20260121 14531600 1.0 TRUE 150000000 5000 FGDC U.S. Geological Survey MTBS Project Manager 800-252-4547 47914 252nd Street Sioux Falls SD 57198 US burnseverity@usgs.gov 20260121 ArcGIS Metadata 1.0 GS ScienceBase U.S. Geological Survey 1-888-275-8747 Denver Federal Center, Building 810, Mail Stop 302 Denver CO 80225 US sciencebase@usgs.gov None Digital Data https://doi.org/10.5066/P9NETC0T https://doi.org/10.5066/P9NETC0T https://www.mtbs.gov/ USFS_EDW_MTBS_AK 2024-08-08 U.S. Geological Survey USDA Forest Service raster digital data A Project for Monitoring Trends in Burn Severity <div style='text-align:Left;'><div><div><p><span>The Monitoring Trends in Burn Severity (MTBS) Program assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (wildfires and prescribed fires) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period 1984 and beyond. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a thematic raster image of MTBS burn severity classes for all inventoried fires occurring in Alaska. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available, or fires were not discernable from available imagery.</span></p></div></div></div> The data generated by MTBS will be used to identify national trends in burn severity, providing information necessary to monitor the effectiveness of the National Fire Plan and Healthy Forests Restoration Act. MTBS is sponsored by the Wildland Fire Leadership Council (WFLC), a multi-agency oversight group responsible for implementing and coordinating the National Fire Plan and Federal Wildland Fire Management Policies. The MTBS project objective is to provide consistent, 30-meter spatial resolution burn severity data and burned area delineations that will serve four primary user groups including: 1. National policies and policy makers such as the National Fire Plan and WFLC which require information about long-term trends in burn severity and recent burn severity impacts within vegetation types, fuel models, condition classes, and land management activities. 2. Field management units that benefit from mid to broad scale GIS-ready maps and data for pre- and post-fire assessment and monitoring. Field units that require finer scale burn severity data will also benefit from increased efficiency, reduced costs, and data consistency by starting with MTBS data. 3. Existing databases from other comparably scaled programs, such as Fire Regime and Condition Class (FRCC) within LANDFIRE, that will benefit from MTBS data for validation and updating of geospatial datasets. 4. Academic and government agency research entities interested in fire severity data over significant geographic and temporal extents. Monitoring Trends in Burn Severity Project (U.S. Geological Survey and USDA Forest Service) U.S. Geological Survey MTBS Project Manager 800-252-4547 47914 252nd Street Sioux Falls SD 57198 US burnseverity@usgs.gov Common geographic areas United States Continental U.S. Alaska Hawaii Puerto Rico Common geographic areas US CONUS AK HI PR ISO 19115 Topic Category imageryBaseMapsEarthCover Wildfire Prescribed fire Fire occurrence Landsat Differenced normalized burn ratio Wildland fire Normalized burn ratio MTBS Burned area Burn severity Fire location Location Sentinel imageryBaseMapsEarthCover Wildfire Prescribed fire Fire occurrence Landsat Differenced normalized burn ratio Wildland fire Normalized burn ratio MTBS Burned area Burn severity Fire location Location Sentinel United States Continental U.S. Alaska Hawaii Puerto Rico US CONUS AK HI PR <div style='text-align:Left;'><div><div><p><span>There are no restrictions on use, except for reasonable and proper acknowledgement of information sources.</span></p><p><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. </span></p><p><span>Additionally, the U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at https://creativecommons.org/public-domain/cc0/). </span></p><p><span>In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. </span></p><p><span>Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English. </span></p><p><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov. </span></p><p><span>USDA is an equal opportunity provider, employer, and lender.</span></p></div></div></div> Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. A Project for Monitoring Trends in Burn Severity 2007 Jeff Eidenshink Brian Schwind Ken Brewer Zhi-Liang Zhu Brad Quayle Stephen Howard publication Fire Ecology Special Issue Vol. 3, No. 1, 2007 https://www.mtbs.gov/sites/mtbs/files/inline-files/Eidenshink-final.pdf ERDAS Imagine and ESRI ArcGIS true -163.20010009305838 -139.70427452542995 67.63462646350274 57.82429733583433 ground condition 2022 This dataset contains thresholded burn severity data available for the specified state or geographic region at time of publication. The dataset may be updated as burn severity data for additional MTBS fires are completed, or otherwise revised as necessary. See https://www.mtbs.gov/ for project information and data access. Refer to https://www.mtbs.gov/contact for additional support. 1 -168.665594 -137.664824 70.217029 56.334741 1984-2023 MTBS analysts examine the differenced Normalized Burn Ratio (dNBR) image for each fire in the context of remote sensing spectral data and any ancillary information available to the analyst. dNBR image data for each fire are thresholded into classes representing unburned areas; areas of low, moderate, high burn severities; and areas of increased vegetation response. Analysts follow guidelines established by subject matter experts to maintain consistency in discerning burn severity thresholds from fire to fire and minimize subjectivity. No tests for logical consistency have been performed on this map layer. Data completeness reflects the availability of fire occurrence information and/or availability of suitable imagery (including Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B), and the current progression of processing by the MTBS program in the specified state or geographic region. In some areas, fires smaller than the specified MTBS fire size criteria may be present. These fires were mapped using MTBS mapping protocols to meet the data/information needs of other unrelated programs but are included in the MTBS database. Each image used to create the burn severity assessment (utilizing Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B), was precision terrain-corrected using 3-arc-second digital terrain elevation data, and Geo registered using ground control points. This resulted in a root mean square registration error of less than 1 pixel (30-meters). N/A For a detailed definition and discussion on the processing and production of MTBS geospatial data, please refer to https://www.mtbs.gov. A synopsis of the production of this layer is provided below. MTBS burn severity class data are derived from data obtained from suitable imagery (including Landsat TM, Landsat ETM+, Landsat OLI, Sentinel 2A, and Sentinel 2B). A pre- and a post-fire scene are used to create a dNBR image. The continuous dNBR image portrays the variations of burn severity within the fire. In exceptional circumstances, a suitable pre-fire image may not be available to generate a dNBR. Consequently, analysis and assessment are conducted using the post-fire Normalized Burn Ratio (NBR) image only. The MTBS mapping approach has consistently occurred in five primary steps: 1. Fire Occurrence Data Compilation 2. Scene Selection and Image Pre-processing 3. Perimeter Delineation 4. Burn Severity Interpretation 5. Data Distribution; 1. Fire Occurrence Data Compilation: Historically, MTBS compiled fire occurrence data from several federal and state databases that maintained little consistency in standards for content, geospatial accuracy, and nomenclature. These datasets were then filtered and standardized internally by MTBS staff to reduce duplication of incident records reported by multiple agencies, and to resolve gross geospatial inaccuracies that were commonly found in the source data. In recent years, significant improvements have been made to streamline the reporting procedures and data exchange for land management agencies in their creation and maintenance of fire occurrence data. Specifically, the Integrated Reporting of Wildland-Fire Information (IRWIN) Project has provided an end-to-end fire reporting system focused on the goals of reducing redundant data entry, identifying authoritative data sources, and improving the consistency, accuracy, and availability of operational data. Through a multi-year phased approach, the IRWIN Project is tasked with identifying and integrating fire occurrence data from several different sources and applications. Fire occurrence data from IRWIN are routinely ingested into a database maintained by MTBS and other postfire mapping programs, and currently make up the bulk of the records used by MTBS to identify candidate fires for mapping. More information on IRWIN can be found at: https://www.forestsandrangelands.gov/WFIT/applications/IRWIN/index.shtml. 2024-08-08 2. Scene Selection and Image Pre-processing: For fire events that meet the minimum size requirement for MTBS mapping, corresponding Landsat or Sentinel scenes are selected based upon the reported location and ignition date. After an initial review and determination of the appropriate assessment strategy: initial, extended, or single scene, candidate pre-fire and postfire scenes are reviewed and downloaded using tools and applications developed by U.S. Geological Survey Earth Resources Observation and Science (EROS) Center, such as Earth Explorer and GloVis. Limitations due to scene quality are common in areas prone to cloud cover (e.g., the Southeast United States). Other atmospheric conditions such as smoke from active fires, terrain shadows and other obscurations also reduce the number of candidate scenes. In addition, northern latitudes are subject to a shorter period of optimal scene selection due to undesirable sun angles in the fall and a shorter growing season. Downloaded scenes are processed to generate top-of-atmosphere reflectance images according to existing USGS-EROS protocols that include geometric (including terrain correction) and radiometric correction through the Landsat Ground Processing System process. Prior to processing the imagery into a burn severity product, pre-fire and postfire images are inspected for co-registration accuracy and corrected if spatial differences are noted. Using the reflectance imagery, a Normalized Burn Ratio (NBR) image is generated for each pre-fire and postfire scene as the normalized difference between middle infrared and near infrared wavelength bands and then differenced to create a dNBR image. A relativized dNBR (RdNBR) is also calculated to evaluate potential limitations of dNBR to characterize fire severity on low biomass sites and potentially enhance inter-fire comparability of the results at larger ecological scales. Processing the Landsat image data to NBR, dNBR and RdNBR is a straightforward series of calculations relying principally on automated production sequences. 2024-08-08 3. Perimeter Delineation: The burned area boundary is delineated by on-screen interpretation of the reflectance imagery, NBR, dNBR and/or RdNBR images. The mapping analyst digitizes a perimeter to include any detectable fire area derived from these images. Clouds, cloud shadows, snow or other anomalies intersecting the fire area are also delineated and used to generate a mask later in the workflow. The mapping analyst may inspect and/or modify an existing burn area boundary and mask sourced from a reputable fire occurrence dataset or fire detection model when available. To ensure consistency and high spatial precision, digitization and inspection is performed at on-screen display scales between 1:24000 and 1:50000. 2024-08-08 4. Burn Severity Interpretation: The process of developing a categorical burn severity product is subjective and is dependent on analyst interpretation. The analyst evaluates the dNBR data range and determines where significant thresholds exist in the data to discriminate between burn severity classes. Interpretations are conducted on the NBR, dNBR and RdNBR data, aided by the pre-fire and postfire imagery, and analyst experience with fire behavior and effects in each ecological setting. Where available, high-resolution imagery is visually inspected to provide confidence in selecting the burn severity thresholds. Thresholding dNBR data into thematic class values results in an intuitive map depicting a manageable number of ecologically significant classes (typically 4 to 7 class values). There are uncertainties in this approach stemming from analyst subjectivity and limited or no plot data to guide threshold selection. Ecological significance of burn severity classes will also likely vary across regions and landscapes and one set of thresholds cannot be expected to apply equally well to all analysis objectives and management issues. 2024-08-08 5. Data Distribution: The primary access point for acquiring MTBS data is through the program website: https://www.mtbs.gov. There you will find an interactive viewer which allows users to search for and download individual fire data in addition to direct download options for state and national data products. Connection information is also provided for those interested in using web map services. 2024-08-08 3 37424 30.000000 38334 30.000000 1 1 0 -18775768.058800 7625343.812500 -18775768.058800 11139723.812500 -15324778.058800 11139723.812500 -15324778.058800 7625343.812500 -17050273.058800 9382533.812500 EPSG 6.18.3(9.3.1.2) Band_1 8 None Raster geospatial data file Producer defined Value Unique numeric values contained in each raster cell Producer defined 0 Background/No Data Producer defined 1 Unburned/Underburned to Low Burn Severity Producer defined 2 Low burn severity Producer defined 3 Moderate burn severity Producer defined 4 High Burn Severity Producer defined 5 Increased Greenness/Increased Vegetation Response Producer defined 6 Non-Processing Area Mask Producer defined MTBS thematic burn severity classes are thresholded from continuous dNBR images or NBR images when pre-fire images are not available. Thresholds are based on interpretation by MTBS analysts. MTBS Thresholded Burn Severity iVBORw0KGgoAAAANSUhEUgAAAMgAAACFCAYAAAAenrcsAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJztfWmTJNd13ck9q6qX6VkxK4YACICCBEokJVKyaPOLGDYlhyLk kP+CHV6+2eGvdvi79Q/kH2CZshi2TC0MS7JoAiAISJRIYIhlMAMMMGuvteSejnPve1XZ1dU93dNb dfdcstHTtWRlZb777nbuuf7V5z5f4xiK4ziHfQpTJa7rIggCVFUl18ZeHz5e17X88LGiKORx/l2W 5Y4/p3YA3/XgOg5cAKdPzSGKwuHnNI/JvxvvBKoaRQWs9nr8C7MzbQSexxeu/wxzvvzx+PyYJEmC PM/huA7CKILvuciLAoMkl9fHUQjf9+DI/4C8LNHrD9Dt91HVjrzGcwHfc+DjmMr6i/9UqBhcNFYR uAiiKJLHKXw8y7IN163e8XV0UPKY5jMoZVkNP8f+5jk0j82FWpvFWtW1KJg8MOHzrXJP2gSt0lAh +eN4rnmdvpYbQBUGemzU8kuetYeqa1RlibriueL4KshTWS/ju7ddQOPPNxeu84RWuObK8jz4QSAr z+729nPGlYNS1jXyvECSZqiqEp7vw2ks4HHZ6tyoINZK6vupcPy+DvKci9+cy1A5HLE28nqnFiXh +VFRnyrICZWmQowrS/M1OxUuaS46tVChLmT5vyrF+MK2i7gsCvSSAQZJBl8sABfsZOXYSpruoz6g mmCtZp7ltGNynq45OpXDo1tIi1PZTUTP66mCnFChcvT7/QNwTWv7/8nPms+ja5WIiwfEUYRWHMHb wzhSYjDfR2LcPInFXMYho+d9P0BeVkO3zqmnLgYZ212GXuLRkEm3c1rPvun27KU45pAMitd6fSS+ hzgMEAaBBL1yP+U1oziCpyE+f2njIxceLRotCEORerfnI/ZL3SmTDBCr0ngNPy8MPKSprjj7kYeu IE1z6DmumDteEAn0QG3GkZEjdKr7LlVVIU1T5IWrQS/dK7pcuu7XiSiI+eFy4Htp4ZgR87y9W6IM /KMw1CB+zDqJgvi+/C6prUaPD11BKNYPjgNfTp7BUZrnyJnzO0Krjjd0sx31pEpVVRgkqfx2Pe7S DL7Vv+fN5T5YVfUwu0XJcsYJtaR4Q77aLmbGF0+YPLAxSKvV2hinGOWhMjL+4flY63roCsIToXJE YYC5TkeCM4ZQrbLE8soK8roamlibS7fvmzahMmjQuYXTfdKk1utBl6vX7yGYnUPt0apUyIocZVUj TTNkEjyrsqRpLr+d2AXyXC0Qg2pXA39mpFRVdi6TkhFWJCkwTAlPiYJYaabamD13PA8z7Ra6/QRZ rpmFqVYOnrfjot1uyd+84XmeiTWcvrM9WKlZG2EatyjFdebto5XoJ4n8XUidZHSVeM1Yr0hSB3Hg aUwiu7sPZxfKsfU5anqaNZtm0DM1CtJc9JKDZs6+EcxNet00CS1fKwol+8Iz9l0HqWvy+0WhNQjj IkzpV9hXqSVzVksaV6rXeYGUG0jDi27eaSqCVLR9X6retvi3f7uN3chGQfpUxCDWF+QOUhYlatdB UZaywwySBAXjkENUjpGxXf+Y5vdtJgbiW3fasWRqKG4UIPBdFHWNQZoiybQ6LMermITYmEWq+bx5 aFjYPfL2p9bvIsmjGr1B0oCK6CvG7QGvLYuM7XYbkd9wiYaZp82uibOrc6Rt8n0fRZVLMXEqFMQW rNK6wnKlMAPJU0smgRfxcDFVk5SDgVwQcGfj5XNGfwusQQNTfi8+79UMAF3EYSQ3nbc7yXL0B4lA O9Z/lq4AKebieEq5DXyXXRN5liH0og0ZJ7sxDavsTRP0RBfObGqei3arpUkDxj7VFFXSJTgjUE7S fZOM7uGLVQ66UeJKWcCfycKNki2aMbE5frnIzMz1+3IDqEg+rUvRiKug4D49QFNDpusabBX4NjNR uxUqCLNfrqPu6+hau7qJliYVzI0o8GWDYqwisewT7i58L4uJ7XYML/PFDZwaBaHwi0/bkrAGjOaX OXJWeYkGDYgG3STd2Hyc//RY2+HNM/AJpi/jMITTQNHa7AkDxcLUAcqKCwBTLx5jBc+TwNou2j0B VxYM5C18hI86shlJjMpd3gAbvTwXBQlDXwqSvE9PEsiLm+U6iHgMwlIY/2CqpOGUGn/TeLCHJmId jIWIglAtgO8yg6+n2jy9Ce6g3lf61D46vi1QsVLsISDUYYiFUgWRAlueI81SQd8WEqs0PqKR9rbH P2gdcuQ7mOiIFtL3EAahLNJ+ry+Jieb5mZftWKgESZqPAIVGUeSa8R+0XFUpSkmAY1kVohySBn7C ZSM4Mlfvj+9OmYIQ+9KUA7/x3MEdc+OdWnb4dhhJ9ZW9FMM0o9hwd1snOXQWReENhBvsyYAsKHlu +LUJ1XbQcgNEoS83fZBxV9aYhpKkqaYi7Y7neaN+DuOZjYX+u7smzsjd5Slw8fEc+eFM0Wq8VUr/ RCsIkSZ9SNWipqtD5R/FC4J/Gl4Dc3bO1ufKu+ELDCRAGIZa7fZ0U7GpWcar1hLTRdqbXqBae0L2 4EjHUnQn0Z4JukMaY+ztfr3ZfdSaiu6SrYjFUQw/Nwp8cWX4GBeDXSjSEDRIUBCNaqJX+bXL062N s8LP4eJsxbGcdyZ1ikSe5eKlW8INnUpPC8KULr8HH3dcVzKTqSQlRiAoe2qbRZy64F104lghIuKi qnWwSlBTYaHX4Emr7FvJUwUZF4MHkqJfFKlPO6Fr7SBEFkjzs6Vbz0VJyyONQVQQ3dkZE/F5FiiL gq4ZrUzD0u3gMyV7OFxwriQmmMa2oENxaYzLyVfyt1yjuhJXixChsizk/VzY/AfRETw0rbAE03Qn ay0QUqk1wHcmXwPGFwJR2Zj2HjZPYe+FtcunCtKQesx6dNodyTgdpljUkoigTulerL9t4q75HmaD jgAEk8SRIpw2BvH8bWBrugfHP6OxyLjQ7WsdLs7QFwUgFIgWpMhz6d3gURmP8WjiepoToRuUipKW YgG5uKkIbq6fw+fjiNbGlc/QijnjLTYojX97ngdjDFUk664dRBrHAmafKsgEsWlaTdUebhqJbT2T ZQMmFl5do0VfnRmlqqX+OTy50Qz4J/V/UCy6la+fmZmR1/InCHzMtBQZQKVh2pMLX+Ix39PCprU0 AsXVDB2thkBHqNC0FiYWoSMUELAosBH+VdPEIPAc9AYpBmm+/hvVbJGt0U9SsTgEGqrTt3f3ZFIT l3zXokQ6bWleBmyHuR5twCgKwhu6w5Npnv9usjdjR53w2KRYyCYXNIANuHh9X2Hk8JARoVAQIa1p WPtuLlP2QTDOoivF4qbv1AhJWOBrwCuwn2Era6PeMPbd7ZnRFeP1k13fxG2MnTynJdbDBvn2CLQq jsPPLlQ5y0IxWyaHyXTvINWD08VrggpH1m/ylZbExcYGQ4nZCJbk8Zh4sM8xXhpeQ14PTJFMQ/1D /GTfk+DyyQ/SWL77ovCPP6hNH2s84Yh71Gm1BPaS5VyAxIapa8bFK0wf4l4xMrWoAGe4w9pUNBG1 jwuEpWV2+F6tITGhwB9bQG0K3Vkn5PE9FIEnCsJzzASrxYC/QopcXDBCkKSeJDURzW4x7c7HtnOt FThJJLH2vzPbxrhu2KQ1rIewA3GKOgo1t226hQ/Qigg+ymZVxHLobkpY9U7F7lYWpyUL5JCh7/aj uQDarVgXuUOYSy3nqvULjQlsKpmv5cIpjeWgJWk+v1NY+VBZt3o94xPWU5je5gLOMvT6BDUyjavd VOLi5YWsESkQOi5m2jHaLr/X5ESKtR6SEjZEDLSiRIgT5+egQEW3r/bhhkw0yBmrlXKnREGUs8kX /5S7hroB+29PuFiYHeFuKawXvqc7HTMtckOfbGWLT+95G7BWBy26oEfumLg/AqL0dJEUuaHXqSX+ EPfCiODj0nTYYHQQQFHGe9IfJOnsAMFcgKJUxZDHBY3gSlDPc9M6jAF9Tjy9YZOtYrtYgM1zsUL8 vrxP9Bb4OJMBlSGJsBsC4SxToSDq83totyLZuYnBYWBm4r59+kzdtYipoosh/vAQU2Vq+GP+63Zj EPHZ2fCzsWp3YGJZO8aLhpLTorsUMMbQ1PCkUMvyZjkHSMDXRE1oLUhjJN8NdC3IydMlIhmcIyQP dBUnYcBUobUeU4jLxsRDIVaBRViJt5h9k9pKLc93DWGdehVqRaZCQSi2Cho0vqD6iOpD7/VtkiJW FOiO6mkwO/aKBl50h8E6cUIoG62l+yDiclMJTZDKG824Kc/hiqZCWAo3nJt57zo4xsb8qkLOfV6X g9TwjXeZbrelDlKpxdI7TjhkUWRaWVLaYgz1nvHa0LqQLcXiw1h8ZbpaEhEmGGe9Rr8nK/2ZWhfz nakoh6ogdsdVdJLePQGLhfSJmXYskQlor1kV3ptqNq95FDGVqzehqggz4TO2YGX/bn5ms1Ki7BsW 2j6egbMY3f0TfiYVRO0EGQT9OEZVFgjEh3a1qr7uHNYvwBGKZBrSI5tATTacmgbjLjTLNBC8VqNl 17gd/E0XTHBhtWbRWnELrSgyr6vE0jNtTbofL4rEexCFMl2PfO/UWJChiFlVDqO5uVlpsEmTTHeB PYTm8QJ2uz1lUgmbmZVGgtYZkYih1oKb3Z1cn+21bfQHAyl2HXh6WtaB4sXsZXG5Y0oQmiv6uPZR ubtruXKmRnlGwhZdZqGoFBWdMLGAuqjpOmlh0SQYmFxwFAPG5xl7sRVaSCF8ehDxMMYqtZkHrglp +K7pUxBrURxNs8202oJ6Jc6I1mSvLD77sfIaWOslyItaGpqolJJ9GqZ7HczOzaDISvS6CaIolgo1 uwFpOaSn+gkInnd97o79GRXNuCOmSSLfywILd6ocBxWM70aE8SZLpOlMiB1MvKCWnP3txKKxuq8J F7kQnqaFZSNjfGXS2MqmONoYmSian52RBBHrJIxxpk9BjFjgGfP3du92SYTAngNjAncjiv9zkGUM 8khLU6MdxfAjgus0epBCoVmMmobWoNZ+OGEXhyGjPd3sjOJTVCgrWo6GE/oEBne3vLwHIcKCSEya taASwKtbKWhjkxXl2hE0gaOFQSqIJB8kQG96DSNCb/b78JrmhWYyp1ZBKNZtYbal5bIPI0BvMBBU aLPnYKcyWjyOKT4VYpL5eW1f+wDEkaqAXneAquQOpcTHZP+j0AyPoHOWArBx0ts+veYqfkz93SEE W62D7JKOBmd0MyrHk35+F5VW0WsgIQx8Ksqv2HPyN64Jxg+si6iOaNDNx6WPQ6BCWruhO6aWxWSo hoMPTIxj3VXTFMe/opAezJRV0selGegK9EN6JJjyq9CXbMMoyN8JVEVe34CQU5hvXxv0ZFcSeDVh 27xsZAM3sQdrNNInYndtR10+EhVpNqlEaZ5n8MxsUu1ofl1eJX329IlNyC/nq/UW+slyRlIH0B/x r50aLgNx4pK8ErMdHyWLfFmNM4GDAQqkpYuchbJOC+26hytkASk9/N3KQBVnm0oyCZd0ELJhW3jM PqENTQzSfWZaJh6x2f7MAqTrhoKCloYz6fkx5NXD3bIpZhOclkLhjlKzYShBWmKKR3uJ46I1ESyQ r7BuOe44I8DwMxVOzroN0/BFrjMxqBROpTt47VK5bCOYKlWrw+wKkKa0BExHKObIZsmCMEaBEgVy tCK+rkLFim/lwC/pVkTInQqlV2ItaqGsSwQRQYIuVnMqqIMOUswQeFg76DoeDj5K2plMAPFuS7ar zHS/AmLIHMYoxch62Cz+FnKkFIRCP5HZh8gvlIXRpvMasy1sb/vOhZRDleTCBTdk2mAVOUL4d4Ug 1AamQsIP08w09KrYvAPEsScLdqXPhiK6Y3yGi7qSwL8u1IyrylSSZibMhYF2UPEGFnDCCgtnY/SX SxRJCeQOkPooEqYuNbWc0msg9N2t4NQl3JI4pRLLcNGrXBRHhPe0NjUL/kjLrLnu9tndihaBDfxF zLnCmbajX0dOQShcuHSBgjAUc8kKKXd+Lk+mOOlvPomSSBMP2VUIiBMgXwCPKFNCUXxe5ApB4ElM IhajdpFno6A2oHbw3FrsofCw2k/l/oZsJHIKZDRKlYdacIJMAgAhXWeXADrOyADCvC9oWq8d4NSp EM5AoTfSWIQKGREGph0bWYF6poOMeN28QicoxI27U0dIqgCpm5lYaTqlNtk33i/GE2VRmfiCu73h ENsHJRfPQ47rHEMFkSKQMl4QZBfSmgTas0DINHse+okG8oKt2cH1lZBWcuXMlBXwvRztiN1yPH4A p/Yw6FuTYXc5/dv3K5xeAHp5G2tJhaXVFD4tjVMjQokFr0baDrDYdZEW+s7QDXF6gUpdoNfN4FUV 5mMHgzDCIGphrQ88WHOQ93xZ6E5IRdFbVrtKBu1emcNs7GBm0AeWu1juA0WfTU0kO6B/bjsLp0ec oXLU6PcHGKSJFOj4DJG87bglirKBD2uPPluQyVYBj5SCNHH7tud43BIYgmim+YQziUHscDcoBT5C mAcxOnnFgZSbwaDHUnzsuWYFnwMfSYuZZcjrEonDwJw+bC1pXc2OcMijB8lxGJIH1qW6vRoFu+mE 865EROZy10UWh+jNeKjn5hFWOao+ay8ZsjrD0hoh5qbi6zl4VHgoSqDMMzxKKuRpBX+ujbgVwk/6 6PWUy9Zh09FsC1e/cAX9R6t4eGcFxVINtzOL0iXn7cDUSA5XOZzhVdYfuS8ZAYOaWbLW0fdD6eDk PRCGf0Lcs1QspVCPmp6NXZ2Lgc9Y3N12rsx0KcgwubR1Lp6LlMGxbdxpvFNAbISr8xi9VKlLtyp+ KSuikg5oX7eiW1NmilhMZHspd2rpo5azaqQJLZRD20WTlIpiG31qlK6DeCaAF1TCHJkMUjDMdn32 +bFbrkRe+vD8AH7kwXErpF3GUyxrlJKtohUUbFdB0J2d2gc4vgt/LkT6aAXdu2tYW8pR9CuEboYq r1AzUTAF/lVhBnjaOJHwF0nPGoyUtqI7Ugy2mChN3+bIskTg75KupSu6B1/HNkdtVw5fQRo6IPh8 Z7SjbyaWyHh84VvCASkuujpnZFBlKOqNZGa0Pp5pjCI+hw1DfC/JDqRAJGwho0UmLhGLSLRc/J+k zXKT7jWNOLKAFR3q831BjVOnmXKt0V9NkXb7COMQHnPs0qQTSFWXsBXXJ5y7QJUWkuAVUmeTAi67 GQbdHLXHVPEoh185JT556wPUqYOgduHFHqq0hzpnNs1ALA6RurWWGoRRCFOoY4JBG6/0/kraVf+h ZHkGmSCvz3NRkMOUw1cQ0zZJM8siIFcGzWyn3d7SPZg0BMVyQ1FoktnLwOC3Tg2Wa4jSJD2Nh/nZ 2SFRmClVSAJAaH4kq2RSr4ESF6hWaZcet3mdiMqRwnw3qxb8L3dCB+1WhfNRjgtxH24QYJW1lc/W 0DofY7nnoLvq4vK1s+gNlrC60kf3USkxy/wM3Tmgm6gCEE8lWTB+BK0mM1fsCOwXWP5wRXLTF87N 4PSZNhL2ePR6WFkq0e1SOQ7XxaoBgeYQgUCLz/sq6k3ohyFt6Hb7ZqY5XVxF6FrCCFrRIFDKpROs IA3/lL51o6PwSaCJtghHE77aWxMoCV0Uq0w04+yZto1DIweNdQz9Nx8XC2aKK7ZtlHvb7PwcauJ0 VteG4MlWDLSjGlU3R5e+lhegcGM8yGosPWSQnuOU7+Mrly/i3ZQJhgpu5KA76KOumdZVizM7w/HJ DmK3JZmwjOfv+ghjB502hwy18HApRWJSouJGCRQgRbZa4MFqgTpjjYjwCpI4kFgBhyaO46BtOIxt wM3/Mkaj4gwGA7kWcr+E12sg7q4S4bGhyQTThwh7OVQF0dh6pA2hFwjScoS1clBUCiWQXd70IquM tQKZ2YwwgSBvgJ1UZAP7Ie0Mm6SG3LrGhXIBNwxQclgPO8nEUoxcQCGXZo2FTTc1ewgYn6gLxCQB 3SqOOmDlPAw9BJGHbpdBvIe2jJcL0Pdnkfb6CDwWBD3kKRt4GN9EaM9UOHsmx8PFElHooR0GTEoh ZVBbOkgzbh6pQiuIOKXuGsaQwYBp7gy9AeAxMTCsiO8d+vlJhJ8uTIf2D3t/GF+kjDGYTieRt8YZ tBiuQ1IGupw+apNtanDNnUALYtjcdfGSNZCDH9kamYmvnxWc0lQJrICLW5i+Jx1GepaZv9LpRGzJ FGSrLeI1yAfEWrGBxhaLlM5CegKqMpFAewgjlxiD0AZIjcFNuxKXSAt0oeci1DRs3nEChJGP2TkX YQfoly3Zxcni0fd9vNOtwfJJHOm4hB6zVKUyEoZRBbrbzFrFfgk/YgDLDSJHntYYSF2GNREmnJn1 cZATkUr/XpDJGv+4bimw95I4symAYTn2HwYiTfeJkB0hXyBim8kRy1Qi1Ksa/4HWT1ytwz3/w1eQ ZkDHfnTD6p3kGRFOCpMgzt91sDA/N+JenRB/DOsftolp7DPU5+0iMUyB7TiW38rAweq4wlfWpSaF 2IBxCV2lDBcvRFjpebh9txAoCS9hxqC4chC0W7hwcQFnzpdwOgmS07OoP11GsTbAWiLkO2jFNQgh Yhaq4wdYyxyURYpuP8XifUJKiL0aoCwS5JEHd6AZL1qKOgkBP4ffcuEHLpIBexcGuHixhXguxk9u 9BCxos5rSbyagBanQEuMCHO9DGflbMJCYpNZgZfX0r/P61/mhcRs4lpJ7LF3IxWOrIIoURfdBM0i SZLUBCYWbUmMPlOGzMRqxDrhOBK/mJ9N8FnSGMNUo6RBlbxMC6sVyjRTn77RycZf9JOZVcrrEB/f z1jAZmgPp+aNrEftnYMMDx4tYXWVnWglyjrFXJ3AjZWjql2nSF1P6iWpA1y7FsBb7mFtNQMyB2jH qNcyxAstRDMhlj9aRMmdFCWCGDhz3sOjByWyrDLIYge54+P+sgePBczSRRWGyFEgqwhx4XsPvxbS FG191YY4plzZ8lrzvjpAq9M2YyWm53ynRkFIZ2n6woY96E3ALf1qtlYqWfKIvIzCBU+TzXRiUzk2 E8VvaS7eEyyTieybA/Oa58ailaGhJCyK7k0YOiiyGnGLOf4aCedYMHhf47QsR7JNcdDFqWcIPPSw mgfAjItThH8UxFH5WGilUhvJQub9PaRSs9EgHilpOWv4VYl2xKE9Lso0EcXUWg3dQO6wHnr9Ckhy OXcqRllx1rfSKE2Tcji25yKOEFaasRJUmmle4oiIJtP9NMjUKEgTvCEy7NPXZ/iagZA4KGcqlYR+ vKRjS2WlIFKzicEa77IYPa4AR1ZqhTHcBv9Cpa+fZ4kCmB7OSMbM50gT5JBHivgsYqNK+CGhvPp+ n7iiTFO9HmshSPDMQoClXoG7K4VUuefrLk7FHtI6QNFNkQ70PTWtZ58bQIneah/osonLQdsrsBAR m+Tis/sDePEsXAImpXioyQMZk0w2dWniojWzV/Jw3ZPNKtl2dJ2tfSgp3c54t06Ugkxc0EOqo8by NgMxubDJPzs/P6+kZx5Z+1i7yKU4ZushmxkR8ctLMmIos15YaotmVRnQo9xI7Szs9QZDhST1P3dl JgPoZlWuh5UVM7qaykMfmtkYp0DoFrgcVbgWnsYcCvR7CcpeG/cHfbTnYzizs/ibTzOsLdIv5yIB gpYDr/KQ5TUyZrgcBwsLrtAgrazU6MwyRR2hVxRYTXJU/K4k/zfYOyKCh337cgFMzWZKpF53LnqD WKzVvw4xVXVUFGRHMUtVYKW7Kohbm0ffKYJXIOPCLM6MChfXCJbiOJk8xrhHbp4ZuxHEAsmVtKpb B2jNtqQSTgYWghnLJEd7PsLcXAuPllK8U+UY9CusdEt48z30SqA3KODWPawtlahZyfcdzAUlLpwP cevTLtKCLVjaT50mAYssCE95yOsBkpUB3KRAmz0xjMuGA3+mcHUdAzmaCiKzJYiXYkZEU4HKsLcz 8gEb0EvWS96oR1exU5wMxxLLI4RCFAUL2hI0pwNHoesuM1MOqlYAryzRCgMhpPssi/DJYo64YGU9 AhbaGJQearp2/RSn2hFWihy+yxRvjWQlI60VKtNgRSuwmgLxQoT2mZDdU1hazlGz0Gg4sXy6i+R3 OmTi7+MqR1JBKJaa1I4jowwZxxvVV6sEG94/6Zh6lOF/bVlSGRKpJAXy1EGLPfIs9DEoHmRAQffI RemEkpJ10xrZmoNBP8bivRznOx5On4pRn55BlbqoHuZwswzBGR9rDwY6l7DwcP9ejowE0pYcAkC3 qOGwT0JGEitnVyLQGAbw2htv+6kpLKhqofWptpxoBRkXi6kajl82j9N92u5iEQCiPZ4pKsr8OwND kbSx66FOHJQD9jab5Bc7G8kh+2kPVV3iYb8AHiayw6/0eJwK3myK09UiLuQFHmQl7mYOCoG9sx/E QeLUSMn6V5kJrXLKWqnv3uuh94ClSLqRjCt425j8rZCzaEkyAiqwDLwhu2IhafNpij+OqhwbBVGp EEctCfykx8DzhOhYCMZYpHoMh5UNFC2nLv/mzOyF+QAzbQ+ry0rwnDqOWA9BjlUVLlydw8Xrc/jo xiKW7ycoCwcuU5Z+iTLxkWcVlu+XWFzOUA58yWA5Mz7OnGXQviwwElaO1S7YRd1Y3FRS6SfSzI90 I3o14thRkmxarIzDchw4TIXT3+LRJIg/ubGJswfJ4mOjILZDjdAUtzJzHiQlSyiHMq1v5XpICpJ8 ScR7KO8kvDhCp00oPImUCYPwBATItcooQFwbapPrwIt9zJ+eQX85x4Cfw9kShK4UuTCjJINC6hWC +SWdTO6gt5yiZK95rbT7o6U8fo7DpvfGQ4TX66kSUsOaiJQUqxJ+oBOdDBvniZV6D45xbBSEIv0f rNjJgnelW418rAKXfkyWxzK9s4ilxcESQTtGbObyFSzEIRZChZGOKdK3u5bi4YMBwpjNUYSEsMrt wS/ZY8IeCHWDHOHbYsYLyHo5HqylkgmzqYDtCzFNQJowJiE5EftTtPc99wjlJ3aNtUOi2U6eVLJp cDOq/tDIAAAgAElEQVSyhE3a97/TZqljpyCjy6E7K7NcJftXhbnbZKo2EXYMzkSRokpdLjtPesRL EiNITBAOe9zJUCKfQV5fLtRuhZWHKebm+ygdFkhq1P0MSX8gjUs5IZRejfmog5UkRc6inlD+DLns TW/Jdt0h+03srPYKAUrMosAKZ5Xz9HJWSKalHr03sh3eLt4rnQPCHiDWkpSfV/gLAo56c06ugkxK cyrcfavGKzufxJVGHipH++wC4vlZLH14G470hKoFcrxS/HuFeJAUrsbCXIRw1odLup5PU3RSQqI8 5B0fM1didD/uo+wra8nqgG6eRvvD2EAI4yw5Nna4pC1uzEHhBRi0Wzh/eg6rKz0sL/XMjlkfG+Uo DErbNwhgy4I4vA4G06dj5mjxa0GIM07jz5OEY8dKQSgbroFRjs2XCVm+WSVnY48uKMYNjlPgmc+f xvKdLrJuJr3urfkQg7VcMFjKnghBH1dJBid14OQ1yrSSnowwqHB5tsJHPqHw6kApc0dTEUxqbA+w B8ReJTReBFIySKciHs54930TGW7EEQWC26NLrKPXBFRTa1tvknHyFDF2nO9uZxhqA9yTTJk5dgqy /f1Sea7omwr0PSJUxbSJdvtiLc5+4Sy6DwfIexJXoxX5SJnCJWu64AUdFG6FPKlQpEAeRijZ8MEu Q/Z9dAfSr6Hn1bAazXOwz8r4sU2KnAYX1mwpXv9+9bnzQYXVRW1hPW6xh8O0u5BLx+glpAmqEJS6 B/DycJIUs5VsdyDWS0g4yPwybIx7Mjl2CrJTEd+UjVgeXSaiBkMWNjBYK/DeW3dRpyUidrz5Hu7d SxQOT23xHJRZjguXZ5D2Sty9nWItV8IrNldVvQo3PnBlV5cYw4wlWC+qTGyoYtaJ6N1JIjeZ9EGP mXlIpWVfvwAZ7XDS4+FhjWpdZLQJIiGk7vVZd+LmorD5PMsE0yYt1TJaeh389YnkRCuI9Ey32zKL jxf+1Pw5rK2uIs1TKfK5GQuFECvBBh8hlq5rzIUxWu0YS90lrH6aI0SG850Eq3kszVMX/BDnAxcP Vrp45LHCLvuc+dSNK1aKkQ0Wk3ERpo8Jc/gmiajccSp9OOa3tFRz+q2OiCYch5uW9rpr/Yj3kD3w xOepu7x7OcEKonBhnZCqi5KUNO05IDT9FG3XwVqXpltbQWdmXARFgdDJUQoHVo1+L4cfl+i0PPQZ nFcFooUQc2dn0L/rYrWXSCFP+7BGTIyjO6/NYRYSM8kd0OernScpjoH1qE3BVjBzZYkkTaT9gAhu DvdUIji12gFpX7nZDbl9d38BTrCCqGTMipgZ7Wu9Vcwv1IjJv0sF8SvpG68z9ox46HQCuFmNIsmQ JCmKml2FJbKaCVuf+UR4bomyVWFwykUxaCMmL1dVIsmoCBtjEC1eqqvFIHI0Zde8wgzr3I6ML4nj 4GFVREAI8ZwCRTn1if1kXLh0qywDDWeCUEu4kdlvbgdV7EZOrILYS8iFzksqRAEV8OhujajlIIod POBwyFSH6VTIkGaeBOPMnPgzQKvyMeiXWO6Rx4osijmiOkd2/xE+XVrCvUUH1894iNsh7rF3nT0e zXOoHfSSFAldOsfB3ExnnXJou7FFKVsAZtMKjblT42vhiGtILWnbSuZASl2jJim5YbDJMwGPVmWO TqslzXM2Czmhk+iJ5cQqiF5GnTCVkiggSTE34wvD+ID8txIPa9U7iMhNFYoVEWJrxuKJTvbQ7r0K flXhXF5jqYiwXNQoWVNxfNxdLVh7l3QwRx0oW7nCRBIG1DUwy1pKi6yImkbTEQ6KHWN2RjvxOFKB FkszaFZ2Osv9yM2D8X14nQ7igoF5rh2IZJkhLMhcE6Zz9wvqf+QVxPJdjQY5bt8dGeXFR9kOpS91 VQkcnQZFN4iE1hKwl4ShaFg4BFCxHwNALyuRcuyAAEBITgD0DJ8uEVxKOKQskjxekg5w+eocZk93 ZBLUozvdIRm7kjeWSDNNE4eVDzdS6Mh4nHKc+0BcDr8hUTinPRHlQOolg7QWd9TOb9knY3nkFWQd NP0xmR5bY5DRWgKN9xska7ogBS6/rs1XW2KVUYXsJqaaITPKTS89pzg5Lh6xM5EZYDICkmNeWANt fwnTVIqyLQq6a8zl5zh9OsTMqQ5Wu3xVdz2tqqE/JaEaF4aMcROiOf29FX/xpF6YaR7MuakYfjLe s5Bxhj5ofm8O79wrOfIKYjsCt/tarhEWBmdaLenvpnVg7YC0l0zGtlrRkGVcg2ezWImeLXTQpzxm +HgFfs7g3PNFiTrtGnFERXKwuKyvG1EosJeDzVEZ+mzZ9UOkq0C6uoaltdHMRQp3SSpAFAdg+YMj A5YTLYIxMI1CH60WwZOT2SYt4NIqxSQu46Mg9aan3FSSzVPoJ0pBNmulHbbNbqvNVin5k4wDckKj CMwm1ej1WUF3EZkcOnE/FEJRJAjUs9Csu0yYNXMmhNbfNFAVOVpsyXVdrMj5KoUp8VI8gMzFyEn4 5mK2HWO1S19OxwAwc8asGjM28kmErEven7GIfj/GQLQojE+Y7mRRUL93La+nZWThU7BK0hfD2Me4 ILR+8tU2ku7Z63s0xBn7DvXm8dikxMVxUpDx1tnNBuvspMVUeK64LRuKH8H2cPGRnXGQyCKm8HF+ XBzHytlkJlCNHF66doaoTgddoMgddPvKEk/Fs+UPQt7pWhGOTyCdrb8wxmCVXLi18lwavJjOlB3f VNe5yAV0qV9UXkvXi4quWS7T6CVVfFcwX74Z9kM3kIUzBrT2tXoJ9TqOW9+joyQbpakclnBQqZtM Gp2cvzv8jlOvILITCiWPDmJpugwUmTnxmE7BTZkc2ZY6hsdgnl18GiNM6Yah7TSUadzD6aiML3xf SZfLgrEN5wx6WBFKIHWvmLXiYuaiJX0PFYQ3zPfoxpFDt0bNsQWETnAUGZXHKoOc7GaFQ76m8b3l nFSZVLGVpE3GQMS0RgxqR8fT5sT1mwrfO16HOYqiX5HXXr9TzvtDd9O0ZXPjcLfpek69gszOzsqc D6H1kWxSrngcDrNxHCwtLclcwichKVBy51EXibhBY+Kz5sG0ovR9KzhO25R0wGTAMdCCns2k85Cs igyoOdvDl95xpnRLcdf6g/6QmE4YBltt+T5UGkK06aapNAqFO/lCxmLZ3ZNMky6HB5nH2q3WiEzO WJPRIJvJ5BZHTYb7iflNxn1eV2m9TlNJC3NgkuV4Zky31aYw9QqyuroqhNNDzt0GSzu/nLC4P+GN HU/yTiJ6JgarYFcgQunpSIpCiKNJjsBzGGSZcMsS2dtNa0TtGD5HSSccIqpwFgm2w1BcIyqDukcF FpeXzHgy5fadNDFtZ32Gozdpdk3/lBR11ZdrRSXhIpH5jmPK0YzxjqQVcRyJFS2LPF1QXne6mK0o 1g1B3OT1mb0jbUEsqRt3XDuByN5Q3vCduleb1Q826xrhJeUCTkkWTer+NIWTAlHOOSMtYaQnRkiC d/ZVcWdi5mroBGtGyrZ7ih9MTTAdj0PS1T3YvMdhJk1eY7pxzNhJtZnzUWQh6aAhtwmnF0aY0UyV oyXEa1ExdEAoJQwibYsRK2FrTBq7bed4U68gFKsgdKtsxkq4pHZhPXYinJ3YH5Arl2hS0pVqFqwu FYJOrBVbdltRIDgt0vrb1Ks28ygXsJ2SK3kwsSIc4zZ63X6KXCUZNEpIuFKlcga8jKYzFplgTeHN 3QI4OdUixdVC7gk3LcaPtavxmHl6xzL1CtIs4tlY4yBvnFXEceHiXuv3JDbihef46SiaaTQqWZuk WTKhOM1yzM7Nyshj7X7bur9jr8VaXmKbiiKXOZBC/m3AgNY1sfN86yOoJNx8ojBq/L27o029gjRj D1vr2HSG+gGfV8bZFoSUyKiw0bzD4WscJcgWV5D1kRYH9gTDoP2wRMdbV5JVm+t05PzZskorJ8/b pEV9tBTEzoPZmzNWSNDUK4iVZpZlKrItpk+BQr++OfSTwh2ZmRPWOXi+QRhqwCgp62Y/yMGLVAUq 9rkUKGOOvR4FqoJpM3NWAjNQ8ygqyW7FpuaPjII8iWyXxHo3wuCbiz5sTEai+0X3qdcfCPaK7Z8s NnJ+u3QPjo2HO3CwockJ0GKQ5MB1Y8GP2d4xiY1EQUJtCjhiSrIXYksKx15BKHuvJKZwCBadfMmr KysK3UCgn6RY6/WlCDjTiuT5yFeCOKGuyTjEMjtUJK5UTGpgMEhltorncd47R2YrAYIohetKz4VN hU6F5d5C9vJaKpznmCvIvokhx2Y+vR2HMnNdK9MuEqJ0h1ASFuUzZOx68zxRjm4yEPaNw1xsdiEN 6VpN8ZWZrMQwU/LvnH0yBsIy7cqx18LvLxAdHGPZr4CeC4mI2tAPEJMuSDBTnM1OLBWtg9KT2hRv Rp8erORmSKXpZ3pGo+l04VxiqHYrHvZZ6Ji02iRH1ILYwuJxF0tSx2TKiVCQvXQPpGDJcWxRKK6T FNsKBuSZwEV4UYfAQhGSSBP3VQotzTTuw1wMmecirpQqx0Iv6gZZxJGvsu9AlCCCxUQd3HqsZbu9 ItsRLgsWK2daIWKywAvit0Q/SdDtJ9obYme4m8Vki5qUaVQOqX3AUnZypokG6bQoQRCMKcwIv3Wc lYTfkehn+Tnsk5l2sYA2mevNhiryLgkSVxlJkpSDP3UOwToynyb5whT77/bc6Pb1BomMY6aFFDod xxEaI75C+uGFenK9ohxHsW4m5amCNGTDnug4EmNEnPzk6chpCmMNZjkEMp+Xki+X/PsEkplpVo6m aAeiMqgQNsPRqC4tRWVmNFIhjkg2a7fS3NyeKsgmbZyCV/J8w9TnDTmtyKxBn53KoY1PG6ngjrKo S6jATIrWRxTFIHNIhJRtSt3FPZDmzHZxtQ77hKZLbFCvfLjttvItEfgms0bMjBEhUjDUPHaUgbTb HpOdlUkGKkldE0ZDQGNh2oDVisgoCBw/saBY0tHaxrOnCrJuBFsgE6aG/neeSy2AZA3SsMUpgIKE JfaKvw1spEG2cByEmTjWajIvQxB4Yin5/aXJaAL743ERS3TB7zrcKA/7pKZBbCsmOxeFC8twUlE5 2HQjIEljPcjuTh4G8cUNh9WBnedjajp7VfPROgCzcymCQsdK6xx6tq0WEsRzpz2OYlsabH/MiVcQ GawyTGW6wwKRTc+OD/5U5dHe+J0uxL0sXO5vTUIZJ3O5Dtp4xAiEbhYtJeMSLSQeT6HnYOX4fsvH iF1YRNlKs1BRYG1tbSPR2iYL+UmUY1Lf906OM5n5fX8URdACQUAWCtMSbAL3upKxZocFYNwqfb4f GbYTqSAygSjSWIN9GdKZSIuxg8U7CX6/3tIcItZqHTv8xn+P/95MQeIoQui1h3P/CN+XmRzuyL2y TDP7rSzNAJr/trGCJcezcZG1/tIpuQf34MQoCC+erQwLHY4gVZUOZjPSuc0UYDNFmJZmrs0szAbr uMm/7WuLopBpTRxcat+qKe3RdThIK8L7Nj8/L9wEzQa65mbF8+n1evJjgZe7kROhIFYpbAMQhTdf diBmpxpDdMYVYSvlGP+M8fcflmylqI9TEqsgvDaDJJHMlWwipo+FMBTCazyXSF/GIgcXrDf5CJrg yaayWFTyXinusVYQC4dQqnx/2ATTNL/NHWg3XYtT1e24TebJx7mERYOAzl4bEnn3KkL6S7EuQtRt i4cTiXTrvUMdZxk++eSTx752L63asVSQcWg2L67t0bA70KSgeVoW97RIRaxZkgyvpVUYe/1YC7IE flou3QgM3TjZd3dy0IkB/6grgIVENNGmzccnWYhp2+2nWcqGtbXX2D5GKDxdLxm5LOhX9uaPxi4f h6s7tQoynhmxOxfFuk2bLfRm0G2txqTXPZXtST2mIMp5W6LMNJOUByHCMEAYcNqTL/PMFdNEwryj XXH3px0wZpXBbcwJbyqPJbYWfqpNMhvHUcSK2iE72xz9wCvGxSvXxiADtvM59aTsXZMpH7kE74PE dlsGCCMqTYjQGw0DOoqqMpUK0uw3sIpig+tJr2sqhn38iT/bjPMSWl3jRowUctQMpTspF4+dv2H6 1JvP84AWpCVv5QiCvUH9skbxzPnzgjheWl7C4tKSVP3HezRs2y9/h1GE69evC9fxw4cPlSXycdej 3viKJveUBW8K871Dpntt4WXthKMkAk/5ckVZAuUzXheZrFO67V2ZzTJxJ0pB7IK37tG4ZRiPJfba WrAg9dKLL+Latau4/fHHeOvtt+Xx0wsL8kMan9W1NVy7dg2dThsFZw4mCd555x157IuvvorlxSX0 0v4QlvHZp5/hww9vYq2ro9YoL7/8Mvr9Hj698+mQT/ZxQqt58eJF/Na3voXFxUV9H4tkwuCo80dY 4yGwMgpjRHFsxkioNe73B7j10Ud4/Y03RgzXu5DaoJuHM2Zt2pz3rBzh2lLTeMYfxis6lXb7n89z n5mZWZfKtevDkvHxHuzlWphKBaFMyjBtpiD7IVSAl196CV/76lfxxps/xI/eektuCG/QpUuX8Mwz FxBFsdQBuBMrU6KDDz74AF946SX85j/5Ft784Zv4+NOPZRHOzc7i5RdeRKfVxs/eex8PFx/JTf7a 176K1ZVVvJa/hk8/+2xb53bu3Dm88MLzOHP2DB4tLiJutXD69GkhpJb6xWAgEHUquR8SMRDIQlpb WxWFeubCBXnuZ++9J/Ca7SrmljJWrBuHzpPfmER13CwIdiRKmNbPLnaNVba+n47JmlkUhJ1LadP3 /Py95mueWgVpStOaHFRcIT4+Z3hIw5TBHsmIQWUGP3v2DP7xN7+J3//9/4ofvPYaur0eLl68hJXl ZXEnuFv/+Mc/xg/fehMrq2v4uS+8jP/w7/8dnrlwHt/9sz/DX/3190UJv/ylL8tOm/T7oiBb4Yls 9u7nf/4VvPTSy/jj734XN969ISyJp08v4Jnz57CwsCAKwmNcvnwZgzQTS3f37l3cvHlTFtI3vvEN fP7zn8err76Kt99+e2jRnvTaOo1YaBzGYsm55cjMgOW5cIJZ5hRaNzJOylAb04+zmfD4/G78zfc2 Y1MhETdx6F7K1CrIVAXcholQfuScOMG2kJ3/t37zW7jwzAX85V/9FT66dVt3MGZ52KYqbKSOsL9/ dvcu/vu3v41//ru/i1d+7hW8c+Nn+MIXXsajBw/w7LVn8Stf+WX87z/908d+z+eeew4Xzp1HxYlU 3R5eeeUVnDl9RmKS5ZVl5GWFxZVVmcEOs0N3Oh1Rhi996UtSaKMV+fjjj/Hbv/3b8jdnM+5mjERt aiKTK9gTCpSCDK5RSTtBX9wisk6SHZ+by2ZKwvVAAnMqyThyYb/WyVQpyEG7UVuei57Q8G9LK2of 4WSrP/j2H+JXv/ZV/PJXviILlxbje//nL0aFs2EkS0b1Pj68+aEsZO72586elfjjzR/9SCzU5z53 Hc9fv447n30mEA/zqRsWmMUX0frYVPf9+/cEe0RLwUD98qVLuHjhAu4/fIDFxSWxhDOdjuCY2p2O pGLJv8ud2wbZexGDOBOwWcIPZkaiTXrfsLWgVIChnxUSLzGwFwsx3JpMwkEIww8uHzYVCrJZNurQ zkdPauKAVDvNY3V1Da+9/oYExL/yy1/B5194Hp9/4QV8ePMjWXij6VWj/6nPXCIMQtn1rz97HR++ /z4GyQCnTs2Lsv3p974nf28WvHL3l5HVzO6ZRXX33j0sPnok8dGDR4/wi198FV/8hV/AD177Ad7/ 4AMJyufm5iSzdM51xaJwp37vvffQ7/XEIu6W9LluJFSaNEHmUg7bmTd7L+MgGeaTFwiovCWVJJAs WJNc+6Dl0BVkHP4xLSKNUZyLbha2zMyQZiFWiskdxbRzge/+yZ/g9Tdex9d//dfxn//Tf8TlK1cw M9NpHkluLmdxPPfc88KhxQlICwun0O608eovfhGnTp2S2OCb3/wmfvT227h//8HEcJXXhylaBtbW 1bh46ZIG2nmOr3z5y/jr//d9XLl8GV/6pV8SX//dG+/JvIyrV6/iypUrkt49I1m4CN///vcl/tiL q14bK0KxaXGbop8ELpz0/uY8FsYUTIB0WpEoc3Pw5rFXkL2uXeyHZFmKW7duScHrzJkz4pp0u31c uXpVAkvOTvz6138dr7/xQ1mg9+7fR7/Xxxr9/zNnhsU4fkemgV988UX8s9/5Hbz2xutIBwkWTs3j 937vv6DX7ePM6dP4B7/2a/i3/+Zf49lnn8Undz4Vd2mScKHReq2srOLsubP42q9+FdUPKiwvL8tx Lpw/Jz/PPnsNRZXjf/zRdyTmef655/CdP/oOLl27ivPnNYa5f//+uu65vSbqC8RV0inF9rnx2IFK o73/+br3ilUpSiEBD5JMrLKM4/ZsnWf9ummil480WHGqgu8thLsYU7Z/+Vf/F/Pzc/inv/lbWOv2 JKV655NPZHE9d/06fu1rXxVrMzs3h//2B3+Amx/dFAvy6NEjKcqFcSQ1iU6rgz//8+/h737y9zh/ 7hxmZzq4ffu2cFFRGRgfvP/++5Ku5c9mCkKxCFrusu1WW1w2ngR33Oeffx4XLlyQmIP1mKvXrkl2 hz203/yN38Df/uTvRbnbHMcQhPuyK9cmtrD/tgoyfr+tpdmsuUmVhOtFuxoJs2exMQwJaRlhvuxr 98MN8w/LakyrYogfbhr26aLcuHEDL7zwgtQ+qCCf3LkjLtDDh4+kgEhXqdVqo7u2hu+8+SbufPqZ 5OlbcSzulM3xs9r9wzfflDjANmudPXtWagHS8luWePuttzRGiXRY6WbC47NVmImCvKAFGLE6Xn/2 WbEkXEDzs3OixD/96Tv46bvv4Mtf/gquXrkiz7E78Nz5c/js/j04ezztqjbxW206/pqIiPFU8HYW tQyzMf07/F2WVBICJHntFPownAm5x0qybwrSDNJsjLGXPLn7rSRchHSnZubm8HBxER/dvi2u1MrK Cta6Wlz74Zs/GpFVC5eWZl4++OBDfPDhh8P4hW4G3QP+nJ2fl7QOB9cwTpDFLsGoLxbl4YMHmqIV mbyJ2PfQStENkeVhfPxnzl+QYJ1xDpXw2StX8Td/87f4/muv4dYnn+Bf/Yt/iTt37uDdG+/i+ueu 48bPfoZRXX93Uq/7wyjt2Hjp4TVuKMdWXZjrrIRwISsNEyvzjKOiONIgfvQOHAkFGQ++p9VibCVc 8LQEs50OFubmZKCMcGHVtezALArSinAn5428dPmiqbI/I9aBbhN9Z8leceqUDPJkAJqPiltMWxpo BhMBa73ucNz1JGlCLGQCksA2+OOhMzOjbl0Y4bPP7qLX60pMwzR0YlzGG+++iwcPHogL+eoXv4i/ +Iu/3Ndr6DQC9kmWY7xNYTui2Dy1TowVZfYji7PSK38E6iDTmpnaiXCHJpzkheefFyWh304LwsVN K3Fqbhbnz54VP38w6MuuT3eH3/n+3btYWVoSOAezV+1WS4CCPCYXM/9m4M59T3L9ElO4YrloYz+8 eWvT8xJISRSJu9Fpd2TRMwv2yis/j5/89B289/4H+Nyzz8rxb9x4F4N+iiCK5HucPXNGrB8r9l7g C2SFrsrjQR5PLnbxUxGs4lsFse4WF7r92ZbYQqwUG2uUdYKIqeEgRBCOOJSnTkGOSpyxHeHNokKw xsCAlzsWax4s4jG9S3zTwqlTOH3mNFqtS5L6HfRVURi7EP5BBpBW3BJCOioLqUxbRlnEh5YsTwif RTFJgbpCjD37479rVtiGwsVEkCLrGNwtaR1++pOfYP7UvMDOGS9pRijDlUuXcOvWbXxw8yOcPXdO Urz83Ju3bsk89EsXLojVieMW/CDY82xWU5prYZJrtVNprioWRkvSwkpNqEJUh1KVZyJjL+KRPVGQ 46QYFJ49sVWvvfY6Xn/tdckKPf/CC4JtmpmdkedsLJKXuSy+q1ev4MrFi4LRmp2dNa2oIxJkOe7w umyCea9dPHz0UGIIvpI74fi1vHjxGVEQ7paEj/zPP/5fklGj0Ap97nOfw7nz53H56lUJ3e8vLYlS MNagUFFZbyHspLvWFbeMsdF+KYjTYEgZT/PuDY+VHo8xYZnQ5crQabcQRVo72a2S7FpBjoM7NbEf RLh3XckIEd7eCkOcXVjAiy+9hCuXL0nlW+AQLltNFRphYwGLMh3tdROuy4SHHChzoRW6EeO3l1aH xcLUDySFywUvn2eCYsZErH3wdZxo1R8MtJfcvJ/uGGOd7uqqwvBNr8h+uVmVOf6kNOxerxceriBq uU8rXsgsF46v2I2S7EpBjnoQvpXExm//xj/6uvxmwM0fxgDzc3MSGGrqkq/e7Ls/ITp2i+eYgZp7 aVaUdm1lRSD5TN3S/WOcZNOhrKxTAXi+tEiqMLlYQVomBuosXhLDpVD94yG1oYZN61yUU9wuzneR Md12I3D2V0GOm0s1Seh2cAH9w69/XaDqrE0Mv6f9dQjf+/btj3H54iU8e+2aLPirl6+IK0XFoHtG MCMTCbQedDcYiDNWYmaLbhSza4yhuHhu3b4tFfjtdBYeNRHCbRubVJVMIh7WTXYg/kl3qTYT7ris eNNaCOHcuhrO4XVXE6z40a1bAhd58YUXxN1iqpjp5DOnFzDoD4bQDSYaiDKOo1j9f1fZJdkzQqTv t7/9h9Kqe5B30TnACVVS7C1KlBUbyEqZ4stKPPt6nP1QkKNW8NsbsR764VMO2IVFSAoDcyoJ3atL Fy8Ktmtu9gpmZmflOSYSCFoM45YAG5cWF/Hg4UPcu3tX0AB3793F8vKKWB459i7RvNMqbDvgck2y THplCH6MCX7cJiPkthTkpFgNil0kLKx9dPsWfvDGG/jwo48O7POZbbr34MG6cxmXNMuk1ZaNToSw fHjzptRWmDljrYFZN2bSeO7sB6GCMM7gb8YcBDqyP2UdLmqfbqtjA+RD5izW+SYFegOOeK4leCu3 eVoAAAFdSURBVGdihV/ctjDsWEGmrU/jIIW+/Ls3bghokAW5gxBeX7o8dz6989jMi+2u48/YQQSw SAQyYwyFo6iVOOlSmwo8fUpZ06iF8I5xyWZX+7EKcpwD8a2EOy4ZSG7ePDjrsRfXmHAY4sD4I38f ceK2/RAqCaE/jN1mOm1REhlxvV0FOUku1eNke/RqeyN7sZTH79dh37/a1D+mSk0doCQDimS5upjp aGFR0QyPUZCnyrFejmPgetDiYDpFYPRlKcVUZimJDh6fvegfNr3OUZCDuMHH+mo7h9dTvq1UcGbi ElMgbuK4REGagfjJSuFuT4714j2oJLkznQpC4YpPSEFk1j4zXNLay9HgT12qp7Lf4kyxcjSFVfdu tycBPKFE0p7wVDmeymGIM2WzHCk8EwbvgzQXvFaLff5PleOp7LfUjaapabcoVAwG7hAAZ60u1lN5 KifJUmAbwv4Sslj+f9Qri/lIR8aqAAAAAElFTkSuQmCCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAA=