{ "cells": [ { "cell_type": "markdown", "id": "573d587c", "metadata": {}, "source": [ "# Ringdown fit from IMR results" ] }, { "cell_type": "markdown", "id": "e371eb54", "metadata": {}, "source": [ "We will show how to initialize a ringdown fit starting from a reference set of inspiral-merger-ringdown (IMR) parameter estimation (PE) samples, as would be produced in a regular GW analysis. This can be useful when launching a first exploratory fit for an event for which we have IMR results for some reference waveform." ] }, { "cell_type": "markdown", "id": "c1adc8e9", "metadata": {}, "source": [ "
\n", "\n", "Note\n", "\n", "The [GWpy package](http://gwpy.github.io/) is only an _optional_ dependence for `ringdown`: you should install it before running this notebook (e.g., `pip install gwpy`); alternatively, you can download the data separately and load it directly from disk as in the GW150914 example.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "c646a479", "metadata": {}, "outputs": [], "source": [ "%pip install gwpy" ] }, { "cell_type": "markdown", "id": "7870d1d8", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "markdown", "id": "6fcdd2fa", "metadata": {}, "source": [ "We begin with some standard imports and global settings." ] }, { "cell_type": "code", "execution_count": 1, "id": "459721df", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'" ] }, { "cell_type": "code", "execution_count": null, "id": "3d500eef", "metadata": {}, "outputs": [], "source": [ "# disable numpy multithreading to avoid conflicts\n", "# with jax multiprocessing in numpyro\n", "import os\n", "os.environ[\"OMP_NUM_THREADS\"] = \"1\"\n", "import numpy as np\n", "\n", "# import jax and set it up to use double precision\n", "from jax import config\n", "config.update(\"jax_enable_x64\", True)\n", "\n", "# import numpyro and set it up to use 4 CPU devices\n", "import numpyro\n", "numpyro.set_host_device_count(4)\n", "numpyro.set_platform('cpu')\n", "\n", "# we will use matplotlib, arviz and seaborn for some of the plotting\n", "import matplotlib.pyplot as plt\n", "import arviz as az\n", "import seaborn as sns\n", "\n", "# disable some warning shown by importing LALSuite from a notebook\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n", "\n", "# import ringdown package\n", "import ringdown as rd\n", "\n", "# set plotting context\n", "sns.set_context('notebook')\n", "sns.set_palette('colorblind')" ] }, { "cell_type": "markdown", "id": "a6472637", "metadata": {}, "source": [ "## Set up fit and run" ] }, { "cell_type": "markdown", "id": "d4d485d6", "metadata": {}, "source": [ "We will first show how straightforward it is to set-up a fit automatically from the PE samples we downloaded above, and then we will explain what happened under the hood.\n", "\n", "To set up the fit, we will use a set of posterior samples for GW150914 obtained using the `IMRPhenomXPHM` waveform in GWTC-2.1, which we can download from Zenodo." ] }, { "cell_type": "code", "execution_count": null, "id": "f16b15e0", "metadata": {}, "outputs": [], "source": [ "!wget -nc https://zenodo.org/records/6513631/files/IGWN-GWTC2p1-v2-GW150914_095045_PEDataRelease_mixed_cosmo.h5" ] }, { "cell_type": "markdown", "id": "0dea480d", "metadata": {}, "source": [ "Now we need only point to the file we just downloaded to create a fit automatically." ] }, { "cell_type": "code", "execution_count": 4, "id": "cc347a2a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:no group provided; using C01:IMRPhenomXPHM\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3936bff0c7a8484e96155d92b60d76b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "waveforms: 0%| | 0/100 [00:00" ] }, "metadata": { "image/png": { "height": 553, "width": 950 } }, "output_type": "display_data" } ], "source": [ "az.plot_trace(fit.result, var_names=['m', 'chi', 'a']);\n", "plt.subplots_adjust(hspace=0.5)" ] }, { "cell_type": "markdown", "id": "b6ef2ff9", "metadata": {}, "source": [ "You can export the settings to reproduce this fit later by saving them to a config file." ] }, { "cell_type": "code", "execution_count": 7, "id": "b234adfc", "metadata": {}, "outputs": [], "source": [ "fit.to_config('config.ini');" ] }, { "cell_type": "markdown", "id": "34cd9569", "metadata": {}, "source": [ "That's it! To understand how this fit was constructed, read on." ] }, { "cell_type": "markdown", "id": "010f083e", "metadata": {}, "source": [ "## What just happened?" ] }, { "cell_type": "markdown", "id": "7050a1e3", "metadata": {}, "source": [ "The `fit.from_imr_result` method loaded the IMR result file that we downloaded from Zenodo; together the information about the Kerr `modes` we wished to fit, as well as the inclination `cosi` we wanted to assume, `ringdown` used the IMR posterior results to initialize a fit with a best guess for the target time, analysis duration and prior ranges." ] }, { "cell_type": "markdown", "id": "06042d76", "metadata": {}, "source": [ "We can break down the logic that went into creating the fit above by examining the properties of the Fit. First, the IMR samples are accessible in `fit.imr_result` as an `IMRResult` object (a wrapper around a pandas `DataFrame` with some useful features)." ] }, { "cell_type": "code", "execution_count": 8, "id": "b5814aa9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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chirp_massmass_ratioa_1a_2tilt_1tilt_2phi_12phi_jltheta_jnpsi...viewing_anglecos_iotatilt_1_infinity_only_prec_avgtilt_2_infinity_only_prec_avgspin_1z_infinity_only_prec_avgspin_2z_infinity_only_prec_avgchi_eff_infinity_only_prec_avgchi_p_infinity_only_prec_avgcos_tilt_1_infinity_only_prec_avgcos_tilt_2_infinity_only_prec_avg
029.1807240.7879590.9242100.3310921.9133041.6760545.3943401.2100512.7753181.800080...0.366275-0.7685741.7944692.104876-0.205001-0.168542-0.1889340.901187-0.221812-0.509049
129.9530470.8641750.6473690.3133051.8410441.8399763.8681700.0248632.6605580.757138...0.481035-0.8753452.0425121.387529-0.2941740.057098-0.1313350.576670-0.4544150.182244
231.4338900.8520290.2056780.8750082.3658951.3696562.9169345.5769372.4937981.272124...0.647795-0.8449002.9629131.292919-0.2024030.2400280.0011380.700753-0.9840790.274315
330.7410310.9803410.7112510.0048001.6724290.6271614.5919010.9338662.9678350.886179...0.173758-0.9437701.6686161.332239-0.0694640.001134-0.0345150.707851-0.0976640.236301
431.2705970.9301300.2506400.2271521.3733581.5252850.6435526.0197263.0745092.418739...0.067084-0.9935520.5601672.3844330.212334-0.1650920.0304530.1436270.847167-0.726790
..................................................................
14762929.7939460.7217850.6948570.4265491.9046541.4404506.2189980.4731102.8183212.597370...0.323271-0.8574281.6785161.946527-0.074705-0.156523-0.1090040.690829-0.107511-0.366952
14763030.4982130.9758760.2706230.1356321.8044542.4105383.7554273.0161182.8363711.129183...0.305221-0.9504582.1591201.654079-0.150187-0.011283-0.0815830.225124-0.554968-0.083186
14763130.1405080.7569050.0751440.7944701.8258601.7799955.7399891.1225042.8955061.330361...0.246087-0.9179191.2947161.8475850.020483-0.217103-0.0818730.5560260.272587-0.273268
14763229.1728860.9914570.2928990.7124411.2397062.1529053.2067720.2912052.6045902.792774...0.537003-0.8442222.8914891.584026-0.283786-0.009425-0.1471940.705428-0.968887-0.013230
14763328.3656950.6776950.7870810.1150662.0235261.9029563.8931340.9568142.8767382.763453...0.264855-0.8714552.0555071.608926-0.366742-0.004386-0.2203710.696417-0.465952-0.038121
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147634 rows × 137 columns

\n", "
" ], "text/plain": [ " chirp_mass mass_ratio a_1 a_2 tilt_1 tilt_2 \\\n", "0 29.180724 0.787959 0.924210 0.331092 1.913304 1.676054 \n", "1 29.953047 0.864175 0.647369 0.313305 1.841044 1.839976 \n", "2 31.433890 0.852029 0.205678 0.875008 2.365895 1.369656 \n", "3 30.741031 0.980341 0.711251 0.004800 1.672429 0.627161 \n", "4 31.270597 0.930130 0.250640 0.227152 1.373358 1.525285 \n", "... ... ... ... ... ... ... \n", "147629 29.793946 0.721785 0.694857 0.426549 1.904654 1.440450 \n", "147630 30.498213 0.975876 0.270623 0.135632 1.804454 2.410538 \n", "147631 30.140508 0.756905 0.075144 0.794470 1.825860 1.779995 \n", "147632 29.172886 0.991457 0.292899 0.712441 1.239706 2.152905 \n", "147633 28.365695 0.677695 0.787081 0.115066 2.023526 1.902956 \n", "\n", " phi_12 phi_jl theta_jn psi ... viewing_angle cos_iota \\\n", "0 5.394340 1.210051 2.775318 1.800080 ... 0.366275 -0.768574 \n", "1 3.868170 0.024863 2.660558 0.757138 ... 0.481035 -0.875345 \n", "2 2.916934 5.576937 2.493798 1.272124 ... 0.647795 -0.844900 \n", "3 4.591901 0.933866 2.967835 0.886179 ... 0.173758 -0.943770 \n", "4 0.643552 6.019726 3.074509 2.418739 ... 0.067084 -0.993552 \n", "... ... ... ... ... ... ... ... \n", "147629 6.218998 0.473110 2.818321 2.597370 ... 0.323271 -0.857428 \n", "147630 3.755427 3.016118 2.836371 1.129183 ... 0.305221 -0.950458 \n", "147631 5.739989 1.122504 2.895506 1.330361 ... 0.246087 -0.917919 \n", "147632 3.206772 0.291205 2.604590 2.792774 ... 0.537003 -0.844222 \n", "147633 3.893134 0.956814 2.876738 2.763453 ... 0.264855 -0.871455 \n", "\n", " tilt_1_infinity_only_prec_avg tilt_2_infinity_only_prec_avg \\\n", "0 1.794469 2.104876 \n", "1 2.042512 1.387529 \n", "2 2.962913 1.292919 \n", "3 1.668616 1.332239 \n", "4 0.560167 2.384433 \n", "... ... ... \n", "147629 1.678516 1.946527 \n", "147630 2.159120 1.654079 \n", "147631 1.294716 1.847585 \n", "147632 2.891489 1.584026 \n", "147633 2.055507 1.608926 \n", "\n", " spin_1z_infinity_only_prec_avg spin_2z_infinity_only_prec_avg \\\n", "0 -0.205001 -0.168542 \n", "1 -0.294174 0.057098 \n", "2 -0.202403 0.240028 \n", "3 -0.069464 0.001134 \n", "4 0.212334 -0.165092 \n", "... ... ... \n", "147629 -0.074705 -0.156523 \n", "147630 -0.150187 -0.011283 \n", "147631 0.020483 -0.217103 \n", "147632 -0.283786 -0.009425 \n", "147633 -0.366742 -0.004386 \n", "\n", " chi_eff_infinity_only_prec_avg chi_p_infinity_only_prec_avg \\\n", "0 -0.188934 0.901187 \n", "1 -0.131335 0.576670 \n", "2 0.001138 0.700753 \n", "3 -0.034515 0.707851 \n", "4 0.030453 0.143627 \n", "... ... ... \n", "147629 -0.109004 0.690829 \n", "147630 -0.081583 0.225124 \n", "147631 -0.081873 0.556026 \n", "147632 -0.147194 0.705428 \n", "147633 -0.220371 0.696417 \n", "\n", " cos_tilt_1_infinity_only_prec_avg cos_tilt_2_infinity_only_prec_avg \n", "0 -0.221812 -0.509049 \n", "1 -0.454415 0.182244 \n", "2 -0.984079 0.274315 \n", "3 -0.097664 0.236301 \n", "4 0.847167 -0.726790 \n", "... ... ... \n", "147629 -0.107511 -0.366952 \n", "147630 -0.554968 -0.083186 \n", "147631 0.272587 -0.273268 \n", "147632 -0.968887 -0.013230 \n", "147633 -0.465952 -0.038121 \n", "\n", "[147634 rows x 137 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit.imr_result" ] }, { "cell_type": "markdown", "id": "79c8a1ec", "metadata": {}, "source": [ "We will walk step-by-step through how this information is used to create a fit." ] }, { "cell_type": "markdown", "id": "f5475459", "metadata": {}, "source": [ "### Fetching data" ] }, { "cell_type": "markdown", "id": "9b00c2fc", "metadata": {}, "source": [ "A lot of information about the IMR run is contained in the configuration file that usually ships with PE files produced by [pesummary](http://pesummary.readthedocs.io/). This includes data information like origin, trigger time, segment length and sampling rate.\n", "\n", "The config file is loaded and stored in the `IMRResult` object:" ] }, { "cell_type": "code", "execution_count": 9, "id": "05605b4f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['accounting', 'calibration-model', 'catch-waveform-errors', 'channel-dict', 'coherence-test', 'convert-to-flat-in-component-mass', 'create-plots', 'create-summary', 'data-dict', 'data-format', 'deltaT', 'detectors', 'distance-marginalization', 'distance-marginalization-lookup-table', 'duration', 'email', 'existing-dir', 'extra-likelihood-kwargs', 'frequency-domain-source-model', 'gaussian-noise', 'generation-seed', 'gps-file', 'gps-tuple', 'ignore-gwpy-data-quality-check', 'injection', 'injection-dict', 'injection-file', 'injection-numbers', 'injection-waveform-approximant', 'jitter-time', 'label', 'likelihood-type', 'local', 'local-generation', 'local-plot', 'log-directory', 'maximum-frequency', 'minimum-frequency', 'n-parallel', 'n-simulation', 'online-pe', 'osg', 'outdir', 'periodic-restart-time', 'phase-marginalization', 'plot-calibration', 'plot-corner', 'plot-format', 'plot-marginal', 'plot-skymap', 'plot-waveform', 'pn-amplitude-order', 'pn-phase-order', 'pn-spin-order', 'pn-tidal-order', 'post-trigger-duration', 'postprocessing-arguments', 'postprocessing-executable', 'prior-file', 'psd-dict', 'psd-fractional-overlap', 'psd-length', 'psd-maximum-duration', 'psd-method', 'psd-start-time', 'reference-frame', 'reference-frequency', 'request-cpus', 'request-memory', 'request-memory-generation', 'resampling-method', 'roq-folder', 'roq-scale-factor', 'sampler', 'sampler-kwargs', 'sampling-frequency', 'sampling-seed', 'scheduler', 'scheduler-args', 'scheduler-env', 'scheduler-module', 'single-postprocessing-arguments', 'single-postprocessing-executable', 'singularity-image', 'spline-calibration-amplitude-uncertainty-dict', 'spline-calibration-envelope-dict', 'spline-calibration-nodes', 'spline-calibration-phase-uncertainty-dict', 'summarypages-arguments', 'time-marginalization', 'time-reference', 'timeslide-dict', 'timeslide-file', 'transfer-files', 'trigger-time', 'tukey-roll-off', 'waveform-approximant', 'waveform-generator', 'webdir', 'zero-noise'])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit.imr_result.config.keys()" ] }, { "cell_type": "markdown", "id": "423fdbc4", "metadata": {}, "source": [ "This information can be used to read or fetch the analysis data. If a file data path is found in the config and the file exists, it will be loaded from disk; otherwise, `GWpy` is used to fetch data based on the trigger time and segment length info.\n", "\n", "_NOTE:_ `GWpy` is an optional dependence of `ringdown`, but it is required to use this functionality." ] }, { "cell_type": "code", "execution_count": 10, "id": "9e1fdd9e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ifos': ['H1', 'L1'],\n", " 'channel': 'gwosc',\n", " 't0': 1126259462.391,\n", " 'seglen': 4.0,\n", " 'sample_rate': 16384}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit.imr_result.data_options()" ] }, { "cell_type": "markdown", "id": "a13e5869", "metadata": {}, "source": [ "Data retrieval options can be updated through the `data_kws` argument of `fit.from_imr_result`." ] }, { "cell_type": "markdown", "id": "3e045fca", "metadata": {}, "source": [ "### ACFs from PSDs" ] }, { "cell_type": "markdown", "id": "6f24f85d", "metadata": {}, "source": [ "PE sample files released by the LIGO-Virgo-KAGRA collaborations typically contain the noise power spectral density (PSD) used in the analysis that produced the samples; that can be used to derive a time-domain autocovariance function (ACF) for the ringdown analysis. With some automatic padding, this is how `ringdown` derived the ACFs for the analyses above." ] }, { "cell_type": "code", "execution_count": 11, "id": "a22670c8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "image/png": { "height": 305, "width": 567 } }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6, 3))\n", "for ifo, acf in fit.acfs.items():\n", " plt.loglog(acf.to_psd(), label=ifo)\n", "plt.legend(loc='lower left')\n", "plt.xlabel('Frequency (Hz)')\n", "plt.ylabel('PSD');" ] }, { "cell_type": "markdown", "id": "4adf9091", "metadata": {}, "source": [ "
\n", "\n", "Note\n", "\n", "Not all IMR results files contain PSDs; if you know what PSDs were used for the analysis, you can provide them to `Fit.from_imr_result` by passing a dictionary indexed by IFOs through the `psds` argument.\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "5e03cbdc", "metadata": {}, "source": [ "### Target time" ] }, { "cell_type": "markdown", "id": "e98561e5", "metadata": {}, "source": [ "The fit target was automatically specified from the posterior samples:" ] }, { "cell_type": "code", "execution_count": 12, "id": "fd33f152", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SkyTarget(geocenter_time=LIGOTimeGPS(1126259462, 408868551), ra=2.0207448950342837, dec=-1.2689317202309085, psi=2.3419094714656477, duration=0.2578125)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit.target" ] }, { "cell_type": "markdown", "id": "2fbb3b21", "metadata": {}, "source": [ "By default, the start time of the ringdown fit is set by:\n", "1. identifying the interferometer in which the the signal arrival time is best determined,\n", "2. taking the median of an estimate of the strain _peak time_ at the best measured detector,\n", "3. identifying the posterior sample closest to the chosen reference time and using it to set the sky location.\n", "\n", "You can reproduce these steps by calling `get_best_peak_times`." ] }, { "cell_type": "code", "execution_count": 13, "id": "9a15216c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(H1 1126259462.423858\n", " L1 1126259462.416903\n", " Name: 453, dtype: object,\n", " 'L1')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_peak_times, reference_ifo = fit.imr_result.get_best_peak_times()\n", "best_peak_times.head().map(\"{:.6f}\".format), reference_ifo" ] }, { "cell_type": "code", "execution_count": 14, "id": "1cc388cc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'H1': 1126259462.4238577, 'L1': 1126259462.4169033}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit.start_times" ] }, { "cell_type": "markdown", "id": "682d5496", "metadata": {}, "source": [ "We can visualize that by comparing the fit start times to the time posteriors in the IMR result." ] }, { "cell_type": "code", "execution_count": 15, "id": "c2a57f99", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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01126259462.4245271126259462.417663
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41126259462.4239121126259462.416951
\n" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "peak_times = fit.imr_result.get_peak_times()\n", "peak_times.head().style.format(\"{:.6f}\")" ] }, { "cell_type": "code", "execution_count": 16, "id": "be3f980e", "metadata": {}, "outputs": [ { "data": { "image/png": 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Q69ev18KFCzV//nyZpqmFCxdq6tSpWrp06YBaG+fMmaObbropr/XNmDFDJ554oqLRqNra2vK6Vilpb2+3/RP+Mbs7FI2Mtx3bvqNDgXi4aGvg/QAn3hPoifdDZh3RuCY0WPZjO7YrnIz4tKLi4P2Anng/wKm390QikZBpmrIsS8lk0o+loQgsy5JpmkokEmpra8v6e0Q0GlVdXZ26urqKscySY1iWZfV/Gvpz1FFHZQyzxowZo8svv1yzZs3S6NGj877PSy+9pEMPPVSRSOYPfa+99prOPfdcrV69WpJ0yimnaN68eVlf/8Ybb8w7zDrvvPN0/vnn53UNAAAAAKhmH330kQKBgFpaWnT22Wf7vRwUyB//+Ee1tbXJNE1NnDhxwK9/4403JEmXXHKJxo4d6/HqSheVWR5ZunRp+nFnZ6dWrFihJ554QrNnz9b111+v22+/XQ899JBmzpyZ132mTp3a59enTJmiBQsW6KCDDlIsFtP8+fO1bNkyHX744XnddyC2bNmi5cuX68wzz6yogfft7e167bXXNGXKFDU1Nfm9nKpmdm/T3x872XZsj3OeVqB2cNHWwPsBTrwn0BPvh8y2ReM68VdLbceeufwoDa6v/Mos3g/YhfcDnHp7T2zbtk3d3d0KhUJqbGz0cYUopFAopNraWtXW1mratGlZf4/YsGGD/vSnPxVxpaWFMKsAGhoadMghh+iQQw7Rl770JR177LH6+9//rjPPPFMvv/yyDj744ILef9KkSbrooot07733SpKefPLJrMOsSy+9VDNmzMjpvhs3btRTTz2l5uZmdXV1qb6+viJnZzU1NVXk36ucpKIJbY+vsh1rHtSoYH1L0dfC+wFOvCfQE+8Hu0QoppWdhu1Y46BmtTTW+LSi4uL9gJ54P8DJ+Z4Ih8OKx+MyDEOhED+6VyrDMBQIBBQOh23//vv7HrFjx46qbTGUCLMKbuLEibr11ls1a9YsxeNx3XLLLXrkkUcKft/jjz8+HWa99957Wb9u7NixOZcmrl27Vm+//XZOrwUAAAAA5O6www7Thg0bfLv/qFGj9MorrxTlXoax+xcjA5mclM3rUqmU3nvvPb3yyit69dVX9corr+jNN99MB0cXX3yx5s6dm9vC4RnCrCI47bTT0o8XL15clHsOGzYs/biShrADAAAAANw2bNig9evX+72MsveFL3xBjz32mN/LQD8Is4qgZ59rsYKl1tbW9GPKlwEAAACgOhiBgBqHDC/a/Tq2tsoyzaLdr9BSqZTt+ZAhQzR06FCtWLHCpxUhE8KsIuj5ph8+vDjfVBYtWpR+vN9++xXlngAAAAAAfzUOGa7vPbCkaPe7/UvT1L55Y9HuV2hHHHGEDjjgAB166KE69NBDNX78eM2dO1eXXHKJ30tDD4RZRXDPPfekHx9zzDEFv9/y5ct1//33p5+ffvrpBb8nUEyByCCNOPUh1zEAQGlKpkwtWblFowfV6ZEvH2r72qBaPo4CAErHdddd5/cSkAU+PeTonnvu0X777afp06fbhsj1lEql9LOf/Ux33313+tiVV16Z8dzp06dryZKd6fl9992nWbNmuc654447dNhhh+noo4/udV2vv/66zjnnHMViMUnSSSedpCOPPDLbvxZQ8qxUXIntH6l27AkK1g7xezkAgH6kTEtT73xer368XQFDemzW4Tpz8ii/lwUAAMoYYVaOli5dqiuuuEJjxozRiSeeqE9/+tMaMWKEIpGI2tra9Pbbb+uJJ57Q6tWr06+59tprNW3atJzvuXDhQn3729/WxIkTNWPGDH3qU5/S0KFDFQwG9cknn+gvf/mL5s2bJ/Mf/crjxo3Tfffdl+9fFSgZZmyHNjxxpmKfvKhA7RCNPOsJ1e5BWAsApez/W75Jr368XZJkWtI3H/ubTt1/hELBgM8rAwAA5YowK0/r1q3TnDlz+jynublZt956q6644gpP7vnRRx/po48+6vOcmTNnas6cOdpzzz09uSdQCtpevkWxT16UJJndW7X91dtVe/ojPq8KANCX5a2dtucfb+/WvPc3UZ0FAAByRpiVo7vuukvnn3++lixZoqVLl+qTTz7Rpk2b1N7eroaGBo0cOVIHHXSQZs6cqc9//vNqbm7O+56zZ8/WGWecoaVLl+rNN99Ua2urNm/erFgspubmZu29996aOnWqLrjgAh111FEe/C2B0pFo+0jb37jLfmzbcp9WAwDI1sfbu1zH7nlxNWEWAADIGWFWjhobGzVz5kzNnDnTk+stXry433MmTpyoiRMn6itf+Yon9wTKydYXfiil4rZjqc7K2TUFACrV+u3drmMLPmjVyi2dmjC0wYcVAQAqSW8zrFHZGFYAoOR1f/Kioisecx03u7fIcgRcAIDSkinMsizp3r+u9WE1AACgElCZBaCkWZaprUv+udevr76zUZI09mvrFawfXqxlAQCytK4tmvH4Txd+qJ8u/FAfXXO0hjVEJEmRSEQ1NTXFXB4AoMw9/vjjWZ/72c9+toArQTERZgEoaZ3LH1Fs4zK/lwEAyEFXV7fWbO6Qgr1/5JwwYYLUtUOSNHzkSK1bs4ZACwCQtbPPPtvvJcAHhFkASpaZ7NLWF/7V72UAAHL09+2dfQZZkvStuc+oPhxUvKtTt184TfF4nDALAAD0iTALQMna8fqdSrXbZ6oYoTpZSffOWACA0rN+R6zfc9rNsAbX1xdhNQAAoFIwAB5AyWp/e47tee1e01Q75nifVgMAGKhPHGFWfdj90fOtv+8o1nIAAECFIMwCUJIsM6Xk9lW2Yy1H/atCDXv4tCIAwEA5w6xBtWHXOe9saFciZRZrSQAAoAIQZgEoSWasTZJlOxZumahgw0hf1gMAGDhnmNVU455wEUtZWrk1846HAAAAmRBmAShJZvcW17FA7VAF60f5sBoAQC4+aXeGWcGM523vShRjOQAAoEIwAB5ASUp12cMsI1SvQKhWwQbCLAAoF84B8I2RzB8922MpSe4WRAAAim3VqlX67W9/azv21ltvpR+//vrruv76621fnzJlis4555yirA87EWYBKEnOyqxA3TBJojILAMrI37NoM5SkzniyGMsBAKBfa9as0U9+8pNev/7WW2/Zwi1JuvjiiwmziowwC0BJicViisfj6t72sf0LkRa1t7crpUZ/FgYAGBDLslwzsxp7aTPsiBFmAYBXOra26vYvTSvq/YBiI8wCUDJisZj2HjdGGza26qvTA7rurN0/9Cx58Q1ddNEg1Yald2+jFQUASt327qQ6E/ZdCpsy7GYoSR3xVDGWBABVwTJNtW/e6PcyCsqyrP5PyvF106dPz/n6KB7CLAAlIx6Pa8PGVr3zxNWq3/FXWZv/mv7asUccqDXnniFJSr3zn5LFsGAAKGXrt3e7jjVEqMwCgEIZNcrfcRx+3x/VhTALQMlprK9RsD2meI9j4dpG1TfUSJK2Rxpkxdp8WRsAIDsfb++yPa8PBxUKGBnP7YynZPJbcADIyyuvvOL3EoCiCfi9AADIxErafwgyQnU9HjcUezkAgAH6uM1emTWotvffoVqSuhwtiQAAAL0hzAJQkkxHmBUI1e9+HGmyfa127GdkRBgMDwClxNlm2FQTUiQY0Cn7j9DJk4a7zu+IE2YBAIDsEGYBKElWMmp73rMyK1AzyPa1UMOeCvT4OgDAf842w0G1IYWDAR0+pkVHjBvsmp/VmWAIPAAAyA5hFoCS1FebYSBsbzNMRSt7txYAKEfOyqxBNfY2w8aI/XknlVkAACBLhFkASo5lWe4wK1zf47G9pTDVuaEo6wIAZM/VZuiYmdVYY6/M6qAyCwAAZIkwC0DpMeOSZf8NfcBWmeUIs6jMAoCS42ozdFZm1VCZBQAAckOYBaD0pLpch4yeA+CdYVZXqyyT3+gDQKnoSqS0NZqwHRtUG7Y9b3TOzIrzfRwAAGSHMAtA6XG0GMoISoHdPwQZEfvMLFmmUl2tRVgYACAbzhZDaeduhj05K7M6ElRmAQCA7IT6PwUAiizlHP5eL8Mw0s8ty/2S+Jb3FGoYVeiVAQCy8HGb/ft4JGioJhRQNJ7S3S+uliQlTfs3cyqzAABAtgizAJQcyxVm1dmf9wi2djGjmwq6JgBA9j52Dn//R0uhJUvRXga9d1KZBQAAskSbIYDS42gzDITrejlxN9oMAaB0uHYydOxcmEk8ZUmhmkItCQAAVBDCLAClJ2X/Iajn8PdeX0JlFgCUDOdOhk2RLD9yNgwuwGoAAEClIcwCUHpSUdtTZ5thxpdQmQUAJcNVmRXpvzJLEmEWAADICmEWgNKTdFZm9R9mmZ0bC7UaAMAA5dJmKIkwCwAAZIUwC0DJcQ+A77/NMEllFgCUDHebIWEWAADwDmEWgNLjCLMC2bQZMjMLAEpCImVqQ3vMdiz7yqwW7xcEAAAqDmEWgNLj2M3QCGcxAL6LMAsASsGG9pgsy36sMesB8EO8XxAAAKg4hFkASo9rN8P+K7OU6JSZ6CzQggAA2fq4zf4LCSsZV10o2zCrxfsFAQCAikOYBaCk1EUkWUnbsazCLEmpzg0FWBEAYCA+dgx/V/sWGYaR3YuZmQUAALIQ8nsBANDT4AwdhdkMgJekVHSjwi0TPV4RAGAgnMPf1bEl+xcTZgFAzg477DBt2ODfL3dHjRqlV155xbf7o7oQZgEoKYMbnUcMGcGarF5LZRYA+G+9szKrY3P2L65vUcq0+j8PAOCyYcMGrV+/3u9lFEXPil/LOagxS6lUSu+9955eeeUVvfrqq3rllVf05ptvqqtr5y9lLr74Ys2dO9eL5aIACLMAlJTB9fZWFCNUn3V7Siq6sRBLAgAMgCvMas++MssIBLW1K6GWZo8XBQBVJBAwNHJoU9Hut3FLu8wy/EXEF77wBT322GN+LwM5IswCUFJaGuzPs52XJVGZBQClwDkAfkBthpI2dsQ1wcP1AEC1GTm0Se88cXXR7jf5rNn6e+uOot3PK6lUyvZ8yJAhGjp0qFasWOHTijAQhFkASsrgBkdlVtgdZhmBsIzhR+uNV5/XwWN372ORJMwCAN99siNmP9AjzAoHAzpuwhDbl19Z16Zowkw/39AeL+j6AACQpCOOOEIHHHCADj30UB166KEaP3685s6dq0suucTvpSELhFkASspgR2VWIMPwdyMYUWDkMfq/t5/TwWN3H6fNEAD8t6M7YT/Q1Z5+GAkGNH3iMNuXl2/qUDSxO8Da1EGYBQAovOuuu87vJSAPgf5PAYDiaXFkV321GbbusPfm02YIAP7rjNvbNpToznziPzTU2H+3upEwCwAA9IMwC0BJGeJsM8xQmbXL5nb7cyqzAMBfyZSp7qRpP9hPmNUUcYZZsV7OBAAA2IkwC0BJGcgA+NYMYZZlmZlPBgAUnKsqS8qiMitoe05lFgAA6A9hFoCSks0A+F2cbYYykzK7txZiWQCALOQSZjU6KrOYmQUAAPpDmAWgpGQzAH6XLR3uY6nOv3u8IgBAtjriSffBeD9tho6ZWRsIswAAQD/YzRBASclmALyZ7FJqxVz9+ZqQZAQla3clQKpzozTs04VeJgAgg46YPcwKBwzFzd3HuhIpzV22znbOCfvYdzekMgsAAPSHMAtAybBSMTXWZjEA3jKl2BZNGmXYgixJStFmCAC+6YjZvyfXR4LqGU2ZlqXWzrjrnJ52xFKKxpOqj/AxFQAAZEabIYCSkWneVV8D4DNeI9bm0WoAAAPV6WgzbHQEVZlkOmdDOzsaAgCA3hFmASgZVmyb65gRqh3QNcxu9zUAAMXR4RgA35BFmBUOGgoF7FW5hFkAAKAvhFkASobZvcX23AjVyTAG9m3KzBCIAQCKwzkzqz7c//dwwzDU6BwCT5gFAAD6QJgFoGQ4K7MG2mIoSSkqswDAN52Oyqxs2gwznff3HX3vgAgAAKobYRaAkuGuzMow/L2/a1CZBQC+6XDMzMqmzVASlVkAAGBACLMAlAwrZh8An0tlltnd5tFqAAAD5dzNMOswy3EeYRYAAOgLYRaAkuEc3h7IJcyiMgsAfOPczbA+nGNlFm2GAACgD6H+TwGA4rBiHrQZUpkFAL5x7maY/cws2gwBALm5/vrrszpv8ODBuvrqq9PPV61apd/+9re2c956663049dff9117SlTpuicc87JY7XwCmEWgJLhrMwywjkMgHe0KgIAiqczluvMLPt5GwmzACBnG7e0a/JZs4t6Pz/95Cc/yeq8cePG2cKsNWvW9Pnat956yxZuSdLFF19MmFUiCLMAlAwvZmZZ8XZZZlJGgG9vAFBszt0M67MMs2od7Yg7HKEYACB7pmnp7607/F4GUFD8tAegZJjdzjBr4G2GkmTG2hSsG+bFkgAAA9DhCKGybTOsCdrHuLbHkjJNS4GA4dnaAKDSjRo1qmrub1lW3teYPn26J9eBPwizAJQMZ2VWLgPgpZ3tioRZAFB8zplZ2bYZ1oTsYZZl7azyaqrloyoAZOuVV17xewlA0bCbIYCSYJlJWfHttmP5VGYBAIov190MnWGWJO2IJTxZEwAAqDyEWQBKgrPFUOp9ALwRCMkYcojufz6l2gMudYVeKccgeQBAcXTE+t7NMBQI6LC9mm1/QoGAIsEMYVY3c7MAAEBm1G4DKAmp7i2uY0awlzArWKPAnifqR48u03fv+5lSG19UYuv76a+bMcIsAPBDR7zv3QxrQgGdesDIjK8NBQwlzd2zSwizAABAb6jMAlASzC5HmBWskRHIrj0lUDPYfi0qswDAF+7dDLP/qFkTtA97Z0dDAADQG8IsACUh5QizBjL8PVDrCLOozAKAokukTMWSpu1YtrsZSlLEGWZ1MzMLAABkRpgFoCSYjjbDgQx/DzrCrFR3mxdLAgAMgLMqS8p+N0NJqnHMzaLNEAAA9IYwC0BJcFZmGQOpzHK2GVKZBQBF59zJUMp+N0NJioRoMwQAANkhzAJQEqxEh+25EazJ+rWBmhbbc2ZmAUDxOXcylKjMAgAAhcFuhgBKgpmI2p4bwXDv5ya7lVr5iB76RlBtT50pdW2wfz3WVoglAgD64NzJMBIMKOIIqLoTKT3y5ie2Y+cdvKdqw8EMM7MIswAAQGaEWQBKgpXotB8IRPo4OSVF1+mofQJKbnjB9WWze6vHqwMA9KfD0RaYqSorZVlas63LdUxy72bYTpshAADoBW2GAEqCmbSHWUag98qs/qSozAKAonMOgG+syb7FUJIiIfvHUsIsAADQG8IsACXBWZnVV5thf5iZBQDF55yZ1RgZWAOAszJrR3ci7zUBAIDKRJgFoCRYCXvbifKozLISHbJS/BAEAMXknJk1kOHvklzztZiZBQAAekOYBaAkeNlmKDEEHgCKzd1mmGdlFm2GAACgF4RZAEqCq80w7zCLVkMAKCbnAPiBthlSmQUAALLFboYASoLp3M0w2MduhpkEaiQzln6a6m5TfnEYAGAgXG2GAxwAXxNiZhYAZBKNRvXggw/6vQwUSDQa9XsJZYkwC0BJsJL2mVkDrcwK1LbIjG5MPze7t3qyLgBAdpxthg0DrsxytxlaliXDMHp5BQBUB8uy1NnZ2f+JQBUhzAJQEvLdzTBQ02wPs2gzBICicu1mONDKLEebYSJlKZY0VRse2HUAoFLU19f7vQQUEf++B4YwC0BJcLUZDrAyywo12Z5Ht2+Q1d6efh6JRFRTU5Pz+gAAfeuM5zszy12BtaM7SZgFoGp99rOf9XsJQMliADwA31mphGTaZ6MYgYHNzFr47DLb85v/9RoNGjQo/WfvcWMUi8V6eTUAIF+dMWeb4UBnZrk/lrKjIQAAyITKrBwtW7ZML7/8spYtW6Z33nlHra2t2rx5sxKJhFpaWnTAAQfohBNO0KxZszR27FjP7//EE0/o/vvv17Jly7Rx40YNGjRIEydO1Gc/+1ldfvnlam5u9vyeQKFYSffQw4HOzJp25AHSjuXp59d8+Uh9//vHS5I6ojFNPmu24vE41VkAUCDOAfCNNQP7mBk0dv5yo2ebOUPgAQBAJoRZOTr++ON7HcK3adMmbdq0SUuWLNEtt9yiH/3oR7r22ms9uW97e7suuOACPfnkk7bjra2tam1t1dKlS3XnnXfqkUce0dFHH+3JPYFCc7UYStIAZ2aFa+uV2NHjeSCh+gaCKwAolo6Ys81wYJVZhmFI8S6prmeYRWUWAABwI8zKw4gRI3TEEUdo8uTJGjVqlEaNGiXLsrR69Wo99dRTeuGFFxSLxXTdddcpkUjohhtuyOt+yWRS5557rp555hlJ0siRI3XZZZfpwAMP1NatW/Xwww/rhRde0Mcff6zTTjtNzz//vCZPnuzFXxUoKOfwdymH3QwD9uDKdOyOCAAorHx3M5T0jzBrUPopbYYAACATwqwcLV26VJMnT+51u+hrr71Wv/vd7zRr1ixZlqUf//jH+upXv6o999wz53vee++96SDrwAMP1MKFCzVy5Mj017/xjW/ommuu0ezZs9XW1qbLL79czz//fM73A4rFXZllSEbvv9E3jKBSDftowQsf6JR/mqRIJCwjbN/9w0p2F2ClAIDeuNsM3d/Hg4ahA0Y0uo6lxext51RmAQCATBgAn6NPfepTvQZZu1x00UU6/fTTJe2sqnr66adzvl8qldLNN9+cfn7//ffbgqxdfvrTn+qQQw6RJL3wwgtasGBBzvcEisVyVlEFwn3+/8sI1So+8lR9879TMkefpoZ9z1UgYt/NkDALAIqrwzEAPtNuhrXhoD5/8J62P7bdCuOEWQAAoH+EWQXWs81v48aNOV9nyZIl2rBhgyRp2rRpmjJlSsbzgsGgvvWtb6WfP/zwwznfEygWV5vhAFsMJckI1tqvmaLNEACKydVmmKEyq1/OMCvGAHgAAOBGmFVgH374YfrxqFGjcr5Oz6quU089tc9ze359/vz5Od8TKBZXm2EuYVbIEWZRmQUARRNPmoqnTNuxTJVZ/V/I/osIKrMAAEAmhFkF9Mc//lGPPfaYJKmurk6nnXZaztf629/+ln58+OGH93nuyJEjNWbMGEk7d1ZsbW3N+b5AMVhJL8KsOvsBMyHLTGU+GQDgqc64O3TKNDOrX8zMAgAAWWAAvAeeffZZbd26VZIUj8e1bt06LViwID2sPRwO69e//rVGjBiR8z2WL1+efjx+/Ph+zx8/frzWrVuXfu3w4cOzus/atWu1du3anNa4ceNGrVmzRs3NzWppacnpGqhOZsL+w4uM/CuzJMlKdcsINOS6LABAlpwthtI/djNMDjCMcrQZtrObIQAAyIAwywPf//739de//tV13DAMHX/88br55pt1zDHH5HWPtra29ONhw4b1e/7QoUMzvrY/c+bM0U033TSQpbnMmDFDJ554oqLR6IDuXera29tt/4R3OjqTikZ6hLSRPRRM1PX+AkndVkoTJkxQl9mg9kSdZEXs15BkxEIKqE7RVFATJkxQe3u7UilvqrV4P8CJ9wR6qrb3w+atUU1osGzHkl3tinZ3a8KECapXXBGz7/bvoOKaMLhWRo/rhBKV8Vmi2t4P6BvvBzjxnkBP2b4fotGo6urq1NVVnbOCDcuyrP5PQ1+OOuqojGHWmDFjdPnll2vWrFkaPXp0XveIRCJKJHYOQU0kEgqF+s4hL7zwQj300EOSpIceekjnn39+Vve58cYb8w6zzjvvvKzvBwAAAAAAcvPGG29Iki655BKNHTvW38UUEZVZHli6dGn6cWdnp1asWKEnnnhCs2fP1vXXX6/bb79dDz30kGbOnOnjKotny5YtWr58uc4888y8ht6Xmvb2dr322muaMmWKmpqa/F5ORdnx5t1qf+e/08+NxvFq3PszvZ5vJbvU8f6DtmON+1+ozg8fl5Xc3aJSN26mQk1jFO2K67Qr5+i5555TfX29J2vm/QAn3hPoqdreD6+ua9Plf3gr/XxQbUgLrzha0WhUxx57rGb9xwOK1NarK5HUA6+ut732S4eOVl04pHh3VHN/eYeMqbt/IXbAyEbdf0HmHZzLSbW9H9A33g9w4j2BnrJ9P2zYsEF/+tOfiriy0kKY5bGGhgYdcsghOuSQQ/SlL31Jxx57rP7+97/rzDPP1Msvv6yDDz44p+s2NjZq27ZtkqTu7m41Njb2eX7PUsOBfEO89NJLNWPGjJzWuHHjRj311FNqbm5WV1eX6uvrK3J2VlNTU0X+vfyUsjYrFV+Vfm5YTWoK914ua6pTZo/zJakx1ClZm2TGd294UG/+XZHwMCke08qVK9XU1OT5BwTeD3DiPYGequX90P1JTCs7jfTzseGIWlpaFAwGtXLlSkUVUSpQq04jaTtPkjqNWikQUkxJrVy9RsZBu78e3KGK+t+vWt4PyA7vBzjxnkBP/b0fduzYUbUthhJhVkFNnDhRt956q2bNmqV4PK5bbrlFjzzySE7XamlpSYdZmzdv7jfM2rJli+212Ro7dmzOpYlr167V22+/ndNrUd3MpGMAfA67GUruHQ2tZN/zWQAA3uhw7GaY006GkhS3fyjfwQB4AACQQcDvBVS60047Lf148eLFOV9nv/32Sz9etWpVH2e6z+n5WqAUWYlO+wEjt5zduaOhlSLMAoBi6IjZN9doiOT4+9KYYzfDbsIsAADgRphVYD1bmvLZjefTn/50+vGyZcv6PHfjxo1at26dJGnEiBEaPnx4zvcFisEVZuVYmRUIOsKsZPWW3QJAMXU6K7MiuVZm2cOsaCKlZMrMdVkAAKBCEWYV2IoVK9KP8wmVTj755PTj+fPn93nuvHnz0o9PPfXUnO8JFIuZKEyboUmbIQAURUfcXpnVWJNjZZYjzJKkdloNAQCAA2FWgd1zzz3px8ccc0zO15k2bVp6Z8DFixfrtddey3heKpXSHXfckX7+xS9+Med7AsVieTYzy1mZRZgFAMXQ4QicGnKtzIq5K2p30GoIAAAcCLNycM8992jRokWyLKvXc1KplP793/9dd999d/rYlVdemfHc6dOnyzAMGYahuXPnZjwnGAzqhhtuSD+/6KKLtGnTJtd5P/jBD/TGG29I2hmezZw5M4u/EeAvr9oMDWebYYo2QwAohk5HZVZDrpVZiW4ZjkMMgQcAAE7sZpiDpUuX6oorrtCYMWN04okn6tOf/rRGjBihSCSitrY2vf3223riiSe0evXq9GuuvfZaTZs2La/7XnbZZXr88cf1zDPP6J133tHBBx+syy67TAceeKC2bt2qhx9+WM8//7wkqbm5Wb/61a/yuh9QLM42Q4PdDAGgrLh2M8y1MkuWmmqC2tFjoDyVWQAAwIkwKw/r1q3TnDlz+jynublZt956q6644oq87xcKhfToo4/qggsu0JNPPqkNGzboxz/+seu8vfbaS4888ogmT56c9z2BYvCsMos2QwDwRadXuxlKaqoJOcKsRM7XAgAAlYkwKwd33XWXzj//fC1ZskRLly7VJ598ok2bNqm9vV0NDQ0aOXKkDjroIM2cOVOf//zn1dzc7Nm9m5qa9Oc//1lPPPGEfve732nZsmXatGmTmpqaNHHiRJ1zzjm6/PLLPb0nUGhmslBthoRZAFAMrt0Ma3KtzJKaHK+lzRAAADgRZuWgsbFRM2fO9Gwe1eLFiwf8mrPOOktnnXWWJ/cH/GRZliznboaGN22GMhOyTH4IAoBC63BUZjXmWZnVE22GAADAiQHwAPyVikuW/Ycgr9oMJVoNAaAYnDOzct7NUO55W4RZAADAiTALgK9cLYaSZ22GEq2GAFAMzt0MG3PdzVC0GQIAgP7RZgjAV67h71L/YZYRVKp2tJa9vU5HfnqMQqGgZARlBIJSICKZ8d3XT3ZJgSaPVw0A6Kkjlt1uhkHD0LjBda5jPQ1ytRkyAB4AANgRZgHwlemclyVJgb6/NQVCtYrvea4uuGK2Vsz7nJpadodVRqhWVrxnmNUtRTxbLgAgg44sK7Nqw0FdfNiYPq9FZRYAAOgPbYYAfGUl7WFWPGnJMHKfteKcm0WbIQAUnnM3w3xmZjkHwLczMwsAADgQZgHwlbPNsCvey4lZcs7NspJd+V0QANCneNJUImXZjnk6M4swCwAAOBBmAfCV6QizonmGWYGQfRYLuxkCQGE5dzKUpMZIPmGWY2YWbYYAAMCBMAuAr5xthnlXZjnaDE3aDAGgoJw7GUr5thk6K7MYAA8AAOwIswD4ylmZlX+bobMyizZDACgk506GUn5hlns3QyqzAACAHbsZAvCVlXBWZlm9nNnjNamYwpsX6aZzAzI2LlJ0W1h1Y0+QEaxxD4CnzRAACqojZq/Mqg0FFApm/n1pLGnqLytabcc+s+9w1YR2n89uhgAAoD+EWQB85RwAn83MLMtMKrTjb/ryPwWltrcUl1S713GZwyzaDAGgoJwzs/qqykqapl75eLvt2LSJQ1WjnmGWYzfDWFKmaSkQMDxYLQAAqAS0GQLwlfdthlRmAUAxOWdm5bOToeSuzLKszHO5AABA9SLMAuArzwfAB2vs10/F8rsgAKBPzplZ+exkKLkrsyRpR4wh8AAAYDfCLAC+cs7MimYxM6svzjBLqTzTMQBAnwbSZpiNpgyvZwg8AADoiTALgK+8bzN0V2ZZVn4BGQCgd163GYaDAdWF7R9RCbMAAEBPhFkAfJXLAPg+BSPOO0gW7SkAUCiuNsOa/CqzJGlQbdj2vJ0dDQEAQA+EWQB8ZTpmZnV7XJkliVZDACigDkdlVkOeM7MkaZCjumsHYRYAAOiBMAuAr9wzs/K7XsYwyyTMAoBCcbYZ5jszS5IG1TrCLNoMAQBAD4RZAHzlbDPsyncAfCAoGY6qAHY0BICCcbcZFqAyq5t2cQAAsBthFgBfmUmPZ2YpQ3UWlVkAUDCdMW93M5QyVGbRZggAAHogzALgKyvRZXue726GkmQ4h8CbVGYBQKG4djP0YmaWYwA8bYYAAKAnwiwAvnJWZnkTZtkrsywGwANAwXTEvd/NsMnVZkiYBQAAdiPMAuAr58ws2gwBoLx0xLzfzbDJ0WbYTpshAADogTALgG8sy3LtZpjvAHhJkjPMYgA8ABRMp7Myy4uZWQyABwAAfcj/V2cAkCMr1S3JHl5l1WZoBGSGh+jDdVu079ihCgYDkrE7m3fPzKIyCwC8FIvFFI/v/N7a7giagmZc7e3tO7/2j3/uEjAMDW+IuI45uQbA02YIAAB6IMwC4Btni6GUXZthIFSn2Jgv6eRvztaKeV/W4JYm29fdbYZUZgGAV2KxmMaMG6fWjRt3HvjGQzJqG9NfP+eM06T179pek0rtbEWsCwd1xdF793sPdjMEAAB9IcwC4BszQ5hViAHwYgA8AHgmHo+rdeNGfe/BJQrX1us/Xvq7zB5Ftpfc+luNbNy5G2HH1lbd9dVT0mFWtgbVsJshAADoHWEWAN9kqswqyG6GtBkCgOcidQ0K1tbbgixJamhoUE39zlbCWJf7+3w23G2GzMwCAAC7MQAegG+cw98VrHH9UJQLd2UWbYYAUAjxpPubdiSU/8fLTG2GluXBfyAAAEBFIMwC4BszaQ+zjFC9J9dlADwAFEciZbqORYLuge4D5dzNMJGyFEu67wUAAKoTYRYA3zjbDL0Ls6jMAoBiiGcIs8JBLyqzwq5jzM0CAAC7MDMLgG9yDbOsVFyhrUv17ZkBGZuXqqsjoto9pu6uyHLtZkhlFgAUgjPMCgUMBYzeK7PiKVMvrt5qO3b03kMUcQRgzjZDaWer4YimGtdxAABQfQizAPjGdM7MyjbMMhMKt72sb58clLb8VTFJNSMPS4dZRogwCwCKIZ6yz7FyhlJOiZSpZ1faw6zDx7TYXtfe3q5Gy1IoYCjZY5Di37du18ia3eFZJBJRTQ3hFgAA1Yg2QwC+sQo2M8vxw42VUiToyaUBAD04K7PymZeVTMQlI6DRo0erublZic7ttq8f95mZGjRoUPrPmHHjFIvRRg4AQDWiMguAb0xnm2G4QAPgJTXWenJpAEAPCcdQ9nx2MkwlE5Jl6pv3PaPG5sG655WN2h5Lpb9+7o/u0b5Dd34zj3d16vYLpykej1OdBQBAFSLMAuCbog2AF2EWABSCszLLi+HvkboG1dQ3qja82RZmmaGIauob874+AAAof7QZAvBNodoMZYQkw/7tjTALALyXMO0zs8KB3NsMnSIhe394LOneOREAAFQnwiwAvnEOgDdCdZ5c1zAMV3VWY613P2ABAHZKOAbAe1GZtUuNo2WRMAsAAOxCmAXAN+42wwbPru2cm0VlFgB4L+FqM/TuFwc1QcIsAACQGWEWAN+YSXuYJY8GwEuSHJVZTcwHBgDPJR1thqFA4Sqz4qlUL2cCAIBqQ5gFwDeWq83QuzCLNkMAKLyk6ajM8nBmlrvN0OrlTAAAUG0IswD4xt1m6M3MLClTmOXZpQEA/+CcmRXyss3QFWZRmQUAAHYizALgG7NQuxmKmVkAUAxFHQCfYmYWAADYiTALgG8KOwCeyiwAKLSEo80w5GWboSMYizMAHgAA/ANhFgDfmMzMAoCylixmZRZhFgAA+AfCLAC+cVZmqZAzs9jNEAA8l3DsZujlAPgIYRYAAOhFyO8FAKhelnNmVjjbyixDVqBWW3d0aUhzvQKGIcn+A5QzzGqizRAAPJd0zLHqbwC8IUP14aDrWCbONkPCLAAAsAthFgBfWJYpK9llO5btzKxAuF7de39Nh581WyvmfU2DW5rcJzEAHgAKzl2Z1XfRf30kqGumT8zq2s42w4RpyTQtBTys/gIAAOWJNkMAvrAc87IkZmYBQLkZaGXWQDjDLIkdDQEAwE6EWQB84ZqXJcko5MwsKrMAwHOFnJmVMcyi1RAAAIgwC4BPzGSRK7MYAA8AnksUcDfDSIZrEWYBAACJMAuATzJVZhVyN8P6GkOWmfTs+gAAKWk62gw9rMwyDMMVaNFmCAAAJMIsAD4xHTOzjFC9DMO7b0mGYwC8JFmJDs+uDwDVLmVacnQZelqZJblbDanMAgAAErsZAvCJlbRXZhnh7FsMLTOh4PY39eVjAjK2valYd60iIw6WEQjvvl7Q3VdoxdtzXzAAwCbpTLLU/8ysRMrUG5/ssB07ZM9BvYZgNaGA2mO7n8cJswAAgAizAPjEuZthINSQ/WtTcUW2LNFNnwtKmxarS1J46AG2MEuBTJVZhFkA4JVMYVZ/uxnGU6bmv7/JduzAkY19hlk9xZKpAa4SAABUItoMAfjCTORemZUNwzAkR3UWYRYAeMe5k6FUgDZD58wsKrMAAIAIswD4xHLsZmiEs6/MypZzbhZthgDgnVzaDAfKVZnFAHgAACDCLAA+cVZmBULeVmZJ7rlZVGYBgHcSKXuYFTSMnVWxHmIAPAAAyIQwC4AvLFebYSEqsxxhFpVZAOAZZ2VWuJ95WbkgzAIAAJkQZgHwhbPNMODxzCzJHWaZVGYBgGecYVbI4xZDSYowMwsAAGRAmAXAF2aiGDOzaDMEgEJxDoD3evi7xMwsAACQGWEWAF+42gwLMjOLAfAAUCi0GQIAAL8QZgHwhWsAfBHaDKnMAgDvOHOlQrQZOsOsOGEWAAAQYRYAnzhnZhWizVAMgAeAgnG2GYYCBWgzZGYWAADIgDALgC+shHMAPDOzAKCcJFPFaDMM2p4zMwsAAEiEWQB84mwzZGYWAJQX98ysIgyAT5qyLKuXswEAQLUgzALgC9cAeGZmAUBZce1mWISZWZKUSBFmAQBQ7QizAPjCOTOrKG2G8R2e3wMAqpWzMitUgDbDSIZqL+ZmAQAAwiwAvjAdM7OMEDOzAKCcuNoMCzEAPkNlFnOzAABAyO8FAKhO+bQZBsIN6prwLU0+a7ZWzLtGQ1uaMp7nDrM6ZFmmDIMcHwDy5drNMIvKrIZISDecOCnrewQDhkIBwxacxZIpNYWzXycAAKg8/EQHwBdm0h5mFabNMOI6ZsU7PL8PAFSjYlRmSZmHwAMAgOpGmAWg6CwzKaXitmOFGAAvR2WWJJnMzQIATxRjZpYk1QQJswAAgB1hFoCis5JdrmNGsM7z+zjbDCXCLADwinNXwULsZihlqMxiZhYAAFWPMAtA0WUKswKhAoRZRkAK2AermHGGwAOAF1xthhl2HvRChDZDAADgQJgFoOjMTJVZhWgzlLs6i8osAPCGawB8sSqzCLMAAKh67GYIoOgythmG6qSY+3jG15tJBTpW6JSDDal9heKpWoUHT5IRcH9LM4IRWYkeryXMAgBP5FKZlTRNfdBq3wBk0vAGhfoYHk+YBQAAnAizABSdK8wKhDIGUb2+PhVTzab5+q9ZIemTeYpKGjTlu72EWc7KLNoMAcALrgHwWVRmxZKm/vDW323Hrp42QaFIH2GWIySLE2YBAFD1aDMEUHRWstv23CjAvKz0tZ1hVozKLADwgmsAfKF2M2QAPAAAcCDMAlB0zsqsooZZtBkCgCdcbYZ9tArmoyYUtD2nzRAAABBm5aG9vV2PPvqovvnNb+roo4/W8OHDFQ6HNWjQIO2///666KKL9PTTT8uyrP4vlqXp06fLMIys/6xevdqzewNecYZZgWDhwiwFI7antBkCgBcMOQqzFCpWZRZhFgAAVY+ZWTm6/fbb9cMf/lDd3d2ur7W3t2v58uVavny57r//fh177LF64IEHNHbsWB9WCpQeMxm1PTdCtQW7l7MyiwHwAOCBUMR1KFyo3QyDhFkAAMCOMCtHH3zwQTrI2muvvfSZz3xGhx12mIYPH66uri799a9/1QMPPKCOjg4999xzmj59upYuXaoRI0Z4tobHH3+833O8vB/gFdoMAaDMhTOEWVnsZpgLZmYBAAAnwqwcGYahk046Sddcc40+85nPKOCYEzFr1iz94Ac/0MyZM7V8+XKtWrVKP/jBDzRnzhzP1nD22Wd7di2gmHwdAE+bIQDkL1TjPlSgyqyIq80wVZD7AACA8sHMrBz95Cc/0YIFC3TiiSe6gqxdxo0bp0ceeST9/JFHHlE0Gs14LlBNqMwCgDKXIcwqWmVW0vR0HikAACg/hFk5GjJkSFbnHXzwwdp///0lSdFoVB9++GEhlwWUBdcA+HAhwyznAHjCLADIm2NmlmFIwSLNzDItuYbPAwCA6kKYVQRNTU3px11dXX2cCVQHV2VWAXczdA2Aj9FmCAB5c4RZhRr+LrkrsySGwAMAUO0IswosFovpgw8+SD8fN26cZ9c+/fTTNXr0aEUiEQ0ePFiTJ0/WZZddpkWLFnl2D6AQTF9nZlGZBQB5C9u/txaqxVDKHGbFKc0CAKCqMQC+wB5++GFt375dkjRlyhSNGjXKs2s/9dRT6cdtbW1qa2vTu+++q9/85jc64YQT9MADD2iPPfYY0DXXrl2rtWvX5rSejRs3as2aNWpublZLS0tO10B1KObMLGUIsyzLkmEUrooAACqeozKrUMPfd107YOxsL9yFHQ0BAKhuhFkF1Nraqu9///vp59dff70n1x08eLBOPPFEHXbYYRo9erSCwaDWr1+vhQsXav78+TJNUwsXLtTUqVO1dOnSAQVoc+bM0U033ZTX+mbMmKETTzxR0WhUbW1teV2rlLS3t9v+idy1x8OKRsannwc0TKG2NkWjUU2YMEHRVL2UcG/7vouVlO31khRINsiQOxQzNcR17rYtGxUI1eb3d+D9AAfeE+ipkt8P0WhUE8aNkdGwO11qqZPqzG7XuWYgqQkTJqjRSOz8upXUhAZ7VVWD1a06M+Q+t4f9Bhm21sJ6xTVhwgS1t7crlSr93Q0r+f2AgeP9ACfeE+gp2/dDNBpVXV1d1Y4yMiy2gymIeDyuGTNm6LnnnpMknX322Xr88cfzvu5LL72kQw89VJFI5h/0X3vtNZ177rlavXq1JOmUU07RvHnzsr7+jTfemHeYdd555+n888/P6xoAAAAAAKBvb7zxhiTpkksu0dixY/1dTBFRmVUApmnq0ksvTQdZEydO1Jw5czy59tSpU/v8+pQpU7RgwQIddNBBisVimj9/vpYtW6bDDz/ck/tnY8uWLVq+fLnOPPNMT9sq/dbe3q7XXntNU6ZMsQ31x8BtXXKNutY/l37edNDlGvSpSxWNRnXsscfqqbsvVX1dX5VZXep4/0Hbscb9L8zcrmgl1f7OXNuhkaf/XqFB+c2v4/0AJ94T6KmS3w/RaFTHXvovMo78fPrYyKYanTnZ/d/8zu1b9OAPL9OXbr1P9U3N6kok9cCr623nfOnQ0aoLh1zn9vSHtz7Rtmgi/fzYsU169pav6rnnnlN9fb3Hf0PvVfL7AQPH+wFOvCfQU7bvhw0bNuhPf/pTEVdWWgizPGZZlr7+9a/rwQd3/qA9duxY/d///Z8GDx5ctDVMmjRJF110ke69915J0pNPPpl1mHXppZdqxowZOd1348aNeuqpp9Tc3Kyuri7V19dX5Oyspqamivx7FVNX6hMZ8VXp5811hppbWhQMBrVy5UrVB6NqCvfeNmKFLHXu8Rkd++Vf6q8PX6nBzY0yQpYMI3OJbSqxVrJ2X6+pJqkaj/4d8n6AE+8J9FSJ74dgMKiVm9tldO6ek2XVhNQVcLdvd5ghrVy5Uh1WWEagVlbE0mcPm2g7xwoH1WUYrnN72pQIaV1nMv18n9jO/140NTWV1Q9+lfh+QO54P8CJ9wR66u/9sGPHjqptMZQIszxlWZauvPLKdIi01157aeHChdp7772Lvpbjjz8+vY733nsv69eNHTs259LEtWvX6u23387ptagu+Q6ANwxDCtZra6ekUL0C4Ya+XxCISKnd92RHQwDIk2MAfDiY3QB4wzDUEBn4x88ax26JDIAHAKC6FW4f5SpjWZa+8Y1v6J577pEkjR49WosWLdLEiRP7eWVhDBs2LP24koawozIUdTdDaWeY1YMZ217Y+wFApXOGWYHCfqSsCQVtz+MpRr4CAFDNCLM8sCvI+uUvfylJ2nPPPbVo0SLts88+vq2ptbU1/ZhSVZSaoodZQUeYlego7P0AoNKFauxPs6zMylVNyFmZRZgFAEA1I8zKkzPI2mOPPbRo0SLtu+++vq5r0aJF6cf77befjysB3Kykfcv1YldmWXHCLADIiyPMCgcKG2ZFHGFWPEmbIQAA1YwwK0/f/OY300HWqFGjtGjRIk2aNMnXNS1fvlz3339/+vnpp5/u42oAN2dlVqDYbYbx9sLeDwAqnaPNMBQscJuha2YWlVkAAFQzwqw8XHXVVbr77rsl7Q6ycq2Cmj59ugzDkGEYmjt3bsZz7rjjDr344ot9Xuf111/XySefrFgsJkk66aSTdOSRR+a0JqBQ8m0ztMyUAl0f68iJhhT9WIkdq2WZve9+aDgrs2gzBID8hHOrzEqZllZvjdr+pMz+gylnm2GcAfAAAFQ1djPM0fXXX6+77rpL0s6deb797W/r/fff1/vvv9/n66ZMmZLzboELFy7Ut7/9bU2cOFEzZszQpz71KQ0dOlTBYFCffPKJ/vKXv2jevHkyzZ0f8MaNG6f77rsvp3sBhWSm8gyzUt2q+ftjevibIWndo+qUNGjKd2UEetnVMBC23582QwDIj2s3w+x+P9qdTOl3r35sO3b1tAn97nDIzCwAANATYVaOnn/++fRjy7J07bXXZvW6++67T7Nmzcrr3h999JE++uijPs+ZOXOm5syZoz333DOvewFes8yUlIrbjhmh2sLelAHwAOAtZ5thgWdmuSqzmJkFAEBVI8wqI7Nnz9YZZ5yhpUuX6s0331Rra6s2b96sWCym5uZm7b333po6daouuOACHXXUUX4vF8jI2WIo+TEAnplZAJAXZ5shuxkCAIAiIszK0eLFi4t+vYkTJ2rixIn6yle+4um9gWIqhTDLTHQW9n4AUOlcuxkWdwB8nDALAICqxgB4AEWVKcwq/G6GjplZCSqzACAvrt0Mi1uZlTAtyeBjLAAA1YpPAQCKysxYmVVf2JvSZggA3nJVZhU3zJIkRQr8ixAAAFCyCLMAFJWrMssIygiGM5/sEYM2QwDwlqsyq8BthhnDrAL/IgQAAJQswiwARWUlu23PCz4vS3K1GVKZBQB5coRZha7MimQKy2oIswAAqFaEWQCKykrZK7OKEmYFnZVZHYW/JwBUKMuyMuxmWNiPlIZhuAMtKrMAAKhahFkAisrZZlicyizHzKxEpyzLLPx9AaACxVOWDMfw9VCBK7OkDK2GzMwCAKBqEWYBKCpnmBUI1Rb+po4wS5KsONVZAJCLaCLlOhYu8G6GUoYwizZDAACqFmEWgKJy7mboR2WWRKshAOSqO+mubC10m6GUqTKLMAsAgGpFmAWgqKyEH2GWe7dEkyHwAJCTroQ7zCpKmyEzswAAwD8QZgEoKj9mZhmBkOJJy76ORGfB7wsAlagrQ5uhLzOzapiZBQBAtSLMAlBUvgyAl9QZsz+nMgsAcuNsMwwFDBlG4cOsCG2GAADgH0J+LwBAdbFSzgHwAw+zjFCduve6UGddNVfzfnmJWpoa+g3FojFpcEOPdTAzCwBy4mwzHMjw97pQUF+fOs51LBu0GQIAgF0IswAUlZXstj3PpTLLMAKyIkO1YoOkmqEK1jf1+5oOKrMAwBPONsNwIPtC/0DA0IjGmpzuywB4AACwC22GAIrKl90MJUVj9plZ7GYIALnpcrYZDqAyKx/uMIuZWQAAVCvCLABFVSozs2gzBIDcdDvbDIsw/F1iADwAANiNMAtAUbnDrNqi3JcB8ADgjaijzTDknGVVIMzMAgAAuzAzC0DBxWIxxeNxSVKi2x4iJVJBtbfvPLbrn4XgrszqLNi9AKCSOXczLFZlFrsZAgCAXajMAlBQsVhMe48bo0GDBmnQoEF65uknbV+//kf/lv7a6NGjJUkpM5XpUmmWZcqIb9G+oyTFtigVbZVlmX2+ptM5M4vKLADIiXM3w4FUZpmmpU0dMdsf07T6f6EytRkSZgEAUK2ozAJQUPF4XBs2tuqdJ65WY32NUisflqIfp7/+w6+fpH/94RRJ0sbN7Tri/DuVSvUTZiW7VPvxg1rwL2Fp9QNqlzRoyndlhBt6fU3U2WbIzCwAyIl7N8PsK7O6kind89Ia27Grp01QQ6T/j6SuMCtcK8vKLggDAACVhTALQFE01teoqaFG7UZKPX8Mqq2tU03Dzm3aO5yJk4c6nG2GVGYBQE6cuxmGi7WboaMCzAgEFU2YGlSUuwMAgFJCmyGAorLMpO25EShOpu6qzIpTmQUAuXDuZhgKFOfjpGtmlqT2WDLDmQAAoNIRZgEoLjNhe2oEwkW5rWtmFm2GAJAT526GRavMyhBmdcT7bksHAACViTALQFE5K7NUpMos2gwBwBvO3QyLVZkVCgTkHM+1g8osAACqEmEWgOJytRkWpzLLPQC+syj3BYBK4xoAX6TKLMldndURozILAIBqRJgFoKgsR5thsSqzOqnMAgBPuAbAD2A3w3w5h8DTZggAQHUizAJQNJZlSZb9Bw//Zma1s6U7AOTANQA+WLyPk84h8AyABwCgOhFmASge57wsybfKLFmmrFR3Ue4NAJXErwHwkrvNsJ02QwAAqhJhFoCicbUYSjL8CrNEqyEA5MI5AD5cpAHwkhShzRAAAIgwC0AxZQizVLQ2Q/cxM9FRlHsDQCXpcrYZFnNmFm2GAABAhFkAisjK0GZYrMqsrrgk2X/gMqnMAoAB83U3Q2dlFm2GAABUJcIsAMWTaWaWESza7Y1wg+25legs2r0BoFI42wx9HQBPmyEAAFWJMAtA0bhmZgXCMozi/UbfCDfanlOZBQAD52wzDNNmCAAAiowwC0DRONsMjSLNy0rfz1mZRZgFAAOSTJlKmJbtWLiYlVkMgAcAAJKKM6wGACTJcvwGPcd5WUawVrE9ztGsH/5eD/z0PA1qrJMRrO3/dc7KLNoMAWBAnFVZ0sAGwNeGgrro0L1cx7LlrswizAIAoBoRZgEoGmebYa7D341AUGbdXvrrR5ZUv5fCg5qye2GINkMAyIdz+Ls0sAHwwYChvYfU53x/9wB42gwBAKhGtBkCKB7nAPiitxnawywr0VHU+wNAuYtmCrMCPg6ApzILAICqRJgFoGi8qszKVYAB8ACQl0yVWaEBVGbly9VmyMwsAACqEmEWgOJxDYAvcqezYwC8SWUWAAyIszIrYEiBIu5K6xoAH0vKsqxezgYAAJWKMAtA0Tgrs3xvM4wTZgHAQDgrs4q5k6HkrsxKWZmrxQAAQGVjADyAorGclVlGbt+CLMuSUlENaZCUjMpMBGSE6mX0Ux1guCqzaDMEgIFw7mYYHsBOhtLO79/O6q76cLDf79+7OMMsaefcrPoIH2kBAKgm/JcfQPG4BsDnGGYlo6pb8xu98m9h6aN7tUPSoCnfdYVVTu7KrM6c7g8A1Soaz68yK5pIafaSlbZjV0+boIYswyhnm6Ek7ehOaGRTzYDWAQAAyhtthgCKxjUAPuhvmyGVWQAwMM6WvtAAK7PyFQoYct6yPZbMfDIAAKhYhFkAisdVmVXkMCvEAHgAyId7ZlZxwyzDMFzVWYRZAABUH8IsAEXjqszKcWZWrhgADwD5cc67CgWK/1HSOTdrRzdhFgAA1YYwC0DxOAfA5zgzK1euAfBx2gwBYCBcA+CLXJklucMsKrMAAKg+hFkAisa5m2HR2wydlVm0GQLAgPg9M0tyD4EnzAIAoPoQZgEoGleboc+VWVYqJiuV6OVsAICTs81woLsZeoE2QwAAQJgFoHhclVn+zsySGAIPAAPhGgBPZRYAAPABYRaAonFXZhW7zbDJdcxibhYAZM3VZlgKlVmEWQAAVB3CLADF43dlVqjedYzKLADIXlfc2WbIAHgAAFB8hFkAisY5AL7olVmBkIxQne2YGSfMAoBsOXczLIUB8B3MzAIAoOoQZgEoHp8HwEuZdjSkzRAAsuWemUWbIQAAKD7CLABF46zMUpErsyQpELHPzaIyCwCy597N0P/KLNoMAQCoPoRZAIrCsizJcrYZFr8yKxBusD03qcwCgKy5BsCXQmUWbYYAAFQdwiwAxWFl+GHDjzZDR2WWFe8s+hoAoFyVRGUWA+ABAKh6xf9JEkB1crYYKvcB8EawRrERp+h7t/1Zd1x3pprqa2UEa7J6bSDsaDOkMgsAsuYcAD/QMKsmFNDnDtrDdWxA16DNEACAqkeYBaA4PKzMMgIhmY37av6bltS0ryItTf2/aNdrI/Y2QytOmAUA2cq3zTAUCOjAkdl/z84kU5uhZVkyjOJXiQEAAH/QZgigODyszMqHuzKLNkMAyFY0XgJtho7KrKRpKZY0ezkbAABUIsIsAMVhJdzHjGDRlxGINNqem1RmAUDW3JVZxQ+zMrUl0moIAEB1IcwCUBzOyqxAyJeWECNsD7OsREfR1wAA5ciyLHU7KqBKYTdDiR0NAQCoNoRZAIrDEWb50WIoSQHHboZmnDALALLhDLIkf9oMQwFDzrtSmQUAQHUhzAJQHM4B8DkOf89XgMosAMiJs8VQ8qfN0DAMRRwhGpVZAABUF3YzBFAcHlZmmYlO1a28Qyt/HpaW/0JtkgZN+a4C4Yb+XiqDmVkAkJPuRKbKrIH9XrQzntTsJSttx66eNkENkYF9JI0EA4qldodrVGYBAFBdqMwCUBRWiVZmmVRmAUBWSqUyS5JqQvb7EmYBAFBdCLMAFIezMsvwZ2aW4ZiZZTEzCwCyUkphlqvNkDALAICqQpgFoDgsZ5thqVRm0WYIANnoSjh3MpQvu9JKO9sMe2pnZhYAAFWFMAtAcZgJ+3O/wqyIcwB8pyzLPQcGAGDnrMzyqypLojILAIBqR5gFoDg8HACfDyPc5DpmJTp9WAkAlJeuZOmGWczMAgCguhBmASiOUhkA76jMktjREACyUUqVWTXONkPCLAAAqgphFoDicFVm+TUzK1NlFkPgAaA/7plZJVSZxcwsAACqCmEWgOJwVWb502aoYMRVFWayoyEA9Ku0KrNoMwQAoJoRZgEoDscAeN9mZhmGqzrLpDILAPpVSmGWczfDHd2JXs4EAACVyJ8+HwBVx7L8bzNsb//HbKxQgxTblj4e3b5Jyebdc7MikYhqamqKvTwAKGndpdxmGEv1ciYAAKhEvlVmTZgwQRMmTNDEiRP1ySef5Hyd9evX264FoESZ/g2Aj8WTChjS6NGjNWjQIL3/0Trb12d9+QsaNGhQ+s/e48YoFosVbX0AUA6cuxmGS6gyizZDAACqi2+VWatXr5a0s+Unmcz9A0gymbRdC0CJclVmFa/NMJFMybSk1//wLQ1pblTqowekrr+nv37nD07Vfw05WJLUEY1p8lmzFY/Hqc4CgB5Kq83Qfm/aDAEAqC60GQIoDh8rs3ZprI+oqaFGHeEaJbt2H68JmaptILgCgL64dzP0aSGSakK0GQIAUM0YAA+gOEpgZlb63kFHcJWK+7MQACgjpVWZZf8IG0+ZiiUJtAAAqBZlX5nVc64NLUFACXPsZqg82gyNYETxodP0k18t1I+u/Iwa6mplBCPZX8Bxb8skzAKA/nR7EGZFggGdsv8I17GBX8d97/ZYUjWh4ICvBQAAyk/Zh1kfffRR+vGgQYN8XAmAPpneVWYZgbBSzQfr/hf+Tzf88GDVtDQN7PWOyiyLyiwA6Je7zXDgYVY4GNDhY1ryXkvGMKs7pWENeV8aAACUgbJuM7QsS/fee6+kncPf991336Ldu729XY8++qi++c1v6uijj9bw4cMVDoc1aNAg7b///rrooov09NNPy7Ksgtz/iSee0Oc+9zmNGzdOtbW1GjFihKZOnarbbrtN27dvL8g9gbxY/s/M2sVZxUWYBQD9c7YZ+rmbYThgyDLt69kRYwg8AADVoqA/Td58881Znfef//mfamlpyfq68XhcGzZs0LPPPmurzDrmmGMGusSc3H777frhD3+o7u5u19fa29u1fPlyLV++XPfff7+OPfZYPfDAAxo7dqwn925vb9cFF1ygJ5980na8tbVVra2tWrp0qe6880498sgjOvrooz25J+AJV2VW8XYzdHKFWbQZAkC/nGFW0McwyzAMKd4l1Tamj7XHct8dGwAAlJeChlk33njjzg8bfbAsS7/4xS9yur5lWenrB4NBzZo1K6frDNQHH3yQDrL22msvfeYzn9Fhhx2m4cOHq6urS3/961/1wAMPqKOjQ88995ymT5+upUuXasSIEf1cuW/JZFLnnnuunnnmGUnSyJEjddlll+nAAw/U1q1b9fDDD+uFF17Qxx9/rNNOO03PP/+8Jk+enPffF/CEawC8f2GWAo75WlRmAUC/SqkyS5KUcIRZ3YRZAABUi7KemWUYRjrQuu2227T//vsX7b4nnXSSrrnmGn3mM59RIGDv1pw1a5Z+8IMfaObMmVq+fLlWrVqlH/zgB5ozZ05e97333nvTQdaBBx6ohQsXauTIkemvf+Mb39A111yj2bNnq62tTZdffrmef/75vO4JeCGS6TsNbYYAUFa6k/nPzPJUrEvqMTJxB5VZAABUjYLPzLIsK+OfbM7p789ee+2lCy+8UC+88IK+853vFPqvkvaTn/xECxYs0IknnugKsnYZN26cHnnkkfTzRx55RNFoNOd7plIpW9vm/fffbwuydvnpT3+qQw45RJL0wgsvaMGCBTnfE/BKbYYirHwGwOeLMAsABs5ZmeV7mBXvsj2lzRAAgOpR0J8mV61alfG4ZVmaMGFCukXw2Wef1V577ZXVNQ3DUE1NjZqbm1VbW+vZWgdiyJAhWZ138MEHa//999f777+vaDSqDz/8UAcddFBO91yyZIk2bNggSZo2bZqmTJmS8bxgMKhvfetbuvTSSyVJDz/8sGbOnJnTPQGvZAqzlEeboZmIqnb1r7XsxyEFPvy1thuGmg76ugLh+qxe797NMJbzWgCgWnixm2E0ntLdL662Hbvy6L1VHwkOfEEJe5i1gzZDAACqRkHDrHHjxvX59V0tgmPGjPFsQHqpaWraXf/e1dXVx5l9e/rpp9OPTz311D7P7fn1+fPn53xPwCveV2ZZMsxuDW00pFSXrH8cyxYD4AFg4FyVWTnkT5YsRR3XsQbw/dsmZq94pzILAIDq4Vufz3HHHZeuzPKrwqrQYrGYPvjgg/Tz/sK9vvztb39LPz788MP7PHfkyJEaM2aM1q1bp02bNqm1tVXDhw/P+d5AvjJWZhl+thnaK7OUitk2lAAAuJXcAHjaDAEAqFq+/TS5ePFiv25dNA8//LC2b98uSZoyZYpGjRqV87WWL1+efjx+/Ph+zx8/frzWrVuXfm22YdbatWu1du3anNa4ceNGrVmzRs3NzWppacnpGqhMtRHHDzxGyN/gyBlmSTt3NAxlOA4AkORNm6Gn4vbKLNoMAQCoHmW9m2Epa21t1fe///308+uvvz6v67W1taUfDxs2rN/zhw4dmvG1/ZkzZ45uuummgSzNZcaMGTrxxBMVjUYHdO9S197ebvsnshONRjVu7wmK9piHYgQiCibqXOd2WylNmDBBXWaD2jN8fRcrKUUj9lA3kGyQocyvcV7Xsgz36+MhGVadoqmgJkyYoPb2dqVSqYzXk3g/wI33BHqqtPeDaVraq9b+PXFIKKk6s7vv1wWSmjBhghqNxM5zraQmNNjbChusbtWZIfe5fQgqrgktNTJ6XCucKN3PHZX2fkB+eD/AifcEesr2/RCNRlVXV5fXOKNyZljOrQWRt3g8rhkzZui5556TJJ199tl6/PHH87pmJBJRIpGQJCUSCYVCfeeQF154oR566CFJ0kMPPaTzzz8/q/vceOONeYdZ5513Xtb3AwAAAAAAuXnjjTckSZdccknFziLPhMosj5mmqUsvvTQdZE2cOFFz5szxeVXFtWXLFi1fvlxnnnlmXq2Vpaa9vV2vvfaapkyZYhvsj75Fo1H98yXH6bqzdldmBSKD1DDpC65zN2/r0Oe/94D+eMeX1dzU0Os1rWSXOt5/0Hascf8LZYQyV2Zlum77u3Mlc3dLSv2EMxSsH6loV1ynXTlHzz33nOrre98dkfcDnHhPoKdKez+0dcU1456ltmNfmDxUzU2Nfb6uc/sWPfjDy/SlW+9TfVOzuhJJPfDqets5Xzp0tOrCIde5fYl3RzX317+UccTn0sf+3+hm3fuFgwf4NyuOSns/ID+8H+DEewI9Zft+2LBhg/70pz8VcWWlpWTCrPfff19LlizRm2++qdbWVu3YsSNdiZQtwzD0l7/8pUAr7J9lWfr617+uBx/c+UP22LFj9X//938aPHhw3tdubGzUtm3bJEnd3d1qbOz7w2PPUsOBfEO89NJLNWPGjJzWuHHjRj311FNqbm5WV1eX6uvrK3J2VlNTU0X+vQolGAyqY/NK1cd3f7sJBEeoKewuh+0wOrVy5UrVBTrVFA70ek1TnTLjq2zHGkOdCmQaNN/Ldc3U32UlOtLnNFitCocHSfGYVq5cqaampqz+v8P7AU68J9BTpbwf2tWllZ32GVldRkSRQN+b+HSYIa1cuVIdVlhGoFadRtJ1nU6jVgqEXOf2JaakVq5ZJ2Py7msN6lDJ/29dKe8HeIP3A5x4T6Cn/t4PO3bsqNoWQ6kEwqwXXnhB11xzjV5++eW8ruP3TmSWZenKK6/UvffeK0naa6+9tHDhQu29996eXL+lpSUdZm3evLnfMGvLli2212Zr7NixOZcmrl27Vm+//XZOr0Vlc+5maAR8/9YjIxiR1SMvt1Jx/xYDACWuO+GeIej/AHj7B/gd7GYIAEDV6L30oQh+/vOfa9q0aXr55ZdlWZbKdXyXZVn6xje+oXvuuUeSNHr0aC1atEgTJ0707B777bdf+vGqVav6ONN9Ts/XAn6oCTt+4CmJMMu+c6GVivm0EgAofc6dDC3LVNDnLMsZZrWzmyEAAFXDt58o582bp6uvvlrSzvZAwzDSgVZDQ4Oam5sVDvfSM1RCdgVZv/zlLyVJe+65pxYtWqR99tnH0/t8+tOf1tNPPy1JWrZsmY4//vhez924caPWrVsnSRoxYoSGDx/u6VqAgXJXZvn//23CLADIXpezMisZ97UiXpIUj9qetlOZBQBA1fAtzLrmmmskKR1iTZgwQf/yL/+iU089VaNHj/ZrWQPiDLL22GMPLVq0SPvuu6/n9zr55JP1s5/9TJI0f/58ff/73+/13Hnz5qUfn3rqqZ6vBRio0mwztIdZIswCgF5lCrN856jM6k6aSqRMhYO+Nh4AAIAi8OUnynfffVfvv/9++jd6J5xwgp588knV1vY97LPUfPOb30wHWaNGjdKiRYs0adKkgtxr2rRpGjVqlDZs2KDFixendzdwSqVSuuOOO9LPv/jFLxZkPcBA1EUcB0ogzBKVWQCQNXeYVQLfM+PuobftsaSG1Dv/owMAACqNL7+62jXs3bIshUIh/fd//3fZBVlXXXWV7r77bkm7g6xcZ1NNnz493Wo5d+7cjOcEg0HdcMMN6ecXXXSRNm3a5DrvBz/4gd544w1J0jHHHKOZM2fmtCbASzW0GQJAWetK2mdmKVEKlVlR16EdzM0CAKAq+FIe0draKmlni+GRRx5ZNm2Fu1x//fW66667JO38O3z729/W+++/r/fff7/P102ZMiXnnQIl6bLLLtPjjz+uZ555Ru+8844OPvhgXXbZZTrwwAO1detWPfzww3r++eclSc3NzfrVr36V870AL9W6BsCXQphl/809uxkCQO9Kss0w0S1DUs/tg5ibBQBAdfAlzOpZhZVPuOOXXYGRtLO67Nprr83qdffdd59mzZqV831DoZAeffRRXXDBBXryySe1YcMG/fjHP3adt9dee+mRRx7R5MmTc74X4KU6jyuzjEBYiZYjdPf/vKSrLjxadbWRAV+TyiwAyJ5XYVY4GNBxE4a4juWqMRJUe3z32gizAACoDr6EWWPGjEk/7uzs9GMJZaupqUl//vOf9cQTT+h3v/udli1bpk2bNqmpqUkTJ07UOeeco8svv1zNzc1+LxVI83oAvBGMKDnkKP1iwQv65rePUl1LUw7XIMwCgGx1JRxthjlWs0aCAU2fOMyDFe3UVGMPs2gzBACgOvgSZh111FEKhUJKpVJ67733/FhCXhYvXuz79c466yydddZZnq4DKBRnmFUKA+AJswAgeyXZZiipsSYkte9eC5VZAABUB18GwI8aNUqnnHKKLMvSihUr9Oabb/qxDABFUuvYWIoB8ABQXrqdlVklEmY11QRtz6nMAgCgOvgSZknSz372MzU2NkqSvvOd7yiVSvXzCgDlisosAChvXUlnZVZpfM9sjNjDLCqzAACoDr6FWZMmTdKcOXMUDAb17LPP6rzzzlN7e7tfywFQQHWO3QxLsTJLqZgsy8p8MgBUuVJtM2yqsf9yhMosAACqg29hliR97nOf0zPPPKMhQ4bo8ccf16RJk3TTTTdp6dKl2r59u59LA+ChGldllv9hloIR9zEzUfx1AEAZKNUwq6XWHmZt7+b7OAAA1cC3Xp9g0F4WblmWNm7cqJtvvlk333xzTtc0DEPJJL+RA0pNnWtmVn7fesxkl2rWPaCn/yWkwKr7tSMYUOOBFysQqsv6Gq7KLO1qNcwQcgFAlfNqZlZXIqW5y9bZjs06fIzqwsFeXtG3ZkeYta2LMAsAgGrgW5jVs53HMAwZhuE6DqAyOGdm5d1maJkKJLZq0ihDim+V+Y9jA0GYBQDZc1dm5TYzy7QstXbGXcdy5QyzthNmAQBQFXxtM+wZYO36A6DyOHczVND/NkMjEHQNomcIPABkVqpths4wq40wCwCAquBbZdbFF1/s160BFJFlplQTcgyAN/zfzVDaWZ1lmbtbk61UzMfvigBQurqcbYaJEg2zmJkFAEBV8O3Htvvuu8+vWwMoplSX+1gJVGZJ/wizEp3p54RZAJCZqzIrVaJhVhezUwEAqAa+thkCqHxWstt1LN8B8F5xzs2izRAAMqPNEAAAlBLCLAAFZSXdlVl5D4D3iBF0DPMqkUoDACg15RJmbe9OyDSZwQoAQKUjzAJQUFamNsMSCbNEZRYAZKU76ZiZleNuhl5zhlmmJXXEaTUEAKDSEWYBKCxnZZYRkGGUxrce2gwBIDvlUpkl0WoIAEA1KI2fKAFULCvlmJlVKlVZIswCgGy5djMskTCrMRJUwL5hLkPgAQCoAr5NYV67dm1Brjt27NiCXBdAbpwzs0pl+LtEmAUA2SrVyqyAYailLqyt0d3VWNu6SmNtAACgcHz7qXLvvfeWYRj9nzgAhmEomeS3cUApcYdZJVyZVSIzYACglCRTppLOoeol9P3SGWZRmQUAQOXzvUTCsthxBqhorjZD37/tpFGZBQD9c7UYSlKidKqfWmrtvyRhZhYAAJXP158qcw2yelZ0EYYBpc1KRm3PS6syK2J7bqXi8rZeFADKn6vFUCqZNkNpZ2VWT23dhFkAAFQ638KsH/3oRwM63zRNtbW16d1339VLL72kaDQqwzA0ePBgXXHFFQqHS+cHZAC7WcnyqswizAIAu4xhVqqUwiz7f1eozAIAoPKVTZjVU3t7u+644w79+Mc/VltbmxYvXqwnn3xSLS0t3i0QgCeslPczs4xASMlBn9bD897Qhaf/P9VGwjkNlneGWaLNEABcupLeVWaFAgEdtlez61g+mp2VWYRZAABUvPw+PfikqalJP/zhD7VgwQJFIhG99NJLOuOMM5RKZfiwBcBfjsosT8KsYI0Sw47Xjx41ZY08XvXjT3EHU9nIUJlF6zIA2DlnZgUNSWZun7lqQgGdesBI25+aUH4fR50zs7YzAB4AgIpXlmHWLtOmTdPNN98sy7L04osvavbs2X4vCYCDc2ZWKbcZSpZk8UMQAPTU7WgzrAsHfVpJZszMAgCg+pR1mCVJV111lerq6mRZlu666y6qKoASY6W8r8zySsZqLloNAcDGWZlVm2cllddcYRZthgAAVLzS+jSSg9raWh155JGSpPXr1+uFF17weUUAerKS9plZKqkwK+I+aJbOUGMAKAXOAfB14dL6+OgcAL+NMAsAgIpXWp9GcrTHHnukH3/00Uc+rgSAS9I5AL6E2gwDIclwtMtQmQUANs4wq9QqswbX2X8xQWUWAACVr7Q+jeSoq2v3D8sbNmzwcSUAnAqxm6GXXK2GVGYBgI1zN8PakpuZZf8lCWEWAACVr3RKJPLwyiuvpB83Njb6uBIATpZjN0MvBsCbyW5FPnlUD30jqMDaP6j9k6AaJn1BgVDtgK9lBGvsQ+pNKrMAoCfnzKy6PCqzuhMpPfLmJ7Zj5x28Z14BmXM3wx2xpEzTUiBg5HxNAABQ2so+zLr//vv18ccfp5+PHz/ex9UAcCnEAHgrpWD3eh21T0DqWq/UP47lwlmZZaWozAKAnlxthnnMzEpZltZs63Idy4dzALxl7Qy0nMcBAEDlKOs2w7lz5+ryyy+XYez8zVskEtH06dP9XRQAG1vVk+RJZZaXaDMEgL6V+sysTKEVrYYAAFQ2336q/N3vfjfg1ySTSbW1ten999/XggUL9PHHH8v6x2/zDMPQ5Zdfrvr6eq+XCiAPzjbDUpuZJWeYxQB4ALBxthmWWpjVEAkqGDCUMndXeBFmAQBQ2XwLs2bNmpWuqMpFzxDLsixNnjxZN910k1fLA+ARy7GboUoszDKC9l2wmJkFAHbOyqy6EhsAbxiGWmpD2hLdHWARZgEAUNl8/9WaZVlZ/+lpVxBmWZZOOukkPfPMM2pubvbjrwCgL67dDEu8zZCZWQBg050s7cosyd1q2NZNmAUAQCXz9adKZ0A1kPNHjBih6dOn69JLL9VJJ53k9dIAeMRZmVVqbYbMzAKAvrkqs8ohzOpK+rQSAABQDL6FWYsWLRrwa0KhkAYNGqThw4dr1KhRBVgVAK9Zjt0MS24AfMixmyFthgBg0+3hboZea29vlyQ1hu2jKzZsa09/Tdq5SVBNjeOXFwAAoGz59lPltGnT/Lo1gCKxzJRroHrJV2bRZggANu7KLP9nZiUTcckIaPTo0TsPnPEvMiYdk/76tTf9m6598eH08+EjR2rdmjUEWgAAVIjSKpEAUFFcVVlS6VVmudoMqcwCgJ5cuxmWQGVWKpmQLFPfvO8ZNTYP1vwP2/TWxmj664ed81XNuOZqSVK8q1O3XzhN8XicMAsAgApRWj9VAqgoViLqOmYEqcwCgHJSyjOzInUNqqlvVENtl6Td/81JKKia+kb/FgYAAAqqdD6NAKg4zuHvkmQYpRVmKRixP6cyCwBsnGFWKVRmOTl3WOxOpno5EwAAVIKSrszq6OjQtm3bJEmDBw9WYyO/YQPKiZl0V2aVfJshlVkAYONqMyyhyqxdasP2OV7djjUDAIDKUlI/Vb711lt64IEH9OKLL+q1115TLGavkKipqdGUKVM0depUfelLX9LBBx/s00oBZMNVmWUEZAT8HxzckyvMkqmaEiseAwA/uSqzSmAAvJMzYItRmQUAQEUriTDrvffe05VXXqlnn302fcyyLNd53d3deumll/TSSy/p9ttv17HHHqu7775bBx54YDGXCyBL7jCrJL7l2LjDLKmJ+cAAkOaamVWCbYY1rjZDKrMAAKhkvn8amTt3rg477DA9++yztgDLMAzXuT2PWZalZ599VocddpjmzJlTlLUCGBhnmOXV8HfDCCrVsI/mvWHKatxH4SEHyDByqxTIFGY11ua7QgCoHM5gKJ82w6Bh6IARjbY/wQyf+QbK1WZImAUAQEXztUzif/7nf/SVr3xFlmXJMAwZhpF+PGHCBE2aNEnNzc2yLEs7duzQBx98oJUrV6bPkXZWa33ta19TXV2dzj//fD//OgAcXGGWR/OyjFCt4iNP1Tf/+z2tOO80NbQ05X6tQEgygpK1u/KAMAsAdvOyMqs2HNTnD94z3yW5r+tqMzRlWpYCHgRlAACg9PgWZq1fv15f/epX08GUZVnaa6+99M///M86//zzNWzYsIyv27x5sx566CH9x3/8hz7++GMZhiHTNPXVr35Vxx13nEaPHl3kvwmA3pgJxwD4QGkOozKCNbJ6DKtvrOWHHwCQdlbCl+PMLGlnoFUXLr21AgCA/PnWZnjDDTcoGo2mK6y++MUv6r333tNVV13Va5AlScOGDdO3vvUtvf/++zrvvPPSYVh3d7duuOGGYi0fQBasZLftuVGyYVbE9ryJyiwAkCQlUpZMxxjTUpyZ5WwzlKTuBEPgAQCoVL58GkkkEvrDH/6QDrJOPfVUPfTQQ2poaMj6GvX19XrooYd0yimnyLIsWZalP/zhD0omk4VaNoAB6lntJEnyqM3Qa865WbQZAsBOzqosKb+ZWYUSDhgKOIpqmZsFAEDl8uXTyIsvvqj29vZ0VdVdd92V03V2vXZXKNbR0aEXXnjBy6UCyIN7ZlZpVmbJFWbRZggAUuYwqxQrswzDcLU/EmYBAFC5fPk0smrVKkk7P3gceuihGjduXM7XGj9+vA477LD085UrV+a9PgDeMB2VWaUaZjkrs2gzBICduhLuQKgUZ2ZJUq0jZKPNEACAyuVLz8+mTZvSj8ePH5/39caPH69ly5ZJklpbW/O+HgBvOCuzvGoztJLdimycp7suDiqw/il1toZVP/40GaHcUijaDAEgM6/bDLsTKf353Y22Y2ccODLjzKuBcq6LyiwAACqXL2FWJLJ72HJ3d3cfZ2YnFotlvDYAf1mJwrQZWlZKwc4PdeohAanjQyUkWXufrFybA11hVk0vJwJAlelK2sOscNBQ0DmcagBSlqX3NnXYjp16wIicr9cTbYYAAFQPX9oMR4zY/aHl7bffzvt6Pa8xfPjwvK8HwBuFqszymrsyi5lZACC5K7PqPKigKhTaDAEAqB6+hFmf+tSnJEmWZWnVqlV69tlnc77Ws88+q48++ij9/NOf/nTe6wPgjfKZmWWv6Gyq82khAFBiuh0zs0o6zHJUZsWozAIAoGL5EmYddNBB2muvvWQYhizL0te+9jVt3bp1wNfZunWrLr/88vTzPffcUwcddJCXSwWQh/KpzLIPyaLNEAB2clVm5TEvq9BqXDOzqMwCAKBS+faJ5Otf/7osy5JhGPrggw903HHH6Y033sj69a+//rqOO+44ffDBB5J27ox4xRVXFGi1AHLhDLPKpTKLNkMA2Mm5m2FJV2Y52gwz7cQIAAAqg29h1ve+9z2NGzdO0s4g6t1339Xhhx+uz372s/r973+vNWvWuF6zZs0a/f73v9c555yjI444Qu+99176a2PHjtX3vve9oq0fQP/KJ8xiN0MAyKSsZmYxAB4AgKrhW89PbW2tnnrqKR133HHatm2bDMNQKpXSn/70J/3pT3+SJAWDQTU1NckwDO3YsUOp1O4PVLuquizL0uDBg/Xkk0+qtpafQIFSYiXsM7NKtc1QhFkAkJE7zCrdNsNaR5thjDZDAAAqlq+fSA488ED95S9/0X777ZcOp6SdQZVlWUomk9q2bZu2bt2qZDKZPi4pHWTtt99++stf/qLJkyf7+VcBkIGZKpPKrBBhFgBk0pUso8osx9qcw+sBAEDl8P3XawcffLBef/113Xzzzdpjjz3SYdUuhmGkQ65dLMvSqFGjdNNNN+n111/XIYccUsQVA8iWley2HyjRyixnm2FNyJCVivm0GgAoHWU1M4sB8AAAVI2S+MmypqZG119/va699lotXLhQL774ol599VW1traqra1NktTS0qLhw4fr0EMP1dFHH63jjz9eoVBJLB9AL5xthiVbmeUYAC9JVrxd0rDiLwYASoizzdAZGJUS59riKUumafVyNgAAKGcllQYFg0GdeOKJOvHEE/1eCgAPuAfAl9S3nDQj6O4rtBI7fFgJAJSWshoAn2Ft3UlTpbtiAACQq9L99RqAsmclnQPgS7MyS0ZQMuzfDndWZgFAdXO2GWYKjEpFpqoxWg0BAKhMhFkACsKyTNfcqZJtMzQM19wsM0GYBQDOMKiUdzMMBQwFHXNWu5MMgQcAoBIVtOeno6NDn//85xWL7fyBNhQK6de//rX23nvvvK67atUqfe1rX1MqtfMDVkNDg/73f/9XtbVsQQaUCmeLoaSSHQAv7RwC33PNVGYBgHtHwFJuMzQMQ7XhgDrjuwO47oRZYkM1AACAFwr6n/d///d/14IFC9K7Ec6ePTvvIEuSxo8fr1NPPVVXX311+tr/8R//oeuvvz7vawPwRqYwy7PKLCOoVO1oLXt7nY789BiFQsGdrYL5cFRmWVRmAYDnM7OChqFxg+tcx7xSG3KEWcmUJO+uDwAASkPBasW3bdumn//85+mw6ayzztJ3vvMdz67/3e9+V2eeeaYsy5JlWbrtttu0YwcDm4FS4dzJUPIuzAqEahXf81xd8F8pmWM/p6YDL1IglF9lprPN0Ipvz+t6AFAJvA6zasNBXXzYGNsfL+dw1YTs13JWlgEAgMpQsDDr4YcfVldXlyzLUigU0m233eb5PX72s58pFArJMAx1dnbqf/7nfzy/B4DcmGXWZhgI2SsFrO5tPq0EAEqHO8wq3ZlZklTrWB8D4AEAqEwF+0TywAMPSNo5v+Ciiy7SPvvs4/k99t13X1100UWyLEuSdP/993t+DwC5ce1kKENGoIRnrTjCLDO21aeVAEDpcO5mWMozsyT3joYMgAcAoDIVJMzq7u7WK6+8kn5+3nnnFeI2kqQvfvGLkiTLsrRs2TLF4/GC3QtA9qxkt/1Aie5kuIszzLJiVGYBgNdthoVW52gzjFGZBQBARSpImPXaa68pmUxKkpqamjR9+vRC3EaSNH36dDU1NUmSEomEXnvttYLdC0D2XJVZRum2GEoZKrO6qcwCgHJrM6xxthkyMwsAgIpUkE8kK1eulLSzxXDfffdVKFS4H2JDoZAmTZqUfv7RRx8V7F4AsufazbDsKrMIswDA2WZYGyrtyizn+mgzBACgMhUkZdq2bXd7zh577FGIW9j0vMfWrfwACpQC1wB4D4e/W6mYwpsX6aZzAzI2LlJ0W1h1Y09w7Ug4EEao3vbcpM0QADyvzIolTf1lRavt2Gf2Ha6akDe/X3XPzKLNEACASlSQMGvHjh3px4MGDSrELWx63qO9vb3g9wPQPyvhCLM8bDO0zKRCO/6mL/9TUGp7S3FJtXsdl1eY5d7NkGAcAJxhUL4zs5KmqVc+3m47Nm3iUNV41Czg2s2QNkMAACpSQdoMa2p2/0C5efPmQtzCpuc9wuHSbmUCqoW7zbC8ZmZZiXZZqYRPqwEA/1mWVYa7GdJmCABANShImDV06ND04w0bNhTiFjY97zFs2LCC3w9A/1wD4Et9Zla4znWMIfAAqlksQxBU+mGWszKLNkMAACpRQcKsCRMmSNr5G7133nmnoHOstm7dqrffftt1bwD+cs3MKvXdDIPuMCvVvcWHlQBAaXDOy5JKfzfDWkfYljAtpUzLp9UAAIBCKcgnkilTpigQCMgwDJmmqfnz5xfiNpKk+fPnyzR3/uYwEAhoypQpBbsXgOw5K7OMUq/MCgQlx8wtKrMAVDNni6FUfpVZEq2GAABUooKEWU1NTTryyCNlWZYsy9Itt9ySDpy8ZJqmbr31VkmSYRg64ogj1NTU5Pl9AAycley2HyjxyizJPQQ+1UVlFoDqlbkyq7TDrEzrI8wCAKDyFKxW/IILLkg/fv/99zV79mzP7/Hzn/9c7777bvr5hRde6Pk9AOTGSjhnZpV+mOUcAk9lFoBqlinMylT5VEqCAUPhgGE71p2kzRAAgEpTsE8kl1xyiYYNGybDMGRZlq699lo98MADnl3/wQcf1Pe//30Zxs4PLEOHDtUll1zi2fUB5MdKldduhpI7zGJmFoBq5mwzrA0F0p+7SplzbhaVWQAAVJ6ChVn19fX693//d1mWlZ6dNWvWLH3nO99RV1dX/xfoRVdXl7773e9q1qxZ6TZGwzB06623qq7OPcC5kFKplN5++23NnTtXV111laZOnar6+noZhiHDMDRr1izP7zl9+vT09bP5s3r1as/XAGTDdFZmGaU9M0vKUJlFmyGAKuaszHKGRKXKOaSeMAsAgMpT0FKJSy+9VPPnz9ejjz6aDrTuvPNOPfroo7rssss0a9YsjR07NqtrrVu3Tvfdd59+85vfaP369ekQS5I++9nP6itf+Uoh/yoZfeELX9Bjjz1W9PsC5cA1M6ssKrPqbc9TtBkCqGJdSXuYVeo7Ge5SG3JUZqVoMwQAoNIU/KfL+++/X5s3b9aSJUvSLYfr16/XTTfdpJtuuknjxo3TYYcdpv3220/Nzc1qbm6WYRjavn272tratHz5cr366qvpCiPL2vmBZNe1jjvuON1///2F/mtklErZP+QNGTJEQ4cO1YoVK4py/8cff7zfc0aMGFGElQBuzt0MyyHMcg6AN2kzBFDFnJVZpT78fRcqswAAqHwF/+mytrZWCxYs0NVXX63/+q//SldT7QqlVq9erTVr1vR5jV3nSrtDLMuydMUVV+j2229XTU1N4f4CfTjiiCN0wAEH6NBDD9Whhx6q8ePHa+7cuUWb3XX22WcX5T5ALqyko524DNsMqcwCUM26HTOz6kLlEWYxMwsAgMpXlFKJSCSiO++8UzNmzNDVV1+tlStXSpJtiGjPwKqnXbOfdp1jWZbGjx+v2bNn+x7mXHfddb7eHyhlZhlWZjEzCwB2c1dmlUubIZVZAABUuqJ+KjnrrLO0YsUK/e///q+mTZumYDCYDqh6s+vrwWBQxx13nH7/+99rxYoVvgdZAPrmnJlllGOYRWUWgCrm3M2wfNoMnZVZzMwCAKDSFP2nS8MwdO655+rcc89VV1eXli5dqqVLl+qTTz7R1q1btW3bNknS4MGDNXjwYO2555466qijdNRRR6m+vr6fqwMoFQVtMzQCMsND9OG6Ldp37FAFgwHJyD+bN8LOAfBbbJtNAEA1KcTMrIBhaHhDxHXMS1RmAQBQ+Xwtlairq9Pxxx+v448/3s9llK3TTz9dr7/+ulpbW9XQ0KA999xTRx99tC644AL+N4XvrETh2gwDoTrFxnxJJ39ztlbM+7IGtzR5cl1nZZbMpKx4u4yaQZ5cHwDKSbQAbYZ14aCuOHrvvK/T3z16IswCAKDylH7fD3r11FNPpR+3tbWpra1N7777rn7zm9/ohBNO0AMPPKA99thjQNdcu3at1q5dm9N6Nm7cqDVr1qi5uVktLS05XQOVw12ZVfrfbpy7GUo7q7MChFkAqpAzzGqIlP73cYnKLAAAqkF5fCqBzeDBg3XiiSfqsMMO0+jRoxUMBrV+/XotXLhQ8+fPl2maWrhwoaZOnaqlS5dq1KhRWV97zpw5uummm/Ja34wZM3TiiScqGo2qra0tr2uVkvb2dts/0TvLMtUZ3EPq8cvxgDFURsIdFvXUbaU0YcIEdZkNavfjXKtW0ZqJkrX7B59tWzYoYg12ncr7AU68J9BTJbwfAvGoJjTsnjc1siaptrY2RaNRTZgwQfWKK2J293EFyQwkNWHCBDUaCdUV6dxRkaRt3aGAqcSECWpvb1cqlXJeqigq4f0A7/B+gBPvCfSU7fshGo2qrq5OXV1dfZ5XqQyrr+nrGLC5c+fqkksukSRdfPHFmjt3rqfXf+mll3TooYcqEolk/Pprr72mc889V6tXr5YknXLKKZo3b17W17/xxhvzDrPOO+88nX/++XldAwAAAAAA9O2NN96QJF1yySUaO3asv4spIiqzyszUqVP7/PqUKVO0YMECHXTQQYrFYpo/f76WLVumww8/vEgrlLZs2aLly5frzDPPHFBVWKlrb2/Xa6+9pilTpqipyZsZTZXKjLXp74/OtB0LjPucGppa+nzd5m0d+vz3HtAf7/iympsafDm3/YM/SPG29PPBR9+k+r1Pdp/H+wEOvCfQUyW8H3447z0tWN6afj7riDH65jHjFY1Gdeyxx2rWfzygSG3fm/N0bt+iB394mb50632qb2ouyrldiaQeeHW97Zj1hxv03DPzfNtMqBLeD/AO7wc48Z5AT9m+HzZs2KA//elPRVxZaSHMqkCTJk3SRRddpHvvvVeS9OSTT2YdZl166aWaMWNGTvfduHGjnnrqKTU3N6urq0v19fUVOTurqampIv9eXkq2t2t7fJXtWCAUU1O47xLYDqNTK1euVF2gU039DBou1Lkps1WKf5x+3qAtau7j3zfvBzjxnkBP5fx+WB8LamXn7p0GjZoGtbS0KBgMauXKlYoqolSgts9rdJghrVy5Uh1WWEaRzk2FLdu6JcnasEVNTU2+/5BYzu8HeI/3A5x4T6Cn/t4PO3bsqNoWQ4kwq2Idf/zx6TDrvffey/p1Y8eOzbk0ce3atXr77bdzei0qi+ncyVDydDdDKxVXaOtSfXtmQMbmperqiKh2j6kygpnbbwfEMQTe7NqS/zUBoAx1xu3zpeojwV7OzF48ZerF1Vttx47ee4giwfx3StwlGDAUDhpKpHpM0qht9Oz6AADAf4RZFWrYsGHpx5U0hB3lwbmTYTJlqcbI/4eg9PXNhMJtL+vbJwelLX9VTFLNyMM8CbOMYJ16DhI0u7f2ei4AVLJo3PvdDBMpU8+utH9fPXxMi6dhliTVhYJKpJK7D9QQZgEAUEm8/eSAktHaunvGBaWqKDZnmNWd8GkhuQg62lW6Nvu0EADwl6syK+zdLyUKrdbZUk5lFgAAFYUwq0ItWrQo/Xi//fbzcSWoRuUdZtnbDFNUZgGoUtGEszKrfMKsOmfwVtv35h8AAKC8EGZVoOXLl+v+++9PPz/99NN9XA2qkZW0z8wqqzDLOTOrm5lZAKqTs83Qi5lZxVIbcnzEpc0QAICKQphVIqZPny7DMGQYhubOnZvxnDvuuEMvvvhin9d5/fXXdfLJJysWi0mSTjrpJB155JFeLxfok+mozOqK+7SQHBiONkOzi8osANWpM560PS/vyiy2ugcAoJIwAD4Pq1at0m9/+1vbsbfeeiv9+PXXX9f1119v+/qUKVN0zjnn5HS/hQsX6tvf/rYmTpyoGTNm6FOf+pSGDh2qYDCoTz75RH/5y180b948maYpSRo3bpzuu+++nO4F5KOy2gypzAJQnZxthmU1MyvkDLOozAIAoJIQZuVhzZo1+slPftLr19966y1buCVJF198cc5h1i4fffSRPvrooz7PmTlzpubMmaM999wzr3sBubASzjDL6uXMEuQIs6xEh6xU3JOdEgGgXCRSphIp+/duL3YzLBb3AHhmZgEAUEnK51MJNHv2bJ1xxhlaunSp3nzzTbW2tmrz5s2KxWJqbm7W3nvvralTp+qCCy7QUUcd5fdyUcWsVBlXZoVqXYdS3VsUatjDh8UAgD+c87Kk8pqZ5WozZGYWAAAVhTArD9OnT5dleVNxsnjx4n7PmThxoiZOnKivfOUrntwTKBQzYR8AX04zs5yVWZJkdm2RCLMAVJHODGFWOc3Mcg2Ap80QAICKwgB4AJ5zzsyKlVFllmEEtKPLHlKnuhkCD6C6OOdlSWU2M4vKLAAAKhphFgDPOcOssqrMkrSt0/7cZAg8gCrj3MkwYEg1zmqnElbnmplFmAUAQCUpn08lAMqGezfDMhoAL6mt01GZ1UVlFoDq4pyZVR8JyjAMn1YzcHWO3QyNcI26k+5qMwAAUJ4IswB4zko6ZmaVUZuhJG2zL5/KLABVxzkzq5x2MpQy7GYoqa0rmeFMAABQjgizAHjOLOOZWZLU5mozpDILQHVxzswqp3lZklQbcq+3rZswCwCASlFev2YDUBashLPN0Os7GLICtdq6o0tDmusVMAxJ3rW/bIs62wypzAJQXdyVWd6EWYYMVzBmePj9e5dgwFAkaCie2v39nMosAAAqB2EWAM8VegB8IFyv7r2/psPPmq0V876mwS1Nnl7fXZlFmAWgurhmZnlUmVUfCeqa6RM9uVZ/asNBxVO7A6w273+zAgAAfEKbIQDPOWdmld8AePvzFG2GAKqMczdDryqziqnOsfsilVkAAFQOwiwAnnPtZuhxZVahOdsMTdoMAVQZ18ysMgyzah3VZMzMAgCgchBmAfCccwB8uXV2UJkFoNqV+26GklTnCLO2ldvWugAAoFeEWQA855qZVWY/P7gqs7q3yrLKq1USAPJRqJlZxVRLmyEAABWLMAuA51xthuUWZjkqs2SlZMa2+7IWAPCDszKLNkMAAFBKyq9mHEDJsxKOAfAez8yyzISC29/Ul48JyNj2pmLdtYqMOFhGIOzJ9Z1thtLOHQ2DtS2eXB8ASl1XgWZmJVKm3vhkh+3YIXsOUjjo/e9XXQPgCbMAAKgYhFkAPGVZpqxUt+2Y17sZWqm4IluW6KbPBaVNi9UlKTz0AM/CrGhcUiAimbtTOLN7q6TibCcPAH4r1G6G8ZSp+e9vsh07cGRjQcIsV2VWufW8AwCAXtFmCMBTVrLbdawcf34I1A62PU+xoyGAKlIJM7OozAIAoHJRmQXAU1Yy6jpWbjOzJMmoGSJFN6afm92EWQAqWywWUzy+syJ1R5e9PzxoJdXe3i5J6X+WOndlFmEWAACVgsosAJ4y4x2uY53uYq2SZ9QMsT1PdW/1aSUAUHixWExjxo3ToEGDNGjQIL3w8iu2r1/9rW+kvzZ69GhJUiqVynSpklEXtn/M3VaOZcIAACAjKrMAeMpKuH9jX44/PzjbDKnMAlDJ4vG4Wjdu1PceXKJIXYPufW2TtvaoZDr72zdp/2G3SZI6trbqrq+eUvJhVm3IXpkVS1nqSqRUV4YtkwAAwI7KLACeMuOOrQBDDbK8nf9eFK7KrC4qswBUvkhdg2rqG5VyfN+uq6tTTX2jauobFa6r92dxA5QptNoWLcPfrgAAABfCLACeMh2VWUa40aeV5CdQQ2UWgOoVT5m255EC7DZYaLUh95ppNQQAoDKU3ycTACXNSthnZhnhBp9Wkh+jlplZAKpXwlGaFQ4aPq0kd4GA4QrhtjkG2wMAgPJEmAXAU2a8QiuzuqjMAlAdLMtS0nSGWeX5kdE1BJ42QwAAKkJ5fjIBULKshH1mlhEq18qsobbnVGYBqBYJ0z3osFzDLOcQ+K2EWQAAVITy/GQCoGSZcWebYblWZtnbDM2uzT6tBACKK+GYlyVJkTJsM5SkWmdlFjOzAACoCIRZADzlHgBfnpVZAcfMLCsZlZns8mk1AFA8znlZkhQOlOdHRueOhoRZAABUhvL8ZAKgZFlxR5thmVZmOdsMJeZmAagOzp0MpfIcAC9JdSEqswAAqESEWQA8VSmVWUakWTLs3yJTtBoCqALOyqxQwJBhlGeYVeuozGqLspshAACVgDALgKesCpmZZRgBV6uh2U1lFoDK55yZVa5VWRIzswAAqFQhvxcAoLKYCUeYVYDdDAPhBnVN+JYmnzVbK+Zdo6EtTZ7fQ5KCtUNtg99TtBkCqALuMMu73302REK64cRJnl2vP3UhZmYBAFCJqMwC4ClXmFWmbYaSFKgbZntOZRaAapAw7W2G4UDlVGZtjRJmAQBQCQizAHjKijtnZpVnm6EkBevsQ+CZmQWgGjgrsyIeVmYVWy2VWQAAVKTy/XQCoCSZCeduhhVUmUWbIYAqEHcOgC/jMKvOMQB+WzQhy7J6ORsAAJSL8v10AqAkVVRllmMAPJVZAKqBuzKrfNsM6xxthvGUqa5EyqfVAAAArxBmAfCUewB8GYdZjsqsVPdWn1YCAMWTcFRmeTkAvticbYYSrYYAAFQCdjME4BnLMmW52gy9D7MsM6lAxwqdcrAhta9QPFWr8OBJMgLefksL1DrbDKnMAlD5EqZzN0PvKrOSpqkPWu3/nZg0vEGhQGECM+cAeGlnq+Ho5rqC3A8AABQHYRYAzziDLKkwM7OsVEw1m+brv2aFpE/mKSpp0JTveh5muQbAs5shgCrgbDMMexg0xZKm/vDW323Hrp42QaFIYcKsgGEoEjRsc8CozAIAoPyVb904gJLjbDGUyntmlnsAPJVZACqfu82wfGdmSVJtyP5xd2uUMAsAgHJHmAXAM87h71J572borMyykl0yE1GfVgMAxRF3VmaV8cwsSaoN2cM4KrMAACh/5f3pBEBJMZ1thoGwjGCNP4vxQKB2qOsY1VkAKp2zMitS9mGWff3buuI+rQQAAHilvD+dACgppqMyK1DGLYaSFKhplgz7TljMzQJQ6VwzsyqszXAbbYYAAJQ9wiwAnrEcM7OMSHmHWYYRUMDRamh2EWYB+P/Zu+/4qKr0f+CfOz2TnpAECBCagCDSREVWCYiigKLAWsAFYcWyYFnb2n7quuqufr/4Xcta1gI2sCuKFFGKgqIoRUDpoZNGSDJp0+75/REyzL2Tnpm5c2c+79drXsy9c+beZyYnw80z5zwnurllVc2sEK00GC5xqmRWcSVHZhEREemdvq9OiCiiRNvILAAwqqYaejnNkIiiXLSNzLKblZe7RRVMZhEREekdk1lEFDRCVTMrKpJZqhUNOc2QiKJdtNXMilclswoqnBpFQkRERMGi76sTIooo6pFZkiVRo0iCJ3CaIUdmEVF0U69maNL7yCyLsvZhIZNZREREumfSOgAiih6yqmaWwRyvUSRt53DUJua8xiTF/pryfDgcDlRVVQEAXC5OVyGi6BL1I7McTGYRERHpnb6vTogooogoGJnldHlgkIDs7GwkJSXh/16Yp3j8w3dfRVJSEs4//3wAwPhxY+F08g8jIooOQoior5lVVuOB0+PVKBoiIiIKBo7MIqKgkaOgZpbb44UsgE0f3Ya05ATIxRsg8lf7Hr/s/K64YtrVqPLasbMGOF5yAi6XC1arVbugiYiCxCsAodqn99UM483GgH2FFS50TonTIBoiIiIKBn1fnRBRRAlYzdCiv2RWnQS7BYnxVsTZldMMDcKJxHgr7HEWjSIjIgod9RRDQP8js2wmCUJWjsRi3SwiIiJ9YzKLiIJGqGpmSTocmaUmme2KbeGu0igSIqLQc8uBySy918ySJAmoKlXsK2TdLCIiIl3T99UJEUWUgGmGOqyZpSaZlNNQhKcKQgT+sUdEFA3qH5kVBZeLVWWKzYIKLt5BRESkZ6yZRURBE1AAPkQjsySTHdU5N+D8P72EHxf+BanJCZBM9qaf2KpzqWqqCC8gu0NyLiIirXlUI7MMEmA0BG+aod1sxF0jugfsCzn1yCxOMyQiItI1JrOIKGhk1TRDgzk+oJBwMEiSBBjtKKkEYLLDYI4PwVlqGepJkglPFWBIDtk5iYi04pbVKxkGd1SWJEmIt2hw+akemcVphkRERLoWBePGiShSyC5VMisKphnCaAUk5Uel7KnWKBgiotBSTzM0B3FUlqZUI7OKODKLiIhI15jMIqKgicoC8JIUMIWRReCJKFqpC8BHRb0soJ6aWUxmERER6VmUXKEQUSSQVTWzomJkFuovAk9EFI3UI7MsxigZmVVZqtgsZAF4IiIiXWMyi4iCQnhdAYXRpRDWsgondd0swWmGRBSl1COzTFEzMqtUscmaWURERPrGAvBEFBTqUVnAyZFZIagAL2QvDNWHcU4PCag6DLchDqaEzpAMoVkRSz0yS+bILCKKUupkliXIySyvLHCoVPmFQOeUuKCumFgvdc2sShdkWcAQLTXBiIiIYgyTWUQUFOqVDAHAYE4AQjCTQ3hrYD32CRbOMQGHPkYlgKTBf4VkCM1IMMmsrpnFkVlEFJ1CXQC+xuPFW78cVuy7a0T30K9wqKqZ5ZUFSqpdaBdvDe15iYiIKCSiZOw4EWlN1DMyK1qmGbJmFhHFCk+MFIAHgEIH62YRERHpVZRcoRCR1mR3pWJbMtlDNu0v3AJWM2Qyi4iilEs9MitaCsDLHiTblKO/uKIhERGRfjGZRURBoR6ZFS0rGQKAQTXNUOY0QyKKUuqRWcGumaWlzHizYruQySwiIiLdip4rFCLSlLpmlmRO0CiS4AscmcVkFhFFJ3XNLFO0jMwCkBFvUWwXVnCaIRERkV4xmUVEQaFOZhmipF4W0EDNLBGCZRqJiDQW6tUMtdRONTKrwMGRWURERHoVPVcoRKQp4VKNzIqiaYbqZBaEFxBubYIhIgohdTIr2KsZaikzYGQWk1lERER6xWQWEQVF4Mis6JlmqK6ZBQCyp0aDSIiIQks9zTBqVjNEfdMMmcwiIiLSq+i5QiEiTQUWgI+eZBYMFkBSrswovPwjiIiij1uWFdtRlcxKUBeAZ80sIiIivYqeKxQi0pTsrlRsR1UBeEmqpwg8k1lEFH08XnXNrOiZZqgemcWaWURERPrFZBYRBYUcMDIrempmAYBkVtXN8nKaIRFFH5esXs0wei4VWTOLiIgoekTPFQoRaUqoamZJUbSaIQAYVEXgZU+1RpEQEYWOJ2A1w2gamaWcZljp8qLS6dEoGiIiImoLJrOIKChk1WqGUTcySz3NkCOziCgKBRSAN0TPpWJGgiVgH+tmERER6VP0XKEQkabUI7OiaTVDoL5kFqenEFGUkQxQ5bJgjqKRWYkWI6wm5aVvAacaEhER6RKTWUQUFOqaWVI0rWaIempmeTgyi4iijNkauCuKamZJkoSsBOVrZN0sIiIifTJpHQARRQc5jCOzJFMcajpNxYRb52PJSzOQkhgPSVXTKtgMXM2QiKKdKTCZZQlyMivOZMTNw3IC9oVLZoIFB0tP1TxkMouIiEifmMwioqAQLnUB+BAmsyQDhCUdu/MBWNNhtIe+Ppd6mqHMmllEFG3MtsBdQZ5maDBIyEwITJqFS1ai8twFDiaziIiI9Ch6xo4TkaYCRmZFXQF45cgv1swioqhTzzRDkyF6amYBQEbANEMWgCciItIjJrOIqM2EkCHclYp9BnO8RtGEhmTmaoZEFOVUI7PMBgmSFF3JLPXILE4zJCIi0icms4iozYS7CoByCSwpykZmGdQ1uYSsTSBERKGiqpkVTcXf62QmWBTbhZxmSEREpEvRd5VCRGGnnmIIhLYAvBbUNbOIiKKOWZ3Miq5RWQAC6nUVcGQWERGRLrEAPBG1mXA5AvaFcmSWEDIk13Gc1h6A8zi8VTUwxKVDkkKYnzeYAckECE/ozkFEpCVVMivYKxkCgCwLFFcp61S1s1tgCFNtrizWzCIiIooKHJnVSl6vF9u2bcP8+fNx6623YtiwYbDb7ZCk2voS119/fUjPv2jRIkyePBk5OTmw2WzIzMzEsGHD8PTTT6OsrCyk5yZSCxiZJRkhGUO3WpXwVMN2+F0s/5sZxv3vwLH1FQhPddNPbANJkiCZ45puSESkV+qaWSEYmVXt8eLlHw4obtUeb9DP0xD1yKzjVS54vJw2TkREpDccmdVKV111FT755JOwn9fhcGDKlClYvHixYn9RURGKioqwfv16PP/883j//fdx3nnnhT0+ik2yK3Alw2grGgzUTjWsbxQaEVFUUNXMMhmi7ztPdQF4IYDiShfaJ9kaeAYRERFFoui7SgkTr1f5LWJaWhpOO+20kJ7T4/Fg0qRJvkRWVlYWHnroISxYsAAvvPAChg8fDgA4fPgwxo0bh+3bt4c0HqI6QjUyS4qyell1AorAExFFk4BphtH3pUS63Qz1dy2sm0VERKQ/HJnVSmeffTZOP/10DBkyBEOGDEG3bt0wf/58zJgxI2TnfPXVV7FixQoAQN++fbFy5UpkZWX5Hp89ezbuvvtuzJ07F6Wlpbjpppuwdu3akMVDVEdWjVYyWKIzmSWZ47UOgYgodAIKwEfXd54OR+3/VelxZhRXuX379xeWonviqddqsVhgtYZuqjwRERG1HZNZrfTAAw+E9XxerxePPfaYb/vtt99WJLLqPPXUU/jmm2+wefNmrFu3DsuXL8eYMWPCGSrFIOGuVGxH7cgsc+iK2hMRaS6gZlZ0JLM8bhcgGZCdnV27Y/rzkNrl+B6/Ysr1wO+rfdsZWVk4dOAAE1pEREQRjMksnVizZg3y8/MBACNGjMDgwYPrbWc0GnHbbbdh5syZAICFCxcymUUhFzAyK0qTWVKUjjgjIgIQUDMrFAXgteD1uAEhY868FUhITsV724pxoOzUKoaj/vIozs6u/Xx3VVfimakj4HK5mMwiIiKKYExm6cSyZct898eOHdtoW//Hly5dGrKYiOqoVzOM1mmG0ZqkIyICELUjs+pY4uJhtScgweYA/JJZThhhtfPznYiISE+i6yolim3dutV3f+jQoY22zcrKQufOnQEAhYWFKCoqCmlsROoV/qJ1miFHZhFRVFOPzDJEx8gstQSrUbFd6fI20JKIiIgiFUdm6cTOnTt997t169Zk+27duuHQoUO+52ZkZDTrPAcPHsTBgwdbFWNBQQEOHDiA5ORkpKSktOoYpE+yqmaWwRKdtaXqq5ll4acoEUWLgNUMo/M7T7uFySwiIiK9459hOlFaWuq7365duybbp6en1/vcprzxxhv4+9//3pLQAowePRoXXXQRqqqqWnTuSFe3ClLdv7HO5XLB4/EAACorBVyWU0lW2ZMG19GjJx+rRPfu3VHltQNuS6PHrBFedO/eHdVyPBzuuAbbCQ9QZVEmdQ2eeEio/znNPW6TbQ2ZvvPWmGsLCZ/eIxsOhwNeL/8YinX8jCB/eusPVVVV6J6ZDCle+PZ1sHoQJ9cEtJUNHnTv3h0JkrvexxttKzzo7ncOAIgXNYiTTW07bgva5sQJRQxpBpfvGEa40L1796B/ruutP1BosT+QGvsE+Wtuf6iqqkJcXByqq6vDEVbEkYQQoulm1Bzz58/HjBkzAADTp0/H/Pnzg3Zsi8UCt7t2GWm32w2TqfE85NSpU7FgwQIAwIIFC3Dttdc26zyPPvpom5NZV199dbPPR0RERERERESts3nzZgDAjBkz0KVLF22DCSOOzKKgO378OHbu3InLL78c7du31zqcoHE4HNi4cSMGDx6MxMTonEbXXFVVVTj//PPx4TPXwW6zwHt0BVB9zPe4lD4EhpR+AIDjpVWY9sB7+Oy5PyE5Mb7R4xafqMAf73ynybbCU42KHe8q9iX0mQrJVP+oq+YetzltK3d9ANlVjhpzNg5n3o5vPn4G9/1rIex2e6PHpejHzwjyp7f+UFVVhfOfWgwp5dT/26NOS0eP9MBagZVlx/Hug7Nw3T/nwZ6Y3Ohx1W2r3R6888sRRZvrhmQjzmxq03Fb0rawogaLthX4HjdIEmae3RmSJMFVU4X5d1+H7777Lqif63rrDxRa7A+kxj5B/prbH/Lz8/H555+HMbLIwmSWTiQkJODEiRMAgJqaGiQkNF6I2n+oYUs+EGfOnInRo0e3KsaCggJ8+eWXSE5ORnV1Nex2e1TWzkpMTIzK19USRqMR+/btQ7t4DxLjjXAcOgCv69QfJ3G202FNqp2eITud2LdvH+IMlUg0N15/pUKqbFZbGZWQXXmKfQmmShjMbTtus9pKJ+B1naorZ6w+isTERF54kA8/I8ifXvqD0WjEvioDJPOpou9DYUO1wRbQtkI2Yd++fagQZkj1PN5Y20rJg32VysLylZINMJjadNyWtPWajQExlMoW2MxGOOHBvn37Qva5rpf+QOHB/kBq7BPkr6n+UF5eHrNTDAEms3QjJSXFl8wqLi5uMpl1/PhxxXObq0uXLq0emnjw4EFs27atVc8lfRNel2JbMjReG0vPDJYE+FdRSWt8oBcRkX6Y1asZRmcB+ARVAXgAcDg9sJkD9xMREVFkis6rlCjUu3dv3/28vLxGWga28X8uUSgEJLOMUZzMUq1omNp4XpmISD/MyhFOFqPUQEN9MxkNsJmUl8AVXNGQiIhIV5jM0on+/fv77m/YsKHRtgUFBTh06BAAIDMzExkZGSGNjQiyMpmFEI/Mkow2ODtMxLUveODtPAnxp18Hydj4NJOgnduiTGalc2QWEUUBWQjApPzsNhuDf5loMxkxbUgnxc1mCv+IqESrcnKCw+kJewxERETUekxm6cQll1ziu7906dJG2y5ZssR3f+zYsSGLiahOuEdmSQYj5LhO+HGvAOydYE7qCskQnj+GDGblUKzUhOgcuUBEsaXC5YUkKS8LrabgXyYaDRK6ptkVN6Mh/J+jCapkVgWTWURERLrCZJZOjBgxwrcy4OrVq7Fx48Z623m9Xjz33HO+7WuuuSYs8VHsErIXEMrpGdE8zVCyKJNZaZxmSERRoLwmMJkTimRWpEi0Kr8A4cgsIiIifYneqxQdyc3NhSRJkCQJ8+fPr7eN0WjEww8/7NueNm0aCgsLA9rdd9992Lx5MwBg+PDhGDNmTChCJvIR6imGiPIC8KqaWfFWCcJTpVE0RETBUV5PMieak1mBI7NYM4uIiEhPuJphK+Xl5eH1119X7Pv111999zdt2oSHHnpI8fjgwYMxceLEVp9z1qxZ+PTTT7FixQps374dAwYMwKxZs9C3b1+UlJRg4cKFWLt2LQAgOTkZr7zySqvPRdRs3nqSWVE8MstgCRyKJVcVAKlZGkRDRBQc5TXKZI7FKMEgRe80atbMIiIi0jcms1rpwIEDeOKJJxp8/Ndff1UktwBg+vTpbUpmmUwmfPzxx5gyZQoWL16M/Px8/OMf/who16lTJ7z//vvo169fq89F1FzqelkAgChOZklGK2AwK/bJVQUaRUNEFBzqkVlWDYqyh5M6mcWaWURERPrCZJbOJCYm4osvvsCiRYvw1ltvYcOGDSgsLERiYiJ69OiBiRMn4qabbkJycrLWoVKMCJhmaDAFFBEO+jmFALxVSIsH4KmC7DZAMtkhhWkUgcGSCMintuWq/LCcl4goVNQ1s2whmmIohECVWzkKzG42hu3zu456mqHD6an9v4WIiIh0gcmsVsrNzQ3aRc/q1atb/JwJEyZgwoQJQTk/UVsErGQYhnpZwlOFuAOv4efHzcDeV1EOIGnwXyGZ40N+bgCQzAmA89Q2R2YRkd6VBYzMCk0yq8rtxdw1+xT77hrRHfGW8F6SJlqUI8/csoDTIyN6J1YSERFFl+it7ElEYRGQzIriKYZ11HWz5KpjGkVCRBQc6ppZ0Vz8HQgcmQUAFS4WgSciItKL6L5SIaLQU08zjIVklmpFQ7maI7OISN/UNbNsUV4zy2w0BEylZBF4IiIi/WAyi4jaRItphlqTAkZmsWYWEembOpET7SOzABaBJyIi0rPov1IhopCKyWmGZiaziCi6lDtja5ohUH8ReCIiItKH6L9SIaKQErJTsR0TI7PU0wxZAJ6IdE69mmEsJLMSrcqplExmERER6Uf0X6kQUWh53YrNmBiZpZpmKFxlkD3VGkVDRNR2gTWzov8SUT0yq8LJAvBERER6Ef1XKkQUUsKrHJkViwXgAcBbyRUNiUi/AkdmRXcBeCCwZhZHZhEREekHk1lE1CbqZJZktGkUSRgZLYBB+UeQt4LJLCLSrzLVqKRYGJnFAvBERET6Ff1XKkQUUsJTo9iWTNGfzJIkCQaTXbGPI7OISM9isWZWgiVwZJYQQqNoiIiIqCWi/0qFiEJKeFXJLKNVo0jCS1IlszxMZhGRTgkhAqbYxUIyS10A3i0LuLxMZhEREelB9F+pEFFIBY7MitMokvDiyCwiihaVLi/UOZxYmGaoLgAPABUuFoEnIiLSg+i/UiGikAqsmRUjI7PMTGYRUXQoq3EH7IuFAvBmoyEgaVfhkjWKhoiIiFqCySwiajUhBISnWrEvFmpmAfVNM8zXKBIiorYpqw4sfB4L0wyBwNFZHJlFRESkD7FxpUJEoSG7ASjnpsTEaoYADOqRWRVHNYqEiKht1COzzAYJRoOkUTThFbCioZsjs4iIiPQgsFgAEVFzyc6AXeEYmSUZrXBmXoo7n/4Czz1wORLttvBPbzTFAX5//3k5MouIdKosjCsZWk0GTD6zQ9jO1xR1EXiOzCIiItIHJrOIqPVUKxkC4amZJRlMkBNOw9ItAkg8DZaUxJCfU81gjlcks2TnCcieahhipAA+EUUP9cgsmzl09bJMBgP6ZoX/M7sh6mmGlayZRUREpAucZkhEracq/g6DBZIUGx8r9SWtWASeiPQonCOzIk3ANEOOzCIiItKF2LlaIaLgU69kGCPF3wEABkvALk41JCI9KqtWjsyKpWRWgoXJLCIiIj2KnasVIgo6ISunGcZUMksKLI7MIvBEpEfqkVm2GEpmBY7M4jRDIiIiPYidqxUiCj71yKwYWcmwIR6OzCIiHVLXzIqpkVmqAvBuWQAW1j4kIiKKdLFztUJEwacqAB/rySxvJUdmEZH+lAfUzApdAfhIox6ZBQCITwt/IERERNQiXM2QiFovoGZW6FcyBADZXYm4fc9h3/+ZgZ3PohRA0uC/1q4wqCHWzCIiPQpYzTCEI7MqXR7MXbNPse+uEd0Rb9HmktRsNMBqMsDp8ZtemMBkFhERUaTjyCwiaj1OM1TwcDVDItKhsurYXc0QqGd0VnyqNoEQERFRs8XW1QoRBVVMF4Cvh5fJLCLSoXJn+EZmRaKAZBZHZhEREUW82LpaIaLg4sgsBW8Fk1lEpD/q1QxjqWYWEFgEnjWziIiIIh+TWUTUegE1s2I7mSU7T0B2V2odBhFRi5RVx+5qhgBHZhEREelRbF2tEFFwcTXDAJ7yA1qHQETUbEKIgJFZsTbNMEFdfJ4js4iIiCJebF2tEFFwydqsZhhJSquEYttTflCjSIiIWq7a7YVHVn6OxfzILBaAJyIiinixdbVCREEjSWDNLADF5apkloPJLCLSD/WoLCD2klkBNbM4zZCIiCjixdbVChEFTbwFAJSJnFismVVQptz2lO/XJA4iotYoq3EH7LPFWAF49cgsyRIHhzMwyUdERESRg8ksImqVpLjAfbE4MqvQodzmNEMi0pOyamXSxmQAjAZJo2i0ETDNEEC+w6VBJERERNRcTGYRUavUn8yKvZpZReXKbRaAJyI9UY/Mshpj79LQbDQETK3Mr3A20JqIiIgiQexdsRBRUCTGKb+5l4w2SFJsfZsPAAWsmUVEOqaumWU1xt7nOBA4Oosjs4iIiCIbk1lE1CrqkVmxuJIhABSVKZNZ3spjEB5+o09E+hAwMivGir/XUReBZzKLiIgossXmFQsRtVmyXbkdi/WyAKCwPHAfR2cRkV6oa2ZZODILAHDMwS8liIiIIhmTWUTUKkn1TDOMRVUuQLIkK/YxmUVEesGRWbXUyawj5UxmERERRbLA5VuIiJohcJph+JJZktECV/oIPPHKSjzylwsRH2eDZLSE7fxqhoTO8JaU+bZZBJ6I9CKwZlZok1kWowGX9skM2Ke1FJtZsX2wtEajSIiIiKg5tL96ICJdCkhmhXElQ8lghjd5AN5eJ0OkDoC1/VmQDOamnxgixoTOim0ms4hIL8oDRmaFdpqh2WjA0M4pips5ApJZyTbl97uHypjMIiIiimTaXz0QkS4l2VTTDMM4MivSGNTJLE4zJCKdCPfIrEiVEqf8QqSo0o0ql6eB1kRERKS12LxiIaI2SwwYmRW7ySxjYhfFtpsjs4hIJ8qqwzsyK1IlxwWO7j1wolqDSIiIiKg5mMwiolbRsmZWpDEkdFJse8o5MouI9IEjs2pZjAbYzUbFPiaziIiIIldsXrEQUZsl27maYR11zSxvxWEIr7uB1kREkSNwNcPYHJkFAClxyrpZ+0uqNIqEiIiImsJkFhG1CkdmnWJIUE4zhJDhqTiiTTBERC3AkVmnJKtWNNzPkVlEREQRy9R0EyKiQFquZii7q2Db/19s+IcJhj3/RZkkIfHMm2Ew28MWgz/JmgrJHA/hrvTt8zgOwpzcVZN4iIiaQwgRODLLGNqRWVUuL178fr9i31/O6wq7xVj/E8JIXTfr4AmOzCIiIopUsfv1GxG1mhAyElS5q/COzBKQ5BqkJ0iQvNUQnioAIoznV5IkCaakHMU+T/l+bYIhImqmGo8Mt1f52Wk1hfbSUECgyu1V3ISGn9/+UmyqaYYcmUVERBSxmMwiohYTLgcMBtbM8mdKVCezWASeiCJbuWqKIRD6kVmRLEU1Mos1s4iIiCIXk1lE1GLCVRawL5ZrZgGAKUlZN8tTfkCjSIiImkc9xRAI/cisSKaumZXvcKLG7dUoGiIiImpM7F6xEFGrBSazJMBg0SSWSBEwzdDBkVlEFNnKqpUjs4THBZMhlkdmBZaSPVjKqYZERESRiMksImoxWZXMkkw2SFLs/gEEAKZEjswiIn0JGJnljO1pdVaTETaT8v8yTjUkIiKKTExmEVGLCWepYjucKxlGKlNSV8W2x3EIQsjaBENE1AyByazK+hvGkGQri8ATERHpAZNZRNRi6mmGsV78HQismQXZDW/lMW2CISJqhjJ1AXgXRyElW42K7QMn+J4QERFFosDiAERETRCucsV2rBd/dzgcEAnxgNEKeJ2+/eX5v8OcleTbtlgssFo5io2IIgNHZgVKsqmSWSUcmUVERBSJODKLiFosoGZWDI/MMkhAdnY2kpNTsO9YjeKxGVdfjKSkJN+ta05nOJ3OBo5ERBRe6gLwcDFxox6ZtZ8js4iIiCISR2YRUYsJp7oAfOyONpIFsOmj25CWnABv3gdA5anC78/eNRLPZ54LAKiocqLfhLlwuVwcnUVEEYEjswIFTjNkgo+IiCgScWQWEbVYYM2sOI0iiQwJdgsS462w2NMU+82oQGK8FYnxViTYmcAiosgSUDMrxlczBIBkm/J73qPlNXB6vBpFQ0RERA1hMouIWiwgmRXDI7P8GazJim1ZNYKNiCiSlKtHZrk4MitJNTJLCOBQaU0DrYmIiEgrTGYRUYupkzSxXDPLH5NZRKQnHJkVyGYyQNRUKPZxRUMiIqLIw2QWEbVY4MgsJrOA+pNZQgiNoiEialxZtbpmFpM2AIDyQsXmfq5oSEREFHFYAJ6IWiywZlZ4k1mSwQx3ytl48b0fcOvU8xBns0AymMMaQ30MFmUyC8ID4amEZE7QJiAiokYEjswK/TRDs9GAC7qnBeyLKOWFQGZ33yZXNCQiIoo8TGYRUYtpXTNLMlrgSTsXzy5fhzm3n4u4lMSwnr8hkiURkAyAkH37ZGcZDExmEVEECljN0BX6pI3FaEBuj3YhP0+bqEZmHeSKhkRERBEnwr4KI6JIJ2QPhFtZT4Q1s2pJkgEGS5JiH+tmEVGk0mJkli6UqacZcmQWERFRpGEyi4haRHaVB+xjzaxT1FMNmcwiokjk9Hjh9MiqnUzaAAismcVphkRERBGHySwiahHZWRqwjyOzTgksAl+qTSBERI0oq/YE7uTIrFqqZNaRshq4vXIDjYmIiEgLTGYRUYsEjDSSDEAEFF+PFAZbqmJbrinRKBIiooYF1MsCwlIzSxdUySxZAIdLazQKhoiIiOrDZBYRtYhcc0KxLRltkCRJo2gij8GqSmY5TzTQkohIO+p6WWaDBHhcGkUTYWoqkGgxKnZxqiEREVFk4WqGRNQi6mlzkjG8KxkCgOyphvXQO1j2NxMMeW+j3GhAQt/pMJjiwh6LWsDILGcZhOzVKBoiovqpR2Yl2UwoDsN5q91ezN9wSLHv+qGdEWc2NvAMbXRJsWF74alplywCT0REFFmYzCKiFlFPM9Sk+LuQYXCXoFd7CXCVQD65LxKoR2YBArKrDEC8FuEQEdWrXDUyK8lqDEsySxYCRZWugH2RprMqmXXgRLWG0RAREZEapxkSUYsEjsxi8Xd/kikOUI1WU0/NJCLSmnqaYZKN32/665Ks/L/tAKcZEhERRRQms4ioRQKSWVqMzIpgkiTBGFA3q1SbYIiIGhAwzdDKZJa/LinK/9v2l3BkFhERUSRhMouIWiRgmiFHZgUw2NIU2xyZRUSRpqxaOTIrmSOzFDqnKEfYcmQWERFRZGEyi4haxMuRWU0y2FIU215niTaBEBE14ES1sm5VkjWyCrBrTT0y61BZDTzeyKjNSERERExmEVELRcJqhpFOXQSeI7OIKNIUViiTWe3iLRpFEplyVMksryxwuKxGo2iIiIhIjcksImoR1sxqmsEWWDNLROBqXUQUuwornIrtTCazFNLizAFF8XcUVmgUDREREakxmUVELcLVDJumLgAP2Q14KutvTESkgQKHMpmVEW/WKJLIJEkSTs9MUOz7vdChUTRERESkxmQWEbVIQAF4jswKIFmSAElVf8ZVqkksRET1UY/MykjgyCy107MSFdu/FXBkFhERUaRgMouIWoQjs5omSRIM1hTFPsFkFhFFCK8sUFyprJnFaYaB1COzdhRwZBYREVGkYDKLiJpNeN0QbuV0OY7Mqp+6bhZHZhFRpDhe6YKsKuPHaYaB+tYzMov1D4mIiCIDk1lE1GyyqyxgH1czrJ/BlqbcwWQWEUWIAtUUQwBox2RWgNOzlCOzTlS7A1aBJCIiIm0wmUVEzSbXlATs4zTD+qmLwHOaIRFFCnXx93S7GSYDLwnVclLtiDMr35ffONWQiIgoIvDKJUgWLVqEyZMnIycnBzabDZmZmRg2bBiefvpplJUFjmZprdzcXEiS1Ozb/v37g3ZuIk9lgXKHwQzJyG/z68NphkQUqdTF37MSOcJWzeFwoKqyAj3T7Yr9mw4Ww+Fw+G5OZ+AoNyIiIgo9k9YB6J3D4cCUKVOwePFixf6ioiIUFRVh/fr1eP755/H+++/jvPPO0yhKouDwVh5V7jAl1N8wxCSDCZ6k/li4ZDOmjh8Em8UMyRBZH2cG1cgseKuRyEFsRBQB1NMMMxPCl8wyGQw4q1NywL5I4XG7AMmA7Ozs2h1j74R0eq7v8bue+DfuWvmKbzsjKwuHDhyA1cqEIBERUThF1l9/OuPxeDBp0iSsWLECAJCVlYVZs2ahb9++KCkpwcKFC7Fu3TocPnwY48aNw9q1a9GvX7+gnf/TTz9tsk1mZmbQzkfkrcxX7jBrlMwyWuFuNxKPfLwRU/48EvaUxKafFGYGa3LAvi7tNAiEiEhFXfcpnCOzrCYDxp6eFbbztZTX4waEjDnzViAhORXfH3Lgu4OnphZ2zb0S18yZBQBwVVfimakj4HK5mMwiIiIKMyaz2uDVV1/1JbL69u2LlStXIivr1AXa7Nmzcffdd2Pu3LkoLS3FTTfdhLVr1wbt/FdccUXQjkXUHN7KY4ptSaORWXogGUyQLEkQrnLfvi7pkoYRERHVUtfMygjjyCy9sMTFw2pPQPsUAfgls45Xe2G18/8+IiIirUXOuG6d8Xq9eOyxx3zbb7/9tiKRVeepp57CwIEDAQDr1q3D8uXLwxUiUdB5KtTTDOO1CUQnjKq6WTntmMwiIu0VqWtmJVg0iiTytYtXvjcVLi+q3V6NoiEiIqI6TGa10po1a5CfXzvlasSIERg8eHC97YxGI2677Tbf9sKFC8MSH1EoRMo0Q71Q183iyCwiigTqkVnhrJmlN2l2Cwyqj+7iSlf9jYmIiChsmMxqpWXLlvnujx07ttG2/o8vXbo0ZDERhVqkFIDXC4MtTbGdw5pZRBQBtKyZpTdGg4Q0u3J0FpNZRERE2mMyq5W2bt3quz906NBG22ZlZaFz584AgMLCQhQVFQUlhvHjxyM7OxsWiwWpqano168fZs2ahVWrVgXl+ERqHnXNLI7MapR6ZBanGRKR1oQQmq5mqEfqqYZFFUxmERERaY0F4Ftp586dvvvdunVrsn23bt1w6NAh33MzMjLaHMOXX37pu19aWorS0lL89ttveO211zBq1Ci888476NChQ4uOefDgQRw8eLBV8RQUFODAgQNITk5GSkpKq45BkUt2VUC4HMqdGo3Mkj01sBz9GAtmG2E4+BEcR42I73UVDCabJvE0xKCqmdU+GRBeJ4DIW32RiGKDw+mB0yMr9mUlWAGEpw5UjduL97coR/lePaAjbGZjWM7fGhnxFuzw2y6udDbYloiIiMKDyaxWKi0t9d1v167puUPp6en1Prc1UlNTcdFFF+Gss85CdnY2jEYjjhw5gpUrV2Lp0qWQZRkrV67EsGHDsH79erRv377Zx37jjTfw97//vU3xjR49GhdddBGqqqra/FojicPhUPwbazzlB1FlUSZuDYZMSO64Rp9XI7zo3r07quV4OILUVngAp2zBmX17oNoLwAsItw2SqP85oYihylM7kiE7O7vhtsbsgPestGA3ZH70RqVY/4wgpUjtDwdPVKF7vFDss3mr4XA40b17d9jhgkWuafQYssGD7t27I0FyI66lbWUPjK4q5fnlasTJprYdN4RteyUBR/zeM7uoQZxcAyNc6N69OxwOB7zexpOBkdofSBvsD6TGPkH+mtsfqqqqEBcXh+rq6nCEFXEkIYRouhmpWSwWuN1uAIDb7YbJ1Pgfp1OnTsWCBQsAAAsWLMC1117bqvP+8MMPGDJkCCyW+lce2rhxIyZNmoT9+/cDAC699FIsWbKk2cd/9NFH25zMuvrqq1v9+oiIiIiIiIioeTZv3gwAmDFjBrp06aJtMGHE4QE6M2zYsEYfHzx4MJYvX44zzzwTTqcTS5cuxYYNG5qs6xVMx48fx86dO3H55Ze3aFRYpHM4HNi4cSMGDx6MxMTYmyZWdeArnFj3/3zbR0oEugydDntc40u6F5+owB/vfAefPfcnJCfGB6Wt8FSjYse7in0JfaZCMtU/kioUMVR5rNjpyMHcuXPxn7+NarBt1d7P4a0u9G3H9Z+DtP5/ajQG0qdY/4wgpUjtDyv3FOPeL37zbXdMtuHzmWejqqoK559/Pq7/33dgsdkbPUZl2XG8++AsXPfPebAnJreobbXbg3d+OaJoc92QbMSZTW06bijbemQZ8346pGh3Rf/2SDZ6Mf/u6/Ddd9/Bbm/8PYvU/kDaYH8gNfYJ8tfc/pCfn4/PP/88jJFFFiazWikhIQEnTpwAANTU1CAhofHaQf5D/0L9AdWrVy9MmzYNr776KgBg8eLFzU5mzZw5E6NHj27VeQsKCvDll18iOTkZ1dXVsNvtUVk7KzExMSpfV1OkfUfhdOX5touPyehjrEKiufGpFRVSJfbt24c4QyUSzY2vOdHctjIqIfvFAgAJpkoYzOGLoc6RI0cabWswVcDtF6utemdM9p9YEqufEVS/SOsPBa5S7Ks8tRhFZrva/6uNRiP27duHKljgNTRef7BCNmHfvn2oEGZILWxbKXkU5weASskGGExtOm5I2xqAE7IFJ6rdvnb7KiT0TrZg3759SExMbPa1XaT1B9IW+wOpsU+Qv6b6Q3l5ecxOMQSYzGq1lJQUXzKruLi4yWTW8ePHFc8NtZEjR/qSWb///nuzn9elS5dWD008ePAgtm3b1qrnUuTzVuYrtgvLNQpEZ9RF4L3leQ20JCIKvcKAlQwbH11LtdrFK5NZRRUu9E6OrEVHiIiIYknTww6oXr179/bdz8tr+o9T/zb+zw0V/6L00VSEnbTjqVCuPlVYznJ7zWG0pim2ZQeTWUSknQKHOpll1SgSfWkXr0z6cUVDIiIibTGZ1Ur9+/f33d+wYUOjbQsKCnDoUG2thczMTGRkZIQ0NgAoKiry3edQVQoGb+UxxXZhmUaB6EzAyCzHQQi58amZREShUlThUmxnJTKZ1RwZAcksVwMtiYiIKByYzGqlSy65xHd/6dKljbb1X01w7NixIYvJ36pVq3z3wzESjKKfOplVwJFZzaJOZkF2wcOphkSkkYKAaYZMZjVHO9V0zJJqNzwy/x8kIiLSCpNZrTRixAjfSn2rV6/Gxo0b623n9Xrx3HPP+bavueaakMe2c+dOvP32277t8ePHh/ycFP086pFZrJnVLJIpHpJJucqVq+hXjaIholinrpmVxWRWs7SzK5NZQgAnqj0aRUNERERMZrWS0WjEww8/7NueNm0aCgsLA9rdd9992Lx5MwBg+PDhGDNmTL3Hy83NhSRJkCQJ8+fPr7fNc889h++//77RuDZt2oRLLrkETmftxerFF1+Mc845pxmviKhhsqsCwuVQ7Css4zfSzSFJEoz2LMU+V9EWjaIholgXWDOLBeCbw2Y2ItGqXDfpOJNZREREmuFqhm0wa9YsfPrpp1ixYgW2b9+OAQMGYNasWejbty9KSkqwcOFCrF27FgCQnJyMV155pU3nW7lyJW6//Xb06NEDo0ePxhlnnIH09HQYjUYcPXoU33zzDZYsWQJZlgEAOTk5mDdvXptfJ5F6iiHAkVktYbRnKaYWuoo5MouIws/p8aKsRpmAYc2s5msXb4HDeer9K65iMouIiEgrTGa1gclkwscff4wpU6Zg8eLFyM/Pxz/+8Y+Adp06dcL777+Pfv36BeW8e/fuxd69exttM2bMGLzxxhvo2LFjUM5JsU09xVAyJ6DKdUKjaPTHGK8cmeXkNEMi0kBhRWDRctbMar6MeAvySqp828VVbg2jISIiim1MZrVRYmIivvjiCyxatAhvvfUWNmzYgMLCQiQmJqJHjx6YOHEibrrpJiQnJ7f5XHPnzsVll12G9evXY8uWLSgqKkJxcTGcTieSk5PRtWtXDBs2DFOmTMG5554bhFdHVMtbcVSxbbC3B8BkVnOppxl6HYfgrSmB0ZamUUREFIvU9bJMBgmpcWaNotEfdeKvoJLJLCIiIq0wmRUkEyZMwIQJE1r9/NWrVzfZpkePHujRowf+/Oc/t/o8RK3hrcpXbNcms37XJhgdMtjSAckICK9vn6voV8R1ztUuKCKKOep6WRkJFhgMkkbR6E+HJGUyq7TGC1jjNYqGiIgotrEAPBE1yVOhnGZYm8zSjiQZ4Y3viSWbZYiEnjCnnQ5JMmoaU2MkgxGwtlPsYxF4Igo39TRDLVYyNEoSTs9MUNyMkj4SapkJVhjVyb+sntoEQ0REFOM4MouImqQuAK95MstkgytrLOa8+Tt2Xz0O8SmJmsbTHJItA6KmwLftYt0sIgqzwJUMw5/MspmN+OMAfdbzNBokZCVYcbS85tTO9kxmERERaYEjs4ioSZGWzNKluEzFJkdmEVG4qWtmcSXDluuommrIkVlERETa4MgsImqSt1JVAD4uq4GW1BDJlgnht+0q+R3C64JktGgWExFFP6fTCZerdnrhkRMViseSLYDD4QBw6l9qXMckG4CyUzuYzCIiItIER2YRUZM8lfUVgKcWsWUot2U3XCUsok9EoeN0OtE5JwdJSUlISkrC+4u/Ujz+/L/+4XssOzsbAOD1eus7FJ3UIcmm2JaSs3C8ytVAayIiIgoVjswiokbJLgeES/mNvSGeyayWkow2HDou0Dn9VPFgV9GvsGYM0DAqIopmLpcLRQUFuPPdNbDExeONTYUoqvL4Hh93w1/R/4EHAQAVJUV44YZLmcxqQka8BSaDBI98aqztpqMV6JqVrmFUREREsYcjs4ioUep6WQCnGbbW70eFYptF4IkoHCxx8bDaE1DlUX4GpSTU7rfaE2COs2sUnb4YDBLaq2qNbTrKKZpEREThxpFZRNQo9RRDyZIEyRyvUTS1hKcGloIleGG6EYYjX6KyyAx7t3GQTLamn6yh348IXNz/1LarmMksIgoPIQSqXMpRV/EWY9jjqHF78cVvBYp9l/XNgs0c/lhaq2OSDYfLTq1ouPkYk1lEREThxmQWETXKW6Es/m6K76BRJKcI4YWxcg/GDjQAFXvgBiC6XgKpyWdqK3Bk1hYIISBJkR45EeldldsLodqnRTLLKwR+L1QWoh97emYDrSNTB9WKhps5MouIiCjsOM2QiBqlnmZojIBkll79dkT5p6RcUwJvxRGNoiGiWFLpCqyFFW/hd5qtoS4Cf7jciQKHU6NoiIiIYhOTWUTUKPU0Q2MCk1mtdbgEkMyJin2uoi0aRUNEsUSdzLKZDDAaOCq0NdrFW2BWvXe/HC7VJhgiIqIYxWQWETXKWxl50wz1zJh2hmLbyWQWEYVBpcuj2E6wclRWaxkkCe1Vo7N+PlSmUTRERESxicksImqUVz0yi8msNjGlK5NZLAJPROGgHpll11HB9UjUUVU3iyOziIiIwovJLCJqlHpkFpNZbWNSjcxyFTGZRUShp05mJViZzGoLdd2sXw5zZBYREVE4MZlFRI3yVCgLwJsSOmoUSXRQj8zylO6F7OJKWEQUWpVO5TRDLVYyjCYdVcmso+U1OFpWo1E0REREsYfJLCJqkOxyQLiVS6gb49trFE10MKb0AST/PyIFXMXbNIuHiGKDemQWVzJsm3S7GRYWgSciItIMk1lE1CBv5bGAfZxm2DaSyQZzWm/FPk41JKJQqwhIZnFkVltIkoSsBLNi38+cakhERBQ2TGYRUYPUUwwNlmQYzPEaRRM9LO3OVGw7C37WKBIiihVVLvU0Q47Maqv2qmTWRo7MIiIiChsms4ioQeqRWZxiGBzW9mcrtmsOrYYQQptgiCjqCSE4MisE1Mmsnw+X8bOciIgoTJjMIqIGBSSzWPw9KOI65yq2PY4D8JTnaRMMEUU9l1fAIyuTLExmtV37BItiu8DhxGEWgSciIgoLJrOIqEGegJFZrJcVDOb0fjDEZSj21RxarUksRBT9TtR4AvYlWjnNsK1SbUaIGuUiKevySjSKhoiIKLYwmUVEDfJWHFFsmzjNsM0cDgcqKipgav8H5f59K+BwOHw3p9OpUYREFG1KqpXJrJQ4M8xGXgK2lSRJwNHfFfu+YzKLiIgoLHglQ0QNcpfuUWybknI0ikRFMsJry8b6PTJEXDaMiV0AKbKnzDhdHhgkIDs7G0lJSbjrXx8qHj+8+SMkJSX5bl1zOjOhRURBcVyVzEq3mxtoGXpGSUJOapziZpQkzeJps8PbFZvf7TuuUSBERESxhWPMiaheQshwl+xU7DOn9dEoGiWDyQZXx0mYcstc7F4yGYkpiVqH1CS3xwtZAJs+ug1pyQkQzhOQd7/mezwjScL+L26EZGuHiion+k2YC5fLBavVqmHURBQNSqqUyax28ZYGWoaezWzE9LM6a3b+oDvym2JzW74DJVUupNm1e4+JiIhiAUdmEVG9vI7DEJ4qxT5zam+NookeCXYLEuOtSEzNgmRJUjxmdR9FYrwVCXYmsIgoeEpq1COzmGgJmvw9sJmUl9NrOdWQiIgo5JjMIqJ6uU4oR2VJliQWgA8iSZJgTuqq2Ocp369JLEQUzSSUVHsVe7QcmRV1ZA+GdlJ+MfHdPiaziIiIQo3JLCKql3qKoSW1d22xWwoaU3I3xban/ACEkDWKhoiiUmI7eGSh2JXOZFZQndclWbHNullEREShx5pZROTjdDrhcrkAAFUFvyofTOwOh8MBAL5/qW3UBfWFtwbeynxAStcoIiKKOmmdFJtWkwEJlsheMENvzstJAXDAt73xSBkqnB4kWHmZTUREFCocmUVEAGoTWV1zOvtW01v1xRuKxx//9wLfY9nZ2QAAr+yt71DUTAZLEgw2ZeKKUw2JKKjSshWb6XYzR9kG2dBOSTAZTr2nHllg/YETGkZEREQU/fiVEREBAFwuF/ILirB90V1IsFvh3fEi4Kn0Pf63v0zEfff1AgAUFDtw9rXPw+vVJpklvE6Yi1fh75MMkApWoeqEGXFdRkEy6q9wuimpK1w1p6akeMr3A8lDtAuIiKKLamSW1vWynB4Z3+wuUuy78LQMWE36/X413mLEkE7J+PFgqW/fd3klGN0rQ7ugiIiIopx+rxyIKCQS7FbEW4UikQUA8Skdalfhi7ciXuOVsITsgal8K/70ByMMpb/CVfgLhOxp+okRyJTcVbHtcRyC4Ig3IgqWVPXILG0/vz2yjJ8PlyluHln/tQL/0E05ypZ1s4iIiEKLySwiCiDXqC7CJQMM1lRtgolypkRl3SzIbqD6mDbBEFH0UU0z1HpkVrS6oHuaYnv9gRNwefSfpCMiIopUTGYRUQBvdbFi22BNhWRgweBQMJjtMNqzFPtE5YEGWhMRNZ/D6YGU2E6xjysZBp/D4cCADAv8K5HVeGR8u+soHA4HHA4HqqqqNIuPiIgoGjGZRUQB5GrlyCxDHFfXCyVTUjfFtqg4qFEkRBRN9hyvVmxLANLizNoEE4U8bhcgGZCdnY2crHTIRfsVj190/e2+hVPOP/98APCtGExERERtwwLwRBTAW6McmWW0tWugJQWDKbkrnPnrT+2oPoo4Dp4gojbaXawcDZQSZ4bJyO8xg8XrcQNCxpx5K5CQnIoVe0uxMf/Ue95jwk344/0PAAAszjIADng8+qzvSEREFGl4RUNEAdQjs4wcmRVSpsTOgOT3cSxkDDtNavgJRETNsPu4MpnFKYahYYmLh9WegG4ZyYr9RxxumE8+ZrLGaRQdERFRdGIyi4gUhOyFXFOi2GeI48isUJKMVhgTOin2jerLZBYRtc0u1cisdDunGIZSlxRlwsrpkVFY4dQoGiIioujGZBYRKblKAQjFLoONI7NCzZxymmI793QDhBANtCYiatoe1cgsrmQYWok2E1JVNckOnqhuoDURERG1BZNZRKTkUk4xlMwJMJhsGgUTO8wpPRXbHVMleE/8plE0RKR3siwCCsCn25nMCrWcVOXorP1MZhEREYUEk1lEpCCcyimGrJcVHoa4djBYlfVWXIe+0igaItK7g6XVqPHIin0cmRV66mTW3uOV8HjlBloTERFRazGZRURKqmSWgSsZhoUkSTCpphq6Dq3QKBoi0rsdhRWKbavJgHiLUaNoYkfPdvHwr3jo9grsK6lqsD0RERG1DpNZRKQgnFzJUCvqqYaewg3wqlaWJCJqjp1FymRWut0CSeLCEqEWbzGhs6oQvPpnQURERG3HZBYRKQWMzGIyK1xMSTmAwXRqh5BRfYBTDYmo5dQjszjFMHx6Z8QrtncVVXJBDyIioiBjMouIfDKTAMguxT5jHKcZhotkMMOU1E2xrypvqUbREJGe7VIls9LjzQ20pGDrnZmg2K50eVFU5dEoGiIioujEZBYR+fTIVE1BMZghWZK0CaYxkgGyOQ278gWEJQ2GuHaAFB0fZ+qphtUHvoKQvRpFQ0R6tUM1ta1dhKxkaJAkZMRbFDdDlE1/TLNbkJmgfL8Pljk1ioaIiCg6mZpuQkSxomeW8g8Koy09ImusGExxcHa+DpfMmYvdS/6E1JRErUMKGnNKT/gv5C7XlMCZ/yNsHc/TLCYi0pfyGjeOlSuTJ+kRMs0wzmzELed11TqMkOuVkYDCilPT9g+WuYBOGgZEREQUZaJjKAMRBUX3LOW2gcXfw85gTQasyqmdVXlLNIqGiPRoV1GlYlsCkGbnNMNw6pOhnGrocHKELRERUTAxmUVEPupphqyXpQ0psbtiuzpvmUaREJEeqYu/J9uMMBl4yRdOHZKsSLRyAgQREVGo8MqGiHy6q5JZXMlQG1JiD8W2q/hXeByHNYqGiPRGncxKj2NSJdwkSQpY1ZCIiIiCh8ksIgIAyC4HOqZyZFZEsHdEaaVyGfeq/RydRUTNszbvuGKbySxtqFc1BICiSlc9LYmIiKilmMwiIgCA5/gW1R4JBluaJrHEOkky4LudymRW5c4PNIqGiPSkvMaN7/efUOzrkmzVKJrY1jXVDqtJeam97kCpNsEQERFFGSaziAgA4D60QrFttGdBMkTmt/nC64KpZD1uH2OAVLwe1YfXQHij69vu5VtlxXbN4dVwl+7VKBoi0otVe47DI59KhguPG52TI2MlQwBweWWs3lusuLm8ctNP1CGjQcJp7ZRTDdfuL9UmGCIioijDZBYRAQBcB5crtk2pPTWKpGlCdsNc+hNuv8QIw/Ef4TzyHYTs1jqsoPp6q4BkTVXsc2yfp1E0RKQXy3cWKncc+Q0WY+Rc7rm9Mr7dV6K4uaM0mQUAvVWrGm484kBZdXT9f0VERKSFyLm6ISLNuEv3wFu2W7HPnNJLo2gIAFxewNrzasW+iu1vQXj5RxARNeyrXUXKHQc2aRMIAQB6trPD4FeO0iMLfPjrUe0CIiIiihJMZhERqvYtUWxL5ngY4ztoFA3VsfX+k2LbW5WPqv1LNYqGiCLdnuJK7Dtepdy5n8ksLVlNRvRMV041fP3HgxpFQ0REFD2YzCIiVOUpk1nmlJ6QJKmB1hQuptQ+sHY4V7HPse0NjaIhokinnmKYGW8GivZrEwz5DMpOVmz/eLAUW4+VaxQNERFRdGAyiyjGyc5y1Bz5VrHPxCmGEcHhcMDcc4piX/X+ZSjN3wGHw+G7OZ1OjSIkokjy1U7lFMNRPdIAiPobU9ic1i4ecapVDV/j6CwiIqI2YTKLKMZVH/gKkD2ndkhGmJO7aRcQwenywCAB2dnZyDnvFjhq/P4YFTL+37R+SEpK8t265nRmQosoxrk8MlbtLVbsu7BHmkbRkD+DQcJp6TbFvnd+OYwat1ejiIiIiPTPpHUARKQt9RRDxHeGZIycZdxjkdvjhSyATR/dhrTkBMhHvoI4scX3+J0TUnH3PTdCkiRUVDnRb8JcuFwuWK1WDaMmIi39cKAEFU5lcmRkj9QGWlO49UyzAaj0bZ+oduPTbfm4dlC2dkERERHpGEdmEcUwIXtRtX+ZYp+U2EOjaEgtwW5BYrwV8R2HKB9wlyPOcwSJ8VYk2JnAIiJguWqK4eDsZGTE84uJSJFoNQbsYyF4IiKi1mMyiyiGOfN/glytnJbCZFbkMcZ3gNGepdjnKtqsTTBEFJHU9bIu7p2hUSTUXCv3FGNvcWXTDYmIiCgAk1lEMawq70vF9s6jApIluYHWpBVJkmDJGKTY5z6xE96aExpFRESRpNDhxMYjZYp9Y5jMikiJNuUIrTc2cHQWERFRazCZRRTD1PWyVv4maxQJNcXc7gxA8itzKOTa4v1EFPNW7FaOykqwGjEsh8XfI9GY09IV2/M3HILHy/97iYiIWorJLKIY5S4/AHfxNsW+b7ZzCfdIZTDZYM0arNjnKd0N4dirUUREFCnUUwxH9mgHi4mXeJFoXO92iu1j5U4s2VGoUTRERET6xSsdohhVrRqVJVnTsPkAk1mRzJZ9ASRTvGKffGwlLFyXlihmybLAV7tYL0sv2tsEzspOVOz7f0t/R9GJUjgcDt/N6XRqFCEREZE+MJlFFIOEEKj4fYFin6XzaMjMZUU0yWRDXJdRyp2uUtyQy49yoljidDp9SY8PN+5HgUOZ+Bjeye57nCKD1+MGAFxyySXYMO9JxWNb8yuQedWDSEpK8t065+QwoUVERNQIfp9PFIOcR7+HM/9HxT5LzlgA72oTUItJEAYbSsqrkZZsh0GSAEhaBxUW5nZnwli4Cd6Kw759sy8ywOs4BCT21TAyIgoHp9OJzjk5KCooqN1xzVOQsk/3PS6K9mNQ18sVz/F6veEMsVESJNjNxoB90U72egAAVz/6Itz2dMzfXITj1R7f49KASzBu8jU4I9MOV3Ulnpk6Ai6XC1arVauQiYiIIhqTWUQxqOyXuYptY2IXWLpcolE0LWcw21HT9UYMnTAXu5fciNSUxKafFCUkSUJc10tQse11ALVD6eIsEip/+n9IueJjbYMjopBzuVwoKijAne+uQaHbhHe3Hlc8Pv68gTjjip8BABUlRXjhhksjKplltxhxd24PrcPQjNkaB0tCIq4aaMFrPx2E23tqSPTyvWXolJ6MlLj4Ro5AREREAKcZEsUcV8nvqNq3WLEvefDtkAxmjSKiljLFt4clU1kM3rX/C1TtX65RREQUbpa4ePx0rEaxL8lmwsAuGbDaE2C1J8AcZ9coOmpKRoIV40/PUuzzyAIf/XoUTg9XNyQiImoKk1lEMabsl38rtg3WVCT2m6FNMNRqts65kEzKP1SLv7oBnsp8jSIionAqqnRjd3GlYt+5XVJhNET/lL1o0b9DEs7qlKzYd7zKjWV7SrUJiIiISEeYzCKKIZ7KY6jYoayLlXjmjTBYEjSKiFrLYIqDrfNIxT5vVQGKlv4JQo6cKUVEFBo/HalQbMeZDRicndxAa4pUF/fOQMckZV2sHcdrgAGXahQRERGRPjCZRRTl/Fe9Kv7p/wCv69SDBguMPadz1SudsmQMBBK6K/bVHF6D0vWPaRMQEYVHYjv8Vlyt2DW0cwosJl7W6Y3JYMDkMzvCpv7Z5f4Zv+bz/2UiIqKG8KqHKIo5nU50zemMpKQkdMhIQsH6/1M8vnBdDVLan4akpCRkZ2cDALwc1aMbkiTB0Gksjp4Qiv2lP/0LVfu/0igqIgq5IVdA9vu1NxkknN05Vbt4qE1S4sy44oz2in2SyYIZH/2GCqengWcRERHFNiaziKKYy+VCfkERti+6C9vmX4ykOGUtlanTZuHAigdwYMUD+GnhrQAiawn3hgjZDWPZFvxpuAHSiS1w5v8MIbu1DksTkikOt77pBST/xWkFipZfD4/jsGZxEVFolFS5gTMvVuwblJ0Mu8WoUUQt4/bK2HCoVHFze1nwvFdGAs7tokxI7jlejb98/CuEEA08i4iIKHYxmUUUA+JtRqDkF8U+U2ovJKV1QGK8FYnxVsTbLRpF13LC64Ll+Br8fbIRhsLVqD6wDMJ/+mSM2XRAIH7oI4p9cnUxCpdMgeyu0igqIgqFp9bsh2S2+bYlCRiWo59RWS6vjKU7ChU3F5NZAIALT2sXUD/rnY1H8ObP/GKCiIhIjcksohgg8ldDuMoV+2wdhmkUDYWCO+c6WLooCwY7j63H0U+vQPmJIl9dNKfTqVGERNRWn2/Px8s/HVHs65eViJQ4s0YRUTAZDRIm9e8Ai1E5inrOJ1ux7Vh5A88iIiKKTUxmEUW5i/tLECWbFPuMCZ1gSuysUUQUTE6XBwYJ6NSpE/pe9zkOHldOR3EfXY0P7+2A9NQkJCUloWtOZya0iHTo4IkqzHhvs2Kf0SDhgu7p2gREIZFqt+DSnimKfVVuL0a9/AN+PHBCm6CIiIgiEJNZRFHM6ziEp65R1VExmGDvNk6bgCjo3B4vZAFs+ug2bP38AXQ9ZwZgtCnajOpnwM7/9sX2z+5AfkERXK7YnZJJpEdur4wp727EiWplbcBLemegXbx+pohT8/RpFwexealiX3GlC6Ne/h6LfyvQKCoiIqLIwmQWUZQSsgeO1Tci2a6crhCXMwZGe4ZGUVGoJNgtSIy3Iim9ExJOnwoYlXVXUL4b9pJvYOSnPpHuPLx8J77frxyV0zvdhsHZyRpFRCG35nUMah+v2FXtlnHFvJ/w/OqdvqnjnD5ORESxin/WBMGiRYswefJk5OTkwGazITMzE8OGDcPTTz+NsrKyqDkn6cuJH/4OT+FPin3m9L6wZAzUJiAKG1N8ByT0vhYwKOvoiLId+Og2IzwndmgUGRG11PKdhXhq5R7FPlGaj0t7pkCSpAaeRXrmcbsArwcbHxgPkbdR8ZgsgNsX70LSFXcjKbs7kpKS0DknhwktIiKKOaamm1BDHA4HpkyZgsWLFyv2FxUVoaioCOvXr8fzzz+P999/H+edd55uz0n6IoSAY+trKNvwtGK/wZoCe9ex/OMnRpgSOyGh9zWo2LkQkD2+/QNyDCj9bCSkcx9C8ll3QzLwvwGiSPXdvuO45m3lSrRmgwTX4qdhHfeeRlFRqHk9bkDImPPfzxGXmIJle0uxrbBa0UYaNB7SoPHomWLGrpfvhNPphNVqbeCIRERE0Ycjs1rJ4/Fg0qRJvqRSVlYWHnroISxYsAAvvPAChg8fDgA4fPgwxo0bh+3bt+vynKQv7rL9yP90LI6vnA3AvxC4AfaeEyGZbA09laKQKSkH8b2uAiRV3TTZhRPfP4yj7/0B1Qe/gfBLdhFRZPh8ez7G/Hc9ymqUv5+PXdQdKNjTwLMomlji4mFPSMSVZ3bC+d3SAh4XAHaXuiFd8xQu+O8veHPDIdS4veEPlIiISAP8Sr6VXn31VaxYsQIA0LdvX6xcuRJZWVm+x2fPno27774bc+fORWlpKW666SasXbtWd+ckfRBChuPXV1Cy9gEId2XA41L7C2BK6KhBZKQ1c3J3JPSdhqq9n0OuOa54zFW4EfmfXApDXAbie16B+F6TYcs+n6O1iDQ276eDuPGjX+GVlauTTuiXhVvO6YT7NIqLtCFJEkb2bIdEqwnLdhZC1S0AAL/mV2DG+5tx7+LfcNOwHNw8rCs6JvMLLCIiil4cmdUKXq8Xjz32mG/77bffViSV6jz11FMYOHAgAGDdunVYvny5rs5JkU8IGZV7PsXRhefh+Krb601kvfWdF1L6WRpER5HClJCNxP43QGp3dsAfxwAgVxfBsfVV5H88Bgdebo/DH1yM/NUPoGzXInirizWImCj2OJ1OHCk+gYe/3Io/f7Al4Hf10l7peGVCL1RUVGgUIWntrM4puGVYVwztnAyzsf6SAUWVLjz+9W50feJrXPzKD3jym934YX8J3F45zNESERGFFr9+b4U1a9YgPz8fADBixAgMHjy43nZGoxG33XYbZs6cCQBYuHAhxowZo5tzUmQRQgAQgBBw1lTCseN9VP/6LLylu+ptb4jvCMPgJ/DoX6/DjFtYJyvWSQYz3GnDcfVD3+Nf1xhxWvv6+4RwlcN9dDXcR1ejejNQAsBgS4c5pQdMKT1gTu4BU1JXmBI6wpjQEaaEbEiWJNZiI2oFryxw8EQ1vth2BPe88hFcWb0hGc0B7cS2r7HkmReQddOphITXy+lksSg93oJL+2RhZI922Hy0HD8dLEFpTWBf8MgCX+8uxte7a7+QiDcbMLRzMgZnJ2NIlzQMyk7Cae0SYDTws5uIiPSJyaxWWLZsme/+2LFjG23r//jSpUt1dU4KH+FxwlN5FN6Ko/BUHIHHcQiesjx4yvLgLs+Dp/wAILubfbyFP8j41+cH4Ki5DgDglflHDwFujxebDghknnUjJM8eiNLfgJqCJp8n1xyHM/84nPk/1fu4ZI6HMb4DTAnZtQmu+I4wJmTXJrziO578t0O9f6QTRSO3V0aBo3Z1uTV7j+OYsxTHHE4cK69BfrkTR8trcMxRg8IK16kRWB3PQH1phXOyEzDivD9BumkaAKCipAgv3HApk1kxzmY24tycVPRLlvHMg3cCg8ZByhnYYPtKt4zV+05g9b4TAPbXHsNkQLt4C5JtZiTHmZBsMyMrwYoe7ezo2S4ep7WLR4/0eCTZTPzCgoiIIg6TWa2wdetW3/2hQ4c22jYrKwudO3fGoUOHUFhYiKKiImRkZOjinNGuuKQAn6z5qp5HRL33jbKEdqZkfPz1R3DLTkD2G7IvSbU3SJB8f45IqL0rnbwBkAwQnmrIzlLIzhOQa07AW1MK4W5s2kj2yVszmOyQUs+AfWw7PDYWKKuowaP/+QofH05Hgr3x2hmlDhtwei4+O5aJhLLIbmuWzRil2vfZ4RS4DQlhi8EkmZFhA9BtiC7eM/+2X5d3Rbz9dCDlMsjuCnhPJlJlZ1mjz2/84HV3jp68/RzQRDKYAaMVktEKyWSp/ddghWSyQTLWbgc+yXjysbr2FgACQsiAEADkk/f9bwIwGAGDCZJkgmQwApLhZHtx8vni5DYA1P4uC3Hyd9pvFGRde4hT27XPOrUtSUbAaIXZZEeGOR0fr14KL2S/Tw+/z5S6cwoZwuuE8LoArxOy13UyDgMkg6H2X8lw8rPFcOrm23/q39rD+70Gv9clFK/hZDvR1GuqO5bfe1D33khGSAZTbV01oxmAoXa1TNkNITy1iwl43RCyB0J21z4m5Nqfh2Q8+bMw1f48TTZIRhsMJitgsJ78DG2C72fs9f3chfCefC1e3+O1P0sZoi6RLxlO/jFe+555hBFu1P7rEUZ4ZMDt8cDtleH2euGRvfCcfKu8QoIMwCtLEAC8ApCFBK+Q4IIFNbDCKSyoERaUu00oqDai2GlE93iBuYOBuz7fjn2VrUsETOu4CxMyDkCqqus3Ai5vBaxjDTin/FWY3AkQMEKWTBCSCYCAQXhO3txwV5cj7nIDhpY9C1u1CZLwAJIEWTLDK1nhlSzwSmZIEHBXleHe8QacU/4irDVmGOCBJLyQhBcGeCFLJt/zqmrcuPViAwZXLIBJToYsWeCVLBCQICn+DwWqq8tw1TkSTq/6EnHCjnK3BOAMRZs+JQuQYnLCVVWO28cYMNQxH2ZPIgQMJ1+bETKMEJLx5Os1oubkcftVfQ6bsNfGCrn2JmRI8J78V/Yd9xzH67C6LIo2gAGy77gmVFdX4+YLDRhU8R7MciIEjBAw1Ns/q6vKMeU8A/pVLoJNxDf6s3TVyEDcQHSvWQOTrD6WcrumuhxXnyuhb9Xieo8r/GKpqSjHVe1+wqjR56NI2o5vijPxw4l0uETTlURqPDIOl9XgcFlNo+0MkoDdCMSbBewmAZMBcMuAR5Zq/xWAxQDYjAI2I2Aznfz35P04v33WujZGAYux9pUbpFP/emTAJQNuWYJHrv19M0q1jxkkwCgJGCXAaKjdb6y7/BK+TzrIdR9vJ7d99xVtpIDHAMBkAExS7Ws0n/x4rYvDIwMeIcF78jXXbRsgEGeqfU1xJsBqhOL3QKh+vkIAVklCV7MZb379NWqE8ndGNLgRsOmLz+mtfc9cMuDyu1+332QA4owCcSd/HhZj4Hsji9pP/4D7kHz7JOnU+2OUat8vQzP21b1/XiH53W98nwBgNgiYpNqfhdEQuA0A1R6g2iOh6uS/bt/PJ/C4dT8/m0kgxSKQYhVItgAJZgHZv63q51z3uW8zCthMte+lzVT72mT1e3jyvZMV25KiX/r+UvDr+/EG4Ay7GS8tq+0T7pO/X7U/z9r3xCgJ3++C4eS5vSf/n/LKtcf2ynX7ah83G2r7pM0oYD35O1j776l9Eur//VG8NtT93xf4Wr1y7b/Syd+hut9NgwSYDMK3bTr5M5P94qt7bxXvl9/jMk7+P6x6HIDqs8CvD/reI6H46Pb/TVTcr6dNu8R4TL1oHCiySUKoPkGpSd27d0deXh4AIC8vD127dm20/YgRI/Dtt98CAL777jv84Q9/iNhzHjx4EAcPHmxxfEDtKopfffUVkpOTkZKSgrFjx9Zb1ytS7Ny/Azd8ebzphid1ipdw74A4PL2lGocr+WujpSSpAq+2+6di36zi+1Eu6k9mhQL7A6mxT5C/1vYHEzzoZ87DePtanGmJvlULy+V4zDr+gGLfq+lPIskQWPMxmritnVDS5S6kHZwLs/NwSM9VJduwzd0d293dsN3VHYe87UN6Pmo5/n9BauwTkaWb5TjemnW5ZuevqqrCzp070bt3b9jt9gbbFRQUYMmSJb7tqVOnomfPnuEIMSJwZFYrlJaW+u63a9euyfbp6en1PjcSz/nGG2/g73//e0tCCzB69GhcdNFFWLVqVZuOEw6z05tuU8dqtQLoiquSC+C0OUMWEzXPt/LNiu0/pZ0AcCJs52d/IDX2CfLXtv5gQylG41t5dChC09zs9EOK7c34U92AwKhlla3oCmCjPAlOOQyfD2aghxnoYXcDONRkcwov/n9BauwTkee9997T7NxWqxVdu3bF8uXL4XQ23h/i4uJQXV0NADhxInx/C0UCJrNawX8lIZut6WWP4+LifPcdDoduztlabrfb9wsVLUpLS7Fp0ybf9qBBg5CSkqJdQKQp9gdSY58gf+wP5I/9gfyxP5Aa+wT5Y39oPiaziJqhrKwMa9as8W13796dHyoxjP2B1NgnyB/7A/ljfyB/7A+kxj5B/tgfmo/JrFZISEjwDeGrqalBQkLjdXr8RyklJiZG9DlnzpyJ0aNbN62hoqIChw4dQvv27dG+fXukpqbCYrG06liRZsOGDZg3b55ve9y4cU0W4qfoxf5AauwT5I/9gfyxP5A/9gdSY58gfy3pDy6XSzG1sFevXiGPL5IwmdUKKSkpvk5TXFzcZGLp+PFTRcZbm1UN1zm7dOmCLl26tCrGaKYuip+VlcX3KYaxP5Aa+wT5Y38gf+wP5I/9gdTYJ8gf+0PzNb1uLwXo3bu3737dCoON8W/j/9xIPycRERERERERUaRhMqsV+vfv77u/YcOGRtsWFBTg0KHaVWQyMzORkZGhm3MSEREREREREUUaJrNa4ZJLLvHdX7p0aaNtlyxZ4rs/duxYXZ2TiIiIiIiIiCjSMJnVCiNGjED79u0BAKtXr8bGjRvrbef1evHcc8/5tq+55hpdnZOIiIiIiIiIKNIwmdUKRqMRDz/8sG972rRpKCwsDGh33333YfPmzQCA4cOHY8yYMfUeLzc3F5IkQZIkzJ8/PyznJCIiIiIiIiLSI65m2EqzZs3Cp59+ihUrVmD79u0YMGAAZs2ahb59+6KkpAQLFy7E2rVrAQDJycl45ZVXdHlOIiIiIiIiIqJIwmRWK5lMJnz88ceYMmUKFi9ejPz8fPzjH/8IaNepUye8//776Nevny7PSUREREREREQUSTjNsA0SExPxxRdf4LPPPsPEiRPRuXNnWK1WtGvXDueccw6eeuopbNu2Deedd56uz0lEREREREREFCk4MisIJkyYgAkTJrT6+atXrw77OYmIiIiIiIiI9Igjs4iIiIiIiIiISDeYzCIiIiIiIiIiIt1gMouIiIiIiIiIiHSDySwiIiIiIiIiItINFoAnaoYuXbrgkUceUWxT7GJ/IDX2CfLH/kD+2B/IH/sDqbFPkD/2h+aThBBC6yCIiIiIiIiIiIiag9MMiYiIiIiIiIhIN5jMIiIiIiIiIiIi3WAyi4iIiIiIiIiIdIPJLCIiIiIiIiIi0g0ms4iIiIiIiIiISDeYzCIiIiIiIiIiIt1gMouIiIiIiIiIiHSDySwiIiIiIiIiItINJrNIE4sWLcLkyZORk5MDm82GzMxMDBs2DE8//TTKysoi8pxerxfbtm3D/Pnzceutt2LYsGGw2+2QJAmSJOH6669vUTy5ubm+5zbntn///rC8Ti3Een949NFHW9QX6m5du3at93ih6lvhosf+4HA48PHHH2POnDk477zzkJGRAbPZjKSkJPTp0wfTpk3DsmXLIIQIe2yhOlY4sU8E93j8jAj/OYPdH3gNcUqs9wdeQyjpsT9s2LAB//nPf3D99ddj6NCh6Nq1KxISEmC1WpGVlYXc3Fw89thjOHjwYNhjC9Wxwol9IoqvIQRRGJWXl4vx48cLAA3eOnXqJNatWxdx55w4cWKjx5g+fXqL4hoxYkSjx1Pf8vLywvI6w4n9odYjjzzSor5Qdxs1alS9xwt23woXvfaHuXPnCpvN1qz3+vzzzxcHDhwIW2yhOFY4sU+E5nj8jAjvOUPxGcFrCPaHOryGqKXX/iCEEPHx8c16r61Wq3jyySfDGluwjxVO7BO1ovkawgSiMPF4PJg0aRJWrFgBAMjKysKsWbPQt29flJSUYOHChVi3bh0OHz6McePGYe3atejXr1/EnNPr9Sq209LSkJ6ejt27d7cpRgD49NNPm2yTmZnZ4GNavLdtxf5wyjXXXIOBAwc2q+2f//xnlJSUAABmzpzZZPu29q1w0XN/2LVrF2pqagAAnTp1woUXXoizzjoLGRkZqK6uxo8//oh33nkHFRUV+O6775Cbm4v169eH7Xdaj58PAPtEKI/nj58RoT9nKH9+AK8hwhVzpPYHXkPouz/UyczMxNlnn41+/fqhffv2aN++PYQQ2L9/P7788kusW7cOTqcTDzzwANxuNx5++OGwxKbHzweAfcJfVF9DhCxNRqTy4osv+jK0ffv2Ffn5+QFt7rrrLl+b4cOHR9Q5n3jiCXHfffeJDz/8UOzbt08IIcS8efN8z23LyKy20uK9bSv2h5Zbt26d7/gpKSmiurq63nbB7Fvhouf+cPPNN4uLL75YfPXVV8Lr9dbbZv/+/aJ3796+Y82YMSMssQX7WOHEPhG64/EzIrznDMVnBK8h2B9aitcQkdkfhBBi69atQpblRs/35ptvCkmSBABhMpnEkSNHwhKbHj8fhGCf8BfN1xDaR0AxwePxiPbt2/s6/i+//NJgu4EDB/raLVu2LKLPGQnJLC3e27Zif2idGTNm+I4/e/bsBttF0n8yzaH3/nD8+PFmnXPz5s2+49jtdlFZWRny2PT4+VAXD/tE6I7Hz4jwnjPYPz8heA3B/tByvIaIzP7QEpdddpnvWK+//nrIY9Pj50NdPOwTp0TzNQQLwFNYrFmzBvn5+QCAESNGYPDgwfW2MxqNuO2223zbCxcu1NU5taDH18n+0HIVFRX44IMPfNs33HCDhtEEl977Q1paWrPOOWDAAPTp0wcAUFVVhT179oQ8Nr32e/aJ0B5Pb9gfQkePnxHsDy3Ha4jI7Q8t4T8lraCgIOSx6fHzAWCfUIvk/4PaisksCotly5b57o8dO7bRtv6PL126VFfn1IIeXyf7Q8u99957qKysBAAMGTKk2fUx9CCW+kNiYqLvfnV1dchj02u/Z5+InONFAvaH0NHjZwT7Q8vxGiLwcT32B//kQvv27UMemx4/HwD2ibbQ2zUEk1kUFlu3bvXdHzp0aKNts7Ky0LlzZwBAYWEhioqKdHPO1ho/fjyys7NhsViQmpqKfv36YdasWVi1alWTz9XT66zD/tByr732mu/+n//852Y/ry19K1xipT84nU7s2rXLt52TkxPy2PTa79knWqc1x+NnROScs7X9gdcQDYvF/lAfXkPU0nN/+Oyzz/DJJ58AAOLi4jBu3LiQx6bHzweAfaK19HgNwWQWhcXOnTt997t169Zke/82/s+N9HO21pdffomjR4/C7XajtLQUv/32G1577TWMGjUKF154IY4dO9bgc/X0Ous7L/tD07Zv344ff/wRQO1/VlOmTGn2c9vSt8IlVvrDwoULUVZWBgAYPHhwg9+gBTM2vfZ79onWac3x+BkROedsbX/gNUTjYq0/qPEaQinS+8O3336Lzz77DJ999hk++OADzJ07FxdffDGuvPJKyLIMs9mM//73vw2uEMdrCPaJ1tLjNYQppEcnOqm0tNR3v127dk22T09Pr/e5kX7OlkpNTcVFF12Es846C9nZ2TAajThy5AhWrlyJpUuXQpZlrFy5EsOGDcP69evr/VDRw+tUY39omddff913/49//COSk5ObfE4w+la4xEJ/KCoqwr333uvbfuihh8ISm177PftEy7X0ePyMiKxztqY/8BqC/aE5eA2hFOn94d577/UlH/1JkoSRI0fisccew/Dhw8MSmx4/H9TnZp9oHr1eQzCZRWFRUVHhu2+z2ZpsHxcX57vvcDh0c86W+Oc//4khQ4bAYrEEPHbnnXdi48aNmDRpEvbv348DBw5g5syZWLJkSUDbSH+d9WF/aD6Xy4W3337bt92c6QHB6lvhEu39weVyYdKkSb5h5FdccQWuvPLKsMSm137PPtEyLT0ePyMi65yt6Q+8hqjF/tD0sXgNoaS3/lCnU6dOGDVqFLp27Rq22PT4+QCwT7SUnq8hOM2QSCPDhg2r90OgzuDBg7F8+XJYrVYAtQUCN2zYEK7wKEIsWrQIxcXFAIBevXrhggsuaPI57FuRQ5ZlzJw5E9999x0AoEePHnjjjTc0joq0FOw+0Zrj8TMicrS2P/BnGJ2C/fnAawj9Wb9+PYQQEEKgoqICmzZtwqOPPorS0lI89NBDOPPMM7F8+XKtw6QwCmWf0Ps1BJNZFBYJCQm++zU1NU229189wX9VhUg/Z7D16tUL06ZN820vXrw4oI0eXyf7Q/P5Tw9oSdHWpjSnb4VLtPYHIQRuvvlmvPvuuwCALl264Ouvv0ZqamrYYtNrv2efaJ5gH88fPyP01x/UeA1RK5b7A68hAumlPwBAfHw8Bg4ciEceeQSbNm1Chw4dUFJSgssvvxxbtmwJeWx6/HwA2CeaKxquIZjMorBISUnx3a/7hqgxx48fr/e5kX7OUBg5cqTv/u+//x7wuB5fJ/tD8xw8eBArVqwAAJhMJkyfPj2ox2+qb4VLNPYHIQT+8pe/4NVXXwVQOxR85cqVzRoKHszY9Njv1edmnwjP8erDz4jQnTMcPz+A1xBA7PYHXkPUTw/9oT49evTAP//5TwC108KefPLJkMemx88H9bnZJ+oXLdcQTGZRWPTu3dt3Py8vr8n2/m38nxvp5wwF/yKC9RUI1OPrZH9onnnz5kGWZQC1S99mZWUF9fhN9a1wibb+IITA7Nmz8fLLLwMAsrOzsWrVKvTo0SPssemx36vPzT4R+uM1hJ8RoTlnuH5+AK8h1G1iqT/wGqJ+kd4fGjNu3Djf/dWrV4c8Nj1+PqjPzT4RKJquIZjMorDo37+/735Tc2YLCgpw6NAhAEBmZiYyMjJ0c85QqCvGB9Sfudfj62R/aJoQAvPmzfNt33DDDUE/R1N9K1yiqT/UXSC89NJLAICOHTti1apV6Nmzpyax6a3f12GfaFiwj9cYfkYE/5zh/PkBvIaI1f7Aa4j6RXp/aIr/dLSGkgO8hmCfaEy0XUMwmUVhcckll/juL126tNG2/qsdjB07VlfnDIVVq1b57teXudfj62R/aNqKFStw4MABALXfmPjHHyxN9a1wiZb+oL5A6NChA1atWoXTTjtNs9j01u/rsE+E53hN4WdEcM8Z7p8fwGuIWO0PvIaoXyT3h+bYvXu3735DCRBeQ7BPNCQqryEEURh4PB7Rvn17AUAAEL/88kuD7QYOHOhrt2zZsog+57x583zPmz59eqtjbciOHTuE1Wr1nWP9+vUBbbR4b9uK/aFpV111le9YDz74YJuOVZ/m9K1wiZb+8Je//MXXrn379mLHjh2ax6bHz4e6eNgnQn+8xvAzIvL7Q1N4DRG7/YHXEPW3i+T+0ByzZ8/2Heuqq64KeWx6/Hyoi4d9IlA0XkMwmUVh8+KLL/o6dL9+/URBQUFAm7vvvtvXZvjw4Q0ea8SIEb528+bNC8s569Pa5MWzzz4r1q1b12ibjRs3iq5du/qOf/HFFzfYNtSvMxTYHxpWXFzs+w9AkiSxd+/eZj832H0rXPTeH+bMmaO4QPj9998bf8FNCGZsevx8EIJ9IlTH42eE/vsDryHYHxrDawh99YeXXnpJrFy5Usiy3OC5PB6P+Oc//ykkSfIdb/Xq1SGPLdjHCif2CaVovYaQhBACRGHg8XgwduxY38oq7du3x6xZs9C3b1+UlJRg4cKFWLt2LQAgOTkZ69atQ79+/eo9Vm5uLtasWQOgtsDl9ddfH/Jz5uXlKZY4BoBff/0VX3zxBQDgzDPPxGWXXaZ4fPDgwZg4cWLAsa644gosWrQIPXr0wOjRo3HGGWcgPT0dRqMRR48exTfffIMlS5b4Cnfm5OTg+++/R8eOHUP+OsOF/aFh//73v/HXv/4VADBq1Ch88803TT6nTrD7VrjouT889NBDeOKJJwAAkiThySefRJ8+fZp8zYMHD0aXLl1C/n7o8fMBYJ8I1fH4GaH//sBrCPaHxvAaQl/94frrr8ebb76Jzp0746KLLkL//v2RmZkJi8WC0tJSbNu2DYsWLcL+/ft9z7n//vsbXbmO1xDsE/6i+hoiZGkyonqUl5eL8ePH+zK19d06derUZMa3uRnyYJ5z1apVjR6jvltDo3MmTJjQ7GOMGTNGHDlypNHYgvk6w4n9oX5nnHGG7zkLFixo1nPqhKJvhYte+4P/+VpyC1dfDfaxwol9IvjH42eE/vsDryFqsT/Uj9cQ+uoP06dPb/Z7npycLF588cWwvh/BPlY4sU8Ext/Wz5xI+4xgMos08dlnn4mJEyeKzp07C6vVKtq1ayfOOecc8dRTT4nS0tImn9+SD5VgnTOYyYs9e/aI1157Tdxwww1i6NChomvXriIhIUGYzWbRrl07cdZZZ4lbb71V/PDDD816bcF8nVqI9f7g78cff/S1T0tLEzU1Nc16PXVC2bfCRW/9IdR/mATzd1qPnw9CsE8E83j8jNB/f+A1hFKs9wd/vIbQX39wOBxi2bJl4v777xcjR44UvXv3FqmpqcJkMonk5GTRq1cvMXnyZPHqq6+26neQ1xDsE9F8DcFphkREREREREREpBsGrQMgIiIiIiIiIiJqLiaziIiIiIiIiIhIN5jMIiIiIiIiIiIi3WAyi4iIiIiIiIiIdIPJLCIiIiIiIiIi0g0ms4iIiIiIiIiISDeYzCIiIiIiIiIiIt1gMouIiIiIiIiIiHSDySwiIiIiIiIiItINJrOIiIiIiIiIiEg3mMwiIiIiIiIiIiLdYDKLiIiIiIiIiIh0g8ksIiIiIiIiIqIIsXfvXsycOROdO3eG1WpFhw4dMHXqVOzYsUPr0CKGJIQQWgdBRERERERERBTr1q5di7Fjx8LhcOC0005D//79sXv3bmzduhVxcXFYunQpRowYoXWYmmMyi4iIiIiIiIhIY9XV1ejRoweOHTuGe++9F//6178gSRIA4D//+Q/mzJmD9u3bY8+ePYiPj9c4Wm1xmiERERERERERkcY++eQTHDt2DKeddhqefPJJXyILAGbPno2RI0ciPz8f8+fP1y7ICMFkFhERERERERGRxn7++WcAwIgRI2A0GgMeHzVqFADgs88+C2dYEYnJLCIiIiIiolbwer3Ytm0b5s+fj1tvvRXDhg2D3W6HJEmQJAnXX399xJ/T4XDg448/xpw5c3DeeechIyMDZrMZSUlJ6NOnD6ZNm4Zly5ahNdVpNm3ahHvuuQeDBg1CRkYGrFYrsrOzcdZZZ2HOnDn46KOP4PV6wxJbbm6u7z1qzm3//v2NHm/Hjh24/fbbMWDAAKSmpsJms6FLly64/PLLsWDBAsiy3NK3S2H69OmKeB599NEWH6Mt738o+0WkCOXv76JFizB58mTk5OTAZrMhMzMTw4YNw9NPP42ysrIGn1dZWQkASEtLq/fx9PR0ALU/25gniIiIiIiIqMUmTpwoADR4mz59ekSfc+7cucJmszV6vLrb+eefLw4cONCs45aVlYnrr79eSJLU5HFPnDgRlthGjBjRrGPV3fLy8uo9jsfjEX/729+afG3Dhg0Thw4datb7pbZ06dKA4z3yyCPNfn5b3/9Q9YtIE4rf3/LycjF+/PhGj9upUyexbt26ep//wAMPCADij3/8Y72P33PPPb7jOByOFscXTUxNp7uIiIiImuZ0OrFlyxbs2bMHBQUFqKyshNVqRUpKClJTU9GvXz/07t0bBoM+BoYfOHAAv/zyCwoKClBaWgpJkpCQkIAOHTqge/fu6N27N+x2u9ZhNmj+/PmYMWOGb3vVqlXIzc3VLqAoon5v1XJycpoc1RFs//73v/HXv/61wce1iCkWqEe1pKWlIT09Hbt379bFOXft2oWamhoAQKdOnXDhhRfirLPOQkZGBqqrq/Hjjz/inXfeQUVFBb777jvk5uZi/fr1yMzMbPCYJSUlGDNmjG+6VGZmJiZNmoTBgwcjKSkJBQUFOHz4MFavXo0NGzaENbY6n376aZNtGjrOnDlz8PLLLwMAjEYjrr76aowaNQqJiYnYt28f3nnnHWzfvh0//PADLr74Yqxbtw6pqalNnq9OeXk5brzxRgBAfHy8b6ROcwXj/Q/lex9Jgv376/F4MGnSJKxYsQIAkJWVhVmzZqFv374oKSnBwoULsW7dOhw+fBjjxo3D2rVr0a9fP8UxRo4ciSeffBJffvkl8vPz0b59e99j1dXVePfdd33bDocDCQkJrYo1KmidTSMiIiL9cjqd4s033xQXX3yxsFgsTX6Da7fbxahRo8TLL78sCgsLm3WOvLy8Zn07nJCQIDp16iQuuOACce+994rvv/++xa/n+PHj4vHHHxc5OTlNns9kMokBAwaIOXPmiC+//FJUV1e3+HyhNG/ePEW8q1at0jqkqKF+b9W3nJycsMf0f//3fxEXU6Tz/zk2NAqnKU888YS47777xIcffij27dsXcNxQjMwK5jlvvvlmcfHFF4uvvvpKeL3eetvs379f9O7d23f8GTNmNHrMMWPG+NrecMMNjY4eOXr0qHC73WGJzX9kVmstX77cd4z4+Hjx3XffBbTxeDxi5syZvnY33XRTi85x4403+kbv3HnnnS0emRWM9z8U/aIpDZ2nKR6Pp9XnDPbv74svvuh7bt++fUV+fn5Am7vuusvXZvjw4QGPy7Ishg0bJgCIM888U6xdu1Y4HA6xadMmkZubK0wmk+/59R0/ljCZRURERK2yYMEC0alTp2Ylmuq7mc1mcdNNNzU5DaO5yaz6bkOHDhVbtmxp1uv54osvRFZWVqvP9fbbbwfjbW3wdbdkiokQTGaFEpNZ0SEYyaymjhuKZFYwz3n8+PFmtdu8ebPv+Ha7XVRWVjYZR0PTpLSKLRjJrEsvvdR3jGeeeabBdi6XS/To0UMAEEajUezdu7dZx1+5cqVvauAXX3whHnnkkRb9HxCs9z/Y731TvF6v+NOf/iTuuOOOFj3vyJEjon///mLp0qWtOm99Wvu75PF4RPv27X3P/eWXXxpsN3DgQF+7ZcuWBbQ5evSoOOusswI+x61Wq++zXpIkUVNT09qXGRX0Mc6fiIiIIkZNTQ2mTp2KKVOm4PDhwwGPd+/eHRdccAEmTpyIq6++Grm5uejTp0/A9EK3241XXnkFPXv2DFmsGzZswNlnn40vvvii0XYffPABrrjiChQUFCj2JyUlYfjw4Zg0aRKmTJmCSy65BH379tXNVEkKj4ULFyIvL893W7t2bdhjmDlzpiKGvLw8nHPOOWGPg/SloSLTagMGDECfPn0AAFVVVdizZ0+97Z5++mkAgNlsxrPPPhtRsbWVLMtYvXo1AECSJEyfPr3BtmazGVOnTgVQO5Xt/fffb/L4VVVVuOGGGyCEwNVXX43x48e3OMZgvf/hfu8feOABvP322/j3v/+NO+64o1nPOXLkCHJzc7F161ZceeWV2LZtW6vOHSxr1qxBfn4+gNqVCAcPHlxvO6PRiNtuu823vXDhwoA2HTp0wI8//ojPP/8cd999N2688UY8+eST+P33332f6z179oTVag3BK9EP1swiIiKiZnO5XBg3bhxWrlyp2N+tWzfcc889GDduHLp06VLvc0tKSvDVV1/ho48+wqeffupb6cnpdLYohuzs7HqTBeXl5di1axc++ugjfPjhh4rjX3vttfjpp5/Qt2/fgOft3bsX06ZNU9TOOOOMM/DEE09g7NixMJkCL5fKy8uxatUqfPjhh1i0aBEqKipa9BoourRv3x5du3bVNIakpCQkJSUp9tlsNo2ioWiUmJjou19dXR3w+Lp16/D7778DAMaPH48OHTpETGzBcPz4cd+xMzIymkz41CV5AGDx4sW4//77G21///33Y9++fUhNTW1VIkqr9z8Y7/3NN9+M9957DwcOHMCzzz4LIUSj78Hhw4cxcuRIX/LsT3/6U0DtqXBbtmyZ7/7YsWMbbev/+NKlS+ttYzAYcNlll+Gyyy5T7H/77bcBABdeeGFrQ40a/FqRiIiImu2uu+5SJLIkScITTzyBnTt34pZbbmkwkQXUftN7zTXX4KOPPsK2bdtw7bXXtioGk8mErl27BtzOPPNMTJ48Ge+99x6WL1+O+Ph433MqKyvx4IMP1nu8Bx98UJFQGz16NH766Sdcfvnl9SaygNrEwYQJE/DOO+/g8OHD+N///V9kZWW16vWEyvXXXw9RW1ICQggWfyeiVnM6ndi1a5dvOycnJ6DNmjVrfPcvvPBCCCHw7rvvYvTo0cjKyoLNZkN2djYuv/xyvPPOOwHFt0MZm9r48eORnZ0Ni8XiW6Bk1qxZWLVqVYPPEUL47kuS1OQ5/Ntv3bpVsa32/fff44UXXgAAzJ07t1X/n2jx/rfmva9P165dsXr1at/zn3vuOcXoJX+HDh1Cbm6uL5E1a9YskVYbOQAAHVtJREFUvPLKK836mYTS1q1bffeHDh3aaNusrCx07twZAFBYWIiioqJmnaOiogL//e9/IUkSbrrpptYHGyWYzCIiIqJm+eSTT3wX20Dtt4bvvvsuHnjgAZjN5hYd6/TTT8eCBQvwySefNHs6Q0uMHj0azz33nGLfZ599hmPHjin2VVdX4/PPP/dtm81mvPnmm4iLi2v2uZKTk3HXXXfhoosualvQREQRauHChSgrKwMADB48WLHCWp261fOAUyvgXXfddfjmm29QWFgIp9OJo0eP4osvvsCf/vQnnH322Th06FBYYlP78ssvcfToUbjdbpSWluK3337Da6+9hlGjRuHCCy8M+L8CANLT02GxWADUJiBKSkoaPYd/ksfhcODo0aP1tqupqcHMmTMhyzIuvPDCRldKbYwW739r3vuGqBNazz//PG699VZFm4MHDyI3Nxd79+4FEDmJLADYuXOn7363bt2abO/fxv+5ALBt27aAUW5FRUWYOHEijhw5gltuuQUDBw5sW8BRgMksIiIiapLX68Xdd9+t2HfnnXe2enRVnSuvvBKbN29u0zEaMn36dGRnZyv2qadHbty4UXHBeN5556Fjx44hiYeISI+Kiopw7733+rYfeuihetv5J4DuuecerFq1ClarFbNmzcKbb76Jd999F/fee6/vC4yNGzdi1KhRvmRIKGOrk5qaiquuugpPP/003n33Xbz33nuYO3cuxo0b56uFuHLlSgwbNsxX/6iO0WjEsGHDANSOuqqb7lUfj8eDd999V7GvtLS03rYPP/wwdu7cibi4OLzyyiuNxt+YcL//LX3vm0Od0HrhhRcwe/ZsCCFw4MAB5ObmYt++fQAiK5EFKH++7dq1a7J9enp6vc8F4BvtnZubiylTpmDMmDHo2rUrVqxYgcmTJ+Pf//53kKLWN9bMIiIioiZ9/PHHyMvL823n5OTgH//4R1COXTfUPtiMRiNGjBiBBQsW+Papv/1U/7GiTn5RrRMnTmDt2rU4dOgQSktLkZWVhT/84Q/o3bt3o89zOBz49ttvsWvXLlRXVyMzMxNDhw7FgAED2hxTVVUV1q1bh0OHDqGoqAhWqxWZmZk466yz0KtXrzYfP1xcLhd+/fVXbN++HcePH0dlZSXi4uKQnJyMnJwcnH766SH7HSFqisvlwqRJk3zToK644gpceeWV9bY9ceKE7/7u3buRnp6Ob775RvH7PmXKFPz1r3/F6NGjsX37duzZswcPPvigYtRvKGIDgH/+858YMmSIb3SVvzvvvBMbN27EpEmTsH//fhw4cAAzZ87EkiVLFO1uuukm33S+hx56CGeffbYvwVXH6/XiL3/5S0Ax9PLy8oDz/vzzz3jmmWcAAH//+9/Ro0ePZrzy+oXz/W/pe98SdQmt3NxcHDhwAC+++CIqKyuxZs0a7N+/H0DkJbIAKGpnNqdeof8IcIfDoXisbkGaX3/9FT/88AMSExMxfPhwzJo1C3/84x+DF7TeabOIIhEREenJ+eefr1ge+vHHHw/bufPy8hTnzsnJafZz7733XsVzb7nlFsXjCxcuVDx+ySWXBDn6lvNfPr65t/reE//lxQGIVatWNXjOhtrm5eWJKVOmCKvVWu95L7roonqXnD9+/Li45ZZbhM1mq/d5AwYMEOvXr2/V+/PTTz+J8ePHNxgTAHHaaaeJN954Q3i93ladoykteW8bUlhYKG699VaRlpbW5M+3U6dO4qabbhI7duxo0Tn8+1JLfm+iRWt+lxq6zZs3r9nn9e8f06dPD9nrC/U5vV6vmDp1qu+4PXr0ECUlJQ22P+200xTv2VtvvdVg282bNwtJkgQAYbPZRHl5eUhja66dO3cqPlt++umngPOOHTvW97jJZBLXXXedeP3118UHH3wgnnrqKXHGGWcIACI+Pl5kZmb62qo/85xOp6/toEGDhMfjCYjnkUce8T3/kUceaTT2cL3/oXrv1fLy8kROTk7A7+KsWbOELMtBP1+d1v4umc1m3/PcbneT7adMmeJrv2DBgjZEHLs4zZCIiIgaVVlZiR9++EGx7/rrr9cmmDZSf4ubmZmp2F67dm2DU0Fizdq1azFo0CAsWLCgwRUnV6xYgXPOOUexJPrOnTsxaNAgvPTSS6ipqan3eVu2bMGIESPw1VdfNTset9uNG2+8EWeffTYWL17c6CqYu3fvxsyZMzFq1KiI/Hn++OOP6NOnD55//vkm6+4AtSt3vfLKKw2uekUUbEII3Hzzzb6pcl26dMHXX3+N1NTUBp/jv6pdcnJyo9PQBwwYgHPPPRdAbc2odevWhTS25urVqxemTZvm2168eLHicYPBgPfff983Osbj8eCdd97Bn//8Z1x11VX429/+hm3btiE1NRWffPKJYoSOOr7HH38c27Ztg9FoxGuvvQaj0dim2MPx/ofyvVfr2rUr5s+fr9jXq1cvvPTSSxE1IqtOQkKC735D//f58y9x4P+zo+bjNEMiIiJq1Pr16+HxeHzb3bt31810vCNHjii2/WtUAMCQIUNgNBp9qzpVVFRg2rRpWLBggeLCNNbs3r0b99xzj6+WSrdu3dC/f3/ExcVh7969+OWXX3wrcxUXF2PSpEnYunUrTpw4gQsvvND3vrdv3x5DhgxBUlISDh8+jB9++MHXl5xOJ6ZMmYKdO3cG/FzUampqcNlll+Hrr79W7E9KSsKQIUOQlZUFp9OJHTt2+JamB2pX9xoxYgR++OEH2O32oL0/bVFYWIhLL71UMSUIAE477TT07t0bycnJcLlcKC0txY4dO4JSIDtWPf744yguLm7w8ZUrV+L5558HALzyyisByW1/gwcPDnp8kUoIgb/85S949dVXAdQWE1+5ciW6du3a6PP8Expnnnlmg6vB1hkyZIjvixL1lLxgx9YSI0eO9B3f//OkTkJCAj744AOsXr0a8+bNw7p163z1qnJycnD55Zfj9ttvR3p6um+/JEmKFQq3bNmCf/3rXwBqpzgGo3+F+v0Px3vvLy8vL+CLs127duHmm2/2regXSVJSUnyf68XFxU1eQxw/flzxXGo5JrOIiIioUevXr1dsDxkyRKNIWsbr9SqWKgcQUEspOTkZl1xyCb788kvfvi+++AK9evXCTTfdhIkTJ+KMM84I60Xze++9h5qaGhw+fBjnn3++b//tt9+OO+64o97nNPVHS0vde++9KCsrQ58+ffDSSy8hNzdX8fjmzZtx5ZVX+uqX7Nq1C2+88Qa++OILHDlyBNnZ2Xj++edxxRVXKN67vLw8TJ48GRs3bgRQezH/1FNP4emnn240njvuuEORyOrSpQueeuopTJ48OeC1b9myBbNnz/aNNPj1119xxx134L///W9r346g+p//+R9FImvMmDH497//jT59+tTbvqCgAEuWLMHrr78ecX+8Rbo//OEPjT7uP2rv4osvDtkf5XoihMDs2bPx8ssvA6itI7hq1apm1XLq06cPvvnmGwC1ieamJCcn++7XV08qmLG1hH/x7sZGdubm5gZ8Nvr78ccf4Xa7AdT+3+P/eufPnw+32w2DwQCz2YzHH3+83mN8++23ivt17Xr37h1QOymU73+43vs6+/btw8iRI3Hw4EEAwI033oiVK1diz549eO211yDLMl599VVf0f5I0Lt3b19t0by8vCY/T/zrkDZVf5Lqx2QWERERNUq9RPlpp52mUSQt88477+Dw4cOKfaNGjQpo98QTT+Drr79WTFs7duwYHn30UTz66KNIS0vDueee67udd955iI+PD1ncDS1tnpKSErY/tktLS9G3b1989913vpWv/A0cOBCLFi3CoEGDIMsyAOD+++9HaWkpOnTogO+//x5dunQJeF63bt2wePFi9OrVy1cs95133sG//vWvBv8oWb58uWKFr0GDBuGbb75pcFrLgAEDsHLlSlx22WW+aYyvvvoqbrvtNpxxxhkteyNCwH/aUp8+ffD555/XW5C6TlZWFmbMmIEZM2Y0a+oKUWvVJSxeeuklAEDHjh2xatUq9OzZs1nP9y803pwV8vwTRf6JlVDE1hJ1Rc2Bto2Y8Z8WfMEFFygeqxvZKssynnzyyWYdb9WqVVi1ahUAYMKECQHJrFC9/+F87wFg7969GDlypG9U6p133om5c+fiyJEjGDlyJHbv3o033ngDQgi89tprEZPQ6t+/P5YtWwYA2LBhA0aOHNlg24KCAt/ry8zMREZGRlhijDaR8ZMnIiKiiKWu6dPUHx2RYPXq1ZgzZ45i3/jx49GxY8eAtgMGDMDbb7/d4OpDJSUlWLJkCR5++GFcfPHFSE1Nxfnnn49XXnmlWaMJ9Oqtt96qN5FV58wzz8RFF13k2677w+ill16qN5FVp0OHDpg6dapv+9ixYwGrTPrz/0PPbrdj0aJFTdZnsVgsePvttxVTC5977rlGnxMudSMNAGDcuHGNJrLUmrNCFlFrqBMWHTp0wKpVq1r05cWll17qGz3466+/+kYlNeSXX37x3W9sZEowYmuJuoRRU3E1xul0Yt68eb7tG264oc1xNSUU73+43/u9e/ciNzfXl+i5++67MXfuXAC1o8FWr17ti3XevHmYOXOm7wsVrV1yySW++03VN/RfJXPs2LEhiynaMZlFREREjVLX9mlJMuuGG26AJElN3hqbqqHm8Xiwf//+gNu2bdvw8ccfY8qUKRg9erRimey4uDg88cQTDR7zj3/8I77//vsmpyUBtYXI165di5tvvhndu3fHiy++6PuWPVrk5uY2azqpeqRbjx49cPnll7f4eVu2bKm33W+//aaYZnPjjTeic+fOTR4fqP22+5prrvFt+08ljRSN1XMiCqc5c+b4Ehbt27fHqlWrAqZlN6VTp04YMWIEgNppawsXLmyw7ebNm31T2BMTEzF8+PCQxtZcO3fuxNtvv+3bHj9+fKuO849//MOXuM7NzcXZZ5+tePzf//43hBBN3h555BHfcx555BHf/s8++yzgnKF4/8P53u/Zswe5ubm+EdX33HMP/ud//kfRpm5UWN207DfffBMzZsyIiITWiBEjfCOrV69e7ZtOr+b1ehVfrvj/P0Utw2QWERERtYjWdXuOHDmCbt26Bdz69++PyZMnY+HChb6C7gBgNpvx9ttv48wzz2z0uIMGDcJ3332Hb7/9Fn/+858bLQZd5/jx45g9ezauueYauFyuNr+2SHHxxRc3q516msno0aOb1T/Uz/Of1uPPf4QEAEyePLlZcdXxrzl29OhRX40vLfn/Ifjhhx/ip59+0jAaijS5ubm+JL96JbdQufXWW/Hiiy8COJWwaO2IJP+RlHfeeSd+/fXXgDYFBQW47rrrfNu33XYb4uLiQhrbc889h++//77RNps2bcIll1zim3J+8cUX45xzzglot379+gZXU/V6vfjXv/7l+/IkPj4er732Wovjba1gvv/B7BdN2b17tyKR9be//a3BWop1o8NOP/10ALWjiKdPn655QstoNOLhhx/2bU+bNg2FhYUB7e677z5s3rwZADB8+HCMGTMmXCFGHdbMIiIiokapp3Q1pxZHpBgwYABef/31FhWtP//8831JkN9++w3ff/89fvnlF2zYsAGbN29WJMrqfPDBB2jXrh3+85//BC12LTVUjFxNXWS4uX/oqJ/X0HRN9XLxycnJLUpIqRNrzSnKG2pXX3217w+Zqqoq/OEPf8CUKVNw7bXXIjc3F1arVdP4qGXy8vLw+uuvK/b5JxA2bdqEhx56SPH44MGDMXHixIg450MPPYQXXngBQO3vy+23344dO3Zgx44djcYwePDgeqcTDxs2DH/729/w1FNP4fjx4zj77LMxffp0/OEPf4DJZMLmzZvx2muv+aavDxkyJCDWUMS2cuVK3H777ejRowdGjx6NM844A+np6TAajTh69Ci++eYbLFmyxJcQycnJUUwT9Pf444/j+++/x9ixYzF06FB06NABTqcTu3btwocffuibNh0XF4ePPvooZEXS6xOs9z/Y/aIpb731lm8V3Pvvv7/JOmLt27fH6tWrMWrUKGzfvh2ffvop7r//fvTt27dF5w327++sWbPw6aefYsWKFdi+fTsGDBiAWbNmoW/fvigpKcHChQuxdu1aALX/n/nXg6RWEERERESNmD17tgDguz344IPNfm5xcbHIy8sLuH333XeKY44YMaLBY+Tl5SnaNnSz2+2iQ4cOYvjw4eLOO+8Ua9asCcKrVyotLRXz5s0T/fv3rzeGn3/+OWjnUr/uRx55pEXPnzdvnuL5q1atanbbb775plnnWLVqleJ5r7/+erOe19zXdv755zfrZ9/c20cffdSs+JrSkvdWrbKyUgwaNKje+KxWq7jgggvEAw88IJYvXy4qKyvbFOeIESN8x87JyWnTsaKR/88xLy+vVcdQ/w405zZ9+vQGj+f/M5s3b17Iz+l/vpbcGoqtzgMPPCCMRmOjxxgzZowoKSlp1nvR1tgmTJjQ7OePGTNGHDlypMG4xo0b1+Qx+vbtK9atW9foe9QcjzzySKv+D2jr+x+qftEQWZbFTTfd1KLrCyGEKCgoEGeffbZYvXp1q84b7N9fIYQoLy8X48ePb/QYnTp1Ckr/iHUcmUVERESNUq+ut2vXrmY/Nz09Henp6UGNJycnR7PpYsnJybj++usxbdo0PPzwwwF1uJ5//vmwTQ0KpdauDhXsVaXUiw+0lX8dNa3Y7XasXLkSt9xyC95//31FvTWn04lvv/0W3377LZ588knY7XZcfvnluOuuu3DWWWdpGDVRyzzxxBO46qqr8Prrr2PFihU4cuQI3G43MjMzcd5552HatGm49NJLwxbP3Llzcdlll2H9+vXYsmULioqKUFxcDKfTieTkZHTt2hXDhg3DlClTcO655zZ6rH/84x84++yzsWbNGuTl5aGgoACSJCErKwtDhgzBlVdeiT/+8Y8wmbT7UzvS3v+mSJKEl19+ucXPy8zMxPr16zUvf+AvMTERX3zxBRYtWoS33noLGzZsQGFhIRITE9GjRw9MnDgRN910ky4W04l0TGYRERFRo9QX9j///LNGkUQOg8GAxx9/HDt37sRHH33k2//1119rGFX0aWo1rpYSEVKoPyUlBQsXLsQDDzyAefPmYenSpfVO36mqqsJ7772H9957D7fccgueffZZmM1mDSKOTtdffz2uv/76Nh0jNzc3qP1q9erVYT1nc87XWgMGDGjTKqLBjK1Hjx7o0aMH/vznP7f5WIMGDcKgQYOCEFXTHn30UTz66KOtem5b3v9Q9otga0siK9i/v/4mTJiACRMmhOTYVIsF4ImIiKhRw4YNU3zDnJeX56ttEetuv/12xfaRI0dQXV2tUTTRJy0tzXc/OTm5Wat/NXZra+Ii2Pr3749nnnkGv//+O/Lz8/HJJ5/gjjvuwBlnnBHQ9qWXXsKtt96qQZRERESRh8ksIiIialR8fHzA6KyGCuPGmoEDBwbsO3HiRPgDiVL+K0qWlZU1uOphNMjKysKVV16J//u//8PWrVuxY8cOzJgxQ9Hmv//9L3777TeNIiQiIoocTGYRERFRk9QjQl599VXU1NRoFE3kqK9GlHqlvtaKpBogWlEnUb/55huNIgm/3r1744033sANN9zg2yeEwOLFizWMioiIKDIwmUVERERNmjRpErp16+bbPnjwIB544AENI4oMdUuw10lISEBCQkJQjm21WhXbLpcrKMfVk9GjRyu2o6G4fktNnz5dsa3V4gdERESRhMksIiIiapLRaMT//M//KPY9++yzeOeddzSKKDgKCgpQUFDQ6ue/8cYbiu3c3Nw2RnSKeqWj/Pz8oB1bL4YOHYrBgwf7tpcvX44vv/xSw4jCTz3Sz2KxaBQJERFR5GAyi4iIiJpl0qRJmDNnjm9blmVMmzYNjz/+eItHDUVKXanff/8d3bt3x3333YfCwsIWPffjjz/GSy+9pNh39dVXBy22uLg4dO7c2bf97bffwuv1Bu34evHII48otqdOnYrvv/++RcfYu3dvRKzOVVxcjNdffx1Op7PZz1mwYIFiu3fv3sEOi4iISHeYzCIiIqJmmzt3LkaNGuXbFkLg//2//4fevXvjxRdfxMGDBxt9/oYNG3DHHXdg+PDhoQ612aqqqvDUU0+hU6dOuPLKK/Hxxx83Wmj8999/x4033oirrrpKkVwaPHgwpkyZEtTYLrjgAt/9vXv3YvLkyViyZAl27tyJ/fv3+26HDx8O6nkjyeWXX44bb7zRt11WVoYRI0bgrrvuwoEDBxp8XlFREd58801cdtll6NWrF5YtWxaOcBtVUVGBG264ATk5Objjjjuwdu1auN3uetuWlZXhwQcfVIyItNlsmDRpUrjCJSIiilimppsQERER1bJYLPjyyy8xc+ZMLFy40Ld///79mD17NmbPno3u3bujS5cuSE9Ph81mQ0VFBfLz8/H777+jvLw84JiJiYm45pprwvky6uV2u/HZZ5/hs88+AwD07NkTXbp0Qbt27WAwGHDixAn89ttvOHToUMBzO3XqhA8//LDegvBtMXv2bCxYsABCCABQxOcvJycnqmspPf/88zh69Kiv+LnH48EzzzyDZ555Bj179kTv3r2RkpKCmpoanDhxAjt37sSRI0c0jrphBQUFePbZZ/Hss8/CarXijDPOQMeOHZGcnAyn04kDBw5g06ZNAYmuf/3rX4oVHomIiGIVk1lERETUIjabDQsWLMC4ceNw77334ujRo4rH9+3bh3379jV5nLi4OFx33XV47LHH0L59+1CF26i0tDS0a9cOxcXFAY/t2bMHe/bsafIYo0ePxquvvoquXbsGPb5hw4bhf//3f3HvvffG5BTDOhaLBYsWLcLDDz+Mp556Ch6Px/dYc39OKSkpIYyw9ZxOJ3755Rf88ssvDbYxmUx48skncfvtt4cxMiIiosjFaYZERETUKlOnTsW+ffswb948jB49GmazucnnJCYmYtSoUXj55Zdx7Ngx/Pe//9UskQUAZ555JvLz87Fy5UrceeedOOuss2A0Gpt8ns1mw8SJE/HFF19gxYoVIUlk1bnzzjvx66+/4p577sF5552HjIyMgJUOY4HBYMDjjz+OXbt24cYbb0R6enqj7SVJwplnnol7770XW7duxX333RemSBvWuXNnfPXVV5gzZw5OP/10SJLUaPv4+HhMnToVW7ZswT333BOmKImIiCKfJOrGrRMRERG1QU1NDTZv3ow9e/agoKAA1dXVsFqtSE1NRVpaGnr37o3TTz896FPxgq2qqgq7du3Crl27UFhYCIfDAaB2Vbm0tDT069cPffv2hcnEAe5aEkJgy5Yt+O2331BcXIzy8nLY7XakpqbitNNOQ9++fZGWlhaSc8+fPx8zZszwba9atapVK1mWlJRg27Zt2LdvH44fP46qqirY7XakpaXh9NNPx4ABAxAXF9fqOHNzc7FmzRoA0T8VlYiIYguvwoiIiCgobDYbzj33XJx77rlah9ImdrsdAwcOxMCBA7UOhRohSZLuf05paWm44IILFIX+iYiIqGlMZhERERERtUF+fr5i1JPJZEKnTp3CGkN5eTlKSkoU+2pqasIaAxERUbgwmUVERERE1AbXXnutYluLKX1vvPEG/vrXv4b1nERERFqJ7KIVREREREREREREfpjMIiIiIiIiIiIi3eBqhkREREREREREpBscmUVERERERERERLrBZBYREREREREREekGk1lERERERERERKQbTGYREREREREREZFuMJlFRERERERERES6wWQWERERERERERHpBpNZRERERERERESkG0xmERERERERERGRbjCZRUREREREREREusFkFhERERERERER6QaTWUREREREREREpBtMZhERERERERERkW4wmUVERERERERERLrBZBYREREREREREekGk1lERERERERERKQbTGYRERER/f927IAEAAAAQND/1+0IdIYAAGzILAAAAAA2ZBYAAAAAGzILAAAAgA2ZBQAAAMCGzAIAAABgQ2YBAAAAsBHr6TK3OAdbZQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 461, "width": 601 } }, "output_type": "display_data" } ], "source": [ "sns.histplot(peak_times, kde=True)\n", "plt.axvline(fit.start_times['H1'], ls='--')\n", "plt.axvline(fit.start_times['L1'], ls='--', c='C1')\n", "plt.xlabel('GPS time [s]');" ] }, { "cell_type": "code", "execution_count": 17, "id": "6c5ab673", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b1d025a9256a462d885aa99ab55fda3f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "waveforms: 0%| | 0/100 [00:00\n", "\n", "Note\n", "\n", "By default, the proxy for the _peak time_ is taken to be the coalescence time found in the IMR posterior samples; for some approximants you can ask `ringdown` to itself compute a posterior on the actual waveform peak time by passing `manual=True` to `get_peak_times`, or equivalently `peak_kws={'manual': True}` to `Fit.from_imr_result`.\n", "\n", "" ] }, { "cell_type": "markdown", "id": "31280fb3", "metadata": {}, "source": [ "### Duration" ] }, { "cell_type": "markdown", "id": "eec50394", "metadata": {}, "source": [ "The segment duration for the ringdown analysis is chosen by computing the signal-to-noise ratio (SNR) that IMR templates accumulate after the analysis start time and requiring that this stabilizes:\n", "\n", "For a number of draws from the IMR posterior samples, `ringdown` reconstructs the corresponding waveforms, and computes their optimal SNR integrating from the fit start time to some duration set as an initial guess; the duration is doubled until the median cumulative SNR at halfway through the segment falls within the 10%-credible (symmetric) interval around the mean of the SNR accumulated by the end of the segment.\n", "\n", "We can plot the cumulative post-peak SNR accumulated by the IMR waveform easily using `fit.compute_imr_snrs`" ] }, { "cell_type": "code", "execution_count": 18, "id": "37e88849", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f01faeecccb64bf7b8106b55d71fed87", "version_major": 2, "version_minor": 0 }, "text/plain": [ "waveforms: 0%| | 0/100 [00:00" ] }, "metadata": { "image/png": { "height": 468, "width": 575 } }, "output_type": "display_data" } ], "source": [ "# Create an inset zoomed into the intersection area\n", "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", "\n", "fig, ax = plt.subplots()\n", "inset = inset_axes(ax, width=\"50%\", height=\"50%\", loc=\"center\")\n", "\n", "t = fit.analysis_times['H1'] - fit.start_times['H1']\n", "l, m, h = np.quantile(snrs, [0.45, 0.5, 0.55], axis=1)\n", "\n", "for a in [ax, inset]:\n", " a.plot(t, m, label='median')\n", " a.fill_between(t, l, h, alpha=0.5, label='10% CI')\n", " a.axvline(fit.duration / 2, ls=':', c='gray')\n", " a.axhline(h[-1], ls=':', c='gray')\n", " a.axhline(l[-1], ls=':', c='gray')\n", "ax.set_xlabel(f'time from {fit.start_times[\"H1\"]:.2f} GPS [s]')\n", "ax.set_ylabel('cumulative SNR')\n", "ax.set_title('post-peak network SNR in IMR waveform')\n", "\n", "ax.legend()\n", "\n", "ax.grid(False)\n", "inset.grid(False)\n", "\n", "# Set limits for the inset to focus on the intersection region\n", "inset.set_xlim(fit.duration / 2 - 0.05, fit.duration / 2 + 0.05)\n", "inset.set_ylim(l[-1] - 0.5, h[-1] + 0.5);" ] }, { "cell_type": "markdown", "id": "729d4b93", "metadata": {}, "source": [ "### Prior settings" ] }, { "cell_type": "markdown", "id": "33ebf9c7", "metadata": {}, "source": [ "Finally, `ringdown` estimated some reasonable prior settings based on the model information we provided (the Kerr modes and the inclination prior)." ] }, { "cell_type": "code", "execution_count": 20, "id": "33cf8462", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'cosi': -1,\n", " 'a_scale_max': 5.941070369343782e-20,\n", " 'm_max': 120.0,\n", " 'm_min': 33.0}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit.model_settings" ] }, { "cell_type": "markdown", "id": "30c360ee", "metadata": {}, "source": [ "The amplitude scale is set based on the value of the IMR waveform at the start time. The mass prior is set based on the remnant mass estimated in the IMR results." ] }, { "cell_type": "code", "execution_count": 21, "id": "3b047203", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 462, "width": 565 } }, "output_type": "display_data" } ], "source": [ "sns.histplot(fit.imr_result.remnant_mass_scale, bins=10, label='IMR posterior')\n", "plt.axvline(fit.model_settings['m_max'], lw=3)\n", "plt.axvline(fit.model_settings['m_min'], lw=3);\n", "\n", "yl = plt.gca().get_ylim()\n", "plt.fill_betweenx(yl, fit.model_settings['m_min'], fit.model_settings['m_max'], alpha=0.1, zorder=-1, label='rd prior')\n", "plt.ylim(yl)\n", "# send gridlines to back\n", "plt.gca().set_axisbelow(True)\n", "plt.legend(framealpha=1)\n", "plt.xlabel(r\"remnant mass ($M_\\odot$)\");" ] }, { "cell_type": "markdown", "id": "62bf5d5d", "metadata": {}, "source": [ "Of course, you can update any of the model settings as you wish before running." ] } ], "metadata": { "kernelspec": { "display_name": "ringdown", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }