20211102 15480100 1.0 FGDC CSDGM Metadata FALSE reach_Necanicum 002 423061.252976 445645.919643 5062754.124652 5099707.721360 1 37.354 Projected GCS_North_American_1983 Linear Unit: Meter (1.000000) NAD_1983_UTM_Zone_10N <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.8'><WKT>PROJCS[&quot;NAD_1983_UTM_Zone_10N&quot;,GEOGCS[&quot;GCS_North_American_1983&quot;,DATUM[&quot;D_North_American_1983&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,500000.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-123.0],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Latitude_Of_Origin&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,26910]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>450445547.3910538</XYScale><ZOrigin>-657.893292373024</ZOrigin><ZScale>4194304001953.124</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>26910</WKID><LatestWKID>26910</LatestWKID></ProjectedCoordinateSystem> 20220822 16143500 20220822 16143500 Version 6.2 (Build 9200) ; Esri ArcGIS 10.9.1.28388 reach_Necanicum -0.206014 831.903076 1 -123.994463 -123.698395 46.048784 45.714065 This dataset is intended to be used with NetMap landscape analysis software. To learn more, go to www.terrainworks.com This dataset was developed using NetMap by TerrainWorks (www.terrainworks.com). Copyright @2013. When using NetMap databases (digital landscapes) and or NetMap analysis tools, please cite as: TerrainWorks (NetMap) 20xx. www.terrainworks.com TerrainWorks (or Earth Systems Institute) assumes no responsibility and no liability for any information, misinformation, or use of information with regards to these data. There is no guarantee or warranty concerning the accuracy of the data. Users should be aware that changes may have occurred since this data set was created and that some parts of this data may no longer represent actual conditions. Users should not use this data for critical applications without a full awareness of its limitations. Regardless of the types of predictions made by the TerrainWorks/NetMap software, and including any and all applications by anyone, no warranties, express or implied, are made and in fact are disclaimed. Applying shallow landslide, gully, surface erosion and debris flow models and predictions requires careful consideration of other factors including the applicability of any models to diverse landscapes (e.g., is model appropriate), other important factors such as climate, vegetation characteristics, soils, land-use activities, wildfire history, and landslide history (at any particular site and in the larger landscape). Model users should have expertise in geotechnical disciplines in order to appropriately interpret landslide and debris flow hazards. Using models to predict erosion is never a substitute for field work and analysis. Models only provide an approximation of on the ground conditions. It is recommended to use both modeling, to provide a coarse level screening of erosion potential, followed up by field verification of model parameters (such as slope) and actual erosion conditions. The boundaries between erosion potential map elements predicted by NetMap/TerrainWorks software (e.g., digital landscapes and analysis tools) are approximate. Actual boundaries between mapping elements in any specific location need to be determined in the field, on site. In addition, not all small-scale landslide-prone features will be mapped and other features may be encountered during field inspections. Moreover, the sediment delivery potential (to streams and to other identified locations) of predicted erosion features, as well as any other erosion-prone landforms, should be verified in the field, on site. The erosion predictions in the TerrainWorks/NetMap software are based on scientific information. However, any erosion prediction will contain inaccuracies and limitations because of 1) the relatively short and unique history of storms that triggered erosion and that are used to create the science (e.g., longer and different time periods and larger storms may yield different scientific results, models and understanding) and 2) the incomplete scientific understanding of all erosion mechanisms. For these reasons, all erosion predictions made by the TerrainWorks/NetMap software will not completely identify all of the potentially erodible areas. Regardless of the types of predictions made by the TerrainWorks/NetMap software, and including any and all applications by anyone, no warranties, express or implied, are made and in fact are disclaimed. Shapefile 37.354 dataset EPSG 6.13(3.0.1) 16760 Simple FALSE 16760 FALSE TRUE reach_Necanicum Feature Class 16760 FID FID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. ID ID Double 12 12 0 Unique reach ID, assigned during reach delineation in Netrace DOWN_ID DOWN_ID Double 12 12 0 ID of the downstream reach CHAN_ID CHAN_ID Double 12 12 0 ID of the collection of individual channel reaches beginning at a tributary junction and continuing upstream to the maximum headwater extent (the definition of a "channel" in NetMap). At confluences, the "channel" follows the reach with the largest drainage area with the smaller tributary then initiating a new channel ID. LENGTH_M LENGTH_M Double 12 11 5 Reach length in meters. AREA_SQKM AREA_SQKM Double 12 11 4 Contributing area (square kilometers) to the downstream end of the reach. ELEV_M ELEV_M Double 12 11 5 Elevation (m) of downstream end of reach OUT_DIST OUT_DIST Double 12 11 5 Distance from downstream end of reach to the basin outlet (km) SRC_DIST SRC_DIST Double 12 11 5 Distance (km) from the downstream end of the reach to the upstream end of the upstream-most reach on the same channel. FROM_DIST FROM_DIST Double 12 11 5 Distance (km) from channel mouth to downstream end of reach GRADIENT GRADIENT Double 12 11 5 The slope gradient of each reach. Gradient is calculated for every node in the linked-node channel data structure. Reach gradients are estimated as the mean gradient of all nodes containted in the reach. The gradient at each node is iteratively calculated by fitting a 2nd-order polynomial over a window centered on the node. Window length varies linearly with gradient, from 50m for gradients of 0.2 or greater to 500m for gradients of 0.001 or less. FitElev FitElev Double 12 11 5 LAKE LAKE Double 12 11 5 If a reach is within a mapped lake, it is given the lake ID value, otherwise zero. Lake locations are from the National Hydrographic Dataset (NHD). MAX_GRAD_D MAX_GRAD_D Double 12 11 5 Maximum downstream gradient over user-specified window. STRM_ORDER STRM_ORDER Double 12 12 0 Strahler (1952) stream order AZIMTH_DEG AZIMTH_DEG Single 8 7 2 Average downstream flow direction for reach, in degrees SINUOSITY SINUOSITY Double 12 11 5 Length of the channel divided by the length of the valley measured over 30 channel widths. Basin_ID Basin_ID Double 12 11 5 Basin identifier - basins generally from NHD (HUC12) MNANPRC_M MNANPRC_M Double 12 11 5 Mean annual precipitation depth (meters) for the contributing area to the downstream end of the reach. Mean annual precipitation values are from raster data available from the PRISM project (http://www.prism.oregonstate.edu/) for the period 1981-2010. MEANANNCMS MEANANNCMS Double 12 11 4 Mean annual flow for reach (cubic meters per second) based on regional regression equations. WIDTH_M WIDTH_M Double 12 11 4 Bankfull width (m); from statistical regression. Regression terms and coefficients vary regionally. See NetMap's Network Variables tool for regression equation and source. DEPTH_M DEPTH_M Double 12 11 4 Channel bankfull depth (m); from regional regression. The terms and coefficients for the regressions vary regionally; the models used are listed by region in the NetMap online technical help. FP_WIDTH FP_WIDTH Single 8 7 2 The average width (m) of the mapped floodplain, calculated at 2x bankfull depth above the channel. VAL_WIDTH VAL_WIDTH Single 8 7 2 Valley width calculated at 5 multiples of bankfull depth for use in the fish habitat intrinsic potential models (IP); VWI_floor is valley width/channel width is used in the IP models.But note that using this index can result in less accurate IP habitat mapping, particularly using LiDAR based NetMap datasets. For IP mapping, you will want to use the flooplain tool at 2 multiples of bankfull depth, and then run the channel confinement tool, and use that output in the IP model interface. For more information, see http://www.terrainworks.com/intrinsic-potential-ip-fish-habitat-modeling-read VWI_Floor VWI_Floor Single 8 7 2 Valley width index (ratio of valley width to channel bankfull width) at an elevation of 5 multiples of bankfull depth; used in calculating fish habitat intrinsic potential (IP), generally for anadromous fish (coho, steelhead, chinook), after Burnett et al. 2007.But note that using this index can result in less accurate IP habitat mapping, particularly using LiDAR based NetMap datasets. For IP mapping, you may want to use NetMap's flooplain tool at 2 multiples of bankfull depth, and then run NetMap's channel confinement tool, and use that output in the IP model interface. For more information, see http://www.terrainworks.com/intrinsic-potential-ip-fish-habitat-modeling-read ValCnstrnt ValCnstrnt Double 13 12 4 channel confinement classes, valley width/channel width: constrained, unconstrained, transition and intermediate - see NetMap tool for additional information DROPMAX DROPMAX Double 19 0 0 Maximum downstream waterfall drop. User-specified parameters. IP_Chinook IP_Chinook Single 7 6 4 Chinook intrinsic habitat potential (IP) from Busch et al., River Research and Applications, 2011 (http://www.onrc.washington.edu/MarinePrograms/IPmodeling/Discussion/IPModels/rra1597.pdf). Index values vary from 0-1, with higher values indicating greater intrinsic potential for development of chinook spawning habitat. Index values are dependent on channel gradient, channel width, and channel confinement, expressed as the ratio of valley width (delineated at 5 active channel depths above the channel from the DEM) to channel width. Index curves are piece-wise continuous linear functions: Gradient <= 0.02, IG=1; = 0.07, IG = 0.05; > 0.07, IG = 0 Width < 4m, IW = 0; > 20m, IW = 1 Confinement =1, IC = 0.25, >8.87, IC = 1 IP = (IG*IW*IC)^(1/3). Source=Busch et al., 2011. River Research and Applications, DOI: 10.1002/rra.1597 IP_Steelhd IP_Steelhd Single 7 6 4 Steelhead intrinsic habitat potential (IP) from Burnett et al. (2007; http://andrewsforest.oregonstate.edu/pubs/pdf/pub3854.pdf). Index values vary from 0-1, with higher values indicating greater intrinsic potential for development of steelhead rearing habitat. Index values are dependent on mean annual discharge (calculated using regional regressions to contributing area and mean annual precipitation), channel gradient, and the ratio of valley width (delineated from the DEM) and channel width (calculated using regional regressions to mean annual flow). Index curves are piece-wise continuous linear functions:However, be aware that IP model results can be out of date, see for more details:http://www.terrainworks.com/intrinsic-potential-ip-fish-habitat-modeling-read source=Burnett et al., 2007 IP_Pink IP_Pink Single 7 6 4 Pink salmon Habitat Intrinsic Potential IP_Chum IP_Chum Single 7 6 4 p_trib p_trib Single 7 6 5 Probability of a reach segment having a tributary confluence effect, based on the estimated probability of tributary effects (Benda et al., 2004 a&b, http://www.earthsystems.net/docs/Benda_etal_WRR_2004.pdf, http://www.fsl.orst.edu/clams/download/pubs/Brenda_et_al%202004%20BioScience.pdf). For each tributary junction, the probability of effects in the mainstem channel is calculated as a function of the ratio of tributary to mainstem channel contributing area. This probability is assumed to decrease linearly from a maximum at the junction to zero at a distance from the junction dependent on mainstem size. The modeled probability for each tributary is calculated for each node within this patch-length distance, and the conditional probability of effects at the node is determined accounting for all near by tributaries. The reach value is the mean probability of tributary effects for all nodes within the reach. Source = Benda et al., 2004, Water Resources Research, V40, DOI: 10.1029/2003WR002583, Benda et al., 2004, BioScience, V54, p 413-427. FlowVel FlowVel Single 7 6 4 Flow velocity (m/s) at bankfull depth BFQ BFQ Double 9 8 3 Bank full discharge (m3/s); calculated using bankfull depth, width and flow velocity StrmPow StrmPow Double 11 10 3 Stream power is calculated as: PgQs where P is density of water (1000 kg/m3), g is acceleration due to gravity (9.8 m/s2), Q is discharge (m3/s) and S is channel slope. Q is calculated as WdV where W is bankfull channel width (m), d is bankfull channel depth (m), and v is velocity (m/s). BeavHab BeavHab Integer 10 10 0 Intrinsic habitat potential for beaver, from Pollock et al., 2004 (http://gis.ess.washington.edu/grg/publications/pdfs/Pollock.pdf). Source=Pollock et al. 2004, North American Journal of Fisheries Management, V 24, p. 749-760. Fish Fish Integer 10 10 0 Each reach segment has a "fish" or "no fish" identifier, defined by the tool user or by an imported shapefile.Note that in the Nehalem watershed, the ODFW salmon (coho) distribution was used to define fish-bearing streams. FISH_RESID FISH_RESID Integer 10 10 0 P_DF_AVE P_DF_AVE Single 7 6 5 A relative potential for landslide-triggered debris flow effects (including scour, traversal, or deposition) within the reach. This value takes into account the number of upslope landslide sources, the potential for landsliding from each source, and the potential for a debris flow to travel from the source to the reach. Values are based on empirical models described in Miller and Burnett, 2007 & 2008 (http://www.fsl.orst.edu/clams/download/pubs/2008Geo_Miller_Burnett.pdf and http://www.fsl.orst.edu/clams/download/pubs/2007WRR_miller_burnett.pdf), which were calibrated to data from the Oregon Coast Range following the large storm of 1996. The values indicate the spatial density of modeled debris flow potential - the model indicates, for example, that we expect to find evidence of debris flows twice as often in reaches with a value of two than in reaches with a value of one, but it does not provide information on what the actual frequency is. Source = Miller and Burnett 2007, Water Resources Research, V43, doi::10.1029/2005WR004807; 2008, Geomorphology, V94 184-205 DF_Junct DF_Junct Single 6 5 4 Potential for debris flow effects in any reach (scour, traversal, deposition) - reported only at reaches intersecting another reach (e.g., tributary junction reaches); the reach value is the debris flow potential originating from the smaller tributary in the stream reach immediately upstream of the junction; see tool GEP_Cum GEP_Cum Double 13 12 4 The GEP (Generic Erosion Potential) value integrated over the entire upstream contributing area to the downstream end of the reach. GEP is found empirically to correlate with sediment yield, although the relationship is generally nonlinear. GEP_Cum thus provides a relative value proportional to total sediment production for the entire contributing area to the reach. GEP is a topographic index calculated for each DEM cell: GEP = A(r)*Slope/Bcontour, where A(r) is the contributing area to a DEM cell from within a radius r (here set to the DEM cell spacing), Slope is the surface gradient of the cell, and Bcontour is the contour length crossed by flow tubes entering the cell (a measure of topographic convergence). GEP values are truncated to a maximum of 1.0. GEP GEP Double 13 12 4 The GEP (Generic Erosion Potential) value integrated over the adjacent contributing area to the reach. GEP is found empirically to correlate with sediment yield, although the relationship is generally nonlinear. GEP thus provides a relative value proportional to total sediment production for the local (adjacent) contributing area to the reach. GEP is a topographic index calculated for each DEM cell: GEP = A(r)*Slope/Bcontour, where A(r) is the contributing area to a DEM cell from within a radius r (here set to the DEM cell spacing), Slope is the surface gradient of the cell, and Bcontour is the contour length crossed by flow tubes entering the cell (a measure of topographic convergence). GEP values are truncated to a maximum of 1.0. GEP_DEL GEP_DEL Double 13 12 4 The product GEP*DELIV integrated over the adjacent contributing area to a reach, where GEP is the Generic Erosion Potential of a DEM cell and DELIV is the modeled potential for delivery of material from the cell to a stream channel with a gradient less than or equal to a specified value. GEP_DEL provides a relative value proportional to the total sediment production delivered to stream channels from shallow landsliding, debris flow, and gullying from the local, adjacent contributing area (the drainage wings). GEP is a topographic index calculated for each DEM cell: GEP = A(r)*Slope/Bcontour, where A(r) is the contributing area to a DEM cell from within a radius r (here set to the DEM cell spacing), Slope is the surface gradient of the cell, and Bcontour is the contour length crossed by flow tubes entering the cell (a measure of topographic convergence). GEP values are truncated to a maximum of 1.0. DELIV is based on the model for debris flow delivery described by Miller and Burnett 2008 (http://www.fsl.orst.edu/clams/download/pubs/2008Geo_Miller_Burnett.pdf). DELIV values vary from zero to one. See Generic Erosion Potential and Sediment Delivery Tools Strm_Name Strm_Name String 30 0 0 REACHCODE REACHCODE String 8 0 0 w10_25cm w10_25cm Double 19 0 0 Predicted single year wood recruitment, pieces/100 m, using LEMMA vegetation data, for the diameter class indicated, using NetMap Watershed Scale wood recruitment tool - see NetMap Technical Help for additional information. w25_50cm w25_50cm Double 19 0 0 Predicted single year wood recruitment, pieces/100 m, using LEMMA vegetation data, for the diameter class indicated, using NetMap Watershed Scale wood recruitment tool - see NetMap Technical Help for additional information. w50_75cm w50_75cm Double 19 0 0 Predicted single year wood recruitment, pieces/100 m, using LEMMA vegetation data, for the diameter class indicated, using NetMap Watershed Scale wood recruitment tool - see NetMap Technical Help for additional information. w75_100cm w75_100cm Double 19 0 0 Predicted single year wood recruitment, pieces/100 m, using LEMMA vegetation data, for the diameter class indicated, using NetMap Watershed Scale wood recruitment tool - see NetMap Technical Help for additional information. GT100cm GT100cm Double 19 0 0 Predicted single year wood recruitment, pieces/100 m, using LEMMA vegetation data, for the diameter class indicated, using NetMap Watershed Scale wood recruitment tool - see NetMap Technical Help for additional information. SolShd SolShd Double 9 8 2 Solar energy to stream reach (watt-hours/m2) as a result of current (LEMMA) shade along with bare earth solar radiation - see NetMap technical Help for this tool result SolShdMax SolShdMax Double 9 8 2 Solar energy to stream reach (watt-hours/m2) as a result of bare earth radiation and a watershed maximum shade value (0.85) - see NetMap technical Help for this tool result SolDifMax SolDifMax Double 9 8 2 Solar energy to stream reach (watt-hours/m2) as a result of current (LEMMA) shade along with bare earth solar radiation, the difference between actual reach value and a watershed maximum value - see NetMap technical Help for this tool result SolBare SolBare Double 9 8 2 NetMap's prediction of thermal loading for bare Earth conditions; users provide input data on vegetation height, width [buffer width] and density. The model uses the parameters of direct beam and diffuse solar radiation with respect to 1) topographic shading, 2) channel width, 3) aspect, 4) latitude, and 5) streamside vegetation height and density. NTLT uses ArcMap's Solar Radiation tool and it calculates incoming solar radiation for every vertex in the stream network (each stream segment may have numerous vertices based on a channel's path across pixels). ArcMap's solar radiation model uses hourly intervals on July 20, typically the hottest day of the year. Incoming diffuse, direct, and total radiation values are computed for every hour, with the bare-earth DEM providing topographic shading. Thermal energy (watt-hours/m2) is calculated as an average of all intersecting vertices for each reach summed over the daylight period. shdLeft shdLeft Single 13 0 0 shdRight shdRight Single 13 0 0 SolDifShd SolDifShd Double 9 8 2 Solar energy to stream reach (watt-hours/m2), the difference between current (LEMMA) shade and the bare earth solar radiation - see NetMap technical Help for this tool result SolMean SolMean Double 13 12 4 Solar energy to stream reach (watt-hours/m2) as a result of current (LEMMA) shade along with bare earth solar radiation, aggregated downstream creating a downstream running average - see NetMap technical Help for this tool result TrbThrm TrbThrm Double 9 8 3 The variable Solshd is routed downstream and aggregated; the values represent differences in these values between tributaries and mainstem channels, providing an index of thermal refugia - see NetMap Technical Help TrbThrmSc TrbThrmSc Double 9 8 3 The variable Solshd is routed downstream and aggregated; the values represent differences in these values between tributaries and mainstem channels and multiplied by the ratio of tributary to mainstem drainage area, providing a relative index of thermal cooling or warming, scaled by the size of the tributary (and thus flow), providing an index of thermal refugia - see NetMap Technical Help FPchg FPchg Single 8 7 3 The difference in floodplain width (2x) from one stream reach to the next, indicating potential thermal refugia (upwelling) - see NetMap technical help tdif_l tdif_l Double 9 8 4 Hist-2040 JJA air temp chg degC-segment scale, from Climate Impacts Group tdif_c tdif_c Double 9 8 4 Hist-2040 JJA air temp chg degC-averaged downstream, from Climate Impacts Group FDSum_l FDSum_l Double 9 8 4 Hist-2040 summer flow %change-segment scale, from Climate Impacts Group FDSum_C FDSum_C Double 9 8 4 Hist-2040 summer flow %change-averaged downstream, from Climate Impacts Group FDWin_l FDWin_l Double 9 8 4 Hist-2040 winter flow %change-segment scale, from Climate Impacts Group FDWin_C FDWin_C Double 9 8 4 Hist-2040 winter flow %change-averaged downstream, from Climate Impacts Group prdif_l prdif_l Double 9 8 4 Hist-2040 precip %change-segment scale, from Climate Impacts Group prdif_c prdif_c Double 9 8 4 Hist-2040 precip %change-averaged downstream, from Climate Impacts Group IP_Coho IP_Coho Double 13 12 4 FISH_Strea FISH_Strea Integer 10 10 0 IP_Cut IP_Cut Double 13 12 4 20220822 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvWmzJdd1JbZO5sn5Tm+uuQpVhYkACQIEqZbULTal7ghbtsPh 8BeHP/iXdIT/iCIU/tIf3LbDg9wa3C3R3ZwHAMRAgCSIQqHmetMdc848jrVP3leQLHZL0QRwCeWW Kgi84d77HnAX9l577bW0Mcagr8+0iqLAH/3xH+HtW28j2Q6wuzNG5HrQ1QD//Pf/EFcvX0UQBL+W 52qaBrPZDG+88Qa+8pWvYDwe49dV/FeJP8u/+/ffxNHpHfhRi+29Iba2hzCNQVW04L9t9+4+wmLa 4sXnv4JXX/3ar+35+/r8l/6sX0BfEDB65ZWv4IP5LTys7mI0cjDyPZQPHuMv/+pP8I++9nXcvPEs BoPBfzJYnZyc4Ps/+D6yNJW//3XV+rG/94Pv4PHRRxhtaeyfm2AwSohkgPxnUUEphd29bRw9uo3D o8cCcL8uMO7r8189YG1IPXPzaTx9+yaq0wyD8yNMggDjnTFuvXeKd979MdJ0iZs3nsP+/sH/73vX TXLT1KjrGo7jwnVd+VieZzg9PcVyucRqtcKtDz/AD374Lbzwwsto2/bX9vpX6QpvvfMWPrjzPs5f 3hOwCiMXTUuwUlBtg9YYOEqhbVqUVYWmrn9tz9/XP4zqAWtDajKe4PL2FXx4egtHsyWGezEmwxFe +a0d/PLd23j9J9+B4zjY3d2TN77uAIlVFDkeP36E48cPUeYLONpFGA3gOg7ms1M8fHwfR9MpTmZT PHh4F7PlI7z/QYQPbj2PMAz/kzs3AuZ8PsdP3noNN164hGgwApSLolgDkoKnIEAFRyFLMzhw4fmB dFx99fV3rR6wNqTYEd28cgM/f/xzvPfgbZzb3sGqUhi4wLPPX8NP3/oZTqcPcP/BAwGsK5cunX3v dDrFX/2bv8BPfvhv4eMUShsEgz14jkeUwPaFbehRgmSrxTP7W1hmCT76+SP8+Z//CXzPxwsvfBGe 5/29QYrdXMVOqWkwn89wenqIJLwMt11BlQpgN9UasI9TnitjYVM3GAwH8IJDHB09wN27d3Dt2lMC xn319R+rHrA2qC6cv4CLOxfx00fvIG9KLFSGsWkQ+i62dzR++u4P8Gd//i1sb+/jf/wX/+Ls+/b2 9nH12hVUxVO4du0Srl3bhXF8pIWPvNpBgwhABRcLeLpCq7axuFng9R++iV/8/G1sb+/iypUrf+fX SYDin9u3P8StWx+iKAsop0Uy9KAdR7om07YwLQSwCG4CWkrBcWxHdePmJbz37of4s7/4v/Hf/3f/ AyaTySfyO+3r81U9YG1QJXGCi9sXsBvuYrZcYWuSoK4LNKWDKxe2sTPZwe5BiXThCv9EAOAfdmev fvV3EMcRfvSDf4O3334Pr3z5Oow/gQ4dKE0AcVAjRlsTcBTm82NkQYpvvfFN6CjC7u4O4jg5ey0G BnVVy/Pw8bXW0lGRWP/Xf/onODp5JCDoB450Uo528MKXnoFyXOGpOP01TSWEO0dThdaOhK0L5Sh4 vofzF3YxO83x2us/xu9/4w8+0999X78Z1QPWBhXHovN753F17ypef/BjXBrtwugagVsg9mv4XoDR aIUPfnYH/9Mf/xFuPvMMnnrqhnRYcTLA8y+8DMeL8O++9x080F8AsgKjaoU4LgUgAAeNdD4tQldj d2sM0yj87Gev4/DhIUajiQDgxQsXMR5P8PobP8Djxw+xs72Hvb1zWCwWePD4Dra2I1x9ahvaJWCS rnKgfR/DYQIHLZr5Em5bQFcZ2iJDWzeyJCRQOdpDoyMgGWM8SpCnJX75y/fwO7/9u7It7Dmtvv5D 1QPWhtXWeBsXdy/h+x98H61RgC7huAUUGjhNib0wxRcuadTqEe4+rHH73s+xMzmPZ575AoaDARaL JfI8xXgSYp7tI5+/j6Cu4YcEFwemAYxjEA2G2C+GmPgJ6jZA1ZZYFY+g6gbv/OS2AEtdLRDrEia/ j9WjQ8Idzo9a7G2FiIIapmnRctNXt1CNgkqPBJhcQTGFxiiYYAAVuXZMJO/VNnDqEm52Cq09hNUM R7NCtGF7e3s9YPX1H6wesDasBkmCc1sHGGGErCjRJAWMS86oRr0s4RclzlOIubWLldE4Pl7gozvv 4uTwPkLPxXJ1jIMdg+0oQ+kewC0DmGaJtgJ0EEkXJwR3kiB9XKEyBl7kQ5kKTVHA913k+Rx5VmJr PMBoOEbiBwgdB9o0CAJfXieFoAQsUVQoRwBOwfJTruehgYtWaUB78jnH7bo7kvT5AqYqoIpSlgIE 49OTE+zu7n7Gv/2+Nr16wNqw4rZud7IrEofj40dIRzFy8j5piWpaYTVtsWqBaOxglAQYRgHuY4rD R/egTIb9vREuPHUVrqrhqRo69mHmNeqigA5jYovAilEamXKR1hlUupDnFk7MdxEfjNCmC+ROC187 GI63ESUjNFkqPJZ0aorDn0FjWijtwUtiKOXCNA1aClLbFtpYwl0eGyTcHf6AqMwAtRPA4edCBcef I83Ss6/tq69fVT1gbWANkxFuXLqB9977GZbnL2JqXDSzFCbTqLzIkuYnR3CXGq7rIB7u4EtPXcFw ZGUM1GqSXXeRo6pJeIfQroZxPaimQl1VAiGX968ir4ozAl9EnaZFUeYoiwzj4RZGyQQROzNXQ8WJ gJ0Q8E1jwattLRBxfOVAyI0gBanGCICRbDdm/fcKqlO86zCUx9FlA8fPe1lDX3+n6gFrAyuOY1w4 uIjVtzKkVYlVrRCVDsLxHtRwG6ascVpOpVvywxjjeA8RdVRNzf/nPg5QLXSbolgcI/QB5YcoSgWf IKJqKLgYRgPEQSQkPGu9daQU4fz2BZEgWLEngYefc1HXFZRq0FTksBpR1fNzVV0KXmlCFLssApni HwtkxKn185yBF8Wklo6Xx+mrr/9Y9YC1gRX4PvZ39jBWI2R1BerF61YhbYGiKVCoCvD4sQYDN0bs GrimQtsqGIJHVUN7SgScrmOE3HZFplCgdjQ8dktKoTYc7xTUx1TzHMsa4ZX4N46FE3Zt7KZEQ0Xu qhHw4dfxjwhCGzsqpmUpGi2tXfgEUdftOjDbfbG7o0rfdVyUZY08pc5sgqeffvrsnKivvn5V9YC1 gcU37iBOcHnnEmbzJfJhggYNWo54ykejeLSsBFDYKbFTYYPCPsa0hCyqxhUaJ0aNFYp0DpgCyo8F aEoCDgFEhJydwpxcVNdhEVD4vyTJn3RdHP+s8JPARGxZ3yK2pkFVlvJYfO080/G0RhtGQtJrDeG6 RJ/VNHZjKE9p5J6wbnqg6uvvVj1gbWiFQYjnbz6Pv7z3V1hGIVqlMHB8hP5IDorZRdUOxZgOVpUj 3Y/v2FGLwLEev9zRAdyCYDMHagXjhWi6boe8lvBWbStjIBsoAtIZ9d39BR+b4FI3tXBmFImuwYln QrLpE/7LPq6rtVBa3Axy1OPrlc6N4Na2qOsGDvkr38NgGCFLF/je976Df/yPf+/vfSLU1z+s6gFr Q4tv3GuXrqB4t8R8r0QVBMIP6dZB4/jw3Fo6LdLn7ISqhmMW2SRLghOAlCng+hPA2YFJCyDPYBwt 7ZhDKYJih2Y7HctV8azGjoVdA9Z1beTErI6Kj8uP2i7LbgVZTjfOrdJUjq9lpJRxs3sOQ8Gq5bLq boPILmxI8WhW4K23XheLndFoJI/dV19/W/WAtaHF7md3exdeqZE1LTJKCLj5q0sYj9BF4wMHocPO hVySQmWsK4IwWex0TAYPDgzV5e4IJiuBLIOOE+GQCETs3NZlgcp2aQQs6Y54N8hxrhsdn3xN183J 99uujF+bpinCKBIwkq+HBSZjCKItKnZ5H5M7+IGPZBghvfUAH310G08//aw4SPTV199WPWBtaBEg eBA8cgcgBZ42Ncq6gs9jQAEMq23i/3kwKNsGtevC51xHGZQx8N0S2tQoqGT3ArTBCGp1SM08tQkA R0J2VTLeOZ0IFHAdbfkp4Zqe6Kik22KXxeeXDsyOnxwZy6oUzy25P/QbuN2ZDTs341peTh5HurJa QFBGSP6s2sXW7gA/fu1HOH/+Yg9Yff3K6gFrQ4tAQHnDJJpgWs+R6Qa1JhLVnXknO5VWOqWWANDw Q66Ag3Q8LfHIku9uk6FVPpyYEoYY2emhHCn7gxFcP5RxjoCy5qX4BNRjNXUrXZPLEY93gHLELHOi lHRKnTShqCo5C/LJTTlO12EZVHVluSzXl46No+7akkYIeMeBH/rYO9jBT370S/H26quvX1U9YG1w ERwuUjiaz1CjguFVDHVU5NOdj41jrYJW3LgZzBsHMUGsrVDmHOWU3Po1dYbG0QhGOxgkA6AskD26 L6c5ju9DR4kA2HocFN6KgCgEux35KPRkyXgoHZ7VVFXc9NU1BnEsgBREofBq8jNwM1jXaEjQUwYh I6YRmQSBjj9jnhf46PYDfOH5LyKK48/2l97XRlcPWBtcfLNfv/IU3nnnXZEOyEExxzB2QZz8ansG w5s+fq5sWlQG8HwHTUMwsJs+EuUFv9ZRSHwtWzvleojPXZJBr6krVFmKYn6K4f6FTsZgRaIc3Tom XtTt1FYJ/8XuqhsYaR8T+YFwZeyY7KRnN4Yk8oWmJzCSEytLmLaBEc8sC3xpmuH4cIo/+CcvII5+ NWCdnp7IQxI4fd8XcOy1W/+wqgesDS4Cx2S0haZoULcED4OagNRxSXZ0s8QTQUm+hkfHjfWoIs6w 0ykogWgMXJeqLZrp2fbI0Ptdayjfl8dpixzVagEvGZ75bBEneetjn6XTZjl8BCsi5VaSNjPac1Hk jfwLZfs++xzSBRrIGEj7GD7mGvDIk+VFicU8xc7WOVy6fFmA6G8Wv4782B//0R8jzedIBhEmW9u4 fv1pvPLyq2chFgSw3p7m8109YG14xWEE11ilOKWjNTd2YuVpP7/e6gkRL5OiBQr+D8WcBJg8z8Vv VPt8Y7PT6ayL1zeEVL+HEarWIJtP4fgBXM9yTtpAdFNUtK83hevvkxdBwSkf8294s3P8k6/pPr62 l/Ho3BA6ApB8TKbmNNyCppW83o8fQIt2q7Ffw+ecns6g3ExI+rw6xc/ffxMPHtyB70coyxIvvfQy rl65JtxfX5/P6gFrw4sr/sALobAS8GGXZSeurufpdFNWtGlV6bLR67gljowlcYWkeqc0l+qAQRxF qclyPSHg7Wg4RTTZhuLBtHRvFHta0agRZb21QKaQlFILeztILVcD43ActeMryXc+51otz5IOi51b B0Z8nPGoxOL0EN/7/rfxO7/9e9je3hZwJNDeufsR3v3pW4iiCEni49yFPeycG8I4LZbzFKen96Ad H8vVClm2lNd588bT8vV9ff6qB6wNL445Hn2l2FSd5fsROBpRpssJDvVUIhMgf2R9qkRx7rqoKSvQ PrTnw+cCsAMq25lZ7dTaa50+Vl48QLngYbULl7yU6549PoWfXtfNEcTY1bA7os6TelJ+HXXq5Lqo I1s/j4hGaebXKeHlJEhslTWcKILatR3Z6z/5vvjLf/HFlzAcDjGbTfHO22/i//33f46Dc/sYJEOM JwP4vgfHUxgNB7h08UDALctyvP3W+3jrrdfkSuD69Rs9v/U5rB6wNrwIJlVeQTv2/o+un47iQXS3 zesAgJ8xsjmkMt2CEPmgkm6gRsFV7I1sh8MuZE2ar2dL25fBglQ0wOrkUCxl9GAIl2MpFeuUPVAE 2jVoLj+v7dhnUMCvfPhBgCzLRJdVV0oI8iAM5OOiqCeD1h1cE7T4OnicvbM9QXmlxhtv/EA4rgsX LuHBw/s4nR1LatD1G1ehKs+OpS4PrnlO1ArIEpQpQP3SS8/gp2/fwttvvyFd2s5Obwj4easesDa5 uo0gKSuesxiUiMIFhglwcjISQalop7Qrd3o0zYtk8dbdE8q9oEKbZchqDwHBoyPTCRYkwtkNOQ6l pBYA+ZxuMhAeKD16hOz0GH4ylBGRvu0UjK5N/AI/EI0VOz8JraitR9ZysZTxkIDJzEN2iTyoLptK RkzWOkmHz78m5J9++hpef+1t/J//179CntVQbovd/SFeevl56aqaxt5IimiVAEojQUA6vSgIJYTj wsUdHB3dw2uv/Rjf+MYfyM/aE/Gfn+oBa4NLtm20gSkbKLdG4Oc4mJxgb3cFXTp4vEyIUWikydLi iRUpjm5Pzl/4GLHnoDYGeUHOyWDoQYSdBAvG20gCc9epsYtqqkoAL9raQ1Pmsj0sFzO43t6Zg4MV g9pILzH1czU838dyMRcASeL4TLFeFqWAF7sty2/JVCvPW5JQpzg1DOU5v/jF53DjZmZHze6AmkBE IETbEft8qR3w8eO046GdDT+yf7CNxSLFm2/9UF7H17/+DSRJ0oPW56R6wNrwYodgVePsjAwi3SIw DQYqw2MTy5u/7DRWPMuRboL/a5ScyRBgCAS6MSirAos2QGYC7FnrT3mjswgQIkPoTnVsdBdPd2Lb 3dGltCPf5TaQvNZaRLouEulVLWDFI2Y+BpXuJOelK3Mc+Xt2W6aycfVcDPDv+Ye6L6b7DD1XfLMI qNR48WaSoOc0/B1YoCLnJXeK5N24HFg7nTKncX8iXdsHt36K/f0DOaru9Vqfj+oBa8OLb0/PU2LW p90avhPCFBpFY89kOI5xC+jTfZQHybI5pN86ie7u5k+U6w0c0yBiJ6I0FrVCSDmDCFCZ4GwlBdrT Z5s8sZ1hB+X5AlbVag43GpzJGiyguNIFiaFfZz/j6fBjd4jdlpGj58c2kwIw/JhS8DxfGjxxKaUZ oeOiQmU7P3ktnYyis1e2C0cLXDL6ii7NBmJwW0kPLoLWyckSP/rxdwQ8r127hiDoj6p/06sHrA0v qxA3cGl57DbQVQ5kJXQVwHG2RRDKOC1W13ysfWHsps5rUXATCANNqYFq4DkNljVQebz749dTxV5J F+PVnpzHrAWcMorRBtnzUWQreAQNyizkWNp2Nex8+Jw+le4xx1Tbia01YtRzrb+WHdP6cyx7x+hI F0agXf8IQtDLkoFTq71nbGvOv9o6nvL3IWuEteUNH4sLAX5fK0T//rkA9+48wre//W9x9+7TODg4 J1tIbiDJmf1tItW+Nrt6wNr0UsCF/RCujrGzxQRmglWJieviGAco+AaWN+56Y2g7KnZaHLOYtFwI nljTPXY0VLurIkPRhnACLT5b0jHVNXKJ+goscU+9ghDqlDwEMEWBbDGHjlpxY+DIKBqsTn5BOQFB oOjkDoZ2OBz3PG15J5k0CVh2nBM1fce1Ecz4evka+XkBwu712iNppusAKndsFqI2MmKemQYSVDkK C39nf3VJEuHaUxfwzutv4e35Ee7e28f29h6SeCihsVevXpcsxL5+c6oHrA0v7Tp4+bltRNEU+9sN AqeBu1BIdIlRlmJWDODAOodao7wnBnsy0p0FPnR2ysZFXbYoViu0PkWjMZRHUCFxXWK1Wp0R9uvz HwIB5QP00eLoWGdL2RiKWymhjdHzHiUHLhz2RHLw3KCurPSBYMIH8rQvvJUNsrAAytFV9FudXMIC p+W92I1ZLZmVabice+mn1ZH+fF6CrACbPE4lN5dW4MqtZY0oCvHCF67j9OQUy2qKD+88FsGppxO8 +OKr+PrvfUOWA/0m8TejesDa8CrLCgoFIl0h8g1048EUIVAYRG0J1XmlW02TJaPl/MbVqBprRaO1 D1M2aEvI8XSVVaLZ8igzqAwUSf2OA4qixIpRBXBqVGVjea3AWsOUUQIzO4bvuXBoFUNFWDf22bJ8 EqdSarbYpfE1cUPIjkjOhDoZgxD+nRaLoCNdWcdDySKRHZemBKPz4KKbaucDJldB3HBylPQDVLUN w5DRlx0df2v0mWeX6YeIfI39S+fgBIGcAB0+OsVrr31X/Leef+75/g7xN6R6wNrg4hv55OQU7/78 Nl56YQVfj2ByDVO2UMeP4CcJdDs8szQmUJColje8Y51CReiZKzC3IghduJ5GMSsxnIyQz+hAWklg qlBTegCfaTxLB4tZKRu5cmXQBi0i3xFy3NMlStrX8FTHf6KWtyc/FIH6KAsbuEpnBz4uAYqjIr/G 6sb0mcaMJdtM7dnXL04OwqbL3xPs2H3Rb4s2NJr+9RwHxQ2Cp0oKy6yG05YIpNPjVtUmTVtv+wb5 cgY/TgSk7WLBw+7+DrQb4rvf+RauXb1K8ceZH1jfbW1u9YC1wcU31+HxQwzjBgFJ58aBSxbaBypf 43DWotGWk6K+Km8NSnZSUAh0J1Ew5IEoIWhRFy3CQWf/UimUywbhQEOHClXRoilaeJFGuijgeg50 oGE59zVhrkR20MYJiuVcujgq4wk+JLqJM0WR2SScitoudlmULNjNIyUM1gTwY55aophnd0UTwkZO f9gtap4Gice8fb0yftb2dfjs+DwbgEFAm09nGLQ5lsZB5DkYDgfQQWQ7PNexywh7x2Sfn+NlpLG1 o3Hrg3fwv/yv/zMOHx/it/7Rb+Orr35NxK59bWb1gLXRZfDg/ofY328xHBJ8WhjmlaYFyukK8wao Ezol0D6ZgyMDvixRnVETWpN/su4MeuiBXg3aA+I9XwjwyaUEDoHJ00h42sPRTCvEY8bJ12hSdjJM fNZYkpcqSO97iMdbyE6OkM1O4SUDuBwTO6bbEupP3BmE/O8Eo6JSl9dAfVcHIB3RTkcG0Xj5/lmX I0k9FRXvWu4kQ4a+0j6ns9MpyhrpcgE3XyDe2UbkRyjSVJT27nJhPb8GieXUyhzKDGRE7WJd4QcO nn7uGlbLDFW7wHx+Kp70PWBtbvWAtYklfBLHrBJZfgfbWwbJwIWqSmBFYsvATSK4NS1gCCkELQo/ uy1g53WVFQXnNSGpa23HwYCUjkRvNdCxtTHW2hFQsVKCBkGo0eZUuysEDmXxDipFwOkkFF4IJ4xR zk4AncMNO8DqbJR96rbOknc654iqtlbL7NSEiGe3aLso+3me7dgD76IxqCS82m4AW8oiHBcjaipa hbwpka9yoKkQuAr+IJatpUfy3PVQ5QUcwzGxxvz0BAE1avEAnk99GFPJOALzd+Zgd3+M8STG0dER wrDj1fra2OoBa5OKnEw+R5sewlFzqKjC0zcW2NtzEfg0uLL3gso00IMQcaWRVzyl4VqfIkwrayBH 41HfxK0Z/bDKCjmlD46DUGbKbgPYqd2FO2pqVNRitZV8fL6aI9ABhtEQHsFO5AsGeQOUlFKQf5JO yD4eCXNXdSJSIb7JSdnHF2K8AyY5oO4U/LIlpCnhOlXaAEXdIq9b6Z7YIvJou2kU4tEITk1hKaDq Fm1ZSK812R5bN1WCW8sUaU+4NnajTZFiPp0imYwQjbdkE0pgXAtfjbJZjINhgvFkhPlihocPH8od ZRj29jSbWD1gbVAVs2Osbr+G5vA1jLZy+DcivPJlmevk86I4Dw2MO4XKWwzCFkt2HDkN9mjzYnkh ShA4BvE4mfd0inMkj4wBpPDhktjutEvWlqaVN3JapShQoqxLHJ4eItKRAMl4MJZOiGEXLs9sOOi1 1r7GZiBawBOw5PhXN5br6ixoOArK7SLPcHSnpBffeXv4zOcXZ4mqxqpqpJN0q7JL7oGAkxNQWe/B V1Tn12KV4/oxgngAk5M369wsTAWPlDyXAeTckiEaTcKfvx97Myn6MmuY2l0GKByc28Hd24/xxps/ kiCMS5euiEKer6sn4TenesDaoDr94E0cfvtfQp++Dv30AdzhdbhBDNphWdDiDY4Lk8RAusBEz7Fy J8jYfBmrJi+rRnRLSRSJEPNsMydK9hwrODIWBvRDZ+dk7HhYtIQqHsRUWBZLzNM5UrUS4GG3FPtM 3LH+73w5jueipMiTYEJ3UlcLOPG5+D2UQFDCIM9/FsJqb/1IwottDsGKGit2fSTfFX2tSkTVCoPQ Q5CM5Dnn0xNkxw9QtgmGyQAt8xZ9jWA8to8nHu+BKPXXxP7axSIcJJifTlHlOcIoFP0YR1K2VgK0 FM+7Dra2RgKqtz64i7/85p/hmZtfwPXrN3Hx4iUbrBGEZyEcfX121f8T2JBi91IcfYRIzbB1YYzV nRNgscDo95+DS5AahNJdKeb9hSHaZgrz1o9xcf8Ig+Qqjs02FnUg7g1ZnsHtEnWE3Lb30PDpve4C pWKUvAefwasuBQItPIduD2HnWroSnRQB6Gh2ZF0QJvsInMBaydDvBq5wV/VqIT7werQlgBGFkXwv uyl2PVZ4aruw9WaQI6NEfdG3qwut4LjJbWGbreAGDgIm8IShAN1Q7yJKApw8zJEuT+GOQiSjEUL6 GjaVbA1pOMjtJANbz24w6dcVuRgPL2E+X+BkOpW0oCSJEcYx8jzD6nSK7YNziIZjxHGAF168IXFl v3jvLfzF//MnePXV35Vznq+88irOnz/fd1ufcfWAtQG1OnmE+9//11i+/n9gFM7g3byJ+CkFZClW P7yN+MUrcBsPOOEdnQOT1cBsCUcbeKe3cRC02B7mWJUeZnWDk3AA7bsIilMY5WNugBm/z4nklpDA VTY1MkWLGevLLhDEMxrjyh0eFeJrC5rpakp4wsH44Mzbym4UrRi0rko4DHntkmzkxCfP7bmNfE1n BdM0iHzySNZWRroxzzuzbW6Zap2vgHgiCTzCNUnGoScRZoMoRBWdR8iNp8/Nn3V7kDtGjpmMJHM9 AUmCpWQqdt5flDqEoY/FMkOWpihOpyJKnZy/hOPTY4zaBkkyEilEEPp45rlruHzlHLI0w1tv/RiX L13CwcFB7/rwGVcPWBtQh+98B97yDfj1I1R5C4bexDu7KB7dg6a3+iCi6hNm7qA9WaKZHiHPazij A3FwcCcBdFRBL1bwqgWiBWAGW1h8+D62ruxixyjcT0M8dM6hUttQTgWHIMDIQjgou1EtoNzAVIjc CHpsu6I0y1AVlYxmcs+nrPCTo5yMeyFHxRb56THC0Vh4KZ7fiNZJPKpshyc8Fe8PxSqmEL6KAPfk DKg5PnjVAAAgAElEQVRFtlxiMhkhTuIzr3pnLX1g98VOkDotpviIJst+r7g6yGkRE6sZfUalP28L rayCnxd+zwBh4CPkGRGdLcTLXsNEI6SrpQB0qAfyOHqgEQYBFosHOH/+Akaj8V+30unrM6kesDag 2jLDYBRi8MINtHUlXI6qFqg9wN/dBsYRw/9AiwWTrmC4zr98CQYByuUptCFv1MKNlJzLuG6F8XaL wNlFPAqgVjlWtBteHCO/8CzaKIaL2OqdXOYUckPYoFI+DD/uhCBN5jlA7rMj4Yhpeae18HMdLMF0 HcQGzcqgzlIsixxK+/I6JMSiWxjwuQhYa0BbrTKkyyVCTWW6I0uDLC+wd+4ctM+4Li4116rztYGz zM5oKnZknVvExzgymxjEb/zrcoqzI25j4PPUhzIJ3lt2IDYZJsjKVeciwfMfm35tz5oczE4XmM1m ohVbmxL29dlUD1gbUJOnXoBaFgjKEG45h2pKGAJX6AtnZbIWRpVosxymKcSdU+/tSFhqrSqo1oUp 6OXON76LOj9FoAy8q/uoZqfIT0+R3fkIOMyxpQqMnr2O2rhQ+gClN4RrGqTMLizIfNNK2Rf5A9X1 PjzotnMKPROErg1KbSo03/iKidQ1if0Mmkk6hdV/8U7RCSKrinddrPJcchIbbgJJvJNoL6kXqxAM xwiiqIsI69TwnW+9PXLmppHnRy3gWMnE+oj6b742lpUvUG3PbtFuNddGfmJY2AW+xowN68I8JLGa 2rVue0rJw+nJCu++9xPpGJnI08eIfXbVA9YG1OTaC1g+ytGezuG7Kzg8cAY1UwGatEI7Z8SXB7Pi Fq6BOxnDFCncYQgHkRw2Y1nAlAa6LRG2K2DZwB0OUZwc4+QXt3D0/n0Js7iw6+CKkyCvCjnxyQYV TssC5iTFrPJgBmPoMIbh4zrBWaCqOD+I6NNKGc78rLiNU7S9oXrdgcoieypUlajKDC1fGyUMriPn P6tVKm6i1EuxW2lcHyU7ONfFcLIF19FoyT/J41tl/Pq5PUVBagdErX1dDLBg2QzEziu+MwgkyFII KqAqm03eMD7ptigglTSgjvSX0ZRjppwiWSFsMkxw8cou7t99iDff/KEcTt+48TTG43FPwH8G1QPW BhQ7Bz24CJRbMM19KiOhXLYwMTBLYVaZbBENu4yJhtqN0D58BEQXRPxodAuTFXDKBnHkIbh4ABUF UE0Fp9VI5zxhyeFFPtyIvlY5InJVTgp3YhAePcDy9j0cziIU11/EzI+wu7NrwYm3eNzAKXJD5Nca m7RM/ovcEce9znVBOC2RAATikNqmHupshSZfyTavhEKaVaI8p40yv5bxF9vhRECO7qdyvkOHh6ZC W9gOyTo1WB5MTnxUK0sAsbPpznrkue1JtNTa1M92Xd0ROE96OkcI1lqwaoHYExCklpYg2MrPa6Bc ha3tLfmZPvzgPr7z3W+KBc8rr7zan/B8BtUD1gYU31s6mKDBBE1F/qaEohVoPpWNnBpQb0Tf8hZq pKACbuhq+RoTGaCkJYwP4xWo8xqmauAMh1BeC2/LweDyJSSnGbRnsH99F9GYpDNDH3huU2OwlWAS tXj4s1swF25gSjEqQa2KRDzK2z/xUO8IbqunspyRAAoJeGq0OukCt4Frp1IvSuDUpc0wdDSG4xHG CTVijpU2UCHf6cXkcTqfK4IkH0O6pU5r1fJepxN7Wk1YLRbOFHpy7HNbCV6U18ZDbUovrArfurZS u9US4HJGkFnFO5/Hc334KkGTNTCBCzd2xbqmWtTys3uRi9APcfXaBdz56AG++91vyubyyy+/Ygl6 bkL/RvJ1X59M9YC1ASUaKT/B1NtH08SIlw+gyhJ+5KNJSzh0D4hDualTPgHLgbo8QtseoTjKoKMd OBVgTg+hshQI9mBm96EuXYAXV7h4Dhg6ByhLQM2WwN55ONtDmHQJdVwj8iKcf/YpZBjh3iCBHu3I ls0mO5PU5jbNFYBhEXwkbzAKpSOh7UuR5+KgwOPnqirORkkeMysngqImC0DmRGh57MizGJFH2DHu TP0uI5r1x7IZilafJa6jLQ+WfbQOnR3sH8tvdXqvDjRsErYdIx1jLZjX4956WUBYczQPqkMsDpnS 48ClDM20WB3myBcFvNhmHqYneTdyuthyd5BVj/G//ct/hbfffgfXr1/Hy6+8IjmIfX3y1QPWxpSC HlwALl6EWt6CeztH/sFtOH4IZzyRbEJaeCpHA/MWcHPUBJ8HS7RDjbpiYEQDsz1CeZqiCV3Ebg5n J4Zf7WPg0EKmRrg3gnbnUDpG2+ZASTtkH/4gweRKjNwEWNDmmFyOdDGNNCpiQexbh0+xgZFNHkn+ 5iyynnlj7Jg8Cjm78a4yFuTEytgLus6nE592o95amS7GgewOJbnnr0fcW/srkcN3oROWq+LXxZFV 4YulTTcGPjkbsiBlJQ+WvLeCVbtFpMkfCfyGOYi+i4yOqGkmvhe01+FIWKzsSQ8BWTsa+7s7GCSx 3Cr+1Z/+hXBuX/naV3vQ+hSqB6wNKu0nSFWEonWwu7sHvapFL8UWzHBDmGVofQ2Tumhnd+Fe3YH7 1HkgGKJ+fIz09jHKwsVoewKnjWAez2ASijAD6B0GmgJ6kkDlGYwXQXklyjZCViWYqgCnoYbJgKha ojEaTpWhrUOoeAiXmqkuF1DkDcIzNVZTVZYCSAy5YFl7mO7YWbyyDPLSWhdHbiM6qrLmWY4Npehi cM66ojVQfRy05Ki6sCr5tXe9JfqtPTNBk04Oa62YBTqi/DrfkLFn1I7VZzyZnAVwmxqzE7T3l0We okGNKAwRc6lBYt+3PNdaKjFsAjTtAEVdIG8WuHv/Q1x6dFmI+D5O7JOtHrA2qjj6OGiMC2crgVpt QTUO6rxEm+eoCVipA+20aKcZcODDiTyY6QpmlgoHU+UGalxD+xFq5cHluU5dAjUPg2txHkXg2yBT d4ijYoRpM0aOCEVEnmwFtTxCRAqbo2BRQZkKphnAOFaVTv7GbuFqCxRdoITophzyWJ2fVQdY7LqK qkESAtx/EugqWj93nQ+L/Je97WsEnOScqDst4uesv7wVkp4FWOguyJUdWdOBisWyM2/59WOt7xhF s8WPE3jJP4nXVis8FvVhGbs0v4ZOHOiYj0IveQpl7Wuw20Wq9g18FeHazfO49cEhptOTM2V9X59c 9YC1QcX/8vuKJyaR+JdjN0B7UqM6nKIpSxR5jaagUwHfvAGKD2eItw1U5QBViPAgsuEQngNnGKNO hryKhvJLKCck+QRTuVCJJacbaEzrCCc0tvNjOCEToulEOoPvaxGAGnYkZQHFjRqth+lg6nEE5dmh BSme6JCkNhzttIeKt4rCI9lcROo8K1EJWCmB2M10ItT1z70Gg7UjKGv9NSTx+X2qYXyYgfIsr2V9 5K3CU6QJkhbNjqxLp16DaJeNyKNnOxbapQHBirKHiqp5MDuxkjMjAioRjUDGH2CtyZLwWHqGcQPK KDGPLhA14iRBGEW9Ev5TqB6wNqiUYrKzj0DFMHkOsxWiPJ0jXy6QzwpUaQHD1JowgLc1RvnhPYRR DH98ACfyEYy4UfRE/sCQCjfMJMfPjWiRsg1VNCJfp1Ke+ihaJosQU5JW7R8BrGgCN4rFRYGx9arM 4FUpVLaAR4W5Y9D6Idx4YMGrcz0witbFDmpo8ZNfE+htq6zPOvMKyWkZJuhIuJgAHfeA69FvrZsS EOtkCutOTGIJG3aPdglgA1yf6K7s77ALXO3iyWQF69iOS/IPK9v5sdY8GDsjEvdFlqEuK+HZ7EmR fQxrwWMNCdd/vbbJefT4GOcPnsHe3kHv5vApVA9YG1TcgDWhjzZnHFeMZnqKKjNo/Bjz2Qna1UrC IBq3xWBrD37aAssUSJiQTFM9DjAUW1ZAfQpVBcCKb2APikJRjoZFLHxYVaQ4LcdYwkHtd/d5Hd8j ZnmdrkoeNxqi8CJxZfDbDJgdQkeJ+G61tJ2RZyTR7iMk6LoGnhjyEXgAowkUbFms9YtYFBNIOmJe Rj4eGgXkhuw5zZm4szP9i6j9cjnG2rAL2QB2Y6d1mCBRb20aRLFuWjnkJgCWFQMqgg7Y7PeJD333 vLKBhINiuULDXMbhoMM8dnLUbhG0rHKfJ1Ly7Uz5EakHsEpXwuX19clXD1gbVoYJNrQ9rh3g4TFW 9zPEozGC4QTOzj62rlwAghaun6LOt9C0Bar5EfJMw6kHGJ3fBXLu52PMv3cHq1mFyc3LGJ4fAQ2N /DhGKVSDCY6zXVRFAlf+NbAdhE1itsERBU9mVMctESTCCKWbQPlDDKsZTLqQrqel06gCAqfFUHO7 SC7pybmMgAMlDVkmqdLsXkRjtT73kTd/CxVwS1gLic8x0AIJuqNpbv4iIf8rFNZSuYsDI8Bxe8rz HyJkUdGEkALXEJHwas7Z8xk6+9Eyugu9EGtoAhMlDhTbukpiwwh4LBkhxVKZQlO78WS3KH5eTS0+ WkePDzGdTZ9Y6PT1iVUPWBtUipHsspovYUYO6qMYcazgjyfYfibG7KM7ePD2zxBuT7AzqLF8MENR tUjO+3C2duBkHrJlDY8g9sF96HSJrE2wWkbYmQaY6BINcqTlALdxCdM6FKtjaUw68prWyizZvmlP upO1eJPbONnCUaGeNnCKTIBC+QO0QlY3cH1PuiSJwxC9E09dFLxgHUqxDp7g1s4S6mu+iZ+XbR1H wqJAxUDVksGrtYDnwGh4YaejEtKfN5YKKd1GeYTNToj3lcaDQ28v5WCVNRiHtGW2dsgcC5VWcASQ qGxX1oP+bFNpaTHLj/H1URRrO771+ZHY13Sq++2dCe7d/SVms1NR3EdRb638SVYPWBtU1WKG6s49 mKO7qLSCmZOv8aQzCsYOtp7eRnTsoF4WWM2VqNofP1phL86wfWDtVPLZAt52jHJ8HotVgDSZYNps 4fhYY8xuwg9wii0skUigBN+AAVXgHXPNzkpARjqfJ+ptEXKSYKfYkwS1DuFXOdx0hoBJ0HyfNylU cHCWQC3wxI6mS5Am4K1PeKyfFnVNvAUkeFlgIjSSJyKw0IDPuPRxJ3Fug1ZdeuKwkaK7atuigAcq PjmCBq6R5KDAcSU4lcPnirmLpT0f4h9yWTX1ZXAwEJ/8ElmaI1stRZjLTlZU+J049skigDjaacW4 BOBto29vG8mQkdDvHUk/+eoBa4NKRKJeKEfC9bQQDquJxzDpHEGpEQ0DBPsjVGGOYtpg0dB+Zh/B 7gTeKEIZjzCvPUyPFA4XIXI9QaFiGePaosIydxCNtlH4A3j0tRJhp0HRKkqY5JZPC9B0wRBrDZR0 Sl0HImJN0mK0ohlCVTk0O0O+kYslmqMajfKgogEQROLisD67WVfOMx2RR2jhrdaRX3b05GbOxnhJ mAXJba0F7Jz0SeBFWbeoeIPpuYh5xsTAVQIvN6QEOwJ+awSMSeuT3fMIuOKDxc6MqfcufK2xmM0R eC4Gw6E4nXJTuCyyswBY/g4EUA1dJCwIi4rf85CtMsRRgiROesD6FKoHrA0qWg5jOAJCD+0iRXZ8 hMi0KLWPKm0RrCL42wN4wwCOKTA4v4NwcA7BdoJ5E2BejLBox0jnJR7PltDBUEY2AoOpmEbTSGwX PahkJGMKjUR3MQnHoJAOy3JHaxmADWlgr2IJapmc2JlROe4mMNrvBJgK9WqGdLpAHHjwBHgIbNbm 2AZKfGycaw0ca3Yq20gm3dChgYMYCW6reGc0GZ/bZhky9UcpKutblAQNRyMh9+SSR7NJ0UzZYZQY pzjZLooOi6BrJPlHxKjCqNk0HkoU6Es/HmwhiQLpwkxt3R3Oto+dQ4RN/bEcH3+n9pbyyc+01p31 9clVD1ibVPwXnm+qMgPylbg4BHRGTlusZjnCkxyTtkV0cYzWbRHub2MZnMNU+TiaB5gZHkBzpPPg +OSNQktWS66fI4e/a18qa8IH8cJyOq1UWhtJTuYbnh2JHDZ3I5ollO0IaekfF6TEa67+xazBxcoz KAIfcQQ4VQ4sWyAeQ+nAJjx71hgvkCAIckocMa14E9wkSrINSfSy2wauCfIWDg+YqcMi8Bp2hA4i 7cCTpYDlnCrGfcnNYneaQ8dTPjZBpevMwLND06CtSuTlSpYJoiPjAbYVc9nDbtkG0h3VjoZr9T6V +WuXCH4dD6p5fE1/+HXgR1+fXPWAtUnFsSlbop6eIl+kGF2+DCc0mN99jOPDHNpUYtuyOxwhO6kx zUs82BqhHZ5HqYwcIasyF+J3SLcGARcreOTbiKJS7VuPK75p2ZEQHDg60VGUPNC8qDEO2JVZsxaS 2BJm2vLQWIgq0NyFYx9dENgJ1Z2fFAO2RjsjuG6D9vQhnCyFpqfWMBYi3dWBCGAJbkyYtt0KSXbH erCLSZ8dufg1HCML5iU63FgaaJ7WOA1qzUAN0+UuuuLHJQMku0UCvsR4EegUGofOFAq+ZDDa0bQu CqSLGeYc5zjKxTEMJRrd4oNSCgIPnTLo/87ujslBIofgLMnqRme6sXI07OPAPp3qAWuDqjU52oZm dgrR3hacUYK0mGH/6QMYdYx7P7uP+ckcy9OVBCVMz13HLAfihJsuhj80KHKu/HkbF0tnQPK4XC3h tA2C4aiL1LJvbI6EVkPJEAcmRiucLFMs9MgGt/JkReLAPNnSZelKUpfTskXhO0hCdkdUv7eI6CvV 1BiSA/cCNMM9JF6IreFEorb8MEK+Wsmfh9MjVOS9tI2lJ07P5zMh2mWLR0NAV6PmeFrXCHwPs1WG RIUYhuzyStncmZoiVoWyrUVnReGtjHQk5JtGQl9z15cROFCdX1ZLnp3kvoPh/jnpQiUmjBo0dpIk 9QWw6FbBCDI6SFjrHI6xLDmq7jar7/30l7h+7SVcvny157A+heoBa4NKuQXUOIJ37aZ0MtMHU7z+ v7+Fvd0I566fg/fieXz47gO8/+YD6J1tnCQTRAMPMTuIgCkztcgQ7CRlFd/srup0aT2bfP+MnxJO yUCAbs0t8d2s0SArKqSG1su1cEuMuBefedgwVcraB67BVugKUc9ADLoeVEUhGYkNDfCKEmMeTXsa +XSOZX0iwOVFIZpjgiVfJ90UvCfeV40jltDUfrH7q+n/RfByXByELgLtiMqdACWdFEfDphXvrrVP +7pECwaFUaQQORVWeS0BsfligTbPEAU+Io56aGQBwe6J4M7ujL8rj8sIikgp06DXOxOARI7x5FaQ f306TbG7d4DxePIZ/VvzD6t6wNqg4lIeWDDHBtrE8PiGGQwxO5qiqe5J9FUTjZBdv452ew8m2ZHb PYogJWFZQha4Ves4Fm79fFrHDOWkp2FeIWPcuTHj91Fw+TEPKcocKBOYBD6008gGjm/KM4tiCbjQ KKuFiDUJFpqBpr5BSwWp1woAsmsKVIgkYQKNhyydig0y8WR84Zy8xpxuDRxJa/7MJPgZrFqjKRVK ChLE/jhAzIwLnvu0WhxBOcquf1ZaLEtUWMOfhY/3hPQWuorpQ6qz5Unn0FWJrTiA3hrK72B93iOc lRxsd7eN3aXSmTEfQVhErDYQgyVJ2G2LMEiwNdnuwyk+peoBa2PKQNULoMlg6B7aKIRa4dK5EPP8 Iqp0hXnjY757FWb/BrzBEJ4fCg9k7YC5aWN3Yp0S1gZ4YnDneOAQpEg2kSvqAhn4Bhdtlai2G+R0 dPBDDGkQ2N3o8ZaQZDXBhQR53VYo6CYheiRyYK24HPB5yAeRy5GRqjtEZkc13N8T7kgHvkgU2DHx 9dFihl0MQZQkeiVYYMNdPc9u+DRV//Ty6u4IRR7R3RzKzwcLXDyPoQxBfh+uRkV9F73DKPwkwK3m 8KNIjpT5fGvBqtwoyimQBTARtq4TgfjzEuy6qLKzjSDtccoKaTrH3u4BkiTpyfZPqXrA2pBqap7Y PATmRzDLAouTE9RHM/gMDy0UymqFejBEs3cBGG/Do4Ecj5y7eKs1QPHNXNYcfxpoCiuzHCUPqeHI m7gsufpnAHIp4aMKJLdby0u1QERJgqMk2UZI/I6zIYFPQpz6J77hI822oxIXBjYhvIMkWK3tWnjU fDybYZmV0CSwOXGmCziHOeq6EIV61RgBIsaUiRSLxHhTSwdG8Wf341iXBLZMcidJrZXd2q3v/awg 1ZoEckfA7So7PQIdA15l1KPkwQ+kI11/n9jKGBu8ylrLONa3htbV1Gqv1r9fikcbkvdti48+vIen rr4k50Z9fTrVA9aGVF2lyE8eon1wD+3RDLP7pzi9dwIEMdKUIQou4nMTlPsDzGvbDQivoj35Y1XY 9k2bFyWcLBdNU1UW4pKgGRnGQ2CqxbW9pfPpmWU4bjWi/ubXD0hKdRokbhA5JvF0RQhr2iHT14o3 elQLkLp2mDdoyXMh8MXkQKHVLmbZCuV0LjgkJ8/cYNZzmCBC5cdoHQ+e54rMgds5cmZMc6aGiik7 1ozPdnKWc2sF+NilybN3t3tWlc+AV1c8wLjRG1BASh5Mbg4725nua+3Ws3OTENWDPciWr+l+BpL/ VDisn0P+o/Ax2xvyeUdHp3j15V2xWe7r06kesDakFGUGVYVykcLMFiJPeHj7EKYxSC7swTl/Ger8 AcJxhHxBkGIXwT+BvfGTNzbBqkKRpvCFtLZOmxyFdBCKwtt37YlJEBKI6OtHp1AHAd+0BDGa/MGm KNuRqEHBropadpFBNBgmvoxtjkPA7CK+OvcD/lmr5GUR4CjkJY3+WrQCNB7c1Uw6Jn+4jSgKEHrk g0R6jqLzoVp3NiLxFH2UiCyEhGfxa9ZWyLw5lE5QXrMvok6GwDLeXrRRWqOoCVxWyiFRXuLPRZ7P JuOsXSPsHaFVy1vBqR0b1+JQq0Vb2zQ7GAyG/XbwU6wesDakyNcEwwHc8+eBJIGJj5B9+5b4sKf3 51ioGohiDJ0Jku7A1tc+fFoos6Nh0k1lkOUNQgOM9vYFJAgeIsSUjZd/RpxbAzwKL60IlMZ4WV1J NL10ba4dq3LhqGzkO9/lQZTInR4TlDmiCXFe1fL6BWDa1oZUOBbIYoKqy7h4hTzLocIh3LpAWK/g 1h4UW5zWnr+Qg7KBp2ubZfsx1tr22Nomc4nKg5tWBKZ0Z6Awln5cDuO6uq+xMWUceUnCU1P2MccG gg9FpRx3qVWjbmu9DZQbQmsO6FO31lk12yfulO98fp5GcczsxaKfWvWAtQHFN0l2+D6w+gBRsILh m3Axw/VrAzx8VKD2EtTbN4D4GoJqJIGifM85JbdpNm+vMuxkGqgqxDiO4LdDUYFn7Uq8zOld5buR nMNQDMoNIDVZ5KXZZXDrRQKeBDrfuFzn09YFaSa6pdj3sJvwUNmI0ya7jTy3Cm9+P4Mg6NRgo7s4 S1liyHZf9uBYTPsILON9tPlcOi16dDWDHaggPEvp0Yw0E4W9PZD2XD4ucZmxZDxc7gIsuu3dOsSC t4JLClnJZwm5r+C1Ht3Buu2ozLrWbJAb1cACJX9m+fk51nZjI4GXvwO+XgIwX7tcDMgJjrW9WTuW 9uc4n171gLUBNX9wC0c/+lOEeB+DrRCt0WiGE1z8qoPtj+YoJhfx4XgLJ2GNJkihEp7bWLM+CdGp GGfVYuCXcsISjLZkFHQaerLzDR+KGtxprKGddAwcsWplb+58HqhYLkcu7aQTsW6eHOfYuQ3oc+Bo mLbCcjlHlmei++J4RFAS76y6Ous26BWVF4WMVGEQSHe35pKE8FYjlLxzrDM4s4cogvEZaBAwM2Mk CILdnoxq3bkNHSnEvli09baoEWNHx2WDOKkaDxV9reQYmvo03u64ohNzvACmk2rQuYGnPASf9Qhq 48D4e7VvDdlKGqtoX8xWePjoCNPZQl7bdLroXltfn1b1gPUZF9XUq/vvwG+OodIFVqcnEjnVrmai Kud/2bPxBTiTXYSOTVymp4GIGIWYppFnC1OuwPdYMBnD9Tk+2bw+4zPUgWOWgdINWhFlWjmDOH2m DsyKXBbFn2xjeMvHNOlGQKfJ+AQKbeGgdFqUOVOWbTgpvavEjobbvpJXzK10ZUo0Uy3aio9RiU/V YODDdTy0GW2R2R2xSwtRcDSsUugyQ+iG4gJBgKSHFvkvjogNNV6VDTelIJXHjwyQkMWDrAXpW2VQ l/QSs9oxdkFU8pOKEg1ZPJD7TNpHK9+S9mfBFuT/Og0XQZN/bQ+0WywXKd78yXtwlI+D/Qu4cO4Z nD+wd4lffGH7LCG7r0+nesD6jItvknC8j/rKK1BzF052H80sxeKkxOmDBeJzezhSY1Q6gc9bPPkv vvWqEi1VJ2RUHSdDR861vpFgRmG25WC48qfg0ka0syvhmQobhLK0PJcVS7rIcutHRcBj+ITTKpRZ gcYNhBjnuGcqUuDdrbDwW46McBWdFmqbHuO0Gi2Jb4Kg041YMktRS2WIYyIIdZ0IIcGuLqBMCcXT GeJQlsmIRvBzGzsGN00unZMb+HDCWHy5xHedXVJlOSytNFC2MoZKN1Yp6FIDlQeXOtUulJXbUR5H U4Mm0g1J4bH+7/kqxy/e/wWytMTO1iVcuXIdly5eEkX7ugscjcYSV99zWJ9e9YD1GRf/iz68+Dzm no8suw+zuCfpNvkiRzYvkG17mNWepOn4H5MvyAjHEIZOoCmix06tLfENXcKLvBG77yCXxTcaRzV8 TL+UUuNV1xJo0ahWOhpTthLFRT2XkgPlUsIm6LhAG2Pl8g3PY+xKXhMlBeSf5GC5LM58ruTIWYCp gjYEVP59g4YdkKlQuSVqbt0UCfoGdZoK/LjiXFFKd2ctspSk+JCLM4a6hxyqqMROxoKWCMaeCGfF FbTTKLQGCbep5KxkDajkOTNye45GXbJj7aK/qGBvWixnBU6Pczzz9HN46ctfwZXLlxExmKO3j/lM qwesDSgvGcPzB3jwIMPpT2/DWZxCzVaovAhzM5BzGV9u3Fy0nWOncCeSvGy7EWt7YsFsfWfHsVdq o9MAACAASURBVMUS0tQXGQG3guGnDD4VvyvGw9PJ05Ld1ERx49aI5xSdOnOUrUIU+/BDbhGXctLD sVJHVvJAtbo0TdAoCUqMBtM1Gln9W3dQcWPgOAkGQ3idI6kVgsrZC1pkTMNJEhkHHZ8jqw1CpRSB G9AWEfzRkOHRKKsCTbaAU66gqmMof4LKuKg9jcbTEldG2p58lkSeuTUG4Qhew99BI+dKjetInJkT idkVWlRsEiV0g3zVfL7Ci1/4Mv6L//K/sur9vjaiesDakIpGWzD+DmZZAOSAG42R71xAee5Zce9c JyhzvLM2xrYKnscUOQKtxCVB4uHL2soAuhRmsSGnzzq7LZEzkL95EucufzobGnZPXWAWHO3LeEXh qIYFO56xrINU5USmkx/IqEkw6EIjxLqmO2uhRKDKc/laWt/YKHtr2kebYX7fcrFAwfGTmrGOE+LG kZtIAm9AR1QJ27HP2/qx1ZCtToSXKpwImY4R8DVbt/buoJv8nUaZ5SiWKYIogp8k0riJPUyWIs1S +Z0E8KFqhZPZFGlR4utffrnXWG1Y9YC1IeUNdnDt1T+Erkvc/tk3cbI9wcn2RWwFl2QcFCFnt3IX spn3ddxeLRbw8yWC8UiIaPm8CCotYIlTZqfkbtdOm22DxXJhz03WcV4Ev6JEmpcIkhGqOkftcbtH Yao9Og6YJlPbsVJuA0W4Sl7Kpt4k3sCm73AU5WgqDgg18qaQMyIC0dbWlrh8ShdI8Oz0VVFs48KE BNeuPK6MrJRPZIWcEtGJglwcN6DskkjM5wS5bI4BSmypDIZJ1e4ILdX54lDqoKoKpCUw3NlGkMSi T2PKzcMHj/HzWw/lXCmMHeztT5CuCqTLBl984Su4TD+yXmO1UdUD1qaUcpCcfwpX/sl/g/1rO6hP vo9fVC6Ogwi+5h/LN5HALhwfmfHhrGaITINkZw86tIS8eEIJt1Vjtlic3cGt0hSL5apzGzWo4WBR d2JPJ5RU51bu73hmYnVbA1/LuMgQh0p7CGhFTB2EOHJaaQDBy54HWbCklirtTO9k5GQYKy1purh4 GzGvkeYrtEULn9IFueFjVBeTpx3kLQMIuQykv5XGaByiXkHM9FzTxXHJIsGHO9pCEcYoVYCoXCLK lzafUO/IyHrrwzv46KMj7I+2sVr+AtPpqXSSUZxgtLWNP/zP/1sc7J/D977/HRwfH+H6lWfx3HNf wJUrvb/VJlYPWBtUHMGC7QPAeQpL/02MHlSYazoXdJ5REtJARbftapgHyPGKyTr0bhfXBs46Mgr6 1g2UmirlIqsVKh2ipWFeCwSDCIOA4aLcOtJd057akJDmhzhuiS0LJQFWS4HSuCgpWWAXVLU2lozi TMnto8o+F83V2lJZhKI8nZHjbkbd8zCa8gFuFFuxZC5qRzrI2gQUJIjK3Y6w5OmsRooAQx9m4b0k V9CS8OJ/xdSfMEKofeQUtK4qoMjRuisB2nt3j/GH/9l/jYO9c3JXSKErgZEOD3S32N7ZkQXBN/7p P0Mpy4JQNn/8WF+bVz1gbVgRHg7TEO89AM57GmE2hZtlgONBeSHcIBGpAlfynu/ArT5meyLaIyt7 oH87OxwKPFdNLXKHQaiFcCf4Rb6PJNA0R+i8njqNg4AdVe/rs5jO392Q2zII6VvVWsdNfqbhSQtl DmUugODGMVzyaqLpsic9NrDURZzYLZtIB2Rcs4r1wGVElw2REOthWuCsnafWfl3r/MKOc1ufAbGs nTGwpDjUT+DmU5jFMYqM1ssOnnv2BQyZ5iyyKz7OkwXFunZ3dz/9f9h9/b2rB6yNKwdV62JWa4xH IVyu3HkYTRFoY6PeEQaoWiXJNI7cxVnQYYdAUlx4IfEZ99DM+XEDnwpKpkojR+RFAlYSeSWWyVYC sV7Zi7FdR/K3le2WqFtyqJXS7PYsSHE4tPd9LozHjo1diy+ygIJkeU3HUnviQ8CyMgo6JdhTIBq1 qKaSMdR3gFVeid0MWzze9jHU1Y6wHFvtUnFtDWO7Lvv84tggDgxGyHjOk6vDQ5wcprh65Skh+kXF Lz9eHxLxm1w9YG1gUdi9qAweMpIr2ULkbsMtMjjpFMgXaLllK3k2UyAW50sjinIS4lah3aDuPKxY mtqqtkZqgDD0EPqBaIpqGts5VqtlfcyZXNONYZIJSJ6KylP7uiQrkEoFEvUVXVHtBk60S2EEj1qs prZgJs0PuyeNJIkFBGnWV0qyNY+RKb3gbaAjEWMBN5cSC8/P2gAMbe+FOgcF61W19qNf5yVa9wZH vLX45ewkSxPjpNSo2gi//erXeiX656h6wNqw4jnJw6MM331rii+Od7Af7qEIriLQK4TNh9DZIZrT I+GnTisNvTsRpbaEw9N/XWQNJbIsE/sZkuEhQxlIUvs+hlFARRM0jxCZutWl56zTmvl9HN249ZOD Ylosd4S5OHqSRBdzv9xuCIkXnCsNkFLIxDMdcmAUupJ78wN5bZL23PImL7eJNrR7YXQXt3j0wXGB SHy3GmQdh0aFPUWs9KiSP3KO9MSoWIDSQDzoGTjB16aMg9PpEmnl4eYLX8ILL7zYb/o+R9UD1iaW qLSBvNFIcQm1OYehO4XyFjB5Cs8soZMtZKWP44qdiY22ovd5QAvjokAUUibQOXC6rpjuta6PrG4R ukY2edb6l/d2HL20gBCFoLFykBfZmR2NPZamPUsL5UHEpSTzZVwTSxjr9574BiYIUXkDId/pgpCW DTLOeUyI5mhXVvDFm8tuFQ00fJ77yAhIfRa1WzXSRmHBA2zaxNDRtKE5n8KAPmBrl4W6QVpUOJxm cHkqE3soqgCPHh7D1xO88sqrPVh9zqoHrI0snpYEiKtdJHkGp3oM7aagJ2irh3DbUgSS47JAsSox Gg4EiGbLHLHYCjuYFbV0XiUlC7wxVC5cnqJQxe5RE6Wt7TBHLB5E82kdawdDop6gx00ZwShPc+HH +EXkniraK/t2G1gwvLQxAk4EKdVWCKg4pzYrLyQNwo8HyEyMug4wiQPUqCXANPQ1koBmgA6KrMtH dDVCJ4InJn7026KAlKR9izII0bguUjpNtA1WWYmjwzke3XqEKxcv487jKY6OHsvu4KuvPtcT6Z/D 6gFrA0u7ESb+AS4WDbzZDxBFzPULZFxCW6HxIji+j5hWyLRQoSiUMxLj3LssQgJRGAaI2AmJEoAH zpXwVlTHm8CepIgPu2+V603aCBjR70qKARZyFN2KDIBgJap3Ju6QE0tT4bVI7tMmmN7mMvqpFkHg YTCI5X6RuigeLceejzCiQkEhIyfHZJyQ939dBH1VyVkOu7h8mWI2n6PqvLmSKEboBvbYm2c4jcFi Osf88Qn+4J/+Mzz77HPSdS3mc9GcjUfj/u7vc1g9YG1g8SA3DgZItELJN7TP8YdyBAdelAB+aB0X uB0jQNSVkNvcBJLjaZQr4RNDP0RVNpJ8w6Juqi5tsAS5K270xGKGPuoSImrFBOSc2OnYg2pu4VwB Nnl+Hh8DmM5n0o1RTrBOmZHncBxMZ49EXc55LoqGCPk6ChsEIR5ant048mdg+hc92FleSKFqg/t3 DrFa8uf1hdO7d/8ehuMQFy7sI0oSCXY9eXCIh3ePce3yTXzpiy9hPB7L+LezTcHok/iyvj5f1QPW hpWMYGWKtJrBUUPAj+SWUIwHuPkLQ+mGqiwXBki6prqClvQEQDue2BdTBkAgo8hTShwd6DDqCpi0 tH7pot2tbbJ1erDJy0ASxyJKlcSaLvKLgMZOiy4IYiFDT63GeqEzXbkheS9EffAEMJjQU5dWxc5u q6rgaSUjIfkyDqOJPCe1VBp3796Hr0d49unnsb21K6PmnTsf4sGjj/DhBw8xnlC2oDCbZtjbOoff /q3fxWQyOXs+AdHOfK+vz1/1/2Q3rCgryPIVsvwIbbkNJwrlbo5eV2tfcXZMIjEAnQsqBI09a6FT KAGGAaYEIcnxIwnUdUDsZHh4s1wsRcFOmxmvuyNcp4eS0PbOzPGsMHPtrU7RKbMHCUwiCpWvsZFX PB4WxweXY2OAMIjkIZnflzNmS1JuahhxtrFq+zWWrnUTBM+jxzN89dWX8MrLX5Ouid3S9adu4P33 f47bH93Co8d3xEni3P5VvPSlV3Djxs3P6J9UX59F9YC1YUVgqPIMoanR1kvATQSMbDACXURrEWRy /Pn/2nvvH0vPMzvwfN/3fvnGCh1JdmCzmSmSCpQoeTRBEsZhBjYM2zDgxcD+yYD9Vxn2D7tYYGHs GPZgws6uEkcSJVEixRyanasr3PTltDjP+90aSSNpZnbNvkXyPWShm11Vt26x+h484TznNCSoXvQp pzsV50o5BvFAZk2sfMRKptctKceVWZjHFpEyBeq25Oi50IGmohjXNspUqnN+JLHtnqtvB3mIzOH7 OpBBEpN1RcazIbom8HOTZCHvk2BX2Q0yVoymfiRbfrAj5n1WnywtRnqUPtBKpoMk0VDsSfDrnzp1 St6ee+6z+Pa3vynf12OPPib3fgafLhjCOmGgDipdLjFVNqrAlSE6B8y2rb3FhbDokOnYyPNKCIa5 fiSKPNMaLLZp4kZKzyvFaPlSbJQlScZ1xRmBCTZuP0Qn8Ymos9TaK2kNezGpzINkjmXLHKsQL61G iFUfNzvH1Z/YFNPJoW2xXB4KYQ0GW3AcX9vShA4qlyc7NqySFRcrRApNtcMpq0JG1y8WC63z+iUf KpLY1772jY39bAw2D0NYJwysiOpihaHXorBc+DTio0ton0K8jqEiSDK0a+Hsh1FbPD7Os0zOYlgR sV0U6UFRSPqyqyKpjCpxK4iOD5TLwtJExNlYHxzhB4G88fnQ2oXuC7qqazCbz4UoqWyXqoxuDa5C kWuy8f0QSp2SoFYOzjn4D8NAyJMbPBKZZwU6BIPhqR6ToOmlRc1Xi8VihiRJMBqNNv3jMDhhMIR1 wtA2VICXcCMfTRT32iY9t2IrtHYWZYvF85qYGYYsVaiDCgO5CyTJkJhIQgSDUqmbkmF9XoqdMUkp yVJk4uOuRHNV9Ep3EtVitexbS1eG4yTEUBTw2rGUe0ep7nwm21DT1aBiZFdT4/DwEEWRII7HGA12 oGhh3B8rc0GQdisEfg1FX/aGRoEubE+T9WQywN6929jbu4MzZ86YbZ/BL8AQ1gkDq6m0qbEKfGz3 Hu4l7VLEMI+mdp5u0boO0ZDhW+JSriOyxAhKE9V6+8c3SS/u5QicSw0CfS7DyknLt5RUP43nIhrQ DcJBkayk1SQJUi7BFpJvttKOpXweBFtDaSNtC56nY7k8l6Z5McJwgCAY6JSdvr3Uk3aa9Nla2V7p oAoUnMPxrtvDndu3cOPmh7h8+QqGw+GmfyQGJwiGsE4YkizB0WoBe5dbNu3eybkUB+9yQqMcbZQn f+agoq6JQapVJa0gic2ml1PXYTgYCJGkaYYyz4SYfN/T8e60Quasqw8I5cdLfDwrOp7J+L44c5Lg xA2B4lQO1hs94GflxcfQZKjktpHD/yDwsEwzZNkKnhv02z8m2DBGrJStIS2WhVpFld8K0XpuiLKD VHp1U2A2O5D20hCWwc/DENYJAgkhSVZYpUucGuwKGYhVTN8G9k4yWmZQVlQyyWyKmii6cbJl5KBa htV9Ko5YxKCTORYfPwo5u9L6LQmnEB8t6se1P7sM6ctSHB1Idvw6bAO1pxVnZdWxoSDnTpLiI3eG awcFYJXMUOQponiIkKaD/VCfX0c7QOg/4x2j7ep7Rp/i0TJA6A3gOa6o3qW1NTD4ORjCOkHgi5Qa rLIp4VG4SbU5K5jefpiEJEfHTDnmcJxCUNqplCSDTrZoWgCqbYips1pLncSJgbd9dCnt2DZWtDiV sxjJI+TnuXo2RjcGzrFITLwr5MZOm+RpbyqZq/F5Bb4o4lkF6jtACk9ZxdFqxpVfpQ3l9yWzMR9R FElryUorDGiBbKOlT5et7xsjL0agQvG+khby5yDmgxKiYeLhP60whHWCIO4GWYq6KWUWxFZJBz70 1sUiJ6BxHn3WC90uUezJGz2POYC2fow0lfdxI8ctISswtm80TWhVC8+mZxbdQl0hNub9UZHFPEG2 bQ71WqJW19UcCZPVFisjtmx6K8kbQ+37wuqrKipkdYrBcIjp5DTqOofvBVq5T8lEUcjjhXSNICn2 glOxiaGLKRWlPNBWFuJhhKLKcXi4j7K8IB9Lwrtz5xZ2dnYxnW5pM0CDTx0MYZ0g0G+cFVZrMSCU jpyWljnQuZNqd4orSWAdzwl9hDzTYXtFqxW2TxRe9mp57b5gIV1lMqvqbAU3CESprtpS8vsqtoOW jUXRaNcF0B99JC1kSy0WWsRRLFFYq9Wqj/SyEU1jCVTl12S1tHY5JX/x7OfOnfeR5StsbZ2D5w1l uM9tIqO8aFPDBUEk4RNOn57joGpKuXPMmWAzDnDv7hHefPM1bG/tiIziJz/5MV599RX87u9+HV/8 4pewvb1jNoifQhjCOkGYz2ZYrGZwfEekCK6toHxbO4N2lDS4Qlx8gfuVL3Mr3vuxTWRlxAG5tiy2 5EUus66ilGE7OOh2XER2iyTN5HF8r58nUS7RFTIc3x4HOJzNsUKEUegBbS3tW1s38Bj2EAZ6Xtaf 56wrJT/05S2Ohzh//grmi30RhpKkqMMabW8LUaVJgt3dXWk5WdGx7CKZMbcwnx+h4oC+buC2Ka69 9wreevNnCMIhJpMdfObJp/D2629iEA3x2c9/zgzkP4UwhHWCcO/ubSTZAtu7U7issNjueT4SrGS+ JRWOHB7r20E5TGb1xEBRKuIZKJrn8j7eC056BwOZU0najk6jkcG8+Ktz4E5lOgWpVNK3KMqMV9Po qhw5HUt1SI22gmF0l+0gXSVS4cnBMonQptpde7Zzi8l2MYqGQlhNnaKpWnRsIasCJZcKnotspTRR Uufll2izFIPxlPoMmbV5ownGD3hoSNJKIRZ7GxfKc/D66z/Ge9fexsOXr+CJJ57C7u6pTf/oDO4T DGGdEJB0Dg73pSWcnB1BcThuccits/5Eld5SDsCbQn3Kpz2yLNQFb/C0YJTzK4pJxQqGMoYgguX6 4qDAFq+hs4NSsrXToRD6JnA9qOdjcPPIzV1dMoKaRKfnV2mei9yB1VEQatkFXf/En71vS9kSzuZ7 KMsccTQRo0F+frpciJdVEA+gvEB7v9eNbCodeLJkUDQMRIdooODQLpkJ03IDqTeRq6pDNBqJkJaK /nfeeAN7d+/ha1//xrG9jMEnG4awTgj4Yqc63LYaRH4gaTicYom+igLPnjRIEpJqQ9LiMJw/xIbV is7xowzBHjpwwwFq24eKAkmf8ZUt6RZZWYvWiqp2fk0+Fjd7Xp+SwyqO1uxsKzkLY0sp3ltKp0/z TayTFd0g9DCfAResssQZAhZ810dDGUVNsasDL4zFC6vmoTZsBFEon+sotquOkOA6XUdmb722Sxwe Oib16NaVlsi1byHeHSBuAmTJCgdHN/AXf/5nQlpcNBjS+mTDENYJAfVXZbmCZzfwuB3kZqwrZF6U ydZQIePZTmUjsHVactUTGAmElYm2cOlQdxZUFEs2YURyYf4fyWx9jyg2Mr7Ml3RCM+UTlC3oA2QJ O6VGSxxKxQ9GfLC4IWRlZDueEInO1tEzJ0m26ToUeYXAG8OKaYnjwvXpq0Wxqo3FYi4by7UCX1T4 lEt4nM1xBkavd4pgtZxBdF1aYipyjUDVyLoataWgOhdDZyBk+tJL38WLX/myBKAafLJhCOsEgCRy /eYt7B/eQ1fTB73CvEhQdDmS1Qql04izZ14liBq6JCgEtG7paN5HQaetXT9Fkc6QiFbi3aOuRKg6 2SpyC8ihPKsXVlZs62SG1HYoG+3XzgqFSTs6BKMFSF69al77a7WYrWaIB/rGUE5yeBpEjVeaIXIs JCQvztqcCEEcS4ApPbL0FlM/DkmSWGvGWJ3JFrQPSBWnLwnPoLBVt5zrANXAaZGUNY4OE1QzniAx tIJBFfpzDT7ZMIR1ApBlOV555Se4dvcA47Ejg/d7TYEBPFRtgVbxhcswU610H7U6qJSDeFZiZckZ lh68S7UlwactglALPFuxfOn62ZeFqmacO+2Lw167Vcogn4fJw0EsxFRkqRAgZQ2yDWRL1xYyI4vC EJU8lk5rdnrzv+FoAouto+eJyZ60eS4j5rVBIP/hnIxkKT5etZ598f2r1VJEpXz+61xECfWh/qtq kCaZNglsWiwPF7h3e4lkUUo4bDwaiLeXNhI0c6xPMgxhnQBEUYjf+gdfwbJMsDi6hrETYZbu46Bq sEX/qrJBWdeogiHaUYS0SlEvu75C4RtkTqV4E9hHt3PmJFmBeSpWLaKVkntEW4tDudnrP86FFmGS OKiA59yMB8vj4Yj5PdKWlkUiN420fAnjUOyOWTUl8zm6qhIvdSVShRyzgxsoa0aNxZiMdhBFIxnS T6ZTbaHMQ2tuOh1NolTqU1HPNnNkj3Q15WjTQt4ozg9S/OgHr6NotaB2PNzCZ555Ds8//3lRz3Mr OZ1sGbL6FMAQ1gkAX6Dj0QgqcnCwaLCtJnjY2kLrr9C1PiZujtvlDHOpWOgaxbmTjqZnO6UFnXrO w+NmptYsswLRQM+62Lbxa4hKnW1VEEh1RQcFkh1bUhmgg5UYL3Y41G+RZQlcxWxC+rXz2LnQrWTd YnY0Q5rlcNoG40GMYDSSFtMPQjhqB1XFiogOEVp9T/Jhy8m2lP8tFst9tZf25z98bM6kSEJ83iTZ u3cO8cE7d/GlL/82zp8/j8l0SzIXaa2jI+h1C2kqq08HDGGdEOiqwoEV7KCdPoPKnqLrSqhkH0n+ CnLOfsIYkePD6xSCWBvsLRdLlA0H9ToRmeTFOXW6OEIRnkbAuzsRvVu9M6n+kXP2JWc5vFYmJFmZ IRW8B3SFCNhyWp7+/XpbydF+nS9EVzWi37xyZICvI+XZ+7W4t38TOeUZk10o5QsZ8euSYMrjVGoO 2nstGCB6s7VNjTy/psbBwQyzowSfefYL+OzznxO5hh7g60rS4NMHQ1gnBBxcy3wnHMMNz6F0piLk dLIES/q8u4DvePBtFz7c4+xAzpRIPpxhycxHiKaWuQ/tYdhK8gVOwmA7KO/n7Ki3q+Fg26e5X6tn YJwDCan1uiztrtD1b40QFaUIKhjoA2lKEnj3SA8tSjNySjB4csONJOdifNOKeJ1zqJOiRQDRD9Ll eSjtZirpPP3HLZYJutbFM08/K6c4BgaGsE4IWMF0HU3tYjgOrYz1Or9TIazGEu8okWlSo8RZVV8B yZ/xFk+83jU50aqY/CVtXlPpsFRWU8dbNB2yyppsfcRM8uG8ioQj5EatF2O+5HEY36UrqLYsMNrZ EYW6eL/35n8ymKeVc1FgEE/FBpluDfweSHR8XyKiVa1cl8fvCWu93eTnKpsOqy6WixRF0mJn+7xJ cDY4hiGsEwIOnZuG6u4IlrUWUAJ1sI1QRVhaKeqOOX69c0ND76xUDptFo1XqW0C2Wnn/flZNJKk1 YcmhMR1Gew0Uqx9xdKDHu2wMuYXTYapMW+bpD9s5VmpCKiQv3jX2Yavc9FEgKs6lliUfW1YFVGcj 8CN4PkmQ1Z0mT75/PPZ7Eap7LIkgj/I5UH9VNA2SgyXm8xXi4RiPXHnU5AwaHMMQ1gnBKl2hcUK4 7pYMqjWY4uzCDcdwqj00XY7WgcTS86ZQbInpi05akA2hLZWP61EpXyPwXbidvjlcC0YrOpJSyiAH 1cdxhDoyrO0QysBbO0VwY0gy6igiJblwTqZcOdFRkjytFejiKU/9V1nAUcBieU/mWhNnF2FAi2Or T3lORdzpK01aWpKhTfroDeG7AepVh2s3b2E4nuKxx5/GI49c3eBPxeCkwRDWCcHtu3eQFEs4091f +PPODdD65zHMbwohVGGF2u6kraJzAm8Dq7IW+5hKCMmGHQ2xOw0xZpBFVUhlQ60UaWWxXIoBH1tB oQmH7ZnWQrELlbRn5g8WuQg2ZYzOk5iahNagrbW+iiZ/ytdJOjIns22MhpQ8BNjqtuW5OzQIJPVZ /TmRbcvjcrgPR8/aqHy3Wht+MxQpw93lIR6+9Cie+/zncOXKIxv6aRicVBjCOiG4vXcHyyrBJPhF tTatZJL4KsLqAEH5Npp8gSSOpC0MZMsGzEsLtcRwVaiKTNq+qkhROBNp89gmsmVLskw0WXx/HEci B5A5WNuJ5IEhEmz91gPzxWIpRMZ5k7iaNi1Gk4kQHvoUHsk+lPfTY96CWzMvMUWSzuEHTP0Zomtp AuhgPJmIt5bf/bUcgWLVrgbYPHoTG02YY39+F8vlcmM/C4OTC0NYJwBc88+SAnA4qA7of4AW69W9 hdYfYB7swiuuw1vOkLUK1WCCkQp0LHzsI+4a2LWD0uu94RcVsjQRIlqfvUicl+dJxRXFoa5u+gE8 W7i208fT/BixPJbWkec/tZzV8C4xHI5RSN5hJ5WXPrVxRVvFFtIpbKTZUpYCeiNJESqgOj1oD4JQ pBf8OmVSo030cqHxGvmu9+4d4nPPfwUXL17c8E/F4CTCENYJAHP8ZuUMS7fAIB/JDyXwzqHlapCw HLThOazKe3CWGcLVXKqkYrANxwsRMk4eDcBLFfTzqoiapwIoOefyhaxYbVEqwa3d2vpYgic4i+JQ vCyQZpmIWNehE5LQIzbN+o0H15Qv6BtATYIkRD6ho8MjaSnjaIS2q6FcVmEKZV2iqyvYEpOjFwxU yrcZtwr0pe/QOtxselLR7e7sil2MgcEvwxDWBkFSIFn98NUfIcMcjl9hlb+LtsnBKZDrn+P1nXys 605RDy5jWSfIkw9wNkth2z5aNYLvWqCevZNbQsbCV+h8bR8jMfXcDtL7qr8lpCyCARfi0sC5Et0+ aQfDrR/LoZ6MSFRaIe+h7jrZKNZFrn20+gBWHROmXR60BzwJKUFZ0pdrAssORGZBkWjXR9UOhQAA IABJREFU0BGVxVonpn52RcL04ASA5ba4c/setqa7GI7obWXEoQZ/E4awNgSdYpPhWy99G99/92Wo bWB7EKNsMyyz98H0wR1LwXJ3aHwFi3bJ3hTd4BRWHTeGwChdAVEpIQ4iNBAHUC3+JJFw0N3VnWzj JFyV3vCUE5DgaI7Xh5uSsDhbcrgNFDJhqUaLG300Lep5y5JTniJJEG9tSctI0hIhaE9yMZOifRfp /FD86ZXkEnZitidyiyLvRaw6YEOI0QFcj9+fjZs3DvDC578qd4kGBr8KhrA2CFZAP/zpD3Ev3MfD 8XlMvRh5U+KwTXGYvgO3A8bD52CxumpLlPUCyqkQj0eo6LawOkJbZ7JdE2fQjvMr2snoZGUhLdsW AuOcjOLUdXsokWJpqgWodG+o9ayqJGHRB55uptRuSZBrIURGWxg+jspzWSnK/R4V+o6DMAoxYBK1 Y2PLOYc859DcQpbqob7kFzIoVQSlPuqi6z3otS6MWi+q2h9//ElJxTEw+FUwhLUhsDXjbdw3fvvr +N++/79rsaXjQnUWWqfFYZ3i+uJV0ScFg8ewahbIqttwrQShE4s0wA1TFJwPeSHazhaxKKsgiZ2n rIA2L6ys6NZZlkJcY6ZBo8PenTuoikKeQ0dtVge4wzENmHXyTp6iLSsoP0AUD+B7StwaSHraVobx Yq4M6LVSvRd0ocXh4S3ZMobhqB/0k7AsbE2nOg26YuVXIZcwWOYjhrJJ1H5YxtfK4NfDENYGwcpj kSxFRMk50EBxoxchqHx0CSuwGa6v3sSE7RjmCO0Mp/0BxsqHZ1voahsZFFrQJVRTjesAyuqQczM3 8jG0adJXi9/UYr7AbHYk2750NoMbhEjzUtwbRtNtmTPNFnN4DGHtWgxHPLFRcB1L+8OHIdq6RZpo y5r1WQ6rL1ZWPAMqswx5kaKuaGGjEIVj+BSLBoGeixUtyqREmiVonQbRMIayFfaPDqRdZXtpfK0M fh0MYW0AcuBLp8+qxCs//THcCaUBTMQBIonycuR9PHOZtwXmyVsYBKcReLvwlI3YtSVb0KXGiuYM NEtw6HBgy2yrbIFAdUgoc7A6lHmJ+TJFuljCpqLd87Bz9py0kbwfpACV5zvSMlKqLvHzzKGoEXgh fDo29O0fZCGorWwIObTm94NW5A3cDI5Hp9A2FeJoCNtyURbaXoYtZ1W0mqysCuEgFKEpXSD27hzi xS99DdPp1FjFGPxaGMLaCLSnJiuOrckWDtoZasbQd9p1IbBdbAdDIS2rsqRFC1FhYAOholc6Z0Gl pOA4VicBD/wo1lrgwJye6F0Dr2LCc4c6y4AshYcObujLMJz2xToVmrmDhcyxdIgEJFFHqhzZKPbH yS0fixESTKjWsgbOwdanNdSPObRfrihfoE2ND9tRaJtOvjcdZU8XB7aBnfi3h1EgX4PnP0eHMwl5 5ebSwODXwfzt2AA4aGYoA1ugJx59Aq/+4A0kRY48poleC5/pysrHljeE3VroaAHjKIQkEAZ9WTUc K4fdFbBWe2irhBoIdJ0jZy78GMumXQzv/xR8FSP0XJlBiecVzfHqRoiJAk6SiQSrMvvPZWvXC0IV nUiZNq1jxMTGRmLjczQScqgJjltEvuVpJuaBOd1J61JslW0Vo4SLpKGeoUbFii4eIBp4cFwLZd0i pyartbG9vWUOnQ1+IwxhbQBrmxe2SQ9fehjutxVWWYGkLTG0IrS86YODoRtKm8iCzFWsRhS6hmPq BlbRIV9QMZ5DtQWshjIEui04EgIx2I3QdZYcQ2vSqWUAT691EtT+/r60YjU3eK2C23mwvQgd28RK uzpUcFA2LW3TpbriYyzTTFT4LMZIYq7jwfW1cHSRJAgHQ3S2hxoNct4I2i4cj3JWMWgXsqTve+PY EvtFIaqk/HgBDo+OxEuLbSGXAcZF1OCXYQhrg+ALcmd7B1d2LuP98jqWRY5p2KBAg8hWMvAmgfG1 zuE28wLZ4hWNg6YaIl3W8LwOrhPAcnyQ6apca6cmZ2NkyxxWZ4kvel1q2xjbVsiTAlVO2mHsPTVR CqrxUBcOrMhGx/DTDkgYbiHxX73tDC1takothqD5MQ+pSWqtCqR6qi0XnQoQBDGUaLNYedlgCBnP jRzPRZl3SHmQbVlivcxEHd9zMJpG+KuXX8L2ZAsPnH8QZ06fFWcHCmEZTiFiVYNPPQxhbbjSoibp xc++iFvf+29YJgWycQW/UfDk7IWJMyIYEDBfMFAkHQud56MaDxB5dGsgX3RCaOw3eXqTHuXYvzZD PAlQJpWQlAoU/NjF6qiAHzJB2YYlqVxMpmnRHKUSCc+hP6PGkjRH2QUoXZ1DqJSHeBRgJ3ahuLnM lkjrFjU9rzwP4x2FyLXEbkb+aSt0ZS1VnEMfetrYFA0cu0BXNHBViJEfobF9uBd2cPtgiWt3Z3jn /TcxHW3h1O4ZbG1t4cKFizh39gGdqGOivD7VMIR1ArC/fwDPG6J0U6zqAgNGubODqnWuF/VOlCxQ UiAunzaJrIWlbNQO5Qy5mGTRVI+fwADVbFmIewK9tezQlUqHYycWWhSExlshqgXj6vW2r0x5pFwj T1dQJB0JRm3g21TJJ1ikJcKtXZwbBWCItKRVUOvFYb/Visxi4FMoyuAKqvL5vBSKEshXFazMQ25T jc+1Zgurq+FaEB1XQ0eKLMX20If1wANwHRfF6giHi+u4futNfPNbf4F/9Pv/DE8++ZRIMAw+vTCE dQLws3deR7BzFtHEQWsdImtzNmsYWlSlOzqbTyzRO5lhtVYraTPhtge741auQmrZKHxu5hwMHQux a2F0NhYbmqzuoMR3qpWW0q4ZNV/BcpgcTUM/hWjoom15qFxguZpBudwpMiqe1VsFq0rhlyu4fGaW I4N5baXMrWYjp0SkS24OxeWBAtPe+phorApWUCCMIlgUjnZaIMotI+UQxwk4XYnO3YHafQDbOyvs LK9h79Zd3Lh5A48++tiGf1IGm4YhrA2CBMQX7FF6BN86jdDbleH2srktQROBreBx0E5hJuUFcr+n Ax3E+YDneE0HmwnPVJ1HlBMwJUffFipXO5dGTouaOi1mCdJBtKtl4E53UdoqU3/FeolzIl+5qNsY qyTVN4S9gp1k11WFkJE2hNH2MZQlMCzV60MxGirpeZNo01G0Ri0ztRYDn/FfEZDZcOjQwNOhgrbN DhzfhVOFULVFXz80zQy1R00XlfoOVvMcX/rCZTPHMjCEtUmIsyd90FsbkTOB7+4iqTgXaiSePm10 Qk2vHpCDZJnh9Ok49K/SufINlMVWz5ZDaIpHKVcQHyy2kLKgkx4PtcUBfIbVcgGHMgKHkfNausAQ VKEixnrRmobkJfeIJCRXtoxCXv18jbeFZVHIYTVzBNd2M3x+bDs59xK9mQK8WDs6ZAc1OquB8kla /D5acWqQeR2PvEHN1hJu3cFzmJ2oyZWkZ2BgKqwNg9WN4w7Fy911xrDrAFlnISNZUZzJzEH6VZEo 6MogpKU/l/KEdSy9LcfDLbp1WARJjPH0km+/ZjwmLXMY3srjpildFBqdYEOZQd1IWGqW5nJawzRp QjZ0ViR/JonN/S0hFwbi+FDVSLtMAlDXga5sTZvedkY5HeyA1qUNOmokVAv4LWxXR8tbqpW7xbxO j/23ujIBWm4/WwzGAX704x/g9Okz2N3dNbmEn2IYwtog+GKneNNxR7AdvtjZbNFdgb57NaDYsmlp gT4uJvn0c6E+gJTVFNsvVAW6LAEitmT0T3fE0pjBEbyzkfkXU3TAzaSH8Xgi20RWRz8fbV+Wtdje 8M/oRMrKh3MwVmvi1lAUiIRAWwnAkMSeNJNWk6RH+2TOpPg5rLqC3kqZoOiUvyeJ8fH5fIqqxHy2 wmKewmFFR7NAtsCWjTyr4Cgbg+EQr//sNXzh818SjZYJUv30whDWBsEX73K5QgMfnUXhJxXiC6Cr 4FqBnOiwIGHlxMpFqEeCIejIwJmRDYsK92iMLpkDywNdgcVDbXksbggkC00YnHtR6Ml5UxTy1Iaq 9UwqLf7K+RkDLZiuHPgeosFAZlx6MK5EzElLGg7I+d/SbnKOFtJHSw/c1WCg7ZFF86UtbqTy68NT Wd1JArVrSUzZvf0ZDvdZTXl44slHkKxWQrYBU6KLHIvlAsPBEJcu0F45MLKGTzkMYW0QfPHOl0t4 YQRlt1gVHyArriO2LExVhLEXo6w1kUi6jZje8VSwQdPkMpRmtdLaY7nXc7I5vOQAbRDDcZXOL5QW TseAcSBPG5eqyqVCYurzaDSS1o7SitlsLpKE0I+kAmM9xts/Vk5CPo5zPGhf+1uJeR9vFF0lSTxr a2X5/voFgXxM73bKm0PP4za0wdFsicWswNUrz+GrX/2q2CL/Op0VK6+1LbPBpxeGsDYIzozefu99 DAYTVO2HWKYfwOmW2PLHiB1fvLE6bsrEg4q3e5rkHLpk0daFcyeqRjlHsnj6rOCyn5StItOiHSEs 7Q7RCnFQDkECciwPdZ3rk53+je/3oghbW9tS8XGgzi/K1o5kpWdm2sFUHEkZL0bi60Wwa2kCrZSl bZN5lp65kYbYKta5jSzP8eHNm0iWNR6+/CRefPHF30hWxJoEDT7dMH8LNgi+8K/duIHpoz6O6hso y3sYOa54tHud3cfX68BUedHbFhwa9YmrAq2M9fv1mZ7eIMqcimRSVZJ0Iy6i3Pj1N4liwGfbYlPD llLi7HtjPpKL3PA5LtKUnvHcRFLzVUtrKWk3dSVtWxhGx26jfHBmJvqeL/8tbStnZv1zJ7nyMfQg P8bt2/fge2M888Wn8dijT/6tZGVgsIYhrA2BbR7Tnm8eXkdTKTTeCpHjIO5cWEUL+iNTx0RyWYeQ aimDaN6FjOT33BDyY/p2iUJQLTDVrRrnVCQttoRaId+Jq4IdUZCqWzaSiijIaYPsaBtkPq74w9ed Hpb3rRjlDUezmbSEuqIiCTLHkHH2bDdLkSHIppBkZStR2PN5MDLMaSPMjhI8/uQjuHr1cTm9MTD4 u8IQ1obAVmyxnOOw3kdUxdgKY0xUjIDFUF6h9vTAnPULbWhIECQRSWMmIfUFiSYp6RX7KooNoyZE Qo/q9YCfFRIH3hyiL5YzISkSiwzZI71JrNsGeZog8JkqrRUREgVGV1T+KhUUZRH1cVWmZ1P6YDrP +Q3oryUJzyIEbY5nW9kqh2152Nk+hcl4sqn//QYfUxjC2hBY9cwXM1RujsDuMFUhxnZIXQHaupQW a+1QIOMrDrD7WZTooEhCFGhS0y7Cq0qM/GymN4s8op8bMaHGqXSkvRCMJh0O+1mhrT3ZSWzcLPJz OLuiHEG+lqjWVZ8/yPOdFsPBQCo6Pkc+Fof2FIryHIczLT6mhLFKZcXtoJYyUKR6/fAAp3bPYntr x8gTDP7eMIS1AbDaOJrP8PaH7yAYBpioCCM7wMDxUdsdMuhtG3VO3OzJS77lgJvHw7pi4hyL1Y2W Z7Vwahq9sBxyjysxHum4riaeVVkilSToQIiK20AOv1kRkXTohEANlZwBcSjPiopfu5+RabW7J6Qj rSmDLSoeTFNsWgmr8tfQD3R1JS2knnGxpaUpIY+3Dw/meP7KE5KMY+ZWBn9fGMK6TxCRJz3NqwoH hwf4/k++j//+0p/g1DM7+u6PRCFEU6OjWygtXvpg0r6r0xVPxyw//YdsE+UwOs9glZl4tNe2h64u kdcZfEtrtfRbr5on6fUPyLMg3gbGUayDUPthPWdSejjPDlO3jFJFSfupZ2aSKcj5lzyS3mDKTKtX 2R9LEPrtJh+L8ywKQVntib+WgcHfE4aw7mML+MEHH+Cnr76Kb333m/jBOy9j+/ltBMEp1F6Bo+oI h0UtinFWO4zospsKO5avNU/aOeZYoqD1SLQdBlySFTqUrovCrlFWGYomR2wPoFrmDTbImlzOdOj0 kBcZlvMFRuOxBFAQ3CRWqZZAkMi4ufO8QASiEmnf3xTysUi+/PokOanIepEoqysO6LlhFEU7BaK9 vEGCV1vg9OktvPPuGzh97qwM3I1q3eDvA0NY9wl8Ic8WC7i+j69/7Rt44Usv4C9f+UsJX/C3HfGY sutWfKSCQSQOBpZ0WtbPVSyUN2jxZset2+E9+NUKTtegCYdiL7NsV7JFpJlDYZdYlgWOZkeyveNc ap4sUaQFzuzuIoxjqaToRUWS4QE0yXI4GkqbKVH1jpY+cFCvN5XUo1JyoQ+Sperj/MtlOk6hFfms 1Dj07z+fbSSH9CTj7dNbyO7exJ/+X3+M23du4Stf/i1xFjUw+LvAENZ9AquRRy5fxoUHH5RqJUlX KNsK/+cb/xW7W1PAteVF79GyWOnbQRJE3hRwoSS7T0JGObbiILss4RUL1JaDJhzDjodyKIxGkwY3 dVmRYbY4Ql7mMrPq+NO2LAzGDEYNhaDyosBquZJWkK2h1mHZenZFM8CaVjaSmiHExZaQz41to7SH 67a0l1hImjN1Xhy6gzowki0frxevdsBwFGD/cB9Hs30hSkNYBn9XGMK6TyD5TCZ/vcanpODRy1fx X1+muR1TJmx4ji/3gyQAun+y2Vq1CYb2AJ7tyXkOSYJxYDxs9pwOK8dHIeGEFRo6M7DCoc9WVWOV JFIl8WuJUrwnIRITH+/g4FA0V2SRMAgxGgylRSThaN2UjvOifotYV1SchfGR+HzErrk3g2Dghajv pW3Ux9Y8wWkaVmQUsNbiOc8Mxp2dMbJsgffefxenTp26Xz8Gg485DGFtCKx4puMp3NZH1TbaQM/x EDDQgRAZQ4Oqa47V4qxm6ppWyY24M5CYrCBA5baoxCZZJ9nQy0qiuBqGVNBdVJsAksTkhjDQvlWi pG9b+Zg4jI7nWSQlObiWwFRdQfHz5SaQG8KmkwxCDuyFIPuQDFZmlD6AuYZdL3ztyYwWNPR95oKB mq3Tp3dw68Yh3nvvHTz37PMy1Dcw+NtgCGtD0PYtIXwr0FqllqHKPmJnoCuarkNSrNBYen7FiqXp GonpYugpCqrRG9iuI1tFmvmRVGjcxzaLlRXbUL6RLGhjwy2l53g6Kqy/QWT1RcJiO0pQW0XVujiJ 0iqGolVbt4h8niQ/EhVzBNezNd4I8viZJMeUV37Nan2fyGqLJVgvHFMMXPUdBJ4N159jPj/Ahx9e w6VLl829oMHfCkNYGwJf6CSJwNVbQMaf+k6IoTcUwiIp1S0Dv2wUdXGsWq9IWEyj6SwM2X7lOWqv ESdRkgMH2yQKViys0NbzJWnvaJc8iKTqmc1nQkBRNMBwEOt7wKLorWAaSYamZmtd+XDWRZcIPjdG 15c8+SnpB6+dHAim44hvPFvG/ld9QM2c1xBpUUDFQ2QVybmFH4dIkzm+/4OXZHZ25sxZ48Zg8Bth CGtDkK0fBZXK6yOwHBF6yk2gbBVrLOsEq2KJOByIlIrV1apIUOYVgsbCFlrUeYqa2iduGVsLnuUC XifV0XK1lK/D6mcwiOEqD4EbokhLLLMFtkZbmIzGQphZlkgVN92ayjBevK5EyqBPf9hKkvRIYHJi w9azqiRElZ9HESqDKTgLoyUNwYqOwtS8ZgsZo7BjnPFcdFYmpBWOJ5Lmc/fmdXz729/EH/zBP5XH N4JSg18HQ1gbAqseuiQkeQa/deGptYqccVwWWquTITqjvErwXKdBQWU6z2Fshcp3cJTUCNo54maA wqVTQu8KaudIm0xe/KJzonCzvwnsmJTj6VZwNB4JYZBsWImNBiPQAF7rrXQgK+PDZPIk7gutVExs C3UFp/VgJCaq3lmRsapKs1zaTXF5YIy9H2IUeWJMaLclGlTwbZ0E3Q3G8LeBH7/2Kh6+8gguXbyE 4XBk2kODXwlDWBsCWybOi6hI99RDUO4OLDvoW8FapAh0TnBtr9/EdRLtzpguJjEzuLR2IzQHB4gt zoRC1LZC2VUS1UX5A+1e1oajdufAZnBq25OPol5KRFUyAytRo0aq20jqUe1WUnZY+bms2tjuMZaL d4ZBINUhKy2ZYSneCSr9eBata1wJtJDDaseHzRkZagnJYKXWWZ1eGNQlfNvGmWkE+8oD+IvvvIzf 9yM8HISGsAx+JQxhbQjSqikXnhUgVFOxL67bEmlXI6szZGUmAlCe4UgVw41bayG0AozUEL7ywSbS duZQtI5xtIEfo8B8RBLswFl3xoF77cBlbJjDr6VQNiUqLNGI+V6H2mlRWBzC6+G+WDFzyA5mGdYo ulKIK3J9VLk+ol6f6rDlaxvOwqL+AElvAvkvTQVt5cPnQJ4yjU7pI2hloaxLWRCIB1cY4eyZET64 /gFWycqc7Rj8WhjC2igsBGqKwImBtkDGzZrVomgLlF2pP6LRFZFs+ZoGkR0hciLYDfP9SBwuKpvS CIWaGi7lw0MI2A0qDrnpYeVpL3dmDgY2u7wOgRtxYiYzM87RqOuqQXlEJa2c6piJ6KFqK5RNBb/f Bjal9tcSvVYfhEoC83onB1ZZ4mBKtxuKYOkOAW43W6DSlHb71l3xaufd5GAQ4ezZ04jiCNtTX2QO 586ckSG8mWUZ/DIMYW0IJB/GacHlHClE0+4j6zI0dGDoZQ+lxGoxhksnKVP/xJgsqtw7yhaaEo4b ovQHKC0GWWhnBKsu4DDYwlYyKwoYG2/bqFqgoPAUCrY3BM+PJf1Gwi5suTcs7VKsjwOGYCgfKWdR qKQt5eyq7hXvIgRlLJhEiVlYltq5lB9T0RWV5Oj78GygylMcLRLUiSUkdXi0FL8tV0XIkgr79xY4 53t44NwU7771Dh744BymkwmGw+GmfjwGJxSGsDYEViFH8yMUbg3wKBm1hIYyLp6gxorxWaxaGLdF 7ZM0bHmJJptBWQ0cz0bjnkIQxTqiS1KgK9RViiAYCeGgKdE1Bcq6RU5hKXw4lotU1Oo58qaFK5Fg nbSitUti5DDdEiKrLQ8F7WP4nKxW2kS2cvR8T6sGjaIXloestVHWgKi5HB9h4GHocW5WY3FY4P13 b+H01oPIixpPPvEZPPXkMzL7ev311/Cz138MPzjCZDKC71V4861XsTXdwuOPP2GOow1+AYawNgS2 XYeLA+T2NeSli4iDaZ7etBR1knS0ywIH3PRPXwdFoK3Q5it08Rj3uhFOj6eomgJtTSNAelgpFDwa bArxryrpW9WUpCbUNOzrauojUJS1BJmumkIqKJImU5cV/0rQ26pmpH0E1bqwCwuZRbcHYFks4RQO aieA5UcYKgeh3aKpSuSKUocECh68mic8ltglHx0u0NYKf/RH/06+97WgVbeUBV57/ad4641rePDB M6LzevvtN7CzfVrEpGwNDQzWMIS1IXCLdvPuTbijBpVNDRQTbnrvKFIHDfqUVo1rB+QOHU/6fL0p XMYKbdjiTnkHRZHJ5/m2D8X0HG74ygZRE8GxHLT2OpcQ6JoS82aOqrXhNFtoWxd5u0KBQmZcU4/K +Appl6MuD6AaBxZDcpiGU+bYLypM/ClGfijzMAc0DrShaDZoW2CWayhbRAs3b+7h6CDDZHQGv/PV L0ooK1X1a0dU4uLFS/hf/s2/xXvvvYu33noDD567gCcfH+ORK1fFX8vA4OdhCGtD4BD9zt4dDCch XM6txMGThngUlGr/dqmo+nh5/sqBtsyolA1q4F1WNLJVpDaL+i3Knwpp74iy0ec5IlPgPxR7ooLl 2Yh9DuK1/7pNYWfRomo6LApPqiaeASVFhqEzwCAYiGtEkifYirdwigp4m2c6nGfpJB5+HW4OeT/o uHrOtVplOLV7Hl//vX+EwXAAz/ubolBKI9j+DZ6OcfWRq6IBO7ZdNhmEBr8EQ1gbgGz9qgKHq32M LgyldWNIKvVX1BTwRSwpNpaedbHCYmvlVg1CVlKOh1QsW3inp1ss8Zti6Knc+TF41UHN9u+4Ymv+ 2r9K8gItaQk5KG/bEo6t48FY6S25zes4k6JlTB88YXfIqxLb3kj847kQYFozsbaOoexC7h5JsMJL NqIwxu7u7m+cRfF9bHslOszA4DfAENYGoNXgKVblChP/lAy9hVREF0XDOz3LInGRqLrWRtfYGDct xm4AxBO4qqJYQMsGOPiu6OSgHUOpPF9XJwwF4zEzB+k/T1hCLBXV6DrbUI6auQWkrUxVS4gET4bq qsBRSQ+sQlwleIyt7Zm1fEHMmsXBQROiSLA6YP/eDFarMB5PzeDc4H8aDGFtAGyhFskCaZNCWTZc HhDTq51H0P3tniYDEgHV4woD1JjAQugGqIZTWNU9LdLsj4/XHup8bPprrRNyWPWsnROoVhfnhL7K WxMXP47kKOnOvBd0OkQ+UFkgTUk1WDZ0Q3UBp4GnIngWyUx7v4tNMiu7psHB4Qx5XqLIgfPnLuPy pYc38b/Y4BMKQ1gbANXcd/f30Di1EJXWQdGZhUJO3uZp1wWGP7hUwtsBdtsUsRwhy1RLt3L07aMu S5JteCLD8xj6V1GBnkvSMgfxBCss3V7qKohEs7aWIeSCR4iykWNpbXVswfdoydyhKxmM4cO16Z+l xACQw3PeE1KFz5Z0uaBXfIUsq/HwpUfx2edfwEMPPbSJ/8UGn1AYwrrPYNt0eHSE6zc/RDyMoSw2 XpQh0OCORZOugtjikTAGXohBa2OoQmbrIOlsdNlSyIdkIeZ69CZlddbHazE8gqc+88UcnccZF0lJ R3PlbDF5W0iFe19tiR1yf6gsuRbrmHlWbcqGLz59bCvpqcW06FYOszmY5yaTn3N0uMSdG3N88YXf xuXLl7G1tW2cFwz+p8MQ1n2EtmmBbMWuXryKt/7qDQwwQVSFsJsWsRvpKoftnGUjcEKE8RDN3h7a wEXhe5itFmiyA3int6gpOK6u9BuPnG1YhYLVuIhomZx3KK1K3yWS1OgB3zXHHuycp7G1E8W7p90d 2Nrxz0mAMgsTo1FO1zo5CWL1R3fTdSvJedV8VuEP/+Bf4cqVK2ZmZfCRwRDW/YSmvcYGAAAM4klE QVTYCkMOhXe2dxG7U9T2CPAHGFjMJaR/VI6cAs/IxyAKoRb7UG6Nxg2QosbMLlBmS0xmDbydXamc OKPCOtKeYq3KQeeWsn1MFitRptO+ZhBMMGLARQckWQZnyUE/HWVc+DxKrpW4RdD+2C5csaqR1J6y Qr2skcwSVOkSb2fXhaguP3QRp7kBLHyU8xle+u5LUl2Z6C6DjwqGsO4jZNPfe6GzgmIbNvCAsq6R yWEy8wi5JaS3eoNiPkPEFObBaYDeVtUSduWjKzIUqxTeFts2W0sYOFinet1yYbNdUzWKKseymXNP CDsYwB7zLAdYHCX4yU/ewPTUENPdEeJBBNvvUFklsjbDIB6gsTo4HpAsM8zupmgTha3RWcQ7ES5e uowbN27g4OAW3v3gfWRpgUWSizmfgcFHCUNYmwKlAAyXsFs4ZY6Uh8SuL5bJgdvCLhO4dQIVnwKi MTrKCZpeOOp6+hTm3j24oxFayhF479fSUYHDcEbM10jyFYpam/N1qkVj10hXJQ5urfDMo5/D9Tvv 4+7tA+ycq7EdsZqCLAIsj0TYonMbrNIEcTjG448/i7Onz8IPfOzs7GA0muK//48P0LY1HnvsaQwH E5w7d95UVwYfKQxhbQAy/JbqJ0XVKQxooLeO1LIa+E0GO5+jczpUPE9xOpR1JX7unDUxPh5OKHd4 SFawokgHnPZ6LjoicItIEmPrxgRmzw/kXCeZFTgzuojf/srv4u1338b1u+9hf3UbB80Mk136yeuU afGBzwukqwy72w/hqSefkqTmNc6ePYvRcCLaraeefFb82ClgNep0g48ShrA2AEoAVlmKRbVCg7Gc ykTdEh7dR8scdpXJQXNCexanQkdleZ2jbPQpDSsmiyk3rkKdrMR9VAWhCD3pnyUCUbRCIJ7viYKc v18eJqhWFj7/5Rdw4aGLePCBh3B37yn81cvfwesfvILDZo5w6CFXBfJFIzmJ5bJDN2Hl9ot/VXiU /Mwzn5HfT6ZTrcw3MPiIYQhrAxA/qbqB8iawrDEyZ4DYVnCrubSBleNi5Q2w8Gr5M6qkWC1xu8eK iZ8v0gN2llSqZ6nYElvK1Wp5x5ZYehIbJQ001+OcKZtXGNhbeOjBh443fA+cfwC+/zsI/BDfe+Xb qBkasWjgpAOcPvcAnNEIo3j6NyonEuCzzz53fAZkYHA/YAhrA+AmME1rnN16CnYzQw4XTg5ErYI1 mMCKxkCxQJnvS8qyeKX3pn4SAd92ErulHT6VzLKGTYtgh06jjk69sW3xdCeZUM7A6sprQvzOV76G yWR6/FzYhu7u7OIbv/v7eOaJz+Cb3/l/8KMf/RD/7o/+DS5ffviYjCQ5+pewFp0aGNwvGMLaAHgf yAgu26Ox3l14lBIke4CqUdtDlLQlbiuEfixzKwl0oIuBqM61ot3hZjFLhZQ6BlDUDeo01R7wFiXw DhQlCdRZ0Riw7DAKx3jooQu/OiPR83D+/AP4Z3/4z/EPv/GPMRqN9GnPL7krGBhsEoawNgCJy6qW WCzfgnMwwba1J+SysAZwOxuxpSRogmS1qBeomXhjaxEnPadA//Suxeywwnk1xe6p84j8EA7Tatoa e9UcBQVW/HhHJzQz6bmkKQ0V9b/RNSGUN0NUBicRhrDuM6hMny3nuLN3DdtRhXFdAlGMwmHWoI9O efBpMey48BkGYdPuZYXO1jeE1FSx6CHp0fN8K5xgN96Bb9Por0PacGjP8xlaz4gnMo7uLXB4Z4Fw uv23Pj9DVAYnGYaw7jN4dJwsj9CtDnDxVAQnHqELR6go+mR0BGdGDDKFQuDasu1TnavJympk8K7t YFw4Awe+G4gdDR3fWVEpJucU2v7l9gf30BUOhsEUz1y9hEsPXkEQGBdPg48vDGFtwGm0ylYIrEpi 42kVw5z5pqKMABBxQNsiaywhLJr6NTYDI/Q/a08rWr1URYVVlkCVHUImR7seCgo+WSnZFm6+v4cL 21fx5GPP47FHH8f29rZs9wwMPq4whLWBDWFRJLDcDnPbwy6DIspE0mjEIYGE1NQoika2h7TpYzJz w1ydipmBlbZ+aVsxASxWC8w6B8MoRhgO0FoKTWdJajMN9D773Ofx2Wc/ZyKzDD4RMIR1n7FcLpAm CwzjAL7rw7McFGz96P4JCkQrCYto2wL3kgau14nvFYkqy1OZTYUBVe4VFsulzLjSIkfhU2PFO0Ib jeVI5uHZnfO48OAFQ1YGnxgYwrrP2Lt3F/uHdzEZx4DyEDoeLD/Aind/5Ry13cCHL8nPbeliVjnw gwZ5mqCqCni+DnLg6Y14WgU+3LbW3lOui7ZoJWyVFjBBMJTTGQODTwrM3+b7PHC/s3dbCOuxnSkK aqU6HRAhRngSGVj1HuwNhr6Pos2xKpimYyOKYiGmtZp9OtUKdDo1CIElGRybRAi4vsL1vVvI6VVs YPAJgSGs+4gsy1Cmc6DO0dJi2HPRUcleASNnhIp3g5QiNBZKq0VhF/CFxzrkjcKyslCiRejwHEbB c11pERFH6NIUTZpCxRFsZcmtX5J9iNVqcRzDZWDwcYchrPu8IbRRwvFtJLYLnitbnSUtIDd8eZtJ 4jI3fAxDrbpSiCmiwr1rkRQNiqqThOjWdpBxu0gveBXBUg2arEVblLCHjrguUO4goaVGrG7wCYEh rPuIu3t3kGULBLEnhKNapuK0UI6ObZcTGqsVlwTLLkXqYDGhmYfKaBBZVK+7EihPiQM93ZmsxSl9 q3yxlbFWGVTIlGgHfuihKHJxHKVuy8Dg4w5DWPcBlCvc3buLl3/2Q9w9uo2toYu4aWHVLbI2h6Uc mUGtaNpH/yrbE0JiaaRTaRo5aB6oEL4XYdmWsBS93FshuA42MttBydSdqgXSFSylEA5tvPP+W9jd Po2HHrygD6FNmrLBxxiGsO4DWR0dHeFPv/Wn+O7bLyHwckzVBGG+Ql47KGn90pQoGm79bIl5l9CI llHwriQsc6juwEHsx3Iz2NUNGkodKp1lqNOWgSr0oYIAKrKQ372L4dDFa2/8EKEbyhB/urWNne2d j/pbNjD4yGAI6yMGB95//Cd/jD+79ufwz7jwoiGO6gbDKkFUZlhaI1gBD451Yg5dRmu7Rtt0kv2X 0+LYsiRRhxH1Ii51dICqyBtojSxRXevW0YFNC5ogwIBupGcdfOsHf4Lvfu87+K0Xfw9/8E/+0NwL GnxsYQjrIwSrpMVygW9+75sYPz7CqeEUfm3D4Z/TLsaxsGU7uHVYw44VVETvKX1+oxQzBinV0oZ9 rKQkPpDmfYzvkmPoPlNQzPz0/SBvCklkwdYWuskEjFEdPXAOd64d4sa9DzCbzUQOYWDwcYQhrI8Q JJ4kTdH5wDgcwKsVmkWFLClwmOb42eECgdXAm2xj+9wOxucG8Klj0HYMMscicZGIaNpXVbW4NvCy sMpKoLOgbCX+V11LGxkbbQm4TYzAAdJmJZVY5Pg4txPi4PYM//m//Cf8q3/5ryVIwsyzDD5uMIT1 ESLLM7z2xqtY1HPY1xrUagunBruIgwkqu8ILn/kddE2Jn7z2Ku7cOEDr19gJxzpyXozzrGNS4fB9 ma/Qtp3cFs5mc9y+vgfX97C9OxHdVVMXaMoOjj2EsgO0FWUPFaBYfQHK73Bv7zr+x5/8N/zLf/Gv jQ+7wccOhrA+QjBRee/uHsZqhAvjC3jkwat46OxDEnDKWdQjj1wVQ73tnXN46eVv496t2yjaDKdO byMa6JkWSYtarGSRYbGXoEwrFlao8g4P7T6KxWqBxcFS67l8D3mWIZ2tUFMy0daIQgejAaPDIuxM pgg9D2+++VPs739Nkm5+lfWxgcFJhfnb+lH+z1UKFx+8iEEQ48ojV3Dp0mWMhqO/MfR+8cUvy7zq 5de+h3Q1x8qpkC6PMNkdicPo8ihFelghtKcYxAppnuHig+fxpRdexI1b1/Ha26+gqNj+eRh5Edwg kONofm5ZrpAdJWg9GxOmTe+GuHPjHm7cuI7pdMsQlsHHCoawPiKwOqLV8Be+8AIC3/+N8yK+79ln n8eZ02dx6/YtfHjzA7xz7WcyA6Ph3t61Gc6OL+Kzz78ggRHzxRyXLl5CGAY4deoUxqMJrl27hiLP ce7cOTz99DNCRLxRZKDEz17/KQ6O7iGp72A0CSSnkH7wfHwDg48TDGF9RFiHRfxdW644ivHww1fk bbl8Dq/89CL+1//jv2A8muKRi4/ixS99RUiKj3f+/Pnjz4uiCE8/9bS8/Sp8+cv/QAjsL/7yz/B/ f+fPoe7YsEoPV68+JoRqYPBxgiGsE4jBYIAvfuFFfObpZyV8gofLJKr/r1u94XCEyWgbThthe7iN //Dv/6MM6c2W0ODjBkNYJ7g6GwyGx//9//fxSFCXLlzExUuXhBBN2IQBPob4fwHF1dYUUv0dOwAA AABJRU5ErkJggg==