{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gridding PFT variables to sparse arrays\n", "\n", "This notebook explores regridding the PFT variables as sparse arrays using the `pydata/sparse` package.\n", "\n", "Inspired by `PFT-Gridding.ipynb`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importing Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import xarray as xr\n", "from ctsm_py import utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining simulation information" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "datadir = \"/glade/p/cgd/tss/people/dll/TRENDY2019_History/\"\n", "sim = \"S0_control/\"\n", "datadir = datadir + sim\n", "simname = \"TRENDY2019_S0_control_v2.clm2.h1.\"\n", "var = \"GPP\"\n", "years = \"170001-201812\"\n", "\n", "maxval = \"True\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/glade/p/cgd/tss/people/dll/TRENDY2019_History/S0_control/TRENDY2019_S0_control_v2.clm2.h1.GPP.170001-201812.nc\n" ] } ], "source": [ "print(datadir + simname + var + \".\" + years + \".nc\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "jupyter": { "source_hidden": true }, "tags": [] }, "outputs": [], "source": [ "# This is an example copied from Dan's script -- helps to read in multiple variables\n", "# dir =\n", "# sim =\n", "# pref = 'lnd/proc/tseries/month_1'\n", "# suff = \".clm2.h0.\"\n", "# variables = [\" \"]\n", "# pattern = dir + sim + proc + pref + '{var}' + suff\n", "# files = [pattern.format(var=var) for var in variables]\n", "\n", "# for multiple files, use xr.open_mfdataset; dan also uses utils.time_set_mid to make the time dims work properly\n", "\n", "# 365*utils.weighted_annual_mean --> weights by days/month\n", "# timeslice: ix_time = (ds['time.year']>1963) & (ds['time.year']<2014) # note that dan's dataset is 'ds'\n", "# plt.subplot(121) #-->1 row, 2 plots, plot 1\n", "# signal.detrend (?)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:             (levgrnd: 25, levlak: 10, levdcmp: 25, lon: 288, lat: 192, gridcell: 21013, landunit: 48359, column: 111429, pft: 166408, time: 3828, hist_interval: 2)\n",
       "Coordinates:\n",
       "  * levgrnd             (levgrnd) float32 0.01 0.04 0.09 ... 19.48 28.87 42.0\n",
       "  * levlak              (levlak) float32 0.05 0.6 2.1 4.6 ... 25.6 34.33 44.78\n",
       "  * levdcmp             (levdcmp) float32 0.01 0.04 0.09 ... 19.48 28.87 42.0\n",
       "  * lon                 (lon) float32 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
       "  * lat                 (lat) float32 -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
       "  * time                (time) object 1700-01-16 11:44:59.999993 ... 2018-12-...\n",
       "Dimensions without coordinates: gridcell, landunit, column, pft, hist_interval\n",
       "Data variables: (12/51)\n",
       "    area                (lat, lon) float32 dask.array<chunksize=(192, 288), meta=np.ndarray>\n",
       "    landfrac            (lat, lon) float32 dask.array<chunksize=(192, 288), meta=np.ndarray>\n",
       "    landmask            (lat, lon) float64 dask.array<chunksize=(192, 288), meta=np.ndarray>\n",
       "    pftmask             (lat, lon) float64 dask.array<chunksize=(192, 288), meta=np.ndarray>\n",
       "    nbedrock            (lat, lon) float64 dask.array<chunksize=(192, 288), meta=np.ndarray>\n",
       "    grid1d_lon          (gridcell) float64 dask.array<chunksize=(21013,), meta=np.ndarray>\n",
       "    ...                  ...\n",
       "    mscur               (time) float64 dask.array<chunksize=(100,), meta=np.ndarray>\n",
       "    nstep               (time) float64 dask.array<chunksize=(100,), meta=np.ndarray>\n",
       "    time_bounds         (time, hist_interval) object dask.array<chunksize=(100, 2), meta=np.ndarray>\n",
       "    date_written        (time) object dask.array<chunksize=(100,), meta=np.ndarray>\n",
       "    time_written        (time) object dask.array<chunksize=(100,), meta=np.ndarray>\n",
       "    GPP                 (time, pft) float32 dask.array<chunksize=(100, 166408), meta=np.ndarray>\n",
       "Attributes: (12/102)\n",
       "    title:                                     CLM History file information\n",
       "    comment:                                   NOTE: None of the variables ar...\n",
       "    Conventions:                               CF-1.0\n",
       "    history:                                   created on 09/27/19 16:25:57\n",
       "    source:                                    Community Terrestrial Systems ...\n",
       "    hostname:                                  cheyenne\n",
       "    ...                                        ...\n",
       "    cft_irrigated_tropical_corn:               62\n",
       "    cft_tropical_soybean:                      63\n",
       "    cft_irrigated_tropical_soybean:            64\n",
       "    time_period_freq:                          month_1\n",
       "    Time_constant_3Dvars_filename:             ./TRENDY2019_S0_constant_v2.cl...\n",
       "    Time_constant_3Dvars:                      ZSOI:DZSOI:WATSAT:SUCSAT:BSW:H...
" ], "text/plain": [ "\n", "Dimensions: (levgrnd: 25, levlak: 10, levdcmp: 25, lon: 288, lat: 192, gridcell: 21013, landunit: 48359, column: 111429, pft: 166408, time: 3828, hist_interval: 2)\n", "Coordinates:\n", " * levgrnd (levgrnd) float32 0.01 0.04 0.09 ... 19.48 28.87 42.0\n", " * levlak (levlak) float32 0.05 0.6 2.1 4.6 ... 25.6 34.33 44.78\n", " * levdcmp (levdcmp) float32 0.01 0.04 0.09 ... 19.48 28.87 42.0\n", " * lon (lon) float32 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", " * lat (lat) float32 -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n", " * time (time) object 1700-01-16 11:44:59.999993 ... 2018-12-...\n", "Dimensions without coordinates: gridcell, landunit, column, pft, hist_interval\n", "Data variables: (12/51)\n", " area (lat, lon) float32 dask.array\n", " landfrac (lat, lon) float32 dask.array\n", " landmask (lat, lon) float64 dask.array\n", " pftmask (lat, lon) float64 dask.array\n", " nbedrock (lat, lon) float64 dask.array\n", " grid1d_lon (gridcell) float64 dask.array\n", " ... ...\n", " mscur (time) float64 dask.array\n", " nstep (time) float64 dask.array\n", " time_bounds (time, hist_interval) object dask.array\n", " date_written (time) object dask.array\n", " time_written (time) object dask.array\n", " GPP (time, pft) float32 dask.array\n", "Attributes: (12/102)\n", " title: CLM History file information\n", " comment: NOTE: None of the variables ar...\n", " Conventions: CF-1.0\n", " history: created on 09/27/19 16:25:57\n", " source: Community Terrestrial Systems ...\n", " hostname: cheyenne\n", " ... ...\n", " cft_irrigated_tropical_corn: 62\n", " cft_tropical_soybean: 63\n", " cft_irrigated_tropical_soybean: 64\n", " time_period_freq: month_1\n", " Time_constant_3Dvars_filename: ./TRENDY2019_S0_constant_v2.cl...\n", " Time_constant_3Dvars: ZSOI:DZSOI:WATSAT:SUCSAT:BSW:H..." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data1 = utils.time_set_mid(\n", " xr.open_dataset(\n", " datadir + simname + var + \".\" + years + \".nc\",\n", " decode_times=True,\n", " chunks={\"time\": 100},\n", " ),\n", " \"time\",\n", ")\n", "data1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert the 1D PFT dataarrays to nD sparse arrays\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def to_sparse(data, vegtype, jxy, ixy, shape):\n", " \"\"\" Takes an input numpy array and converts it to a sparse array.\"\"\"\n", " import itertools\n", "\n", " import sparse\n", "\n", " codes = zip(vegtype, jxy - 1, ixy - 1)\n", "\n", " # some magic from https://github.com/pydata/xarray/pull/5577\n", " # This constructs a list of coordinate locations at which data exists\n", " # it works for arbitrary number of dimensions but assumes that the last dimension\n", " # is the \"stacked\" dimension i.e. \"pft\"\n", " if data.ndim == 1:\n", " indexes = codes\n", " else:\n", " sizes = list(itertools.product(*[range(s) for s in data.shape[:-1]]))\n", " tuple_indexes = itertools.product(sizes, codes)\n", " indexes = map(lambda x: list(itertools.chain(*x)), tuple_indexes)\n", "\n", " return sparse.COO(\n", " coords=np.array(list(indexes)).T,\n", " data=data.ravel(),\n", " shape=data.shape[:-1] + shape,\n", " )\n", "\n", "\n", "def convert_pft_variables_to_sparse(dataset):\n", " import sparse\n", " import xarray as xr\n", "\n", " # extract PFT variables\n", " pfts = xr.Dataset({k: v for k, v in dataset.items() if \"pft\" in v.dims})\n", "\n", " ixy = dataset.pfts1d_ixy.astype(int)\n", " jxy = dataset.pfts1d_jxy.astype(int)\n", " vegtype = dataset.pfts1d_itype_veg.astype(int)\n", " npft = vegtype.max().load().item()\n", " # expected shape of sparse arrays (excludes time)\n", " output_sizes = {\n", " \"pft\": npft + 1,\n", " \"lat\": dataset.sizes[\"lat\"],\n", " \"lon\": dataset.sizes[\"lon\"],\n", " }\n", "\n", " result = xr.Dataset()\n", " for var in pfts:\n", " result[var] = xr.apply_ufunc(\n", " to_sparse,\n", " pfts[var],\n", " vegtype,\n", " jxy,\n", " ixy,\n", " input_core_dims=[[\"pft\"]] * 4,\n", " output_core_dims=[[\"pft\", \"lat\", \"lon\"]],\n", " exclude_dims=set((\"pft\",)), # changes size\n", " dask=\"parallelized\",\n", " kwargs=dict(shape=tuple(output_sizes.values())),\n", " dask_gufunc_kwargs=dict(\n", " meta=sparse.COO([]),\n", " output_sizes=output_sizes,\n", " ), # lets dask know that we are changing from numpy to sparse\n", " output_dtypes=[pfts[var].dtype],\n", " )\n", "\n", " # copy over coordinate variables lat, lon\n", " result = result.update(dataset[[\"lat\", \"lon\"]])\n", " result[\"pft\"] = np.arange(result.sizes[\"pft\"])\n", " return result" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:             (lat: 192, lon: 288, pft: 78, time: 3828)\n",
       "Coordinates:\n",
       "  * lat                 (lat) float64 -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n",
       "  * lon                 (lon) float64 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
       "  * time                (time) object 1700-01-16 11:44:59.999993 ... 2018-12-...\n",
       "  * pft                 (pft) int64 0 1 2 3 4 5 6 7 ... 70 71 72 73 74 75 76 77\n",
       "Data variables: (12/15)\n",
       "    pfts1d_lon          (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_lat          (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_ixy          (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_jxy          (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_gi           (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_li           (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    ...                  ...\n",
       "    pfts1d_wtcol        (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_itype_veg    (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_itype_col    (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_itype_lunit  (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    pfts1d_active       (pft, lat, lon) float64 dask.array<chunksize=(78, 192, 288), meta=np.ndarray>\n",
       "    GPP                 (time, pft, lat, lon) float64 dask.array<chunksize=(100, 78, 192, 288), meta=np.ndarray>
" ], "text/plain": [ "\n", "Dimensions: (lat: 192, lon: 288, pft: 78, time: 3828)\n", "Coordinates:\n", " * lat (lat) float64 -90.0 -89.06 -88.12 ... 88.12 89.06 90.0\n", " * lon (lon) float64 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", " * time (time) object 1700-01-16 11:44:59.999993 ... 2018-12-...\n", " * pft (pft) int64 0 1 2 3 4 5 6 7 ... 70 71 72 73 74 75 76 77\n", "Data variables: (12/15)\n", " pfts1d_lon (pft, lat, lon) float64 dask.array\n", " pfts1d_lat (pft, lat, lon) float64 dask.array\n", " pfts1d_ixy (pft, lat, lon) float64 dask.array\n", " pfts1d_jxy (pft, lat, lon) float64 dask.array\n", " pfts1d_gi (pft, lat, lon) float64 dask.array\n", " pfts1d_li (pft, lat, lon) float64 dask.array\n", " ... ...\n", " pfts1d_wtcol (pft, lat, lon) float64 dask.array\n", " pfts1d_itype_veg (pft, lat, lon) float64 dask.array\n", " pfts1d_itype_col (pft, lat, lon) float64 dask.array\n", " pfts1d_itype_lunit (pft, lat, lon) float64 dask.array\n", " pfts1d_active (pft, lat, lon) float64 dask.array\n", " GPP (time, pft, lat, lon) float64 dask.array" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pfts = convert_pft_variables_to_sparse(data1)\n", "pfts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### test out a plot\n", "\n", "\n", "xarray cannot handle directly plotting a sparse variable (yet). This will be fixed soon; instead we manually \"densify\" to a numpy array using `.copy(data=gpp.data.todense())`" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "gpp = pfts.GPP.isel(time=3606).compute()\n", "gpp = gpp.copy(data=gpp.data.todense())" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Vne17gpnVa8xkz2us9WrUYxtYd+0Yy2zFeT9P8+dQdT8UkRGpESXTQdZNbt5kVqKO89L9P1lMuluThbn11tfi7mfUi0g0cLt4rE/EzF7CUNep97n7fzZYz78CN6SH/1qj3EOJNPFQldI7za3znfTwn8ysu2rfHPDm9PDHNbpM/m+6f7KZPaRG9d5C/AjbwNCPzbHIyn+BDZ8wNJP9uL/G3et18crmEzszPZzMhBKZnxLZHwHeWL0y/WjP0tr/coT5v1rOzJ4IfJ8Yf/h1Iv36uKWkKD9LD99SHTVNn73sOf9vjfGRLfks1JFl8DzOzJ5eY/2riAbaXuCHVet+S3S1zBGf92HS9yJrNE5USv3nmdmRNY79GCCbaPz7Yyxz3Of9NM1Es+fQVc0eV0QOEu6um25T+gZ8kOhK83ti7FGtbc5I26ytse7itO6lY913tDKIf/B3pHWbiGjDrIr1hxE/gK6td/ypcCPmBSqk5/GRJvZ/WtrXgY8As9PyBwDXpOV3AJ019l1KTNTpwC+BlWn5YmIOLwf6gRNq7GvA5WmbO4FT0/JO4K1EUoxsXFczr8sMYG0q4xrgAWn57PQ8s+f85FHKeXXargAsa+H7Np+YcDi7ZfX5f1XL22vs+4aK1/Yfs/eGmDj6J2ndAPCwJuo1t+r496Ty3lq1vLNqv0cRkS8nGha5MR737Ow1qLP+Iek5efpsLUrLV6bPngPbgcWt/ixUrD+7zvrvpvVbgKelZXngxUSKcgc+WGff16b1xfQaZ+/lI9P3woE/NPOajfJ6Z6/HDmKc2GlpeQ54BnFOdOBXNfZdNcp79aS0fiuwulXfmYm+VX2+H1bxvp9atW5Mn23ddNNt6t0mvQK66TbajehW1J/+EQ0S2Z7WVv4oYJIaUWnd0cS4hOyfZTH94++tWObASyb7tRzh+d1ZUc+No9xOq1PGuyrKKDDUMHJiPMQDRzj+k4jB9dn2O4juPNl7/sIR9l1RVf/daZ/s8efG+do8hPhhm5W3k6HGWQl4ewNl/DFt/7MWv29rqz5j9W5n1Nn/CxXb9BPRqezxAJGVsZl6XdxgvV5atd9FVZ+ZkT6Hz6tx3LOz/Ueo20sqPh+l9FnLjtkDPH4iPgsV+5xdZ/0costgtt0eItFJ9vjHQNsI5X+x6r3bXfH4DuDQOvuN+po18Pl7JUMNpt0MP/etocaFA0ZvRLUTU05kr+2mdLy1wIpWfo9a/J1s5HPvwKrJrqtuuuk2vpu688mU5+63ED+yf0H8aFlKXC1fMdJ++4vHJJMPI7od/Y74ITqHaEjcyNBcS9+crDo2oPJc0NR4K3f/APE+/ZS4mt9JNG4+TTSg/lLv4O7+ayLD2NeAdcRV//uB7xHRpf8ZYd91ad8PEQkA2ogfcr8D/p+7v3akJz4ad7+BmGT50+n5dBKN5J8CT/JRuj2a2dHEPDkwNbrylbn7PxLj+X5FvGazgHuJep7o7hPV/aueys/hIkb+HI6Uarwud/86EaH5HvEZm0E8568CD3X3i0bYd1yfhVHqtYv4nLyd6B6bNWwvJyKFz3T3wgj7vxp4HvG57yG+B7cQkfyHuvv6OrtmXRPHM07vduDhxGu4k4igrQU+Bjzc3cecEdDdB4EnEOfN+4io6+HppszCIjLpzN0nuw4iIiIyCczsFiKr4DPcvebcZSPsu5Zo1DzOmxiHKSIynSkSJSIichAysyVEA+rasTagREQOdmpEiYiIHJwek+7fN6m1EBGZhtSvWERE5CDk7t9nis2/JyIyXSgSJSIiIiIiMgZKLCEiIiIiIjIGikSJiIiIiIiMgRpRIiIiIiIiY6BGlIiIiIiIyBioESUiIiIiIjIGakSJiIiIiIiMgeaJOsCZ2V3AHGDtJFdFREREDgyrgF3ufsRkVsLMvgUcN0HF3+LuL5igsuUAoEbUgW9OjvyCbmYvmOyKiIiIyPS3h92UKE52NSAaUCee+KDOlhZ67Z/7W1qeHJjUiDrwre1m9oJT7ImTXQ8RERE5AFzhv2E3O9ZOdj0ATnxQJ1f9amVLy3zEk+9RQ0pGpUaUiIiIiExLjlOi1PIyRUajxBIiIiIiIiJjoEiUiIiIiExbRW9tJEqkEWpEiYiIiMi05ECpxd3v1JlPGqHufCIiIiIiImOgSJSIiIiITFOtTyyhWJQ0QpEoERERERGRMVAkSkRERESmJQeKrjFRsv+pESUiIiIi01arE0uINELd+URERERERMZAkSgRERERmZYcKCrFuUwCRaJERERERETGQJEoEREREZm2NCZKJoMiUSIiIiIiImOgSJSIiIiITEuOT0CKc0W2ZHRqRImIiIjItFWa7ArIQUnd+URERERERMZAkSgRERERmZaU4lwmiyJRIiIiIiIiY6BIlIiIiIhMW0WFjmQSqBElIiIiItOS0/rEEmqTSSPUnU9ERERERGQMFIkSERERkWmriE12FeQgpEiUiIiIiIjIGCgSJSIiIiLTkjuUWjyIyTUoShqgRpSIiIiITFM2Ad351D1QRqfufCIiIiIiImOgSJSIiIiITEtO6xNLqDefNEKRKBERERERkTFQI6qFzOylZuaj3IoV268aZdvvTObzEREREZnqSm4tvYk0Qt35Wut64Jw6604HHg/8vMa6G4ALaiz/S0tqJSIiInIAUnc+mSxqRLWQu19PNKT2YWZ/Sn9+scbq69397ImplYiIiIiItJIaUfuBmT0QOBW4D/jpJFdHRERE5ABhFFs+OkVd+mR0akTtH/+Y7r/i7sUa6w81s38EFgJbgT+5+437rXYiIiIiItIwNaImmJnNAF4IlIAv19nsSelWud/FwEvc/Z4Gj3NNnVXHNVZTERERkenFoeXJIDQmShqh7HwT7/8B84Cfu/u9Vet6gfcDJwHz0+2xwO+AM4Dfmln3fqupiIiIiIiMSpGoiffqdP+F6hXuvgl4T9XiS83sycAfgFOAVwKfGu0g7n5SreUpQnXiWCosIiIiMl20OjufSCMUiZpAZvYA4DRgHfCzRvdz9wJDXf8eMwFVExEREZn2HCh6rqU3deeTRqgRNbFGSygxks3pXt35RERERESmEHXnmyBm1gW8iEgo8ZUmijg13d/ZskqJiIiIHFCMklKcyyRQJGriPJdIFPGzGgklADCzU8yso8byxwNvTg//Z+KqKCIiIiIiY6VI1MTJEkp8cYRtPgyckNKZr0vLHgw8Pv39bne/bGKqJyIiIjK9Oa1PLKExUdIINaImgJkdDzya0RNKfBN4FvAI4KlAO3A/8D3gv9z99xNcVREREZFpK0ss0eoyRUajRtQEcPebaaBDrbt/hebGS4mIiIiIyCRRI0pEREREpimj1PJEEEosIaNTYgkREREREZExUCRKRERERKalSCyhMVGy/6kRJSIiIiLTk7c+sYRaUdIIdecTEREREREZA0WiRERERGRacoxSy7vzKbGEjE6RKBERERERkTFQJEpEREREpq2iK3Ik+58iUSIiIiIiImOgRpSIiIiITEtZivNW3lqVnM/MVpjZV81svZn1m9laM/ukmc2f6HLM7DQz+5mZbTOzXjO70czeZGb5EfZ5iZldaWY9ZrbTzC42s6fX2fZkM/sPM/u5mW00MzezdRPxXKYqdecTERERkWnKKLU6xXkLEkuY2VHAZcAhwIXALcDJwBuBp5jZo9x960SUY2ZnAj8A+oDvAtuAZwCfAB4FPLfGcc4F3gqsA74EdABnAT82s9e7+39V7fIPqQ6DwM3Akol4LlOZIlEiIiIiIq31WaKx8AZ3/zt3f7u7P55oyBwLfHAiyjGzOUQjqAic4e6vcPd/BR4K/Al4jpmdVbXPaUQD6g7gwe7+Znf/Z+AkogF2rpmtqqrXecCJwCx3f8hEPJepTo0oEREREZmWpmJ3PjM7EngysBb476rV7wX2AC8ys+4JKOc5wGLgO+5+dbbQ3fuAd6WHr60q6zXp/oPuvr1in+y4ncDLKndw9+vd/Tp3HxjpOYzzuUxpakSJiIiIiLTO49P9r9y9VLnC3XcDfwRmAqdOQDnZPr+oUd6lQC9wmpl1NrjPz6u2aVarXpMpQ40oEREREZm2im4tvSXHmdk1tW4NVOnYdH9bnfVr0v0xE1BO3X3cvQDcReREOBIgRX6WAz3uvmEcdR1Nq16TKUOJJWTSvfK2tQB8+ZhVk1oPERERmV4co9TimICPP7HE3HS/s876bPm8CShnrPu0qq6j2V/H2W/UiBIRERERGe4Wdz9pgsrOWmnjHn7VRDnNHrtVmd/radVrst+oESUiIiIi01ax5SnOxy2Lqsyts35O1XatLGes+4y2/WgRpEa16jWZMtSIkgn1y/U3ANBT6uPZK04Ztu61a24HYEcxErE8/a+REGZnYSYAv39wJ6PJ9ukvtQMwK98HwA+PX8SLb70XgG8ce9i4noOIiIjIGNya7uuN71md7uuNDxpPObcCD0/7DBu/ZWZtwBFAAbgTwN33mNl9wHIzW1ZjXFSjdR1Nq16TKWPKNd1FRERERBrhQAlr6a0F/cl+l+6fbGbDfmub2Wxiwtu9wOUTUM5F6f4pNcp7DJEB7zJ3729wn6dWbdOsVr0mU4YiUdIS59wVFztefMXLATjirIhAbSr2AHB3oZN/ueMmAHaVZgCwpxSRpp3FiDy1WyHuc3F/0vUdAOwtttOZls3Mx3QEg6U8ANfvjijTnLaIQM1v7wXgaTftZN3gAgAecl0u7RP3/aX42O8udAHQW4jjtFmJdT0RZc5ZnEJn/s2dTb4iIiIiMuHcWt+dz8eXWMLd7zCzXxHzIv0z8JmK1ecA3cAX3H0PgJm1A0cBg+5+R7PlJOcDHwbOMrPPZHNFmVkX8IG0zeeqqvx54EXAO83sgmyuqDTB7j8D/cDXmnktxvlcpjQ1okREREREWuufgMuAT5vZE4CbgVOAxxFd1t5Zse3ytP5uYNU4ysHdd5nZq4jG1MVm9h1gG/BMIs34+cB3q/a5zMw+DrwFuNHMzgc6gOcBC4DXp4l3y8zsOODtVXWdb2bnVTz+F3ff0uxzmerUiJKWyKfgdxaByvR6LF87uKgceRr0/LBtillCFo+PYyldUVrQFhcjNpTmlqNH2b7tVgRgXvteAJZ3lifYjuOWOtheiLFWOWJOtxn54rBtCvnBtD7qWMJY2r0bgL5iHG/gt4elOkUd2554z0gvg4iIiOxHDhRbnuK8BWVE5OXhwPuIbnJPAzYAnwbOcfdtE1WOu19gZo8lGiXPBrqA24lG0qfdfZ+n6O5vNbMbgdcBrwZKwLXAR939JzWqthR4SdWymVXLzgbKjahWvSZThRpRIiIiIiIt5u73Ai9rYLu1UH9yqkbLqdrnj0QjZSz7fB34eoPbXswIdR5hvzE/l6lKjaiD3LlrL6fdIlLzxsNPG7buDbdHIpWONFbp3KNO2Gf/bJxTFhn6wj1/AGB2Lq4KbSvG96tIjnw6TqbPI6PelsHZAMxt6x1W1hGdmwCYmetnW2EWAD3FiGbNysd4yF3FGWl5jG/K+kXPzPeXZx3vL8WYp1w6fja+qtruwS4KadxUdt+Wi3268rFPxx8WxXEHox6lx91X3r/3l0fGtm0R4dq+N8Z6FYpRVntbsfz4kGfeUrMOIiIiMjalcY5hEmmGsvOJiIiIiIiMgSJRIiIiIjItOTYBY6IU2ZLRqRF1kNtUnMWcXN+wZVlK8PWD8wFY0BZpyl93+xoAdhW76M5Fd7pVbTsAWFuYB0CR6JKXoz89jrKOat/EPYVIOd5l0d3t/kKkE3/YzLsB2FyIbn1Z1727B6Lr3MzcQDmxRNadL7ufn7oAruuLuu5J6coLpTyz2uN5zUgJJDpT17zqJBUdqXvfQCnPQEqdvqgrklrsGohuglnK81rW/eCB8Vp0Dh8Pubg7XrdZ7ZGWffPeSHSxfc9M7vu/6Bq5/O9vqluuiIiIjK7U6hTnIg3Qp05ERERERGQMFIk6yH3kqAdy7tqYHPq1a24H4PKeo4GhCNQh+d3D9jm0bQe9KVnDxmJEjbJkENngzletfPQ+x/rw2isA+HP/CgD6SpFYYndKDpFFhrYXIiHD1sGI3LTnSvQWY9tCihTtKcbx7++bA0BvIdZn6cq72grsGIhyBtoiErQ9Pc6iSlmE6v69s8t1nNfRN+w4mR39EZGamZJGZJGqTRet4Lj2SIDRkYvXIEuPniWl6BmMut67MSJxc+b20tUe267/YUSkDn2WIlIiB6rbvvQIjnnVVTXXZZHsYkpmk7sxzqm5Aiz/j8v2TwVFRpAlkKqVXGoqiBTnre1+14oU53LgUyRKRERERERkDBSJEu4rxtikjWlc00O7Y0LZL66OlN3vujMm0M2u9KwdXFget5SlLV/SthOA/zj6wTWPce7ay7mnEOOWsslvs4hQOzEmaWeKSGXRrL0p2jToQ5Pk7hyMbXaksUp7BjuGlZVdzbUBZ2FXjJfKokpZivPs8X39EZnqK8TXYM9AZ/k4+RRFWpDK6OmPdVkK9Y40ce+hM3fRm8ZhreuJ13FWR4wHI1U7q+PceVHW7p4uSpuj/j43Ilt3fOth8dwHo/zVL72GZt32xZPjD3NsIJX3z1fU3LZ83P54TawnXouj33B508cXkeHqRaEAin+NaHqatYFsJoj+BQ4XRdR+zU1xv/r1+l7K/vEvd9zEnlL838t+G7zw1pjSY2aunzvO7Gf3lOlAYRMwJkqJJWR0akSJiIiIyLSk7nwyWdSIEj5x1PEAfGhtXC3tS9nr3nbHLgBu6V8GwKqOzQDctHdFed9FbTFeanNhTs2ys4x+W0sz2V2KKFKW2W9Dyv6Xja/aWYj1uwoRpRlI9egbaC+PK8qiOv2DsW6gGBGUtnyKMqWJbTvbimxJ2fCyqFKmM02cu2NvOt7eON7cmX10tMW63X1xBW7HnoUAWIp0ZcfZ2jezXN7dO+N5bNs8NLYKoH191LV4+F4APEXYLOeUZkaYykqxLIsEzb4x9ilHiHpirNfcZbvYuS2ez9IlEfXbtC2ON2NmjPnaszOeh/VEmd7pWCH+XvNfpwCQS5GutrQN96d/FTPiec1cpx6+IvvTrAj8s/BL+45/WvP1k+KPjtI+60Qm0oC3Mejxf3Z2Lv6HZY/XDSws/y1yMNO3QERERESmLaU4l8mgT52IiIiIiMgYKBIlZe9Y9QhgKJ1pNqh0cVt06xtI4fsHz7iHHcWUftyi+9s3jj2sZpnF1IXthr2Hl1OYd+YimUJ1N74sTXqWenxLXySv6Cu20Zu68WVJJzJZ97ostXlnW5SRz5XYOxhd4QYLcdz+gdQFsDeWWz72ybVFGZu3zsZT9zqyrnep/PbO1AVwT9R1RmfUceP2ORTvideC7th20VVxbWLWuuhmd/ffxuuYdZXr3ujsOiJ140vfwHxsyuDc9LzuiuMUZkQd+65bQHtUm82bFkfd0iWQPZ1RvnfG8a0t9rGBHG09sVGpI5Z17IjjpvmFyQbP5nbkh9Vjz3NPpfv7GsQukjn1hvjSXP6Q5v5t7v55TB2xeXt0w138k+h+m2Z6YNFl0S14y2nby/scu3IjALfevWxYWVnymNze+H4f/UZ9V6W1Pn30sTzr5i0A9KcPafa/+2cnzGWn5+vuu7+5WznpUyvLFBmNGlEiIiIiMm2VlE1PJoEaUbKPG/YeDsCxXRsA2FqVzrzdinz7uLgy+vi/xKSzz7slrpj+x3VPAeBvVt8MwPqUPKIzN8juwbjyel//PGBoct09hYikZFGmTSkCtXF3XLFty5eY0xmT4PalSXV7B1LShpRIItu3qyOulPUNtJeTQQz2p4/5/XF8utPEwCkSNeeqWD44C7q2xia9S+K+75DYdiCfolfdUf5Aimr5xhl0r4tjz78jTeK7LhI/FOZEuct/F8v3pIvJxQ5j0Y1Rbq6YUrN3xPMYnJn+EWT/D1Leh4E5QymQ3dLKdOGtd2lKJJHLpfWpCIcU3KN7fSycd3vUf+cR8Xw6dqUyU1n5eJnZsyRHiq+JCLCkPb7Xh/zpKDpSKHfdKT0N7z/7qTGZeZZ+ZtPrTwOgN50X/rIp/lhKRKLuOf9BlDZl4eY4V675zKkA5FNimFJKCHP3B6Ksw991GXd9+JGxTzoRZN/plWdr4l6p79Vr7gSGkkfkKVFM/2TWFWOi+JzH5+15t2zktr8fnEIpzkUmhxpRIiIiIjItObS+O19LS5MDlRpRB4G5DyjCzY1vn0WIrt5zBDAUgcrGHS1p38nT/xpXS/Np4ty7+xcB8ISjbgOGJsXt84h49Bfb2V6ItOBZFp1sm+40EOf+vrhGu35nDAzqaI+rvd0dA7SlNOWbtg9PI55NTpv1Xx7YFGV6R4lc7/A+2/Nui20O+VNcPbaBKN9nRB09l2NwfkTFujekCFd73PfNj/vtxw9PEd61BQ69MHIU+5yI3RTmRR1yg1Hn7nsjWpfvi+WdW/rI9UdEaM+R8VwtRaQ6d8Y+g91xnK5tWcSqjc6d8XcWaepbkCYRHhzejSH7X2JFaE8XygcjuMeulfFcu++PsnYenqKBh8Xxj3rrnxCRfV34gJju4KTrtzM7hXfWMaOhfdd8/SRWv2T4BNqHfCYiQ2u+EOObZqfxnNt+egwAA7d3410ptXkaq5lNxFucE+eubAqDLOJ89wdOw3PxXfb2NO3D7Hh8+6ciipUC9Bz1Jo2jkn0jULuL0YOiz9s5tD3+z6/o2AbAuoGISPWX2ilOoTFRIpNFjSgRERERmaZsn6RTrShTZDRKcS4iIiIiIjIGikQdBEpYOQHERQ/cN13A6TdGxoL2NFh662B0mduVEkFs7o++YMtmxMDqzQOzmZG64PWnHN3VXfR6UwKIO/cuLh/nrp7oDjOnPY7Xlium5dFFoFCKMvbsiS51uwei+9/uWQN0pq59nSm1eO/O1I1mb3Qp6NoY9+27Y/H8Nc7MtUOpggG8I+qa600ZGgYGhq0vLuomNxBdYGZu6o3ntyTqMOv2eLzo2pQSfGc8plBgcOUiasn1F4Zve0jUOb97bzkJxKybNkfdZsZzHpwXr7mlbpAd26PbUNf6QfauiPfF21NXwtTVr3t91Ll/Xjy/voWpC+JCKKUeF53RG4PZ98br1/GLq+I4z4yuRL0nD9Z8DiIyXJ4Sc/PxnV5xRZzfRkswkU9d62pZ/Y9XDnu85nOnADBzQ472PfFdzroMrz9zJQClfCxf8e1IVrH2lZE+vdQBuYE4PwymKReyx74wznez5uwF4O7vPTi22ziTo9+grn0Hq93F+L+0sxj/6/pSt77Zub5yd/1Sui+m6EzJc1NqzJBDOQlGK8sUGY0aUSIiIiIybbW+O5/I6NSIajEzWwscXmf1/e6+tMY+pwHvAk4FuoDbga8Cn3H34njrVHIrT5aXWX11RD7yOGv2RHKDLf0RpepIEaK+Ynw8ZrXHFczrtqwAoKttkBXdO9M2UW4hRaKuuz2ulD7kqHsBuHXnIbFPvsCt90Xe8MOXRB7xeSlteRaByrLrdHdHpKjQFY/3bp5Jf8UEsgD5dIW2PSV4mLEpnlc2wNpzQIr2UIyFtjdFW3LpZNsWz89KcZWtbdueoWhVSvww456Urnx+XKVr25xCXbvTlec5s2nbHlelrVBKx86luqbjedS9rTclhmjPYyka5p2Rqt12RLkde+I1ad8cr2tpdrxPA4u66dge70Nhdqxr3x5l5Htin2JXpJPP0phvXNBJW0pvnA1IzyJQma4fxVXwI3/EuK390GnlNOyHnaN0ynJguvKheSCi6s+5OQbl/+YPDwBgx6O31Nxn3kUzWPPZiDCt/qcrRix/9Wtjfe+zT2X2L/8CgKdz1aHficQ9hc3pOEdF8p9V314PQGn2TDY+Zh4Ae9qyibZjU+9NiQM8TRg+K76sA93F8uS9bbsidN22J86RK9+r7/GBLksk1Zs+KNmk9zlzDkuJJe4ciIhrbzH+Hy1q383UikWJTA41oibGTuCTNZbv0+fDzM4EfgD0Ad8FtgHPAD4BPAp47oTVUkRERGQac4xSy7vzKbIlo1MjamLscPezR9vIzOYAXwKKwBnufnVa/m7gIuA5ZnaWu39nPJVxjO2DEUk5/Mq4knRfb4xz6sgVyxGnmW0RwcgiQ0fMiojRn7cdCkB/IbYbKObZlI+rVW0pxJFFoo5dFRP0bu9P6bzzMS6or9hG14yIpORSjt2ewbjy1dUW2+wdjAjLQCGujA3cF5GxWffmyAJpabhWOc13Lg1rmrM26t61Ofr7W+8APjNdgm3LIlIpmpWNiWpLA4ZKaXmhhA30D3/xBqNubZurAoKzU85wd2wgrctlaYhTRGpGHN8700S9xVL5OBnrT0+gI9U1jdPKTt+5vVH3XGcb3pbGY6VxW/kde1IdU9Ts3mijbz5lXrwmdzvd6+P5dGyMSFeBiXPopYPsPKp99A1FDhBZqvNHzFsLwGe//liAfdKZL/jyZSw+PlKXr/lITIa77E/xPZ7xw9qRqTl/uHPoPFNI39w00XbbwoiEsSPNlp3OPX73vcxdeRIA+TQheN+CtEl6XJgV972rUsS8owgDcS4sLohziVt8j7OJezt2RvnLPzR6ZOre98bEv31L47yYRdZkaurKxXuepTH/n2OXpzWz+CEx3vf5t8T/9SxKNeh5xaFEUHa+yfYcYDHwnawBBeDufUT3PoDXTkbFRERERKaDoltLbyKNUCRqYnSa2QuBlcAe4Ebg0hrjmx6f7n9Ro4xLgV7gNDPrdPf+Gts0pPfmoex5WT/mBZ0RxVjY0cvde+JS5Y6ByAy3M00Ku7grIhvzOiO6s3J29I++ffui8ripw7pj2UDK0rc7ZfSb1xFXaHcNRuSr5MbyeTvLfwPcvztlAdweEad5C+N4g31RlqcJJbu2tZMSBzJ3TcqatzDK7dyaMv2lcUnZ+CNKju1JL1lfivakjFa0R/k9x8bzzian7dw+SMc9cTWuNDsid7me4VEl25WiPymq5PNnM7gwtu1YtyNei0NjjFlbT8pguDDLuBe7tnW0laNTuTQGyjvT+KzC8I9IdrxcZzvkU6Rrb7wYhUXx+mVjonIbY5xE5845AMxZs5vStTfFtky8jl9cxeLRNxM5YNw9EBGhnWki8RMOj7FJa394AgA9W2P5Ma+8CtsS55ZFN8Q+9z88oj+rflin8EIRL6Sxk4PDx1eW9sY5uW159BLwnjSh99IldN8YUQMbjOG3ljKl9hwWRRz5tTRe9U0xxrXYXYQ0QW8KdOFpDGrqpEBhYN8flev/LSJOe1ZkAy5TpL0vPU7nu2yS36xMcpDrT1H1FGnPxmyW2mObI9+276Tfd3zrYQAc9YLr9lknzRuKPO3rnLsiorq7FP/DZufjc/fF1UeyawpNtuve+sQSrlCbNECNqImxFPhm1bK7zOxl7n5JxbJj0/1t1QW4e8HM7gJOAI4Ebh7pgGZ2TZ1VxzVWZRERERERaYS687Xe14AnEA2pbuBBwBeAVcDPzewhFdvOTfc765SVLZ/X8lqKiIiIHABKnmvpTaQRikS1mLufU7XoL8BrzKwHeCtwNvCsBovL4tOjBpbd/aSaBZhd03Wcnbg3ZWa4aVt08Vg0I7p/LO7oKSeFyBI+tKekEdvTZLdZ4oesC9+jl91ZnmQ32yZbN7s9upZtTenSK5NV7OyPLgF9qXtJ6feRkntBGhu9/fSURvye2O6Qv5TKr8K8GyLJRdYNrn1X1Lmccjzr7lLZHW5mmpA3SyCR1vWunhf1mBdlzNiaBsvObqP/wZGGvWtrSoKxM3Xfq0gGEWWm7nfbd5P78y2xSVqVX/Cg2Dd1J+xMy/sOiednAwUsdTH0OWlZSqlOKdU/G0ieklTkN2yltCjqXZgfr08+desrp3JP6dLn/iIFLU39usfi9m+eCMDMlPr50GfdNJnVkWmgevLyB14bcy2s3R5dhWctjG7Gd3zrYXB/fG+P/m6cU/rnRtKIjW+KbnFLP1mVtGFGF/Sm6ROI856nbnz5BXHuLNy3ftguub5+Sv1xDu5cMg+AmX+N7n2lp8X0ExufGv36ZmyMffYcbmT/amww3Wf/cUrxuDgjFmSp0K1g5PbGsmwyXwbjPFuamaZyyH6HzsgS78T2+Y4ixSyRxazUnS8V0TEnvnt3fvuhsb4/tuuYOUhXW5wz7/u/6Co5d2Y8z1lPuYPR9P96FQB7B+J1fMSSmLw4S5Tw15P2R4fn6ee9R9T8WcFJ1zt/Pgt2j9g/RuTAp+b2/vP5dP+YimVZpGkutc2p2k5EREREEscotvimFOfSCEWi9p80HSyVly5vBR4OHAMMG9NkZm3AEUSA487xHDhvJQZLcUUvSyO+Mx9XRe/rm0dXPqIguwYiZrK0O6I7CzviimkWbcoGbs7K97O7EPvfsSNSoM5ME/Iu646wUpb6/O5dccW0q32Qw2btAODennkA9BwVVwGP/3gkRBicFVGgFOQaSibx5y1DEaZsDHNK712aFy9nLk2+m6UkZ2Bwn5Ghgyvmp3LT4Oz2eD4Ds2PfeX/Zwd7lWbKGlJQiJaHwGSlNeSrT2+P1LCeaANqOXBV/7IyrxYVbbx92/K6THhjHndGOz0rRpJ0pIUZKelFcmuqYoli2PUXaZg99bDruizZ1cW6KYu2KMkqL58UG86Ptndu0jVxKNZ8NRJd9rflMDHynFN+D3ntmT2JtZDr7y4lxnvr7G24A4Kf3RtRk5fLt3HHHKgCKXXFOWfrNPwOw4+mxzdZXR0Rq4Y3pnLJlN745IvCWzkOWTYWQpmXIdaakNV1x7vbBQfLz5sU2t0UCiewctvTC9G8kRbezaDozurj5X+M8XmpPySHahp87vRDnys65cV46bOF2imkqjLvujgnVLZ/OjcUUzUr3WTQr35V6NHQUGMiSAHTE61UazCJSUca8OXFO60uRo/ndvczuiGNv2B3nt+x/zkgecl2Ue9F9ncPKzyaOP27+/WnLfaZwFOA9d0YSj43FuM7bW4rX8fa+JdgUS3Le6sQSIo1QJGr/eWS6r2wQXZTun1Jj+8cAM4HLxpOZT0REREREWkuRqBYysxOADe6+rWr54cB/pYf/U7HqfODDwFlm9pmKyXa7gA+kbT433nrtKXTy160R5Zk/I67wzeuM/uR37FjE6nmbAVjRHRGObIzUjHxc6Rtsj8ftKbrUU+zkqJkRWNs7P64UXrch0qTuGYgrpVnka9fOiIS0dRSZm9KeHzUnIk8bF8UVxbufHXVb+eNYno17KkeSBgbLY4TKKcBT5MaziXSzMUWZjvby/oUlcRWtb1FcRSvMSP3802Cltr3xuHfVHNp60pXRrA6ps36WKjwbGVV6bIyfycZDARTuXBvlZRGpaukKcK53oBylKpz20GGbDKaJMNvTOC6fn1KtD5bI70gRp1kpbXxXbFNONJu9XikVOnNnw6FxhbltXbxf3hsRqeLu3cOO23bosqjP+g216z6CtqOOiH3vuGvM+zYruwKfWzAPgN2nHA7AlofEq3HYOcPHl6z53Cl0bEljNtJbW5ibjZ9LYzXSVfh8Gm93+6dP5eg3XD6snEMvj8/srdvj6vvspw6PNooA7C3FeXBOOs/ecu9S2rPgy80pQpSiR/N/G2N6SjvTdzJNnFsqDV3pt870nc/GSOVTJDyVUTn+MVtXSt9xz7adkUWt4r48VjSfo2NTNhFvitLPTRN4z436H5qmp3jgvDS+ynPlcbFHPCiiZddsjLFWPbuj/OLeWJ9L0aaurihzsJAn3xbLZqYJ2AfSeKoZnbFNFuVaPCsiRCWMXWlM7azOuKbYn/Kvz6C+Gx4W3+mF3ArA6quHptwAmNce58Pj/xLRv18/cNYIpR181gzEGOp7Uir/u/fGWL8r7jucHf1XMJVGGigZhEwGNaJa67nA283sd8BdwG7gKOBvgS7gZ8C52cbuvsvMXkU0pi42s+8A24BnEunPzwe+u1+fgYiIiIiIjEiNqNb6HdH4eRjRfa8b2AH8gZg36pvuwwfquPsFZvZY4J3As4nG1u3AW4BPV2/fjJw5xyyIaNOmvXGlLbsSN1DMs6gzrvbtLcYl+q39sc0F18bkho95YExjtak/xooc1r2dK7cfMayc+d1xRW/zrti3PGFuMa4OLV2ynXt3zQNgVntcSTx+SfRHv+GwuJa49RFxtWvhtTsA2Hh6jA/qWzCPZX+KK5Zd6+LKl6doy23/GFcnj/3vFOXZnSav7chjPSmTVZqIt3tPlLH1xIhMtffESzt3TTz/nsO7y5GonlVRp66tEQGr/qJkE+mWGIrElLPk7emlltJ1fy3vU3bZ9cO2yT8mXvNSR5RVSpG29g1bGTx0XhxmIOrYvn7HsH0tZRC0LWn5nFlYbxo3MC8iKKXDIuqXv3UtAF6MsqojUDtf9EjmfnPfCS9r8e1xvLaVK/CUcaxYEaFrNWtrL19VzzIwtqf3beaGuKJ913/E+JIj/j0iUvmeXDlbWDaxZ64vjUObFe9xlgmsNDdNpDy3KroJ9KXvyBFz4+r7ltY8JTnAZBGQB1wdKfDWrlvM3DuqTuXZ+SKbQHcgZQTNokv5XHldcWeMNc2iTNnku9njbDvL5fD+/uHlzYxotu+Nc6NlY6Hah85q2XeifVecV/tmpfGvXVHWrDT+6A8bjyzvs2hmRG+yzKxd7elcOS/Of9s9xnEuWhARsc60fndfJ7NTuTNS9tbdKcrUkTLBZpPCd6bHO/tm0JVlek1f5O5Up6UpOpx9Nw+dsQOAmbkBrnzo8Alh1zx8eM/4598S709vihyCIlGVvn3csmGPD79y+KTMU4UT0cpWlykyGjWiWihNpHvJqBvuu98fgae1vkYiIiIiItJqakSJiIiIyDRlFFuenW+KhdtkSlIj6iBQcqO3EN0VjpuTJoPcs6C8LuvGtzl117tty2IATjshBs135qNLxX27oxvc7sFOjpgduTNu3hbdw7pT+tksBm6bU8rdedEFI28ljk1dCrNuF31pYHA+pSffuyhOWvc8LbrxLf/P6Iq15nOnsP70qH/Hzqjb3rjjqG9EF5Xcph2xIJsEd2CA3uNjUOzA3DSxY2fqqrIg7g/7UXQn3PXgKGzH6hxd86PexdS7o2N37cGqpWv+Uv5771HRDXHG9XcPq0OWrIHUZY7umeXHhbtjcHnhSQ+PbX99dTyPSyOlbO7ESHu8d3l0iRlcNpf2O9LsmNmA8KxPRdZNJ9XH+9LjQqHc3aewYeOw+hce/dA4Tl+q29WRbrlt4cL0vEu0LVtae9+qOtvs6ALjc7onpBtf24pIWuI70iDmtjb88EMB6F2Zut+kz132uSh1xoLbPx3py60IudTLMntvsxlF8zNSd77UBbXtkOgGesRZN5TrMO8PkaAjlybn3HLa9lY8NTnALe/cEX/05Vn4w+jOm3VB9d0prXb6HudSF9VS9n3O57Hc8HVZV71cd/rOZeeWgZSQoadnKOnKjDhOlhY927ecgMZSl+GujnKSnVJH6q6VUo5v3hTn/K07hndzKw3mmHFoHLMn/XidnZJoHDs3/sdsmBv7zmqL4/amL17/YBue9smSQ/SmVOZZCvLsf85AMc5fWVe+SnNSt/CNe+P/Vjatxt6ONJm7G0NToNdW3V1NRnb3ydGFc/6vesnnS6Nsvf+4tz6xxPgHUsjBQOlMRERERERExkCRqIPAQDHP5r0R0djUG1cUD5kZV0F793aUU5ffsC6u7h++OEWZUlr0HWvnAdC1LK5C5czpnRFXFbMJD7P0s7lcNpFtuozTF1cS125YxF2FSAtN2obdKZ33YFyV3H1MXDU85tVXDqv/6tdeUU4bXlwQVx3veWrcd2yMAdd/PTvqntsTxzv289uyQANtfSnVeVea9DFdkL3jJfH8BhalAdE7cli64HnoR4enyB5J+68iIsOxR8dxUvryLKpT1pMm0Wxvpy1t2/aXdbHskAih9D04UnV3bE3JMK5cG+vz+aGB6H3xBAqbIrKXn5WuSq+I1zeXTaxbKpWjYG2p/Gyf9jvTJJPZlewUdcqOUWozdjxmFQBzb4yB28WbI8FI1mui78xTYpc0eXHHT4e/b+OVvX6VESgAmzmD4g1xVX/74yKBhGfjx1Pdcv3xx5xb474wA3Ydk55r+vxZd0qbn/aZvyQGwC9+xr7RtPtTQpaBQqrDLyKSO+spdzT/BOWA99eeiHS07coNTcvQPzy5ASlanEWbskvgWUIIAE9JIrJkEZYiT6WUxCaXotz52bPL0xfsk1AiS1iRnUeyKFf/IKvPi/PCmpfEeaI0M02cmybZLRaH/1TIdRYppCjR/Zvj/JB99+akXglLuuLc/JftafqEtH0+52zakc7fHfEdnDMj6jivK85dWQSqIx/f2b5CG6vT1BibU+KjLMFElthi+cwdwNBUHH89aeQolDRvw5Z5DBbyo2+4H2myXZkMikSJiIiIiIiMgSJRB4n1m+YBUEpXj7bfEpGHXAf8aNdDAOi+PfqS335URJmskOWEjquSe7dFH/vBnbO4akG6+pj6RR9ySFx1LKaU5vmFKc3uljQ2ak8b+V3ZWJ64K86MfQcWxxXD2bfV/zhmE9lyZ9wdloI/2bXGtm2PBGDxg+KK6i1vmwe5KP+wH6SrqR0pbe/6uHK5d0nUZ/Vrmo+gtC05hMHVEQUr/OH64XXeunX4totjXA2Dg7Anrrh6Sj1uO+L167phLQADJ6wEoOP+9JoVi+UJh4tHpKvb7cMnBLYNcbzSrnQlevYs6MgGAKU6HJ1SFKdoFnNSFCuVUZwX7/HcS25n4AFRhywC1ffMk6OOm4ZfSW/viXrlTziG4k23MV75BTEmLrtib3PiqnU5ZNSWZ9PrIgJVTBfvi2nGzcLMbLxH3PUuSZGobi8v61oUr/1gmuAz69tfKwKVWdgVV/z/sj5e+640KejS30f0rysfjzVWSirdtCXOs7PuMcyGX7MsD7lIESHPJhRPE+sCuA8fd5JP44zKu6bvb3XZlbIIVHFnRHTzpfg+lcdQzphH3/IUbe7Ocp2n2pVnF09R/BTpb5vbz449aWxXbzpvd6bJ2AfjnLOpN86L23oiIjazK01T0VZgdndEnma0x/cmm6Q9Z1kK7bjf1Rdf8Bkdg9x1cnwHF/wxjttbaB92f8cj+uq+BjK6p/81zl096aT6pUvOgO74XzlvUfRcedgh0XNiQeEetncMMLDfa1mbUpzLZFEjSkRERESmKZuA7nzqHiijUyPqIGDmdM+Kq/p77oormXuXxVXDo994Ofe/Ma7q71kR116OedVV8fg5kdnM06dk2/FZljtY/YrYJv/AYwG467nRlz53QkRBSunKZTlb38wCxbTIUjY0UiTAeuN+YPhF1jE58m0xMeztn4o6zz1iB7t2xhXQe5+SIjVpMtpDvhRXZHcdHuNa7vxoRLGO/NfGJpcdplSi/bb7gNHyQEFhc/Tpb1u4ELKxDTuycTrpSnLK3NVxW5r8tn/oWl92VTp3S2QBLKQry3Wrlo2NqpBFebIr16WtMf4ty+DVll4zOjrIXXLt8H37hl8V71q/Oz2HnvKytuUpKnff+hHrVkvbyhUA+LYdQGQaA2jLspktjUiebdlRjkBln5ksq1huIEXUUpSz79B4fT0HHXPjO5BFkbL7kSJQHRdH5Om2LQuG75uuoGdX3XtO3zTGZysHgx2bIupz3Lf+Wr6yXZ4gN5v0tlT7mvewKFS+avxJil6VI1C5oR982Xe8nLGv6jyRZfTreWRMEr5zVZ6eI9ME09l41WzcYMrSV058liJEhf48bZ3p3JWd2NN5fM09MdZ07oI9w467bVtEzWZ095ez82URp96+lEEwHT4bP7V4VpwD2p9091A5jxoe4deYhPE56fp40TcNRDTy8q2rALA5g8y6PmWMfGy8X3fsinPw5l2z6B1s3881FZl61IgSERERkWmr1SnORRqhT52IiIiIiMgYKBJ1EOi4p8jyf4/OZre9KwbfHv2C68rrl3xqeDrvnrOiS1wupfvuXRBt7Zmph1lhBux+fmyz6/BYt/K9w8toOyq6ihQWR1eBts27KC5KCQJSn438fWkS15Qw4L5nHz68jNS9i1weSsVh6/qOjQHb2YSvmaPfeDkAO3+2mlw+dU/8SDpOe9uw46/47l3xHE5dSbWtr44ujgu/OEqq85LjxZQiPaXkrk4osc8uvb3lweM2I7qteEoGkQ0ur9kVLw0qr+6eky23bDLfrE9MLhdpzrO/K9YV1m8YXnh2vBG6CJZTuSfFOts1ovzeDg51gixtia6Fpd7eYdtmadnbssQSs7rpPTSlYG7Luh1lfUXTXepOmuuJ17fU4Qy2RfeTxc8bmkR3NNmg9fndUaf5nfE6daXJQHc8ekvDZcnBx3oqEsOk72B1soihyW+z/PxDXfOy7npe/W1LXQA9Jc+x0lC3PksJZ4o7I1lNluo8+17lZsV0F72HDCX6seLw7tflDoap211ub9YlOnWX3d5Jf0okkU0lkZWRDU3JUmC3pcQtc+bG8We0F9jdl5JdpHoXU9duT8l/2tvi+VZ245OJcc1D4zXvuiQmNb/zprg/9uxb2fmk6K4/77U7YuOUhr+7ZxPrtxSoStY/aZzWpzhXYglphBpRIiIiIjJttTo7n0gj1Ig6GOTz7Fk9DwDbMPoEefn+uAbTfWdER2b+ICY23fniSMBQajN6D4kriO0paHD/myJys+STKXKTUmi3bUqRjf4B8mv3lOszTIq+9MYY/qEoRTbguq936GptiuB03RTJHFixfHhZgxE+G/zxYlb/PkWE9sRxPUU9LCUqIKX+nXP5PVGNimJGjUBlVd+6dWggd7qCXJ4kNl1xLm6L1LG5GXHc0t695chPnnmxbUrnnZs7Z2gbhqJMpT29QymKZ6cUxfOHZ+LwnuEDuYelPU4Tfe7z2tfRtvooSnNSfa/5S0P7DNs/JZjI3luyNOVpUHs5ecSOHeV9clXp2DPZZMKZm996CKRJOPNzIvFGcUtcIS3OTe9xf0q1vzddyZ9VYPXLh0fSGrFnID4j+XTFv++xEdVUMmVphM9On/9cDk/nROtO559iVXQpixYXKyJTWbCoI6JL5TTo2fc42yeLOJe8HLXPEkiQTe57akxlsXdufM/a98Rxip2GpWp6+grmdsZPg+zi/lC0Ke7ze41iltwiRX2zqLAPRJ16d8XzbOuKc3J7e7H8tLJIUzaZbmdKf97dGffZJO6y//z5zvi/e9wXh6ZpmHdRTBxfjnz2ph4Lyw6BHfnRsymJHODUiBIRERGRacm99SnOveUp0+VApEbUwaBYYtZVEW058kcbR9280JX6wa+7Hxiab3HuN4ZSgO/9u1MA2L0yrojO2FLVgziLHGVXTAcGyhO/Zumv25bERKWe0niv/lhM1OopNa+liWjp6mTv8RGmsnRFrOu2+4eXn00e2xnHWPqDNZDGApTHGqSruNl4oHLEKF2p3fPcU+n+/uV1XpX6skhTJj9v3vDHKXJUvmq8d295G0t9zG1u2qZn+HigLPrUtnBhuT96+apzel6F5fE82rbG+sKtt4/5OVQbXDIby4ZTPeQBsWxBXFnO/+6a0QuYFa/94OKIIuUG0/iJgXgPcuk9zxeL5ffQU9r3Pc88Me6Xxeu195BUpzmpjNkD5NuHX8XvXBZRuIHe7Ip9GrvRlT2J5nq4ZxOKZjb978MAOPIfrqu1uQgAD7w2PtP3fT+l7t67dyileTGlE0+RIquOJpUjUkU8i/ZkOaDSvuWyknJZ+Xx5KoVM+Xx0T6Thzy2L88WeZXHOyQ8MTQ2QTWeRRZzKqaey35PZWEMH9qTxTHNT5CuLWqWJevOdw8MU/XvTWK1Srjypbh+xbF53RDhmPeUOZHJ03hOfVdsa/3O8WKKU/q/2n34CAF1XrImN7988bDyryMFKjSgRERERmbZaP9muyOiU4lxERERERGQMFIk6GORzFDaM3o0v07k99eXI1b+yM+OCKwAoPTdSnc+5NboAlDtZpa5mvjmSO3h/P7k5VW32QmxdmVwAoC1LFpFS9W47dQl7F6buWTHOn66VhwHQvSm6FHT/Obr3+Y6d5br3PCzSoHdtTkkudkY6gLb7tw6rY3FrDwADs4zuus84ZEkkqrvwwVASiKwro6ckF5a6MWbPs23xonL3mywZRHH37prHKyej6O2FLPX3cZE+vnRdJPzg7nuBsY3xbTtudexzy5rhzyFL4nD97RR74nXJunNmKSns5AcBsPuIeLVmfbd+F8hCd7yHXevj+dmWHbEidVmyWd3lz2b/008GoJi6k/ZFryMGFg9/Zu0dRSx1Bzr8/90IwL3nR53yHfG6FrKUz6nyq1/aQBfEWvXvT10N01XO9i51YZHR/eXElKL7dfHYOjrKXaMsJbSxYp1rmOVU50Prs4QSWYKYcoKJrFtf1rUqn6dt8SKAcre+bMqFtmVxPsy+kzO2RB33LG0jn/I4ZN0Hy91fs96DKcW5V3bvyzbdk6YR6E7PeUac92bO6B/2dHasi/NjwQbItw1P864owuSbf0t6T7KEJKVSufto5+b43zP4kCMBsGIJv64DevZ7NevSZ0gmQ8sbUWZ2HHA8MMvdv9nq8kVEREREQPNEyeRpWSPKzB4KfBl4WMXib6Z1jwV+DjzP3X/cqmNKYwoz2xjLrHgdv7gq9quzPj93LrZ0MQCdO2Kr4o03Dz9mSh5Rnuhx715yWZrrxKtT/CalNLlqbl5cufTcEmbfG9sOzopLoQt/szYeH7kk9rl/U/k4mVm/uxWgXNdSdwpjLYkQh+2ObbMEE4t/dFvd59x2aCS28L6IZpUHaxcK5YhN+QpeuvSapSHOEleUn/fevqF9kiz5RHVEKns+uRkzymlmyxGoUbQtOaSctKF6ct3SnTGJZXZ1upy0IouQbd030lau/5V/BmDWlamsMyIRRO7ia8vb9B8a713fwjjFdK1PK7IJgfemJOEd7eXXvye9twOzqv4ZpiQRq18X0c9NPzqOQ555y7BNZqYUyT298R7nUgplLzX/j3XNVx8+9CVIE4ke/qJr6+8gUmXhzfHd3/03D6T7whQNTYlzyklxqs+DWVKeUmko2USmnNrchm+bJhL3gcGh6QyqZBHfjrStrYyIVc+hbeWkENkE69k5bHBuOn6KRBVmxvFyA1b+lVmclZJapOQUxcGUECYXEfhseoAsuuVFo1BME/F2xr5znzY8Ii7739xb04Tv/VlY0sufJVsTiana0+et93EPwPOK/Ii0ZEyUmR0DXAwcC3yKaDBVuhTYBjynFccTEREREYGYbLeVN5FGtCoS9V6gAzjJ3W82s/cCT81Wurub2Z+AR7ToeDIG+W095f7r1ROX+sAApYHaExtm43/6Hn4UAO2/islK/agV9C2OiEL7L68a8djZmB7L5SjtqoqyVE0OW65TNrFfilYs/PkaNj3zGAAWfCUmwc0CBLksAlUjqpWNQcql55c7LCaArU4Bno0hyI5XKXsNSikyk1swL1akq8mes3K0LUvRTZrkttQf5eU6u4bde63UsLmRr2eU9u4tHycbM1bKJvFdtCCe1z3rhu1TSK9NzfJSXUvp6nTuxBOGrbeZM+Dm20asU7nqKQLVdvhh9B0TkcHCzLjSPP+SiHiVr5hn92nC4x2PWMLcX0XEMLsa7ulie8eOVNeOWHDHtyLI3da373u98OlRxt4fPBCAgWKM+5g5O96D+y88niVn3rzPfrWs+Vyk77ceo5RSpOe7B0faRaSm/G8j+pQ78xRyc+Lcm42nzCKwpWzsZEVq89g5PzSuMluXfX+yCXnTuNFs6gfPQ2lX7YEq2Xkumw6i/Y40TvaEI8tjTUtd2ay6WXlxPzA/vgftO9O5rcMpziwN25YZ2b5xV44O78kGsg59bz2NWVz1PEV2p4reFWmM69aYWsR37saPibHHubuiJ8PgsTEhb35vaWiwrMhBrFWNqCcA/+fuI/1KuQd4UouOJyIiIiIHOWcCJttVNEoa0KpG1Dxg3Sjb5IholexnlssP9V9PY3GyMTilgYFydKq8LssylzLzZBPsZdcRS9felKZIHF2WGaoWL8RV1urjZ8uLu9PV/549LDwvxsNYNoFuqls52pMy4NWKqpVSVrtSikC1HRJjpApp7BXzsudbHMqGl8Yiebpqm11FzsYVlF+jyuPUiehldawlG5PkvWnsUzaGrLd3n23Lz6Nqne1s9N2oz26LiFE2+a8vXUTbkatiZTauak3tiTCz8WHbT1vO3Ft2AdC1OWVJzFJzZZMhpz72W06P573w6m3sOf2YYeUVsuFZ2dzCHWks2EDUozjCxLl7e1Lms7TN3uwquMOSOvus+cLJ5W0ArD9lIusskdubrry352vtKtKQrguvYMPrTwNg6Rcj+pKdG7NzTvU4ydyMGUNRqRnDJ30uZz/NMv51pIgU+fL5M1M+V6VoFW3DI16z7x6kZ0Xsn9+VjX1K+6bxiFl0uDA7nXe7SuWxivmZEVkvDcZGnV1x/N098QUupgmwLU2Q7YVceSDB7d+I8ZRHv1gRqcnW9aMY5Jr1k+h/xsnkBtLk9nuit8OO1fE5nP+1P2FeuyfJpPAJyM6nzBLSgFbNE7UJOHqUbU4A7m3R8UREREREpiwzW2FmXzWz9WbWb2ZrzeyTZjZ/ossxs9PM7Gdmts3Mes3sRjN7k5nVvSpoZi8xsyvNrMfMdprZxWb29BG2n2Fm55jZrWbWZ2abzOx7Znb8CPv8rZn9yszWmdleM7vTzL5vZo9s/BWZGlrViLoIeIaZHVtrpZk9gujy98sWHU9EREREhJJbS2+tYGZHAdcALwOuBD4B3Am8EfiTmS2cqHLM7EwiqdtjgB8C/030BvsE8J06xzkXOA9YBnwJ+B/gQcCPzex1NbbvBH4NvAfYRSSW+w3wLOBqMzulxj4fBn4CnAj8Iu1zLXAm8Ecze2EDL8mU0arufP8BPBe41MzOBg4FMLMTiDfwvcBu4NwWHU/GwH0oLp1/YLRzBxfFINK2Xf3YfSml+LGrACimrkuFWdENo/Oq1qafLacHT91J6k00m8l1dICnbiQ1urnB0IS21OlSV6mcwjWp1U0t61aXDejOJq7MFHfuLG9Xr04jyQaVZ5PtZunes4k4c6m7YtY1p146+Mq6jEe5K1F2X/V8R9w3JfCY/8d1eJbCPNV309NicsasO1B/Nh9xejz4mIV07E6D12entMoxrplCdywvzMlSKMd7ceTLrh+1Th1pws+BvfEZruwudOe3HxpV7EtpodMg/VxvmjR0Rrz25lCaGcc+5mVXj3pMkZEU0ymqugtyzUQzRJficiKgLNV5VQKabLLdrAwfGNhnuoSsq1+5a60P76eUKzr51Nu2lDbNpXTlpfb03RhM3fxSyvP8nEGKA8MvZh9ySJyHsomu+9alus9OdetNPzc6S3gqj8L4uyLLxOjc3E/+L/G/sf+RxwGw6PsxvQVz52K780N9/KWezwKHAG9w989kC83s48CbgQ8Cr2l1OWY2h2gEFYEz3P3qtPzdRNDjOWZ2lrt/p2Kf04C3AncAj3D37Wn5R4kG3Llm9hN3X1tRr7cAjwLOJ6YwKqV9vgtcAHzVzB5UsXwp8C/A/cCD3b2c/crMHpfq9j6i8TYttCQS5e63As8mWrn/BbySyNFzI0Ot379393tacTwRERERkWyy3VbexjskysyOBJ4MrCV+B1d6L7AHeJGZdU9AOc8BFgPfyRpQAO7eB7wrPXxtVVlZI+yDWQMq7ZMdt5OIhGX1sop93pY1lNI+FwK/Bx4APLbiGIcT7Y4rKhtQaZ/fEcGWxdWvwVTWssl23f0XZnYE8BLgVGAhsBO4HPiau29r1bFkbKy9DdJYY09Rpvb7I+JQvPm2cqbStmzw8vz4LubbW9Xbc7gsctEo6+rcJ9qSpR7P0gVXD8oe8fhVZdVK5tBodKmZKFQcNF2JTRNflrJoXFV5WdTO+/uHTSQ8FXnPnkhiAtz16ohA9S+IT1dbT4ropYHpXZuyVMnQPy9ei77UISFLt5xFq7JJOrNkSWs+ewqr/+mKOpVIV8zTIPd8+9Cl0tu/GYPYvT99rlPkiXS13eekdNK709XxmUWOecXIKfxFGrXsYzE9w84XR7f/BT+KZLaWTXCdEppUJqipd17LIlSlNC1DlhCm1vkoi6KXU5xnUqKJvYvbKaRTfzGlKc+nyXUtTTKdJXehM76/xYGh/w35tli2bVecRwt7IsJW7hDVl75n2YL+HJbSna9+lb5fU9WO42Yy7/L4/HWtTb+pF6bhN/k89LZNqUiUtzqxxPg9Pt3/qrKBAeDuu83sj0Tj6FTgty0uJ9vnFzXKuxToBU4zs053729gn58D707bvDctOwpYCdzm7nfV2ef0tM/v0rI1wABwspktcvdylxczewwwm4hgTRsta0QBuPsOon/jp1pZroiIiIjIfnScmV1Ta4W7nzTKvlmOgHoTLq4hGj/HMHIjqply6u7j7gUzu4tI9nYkcHOKYi0Hetx9Q51jkI4xlnoN28fdt5nZvwEfB/5qZhcAW4kG2TOJ8VX/WKe8KamljSiZmnxgoHwVsHTdX+tvtyvSU2fXGPesir71HSdG4sX872qeSyZczTE/xdbN9Odp4tz8gvlDk+imq8MTFf0pj3FKYxnqpWjPonZtixdN+UiU5fOQJgfNxlb43HhcSEnx23flhq3fs8Kx9FJ49sGr6keRjYXytrSio/57v/qltccu3falR5BLl007utNrnDo/FFPUKrvGd9TrL69bvsh4zf3GnwDY/IpIeb74/JuAoUnGG1FKqc2zMZO5NHVAfsH8cnS+WjkilcZjZtHwUp7ydzDfF8uKM6MuVu7BkOqWorheMZRpMH23c9vTwrYsmpWiz+3ZmMaULj1nrH7NlQ0/V9k/tr4qPo9z18b5cd55fyqn36c/Pm99x8bUFG2/vpqhAMZUYJRaPq/TuMvL5kGpN2g5Wz5vAsoZ6z774xgAuPsnzWwt8FXgVRWrbgfOq+7mN9U11YhKYbemuPulze4rIiIiIrIf3NJAxKlZWStt3MOvmiin2WOP+xhm9jbgQ8CniRwKG4HjiAR13zKzh7r728ZYr0nTbCTqYpp/4zVr5RRVvoKZ7mesvbvpsrIJHrPsUdufvJrZ3x7/Ff5sLIA3kIWvzFKIw2tHMMpRoYHBMY2tagUvpIkqR3k+vrevfAV5pAmMJ9WMLtafuRKAgRXxfLpmxf3eQrwHA+mKdj6NkbKClcdbeD5d/a4+s6RxGeXlXWM/9VibM2t2jB8ZLMQpaCBl5zvqhdeNuTyR8VrwlRgj1fuMmOx55u+il0CxZ7DuPpnyROXliXTTWKU6UajKbbNhFZay6C387Vq2H3dErMt6LKRor6VolaVTp6fxifldbeUuC9m2uewU5tl4qniY64sNj3rLn0Z9XjJ5Fn7psn2WZWPtCvesA6At3U81WWKJVpc5TlkkZm6d9XOqtmtlOWPdZ7Tta0WdxlwvMzsD+DDwQ3d/S8W215rZs4iugW81s8+7+511yp1Smm1EvY99P2OnAE8h0iP+gWhdLgUeTfR3/DmR315ERERE5EB1a7o/ps761em+3pii8ZRzK/DwtM+wcRhm1gYcARSIuaZw9z1mdh+w3MyW1RgXVe8YY61XNmnv76q2xd17zexKYo6ph2V1m+qaakS5+9mVj83sVODfiYm//rsyg4iZ5YDXA/9JNL5ERERERMbPJyA73/hDUVlD4clmlqv6XTybmF9pL5HButXlXAS8gAhsfLuqvMcAM4FLffjAtouAF6V9vla1z1MrtsncAdwDHGNmR9TI0Fdrn5R7t24a82z5GLoaTa5WJZZ4P/CbyknAMukN/5SZPYloRP1Ni44pU1h1MojZ3268C5q1xQDlcje7ym54+TQh6li63dXpxle9vt6klxPBU2piGhxMXuzpIZ8my2xbsRyAwrr7JqRujWo7JJ3vUpfNe5+7kr60KLcjlvXvjHvLJu1MXXusFM8l3zf0d5baPOu2l030WZb9jxwc+z/LfGeR3dsjBfNqTZwrU0j35XHB1dP3qHz+Kwx168umYcgS6mQT9pa7NadzQ3727KEuwtWJaLIJe1MSivIE3+60p9Np/8LUpbYw/DtWTvoymP4wyObGyLbN0qMPdf2L5erGN7Wtf1sklFj5fxsB8I0xrt9LpRG7h041re7ON17ufoeZ/YrInPfPQOXv43OI1EZfcPc9AGbWTvTaGnT3O5otJzmf6DZ3lpl9pmKy3S7gA2mbz1VV+fNEI+qdZnZBxWS7q9Jx+6loXLm7m9nnifFNHzGzysl2zyTSm/8VuKTiGL8HXge82sy+4O7lHzFm9lSiQdgH7Nu3dIpqVSPqZIa/sbXcQLx4ozKzi0bfalTnufs3WlCOiIiIiMhY/BPRIPi0mT0BuJkY+vI4opvbOyu2XZ7W3w2sGkc5uPsuM3sV0Zi62My+A2wj0ogfm5Z/t2qfy8zs48BbgBvN7HygA3gesAB4fZp4t9LHiS56zwGuMLPfEnNHPZeYi+rlVXNbnQ/8BngikVr9h8TQn+NTOQa83d2n6MDvfbWqEWVEC3okR4+hvDOarwoQgdiLx1mGTBLrSFdie9Pg6Xnzyqm+a6Y7b5Hs6u7+VI62NaCYJuTN54ZPgpxNyDvWSYzHLV0V98WRCr/UAcUZsSxLZd6WLob3HZKuoKfoUvvuLN0xWAoA5lOoaXBOlm85HSdLaZ5Njls07r/weACWnHlzQ1U98h+UPEKmpiz1ePFxkQSs4+oYQlDcPRSJqjepdxZtylJRZ+eIWsrnjzRReZbi3Pv7hyJNWbAqpSfPkr1kshToVhqKHJO+v1kgIItI5XItGJovE64r/Vz1LdviPkUsm55IfhI4re/O14pPb4oiPZzohfUU4GnABiIz3Tnuvm2iynH3C8zssUQD69lAF5FG/C3Ap919n6fo7m81sxtJ0SLijHAt8FF3/0mN7fvN7InA24F/AN4M7CImzH2vu/+1avuSmT2NiGydRYx/mkk08H6W6vWrRl6TqaJVjajLgGeb2dNrvdBm9kzg74mJtBp1trs3NYbKzEbpvyUiIiIiMnHc/V7gZQ1st5YRJqdqtJyqff5INLjGss/Xga+PYfu9wHvTrZHtB4FPptu016pG1DuBS4ELzeyS9Pf9wBLgscRAtr1UhRxFaqm+ArbfIyz7QTMT52bjIqqjcTYzrkLn05Xl8fRjL49zyuchRckKmzbHotkRccquaGdReu+MyGGpHTp2pDFPWdb4uXGxq9SVrmuk8RP59PSLXUPjLUppks62PWmSzsXZxMfp6nd2ZTsHheLwaJzIdOf59PspXSDOzZgx5vNE5T7Z2Kpy+dkYq2wcZpa+3HJ0pevY3hbLCmmO1ey72bErnVtiTnAOf/dlrP3QaankFNHKJtntT4+n1hAVqeOQn0c+gMK0/j9rEzAmSh9gGV1LGlHufk1KHPFVoiveGUQ0NPsU3gq8wt0b7VPzXGJAWrPGu7+IiIiITAP7dk4TmXitikTh7pcBx5nZacCJxARcO4Fr07qxlPWDcdZlXPuLjCYbg+CDhWFZtCZSdYSubfEiAArrh0/pUB0xakTbsqXxR4o++e4eSGPT2o4+cvi2s7pjm55IBmSFiDINzPXyFetSugju2bimdDklG2tR6E7LHQpz0sSeKetebiBtvCdOT/n0uDQzRb7aS/T3dTT83ESmg7bfRNbIwceeCEDukmsb3rcyYlWekDxlGy2P9UyTjpfS9zY/N82Fmc8xY0t8txZ9rvEJ0Ve9o/a/9bs+/Mgodm98b9f/W0SsDv3wtEm4dXDpnzbZpEWmnJY1ojKpwaSzpYiIiIhMKAdKLe5+p8CWNEIDC0RERERERMagJZEoM3tPg5u6u7+/yWM8FvhXYk6q+dRuALq7tzy6JlKtmcQQrZalRs664hU2xGSJex99HAAbTmvniPN3AGAD0bXHu+LrcesbI0nFcW+JOf28PyYuv/3fYt/cgNGWpu4rzhh+3JXvjUBzlugid19Mznjsp3u47fUrorzsomDVxUFLSSIKs1Kq811Gvje+ylmyifI3uDt1R5qdHvfkUxk5Sp0a9CsHprF046ulnKK6esqGLBFM6n6bfeeLPT10f3/zuI5ZKZt8t2NHPM7tvznMpQF7n3UKADN+eAUAha3TZkqe+rz1Kc4VipJGtKrBcfYI67KPoqW/x9yIMrO/JfLO54F7iEQVU+7UbGYLibz3fws8iJg8bQD4MzHT89cqJx5LM0HfNUKR33X3syaswiIiIiLTXOuz84mMrlWNqMfVWT4PeATwBuCnwOebLP9sYBD42yk+Eddzgc8Rk6D9jmjwLSHmyPoy8FQze26NSc5uIBqJ1f4ycVWVA0UWgcp0/PwqAI7683I2nHk4ADselCJRKX5r6Wr1Hf8Sk9bO2JT23RH3fSfspbAlZtPs2Bo7HfH5mATU02SdWSr1XHdEpAp338uK30RU7O6nZ6nOU7KIlFCi1BEf/Y7tQ5PtlifgTUkpBrPIV5ros7M7Bj4Pbo0B86XuIke/aHxX60UOVI1Oklrs6ZmQ43emGRY6d6bpDdr043YqySJQIjJ+rUpxfskIqy80s+8CVwLfafIQDwS+M8UbUAC3Ac8EfloVcXoH8fyfTTSoqrMHXu/uZ++vSoqIiIgcKJTiXCbDfhk/5O5/NrMLgXcAFzZRRA+wrbW1aj13v6jO8o1m9nngg8QcWkrBLhOusO4+Fv/3fQAsrlp350cjDfHAoohQrXrHlQDc/ulTgeh7e9SbIt3xmi+cDAylTKYwvCdteWzWyhXc97DIbZ4biP9o2QScltKUF2el8Ri78+X9PUWisslGPUWg2Bvb9PdEpMtSWatfc2UjT19EgFxnF7DvGKlsmoZWj+9cdm6MmcymYNhz6pEjbS4iMm3tzyQM9wDPaHLf3wKPbGFdJkM2mVCtsVyHmtk/AguBrcCf3P3G/VYzERERkWnIaX1iCQW2pBH7sxF1CtDsJa9/A640s3cBH6wxpmhKM7M24MXp4S9qbPKkdKvc52LgJe5+T4PHuKbOquMarKaIiIiIiDSgVSnOV45Q/mHAq4BHA99rsLyv1lh8E3AO8HIzux7YUWMbd/dXNHKM/ew/iXFdP3P3X1Ys7yWyFV4A3JmWPZhIpPE44Ldm9lB337P/qioHg67NcdXu0H8d3jWufWkMSs/dNGtoYbpkUdyxo2ZZbSuWxx8lZ2Bu/Jnrj/LbUgKJwsxU/vboouepN1+xw2nfmZJORE/AcjKKtp2x0cz18bhnVVTk9m+cCNs7ADj6jZeP/mRFDmJeGKy5fKKnaci6+Xb+eMuEHkdGl589m+Lu3ZNdjQlkrU9x3uLJe+XA1KpI1FpGjn4asAb4lwbLe+kI61alWy0OTKlGlJm9AXgrcAvwosp17r4JqJ5j61IzezLwByJ690rgU6Mdx91PqnP8a4ATx15zERERkSnOJyDF+bTq7ySTpVWNqG9Q+yNXArYTmekudPf+Bss7okX1mlRm9s9EA+ivwBPcvaHkGO5eMLMvE42ox9BAI0pkLA79yGU1lxeLERU6/JyK9YO15rUeUlh3X/nvjp2RUn3unZFAomd5mkg3ffMH08S5pYozjw/lmEgL4i43MDwCRcp3eeKR93LdVUePWCcRCV4sTnYVZJId2FEokcnTqhTnL21FORXl3d3K8iaDmb0J+AQx19MTUtRpLLIp5LtbWS8RERGRA8n0GikvB4pWjona4e67RthmNjC/0UQJVfu+B7jY3S8dYZvTgce5+/vGWn6rmdm/EeOgrgee5O7NdAo/Nd3fOeJWIk3oOSs+Xj2HRqRo6ccj8nTk86/fZ9vVr2983NHy/4hy+p8eadHz/cOjWLk0PMPSxXHPGZYiTMXIxExbGqpx2DnDo2VZWva/bFzKUW/+U8N1EpGJt/EtpwGw4lu3x4K29PMi/botHhYTLfgVSjwrIgeGkfvpNO4u4E2jbPOGtF0zzibmVxrJY4D3Nll+y5jZu4kG1DVEBKpuA8rMTjGzjhrLHw+8OT38nwmpqIiIiMg0l6U4b+ltsp+UTAutGhM1FdKYtFEeNTE5zOwlwPuAIvB74A1m+7w0a939vPT3h4ETUjrzdWnZg4HHp7/f7e61B6+IjMOs70R0afu7T2t4n4GnPgKAjp9fNWx57iEPiPstO8oT8e6eEwOdcgOxzWBK9lceC5Uu31hxKDrlaV11BCpTnBFf7+LuzobrLHIwyS+YT3Hb9kk5dhaB2nFGTK4773d3ALD5b2P84uJfro0NV66gcM+6ffYXGY/WZ+cTGd3+nCdqCTCRqbpPAiY7l2qWECNP/cjcJcB56e9vAs8CHgE8FWgH7idSwf+Xu/9+oioqIiIiIiLNaboRZWYvrlr00BrLIBoUK4n03n8eQ/kXVS16qZmdUaf8w4DDgW83Wv5EcPezia6HjW7/FeArE1UfERERkQOdut/JZBhPJOo8hj63DpyZbtWyGGsvMVluo86o+NupPz9UCdgKfJehcUQi0oDD3t94b9Hqbnz28AcBUJwRp5HcFiCfuvENxqkhF7376N4Q94UZcTroT5Py1ku1Xsvq113R8LYiB6P91ZWv7egjKdw+POdR4f5IQDvru3GfvvrM/1okmt39rFMAmH3ZXbQtXBjLTo+ufjMu0HdbRKaf8TSiXpbuDfgqcAFwYY3tikQj50/uvqPRwt29nPTCzErA2VMh856IiIiITB0aEyWToelGlLt/Pfs7JVS4wN2/0ZJa7etlwHUTVLbIQa/t6BgMfs+zlgJw6EdHjxD51dE7N58SS5SWLqB0zV8A6P5+hJ7qTXLWtiyOU6izXmTNFyJNftuOiG4e8W9Kaz9lDAyMeZcZP4xo0+6/O4V8ilTPvupeYOg80Hb4YQD4zpgctrhjx/jqKQcHp/X9+dQ/UBrQqsl2H9eKckbwNaK73vMn+DgiIiIiIiIj2p/Z+cZjF7B2sishcqDKxjes/H7MgjuWCFHphr+O/XgbNo55HznIdMVn0QrT5d/U5MvPmwe56NbU6PiothFSjmcR6urxTxSKTdexcvzTPueZvX0A3PLhYwBYdEVEIfcusjGNn5SDjU1Adz51D5TRNfXfyczuJIKdT3T3u9LjRri7H9XEIa8DHtDEfiIiIiIiIi3V7CW+HMN7jFY/rqfZpv2HgR+b2ZPc/ddNliEioygsnQdAW6k4IRNibntlTO674MvNX1Xe8C+n0RFDJlj4BV2dPlCtfsk1k12FaaepMUSDBTb8S3wvs4mvl//wnvijd2/NXQrrN4z5MLte8EgAtp1grHrH8O/t9pfHuu77I8J11LeiInuXRH6puXf2c/f7oo79y2LdMa8ani1UDl4OeIvHMGlIlDSiqUaUu68a6fEEOAT4BfBzM7sAuArYSI3P+QQmtxARERGRKUbZ+WQyTJfO5ucRDSYD/j7dYHgjytJjNaJERERERGTCtKQRZWYXAeeNFAUysxcCL3f3xzdxiJeNvomIjNvlNwATl3p8PN34MsvOVRc+kbHKz5sH7Nvlr7BhI8vOHZ7opd73/55zokvdyveO/Ts451uRon5OjXXzv1o7fX3lFAmHXzzmQ8rBRJEomQStikSdAVw8yjaHA49tpvDKOalEREREREQm0/7szjcDza0pclBpO241AKXuzrhPk/E2o/DEhwOw4bQOCjNj2RFvV1RKpBGtmLi2f9FQavPeZ58KwMwfXD7uckXGxVufWEKZJaQRrWxE1fzImZkBK4GnAfeO5wBmNpMYD/UwYB6wE7gW+KG77xlP2SIiIiIiIo1ouhFlZiWGN5zONrOzR9oF+NA4jvc04OvAAoanSnfgE2b2Mnf/SbPli0jr3fOMxQCs/OqtAJTGUdbule0AdOyCQrcuE4qMR372bACKu3c3vE/brpj89p5zTmNmGkaVgsLc/6YYL7Xkk4oOyyTQvwSZBOOJRF3K0Mf2McA9wNoa2xWBrcBvgS83cyAzOxH4PyAPfAu4CNgALAMeDzwfON/MHuXumlxERERE5CChFOcyGZpuRLn7GdnfKSr1NXd/XysqVcM7iQbb6e5e3QH7PDP7byKxxTuAZ09QHUQOam0rVwCMaRLeQz8aV6VbMRhy7p39APQtaCeup4hIs8YSgcoc8e/1o0xtaV7ejW+OiNTSTygiJSIHtlaNiToC2NGismo5Hfh+jQYUAO5+hZmdD/zNBNZBRERERKYadeeTSdCSRpS7392KckYwl9GTUtxD7SkoREREREREWqalKc7NbBnwBGA50FljE3f39zdR9Hrg5FG2eTgxTkpEJkJ7+6QePnfxtQD0v+yRWOofuOMljwRg15HRH76ZSUBFpDFtyw8t/124b/2wdcV0elA3PpkMGhMlk6FljSgzOwd4e1WZxlCQNfu7mUbUz4DXmNnbgY+6e3myCjPLAW8Gngh8vomyRURERGQ6clrfnU/dA6UBLWlEmdkLgHcTWfP+G/gBcB7wK+AM4BXA94EvNHmI9wN/B3wQ+Ecz+z0RdVoKPBpYBWwEPtBk+SIyik1nLANgwR13DVueXZ2uvjI9UXauNgrdkSx9+cXxn677PiWaEJlo2Xe87ZDF+6zrW7S/ayMiMrlaFYl6LbAOeIq7F2J+Xda6+3eA75jZD4GfAt9upnB332hmjyIaYU8CDq/a5NfAa9xd3flEREREDhrG8OlDW1WmyMha1Yh6EPBtd6/MZFy+NOzuvzSzXwL/Cvy4mQO4+1rgb8xsOfAwItnETuA6d7+v2YqLSGPae2tPlevzY9LOPY88le7zaybQpO241bFtPv4xFW+6rel6rHrHZRSe9HAA+ufFKWzm5uJIu4hICxU2bd5nWd/ywUmoiTSibfEiCpu3THY1RA44rWpEtRMT6mb2Eo2cSn8BXjPeA6UGkxpNIiIiIqIxTDIpWtWI2gAsq3h8D/Dgqm2W05o5N0VkEuxdnANg/rKlABQ2bATAdvcCMPdPu+t+wQu3rGlpXbruiKuqXT17ALj/zIh0zWjpUUSknrZD41/+jtOjd/3sW3JjLiM3I76xm7+/EoCFT7+17rb5efPij+WHAEPR7LbDDwPgzpfHvTJ07uugiEKpESWToFWNqOuILn2Zi4BXm9mLgP8jkks8G/hjswcwswXAy4lU5/Op6C5Ywd39Cc0eQ0REREREZDStakT9BPismR3h7ncB/wk8j8jQd17aZhB4VzOFm9lxwMXAYkYe7adrESIiIiIHE80TJZOgJY0odz+PocYS7n6vmT0CeCtwFLAW+Ky7/7nJQ5wLHEI0zr4I3Fs5V5SITLxCmj7bUxe68vK77wWgbcVy2lauiGX3rJvQuuw8MboUzv3jWgBKaaLPvjNPAaDrwism9PgiB7vC+kiGO++P0Y1vy8cix/m2V5wGwIKvDO9WV7m8bXFs64PRATjrxpdNl+C7dgNQ3L27vH9xx474I7vP6pHOP0d+Ja6hasyAiOwvLZtst1qKSL2uRcWdDvzU3d/RovJEREREZJpzwFvcD0ndmqQRE9aIajED/jrZlRA5mK389t0AFCquDlcqrNt/STOzVOq7/j4iT5b+43Vt6ttvdRCRoe997uZIMLH14dFJZM+hEXnqTrM3FrriftMbTuPQ82PC7uoOWNlkvve9PfZd/p+NJ4mY6Oi3iEi1phpRZray2QO6+z1N7HYNcGyzxxQRERGRA5DT+tCRQlHSgGYjUWtp7iPmTR7zfcAvzewMd7+4if1FZLxyU2fgbu+zTwVgzhUxHmJmuhpeHhM1OdUSOWitPDuiRrufH9/NUlv8RFh4SXxH1/xzpCCf/1cYWB3p0XOXXFuzrLFEoGRstrw2onyLPneAvcZKLCGToNlG1DfYv+30w4ALgV+Z2beJyNSOWhu6+zf2Y71EREREROQg01Qjyt1f2uJ6jOY8otFmwIvSrboRZ2mZGlEiIiIiBwFjaFxsK8sUGc10SSzxssmugMjBLhu43bYs0osXNmyckOPsfPEjAVh4SRwvS2FcaeYPIrFE71MfAYCdEKmRtzww5uBeceGEVE1ERjH725cPe5ylHG/riaHUO4+CeV+v3Y3v9m+eCMDRL6q9fqx2vSDOJXPu7I0Ff7qhJeVOZwdcNz6RSTQtGlHu/vXJroOIiIiITEFKBCGTYFo0opplZm8E3ujuR052XUQOGPn8sIdtSw4BoHD/ppYUv2dpdKTofGhEl2al5bUiUn0L4xS2e2Xss+KDusoqMhUd9v74bt72pUfU3WbV13MtPWZ+IP2y1g/sA58SSxy0zOwI4F3AycS3/XLgQ+6+dqKPfUA3ooB5wOGTXQkREREREWkdM1tONJoWMTSU7YHAM83sJHef0AksD/RGlIg0IXfSAyld85ea66on1d3++Aj0zv72+CJR/U8/GYBDPxJXrNe9M1Lxei4iUoOnrwBgzv/8qbxP9veccR1ZRPaXY151FWs+E2nQV79++Piptt9cPer+baviumhh7d2jbtv9/ctH3UaG2/DWOO92bXc6d0YILxuDOmVpnqiD2b8Di4HfAl8kGlL/CDwurXvdRB68tbFzERERERHBzFaY2VfNbL2Z9ZvZWjP7pJnNn+hyzOw0M/uZmW0zs14zu9HM3mRm+RH2eYmZXWlmPWa208wuNrOnj7D9DDM7x8xuNbM+M9tkZt8zs+NHeT6nm9kPzGxDej4bzOxXZva0xl6RsicBtwFPcffvu/v3gCcDa9L9hFIkSvax5zlxlbBvfrSxF35J40wONvWiUJW2vCZN2vj51nw+On9y5bDH2fim7S+PDFvzv/qnffYRkellzRdOZuGV8b8ld9IDAbjr7yKWfPi7Rz+XNBKBkub1Lo8QzLKPTbPz7RSMHJnZUcBlwCHEXKe3EON23gg8xcwe5e5bJ6IcMzsT+AHQB3wX2AY8A/gE8CjguTWOcy7wVmAd8CWgAzgL+LGZvd7d/6tq+07g16m8q4FPEfO6Phf4WzN7vLtfUeM47wLeD2wBfgJsILrjPQw4A/jZaK9JhcOAL7t7MVvg7kUz+yXwqjGU0xQ1okRERERk+pqCjSjgs0TD5w3u/plsoZl9HHgz8EHgNa0ux8zmEI2gInCGu1+dlr8buAh4jpmd5e7fqdjnNKIBdQfwCHffnpZ/FLgGONfMflKVrOEtRAPqfOB57l5K+3wXuAD4qpk9KFue1j2XaED9Bvh7d99d+UTNrL2B16NSF9EYq7aVaAROqJZ15zOznJm93swuTyHAQsW6h5nZZ83smFYdT0RERERkqjGzI4nuZGuB/65a/V5gD/AiM+uegHKeQ4wT+k7WgAJw9z4iix3Aa6vKyhphH8waUGmf7LidVMzZamZWsc/bKhtK7n4h8HvgAcBjK/bJAR8GeoF/qG5ApX0Hq5dNZS2JRJlZB/BzIgy3DdjNUGZigLuAlwObiTddprDu82MQ6dZzorvWwsmsjEyathXLgX0TSWRa1Y1vNOrGJ3Lg6NzYxp7IFcOsjTOBxrrxyf5x1Fum6fl2YlKcH2dm19Q8nPtJo+z7+HT/q8oGRtp3t5n9kWgcnUokRWhlOdk+v6hR3qVEI+Y0M+t09/4G9vk58O60TfYb/ihgJXCbu99VZ5/T0z6/S8tOA44gIlfbzexviUx6fcCV7t7sh++MaNMNXwbl6Fv1Snf39zd5rGFa1Z3vX4lMGGcDHwDeQ7zgALj7DjO7FPgb1IgSERERkQPXsen+tjrrs8QHxzByI6qZcuru4+4FM7sLOAE4Erg5RbGWAz3uvqHOMUjHGEu9qvfJJom7H7gWeFDlDqmd8Bx331ynzHrOSLdazqn424kGlRNdCsetVY2oFwB/dPf3AZhZrd6pdxGD2qSKma0A3gc8hQj8bCD6k55TGVbd31a+V1cHD2ZZBKpteVw2Lty3fsTte553KrO+O8VT4YrIpLjz3EgQk++F/iMHANgyEEMWDv3ppFVLDhA1f3WO3y0NRJzqmZvud9ZZny2fNwHljHWf/XEMiHFdEN0A7wKeCFxBzOf6MSLQ8n3qN4hqOWf0TSZOqxpRRwCjnQa3AQtadLxGXbyfjzdmrcreIiIiIiLTQtbFbLzNv2bKafbY4z1GvmLdc9z9hvT4JjN7FhHVeqyZPbLRrn3ufkA0ovYyemt6JbCjmcJTTvtOd++tWv544Eyif+cXq/tluvslwCXNHHM/alX2FpEJkUWgBv8mIvHtv7yq5naltrH3Sb/33adx2PtrRzzt5Ij09y2ZAUDnjyMF+u7nn8qWh0ROnCPeXnvfde+K8XwdO+LxIf+lqKrIZJp5X5wfeg91um9OEaiP6HspLTA1J9vNIjFz66yfU7VdK8sZ6z6jbV8r6tRMvbKeVXdWNKAAcPe9KS35K4hAwrQYnNeq7HzXA09OCSb2YWZziTDdlbXWN+BcYFsqJyvzLCI//euBfwOuNLPDmix/UrQqe4uIiIiITBm3pvt6WalXp/t6Y4rGU07dfcysjeg9VgDuBHD3PcB9wCwzWzbeYzSwz446+2SNrBl11tdkZkeY2VfM7M9pQuEvmtmqsZTRrFY1or5ETHj1rZSfvszM5gHnAfOBzzdZ/mOA37l7ZYv2vcQb8WLgbUQk7C1Nlj9ZRsy6AvwRmElkXTlotC0/tDwOR6aO9l9eVTcKBdC5q1h33aY3nMamN5y2z/K23hobJ4NzOhmc08mmh7ax6aFDQfMtDzVKnU6p01n3rtPKUadKpba49S2Km4hMrqWfuIyln7gMPCJQikLJAS7LSPfklNq7zMxmE/Mr7QVGG0jcTDkXpfun1CjvMcTvyssqMvONts9Tq7aBmE/qHuAYMzuiwX0uJRpvq+sEXR6Y7tfWWFeTmS0nnvtLiWQZDwReCVye1k2oljSi3P3bwNeAZxNpzF8LYGZXE0kSzgQ+6+5jmYW40mHA7dmDFME5FviMu/+Pu59LpFOs9eZPZc1kN6nJzK6pdQOOa0VFRURERKYi89bexsvd7wB+BawC/rlq9TlAN/CNFAXCzNrN7Lg0Tr7pcpLziQlozzKzh2cLzayLyKAN8LmqsrIgxzvNbH7FPtlx+4nf+Vm9vGKfj1Q28MzsTCK9+V+pGFLj7luA7xJdAN9TeXAzexLRY20ntdOs1/PvxJxYFwHPA84iGp6HpHUTqlVjonD3V5jZ74mECA8mBo6dCNwEfNzdvzbS/qOYA+yqePwoosdq5Qt9E5FmfTppVfYWEREREZk6/olIHPZpM3sCcDNwCvFb9TbgnRXbLk/r7yYaTM2Wg7vvMrNXEY2pi83sO0Ryt2cSF+/PJxozlftclsbivwW40czOBzqIhskC4PVp4t1KHweeTkzue4WZ/ZbIf/BcIlfBy6t7WaXyTyEaa48hhvkcDjwLKAKvcvcdNO5J6TV4irsXAczsB0QD7sljKKcpLWtEAbj7ecB5ZjaD6L63s6p13KwNRB/OzBOJ8GXlJGiziDDhgaThDCr10nCmaNSJrazURBstlbZMTVnih1oO+XTtrjvLPrbv8rZDU5fs38RE64f9Jh5ufHNKFrETip2xbMUHape78ux9l9/9/thfE3uKjM3dH0jfnXc1/91Z85VITHPIxeOry9oPRV1WvSPqsv5f4/GhH9X3+uBlEzDZ7vjLc/c7UiQom8LmacTv2U8TU9hsm6hy3P0CM3ss0cB6NtBF9Oh6C/DpFEmq3uetZnYj8Drg1UCJmM/po+7+kxrb95vZE4G3A/9AJEPbRUzR8153/2uNfTaZ2SnAu4iG06nAbiLD93+4+1jnSTkM+HLWgErHKKYkFa8aY1lj1pJGVGpNrnX3eyCybBCNnMptDgOOcPdLmzjE5cAzzezpxMzGzwF+6+6DFdscSQyMm05alb1FRERE5OA0MfNEjZu73wu8rIHt1jJCy63Rcqr2+SPR4BrLPl8Hvj6G7fcSOQreO4Z9thGNuVbkMegiui5W20pE0iZUqyJRvyP6Zr5vhG1enNbnR9imng8R46ouTI9LROpvAFIyizOA7zRR9mRqVfYWkWlhx0tiws15X6+fvbSwvtaE6TEwfTwUgRLZ/7IkQatfEUlpBp76iHGVl0WgMlkE6v43RURqxub4NT3nW81lSM4S4JTSr6OlH9d5Q0Rqa1UjqpG4p9HktQJ3/3MK/70kLfquu1emCXswMfDu282UP4mGZV2p7Ds6xuwtIiIiIgenKRqJkv3iDLN9miFnAJjZu9m3jeLu/v5WHLilY6JGsZLo99gUd/8z8C911v0B+EOzZU+W1M/1V8Tgt38GPlOxOsu68oUWjSsTmXQjRaBEZGoaz1io6jGuHT+vP03CeCz5ZNRx4CkR6Wo7/DAKd9/bdHntPSOvb1u2FIDCho1NH0NEWuKMdKvlnIq/naGAzuQ2oszsPVWLarUEIbrvrSTSDrakoZPSL85KfUSnuzFlXRERERGRIa1ISy7T0jmjbzJxxhOJOrvib2fkliBE0oe3N3swM5tFvFgvIHLCO6n+qavfe4F3ufu1zR5jMrQqe8vBatProv/6If+lfusyJBvXUC8r4EhyD3tA3G/eSWHddMtVIzK15BfElDPFbdv3zwHHmVSt0XOGIlBTiNP67nxqlE0L7j5tG1HZnExGTHJ1HrUzehSJLBm31sgX3xAzm0tEsU4AricycRxfscmfiYm9nk+kY5xWmsm6IiIiIiIik6PpRpS7l2chNrOvAxdULmuxdxINqJe6+zfM7L1UzHbs7r1mdgnwhAk6voiIiIhMRYocHbTM7LXEVEEfyYI1ZvZG4I01Nr/E3VsWtGhJYolWVqiOvwd+6e7fGGGbu4Hx5U6VaUfd+KSWZrrxZfqWdgNgi2dSPHE5AF0/qj+RsIjUl3XjazsuZuwo3LJmQo+XJa4oTOhRRGQqMLMTgf8iJuqt7O02D1hVY5fDzexT7n59K46fa0Uh+8EK4MZRtumh/qS1IiIiInIAMm/tTaaN5wMDwCdrrMtyJ7Sn2yFp2xe26uAtS3FuZsuAdwF/Ayyn9kzB7u7NHHM38eRHcgS1Zy0WEWlYZQrmwt+dMok1ETlwTHQESkQOSqcDf3L3mr//q6JTW8zsN2mflmhJI8rMlgNXAkuAm4BOontdP3BkOs71wM4mD3EV8HQzm+3u+8w1lRpwTwN+0mT5IiIiIjId+TjTMsp0tRr4Zo3lRu1cnWuBR7bq4K3qzvceYCnwFHd/SFr2NXc/jmhE/RKYQYxtasangIXAz8ysMisf6fH3gS4iLbiISEvMuOAKZlxwxWRXQ+SA0XbkqsmughyIvMU3mS5mE73Vqn2NoSzilXakfVqiVd35/gb4hbv/pnqFu68zs+cCfyHmeXrDWAt391+a2dnE3FR/AQYBzGwLMJ9obf6buyvLgIiIiIjIgW83sKB6obvfTfSIq7YA2NOqg7cqErWU6MaXKRKRJwDcvQf4NXBmswdw9/cRKcx/BGxPx3DgZ8AT3f2jzZYtIiIiItNPq5NKKLnEtLIWOHkM25+c9mmJVkWidjE8kcR2IrlEpZ3A4vEcxN1/B/xuPGXIga1tRXzsCuvum+SaiIhItcKdaye7CiJy4LgEeKOZnerul4+0oZk9EjgJ+ESrDt6qSNTdwGEVj28AHm9mMwHMLAc8GVjXouOJiIiIiGhM1MHrc8Q79m0zO67eRmZ2LPC/RC+2z7fq4K2KRP0WeLWZtbv7IPB14BvAZWb2a+DRwAnAh8ZzEDN7MPAPwPFAt7s/MS1fRYTofu3u28dzDJme2pYfGn8Ui5NbERERGVH+QfFbp/jnWya5JnKgUPe7g5O7rzGz9wPvBa4zs+8TPdbuIxpXy4mhQM8hMoef7e4tm2+hVY2orxBd+BYBG9z9f8zsJOD1wIPTNt8BPtjsAczsfcA7GIqeVX5lcsC3gTcBn2n2GCIiIiIiMj24+zlmBvBOYiLdF1RtYkCBaEC9r5XHbkkjKrXqPly17M1m9iEixflad7+/2fLN7CxiIt9fAv8GPA94e8Wx7jSzq4FnokbUQalw3/rJroKIiIwiP28etm3XZFdDDjSKRB3UUkPqG8DLgdOIhHcGbAD+CJzn7ne2+ritikTV5O6bgc0tKOoNwO3Ame4+YGbPqrHNzcAZLTiWiIiIiIhME+5+F/Du/XnMCW1EtdCDiFbkwAjbrAeW7Kf6yBTVtmwpAIUNGye5JiIiUq24Ywfs2DHZ1air7dBlABTWb5jkmsiYKBIlk6BljSgzW0CE0U4mJsDN19jM3f0JzRQPlEbZZgnQ10TZIiIiIjIdTcS8TmqUSQNa0ohKaQUvJuaBshE2bfZjuYbo41jv+HkiA+BN9bYRERERERFphVbNE3UucAiRXOJIoN3dczVutaJTjfgecKKZvbXO+n8HjiZywMtBrLBho7ryiYhMYfnZs8nPns22V57GtlfWvT46KQrrN6grn4g0pFXd+U4Hfuru72hRedU+CTwX+IiZ/T9SRMvMzk3HfjhwOfDFCTq+iIiIiIgI0LpGlAF/bVFZ+3D3vWb2OOBTRP73LKL1FmKs1P8Ar3P3wkTVQURERMavuHs3AAu+fBkAG98U0ShPv0iWnXvZpNRLpjGNYZJJ0KpG1DXAsS0qqyZ33wm81MzeAjwCWAjsBK5MqdRFREREREQmXKsaUe8DfmlmZ7j7xS0qs8zM7gR+7u7/7O7biEl3RUREZJpb+snhkafSGScCkLv42lH3zaa1IBdDvKfCxOtbXxWRtYVfUkRtf2l5dj6RBrSqEXUYcCHwKzP7NhGZ2lFrQ3f/RhPlLyaiTiIiIiIiQ9SIkknQqkbUecRH2IAXpVv1R9rSsmYaUTcBR42jfiIiIjINZBGoTa+PiM6SK3bjV/659salNIVkrlXJhsev57C4Xzi51RCRCdaqRtTLWlROPZ8GvmxmD3b3Gyf4WCIiIiIyXSgSJZOgJY0od/96K8oZwTrgN8AfzewLwFXARmp8bdz90gmui4iIiIiIHMRaFYmaaBcz1F3wLYx8zaHZCX1FRERkijjkM5GYYaR/+IX7N+2fyozB4e9RQon9yicgsYQiW9KA6dKIeh/6SIuIiIhINf1ClEnQVCMqpRx34Inufld63Ah39zEniHD3s8e6j8hY3ff201j+n7qCKCIy1bStWA5AYd19k1wTEZHQbCQqx/B2f/XjeqzJ44mIiIiIDGO0vjuffqxKI5pqRLn7qpEei0wnd37kkQB07pjceoiIyJC2o44AoLTh/nIEKn/CMQAUb7pt0uo1mrs/EKnZD3+XejaIHMimxZgoM7uogc1KwC7gZuCH7n71xNZKRERERCadxkTJJJgWjSjgjHSfZeirVrn874C3m9nn3f2fJ75qMt3l++Oj075rkisiMkk2vDWunC/7mK6cy9RRuOOufZZN5QhURhGoSaBGlEyCZhNLvLjZA7r7N5rYrQv4DnA88H7gD8D9wBLgdOBdRATqdcAJwH8CrzGza9z9q83WVUREREREpFqzkajzGN7uN0a/DpBt00wj6t3Aw4EHuntlvOAe4Ftm9hPgL8Br3P09ZnYtcCvwCkCNKBEREZEDkeaJkknSbCPqZTWW/T3wDOASYnLcjcBS4HHAY4AfAT9s8ngvAP6vqgFV5u47zewHwAuB97j7VjP7eaqPyIjyfXFfmi6dW0VaTN34RERExqbZ7Hxfr3xsZk8DngKc6e4/rtr8HDM7E/ge8PmmagmHAoOjbDMILKt4vI7oBigiIiIiBypFjmQStOra+zuJjHjVDSgA3P1CM7uA6Jb3iybKvw94hpn9u7sXqleaWTvwTGB9xeLFwI4mjiUHmRUf1FV4EREREWlcrkXlPAS4fZRtbgce3GT53wSOAX5tZo8ysxyAmeXM7NHAr4Gj03aZ04CbmjyeiIiIiEwH3uKbSANaFYkaIBpSI3kIo3fJq+dDRGKJpwGXAiUz2wYsIBqCRkS4PgRgZsuAG4AfNHk8EREREZkGWp5YQqQBrYpE/RZ4mpm9zsyGzeNk4fXAU4HfNFO4uw+4+9OBlwC/A3YSDahd6fFL3f1p7j6Qtt/g7s939/Obf0oiIiIiIiL7alUk6u1EFr5PAW8ys8p5nB4NHAFsS9s1zd2/yfAueyIiIiKTpuesUwHwXFxDnv2/f5rM6hycFImSSdCSRpS732FmjwT+G3gicGTVJr8G/tnd72zF8URERERERCZLy2bGcfc1wJPNbDnwMGAu0e3uOne/rxXHMLMHA/8AHA90u/sT0/JVwMnAr919eyuOJSIiIiJTnCbblUnSkkaUmV0E/NHd350aTC1pNFUd433AOxgax1X5Ec8B3wbeBHym1ccWERERyex46SMB2LXKWHm2psmYdGr0yCRoVWKJU4F8i8rah5mdBbyL6Bb4UOA/KtenboJXE3NFTQozW21m/2ZmF5nZvWY2YGb3m9mFZva4Ovu81Mx8hNtr9vfzEBERERGRkbWqO98a4LAWlVXLG4h5ps509wEze1aNbW4GzpjAOozm/cDzgL8CPyMSaRxLNOyeaWZvdPdP19n3QuD6GsuvnoB6ioiISBPWfug0AKwYj3MDk1gZGaJIlEyCVjWivgycY2Yr3f2eFpVZ6UHAeVkK8zrWE9kAJ8svgA+7+3WVC83ssUQE7aNm9n1331Bj3wvc/bz9UEcRERERERmnVjWifgw8CfijmX0YuArYSI1rA002sgwojbLNEqCvibJbol4jyN0vMbOLidfnNDQBsIiIyLSy9oMRgVr1Do1/mops9E1EWq5Vjag7iQaTEXNF1eNNHnMN0QCpyczyxHxUNzVR9v4wmO4LddY/1MzeBHQRSTl+5+7r9kfFRERERKY1deeTSdCqRtQ3mNiP8PeAD5jZW939YzXW/ztwNCM34CaFmR0OPAHoBS6ts9kbqx4XzezLwJvcvaHompldU2fVcQ1VVEREREREGtKqyXZf2opyRvBJ4LnAR8zs/5EabGZ2LnA68HDgcuCLE1yPMTGzTuBbQCfwthpzWN0FvB74FbCOmFvr0UT2wX8E5hDzYskE2PAvEdxcdq66Z4iIyJB7zon/D6X0K2nVO/V/YqoyWj9PlLoHSiNaNtnuRHL3vSlN+KeAFzCUTv0txFip/wFe5+71uss1xMzWAoePYZdvufsL65SVB74JPAr4LnBu9TbufglwScWiXuD7ZnY5cAPwfDP7sLvfMFpF3P2kOvW4BjhxtP1FRERERKQxrZonqszMVpjZM8zsRWb2TDNb0Ypy3X1ningtAZ4KvBB4BrDM3V/i7rtbcJg7gFvHcFtfq5DUgPofInr2PeCF7t7wdRJ3v5dIkw7wmGaeiIxu2bmXKQolIiL7sELcVr3zMkWhpjqfoFsLpN/EXzWz9WbWb2ZrzeyTZjZ/ossxs9PM7Gdmts3Mes3sRjN7U/qNWm+fl5jZlWbWY2Y7zexiM3v6CNvPMLNzzOxWM+szs01m9j0zO77B5/WiirlRX9nIPlNJyyJRZraS6E73pBrrfg28xt3Xjvc47r4N+OV4y6lT9hPGW4aZtQH/SzSg/hd4sbsXmyhqc7rvHm+dRERERGT/MbOjgMuAQ4j5QG8BTibGwT/FzB7l7lsnohwzO5PIBt1H9IbaRgQePkH0kHpujeOcC7yVGF7yJaADOAv4sZm93t3/q2r7TmIKn0cR85p+ipgz9rnA35rZ4939ihGe12HAZ4AeYNZor8NU1JJGlJktBf4ILAfWEgkUNgDLiDE+Twb+YGYPd/eNrTjmVGRmHUTk6Uwi2cbL3H201Oz1nJLu72xF3URERKRBGhQzvUzN7HyfJRo+b3D3z2QLzezjwJuBDwKvaXU5ZjaHaAQVgTPc/eq0/N3ARcBzzOwsd/9OxT6nEQ2oO4BHZGP4zeyjwDXAuWb2k6pgyFuIBtT5wPOy37tm9l3gAuCrZvagWr+DzcyArwFbgf8D/qWB12HKaVUk6t1EA+rfgI9XRl5S2PDNwEeAdwGvG60wM/tqk/Vwd39Fk/uOS2qR/x/wNOArwKtHa0CZ2enu/vuqZQa8HXgksIWYxFdEREREamh1YonxMrMjiQDCWuC/q1a/F3g18KKUdXpPi8t5DrAY+EbWgAJw9z4zexfwW+C1wHcqysoaYR+sTILm7mvN7L+J3/kvS8fMfqtm+7yt8veuu19oZr8nEr89Fvhdjaf2BuDxwBnpflpqVSPqb4FfuftHq1ekBtW5ZvZE4Ok00IgCXlpneTYXVb3lDkxKIwr4PNGA2kLM9fSe+IwNc7G7X1zx+FIzu42YnPg+Ijvfo4AHEkkmXuDuuya43iIiIgeszf8UmfYWf7bxsU3e8hHjcpDJGga/qr6g7u67zeyPROPoVKJR08pysn1qXYS/lPh9eZqZdbp7fwP7/JxoRD2e1IgCjgJWAre5+1119jk97TOsEZXGS/0n8Cl3v9TMDvpG1FIilfdIriFanI04oupxjujHeTrwaeBiYGM67uOINOGXEqHFyZLVeRHwnhG2u7ji73OJfq2PBxYQmQbvIa42fNzd1ZVPREREZCQTE4k6rt4cnPUyIlc4Nt3fVmf9GqLxcwwjN6KaKafuPu5eMLO7gBOAI4Gbzayb6E3W4+4b6hyDdIyx1Kt6nyxvwDeJ37rvqLPvtNGqRtRORk8NvjJtNyp3v7vysZm9mWhAnVi17lbgEjP7OtFIO5OYU2q/c/czmtjnXyegKiIiIiIyeeam+3q/e7Pl8yagnLHusz+OkXkP8DDg0e6+t86+00arGlF/IAaqfdbd94mXm9kpRLaOnzZZ/quB71U3rjLufpeZfS9t98kmjyEiIiIHmFL72PdZebbSmk8nEzQm6pYGIk7NysZ7jLfmzZTT7LHHdQwzO5mIPn3M3f80xmNPSa1qRH2QGBd1iZl9h+j/uIHobncG8Hyiq9qHmix/FbBjlG12pO1ERERE5GDQwnmdhpU5PlkkZm6d9XOqtmtlOWPdZ7Tta0WdxnSMim58txHjqw4ILWlEufu1ZvYc4DzgBcA/VKw2Ij/9y929Zt/SBmwB/gb491orU5aQvyFSJYqITFkb3xyD3Jd+Qle6RfaHJZ/Sd032u1vT/TF11q9O9/XGFI2nnFuBh6d9hv3uTo2ZI4ACaQodd99jZvcBy81sWY1xUfWOMZZ6zarYtq9G4jWAL5nZl4iEE2+qU+6U0rLJdt39J2Z2ODEu6USidboTuA64YKQUjg34PvCm1GXv3yozgZjZEcCHgQcTySdERERE5CAx1VKcM5SR7slmlqvMrGdms4lMzHuByyegnIuIgMZTgG9XlfcYYCZwaUVmvmyfF6V9vla1z1MrtsncQSSHOMbMjqiRoa96n35i+p9aTiTGSf2BaJxNm65+LWtEQbRmgf9Nt1Z6DzFp73OAZ6UW8/3AEiKjSJ5IE352i48rItJSikCJTLy25YdSuG/9ZFdDDlLufoeZ/YrInPfPwGcqVp8DdANfyAIMZtZOpA0fdPc7mi0nOZ8ILpxlZp+pmGy3C/hA2uZzVVX+PNGIeqeZXVAx2e6qdNx+KhpX7u5m9nlimM5HzKxyst0ziWRwfwUuSdvvBV5Z67Uys7OJRtTX3f3LtbaZqlraiMqk2ZLnAjtbMc+Ru/eY2aOJGY1fxlB+eoDbiTf2Y+4+MN5jiYiIiMg0MvUiUQD/BFwGfNrMngDcDJxCTM1zG/DOim2Xp/V3s+/4/rGUg7vvMrNXEY2pi1Ougm3AM4nU5OcD363a5zIz+zgxVdCNZnY+0AE8j5iC5/XuvraqXh8n5n99DnCFmf2W+G3+XGIuqpdXz211oGnZdHJmljezt5vZ7cB2Ynbl7WZ2e1o+rgabuw+4+4fcfTUxYO0wYI67H+Pu/6EGlIhMZZvecBqb3nDaZFdD5KCw8ZmrJrsKsj95i2+tqFJElB5O5As4BXgrEQT4NPBId29oHH8z5bj7BcBjiTlUn03MpzpINJLOcvd9nqW7vxV4KTEP66uBFwM3Ac9w9/+qsX0/8ETgfUQq8zcDTwIuAB7h7lc08vyms5ZEosysg5jl+LHEx+9eIjvfMqJF/UH+f3t3Hi9ZUd99/PNlWBSQYRHFJQmKGpWocUEQNxBFTVTccSO47wpqHmPcwKhPjBjE3bggLknQoGI0Lig6oCIibjwEEQVGBVH2HQZn5vf8UaehafrO7b7Td+72eb9e53W451TVqVN9uNO/W3Wq4FFJ9p5EsFNVVwJXrm85kiRJ0myoqt/RRlBNl24lN0wLPuNyBvJ8H/ibMfN8EvjkGOmvAQ7qthmpqoNZoK/jTKon6tW0qcz/B7hbVe1YVQ+oqh1pXYdfpo2PfPUohSU5LclLZ1qZ9c0vSZKkhSE12U0axaSCqGcApwKPr6pf9Z/ouiGfSOsSfOaI5d0VuOV61Gd980vSRK3dqG2SZt8tP+QELpJm16QmlrgT8L6pXiCrqrVJvkYbkzmqPaaYR34U/h1BkiRpsZufi+1qCZhUEHUdbSGtddmC9lLbqPboNklasC56Ybe47mH+ZVySJi1AbjpPwnqXKU1nUkHUKcCTkxxcVRcMnkxyS9oUiD8fsbw9J1CnlRMoQ5IkSZJuZFJB1PuBI4GTkryNtsLyecAOtN6kNwLbA68cpbCqOm5C9ZKkGVm27TasufiS9S5n7SYTqIy0BFz44tZre8sPb5he2wte2q63/QftJV7wHH6nOTCRIKqqPpfkr4HXAR8ZkiTAO6vqc5O4niRJkiTNlUn1RFFVr0/y38DzgHsDy4HLgJ8Ch1fVDyZ1LUmL16X7PwCAdNPULP/03PzqyLJl65X/ukfvAsBG82QZ8D8ecMNCv7d+j3951/yz8TUbtjvBHqjFw2nJNRcmFkQBVNWJwImTLFOSJEmS5pOJBlGSJEnSBmVPlObARIKoJE8BXgI8q6p+P+T87YBPAR+oqi9M4pqSFqetPzk/Rv6uvuDCsfNs/Bd/xurf/K799zVrANjuoz+aaL1mqjeE7/yX7+4L9ZpXLnxJex6XrZqdb8KX/V0bIrz8U/Pjd4smrGZhOJ9BmUaw0YTKeT6w9bAACqCqzgW26tJJkiRJ0oI1qeF89wC+Mk2ak4HHTuh6krTBXfOEXQG4+Rd/OPR8rxcK4JrtNwXaKuPzybI/4V9ZNa+kddqy0Z9mt3wtYv5O0xyYVE/UtsD506S5CLjlhK4nSZIkSXNiUj1RFwJ3nibNnYFLJ3Q9SdrgpuqBunLf3QBYs2mun5q91m+G9Fmz0Z+KjVa3/+5NJ7924wCw7cd9R0pzoNeLkNkpfqt/912oxc4pzjUXJtUT9X3gcUnuOuxkkrsB+wDfndD1JEmSpBaIT3KTRjCpIOpdtF6t7yV5ZZK7JNmi2x9AC56WdekkSZIkacGayHC+qvpRkpcCHwDe3W391gAvqarhY2EkaQFbu0kbh7T80/N32NBFL2jTSG9ydbHxte1Prddt6TA+zb1l1/mnf60fh/NpLkxssd2q+miS7wEvBXYFtqa9A3Ui8KGq+sWkriVJkiRJc2ViQRRAFyi9YpJlStJ8t9Vn5m8PVM92H229TZf93QNYu0k7tpFTP2se6D2H1y+2u9u92v7En89NhbSwVLVt0mVK05hoECVJkiRtKGHyw/lmaaJILTIGUZI0Q1c9uU1t3vsXd4v/OnHuKjOFXh17U65nLVRX34XQg6bFb6PV7RvwslVtfYCNftJG/6+dsxpJ0vQMoiRJkrRwOfpOc8AgSpJmqDeEZMuzrwTgukfcj42/efIc1ugGvQWAt/j9KgBWbbspAHVdsWYzB6toHun+P7r+nai73antf37alFmWbbvNjX5ec/Els1EzSZqSQZQkSZIWrDj2U3NgUovtSpIkSdKSYE+UJM3Q5p9vE0lc94j7AbDs2rmfM3zVY+8PwKaXt7qs3bj9raw3mcTNv+ia55pftvxs+/9o7UPv0w5sPP3fd7OsmyllrS/DLHnF5N+J8rHSCAyiJEmStGBNeopzaRQGUZK0nubDZBLL7nFXADY/+zIAVu1wCwBWb9n+Yn/zo+2B0vy20aqu9/THp06bdvUFF852dSRpnQyiJEmStHCVXVHa8AyiJGkB2+g+OwOQy68BoDbfDIC1m7SXoDb78klzUzFpRNc9ahcAbvbHq4B1L7K77Bath3XNFVfMdrUkaZ0MoiRJkrRg+U6U5oJBlCQtYLVRm8nsutsuB/q+TPilQvPcnx7ZeqA2Wt0e1rU/nXpx3Z5sv137D3ui1M/fd5oDrhMlSZIkSWOwJ0qSJEkLU83CcD57tjQCgyhJWsD+tHWbSKKWtYkkNvnGjwDYdM5qJI2om1Ftk0tXtR9HyLL6rJWzVx9JGoNBlCRJkhYupzjXHDCIkqQFbONvzf1Cv9JMbLSmffGtk//fHNdEC1mY/HC+TLY4LVJOLDEhSXZMUuvYjlxH3v2TnJTkyiSXJVmR5DEbsv6SJEmSRmNP1OT9HDh6yPFThyVO8i7gNcA5wEdprzI8DfhykldU1ftnqZ6SJM2ZZcf+eK6roMXC0XyaAwZRk/ezqjp4lIRJdqcFUGcCu1TVJd3xQ4AfA+9K8pWqWjlLdZUkSZI0Jofzza0Xd/u39wIogC5o+gCwGfCcOaiXJEnSgpCa7CaNwp6oybttkhcB2wEXAT+oqlOmSPuwbv/1Iee+BrypS3PQdBdNMtW4iLtOl1eSJEnS6AyiJu8R3Xa9JCuA/avqt33HtgBuB1xZVecNKedX3f4us1RPSZKkha2AtRPuPrI3SiMwiJqcq4G30iaVOKs7dk/gYGBP4Ngkf11VV3Xnlnf7y6Yor3d861EuXlX3HXa866G6zyhlSJIkLTgGPZoDvhPVJ8nKaaYpH9w+08tbVedX1Zur6idVdWm3HQ/sDfwQuBPw/BlUy18NkiRJ0jxiT9SNnQlcO0b630+XoKpWJ/kYsCvwEOA93aleT9PyoRmn76mSJEla8pwMQnPBIKpPVe01S0Vf0O236LvWVUnOBW6X5DZD3ou6c7c/Y5bqJEmSJGkGHM63YezW7c8aOP7tbv+oIXkePZBGkiRJg6omu0kjMIiakCS7Jtl0yPGHAa/qfvzMwOkPd/s3JNmmL8+OwMuAVcAnJl9bSZKkRWDCa0Sl8G10jcThfJPzL8DO3XTm53TH7skNa0G9qapO6M9QVSckORR4NXBKkqOATYF9gW2BV3QL70qSJEmaJwyiJufTwBOAXWhD8TYB/gh8Dnh/VX13WKaqek2SU4CXAy8E1gI/AQ6pqq9siIpLkiQtWPYcaQ4YRE1IVX0c+PgM834S+ORkayRJkiRpNhhESZIkaYEqMvHJIOza0vQMoiRJkrRwrZ3rCmgpcnY+SZIkSRqDPVGSJElakNq05JMdfhdH82kE9kRJkiRJ0hjsiZIkSdLCZc+R5oA9UZIkSZI0BoMoSZIkLVxVk90mJMntkxye5PdJViVZmeSwJNvMdjlJdk/y1SQXJ7k6ySlJDkyybB159k9yUpIrk1yWZEWSx6wj/c2TvCXJL5Ncm+T8JJ9LcrchabdL8vwkX0zy6yTXdNf4XpLnJVlwMcmCq7AkSZLU0yaXmNw2kTolOwE/Bp4DnAS8GzgLOAD4QZLtZqucJPsAxwMPAb4IfADYtMt75BTXeRdwBHAb4KPAZ4B7AF9O8vIh6TcDvgm8GbgceA/wLeAJwMlJdh3I8pSu3F2BHwKHAZ8H/gr4GPC5JJm+ReYP34mSJEmSJuuDwK2AV1bV+3oHkxwKvAp4O/DiSZeTZCtasLIG2KOqTu6Ovwn4NvDkJE+rqiP78uwOvAY4E9ilqi7pjh9CC+DeleQrVbWyr16vBh4IHAXsW1VruzyfBY4GDk9yj95x4AzgccD/9B0jyetpweGTgCfSAqsFwZ4oSZIkLVzzbDhfkjsCewMrab1A/Q4CrgL2S7LFLJTzZGB74MheAAVQVdcCb+x+fMlAWb0g7O29AKrL07vuZrSesF690pfntf1BUVV9CfgucHfgoX3Hv11VX+5P2x3/A/Dh7sc9BttgPjOIkiRJkibnYd3+mCFBwxXA94HNgd1moZxenq8PKe944Gpg92443ih5vjaQBmAn4M+BM6rq7BHzrMufuv3qEdPPCwZRkiRJWpgKsnayWzdl+l2T/HjYNkKt/rLbnzHF+V91+7vMQjlT5qmq1cDZtNd57gjQ9WLdDriyqs5b32usI89QSTYG/q77cVgQN2/5TpQkSZIWrgnOqDchy7v9ZVOc7x3fehbKGTfPhrjGuryDNrnEV6vqGyOknzcMoiRJkqQbO72q7jtLZfdmoVvf6G8m5cz02hO/RpJX0ia0OB3Yb8z6zDmDKEmSJC1c864j6vqemOVTnN9qIN0kyxk3z3Tph/U6rff9JXkZbVr004C9quriqdLOV74TJUmSJE3OL7v9VO8E3bnbT/VO0fqUM2We7v2jO9AmcDgLoKquAs4Ftkxym/W9xjry9NfjQOD9wKnAnt0MfQuOQZQkSZIWqCI12W0CXVvf6fZ7J7nRd+0kt6Ctr3QNcOIslPPtbv+oIeU9hDab3wlVtWrEPI8eSANtPanfAndJcocR8/Tq/Q+0RX9/Rgugzh+Sf0EwiJIkSdLCVEx+naj1jKGq6kzgGGBH4GUDp98CbAF8qusFIskmSe6aZKf1KadzFHAh8LQk9+sdTHIz4G3djx8aKKu3TtMbkmzTl6d33VXAJ/rqVX153tkf4CXZB3gwbZjecf0X6Rb8fQdtAd+9qupCFjDfiZIkSZIm66XACcB7k+wF/ALYFdiTNsztDX1pb9ed/w0tYJppOVTV5UleQAumViQ5ErgYeBxtavKjgM8O5DkhyaHAq4FTkhwFbArsC2wLvKJbeLffocBjaIv7/jDJsbS1o55CW4vquf1rWyXZH/gnYA1tMd5XtjV7b2RlVR0xeHC+MoiSJEnSwrV2+iQbWlWd2fUE/RNtmNzfAOcB7wXeMupECjMpp6qOTvJQWoD1JOBmwK9pQdJ7u56kwTyvSXIK8HLghbRW/QlwSFV9ZUj6VUkeDrwOeAbwKuBy4GjgoKo6bSBLb9jfMuDAKW73OOCIKc7NOwZRkiRJ0oRV1e+A54yQbiU3TAs+43IG8nyfFnCNk+eTwCfHSH8NcFC3TZf2YODgceoz3xlESZIkacHK/FtsV0uAE0tIkiRJ0hjsiZIkSdLCZU+U5oBBlCRJkham3hTnky5TmobD+SRJkiRpDPZESZIkaeGah1Oca/GzJ0qSJEmSxmBPlCRJkhakUBOf4jy+FKURGERJkiRp4XJ2Ps0Bh/NJkiRJ0hjsiZIkSdLC5BTnmiP2REmSJEnSGOyJkiRJ0sLlO1GaAwZRkiRJWrhcJ0pzwOF8kiRJkjQGe6IkSZK0YE16nShpFPZESZIkSdIY7ImSJEnSwlQ1C1Oc27Ol6dkTJUmSJEljsCdKkiRJC9dae4604RlESZIkaeFy+J3mgMP5JEmSJGkM9kRJkiRp4bInSnPAnqgJSXJEkppmO3Ygz7OnSf/iubofSZIkScPZEzU5RwMrpzi3H3BH4GtTnP8S8LMhx09e30pJkiQtWsUsTHE+2eK0OBlETUhVHU0LpG4kydbAa4HrgCOmyH50VU11TpIkSUPVLMzOZxSl6Tmcb/btB9wc+EJVXTjXlZEkSZK0fuyJmn0v6PYfWUeav05yIHAz4FzgO1V1zjgXSfLjKU7ddZxyJEmSFpRaO9c10BJkEDWLkjwAuAdwRlV9Zx1JDxj4eU2SjwEHVtW1s1ZBSZIkSWMziJpdL+z2H53i/NnAK4BjgHOA5cCDgH8GXgRsBTxjlAtV1X2HHe96qO4zepUlSZIWEKc41xzwnag+SVaOME15//aZdZS1HHgq65hQoqqOq6r3V9UZVXV1VZ1XVf8F7AlcAjw9yb1m4VYlSZIWvqJNLDHJzZhMI7An6sbOBMYZPvf7dZx7FrA5cOS4E0pU1e+SfBV4JvAQ4Ofj5JckSZI0ewyi+lTVXhMsrjehxL/NMP8F3X6LCdRFkiRpEapZGM5nV5Sm53C+WZBkV+BetAklVsywmF27/VkTqZQkSZKkiTCImh29CSXWNa05SR485FiS/CPwAOBC4OuTr54kSdIiUTXZTRqBw/kmLMlWwL60CSU+OU3y45OcAfyItj7UcuCBwF8BVwPPrKrLZ7G6kiRJksZkEDV5z6S9xzTKhBLvAu4PPAzYFlgL/Bb4AHBoVTmUT5IkaV3sPdIcMIiasKr6EPChEdP+n1mujiRJ0uJVwNq1ky9TmobvREmSJEnSGOyJkiRJ0gLlFOeaG/ZESZIkSdIY7ImSJEnSwuXEEpoDBlGSJElamApYO+EgyphMI3A4nyRJkiSNwZ4oSZIkLVhVE57iXBqBPVGSJEmSNAZ7oiRJkrRA1eTfifKlKI3AIEqSJEkLl7PzaQ44nE+SJEmSxmBPlCRJkhamAtZOeGIJO7Y0AnuiJEmSJGkM9kRJkiRp4fKdKM0Be6IkSZIkaQz2REmSJGlBKoqa8DtR5UtRGoFBlCRJkhamYvLD+YyhNAKH80mSJEnSGOyJkiRJ0sK11q4jbXj2REmSJEnSGOyJkiRJ0gJVUBNebNeXojQCgyhJkiQtTAU16eF8xlAagcP5JEmSpAlLcvskhyf5fZJVSVYmOSzJNrNdTpLdk3w1ycVJrk5ySpIDkyxbR579k5yU5MoklyVZkeQx60h/8yRvSfLLJNcmOT/J55LcbZL3Ml8ZREmSJGnhqrWT3SYgyU7Aj4HnACcB7wbOAg4AfpBku9kqJ8k+wPHAQ4AvAh8ANu3yHjnFdd4FHAHcBvgo8BngHsCXk7x8SPrNgG8CbwYuB94DfAt4AnBykl0ncS/zmcP5JEmSpMn6IHAr4JVV9b7ewSSHAq8C3g68eNLlJNmKFgStAfaoqpO7428Cvg08OcnTqurIvjy7A68BzgR2qapLuuOH0IKedyX5SlWt7KvXq4EHAkcB+1a16DPJZ4GjgcOT3KN3fMJtMi/YEyVJkqQFq9bWRLf1leSOwN7ASlovUL+DgKuA/ZJsMQvlPBnYHjiyF0ABVNW1wBu7H18yUFYvcHl7L4Dq8vSuuxmt96hXr/TleW1/oFRVXwK+C9wdeOh63su8ZhAlSZKkBapmYTjfegdSD+v2xwz0xFBVVwDfBzYHdpuFcnp5vj6kvOOBq4Hdu+F4o+T52kAagJ2APwfOqKqzR8wzqTaZNxzOt/jteBVX8MP61lzXQ5IkLQJXcQXAjnNcDaDVZdLfcbr7u2uSHw87X1X3naaIv+z2Z0xx/le0Xpm7AMdOuJwp81TV6iRnAzsDdwR+0fX83A64sqrOm+IadNcYp14zyTNKm8wbBlGL3+VrWbP8Ci5dBZw+15WZ5+7a7W2ndbOdRmdbjcZ2Gp1tNRrbaTQzbacdaZMJzLXT17KGK7h0NsrecT3yLu/2l01xvnd861koZ9w8G+IaM80zrxlELXJVdYfeX1JG+MvJkmY7jcZ2Gp1tNRrbaXS21Whsp9Es9HaqqmfOdR1mKN1+fccNzqScmV57tq8xqTbZYHwnSpIkSZqcXq/K8inObzWQbpLljJtnuvTDepA2RL3mPYMoSZIkaXJ+2e3vMsX5O3f7qd4PWp9ypsyTZGPgDsBq2vpMVNVVwLnAlklus77XmHCeec0gSpIkSZqc73T7vZPc6Lt2klvQ1le6BjhxFsr5drd/1JDyHkKbAe+Eqlo1Yp5HD6SBtp7Ub4G7JLnDiHkm1SbzhkGUJEmSNCFVdSZwDG1yipcNnH4LsAXwqa4XiCSbJLlrkp3Wp5zOUcCFwNOS3K93MMnNgLd1P35ooKwPd/s3JNmmL0/vuquAT/TVq/ryvLM/KEqyD/Bg4DTguPW8l3nNiSUkSZKkyXopcALw3iR7Ab8AdgX2pA1Ze0Nf2tt153/DTWcFHKccquryJC+gBVMrkhwJXAw8jjbN+FHAZwfynJDkUODVwClJjgI2BfYFtgVe0S282+9Q4DG0xX1/mORY2tpRT6GtRfXcwfWgxr2X+S4tmJQkSZI0KUn+DPgn2jC57YDzgKOBt1TVxX3pdgTOBn5TVTvOtJyBPA+kBSUPAG4G/Bo4HHhvVa2ZIs/+wMuBuwNrgZ8Ah1TVV6ZIf3PgdcAzaAHU5cAK4KCqOm192mQhMIiSJEmSpDH4TpQkSZIkjcEgSpIkSZLGYBAlSZIkSWMwiJIkSZKkMRhESZIkSdIYDKIkSZIkaQwGUYtYktsnOTzJ75OsSrIyyWH9q1EvFd291xTbH6bIs3uSrya5OMnVSU5JcmCSZRu6/pOU5MlJ3pfku0ku79rgM9PkGbstkuyf5KQkVya5LMmKJI+Z/B3NnnHaKsmO63jGqlvwcKrrLOi2SrJdkucn+WKSXye5pruP7yV5Xv9q9gP5ltRzNW47LfFn6l+SHJvkd107XZzkp0kOSrLdFHmW1PPUM05bLeVnSpo014lapJLsRFsV+lbAl4DTgfvTVoX+JfDAqrpo7mq4YSVZCWwNHDbk9JVV9a6B9PsAnweupa3sfTHwWLrVvqvqKbNY3VmV5GfAvYArgXOAuwL/XlXPmiL92G2R5F3Aa7ryeyufP40bVj5//2TvanaM01a5YbHEn9MWDhx0alUdNSTfgm+rJC8GPkRbNPE7wG+BWwNPBJbTnp+nVN8/OEvxuRq3nZb4M3UdbaHP04DzgS2A3YD7Ab8Hdquq3/WlX3LPU884bbWUnylp4qrKbRFuwDeAov1y6z9+aHf8w3Ndxw3cHiuBlSOm3Yr2D9Eq4H59x29GC0wLeNpc39N6tMWewJ2BAHt09/OZSbUFsHt3/NfANn3HdwQuon3J2XGu22EW2mrH7vwRY5S/KNoKeBjtC+tGA8d3oAUKBTxpqT9XM2inpfxM3WyK42/v7u+DS/15mmFbLdlnys1t0pvD+RahJHcE9qYFDh8YOH0QcBWwX5ItNnDVFoonA9sDR1bVyb2DVXUt8Mbux5fMRcUmoaq+U1W/qqpRuqFn0hYv7vZvr6pL+vKspD2PmwHPmWH1N6gx22omFkVbVdW3q+rLVbV24PgfgA93P+7Rd2pJPlczaKeZWPDtBNc/C8N8rtvfue/YknyeesZsq5lYNG0lTZJB1OL0sG5/zJB/rK8Avg9sTuvuX0o2S/KsJK9PckCSPacYK99rv68POXc8cDWwe5LNZq2m88dM2mJdeb42kGYxum2SF3XP2YuS3HMdaZdCW/2p26/uO+ZzdVPD2qnHZ+oGj+32p/Qd83kablhb9fhMSetp47mugGbFX3b7M6Y4/ytaT9VdgGM3SI3mhx2ATw8cOzvJc6rquL5jU7ZfVa1OcjawM3BH4BezUtP5Y6y26Ho3b0d7z+y8IeX9qtvfZTYqO088otuul2QFsH9V/bbv2KJvqyQbA3/X/dj/Bcznqs862qlnyT5TSf4e2JL2ztj9gAfRgoJ39CXzeWLktupZss+UNCn2RC1Oy7v9ZVOc7x3fevarMm98AtiLFkhtAdwD+DfamO6vJblXX1rb7wbjtsVSbrurgbcC9wW26baH0iYQ2AM4dmAI7VJoq3cAfwV8taq+0Xfc5+rGpmonnyn4e9ow9ANpQcHXgb2r6oK+ND5PzSht5TMlTYhB1NKUbr9kpmasqrd07yP8saqurqpTq+rFtIk2bg4cPEZxS6791mGmbbHo2q6qzq+qN1fVT6rq0m47ntbr+0PgTsDzZ1L0RCu6gSR5JW02r9OB/cbN3u0X/XO1rnbymYKq2qGqQvsD2BNpvUk/TXKfMYpZEs/TKG3lMyVNjkHU4tT7y9DyKc5vNZBuKeu9zP2QvmO23w3GbYvp0k/3V81Fp6pWAx/rfhznOVuwbZXkZcB7aFMu71lVFw8k8blipHYaaik+U90fwL5I+7K/HfCpvtM+T32maaup8iy5Z0paXwZRi9Mvu/1UY5R7M/VM9c7UUnJ+t+8fvjBl+3XvLtyB9vL3WbNbtXlhrLaoqquAc4Etk9xmSHlL9dnrDae5/jlbrG2V5EDg/cCptMBg2GLWS/65GrGd1mXJPFP9quo3tKBz5yS37A4v+edpmCnaal2W5DMlzZRB1OL0nW6/d5IbfcZJbgE8ELgGOHFDV2weekC37w+Ivt3tHzUk/UNoMxueUFWrZrNi88RM2mJdeR49kGap6M2EORh4L6q2SvIPwLuBn9ECg/OnSLqkn6sx2mldlsQzNYXbdvs13X5JP0/TGGyrdVnKz5Q0vnEXlnJbGBsuttt/zzsD2w45/he0mYUKeH3f8a1of5FblIvtDrTBHky/2O5YbcEiXZhxhLbaFdh0yPGHdfdcwO6Lta2AN3X3cvKw/998rmbUTkvymQLuCuww5PhG3LCA7Pd9nmbUVkvymXJzm40tVb4LuBgl2Yn2j8etgC/RpuLeFdiT1u2+e1VdNHc13HCSHAy8jtZDdzZwBbAT8Le0f2S/Cjyhqq7ry/N44CjaPw5HAhcDj6NNpXsU8NRaoP/zdPf2+O7HHYBH0v7y+N3u2IVV9fcD6cdqiyT/CrwaOKdLsymwL218/iuq6v2Tv7PJG6etuumBdwZW0O4b4J7csH7Km6rqbUOuseDbKsn+wBG0v3a/j+HvR6ysqiP68jyeJfZcjdtOS/WZ6oY6HkJb4+lM2hf1W9Nmkbsj8Adgr6o6rS/P41lizxOM31ZL9ZmSZsVcR3Fus7cBf0ab2vs84DrgN7SXmNf518/FttH+MflP2uxXl9IWtbwA+CZtbZZMke+BtADrEtrwx/8HvApYNtf3tJ7tcTDtr4pTbSsn0RbA/sCPgKtogetxwGPm+v5nq62A5wFfAVYCV9L+Kv5b4LPAg6e5zoJuqxHaqYAVS/25GredluozRZvu/QO04Y4X0t5nuqy7n4OZ4t+wpfY8zaStluoz5eY2G5s9UZIkSZI0BieWkCRJkqQxGERJkiRJ0hgMoiRJkiRpDAZRkiRJkjQGgyhJkiRJGoNBlCRJkiSNwSBKkiRJksZgECVJkiRJYzCIkiRJkqQxGERJkiRJ0hgMoiRJkiRpDAZRkjRDSZ6dpJI8e+D4yiQr56ZWG0aSI7p733Gu6zKdqT6nOahHJVkxl3WQJE2GQZQkLRJJ9ui+qB8813VZCJZCsCtJmh0bz3UFJGkR2muuK6Ab+SJwInDeXFdEkrQ4GERJ0oRV1ZlzXQfdoKouAy6b63pIkhYPh/NJmhNJ7p/ks0nOTbIqyXlJjkny1CFpn5rk+CSXJbkmyf9L8o9JNpui7Psm+XyS87uyf5Pkg0luMyRt792eOyZ5RZJTumus6EtzpyT/leSSJFclOSHJ367j3m4yTKz/vZwkeyZZkeSKJJcn+Z8kdxtSzl2SvCPJyUku6LuXjyS5/eB9AN/pfjyou1Zv22Mg7dOTfKe7n2uT/CLJG6dqz3GN83n12irJ5kkOSfLb7j5/neQfkmRIniQ5IMlpXf3PTfL+JMuna/vu5z2SFPAXwF8MtNURXZod+38eUocVXRmDxzdN8qYkZ3b3cXaSt62rbZNsnOSlSU7snoerk/w0ycuT+O+0JM1D9kRJ2uCSvAD4ELAG+G/gV8CtgPsBLwU+15f2/wL/CFwI/AdwJfBo4P8Cj0zyiKr6U1/6xwCfBwIcBfwGuC/wEmCfJA+sqpVDqvUe4MHA/wBf7epGkjsDPwC2A74G/Ay4E3B09/O4HgPs0+X9MHB34G+AXZLcvaou7Ev7RODFtODoBOA6YGfg+cBjk9yvqs7t0h7d7fcHjgNW9JVz/f0m+TjwXOAc4AvApcBuwFuBvbr2XD2D++qVP9bn1dkEOAa4La1dVgOPB94B3Ax4y0D6D9A+z98DH6G1y+OA+3dlDZY/aGVX5oHdz4f1nfvZNHmn1AV8n6N9vmcC7wc2pbX3PabIswnwZeCRwC9pbXYtsCfwPmBXYL+Z1kmSNEuqys3NzW2DbbSg4U/AxcDOQ87fvu+/HwAU8Ftgh77jG9O+eBbw+r7jW9K+vK8BHjxQ7j906Y8ZOH5Ed/xc4A5D6nNMd/6AgeP7dMcLePbAuZXAyoFjz+7Srgb2Gjj3z9251w4cvx2w2ZA67d3d44cGju/RlXPwFG3fq8MXgJsPnDt42H2u43PstduOM/28+tqqaIHrzfuO34oW4F0KbNJ3/MFd+l8CW/cd3xQ4vjs3VdtP+zn1nduxy3PEFOdXADVw7Bldnh8AN+s7vi0tqCpgxRTt/j5gWd/xZcDHu3P7bKj/P93c3NzcRtscJiBpQ3sJ7Uv1W6vqfwdPVtU5fT8+t9u/rar+0JdmNfAaYC2tV6ZnH1qP0Wer6rsDRf8r7UvzI5L8+ZB6vbOqzu4/0A2ZewRwNq1Xob+eX6L1+IzryKo6duDYR7r9/QeucW5VrRosoKqOAf6X1nsxjgNoQdxzq+qagXNvBS4Cnjlmmf3G/bz6vbK/TlV1PvAlYDnwl33p9u/2b6+qS/vSX0frAZtLz+n2r6+qa3sHq+piWvveSDdU7+XAH4BXVdWavjxraG1WrN9nIkmaBQ7nk7Sh7dbtRxkKd59u/+3BE1V1RpJzgDsk2br7Qr2u9KuTHE/rYbg3rbek30lDrn/vbv+9/i+4fVYAD53mHgadPOTY77r9Nv0Hu+Fhz6T1pNyrO7+sL8l1o140yeZdGRcCBw551QhgFXCTd7PGMO7n1XNZVf16SHnD2uX6z2RI+hNpQeJcuQ8tUBxWtxVDjt2FFvT/CnjjFJ/JNazfZyJJmgUGUZI2tK27/bnrStRZ3u2nmpr6PODPu3SXjpi+vw79/jDkWK+8P05R3rA807l08EAX4MGNAySAQ2nv7ZwHfIPWZr3emmfTJkYY1Ta098S2Bw4aI984xv28ei4dlpgbAqL+dpnyM6mqNUkuGqWis2Q5cHHd9J0vGP6sbNft78y6P5Mt17dikqTJMoiStKFd2u1vB5w+TdretNQ70N4pGXSbgXT96YcZTN/vJjOt9aW79RTlTXWd9ZbkVsArgVOB3avqioHzTx+zyN69/LSq7rPOlDM37uc1E5d3+1sDZ/WfSLKMFpiMEqBPZ223n+rfya2HHLsM2DbJJkMCqWHPSq8dvlhVTxy/ipKkueI7UZI2tBO7/aNHSPvTbr/H4IkkdwJuD5zdNzRsXek3Bh7U/fiT0ap6fXkP6r6gD7rJdSbojrTf0ccMCaBu350f1BtyeJO6VtWVtPeodk6y7YTr2jPu57U+13jQkHO7Md4fB9cwpK06l3T7Pxs8kWQr2lC8QT+hfWbD6rbHkGOn082O2M3SJ0laIAyiJG1oH6IN03pTkrsPnhxY/+jwbv/GJNv3pVkGvIv2O+zjfemPps369/Qku3FjB9ICj29V1eD7UEN1k1x8E7gDbQKA/nruw/jvQ41jZbe/UQCXZEvgowwPFnpD2YZNnAFteOCmwOFJth48mWSbJOvTSzXu5zUTn+r2b0jSG9pHkk1p06iP4yJg+yQ3HzzRBa6nAw/sf067ezkUuEke4BPd/u1JbtaXZ1vgjUOusZo2K99tgPcOq0eS2wz7/0SSNLcczidpg6qq05K8lLZG0k+TfIn2Yv12tHWirqCtkUNVnZDkncBrgVOTHAVcRevF+ivaC/yH9JV9ZZLnAv8FHJfkv2gTSNyXNi34H4AXjVnll9GmrD4syd7Az2nrRD2BNm33Y8duhBFU1R+SHAk8DfhZkmNo79w8graO0M+Avx7I9kvaULanJbmOdu8FfLqqflNVhye5L20trjOTfKNLsy0tUHwILRB48QzrPNbnNcNrHJfkI8ALgf9N8nnalPmPpQ2P+z03DMWbzrHALsDXu0lHVgE/r6ovd+cPoQV93++epd76TZvQnoN7DZT3n8C+tDWrTu2e7U2AJwM/AnYaUoe3duW8mLb217dpn+GtaO9KPRB4A3DaiPckSdoADKIkbXBV9dEkpwJ/Txvm9HjarHGnAB8bSPsPSX5K6wn6O9qX0jNpf9n/125q6/70X0ryQOD1tCnAl9OCpw/TplX//Zh1/VXXq/UO4OFdfU/p6rw9sxREdZ5He+9nX1owdwFtceI30xYUHqzrmiRP6Or6VOAWtMkkvkdbdJiqelmSr9G+tD+c9m7PxbRg6hDgM+tT4XE/rxl6Ca2X6EW0+7gI+CLtMz+H4e9jDfM22v0/lhasLAM+SQuO6YLOAK+mTa1+CW3a9dczvP0ryVOA19Em/ng5bTKNTwD/RAvCBvP8KcnjgWd1eR5Dm0jiAtrU+m8C/n3E+5EkbSCpGvYutSRJC0uSOwNn0NbiGnfiDUmSRuY7UZKkBSXJDt1Ctf3HNgcO63784gavlCRpSXE4nyRpoTmQNnnICtpwuR2AvWiz/32N9k6cJEmzxiBKkrTQfJM2GcPetEkxVtOG8b0XOKwcpy5JmmW+EyVJkiRJY/CdKEmSJEkag0GUJEmSJI3BIErSkpBkjyTVt50+Yr4du/RHzHIVNc8kefLAM7NiruskSZofDKIkLTXHAW8B3j/XFdHc6gusD54iyWm0Z+UtG65WkqSFwNn5JC01K6rq4LmuhOa/qjoNOBggyUFzWxtJ0nxiT5QkSZIkjcEgSpKAJLdIcmiSc5Jcm+T0JK9mHb8nk2ye5B+T/CzJVUmuTPKDJE+fIv1mSQ5OclaSVUnOTvK27vhN3rnp0lY37OwZSX7YXWPlTOvQ5Xlkkq8mubCrx5lJDkmy9ZC090zyn0lWdmkvSPKTJIcl2WT6lp2yDk9P8p0kl3Tt/Yskb0yy2ZC0j0/ymSRn9N3jj5O8MslNPp8kt07yriS/7NJf2v33EUnu2KU5AvhOl+WggXef9pjpfUmSlgaH80la8rov7scCuwA/B/4d2Bp4E/DQKfJsDXwbuDfwE+BwWsD1SOA/kuxcVW/sSx/g88DfAr+ivZO1CfBsYOdpqvga4BHAl2lf/JfPpA5dnjfT3vG5GPgKcD5wT+Dvgb9J8oCqurxLe0/gh0AB/w2cDWwF3Al4KfBG4E/T1P0mknwceC5wDvAF4FJgN+CtwF5JHlFVq/uyvANY29Xl3O7+Hwa8h/aZ7ddX9ubA94GdaIvyfhkI8BfAPsBRwFnA0V2W/Wnvya3ou97Kce9JkrTEVJWbm5vbot+APWjBwMFDzr2+O/d5YKO+43egBRsFHDGQ54ju+GsHjt8M+DrtS/9f9x3fr0t/PLBp3/GtgdO7cysGyjq4O34VcO8h9R63Dnt26U8Ath7I8+zu3Lv7jv1rd2yfIdfepr+txvgcetf5AnDzKe73gIHjOw0pZyPgk136XfuOP3bwPvrObQrcYpRnYkjem3w+bm5ubm5Ld3M4nyTBc2gBx2uram3vYFWdDbx3MHGS7YBnASdX1Tv7z1XVtcA/0Ho/ntF3av9u/8aquq4v/aW0Hph1+UhV/XQCdXhlt39Bd93+PEcAPwOeOeT61wweqKpL+ttqDAcAq4HnVtVguW8FLhqsQ1WdOeT6a2k9UdB63kap83VVdcUM6ixJ0o04nE/SkpbkFrThab8b9mWdNsxrcGa2XYBlwFTTY/feFbpb37F70wK1E4ak/9401TxpyLGZ1OEBtOF3T0nylCF5NgW2T7JdVV0EfJYW9Byd5CjgW8D3p2inaXVD7e4FXAgc2EY43sSqgTr3Asb/A/wNcEdgi4E8t+v77+NoQ/5el+Q+wFdpw/t+VlVrZlJvSZIGGURJWuqWd/s/TnH+D0OObdftd+m2qWw5cJ2L68bv+vRMde1J12E72u/96abr3hK4qKpOSvJg4A3Ak+nePUryS+AtVfWf05QzaBta79j2I9SB7lpbAz+iDa08CfgUbYjlatpQyAOA6yejqKrLk+xGe+/rcdzQS3Vhkg8Cb6uqsd/jkiSpn0GUpKXusm5/6ynO77COPO+uqlePeJ3LgW2TbDwkkJrq2j01oTpcRnuPadsR01NVPwAe002+cV/gUcAraBNXXFBV3xq1rL46/7Sq7jNinufTAqi31MD6XkkeQAuiBut8DvC8bjKPu9MmoXgZ8Gbau1RvGqPOkiTdhO9ESVrSundkfg3cLslOQ5LsMeTYSbSheQ8e41I/pf3O3X3IuQeNUc761OFEYJsk080GeBNVtaqqTqiqN3PDu1X7jFnGlcD/AjsnGTWQu1O3//yQc0NnTuy7XlXV/1bV+2izGwI8vi9Jb3jfshHrIkkSYBAlSQCfoP0+/Jf+dYeS3IEbAobrVdX5tGnQ75fkTUlu0qufZKcuf8+nuv3bkmzal245M+gZmWEd3t3tP5rktkPSb9ENhev9/OCufoN6PWdXj1tv4FDau1eHT7Eu1Tbdu0w9K7v9HgPp7g3845D8f5VkxyHXHVbni7r9n49Qb0mSrudwPklqU3k/HngS8JMk36C9w7QvbUryxw3J83LgzsA/Afsl+R7t3abb0iZG2AV4Om1tJWhB1NNow+FOTfLftMkfngScDPwlrWdpHGPVoaqOTfI64J+BXyX5anduS9o6Sg+lTXLxqK781wB7d4sAnwVcSVvT6tHAJcBHxqwvVXV4kvvS1pk6s2vr3wLb0obtPYQW1L64y/Ip2qQShyXZk7bG1p2Bx9CmSd934BIPBw5NcgJt6vjzgdvTes3WAof0pf0lbRKKpyW5rqtHAZ+uqt+Me2+SpKXDIErSkldVq5I8nLZO0b6092xWAm8DvsiQIKqbwOChwAtp04g/ibY+0x9pX/RfRVvstZe+kjyBtibVfrT3is6jrXX0QdqX/MvHrPdYdejy/EuS79N62B7UXfcyWjDxEeA/+pJ/kBYs7Qo8kPZvxjnd8X+daaBRVS9L8jVaoPRw2gQRF9OCmEOAz/Sl/X03ucU7uvo+khYcvZQ2W+BgEPUN4DBaMLYPbXHg87p2OLSqrp8dsarWdJ/JO4CnAregTXzxPcAgSpI0pVQNe19ZkhaXJHsA32HIBAVzLckjgGOAd1TVTYaoae4lKeC4qtpjrusiSZp7vhMlaak5KEklOX1DX3iK95C2o/WEQOv10jyR5Mnds+JfGyVJN+JwPklLxUra2kE9F85BHQ5Nci/agrsX0N7VeTTtfaB/q6phi+pq7pzGjZ+ZlXNUD0nSPONwPknaQJI8FXgJbXKGrYFraVN+Hw58rBbgL+RumOQeIyS9tKoOm826SJK0oRhESZJmLMnBwEEjJP1NVe04u7WRJGnDMIiSJEmSpDE4sYQkSZIkjcEgSpIkSZLGYBAlSZIkSWMwiJIkSZKkMRhESZIkSdIYDKIkSZIkaQwGUZIkSZI0BoMoSZIkSRqDQZQkSZIkjcEgSpIkSZLGYBAlSZIkSWMwiJIkSZKkMRhESZIkSdIY/j/DQLeZz2tFdgAAAABJRU5ErkJggg==\n", 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