzzMdF9piGtCxAO+cGm1y3qmMwnEkX0fuDIACxokRGZrnpZNGW0ZLE5M+Whvtq/amo+hG2SKRSCQSiUSitVxCECdLY4SCRmNHg+nkIebsRsEIy6aJB2eENDfKYJtIlSKYjfRKU+ccIyxhgYVuNTZTva3Z3lqzDmrgGjI+9EK0YEjOGahzKjdHd0ZI4tsYIU9xCN4jnMayQJdH4B/jDAAVN0tLOF+FSwWrmBXKZXGjPRYKFDXpD4Z/eTetcClAzpQvut6G/aL8lb0iycvkjjFL7kyLWqP+pPhik5NHUMkJ6RxodNcrBbGxc+4jQL7pkjUprmxZi2kx9XX6YzEJuLEeSS3TWUIQRSKRSCQSiURIQhDHI20fIEdTXftzzIANZUO4Cjuw2jBCJ4YCRs48UGzLzRHEQihYtLCjMOgQmyzAcyl5RkjAnq+TNEHwnvbt+iOlQBHl1hELjFrETdBKmLJOLbgRGA35Ni125+OIVDlCxuUSRIfTCRKrKM+jLVbNSg6E2QJkiHKrINGlB00oAKhI0KF15lGl44sE/mFG6PvDuUVLWAhfTCFD8ysu6w88zOSBYpyuw7KWHSp8Z0bIEFXmLLIBpMaHxms4vthkDsMXoykSrvl8d0qbTihXWeHfnWEY4JS8am+1khBEkUgkEolEIhGSEMSxSBc/SFlNEiNMpw3dAfq0OUmkEBLPeRq7dXr7SLSImJSKER1bahAKWAIUUyyw5mqGdpLHgL2atOhyuTXOhD5GubmyXPcKcnEl5OxFZBepDS0XKXVzJ143gmwCBfMEMYJ/qdzml6rIJBTQ5SKAZ1Cio1xVEy9onU1/NHHG3cPwz9VGqaQ9iirwcaXo6XImixupM6KByTPvbxjECIEclKsoKJ4KSSTIkA8YJPSRKKJ/TQcSZNF2LwX00BnIhxVGYJqtsuwbP/FRyX/c8h0oq6OLU6KgbHMjBFEkEolEIpFIhNRCEPfaa68777zz7W9/+/nnnz9r1qzY4b3vfe+ll176wAMPzJ49ezw9XGM0NkbIFU7BttF0AAOzQers9J5iAJbzRf48UOSxXxYKdtqn2qXnNwv0/clHBHJl6wRQZIMOCRqEKKrPmmDNHFAkAK/mCSLKJfAvYpPI5PqDzESEIp8bXT7GOU5nRXLz9zrJjUMDw/Q8QcxHGVZZ06U4BlZxoLHyK5FV2ERF6sRlSXRjUzOGcJULSdQ6znUpNWaEDjTWGBlW+NASjJB3hqwza/pSuNt+d2sMcWlIIr64AwQsRv1Ofd8hZ98iRxYV/hXzxDBPh96D4b2oFDpGHocmNGl8MS/ZB7GFID711FOPP/74V77ylT333PMvf/lL7LBixYrHH39cSzCnSCQSiUQi0Zqi4/utiQAAIABJREFU9hjEDTbYYNNNN73pppve8IY3/PCHP9x2220noVtrhJiHjxEywnTy6FBlxKIGqTMbVsiXTbE3Wq4MGdJcdCzD71no/AdbehytREYQjiWISRPzPAIaU2GF7Drlmpbl+1NTgui7EZdlSWEqfUCgCDrsQNjnTDpr5hliilQ1ZgtBVGHuMMjQmfn0iCBavkidNaCgQ1TW5AaMkGmIkMLKfRSdm8YmAAT3D6GAGpvNqmoczZd2Ng1ZZ7r/Iuot2QlS43T/gj4CFIEwQ/QDSC6LDEkutdB9AhEjVKQkRxbzAYtBgyruhV8QrYgzr+iPBP7a5H4m/66MnS8WSfZBbB8gzpkz57bbbnv7299+4403Lliw4Iorrthjjz0moWfTXSMfCw4wEOzQDW6cNGCdo9+SxrolaikZEZKyHUeEuZGfT8cDx/yIML3QBHUgsQqEN9m5Y/DDOFzKD+MURMM1MmtMZpypMx0+pqoi7bozwKQHo0yNTZRLRoTRBdLIxLlh61xOXvaPNzc3GA1TFM51wxFjoqroYC6R2wzI8EjLpVc4nQ7jUs54o2yb2/S2ataX4Fw8IiQDwSrqXo1NjceLedNMW+vEHuBl89FobEfGi2CHjP6SKt8NV1zhSx4sUoG4e8Ht1Wm8GPigBO5uw27ReBX3x785mhE/agzqwlVFvSuoMzFqpKX5YW3ZaG2ov7xYskilaJHKJpts8tOf/nTRokWPPfbY3nvvffHFF4+7WyKRSCQSiUSiqVLpNjfrrLPON7/5zZe+9KWLFy8+9NBDly5d+vGPf3ycHVs71E7OjDo8x7ThPWINgSGzC03SZUuQIc8CAzfyEM/kDocM+arYBSVM+ggnkbsjQ4IGgUF3HDIsBYqQLds5FywjDHbS8YncMWpsmlyEOLyJyza4EZA0Tegmxc3rkUnk5PvxzJwp3qilwggquaAE88XUnHLECAtyKQVETVR4Xxs6W+1zMYzEoNGd8xqQmw4OPDYrzPPIhLjGRDOBcq3ZsEl75jFmiyamXaXok8sCRfJlVAwUTXME7yny0+T480OIHj63pCgBirRixXw0UvAv6qXmvHC7yTr5HE1++j62Fh2ZdPoP0NqjDtvcKKU+/vGPf+UrX5kxY8bixYuPPPLIVatWja9nIpFIJBKJRKIpUeeNso844oitttrqoIMO+upXv/o///M/66yzzji6tQaKexYZIynEfoSfDVj5VCLDnNsA607I03AhMgwTYTxb1VCTJ4hgcxlkyCG67sgQY79+KreTMyaFwS8aHxQyNZcbIChgTE9iEBchcIUFil1FkCFgs8n1u7ogYNYQRI1NzOTIptbp9SXoF0IWXW6vC0HsYSbXhgzzuSQd1RkAYERYdVCk1VTN1XQHjviiImbwf1hJA9IoUAwzKVAMYFc7UKQhq8lclhTatFEARV+++Q2d+TKg6CrhDwqz06jFospZrEirGSZgMS9ZpDLIm1Te8pa33HTTTfvss8/PfvazkXdIJBKJRCKRSDS1GvBVe9tvv/0vfvGLAw444Kabbhpth9YcUcrFagie19LcKGqeBGTIVRHzTuLGVpLa3doyVOzsMjXzkJp/011cNs8I2dz0m+4Iz1MQocooIhDRR7J42bv5UgpynA9yuZrLtTX3G2fNlu1TgoiwX2BiN2P6kERCEHNmwwjtFdY0F/2MbtgcQFH4V+UN5f4Hz5ycaQiiCp1TZtXwRcwCvWlyI0SnAAB6tj8ECvaVAosGIVpr3MPONQGKQRGIkKHJpdGf9H1z7hKohGnOAwKKrs6qwCTfegr/4pFhswW2/aQkdsMB7GZRle2tubg+hBQBxea31JJnitc0KktAI+jQW2fdJg0opioZSahiomY+hw0jTgYsdpCSjbJbBojXXHNNXdds1qabbvqTn/zkqquu0lrLRLNIJBKJRCLRGqOWAeKcOXMyubNnzz7ooING2p81RDrB1cqLd3WaLsiwScwiQx1l5LmjBXt8E4kIxQ6MMGkm0muuVJoCorNHIgIJfSQhiRHeY/giAPRJqCKP/VBuP2umkGFUlQaAPmATk8KgTpTe13VoEkbonVUd52rAZf3eb6gqAMJF0E2Z44dBgqImJoiUEVYQI0PdhMARZEjK9nBZuiuhLdVTCgD6Dvs1jNDxRQ0WDbp0hxvrLFDU2KyaXtlc2pDJpXS2+aT4K0I+UDo8RhIRqIN2vYkZnON5DQPDaNAtCa+5XPBBhxgoJnfbxnyx6YEzFIBfmEzvlwFjE4OGHN/0XvjLc8xAkXHjKgncUI6ix8gWKaqZzRlydwIjno2tTeqwilkkEolEIpFItDZowBhEUZvUWo8Mc84EGfr07DFq6kYoIG4Rl2JjDV16nhGmQhJH8jYU4px/013ECPNokAeNPDLEnc8TxMDUGdMGHWrWdBSwrzEUhNo5O9OQQudWe0ZYA4BWBDc2pqEIrqyFCimgCDgXSbG/RsgQsFlBFZqUIOoKACpVhc4BI6xcIgD0dB2axq2HAxYBoG/S7aVvgg7twRgoWLvAwQYoqjC3cNVwD+MzEvtoT7TDn5pNtyYBisgMGEYDCY1hERQCeCph2gA4TAqtK1nFTIMO6ftLEKQMcBr6olM4YJHeViwy9DgafZ+Q/REVjeykPyFwS26LuCYDxbL8Vmn6V2MtlAwQp0ZTOyJsbZ0d5CW+iOLEkuEj/o6jNUfDVjyZkp81DhNdelQVP+ZDIwjgZ4dJrnMoXJVCNspmx3ypGWc7mONHfmS8SMZ8qQEiaxaOCMm6kz43idxPDBD7eCLYm6rGuWSAWANAbQ8uGiCaGefceFEH1xPQfcuOF6kUHl34v+nNDzwiVGROGY35iFlBD5t4gGhMjUz3DNDDA0f3+NHDZt0MK8GaAMHw0U4iK2yi3B4xbVXsKJsMKdzPaGNzhcvxIxN2zYrPxat2zJCHvBu69pkagnl5ukgFv12QrndIDh/RIM+mk680d/qwc/MFmxok4SbQiNTfbM0+O841OwSkp1ijqgA780X8b/jYVtPxYmfp4Dt8rZVMMYtEIpFIJBKJkIQgTpKmGzL0ZVO5BBn6dHoU5T5AZ4c1n05yh1mGQs4PXV/Clk1MMRebviGXPswylORUMnUrMhNAUWNnSwEpMjSJte0AIYgG/lGC2FcICvYpMuwDQN8hQ8URREDOATJES1giE90LlB1mAUVqTtnOOFcJ00DBhrtZvogJomOEqhc693UFAD07H92HHgD0rLM7q1Vzh7h0gwzdDVxBAHHNipZgGyAFdkduZ0azxo16XUynBDJ0QoywcXYLX8icMgZ4pttuurzBeYk1K81ZIqQwqrPmtteucJ1uwU1zrT3ew73VGoKJZY2RocV7+CajC1zc1LPjne5ATSXkS5IBiqnpaUw7HQxFp6WV2DmWixymM1CURSpCEEUikUgkEolESEIQx6XxIcOW+jE/61iJfb7kah/UGT/X6s65LiW/DAUSjLDTupN8WXb/GohiEBOvy0OlOr0uL7Vmhd2/xqNBcG44vYAgppEhMQkj1O5/AKg1iiY0v/QVMsHSwVoRgthHuR4ZMmZAEA0jbHJ1s6s2DxTNlfQmvo8pkKA2QoZBiBmDDJV3RlGGBCg2BFFZZGgYIc51aLDS5iz17IG7tSxVeFBaVwBQW+5orkgPEne7qQEfaAAFma8UnTULxAXqkag+70sC9XCAYLNyAhVx0Z81F3SYikGskstQAIIPI3l9Hx+qiJewBLiP8EV8vKao/4LlzkPQEvqZwo1NVaQmdyegjAgoKtwsAoppCujLgrs0ivNpA37DA8XBJItUhCCKRCKRSCQSiZCEIE6uxoEMm2xV4paigBG6Y0sxoX6tzvmQRPLkzOcGsTphekzyUO4Qe18XvkOvJegQkzxNKSDhi3wcYUnQYeq1eC4GsdM65X6tkYk3u6bIsMmteRMwQWyWJ7toQsQIXXpfEUY4gU2OIPpdtXEMokYRinRfG02AovUKf1gp8qu/Uw3tIUGHjppU7n/3SxSDiAkiRYbNV7Qhi7VucnsKB1/a9LpxsxAXNAD0/D4pFUDwBrwGN6Ij1xhc9Rw4MtCrYj5rXZX8FiKQiSwEbnxRVB8JSaRQ0BZtFneXxSC6zyZ5I18LIyxZ8pwq2xwJRoYqSjdhhaTBVK4pixkhZY/uNiavBMw7W8IZOgdHwcvuHO78NM5FGjlQHEwSgygEUSQSiUQikUiEJARxzJpqZJjwyYUPcqVy7LCEL5KqNM4gK465SnKcj5Qdd9AhIYUuhZqJUkVBh4CqanlpHkaVBBw6h+Q65dok6jCXmuBMTBANIySmPdYJ1QcEFPtgASEgZIgYYZ8iQ44gakIQbdBhs1A6QRA1ujJ0FTP9MOSgRkBcDBfBpsIxiHhrw9oDRYwMFUMQK9t5Y2o1wx54DWg9MjoKbWMTNTs1YNc+g4kH1SlMQACeAgjuG3MGCglDImou+jbkQhLxQuDANxeSWOElxnWQCOkYRLLxYV0QkpjcFjGLDG1EIO/chA9Gq5jpTdhca1wqEXRYtsYZNZTcNBH9CD8LGujtQhV88yt8ZKsjUJQYRCGIIpFIJBKJRCIkIYjjkYbo0X3E7DDvk8zNlu0EGvNQMLA4zqdRbh4cOofkxodNbhEUHIQvZk3XMWLWCb5It2nUzKElVzFrX6H7haxWJmVT6eRtKMmgQ8DIkAs6nKCMEJu2MgsUJ5pchRihS7cbH06EuSlkGJk1eFLYsENiRsiQAMUiRciQAEXLCFUVmjXe+NAQRA1uJTILFJvO92BGcAjW9ODQHqPC95OpzTM4xHgadmivF0WJPMBLADOUR0XTVSLdcCwSkugKKcTVmovn4ynNoWEoiHdJJMujA9NWwlFAyvlGF5IYADwGncarmO12iU7mZrMnhJRqPNwd4mt2bsOscfbH5HuNDrmEAnrcyJ4u7Fb4F9TeA/bQOnysaaPyJhUZII5FOhrEtPpns9tHhOmq0JdK29AQD7wS3sQ5GvNxVdE66Rgrn8u/TM/ndh/zpcpyk8gu1743GZmuY/Rleok66euVgyK+CTJAxIeWnGJODBATa1k0bsKagHLzc8oT2WUodkSI5o7NhDKEI8VmEpmYdsynGTMaEbopZjMBnZpTNs7oTtH0xoGEqViz+aPotz4xE2d2pNXsD4KmmN2IUOFNeRSeRG7mxxXqfM9+Y5tu9/ynDX/8FLLotB/5s5edcfYj3+bQ3N9dbHJfdyr6pSVdAYQrAzTyqUMnl00OzSqaYjaXqTHN6wRrb9omNDKBXcsyvhlnMhbHgzmwt64iFzdZCtdp5pRJ3/03LRqwhvUBFC1hcf0J9v1B3cwP8tAg13+6NONj1fb3lf8T00nDlF0zJFPMIpFIJBKJRCIkIYhTqRJw2OqWyEUzmAVl29khcdY4I5lLK+yQO8yccuBdUJaakDAV2yt21rhOVUKZJXKOkKFicgciiKltbiITIUNDWfz78fAUc93sfZ3YsAbInDKaSk4RRMMI+9h0BNHAwpoiQ5fLrkohM84EGWKg2AYNFGYc9qIjoOgJIl6kYraVUXZKt2oIoluVgogmu603RYNM/5hsylow1XLhBNB0T/O5icoyvehMEIlpOF9i+oPAUPuxQijK3fNVk4gnbYlpU+jqEEzd8ozQHwJmhLrTjDPmnXReODHjnF7aEuTiidf8AhffTXS2APLO+LfUEha2LKkkuQk5bqjojhxIMsUsBFEkEolEIpFIhCQEcVI1EmTY5oMoV1vZHDiM6uCCSqJQmAQd7JCb7J4vxWBIlhQyddKARcU5qyLTITpsErf8zjU0YLHJRWVJSKJzJotUNDbJ9toBUNScySND80uADJFpgg7dtjJ9FigqHIMYEcRmkYomq1L4RSpaY7PZ7YUQRIu98FbYDVksjEFMsA5yx5JX7WlMEC2YQatSAr5ouBG6Yv5lcXirZwKVWrCJj1NToWX3FmnsPmZQJt1dXLtHD+oOgX/BluAam6gXpFSNTZdb4yZq5NWgzeDmZzEbuk4VXmbhP0cKAKDyaJBQLmQGF5e9JxyTUwA+6hE4V7r8xWVzIYmpdvMhiaW55nphOKqSLZoKrUkhOo8SbTdcKQZu529ncn4CgslXkqlqMMlG2UIQRSKRSCQSiURIQhAnSSXssAQccm4IA7CVpFigd+DpCc/kAKONUQYdNj8obmyYXCIiZIBVzKQnhE0maybOSTe2P9gk8YsJRkigoA58UmXjGETbomZNgiHpKmbAfLHZiRqZni/inbHr5qV5aP8asgM2+ChDbCYJIhODqEtjEM35IqcTnZbkpxDfsTRczCdUuEUD4cj1tLveNNStF7YT3H9cT0hQFo4hc794UykI3s2ozAXSvLO5mm5Tnr5CkMmuYkYdqfzZQ3FpJHY24o7oCD1QVNjEZ4HwxebiJRihRl+K9iOJiVmwQhxVSra/Uak4Qu377CvpsopZERKW2jeb7MLj0ruEJPK5Hn/iztJvfnOjun76Chv3wI2mklIxHg1+5OGf7/zo9sTJSzbKFoIoEolEIpFIJEISgjguFeHAoYIO0aNrqga2lE5kEyhI0yljSXRe53Jbgg459pbqZ+nWhiQXlwVs8rTPmyo2003gUr4tpgmybJkCxayZAop5shiUwoywiUHEuR4ZagCo7TrcBihSE78WD3Km+4XsfU2WLftcYyZy+Z2xMTLUgAkiDddt/SziT5BCUEzh7ZotmdG8CWFRuwkf7kedeKdfTdrFUY8Kp7uVpHUDrmps4lx7cJW59AodWkRKUTxanhRCdLcTtzpraryomQTGWaDoTMTPCFAMfUI3dhUzjfv0ONYwywQjJOqyipmCTNc+F+ZYGHRIdkkkuYmQxKCREDcG9YV+trYcFAwooPmB7pGoSl5N0bI1zgNLSwyiDBBFIpFIJBKJpp1Wrlx51VVXLV26dOONN953330333zzjPPdd9/90EMPhSlVVe25556ZIjJAnFwF5AHannLSuQgylZV1T5l5fw4c2t9SfJF4s6+QyYckEjYZg8PEWmPsVRihWMIXKRpMoUpcKkk0iXM7UMx3j4+hS0QTgn30D9x07KaxM0WGLSYmiN7scyaCf6n0ABnWKJcGHRqgWLMmWcUMnVYxt8hxNUTBLN2xPM8ARU9mEMWhDeIIPHM/V/7AFeSAIiGIzuyHZhNl6K8ICpG0MZG8WQXdcP0MeqMBoMJEivBF9ws5AamgQ/6zUIgMaa4pi5gTCVh0IquYo0XNnOFqwRROJZBharGzlbm4/BebL4qxKBt0GPQW1Rl9SWNGiBEm+UnfpIKvSGrjQx4luu7gNc7cJ4MqgJH4xsnjxw5Sq3kM4kMPPfSWt7zl3nvvnT9//qOPPnrCCSd8+9vf3meffVL+Z5999te//vUwZdasWStXrsw0IQPEydJQy1CMWgZ5ibK50WRqzEc+ph2Hj8gs2eYm9VpCnSjFjgWjPySpOkn38EGRY9HEme9VtNKFcUs52/EZurjJWWNSFVszLutqi+q0F1mjcVINKLfGoygzm+xGYHnTbvXMvxm59qM67gXKvlQf5bLOiTnlaF8bbuvp5J2bEvlLhsY8itxOZmEHGYiQP7y+CBlr9oP6+SN1k/vKv4s5dwmMf6WJ6UaZCplmzOfuBJPrDpzc82b8yh5JfMeScRvJxXc7HT4q1D3fBB7T2JrR9KvGHSKDS1dLap2JKeXncLlZYzoMSq07AeKHqiLftwrfk6S3+W1uyOAuGFShQ+OHgPi+DfqMR5PkULJjvtSAyy58yY3Fk2Vx5+3kdeEf27S0/85cPfXud7/7j3/8489//vPddttt2bJlb3vb2xYtWnTfffdtttlmqSKbbrrprbfe6kzyEsVYskhFJBKJRCKRaNpo6dKlV1111WGHHbbbbrsBwNy5c88666ynnnrqoosuypTq9XrbBnrRi16Ub0UI4pg1BXtf+yKpUi3gkKmuA19ku5mmNEzN+e1svBtOT3K1fJ1cfwgXYalnthTjRoEirrSl87hpwgjzfDFI1xm3YE8cjXM1LqshmGKOTMyr8IwzCxSZdMIINQJJkbMOTcA0lEAo6xafIV+2QJgjYbphN8p2qsL+2Bln11tDZhAjCwhQBfFZUkUn051zM5tc0VxtO6djk1xrcie4LdwrPL8Z3WPMJ8K7Ze/YFr4ISNG+NoiBpXa9IZu8kC2gycffJZBSNpH0h0eGVNk1K/ntb/IkT4d5YSXBIbhSeUboKyvAjUyLHBRMdS8sEpRiOGKqLHHLL2Ep1Oq8SGXJkiUAsO+++7qUPfbYY6ONNrrmmms+8pGP5MuuWLFivfXWK2lFCKJIJBKJRCLRtNHSpUsBYNttt3UpM2bM2HrrrU16SsuWLdtwww3XX3/9DTfccNGiRffff3++FSGIY5Ka4qDDbg1huJRwJnwxxc8aF41yo4Z4FpjqAAsPkk+S3Dv38kCRQsGkyZ/biItgfIJPVwQFFcQdyHe+1MTI0DXB90dnTIIMycvrAnzFIqiaNV2HEqVwEzSy0nW/5szUIhVclr3pE6kUrpBNPui7vyxTMRyU3qfOxis9CP5sogmrsE5ylvw5VPxJbrsiTBMBUARcVnFm2PfAVExZ59DpfgaSSz5lOG6P9gdHGZJdb7gQSQXR6pAohLQxG9yYJVX+yvN7O/N0LV0JOijbrD23LF8ccKNs4ozsCDeGvUssRvHwUmecfSlt+jxISCJtd4iFJqvzIpUnn3wSAObOnRsmzp07949//GOqyPOf//yjjjrqFa94Ra/Xu+mmm77zne8sWbLkl7/85Qtf+MJUERkgikQikUgkEo1RN91006c+9ak4fYMNNnjve99bVRUA3HLLLQceeGCmktNPP/2oo44CO3quazQN3u/3TT2sPvOZz7jfjz766Le+9a2HH374Jz/5yS9+8YupIjJAHIt0+nGQuCU0RNBhW9WMc+SvWTdKufigw/yyZdBcos9M4E8WCuL+pDhEyZJnoIQjFeyIK8nva9Olt0Gd2JmY3FEknUm6L6UhpCmYXvCkCoeFkTsioDiYgeFj5WuO+529nnwTeHOT6EZN325MoZyr+zW6pXhuYomUTW0okrvpmGOJ3svHH2nDnPCZD37BbuTs+XfHkd6iQyHL2+2Zzwf5oUmP+CanncOnreiTko9QpP1xx4Kas8405o+4WWfEsUgYnxO9JbhNralcJmFy/KJmsFGqqE5fh0b9sJa7IhoyS57NoZEbleTiJrn38nEHm6jT3pnBgcVF3Qdl6JDEAaRHHYO4ZMkSEzhItOmmmx588MGGBb7gBS84/PDDM5Vst9125peNNtoIAJYtWxbuffj444+b9BIddthhJ5100g033JDxkQGiSCQSiUQi0Rj10Y9+9PTTT8/7zJ8//5Of/GRJbS9/+csBYOnSpdtvv71JWbly5Z/+9KfXv/715V3aYIMNnn322YyDLFKZGmn+yUaZf1rn8GFUVqGHKK5q7ZOVeVDU7gku8E+6lR8SUxUAgDkirWlvGzetzD9apS2lAf0DrRguaBIDqsf/ww2R+s2PyFTmX7JmdGjBQ7q20CG6QMlDY88e6Q/51+kyRXWSm42tk7Zpz7T1RbVGHXSdDy3SfvDPyHeEOSHOjg6cvebk1A90vvKin6ii/rR9Bo3Jnxbu3vE3B7UTdwp7NVNnvuQ+6Xq22Bul8LNAaoj6oRrWhtLRF2zqwvjPO+kPNqMTorRWcZ34jKvMd1fy0Iiv/baMznxwaInbqdMFC3y5PwfM9cr+1WAvUCqTHjL3BVtWdhjF32UD/xu59t57b6XUlVde6VKWLFmyfPnyv/u7vyus4be//e0f/vAHhyRZyQBxUpX+eDKf7bay6NOS/DYJPrrc2CXrhrPzXwSaVsLXzH9vRpXk+5P6FkZlE9/FiSZIb3Nm/lQX/vOHRv7qlHSg7Oy1dYCURcMCkp52Rv9szfyt0e17PPlRyR/6tFD+Apb5ZqtuuwTkqg126fluDnTvDXTzNz/4L5ZuHcA3dL4Dgdq/0Ny30AC9GvI7M99bUpYHB9n+MD3Iuxlf+uWJ/4plb/agbO6PwghVgxrVv5H2CwBgm222Wbhw4cUXX/y9730PAP7whz+ccMIJm2666RFHHGEcli1btv766y9atMiY999///HHH3/LLbf85S9/WbZs2Q9+8IN99923rusPfOADmVZkilkkEolEIpFoOumCCy546KGHDjzwwNmzZz/99NPz5s37/ve/72IQtdYrVqx45plnjDkxMXHeeed9/vOfd8Xnzp371a9+de+99840IQPESVLiyQbFdBeXxSs2uLJBGrO8o1Uae9ufY1iVwu4jEO0urjPOgX/UbLosPjRyusgp1W3ngZyuZFfS3Uv3h3PGZlQ9SxnaOpAtoJM5yRqGVTLwXcVG6lWNPj80Fbmc2RD79v4RS+XccGKUxzmPHj1kjlPHufmbOl8Vd24VdLyfk58UxTgn28+WJasx3OfdbmTjTOV8nOibkQd6t16TliqbSNfc6559d/AZsJb7i6MBuq5ZaVF0ytHpopfCcEv6wUEXOdWiLct/cvNlO2k1f9XeRhttdN111918882/+93vNt1007322mv27Nlh7t13373BBhsY88UvfvHjjz/+y1/+8n//93+fe+65rbfeerfddlt33XXzTcgAUSQSiUQikWiaSSm12267mbftEfV6vVe84hVhyvrrr/+mN72pU/0yQByv0k8g+edfNrMdHAalEuCQsDFceUtvs/1LgweuJ4mynQAnZYTOzO5uap1zQDFIzrHJ/FGk3q3XQuyyZzWTCInTkklPVJLnOIMrT/m82bAVdPZoWUc+6LVWvgqwr7ZLMkIELdiNfLlOdmeEHnUwZV2m4j2iOtKtZtK7iL/0OnEpQqfWW26A+5nYyVkE8z9lhBgNWmcyBRC9RM7VqaAJE/buFBniuysNvRhkSFSI6Kh/kvMxAC91BhIfsASbTKFBfIFaHj8WAAAgAElEQVTIT82dHwJrCXJO9o52KN/bwbV6A8TJkCxSEYlEIpFIJBIhCUEclxIPHyjoo/gBpVPQIZeeBYepYnmI1FJJCzDLRRMyvCEbqpiXzp7qElzWSjhSPLJE/Pkp4IiTqxQDY5NImGAKuaFfXESRhYLELWt6ZGjunxyxCyyCpBAha7u3CB8p6q3CvS06tMRZSjg7I3fg6UNjIeZki376Eiyo24cC00/y2sAIHKIGU2a+rPlN5c9l6jV9iU22FRu56O8uBqGpxNkr4YvezGJ8ehqTRJNpwqfT08We7ERVTfdUlNNWRVYaoF6NX7U3ORKCKBKJRCKRSCRCEoI4aWoiVoza4sBKE5MNJYpp1icCjcQt8UyfyOWa0Mxv3mo9tJIIp5TJnpCoxfyxWLMgupHrAM4tIJppuzxzQCkOI0U8KkfIFHVzmEQB+OBChdMDkqfCXFKJbpzdk60JbKqsocOyWlUAoHRtc40bfoHWgIwghegqQIeGzebQKlzKmoo58NSpZp1deukVoU0wB0bx50jVcgNT7JerQRMcTHKxmToSsi6YgDNrooQ0ZkOIbgAlO5ly6xICSI5luEXNfBP09HWCglx6/oQErLS9oUKNY4Pr6SUhiCKRSCQSiUQiJCGI4xZ6KOsIDouCblqIXbcWBxdhhGlCxj0HloHVth4UEQ4W0aUAJ3ssTCe7cb7uJGbSH2RjPMAYrZSLY4ER62qYYuBWAYC2nE8pY1pqrTEMMrkamcHpqn2iR4kIN6Yjr1Ji2CE9AyrFCKuwBoX5YkMWXW+JMxBn0xDFewNckdSBWedJVyvo44UuX545pdgkuQMoFCSIjqukpcupoMMyRaiM5JrrxUfgkYDb4S9r4VWK3BK4kfdOwNp8Q81pUlFOZ9XtLmu4ZIA4PnUYseXHEFOxKkXFTeD3I2WOqP3AW47Xt5jqbYfvt/yMc6J7A401u2UX+Y5kZMiMERiT/z6NchVAME3c/EV1pvkTZYc4dDhi0itsuqGPGxjVoWlfrWW/q5thkw5z/YCM/RNuy2pclow189u4EEUjwtSkLR7zmSNV+PzgESE5XUEuObfopAFNJ+NF1KK/Xs15SA0fcwdeclMNqbKvjrIqco+lrUMcd8jM2Is8StFttMu6WbhvdjS6yw2b2O4BdwhgL6t70GriOEiu/+Bw7dmkSVizUjTjjHs1mPIxRWuDZIpZJBKJRCKRSIQkBHG86hjl2g4OW0uVKDmjOsyTf3aaOPUo1mkqXHcyyxaU5NPbJ5ez6TmNf9Z4sGtJkCHBACq7kIJFhhQo4pnloE43EWxKuSZ1mGtRN8oNuod62/Ay1x9dM2Up8Gi7MHTnEsTO7MwvJnaAGGE045zgi4qcPRYoonPoUiLQWHi9EDji74Qumgz8MtB8dApBcVPKSbOk/dTb80rUekB8f/zHiuOdA07fkzLoQzfotDVDNIny22jnFZUdRDLFLARRJBKJRCKRSIQkBHE80kWIaDCKhJ/wo6r46MOWx6gWcsZHlZT1J1tzcmfsTiqJFEkCzu4PmIlejoMJ5p+cE1FiqTR0FX24GA5/atAU3p44HXnWGShWfo2Io1w9CJChJWcoytD4uLYVuZwJyNPsWuJjEMmhcTGIZbdD4e4zza43Cp2BgC/23PGCP/Be6JwGir3QrIZAhtGRoITgPiEmxGXRGUoZrekDEql2JfHZgFyNC4wrDWzEn7oyEW+yb3YZ4HTf4Tp0tmUdRtcQsLcSkhr2i22CxgfnnQtUetEG/V7WGurxz/Os5hKCKBKJRCKRSCRCEoI4FrUCxDzH6rRsmc0OLA7sMThtiMd1FJHS0rF0WpA7isXLLS0mT1f3qro7jEnkodwzOZSbKsf3OsGcXLygBoBKWUSnKwC3WLhhhJrCLQu9VM+WMrjC1dkDgMr2p25iEBFQrOzBuC2wbXdNFJ07EzU6dLPNjcc2GhWl9zHlNfinYlMb6oa3uVGJGERjuvPAIsMKErgRmLIQn2RMbSttTNt5zQWJ4mudFpObwKoQna7JVh56dQKI1JkGvHaoKqra3U04fDARVsj3h2QoJs23MOD1MNM+9usFByOqlFtLd3OZwy9qHkxrPUAUgigSiUQikUgkwhKCOKkaABxy4qBgt3aL3PIbHxbXOQLsN4qnQL4bBKmyzaWvyxBABEf0JCviQpUU49LeJxo46GmFdolgQVpkOmdTNgEUocYmoVk9QERB23TjYKMMQQNA7diYBmCQYWNXDbZxeIKgK5Pt4ytd/a4nwbklVyJBEInZUBMSt0fenpeIIyRBhxQoGmTYmJYFIpOEJDqH5B6T9HphZKhxlGHgkzTpncCr0x1LOVHWeTgmScLqrIHvlxQyZDf2C7w6B9X5DiR62bESc0Vy353k0AJTAWS2Rcy328EtYszZ3pLPMb58Q7HbhCQGUQiiSCQSiUQikQhJCOI0EPsYk4qVygOzKF5w2NDDuJKW3jY2jUjp1KDOm/mYyE4tTbo6hX0Vmmy4nMI3SoovVs0yc4TonFkbZ8/z0IvvdPOWPBtrCBqCeDiHDLVCJM8EWlUYCLhHeYIMm3S/3lKFJnkDhH0eti/x0xogjGvtdNXxSfXhUXgRcZOeepMKRoYYKJr0yvPFXpircC5ZxUzSTeihM/0OlKAgCEmsODPFCOltg22S6zQY/C7JHaOGYFP0W7qshsJ2mipx5GIyjI8mErhe1iQSYqW2/STjLVvUnOrt1KjjNsZroGSAOEkaxeTyIJ+XoSeXC5yHaZj4dluGUjijlW1xqIbGJYW+86PcvJmY9ErNU9u5QtSiG8TZiSoySkBjCLcMxf4NcJPIZrDipnTRVLL7u9KMZnysew8Aaj9CRd01d2atXAd8J90v3jQDI7uGxnRM29ES4OUvrnloEZlLRWMhhQdKdkRITDJexLPGCk8WY7NSFcrF0/eukmjXGzJ8dKVyF9cOH/FxZoeAyfGi4t1Yk6QPs9HxoEJzl3kVjx7ZKdQODQE0X4+avnkvXyRqKkhWCZPWYYIvVJFzqokyFZ2QYdaslEvbB+C1WdNpgLh06dJPfepTv/71r2fPnr3ffvt94AMfmDlzZsZ/+fLlZ5999jXXXDMxMbFgwYJTTjnlBS94QejwyCOPnHHGGbfeemuv19tzzz0//OEPz5kzx2RNTExcd911V1555T333LNixYptttlmv/32W7RokZqKryuRSCQSiUSiydS0GSD+93//92677bbhhhsuXLjwkUceOemkk6699tof/OAHVcWHUT7zzDNvfOMb77nnnkMPPXTdddf91re+dcUVV9x2221bbLGFcXj44YcXLFiwfPnyQw45ZOXKlZ/97GevuuqqG2+8cb311gOAH//4x/vss8/mm2++0047bbXVVjfeeON3v/vdn/zkJ1/72tcm7ZBTE8Ft6yq4IeyAqNyBmVwuq9TalHxHkrksWRwD/x9JlSwriBrp8KSRRCwcmCFzxz7dkyG87qTZProxLZNzuWb+F0MmMqfsbslmmxtnIjSo/TIUCEtFR4V6oLFp2GEFfVuJQYZuu5uaM6vQDJAh6kA+nD/RS2cgVha9XTA3xazwvtke7+E5ZTvj3HxjN0ARr00J0t2+NsRE29ywM87B/Dg6NIobyb7Z+DwQvlhIFolzR+Uu30ge60c/75mcDzanPgrMyXaAzfXvmeTPD0/sbFUdASfxpR01H1WMP/HHXOWdJ12ySGXaDBCPOeaYXq936623mhHeTjvtdMIJJ1x66aUHH3ww63/OOefccccdl1122YEHHggA7373u3fZZZdTTjnlq1/9qnE49dRTH3zwwdtuu22nnXYCgH322Wf//ff//Oc/f8oppwDAC1/4wksuueQd73jHjBkzAOC5557bf//9v/71r7/nPe/ZfffdJ+eQRSKRSCQSiaZE02MV8/3333/99dcfcMABjv/98z//8zrrrPP1r389VeQb3/jGFlts8Y53vMOYO+ywwxve8IZLL730mWeeAYCVK1decsklu+66qxkdAsB+++23zTbbuAp33HHHhQsXmtEhAMyaNevoo48GgDvvvLNTz7X9Z6XsPwAArZt/kybbmaYbUfe61RKnFcXs6BbnAXtVpvHV3Ekq9U/lgq5CnzAaTJEbq0lvHKlbk2HFdSBIUQpUlfintFJaKajMP5teoX+61/wD889l9RL/ZlQwo1LNPwUzVGBWqlcp6wPOtP9oblBE0ZppLk5Xqhf+KyyFeuVyS3uLKqQH3rilTho+q/6c42thrpq9XubyJS+uvcsSN4ZJ9LdRmMvegal7VSlUefLmZzsQ3fZTpXF/ZbV/Wxb0IFlV994HVaG/JpOm6K8n/ts6iiuiR/dvmmp6DBBvu+02ANhrr71cypw5c173utfdeuutrP9f//rXe++9981vfnMYMrjXXnutWLHiN7/5DQDcc889y5cvDys0DkuXLn388cfZOp944gkA2GabbYY+GpFIJBKJRKLVWtNjivnPf/4zAMybNy9MnDdv3g033LB8+fL111+f+D/wwANa69jfVLVgwYJUhQBw//33b7zxxqTCp59++qyzztpiiy3e+MY3FvY5emhQ2Vy2bKIIH33Yof5ku8m4xnYzlZE/Dy39KXcdey35qlE8Fg2c0chqdp0lvUo540y80zPNJTWRUt5s9kNG3azso5TGHWhCEt3CSa0gCGIjq4aNuzs0W8qFJJqq3SrmIpl+1RodlvYv98MmjUGssIn7g00ija9QenWaOZmoe3TZso/bY9YUK/y6PB9NSHa94YIOmRhETdYpM9vcpPa1oTsc4UNrFjXjTYpDWBiWpbdcfMrYdK4UPe+J60DKss5R0XGSnaEjFpOxfEmhbwfaPtcf0kSL6b52uhxUdBTuq0PH3Yt2vUk4c00Eucx5GEwSgziVA8Tnnnvul7/8ZcZhiy22mD9/PgA8/fTTALDhhhuGuWYYt2LFiniAyPpvtNFGLsv8b1KIw4oVK0htWuujjz763nvvveKKKzbYYIPSwxOJRCKRSDTVWrFihVl+KuqkqRwgPvzww/kFHx/4wAfOOeccAJg1axZEQ7ennnoKAJ73vOfFBVn/5cuXA8A666zjHEwKcYgrPPnkk7/2ta996lOf2n///cuOrECT9miiSWuFT395t1xup3frFZ6GqX6Q6/4wH4g8jg+iLnyRIYgcMgxKKYBm12iX7l5tZ5IDZIjq1hgZmmRnaUwj6RM9pQVIDlw1W5Gpfphea7uKWRmE6YBiDQgo6jDXRmppbEJoQsqk2ASBtMBUoTMliOwqZrpsGW1t6DfKxnyxRwiiTpDFoYCi6RU6QnrbQCKXA4rul8EYYScNV8cUf98UwsdOjLLtzXv5b6nC7zDzYXSzDV36N4wKGhpsdDjVf3emXlM5QNx8883vuOOOjMNmm21mfjH7Fz722GNh7mOPPTZ79my3c2Eo1n/ZsmUuK1Why3I69dRTzz777I9//OMnnXRS4XGJRCKRSCQSTWtN5QBx5syZr3nNa0o8jdstt9xy0EEHmZSJiYlf/vKXr371q9l9EDfbbLPNN9/8lltuCRNvvvnmXq/3qle9CgBe+cpXzpgxI3bYbLPNttxyS5eyePHiM84444QTTjjttNM6HpwTeq4pfkOJYtJbYvta0lt8Biw2ZFGXXfSYOczz3AifBTHO8ynZqMJkJQ1TSYQVluT6Ojm+GJcib6urNAJpJsjM3ahkW8SmrNsHEVBV9k0PvilUWCXuYEV+KACo7U6HgCPharOXoUKmAYdg2aHfl9GwQ//qP5RrfRBQbIfFXG+DTNNb97IWZJJcgxKDt570MmYUdGhYII1B7GFG2COMEIeQkijDSmGTzwWbaw7B5QKkbzn6SxlQ7JTLquUatrkNoGHmGkrL4nfrDVYJD90GKjxMi7SsD18mzphNsvXTEMn8F3C7tMQgTpdFKq961ate+tKXXn755WeccYaZI77qqquWLVtm9jg0+tnPfrbuuuu6OesDDzzwvPPOu+uuu1796lcDwGOPPXb11Vfvtddec+fOBYANN9zwLW95y09/+tNHH33UcMq77777rrvuet/73ucq/Ld/+7ePf/zjxx9//Gc+85nJPNju4j4vHe/sTutRRv6p6Vrh+D62/FDPfRWSL7lEP/KVKDrpz9WAnZNzcAUzzkw6VypYpKIhfNWe2ZiaDO3wW7c0oIe01Iwz+RPhD6r5a+BGhCbXfc33IXjnlYI6NumcssZrVvxO3WhVSmPihzbNvtyQ65i1FU5EJjsipDPOeAgYmGhOmS5h0T2w4z8Ix5FkZ2x+Thm9as9dejrF3OxJHhxYbpGKOxn+FxVda1KKZmfrzCvv3FIHc0m7VzKEOk4WNxr5uFYNZBZVzRRgvyZXL8kAcXoMEJVSn/70p9/+9rfvs88+xxxzzIMPPvjRj370ZS972Xvf+17nc+CBB86fP/9Xv/qVMT/ykY9ccsklBxxwwOLFi2fPnn322WevXLnyjDPOcP5nnHHG7rvv/vd///cnn3zys88+u3jx4k022eTkk082uVddddWJJ5644YYbrlixImzlbW972wEHHDApBy0SiUQikUg0NZoeA0QA2H///b/zne+ceuqpBxxwwIwZM/bZZ59zzz03jDzdbrvtwtnhLbbY4tprrz322GOPPPLIuq5f85rXXH311bvssotz2Gmnna6++uoPfehDBx98sFLqjW9847nnnrvVVluZ3H6/v/POOwPA7bffHnZjhx12KOuvIk9MxZPLA2SXFC183hvioTQ/A64T6S0a30N7WbOF3c0+DCcy3XQJmrnjN8HBuS3z0c6kbo79aAiXoZgO2CbtvjaWKpGFJtr44Hs76CBEQBHlhd4e1CjUW6WAmWJuoGAzxWybqNl36ymyr41bwsItUglIJtvthMg8FkaGeJI9hQwVkCnmnNlMMWts4nfogZ9TziJD25/8qhQ6iYyO1/JFSqkTfJGQRYC8G6vEGiz+qnVChi2aoi+hrl9DDdhL8Pvo0ze+/rim9IDtuBYShfMIs/nG8tmDX7+1HiBOnwEiACxcuHDhwoVPP/30Ouus0+v1SC4JKASA7bfffsmSJatWrer3++xi5ze+8Y233377s88+2+v1Zs6cGWbtu+++++6772j7LxKJRCKRSDQtNJ0GiEazZ8/u5D9z5kwy+CNix45TpBSYAS69yDlRdjAN8yhdVDbFHYucy5of5gwQFpiM0Wk4n4t1Q8dOgqUILGwJ7WKDDrO54FeluHQGGQZARoEDh7Z2smaFdDc4BJNvt5XRVezsLRxUHoBP5ToJAEohRqggZ9qNsjVrmiaDXBSSGIkGkSbccG/xBaQxiNQsIIjJ8EEmN5Xe88gQr1lRDFBUNNceJ0aGLFD0m2zztC+6Y7vnEsVhjigXf1kqmhv7D6tONbTgvCi7LPhvmK+6ob4mS7oXHVMON6acJw3tSQzi9HjVnkgkEolEIpFo0jT9COL0Uv4JZCzPJ10qzUdGdqqz0/7Yq7m6PqiyZFHjbAfMWDCVBIq4LI9YsrnOwS9M5itxGAmF8JBFzTZGEnVS0/Pl3FG8IG3YgxEVp3smpxUA1H7xrAKA2mO2PEHUOJesYsY/kU9SdBVz01tiut6SGESGIAbLllEu2eOahiRyO2CD3cimh+ssXLZMkWGQCB4ZOrLr//fOuCyzUXbwf6oSkkvUhgzj3xjnTl9Va873WvAtnd4xu/EzTjkXPANQ0nZrnZ2U568jWeu91gNEIYgikUgkEolEIiwhiGNW6TMIes4hiGO4ZgufoFTXBqfp0xXFe2FqlME6c765p2PKFylQNME3KnTuBhSzuXGdzUMh5n5+813DsXyEIo7eIksENTq06HzhkEQDzNw7/RII03RAe2RoSiFGqPw65colQgIZpoIOI1iooeCuZgFW2z6IKaCITAMFo30QHUll1yPTVczROuX2oEPCCAlfDO4uBdFdFNQMEF9TetIGWcWcqhNw1BrdNJ6pKRS6zinn3Oe5NHm1VifMRoKYCz8r+d2tC5tk980uDVEf9MJoLTGIQhBFIpFIJBKJRFhCEKdGAzyZpKDg+B5yGMAydCXDVDVCJYjhUJUMZpKND1n0QoBiKpeuYqaILlEKAKJ3pdDHbsI/E2ucaVkakmhsuwDZQq8GTfmFyQoAag8UtfMBG0TlytqgQ0dFOxFEcmyhT1IdYxA5gqhJbi4GMXjxHWaBzWplVBXnRhghJog46DAy/f/gkaE9zjwy5HK9WxIKZuvk3JiLMag5mKYcHKZI2QAhf6ko6nEcY9S9wXHjqIMbg5pH+Idqym+UgSQDxLFIj/beKmxywMw1XdnpiNRAiPXR5LsoMehrXqbnTTOHq1AurtzXpLxPnEu/uFln3A0mneYqAFBK41zbW4VnwDUarlV4xpkdoUbH4va4diM0MleIWmzmlLUzKwColNs320wx2zGfxuPFZvbcnTAyicyYxVLoR8okW/bgIaDCwzVFR4TM8DEYETIDQUgOAVPjRYDgaipiKt9Jb/ojRCPjwuUmqXQ+F5ugmFxSNrgAGpuZMqE0n8wVavmLPz0HBMOrZVg51lEn2+AQf/zqdpc1XDLFLBKJRCKRSCRCEoI4tUKPUZr5rbOKinauP/u4N0V8kvC7EdapE8+5qffgsYwygRetaW3L4CzA0wqKGSHgFSMRGkSVtE0iR+AxTKcv8GpMs/sM2VU7eSJS3TNVOa7WAEULBZsJaDRNrPEEtGOEmgDFZhENLhscW2wWK0cQS9esaEwQgSOIGpspUug5H4cMKVAE7Awo1/bd4kaXC7gqwM7+fwA6/9sCGjvxRXx/5tempL62kulZ6jVy5jWVkLFlXpb9SstWlautoMGCJjS+T8bzFwBplItUpidRFoIoEolEIpFIJEISgjipmsR1HrkHlrEiv+kR7+gfPpknW5/JPUjHZ9ZWgR6l8xHfeTO5KoXDeSm+6B/+NABAnQWNVeqYVZho03H/dIRDQ1+nZvWJ3+DHgRmMDIlJgaLBfo6QaQCoKSPEQYe+Ozrslgb8k+kyo+jqK5xIGCEqRAgigYJ2g6EcMkyaib1p8hvZKFrWFwGPDElV6Dyk0KBtCOUmSyVMtqxP726S3aFjwEnF9SeQzmevVhoK4BXUnK48x/lSZQfo7TiCG1uXrK3xEoIoEolEIpFIJEISgjiNNI7Hv6ZO/kFpbX984p9rU24a256UNSYOqqO5rgm8ahj3Q2FCRxAG5YuJdIoMASCItiFrVyvCMDniqWkMIjJdURLQ446ibg5NWbMdKPoow4Zy8QTRBiy6NvMxiD4xKxY20wsV/kjveoPuhBK+GMQaAjJxqYDzmVKN+BhEj/2I6X0AKN4jMYgtaDCbXhiSSJcYE7zXmDqTm7Co2sniWqIElLN4L4cGB9U46hxQerQxiOPRgw8+ePvttz/00EPz5s3bf//9W/1XrVr1zW9+8+abb+71em9+85vf+c53KpW7zWWAKBKJRCKRSDSdtNNOO915553m91133bV1gPjMM8+89a1vvemmm3bfffeVK1decMEFl1xyyXe/+91er5cqIgPEqRIatpOoqG7KllrtH4EGESV2RSqLUclWbVmKo1mIdQQUMFdlS7AU9s5vo42K2A44PlST9CwyJAGLro3aUCXSA7zk2UUEkrIKN0TOUu2TFQSxiU0LWaDoYwwJUOSCDtPIkCWIKDcQf9t0I4gam5QRMrkEGUZ8EUITHBR06YpxI6GBqaDDiC9CXLYFKOKycTrljoCUj1BsKQuMkkCRxIqmVEQUB/nGXSNRZUsc4RBhhoO8eW8gTfZmxh215557vutd79p5550POuigEv+zzjrrxhtvvOSSSxYuXAgA55xzznHHHXfRRRe95z3vSRWRAeIkaYA7bQpvzk5Nr94fokYDfRFB/mssNR2icKnG8N5mEIMGDWSTmeCNAugPPBljEZMOUvAQkCw78X+8NUA4XFM+0XnXbgxBZpzNpjx0VtseON1GW4PdGSesoyZVZseL0SSywmbQbrQMRQc1oCZJeoHo+1ToNHHgiAYxJWbRiFCRoZ53M2VzJp2AxsM470zHi/Z4kHNwnJnBXBe3aMyHR2/sGNx+6FK73iju4hZ8J6BS7JA0Za7m6jQ8G2AsNxKVPdPzpUbS29V8o+zPfOYz5pcZM9oHcnVdf/nLX37lK19pRocAcOyxx5555plf+tKXMgNEWaQiEolEIpFItMbqvvvue/jhh9/2tre5lKqq9t577zvvvPOpp55KlRKCOP3Ego5S+jEQ7tNZc4zPlilG1yIzKaloUvdmiZ2acVCsW0Tywp6ohNkUwdktprUM8iFzxxAxwgoDxRqDIpNe47KuDXbGOZpq17ZmxZi2HdJPjdMJUAy2wlYQMUJKEGnIBh/BobMTSEUTzD5XYRP9WkoQVbszgX/BpUf3YGor7MjknClQRGY0Pe06gA6c5Yuxm+Lcites5EzFZad2zCHKt1ioqGz3b97xIjv0LVDypdSugmIpKDhVkDKv1X+RSrl+//vfA8CWW24ZJr7whS/UWv/xj3/cYYcd2FIyQBSJRCKRSCSaYtV1/eyzz2YcZs2aVTKhHGv58uUAsPHGG4eJm2yyCQD89a9/TZWSAeIkK/GM1PKk0unJanV7DBtG6IGzkCfyL8RLFW4epIvCXSKyaJ+/tcq4JTpiI6swQIvebKcB/BYwbP0pvujiCGuMbVg3jwxxpxu+iE+PxmczAswkF6NcjQ/cPqb7FjBQ1Bh/NoyQnETbhF2zYk0uGDSgJiP7pKSQoU1CPIvsK5FChk3QYQIZRkDR/sIzQoXNTrm2P8onQoIRepOrinGjZ4Aciw5NIPSxMXGEIhYFiiQ3WptSdkPwMaz5KrJ7iaSKrkH8CoAwy6Gced5Y+Pehg0a7UfaPfvSjRx55JE6fM2fOJz/5yVmzZgHAjTfe+KY3vSlTyec///kPfvCD/397Zx4fRZH+/+rJCXie2ZIAACAASURBVCGQgyNcIgEBgSB3BJRTVvDEC0FA4YeAF+4iynIoq664un5RVL4iKrAqiwss4AG6+EUFQQWFRQRlEZAVEBAh4cgB5OjfH53p6aquqq7u6Ukyyef98iV01VNPPX3MUPOpp6o99G4sVb5w4YK10BiMxsXFiVphgAgAAAAAEEGOHj26bds2e3lSUtK5c+eMAWKbNm0WLlwocZKdne2td0MszMnJsRYah0YVFwwQI4v8B0j5/0KsYr9Jrfj5+9GlL4FkKNMIRTKEQ2oOqy86H5olzPY3jL7IHtIRMG/asxxqxLqKWVqrlUmVptoXDI/eK4cRFJktLfSyWkojDN0lTSOW3/2seEnH5+NnwaZyyTRCptymL1LGcsmQWfJMOMIep5VoFbOtlj6U6ovsIRsGZaZ4yLblHTJ2jCvuMmROWznurMvHU2WknPMI5ZM+3lZAMx78zUG8++67n3zySblNgwYNxowZ42evQdq1a6dp2s6dO62F3333XVJSUvPmzUWtsIoZAAAAAKDqoOv60aNHc3NzjcO6detmZ2d/+OGHhYWFRsnx48c///zzwYMHY6NsEB24ylLxqhly1jirNCGEzY/TWQNazuEtXiacX7Q6Iey2iIz4x7yYz9UaZ0tYZYds0qH8kBBiXQFtHAY9G/ISs4pZF6xxDmqBwUXNoTA1IhYUy5IOaf01pC8ytYSCnxypmYe+6QPiVczMIS0K0nIjq5Cxi5ppY1owE61u5q9TZgOgnIjESFeHtnOhyx0OdUlt6KNBqAxF7jW3BxBEoCzSjyhXsxQdlsM65aotQFZCKvmE22effbZ3715CSH5+/q+//vraa68RQjp27Ni9e3dCSE5OTqNGjYYMGbJq1SrD/k9/+tM111wzcuTIp59++sKFCw8++CAhZNq0aZIuMEAEAAAAAIgmFi5cuHjxYuPvubm5EyZMIIRMmzbNGCDaGTRo0Pz58ydPnrxy5UpCSN26dZcuXdq5c2dJFxggVkaUfrhU8l83UtxqfwJl0fyhrzm6Yn58i41dJNJozN8EkmGZZmmKGHQPzKtJmLa2clr+NAQPut9QrqFZohEifuWdceiQksgkUNJxMvqiaI1zUO2jddbgy/QsZtTd1OkXzzCSIRMVu6jZ8CxcCqn8UHCQSYahclojZIxZaUzj1JqPDVcSCy1AZnsUmHElQ8GhStKhqF/utoiis2BEOEVR0OohVBuq5miE4n4VkT0h3PDkxiKHrgKLbllRLVWwnLMbTUor97v23n777bfffltUm56efuHChUCASiMcN27ciBEjdu7cGRMT06FDB2NljAQMEMuNcn28K/Vz7RNOo0zB8JE72BT5Yo3pSVzpVbZP9fJ7KOuCmsVmF5rojDE/dr5xsMSchSzlHconoHVBW2ZLcJ2/SMWMgjNeJKRsEx/WLDQkZcaLRqFoGYphQ10+7qCWh/PPDOL0MeaOaUKH9KwxY1M2O8yMeEJtQzahWmaMKTCTvyKZcRKOsaitcPwqr+Uf6tZDBtvwmmMjayb8aDkV2spVviOie2ynRviLRdzD/Yr3QiV/1Z4j3P1ratasqb4UGotUAAAAAAAABRTEikGn/hDBUUeErtxR9X+7hmQ+/gWiftn6cznopSTCCXFmJ2jWiJ5EDsVo6UG6ZoXRAokpB9IrS2wLTShjZk7ZwZjuX7RSRKd3vRGuO3EQFI1C+qOhU8b8PU5El5xpqwYzy2+rlR8G5T3eTXYlGTLGpr2iKFhWy/ZYhsqMsytj4VlIV6VYLhJnAtp2BWSTtmxHdK2oXHRojUqEqoQZ3TBfQEpwZ41tcqPUM/2d4P+V1h3exlkdgIIIAAAAAAAooCBGiur00yPcHGLR78RQOa2ByXNMVDJQRL9cbQX0j1M3N1Vj/sYkMhJa8WA1QspWmMej6YSQQFBtY5RCuU/FHETG2LBlV6Uw8h4dgEj8syQjcnITLYccOTa0FTaTBMk3Zv5G/8HeJy9ozF8F8iJXgmLEP6acla9YdY0VI7m6mreVJUyPrhIWuYe8eDiiIKEPRdvcsJ4ZfTEcVYltJlUlvbmU10qD90kqqy7/QHk+Tz36cxDDBwoiAAAAAACggIIIwoWrW7n73eYs0UlFSkOHkL5CjZUM5aIlLVUKgrGIG6wTZjUvLypG3pN2IVrUzLjQ2NfSlf3J3eZGXsukFYZ+STOCIn2iOq0+BkItQn9hBEViyn6s0KhbmzEb2XAXL1v+wpUKg4qmVIJxfG7Dk4I06yEjibHiH1tLt+WpfZxyRiO0FIZ6FBzK977mJh3Kay1dCHIB+T6liYOirwTWMy9/MRSVQ26i4JgqVBEUQ7WCj30Qaa0nCZGrzgI5lXybm3IACiIAAAAAAKCAgggii5qgKMyys7uye5N3UfbqNl3k2ZAW+LU+/n60bYtIy5ACea9sUbNgk23brm10NW0mSjrkZxkygqIgy5BZ48yqjzx90baptaCcfkGfLhAFmRXQwUrKl+jOixNPVdCEB/Zy2w2xHmr0sUW+0qS1hNiUOVG5NxXQVYaiZrGRhidX8qhDwhir6IsRUMbYaEM1sgdGk2qEUi3SEaUHFRph+FR3/RAKIgAAAAAAYICCGGlc/JAL7/dK+f1iVBMFvbcVKYUeOlLsS5QgyK6SNUQL27JYVgUsK7Rl28m6pD2aHbqJnn0pC6NcqaUkWm1MZ8ybVJhnjd0HUSDv2ZYn88sFtXxRUJ50qPwOFQNNwUbpYybM99KYWkoj5MqNjGDG+FRVEGknwkNCiOPCZEFbBzHSfK4VAhBdEMKrdXDlkOzIfNrsd1fnFQpsvdr45SScvspRbvT+7a40zeRHR2bD0mqvIWKACMoHl59uGu6YSvQFIB+DMt3rPGt5iO6/M3ijKqbWfOUu/2TkI9Hgn+zcMX3N6eslHAJaQybs1QwwYz6FSWTxhtj8cv6qFHahCVVrnpvOu9I696/2++h9htl+a7ljm2CddMTDmLkdPqoNm6SHorYaIfa3Kju4osaFyj0KRpOEtna4YV7+aeeOUIXfBtzwpJ5VCh1dOVHdxzR+oWORCqaYAQAAAAAAAxREEDZyyc5qYzNTaWqxFswdKtRqoTe88WppxUwn1iOhFMfONQt3vREXhsqlGmEIWUf8uWNiE9aYeBiN0DhSEwW5W3Mz3coFRXs5VyMUTUBbO7K3tRlzpEX2XrtEJhXaRUHmUCpDijaykdfKJUOusXAbbbqtir5oaavb2xLnlS46p5btQhe0lccj64g/s2wrZ2RRLvK1KQ4pIw4JJTZzF6Vezao3rl6/WSWBgggAAAAAACigIJYrOudvUYA7nS+cjgwFSGU1ga8picJ9s2nPDMIw6S6DIpNA4GTj46XRuZEb2e1sQl3IUhJtuZmi8CjjgEK+YKgRz9i0Fy0osb1Mjw5H2paBfa5499XxIVdRXkSbrfAFRYEGxrjithUtUpGnKvqkEXLb8tMH2YUvrE81UZDRF/nxUK5CSG+bSLOUW8vNuLWiJqqqX3nJflEmL9JfAj4Gj1ftQUEEAAAAAAAUUBBBpYLSi6S6XghXgiIp0yE0ldqyopAvOjyRsMc053XhkEGkKoc6pySaUZcKzkLp8jG2tBgq0gJFSYdlrkROeLWErpUnHTLGRM3YF7jClaJupPGMmTw5odbln2RI3Biz8p4pLfPa8nyqiIKsEwGcG2u7XHypknUi1HRlXTjVKpU72UTVxFP0g21uoCACAAAAAAAKKIiVk0gngQjSxCKGohao6oQreoXseIlpZqUhI9FtGSVGlATJeGQXwNLCHvt+PKEx5VpnVC+VjQ8dV0BrhNhfl2fqa7pGiNPmhTpVqyhwahYPIs8hM0EXTCvGJzcKIoqWa+wrDplnPIlOeCgVFOULkzVbuaQ2vBxEXaktW07fGQedjbpj3pYts7dddPUEgXC74NaKG8vXLzv3rkg5faFTlEd2enl1RIFVzFAQAQAAAAAABRRE4A+mKmC8FU0sGXJSAN3ri7I8QqlCp7jG2VQ4qB/kQeWSX8t3YesjqJqIUvOYxrIsQ531LDMWXwKe4MoYC7RTRrLk5iDKBUWzlShq+Rpnq409eO7jVJEKIlOuICiq7JVoLfcgGboytih2uhtjs5yj5ImdqGQocmVivkbItg3VU8+o7WLyHxl399qh0MVTKRaenZMg5Zs4Ais6IaXVfh0zBoiAQwQFfQXX9k2tVeJhxz/mvxzGFKpTF5QT818G2cSUdQKC+oeFnfllRpncSOTjM7UZZ76xyJ47TAyVU//UM5WiISDz7yt3vMjUEtups3PKvDNmxs+CbALBADGS/0Ry5xkdBg2i4RrPs8hG/vo+V/PRGm1nqdWtrrhDQPlQjwhO2eaEnraWjlAZbOcin8tmB5f8YZzgBknCsPSocwqdsN16tUdW0bt7Iua4soNFKphiBgAAAAAAFFAQgRhW7eGJbeWfOWxKYNIIRJV8+UzoiTeXbbO2KYuMc+k0MVuqUQ7ZcJ1nnNkXAJr2bibdQwkD9BIWbpNQODpdyRUU7a0stcR+c+kOy+aj6S5E+iJXeRHJjb7A962mEZYVhiEomiV+CooCDcxJ3jMObfO/CkKjKJ4givE464ucCvbIedJWXu7wrEm1yXJA3K8ur64e6FAQoSACAAAAAAAKKIiRo5r9/vKkKHL1PGWoxSLuMhTNck0nokzE4N/kmYgklIwYzDuiz4oNjNX5OPGx2Yts9G6lQekyEKnOF1zCQlmLxD8nV5xas0Seqhg65CmLGn3M9Cg0jhgOIpNQr+IdSrVARTNFyTBYq3NrXWmTXC1QHIAs6ZBpJdIsuRddFABhwuM0tYYnqnWhLwrMwhKovDzJ1ewfJUJIOOeMbW6gIAIAAAAAAAooiFUG/ppZH/EkEbIaYfCQHy1rTK81VgyAVSV5y4SFgiK/kIrWLlAxy5Z1WivjLmoW3i06WpE4SIVPC3r2JD/h6mZrh0wfoixDwhFcmVQqoaBI1zIypEh3ZERS5gow4q4ulXwcnlsPj7WXSgfxT2jGM7Y39bDY2VJL6Wrctqzqxup5fC2Q7VEkNLLmsvD410e6bNlxg27GiSgSx0JCiIf1y7YHgzv3YMeW6Knco5p/f4kaTU7HNjdQEAEAAAAAAAMUROAzxu9vXVFjCeP3ZMiHIS1IVTebhEnbSLdFtCyXNjQMW0IT7ZRVudhaSvbjJzgKttG2rVMmtGtabWOdUmdhqeW1EomCdHjBWvrKS9vaMyEZCZO/8SGjFAqMuc+cLrgvLIKLx7NSRf76Nbnko7KNNnOp7cYqkqG4Vt7W+FOWPigWOHW+GU+kFG6FzfbI+SAJtUlhLZXxKuhRSV90kBt5Yq1H1Lxgi2wPlCruQFl1gYIIAAAAAAAooCBWTUQKHVc/k5RHL+wVEGhyfEGRaSs1Jrbf8DrhC41Wn5pA9mNX2vISKHVG4QgFxElJ5MCeBt2K25a+IuJra9RSccn3OyS2xEGd41KYsEhonwxMW4mNiVtRx0NuWaicVyEUojRpLe1TbKZb++XqfMov4tOltZzgzVauBE65OMqGIQpAcdmycqGw3NOrU6ILX65eVIAcRAwQqxluRoKicZKDS4UuLJ6psRS7XsG0pzejYXooOxS93ZjnUzgElO56I3oXMzu1qodOzaxgloxY+jX+zda4tcETlp2a7WSCf2hsa5VWTLSi1xzTbYOVZWNd/gS01Ybqj3fKzCBYZCzyyQ2WtdGFNq6RTkoKbAXlovGZJ2PRNtfWQ5db5NAPu2CsaauVzUTb4pGturDEw5sfZy8uP1rbEfWrRTyg53yQHIfsdLXs1EQdOQz35T0KvITzoPrXoJKj69V+gIgpZgAAAAAAQAEFsXLCnUmLXEdOfdHKWTi/Ldnf4G4UTVd9sYpUqJozaWsLj9IIbXpe0MgmJZaVa6F+QoWmE94ph1Q3NzPOtlrGMz2LTRezsmiolqcKMv3TPYZc8aVKvqDIBMBKvIIrwN3Ihr1BgivP9FsOHy+2BwUVRygX8U7c7iF8ydAmCnKmki21RFJr09Ydd+FxnlNml8XYuuaHR/fLhucgxKrNGqvKfv7BcxpeR7b8DxXj8qA8+wqBKWYoiAAAAAAAgAIKYtQTAQ3Ohx5N4cFhvxupZ1ZVCkl3vFrzUBqfg76o0BHjSlQu2hyHm7BoortPSRQJnBwpscxACwZFmdPRy9qGjhQERdtNtOU8ljmhwhfmINKNuUq7YBtvFh8/MoqPuINkKA3bdkhdXAdjN5KhSKITiHBUF2yGoi0AgaLpIulQFLztSHZ9JIWcMAR27jRCBQHYeRd0eRf8bpUe88iL6RXfo1t0Qko1KIgAAAAAAABYgIIIIoNIXuNZid68Z8OFmXxltMPO2OahyqJmQjSd6oSbGugyYdFLSiKLQAcNiivcDCu2rbUfQt0pEnJCi5GMoMhi707jVFhkSI3YnyaBvsh2q3Nq5dH5C1flkgdgU5X44Qu1KKlSJZCslCRDvqvQjeClD9oEPO7ZCbtwEx4rfzoE76wvmuHZm6vbsB86BxRFbb7gyqfyK3WVGOQgQkEEAAAAAAAUUBBBCDXVL6y2XMnHbceszsc9VA1IJ4R9TZ+tKSX+cVIky7p0MAv6ogROyoNASlRMSQwqhTbRwBaB9Q9GC+JuNCjwEPybIEtLZYNDS9g234aZIeIyTmiVUhgdT0ZiCDMT0ZVAIzCmRC+5sWN6HF/BEkhl8s2umSZcyZAxc0h2JKRM3RfGwziRJR2ygiLXmD2knnn7FeaK6mKxVuHZkZ6p6FDRp1pxGB351zY6wT6IUBABAAAAAAANFMTqiFul0KbJcRePitoaCpCL35/28FjdUZ57R7fm6nmKh2WFprAg0LoYbUNXWLas0+GFgtZCNpYzUUxJpOOS5yay9z6oK2q0Jsd7RNj0RdGr7uSv72PiYaLT+SbBFFLKqTAHkR+dJjzwAY4mJ0JFI5S7EmpgrHAlW1zsIG6xd8eLK8VEvbCSDnmuXSmXoUM1iU7tUHFORDGR0QWK65eDKCVlCjqqsiAHEQPEqgc1bOAPBjgjRBdjPqEPiZ3UKORKaeTHD0B4qDKJrPamZu4b/4h9gGKM4pgp3FA8dIdhzzgzMNGa7kTDWZsrZmim2csY2Bcls6dmRkENPUPx8Ca15W/Pkw8fReExVmFOLnMDCMdYcQhIlQtHPIIBh8qIkDXmu5JPiJeN7WyTsOyYj3XlfU5ZMHzkR8u1sQfMv/iKgznp+huZfwk8n2JjX0y4CD5+Ds59/MCVBzoGiJhiBgAAAAAADFAQoxZVBa+8UVAMVWOXa4Qi2c8DFpWLdswNQ7pFjqWVqVRRyhkTsocZZ0uuvUbEUqUlMkajk8op3L4YoUbxtrEOZTPRbP/0SbnSF9n+RN3yi32Fp9gphsGW810JJnylrbxJhmWHQs2Sjkdw4lxvinon2wVdytcmpediCUNRFJQ58YIfM8v+mpU3lTQsQgghGjbKxgARAAAAACCqOHPmzPbt27du3bp3797mzZv/8Y9/lNsvWrRo8+bN1pLY2Nj//d//lTTBALES4ZBKJa0tB2wBsFlt0raUnCbvQncpDTIiE//QfJ0XLyHPcqhbg5R7dlgHE9IIKZ9WG9PMVUoiEy2T26+Jbg+d6UUESZli/c9yrLj6hOvQLKZP2VYtvfVSfZHtWSA3Rg6Poo4mrWVS7hgbaVtlSYzXhZpnbyIcX+ETioKypEMGtVcR8lMkxYcK5yh9baDo0AGBT76th7UpobYVjDetPWLoOikp7z7dkJWVdfDgQePvPXr0cBwgrl+//u9//3uzZs3Mkri4OHkTDBABAAAAAKKJKVOmNG/evGvXrl26dFFsUrdu3f3796t3gQFi5GClmYjiTV8UtZILM0y1g7G0J6W2jkqem7XPSl2qSZjy7W9sGiEVvlhuNDzwUxKZ4JlkR1ZUEZwjk0kpSGOje2QaC5cxypRF210TdEhfAcaWynET1UoDkJq4xoXE7dxEtkbVJgPzXXGbi7aq4TZRlAzLohUYO/nkByDwJks6ZIw1+lvJQcZTVXrVnhFX3/SRyz508y2o2Ir/UCn140+r8PD+Ea/kq5jvv//+SHeBASIAAAAAQBWnpKRk48aNx44da9SoUdeuXRMSEuT2UTNA1HX9vffe++yzz3Rd79Gjx9ChQ2NiYuRNtm/f/s9//jMnJ6d169ajRo1KT09nDNavX7969epz58516NBh5MiRNWvW5PrZtm3btm3bGjZseP311/tzMkRZOos0rPbiIq3QneNQOSchT9zWTZqjop5HJ/nZaulDpRfx8fdHlKckElEAZXKjNFqpYmaJKtiKviAslG8nIZYvKAqOXaUqirqg2wavLd+XQKRUetgig4scL7moo6jiCIRGXyRDw9iF9MVscGgaKAfgnHRo0SxVkg7NHhUzJqV5n/Jo+f0SN7hIPSS2u+yqrRoKgnyFELFIqt6r9k6cONG7d2/j7/Xr1587d+5tt90msY+OAWJpaemwYcOWL1/esWPH+Pj4uXPnLliwYM2aNZLx7/z58++///5GjRq1aNHirbfe+utf/7phw4ZLLrnENJg6deqzzz7bunXrevXqzZ8//6WXXtqwYYN9EHn69OkhQ4YcPny4b9++4Q8QnWb/GMIZrlFtXfbrrS/vH1NheHSFeMzHtHYeECivWXE+DMVDb38jHnQa/XqfcbYMAel4RMM1d2tK6OrIjRfpDrlJDqFywRiC26EtqAr7HaYyCehu1MiU26rNX0kSJ+UyIjRqBR9oh2Fc5OaUHcbrymNBa3iuhnFuZpYFHn0aDkXqQ2ELL5yOXLT1fZSo63qpXt6LVPLz8/ft2ycxaNy4cd26db05Hzhw4K233tq+fXtN07744otp06YNHz48IyPjyiuvFDWJjgHiggULli9f/txzzz388MOEkMWLF48aNeq555579NFHufb79++fOHHiwIED33vvvfj4+H379nXv3n3MmDGbNm0yDD777LNnn312woQJ8+bN0zRt06ZN/fr1mzx58t/+9jfG1eTJkxs1apSfnx/J8wMAAABAlWXx4sVfffWVvTwpKWnx4sW1atUihGzdurVv374SJ3PmzPn973/vLYCRI0eaf7/44ouzsrI6deo0e/bsqB8gzps3Lz09/Q9/+INxOHLkyKeffvrVV1+dMWOGxpMlFixYUFRU9Pjjj8fHxxNCWrZsOWbMmOeff37nzp1ZWVmGw0Ag8PjjjxvNr7jiisGDBy9ZsmTOnDkpKSmmn08//fStt97asmXLwIEDy+M8vcHOq/ns2O6bUbm48h5rzB67UPtISASSKTKKE77cxq52xrafqasAbBqh7NSUZpyFxmaPnKlnRnd0Ohez1j9BMdSK59Pb88zzFGH5XDEKj8Ya52/WI6HmThW6kQwFC4FE+hk/HmEXDjqfbE7Z1ioCc8qujN18FJRe4heCM1stN7Y75T8JgmMlY3+JrHd/8HeKuUaNGqmpqfby5OTk2NiykVinTp1MGYtLZmamX/F06NAhMzNz165dEpsoGCCePXv222+/HTZsmHkRCSHXXHPN7Nmz9+/f37JlS3uTjRs3pqend+vWzWr//PPPr1+/3hggbty4sWvXrhkZGVaDDz74YPPmzYMGDTJK8vPzx40bN2XKlE6dOkXq3AAAAABQ1bnllluefPJJuU3t2rV79epVPvEQQvLz862KmJ0oGCDu379f1/WmTZtaCy+66CJCyL59+7gDxP379zdu3DgQCL1p2mhu7ACUl5d37Nixnj17ch2aJVOmTImNjRXNYlcgkVAMbT7DTStU6Y7YzkJ4anSFhyvAioKhQxdnypUGeT5Dxqa90LhM9gsaE8qYjT7oUVbtrJjRIahczVAPYWQoCqPi9R2KTuNYeHoSHPTNMPD2OZHfZZFGKO+Rq8a6EsyUWzG13rIeZZKhIAzfkg7tl0p0KQRwdL5IPAny4nJR4lzJn16onHpiaeXeKFtOcXHxl19+Wbdu3bZt2xJCSkpKAoGAdcZ15cqVR48evfbaayVOKnKAWFpaqrP/1IXQNM0Y4eXl5RFC0tLSrLXG4dmzZ7lt8/LyWrVqJbJXcfjll1/Onz//448/TkxMdHteAAAAAKgknDt3rur9U7506dLt27cTQk6fPl1aWjp16lRCSN++fY1Z0NOnT/fp02fIkCGrVq0ihOzdu/fGG28cOnSosVT3yy+/XLhwYVpa2rRp0yRdVOQAsWXLlgcOHBDVdujQYceOHYQQYzubCxcuWGuNQ+uks5VAIFBUVCSyNxxKDAoKCkaPHn333Xf379/fw3lVLB71RffNGA3MlLW4kg+rarnMCwvm7ZnpdZwfnHIBT9GzfFFzsFQWhr2Vh5REh3f9hYyNWtkaZ7PElmepU4duVjozp2apUBACRCYOeqfIiRvpgfYUjqDo0KtaUN40wrLaMEQmXyRDB/2MJ+8R2ykzUrySdqho7Crnz9UFcfXiO09huJPThOost1ymv3rrMZKNfMPxnXI89Eq+zc2WLVvee+89Qki9evUIIcuXLyeEpKenGwPEmJiYiy++uEGDBoZxSkpK06ZNX3jhBWPFbVJS0vXXX/+Xv/xFntRYkQPEWbNmnTlzRlRr7jhjaHs5OTnW2pMnT1pt7G0Ze+PQsK9Tp05MTIzhgevw5ZdfPnjw4MCBA9etW2fUFhUV5ebmrlu37uKLL+ZOagMAAACgEuK4a3I08vzzzz///POi2pSUFKsAl5GRYYxnTp8+ff78+bS0NJG+ZqUiB4jDhw9XMWvRokWNGjWYtTa7du3SNM2YXLfTtm3bdevW5eXlGUvHDXujnBASHx9/ySWXfP/994xDQki7du0IISdPnjx//vytt95qNdixY8fAgQOnT58+a9YsxRP0jEcVkA+VcObNs6/xMJ4pwczanWOPqmYaIdQ6XGkThZ2xLdIpX3fkhudLSqLTsmWdNqYFGofrxRcUGfVRxtnACwAAIABJREFU1pIjKDIdS3GtLIaqVfVoaapXeAqHiw+HYkfsXVNzYivXibLcyL08wnxBrgdhRwIhTSWP0L+kQ7ZcoNiJZVHZXVa8FJImbs24O2P7grfPAvfZ84WKUh+jOgdRRJ06ddSNA84mFU1sbOzvfve7DRs2nDhxwig5d+7cmjVrsrOzzR0j8/LycnJyzIzGa6655ty5cx9++KHpZMWKFXFxcVdffbVpcODAgX//+9/Goa7rK1eubNCgQdeuXQkhU6dO3U+TkpKSnZ29f//+yZMnl89ZAwAAAABUFFEwQCSETJ8+Xdf1O+64Y8+ePQcOHLjrrrtycnKs64tHjBiRnp5+6tQp4/Cuu+5q1qzZQw89tH79+uPHj7/wwgsrVqy45557jKl6QsikSZNq1649ZsyY7du3HzlyZPLkyd9+++306dMNITotLS2TJiYmpkaNGpmZmczSlvJCL59N3LTgf3S/brt200Rz8fMwFJ6mS37N21xy4mHOlD3x4DHjintoxmO7eoJWmm7VOfhdmAGUGZf9xw9Ao/8LtdaD/zEBEE1jz9H2H9VW3IX0vzJ7nfqP8azqRPqfoivhOYb/n6cA/Dy1YCTM1fZyv5iuZedoeVApzUzYlteF7bMQfNqNh59+UL19rBQ/sPxD2o5pZXcShPraEdiY1bIvEHErV9aev8lDOqu7DsOinP7Jc4hAL/Xrv4o9F89EwTY3hJDu3bu//fbb9957b5s2bQghSUlJc+fOlSzPrlmz5po1a4YOHdqvXz9CSCAQGDly5HPPPWcaNGnS5L333hs5cmTnzp0JIXFxcVOnTp04cWLkT4XG/LSF/1mgXXl07KmZ0cjjvtllhdSMqnI4LpbFiNasMPEED5kCmedQq0jMOJcZBw91Y5qPikq+hMU8HfqfbzaykBOd8Umdh4cVLSyhptz5aFt47nxGAYrBCsyCz5igmlss6lFl3QnrRKMP2S5kU8niVnQAnMdTfBiBOWUbLlq5i0f6yRG3dbGqiS0U3AglPH3KRAH4QMQ/9X4uUomqr6gQ0TFAJIQMGzbsxhtv3L59e2lp6WWXXZacnGytXbp0aXFxsZlxSAhp167drl27du7cefLkyVatWjVu3Jhx2Ldv3//+97/bt28vLCxs27at/P2G+/btq5JZrgAAAAAAdqJmgEgIqVGjBrO7tQl3iyNN0zp06CBxGBsba33bigT5buNS+FoUV0hzhUuxjwrDm74YTo/KrhkFTWauqFmybelNZxzC4a1Z4ZixrQghnFfeUcEL4hEomqaxcexiCUuohBUUNW4r9tqzgiLliysoMmIkUy687iJlkS2L0p/iIVT1Kqmd3ImybuRdMrQV0q5cqYD0MhTVVgpaHROPonBoaSW7PmwrhU22w9bz6G869SZqePtocR+qiPao5sSHGepS3bdFKlEqL0VHDiIAAAAAACg3oklBrG540/l87NrSr7OwxwpXtCgg197E/ao5Kcub44enEoBDPNLkQnsXZXv3BCUgJquPH4BaSmJZYUh4MNRH81B2g2y5ifyIVARFu2+uK7W2Cs8WVS39NLCVlURupLUutaBUrIQamKCCm77mStYSSobStiKRSZ5uyHflLunQPPQhVZEfj9yVCKk26apc1ZinoYpRulyRpsI/vZV8o+xyAAoiAAAAAACggIIYRahl9YlUOFdInbjsQaYQsa7KsveoHDu3KGmWgtflqUiVyimJwbMoOylZK1tKolnLSe+zqY/msU4sF00oQ2p0j2UCJ6OayARFpyxD3V5IiG3tM0M4+iIdnrPTCsXVMy00dhCEXGhgriRDtly1rVnCyaKLiNzIk+icxTYPwp67ZcteUMzRtOFfLqAvUp6Sk0r1gdV1/3IQoxQoiAAAAAAAgAIKYuUgDNlP3tRWSykyotw7Qa2ShBnM26Mayzty7FhazGYKEl4aH2tsHvIWNTtES6ckOndBh6fRfXkQBUW3wfaaPtNM2iOjRfknKNoQKItyNUzhiVGT1isdKtE6JSzKlEJhuR+SIdeDUPyjLRSj9SI3utnpkBEORQijdbWMWhqevCNXj7UrediGi9RDXm2YzZUpr885chChIAIAAAAAAAooiECMVElzJwpKVR6x2sdPRuSKphZJzBDB+KobF5fvWTGPKVHQKSVRKTxbF8b/qPCclFQ2AFFuIhdXgiILXVx24s7PB8dCNXPR2VPlw5Oo41GPcaPY+SEZmuUc8U9kz/oUPDEq2qFyeKEmLrqQhid35SG70dUbUyxUstTDqEUnpDRqX5HnFxgglhucbHvFMZbKEIffRrmZly4U+6ddu+vI5VnQeBqSKsw4c9oqLF4RzThzJ8TZuWM2PNOVbO44VM6OMqnT4oZna8uEQv0hf/Ufd9RIxANH21nI7j0bQBj/pLl6xHz6p1M2aHDXr9oF4I4IRZOScod8V2G0VWweiTll1WEl75H1cbjm1rWDufS3QTi48+kpApVGYhvf/h3TCRapAAAAAAAAYAEKYqTwXZOzIJKKhNhEL3dp/cYvez10MlQALs/UEC2UxDZLOTVJajUWOqGrHYyZwqC1rsuMWZ8RmHHmt6UFPdFr+oRhlx27UQFpXzYVkHFlD5xXK1IWmWMetvDCUph54bjDF53G1XyfB6VQWOuL+uhJO3SlyalM2jq0dVpWwtcdBU7VBE43xqFyF5PLilq7kyv6yrh7oB2uqgcnPhLOpIJe7aeYoSACAAAAAAAKKIiViTBS7sLK1oskXD3PmxfRBtoOqpt7ic6SFKURgY5IbDpWOCmJhF4lwyYCijoqC5JKLrS/cI+f18hoG1JBUeiKERSZcMt0X7pWLTyRvshWKjxOIpNIpGd56cKNQKVgoyQWSrqQp3a5kgxF4SlqcgI9z5O65jHp0PhTSSFTETjlTkQbYrtDTQ+uKMKKqtxPCdvcQEEEAAAAAAAUUBCrOrToJEpTs9aKDEQym1HjUiI01x46p+vxKjiqk6hVUMI0o5ZFa7s+zhmTxKaBeUhJDJVrlOrmco0zVcDkJjL2rgTFkNCp4IpJDLSpfYxnuy5AFTjk2zpqaJK2PortatqGBwVEnismNFPLCHSq9UUyJMSz7Fd2rPPL5T3SbbnGCvKes3boS7RsorRaK/pzpoT8cfL6nAgMKqeG6QbkIEJBBAAAAAAAFFAQyxm+biIX9pgGXAHPlSvHeBQjUCfU1E18yjqfqwDcpyRKtUC7E5s45yIl0YdozXI6N9GVCmjbbZt66ER7bjtIdxplY1MWqWNFfZHTUl25qCCFw6lbF2Kbu6vjZCPa3VrSykF99CiJmcce0vi8tw2VK7xDj1Mu7VGOy2sr8xLWQ+2usbt/BgS+XUiYrlz5AVYxQ0EEAAAAAAA0UBABB7lEx03UYwQqh19zIdVLluQnb+5qUbMNJemUdaWgBYrKdVqGZOIQSXGMpGGcKZPQJVQuzXI6Xi+Covm3MpWCERTNSs7FdM4I1KRmPH3RhXNJcXng/EQ7BBfeKcm1Fv4TIG0bYcnQ+F8FJSxamsvtaZS+tATx6LJqZ1cyxdcpAD+zD6sqeJMKBoiRxeOsrIc5VDdx8KJyMzvHD8/TkItuy12zImxlToPS7xpmghAM16hq+W2SzzgLuxA50ejxtC41Zst1OnhNakzh43gxVMvOalEnE2zr8FA5PHl0hcOIkP+RcVPqCXf/gkqtXblSMKbnPXkNvA4OvIwI+bVhLUOh41HskSl0My7kDmqdu2DLXc/mW8rdJCGwCJ76MCaXRRfET9z79CEKHYtUMMUMAAAAAABooCBWIhTlRvkMrytX4cTjT60brVToU+pESc9zelsdvzakfGiEOO+nTTuhZpccp62ttYzyEZIGeZPXclfEk6Do4JPVVPjz0U4znEo9skjlRqltBAmnI+W2SrPGKj4rrWTI1irIfkIpjv9p83IWrjRLebXr54TV7137DKdWEfnj5KqvcvvAWoCCCAAAAAAAgAUoiBWFm5w/H6FFMJvKpZRHKGpMH7s5tZAr40e54Ac+bc7NzyO2ZES2B64rtW20xa4McSUYNk9KdMgylC5hUXXFU0Plruy1rgRFtq201iZ42B466khJX+Q5CFERkoMLXIbH06ucXIQnEfknGYYqwsgUtHhw7IjvKlQaTtJhOPKnl/RB9iPj8bHmde1j6qFrbz7iYzqx1am/29xU8m8jPlAQAQAAAAAABRTEcsJNrh3dRtBMUfzz0q+gL6FOoxFik81U2/Jrqdw+SXOOL8Jf1KzW1EtKIrHdguAF4dwIuzdWyXOzxplxSLWxtqJlUaUwzHLeHRCdmopnO7QzfuaiyLlhHpW/zUN4XxPq9sQdlCrBC/GU2vJLZWKbqk9a9vOmXHJPzfHquUo6dHLlWvzjaO1CA/e1vj5Xbpy4ST30FIeP3wa6nzmIMf65Kj+gIAIAAAAAAAooiJURX2Q/5w4sfdh6dJ1HKFc0VbP63Jy50KeR6+ZpG20PKYkin5Y33bnW20RrnCVNJOXMYudwBMWyWlqPsYjHMj3PW6agQF8UeJU+OS4/UPJr4KKly2rPtuqtKKnbmxNvkqG81kO6oTdN1MekQ7lP+VbYrm+u5iEM14uFeXgXuX2h4pIboSACAAAAAABgAQpixeJm1XBF4yCkOepsXnqikhHdJTJKUxKVFc1wUhKD5YaApws0F24T+jCkaBoSHT89T1kU9F9Q5NeorIAW+RR17dDWz8yqCCr4BuF85pXbylYiu/IZCcmQraUTXsPLsSunpEOxGipLOlTzKVMfPRL51MNyJNKfUN3XV+3F+eeq/MAAMRqQDkxUZofls7FmhXyaWBF5PEozvJwa45ua/geE15ZTG7EZZyo4J58a7URxmthWS90nbztjs7VhjxeFZtzbFXq2ZDPvIuTXpwrgdU7ZxWBCsYvyGBHSPsPxHPx+cTGnLOyoguaULbW+rEoJf3LZy8yyuKOIL0+JANgoGwAAAAAAAAtQEMsVV9OwPs7Z+otcdRNUh7FmJWTnw4xzcOLThxlneY9iRZOatwpnyxidkV7KS1BU7IJrE5I/RTdQZ1p5ERplAZQjfuggbubpPQXgpOVUUsmwrNbT1jxy7dBrPP7NKdOl4QiHihFUFsGOxlVUETkFPxepRCVQEAEAAAAAAAUUxEoCR9ZikcpciumDQvGPrlDJa5Rj5sWLNtBWcmJ2zwbkPSUxuIu1y+tjreUsEpHJog7SnfslLMJa3ibbouYeBcUya5mepZiwKLIvyxkV11NtFeStyqmOEEJcrSDh4rapolioYuxOmwz9LbLaoUexzaNWRznh3kePSYfsR8A7Xp8uH7MPI5F6WE6zAnp5dVRpgYIIAAAAAAAooCBWDA5pfNGAYoqkXIx0dx38y8q0CJzhpCQSytDNbjjCNc5GeiU/GYkfla3WpotIcxM9dMG+zU+aRinvV2Ivb1XWViYiu4TNfRT0SP0Rcbz1o9rKzbvjvKQz2kS+8CRD408/Vvj6o5WGKwCr96Rm7nrZciUW110QybNADiIAAAAAAAAWoCBGAa5kNpX0QaFCRleEm9coq3axqFncRRiLms3asFMSLXGY18n5pMS1lCzBzU10p/Y5LXZWceJwBZg371k6lgfmogs1whIS/Uv8UuinfJ2orUR21YUgZ9adiKWkcnlap8yv9Sc8WdKhonPz8x1GWzYeV1685Au6Sz108ibF94/hxo0br7zySpeN9Cif5PMBDBD9p02bNud7trh5xeKKDgQAAACo7tyzpJbbAWLz5s11vdivAN57772WLVv65a3c0PRwVpkCAYWFhefOnavoKAAAAIDqTkJCQs2aNSs6iugDA0QAAAAAAECBRSoAAAAAAIACA0QAAAAAAECBASIAAAAAAKDAABEAAAAAAFBggAgAAAAAACgwQAQAAAAAABQYIAIAAAAAAAoMEKsXe/bsufPOO7OysrKzs5966inH3bzPnj07Y8aMrl27dujQYdy4cQcPHmQMjh07dv/993fs2LFz586TJ0/Ozc0VuXrqqacmTJiwatUqH04jmjl06NCECRM6dOjQtWvXadOmnTlzRm5/7ty5WbNmXX755e3btx81atTu3bsZg1OnTj3yyCNdunS57LLL7rvvvqNHj5pVJSUl69ev//3vf9+vX7+srKzrr7/+xRdfvHDhgv9nVVlZvXr1oEGD2rVrN3DgwGXLljna/+c//7nzzjvbt29/+eWXcz8gX3311c0339yuXbvevXvPmzevtLQ0zB6rPKtXrx48eLD6BXH8jtq8ebN5C1555RXrLcjNzX3zzTeHDx/etWvX7OzsUaNGff755z6fT7Rx4MCBsWPHdujQoVu3bo899lh+fr7cvrCw8Mknn8zOzs7Kyho9evTevXsZg5MnT06aNKlz584dO3acOHHi8ePHRa5mz549YcKEJUuW+HAaoELQQbVh165dtWvXbtq06fTp0//f//t/sbGx/fr1KyoqEtkXFhZ26dIlMTHxnnvumTJlSoMGDerXr//f//7XNDh27NhFF12Umpr60EMPTZw4sVatWpdeeumpU6fsrpYtWxYIBAgh06ZNi8i5RQkHDx5s0KBBvXr1Hnnkkfvuu69GjRqdOnXKz88X2RcXFw8cODAmJmb06NEzZsxo1qxZrVq1duzYYRqcOXOmXbt2SUlJDzzwwMMPP5yent6kSZMjR44Ytd999x0hpEGDBtddd9348eM7depECOnTp4/kplclXn31VUJIr169Zs6cedVVVxFCnnnmGYn9zp07k5OTrR+QAQMGFBcXmwZr166NjY1t167dY489dttttxFC7r777nB6rPLMnz+fuSB/+ctfJPa7du1ibgHzHWW/BWPHjjVrJ0+eTAi57LLLRowYMWrUqLS0NE3T5s+fH8EzrNzs378/PT09IyPjj3/844QJExISErKzs8+dOyeyLyoq6t27d1xc3NixY6dNm9akSZM6ders3r3bNMjNzW3VqlVycvKDDz44adKklJSUiy+++Pjx43ZX//rXv2JiYggh9913X0TODUQeDBCrEQMGDKhdu/bhw4eNQ+Mfs0WLFonsn3vuOULI0qVLjcM9e/YkJCTcfvvtpsGECRMCgcDWrVuNw3Xr1hFCZsyYwfg5ceJEgwYNHnzwQQwQ77jjjri4uO+//944NPRUyRji7bffJoS89NJLxuHRo0dTU1P79OljGsycOZMQ8uGHHxqHO3bsiI2NNUcthw4devPNN8+fP28clpaWjh8/nhCyePFiv8+s0nHixInk5OQ+ffoYI7zS0tIhQ4YkJCQcPHhQ1KRfv37WD8i8efMIIW+++aZxWFRU1Lx58xYtWuTl5RklxnBk06ZNnnus2hgXpHfv3tYLEh8f//PPP4uayL+jiouLMzMzMzMzzVvw8MMPE0I2btxoHL7//vvm15Gu68eOHWvSpElycnJBQUEEzi8KuOmmmxITE/fu3WscvvPOO4SQF198UWT/2muvEUJee+014/DgwYPJycmDBg0yDaZMmUII+eyzz4zDr7/+WtO0Bx54gPGTl5eXmZlpfOdjgBi9YIBYXfjll180TRs5cqRZUlhYWKtWrX79+omaXHbZZfXr1y8pKTFLrr322oSEhDNnzui6fuHChZSUlMsvv9zapHXr1hdddBHjZ/jw4dnZ2cbcaHUeIObn59eoUeOqq64yS0pLSxs3bty2bVtRk6uvvjoxMfHs2bNmydixYwkhpo6bmZnZvHlza5M+ffokJyeLRIIdO3YQQh5++OGwziQaeOONNwghf//7382StWvXEkKee+45rv2hQ4c0TRs1apRZUlBQkJSUNGDAAONw/fr1hJAnnnjCNNi/fz8hZMKECd56rPIsWLCA+TXy8ccfE0L++te/cu0dv6M2bNhACHn88cdNg59++okQMn78eFEMkyZNIoTs2rUr3JOJQnJzc+Pi4q6//nqzpLi4uF69el26dBE16d27d61atQoLC82SO+64IxAIHDt2TA9+X1166aXWJt27d09LS2MmJe65556srKxDhw5hgBjVIAexuvDNN9/out6/f3+zJDExsWfPnka53b6wsHDnzp19+/Y1poYN+vfvf/78+W+//ZYQ8uOPP546dcrqkBAyYMCAgwcPWtPgVq9evWLFigULFlj9VE++++67wsJC6xXTNK1///67d+8+e/Yst8nXX3+dnZ1dq1Yts8Ro/s033xBCTpw48dNPP/Xr18/apH///mfPnrWnKhqcPHmSEFKvXr2wz6ay8/XXX5Pg5TIwHuYtW7Zw7Q3lyWpfo0aNnj17fv3118YHxO4wMzPz4osvNh267bHKY78gffr0iYmJEV0Qx+8ou8PmzZtnZmZKrnBOTo6maXXr1g37bKKP7du3FxUVWS9XTExM3759v/322/Pnz9vtS0tLt27d2qtXr8TERLOwf//+paWlxhfOL7/88ssvv9i/83Nycvbt22eWrF+//vXXX3/11Vfj4uL8PytQjlT3f7OrD8b6kvr161sL69evn5eXx11Zcvjw4dLSUmYk0aBBA9OVyKFZRQg5ffr0vffeO23atHbt2vl4LlGK6Irpum781GY4e/Zsbm6u21tgNWAoLi6eOXNm7dq177jjjjDPpfJjKILWqxcfH5+amsq9MkR8d86ePXvq1CnDod2gQYMGpkO3PVZ5/LoF5ncU9xbUr19f5HDHjh1Lly4dMmSI8aGoboguV0lJyZEjR+z2J06cKCgokHyfSL7zzW+wgoKCcePGTZw4sWfPnj6eC6gQYis6ABAWRUVFX3zxhcSgUaNGrVq1IoQUFhYSQurUqWOtTUlJIYTk5+enpaUxDQ17w8Bu7+jQOJw0aVJSUtLUqVPdn1k0YUw+iqhXr54xPna8pAzcK2wcFhQUiBwaBlyHjz766KZNm+bNm9ekSRPns4pyjNlJI03eJCUlxbh0XHtCSO3atRl7Qkh+fn5qaqrR0P7Amw7d9ljlKSwsTEpKio2l/pVxvAWS7yjHW2Dl1KlTw4YNq1mz5ty5c8M7j2hF5Sta0d76hSN3OGPGjAsXLvz5z3/27TRAxYEBYnSTk5PDzDAyTJgwwUj0TkhIIITk5eVZa42ZzRo1atgbOto7GnzyySdvvvnmhg0brBMWVRL5LRgyZIixGMXbLWC+yo3mxiXlOjQO7Q7nzJnz7LPPPvjgg/fcc4/aaUU38fHxBQUFpaWl1tyGM2fOpKamcu25V9v+wDMGZ86cMR9vtz1WeUQXpGnTplx7xe8c+z2yf8Pk5+dff/31Bw8eXLt2baNGjXw4mSjEx+98yReO1eHmzZtffvnlVatWWbNiQPSCAWJ0U7du3V27dkkMTGkwIyODEJKTk2OtPXnyZEJCAiNBWe2NlDWrvVklcmhWzZo1q3HjxqtXr169erVp+dlnn02dOvWuu+669NJL3Z1qJUZ+C0xRyvGSMiQnJ9esWdPxFqg4nDt37qRJk+699945c+YonlS0k5GRUVJScurUKfMjUFpampubm52dLbInvOc5MTHR+ICYVzszM9M0yMnJadiwobceqzwZGRnGFUhPTzdKdF3Pzc3t1q2byJ5Iv6PMW9CiRQurgXkLDAoKCq655ppt27atWbPmiiuu8PWcognR9dQ0jTvnnp6eHhcXF853/rPPPpuenv7FF18Y81rGUP6rr76aOnXq7bffbmyzBaKJilsfA8qVPXv2EELuv/9+s8RY0da1a1dRk8zMzBYtWlhLRowYEQgEfv31V13X8/PzExMTrTsg6LqenZ2dlpZmLHweOnRopgVDNkhJScnMzPzkk098Pr1o4OTJkzExMbfeequ1sG3btk2bNhU16dmzZ2pq6oULF8yShx56iBCyc+dOXdeNJNGOHTtam9x4443x8fHGSnODN954IxAIjBkzxrogvcrzwgsvEEJWr15tlmzevJkQMnPmTK69sazHumGH8QHp1q2bcfjBBx8QQubMmWMa/PrrrzExMXfccYe3Hqs8xq+RDz74wCwxVpM89thjXHvH7yjjp+YLL7xgGvz2228xMTHDhw83SwoKCgYMGBAfH79mzRqfzyfaOHLkCLMwX9f1zMzMSy65RNSkU6dOzM4V9957LyFk3759uq4XFxenpKT06NHD2uR3v/tdjRo1jIXPY8eOtX7nN2vWjBBSu3btzMzMd9991+fTA5EHA8RqRFZWVqNGjcwtxNasWUMIefbZZ02Djz76yDp0e+SRRwghX375pXF48uTJlJQU67Y4N910U0JCgrlp2X/+859AIDBu3Dhu78a3f3Xe5kbX9auuuqpWrVrmvrJbt24lhDz00EOmwaeffmpuaqjr+uzZswkhq1atMg4LCgqaNWtm3WbCmC/+7rvvjMOjR4/WrFnzhhtuMA0WLlwYCATuvPPOajU61HX9wIEDsbGxw4YNM0seeOABTdPMbcZzc3Pfffdd6wYo7dq1a9y4sblvuTEcMfdkMTIRu3fvbl5JYwC0cuVKxR6rG8YFse6cOnHiROsFOXXq1Lvvvmv82jGQf0cVFBSkpaV169bNvAUvvvgiIWTFihXG4fnz56+99tr4+Pj3338/0mcXFfTq1Ss1NTUnJ8c43LRpEyFk+vTppsG6dev+9a9/mYdPPfUUIeSjjz4yDs+ePduwYcPOnTubBqNHjw4EAnv27DEODx48GB8fP3ToUG7vx44dI9jmJprBALEasXbt2piYmCuuuGLZsmUvv/xyenp6y5YtrVJTvXr1WrdubR4eP368UaNGjRo1evXVV995553OnTsnJCRs3rzZNNi1a1etWrXatm379ttvL1y4sHnz5unp6dZXrVjBAFHX9W+++SYxMbFjx45Llix57bXXmjRpkpGRcfToUdOgffv2KSkp5mFeXl7r1q1TU1NffPHF5cuX9+nTJxAIWKWRQ4cO1a1bt1mzZgsWLFi8eHFWVlbNmjXN8eK///3vQCCQkJAwduzY8RbeeOONcjvlCsTu2ZneAAAJbElEQVTYyPqee+5ZtWrV5MmTDRnVrDV27pg0aZJZYrz74corr1y2bNlLL72Unp7eqlUr6yaUr7zyCiHk5ptvXrly5axZsxITE/v06WMdect7rIYYG1kbF+Thhx8OBAKjR482a40fSH/4wx/MEsfvKGP38ptuumnlypVPP/10YmJi7969zVtg7HrYrl278TQ//PBDuZ1ypeKLL76Ij4/v2rXrP/7xj3nz5jVs2LBp06YnTpwwDVq2bJmRkWEenj59OjMzs27dunPnzl22bFnPnj1jY2PXrVtnGvz000+pqaktWrRYtGjRW2+91aZNm+TkZOurVqxggBjtYIBYvfjggw8uu+wyTdMSExNvueWWQ4cOWWv79+9/0003WUv27t17zTXXxMXFBQKBbt26mRvom2zZsqVXr14xMTGxsbH9+/e3igEMP//8c5cuXV5++WX/ziYq2bBhQ/fu3QOBQFxc3ODBg83f4ga33XZb7969rSWHDx++7bbbjCTxDh062Gdqvv/++6uuuiouLi4mJqZHjx5fffWVWfX111934fHoo49G7gQrD8XFxX/+85+NBLV69epNnz7dfKmMruvff/99x44dZ8+ebW3y/vvvd+jQwfiA3HrrraY6bvLaa68ZCXC1a9ceP34882JJeY/VkOLi4qeeesq8INOmTbNekB9++KFjx47/8z//Y23y/vvvS76jdF1//fXXjVuQnJw8btw46y2YOXMm94G3/qytbqxbt65Lly6BQCA+Pv66667bv3+/tfaGG24YOHCgteTnn382poY0TevYsaN1QsNgx44d/fr1i42NNYbyxi6VXE6ePNmlSxf5yxVBZUbTeZskg6rNhQsX4uPj1e11XS8uLpbselpSUqJpGrbCVqe4uDgmJkbTNPUm8rtWWlpaWlrKbCkCDNw+8I724RtUN8r/FgArRUVFsbGx/n7h6LrObOoEqhgYIAIAAAAAAApIPgAAAAAAgAIDRAAAAAAAQIEBIgAAAAAAoMAAEQAAAAAAUGCACAAAAAAAKDBABAAAAAAAFBggAgAAAAAACgwQAQAAAAAABQaIAAAAAACAAgNEAAAIcejQoeXLl3///ff+ut2wYcPy5csLCwv9dQsAABECA0QAAAgxbty4YcOGlZSU+Ot2z549Q4cOfeGFF/x1CwAAEQLvYgYAgDI+/PDDa6+9dsSIEYsXL/bXc1FRUZs2bY4fP/7jjz82bNjQX+cAAOA7UBABAKCM6dOnE0KmTZvmu+e4uLjJkyfn5eU988wzvjsHAADfgYIIAACEELJx48bevXt37dr1m2++iYT/nJycRo0aJSYm/vLLL0lJSZHoAgAA/CK2ogMAAIBw+fjjj0+fPt2kSZMePXowVZ988klOTk7Dhg2vuOIKuZP58+cTQkaMGMGU79u3b/v27YaHQ4cOLVmyZO/evbVr17766quvvvpqw+bcuXPLli3btm3b2bNnO3fuPHr06Fq1ajF+0tLSBg8e/O67777zzjt3332397MFAIByQAcAgCjnn//8JyEkISFh27Zt1vKPPvooEAjExsZu2rRJ7qGoqCg5OZkQ8sMPPzBVc+fOJYRcd911S5curVmzpvX7c8yYMSUlJbt3727ZsqW1vGXLlkeOHLH38sorrxBCBg8eHOb5AgBApEEOIgAg6rnlllvuu+++8+fP33777WfOnDEKjx07NmbMmNLS0lmzZvXq1UvuwRD/6tSp07p1a67B7t2777zzzqFDh/7f//3f+vXrp0yZEggEFi1atGjRouuuuy41NXXhwoUbN258/fXX69atu2/fvkmTJtmdXH755YSQjRs3FhUVhXfGAAAQYSp6hAoAAD5w7ty5zp07E0KGDh2q63pJSUn//v0JIYMGDSopKXFsPnv2bEJI37597VWGgkgImTFjhrX8/vvvJ4QEAoGsrKzCwkKzfO3atYSQuLi406dPM66Kiori4+MJIVu3bvVykgAAUF5AQQQAVAUSEhKWLl1au3btZcuWvfHGG3/6058+/fTTjIyMRYsWBQLOX3SHDx8mhGRkZIgMkpKSjDXOJrfeeishpLS09IknnkhMTDTLBw4cmJKSUlRUtGvXLsZJbGxsWlqa2R0AAFRaMEAEAFQRWrZsaSw0mThx4tNPPx0TE/OPf/xDMuaz8ttvvxFCjNEbl1atWjEJiI0bNzb+0rVrV2u5pmmNGjUihOTk5Nj9pKenE0KOHz+uEhUAAFQUGCACAKoOw4YNGzVq1Llz50pLS2fOnNmnTx/FhsXFxYSQmJgYkYEx5rNSo0YN4y/2ja+NKm6iYVxcnKgKAAAqDxggAgCqDmfPnt2yZYvx9wMHDqg3rFu3LhFofgaapnmosnPixAlCSP369dWbAABA+YMBIgCg6jB27Ngff/wxKysrKSnpb3/721tvvaXYsEGDBoSQkydPRjI6QoJjUKM7AACotGCACACoIsybN2/58uV16tR59913X375ZULIfffdt3v3bpW2Rh6hfVmJv+zfv7+goCA2NrZjx44R7QgAAMIEA0QAQFVg165dkydPJoTMmzcvMzNzzJgxo0aNys/PHzp0aGFhoWPzK664IjY29vDhw7/88kvkgjSmv7t06WJsyg0AAJUWDBABAFGPORC89957hw8fbhTOmzevTZs25sBRTq1atYx38X3++eeRi9NwPmjQoMh1AQAAvoABIgAg6jGmktu3b2/sd22QlJS0bNmyGjVqzJs375133nF0Mn78eELIkiVLIhRkUVHRihUrYmJixowZE6EuAADAL2IrOgAAAAiL3377rW3bts8888zNN99sbj1jkJWVtWLFiu++++706dOOfm655Zb69euvXbv2+PHj1lXGPXr0eOaZZ1q0aMHY16lT55lnniGE2Dfinjhx4rFjx9q1a2ct/PDDD0+cOHHDDTc0a9bM1QkCAED5o+m6XtExAABApWDOnDmTJk2aOXPmE0884bvzfv36ff7551u2bGE21gYAgEoIBogAAFBGUVFR+/btjxw58tNPP9WrV89Hz59++umAAQPuvPPON99800e3AAAQIWIef/zxio4BAAAqBTExMe3bt9c0rX79+s2bN/fR87p16xo2bPjEE0/Url3bR7cAABAhoCACAAAAAAAKrGIGAAAAAAAUGCACAAAAAAAKDBABAAAAAAAFBogAAAAAAIACA0QAAAAAAECBASIAAAAAAKDAABEAAAAAAFD8f1bxO04QX3zTAAAAAElFTkSuQmCC"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap(xvec, zvec, Array(Ex[:,iycenter,:])/1e6,\n",
    "    xlabel=\"x (m)\", ylabel=\"z (m)\", title=\"Ex at y=0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "baba4eb6-97dd-4e47-bd08-2845e129005e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yNm3atGnTpjm7CtwcAREAcHMvbjOr+Ig/NPGsyUivt9KO5Rh/3K7WqmjFRXnW/ztwHX/6AICb2JAhl52Wr7fU/DysJ0ER4sM2Rucw+askc0820xHhoQiIAICb+NM2s2GQ8oSbbm1ze16qmNNVVvdTBq4y0686uxrAGTzxzgcA3N6y03JrpnzzQVX31HeJIC+xrI+Wb8q41cZVw9nVAOXOU299AMAtSCEm7zSbV1KG1PTo94jaAcp3vfT9F+ToJNNiqBkexqNvfgDALy0/LZOz5KSWbrwxdkl1CFNmdtEWnbReSTadXQtQrjxs7jEA4E7e2GW2qKQMYgGvEEKIUdHqwRz5111Ww2BldB1+JvAUBEQAwP8sOWVtPye/66XRfXjd5FbakUti3Aazhp/SNdxpP5iwsLCEhITo6GhnFYByc/Hixfs8ePD+ERABAP/z111Wy8oK+//dSBFiZmftyEU5cq2xbZBew98579wzZsyoUqWKaTLYfXP5+fk+Pj7OrqLUREREqKozb0MCIgDgPxJSZXKWXBBD92FxvrpY2ltvvdgYtMbcOFB31t6Qb731lnP+YTvIzs4OCQlxdhXug8+IAID/mLrXrF9RGUz34c2E+4rFvbRDF+WYJJM1zXB7tAIAACGE2JklE1LlC01VVi/fSqvKysedtIUnrck7GeeFm2OIGQAghBBT91oRvizUvYPRddR92fKNnVaTYOWhWvys4Lb44wYAiOOX5YIT1h+aqN6as0txeVNaa/0ildHrzR1ZDDXDbREQAQDivb1WgEOMb8Cbwp2piviyux7ppwxPNLPynV0NUDZoCwDA02UXiFlHrF83UAMdzi7FJoK8xOJeWnaBHJFoGJazqwHKAAERADzdxwetAlP8tiHvCHehQZAyt6e+IUP+cSsLVuCGaA4AwKMZlvj3T9ZDtdRaAaxevju9qylvPqj940fr/w7Riwh3wypmAPBoC05aKbnym8YsTrkXLzVTd5+Xv9tsPhCstAslYcN90IMIAB7tg/3Wg5WVjmGEm3uhCPFpF61hkDI0wUi7yqJmuA8CIgB4ruQsuSVTPvcA7wX3zlcX3/XSDEs8lGAWMB0R7oJGAQA81z/2W+G+Ynht3gvuS5S/MqeHnpwln95MQoSboFEAAA91vkDMO2E9WV/14q3gvvWIUKa10WYesqb/xIIVuAMWqQCAh/rssFVkiXH1iYel49kH1L3Z8g8/mI2DlW7hzOmEvdEuAIAnkkJ8ctDqX0ON8ifKlJrpHbUWlZRhCcaJyyxYgb0REAHAEyWkysMX5QTO1itVPpqYH6PpqhieaOYZzq4GuA80DQDgiT46YNUKUPpWp/uwlEX6KfN66nuz5YSNLFiBjREQAcDjpF8VS09b4xuoKvmwDHSuqnzQXpt91PrbPhaswK5YpAIAHmfmIUsR4ol69BGUld82VHedly9tN5uGKDHViOGwH1oHAPAsUojPDltxUWpoBWeX4tb+2UFrU0V5eC0LVmBLBEQA8Czr0uTxy/JJdrcpY16qWBijV9CVwWvMXBaswG5oIADAs8w8ZFX3U3ox7ln2wiqIxb20I5fk6CSTXkTYCwERADxITqFYdMp6rJ6ikQ/LRcvKyr87aotOWlP2sGAFdsIiFQDwIF8ftfIN8TjLU8rR2LrqrvPylWSzaYjSP5JgDnugjQAADzLzsNU9QqkdQEwpV++31XpXUx5Za/x4gaFm2AMBEQA8xZ5suTNLsrtN+dMU8WV3vYqP8lCCmVPo7GqAEqCZAABPMeuIFegQQ2vS8jtBJW/xXS8t9ap8ZK3BihW4PpoJAPAIhiW+PmoNr61WYPK5kzQJUb7oqq06Iydu4xQ+uDoCIgB4hDWpMiNPjK5Ds+9MQ2uqr7VUp+2zZh5iUTNcGi0FAHiE2UetmgFKl3CWpzjZpJbaQ7XU3202t51jpBmui4AIAO7vUpFYfMr6VR2FeOh0ihCfd9HqV1SGrDFTc8mIcFEERABwf/OOW1cN8SvGl12Dv0Ms7q0ZUsatMa9yCh9cEo0FALi/2UetdqFK/Yp0ILqKKH9lUYz+4wU5dj1rmuGKCIgA4OZOX5HfZ0iWp7iaDmHK9A7a/BPW27tZsAKXw24HAODmvjkmVUUMq0VAdDlP1lf3X5CvJpt1AsXI2vyC4EIIiADg5r45ZvWqpoRWcHYduJn322pHL8nHN5i1A5TWVZgDAFfB5xUAcGcHc+SebPkwvVOuSlXEl930Wv7K0AQz/aqzqwH+iyYDANzZN8esCroYzPF6Lqyil1jaR8s35aA1Rh6LmuEaaDIAwJ3NOS4HRKqBDmfXgduqHaAsitH3ZsvR602LVc1wAQREAHBbyVny8EX5cDQz22ygU1VlRidtwQnrtR2c1AznY5EKALitb45ZFb1Ev0j6AuxhTF31YI58e7dVr6Iypi6/NTgTAREA3JMlxdzjckhN1Udzdikosbdaa0cviV9/b0b5K105OBvOwwcUAHBPm8/KM7mS3fXsRRFiVjetVWVlaIJx6CKzEeE0NBwA4J7mn7RCvEXPCHqhbMZHE4t66RW9lAGrzKx8Z1cDT0VABAA3ZEkx/4QcFKU6aOZtKKyCWNZHy8pn4xs4DS0HALihzWdlaq4cwfiybTUKUhb31ndkyTFsfANnoO0AADfE+LIb6PLfjW9e2sbGNyhvrGIGAHfD+LLbGFNXPXlFTNph1vBXnmnMrxPlh4AIAO7mv+PLbG/jDl5roZ7Nk8/9YEb4iodqkRFRTgiIAOBu5p+0ghlfdiMftNNOXpaj15vhvkqHMH6tKA98FgEAtyKFWMD4snvRVfFtD71xkDJwtXEghxUrKA+0HwDgVrZmyjO58qGaNO9uxd8hVvbVK/sovVaaKblkRJQ5WhAAcCvfnbICHCKmGgOR7qayj1jRRzMs2X+VmVPo7Grg7giIAOBWFp6UsZGcv+yeogOV5X30k5floNVsoI2yRUAEAPexL1seuSgfqkn3odtqVVlZ3Fvfek6OWGsYlrOrgfsiIAKA+1h4UnprIjaStt2ddQ9X5vTQVqbIxzeYzEZEGaERAQD3seik1auaEuBwdh0oY4Oj1A87aF8etV7cyiErKBPsgwgAbuLEZbknWz77ANMPPcJvG6pZ+eK1HWaQl/JKC7p7UMoIiADgJhaclLoqBtYgK3iKV1uoFwrkqzvMYG/xdCN+7yhNBEQAcBOLT1ldqiqVfZxdB8rR++20i4Xi91vMQC8xug4ZEaWGgAgA7uBcvthyVv6tHePLnkURYkZn7Yohnthg+moc1oxSw18SALiDpactU4qBNdjgxuNoiviymxZbXX1knbk8hWXNKB0ERABwB0tOyWYhSq0AAqIncqhibk+tS1VleKKRmEZGRCmwWUC8dOnShQsXTJNV/QDwP3mGWJNqxUWRDj2XjyYW99ZbVVbiVhsbMsiIuF/2CIirV6+Oi4uLiIioWLFiSEjI4cOHnV0RALiQNanWVUMMirJHk44y4qeL5X30JiHKgFXG5rNkRNwXe7QmW7duPXbsWExMTLdu3ZxdCwC4nCWnZXU/pWVlehA9XaBDxPfV61VUYuONHzLJiLh39giIr7766o8//jhr1qwuXbo4uxYAcC2WFMtPW4OiFOIhhBBBXiKhn14/SOmzkoyIe2ePgAgAuJUfMmVGHvtj43+CvER8Xz06UOkbb2wlI+Ke0KAAgL0tOW0FOkT3CDoQ8T8h3mJNPz06UOkTb2whI+Luuf9G2aZprlu3bty4cTdebNGiRbErnqmgoKCgoMDZVeAuFBQUsIrfdsr6RltyUu0VIWQRN3OpcY8bzV+IFT3FwES11wpjYQ/ZNczNYyLvaLfh7e19t09x/4AopTxz5kxiYuKNFzVNKyoqclZJrsMwDH4O9mIYhpRSVen7t5MyvdFO5SoHLno928AoKrJ9oHEdbnOj+SliUVdlcJI+dK0yv4vZKdRydkVliHe02yAg3oSu66NHj54+fbqzC3FFhYWF/v7+zq4Cd0FRFF3X7+FWhxOV6Y229pSlKuaQOhX8K5TRv+CJ3OlG8xcicYCIjTce2uBYGKP3qe62UxF4Rytdtv94BACebOlpq20VJZR0iFsLdIjVsXqHMCVutbHwpDt3IqIUERABwK4uF4kNGbI/65dxJ366WNJLj6mmjFxrfnWUjIg7s0ezYhjGhQsXLly4kJeXJ/574N7FixedXRcAONOaVKvAFANquO2gIUpRBV0s6qUPilLHrDf/9RMZEXdgjzmIGzZs6Nmz5/Uv27VrJ4QICwvLyMhwXlEA4GRLT8tIP6VpCAERJeKlim97aBM2iqc3m6dz5ZTWmrMrguuyR0B84IEH5s6dW+yij4+PU4oBAFdgSbEyxRpaUyUeouQ0RXzSWQt0iKl7rEJTvN9O4+8HN2WPgBgaGjp8+HBnVwEALiQ5S57NEwOYgIi7pAjxt3ZaZR/llWQzK1/M7KI5+CPCL9gjIAIAilmRYvnqons4HUC4Fy83V0MriN9sNM8XyLk9dT/iAH6OTw0AYEsrUmT3cKUC7+u4V+Pqq/N6amvTZI/lRmaes6uBiyEgAoD9nMsXO7Jkv0jacNyXITXVdf3145dluyXG4YtufhYf7gqNCwDYz8oUy5IiNpLxZdyvdqHKhgG6JUWnpcYPmWRE/AcBEQDsZ0WKbBik1AogIKIUNAxSNsdpkf5KjxXG/BNskQghCIgAYDuGJVanWv3oPkTpifBV1vfXe4QrI9ea7+0lI4KACAB2syVTXigQTEBE6fJ3iMW99d82VP+0zRz3vVlISvRsrH8DAJtZmWL5O0THMHoQUco0Rfyzg9Y0RHl6s3kwRy7qpVfhSApPxQdQALCZFSmyVzXVm2PSUDbGN1CX9NL3ZcuOS40DOSxb8VAERACwk9RcuTdbMgERZSo2Utkcp1tStFtsLE8hI3oiAiIA2MmqVCmE6FudgIiy1ThY2TZIb11FiVttTNljERI9DQERAOwkPkU2Dlaq+xEQUeZCvEV8X/3pRuqft5sjEs0rRc4uCOWIgAgAtmFYIiHNovsQ5UZXxT/aa1901ZadttovMY5w2orHICACgG1sOycvFIi+bHCD8jWmrrpxoH6pSLRebHx3iv1vPAKtDADYRvwZy08XndjgBuWuVWVlx2C9bRVl6BrzpW2mQUp0dwREALCNVWdk9wiFDW7gFJV9xIq++l9aqO/ttWJWGmlXGW52ZwREALCHrHyRnCX7VKfdhtNoivhrK215H/3HC7LFIiMhlYzotmhoAMAeVqdalmSDGzhfbKSyc4heJ1DpE2+8ksxws3siIAKAPaw6I+sEKnUCCYhwvkg/Jam//kIT9e3dVrflxukrdCW6GwIiANiAFGLVGasP3YdwGQ5VTG2jxcfqRy/J5ouMeSfoSHQrBEQAsIHd5+XZPEFAhKvpXU3ZM9TRtooyItF8cgObabsPAiIA2MDqM9Khim7hNNpwOWEVxIq++t/baV8fs1osMn7IZLjZHdDWAIANrE612ocqAQ5n1wHcjCLEcw+oWwfpvrrovMx4dYdZxICzzREQAcDV5Rpi01nZmw1u4NqahijbBul/eEB9e7fVbomxL5uuRBujuQEAV7c+XRaYonc1JiDC1Xlr4t022rr+ek6BePA74+3dFpvg2BQBEQBc3ZpUq5K3aFWZgAh76FJV2TNUH9dAfSXZbE9Xoj0REAHA1a0+I2OqqSr5EPbh7xDTO2gJ/fTsAvHgd8bknRazEu2FgAgALu1MrvwpR/ZmgxvYUI8IZe9D+m8aqm/sMluywNlWCIgA4NJWp0ohmIAIu/LTxQfttY0DdSlEx6XGM5vNS+yVaAcERABwaavPyEZBSnU/AiJsrH2osnOI/npL7ZNDVsN5HLtiAwREAHBdlhSJaVYvxpdhf16qeLWFuneo3iBIjEg0+60yjl5ixNl1ERABwHXtOi+z8kWvarTVcBP1KioJ/fTZ3bSdWbLJAuO1HWae4eyacDM0OgDguhJSpZcqulalBxHuQxHiV3XUQ5qNA3IAACAASURBVMMd4xuo7+yxGi0wFjDi7HoIiADgutakWu1CFX9O2IPbqeglPmiv7RisR/mLYYlmzxXG/guMOLsQAiIAuKg8Q2w6KxlfhhtrGqIk9dfn9NCOXBQtFhm/3WSey3d2TRBCEBABwGV9f1bmmyKGDW7g7kbWVg8O119toc0+YtWdW/TuXivfdHZNHo+ACAAuKiHVCvISrasQEOH+fHXxWgv10HB9cJT65+1mg3nGV0ctizFn5yEgAoCLWpMqu0eoGvkQHqOan/J5V23HYL1uRfGrJPPB74xVZwiJzkFABABXlJkn9pyXvRhfhudpXklZE6vH99WlEH3jjZ4rjK2c0VfuCIgA4IoS0ywpREwEAREeqk91Zcdg/avu2qkrot0SY9Aac282MbH8EBABwBUlpMqaAUrdigREeC5VEaOi1QPD9I86aTuyZItFxsi15k85xMTyQEAEAFeUmCbpPgSEEA5VTGigHh2hv99W25BuNVlgjFpHTCxzBEQAcDlHLspTV2RPAiLwXz6aeO4B9dhIx7tttMQ0q8kCY0Qig85liIAIAC4nMU0qQvSIoIkGfsZXF39sop4Y6ZjWVtt41mq+0Ihbbf7AEpYyQOsDAC4nMU02DVFCKzi7DsAl+eriDw+ox0c6Puyg7c2W7ZcYPZYbiWd1cmIpIiACgGuxpFiXZvVkgxvgtnw08XQj9cgI/fOu2tk8MWJThRYLjS+PWkWWsytzCwREAHAtu8/L8wWiJ+PLQAk4VDG2rrp/mP5V+zx/hxidZEZ/a0zbZ10sdHZlNkcDBACuJTFNOlTRpSo9iEBJKUL0DTc2DtQ3x+ltQpWJ28zIb4qe3WIeu8Sw8z0iIAKAa0lItdqGKv4OZ9cB2FD7UGV+T+3wCP2J+upnh61684yBq401qZKceLcIiADgQgpMsfEsG9wA96V2gPL/2mlnRjmmtdUO5ojeK41G841//mRdKnJ2ZfZBQAQAF7IlU141RAwTEIH7FugQf3hAPTRcX9ZHr+kvnt1iVvu66Dcbzd3n6U+8M93ZBQAA/icxzfJ3iLah9CACpUNVRP9IpX+kfvSS/OiA9dlh6+ODVttQZUIDdURt1Y8cdAt8SAUAF7I2TXapqjhom4HSVidQmdZWSx3lmNVN0xXxxAYz4qui324yk7PoULwJGiEAcBWXi8T2c5IDVICy46OJ0XXUjQP1H4fpT9RX5x23Wn9nNFtofLDfysp3dnGuhGYIAFzF9xmyyBI9WKEClL1GQcrf22mpoxxzemjhvuL5rWa1r4uGrDEXn7IK2Wr7jnMQz58/v379+qSkpN27d2dlZWVlZfn7+4eGhtasWbNLly7du3dv2LBh+RQKAG5vbZpVyVs0CyEgAuXEWxMja6sja6spuXLWETnriDV4jVXZxxxZW/1VHbWdB88GvmVA3LFjx4wZM2bNmpWf/7Mu13Pnzp04cWLr1q3ffvutEKJhw4Zjx44dP358cHBwmRcLAG4tMU12i1BVz31LApwm0k/5S3PlL83VLZnyy6PWnGPW9J+s6EBlVLQyKlptEORxt+VNAuLmzZuff/75rVu3CiE0TWvSpEm7du3q168fEhISEBBQUFCQnZ2dlpa2devW5OTkAwcOTJw48d13333ttdeeeuoph4OtXQHgXpwvEHuz5a/rM/MHcKb2oUr7UO3v7bSVKdbXx+S0fdZfd1nNKymPRKsjaik1AzwlKRYPiM8///z/+3//T1GULl26jB49etiwYUFBQbd6smma69evnz179sKFC5977rl//etfmzdvrlSpUhnXDABuaF2aZUkmIAIuwUsVg6LUQVHiUpG2+JQ155j1SrI5cZtoXUUZUVsdXkup4e/mt2rxj6rJycn9+/dPTk5ev379uHHjbpMOhRCapvXo0eOzzz5LTU2dMmXKuXPncnJyyrJaAHBba9NkNT/FA0eyAFcW6BCj66jL++jpjzo+6qQFeomXtpk15xhtFxvv7rXc+KxnRf78fMK0tLSIiIh7e62cnBwvLy9fX9/SKKzU1K9fPyYmZvr06c4uxBVlZ2eHhIQ4uwrchdzcXF3Xvb29nV0I7kIJb7QG84w2VZRZ3bRyKAm3x41mR+X2jnYuX3x30pp/wlqXLoss0TREGRylDKmpNq/kVp/uig8x33M6FELcvrsRAHArqbny0EU5sRkTEAFXV8VH/LqB+usGanaBWHLK+u6UfG+f9cYuK8pfGRSlDIpSO7vFXvccMQMAzpeYJoVgAiJgJyHe4rF66mP1RK6hrTpjLT4lvzpq/eNHK8hL9I1UB0QqfSPVSrbthiYgAoDzrU2T0YHuP+0dcEt+uhhaUx1aU5hS23RWLjttLTkl5xyzNMVsF6r0i1RjI5XmlRR73d53DohFRUXz5s1btWrV8ePH8/LybvqY77//vkKFCqVdGwB4inXpsnc1e719AChOU0SXqkqXqtq7bcTRS3LZabkixXpjl/mXZBHuK/pUV/tWV2Kq2aNb8Q4B8ezZs/369du5c+ftH2ZZnEoDAPfo6CV5+gpHMANupU6g8twDynMPqLmGSEy14s/I+DPy88OWqpitKit9qiu9qqntQ113tuIdAuIzzzyzc+dOPz+/X/3qV61atbrVMhSWegHAPVuXJhUhuoW76hsFgPvgp4u4KDUuSgghDl+U8Wfk6jPW3/dZb+6y/B2iS1UlppraM0JpEuJaY9C3C4iFhYWLFy/WdT0hIaFdu3blVhMAeJR16bJBkBLuWluEASh99Soq9Soqv2+sFlpiy1mZkGYlpMo/bTMNS1TxEd3C1e4RSvdwl9gP9XYB8cqVK4WFhU2bNiUdAkAZkUKsS7MeqkX3IeBBvFTRNVzpGq79tZW4VCSS0qy16XJtmpx/wpJCVK0guoarXcOVLlWVRsHO6Vm8XUAMDg729/c3TbPcqgEAT3MgR2bkie7hzu8wAOAUgY7/jUGfyxfr06316TIpXc49bkkhqviI+L56y8rl3UTcLiAqijJs2LAvv/wyIyOjatWq5VYTAHiOtWlSVUQ3VqgAEKKKjxhWSx1WSwghsvLFxrPW+nRZp6ITPkDeoUl65513IiIiHnnkkfPnz5dPQQDgUdalyaYhii22vQBQnir7iMFR6t/baYEOJ/zrd1jFXLVq1S1btowcObJ27do9evSoW7dupUqVfvmw559/3uFwRvkAYGeWFBsyrDF16T4E4FruEBCllDNmzNixY0deXt533313q4f97ne/IyACwN3aky2z8kV3NrgB4GLuEBCnTZs2efJkIUT16tWbNWsWFham6zd5yk0vAgBuLyld6qroXJUVKgBcy50DohDizTffnDhxoqZp5VISAHiKtWlWy0pKRS9n1wEAP3e7cY2cnJzMzMwaNWr85S9/IR0CQOkypdiYIbtH0H0IwOXcLiD6+fmpqhoVFVVu1QCA59iZJXMKmYAIwBXdrmFyOBwdO3Y8cOBAUVFRuRUEAB5iXbrUVdEhjB5EAC7nzvsgXrx4cdKkSeVTDQB4jqR0q00VJYAdIAC4njssUqlUqdLkyZNfe+21nTt3jhw5sm7duhUqVPjlw1q0aKGqjJIAQEkZltiUIZ9pTMsJwBXdISCOGTNm+/btQohVq1atWrXqVg+7cuWKn59fKZcGAO4rOUteKhLdOWEPgEu6Q0AMCAgIDg6+46soCnNoAOAurEuXXqpoH0rjCcAV3SEgJiYmlk8dAOBR1qVZbUMVXw4ZAOCSGN0AgPJWaInNZ2X3cLoPAbio4gExJyenc+fO77333uHDh51SEAC4vW2ZMtcQ3ZiACMBVFW+epJQbN27805/+VL9+/ejo6GeffTYhIcEwDKcUBwBuaV269NZEuyr0IAJwUcUDYnBw8P79+6dMmdKxY8cTJ0784x//6NWrV3h4+JgxY+bNm3flyhWnVAkA7iQp3WofqlRgAiIAV3WTAY7GjRu/9NJLGzduzMjI+OKLLwYMGHD58uXZs2ePGDEiNDR04MCBM2bMyMjIKP9aAcANFFrih0zZjRP2ALiw27VQoaGhY8aMWbp0aXZ29pIlS0aPHu3t7b1s2bIJEyZUr169U6dOU6dOPXjwYLnVCgBu4IdMedUQ3SMYXwbgukr0EdbX13fgwIGzZs3Kysr6/vvvf//730dERGzatGnixIkNGza8NlVx48aNUsqyLhcA7G5dmvTRRBsmIAJwYXc3xqFpWqdOnT744IPTp08Xm6rYuXPnrVu3llGVAOA21qdbHcIUH83ZdQDArd37HOnGjRtfm6148uTJxYsXL1myRNeZcQ0At1Ngih/OyT83Ix4CcGnFexAvXbr0+OOPb9iwoeTjxTVr1nz22WcTExMffPDB0i4PANzKlkyZZwi2yAbg4ooHRNM0P//8865du0ZFRU2cOPHkyZPOqAoA3FNSuuWri9ZMQATg2ooHRG9v73bt2gkhUlJSpk6dWqdOndjY2Llz5xYUFDijPABwK0npskOY4s0IMwDXVjwg+vr6btmy5eDBg5MmTYqKijJNMz4+fuTIkVWrVp0wYcLGjRudUiUAuIE8Q2xlB0QAdnDzdqp+/fqvv/768ePH16xZM3r0aF9f35ycnBkzZnTu3Llx48ZTp07NzMws50IBwO62ZMp8U3RjAiIAl3e7D7KqqsbExMyaNSs1NfXjjz/u2LGjEOKnn36aOHFiZGTkwIED582bxzHNAFBC65mACMAmSjTSERQUNH78+I0bN+7bt++Pf/xjWFhYYWHhsmXLRowYUbNmzZdffpmYCAB3tC5ddgpTvBhhBuDy7q6heuCBB6ZNm5aamrpmzZrhw4d7eXmlpqa+8847LGEBgNvLM8S2c7IrExAB2MF9NVWcrQcAJbQ5UxYwARGATdzd2Sd79+797LPPvvrqq3Pnzl27EhkZ+dhjj3l7e5dBbQDgPq5NQHyQCYgA7KBEAfHChQvz5s2bNWvWpk2brl3x9vaOi4sbPXp0bGwsJ+wBwB2tS5edqzIBEYA93C7bmaa5bt26WbNmzZ8/Py8v79rFRo0ajRkz5sknn6xcuXK5VAgAtnfVENvPyUkt2SAbgD3cPCAeOHDgiy+++OKLLzIyMq5dCQoKGjFixIQJE1q2bFmO5QGAO9h8VhaYHMEMwDaKB8SrV6927do1OTn52peapvXp0+fxxx+Pi4vz8vIq9/IAwB0kpVv+DtGqMgERgD0UD4gFBQXX0mHdunVHjRr12GOP1axZ0wl1/cLu3bunT59+4sSJypUrjx07NjY21tkVAUBJrc+QHcMUBxMQAdhE8eZK07THH398w4YNhw4dev31110kHa5evbpNmzbr1q2rU6fOqVOn+vXr9+677zq7KAAokTxT2cYRzABspXgPYmBg4KeffuqUUm6loKBg3LhxtWvXTk5O9vf3tyxr+PDhr7zyykMPPRQdHe3s6gDgDrae1wotdkAEYCc2+ESbmJiYkpIyfvx4f39/IYSqqs8//3xRUdGXX37p7NIA4M42nVOZgAjAXor3IObm5j7++ON3+yqzZ88uu72yt2zZIoSIiYm5fqV9+/YBAQGbN28uo38RAErRpiy9c1UmIAKwk+IBsbCwcN68eXf7Kp999lnZBcSTJ08KIapWrXr9iqqqoaGh166XREFBwYULF2684uXl5efnV2olAsAtXDXErgvaG7WJhwDs5JYbZbdt27Zr166KUqIxEYfDUXolFXf16lUhRHBw8I0Xg4OD09LSSvL0oqKimTNnzpw588aLcXFxn3/+eenVaFc5OTkl/BXDRVy9elXTNA63tJGkTL3Q8mnlf+XCBdPZtaCkuNHsiHe02ygWokqieEDUdT0oKCgnJ2fr1q2ZmZljxowZO3ZsrVq1SqnCe3EtfV69erVixYrXL+bm5pbw1tU0rWvXro888siNF2vVqnXjq3ks0zT5OdiLruu6rvO+ZSPbj1p+utU5yp8hZhvhRrMj3tFKV/GAGBAQkJGRsWTJklmzZsXHx0+ePHny5MmtWrUaP378qFGjri0TKWehoaFCiOzs7Bt/8dnZ2SXcgkdV1caNG0+YMKGMyrM1VVVVlXctO1H/y9mFoKTWZ1jtK5veOgcN2Ak3mh3xKytdN/lRent7Dx8+fOnSpadOnZoyZUrdunV37NgxYcKE0NDQESNGJCQkSCnLs8RmzZoJIa4f7iKEOH36dGZm5rXrAOCyrhoi+ZzsUInBZQA2c7usHRER8dJLLx0+fDg5Ofn3v/99hQoV5s2b16tXr6ioqIkTJx4/frx8SuzXr5+Xl9ecOXOuX5kzZ46UcsiQIeVTAADcm41nZaElOlUhIAKwmRJ1xrZq1eqDDz5ITU2dO3fugAED0tLSpk6d+sQTT5R1cdeEh4c/++yzCxcufOGFFzZv3vzhhx++/vrr3bp169OnT/kUAAD3Zn265e8QTYMIiABs5parmH/Jx8enS5cuKSkpe/fuPX36dNnV9Etvv/22qqr/+te/3n//fYfDMWzYsOnTp7NYCYCLS0qX7IAIwI5KFBALCwuXLl36xRdfrFy50jAMIUT79u2feuqpMq7tf3RdnzJlyuTJk8+ePVu5cmVfX99y+6cB4N5cm4D41wc1ZxcCAHftDgHxxx9/nD179qeffnru3DkhRERExLBhw5588smmTZuWS3k/4+3tXaNGjfL/dwHgHlybgMgRzADs6OYBMTs7e/78+R999NGuXbvEf9c1jx49OjY2VtfvYlQaADxWUpoV4BAtKymXcpxdCgDcpeJpLy8vb8SIEfHx8deGktu1azd27NiHH344KCjIGeUBgF1dm4CoMwERgA0VD4j5+fnLli0TQrRq1Wrs2LENGzYUP9+D8Ka6d++uacyzAYD/uFIkkrPkW0xABGBPtxwv3rFjx44dO0r4KleuXPHz8yulkgDA9jadlUVMQARgW8UDoqIo93CiMzvOAMCNktKtAIdoUYm2EYAtFQ+IQUFB2dnZTikFANxGUrrswgREALZF6wUApexKkdiRJbtF0MACsCvaLwAoZRuZgAjA5giIAFDK1qdbFb2YgAjAxooHxEmTJm3btu0eXigrK2vixIkpKSmlURUA2Ni6dNm5qqKRDwHYVvGAuHbt2rZt2w4cOHDlypWmaZbkJQ4ePPjSSy/Vrl176tSphYWFZVAkANjG5SKxM0t2C2d8BoCNFV/FPHPmzBdffHHJkiXLli2rWrXqsGHDOnTo0KZNm+jo6BsflpOTs23btm3bti1btmzr1q1CiFq1as2cObN27drlVzsAuJ7vM2SRJbozARGAnRUPiPXq1Vu8ePHatWunTJmSkJDwz3/+85///KcQQtf1kJCQgICAgoKC7Ozsq1evXn9K7dq1f/e73z311FPe3t7lWjsAuJ6kdKuil2jGBEQAdnbzk1R69OjRo0ePo0ePfvrppwkJCbt27TIMIzMzMzMz8/pjatSo0bVr11GjRvXu3VtVGUwBACGEWJcmu4arTEAEYGu3PGpPCFGnTp2333777bffvnz58v79+7Oyss6dOxcQEBAaGlqzZs2oqKhyqxIAbOFiodh1Xo6qw2dmAPZ2u4B4XUBAQPv27cu6FACwu+8zpCmZgAjA9viYCwClJindCvEWTUMIiADsjYAIAKVmXbrsUlVVyYcAbI6ACACl42Kh2HNedo8gHgKwPQIiAJSO9emWKTmCGYA7ICACQOlISpeVfcQDwQREALZHQASA0rE2TXYNZwIiAHdAQASAUpBdIPZdkGxwA8A9EBABoBQkpVuWFKxQAeAe7hAQLcu640tkZ2cbhlFK9QCALSWly9AKomEQARGAO7hDQBwzZszu3btv84Dt27c/+OCDBQUFpVoVANjMujTZPZz5hwDcxB0C4uHDh9u3b//BBx/88ltSyg8++KBjx44nTpwom9oAwB7O5YsfL0g2uAHgNu4QEHv27Jmfn//cc8+NGDHi4sWL169nZmbGxsY+99xzhmG89NJLvr6+ZVwnALiupHRLCiYgAnAfdwiI77zzzsKFC4ODg+fNm9esWbMtW7YIIZKSklq0aLFq1arKlSsvW7ZsypQpikKzCMBzrUuT4b6ifkVaQgBu4s6rmIcMGbJ9+/ZWrVqdOnWqW7duDz/8cExMTFpaWrdu3fbs2dOvX79yqBIAXNm6dNk9nE0hALiPErVo0dHRmzZtGjNmTGFh4bfffmua5quvvpqQkBAREVHW9QGAi0u/Kg7lcAQzALdS0o+8mzZtWrNmzfUvN2zYkJGRUTYlAYCdXJuAyAoVAO7kzgHRMIyXX365V69e6enpffv2XbRoUbVq1davX9+iRYv4+PhyKBEAXNnaNBnpp9QJJCACcB93CIhnzpzp0aPHO++8o6rqpEmTli9fPnjw4N27d/fv3//cuXP9+vV79tlnCwsLy6dWAHBB69JlD8aXAbiXOwTEkSNHfv/995GRkUlJSa+//rqqqkKIypUrL1269N1339V1/R//+Ee3bt3c+ySVi4Vi/wUpnV0GABd0+oo8dokJiADczR0CYlFR0YABA3bt2tWxY8cbryuK8uKLL27atKl27dpbtmxx75NU4s9YTRYYVWYXDVlj/r/91u7z0iItAhBCCLEuXQohujMBEYB70W//7UmTJvXr1+9W2xy2bt06OTn5ySefLIPCXEif6uriXmJ9htyQLl/YaplSBHuLrlXV7hFKjwilcTCbQAKea22ajA5UavjTDABwK3cIiP3797/9A4KDgxcsWFB69biiIC8RF6XGRQkhxMVC8X2GTEq31qXLP/xgWVKEVRA9ItQeEUpMhFIzgDcJwLOsS5N9I7nxAbibOwTEkvCoY1QqeokBNZQBNTQhxIUCsSHDWpsmE9PkN8csIUTdikpMhNK7utIjQg10OLtWAGXs6CWZkssW2QDcUPGAmJ+fP3ny5KCgoJdeeun6RcuyfvrpJ4fDUb9+/WKPHzlyZHZ29tKlS318fMq8WBcT7C0GRamDooQQIv2qSEiz1pyRi05a/z4gdNXsEKr0qa72qa60rOxJCRrwJGvTpCJE9wgCIgB3Uzwg5uXlTZkyJTIy8saAeOXKlSZNmkRERKSmphZ7/Pr168+ePWuaZplX6trCfcXoOuroOkIKbW+2XJMqV52x3thl/iVZhFUQfaur/SKV3tXVIC9nFwqg9KxLlw2DlKoVnF0HAJS2Uhhixo0UIZqFKM1ClBeaqLmGSEqXK1KslSnyiyOWrpqdwpT+NdS4Gkq9ivQqAvYmhUhKsx6qRfchADdEQCxDfrroH6n0j9SEEAdy5LLTcnmK9eft5otbRf2KysAayqAotX2YopEVARs6kCMz8gRbZANwSwTEctIwSGkYpLzYVL1QIOLPWEtOy08OWdP2WVV8RFyUOqSmGhOheGvOrhJAia1Nk6oiurFCBYA7IiCWt2Bv8Ui0+ki0KLS09enyu1PW4lNy5iEjwCEG1FAfqqnERqq+/FoAl7c2TbaopIR4O7sOACgDJBGn8VJFr2pKr2raPzuIrZly0UlrwUn5zTHLVzf7RarDaykDapAUARdlSpGUbo2rT/chAPdEAHE+RYh2oUq7UG1qG7H7vFxw0pp7XM4/YfnqZlyU+nBtpW91ldFnwKXsOi8vFIgebHADwE3dMiAWFhZe/++ioqJfXkRZaF5JaV5J+2srsSdbzj1ufXtczjlmBXmZQ2uqo+qo3cJZ0QK4hLVp0ksVnatyQwJwTzcPiCkpKd7exWfWpKWl/fIiykizEKVZiPbWg2LbOTnnmDXnuPXpYSvCV3kkWhlTV20awtsS4Exr06y2oYofYzAA3BTjI66uTRXlb+20lEccCf303tWVTw5azRYaTRcaf9tnnc1zdnGARyq0xMYMyQY3ANxY8c+/AQEBy5Ytu9tX8cBz9sqZpoieEUrPCO1fHbTvTlmzjlh/2ma+tN2Mra4+UV/pH6k6iPpAedmaKXMNJiACcGfFA6Ku6/3793dKKSiJCvq1XXLUtKty9hH5+RFryBortII5uo766wZqfQ5oAcpeYprlq4t2odxuANwWn4DtKsJXeamZemCYvmmgHldDnXHQajDP6LrM+PKole/pJ2MDZWttmuwUpnjRfAJwX7RwttchTPmks5Y2yvFJZy3fFKOTzOpfF/1xq3nkonR2aYAbyjXE1kwZU43GE4A7o41zE/4OMa6+unWQvnOIPqyW+slBq/48o89KY8kpyyIoAqVnQ7ostER3VqgAcGsERHfTopLyUSftzCjHhx20M7li0Bqzzlxj2j7rQoGzKwPcQmKaFeItWlYiIAJwZwRE9xToEE83Un8cpq/tr7eopEzcZlb/pui3m8yDOXQnAvclMU12j1BV8iEAt0ZAdHPdw5UFMdqxkfrTjdQ5x6xG843+q4y1acRE4F6cyxd7s2VPxpcBuDsCokeI8lfebaOlPOL4Rwft8EXRc4XRcpHx9THLsJxdGWAr69IsSwoCIgC3R0D0IP4O8btG6qHh+oIYrYIuHl1ntl7j/8+frKuGsysDbCIhTUb6KfXYcBSAuyMgehxVEUNrqpsG6t8P0BsFWr/fbNacU/T2butiobMrA1xeYqrsWY10CMD9ERA9V6eqylftr+57SO9TXZ2004yaU/TqDvM8i52BWzhxWR6/LGMYXwbgAQiInq5xsDK7m3ZouP5wbfXdPVatOUUTt5tZ+c4uC3A9iWlSERzBDMAj0NJBCCFqBygfddKOjdTH1lU/2G/V+rbo5e1mNr2JwA0S02SjYCXc19l1AEDZIyDif6r7KR920I6O0B+rq/5tv1VrTtHrO81LRc4uC3ABUojEVIv1ywA8BAERxVXzUz7soB0ZoT8crb6926o9p+i9vVYeK53h2facl+fyRQwrVAB4BgIibi7ST/m4k3ZgmB4bqU7cbtadZ3xykH0T4bnWpEqHKrqF02YC8Ag0drid6EBldjdt1xC9eYgYv9FsstBYcoqQCE+UkGq1DVUCHM6uAwDKj6wb5gAAIABJREFUBQERd9Y0RFnWR18/QK/oJQatMbstN5KzOKwPHiTfFN+flTGsXwbgMWjvUFJdqipb4vRve2gpV0TbxcbY9WZqLjERHmHTWZlniF5MQATgMQiIuAuKECNqqweG61PbaItPWfXnGX/dxfoVuL+EVCvQIdpUISAC8BQERNw1L1W80EQ9MsIxuq46eafZaIGx4AQTE+HO1qTKbhGqTnsJwGPQ4OEeVfER/+6o7Rii1/ATwxLNXiuNQxcZcYYbOl8gdp2XjC8D8CgERNyXZiFK0gD9m+7aTxdE0wXGn7ebuYw4w70kplqWFBzBDMCjEBBxvxQhHo5WDw7Xn2msvr/PajzfWHqaEWe4j4Q0GemnNAgiIALwIARElI4Ah5jWVtsxWK/uJ+JWm0MTzLSrjDjDHaw+IzlABYCnISCiNDUJUb4fqM/opK1PtxrNNz46YFmkRNjZoYvy1BXZm4AIwMMQEFHKFCF+3UD9aZijb3X1t5vMrstZvAIbW31GqoqIqUZTCcCz0OqhTIRVEHN6aMv66Kcui+YLjal7OMcZtrQ61WpZSans4+w6AKB8ERBRhvpHKvuH6WPrqn/ebrZfYvx4ga5E2EmhJZLSZe/qjC8D8DgERJStQIf4qJOW2F/PKhCtvjPe3WuZpETYxOaz8kqR6M34MgDPQ8OH8tA9XNk7VB9bV524zeyyzDh2iZAIG1h9xgpwiA5h9CAC8DgERJSTAIf4uJO2sq9+6opovsj45CBzEuHqVqfKbuGqg2YSgOeh5UO56lNd2TtU7x+pjt9oxq02z+U7uyDgFrLyxa7zTEAE4KEIiChvId5iTg/ty27ahgyr6YKiVWcYboYrWpNqWVKwAyIAz0RAhHM8WkfdPUSPDlRi440/bjULGXCGi1mdKmsFKPUqEhABeCICIpymZoCyfoD+Wkv1g/1WxyWsXIELkUKsPiP7ML4MwFMREOFMmiJeb6kl9NPT80TLRcbc43QkwiXszZZpV2VfAiIAT0VAhPN1C1d2D9E7VVVGrjWf2WwWmM4uCB5v1RnppYoeEbSQADwUzR9cQmUfsayP/k5r7aODVqelxsnLDDfDmeJTrA5hSoDD2XUAgJMQEOEqFCEmNlPX9tNTr8pW3xkrU8iIcI4rRWLTWdm3Os0jAM9FCwjX0rmqsnOIo0mIMmC18fpO0yIlotytTbMKLcEKFQCejIAIl1O1gkiI1Z9/QH1jpzVojXGx0NkFwcPEn5HhvqJZJQIiAM9FQIQr0lXxXlvtmx7aujTZZrFxIIeORJSfVWdkn+oq8RCAJyMgwnWNrK1ujtMNS7RbbCxnSiLKxeGL8vhl2YcDVAB4NgIiXFrTEGX7YL11FSVutfHuXnZJRJmLPyM1RfRihQoAz0YjCFcX4i3i++pPNVRf2maOTmKXRJStFSlWmypKJW9n1wEATkVAhA3oqviwg/ZxJ+3b41aPFUZmnrMLgpu6aoj16TI2koYRgKejHYRtjG+gxvfVD+TItkuMHy8wJRGlb22azDdFv0gmIALwdARE2EmPCOWHON2hio5LjYRUMiJK2YoUq2oF0bIyARGApyMgwmbqVVR+iNObV1JiVxkfHWDZCkrTqjMyNpINbgCAgAgbCvEWq/rqw2qpv91kTtph0pGIUvFTjjx+WcYyvgwAQujOLgC4F96a+Lq7FuUv3thlnckVH3fSdD7s4P6sTJG6KnpV4y8JAAiIsC1FiCmttUg/5dktZkaenNtT9+PPGfdhRYrVIVQJ8nJ2HQDgAvisDHt7upE6t6e2Nk3GrDDOFzi7GtjW5SKxMYMNbgDgP2gNYXtDa6qrYvWDObLzUiPl/7d35wFVVH0fwH9nZthlVdkJ3BVJRUNxyRTcNywXrNT0tXJN6tFKM80llWwxbfOxxaVScClUFEVxTUVNBSVFFAREJUBQkfXemXn/uD2EKAgIzJ3L9/OXnDt35scyx+89M+dMHu5IhOrYf1MqlrDADQDAPxAQwRD0dGRHhgh3i+XuO8XLd5ERocrCU+VnGrB2dgiIAABEagmI0dHRU6dO9fHxMTExYYxdvnxZ6YpA77SzY8eHCsY8vRCuPZuFjAhVIMm054Y0GMOHAAD/o46AuHfv3pCQECsrq3bt2ildC+ivJpbs2BDBwYz57dYeTUdGhMo6kyn/XUBDnlFHfwgAUAfU0SHOnj07Ozs7Kipq0KBBStcCes3JnI4MEdrYsoF7tfvSkBGhUsJvSBYC+TljBBEA4B/qCIgNGjRgDH03VIqdCUUNEro5sGGR2t+S8agVeLJdKXIfF86UV7oOAAC9oY6ACFAlFgLt7Cv4u7AxB8WQRGREqMiNPPlCtowbEAEASjP8lYUlSTp37tzy5ctLN7Zq1SogIECpkvSHKIqiKCpdRa0wZrTdj71ymMYeFgu10rjmBvLfvyiKjDFD/a0pYleyTEQDXCSx1p7aaMAnmqHCiaZGONEqwPNVvkSiZECcO3duZmZmea+6ubl99NFHT38UURTPnz8fHx9funHgwIH+/v5Pv3O1y8vLMzY25AdH/OhDU8nk/47x2XlFk5pplS6nBuTn5/M8r9FolC7EcOxINulgy6ykwgcPausQBn+iGR6caGqEE60C1tbWVX2LkgExLi4uLS2tvFcf1FBvbWRkNGnSpG+++aZG9mZgRFGsxh+NuvzqT/wR8d3zxpbmZm+0Vv09FYIgCIJgYmKidCEGIl9LxzI177XjavVEqA8nmoHBiaZGONFqlpIBcdeuXQoeHeoJgaOfe/ECo8l/iERkABkRatD+m1KBloZhgRsAgIcZ/j2IADyjdS/wjNHkP0StTFPbIA3AP3akyM80YN6NDOQWVQCAmoKACPUCz+innrwk04wToglP/9cSGRFIlGn3DWlUEw7xEACgDHUExIMHD5aeU+Lp6UlEDg4O6enpyhUFKsMzWv8CL8n0xjHRmKOxzZER67voDDmjgIa54y8BAKAsdQTEZs2aBQcHl2m0sLBQpBhQL57Rxl68RDThiMgxeqUZkkG9FpYsWRtTLycMIAIAlKWOgOju7v7+++8rXQUYAp7Rxhf4fC1NOCJaCBSA0aN6bFeqPNCNM8afAADAI9A1Qr1jxNEWP97fmQUeFCNv4nnN9VT8XfnKPXnYMxg+BAB4DAREqI9MePqtj9DNng3frz1yGxmxPtqRIhtxNNANfSAAwGOgc4R6ykygHf2E9nZsWKT2TCYyYr2zM1V6wYnZ4LELAACPg4AI9ZelEe3pL3hYskH7tJfuIiPWI7fzKTpDxh2oAADlQf8I9ZqtCR0YJDQ0YX33iNdzkRHri9+TJVmmAHfcgAgA8HgIiFDfNTalvQN4xqhfhPh3gdLVQJ0IS5F8GjM3CwREAIDHQ0AEIA9LFjmQzymSB+7V3tcoXQ3UspwiOnxbfskDvR8AQLnQRQIQEXnasIgBwtX7ckCktlBUuhqoTTtTJY1Ewz0wfAgAUC4ERIB/+DRm2/yFE3/LrxwSRdyOaLjCkmUvW9bKGgERAKBcCIgA/+rvyta/wO9IkWacwCiiYcrT0r6b0osYPgQAqJA6HrUHUGdebsblFNH0E6KTOVvgjU9QhibihlSgpRFN8JsFAKgIAiJAWdM8ueQH8kdnRWdzer0VkoRB+T1ZbmrJ2tthBBEAoCIIiACP8Uln/nY+TT0uOpmzwW4IEwaiSKTwVOnN1gj9AABPgI4S4DEY0U89eT8nNjpKG52BGSsGYm+adF9Do5ui3wMAeAJ0lACPZ8TR1j5Ccys2fL82CQ9ZMQjbrsseluy5xhgSBgB4AgREgHJZGdGe/rwxxwbtFbOLlK4Gnk6hSDtTpJEeDPEQAOCJEBABKuJiwfYO5P8ukIfv1xZh6Rs124frywAAlYa+EuAJPG3Y732FUxny+CNYP1vFtuL6MgBApSEgAjxZLyf2XQ9+S5I0/0+MIqpSoUi7cH0ZAKDSsMwNQKX8X0su8b68NEZqbsUmtMQnK5XRXV8ehevLAACVg4AIUFkfP8cn3qfJf4julqy3E4ai1ER3fdkH15cBACoHn6cBKosRrX+B79SIjTigvXIPtyOqRr6WdqRIgU1xfRkAoLIQEAGqwJSnsL6CjTEbsk+8g4VvVGJXqvRAQy83Q3cHAFBZ6DEBqsbejHb357MK5Rex8I1KbEqUW9vg+csAAFWAgAhQZW1s2GY/4WSGPPU4EqK+u1tM+9KkVzB8CABQFeg0AapjgCtb6cuvS5A+vSApXQtU5LdkqUikl5th+BAAoAowixmgmmZ4cpfvynPOiK2saZg7PmvpqU3XpM6NWXMrBEQAgCrA/2oA1bfKl/dzZq8eFi9kY1KzPkovoMO3ZUxPAQCoKvSbANUncLTFT3A2ZwH7xcxCpauBR2xJkmSiUU0wfAgAUDUIiABPxdaEdvbj7xXLIw9oi3E7op755ZrUy4m5WCAgAgBUDQIiwNNqZc1C/YQTGfLkPzCpWY8k3JPPZMrjW6CXAwCoMnSdADWgrwv7vAu/PkFa/RdGEfXF+gTJQqCXPNDLAQBUGWYxA9SMmW25i9nyrFNiGxvW1wXXNBUmyfRrojzcg7M0UroUAAAVwmdrgBrzbXe+uwMLPKi9iic1K+3wbTn1Aa4vAwBUE3pPgBpjxNE2f8HGmA3bL94rVrqa+u3na5KzOfN3xlAuAEB1ICAC1KRGprTNn099II87LEoYRlRIvpa2X5deacZ45EMAgGpBQASoYR0bsZ968uGp0oKzmNSsjN+TpVwN4foyAEC1YZIKQM0LbMpdyJaXxUjP2rHApogpde3HK1KnRuxZO4wfAgBUE/7rAqgVSzrxg9zYpKNiLJ7CV7eScuXDt+X/a4nODQCg+tCHAtQKjtGvvQW3BuzF/WIWnsJXh9YlSKYCvdIcnRsAQPWhDwWoLdbGtKMvn1Mkv3RAq8H62XVCkmlDgjzCg7MxVroUAAA1Q0AEqEUtrVmov3Dib/nd05iwUhf2psk38uRJrdCzAQA8FXSjALWrnwtb3IlfFSdtuIpRxFr3U4LUzIq94ITpKQAATwUBEaDWze3AjWzCTflDPJ2JCSu1KKOAdqVIE1tyiIcAAE8JARGg1jGidT35FtbspQNieoHS1RiudQmSRDShBfIhAMDTQkAEqAsNjCisL1+glUce0BbjUnMtkGRaGy8NfYZzsUBABAB4WgiIAHWkqSUL8RNOZcozT2DCSs3blyYn5cpT2qBPAwCoAehMAepOXxcW7MP/N15aG49RxBq2Jl5qZsX6OGP4EACgBiAgAtSpWc9yE1tyM06IR9MxYaXG3MiTd6dKU9pwmJ8CAFAjEBAB6to33fhn7diYg9pb+ciINeP7eEngaEILdGgAADUD/SlAXTMT6Pc+vFailw6IRbgd8alpJPrhijSqCdfIVOlSAAAMBQIigAKeacC2+gvnsuQpx5EQn9a269LtfJrmid4MAKDGoEsFUMYLTmylL78+QfrqL0xYeSqr/pI6N2Zd7XH7IQBAjRGULgCg/pruyZ2/I//nlOhlx3rj6XDVEp0hn8qQf+3NK10IAIBBwQgigJK+6853tWcjD2gT72PCSnV8GSc5m7ORTdCVAQDUJPSqAEoy4miLv2AusJcOiHlapatRm5t58m/J0nRPzhg9GQBAjUK3CqAwRzPa5s8n3JMnHhExilgl31ySeEZvtEY/BgBQw9CxAiiviz37rju/9bq09DwmrFRWnpbWxkuvNucaY3UbAICahkkqAHphQksuNltecFZsbUO4o64yvo+XsovoHS/8rAAAah76VgB98VkXfoAbm3hUjMvBpeYn0Ej0ZZwU4M61tcXsbwCAmoeACKAveEa/9BIczdiL+8XsIqWr0W+bE6WUB/J77dGDAQDUCnSvAHrEzoR29eOzCuXh+7XFuB2xHDLRpxekF5ywODYAQG1BQATQL61tWIifcOJv+T/ReArf4+1KkeJy5PfbYXFsAIDagoAIoHf6u7Lgzvw3l6Q1lzGK+BjBsVI7OzbADcOHAAC1BbOYAfTR7Ge5uGx55kmxhTXzd0YS+lfkTflkhhzqx+OHAgBQezCCCKCnvn+e7+bARkVpE+5hUvO/lpwXPW3wbD0AgNqFThZATxlxtMVPsDZmwyLFHExqJiKifWnyH+nyRx05DuOHAAC1CQERQH/Zm9GufvytfHlUlFaD2xGJFp4TvWwxfAgAUOvQzwLoNS9btsVfOJIuT/mjvk9qjrghR2dg+BAAoC4gIALouwGubKUv/1OC9OmF+juKKBMtOCu2s2MveaDXAgCodZjFDKACMzy5hHvynDOiizE3oonS1SghNFH6M0sO7y9g+BAAoA7gsziAOqz05Qe6stejhVNZ9S4iFUs070+plxMbjLUPAQDqBAIigDrwjLb4C1428otRVN8Wvvn2knQ9V17ug0enAADUEQREANUwF2hzD62FERsSKWYWKl1NXblXTEtjxFFNOV88eRkAoK4gIAKoiaOZvMNPyiyQh0Zq87VKV1MnlseK94tp2XPorAAA6g76XACV8bShHf2E2DvyqCit1tCnNV+7L38ZJwV5cc2sMHwIAFB3EBAB1KenI9vYi9+bJk85Lhr23YgzTogNTdh8b9x9CABQp7DMDYAqjWrC3faloJOivSktM9DZG78nS/vS5F9785ZGSpcCAFDPICACqNXMtlxWobzkvGRnymY/a2hXAwq0NOuU1MORvdzM0L41AAD9h4AIoGKLO/FZhfTeKbGRCU1oaVBBauE58UaeHNZXwL2HAAB1DwERQN2+7sZnF9Hrx8QGRjSyiYFkxD+z5C/ipNnPcu3skA8BABSAgAigbhyjn3vx+Vp65ZBoyrMhz6g+UWklevOY2MySfdTRMO+tBADQfwYy3gBQnxlxtMWf7+XERkVpo26pflrz0hgpNlv+sSdvinwIAKAQBEQAQ2DKU1hfwacxGxapPajmjBhzR14WI87w5Lo7qH4oFABAvRAQAQyEuUB7+gsdG7Ghqs2IeVp6+ZDYxJIZ6sI9AABqgYAIYDgaGFHE/zLigZvqy4gzT4jXc+XNfrwF7o4GAFAUAiKAQdFlxOcasaGR2l2panoSX2iS9FOC9IkP790QF5cBABSGgAhgaBoYUcQA4XlHNuKAGJKojowYf1ee/Ic42I3N9EKnBACgPPTFAAbIXKBd/YTBbtzYw+J3l/U9I94tpoD9op0JW/8ClsUGANALCIgAhsmEp63+/Njm3LTj4od/inp7Q6Io05iD2lv5clhfvpGp0tUAAAARYaFsAAMmcLTuBd7ZnJbGSDfzaW0P3kj/PhLOPiVGpsnb+/B4aAoAgP5AQAQwZIxomQ/vYsGCToopufLWPkJDE6VrKuWTWOnLOGlJJ/5FD/2LrgAA9Rg6ZQDDN92T291fOH9H7rJDe+muvlxtXp8gzT0jTvPkPvRGRwQAoF/QLwPUC/1d2fFhAhF13aH9LVn5aSshidLrx8SxzbmvumJNbAAAvYOACFBfeNqwUwFCVwc28oD4TrSoUS4l/nBFGntYDHDnfurJc7jzEABA/yAgAtQjDU1oT39hYSf+q7+knuHaa/cVuNz8Saz05jHx5WZciB8voAcCANBL6J4B6heO0QJvbu8AIfUBdfhNu+ayVGchUSPRWyfEOWfEt9pyG3vp45RqAADQQQ8NUB/1cWEXRwhD3bmpx8X+EXUxlHgzT+69W/vdZSnYh1/VlceFZQAAfYaACFBP2ZnQ5t58qB9/MVt+drt20TmpUKytY+25IXcK0165J+8dILzfHt0OAIC+Q08NUK+NbsrFjzJ6ozW35LzYaqv2xyuStkYnr2QU0CuHxMH7tM2s2LkXhT4uGDoEAFABBESA+s7amFZ35f8cLrS1pdePiV7btesTpKKnHk3M09JnF6U22zThqdJX3fhjQwQ3C6RDAAB1QEAEACKiDg3Znv7C0SGCkzlNPCo+E6JZcFas3r2JmYW04oLUNFTz/mmxvyt3aaQww5PDcjYAACqCR+0BwL+ed2SHBgvn78ir/5JWXJCWnJe8G7JRTbneTqxTI1bxvOObefLh23JIkrQvTRZlCmzKzffm2tggGAIAqA8CIgCU5d2QrevJf9GFD0uRtl6XFp0TPxDJXKAODVkzS+ZhSc7mTDci+EBDqXnyzTw6myVfz5WJyMuWffwc/0oz5ooLygAAqoWACACPZ2tCE1tyE1tyeVo6nSkfS5fPZcmx2fLOVPle8T/bCBw5mrFnGlAfF9bDgXvekTWxRC4EAFA9BEQAeAILgXo7sd5OZZNfThFZGxNuLgQAMDwIiABQTbYmSlcAAAC1Qx0BsaioaP/+/SdPnkxLS3NwcOjYseOIESOMjIyUrgsAAADAAKkjIHp6eiYlJdnY2DRv3vzAgQOffvqpj49PZGSkjY2N0qUBAAAAGBp1rIPYt2/fY8eO5eTknDlzJjU1dd68eWfOnPn444+VrgsAAADAAKkjIK5Zs6ZHjx66f/M8v3jxYmtr60OHDilbFQAAAIBBUkdAfBTP8+bm5kpXoW7Xr19ftWrVnTt3lC4EqmDz5s3h4eFKVwFVkJSUtGrVquzsbKULgSrYtGnT7t27la4CqiAxMXHVqlU5OTlKF2I4VBkQN2zYkJ2dPXr0aKULUbeEhITFixdnZGQoXQhUwdq1a7du3ap0FVAF8fHxixcvzsrKUroQqIL//ve/ONHU5fLly4sXL8aQRw1ScpJKfn6+Vqst71XGmKWl5aPt8fHxM2fO9PHxmTZtWmWOotFofvzxx02bNpVuHDhw4HfffVfVgg1MXl4eEeXm5t67d0/pWqCyRFHUaDT4lakITjQ1wommOjjRKmZtbV3VtygZEIcNGxYVFVXeqw0aNMjNzS3TeOPGjYEDB9rY2GzZsoXn+cocheM4T0/Pvn37lm708vJq0KBBNWo2JGZmZkRkbm6OH4WKcBwnCAJ+ZSqCE02NcKKpDk60GqdkQJw7d+6ECRPKe/XRZQ5v3rzZu3fvoqKiI0eOeHh4VPIoPM937dr1k08+qW6ZBovjOCLieb6SURv0AWOMMYZfmYrgRFMjnGiqgxOtxikZEP39/Su/cUZGRr9+/e7fv3/48OEWLVrUXlUAAAAA9Zw6FsrOzMz08/PLyMg4ePCgp6dnld4rSdKlS5fWrl1bS7Wp119//UVEW7ZscXJyUroWqKyMjAxZlvH3rCJxcXFEFBoa6uDgoHQtUFmZmZkcx+FEU5ELFy4QUUhIiL29vdK16B3G2BtvvFHld8myXBvV1Kx27dpdvHhx6tSpHTp0KGk0MzMbN27cE9+7fv36iRMn1mZ1AAAAAHqK4zhRFKv6LnUERJ7nJUkq0+jg4JCenv7E90qShDlNAAAAUG/Z2tpW9S3qCIgAAAAAUGdUuVA2AAAAANQeBEQAAAAAeAgCIgAAAAA8BAERAAAAAB6CgAgAAAAAD0FABAAAAICHICACAAAAwEPU8ag9qEt79+79/fff7e3tlyxZonQtUC5Zls+cObN9+/Zz586lp6e7urr26NEjKCioQYMGSpcG//rll1/WrVuXnp7u4eERFBTUr18/pSuCily8ePG3336Ljo5OS0tzdHT08fF55513GjdurHRdUCnp6ekfffQRES1YsMDFxUXpclQPC2XDQzIzM9u2bXvnzp2mTZtevXpV6XKgXPfu3bOxsbG1te3cubOHh0dcXNzx48fbtm174sQJKysrpasDIqIFCxYsWbJkwIABnTt33rdv3+nTpzdu3Dh27Fil64Jy2djYaLVaX1/fpk2bpqSk7N+/39nZ+eTJk25ubkqXBk/20ksv7dixQ5Kk2NjYdu3aKV2O+skApQQGBvbs2bNdu3bNmzdXuhaoSF5e3tdff/3gwYOSlsWLFxPR8uXLFawKSly5ckUQhDFjxui+1Gg0Xbp0sbOzu3//vrKFQQVWrVp1586dki83bNhARFOmTFGwJKikkJAQc3Pz1157jYhiY2OVLscQ4B5E+NeuXbvCwsLWrFnDcfjD0Hfm5ubTp0+3sLAoaZk+fToRxcTEKFcU/CskJESr1U6dOlX3pSAIb775ZnZ29u7du5UtDCowc+ZMOzu7ki/HjRtnZWWFc0r/ZWVlzZw5c9myZU2aNFG6FsOBHAD/uHfv3tSpU+fPn9+mTRula4HqyM7OJiLcL6UnTp06ZWJi0r1795IWPz8/XbtyRUHV5OXlFRUV2dvbK10IPMFbb73l7u4+Y8YMpQsxKJikAv8ICgqys7N77733lC4EqmnevHmCIEyaNEnpQoCI6MaNGw0bNuR5vqTFwcGBiFJTU5UrCqpm8eLFRUVFkydPVroQqEh4ePi2bdtOnz5d+nSDp4eAaMhiYmLu3r1b3qtGRkYlwxsHDhz45Zdfjh07ZmRkVFfVweNFR0cXFhaW96qlpWWnTp0ebf/mm2+2bNkyb968Dh061GZ1UFkFBQXW1talW8zMzExMTPLz85UqCapk586dn3322dixYwcNGqR0LVCue/fuTZkyZc6cOd7e3krXYmgQEA1ZUFDQ0aNHy3vV1tZWd1Hy/v37kyZNeuutt7p27VqH1cHjjRo1Ki0trbxXO3Xq9Oeff5Zp3LZtW1BQ0IgRIxYtWlTL1UFlGRsb5+bmlm4pLi4uKioyNTVVqiSovIMHD44ZM6Zbt25r1qxRuhaoyNtvv21hYTFv3jylCzFACIiGbMOGDXl5eeW9Kgj//PY3bdp048YNjUYzZ84cXcutW7d0X3p7ewcGBtZFrfA/UVFRGo2mvFfNzMzKtGzfvv3ll18eOnTo5s2bcYVFfzg5OaWkpJRuuXPnDhE5OjoqVBFU1h9//DFs2DBvb++IiIjS88BA36Snp69fv75fv34LFy7UtRw/fpyIVq1a5eLiolvYAaoNAdGQeXh4VGYzMzOzJk2aRERElLTcvXtXluWtW7dqNBoExDrWsmXLym8cERHx6quv+vv7h4SE4PYAvdKhQ4dgVavRAAAMz0lEQVSoqKjY2Nj27dvrWk6cOEFEHTt2VLQueILo6OjBgwe3bNkyPDzc0tJS6XKgIrIsN23a9Nq1a9euXdO15OTkEFFkZKSNjQ0C4lPCQtnwGN7e3g8ePMBC2Xpu3759AQEBPXv23LlzJy5c6pvo6OiuXbvOnj37008/1bW8+OKLERERycnJGETUW+fPn/f393d3d4+Kiiq93g2oxaJFixYuXIiFsmsERhABVOn27dsBAQEajcbR0TEoKKikvUWLFrNnz1awMNDx9fUNDAxcuXKlIAhdunQJDw8PCwubP38+0qHe0mq1/v7+OTk5fn5+c+fOLWlv2LDhsmXLFCwMQBEIiPAYbdq0KSgoULoKqIhWq/Xy8iKiS5culW6XJEmhiqCs9evXu7q6fv/998HBwa6uritWrJg1a5bSRUG5dNcriSg5OTk5Obmk3cnJSbGaoIqcnZ07der06L3aUA24xAwAULuKi4uNjY2VrgIAoAoQEAEAAADgIXjUHgAAAAA8BAERAAAAAB6CgAgAAAAAD0FABAAAAICHICACAAAAwEMQEAEAAADgIQiIAAAAAPAQBEQAAAAAeAgCIgAAAAA8BM9iBgAo19WrV2NiYtzc3Hx9fZWuBQCg7mAEEQDqkVmzZk2dOrW4uLiS24eHh48ePfrzzz+v1aoAAPQNAiIA1BdxcXFffPHFhQsXjI2Nla4FAECv4RIzANQXO3bsIKKAgIDKv2XIkCEuLi5ubm61VhQAgD5isiwrXQMAQF3o3LnzmTNn4uPjW7VqpXQtAAB6DQERANQnISEhNjaWMRYQEGBkZFT6paSkpLNnzxLRsGHDTExMStpv3brl6uravHnzhISEksa0tLRdu3Zdv349KyvLysrK0dGxR48evr6+gvDP1ZWrV6+eP3/ezc2ta9euZWqIioqKiIjIzs52dHQMCAjo0qXL5cuX4+Li3N3dO3furNumoKAgPDyciEaOHFlQULB169azZ88WFRV17Nhx/PjxZmZmus2OHDkSERGRkZHh4uIyfvz4Fi1alDmWLMtnz549duxYamrq3bt3ra2tu3TpMnz48JI9VJJWqw0LC5NluX379i1btiz9kiRJYWFhoii2bdvW09OzSrsFAAMkAwCoTU5OjoeHBxG9//77pdtzc3Nbt25NRNOmTSvzlu+++46I3nvvvZKWtWvXmpqaPtorfvjhhyXbfPHFF0Q0cuTI0rsqKCgYPnx4mXdNnz596dKlRDR+/PiSLdPS0nSvPjps2bp169u3b+fn548cObJ0u4mJya5du0ofrri42NHR8dE6XVxcoqOjq/qje/fdd4nIzc0tKyurdPvChQuJyMHB4datW1XdJwAYHgREAFClU6dOGRsbM8Z27NhR0jhu3DgievbZZ/Pz88tsP2DAACI6fvy47stLly7xPM8Ye+edd44fP37t2rXo6OjNmzcHBgZ+/PHHJe96bECcOHEiEdnY2KxevfrSpUuxsbELFy40NjZu3rx5eQHR09PzueeeCwkJiY6OXrt2bePGjYno5Zdfnjx5cqNGjYKDg6OiosLCwrp3705EjRo1ys3NLdlJYWGhlZXVlClTQkNDT548ef78+c2bN3fp0oWI7O3ty+S8J9JoNLqjDBkyRJIkXePhw4d5nuc4LjIyskp7AwBDhYAIAGq1YsUKIrKzs0tJSZFl+fvvvyeiBg0aXL58ucyWubm5pqam9vb2Wq1W1/LZZ58R0ejRoys+xKMBMTY2log4jjt69GjpLdetW6fLgo8NiG3bti2dWSMjI3U7EQQhNja2pD0vL69hw4ZEFBISUtIoSdKDBw/KFKbRaHr06EFEK1asqPhbeFRqaqruKF9++aUsyxkZGc7OzkS0YMGCqu4KAAwVlrkBALWaPXt2QEBAdnb2uHHjYmNjg4KCiOjbb7/VXWUubc+ePYWFhUOHDuV5XtciyzIRlXxZeZs3byai/v37P//886XbX3vtNd0I4mMtWrSo9P2Cffr0sbW1lSTplVdeadeuXUm7ubn5oEGDiOjcuXMljYwxCwuLMjsUBOGNN94gokOHDlX1W3Bzc9uwYQNj7N133z158uSkSZNu3brVs2fPBQsWVHVXAGCosMwNAKgVY+yHH344e/bs0aNHu3Xrlp+fP2nSJN1V5jIeXeCmW7duRBQSEmJhYTFu3Lhu3bqVTEypmG4GTMk0lNLFdO7c+dq1a499l4+PT5mNnZ2dc3JyyrQTkaurKxHduXOnTHtWVlZERER8fHxWVpau5fbt20R048aNypRdxuDBg99+++2VK1f26dMnPz+/cePGmzZtqkZcBgBDhYAIACrWqFGjDRs2+Pv75+fne3p6rl69+tFtNBpNRESEubm5v79/SWO3bt0++OCD4ODgH3744YcffrCwsHj++eeHDh06duxYKyurCo6oy2ePXRnxmWeeKe9dTk5OZVp0A4qPtuvmzWi12tKNy5YtW7x4cVFR0aN7zsvLq6DaCgQHBx84cODixYtEtGHDBhcXl+rtBwAMEi4xA4C6RURE6P6RlZV19+7dRzc4evRoTk5Ov379zM3NS7cvXbr03Llzc+bMad++fUFBwd69e6dPn+7p6RkdHV3B4TiOIyKNRvPoS2VSXWmMsSq1l/b999/PmzfPyMjoww8/PHnyZHJycnZ2dnZ2tm5YtNquXr2amJio+/f169efZlcAYHgQEAFAxXbv3v35558bGRl16tQpIyNj/PjxkiSV2aaCB6i0b99++fLlMTExf//9988//9y6deubN2+++uqrcvkLxOpWnHnshd3U1NSn+mbKsWbNGiJasWLFkiVLfH193d3dbW1tbW1tH70MXXl5eXmjR4/Oz8/39fUlolmzZsXExNRYxQCgfgiIAKBWN2/enDBhgizLy5cv37Nnj5OTU1RU1LJly8psFh4ezvP84MGDK9hVo0aNxo4du3fvXiJKSkpKSUkpb0tdonp0aohGozl27Fg1v5MK6cb5dMctTXc3ZPW89dZbly5d8vLyioqKmjJlSmFh4ahRo+7fv/9UhQKAAUFABABVkiRp/PjxWVlZAwcO/M9//mNvb6+bZvHRRx8dPHiwZLOYmJjr1693795dt/Rgicfez2dpaam75iuKYnnHHTt2rCAIJ0+eDAkJKd2+fPly3ayRGqebwlxm+ktiYuKGDRuqt8PQ0NB169ZZWFhs2bLF3Nx85cqV3t7e165de/PNN2ugXAAwCAiIAKBKuiDo4uKyceNGXarr1avXnDlzSoKjbrPyri8HBQUNHjz4t99+u379um5xxLi4uHHjxsmy7OXl1bRp0/KO6+Hh8cEHHxDRuHHjpk+fHhYWtmXLljFjxixcuFC3eHWN69+/PxHNmjXr8OHDWq1Wq9WGh4f7+/tX9Tl7OiVB8Ouvv27Tpg0RmZqahoaGWlpahoaGrl+/vkZrBwDVUnQVRgCA6jh06JDuyR9RUVGl27Vabe/evYlo4MCBuseEeHt7E1FCQkKZPegWEdQxMjIqWePG1dX14sWLJZs99kkqkiTNnTu39LI4xsbGq1evnj9/PhFNnjy5ZMuShbI1Gk2ZAp577jki2r59e5n2RYsWEdFrr71W0nLz5k13d/eSUnWL0bRq1So0NJSImjRpUvmfW2Fhoe4HEhgYWOYl3fqOFhYWly5dqvwOAcBQYZkbAFCflJSUpUuXtmjRws/Pr3Q7z/MbN2789ddf6X/zSGJiYtq2bduiRYsye1ixYsWQIUMOHz6cmJj4999/m5iYuLu7d+nSZfz48ZaWliWbde3adeHChW3bti39XsbYsmXLpkyZEhkZeefOHScnpwEDBtjb248dO5aI7O3tS7a0srIKDg6m/819Lm3GjBnp6eleXl5l2v39/U1MTEq3Ozs7nzt3bvXq1YcOHcrPz3d0dOzTp8/rr7+ek5MTHBxsbW1d+Z/blStXAgMDx4wZM23atDIvjRkzJj8/PzMzMyEhQTeyCAD1GZPLn6wHAKBqX3311cyZMz/44IOlS5fW9rEkSWrRokVSUtLvv/8+fPjw2j4cAECtwj2IAGCwioqKRo0aFRgYWLO7zcjIyMnJKdM4f/78pKSkhg0b6m4ZBABQNYwgAgBUzZ49e0aOHDl8+HAvL6/GjRtnZWWFhYWdPn2aMbZx40bdhWYAAFXDPYgAAFXTsGFDa2tr3ayOEk2aNPn0009HjBihSEmJiYnLly+veBtjY+Nvv/22buoBALXDCCIAQJXJsnzlyhXdU+/MzMyaN2/u5eVVmefm1ZITJ05079694m1MTU0LCgrqph4AUDsERAAA1ZNl+bFLf5fGGDMxMambegBA7RAQAQAAAOAhmMUMAAAAAA9BQAQAAACAhyAgAgAAAMBDEBABAAAA4CH/D5yIAUgOIimzAAAAAElFTkSuQmCC"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(xvec/sigma_x,  Array(Ex[:,iycenter,izcenter])/1e6,\n",
    "xlabel=\"x/sigma_x\", ylabel=\"Ex (MV/m)\", label=\"y=0, z=0\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d81f293-7260-4089-9fb6-5f1c334813de",
   "metadata": {},
   "source": [
    "# Explicit FFT convolution (CPU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24340507-85ff-4b37-ab4d-4425d0d3f8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "using FFTW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0bf134d3-e073-40ee-b7c9-0bacfe698e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1.305497 seconds (135.10 k allocations: 7.404 MiB, 4.93% compilation time)\n"
     ]
    }
   ],
   "source": [
    "G2 = Array{ComplexF64}(undef, 2Nx, 2Ny, 2Nz);\n",
    "R2 = zeros(ComplexF64, 2Nx, 2Ny, 2Nz);\n",
    "R2[1:Nx,1:Ny,1:Nz] = R[1:Nx,1:Ny,1:Nz]\n",
    "@time fillgreen!(G2, f, DELTAS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "997a34bd-2bbe-4249-a17f-76494ffed814",
   "metadata": {},
   "outputs": [],
   "source": [
    "res2 = real(ifft(fft(R2) .* fft(G2))[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa4d61d9-48db-4dd4-93b2-a2669be1996b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  20.656 s (118 allocations: 2.38 GiB)\n"
     ]
    }
   ],
   "source": [
    "# in-place version\n",
    "@btime res2 = real(ifft!(fft!(R2) .* fft!(G2))[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "519c860b-a1d3-483a-886c-dccaacb78e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that results are the same\n",
    "res2 ≈ res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1751d30d-d9ca-4c6c-9abe-d86231053f23",
   "metadata": {},
   "source": [
    "# All-in-one function (CPU)\n",
    "\n",
    "\n",
    "Encapsulate this in a single function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7140f5a8-e664-4431-8148-2c125eb5af39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fieldcalc (generic function with 1 method)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function fieldcalc(ρ, Δ)\n",
    "    Nx, Ny, Nz = size(ρ)\n",
    "    \n",
    "    # Fill double-sized arrays\n",
    "    R2 = similar(ρ, ComplexF64, 2Nx, 2Ny, 2Nz)\n",
    "    fill!(R2, 0)\n",
    "    R2[1:Nx,1:Ny,1:Nz] = ρ[1:Nx,1:Ny,1:Nz]\n",
    "    \n",
    "    G2 = similar(ρ, ComplexF64, 2Nx, 2Ny, 2Nz)\n",
    "    fillgreen!(G2, f, DELTAS)\n",
    "\n",
    "    res = real(ifft!(fft!(R2) .* fft!(G2))[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1]);\n",
    "    res\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6b1f9763-3656-4767-969c-12b6e0c3592c",
   "metadata": {},
   "outputs": [],
   "source": [
    "res3 = fieldcalc(R, DELTAS);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e7c8eec-ccd1-478a-b6c7-2b226681c673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that the result is the same\n",
    "res2 ≈ res3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708e95ad-881b-452b-af09-215c5ceb22f0",
   "metadata": {},
   "source": [
    "# Green function (GPU)\n",
    "\n",
    "Do the same, but on the GPU.\n",
    "\n",
    "Here we will specialize fillgreen to work on device arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3a67f7f9-79d8-4c02-baee-5c2a53604b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fillgreen!"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Fill Green function in double-sized CuDeviceArray A.\n",
    "size(A) == (2nx, 2ny, 2nz)\n",
    "The center is at A[nx, ny, nz]\n",
    "\"\"\"\n",
    "function fillgreen!(A::CuDeviceArray, f, Δ)\n",
    "    index = (blockIdx().x - 1) * blockDim().x + threadIdx().x\n",
    "    stride = gridDim().x * blockDim().x\n",
    "    \n",
    "    nx, ny, nz = size(A) .÷2 \n",
    "    for i = index:stride:length(A)\n",
    "        ijk = @inbounds CartesianIndices(A)[i]\n",
    "        # Symmetric vec\n",
    "        x = Δ[1]*(ijk[1]-nx) \n",
    "        y = Δ[2]*(ijk[2]-ny)\n",
    "        z = Δ[3]*(ijk[3]-nz)\n",
    "        @inbounds A[i] = f(x, y, z)\n",
    "    end\n",
    "    return\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3c14fcad-72a3-4304-8355-7ec6e5d2cc70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134217728"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up device arrays\n",
    "Rcu = CuArray(R);\n",
    "Gcu = CuArray{Float64}(undef, 2Nx, 2Ny, 2Nz);\n",
    "Ncu = length(Gcu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5f22591-0c96-4ca3-acd6-9ba136d74476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(896, 149797)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the maximum occupancy threads and blocks\n",
    "f(x,y,z) = ixlafun(x, y, z, dx, dy, dz)\n",
    "kernel = @cuda launch=false fillgreen!(Gcu, f, DELTAS)\n",
    "config = launch_configuration(kernel.fun)\n",
    "threads = min(Ncu, config.threads)\n",
    "blocks = cld(Ncu, threads)\n",
    "threads, blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2233029-b5f0-4cf5-8317-4924db3c917c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0.923052 seconds (3.30 M CPU allocations: 176.949 MiB, 6.34% gc time)\n"
     ]
    }
   ],
   "source": [
    "# Fill on the device\n",
    "CUDA.@time CUDA.@sync kernel(Gcu, f, DELTAS; threads, blocks);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "52942729-3395-47b3-8da2-32957974e935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convolution\n",
    "res4 = DSP.conv(Rcu, Gcu)[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1];\n",
    "\n",
    "# Check that this is the same\n",
    "res3 ≈ Array(res4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa5e480-8ccf-4f1a-b6c6-b365c3f72634",
   "metadata": {
    "tags": []
   },
   "source": [
    "# All-in-one calc (GPU)\n",
    "\n",
    "Encapsulate this in a single function, specializing on CuArray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6d6cb03-713f-4d13-9e02-da1c29e86a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fieldcalc (generic function with 2 methods)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function fieldcalc(ρ::CuArray, Δ)\n",
    "    Nx, Ny, Nz = size(ρ)\n",
    "    \n",
    "    # Fill double-sized arrays\n",
    "    R2 = CUDA.zeros(ComplexF64, 2Nx, 2Ny, 2Nz)\n",
    "    R2[1:Nx,1:Ny,1:Nz] = ρ[1:Nx,1:Ny,1:Nz]\n",
    "    \n",
    "    G2 = CuArray{ComplexF64}(undef, 2Nx, 2Ny, 2Nz);\n",
    "    N2 = length(G2)\n",
    "    \n",
    "    # Prepare and call kernel\n",
    "    kernel = @cuda launch=false fillgreen!(G2, f, DELTAS)\n",
    "    config = launch_configuration(kernel.fun)\n",
    "    threads = min(N2, config.threads)\n",
    "    blocks = cld(N2, threads)\n",
    "    CUDA.@sync kernel(G2, f, DELTAS; threads, blocks)\n",
    "\n",
    "    # FFT convolution\n",
    "    res = real(ifft!(fft!(R2) .* fft!(G2))[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1])\n",
    "    res\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc424979-4014-4d05-88e8-fa9fda19c793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res5 = fieldcalc(Rcu, DELTAS);\n",
    "res2 ≈ Array(res5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91db8bc-32f7-4763-9eec-d5a99a71a000",
   "metadata": {},
   "source": [
    "# Timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4081adeb-9952-403d-a137-5364cec565ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.060587185"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_cpu = @belapsed begin\n",
    "    fillgreen!(G0, f, DELTAS)\n",
    "    res = DSP.conv(R, G0)[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61256b98-eec7-4d4d-878a-8f8fa2420134",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.256222967"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All-in-one calc\n",
    "t_cpu2 = @belapsed begin\n",
    "    fieldcalc(R, DELTAS);\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8c02bab8-25af-4a01-bfca-e70038520235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.388237221"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_gpu = @belapsed CUDA.@sync begin\n",
    "    kernel(Gcu, f, DELTAS; threads, blocks)\n",
    "    res = DSP.conv(Rcu, Gcu)[Nx:2Nx-1, Ny:2Ny-1, Nz:2Nz-1];\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b06c9181-e54b-44d4-84c2-7db98f57dcbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.123009048"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All-in-one calc\n",
    "t_gpu2 = @belapsed CUDA.@sync begin\n",
    "    fieldcalc(Rcu, DELTAS);\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa2174ff-3c01-4ba8-b0ea-a79b7008b594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28.060587185, 0.388237221, 72.27691129851766, 197.1905592424388)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_cpu, t_gpu, t_cpu/t_gpu, t_cpu2/t_gpu2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0631be-16da-42fd-b422-82f24cc0d664",
   "metadata": {},
   "source": [
    "# CPU and GPU info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7bc61e6c-bf94-45e0-9ebd-3878dc584b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMD EPYC 7713 64-Core Processor: \n",
       "        speed         user         nice          sys         idle          irq\n",
       "     1727 MHz       2879 s          0 s       1846 s    3614770 s          0 s"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sys.cpu_info()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3570d01f-3d27-456b-9890-dbebe85d8515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Jan 15 19:53:35 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 450.162      Driver Version: 450.162      CUDA Version: 11.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  A100-PCIE-40GB      On   | 00000000:C3:00.0 Off |                    0 |\n",
      "| N/A   44C    P0    58W / 250W |  15127MiB / 40537MiB |      0%      Default |\n",
      "|                               |                      |             Disabled |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|    0   N/A  N/A    124991      C   ...lia/julia-1.7.1/bin/julia    15125MiB |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Process(`\u001b[4mnvidia-smi\u001b[24m`, ProcessExited(0))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run(`nvidia-smi`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6d18156-6867-4749-a2d2-926e9671e44d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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