Exploration of wear characteristics in aluminium-silicon carbide metal matrix composites for aerospace and automotive applications

Exploration of wear characteristics in aluminium-silicon carbide metal matrix composites for aerospace and automotive applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploration of wear characteristics in aluminium-silicon carbide metal matrix composites for aerospace and automotive applications Bharath Srinivas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3993647/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this investigation, two metal matrix composites were synthesised employing the vertical stir-casting methodology, amalgamating aluminium with silicon carbide (SiC) at concentrations of 5% and 12%. To assess the tribological properties of these composites, a pin-on-drum wear analysis was executed. Throughout this scrutiny, meticulous monitoring of wear losses was undertaken over a predetermined traversal distance while subjecting the specimens to incremental loads spanning from 1, 2, & 3 kilograms. The results unequivocally demonstrated that, in concert with the increase in applied loads, the composite containing 12% silicon carbide exhibited a conspicuous mitigation in wear losses vis-à-vis its counterparts containing 5% silicon carbide. This augmented wear resistance observed in the 12% silicon carbide composite can be attributed to the heightened mass fraction of SiC particulates integrated into the composite matrix. These SiC particulates, functioning as reinforcing agents, contributed to an amplification in the composite's hardness and wear resistance. These findings not only yield invaluable insights into the tribological behaviour of Metal Matrix Composites (MMCs) but also elucidate the potential impact of supplementary performance parameters on their wear characteristics. Metal matrix composites (MMCs) manufacturing and aerospace domains stir casting Mechanical properties Anova calculation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In an overarching context, materials enriched with a multifarious spectrum of reinforcing phases are encompassed within the purview of metal matrix composites (MMCs). These reinforcing constituents manifest in diverse forms, spanning particulates, whiskers, and continuous fibers. The production of MMCs entails the application of an array of methodologies, including but not limited to powder blending, consolidation, and physical deposition techniques. Presently, there exists a discernible and burgeoning demand for aluminium-based metal matrix composites (AMMCs) owing to their extensive utility across various industrial sectors, including defense, automotive, aerospace, and aviation. AMMCs have garnered commendation for their superlative material attributes, encompassing reduced density, elevated specific strength, corrosion resistance, favourable wear characteristics, and commendable thermal properties. Among the myriad methods employed for the synthesis of AMMCs, stir casting conspicuously emerges as the predominant technique. In contrast to steel, aluminium exhibits a diminished propensity for wear resistance, thereby rendering it a favoured choice as the matrix metal in select applications. The primary crux of this research endeavour is to conduct an intricate investigation into the wear performance exhibited by AMMCs, with a particular emphasis on those fortified with silicon carbide (SiC) at concentrations of 5% and 12%. The investigative approach is centred on the utilisation of drum testing as the preferred method for meticulously scrutinising the wear attributes inherently embedded within these AMMC specimens. In the research conducted by Xu et al. (2009), an investigation into the intricate interplay of plasma electrolytic oxidation (PEO) technology on ceramic coatings applied to Al-Si alloys featuring substantial primary Si content was undertaken. The alloys employed in this study boasted Si proportions within the weight range of 27-32%. A discerning scrutiny of the PEO process unveiled four discernible phases, with a particular emphasis on the consequential impact of bulk Si on the morphology and compositional attributes of the coatings. It was noteworthy that, at elevated voltage thresholds, discernible transformations were observed, giving rise to the formation of Al-Si-O compounds[1]. Adnan Mehonic et al. (2018) delved into the burgeoning realm of resistance switching, an area that has garnered escalating interest for an array of applications encompassing nonvolatile memory devices, hardware acceleration in the domains of machine learning and artificial intelligence, as well as neuromorphic computing. An intriguing facet of their discourse centred on silicon oxide, an exceedingly CMOS-compatible dielectric material. It is noteworthy that, despite its inherent compatibility, silicon oxide had hitherto received relatively scant attention as a candidate for resistance switching. The discourse proffered an exhaustive classification of the mechanisms underpinning switching behaviours in silicon oxide, whilst succinctly encapsulating the contemporary state of comprehension regarding fundamental switching mechanisms and their prospective device applications[2]. G.A. Fontalvo et al. (2005) embarked upon an exploratory voyage to elucidate the intricate influence wielded by oxide films in the context of metal wear phenomena. Their investigational focus zeroed in on the role played by aluminium and silicon, alloying constituents, in modulating the oxidation and wear characteristics of hot work steels. A battery of meticulously executed ball-on-disc experiments, executed under varying temperature conditions, unveiled a salient correlation between the presence of aluminium and silicon and the consequential reduction in oxide film thickness. Paradoxically, this reduction correlated with heightened mechanical wear, particularly at elevated temperatures[3]. M.G. et al. (1993) undertook a comprehensive analysis involving ball-on-ring wear assessments, with a specific objective of delineating the intricate relationship between humidity levels and wear patterns in sintered silicon nitride materials. These rigorous investigations yielded intriguing insights, revealing that wear rates exhibited a multifaceted dependence on testing speeds and the prevailing humidity levels. Notably, the primary mode of wear was identified as the tribochemical oxidation of silicon nitride, resulting in the formation of silicon oxide[4]. The research endeavours of Olufunmilayo O. Joseph et al. (2019) were motivated by a laudable aspiration to foster sustainable development through scholarly inquiry. Their scholarly odyssey unfolded in the context of metallic matrix composites (MMCs) and aluminium metal matrix composites (AMMCs), with a particular focus on the reinforcement constituents employed therein. The collective wisdom gleaned from a comprehensive survey of extant literature illuminated a promising avenue wherein agricultural waste materials, typically regarded as byproducts, could serve as particulate reinforcements. This newfound potential was particularly evident in materials such as groundnut shells, coconut shells, and rice husks. Remarkably, these agricultural waste derivatives, when processed into powdery particulates, demonstrated remarkable reinforcement capabilities and imparted enhanced mechanical properties to the resulting composites[5]. The meticulous scrutiny conducted by B. Kaczer et al. (2007) centred on the indispensable aspect of gate oxide reliability within the context of scaled-down logic devices. Their analytical foray underscored a pivotal consideration—namely, the influence of elevated operating temperatures in conjunction with the commensurate amplification of adverse effects on exceedingly thin SiO2 films. What emerged from their discourse was a nuanced perspective, challenging the conventional notion of an Arrhenius-like linear decrease in time-to-breakdown. Their research illuminated the notion that oxide defects arising at distinct temperature regimes were not wholly analogous, yielding a non-cumulative effect. In their scholarly purview, microscopic models were explored as explanatory frameworks, and a reliability projection for ultrathin oxides operating under heightened temperature conditions was cogently presented[6]. Materials and methods Exploration of Tribological Characteristics in Al 6061 Alloy Enhanced with 5% and 12% SiC Through the Utilisation of the Vertical Stir-Casting Technique. Material selection Aluminium matrix composites Aluminium 6061, an alloy distinguished by its multifaceted properties of excellence, commands substantial significance within the domains of aerospace and automobile engineering. Its ubiquity in various pivotal applications arises from the alloy's juxtaposition of lightweight characteristics and structural robustness. Within the aerospace realm, Aluminium 6061 serves as a pivotal constituent in the assembly of aircraft structures, predominantly attributable to its remarkable strength-to-weight ratio. Its innate lightweight constitution invariably contributes to the curtailment of fuel consumption, thereby elevating the overall operational efficiency of aircraft. Furthermore, the alloy's inherent resistance to corrosion endows it with an extended operational lifespan, particularly indispensable given the taxing environmental rigours inherent to aerospace operations. Components spanning aircraft frames, fuselage panels, and interior fittings all derive pronounced benefits from the alloy's enduring properties and ease of fabrication. Similarly, within the automotive sector, Aluminium 6061 assumes a paramount role in the pursuit of augmented fuel efficiency and enhanced vehicular performance. By supplanting more weighty materials like steel, the alloy effectively mitigates the overall vehicular mass, thereby instigating advancements in fuel economy and concomitant reductions in emissions. The alloy's remarkable machinability further facilitates the realisation of intricate automotive designs, ushering in a new era of innovative structural configurations that judiciously balance safety and efficiency considerations. Moreover, Aluminium 6061's innate thermal conductivity attributes render it invaluable in the manufacturing of engine components and heat exchangers. This, in turn, engenders an efficacious dissipation of heat, thus augmenting engine performance. Its innate resistance to corrosion, a distinctive hallmark, certifies the enduring durability of automobile components, even when subjected to the gamut of environmental exigencies. In summation, Aluminium 6061 stands as an indispensable material within the aerospace and automobile sectors, furnishing pivotal support to the relentless pursuit of lightweight, enduring, and fuel-efficient engineering designs, thus indelibly shaping the trajectory of future transportation technologies. Silicon carbide (SiC) matrix composites Silicon Carbide Metal Matrix Composites (SiC MMCs) have garnered substantial significance within the aerospace and automotive sectors due to their extraordinary attributes. In the realm of aerospace, SiC MMCs are ubiquitously employed across diverse applications, primarily by virtue of their lightweight yet formidable character. They find employment in the assembly of aircraft components, encompassing engine constituents and structural elements, with the primary intent of diminishing overall mass, augmenting fuel efficiency, and bolstering longevity. The superlative resistance to elevated temperatures exhibited by SiC MMCs renders them eminently suited for deployment within turbine engines and exhaust systems, where they confront extreme thermal vicissitudes and mechanical duress. Within the automotive domain, SiC MMCs have assumed a pivotal role in advanced engineering. Their intrinsic lightweight propensity serves to ameliorate fuel efficiency and curtail emissions, congruent with the burgeoning demand for ecologically responsible vehicles. SiC MMCs find application in pivotal vehicular components, including braking apparatus, clutch plates, and suspension systems, engendering performance enhancements and mitigating attrition. Furthermore, SiC MMCs play an indispensable role in the evolution of electric and hybrid vehicles, leveraging their lightweight constitution and heightened thermal conductivity to facilitate efficient thermal dissipation and amplify the efficacy of battery systems. Moreover, the coveted attributes of SiC MMCs encompass their exceptional resistance to corrosion and abrasion, rendering them eminently apt for mission-critical functions across both industries. Their capacity to endure the rigours of adverse operational conditions, encompassing extremes of temperature and corrosive agents, underscores the protracted operational lifespans of components, thus culminating in the diminishment of maintenance expenditures and replacement outlays. In summation, Silicon Carbide Metal Matrix Composites occupy an instrumental niche within the aerospace and automotive domains. Their lightweight character, robust resistance to elevated temperatures, and intrinsic durability collectively coalesce to engender improvements in operational efficiency, a curtailment of ecological impact, and an elevation in the comprehensive performance metrics of multifarious components and systems. As such, they substantiate their status as indispensable materials within the purview of contemporary engineering applications. Stir casting process The vertical stir casting process assumes paramount importance in the manufacture of cutting-edge composite materials. It encompasses the liquefaction of a matrix material, frequently composed of a metal or alloy, within a vertically aligned crucible, concomitant with the introduction of reinforcing particles or fibres through the agency of a meticulously controlled stirring apparatus. This method ensures the systematic dispersion of reinforcements, culminating in composites endowed with augmented properties. Its indispensability reverberates across sectors such as aerospace, automotive, and defence, where it assumes particular significance in the production of both metal matrix composites (MMCs) and ceramic matrix composites (CMCs). In essence, it stands as a meticulous and versatile technique for the formulation of superlative materials across a multifarious spectrum of industries. Pin-on-drum abrasive Test The pin-on-drum abrasive evaluation method stands as an indispensable tool for the assessment of material wear and resistance to abrasion. This sophisticated procedure entails the placement of a specimen, often taking the form of a pin or a cylindrical sample, into contact with a rotating drum sheathed in abrasive material, typically exemplified by sandpaper. Under meticulous control, an applied force is exerted, inducing interaction between the specimen and the abrasive surface, thereby emulating the wear encountered in practical applications. This rigorous examination encompasses the quantification of pivotal parameters, notably the wear rate, indicative of material loss, and the coefficient of friction, signifying the degree of resistance to friction between the specimen and the abrasive substrate. These metrics furnish invaluable insights into a material's capacity to endure abrasive conditions effectively. Diverse sectors, including automotive, aerospace, and manufacturing, universally employ the pin-on-drum abrasive assessment to scrutinise materials integrated into critical components such as bearings, seals, and coatings. Its application empowers engineers and researchers to engage in judicious material selection and design, culminating in the development of products characterised by heightened durability and unwavering reliability. ASTM Standard A514, traditionally subjected to quenching and tempering processes, represents an alloy steel plate prominently employed in structural applications. The A514 family encompasses diverse chemical compositions denoted by letters such as B, E, F, H, Q, and S. Notwithstanding the nuanced distinctions in their chemical attributes, these grades collectively exhibit akin mechanical properties of significance. The canonical material adheres to the archetype of low-alloy steel, distinguished by a hardness rating of HB 269, and is bestowed with the esteemed ASTM A514 nomenclature. To accommodate minor fluctuations in the abrasive characteristics of abrasive cloth—whether arising between different batches or within a singular batch—the conventional practice entails the utilisation of standard specimen wear for calibration and standardisation purposes. Procedure The fabrication of composite materials entails the meticulous selection of an aluminium alloy, specifically Al6061, in accordance with ASTM standards. The fabrication process employs a vertical stir casting machine and employs the gravity moulding technique for optimal results. The stir casting apparatus is meticulously preheated to temperatures ranging between 750 to 800 degrees Celsius and maintained at this temperature for a duration of two hours. The metal matrix composite is stratified into two distinct groups, where the foundational alloy is composed of 95% Al6061, with the remaining 5% being attributed to the inclusion of SiC as a substitute compound. Subsequently, the final composite configuration is attained through a composition that consists of 12% SiC and 88% Al6061 alloy. This composition is meticulously achieved via the gravity moulding technique. Following the culmination of the fabrication process, the resultant composites undergo a battery of rigorous tests, including a wear test, to comprehensively assess their mechanical properties. Results and discussion Table 1: Process control parameters and levels S. No. PARAMETER LEVEL 1 LEVEL 2 LEVEL 3 1 Load (N) 10 20 30 2 Speed (rpm) 40 50 60 3 Track Diameter (mm) 50 60 70 Table 2: Output Responses for L9OA Trial Load (N) Speed (rpm) Track Diameter (mm) Sp. Wear Rate 1 10 40 50 0.000163 2 10 50 60 0.000138 3 10 60 70 0.000127 4 20 50 70 0.000065 5 20 60 50 0.000091 6 20 40 60 0.000079 7 30 60 60 0.000053 8 30 40 70 0.000049 9 30 50 50 0.000069 Taguchi’s Single Process Characteristics Optimization – Signal to Noise (S/N) Ratio Approach From Table 3, Load has the highest “max-min” value of 8.0572 and this parameter has considerable influence on the specific wear rate. The next influential parameter is Distance (Track Diameter) which has the next highest “max-min” values of 2.6962.Based on the results in Table 5, the optimum parameters for (SWR) are Load (30N); Speed (60 rpm); Track diameter (70 mm). Table 3: Analysis of S/N Ratio Response Parameter Input Parameter Mean S/N Ratio (dB) Max - Min Level 1 Level 2 Level 3 Specific Wear Rate (SWR) Load (L) 76.9557 82.1843 85.0129 8.0572 Speed (N) 81.3538 81.3841 81.6633 0.3095 Track Diameter (D) 79.9413 81.5741 82.6375 2.6962 Total mean to S/N Ratio = 81.4119 Optimum level = L3 N3 D3 Results of analysis of mean shows that load is the most significant machinability variable followed by Track diameter. We go for Taguchi based grey relational analysis to confirm the above prediction. Table 4: ANOVA for SN Ratio Factor DF Adj SS Adj MS F-Value P-Value Effective Factor (%) Load (N) 2 100.257 50.1287 173.18 0.006 89.58 Speed (rpm) 2 0.006 0.0028 0.01 0.990 0.01 Track Diameter (mm) 2 11.066 5.5331 19.11 0.050 9.89 Error 2 0.579 0.2895 0.52 Total 8 111.908 - - - 100 Taguchi’s – Grey Relational Analysis One of the most proficient means to manage imprecision, multi process characteristics criteria and distinct data is Grey theory. The step by step procedure is as follows: GRG is shown in Table 4, which confirms that the machinability parameter points of trial 9 have the highest GRG Table 4: Normalised Data and Grey Relational Grade Trial Normalised Data Grey Relational Coefficients (GRC) Grey Relational Grade Rank 1 1.0000 0.3333 0.3333 9 2 0.8661 0.3660 0.3660 8 3 0.7962 0.3857 0.3857 7 4 0.2421 0.6738 0.6738 3 5 0.5205 0.4899 0.4899 6 6 0.4052 0.5524 0.5524 5 7 0.0710 0.8757 0.8757 2 8 0.0000 1.0000 1.0000 1 9 0.2884 0.6342 0.6342 4 Table 5: Analysis of GRG Response Parameter Input Parameter Mean GRG Max - Min Level 1 Level 2 Level 3 Specific Wear Rate (SWR) Load (L) 0.3617 0.5720 0.8366 0.4749 Speed (N) 0. 5838 0.5580 0.6286 0.0706 Track Diameter (D) 0.4858 0.5980 0.6865 0.2007 Total mean to GRG = 0.5901 Optimum level = L3 N3 D3 ANOVA for GRG is shown in table 6. Table 6: ANOVA for GRG Factor DF Adj SS Adj MS F-Value P-Value Effective Factor (%) Load (N) 2 0.339824 0.169912 17.22 0.055 79.42 Speed (rpm) 2 0.007650 0.003825 0.39 0.721 1.79 Track Diameter (mm) 2 0.060689 0.030345 3.08 0.245 14.18 Error 2 0.019732 0.009866 4.61 Total 8 0.427895 100 Confirmation Test Load (N) Speed (rpm) Track Diameter (mm) Change in weight (g) Sp. Wear Rate 12 3 96 0.8250 0.000045 Conclusion The presented work elucidates the successful synthesis of two metal matrix composites utilising aluminium and Sic in varying weight proportions. The outcomes of the pin-on-drum wear test underscored the superior wear resistance of the composite containing 5% Sic compared to those comprising 12% Sic, particularly under escalating loads. These findings underscore the efficacy of Sic particles as reinforcing agents, augmenting the mechanical attributes of aluminium-based composites. The devised fabrication methodology holds promise for engendering high-performance aluminium composites endowed with distinctive characteristics, notably a commendable strength-to-weight ratio, excellent thermal and electrical conductivity, and remarkable wear resistance. The incorporation of Sic particles as reinforcement agents represents a significant stride towards fortifying the wear resistance of aluminium-based composites, potentially engendering robust yet lightweight materials suitable for diverse applications across aerospace, automotive, and military sectors. This research not only unveils novel insights into the wear behaviour of aluminium-based composites reinforced with Sic particles but also introduces a fabrication approach that holds the key to crafting bespoke materials tailored for specific industrial demands. In conclusion, this study furnishes invaluable insights into the wear dynamics of aluminium composites reinforced with Sic particles. The demonstrated enhancements in wear resistance, coupled with the innovative fabrication technique, herald the advent of lightweight, high-performance materials characterised by specialised attributes, poised for multifaceted industrial applications. To further broaden the horizons of these composites' applicability, future endeavours could delve into refining composite compositions and scrutinising their performance across diverse operational contexts. References Eshan Agrawal, Vinod Tungikar, (2020), Study on tribological properties of Al-TiC composites by Taguchi method, Materials Today: Proceedings, Volume 26, Part 2,Pages 2242-2247. Ajitanshu Vedrtnam , Anuj Kumar, (2017), Fabrication and wear characterization of silicon carbide and copper reinforced aluminium matrix composite, Materials Discovery, Volume 9, Pages 16-22. K. Senthil Kumar, S. Karthikeyan, R. Gokul Rahesh, (2020), Experimental investigation of wear characteristics of aluminium metal matrix composites, Materials Today Proceedings, Volume 33, Part 7Pages 3139-3142. J. Dutta Majumdar, B. Ramesh Chandra, I. Manna, (2007), Friction and wear behaviour of laser composite surfaced aluminium with silicon carbide Wear, Volume 262, Issues 5–6, 28 , Pages 641-648. T. Sathish, S. Karthick, (2020), Wear behaviour analysis on aluminium alloy 7050 with reinforced SiC through taguchi approach, Journal of Materials Research and Technology, Volume 9, Issue 3, Pages 3481-3487. S. Suresha, B.K. Sridhara, (2010), Effect of silicon carbide particulates on wear resistance of graphitic aluminium matrix composites, Materials & Design, Volume 31, Issue 9, Pages 4470-4477. T. Lin, D. Bhattacharyya, C. Lane, (1995), Machinability of a silicon carbide reinforced aluminium metal matrix composite Wear, Volumes 181–183, Part 2, Pages 883-888. A. Radha , K.R. Vijayakumar, (2016), An investigation of mechanical and wear properties of AA6061 reinforced with silicon carbide and graphene nano particles-Particulate composites, Materials Today: Proceedings, Volume 3, Issue 6, Pages 2247-2253. Md. Habibur Rahman, H. M. Mamun Al Rashed, (2014), Characterization of Silicon Carbide Reinforced Aluminum Matrix Composites, Procedia Engineering, Volume 90, Pages 103-109. Pradeep Kumar Yadav, Gajendra Dixit, Savita Dixit, Virendra Pratap Singh, Surendra Kumar Patel, Rajesh Purohit, Basil Kuriachen, (2021), Effect of eutectic silicon and silicon carbide particles on high stress scratching wear of aluminium composite for various testing parameters, Wear, Volumes 482–483, 203921. O.P. Modi , B.K. Prasad , A.H. Yegneswaran ,M.L Vaidya, (1992), Dry sliding wear behaviour of squeeze cast aluminium alloy-silicon carbide composites, Materials Science and Engineering, Volume 151, Issue 2, , Pages 235-245. Adem Onat, (2010), Mechanical and dry sliding wear properties of silicon carbide particulate reinforced aluminium–copper alloy matrix composites produced by direct squeeze casting method, Journal of Alloys and Compounds, Volume 489, Issue 1, Pages 119-124. P.B. Pawar, Abhay A. Utpat, (2014) , Development of Aluminium Based Silicon Carbide Particulate Metal Matrix Composite for Spur Gear, Procedia Materials Science, Volume 6, Pages 1150-1156. Kenneth Kanayo Alaneme , Tolulope Moyosore Adewale, Peter Apata Olubambi, (2014), Corrosion and wear behaviour of Al–Mg–Si alloy matrix hybrid composites reinforced with rice husk ash and silicon carbide, Journal of Materials Research and Technology, Volume 3, Issue 1, Pages 9-16. Xiang Zeng, Jingang Yu, Dingfa Fu, Hui Zhang, (2018), Wear characteristics of hybrid aluminium-matrix composites reinforced with well-dispersed reduced graphene oxide nanosheets and silicon carbide particulates, Vacuum, Volume 155, Pages 364-375. 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(MMCs). These reinforcing constituents manifest in diverse forms, spanning particulates, whiskers, and continuous fibers. The production of MMCs entails the application of an array of methodologies, including but not limited to powder blending, consolidation, and physical deposition techniques. Presently, there exists a discernible and burgeoning demand for aluminium-based metal matrix composites (AMMCs) owing to their extensive utility across various industrial sectors, including defense, automotive, aerospace, and aviation. AMMCs have garnered commendation for their superlative material attributes, encompassing reduced density, elevated specific strength, corrosion resistance, favourable wear characteristics, and commendable thermal properties. Among the myriad methods employed for the synthesis of AMMCs, stir casting conspicuously emerges as the predominant technique. In contrast to steel, aluminium exhibits a diminished propensity for wear resistance, thereby rendering it a favoured choice as the matrix metal in select applications. The primary crux of this research endeavour is to conduct an intricate investigation into the wear performance exhibited by AMMCs, with a particular emphasis on those fortified with silicon carbide (SiC) at concentrations of 5% and 12%. The investigative approach is centred on the utilisation of drum testing as the preferred method for meticulously scrutinising the wear attributes inherently embedded within these AMMC specimens.\u003c/p\u003e\n\u003cp\u003eIn the research conducted by Xu et al. (2009), an investigation into the intricate interplay of plasma electrolytic oxidation (PEO) technology on ceramic coatings applied to Al-Si alloys featuring substantial primary Si content was undertaken. The alloys employed in this study boasted Si proportions within the weight range of 27-32%. A discerning scrutiny of the PEO process unveiled four discernible phases, with a particular emphasis on the consequential impact of bulk Si on the morphology and compositional attributes of the coatings. It was noteworthy that, at elevated voltage thresholds, discernible transformations were observed, giving rise to the formation of Al-Si-O compounds[1]. Adnan Mehonic et al. (2018) delved into the burgeoning realm of resistance switching, an area that has garnered escalating interest for an array of applications encompassing nonvolatile memory devices, hardware acceleration in the domains of machine learning and artificial intelligence, as well as neuromorphic computing. An intriguing facet of their discourse centred on silicon oxide, an exceedingly CMOS-compatible dielectric material. It is noteworthy that, despite its inherent compatibility, silicon oxide had hitherto received relatively scant attention as a candidate for resistance switching. The discourse proffered an exhaustive classification of the mechanisms underpinning switching behaviours in silicon oxide, whilst succinctly encapsulating the contemporary state of comprehension regarding fundamental switching mechanisms and their prospective device applications[2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eG.A. Fontalvo et al. (2005) embarked upon an exploratory voyage to elucidate the intricate influence wielded by oxide films in the context of metal wear phenomena. Their investigational focus zeroed in on the role played by aluminium and silicon, alloying constituents, in modulating the oxidation and wear characteristics of hot work steels. A battery of meticulously executed ball-on-disc experiments, executed under varying temperature conditions, unveiled a salient correlation between the presence of aluminium and silicon and the consequential reduction in oxide film thickness. Paradoxically, this reduction correlated with heightened mechanical wear, particularly at elevated temperatures[3]. M.G. et al. (1993) undertook a comprehensive analysis involving ball-on-ring wear assessments, with a specific objective of delineating the intricate relationship between humidity levels and wear patterns in sintered silicon nitride materials. These rigorous investigations yielded intriguing insights, revealing that wear rates exhibited a multifaceted dependence on testing speeds and the prevailing humidity levels. Notably, the primary mode of wear was identified as the tribochemical oxidation of silicon nitride, resulting in the formation of silicon oxide[4].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research endeavours of Olufunmilayo O. Joseph et al. (2019) were motivated by a laudable aspiration to foster sustainable development through scholarly inquiry. Their scholarly odyssey unfolded in the context of metallic matrix composites (MMCs) and aluminium metal matrix composites (AMMCs), with a particular focus on the reinforcement constituents employed therein. The collective wisdom gleaned from a comprehensive survey of extant literature illuminated a promising avenue wherein agricultural waste materials, typically regarded as byproducts, could serve as particulate reinforcements. This newfound potential was particularly evident in materials such as groundnut shells, coconut shells, and rice husks. Remarkably, these agricultural waste derivatives, when processed into powdery particulates, demonstrated remarkable reinforcement capabilities and imparted enhanced mechanical properties to the resulting composites[5]. The meticulous scrutiny conducted by B. Kaczer et al. (2007) centred on the indispensable aspect of gate oxide reliability within the context of scaled-down logic devices. Their analytical foray underscored a pivotal consideration\u0026mdash;namely, the influence of elevated operating temperatures in conjunction with the commensurate amplification of adverse effects on exceedingly thin SiO2 films. What emerged from their discourse was a nuanced perspective, challenging the conventional notion of an Arrhenius-like linear decrease in time-to-breakdown. Their research illuminated the notion that oxide defects arising at distinct temperature regimes were not wholly analogous, yielding a non-cumulative effect. In their scholarly purview, microscopic models were explored as explanatory frameworks, and a reliability projection for ultrathin oxides operating under heightened temperature conditions was cogently presented[6].\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eExploration of Tribological Characteristics in Al 6061 Alloy Enhanced with 5% and 12% SiC Through the Utilisation of the Vertical Stir-Casting Technique.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAluminium matrix composites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAluminium 6061, an alloy distinguished by its multifaceted properties of excellence, commands substantial significance within the domains of aerospace and automobile engineering. Its ubiquity in various pivotal applications arises from the alloy\u0026apos;s juxtaposition of lightweight characteristics and structural robustness. Within the aerospace realm, Aluminium 6061 serves as a pivotal constituent in the assembly of aircraft structures, predominantly attributable to its remarkable strength-to-weight ratio. Its innate lightweight constitution invariably contributes to the curtailment of fuel consumption, thereby elevating the overall operational efficiency of aircraft. Furthermore, the alloy\u0026apos;s inherent resistance to corrosion endows it with an extended operational lifespan, particularly indispensable given the taxing environmental rigours inherent to aerospace operations. Components spanning aircraft frames, fuselage panels, and interior fittings all derive pronounced benefits from the alloy\u0026apos;s enduring properties and ease of fabrication.\u003c/p\u003e\n\u003cp\u003eSimilarly, within the automotive sector, Aluminium 6061 assumes a paramount role in the pursuit of augmented fuel efficiency and enhanced vehicular performance. By supplanting more weighty materials like steel, the alloy effectively mitigates the overall vehicular mass, thereby instigating advancements in fuel economy and concomitant reductions in emissions. The alloy\u0026apos;s remarkable machinability further facilitates the realisation of intricate automotive designs, ushering in a new era of innovative structural configurations that judiciously balance safety and efficiency considerations. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, Aluminium 6061\u0026apos;s innate thermal conductivity attributes render it invaluable in the manufacturing of engine components and heat exchangers. This, in turn, engenders an efficacious dissipation of heat, thus augmenting engine performance. Its innate resistance to corrosion, a distinctive hallmark, certifies the enduring durability of automobile components, even when subjected to the gamut of environmental exigencies. In summation, Aluminium 6061 stands as an indispensable material within the aerospace and automobile sectors, furnishing pivotal support to the relentless pursuit of lightweight, enduring, and fuel-efficient engineering designs, thus indelibly shaping the trajectory of future transportation technologies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSilicon carbide (SiC) matrix composites\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSilicon Carbide Metal Matrix Composites (SiC MMCs) have garnered substantial significance within the aerospace and automotive sectors due to their extraordinary attributes. In the realm of aerospace, SiC MMCs are ubiquitously employed across diverse applications, primarily by virtue of their lightweight yet formidable character. They find employment in the assembly of aircraft components, encompassing engine constituents and structural elements, with the primary intent of diminishing overall mass, augmenting fuel efficiency, and bolstering longevity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe superlative resistance to elevated temperatures exhibited by SiC MMCs renders them eminently suited for deployment within turbine engines and exhaust systems, where they confront extreme thermal vicissitudes and mechanical duress. Within the automotive domain, SiC MMCs have assumed a pivotal role in advanced engineering. Their intrinsic lightweight propensity serves to ameliorate fuel efficiency and curtail emissions, congruent with the burgeoning demand for ecologically responsible vehicles. SiC MMCs find application in pivotal vehicular components, including braking apparatus, clutch plates, and suspension systems, engendering performance enhancements and mitigating attrition. Furthermore, SiC MMCs play an indispensable role in the evolution of electric and hybrid vehicles, leveraging their lightweight constitution and heightened thermal conductivity to facilitate efficient thermal dissipation and amplify the efficacy of battery systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, the coveted attributes of SiC MMCs encompass their exceptional resistance to corrosion and abrasion, rendering them eminently apt for mission-critical functions across both industries. Their capacity to endure the rigours of adverse operational conditions, encompassing extremes of temperature and corrosive agents, underscores the protracted operational lifespans of components, thus culminating in the diminishment of maintenance expenditures and replacement outlays.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summation, Silicon Carbide Metal Matrix Composites occupy an instrumental niche within the aerospace and automotive domains. Their lightweight character, robust resistance to elevated temperatures, and intrinsic durability collectively coalesce to engender improvements in operational efficiency, a curtailment of ecological impact, and an elevation in the comprehensive performance metrics of multifarious components and systems. As such, they substantiate their status as indispensable materials within the purview of contemporary engineering applications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStir casting process\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe vertical stir casting process assumes paramount importance in the manufacture of cutting-edge composite materials. It encompasses the liquefaction of a matrix material, frequently composed of a metal or alloy, within a vertically aligned crucible, concomitant with the introduction of reinforcing particles or fibres through the agency of a meticulously controlled stirring apparatus. This method ensures the systematic dispersion of reinforcements, culminating in composites endowed with augmented properties. Its indispensability reverberates across sectors such as aerospace, automotive, and defence, where it assumes particular significance in the production of both metal matrix composites (MMCs) and ceramic matrix composites (CMCs). In essence, it stands as a meticulous and versatile technique for the formulation of superlative materials across a multifarious spectrum of industries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePin-on-drum abrasive Test\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pin-on-drum abrasive evaluation method stands as an indispensable tool for the assessment of material wear and resistance to abrasion. This sophisticated procedure entails the placement of a specimen, often taking the form of a pin or a cylindrical sample, into contact with a rotating drum sheathed in abrasive material, typically exemplified by sandpaper. Under meticulous control, an applied force is exerted, inducing interaction between the specimen and the abrasive surface, thereby emulating the wear encountered in practical applications.\u003c/p\u003e\n\u003cp\u003eThis rigorous examination encompasses the quantification of pivotal parameters, notably the wear rate, indicative of material loss, and the coefficient of friction, signifying the degree of resistance to friction between the specimen and the abrasive substrate. These metrics furnish invaluable insights into a material\u0026apos;s capacity to endure abrasive conditions effectively. Diverse sectors, including automotive, aerospace, and manufacturing, universally employ the pin-on-drum abrasive assessment to scrutinise materials integrated into critical components such as bearings, seals, and coatings. Its application empowers engineers and researchers to engage in judicious material selection and design, culminating in the development of products characterised by heightened durability and unwavering reliability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eASTM Standard\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA514, traditionally subjected to quenching and tempering processes, represents an alloy steel plate prominently employed in structural applications. The A514 family encompasses diverse chemical compositions denoted by letters such as B, E, F, H, Q, and S. Notwithstanding the nuanced distinctions in their chemical attributes, these grades collectively exhibit akin mechanical properties of significance. The canonical material adheres to the archetype of low-alloy steel, distinguished by a hardness rating of HB 269, and is bestowed with the esteemed ASTM A514 nomenclature. To accommodate minor fluctuations in the abrasive characteristics of abrasive cloth\u0026mdash;whether arising between different batches or within a singular batch\u0026mdash;the conventional practice entails the utilisation of standard specimen wear for calibration and standardisation purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProcedure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fabrication of composite materials entails the meticulous selection of an aluminium alloy, specifically Al6061, in accordance with ASTM standards. The fabrication process employs a vertical stir casting machine and employs the gravity moulding technique for optimal results. The stir casting apparatus is meticulously preheated to temperatures ranging between 750 to 800 degrees Celsius and maintained at this temperature for a duration of two hours. The metal matrix composite is stratified into two distinct groups, where the foundational alloy is composed of 95% Al6061, with the remaining 5% being attributed to the inclusion of SiC as a substitute compound. Subsequently, the final composite configuration is attained through a composition that consists of 12% SiC and 88% Al6061 alloy. This composition is meticulously achieved via the gravity moulding technique. Following the culmination of the fabrication process, the resultant composites undergo a battery of rigorous tests, including a wear test, to comprehensively assess their mechanical properties.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eTable 1: Process control parameters and levels\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"317\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.793650793650794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eS. No.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.06349206349206%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePARAMETER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEVEL 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEVEL 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLEVEL 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.793650793650794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.06349206349206%\"\u003e\n \u003cp\u003eLoad (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.793650793650794%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.06349206349206%\"\u003e\n \u003cp\u003eSpeed (rpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.793650793650794%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.06349206349206%\"\u003e\n \u003cp\u003eTrack Diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Output Responses for L9OA\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"369\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoad (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpeed (rpm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrack Diameter (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSp. Wear Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"31\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"37\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.531165311653115%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.428184281842817%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.08130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0.000069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTaguchi\u0026rsquo;s Single Process Characteristics Optimization \u0026ndash; Signal to Noise (S/N) Ratio Approach\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom Table 3, Load has the highest \u0026ldquo;max-min\u0026rdquo; value of 8.0572 and this parameter has considerable influence on the specific wear rate. The next influential parameter is Distance (Track Diameter) which has the next highest \u0026ldquo;max-min\u0026rdquo; values of 2.6962.Based on the results in Table 5, the optimum parameters for (SWR) are Load (30N); Speed (60 rpm); Track diameter (70 mm).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Analysis of S/N Ratio\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.729684908789388%\" rowspan=\"2\"\u003e\n \u003cp\u003eResponse Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.729684908789388%\" rowspan=\"2\"\u003e\n \u003cp\u003eInput Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.946932006633496%\" colspan=\"3\"\u003e\n \u003cp\u003eMean S/N Ratio (dB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.593698175787727%\" rowspan=\"2\"\u003e\n \u003cp\u003eMax - Min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.764119601328904%\" rowspan=\"4\"\u003e\n \u003cp\u003eSpecific Wear Rate (SWR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.764119601328904%\"\u003e\n \u003cp\u003eLoad (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e76.9557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e82.1843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e85.0129\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e8.0572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.20545073375262%\"\u003e\n \u003cp\u003eSpeed (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e81.3538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e81.3841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e81.6633\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0.3095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.20545073375262%\"\u003e\n \u003cp\u003eTrack Diameter (D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e79.9413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e81.5741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e82.6375\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e2.6962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.102725366876314%\" colspan=\"3\"\u003e\n \u003cp\u003eTotal mean to S/N Ratio = 81.4119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.897274633123686%\" colspan=\"2\"\u003e\n \u003cp\u003eOptimum level = L3 N3 D3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults of analysis of mean shows that load is the most significant machinability variable followed by Track diameter. We go for Taguchi based grey relational analysis to confirm the above prediction.\u003c/p\u003e\n\u003cp\u003eTable 4: ANOVA for SN Ratio\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"465\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.21551724137931%\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.896551724137931%\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003eAdj SS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003eAdj MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14655172413793%\"\u003e\n \u003cp\u003eF-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp\u003eEffective Factor (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.21551724137931%\"\u003e\n \u003cp\u003eLoad (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.896551724137931%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e100.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e50.1287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14655172413793%\"\u003e\n \u003cp\u003e173.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp\u003e89.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.21551724137931%\"\u003e\n \u003cp\u003eSpeed (rpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.896551724137931%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14655172413793%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.21551724137931%\"\u003e\n \u003cp\u003eTrack Diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.896551724137931%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e11.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e5.5331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14655172413793%\"\u003e\n \u003cp\u003e19.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp\u003e9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.21551724137931%\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.896551724137931%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e0.2895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14655172413793%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.21551724137931%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.896551724137931%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e111.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.008620689655173%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14655172413793%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.224137931034482%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTaguchi\u0026rsquo;s \u0026ndash; Grey Relational Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the most proficient means to manage imprecision, multi process characteristics criteria and distinct data is Grey theory. The step by step procedure is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABC0AAANOCAYAAAA1S/ZxAAAgAElEQVR4Aeyd23UkubVt5YJskAvyQSa0DXJBHujj/MsDOXDbAjnQDsgD+VB3zDpnqlfvBiIjmRlkkrUwRjYigY39mHgEAJJdf/jWVAIlUAIlUAIlUAIlUAIlUAIlUAIlUAIvSOAPL+hTXSqBEiiBEiiBEiiBEiiBEiiBEiiBEiiBb7206CAogRIogRIogRIogRIogRIogRIogRJ4SQK9tHjJbqlTJVACJVACJVACJVACJVACJVACJVACvbToGCiBEiiBEiiBEiiBEiiBEiiBEiiBEnhJAr20eMluqVMlUAIlUAIlUAIlUAIlUAIlUAIlUAK9tOgYKIESKIESKIESKIESKIESKIESKIESeEkCvbR4yW6pUyVQAiVQAiVQAiVQAiVQAiVQAiVQAr206BgogRIogRIogRIogRIogRIogRIogRJ4SQK9tHjJbqlTJVACJVACJVACJVACJVACJVACJVACvbToGCiBEiiBEiiBEiiBEiiBEiiBEiiBEnhJAr20eMluqVMlUAIlUAIlUAIlUAIlUAIlUAIlUAK9tOgYKIESKIESKIESKIESKIESKIESKIESeEkCvbR4yW6pUyVQAiVQAiVQAiVQAiVQAiVQAiVQAr206BgogRIogRIogRIogRIogRIogRIogRJ4SQK9tHjJbqlTJVACJVACJVACJVACJVACJVACJVACvbToGCiBEiiBEiiBEiiBEiiBEiiBEiiBEnhJAr20eMluqVMlUAIlUAIlUAIlUAIlUAIlUAIlUALvcmnxt7/97dsf/vCH058///nPn7Znfvnll2//+Mc/vv3000/f4/3Pf/5zM5aff/752x//+MfvH9pn+uc///m9/E9/+tO3WZdyH/3873//+3vcf/nLX777a3/j91//+tdvf//737+9ar/CXn9XuTE8wv9f//rXdwbwQd+zE/zRi/+MmVdM+IhvjAd8fYaf6ILte6crYlnFwLyhT5k7Z9aSlY6WlUAJlEAJlEAJlEAJlMBnJvAulxZsuDkYckhhs5+XGB5kOXh40Kf+MyZiyEOvsd2KhYOs7TikZMoDNXxeLdGfHBz1n1i4hDHBBA4evCx/tdzDoXFwQOSTY5W+eMvFxdT9jPEN8zz0pw364BWTbM0fPYQTP7o+4tLCGMwfjWXXX+r/qDh3frW8BEqgBEqgBEqgBEqgBN6LwOWXFhxq52HPnwizEZ+HdA64H3EIeRZwDuweNGZsOxtHv2mRh+ZXO4xyaMxLlTxEZ6wc6JB7xmE99T7zmd+Osd8m5xyvXBa8JeW4yEude3XB0ss95paJZ/3c9YOyH5k/6wLLC4uPPMw/K5aj/vAyCltXXYwc2W9dCZRACZRACZRACZRACXw0gcsvLThc5CUEhysPh+TzJ9evfLA901l5yZBxn2m7k/GQevYSZKfnmeV5aKQfbx2U528GPNOXZ+jiosJxOTln3bzQOGs7L0XOtplyMPaSiEPsZ0z6PxnfEwtrhnros2fNs3t8QFYfHonlXpuVL4ESKIESKIESKIESKIEfjcDllxYTaB522fTfShwIkPNv4D1YzkMyBxkOxh4kyHeHCWQ5RHoY9SeY6Rv1M3Hhgg1/ok0+f2ruT18zNmOYPy1FHzbVSb5KXFpgSz+RUSc80Iu/tJ+XQCt9j5ZxSLQfyM/82Qr+6hs53/HbSwAveyZPfMUeNuxb2sAi9eRlF1yRnbx3caMr48lD8KxLO6mPfnQ8oWvKWbdiRRzWw8Vn+pyED8aefjomYIY94rVN+kZ7edMeXdM/ZNDjWLSecspotxuft/zXlxw3tHlLSluyyP5C5yOxwACGMOJZGzP2GQs2cy4zBk2wRA/1Ju1Qjj1kmMOuO8TJs+P+nrbaaF4CJVACJVACJVACJVACX4HAu19asAnfHQQSKIcADmFu9tnEs7m3bR4K8nCBftqy4VeWHF0kNv+rcuo8nFE/D1W08yDsIcnDjX7jk7o9nKZO6vLwoax51qETOxxg8T398RBEHbEi56FWX67MPVTrd/bFLbuTvwc1dXG4NhEbHIkfG8aNLHK2ISd+U9bZV9at8hw/qQfZ1EXdjJV+wT/6OccsPpko11cPpdbJAx3Ioce+5DmT5enj7IvZxnlDG1hgw3kET9PUA5OU1f8Z/z3+23/pv/bP5PiDn8SkP+Szj98aC+zQR64t7cy5OWOZNrOf1YEMyfEGB3jyof+NhXhsYy73W23PcKxMCZRACZRACZRACZRACXwmAr+erN7JazbqbsTnQSBd8ADh4Z8623HoMnG4SJ1u7nPjP+144EBfHpI9zM1D1TyYYTt16Ity6OXQwnc+efDNwwztPKzQhlgyEXv6Z53xJhsOM+i6Os0D4zwkn7HvwYuY6SfjkRs6YEE81MnFMYEc/Uy5fUYZvmXbLDvyK/tSpuhPe9hRv7r4jn8eRinXH3JTjgvHJ3W2Tz9zrNDOhCxyfPTRuvQftqbUn+MoD9jKkmc/GD95yucFQernmbTznzrZvGXM0Nf4oR1ZkKdP3514Qyz2ET463jLu7LddLMxt/co+Urdl5MilLWxSpm2/U5bz+kxbGTQvgRIogRIogRIogRIoga9A4F0vLThwuKknnwcBgeaFg4cU6mybhx4PBNR5KEA2D8Z5YKMuD6Pqxxf1p570OQ9HHlBSd+pF1gNkXkxoz1g9KObBxLpd7uEPf/Ngm37v2j5abtyySvtndauDODhwowOOeUHh4Uz9OSaSeR7Ys394Rv+ZlP1jXObU2Y+pi0Ol/eA4xi/bpS/GMv3xUJz9ljrUi12ZoZ/nTOqnzkMv9caVXCnXLvKmHP/Uw1X7xpTyqeeM/zmP7FNtn8mxke3Sp2SNrntjgZnzMNnKafbbLpa8aKBtJr7rf64TuZYlx7RBX5jOtFW2eQmUQAmUQAmUQAmUQAl8BQK/nlreIZo8kB0d0j0spExeQrj5x+XUybMpN/d5EKHewyYHFVPqSXkPhPMQYrvMPfigXx/zIJP2aJcxpe+pc/WcvnJ409ZK9tll0/a8hOEAmQfKfPbwL1PyPJDpqzrgxQGU/pDt5JT+5OEVW2e4ZP/gK/FgT7/ngVUftUsMtPHyBD+NE9nUn7EaI3ZS3ouGaVdm+qgfqT/HKLEbQ9qlneU5HlMePfqUfuKDKcuVpW7nv3ywjc/3JOby7EtjIJ/p3ljsy/QtuebFAraOYtGv7Ava7NghP/VPGzmuk/uu7eTR7yVQAiVQAiVQAiVQAiXwmQn8fsd/YTRs5N3Uz4OUZvMnjHl5kJcQ/gSYNinv5p4Dh4dc7O3kPSxQn/LoJFGuv/OwrL/m6Qe6tMmBTh3as03GpE3rbuXZFv3Gvmo3Dzr6s8vngSt15gGP9qsEfw+vyHBgy4OqrJGRU+rJAzqy+MNYWMmmPzle8pCYuudzHnC9KMh+x/9V3xgD9bTDHroyTmyl/jzcGyN6TGl3jhXtpfzUn/En//Q/x2My0h/iyf7PA3rqVz79OfIfRujGr3sSNml39Jn69O1sLLJNHtlvPGc6ikU/kwvMp47UT5u5vmgj9ejDrbbKNS+BEiiBEiiBEiiBEiiBr0Bgfeq8IDIOc27oyXeH7DykKENbDxZs5mfyAO9FiN+xMw8LeQizjjL15yEhD3j6Mm37Pf3OA2f6kodW2h0dTNS7y2GSB9M8cO3aPKN8XoCsLhKwY2z0Qcad7ZNT+uY4sT+zbj7npYUHP/oibc42+T37J/1JtujLlDHcsuMBOscVuhxveUGQsTg2kc0LsdnP6b+XEznX5nxJ+RzT+gP77NPkkOXK3+v/ZJlc5zMMsk+sT/74O5O+nYkl2Tp+0EdcjsOMO+VXsdhGv+iL2Wf6m32BzybsqefetupoXgIlUAIlUAIlUAIlUAJfhcDvd/wXRcbhzo14btCnOQ95bvqpz7LVIYaDBIeMPGjQJg9l2slDGAcK/MrDYh4SsjwPkerKPG3nIScP79gz5eFnFZNymdM+Zfmufuy/V9ImfbQ6tGdsyGTceWm06p88kJ65tEh5+gv22Ye3mGQs2cfpJ2MmU46LVfwp6wE6fUo+6CJlGcxyDHE4pozPPCirnzhMySTt5mE4x0vaTnn6TbvJIOXv9Z+2ZxJy2MyxY7uMD/8ypW9nYkm26CVlWXKddatYch1AnnGkXr6nT36XMd9JaT/H5Jm2/6eiWQmUQAmUQAmUQAmUQAl8GQK/3fFfGFb+VHFuvtNsbvo5FLBpR96NPd/Z1Htg8HDp99Q1n+ehjTa0R6f6OYTxncMoH8s5HHr4QA9yGYdyebhLe8hiD99J+k077KGb/Cghg3we5PCDsvTlSMcz6pJLxqvujC0Px9Qjj7/zMGhbGFHPB+b2KyzhMw+yMkHePkH2TMr+oX22Sz9mXR4q0x/a6AP2k5P9zjwgDnTyod/oTzg59mFDGRxJyCiPDXQRd+pHr20yLvwxOQfhuhpD2MgxmH6iR5vJ/Iz/9jl2ScRgbPqWOfWwSB+zPu3jcyb8lNWZWFIertgmJnxFD8xkjp1bsdiHtMXPGSfl9JvJWGSTNpClL/HRPr3VVr3NS6AESqAESqAESqAESuCrEPjtjv+iqPKQx0b86EAyZTkk5MGCtnkQQJ8fZDlkoGN14PGAkPLITf3oIFHn4cU25hlDHh7TtyzHNw946M7DDfay3a4bZIMuDjH454GW2N4z5YGWWDjYkfCLQ7ecYGvCX8tlbF3mxKdc5ujVjvLZp9TD6EzCz2mHskwZR/Y3cmfGBX2q/8SLDg/Ss32OFeqQ1R+e1YNO2a3GLYxJtiGnzP5y7GScyUGb1E/9OUbP+p99Dk906FP6wDOyjvG0lXLIOOZlIlPk7o2F8aQecuKSFd/Rh7/6p+wuFrkjN+PUFjacrzLOeaINcjjgz9m2yarPJVACJVACJVACJVACJfAVCFx6aZEHytyI++zGPUFyKPHgweGM7x7o2Ozn5p52bOrVl7mHj9TNgYzDBnW0QzfpSD+HhTwkcRDJQ9L0IWPSHn4Zi/54WFnFpMzMaYOv5B6o8SdtzjZXfic+4rK/5I9v+AXjPATnYXAyTD8nc/QTs/2VsjnG6KczCd/0deb4bCK2WS9r+iHjXo0LYqR/0UE9cZlkASt0kdQ3ZR0ryPJsop365/iCVY5bdK+YI2eM6M+k/tUYPes/+uW90pP2lNOfjBW57GtlzPH1rbHYz3JnzK64noklY8j+xn985FIGdvY1zGe/qCPHxtm2ybPPJVACJVACJVACJVACJfAVCFx6aXE1IA4AbPDJOWyyyfcQQ87Bo+lrE/AgS99zqGwqgY8k4IUDa1JTCZRACZRACZRACZRACZTA4wQ+7aUFlxRcTORPxsHBTzf9KSb1TV+bAJcWXE7Nn2p/7agb3asS4NKC39xoKoESKIESKIESKIESKIESeA6BT3uq9yea81fIweJP3/ubFs8ZJK+kxV/l51fq+c0K/gSCX51vKoH3JsBFGWsMl6SMRcYh61JTCZRACZRACZRACZRACZTA8wh82ksL/6aeQ0P+TTh/j+6FRg+zzxsor6Ip//yH5+z7V/GxfvwYBPx/jTgmvbz4MaJvlCVQAiVQAiVQAiVQAiXwPgQ+7aUFeLiU4CftXFx4cOCZMn7bounrEfDPgriY6p+EfL3+/UwRMf74f6mw5qx+4+szxVJfS6AESqAESqAESqAESuBVCXzqS4tXhVq/SqAESqAESqAESqAESqAESqAESqAEHifQS4vHGVZDCZRACZRACZRACZRACZRACZRACZTABQR6aXEB1KosgRIogRIogRIogRIogRIogRIogRJ4nEAvLR5nWA0lUAIlUAIlUAIlUAIlUAIlUAIlUAIXEOilxQVQq7IESqAESqAESqAESqAESqAESqAESuBxAr20eJxhNZRACZRACZRACZRACZRACZRACZRACVxAoJcWF0CtyhIogRIogRIogRIogRIogRIogRIogccJ9NLicYbVUAIlUAIlUAIlUAIlUAIlUAIlUAIlcAGBXlpcALUqS6AESqAESqAESqAESqAESqAESqAEHifQS4vHGVZDCZRACZRACZRACZRACZRACZRACZTABQR6aXEB1KosgRIogRIogRIogRIogRIogRIogRJ4nEAvLR5nWA0lUAIlUAIlUAIlUAIlUAIlUAIlUAIXEOilxQVQq7IESqAESqAESqAESqAESqAESqAESuBxAr20eJxhNZRACZRACZRACZRACZRACZRACZRACVxAoJcWF0CtyhIogRIogRIogRIogRIogRIogRIogccJ9NLicYbVUAIlUAIlUAIlUAIlUAIlUAIlUAIlcAGBXlpcALUqS6AESqAESqAESqAESqAESqAESqAEHifQS4vHGVZDCZRACZRACZRACZRACZRACZRACZTABQR6aXEB1KosgRIogRIogRIogRIogRIogRIogRJ4nEAvLR5nWA0lUAIlUAIlUAIlUAIlUAIlUAIlUAIXEOilxQVQq7IESqAESqAESqAESqAESqAESqAESuBxAr20eJxhNZRACZRACZRACZRACZRACZRACZRACVxAoJcWF0CtyhIogRIogRIogRIogRIogRIogRIogccJ9NLicYbVUAIlUAIlUAIlUAIlUAIlUAIlUAIlcAGBXlpcALUqS6AESqAESqAESqAESqAESqAESqAEHifQS4vHGVZDCZRACZRACZRACZRACZRACZRACZTABQR6aXEB1KosgRIogRIogRIogRIogRIogRIogRJ4nEAvLR5nWA0lUAIlUAIlUAIlUAIlUAIlUAIlUAIXEOilxQVQq7IESqAESqAESqAESqAESqAESqAESuBxAr20eJxhNZRACZRACZRACZRACZRACZRACZRACVxAoJcWF0CtyhIogRIogRIogRIogRIogRIogRIogccJ9NLicYbVUAIlUAIlUAIlUAIlUAIlUAIlUAIlcAGBXlpcAPUqlb/88su3n3766dsf/vCH758///nP337++edT5v7zn/98++c///mNNn//+99PtanQxxKgz/76179+++Mf//i9v//0pz997zv6kLJ///vfb3Lwb3/723d9Z8fOm4y0UQl8QgLMKeYWc+xHS4/E/kjbr8gZHrxnWbP/9a9/nQqxDE9heqpQ90XncF41Nj/7XuTRvdg5+r+X+ii7v/fk/pK3rI1p5WjOXjGerhr7GdMznzknem54pt5X0fUlLy0Y1P/4xz++/eUvf/nvAZ+NKAd+Du6fMTEQiYGcRBxsiPicSfBgoePCo5cWZ4h9rAxjmP6i30hsfBnPjAH6nJzF9C3pioX9LX60TQm8GoHPtkF5Jr9HYn+k7TNjeAVdrN28Y1mjed/20uIVemXtQ/dFay6z9Kr5/dn3Ih91efBRdue4uPe7ayN72HvWxrSTZ7t5lrliPF019jOmZz1zPoSJP9h+lt5X0vPlLi346TGbBSZ1/iSZgWdncvhj8nymRDz4fTZxqTE3S8Y/J/pZnV9BznHw6rG4+L66n2f8+yzMz8RypUw5XUm3ukvgfQn4Q5P5HsYLfhLW9BoEvsq+6JXfH6/s21WjsHN8T9Y5t1ob961+raHdFT+AfZU+e9QP3z2/Ervv6ZXn65e6tOCgzkDmgL+7lDgjc1/3vo80cZ29tCB2bjLnguBC8SNfWvDbNp8h/kcXnfcZleesfBbm56K5TqqcrmNbzSXw3gR83873MD8N433e9BoE7KfPsC84IvbK749X9u2I6Vvr/G2At7b/6u2cc3NtPBv3FZcWr7IuP2PsPHp+eOX5+mXenNwM+euYPB8lbrHYNNAxnyWdvbTgwsL45oLgQvHZX85v7TMWAzh+hvgfXXTeyujZ7T4T82fHfo++crqHVmVL4PUJ+L7N9zB7E36o0kuL1+k/++kz7At21F75/fHKvu14PlLOfOcscvaHjI/Y+qxtnXO5Nt4TC+2euZd/lXX5WWPnkfPDq8/XL3Np4UGdDcGtxJ+NMOD5eMHBLRs6WGxIyLi5YACsfnODNlx8qItnys4k7GqP9vxmxOqlqe6Zr2TR6d+KpbyyLhTkeblBzPwGyiohq86dj7Md7GDhos13dSTLZEz9it1ZTvjAn1R4cUXf8d3YncTJRf+m/3yHB/UrVujNNMeOf9qRl2LwTpb4Sf/PcbXyE5/1ddpKP3he2XCMU8+zMqsXRvbJysdpP+XxMeNZxWIc02++o+tZ4wY/0WV/80xZJueAY2YVLzKMBcZTjgVk+azmzdE4TPs+n+GkD0fxqC/z2V98117OueS1i8txc+TD7MNsM+cNfqZd9B6tMTnW9CHzHM9T76r/k5PP0/9dOXL3rmnMPccGelMH4wufTXPcUYc9+ibjTCbUrdYUdE4e2Jtjdxc7vsy5xPfpB2Wr+U37HAc7P/HRd6J88JM+Ri963pJmXMk99SbLnBvEmeOMWEy7cuqRo15O6Cf2bDPrdwy1Rw4HNpb4iG645Zye/Tq5PvJ+0o9Vf+Z7ZiWHv8lOGecELOhvvk+5MzLqyxxWjin060OOJfuJXHlk6avJEt05TvSZskx8ty/Raf/QbyTtOB52c2IlO2NQN774oSwTsdGOettn/S1/sz7bEQdxapdnPo75I9/u2YugP/XqA/2TcTFOkDuTkon8cwxnzKs+nPMKm44leZinP+h1XdMu+k1TLzEZPzKz3nbkqZs22KHsTDrTdtrONvR1xqFNmOYcRI4+wj/HibKrHJ3ZV/S3TCg3HY2no3FCDM5D+yt9ox5/c07hEzrhiw98N0Z0rdYN/Mw47HvHHHVp32fj2+UwlCdt8MPxNdukfWRpR5+ajuYrMtnftL9nfGnj0fzLXFo46OiUM8kBgTyd5iSgnAFH51FnJ9K5mRzoTjq/44eDMOXzGXvIoZ/B7oB3EKWsz9TlpLF8leuzvimDPfQQKwMbn/m46OfgpQ0DEjl9dGLQfpfQpxx+IIvdnFjUwxjdlOsXfmS6hxM68Ff26CdWyk3ayTLrMleX7Y2DWBxn+E6aY4dNCXXI8SHBD9+IW/+8zSRm6meyD7N82so6nrGbDLSBH/YhsRnDHB/El+3lRRlp2j8zT9RBfpSeOW7QRYzG53fK5I8vxAV/y4ifPrdvkYEhcs6b1ViAi4k4k+FqHCqb+REn+9V4yIklY0xdPq/6C1201x46kGNswgl/KSNeuaBPNvqALDI5ZynLuY8tPthCDnl4mtCPLdo4Ph33+JFJjraXATrRn8n+Tl+xwydjyjY8T/+tn+WwwCb6jZccHydf+SArQ3xGJ7Gix5jxz7E0xx36lZMB+nKsoQvdlGVCJ7ptB2v7Q3YzxmyPXeKyj4wj+coB2Uy0wR/qZY8f+IkP1JNWYxVdyQcd96YZ11v6Dpv4aYwy05dduf0hJ+WJnU+m6WfW5TMM0Ut/ooMxB1/K6CN1O38mV9gjR3s+pLN9pB+0z3Fnf6LP/kQWmRw3cqIPTPidupznlJvOyCibuXHpA3X6kOMU/XDDL2TpCz7OEeck7SlHVv9cwyhzfM++RCcfZLBPImb028Y5hVyme2PQr9Rxqx9u+TvrU/ettQFZ+aZvxMV3x/GcI4wD6iynD+DHxzLylOE7TLNv09d8hnOOuzmGZ8yzD+e8St34gZ8rP+jntCsbykhT75yvsz7t4jN25bwamymfz2faTtv0ETFijxzbjm916wPlPNPvzoXsS+VXOWz42O/2VcZ6NJ7OjhP08ck0x4F1+IBPyNOnxE/OxzENr0y3xhyyR2Mndfmc/QYDGOvXjAXfKJOjbZkzmRyTjiPrlLfcvkUnz++VfttD72X1yXYABjg+Ar1lwoGVk0wdvvDRwUCwXJ2U0T7lqHNQMDiP0m5hdeKvYsAH6s8k9Tg4bYNe9GTM1LkIpF2e52BOFkeD1P6gPW0yyXL6ps850e/hRPv0H5t8zzKesZ9l6Vs+u3jgQ/rEszFkuWWwJMFARowHxstkoT+rfpVH+uSzY9fv5OrSpnX4j3yW0//4m31gvCmHDhfAHOvGmmU5NrSdfp1hjm10PzJu8OPM3DTe9MsYsJ9JtrDIPnS+p44z4zB1+6yN1EUdjGdfUa7/q3GlTnPiQUeOV+ocY9OmvjiWkaX9HKeOjRwz6Vfa84WXOrRDG5Nycw0lztkvskmdZ/tfezN3fqdOZJ4xNtGDXlgydjI5J3Ntlk/OB1nJOdmjzz5xbspj8pRd+rGLfTX+iENfsLtre+/ahy0++o9u5yXlb0nP6jv7gzyTfTHLlU9OtDPG1MHzjuGU47u66Ydck3yXM18yadM5DRPHzj19pF3baoMxik3LkZvzNftROfxHNhPfs+yMTLb3mXbTBxjjJ/PEhC345NyjTpbTlzkOfQ/kmHVMIItNEjkMrEu9spn+3htD6sQm36dObeGb/aBPK3/1nTr8yUTZHN/IZBk+IDd9Q4/rXsrrS/JElnGKHt4RJPRNf2g7y74Lx3/0x9itglOOYf3A5uxD2yBPfSbbTT8sn3bnmo0udPJZzVfqV3axN31Zjc301ed72upb9k+OKXWSoxeu1GcyZpgcJftK/soa1xxTq/F0dpwYlzbMsU3d7E99I5aMb+WbsrPvd2Nu2tKXzLHJOJjr1q4vVjHYD+mXvpJnumeMZLtnP/92tj1b+zvpczGgUybonQt2QA6O3aBVVl1MVmRn0g8G0i7ZduWnG/ZV+9WA29nQ37kg7AajfqdPTKb8ri11u5haPvOdv7af8pbr872caI/POfl4zhh28U9f+L5iopwvz9S9Gzv4sGORi8tclOWhzcxXdYwZys8kOciaNsS0aq9sLoy7WFd+2T5ZHfm4Y7XSjR7LjcVxM23Yn84t+oXnOY5Xse1iUGfGhj+3xuH0je87G+jCp1XyhTNjmLIymuXalJ31lmdc2MoxgKx6s71MqJtp9q3rXY79VftVGbrdTNinlJ3t/+lbfp9+Wrcrl4Ny5pYnH8uUMXedyL5e9ZZrFzsAACAASURBVIPyZ+crYwOd6YM6VvkqRsro+9yYwXnqnG2NaTUWdmsfOvjMtOM25Xbfp2/K7fRanjHu+gMZ9Od8Qb/yqYPyXYzWrXjpr/lON/WuGWl3Z/PePjr7nsGHyQPf5Oqaxfdb6+UZGbmYuzasfFDGXJZTdtWvHEhyvUHHqr1tV3159t3zjBjO9sORv3JazSHKbq0NKz7qtC7HKvrQeyut2tKGtfEonR3DZ5g4ntPert3ZNRtdu/mqnZXds2NTHZnf03bn2/RJDvTTTLu+SzneEbu+2ule6V2VYWeOk11cyFI357J6yTOtfNvFke14tu20NeX4rn3azDT7gvqzezj1zrjuGSPTn2d+v70yPNPaRbp88TKwJuidSV/suRHfDdo5AOxU5Vf5zi4TBXlvi6ecunIjjwzlZwYysvo7B7N+T0ZOlCzXj12esjOGI3/1bbaxXJ/v5eQBiMWBycWYmGkX/5Tj+4qJch4Esj/kpIy5hyh8WiXjdhOnjOV+z3zWubnZ2ci2PMtB1pSp0zhmTr3JOr+bq8Pv5Nq6NV5sg+60ZflKN3WWG4v29HGVq9OclyP95CUAbTKpc8awGiNnxmHq9nllw3UNv1bJNrmGreRkNOtsLzvrLSdfJWJ0YwmrbC+TVR8iuyrHBrFiz3U55RzfWaZfU6e+U7772HaXT53K7crxi7qZLE8+lk1ZvrN2oUd5Y1n1g3qQX32oJ9lPeeGwsm0ZumxrGesK5fQN69RO12z7lrXPWLRtbrx+vzefvtl+p9dy+wL5XX845mc/KZ860LOL0brJX18z3+lGxj5Pf3Y27+kj5+GZ94z2drm+nVkvz8gkG559R9P2VpKlPim/61fr4cE+xXmb7W17qy+P3j3PiGHH33J9PuMvbWY8Z9aGHV84WpdzBJ67d57syXlnyJ5+SB0pl8/3jOEzTFwn0saunbKyn3mytS715rO6siyfj8Zmyq2eb7Xd+TZ9cmys5uCq36cvzvvVeiNjx69tV3rPjpNdXOimLvuHMm1NH6Zvzx5zxup+dfU+nn1hG3PY+p4gNnw27eKynvzWGEnZZz//fqf1bAvvpM/FazXAVy44QPOwaNmUnwNgfp/yR99tm3ZTXh9yEFFPOW3PJG1MHbvBOCfZ/H7G5pTZ+atvU95yffb7PZxoazvsz7Gwi3/6wvcjBtZhy2S/+d1cmywQq6S/yGWyPMt8nnX6s7NhO3N9op1p11/WZ76LdfpFG23N+FJfPu/8WOmmneXG4vfUuXtmsccv1g42Pb5c8CHTLga5z9go1w90zXGYun1e2VA//q2SbbB1lPRlythedtZbTp6JAw4HV/Tx0lNvttfnlU+rvmVD4cYfex6iZnsvM/IFbX+ln/qUft/7vPITHbvynU3Lk49lK5+sU37XD0e+TL3qhNWZtIvRvqee8cj3mWZb/d+tS/qW/YcOPjMpO8vPfp++2W6n13L7AnnjSX8pd8zPcuVTB/K7GK3D9q20000769KfnU1lz/SRce5k9Vm5tG/dKkde3vi5Wi/PyKRu49rtH1ay099dHPrCmsQ8wF/8zva23fXlmXfPozHoQ/qVceezsjt/kSXGVf2ttcE4Vn5Yh30TdnbvPGXMfX/QRv/yHaGcuXHeGsPIK7uKWX3UYTfTrp3+pezu2Xh29Su7yGKbuqOxudN5tu3Ot+mT39E706rfdzKrcSPjWbfTe2ac7OLCr1XfaWv6MH3z+7PGnJyO/JW9subOVep3e7hdXOggFtq+ZXzpw6P5b2fbo9o+sD0bXzrxzA2tm13kGcym3SCYA8CXVLZVx62cgYudOdBtpw9z4aUcP84k/WWAZdoNRieVPmEbezB9a9r5q29Tr+X6/FZO6EWHh5yMYRf/9EUdxCCTlJFX6kaWz0xsmnYskDVuFpBMlmeZz7PO8UzMZ5IcZE0b5s3Z9rtYp1/o1daK48rXHauVbtpbbixn5yZjnJj55DxexbaLwXGwi4361Thcxb2yYb/i0yrZZrXJT3kZZRnPtped9ZaTmxjrbCRTVr1ZxvOuD2c58aET3a53u/ZT1v6Dr23x9Wz/G9cqn34qsyuXg3Lmlicfy5TJ3DrH46oflD87X11HV5cM6sp8F6MyrGf0GXJT52z7lrUPHXxmks0sP/t9+ma7nV7Ls+92/eGYzfmCfuVTB+W7GK3D9q20000767J/djbv6SPXo1vvCeYj9vL9eCse6uF0a708I4Mu4zrjg7zIM636FabEhn7Tqr1tV33p2nXr3fNoDPf0w5G/xkncq3isx9/V2rDiYxvrsG9Sh+ug5Uc5ss5ZuOY7IdudHcO0OcNEm2lj1+7smo2u3XzVzsru2bGpjszvabvzbfrk95wr2lz1u3XmyqwO+zJGJpNtcjxl/dE42cVF+9XY19b0Yfr27DFnPPqL/plkn+Vn93C7uO4ZI2n32c+/3x0828I76WMw2om7AasrdB6yc7Nve+XM5wDwZTLbKz8HseXkdjwL2Crhw6qOcvw4k/R3ctgNxjnJsMGLg8/qxcELYbUQpW87f/UtZXm2XJ/v5TT9cWOAH77AdvFPX/i+YqKc/Y+PJuzwmSnHpX6kjHHPOstT1udVnS96+SlLzqKWvsohZT3czMsT9eSY3sW68ktb2V6dqxzd6JlppRsZy43Fvrk1Nz3cznhXse1iWI2RM+Nwxsb3nQ038dPPbLOqSxsyyrJsLzvrpy/oh8tkqt5sLxPqZpp9yzpHWY79o/aMUW3SjnV8rk9n+3/6lt+nn9btyvVJOXPLk49lymTOHM4D4eyHlD07X+WB3VWafbqKcY5p1hN8ne+p2fYtax86+Mx0xG3Krr5P35TZ6bU8+27XH45Z6jMpnzqo38Vo3a6vzuhGxrGRc2Nn894+OvueQY5P+qD/zHfHlHnWzXXhjIztzT0o4EOuL9ajU9/sp9l/s1/RA8c57lftbbvqy7PvnmfEcLYfjvyV2WoOzb5ZrQ0rPuq0LueI43f2B23oA8unberd2x+9E2FCLGlTf/DfvdIZJq4TtifftTOunW/GhY7dfNXOtHvP2FSH+b1td75NnxznxD3Tqt+njPsO+msmGSczZFZ6z46TXVzopW7OZW1NH1a+PXPMycJ1chXf7AtZzve9cvhsWsV17xhR1xX573cHV1h5J5150AXyKh3J7AatHas+dDsI54BlcLBw7lK2zYGCvIN9Lmq+vOak2dlwcVT/vS9n9DpwmRjYN+E/fqjT8pmvJjkyk6XtLNfneznRPv1ErzGgi8Tkxi/7jHLr9MPcvkDvTJTl4YL63dihzti0qz5jnAsJ9Y4vZTN3scoyX9a0Sw70E/YzTl8mskaP8dJ+jj/kfZEju4vVONOve5irGz0zrXQjY7mxyDT7WV05N22XsdI2Y5OZ42j2n8yyHL3JH9u2V5/+ZL7jZFv0zkTZ3EBPGb4b66xTt+yst5yc5Pc5Th2HtDc2maz8hW2Wy9q22KI/Us46bE/7+ps58s4d/bc++9+ymdM+7Wf9rnzH1/LkuyrDhtxynsl9xpHyt+Yr81/O873EmEvdu9hZ6+wHeRBHjr1dW+NNO+hAHt9nn+qrdszV43dy7K/iTxmfn9F3ztH0mTj0bcaIHHaz//HH8alvvkt3DJXLHFvonjblOvt6xxWdO//VlfGefc/oH32U66G8jBnbWY8/tkVW/27JfBcc//HCFx/UhQj9kQcp7U2WzknL/Z7jHn32s3LYUpb4ZpL3mXfP2Rgcm+kDfvCdvr/VD0f+4j+60DPjObM27HxLdtg3uUfHXq6H+MD4U5bY0J3JOJJt1vN8dgyra8ac+uCKn5kYq5MV4119qzWLMZSx0n7qTRvTrrrPjM3Uw/O9bXe+Oa7VLwfksZHJOZMxZ73PrpWOa8vdJ8x1Tr1p7+w40ZY2XKN2Y9+5NX2TZ5afHXMyyzGnH/pl7rzCb3zM5Piwrb7CJ5Ny+KwO9eo/5f/v//2/7+PxLeMr7T3j+bez7RkaP1gHk4BOBG4uXHSeHccLyw7SXQcLE4xnE3IO5pxgPDt5qWeQYXO1iKvL3EFNOycX/tF+TkLa6Dfy6Zv6Zq48umDg4PMyY8bvRMe+XMj5bow8EyM+zBfFtO+gR9ZJgwy+UzYXMWz5ctZX5O/hhG/oSJ58z82J+oiF5xVrY1EWX40XP118tGNccsoxoi7aGffUlcyVT9tTX47THN+zv+DBB79SR7KeC5ix0QZ2Mk2GaT/HYsaY9ozlDPNnjRvs2x9wJw7sExN+krRFORxpk/HzbF/lvLGPyJ1n2Ycyc3ww/uc4TB0+H3GyH/Ep/Se27AN1ZZ79tRsvOedom/FiTx3YgxN68IW44Iw830lyTdaUGx/yrglph3r8sAxbPBsf7eCMjB98oZ36vjsQv81Gm13/Kztz/add6t2V4x+y2MIXE9zkk3ztS2JRP+300/bkssjxlfUwxy4fbDn2aJcp50PKTb27GNGP7vSXMuRNu7ZwkI/ylOH7tO84Q7f9jv7UQSwkmCHHh5iO0s63e/uO+LWJ/6yfcGE+UE6c9DX+8rH/5zrrGMAv4jGmnZ+r2LCjTccd/sEUP7BvSq7aso48+d7qI2SxIQdiMZ7UPeVogxy+aQPblMEpY+B7juEzMhmPz9m/2EWPfHJ8Oc/Ik9tqX4QeYoc/PiMjD3J1ZF+mLXyzDvlb756zMTgf0Mmz6/HZftAn4pv+ps/Uuw5QDgu4WqYf2ceWrXzbzZFc22iHDWwbF7bpA8q0RazwR2f2I7KZJhN08yGWHMO3mMDJeZDvVmxRjm+UM0bkk3HhJ3bneE+96Y8xZH3axR52b41N9WR+tm3a5tkEU3Wkz84h6vTLfsNX+tb+U1fmxCdj2DGW0O+4US/2+ViOXZP2tIPcapw4BpDDhnHkOLAf0Y0OfCPPhD1jwxaJnFiNZTfmkEWGuObYSRs+q5O48Rc++GNf8Iwe+41y5CiDp7yQc27t5qs67cfd2qdvV+Vf7tICUAysBOogoGPokJkcZA4ocgbVrtz26HLQ0IZOd5Aqs8sZRPijTfQ4SbKN9TNPmfmMD05AfZrt+e7gnHXqQw8cHawMcAb7UdJu6oTjjuXKB3SYznIizrSNz9ic/UE8+IZsLrraM9cv9GY/8ZztdnGpxxw/0JUsV/5lDMkQf1a2KDNNG+iincmYUi8ymVigXchkaP3KPu135bY7w3wVN3p3um/FQv2tuUl/wII4faHpK3ZJycrnlW3qKD87DmWTubZhkWMMGfzJfsFOvkBTj8/3sMN/kjFmTjl8HLv4yVhjLUCOOcH3XR/uyvHfOmKDH4l+w1auNcqlX/mMT5nO9H/K87yygZ5VOWzv4YuOtJHzjNjRlSlj81k+KZd65nxNOVg6H5Sjz0yrGLVHO9viC/7mu+qoLfrnumS8aX/HcldOW/TwwaddWvmGzp1eYpa3uX2HDeJ2HjjuaSMT/FrpSB+Z28jbD+hd+Sn/VWz6T27foI914QzXqZM2tDU2/EN36rLNlMX3la/IMS9TZ85p9J1ZL8/I6NvMYZ3vb3TlumkfZ37Uf9Q55ogbXXyIkX7A3qovYZkJP7BJu927R/lbMSi3e3/c6odb/q7q7e9ba8POtxVj7GSCmazJc81BDm60cXzBc/Zv6svnW2N4FXP2Ic85ZnjOetcJ/J5j/mjNXulNLqt67Z4Zm8kgn8+0XdnGt125+jNeedCGsQMn+uIo4VuucbTVX56Zf3yf/SG3s+PknnV52uL7ygfLie/WmJPB0dhRxnzObWJ2DWLdy7EHB+fKbg+n3tVaInNiws5q7bP9lfn+bX+l1eougRcn4ALEothUAiXwsQR4kfPSZV7yzLz04wGAl+irJ172vPSbnkeAjfCPlhj7uSH+0eJvvCVQAiVQAj8ege6efrw+b8QnCPTS4gSkipTAOxDgpwX81OAo+ROGI5lXqOulxXN7gYsqLq1+tNRLix+txxtvCZRACZRALy06BkpgQaCXFgsoLSqBDyDArzQeXVr469Mf4NrdJntpcTeybQN+NRaen+E3bLZBvLGilxZvBNdmJVACJVACn5ZALy0+bdfV8SsJ+Ddd/C3drb+5u9KP6i6BH52Af8/KnwEwL/0zEQ5u/JSdg+tnmKMcrv2bUv7EpentBGBJ/3+Gfn97lOuWXtbw5yE/4m+ZrKm0tARKoARK4KsT6KXFV+/hxnc3ATaD88NvXjSVQAl8DAH/Z1o5L7ms+CyHf38yPv3/GJq1+lkJ+BuAOY54biqBEiiBEiiBr06gb7uv3sONrwRKoARKoARKoARKoARKoARKoAQ+KYFeWnzSjqvbJVACJVACJVACJVACJVACJVACJfDVCfTS4qv3cOMrgRIogRIogRIogRIogRIogRIogU9KoJcWn7Tj6nYJlEAJlEAJlEAJlEAJlEAJlEAJfHUCvbT46j3c+EqgBEqgBEqgBEqgBEqgBEqgBErgkxK4/NLCfzry559//qSI3t9tWP3000/f/ym/97f++SzyT8DxrwjwTyPyf+lv+vbNfxKQfyay//LJjzUiPkPf+89/Mmc/Y2KN9p9i5V9vYL3+5ZdfToeyW+PRwT9jyT+N2lQCJVACJVACJVACJfC/BD71pQWH1d0/hUc5G2MuTT5TcjPLRph/0u9HSqt/FnD+027zO3zoaw8QvbT49o15AQcOPvDqpcWPM4vsey6rXrnvP/OlBRek8IW1vJlrXDacSbs1ngsLL/npu6YSKIESKIESKIESKIH/JfBpd0Zs/Ngo8mETySbYxCGNn3x5wLX8FXP8ngdt/y32H/HSgsuH2ZerfqTPKVfWC4/J8iP7HB8/8sLAA9BH+rDjf/aAt2vf8mMCzodX7Ptjz1+7lksK1p1H15mjNX613r02lXpXAiVQAiVQAiVQAtcS+JSXFh5YOeCyidwlD227+lco53JlboCPNrSv4PNVPsBh/oq1LNjIz0T/eijzkDZZzjbv9Z1x+dF/miETGb1X7Lfs+NtRt+Ra/3YCr9r3b4/oNVq6Hj1jnWFNW11MU7Za716DQL0ogRIogRIogRIogfcn8PuT4Pv7cJdFfrLur737U/YjBWwA50H4SP496zi8sTmdG2A3xqsN7Xv69962Jgfsy2K1iaeOD8lD2krHe8fBhQW/SYDP+vfePiSTj/Rhxo0vzN8fbWxPDld/dz68Ut9fHfN76Ifnas1+i230rOYBZav17i022qYESqAESqAESqAEvgKBSy8tuFRg87z6iTO/LeHfXZPzU3N+6+BW8jB49tfL+TOS3Lj798RsDDlcukHkAsHkodPLEX6jg3YmNpT5yY2nm1rrbTNz7SpHrh51+B2G+gKnVcI//78OyMKHOGZCd/7pzIwNedrRP9Rhm0sf+sjDZvrMc/LNuGj7aJIFdo4StpAht//4js/EskrI5hg86y/6kyHPfPCVMa9O7PtJ3fpnn84+oJ7xiB50wl+ulO3imTFiE/vo4OP4QEeOZ+X01Tbq0zblGYf1mTM+My6+22ZlB52Zbo1j4mBsr+YGfQC7W+keHXMNgz3xwTBtOV9kiC+7y1LKqVeWfln16ZTb6cRHmMgdNuq377VFbn/AaVeObXUiRz8ii17TrXGs3Cqnn9FlPyqDTn3XHt+J41aavOijjNX2sKYO/eTElnEplzn2k1U+p288p/9zbqdOdMz4qaeMulW6Nc5WfqJnNff0e7ZB/kw/0I4xd4vdKo6WlUAJlEAJlEAJlMA9BNY7o3s0bGTZ9LBRciPtBglxNzuWkbN5XG3gUj2bczeLeehKmaNnN8puFtmU8+G7Gy9s4LOXGGyEPexpU/9px4Z3JuSoo+1RciM5N9bo10f0s1GlzM2svqkbGXzEd5J6KcuEHmIjJyFvbKmTZ8vd0Gsb3TLDR/p5JtrKc9bd+10W2DpKxoy/+Ecf8PFwMvsCH5HD/9yg0/5WgkW2pQ3+4atJXllG3a3xRT2xOG88pEzu9qH2VrlMyPGHPA9U6iD+Z/Qp+uHqOEQ/XCg32Z/4MxMcsz3taE8ZCX36T3v7Gjn7GdnVmNTWPTrQg277gmc4acsxRRl27Wty2vCxTPu0odz5hg31od/E2M32fqdMvsg6nuDC8+xL7VMuu7SDjlmOj45p+OMrMWKbT9o1DtrAAHl8PUrUa3OOA75jC3/56Idx7PQmB9uiC38c57S1b9RHDv/px84O8uicDJHHDny0h08ykVPqRc/Krn6nLM9nxxm20M0HFib8shzfMtEncLAcH271g3YcE6mvzyVQAiVQAiVQAiXwTALHJ8EnWHJz6iYRlWz45maN+lk2zbOxctOV+qbc0Xc3nejx0EHu5s7NWurQLps6kxtANnYzUbcqn3JwwI+5AdZH7OkXbfET+eSkrJtNbbhZzg30yhb62XRSl0z1LX3IesppM+1in7pVub7dkxsfto6S/jLeMrmxTsY842MmOGBjF1PKIpMsqKNPsozvO7k5Nlbjy3jQk2PAeM4cFNTBwS/TTsejfYqvyRmbfM8y+xPZTJbPcTPHMW3gmodDymCk/8R3K92jw750bGHLtcN1IPse28aDn/YfOd9n/6vDflLOuWssHuCzPb4RtzaUlVv65XjI/khfZzmM+MiTvrF/sJt+oGc1jvVn5qu1DBnspc+UYWuWTX3GlnL6k34ih75MtJllWZ/PyOLjZAWXVbl9uYqLspVdyqjL5BjJ+KjXnxxnlKvDcaoux4X9aDnx5Hhb+YvOtE9sjI0sU1/zEiiBEiiBEiiBEngmgd/ujJ6p+f90rTaTqzLEc3O5csV2qw1VyrthQy4/yLjJQ2YmN56rTZh6chPoIWluAClPuWnH78ZDnunIR/xI32GW39Wjbg9ayNF2FZuHIWXRYfvpm/rdRM8+ozz1KP/WXBb4fpR2/to+46B/8rt6HTce0iyfOb4QYx4UiTvZqivL7hlfxpPt9cNxt6pThvxeHY/2KTHjW84HnpM1Ps8xjK9nxzGyq/aU6/9qPlCf6R4dq75Ul33h98w9IDqeyLF7q9+MI3XxLDsOqPk9+dpm1feWTXn1znJ85TPTPeN4ts3vqz6g7NbcSh0+e0GR665x5XiQweyDuY6pd+bqnKzuXV/Ru4qfcsdb2r5nnNHOMTTjMv7pP2M117O39kP63OcSKIESKIESKIESeBaB3+9In6X5//S4ScpNIpteNt5sjNhUZd2ReTf9tLvVxs2lmy/1Wp4b2VlHm90n7a42htSzATyTZDM3kEc+4lf67gZ356+ybnpzY6qP2vMwRPnON9uQqzMPqdjjAPGspG/Ed5R2/to+Ge9YWZ6yK5te8hA/Y3LF1H7BvklftLPKlTcev6uDnEMdbW/5eaTDGKaOR/rUgyPjCP05LvRfBo5Ly+W1YkJZys/v6uDAanvLdvk9OvRt9oWH9918l7+XePbbarykn7YzllWOvH24mm/qSJ8tI89kn8xy7aYsz8pbv8rT7mzvd9plv1JuTEdzy/a7nH4hFsdy2qDuLe8ebRn7ZKWtVd/aJtdX9K3ip9zxps17x5ntsDdtohtf+ZgYP/Ny4xn9oP7mJVACJVACJVACJfAogeOT4KPa4/A7N7FsxPzplJu31YYvXchDiT+9zPr57E8607YbyNzI2o6NKL6kvHW73M0q8ZCIicuMM0l7cwN85KOs1D+/Wz5z5PjkTyKV0R71pp1v1pPPSxsY5GY4Zd/6vPJtpWvnr+1lPL+vdJ0pI3b7noPB7HMPHtgz6WOWWTfzI1nrjGm29btyK3vWTR2P9im2jJ3xxOEnE/WUz/m3Kst2+XwkSx2fW+keHcYzORrLPBhqW8bGqp7VHLQNuXJZtnpWbvqFrLazzrLZ58Yxy3cs1ZO6V/7dKtv1wa25tdPrO4X+wEfHsvxtp5zxUX/r3WPbW6xWfWsb7GXaxW+/Kmv7s+PMdl48uDaR85nlXKat/Eb2aI3TTvMSKIESKIESKIESuJrAb3dRF1i7tcFlA+kmbf6K6sodN1G7n25mG/Xm5toNIHUz+ZNiN3mzfvXd+LisIBY2lmc3wLYlz3Tk49zowuHMRQF+0TZZaFN7yXTnm23IiVO9HgRmLCn/lmd9mxv+qWvnr+31C5/RNX+yOPWd/c7lmQxy3KzG3j3jy3hW/WVd2lv5q9yRjvlT+mf1KTadq8na/pjz7+w4Js45BzJ26s7Mh3t0rPoSmxz00MNnleTvxY2/aXGr3zxUMqeOkn6tLnC1nX1vGXkm+2SW72K7Zxynnfl81AfI7ubW1MN3+oJ5yFhz/TWuOdZsf++7h3bqnKxcA5K3dmyT6yt1u/jtV9vfO85sR3xpA/uwyXKep1+2N7+nH2zTvARKoARKoARKoASeSWC9236iBTfKuZlbbbLZbLLBmoeo6YobZmRXelLezV/adgO52si6Odxt4miburCVhzxiyANa+rJ6ls3cAB/5mJtQdHoQ2nFTt3w9QKU/2su6nW/Zjmfl8IONO5vgZyZ9I+6jpB/Gq6ztsxw/d77Sn7fG1az3wJTjZjX27hlfxoP/M9nnt1jf0gEDD3hpw3b39unkgm6Y0HfasT/m/DOmW+MYP+cc0Hf5npmD9+hY9aU2vZhZ+S1H6+Czs4s+559yfteWOXqVRx/sZtJ2jh/LbG8b+2SWo5vPTHLO8Z4y6Eu7WZfPKxZzDK3mVurweY4zyo0rx9rUj5xro/2kzlWuzslKHas+s82sW8WPTcdb2r9nnGU7uXBRlj5rA79Xl2iT09l+SNt9LoESKIESKIESKIFnEfj9jvRZmv9PDxs1Nme5iWXzNDdFbuzObBzdIHLoSr3TdTdmKaOd3MhmO9twEPCgRT2btl0b4iFGPsidTTCgjZtJ7PE58hH59ENZWEx2sHdDmnIZF75ifx7ijUnfdjGhi7b4tTo80Q4m6Jl2dzqzXL/Rf5R2/to+41CWDX32F/7B9tZlAAeIGQvt8hDnIRz7JHUiJ6vUMceXPqbf6KENvJkDG1Ds3wAAIABJREFUt5I69EF5fMGHqdt6bein5bdyYkueyOuDsVKPXmRN+GM/3RrHtKE9n5m0JetZn9/v0WGfTY7o02bGox3KckzIHduz/1gL7I/kb5k6meO2lSX6pm+uu85/2rve5OEZW8Y3be0Yocs296yTxkCOXfRPbmfmVurxWV8dZ5TDKm1QR4yPvHtkOFnl+E0f8APZub7u4kfeiwZjU0fGknVznGUd/U+7aT/Lp7+0P9MPtIPHnPNpv88lUAIlUAIlUAIl8AwCv9/5P0Pr/+lgU+NPiHKj7CbOzSNybH5XG6WdO7RlI8aGjE18bpw4HGR9bugpdxOXbbRDmXrdJLpBX8nTDv9pkwcU9R3lbnRpx7OHEX2ERx7A3ITjV5bTjjI+tMFf8nmJAHdksGd77OJ7Hm7w2UM3ssR3lNQ7L01sIz/k7k36gd/Zj1OPcuTpr4e3jIN6vsuMZ3yEA+xvJdohnwwpy7YyoW/gYuxnx5ft8cm4sYevlGWMO39zHNk32EeHY23XVvu228llueMu/V2NQ1gRA7rpHzmeHcf2G/JyMFY5p1+r57M60E8MyO+YOb6nP8Q41wwPi+jLuUqfGAv+phx6sIEMbVLO8Y0MscOenO/YoI3jEs4ZN23RSz9Qrg7047eyc23Av7PjeMWeMvsLm/Y/5djEJ8uIhzJj2OnL+S8Dy7DBMz7LRn3ESt3kurKjLP7M/kIe3dal/9ifDHfxJ/c59+4ZZ/qPz9ifY3dXbrsz/ZAx2K55CZRACZRACZRACVxB4LJLCzebbH78sOkisdnhmc2UdWyq3OidDRT5lS43XGzK2QSa3PRpk5yN5kzodcOrrtQz5fmO/NyYruRmmYcOfMPGzsdVOYxNcPBwBddVXMiyEWbDLQP8Tj3IWJf5lNEuOX5je5eMcW7Cd/KUewBIH3ye7SzPfDX+qDexaccvxyD+n/UPfsmQtrPv0W+f5WEW+2fGl/GTa8vDB7rPJvzKsYyu6etK160+XbUhTmOGteNw+ot96lbMz4xjdBNH9h/fz/Yfvp/RcWsMJQP6iXiM+2g9yzm4Y4Ru7Nv36J3jSPvJTKaOG1gnf9mjj3FBHXYcw3x37CHjh36d6cw4nm34nmNE/fhAIt6MWb9WerIMX9RLm9QHY8fG6n1x1FfaQJ++Zj65ZN8it1pf9TP1wPwMd2SIj7bEdcZ3ZFbvr1352X6AiT7IqXkJlEAJlEAJlEAJXEHg11PcFdp/EJ1s9NlI5uHgBwn9e5huuH+kmK+O1QOMh6+r7U39r9ynHNjmYXH6f+v7M3TcstH6EiiBEiiBEiiBEiiBEiiBxwn00uJxht9/cs1PrX7UxIUNP+lseh6Bj760eOU+fcaFwzN0PK+3q6kESqAESqAESqAESqAESmBHoJcWOzI3yv11W3/L4kc6tBOz8fLr1j/yhc2NYfLm6ve+tPhMffqMC4dn6Hhz57ZhCZRACZRACZRACZRACZTAaQK9tDiN6ldBDuocevh7Xn4izd/W/0iJuImfvz3n2QuMH4nBlbFygeDfvb/XhdBn6VP+nwGMPT5vHXfP0HFl/1d3CZRACZRACZRACZRACZTArwR6afEri9NPHHo45PHhAuNHSx6oyd96cPzRmJ2Nd/c//Dvb/q1yn6FP9dFLC3J+I+We9Awd99irbAmUQAmUQAmUQAmUQAmUwGMEemnxGL+2LoESKIESKIESKIESKIESKIESKIESuIhALy0uAlu1JVACJVACJVACJVACJVACJVACJVACjxHopcVj/Nq6BEqgBEqgBEqgBEqgBEqgBEqgBErgIgK9tLgIbNWWQAmUQAmUQAmUQAmUQAmUQAmUQAk8RqCXFo/xa+sSKIESKIESKIESKIESKIESKIESKIGLCPTS4iKwVVsCJVACJVACJVACJVACJVACJVACJfAYgV5aPMavrUugBEqgBEqgBEqgBEqgBEqgBEqgBC4i0EuLi8BWbQmUQAmUQAmUQAmUQAmUQAmUQAmUwGMEemnxGL+2LoESKIESKIESKIESKIESKIESKIESuIhALy0uAlu1JVACJVACJVACJVACJVACJVACJVACjxHopcVj/Nq6BEqgBEqgBEqgBEqgBEqgBEqgBErgIgK9tLgIbNWWQAmUQAmUQAmUQAmUQAmUQAmUQAk8RqCXFo/xa+sSKIESKIESKIESKIESKIESKIESKIGLCPTS4iKwVVsCJVACJVACJVACJVACJVACJVACJfAYgV5aPMavrUugBEqgBEqgBEqgBEqgBEqgBEqgBC4i0EuLi8BWbQmUQAmUQAmUQAmUQAmUQAmUQAmUwGMEemnxGL+2LoESKIESKIESKIESKIESKIESKIESuIhALy0uAlu1JVACJVACJVACJVACJVACJVACJVACjxHopcVj/Nq6BEqgBEqgBEqgBEqgBEqgBEqgBErgIgK9tLgIbNWWQAmUQAmUQAmUQAmUQAmUQAmUQAk8RqCXFo/xa+sSKIESKIESKIESKIESKIESKIESKIGLCPTS4iKwVVsCJVACJVACJVACJVACJVACJVACJfAYgV5aPMavrUugBEqgBEqgBEqgBEqgBEqgBEqgBC4i0EuLi8BWbQmUQAmUQAmUQAmUQAmUQAmUQAmUwGMEemnxGL+2LoESKIESKIESKIESKIESKIESKIESuIhALy0uAlu1JVACJVACJVACJVACJVACJVACJVACjxHopcVj/Nq6BEqgBEqgBEqgBEqgBEqgBEqgBErgIgK9tLgIbNWWQAmUQAmUQAmUQAmUQAmUQAmUQAk8RqCXFo/xa+sSKIESKIESKIESKIESKIESKIESKIGLCPTS4iKwVVsCJVACJVACJVACJVACJVACJVACJfAYgV5aPMavrUugBEqgBEqgBEqgBEqgBEqgBEqgBC4i0EuLi8BWbQmUQAmUQAmUQAmUQAmUQAmUQAmUwGMEemnxGL8fsvW///3vb3//+9+//elPf/r2r3/966UZ/POf//z2t7/97aV9PHIO1n/84x+//fnPfz4Se4m6//znP9/gja+Mj6+Wfv755++x/eEPf/jG56effvr2yy+/vEyYr+7fy4DaOPLVx+8Mm7X7Pecp6/ArzZfJg/nDnP7LX/4yq37znRj++te/fl+Xf1PxTl+wj5+uQ6y3+P5q6T32CczZf/zjH++6DyEuxjK2m74egaNxyzxj3n3EnvLs++nI/8/SW8TwWfa9r86UscqYfcV3xFvY9dLiLdTe2IZBw2bn1qbojervboYfbnzO5MizcLLRZUGhzXtcWrhJ0yaXJfjg5uEocDZ3tjPGI/7oVc78SP7I9jPqPtPizeaRDTTc3vMw9AzOt3RwGcO4Y/znHGA+v0J6df9egdEtH77y+J2xM14cu6zhrnWrnPWTdZQ2jyTmDRuoR/U84sOure9m4j9a73kXuQlE9r0T9ukPcpLznrXplVKukXC6Yp+ADfpKFquxO/tzJfOWdxXvZeaEtl+JfX15O4Fb4/YjLy14PzHeGcO7MXvL/7eTed+Wn2nf+75k7rfm+6qXFvez+yFb8FJj0OTB+WhT9J6Q8IONKwudyUN7+kg9MeTGyMXzis2IvpDDz00zCxmJXD9ZwHcJv/UZPfpMGzfsq7a0Y0NCW22u5J5dduTTs21dpc9+2b1Ur7J7pV7Gw9FG4UrbZ3S/un9nYngVma84fidbDrqsbzMZe66pjC02y76/Hl2j0MfF5iteXMCD2PPdNxn5HbnkZPnVOezO+He1H2f1+8599j7BcTQvDXhf8962H5GbyYv1Ry8dsI2t6cO01++fj8BV4/ZREl4w39pfeVB99rx71P+2v46A56LrLLyG5v2J7zX8+/ReuMlw0+fL9BUCO9q4rjZGxGJ6r0WdTQGfVWLje7RxpH4u7m5YaHe0caZuxWDlxzPK2PgcxfIMG++h4+xL9T18eZaNV4/p1f17Vj+8hx4P7nPdeA/b72HDdWZ1Ges4Wq1DrrXUIfdIoj3vw5UPj+h9RlviO7Pu+/57hs17dJz17x6dV8o6nx4dM9NH9i67CzT7ZjWHGf+MvaN3/7R19B0buS86km3d5yFw1bh9lIBr9Gpsp+5X9T997PNzCbAm3hoXz7X4Mdp6afFO3F1EXmnTsRrg+rnauKW8G4Nnb0Zmd9zitfJTHWwm5sYY+by42P2UhLiOdGvjGTk+6tMz9H2kjrMv1Y/08V7brx7Tq/t3L++PlHf9y7XuI/15tm3WmdVlNXYcR6y5q0Q5n2ewwY/3Wl9XsezKbr1vbIfvO07KXJGf9e8K22/R6XxibD0r+Sv6u3e3fTPHKfLP/s0IfpPjWXPiWXyq53ECV4zbx736dY2eY3vqflX/p5/9/hwC/Dbkj7IOrXcnz+FYLUHAReTVNx36eWtD6caAzQibAb/vNgW83PnJiL9xwqb1zN9YKb/7ychu8fYiILrg+yN+4jO+0Be7n/ghc4sBNrBPzMjjI/r47q+lEqMXEtibcVNvjNT7QR+Jeg4ZK1+woX1jIS5tz9j9rg3z1I1dy8lN9DF+WEeMK/a2t47cNuTGpZx1lmvvzHhBJn3imc/Upc7M6bscj6t4po/6mnGkTp/zz8Hob77DYeqTEe1Sd5bDXT+R89c+jTPb5bMMzvYbuudY1YZxmaccY/fMmDuKQ71nxnPGmOOWZ+uyXN3ps3KZy8vxSu4YRI445xo05yZtGEfI00+rlH7s2GFHPeTooi8ynZFJeZ6xjW8zDuVyfFpmDgt55fjMesepvI7GhZwZF7dSckU3LLIdvhETcw1Z+w2+q37Tnm30V59W48c25o43v5PLx9wxRd2chzOGW+8Sdc4cn3cpOdBuNd6m3fSTsZectTP1Tp+Sn0yThXrSFjomE+VmTj/j2y7ZN8mGsb+LZ+rJOYpf85095bEH213KeSUrZGVjGbmcZht1T/Yr3/AflviFvDw42Jjewp42znH0JCdtqd/8zHsWH52LMMEOfQVTOMwxKgN45fy2nDLqVmtw+ozMih++2zf2B2XTT2MkR9fRJ9lPP3c+IIcfsEA3ue9/yo9S+k8M2FAHDEzKpe8Zs2OH+pXNrEfGNPVaTk7/6g99xZjie6Ycw5ZP/skxx4Hy5vgiw4zTZ+V2ebZHz4oDbZ1Tjj9iYlzThjSZyJlcX8gt15+0T/1cJ2cfIEMZac4ddZq/ZX6e5a6NZ+e/jrJna66+3xDIAeuA+o3Ai3zRz1s+OlFYjFl0mGhMUCYMkzYTE4MyF+5ctHIBzTY+64+TFV1nEra0l/L4ja9MPBdOcr5nQuaIAfL45gLFMxxcHImR2PCbOpIcKJtxUMYnkws35dMX/WcBUxfxIosPM57Ui7yx034m7BKXesn5jix6+dj/9HkmuOGDMVNHe7lQb0LPqlx79t9uvOADzPXJF3ra0Fbm6CMefLQtevB7xWMVU+rLZ3TCVnbOCXlgDxuTETpmOX4aE/LwwE9852Pa+SfHM/2Gn+iUHbaxyccy7OFPxkdcyFC2S2figAs68FV2u/GszTknlJ/l9oHjiXiIFb/tF31XN3HCmrnAx3FKLKQ5N5HlQ3tltafuM+z0Tebk6MuYzshoM3PHlzFkHc/otc9nnVyot39Shr7DT+sctzBZJfihC7mjpB552A5bJjhjX32woh0f+3nGbJ3rl0zRkay1MXNkkM3kHCJnPJvwOeeW3ymDF7Lw1Vee4eY4St/P+oftW30y7cpR+9jCp0zOU3TrO74qm3HTznFj/6lLBpb7XSbKzRwW2GIs75J9g22ScTk2d+0oxw/025Y29suuvWOUtruED+jlk4xcmyif+tGXc0pf0EWCheNe2+TOczjQN/aPzJAhpnvYY8s48RW/0Q8neatfBrTBDjLEzGf6gqz9g15soEed//M///O9vX2ALPXE4HyjHbFQThl1cuG7iXJkz/QtMuq1ffqpDuuwl/1KufaoM53pQ2Vpx8d+wj4+ZQzKzlz/ye0n+NheLtkn1M0Y0IsPtN0l/FNvyjhXqTOhn74kFhI87Gtl4Kav1JmSP+OEOnI+jg9sZmK8UbdiaFnKz2diRwd+89EvbGZyLOKjso71jJ94XdfTPm1W5dihvbKOKWQzZX9bjk7KZaMO6986P89w18YV+a+j6Qrt1flfAg4qBmBOxP8KvMiDft7ykXpicfHRfSd1ThBkmcCZdpMvZXx24mKPDz4yIY8Siw0LxEz4om+5UMxFGZlbDNAtB9uj04XTuvTBWHxpWGdsfjd34Z++uBhPDmf7j/iwOfViF9+yX9UpN2Tsv9mv6qVNJlmkDupXupGderWXizX+T320nWXpB8/oWMWtj9P3XUxTL9/RMdvzPct4dhynjp0dx4Z9wrjOsb1rp53kIcfka/s5JpHBNm1IyqVtyplryM32GRvPR3Fgi5frmfGsH6s+xMYsR2+OG3zB15WszJzPxgB75LMf9QP9znnkZZx+KHuLHfqzHfpom2VnZPQ7czcvWZbP+kicJsocB7Rf9bHtkg39iJ7JXb2ua7t65VZ95HhLlthGlrocQ6636Zv+Op+0teo362ZOfyQn4qEsxwFt8GXFTb9yHqrTsUfbqW/FY/rGd2PMuHd9ot30BR1yRpfJeTDHAf2IbylLG/sly+9hol1z7Wdc1pkbDzLEhG/YPJNsm7L21YxZGX2a/Kw3V/fsUznneKYN/qdN2k8bjtmcR/Y9/aEtchg8wh6f0Mkn/UKn5cZKjk/4PJMcsg95Rkf2VY4Z4qZ+zlnbMWeyj2XgXMIH7aY/u75Vb/pAO/s6fbc89dKXzHs+9gH1Z/tQ+9mW9vo77adtnm2PfCb9x69McIfvHIPIULcqz/ar/qd+Mrdfsq1jMsuIG520z2RcZ9Z5dcw541g6w5DYM+VYl4l9vdIn19Qhkzm2jC3LVwxW64VtVz4wB9CTevEH3yZfyvUvdan/DPeM9YrnX3cnV2ivzv8SsNNXg/C/Qi/woJ+rwZzuObDnRLC95Uzo1YRBF+V85sKcdnxGn5OVNiy62MoXlbLYRHaV8FvfqMc2utCZCzwytxjQfseBOvTNl4N8yDPJIst8pi59kWmWKZuL6i2uLqjoyzQXJjdGqQ8+0y90WD7j23GSh31ibH5Pv2SkH3yfmxU2U6u26qGedtM/6o1z9tkuJnVmTpxwTaY8pz1jzjJ07OwYd9rJ510745FX2six40stda6eedlnO2WMJzeI1mW+i8M+X+lejWfjXcljI8t3sjBBdva1sZzpm51uYp5+nGWnfXRnyo3XGZlsy7Px4tcuGQ8y88NmN8d06qAcjvNAoY6Uzedb9ciyFs1xRf/SNhnJ5Ey/2T598Rm9OX4sn3nqYE3h++pd5Hoz28s6x586M67Z7qx/9/TJzq5M05+drGvIHAMrHfcwmfFrZ/ZzyukjbB1j+W5P2fl8zzvbtvYldo+ScedcRl5GM6Z8D7tGZl9oyxhd54/80Qfbmtsmx6N1mWsry3iWueXamTFR73spbe0YqM/6Gb9+TzuWZ5/c07f32tNPc3nAwXS2D1lHYJO+q8O4ZrzWm+/8p959X7K0v+bYpHyuv9rI/Oy42Pk/7aIbnZOBcc34V3p3sl7c3IoLTtMOftm3rnWMK3ylf2dSNsstS/7U62+WP/r+2+m1v1fx3TM/V9wz1iue9zuYK6z9wDodkKuJ+EpY9HMuFtPHsxPPQe2itspzkk478zuTzUUXXfliV5bFxAXFMnP8nvacpOhDPwmZWwyQ23HQnjkbChZmN1JzsZCL8plTl7644Ow2Yvq0Y6Bu9eQLg7jzu7LmLMz4bh+kX8jQHn9nfPo02TveLLe9PFa5sr4s8IVYV4cG/TYnNnTS56ukPTeAyOjTjGnV3rFEP+Pf6kVmzFPfzo4+reyd9e+o3/CVeXQr2Yf6M/M5FqY+5We54/DseJbTyh42stzDepZpf8pSfk/f3OMH9o1/lesf/eQawVjFxkxnZGYbfWWu7JIy+Edy88z3W2uJOmlDfzKejNO6mVu/inHK8p255aGVttnubL/hH2134506+2Llg2X2p2vQbu3RL2Nd5VNnxmWd+Vn/lCe/1SfGMu3qe5bLP8uwsZLdlSu7YmFZ+p/P+soY2yVlsOPagt6j99pK1613tm1gcctvZZnbfDLhL/My5yZjPf1NG9qauX2iLHpneoQ9urQ59crc8nvfs/pFvkrWG6MyxjrbWb5iQNtbfXuvPf0hZ62E0zwU65MMVzky7iFW70N1zHjTPs87/6lzzZo6GH/4lHsW+OHPrWQsU26OC+p9N1B3NI/ROfvPuKbvKy72wxnZ6TffjWmXq1duKx2r+C3D50zGNsuVecv7j7Yrvc+anyvu+ntV3kuLq8gOvQ6c1UQcoh/6VT/nYjGdOjvx1LebiFPv2e/qhWe+3GnPopgLb+rE75UvubnhhYbMLQbo3XHQpnpY2LCxe2G4MNou8zlmjH2+FG2jT8gdJTazHpDcdKOT+GeCJ5yRR6+8JiPixd9pW58me2OxfH6ffszv+OFLA9/4fpT0Y3cIsx/0B127mHZ2kNcO+ubmwxgno50dfTqyt2KO/Jl+o+3cSK9sITf7eyW3K9vFIY+z41lOK19WPjo+HOP4xxhHdvaBvsxybWa5ZWf8WPm142SfyQv96Tvtzsik/iNflVMGuyY30JRRv0v4BxvGEeuEfFPXbGt8R3pp4xyHA/6QT3/O9psxrvoMW+jd1aX/+uDGmHfO7CPklcu2u2dlj3ic9Q8bZ/tkZ1em6Y/r/lzTmLv0/WSw0qG9HYejctumT1NeGWyT9Bl2jM3p42yPbnTcemfbDnl087mVfP/7niLnM8vne3jFcWdLf4hhJtnM8rPfd3FOvX4/+541Pvts+mP97Hdjne0snwwop+xW395rT39d99A/x9lOp23NlZsxUW9cqzrbk6sD+ZmsmzqcJ+6neccQx5l0dlygK9cl2mFjteekbvbfzvcVF/xHx7ygds1esTHWlT7rZr6LHTnnQbaxbNo3tllOv8CIdve+/7C70qsPj87Pezglg0eeb6+yj2hv2/8ScOCsJuJ/hV7gQT8Z1EfJQT8nmO0td9PLxHtLcgFdtXWRhamJhWp3+EEGv/XNNubYQhebLxcH63b5jgPy+pcLg3zIMx0tfHPMuOju+kifiOFWSn9gt9LJC8XDiC9iF6spb/mMT58me+1b/tbxAhN8hNXRWGNsIDP9k5P9YJyU72KyzS6nHS8bdOY4Nubpw86OPh3ZWcV0tt/kRv8fJV7+ZzcxKz27OO4dz3KaYw+b2JjlkwN9ayzZz7S/p2/u8UN7Ky67MucjMdF++kq7MzLI6St9vUvKYC+TByrarnyQJz7mGNr1t7qtx+4uMW+wmzKrteRsvxnjjgM+zfGz8k0fqPO9kXPcNrJLLtbNXJ0Z65Q56989fbKzK9Ppz+yT1XtOv1c67mGiHnN9zXeqdebKYNvkGgC/3VxCdhWLMaQ+9ZI7ptB9KzEOsg/1Jct5pjzTPe9F/YHDTI+wRxe+r+KUufbufc/eYmz9HIvGOvvG8mRwT9/ea4+4nXPwmX5Sf7YPtb3axxrXjFfu5upY+WHd3B/iP+si/jMGmee37Gjv7LhQnhx7jkfsMkczoTP7jzp9n37tuMw+dx1YsU3b+Ib91ZqecjzvYqduzossm31jbFk+19pde9tOLshbl3qfNT933LF7Vbq9yl5l+QfT68BZTcRXQqGfc7GYPjoZcyIgY3vLWSSIeb6E1YecspZlfuuQNBcMFkEWql3C7yN7xsUieosBNpSfOl30ZtzyIc8045h16YsbHNpgZyZ9WtVNWXUR744dMUxbxEtZ+oVuy2d8+jQ5ycPye8bL3Lj6QprMM2ZfYjsZYpp1u5hSr8/TJ/pg8jPmyWhnB5/47NKu3bRLe2Wz345eYPivn8rNzY5+Kef3me/icAxSvxqzjh3rVjFoCx0Zm+X4rh5k2Axgd6Z7+uYeP86ym+MH//AVn+V+RmbG5bxCzy4Zz0rGsTTnBrrceOqf+tGz0nW2Hn20R38m+xF/TWf7jTGkXzCZibrV+Jly+kA5OuUz+4bvqxjUl3NGnRmXcuZn/bunT3Z2ZTr9gRvvZS9kyXfv3JWOe5gYt7nzKLlZZ248U8b3AwzpL9cT2zk25hg3hqnPds4b7J5JjhWYpU79Zr5Pns7f6Zv28MF+OvLnEfbYgh2fmfTd8nvfs7cYW2+M2jHW5Eid5fbJvX17rz1sOuemL/p6tg9d99iTzWRcOxvK7/ynnjmE7jn+qbOdMqt3pDYyPzsu8B8OmRwrc51Hp/2nvP7N+HdciJE545wjbtquYteGObJ8Vgxo71qv7vn+Q8+cF1mGz5mMzXLHweSiTuXQYdvJJetSXua79QT2WbfTj05kV3Yztmc+/371eab26vovATt9NRERYiK7CZiD9L9K3uFBP+diMU2vJg4yts8JoiwLYS4WxHzLDrxcHKYPHnZSBwzTxmyDbPo262nrIpR6p5zfkcHHqdPJnBOfNvPFpq8sjugxuVBSvxoz2p2LBfLoumcMeSiC3Sphn4++IuOCKiPrLJ9+udnMhT3HfPIztlvjZdXXtJ3MMyb5EE/aRMY+Sx8pd6M3Y0q9PmN/vpSdEzJSX/YRdcY97chfGzNX366ddmln/2CLRJ0vMOzwbKKOsSEn+TC+JiNiybbqyPwojl3s+DDHM3zRNfvZuWVs2qY8WVu+yu2rydLYs9yyaQ+9+Jflyt5ih/653tlW5mdkVrFhG792STsrGdYj2zMmMtl3+kcd/YYedfE9k2v3bs1BljhpP/vO9Rl/1asseSZjynLXouwf2ujzkU/q1ge/J5+cB+iUW/pAO3glSzni8yo57qffK1l1nekTZaddmWa5ccp9ZTvLnJOp4x4mqYvn3VqXcsYzeSODbfuOfsm1Gh8Zb7t1RX0zdtfP7Mv0Zz4rj314mrJ82kDGuG69F40D+ZkeYY+unNM9HwLMAAAgAElEQVSpW98sSzvZ99TrX45Nx5qM1WNu/U7XbKcNGfj9bN+uxi2+4DMMpj3Ltaff5LBwTZfTrT48WjOwf2us7Xi57k7/9Tf7DR/PJv2lvYnxbLll9MNKLzHlOo8eyiZP45r+27+znP7OcaYfZ3JtoSPXCXzDL+duymX8PPMuIY5MxE9Z+oV+ZYmFpN7kQjn+0B457c11kXLrVmOZOvtGe/ooy/RPX8gzKTvLU+bZz7+l+Wzt1fedAAPExYrBxmBxwIvIQUG9A9K698rx04lDPn3Uj5TLwZpx5qLKhHSCEBss5JGLgfozlwcTPWWZUExe9FpOvloQ1Uc9+tJn6zInbvTi41FKDhmvbYwZe0xuFg8XHHJ8RQdJHiw+LPZ8SC5G6Mr+oJ36fSFShh/oVu93JTf+c2vhcZElR5Z4LMMHnu0Dy4knfcgNGRzwkw+66BN8Ng50GdvReLFOLsahnl3YymGDZxI68GH2IzEY0xmuxM3cSb18R4cJW/jOB3vwoB1jmjL8ggu2HbOUOybUQ37kn36T489Rv+GHPhEn/uDH5JFyxGW8GV/65/OZOOxz+4/YsLfijm38xT5xIZM2YMp3EnLUI+cHljBx7OhnMsO+CX3qsRw/KcMXy5BHL+V8Uv8ZdvgHh2SAT2njjIx+Z659x2bW8Yxe/c4Ni3LOYWQcr9TJAca0Q05byPJsPOqSEXW7ZH/CA53oRt6+h4vts99SnzHlGKJPHGvGgW6ejT/HT+rjWb+QTZba0l/bJTfqsIM/2aeMH+MyJtubp37HtnUzP9snaXf2kTzSH32AD89+4JAs8Cd1I5/pDJOU99m1E99WafZtzkvlicd+dnzK03FBXMSD3/QVcuSMs6lTJsR0JtEeO8mVdrtydeKj/uEPDOwj/UfWvkc2y9XzVvboklvq1W/qkgH8KMMPxwb9A8cZu/OXusmX786NOY7sy9kvMqAdNkmyu9W3R/b0E+76SY5uPtqSNbn2eIabfhz1IeuRrIkRfrCVAzrQqw9pj+eMH13aXrGfbR3Ptpv1q+/2A/7RHj746/iEG/ocE/Sjvmsvx5T+T6byJ8+kjhw/2kKWej/6of3Uk8/Uo89+4Jl48An/TClHHbH5MX5lyZ1/ysKOT8aAfucbcrTBb+QcA8TFd5Kx4iPPluOb8nPu2Ab9PJNuzc8z3L8ruvA/vbS4EC6qHfC7XPMM0N3gUubK3Mk1/aQ8kwN9yq3Ksy2TgQFvO+pykUob+czkY+IxabM9rKxTnkmJ3CppN3Mn6koe39L/KbOKF92ZkLFP0QUDPiwSLC4Zv/1PHYsXiTbpL8/pM1xgQBvqsEVbyu9N+INvq0S5vmBDH2iDbV9u09fpL/1DGW1cQPEX3erQPjazv5FJXshhn4928W3X/+o1R1fqR89su+tjfNkl+kNWxrrqE2zZb/hBnzleqOM77YzNPG3f8u9svxkL9hyvRyx5oSpHDLQ7SrfisO0949n5Ahf6zrGhPzmWsz/kmLljMct8PmKsjDlxrmwln1vsqEeHYwPdjKmM54yMTDM3Ftpnstw4MqcuE75kvePRcvxWv/M941eXY8J+s3zm6JIF+hgjrBX44LxJf3zexWQ8c33Bf3Rji+edX/qtHXIZZJnPxoPdXKu0R/3OV9uSq2/mKTOfb/XJzu6uHP2wtz+mL3xnXYDdkQ79RGbHRJlV7vyYdSt/KLN/kOd5J0ff4pNrG7KMEz7EnOtM2t75kzLzeTfGduW2n+MW2zlWV/Gt5t+97Hfjfleuv/h26z276g/8I5Gv6qlblSO/YkA5n1t9e689/Mj4Vj5Rhl7TrT5UjjbOD8Zfjk+e0XOU2Eekb+iae5xVe/oMTvck1k6501Y7lOWYJibK7AfY8D35qCdZUp/ffT4qh0/asY05TPVzFytx8d5xzUPf3KvSFrlcb+kfkrFM/b4b0csziTbIp/4z7z91qxMduzWYukyPzE85Zp79mHae+fzbU9YzNVdXCZRACZRACXwwATYmvPx5ofLM5sCPG41bG8APDuFp5tm0sJH96MQGem6gPtqn2r9NwM05c4mP88icTT1z6sqEXTbKbLg/OsEDX4i/qQS+AgHn8leIhYM8lwCsGewBjI2c9w/r1ZUJG6wPTc8jUJrPY1lNJVACJVACL0SADcutQzobi1c4AL0HNuJkE/WRlzTY5idMH+nDe7D+ijZ2v21grBwObs03ZR/JsXH15cgZ/7gEhUlTCXwVAhzkv8LazIUFFxVHiffQlamXFs+n20uL5zOtxhIogRIogRcgwKbk6BDlr0e+gKvv5gIHLTZTH5WwjQ9Nn4sAB4Bbv+HARQIXF1cnfsOBy4L3sLWLBR844P0oF547Di3/3AQYx15SMMdf4TLwUaJezh9dWvhbl4/aOmrfS4sjOm+r66XF27i1VQmUQAmUwIsT4GDDQYvDhT954aDDr4eyOWNTwabtR0ts2D5ic4rNXlh8ztHGby0xl/hwEcgcoszNP3PtPfuWecv8/YhLg4+0/TlHT71+VQJc7DOnmb9f5Tfg/G0+42KtYm1iveL5PX5TizVCtu+5Lr7qOHuWX720eBbJ6imBEiiBEng5Avy0xZ94eOji+4++keCwd/STqGd3JJvFjzhgPjuOH1kf/cfFE5eAziWeKfOnte/NhzH8nuOKOBnLP+Jl53v3be1dT8B3I/lHzeEromR+8oMKf3DBeuVvXl79G1qsD66P5vBtepxALy0eZ1gNJVACJVACJVACJVACJVACJVACJVACFxDopcUFUKuyBEqgBEqgBEqgBEqgBEqgBEqgBErgcQK9tHicYTWUQAmUQAmUQAmUQAmUQAmUQAmUQAlcQKCXFhdArcoSKIESKIESKIESKIESKIESKIESKIHHCfTS4nGG1VACJVACJVACJVACJVACJVACJVACJXABgV5aXAD1M6vk/6j/lv+rPv+X3mf8H7z95whflaH/93T+L8SZ/D+K839Sv/r/TJx2V8/8X5PpQ/6vyfxfjK9MrxT3o3ESC+O4/1f4R0m2fQmUQAmUQAmUQAmUQAk8j0AvLZ7H8tNr4p8tmxcWHOA4+PpPnHFYR24e7PjOgW+2vwcKbdH9qokLC2L0nzDSTxn5bzJ/9KUF/wSc/8zTlZcWrxa3/fFIzsUF/4b3My7gHvGjbUugBEqgBEqgBEqgBEqgBP6XQC8tOhK+E+CygMPuTB5++TeGfebQzvPq4oLyt1xc0IbD4mdI89JCn/33rt/70gJ206b/TvSVlxbG7UXO9MH6R/LdJdYq5kfsZFsuLLik68VFUulzCZRACZRACZRACZRACXwMgV5afAz3l7LKZQWHtJk4GM7fquAg528UrA6UHFyp5yfWZxM6uQi4p81Z3VfIeTkxdXtRcMXhfdryOxdHqz9J0Zf3uLTQ1rPjdlwYq/kuZuufkRMTF3BNJVACJVACJVACJVACJVACH0uglxYfy//DrXNRwCXD6rcsVpcSOMxlBpcMHN5XicPerm4n/1l+ywL/X+XSgsM7fURfzAsDLxI+66UF45JxRGyZjmJOuUefsYPt9+D3qK9tXwIlUAIlUAIlUAIlUAJfmcBvTwRfOdLGtiTgr/bf81sOHuh2FxMemM/8ev3PP//8/XDIRcgqUe7/T4Mcf/OCg/Z81xe+K08ZvpIo9xBM/fTNw7C/RUI+f8tE/95yaZH2V7rxB3vUkeyXjFX75PSXcXK49uMh2z4gNzZk0L9jjaw6ydWVdlfP2uLihE9yJu5VOrJFG/w0JvMcC5aRp5/Gant8SR+oRw/ltIM7sSI/L37oZ8qbSqAESqAESqAESqAESqAEPo5ALy0+jv1LWOZQdu/BjIMfh0UO1qvEIfGoPttwKEd2XiIgwyEyD5N854CZFxS2pwx/OIgiZzk5h1QuBCinHnvoycQhljIvb7w0oN1M915aoAv96tYHykjErj1847devMC41Tf6QmyZtIFedNEnfIhxxRtfkKNv+ciP9reStsjxh9z22JqXJGdt0ZbPTLuY4Qsvf2sIrthChxcX1FlGbPipPttpzz6xreXNS6AESqAESqAESqAESqAE3o/A708E72e7lj6YAIc6DnQc2u5JHEJp5yF8tlXvvBiYcnznkLk6mFLnITjbcThPf/GB9tjisJ2Jcj7zQO8hFT9J1COHPZMXM6sYbK+sOe2nPXVPVh6c80Cvvx6eaTPbactcX2aM+sKhPBO6Z6zIzjiNH9lbPmhrXnBoKy9e7rElj/Sf513MlM9LJi4c0JPx6W+OmckPO/o/dU5/+r0ESqAESqAESqAESqAESuA6Ar20uI7ty2v2UMZh756EPAe/o7Q7cGYbLzeQXSUPl/NAOQ+RtF/FQNlKt+Xq5VCeP6HXl10MtlfOfOUvvq58UzYvFXb21L/K9cVYlFE/eSbksJPlHN7zu/LqZpwcJW1NH2iDbuxZd4+tHQ/9Uid26MO0k/6qx0sq/V3FnO1kteq/lOtzCZRACZRACZRACZRACZTAdQTWp8Xr7FXzCxHw8DYvAY5c5DcD8qC9k/WgmAfLKeuhkIPsKnmZgC583OmifnWw9HA7dVu+0sdvGBCjvwmB7plsP8vlmXqVlcfMqTdZ5/czufrTJu30ZR7MkcNOlmt3l6fsyidtTR+Q9U8s1LGzYblytLVs2lzFbFy2WeX6p79pa9rge+pc1besBEqgBEqgBEqgBEqgBErgegK/P5Fdb7MWXoTA2cOb7vKTag6M888wrM/cQ6MHxazz2UNhHtytM+figgsL9a3sU7fSQRl1M1mevhETPPiNC+wd/RaI7adeeabenW+zLd+NcVW3K9OXtImsvpBnkrnl83vKnn3W1vSB9taR32trx2MVs3ZWPsw4lCU/Svq7GkNH7VpXAiVQAiVQAiVQAiVQAiXwPAK/P9E9T3c1vTgBD29nfnOCQz1yZy4sCNsD59Eh0kNh/j8Pdsi4vPCwym9BpB/Yom4m5Xfl+oYudPLBjskY/G6+0ytP9SKPzt1vkqjPfGfP+lWuL2kTOX2ZB3OZW07s2L3nt22mH9qaPqQf/L8l7rW147GK2f93Rf4/QqafftdfGVg+c1nhR1MJlEAJlEAJlMD/Z+/criS3lW2rn2OAbJAL8kEmyAa5IA/kgf7Opxy4bYEcaAfaA/lQd8zeZ2lHRwMkmK9iVk6MkUUSj0DEBDKJCD5KAhKQgATeh4Cr8ffhfopeV99pgbOJU8t2Nc0cztp+624G6o3epZC7Lup/dKAvHNme4tzO8uNk5xGGKpM2MxtmcuMMRy4yCPQgp8uOTtVxnvWXuqNtdKl9Ui+6VPnkxxGv+QSN+NSATfpizEfjkPLaV9eBMuxHdubOkb5mPEY2Zy4RJBoldIt+Mza9XVjRn0kCEpCABCQgAQlIQAISeB8CBi3eh/speo2jt+WUbQUscHKr8xujyMfhXLnDACeWuqOE7O4wx5GsQQDaj2yIc9tlJz9ObI6rTOyuTnOcbmThGI90TvAjcqkbfbGzyqeM+vXOgNpf13l2nKBI+kzgYeaYR586bqmLXcyJJGyGTWQmv2/TPjqkPPPg0r763IgeM5szjpTX8cImypKib9UrZXXL2DAm9S6UjPFe2yrHfQlIQAISkIAEJCABCUjgcgJjb/FyebZ8MgIEFkYOOGbg7OHI8sHp6x/adUecdnGMq7M3w5I7J7rDS30cQxzXBC5wRHFI0TlOKWXoQb04tdE9Tm+VTbvYHMczMrATe3BWo1ec1uiQQE+3vcrFsa2pyqJvOLLFlqQqtwYyUj7bYkN0RPfYFMe+O/BxurE1DNlyjBw+Ge/KftY/+eGHTZkPmTt9DhzpC07og3yYhMvMZvrMmNMu85V9ypLCpjJIWd2mn/RLGbLyqfOttnNfAhKQgAQkIAEJSEACErgdAYMWt2P5lJLimFWnDkNwyKoDGEetbnFSR2kmc1Q3AY4EBWod8nA8qx44wXEW49RWneg7/dd86qavnk+fCSxURz0OPvJIR+TSR03YAi/y6SMyt+TW9rN9ggDhgA0cV/uyP7K96kg77A3rGoCY9V3zcewTDEAuAYHq7Ne6q30xJ9Gj8xrZHPnMjaoHbOrcDo+6rUGtyGFLW/quCdnk8Zm1q/Xdl4AEJCABCUhAAhKQgASuI/CtZ3WdLFs/IQEcQBywUdDgUnNypX61Pc4hzqBJAmchwPeCwEYNLlXdCBAZtKhE3JeABCQgAQlIQAISkMB9CBi0uA/Xp5KauwBuoXTu0MjdECsyuRKOg3ikzYpc60jgUgLcJULwbZYItJkkIAEJSEACEpCABCQggfsTMGhxf8ZP0QNXjmdXlY8YgDM3eyxgSw5tdAS3CFn2KALcZcFjKfWxkto335W8u6Pmuy8BCUhAAhKQgAQkIAEJ3J6AQYvbM31aiThjlwQcYvC17ekbGSYJvBeBvC9jFrAgsDcrey+d7VcCEpCABCQgAQlIQAIfmYBBi488uhfYxhXkS64i38qZwyG85fs1LkBgkxclwONJzGMCFyYJSEACEpCABCQgAQlI4BwEDFqcYxzUQgISkIAEJCABCUhAAhKQgAQkIIFGwKBFA+KhBCQgAQlIQAISkIAEJCABCUhAAucgYNDiHOOgFhKQgAQkIAEJSEACEpCABCQgAQk0AgYtGhAPJSABCUhAAhKQgAQkIAEJSEACEjgHAYMW5xgHtZCABCQgAQlIQAISkIAEJCABCUigETBo0YB4KAEJSEACEpCABCQgAQlIQAISkMA5CBi0OMc4qIUEJCABCUhAAhKQgAQkIAEJSEACjYBBiwbEQwlIQAISkIAEJCABCUhAAhKQgATOQcCgxTnGQS0kIAEJSEACEpCABCQgAQlIQAISaAQMWjQgHkpAAhKQgAQkIAEJSEACEpCABCRwDgIGLc4xDmohAQlIQAISkIAEJCABCUhAAhKQQCNg0KIB8VACEpCABCQgAQlIQAISkIAEJCCBcxAwaHGOcVALCUhAAhKQgAQkIAEJSEACEpCABBoBgxYNiIcSkIAEJCABCUhAAhKQgAQkIAEJnIOAQYtzjINaSEACEpCABCQgAQlIQAISkIAEJNAIGLRoQDyUgAQkIAEJSEACEpCABCQgAQlI4BwEDFqcYxzUQgISkIAEJCABCUhAAhKQgAQkIIFGwKBFA+KhBCQgAQlIQAISkIAEJCABCUhAAucgYNDiHOOgFhKQgAQkIAEJSEACEpCABCQgAQk0AgYtGhAPJSABCUhAAhKQgAQkIAEJSEACEjgHAYMW5xgHtZCABCQgAQlIQAISkIAEJCABCUigETBo0YB4KAEJSEACEpCABCQgAQlIQAISkMA5CBi0OMc4qIUEJCABCUhAAhKQgAQkIAEJSEACjYBBiwbEQwlIQAISkIAEJCABCUhAAhKQgATOQcCgxTnGQS0kIAEJSEACEpCABCQgAQlIQAISaAQMWjQgHkpAAhKQgAQkIAEJSEACEpCABCRwDgIGLc4xDmohAQlIQAISkIAEJCABCUhAAhKQQCNg0KIB8VACEpCABCQgAQlIQAISkIAEJCCBcxAwaHGOcVALCUhAAhKQgAQkIAEJSEACEpCABBoBgxYNiIcSkIAEJCABCUhAAhKQgAQkIAEJnIOAQYtzjINaSEACEpCABCSwSODvv/9++/XXX98+f/682eKPP/54++23396+fPmyWe/ZClftx66PyuDZxkx9JSABCUjgcgIGLS5nZ0sJSEACEpCABBoBAgQECv76669WcptD5P78889v//zzz1AgDv0PP/zwb/8c//TTT98FOJ7Vmd+zHyjYzxgkzRik3K0EJCABCUjgzAQMWpx5dNRNAhKQgAQk8EQEuPNhFCC4lQk43z/++OM0YEE/f/7551envd6FEae9BzqSX+veStd7yFmxH1sIWsChptjaGdQ67ktAAhKQgATOSMCgxRlHRZ0kIAEJSEACT0YAZ5iAAs7xvdIvv/zynTO+2hd3Z3B3RU/P5MxfYz92zxh0Jh5LQAISkIAEzkTAoMWZRkNdJCABCUhAAhsEeDSAwAAfHsP49OnT1zsbuLIeh/z333//t87oDgLqcTcEbdjyGAFtkgg+4BzXq/U1b3alHrm8Z6IndKCfGtBIHk70asJedNp6PwXlfKo9kc+dB+gxSvd25tEHvSqf5DGGK2nF/ozbjOsWgxUdrCMBCUhAAhJ4DwIGLd6Dun1KQAISkIAEDhLAaSXAQNAAB5ggQd5bwDbOOncO1Dq1G+rV4EEepaiPEuBME1RgS11SXnqJUzxLBARG77GgLfrgSGcfOdhTnfiZ3OQjGxu3EnpTZ6QHXCgbBV3u6cwTlEA+W/pHR8YOHcN6y6aUrdhPXcYs8yJts91ikDpuJSABCUhAAmcjsH32P5u26iMBCUhAAhJ4cQJxzKvDn6AFTmkSDnINRsRhrQ598pDZU8qQvXc3QO4CqP13ebnDA72pP0oEM2aBEdrPyiIrOm/ZM9Ix7UYBjci+xZYxgSfBilHCRuqM0or9tOvjXmXFzhGDWs99CUhAAhKQwJkIjM+MZ9JQXSQgAQlIQAIS+JcAgQgc0+r4d2d/5JwSLOiPR+AI526KfzsoO3GyS9ZwN/0NC/8vM3Vq0KTXpz90GqUVp506yBil9M+2p60y6kYusmefmd61L8apj0EvnwVmVuzfs2OvvOrivgQkIAEJSOAsBMZn9rNopx4SkIAEJPAyBLj6vHdFfwsGTjwytpzC2h7nHwdxy4mu9c+yz5X6/s4CHOl6V0Wc7Krz6LGBPLJR62WfuxVoM7srIPXYxhmueX0/jzdcOsbY1O3uffTgTS2Pjmx72irrdS89ziMyezbM5K/YPxr3Ku8Rdtb+3JeABCQgAQncgoBBi1tQVIYEJCABCVxMACccZwqHK9vqgK8IxiGOU17vQNhrSzuCHDi79340YE+X1XL0hVnSyBEdOe8ENmCclHcs1LyUwYI7MwhYrDjZ0WHGkAAIOqPXKAiSu0fQcSYjQY/oONp2G2udLR1TxvZeCftjZ7cRPujOZxbUWbF/NO7VntjZ+6913JeABCQggTGBoxdHuhTOAfxO83tuOkbAoMUxXtaWgAQkIIEbE8CBwnHmRI5DPnKit7qMM7eyCEA+jmENbNA/jjmfsztz6I3+NagTR7gyog7BAZzhBAkSnKEejjGcqQe3fMijjIAFbRPYoF+cbvJGKXp1p58xpYwtbNEFPdinj8qbsq0ASfpgO0px/GdOP7bR9yjdy5lHLnYxRnywtzKv4xjWlUnVdc9+6u7dGbPFoPblvgQkIAEJfEuA8+Ts4gi/35wjs8bgd559fnN7Qg5lOS/2co/HBAxajLmYKwEJSEACDyIQBxaHFUeWE/pqwpHbc9QiK07fyHE9Iify3mMbx7YGD2DWnX0cZRZNdVGUBRe8spCiHfWoT0IW5XUMUqfmjWyHa6+DLPKjbziTn7zIQtfokby+pU5072UJ3tDHKGHHrC35o3kxknMkLzpVuzI2LHBrQoc+jrWc/S37E7TpY1BlbDGo9dyXgAQkIIH/EuB3NQHn/+b+Z4/zJmVsc/7h9zjnTvJ7Yt1DOZ9ZoLq3efVjgxavPgO0XwISkMA7E8B544o0ThtbHL16BXpLPZw4Fgu3OOnHmUQH03ECjB/jcWmaLQirPMaGgMdovFkYzgIPBHsoG7VD/hmcedj1QEa1nf0t+xMgyaK5t91j0Ot7LAEJSEACb18DEZx3avC5cuG3e3TuyUWG2RolQfyZ3NqH+29vBi2cBRKQgAQkcAoCnLg5ya8mTvgsBvYcvfeSt9rvR6lHQICF3SVBH9owljOHuzLiile/OpX2o7sMuOLFgrLf2RGZZ3HmV4I26DyyH27YOPsu7DEIC7cSkIAEJPAtgWsujvC7zmd2XvRiybest44MWmzRsUwCEpCABA4RwHHFceIk3R8BoCwn/yPBiZkCubI8clRrmzi0WTzMrrbTBscPvU2XEbjUOeYuDcaHsWERtzVGaMaYcmfF//7v/35tx7ghoyfmCPNxFgy5VN/ez7XHmaNs0ZntVor96J/vGnaOuO0x2OrHMglIQAKvTOCaiyP8Hu+tO66R/2rjYtDi1UZceyUgAQnckQAOJw5VnKk8y8nJm6vjeyfwI6olAEJfeym3aeLcbiX0Rcc9pzEyqBebjmxHDnZkPvuWRRgO9F4wqdrJGBIsYnxW2df2l+yfyZnP9wMG6GWSgAQkIIH3J8DvMef2I+ezaE0b2nI+3EpeLNmi898ygxb/ZeGeBCQgAQnckABBity1QDCg39J/bVec6FkQrKQEFxJEmbXJFf9LFigzmeZLQAISkIAEJPB8BI5cHKnWEYhm/cOH/a109GLJlqyPXLa22vvIBLRNAhKQgATuQiDPanKlggDD3on7qBK5s2GlXa547F3FTtDiI98JscLLOhKQgAQkIIFXJ3Dk4khlRbCDgMXKnaBZd3ixpBL8ft+gxfdMzJGABCQggRsQSKBg9cR9tMsjQQtuz6T+3qMHWTycMWgRe93+58Vmcrgth6PfP+tLQAIS+OgEcp45YifrjSPrnjOvO47Yfe+6Bi3uTVj5EpCABF6UQB7J4I6LrZR6ewGFLuPIYiLv0+gy+vHRxUN0jy6r2zMGRToLjyUgAQlIQAKvTCDn9FUGRwMWyD267ljV5aPVM2jx0UZUeyQgAQmcgEB9sSC3SW6lnLCPPj6yetsmcll4ELjYS9HF2zT3SFkuAQlIQAIS+NgEjgQtLn1pZ9YdbE1zAgYt5mwskYAEJCCBCwnwYinusMhLrC4Us9kssveeGc3dEHtv8KYzX4i1idxCCUhAAhKQwMsQWL04knXGJRc8ErS4pO3LDMTb25tBi1cabW2VgAQk8AACBAfyn0JyMp49+pGrGJdcYVi9qhEdVhYELFB4FtUkAQlIQAISkMBrE1i5OMLdnKwbZv+djDXR1sUVL5aszTGDFmucrCUBCQSEVoAAACAASURBVEhAAjsE8t6I+gIqTtQEJjhpc2JnAfDly5d/JX369OlrOdujCTmRvdV2ZdFB+1V5W31ZJgEJSEACEpDAxyCwcnEk/ymNNRDrjfrJumh24QZKXixZmysGLdY4WUsCEpCABDYI5L0RnHz7FYVcRaCsBycStKD9JSkBia32uZtjT34WHluLiz0ZlktAAhKQgAQk8DEIrFzMyBpjaztbV6zI/xgkr7fCoMX1DJUgAQlIQAIXEiBQQDDj0rR3ws+dHgQ3ttKenK22z1TGIzJ7LK6xh4UZQaoeuIpM+s5489gOd+DAfiut1tuSsVdG0It+mI/3Snts6l1Dqzav1ruXTcqVgAQk8NEJrFwcuZSBF0vWyRm0WGdlTQlIQAISuDEBbp3Ecb0m4Yjj8I3eWZFbO9nOEg4revDZumNj1v5Z8uF8LestW+G/x7A/94sjP7o7p/ezWq+3WznO+I/mz0r7lTp7bBJcq/N01ebVeit6WkcCEpCABL4lcK+LGveS+632H+fIoMXHGUstkYAEJPB0BAg2VEftUgNwCnGIuWpRAw840fQxu5ofh48rKbXdpXqctR1X5O99hwX8L2GYMdhru1rv6BjABT73Suj9rGzuxUS5EpCABJ6JAGuM2cWRS+zgfPcKF0suYTNrY9BiRsZ8CUhAAhK4KwGcORYBvNeCBQGfaxKBifq4CXcVbC0yCJbgsF7b7zU6P6ItiyOc5tEjG7k1tb71PHn9/SNbusJxK/hEUICx4DMKTrB4WwkcrNbb0rWWJaBQ87IfDvdmAzu4YNsordq8Wm/Uh3kSkIAEJLBNgLXC6OLIdqvvSxOA57d/dD78voU5EDBo4TyQgAQkIIF3IZArDVkE3EoJ5MUJPOJ436r/s8lhoZX3SFTdYEOggS28CGoQOKA+DvsoyFHbZz+3uM7uZkk9nP+RHpSjx6ws7Y/Uq2229mePzDyaDXN29ujOe7HZ4maZBCQggVck0C+OHGXA7/krXCw5ymWlvkGLFUrWkYAEJCABCTwpAZxhFklbiaAF9QhWjFLulBiVEeSg/V5Ch3rXQq2fu272rjqt1quyt/YJlOzd4fEINvTBYnaUVm1erTfqwzwJSEACEpDAmQnsrzLOrL26SUACEpCABCSwSYBgwZ5jTp2tOx0o5zNKyJ6V1fo45jM94nCz3Uq9Xo6RPfts6UabvT5pf082sWGmx155eK3WS323EpCABCQggWchYNDiWUZKPSUgAQlIQAIXEMDpngULEJfHdGbvVNjrciVosedQ75VHh9V6qb+33QtaPIIN/NBjllZtXq0368d8CUhAAhKQwFkJzM+SZ9VYvSQgAQlIQAISWCZA0GL22AdCeCyERxNwnPvjGflXnJTN3g+C070X8DirY75l16PYMD58Zmk1GLFab9aP+RKQgAQkIIGzEjBocdaRUS8JSEACEpDADQiM3mmBg0sgg2AFH4IVOPC8n4JPfb9CXtTZAxpRbeWdFrzLYiuwEYd71kf6Wq2X+nvb0TstHs1m70W0qzav1ttjYrkEJCABCUjgbAQMWpxtRNRHAhKQgAQkcEMCBBX6OxlyZ0W9A4N9AhcEOWrau5Ni5b+HbP13DPqij65j1SH7q/VSf2+Lrd3eR7LJnSyM0Syt2rxab9aP+RKQgAQkIIGzEjBocdaRUS8JSEACEpDADQhw9wJBA67EX5J4dKE79l0OdXCaR2nFMecujFn7KnO1Xm2ztQ8T2Ozd4TGTcS2bBEi2/l3sqs2r9Wa2mC8BCUhAAhI4KwGDFmcdGfWSgAQkIAEJ3IgAAYGt9yZsdZPHRrbqzJx/ggE8GrJ1FwWPn1C+FzhYrbel56hsK+Ayql/zrmFDoAK7twJCqzav1qu6uy8BCUhAAhJ4FgIGLZ5lpNRTAhKQgAQkcAWB0aMQe+LyngS23BXAdpZ4xIGr/Qk+EAzAqSdowd0Wo0Q+jvusPG1W66X+kS36ovfWIxojedewgUsemQmv3seqzav1unyPJSABCUhAAs9CwKDFs4yUekpAAhKQgASuJIBjfuSOizj0ONgELfYSjvxWkKK2Rx6BlK1HI6i/Wq/KPrqPndyNUt/xsSfjVdjscbBcAhKQgAQkcG8CBi3uTVj5EpCABCQgAQlIQAISkIAEJCABCVxEwKDFRdhsJAEJSEACEpCABCQgAQlIQAISkMC9CRi0uDdh5UtAAhKQgAQkIAEJSEACEpCABCRwEQGDFhdhs5EEJCABCUhAAhKQgAQkIAEJSEAC9yZg0OLehJUvAQlIQAISkIAEJCABCUhAAhKQwEUEDFpchM1GEpCABCQgAQlIQAISkIAEJCABCdybgEGLexNWvgQkIAEJSEACEpCABCQgAQlIQAIXETBocRE2G0lAAhKQgAQkIAEJSEACEpCABCRwbwIGLe5NWPkSkIAEJCABCUhAAhKQgAQkIAEJXETAoMVF2GwkAQlIQAISkIAEJCABCUhAAhKQwL0JGLS4N2HlS0ACEpCABCQgAQlIQAISkIAEJHARAYMWF2GzkQQkIAEJSEACEpCABCQgAQlIQAL3JmDQ4t6ElS8BCUhAAhKQgAQkIAEJSEACEpDARQQMWlyEzUYSkIAEJCABCUhAAhKQgAQkIAEJ3JuAQYt7E1a+BCQgAQlIQAISkIAEJCABCUhAAhcRMGhxETYbSUACEpCABCQgAQlIQAISkIAEJHBvAgYt7k1Y+RKQgAQk8BACf//999sPP3haewhsO5GABCQgAQlIQAIPIuDq7kGg7UYCEpCABO5LwKDFffkqXQISkIAEJCABCbwHAYMW70HdPiUgAQlI4OYEDFrcHKkCJSABCUhAAhKQwLsTMGjx7kOgAhKQgAQkcAsCBi1uQVEZEpCABCQgAQlI4FwEDFqcazzURgISkIAELiRg0OJCcDaTgAQkIAEJSEACJyZg0OLEg3NW1b58+fL2xx9/vP30009vOAmmcxH49OnT26+//vr2yy+/3FwxZPOiw99///3msq8R+NHmZL5fsP7xxx/ffvvtt7d//vnnK6LPnz9/HV/K+Pz8889vf/7559d67F+aGFPkMcZnT3/99dcbn55Wgharc7j3MeJDHuMxSujCOF6SVnW8RPYt2jz6+wZjvgN8F86U3lsvxgEm13zvz8TzvXUZfcffWyf6v/S3ZOv36Qx2qYMEJCCBIwQMWhyhdWFdHAoc/JEDcqHImzTDqY3js7KlPo4TC3EWSrR5dNCCRRonYhZp0RldcNJxMtCP8rOlOFPRuW+rDdfojrMDC+S/StDiFnOSudO/D5lTjAcO06MSfdE3ifnOMeOJjjhJzJU4yuTx28KH/Gucl7Mu2Dt3eGD3KOV7NipL3kpAYNTHiE9+b7b0YSypdySt6HhE3i3r3uL7dkQf5nrY8z04S0IvzoX5LX8PvQxa3JZ65hnfv7Mkflv4PeqpB7ZHAdK936cu02MJSEACZyZwnhXAmSldoVscjixsssW5OLqQvUKNYVOcNPSremQRVh3enPhwjJLi4D0yaBHdYMeiInqz5TiBjKp79D3LNjbUxTf650o5+aMFyhH947hdw4HF8GgRdESPR9e9ZE7ieMThx17sTmJORWYdr5TfY0uf9DX7XjHHrxnXe+h8qUwW4zM7ZzL5bvBdmaXM/Vn5Sv5eH10G31/GZRa4IJ/y/F719s96nO/G0TG81F6+F9d+D6/9bR3pHg6jMvPOSeBZzm/8diSAXUkyj5l3nLPYJ2DNd4OgS097v0+9vscSkIAEzkrAoMUdRyYOSBZbfTs6wdxRne9Ej06GcapHjhEL76Qs1B61YOXEDL+tRScnZ/Qa6R6933sbpwpbemKBkjlyLVfkXMOBufFsQYvM3VV2BCxY7NU7F/qYcJy5Nyq7dd6eDdeO6631vVQe31WCRatjRT+5Y22rz3y/tupsla30MWpPv8yjGvSq9fguvvfvfdXnFvt7c/UWfVQZMBz9btY6W/uM7TW/iTPZ1+o1k2v+/Qg8w/mN8xPzvf+m8FvTA6SpO/t+7P0+3Y+0kiUgAQncjsD3ntPtZL+8JJx8Fkq5wsY+J5V87rGAOgJ95JRmITrSrdbPQo2T4b1Tgj/1To9Zn3GGZuXvnQ+vjP9Il5RV1qN6e3nIGY3hXjvKM0+v1WGlr1vWydxdmZPMk1yd6gvAkU6XshzJ2srbs+Gacd3q95FlsE8gaGWs0I2FO+PF3NxK+X5t1ZmVrfYxa8/v/Wye5Dds1d5ZH2fK35urt9Y155xL5MKd+TMbn0tkps01ekWG28cReJbzG78nowtLPYgRcnvzcOv3KTLcSkACEjgzAYMWdxwdFqo9ZbF+VucjC9G9xV1OkCwGifLnmMACxz3FUYmjyAl0xKe34xiZ8FpxLqlfr2jSL+3oD9vQDXnoUR0IdKEO/VDGONGWRD3y6wdZSWFG+R63Kivts6W/9FHlp5wt+rOQST32R7xnuiA3PKnT22ccI7/K6SyrXuyzmIJbxph+uh1dBsdpQ7vRGNexQZ/Z3Mk41HHtOuY4ddFxJaFXl8txHYuZXsjfG7foU7lnn/bZ79vwhRG6zOYf9TLumd918ct+6nQ76b+OQdozdknYl3Hs9dEpdeknelRbYkfk9S3faepXnXsdjtGderPU51+tt9UH7XB20H3EBznYQN+j7yPllDFGe2lLR2T33ymOV1PGGF0yjp1pHWvqzeZ17B3x6DKwO/X6b0x0jzz65NNT2vX83lfXt8sdya8ywiVztvaHDfU7z5zPeNR6fT+69767brUd/de+2K8cqYve5CE/qc8fjvPdxLbRbyxt0WX03ew6p5/RtspAFsejhN6VCTbw/WJL6lwyd9hGH7bJTx+1f8qRV7+Ptc/ICbu938A+9n2eocOl7KN/tvBBv9lYpV7dwju21Pzsh2nlkTK3EpCABJ6BwPcrg2fQ+ol1zImDE9LeVcP3MDP6bZ380Csnf2xgQcQJnRMsdrEwqonFAHmxty68OTlvpchELguCo4k+s6jEKckCr/InnzpZvIcBeUnIoQ2fkc7IYNGwp2NddEV2tumXPqJLytjSLxyzUMsxeb0+MvoYximr7amH3jVFD7Y1VZa9jDFFD/JhwId5gfwsRJFVZaAPOrLlQ3vq10UVNpKX/jKXyOs2U4f82Fd17/vYTF10vCQxL9E3i0p0yTzDxpoyTtErx6Nx27MBnfu4Ig/GozL0wMY6vzOX6T9jRb/hHz1jA2NT20fHfD8YL+rQPx+YoCP12JJX5wByk9/7Sp99i2589hLy6G+W6vxDv5pmfTC2W3wiI3MVFqOU+bH3GzHTkXbomPmFXuE46q/n7c0D6seGsKEP+oQp+zVRh/w+hvluJJ/5Qb1aN+NEXk21bs1nf2Trqr7pDxk97c3v1K99MRbwyJh2O9KmbqNDrzuzGV0Zs3xH8x0LV/QZfe/r/KENctjyyVjSZ030Q1lkIwM9+SSv1h/tw6LqG93ot6bogf6kyrGOD/n5na46wGOUj9yqb8Zr5fyGzK3veOb03u/9Jewrm+yHXR+nlPdt9Ovf0VovPPp41DruS0ACEjgzgW9XDGfW9IPoloUeCwROlGdL0a8uHkY6Us4CIQvo1MnJti4yqMtipqacQPuCotZhPwsR+ro0xSb6CvPoxxbZ/WSfxWgWKfSNDdStedGJslF+yrNNf9Ue8iKbeTGSg96jsvDpfJHfx3CUFzur/eHFtqcsZnsZbHt/tM08qfUjn74zHtSNLbVu2lc9Uq9zityMbW1T9+kTFnxqX7XO1j6sRm0zRpRFh+R1XWNDH7c9G5A94hzHp5dFXh1fbGO8mE81f/TdzXyt9WifeVPtQjc+Na+yrkwzruFUy/r+zLZej+PoOypL3mgOr/SR7+hM58iY/aaFb+UTnfp2pOPItsyv3r4fr86DjEttn7na9Y7MyiM69rphF0cV+ZkvtS/2RzrM8kd1R/pGL+rXlPy9+R3OjGFNs/ld69T9IzZTt7JFDvrXvMy5blfGZuU3NjL6b1HGDFl7iTp93lc24ZvzfrUB2ZRjb7eD4xGH2FfljNrndyr901fajuzKd7TKjW69fuZE1y/yV9jPuPLbjNy9hG4Zp74W620zzn2cej2PJSABCZyVwP6v4lk1f1K9chKti7czmZITbl88dB1XFxM54ddFQGRxUubDyXSW0s/eCTyy6jY2xKa+6KBPTvipV3VIm7pIjS39pE8+i4yVBIeqY91n0YGsUcIJoG5Pkdf7p263i7lX7UFW+Nbxie0jXumvlkW3mhc9s0it+s3kj2TjgNS2yJ21T361JXrUbfqB0UjnWne0n0XiqJ84TOEcNl1OdJjZNpKNjNG4RvaoDPl9HqR+3474Hfl+0D+fnjLHan7yZnbWunHgV+wI19q+76dOHfuVPkZ8uuwZA+qlfe23t8/xSMdRHvUZo720Og+u/b7F6dvTh/IZq8yNLmOUv6pv2PU5tDq/M3bI6WmkV6+T4yM2UxeeOMdJ/J50HajX7Yq+fa6FQ82f1c13Ir9l0WG05ZxYZaZO2CCLxDmonz9Td2RH2nebo3PNv8X5bST3yO89tkRG5zFiH9vrNsEFeGylyKNePnu/Bam3JdcyCUhAAmclsP2reFatn1SvnGRYaJ015YTbF0Fd39XFRGzOyXK0pc4sZRFMu70U3ambRRJtkt8XEZTFjpFe5HUOWcCweExC7uqYVh60r1drqs6RnW1smOlJfk0j3Ws5wYTKto5B+hrxiv61LExmgbjonODUTP5IdtWZ9vSFA4bMqgP1IrfaUttnvy4Ku4zU2dqy6Kb/6kykfmxIMCI6hcFom7ZsU39mA+37nEz7XhY7V+fmqO8j34/YFn2yjYwcs03ezM5aN3rtLchpE/61fd9PHeQmrfSROls6h8GoTtrPxi+6sB3pSH6C3siov0G1bd8/Og/S/pLvG/MeHVdSWPW6mRur+am3pW94dvbpK7r0beqH++g7HxnRY2sb+b3OSAbfW+rze8O5YdQ3cqgTPSM3c41tTeFQ85GNjJpHm1HdKqvux67ZFtkJ+nddI2dkR7igS03Io37PT51Lz28juUd+7+k/MtjWtMoz9WbBnSqT/cz7sO/91vqpM+NW67ovAQlI4GwEvvV0zqbdB9KHBQcnoZWF93uanRPubGER3VYXE5F36UkyCypOtpyctxKMc1Ku9aLD6GRO/T1bq6wsvOqCgv3ZHRK1LftZkNBvUu5GIG/GKbzTZm87swtHB32RR7+RW/vd4hX9K8vImAVd0KXaNpM/ko2d5NMHeqN/FvNVB+pFbrVlxik6dRmz+jU/bUfzMTZQhxQ2tf3W/p4NyEXmKPWy6LJypRR5o767zFG/yaMun55GDJK3MlbRa2WsYnPXoR6nTpW30kfqbOkcBqM6aT8bvz0dKec3DjkJ3PGdGM3DkazVeYDu6HjJ9w37E7CrOoz2w6qXZW6s5q/oSx366+xHeb1fjme6UjbT94icmYz8XtM/XEeBqpENmWtsawqHmp9zWg825dxLm600kjmqn3p9DFJ3ZEe4dB1iX88PL9pdcn4byc3Yj75nsYk6SZFRGVOWuj0/7bJNvRmn1OvbjNdWu9jSuXVZHktAAhI4I4H//tKeUbsPpBPBirMHLMCdE+7WiY96lHMC7Ce/tE9+HPLRYmtleLOgoq+VK8Y5KVfZ0Wm0WGChxuL8SIrt2MRnj1WVnQUJetYUR5yF6eiKWspXgyPI73ox/5CfsaH/2FLztnhF/8oyd2zUvGpbxiR2zeSPZMOX9jUgMmuf/GpL1aPuR+e+UK91ZvswRKdRP7Ehco+O254No3GNnr2MRTZ5q/N71PeR7wd98ekpc6zmJ2/EsNZjP3qtON3h32XU49RBbtJKH6mzpXMYjOqkff9eRoe6HelYy/kuZW4xH0cOVeofmQfXft/y3Vj5nQqr6Jlt5kaOsx3lr+obnp396vyOriPOI72ic99GTs/fk8HvX9hic03I7HZlrrGtKRx6fueInfS38p1jLqLD3vomfXddo9/IjnChbU2xr+bf4vw2khvuta/oEpvye09+ZHTGqdvzIyvb1KPfo4k2M77Iyvwb2XK0L+tLQAISeDSB71eYj9bgBfrjJDU6oePQ753AHo0HfUaLh67H6mIiC+Z6Uq+yOHnunUCzOEev0aKxystJuebFphHrOK+MxSiN2mRRgTMIh76IHMlJXtqiZ08wIn/EKldRYDFKXU/k1MVLgke9/Wgct3hF/9pfFrwjvdG12zST32VnMdzlztonHzl7KX2h20p95l3Gme8y7TpL+ozclB0dtz0b+rhWO0dlWwvuahNyRn0f+X7QP5+eMsdqfvJW2Idhnc9VVt0P/5rX91MHe5NW+hjxSftsZwwoT/vab9r17UhH8vrvX757mW9dTo5X5sEtvm+ZLyMbkV/zZ6wyN6J7tj3/iL7h2edQ9N37/c9vM/Okp65XL6/HR2zufTH2jGP/PURmt2s218KhjgP6wRK5sZN+qEP+SqI+n1GwChnYkvFC35HckR1hi941xb7k3+r81uXS55Hfe+pHBtuaZuxrHfYZ59k86XX7MbxGa83Uu1Ru2ruVgAQk8J4Evl9hvqc2H7DvLCpzsujbnHQ5UeEEU763AL0nppxw+yKo97m6mKBd6rJArIsVbN7rh/a0yWKK7WhhFP3CN8dsY1NfRFCWhQQLrr5wZRziqFZ57Mcm2h1J6Q89e8Iu5FHWFx4wSFm3A71rfeoio7INgz63whW9MjZx4NIP+SnL4jBl2EBZdMt8jm2xt7KNLlUG9VM3+TlGx5qwAftSL7olv+tQ29b96IHuzMVZoqxyi160S99pi0zyM0crm+ibun3cyI8TNbIBPfq4Rhb9jMqy4O42oh/zo+o/4ldtrWNIv9Sv3w/659NTvis1v9sZXrVO9rfsTp1sw6HalbJsR3N4pY/Ml9HYIBsbsH92Z0vGojKLTn070pF+4dYTfdb52cs5Tt9b8yBjfc33rZ7vqp2MBzpUduiC7nWsaJP8bkd+q5J/RN/R+DJekUGfW/M7v4nUq/qiS/TamsPRObZVGTObmUe1HjL4LtXxoRyG9beeepmr/Tcn9vZ8ZHb7o/PKNv0hB9ZJ6IduYZPfAuZCTaPxoTy/E1U36matlPmU/vv3IGNDvbDMWIYB+Snb+w1MveiODMY09pEfXSI/dWfsU163mSc1b28fHZgLlX9tk/LZ71Ot674EJCCBMxL4foV5Ri2fVKecpDiRzD4xLSe61MvJOOWP2HJCzmKAbT0R1/5rvXpiJn+0KOEkmpMw9lEn9WYn2Nof+8jOggJZ9Fv1Q07K6aOmLHxYwCCnpyzoaYfd6MZ25CCkbca2L5JSPtvWca4LsdSvi370qHVqGQwoxyZ0rXZlUUadMIIP9pGHHORiN23Jx9YsJGMbstlPPjqGJX3XPtMG+eyT6BsZtX2V0fmGTR0n5KEfZciFN+XksUUGevCJLUfGJH3SD/t1PrJP3khe2qFDGKNf+H4F8H9/VscNObGhM0NU+qSPqidlozEnHy7hBTPGjQ/76JW0xQ9dqM8H/WjPto5f5hd1qm7IzRjW/mILspmLHG+lcNmqkzLsrX0lP9vZHN7rIzpnfkdetuRj/2jsqBPumS9pN9qOdIx85iNcSdGpMh/JW50HGSvk0t8l37c6XxgL7EZu55J6cKc/6jFu4QSD/P7V+ZU87FzVl7qMDfVpj10Zh+ixNb9pn+8R+qInfNAxOlR9R2NAXvpasRl9YBE9M/5815Nm3/vMH7Y1Zb5gS+ZQ5FKX8nzgRFnqVTl9v8+vOu5VX8YxvMIAJvCMvVV28mnDmFGXT7UD+Zkf1KMNulOPPpCLbRyTYi86sp98bEh9+qqp9lfHI/3VukfY13Z1H53QG/16Qn54VF2wB9tnKXbH3lk98yUgAQmclcC33t1ZtXxCverJmZPP6MNJJon6sxNm6txzm4Vi15P8mnLi6/VG+bUtJ9eczGlLGTYfTbRhQdH15SROHguYnMiR3fXkeLQQoF34I4tFyl6iXu1rq/6IT3Tr+mTBkvLKkbrMm5RRty4qOxfqxRZsRGfyYEg7FnccMzZVDuXkIy/jxHH/VJupV8d4tIjq7TmesSGfT8YFXeDNBzuQT5+z9lW3rX3kYW/linxsQfYswa622aqPnFq3j1sWxZ1Pxq7n5xjdRmNe9WZc6S9jT/1azn7kZUudmra+HyPdaT/LRy46Re/Oovab/cjKXEz+aMu4MGfqfE692Fe3KdvrI+WVXdqyTflMR/pkjuylqlv2aUO/MMv3gTKOZ/r0fvbmQfqIfGRf+n2DReSwHTlTdQ7UOvTLnAjHcA2L2L2qbzigA98B+mKO1LQ1v1MPffmdqN8jdERfxrXLTLu6XbWZNvxe1N+Myohy+q1M2B99l/fyGWNkd1k5xt7R+FW72O98RpypF2bIr3U4xqaeci5CD/ZJzAnqVubXnN9G3Lou9FXHY/R7H2Z1O5JN+db3Nm2wqSfGoo4X+3xfGMetlO9RvldbdS2TgAQkcEYCBi3OOCrqdHoCLCr6oub0SqugBJ6UAA4RTstoET8yiQV66q8u0vf6yKJ/5mzg0Mx+E3B49hyVkR3mSeARBAgGMEeZ23zHmOv5MKdxjO+d+H7Mvj/37vuM8mGxEuRc1X3r92lVhvUkIAEJvCcBgxbvSd++n5YAVzZWrj49rYEqLoGTEcCZOuI8ceWR7yltcMBW0lYfCVqMrmiSR5BkVEa/6JGrxCt6WEcCjyLAvNwLBjK3750MWnxLmGArTGa/Kd/W3j7a+33abm2pBCQggXMQMGhxjnFQiycgkCu2bI84T09gmipK4CkI4PyvBiAuNWjWB87d7HvPVdFZEJOr11zl5E4OkwTORCCO8VbQgnl97+8cTAxafD8zYH+Lu0+2fp++79UcCUhAAuckYNDinOOiVicjwG2aLKpwWrjqVJ+lPZmqqiOBD02AoMIsGqLP3gAAIABJREFUQHArw+mDhX4eMSHgwHd/9L3f0genEDkGLG41Msq5JYFcgefcRmCN4ATfLeY5+5z3mN/3TgT20IHPLe4suLe+j5TPeFwzBlu/T4+0w74kIAEJXEvAoMW1BG3/EgRYwCVoMXum/SVAaKQETkAAp2oUQLilajgLBC3i0I3643chd2D1vvN+gJ7vsQTORICAGncREbRI4IB5P3rR5D30JqiXfrPle2X6LwF+Y7buhvlvzW/3tn6fvq3pkQQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpCABCQgAQlIQAISkMBLEjBo8ZLDrtESkIAEJCABCUhAAhKQgAQkIIHzEzBocf4xUkMJSEACEpDASxH4448/3n755ZeXslljJSABCUhAAhIYEzBoMeZirgQkIAEJSEAC70TAoMU7gbdbCUhAAhKQwAkJGLQ44aCokgQkIAEJSOCVCRi0eOXR13YJSEACEpDAtwQMWnzLwyMJSEACEpCABK4g8MMPP7wRdLgmPUPQAh2x1SQBCUhAAhKQwH0JeLa9L1+lS0ACEpCABF6GwN9///3VkWd7JCUAQBBg9rk2EHJEn5W6vHPD926skLKOBCQgAQlI4DoCBi2u42drCUhAAhKQgARuTOAZ7rS4scmKk4AEJCABCUhgQsCgxQSM2RKQgAQk8JwE/vrrr69XwP/8889dAz5//vz2008/vVH3n3/+2a3/ChXg9+OPP37lApPff//96zF5X758eUs5d0R8+vTpXyS//vrr17skfv7553/zaEs9ypA1qvNv5bLzqKDFnn4pj+2oyJzJ3SCxH9u464J85hJ14MAxMkwSkIAEJPD8BDgH8pvOuuGSxPmBcwXnUdMxAgYtjvGytgQkIAEJnJRAHEcWE1sLAspxJll8kHiUgUUE+Tibr5wSpMhjHnBhn3yY4YgnGMQ+5TWRFyeddoxDHv347bffvjLH0UcW5bP0iKDFnn7ddnRKig1wISVQkXkEg7DEVpMEJCABCTw3gQTscy6r1nBOID/ri36+rHWRQz3OFzmH1HL3xwQ8k465mCsBCUhAAk9EgBM/DjOfrUUAgQoWEywYemLBwRX1Vw9cwCVOOVsSTOGWgAR5MKyOPHnUSZuvDd/evrapdypE9tY4PSJosadf7MicqUEW9GOu9ZQ5mHx4jeql3K0EJCABCZyfAIEGzm9se8odhKwhcl7bu9uOejlfpE2X6/G3BAxafMvDIwlIQAISeEICOIfVMb7UhCwiLm3/UdrhlNfATgINuTtl5Mjj1LOoS52w4GoSi7kk9s/kyHf9mEvV9ixWoz/b3iZlfVGLnBroST23EpCABCTwHAQ4p7G+mP2Wcz6o54xYlfMm54VR2pM7avPKeWOKr0xE2yUgAQlI4KkIxFmeLSiOGJNFRr+D4IiMj1C3O+XdkedxiL4Q64GOcDi7I9/1I6BSgyxcRYNHTb0NZZmHCdqwpV7u2Kjt3ZeABCQggecgwO8/v+WX3BFBOz6zxLmV8non36zuq+fPKb46Ge2XgAQkIIGnIICDyUk/zuJI6TiUWUBsLT64YjK6ajKS+1Hz4JR3V2Bjd+TzeAQcEyxiYceHcUheuNdHbpDN3Qt83nuh1vXDnuiXscUm7MEGmKQN2xxTFyZcjUtKvfDYmnNp41YCEpCABM5DIMHnGshe1S4XQbbWE9fIX9Xjo9QzaPFRRlI7JCABCbwoARzFrUVBsKwsIKibIEh1tCNjdRuHNUGS1e0Z7vCI7rE/jnwNYoQlDn3q5TGKBC5g1R158giAwCOBjVWm96jX9YtdsYk+0RN9mRew4IMNzLvKBLu5KyOJxSh1+MDGJAEJSEACz0WA33h+/4/+htfzRD2fjKxn/cJ5wrRNwKDFNh9LJSABCUjgxATiYFdncabuat0sUs4QQJjZYr4EJCABCUhAAvclQDCaoMVe4CFaEKxmDUEgogb1Uz7a5kWerFFMcwIGLeZsLJGABCQggZMTyNX9lQBD6tar4yPzEty45HbQkTzzJCABCUhAAhJ4PgIEHwharKQEOKjPnRMEI1YCEaxfLrmbY0Wnj1RnbRQ+ksXaIgEJSEACH4ZATvYrQYs89rG3iEjQggXIMyQWO36ek8EzzC91lIAEJPCqBHJuPWI/j4ZwkYTABe33HoU8so45osdHq2vQ4qONqPZIQAIS+IAEEkjoAYcjJ/u8S2EPT/q6JmgRGVnwrG5Xgi97+lsuAQlIQAISkMD1BHLuvkQSj5SkPY+NzNKRdcxMxivkG7R4hVHWRglIQAJPTiAnda5g1JT8PWefdiweCFzspQQcrgla7PVhuQQkIAEJSEAC5yaQoMOlWuZiydaLPFfXMZfq8FHaGbT4KCOpHRKQgARekEDeU7EXtEggYuU9FUfqviByTZaABCQgAQm8BIEj77QYAcl7LvjPVLOUoMVWYGPW9pXyDVq80mhrqwQkIIEnJJArHaPARAIMe/895MiiwP8e8oSTRJUlIAEJSEACNyaQoMPWfw9hjTK6MzN3ePJui36XaFXT/x5Sacz3DVrM2VgiAQlIQAInIMAVChYFsysVLAi4GrKVVhYeaZ8Xdm4tUlLXrQQkIAEJSEACH5NALmJs3QVRL6wkOMH6YeXREKixfmEdY9omYNBim4+lEpCABCTwzgQStMhioKuTIMPWi66yqOhtR8csIPaCIKN25klAAhKQgAQk8HEIsK5g/bD1aClrFMoTpKA+QYiVf3m6Iv/j0LzOEoMW1/GztQQkIAEJ3JkA/y5sK4iQR0Rm/1Ysb/Ae3b7ZVU+AZPQoSq/r8X8IEEyC14x/Hs1hcQbflYWcbCUgAQlIQAJnIJA7NWcXTq7RkfMmQQ7WMaZtAgYttvlYKgEJSEAC70yAqxdbVzlQb+tui9zeyXYveZfFHqFvy1nEMT5bt84SpKi3vhK82GvzbS8eSUACEpCABN6HwL3uhriX3PehdP9eDVrcn7E9SEACEpDAFQS4CrEXcIjzjDPcr4aQhwwWCFuJwAfOte+y2KL0bRlXoC65K4UxWmHtFahveXskAQlIQAKPJ5D/VLYVoD+i1daa5YicV6pr0OKVRltbJSABCTwZgTz6wWMFLBa2FgwsAnCiuVsit1rmDoytdgQz0s6AxfoEgXG9g6K3zNgReGD8euK2WLhvJYMWW3Qsk4AEJCCBRxFgHcE5j3NXvzhyRAfOjaxTOP9dI+dInx+hrkGLjzCK2iABCUjggxLI1YgsFFbMZGHxP//zP1/vruAui5HDHDkEKVg8cCeHi4dQWdsSEOKzlfJozugul7xrZFQWmQYtQsKtBCQgAQm8NwHOVwQtWDdckjgnEqzYupByidxXaGPQ4hVGWRslIAEJSEACVxDIXRNsk1i07T0aQvnW3RizuzDSh0GLkHArAQlIQAISeF0CBi1ed+y1XAISkIAEJLBEIP8BpFZeCShwRWnrERBk1MAHx3ufqoP7EpCABCQgAQl8fAIGLT7+GGuhBCQgAQlIYEqAx2Ly7g8CBuyvPCpD3XrnxagD6tSgRK+zUr7XR5fpsQQkIAEJSEACH4uAQYuPNZ5aIwEJSEACEjhEgCBF/usKz+vyOEd9VwXHBBd4jrcm8rbeFzJ6pKS2Z/8MQYu8XI3HXQjWYCc284FHykf29oBP//eu2JiAUP4DTt6jsnUHSufksQQkIAEJSOCVCRi0eOXR13YJSEACEnh5AjjjcaiBEac9YHDMRw773jst8hLOrbs2RnLTL1vK73mnRYIUCbAQSGA/NhPMCRv2e6CBPD4EN2gDk1qHoA7ykJGgCOXk18BQtdl9CUhAAhKQgAS+JWDQ4lseHklAAhKQgARehkCcdbZJ/f0VqdODDzjdW443dx3gqM9S5OLwz9K9gxbplyBCDaAkaFHvLulBGgIR3I1RuRDAqG0iHxuRT8Ci1k+5WwlIQAISkIAE5gQMWszZWCIBCUhAAhL40AQSONgKWhDEGAUfaNOd9gqLsq2gBs59vSuhtn30frcxQYwEVBJ0qJwIUFT7Euig7SgRtMhdG6Ny8yQgAQlIQAISGBMwaDHmYq4EJCABCUjgJQjgTNeXZRJMqEEKAgvVOa9QKKttU0YecuP0Jz9b8glq8H6HM6RuY2dAsAF7aurcYvPoTgr49SBHleW+BCQgAQlIQAJzAt+egef1LJGABCQgAQlI4AMSwGEnSIGzTTChPwYR55y7DPqdArTBGedllaQ47uTN7jjobc6AFBurbT3AgF3koXse/yDowiMwJNrCETmp8//+3//7yoOABXz4JBgUGWewXR0kIAEJSEACZydg0OLsI6R+EpCABCQggTsSwMmOw40j3h1qnG6ccfKp2xN5OPW9Xa/HMYEPHP36mMWo3iPz0AX7ctcH9vQgRh4XgVPqEYSgHoEIysmHH8fsJ4CTgA5yKTvTHSaP5GxfEpCABCQggUsJGLS4lJztJCABCUhAAhKQgAQkIAEJSEACErgrAYMWd8WrcAlIQAISkIAEJCABCUhAAhKQgAQuJWDQ4lJytpOABCQgAQlIQAISkIAEJCABCUjgrgQMWtwVr8IlIAEJSEACEpCABCQgAQlIQAISuJSAQYtLydlOAhKQgAQkIAEJSEACEpCABCQggbsSMGhxV7wKl4AEJCABCUhAAhKQgAQkIAEJSOBSAgYtLiVnOwlIQAISkIAEJCABCUhAAhKQgATuSsCgxV3xKlwCEpCABCQgAQlIQAISkIAEJCCBSwkYtLiUnO0kIAEJSEACEpCABCQgAQlIQAISuCsBgxZ3xatwCUhAAhKQgAQkIAEJSEACEpCABC4lYNDiUnK2k4AEJCABCUhAAhKQgAQkIAEJSOCuBAxa3BWvwiUgAQlIQAISkIAEJCABCUhAAhK4lIBBi0vJ2U4CEpCABCQgAQlIQAISkIAEJCCBuxIwaHFXvAqXgAQkIAEJSEACEpCABCQgAQlI4FICBi0uJWc7CUhAAhKQgAQkIAEJSEACEpCABO5KwKDFXfEqXAISkIAEJCABCUhAAhKQgAQkIIFLCRi0uJSc7SQgAQlIQAISkIAEJCABCUhAAhK4KwGDFnfFq3AJSEACEpCABCQgAQlIQAISkIAELiVg0OJScraTgAQkIAEJSEACEpCABCQgAQlI4K4EDFrcFe9rC//nn3/e/vrrr7eff/757Y8//nhtGBda/+XLl6/s2JLY/vjjj1+ZRuSff/759unTpxwe2v7+++9vP/zww8XtVztDv19//fXtl19+WW1y83qfP39+++23377yu7nwgUDmPWOVsRtUuShrNAcuEmSjD0uAuc73je82H+bikd8I5hi/2T/99NPb33///XScrrX/6QxWYQlIQAISkMAHJ2DQ4oMP8HuahzPNYplF80cNWmAfwZl7JBbeOPlV/sxhhTUBiKPpEUGLBCyYB+8VtIAlczBO3FFOl9Q3aHEJNdtcS4C5TrCMLYnAMcEHPiuJ3xu+K8jg+/JsQYtr7V9hZB0JSEACEpCABB5LwKDFY3m/XG9xFD9i0ILFPIt6nIJbJxbeRwMi3EXA571TrtJWPcLqvYIW0YX+GbNnSWcYz2dhpZ7/IcDvxtHv2Wie5bvybEGLS+x37khAAhKQgAQkcG4Cz7N6PzdHtZsQ+MhBiyzqV69gThB9l82VTmQeDYbQjqujR24D/67zG2RwW3oPUhm0OA6WwNUzBViOW2iLexBgzhwJWszmWX7fni1ocdT+e4yBMiUgAQlIQAISuC0Bgxa35am0RuCjBi1Y6BNYuMct1DyygdxLUtrWR0oukXNpGx5TwWkwaHEpwf+0424VrhgbtLiO4yu2PuK0b80zgxavOHu0WQISkIAEJHBOAgYtzjku765VroyzAM4HpRKESB7bXInrbWp92uFIcxsybXDKZ3cSUJeAAPXYdgc470hgUY3MLK5xmJPSV4IKOIC3vAMBO+gvPLi74BYJvbF7dLs28mFMGTaPUsagshjVIy+PccA4Y5i6eQ4+Y0AwZM/GjANt8ome0SvHcMvYIHuUCAzVlwmyT95qos/aHm6zQMBWX9E9NrGtczJzgPzYhzz6GwWfmIfRgzZ9blIeNrXPjFGd/50F8wd98v1BDnqQX1OXUdvMxqO2z36CZLGD48qGeke+i+hV5xHjx1xmS6pl9JlUx6Dmp3xrfKnTvwu1Piw5HqXKLayR1VOtN/pN6/Xr8eqY1rlS9/t4RPbePAtr5h3253jG48g4Rwe2nT2/PbCknzpv9xhWm+t+tb+OK3X6bwr90T/fSdpRHz3QJ98/dIZdvsMZ96or7er3v9aHY60bFl035I/Okb1etyHyRtvV3/Wq78i+yI4u1IFndIYZCYZ1LMKQ7Sg/cuETfpGLTjVxjO3wJNX5MfsNq3Yhl/bRqcveGt9a130JSEACEngMgf+u+h7Tn708EQGchSws6iKLhU/y+wKdRQELluRn0cIigkUI5amDDBY9NbFQiJNFnywqqJdFCG2Tx2KFunyoQz6JvllExXGnjyxAaH9tinz0Y3/GYqWfsEzdMI/uyWdL3Wp7Lcs+OqEP9m4l6jE2WWzWhRv75CePLWOaxeGW3Iw325qQgV7IYCyxhTyOye/2Mk5VhxyTl7lV5fd96iMXPTJOmQPk1xTZsTfHtS/KOB7piixswhb6Yr5xTN1RX+SFT+YSed2uWfvZHKBvbKQ8sjKfGD/KSdhXZeQ7hE7Uo98+HpVX9qlPf+krczm2US/2Rd7WdxFmMEa/tM2Y1bmXuYSeNSF7xgy5s/GFCzrX8aVf8vLbQllPlFX7w5q6YU0b6lCXPD5hj717ifq03xvTKgcGlVctG+2PmFGvfjfRH34Z487jyDhXHbCvsg/zzEPGlHSE4cz+fK9n84B+GEP6QgbjA/fKgTrkUyfzHp2pTx6pf/9hhgzqRRZya6INTPM9gUsY0C5pxYbU7Vvspo/Yz5Y+0KmmPftSF7uiM/ryyfcFHklwii3pm7JqY81fmUuX/IZF3/RVfy+Sh16r9sc+txKQgAQk8BgC/z2zPKY/e3kyAllkZfEY9bOwy8It+SywWBwkZUHXF2lZ4NcFGfssbmpiYcMCiE/6YoGRvOjFlrokdGbxVBOLHNp0+bXO6j6yq95ZqLHYmaXo2xeIvT6cqFsXUbUODCjfksNCkjrhVdv3/VF/2Nblo0/P67I4pi19Vz7kZ8zgn3EiPwvHKptybKjziLrwRXYfW8pqSvs+58inPZ+k1F3pa2YbstApczGyMw45ZoudtX/yYlfXoesaOSNmlKEDfWJTTdG7Ms54UL/qne9JrVtl1X3q9HHmuOZRp49X+qjfxeT1eT+b7zM29Ff5HhnftO365reu6ham/TuGTTBNPvWqnfCr8zD1Kte6f2RM0w77sWU17bGMIx15o9+M1XGOjL4N+3xnYZR5eZThyP4j8yBjW3+rMvb53vRxyxyp3+FwrXl17MMguvV5Rztk5LySelUeMvL70dtHfrbY1ecF9tS8Vfuwn3mOzJ7ght41ZXzDMWVhXfOp223J70P9LkXXld+w1O3s6AddkU9KvZXxjQ1uJSABCUjgMQS+PbM8pk97eSICWTj1RUQWG33RwuKNxVXSrF4WB7U9C5J6HBlZ8GTxnLZ1sZW6LDZYhFCnJ/L5ZDHcy1eOs3CsNkYfFk81P/KwK4sl7OssU48tMmb6px7lI9tTHl4jBqmTbcan1h3lUX9L7y6vj2MYjfTu9mTORWa2kQGjrTTTnzZhk/ZH+srcqgtn5JA/sqv3RV0cjK5/9O3M4MJnlDqz6DbSgzkZWZn7YTmq32WP+iePtrCg7yT2Y0d0qnMr9bo+/G50rrVu1zPtUydb6lGWdGR807brm/Gp+Yxh1yl91i02hUfNT1/5Tatl2Q+/UT+jMU271fGr9Suz5EfHajdlnUf07PWom3HKvIvsvp31Rb2jDEf2H5kHsW80bvwGjsYjbRJ0qbbPbE1+AvgjfqnD9ogNtV32o2Pvp/6ur9qXQAlj31PGsuYnr/fddToyl5A1Gmv67fkJtFWdRvur9o/amicBCUhAAvcl8N/V3X37UfoTE2CB3h0tFiEsJqujwdWKugDC5CxK2NaUBUfNzwJ3tk3dtB0tHlM2k0E+dS5N6NBtRNboShv5LDQrI/LQYbTYS9lWeeqMbKeMlAVieP1f9nBDnc4E3Rhv8rH1CK/I631nXEZ600/NjwzyZ5+hMf+XmbEYBZDCJu2P9gUPdGJck5BRj5Pf+0p+tjhyyAvrziy2p37dUlaZxaHJVdlal/3oEif5yHh0WTnO1U/0p98+p9NH7BhtqRNHpdqTPth2W5NHfk+xM/lHxjdt0ammyEg+40bfM9a17cjmmtfHvLY9OqZpi/wZy9Sp2+hT89hf5bE6zl1+PZ71RZ3oN9t2hiP7M4YzGeQnpW6XS3n0nMmp3FMncrONjBzHoR79XqUO2+gVuaNtrd/3V37Xo9tINnmxj3Max6MUGbUsefkOpSw2Jf/IXErd6BSZbKuuHPMbxXlhL0XPPfv35FguAQlIQAK3JzA+69y+HyU+MYFcVYljxpZPz2fx1a+oZVHSF4BZcCS/H2/hSt3RYiX9UeceicVPd87oJw5GD1DglPKpiTphib61TRZLtX7f7wuyXp6FV9j28no844WNcdDT396iGrmR1/veGrPIj17RP8dHt1sMu+x+vNdX7KgL4Dp+tf1MNjIoyzzI96gz27KjMwv3eqV3pEv6iB3o0VOX3cvrcWyJrtWRj07U2UpbutBupE/663I7837c69fj1O36djui74x1ZKZemCd/dZt+Z/1E3y5/xGurzz2WezyiZ6+31Wcviy1dxiUMR/ZHfu93dBx7OlfqjmSPZKQu9XvquuS4nzv32vXyleO93/VV+6g3sg0dYk/VJ3l9fMM6+f24yuj7mRvI7qnbwTHn7r3U2+3Vt1wCEpCABB5H4Psz6uP6tqcnIcBCp57McdhwYGs++9WRi2lZhPQFYBYcyUcefXQHP3LqNm1Hi5Vc/U1QoLa7dh+ZW/qxKMKGLMDoDx1jY/pP3ijQQXs+WwtYyke2V/nU6f2mvG6pQ92qcy1nXOmLOhn3Wt73I6/3vTVm3Z448fR9SUIenxHD2BK5l/QVGdjEGHZbIzv1csw2Y547HsibMYsdtX32O7PcYk6foxRd+rPbo/pd9khez4MFQRja5juy+l3cmhv0M9KHPD49xc7kHxnftEWfmjI+yWde0fcsWJW2R37T0qZuj45p2o54pWy03WMZu9O281gd57QfbWfsL2E4sv/IPIh9bHviN3Bv3NNmj2vqEZSiLr8NW+mIDVtyKJv9rq/aN7MN2RnLqkPybjmXkDUaa/rt+Tk3751TVu2vtrkvAQlIQAKPIfD9qu8x/drLkxHgZJ6FVV3MZTGCozJadM0WgFlwVFksLPiMFhYsXuPopS199xSHAn1HibZ8LkksVrfaxtZ6ZRQdq430W/Moq4vgLK62+ukLsm5LxmRLRtpE51o3nFOHLeNLv3F6a1ndj7xuM/Jnevf8OGss0kepy+51MldHdoRN2lzSVwIPjDN9jeYr8ntfccD63JwxgwufUerM0CH16aen6JKyI+PRZeW480V22LO/+l2k7pbu3Vb6T/3okm3szPGR8U3b+l1ATsan5m99T7E7v4Wrv2nRt26PjmnajnilbLTdY1ntpn3nsTrOo76TN2NP+VGGI/uPzIPYx7anBBhmv4O1zR7XyI5uMBil/A6mXo573dp3L+OY9j313/VV+/I9H3HIWNa+knfLuYSs0VjTb8+PXSNG/P4kP/VGdiE39apt7ktAAhKQwGMIjFccPGB4AAAgAElEQVTEj+nbXp6IQBw1FpDVSav5nPx74iTPAqKf7LPgqPmpy4KIhXAScln0pN+0nS3yskBiAVJ1QmZtkytXVYf02bcsYmrbXs5xdTKiK4tCPjURpIhTQ981aJFFU1/c1fZ9QVbL2EcedaJDL6/HYVD7Q6e+wA3z2WIuMrOwDlP480n7EcNuD/XjFEZO5NN/55mybKMDMur4U57Fdthc2lcYb+mSvqJXGJBfU8YgtkbnMEjdqnNnRp3M+8hJu9hYnZ3osjIekdO3tK3fU8rpG91iQ3Ta+y6mXucZh7jrGTbph775TiU/usZ2dOpc+lyKDrCpKTbVfPREJv1VBowRcqJX2u79ptX+6n506rrHrjqmtJvxqjL7fmeWeZa+q920jU01P3X3xrn3neO0rzJTlv5WGM7sD6+VeZD+OnP0Qb+Me/8tZCzyu05d6vHpKbYmH96p2+c/v2XR44gNkV23yNn7XV+1L4wYk8x1+mI/v42175zXKjPGKnXruIfP3lyKrtTvCZ41P+sU8usYoS/M039k8p2ouiK/ji9jRh30z/el6+CxBCQgAQnclsD3Z9TbylfaByGQBVNfVM3yY3YWK30BwgKABURd9CCL4yzg2GfhweKgLraqU1odhvRJXhbiWbxkIVTrpx+2ewsP2mdhk35G29gbTiyQ6uKJNrU/Fn8sfJKyuMpCNfl1G5tqXt2nvMqsZXUf3lk0VueHvitz6mEXddnfSln0MXbsh0PGDBmVNQtD9K1MkB8O5KMLDJG5ogPtM4+ojyx0wYbMC/azKL2kryzaZ3OCeRa70g96pX/a0xbu0ZUteoVx5izs0JEPKSyRVVnSLvKpQyKPMUB25FYZnSc6Re8q+6uw9gf9aB8G1OcYG5JWv4u1HjLgg94Zm/4dogw9U5dy6obZ0fGFDbKQGXaxITIzl8mnfsaNNtRJvYzTqB5tqMc49X7SX93Sz+qY0i7zkjb1t67K7PvRG33QnU/lgcwk8lO/8qjj13ns6VH7qjJrn5X1FsMt+7EL3fjABzuQxbijQxJzhzqU1fyUZ+5Rh7bIYdvnffqq9iMv41nnSdWtyuw61HpbNkTXuoUNbTLv0AWdu/0r9tEW3cKS37F8Mj9q39Gb/qlHH3wyXsiKXqtzifoZgzpOs9+wahf9oSf6kF9TrVfHoo5v9Kb/3r7Kcl8CEpCABG5HwKDF7Vh+eEmcnOsCLAbP8rNoq9u6oKj5kcXig0UNiwnKWTRUpy8LotqWBURPOFBZfFKXdl13yumHTxyvLofjmc5Vh76PzCyksCHyYVUXOehOeRJtaFsXSCnLNvbkuG6jaxaAtazup17VG0Yk2mZBl3J03nNiI5/xi44wn43ZKD+ckMV+FsbIQ4cwTV+zbZ9H9BVdYFvn1CV9Ib+OW9WjLmjDL3MUm2gXPjDlw5hja52j7FOXsrTfY4ZecKINfdCetpXbSAZ1ZvnVtrpPP7VN9Kx9UX/lu0i9jE/0zhiFVe2bPtI3NsYBJA+9Kkfabc0lyuijf2b50aOzpm/a9ES9rd+0Xr8f935GY0qbrn+Ou7x+3OfZzO5RPjYnrY5z6rMdyUTvnlYYxt6+rbLob+s3pbflmDY98RvJOFCeeZ86fJe6HDjN8tOO+R7dIhO7e9qzodfP8ZHf9S37Ii/zsjPAVvJ6ynkB29gn5Xcn3/W02ZtL6aNyjqyaxz75Sexn3OrvRsqz3bOfMcAOZNTvQNq7lYAEJCCB2xP4/sxy+z6UKIFTE8DJGS1Mb6l0FlJ1gcMCKvksfpLIZ0E0S7Spcmq9OEejxW6t574EnoXA1nx/FhvUUwKvQiABhVewl+DK7Fz8CvZrowQkIIFHEjBo8Uja9nVKAmdbdBBw2LoKhBNHoGWUaNevWo3qmSeBZyFg0OJZRko9JfDfd+u8Agvu8MpdXq9grzZKQAISeE8CBi3ek759vzsBnP8zOvm5ZbvfMcExTtxoocQdGrNgxruDVgEJXEjAoMWF4GwmgXcg8Cp3WuRxtndAbJcSkIAEXpKAQYuXHHaNhgBOfn/2/UxksijiUREWgtyKyrO2PPfcE0EMAxadisfPToDHtgha8GH+myQggfMSIKjO+WoWWD+v5sc043eJc7FJAhKQgAQeR8CgxeNY25MEDhNgEZgrVywGCUz0uy8IWJzxbpHDxtpAAoVA5n2CFmwJNJokIIHzEeC7Wb+r7PMdNklAAhKQgARuQcCgxS0oKkMCEpCABCQgAQlIQAISkIAEJCCBmxMwaHFzpAqUgAQkIAEJSEACEpCABCQgAQlI4BYEDFrcgqIyJCABCUhAAhKQgAQkIAEJSEACErg5AYMWN0eqQAlIQAISkIAEJCABCUhAAhKQgARuQcCgxS0oKkMCEpCABCQgAQlIQAISkIAEJCCBmxMwaHFzpAqUgAQkIIFnJMC/Vf3999/ffvrppyX1+beH/IcE/oOP6RiBV2fNf4Ea/fvqGcVX5zXjYr4EJCABCbwGAYMWrzHOWikBCUhAAhsECDzk3wrjIK4m2hHkIHjR/x3xqox71su/jq19jPJq+SX7OOCr9n9U1ivcPn/+/O+/rmYcOGa7lR7BazQnRnlbelomAQlIQAISuBcBgxb3IqtcCUhAAhJ4CgI4hT/88MPuHRO//fbbsF6umh9x3B8FZuR4jvIu1QdZBHnYwuHXX3/96ojP5K2yTj2Y13Rm1lXPrf2///776x09BMngRuBilsKB7VZiDJjD/S6hVV6jOTHK29LBMglIQAISkMC9CBi0uBdZ5UpAAhKQwOkJ4OzhPPJYyF7CIcQxHN2JcUTOXj+3LB85nqO8S/vE7jxSQ9Dm06dPU1FHGM0CRAg/ImeqzDsWwIgxYD7BDntG6YidCW70IA9yV+SM5sQob6SneRKQgAQkIIF7EzBocW/CypeABCQggdMSiGPGFelrEw4oQQ2upJ8lxb6qzyivlh/Zz+MNccC3bE+/H5X1Crf+eAgBBbiM0iN5pa+qxyivlrsvAQlIQAISeBQBgxaPIm0/EpCABCRwKgI4jAQZRlenL1H01vIu0aG3GTmeo7zebvUYWXG8CUbAcvS4w63Z3Freqr23qpfHNmbybm3fnrzRnBjlzfQ1XwISkIAEJHBPAgYt7klX2RKQgAQkcFoC/PcPghZ77wugTj5bjz9gKHcc8LjJWdLI8RzloW+CDtiKDTX4QFnajRjsvc9jlXX6QIe9R3bOwDp3mqBvZ0AZOlIGvyNpldcff/zx79yk/620xSvca/tRXi13XwISkIAEJPAoAgYtHkXafiQgAQlI4FQE4pRV53ykIA4njicfrlhvJV5ESb2txyRq+8g9skXv1RQba/1RHuUECdAbHuiDLSTsxyGOjkcdcGSkzz3W1E1fe8Gko6y/GnPjP+jAnEiQgS0JOwn8wGwvmDBS6QivPJa0d8fQFq/0V3UZ5dVy9yUgAQlIQAKPImDQ4lGk7UcCEpCABE5FIFfBV5TC+Vy5gyJXvvcc7pU+b1Fn5HiO8npfONqxF2eX40uCFZF7hHX02wtwnIl1AluwYh9794IIYTPaHuEVDgmYjOSRl3qjuRnmte0or5a7LwEJSEACEngUAYMWjyJtPxKQgAQkcCoCuXNgT6m8DwCHdC/FMWR7hjRyPEd5XddcvccRxoG+JmCB7FXW1KW/BEy6XvX4bKwJ7KA77FbmSrWl7x/hRXCE+qtBntHcHM2JUV7X02MJSEACEpDAIwgYtHgEZfuQgAQkIIHTEVh1DLkyTd2Rs9eNOpsjPXI8R3ndjthM8GDPGe5tR8errBMgQse9dIR1+j+yXdGh6pjgwbV3pSAzelb5s/3VuzK2eI3mxChvpoP5EpCABCQggXsSMGhxT7rKloAEJCCBdyeAAzgKOKw6hrnrYOU9FVuO4QhEdDiyPeJMjxzPUV7XDVvRCdtvkWLfnixe8knd0Xj1tkdZ9/a3Po4+oxeV1r7gvzeGq7zyWMqePPqPfiO2ozkxyqt2uC8BCUhAAhJ4FAGDFo8ibT8SkIAEJHBTAjjU3AmAg4eDNbojIM73yJFcvULNlXP6WHlEIo7h6L0BNzV+UdjI8RzlVXHYic2wpe4t0irrBIhG49X1OBNr5l5sHAUFqu7Mpb1gUGTVdqP9BHn25NF2i9doTozyRjqYJwEJSEACErg3AYMW9yasfAlIQAISuDkBHDAcOx4nqE72SmAhysQpGwU7UoctTiZ9rSTeZUD9lbsyVuRdWyc2VjmjvFqODTjBe/Vqm739yNpjnXqM6146C2vmHPMjAQRsuDaFwx6vrUBE12GLV/qrbUZ5tdx9CUhAAhKQwKMIGLR4FGn7kYAEJCCBmxHgLoB6RTsOY73DIU4X21HKv6msbXq93Kmx+p8gcF7R7SwpDKo+o7yUY2feyRCHeCsAA/fciYLdsyv+K6zR4UiA6AysEzDL/NhiG57YuJdWeaW/lSDPFq/IqXqN8mq5+xKQgAQkIIFHEdg/cz5KE/uRgAQkIAEJLBBIIKE70ziDNZCBQ0nezJHOSx/jcI66jqOJE7mXVuTtybh1+cjxHOXVwEOu7rOFH3xgSbvqHOdlnWGT+qMg0AqbjCt3BOylFXl7Mq4pz9yCT4I8yKvBBnjArCbq8tlLq/bR/0qQbE/eaE6M8vb0tlwCEpCABCRwDwIGLe5BVZkSkIAEJHA3AnFuR0GL6vDGseRugFmKY0bdUUp572tUl+AITuRK3VH7e+RF/yq754UTV+ITsEj9PFJAWeWIE9zvdqEN9tcxiBy26XfGOgGiGniq7ev+e7POnT3YVO1hH1ZwoKzzJGAxC6JV+1Z45XtAP3tpj1fGpsoZ5dVy9yUgAQlIQAKPImDQ4lGk7UcCEpCABG5CIM5aDw7gKNa7JuJY4mDP0t4VaBxz5O6lPTl77e9VPnI8R3lH+88dBZ1tH4Mqd49RAiR9XKsM9vfk9PpnOU5waHQnykjHPTszBntBnj059D2aE6O8kZ7mSUACEpCABO5NYH8ldm8NlC8BCUhAAhI4QCBBi3rln+Y4zNWB4+oyV733Uh5z6M5kHneY3TkQuTijue2/XnVP+XtuR47nKO+ojsjobMMrj4uMZM5YU5cA0d6jDmdmPbK35q0E0Wp99rd4JcjT7+aoMlZ5jebEKK/Kdl8CEpCABCTwKAIGLR5F2n4kIAEJSOAmBHLluN5mPwpk4HTtBRyiEM4hDjMyE3jILfU9mJE2bOkX552+0q6Wv/f+yPEc5R3VExl8auIuFxjucRixjkNf75Spstk/O+uubz8moLYXlOltOB7xgjFBuh44qu2P8BrNiVFele++BCQgAQlI4FEEDFo8irT9SEACEpDAzQjgUOEAcpUZB47gRH/BIU4dgQfq1ADHTAmCIdTDEcRR3HrUARncUYAeW0GNWV+Pyh85nqO8o/rk0YRc5c/xKovKGhmMJeM3C3g8A+s9hnCPjavBtMisvGCEnMz/1Knbo7xGc2KUV/twXwISkIAEJPAoAgYtHkXafiQgAQlI4GYEcNy4Kk9ggQ9OYHd4Uz4qmylCXeQRuKiPmszqnz1/5HiO8i6xI3eiwAsnuj+usyczgQ6cb8aqj99e+2crTyAMVgn2HLGBNnW+XyJj1t9oTozyZu3Nl4AEJCABCdyTgEGLe9JVtgQkIAEJSEACEpCABCQgAQlIQAIXEzBocTE6G0pAAhKQgAQkIAEJSEACEpCABCRwTwIGLe5JV9kSkIAEJCABCUhAAhKQgAQkIAEJXEzAoMXF6GwoAQlIQAIS2CbAewx4PwbvIsh/xsj7OHiXA/l5SSj57Pf8UQ+8P4J3I9R3HCSP/xqRlL6o11/cSFneW3D0fRSR71YCEpCABCQgAQncm4BBi3sTVr4EJCABCbwkAV7kyYfgQAIM/BcIXvbJSxTJT0CDQAMBBLa1/uhli3kBJkEQ5JGSR3AieclHZl7iSN+k2gdtODZJQAISkIAEJCCBMxIwaHHGUVEnCUhAAhL4UAQISHCnA9saVMidDv0uiPznk3rXBEC4I4IgQ4IPgUQ98gmCzBKBE/oh0Z5jgxUzWuZLQAISkIAEJHAWAgYtzjIS6iEBCUhAAh+WAMECggo8LlJTghb936smv99pkTszauADeQla5BGU2kf2czcG/2oUOQYsQsatBCQgAQlIQAJnJmDQ4syjo24SkIAEJPD0BAgwzO6CIJ+ARg8gkJe7IgKAgAf1+10WlKeMgMQspQ5yezBk1sZ8CUhAAhKQgAQk8N4EDFq89wjYvwQkIAEJfGgCCRZwp0NNec9Evzsi+dxtUVMeGRkFJlK2FYzI3Rhdj9qH+xKQgAQkIAEJSOBsBAxanG1E1EcCEpCABD4UgTyW0f9DB8EH7pzoQYjkzx4Z6e+54C4N7p5A1ixRJ++06MGQWRvzJSABCUhAAhKQwBkIzFc4Z9BOHSQgAQlIQAJPTqD+55BqSu6O6EEIHv8gANHz856Lnk9wg/pbwQhkEjyJjKqH+xKQgAQkIAEJSODMBAxanHl01E0CEpCABJ6eAAGF0X/1yEs1u4Gz/AQcatCCx0ES5Oh3ZkQuwZH8p5AEOKqM1HMrAQlIQAISkIAEzkjAoMUZR0WdJCABCUjgQxAgOEDQor88k8c1yCeY0FOCFtThfRhpm8dGCEKQkE0gI/8GlTspaEN9ynKHR33xZt6XgQzq0r7/J5Kuj8cSkIAEJCABCUjgPQkYtHhP+vYtAQlIQAIfmkDubOh3QSSYkQBEhZAXdxJsSHAh5RwT7KAMmQQe+CRAwZYgBnm5w6O/nDN3ZhAc6e/ZSD9uJSABCUhAAhKQwFkIGLQ4y0iohwQkIAEJSEACVxHgrhHuOCEgs5K4e4W7TQgUfZSE/UeCUTL7KCOvHRKQgAQ+LgGDFh93bLVMAhKQgAQkcBcCuVOk3kEyyru2c5xvnPCVROAhd6cceeSFdgQ5CF5wh8qzJu7CYQwYk2z7f6bptt2b2WhOjPK6Xh5LQAISkIAEKgGDFpWG+xKQgAQkIAEJ7BIYOZ6jvF1Bkwo423yQiRNO8GL0KE2a43zzOAzbvZR3htTARh6x4fGaZw1coDesCL5gYw0ojZisMssjSZ3tCrPRnBjljfQzTwISkIAEJBACBi1Cwq0EJCABCUhAAksERo7nKG9J2KQSTjLv38jdE7NgAsEH6qzckUHdvOujd3tETm97hmP4wIvAC9seZKg6HrF1FOSJrD05ozkxyos8txKQgAQkIIERAYMWIyrmSUACEpCABCQwJTByPEd5UwELBdwpgAPOnQPsz4IWlBOImJUvdPVvFQIfyMKWZ0v98ZDcrTKy41HMRnNilDfS0TwJSEACEpBACBi0CAm3EpCABCQgAQksERg5nqO8JWGDSnG4kUnAgu3o8ZDcOTEqG4jdzbq1vN0O71Bh70Wct7ZxS95oTozy7oBBkRKQgAQk8IEIGLT4QIOpKRKQgAQkIIFHEBg5nqO86MI7KfKYQQ8w8BgDj3dwV0VPey/iJLix8i6L6Ebdvbsy0BN93jsRrEGPkX25I2TlkZhuxyqzsGLLOGylGbNwx5akUV7K3EpAAhKQgARGBAxajKiYJwEJSEACL0MAh6o6aKv7OF+vmkaO5ygPPlyJJ1CRdy7A9/Pnz1/REbAI70sc8DzmEHlb44HjTV842FuJ90FQ7z3HFy4EF2DWAwKwDLO9YMLIzlVm9J1+GMOtNGM2mhOjvC3ZlklAAhKQgAQMWjgHJCABCUhAAhI4RGDkeI7yutAEDnDICTTgFG+9MLK378c49MhYSdEPB3srJYh1jV5b8o+W5a4KeMGNuy9WgjSzfo4wg+3KXSczZmHunRaz0TBfAhKQgARWCKyd6VckWUcCEpCABCQggZcgcKkzmqv33C3A4yA44dek3AmwIiN3dez1GQe8Otor8u9VJ4Ee9Lk2YIGOq8zyroq9IA8yZ8wunSf3YqlcCUhAAhJ4TgIGLZ5z3NRaAhKQgAROTiDO4bNsj+C8xhnNlf7+bosj/adu2OZ4a5vHKtB9K80c8FGb1I0eq9s9HWpfCR4g+xZ3f0TH2sdoP0EebNxL4dDrXjNP9vq0XAISkIAEXoeAQYvXGWstlYAEJCCBAYE4XHHmVrdHHM9Bt0+ddY0zmncqcNfFtSljtSKHOzuov5cyH7oDvtfunuXoPXpRae0zY7I3L1eZ5bGUPXnoMGMWnSrLUV61w30JSEACEpBAJ7B/9u4tPJaABCQgAQlI4KUJjBzPUV6HxNX7/EcM6l+bctfGnpw8lrLn+CMnDvgt7mrY02ulnDtEwmyrfvTeCwatMkuQZ0/eFrPRnBjlbdllmQQkIAEJSMCghXNAAhKQgAQkIIFDBEaO5yivCuXlkTjfRx47qO1H+7lrY+/FlNFt5ZGU2X/CGPV/7zzev0HwIAEJ7Lg2rTLjjgwCHCtpxizcvdNihaJ1JCABCUhgRsCgxYyM+RKQgAQkcBWB3F6eq9s4LjitfHi5IFdw4+xQp1/R5bheZcbZ4lP/zSPOKo4VMuPQJS/9XmWEjYcEjjqjNWCBQBxixnKW+tgzlqPABE49svbuiojTv1cPfTKfZro9Kh9dmdfYPeJd9cgjHzU4UMvr/gqz9LcS5EH2jFnkVL1GeVU/9yUgAQlIQAKdgEGLTsRjCUhAAhK4mgAOF5/6nw/iMOLg4ITiEPGSwdTBmUnCaaUOH+qQEuCozit5qZt9nGHacGy6D4GR4znKy9jiVFcHOGPJODEvCHAlZTz72DNveqJ9l93rcMycoF6dO6N6q/JGbW+Vl2Af+uY7g2wCGGEAyxq8C+eaN9NnxcYEefb+0wp9bMkbzYlR3kxX8yUgAQlIQAIQMGjhPJCABCQggbsRyFXd6vzkqizOKSlOTHUo4zThECWRh+M2SpQhF2e4thnVNe96AhkzuCeN8uKA16AE9anLWCbgkLlAGXUpq3mZD3WOpN8EJGr9lGVLP3z2UvRFv/dKBGuwvwYs0CXfJcoqd8oStNhiUO3ZY5byFQ5bzEZzYpRXdXNfAhKQgAQk0Ansn8F7C48lIAEJSEACiwQIIuTqcJrgPFanKw5pytnimNUr8+ThSM3unogj1B29KvMj7cMUju8VoAnvOo6jvEuYj8Y+c2RkL3mw6PMlfRPooJz5s5X25Gy1fe8yAgf9e7al056tjAHM9tKenNGcGOXt9WO5BCQgAQm8NoH9M9Jr89F6CUhAAhK4ggCOFA5VUhyWesUcZ7I6lHEyawCCK8g4UfWOjchkS13KV26Pr+2ecT+O4hEn9dZ2ZhxvHbSI3Dr26E6wCkd6ljL+vR31c4fCbO5Qh/mVx5FW71aY6fIe+eg+C9rM9Jkxy/dvFiCMvBVmGc9bz5Po4FYCEpCABF6DgEGL1xhnrZSABCTwcAJxrqsjmSvmVRmCDSOnBocnaesWdJwsHDYCHzVAkrZub0/gXs5o5Naxzzyqc2RkEfOMwAZzoAYecOiZY8gZJfoiAMT8qe1Gdc+atxXQ29J5xCzftfq97TJWmWU869iN8rp8jyUgAQlIQAKVgEGLSsN9CUhAAhK4GQGcHpypelcFV2/rXRVxYLhDgvp8upOKwxNHijICFGyRky3OJnVwPtmnn2d1QG82AHcUlHG7hzNK4CFX+RnfI3dAUD/zAPOZK8zBmQPO3RfMo1n5HRHeTHTGon6HjgivzOAAr627No4wi273mCdHbLSuBCQgAQk8NwGDFs89fmovAQlI4LQEcB77Lf25El6VzpVw6iexj/NEEAJnDMeKtnziYLJPeYIitU7yIu8jbOMAwoXPewZloss9nFHGjnHFRsYYB/qorbSjPXProz8yBBvsxOb6HToy5wkSwQvudUyPyBjVvec8GfVnngQkIAEJfEwCBi0+5rhqlQQkIAEJfEACOOBxLj+geZokAQlIQAISkIAEviNg0OI7JGZIQAISkIAEzkkgV67z+MQ5tVQrCUhAAhKQgAQkcDsCBi1ux1JJEpCABCQggbsSyDsHtv4Txl0VULgEJCABCUhAAhJ4MAGDFg8GbncSkIAEJCCBSwnkxZLccWGSgAQkIAEJSEACr0DAoMUrjLI2SkACEpDAhyCQl5Z+CGM0QgISkIAEJCABCSwQMGixAMkqEpCABCQggfcmwH+JyH/EeG9d7F8CEpCABCQgAQk8ioBBi0eRth8JSEACEpDAFQTyEk4eETFJQAISkIAEJCCBVyFg0OJVRlo7JSABCUjgqQn88ccfX++04GWcJglIQAISkIAEJPAqBAxavMpIa6cEJCABCTw1gV9++eVr0OLz589PbYfKS0ACEpCABCQggSMEDFocoWVdCUhAAhKQwDsR4H0WfEwSkIAEJCABCUjglQi4+nml0dZWCUhAAhJ4SgLcXUHAgrstTBKQgAQkIAEJSOCVCBi0eKXR1lYJSEACEnhKAn/++efXoAVbkwQkIAEJSEACEnglAgYtXmm0tVUCEpCABJ6SwM8///w1aPHly5en1F+lJSABCUhAAhKQwKUEDFpcSs52EpCABCQggQcQ4F+c8miI/zXkAbDtQgISkIAEJCCB0xEwaHG6IVEhCUhAAhKQwNvbjz/++DVYwV0Wnz59EokEJCABCUhAAhJ4SQIGLV5y2DVaAhKQgAQkIAEJSEACEpCABCRwfgIGLc4/RmooAQlIQAISkIAEJCABCUhAAhJ4SQIGLV5y2DVaAhKQgAQkIAEJSEACEpCABCRwfgIGLc4/RmooAQlIQAISkIAEJCABCUhAAhJ4SQIGLV5y2DVaAhKQgASemcDff//99SWdv/766zOboe4SkIAEJCABCUhgl4BBi11EVpCABCQgAQmci8Aff/zxNWjx559/nksxtZGABCQgAQlIQAI3JmDQ4sZAFScBCUhAAhKQgAQkIAEJSEACEpDAbQgYtLgNR6VIQAISkIAEJCABCUhAAhKQgAQkcGMCBi1uDFRxEpCABCQggRD48uXL22+//Wi+uQwAACAASURBVPb1UY4ff/zx7fPnz1+LPn369Pbzzz9/zf/pp5/+zeddFaP8yOMdFj/88MPXzy+//JLst3/++eft999/f6MPyv/666+vZfSXNpQl/9+G7khAAhKQgAQkIIGTEzBocfIBUj0JSEACEnhOAgQSCCwQiCBIQTCBd1Hkg1W8k4J8AgsEFAhw0I795HfrEwRBTlLa05Z29EvAgi2BE/bJJ0BikoAEJCABCUhAAs9EwKDFM42WukpAAhKQwFMSyH/7IIjAHRFJyecuCAIPSQQaEnxIXra0p4y2o0QZd2vQF0GMJPL5mCQgAQlIQAISkMAzEXD18kyjpa4SkIAEJPCUBHJHRb/ToQYtaoAh+TWQEcMJRhB8qPVTljsqKCfwkZQ7MHr/KXcrAQlIQAISkIAEzkrAoMVZR0a9JCABCUjgwxDIIx39nRL516X1UQ+MTpBj9C9NCUhwJ8Uo5bES+qspj6f0/FrHfQlIQAISkIAEJHBGAgYtzjgq6iQBCUhAAh+KAHc4jO6O4E6KflcEhie/PwKSOzBmwYcERwhS1JRHSnrQpNZxXwISkIAEJCABCZyRgEGLM46KOklAAhKQwIchkEczRndH8C4LPj2RRzCjp9yZMQs+5D+P9EdHkl8fGemyPZaABCQgAQlIQAJnJPD9iuiMWqqTBCQgAQlI4EkJzB7NmL1sM++l4N0VPeV9FtTpKcGR0XsrCICMgiNdhscSkIAEJCABCUjgbAQMWpxtRNRHAhKQgAQ+FIHZ3RF5/0R/n8UsHyh5zGQEaPboSPJHQZCRHPMkIAEJSEACEpDAmQgYtDjTaKiLBCQgAQl8OAKzuyPynon+/okELSjnbgzeb5F3W+SxEe6qoF797yIJjvSXd9agBe2Q2+t8OOgaJAEJSEACEpDAhyFg0OLDDKWGSEACEpDAGQnwaMbo/RQJZvT3TBBYyDso2NagRgIaBC8IPlA3KfIS4Eg+25RxpwYyTBKQgAQkIAEJSOBZCBi0eJaRUk8JSEACEpCABL4hQMCH4M3oPR7fVPy/A+4wIYDzkQI3BLZq8Gpkd/LkFRJuJSABCUjgmQgYtHim0VJXCUhAAhKQwDsQyKMn9S6OUd61qhGAqHeWbMkj8MAdJ/yb1363yl47ghz/n71zPXLjWNKoXOC//S8X5INMkA10gbEOyAN6IAtkgRygA/JAPszGmb0fbypZ1Q8AjWkApyLA6q5HVubJ6nqhMeTwYutmf0neR+WhP3bHDn4qNPoDrdHvHrxGfWKUFp2MJSABCUhAAlsIeGixhZJlJCABCUhAAi9MYLTxHKVdioiDBw5EkJl46e9usAHnJzdb3pjIHy+tBxv5Cc6etxQute2oevWtif4zot7mvXiN+sQorevnvQQkIAEJSGCJgIcWS3TMk4AEJCABCUjg/TCBQ4Kj3rTgEIHNLW8NcMjA9SywWc/f9JiVSTpl0Xv085E9ciLvTDFvVYQXb6hU31Q999h5La/RAcUorerntQQkIAEJSGCNgIcWa4TMl4AEJCABCbw4gdHGc5R2KSYOLfh5A28MEC+9QcFGnYOIW/y0g81+P4y51IZ714MDhwzEsOBtldHPQ+7Ja9QnRmn3ZmV7EpCABCTw2AQ8tHhs/6m9BCQgAQlI4HACo43nKO1SRfrPQ/hpyOjnIXkTgPK3CLeWdwud9spY+onLre1bkzfqE6O0vTZaXgISkIAEXpuAhxav7X+tl4AEJCABCawSGG08R2kRRB4/4eAthv7WRN5uIO6BtKU/xMlBxkhml8NPJSiXz9JbGfx0BF0/MuRNCfRFl/rGBHl5W2KJzUj/e/Ma9YlR2khX0yQgAQlIQAIzAh5azMiYLgEJSEACL0Mgm9s9MRvJVwmjjecoDR4cUrBZZrPdDwR4QyKM927AkZ3Ne93Uz3yAfNoa/T2LWoefo1Bu9jchatmjrjmsoX3sQhd0IsCQNynCjPs94d68Rn1ilLbHBstKQAISkIAEPLSwD0hAAhKQgAQksEhgtPEcpXUheauCzTgHGf0tgl5+7Z4DCDbwW0LetsgBwKxO7OhvhMzKH53OIUXe/ED3pZ9/rOlyb15hWQ+ARmlrepsvAQlIQAISqAS2zfy1htcSkIAEJCABCbwUgdHGc5TWoeRtB8pee2CB7Lxx0NsZ3XMIQXkOS5ZC7CA+Q8hBD3pz6LD37Ypqw715haWHFtULXktAAhKQwLUEPLS4lqD1JSABCUhAAhMC2TSeNZ6o/UPypZvR/OFG7L/Fmwzh+IOCg4T8FKVuoAfFvv93rlsOLfJzi+ixNR61O0vLYcu9D3luwevSfjJjYboEJCABCUgAAh5a2A8kIAEJSOBhCLBp5HPrsHXzWcsdocet7bqVvGs2ozDjJw63COG/RVb+FsRa2di25dBiTdYt8vOzFt64WArRe6nMvXlFp3pQNEpb0tk8CUhAAhKQQCfgoUUn4r0EJCABCZyWAJuwtc3caZV/YMVGG89RWjeRb+95YwC/3SJs/RsN/KSCNrcclsSOW7wJcq2N+cObMFs7FCN/zb578wpLDy2u7QnWl4AEJCCBSuA2q4gq0WsJSEACEpCABJ6KwCWbUf4mA5vqUd1L4bBR5zBi7X8PydsKHJqshTP87yHREV04lIudSb80jpx78Rr5epR2qT3Wk4AEJCCB1yTgocVr+l2rJSABCdydAN9k8w0yH/7WAX+kMd8Es7EhsGFLmbrRysanfmNPfuojC5nZpBEbbkcg/Ld+gx5f46McIMTHI60ol8MDfMz16A9QchBC/tpbEdF3rRy60Ifocx8dOGDhkAe7o3/lHf3CEw6j/JQjvjevkd6jtKqj1xKQgAQkIIE1Ah5arBEyXwISkIAEribAgQKbsry2z0Ym34ITswHjwIJNWC1TG2ZDV1+Hz8aWtHw7TTtsVLds6Kpsr5cJjDaeozT8AHs+9cCAQwEOBwj4m0OmBA4syE//wP+UTf9IOWL8i+xRXi2XwytkL4Wt8pZkXJtH/8UmGERf4tgJD+xB14RwJm8pbLXvVrxGfWKUtqSzeRKQgAQkIIFOwEOLTsR7CUhAAhI4jEA2Yxw4JLABZYNWvzXmnm+Ja8jhRE3jOhvibOByaFE3eb2O9/sIjDaeozR8hD/qgQUt5Rt/8qhXA3X6mzHcU3YUssGOv0dl6D981kI2/7XvrdW5ZT42oCeHNDmwiPy8eUJePeQhH73htiXck9eoT4zStuhtGQlIQAISkEAIrM/oKWksAQlIQAISuJJANq/1QIFNFZ8ENpBs5OpGMpu7vhnOIUhNZ0M32/CmjUeNc8BT7b2HLaON5yhtry4j/yGD/pA3M7rMtbcHIrP2qS6D+zU5ozpnSePAYu1tk+i6ZucteY36xCgtuhlLQAISkIAEthDw0GILJctIQAISkMBNCLDR6t8Qc0BR36rIJqc2yDfNlKuHHeSzee/pbHbrmxxVzqNfY1u39x42xSf1IGmUtleXyOh+5dBpyYfx++jwJgdjtU91vTgEox/yWXpjo9c7w/3sAG9Jt3vxij9v3U+WbDNPAhKQgASen4CHFs/vYy2UgAQkcBoCbLrrN8Sjtyr6mxcoz2Zo9PYEsuo38vlWeWnDehoYD6TIUZvRyK0ossGuG9+an2vK0Sd4s6YePHAQsXSwg1z6DP2s1ovcs8d5ZnhDYk+4B6/4s/pulLZHb8tKQAISkIAEPLSwD0hAAhKQwF0IjA4U8q14VYANJxtRNmXEBDaY+Va8fgPP5rPeZ0NHzCaNj+F6AqON5yhtb0v5aUIOmfAbBxH1YGtJJn2KPpKDK+rRf2Z+px360ix/qa2z5IU7By7Y299SWdLzaF7RzUOLJS+YJwEJSEACewl4aLGXmOUlIAEJSOAiAvmJR/2GmAMHDiNqYBPKxrN+E84mkzTKpn5ek8+GFxmkUYaNLxuoZwnYnk//o4z3sPHIzSi+xV/Yx+HDJX5LfXz/EXzu4YO0Qf/HXlhdevhyFK8j+0nsN5aABCQggdcj4KHF6/lciyUgAQlI4MEI5ICGjf2eb9YfzEzVlYAEJCABCUhAAj8Q8NDiByQmSEACEpCABM5HgAMLviE3SEACEpCABCQggVci4KHFK3lbWyUgAQlI4CEJ8HYFhxb173c8pCEqLQEJSEACEpCABHYS8NBiJzCLS0ACEpCABO5NIH/T45K/93BvXW1PAhKQgAQkIAEJ3JKAhxa3pKksCUhAAhKQwAEE8sdJ6//KcEAzipSABCQgAQlIQAKnI+ChxelcokISkIAEJCCBfxPgf8Xg5yH8QU6DBCQgAQlIQAISeCUCHlq8kre1VQISkIAEHpIABxb8F5cGCUhAAhKQgAQk8GoEPLR4NY9rrwQkIAEJPBQBfhLCocXnz58fSm+VlYAEJCABCUhAArcg4KHFLSgqQwISkIAEJHAQAf74JocWX79+PagFxUpAAhKQgAQkIIHzEvDQ4ry+UTMJSEACEpDA26+//vp+aOEf4bQzSEACEpCABCTwigQ8tHhFr2uzBCQgAQk8DIFPnz69H1o8jMIqKgEJSEACEpCABG5IwEOLG8JUlAQkIAEJSOCWBL59+/Z+YPHbb7/dUqyyJCABCUhAAhKQwMMQ8NDiYVylohKQgAQk8GoEvnz58n5o8ccff7ya6dorAQlIQAISkIAE3gl4aGFHkIAEJCABCZyQAAcV/q8hJ3SMKklAAhKQgAQkcFcCHlrcFbeNSUACEpCABJYJ8FMQDit+/vnnN/7nEIMEJCABCUhAAhJ4ZQIeWryy97VdAhKQgAQkIAEJSEACEpCABCRwYgIeWpzYOaomAQlIQAISkIAEJCABCUhAAhJ4ZQIeWryy97VdAhKQgAQkIAEJSEACEpCABCRwYgIeWpzYOaomAQlIQAISkIAEJCABCUhAAhJ4ZQIeWryy97VdAhKQgAQkIAEJSEACEpCABCRwYgIeWpzYOdeq9uXLlzf+y7yEr1+/vn369Ontr7/+StIw/vvvv9/L/fLLL8P8SxL//PPP97+Gj05rgfb5i/n85fw1XddkXZO/hQN2wYm/9M+Hv/qP7lxvsfUa/Zbq0jY6oN8rhm/fvr19/vz5vR9X+7f4tJbfek0f4NlC/hnDpf1hxnFm41F8Z+09QnrGMp5H+gj98p9//nlXHb75n0LIpx9lnL5m/L3U3/fg+RF9hHEQzr/++us9THypNvAnffysY9+lzsAenqM8q5fKedV6cGP9yThG/6hh77xS6265zrga3+FLnv1XfP73rL23sD1bmaP70tnsfXV9PLR40h7AQMWEm8BCmUUxn7XFBfksrq9ZNKfdxFsHTiaZusg/86EFEzIHK+gcvcMNzpV/ONwrPvOm5WgGTGL0ofT32t4RfRv5PCvPdmgBx/QjWG4JR/Hd0vYZyzDuslkmwCbjMGMHfOkzxISMJ4wpGUfeMy74J35j3D1buHcfgUEOhl5x03Kk/+m7MM3msLZV53HKfORcXvWq1+hE3+ib6pShr5KfZzTpxusEOHxlXmTuqHxhmfFp67yy3tq/S9Bu+iTtMZ7SFuPvPQM60GZtnz61N9T1DHbwQWZsXJK3de29JOOsefi2sjmrnup1OwLbVqK3a09JdyKQE24GtTpBkP4IIQPRvRY6eyczuDJx1Mn4I7gyAX60Dh9h95Y2WSjjo1sGnp979clb6n2NrCySrpHxinWzWJz1FxbWz7KJPtNzMRoT8QH9+Fl4n+F5YsNQN4dVp2zU0vfZwMJ/zyHakX0KvVgXZTO5NIdiJweJxIZ9BLKOG/E9cl7BX/QfxoL4eNZX91m0vTRrRNrkgx7c57ngfmugXmwIM+K9a9at7T1iuSPWeo/I4RV0vu2K/hWIPYiNDHR5kBnwGOAeadLNZJdFz5HY4cIksCdkETyajPfIubbs0rdE18p+9Prp/7eyg2eKxdA9+uStdL6FnCyUbiHrlWSsjWFwfYZN9Nmei9GYmPH6GXif4RmKz9kY9pADip4He9Yi1F0LkX/0WBtd1+Zx8tl8GvYRyHM34nvr+blqRt/LRh+/4ed7B8Yhxvh6QEG/Rq894xC6j/jd254zt3dkXzqz3a+o276d2isSemCbGSA5rNiySDibmQzSDPhHL1qYUJjUaGtPWJqM98i5puzWBdc1bTxy3VtOZHmW7tEnz8Ycm/c+H2ez4SP0WRvDYLpn8foRNqy1ebbnYjYmZrx+dN5r/rhXft5SGLWXzWLPYyNJn1/bgN2zT6VfbNFpi+7d5le/X+J7y/l5xJn+1g/ORuWOSMsXYaPxhjU5fQk2WwLr00dcw2+x7VZlju5Lt9JTOdcT2LdTu749JdyRACe99ZR3a9P5DXAfcBk4c3rMoMs1n7XBl3pMHgy+o8UBOjKQZ7FDu2mny86CJmWR2V857frTJt+QozOLrQTKRQ55+aTNLod65KVcj8lbs7Xrj17oR3oNVWfagUd9UyaDdNUh/oJn6seWKpu08KX+iGG3o+oNsz2Lge7f2Fx14rrypr3YuOVbkm4T/Qm7sK+G2kZNp73KhGs+yEX/9J/KG8YE/JL+W2X2dNqOTthGmz1Qp+pR2+M6C56kRwfkJI24pi/1B9qLTvg13KpekUsaNoTFyIYRX+yszz/34bXUl9KH036Pq46z6yoj9sGjh95/Rs9E6nQf4S/SEmiz65p7yuS6x/HZiGFkE6/ZtORv6td+GCa1L2JL/NPLV5+vPRdV53o9su/SPlLlZryoXEkj4F/Scw9DbCetzglV3pqfa9l6Xe3DruhVx7HqQ3TofehSHl1uZcF1DWv9oJat1+iGLPpID8gkL5xrfnzA+DELW/oU7Vc7R314Jr+nRyfkrQVsoq1rQ9W9+2evj/qzXvssnOu4FL3hl+eb9vpY1/seMpCF7fAi1HaQQf6I4RLfPBfRK/eVSfKIa3r0qPn1mvzZc13LHXVN2+g7YkIaeaPnp+vDvElZ+FJ+ze5ev/uy5uPDtbm/lue6+qA+49V3NZ06W9vZOh7BoK6R4BI7ur7ePx+Bf89iz2ffy1rEYLdnYxlQWXAxOPXBh3sGCAZCPhmY1wZSFmsZVPogzqTLZMggxDVyaSODY5Wdsln81cEQvQldf2TxoV0GfuSmfmxOW7kfyal5XKMX9bo9S7ZiGxzCEDkZfCvrzhWbaAv9a8jkV3WgDe5hSp3Kj7r0CfLSN2Aa31Qu1Q70QT9iPpEN/7VAGcqjE7pV/2J7wsxv2FDLpXyNwydtVJuon9DbSDrxlr5Nmc4U++Kv2lZPhzf10TFyul1hlf5R7aBOAgzTb5BXQ09f6g/k4Zv4nfaiW5WJXXywgfboU7G52jDje0lfggG6pf8iI3okreo4ukYGfRu7CJGBXGxP2PpMUB4bq165Jy3tRC6s0HmmL3nVr5EfH/Y88tdsWvI39fFbZRIdSSNc0m/TZ2Z2vgv+zz+37CNVbq5jD3EN6BbeMMDnpEX3PAOpE7/GptyP/Jw6xN0+/MWHtvOs5Nmpssmv4/vRz8xaP6g29es8R50Z5cI/tva62MlnLcQvYZTy9G/6KvLzvEUf+NXnOnWWYuSjT+8vozrxGz6+NNAX6vgR3dGh27rmo/6sIws22JI+R1s1wIy0+I7nnTq0H7sihzR0gHX8QV5kkI4OfJLPc1XDEt/USXnkRhfiHmgHnaJ7z6/3yN4b0ndpY8tnqc/EtpGuYbJFx6xZqz7hvsW+6suqLyxrP4B9dF6TG05df9pCz5q+tZ21vh6d6KO0gQ7Irn2GdMPzE9DLT+rj0aC/1VQmsj74UJe0PrEyQPW0UTsZ0OrASTnqjxYbmbyqbMoyGdeQQawu+DIpMChjS0LK1kGVPOwaDXgzDtRJG90e8tZsjT7EtIGe1V8j9uHBIJ2QyWOkAxNb9xd1SevlM7H08pFP25RJyOKty0l+jfFL500+aV2XMCU9fiOubVfZXEd37K2BdOTwqWHmU8rVvkYddKxp0bmmRTY+7G2RFx3qYm6mW3xWfRyfddnxTfdBGPb0yK66p2xsIA7PmhYb4pPkjWye8Y2+W/pSZPRnPQvxblv0qXHaqyzJpz+id9LDt8sMB2wPs6RVXyIzz0PXNzqkftWPa2SPno3Y3/MiL7pHXreJ9CV/9/oZW6pd8XlNm/XbpeciOtZ4zb4tfaTKq9dh1P2Zvg4r7EgY6bLXz5GVOG3BEPkE4rQ78nt8UH0TW7bwiB29D46emehX20LH6FB9HptqPOpbyR+1lzzi9KvZM5Gysz6FfJ7fsEz5sOrPTPJncVj0/jIqn3m9Mx6VHaUd5aOw6nrFn5U1ZXu5rIt4NhLCsz4vkZO83FMnMrrsJb7RO20S0yfpI1WX5JM3Sk9+YvovvvrIgJ7YURlFnzAhf0vAbvjCNs9PHxOW5KTf1j4eHWq9jHs1bXSduqNnDf1qespWOb2dlFkbj1KP8acG0sOlpnv9nAS2PTXPafvTWpUB7hoD++CDLNIYMBgkEpggGHTWQgam0cBZ0yKnT4yZzEZtZcDKAjFt1cEzcmd2kT4Ko/KUSxsj3Ud5WbCMyvd2mZD6wJwJvtofRiOZyavlM+nVtLSdjVdtNzK6/JF9kVNj+gb8en3KZJHDAjQhckd+S5keR8eRTWHW64x8Stpa3468vW0hu4fIqumUG5XN4UD6N3Vid2cbhj095avus7J94TnTa2QDulGevBrSftdrpMOsbBZftY/WNuo1zLoONT/Xe56J9OfUTRwbal8mL3ZU5qlDPOKU/FHeVptmbWPriEn0rFz3+Dz9YGZnbKrxyL7oQVxD+Pb0WibXazJG9ndd9vo5bSeOvqO2KHPt+B75lcfM7tEzs6cfxKYaZzwa+Tt9oepW66ZfjerWcpFTy2UNMOJaNy51nKwyR9cjlqNypKXsqP1ZnZp+lI9GrGg37YVh+OW+6ha/hF3qjvyYuTtlkTNjk/SRnOhd9eA6YzLPYQ2kbzmM4PnqG+Aq5x7X4TliHSaU2Ruom+dvxHQkL+3V8qM06sJ4LaTu6DnAppqesrXt3s7W8QgZyEdmD7O+1Mt5//gE9j81j2/z01vAw71lcF8C0QcfymZjyyky8uvhxZIs8kaDV+QxCfbQB6jUR6/ZJ4NZytbBM/JHdkVeytR4VJ78tNEH41leFo8jW2t7/ZrybCaiY2ykXBiNdEheLZ/T/5HfYk/deEVGl5+yPb3rnsXHzObYlMVP5I781mXnngUKckY2zSaykU/TF5f6duRVptEjeblPHBtzn3hUPouRlEk8KrvXNynfdQ8/2uiLxLS/xwbqjPim/d5n4vOanmelpiF3VDY61pj+hA74dC3seSZiQ3iM4tpeynfmKTPiNMvbYxMyRm2nH4307rqkTPRJHBm5J07azM5aNte9PdKjM3ENW/2+RQa69tB1iR5hMIq7jHoffUdt1XJcXzK+R37ltOeZib9GdnUWXV/uU2+0McxcVXWrMlI3Y37Nq9fRsfapHCbNnuvUgcXWMGI5q5uy2HBJOMpHsbuyQr/046RX/eOHHqds6s78GPvpA5TJOIouNaTNkZzoXctzncMVZCYwvzM/jub5lElc1zBJu3ecuTw8a/thcqmemQsqnyq/X6e97oMtc3+XxX3kdV+TR3/q6WvtpB/0vpj7yIucUR+IjJG+pj0XgctG3+di8HTW8ACPBss9ho4GH+qzcMgExaA72+j0tjLQ1YEzA81IV8qhQ/L6fZdf79NWBruaN7Irg2Mtl+tRefLSRrUndUZ50X/rgiqcsYGF7YhVZI50SF74oVvsHC0YozNlEiKjy0/Znp56iaPzzOboEx0jd+S3yOxxZPR07tN+z6POqI0wJ3/UtyMv+la5yatpXM/0G5WfHfKgS9d3r29SvuvOAoA82kBXnu3eP/bYEJuv0TeLVhYpNWTR322oZbhOP6pvDvQyuY9t3eYqhzKEkc8iZxTPmKcscjunWd4em5AxanupvbSbOFxyn3jEIGlrfokM4pEu0Zm4htje02uZXK/JGPHuusSeyNwbR99RW5GVsYYye8f3yK889jwz3d7otDWe9Q3qh//oYCE6Un8txAe1T0X27LlOncplrZ0Ry1mdlN2i/0hG7N8yru3xUeyurGg/vJLe70c6Ji1lZyyxhfmKeYMy9OeRzmE2khO902aNMxcil0BM2lqgPeReEmIzdmz5jGxKu7Ft9IXNjFXqbonDZ0vZmQ+2zP0j+ZE34jzqA2vtjOqM2o1PRnnhPcoz7bkIrM8ez2XvS1jDA5zB/lKD1wYSNhDZ6GxpKwNdHegz0Iw2tZlAqEdg8EenPW1tHVSXBsMZh5E9YT3Ky4Zry8RLGdjGduSGVU0Lo8o0OiSvlo+/alrKk4atdUEVGV1+yvb0yEq89Vu3nJxH7shvkdnj+G606QyzUZ2lNmZ9O/JG/JI3agsdexiVhwP8+bAoJGRx0u3b65uUH+lOO7Sdt03oJ7W9MN5iA2Uo3/mm/d5n4vOenoVdxgb0Qa/ZhqXqRll02PJN1J5nInzim9rm6Do2z5iPOEVOz9tjEzJGbdOvtjChPu3z6WHUb5M2s7PLiPxr+8hIbuzu/Sn9rLc50mWvn7seS21R9trxPfK7jVufmT39oNsWXvSNOkakXObpEefoTftrYdSnMoeOZCMvdUYbxVl70amzHJVP2dFzMSo/SjvCR7Eb/WrIs5D0+GbLGip1R1wyFtOP1+buMBvJid5V51ynXsYr4tiRMqOYMqkzyr9XWsaQkd1hS5lLAzK22hmWI11of2nuH+kXeaPnkGdjlL7UztbxCNmzcWepL41sMO1xCfy4KnlcW9T8PwRY2DOhXBNGg082EJGbyWvLIiQDXR04M7CPNiIZ2KlHyKJ91hblUjZtjQbPkV0ZDGNXjUflyU8b1Z7UG+VFfzZJmehTnhi2bIaysOgTWgbl2EidMBrpkLxaPhvgLhtZ0bnmRUaXn7I9vdrDdRZoM5/BtuZF7shvXXbuqY+c3jfJD7OUypv9RwAAIABJREFUTTzyaa8/6tuRV5lGZvJyn5i2+PQwK48e2JTNNOVG7e31TcpXWVxjZw3xWe0He20Y8U37vc+gA+V7Os8IHOJfeFBm9OxU/XMdftXe5GFzFu57ngl8g66VTWQSdxu4p/xIB8qPOEXeKG+rTdGlt51DxNmmrupPXT49jPpt0mZ2dhncj+wLr6oHZWd9ZCR3TQa69tB12evnLi/6jtq6xfge+Z3T1mdmTz/otnG/1A+TT5kewrXr3ctxP+pTzI/pl6NxIHVGeaM2SJuxHJVP2ZFfR+VHaUf4KHajXw15FpKeNQhj6ihQLmVTd+SrjMmVM/X6c0QbSR/Jid4jXUhLPmP01g16bNx6sDxr+9r06DFa2+b5C+tL2oLJbB7q8kY+IA0daxjN/TU/15E3eg56H9jSTniszUvpd4wjPaSv9HTvn4/Aj6uS57Px5SzK4oA4EwsDFANI7pegUKYPPpRn4uj1GSxmk2BtI4u1OnllYKctdKuBAZn0bC7Iy8DEIFf1QE4dQPcMqsjNIiztZ8KbcaBcGFd7Un9kK3nwwyZ4Vf3RN5Mb8ijTJ6QM2NWHXQdkRm74Va7hgr0pF51pl/TYTnp06TZGTk+PrMS0EbZVD/Ijo05USau+jKxZHAYjm8Ks2oRO8O1tbOnbmVxjS5WbtrqetMWnh/Tlmg4L5GwJsbv2E2yL3O6bWX9Iv6tt9v63x4YZ3719CQ61b1T9tlznMIJ+URdn+AxG6ElInxv1H3QmPX6mDvfw6HzRlTZr6P2l5mXs6/2QMjOGW21Cxszf6I4NnS3l61i7x+fdzvCq9tbrmX17+0iVmes8F/EPbfGJn0e8sbWmU36Pn9N24qW2YmN9bqmX8YO6tE9I2djS5ff0rc9M9NvSD9Jmjbu/ax7X0bv3MRjTZuzr9ep9byN9Chmj5y8+61yrzNF1WHSWo7LZ1NXnPM/ZlvrIPMJHYYItNcQPNT1l4Vv90NdQqTuyK2NDrY+v63OUvKSP5KTPV53rdXyDXJ7rrYE+huyM++iCrNxvlXNtubAOC+RxjX7k1UA6jKqO9Pnqu5SnzGi9kvwej3yAXPpAD7Bee4Zon3IwriHPQrVtSzuUQd7aeJSxfTSGpC9lnKh6ef1cBH5cTT+XfS9pTQZGBoL+4YGvA+MIUB0c6iCALAakpGWw2TKhZBFC/TqIZ6BDLwZtZBJzT3sMRpGP3kmPLsjjutoU/fvAHn0pHxuwPzKox8Iki/fIoc1aHv1jD/pVe5CXvG5r1R+Z5FOf6+hPjH6koQcTDoukHHggO4um2IMMrpOOPinfJyDYIp86sYm6aa/2h9hBXEOV0W2v5biOjsjnmkC7tB99U6fyDo/kLcXIwiZshhntoDNtks41HAm1jdhPOuXwR9KiN+UTYjd6I497QnyGjLTT06s9MItu6WuUp33SkZsP7aNLrU9Z9KQ9PuiDn6lP+6RFDm3N+kNspG78GBvTXrWN8gm0P7JhxndPX4pe1AkHYmwjL7pGl1FMmfQLeMCGD9eVOXVj85ZngrrICGNkUq+PNfDJM9j7eW0ThmEdO2YMt9pEubTdn390if6UQX9iWCdUn1fdkDvyefj15yLyejyzb08f6TJzn76DT7gO+7SJrXnGqZPnBSY1fauf026N09bIt2FLHm3QPjrGXzCIznt4xG7qbHlmaGOtH1Sb6nXY0M4s5JmAKf2Gfkh72LslzPpU7YNwJpCGPbTJ9Z4QDjwHa3WjE/YnhGHvP8mv8RE+Quf0nfBImxnv0p9Ip//lGUZnyqRcfdbT90ZMk0eMTXBJGrK5jqykd755Dtb6RHRb801sJo6fqm9yXVnUOkdc0/fDA/25xx7S6lhTdSY/IX0TH9Dnwpr78E3ZpXjkg/TFpbl/SWb6HPrCOzqFM3LRcWs7sZX6yEYuMbrXQDspEyaUSZ/meusYU+V6/TgEPLR4HF/t0pRBMYNVBpLESwM3g0XKJWbgITBgZNAgj0GlTuAzBSOnxrUsk20GQWIGnQyEyK8TVrcLfesAPtIfWbN09KA+7TLwUZYwKg+HDMLVFq4pT+jp3NdAW9Uv+KJPYPDIIJxJBSbIom7lkcUg7ddJouoR3aIHsqofkRkfp0ytn+uZ7b1uZCTuNtN27zcj3vFF5MxieMAhzMKCGNsyiY3aiO5b+jbtRAZ+4x4dwycxaaN06s7SsW2UF5nE1K++h2FsTr/AnjyXlB35DDkE8rjOs5c2wmSkz8wGypJX9eV61P5aOs9D1anLxObef0Z9A/vxUxihX2zr5bc8E6mDjPr8pC8kf8QNG0gndHtyT96MYWSv2TTijcwa6ngLm+hFmZHu1J+lUwedondnUdvlOuViM/FI57X0Lrfe9zFx1Cb2jNJr/1jzc20z1yOZlS/ltozvlU+ulzhd8sws9YPYM4rxN/2GMWcWKFPHZMoyD2wNS32KvPpcM1bAmPStYcay+6rKw7fYXQN2kcan9p1aJte39tHMhll61QO906+wq/om6TWutmFH+jnsk8eYCIfMt7V+rikL49wnRt4o0Efx9d5Q+3baSIz+9wq0FdawmY2PMEM/npkEfBLO5MEXu/b089hcY+TjB2TXeZb7+DI6zGJ0S130Sv/BRvwbxnvaqT6LnN4+ttdxBZ3DCc7pe72e989D4N87quexS0smBHioedANEpDAuQgw+TIhM9HznDL555OJestm/VxWXaYN9sIAFixmwoE4i63LJFtLAs9J4J7PDM8hG4tXCWyW2Phh9yiwGd2y4bunj0Z6Plrano30mm348Jby1tozXwISuD0BDy1uz/T0Ej20OL2LVPDFCPDNBN9cLAUWzGzgnz2wsF+z85U2TM/ub+27nsC9nxk2gIxXr3KIip18ozwLW9ZU9/bRTNdHSd8yJ+61hTl0y+HSXrmWl4AE7kPAQ4v7cD5NK0wEl7xudxoDVEQCT0iAVxvZBLAZGIV8SzTLH9V5xDTeNuEbzaVDCzYQs288H9FmdZbANQQ+6pmh3aUx6xqbzlQ3BzTYOwqsp9ZeS/8oH430PXMa69PMccyJtz4Uw1e0YZCABB6TgIcWj+m3XVoz+PNhoc+3BZ4078JnYQkcToBv4dis8wYBC6t8I8Tmnbz6u9HDlfnABlhQwgAW2AwHFq75uQzjmIeuH+ggmz4dgY98ZtiM85ZBNpqng3OlQtiFfbMDC8anWV5t+iN9VPU483UOdjL2L73ZstUO5g38xxzKZ8sbMVtlW04CErg/AQ8t7s/87i0y+DMR8K3I2jcCd1fOBiUggXcCPJtsyrNpz+KNhfGzbgpGrsfWHNTAgA9MYOOB64iYaa9O4COfGdrmcPXZvsHGnluOvR/po0d4PuDNGpXxnvEfXtcG5tTMIRx230LmtTpZXwISuJyAhxaXs7OmBCQgAQlIQAISkIAEJCABCUhAAgcS8NDiQLiKloAEJCABCUhAAhKQgAQkIAEJSOByAh5aXM7OmhKQgAQkIAEJSEACEpCABCQgAQkcSMBDiwPhKloCEpCABCQgAQlIQAISkIAEJCCBywl4aHE5O2tKQAISkIAEJCABCUhAAhKQgAQkcCABDy0OhKtoCUhAAhKQgAQkIAEJSEACEpCABC4n4KHF5eysKQEJSEACEpCABCQgAQlIQAISkMCBBDy0OBCuoiUgAQlIQAISkIAEJCABCUhAAhK4nICHFpezs6YEJCABCUhAAhKQgAQkIAEJSEACBxLw0OJAuIqWgAQkIAEJSEACEpCABCQgAQlI4HICHlpczs6aEpCABCQgAQlIQAISkIAEJCABCRxIwEOLA+EqWgISkIAEJCABCUhAAhKQgAQkIIHLCXhocTk7a0pAAhKQgAQkIAEJSEACEpCABCRwIAEPLQ6Eq2gJSEACEpCABCQgAQlIQAISkIAELifgocXl7KwpAQlIQAISkIAEJCABCUhAAhKQwIEEPLQ4EK6iJSABCUhAAhKQgAQkIAEJSEACEricgIcWl7OzpgQkIAEJSEACEpCABCQgAQlIQAIHEvDQ4kC4ipaABCQgAQlIQAISkIAEJCABCUjgcgIeWlzOzpoSkIAEJCABCUhAAhKQgAQkIAEJHEjAQ4sD4SpaAhKQgAQkIAEJSEACEpCABCQggcsJeGhxOTtrSkACEpCABCQgAQlIQAISkIAEJHAgAQ8tDoSraAlIQAISkIAEJCABCUhAAhKQgAQuJ+ChxeXsrCkBCUhAAhKQgAQkIAEJSEACEpDAgQQ8tDgQrqIlIAEJSEACEpCABCQgAQlIQAISuJyAhxaXs7OmBCQgAQlIQAISkIAEJCABCUhAAgcS8NDiQLiKloAEJCABCUhAAhKQgAQkIAEJSOByAh5aXM7OmhKQgAQkIAEJSEACEpCABCQgAQkcSMBDiwPhKloCEpCABCQgAQlIQAISkIAEJCCBywl4aHE5O2tKQAISkIAEJCABCUhAAhKQgAQkcCABDy0OhKtoCUhAAhKQgAQkIAEJSEACEpCABC4n4KHF5eysKQEJSEACEpCABCQgAQlIQAISkMCBBDy0OBCuoiUgAQlIQAISkIAEJCABCUhAAhK4nICHFpeze9maf//999vvv//+9vPPP7/99ddfL8vhHoYfyRrZnz59evvll1/uYcquNv7888+333777e3XX3/dVe+jCx/pr2rbH3/88cbn0cPXr1/f8PUl4cuXL28//fTTxfUvaXNPnW/fvr19/vz5/RnbU+/Ssmd9Zu7NofODC/2E/mK4ngDzBfMGY91HBNYcrD/2hmvGmr1tWV4CEpCABG5PwEOL2zP9QSKbCzZfLJz4MOlfulD/QfgVCVWn6LYUU/6ff/55XzCwaKGshxZXOGCl6tGsz3pokc0X/euRDi3iLw7zjnw22AgvHViQx4FPnlF0Ycxh08YGknxiAs8v+bMPMpC11F7txvQp2qG9yKwyYNQ3j2wmelqVObumDm2cYSztOsI3+qHj0eGszwwc2GCmLxzNYSQfNrR/SR8byXv1tI88tGAcYvxbC4xZo+fu0rFmrT3zJSABCUjgeALHr6aOt+HULTDBZsHW449ebLMhRD82EglZYNbNYjYabMYSyMceDy1C5Lj4Fqy3LPSOs2C/5Gymaz/cL+VjauQZOuLZwI8svEeBDWIOC9AhBxOU7ZvHrlt0rgt9nnvayuHHWh+KDHRgbMu4Qsx9dBv5FNlr8kc2nz0tY/499LzFM5M3hW6tb8awW8tV3nEEOCDo48Rxra1LRh8OI9YC5Zaeu2cda9a4mC8BCUjg0Ql4aHGgB1nE88ninQVANgBMqqPF+4Hq/CB6tADIxmOkG5uOhCxCz7SoiW7PFscnl7Jmw1o3o4/C5wzPyCWsrvXXrE0OEOrBYS2Hjxlb+NTDilqG63zr3PsS97OFft0E9HqRz0aA+ksHD4yDjBujsYU8dP/og9zYc6s44+St5K3JufaZYU6g/9463JvDrfV/NXk8j2f6+WfmsLWfpJCfw1GehVF41rFmZKtpEpCABJ6JwHhUfyYLT2ZLNjRMqKNDg3uqO1qcRr/RxqKWzyJ0tom5px3P3lZ8cgnruoh7NE7XbsA+yt5r/DXTGT+yqZ+9ZZGFOgcMawH9upylQwvk4Qs+dQxIOzkImR2opBxxNkM1Lde8vo+NlHmWkHHyXvZc88zQJ2Y+vlb/e3O4Vt9Xrs/zl0PIS+acI9gxvm1ZL9HP1sYy9HvGseYI7sqUgAQkcCYCHlrc2RtMvCwM174RvbNa35vLhovJfylkEcoCgW9Bcs/GZfRNbxZC2I39LEL2fKuKzLCjfv+gN22waUM299RBH9qsiy/azSaPPBZo2SjF/iq/1o2d5FN2Fmgbucgn5PftdeG1lUl0qnogszPB1qoTdob3yB7y0Wfka3RDFjKp2znF7m5nZYvccE35bvNMLuVpd6RbZPW486g2c409tL/WR7qczrW2y2FC/Bx900+7v7rte56B9B/a6yFvQqDnloAetZ9QB13Dq8ugfPJ6Pcqmj2w5MKE8toxCdOgHKqOycEj/7JzRIzoR01597kbyktZ9RP30m5QhTtvhgnz6TQ8ZL3p6l5HnIP5NvchPfdpNGnEPpI2emTV9e3sjOVVGuPT2uccfeQaQw/OR8XZUPmmUzafaUHWr6dSDeWSHIfcJ+LM+70nv/ac+89g28mXvG9E1cdctbdW4y1gaAxhLq+0w5dlIX6556JCw1Ed6+2FGOgEu2B+bEiOTAJeMd+8J7Z/u+5F9e9nDAT3WxhfYRM/o3dT7frtnrPleyQsJSEACEvhQAv+d6T5UjedvPJN9JtO+0D4LgSx41hZgWTCxUGARgz0sKrCPhVANLFJIy2YEFllosiBZC5SviyvkpX7VE/lJz0al6kk7pFMGGYTYSxohi7r4KYu598z//EPZLBxreq7Rl3YiI4ywIWz2MImOtc+kPnqgI5/Y2hd30SP6EcOduuRVhuQhKzaGEzZQlgVtmHQ7aRdZ6BtdOifkIiNyw4k+1MNIt14m97fqI3u41rJcwwU7wnvkr0ufgdp3YnPibDJgeWlA1+jdZaT/kR+/pUyeefLSL5K3N6Y+cvIszupTDp1gQvnKmWvSk0YMn97HR7KRS9v4MLaMnpH02bSRTRXt9JDnoKfTBm2FZ54vdE/byMc+PjXQ10fplCG927pV3/iZuIfKBf3Cpfe5sEAG5bCPujN9ezvRodsQPjUd+fDKM0VbnTd5aT92UY/r9J+UIS3PL3k1UAc5fGiH+1qW+7VAvaovfoxufR6EK2WTXjlWBnv7CO1tGX/DMX0c2/p43+1lLEDnzD9V5/joEvbpa7Q/C2GZ/LX+hh6UgYdBAhKQgAQeg8C/V0OPofPDaVkXFplMiTO5n8mg2aKx65hFTRYjyc8Coy52KMsCr4YsbkcL/VqO68hkEZTAdVgmjTj6IzcLyegSP1Q51MnCsfqD+sjvZSlfF3217X4d/cIIWZG3h0lsih20M0oL0846enT9WOiRhy41UJ/FZ/glL2328pFf+WVRSF5C+CMnIeVG/WCkW+r1+FZ9JDZW1jOucKj9LDqlP1UZe/wdOYlnfiI//GBVuabu1ji+6f6qG7Pq38jNhrjWS94lMf0OWXlOlmTE55UzDHr/JL+njeTGnzUP9uhUNzfo1+XF511vynU26WO9LH2Jtmo6dXt99BvJJX2k2yhtpG/06v2I+/581n4XfUlDf/xSQy1b00fX6YedL2W7HSlb5USHmsb4S91uVxj28TJsar+KjP4MwAXZtWxtu17TXm8rY0vlm7QuE86dAfJJ49ND7Et6eFUO8U1tn/Kp23UgL89o5BJHtyqb9PijM4r8zmPEftRebZtr5PGsJsyYJJ84ctN/a57XEpCABCRwPgI/znTn0/EpNGJCrQv8TKpnmzBZdKAbi4ClkEVHX9SkftKzmMl9lRkGdbFR83OdcrlPnEVHrZ/2++KJOiyQRnalTl1sszil3b6oIr2Wiy6jeKb3XibRrzLMwrbaTj5tdhtneqBzLx/dugzKZoFLndruTH76SNggG5/lECfps/pdt5QfxTMZe/vIVq5hPepn3V9hWv0XG6J35Zm8xNkwjXwSPZAz0iUyolPaSxydqpzkJaZ9bBiF+JiySyGyajyyJ/Ki15LM2FTLjtKQ0Z/jLhf+6LbEMHXYWPUxYKZ30lOXmD45sr2WyXV45T7xSC55lO+yt+obdp0BG9qeRlvRIc9z6ld/rOmb/MTph90G8rttKdt1676elYv+Xd+RHbOy9AP0CoPY0eM9YwD+6ocIkdcZhAvpPUTnpO8Zf1O3s0FW8iKXGOboMCqfdU99ZiKjl+/s81yO7Ev7yO/8Kb9Up9rRdYhcYwlIQAISOBeBH2e6c+n3dNowCWcjxaTaF1wfbXAWDSwqlsLWRQcLgiwgZvHaoiG8uj7RoaZH/xHXlJ/p0W1m4UjZumGjDBvbLSHt9LJ7mcSmGSf0o0z07XbM9EAv8mr5HNawEBwFylKnLhJn8lN2JIcDENrKN2vI6KHr1vPr/a36SJW5xDUL8VFf6P7a6++qA9eR1zdj5NVFPeWWAvakjxDjg4SqI2n1G9Lq65RPnE3byH8pkzh2UHYmM31mzRZkRl59LrAxfQFeNS96jGL0Qa+RP0flk0b5yqC3F3tSPv6aPV8plxidRmy73FqevFlY0jc8O/voMItTPs9y7VfRY6Zv8hOnH45soP2enjZJZzwZhciMnikTnbrPwqGmx8c1DTmjspFf4+gwY0g6Zei/Izsja5QXmSmTOPblvsZr42/qdnuRkbwqL+PKyPexnecyITK6/M4zdZE/CuQjq4cZk1ouOvR+Uct4LQEJSEAC5yHw407hPLo9rSbZGDKxbl283gtGFg2jhUDVIRP+2qIj8nq5KmvtOt/i9A0Fi6CuZ9obLURGC76ltuOnbBaz6VuqU/NmC6fouJXJrDz6oBscKBN9O5OZHujamaSt+q1YtSl+p1zCTH7Kphwxi1rqojO6ZxOHjB66bj2/3t+qjyBzC9fYNvJhGCav31e9t1ynPvEowBJWM5/VOjls6bLQtfuR5y1psaXK4jqbfcrhy6WA7yNvVi5cu36j8uHSdYv/0hYyR5upKjOyZocptSzXPGtspJANp+jddUl66ofzFl9RJzakfuIuN+mUJ6+HLfqGQWUffWtal537ma7kz/RN3cRpb2TDyLY6npCPT3o/jMxuQ3Qiv4ZwqOkZW/t8jR95/rb2ryqztpnr6DqynzIjBjPusS+yiSuvpfE3dUf6Jq/KjQ6dPWViE2USIqPL7+xTl/I9YAuHVjzvPUSfnl7vo0PvF7WM1xKQgAQkcB4C/51FzqPTS2iSSfVsE2YWDaNFQnVMJvy1RUc2Piz6Lg1ZnNQFSjapfZEU/UdcqT/7xmakG+1mQ5iN0EjuqC5p8XHP38skNlXW2J2NP3oSZgu8mR7Rsfo6m9CaVvWP37EhYSY/ZVNu5EfyZvVJn+kRmYlHsi/pI1u5xrbRJrf7a6+/Y1PiyJttdGMnvNIXUrfHkUVcQ/oOMmrIIcdsY8ZzEf/1DV2Vk+uUzX2Pw7Xr18txH1vqc1HLoVvk8ewvsUm/h+VaoAw8artpp6YhJ+mRSf+CwdZxaMary418ypNXw1Z9w7Oyhxkyt3CJrn1MRpeZvlVPrtMPuw3kjWxLffSsfbXqEJnVLupFp+6zcOjpnWMOMkZjQPRKvHUMiK4j+5E1YhDuaStx7Ms9jHgO6jwamcioIXU7A8okr5bPPDkqH5toNyEyevnOPnWR30Oe2di/FPe63EeH3i9GZU2TgAQkIIGPJ/Dvmerj9XkaDVggsDAdffOThSCTbF1cncH4LBqY0JdCJvy1RUcW6XXBUuVSv8uo+blmgYKMLI5of1Qv+o8WImz6YF433JFPPKoTeflGbfStTpVRr7OIqmlc72USHaq9sEA+fSmBfNK672Z6UK+Xr5vQKjttxO81byY/ZVM3m4rOf1a/6xY5s/gWfWQr19gyOkjo/trr725fFufdrymHz/JcjPRJOeLoRlxD+g7MewgT4lEIC+qujWczX0du+kzt68nrcWypZWHVA5tN2u39rpaLj+BY+3bKIBfO2Xxicw0zvZNey8ZXVe/ko0c94J3xGslFBuXJS9ijb3j2voG+fEZjH6zCPP0k99GBeKZvLcN1+mG1IWW6bZTt/S0HCdU/kdntik7dD+HQ02krczq6cF19FT1HcfrX7BmiLT7wjM9H/bAzoK2U7+3GvqTnOe3Pwah+6nYGyEpe5BLnGavck48M2qh5kdHld/bhRv0eqEv50Sc2Ja/X5X6mw6isaRKQgAQk8PEEfpwJPl6np9AgE3WdPFmE8MkET5yQBVGf3JN/rziLBib0pTCb8FO/LkZSlg1VXYhh81o76MAia7bY6zqmfeIe4hMW4H3hxoJqtABF32wy1jaEvb34vqdzv4dJFpuVaWRXntmghGnyon/0yOaDfOSkfPKjW2cYFnXxSZ3okvqJI6ffV/bRARmE6Jz0rltk9fhWfSS2RA/aGXGti+nqF8rHX7U/hcUlz0DaWmKRzRr6jzaN4TV7PvJsxA8pT1wPReqYlTKwyoaVOP0r+TUO35pWr9kIUmZJRsqHc+WPfd3+2Fb7XWTUOG1jQ/U/9fPsh19/BmI/ZWvdpNd2Mv7zXOLbBGzGx7V+nt2ahq+TnrrElIFd7Sd79IUb9akTeciMDGyp+pJHW/FV6qNb1RdZ4ZCy7w0M/klfp3wN8XW1rfqllsWG6p88v7ErZZFFWeTUEHtrOnqP7Kr11q7T3toYkHL9WQubyoA20xcq81Efidz6HFAHBnwIkYGOlU31W3xZ7YUV5UeM4El6lRFdKmPkjdjHvtre2nW1aVY2z3vVa1bWdAlIQAIS+HgCHloc5IMsMDJ59rgvSDJZp1yfzA9S819iWbBkIieeTea1HHonkJ7FSLUPFll4YB9lUo68tUBZ6tNWPiyQYdTrZ7HFwioLsCo/Gwb0wEZkE2dTUsvmOr6pi73kzeLq/7p5TfmtTCrruhCPncRwQMekwYrrsAlrmKFL9KmbjOpr2oy/KEMgDXada7Uz7aV8ZPT2kAFL0qs/uE57M93Cr8e36iNhuIVrNlLpm/FD7MbO2LPV392u3Oe5zP0opv2Ug0ftr/gP3uhE36e/1JA+Tl6tlzLUJY9Pl00Z5HcetU9hf/KRMQvkYcNaoL3YWp8L7IB/uFMOX1KW66VQfYQM7IQX1+nbxOhIGkxgRb+NLrTFPSFlKV+Zokf8QB7t8OEamTXk+UA+tlGOMilPe5E9emaiwxZ96T/ogG5cx46uL/m0j8xwjs6xC33REznoSFlkV31Tp8dhSRvYjMzYgQz8zX305T6+TT8mP4E2qYe8lCNOO92GsI39yIlc2uI6H3TgsyWgUzhEn7RV9a3l4nd0gWfq1fa29pH0D3jSZ5CXusjlOixiL2mU5Z6AbpTlk34XXVIJF2HVAAAgAElEQVQH+Xn2YZO+l3J72UfHrZxpJzqmzVFMGfgaJCABCUjgMQjMV4+Pof+ptWRSzyKOCZLJmwXUaPJlMZBFVF2E38vALJ4y2ScmvQZ0T16NR+m1LouYLB6pR15dqNU2+nUWQ7W9eo0sFkI1Ldcj1izMwhqfZEHW2819fJP7tXikb2WR+mtMRkyxi0BdZHKPLbGT/oZNdUEZ/autqRtOxJGBfHiyWKRO2sAu0hNmds7SqZcFKHKzQKa/0wb1CCPdkpe2ezxqs9qGzC19ZA9XdKh9CT9kgY8f2BRUXmv+7jbV+9i39szQHu3yrKWPhwM6ZRMS2bM+Rp3aHygf30UeTHtAP/zZfYi/SYMXHEYhuqRfjMqQlnLRgzi6UJfr9Fvy0HvWZm8D/es4NapLG5GPrTDH77RFXe7jr5GOtEkZZEcOOnfeKUcecvAnviWQRv30h5Sp7eWZ2aLvu9DylhDyIjt6YGv0TV9PvcTYVctFDjFs6riUOj2m3fRd+mz0oG1sii/hhdyUxXbuK8fKI9ej/kPeLB390Du2R06N0SF6dnvq/dYxAFnYQhuVNfek1wDzWnbWR6hDn0EGtuQ5w1+kpb9QrsqkDvejPl3rUA9O+Ax5fPB59ceM8SwdmcmLvqSthbQ/K3eJzJks0yUgAQlI4D4EPLS4D2dbuYIACzgWViw0WBSxUMonC+Qs1K5oZlo1bU0LmPHhBD66jxwNgE1D3Wgc3d5HyM+zjK0GCZyJAH2SzTtzEJ/MCYk5WCD/6MBmvB9aHN3mGeRjMwcgtwqONbciqRwJSEAC9yPgocX9WNvSBQT4ZooF4VJg4bjnW5glWaM82s+3e6N80z6WwBn6yD0I0MfXnoV76HFUG9i25Zv4o9pXrgRmBOobH6MyHGTcclM9aoO0Vz204FAa2281DzvWzHqY6RKQgATOS8BDi/P6Rs3e3t4XgiwwZt++ks63MLP8SyAiK4sjNor3+AbtEj2t8/8E2Czcu498FHv6Iod0zxawyefs2bz6HPYwB7BhZuM8C/RdDi6ODq96aAFX3qa8xVsmjjVH91LlS0ACEjiGgIcWx3BV6o0I8BonCzVejc+GjcUhC0ny1r4Bu0SN/HYZ2VznAOMSWdY5nsBH9JHjrZq3wHNw5M+h5i0fk4MtHlgcw1ap1xPg7R/mID4ckLLpJY1+yzXzxD2eR+a96PGqc9K1Y8W19a/vTUqQgAQkIIFLCXhocSk5692NAAtEFos5TGDhxkKRBeMt37CIQXybQxvEr7o4DItHie/dRz6aC/byefTAJuIZ7Hh0P6j/MgHesuBgjTe6cnDANWn3mCMyJ6VtYua/Vwz4gi8t9gbHmr3ELC8BCUjgXAQ8tDiXP9RGAhKQgAQkIAEJSEACEpCABCQggf8Q8NDCriABCUhAAhKQgAQkIAEJSEACEpDAKQl4aHFKt6iUBCQgAQlIQAISkIAEJCABCUhAAh5a2AckIAEJSEACEpCABCQgAQlIQAISOCUBDy1O6RaVkoAEJCABCUhAAhKQgAQkIAEJSMBDC/uABCQgAQlIQAISkIAEJCABCUhAAqck4KHFKd2iUhKQgAQkIAEJSEACEpCABCQgAQl4aGEfkIAEJCABCUhAAhKQgAQkIAEJSOCUBDy0OKVbVEoCEpCABCQgAQlIQAISkIAEJCABDy3sAxKQgAQkIAEJSEACEpCABCQgAQmckoCHFqd0i0pJQAISkIAEJCABCUhAAhKQgAQk4KGFfUACEpCABCQgAQlIQAISkIAEJCCBUxLw0OKUblEpCUhAAhKQgAQkIAEJSEACEpCABDy0sA9IQAISkIAEJCABCUhAAhKQgAQkcEoCHlqc0i0qJQEJSEACEpCABCQgAQlIQAISkICHFvYBCUhAAhKQgAQkIAEJSEACEpCABE5JwEOLU7pFpSQgAQlIQAISkIAEJCABCUhAAhLw0MI+IAEJSEACEpCABCQgAQlIQAISkMApCXhocUq3qJQEJCABCUhAAhKQgAQkIAEJSEACHlrYByQgAQlIQAISkIAEJCABCUhAAhI4JQEPLU7pFpWSgAQkIAEJSEACEpCABCQgAQlIwEML+4AEJCABCUhAAhKQgAQkIAEJSEACpyTgocUp3aJSEpCABCQgAQlIQAISkIAEJCABCXhoYR+QgAQkIAEJSEACEpCABCQgAQlI4JQEPLQ4pVtUSgISkIAEJCABCUhAAhKQgAQkIAEPLewDEpCABCQgAQlIQAISkIAEJCABCZySgIcWp3SLSklAAhKQgAQkIAEJSEACEpCABCTgoYV9QAISkIAEJCABCUhAAhKQgAQkIIFTEvDQ4pRuUSkJSEACEpCABCQgAQlIQAISkIAEPLSwD0hAAhKQgAQkIAEJSEACEpCABCRwSgIeWpzSLSolAQlIQAISkIAEJCABCUhAAhKQgIcW9gEJSEACEpDADQn8+eefbz///PPbX3/9dUOpipKABCQgAQlIQAKvScBDi9f0u1ZLQAISkMCNCXBI8euvv74fWPz0008eWtyYr+IkIAEJSEACEnhNAh5avKbftVoCEpCABG5M4MuXL29///33+2GFhxY3hqs4CUhAAhKQgAReloCHFi/reg2XgAQkIIEjCPDGhYcWR5BVpgQkIAEJSEACr0jAQ4tX9Lo2S0ACEpDAYQQ8tDgMrYIlIAEJSEACEnhBAh5avKDTNVkCEpCABI4j4KHFcWyVLAEJSEACEpDA6xHw0OL1fK7FEpCABCRwIIGthxbfvn17/xkJPyXZ8/n999+/a//HH3+8ffr06f2Tv6fB/1yCvFruewUvJCABCUhAAhKQwIMR8NDiwRymuhKQgAQkcG4CWw8tsOK33377fmBBvVkgj8OJehjBIcXnz5/f/vnnn+/puSemrOE2BGD89evX9/8ZhsOmtUBZ/icZDpUMEpCABCTwHASYd/mj23w5cElwbriE2v/XcUVzOTtrSkACEpCABH4gwIKmHi78UKAksBnOYcTaIog3J0Zy88YGByAJHFqsyUtZ42UC8IUlhxCzgyUWsvimMufAIvXws0ECEpCABB6XQN5sZH5lzF8KHE6M5mvqODcskZvneWgxZ2OOBCQgAQlIYDMBNrUsUvpn7WcaWdzMFjhRoL5RkTTi1K+LqF9++eX9LY5azuv9BDiw4FCJRepSYBGK/3o5fIYv+HhwsUTQPAlIQALnJZAxnngtZN5YmtOdG9Yo/pjvocWPTEyRgAQkIAEJ3JVAPfCohw9dCcr1Q5D+VkW+9ecww3AdgRw4XCMFf3DwwRs4BglIQAISeCwCe8fwzBtLhxYQ2Cv3sajdXlsPLW7PVIkSkIAEJCCBXQTyEw8WORxM7An8BKF+w59vhJBpuJxAfo7z559/Xi7kPzXzk6HZz0uubkABEpCABCRwCIF8qbDlbbn8vQvmjbVDC5R1btjuMg8ttrOypAQkIAEJSOAwAlm8sNDZ8goqiozeqshmm3wOMyhj2E+Aw6D6NypGEsIan/Ht2izET/VwaVbWdAlIQAISOAeBPWM3h9LMBcS57m9Gdqv2yO91X+3eQ4tX87j2SkACEngBAnUzySJi6+cjvwnnWxw2yejKzwm2fKuTb3PqWxX5PS2ybvGWwAt0lx9MzJsvWw4Zcti0VhZ/4FeDBCQgAQk8BoH8zai1LxKYr+vPALMGWTu0gIJzw7a+4KHFNk6WkoAEJCABCRxOIIcQHFzU/w3k8IZt4F8EsuDc8ndBtpbNf2/7kQdj/zLSGwlIQAISWCSQn4bULwZGFRjf69t2mRe2HFo4N4yI/pjmocWPTEyRgAQkIAEJfBiBLGA4uHCD+zFu4K2JrfxTdm1Rm0Xs2jd2H2OxrUpAAhKQQCeQtx97er3P2xh1Dsh4v+XQImWdGyrVH689tPiRiSkSkIAEJCCBzQTY3G79bBHKb1x5zRSZLmK2ELt9mXy7tuXQaMuiFg2zMN2yiL29RUqUgAQkIIG9BDK3z+pxUMF83cf1Ot7z0xHmlNl8UsvO2jH97c1DC3uBBCQgAQmcngCTPYuH2aTfDcgiIAuOrfFW+b29W95nEcQiZ2+I3Rx8GNYJwHjEmbQt/Y3FKOVGMnrr8Q2xQQISkIAEzk8ga4eZpvXNyJSdxbP1hXPDjO6/0z20+DcP7yQgAQlI4IQEMqlv+eOUJ1R/s0rYx+9i+ebmEltZQFH32sDBCRvxZ99gs7jkD2n2sPXQIn+DZCSjy0wffnam3W7vJSABCTwqgRxA7NV/z3i/p+xePZ6pvIcWz+RNbZGABCQggYcmkP+JYvaNzNHG8YYGf6MhP3l41Q12/k7Fmh+y2NzyM549ZY/2s/IlIAEJSGCdQObC9ZL/LpHxfsscmrJb5pF/t/Jadx5avJa/tVYCEpDAwxHINx1bJv+HM64ovOdb+1Lt/ZLNdThd89+c8gfFslFH3rMyzyIRG0ch+Wv/e0jeyNjyc5y8Rhy+o3ZNk4AEJCCB8xDIGF//yOYW7TKHbJlDnRu2EPVvWmyjZCkJSEACEvgwAtnMX7MZ/zDlNzacP75Z/8u0jVW/F8tfMN+ygf5eaeHimQ8tMBvWM94sULGfNy6WAmW2/hyHb+y2ll1q0zwJSEACErgPgcyre9+CyFuTa3MIVjg3bPPl+CuGbXUtJQEJSEACEjicQA4tLvkbD4crd6MG+DaHDe3agQMLIcqOAt/o9E1xvu1hcz37zL4Jovwsb9T+o6VxYLH0tyhYSPKZhbzdMvNHrYdf4bllAVvreS0BCUhAAh9H4JKxu8+7S/PoJfI/jsbHtuyhxcfyt3UJSEACElghwMZyafO4Uv302VngbPkmBw6zTTLps7xLIDzzoQUHYNi3xDx+mb3hk2/glhak4Z5v3fxpSIgYS0ACEngMAsyrzBdHfHHi3LC9D3hosZ2VJSUgAQlI4AMI8I34s35DnW/rt9iXTfTsYOLWhwy3lvcBXWfaZN7eWXuzZelti/wOee23zn6TNnWDGRKQgAROT+CoMfwouacHeqGCHlpcCM5qEpCABCRwHwJsntf+IOJ9NLltK3xrw6YY+/Z8RgccOfzo3+TnoGNJ/uxNAerM8m5L4v7SsKv/lGakBQcSlOvM86bG2htAlMvfzjjiW7qRzqZJQAISkMBtCfBWHnPi0tt5e1p0bthD6//Lemixn5k1JCABCUjgTgSyGeebcRYLt1ow3Fr9vOKZP+zIIQubXT6znxewEV46TJjljQ4S8lOFW22M8yYCbxPcSuatmV8jj7dV8BW2YeNS4OAiP8vhm7EsNvHt0lsW9N3Ue0aGS8zMk4AEJPBsBFh/MO4z318zpjs3XNYzPLS4jJu1JCABCUjgDgTqBpGFwhlDDlOy0edQIW+GZNN6tN5svNe+9d+iA7qPDktmP0nZIvOMZfAZdnJwsXTwEN3ph//7v//7nQ28l+rhf5id9ZAtdhlLQAISkMB2Ahxcsxa5dL51btjOupf00KIT8V4CEpCABCRwAYG87ZADC0SwsOk/LbhA9GoVvv25RzurilhAAhKQgAQkIAEJ3JiAhxY3Bqo4CUhAAhJ4TQL9bQe+nefb/HqIcQSZvB3BN0AGCUhAAhKQgAQk8GwEPLR4No9qjwQkIAEJfAiB/lZFfoKw9DOCaxTNYQU/cZj93Yxr5FtXAhKQgAQkIAEJnIGAhxZn8II6SEACEpDAQxPIf11W/4YBP9fgZxsGCUhAAhKQgAQkIIHLCXhocTk7a0pAAhKQgATeCYzequAPMfLhZyJr/0OFGCUgAQlIQAISkIAExgQ8tBhzMVUCEpCABCSwmQB/Tby/VZGDDA4ujvqJyGYFLSgBCUhAAhKQgAQelICHFg/qONWWgAQkIAEJSEACEpCABCQgAQk8OwEPLZ7dw9onAQlIQAISkIAEJCABCUhAAhJ4UAIeWjyo41RbAhKQgAQkIAEJSEACEpCABCTw7AQ8tHh2D2ufBCQgAQlIQAISkIAEJCABCUjgQQl4aPGgjlNtCUhAAhKQgAQkIAEJSEACEpDAsxPw0OLZPax9EpCABCQgAQlIQAISkIAEJCCBByXgocWDOk61JSABCUhAAhKQgAQkIAEJSEACz07AQ4tn97D2SUACEpCABCQgAQlIQAISkIAEHpSAhxYP6jjVloAEJCABCUhAAhKQgAQkIAEJPDsBDy2e3cPaJwEJSEACEpCABCQgAQlIQAISeFACHlo8qONUWwISkIAEJCABCUhAAhKQgAQk8OwEPLR4dg9rnwQkIAEJSEACEpCABCQgAQlI4EEJeGjxoI5TbQlIQAISkIAEJCABCUhAAhKQwLMT8NDi2T2sfRKQgAQkIAEJSEACEpCABCQggQcl4KHFgzpOtSUgAQlIQAISkIAEJCABCUhAAs9OwEOLZ/ew9klAAhKQgAQkIAEJSEACEpCABB6UgIcWD+o41ZaABCQgAQlIQAISkIAEJCABCTw7AQ8tnt3D2icBCUhAAhKQgAQkIAEJSEACEnhQAh5aPKjjVPvt7Z9//nn7+vXr288///z2119/fTiSv//+++3Lly9vnz59+pcupP3yyy//Sjvi5l7tbNEde9HnbAEfff78+Qcf3VPPP//8873P/vTTT+/94tu3b+/Nz9LvqdtaW7///vs7O3T/7bff3p/BtTq3yr+F7x6B8a147ZVzC75723yG8mcadx+Z5x9//PH266+/vjHGGM5J4KPmdZ6xrWs81oSM81vDIz2/szXmzC+skVnvMF+zLo2txJeGs627L7XDeo9JwEOLx/TboVqzcGCQq5+tE8ahijXhdQN1Bv3YxIVZVTUTRU074vpe7WzRfTaJbql7ZJmZj45ss8qmn3LIxsTPYQXX+G2WXut+9DXPG/wIbDBYBO1ZHF6r/7W+ewTG1zK6pv61fK9p+5HrnmncfVSOPJuMJ8yfHlqc14v3nteZJ2lzqU/0/NRhs74lPNLzOxujZ35hLc8hDgE7ecZmZbewoszZ1t1b9bbccxDw0OI5/HhzKzjRZQGxdeC/uQIbBTKAoieLnjOEHPgcrQubxaWJ/Oj2H0k+k3QN9/JRbTPXLDpGfpulp94lMYs3bL1VYMHz0c/ZNb57BMa38tVWOWd6NrbqPCrX7RiVeYW0HIA+gq3dZ4wtHlqcx3PdPx+hGWN2Nt2j9rP+63NqDi44XH+2wDPOc7IW+FJkS7k1OaN81p9nWnePdDTtOQms9/zntFurNhB4hAVEJq2P3kwF5zWbqsjYEnOY1CfqLfVerUwO36rd9/JRbTPXs2dqlp56l8R53fqSur1ONhQf/Zxd47uzM+7Mj74/27Nxqb0jOy6V9ej1zvJTyTWOI59ljHFeW6N3fP7IP8e3+u8W6A/051lgU87BymxcxwYO2jnAeKaQNe+aTVvLrckZ5edZ/ej1wEg3056bgIcWz+3fq6ybTQZXCb1x5QzMZxk8r9lUbUXDZvQRfLPVnqPK5dsWWNVwDx/V9ur1zG+z9Fp3zzULOhZs2HqLcJZFyjW+OzvjW/hpq4wzPhtbda/lZnbUMq9ynd+un2UunHGf+SxjjIcWM3L3SZ/55z6t/7cVxvqltyzIz9sEsz5DmVnef1t6rKusede03lpuTc4oP8/q2ceake6mPTaBf6/mH9sWtb8xgb7I5+Q6v4vjmlf3KMNpOPezkG98GUTzalt+C8+kEznIYuHFpJlAOeSTl/zkEWdgXho8kZcFXfRNefKYGGmDtqJL7mljS5no1DdVlVnKEFe72VzCpQbsIh19iTN557W88CDGllk71U+UxT7aTqAubNCb63xzwf1SQEZsxb9V/yoTGZ1f+hB2pR+kLe6jAz5Adg2VG/b0/lLLRk5YxabojV65pmzvw/ggfa/2h9pGvUYe+sRvyA7r9NPoQkz+LB25S/JG+fiWOtgRHdIe7cxC9Tv1OtMwiiziJXnkzbjVfhMGS9zxSxiif3RZ8121dYkx/S26og+219B9sIUxddLHkYkNtZ/X54P+DfP6/NT2uV57huv4mrFszUfXPhv1OUX/pf6ADeRTLs8Z93BAz3yqjKTh71l90md2pE58W59feMY/tR/RFvfolXpp/12JwT9VVrLTZ7CB/gQrZI4CZeMzykUvWNU+Q11kxF7y63NKvTBLPGqPtOiHDMpiY56x1E065Wta7LjmOY4NkRvGyCYt/SD5xCmDPlwnb2bjqL+lLFzjX+TUZx4duA+TlMvzia/CLXpG7hLXlNkSIzftEvd+wH0YEnPPuEQYsQmrynDJf5Hd61U21Q7SU6f3yz39u8rkuaJ9ZI8CjLImolz3ReqEZe57fO3zW+tja/gTc4/+8WXlHz2W2KUMvgpf/IxN2JyAjPTZpMV3NabcqCx1ap/Ch50nttA28tInuUbeLCz1sbSJLNqjbJ6xmTzTJQCB//Z8eUigEWBQqoNXBq2kM5AxaGeiatXfbxnUyKcOgzblGYCZcLhGJjEhZbOIIJ16mZyIua8b2QzgS4Mn8hgc0ZcP13wIqY9cyqVMJh902FLmXVhZNOS+MksaMmHS7co99qEPAzkhMion8me+STvYUtuhPuxJi6z4Bh5pnwmsc47MxOiU8rCnfEJkZpJGz6TF3+hGfvxAXdqlXHwZu8MBnVOf8r2/pP0ax4+jNPRCD+TSbvodZZlA088oQx42hluVl2t0i+6Ug3W1j3Ldb6k7Sl+Th/z0WRghgzoJ3McHSetxGEbv3CO7BtKRl3I1r16vcYNHdIzOnTttww/ulMc3CfHnku9StsedMW2Qln5MzH38Tv1LGKNj/FL7Dv2bkGcB22BA2TDpOlOf8tEx/Yo0ruMv9EbX+Ac/kEaZWQjLmp+0Jb7YgXx0I4y4VZnYiK2U54OtyCdE/24/PkjaUn1kROfa5lI/RC5s+GALOqUvIiusyaNM7Q+1Da6rrORRP7rDH9vjl5RJDIf0B+pQHn3Qg3oJ4RQ5ua/PKbLQN2VSt8e0kzK0h4y0Rdv0R+SEAyzRp/YlysfGsLvkOa66oRPtpm9EN/jUQDrto+soLPUX6lbbet+NL2CS5zVlKrfR81Xzo3u4jvQcpS31W8rTF9Ex/gsz/JOA7tiYACfSapk1/1G2ykBW2FQ5tN/14Z72CFv7d3RNjJ29/eTh3/Q90iiXPpMyifMM449RQA71a1t7nt9af+9YssYOfbEVnnkWqdP9O/ILdTMeVLtHZdGbfpfnKf094176T+Y0WMZu9JmFtT6GLthHQNfar2YyTZfAf0c2WUigERhNBqPJjLSlAYeBDVkMjDVk4s+kUWPKMaCRVicc7usElYF5afBkgK0LqtSJLrmvMmiztrWlDPJmfJCVgC5Vn0wKmZiI62In/KJf7tGpht42fKscysau2j66df9V22sbuaZ8bb/Ko0yXOeKXtMhE10yUpGXCzsS21l8ip8adCXmztDDAH+g/+lSbaztZHI3qxG+UJ38ko6evyYNT9y0+oK8nIDM2Ja3HLIC677JgrL5In6u2dFlbuLFQqXLRLzqmb9bnnbLUSaAsdtVQZdT0ft0Zr40vtL2XcfxWbUAP5FRZ6JKFfdez3m95huOb2q+StuSvGcs1vlk0U65+4seqP9fogO1hQlx15Ro5yadOLb9Wv9uxpR/2OrRJWrcBvaqu3bbUo1wC5XkO0YNAP1ryQ+yvZZIWmVue09SpclI/cfpn9Vuuaz3GBNKJ+/iArCOeY9qnTexISFodM9Cn3qdsYurU/lP729ozjwx0WOsH0YuYsJVrdBzFa/2W/M4dOb3fco8NNfQyXU7PH8lAXmdzab9MX6061uulfHTLsxWdap+pcrqfal6uu63I2vP89vrIJY1PDb1vb2GHjP78cd/92/1CuzOGvezaeI6cOnchm+cPOen/1c5cr/WxXr/bGTnGEqgE/j2y1RyvX54Ag0qfDLYO0BVeJo6RrJ5W6+U6iw4Gzq4T9fvgl3o9ZmHBZER5PgkzGQzmmXi2lEHejE9tjzJsRtYCCywGcgb/auMSz95O9K9tVbtIp04vR9qSb3KAgE8oVxcRI5kjfkmLbrSJj2YBHZd0GtWjTmVCmVlaGITvSN4sDb1Sf1aG9BnXnr4mj3z8uBSQuaZTbxd5WTxX1mFCPAspM8tPOvJZ9GSxFB1Tv/el1COmLDrXQFpk1PR+PbKVMrPx5RLG1On60Uael+hEmS06z2yrz3C47fUXsruus7SqK3WW+kFsTAzfjGGMv70u/iY/i1bya3tr9bvO4ZH2R3GvQxnSarukYWvlukVWNrCxCf2XQvpM5ZK01Bvp0Z/T1KlyUj8xZbqNyasxsvN85tC45nNNmVs+x/EbOtaAvtk00SbXxLOw1l+oRxnaGa0pYN0Zdf7RNay3cp3pTHpkzsokP22mHLpWfblG3xp6GfJm/iNvJIP0zqZzidyaDhvuq95Jo/wozPJJ72uE2laXNWNWy3Vb9z6/vT6yR7y7nv2eevikptfr6DxiQznarGFUjvxelvvqmyqD65EtW7hSd6mPZXwhXjqE7Pp4/9oE/j2yvTYLrW8ERgPm1gG6isoAxyBaw2gwrPlc55tG3j5godF1ysC8NOiy6GJxwqKYCSl10lbuuwz0y0J6SxnkzfigdwJlsghLWo0Z6MOGwTyTaPRb4tnbYULoIbKTTh3Sauica16u64KVBXpd3HaZI35JizzqhHfSatz1rnmza+ogt4ZZWhiEb3jXurNrbIEBvlsKM649fU1e2C1tiJAZm2Y6UWZ0gNb12cJkSxn6DH2fNukv6BcdU39pAUNZdKuhyqjp/brbRP7S+HIJ49Tp/SDp0QldYnfSRjFl1p7hcKONhKQRz8KM5RrfEcdZG0mnn/Js84xQvz/n4ZNyvQ8kfVS/23GN7d0nW2zt7WMzOtQD8m5PuBDH9uqrpKUceqw9p6lT5aR+YspsGacyB9H3RuWPeI7jN3SsIem0yaf3nVo210v9ZemZpz6s1/pBdArrrVyj3yjuMnuZWQgahqYAACAASURBVD66Vn1H/bGXWfIf7Y5kkN7ZXNov01e7jbmf5dPe0if+iJwZs+QTj2yl3tbnd1S/86Yd9K59ewu7Xgc5IzaUo80aRuWiRy07aqPKGdmyhetaH2OMQcccHDLW9Hmz6uG1BCDw79WfTCRQCIwGMwYw0msYDWo1PwNcHbDJZ/GBLGIWGQlMFoT8tq4OZF2nDMy0MQssuuqCL3VSPvdVBm3SFgMvYUsZys34VGaZDLG72ha7ietGJfyiX+7RqYbedvjWwwTKZ9OYuuhG3Ro655rHNbIT0AfGVZ8uc8QvaZGDDOrlZzKk0y/iu9hDPOovkVPjzoS8WVoYIBs90Kd+q4OdVbfaDv2EOvit8sbGek+ZyikyevqavDwbtFdZwCr3yIxNaafH9AU+tR/G/m478tIHuxzuU2/GDQ7IqHLRLzomn/q1HcrnOaQsMmqoMmp6v6ZeZR+G1fZaJvl7GMdvvZ/QZ+szTTuxu+tZ79Pnax8ivz7DsKp6k5+0yrHK5XrGElk1dL60TRlsDDtidB0FdIj/Uq63QTp+Z+wjrmGtfrdjrR8iu9dJGuk1dK41L9ddFn0szyAxfu9yU5eY8rRTfZW0lNvynKZOlZP6idM/0an2KermHl+gL/d88AflSSeQhr63fo7Re8YbfWDQ9Y5dNV7qL3mmYwv1epvcd3/1MtE1rLdwrTqOrtf6bdrMsxQZ6Fr1ZQ5A3xpqmTX/UY/yXQbpnc2l/TJ9tepYr8Oz+qnm1+vum5oXZnkea16uu63olvJbnt9eH7mVd9rpem5hx7OHrBpG7LpfKD8qR3ovix6kzcbzjMnVF+Ga/l/143pLH6vzRZ7Lmbwu3/vXJfDvke11OWh5I5AJtA4sFGHRwABXBzDS+MwCixvqMIjWQBsMyuTVT9rMoMsASHuZyEjPgikTdO6r/FwjOwcCyMokQ/vcpx3apR0+XFebtpShvRkfdAizDOjVZiYO2iCgH/eUR8fYyIAeO6lLOrKyiOlthy/pXBOYHGAeXYiRVW1NGvJnAR0rL/RFNiH1q8zYEP0pl7Tokomrc8lEFntqPtfpLyNd4zfaiD2dE/VIG+nb2wrHUVuRW+vAJYG6I31n6UvysAfZva26wOEaGZRN34ouifFHdKIcH3hWOZTNs5e+lvo9jk+rXlxjI37kOv2EtrGBttIveE5rXfoqZdCLECa5TxrpS2HEOH2DZwh5sZF09CHtEsbogt7pt8T1HrnY2BmP9Edv6iKTa0J/huPD9G/KJC1cR7JjP/qk7ha+kV39xPWsb3T7o3/XKfoQ17BWP/WqHUv9ENkjO/E16QnxU9gkvcddFvqQFn/RpzP/9LrcR9fqq6ShAyHM65jbn1M44QfKwjjt9zajb/UfthNoj/yqS+TSX8nP/TXP8chnsXHEO21ueWYoW5+32t/S7tIzDxcYJMCRtKpXdK2clrgiA53W9I/fq2+4ji9po9pGOmlVbrcR+zOOUS4sl/wXGfg7dnPd2YTDUr+MTZVV0pA5CmGOrmsBndB3FEhP3x7lkxa/RRfqkBbma89vr4/MLWPJFnbxAzH60W/TXuwa+QUd8EntO6SNykYPytZPxvP0FzjABBlcUxYdRj5KnaU+Rv3kYxf3xAYJLBHw0GKJzovmMbHVwYtrBqGeDp5eriPL4EU5BjgG3xrqIMxknMmPMgyQ1KEubXPPgE0a9TKgR4fR4ImclEM+g2QG6UzGyadtyiCPQZnBOWFLmRGfURoy0TWTD20iPwH9ogc6Yiv3yIpOmZBiw6wd6maCiV2ZjGkv7IiRUf2VtOhVY/RFz/gneoxkhl3aoo2ub3yHfyKTmDZqWOovtVyuwy59prdLueiVOHWxKX7AV12XlEtcJ3NkwZ32CZ0B+aTN0qmzJI98/Fh9W58d8rMJx+b0G9J7wK7aF7G7lu/M0D3+6rK4X+IWWfgDGSmbBRLtJo12KB+GqUs6H0KuE4/0mTFeG1+QdQljbKhjCWwrr+hK38J/awH7q5+5zjOM3MgjHvUp0kbhmmcDf9XndNYG7aIjfSxjVp7FrhPcsCG2JX+tfrcj9Wo/qs/vln7UuVJnFEayYIFOySOuz1OVQ9nqP9pNvaSTRlh7TmkDO+lXeZ5qW7mmXO9P6Euobad89Ehcy13yHFO/+6zzHvUnbFuyK/oia9bf1p752EgMi64Xad1n0XWJK/YiM7yi6yie9VvK0kZ8FFnc80moZUhPX+T5636OjLQZvt0/yO5s0t5Sv+ys4Bn9I4+0UaBc2I7yk4acWTn6ObbNQteFcsgKM2RTBqajMKofuxKP+lBkLbFLGfTJOgRbuMeucEs70ZV6NS3XPR3dE9bGc/Skr6SN6J3+Ejk1DptZH8OO2LY2ZlW5Xr82AQ8tXtv/Wl82lJkERlAYXBmwl8qM6pkmAQlIQALrBFgIsxg3SKATYNPIxubRA3381msINod1A/rojKI/nNjwXho4oKLP9EPQS+VZTwIS+HgCHlp8vA/U4IMJbDmQ2FLmg82weQlIQAIPSYBNaX0b4iGNUOnDCPCWwNI35oc1fGPBR9jwrIcWoMfvfC4JHIDm5weX1LeOBCRwPgIeWpzPJ2p0ZwIsJHiLgm9BZmFLmVld0yUgAQlI4EcCbCzy+vIzflv8o8WmbCXAhjN9g2/MZ6/ob5X3keV4awB7jrCBtxE48HvGkMPMPQcX1GFc2VPnGdlpkwSekYCHFs/oVW3aTCBvUGRxNHp1c0uZzQ1aUAISkIAE3gnkMJhNxhEbOjE/LgG+RGBeZkOev8XwuNYco3nWLYmPaeXjpbIGG63NRppxOOQbFiMypkng8Ql4aPH4PtQCCUhAAhKQgAQkIAEJSEACEpDAUxLw0OIp3apREpCABCQgAQlIQAISkIAEJCCBxyfgocXj+1ALJCABCUhAAhKQgAQkIAEJSEACT0nAQ4undKtGSUACEpCABCQgAQlIQAISkIAEHp+AhxaP70MtkIAEJCABCUhAAhKQgAQkIAEJPCUBDy2e0q0aJQEJSEACEpCABCQgAQlIQAISeHwCHlo8vg+1QAISkIAEJCABCUhAAhKQgAQk8JQEPLR4SrdqlAQkIAEJSEACEpCABCQgAQlI4PEJeGjx+D7UAglIQAISkIAEJCABCUhAAhKQwFMS8NDiKd2qURKQgAQkIAEJSEACEpCABCQggccn4KHF4/tQCyQgAQlIQAISkIAEJCABCUhAAk9JwEOLp3SrRklAAhKQgAQkIAEJSEACEpCABB6fgIcWj+9DLZCABCQgAQlIQAISkIAEJCABCTwlAQ8tntKtGiUBCUhAAhKQgAQkIAEJSEACEnh8Ah5aPL4PtUACEpCABCQgAQlIQAISkIAEJPCUBDy0eEq3apQEJCABCUhAAhKQgAQkIAEJSODxCXho8fg+1AIJSEACEpCABCQgAQlIQAISkMBTEvDQ4indqlESkIAEJCABCUhAAhKQgAQkIIHHJ+ChxeP7UAskIAEJSOADCXz79u3tp59+2v35/fffv2v95cuX9/q//PLLe9rXr1/fPn369P75888/v5fzQgISkIAEJCABCbwaAQ8tXs3j2isBCUhAAjcn8Ntvv30/tPjrr7+m8snjMIJDjhxa/PHHH298OJxIOocWhJ9//vnt119/ncoz43oCsIdxmK9JxCccMv39999rRc2XgAQkIIEnIsC4z/jPPLAl/PPPP+/zesoyzzDfMO8Y9hHw0GIfL0tLQAISkIAEfiDAwiSHEWuLGQ4rcjhRBbGYIb1unpH1+fPnWuxhr2M3NubTjeFQh7zRQU3qJL6WCz6jHRgvLSBphzZThkUrafg7ad0O7yUgAQlI4LkIMN4z7jP+10PrzFuZm3pMnRqQw7zD/MM8ZNhGwEOLbZwsJQEJSEACElgkkEMHFix5i2JUId+89DK8rVEPPFKuHmKM5D1SWt5Iwc7RYo1FHPxGhxb1Zzi3OLDgpzh8RnpUpuiKTnWRSj4LT9I9uKi0vJaABCTwfASWxvscWjCfMHf1D29m9MC8s3UO6nVf9d5Di1f1vHZLQAISkMDNCWTTPdrk1sYo1w8t2BzXzXgWSWzWnyXkbYvRIi4Lv9mhBQzIY6F3baB9vv3qBxF75d5Kzt52LS8BCUhAAvchwDzBfDGat9AgcxfxnrAmd4+sVyjrocUreFkbJSABCUjgLgTq2wAcTGwNLF76t/b5CcJWGY9QLocW/cAG3XOQMzu0yCHO3oVh55IF5mwB2ssv3edtmD2+XpJnngQkIAEJnIsA4zvz0uytvMwpl8xNzEPIvqTuuSgdr42HFscztgUJSEACEnghAlmE9EOIJQTZkNe3Klgo8WGhxM8qniHMDi1YsPFNVhZ//RAABv1NlEt5cBiEb9besqBMPkv/g8tWeZfqaz0JSEACEvgYAvlCob4F2TXJvHXJwcMW+b29V7330OJVPa/dEpCABJ6YQDbH2XRujS9ZdHSM2WDTJhvx2bcztR4HHZStIQcZbODrYUYt82jXsam/aYGNpGXx1w8tyIPP2kHDFh7I4QBkLeC39JuldmPTM/3tkTU25ktAAhJ4BQL5W1WM87OQeevS9QPzUZ//Z229crqHFq/sfW2XgAQkIIFDCOS/L2XT+yxvSdwCVBZ39VCCNBZsHBKM8kknvx90XKJP5G/1SQ6eltrKT4KqTUvlzZOABCQggccgwLjOPLD0xUGdV/ibSzns5nrpsCME8geqkWOYE/DQYs7GHAlIQAISkMDFBLIQYQHjYuT/MWZxVzf4XOdAYpTPa7l8E7XljZU1Z+WtiLS3VD6v7W454MDHW97eWGrPPAlIQAISOBcBxnXG96XA3MQBBfNK5ikOOXKAsfb3k6hHG1sOOJb0ePa8ZS88u/XaJwEJSEACEjiIAJte3hBwMfJfwP1Qgvu8ZUGpnp+3GJb+psR/pa9fZXG45dBizwEHPl5b2K5rZwkJSEACEjgTgWvGdg4wUn/pJ4Z75qUzsbm3Lh5a3Ju47UlAAhKQwG4C2cwSbwlZBGTBsDXeKn+LDmy42ZDXtwq21KMMdZ7xm/v4MUzqWxbYPcpP2c4uZfd8O5V+seXQIn9QdUufSP/qOnovAQlIQAKPS+DasZ35CxlLc86eeelxSV6vuYcW1zNUggQkIAEJHEwgk3pevTy4uavF53XR+hbBHqHU2/KzhDWZvKHA4ceWjfearFvk56CBhRzXnU/PZ7E3+y1x/kDaLH+kb/rR0gIy9fJq75Y+d+3CNm0aS0ACEpDAeQhcO7Z7aHE7X3pocTuWSpKABCQgAQm8E9jzLf0RyNj8s1jK73G5P0tgEYhufPrhQQ4t4Ifua78F3mvT3p98bH3bBZu2lt2rs+UlIAEJSOBjCGQOvbT1HFos/e9SOUzf89bgpfo8cj0PLR7Ze+ouAQlI4AUI5JuOvsE9q+n5n0Mu2XBn8YLNW77hnzGgbX5Dm0OAsx1a8IZFf8sCW6LvLD/2pk/sZRz5a2+xpBx/BHQt5O9usDg1SEACEpDA8xDIocPSG32UGY3/zOGZq5b+pkX+aPeZ5ukzetBDizN6RZ0kIAEJSOA7gRwC3OqPMX4XfMBF/vgmPy24NLCAudW39tl8n2kxlEXc6BAq+lJm6ZupHBRc8s0UByJrfHN4tKRD/Ju3N7aUTR1jCUhAAhI4PwHGdeajpbkmBxsccufLBuao/MRwqS4EmI+YlwzLBDy0WOZjrgQkIAEJfDCBHFpkMfDB6iw2z+KFxcfStyoI4A2B0Tcz5JHe3wTIoigb/lE8OpjIIcAob9GQAzPRHUYjf0bftUOFlFv69mtmAgtLdFjyUXhv4bZF3kwX0yUgAQlI4LwEmCeYL5beumONQn4OKTLHMY+vzSFb5J+Xzn0189DivrxtTQISkIAEdhJgg7+2id0p8pDi+XZ+7VsVGsee2aEFC57RWwiXKJ3N/drC6RLZH1knrGc6LNmdvKWflnCogh/WQl7/nflyrb75EpCABCRwbgI5xB4dtF+rOfMQc82zzdHXchnVX5+RR7VMk4AEJCABCdyJAN9eLH3LcSc1FpvJRniLntlwjza6kXOrBcyt5S1CuGMm7Eb8okIYzxaZ+ImF4uhti/z0pL/tEtk1XpJTy3ktAQlIQAKPSeCotyGOkvuYlNe19tBinZElJCABCUjgAwmwuTzz3wtgY8ybE+i55zM64Mhmu+PONz1L8kcHHc96aAEHWF0a8BmHYXz6wUa++Vp7Y4Z89Fgrd6mO1pOABCQggXMQuPV4vzQHncPi82nhocX5fKJGEpCABCTwHwLZdPObURYNZ9wg5tv2pQOFUd5o0823+2ykbxWyAR+1das27i0nb0LM/jBrWK/ZzKKRwyAOnHLgk4Xp6EApdlIPrvyE5Iz9MXoaS0ACEpDA7Qgw3jPuM/73w+49rTDf5Cei18jZ0+YzlPXQ4hm8qA0SkIAEnpQAEzqb+CwUntTM72Zh59KG+XvBlYvZmxlrG/kVsafIzl9zH/20AwXzh1tnhxrdCBai//M///P+1gQLyTVGlGHROmu/y/deAhKQgASegwDjPuM/88AlgfmL+dkD7/30PLTYz8waEpCABCQggZsTyBsELmaW0fI2ytKCMYcWfoO1zNFcCUhAAhKQwKMQ8NDiUTylnhKQgAQk8LQE2GCvbcaf1vgdhuXnQksHO9d8C7ZDFYtKQAISkIAEJHAnAh5a3Am0zUhAAhKQgARGBPJTDg4teNvC8COBHFZs+fkGPye6xU9sftTCFAlIQAISkIAEPoKAhxYfQd02JSABCUhAAhI4hAB/iPPM/9vMIUYrVAISkIAEJPDEBDy0eGLnapoEJCABCUjglQjkjQz+rgU/IVn6GckrcdFWCUhAAhKQwCMT8NDikb2n7hKQgAQkIAEJfCfwav/bzHfDvZCABCQgAQk8MQEPLZ7YuZomAQlIQAISkIAEJCABCUhAAhJ4ZAIeWjyy99RdAhKQgAQkIAEJSEACEpCABCTwxAQ8tHhi52qaBCQgAQlIQAISkIAEJCABCUjgkQl4aPHI3lN3CUhAAhKQgAQkIAEJSEACEpDAExPw0OKJnatpEpCABCQgAQlIQAISkIAEJCCBRybgocUje0/dJSABCUhAAhKQgAQkIAEJSEACT0zAQ4sndq6mSUACEpCABCQgAQlIQAISkIAEHpmAhxaP7D11l4AEJCABCUhAAhKQgAQkIAEJPDEBDy2e2LmaJgEJSEACEpCABCQgAQlIQAISeGQCHlo8svfUXQISkIAEJCABCUhAAhKQgAQk8MQEPLR4YudqmgQkIAEJSEACEpCABCQgAQlI4JEJeGjxyN5TdwlIQAISkIAEJCABCUhAAhKQwBMT8NDiiZ2raRKQgAQkIAEJSEACEpCABCQggUcm4KHFI3tP3SUgAQlIQAISkIAEJCABCUhAAk9MwEOLJ3aupklAAhKQgAQkIAEJSEACEpCABB6ZgIcWj+w9dZeABCQgAQlIQAISkIAEJCABCTwxAQ8tnti5miYBCUhAAhKQgAQkIAEJSEACEnhkAh5aPLL31F0CEpCABCQgAQlIQAISkIAEJPDEBDy0eGLnapoEJCABCUhAAhKQgAQkIAEJSOCRCXho8cjeU3cJSEACEpCABCQggack8O3bt7effvpp9+f333//zuPLly/v9X/55Zf3tK9fv759+vTp/fPnn39+L+eFBCQggTMT8NDizN5Rt9MQ+Oeff97++OOPNyb9uhg4jYJ3VgQOLHr+/vvvO7dsc7ckgP/ozz///PPbX3/9dUvRLytrbaz4iGdnTaePdBabJjZlbKwuCWe27RJ77lGHjfDnz5/fx/B7tHfrNjJm0W+Yh7CFfjAKH/G8jfToafT733777e3XX3/tWT/cUy4HF0vjNHnwoGzWKaxb+OQ5I51DCwLj/pb2f1DIBAlIQAIfQMBDiw+AbpPHEWDSzuRe40zgNa1eV40oW/O4ZpL/v3bORclVHdmC///XN/LcyIjlGoHBTbux91KERyCV6pESqNDpPSQ/XKsrx9zt+rc4GOddE0H9a/2cAEk+a9kkdy8Zfq6tEhJ49q74i2cHn/g4ueP7y4+pVw8tnvF2Xlr/PwEOLPwv76yHTyscUPART+HQlXvi4MN8Vf7ieVv5kW2sef0+cmjAu9r3NAcNe8X8ZeYpPCdworagCz++oRg3MfqbcZkXrZg7xvrOXFgPPMOrOGbMvS+BbyLweTvWN9FvLL9CgBe6BwxsQCRpWXJzmxu7ciRDJAkkR+ijOG5rjGPvUv8Wh7vE9+1+kIRffZCwSsT8mL3a1rfPz158d3xXmLB/yvtrj+/se4X36lmYer/53o+zT4rRA65veVcxB0c/PD10YMzeM8y+v5Ihl8kDD+XyEOOT1sLKV/8ihTjN21LOvW7FPP8Zzp3fDeQFHmCt4sh4e10C30aghxbfNqON5z8CJuhs3qt/wuChxt6GzcaQY19JjP96On6Dw1/H9C/YJ+Ei8boyOTcpm/xM5K60NW38a/c+d3sfF+9mckefrmJw9t3Me/9fT/h97q+ag3focZ6/5V115tACvs7ZVl7jHCA33z3zryr4+EXP/I866vjE2vWx+qst3397zOkjN7xjYZ6YV/xzHVC3lMC/RKCHFv/SbP9jse4dTLhhb21Q/Bcd/wRVbG6IMxmw/6711RzuGue3+MWBBf+lhwTqquScwzfXweRkAnSVran/X7w3Qb7Tu+KOPl21Ns68m+HAgfS/nvD73F81B+/Q4zx/y7tq7wN6xdOD57PjeP8zhrzHwh7Dc/BNxfWxeu+y3u1fPfvmhHddW+Sj5KUU3+WrOL5pPhtLCUwCPbSYRHr/NQT8c8r8k0iDcxNnI8+/prCfDT03eNrd8Kjzw5KNf8qqh03Gj0Xk0Jt/tkgSksmD/9Y4D0y0xXj8RZ+bl3b26qs5wAs/jyQ8+A4bfIYb8TIfjM3k4BknZOHkJo0uecAqmV5lE6b4jr9wpybunBu540/KcZ9lznPGS0z6D1v1YNOf+pDDvu1c80uWaZdrbMnKcdSOwb73+Ok9fnA/Cz64Zhl3dD0Sm5ywjW5j2bPlGGwRx3yG8G/yzedorgfk0SkTZC1pK+fF/vQZf/CbMbMQH/2zb/qZ43ymZMr9HH+UPXIZC37KZOpMH7jGfv6IxXiyPfVkO7KWXOeruVvNjWOpJ++0w7U+UHsvI+6xme9m5aYebTrWtbHyWdmjviE/9R55Zp4xz1hYqxbamW9j5BnD11l8zm333nG2px36ZpkctuzNcd4Tp+8B9K/YTB/0ceWPeqnxzXdVtu9dP+M312zO7Vxvacd9EJ+RM6acu5TfuvY5Rk+u7S152pFDPtcBdvnhP/y/ociUOgtrDOY+U5M5DHhmWCufULbi+ATf62MJ/ITA/+5AP9HWsSVwIwJsRGzUc7PGRT/k6SMJyMI4NrhZ3BCRZ3MjIedngpgJAWORIwHzUMTxtFGQzwQEn0ywtM9YrulzDOPxG9tHypUc2CzT52f28Vt/GUdyRMKA/8b0jBMJl0ktY5GHEzxljw3ipFxhEz2Z6HiPvZnwYBt/sM9PX/GTMueZeNCB/7KYSaPt+JCF9rSFDVhOuRzjNXL8ZtEW3NCNLpNc16FjXl2PcCFe9Dn3cKMNm7RNW4xBBjY+Q/iILPNAP2XyNQ708VutB+LLD2pkYIk/xO8c4pvF2OnDNj/ZoS8LOvATfZbpp+3UyBGrcco/x2sfXynoYwx25ruAdn6uC8YgN31KH7xmjPOUerFn+4wXFujWHrrgmTERCzK0WXJuMlb6ted6J37GogPuWdSNTeTxm5/vB3RZnJupg370M8Z58PnKdYDcGd/OzJs+WudcOO/2UeMfcfgs6K/zAAN4EdMsrt1slw1jshAvbbMd/ayJtMc9PxmmnnnNOkLW9ZRzvIrXedbe1Jf3+CyP6XfK5bXy6l/xyzUrf2p+xIKtXG/ot8840a/sah2mT/OauXZdo8O5n3J5r/1swxd8xf70N+U+6dqY5ruEGGmDuzFnXPTB8siazXF57dpE/5mfay11PbveiuPZuPaXwKcTeNyZPj2a+l8CgwDJJhvITDrZ9E1I2KyysPFNefrdlOYHJkkMNui3uKnMTdCk2+QFeTc4kzTGOI7Ndvqi36tEVPuzvpIDuk2app3VvdwYY4LlRn2WE3OV7DKBkx8+XGETHTOhxN9sQ2bOAz45p84jPtk2/bc92WGDdjnZt2pDdsopn/XKDv3aSn60++GeupH9yXrU1tThc5G2kFkl5c4turIYn3HkcwRz+ueziz3amcOcK/TSnu8G7aaPPoszHvUyZhZ0ojsLsUxZ7rPtKHvGoH9+iPDhQnvqTB/yWh2ytE8duYbpI95kYPyTqfOc47Gx8sv1lzq4RpZfFv2d87vSrW9z/diefHyW5zN+xrej85bx5LWxpV/2wzznGS4zLpknR8YjNznSvuK7kofNfB8j5xrJ9UD7LM7ljEu9+MGcZJHFbE+Zeb163qaM90f56Qds8ddi7BmT62o+S7475nypa692LP7ONb837tv7ZJ1MaWMNME+rftdbztndOa3iuLvP9a8EriDwuPNfobE6SuBGBNzc3bRwjSSPZINiQoechbZMBG03UZmbmxtItpOw5cY5dWSisZUkmtShfxbHrPycstxfyQF9WwnvyvYWN2TPclox9YM0+66wqY7JP5NxPmZy3o1fPpmoOmfKWCvrPbVt07ZJaibKxD/lUpfXz+xPHTP+K9bjVlxbtpCfhdiNJde/bVOee2KjfzVXtK/sTH0+Q2lTvXO87St7Mkg/aZsHJ/B2/FH2JuDTn2cM0heutTc/1o1r6ueZoM9y5rlWp7GqY/K33Y/QnAfGruZ3pdu2GQMxozufWWyu/Fi1ITt9k2OyMQ51ZBz2Za2OORe0zxjYu3JvQQ8y2Jo+2J62uNav2T7lfe9OOfnCYq+wRlZ+McaP/xmL8zxjOxAoKQAAEAhJREFU2bMz/d6TPcpPP+aaNfZs37NP/PS/UmDjXJ3h8YqtTxkj/2TKtfOx6mcd8mzlnnr3eFdx3N3n+lcCVxDoocUVFKvj1gTYkNjc/a97JEQmptT0mRyRQM7k0OBeSVRMKmadm6p92rF2Y7J/VSNztFzFAXv4jz9Hyha31LOKjbbJKe+1zZw53rYrbPoRg+75UaYd7W7VJkvIK+NY6xVL2+b8mswzl6zdM4nWM/vTlgxtp1bHVq2ssc16K65pyw8i4l0V9fgcI6NPK3l9z/lQjnHom2VPH2sDXT5Tc/yePX1Pex6I8KFHzOjPoj59WtXIqGfFTR0rBmnLaz+K8qPawwjs6yNrcL4zjXHlJ23Ja8svDwD0x1rd3lO7fmZsK922pQ+pi2tiYg16qI3PWY76pq0tDrQj86z4ge8ehjyx5v3UwVpwDld2VhzRoa9T35SXufKreurIe5+d1TtMbvPgQ5tHmGlr+m37s3qPn35QZ9Fv24kNLqyjVaFvbx2uxtiW+9PeOlD+X6jlL1PuWUOusdlv7sBcf1KZcXyS7/W1BH5C4HEn/ommji2BmxIwwXAjy02Mjd9ki2uS/fwQypDUY0JinxtItp9JRrSvPmvtof+Kor6fcsAXdOD3kaLd5OO4s5z03fHWk+FVNlkTfjDo60yAVnHpV9bTR/tWLG1bzT0Jqgk/a/lowvrM/rQlQ9vnvf6fqbfimrq99zBx2lAPcpat+OgnBvpTPsehb5aVPtcD3NEFe+Tm+D17+j7tMcY+dObBgzyciznWe+VWce755PisPQBh/VOMfbbzvpzvzBWT1J3XW3753M0PCthP3ltxr3TbNnXgE882urCBfT9qiCfLUd/0C5s/KfqcH7/zoEj9vh+ID3auqemD7Y6zJtYZL31Tft47/mitnTwUc6zxTj9e4XnWzyP89IM6i37b7j0+rArxbfWt5LMNbqtnIWW2rrG5tX62xnxC++RNnM4F/q/6t/gre3R/dU24ro/W2Dlb9G3L97P6Kl8Cn0LgcSf+FK/rZwmcIECy7QZCcj0/hPyvUWw6JAHIr4qbUm6CyLmBZDvJ5dGkQN+mTT8Ojm6ac/y8v4oDetks8ftI2eLG2LOctjZpfEneV9k0PtgZMz7zccMPu37UKbtVb82zenOcbXsJDWuZ9YreI2vkmf1pS4a2X7Eet+KatogNf5FfFfXkB+1WfIxfPaPq3bIz9fmRwHzPg6vp5549fdf+rBnLWsa+a+soeznOd9wzBtMH7/GDNUa8+OJayHau5eG4M8/1Fit0ooef72R8gMv80DVu6iwr3bbNOVvZQ9dcB7StZFe+HZ239Hnr2nWD/zzvM1bG4QPzhYwlx9lGbXu2cb2KdyXPoRqyzs3U8+zed1f66hjniLnP4jyvxqRcXm/FmTJeH+WnH3MO9Nt274l1VeCHf2eL689n8+x4xq3eEWf1sL55/s/Mx1kbZ+TlDVOuJ5/ZD//5LtGee9BWv3J/UWccf2G/Nkvgrwgc++r4K+9qtwQuIuDBBJtUfuigngSQdn57G/nRRAWd2pu2DMekhntt22fNZknfTNzsZ+Pid6boF3qnb0c5YO9MIrjFDT36M30xpslpleDJiYTTcoVNkpZZ/DDRX5IifqvEncQydWzN84qlbXN+Ux++ETv2t9ZI+v/M/rQlQ9vlvGULOWXTbl5vxTVtwVN/4TiLerJP+SnLPX7Rn+tJOdrRN8vUR9y0pU31zvG2r+zpe9qb84qNtHeUPesSH1kTs+z5NGW998MU//Ld6HzxPORz57gzz/WeX9iFA/E4T8jPoj+T90q3bXPOjNVnWxtzHdh+xLej86bOvdr3M2xhMt85zn3+hQ76XG+Tm+3T5la8U574kZ321DfnwnZr36Wr8c7R7HOeZyzqXNXT75UMbWf46ceMUb9t5zmWJ2thFvrw72yBC2PPcDhrY08eu/jNgcVf+rHyUab45zwo5/zAD9/n+lLu7rVxvLJ27h5b/SuBPQI9tNij076vIWDCt0rmCdKkGLmtcjRRYbybCnpnEsxGmXbYZPmtCpsSfSSq+bFEAvTKhnUFB/zUr5XPs22LG3JXcFJ/JvG2Uc9y1CZjScyzONY51Q4fEZmUMlcwSp+25nnF0o8+7FHUs/qv2ozH/rPiGldOndrXlv3Glu3KvroeHZ86sbdni74ssCWWmXBu8WUs9uifuuijHb9mmfq8z+fQDx3H22f7yh5zha4sjM/1Q59M1Cm7Z+yd52lbn1aHDOlLXrNG8BWd+c6ynb45l4yXN+N8VtQ733/6tfL3yLpGr6ymDv3Idjjjt3PGeOKRb/oLe2T5UZwLZI76pt5n8/afgSf/40fiag5lMJ8L1xss9B8ztk+Trp+UZe5tV55+25Iv/fBZ+ehYaucGHWmLPvTR7jvKcfO9aPtevRXnHHOGn7IzbmPKdn3O9YZtYmZdMadnis/LnOcjOvQbu5P5kfHKYJu5MV7quxRiY+2s1pX+bvUbAzr4vcJYHb9ZG8dcU79ps7pL4A4EHjOnO3hUH0rglwiwUW0lUrTTv1dMPmbyycbGBkdylIkAOt38SEzYYKgZbzGBRi4/CrIfv9SDDn7cz48cxzyrf8oB/frwzBb9cpt8HHuEE7IyQF7O/te+TBKvsolOWHlwgU1iYQ61T21SjH9cwybH4U/Oc84b453fnH+TS2IlSTU+bKDfZN7kRR9luqqdM2SxxQ/7xINebTCWduXxwYLv+qsvymVcymedtqa/6khbyUZ52pCZayn5Jkftuwaxgw4LssQBA5nSl/pMyNVBTRu8bIMJ1zKwfdpLvcyrBTl80Ba+cI8eC2OPsPejhrhghU7idJ7Rge/JQRurWt9m31a7ctjGB/kqnzEhu8UKeX3FX36sA+KRs7bUQZ1xbb2b8QndsPIDDN20s7Zoh1nGwLXr8IxvR+fNWPZqGOCj6yRlXVvEhe/EgM/OO2y4pyiLLuSyGDPjsEes6KNGHj2OoZ02fthFBn6MzXlI/XltPIzx+SM2Y0hZnwlsGUf2r6734pzyymp7j1+ut9ST8Rg/fqMTv+GDXn7ypJ01iP1nRV3werXgO/NzRWGu8H+1Hq/Q/4oO/OHHXMyiv/T7LE8Z7l0LrO87Ft9rxHFk3dwxhvpUAq8Q6KHFK9Q65iMJ7CUGvPj3EiE3wqxzA8z2hMPGaNJI4pIbqQlOjiWRmYVExSQJWWR+slH9hIO+mXB5v1VnbF6vEpw9TupmPMka/psEcm8CnXLasn7FJj4Rp7bQxRphPrKQnKZPzHf6tDXPW+3oRqeMsWkCTLz8jAtbRxMr1gzyrsOt9btqxxfLK+txpZMYttq1RdzE7xzgP9zkgdweR/pllTV25Zvt6Frpo424HYMf6KAwH/jnnKc+r5Hd0osOYlQ3Y5yjjBO5o+yx5zpRF23ym2v4v0A2/of1tUrwt9pTzbPnWj5ZO37FK+XgBZ9s85pYvc5a3fgOF3g4b/QxD8jTZ8w827Thj+Wob8ofnTflt2rixeetgs/4jr/4jTzxcc8ewv3Kd1hakHEtYsv3C23wmXtPrjXsIIOOowX/XKv6ic4sK5+RzTlJea5XY/bkGXOEH3bnb2u9GcecfxkxVyumMxbvmQPGPHt+mfucU8dT085ayELbjGneG0uOM+5VX8q98xq/YbRag/q79wzhq3Jzrb8zjmkLX7bmiXi35nvq6X0JfDKBHlp88uzV9xL4IwJunu80TzLSjfmdxGurBP6OAEk6H198QPBhywenP9pJ1P2gfreXd/bt3Sxq7z0EPIQ5sub5KN/aK9lHnx3eHI3Ij3vqbyqy/qaYGksJfAOBHlp8wyw2hhJ4MwESomf/teJql3pocTXR6iuBexLgvyQ/e7/wYeFfQ7wzijv79k4OtfU+Ah4O8FcZz4of3KtDC/Vcdchwtb5nsb2rH3Yrfu+yXzslUAJrAj20WHNpawmUwA4B/pz3SAK1o+J0Vw8tTiPrgBL4SAL+u/vVn3gTkP+EYav/N4O+s2+/GXd1/w0B1jgHeOx/Z36r/dkDjRkJH+jPdK8OOr710AIWsGopgRK4F4EeWtxrPupNCdySAIcUJE4kKfxp9JF/V3tlIP67bJKJZ/+e90q71VUCJfB+AvzzD5513jN8fPEBwbuHv6ygj/cR76G/KHf27S941ObvEmD9PztQWPWvPro5cOPZuar4LKxsXWXj3Xp4r8CTnKOlBErgXgR6aHGv+ag3JXBLAiYnbOYkPu/8YFj9V6BvSpJuOeF1qgT+mAAfDbxrOLjwo4wPLp79v/gLi8RxZ9/Sz16XQBLwEDDbXrle7ck8o9+wL3MwSiz9jyOvrIyOKYHfJdBDi9/lW+0lUAIlUAIlUAIlUAIl8GcE/AuCI/9Hnn/m5A0M+8+/buBKXSiBEhgEemgxgPS2BEqgBEqgBEqgBEqgBL6BAH+Z1I/x5zPp/0dHD3aes6pECfwFgR5a/AX12iyBEiiBEiiBEiiBEiiBXyTgP+V49z/r/MWQLlftYQX/v13f8E9cLgdUhSVwEwI9tLjJRNSNEiiBEiiBEiiBEiiBEiiBEiiBEiiBRwI9tHjk0bsSKIESKIESKIESKIESKIESKIESKIGbEOihxU0mom6UQAmUQAmUQAmUQAmUQAmUQAmUQAk8EuihxSOP3pVACZRACZRACZRACZRACZRACZRACdyEQA8tbjIRdaMESqAESqAESqAESqAESqAESqAESuCRQA8tHnn0rgRKoARKoARKoARKoARKoARKoARK4CYEemhxk4moGyVQAiVQAiVQAiVQAiVQAiVQAiVQAo8EemjxyKN3JVACJVACJVACJVACJVACJVACJVACNyHQQ4ubTETdKIESKIESKIESKIESKIESKIESKIESeCTQQ4tHHr0rgRIogRIogRIogRIogRIogRIogRK4CYEeWtxkIupGCZRACZRACZRACZRACZRACZRACZTAI4EeWjzy6F0JlEAJlEAJlEAJlEAJlEAJlEAJlMBNCPTQ4iYTUTdKoARKoARKoARKoARKoARKoARKoAQeCfTQ4pFH70qgBEqgBEqgBEqgBEqgBEqgBEqgBG5CoIcWN5mIulECJVACJVACJVACJVACJVACJVACJfBIoIcWjzx6VwIlUAIlUAIlUAIlUAIlUAIlUAIlcBMCPbS4yUTUjRIogRIogRIogRIogRIogRIogRIogUcCPbR45NG7EiiBEiiBEiiBEiiBEiiBEiiBEiiBmxDoocVNJqJulEAJlEAJlEAJlEAJlEAJlEAJlEAJPBLoocUjj96VQAmUQAmUQAmUQAmUQAmUQAmUQAnchEAPLW4yEXWjBEqgBEqgBEqgBEqgBEqgBEqgBErgkUAPLR559K4ESqAESqAESqAESqAESqAESqAESuAmBHpocZOJqBslUAIlUAIlUAIlUAIlUAIlUAIlUAKPBHpo8cijdyVQAiVQAiVQAiVQAiVQAiVQAiVQAjch8H89qIxqiMaVawAAAABJRU5ErkJggg==\" style=\"width: 1045px;\" width=\"1045\" height=\"827\"\u003e\u003c/p\u003e\n\u003cp\u003eGRG is shown in Table 4, which confirms that the machinability parameter points of trial 9 have the highest GRG\u003c/p\u003e\n\u003cp\u003eTable 4: Normalised Data and Grey Relational Grade\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"473\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003eTrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003eNormalised Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003eGrey Relational Coefficients (GRC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003eGrey Relational Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.3333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.8661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.3660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.3660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.7962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.3857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.3857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.2421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.6738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.6738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.5205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.4899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.4899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.4052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.5524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.5524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.0710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.8757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.8757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.169491525423728%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.491525423728813%\"\u003e\n \u003cp\u003e0.2884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.86440677966102%\"\u003e\n \u003cp\u003e0.6342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.576271186440678%\"\u003e\n \u003cp\u003e0.6342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.898305084745763%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Analysis of GRG\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.729684908789388%\" rowspan=\"2\"\u003e\n \u003cp\u003eResponse Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.729684908789388%\" rowspan=\"2\"\u003e\n \u003cp\u003eInput Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.946932006633496%\" colspan=\"3\"\u003e\n \u003cp\u003eMean GRG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.593698175787727%\" rowspan=\"2\"\u003e\n \u003cp\u003eMax - Min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLevel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLevel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eLevel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.764119601328904%\" rowspan=\"4\"\u003e\n \u003cp\u003eSpecific Wear Rate (SWR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.764119601328904%\"\u003e\n \u003cp\u003eLoad (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e0.3617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e0.5720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e0.8366\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.617940199335548%\"\u003e\n \u003cp\u003e0.4749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.20545073375262%\"\u003e\n \u003cp\u003eSpeed (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0. 5838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0.5580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e0.6286\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0.0706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.20545073375262%\"\u003e\n \u003cp\u003eTrack Diameter (D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0.4858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0.5980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e0.6865\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.448637316561843%\"\u003e\n \u003cp\u003e0.2007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"63.102725366876314%\" colspan=\"3\"\u003e\n \u003cp\u003eTotal mean to GRG = 0.5901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.897274633123686%\" colspan=\"2\"\u003e\n \u003cp\u003eOptimum level = L3 N3 D3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eANOVA for GRG is shown in table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6: ANOVA for GRG\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"483\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.273858921161825%\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.639004149377594%\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003eAdj SS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003eAdj MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.655601659751037%\"\u003e\n \u003cp\u003eF-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.033195020746888%\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\"\u003e\n \u003cp\u003eEffective Factor (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.273858921161825%\"\u003e\n \u003cp\u003eLoad (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.639004149377594%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.339824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.169912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.655601659751037%\"\u003e\n \u003cp\u003e17.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.033195020746888%\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\"\u003e\n \u003cp\u003e79.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.273858921161825%\"\u003e\n \u003cp\u003eSpeed (rpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.639004149377594%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.007650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.003825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.655601659751037%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.033195020746888%\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.273858921161825%\"\u003e\n \u003cp\u003eTrack Diameter (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.639004149377594%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.060689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.030345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.655601659751037%\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.033195020746888%\"\u003e\n \u003cp\u003e0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\"\u003e\n \u003cp\u003e14.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.273858921161825%\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.639004149377594%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.019732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.009866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.655601659751037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.033195020746888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\"\u003e\n \u003cp\u003e4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.273858921161825%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.639004149377594%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e0.427895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.485477178423237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.655601659751037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.033195020746888%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConfirmation Test\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoad (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpeed (rpm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrack Diameter (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange in weight (g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.102292768959437%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSp. Wear Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.516754850088184%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\"\u003e\n \u003cp\u003e0.8250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.102292768959437%\"\u003e\n \u003cp\u003e0.000045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe presented work elucidates the successful synthesis of two metal matrix composites utilising aluminium and Sic in varying weight proportions. The outcomes of the pin-on-drum wear test underscored the superior wear resistance of the composite containing 5% Sic compared to those comprising 12% Sic, particularly under escalating loads. These findings underscore the efficacy of Sic particles as reinforcing agents, augmenting the mechanical attributes of aluminium-based composites. The devised fabrication methodology holds promise for engendering high-performance aluminium composites endowed with distinctive characteristics, notably a commendable strength-to-weight ratio, excellent thermal and electrical conductivity, and remarkable wear resistance. The incorporation of Sic particles as reinforcement agents represents a significant stride towards fortifying the wear resistance of aluminium-based composites, potentially engendering robust yet lightweight materials suitable for diverse applications across aerospace, automotive, and military sectors. This research not only unveils novel insights into the wear behaviour of aluminium-based composites reinforced with Sic particles but also introduces a fabrication approach that holds the key to crafting bespoke materials tailored for specific industrial demands. In conclusion, this study furnishes invaluable insights into the wear dynamics of aluminium composites reinforced with Sic particles. The demonstrated enhancements in wear resistance, coupled with the innovative fabrication technique, herald the advent of lightweight, high-performance materials characterised by specialised attributes, poised for multifaceted industrial applications. To further broaden the horizons of these composites\u0026apos; applicability, future endeavours could delve into refining composite compositions and scrutinising their performance across diverse operational contexts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEshan Agrawal, Vinod Tungikar, (2020), Study on tribological properties of Al-TiC composites by Taguchi method, Materials Today: Proceedings, Volume 26, Part 2,Pages 2242-2247.\u003c/li\u003e\n\u003cli\u003eAjitanshu Vedrtnam , Anuj Kumar, (2017), Fabrication and wear characterization of silicon carbide and copper reinforced aluminium matrix composite, Materials Discovery, Volume 9, Pages 16-22.\u003c/li\u003e\n\u003cli\u003eK. Senthil Kumar, S. Karthikeyan, R. Gokul Rahesh, (2020), Experimental investigation of wear characteristics of aluminium metal matrix composites, Materials Today Proceedings, Volume 33, Part 7Pages 3139-3142.\u003c/li\u003e\n\u003cli\u003eJ. Dutta Majumdar, B. Ramesh Chandra, I. Manna, (2007), Friction and wear behaviour of laser composite surfaced aluminium with silicon carbide Wear, Volume 262, Issues 5\u0026ndash;6, 28 , Pages 641-648.\u003c/li\u003e\n\u003cli\u003eT. Sathish, S. Karthick, (2020), Wear behaviour analysis on aluminium alloy 7050 with reinforced SiC through taguchi approach, Journal of Materials Research and Technology, Volume 9, Issue 3, Pages 3481-3487.\u003c/li\u003e\n\u003cli\u003eS. Suresha, B.K. Sridhara, (2010), Effect of silicon carbide particulates on wear resistance of graphitic aluminium matrix composites, Materials \u0026amp; Design, Volume 31, Issue 9, Pages 4470-4477.\u003c/li\u003e\n\u003cli\u003eT. Lin, D. Bhattacharyya, C. Lane, (1995), Machinability of a silicon carbide reinforced aluminium metal matrix composite Wear, Volumes 181\u0026ndash;183, Part 2, Pages 883-888.\u003c/li\u003e\n\u003cli\u003eA. Radha , K.R. Vijayakumar, (2016), An investigation of mechanical and wear properties of AA6061 reinforced with silicon carbide and graphene nano particles-Particulate composites, Materials Today: Proceedings, Volume 3, Issue 6, Pages 2247-2253.\u003c/li\u003e\n\u003cli\u003eMd. Habibur Rahman, H. M. Mamun Al Rashed, (2014), Characterization of Silicon Carbide Reinforced Aluminum Matrix Composites, Procedia Engineering, Volume 90, Pages 103-109.\u003c/li\u003e\n\u003cli\u003ePradeep Kumar Yadav, Gajendra Dixit, Savita Dixit, Virendra Pratap Singh, Surendra Kumar Patel, Rajesh Purohit, Basil Kuriachen, (2021), Effect of eutectic silicon and silicon carbide particles on high stress scratching wear of aluminium composite for various testing parameters, Wear, Volumes 482\u0026ndash;483, 203921.\u003c/li\u003e\n\u003cli\u003eO.P. Modi , B.K. Prasad , A.H. Yegneswaran ,M.L Vaidya, (1992), Dry sliding wear behaviour of squeeze cast aluminium alloy-silicon carbide composites, Materials Science and Engineering, Volume 151, Issue 2, , Pages 235-245.\u003c/li\u003e\n\u003cli\u003eAdem Onat, (2010), Mechanical and dry sliding wear properties of silicon carbide particulate reinforced aluminium\u0026ndash;copper alloy matrix composites produced by direct squeeze casting method, Journal of Alloys and Compounds, Volume 489, Issue 1, Pages 119-124.\u003c/li\u003e\n\u003cli\u003eP.B. Pawar, Abhay A. Utpat, (2014) , Development of Aluminium Based Silicon Carbide Particulate Metal Matrix Composite for Spur Gear, Procedia Materials Science, Volume 6, Pages 1150-1156.\u003c/li\u003e\n\u003cli\u003eKenneth Kanayo Alaneme , Tolulope Moyosore Adewale, Peter Apata Olubambi, (2014), Corrosion and wear behaviour of Al\u0026ndash;Mg\u0026ndash;Si alloy matrix hybrid composites reinforced with rice husk ash and silicon carbide, Journal of Materials Research and Technology, Volume 3, Issue 1, Pages 9-16.\u003c/li\u003e\n\u003cli\u003eXiang Zeng, Jingang Yu, Dingfa Fu, Hui Zhang, (2018), Wear characteristics of hybrid aluminium-matrix composites reinforced with well-dispersed reduced graphene oxide nanosheets and silicon carbide particulates, Vacuum, Volume 155, Pages 364-375.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metal matrix composites (MMCs), manufacturing and aerospace domains, stir casting, Mechanical properties, Anova calculation","lastPublishedDoi":"10.21203/rs.3.rs-3993647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3993647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this investigation, two metal matrix composites were synthesised employing the vertical stir-casting methodology, amalgamating aluminium with silicon carbide (SiC) at concentrations of 5% and 12%. To assess the tribological properties of these composites, a pin-on-drum wear analysis was executed. Throughout this scrutiny, meticulous monitoring of wear losses was undertaken over a predetermined traversal distance while subjecting the specimens to incremental loads spanning from 1, 2, \u0026amp; 3 kilograms. The results unequivocally demonstrated that, in concert with the increase in applied loads, the composite containing 12% silicon carbide exhibited a conspicuous mitigation in wear losses vis-à-vis its counterparts containing 5% silicon carbide. This augmented wear resistance observed in the 12% silicon carbide composite can be attributed to the heightened mass fraction of SiC particulates integrated into the composite matrix. These SiC particulates, functioning as reinforcing agents, contributed to an amplification in the composite's hardness and wear resistance. These findings not only yield invaluable insights into the tribological behaviour of Metal Matrix Composites (MMCs) but also elucidate the potential impact of supplementary performance parameters on their wear characteristics.\u003c/p\u003e","manuscriptTitle":"Exploration of wear characteristics in aluminium-silicon carbide metal matrix composites for aerospace and automotive applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 04:27:41","doi":"10.21203/rs.3.rs-3993647/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d758138c-a0c1-40f9-9d84-199afd2091c1","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T12:58:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 04:27:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3993647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3993647","identity":"rs-3993647","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}