Statistical approach to determine cutting conditions and cutting geometry for edge trimming of G/PA12 plates

Statistical approach to determine cutting conditions and cutting geometry for edge trimming of G/PA12 plates | 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 Statistical approach to determine cutting conditions and cutting geometry for edge trimming of G/PA12 plates Petr Mašek, Jaroslav Kovalcik, Pavel Zeman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5367604/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract The G/PA12 composite used in this study is made of glass-woven reinforcement and semi-crystalline engineering thermoplastic. This type of composite has potential applications, for example, in the automotive industry. The relatively low glass transition temperature and low stiffness of the matrix compared to the high abrasiveness of glass fibres make this type of composite difficult to machine. The workpieces from this type of composite are produced in a near-net-shape, but the free edges of the workpiece must be trimmed in order to achieve the required accuracy and quality of the product. This study recommends cutting conditions and cutting geometry based on statistical evaluation of force, quality and temperature measurements. The double-helix cutter significantly improved the machined surface quality compared to the standard PCD cutter. The PCD cutter was used in this study to identify key control factors for cutting conditions. By selecting the optimal helix inclination and angle, surface quality improved by up to 80%, with only a 12% increase in temperature. Increasing the feed per tooth also contributes to improving surface quality. In addition to improving quality, increasing the feed per tooth significantly affected the cutting forces. A cutting force model was developed specifically for machining this type of composite. The model's accuracy was enhanced by incorporating the effects of face angle and helix. The temperature measurement method during milling was designed to monitor critical temperature limits, such as the glass transition and melting points. An infrared camera was selected and the emissivity of G/PA12 was determined experimentally. The measurements showed that, while the glass transition temperature was exceeded in all cases, the melting temperature remained at least 47°C below the critical limit, even in the worst-case scenario. Composites with thermoplastic matrix Edge trimming Temperature Delamination Cutting forces Cutting tool geometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction Composite materials are heterogeneous materials made of two or more components, where each component has very different mechanical and physical properties from the others. Composites contains reinforcement, an internal support structure, and a matrix that binds the reinforcement together. Matrices are usually much softer, tougher, and their tensile strength is several times lower than that of reinforcements [ 1 ]. Both the reinforcement and the can be composed of various materials. Their appropriate combination and the method of depositing the reinforcement in the matrix give composites their unique properties, which are in high demand. Their excellent strength-to-weight ratio is used in the aeronautical or aerospace industry [ 2 ], and they are also very popular in the automotive, defence [ 3 ] or sports industries [ 4 ]. Matrices can be made of metal, ceramic or polymer [ 5 ]. This study is focused on polymer matrices. Polymers used in composite materials can be thermosets or thermoplastics. Thermosets are heat-curable and cannot be heat moulded. The main representatives of thermosets are epoxy and polyester resins. Thermoplastic can be shaped with heat. The most well-known thermoplastic polyamides (PA6, PA12), PET, PEEK, PEKK, etc. Reinforcement for polymer matrices can be glass, carbon, polymer or natural. The reinforcement typically takes the form of fibres, which can be short, long or continuous, loose or woven [ 6 ]. Properties given by the combination of matrix and reinforcement and its internal structure give composites different machinability. Composites are typically produced in a near-net-shape, but in order to obtain a product of the required dimensions, it is necessary to process functional surfaces, holes for joining or simple trim the free edges of the composite workpiece. [ 6 ]. Composites with resin matrices usually have worse machinability than those with thermoplastic matrix. The reason is the brittle fracture of the matrix and dust-like chips generated during machining [ 7 ]. On the other hand, there is a risk that the thermoplastic matrix will soften during high temperature and the malted matrix may create build-up or permanently clog the cutter flute [ 8 ]. Therefore, different types of cutting tools are recommended for both types of dies [ 9 , 10 ] The reinforcement in fibre composites is very strong and hard. Because of this, these fibres are very abrasive, and tool life is very short when using standard cutting material such as high-speed steel or tungsten carbide [ 11 ]. Therefore, recommended cutting material is polycrystalline diamonds (PCD) or chemical vapor deposition diamonds (CVD-D) or their thin layers on tungsten carbide substrates [ 5 , 6 ]. Glass fibre undergoes brittle deformation, similar to carbon fibre [ 12 , 13 ]. Fibers can produce dust particles that may be respirable [ 6 , 14 ]. A smaller volume of these particles can be observed in fibre-reinforced thermoplastic composites (FRTCs) because they tend to form regular chips that are able to bind broken fibres This risk is much lower than with thermoset composites but is still present. And for this reason, it is necessary to vacuum them to avoid harm to the operator [ 15 , 16 ]. A quality problem caused by the machining process is delamination. Delamination is the separation of adjacent layers of the composite [ 17 ]. Delamination is caused by forces, their magnitude and direction acting against the fibre orientation. The magnitude and direction of these forces are determined by the geometry of the cutting tool and cutting conditions, as demonstrated by Hintze[ 18 ] or He[ 19 ] in their studies. The favourable effect of a positive front angle and a higher back angle on the surface quality was experimentally carried out by Vos et al. [ 20 ]. A numerical analysis of the results obtained during orthogonal machining confirmed that a larger face angle had a positive effect on reducing the cutting force and damage of the CFRP composite [ 21 ]. Similar results were obtained by Sheikh-Ahmad et al during milling of UD-CFRP, showing that a positive rake angle reduces forces [ 22 ]. Chatelain and Zaghbani tested special cutting tool geometries for milling composite materials. The compression geometry was less stable than other geometries tested but had a positive effect on thrust force [ 23 ]. Puw determined a specific cutting force model and determined its coefficient for C/PEEK and C/ABS and for different fibre orientations using the Standard Kienzle model [ 24 ]. Kala et al.[ 25 ] used another approach to determine a specific shear coefficient. They applied the Mechanistic Model presented earlier by Altintas[ 26 ] and neural networks on C/Epoxy composite. Karpat[ 27 ] also used a mechanistic model, but he developed it specifically for the geometry of a double helix cutter. In our previous work, this approach was improved by considering the axial positions of the cutting tool relative to the machined surface on C/PPS [ 28 ]. Davim et al dealt with the statistical evaluation of cutting parameters to obtain their significance in the milling of CFRP materials. He found that the feed rate was the most influencing parameter for the delamination factor Fd , surface roughness and IT. [ 29 , 30 ]. Feed per tooth was identified as an easily adjustable control factor with a significant influence on the CFRP cutting process, however, its correct selection and to keep it is essential [ 31 ]. How to maintain consistent feed was solved, for example, by Vavruska [ 32 ]. For different cutters, Praveen et al presented different effects of cutting parameters on the quality of the machined surface. In this case, the geometry of the cutting tool probably had a big influence [ 33 ]. In their study, Jenartharan and Naresh evaluated data combining cutting tool geometry and cutting parameters. The effect of cutting parameters was higher than the helix angle, but the fibre orientation had the greatest effect on delamination [ 34 ]. In another study, fuzzy logic was used to optimize cutting parameters for GFRP milling [ 35 ]. Composite materials with polymer materials are relatively less resistant to high temperatures. Accordingly, the effect of cutting conditions on temperature has been studied several times for various polymer composites. Rahman found that while milling the C/PEEK composite, the glass transition temperature ( T g ) of the matrix was exceeded at a cutting speed of 75 m/min. In this study, the melting point was not reached, and the maximum temperature was lower than 250°C at a relatively high cutting speed of 200 m/min [ 16 ]. Kerrigan used an integrated sensor in the cutting tool when milling C/epoxy and found that the cutting speed was less affected. Nevertheless, workpiece thickness was identified as the most significant controlling factor in this experiment [ 36 ]. Yoshiro et al. measured the temperature in a full section of CFRP using three different methods (infrared camera, embedded thermocouple, and tool-workpiece thermocouple). The cutting speed was increased up to 300 m/min, at which point the glass transition temperature was reached. However, this cutting speed was recommended as the matrix was not affected [ 37 ]. Jia et al. also used an embedded thermocouple during CFRP milling under cryogenic coolant. Liquid nitrogen was able to reduce the temperature from 135°C to -50°C [ 38 ]. In our previous study, the temperature of C/PPS was measured with a semi-artificial thermocouple. In addition, cutting speed was identified as the most influencing control factor. The glass transition temperature was reached in all measurements, but the melting point was not. Thus, the cutting speed could be increased to achieve higher productivity [ 28 ]. This study deals with the cutting of G/PA12 plates. Unlike the previous study with C/PPS composite, this composite requires a different approach to temperature measurement due to its insulating properties. The main objective of this study was to identify suitable cutting conditions and geometry to achieve a high-quality machined surface, optimal cutting forces, and safe temperature for the FRTC material. Additionally, the study aimed to develop a cutting force model and determine an effective method for temperature measurement during milling. A two-stage experimental design was used. The first step was aimed at determining the significance of control factors on the cutting conditions when using a standard PCD tool, while the second stage compared the most significant factor form first stage of experiment with the effect of cutting geometry of the double helix cutting tool. 2. Materials and method 2.1 G/PA12 composite The composite material was polyamide 12, reinforced with glass fibres (G/PA12). The matrix was a semi-crystalline engineering thermoplastic, commonly used in the automotive industry. The glass fibre was of EC11 type with improved tensile strength stability. The reinforcement had a 4H satin woven structure. The plate had a thickness of 3 mm. Other specifications of the G/PA12 are provided in Table 1 . Table 1 Material properties of the G/PA12 Property Unit Value Ply thickness mm 0.375 Reinforcement Ply orientation [[(0,90)/(± 45)]4]s Tensile strength MPa 1,900 Elongation % 3.7 Tensile strength modulus GPa 73 Density g/cm 3 2.65 Matrix Polymer volume in composite % 50 Glass transition temperature °C 50 Melting point °C 170 Chemical resistance good Moisture uptake 23°C, 50% RH % 1.5 Mechanical properties of composite (bending) Ex MPa 59,000 Ey MPa 43,000 Gxy MPa 4,428 The G/PA12 samples were prepared in dimensions 40 x 70 mm. The samples were clamped in a fixture which was bolted to a Kistler 9255B dynamometer. This fixture allowed to clamp up to 4 specimen at once. 2.2 Machine tool and equipment The machine tool was a 3-axis CNC machining centre with linear drives on each axis. The maximum axis acceleration was 20 m·s 2 . The maximum spindle speed was 15,000 rpm and the spindle power was 18 kW (Fig. 1 a). Cutting forces were measured using Kistler dynamometers 9255B and 9123C (Fig. 1 b). A preliminary experiment performed on a 9255B Kistler dynamometer with PCD tool resulted in the measured force components F x , F y and F z . From these components, the resultant cutting force F was calculated according to Eq. 1 [ 39 ]. $$\:{F}^{2}={F}_{a}^{2}+{F}_{p}^{2}={F}_{c}^{2}+{F}_{f}^{2}+{F}_{p}^{2}={F}_{x}^{2}+{F}_{y}^{2}+{F}_{z}^{2}$$ 1 Where F p is the passive force and was equal to F z measured by the dynamometer, F a is the active force, which in this case is calculated as the vector sum of the forces F x and F y measured by the dynamometer. F a can be defined by the cutting force F c and the feed force F f , both of which can be determined based on the known position of the cutting edge and at engagement and the force F x and F y . The 9123C rotary dynamometer was used to describe the coefficient of tangential specific cutting force for different double helix cutting tools with CVD-D coating. The main comparison of the control factors was made through the resulting cutting force. The surface quality was evaluated by the average delamination length ( ADL , a method previously employed in our earlier study [ 28 ]). This method requires taking a photo of each side of the machined surface (bottom, front and top), see Fig. 1 c. The resulting delamination coefficient is calculated as the average sum of squares, accounting for the deviation of the burr from the machining plane. The photos of burrs were taken using Canon Eos 550D and the magnification of surfaces were taken using microscope LIM. For the experimental evaluation, the method of temperature measurement from the thermal area measured by an infrared camera was used. This method was the only option for measuring thin plates made of electrically non-conductive composites, in which it is not possible to place a thermocouple. There are certain limitations with this type of measurement, such as the emissivity of the measured objects, the response speed of the IR sensor, the limited field of view of the infrared camera or the resolution of the IR sensor. The temperature was measured with a Flir T640 infrared camera. This camera was able to measure continuously at 30Hz with an accuracy of 2°C. The monitored area was the machined surface near the cutting tool (Fig. 1 d). 2.3 Cutting tools A standard catalogue PCD tool with a diameter of 12 mm was used for preliminary experiments (Fig. 2 ). This cutting tool has two PCD diamond cutting edges brazed onto a tungsten carbide body. The angle of the rake of the cutting tool was 0° and the clearance angle was 10°, the tilting of the PCD element was 2°. The main block of the experiment was performed with 4 non-standard cutting tools with double helix compression geometry and 5 cutting edges. The face angle and helix varied for each cutting tool Table 2 . The clearance angle was the same (12°) for all cutters. Table 2 Tested compress cutters with various tool geometry 2.4 Cutting conditions The preliminary experiment was carried out to determine the significance of the basic cutting conditions. Different cutting conditions were tested in a full factorial design of experiment (Table 3 ). The most significant factor for a given measurand was planned to be a comparison factor for the second block of experiments. Table 3 Cutting condition used for preliminary experiments with PCD cutter Factor Units Sybol Levels 1 2 Feed per tooth mm f t 0.05 0.1 Cutting speed m/min v c 100 300 Radial depth of cut mm a e 1 3 The second block of the experiment was based on the results of the preliminary experiments. The basic problem was the completely different geometry of the cutting tool, which could affect the results. On the other hand, the preliminary experiments helped to adjust the boundary conditions and reduce the number of experiments needed for the main block. The main block consists of the most significant control factor from the preliminary experiments and the geometry of the cutting tool, see the design of the experiment in Table 4 . This comparison provided information whether the change in the cutting geometry had a more significant effect than the change in the selected control factor of the cutting conditions. Table 4 Cutting conditions used for testing compress cutters Factor Units Symbol Levels 1 2 Cutting speed m/min v c 100 300 Feed per tooth mm f t 0.05 0.15 Radial depth of cut mm a e 1 3 Helix angle ° λ 5 15 Rake angle ° γ 15 25 A full factorial design with replication was used for all experiments. The Results were compared using analysis of variance (ANOVA). When the data met the assumptions for parametric tests, ANOVA was used. In other cases, a non-parametric test was used. The results revealed significant control factors in the experiment. 3. Results and discussion 3.1 Emissivity of G/PA12 Emissivity was one of the key parameters important for correct temperature readings during machining tests. The emissivity was determined experimentally in an electric oven, where the G/PA12 sample was heated to 150°C. Natural cooling was observed using a FLIR PM675 infrared camera, while the ambient and G/PA12 temperatures were measured. These data were compared and evaluated to determine the true emissivity of the composite. The temperature in the vicinity of the area was measured by a thermocouple K (it can be seen in Fig. 3 a) and the ambient temperature was measured with a PT100 thermocouple and recorded using an Almemo 5690-2 data logger. Heat was generated in an electric furnace and the natural cooling process was monitored at spot Sp1 with an infrared camera (Fig. 3 b). The preset emissivity was 0.96. The cooling curves measured by the K thermocouple and the infrared camera were not the same. This means that the preset emissivity was not correct. However, the curves showed a similar trend (Fig. 3 c). The emissivity estimate for G/PA12 was calculated using the Stefan-Boltzmann law [ 40 ]. $$\:M=\epsilon\:\sigma\:{T}^{4}\:\:\left[W{m}^{-2}\right]$$ 2 Here ε was the emissivity of the given grey object, σ was the Stephan-Bolzman constant 5.67·10 − 8 WM − 2 K − 4 and T was the thermodynamic temperature [K]. $$\:{\epsilon\:}_{1}{T}_{1}^{4}={\epsilon\:}_{2}{T}_{2}^{4}$$ 3 ε 1 was the preset emissivity, T 1 was the temperature measured by the infrared camera, T 2 was the temperature measured by the thermocouple K. ε 2 was the emissivity to be calculated. According to Eq. 3 and the measured data, the average from the calculated emissivity values for the selected temperature range was estimated as 0.89 (Fig. 4 ). This value was the input value for all temperature measurements. 3.2 Cutting forces The calculated resultant forces reached up to 75 N when the chip thickness was the highest. Machining was stable with no audible chatter under all cutting conditions. After machining the collected data were statistically tested for the possibility of using the ANOVA test. The ANOVA test could not be used because the condition of the normality for the data was not met. the non-parametric Kruskal-Wallis test was used instead. The Kruskal-Wallis test for the resulting cutting force revealed a significant influence of the control factors f t and a e (Fig. 5 ). The p-value of these two factors was less than the chosen significance level, which means that the medians of the two levels were different. All measured factors increased with increasing level of factor. Both tested parameters f t and a e were statistically significant also in comparison with the cutting geometry, see Fig. 6 a and b. The main effect plot in this case revealed the higher the factor was the higher was the measured evaluated forces. That made sense because the chip area increased with these two parameters. However, they were much dominant then the rake and helix angle which decreased the force when were higher. The rake angle changes the direction of the force and affects the size of the primary cutting zone, resulting in lower cutting force in general. The fibres bend less and also compress the matrix less in the primary cutting zone. As a result, less energy accumulates in the cutting zone, which has a positive effect on cutting forces. The helix angle increased the smoothness of the cutting edge engage and that could have positive effect on cutting force as well. The response of the F to change in feed per tooth was predictable. Similar test with similar results were measured by Davim [ 30 ] or Sorrentino [ 41 ], for example. For this reason, the cutting force model was created based on control factor f t . 3.2.1 Cutting force model A cutting model for G/PA12 and double helix cutter was developed based on the Kienzle model. This experiment was designed separately from the main experiment because it needed a higher number of levels to establish a reliable dependence of average chip thickness ( h D ) on cutting force ( F c ). This experiment was conducted with a rotary dynamometer Kistler 9123C. The advantage of this device is the ability to collect both tangential and radial cutting force data. The tangential component is essential to determine the Kienzle model. The f t levels were set at 0.05, 0.07, 0.1, 0.13, 0.15 mm. The h D as well as average chip width ( b D ) were calculated for each f t . The basic model Eq. ( 4 ) was derived from the Kienzle model: $$\:{F}_{c}={k}_{c}\bullet\:{A}_{D}={k}_{c1.1}\bullet\:{{h}_{D}}^{1-{m}_{c}}\bullet\:{b}_{D}$$ 4 Where k c is the specific cutting force. A D is the average chip area. The coefficient k c1.1 is the specific cutting force for h = b = 1 mm, where both the face angle and helix angle are equal to 0 [N/mm 2 ]. The coefficient m c expresses the effect of the thickness of the cut layer on the cutting force, respectively to the specific cutting force, and is dimensionless. This model Eq. ( 5 ) has been adjusted due to the helix and rake angle: $$\:{F}_{c}={k}_{c1.1}\bullet\:{{h}_{D}}^{1-{m}_{c}}\bullet\:{b}_{D}\bullet\:{K}_{gl}$$ 5 The \(\:{K}_{gl}\:\) factor modifies the basic relationship due to the influence of the rake angle and the helix angle. In addition to these parameters their interaction is also considered. $$\:{K}_{gl}=1+A\bullet\:\gamma\:+B\bullet\:\lambda\:+C\bullet\:\gamma\:\bullet\:\lambda\:$$ 6 The extended relationship for calculating the cutting force with the influence of the angle of the face and helix is: $$\:{F}_{c}={k}_{c1.1}\bullet\:{{h}_{D}}^{1-{m}_{c}}\bullet\:{b}_{D}\bullet\:(1+A\bullet\:\gamma\:+B\bullet\:\lambda\:+C\bullet\:\gamma\:\bullet\:\lambda\:)$$ 7 The coefficient A represents the effect of the face angle on the cutting force. The coefficient B represents the effect of the helix angle on the cutting force and the coefficient C represents the effect of the interaction between the face angle and the helix on the cutting force. The coefficient values were estimated by nonlinear regression in the Minitab SW program. Table 5 Calculated coefficients for the cutting force model Parameter Coefficient P-Value k c1,1 101.856 1.2278E-13 m c 0.269 8.9735E-10 A -0.0259 1.5191E-08 B -0.0451 6.6817E-08 C 0.0022 2.0583E-07 According to the P-value, all estimated coefficients, including the interaction between the helix and face angle, are statistically significant (Table 5 ). The resulting Eq. ( 8 ) for calculating the cutting force, with respect to the angle of the face and the helix with the obtained constants, is: $$\:{F}_{c}=101.856\bullet\:{{h}_{D}}^{1-0.269}\bullet\:{b}_{D}\bullet\:(1-0.0259\bullet\:\gamma\:-0.0451\bullet\:l+0.0022\bullet\:\gamma\:\bullet\:l)$$ 8 This model equation has a very high coefficient of determination, R 2 = 0.994. The scatter plot (Fig. 7 ) illustrates the correspondence between the measured and predicted data. 3.3 Delamination Delamination of G/PA12 manifested as uncut fibres and matrix. In the case of the PCD12 tool, the cutter was unable to cut the bottom and top burrs at all (Fig. 8 ). The ANOVA test showed that the a e was the only statistically significant control factor. This finding numerically supports the mentioned hypothesis about the unsuitability of the PCD12 tool for finishing the machined surface, see Fig. 9 . The results of the preliminary delamination test did not identify any control factor for the next experimental phase, for this reason. All control factors had to be included in the next experiment. To reduce the number of tests, a separate experimental design was used for each factor, rather than a full factorial design. The machined surface of G/PA12 showed uncut burrs along the edges of the composite sample and, to a small extent, pulled fibres from the surface, see Fig. 10 . The low glass transition temperature of the matrix and its surface ductility probably limited signs of fibre pull-out. An initial assessment at the quality of the machined surface revealed a very high effect of f t . The volume and number of free fibres appeared to be less for higher f t , see Table 6 . An analytical assessment of ADL confirmed this result. The difference between the two different cutting speeds or two different radial depths of cut was not noticeable upon initial inspection of the cut edge. If the cutter is able to cut the top and bottom layers, then the decisive value for the average delamination length is the minimum chip thickness associated with the high elasticity of the machined surface and the actual position of the cutting edge relative to the fibres during cutting. In this case, some loose fibres were bent rather than cut. A higher f t reduces the probability of this happening. Table 6 Comparison of delamination – an example for cutting tool R25-H15 A comparison of three cutting condition control factors revealed that the f t was statistically significant. Other factors were less significant compared to the cutting geometry control factors, based on a significance level of 0.05. This evaluation supported the estimate shown in Fig. 11 a. The second most significant control factor was the rake angle This control factor was statistically stronger than v c , radial a e , and helix angle (see Fig. 11 b and Fig. 11 c). The high rake angle allowed to create a surface with less fibre bending or pressing the matrix under the cutting edge, meaning less burr. The helix angle was statistically significant only when compared to the a e at the chosen significance level (see Fig. 11 b). A higher helix angle helped compress both the bottom and top plies, improving the quality of the machined surface. 3.4 Temperature The temperature measurement method was suitable and with high repeatability. Nevertheless, the temperature was measured only on the visible surfaces and was certainly higher in the cutting zone. For this reason, the temperature was measured only for relative comparison. The cutting tool PCD12 easily removed chips from the cutting zone as is shown Fig. 12 . However, the temperature of both the contact zone between cutting zone and machined surface and the chips was always high above the T g . The less positive cutting wedge of PCD tool resulted in higher deformation accompanied by increased energy in the cutting zone and this energy was transformed to the heat. The conditions for using a parametric ANOVA test were not met and the Kruskal-Wallis test was used instead. This test performed for all control factors showed that feed per tooth and radial depth of cut were not statistically significant in affecting the temperature. The cutting speed had a significant influence on the temperature, with higher speeds leading to an increase in temperature (see Fig. 13 ). Feed per tooth was evaluated to have a decreasing effect on temperature and the opposite effect was evaluated for radial depth of cut. The double helix cutters had grooves crossing in the middle of the cutting part. Chips were not smoothly removed from this part of the tool. It was observed that the crossing of the flutes became clogged with hot chips, leading to the gradual heating of the cutter (Fig. 14 ). In the second phase of the experiment was found that only v c was statistically significant based on the 0.05 significance level. The geometry of the cutting tool did not significantly affect the temperature. The main effects plot showed that the higher the cutting speed, the higher the temperature. However, an increase in v c by 200 m/min increased the temperature by approx. 12%, see Fig. 15 . Conversely, the higher the helix angle, the lower the temperature. The rake angle had little effect on the temperature, but a higher rake angle led to higher temperatures. These results were consistent with those of our previous study [ 28 ]. 4. Conclusion 4.1 Forces The cutting forces were primarily affected by the feed per tooth control factor. However, the control factor ae also had a significant effect. The cutting speed had only a marginal effect on the resulting cutting force. Both factors were more significant than the selected element of the cutting tool geometry. Decreasing f t and a e caused a large reduction in the resulting cutting force. The rake angle and helix angle were statistically insignificant compared to the cutting condition factors. However, both of them with increasing value of angle reduced resultant force, so the most suitable for reducing forces was a cutting tool R25-H15 The cutting force model was created based on the Kienzle’s model and improved by the influence of the cutting geometry. This model had a very high coefficient of determination R 2 = 0.925 for the range of factors included. 4.2 Average delamination length The delamination length correlated the radial depth of cut. In contrast, double helix geometry resulted in a relatively clean cut, though both cutting tool geometry and cutting conditions had a non-negligible effect. When the rake angle nearly doubles, ADL should be reduced more than 3 times. An increase in the helix angle also contributed to a decrease in ADL . It follows that the R25-H15 cutter was the most suitable for ADL reduction. Among the cutting condition factors, only the control factor f t had a very significant effect on ADL . This effect was even more significant than for tool geometry. As f t increased, ADL decreased. 4.3 Temperature The temperature measured by the infrared camera did not indicate the actual temperature in the cut. However, for a relative comparison of different cutting conditions was sufficient. To reduce the measurement error, it was necessary to determine the emissivity experimentally. The temperature was consistently measured above the glass transition temperature ( T g ). The melting point was not reached in the measured area or on the chips. Cutting speed had the greatest effect on temperature, while other control factors were statistically insignificant in comparison with cutting conditions and cutting tool geometry. However, the higher helix angle could reduce the heat in the cut according to results of this study. Abbreviations G/PA12 Glass/Polyamide12 composite PCD Polycrystalline diamond CVD-D Chemical vapour deposited diamond T g Glass transition temperature E x Young’s modulus in axis X E y Young’s modulus in axis X G xy Shear modulus RH Relative humidity F Resultant force F x Force in axis X F y Force in axis Y F z Force in axis Z F c Cutting force F f Feed force F a Active force F p Passive force ADL Averaged delamination length f t Feed per tooth v c Cutting speed a e Radial depth of cut M Radiant exitance ε Emissivity of grey object T Temperature σ Stefan-Bolzmann constant k c1,1 Specific cutting force h=b=1 m c Chip thickness coefficient h D Chip thickness b D Chip thickness γ Rake angle λ Helix angle K gl Modification factor for geometry A, B, C Coefficient of cutting geometry Declarations Acknowledgement Authors acknowledge support from the ESIF, EU Operational Programme Research, Development and Education, and from the Center of Advanced Aerospace Technology (CZ.02.1.01/0.0/0.0/16_019/0000826), Faculty of Mechanical Engineering, Czech Technical University in Prague and National Centre of Competence in ENGINEERING (TN01000015), which is co-financed from the state budget by the Technology agency of the Czech Republic under the National Centre of Competence Progamme. Funding This publication was created with the contribution of the knowledge obtained within the project, Development and Education, and from the Center of Advanced Aerospace Technology Reg. No CZ.02.1.01/0.0/0.0/16_019/0000826. This work was created also within the project National Centre of Competence in ENGINEERING (TN01000015), which is co-financed from the state budget by the Technology agency of the Czech Republic under the National Centre of Competence Progamme. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authorship contribution statement Petr Mašek: Methodology, Writing, Visualization, Editing, Data acquisition, Data analysis, Jaroslav Kovalčík: Writing – review & editing, Data analysis, Visualisation. Pavel Zeman: Writing – review & editing, Resources References Harris B (1999) Engineering Composite Materials. 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J Eng Sci Technol 3(1):58–64. 10.25103/jestr.031.11 Jenarthanan MP, Naresh N (2015) Process parameters optimization on machining force and delamination factor in milling of GFRP composite using gray relational analysis. Indian J Eng Mater Sci 22:313–320 Jenarthanan MP, Nishanth G, Bharatwaj R (2016) Optimization of machining parameters in end milling of GFRP composites based on the Taguchi method with fuzzy logics. Int J Knowledge-based Intell Eng Syst 20(3):123–134. 10.3233/KES-160338 Kerrigan K, Thil J, Hewison R et al (2012) An Integrated Telemetric Thermocouple Sensor for Process Monitoring of CFRP Milling Operations. 5th CIRP Conference on High Performance Cutting 2012 Yoshiro T, Ogawa T, Sasahara H (2013) Temperature measurement of cutting tool and machined surface layer in milling of CFRP. 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Processing and manufacturing of composite materials - ASME Cite Share Download PDF Status: Published Journal Publication published 20 Mar, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 10 Dec, 2024 Reviewers agreed at journal 31 Oct, 2024 Reviewers invited by journal 31 Oct, 2024 Editor assigned by journal 31 Oct, 2024 First submitted to journal 31 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5367604","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372757934,"identity":"9264642d-98ec-4364-a4b5-c8b88b81d581","order_by":0,"name":"Petr Mašek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPCCBDnStRgDCcYGEJOHWC2JDURrMWfgTvz4oyItfXv76fQHDDWHE/czMD/+gE+LZQPvZmmeMzm5c87kbmxgOHY4sYeBzcAAnxaDA7wbpBnbKnJnSPBubGBsSANq4WFIIKBl88+f/yrSJZC1HCCgZZsEb0NOAlSLDUgLJBxw+qWZd5s1z7E0wxk8uRtnJByzMe45zGaMTweDOXvv5ps/apLlJdjPbvjwoUZCtr29GX+IGTAj8xJABDNWhUhaCMiPglEwCkbBKGBgAACTlUTg52fPngAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3271-7394","institution":"České Vysoké Učení Technické v Praze Fakulta Strojní","correspondingAuthor":true,"prefix":"","firstName":"Petr","middleName":"","lastName":"Mašek","suffix":""},{"id":372757935,"identity":"840272db-c74f-4e86-9c90-0e19fa8b9f8e","order_by":1,"name":"Jaroslav 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1","display":"","copyAsset":false,"role":"figure","size":404952,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup: a) MCFV 5050 LN CNC machine tool, b) Photography setup for delamination measurement, c) measurement of temperature field, d) force measurement setup\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/ccb8ad7b500c7f8b527d8bfd.png"},{"id":69447580,"identity":"efa7aca4-4fea-4fc7-9e14-b5cddfb9970a","added_by":"auto","created_at":"2024-11-20 12:09:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19447,"visible":true,"origin":"","legend":"\u003cp\u003eStandard PCD cutter PKD FRAESER 05492-12,000 (PCD12)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/ba78aab4898218592103a2a2.png"},{"id":69447584,"identity":"e5c0b854-f342-4c87-866d-d7b0615108f4","added_by":"auto","created_at":"2024-11-20 12:09:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":272147,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental investigation of the G/PA12 emissivity in electric oven: a) composite placement, b) infrared photograph of the same place, c) recording of measurement\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/36daa5d8f5f2e383c66cd604.png"},{"id":69447583,"identity":"724ce919-b9ab-462b-824f-1bac11f9c8a1","added_by":"auto","created_at":"2024-11-20 12:09:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38616,"visible":true,"origin":"","legend":"\u003cp\u003eEmissivity evaluation dependent on the temperature\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/7ffe763c283088564b548579.png"},{"id":69447590,"identity":"93fd8d67-aa96-4321-b984-42b6904e06d7","added_by":"auto","created_at":"2024-11-20 12:09:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46380,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of control factor via Kruskal-Wallis and Main effect plot for cutting conditions control factors and F\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/932d4255ea2245ecdaa11e9b.png"},{"id":69447585,"identity":"68f69c54-dd44-423d-b678-82dfdf9ace20","added_by":"auto","created_at":"2024-11-20 12:09:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60569,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of control factor via ANOVA and Main effect plot for F and: a) f\u003csub\u003et\u003c/sub\u003e and parameters of geometry and b) a\u003csub\u003ee\u003c/sub\u003e and parameters of geometry\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/49239fc24cc0857c298e85de.png"},{"id":69447872,"identity":"b0a17be6-7103-4e46-a147-224e88eb2d05","added_by":"auto","created_at":"2024-11-20 12:17:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":14303,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot for the cutting force model\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/45d52da79cba29bacfad4a8a.png"},{"id":69447600,"identity":"87566b5c-71c3-4e2e-8cac-e9636d06dcdc","added_by":"auto","created_at":"2024-11-20 12:09:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":153887,"visible":true,"origin":"","legend":"\u003cp\u003ePhotography of delamination appeared during milling with PCD12 tool\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/574713a05cbfe0beddb525b8.png"},{"id":69447871,"identity":"12be8547-518d-4be1-8ab9-4a45ad777d49","added_by":"auto","created_at":"2024-11-20 12:17:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":37470,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of control factor via ANOVA and Main effect plot for cutting conditions control factors\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/ce371764e94ab36dd24b9cc8.png"},{"id":69447589,"identity":"a11133b8-45d9-476d-974b-9eeea8ee9a49","added_by":"auto","created_at":"2024-11-20 12:09:07","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":222870,"visible":true,"origin":"","legend":"\u003cp\u003eAn example of surface quality made by R25-H15\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/585d814c77fb545a3250e287.png"},{"id":69447587,"identity":"53fbae22-c165-432b-85f1-f8b1f5f4f141","added_by":"auto","created_at":"2024-11-20 12:09:07","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":98497,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of control factor via ANOVA and Main effect plot for ADL: a) f\u003csub\u003et\u003c/sub\u003e and parameters of geometry, b) a\u003csub\u003ee\u003c/sub\u003e and parameters of geometry and c) v\u003csub\u003ec\u003c/sub\u003e and parameters of geometry\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/75499c8b99c78e00187b6801.png"},{"id":69447597,"identity":"c09da36e-f696-48c7-b4a1-458dbbcbde94","added_by":"auto","created_at":"2024-11-20 12:09:10","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":108256,"visible":true,"origin":"","legend":"\u003cp\u003eInfrared image of PCD12 cutter during trimming\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/f9f3f1fee62139de2ad22f7d.png"},{"id":69447596,"identity":"7d460f59-0e9c-4935-946c-d6ec8bdc96cf","added_by":"auto","created_at":"2024-11-20 12:09:08","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":138167,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of control factor via Kruskal-Wallis and Main effect plot for cutting conditions control factors and T\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/b4e5cd78d3f3812ce4f2da88.png"},{"id":69447594,"identity":"91691c41-c160-4684-bc5e-aab51dcbe380","added_by":"auto","created_at":"2024-11-20 12:09:08","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":155964,"visible":true,"origin":"","legend":"\u003cp\u003eAn example of machining progress during trimming with compression cutter\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/2f5c4e76367df25932f36b72.png"},{"id":69447598,"identity":"9218839d-64c3-4199-89f7-6d14f7079083","added_by":"auto","created_at":"2024-11-20 12:09:11","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":117091,"visible":true,"origin":"","legend":"\u003cp\u003eInvestigation of control factor via ANOVA and Main effect plot for T and v\u003csub\u003ec\u003c/sub\u003e and parameters of geometry\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/64f3939f84da96dfce1c8c6c.png"},{"id":79121211,"identity":"036cf32a-107e-4b56-b1a5-0d639f1de2e8","added_by":"auto","created_at":"2025-03-24 16:11:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3089757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5367604/v1/bc85b697-e2aa-446e-b15d-11b2ca8eedbf.pdf"}],"financialInterests":"","formattedTitle":"Statistical approach to determine cutting conditions and cutting geometry for edge trimming of G/PA12 plates","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eComposite materials are heterogeneous materials made of two or more components, where each component has very different mechanical and physical properties from the others. Composites contains reinforcement, an internal support structure, and a matrix that binds the reinforcement together. Matrices are usually much softer, tougher, and their tensile strength is several times lower than that of reinforcements [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Both the reinforcement and the can be composed of various materials. Their appropriate combination and the method of depositing the reinforcement in the matrix give composites their unique properties, which are in high demand. Their excellent strength-to-weight ratio is used in the aeronautical or aerospace industry [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and they are also very popular in the automotive, defence [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] or sports industries [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Matrices can be made of metal, ceramic or polymer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This study is focused on polymer matrices. Polymers used in composite materials can be thermosets or thermoplastics. Thermosets are heat-curable and cannot be heat moulded. The main representatives of thermosets are epoxy and polyester resins. Thermoplastic can be shaped with heat. The most well-known thermoplastic polyamides (PA6, PA12), PET, PEEK, PEKK, etc. Reinforcement for polymer matrices can be glass, carbon, polymer or natural. The reinforcement typically takes the form of fibres, which can be short, long or continuous, loose or woven [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProperties given by the combination of matrix and reinforcement and its internal structure give composites different machinability. Composites are typically produced in a near-net-shape, but in order to obtain a product of the required dimensions, it is necessary to process functional surfaces, holes for joining or simple trim the free edges of the composite workpiece. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Composites with resin matrices usually have worse machinability than those with thermoplastic matrix. The reason is the brittle fracture of the matrix and dust-like chips generated during machining [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. On the other hand, there is a risk that the thermoplastic matrix will soften during high temperature and the malted matrix may create build-up or permanently clog the cutter flute [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, different types of cutting tools are recommended for both types of dies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe reinforcement in fibre composites is very strong and hard. Because of this, these fibres are very abrasive, and tool life is very short when using standard cutting material such as high-speed steel or tungsten carbide [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, recommended cutting material is polycrystalline diamonds (PCD) or chemical vapor deposition diamonds (CVD-D) or their thin layers on tungsten carbide substrates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Glass fibre undergoes brittle deformation, similar to carbon fibre [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Fibers can produce dust particles that may be respirable [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A smaller volume of these particles can be observed in fibre-reinforced thermoplastic composites (FRTCs) because they tend to form regular chips that are able to bind broken fibres This risk is much lower than with thermoset composites but is still present. And for this reason, it is necessary to vacuum them to avoid harm to the operator [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA quality problem caused by the machining process is delamination. Delamination is the separation of adjacent layers of the composite [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Delamination is caused by forces, their magnitude and direction acting against the fibre orientation. The magnitude and direction of these forces are determined by the geometry of the cutting tool and cutting conditions, as demonstrated by Hintze[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] or He[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] in their studies. The favourable effect of a positive front angle and a higher back angle on the surface quality was experimentally carried out by Vos et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A numerical analysis of the results obtained during orthogonal machining confirmed that a larger face angle had a positive effect on reducing the cutting force and damage of the CFRP composite [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similar results were obtained by Sheikh-Ahmad et al during milling of UD-CFRP, showing that a positive rake angle reduces forces [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Chatelain and Zaghbani tested special cutting tool geometries for milling composite materials. The compression geometry was less stable than other geometries tested but had a positive effect on thrust force [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Puw determined a specific cutting force model and determined its coefficient for C/PEEK and C/ABS and for different fibre orientations using the Standard Kienzle model [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Kala et al.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] used another approach to determine a specific shear coefficient. They applied the Mechanistic Model presented earlier by Altintas[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and neural networks on C/Epoxy composite. Karpat[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] also used a mechanistic model, but he developed it specifically for the geometry of a double helix cutter. In our previous work, this approach was improved by considering the axial positions of the cutting tool relative to the machined surface on C/PPS [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDavim et al dealt with the statistical evaluation of cutting parameters to obtain their significance in the milling of CFRP materials. He found that the feed rate was the most influencing parameter for the delamination factor \u003cem\u003eFd\u003c/em\u003e, surface roughness and IT. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Feed per tooth was identified as an easily adjustable control factor with a significant influence on the CFRP cutting process, however, its correct selection and to keep it is essential [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. How to maintain consistent feed was solved, for example, by Vavruska [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For different cutters, Praveen et al presented different effects of cutting parameters on the quality of the machined surface. In this case, the geometry of the cutting tool probably had a big influence [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In their study, Jenartharan and Naresh evaluated data combining cutting tool geometry and cutting parameters. The effect of cutting parameters was higher than the helix angle, but the fibre orientation had the greatest effect on delamination [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In another study, fuzzy logic was used to optimize cutting parameters for GFRP milling [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComposite materials with polymer materials are relatively less resistant to high temperatures. Accordingly, the effect of cutting conditions on temperature has been studied several times for various polymer composites. Rahman found that while milling the C/PEEK composite, the glass transition temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e) of the matrix was exceeded at a cutting speed of 75 m/min. In this study, the melting point was not reached, and the maximum temperature was lower than 250\u0026deg;C at a relatively high cutting speed of 200 m/min [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Kerrigan used an integrated sensor in the cutting tool when milling C/epoxy and found that the cutting speed was less affected. Nevertheless, workpiece thickness was identified as the most significant controlling factor in this experiment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Yoshiro et al. measured the temperature in a full section of CFRP using three different methods (infrared camera, embedded thermocouple, and tool-workpiece thermocouple). The cutting speed was increased up to 300 m/min, at which point the glass transition temperature was reached. However, this cutting speed was recommended as the matrix was not affected [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Jia et al. also used an embedded thermocouple during CFRP milling under cryogenic coolant. Liquid nitrogen was able to reduce the temperature from 135\u0026deg;C to -50\u0026deg;C [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In our previous study, the temperature of C/PPS was measured with a semi-artificial thermocouple. In addition, cutting speed was identified as the most influencing control factor. The glass transition temperature was reached in all measurements, but the melting point was not. Thus, the cutting speed could be increased to achieve higher productivity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study deals with the cutting of G/PA12 plates. Unlike the previous study with C/PPS composite, this composite requires a different approach to temperature measurement due to its insulating properties. The main objective of this study was to identify suitable cutting conditions and geometry to achieve a high-quality machined surface, optimal cutting forces, and safe temperature for the FRTC material. Additionally, the study aimed to develop a cutting force model and determine an effective method for temperature measurement during milling. A two-stage experimental design was used. The first step was aimed at determining the significance of control factors on the cutting conditions when using a standard PCD tool, while the second stage compared the most significant factor form first stage of experiment with the effect of cutting geometry of the double helix cutting tool.\u003c/p\u003e"},{"header":"2. Materials and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 G/PA12 composite\u003c/h2\u003e\n \u003cp\u003eThe composite material was polyamide 12, reinforced with glass fibres (G/PA12). The matrix was a semi-crystalline engineering thermoplastic, commonly used in the automotive industry. The glass fibre was of EC11 type with improved tensile strength stability. The reinforcement had a 4H satin woven structure. The plate had a thickness of 3 mm. Other specifications of the G/PA12 are provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMaterial properties of the G/PA12\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePly thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eReinforcement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePly orientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e[[(0,90)/(\u0026plusmn;\u0026thinsp;45)]4]s\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTensile strength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElongation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTensile strength modulus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg/cm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMatrix\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolymer volume in composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlass transition temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMelting point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemical resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoisture uptake \u003cem\u003e23\u0026deg;C, 50% RH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMechanical properties of composite (bending)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGxy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,428\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\u003eThe G/PA12 samples were prepared in dimensions 40 x 70 mm. The samples were clamped in a fixture which was bolted to a Kistler 9255B dynamometer. This fixture allowed to clamp up to 4 specimen at once.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Machine tool and equipment\u003c/h2\u003e\n \u003cp\u003eThe machine tool was a 3-axis CNC machining centre with linear drives on each axis. The maximum axis acceleration was 20 m\u0026middot;s\u003csup\u003e2\u003c/sup\u003e. The maximum spindle speed was 15,000 rpm and the spindle power was 18 kW (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Cutting forces were measured using Kistler dynamometers 9255B and 9123C (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). A preliminary experiment performed on a 9255B Kistler dynamometer with PCD tool resulted in the measured force components \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e. From these components, the resultant cutting force F was calculated according to Eq. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:{F}^{2}={F}_{a}^{2}+{F}_{p}^{2}={F}_{c}^{2}+{F}_{f}^{2}+{F}_{p}^{2}={F}_{x}^{2}+{F}_{y}^{2}+{F}_{z}^{2}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the passive force and was equal to \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e measured by the dynamometer, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e is the active force, which in this case is calculated as the vector sum of the forces \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e measured by the dynamometer. \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e can be defined by the cutting force \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e and the feed force \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e, both of which can be determined based on the known position of the cutting edge and at engagement and the force \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e. The 9123C rotary dynamometer was used to describe the coefficient of tangential specific cutting force for different double helix cutting tools with CVD-D coating. The main comparison of the control factors was made through the resulting cutting force.\u003c/p\u003e\n \u003cp\u003eThe surface quality was evaluated by the average delamination length (\u003cem\u003eADL\u003c/em\u003e, a method previously employed in our earlier study [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]). This method requires taking a photo of each side of the machined surface (bottom, front and top), see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec. The resulting delamination coefficient is calculated as the average sum of squares, accounting for the deviation of the burr from the machining plane. The photos of burrs were taken using Canon Eos 550D and the magnification of surfaces were taken using microscope LIM.\u003c/p\u003e\n \u003cp\u003eFor the experimental evaluation, the method of temperature measurement from the thermal area measured by an infrared camera was used. This method was the only option for measuring thin plates made of electrically non-conductive composites, in which it is not possible to place a thermocouple. There are certain limitations with this type of measurement, such as the emissivity of the measured objects, the response speed of the IR sensor, the limited field of view of the infrared camera or the resolution of the IR sensor. The temperature was measured with a Flir T640 infrared camera. This camera was able to measure continuously at 30Hz with an accuracy of 2\u0026deg;C. The monitored area was the machined surface near the cutting tool (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Cutting tools\u003c/h2\u003e\n \u003cp\u003eA standard catalogue PCD tool with a diameter of 12 mm was used for preliminary experiments (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This cutting tool has two PCD diamond cutting edges brazed onto a tungsten carbide body. The angle of the rake of the cutting tool was 0\u0026deg; and the clearance angle was 10\u0026deg;, the tilting of the PCD element was 2\u0026deg;.\u003c/p\u003e\n \u003cp\u003eThe main block of the experiment was performed with 4 non-standard cutting tools with double helix compression geometry and 5 cutting edges. The face angle and helix varied for each cutting tool Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The clearance angle was the same (12\u0026deg;) for all cutters.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Tested compress cutters with various tool geometry\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cimg 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ugF1qEX+u5znjzyUvzjeUsjrtfj3e7kg/X3Nh3Sng7xtOY7Xj2/O3B8vJeOpjInacby4jKX6BZnog8V9SP55OYuf1SJvT/JgPFBmXk4exlErV/7ZAbWfi1p9l7ikrx1v5E15teMV5O26WFbyI49cFtqLfE9WJ5DXi3JPF6ebD+lPFWuO6AAak0GXDwAOOoFGOV5H5AOTgw8FoAPJZ/EHI/8g8Kw2v8XheafWHsiQx6s9lorLARiA+cBa6jiVAUYetYO8FnxAyCPPjw9U7YejFqcS93j1CwQCgcD6BN/jy8FSvwMchNcCRWtxHH7P8vT8buVKHb9f3PN7wu/Y4nSEHS88B9f57ym/ZbUK4vFwOvKc6Pew9rd08UHe+W/rUs9rgTzIQTjn2vqeCLnMp/v7TPr7ms/iupwq8jKXOnI5ThSnFicaE0vlkY/hpcJrx0EeDyw1Dk6EnJwhV96f5ME9+urx5KotPz/y9LWEb6kjJ3FLyZq3KWBc5nGQZ7EufDzkMufynA5ONx/Snyrq+E8JzjiYr7pCWa9LYCZ80Yte5Ne1e+zgoQUzIp49XvnKV2ahgUAgEAicOcRv8pkHa4XZm4dtG3D0k3tcA3/zN3/j04RWct0D5eMQQgrocV19nwqY9sVeNfclnxhX9x/oJ/RH9s7BQ3HtVLPvfe977piB6WwrhXyciKAsa4rmUmDcnE4+yxl3QXTuB0By2KjzeGty8DBz9913B9EJBAKBwIogfpPPLCAwP/jBD47reID1D7/yK78SRCdwRsDaHPr6RI4HWAt+qo4J7gvWK9GJDUNXGAxOvG+cCDgDWMq5QiAQCAQCgbUHXmCeCHhSy73JBgKnC7ytncihAbrmlVdemd0FahFEZ4XR0dHh3s6uuuoq/2LEswqsmIO3MbgGxJJzJl1eBgKBQCAQWDkwLQ1PqrxF57c8/13nN54w9iw5HSvLcsBbdt5wcyDDqQCZ8zSkD6xtMN5wpX3eeed53zETiL7GYpjrlqdrZTlVYFnKxw6ynAqQNU/DcX8ipq7dD8C15fve9z6fP3nbbbd5GIP2BS94ge8yHSQnEAgEAiuJ+E0+s+Dt+p/+6Z/6WhwUUMBLTbZp4OXlmdw/ZS0jxtX9B0jN//k//8e++MUvZiFpO5aXvOQlp7VdyXrEcsZdEJ1AIBAIBM5yxG9yYCUQ4yqwGljOuIupa4FAIBAIBAKBQOCsQxCdQCAQCAQCgUAgcNYhiE4gEAgEAoFAIBA46xBEJxAIBAKBQCAQCJx1CKITCAQCgUAgEAgEzjoE0QkEAoFAIBAIBAJnHYLoBAKBQCAQCAQCgbMOQXQCgUAgEAgEAoHAWYcgOoFAIBAIBAKBQOCsQxCdQCAQCAQCgUAgcNYhiE4gEAgEAoFAIBA46xBEJxAIBAKBQCAQCJx1CKITCAQCgUAgEAgEzjoE0QkEAoFAIBAIBAJnHYLoBAKBQCAQCAQCgbMOQXQCgUAgEAgEAoHAWYcgOoFAIBAIBAKBQOCsQxCdQCAQCAQCgUAgcNYhiE4gEAgEAoFAIBA46xBEJxAIBAKBQCAQCJx1CKITCAQCgUAgEAgEzjoE0QkEAoFAIBAIBAJnHYLoBAKBQCAQCAQCgbMOQXQCgUAgEAgEAoHAWYe6BSG7PqOoq6vLrgKBQCAQCAQCgUDgzOBU6UtYdAKBQCAQCAQCgcBZhyA6gUAgEAgEAoFA4KzDik5dW6GsA4F1g/gcBAKBtYD4LgqsBGJcBVYDyxl3YdEJBAL3hr4//Dsk+x5ZmE/nUwHp8mNe6fLr9GxBx1zNsYyMA0eR2jFr1Ay1YfkjD0uXR5/Nq1Pm52vj5jESPM0Sz/Lw2uNUUZtmbm7OjxMhz1sp/OzQJcF5PtSD+3QmLIsn1F6DPD9Qe50jzzN/tlScWtTGDQQCgcDaRVh0AoEVxHr8HMxLB61vyG6Og9mZOdu378DRuhWamhKrEbZt36yK++VxcYzg1KuNssvAKSNvd8ZXjmPjrO6MtCn55eO3tpz7AshIfq6vr/djemrW9u7da21tbdbX32uNDY0q6+Rjb7VxrJ3v2f5rHevxuyiw9hHjKrAaWM64C6ITCKwg1tvn4N++/i37xr9/15qam6xess9Vq1RC1w02L3LS2Nho1eqczcxM24EDB21qaprHUlwbbHZ2Rorsgp133m7r6Gz3eu/cucMufvjDrEX5dXS1W6FBWuwJdMP70la08QMRtFVe92PtpntxionJkpVLZT0XqVBQnYhFHqfY1motLc0iFBj0yYPQNE7va1seLZ+ysg4+JtMxEFYvgUZGxuwP/9eH7d/+7d+tvb3Nenp7rVUyNTY2WWdnh3V0dIoAFa1B4w356zVukK2xoaDzgvX19duWLVt07tW4lNyqn37MvC7Eq3O2NG9NhSabE3NvbCxYa2uzNXidGa/ZZAaXN6VZDvK63df2Wg0g61J9EgicDmJcBVYDyxl3QXQCgRXEevscvP99/9P+9//+CynCLdITF6QqzkvRRClEoVM9VB/VyObm8ilIVZufm7fZ6qyu5z0WZKhFSmVTU5MU2B7bMLBBSmaLXXjhBdbT02Pd3T22a/dOKbQosy22Zetmsr0XaDbCab/82qcvoZy7FJwFZLpH+iUyOyHua/8st5yVQmofzhCb4cEx+7vP/p3ddsftVq8HDVLq59VXPg4l8sDAgHV2d1mhGcUf8pBMKE26LxQardhatH7FaWoSGRLLgBQ061mvSAUE+FThMnGImHBeUNmQ59nZqv3RH37Q/uAP/shKImQ+gRo5JQdjpq2t3Qb6B2zjpk3Wrmvk4hl97v3ucZGpxQoiMGkq3jytoDIYFxqzijcnQl5saxMpn3Fi19nZ5fUFkDznOpJtwa1Nyll1dWJIfZsK1tberrHaLdLekcrVX7vCzjvvHBH3Zpcj8aWMNK1xxG9yYCUQ4yqwGgiiEwisEay3z8Hb3vZbUkD/QIpes+4ku/5QlHXin9dHwU5yqJfUQldS5+fn9IxpaOm51GN/y44Szdt1piWhOEOg2js6rLenzzraO6ynr9t27tphnV2dtnnjRnvkJY+ytmKrlNgmkaPWe09jQg6fBaULdFaV7XLVJ/LDvwVp1DS5i000D+NMgmPwMM9Hx30C+d0zz9VBmhbGdMC6uka74/Y99ld//hd26PAhqxP5rFc4/eMNobrSF04LFtRHIqXcY/WAPEAQiNegMAhrQQo/5ANrS29PrzXonjo7AdLzix50kW3fsd3bsl4kotDQZE2FgtJmlrtFzTM7PWtf+tI/2+/8zu/YXXfe4YRDnFnQf8oEi2Cj0nd1dFp//wbr7+u3rq5ua2ttS2NN/yDWcyInjfUFauHkid5HJrKqzldFcoijMakACDjhtA9WSUjfDNbHhdQmszOzbqGcrVZFwmYVc96mKmWVpfpIlgbFmXZr5Zw95KEPtbe85U22a+d2v1cU5dHg+S+q6ppD/CYHVgIxrgKrgeWMuyA6gcAKYr19Dt78pjfb733g961FpCRJjSJZ72/KUeR4K68KWZUpbQKKoNdRCqpPB9Jz7kE6wzb8zo86ZVAvJZhnDfWJ/DBdqVhskULbYX39fbou2tatW+ycc3b5G/WtW7batu1b/K3+5k1brLEp5X9iZNJL1iROnmZxX0i+Mw7KqJVx8f2ZBkQH4kJdG+32W++wj/2fP7dDBw9YnfqFRVdM90r9oz5AOyeuiAJssAGCoHC33CmcqWBYOZiOWBDhLIiMQHa9n1USJGNO+UIMICVYVortRSexXZ1dIij91qRrSEwB615Lq/X29dnY6Jj9y7/8i33xi1+yu+/aQ9EuF+SmivwiJImIzDvRYmx1iBR3dXZbpw4sLE2NIjeSrwHLC8RasjMtrU4HxKyRemChop7UQ+1OfZyYZ+Qc6w/EZQ5nGCrbiZOTJyyUWCbnbGq6LAI0o9Gh9Krz+MSEDY+OSIaiSNo77SlXPlFpaVc9DKITeAAjxlVgNbCccRdEJxBYQay3z8GbfvNN9v73vT9NXatR3fxNvxRT9GaU4DkppA0iLIVCkyuO1BPFeEFx9E8HSjfKMfHVBqQnrdoCS48zJtqF9tFRp/zqsCgoDYSGqVJYEorFVpGffinPA9be1ma7d+32KVQdHW128cMvto0bN1pzU6Pu25OgS4AyF6RQI0UiBSCr2wImo+w6AzKfHvIyTgYKOtXCTjSGyCMnOvU2eHDQ/vIvPibCc6vaU8/c8qA21iXEAEtGg/qK9S8E+p/C6T+XSNf+p2dpjUyy9FRFAMqVso2Pj9uBgwft4KFDNjE5qZLrfWoiBJWpYqy36erp9nv6kLFUL1J74MB+u/P2221UhAfSgQUGYsN4qkpGLC5OOOZSXbiGnNWJ9JB/S6vIlIgxBIi1N1iisDjV6wxhxgLYrGuskQUINONMRxp7idDQihA1SEq1OutWGYZhtZrJkN3Pzk6r3RQ2V7VZnSF1+w7ss5Lq//Z3/Hd79lVPd3l9TInorAfEb3JgJRDjKrAaWM64C6ITCKwg1tvnAKLze+9/vyuoyK4aeDhTgHg/jtLoxEZKKp7WUI5RVlFImcrkcbEeeBynRxmRgf6kdkCJls5JRG8bqbTWIGWRN+xE8TCdKR4Z0tS39Ma+0NhkrVKgWai+YeOAdXZ0Wo+U6kc+8hHWi4Ld1WVbtmyz9g4p3cU26+5q8zIXg1JdbijZPbrnHjdCJrOTttQWSyNPl8dZnM9SOFF+i3E88kQe98xnYnTS/vrjH7e77rrLCmo70roFRH0Gv/T+k3zuoADmmgHFnTvqOS/FfkZHBWIzMWmjY+M2PDxiIyPDNj42ZuWpKV9rIxaU+ooclTckFQuLO7NQ/pCctBarzS0knR0dtmnjJl83M6l8KlNlqyofiDMWFawv877+i+l2EBKmool8+PDRuFL+OMbgtoFyIDwFrEZFX89TVHltLa0iRa161uJEKFmHmt1S422QdbgTHYiVyqEs8se6BKlJYxDSpXZgHZru9x84ZHfuudN+5b+8yq6++iWWaKHyQphFfbAWsd6+iwLrAzGuAquB5Yy7IDqBwApivX0OcqKDRQXZawE5UI38jTtvy4njFh4Igyu4IjpopFhP6hRGetXdSYLiowuiuKIAuxJLngrwxeCKh5JJXCch2dv39J8S6t+8NGn++OcWCuXDuhLe5re3Yz1oFrHptIGNm62rs9d6+/tsx/ZtCuuw884/z849d7cruqwfwSK0GPQT8lDo0S5zS5BOUq6RDRDvnm1DmhweO10uQp7nomZdBCItFaG2jBzHykImyMHExITdddtd9oVrrhExGUrh7igCS8WCWyjmIBaQCrX7lAgHpIZrvOZhtanqPCUiMzU9LaJTsZKOytS0P0Phx8FB1dsp1Yk2RdtHciwi9A19nNqT+qZ2q1cSLG+7zznHLrjgAhvo7bGZ6SmbFJHCMlSeruietTNMjZS8qhtyucwiwaylcej5vFtgsgbVibVgWHGaC3hXa02HyE+rSE9zUdfNrYrTpDEiwpxNt3SplS6NRf0pP/KFdDEWkYOxOQvR0fOhoRH7xre/aZdfcam9+13/3adaUrZXzJHquVYRv8mBlUCMq8BqYDnjLohOILCCWG+fgze/CaLze05ifNqQ5EcRdEgzpCqsz2AKUVtr0ddutLS2+NqJ+rpGTwORqG8kHYSAZPrTRXJnnK5ducwUzPk53UuJ5s160hyl1vNWX0ozZTLNCiUXhTN/+45CjRzEP0qU8rRSrAuNLVJom9w7VrGtaL29fTYw0OcevPp6e+3ihz3MNm/ebNt34qK4x60Q3d1SXJcAMia1WHmrLOR3wXgipVhV9nahn6m/K/265khxFVtnJ3UuIm1AWEqTRfHr/FmKl+qTl5UIQxY5Q22af/7nr9hH/+SjNjoyIvIwYbNOGlJbeTwd3KepW7QW09GwWkAq6AfID+2qs8Lz8n06GelVPNYRpp1RaZ9ipj7wflX5tBF5YMdDntQtELBEpLCAJKtco4hop513zrnufpxpiY2N9W7lmZ6ZtmnJPSOSNT03q3PViU51TiRMhI1M56qSRQVURdCwKkFM9J/qxmPqSR+JBDO10tfuZOOg2GbNLS26xuKjMaswiA91yi1Rbi3yuiWi5e3GOFbYoNr1y//yFWtra7W3vu0N9pSnPDE9d2JPS/GfX6xJpDEoeQOBM4gYV4HVwHLGXRCdQGAFsd4+B29585vt9z/w+z51rZbocGa6Ggovb/D7uvvsgnPPt+3bt1tvd4811Sc3v8TjuVRltwCg9qFUo4yifOoLh0ZJiqTC3NGBwkh77C16UpB5q6+oim9SgEV0sEzMozRjddC9lFymNaEU51YLvGlNTc962qOHwl2JR3aRMdZw+OL5phbbvGWT9fR2uWevc849R4Sox6dbnXPuubZp8wbf44V1QseD9yx1Ooqk5Es7Phqc7lOdElJAGhvpOc+4B3m09CxLLNRcOjyewgg+fPiIvf3tb/eF/qTBwkGeEBGIhvdLlgHt5KQSUuYcAZU+9Yl7IjtKGQXyWKgzvDL72h36UH9MUyNvcoQYIQtp3SKifLC0FQotqVylg+BAinFc0AbZaGl173pMgyPxhg0DtmGg37rVD0w7g3hArNRjyXEA/Zv3MQROxGZWY6EqcgRZY/wgD33NGKDu1JF9oKqzGkeqAH3PGIM4M7WuSbI0tySyg0vtVh1YBX2NTyERJPc+p/icx8sl+/wXvmDjE2P2+v/2a/bin3+eBKRNoIk52YGMrk3QF8fGYCBwZhDjKrAaWM64C6ITCKwg1tvn4K1veYv9z9//n/cgOiA/YxHA2tPfy7SwnbZ923brYG2ElNqGAi6JC55OamaKL2WTN+9qBH8rjvveZL2RoiqF1KQckzf58gzrAe2FIpuUVymrOlBQq76GByUc8qU83BrBeoo8HgRI4YpBvrMiOVxCdjwPKeELmaIuYUQG0KR1ltwFKbzskcICetZ+tHe0u3KOs4OLLrrIOjuKvq/Lps0b05oQtUFzM66WVx+HDh22P/7In9jnP/95m5ycVJ1QuznRluoDtTdtSr9AOiCHao7UvsQlEuNU9xAZ4rhlxHOhqdRGqdW8zQlP+ae0PjYoR+V5GcqjRSQG8kA7QyzSOpoWa9XRXmzTUbTDRw7b4OHDNjI85KS1u6vdekSam0WCunq6rLenx1o1tliTxb42PiUOGRBeBAPSm48nJ7MK5wwhpu5cz2k8uCvp7NmMSPDMTFUESPElNOHUy9fyYP0RwYKEITtT3NrbOqylgFv0VsPP4D995V9seGTQ3vDG14voPF8hCakduPL/1iTW23dRYH0gxlVgNbCccRdEJxBYQay3z8Hb3vo2+4Pf/31XLlFaa5H2xKl3QtDR1gbncCWWqUDuJlrPmQ7UKpJUaMYjW9qjBdWvUWE4FAC8rUdV9jf/9clCRBPRTryVT0ibXB5VxhWCYooy7k+lQJMxCrma+Ci8rXVfh0Mxf9ue1gMRqUHlY1XAQkG/eDxdu9VHcVgHMj01I8W4apWpio1PlqxcKTnpwgXzwIYB27Rpsy96Hxjot3PPPcfdYbMRJVPjKJtpTdQH8XLk9XLFW2RrdGzMvYhhLZqqTNvg4LDaCi9oBVf6actCY1K4WSszMTnhm2iWSpNS4rFWJPKC1QQrxDe++U377Gc+644DnMhkyj8VhHx4/YVEQCWP3+HeW+2ONcLbMGtECa4gb1ciQh6PrbVKY9m9sCktDg7IuaU5WW6IBqFxj246u5tnta9PC9M9jgNwXgE5rZTLbm1hI89u9szp7bfunk4P37d3r+3Zu0f1mVJYj6+1ws000yVbmvHilnlYY0yJjNAOtDfuyp0Qq42Q1/+oA0RX7ZE8u4n4qn+ZJkc/c12VHDNqs6npKQ+nUqJn3m5YdpjO1sznQXW44ce32NjEmD37Oc+yF73oBbZz53Z9VvBQuPax3r6LAusDMa4Cq4HljLsgOoHACmK9fQ5++7f+u/3RH/6hK5I+fUiyIz81gCQQBtFh53yURIgByqZU3yyulN1GrCbYVXjDj9cvPUUZ5Ur3uUc3V5Z1jwJeYJ2ElHu3KDDtSaQktRqKMqwhyUAm3LvyzeGgjROZEW/ya/T0tEdLUtZz6w+ZYEnirf480+Ak8FSl4p62pqTsVspTNs311LTiz7rnMZRkXGQnawfTmPD2law+eHnr6upxSwV5cKaQupoF6mmNSrIe8Iw1KLQj7TAzIwVbZCZ36Tw9Pe3tAZlAycY6QfxCgTUs0163o+RNeREX5RzLhveTjjkp+ij3tBWgLM/Tj6yNITlOgtjBJllw3NWyzikf6pvGLnJQHq6m2cjVp2upSxohFmpPHFOQHwUyRlLfiFTp5FO/lI7nbhnyvZOSM4D+vl7btnWrbRzY4GGsJZqX3NMiHAcOHLBvfufbduttt3ob4bKatVZY3CA7tDMWorQGq1XhXLf4mhzkLWgMpk1LsSgxNtUTagfkQU7qm5Nq6pamOaq/1Y54h6NMiBbj2y2CagfGxQ033WKT5YrtPmen7dq1w37qp66yy694QiKL3gZrF/GbHFgJxLgKrAaWM+6C6AQCK4j19jl49zveZX/84Y/4uhSUWawZTBhCWZ2eqbjSz1qKzZu3SLnj+byeibhAAFRXNEq3/NTNueKPcksetAEK5GxmISAc717sX+JKO8ooCqhIgVsMpKQmq0KyJKCkm/JEGVeEdK/4TnA8GopruoYwIDUWGicpPJecrOlwhV5lYMGB5HCPHL6eQ+EQIrfu6Drlk+B7AUlzp1zKIl9AXXF6gBwou27FcCsJRIRqiehkclEeeULAkDIH9cPyAmvIN2ZNz7Fm6QRjqMOexTQ/8pcsalMe6tL3GWJjVdUmycBzPW5oSJtpumz6y3NOpIa81Z4LkD8Uf3/kpMjLUJgTT/0lookMKU6y6BAvBTj5U1wnrg1N7nTAvfA1sQePclB4SzPe0IrWKzn7RVo6RJbbRVxY/wIpYx8l2oC+YIxMa6yMjI/Zj2+92a7/j+/bwYMHfQy2tbe51QyrGk4NIDOE+7X6oSiCzgFxgvBg9WmSHL4uBwuQ2oM6Qs4hRcnKxJomyQoxoqPSPyfyjO+0x868jYvgXPOFL9mhI8O+oW1Pb7e94Gd/xp79U8/0dkjNkTXSGkT8JgdWAjGuAquB5Yy7IDqBwApivX0O3vee37X/+7//zKdWAX+TLyWwtbno7n/ZMX7n9m22ZeOmtOYFRVhEh4XerHmBf/AGHWUeBT8pzkn5Iy/fK0dpIAI4GGA6F60DKXLlG2Vf7UUSFHInH1J8UbhRyj0P4kEMiOOWmpQH1gCUZsgCzyAtxE/5ImuSQdm6Uk0qCANTmlBt86lygHKRI+87TulQmOdFvybnC+k5Mqe+zsNACkt5OlHi3id8KU7KGin87HH5k4Bcc+Ty6JL/PSb5UAnKQsHfvXu3k52Z2Wknce4KWxFJQj1z0uVkTu3smSltY1OD2p9pXhCZ1E+kzaf31cKtQnpO+d6niuPkQGcW9rNxrG8uqnywzjkBEcFo9bVPbdYnUtDR3uHTGnlWJ/l9ilnWxz6FTMfUzLRVpqf8KKs+Y5MTdvfde+2GH91gI2Njqm+DtYuEd7B2RmOUMpGJKiExljYIDuQH4g3haiqwj47kg6BpjEJ4mFYJ+SFublWE9DS36MBJQXOz14Pnqqw1tbTahIjOX3/6/9nBQ0M2MLDBuro67ed//oX23P98lcugyqiNIEtrE2ksJkkDgTOFGFeB1cByxl0QnUBgBbHePgf/6wO/77vqs8EjQNHGatFabLdyJe1zct45u2zzxg3pjbeqxlQuqZBSOpkmpETU15XlNLWKc94OnAHKNySH9BAT38sFpZx7/ecKvs64FobMoLDi1csdFrjlIr1xZ2qXG0hULop8/Xydu6GeVd5epsKS5QYSg3LNVK18OpsOPefaw3KiA0nLiZfSJsIk2fWXT4OjFshLdXCzLGGP1hXkdU31TkQDIbEeuFWK3HSN7MgH8rZJlh8sM1iDkmyJZKQ2zIkOYAoXzhJa21p9zxviN6g4nyqmP+pEnSEn1If2Q04vv4H6UMcUlyltQNTViQziQAKAW0KwmiifxoKIAYQhC2M6G6QHMsGUw2JrsxVFDFrbitZWLOpehCQNDD/m6UOVO+3TA3EMUHULDvKzd89kqWTjpUkbHR+1Md+sdNRGRkZsdHTU5adcpqwxhbIVj3iSlfGhHzMurVlyQL4AlrzUrJmVSQfEHTJXFdGuVxtg2aFNIEbUJ3lewxKkA9KkA7fUIxNl+9drvy4yVlW5bdamev3iL73EXvTzz8+oK+OCdkv9uNaQxmIaN4HAmUKMq8BqYDnjLohOILCCWG+fgz/+0IftU5/8a58axJtsFEv3etXcYmNjE64gPuSiC22gr8dmZ6alPKO6Use0NqdBmjuKfJ0U3hwo2ijZKJOuZCsyJMGVdd27RSZrI7c4CNw5EZEizCOU1IUFKadZ3FxpZ02LSyBZ1dSuaOKNy60EGSnxsgTSkCd94iQre56Xn4iXiE1GqBAC4sDaDfZuYXoepAUPYeTFQZnIl9ePexImEiRpFFbb//ccD6ndIBl5XvyHRQckcsM1D2gvyaoEtIPXWf+zTuiCc85TZBO5S+6XJYW3LRYqCIV7t6OUOhwBUP9klaF/yYMSGrAC6Z5paPXqO/qZOFg+qHdTATfMyRrjTiZECHCgwBmLia+d8XUzbNrZ5Ondq51KcLLpBwv/Z32TUuTwqYxqZ9YYJYIzaWPj41aeqtjQ0LBNTk7oKNnkVNnXTPkaJXKU0BBr5GEqW5PK5Jo88zVAEBTq4sQmI33Ux9eTqZ6MEVoXZxSMFUgSeUPg+EuEKLnoxrHCgtrn8OCw7dl7QHGYqrigz0TBXv7yl9ovv+JlPj5TSyIfrbr2sN6+iwLrAzGuAquB5Yy7IDqBwApivX0O/urP/8r+/m//3omOK/XVeZ96hCI3NjruCuSjHv4w62gr2nS5pApKkaeOWDxc/4Y0SKmVsuj11oGS6wq2lN+c6PCmHUV+Xrf59DPaCnB2K4zCcksMWc1LKSVP7gnnGotPHkZqCAb5cUN6JzsqnwDF8jB6w61R/CmdWzUyGSmXPNmgNJcDRXhGebIoHUFQzklDeckKgnwqX/GpH1Ps8uln+s+fO3QijLiE8ATU5qFkLgsPnbzxXI/r5iFDkLBE2gDPcIO8ceOA5JlzZwos6K/zmXiJuFGey6HYKT/KTgv1mWbFo+QwIFu7wt4xkBidITFM4TpmsUmkBu9wrP/hvpUpXkxR05l1Or6mK+tTNhb1dVgZMcT6NqMDuXDAMCVCg6OFcqVsE5OTNiGiUy6XrVSp2IQIT6lU0n3J07e2tXl5IyOjXsd6yVMnUuIyQnRUPvVkKh9jtUnyEz9tGEq9mIIGOWkREZOskhGrDaTHm0dwosueO7qnD3BMwDhlPJRFxg4Pjkg2nEUURMxmfBrda37lVfaqV/+S503bpnbOMlxjyMdeIHAmEeMqsBpYzrgLohMIrCDW2+fgc397jf3rl6/1qWu+EaOUvEJTsxMIXP+y2ePFP/EQKYuNVhof8zSoeCi3WBvw+KUKi0KkaVHofmoCV3QBKiBt4qqgnrmFgj/OpMvicU08kCxCxFK4E5NEOMiY+J5eZ997R3L6I/1BuNxiw6J7nhOXcshTyjPpkZR6kmciVsiQyJGTrSzdrOJgyXES5/EgI8hHnAUnCZSJzFgujva7ouT19XbSmXzJR//xz5/zIG8DDxRh8jZQfLcUueUB4pbahzCmg6GIezzlMcv6J8VL1hlkIW8sE0pfj3w4TxCpyawarEFxt+BOCNJULYhNsuSktSpMU/MpXNRPGaSpa+QBOcrKUL7uwY6+kYxpOppkZQ2W+m52himGWG+Yojbt1hvIzVRZREcEoiQyU2ZNjggOJGdaJIiDcnFe0Dcw4BuKgltvucXuvPNOGxoelgyQsmR5qcvkIg1t49YcCI7XJW0A6oeTuOSIAKICiUtrc0SU9Ed4UWOcNqUuWHhKpYrdevvttmffPvWd2rKh4EQY0vfCFz7fXvtfXmXtHa0+JlK70yJrD8jl4ysQOIOIcRVYDSxn3AXRCQRWEOvtc/BvX/2mXffd71tbR5srqCiirOFgzUtj3YL193TZlo0brF5K9ZQUVOqGMgt9gCyg5KrSUgiTQg9pUEBS6qWU61F6i+6KPCSElKmdnACgKDuZSEojD528eC60ZbK8pHbVM10ncpNIgiubCkfmnPSQH2mAW4iIl8lCPjnRSelVH4+TCI7LpLMr7hmp8PoqDkqw569r9nAhL1Rc1hIRjiB4FQO5BYhCyYNrJ4IelJRjknCQ2PORLNQXRR7lnTr4cz1VdA/DylSaLLk7ZEnrhMY9n0k29i1CRqaRQVYhAWnKGUp+k7VIqXdHAk54smdsnOnkACKTkQYVppPCU98hJWuA1Fpu7YAMO8GB1Oh6GkuI5MJiMzuDu+gZdxnNPjVMQXO3zbPTNluZ1lnPMgcE7uBAMmB9YaPWTRs2WFdHp8sCEcHZAut3Duw/YNd//we2b99+d2LhMvq6oUR6EsnB8pTajXZwguf1U311pt5NCs/X4FB/2p31Pz09Pd5G9AneByFo3/2P/7DbRLDq6vAwmPaGQq6feu6z7dd+7TVOdLxfs75ci/Ax5gMzEDhziHEVWA0sZ9wF0QkEVhDr7XNw0w232B233mUNUvSYnsP6jjprdAIzX52yHimeA709Nj87ZbNSTiEOvN2ekWKbrxFBC/Q66+CcHzzzt+9qk2PAUpFIDUopVAHiwDQif0r7QTg4YDCCkyfy9xuIhVRuPcPawzPW6fhUL8iKivKzkwSFuVKOQprkg2hBKHTj2eFK2K02uoaI5H2H7C4T8Wtk87SKrxtXsD0nyUA6FG+IAYTALUA8lDzUkbp6myq9K+oQBz3Px0suk/iaCKYzjNQOekYY5AMCQ56joxO+YL9eCjxHc6HVlXe3yEjZd4tFc5relUhMsmg0N0nhFxFSboqnNvM6SBbFYX0NZBG5XA4XnkvITdqzh/U2TOPjzIFVZ3o2uYZm/Q3T0MputRGh8alqTPmbs4qu5xQ2OyUSreJxWNDV1W1dItGcO7s6ra2lNXlJo029zmmtEfKpEWxiomQ33Xqrff+HP7D9+/aldsWCo7pC9mgfLDyIzTQ38iGsmf2hlAekJ/fQBmFx99NqExwcsNaI9qKsVhHESRH660SshkZGrU4kkil/9BUOEa644lL7jTf+qm3atMH7mDbkWIvIx1YgcCYR4yqwGljOuAuiEwisINbb52B4cMRGhsYleKbcV3EOUKfznM2J2DRK8S2IJMxWSlJU0waWvsO8lDysPr5+RQq8HriiSP1dOVcuEATuCadNvG30h6XEw50U6E/PICeEkTC3tgDPT7Lk+XvcowQgEYSGrLl5lihJyoM85yAyxMmIBYSGtA6dkcUfCJAmv/S2SNYLyIY0aX+OrKlOyJWHkVzKrhJiRfB7FGA9Ji/PWf/5+iRKV6BvNMk98Xiua2QkYmqvTNnXc0L53xV5KeZYjEZHxm18YsIapZQ3SNFvaW61Fl1jnUnWiqTUE5/+ytdKofA7jxHcS5uucyLqbS/QnsmKluo+I7JBu0D6GBu4iE7rbEpu0SlXpn36GcSn4mtwcBgxlSxOSu/EWefO9g7r6Rax6e50ZxcdItBsBJqTFNbP4ILa+1iiYJnC+QVoLDR7u+GefP/BA7bnrrvsxzffZIcOHbEpycM0NvcI11DwPqNirCui/vmYZF0SU89ySxbhzWozPMQV29q8XXnGxrl4g/uP//i+VVRevfLwfYZEDpm++YQnPE5E53W2detm7yOQt91aA3LlMgYCZwoxrgKrgeWMuyA6gcAKYr19DtgbxxV/KeYYPVB0UeZRahv0V5Xyenj/PpuenLDqDBt+sh4jTfeaRZEVCUExllbs+aHy5dPT9MCVXZTpdKt7LDF58+hMTP9ToLedK/ypHQHhKNy87T96Tz4KmyeOrqFLLrs/01lRuUZBh+ig3EudllySFUUacfVHZOoLqUl5J4uOS8AzjkVkzcvQY0hDIkGSeS7JhALtSRXu8XWdtwfSp/1+JCuRMtm9rAyk87JoLz1wpV2xcYmcHAkkC1i5hNVk1kZFdmamq7ZhYMC6u7oypwHHrDW50wP2n2GhPeXSJsTBNbiTII+B/Bk5nE3T+mZEViC+TOOCzDJFDW9p5UqajjY9XXZ30eUpzhoXSuOWHaXJN26tivRUSiU7/5zddsVll/n0MQgUZTIm5pR3aotEwhp1QMrcQxrjkXvkwwykC+rElEL6fnh4xG678w679a477Y477hDxmyQnpWn0vH1am/rDSZ7S5S6mc5fZTnQyq1exucVJDtPlIIy4VL/xpltUF/pX5FUkiPGFG+3HP+4xbtHZtm2z1zEnUmsRyMW4DATOJGJcBVYDyxl3QXQCgRXE+vscJCU903hdfhQ4FEo8WU1PTtqhu+92j2tzUnhZ64JCOzMrZRhrTkZMPHF2dkuF8uNRaouUuV9KaUexRmH3M6/veaqHWGiO3uuPdORDRpyQjbyJy5mHxIMEeTSFOTnRtQcIKPZSdZ2wJKsRBER/WR7kP6cy3eKSBPYzspDOyY/ycWKjZ0xvQ7lNIL5Oip8cAFBOIk05McJyQn6IQ9WITjh1cfldhmRRQVY0fAhTqksiWcAVdtE18p+crLgV4+69+2x8bNy2bt7sG4gyLcstNJnZxmXSGWUehwCURX2IQ94c9HVuseHaN1QVAWG6GWtsWBNTyaajuVtokZs0xTE5HsCbGm6gKciJXFZ2CdfRIiP9Pd327KuusnN27/I2pgWwWOGdDdLDdLbUbkxBVBt4DMmhP9ZBuctq2tLbifqQB+1VbzPVGRsdn7AjQ4NOdn544w3uKRAZ3YmDymMtj3uZYzobh1uymAaIF7mmo2uZcFHNfRsWHaU/sO+A+po+ZRoh7T7nLrWvfPIV9vrfeK1t2NCf+lXi5H201oBcjLVA4EwixlVgNbCccRdEJxBYQZwVn4Nq2tyR6USTI8N2eP8Bm6pUpOylaWq5IulezyAHAifqvhgo1sSpXZOSwpOyX3vv5EWAJEBKpN36tSIRIVPUs/IyRRagQBPKna+zSYH8T0SlVbgUVbcseT6o0Skff+gpj1251aamLCcZyKQw8nEiomc4PaBs3vbzvLbva+sGuCQ9j8kPUIbLo+u8nslxQ53alylYEKwGl5e0WCmwoA0eGbLWtnafQkZfsIlosdjmaXH9DMHx9iTvrK5MLcNhAAo75flUNDyniThhwWEK3zTWGaadqf/LPj0teUSbFUnCglStITeckZdpaMjO+qA0lW3KJibGbXjoiLUXW+0ZT3+aXXzxQ/jhcXIBKWWNDOlpH8aGEyzGicr1KZNY3rwZ1YZZ30A0aTfSuUtpXXsUpYN0Q9ZGxkbt0OARO3xk0B0XsEfP8PCwjY6NuVUNCyDEqV7l45CBqX6F5rQGB2907B2EVac0WbaJ8bLkwxKJRY32TNPrfu5Fz7PXvPaX/RrQr2sVteMxEDhTiHEVWA0sZ9wF0QkEVhDr7XOAYwFfM2NSJCU3ivCslFw8e6EUViYnrSRFcUYKtSukxnQ1prAlawiv4FGa8xpT/3QmRCr2QlK6U9i924Z4eRB6P3qtExcFLkjhBU4CABEJ1+XivGpzJRwlH7iInnHKJ88bpGdcH8szz5+DqWNYmFB0SVtLdDwHxXXSorMTnoykHM2f/AD3us7lILxW/jwvynBri+LRziT3clUm9cESMj1TleI+bs2tRXcw4OHYeohHZ+iednMLjfIhLw5IirveFpHw6WiqmzsV0DWe0HAiMDWbnAgki50IhAgOntQ8rYgE3JO6t7S02sDAgO3evdu9mkE+SpMVGx8fs9HRURsaGrQjBw/Ygx98kT31J59ibe0tTgbd4YFkgbxhTSLD1CZYWBJxOdYnGpsuKxu5Kg6ET+eq5II052NCWXofYFmk3kzfo1khXKVy2cYnJ2xsdMz27d9vt9x2q7upxtkATiqUiROcVpFESI7vKSQS5F7iprEw5RYg+gxLW8Guvvql9spX/6KHUQ7P1yoWf0YCgTOBGFeB1cByxl0QnUBgBbHePgd33H6XHd4/mN5aSxFGqUV54+17VYSnRYpsvRTMqUrZFVBXvKWSHvVQpjRYXxI9OFZ/nQTOEB1/lJRY3fA8V1SJc6y98rRJuUSxBbUWG57lhIG4Hp4Kc+R5LT4fJRC6R6n2AoSUVyqTzPzkmWIFStPKSMsZkkP83DIECCdPzsnVcyJEtc+IS2nU3ePp0H8uO8/ys7cJ8T2OqI7y8bhZnER6pMSrX1JjNCTySaAr+lhEEokhnKmFWDqSq2zW0OAtL6218Slnes41VpxpkRqPg/VGx9yM0iguzg26Ojuso6PDunt6rKOzy/r7B6xL90yJo35OnkR8IUZYQyA8kxNjLm9HR9Fam5uUrt0629qtvaPdyVKjmpC1MBAO1gsxTc1JpNow1c1bRTKkNufM+h8ndhpTjEUIDuPU6uhX+jKRUPfElk07hNR5GyoeXuEOHjniXtuGhoZsr87DImaTE5MYMa3QULAGjXdo+8IC0weTla5O+WPdYU3PL/7SS+yVr3yZyka6NE49/zUI5MrHfyBwphDjKrAaWM64C6ITCKwg1tvn4Btf/5b98Pof+5SnuZmqv+Hv7elyl7ulyQnbuGHAGqT4YeVpbi5gM3ACgBtndD13HiCF0t/QC76Q3vXKpPAftaDonBR1qZFZ+9BWi9srv0qqY0YYKMLj+p3KUrjSkAwK4G/d8xTIczQXynAp/drj678TvYU/Fh+5VE5WF2RH8QdOlAQnJgIKOvXw+6xO1D0nKrm1AgFy0pbfE58zcuXtkR6RnnxRtj3I5VAOViozvUwkQWGJ6CSHAT7tSwf72jjhwVpXnXWLDaQCL2IVNvOcndY9a2uOrbVxRwK6xuKC++eOYrtt3bzFNm/ZbF1dXdba0uqyMGUR8uPGI3WMi6ZwiErqB9bfKD8nT1NWKk2k6W8qk75nHUxnR5v19fVYS3OLxlmzMoA8JosiOXifQi7oJzUR087mmJ6WtQPuwvlzElPvLeltg9WJ9TZMqaSdUz/Rprr39TnqR41P2haSh+e6oeERGx0dtz177rZb77jTXUpPVKasocCGopnXOpVL3flM/MJLXmSvevUvOtlNfcazTLA1BuSq/WwFAmcCMa4Cq4HljLv0yxy4T6Chj3f09vbaM57xDPvEJz6RxV4a733ve+288847muYtb3mLv108HshvcVmPfvSjs6cnxuJ0+YEMOZZ6zvG1r30ti5GAjKRD5pPhdGQO3L8YGxu3ffv22b69+23/gYN25MgRGxwasTvv2mN33323jYyOqO+HbXx8wirT075IfUbKMwo2nrZ8rYeUQZ8KpXtISH4sSP/zOIRLMfWNQfVFlZMAvrQSmUiKIoosb9P9IK5IC1OSmFrn11Jgjx4eX4RC6ui8EpIN6yj4ivPNQ2EBfp3CXTn278j0rPa4ZzqmpyGjDpcvHVyD/N4PxYWM+LWeUUevm9oEpRrvXp5WeeXrbGi3o1PKsnzysvIwFHbIpNMIKdg0JASHdpqambbhkWGbKJVsQor6qPpnmGNkxBfls0bl4KGD6kv156EDtvfAAdt34LDtO3jQDhw8ZIcPH9bBOpYh9fOQT+uivK7OTjt312579CMfZU9+0pPtqmc+wy59/OPtgvMvsA0DG3xNSr4/TmWy5GeIMcSKOkGa3Pqiaz7v7OXT0d5pAwObbMfOXbZr13m2YeMWaxJhGh0v2Y9vvt1+eOPNdvMtt0vWIzY8PGFl9t1RXeshTY0iU1is1D8NvoamSTLgXhvvbGYFkaC21mbf94b1NoD1Pw30r69HmlWHQHySq+0GydQgwtOg9mX7zzYRrI19fXb+OefYIx72MP/ufulLXmLPfPrTrLu7y/skjcq8zyFY874eClKm0ETssrEbWNu4P393AwH0J75TanWt4yEfl7XHNddckz0N3BeERec0cf3119vDH/5wv7722mvtsssu82u+GF/zmtfYiBSOV7/61fbBD37Qw2vB8w996EP29Kc/3b9kv/Od73j4JZdcYl/4whesTz+8i8GX6+Lw173udfasZz0ruzsx+MA95znPOaFcxLn88sv9+j3veY+94Q1v8OscfOj+4A/+wL74xS/6/cn6+XRlXs9Yb5+DT//N39o//+NXrNja6mQB5W2gv9/GRkZtqjxpD/uJh0qxrLPqzKzvII+n3+S6N+1/Qk2xuKAso/IlJTBNFXMrjJ7RHqThOde+0F/Xeqo/3SueHhw9p4g8S0D555GHeBTipLJBnRRQ7pg6lhMSSEOy3HgCf7ufEwnCU7QUN/9x4VZSpPxVDa6R32VXGlfide9T1wRIGgTG64H1oCHlAclD0Wc6IAQA6weSJAtNJhMR/TpdUhZCeTlOvtIUuaPjiax1zdqT22693YlglWloszNusXEHAbNV95LG1DO31DgBgYykeFhFCMOBQGtri/V0ddnmzZusu7PL97VpVzjTs9h4k41FKRvlnilvENxSueJtiHWDvs8tJG55UYP5uKgTjcC6onSUS808jqL42iaRUqarzVQhTRWrlEtWqZS9LkxDw2rS09Np/VJGi8XWNI6c+Im4kImv7VFrqR6sWaIsCeOuqiHUyER7QSYBdYVwUhcXk7blma7doYYu1IXuSpqevO2OO+xv/vbv7OCRUckjsuVQBNWPNvvlV/yivfzlP5/VmXHjEdYkjo6dBzhW43f3bEaMq+Pj9ttvt0996lP2O7/zO65zLaVP1QK98f3vf/89xlp3d7eHB+6J5Yy7IDpnADB1lP5aogNqCcN1111nF198sV8DCNLVV19tn/zkJ+2cc87xsFoS8uEPf9he+cpXengOBvuePXtO+EE5FRxP3lrQf+C22247Kt9i8OaB5yfq5zMl83rFevsc/OVffNw+9w9fcK9d6HMsFN+yeaNNjI/b5NiYPeoRj5AS3G7lUtnfjLMXSlOLlHgUeyEp/U5BVG/nB26Z4Q26GkP/kuLpU5P0VzvtLEe65H/PxZ/RjrnSmrdnmsp0LO0CairtPef2Di8vn+KGQu5QZGRAHsK8FCJw1uEhWYacUOD9Whm6tUmB7L0COYJIUAsIDG/89TD7p/LrRPxE/gj1siU7ZUIAkU2pFJ6moqVE1C9rDz1NZSGbnhOORzQ9SaSMOqCoN/gC++9+9z9sZGxE7byQpp+xfqU66+Qm3/fGFf1MNvq2VQSmC0LT1mabNm+2gd4+n4rIbv+et2RwV85I6iKS0kSaZqyio1Qq6T6Txf8SIJd1SkdfpbpBgtJ9svKwXkitrAS+gaeIUJPKpV6FjMRgpZqZKtvkZEmEreykTQ+ss7PTyU5bscVamgpONFpbRMSUFi+AjAefMkf/Kq9ZkSHk832caE+F43KbfYgY18kNORZByeNtbTYjwuRER/8RdosUlb/57N/a0MiknrNZaIqHtQ8S9vKrXyqy89LURhny8bTW4J8NKvUAxmr+7p6tiHF1cjCGfu7nfu6kRAdCfTyyHbgnljPuar6eA2catSQit37k+MY3vuEDupZEED+3sHzmM5/xcy3e+ta32vOe97zs7v7B8UgOgOicDKshc+C+g7f/k5MTidiUJm18YtyV2qnpSlKefWoS7onn9LzkaxuYqoQyjxLrFgt9+biyqDgzTF+SjunTzbJwvppQLllMPuuugAnjS0sKvc4+tYtrRczjo+S7S+DsSNO9OKc8fUpblXUa84qnNFJiq9lzX7TOcz1AgfWylZ5pUZTLlCiUYF9AP8OaFoiC4ivujOKSTlkoL+TWMUs5aocsDumon5/nVe5cXcrL88FqojRV2oM2IlyHAikDy0FKR3tkzzxdKjeVLVm9HEgL+WV1zeKMqq/27L3b9ty91+7et0/Hftu7/4AdOjLoa23wIrZp0yZ76EMfao977OPtissvsyuvfIo96fIr7NLHX2rnn3Ou9fX2WrtID+S1wH41EBW1Tr3IEz8otD0EpCRCwRQ5LEKs90kUJ5EgyAxWGCeQkpdxQM/NL4jciKjVs36mjjFRsXJFY2t8zCYmx13BnFSeFeUNMYOMdHR22+atW23Hzt22Y9c5NrBxi/JpsMHhUbv19j32wxtvsR/puPmWO+zAwUGRr1kRGKxmBe9TNYuTKNYIIQZrchrxeCAZocH5+JtmfCoCktLG/uNJjRRGOGPex5oCvV5ORJWD6qmoyqeahQXWA9bL727g7MK2bduyq+MDMrSU5TBw+giicz+BBby14K3RUgM6/0BgrqwFHwKsJ0972tPc9M5bqLWO9SjzAx0oeWiGLGhnnxzTmbfpWElmUOxRvKX1semiL4aXjofSzf4tKOF1UkaZjuQv0PXn612UH+Ro1p0bQBASWZqToq5Hnp+v7dHhCrKnS8KQ3smIDhRN4rrSeY8jKaqueXIIHqZAlFQvW4otYdgqiAvhqExV3DXzocODduDAYRtmc0k8f6lMFOYGKc51UpYV1RXjeREWnqm6XmcIzKxIjxMZ2kEEh/IgPVWV5YQway8sOSjWhClKiqOEtAdxaDsnRToSOUrpmH7FM98bhvxULu2G/JAC5B0eGbWDh46IkJa9fnyvPPiiB9ljL3mMPeVJT7ZnPvVp9pNPutIe+6hH28Me/GDbvX2XbeofOEps0lQutY3qhQULy4i6VoDo0MeQr7SPDgv2h8dGbFQEmPuKCDAkeG4e6wmkAaqqvlJd6Yl61cGdCugZPYklj31vmNVnCyLOM2WrlCdEekbdFTV73EyMT4hsT7olB6tQW1u7bRLR2bZth+3edZ6fu3v6VGa9HTg0bD/48W0iPbfbj266zW6/a58NjU3aZGUqOVtgupzGZKGxWeREJEjypPVRadx5X6jeEE+IHA4Tptyd9LQTL7cmUY80rIR0ARFifLnDB9UVkFdgbeOB8LsbWJ+AUDOlkhk3H/nIR064ZiywPATRWUEwWHM89alPza5ODc997nOzq4SvfvWrdu655/qXLh8GpsTxxbuWPwzrUeYHOnAf3draasXWorW1tFlba7vh4peDqVKJlEBQmAbU4soe85DqdI8C7mQEUqAzi8hRVFHuISycZ9zzV1Iw+frhuZMZ3UNDUJOxxuRnNG637qCQikBBKCAbftSk5/nRvHRgaSEtYb4TPnvMKA6K9EERm71799m+/QdtZHRMhGHWF7e3SfFnWldzE3uoJK9caV0M+UJ2sAwhKGQrkZB5XBmLIVCc/pd8Unx57nIm5detB6wZgayQj547+dJDnZQ+WSCw0KAy+73OPPcw0ug/J4acRa4gXcwJrNffju3b7fGPfZw7DXjWM59lT//Jp9oTL79CYY91a81Af691tbdbi0hNs+pVgMxIhrRnEWemp6U1NU4GFc4aJuoNcaHevgeNCAibcB4ZHLT9B7AYiRziCGEyWWNYWwNR8LU4tIHSJVfXGelRvoXGBmsrNrunNaagFZo0rtRueH2bLI3ZmPIfGh6y0WwPnvEJXFMn4lNVvvRjj0jOlq07bOfuc233eRfaVhG3prYuEZyK3Xbn3faDG35sP7zhJvvxrbfaXXv22qHBIclYcaI9NyuSOF1VeaxfgkBihUyWSI5KhaPsVqZDhw7ZoOqYExk1kwPrYLI6LrgzAjY81eXR54H1i7Phdzew/gCBzmfIMPvnVa96lZ1//vlBrM8QguisAJgHjHcNBiv4+Mc/fsIpYLXYu3evL/JeTIwwrd+qH+5BKRnM8yQOX7wvfvGLsxhrD+tR5gc6mDZUbGGvk1Zrb2uXMtrmJIZFKsXWNhGCVleysTDgVrhFhKheynOdu+ttUDhEJlkhcmKAUsizY8ThmEboxERqcJ2TogYFuI7shzR+PaFsyIvHlkosZZ9InF3ZVJxMUZeq6dd1XgbrY+pcjtERPMkdtLvuvMsOHjjk+6ewyeXGDRtt+9ZttnnzZuvt63OiA8Ehj1z2NAeYg7wT4XG5IFG+hgUyACnTIRko0xfF80xhEJu0TigRB8hXImlA1xAitaeTNR2+GMh9hSXLGPD2oW0UH/KXCCBlsiC+zR71iEfZk654kj30wQ+1bVu3W19PrzUXmrI1KMoDITwf2o6SsU6x8SaL9SkbRT4RHM+6XkQR0qpQiMDEZMnGxsdtVIQGEgLBuWvPXXbHnjvs7v17bf/BA7b/0EE7OHxY8UatVJlUG0/a1HTZcHs9zTGbLCMQrAaJwxqhtmKrdbS1WmdXm3V1dViTCAPT3LC0YdXBA+CRI0Pu6W94ZMhJEFPdIF2sFWI9EONz44ZNdo6U0Qdd9CA797zz1ZcD4oD1kqUi2Y7YTbfeYdf/4Ab37Hbg4JAdHlK+I6MiUxA3ps6N6jyhMibdqjc0NKZjxA4fGRT5qjgJ5z/WSYlm6jhGEFtF2jgH1jfOpt/dwPoD0yeZUsn3CmuncZTByxaIdZCd00c4IzgDyBf3L8ab3/xme/nLX37KJAfA6vFodjJvLnjzeMELXuAeYyBSL3zhC7MnJ8fx5F0KJ+rDPJ9T7efTkXm9Yr19Dv7645+2L33+n61FijIKOm/2e7q6fePHqZlZu/hhF4sAFW1yYtwXr7d3FKVwJgIwPz9rU+WKyFKTCAPTvqSaOwHJPGwpUq1S6Ne0T9L6pT7qVodvVqq45Hk0us7szePWE5EQ7ABo5RChvBwUUKYa4Q2sLAV1dmbGD9ZQ4PUMj1tFHW1Srn0jSdJQvp6nPrp3X3EP6cn7kcMdLqhMrDdOQnxdCmQOGfSIOFm9WXuDgBLbQVyfOkWldUb+vI3ysiEnPOce4klwcltNQ6UGgUyB1D6U4bfeBskV93y2zoZ8k8c56uBkRoGUQWzqgjwepv8oE3lYfzQ9M2WVypSN4bZ6YsytNwcPHrL9+/c7EYGX4SCARflt7WrbVp1b20VeOqy5pdktHVg8mhqxkDWk9T/ugEC1pkAIKX1NGwnV2fk0HY5pZ+rHSnna60ED1DfWe58lCwr5FdwjHNY3yuEZbcXUOOpQrYowKZ9ypWRljV3IE1Yn6osjgoW6eSs04XAhEUj6x12ii6jPzExrrCf32EOq8w9vvMnlYuz5mKtv0piYt/7+Xvv1//oae+5PP9vlp5/ydlyLyPs3cG/cH7+7ZytiXJ0ckBVIy8mcEdSCGUG8LMeiCNkO3BPLGXfpFzZwRgATp+FxUwlYl7McksPAZi7wqbisJN+PfvSjfv3Zz37Wz8tFLu9Sx0rgTMgcWFk0iKC0NLdZQ1Orr1OxukZrKXZYW0e3Ww8gDJCFtGEkCmu2Z4kUzKamFiuKBLHpI4qtP5fSjHLJGWUU5OEo2xAC1mvUocSzjgNFHAXc89Y49PB5Jw/Er19QOay3UF7kM1cVuZISeujgQbvl5pvt5h/f5B6SJkoT1lBo8D1Qtm7dYjt2YLnZaD09Xa4gUy5Exd/SK/90zHmZTgr0PCcH1A3lGPlTXaQg65o6cLBvC6SChfjN7opZSrcOX4+i+E06GqWoQ+CYHsbZFW7SuSVM95431zQG7dngijvlkLahoLyaUOaTUp+eK77y8ulyPr0qtZ2fPZtjBIYwFeP1VkW9nPya9s0/8zT39HTVpmanbbJUtpHRURseHrahoVHbt/egHTxw2HbvPtee+MQn25ZNW2x0ZEzkB2sZ+yzts7vuvttuu/N2nXW/d6/dvX+f7T+03wnDoPIZE0GusA5GRAIyBxHNiWShiWlp3b4p7aaNG2zjxj7r6mxXfzXZfHVeZLtkQ4NDdvjQITvi+/8ctsHBI/7mk2N8fNzGJ0oiNVh7GkXC223Tpq123gUX2IMf+lB70EMeZDt377T+Df0+hpl+WSrPqH6jvk5raHBUeZR0VDSmpt2NdnV2wce9uwtXG/raLydmdbZB+Vx40UVZ+9LOeVsH1hPu79/dQOBUwHoySDXTJpklFLjv4Ns7cIbBFyEm7je+8Y2nbHZkIOPx5e1vf3sWcnLgrhoTJ3PZczBljh/bxcdawVIyB9YO2oqd1t2zUQRho4j6BimaA9apo6d3g2/w2N7R4Yq2u5YW6UmKfnqTzttxyA5v23Nyg7LO4cq8K/eJAPmYdMXQRBJQ7FG8CZcSfvRIBCrPo4k3+fUFV45Hh0Zs/759/qbrlltucWWb/ViYArVp40bbsnmTbRwYsP6+7kxZTi6Mk2lFin2dSI3KxIUy1gXK9sd6CkHwsutUhzrJzVlEJxGP5KQAgtLIGQ9lqj+L3Zt0L0qTiM/RI1kQiMcifMpr0plr1qtAYghrRJbsuqCzOwkQuWGnf5dTcvihNE54mkhLeyIxlhmsSqwBEilEdm//1NYNECW1/z2IjxT2NK0QqxSKO0GEzfk6G998dCxN4RoaHBa5OOz5XXrppXb55VfYYx/7OHvOc37KlcMHXfRgK7a02fjYpPrhsK+B2n/gkHt/23O3jr377c7MI9we9wgnwuQkZfCo04F8jQykh3bA6tbX1+tkAoK6adMG69V3KmupIKWl0qSNDA+K7CQLE+tpGANsZstUtOFhER/Jw5oiSBVu0Pv6+m3Lli22c9dOu/CiB9mFFz7Ezjv3Qtu58xyVsc36+zdofHep7A5rbmrXjyPTONts88bNvk+Um7Dc+sVYqfO9ho4tbFfrJq4YWEc4U7+7gcBK4LWvfa2f+T4O3HcE0VkB8EX47ne/26/xz4+5+0RgYSNvlf7yL/9ySY8wJ8KVV16ZXSXs2LHDv4QXH2sJi2UOrB10dvZKGdyVjq3p6O3bbAMDW2zblq3W091lLU3N1t7e7m/F3eIgpRxrBJqerwvRkZOcnNxwDzijULuyCPkhDLIhZuFKPPlBAqTQ+h49yoO4rP05dPCQ/fjHP7Yf/fBGu/OOu3z6FNOmNojQYLXBhfKGDRtEbnqts6Pdpzmxgz/luHIvksPJIA8qD3IDSTDWXUgmlzGTC7IBeUAelGtICfVCRicYkBfVC5KHNafgBEX19QJUhOLyrMmfk0b56Dn3xEtEBgKnumKl8fRZPJWnrKU6S0ZdJKtPUq4py0mR4kNgfAoY5SE7oB4K0iMTt1K4jvTkKPI0kBpIH20DwXAvblMzTj7GRsZERIZEWA7aRKlku3busic/8Un28IsfYZ3tXV4HiMEjH/4ofb88w5797OfYTz7lJ23b9m0+zWtwZMQGRTYOHjksYrPf7hLRuePuu+32u+6w2/fcZXfiDnvPXpGUgyIph0VOxpRu0kq+tqdi1eqM5Jr3NTD05cBAv23ftsV2bN+qfoaIaxy2MIVs1uUdGhp0onP33XtU3t5EfHTs27/Pp9zhdAKnCbjbFleB8llTS7PGtsb7Vqw+59mFF10o8nOhiNtFfj7/3HPt/PPO86NfJAlrDsA7oJKn/s7bXTk6kQ6sG5zJ391AYCVwvH0OA8tDrNE5AzjeBpx5OFPZvv3tb2eh9wRftm9729v8jdJyv2wBc4TxFHNf1uicyoahJ+rD5a7RyXFfZF6vWG+fgx9cf4fddushKXGNNjvDPinmC/dxN11vM9YhxXN2eloKZlXP0nqQ5HWMHffTuhDWq5Awr/tRJVzgHi9nkCCfHoY9YT55K3MLiJR+1oywCH5yYtLfZE1MjNtEuezTl3xdhhTUNpEsFN20+36a9gRJcMuFwnzzSIWnP+Rb8BfyLhPKve7duINM+oMOYLGgq4jj49+vE3FzxwR4WFMm1AeygEexFB8HA/OZ0nvPOpOPUvn56DjQiTLz+7w8X4+SgWe1+UBK3OKle1xYQ6So5+xscoOMku35qVJJDpWp+rj1ai5ZeY7KkOXNVL287OSuei5NDxse9oX6WFpoE6brbNy4yccE/hEQkzQJ1ATLEPnNuxXotjtFZm5nf5t9NqZ7SBTkjf5xsiaC0Kr7ttaie/g76uWvrUXXTItsthbC1b8QyqbGtOYLMo34VZEbCMvMNKSs5ONjejptjlphbCoSJJDxi7WtX0S4ra3oFsE2EXQItFoiEUzGoa5x3gDhZrR4++DGW+1DvSZnpu0L//gF++a3/l31ZWylcXf55Zfau9/1362ntzM1xRrH0f4PrMrv7tmKGFcnx31ZowOwOP7Mz/xMrNFZAssZd8c0kMAZB2+KmMLGwkVcUi4GX7Z4b/lv/+2/3evLlgHO26YTgTjM31zJL9uTWaOWi/tD5sB9R19fl+3ctcm2bOu37bs22LZdA9a7oc36Brp8fQvKId6xsE44scCyIMUaBREnARAKFOLckuPKrZ5z8MVEGGe/RiFHYXfFvM69e9111932oxt+bNd//4d20823+KaXuJHu6e61zVu22MZNGyVjrxTWNmtta3MFtsi1FONmKcaNTnggIuTI9DMsIbpXubkM0mtdgcVVttiCZGJaW3qe5KM+yIT1iVYRXP+FsGAxIZ2r967kK7qH85fXi7KJX0techAvR94WS8W9Z7tRSBYumZHR6+j1SWUzxS23/EAQOCMJUQBrSyjj2MGifSw5czYlgjCKFWYQ72bj1iFCcNFFD7LHPPaxvscIDia8ohKRNoVCuXVJYWpBl6mhvmC9vb32iEc80p75zGe4q+tLH/cE27Rps4jItHu7w2MaG9EOqay9Bw/YHXfdabfcdqvdfOvN9uNbbrWbbr3NbrvjTrvz7j1219673cPbuHtam7RSacJmZio+pY9+7+7tsk2bN9j2ndtt246ttlHXGzb1W1dPu9oBceuUbsL27LnT7rjjdrtTZd111x7bv2+/vntZdzRkQ4ODXl8cLlQkGw4M3HOg/lg3BfEa6B+wjo4OHxNpXKc+Sf2TNW5g3WA9/O4GAoCZQTjJCJwewqJzmuBL7+EPf7hfv/rVrz66w3KOa665xq666iq/ZmHZm970Jp/axpctFhEW0eb+03PwDHLEHHa+iPnixfsG6d/1rnf5G1by5QOwXLP7yeQFtTIf7w0EBAhLFfIvZRk6kzKvZ6y3zwFK7+ystFlklg7HviOV8pRUvHqbKc/YkYOHrTw5qQcs4tdpAZe7eBfDy1XFp/P4m/8aZR64Qp6B9uBgTQabXJbLFY2jYd+MkkIhLUxJa2puFqnibX5SLhOBQtnkTTxTupLyj7KZLC+USd78jzKuA+VcAdWFpNxj1UkL95OiDsHJgSLr736QT1e+6BxFXvGU2tNRi3qfxlaXpjApLsov9YHsofy6y2uXgciJaHBALEjvees/5CEc+bkmf/IC/gz5qGMSxs8o715pgXTs3D81VVa6VLbnpz8nMsSkICXkDiKa1x3Mz6k99Jh+mCyVfKPMpuYW6+rutvYiDinwnEd9kJO2SXXIQRtQTo68TDzZSRTfT2d6ZtodEXz9a9f6/jt4Z8P7HYQZK9L46LikW1AfF0hp8yJekOOC+hcLFmOhq73D5eno7PDx5SS32Ko4TB1LpJo8ptm0FmsPm31CqkqsNZpMe/BoXLPhKi68C00aVzpaRIqbNc5aRZqwErY0tbjjA+rKuVX3OL2ob26waz7/OfvKv3xZLA+Lo8aEwp9y5RPtHe94m3V2tXv91zoeKL/JJ8Jq/e6ezYhxdXK85S1v8THEWPqjP/qje40dXhAB9DEINPrV+973PrviiiuCUB8Hyxl3QXROA9RxKSwmB1hz8L2fgzUz+Zfq8cAHgl2ZAXF5A8U0MQDBuPrqq90rx3JwKvIeL04tmcmnrNWCOuEHPseZknm9Y719DhAVaRkGrqxLc+WtP6RgYrhkd922x6YmSwrLd8KXiqm4bJ6JwoyyzdoVwrkG+ZhCEWY6FNOZWMiLAsp+PL54X4qvOxxgPUw2pQilFkUfsgPB4U0+Xsw8P/4dVewT1NLpTwK5Pq7ysGhQru9gLzXclXApshAcSJATGD1zkqAwyApT58g4JzqU5deu8FOQ4mXkhHyRjXZzYkI63RANKxIgno8DkhLgmajNZmlDiFKajgahSNPnqEOSwdtQ0esQxe/Jn9RJFrzGQXbgQ4hKfRXLCY2Xp0gupwgAZ19nooOpgnQcU7uYfkg6rDYQCJ/ypmju4lrhNEejlwtRUBnKk7xdxhqQv+9tpGtqSNu6TPrvlttvta9e+1URhAaRlxZrF2np6OzSGGPdU6PKbaNAJ1+QNkjLuE97q9rE+Ji3lTu7EPltb2tzS3lRskJ6WCvmnvT0HHIErZvTuGL6I2uOWMtVEgmfm1FeEyJ0Il8QH/YpQlrkhvC0u9OKFmtRXmwci2dB1orVN9U7yfn3b3xdfUp7JbL9k095sr397W8JorOO8OhHP/p+/9092xHj6vjIp6wtxuKXw4w59EQION9tkBvGGS/FA0tjOeMuiE4gsIJYj58DpE2quKCbmUpVivaCDR8Ztb133G3V6bQ3TdpXhDUTIhFSFlVVKY1JcScPV7CrVSc0pRJrKSac4KCEo5j6dLPmVic5udMB8sByc2zNTYNbb1x9d6FSW3pcnT1MByfKI+/ZubSPCmtTIAocKOikhOgAyAA5oMrPiSyg9OdWC/qL/HOQL4lJ4Yqxjrxf3UrjUZU+CeixyBvFm3LZ/4dQ4sJbyMnzkYx+5/IlhTtllZctKaX085eGENPmeJbK5IDoEKfQmNL4njgQPdoe640icU1/pTYgbSIXad0MZDJN26O98vpQHnVUU3qeLrcLkdqJK4hQDtrCD9LrnvxYJwM5IZ/K7JR949//3b573fd8jUx7u4hOV7cTHCxm7JnTKAJZFInp7Oiwbj1jQ1HkpS/ZD+fIkSN28MBBq4pQE57cmNdZp0hTZ2d32tNHeTdr7BQa05obptSRnraYnZ3xqWm4jq6ISLHX0tR0It5TjGnaVH3I2GxpKTrhYVpjW2eb/cf1/2HX6Ugu15OTjSuffIW9/R1vdU9/6wH5mA0EziRiXAVWA8sZd0F0AoEVxHr7HJRKM1Iqp11uafQ2PztnU+UyGrJVJks2NjSSEZ20kB2m4URHSi8WEiwceM1i/xUUSKazYekBKNesrcC5QfJk1ijFMllXIDO8+XdPZw0iOFKS63wtCkWg4Gc0A6Vdf9xzJc3crUK88YfkQCrm0FiFep19WpvyZ8qSkzHICIq6ki5g5eFWcRMZUCAFKt9ELlK/+Vnp/SRFHlKCsu9WEaIrDclScl1nBCCP41YYhedWLi9B10wlo065JYgMZhXGtY8b8iCNlGoKYpNKwr0tnGTiwKHO25TOmpujXxQ8T/1SfZhipciumFMO98iVxCafVA7/0a7Uz2+V1uX36W2J6BBXzXQUbtXyeMikHHW4WBxK0CixU/kp7yODh+zvrvmc7du3z605BZFc0pRLZXcFjQtuxgJWlZ3bt9nuXbts6+bN1iRC7G6wVSfG0tjYqAjzsE+3q06JuLiXtjmf7gg5YkPbtja8AraI/HQ5mfMpj7S9DixgVRwXYDUax9nFpMZ82Uq+Pmfa1ytRTs7jmOq29wCusvdQa9VEY1Xt+aQnPdHe+Y63WVd3WHQCD1zEuAqsBpYz7oLoBAIriPX2Objxhjvs1tv2J0VYf21SMptweywFsZ7paayDqM7YvJRF91IlZRdFkzflKJ4jQ8PuBatarbqCiBKO5YZpaSjLvC1H8XQ3zSI0tA9Ko7t61j9IQVL0EQBiokRqvnpvwqSI413LDym3LByfrWIJSQq3T82yRJ7IgrRMoWI6m08/g3RkeaHE2wLKvuIoMFfamfaEYu6KPMRG5IPMqu5pjpS0DMQpTd3zOiA/oTpBI5zEuGWL8pySOZmC7PmYUL5pvYwL4/VEEUcJdzIkkkG6vF4qSOXpWm0F0UnEqGrNrQXr6+lR+gUp71PkpHyaRSJx1d3keZGWevuhS0qkftlFuial14Nyk3WJM23kZFGkExwby2manYpzII63CnVTFK7555ugioxBVCS2k4W/+7t/sCPDg9bUhjWnUc+UgfrH41J3XWOZaRVR2bZ1q517znnWKeLCGMLTHWMLt9JYY/DgB5meFBkfE/mplEsuI5bAQnPBOpQOSw9rg/DyBvlx1970reScFmkva0wzpbLiR0Vkn4O1YyWbVv48h+gcPHjEK0p7sZfSE694kr37d97qU9cS8VcFqfQaReqbvP8CgTODGFeB1cByxl0QnUBgBbHePgf/+pVv2be++X0JnvYJcY9TUkghO82N9SIBs1L4WfBd8aloKIh402KTSRRzX2/RwjqHJinhyXLjFhulR4lFaUahdwVc4DpfTI7yzr1PiZP269YZziIEUxAsyM0MBAdFV4q2iIRycLIBIYFUIDihbkVRNk5GlB6LCkTHiVAeHyLhVqNkeaGfUPBzqwr0BJfWSqk/sxmUdU8uUqE2SIotT6TWQwbyP+Iodb7mh/VMiuxxCVNUv09WCiIzRWzOLVz5uiBvBydIEB2Vg7w8Uzj3yEcZ7W0tvr8Mi/ehValtmQZIvFRnb2vK93ulUr7kjGxOmJAMOYTF4zWRMYqlnY7FTdkpB5WTR/d7FeWux0UgWcuEXIwJptjhuKIsQvy1r3/dfnTjj61ZJLhBYwz30eRBGciKZdAtUGpI1mpt7N9g2zZttaJPdYTs0LZqEsk2S770mcYlLqeZnlYW4cZCg1MCNpfFwcKCxkxLa7MVNTbxKMeGpK2QbuVPZRgXTqJF4rHsQNzZlJQ1PuPK67rv/8juumuvyqXiyNpoT3j8ZfbOd/9/NrChy/syb/O1isV9GwicCcS4CqwGljPugugEAiuI9fY5+LvP/qP98z993dfIoFhiLejq7HYFsdjc6Mr04cOHfRoSb9Z98baetUqJxFoDsWHBOWtGeENP/TmYOqTWSIps0vSdqGCpUQt5G0E+fI2NyExlpmLTlWkvY4apRpAbxUGJbpACjTcsqZtZ26ZpaVUWlEBzRBrS4Y88DmkhMSj5UkdTvlKOie9e4nimBK7w6o+pUBAN8rhHGQp364nSE6qHXidIA9FQliEPqd7+ODlpwBKjdsnhC/2VH3FIn1t7aAMKRdH3eLrPPccRxzV8XZM/jAqX3w976EOsqYAMibxQpsukKCm/RChpg9TO1BRrC5a01IapjmSdznm/eZkC+dUCxZ42SeQkOVBweJ3VTnjuS9K4laRcnrTBoSEr6RrvehCJBdWlPrPisJaGsxO1WsKQZbuxv98tM/RVE97SRHgAVjamTRLdCY/GDueZmUSMsfbQVxAfljFV2RtKVWGsFhrqfE0QFkem2DFucXzhRFD1YaxVROBLGof/eu3X7Qc/+LHEmfd4EOwHPegh9tu//WZ76E+c7+2Uy3xU9jUG5DraT4HAGUKMq8BqYDnjLohOILCCWG+fg09+/LP2uc/9k2/I2SzS0t/XZ90iOlhkirpvknKM4goZwHsWJIc32SjNudUGZbdGp1cYSnOuUEMeEmFAOXQLkZTPGSmjbPCIolpFYRXhmfOV8EnXZcoSb9N9g1Ip7z4tbC5ZQlCnyWvGyQkkgDi69mlXaS0PeZION9h4WQNYAdLUrExu8vdyk2wpr9R/3of6NyMZqTt/KNTEyzcmJf/k1U0NgK6rNBANykF29+CldoLgQDrqdEbpToq94us869PpFtIifiqujBCNqVi51YEyyZ8kGzcM2KWPe6yv1ZEEng+ick5WLdqemEqnB7mly+Po2p/oJq+n5y1wXzt283uOY22TtZsAmaCu07MzTjKmp2bdAQUWFs7uhlxZYznz6YikVX1TnXSrP/LyMj1H5e/nJAMEmkX/uIUu4gqazUTZL0lpcGOthN4f9GdVB/khB9eMNeRYcBKN9znW6DD1DbfcieAwJhpEFltF3N2tebPGeiG5SZ9Rvl/44j/Z9d//keIrZ/qmrmC93b32q7/2Gnve867yerjM3pdJ8rUGb9usPwOBM4UYV4HVwHLGXRCdQGAFsd4+B3/1sU/Z3//dF5zoQF7wftXZzmaJZi1SNnu7u13ZQ/HEra+vm1D1VE2vKwcKH4SGN+K5ZQQLAIon1g0UU8JRSlFA56u8J08Kd2orMhMhENFBefXCea6/oyREfxADn1qlNLyFd/Kka7W4wpNzAuJgq0GRJR0Krlt1FO7ERuFMhUPpJ1fWh0CGyJ70yIPyioI+N4uXNHKnyGPT6iAl7tJa9yjxWCdcWkiJ1z2RB28r/bmVRSxGKnEmi9pN5dOIMyJ6VBeF3qdD6Y9+QKH39vE8lV5nSMvWzZvsyic90S0cTjm8/TMQT/duTfMk1CU9ymXSlcsDSaVuXgdFqh2zeb9yIAvP2JOG+jNlkeld7IUE2WGNUXmqrGzJw1OrXPrBL/0/rwc3HidZxGrP9Augrt6OLvu8OxrA5TPe1tpEspuaE0nBwkdf0Y5VqoWsSpSmOObjTOMhG3NMJUxjcUpESONS9zz3PlWc6hxu0lO70f44rPj3b37LDhw4LDKkdmLsWKPvu/PsZz9LZOeV1tPbpbypA8Un+dcaFvdrIHAmEOMqsBpYzrgLohMIrCDW2+fgL/7vJ+zv//bzvj8Jb7uLUixRMP2Nt6qxZfNm1cncMxVKL3uasM7h6BQoKadYXeZEYGZRpj0skR6sISi9KI8o1OltfFL8aSfXfRWOfou+ekwZT9co0RAL4qHAQiqcNEEIRDQSKSIxa2jY0DQRDeLm9yipEAivD1Ou6iE/mUKs9A28xUcGFGLSuAxSoN35QVVkJRGCfO0PSrNbZJQXoG7S1LOydEndpDRz70pzRmxyoPJ7UsmPck1a1gVh+fH6K4gpbbSrclQ76X/FdWuP8tqxbZtd9Yxnqo9ayCxrRz3XyUVPqbx8t6Y5uKf9s1uB+PQHzzjoL+45aC9IklunnDxUfZ8b9042rX52K5fypONScrUPfMUr5kSTficP2oqm8TVRbohRZNIIjLcE+i3VN1kAk7xYcLAyMqWylTVgIneQbd9UFjmVhjZOpJP+pN/pJ2SH7DINMt3PMD5Fhr0+XicRXt97p+Lrz9iEdXx8zIaHh216dtr2HTzsJJT1Y6qFZGoQ4Wq1xz72Envr215v27ZtUbnZOF6jWG/fRYH1gRhXgdXAcsZdEJ1AYAWx3j4HH/vzj9vffvYfpEzistikWLL+psV3zZ+qVGzH1m3W3t7u05FY7N0mosOULK8nSr3+kpKepoehxOakAVfVtASKPool3rh0coKRlGQISoqDdcXJhGvrmSVH+bjSjdKqe8p0hwVSXrG6eFwlzskN8WfnWYcj2bI8kuKe1tgQmb95CI3r28qPPy8n5UHfuZLv1xCIpDQjh4pXPpJU5eFJTjX3sj0fqqMS+B8CQ5lK6Uq73zMmUOJ1Db9JY6TOLSv+yP9TiApxA4I/pnyRh/yZFPZNmzbYzzz3P1tPd6cIEm3OeNP/yhTZkBNCmiORmXROZIc80xjlTDhnLB/TM9Neb6xwTjIVh3Uv3jZqP5qbvL1uyov2QTDIAJYVLC2QUHqTxnCnDIqD9zj6LO9P6uUbxorQJfnpe0ZAOivAD+Riyhrku1lEp0VEh6lmuVc/32CWOlBB6sA0RZXBgexVyczUQMYaUyPZNJRx6eGql09xq84ofEZ1x/PapA0ND9qBwwdt3/4DGdGhbZm6xt5PLfaYxzzS3vrW19v2HVu9HVSsy7kWkfdzIHAmEeMqsBpYzrgLohMIrCDW2+fgY//3r0R0/j5TGhukyKa3+lNTFauUp2zzps2+Nwl7n+B+t62jw6e5AXRSd90rpdWVSymquVLsU9iIoDAUYDWKrlhMLmUT8oICqj+8maEUo1BCNI7u6K9r4jj54E/paVsYBfmTL2E0Nfn5m3ydia+IyjL1A3kRB8Wca9KxzicHMqIUE9fTer4QFeVHGuT07kxTvPL8aKcUL8nFGeTEIZcPpFNSipHDH7t8WG9QolNb+nNvDKxAUt91mdY66az/sGL19vTas575LNu2ZbMTHdR8FHkcPVAPys9dWrMWCVIHOSGs6mQF+VO8/J4zJSflP5+ud0x24hwLYfogedCWtIr6yAlGahvWRrk1h74Q0cEBBIQBF97eRvXzItPJkQXrvSBe5MwjyqJ8bx7dcE87Q3aSZadozQUID/fN1qGx6I4uVCZjEAtQmiJJ39H3qQ/IE+sNzisgUpAu5PKxqOuqyI47Ipgq+349+w/tt1tuu81GRyYlgGRRW7JGp9jaZk+84gn2G2/4L7Zp84DnT5+l/l57QK58DAYCZwoxrgKrgeWMuyA6gcAKYr19Dv78z/7cPvPpv3XFk6ldriRKGWSKT3mi4lPVOjo7UUVtujLr1p1ie9EVcdYooEDjwUoqsk/3cnKColt1ld3bgmlXtIsr08rXrT0qg/tEZEiTXUuBRhn1tTUeJ8GVbeVBHA4AmZlXfijbtLiv2ZGCzXVuNcnrQ2zgSrXuITGpn1DQc8uPynYZUGCTpzbCGqVAe1qFkyQ/EyfJlMgP1zzL17UABfG/KuARdCTlmzC/1X+Un+fFs/zsKf06C9e5p6vbnvqTT7Vzd5/rlgzaljajTPoB4nSMdKUpeMkLHNP+EpGjbCdGkjsvKy8XOMmDeHmbZO3lpIH2SVYTJ6JK4+td6FMRCfa5cccSvvh/xtOxpgeSwfQ8SAtTzzo7290qUyzi/Yx1MWnqG9wJqx5ji7i4GGdtDWOT+JCd1uZW3+sJotPiHgA1bpVHo+In61jqVyfZkp9rCFwaB8mayLilDhAdpqn5uOFaso5PjNqhocP2/R/80A4dGkzTEP2vUQS/3V7yCy+0V73mZV4WoM3ydltryPs2EDiTiHEVWA0sZ9wF0QkEVhDr7XPwv//kz+zTf/PpRFiyaUkohSj542MTCi9YT0+POyPAQxrnVimoWAsgGqqxK8RzdWkaEOshcisDQOFMlh0BJXM+ER0UWuDe0nTGW1aKyzSobOpb1o751DeUdLLKyYxa2hZmpXTXkz/WISwPlIFSnh1K4NPLlAdIyrT+0y1BSYlPz3OlPgfp0z15eUjWv8n6RHuB2RnJpvCc4HhZIiUeX/9DorxuROaR/4cVJlm65lm8wiPPezH5SBYdEnPdVmyzp4voXHTBgzPvb3qkvH2RPuQla3fi4i0PguWew5w8QIQavcwUR2U4XVKfOwlK9fJ6cyj/ZJ3DNfeMT13kOYdfK41bRvR8aprnUzYt4oNVzvuZaWGsP1L9mxs1bkRQerq73ZsahLnQxJhLFrA0ZhLRYR0Q9RgZGxbRmbW2tnaNwW532FBQHdrVBkxhY4NarDtsClooNPo4YXok9aEOgHrwxz/GDfJoaNEskpN1OiLU+uPse+pMTtqR4SP2ne9eb3v27FU3prFS39AkgtVpv/Zrr7QXvuins76hrNRvaxH5OAoEziRiXAVWA8sZd0F0AoEVxHr7HHz4jz5sH/vzv3RlFCWYs2ohZXPaxsYnfHoR06WamgvGZqGNKKwtrapnmuIGUNyx4qCkUnM9SYq92iEnL94m0mSZ1oSXs2QRSCSDZ7nFwMmGCIu7lVa+5OeOB1wBl4aq9iUwWVGknFOGK+wpHe0PkaFcFFSPK7mO9QtlkRblnzCIDGfPKmXv18TRIyFvG0geFgSmTJEXFoduKe7t7Z2u+I+NjSY5vS7m06moJ4v3yTjlJ0KVTe0i32Q9QYa8bMrNZSRBIgJcQwcapehf/BMPt927dvvi/lbJ06xyIKRYaYgD4aK/8qmITsi8TsnahIKP62VcY1M+bY3Vw51H6B7iwpoWiAsEB6sNViEcEkAGIAXuItzdOVO/OZvimnRY9VS/xkKysDBW3HuaiEl3Z5dITpe16B7SgjmKqWdUk/5kfJXKZZscH3fHBxwguZruFtnp9TVizYUWhRXcC1qLW3uw7NA3TV5fyLPDhwqDILVrTpjdWQFjSs3ulicJkKayzajMig0OD9l3vnO93XTzLar7jHPWxsZm9TXupV9tP/v8Z3uegL5Zqzg25gOBM4cYV4HVwHLGXRCdQGAFsd4+Bx943wfsz/70z6xQaHLFG/lZ6I3SOi6FkzCUTBaDM8WHe6YNKaJPMYLweJ2lkKPU+54prsgnsgGZQYkFqITzdUmx5rkfSuPKp65RS53oePrUlmiZkBjiQJRQjmle2jgnBeSsZF6OUpONx0MU0ifrUeqXpJbqf+XNH9dYp9zNs8IgDE5oUKCLInm9PW5R2Lp1sz34wQ+xzZs3ujUCJb5UKjvRYe8hpm0dPHgYSV2Rhuy0SAGHfDE9ystXGNPJnAwhJHVTXakCZXt1qb8qTxUSkoz+TBFZ63LjDTfYVHnGdu06x6ZKFWtUPs0iPEyzok/c2qb47hlPqfO29jz1YPqoswEcD1R1P+VT3MoiFjOS1UkN1hkOXUM4pkVCSIN7cCwuTC3k8P2PvB/mRcIKajOsLSI37UxRa7Oujk7r0DUb0La1FH0TVeSgryCHEKXJyZJNlnRMTnpfMca8QyUr5MSnREr+ltaibdq4yfr6+5NzAiw6OlqVd3NL8siG1QhZjhFJ1oipnetEgueVr9rFxxL5Ml5oU+qgse2eAnWUKyURne/Zv3/72yL74+7QoKOjy3afe4791//6GnvaU57odWD/JojoWoV/Lv3zEQicOcS4CqwGljPugugEAiuI9fY5eNc73m0f/vAf+6aJKMYcrIlQRaw8Melvurs6Oz3MFU4pkc3NLb6eJ228maZvJYVP9UbJzImO2oGDNRwo4eTtjgj0lxR6Foqzn0mKl44sG+Ior6Nez/QApRckKwjhiEkCFNh6KclVpdM9SrLgpEv3pEIlhcRgDSAdSrDXoaHBrQSbN22xnt5uK7a2246d223TpgEpt212wYUXWGd7m1shOjs6rLEAiSDH1M8QF/LiGlKTSk7IxPVnx0Bg7f29kUfPkntsz0sXXI8Oj3pfVCan7dvf/J5NV6a89Smdso6mU2Rf76Q2RpGnzaayKWgVd6ms81RZRAYyM2Xlctk9r5H3lMgPlijiYrmhzRdUCH3AfkezIkAQndT0lJhIL0THp5p1d3p79apNuzvbrUNhpGNaW0Xyjo6O2cTkhOSY9vakyyAa1LOhPlkWGSsQN6+HyAjyYJFq7yD/buXf6Vai3p4eX0uG9ZFxCqlK1sJja588EwrhOm8b9ZdPY9SNE3K1EVMmIabfu/46+9evfs2ODI+I6FTt3HPPs1e+6mp7znOeIQKXLHpe7aOtvfaw3r6LAusDMa4Cq4HljLsgOoHACmK9fQ7+x2+/3f7wf33QrRi40WVRNwojKJdK/na/tYU35S2ueEJQiIsbatwDQya8vjrQIxfwwOVEBqKSkRfl5Qqy/iAt/gZdR3rzTlISpjNxFTWF6a6efW+qrsIrLBEUFHbkUPKUpm7e12ag4lNunaX1GrhmbkLOggiOFOCWVhayF62tvcPXiTzkIQ+1c87dYe0iMtu273DFnLUeKLJsTgmJ8SlhSaNdEl6+y4qin2SvBY/ysPya+PeMtyjRceDpaaEFxRfB2HPHPrv2K1+z6SnWwlSdUDJtiylo6R4ykvbAqTiRmbIJ3ITPQHIqVnbPehUp9qxP0aF0ebuj+Hs5EhTpaN98DZI7gPDn9IefvF/UWx6noYE2Y8qZ2j07Wpua1a6d3v+4LS/rYBzhQKFBbQzhwLrn+euafnQSItAHfK4YM9SL5/QJZbW1ttnG/gHbsnWbW9aYGkd/4p0NgoJcBeROgxNJlS6zqinMrX16lOfNmXVGP/rxj+2fv/xlG+czoLCrrnq6ve3/e7MN9Pd6P9Aop9Zrq4f4TQ6sBGJcBVYDyxl3QXQCgRXEevscvPMd77IPfvDDUgzZxV9qYF1aj8Ii9PLUlCt+3EN0qBcH09wgDoXGJr5RkrVASiTEhfhqAgfXaJG86efslhkpl/Os0UGZlSJd21aZXuttmJACiMKztNZGCrWuUWB5wDMU3samtE4lkZpW65bCu2XLVpGadtswsNG2bN6YrDLtRbvowRdZT0+HT0dqLaY9gVioz/lo0d4auXzpOn+Wy8x9dulIfc9VHpinSXLWpj9Wx3si5Z0/45q2AylP2tg35lT6G35wo/3F//mYjY6XfG0Ma1yw3mCZYQqaTzPDc5mIjG/0qWest6EIyqEffLqfSIO3AW2rMtxypoM2TsQlHawB8rVPC1hadJ/F8bisBapXG2ZxiVfCYiMyhaWGNF0d3WmKnQohDkSn3kkM5BXyoj52UkttJZ/GD+FOwlWGO0iAbHsFkitq4qp0a9GYZO1U/0C/W3p6OrutraPNxzMEFk9trbinVl7KxV1zIzftSX6JQCWr0ayqeNMtN9s//vM/2fad223XubvtWVc90y6//PFKC/FL/XeiflwLyGUMBM4kYlwFVgPLGXdBdAKBFcR6+xy8/3ffZ3/6kT91r10orciPQsnUpDKLz6Vs1kszhUCwLgXFmDOkAqKiGuucCA3pURjJA5LCNaA9eAbxQZlEO2UhvB64Ips/92bj4JkUaQiNh6EIq1ymmWFF8iloIlqsx+juZtpSh3uCg9hcfPHDrK1NhKarw3bs2GaFpgZ/w9/R0a4ykkJPXY5ZafK+ygtPR6pbImiL+zTdJzFrQZw8bmoDD9WxdPxTQ542kcwcWKtuvOHH9tY3/5b94Ic36h6yAAnAWgZZkAxY1pSEa5DSJ/KV54kVzwmM7p20qN6cOSC4uYMKDtw4NxUa3VqCUwQsJVhk6JdkxYH0KJ3avFGkB8J100032vjYmBXVJ1gKvf2Vhv5FjkalpZ0hak5i1enkiaSMM+JDihAQgsI9Y0bVcJfVHqp7xlbyQscmpAvu/KBJcpIfbqi7u5iWKLJTLLo1b2CDCFF7RyJcGg/kCyHzvCX7D350o33lX79sP/8LL7af+dn/rPHUKXnS5yNH7fVaBPLVjplA4EwgxlVgNbCccRdEJxBYQay3z8H73vNe++hHPpo2XESZlQLLAnEsOpUZvGyl6TyFQnoDn8gKb98hAYk4oKj6SQqmK9mqP0daI4GOiuLvuqpiJKtB+o9zissz6blObnxRfX1BirSUVSmpEJdNmzf5m3re2O/Yvk2Kc9H6+wbswQ++yDq721werE68vSc9crkSrXKTjBSFHKm8XF7ESE/T/zmQJ0ee/hi4XxxWi2O53neoIRy0cwJKe8obmRpscqJkb/8f77K/+vhfe1hepMvL4nudc0ngdSTHaoP1DXLjgJionWgJiAYEhjEAAcCDWXJskNqTNGwWu2nDgDVlcepFcAqFZr+GFNG27uoaAqU+ueuuO+0HP/i+N6hGlbcrRBMyjfMC0kGIPZ3y92mJIlt45nMyqn/IlPogEeL8TBqojj9jahrVgSxpjPKMMMhLcppBeo/hZeLMIE3TbLWNqg9EiPILTqDbbf/BQ/b9719nr/u119oLX/yzSpfGDWcf7wiWZ7lGQZ2TzIHAmUOMq8BqYDnjLohOILCCWG+fg/e86z32Jx/6iFtIXHZUOPRGKYmsVcDrFlOcIEHse5K/PVdNXWFMCqfUP4WjvBLub8XTpSNX0FEk5+YpA8VT6RSPMwotSjJKJ9OPcCPc09NlmzZttYc8+EHW3d1lu3fvdHKD44Devh5/u07ZyTpDKRRGoXnBKKY63QMp4KgDg6MgfibsPVCbwVLPVxB5NWpAO9I3PEgWpwX77Gc+Z7/9P97ui/tZL8XzYwfxlEbhruzTIBAChdPmiYg2JOKhvsitMljMILWMYzyTQYQaCqQXSVE/7dy50wZ6+9xqAymq53ByyfQ1ESPlQVrGDO6ZhwaP+Hqv8dJE8tIm8oFHv7GxcatUKu7sgOoSjohYdKhjbnXjzFgjT67zzxdkA9fisDjvUh1eb/5SUrcEIhNusj1vEXZoiq8xUltQX7dmkoDnqkuhqUXkK7lB/83f/G/2sy98XsrcD8rQ2KH9szLWKuI3ObASiHEVWA0sZ9wF0QkEVhDr7XPw7ne+xz7yRyI6UmwlvGRHMUaDq3cPVCiqvjZGIRAVlDwU5GTVyYlOsoyg/FF/V1i5d5VT/0svTIo05KTJLQDso8LCcTxm9fT22q6du3x60O7d2+2Rj3y4SI/iiHzh+QwlFOWXvNPhmbusIBEXv1IY97kGqjOP8tujWDIw4VhWq4vjyp0e5PW88cab7Vd/9fV2yy23OhkgDdXnoG+O5aN2I5EOyALPsNZBAhp1iK14XJ+uxr48Dc1u1cO6V2gqKD0EN3lw27ljh23butUKIjptxXafDobjgXzaG2UlspssZxAextB83by7b2ZTzgMHD9jtd9zuZT7hskt935+xsTGfwjY4OGT79+/X9aw7TZiennFCxLjC0QKJKEel2UI9DcFohcylseHT8XT2saw/6oQ8bCSLVYowb4eMPHEgNU1FHsSD6ONe/P/7rbfYz/7s8/UEEEP5U0gQncADFDGuAquB5Yy7IDqBwApivX0O3vX299iHPvjH/lZfwhueqNDfUAjZBJQF7NVs+hqbO7JughhOdBTRFVusBbpGOfS3+rwVh5joGdesj+nvZ7PHovX1DdiFF13o6yP6N/Tb5k2brKOzwzYMDCQHB01pWtEx3LMtj7Vt0jK597UmCODqKmFelYTFXZGHr3Ug9yJZk0UnWaqw6ND2E+Mle+97P2Cf+vSnvZ8gO/RNPg69T0jLnyv+JKbLWBeDlWNBfVVwpwDYOrDOEH+uqvheFhYRiEraVHR+fta6urutq6PDSQGbpfZ097hLZ9ZJ4b4bkoocrepPrD5YTxhLI2PDdvDwQTuw/6CNTIz6fkQvfelL7Nk/dZWvk6EM6oal5/DhQ05wcA5wZPCwE59Kmb19cE9dtoOHDtq+vft8751kFUoe5iYmx90RA22ANQdSDpkj/wUfp2ltEO3hDhWc+DBeRH9UT+pEm9CWbe1t9s53/g97/vOf58/XG/IxEAicScS4CqwGljPugugEAiuI9fY5eO/v/L79yZ/8mTVKEUShde9pUo7dOiCg8OGm2Nc6iNmoerrOlGZdY9Vh6g/rKFpbm333eN7ys5fK7t27RXD6bMvWzfbIR17sb/2bRGJYDE58lEzKoc1QMJl/xB+LSVI70p5JqdeVy5POtG9+n8dJ53uith/yNLWRap+D2vvFce9vSBYXRzK4GKltfGwRtJCmDFLnG2+4zb7y5a9YqTyp+2Rxg/RBiug/pmF5G2V/LP9RVysc8jJrE2OTIiHjViqVlHWdxyedu3yGpDDdTIR31sPTRqFYewhnHDAGGp3gpjU+kGY2XGXdCw4IiDNZwoojsjJddhKCI4mrX/HL9gsvfbFv9JlAhb2yHif/HHn5Opy8SHauy+WK5J10coO81JnpcN/+9rft8JFDXhf2AILMQYb27d0vUjihtKqzxvOM0rl7aeXl69B0UFrubnpWZIkx/IEPfMBe/OIXqv2SXOsJ8ZscWAnEuAqsBpYz7oLoBAIriPX2Ofjqv/6blMPrXIHFBTFrYJi65mtupAgyfQ1lF1e/wBVdKZblspRi1ZVpQcViq3X3dPsO+Dt27nTXzoWmZtu6ZYsV21qczLiLYDJYpC+iBKfAY21GvvfGsTCad8koJ0Se/8kSnmq81QFEBcmODbFkXcOikdpSqKlCinc08j2hYPIrTVZ82lhlakpJsj1mRC5zssEGsJzJn7148KI2PjHhFh4IBZYTxgvkaF4kGCYFASlXyjZdmbYJxcUKM1kad8sO8bfv2G5vfNMbbdeunU44vF5HO5U8INXZFDghTYm7JxbXLW+HRIoIR+YFJzz79u33KXM8832G5kTcZudsaGjI646MrEfzjVA1xkkDYcOac+mll6YC1hnW23dRYH0gxlVgNbCccRdEJxBYQay3zwHTg1D8fD1Gdd7XSqBSur6KoiiFUVWS4ohimZRddEuUQurKRpK8xW9WOqb+MO2McH9Wo5zmbbK4fZa6D9z/SH17rB/APW/TDXEgEVhQnAyJCKvXGCk+VohGTIjPlMYW5AfSAFmuVkXGFI7VBEK9bds2J8rgeP2ey3S64yInbaD2TDgH5J2xnRMrCBFAztxRx3rD4s9WIHAmEOMqsBpYzrgLohMIrCDic3AMi9uBtjke8rgnihNYn0h9S7+mPuaefl4vfb1exybyLv4MBgKnixhXgdXAcsbdve3/gUAgkOFUf7/4wsmP44EvptrjRDiVOIHVQW1f5/19on5fjNSvx9Ll/bycPO5vLK5njM1AIBBYHwiLTiCwgojPQSAQWAuI76LASiDGVWA1sJxxFxadQCAQCAQCgUAgcNYhiE4gEAgEAoFAIBA46xBEJxAIBAKBQCAQCJx1CKITCAQCgUAgEAgEzjoE0QkEAoFAIBAIBAJnHYLoBAKBQCAQCAQCgbMOQXQCgUAgEAgEAoHAWYcV3UcnEAgEAoFAIBAIBM4kTpW+xIahgcAKIj4HgUBgLSC+iwIrgRhXgdXAcsZdTF0LBAKBQCAQCAQCZx2C6AQCgeMif2Oy1JuTU3+LR7x5HXPZmftFae9xmz8/0RE4FdBFp9xNx8GCt/e8/qfvUv8dC+MvnWvDj8XLQ7nSdTotA8pbidJYO96RcKrjcXHqex2UpzN3p1p2IBAIBNYmYupaILCCiM/BvYFiXGf1ahddL+habYTOSCtxWVeXK5fH2s/j3APcH+/ZAxupTdTKcA210bHmuWc7EU77Lw5PPQEIT0r/0T7yIF3orJz9qZ8pM0uSwomWyp6XIHX1uiCdRyFmenZyUA8IslLU5e/ljvV5yjLllerNuLpnndKze17nZZOG+3rJx5n7PD9IViqHOClBeu5X/izJlGW2xoG8x+QPBM4MYlwFVgPLGXdBdAKBFcR6/RzMzs5ZtVq1esk/PTvrpt+mlmY0PT2rWkNDvTU2NNqM4lRnZnXf4MeClFoUW1ePlZYT9Xd1WMp3odBkLS2FVEgNEuFBoaz3ZPPzqc1oP8LvDZTL9aFg3v/ACpKU/fWkiB8fkB3qJHoMYWJQZXXKP1p1dc7qHIQxhhg7CXkasPi6Fnm+i8OPD8b2sXLWNuI3ObASiHEVWA0sZ9wF0QkEVhDr8XNw6OCg/fu/f9fK5Yo1FQo2ODjkxGbjxo02W52x8fEJKxZbrdjabqOjozY+MW6trbovFm16atqmZ6ZtTgSIelP1ubk5Jy4Qlg0bN9mDH3S+tRYLHr+zq8uamhqdADU0nEhhzNswP59cIa1td/rhgQJIDnWnzaenp7O21zjkofgA52OtkfroGPKnKXx+Yc6D6jNrCk/Jc0bktlBotOamJhHiObfYTIyXUtkqr6m5yZqbCzYjktzU2KRi550YY82bmJiw6pzGx5xCkdPTJIKcCC4HVpYG27BhwDZt3uT3ywN5nKTPaQuVz9+cym6sr9f4nktjN0vrhFFth4zOpRSMrDxva29z4rVehlb8JgdWAjGuAquB5Yy7IDqBwApivX0O5qTo/dM//Yv97nv/wBXZeil/KLIofJARzlh6sN7U6RlK4ZyUVpRS4qLkohQSJxGc/Lqqtqi3jo4O6xa5QUlEid29e7f19vU5ierqbLeWlmaRoBbr7e22gghQsaVodQ2ZcIFThBT3qtmPbrjRbvjRjd5/PudM7d+gPkJZZ+qgW0ecWEBkpLD79K1kNWHMcvi0M0VrEEnJx3KpVBIJLjtRbWsrev+S45HDg4msiMB0dnU6GZ6aqqgPm606PydSVLCp6Rm7a8+dNjY+5oSnVC7ZVGVKBKNqVZGifKwBxtgjHvlIe/azr9I42WUFESdJ6XIAJGe8cTUzWxUBm83qUa/8R1wOn8qmOKSBzEDX6lVHxujsjMpU2OzsjNLOWKvknJiYVN1Kaewq7qxk8nbg3ptq3okejfKEJzzBzr9gt78EWA+I3+TASiDGVWA1sJxxF0QnEFhBrLfPAYrdJz7+1/arr3u9kxLedqM88iYeRZFZZF4n/0PtPLa+AfBWnCconjnR8XOdyNACSrYUbKVvbm6R4tti7R2d1t7eaV3dXdbR3q6jw7p6umzr1i0iPp22Y/t2G9jQ76Snp7vXurs7rbGxwZqaG1OBJ0B6G58roXkfSGIJKxEycFHz7Chq++x44bWojbO6QMLqzLz9v0//rX3t2m+ozxI5aGhstKbGgvcXBMDJquLWi8Q0eFg6nADpCX8eV4o8BCnVkLZjHUwiD/V6xj3N7MRinj5esEblTQK31CzM2ezcrMbWlI2OjdvBI4fsyJEjNjI6bBOTE1YpV2qIsYiO0uTkpL+33x4psnPJJZfY5q2bnWy7FKok9XRirWN6ZkrHjAKQcsEmxsc9XJl4ZOLPw/70rJ5rBXBQppetcgtqh+mpKZGxaZd3DsKuz4OTN11TP+Ini1bVnv7MZ9jzX/DTGr9tnu+9kVpsrYD2jN/kwJlGjKvAamA54y6ITiCwglhvnwOIzl/+xV/aK17xKuvMiA5vsetZP6OrozVx5TmZWqgjwKLTKGUa682MlM4qiq/nIKVS2iUkibZAeWYqVGoWrhutvhELQ8GaCs3W2lK0YlubtbW2idh0W1tbh0hXu+3YudO2iQB1drXbps2brb+v2+NhCWpuaVb6BmttTYrwYsxlU7BcVhWcTghAPVIc1SCrS6pl+p8Yi5HNYTqKmuuUZXa+d8ozh1w6sLgctbhE/Pw1X7bvfes61apRMUR0GnRAWOgI3dfrnn7AAoKoxEkic055+jnLHoLALf0MIAeQBXLTlTXQpyICEJXqvMiDr/OascrMtJUrZRufHLPB4WEbHhl2i065NOlT4CAgZNmgzAuFgk+Ha2lhamSrdRY7rLun17ZBeDdudLI9JhKDVak6C/nGUQECqsLI4mTae9LrRUNQB6bJQdbB0RmSGi8MSggRpA8C7pYbiI1qJBrk5TFMUniyAM3P19lkacoe9ehH2yte+YvW09OjOJST5XsUqZ3WCrwdfMwHAmcOMa4Cq4HljLsgOoHACmK9fQ4gOn/xF39hr7j6l629vT0FuuKYFNoENLpjynC6T1ONCiI6s9VZV2DzKUik9Lf+WbzkdAASxDO1D1eYBLwAYmH1ScSnXnk2SCFtamoRqWEdUKvLxXS37s4e6+3ttc1bNllPrwhRsWg7d2+3ThEfZNmyZYvSiTw1Nfr9YixAflw2JOFIimneXS6t/yfUdmEd6WqV2DyScI94tTdnAjXleEF5/oQvflZn3/j6d+2G798kLV2yuqI/5+QGIoIaj8IPUaH6KPRUnLHK9Zzi59YVv3YCk0gA0xvnZqtu5XALx+yMzYisVNWe9Psc/T83Y3PTOk9PWVlHSaSmVKlYZarsVhLGA9Y/+oYpcF2dHdbZ3qajw4oi2F066M/mZhFXCTijMlkzdnhw0A4dOuxkCUsQTjN8rDSmqZONGit1DY0ah1iuktWKqWX6odN9aiP62/ucI+tsJ2+p2RSk+qouECPGJiOX+s8tpHVnff0brKGxxc6/4EL75Vf8kg1s6PPw5BAhlZFQO0ZWH+vtuyiwPhDjKrAaWM64C6ITCKwg1tvnoJbotGVER1UQVIdsWhB1gpi4Gqe65fXDmsMbeaYQkU8OntexhocUWV7+50SIAB1SNInneYkgODGaz57p8OlS/kZ+wctpkCJbqG+y5pbW5AhBR1tbWvfTJmUZJfnCiy6y9o526+/rtV27d0ipbnLLEFaillYsQMpuuci70uuxGIv7efH96WKxwCn/rPlTvygsd4X8nW9eZzf96BaRDRGRqWmdK96XrJeZqc648o5zCSwaPoVLZAWPejMzydkA92kNlp6JzCQHAizW1z2ERqSHtTUQW19jA9HJ8iEe5MJ7z/9bUPs3W0ux2TraOq2ns0uEtMOnfbUXdejcpj6E2FaVdl5lzKjMifKkW38Gh4bsoAjO4NCgTU6WbVrkCacX83NUnvGR1owxThrqGnUNYa6zgogu0+gaRXbIO28rJ766gbghJHEZk34j+LWIS6I5egbp03gl/YMe9CAb6Be5Hhiwl7/8ZXbOeTtTCpJ48jyflNdaQfwmB1YCMa4Cq4HljLsgOoHACmK9fQ4gKB/72F/YL1/9CncY4Oqi6uDwenDPCRVQQa4ApnAICJ61cqKTvUBP05tQNDNFnfQokrnFhwCUVNZb8Ec04pJv3nScEzHJJyApmS5Qav0ZxMvf5DdbQ32jT31qF+FpbStaR2e7bRQBKra1S0Htt+3bt1lXT4/1iQBt3LhBJImpcq3W09PhZKihEeX2eEgCIXpqB2TLyF8mGfL4bVbfvA4p7Pg4WbxEYLJIS4B05AG9IOY3v/5t+9EPfmyjw8M2eOSwjY+NuZVmZh5Ly7QvwIfwkAhy4WtVdIa0QGaIm0/jqorMMCUxXz/DGfA/be/uxgtqf7Uf62haffpZixPL1uZmJxxOanS0trZZUf1DWwMsPNMiTtNTFV+zU5os6TxpE6WSjU6M25iOyYlJG5+YsCkRNpdLtTz6ucpPah93pgBJpi0URr8wJc6JT8ZsnbwojhNqrhmoyquOqX36o/FSfC750wXPVWemSD784Y+wzZu325Rk/sVfeok94lE/4eVAGNP6p1TKWsN6+y4KrA/EuAqsBpYz7oLoBAIriPX2OciJzit++ZVS/ouu5i2oDr4mJ6uHvzXniU/NQkFkehAL2nlLbjY9O+0acAPTiaQ8+kJuHugfVgQu/M+zS3m44ooS7YqmypPSyCPiEZE2dDH4cwVVQMtWIIvtUzxCSY8DhUZPX19ACRcJa2AKW3KAUBQBamlps77efhvYsMEdIPT29diWLRusu7vLunu6bMumjT5tiqlxGzb2UdoSUAG0D/J5+ene5aRsyBtBGbjm2fGQp8tRe5+nq32eY3EaROF8/fd+aN/+5rftzttvtz133mljo6M2MyeioASsbXFrjdo8lzMRm3TOp7LRdyzOIb987yR3Ky0y0yQCA5lpbeUoOqEpNrc6GWhuErkp4DSiWd2RprhBmshnVtdT07M2NDJiw0NMQSuJwEzZZLns3txY3wUJY/8mCBAuqpEDMpbaWpnkldal/nn7+1jVuOEz5587zh4phaXPoWgL49QBMYH8iCTrOWM1rTtLcnqOSuPj2q/nrbO90x716MfY1q3bRR5H7Odf+iJ7/BMucZJPu7klksRrEOvtuyiwPhDjKrAaWM64C6ITCKwg1tvnAKLzV3/5l/bqV73aOju7fYqPE5vGgit8KIaFRqYHidzojNLbUGB6UMHfmFdZgK48fKE78TOdMlkIsBSIzKg9kkVAbSOlkqlUvgZE4a5fK9wJEWtDiCsFk8MVc66lRxKuZEqt/2hfFFNX1InP+gpB/+Utz5t+/og6r2tka2T6m+/f0yRFvdm9zDH9raur0wb6B/x+65attmv3Trf4bBjYYJtEgHB4QBymx/HWn7JPBchyf6rAN99wq3392q/b3rv22KGDB0Qeym6VQZI5BFFjOFcVfO0NBEf3GrJqH5wXsLaJvmeNVMFJTXMj++NgtVHbMVVR4QW1n3tf01+98qyqnzw/9RXTzyAxTDWbnCxZZapilUrFDg2N2P4DB9x6U51LRAYyxHog+p/GSgRZ0LUTEheWMZNIB+TGo6ZgJysQHcDnzgPz/z1eIjae3p8Qj7zTGHeyozNEmrLIAxLt+SoIww/rwh76Ew+3rdt2iqSN2It/4YX2hMsf658DnwbneSodGa8xrLfvosD6QIyrwGpgOeMuiE4gsIJYb58DlNRr/v4f7F1vf6f19KdpXUwJQ3/k2YyUVrxkoegWpfgWpPQyzae+Pq2LEJfxaU7MK5uDdEh5RkH0dSAZyYGkoKMm5TO9qceC4E4KpGkrN1fI/Q25/iBFrghDZJTWyQ4CSSn1N/0sjBfhQS5fKC+i49YJJ0+KI/2TeKTJFXAWzKMoI48ruRJnYaEhlSE5eUOPB7hisc2n8LXqvGnTZh2bnORs2LhBxKfPn+3YsS0RH5Gkvv5ekYLcYrC6+P5//NB+9P0fWGVyUmSDzTxpE/pAbabmc3fLag/devswVsECJEeEIBEAxjDEVgcdSX94Hypd5qSATTYhK7Os68ERAc4JRHanZmZssjRpExNp3x3W1LD2Z3xswobGRp3wMA7oD7cOqrA0vSzJQdkU6Z7inOgQlkhEIr/qOyJ40tTm/lkjjt+le8iLBxBff4qR5cG9rr1elMOGpjxN5YrjaFyIrCsu9YX0d3X12pbN29TPG1VXs1e+8hft0sse44TQBRHydlxryOscCJxJxLgKrAaWM+6C6AQCK4j19jngrfp3vvFN+9TH/8a6ewesrdiuStRbaWraxsfHbXR40DdU5I0/Gyw2ivAA9mgBczPzPuVofm4mW6vDYncsLNmaG8iPmoN2yZVPf5Ova1euk+qpcN6OJ+U7b0OmIqHgwnF4xjQqnuVTmsjLSZeU3rzN66S8NkJksEwwxa4xefsiHmSNMrFYQMQgRKWJieS+mDUiE+M2MjriynplZsrTNDW1WnNzu2+Uife3YqsI0OYN1tPdY106tm+TEiwChLWDuvq6pQLunRcSIVDb5KOBJkBMzlw4efD79MC9neUKvcLq8e+seyeQRBSSBetYO3Eolc1Mz9qcymprabWCN6fIp+cpUigyiRczJz2K6wQQK5gTniRHPtWMM4QGWehL7ysRQYgNBNOnmemgXu6YYIG1Nhork2rHiUkrlaedILNHTU9vn3tVIz571UCKcFyAswTPz8cJdVINvD9wasDaoMxqJziFob0ELC/88Y9+TpaX1Fb6T+2FlQaClo+qFJ6nz61QKRhyp3HDn8p24qLwBTFC6o6cjSK+HR1d1txS9HHQ2dFnr3r1L9pTfvJyt+ggCOWn6XBrD9QxHzeBwJlCjKvAamA54y6ITiCwglhvnwPe0P/wP75vX/rcl6ynZ8CaW4vGdKbJUtnGRsdsZGRI16Mej+leLc0s/mcKm85SZtnZHs9d1eq0zaIAZ2/43VLi1gMU6dQuPtVH1+6li8KlWKIkzmHVUZv5i3jFyw+fVqQ/LEy6dWUUj1p404L0QLqadDQ2Mh2t0fdjYTf9Jp4rnJ35ITeQD86sI4HkYEyQOit55tKGkVLUK5Wy79w/NDxsR4YG7eDhg3bg0EFdj9jEZMWVepRkymFRPRtxFpQfDhA6Ortc8aXfnehAekQeUPgTMfHaHmsDaq8w/0uN4/c+bc+VfsWh/pgZFMfbMcsDpGu1l/64guiQxbm7dtuF55xrHcUWES89dRkgF+RRb7O0u8JIjzIPCUoH3tQgNBBWtYvizNYSHbVTiqN+5jln+l3x2TNnVOOkXJmyTrXDtm07fNNPpgi2FztEMut8eiQH+SSPbYyT5P2N9TvUOX+WEyq8sBFOC/Af7QQxYvNO5PLW0H/UJ0VK0zAhMzQPZK0RMqSxgMUP0gqx8XEFEdI5n5pJXvVqr4LGBnsBQRpZK9SgcYVlr66uoDiN1t2zwV70ov9sz3ves9Tvxawv0xhei6COteMmEDgTiHEVWA0sZ9wF0QkEVhDr7XOAIvv973zPPveZa6y3d8CamlttVgrl+MSkFNhRGx8ftXJ5UjEXrK2tVcpri7VIwW9uLor0oFCntTpYCFC50TldsUSh5o91NVgP1CRYFFCicwLg1ot5Kf8ezpt5RUKpRbCMAOQKqiunum6UMtooJRoLCgQH6wnkpYDzgeYmJwd+iCQR3/+Upyu5yIbSDjGbT8o29WejU8gaa4KmdS6VyzbKdKvRETtw8JAdOnzYRsdHbHJyQkpw1eVKQiYlF+JD/Tjof8pP98nagJKOLNynMCVUXRVT97SZnpEh4XouEf15UqB5lvLi2dG8FeZTtKA5CqAdtm3eYru2brXujg5rFcnD1uPEhMjW4O2t2vs9xIWyCWPqnhNTj8t1Ij6Ume4hCvVumXH5lZs7E9AYmSpPWW9fr+3YudsuOP9CEZ3t1tPV7UTU1LfVaUhvIlz5ep1SuaRjQtdTbiFKeSpX/YMCI0eqpAvuJ5dTckCGfLriPJaqJG9qIXMSRFuQACuWkx7SKx0bfzrppp70h/dTsgDRIXMaDznJnKrMSLZpd2XeUmxX3dW/IjqsYXvuc59pP/8LP+Ousr3vEDt1qMuwlrDevosC6wMxrgKrgeWMuyA6gcAKYr19Dnij/91vftv+3yf+n3V39RqWD5TcsYkJ36RxcnLMpmam/W13e7Hoe5+0SLFnOk+riE5zS4tbMNDJk0MCqay6aYS0MDFNJ1eU9edrbVDMUbKlbPKXlGzukUYh0hf9Ce2ocJ2Ub6Py4SIjMApMCr9yVZlEYk0JhUFaaH8OlGKsD5SP0j3LmpHZGYVX3fo0U512BRjFmTUmpGGaGOTHLRqKh2I+OTnpG2Di7pi1JxA14ERK5bK2xafrYQpz+RFJcuqK/YRQiJlG58PCZaMdULYhOlLIqavaJSX21FkeWT4erBAO3eiW1LpMcal7u8hNf0+f9Xf3WFFklDwB6UjhudNmdYn8UIeUh8KcAKR2gyh4/b09KCcp/56H5KZNpkRQIDodbe12wbnn2oUXXGCbNm+1LhGcgsinTxGExClfwPgAKX/KxgvbtLelH2wsWq4ka43y57mPAcV32fTn1xBm2sLHClYm9YMCiIEFh/HiMtO+XMMDBfrGx5iufV8g6kO9eej58jlALpEdnaenZt3VdVVlNza1Kl6DGq/Rp7E9+z89zV76shdYV3cnOXvf0AfpWFuI3+TASiDGVWA1sJxxF0QnEFhBrLfPAQrvtf/yVfurP/tz27hxq09NQ9mckAI6NoZFZ8zdRzc21luxFS9cmfct3A3rut5JDtYSlHqpwzoAC9nzP2mTSdF31ZJ7FNJM2cSikyme6MXE8XD+CMj0x5QPbYvi7U+dMLnFQe1NHhwo6P72X6SDt/6sJcFSgwI/z7QkkR3qN8NieinKaepdmlrlThJyJVul0Y90JWtHPB6L70mjeBLS5UwL+I/VAdReJ6sMdUoEjTyPWbBcz3bS5jWX0p4lO/qMcwpLcTgfe5ausVZt2bLFent6nISyyB5yR7iPRcVxIkbyTOlHRm83heX19DPPdEbxx+rDHw/pi9mpaRGUKd+zaNeOHXbh+efbjq1brdMtOGmaYBJOclGWrqk/YwFSxTXjw+WiL5W3E4wZNiFl81K1L+3MFLasX3BogCVpepopb7NuAXKnBiJsUEknb1SC9svGS+p/6otVRwRbfQ1J5s8Jj8IRk/u87sBJFfLMVq08VbFR1m6J9MxpjNbXF6yzo9ue/owr7Rd/6eesr6/b06o05UUfK8M1hny8BQJnEjGuAquB5Yy7IDqBwApivX0OIDpfuObz9vvveb+U5a3W3dnt63SmZmdsks0bxyA6U1IYzQoo9RAUKaxYWZLiypt7qYzoeVJw0wV6JaqsbpUGRTdNJUvKPkopR2or4vA8Ta3yqUdZDB4mQ48UVhRZnZ2MSFknjDjIzz3p/DnrO1BySSalluduQVLePPcwxZmR0jytA+U5eQ9LBCbPJxWbZPR66uzWKeXs63yQV3nlz3Jyk+qU0hGWgzB33a347v1M8Di0p7cUZab0nEE+lU8S6JqDMA9Q9CRXyrfONm3e5PsDsY4J8akzBAoiQc4uE9lR/5qwnPQgqsur/AlLVjDaX+2h+ExRq4pkDAwM2G6RnN07d9vmTRtFetJaLeRiupp741PeaT0V0wshW8hM4dQPC5j35tF6QTColvMVgWu3/vnaIvoEBxSMjUR+cFtN/5VLZV9flQgS/ZemtdGmVBDpE8Gbs8aC5FABkDuHzqlNkA2JsQgmIRbUP9Ma/4eHh214ZNSqyq6+vsna2zpEdJ5iL7/6Rdbb20UWQk5qOdYWkIs6BgJnEjGuAquB5Yy7IDqBwApivX0OUIT/9Z+/Yh/5ww9Zd3daW1FoanHywQJ9JzrTFZtFiZRCmSwiSVGmrnVSaHXlRMVnCi1IedUFymY+fSqtJTFfR+JpUNrJQdcQCxRySACyuLINGVA4CnYOzwGFXOEsSk8CJPlRmrnN3+yrA4jt/eAExIN0jQKsOBAdrDkzTpKSVQHFPr9GLoBCzsEanGOkgv14mIol5VvxCacMyEO+DojyaT9IAmUhEyKRjjhYmohHW4D0vyKoLWkZGtDl9nDJoTLzKXuehkPRyV9f6B6nq7PTWovqN8Vzq4wOcmZ6F7o861ScHCmMZ6SiLb3NdXZrhqdJJIMYbt1QX0+VyzYnMrhxw0bbsX27DfT1O6lqaU6OHprUr1j4mptbNH4a0vQ1HEGwnor9llQv2o3xkAib5BA5Y6wl8fkPuXTmmdrIrXbI49VN44d0TGlEZqYWenylpY0hQoxXpr+x+ShxfJqiyFmyEM1m9VMeOlMvHzeUQ/0pX+1TJ1JEmzIeBsdG7dDhQZtSfg2qR0tz0Z7+zCvtl1/xC9bT04WoQsozHWsL3l6pgQOBM4YYV4HVwHLGXRCdQGAFsd4+ByiE3/3Wd+xTf/VJ3xcGxRFFs9DYzLeFnqe1K3jKKpdKNjHJTvZ4VpvWMwhDmmYkFdEaVHfWwrjiqhMWCSwL0BRUScITyUGRTyQGxwXeXhAD8tEZRdkVVZEq1x+Vr+fKq3XlgSKeHnCHvCj3SUFXcs8D1RhFOCVPfeLkIbvmD2XZKZQiYfGB6LBWRzeJXGTKOWfvUdJn5MfrQFkKrrAIXvIWpehjwXKLkrKlfMgh8rhCjaxKwT1NlcvCGbB43kmhCoNqOHlUfoRRZpIz5UOL6FJpVQ8p4sSBSBLfN1pV+5KvW2YU32XycpBA+SuxW8cy61IilqI3OjvRwXoikoDFhH7oaG0X0dkgMtzjUxjxuuebijY3ORHE6xzOKZpFeCA/ube71tZWnZPzCHcgIXKTyKLkoB0lDOGsM4KH+PCRhL7Wy9tHdyIgkDUkp+b01azaExfnuA5nip23mcutuuo61Y2xpXE1pzYS6aFe0xWmwk17WvIjjhMltRPT51jTBcFmvI9MjNtNt95moyL7Gt0icq32DBGdV7zqJdbbG1PXAg9MxLgKrAaWM+6C6AQCK4j19jmAiPzw+u/b5z7795nCWkiKaAOKakP2th5PZ/VWLk/b0OC4v6EvNDEVaM6mKiVXitkJf6rMhpBS+FT/5OZXiqfyp01QPJli5Aq6jtx6wLQiPXZFc25WirYU98bGgr91x4rge59ImaVNqyihOkNI/Kw8eeZTySAeKPfwAWUIQUFxpS5uwVB8zrjG9qlset6gcph2RVzf38UdFUAxEiGB4KBBoyRTtq/HkQKOwwLy8M1T9VcSAUQNb28rZvKxaWpyq+xT91QH4ifFG2KTWTiUFyQREqN/QrJskSeNUlefSBxKtsdXMEo8MpGP/vn1yPColWh7mh6KRNtm9SUKFi2/51AI7YOXOe8fMsme0ebuoaw661Y81sNAokje0lJU/dqttci+QiI5IjqtxaKTKwguFp1WESA88kF6cFLRhHc+H1N6pnMjbQE50eFxcPfdwF5FTR7PCYOERST6BesXfXuU6FFBPxQnO6d/SEiISA9XXifaSmH17G8EaaLN673eECInV2pTj6uUpKa9IIlVkXjqfmRkxP7lq9fanXfv0dMGa2/vFNFJU9e6e7pS+UF0Ag8wxLgKrAaWM+6C6AQCK4j19jlAubvlxpt8+hp70OAuGgWTN+VOepzsNLiTAvaTGRoatQ4pfMVis1LjknmGTGxUSuHI6GiWLq3TyOwlAgp9emueFFAdmdLPHwpovvicvNyCQhvqgFxwTbu6Mq4DUpKmmWGNqEvKttJQhiJ63n5WHJRqV2AVhhLrZUoAJzGQCCcSybIFGXM2IcWVtR6UqX8+HapSFplROCSMssmz6Hus1NnI2Ji3G5uKso6FRLQhBMnXryhP4ud14SAfiBkyJcsGliyRLuSifYivdqFtUOCRI40tqqyzMx4U+Qa74aYfSykfdhLHOhVAPBwxcEHdCsqXPJ3YZBF8uh7tLmLja1t00B9NKrOt2GIDGwZs85at7iHt4P6DNjg0ZJMitrjgbmoSqWnBWoMlB3fjzem6VUQI4iKi42dIkdoAi5ATF8nRpHFGvSBAyEX/5RagJhFqpr2xDxLt5eFuBcIalAi4dxh1VztAKBlQTnh1QHTSlDYajHiMG6Kn+Kn2uuaJtydjjD5SeU5YRPrUDoyFwdFh+4fPf8FuvuU2q29s0rjvsmdd9VR76ct+1r2uQRRVmsuZFbamkNcvEDiTiHEVWA0sZ9wF0QkEVhDr7XOArCixt9x4oyuhyJ+US5Q31Ue6H9corVNTMzYzPefTlSrlcSmEMzYvZX5mqmIjI+y3U0mKJ2Rhbj69qW9tlqLNzv3Tnrf+c8uBkwo1E8oiyjVv2lG466SVQq4U0WVLqiecRCTBBapzpRyFPlkA9MxlxUoheRbY8JHNKKWEUpzSHV1wrvzyslF1yQ+FGkCOfOqbFHDK8jUcEAOPX/WNRVkHwh4+bl1QXij1yADRKeqazUuJhyXMrVBKyzm33KCIe4MKBbUNThCYFladnXPlv6Ojw9Owjw8kiM0qPS/JnshONra4r1N7KG5jU6N9+dqv2l133+355lPWfJ2J2pO2RXHHTsQ1G2IyXZB+oq70UZvKwVU48hVbi7ZxQ79tHBiwvv5+ydTp1jpcjeNe+8CRI3bw8CGRnhE7dPCQT21rEqmBpEFqkJn9jFpbit4etC9EqIWpbiJ3kBnqBNHButOsuIw7iAxjDFKd1vdAdBKxJI9C/lyHW2ca1YYQJeWBxYo68yyNp1Q3H28+CGh22ox2BIxBETsuaU8/QzCxCiq9xgLPhsfG7XNf+ILdeJOITkOTdXf32s+96Hn23J95pm8YSlm5i+9k1VlbODpeAoEziBhXgdXAcsZdEJ1AYAWxHj8HEIOpSsWVQWRHMceOgGIMUJp52y2130kAU8rGRwatWirb2PCQlSbGbXycPWYqxEiERenailL+pcCz7sVVTrVNPn0L+G77euaWDZEJwtEbIVYowa6W8jpe6bj0PW6kXPqbe0iEwlHYSYelIq1HwRNb7n1LNfAjWWxwu8yNW1hEfsgLRRnFGMWbcpyAobQiKxYgCJDynZZCz3Q3plvVS1En3xYp+BCZ0dFxa29r9TZiyhOeyMiDfVmwQDQUGp3oAWRGsUeBdwLl1qKKFOd2KxaL3nZsxEn9fc0U8SU3faLmSu2YMlJd9E8N9nf/8Pd22513eN6QVNbONIkAUVc85bF2pkN5t3d2utWJfN0xRL2eS44GkSbiusVE8SGa7SI8EJe5uQUnY5DJesWdVbtXpspWqky522cI7g0iyVj0JjUeaHMnnpKFunMN4WEKJHVya6HOkBbueU5/QlqadKRrrETZPZYcycM1bYYTA/Kgfk50dMaKCPFj6lwiUljCaLNEdiAj9G9a85SHpSM1JeMoEWnWKTEW5zQ+BofH7O+u+bzdcNOtSt9s3T29dvXLX2rP/E9Ptmb1cSI5MXUt8MBCjKvAamA54y6ITiCwgljPn4Oc2HCV1DZIAnRD4dRJirFqaNWpik2NjVu1UraDe/faBPuNjE/aZHlKineDeyOTrulv2FFM0xQfphQlC0nKd97bCWLAlDNvMh1YZFC4Wd+BBSBf28IbeoQijSurOlxe0pCv8iMe4WTm15AzkRTqwFQvV2CVH4o0+6LkU+noszRNr1FEIxEkJ0O5e2Plj4wAYgVxwbLDQnvEHhketo52SInIjYibT7uSEgxpQkGHGM3OsF4oU4j1z61CAiQTWckLpR8ZsfRQBoo8PM8tQUqEHCregWKP3NOK+/3rvmfjpUknBOzYX2xpdQ9s3HeIsGANoS0bRIAa1DG0G+UgqztMUPlcQ2i9H6ijyAQ9n6b7meRrc+sc7UsPkjfTuXDDjGc+NhA9dOSITYikjY2O+TUEcXhoxErlkreTEwLJT1ocFEBcUltgxUmWsiaRI9rMCZjaC1IIGYXAQF4I83VAIji+foc0SkscntO3DY1MA8QqpHQQTcrM2hPSRd8mApUIJH9OhDS+sZLNztLndXZoaNj+/vNfsptvvVN1bbYNGzbar7z2VfakJz/Wy08WHXojq9saQ/wmB1YCMa4Cq4HljLsgOoHACmJ9fg5c9fWrpLeh8KoOqot7vMpICUohGmt5dMRmJydsVoTn0IGDUnQnbKJUsVKp7EqjUiTFUgoo1+4NS8p0IjkpfwiHW2MUxmaQytytSKIrFKtneBFrMPEeT+/Tm6TYoqAiI3EgAViEkM3vIQJOUFI5KMFM1VIVPD1WFRwUoBhjmanieMDJgxRjpkspHLncMqQwLD684XdSQV4o/ZKwiXUnSk8e1Ks0MeEWGTx1QRrwIMb0MPJ2q0NzQemS8k1dyJM4VASPXyjJjbSVyqQupGMEJS9piejwLLUfJAySk8heRX1wWH1Adk4CWlqsDcuG2g67XFGyOrL+XJifdXJSqUyJnFS8XXg0p7bALTNEr61YTCSOB5AClUd/QATztkkWEP4E3fPHM6boVUR6cMnMVMLRkWGb1NgYGhmxAwcOuNWPzTghRAveBunAwUWdqod1hjU59B2kzEkRJJRwtaVbbBgLfp+RFSdHjT6G2NCWMKxaiTg1+LQ5+iERJyxZClee9DnXXmYhlcV4pQ8ZQkdE0v7pK/9qd+05IPmabMuWbfZff+O19vjHPcJl8/ZJ1ed/v15LiN/kwEogxlVgNbCccRdEJxBYQay3zwGyzs5MW6U0wV1S/P0KHVfqq5RQ3lXnb+HLpUk7uHefLUxXrKoD17vlStWtOSjd0vylhDLFKynbTCPz3GAb0mSl5zuZoJ1QnlGQmcLFPVPjJJBiQ6qkXEsxJR38Ktcl01t4lExISlrbo6SJHCgtSjFEx6ffScnFEkN4Wr/BniuzrrCj+CbXw4lYuTUAxVc3pKlCSJBbIrGHSsoLJX/h6MJ7XCmrYN+pH2Iwp3acVX4FKcF4cCMfFGmeKRMrKB/KSlXhQuTA654r4JJHRbpXOf3lYykRnbTmRyKncARTeFntfuTgIbUT63xEqlT/ZqZ1qTzaEFKAHLQZLUx5ytKm1Vesr1Hmypf2Tk4WAESOOvo0PsHTqlzycwsQ7SDUKW/anf7kzBhJVijGEHXRM5VNnZCB6ZEQqUOHD9v3rr/ObrvjTvfcJtqnuje40wJqlqxaMwpPbaBAXaXynfDWH7PG+HQ41dmntUlO2op2hugQp6B2S9ahlNbXCfGMspQvYcmaB3mkUipJ/+g3Ngv9znevs4NHcPTQbBs2brZf/fVX2RMue7TyJnICfbMWkY+fQOBMIsZVYDWwnHG3Nr+R1zHe+973egcc73j0ox9tr3nNa+z222/PUtwb119/vb3whS+8R5qvfe1r2dOlcd55592jHI5rrrkme3p8nEjeHMeL84xnPCOLcQz3VY7A2gBfHMOHh+y6b15v//Gt79v3vnGdXfedH9gPv8fxI7v5hzfb3bffbYf2HrK9d+61m2+4xe6+U/eHBm3P3QfsyOCITZQrVpLCPT1Ttcr0rJ9npNCy3w7WGqYCsU4HiwHKtVtNdKBIs3EnMkB2JkslEadJEacpj086rAQ+7UwHRIM8IBbuvMBJDmMO4pPqgxWDN/8owFhikoKLUswUKJGAllZXxpPCDJliqlN6yw9QylHTsVgw7QmF3xfMo0QfzSsRE1eydaBQYy3A0xgL71G6ccfcyr3KQqluknJOeSjp+doTiFyShfQpbz8rP8LJ1/P2s8rM7xXH5SYPlGx9dL0dkJ82zaYDVtX+PiVN4clyhpWlKrJa8TamD5hSp8S+ZganBDhV8HVLWXuSDgtHmuIm6iEu5KRS9245UzzKJoz+cWLj/SsypDjI2FxotJ7OdtuyeZNt37bFHn7xQ+2yxz/O+nu7fIrj5o39dv65Oz38cY+5xC5/wuPsSU+8wp58xeX2mEc/ys475xzbsmGD8lB8kaIZyT7p68LGbXh42IYGB21QxxEcJRw6ZIeO6BCZOqTrgzofOHzQDhw6aPsPawwf2m93H9hrd+2/2/Ye2O/Hnn177c6777Y79+7V9X7bf0hplNd+pS1NTaktVD/VMbkVTw1Dnb3yR4/AWga/aflvVW9vr73lLW+xoaGh7Om98YlPfCJ9pmoOfpcDgfuKGFP3H8Kis0KAqHzyk5+0pz/96faFL3zBwyA3b37zmz28p6fHvvKVr9jFF1/sz3JAcp785Cf7ly9fxN/61rdsZGTEn3384x/3fBeDD8z73/9+6+vry0LMd7Un/FTAF/zb3vY2+9CHPnRcuYjzK7/yKy77ueeea1/60pfsHCkctThdOc5GrLfPAUrpzT+62f7xc1+W8ElxbW6BKLB4u86V9c6uTmtvb/M3+azHWMDj2cKsjWqMML2qsdBikyI7yXozb02NDVL0paiLELAGB7Bwm7bBwmAiEblVgLUOTD/D0xjTnVCgsUxAGHgr7+6HlR+C0azkwZt+7Eys5wHsh4OHMdwEIzeWBl9Lo3KIQzqIiltoBBbcM90JxdXz5c/z1gHhycqBUECilM3Rsj0LxYGs0DZo/jgUaJXMTiZE5PK2o45YnnwKn/KBbHly1QfSABGA7DCtjfguQGbVSexEF/oHseIVVapuqgWWHcJLkyUp+IfUj7PeZm71UD9C6ohI+Uxng4j4BqzKGC9qrAOqhzipjhC5RGDInyhYw3BVjfwunQfn0wRzAkZbOc9SmTzDkgfJwzKF6AhAvZBYlEzlsb5HP/B6XlFff/3f/s3+9dqvW4cITD9EprvX+np7rEfjrburyz2+UR/GRUXjg6mRY6PjNjoxJrJbtsMjwzYyPOJlY4Fj+p07oPCKU1PaVf2tunDNuidvc7c8Md0Nj3soHIzDZieSEDpIK5ZBnEwcPHBExJ01YgXbMLDZXv/619jjn/BIjU3lRW1IrDzSsbaAbA/k3+QcvGjkt47fZn7XvvOd73j4JZdc4r/Vtb9fOVBAF4e/7nWvs2c961nZ3QMXMa7uG2JMnR6WM+6C6KwQsMBcfvnl9yA6ObCEfPGLX7QXvOAF9yIBEBmIzitf+Uq/X0xCeGO5GHxgjvcFfao4kbw5eAv2xje+0V796lfbBz/4wSz0GM6EHGcb1tvngDfv133n+/Y3f/XZpKBLM/T1DSImXKMEd3S0+xqUZC2oWnNBymd5wgalYDOdram5aGUp+CjQjQ0L7uWL6VOQlpnZirdJbsXwKUJSqJkm5YvJ9WxaiiprRlBY6xqSZzN3Taz4kCWU1QUUamnVvo5CZ/eyVsUaRD4Qnaor2SjTiuB94Oqt0hKfTS3R47GAkB9e13xdiMpzMqPnTkwkl3sIU9mEOZSA/DweRE1adFNDkxWLrVKMk0c29p1h7U1ZCjgKM8QK4MYYogApwKLjajgEAXkUH2cNvt5F8jAdjTIU0ePxj1wgNAwpZPDDJZA8ijclAjA0eFhy0C+pXLf4kL/qgkUHqxTp3JKmPqHPUzF4ZFPbzaXpi3V1KO8p73pIgLcdRKHJSRB1RRDvA6VlfJAP8mHNcTKrP+rv+Sgu9cS6JPrl5IK2xFqHYWTv/gP2xX/8R7vt9jutr3/Aurq7rbO90wb0fdLb12ubNm6yPn0HMr0NOSBnfojUzorYlafSdEmmw41PiACJmECGplUXiDPe7KruchwCOuVtgSxOHL1uqR/8EEmug7zRrpKdsQQZm5yYUr3po0bJs9l+7ddfY5delogOm90C7zNPtbZAveiDBzJ4kXj11Vf7C7v8RR2/fc95znP8heKHP/zho7+9OfiN3rNnj73hDW/IQgK1iHG1fMSYOn0sZ9zxjRy4n3HllVf6mS/bxXjkIx95jy9aSAOkAisKX8SLp7DxgVnqzcBKYteuXdnVMayGHIGVAURjrDTp03RYKI7SODw6aoMaf2xEeXDwiB06fMRGRsfcgsC0p2Fds4ZhdHzCJsqEVXyh+aSfyzY5WbYJKZ1TPnVNiubsnK6rIjQzNi1FlT1pfBqanmPcQDnGfTFrYVCiCSNOFbIkrXh6hv1lpn3TUs+T6XEzIl7SNVHU86ltOBuoiHgwva2kA2V1xqfUVfUcOZTOp8QpvhOxNMWOvWxwDFApT0lZZqG+5FT4jMKm/Vpn4up+lnxUDvfTyjfVQ2nJu7rg8kH8ZkQo5qUEV/XlPKX0U3OK6zLQJkqrgzYhbzzVzVJPtdXUFHnqOWlURywKeVkzek57zlJ3hXk+kg+L1pSIJtYl3D5jSRvPvKGlNT/YH0Rg1L6sUcGaw/Q3fhIIp82doEGSnMwl99CNhWYnfVjefBPQlqI1iLRiaSGOaI1+fPgRUr8pL/rApyzSZt42yMUarhkbHSvZ0NC4DeoYHp5QoiZ9t5xvmzZtF5nutWJbt+Ro0vibsv0HBkWA9tieu/cr7qiPG8hicyvOFoq+AemGvn7bvnWr7dq+3R5ywYPsMfouvfQxj7UrLr3MrrzsiXblFU+0Jz/xCrviCU+wJzzu8fYoPb/4Jx5mF11woW3bvNW6Ojo9r6KIuWiuzUxpvGosMy2O6XDjGuOQGf2+OlHmD/IIseEHN5S9tY9vfOMb/jKudjbCZZdddvTF3Wc+8xk/1+Ktb32rPe95z8vuAoHTR4yp+xdBdFYRWGgW43gMn2lsgE0Ea8EHBmsPVqKPfOQjbgFaDawVOQJnAmmalytyEAcp6wtSujmqUlKnRDDYIwXLhe/7IgIxNi6FdXRERGdcBCknFbxhV9xKyfdZgUTMKi/IDGtzpqR8E16REuxkROcqZaAcizigNmKHgUiNiUCVRJzYlZ/1PpAeLAEsvp+YEIkSkeKNvq/j8TxEEFypnlW+Cle5CnJiMCUCQtm8+WeNCuX6eiIpz5CacfLTAXGifAjHlJT1qSmm1Ilc6ajomrwhIBAN6uZ5ZOHkNaG8IDqQEpwzuIwo/RJkUnUqQ2CUdyJtqa28LUSUEtmBdEIKWEOj9iJfDj2D7EB6XC61w4wiz1E3SJbanj4TE3CrVZ2ICXYksRFfk9TU1GJNWNla2cyz2a8LIjAQy/k6CI/IjOLUi8CI2SiDRm8L6oElJ5XB+qpE/jgj98TktMhLWYRA59GSjY1Nqy1nbGS4bIODkzrG7cjhUZGGCTtwaMT2irzsOzhsh49o7IjolCuzTnIuu+zJ9pjHXmoPv/gSe5iOix78MDtn94XWP7BF5KvFyS3jDRkgstTRKYbackH19g1RRURaCgVrL7b6nkFYgrZu3iIitN1279xpF114oT3iYRfbox91iT32kkvssksvtSc5EXqiE6FHPfyRdv7u3U6ctuno7e1R2cxZFLGB1OhvXkR1bHTULUM+dxArXHCdNQ1eIi71Mm7btm1+Zqp1LXiBd9ttt9nTnvY0n/J2srWygcDJEGPq/kcQnfsZEIA//uM/9uul1tucCFh1atfO8AHJCRBT4V71qlfZ+eeff79/cNaKHIEzBF+ggWrMInOmJ+H6ucEaIT9S5uYhE1JwmdrFG23IB1ODcCk9Njlpk1hZnMhICZYyioIMOYHUjE+UXHHH2oFCX4EoSUnGyuLWEicgyZrB+o2qFFeUfKxBE1iGVMbkZElK7rTEqpduybofyAyEa8ZJURkrBsSD/LAiiFBAcBYsWYawKrgFSeVILKsu1CcShAwiLjMiHpAaJ17zqLR1Lg9kyc9Km9cNpX9mlulf7AE0p7IhASIzyos4yEDeEkd1xcHCtI2JRHGNjGXyIK7icUZOtwLpGtIEqfHnkCra0UkVcXS4HIQlSxTkB9nZu4i2W1hokMwqm37SV31jc1FHm5MZ4kDi5lT3ZCGCgIqQSZ6Sypoozagv6c+yjUhezmPjJRsembShkQkbHBnXedJGRied3IyNY/mjf2cVl35QflOU3ag2xlLEmpc2ayh0iGx1WUdXn/X0brKBDVts4+ZtfubYum2XXXDBg0RszrPt23faju27bOeuc2zn7nNs+46dtmHTFuvs7rXGxla1SdXGVTbjI+11lKaM+RidFwHS2HXPdBq/7gyBOMRQnX0vIZE71isVi23W1dltGwc22KYNG22riM3555xrD7nowU6GHnvJY+wRj3iE9aMgZ2QG6w3j/oc/vMEOHx70vCGWlB9Yv3juc5+bXSV89atf9d9dFFNe5DG9G+U0XuQF7itiTN3/CKJzP4FBjPcxLB4McBY+vv3tb8+enhy33nqrveIVr8juEjC5Y4bnR/faa6/19TVMb+ODc3+SjLUiR+DMAMWwOjedHXjs4tC1DvaGwSMaLn/9qKYNLZkaxZqais5M3WL9B2tOsLBgBWBqGFO0IDxMnXJLgOL59DGu9Qxl3omRzgtzUlal8ENI3DIhPRLLgRMMHZAkpnexjgSlnalWxIOEQG6SpWjaiQtkyRem61naA4c380yxIl/WZbD+RqRJxCn3gkYcJXNi5GRI6SkPZwtMzzJr9GloPk1OkYiXT5eDkHBPfm7FEjHhGZYR9O2SFHPqzLog6oKiDrmBjPjUOSd8c6pjmuZHuE95Qw7Km68TQZH8TmggKxgzlN7lzQiZ4pCnkxeRqymdp0SCxkQ0R8chKCInIjDDIi2Hj4y6tzwIzLCIy8iwjjHFG2V/mynJKzloB+VZndNPRj2OIdqspbXdmlpFXIpd1tLW5QSkSwSmu6ffunsHrKunz7q6+61L1919Ojj3brAeXff6dZ91dHS784G29g5rK7Y76WA9TEH94I4nGnAHjQOKbJ2U2r5ObQ+Jo/8glyMiXeMTkz41UB3JP5G4BNw+s3ZILaMDopPICLwkucZWu9FXJKD91Jjc4yq8s63DukWAelUvzoWGpqytmfanvtMYvOnmW+3uPQecEOfre8g/sL6wd+9en2Hx1Kc+NQtJYEobv7148XvPe97jcVBOX/ziF2cxAoHlIcbU/Y8gOisMLBz8+PX399tVV13lYXhP+/a3v33K61kwdfJhONHCtZxssJgSvOxlL/PzcpHLu9SBI4KT4UzJEVhNqL/rIQANvnbD12AwhYk1HCIAKPsQBAV4XJwSoGD7ho/+Nl3R6tm3BMLAOo2k6OMimY01SZO8rJEPyrvIgJgBLnu5Z0ocVhr3eCWFt9jaZq2tRV8zoqyVTIqkDqauVeerEinJimcsHB+wR07a8DJ5BUNBZsE5RGlGyilxmcbFuhKUatJBbNhwsoE9VSQna1Gc8ORxqb+3iYiQymhpafX6+boWxcE6okzSHi2Sk/tkGFOF9I92oTwW4COjO0pAmVcZOE8gf/KDKGG1os1UoMpLzgTYG4b8IUf0AeXWS/EmHVPTaG88gZEf1qSREaaOiaj4NK+KExrWwQweGdMP7IQdGRqzMZEdLEqzswiI3Exja7fWYqevkYGodPdu0nfXJtswsNUG+jdbX/9G3W/Qd9dGEZoN1t0tQtPVa52dvdbe0WXF9jaRlnaRoFYrNNOOjdbYxBhirNAUGgtqGIgeDid8M9GyDq7LZZsqlWyaaZF4gnNiPONrvMoKK01O2sQ4bqQnbFLEZkKEzcnvVNUdD7B2hzFBbfi+os8552tnKBv7XOqQ/HvtWFxuuOYZlMj7SP3tkTJLTkpKPJFqDXT6inrgaCOwfsHU64997GPH/U0mnN9fPLTxkpLfSX6XA4H7ihhT9x+C6KwwsG7wQ/u5z33O77HmLH5rdCJgCcJl80c/+tEs5MRgDjLe3CgHDzPLRS7vUgdvH04VpytHYPUAMWgutFlLkwiGFN9mHU0t7VZo6bBm3uBz3VzU0ZLWf0jJ4+16a1NR6Zp9V3/fvR7lHbIghR5lHQUfRRylnGvIAlOIXJmXosl+La7UQ6bIE7OIlEn2dOnp7nIXw63NzVk8prVJqVU81p1QJgpqs/LFKpAsA5CBercKgDmmkUnBLij/FhEud4ssOX2DSEiH8kikCBfL7JdTdE9vyAPpYaqTe35TvZKceH2DoOiacB2sf0EGyJanEXnBaxz1Zd0TUwBZ8I71grq3KH6biFyxpaj4BV8zg4tnXGpT79bWFtVZ+dWn8mlfNrkkLfFwENAkglJoSsSrvb1LsjSLYKGwQ6SKykPtpqO9o09ERWSlD/KSSMvGjVts6/adtmnTNtu4YYttEKnpFYnp6R2wnp5+kZget7rQnvQXU72aCy3eRrR3IgAirkydE7HEsseULpweTIqYjE1M2PiYDpGTsfExEZJRfacN6zzirsnHea6DuKVyScSs5A4wWPs1pfOEnpFufHJMccb0fFzxJkR+FF+kiHKwKJbL0yI6IypnMlGZ7Dvr6JS1jNA4hdGZZ4Rh7eE6n+qWrD/ULJEZAOnJyTJpNfTU94kUEcfb4v9n7z/gLMuu8m54Vbh1b93KXVWdp6d7kiISyoFRTkjIIgkEJhkkJBSRkABjkzO8gG39XiMDtj/7+yyDbJmP14AkZDBG2CjnMBppNJrQ3dOpunJO7/N/1j1VNa0ZaUrqmuoa7efWqXPOPjusvfa+Ves5awcvl05Rmb5g74A5pcyXuC/L+rKIQfW/+M/+7M98Lij4WlD61M6jEJ37CfwRZVlmhnRtx0XJ0tJvfOMbv2Rfmy+H17zmNT5jJFTIf8p3P1gueidxT3IUXNlQz7DB3dsjw1jGcbPRv0F2GiI63SI6HM2e/mg0IT5JePC6NLt7o1skCOMdDwTeIG+U2d0to50lprsUpxl9vb0y9JWHDGdIQR3iIIORTeghHz09kCwRF91jQK+vrCpOV/T39rVWxuqWcQ/ZURp9IBj2kkCqyEdkoldxmyITLB8NUWGvHMgNe91QJgZ7TXmmZ0Vx8OZwL+JAHpCeroZIhYjGxgR+Hfb+6LsD2XJeSsc+QZxZehvSg1eHPWyoB0tOQ17QAaSFtMjT02QDUYVLJtKx7w7EiDT79g3ruUimnjeVBrkZvoVOqHu9i/x1SJbq6NYzdNQtfZP++IkTcYK5LTqOHTvh48jRq+LAocMiOaMiOyMxOIQnhv1paM8WiVG+1GGTAKyJUOaqbYvLIjHzi8FCEzNeSU8kReRjcnIiJibH9bdNJEZko9qs88KFizrGREAuitzwnLh4Y0RQlMeSyMySPTdLJhkQEgaFQTpWVlZUZuv5UnWwueyySBwLVYiUqMNw3cZAMrUh3jDIE56glJ19fxiuKBJCG7fIiZfBprO15fwdVlKriI/DBUvCM92viyxxzX4/pHeY7jva8Ti223sIgary2ETmVXDlgpdwrLS2nWHk/C/mhSCkvaDgcqD0qZ1FITr3I/hjyiQ0XJTsxPyVgBvzqU996rYXLWD42KXgS3TpcezYsdbTncE9yVFwhYO31SYOGOgyqlmZS+SkoaNbxneu2NVtEtHXh5dg0EOX9u0b8fClvl6FiSRBeDiIhwcDotNo9Oh5nw+IAN6JpvLq7RF5koEOYYFAeHiXjO6GDH3OEA/ZlSYIxOvtUb4tYgWpgsTg5WgoPzwzeFvwolBOR3tN9ma7vR49StcN+VE5zNvBA8HCBmfPXYiTJ0/HnSdPxp2nT8Wdp/I4fdcZG+t4I9ifBbMVcoWDiFW9MJwhhSYxqk9DZAVigjemJuKD7Gys6s1OHU/3Ij7YwnhjIEq5PxDy5oajTeXlfYp6mr6GCLHZKiSnG73URbC4hhwpvBvCowOiVetiaFxH7D84GgcPH4iBIYagSf8iMZSNHAwla69h0YsIyMBfFFFYWln2kK/5RVbLY7EIkREd0yIxU9OQk6kY5xifjjGRmQuQGZGX82MX49y583Hu/Lk4d1GEZvxiTHBMXIzp6alYWJiLlWXmdkFOlk0C2JMIudl3pl6HvOEFgxziIaON8Z+wEIYIzPqK2pY9ikQ8lRbPHPsx1aWDTM+wOBZZkK6bInrSCQQET48uNkiHCQjER4QliQ0H5eijMiEvJjH6KNhp/eGsY1WEiaXGCWOfJb4kkCMWQKBTrEqHeLZ8w8Gp4IoHoyXw5rz1rW+91yFr94Zqi4iCgsuF0qd2DmXD0B0Ck/DvaQPOKhwwnO3e3OXVWM3tkhzAW6rv/M7v9IS3+4p7k3crqg1DGcJ2Xza6+mrkeKBhr30PeIP/mU98Pt71538fNRmkOSxHPzIIqQZvySEjgwP90d/XE/Nzs37bPi1DmCFJ1JWJ5SwPzeaMIYOw1ilDtR0vSe4+z0vz1TUZjioLEoMHASOUfV/S+GSokMJ4y+95KuiRIUJp6DLPB7uSeT6pX5nGGLFr6/YiQTJY8ACDk/18WP4XA98LDCyx0tuCFzNgXgieCpalZoEFysH8RQfIabJVE8kQSWCTSkgLXhYIDZuoUlfIF5unMtQNzw11mpGhDamQJRxs1IlxjGwYzHgA8DzgaWIfGuKzrw4EBUUzDBDiw5A75JcqyEb6ka50xthmXxuWOmYOEzKkka5nuubjpbmlE5ZexiMG0A/18gR8PCbSCTpinonnWEkmwjNPEQHkkk5y/hW6znCAPJWhz5LjFI6XxEP5VF8IiffgUZ3w1HkvJKXznj0imZBFFaB0SgvIr7p2e2Y5/t6oHuhKN3mvciiXvuFNYiUfArA6IDpaWpp3fxsdHTF5oj7I6/7hPBzd9eMSnRBO2iQrLLhAXixhLv2RWPWC4L3/Qx+O02fOK757qerXHYcOHIwf+7Efjqc+43FuQ/I3cSIrzyiT7FcIvt7/J28FJIfRErx83C7JAfxfZoW2r+b/8wMNpV9dHpQ+tT1sp99dOX+Fv06Al6Oa6/L93//99zh/BZLzyU9+8h47PMsQfiX8xm/8Rrz5zW9u3V1+3Hbbba2rL4+dlqNgJ9Bm782Bw4djVEbcyP79MTKaq2QN7xvRMRz7FXZYzw/sP5BDxBrNGOjvj5GRYS/B653sBweiXwSgGm7WB0mQ8Y/nwh4KhqyJsODFYA4KZGFokPkgfSYYeHUYYgY58vwZEQ48Tcw9kW0qIlKPXuXb6O6RoZ8bXa6LdLBqFssyL+qYnZ2X4ZsrwTEpf2JyMiaYxD7NHJBlkzg2vBzYNxQHDh7y0K6jV13lDXGv5jh2tffXoJ7Dg/s8lwYPEkPecoicytQfWoxoz/fBoNcfX883anlqeIYNj9GtP8uK1x49PU17H/AI2XslnUAAGIhXU3pIIWSBuJADkwg8Hjo6awyzY05QDsGqViWD3HFAvMRXYmlhKRbnmciverOZ68VxH5DRC+fG4vx59q8Zi3Nnz3t55HPnL+TwsrGJmByfjPGLEzFxccoeLxYCgNCmhwbyumICVu/qiGazK/p6GiK+fbFvqF/9Y0CGY+vQ/eBAbwwM9ES/jr4+tXU3BLJD7cZ8JNWxpvrWmZOUHhrIQq1eE8nWwb2IbQd1Vlkdit/OcDG4t/5zteka5a4sL3gRg7nZabU7S5uLyM7OeGgcLKfiUPRtM5HWP8ekNeZObjeOKk4O2cuVBZdXWP0uF8egvZN0q72Irx/ag8vqny65FFy5gOQwfPxNb3rTl5Ac/h/j5flyIA5zT4tBWnC5UPrUzqJ4dHYIdNi3ve1tXi3tb//2b79kjs3jHvc4r7bBc5YbZIEC/uhCcr73e7/XnpVL8YEPfMDzfH7t137N9/v27fOZ9JR36623xu/8zu98VcPdvpK81T8Hht0x/O7d7373xu7Sl1OOBxr22vcAWS+cm4xbbrnLBjuS4+XgyvVYX4tmd93eHAy603eeirmZGYcz74K34qxKxgRyhi9h3HfI0vR8mpYu/Ba9ZYAqVxmNSQww5FfWVr3qGl4PymZ41foaBj1zY/AE4cVJ475dR24OypwOVnbDY5HeAN64V3krVEZpeqZygQIZzSqP4W65elkSBg4KgVDZO2HioiDVCQ8T6T0fRIJ7zooMaeqDN6fZbPrAIGaVMObaUL9lxbOHQmnxgkDclLM9CszlQRYKQTeQIoZ5QcCYM5SFp9cBveK5AZ4Ur7OHTlle7lVf1z/i7Hkm5U85rvcXkjy5il0O3UJfbgfpx0SMcpQVxMseKB3WnfSMJ8wkDR3pDCGzxwmSJQPfc5ZUjhdnINysIskA+eAZ8eu0qh6QBWTVfXqMkCLLq7wo5EBbetluyWrPngKp77J0jjeKBQhS/xAuhu7VTZKRtaH+SX/Fq0Tb4bUiL4SAKNkTRf0Vbk8esXmMkDrs9Vol/4VYtkxrMT45HZ/4zGfjzJkLqk/qgRXvDoj0v/wVPxxPb3l03E7ok6yKR+eKA//H2OKBubLV3m8VeMb/ZOaY8b8YwsOecCyq8+u//uv+f8cWEby8+2qGuz1QUfrVfUfpU5cP2+l3hehcZlTDu+4JW/UBg3/GM57hP7gVSPuVhoR97GMf2yAhkCI8POQBQYFUsNrZpSTly+G+yHtvcaphbpdDjgcq9uL3YMnekEWE92GbjV+QE50xdGsyfFeWV+P86XOxvLCksHYZoUvez4W9a86cOxvTk5MyqpWPDPxchlkGpoiJDU0ZmRiXEBm8EH5L3gkpUfnKFwM8DXMFtNRXXSZBUkwZ2yw8ACmRJS4R05tiIiN5MLxzlTc9UbiNcRMdGeFcOz1pMNCzigBjHyPfhIJrnZkHhDDrTGCPVc8DYeI7xjJEh6FqzZ6m6qtnIn69MrqRh4n8kDq8AsrS8mAMU38P41PeGNaGzqRHkI6uXMXNQ68Ul3geRtiSCUAOdJP36IR4+py66644d/aMCGkz5kU+5+bnWvqEpCmhDuK1oaNW3SCA6MxnkQXrhGfWoa71LHXSIlbZCBKUuqSM6IdJ+5RVyWdPiMJX1c4QLIbIQTzYX4l4SWTYIJbhdGyYmp4TCBA6w4ME0SDOqp45b+XXFJkeHBj0Ygojw/tMcvAKUj3qBEmx3vHI6PA+StRfbQBRom8xRG1pcdNTox8phsUF2Gh2IZbUdxm6BkfCI3jT524RibygutNfmR9Ui4P7D8YrX/3SuPHGx3i+EG2LjhLOMC+vAOzFv0WXG9ULxnsDBmg1bHzryz3AEsAve9nL/L+tYBOlX913lD51+bCdfleITkHBDmJPfg8k7laJVYW8168Ns00G7fryWkxdkLEvoxVy4NWu9GhmdiFOnborJi5ejIXFWROTNfKQHnyttLy9Z64DhAWjmzfuVpOMVG/MqZucqyGjGh5jAxISg/EtsgJJwfjGSO/AG8QQNxnuvMWXLMgMscGItyeEAAHPhN/gK0/96Dq9Pn5K+frl9H6az8gnd9uXsb22HPMLueIYQ6SQGXIAyWEVOYxqvCkshECeS8uLJggmGvqwshqyYMhDeoiDVyrLRjcq096T9ColQUEOyIbqhizUhTzQky/JRQf61dWZMydjanI8BgYG7HlitTI/UCEV2TMh5FBZXjpZMlbkEHBfzf1BNpMBDpE85v5YFxAX1YO5PXhYklDoWoRlWYQmN5tVfIXT/ni3vGcSy1Bv5EE/wIPSIja691ybxUXHh0R2o9tmM3oazejr742BvoEY6u/3UMdmD/OmcvU79MDKcMiQ82yUP32NcnSPnulvJkytMPrsCvWwvMgkORWW3hz6M3muiDAumejcdfas2wEPYWdXPQ7tPxSveNWPxI1PeYzaEz+Seo7bA2T/vVKAfsr/5ILLjdKvCnYD2+l3hegUFOwg9uT3AHErW+2ewHMswpW1uHh2IuZnZnUrg1WGJG+755dW4uy58zExPub9TjD19NSGsrRhg9NL9yoLn8mQa8XL+Q9pcNszIwO/MsAzXIZ5ZagrLIdIteJ1snFnJX6LpKh0DPYMTw8RbQKUVIHE2zTokcVx8pHi8iA3NWXltVk2q5yd9j3D7UyJVA775rBgAXM62BcGwxz6hIeCIVZ4QCAvzM3B+MX7Qj0wwvEOYBxDcgBDxCgWD4ceiKyxUSoECF1A6Dp0v7ZBCPH2QCCJqz/o0vtZxYvYN8SQUmqUXjOMfHscVJb7JZ91EQClSa+GcsXgl+FPW+HNQP5ltSdEhs07IQFLkqsiNAwlZFgcG7FCVFZFTrzIgfTDMESe2wuj8rzvUWrWdaO+nFleGrlYKpoyu0UYDx08ECMsgT3QL8KG56bfq9vhOWPlPYardardycuNhOwmNshPuUkCITD0TfTszVsV30PkWnUk3IsOKIyDcOqMrMgPYZubX4xF9fVP3/TZOHX6tPSM94v+1hWHDx6Ol7/in8RTnvZY9QfkoT/qZNCjC9EpeGCj9KuC3cB2+l0hOgUFO4i99j1AVIatTYxNy4BXAGwAO1KX7bIcZUv7YO8YjOfTJ0/F2NkLfvtOPVkYoLPGHB02ihQJEjmQZSk9pA4wprF7E2lwQwC8KhoGvz0MOeQMgmOvg4mIPhj0EsfhSmdhIUYmOzyToekw8kVOzpSrG9WATxILF54VkWwVMMI3hsv5rt3GvQkOk/LZuX9h0YZyErBO1WXZaZl7w/LVGNkMaWORBsgYhvOsiNHa+orIEAsZpOfBXiilN7nRD54qyA+yQoDQ5aJ0Sjl4NVLu1A3eHYbQQQow7tEJpNHD21SlhYUpkZyBGBoadD5tbVknG/IQFw7IgAx8vDOUY89Pi9Rg5LPkNMPLIDX5DLIjg19x1mT0IycaIn/alAUjWIkOGZGX9sRLMzcvvc0veEU9CATD6Ogrq9Ir9cc71ytSyIIVXtBidCT6e/tjdP9oDA0M5fwbhvHh/XJzMXQtm07Uz2UnSRKR0TXl24ukdqTfUGd0VxEZcw+lp44ohbpz3ogDuRHxwgu1ItK0tLQaCyJysyI7nxLRuevMWfcv+iV7MB09cixe89pXxOOf+AjVh56QfZUfF5QXVwTQzV76W1SwN1D6VcFuYDv9rhCdgoIdxF77HvBm/67TF+OmT92W5ANy0XrGWTzExmdPs1vGbVucOX0qLo6NxboMTXbEZ34Nb9whCDPTGPgYpmv2UjD5H1XYwJUxj7GIfjAYKQsDHu8LnovqWXWkADKtlQHaxJiUeWqS42FdJjqZhgicCCONfUaSAWOWZYwxkkG2i4xjfkt+3ykORi6GPeRmYmrC5Ib5Rx5WRnk2sJWvDogOBjxGdb3ebWLDXLXBoSFvCkp+DIPC0OeaBQi8OhpD7VRPG8VrqnPLiyOb3fmjBwx3hkgRj7TEhdC5/opn4sYNIfqB6PF0bW3BK6CxutuiyElu+NkiOBAX2qk11MxDyRSeZGfZZAsysaRn1K8ifrQRpCuHCtJWrJpWj5quOXfVayYqrEpXETOW8R4X6ZuemRbxHY+piak4fdepGL94MfYN9MVjH/sYz7PZNzjkOU0MQcNzw3LeHk6njxSsWtN+rcN1zWt0QcVzWJ3UuEF0VMe1FeUholmRG4Fw+imeHF+rL1B36ktaz+NRvniraC+GvZF+UeRvUn350zfd7D2XVHnrGo/e0aPH4id+4jXxjY9+sIJpjGyjKxHoJvt8QcHlQ+lXBbuB7fS7QnQKCnYQe+17gIF6y+dPxd//3SdtvDIZvTrY7LHRqHmZ6K6ajG8Zw6dP3SlidErpMNNJvy4iVI+2jjSSMeqdVuSoi6FaUkWNFdRkLFZDqKp5Jxz65Wec0Ru5VjpMYx8DFzKUcSAeSRgc0feKzI1+dCad01SGcnojWCCBa/JCZgxdViibnZ2J8YmJmJZhiweHIVmQD+Xs9Db2RT4YyoVHRKaxiM6SvQGSHBFiTIb84MCQyM6gDeqFhXmd8RwxR2aT5KETJguRb60mnUFuJAt1wZD2kD2dPbRKBCWJYNYbYJTnlWuUz1XP1eV53WPod9pzghcGo91DzXTGI7MC0cGrAYFxfrRHi4RKn17K2p4UPCp1E1UIaK1D5KaTBR6QjeWgITxs1lmLJsuCdzSs10k2DFXd2UR0fELHhfMiCWfj5J0nVfZ8PPsZz4gnPeHxUVPerFBHfpBA1w9p1I6097rqjt4gIQxHQ1R785AVcqjGwXNEMrevmtxzbSAuSok3Spf2pBGPpbHX1Vb2UKl/0v60nYmPdO9FEuzVaRFD5bUoXU3PzJjonD5zoTU3jKWv6yY6b3jDq+KRj3qQwpCDPsBy51cequ9RQcHlROlXBbuB7fS7QnQKCnYQe+17gDH4qU9+Id75F/876t7BnqV72emfN/oQCTa0VJ1kALLr/eTkhFcg6+xQnM40iD10qYu3/xjOkByWKM55DagComDSIt3w4afS0brC9MDx/IZeRi4GuN+SKwwy4P1llDn5Zx4Yx6RXRB38htIYysjDm8iDaCY3MqI9LGkp5mbnY2pqMqZkyPLWnoUEWKnMRrVie7K8LjDqkRlyASlg6BOeAIx674yP3MgsI3lCRAkPRU+zR7qZM8mhfkQxwTFRrPk6jeyKHHa0qpB1wJBHbxCTHC7X8uZIB5C3auPKzJu2QVsSY2lRRvuyy2M1POaapF7boxPy6jlB0p/SdqhtkYXys40ZktUiVPphSB4EpPKW1GqNTAcR00FcvHHMnYHAoZ/x8Yk4ddfpuCCCMzvHqm+zMT52IS6cPx/jF8fiUY/4hvi2F/0j77WEOpnfhOAMRcObQt2pC54yNgKFeOBpopzsJ8TIhRWqvkQ86okMPE7yyhyjJeWqOOjI6VikIIezQW6Uic8MpyNPlin3HB/FgeQu4g0TKZoWAf7szbeoXmcsZ7vIOnswHTt2Vfz4j7+q5dGBWqkUzx268rDX/hYV7A2UflWwG9hOvytEp6BgB7HXvgcYeB/78E3x//zpX0dPb6+MN954r8S8DNbp6clYWpiVsdvht/d4eGy4dzU8+b5R7zbJwTj2UDTZlbmQQA4psyEuVUA0/IYeAqIA9IPzxXYoZEQGNGlhGEriMjo6W9fM4ZEhyXMMbkiIM+Va5n9mQjm8XU8CxG8M95U1Vs+a81AyNg89h+E9Ph5T0yI3C8zLYJgdk/BFLHTYq2AZlV6FM5l9Rfd8qknuECrmvVAHSCHkiU02vUpYd1Nl5dwXnpMnxjnEAS8IknofIMrq6EzJpStMcrwsbETpJabtiVjzMDGw3k4M6VByUXXqihGPF8iEQNejw/sUn81Je2T819QQyk/GebvapaZ45GWDXBnQZg0RHciTKmy9QaaUjdufM/IjOx4fgG4gOYSZ5IisTE5NeNO7z37+c3Ee3Uqvjis5WOlsbm42DoyOxLe+8EXxsBtusNzojmWw0eeySAXlkHc1lwiyl6uhVSSCekCYs13oFCaMy6sxIzJCe0EyvRgB7USbrqXXJr1EuldnQ1+E4dWiHHRncuQjSZA9OzpDdGbm5uKLt90ed546Yw9POwRR/f7Rj3pUvOa1L49rrj0sYSoZ6XFXHsr/5IKdQOlXBbuB7fS7QnQKCnYQe+17gPH3kQ98PP6f//buGBjcZzKC4T4xMR6TExdjQUQHr06fDOheHc3unhgcHPQeJo2GiA6egXq1Glke0oCNd3688prOlMO1oYBOGYcMjVLmNvZZFtrpSYbh7TzSGwN/gRjpkcL4RZ6KoQOvE2HEhQVhyC6J2DAU7byHT53z0tDTs7MiNwsiJoutVdFkwKtMl6+MMa6Rjw9ZUdrGkCYRJntzKNcx1j2UDXkpm8nseLcaIoMrTO4nL8Wlzngc7IlQJSAAJizKFwPbZQl0F3tWaq2hXAqAJOJVsj4xpHVvo9wJ+EU4G6iuRo+Ix6Mf+UgPMcTr0tVqE/JjLlD7uoij9dgajid5GK5GNl7cQHlTXpvIJeSAldkoBJJDfNJRD+JBcJDi9OnT8d73vzdO3nlHTMxMqc7ZzsgP8XXmSvuExz8+Hvfox0SPiAJl0L4QMeSGzEGKqQsLH2T9sjzSQl48pE51ol/ifWTODcMj9Tjm5uc9J6mnt8eEz8tbL7EB7YJ1TV0QCqKMQOSPx4d+wGIHkBsO6miiI5nwBFEO5Obs2FicOnM2JqZmqJjS1OM5z3xmvOo1Pxwjo4PKmtagV3JceSj/kwt2AqVfFewGttPvCtEpKNhB7LXvAcbdxz708fjzP3139PYP2tCbFxFgSNfk5HhMT03KGF4SwWl4pSxWGhtUvF4ZlzaoZfyxO70NcqxY1z0JCvfowteE67GHQNnQhgCkIWrvA8atoBROz1t2jFM/B86XI4NUWssgXYyZ6ZmYnZ/1xp3Mt5mUzJOSn0nxiwsyfmUkQ1TwxyBDLjssmWxkYwDLgFb+HOgD75ONbSEn9zPnBcN8s3yyyijsJ7SiMDwPGPhpoJMm687Qu/SEcZ/eiS1lQTSUD+EQF7ImLiQDXVFPClJ01140wGmtAeWNZ6S3uxlPu/EpcfjQQa+UZyhNvSHiA9lRGEO9GPZlMmnBk1BkXrSLiIzawVWTvlxHgbazZ6rW4eWeZ+fn4lOf/mTcdPPNcVYkYH1ddZeskFaTIeVpcqczBOOgZNqn/lJr6YGFLWh3SAj18/A53VMcerA4pKXvEKYPniTkZGlqntPfYEzobnl5Uf2QFfs6RWIXrA+GIpq46XA5eka/JjHqYYicCazzM3V0HWkPhrGxIMHC4lLM6ZicnvVeOnP6TkCMv+UF3xyvfvWPxPAww/AghOiSssglJb5SsNf+FhXsDZR+VbAb2E6/K0SnoGAHsde+B7zRv+mTn4m/edd7ot7s9XAulgeempqOC2PnvWT0+tpKdDe6opeNHLt7Y6A3d6Wv1xs5bE0GYBrQGIz5Zj9X65JB2rqGU2DQsszyhi0oNdmrobQM3cIIxeBkuBEGq4cGkZlAOJ4SPCkMiZqWfBfHxz0kbZqhaCxpvDDvt/XeRFL1wLhltTC8Ap6s3moX8kIIZOaa9iI+56ps5CI6hq+NYBn0io5J67x4yB4/GLoYzACDOr01kKIkDSZ/nLf0CwgGT12u4hLKc/SD/iAJTH4nLPMQIWnF5+x6OT+YwZpXPnv2M58VD77uBg9JczzKaOUJiVIiG/3A94Jlov4ilgylo72SBFE9pEp9rCsd7by0vBCf/NSn4/O3fF6EZ971hYRAVryppuWmjSGzClP+kJsu4tEHJDv5MByRZ0l4N0mvgiQU9SV96qjKk0deKU9iecPSFpGEUFK+5zbRTmpHlge3DErHstomkfqQZsUenPQO0c54ftAnYZm/6qyDuUbtnZK11hWf/uzNMTY+YW/Wt7zgufHyl/9gjIwMKQ/aHVmzjSWhjtTflYCtfa6g4HKh9KuC3cB2+l0hOgUFO4i99j3A0L3jC7fFh9/7URl2De9gPz03500wIRKzOi8uzukPx5pITreXUO7t6fWywMy1wMjDgKbe3sNGPxALjEt7JGQDpnnO8zRu05ORxIKPMseyFpFJT4jnUXj540UbrSx+wFLW09OzXqoYT82cDFGGjPF23gsFyJDng5FKObgHkti0CA1XLUPfRKcydiVHtMnIJa7CeZYGc3o1PJwK8kK+zoP2zThZH+bUpBfH2VNf8nLZSWp8T2YC1+ndwquhewJbaZy3ygPo1OkUA6KDxyTl0aHy8f5kXm3REOF80Qu/NR503YOipnQMKcSAxzPB2UO/1K4bpEEFsydPeuCsGevI5ENxkY1+gcRecawz8/rCF26JL9z6BRHJFUVh3lZ3emQgBE7HYggQJurGnK3sG+RNOPmZ3NSY99MidMTRmfBKB1zrxtc5rFFESNfZBpJfjyEZbZ67lCTbHju3W+4DRP4Ql+wPSZTw4nhvJJb+pn1bnMSLESgtdVxaXvTwOIY3DgwNx8DgUPyf978/LqjfPeqxj46XfNd3xGMf9w3BghsSSch2SRSiU/DAR+lXBbuB7fS7QnQKCnYQe+17gGF94a4zcftNX4j2rnrML63GJEPBRCTGJybj4sULMT0xLkN5ObobzEOp25szMDBgosMwIoxPjM3K3uNtOt4ZgD5yB3w8HUkIqjfrGKe+XmVjSTbpnPGwoxkRG5MZNu1kSJqM0+WVNRuwPMcwxUivdG1jFkOdcikTSXQB0TGB0UE4RjVnEx0Bo5jD829kpGLzK4rltFfGyIqk54NbyiQgDWieb8jgYjG+McqzDMJSGdVQMYXpF7JBQkhLdtQF8Mz3OhgaZeLTIgQVUeNhi58Y9Vojvv1bvyMe8bBviEaNhQa6TNQoHO8JGVLnTUIBUVP9rDNlIPkqsqqS/Bw5WWyhprwWlxbi/PkLccedJ2NWxJPNXcmb+Vr1OktPY+zTB1gyOoetoYP8KCrliixZM0pHHB64NPcbboiZOqrC3L76eH5Rq+5+jt4cVzRHrIe6oSef9SE7TlKm5bJuVSHa1Z6gqi2ppK5IxwIRLMu9MD+XCyOorzVE6muq3zv/6n+Y9P/Em94Qz3jmk6PeYH+o1JHrTl7+RZ9BoVcGrD/XsaDg8qH0q4LdwHb6XSE6BQU7iL32PcDoGz97Lu669TYZdn2qQE0G36qIxqwn8rMXyvjYmHf75009mzua6LAgQZN5Orxtt7Wc9iVGpOq/omMNMsO1N7Bkfxcdi4smNazqNjUzJVKVQ8+mTXIWvMTvwtKiPRBJhKrJ/RirGL8qxL98S7H5Jl+GJ94IjF/KzzbAOGZYWhrAJjoKt/fFhjPD1HLeB2/jbSjrJgmJUvieMvEOpWGLAU502tmrmOncUWNoFvNzRC5I3yFDPiM7TxJQPml8L5B/m4z/ajgWdcGx1YrsuE5lEkZWKQfBMvWR1mHol3q98PkviCc+4UnRrHd7hTX2h/GwNYbtQWooT/GS0JAzXqo1kxlkY38fvEOGBCFezp/p8Lyn02fOxKLarY0V5ESICe9qNFRXDH08NkmikpC1CJPJh3J3XTfrXpEDe83UXtTOOoDYQXJasgLSkD9a5zdtoxMLq0UbvIKzyspcMo1q5vQsekD2yEV29joJ9BHHVlY8cyTdIye6MhFVv6DNp0S4//wv/jzGRPp/8zd/Lb7xMQ/N+khHVAc9uX1cdCE6BQ98lH5VsBvYTr8rRKegYAex174HkIlbb/58fPx974+R0YPRPzAUfQODfms/NTkV58cuxKQM3YsiOyzLjBFcZ0d7kZ1cyjiHJDEsiLfcEBVP9ha5YWnn2bmFmMNDMzsZM9OzMbcw78UC5mbngt35mew/vzRvUoHhiOGew7d0SJf6ZeMTwoE56/kqCuaJYyk+xjDkxXEwlPWQ/IirwNYzjF/loCDPwVFcht3Z80GZrSM9OeRRtSPEQAa8DGLPSamxqlmXdTA0tM8r0A0ND0dPd7dIyXr0SS9svOoNRpUX3ifSMeyPupIvclB3hlBBMsh7QSRiWbpDp5BC9Mjkf+qxsLDoCqMX6gYhYZd/YF3p+sTVJ+L6a66PhtqtXqunVw3ipfjU33pRVAxzPCIM12PYHvrA2G8ttGYiwT0EiVXtlkRML14c83LLLJNNv2AvHsit9wFqz/aCK5B2jXkvlCW9Ue7Sguq5LOIqXaAT61g6p53dbj5nGHNv1iSk5VY4+boJqDNxqKquAenpCNQJfRKNcqmfiazaFuKLQ6tDfZRy0Ql9wt4/6sn8Ij3zynSqj/sy8SlPF+ie/XT++n/9reck/dqv/2I86tEPt3zkVxGnTUhu1ftKQfmfXLATKP2qYDewnX5XiE5BwQ5ir30P8Jj89bveHf/6X745Dh446CFpIyOjMTw8IjLT52WKebvNRpgz09MyWkVcFhZs0HrYmow9hvlguGM4Q2pm52btleGMZ4h5Niz5vLSEh2ctV8+S1WrSIuMQwxRUusPwtdVtwuHfvrNedfiJ4+pC6Qn388oSliGLJ6SV2FlVngF+/NbecyuYq6L0HMoDQpK7/3d5k8/uenc0RTx6pIdmoxndPc04dPBAHL3qSPT398Wxq4/G8MiIl932SmACw/k8p6W1ohfzPjCmWQFtWfWnTIaT4XExgREwuqt9ZLqb3T4viRSwAAS6QG8JDH9X2nWnegBi8plP3xSzIpI9XU3vm8Ozrs4ulwXJABAC5rrwkBPtRVksEMA8I3KuPDzLq7T5bJw5cyZO3nnSS1WPqE/0DfRHXXqxtwjSwEdsw4SRlG4DX5qMLKqODD2cncv5VvSVFfWnZciOymDTz/RqkQfEy74m3asNlRd5QJrIk3ZiBTkPC6QMP8m+gScJCZiPRL9SDnoGSeWpxXGc9AhlOgifSZnCaHufRXhY8Y1NcnnOMtOf+NSn6KXxy7/6c/HYx32jUiIf2VxKaijFgl0R2Gt/iwr2Bkq/KtgNbKffFaJTULCD2GvfAwzdt/+X/xJvev0b7J2oy6DtYslonZmTg8HfIeOXeql2NhTx2EBoMFghCuxbAmHCpsRYXYJkyODEyMeAt2EqnUAwMJAzn8xPERWO3lJ3GIvVG/M0HB3bZit5EE40jHETFpI4TZ4J8Nt+AW+KvRdO0yFD2cEGBI25LRj53d0NL64wODDkzSdHR0fiwP79MTqyP46fOB4HDo54LgrGcI/IDgerjTGMr/IC7BiyKijhbqCKW8v9n3/9nrjlc7fEvv59HjK4uryUnie1H8Pc8GJxZqI9G2Ji9AM8awwNxDuBPiG1M/Nz9uSdOn0qzp07b8KKN6epeg8ODIoI748B6YtFKTxPq6vu9uxSn6ENOegn5Mn8q4X5pVjAsyeiA/GB7CywgESLaNnzJZmQwyvr6aCpIED0K/cF5BXxgVghZxIUBUOI6CG6h2NB+kx6dEOy7Ge0P/F1kBWeKB7SLyQnbYmCFSKCwwpuHSJMosI6Ly4ux6233SHC3xG//pu/FI9+9MMVK71+9N0rGdS5/E8uuNwo/apgN7CdfleITkHBDmKvfQ8wNP/r294WP/G6H/ewKd7SqxYmC63ZB7wKN2Fgk8WuesNG7ILIDZ6GhcU0XjMab9Cz/u2dXWlMkoOeeTiS8vMbdYcRjysISxqkhG1Az3gsm9Y3vJlXTA916kA+3WWekk3pVtmwkhTKH4MX+5ZV3PDQMHQJo5/VydjdfkiErt+eq+G49trrTPCOHTsSI8PDnny+b9+QiE+vN0NtNkVqavdm0CIhMqccHJe2faUPjrvVrwWi2zEgg53Hl8arsstneQZVyRX+8i/+Kv7nu/9G5K0r55eIYLCABAlIBynFe7K4uLRBPnO5bu5FJshfiuPeS3UvLdqrxIp2kEIIgeftSJeQCYgwc3V6m73R3dMd3dIteuxS+QzrYwhfj0hjrUu6VxtQ3tqKiIvkos8hA+QLjwllMGdrei73QZoWsWKYHwSa5cKJ7zZVjZMoJ+hL0lrLM8NQvexHrozqTbsTe219VQSGBRCyj5FH6jKXv8771Htnh/q4wtiUlKFvcyJod4nsdfc04rd++1c8dC3bG9xbv7gyQH0u7Y8FBV8rSr8q2A1sp98VolNQsIPYa98DjMg/ffvb401veJOXjkb2lB8zOofoqFL6JZIiAxeywzAm3qoz/Ip5OCww4OFniux5MIpuI5RkmKI6V3nmcB/i8RadsnKIF8VVw8k8dM1F6tqbeyq/DoYxMbcmSQXZeW4GSTkrLkZuvYuhY+zvUvO+P/39kJo+e2yOX30sRkZH4sE3PCj2H9wvElOP0f0j9lY0utOL5bIwlo0s+95QtXPWz1eWv8Lm8+wTW/tGXvvS6bdiM27WFTm2xqGuYDOsLd7+X/80/q/f/p1YnGUPmU6pTm2nNoIekg05wf7sBdNl5fUClkmZQRghOxwQG5MIzhAd2pXIEE7FZ+gieXiYl0gBaZNY5EalDZGdHpEdiCLeH0hQr67xEDZ04EWhJzCs7dzZc3Hq5Mk4e+F8jE9OebGKFfbKEQliSCSeQXsF1cc8n0jiMozN4iB3JaMlpF4ZBvmBnCEzhIZH9GGIEXE9F0kHeXhOD3mvsCocm6OK0Cvt5DTLUa/EvuHBeONPvSGe8+ynqJ9UBKcQnYKvP5R+VbAb2E6/K0SnoGAHsde+BxCd//Zf3x4/+YY3yCjttSGMcayKbNTDxq/OuYRzp4dDYUj6Tfz8oojOgj0GRFcyYrYMTdumMiCrvNIw5JoNMcGaDNr01mB66kMGMmqJzbwaG/sy7Nl9n3zzSFkYYpfDs/Ag9Of8omERmJ5e78g/vG8wTpw4EUPDAzKua3Hs2FEZ2l32NuBp+FK06mtZ87A8GytpEZZ1yXbOsKrNM+59Q8avrpWrrjO/vAab4Rj1GZhlQDaqePxqi//5N/8r3vTGn4qzd52VThhCxmIM6JNMc8NU+BHGPASAtHlOYkk87lOI1nwmriELtKXJZJIcntFvXO82vCUiviKheJJSRRAPlaV0zN2BeDGvh36D7gf6B0RIu0QYOkyUxy5cjOmpSREKVuYTgW4NV/OKezoYckdHYjNbdGEd6Dn9AxDXBSvcxAb9kERXuVcPXr5MZ1KE3EpDOAsRWIMKg8zxgeTV1GepxLKIT6PRE/sP7I9vf/G3xj/5oZeon/VleYXoFHwdovSrgt3AdvpdIToFBTuIvfY9wGD9r2/7L/H61742ekQQKvs2z2nYbr795613rrzFG3KeMYSNIUY2pB1f5p90gJEoe1HAgN7USXoUdE8UCpGx6InnSp/xMpRf2OgdMoYxwDs7mAvS8PC6wcGB6O3tlsE5GIcPHzLBYYGAg4cOxL6hfSY+o/uHbPDn0LNcGS6HPSFHEof1dcrWlQvknDJ+KVoRLhso597K2orKkN4aF1kuvY+47Yt3xo+89OVx8003S/+qayekFM+F4qJXVdmaVWU91EvtQ18liCFkXEOCUMh6iLCQra7tXHOyJDrVcthe3EH5QpJMGHyNXnPj1+qavMmAuTRkmUPgWtf6hUeo2eyJ7kZDMneJvDb8gKFtC0tLIho6dL1xrLI5rPJWhVilzZrQL/oPfYo9d9wPHY7gScw2yCIF84tKIIeDHCiwKIF0rnTt9AXn1Rk9zf44cPBAvOhbvyV+9OU/GENDA4pLCYXoFHz9ofSrgt3AdvpdIToFBTuIvfY9wHh825/8Sbz2Va+1wYlRrArYjKMeHKqSwC8MQZmFMlbxpvB2fGlpMRbmITq8YccbhJHYiouBrSuMRxunzgeDMw1lzwdqY/+ZNJghT8z7aDTw0nTbS8Ncmt7eHhGYkRgY7I+DMjhPnDjuoUQsCnDgwKg3MWXoGWkZEkUbpMwVkIKAXH0sQTu1ru4eWdh6f1/a8tL0Xwn3JU9w3/MdvzgVP/IjPxr/8N732gOHatN7gd6T0FEsLVLtfUM8z50xcXA0h+NBoT29Gl2LFDplpTCfUsdJIygLQpryEs0eFH0cRrx1FqJQHApxKsKQpT2ajW6R16E4sP9gHDxwWG1Y8yp9c8zbmZmKuYW5mPNS5fMi1vOWd8XenmUvfpELD1REWwSI+Vouhkq5JH/4AR6GlwJYNu4TBEpq4rVD4NBTTX2x6ZUIv/M7XxSveOWPFKJT8HWN0q8KdgPb6XeF6BQU7CD22vcAovMnf/wn8aofe230iFBgEGKg2jSUAYk9h9HsUNXNQ5w626KznXkwnTY055lT4bf4DENL489D0zAonQ696BpjWDd4hDynQ4ZvrUtGrshMf/+A943Zv39Ux34PcRoZGYnh0WEZloMiNCI6A7321rAEM5PFc3gcxulWYJijf8hTS3YRKWR3OG/5ga3ZivhszYPrrffEuy/YXaN3bm4pfuaf/rP4Y7Uliwgk0cl6e+iW4ljnDu9QFVVH2lbPGFLG6nOsMpZGPkSAZZbRb6tetJ/SOD/aUTqijzAEkPSsStcQYWGoGnOjsgj2psnV907eeWeMjY2JtPZFX3+/V/er8oYgMwRxdHi/+sI+txGbzLJAwcTUuOfp0E9n52dMdiA5q6zQtrJqor20JKKtvsr+Q8TjOd9BDg+b5KNrWpVz1p8bHTp7SKbqB3D8oJpop29IB8FqeyxQMRzf+49fHC9/xY/Yo5j9rKWbKxS01V76W1SwN1D6VcFuYDv9rhCdgoIdxF77Hiwvr8R/edvb43Wv/QkPC1MF+DGqatj4lSHp9QNsBDLkKFff4u06m0oyn4JlgnlMmHKRscvqVSzX2xld9e7objSjr0+Grg6GlI2Ojsbhwwfj8JHDcejQYRvLI6NDsW+QDUtrUbeXhqFy5INR2RKsslC5kpCVzvOcczHWIDeOUeHud6DNW+tfCuJxbJZx37Hd+F8J96UfZZkY+u94x1/FX/7FX3oSv+fncCgLb3YqUmEio+gY+ugI8gnBGBpkk9h+Lw/toYWKW82pYSgYcF7QG6Wzp4hD4QyRY9gZq9WxnxD9qbevZ3O/HsVbWV6NT3/ypnj/Bz4Y01PT0T8wGA2Vixy0G/0FDxKy2svE8Dh9WBVuYW4un6n8JZEfljaHXNPOEFmGtrFwAEMoWTEO7x6kDRJeET76VXqWqAOV0Y/6B3sMQaooi/z9QDIgerRnHxalV1ntXkb7Oc97Ztx44xOdX7ZNIToFX38o/apgN7CdfleITkHBDmKvfQ94K/7X/+N/xe/97r+WMdeQ/DLeVAdQ1cVDm2SAYiyvyyBkdS09sAHKwXyK6i06KSE8DHvq6srNN9mXZnRk1EPQDh0+FEP7BmyQHjlyRPf7bVBzUJ5X47LRWSF1aRlaxmVLPEGGqK/5xbPNc7YBzzP9l+JLy9jE1vx2E/cmOx4KSWfx1CYOiZibmY2JiYlWO+jhhvh4LIgvvXDXUqANeT0wGdVRtTeP7bUxmWllspFXpmsV7mB7jnQQf1Ouu2N2diHuuO1k3HrrrSLI7D2U84Ro12zvnJeTRJU7vFCqp547P3VLdwEBYuKCVB578Rw4sD9mZmZMlAaGGOrYG82eRszPLTpuj66rOlfIOrRuLsFGcOvCK/opfb/IoFdjs7wpArg07ysFVXsWFFxOlH5VsBvYTr8rRKegYAex174HGJon7zwdH/3op/0WXOILG+at6sIhC5MqyTBlvg1GMZPMl05Ibt0AAOYlSURBVNhjRQn8Rt5v2XM+DsYlOmB3eTIa3T8qojPi/WhY5hkPDenyTfuXvhWv9JeykNeGNDrymvT3DZlXle6BgapOIOu1qZndx2b73V0ivE6sspaPW8+4UTzu6DcZ2rpvRclniY08W+l4wEp6eHl4Qh9m3k9B6qr8Ty643Cj9qmA3sJ1+V4hOQcEOYi9+D/DqMLdB0ttYvBRba6Pq5Zt41dEEqErBvU68+a7i401BFRCj3H2+oOD+AD3wnnryPWE7cfcWyv/kgp1A6VcFu4FCdAoKrhCU70FBQcGVgPK3qGAnUPpVwW5gO/2u+PQLCgoKCgoKCgoKCh5wKESnoKCgoKCgoKCgoOABh0J0CgoKCgoKCgoKCgoecChEp6CgoKCgoKCgoKDgAYdCdAoKCgoKCgoKCgoKHnAoRKegoKCgoKCgoKCg4AGHQnQKCgoKCgoKCgoKCh5wKESnoKCgoKCgoKCgoOABhx3dMLSgoKCgoKCgoKCgoOBy4r7Slx0lOjuUdUHBnkH5HhQUFFwJKH+LCnYCpV8V7Aa20+/K0LWCgoKCgoKCgoKCggccCtEpKCgoKCgoKCgoKHjAoQxdKyjYQey178Hc3EKcuetstLd3WPbOjo5YXV2Nzs5OPW3T9Uqsra3F2spKrK6vxdj4eIxfHFe8zmh0d0dvb2/09fVGvV6Prq7O6Onpi0ajHsqG5BtYV1r91pH64aC8tjbKdRTjUt0Rp+D+QaX7qg9fer433Jc2qvIB9Kf29naHVflemkcVTrzEvZdPn9opuO+35AXIeV/qeyUAOb9cuxUUfDUo/apgN7CdfleITkHBDmIvfQ/W1tbjk5/8VPyb3/93Ue+qOaxDDGVd4R0QHdlzayI9EB+O5eXlmJya1DFlw6+jsyO6G90xMDBgwjM4OBDHrj4Wfb39MTw8FKOj+0WG6jEyMhw9zW6Uo/y/spF4T/pDrwVfn4BotLWJGOm6Xd3g0u6xuroei4sLsbCwpGMuVkTKRUdirRWR69W1VferrUd7q08lCecaEgOlIt16DA70x+DQkPs6/a/qg3ulL+6lv0UFewelXxXsBrbT7wrRKSjYQeyl7wEG5Lve9e546Y+8Irq7RUREcDDwqIPr4VhtMgh1pXsCMnw9lleWY1GGJXWtdXVFT09v7Ns3aHLT39eX5Efn7kY9Dh48GP39vdFVr8ehw4fiwH4IUHfsHx1VWlw/9wCVlQZnopIHE7PSr6/1C0NWgUTys61twPWlqOLcN2zKcO+4r3ldHmRVK7kub9mQ3GURBQjv0tKy23l+bj56e3qsMzx/9XpXS78iE+pDK6sr9vwtLa2IDC/F/MKCCTIEwiRivV1dKz16q6tr9gAeOXLY5S0vr8Xk5ETMzk6rbLrg2gapnlO5Z8+ejZmZqVhdWXf+kBiek/8aH6WhzNmZWeUxF9PTc06LkiA6lUfGhL0iO6oj3kk7aVQNCL8vhOw74Tp94yO/IX7wh34gjh074nSQrXTs3L/t/dVi6/egoOByofSrgt3AdvpdIToFBTuIvfQ9WJPR+Rd/8Rfxj7/3+6LR8risr0p2yd/enm+3Q8Zdm6y7Th1dnZ0esoadN78wHwtzMmhlMLbJKK511aLRaIrk9Jj09Pf2R10EqEtGcaPRiK6uetRqXSY/PT3N6NXzw4cPR09fU+RoJI4eORo1EaGBfpGjeiPaO1TuFg6EStGqy+ODbAr0PW/cW3EscqsNOINL77cHDPSvBJealzsEjHWQdUEXfCAFIhrLKzLwZ2J+Xu2heNQVXa3bwBdxEWGZm5uLiYnxmJqalhG/bGOfuKvEt6FPf1iJRcVdEFHpUDtzXhHRmZmZcXt6eKP6hYmO04jAKD3EaHFpSUSEY1HXKQeCVt5AZFxcJnzdXsCHP+Lh6iv9MTs9HefOnY2L4+OWM/Nbdl4Lqs+0nqccEKklyZ75bdSzdayI3JD3ivSxJkIDFGwtqZP4mr7i+GqqtfX0+vChLlU+NCNnZHjIgx8Sv/d7vxtPecqTpfdN/e+Vqa5Vvy8ouJwo/apgN7CdfleITkHBDmIvfQ8wGP/iz/8ivuclL7GHZQ0vgYy+dlmCJhoiEOsKa9N9Q0SFuTfM4QEzM9MyrBdlamddqTcHw3y6RHAaXQ0TlnqjS4ZxI5rNZnSLTDXq3VHrrNu7U1eenbUOP+sf6PfzkeFRD4Mj7OCBA7ruiabI0wjD4Oq1u9mYlGxDlh90rh+JYDn8XGHVdYVsH4ZC+c5hm+d7AqV8JXy59JcHGPZwB4b+ra21Sffz8d73vj8+9KEPinQuet4UpAAvjA33Fglal9GPh4NjAXK6OO+87GVpkQXfpxJNfIjbWatZV/Z2SF+dHbVoF+klXmetUzVut9ens7MLpSbh1Bn5qnTks7i4GHPzs7EsGRdFgtZEIFDX8MhwDA/us6eoprxn5udiXGRnYTFJDYTFHhhKVN6WVXnipam+X9mWZJeeImTLJ/yGwuRVDk3jnL/cz2PVdSCs9VhJRKBacVZW1mL/gYPxi7/wc/Hi7/wO9X2RO+kUr44rsAeQ+qFCBQWXD6VfFewGttPvCtEpKNhB7KXvAUTnz//8z+O7vvPFIhlNG4D4RjaJDgasyII+vc0eGaVNL1LA2/vJiQkbsuuqr+GTfrXq7jk8eFpkF5K+JsMZAtRV61JZIjLdTXt+ID4QKJ6RN14fyBTGNGSH8KaM4aGhIcc7fvx4XH386kzbjadI8RW3/W4j4JABYzuN762o2odgRM3H99ZeV86b+yQlKT/HTZ/5XPzKL/2qyM57N+rkIWRbSAthGOdV26yvr0pPUpTCUy8iJlYc7d0hAtqh+LrW2W1gD15NRCSVi5HfoX4BwSEdbcpRlY8WKW9J5GZxMUnOssjNqogLpXXV2tWPumNoeDgGRF4H+vujr2fA7YxXCK/OXWfPxJnz52Jmdi5JtPI2EaMalENGCq7m33h4HMSu1dbtCl5pDVGDmEHAGLZGPsQhmUL0IQ7p1cZKz1BA0SWno90hOiys8dKXvjRe/eofi6F9AwpHt6m3vYCqXQoKLidKvyrYDWyn3xWiU1Cwg9hL34P06Px5vNhEpzvnu0j+Dhl/EB3d2FCEsPT39sXg4KCN36mpKRulNhyVD2mw/fAAAdJAltZkWNsqxS7UCYMVAmTvQHsa0fXO1tC2jSFuXRukiDNGNdcMfaNsvD29fb0yyrvi2LGr7BkaGR6J0QP7o6+/L+rdtTh4cL/i1lWGxVF7+LeOTfJjmTeAAXtP2Ixz9/hfLSzIfUBLYVuAh6SSm/r89f/4n/HaV782Tp85bT1ZzZaRYYfMJYGy6tNJGt3rqIkQMiQNAstE/GwDwkRcRG7aRF46W0SzLlLZkG4ZTtYJWxVIRxt0KF0lC+UxLI0hbpAlhjjqgQkughKH1fj6RWZ6erpFahrOt1ZTeWS62hbMvZmZn43p2dkYuzge58fHYlxEem5hXn2oVZ+2Dh/IwIGOGF6J7HbQCNRJ2lG/VoD6oIm62ntlBe9N9sfKK7S8xmqCxMvva3p98BxZkTqHZKzHd33Xd8Xr3/C62L9/WHHoz9Kr633lAznL/+SCy43Srwp2A9vpd4XoFBTsIPbS96AauvbdMuYgOmuy3zAIMUHTG9PuSds1Gbf7hoZi38CQ3/aPXbwYF8fGNo3QlnGL0Vu9acf45O1+DhNLnVR6wYjE8Oa+DVJFWQqHXHW0jFkMWAxnznUZ38wBghgxpMphXY3o7e/x8Ldmd4/JT3dvMxrNrrjqqqticGhfXHvttTE6MmxvUFP1uxTIYbGFL22zNNI5g7yusPV6a7rN+He/rpA6uHtem9h8lvraGm29ZZCjq5mZ2fijP/qj+JVf+VXrAiJIeJfIn4mI9dbpMDwv1bW9NnhgJJaf6Z72TIIgAiqymWnbo6fZE8PS4eTkpD0y2UJZI7wdEobaiNh0uu/gqekWUWVhinYPX1vTM6VR3h20p8pbFbmgDqyMtrS45IOFC6ZnZ2J6ajpm52Y9hI0lz2cUvryypP7HwgMq1G0lHfiToK6ul8IhbNaBZLeHz/XOPpx6yGuzecmwxDylDZLDwVA/iBDknWXVKbA9nvvcb443vvHH44DIc87ToW5J/K50WF+qW0HB5UTpVwW7ge30u0J0Cgp2EHvpe1AtRvB93/u9UWeOjow9jEa/a8dAlKHIG/FGvRGHDh6Mgd4+m5l3nTkTExMTIka8VU8jEvBsVXXHJPYbdKVVlNaz1dQNtiLGJrF9Tn1x8Jw0vnaMzBNSxKAqMjM5UXmVQW9jFwO+s2YjmwUNeppNryKH92dkZCROnDghQ/WAJ78fO3bMxKenpyf6+vqUPg1ij+DaAr/ZtwQteVR21iXl5f5SfGnbU6/N8CpNFWfrPdeb3ibC/GjjWcZNI5u5LP/yX/6reOtb3+oVzLrUPp3teHUyrfUlUkhcD0HzdZJHe3AgN7Rbi9SYCCkupAei6XCRExaTgIx46JfLJg+IRC26pefuHuZcMR8rvW3WmVgJ/WhldUmESESF1diWc3EB5uiwiAWroy205uzMzs/qWIj52dlY8EIGS7G8vGovDCTHOnBbpC6oIH0CWbiHnHANEUv9JRkE7suqGyQZgoeMkHb6aw6RVL25hzxDygTXT+SNfja/sBwPeshDRXReF9ded43yLh6dgoLSrwp2A9vpd4XoFBTsIPbS94DVqd7xF38ZP/B93+/FCIDffKsOkIl8A77uCeOHDh0yOWD+xem77vJKXMTDKGbVtarOnDE+Mbw970FGL2HEBRijeZZhqnB7fUwmXJSNZNmsmNWOi5ErjW6k21pOdcYOtnGL3NA0nSEwxMCw9Tyg7oY9DwMDgyZChw4dNumhTqz+dvToUXt9OjvbYnBwXwwODSjPJA4YyJueKcrK86Ug7t3D8wbCsVVe4rmSRuqmGpqWz+/ehzI+afFOsMnrfPzxH78t/sN/+P9El410jHcZ8gwps/ekUzIzPDCHkuHVYVgaRjxzpCAq9uJg1JvUQBOSGPhMfAiD7u0Z0XMPJYQo1HRfS13DDZByXe0NMVlahqQsx8ryokjLgufoLIooQWgYnrbI4gQsPz3HMx26n12YM7lZIe0SaXWg65Z+Kn1U13Bj+hz9gTZ3v5AQkHaGljH7hnu3kdLwnORZp+yva5K1g2v6KPWrZb26IEToTe11w/UPjk4RSBZ3+Ik3vS4e+9jHOJ9Klr2AS/tRQcHlQOlXBbuB7fS7QnQKCnYQe+l7gBH4zr98R/zg93+/J4QDTF42C803/enmGBgcjEMHDnho0szsbJw8ecpGrd+QywA2VG8MT2q+iiHJtQzWfOOeUVCLDU99IDpAJrIn0KM3DohO7sGCvaqPnmHEkk8arRln61t+E6aWzmUWO55laYXnUKN8TnhbW3o5bNjK6Mfzg3cHLxDHgQMH4/CRQ94P6GEPe2hcf8P1JgjE6xZhqoYuUW6n9KQAVw7ZGPLEGUPbcbC7JS9xso5UjHNKlDJmFi0RLT+X6Kn14+cmnzqvrqzF3/2v/x1v+f0/cBxk87A/tR7XnttkgiLjXQa8kunA0E9vjMmp5HO+Opvg6AYDH2IESfRiFNSJHqH0rL7H0uMefqZWw1ODxwYiYIKzAklhCWhWd1v0MtCQGogOc21WRHSWlGYBzw4EyHvi5D49HG5f5tYglNsZz0ySE8AJPXq45BaiA0T/dJ/9o+pLwHvtkKbVVxxKXclDl2u0g8rgKXXiN7pgjs/zn/f8OHjwSNx+8mS8/vWviSc88fHSG3rKvPcCkLX6XhQUXC6UflWwG9hOvytEp6BgB7GXvgcYhu98xzvjR37oh0x08o1+vvXO4T0yinXs2zcU+4dHo16vey+WU6dP2+DkLT+T1u35Eag3B/kCTMKl5UWe2IjGJCUdhj7GJ2FtHRjPvI1PnXGGKLG0cHWP0Zxv7DfzT+KjOJKZ+6pMfhPH3iTut6TLcOqNZJAGaksWSYpyLgv173Td0Ankphrq9uAHP9ieHzZDheTNivQdPXrE1+wfQz4MzUJ3jUa3DfGJiUnvYcOKceiPIVPki/wczFdhmW7KZRluwpgXQ57IRx7IwkIN5MnqZFTsi7feFp/5xKc9/AovBJ4ZyAnnWjv1yOv2jiQNtKmq6DPPaAfyd6CetzThPmBdQWRYZGBVJES6TUJSkRNWU0vvDeEMTVvxs9VYZrU13S+pHhAbns3Nz7l9kuCIBLXiEubiKVeEAxEQCSLCNXJmK26RFSkhkdz7pk3kTHVz+xOWBJv4lENdKl0D7vEeOU88YGqzVfVF+pL7TEump9741BjZfyjGxi7GT/7UG0R0Hmd5KgK7F4AOqG9BweVE6VcFu4Ht9LtCdAoKdhB76XuAofnud70rXvYjL7Uh7fkbMuY83It5F10M7WmL/aP7Y2Ro2IbehbELcebcOREgvAWshpZzHfzmXMYjBAZDk6FB+rFxqwBdY1zLGOUtOte6tIfC1CR3oofQENdv4m24Yo6mh4hDAugew5WVtJasZ4z75ZYhi6xrayICQhKqXHAB0iQb1meCeYufZAdZMx4GcgXKph3x/GDYco0hDvFDT2x4yvwijP7+fjbSZOWwJeuHMOJDlgBDtCjCXhLlUZEpyiXc+hHQFc/wlCyLYGSZaVhX5fOcdOjvwMhoPP4xj42+3j7pUESGOMqDNNSMNK6YCV/qjGf2kCmPJBl5RpfpNYPwJDGAuOUGnZKHtrHnJo9lt4cIEIfiQl4WibPM5qW5RPO82gF9L8zPO460asKUXhaGuuWGne3ryIvgIismXPQiheMp0xV9xvVqEVNk5+N66p6D/poevtRT6ksERmVVanCbUn/ll/v75OalCra+Wfigqnuz2RvXXnOdSHyPsuyIn/v5fxrf9OQnRkdnypS6vfKBnO7fBQWXEaVfFewGttPvCtEpKNhB7KXvAcbf/3j3u+OVL39FNLrZDDSXfGbxAYZomfS0d8aB/QdiYKBPRuBanL94ISYnpzYmoHuOSEeHjVpV3oapFyTQNUYjcy7WlQ4QVgENMfmdZxi/GNXIg4EKSMv91sP5KyX6xVAmjHkmpMf4x4jlGXv7LJOvnjPcSkLpWrKs5xv+Kv+8TuMZO9m3pHcZrMCltiQyYTqQn8OEwWHY56qT027GySROKeOaG+UD04LktZ5tPG/FtxFOHX2jOBIon+VBXapyMdAf+qCHxku+8ztjsK8v669Y1JF0iqF4ld5Ub5ZSbtUXQsNmnBBCDH7isKqah52t5vAzCCeEBnJJGEPTVkVg7MFppYO0QIzIf1VpuMYz4jJoh/a8XlhYyP7hNqrqKn1IBjRg741+kINK4AVCS/Qt9sNRJq433r9KZ4Awt4XCnZ+eMdyyVYRBmIfj0UfIS8CjxtBMyBrZVfVfZRjewqIXSujpHYzR/YfpoCaoP/tzPxVPe9qNuQT3lvyvdKCfrTorKLgcKP2qYDewnX5XiE5BwQ5iL30PMPL+5q//Ol7/2td5wQGMR3apZ5gWZMfDonTsHx0NJupj8J67cN7GKxP6GTLFylwYsksrrR35VfUVLwm8Zq8ABi4sohoyxev5NMDzDXubzml4p6HrcH3sIdI1uqxID9c4YtAxRIdwG//JNFSjzJsyljHC9RyDmvjOQ0QnSU3madmqe86Sc6McnXkOYVCGDrNF7h/qSXgO80tPVcq18VwfvBCuk+Ny2eobRNM5aU/rmkuIEP2Hj8OIT/Sso70WpFV2D77hhnjJd4jo9PZZ9xjra/ZM4GWR7lRXExh7iFqERvWBwHCN/hg+ZkKkcLxiDEeD3CAvXpxF5QUBJV0OH0RnykdCEUYbs+gB+/P09Q1Er2QBtLt1qXxYPhoSzNLRyI/HC9IBkUHvog72yLjdBJYzZx4Qdbeckle19r1VxIEadJ9erpyvA8gnvTapT7eRZGzvhCgxNG49l+CWvPQylOn5aLrs0LOFufmYmp5V2nr09O2DTemnLX7hF/9pfNM3PcmEink6ewXZf9BYQcHlQ+lXBbuB7fS7QnQKCnYQe+l7gIH493/3nviZn/7p6Bax8RLNIjpMzK/Xc0gab8D3Dw97KBHzT8bGLvhtfq+IDvNOupQGE3RFYcurMmLxoMhoxCBmYjoGMRPcmUAu5cjYlfEq/dh7gZoUFwOYeJYJY5rnMlJRo3Up2xLzEuMbAxodY3gz18LkSU9zeJryx3Oi5+SGN6fyZGQ+GN+QGuWt8m2s6544yG+ReN4iUTbuCVd8zpQLeEY83+vHeQs8xbYmWCXyK0Mdh+uUg3RVftUBsi7EbeWn8LzGI4ExDwHLZ9eeOBHPfcYz3W5eqQyiI7m8ctlKLuuM/MhqAkNdlJdJj84QDi/hLL3lnCgqz1ycVjrd055JXnUoDK8Zw/66urtNcJlrdPDAoRjdfyBG1EeaIsgMXZuemozzYxe9sSwrqjE8b2kp98WphrJBrBbUn0RNTCAAv9eSC1sX9Ct0b3239IUeIGsZn7TZ/r7X82qoH7J6YQHVx3PN1P/QCcTGQ93UnyFKngultKsM1VtciPl56Wq9M+rdfcqD5x3x0//0J+zRqXm1ORe1J7DZfwoKLh9KvyrYDWyn3xWiU1Cwg9hL3wOMxg+8733xm7/+G9HbaEatjTkiIjG9vTE4NGiDkJW72KtlVUYvk+8nZcQyV6GPFchqdeVik96/K8Oa6pM3Rm8amKIdMhAxnivDGWcHpCi9HW02wDHWK91h6JIH8ngFMKW3sV/l3SqrAsa+PQQqb13GLs/JC4NXCRxHPEjJ8Rikd6IiDTa8icOPgph7giGONwRxTMSUxt4PXRMOMKQrrwhyue0VzjnlhZi0CJLOSeAYipXyEY6hzWT6aqln6pBD2NJbZO0ihO49B0npkGX/yP54xMMeLqKpNpMM6SmRfFyLAJr86B7ZrdsWabHHBiKqZwwNo+7Wo55RDnJRP/QPKWABhbqIAvOxWIRhcGgohob2xT4RGzYVZePWeqOBOkwc2kV8KWN+fsEbm7IUNosRXBRB5gzhmYdUeDic9IsczBGybiWviXLqjH7G0LPcaDSsH/cnxYPAEOZ2pFV15hmgPiyxjRcH/UGyTWjUDPSxmsgLS0q3i8gjs3KR3kTwJNPk5Izavj26Gr3UyCTpZ3/uJ+ObbnyS8s923Stwf3TfKSi4fCj9qmA3sJ1+V4hOQcEOYi99DzAsP/LhD8e/f8u/jUP798ugrXuFMFZSG90/ao8OywOzwz1v4ufmZmTAznoYG3N48NRQVxv0laGqfG38K6wiNRilsig9rCr1o7LxvPDjUxrwGOSkr97wkx+Qeel8+JAeTxEGK7EoN8tQeoVh0OISwGAngr0QyNGKS7luH+WN3C5f4R4yBfFqyWx5ZPBjVJtA6DmZYERjpCOTMjehIA6yuG7KH3JIXORhorsLcbkWKQmMbohfDb/i2ht5QsycNUSnNWROP4TZyFc9l0QMIB/7Rw64vklgkughN3XnTIEmMmvpWWOomY+WXqvflfeju652Vduy4EIXG6+K4PY08r6pMwsvNEVuIMD20CEXhAiiJp1LYt3i9aEOHaq/ZLU3ZzkWvG/OkvoPG4bOxuwMfWnO4ZAw5HU76pz9AD1CcAlLTx7dgmFnbmNiqPz0gqWHCu8j/YL+h+4486zSs9tZ9e+qd3kT0Q68kWK/lEk5ayrn/NiEZFqLzrqIjvKvd9Xj537hTV6MgHxonr0C5HX/KSi4jCj9qmA3sJ1+V4hOQcEOYi99DzCQP/Opz8Sf/ue3x9VXHYl9+/bZaGd/GQxdhkFNz0zFsozVhdk5GadTMkqXolcGMIsRYHhSV4zLynsB1cFwtAogDS2jGjKDQYsR7HgcMjKJRx4ckA3CrUM+uiYcQzyN+BW/hceohUBUw5QgG2BJYQytAiY1kBP4CYUIKs7yQRraya/KXw8YTuc0kAYWIsDJoeerzOtRGGlUmHXBvBOG+aVRvmRyQlxgc19GNs8WFJfnJhImM8xIyT5SHZAXjHXmPeGBGVYbmDS0y9DHqlYc8iY9hj+6ZigYk+YzLIdeuXzVwcQM/aN7hXsTTdUB1ChLsjE8saH2pQ3ZSJVlq7u7m9HUQdt2N5uWzXN8pFP0a8+ODnRO/Zibg+ysBEe97EGRfMhoIqdwdEs7QW7sqaIB0JXIBsPPXI+56ZiBQKs+yyLVJocicpKc6mQbQIRoB4iLyqfe8A1k7PQ+QeiSJb0bir8qwlNT2lxdDZAHeksyZZWKh7HwhgiT0iFnu9Lh4Tp/cUIyrURHF/tKtUlHtfjZn/+peNKTnuByVD0j+yWav3KBfFXfLyi4XCj9qmA3sJ1+V4hOQcEOYi99D5DzzKkz8Yn3fyz6+nuj3p0rUrFRJsbq1NREnDtzJibHJ2J+Zjrm52ZlZ657Do/ftisPEwMZkB5OJAOWYUYQFpmESXKkCgzgytsgBWUcPAt6hqqQgxW7cu7MqsoQWSFzhaNJ8nEeOjsfzsorvQoiWkpjr4bCVuAbSpurnBFXhv7yquzZHIrF8DFJ7eyB83K+WRcb5Lpn5bZcGUx1koFtUiEDn80w8XpBBvF2QQLwMqgw14XhTdSHZzmRH5LSZnKRJCwJTtYs7F1Q5ePc+Qv2VBw4eCA9O5I3l1jO/Njok4Ub8DIhw5133hlzM3PRhndFsZyb4pGOfPD4YPB3N0VQGuzfo+vubnvjmo2mDPiu6NF9J5PzVTf0QxtQH/gIm5LOzM7ExOSECQgkggUquuq5+ABzuBjOlosR1FRuh4fgQR5MfiQDeUICV1azbZaVJ/1keUmkAz1bap7lggmL7MEj8sPSz5whf7mwAe1DnJxrhHzqXdYLOuI5OmWp866uTs8dIl+u2xWZcojHkLwl1cVzsFZUttLQJr5v5X3mwnjMzq/Yo0Pr19UWP/8LPx1PevIT3LaUk6ouRKfg6xOlXxXsBrbT7wrRKSjYQey178H8zHxMnr+Yxi1zIxTW09MrY7gRs9NTccdtX4yTt90e0zJ4F+fnZex2Rf/AgIkOBADjEEOTN+YYsSxlDFlhPS0boPoQ12/XW3rByCUuelrXs4qkQEooH+8PMTHaISyU09nO2/f0HhEfkD6HqKXOPXxNpKGNuR285VcuyIBnChkw6iuvUZaSsigk1mT4kiv55PMcTgfw9EAeMOi90//srMlItSR2R+tNf3qesjwb5KStiI5IgZ9D5CBoyp8PpGFRBv3Zs2e9MWlff3+LlOElScKDZB6uRSMp07GLF+JjH/9EzM7Ni6w0RVAb0RRp6ent0X3D86cgo3hpekRs8MTYSyQ9mTC19AYXRH706T1zIDrSL8esyNyFi2Nx/vx5EwyGrFEG83GabH5q4sP8ndxLCd1s7Kuk6w6RVUhQu+RGD54Lo/IhfqyyB5lZgpWqv6Aj9CHF8NvdBLnm5lkcYN6EZ2GRMwsZQJLpd+hYfUEfdEoh6M1DKqU7HyI3LJrAULs6pEf5q5e43jlMUcl0UMbiEmUsxF3nxmJyZklEp+m+0aX2+aVf+pl4/BMfp/aQcEi65TvO9ZWK8j+5YCdQ+lXBbmA7/a4QnYKCHcRe+x7gtcAwB9XcGKxNJmyvyKi//Qu3xuc+8+mYnZz0PJ2eZm8MDg7akMRAhcBgcGIcY5wy9MjkQXlgkEoZfqtuAxXPiPRj74zC8U6YIQgMNarUxjODfLBERRA6RZwAzxi6JkXb+8C8H4azAeaCmARBEkQusGi9EpvSMAxs3YezVbjpTcZXXhjggGfUIwkAZUteHXiD8HzgaZgz4atnXTGydXSKXNH2nswv0kC6qi9whqggu71TDPVSXhjpkB8M+ImJiRgZHU29Sg4IEH4L4gHSSwpdtcWk4t56663e+2hY5IihZs2epkhSXzTsZZG+lB65qQJtbI+Z5NxoI52RnXkseDpWRDq4pn7TMzPeL+nCxYtxUWRnfn5RZbC3UnfO4zGBUpkcJjwQna7o0TUECA9SR4fCOmquj71h1k8ebDZLe+NBY/EBhrct6jBx1kHr2/sl+akvbUL/on1NkJYWVQcI50z2P5El+gRtiN44k39HZ1t0Sy7CIDusmoY+u9V2XV0spCH9KG/qDKmdV953nj4T58amYmW9Zh11dXXEG9/0unjWs59mD1GF6jtO216pqPpfQcHlROlXBbuB7fS7QnQKCnYQe+57IFExvjdIDizHizMrXEbnHbd8IW761KdiWkRnaX7BK7INDQ5lPbGiBXsDNogOhmcOFfIbd/K2QZlDwKScNPoVnquX4bFAjBx2Rfk8A5XXxxP0lZ45HhiwczJIdVL+GOmQjJz/ggyed6G6pDGdOePNcXqVle1DsAxqnTH+8QRg8RKsCE5nr0FLdg8Hk4GsRzaKKQcvCca25dAZ7wEGfQ53y7xcD12gWz+jPjrIL4mOHqssD9FSvn19/VZ/m+qDjvA58VG1c2NWyQ+pS72vy2BP7wl5K5YSppcC4mXiprwhgx46pscQG2RaVBjR8lkurkAh83PzcXFyIi6MXYiL4+MxPTdrAoZ8NZfV5cn5jTokB8LTHb06IDsMZWMvJvSCl6lRq1s2PD0e6tZIjxb1znk1eFzw9ilcNUPXzNuB9PhQX0E2iAiDJKVBr0zHJrP0EjQ8J/msO8kPCacfQuLoZ4siQ3gW7YWhsvoFAaQvWFb1Y8hZzrvBO7kcM3Nzceb8WNx1XnW2A1J9VRnc+JQnx/d///fEtdcdVx58P2g5zlc2yv/kgp1A6VcFu4Ht9LtCdAoKdhB77nuAqByy2yoDGl5BGMvt3nrz5+Jzn7kpFmbmvCdKX7MZg/39NkAhMkTEMK3mt0BeyI7J/pAebtAHBAWDn/kQGLt+m+/yXKLjoDv90rXMUv2q8sRwXWmtHEYaiBR5YCBDUrq8B1ASA4aJ2Sy2QQppEpFpS4Jh743uGTqVE+NVR+XJUK6q7Kr9NoiOZMZAZ/6JLj3Eief1es1GNcKuLovAKR0GM+VkXq6Y4zoP3eI1IE+Iyea8GFYmYxU3kZmuLv0WMMhFaBCX/Ijrpb6dDk+Nnsvg5xmky94RlyXdwLyQSTrzkDS1TRLKtljUOT1iIgKQHZMJFjfIvW4ujk/E2Nh5kZvJ8AafyqsuIkM9x6em7FGBoNSlbwgP5KshAoMnp5vV2VixTeEMl2OYGyu1MXQM0gYBStLDQghJfljQoaODgyW201tCj6Au6AUvE3IyR2hpMT2HeHrQW035omZDF9VqcotL6V1cWFB/VZ0Y4sacqjXqq+c5lBAPmXQqGdAjnhrqyhDCsfGZODs2GfNqU4a3UWZPT1+85LtfHN/9Pd8aA4M90qQEdItd2dhzf4sK9gRKvyrYDWyn3xWiU1Cwg9hr3wP2Obl4flwGK5t/yoiU/YYR2CWjmjfmn7/55rjj1tu92hjDhBiy1NvdI1MPIiLDWoagjW0ZfjayBetAzxgShwGpXwqtnuU8FtJaSyIcEBeF6BryhHGeRi3hmJMYwgzDQjiGkGHwsyqcjX4ZvdxDHtL2hFSRcx6U5TlDOjDs7dVQGeSMnJZD8U1StpypE/lV3h6MdUgUXgPqhL4gbS4Fy5yLFsiTfDiqe5+pk8iO66DDxE+GN0PdcmiaCKLSrOoCT4M3XxUw6r3pqtoF0gMBWFdeEEHyy3ZIEmWvjtLl0thJeKxL5QNRheTgLWGpZw/Xmp8TqVmIqclJEZ2LMcOyzyIG+wYH44Ybro/jJ06Y6Nx58qSO03Hu3PmYmp2JdRGoToas6WDTUobKMZ8FssbCBd211lA3VnVjuBv9yx6hLpGg1nVDhKeTOTQ1pVN7ogPa0W2j+rqv4JWiDnhpIGQiPZKdulZ6pe1NnDrqrf6RpAkSR/tDdFaX2KB0Tn2SRSLYx2fRzyFCdC08PeQ3O78gojMV03MiSbrn+4HOXvD858Ub3/TK2H9g2O2PfFc6yv/kgp1A6VcFu4Ht9LtCdAoKdhB76XuAnCfvOBXve+8H48DISPQ1cyI3b+l5Yz8/OxunTp2KyYvj+VZ9YcEGPmTHQ7GUHvIDZeGDQbhZc12ttSbft3RiD4QMewxVwiEbDKvycDelRXf65WfscZIT25lIDqmp6ZGIGB4bRUNOLiAHbDDpkz5reJEEb4bZmuhu0iKjlvjMG+GMPJCKDWNZ11W7ccawrsgK90mmIuure+Z5UB8EIBllV+mrtFVfcL0EZEMWG/R67g1ElQdxIDDOT8DYJw3l2Bukgpncb8Kne8iWCY6MdPKrvDQetqU8yJfwitRA8DwsTOUveH7Lcizp+cLCUkxPz8SkSM709FTMiwh0Se8njl0Vj3n0o+Ka49d4iBdNyeIE4+oHY5MX4+Tpu+K2L34xLoyNiRiIRKhMSBhtlvvTIK/aT7pmLhNkqCkyxKptTd339jQdj34GCapzrbPJq8lcen7Ir0N68Ec6oL/Qpp7TY7KSq7RViz8gZ1t7LhvtBRKUX3rJ0J3a2nHQ34LSz5o4MT+KeT/oCuIzx4p2p8/FxOSMiTsbn1Lec5/zrPjJn3p1HDg4QkvqKESn4OsTpV8V7Aa20+8K0Sko2EHspe8BHoFPf+qm+JP//F/j6MED0dvdsGHYz0aRMkYx6qcnJr0oAZO9Z2dm/da+T8/xMgAb3KovdebtuedVVMa7jFPKWFYYxiiGKXMqMMg99EzP/WZch1ft6sqVzTBKMfA9VIs39DLy22XAolZv7CiLNnWcFEsWrO6zTAlqgxXjFEMVIxwCwWIEXoELoqO88XxQNh4VNdlGu13afhAWgCykwSOARY1HIsM3DV4PtVNZVf0hJWTlPHRRhUNyKId8MgyRFFfXXkBA6TDW0SdDxZCThRQoi/vpqQnXz0PhrH/mRjHUK+e2oF8Ohprl/KkkBNR1DmKgZ6wwNi0iOzU+kYsriMBeddWRuPb48XjwDTfEwf0HvKKa6EGLxCGuylF5CytLIgJTcebsXXHqrrvi5KnTcebc+ZicmUrSI111tNdUl1yMADD0rtEauoZ3h6FsTfob50ZD/a1H4bmSG0Pi8PDUaoqnNoN8mUjRBkgjHaFD6s2wQepo4rNI/0oSmLqlH0lnECeV7zxNoNUHOrKNKyJLHp7vI13d9Llb4vNfuE3XudmpYsWznvn0eMMbXhH7Dw47b+S40rGX/hYV7B2UflWwG9hOv7vyX0NdwfA/2Hs52Gzxm7/5m+NP/uRPWrHvHWNjY/Hbv/3bTvOVQH6XlvW4xz2u9fTL49J01UHZFe7pOcf//t//uxUjcX/JXHB/Yt1L695++51xy+e/EJ/7/C1xyy23xq233hGnTp6JC+fHvDGlh0DheZFRuSDDb17EZ0FG5ZLuPdEdMuM9UphbwZAh4q7ElIjR1Mx0XLw4HpPT0zYamUzf29cfvf0D0T84GIPqT/uG98Xg0KCXVsaD4L1ePOQJAxVDVZKus7DBsox7hmzhnZgX6ZBhOjfr1bempyZjZmoqpqamZYRPxhQeCtWNvtfoSm8Cw6QgVflnMI13DN0kM/TTioBU15uHiRkeJX1Y9jnDcmW0WM88c4J9Dq0jD8KIx7VU5PLsnbH3A+8Mnh3ykCw+FBfPDeGtMnPIHOSPNDVPyJ+TMT+hekJUaAuWYYasMARtWjqfnJyOSeliWrrnfHFiIi7oOD8xHmPj43H+wgUPQZscGzcB+MZv+IZ4of52fcvznh9PfPzj48ihw/ZYiQJY6DUv3LAimZhHVPMcreMiRY9+5CPjuc96Vnz7t74oXvztL4rnP+vZ8ZDrrouB7m46QSwtzMXCvI65OfWjGbfJxYmLcfrc2Th19mx88dSpuO3kHXHryTvj87d9Mb5w+21x+6k7487Tp+PkXZCnu+LchfOS+aLqMeFjfmFWxA3yloSz0d2lftMTQ0MDMTK6Lw4dGo39+0djcHBA5KkuXbI6Hxusql9Q/zHV/fxZ1f+sPVTzc4vSvEhYoxn7hkZE9o7H0auOmoiJs7kdTJBFvmiegr2L8j+s4P4C9hP24FZb695wnf5mXtrX3vGOd7SeFnw1KB6drxEf//jH4xu/8Rt9/fd///dx4403+po/jK961atiXIbEK1/5yvj93/99h18KOvCb3/zm+Ku/+ivffyWd8cd1eJi3iJt43eteFy94wQtad18efOFe9KIXfVm5iPOUpzzF17/1W78VP/VTP+XrCve3zHsZe+l7gDfh/e/7UPzWb/xe9PWwMz5v0GvR29MXg+zDwtwKkQ6GGjFsbWpyys/7+npsgFNN6opHx8OsZAgzLArjmw0YGTbmzTRlLG5sLCmDnQMDEoKBeU9Cz8HB3Jcx6dB1rMyWHimHYWh4TJSn51bgXWA41kJ6KAivvCucO2WcQia8qSXEgQ9lkKXOeAN0YZkJh+tkfcihVa4AeeEezwMkCwO7kp3I5EvdMYJzdbhNfWApo2Myt6eGawXn0DXd45EhjuKSFmJhD4TJEJ4GRSdPpc261FznW794a8xOz1hXLKrAECzyYuEB7wkjwx6iyXCsBbXDrAiRh7upLLw+kLHe3p44tP9gHL/6qjh04ID3Rqp1dUQNQkULqExapGqDdfZYokChXUHICYiHJvHWscHonIjx6bvuijtOnorb77jTJHdGZJQ2WseTRq7URzJ4zpHygcTRN9jrBkLa22x65TZWRcPbg6fHq71xLRJVeX3wKhKGDEkIJRPCSH6GSOKBWlxisQc2H825PVzj9aL/UJ8uNkz10ZlD7Hr7RPg/Hx/66MfSQ6V82EPoOc9+Zrz8x34gRkeHlD86sXauaKCXr4f/yfcF5X/Y5UPpV/cOlv1/+9vfHr/5m79pm+ue7KmtwG783d/93bv1NbZvILzg7thOvytE5zIAps4fzK1EB2wlDB/72MfikY98pK/vCbD4L3zhC19WZ3T2O+6448t+Ue4L7k3eraD9ADJdc801vr4U96fMexV76XuA4f3ef3h//Mov/ZaHDnXV0hNRl5Hd39sfI8MjsX9kNPr7+/xmfnpqykYnywin1yLzABiOzPtgXo+NcjZoVF6dIkbV8CB0w4FXx3rCYLSq7MtwXqy2hYFazV1JVa7KOF30PcOSWBZ5VeE5bImhRW0eaoexigHdIXLV7qWE0wvDvjXk7/xEljxMDKM7Mzex8OR/lclRtSEEhHpi1PuNvmRjIntVd2ShPEnqg/oqsdKmTlL+JEOun4xm6kz6NYbg+T77DEPrkItrGAx1wQuUQ8EgA5JBZJHyP3vzTfZckY+HbXlYGpPtF2NehNRLRivcm20qPpt2Nlqr5e0bGowDBw/Evr4BXQ+ZzHoejNgL0rAPT3q40AskTnK3MRSMOOhM+lH1mLwPOXP9WnK7bdQHaMOFpRUvUX3u/Pm4/eSd8bFPfNwewWWlZ0EBZa600pjypK1ZcpohinjwGEZnj5LyxAvXaKSHDwKEp8XD3hTGMEv3W53plxAfCCJpkM3kR+Ws4m2UnNaVCM+i+gxD9zx3qUWSmSNmvYtQ3aq/Xwxdm52fbRGdZjz7Wc+KH3vVD8bIyKB04VZDRVc0qn5csInyP+xrR+lXXxn0oe/93u/9ikQHQv2ud73rS0h1wZdiO/0u/4MV7Ai2kojqzdG9gT+4Xwk/+7M/Gy9+8Ytbd/cP7o3kgCtV5oKvHvzdwEBmZSp22p+emYmJyVkPg1pcYLI7xj8Eg3kgzH1hr5OcBF8ZuevYv8rL+9XoYHhV5dUgLgZmlYblmRcXZ0WccsjZzMxkTE1OyCge86pfTIyfnJiK8fHJGBsbjwsXxmLs4rieTcZFhU9NzcXcIp4QEQsZtH0y2Pv7B72RaVcXq38xt6MhWbpkt7LwgEgJBrn/9OGp0JljTQeGNktUV884K2yd3U45q2LQlPyzyWyV9liGYbVDbmTcd9TscSHP3JiUdDorPXyNPDhkx5uYLaPH9Q7lwd5D67GieKvIEXjHOkRQ8I6Qt8pRGsqoZKe+iyvSvTKeE5nBS8LQNIYGTkxNxoWLY3F+7EKMT4xbh5CfgYGBuO6aa+NR3/ANceMTnhDfpOOxj350PPT6B8XRo0elt17pi3rQvrQnQovutIgabe4VxqiPyl1dVhz4gOShvTkght6PRwf9AZKLV6ZfBOTIoQPxyIc/NJ5645Pj4Q9+SFx77TVx/fFr4vhVx2Nf/7DIS1Pkms1FpUfly4IX9MHxCdVHbT8xMa12n4gzZ8/GydOn47Y7T8YXb78jvnjbHXGbznfo/o477ow7T56KO0+dsifp3Hn2ALrooWqsGsih2on0QKBqMTjUF6MjQ3Fov0j8gdEYHd0XgwN90dfTLYLEXkJKp35ZDY3Lf666QidcFOxplP9hBfcH+Pv6lQAZuifPYcHXDv6rFtwPwMj4WsCXgDdPz33ucz0k7tI5M1ci9qLMX+/AkPOQMt588/pefyJyIjhDgmSkt952Y+etrqznG3BbfunRSCtQP0rLJW/byRNDe1rG5qSM8PGJizJaL4q8jMW5c+fiLhmkd505HefP3WWCg3E+IbIzPT0t4jMnYzNXwYIgYFR3dLB6Vm90dw9Eo9mnM/N4+qPe6ImuRjO6m02Tj04RnY5aF+apEqY3x4aqasWhmzx47pDKiMXTwyM8O+nV8uG0LU8GnoeNe/LNeTTEk4p8kKNO9h5wrg7IypoIEOpCd+gZgkC56UVS+SrCciKL8sx4Sq0y/ZxsdM3yz+fPn4uLFy/aW8KZoWnkw5CH48ePx6Me9ah45jOfGc9+1rPjGU97Wjz8IQ+N40eOxqj+ofZ0d9s71M7CCiJOVJODOtIHWK3O5XEgr4guSmJg3ZYK2UviBRDoHwpZkQKdVpJ6xTvP61E66Qqv0ejIaNTaatHsasbI4HAckzzXHbs2HnTdg+OhD3qIZHxY3HD9DXHowCHJ2EORMT/P3JrZmJmdUx+ZirMivWdFZCA0zOG57c474hbm9ui49fbb44t33O6FERgyx9C5u86c8cpwzM2YVP9ib6DFxXkk9DC93p6GPVz7RXoOHT4QR44cjCOHD4qk9dqblMuGUwc8euklKnhgo/wPK7i/AKF+y1ve4hE3f/AHf+C/UwWXB+Uv9Q6CzlrhOc95Tuvqq8N73vOeuPbaa/1Hly8DQ+L4w3slfxn2osxf78BYr8v47G5CHnqiv79fJH3Qb9tZ5pehWTbIbfAxET/n3GD64sFYlqGLPYjxjwHMxytbifAwlCh30md/k5qNcRvyeD0Umzxcvp7jkWFjRoYJ1Rvsv9JjOTjw2PTp6BL5WlxkiBZLPOOpUWmQGZOcuofEeegaQ5aQEZn1PIewZZnUA3mDmSiEy4hdU9w1CIVlSjIDqSF/XeY1FzxXWhM93ZuwENe3kJN8Xh14eTCSvTSyAvgkeSE68imeEuU8HeUBmXLOrfLMfsifiIq/1hZT0zOe98KQvb7evjh+7Hg84hseGU960pPixhufEo9//BPjoQ99aFx11VWedN2tdmQVPea20C74rRi+RXkmsjozt4d2tEdK+qpIlooTsc3hgQw7W1HkFYXj2YOIQoBJb4+f0qA/zuzhs6g8Gb5G+qmpWfWBXhGLfpGKQ3Hs6PG44doHxyMf/sh47CMfHY/R8Sgd3/SkG+MZT32GjmfFEx/3pHjIgx8aBw4cNvGhPVm5b3YWr+Osx79XQ+POiDzj0fnibbfHLbfeKsJzR9ym446TeHtO6nwyTp4+FWcVj+PC2MWYmhSpZoGMxQXpYT3q3bUY2jcYBw8c8AIH1IZ5XKgk+5D+dTI56W649L5gr6P8Dyu4PwCBrryLjP75sR/7sbj++usLsb5MKERnB8ACBayuQWcFf/zHf/xlh4DdF7BowC233BIXLlzwOM+hoSH/4f2+7/u+VowrD3tR5gIM3jYvJdysd/vgjT8mXHdPtxcTwPD1G/8OGXwiEvwZwfhvgwTpmhXZIDEQIfIicb295k0jO9s6o97JJPL0wgz07YuhwWERmOEYHNgXzQbLCjdNcjDKu0W2Gj2QHaXtqkWtkRtJ1kW6IF5Y03h5mIdBOu+XAskRmcAY9gHB4MxHBiof0mDYm5BBPCAmrTp4zhDheq4ESUyUYiOMa33w+mDU2/pVWnTE8C7IEvU2mWrZvoqxWTaBTgNZIWfii2TxVGUgqzNTHGTEm6AYJpU2slVv8iKbTun1+NXXxuMf98R47GOeEA972CPixIlr4/ChY9LrPhNGyCWGOXNfGHLmIXbKD68SRIZ8lpegaGrb1TaRFhXNMDq1I/NnFkVQZhcWvEfOkkjLguq4DMHRmeFzi6simpAyHXhwyAkPD+mY/zI7hydmPianZuL8hQkRk3mR1aF48IMeGtecuC6OiuhcdfTqOHTwcIwMD9uLM7pvfxwYORBX69nDHvKwePxjnxBPe8rT40Xf8qL4thd9WzzzqU+PRzzs4XHk0CERvF7rkiGRDLmcmmKo47i9N7kQwp3xhdtui5u/cGvcLOJz6x13xq1fvC2Hu5067SWx7xTxOX/hogxYVmIbi4mJyZiZmY755XmRPFxZa9Jh9mWaBm9ZrSOXFM/WBTwpeCCh/A8ruD/ANAfm5vASi7nTz3ve8/w3DGJdyM7XjrIYwWVANbn/Uvyzf/bP4qUvfel9IjlVHvdVZ6zm8ZKXvCQ+9KEPmUh9z/d8T+vJV8a9yXtP+HLy3J8y71Xspe8B5OSDH/hIvPn33iIjWYZcjU05w2cme19z4rgNPvaOWVnGqJw1GcGYpp5Mhqeqa+s5/4awHIq1FjUZ2jyvwl2eTxAEGEMEyyzbGFcIK6MRH3LhzUSVcaVHVgnDOwLhYnUvQrvqm3u0kH9VtqeZCKStZAQQFMhCZZyyShlXzCcClYyukH68gSk8pxMywupcNQ/Hw7BmCWzCGMbH3A2ICWkqGagzz70JaKsOLBDAPCVPsBc5ozSG/lkGhRF/Q15kb+kDoB+ec1y8mMOwRkZGlFdL7lZ6SFNuxJpGOqQKeTZ1kCvTQc6QkXbmjBzdau91nS078b0xa9ZLrMt5c4/cEDfy4uDeHh2VC/Gw10rkiD1s8BBJLKVrkVC8gW7/XGSA7NnPBlJmMq3bqt2Rg7P1KKLLHBvCZqan4rx04CWzL5yP8yyTPTUeM7Oz1hs8FFmrvpwb4DasA0gx3kM8jNSXhQzo52xcyuqCPOuQXB//+Kfj5lvYR2fV5K9P5Py7vutb4/t/4Nu9lPUm8GFl/7kSQXtV/a8gUf6Hfe0o/eorA7ICaflKixFsBSOCeFmORxGyXXB3bKffXbl/lfcgYOIo/rGPfazvmZfztXpy7g3k+2//7b/19Z/92Z/5vF1U8t7TsRO4HDIX7Bz4w8GeI8989jPjkY96VAwM7oujR6+KR33jo+LhD3u4V13DyFQPcXwMRUgHYVxjPObKWLWoN5hUznCwNEyxMulXXBNOWcSr1TI9K4lh2EIgml3dUVO+DDGDIHVx7lDcjnouDa20rKLWJYOUpa17+1qenBreHgxa/qwxvKiVd0sGyuXgmv1fqrL9jHhbnrsMpcNjxT1xMXrxpnR0sBIZ4ZTJNfUkHWQLg51n3Gf90xMjHaxTx9aha1ZWwwBnaWTLxNnxkxRCjHyo7K4u8kqykfLg1aEMGeUMQ1N9SFO1A22U+91AGKT7DWKHpwaDfdVEC48NBAUw38fERvFysQlWeGOooloGViqZvSCByOPK8kosLSi9jvmFpZiaZr+emZieno+pqRmF4xbK8pjf1YV3sNknUtETjS6WimbeV1Vv6kF/or2le6VCN2YpOrimTh4myQINyKBHEJb9Bw7GQx/y0LjxiU+O5z3rufHdL/6u+LZ/9O3xnKc/Ox79qMfE6OgB78eE3qjv3Ox8TIxP2Gtz/vz5OHvmTJzWcfvpk/H5L34hPnfr5+Pmz3Pc7GWlP3fLF2JM8VsqsrcOWTxXDfkKvq5Q/ocV3F94xSteYVLNsElGCRV89ShEZwfAH0Jc3D/90z+9o25HlqvGxTkxMdEKCQ+Z45/6pceVgnuSueDKAcOdnviEb4qHP/wRcd3118fRq66OwaHhGB4Z9XAy5sZ0ytC3wd8OWcg9R3zIILfXQwY5JMXkQYYhxjbGO6Qoje3cO4YwtmVheWKfFU5fJQ5dlnuuTUB07hJxYEPLThu+kJeKBOSb+ZQD45mDt/hdJg/eTd9HGtYuW3k679YBoYF4Vc858lnKU8mAJwIyshmvIlYtYgQpU9keFlcZ6LpnmeMqvsuSXMjH8406t47MZ7MMpycf6ixS0AVB0zXloA/aYX0VIpMeNeUAzYu1ZVZAW8mFApgjs5DLTuNx4aheakBkcggi4O8FhCbJEOmJ46Wgdb2mclaU79wsm5GK2OiYn4csQcCkP/QtUkMfqdVEwGo9ko+J++iJukJ6OadXyfO8VDewoQfJ74n/AvVhTtDaiuQUwTIBY66PwvAY4WFxfZWOZc5H1Fcf+uCHxDOe8Yx44fO/Jb7nu18S3/0dL46nPeVp8bjHPT6uuuqYCTtDM1dF6mbn5rwq3fjFizF24UKcPXsuzkB8br9TJOfWuOmzn4uxiamcc1R56wQ8Vuil4OsP5X9Ywf2F17zmNT6zME/BV49CdHYA/CH8jd/4DV+zOSfu7p0CqyltxbFjx/xH+NLjSsKlMhdcOVhYXIy77jobTC6/7trr48EPeXDsP3gwRg7uj6tOXG0Cb89C64MhnsZfDjXCAMc6ZSgY95CMeh0yJONWj/DcVN4HjFmGqim7DUKyafgnEeE671VWjRXgRBI4FA/CQRzK50gygHcAwtUiBjpXxGCDtPggX4zrysAmPXllmZtlb95TIcqhXmBrPEP18XMdJhD6QFIYZkVyacU6QH6Hi7TcLb1gGVpyWrc6WyeEdSAjz/LgmrpVMq2LBEBEyA8vjcmK2pFNMWlPPVCeNeUDqcHTAjlkor1El+xcc/ZiBCIBEBuWrp5j/52FuZidnTFZWl5echtDXFmRr15nE89uycKCFcpTB2TGPABvkEgXZZscUBr9wPXWhZE6q8gVnhIIDSSGgyXNOXIRBORajYX5RXuj8EC5rqof/QmvTV6veVnrqw4djRuuvSGe/rSnxQue883x7d/6bfGif/SP/DfxxiffGNdfe52HaXaJuK+IQM1MTXs+xrlz5+PseRYrOKuy5iUisraG1KlKLJJB/yv4+kT5H1Zwf+De9jks2B7KX+odAm5H/pkyoQz3407hIx/5SPyTf/JPWnfhMcNMarv0uJJwqcwFVwhkwWHAHTl6OA4fORjHjh/29dVXH4mjRw/FgUMjsW+4XwYyZANSwFv2VZ/b1lejQ2eGnOFx4Q8Lczd4V98tY5g5NDJbVYTIAAZ7J4a7jGWRFwz/Tt9j/CKG0ilOEoQ06pMoySC2gd/ycFCOEmAkc84q6DneJMJtQBOaZMpx7D6qXEgQEfJrkQlHTqIA0tvBMCkIDHVSuK4rg7yal8JBmuogD/K2bUx9ZRCnnM7VeVJHy0eeRNPHxn/rUw2Z892WvJMgKD4H5Siv1VWMezwv3OK90T3torjUC9KI0c+yzvYQSTDyQm68IvPsmTQ7KxKz4E1FZ2dmPKHfG2SKnBDPniq8UJBMtWWju+FFDnJujeT0HkXkaRW5RtSQfzHsLeS5QmuSUX1gTfKyRw/xiJ9kRiRlieFyObdnQaSGg3lPXtltlY1HVVdyUJ0t69x8rPFcxGerhwp5V5eT/LDXD+f2tfZo1ptx9OAhE58nPe5J8dxnPTe+69u/M/7xd39PvPCbXxhPftwT48HXPzgOjbKkdXemFeFyvcjXB7dtMTKyz0Mn6Vtu74KvK5T/YQX3BxiyxhydQni+NhSis4N461vf6jfgTFxkScrLDb4EjN/cyQmRl9sbdX/IXPDVAbO0t6cZ11x3NK46diBG9g/EwFAzBod7o3ewO5o9XdE/2BvNXjwvWNoY2StOiTFdGZsM87JBLUMbExDDk3Di+RpDVIY4xjNDiJiXwdv4VRnoGI32VoiAQGpIZwMforGuMAxNPronR6jBhhdG1w5EBl9j9GJYs8CBbqmgH28axRiyK54sTwzJqjC8BClvLhqAsU8cQ8HUM+Mn+SBqLq8sw1hGN4a1jW2H6Rqy1EpMvdMLoXLwWJCXh2Rx5jnDopCdlcu4zvko9n5xtnzKF4/HSpIsMucZ5VREEA8Z5IaJ9l3SMfoiHWWgjwVWQpuB1MzF3OycPSQQBsplNbuGSJGX+G6yxHeviE2PiE1DRLZL7SBSiIZVdz5Zvn65hpJPJAZZkI29eVi8whuQUlETHuTnQA/oKvWEXlilbVHhKyJDK6tLIjqLIjlKL5K0sr4k0gOJy3QsiLG0xB5LEKeKcOQS2ZCs3KtIeYuwMAQPWbKdRHz0HOK2b99wHL/qRDzhsY+Pb3n+t8R3fOu3x7d/27fF85/3LR7u9qAbHuRhj5Ar2oz+BwllGKS6t9DqVAVfNyj/wwruLzAy6M1vfnPrruCrRSE6XyP4o1etYPaf//N/9rkCO9z+p//0n3zNkpT8YST+pYBMfOADH/D1Pc3pYfUNjArSV8TjHe94h+cAbddb8+XkrUDeFd7+9re3ru6O+1PmgvsP4h5Rr7dFV6M9uuodIhwy7DggNnrGfjgMVUpagZGfBmZ6Dnhzj0GY3oRq6BUGLAeorukboCIRSTyUk7IzkcDoX1rd8E44X0UgnCFaJkatcK/6pWvn3Yqf+W09IFdJzMjTQ7OIX+Xt/PMMuajkJN81GcZO44OwzCsJRxIjrpcWmTuC8c0hoxoDu5UPxjj7vvjasmLIL3vVuEUZ7Mhij4UO5EUvGPcQIebEsDkrZVA+eVRkijZgfhJDx9A1QLUQT4A+lZ3iM0dnwWO9p3SwWSZD0GiHhtLiyWP1OBZQYe+kvr6+6OnpNRkgr2wvyKX9SM7TbdWSlRXoqLflg1SoXtQt6wsRScJKugpJctAluhCJ4VhejDUdeH8oAE/hmtpsVQQHT9DqKuWw0edci7ioPdbTm2XdKV/LpnP2LR20Pfm4z6iv0L+IxL3IovuH4jMMb3hkJK6//gYvKMNwt4c+5CEie9Kt4uAVzKGMOrgp2NMo/8MK7i+8853v9BlP4D3twcQeZxxsUAvob7wc/7Zv+7Z4wQte4LCCrx5leemvAfnP/0tx6RKCdFiITgWGtFV/KO9pqeetzwFfDNbtr+LxT/hlL3uZh8dtB/dF3nuLwwptlfv0/pR5r2OvfQ+Y9zA5MSVDTkbzer71Zi4JxII5CePnx+NTH/tkXDw/5vkMNT3r6+sVGcrVsiAADEui3hWB8INI8gDRqIZlYWhCJDBQIUqdMqjti9EZ4xdZPMdGhrx9B9YlpCtJFXmxHDRxyIOi2A8HT5LjKkAn5Zmy8FaeOwxd2oQhdG4aHdA2LFmTKeeVBjARuIZMMRCP3ywC0MFkdoUvijwAvCYY98Rva1N6hstJDkWxUUx6S6Jicm5Km4zwlCPrk/Xz0su6Ji33TqB0yEfZCs5wtQ31ZtgZhAKisrDEZpd4uPAG5aR9r1CmjyVSu1RkyPrmoN1aYRVcb4VJsyYLtD1D7Vi0wLogP+nSS3IrKkPLaFsPydOHYYaEu261TpfloYTKA92nHpIUWzaUJED2nD9VFkgPGYJE0dYQWHTMnBlIX09PMw4cOJjzZTq73AfdrwTqSRxnRtkKo39VeXthCeqtAHseWREPtapM2o+6LIlIffxTn47/894PxMTUrOup2N609DWvfWk881lPcv9PkDH1uLsuryT4O0HDFJT/YZcRpV/dO6plpS/FVnsKQHCwE5nqwCggyDX9jPneBfeM7fS7QnQKCnYQe+l7gJxTkzNx+uQ5G41IzVt6DHrMty4RlKX5xTh9x8mYGBuPifGLNrwH+vui0ag7DwxR8kkjX8awDgxd/aXxQgeVtwe9QHQgARjJeIgwkL2JpUC6JYiO5CA9eZog6TqHtGG4y6wUWcFoxiD38sPKN+fryIS23ZnGrMtQHmTPjv7EYMhbGuwyYJUn8JAoGdTkjSFPMMa5Nw0lnvKqVpMj3uICE9XborvRsGHNsCzmGm14lZSn66oL9rJJTwtlQRZST9aPQ3iiT0sWx/NlriqmDH1rnqTYhOElWlyc9+prSfbQdy4BzjULBjAHCRlJXBEcPHQStZU/vyg99c6RJXXqmuFs6R1JEovceJjw0KkU1RNvCdGrulQkAhLqsz5uE9I6b9WbNlM6yIR1pXDayMPSlD/X6JP7xcUF9yuu0Z+H5bGBrOrWJYLT29cX3ZAdhVfLfYOU1WJa93RiyqV8+lvKJtFpW9oVSXVO9a+rfVfio5/6ZPyff3h/TJro0F6dcfDgQRGdl8XTnwHRgVhly13p2Et/iwr2Dkq/KtgNbKffFaJTULCD2EvfA+QcvzgVn7v5Du6C1dEwoGEHGKIM4ZoenxDJGYu56emYFNFhqNHA4GA0urttzGKQVnXGOPbcGhmUNmpbe7bYkNcPngK8F/nGfk3ldcWiiBVDrNgbhqFExMHQxTjHuK3yZjgSeSNjpl/1NfvsVMBT4SFKKow0mLfVkLeWWWujmgMiZvIkmRl+RbJMI6j+zkvpvKiAZKFOGMILIjrI1Kh3ecgWCTH2RZVUlrLVNfM6AHOSgGXiUO650AJ71STxcTBlSD8Y7Ckb5IF8VAN01plGNfKSbkn6YcU65uMgF4e9GzLCka06TFqoOe1JBhTSAuW4vjqoR8qi9oTQEGYSkrqyZ0xhmRxPHzrJoYqQjfRIKYyn5COZ8SZ5rpSOpZXsBxCzebW1hy5SL8VdYhjfag4TpEzq4rpBbESE+0SqBweGotloevjezMy0ZaXv6KQ6tbfiolPdcrgP0tZqd+WX9UzS53O10IXqi2KSECu+bj7xyU/GP7zvAzE5PSuZ9Vxtcd31V8fr3/DKePRjHmH5tqjxikb13SkouJwo/apgN7CdfleITkHBDmIvfQ+Q8+bP3hJv+5P/brmbzUYcOnTYQ4NY4arB6mkyDmenpmJxdjZmdF5bW47enl6REt7+U880cjFSIQYYnDhWPF9CYRAmjEgVtlEmRi1GOMYqhi+72veozGZPj9NAkBguxht8v9VXnirEeUHEFnnbr3ieT+IVwNLyrDw9yKQb33sJZslJ8ZSNgb3OogqKZxKmeHhqyA8xqRJDzPDSMP+HFcuwhymf50y0Vw6WY2WJxRT0SDqSIC0juJWJy4dsQLSSKEBUIE4s+WxW0HqO/gggKUB/9j0oDcY4K9ShPRMdzspv/8hIDA8PZZyWYS/moKf0v1Zci6GcfC859FQFmiigZ+psIqP8eG49tA50zRlQD7w2EAT0UmfzT7U/+4OaCEoqz41RfstqO9IxT8dzatgDR2f6jZexRu8qzyQRPUGilL5LxLHZnX1geHhfDPYPWK+s3tdVa5j40XbkideR8jw3Sm0EklB3RL0zlzY3qeGjelkrOpMfHkQ8XptEB6VnHHTxiU9/Ot7/wQ/H9Myc+ghxO+IF3/Lc+NGX/2CM7t8nXaBv0nBc2UBO6l9QcDlR+lXBbmA7/a4QnYKCHcRe+h5gHH7ggx+N3/r1f+Xlg7tkvHc3u73aFsZqT6MRVx0+En09TS/rOz05KQI0G729xOmyMYwXAjMRrwhGL8YwRCT1IINW+aAODHEMU8q0p0PPMTbxzjhMBiVpuEZ/eHQwnPH4YAQTxjO8PsQjU29C6XJ0wtzWNW/9ZdsbPOcZ6exdIA8Ma8rgAylSVmwOibchPRHIDPlol1G9ZMLhNOSnsIogsVKZiYDSmuB0qO4qD9FyOJaTZNmk1z2bfDLcLg3xHHbH85QXosOqYSIgCkNfHnYluV1fqqwP1zXp78Txq+PYVUdEGJayHCqqcuzBoB7UhzDVwMQGciF9EobcJps6c511x3OjurWeM/QLkgcpYxUyvEXsZQTRMXMSFtQ2OcyMBQiUt3TP0DrqsLSWiz+sQnI4i75BSkjp/KWrZr07Bgb6YmhgKPr7+jfK6Wn2eIiYiSo1UJ3XVSYeJwLw1JAHdWF5bOqFV9BkSM+5r0gnxJtrwNmH9M8zPta7PuiWOTkf/9Sn4oMf+ajIN/mux/79I/ETb3xtPOUpTxC5w7NHTpnuSgd1zD5QUHD5UPpVwW5gO/2uEJ2Cgh3EXvoeYAy//70fil/+5f/Lw38gLbztZlUuVSPaZccfGBmNo0cOefL91MRETIxfiL6+Hnt9MED1a8NDQ92BDWeMUv2gCxMeGcGQGgJzZS+8QDlfA+MTWTyESuc0SNtiYZFVvZajVs8hWYBnDFfrUB4uYA0igr4hCiInNoAx+ImfRALjmDCu7b3QWa1kmaEwuUJXEiHkRX5fc25dkwknEx1dMFcE/oIINpj1Y314CW48IZSPjtMg59H6qsiDdIzxbY+DAsnfz7IKG0QNkgPZ81LJLojnkI+cH/Swhzw4rhHZWV5adP6WW2nTk4PsGYbHaFkEy94VyQXhy2FiuZACZATb3cWLe3k4GnNhRDQgrZAdt7HisRDD3PxcTE1PiWDoem5e15MmN8QdHBiInp4e1x+Z0TFEhzqgs86umvsNq7wNKe5Qb4vgiMjSJ0iHx6S9vWbZK7geOmhT2j+9Tak36kV9NvtQenrQQw5JTOJGa/Cc+kGoqiMJYM6zoj0+9olPxEc/+SkRnZxf9pCHPCh+5p+9MW540LUqI9sx29YXVzTK/+SCnUDpVwW7ge30u0J0Cgp2EHvpe4Dh9/73fzh+7Zd+V0ZfbgDJvBf2uoEnYCwzTO3ooUPR39sXs9PTcf7CuehpNqKnt6l6VqQmhy05QHlgdLKzPUa2bg2eQ3TQDwYmOkpCIENzdXM4EkY2Bq3vMaB1zzApPBV4DTBMc8I93pwOmZuUl2eM4Ry6hhgYvunZwAPBc4xjysSTgax62vLmpPchE8p4Zpln4igpcXnTj7zUhZxgBRjMEAsnUVnklkSHrDHWHS3LJ60+OtkZwhAq9GBPBREBD8ldeVGHdtctw1d0TzgC4OEZ6O2NJz7+CXGtiM6SdGqvGXWB6Kg860HXy3qGF2XJeksSSTuYTClrZIB40fYQFVYUM5HQQ+rPsEI2FoXcTE5OelUq8pudn/c5dZn5Iluf5GIFIZM4yJnyYehjP8PRRoZjeN9wDOr5QP9A1DqZD9al8vGc4YFhPhPkjEpn+fZoVQHKH9mRD7kzLu1L31s1QeeevCh6o67Kx31JpMdD9RROe3tYXFeSezxsnvulOHh0PnPzzTG3kOT9xm96YrzpTa+No8cOq0xlbFh7eXkFYy/9LSrYOyj9qmA3sJ1+V4hOQcEOYi99DzD63v++D8dv/MrvbEz+Zp5Dp4zGzk4Z1rLr2A1/BAN1YMBv9MfHL9owbnTXZWTnPAyMTDwEGIZY9x7iJYM8vQotcqG87EWQfWhj2m/89VExGPYYo6RBhpz0v+h4DJHzfprS6YZHiDfxMloZBlYNZeM5h41ufBRcY/FKHgx/LiFlHlam8jK+SIEiUD7hQKGOR1rLr2sMaeJRDuWTH0QEEkTYRn2QQx/KzjkzLbmUD3HQp71J5GPvBPGlA92iC2DdqTyMfGQg1IRN2UKMIH8M9XrWM54R111zwjpBd8wdYpgfHhR0g5eD5bpdD+qpw3NTlC8LF9TIn/KUZxt1lO4XlxecZmFxwUPBlkRAkW9ubi4uqt1ZRc9DFdGp0uAFQXauK/1X82NWlha9/MMjv+ERcfzY0egTuWGlvmazx0SV+qVHEGKXfQGQB7JTBsPmyJt+k0QonxOWSVNHLJqh6CmHmKT3XiLAcVv9TrXF81QtjgA5bFtP8oxO9CNyMx9f+OLt8cU774x56YG2ev7znxOve90rY3iU+TmbxJYWu9Lh/mhFFBRcPpR+VbAb2E6/K0SnoGAHsZe+BxiGH/rAR+K3f/VfRGdXLhWM/NjoDF0KmarMy+jvG/RwIwz7C2MXXD8mjTPHBaKDwc5mlBAI8kxiIQtTeSlyMKkfYx2jEtgLhOWOEQ6F0CXlYngTB8MdDTLhH5lY9QsygqFK2UmkVj0pnmF2euwyvcoW7gjlZXLCG32Vw8peJl4uj3I3V96yoVwRHYtLPpSOeHqmdK5TphRoX84QlyQ/be0Y1hjfEB7HsBGOHFVfSKKTZWHUUzZDpXiO/lx/Pa/ygATY8ySY6OhMOoz6wb6+ePYznx0Puv6GmJ6atscFYmJyIt0ByBI6g3jgRqJ8iA5eC0hR5e0wCVVc9G5PD40vGTjVRGYhGzMzMzE1O6P0rSF3kg+9OC5QQEWmrD/6ybkzMTo8Ek947GPjwOiB6FN/WZMceHCoD2Cfm8563W1Fo6SMOTcrkXHJG32kbrjf9NyRjtzsedOBTM6PfuBwdMq1+p/yJS88ZNapCBFeHutBYdOq4xduuy1O3XXW84/AC1/4/Hj1q3809o0M6q4QnYKC0q8KdgPb6XeF6BQU7CD20vcAw/qD7/tQ/Pav/a4Mf4y3dRGbnLDtIWMiGrxJ72n2Rk8vK6KtxbkLY37j3qt7jE8m7FNfVtWqSI5XMFMe6cmRic5zHR4+ZCMTsz2N0DURJbSVpCMNWJ2w9J0OfWJXQjSWlypixRv4nKuyvob5n0Ynz2y0ruScFJcvY5ahcey3gtHOkDNWdCNfe0PYmZ/cMdQruXSmTpVHyvXjXmkyjspvfWxQtzacTI/JpgFMPQh3HTDSlVa9Q0+4VzzVgfri3eCevDlTru8lf+YDuRPB4FpGfH9Pbzzjac+Ia669Ji5cuBBTIjuQF3t2pHvyWVpadDuhK+4hIZTLtctUuMuVzB66hZelg3krrHLGAgQ1tXmv239qasokAZnwZDHcjfZIKExcyl4y5U2ePDp918k4eGA0Dh8UyWn2xdDgkONAONMT1Koveam+lay0D8TPBE3wMD99KJMzciC/MnB6aI7bBh1ZZzlMkjYC9AHkYXil0xGGHOhB/TkJN2Er9mTdctutcevtt+s6F9R40Yu+JV75KhGd4QHdZxl7BciKXgoKLidKvyrYDWyn3xWiU1Cwg9hL3wMMxQ++74Px27/62x5WhFGeQ4/SEO1or+mcpKdeT8/J2Pi452h0d7Pkb7uN61URCTaklB3qPDmwHjEycxhSGojce4gb95Qvoxcjk1/ptUi9YXyTBk8O5CLf5pMfebeMdBYkkBFM/jZ4FRfDFc+Eh0XJMK6ITWdnXfLXbbxjAEPOpmcmvSdLytciGDaG0/it6qBAfiSzLm3j6hf3emzCRSyl2yQ0hOQ55cq6E1zt2+JEgDhb0lVxq5XWSF+Fw/bQkJQSvY3ueOhDHhIHDx8SCZlUXWZt3KPbasEBkxkV1CmC19YGiZFOpC90hjfLk/JN/nL/nY6O3HyTYWX1epdXPuuWzlS4hxGa9FoWBEe/yM91npDRcqIbnZdWFpSmPWoiLczHqfZEInZFONSzNq5zgYrWPB2FQWAAbSYVCSxiwb5BmZ79e+xBUnh6cHKfnNSDE+hJm+RIjw1erWzTBGkqeVkswW2/vhJfvOP2+NRnPuuVA+v6TrzwhRCdl8bw8KCi5kIZewUbfaeg4DKi9KuC3cB2+l0hOgUFO4i99D3A8Hvv/3lf/Oov/qqIi4xIjEkZlszPwTjMCfEYo+smOiwJfHFy0nM22N8EIxnPAfvedNaSkEBEIBusukb+JjZKj33IPfmiH58VnsOfKoJgU97P8y6N5jR6IQE8w3xteQT0zB4EpJQhz+RyliXGaOZg0QPiISdx2OxzenoqpqanY2Z2RjKkEbxJKJARIpVtCMmzV8WyqVRVAgPbZeOZgOjokQ1pI419ZEQe8q0O0iQxQm7dE5sCXe6mEZ5y6FqPMOjxZAB0gB710POmBvsHo7e31/H8FHkVl/k3lI3RDxn0fBgIHzrhGQRQOmIp8YrkEMeLAyhf4jAHqltGPnGXFhjCte57ZKOBqIOq6DpUOqB8z7XhRvEhqW1tK45rXwwdoBWfJaFXWQkOkioiQhqIkEmYDjVqLCzOe04YMtbrIkn6QGKoLr0jiRHt0WUPFHNzct4U85MqvWfPgfwpicMIwfME0bFuLTd9MYf03X7qZHz6szd76Bqb2L7oRS+IH6uIDjp2/YAVL1T3Vx6yL1VyFhRcHpR+VbAb2E6/K0SnoGAHsZe+Bxh+f/u3/yve8ONvjKYM2U4bjmlsehhSy2vStt4R/f1DIjeNmJmfjemZKRuVeAYYjra8vLBBcjAYOUdbLhEM0cET0+bhXRCLNOjtOdEZVUEo0FsFe3ww6mVVc80lz+2RQCaMch25RHB6ayqDGC+OvUsyQJGDfV4gZnhvWDmM+SxJxO5OLhJ5ToObYVQ1kwlWEqOs/v6+OHr0mM+kqQ7y2jzSyNalnoU9XizDDBkiX4b14VGiLMIzLnWlPdgXhh35MyyJThKEJFPUiTIi5mbnRAQW0ysjQx+jHf1g/KObrEMu1sCGq4R3ORwiqPDWmfzRHfEgOBAIt7mEh8wtihyC3p4exzWtNLnM+qIy31NZgD4k//JqLgDgNDp4jEfLOlBdSJokWPqSTiA/ylHxRGj0HBJEu4GBgQGlSy8fni3L1tLDukgReVMG/UC18TX50sfQDSA/t7uu76Zz+qb1ysp/C3HXmbPxxTvujBmVjawvfOE3x6te/bIYHoHoOInLp26JbJ8rEVXfLCi4nCj9qmA3sJ1+V4hOQcEOYi99DzD43vWud8cP//BLN4iO+IuNZgzU3HCT5aAbMTqyX0Z/n4mCiY6f53A0WZQiIzkszcO4nHuSJbw2zH+AAOjKZSph65nSKz5v++094dkWkH81BA0jloUH8GZAaDgqo7zy3HAsLrLa2KKXRZ6dnbEHB08OnidkpQy8SJA06pBGfpff3vf19XmfF8433HBDjIwMx4ED++PoVcdUbnqJhkdGZPQ3XRbNnB4pQTfUmzv8UvY8KQ7Ehv2A0lPE6nTMU8kFCZATIkcKfrhmuWglbN3ncsoAXfmscDwb73nPe+Iv/vs7kqBILuKhi9RJzTpl3g3kBQJEnpAb9uYhHqvZsReQ9ac0JjmqI/nQfz3HB1K2sOA4PdIPZIP2YBihqmfiQoWte32QLT15rcUNaFs9owaoCeJMvagev/TPiEwch/6AzBAPyqV8FlcAkEUO2oo8iEedaEt6VQXkxtvjVesUh/jI3uztcXq3PWVVcXXh/su1ZF5TPz134UJMTs/ExYnJmJ6aiuc//7nx6tf+aIyO7lMqBG/JX4hOwdcpSr8q2A1sp98VolNQsIPYS98DDL93vvNd8YM/+EOe2I8xivz6aRm0zAuRgVyrx/DwqAhAv9/wQx54TjWZn4N5ybLGEB4y4e34qogORisGIXYhc20UkIY96VQ2euLIMvOoiE16a5KAQHC6daRRn16HyiDH6OdIYjPnA+8AG1qurbFQAsOc0rgnXY9ISv9Af3T3dMehg4dEZA44/Pjx43Hdddel96beFQP9fTb8Me6ZJyJxdU3d0I0t3Q3ZL0UVVD33kDMqLaPYJGEjXRrbXOaZi4yZ4Crbg+eMaKtC3/N3/zv+xe+9WVe5L1GuiMaQNJE+yVmRGaW2rqr649XB48NwNUiPw5w+vTgs4gBhtQdE53kRW0gI+mB4GbK7o1Cy4pM3bUYdOegzkIdMn6u7ub5KZu+UPTI6696E00HKQ3lt5McZeaiofkEO6Tcsbz01Oek4kPDF5Vx8ITcZbQ23tCwQSYhiykq+zNtBDurp4X061DVM6l0biLjSsVdQvbsZY+MT8cXbbo+nPOXJ8cY3vjoOHhpFGOVFZNBqjEJ0Cr7OUPpVwW5gO/2uEJ2Cgh3EXvoeQHT+6l3vjpe+9GW5uACyY4Ei/kY9cghXf+9A9PcP2DszNTXhDSOrSft82hiypYO3/Mur7HcDESK9M/M1cbnfVA9GMvmnV8ZehrqIjchNU0d3s2mShVGOXjFkMZwxoiEz1ZLKeEbSoGblM7w/XTbKh/YNmCBBXo5edTQGBwdiZGQ0Tpw43nq+z2HMSWpXGZaltajCvbUhclyKe2tzwsgzr4mHse07h4EMz2vyQUWXlpFxNsuAvHzg/R+Kf/kvfl+6WIlGfXN+C14MDs+1EYlJMkPd0ktnoqNwSBHLO3dwTXk68MBUusQLNjs7HePj4x5CNjQ0IH0Nql2a9v6YrICWTGnwi4BwqTCWsc62Ya4NC1BAJFqT+SlP/QR5WSRBN5aNZ5BIzjzDI0W9iW7ig4zqc9TP3iZW8VvNcuhbEDPKg3ARZ2mJ4XPorTU3jGpKRgjQiupKHPJ1v6EM5c9y3VefuCamRZg//OEPxzd+4yPiTT/543HimqPOtyK5SXSoe86huhKxtc8UFFwulH5VsBvYTr8rRKegYAexl74HGIfvfve74xUvfbk9JxXJoQ5+rnpg0PL2nFW4BgcGY1UflhvGuKwm82MoYpxyzdt0CBAfskEVG+pIS9P522MjEtPdaEa9gfemkd6b7nowqT4XBGj33jlMSodgzYncMASpMjXxtuDt6enp8dHX1ysScyIOHjwYBw/sj2PHj5kEsHrWwEC/h7957k0tjWaJba8CSDlT5qr+CmmdE/k8PQSXomrze3oG8nHlBdiMV6W7L3mnoc0E+oiPfuQT8fv/9x/FzPSM57AkeRHR0YFuOeM9YeUzNoBlIYmcuJ/eDEQxucDQV76Q1SUTBZFHkZ3x8bEYG7sQF84zlGvc+h0dHbVue3t6RRKSVC0uLJl40o7NZi/Smu6sSmbai6Wv8bLlfJyso6ujX5At2hWyY9IjeQgnDnkzH6vy9OClAvbAdOZKbp5/pDrS/zLf9PBxhuTMzc2qT6pmK6s5N0vEyCRH/VZXYjaUh14lg/JYlLwzs7PxhCd9E8wq/uEf3huDIsu/+Iv/NB7xyIepDL4LFkOgLTlY6OLKRKWTgoLLidKvCnYD2+l3hegUFOwg9tL3wETnXe+OH/3hl9mLgsGI4bdp/GN0Yra2i4Q0Y3BoQAZnR0zPYLzOysBtbRKqRLytZ3ln6p/gmjP6SK8NniHPt8F7IwICqWHpZwxeAHFaFlliXovJE8siY9i2rZus4H3pkZG9b9+g589AXgYGh+JBNzzIZAlvw/DIUO7x0/IMVKjkommyfdJTUCGbjF9paN8bvtyzywcM6E0gb9UWKXdb3PL52+L/++/fGmfuOht9/f32yiDa1mFptAu6w+cAkdwYoibdoGOGFkIcSQjJmV+AmEzGxYlxbwx78eJYTExOxJLIDwSj0d0wmYToMI9pqH8o2kWkkGtQZIu9cuoqhzLQJIstMKkfMgbpsBdQ9VhbU59Qf6EveAlxYuuHxRcY0ggBM/GRXLRhLn+d5Be4niJ2eK28HLpkq3V0uV7kSZ+hnFxOXJHxBHKI6NBvx1WnheWFWFxOD5b77tJCTKvuLCv9zGc9x6Twve//gInOz//8T8bDv+GhzgvdU362EbUsHp2Cry+UflWwG9hOvytEp6BgB7GXvgcQHebovPQHX2rPCOQCydOcTAO7ZVqKSOQQsM6uTr+px3Dl7T9peEvOYU/OehqC0oKM0xwShiGKAboxZEoGKnrCiMV7oxQ4H/ycBQ8gMAwtGx4ejmPHrnIYBvbVV1/leUIQmd6+Hi8cQEneALTFaSr9W3YEaYVVyOFzvMwnbDP8ysLdiQ7I+iTRAWfuOhdve+vb43M3f97zjuxNE3FhFTaoKbqHzCSRWY9mb9MEyEstK69FhbN0NEtts6Hq9Oy025WhalPTUyYAS6u5GSz6oz2BCaTuOjtqLgsvWbPZE/0iW/1qm33qIz1ql5rCaU8lj/n5xZhfnPdCCww3W/KQMz3QD9c8mxchwjPEc4a5AQgPMnfU0ntEnehLbG7bzZBG5l5BgiSf5295cYUkP3iqIEIe+qg4ED8P1dOxLEINKWKPHfYIgmAtzM3E5MSEh8Q9+KEPi9m5hXj/Bz8Yj37MI+ONb3xNHD5yUH0n+3SiaqPq/spD9V0oKLicKP2qYDewnX5XiE5BwQ5iL30PMPrf8ZfvjB/4vn8iIsOGjsgtQ1ryUwdoDmfqhFE7IGOWeCsygKv5MTawlcwkqVVt4mMQY+hCZjA+bXDK8MQzA4mBrECc8BT19/XFyOhI7N9/II5ffSyOHD1i45m5JSwcgJGab+tzDkerFB1ZoA3jdTwLKQdlVm2ALPeEDL7nZ7uPeyY6KS9Esi3On78Y/+k//km87/3vNeFbWVoWcVnMuqtdpSkPF4M8rMWqF1YAMzOzHtbFXBbOszLwGe61tMzGr4pJe0uvXga8xas8fKyld67RO4TWDQ6xVUiS2A4T4rr6Sq9k2rdvn9pv0ESDfgKYu4OnB7kYEgm4pi8hB+WyIAIEzfLg5RExyf6l8lRYh2RBDvpFenpYYS73CoJ4MUeL4XAe3tbZMOHhHtLE/k8dba35S1yLHIH2dtVb+UOQCDt5+q64886T8R3f+aL4gR94ierR4zpv9r9CdAq+PlH6VcFuYDv9rhCdgoIdxF76HpjovOOv4od+4Ee8GAFG5IZBiSHbqgumLEZiXXHYOFKB9ghgLOeqV+mdISaGInuuNGRsMueGyf9DQ/v8xr2vvy8OHzpkT83w8FAcPnLERmmjqXgKa3azSSVensqY5LypS8RKMoahnWHAMm7xdgCJ1Dpvhm2i8vi0bg1uLo2bZV0+3Fej+J6IDr+zPdD12Nh4/OEf/rt4+3/9U7UNyzKv2hsiluPIa8trscKqZK3hYOGFENZjaSU9OsxTsc6I39InRaB7SAFkifj5UfiGsnRWG9AMqfN85ueKT1+hvDbJ4yFlIhuQ4271CfLJoWnsyzQQAyJB9AuHKz5Ehz65LBlX2IhWhMd76qivMUwS4kYclp72nB8RIfqr+6qF1IH89B/JY4+QylKI5fMwN4gMezG1swS3nkCCWnX2XKZW+NT0XBw8eCD++T//6Xjikx+jdNRxs76F6BR8vaL0q4LdwHb6XSE6BQU7iL30PcCo/MAHPhy/9iu/HYvLC/rjsOa5M8ylYNUt6qIabdSJpZ0x8HgDjweB5X0xNPH2ME9kdHR/9PT1xDUnrhVxGfKqaYcPHYxDOjBwu7t7PCyNFdXEm2RgygilCEqpLlpIFW6SkUqll94DwjafI+vdwypsps0H1X3ibjctXJLB14x7KuOe8KXlZp2yHTjPzMzFf/wPb41/8Xv/Mpf75pnjtdKu4ZUR8aFIPfOKeITrFwY7dYcoYOATbg+O0KI1ep7kJ7GmvqKTImb57fCJDOCHzFrAa8cCAV7ogHwgD2psNl5lqWo2ex0V+WVRA/ZEsizKzIRKhKXy3iAP/ZN+4z19ID4iPBCcDW+iSI+hdE5vMSS0fpCJfPlU9+yd5KFwIjudbSLuPSxZvrmXkEmQDi/4IOkf+7jHxD/+vu+OI0cObujV+bq+lXI2636lATk3+kNBwWVC6VcFu4Ht9LtCdAoKdhB77XswMT4Vn/n05+L82HnZbmueZM78jMnJKdeFt+CcvWSvjECGFTGvwUaovQK5Alav0h0/frVJzP4DB3TPULg0CjkqbF5j2KbRrtAMKrhPQG8Qj49/7JPxh3/wR/Gxj3/cdnfOk6q8HOk9YT21jF95ILINULtXQcODprBqaFnVPjb4RUKq4YZM7IcE402iH3DGe5d54UXBo7ce+0V2WZ2tIhDV/BiGKDJUTtG8oAT5Vd8T4mUeHe5TFSSd4+CZIpwDmSE8VX1S3qoem9895NFDE66NctSXITSUw9A15nt5qB3eR4a7eWgkHhqIXFsMD++L4ZF9km9LHpJ1rwDdVHIXFFwulH5VsBvYTr8rRKegYAex174HiJpv0FNmhvFgQ1aGpN/ct+qEIckPjxTiD2HM0cE+TFJEKv8SksxUQDeb+uE6wwu2B7eFlLewsByfv/nzcfPNn/MQL+sVnbZ0jlfEpOeSdgC+a+WDV6Xy+HAPTFJEcliKmqWlG/V69LCaXYv8VHEASfDa0GcGhwZjcHDQRMGeH0WBnNCvyJm+whXybMjgTibotK5+SH7ZT1pnP0widrdqEK91yVUle6WfTK97cmilq7xReH+Qwylav1rJN5Blpe6qvJMI7Q2kDrYqrKDga0fpVwW7ge30u0J0Cgp2EHvte4CsLBGdS/I6xOHUowJx8v5LjUHAc9JiUGI6kkO759KATIARTB6VobiZZ8F2sdG/0Ll4jEmOtZ7txuE4cJyWiqvWSHDHgzxX8d0crYjiA/xWOIs7JIH1c4duXNwDvrSPIMqlYVtR9R+wVRbCcvJ/66ELrK7B1muefZlCLoH3ezIJ3JS36o+Vfr3ggrAp02b/3Quo5C4ouJwo/apgN7CdfleITkHBDmIvfg88hEk23T0Nb6Iq1Cfv89iKJDBcbdY741R5EV6FbeLS+4L7jkrPVbtU2AmdZllVvlnu1rYlbLN9M261xDhIGbnK/rGVKGztb/dUlyrtZh6byPC83kxLuXcnIjwDVd7cmry1sSy6yle48yIOH13oia/X1zeHqVVlVMdeAHJW9S8ouFwo/apgN7CdfleITkHBDqJ8DwoKCq4ElL9FBTuB0q8KdgPb6Xd7x+9eUFBQUFBQUFBQUFBwH1GITkFBQUFBQUFBQUHBAw6F6BQUFBQUFBQUFBQUPOBQiE5BQUFBQUFBQUFBwQMOhegUFBQUFBQUFBQUFDzgUIhOQUFBQUFBQUFBQcEDDoXoFBQUFBQUFBQUFBQ84FCITkFBQUFBQUFBQUHBAw6F6BQUFBQUFBQUFBQUPOBQiE5BQUFBQUFBQUFBwQMObetC6/qyoq2trXVVUFBQUFBQUFBQUFBweXBf6cuOEp0dyrqgYM9g730PkPXeXlJ8uWf3hq8mTcEDDXwHrpyXX5f2yeqec4X7Iuve6tvlf3LBTqD0q4LdwHb6XRm6VlBQYKyvr8XaWv7h4O8H14Rl+Gqsr63pgY7IMP7IcFTx0vDL52vE1T3njLM1fuYPqvPlRSXHjmS+S6jqdG9IvVdx0HOF6npT51xUbaZ2IY3b04+/LIjzZeM564xAf6FvVH3qysGl5KS651wd9wzXn/5OJ+Z4QPWxgoKCggceikenoGAH8YD8HlCde7cF7wYbhcSVDtAD+gCtk8C7FjLk2eV871Lp/J4EvbQ97mNldh1fTvFJcMCl3W1D7/4hPW2hqzbOGQds1f/Wtrq3vKvHhHHtPPVxf/c9kduivb0tJien4wMf+GCcOnVK/3QI69CjdhNgxcx84A5c64c0Ll9hq4pDUf4uZUynT/kkp+IqQOf2aFcYoWsqm2vitSvcUOJ6Vy1qtfZYXFqJ1dXV6NCzRnc9uhvN6B/oj96e3uhUnLm56Th1+nRMT065HuR53fXXx8Me/pDo7Oww0YEgUqZKyfyvcFh/bpOCgsuH0q8KdgPb6XeF6BQU7CD2yvcAGRcXlmNxcTmWl1dk6M3aCOXAUFQ1bOz19w/E4GCfjdLJ6emYkiG4srKkYy06ZAC2dxA3Dc4D+w9EvbszC9gCyuIgT1ufyhmjO0mPA1u4J71Vz6tnW+NvQWbburg0n0vT3EseewqtOrq6+qUqcWnioY/Nf3ScgToTYSNSHhVa187G15tkpMLms62gr989I+IsLi7FX/7FX8Uf/eG/jS/c+gUTnTYIiB6uqSPRX0hFn6AkMt9KTtZESCgd8kO5ft7Z6TSQHKdX/Hb6n4mH0ohgU/NarRadOiyVfvU0GorTEUtLi47Tof7aUFhPb090d3dHU4Sn3qjH7MJMjF24ELMzMxQoGdbi+DUn4rtf8pJ4ylOerDRdSr/qvJzxHkD5n1ywEyj9qmA3UIhOQcEVgr3yPVhaWoq/f8/74qMf+YTfdE+LxBC2LBLToTp0yrDkOHDgQBw+clAp2uLixXEZg+djfmFeaTCnIS8d0Snjr1brjP0iOkNDA9FoNuLIkSNx4OD+qNdrMTy8Lwu9GyodbTUaMXu536o/SFLGkVjCvbxNJ8lGVpfqf2sZFe6tjWg/yqqe31PaKwiIyXEFOBkWF1dEhCfittvujHe8453x0Y9+NMbHxmNufkZEeiYW5hdiYXFR8iahxnODfmlXkxed6VM0AFUygRaR2eolrNqDuPbyQIbwtugRT4ifnCnjdXRAvOlDSbDadSiR42Q7pweKnO1V0ifLVFoRpuc893nxuh9/dVx97Kj6/LKe4Vm6ApR9H1D+JxfsBEq/KtgNbKffFaJTULCD2Cvfg7m5uXjzv3pL/Je3/beoidCsrq7Fysqy31pTh1qtwx6bZrPpgzfhGHp4gfI9fL5Zr3XWbGxiifKGvFbriprIzdDQvhgYHIj+vr647rpro6urK3p6euKqq46I+Azruhm1Lt6O3x2puq36q67TcK2AjNvC1iydlICtgQnKv3vWadTSptsuc4ewtXtVIp0/ezE+85nPmEx0qk1cN7VPl4z1er3umg4N9nnoFsCjUe9u+JphX5DMjg7aAxKgTNWe0A23sfLAw7GystLqH0k+FhYXRGpuizNnz8SiSPKZ02difHxc8inu8orzXllZjZWl+Zifn7XXcHp2Rn1oIeZm5kyYF5XH3Py841HGqvJdXV+1vpFK0ujX3fWe3681e4hSzs2hbv7+2bvTZjK13mI0hKc3hrTEpg9DrpRal9SJ0Hbdk3ZdBL7eaMZDH/7weP1PvD5ufPITyNHx9Esxr3xYF1s7S0HBZUDpVwW7ge30u0J0Cgp2EHvlezAzMxu/8su/Hn/4h/82uutp8Ep0/co6bL4VZ0YCxp+uOmQC2nDkJ4cbYQR3dGBY4wXqiK7OrmiXwdwuY3tjmFBPr41tjOt9g0MiOiNx5MjhGB4Zdh59fT1x+PChGN0/qusmBW+gUuVWlWLY2sht3VWXxEn9c4fRSxg3rQg6VanS0N1E1WakvzusiCsCVV04ZfsoTNU4ffJc/OVfvjM+e9NNsbS8bE9cpQdq01nrFMFpi2a9SwHSnh7UdE17MkxsWQRmWSSX/PHUdbTXnAeePvKB6EKCp6dFUhYXY1llcF4QQRm7OGZvICSYcEhvb28zRvaJ6PYPeD5MXSS33ln3kDJzKBERiBD5k+/M7JzJz/zifMyKDM3MqBy8i5Sj86rODHlbaXmBPM8HvkFHBG5XPomq3l4UAUWZyLQ8NXqYqZjTw5yhJG4dIjaE6R+kn6qHRb3ZGwcPHYlXvfrH4kUv+haR9Y58DtnZA8g+kPUpKLhcKP2qYDewnX5XiE5BwQ5ir3wPMCZ//md/Md7yln8jA05ERwZwzXMeNsxF1WPNRin14W02cyKYa+Hqra8qTQ4FggrZqyN08NZcBEcPTUY6ZGS3y4jEW1DrktFbYyJ4I7q78RT12QvUK6LD8Lb9+/fHoUMHTXyYJP6whz9M4UPR0VnJlKB8ZZ1ySC50nvJu6r265FE+zzxcF19uNVZJm1cZtwKBW+93H8jPUD48cu9/3/vjps/cHLffdnvcpmNyctKEAALBcC5Igb10Ii9JMFb1DK+MnimfVd1T8WVIisgO9xlXxEjttaFPdEJ85eu0rTNemNRRRqHpIbudtY7obXbba9foVlt3NaOnu0ft3i2y1TDxajTq0ejqdjn0HYY+ZlbrnuOT+S/HvMgU82sWl5ZjjqFvuocQLawtmdThZdqQWzJCbiAvrUa2rO6juidP6oRnB/6zihdJYe7HamdSJJFpk34UJnI2ODQSP/TDPxTf+70vjv7+u5PwKx175W9Rwd5C6VcFu4Ht9LtCdAoKdhB75XsA0fnZf/5z8ZZ//RZPxsbg7OyEuGCxpmG7vppvz9dFajAOmbOANZjLFLfeoFNfbreAF95KxSt1/VYsGc4QqHYZ6Dxk3gRzeyBFnbUue3souy4juK+v1x4EhrkdOnQ4Bgb644YbboiDB/d73s/BgweVvk1xu0ScvnThAwxlG6+ta9rD8AlpaB9uMH4Joz6b7bUR/wrG3Nx8/NIv/kr8zd/8rcjNhIx5DP5V1SzJDdXBYEcL2VY6492gTSECtA2aUFUV4ud4edAb9bcnpAW8ctzTL/DUpa4yfWaNvvCWKKw1r4nhYElgRHyUvqvWsKePIZKNTrxFNd83RXLpe7R3Q9eNri57FyFCDIlk6CTl44lBJNpuFWKj8lfb1mJJRMieIR14pJB/BfJDmOKtiNRBxdZ0jWQQnnVkTDGtK3SSw9ZaBEl9ngU2VtVnp2YWYmp2IZ73vOfFq171MvW//Zlwj6D8Ty7YCZR+VbAb2E6/K0SnoGAHsVe+B0l0fjbe8vu/b69KEp0c8oQtqJPqUREd3tRjeHbaUFyTAQlRwPjFUKa+VZVJBwjLuR+JJBgZN3Ukg5pyZMRizDLXwmUr31pXzcSoVoMA1UR2Bkx8WBgBr09/f28cPnwkTpw4YaO1t7cvrrn2eAyPDGEKt4zsrR4bsCmLLy3DZhj1xKDeikvbkvsvh+3E3YpL022934oqzw99+MPx6le+Nj73uVusu7X1ZT3jSctox6BX2+HVoU6kM3nRDxrOSzwbqXPaqcqbUxIezklYkIe4iEW+qSfdiyeQCr5T5UVclw2ZdJuSJuVyKmRQYKfy7SBMZw9ta4jgdLbFQG9P9Pb1uS9ChBgaWRMp6hAR7hIp7hIpNgHSc841kWZ7bCDSrbqyOMamDvFEIQ91oK+pb1hO1dGEG9kIU8w1hu+xmqAIfWc9bvrsF+MjH/9UPOnJT4rXv/5VcfXVh1p57g1U7VFQcDlR+lXBbmA7/a4QnYKCHcRe+R7cnej02LDk4N02xigWLMYwE8S56mrUZZTmvA3Pa8ColHHpiDKIVW3FctQvqX+Spa3GMs8hQgrXB51lPHJr5akjs8GAxWDOt/vI2Gh0R1/vgM94gyA6/SJD/X09HgJ14pqr4xGPeKTq1aX7DpGkvjh06IDi9aZBXBWxAd7mK8hy3F32S1HJWuHLtfWlce8J95ae8K19qbrGWP/Pb/2T+Pmf/4UYG5uwTjDWt8bTL7Tne665RM/r67lAACBeygf9qNqgklf3KidXMyMlQXnGI1QRHSuNK6WFQJCXSZMUjLcuVyiTvt1uEOOIWntn9Iq0jo6Oev4WRIY8WZhgbPxijF28EPOzsyZAPU2WgMbDw144eTREdjzXR3kz1BLvjwmQwjrVP7vUP7jPMmtRJ677UJKc6oAckdZ75Og5Q/nUCxQPotOpenTGRz95c3zikzfFNz31KfHa1708jl3F6oN7B+i+6hcFBZcLpV8V7Aa20+8K0Sko2EHsle8BROfnfvbn4vf/9b+ORnfTGyvylh2Sg6eGN/AYvLwNxzBsyujEtl1czmFAnSYfGNnYu0liiACp0Y11UOkh50zoQrfoh2FWzBUhzMXoAmM3zXPmjuQQqkqNeA42dUq55IMhToYYtBivOa/ERKje7bkhXfUuG9P9/f1x8MCh6OtnThDEqD9OHL8mjl51SEZxPa659moZ1XXnDVFKmRJrrADGhcr3sD6XvxkhvR9ZOU4pZsoKObsU1CPj5/VmvRJb+08V17c6kwo95yISf+QJ/NQZcaq8TFCkMAx7twWJnZ5LkVQaGF0rEXOneOycnX/qv/KQ8IEwkJ7FDNAVopM3cnV2dplIkJzV9hh6SF9ABuu+i7lYDE/TM5FO5l01mZ/VpTYQ2Umi0el2ZLjZwvJ8TIxPxF1n74o77rg9zp49G1NTU54b1mVSJIJCfe2BkXTIhUeHdheB4nlnB/04vYQMf5MoBuXUVS7emiQ7qj91sv6UVytPhuC1Ka/F5Yjbbj8dZ8+PxXOf+5z4sVf/aBw9OpqZ7RFs7UsFBZcLpV8V7Aa20+8K0Sko2EHsle8BROcXf/4X4t+85S0mMRj4EB1gA9cX1GXNixUwnAjSw2pbAMMRgxFQX+rNAZY94R3DO9/4Yyiv6rryFK2s5IT4yitEent9lMZzRRR3qw7J1kOvMMNbwTbihSpelk1E8tMlK8RlLexd8Opwkjk9A13RrTr3NLs9P+TEiatjcGDIiyBce+01sf/Afnt/9u8fjmPHjjoPQL6tKu4axkUEXv6jr4z3/N3fuc4sF42eK08K+sCAh1xaDXrOimKE+ZCBb5KDTjq6fIa4MFwQjZFnemyS0Nh7oox8Lf0BEwXrFKJTN6lpiiw3pEvi0JfwtJmctMgG86rQPZ4XhqwlUU5lInP2gSTEK8tLMTs7G2fPnYvTd52OO0+dipOnTrpfQZhMSiQDRM2El3apZNfhPZDcVgzLW7HsxEMX7SIx6QVLXThxVH0TzxQrCXarrzRianohFhaW4hnPfHq87nWviKuuPmSd7nIXuM+gjlu/RwUFlwOlXxXsBrbT7wrRKSjYQeyV7wFE55d/8RfjD//NH9hIZblh5jsADDnqUQGPCPNklpZXYn5+wfWrvCdVfas6cw8JqQ4Mz5wgL0PcMWTIrq76wHugBA7bSGNTMo1f7nXhOCZB+vB445lQnYFFWE95CHeZrfwzGwxcIimMIUoYwBjwXk65y3v9NJu91sfg4GAM7dsXR6864vlBD37wg+KRj3iE4shwx1BWLhXRg7gByqBcyrQeCNBBibLrW3GQjZDKuE85vY+RDHm8Eiz9PTM946WbWRmsW+WP65r9Zi5cuBB/9v//szh/9qzLJ1uGg3VKLi8ZrXItl/JkXouHkCkMUoDBbxKj4onnVfR0BtXKZNaZ4rBpLPVkfgwEmLa2frxaXhIaSA6EpsqXY4X0ys+ERnHcT1QnVs6D4CBPiNBAaipdZf1bc6vUPix5je6qZaxn5+firnNn4vOfvyVOifiwsa0iKr1OyEp9GaDmOrTytNZpafpqJ+owkJG+xPweByFvixjxi35JPs3uAclAnPZ49rOfFa981Y/EVWXoWkFB6VcFu4Lt9LtCdAoKdhB75XsA0fnVX/rl+Hd/+Ef2XjDUC2PXngE9t3cAg1CG6aHDh2JQRGdmdl7H7IZRC6hvBerNPSEYqZXxWj3jwLiFGJj8EN5KXj2vDuJw3gruPUcorVJfO77vdF7Vlb1B3CWqXfOJtyErxvTaShIRgeWUMZSZQJ9v/mt5lqHv4VU6mAfUp6Mp0lH3ssg5Ob5dcZfYW2ZlsUXoyDHr4OF50LNWOZYVrwVnyQm54ZBAKk8xWxtlpo5yFbFKZvTB/fLyUvSzMp0IB2QCcspK386bqDqoMyTHHhzVg2eeL9P65I+eK2+GfZmMiJTk0tAM/WOVNMhfDi2jnh7Sx0ft3kmeKdZGHMpJ8qC2k25z81i8RMimeqAHyeXCTfRYIQ79JCGkLdXjNurMmYN6Qz7w5swvLtjLc+rU6Th39nxcnJxw3vRRaxhdQVp8zhMeHIAOyBc9kG+SKj8yIKLcrrhN1r1ZaEdHt9JFPO95z41Xe+jafpezJdkVDepLvQsKLidKvyrYDWyn3xWiU1Cwg9gr34NZEZ1f++Vfjf/47/+9N/HEwAU5nAyPBZ6Y1Wh01ePYVVfZqzOpNLMiOxjExKGeGH3U2R4ajFOF5XCqlvGqZxUp4p4D45V063rGJHBuiL++AlFIA5d8OFdlKCMTHBv8ioPFWT2vznxMYFrkwfLwjLL0HOPaJ5Mawqs4CpHhS1yny0guz3ISaiFy3keXdFURFLwFWQaGOvlJCmTCuM9Eea2zc23lDZDXoS7AQUrTKpPsLSePkTvlWpf+jh4+HMeuPm5C0rGenpiNj9qG8iAZ3uW/VRxtlsPUcsgZnimvfCbChucFUkTB6KEaqpb5bg5V44b6d+oZQ9rcT1ppqANZMERxUWQs92RKPfMQPUMg0KqHqCl4vY12Vp9wG3DouR6tKA09JvtA7vPDGY8XXkX2z1lYWIjz58/HnXfeGRMTE7GkfmEhqLOEom95ThVFGpKBewEdMOeo8jYSByJH1GXl39FZj0ajV/XuUn9bjac+9anxhp94VRw/fsRxyIW2qNr3SoX1sKW/FRRcDpR+VbAb2E6/K0SnoGAHsVe+B8yB+K1f/41463/4/8XwvqF8+y7ZMV5BRTjwZBw/drW9PhOTkzE7P2tjl7gY+7zFx/uB9wIjX8EyBqUDnkNoCBCSROS5OmRxWlcZRUatDGHS8Mwa1BkChcGbRjNGr8pRuRjzNn5lNEOeMnoV3spfctioVjrSmtyYMBBbZ6Xns1GefimlLvCsZP5+xkPLCAHMvKgXBIDhXJAvZMh8KY4J/WRHgA7FxeORz/J5Sy3O133G8mYGbgceeOiUZFJ9OZMDMh8XyXnc454YPY2GHrJUc6dJC3oCSTaVg/NGzS0yoxvkMIkhL+eaugWIyDXD3PDSQIKID7GhX5De7elilFqXFRlxWQrNxRvUjm6DJB/kyRA8SMPG/CHloZS+dhu32itE3Lz/jcKtewWRzm29tOL2Z/8ciI49aSJVbJ7KfjyQK/bRqfoccdh8dG5+zjKz1w4FW04OZFQ784wki8ur3pS01tWMZs+AwpLEPuUp3xQ//vpXxTXXHHVcZ7AH4H7V6hMFBZcLpV8V7Aa20+8K0Sko2EHsle8BROf3fud347/9ydti/8ioiQ7GNnMsPD9DxikGPKtjXXPiGs9TuTgxbuORt/uKYqJj41fGa3p0ZNTKONYv6UDPZayiD4zDNGSTJPjAyEQQP2+FEaNFGrzcL3FaB8+QCcOYt/cYzmAFjwBEYEseaXxneeTl9JKN/LzClqxn0mzm7ZLzvmWoU47j6EA+Fe7niLu2ql/6YclkVvnCqMcYJxr11SOdbcbrOsmB9QCUnQmJWUsuFmAVpDYE6Z4cSN+Smeg5rE59a2UtvuERj4jnPuu53lzTdroIIs9oL+I7Y37roWVRm1gGC4hsSXQol0UCcuiX8m7LeUsmZTrsjSGN5HBkLrlGWvTSjtRJatCZsnLdSLfqdsk2wqvkeqgM5GDImvuL4ln/SkM6Vl5DYsgMZIdnzMdZwdOncpe5VjrqSTh9kRXWrrnmRFx91VXR7JY+lGflyXF+5O0+sGpvjfeBgnQpDJK+rGvyXdAxOz8vMj8Tp+46H5NTIkcsdqDP8ePH46Uv+8F46tOfHD1NkUuUtwfg/kL7FRRcRpR+VbAb2E6/K0SnoGAHsVe+B7wF/zf/91viz//sv8fB/aP2TtjwlBHHW/dOGXkYk92N7rjm+Amf2YV/YWnRdh5VtCGs+lJnv6WXMWqPkIzkHBKUesjnkAje7pM4dcQwJ6KQn58rDwxU8rFhKmM0jeXMCxllqvqtvT0GCiOezWxFocgVZKANdJNeAaVXWHp+ctnlNurpvClfhwQgbb7xV/1lPCOPCZcOrgmnPPMRxUcWLjwcywqRZMqLsu3BUBqukdtDpZTnRpiojAmJrj2/JZXi/PI5j1T2Ws5zovz19XYRiQ7JvR4Pe8jD4rnPfHY0G818pvohQ5IyEqODJCpkV+VJKYQrIhpDNAVkHJMqlIg+XCG1Gfrh0hGJgxzSeeva6SCGreeQCASgbm4XDgU521abo0PaZRlSRjrCRHyoJ0PSlpZVb+XBHKWK0LAgwepKLk6A5xAdQFbYJ+mG668T0bkmGvUuE0SG65k86kP+1J0NScmPMj00T3Jz3VnrcrskaWuLJZ0nJ2fj/R/8WHzqps9FR1cd8ewpuvbaE/Hd3/Xd8dxvflo0e0R29gCoe9U2BQWXC6VfFewGttPvCtEpKNhB7JXvwfz8fPzHf/8f4q//6n/E6PBIyi0DUCcbn3h1lhaWvffJNceP21g00VmYd3riE5m6Ylhi+GKY2jBWOEa936wLxDFh0Tnnc2BUp8G8YcjrmvgY3NUbeJMTLGU9BxAQwhjCBKGCKGE0u0xhXSzNUSmPtDKKXS8FQY54ww+poGTyN1nSgVEM6cGLQB7MZ+Ee2TYJE3HTOIcrVMSQMohLHqk8nuWQp7xmrorIjK4XJQMeFDxBayoH2VhQwHp0/BwOx/LJyLgi8pSEbFV/uEV0kF36uP7E9fH0pz01+tQ2lm/Flc76WLcQuiSXlgh9kAeakPDUveqjqWulb5EU16VVD7cN8WAGji4CojAuKYt83cYckov2YCEC4pnQVHmrgZBxyfqVjORjItPSa4uEcL+kA08NpIb+xEaiy8uLSUYUF7l7unvi8P6D8aCHPCgOHtzvld/wVql7WHZ05EUfVFf8Y8xHsoitsqm/Ca8IqEkmFdIJ787UzGL8w/s+HJ/41E2eq7O0KvK1mF7MZz7rWfH6n3iliNVVKOOKh3VB+xUUXEaUflWwG9hOvytEp6BgB7FXvgcYk3/6X/5b/MN7/k8MDg225LYZKJuPIVkdMTsz600frz1xwhPwITrVPjqqqQ1GYKNR6TFMOfOp5j6gC7+F39AJBj5nlaSLNITTMNaVr8mnGrqUz0kF2mwAM0fDmejHucoQ59ZFiBBUxjRpScecniQkLIKgEMmL5yeJShr9GPyWVeEmCbrP4VWtZ0inpBjoGNK1TshMDqNCJobEYVhTNqQQKJqHtnl/Igm4sLgko7szOmrkDxlJbw5lEA/dUQgeIKqShETyV3pAChnjJ45dHU964hO9GIGJEEO09NyeMBEW6oU+zV3QBHmoPMpxCPF0cM8mohTGUDs+hCMTsrt8xTeB0pWJKHnpgy7Tk5Rp0JUJoctOvaKbnE+VeoXs4KVbFxnCU4MHh3TLSwsxNydCI/1ANhaXFk1KIbV4XND1vsHB2LdvXwz1D8YhkZz9w6PeB4ny2zulN/ob6hOsResS4MGBZq55wQ36FSQZMurKKR7P2zrwGK3E9Oxi/N3ffyA++olPR62rW1HaFDbldn/6058eP/Mzr4/rrj/unK905HeaShYUXD6UflWwG9hOvytEp6BgB7FXvgdLMir/5q/+Jj7+kY9H30CPjD4ZsVh++sGwrMk4n5qYjE4Z3UePHPFciOnpyTT0MWZl/HpeCjxEvzo7a1lvqq57kybdSx0yLvNNP3NjACeMXy/liwGvNOTH3AyAgWwjWeF5ljHL63rBc3OUKcYtb9kJxxglchIbjPG8BnimSI9BT14Y5RAjYqZBngQCwx05kAcpvRoYxrsdUasmMnzmZZBTNzbYTNmUx5KITiWfErBAAf0AUAevbib9eH4Te9t05gpvbMTKcDRIgFdJg4CZjCV5VEUtE0SHIwVbi/0jI/Gwhz406o1GkhyGe0FKiKc4tCOypURkoY/biUO68UUSkrZUv+PmM8pBPuZXKUz19pCxFulB15ATvDDoKufitMgN6blWXMrPoWZJGCERxMNzMz8356GTkB2wtrxkYkN9IJnor6enN7q7G3H86mMxNDQkUtMTTZ5LKPdPlgDXB/1DdjwETR8AcUHOar4RciB7XW3G8DrUqI5jQkl5KJYhbxCvyem5+Lt/+GB89OM3RVe9aV3Nz884jxuf8rT4pz/zurjhhhPWpbXRaufUYnV9ZaD8Ty7YCZR+VbAb2E6/K0SnoGAHsVe+BxieH3n/x+LWz98a3c26DXmMWYxHDEJVIuZmZqJRk9HZ2yeDmtWtJu1BsFErwoFZp1vZzBiaHSYuPPccGOWFkUsklgvmTX2SozR+mduSe5YkIeLAUMbw5Bq7HmM/V//q9JA3DFfKYWgZG2vaG4KngLKUv1dsU1k2rCEuyhsjuGoPziY7lNManmZjX88gGCkLNCHrRB3gRRjHkCqM+IX5OeeDYb1BdKTLThnnTqv4yEU98Bygj5yH0xELiwu+xlCnLDbjZId/5yciZGMdQqg+5GFVSCYBGIZFGGQEAtaod8fRo4c97A3itrqMgZ91NMnTkbpXPg6mLq5Vysu8Il9nXBMkzj70hDzRqQ7FEElFT7QP7ZptDKfwHCrFWVnNYWiQGFY5s6eGaxG7xaUFtw0LCiypDxGfVfvqEDuREDai7Wl0R0P6G90/Go1Gl/ewGRrs17X6Jd8npfXwM8lQfb+Ya4ROU1d4wSSSZCMc7xjl02+kal/TryGnxK/m5NCXqBf5Igs94czYWPz1//o/8dnP3yH9NlUWc4fmXb+n3PjUeNNPvTauu/5q68lLgSu/1D1HRS2vDFS6Kii4nCj9qmA3sJ1+V4hOQcEOYq98DzA+b7n5i3HmrnMtgxIjGUMRD4SMPhGSxbl52W9pzC0vLcqIlZHfMnrZzwVjneFIOSQph0/xZh6CgQowMNMQ5lA8PWfYGXlgWGLHM0kdgxSjk71M8HZghNYY8gXJURjXmJM2bD0KjKFyqWvgIVQY8Myh0Qcjn2WHXT5l64wRbpKG3CofL4sJl67JBnltxOvgpsoHj4aHOplUsQrYYoYTF8Nf+VI35KfdPSRK+VXeJuYG4a0ByIDBnoSG+kCA6C85l4cy0W21ZDdmMx4MnlNT8vfkfyXGy+ElpU0YIZetOigPXdmYX1ce6MYHU/x1dtshN3m16uB2RBc6SOu5TpztrZGhLyIDCYWkLi8xZ2XJRHl5OckNBHNJhIZ7Lx6wvGh5GMLnYXvKq6+vT4S6obB6DEr2of4BDyXrZxPW7qb7Q6NOm0MgIcWsioYOqH96DGnv9DShjSSC9AeIJWHIih7Vo1w3+k9bu/qvnqNH4hFOXJMc5VK1dz7viDvPnIl3/c3fx+23n5V8DfeplRWGa7bF4x//pHjTT746HvSgE9KbgpQDeVK/PArRKXjgo/Srgt3AdvpdIToFBTuIvfI9wMAbH5+M+VmW6JVBKLkxujEAMdcwXC+cPR/jF8ZibjaHGa2s5Bt5hvF4cr8IDsQmn0FuMKaVt66VnQ1M7D+MaWVuo5e37553UeuykYhBDxHAS0PZiTRKdbIBjGxkZLWSr+MILV1jpBIFz8c6u122ysTYNhlRXZGRhRTSo4Ss6T3iGnCPEb1BBsiEAhUnvUkdTsscJZWadXO+DOFaJZrk6bAulYONbOqAnjCgLbTys35NdJIU8UxcL4lKK1/kIkHWPcXIemPor8f09LQn4CMXZWN1E551TT1R58rD5DDi6YZr0nAQB1KBx41rVp2jPdEJhJCFADxfRuHOW+EQSABJUxITHogXdcaTMzs9I11FPOiGG+IhOiA4EJZmb7eITF3XSqcyIXwmqALDJCFLHcqzZn3gXUziTL05oyfrU7JkX6VvQUKz7akXMLkhUes6G4aTFehn6A09W0fonE+rbb548lT893f9zzhzZkLp6/ZWrerg2aO+8dHx+p94TTzs4dcpR8qjD4lMSjdkr1L4dcWg/E8u2AmUflWwG9hOvytEp6BgB7GXvgcYeshbybw4vyhSMx8Lc/M2WO+47Y44d+as7mXsMjyJAwMUorCKQZ1v2FVr5YGRD1kRkWkZ6hAYPCEYmxiKOZSoIjTEST0RViE9E5if+QzZWhf/b3vvGeXZWZ357q6unENXB3VUtyISwsYwgDE2xibPOAzMMix7rWEZbEQwM8Zz7RmHLxdjMFx/uIzHNr5cwIzHxjPMMjNjki6gIRgjIYICyjl1qOrKOXTd5/fsc7pKQhIq1KXqat6n+tQ55w373W/4V+/nv9/AL12U60QyMvWuR09d0iPeDoDh6fvarEo3Pzvn6VPU2wazuUqWQ5ifJRfz1/JpFyXCMMYYn5+fU/tMeQ1Js4x28nAGS20wUy88DzbaVSf0g0gASsEDgTL2DunR0+EoU88c0onxnUTHKbOe6MBdEhobmR4XcfT4MRGQBcu03iIlTqFIEzU9kzA9NNlHXi+D98VkJXc586X2QNaSrhnVb4n1RisN0SKSgMelsWm7N6LoFGHB80JftbW3S0+1h+SMT03FzORETOo+MnIypvR82SUXx8tf9tLYu3uP9UAO7YOeNcFwv1pfkULJwitlL5j0Jc5EjEppQOBFgxzSvnU/UU/aljrnGFFi3T0FTXGEQah9hpDbIMkIMkx0nE6yTFIkW5H83HLXXfEPIjojY7NK22ySw7gnzeWXXxH/7t+9I6541iWWhW6UU4hOwQ8Tyrgq2AysZ9wVolNQsIHYap8Ddrw6LsP5nnvujYcfPOrF9hi6ISKzMLcg45hvvGUUqk4rKzKUZdRBADDovdj79CWjWJeiZCBKsIxAG+y6sEHzG/k0YrkkzIamhK+2GQZjqiWstmFOL8v8GMwYxzxhwnqTAvLLME6Ck2koAvkmOxJlcqEyMPIJX1jKKWjWhSQuWwYx9bSU1Iu6YxyzrgYvFxsKMP0KEgeB8HQ4yVsUAaT+XvsjA7wmKxjw5MWgporAcZKNwT0/O6s0S9HaColo8VQsDH70ZloagMgB1vGgz8233RKjE+OuD2VTD+5c9rJJnnctE5HBk7XqqclpfCZAEp07rW2LZunB4v+e7h6RmbZoa22NDpEazqnBI9NKWFurtxqnhefnZmN0dCyOjY7EieHhOHb0aDz88MMxpvfz9uyOf/7KV8QzLrnI/cNQaGA6n8dBeqQo3GOBSvmZvmHcMO0s25/4mrCwvTTd1NzUrOTZx4ihHrQBcR5D/NAmVbu5LMnx5g8KY8MJyLbTqa1Iy3iGxCTBjrj+5lvjU1d9IWZmTylfiwkXnkx0O3Dg/HjHO94SP/Gi50ZTc5IudKcOefF89sBtQqUKCs4gyrgq2AysZ9wVolNQsIHYKp8DjLSjRx+K22+91UbqPffeFyNDJ2WsN8WuwV3RyS5XMnZbmli/k9Nz+AEYqOlFkYFoox1DUeRHpIOaE49fQU3hN9ojwzAm0zAF+ZwvGJ5pxPpNcXmvn010lBQCkJEZDtFBRHqWKK0qT4HUse4LG9EKYyoZZOb0GTy61+ntDajSmwRQJxVqLxRehwXW4yylpwqCpbQQHcBWyN5AQHIwwG0AE6+Lsk1ESIuyAu8QIIgQZKQJL1FrS04j1M8pSIHqmCQg9cPQXxCRufpLV8d9D9xvj4bl2hjPXc7snZDcZpEil6Xq4FHBC9Xa3uqNAJDT2dERXR3q35ZWkZj26FJ/d3V3RxvrapSftS38oCNl0yzoPzk9FSdOnIijx07EwyeGYmxyyt6/qZkpr+u67NJL40ef9azo6myLRnTx+EiSlmtxkuAxna1RcW5vhZGGQiiLFjUZRHkNCaVymxHWUKWHEXJjnNVxq8i+83RKiI4HThIjynZ7LYvIKA8EFQ+lvZQi+N++6ab4H5++KhYWpZM+C4yR5WWITmPs2b0/3vq2X4ufeekLc+qdxk6WK+G+CtEpOPdRxlXBZmA9464QnYKCDcRW+RwwHehLX/xCfOeb3/F5IWMjY16Lwxa+u3buinYZwZ3tbTKKm0xqTAiYqqO8+aW8jDy95XoTxcvg5t1QG0CK6mYg1I/6tZZYYEh7e2O9E05YIt/JSRAkp5bicIclkTHJUjTGq9d8oIt+bCYrbb3lMQYpC90hHdQCwoTha++GQHzqm7rxDtkRdXEeSBwL8ZFtGUyTU3oMd+tC0VU9qA/5a5nW7fQ95UNMAPojgzo1cfAlzyincHY7w7MByAcpGRsfj//23/57HD1+1LvhsUuZt15ua/P2zBCTVt3Zzaz2PEFyIDst7e0iF6qR6tKqOPoWUkMdTTSpHW0KiTNPoy500DZPc+NcpaGTJ+OB+x+I8YlJkYjmaG9nDQ5tKuKgSjB+2ttaXQ9vwWCvUW7+gGfI40lxnlZGeUrSqLAWkTzIkCoc203WOD9HElU8+urVfUtzNGynTSA6EMnV8cSP21yxqE17k5dSTLAkt17HRF3dX4hVnak+jqBvfuc78fef+mzML9L/jRXRYUv1bXHwwPnx9rf/WrzkZ1/o9kSG+8tSuArRKTj3UcZVwWZgPeOuEJ2Cgg3EVvkczM/Nx2f+1z/EDd+6IQYGdsbIyEhMjI1Gd2d3DO7coXu7D2SsDToMcYgOdWOdByYdhiNRaShi9BEGAXERNv1O32kT/8Ps5V2/Sc8vQhVP2/GThAG7NsMwfAlgW2S1rgxsZaEwyXB76wdPC4Izry4pwZQ3SA7GPIY062rqKWzkg+Skl4WwJE3IIi43TsCDIJNadwgDazVY08IUNfJmHtVB5MF6WEcMY7aIJk+jwrJ9MMC5Ew9RguiwTojq480hf67TSa8Qa4MWFpecL/Pm+TzHTxyPO++4M5raWmJwxw63X3pl2kweIHtNLc1eU9SEd4j8VZuqUqqdG0khqS/loiS9UMNeD9IpCxF4ik6eHPV0teHhkzEhwtPf1x8DO3aJMHFoJ6TAEj0O3KZ139DPkoUWtCNvdB/h1MkFK5zc6mjLcLvqB3KJh4u+WJ32SDIxW91bRPKQSb2TwCpYCWgv3jV8pJolWgb1plKLGhOE0x9kgvzgHYMUf+uG6+OT//C5WFimL5rcl2z5DdHZveu8eOvb3hQvf8WLNZagcVX7IcQXNTt7kO2PXgUFZw5lXBVsBtYz7s6uv8TnAN73vve5Ax7veu5znxtvfetb4+67765yPD6++tWvxite8QrL/H644IILvqesT3/601Xs4+OJ9K3xeGnQ7dH4QfUo2GSouz1NSgbcKaZjYRDaQNWFse6F3nq14cpFOtIoa3XHTiXM37SLfTTgTbEIGbEy5JHLtcKlxGkwVhllXBLuzQ0oQ5fP1lGh3spYF7KVRIY207uWYn5hMeZFNNjpjGteYXPzCzE7Mx+zs7rmZn0Q5cTERExNT3ttCp4BSA5eDqagMT7tQVF523RBoqS13xsVrhS+zKVogMpQRid7WuAJpFcCyEPqzroYtVf1Q+NSDmGeHqcfL2qnDTCqFZ4yMg2kB0KCByfjICb5hx0kqVLx+oVhf8EFR+KZlz4jDh86P84/dDB2Dw7EQH+PyWlPd2d0M+WQKWoieE0mEfmHv0HlbIekpvqqp4dBbINgqJ18F2kA9qToZ3l+OcZGRmNkaCSGTpyMFhGbi49crHIPx+DAjujq7BAhbjUppo0hH61tuuu9ReHtxIs8t3V2RXMbnqeOaG3vVHh3dHT1+mrvUHxHl2W3NHeKPPHcob5rkSKNag8Rw1MifhofcwunYkZ9Pz27EOMTMzGma2RsKk6cHIujx4bjgQePxUMPnRAhHIvjw2Nx7MRwHD8+rLDj8fCxEzE8Mh5TU7MxNTMrWUsxq/HDOOL8H9an0dZ4mOwJUl/RTW4/jRMIvjeXyG5xfxSDb+vg5MmT/r+tv7+/Cnl8fPzjHz/9/1l98X95QcEPijKmnj4Uj84G4XWve1383d/9Xbz85S+Pz372sw6D3Pzu7/6uwzn34uqrr45nPetZjlsL0n3iE5+I9773vTE6Ohp//Md/HL/9279dxX4v+MD8yZ/8SQwMDFQhEb29vQ5/MuAP/h/8wR/En//5nz+uXqR529veZt2PHDkSV111VRw+fLiKTTxVPc5FbJXPwcL8fPx/n74qvn3NddHd0yPecco7rLH2YIf6s7Oz8/TWwPmtPAZ/fjNP/TzVSQaf/lxbHga/PRGVYW8SoMuGNOH80Cx6xtjnBameKoUAESVFeU0F8iEPnOeiEP1wE1moZUMGeJfhzpSq9JSILCgsvTcytFta8hLJYV2KoiyXE/Ctv7fDNt2wPtxZh+GpcNKhBu/2AulnScYwOnLwpKdA4RlwTnRTXRugDi5IhjKHVrJmJmXMzEy7npAtzh1i9zf0IJ6NHdo7OvyOFyoXzJNRotRI6XlYEZFoEbHjbKOwR4G1NvZ2BZsq0H70Bh4NptZV9USSCFqOS7WfZNoTRy7pzLtBXi6KomdUxuLCckyMT8Sx40PeVa1dY6Knpz86pCvwuFAey0NX5cm+gJgpTkAvRwDdcsoabc6lX8pPPMQCvSHfaIfetU6QbcSsereQp7G4Rnf3pYiQN65QEB4dxgviYd8QFAilN9FQHNtis4V1bo0OsV9wHUYnJuOWO+6KB0SWpmfn3M+s9YH69vcNxFve9qvxL37uZZ4y6Ao9ArVCZweyzx+t4w8n+PLtAx/4QHzuc5/z+/drFwzQtf+vgXe84x3xqle9qnr74UUZVz8Yyph6aljPuCtEZ4OAN+ZFL3rRI4hODTwh/IH9pV/6pSckAcS9/vWv/75Ehw8MZTz6Q7MePJG+Nfj263d+53fiLW95S/zZn/1ZFbqKM6HHuYat8jlgW+SvfuFLcd3XrxUZaDNBGRsds/79Ir+dnSxW74yGpoZYXlk0QTAZUV6MytqTgf+D+iqbw1x3pcU214PTYnwj1+QFooEMDGKldfKqzQjjnnKYegapwSCF1CzKyGeba73zrbrAN+wYzs2sN2lujs72di+y9yGjTaxFkVEr4mPVK9noaStbhjKEyWfGKAyPinXxNDSLt95Y2BjGkuDtpSEWkAxvZY1enCNjo1ztRKWVGcOfqVa1JwbiMi+CYm+BZFEuJCi3l6ahVqJdurM7GcY/Rr9JotsFudkeLZSrfmKtEVs+N25TPVnXAjuRDERRT6+ZQTfLFiQHsqYA62pyozATDcF95j5SXXVBnObmFmLk5FicGBpRum0iN+y+1mpvB1MAOV+Hyq3upoeg6qqe3ecOU5nSO8GzyEOlH2m4ktylpwmymjpVMqp+pi8pi7h67ZIeMo52lUzFJvFydr1LrpMpDI8a20gTjvfM/SPZJtC6aFtxpXjg6LH42tevsQcIiRAdNOMLgCvfIqLzL14Wbe1s9U0h1AGtK33OIuT4yXYsSDAD4a677nrCduH/4fvvv/8J/w/+YUYZV+tHGVNPHesZd/xFLnia8ZKXvMR3vCNPhH379lVPjw8+MI/1zcBG4tChQ9XTKjZDj4IzB/5eQEGam0Vy9Ox1NzZYMdh06W6vxdKSDeSleU7EnxdBWtJ9ORb1jmeCb8Y9jWxOcYu5jTHTgRbwtoiU4HFZkFE/yxk0yj8zsxDTM7rPLni62dTUTIyPT8TQ0El7Do4eOxYPPvhQ3HPf/XHvvffFvbrff/8DCj/uNSLzc4siMc02vHfsGIzzztunz83+2LVrd3T39MoIb5NRDyHgG30IUhImkyZdTINTdXxhv2J+K9hx2OL5zT9GMOt++DZfd13LYkOnRDow0HlfXFTdlW5BxvKCZZJHF94B1XtO7WFPgcKYQod8pkex7oZ8TMdqkJ5sy43s2VmIHJ4q/piLdJ1SWUzdo1x0VR50h6VAChpEcryeRxdMzdMKdWctCem444XwpWdPP1Q9HQ55Qgfppi4WMh3kgPcZ6eJto08MR4PIRb8+4x2dnS5P1Y3ZOTwdGiKUrcGT3iQ98w5hlLRtkENdTIVjzU8j3ieRMuq7Tdd2ESbWPZHO5EV50IcDWZl+yDbeXLxDbpmyyBijXWlHiBjPjEn0mZ3N9ExxXFA8aZbUR7R79qUU1zPtgFeoRWV3iRTjuezp7o7enp7o7x+MwR27YtfOndEK+adufBr8mWAcMfWS+tJmGZ5xBVsFEJ3vh9///d+P1772tdVbQcFTRxlTTy8K0dlEME3sqYIPDFPO8BJ98IMf9BSzzcDZokfBDwZIDNsE3//AAzIEl/0tPYvamX42L4MRAsOamDldkBWM9vREcAaNyIziMcwxOEk3IwICeZlmrYzurJEZHRuP4ZHROD40HEePn4iHHz7q8iAwd951T9x2+x1xxx13xJ133hX33XdfHBPJGR4eseHKN/QYoQODg7Fv/77Yv/9AHD58JM4///yK2OwS0dnh6XU+wFMXBECWswmc7HABzwg7hqEz08XYHhqjV0RF/G0BEiNCAYHhnQ0AIAJcEiQZ6aXBSM5vkpJ4SJTeyYMRzRbFeMggDSqL9R5qC6Z9YWjbuwARoSwRkPk5SJAuiI3SnloRGWhojmXLU1mLSaRk8/tK4pNkh93t/CdcaeufJfWFPWXWL2sNxalRfwvGRV1IiyfHySUTb1eDCECuVdomEjodJ9Rfw8PjJiZtbR32ipgASqGlFZFclYm8BRHbeT1zcCgX7bTIGFEZjCmmFtprhscEZURoGppEcpqb7Blq8q5wLSI9GnciVHjmmG5I39dXvY0509KY7geZmZ6e0TWrMZdjjbacnuYw13yfEZGeExGfVfyUSPTM5GQs6pnDUDkTB28mY3xudlb3WbX5Ip2v/st2atyu8SRiBiHinbVltBt1qtsYEFdwboEv8PD4vOxlL/PaWmY+FBQ8FZQx9fSjEJ2nGRCAv/zLv/Qz63ieCviA1N9IMRXuyiuvjAsvvPBp/+CcLXoUPDVgsB47fixuu+32eOjhhxWyLVrZgljGJsY1nopc7I/3ZTEmpxZidHQ8RkbGY3RkIoZOiMCILD10jIXex+yFufOee+Kue+6Nu0VkIDP33Ht/PPjAwyIxQyY90xxIKgOS0/V7e/tj9+7z4uDBQ3HRRRfFJZdcGs+84llx+TOviIsvujQOidTs33cgBgd3RV9fv7dM7uzskjHMDmOstWBaEiQgDXfd7FHBM8CzbFMTM09PUwAEyOlJhwFLI2Crqq78pKGrV9ISSRzhhCkNBAODm1lrnv9FPhnh/JCULBATCIoqqb+2kKgMx5DHsCe8UcZ9qwhEw7Ymk6XaE0OZ5MezBJFyeQrLtUq8o1d6GTDCAXXFxQApgKQS7mfda5IDyJO7l0FoOB+nQfo0hrdq1j+mbc3NzuTuexOT6h+8HV2eRoZiK9uYEifZDZSPzst5xtD2lOepaSIE0l7kCeKVl5RGPdUrSQJhlEWeegvpPKcIBxAexmZvasBlosMPZKeanoY+Tq87XiiqRxukJ01k1gekJhGzN1HkyJtUqG6QG6bcTU5NmzBNTk0lmed5ciqm9M70SLZZ97Q26q0frx2q9HR59Ek2q8spOHfw5S9/2WtSMUz5Io/p3Rin5Yu8gh8UZUw9/cj/HQs2HAxiFkDi8WCAP+c5z4l3vetdVewPhp/4iZ/wmhj+c/3KV77i9TVsXsAH5+kkGWeLHgVPDRi6nMWyvaExpiamTVY4CBIysiQjEcNxbnbexOahh4/HAw8+FA/qfvToCRGX43F8eNjnqZAeUoQh397RJQLTF4O7IDDnx8UXXxyXXf7MuEIE5oorrtDz5SY0F110cVxw5AJ7aPaet89kpr9/ILq6epLINDbJ6G31N+tNIjVMecLgRyeMX4xPkxE9QxJsU/tVhrfi0iBNw7qeYuTpVYq3R0P5PAVMaQnPZ6aE8ScSeQjgD6YMbMWzq9wpSFNlUHsKFJqobAxuUlImW0XjrfC2zsi2fIiQylOd8Kow5cprc6RTrhPKzQRqQz7Xoig/5bo+SWCcRuSAZ4kzyYAcJNHIenotDqXBX3SdJhy8K8pyJI84CEdF92ToT8bQ0FBMTE14oT2bUrD2JY37JCanWLAvWZCk7ay1gtjQvpLhNVyuJWWk/HxHF8gCKqgspVe04zhg9NSpRSmCnNw4gLyUy53tuSE86bFjqpvaRoLY8hyCVqdzXZEoGUwbZP0Tmz8w7W1R/YVmJuwiQUyXJGx2jk0IRIzm8cAtiQjNedc1psCZ6KqP6U/aFF2TtGU/uIkrZHzBuQLWot55550xrL9trJVlFgbG6S//8i9XKQoK1ocypp5+rPkTXbARwMPBf35Mq3n1q1/tsL/927+Nb3zjG2d0PUtNNv7iL/7C7294wxt8Xy9qfR/rYiOC74czpUfB0wv6t6WlPXp7dmhc7rR3pc2HPzbH5ARbNM96W+B9+w7E3n0HY3Dn7tip6+DBg/526qKLL4lnPvOKeMall8UznqHrssvikksvFYm5xCSGNAcOHIhdu/ZEX39/9PT2Rk9PX3R24ZFJ47VV9xaRGdZDcPeBkZVueB84r8Zny+jyVtiy1HnH4OTHO3Q5DBIAKYAgJLFYS2KShDCmFS6ZufOWjGYRqEaRM68fMZnC4yGDurFFhrzelReSsx25LifL5Xwa6xbSi3JNdkQ8RHCSoKRBDLhDXriz+UDuBpdGe2PTdpPN7Y0Y7Ulc7DlwHHpi1G8/beSzCQKGvo17kQa8JLQVBj9kAmYmNdU8EDnIjQiGCEGD5HPniwkfgqoy9KB8ah/9TM9MxsnRkzE+NamypB/bPKsujldZeFPQq1m61h4cVjdBsiAB25WG8GyfitxUl0lCpSt3msWXsiq52jbzuF6uQ1414TFJQmc9Uz7bV7eJ/HBuEJsjtLaxdXh10bYmaBBX+l6yJN9ePRFBpmPiMYPseHrbHFuU53oyymR8ML1wAUInUB/U4QYxpQ8hXg4Q7CWEsBacc+D/ahaOX3fddf6Skv8nmYJUUPCDooyppw+F6Gww8G5gUHzqU5/yO96cl770pX7eCLz5zW/2bm6Uc/3111ehTx61vo918e3Dk8VT1aPg6QZEpy26unpFPnpFRnbEoAjPzsFd0dndGysy4FdWOM2+M3r7+rwmBrIzsGPQafv6BqKru9seofb2ThmA7TKGm73m4rRhj/GLASsD36RC8c2NIjkiEk16Zi0ExIN1NSYkJiOZtknEh/R4WdCVywvZrRfvyMeolZyKuOThknzrL/JBvMpmwTuXPSUyfjGjIS08Q1ialbepilPyaDaxgIDkgZ/sbtaEkS1Dn0M4OZumhR3dZETbCMejglGuMltFmBp1x3RnNzSeKatF4ZA6jOSuzs7o7hHZw2uCbF21YQ6ZYCoX5VGW66L8yEFXDG8WxC8vL6geGNlLykebiWhUbc5FfZFnEqRwe7ikU24GAJnM9mS22djoZJwYHomJySnVu1VjoSda29tMqrjYvS6JWRKuRpEmSA160241CbPuTqP2Ia/uJkZNSqs4SJvU0JWkBb1JTzwXZM/T05yONoU4QrTc1X6GoNkbpatJ6ZstW+3b0ijiIwLU1hQd7e0al9XV1end0do7WzVO23XPA3DbO9QXCk/Cx986Np1g7dSMSNCUp7zRRDlxjXVYkBnWQ4kYzc/rOZHepPJf6rkMjlT40Ic+5OdPfvKTvhcUPBWUMbXxKH+VnyawNzrbMjOla6NdlG9/+9t9Z/pJDYycR19P5iDSp4LH0qPg7ATjoampPTo6eqOnuy/6RHIGd+8VidkpA7FPhjgHNjYpHZ4NDFamD6XnAwMxp1ex7gRyk56M0wY74RCbioQkyUgj3N/66+LQSlmYsif1XIfJEiYtYbIvU0/92LsDIalIS01cQJaLoV2TK4xgGeePMMAr4xvjHINZukBcuKtY5UtvSuqRMpGX3hWlE+nZzl2GNXwsF/NjsFdkQvltzEv/ZunSyhQ+PCMQH/SXPB/gacKiNAqHuLgeCsNHUhMa6gtpch2kL2nRhbN7IE8sqF9elAku25u+0G8RFpEWvZ++RGDwTLBxwsIC07nYiIDF/MtewD8+Ph3jY9MxNHwyJqdnRBC6Ytfu8+xxo69HOTvnxFA8fPSYL3bCO3b8eDz40IMKPxFDI8MxMjYex4eH7AkaHhmJk7pG9LdufGJCMqdimjUxiwvenGBRCuVZRRAsiAxXepXcfnqmLVpbcgOAehwBwvGCZX8ksaJv637NPtYlmSZeIj20G2na21p1teW24yJv3LtEcnIbcq42E6N2DjzlXcSIDTA4AJVy0VbdLB1rXSDmOWYA3pz6ueDcBWfM8YXg2NhYFVJQ8NRQxtTGohCdpxGsyWEKDy7K3/u936tCzzyYPvZo8CF69MVUoo3EY+lRcHYCAxNPzMDAoKeXDfTvkqE7EP07BmNgx47o6e2T0dhmg5TzXTCqPbULI1V3rEC+7QYY/RjozRjuSs+33Fz2nCitPSxr3jHoTYAwZrEkddnAlfFaSbQBmekxfGuPC2Xnn7D62fnqOOmGwZseA+LQqp4GBcEQCZHRzDQndBUnclmUgeG8WgakK+XhPcALwN2muhQzAdKF56GxWXIrQxyiBBnB4CYvZWCkJ+lTHl1MdyKd66Qy8PJgjDuPZafxTjvlVDvqlG2IHuzkNuvdwtjOe8lrStj5bmJ8KiYnp70j2biep6Zz6+6x8UmRmskYOTkR45Mz3mmOra0bm1qld4c9c3joensHoru3P3r6+kV69tizQ1+wcP/40FDce3/ulHfnXXfH7Xfc5R3zbr/jjrjtNl233x533Hln3HH3XfHd226J62+8QdeN8Z2bbowbbropbrzpu3HTLbfGTTffHDfezPPNvpBx9z33xoMPPhgnjp+IoyJT4+Nj+s9/1NeESBNnF+FJYT2Rx4qwTSwZzxKet/a25uhQf7Z5ShskqPIq2bOU3qhWwkWAmkVYSUd8m/J0iPS0teVOb+0iQhza2tHZ4TttnYBE5rQ12mrnzl0eJ4Awxn7BuY/6iIiCgjOFMqY2DuXA0A0Ci/Af6wDOOhwwne2JTsGt036/A0MfDaaKveY1r/GCtyeLx9N3LeoDQ5+sPj+IHucatsrngO2Cb/vug3H//cejrQnDLT0qHOjJOSQzU5P2OmAsnlpmJ6sFT5vCGGfhO5N5APU9JVlJENLw4yL80XC47ngwlCDbSY+EetE8z4RBBiQL1FJYYwFcHuXrqolJgjaXASxygGyIictQujqPCYaIBIBwsM0waSA2LGInDURjbRkNJjXKo/TsyrW4sOhv/11P/eB5oWjkka/OC8VCN08dU5i3WlY4Hg7k4RXaripxiKXrXAFPAhdtghcGnZ1/Zcn3oeETKmxZBj5bMyud0rCuhq2duSBEEKwVyJ7K96J6yUUe+uBZod0sU3FM2UJXFeQwp0ERBeUW0Szsn43Z6WnvyMbFLmZTIkBIJg0bKpCeNs+ND7inEIfRHuqWmugSRf3ZyIGEJne0MW3CtDXlV4x3qiMO7wx1NMlWXE2SWUfU1s46rxbnYb0PpLy5pdnkBTJJvZHL5hDUm/JBPRbREf24L7PmSWkgh5/7/BfjW9ffqODt3labKZmvfOXL4sq3vCH27d3pOtCq1DfHYEo7m0CbrB1bBauHd6+3Xdgx9Rd+4Ree8s6p5wLKuDozKGNqfVjPuKutgoKnCXg56rUuv/Irv7Ih61fe8573xAc+8IHq7czj3nvvrZ6eGButR8GZA380OARycHAw+nb2Rf9gfwzs3BE7BnfG4K6dXouDEYlhCTFpkoHIhaEIIarXt3i6lqe0pQGJXOw+DFburGPxWhbCHK4wcwOMYwxg0nHJiFV+yqoJDBcGqi/LJUWW8Yg0eleo/gjKgMa0lbw6Pnc/S6+I08lohZiJH8hgblYukTPZudSJKUp4USAAHILKtby45NPz+QPrratFDHi30a707MbGOTkrkp2X6kY5qizL73P61pKfldNGN/nFevQmUslieF31H3CXozgIBoRmm9po+RQejdShtaUtunv6oq2jO1pa26OljXUnqkdDo7eEZttqWjHrJEKlt0XqLPHcmb4GyaU45Nn7Rt1VDy52kyPTypL6TF3U3NAUfV09sWfXnnjGxZfGc5/9HF3Pjef/s+fHsy67Ii48fERxu6Ors0ttyWGlSyJFnHMzHZPTkzE+OR7jIoijIg8j4xNxcmIiRnSNjo/H6MRYjE9PKXwsTowMx/D4aDx0DM/OUDx44njc//CDcc+D98ed998dt9x1W9x02y1x4+26br05rr/5uyIi18e13/hmfO2aa+Pr131T17fiGu7fuC7+6ZpvxHXf/nZ88zs3xHe+e1PcpDx33ntPHB06FseGTsTI2EhMzkyoXRdEYjS+Wxp0sW4NL1tOz4TEsIkCbch6pcOHDkZfbxdDzXGA9nNAwTkL/s9m7WkxSAvOFMqY2likpVBwxvGnf/qnvl977bXfQ2bwhrDLBut1fvqnf9o7bTzWHuqf+cxnfP/Wt771mPH9/f2+6p067r77bu/HzrcCT+Qpeiw8kb6A8r/4xS/6+aqrrnJZNc6kHgWbAwhMb1977N0/EOftHYzde/tjcHdv7N7TF/v274xDh/fEjp29MqJJLeMX4x3SUBMIvXMH9XN9z00Dqm/m18QlYVmbTvllMNbbF3uqluQDf8uOIY79r4vwtfnqdHmIo3Sw7CwPmBhUMkwiCNctSQReiEW9QjZYy5LnrpAA0mFPRnX5UMx5ER4RALwQkAUOxMSDQzxbFS+KlODpgUD4vSITa+XgOeFgSvKa/Oh5bmkhNVAbuJyF+ZjTxdkv7AZGOsJTnsqHYLmekLEkRLDGReli7fWLJ8rAm5G6qX1oL3Uk+dx+imP3MfJ6gwK1Ja+0kWLdhm5GpeWyh0oBeKcgtd1dXbFLhPjA/v1x+eWXx/Oe97x44Y//ePz4818Qz/mRZ8eFF1wU5+0+L/q6+6JdZKyN3fU4GJQuWFiKxdm5mJ+ZtUeNQzt9xg3bPxOuMNb3QJSYhjc7PRvjo0y/m4jJicmY0J2/o5CmYV3H9fzw0FA8cOxY3H/saNz38MNxzwMPxh333BPfve32+O6tt8Qtt94eN9x0s0jQN+KrX/t6/OPXuf5Jz1+LL331q3GNiNG3v/PtuOHGG+K222+Nhx56yNMD8SC5mWgXtZ03R8j5jg5bey84+8H/U/x/Bx7rGAQOvqY/MT7r/+84IoJZDY8366Gg4IlQxtTmoExdO8Oop3c9Fta2B2QCksN/0jU4gwaPz9rpbWtRx9eAWEAokMFe7Hx42O2MhW1PFk9G38dLU09zOxN6nKvYSp8D28k2jVNntGbiko03xQ0fG467br87JsensKBl9KUno/YukA4j3oSlQtYdcpFpgOXV9zVtQ3y9sB/5xNu41DNeF1JSniKQ6HS1LJ4p23lUfpbFVC9HV2X5nwGxU6jT2StTqVwTEdJ7HQjfzusfxIV64gpp8VkuLTE5NeGzVjq6OoLd4iiStUDk4/T/9ArhFYGQ1KIol3qKHClcjMxhJlvUR7HcLYz0iofEMe0M3ZHp+uGSIbFCW7xoH4+UyqI+WOOKZ8MCy4P4KHxhcSmY3sYUMOdUGBd15U697fly2yjc5dBviIPMZXsQ68ttxBhQmNK6fcRE9dubKeCZQ25N6KZnkrAwLXBW5IWNClhbNDXFmqFRpVH50o0NLZgyhtcNLyGFoRJrczjAc1Zy8syfiAW9z0v+Cu1ImUrImAG0EVPdSIfWtANTC+kjAiGVtCu6M82NipKTdVJqZuuPLHGxeOjhIW+4wMGyaqbo7+nX37g3xL987aujra31dDuezaj7uWB1ytpaPHraNl/usXFQnY4vJ9/0pjf5/7aCVZRx9eRRxtSZw3rGXSE6BQUbiK36OUDl9KxAdBSwrTHGhkbj9pvviLHRCRuKDlakDdzqGU9F/QyoO3IgJuRIIzrDecQYrZ8RWokynFf303mqOMLwXhCPYU75XPV7rRO51PpOT+aUlcSJ6WhJyEgry1WkLdOhAyQkp7OZhPjQySQvbGXNbltsPjA5NWVDm8XqTOFjTQdEBBnol1PwJN9lpSapJ2s8mmy4L8zTxs5StVme4I88kxtFYKzjdbEhL8O7bmdkeiex9lbXmwX3eIbgT5TPWpVT0t3tK1kQnZbWFhWEoZ/tXl/IomxAnfGcqcYuq25PiEKuaVI9VAgkBGJFO1I+FXGbc1cZzdubKg8IciWH+vhJfaN8EBvWEc2K7IycHIm777pbw4wpY63R1trmerOxAzur0Ue0CW3G+jDqydbO4+PjMTIyZi8b68mWfehnesGWVN9tai/I2rzaCzJJl9vzhSYohz70DfXQM+3rDR+kBz3GqJiZXYzRsWn3Czlp38H+gfj1N/3r+MXXvNrn92wF0I/Us6DgTKKMq4LNwHrGXSE6BQUbiK30OfB0KBlxtb4Yl6wJYUE/xu9JER08OhNj4yYoAIOf9BjCAK9ADcIxThc57V4Gow1oPeVCfVKkBwhDlPcMx7DGg5Dy6/ZLkzTBovzaS4GRXZOr+r1+Bs6nR3sAdM9QhfOgSG424hXJD+wGI54wCI4JlcvD06M6op8N4UZPZ+KEfdYuccaN66If9IEYsS6JwzNrT4m9LZJDuSY6CxjtMvYxyBXG9DLCMdJR1t4IqWQiwfQ/yI/CkAlBgCx0dbVHX1+vCQ/n7VAWcSzQx0uBcc90Nyx86mrSxDP1Vd14po1zkwT6Lzc5oGWoR6PIynZ2HFOQew9ChFJ6rvu+BtPcAB4Th6vdXJZ+3I+k0TuXCk+5gqWpfjfecGPcfd99atO54Eyn5qZ697O26BCZbG9lm2d23JPOIikQlJqA0WcmOQt4iKbsPYIUscRoQfJ4n1tYkEq5CQR1m5ubcx+eUpWQkf23HIu4cNz/TFE8FTNzi0qnNlS5gG26BwcG4s2/9ob4uV98pXft2wqoP0sFBWcSZVwVbAbWM+4K0Sko2EBslc8Bxt+J4+MxOTFvnXM6EvrLWJX64jsxMTEWw8eHvI4C85Q89lQonmfqmYuy01AmXGL8zT0hCFzBQNeLp3TJuDRJIaHzVgaxysypWbpOy8pv3KE8PC9hwfKm9BijWW6WCWp9gGViDCvMBEr/6rVFeGmSbGU8z+yIBlnId3YhSwLBN/rWQwY+hGHp1JKnXkFiGkVQSLsokjIjoxqjGyLAVCmmbXnKHfrpJ6efiayI6MheV75a/7AnY0lyqDc7j2UdyJM7hlEuU7qyaqeiu6sz9u5h8X+HCNO880DEOG9HFbAc66177Y2RMJMcn9VDO+gCbNOMHtSXrbLZ2W37NnSgw2hHEYyKXIDTREkXHhZv8KBnx9FOdLSKo409NYxy9c7ltpJe82oD1uJMz07HHXfdGTfffHuMjY9RmA9VZcc4zrYZ3DkYO3YMxN7z9kQvmy+0tKgvk5CiXIOE0oeQQNdT+YmjH+k7P+vCu0ic10TNJVHFEwehZXoiJIttrKdmpk0S50WGhk6Ox8mR8RwrKgeuunvnzviNt/96vOJVL/EUwa2ArfK3qGBroYyrgs3AesZdIToFBRuIrfI5YHesG6+/Ox6493i0tjXbQ+GDQGW8LuMRkCHIzl8rLNqXAYyRm0QgiUs+E5aeE+rN5XesZCxe3pWeBEl00gDl23naKL0dGNvQAYBh6gchwzHCCVI25818WS6wvKrNPUVJiZ3G6SBcinNK/ZbhzdofDFrLlP4YxqwpwRBOoiVIHoQMz0h6SyAqluA1Iw3SH+/ErE/Sn1U6hal+eGFoRwzyugnQIc8BEjmAgFj3qhwlYP1PtstKMEVORVt/725Hm6ms3EUu15XsHtwZF114fnR2dMoonzYxqMkLRbqdpfP2xjx8U0IkT3pIp5roUDfK2d4IEUldaEMEUC5UIoPV+lV+dEVHdKEcvCMQCV7Ymc4ypSPhk1NTblPAJgPsXscmAxwkPCVSyDqnkbExr9lhGlvdp5AuyAvdgDoc3rl333lx6MBBX4OqO+fimKjqQg/rn43m/uEd8uXqcFco7VCPHca3xylpFQaZYc3PnM8lSuJz6213xTe/dX1MixjZ+yOSvWf3nvg3v/HmeOnLf8pepq2A+nNRUHAmUcZVwWZgPeOuEJ2Cgg3EVvkcLMwvxtVfvDZu+e490c3iehmATc0cbrndu2HJCvQakC62mJa9jOeiJhfUD8PW3hCMTr1zYRRDMPBg1IYrZAiDFJz+Rl7Nw5138uWaDgxKwipjtWrHFZWpm99TZualXOdBnqBol8WZJ2znnGlrzwqGLTqSPtduYLzKvLYcppLhcSANbqRlGeYLnBsEKVImPAboAdghDOOYKWc5lW3WutnTocx4YIDr553Rlu2pYM2NyRTrl6SX80i3JrUxxv3SkoiICJVq7TaEsDSKOFk/xXPIJVt6HzywL5777B+Lnq6u2IZO1Pt0G6CBylY1OOwVxkAcfUtbEpFrhXK6WoZnG9R9gd65e1zVv3qeFXmxjirPYbqzIxqkAFkQGfeJZLBr3cz0tMnD6vQw3ZXOu9fRLqRVOLu++Zl6CNnv1EBlabzRJ5TbxTboO3bE+YcOxZHD54t07LL+1lv5lVHNkESbflihTVSeyR/1rlDHcwc8Ww/l5TdrgZhGeNN3b42rv/SPMS5iBtFBxP59++N3fvvfxote9M/s/doKoJ7u94KCM4gyrgo2A+sZd4XoFBRsILbK54BvsD/595+L6665IQb6+m30ccgihzMuLszLwDwVba2tMTjQL0O9RcYo39rzLX2SGsgB3g5A2nyWwSk5aoU0cGX4Yux6GhPGLkQGY7kygGsDnbLJo1gZqHiQeIO4YMjmVDKmU9lDIzKFIc7ajCQx2eYUAcHxNs0mZRCsXB9CbyB7BeIhHZDr6WI27qW78pjsiISwpsPbHctgRw8upqlBivAGTTPFSemaG9UmyzLm9Zx9nno0sY2zfnI6Wk4R8/qZ7TnVDaKj1C4XgxwPEOmtL8QE/fwMEUkSxA/kEfv68MGD8bMveUn0d/eKCKleCmNaH31C29MmyGbbaCo+Oz/vNgPoz8J9vC4ms0pPm1JXCAtkFg+M+0bEi/6GgHmhP32hMviBkHr9i/Ss37P/0mOCLoTUnqskU1kn8jRUBMfSlCYJtFvFZAlSyZbdkBWmplGRFekOYRwQ4Tly/vlxwZHDsaO/fzU/9Vd/eyxIB9raxFttwx1PlctTunrcKINCst+oG2MPgnvTzbfHF/73V2J4dMTrdBh756vdf/c//FY8/wU/6nxbAdRrK/wtKthaKOOqYDOwnnFXiE5BwQZiq3wOWCfxN3/zifjK1dfE7p2DNrjZNpcdpRaYxiMiwS5be87ba+/BKQxfvCEyIDFSuVRRG7D1FCAbwqp6egdk2Mq4tCnJP+dJ70wa0kk2kurkN/ImOkrjCUfIXkQmeSq5yoeRidHN+gpZrLY56zJ9no3IB+fRYKD7G331h0uoCAPGN9OeMMWth6752emYmJq0Hqzp4Cwb9MDAhXTgpamnhM0vQgIx6GUA647N7O2NVRHSYOgD2gWDXoE2snNKW8rH05LkgCoor1SkbSBl6QXJNTa55gVNsw15P3zwUPzsT/109PX2ZpzqvUDfkAd9lAaCwHk/3KmLx6MuWgLvS+5kpvrRh0pPu6hg1ZX2yXp72peDTRGdDkAmICPWHxKicJepd+4e//ohD3Wp2z/JiP5RDvq4PMlAPvVQfg5aXVqRbtIx+0jtzBhyvMaL6sMz5/Ls0piF8HCWD+f6IDILkFroRQBl6x/9gBb0aeqnYlUenpmc3pb6Q6qXTi3GrbfdEVd98UtxnLPMTNy2xYF9++M3f/Ot8VMvfoHGQ0WUznK4rtkwBQVnDGVcFWwG1jPuCtEpKNhAbJXPAdOu/uqjH48vf/EfY8/grmhsavR5IpzRwjf+bOWLgb9v734RnR5Pu/L0NRm5WI3U8ZSIiD0Tek7jN8kOly1OtQXgt+NkaNpwVjz5ZAdLJl6YeSVYMWFw2+kZgxjTHOOdRfe1F4Y7MZAdPBEL7JQlox19fdiniESmq7wiMpQhCHhHvFWzjGAIhY3bpYWYUV1npifVHtOSKuNX8ZSBGuTH+KcG8BGrRvl6IL+nsym9p5ydQi5refAgqY0gCx4Lys1ubBKAJOpHuN05tF3VQCYFWdJpImAPD+2g92z2bTHQPxD7ZHTvP29f9Hb3uj6ZXOnIQ/nuE5pR9VEeEyaVhB5uX717ipgft0Wj9OZuDwwkjNTKwlokZbdWSdygOZJhaUqn9FUNFMC73sgi2XU/q0Eswm1FGl32/inenivFeYqgwiCmp1S+p8K5nRlTaINuEB36RTIVjvelvb09dg3uiIsuvFCE50CwdoapalBm9zFtzju8BMUAeRlTIk21p4tDlbbhZVLc0qn5uO3O2+Kqz18dR0+cVBq2ul6JXbsG48q3vDFe9aqf1eekOfOd5cjxV/VPQcEZQhlXBZuB9Yy7QnQKCjYQW+VzANH56If/Nr509Vdj946d0jt8fgnT15i+BNmhHn29/boG/O0308WoWtYRD8qC7MYVL3DH4MU4VayfbQjrPVtCIZXhiwGMUasgvytERueCdzNDLh4OPBE2mmWc4t1YWJjztKtZ3b02RMQHgoSxunhqKVb4Nr6SzUGgkAr0qHWF4GD4Mg2KeqC7jWYbz1lXiFyd3t4FnuECuDWoRGUTA8oBtA9XGtWSjXwTjYrMcDkhfGgN0UGYo1ZlIzJlEa4UyIZISFfkkAzPBMRtu+4XX3hpXH7ZZdG0PTeSgITgEeHOzm/K6rpyRoztfMlTJMKtY32hi3UVuNPmpxqSJOY0OLxT0quKN2miDspL7yYpRLsswwRVaWqyRl3S20eHK450utfjwMl0d5srzkRHpNXeHYVDgirxHgv0uS88WIqgfm2tLdHb0xVdnV3R09kZnV0d3qQB4kTeRnSVDtSH8c2UTBM9VQWCj2ps3gChWV5ZiNvvui0+e9Xn46GjJ9RGzdZhYEdfvPnKN8bP//wro62tbC9d8MOLMq4KNgPrGXeF6BQUbCC2yucAovOR//ev4+rPfyl27dhlnZmSw6J3pn1BPFiQ3traHjsGBmwgsgYGI9VERVXEeOSZNSzARmlNc2Q9JllJA9hTl+pwGbUYqovIqcLwxpAekpJrRealAyQkPTWLVVmQIIxovAE24u3RwFhPA571GTLH/ZzdAHmgDKaDrXhtCqf1z8/PZXplhOQwncvGLzkgHdz1wOwz7uQlLQStfqZeXHhyeF9LGiBenpZmSTku9DsN/uqdH0uT/gheHTfIyaf0slQpq2gI0BWXXRE/+cKfjI72Tntk7LlyfVT3itjhkct2qPQnry5P7ZJM6+67LvqKZ/p3RcTDfZcF0l9oACGxl0RhyILsIDnJCORHaZSPiwRuI96JU3rKIj+ySAuRSQ+MpYk45XQyPDqq0Gmis7SYXjqPA9ZgiXBThsT57u2hpyZF+Nhmu8nkxWVIKvVmRDD+KLu5uTla29q8LTft4DqoPXNnPHYebIiR0ZG4+bbbY2yC6YxZ706RqF994xvida/7xejqalP42Q+3f9WHBQVnCmVcFWwG1jPuCtEpKNhAbJXPwczMbHzoLz8SX/zs1TE4OCidWY4gw1dEAUsWg5JF6M3NrdHX2yei06Ywvk2HEGBCppFZr3vQLxvxJkGYlxjXMiCRyzobf2O/LUmOv8lXKu/iJXn22qg8PCsSag/NgggN3+zjefFaHzEOr4WR4VkTlCw2jVUMZgxpDFWZ1FU8etbmrmRKHmcDjY2Oeu1KrqfBMMdQh4Co79h/a033YYavUgRS8OwH9zOXyYX0S8LCO3eu9IzkM0CPHB8OdVzKIA2x/KvzuMTTJKWSY1KyEs/50WfHz//cL0ZPV7fPlGHNEDpAAt0vygelgCwsMaVPMpaW1ad6tjdEMug7JTSxpS9oA4iF12Hpx88Od8lUx7JrbSUiViBNp8Noc93VHnhRGpuSfNEj9kThZdG9WUTEUwn17OlmpGcdFN2s9O5z9WMSKDeJiS2eJd7QgbVYENZjx4/HyaGhJMqKo3zIFTrxnpxOJEqyEObpcQpncwMSm3xB0CSa7b3Rk/VrkBx7LGlPXc3NLfHyl78irnzLv479+3ZLD8TVfX12At1ybBUUnDmUcVWwGVjPuCtEp6BgA7FVPgcccvnBP/tQfOZ/fTZ2DPTnFB7pzbfibKfMt+acdwJp6OnusWcH+3FJBIQ6YvnmdLGcQiSrFfPRJMbxAgZpTSLyOS/SeNc2Fn/rGVLlNRgL9bbHMo1l0NfrTzg8E7KDEYxhmkTHKtgDgBcIox7egnlr47oypO1BUGKM15GR4Rg+ccLnuSytLDi/yYWhJ0iFCsz64cVoUB2TZEEMbDRbJjn1rHu9+QB66s35Qa71kQysfEoimPz80gtehEqMykiPSS0XnRADvUlxxKdeJjOLy/GjP/Ij8dp/+VofpLk0N5vEUMb+bDXt0Gf2SFyeEZRrm+YX50xyMPrdL5KcxEbvVd3R6xREyGudKBeSgQ5MCVT9rEv2Ee3LjnJMXXS835PI4DFhKiREBhLDe33AKAduNjW1eMpdo88OQh5eF2nk9hKkPBqynoY2QMeFerqi6jQ8PBTDQ8MxOT1VkTGlk7KML7egG46LNlO4KlETZI8/2kdp0MeVVD7iVQsTPzxEeP2QDcFuamyJ5z3/BfHOd741LrzwgNJJustQ9rojzzKgV61jQcGZQhlXBZuB9Yy7QnQKCjYQW+VzANH58//0wfgff/8/o7en11N++EbdJ83L4MPAxOvD2SftbR3e2tfeBNUNA9fWo8A363Vtiavrj8FpD4HIjL05CsPIxsAkQz0FDSaQRj4iITHsUCaDWs8+JNOG6Cph4RwY0tt7ozhPX1NeCEfDNhm4DZLpfKxTWbGnaGJ8NE4cP+opSbPT0w5jW2YTKdnF6OY6L6M/U73YAQ3jXEZ8Y6tkN0dbW1t0dXXFvn17Y+fOnSorDWSXq7y5zbSDXK49BbogctQX4pNTuLbZ0wEJwnNCDvKQmfQq3PrwQxh6AIgJTYG+9MNAb3/s6B8UqZmJiYmRmJmajmn6a5HpXSID9pRVU790t0y1D+2fZCD1tCeLekBi9Jy6O9qQFi6bdqFvqHfuQpekCy8ImwK0q306Ozqiq7Mjp4WpLMYSUx5Jj4fHfUV7+dI7/ay6UO26LHRcWdrm7avZ5psDWicmJkVOp2NqZlphsyZxp9SHHktVPhNo5a1B3XOTiFWCTT/RDiDHZ45XAIFDke0NTW7jqampGDl5UmR8wSS/o6MzfvKnXhzv+I03xcFDe53nbAd1q+tbUHCmUMZVwWZgPeOuEJ2Cgg3EVvkcQHQ+8H//p/ivH/+v3j7aBEI2X17UgVPtOXNFhp5IDsYewEDEuOWbem/Ja+My09c4BYnBayCLkW/EPW1Icmvvh585YwbDuSIKmPvIxJDGUEYRdCGMheLWST8Y2xjMkCG+f7cHSs/k8RQkyWFa2sTURIyMnIyTI0MxJoIzI0KwLBKwzPbQkru0DHmiPMpRmap/a2tbtLS2ymDvjL7+Phvuu3YPxgte8AKRm91exL5j50D0dHerZFVY//AoWdGKMBjZJH51u8ArULZCkikM7dUsfnCGvLjxjn4Z5BD9AdclgnHrzbfHtdd+IyYnJmJM9RwbH1OfzsesvTt5WCfky7u22ahHF9ovx6enEDKlUHG0Le3Osxf84/XRszcJcMYklva4SCfGQ3pykgzioWlpbokO74K2Mw4d2B87dnA2U/YhJIfq0z880NcrVXk0G+WxJmxiejJOnBjyhhN4/CYmpxxHnzKdbUnpCfc0ReRJnOupO+2j6rhvk8DireGuQMF1py2puy7u9fohQwJT30aRqFNux/Hx8WADjra29ujUZ+S1/+pfxRt/9fUaG/T/2Y/yf3LBRqCMq4LNwHrGXSE6BQUbiK3yOWB9wx+/9/3xnz/219HLOo/KEJf61t8GpC6qwgLuLtLICORbdBMLEQOMRJmqVVqM6UyvCBuVtAVpuUNmkkxlmEmPDHbuxHnak+JNYKq8Dm9MMnQ6ne65xiM9P0yVAnMzczKMx+Pk2HAcGxqK4eFhvU96Ch56qVquWyPGbENjNLewM1dzdHZ2xu7de+yt2XPe7rj44gvtsdm7d3ccPLTfurS1t1g/hJiUIaiCn/hlC5sXnnWtfX40nL6616jfq/SIA2uCdNeT/qHTN6/9dvz1f/mbmBifiNHRUV/euEEkB48VRjx61uSFPsLLkfrTRenlSLl4QEhfvflfkpusa9WXVR/ZS+O2z7U2xG3zFs0aKwpr0XjZe96eeMZll6ptdwYHxvqMmkUOemV6HX01ZUIDUZucmvI234yt2vuUZDD1XBGTgTCjLmVlf0J2sn7WmjrpWdXMNJlYMUnyiENn4ijHeYit8psUueIrsbSQW5b39vab/J7QeGpQvX79zW+M17zmn0dHR2uloxvsrEXdVgUFZxJlXBVsBtYz7grRKSjYQGyVz8H01HT84bveHR/+0Eeju6tDIdIZtb1DmS6TFRmGMmQxcHt7eoJ1CuyIhVHJt/lY4TyTnnrbO6OfFhEIDGq8BBAVT12yUZyEZZW0pKHsdC6HaVFNmUbytjeJ1MhobmxmEXsSDJcjUsYZOtMykD3FaGQkhmSMjo2K3MyMK47pdOiTa0GQ0dHe4TNoukXY8MhcetklMsLPE9HpiCNHDkVHZ4unp3V3dykf07RgfmoDDGDaQwZ3rcP3B2lozPpeY21eZFaPwveIpWHJ63DpUMlhqhcE5IH7H4x/+29+K2644YbKO5NEE0BoIAr6536sda/FsQW3WtJpMfCpU3o2aDXk51qjur65ccA2L8j3gn31D/F1PyLtlDcKSE1dlxURBbXz/gP7o1XEaH5+1mOOqZB4aebmF5SXEpNAAp/FBPGQKAhLymZd2LJ1oC7AeunuNsmgRzyDui1QBpkmeXp2XfVe96M9Xkpb/7CXhadVLi7FMy69PHbv2R/fueF66bAQv3blr8arXvUyby9Nnic3FjYP6He6HQoKzhDKuCrYDKxn3BWiU1CwgdgqnwOMznf/4R/FRz70kWiTsS+1BZl6Nvz4ljtJSmX6+owSDEwWgjN1ioNFITssOIcI4R3JKWds8duiNMqPB6AmOXomP14dr51QgXhj/G5CQtpqOpTClNykyetKZKSy2H56eipOjpyM0bGxGOMaHzfRYec25DGNqrWt3dPPOjo7fe3duycuvPCIPQwHDx7SfbfKa4j2zjwcVQqfNqizDXim/tkGdRyo+5Wy1guyrjtbPYyqfGtfh06Mxjvf+Vvxla982X3mRfWKSZKTBr1GotPiFYFMQg9y+hgEBp2Uj1TKShqU9JogPeYUwSQhEMXmaq3NihKzSP/0hgfKA3E4taQyRHw4w4cxwWGnoLW1JVrUL+yk5x3aJNDrk5SHZ/SBMKFQhqdHh8qiU10XwkyASeucqFu3iCVlnIK8lkhy0YsUdR961z6KVSh5a5KuGAIVekpEh/ose8OH5z33BXH+4Qvja/90jdKeiv/j3/9m/Nizn+m0pP5BxsHTibrdCgrOJMq4KtgMrGfcFaJTULCB2CqfA6auvf8974uPfuSjIgct6cUwOWGtC2tgmvwNvg1jARKi2mEi2qBMAkOa3JKX3chMfBpZv7Pq0ckteyVX5Ii0TUoDwTGhaZZh3Jzrcfwtu8rCAIZMzczNWsfJqYkYn+Ca1POk10ywGxheh/b2tujp6fP0M6aeHTy4zwc79vb2xiUXXxSDuwZtaPf0sAapmmLl6tQGKgZv9ViBNAki0sg+O5D6oC8azc0uxvv/r/fHxz72nz0dDOOecYe+tCV9VHujTH70Y+Ii0De0dY5TxaSd7xL4hQxPTaOPRAazDVa8c523HV/IndySKEme4k2kNB5MWJWXdTTowDQ3bw9uCaRO/XDOeOc3dHZ8TrHj3cSEekjv1JFs0G3ypAcGlXzXD2MSHflhil7dZ9whM/X7aSgfefkB7NCXMkWWpTMeHb3Gi1/8M3HFFc+Mf/ra12Nb47b49//hnSLNh50WfI/cswyuf6VrQcGZQhlXBZuB9Yy7QnQKCjYQW+VzYKLz3vfHRz/84WjvaPM39u2t7fbGqBKVkdpm7wrAQ8MaHeIwRDGavSsahKcBr4zuJi94dojPTQWaRHwwJHnGALY8yaCdMGTZYnp6cjImpqdiambGC8CnZ2diSvoxPQ3zuFEyIFatba3R29cTfSIyF110YQzuHIhLLrk0OjuktwzygcH+6GjHm4T8tN716HfkAPom3x9ppNZ9lnGJ1bRnA2r9+U39Iv7u45+IP/qj96jNJtxfuQYn9a3rfxrKl2tUqnDfsh/qq24T+tf9JdICGeFwWTw4bE7hPhcJ9nQ2/TAtjWmBbSKdHW0d0d7Z7k0b0FOfBI8JiDO6efMKRdQbHdC+hCET0sG5TY4zmRHhhXCIuCzpzjbZ3EkPKeI8p5r0cK/7iTvvwHFsUi1d6jOOkFvnyfqTskHp9K5wCJvJktJzbs6zf+zZcd23vhHPuuKK+KXXv1Zjr4cMWwK0BXUtKDiTKOOqYDOwnnFXiE5BwQZiq3wOIDrvffd7468+8lfR39/nHbOY8pXfm+PBYUvlDoU1UymRGQgN6zNYr8HUJ7w16aXZzu5lMqIhIy0iMi3NPENq8AbIcFzC4MTAXbbBzLbVE9MTMTk5lYvSp6ZiVqQGg5gtiTGWu7q7RbRaY/fu3dHT2x0XHDkch4+cH7v3DHqHr56eThviqQsa0+a1wX4uIr0Q1BFDHHLw+c//73jX//muuPfe+9zeq+MOMprjkPbgwuPTuG07UW4piEWSHu7kqfuULbXp4zhNcCAYdX+ylXRnZ5favyfa29qju61LJEdjR0ST3dgYDx4LkFrLgSBTjMrRD7JxITG1DVJBuKfWaVyx3fiydSGlyAzkaEVkmO2yFxaVJvUjyTZPMYOc5EYFgLLm5tFXaVVXdIBIQ5jxBLJVNYfG4pVi3Q/5uUyHdCd8dnpWaUSiRKhe+MIfj5f8zM/YY3bpMy6KfQf2Sq4V3BIo/ycXbATKuCrYDKxn3BWiU1CwgdgqnwOIznve/b742EdFdHr7ore7J4mO9FcFZDQ2ynBti9a2NhuvkI5c2M8GAjJe2bmM9TkyJiE/rI9Jo5Y4malMcZKB6nNQVBY7oE1OT4rUTMqg5BR7yFRLtGIki7js23de7Nq1Mzq72uPIhYdjcHBA5TX63JqOjg6lxWDHQJZ++S8NXcFTqAjHQPZ9tR/q93MB9biqz55hx7Ur3/y2+PrXrzFJTHLjJALEKJ9oA9ZFbYc+EKh3OAb9pWRuR6/JUT/WnjnIwdzcrD18TBHs6+vxNMGu7i69c66S+o4xIRmexggRpgzLyKlqOU4UL9KBDtajItIun1+qR1IgPSo9Xpz5JRETxoj0IgkbAzAGz7/gUBxgc4M2jVOlzVxZj5ZWjU0RMUgZ0+q6ujvtZarXCoFsj9X3tSCOcTk6Omaig5yBgX5/CWC4qCSQWwX1Z6Cg4EyijKuCzcB6xl0hOgUFG4it8jmA6Lz7D98TH/p/PhKdMhzzW/nWaBZhYZcz1lawRicPg2yPthZdbZ02cEnHDmUQEc5qwSjkW3TWcOCt4ZqdnY/Z+Vmv5+BbdYzQZhamtzTGwI4dsWv3HpOYI4ePREd7awzuGhDh6pZBXn/rj2GsdtxWreOQxVvvusVmCDRx3dbc4TwYoeSpm5/wcwk2/Ks6eYzp+o//8S/iqs9+IU4MD3u6oAmP2iqnilVtRwa3p2TonazIgayykYHX8DievJkWDwppW0V+IbRMR4Ms0fd40ryGa3uzPRy0O+EQVpMbZEmPJMgKUz7IiMukDHRUMXhcOBhW/6zT4sJcTE6Ne0tnpiH29PbEwYMHdO8VAe6MHYN9GkPVmqGqjyuuY0hSFaw6iwzyDFFC9lqQ3VA44wYZp8OM1QyZd/Xd5HCLgHbaCn+LCrYWyrgq2AysZ9wVolNQsIHYKp8DzjB5zx/9SXzkwx8zecHY45t8jE97aiA8esfLw/S1lqZWESIRHb1zgCLGKJ4UDlacmZn21CC8AEz5ae1oiSYZvb19vTEwMCAjtSMOHTwYu/bsVL7m2L1rp703p700KMQvkRRutB/tCCjDd10YspnAiZWmTptnrpAqv8HPvKv3cwtr22d8bDJuvPHm+Pa3r4/5+QWTiu7OTpOXycnJWBQBrcGp/yY6IowmNA0iGrL0mTpGvyHXJEQXa3PglRATniEwTCVrF8GdEaGlW/Dg+FDRaiodG05kn5AvvUje6aySmUSUsG3RBANRGnQhHfWZ1Tg6tbIQz33uc+Kiiy+Knr5uERXyZF3pf/czr7pXwXpk3OQL9Ts9vay6PRYsZw2SIGcg5aW3cNWDsxpXiE7BDzfKuCrYDKxn3BWiU1CwgdgqnwMOl/wvf/2J+MynP18d1JkHPTbI6ER7DGAOeOSbfIxUphLJPpXhmZsQYABj+LJIfLsiBnaI0MjA7unpjiMXHoqu7o7YtXNP7Nw54PSQJwxcSVAb+beeZUxuY41EIsNqQ1LPVkT/CCakuq+iSnBaAlibiOfvyXTOAaMcz1l6fMIbQDAGl08txcoyNEDQL++0BhGk2d2YxGT7kJ63miRAcugPXjMu0zaa8EgAY1yRtQct05HTvxIZICAz5UJmjfpW3c0f9IwcpqYx5uzBczZ+Qe5SL14bfHfkGUKliLH2uQZlncnyNh7l/+SCjUAZVwWbgfWMu0J0Cgo2EFvlc4BRfP99R+Ohh457ITbT1PDW4NHhW3HimZLGInNVKeZmZlUvSIniMJ5lcUJA+MYbErNr56DXb+ClaW3jfJpsC0xD2uO0iUjbnH7hnJzqUaCcRwQYtGUV9ugog/i17b020WNmOKeQu4ixxgamQH0hHtlmtH8i28dNvyaIvsw0VaCTrU1U38mbafPuEF16r347q59X81QChcfyglRxp1lsJUk3ylitU51kzRgCK0yFq57POPDmrAUFbVhhG4at8reoYGuhjKuCzcB6xl0hOgUFG4it9DlAz1Vd05CT+kYd/Mj3x6tXGtV1WqY65bN+KaPbxG8E8pRvJjr6vRa1jMTa2EdEFJxG3Yd1myZo8xqPjF+Txr9yvPJ8+vdpL02mdbwe84waB1WgX1cDMm6V6pj+iKW49x+RDxBXPVY61InSM1WPqTqulp9Yq98Zw6oKgrWvXqvAR8Sf/VjbdgUFZwplXBVsBtYz7grRKSjYQJTPQUFBwdmA8reoYCNQxlXBZmA94+6x5hAUFBQUFBQUFBQUFBRsaRSiU1BQUFBQUFBQUFBwzqEQnYKCgoKCgoKCgoKCcw6F6BQUFBQUFBQUFBQUnHMoRKegoKCgoKCgoKCg4JxDIToFBQUFBQUFBQUFBeccCtEpKCgoKCgoKCgoKDjnUIhOQUFBQUFBQUFBQcE5hw09MLSgoKCgoKCgoKCgoOBMohwYWlBQUFBQUFBQUFDwQ4tCdAoKCgoKCgoKCgoKzjls2NS1goKCgoKCgoKCgoKCzULx6BQUFBQUFBQUFBQUnHMoRKegoKCgoKCgoKCg4JxDIToFBQUFBQUFBQUFBeccCtEpKCgoKCgoKCgoKDjnUIhOQUFBQUFBQUFBQcE5h0J0CgoKCgoKCgoKCgrOORSiU1BQUFBQUFBQUFBwzqEQnYKCgoKCgoKCgoKCcw6F6BQUFBQUFBQUFBQUnGOI+P8BrCbStlafJXUAAAAASUVORK5CYII=\"\u003e\u003c/p\u003e\n \u003cdiv align=\"center\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Cutting conditions\u003c/h2\u003e\n \u003cp\u003eThe preliminary experiment was carried out to determine the significance of the basic cutting conditions. Different cutting conditions were tested in a full factorial design of experiment (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The most significant factor for a given measurand was planned to be a comparison factor for the second block of experiments.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCutting condition used for preliminary experiments with PCD cutter\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSybol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed per tooth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadial depth of cut\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\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\u003eThe second block of the experiment was based on the results of the preliminary experiments. The basic problem was the completely different geometry of the cutting tool, which could affect the results. On the other hand, the preliminary experiments helped to adjust the boundary conditions and reduce the number of experiments needed for the main block. The main block consists of the most significant control factor from the preliminary experiments and the geometry of the cutting tool, see the design of the experiment in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. This comparison provided information whether the change in the cutting geometry had a more significant effect than the change in the selected control factor of the cutting conditions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCutting conditions used for testing compress cutters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSymbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCutting speed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003em/min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed per tooth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadial depth of cut\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHelix angle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lambda;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRake angle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026gamma;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\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\u003eA full factorial design with replication was used for all experiments. The Results were compared using analysis of variance (ANOVA). When the data met the assumptions for parametric tests, ANOVA was used. In other cases, a non-parametric test was used. The results revealed significant control factors in the experiment.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Emissivity of G/PA12\u003c/h2\u003e\n \u003cp\u003eEmissivity was one of the key parameters important for correct temperature readings during machining tests. The emissivity was determined experimentally in an electric oven, where the G/PA12 sample was heated to 150\u0026deg;C. Natural cooling was observed using a FLIR PM675 infrared camera, while the ambient and G/PA12 temperatures were measured. These data were compared and evaluated to determine the true emissivity of the composite.\u003c/p\u003e\n \u003cp\u003eThe temperature in the vicinity of the area was measured by a thermocouple K (it can be seen in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) and the ambient temperature was measured with a PT100 thermocouple and recorded using an Almemo 5690-2 data logger. Heat was generated in an electric furnace and the natural cooling process was monitored at spot Sp1 with an infrared camera (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The preset emissivity was 0.96. The cooling curves measured by the K thermocouple and the infrared camera were not the same. This means that the preset emissivity was not correct. However, the curves showed a similar trend (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). The emissivity estimate for G/PA12 was calculated using the Stefan-Boltzmann law [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:M=\\epsilon\\:\\sigma\\:{T}^{4}\\:\\:\\left[W{m}^{-2}\\right]$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere \u003cem\u003e\u0026epsilon;\u003c/em\u003e was the emissivity of the given grey object, \u003cem\u003e\u0026sigma;\u003c/em\u003e was the Stephan-Bolzman constant 5.67\u0026middot;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e WM\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eK\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and \u003cem\u003eT\u003c/em\u003e was the thermodynamic temperature [K].\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:{\\epsilon\\:}_{1}{T}_{1}^{4}={\\epsilon\\:}_{2}{T}_{2}^{4}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026epsilon;\u003csub\u003e1\u003c/sub\u003e was the preset emissivity, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e was the temperature measured by the infrared camera, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e was the temperature measured by the thermocouple K. \u003cem\u003e\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e was the emissivity to be calculated.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAccording to Eq. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and the measured data, the average from the calculated emissivity values for the selected temperature range was estimated as 0.89 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This value was the input value for all temperature measurements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Cutting forces\u003c/h2\u003e\n \u003cp\u003eThe calculated resultant forces reached up to 75 N when the chip thickness was the highest. Machining was stable with no audible chatter under all cutting conditions. After machining the collected data were statistically tested for the possibility of using the ANOVA test. The ANOVA test could not be used because the condition of the normality for the data was not met. the non-parametric Kruskal-Wallis test was used instead. The Kruskal-Wallis test for the resulting cutting force revealed a significant influence of the control factors \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The p-value of these two factors was less than the chosen significance level, which means that the medians of the two levels were different. All measured factors increased with increasing level of factor.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eBoth tested parameters \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e were statistically significant also in comparison with the cutting geometry, see Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea and b. The main effect plot in this case revealed the higher the factor was the higher was the measured evaluated forces. That made sense because the chip area increased with these two parameters. However, they were much dominant then the rake and helix angle which decreased the force when were higher. The rake angle changes the direction of the force and affects the size of the primary cutting zone, resulting in lower cutting force in general. The fibres bend less and also compress the matrix less in the primary cutting zone. As a result, less energy accumulates in the cutting zone, which has a positive effect on cutting forces. The helix angle increased the smoothness of the cutting edge engage and that could have positive effect on cutting force as well.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe response of the \u003cem\u003eF\u003c/em\u003e to change in feed per tooth was predictable. Similar test with similar results were measured by Davim [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] or Sorrentino [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], for example. For this reason, the cutting force model was created based on control factor \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Cutting force model\u003c/h2\u003e\n \u003cp\u003eA cutting model for G/PA12 and double helix cutter was developed based on the Kienzle model. This experiment was designed separately from the main experiment because it needed a higher number of levels to establish a reliable dependence of average chip thickness (\u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e) on cutting force (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e). This experiment was conducted with a rotary dynamometer Kistler 9123C. The advantage of this device is the ability to collect both tangential and radial cutting force data. The tangential component is essential to determine the Kienzle model. The \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e levels were set at 0.05, 0.07, 0.1, 0.13, 0.15 mm. The \u003cem\u003eh\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e as well as average chip width (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e) were calculated for each \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003eThe basic model Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) was derived from the Kienzle model:\u003c/p\u003e\n \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:{F}_{c}={k}_{c}\\bullet\\:{A}_{D}={k}_{c1.1}\\bullet\\:{{h}_{D}}^{1-{m}_{c}}\\bullet\\:{b}_{D}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e is the specific cutting force. \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003eD\u003c/em\u003e\u003c/sub\u003e is the average chip area. The coefficient \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003ec1.1\u003c/em\u003e\u003c/sub\u003e is the specific cutting force for \u003cem\u003eh\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 mm, where both the face angle and helix angle are equal to 0 [N/mm\u003csup\u003e2\u003c/sup\u003e]. The coefficient \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e expresses the effect of the thickness of the cut layer on the cutting force, respectively to the specific cutting force, and is dimensionless. This model Eq. (\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) has been adjusted due to the helix and rake angle:\u003c/p\u003e\n \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\:{F}_{c}={k}_{c1.1}\\bullet\\:{{h}_{D}}^{1-{m}_{c}}\\bullet\\:{b}_{D}\\bullet\\:{K}_{gl}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{gl}\\:\\)\u003c/span\u003e\u003c/span\u003efactor modifies the basic relationship due to the influence of the rake angle and the helix angle. In addition to these parameters their interaction is also considered.\u003c/p\u003e\n \u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$\\:{K}_{gl}=1+A\\bullet\\:\\gamma\\:+B\\bullet\\:\\lambda\\:+C\\bullet\\:\\gamma\\:\\bullet\\:\\lambda\\:$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe extended relationship for calculating the cutting force with the influence of the angle of the face and helix is:\u003c/p\u003e\n \u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$$\\:{F}_{c}={k}_{c1.1}\\bullet\\:{{h}_{D}}^{1-{m}_{c}}\\bullet\\:{b}_{D}\\bullet\\:(1+A\\bullet\\:\\gamma\\:+B\\bullet\\:\\lambda\\:+C\\bullet\\:\\gamma\\:\\bullet\\:\\lambda\\:)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe coefficient \u003cem\u003eA\u003c/em\u003e represents the effect of the face angle on the cutting force. The coefficient \u003cem\u003eB\u003c/em\u003e represents the effect of the helix angle on the cutting force and the coefficient \u003cem\u003eC\u003c/em\u003e represents the effect of the interaction between the face angle and the helix on the cutting force. The coefficient values were estimated by nonlinear regression in the Minitab SW program.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCalculated coefficients for the cutting force model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003ec1,1\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2278E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9735E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5191E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6817E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0583E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eAccording to the P-value, all estimated coefficients, including the interaction between the helix and face angle, are statistically significant (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The resulting Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) for calculating the cutting force, with respect to the angle of the face and the helix with the obtained constants, is:\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$$\\:{F}_{c}=101.856\\bullet\\:{{h}_{D}}^{1-0.269}\\bullet\\:{b}_{D}\\bullet\\:(1-0.0259\\bullet\\:\\gamma\\:-0.0451\\bullet\\:l+0.0022\\bullet\\:\\gamma\\:\\bullet\\:l)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThis model equation has a very high coefficient of determination, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.994. The scatter plot (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) illustrates the correspondence between the measured and predicted data.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Delamination\u003c/h2\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eDelamination of G/PA12 manifested as uncut fibres and matrix. In the case of the PCD12 tool, the cutter was unable to cut the bottom and top burrs at all (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The ANOVA test showed that the \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e was the only statistically significant control factor. This finding numerically supports the mentioned hypothesis about the unsuitability of the PCD12 tool for finishing the machined surface, see Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. The results of the preliminary delamination test did not identify any control factor for the next experimental phase, for this reason. All control factors had to be included in the next experiment. To reduce the number of tests, a separate experimental design was used for each factor, rather than a full factorial design.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe machined surface of G/PA12 showed uncut burrs along the edges of the composite sample and, to a small extent, pulled fibres from the surface, see Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e. The low glass transition temperature of the matrix and its surface ductility probably limited signs of fibre pull-out. An initial assessment at the quality of the machined surface revealed a very high effect of \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e. The volume and number of free fibres appeared to be less for higher \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, see Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. An analytical assessment of \u003cem\u003eADL\u003c/em\u003e confirmed this result. The difference between the two different cutting speeds or two different radial depths of cut was not noticeable upon initial inspection of the cut edge. If the cutter is able to cut the top and bottom layers, then the decisive value for the average delamination length is the minimum chip thickness associated with the high elasticity of the machined surface and the actual position of the cutting edge relative to the fibres during cutting. In this case, some loose fibres were bent rather than cut. A higher \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e reduces the probability of this happening.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e Comparison of delamination \u0026ndash; an example for cutting tool R25-H15\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eA comparison of three cutting condition control factors revealed that the \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e was statistically significant. Other factors were less significant compared to the cutting geometry control factors, based on a significance level of 0.05. This evaluation supported the estimate shown in Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ea. The second most significant control factor was the rake angle This control factor was statistically stronger than \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e, radial \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e, and helix angle (see Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb and Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ec). The high rake angle allowed to create a surface with less fibre bending or pressing the matrix under the cutting edge, meaning less burr. The helix angle was statistically significant only when compared to the \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e at the chosen significance level (see Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb). A higher helix angle helped compress both the bottom and top plies, improving the quality of the machined surface.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Temperature\u003c/h2\u003e\n \u003cp\u003eThe temperature measurement method was suitable and with high repeatability. Nevertheless, the temperature was measured only on the visible surfaces and was certainly higher in the cutting zone. For this reason, the temperature was measured only for relative comparison. The cutting tool PCD12 easily removed chips from the cutting zone as is shown Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e. However, the temperature of both the contact zone between cutting zone and machined surface and the chips was always high above the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e. The less positive cutting wedge of PCD tool resulted in higher deformation accompanied by increased energy in the cutting zone and this energy was transformed to the heat.\u003c/p\u003e\n \u003cp\u003eThe conditions for using a parametric ANOVA test were not met and the Kruskal-Wallis test was used instead. This test performed for all control factors showed that feed per tooth and radial depth of cut were not statistically significant in affecting the temperature. The cutting speed had a significant influence on the temperature, with higher speeds leading to an increase in temperature (see Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). Feed per tooth was evaluated to have a decreasing effect on temperature and the opposite effect was evaluated for radial depth of cut.\u003c/p\u003e\n \u003cp\u003eThe double helix cutters had grooves crossing in the middle of the cutting part. Chips were not smoothly removed from this part of the tool. It was observed that the crossing of the flutes became clogged with hot chips, leading to the gradual heating of the cutter (Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e). In the second phase of the experiment was found that only \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e was statistically significant based on the 0.05 significance level. The geometry of the cutting tool did not significantly affect the temperature. The main effects plot showed that the higher the cutting speed, the higher the temperature. However, an increase in \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e by 200 m/min increased the temperature by approx. 12%, see Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e. Conversely, the higher the helix angle, the lower the temperature. The rake angle had little effect on the temperature, but a higher rake angle led to higher temperatures. These results were consistent with those of our previous study [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Forces\u003c/h2\u003e \u003cp\u003eThe cutting forces were primarily affected by the feed per tooth control factor. However, the control factor ae also had a significant effect. The cutting speed had only a marginal effect on the resulting cutting force. Both factors were more significant than the selected element of the cutting tool geometry. Decreasing \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e caused a large reduction in the resulting cutting force. The rake angle and helix angle were statistically insignificant compared to the cutting condition factors. However, both of them with increasing value of angle reduced resultant force, so the most suitable for reducing forces was a cutting tool R25-H15\u003c/p\u003e \u003cp\u003eThe cutting force model was created based on the Kienzle\u0026rsquo;s model and improved by the influence of the cutting geometry. This model had a very high coefficient of determination \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.925 for the range of factors included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Average delamination length\u003c/h2\u003e \u003cp\u003eThe delamination length correlated the radial depth of cut. In contrast, double helix geometry resulted in a relatively clean cut, though both cutting tool geometry and cutting conditions had a non-negligible effect. When the rake angle nearly doubles, \u003cem\u003eADL\u003c/em\u003e should be reduced more than 3 times. An increase in the helix angle also contributed to a decrease in \u003cem\u003eADL\u003c/em\u003e. It follows that the R25-H15 cutter was the most suitable for \u003cem\u003eADL\u003c/em\u003e reduction. Among the cutting condition factors, only the control factor \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e had a very significant effect on \u003cem\u003eADL\u003c/em\u003e. This effect was even more significant than for tool geometry. As \u003cem\u003ef\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e increased, \u003cem\u003eADL\u003c/em\u003e decreased.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Temperature\u003c/h2\u003e \u003cp\u003eThe temperature measured by the infrared camera did not indicate the actual temperature in the cut. However, for a relative comparison of different cutting conditions was sufficient. To reduce the measurement error, it was necessary to determine the emissivity experimentally. The temperature was consistently measured above the glass transition temperature (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e). The melting point was not reached in the measured area or on the chips.\u003c/p\u003e \u003cp\u003eCutting speed had the greatest effect on temperature, while other control factors were statistically insignificant in comparison with cutting conditions and cutting tool geometry. However, the higher helix angle could reduce the heat in the cut according to results of this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eG/PA12 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glass/Polyamide12 composite\u003c/p\u003e\n\u003cp\u003ePCD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Polycrystalline diamond\u003c/p\u003e\n\u003cp\u003eCVD-D\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Chemical vapour deposited diamond\u003c/p\u003e\n\u003cp\u003eT\u003csub\u003eg\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Glass transition temperature\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003ex\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Young\u0026rsquo;s modulus in axis X\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003ey \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003eYoung\u0026rsquo;s modulus in axis X\u003c/p\u003e\n\u003cp\u003eG\u003csub\u003exy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003eShear modulus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRH \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Relative humidity\u003c/p\u003e\n\u003cp\u003eF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Resultant force\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ex \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003eForce in axis X\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ey \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003eForce in axis Y\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ez \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003eForce in axis Z\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ec \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003eCutting force\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ef\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Feed force\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ea\u0026nbsp;\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Active force\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003ep \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003ePassive force\u003c/p\u003e\n\u003cp\u003eADL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Averaged delamination length\u003c/p\u003e\n\u003cp\u003ef\u003csub\u003et \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003eFeed per tooth\u003c/p\u003e\n\u003cp\u003ev\u003csub\u003ec\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cutting speed\u003c/p\u003e\n\u003cp\u003ea\u003csub\u003ee\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Radial depth of cut\u003c/p\u003e\n\u003cp\u003eM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Radiant exitance\u003c/p\u003e\n\u003cp\u003e\u0026epsilon; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Emissivity of grey object\u003c/p\u003e\n\u003cp\u003eT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Temperature\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026sigma; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Stefan-Bolzmann constant\u003c/p\u003e\n\u003cp\u003ek\u003csub\u003ec1,1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003eSpecific cutting force h=b=1\u003c/p\u003e\n\u003cp\u003em\u003csub\u003ec\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Chip thickness coefficient\u003c/p\u003e\n\u003cp\u003eh\u003csub\u003eD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003eChip thickness\u003c/p\u003e\n\u003cp\u003eb\u003csub\u003eD\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Chip thickness\u003c/p\u003e\n\u003cp\u003e\u0026gamma; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Rake angle\u003c/p\u003e\n\u003cp\u003e\u0026lambda; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Helix angle\u003c/p\u003e\n\u003cp\u003eK\u003csub\u003egl\u003c/sub\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Modification factor for geometry\u003c/p\u003e\n\u003cp\u003eA, B, C \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Coefficient of cutting geometry\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors acknowledge support from the ESIF, EU Operational Programme Research, Development and Education, and from the Center of Advanced Aerospace Technology (CZ.02.1.01/0.0/0.0/16_019/0000826), Faculty of Mechanical Engineering, Czech Technical University in Prague and\u0026nbsp;\u003cem\u003eNational Centre of Competence in ENGINEERING\u003c/em\u003e (TN01000015), which is co-financed from the state budget by the Technology agency of the Czech Republic under the National Centre of Competence Progamme.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication was created with the contribution of the knowledge obtained within the project,\u0026nbsp;Development and Education, and from the \u003cem\u003eCenter of Advanced Aerospace Technology\u003c/em\u003e Reg. No\u0026nbsp;CZ.02.1.01/0.0/0.0/16_019/0000826.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was created also within the project \u003cem\u003eNational Centre of Competence in ENGINEERING\u003c/em\u003e (TN01000015), which is co-financed from the state budget by the Technology agency of the Czech Republic under the National Centre of Competence Progamme.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePetr Ma\u0026scaron;ek:\u003c/strong\u003e Methodology, Writing, Visualization, Editing, Data acquisition, Data analysis, \u003cstrong\u003eJaroslav Kovalč\u0026iacute;k:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Data analysis, Visualisation. \u003cstrong\u003ePavel Zeman:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Resources\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHarris B (1999) Engineering Composite Materials. 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Processing and manufacturing of composite materials - ASME\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Composites with thermoplastic matrix, Edge trimming, Temperature, Delamination, Cutting forces, Cutting tool geometry","lastPublishedDoi":"10.21203/rs.3.rs-5367604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5367604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe G/PA12 composite used in this study is made of glass-woven reinforcement and semi-crystalline engineering thermoplastic. This type of composite has potential applications, for example, in the automotive industry. The relatively low glass transition temperature and low stiffness of the matrix compared to the high abrasiveness of glass fibres make this type of composite difficult to machine. The workpieces from this type of composite are produced in a near-net-shape, but the free edges of the workpiece must be trimmed in order to achieve the required accuracy and quality of the product. This study recommends cutting conditions and cutting geometry based on statistical evaluation of force, quality and temperature measurements. The double-helix cutter significantly improved the machined surface quality compared to the standard PCD cutter. The PCD cutter was used in this study to identify key control factors for cutting conditions. By selecting the optimal helix inclination and angle, surface quality improved by up to 80%, with only a 12% increase in temperature. Increasing the feed per tooth also contributes to improving surface quality. In addition to improving quality, increasing the feed per tooth significantly affected the cutting forces. A cutting force model was developed specifically for machining this type of composite. The model's accuracy was enhanced by incorporating the effects of face angle and helix. The temperature measurement method during milling was designed to monitor critical temperature limits, such as the glass transition and melting points. An infrared camera was selected and the emissivity of G/PA12 was determined experimentally. The measurements showed that, while the glass transition temperature was exceeded in all cases, the melting temperature remained at least 47\u0026deg;C below the critical limit, even in the worst-case scenario.\u003c/p\u003e","manuscriptTitle":"Statistical approach to determine cutting conditions and cutting geometry for edge trimming of G/PA12 plates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 12:08:32","doi":"10.21203/rs.3.rs-5367604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2024-12-10T15:22:50+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-10-31T21:27:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-31T21:23:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-31T15:28:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2024-10-31T10:29:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1585887-09c7-4cbe-b120-fc517c7fc69a","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:10:03+00:00","versionOfRecord":{"articleIdentity":"rs-5367604","link":"https://doi.org/10.1007/s00170-025-15405-1","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2025-03-20 15:57:13","publishedOnDateReadable":"March 20th, 2025"},"versionCreatedAt":"2024-11-20 12:08:32","video":"","vorDoi":"10.1007/s00170-025-15405-1","vorDoiUrl":"https://doi.org/10.1007/s00170-025-15405-1","workflowStages":[]},"version":"v1","identity":"rs-5367604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5367604","identity":"rs-5367604","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}