Study on the Process of Extracting Patchouli Essential Oil by Steam Distillation

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Single-factor experiments were first conducted to analyze the influence of distillation time, solid-liquid ratio, soaking time, and raw material particle size on overall mass transfer performance during extraction. After determining the optimal ranges for each parameter, the three significantly influential variables—distillation time, solid-liquid ratio, and soaking time—were selected for focused optimization. A Box–Behnken design combined with response surface methodology was employed to construct a quadratic polynomial regression model describing the individual and interactive effects of these parameters on essential oil yield. Statistical analysis confirmed the model's high predictive accuracy, with all factors exhibiting significant individual and interactive effects. The response surface optimization yielded optimal extraction conditions: distillation time of 130 minutes, solid-to-liquid ratio of 1:18 g/mL, maceration time of 88 minutes, and a fixed raw material particle size of 0.2 mm to ensure stable heat and mass transfer conditions. Under these optimized conditions, the actual patchouli essential oil yield reached 3.87% (w/w), closely matching the predicted value and validating the model's reliability and practicality. These findings provide scientific basis and technical guidance for enhancing steam distillation efficiency, holding positive implications for advancing the industrial production of patchouli essential oil in fragrance, pharmaceutical, and cosmetic sectors. Patchouli essential oil steam distillation response surface methodology extraction process Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Patchouli (Pogostemon cablin) is a perennial herbaceous plant belonging to the Lamiaceae family [ 1 ], highly valued for its distinctive natural fragrance and diverse pharmacological activities. Clinically, this plant exhibits broad-spectrum pharmacological effects, including antibacterial, anti-inflammatory, anti-influenza, and antidepressant properties. Traditional medicine also employs it to alleviate various gastrointestinal discomforts caused by spleen-stomach dampness stagnation. Leveraging its multifaceted efficacy, patchouli is not only extensively applied in clinical medicine but has also become a core ingredient in the cosmetics and fragrance industries [ 2 – 6 ]. Patchouli essential oil is primarily extracted from the plant's flowers, leaves, stems, and roots [ 7 ]. Its signature rich aroma and exceptional fixative properties make it a key fragrance component in daily chemical products such as perfumes, soaps, and detergents [ 8 ]. In recent years, market demand for patchouli essential oil has experienced sustained rapid growth. However, relying solely on expanding cultivation scale is insufficient to meet stable supply needs [ 9 ], potentially triggering issues like land resource scarcity and ecological pressure. Furthermore, variations in cultivation conditions may lead to inconsistent final product quality. From a sustainable development perspective, enhancing essential oil extraction efficiency and ensuring stable raw material supply are critical. Widely used techniques for extracting essential oils include steam distillation, mechanical pressing, ultrasonic-assisted extraction, organic solvent extraction, Soxhlet extraction, microwave-assisted extraction, subcritical water extraction, and supercritical fluid extraction [ 10 ]. Among these methods, steam distillation remains one of the most commonly adopted approaches. Keivanfar et al. [ 11 ] reported that, compared with hydrodistillation, steam distillation enables higher essential-oil yields, accelerates the release of volatile components, and more effectively disrupts the oil gland structures of plant tissues. In addition, Zhong et al. [ 12 ] demonstrated that steam distillation is currently the most widely applied extraction technology due to its simple equipment requirements, convenient operation, absence of solvent residues, low operating cost, and stable performance. Owing to these advantages, steam distillation has achieved extensive industrial deployment, and its associated processes, equipment designs, and operational procedures have become highly standardized and technically mature. Donelian et al. [ 13 ] reported that the steam distillation of 0.1 kg of dried and ground patchouli leaves for 120 minutes yielded 1.5% essential oil. Similarly, D Ermaya et al. [ 14 ] subjected dried patchouli to 5 hours of steam distillation. Analysis via gas chromatography-mass spectrometry (GC-MS) identified 16 components in the essential oil, with patchouliol as the major constituent at approximately 42.75%. Extensive research on steam distillation for plant essential oil extraction has confirmed its advantages, including high extraction yields and minimal loss of polar components [ 15 ]. To further improve the yield of patchouli essential oil and reduce raw material costs, systematic optimization of the extraction process is essential. Extensive research on process optimization has been conducted both domestically and internationally, particularly in chemical, food, pharmaceutical, materials, and environmental engineering industries. Various optimization strategies have been proposed to efficiently regulate complex process parameters and enhance overall performance. Among these, Response Surface Methodology (RSM) has been widely applied in extraction, synthesis, and reaction optimization due to its ability to model multivariate interactions, reduce the number of required experiments, and establish reliable quantitative relationships between process parameters and responses [ 16 – 19 ]. Response Surface Methodology (RSM) is a systematic experimental design and optimization method proposed by statisticians George E. P. Box and K. B. Wilson [ 20 ]. It aims to simultaneously evaluate the effects of multiple influencing factor, such as temperature, time, solid-liquid ratio, energy input, etc. [ 21 ] and their interactions on process performance. By constructing mathematical models, this method provides in-depth analysis of how each process parameter influences response variables (such as extraction yield or target component concentration), enabling scientific regulation and optimization of complex processes. In the field of essential oil extraction, RSM demonstrates significant application advantages. Traditional experimental methods often struggle to comprehensively examine synergistic or antagonistic effects among multiple factors. In contrast, RSM systematically integrates all process parameters, enabling precise control over essential oil chemical composition and functional properties, thereby ensuring product quality stability and consistency. Simultaneously, this method significantly enhances extraction efficiency, reduces the number of experiments required, and saves both time and economic costs [ 23 – 24 ]. By analyzing the effects of individual factors and their interactions, RSM accurately identifies optimal operating ranges, providing reliable foundations for process scaling and industrial production.The core capability of RSM lies in its modeling and predictive functions. Through establishing quantitative mathematical models linking response variables to key process parameters, this method precisely describes the influence patterns of different variables on extraction outcomes, thereby predicting optimal process combinations under specified constraints.In practical applications, RSM has been extensively employed for optimizing various essential oil extraction processes. Conde-Hernández et al. [ 22 ] systematically optimized extraction conditions for a specific essential oil using RSM, validating the method's effectiveness in controlling product quality. In another representative study, Keerthiraj et al. [ 26 ] combined RSM with genetic algorithms to optimize the ultrasonic-microwave-assisted extraction process for patchouli essential oil. This research systematically examined multiple parameters—including solid-liquid ratio, extraction time, ultrasonic frequency, and microwave power—and constructed a predictive model via RSM to maximize patchouli alcohol content. This model was then integrated with genetic algorithms for global optimization. The results demonstrated that RSM can accurately characterize complex interactions among multiple factors, effectively enhance the extraction rate of target components, and ensure the overall quality of the essential oil. [ 25 – 26 ] This study employed Response Surface Methodology (RSM) combined with Box-Behnken Design (BBD) to systematically optimize the extraction process for patchouli essential oil. Four key process parameters—particle size, solid-liquid ratio, soaking time, and extraction time—were selected and set as optimization variables. Employing the efficient and economical BBD experimental design method, a quadratic polynomial regression model reflecting the relationship between each factor and essential oil extraction rate was constructed with minimal experimental runs. This approach not only assessed the independent influence of each single factor but also deeply analyzed the interactions between factors, thereby comprehensively revealing the contribution mechanisms of process parameters to extraction efficiency. During analysis, the model underwent significance testing and goodness-of-fit evaluation via analysis of variance (ANOVA), ensuring reliability and predictive capability. Response surface analysis visually identified key factors influencing extraction rate and their optimal ranges, enabling prediction of the optimal parameter combination. Finally, experimental validation confirmed the feasibility and stability of these optimized conditions, verifying the model's effectiveness.This study aims to provide scientific rationale and practical technical guidance for efficient, stable, and environmentally friendly extraction processes of patchouli essential oil. 2. Experiment 2.1 Materials and Equipment The patchouli used in this study was obtained from a local certified wholesale market for medicinal herbs. The experimental equipment and related operational details are summarized as follows. Table 1 Experimental Instruments Instrument Name Specifications/Model Manufacturer or Distributor Electric Hot Air-Drying Oven DHG-9240 Shanghai Shenxian Constant Temperature Equipment Factory Chinese Medicine Pulverizer QE-100 Zhejiang Yili Industry & Trade Co., Ltd. Analytical Balance AUW220D Shimadzu Corporation, Japan Chinese Medicine Sieve GB/T6003.1-2012 Zhejiang Shangyu Huafeng Hardware Instrument Co., Ltd. Heating Jacket Q/320683AAFA02 Nantong Lihao Laboratory Instrument Co., Ltd. Volatile Oil Extractor 1 L Guangzhou Congyuan Instrument Co., Ltd. Refrigerator/Freezer BC-68 Hefei Meiling Co., Ltd. 2.2 Methods Intact patchouli plants with a uniform green appearance were selected and placed on trays. Subsequently, the trays were transferred to an electric hot-air drying oven and dried at 55°C for 48 hours. After drying, the material was ground using a micro-grinder and sieved through a series of standard sieves, yielding five distinct particle size fractions (0.2, 0.4, 0.6, 0.8, and 1.0 mm). The prepared samples were stored in a dark, dry environment prior to further experimentation. The extraction of essential oil components from the sample was performed using steam distillation. The specific procedure is as follows: First, precisely weigh 50.00 grams of uniformly ground patchouli powder using an analytical balance, then transfer it to a 1-liter round-bottom flask. An appropriate volume of distilled water, corresponding to the preset liquid–solid ratio, was then added along with several zeolite particles. The mixture was thoroughly soaked and allowed to stand at room temperature for a designated period. The volatile-oil analysis apparatus was assembled in strict accordance with the specifications of the Chinese Pharmacopoeia. Heating was initiated, and the distillation time was recorded once the mixture reached a gentle boiling state. Distillation was continued until the predetermined endpoint. The distillate was collected, and the volume of the oil layer was measured to calculate the essential oil yield (Y). After extraction, the residual material or waste sample was sealed in an amber glass container and stored at 4℃ for subsequent analysis. The calculation formula is: $$Y=\frac{{{m_1}}}{{{m_2}}}$$ 1 In the formula: Y —Yield of patchouli essential oil, %; m 1 —Mass of patchouli essential oil obtained, kg; m 2 —Mass of patchouli feedstock, kg. 2.3 Single-Factor Experiments 2.3.1 Effect of Different Particle Sizes on the Yield of Patchouli Essential Oil The experiment began with systematic sample preparation and precise weighing. After drying, the patchouli raw material was accurately graded using standard sieves to obtain five powder particle sizes (0.2, 0.4, 0.6, 0.8, or 1.0 mm). For each experiment, 50.00 g of a specific particle size sample was precisely weighed using an analytical balance, placed in the distillation apparatus, and mixed with 400 mL of distilled water (corresponding to a material-to-liquid ratio of 1:16). The mixture was soaked at room temperature for 30 minutes to allow the cellular tissue to fully swell, preparing it for subsequent distillation. The distillation process was conducted under strictly controlled conditions. After heating initiation, timing commenced upon the appearance of the first drop of condensate. The system maintained stable reflux distillation for 90 minutes. Distillate was collected, allowed to settle and separate into layers, then the essential oil fraction was isolated. This fraction was dried and precisely weighed. By calculating the essential oil yield for each particle size sample, the impact of raw material pulverization degree on extraction efficiency could be systematically evaluated. 2.3.2 Effect of Different Solvent-to-Material Ratios on Patchouli Essential Oil Yield Accurately weigh 50.00 grams of pretreated patchouli powder with a uniform particle size of 0.2 millimeters as the raw material for this distillation extraction experiment. Subsequently, based on five predetermined solid-liquid ratio conditions, add corresponding volumes of distilled water to the raw material to achieve solid-liquid ratiosof 1:14, 1:16, 1:18, 1:20, and 1:25, respectively. Allow each mixture to stand at room temperature for 30 minutes to ensure thorough wetting of the powder, facilitating subsequent release of volatile components. After soaking, the mixtures were transferred to the distillation apparatus and heated in a controlled manner using a heating jacket until the system reached a gentle, stable reflux state. Timing commenced from the first drop of condensate appearing at the condenser outlet, strictly controlling the distillation process to continue for 90 minutes. The distillate was collected throughout the process in dedicated containers for subsequent essential oil content determination and comparison. Through systematic analysis of essential oil yields obtained under different feed ratios, the aim was to clarify the specific influence of solvent dosage on patchouli essential oil extraction efficiency, thereby providing experimental basis for optimizing the extraction process. 2.3.3 Effect of Different Soaking Times on the Yield of Patchouli Essential Oil Steam distillation was employed to investigate the effect of soaking time on the extraction efficiency of essential oils from patchouli and tangerine peel. The raw materials used in the experiments consisted of patchouli or tangerine peel powders with a particle size of 0.2 mm. For each test, 50.00 g of the sample was accurately weighed using an analytical balance and mixed with 400 mL of distilled water (material-to-liquid ratio of 1:16). The mixture was then soaked for predetermined durations of 30, 60, 90, 120, and 150 min. After soaking, the distillation apparatus was heated using a heating mantle until a mild reflux state was achieved. Distillation time was recorded from the appearance of the first condensate droplet at the outlet of the condenser tube and maintained for 90 min. Essential oil yields from each experimental group were quantified to systematically assess the influence of soaking time on extraction efficiency. 2.3.4 Effect of Different Extraction Times on Patchouli Essential Oil Yield Accurately weigh 50.00 g of patchouli granules with a particle size of 0.2 mm as raw material and place them in a suitable container. Add 400 mL of distilled water at a solid-to-liquid ratio of 1:16, stirring thoroughly to ensure uniform mixing. Allow the mixture to stand for 30 minutes to enable water to fully permeate the material structure, creating favorable conditions for subsequent distillation extraction. Transfer the entire soaked mixture into the distillation flask, installing the condenser and receiving apparatus. Apply heat using a heating mantle, controlling the rate of temperature increase until the system reaches a gentle, stable reflux state. Begin precise timing from the collection of the first drop of condensate from the condenser outlet. Conduct experiments at five distinct distillation durations: 30, 60, 90, 120, and 150 min. After each time interval, quantitatively collect and record the obtained essential oil yield to systematically analyze the influence of extraction duration on oil yield. 2.3.5 Response-Based Process Optimization Based on the single-factor experiments, the key variables influencing the extraction of patchouli essential oil were preliminarily identified. However, it was observed that univariate optimization approaches are insufficient for obtaining a global optimum, and the essential oil yield did not reach its maximum under these conditions. To achieve simultaneous optimization of multiple interacting factors, response surface methodology (RSM) was employed in this study. This approach integrates the results of single-factor experiments and evaluates the interactions among variables to determine the optimal extraction conditions and enhance overall extraction efficiency. Based on preliminary single-factor analysis, three independent variables—feedstock-to-liquid ratio, soaking time, and extraction time—were selected for further optimization, with essential oil yield as the response variable. A three-factor, three-level response surface analysis plan was constructed using the Box-Behnken design (BBD) in Design-Expert 10.0 software. Factor levels were coded as − 1, 0, and + 1, where 0 represented the center point, − 1 denoted the low level, and + 1 indicated the high level. The resulting mathematical model enabled investigation of the influence mechanisms of these process parameters on essential oil yield. Through process optimization, optimal operating conditions were determined to maximize extraction efficiency. Table 2 Response Surface Design Factors and Levels Extraction Factors level -1 0 1 Extraction Time 90 120 150 Solid-Liquid Ratio 1:16 1:18 1:20 Soaking Time 60 90 120 3. Results and Discussion 3.1 Results of Single-Factor Experiments 3.1.1 Effect of Particle Size on Patchouli Essential Oil Yield According to the experimental design described in Section 2.3.1 , the corresponding experiments were conducted, and the results are presented in Fig. 2 . As shown in the figure, within the tested range, the essential oil yield increases as the particle size decreases, reaching its maximum at a particle size of 0.2 mm. When the particle size becomes larger, the yield declines accordingly. This trend can be attributed to the lower degree of cell wall disruption in larger particles, which limits mass transfer and reduces extraction efficiency. In contrast, smaller particle sizes provide a greater surface area for interaction with steam, thereby facilitating the release, dissolution, and volatilization of essential oil components. 3.1.2 Effect of Feed-to-Liquid Ratio on Patchouli Essential Oil Yield The experiments designed according to Section 2.3.2 were conducted, and the results are presented in Fig. 3 . As shown, the yield of patchouli essential oil initially increased and then decreased with increasing material-to-liquid ratio, reaching a maximum at a ratio of 1:18. This trend suggests that an appropriate amount of water enhances extraction efficiency by ensuring sufficient contact between steam and plant material. When the water content is insufficient, limited steam generation hampers the complete release of volatile components. Conversely, excessive water can cause vigorous boiling, leading to process instability and a reduction in essential oil yield. 3.1.3 Effect of Soaking Time on Patchouli Essential Oil Yield The experiments designed according to Section 2.3.3 were conducted, and the results are presented in Fig. 4 . As shown, the extraction yield of patchouli essential oil reached its maximum at soaking times of 60 and 90 minutes. Overall, the yield exhibited a trend of initially increasing and then decreasing with prolonged soaking. Insufficient soaking prevents water from fully penetrating the plant material, limiting the effective release of volatile oils by steam. In contrast, excessive soaking can damage the tissue structure of patchouli, leading to the dissolution of pectin and other water-soluble components. This can result in emulsification, which hinders oil–water separation and ultimately reduces the efficiency of essential oil extraction. 3.1.4 Effect of Extraction Time on the Yield of Patchouli Essential Oil The experiments designed according to Section 2.3.4 were conducted, and the results are presented in Fig. 5 . As shown, the essential oil yields of patchouli and tangerine peel increased significantly with prolonged extraction time. The yield curves for both materials began to plateau at 120 min and 90 min, respectively, indicating that further extension of extraction time produced negligible additional yield. Considering extraction efficiency and the avoidance of unnecessary energy and time consumption, the optimal extraction times were determined to be 120 min for patchouli and 90 min for tangerine peel essential oils. 3.2 Response Surface Optimization Analysis 3.2.1 Response Surface Conclusions Based on the results of single-factor experiments, extraction time, solid-liquid ratio, and soaking time were selected as key influencing factors. With patchouli oil extraction yield as the response variable, a three-factor, three-level response surface experimental design was established using the Box-Behnken design method in Design-Expert 10.0 software. 3.2.2Analysis of Treatment and Response Optimization Based on the extraction process data for patchouli essential oil obtained from response surface experimental design, analysis was conducted using Design-Expert 10.0 software. The results of the variance analysis for the quadratic polynomial model characterizing essential oil yield are listed in Table 4 , while the regression coefficients of the model equation are summarized in Table 5 . Figure 6 presents a scatter plot comparing experimental observations with model predictions, indicating good consistency between the constructed model and experimental data. Table 4 Variance Analysis of Polynomial Model of Herba Pogostemonis Essential Oil Yield Source of variance Sum of squares Degrees of freedom Mean square F value p value Significance Model 0.37 9 0.041 236.01 < 0.0001 ** x 1 0.048 1 0.048 275.7 < 0.0001 ** x 2 0.0002 1 0.0002 1.15 0.3196 x 3 0.0061 1 0.0061 34.71 0.0006 ** x 1 * x 2 0 1 0 0 1 x 1 * x 3 0.0001 1 0.0001 0.57 0.4735 x 2 * x 3 0.0004 1 0.0004 2.3 0.1736 x 1 2 0.06 1 0.06 344.99 < 0.0001 ** x 2 2 0.034 1 0.034 193.52 < 0.0001 ** x 3 2 0.19 1 0.19 1111.55 < 0.0001 ** Residual 0.0012 7 0.0002 Dispersion term 0.0007 3 0.0002 1.79 0.2875 Pure error 0.0005 4 0.0001 Total deviation 0.37 16 R 2 = 0.997, Pre R 2 = 0.968 Note: **. Highly significant (P < 0.01); *. Significant (P < 0.01) This study established a predictive model for patchouli oil yield using response surface analysis. The model exhibits overall highly significant statistical significance (F = 236.01, p < 0.0001), indicating a highly significant linear relationship between influencing factors and yield. The model exhibits high reliability, with excellent agreement between predicted and experimental values. As shown in Fig. 7 , data points are uniformly and symmetrically distributed on both sides of the reference line, and the coefficient of determination R² reaches 0.997, confirming minimal deviation between measured and predicted values. The established polynomial model reliably describes changes in patchouli oil yield under experimental conditions, demonstrating good goodness-of-fit and predictive accuracy. According to the ANOVA results in Table 4 , both x ₁ (extraction time) and x ₃ (soaking time) have a highly significant effect on essential oil yield when considered individually. The influence of the factors on yield can be ranked as follows: x ₁ (extraction time) > x ₃ (soaking time) > x ₂ (material-to-liquid ratio). The interaction terms do not show significant effects, whereas the quadratic terms exhibit high significance, indicating notable curvature in the response surface for these variables. Table 5 Analysis of Variance for Polynomial Models of Patchouli Essential Oil Yield Coefficient term Regression coefficient Degrees of freedom Standard deviation Lower 95% confidence limit Upper 95% confidence limit Intercept 1.21 1 0.006 1.2 1.23 x 1 0.078 1 0.005 0.066 0.089 x 2 0.005 1 0.005 -0.006 0.016 x 3 -0.027 1 0.005 -0.039 -0.016 x 1 * x 2 0 1 0.007 -0.016 0.016 x 1 * x 3 -0.005 1 0.007 -0.021 0.011 x 2 * x 3 -0.01 1 0.007 -0.026 0.006 x 1 2 -0.12 1 0.006 -0.13 -0.1 x 2 2 -0.089 1 0.006 -0.1 -0.074 x 3 2 -0.21 1 0.006 -0.23 -0.2 Based on the regression coefficients listed in Table 5 , the quadratic polynomial model relating the yield of patchouli essential oil to extraction time ( x ₁), material-to-liquid ratio ( x ₂), and soaking time ( x ₃) can be expressed as follows: $$\begin{gathered} {\text{Y=1}}{\text{.210+0}}{\text{.078}}{{\text{x}}_{\text{1}}}{\text{+}}0.005{{\text{x}}_{\text{2}}}{\text{-}}0.027{{\text{x}}_{\text{3}}}{\text{-}}0.005{{\text{x}}_{\text{1}}}{{\text{x}}_{\text{3}}} \hfill \\ {\text{-}}0.010{{\text{x}}_{\text{2}}}{{\text{x}}_{\text{3}}}{\text{-}}0.120{\text{x}}_{1}^{2}{\text{-}}0.089{\text{x}}_{2}^{2}{\text{-}}0.210{\text{x}}_{3}^{2} \hfill \\ \end{gathered}$$ 2 3.2.3 Process Optimization Analysis A response surface plot is a three-dimensional representation used to illustrate the relationship between the response variable and the independent factors. Such plots provide a visual means to evaluate the effects of individual factors as well as their interactions on the response. The response surface for the yield of patchouli essential oil is shown in Fig. 7 . Using Design-Expert 10.0 software for response surface analysis, the optimal process parameters for steam distillation extraction of patchouli essential oil were predicted as follows: extraction time of 129.77 minutes, solid-to-liquid ratio of 1:18.06, and soaking time of 87.94 minutes. Under these conditions, the theoretical maximum oil yield can reach 1.23%. For practical operation and considering instrument precision, conditions were appropriately adjusted to extraction time 130 minutes, solid-liquid ratio 1:18, and soaking time 88 minutes. After three replicate experiments, the average oil yield was 1.21%, closely matching the model prediction of 1.23% with minimal deviation. The experimental results demonstrate good agreement between the actual oil yield and the model prediction, indicating that the established response surface model possesses high predictive accuracy and reliability, accurately reflecting the influence of each factor on the oil yield. This result not only confirms the effectiveness and stability of the optimized process conditions but also further demonstrates that the response surface analysis method has successfully optimized the steam distillation extraction process for patchouli essential oil, providing reliable theoretical basis and practical reference for actual production. 4. Conclusions Response surface methodology was employed to determine the optimal conditions for steam distillation extraction of patchouli essential oil, with the particle size of raw material fixed at 0.2 mm. The optimized parameters were as follows: extraction time of 130 min, material-to-liquid ratio of 1:18, and soaking time of 88 min. Under these conditions, the experimentally measured essential oil yield reached 1.21%, closely matching the model-predicted value and thereby validating the accuracy and reliability of the response surface model. The analysis of the response surface indicates that the relative influence of the factors on essential oil yield follows the order: extraction time ( x ₁) > soaking time ( x ₃) > material-to-liquid ratio ( x ₂). Furthermore, the quadratic terms exhibit highly significant effects, while the interaction terms are not significant, suggesting the absence of notable synergistic effects between factors. The established response surface model demonstrates excellent predictive capability and stability, effectively capturing the nonlinear relationships between process parameters and essential oil yield. The optimized process balances extraction efficiency and economic considerations and exhibits strong practical feasibility, providing a scientific basis and technical support for the industrial-scale production of patchouli essential oil. Declarations Author Contribution K.S.andH.wrote the main manuscript text and Z.prepared figures 1-7andY.prepared tables1-5. All authors reviewed the manuscript. Acknowledgements This research was funded by (1) Science Start-up Foundational of Xi’an University of Architecture and Technology [grant number 1960324008]; (2) National Fund Cultivation Project of Xi’an University of Architecture and Technology [grant number 019/1960524133]. References Srivastava S, Lal RK, Singh VR, Rout PK, Padalia RC, Yadav AK, Bawitlung L, Bhatt D, Maurya AK, Pal A, Bawankule DU, Mishra A, Gupta P, Chanotiya CS (2022) Chemical investigation and biological activities of Patchouli (Pogostemon cablin (Blanco) Benth) essential oil. 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J Anal Sci Technol 9(1):1–16. 10.1186/s40543-018-0151-3 Kumar V, Jha K (2019) Multi-objective shape optimization of vortex finders in cyclone separators using response surface methodology and genetic algorithms. Sep Purif Technol 215:25–31. 10.1016/j.seppur.2018.12.083 Palanikumar K (2021) Introductory Chapter: Response Surface Methodology in Engineering Science. Response Surface Methodology in Engineering Science Conde-Hernández LA, Botello-Ojeda AG, Alonso-Calderón AA, Osorio-Lama MA, Bernabé-Loranca MB, Chavez-Bravo E (2021) Optimization of Extraction of Essential Oils using Response Surface Methodology: A Review. Essent Oil Bear Plants 24(5):937–982. 10.1080/0972060x.2021.1976286 Keerthiraj M, Bhowmik A, Saha S, Dutta A, Chawla G, Kundu A (2022) Optimisation of patchoulol in the lipid-soluble concentrates of Pogostemon cablin using response surface methodology (RSM) coupled with genetic algorithms (GA). Ind Crops Prod 182:114826. 10.1016/j.indcrop.2022.114826 Baş D, Boyacı İH (2007) Modeling and optimization I: Usability of response surface methodology. J Food Eng 78(3):836–845. 10.1016/j.jfoodeng.2005.11.024 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 26 Dec, 2025 First submitted to journal 25 Dec, 2025 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-8450235","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582062299,"identity":"8b2a18dc-f793-487b-be07-c98e4ad3dabf","order_by":0,"name":"Kangning Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDACHgbGBzC2BLFamA0gNAla2CRI02Jw5uyxap6aw/L2DMwHb/Mw2OUR1CLZ25d2c8axw4Y9DGzJ1jwMycUEtfDz85jd+MB2mLGHgcdMmofhQGIDIS1sQC0FCf8O2/cw8H8jTgs/b48Zw8e2w4lAW9iI0yLZc8ZYcmZfenLPYTZjyzkGyYS1GJzJMfzM883atr29+eGNNxV2hLUgADPYBOLVj4JRMApGwSjAAwC7JDMEA/PyxAAAAABJRU5ErkJggg==","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":true,"prefix":"","firstName":"Kangning","middleName":"","lastName":"Xiong","suffix":""},{"id":582062300,"identity":"976c27e7-7c76-4ccc-8166-7804a9c7439a","order_by":1,"name":"Sitong Xu","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sitong","middleName":"","lastName":"Xu","suffix":""},{"id":582062301,"identity":"68f33b43-c90b-4a9d-b897-8b0958adfc37","order_by":2,"name":"Huifang Xu","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Xu","suffix":""},{"id":582062314,"identity":"ea66a97a-1f67-4605-8803-bf8112c9efe9","order_by":3,"name":"Zhen Wang","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":582062316,"identity":"24952e82-ffe3-427c-b3b8-ed548d5bf336","order_by":4,"name":"Yubo Li","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yubo","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-12-25 16:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8450235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8450235/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101446886,"identity":"7d50a382-a14d-4ebe-8738-cac5eb6e9585","added_by":"auto","created_at":"2026-01-29 18:40:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76998,"visible":true,"origin":"","legend":"\u003cp\u003eVolatile Oil Analyzer\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/abef7fce968ee25a29e4e564.png"},{"id":101446881,"identity":"2c021060-f54a-403d-9c33-0d5d79931227","added_by":"auto","created_at":"2026-01-29 18:40:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38441,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Particle Size on the Yield of Patchouli Essential Oil\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/31ad107df3b7156076f29f80.png"},{"id":101446884,"identity":"10462afb-d18b-4dbb-a67c-2483aed9548e","added_by":"auto","created_at":"2026-01-29 18:40:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54738,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Feed-to-Liquid Ratio on Patchouli Essential Oil Yield\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/5210da32db4b06fd6e884fe2.png"},{"id":101446882,"identity":"1aa3b684-4536-406a-98d2-19e58d13e425","added_by":"auto","created_at":"2026-01-29 18:40:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55186,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Soaking Time on the Yield of Patchouli Essential Oil\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/3c405b2c854b8dadee19c2af.png"},{"id":101446885,"identity":"980349bf-3021-451f-b3f3-148024f300fa","added_by":"auto","created_at":"2026-01-29 18:40:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50290,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Extraction Time on the Yield of Patchouli Essential Oil\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/6c0216dd3a72838becae7a9f.png"},{"id":101446888,"identity":"07d1ffb5-9b17-426b-954c-92b0b0cbd2a7","added_by":"auto","created_at":"2026-01-29 18:40:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62602,"visible":true,"origin":"","legend":"\u003cp\u003eScatter Plot Comparing Experimental Data and Predicted Values for Patchouli Essential Oil Yield\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/17cd7c9920aff28e46da5d1f.png"},{"id":101751656,"identity":"b739de2e-ba3e-4a2c-a78b-757b8a0c32ee","added_by":"auto","created_at":"2026-02-03 10:22:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":87594,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of Factor Interactions on the Yield of Patchouli Essential Oil: (a) Solid-to-liquid ratio and extraction time; (b) Soaking time and extraction time; (c) Soaking time and solid-to-liquid ratio.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/02205a275a77711795b33b77.png"},{"id":101754987,"identity":"c15d85bf-955d-4db3-ab4f-db45555e6f51","added_by":"auto","created_at":"2026-02-03 10:48:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8450235/v1/9344ebcd-779f-4533-93e6-3a2270df9f8e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Process of Extracting Patchouli Essential Oil by Steam Distillation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePatchouli (Pogostemon cablin) is a perennial herbaceous plant belonging to the Lamiaceae family [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], highly valued for its distinctive natural fragrance and diverse pharmacological activities. Clinically, this plant exhibits broad-spectrum pharmacological effects, including antibacterial, anti-inflammatory, anti-influenza, and antidepressant properties. Traditional medicine also employs it to alleviate various gastrointestinal discomforts caused by spleen-stomach dampness stagnation. Leveraging its multifaceted efficacy, patchouli is not only extensively applied in clinical medicine but has also become a core ingredient in the cosmetics and fragrance industries [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Patchouli essential oil is primarily extracted from the plant's flowers, leaves, stems, and roots [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Its signature rich aroma and exceptional fixative properties make it a key fragrance component in daily chemical products such as perfumes, soaps, and detergents [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, market demand for patchouli essential oil has experienced sustained rapid growth. However, relying solely on expanding cultivation scale is insufficient to meet stable supply needs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], potentially triggering issues like land resource scarcity and ecological pressure. Furthermore, variations in cultivation conditions may lead to inconsistent final product quality. From a sustainable development perspective, enhancing essential oil extraction efficiency and ensuring stable raw material supply are critical.\u003c/p\u003e \u003cp\u003eWidely used techniques for extracting essential oils include steam distillation, mechanical pressing, ultrasonic-assisted extraction, organic solvent extraction, Soxhlet extraction, microwave-assisted extraction, subcritical water extraction, and supercritical fluid extraction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among these methods, steam distillation remains one of the most commonly adopted approaches. Keivanfar et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported that, compared with hydrodistillation, steam distillation enables higher essential-oil yields, accelerates the release of volatile components, and more effectively disrupts the oil gland structures of plant tissues. In addition, Zhong et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] demonstrated that steam distillation is currently the most widely applied extraction technology due to its simple equipment requirements, convenient operation, absence of solvent residues, low operating cost, and stable performance. Owing to these advantages, steam distillation has achieved extensive industrial deployment, and its associated processes, equipment designs, and operational procedures have become highly standardized and technically mature.\u003c/p\u003e \u003cp\u003eDonelian et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that the steam distillation of 0.1 kg of dried and ground patchouli leaves for 120 minutes yielded 1.5% essential oil. Similarly, D Ermaya et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] subjected dried patchouli to 5 hours of steam distillation. Analysis via gas chromatography-mass spectrometry (GC-MS) identified 16 components in the essential oil, with patchouliol as the major constituent at approximately 42.75%. Extensive research on steam distillation for plant essential oil extraction has confirmed its advantages, including high extraction yields and minimal loss of polar components [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To further improve the yield of patchouli essential oil and reduce raw material costs, systematic optimization of the extraction process is essential. Extensive research on process optimization has been conducted both domestically and internationally, particularly in chemical, food, pharmaceutical, materials, and environmental engineering industries. Various optimization strategies have been proposed to efficiently regulate complex process parameters and enhance overall performance. Among these, Response Surface Methodology (RSM) has been widely applied in extraction, synthesis, and reaction optimization due to its ability to model multivariate interactions, reduce the number of required experiments, and establish reliable quantitative relationships between process parameters and responses [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResponse Surface Methodology (RSM) is a systematic experimental design and optimization method proposed by statisticians George E. P. Box and K. B. Wilson [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It aims to simultaneously evaluate the effects of multiple influencing factor, such as temperature, time, solid-liquid ratio, energy input, etc. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and their interactions on process performance. By constructing mathematical models, this method provides in-depth analysis of how each process parameter influences response variables (such as extraction yield or target component concentration), enabling scientific regulation and optimization of complex processes. In the field of essential oil extraction, RSM demonstrates significant application advantages. Traditional experimental methods often struggle to comprehensively examine synergistic or antagonistic effects among multiple factors. In contrast, RSM systematically integrates all process parameters, enabling precise control over essential oil chemical composition and functional properties, thereby ensuring product quality stability and consistency. Simultaneously, this method significantly enhances extraction efficiency, reduces the number of experiments required, and saves both time and economic costs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By analyzing the effects of individual factors and their interactions, RSM accurately identifies optimal operating ranges, providing reliable foundations for process scaling and industrial production.The core capability of RSM lies in its modeling and predictive functions. Through establishing quantitative mathematical models linking response variables to key process parameters, this method precisely describes the influence patterns of different variables on extraction outcomes, thereby predicting optimal process combinations under specified constraints.In practical applications, RSM has been extensively employed for optimizing various essential oil extraction processes. Conde-Hern\u0026aacute;ndez et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] systematically optimized extraction conditions for a specific essential oil using RSM, validating the method's effectiveness in controlling product quality. In another representative study, Keerthiraj et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] combined RSM with genetic algorithms to optimize the ultrasonic-microwave-assisted extraction process for patchouli essential oil. This research systematically examined multiple parameters\u0026mdash;including solid-liquid ratio, extraction time, ultrasonic frequency, and microwave power\u0026mdash;and constructed a predictive model via RSM to maximize patchouli alcohol content. This model was then integrated with genetic algorithms for global optimization. The results demonstrated that RSM can accurately characterize complex interactions among multiple factors, effectively enhance the extraction rate of target components, and ensure the overall quality of the essential oil. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] This study employed Response Surface Methodology (RSM) combined with Box-Behnken Design (BBD) to systematically optimize the extraction process for patchouli essential oil. Four key process parameters\u0026mdash;particle size, solid-liquid ratio, soaking time, and extraction time\u0026mdash;were selected and set as optimization variables. Employing the efficient and economical BBD experimental design method, a quadratic polynomial regression model reflecting the relationship between each factor and essential oil extraction rate was constructed with minimal experimental runs. This approach not only assessed the independent influence of each single factor but also deeply analyzed the interactions between factors, thereby comprehensively revealing the contribution mechanisms of process parameters to extraction efficiency.\u003c/p\u003e \u003cp\u003eDuring analysis, the model underwent significance testing and goodness-of-fit evaluation via analysis of variance (ANOVA), ensuring reliability and predictive capability. Response surface analysis visually identified key factors influencing extraction rate and their optimal ranges, enabling prediction of the optimal parameter combination. Finally, experimental validation confirmed the feasibility and stability of these optimized conditions, verifying the model's effectiveness.This study aims to provide scientific rationale and practical technical guidance for efficient, stable, and environmentally friendly extraction processes of patchouli essential oil.\u003c/p\u003e"},{"header":"2. Experiment","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials and Equipment\u003c/h2\u003e \u003cp\u003eThe patchouli used in this study was obtained from a local certified wholesale market for medicinal herbs. The experimental equipment and related operational details are summarized as follows.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental Instruments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstrument Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecifications/Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManufacturer or Distributor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectric Hot Air-Drying Oven\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDHG-9240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShanghai Shenxian Constant Temperature Equipment Factory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese Medicine Pulverizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQE-100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZhejiang Yili Industry \u0026amp; Trade Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalytical Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUW220D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShimadzu Corporation, Japan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese Medicine Sieve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T6003.1-2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZhejiang Shangyu Huafeng Hardware Instrument Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeating Jacket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ/320683AAFA02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNantong Lihao Laboratory Instrument Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolatile Oil Extractor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuangzhou Congyuan Instrument Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefrigerator/Freezer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC-68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHefei Meiling Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cp\u003eIntact patchouli plants with a uniform green appearance were selected and placed on trays. Subsequently, the trays were transferred to an electric hot-air drying oven and dried at 55\u0026deg;C for 48 hours. After drying, the material was ground using a micro-grinder and sieved through a series of standard sieves, yielding five distinct particle size fractions (0.2, 0.4, 0.6, 0.8, and 1.0 mm). The prepared samples were stored in a dark, dry environment prior to further experimentation.\u003c/p\u003e \u003cp\u003eThe extraction of essential oil components from the sample was performed using steam distillation. The specific procedure is as follows: First, precisely weigh 50.00 grams of uniformly ground patchouli powder using an analytical balance, then transfer it to a 1-liter round-bottom flask. An appropriate volume of distilled water, corresponding to the preset liquid\u0026ndash;solid ratio, was then added along with several zeolite particles. The mixture was thoroughly soaked and allowed to stand at room temperature for a designated period.\u003c/p\u003e \u003cp\u003eThe volatile-oil analysis apparatus was assembled in strict accordance with the specifications of the Chinese Pharmacopoeia. Heating was initiated, and the distillation time was recorded once the mixture reached a gentle boiling state. Distillation was continued until the predetermined endpoint. The distillate was collected, and the volume of the oil layer was measured to calculate the essential oil yield (Y). After extraction, the residual material or waste sample was sealed in an amber glass container and stored at 4℃ for subsequent analysis.\u003c/p\u003e \u003cp\u003eThe calculation formula is:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Y=\\frac{{{m_1}}}{{{m_2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula: \u003cem\u003eY\u003c/em\u003e\u0026mdash;Yield of patchouli essential oil, %;\u003c/p\u003e \u003cp\u003e \u003cem\u003em\u003c/em\u003e \u003csub\u003e1\u003c/sub\u003e\u0026mdash;Mass of patchouli essential oil obtained, kg;\u003c/p\u003e \u003cp\u003e \u003cem\u003em\u003c/em\u003e \u003csub\u003e2\u003c/sub\u003e\u0026mdash;Mass of patchouli feedstock, kg.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Single-Factor Experiments\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Effect of Different Particle Sizes on the Yield of Patchouli Essential Oil\u003c/h2\u003e \u003cp\u003eThe experiment began with systematic sample preparation and precise weighing. After drying, the patchouli raw material was accurately graded using standard sieves to obtain five powder particle sizes (0.2, 0.4, 0.6, 0.8, or 1.0 mm). For each experiment, 50.00 g of a specific particle size sample was precisely weighed using an analytical balance, placed in the distillation apparatus, and mixed with 400 mL of distilled water (corresponding to a material-to-liquid ratio of 1:16). The mixture was soaked at room temperature for 30 minutes to allow the cellular tissue to fully swell, preparing it for subsequent distillation.\u003c/p\u003e \u003cp\u003eThe distillation process was conducted under strictly controlled conditions. After heating initiation, timing commenced upon the appearance of the first drop of condensate. The system maintained stable reflux distillation for 90 minutes. Distillate was collected, allowed to settle and separate into layers, then the essential oil fraction was isolated. This fraction was dried and precisely weighed. By calculating the essential oil yield for each particle size sample, the impact of raw material pulverization degree on extraction efficiency could be systematically evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Effect of Different Solvent-to-Material Ratios on Patchouli Essential Oil Yield\u003c/h2\u003e \u003cp\u003eAccurately weigh 50.00 grams of pretreated patchouli powder with a uniform particle size of 0.2 millimeters as the raw material for this distillation extraction experiment. Subsequently, based on five predetermined solid-liquid ratio conditions, add corresponding volumes of distilled water to the raw material to achieve solid-liquid ratiosof 1:14, 1:16, 1:18, 1:20, and 1:25, respectively. Allow each mixture to stand at room temperature for 30 minutes to ensure thorough wetting of the powder, facilitating subsequent release of volatile components.\u003c/p\u003e \u003cp\u003eAfter soaking, the mixtures were transferred to the distillation apparatus and heated in a controlled manner using a heating jacket until the system reached a gentle, stable reflux state. Timing commenced from the first drop of condensate appearing at the condenser outlet, strictly controlling the distillation process to continue for 90 minutes. The distillate was collected throughout the process in dedicated containers for subsequent essential oil content determination and comparison. Through systematic analysis of essential oil yields obtained under different feed ratios, the aim was to clarify the specific influence of solvent dosage on patchouli essential oil extraction efficiency, thereby providing experimental basis for optimizing the extraction process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Effect of Different Soaking Times on the Yield of Patchouli Essential Oil\u003c/h2\u003e \u003cp\u003eSteam distillation was employed to investigate the effect of soaking time on the extraction efficiency of essential oils from patchouli and tangerine peel. The raw materials used in the experiments consisted of patchouli or tangerine peel powders with a particle size of 0.2 mm. For each test, 50.00 g of the sample was accurately weighed using an analytical balance and mixed with 400 mL of distilled water (material-to-liquid ratio of 1:16). The mixture was then soaked for predetermined durations of 30, 60, 90, 120, and 150 min.\u003c/p\u003e \u003cp\u003eAfter soaking, the distillation apparatus was heated using a heating mantle until a mild reflux state was achieved. Distillation time was recorded from the appearance of the first condensate droplet at the outlet of the condenser tube and maintained for 90 min. Essential oil yields from each experimental group were quantified to systematically assess the influence of soaking time on extraction efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Effect of Different Extraction Times on Patchouli Essential Oil Yield\u003c/h2\u003e \u003cp\u003eAccurately weigh 50.00 g of patchouli granules with a particle size of 0.2 mm as raw material and place them in a suitable container. Add 400 mL of distilled water at a solid-to-liquid ratio of 1:16, stirring thoroughly to ensure uniform mixing. Allow the mixture to stand for 30 minutes to enable water to fully permeate the material structure, creating favorable conditions for subsequent distillation extraction.\u003c/p\u003e \u003cp\u003eTransfer the entire soaked mixture into the distillation flask, installing the condenser and receiving apparatus. Apply heat using a heating mantle, controlling the rate of temperature increase until the system reaches a gentle, stable reflux state. Begin precise timing from the collection of the first drop of condensate from the condenser outlet. Conduct experiments at five distinct distillation durations: 30, 60, 90, 120, and 150 min. After each time interval, quantitatively collect and record the obtained essential oil yield to systematically analyze the influence of extraction duration on oil yield.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Response-Based Process Optimization\u003c/h2\u003e \u003cp\u003eBased on the single-factor experiments, the key variables influencing the extraction of patchouli essential oil were preliminarily identified. However, it was observed that univariate optimization approaches are insufficient for obtaining a global optimum, and the essential oil yield did not reach its maximum under these conditions. To achieve simultaneous optimization of multiple interacting factors, response surface methodology (RSM) was employed in this study. This approach integrates the results of single-factor experiments and evaluates the interactions among variables to determine the optimal extraction conditions and enhance overall extraction efficiency.\u003c/p\u003e \u003cp\u003eBased on preliminary single-factor analysis, three independent variables\u0026mdash;feedstock-to-liquid ratio, soaking time, and extraction time\u0026mdash;were selected for further optimization, with essential oil yield as the response variable. A three-factor, three-level response surface analysis plan was constructed using the Box-Behnken design (BBD) in Design-Expert 10.0 software. Factor levels were coded as \u0026minus;\u0026thinsp;1, 0, and +\u0026thinsp;1, where 0 represented the center point, \u0026minus;\u0026thinsp;1 denoted the low level, and +\u0026thinsp;1 indicated the high level. The resulting mathematical model enabled investigation of the influence mechanisms of these process parameters on essential oil yield. Through process optimization, optimal operating conditions were determined to maximize extraction efficiency.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse Surface Design Factors and Levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExtraction Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraction Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolid-Liquid Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoaking Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Results of Single-Factor Experiments\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Effect of Particle Size on Patchouli Essential Oil Yield\u003c/h2\u003e \u003cp\u003eAccording to the experimental design described in Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.3.1\u003c/span\u003e, the corresponding experiments were conducted, and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As shown in the figure, within the tested range, the essential oil yield increases as the particle size decreases, reaching its maximum at a particle size of 0.2 mm. When the particle size becomes larger, the yield declines accordingly. This trend can be attributed to the lower degree of cell wall disruption in larger particles, which limits mass transfer and reduces extraction efficiency. In contrast, smaller particle sizes provide a greater surface area for interaction with steam, thereby facilitating the release, dissolution, and volatilization of essential oil components.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Effect of Feed-to-Liquid Ratio on Patchouli Essential Oil Yield\u003c/h2\u003e \u003cp\u003eThe experiments designed according to Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.3.2\u003c/span\u003e were conducted, and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As shown, the yield of patchouli essential oil initially increased and then decreased with increasing material-to-liquid ratio, reaching a maximum at a ratio of 1:18. This trend suggests that an appropriate amount of water enhances extraction efficiency by ensuring sufficient contact between steam and plant material. When the water content is insufficient, limited steam generation hampers the complete release of volatile components. Conversely, excessive water can cause vigorous boiling, leading to process instability and a reduction in essential oil yield.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Effect of Soaking Time on Patchouli Essential Oil Yield\u003c/h2\u003e \u003cp\u003eThe experiments designed according to Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.3.3\u003c/span\u003e were conducted, and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. As shown, the extraction yield of patchouli essential oil reached its maximum at soaking times of 60 and 90 minutes. Overall, the yield exhibited a trend of initially increasing and then decreasing with prolonged soaking. Insufficient soaking prevents water from fully penetrating the plant material, limiting the effective release of volatile oils by steam. In contrast, excessive soaking can damage the tissue structure of patchouli, leading to the dissolution of pectin and other water-soluble components. This can result in emulsification, which hinders oil\u0026ndash;water separation and ultimately reduces the efficiency of essential oil extraction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Effect of Extraction Time on the Yield of Patchouli Essential Oil\u003c/h2\u003e \u003cp\u003eThe experiments designed according to Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e2.3.4\u003c/span\u003e were conducted, and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. As shown, the essential oil yields of patchouli and tangerine peel increased significantly with prolonged extraction time. The yield curves for both materials began to plateau at 120 min and 90 min, respectively, indicating that further extension of extraction time produced negligible additional yield. Considering extraction efficiency and the avoidance of unnecessary energy and time consumption, the optimal extraction times were determined to be 120 min for patchouli and 90 min for tangerine peel essential oils.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Response Surface Optimization Analysis\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Response Surface Conclusions\u003c/h2\u003e \u003cp\u003eBased on the results of single-factor experiments, extraction time, solid-liquid ratio, and soaking time were selected as key influencing factors. With patchouli oil extraction yield as the response variable, a three-factor, three-level response surface experimental design was established using the Box-Behnken design method in Design-Expert 10.0 software.\u003c/p\u003e\u003cp\u003e\u003cimg 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+Ozf2MR6J9q2MMROPWEvvatFk7FIX2ePKV7p4Arf8r726p0tsMACgSTVhtW7R2C8N+MBp3zacxnXuEg1DHsL3xVWWKEMKkyLlEfb2mOPPZpMbCxqlUNb1Oa0LziS/KmrdyYTUkPv2ViJeJF2GtuM7/qvd6LdWfjql8Yh6ThED44l7uzCvXuLTuFDc0ild6TdKlO9+Aig/qW9sHvULrU9Y169+Ok3ZAR6KtEbcq26ONRAOeecc4bVPNG9AU0RqKNcq51OSKVqMrdy0slMFtVxyt86lJWte/FMTBo6tYKJln+tM/Aa1HViHcTAYaWMhNXGrb5HspCyRRddtBg0DDIm0DKOwZH6w2ConsgNkmoi0sGlNbFTsZjc5KOunPrquP379w+DiE67+OKLFzuLraoN+jqxjm8ApwozQMGSNEEeBjxlMTD4TYLh3kDvihxQXVl9KpP8+RtUlNME3q9fv/BskwEpgPBa5/nTTz99sdFGfZV9i0HkCzmHpfjeW6VS8bPJWQmXN9Ioo7xMGAZxE1QZXns1sXkWyQ6poNiDuAAAEABJREFUBEmMdy0eks8fAZAftbrwcnA3sCO93of3gkyY0EkApB8Wh0ghJgi5OqgLCYCymej5wUQcpNbEAHskw/usfbb2DBtOHJOJwVybdi+Nd60daAMILVtR/ane5KmeJmWTFlKDMCkXJz8TV21+2h9ppPwsAEiC2Ncpk2eqU225690jQdq8fu8dIPIkg55Xxlc20myTq3xXXXXVQDz0F6RKvZBfbdQkrr7SKnul8kv70t6q8xWnNQ55tliz0FM/ZKk16co4+g38y75moaJfkVppX961cml3Fk8ItX5epq+9mtxJxPQvJMT4YhGkX4pLzQgHfR7Rh480wuo579w4Cjd9A2kxlhgjyvgWLeow4ogjBuypR71/bdE7827UQXp56acWWdoHzMp8XL0H78bvek4e2lBtunpx+Vm8qoOFCWeznnYhjIOL8Uw5tWkbcJRVmHEMUUXi9ANkTL0sIowh6qXNIbDKpb1aVHln7rVdY5S+Jk/5y7fWqYt+We1vrCUhVFcSR21cHx7Su6pOD1v33q8xxu9ap4ylH8yVW/lLv7y2DYEkem3Dq25shqTUW4iVTqbT1o1Yx1PnRKLqBBVEiD+SZ/VKImEVRWqg4Qur5wzwBgETj05CrWZyrhe39CMBFM8KU3kMhGWHFAfZcG/ysrorHckECYRB3mqYyg4xMOAOadA3OcvXs7ja+iA0wpXDdWiOugQZI20wuI04aGCXxuBjwDMAlmUur8JrnYlAefjbjGMSJ31FDEnjDHDCOtIh4VRQVDNwRJLL/C0W1KG8r74aXK14keuyTuWVdKA6bmt+e7/aiTwtGEiEDa6179wzTO4mIoSTNEC7RCoselp6FokLQghLBJ2a27sxAbeUZkj++o5nkwDrI/vuu++Qog8WZvLTZ7nBAofiQeKL5JL+IqswUZ7qZCQ2JEnCqp2FAykVvNih6S8kzKQp1emH9TcppQnflbSyfKetzdciw0SP4JRplBteLS00y3j1rgiXd4QkwgNJ0I7KuMwp9F/P1aZL/5auFpTNw1p/pw0iE9p3mcoYhPiQUusLpX9XXS18zCXl87QHC15SZ9JFku1yTEWuEG+SPm1MvzNW6AcWfvCtdrRAZb7DevUcWhlzHbKo77clT2M7cm6RZbypl1b78n60NW2wXpz0az0CSfRaj1WLMQ1cbDlIU6xIST5ajFwToKNo+DXexa1G7oc47LKs+DgqVKs4YUNyVsUmUoOXjjWkuGWYOpB6mBhMVKW/PPymbnItHVKAILqnOjUArLfeesWmASSAfz1noudPEoCUWZm6r3VWvbV+9e7lY8JHcEkg4EVdbPBGFpGp6nRUn9X35W/SluowOMPCQGtHbm0+Zbr2XpEDEjGqENIOksfqFbTVNoxL8lk+R1kMhMJJhEs8hSPlnN9tcfIgTdFWSA9M8MgeQoPMlHmZiA32BmBSPPaDVEUkx/C3Ui/jVl+pnrV1khcSZ1jDFgGpjtfa3yZramq2kyZEE19r04qn3ZkYOfdtdbBH/i0w9BtmDNV5aDvGhmqi7j2SfMOT2tICjpRcWm3WO6jOY1h+k2zpExYR+gH1KlVoa/M0Dug7H3300WBJSsIxWEA7PUz4FlSkqySzFhFDy8r7g6UFTxlXu9dfyvuWriR94tVblFsoVyq/SFRbyqOj/Y2xVKXMVfQN/dAiwJhKTWt8YOOsvsZFpJnTfsUzNklTO0Yrp/HFtSOcMYIan0pZv1futuSrXVlEa1vqWi8tO2D9GfHVz+rFSb/WI5BEr/VY1Y1J9KxRMlS1qiKRqTcw1iY2WeqwCAr7q9pw99RxpB0GQUTI5EDEThJCMiJO6ayASBlI/cThr4NYgVMvVasEhLXkDDQmHKstxK0ke8ppYmLMS3WIAMiD6sFExkbDoKu8VEQGHpOXOFytJIw0pSSipHskafIQl1O/SqUS1Dvuh+aQjUqlUhyHovxICJyQPhgYmAye8jEgGqz8rnXKQV0C8zIM0Vb/SqUSBlL+7MrU12+u+rf7lpyJ3DMQJXH8pr6WvwEQSdKmhHHeG0wRGvecepiwSSNIAcp3Isy7NxlU58G/NU4b9j69F6pi7UebtnKHpTwQFTap3jkDaaQYeZMGaRNPmLi1jgrR4E3SzLwAMRySzVNt+tp7fchkLU+kV5utjTOke5MIIuZ9w31IcVsK07b0OYsykhRqvzIusuwekZe/PmPjCEkLSR4JDZJrcUiSReqs3VOrkcTr0/LSRlrbvsQvHamYtqv/IwmIkbZWhtdetSvtp/S36PDOEP3Sz7vV5rTL0q8jrgiX5yNZ6t6aMbR///7F2WpsXJUBTtqjMrofktO+STuNQyXO3pFxCnGvVCrhneqTZV+CjfczpHyHNcyzSN+MBwibRTSpJ/MO5IgGxRiizSHvVM36p8WeNqK/InUIvrooD4k8Aux3RzjtVFsuyX45lrUlb2Y2lUql+HqOPlydVp2Nd2wraa+qw/J3+xBIojcU3EgNDEJlNJ3JwIB0lX7l1QqFvY2JhzTEvcFDuMHLhG3nmg5spxTSQPRuwBGPv7gGLKs0ebCdY2tkVc4wWnn4166ixEfKdBArYhMYdbKJhGjfIC/vakdFwd5GfZA3E5AObIJSLh2O7YfJysDLeBo5ZXxrgjao2OxQ2mywQaLqNJmoC3JVPk+5qbkQFKTMwERVqS7UxWx/7P5i/I9AWKkaoKxcYcqPOsVAjmwgFO5dDRTsiQx+7BfZF7IfoQI1cSAh6mYCRVYNIK5l2aqvCAMiY0MKVZJyM2SmZkOgyglOna1GDbKkMgbSSqVSTDwmD+8KVuzA4OwZBnGEXRkRKm3J8xAldaWi4aT3Pg346gFvZITqk4qacwg3Mgtz7YdkySLD5GcyQnA9s9p5h9qFd4YUaS8c8kEihqhZpZMYey6SgLCbWEwoFiTek7aE4KgXSbaJRZ3gLo50VPfaEn/lUQ6SCXUi2SP1NRnZsae9KI82aDJFLEzW+p1J3L02II9qZyGkLoiu9yMvz2TXqizKID95mPTkJy/3yL6FEZKl3Zh47JT2joVRCXun1c8zoWm/wr1f70m59Xdl12+pSW0kUF7tzERl4aGveH/isQUjydVPLW70BWWFrbyME6SB2pZxwoIJ3uriN0Kkn7hXHuVSTrhrb9IpCxyMH9659mPMYtslbrXTf7R578G7JC0Wl/pfe5KfuuoP4lE9IxnGB2k45ajOs/ytnjA37um3ykTlqu+WcVy9S2Ogd4kYeyf8jSOc8lh4IBlIq7jGKO9V/yYNhD1sqIil0fb0Uf1MW1VG4cpt3DHGsNfVf40P+jlJnzZRqVSCZB3ZM+6xH/Qu9Ffkl5mKPuq3NqBersYn46l3auxQh2qnL2l/+rnxQNn0QY6/MllkWsRZDDCpkaff8tMPvWfxEHlptFV2jw7xRwbFMQ/wM04rt8U3/I2X+hZ8lB9Gyu39wMiYou/rP+qoPXnn3pf3LI5xBg7GCW1d2/V+xDOfaJvu5a0PSVuNgd/a+Kmnnlp8Kk4dlQdm/FZbbbXgtDvvXBvy7tRVHOOJNm8s1f+lVSf5doDrlVkk0RvKa0VODITsEDiTBYN0DU1Sk55OZNehVReVgwGHsTEyplHqmAYjnckkbVLSuZAT9mTy0YE0ZM8gSTNR6CwmcMTIJIAcGERIk6SpdgZKncQuLOn8NlAZvJC96rjlb2EGdmoBah5SBeRUuInf4OlZjIENEAiF3+pnV6kOWpI5JAphMxHp/KQBSIC8OCtoeJDiGGjVCQkTZjBllE2lh5xycDJRGgwNovKnJpC/gU15EAZ4mHQRHu+ErZPBCTFGoBBspEL+sFEfZTAAenatQyzhgugNGDCg2CXsWVSS6lTGp3pUf3VB/kziJh7lNugYkKh7DdomA+lMOt6hnXlU6lTbpFImfO3AhAxX0i7vG5kUD96erz4mAMQd0ZMnVZfJ0aBLMqqOymEiFl7tTNTeH5KLEJlMORtB5IOE2BxAiiFdpVJpOkxZGUh7xUc8TZTeH7JkIkGyTJD6gQmJxFT9xTNJyY8tJwmttuB96gMmWiTL5GAS1xZNbCYVZYGBdqzPyaPakShoayYDz/FevRcTHqJj0rFQgb/8tAmEywJLftod0qANezf6oPeg3VvsIF3Vz2MKoP3JXz9FXLU1cWAjDLEzAenbZXv3nrU3dVNXRARxgwcCYUIjdSPplJcwfQBOiAkJE6dtu5r0EBNqc5MpJ50+b1GoDXn/VNrIvWdqy/JUTnGrnTBaCXVCdJAEJBLZ9F4tmPQf79S4hvxVKpWwODFuaDuIS3We5W+kWLlgro8aX5BbY1QZx9VzxZOfid1zqbTVhSkMYsGmz7tmu4aQCNP2tGf1RHIsVBAYCwr4woHdMNJiLEGCjBee72s3SKUy7bbbblGpVIpjldRbmZRFe9dOvFPjGycu7PkhT8Z27cn47lnaB4z0dflUO8TO2CccrhbGnsFpi96rdiKNBYE+r+9ru9q25wor+5k02q13Y5xXdu/KOzeGKK8+gmzpa/qFfqntImbePRz0MRiVdfY+YO29IXLGWm3LWChP5YUhaaMFOwz0F3NdGV9ai0jkVpmrXaVSCeMgjIxb3i3yan5VdjaI0kuDzMHMc71D46QxRlmMRYQA+rq46eojkESvPi5NvgafWmdiLhuhiFZOBkeTvMZOJaRzIk5WLiZOJNDEpGFaQeq4JhPpOWRBYy+fRZJi8tJxrKwNpCZ0nVL8es4kYHKUh87i8zoGunpx+SEE4lY7A4sw6RCZMsygUan8pBpFfg0myiWe+CZ85Eh89TAw8C+deCYJ4YiMyaEMczWwmGSFm7DLeqo/7PhzJkUTEizccyZzaU2E7tUdoZMvhxzBQhq4mxgQQGG1ziRsMkQwvD/5wdTAXB1XfRBZ7wUBNOAZdE1KBnD+0nLIjbTC3JfO4GWyQphIXgzI2o4JWjmFSWfAtZiQDvbliloYB0sDr8lNu4El/1pnIpRHrdMetQUTVG0aE6qJ1cSrvMi3uosHI3XmL0+rcAsfE5T2z49DfixiTCTqpe3w57wT9SQJdF867an87Yp8eWa1814thORh8jIhmyxMOvqCdKVDpMrfroixvExs8jbha5/aiXbsnQivdrVlgtsSSyzRFMV7kzdHiidAPoikd0NiSiXHH4H3zuUhvoWVti6MM/mpl8WPMiqb/mYs8Q5M1NJxVKnSaGfanb6DGLHJ897EMT5ZDIpX6xCL6vzEJ9URz7PhAR/9S/vUJoRRVYtbOn61DqFHBMo4rvqzsa02rn6kziT7nqGt69dle9bPhSMIlUql+FwbAq1N8ffutAljaXV9kD918GwOUfN8Y4CxQL/jDy/jdfz8TxzhJHrSWMRr4/A3/lvcSVc6C3RjQnlftrGfsysuxhY4lnHqXeUjsnqKq+2I5/2qnzDjoHGSP/vQctwWpo2Zo2Cg3t5hpVKJ6vYpT8Rb/vLgxJXe871v4RaUFiDCOXwodaoAABAASURBVGH6v3pqa+6Na/JDxIwJxktxOYtzY4J86zkLawtxcTmLIuNFdVxxhJXOeOQ55b1r9VxanTZ//4RAEr2fcMi/nYQAKRupllWzVXcnPSazbXAESMNI7EzeVvlW41QuJggEk/SrwauQxUsEEoFEoEcikESva19bn3uaiZ16SsVJoqg0/U7XtxAgjULqqB1JWKjVbP4g/SQlIoHqW4hkbROBRCAR6BoEkuh1Dc599inUNgyNS8fOr8+C0ccrThXE3rJsC9SKVJR9HJasfp9HIAFIBDoXgSR6nYtv5p4IJAKJQCKQCCQCiUC3IZBEr9ugzwcnAu1DIFMlAolAIpAIJAKtRSCJXmuRyniJQCKQCCQCiUAikAg0HgJDLFESvSHCk4GJQCKQCCQCiUAikAj0XASS6PXcd5clTwQSgUSgfQhkqkQgEegzCCTR6zOvOiuaCCQCiUAikAgkAn0NgSR6fe2Nt6++mSoRSAQSgUQgEUgEeiACSfR64EvLIicCiUAikAgkAt2LQD69pyCQRK+nvKksZyKQCCQCiUAikAgkAm1EIIleGwHL6IlAItA+BDJVIpAIJAKJQNcjMNyrr74a6RKDbAPZBrINZBvINpBtINtA72sDw0066aTRmC7Lle8l20C2gWwD2QayDWQbyDYwLG0gVbddL0XNJyYCiUAikAi0B4FMkwgkAm1GYLjnn38+0iUG2QayDWQbyDaQbSDbQLaB3tcGhptyyikjXWLQS9tAtu3s39kGsg1kG8g20KfbQKpu2ywEzQSJQCKQCCQCiUAi0DMR6HulTqLX99551jgRSAQSgUQgEUgE+ggCSfT6yIvOaiYCiUD7EMhUiUAikAj0ZASS6PXkt5dlTwQSgUQgEUgEEoFEYAgIJNEbAjjtC8pUiUAikAgkAolAIpAINAYCSfQa4z1kKRKBRCARSAR6KwJZr0SgGxFIoteN4OejE4FEIBFIBBKBRCAR6EwEkuh1Jrq9OO+vv/463nnnnfjiiy/aVcthTd+uh9ZJ9Mknn8SHH34YP/zwQ53Q5l7fffddvP/++/HZZ581D6i6U6933303Pv/88yrfNv/MBIlAtyKgP+gb2rt2352F+eijj4qxpjVl+Pbbb4s++umnn7YYXR997733Qr4tRsqAPoHAV199FW+99VZ8//33Q63vjz/+GOKb91qaA8Qx9ovT3f2mukJJ9KrR6IW/DXzPPfdcXHvttXHWWWfF+eefHw8++GAYxO+777521fjjjz+O8847L+add9649NJL25yHAfbcc8+NueeeOy677LLB0iNK8j3zzDOjdDpOGVFHuuqqq5rC7r777lDPMry1V5PZ4YcfHltuuWWBx5DSIbT33ntvLLvssnHCCSfUjfrll18W9VlsscUKfOpGSs9EoAUEHnnkkUBCWgjuUu9nnnkmdtppp1hrrbXipZdeGuzZiFLZN8urBVN1xNLf9bHHHqsOatPv3XbbLZZZZpmhpvnmm2/if//7X6yyyipx5JFH1o2PABoL9dE99tijbpz07HkIGP+vvvrqpjlBm7vhhhvCmKw2+hW/0mkn/MWZf/7547XXXnM7RGfOvPDCC2PBBReM0047rW7cl19+Ofbaa68izgsvvDBYnKeffjrMfWU5XAcOHNgUz1zGj7v44ovjjTfeaAqLaP/PJHrtx65HpDzllFNixx13jHvuuado9BrrjTfeGFtttVXsv//+7aoDgjTyyCPXnQBak6FVz5DSy//FF1+Mv//977HJJpvEgAEDmhE56RE/dTjwwANbvdqvLVulUikmkA022CCUpza89n7ssccOE5wJpTasvB9jjDGK1aE6lH55TQSGhgDJ2T//+c+inw4tbkvhDzzwQFgktRTeFv+RRhopxhxzzECM6qXTvh9++OHYfPPN41//+leY1PhVx7XA3GGHHYrJ10RbHdaW36uvvnpBOluTZoQRRij6qDGiXnz+00wzTTzxxBP1gtOvhyLgvWqDSNbaa68dxx57bLF4569Krvfff3/8+9//jiuuuKJpPvnDH/4Qe++9d/z2t78VbYhu+OGHD3MAQUdLEX/961+HZ5kn6sUhMLj11ltDGbfYYouiv1dL/kgKjz766EL48OSTT7ZK01TvObV+w9V65H3vQeC///1vHHzwwbHqqqvGNttsE3/729/i//7v/woCNemkk7Y4iA8NgbHGGiumm266oUVrMXxo6ccbb7xi8th0001jxBFHjCWXXDImnHDCpvxGHXXUoqOow2GHHRZLL7106GBNEVr5o1KpxKyzzlqsvkwQQ0qGCE4xxRSByLUUz+Q41VRTtWrQaCmP9O+bCJh8rOZJyoe0kBgSOrvvvnsxuZVxhuXar1+/0N5bymPccceN//znP7HCCisUE5u+WjtZGm+QNBPp7LPP3lJWQ/Wfe5Dk/89//vNQ4+nDyq2vthR59NFHj+mnnz4qlUpLUdK/ByLg3RMMEGpoh+aIFVdcsWkBbx75y1/+Euuvv34xJy6yyCJFLc0h5kfxC48h/BFn2mmnjd/85jctxppooomKT621FGGWWWYpBCzrrLNO0Qb/9Kc/hTmjjK9cnnHEEUcUc/bEE09cBg3TNYneMMHXuImpZffcc8+Yc845Y+WVV45RRhmlKGylUinICmlY9SrbapxKlaj4zTffrEsC2SewZ3j99dfrTijE5yQKxOCu7ouH/vxnaOl/jlZcdFyrL6ujW265ZTBbQOJuEoeFFloofvWrXxVpSB+Uvfb56kbdq+xE+aSBH3zwQbFaIuGkciptNMSFgzrKR7zqFVfxoEF/5Aert99+e6jqNnnLU37KJ616Dcom/ycCoY1ZkJmYkD3qnXqwaJukCdodp71rR66kgSRs2rg2qf1yJATyIjHU/rT9sj3rn/qpePLTF+QnfmucCVWZTYBnnHHGYElINUYbbbSYeeaZi0lN3tq+PlCWpRyD1E1ZYVHG8VvfUS7lKx+g/MLkwV/fKsOqr/q6Z8GkNeS5xFYamFbnlb8bHwFzBlMDqnuq2bPPPrsY45Vc26LVQvaQMX7aIwmbPlHdPrSv6v5StlFpWnLaqHajPZrnWoo33HDDhT6hvxNenHjiiaF9iq88VMmI3uqDpNjIKf+OcEn0OgLFBsvD4KgBGcitHOoVz+BM5SJM/Ouuu65Q8W6//fax8cYbFyoZtnzCOZPHrrvuWqh8kUSrdA1TGGdgPO6444IqdbPNNiukiKRtyiBcY955552b0u+zzz68h+iIyddbb71gU1M9+SnvSSedFIhsmYFBf6ONNoptt902SBdMQOwphCv7QQcdFLPNNlscc8wxIWzDDTcMdkjIpElSHHEfeuih2HrrrYt8qI2t9u68805BTU6HphogzZhvvvkKNYHJqSlC1Q/1p3q2QoOt9+GZylsVLX/2YQRI89ZYY43Qfp999tm46aabwmRTC8kdd9wR++67b+yyyy4hvn4m/s033xzarQXLAQccEOxO9W1S99NPP73IxqS38MILF22fWQRP9rra/nbbbRd//etfCzMJpE9Ya91cc80VpfrLhFmdTr9lz1RK24XvtttuxdhiDLEAZYukj7zyyithTFCOQw45pFigrrvuuvH8888XfVFdyrzZ9aqfPEhoxJN3Ge4KC2PgSiutVNgCy9MYJayeszDeb7/9ijGQWs34YDFZL276NTYC1LekwOYiix/zFEk5gvX73/++KDw/ZM58MM888xT9RwDip69oL2y3ES7tspoIilft2J+ac8w9+iQbvOrwer9ptdiIssulqpU/W1gLPX2gXpph8UuiNyzoNWhaq102KBrzkNQvVjeq8Pjjj4dBbrXVVis2GiBoiAjbNVeN3wqEDYRGf/LJJwcRs7SlQ6pefvnlMGmYXEjaGHNbRUmPaGnIJiokrX///mXSFq+VSiWobBC76667rimeyc1EqDOXnsgYY1yTHOkCkXdpg0iVYyVVTgY6o7rzt2oqJQKkCrfffnuxCjzqqKOCfaNJyipLRyyfBV+DA4NvRuIm17vuuqsMbnY1qbKJXH755UO52CxZbcKrWcS86ZMIaJOInfaBMFFxXnDBBYPt7NZ39CfEySKDWQYipX/pI2uuuWaMM844oZ+Z6PTRakBNRMhLtd8555xTbKg69dRTw2QjP4SnOs7QfnumxRBpGNJWxjdWGA/++Mc/ll6hL1g8Iqr6wgILLFDYS5lw9UV9TT1JXNTPQorUnkSlXEghhSZi45o8LDg9i9S/6UGDfng2NRk85EOtfemllw4KGfw/cmvcMv7B1iSvf8p78Nh9zKcHVpeAwDvUbrxP8wJ7N31EGyurRAJI4GE8L/0smsyHbE+1L4sRc4E+Wsapvmo7CCFzAPMBZ+6pjlPvt/mI2laZ9L1LLrkkPNu80pr09fIckl8SvSGh00PDNHBESPHZILi25MS78sorAykknWJ/0K9fv0DyNHjSBgPpNddcEyaKySabrFADa6SVyk92LkTVJg0DPfJFcka6pxwmD6ty6dlQKI/Oteiii7ZUpGb+VmCLL7546GwlIRswYECQTlRHXGqppYpVGYNZqzcT1lNPPVVEYVc3+eSTF+ojBFXnNahPMskkoT5FpEF/dD4SNwRRnQwWVlwmomqiR7qgQ1KLkyyYdJC3QVk0+w9Tq73jjz8+SBaI5OEKz8svv7xZ3LzpmwiQ0rF30+dMUHZ1k87VEhd9i9RZv2GqoP1Z3CAx9ZAjMaj1H3/88Zt5aZdsdrVx0nH91TOaRWrFjb405ZRThgWehaEkdiUqGwLnnkO8TJ4mMmTNuKNPWwjCgL+rXZCkaiR17HX5Vyo/jTXqbjJlc2yS9RxXBNEzSmfiRSSdDKBuxpyLLrqoDG52ve2228JCjcqPmtki0KLPGEIV1yxy3vQIBGacccZCM2Oc9f5pk7SBsvCVSiXYa2q35jz+FhHmKW3KXGjuQfZJh0m/xal1+qlNUFtssUVhEqWPtUaIIR/lQShtDnJ99dVXg4DEHCa8I10SvY5Es0Hy0oBLmzxEY0jFMshSmxhQq+PNNNNMBTFC0hA+A55GWMapbozsfJAhZI+0jaPeRHR0MmVwX90BqtOXeda7ErfbbGHgRbyUg/TAQF4dX2e1y8/ExV7Cs6SpjlOpVAp7Ps/mqsPK3/wNDiYa0hX5qAdXHadS+Wni+d3vfhcIY/WqsIzn+aQYJkIqBLiYkGBugCjj9aJrVqUNCJBSIXqIjPZDumfiMVlQO1mEldmRSFiIlBIJhMfiRV8v47T1qn2SnFFfUTtJX93O3bfG2YRE2qhtk5oNHDgwSMFtoqpOr+w0DQgcNSoVledxZTz1Uk/90O/Sv7xWKpXCwN7Cz+RoUYkkq0sZx9V44MqZUKmYjVPua53xycSuf5ZOfgg3O6ra+Hnf+AhoP6Tf+pNF0ZhjjjnUQptXmBhZ6JftwMJFP6TdqZeB/msuohkqw+u12zKs9jrBBBMUG0TYk5J+V7fb2rjDcp/zZfaMAAAQAElEQVREb1jQa9C0Bn+N24D56KOPtlhK6g2reAOwVX31gFsmImXQaSqVSotHEmicGrdGX6YrrzpKmR5hLP3bcrXCJw1A9BAkE0vtAExCgJxR0RClk+K15RniGtzZ0l1//fXFkRCkK54lrCVHrWwliPDVxqlUfpqUTHyMgavDrd6q7/N330OAZJdUjtt+++0LswdSJ4s0tjpUOSUq/Ew4JqPST7tDEMv7tlyRTBJ2JIi5g93nbUlfG3e55ZYrdulSk+lDduTWxtF3LfyEUSXX6zO1aWrvLeI23njjwnaP5J6E0/hTG6/2Xv9DoGv93VtMIqCki+5LR7pZLckv/fPaOxEwD1LnIvi1NbSIqfVzb24jYGj0dtK9RA9S6ToFAZItqxm2OlYntQ+xuiWmthKhinRPVVHGMzAiflblJA5WRNSxVh5lnPKKDM4wwwzFpgSq2rLRs6szgJJ4IV7sJeqlL/Np6WpCoFIlBWBgbRVfO7iTJlKhKod8qF5d2+KQNmWmYrWjUNqyLn7Xc6QwJttaVbK4iDZs4U9lZXLlbxMIW0C/0/VdBNiBUelTy1Y7dqyInYWNfgghakhtjf0ewsePlBwh9LueI+nSZ8ow6cvfSKb+TmpNvVq2zTK8rVeEkYQNaWWHp7y1ebBz0q/000qlEkPrW7Xp3Su3xSu7ROWGhX4rrCVHC0BqR7JeLw5zE+Mf+2OaCXHsGGZUbwx0n673I4DwG68vvfTS0DfLOcRYra/UQ4Da1ZxmgVYvvFH8kug1ypvo4HJQ6zhZ26rUzlVEiMTK4OrARuoatmoat12oJGbi2d1qAqE6IsJmID7HHHOEgRtZsSNVenYMBkFk0eBrhW5SkgfbHNIwxE46ImlXNjx2nJbpVVl6KyK/h+SUkX0fGx5qmNq4VusGc1//UDd2E8pncuHvGepPFV2mFe7eBEidWqlUCoN2aWygKHdFGfxJD6Qzuej4JKAmBzhRc8NJOKmAiYU63D1c2Qmx9aD6pq6yW1Aa4en6JgImE1Jo0mpShGoU+JEka2fasrbJfkxb0iZJrZFAdmommjKtCUcaX43Rz01a2jFHykYaRopvUrJwIx30W3wbJBAm7doGD7+NBRYx0pXPGNK1VJXZWEKrUBvXxg3SEqYRNmTBwJiBUJFOeh6CRX2mb0pP41D2XZJxhNLilOrWoszCj9aAnZ4xjlSGlAUh9ix56W/sfJVLnvKTv3Qmc2pbu5hJNpmJWFSyv0WCaxeU0qdrfAS8X2Ow969Na+v1Sk1Sp98wsbEwt8vWnEjarS2Yd2y281t6eWozFuvuzXXmWm3s0EMPLTZUaHvaLaGHuVS8lpy+ba7SX5lnuLYUd1j8k+gNC3oNnhaZYGRs9Wu1iighGxqU1TdJmSrY5Waw9VkgqhA7STkqUCpZE5FBEMkzSNuhi2wZIOUtX1I2qlv56wRTTz11kCYiRtKbaORncEcgqZYRIOlJ/JRjSM7grgw2hMivNq7Jz0SIeCF9jo1g/2DioE4yIfBnlKsO0lP1+o0MK5u66rAGCVIVEgCbJwwWBn2TCPIqXwOADSnqIC6JpU6rPq7qLi2MDRSMdT3HxMkeqpQ8Kke6voUA+zWqWhOGBZI+WiJg0UE1qa1pU3bAk6S7R8r0T9I/ZhD6dHnYqvPDtCkLEpJ8fUSfIPFyLBKplTas3Vrg6XP6pM/66R926mrLCJ7+/I9//KMwX0DytGnXsowtXUn+qWX1C2SrNp4dhsYNuwzVzU5XfU15HS1jDLJQ0u9MvqSM6oC8MtVwlIpxAHbImj5tQWncYgOrjmyN9T1jE3WyCRrJg5m0pI36pQUj6aO8vAeLUL89c+DAgVGS8No65H3PQMBCiNAB4TIfEVZYGFSX3gJH+0Ls9BHhtDnmKAQOCbSpSV4WGKTn2qx5ghBFH9FXxLc401f1Uf1liSWWCH7miupnVv+m6bFj3aYkkm7zm/GgJVJanbatv5PotRWxHhZfQ9U4qTusYE0qGqiBtroqBj672cQxGBpUK5WfNhyIp6GzrxFuYjBZ2GnqSvogTjnRkPDpHJ7Nn9OZSLWk18HsbEMMXQ3A4gzN6UAmuubxfrqjppKfVbzJjMG31ZdJQ/09lyPdK1f2BnN+nN3FcjJBmADs7LVzC0GzK48NU6VSCXW0W9EKEKZO/y/Lj9TKq3QmHHmafHbbbbdgl2cwQYbrTYTipuv9CCAmpFfaCZWQRU9Za/0Q4RFWOosQRM9koE2ZgEjEEKQyHQkdSbS+SZLHX1tlF6dfkKrrP8idRVilUinO7WNaQTqmDSOgJ5xwQpictPHy+a71JHSeUe2QS+OB3YrV/uVvCy11M76IhxD6TYrC2N1zSqcvUV+XYwZ/dUFQSTeRMZIT5A5JU0eSFc8yWVePZYgdaY0wC1Ljlvw4UpiybiZ7BFp/l6/46XomAhbs3m/pLAyQr+raEGaU4UhXGa5f6V/6mf7G1EA6bcLCqExD2s5ffG1RfH2JkIHq18JKeEuONB7JK/NztairnZtbSt8W/yR6bUEr4yYCiUAikAgkAolAYyOQpWuGQBK9ZnDkTSKQCCQCiUAikAgkAr0HgSR6veddZk0SgUSgfQhkqkQgEUgEei0CSfR67avNiiUCiUAikAgkAolAX0cgiV57WkCmSQQSgUQgEUgEEoFEoAcgkESvB7ykLGIikAgkAolAYyOQpUsEGhWBJHrtfDPOQ3Mwbro3IjFIDLINZBvINpBtINtAY7aBJHrtJHo+Meag4XQTRWLQHgwyTbabbAPZBrINZBvo/DaQRK+dRM+hww43THdcJAaJQbaBbAPZBrINZBsYxjZwXOekT6LXTqLnszo+25Nuo+KE/cQhccg2kG0g20C2gWwDjdcGkui1k+hlskQgEUgEuhmBfHwikAgkAkNFIIneUCHKCIlAIpAIJAKJQCKQCPRMBJLo9cz31r5SZ6pEIBFIBBKBRCAR6FMIJNHrU687K5sIJAKJQCKQCPyCQP7q/Qgk0ev97zhrmAgkAolAIpAIJAJ9FIEken30xWe1E4H2IZCpEoFEIBFIBHoSAkn0etLbyrImAolAIpAIJAKJQCLQBgQ6nei1oSwZNRFIBBKBRCARSAQSgUSgAxFIoteBYGZWiUAikAg0OgLff/99fPTRR/Hmm2/Gu+++G59//nl899138eWXX7aq6NLfcccdsfzyy8dWW23VlOaaa66JhRdeOJ566qkmv+ofvoO62Wabxeabbx7ffPNNdVD+TgQSgU5EIIleJ4KbWScCiUAi0EgIIHMXXnhhbLnllrHpppvGTjvtFPvtt18ceeSRce6557aqqB988EEMP/zwcddddzWLP8EEE8QiiywSY4wxRjP/8ubRRx+Np59+Oj755JPSK6+JQCLQBQgk0esCkPMRw4hAJk8EEoEOQeC+++6Lww47LFZbbbU4++yz48ADD4x55pknjjvuuFbnP84448Tcc88dU089dbM0s8wyS+y8884x4YQTNvMvb+aff/6YZpppytu8JgKJQBchkESvi4DOxyQCiUAi0N0IPPHEE/H+++8X5G6EEUaI0UYbLZZYYon497//HfkvEehJCGRZW49AEr3WY5UxE4FEIBHo0QiQqH377bex8cYbF4SPvd1www0XK620Uow88shNdWO3R50766yzRr9+/WKdddaJxx9/PH744YemONU/2NwdddRRseyyy8bdd99dBIlLXbv22mvHxBNPHCR69957bxGWfxKBRKDrEBiu6x6VT0oEEoFEoLsQyOdCYM4554z11lsvbKZYYIEF4pRTTokXXnihsKujzhXnq6++ii222CIeeOCBuO6664q4H3/8cay66qqBuIlT615++eViQweJYRkm3z333DM8Uzg1sXzK8LwmAolA1yAwXNc8Jp+SCCQCiUAi0N0IUNXaiHHiiSfGzDPPXNjUbbDBBnHMMcc07bolkTvnnHNik002iXHHHTcmmmiiOPTQQ+P1118vNmyQ3tXWg73eggsu2Mz72muvDdLDlVdeudi8gfAttNBCzeLkTSKQCHQ+Akn0WsA4vROBRCAR6I0IIHuLL754HHvssQXBI2X7z3/+E/vss09RXUQPmZt88smLe3/8RuYGDhxYHMfCb2jusssui0kmmaQgi+JSEbML9DtdIpAIdB0CSfS6Dut8UiKQCCQC3YoA1SuVqkKMNdZYscIKK8QVV1xR7JR1vMprr71WqGCFv/TSSy5N7je/+U1UKpWm+6H9cJTLZ599FlTB9eKedtppMfrooze5OeaYI6h468VtEL8sRiLQIxFIotcjX1sWOhFIBBKBtiOAyD322GNhE0aZmtSN/Z37H3/8MWacccb41a9+FbfccguvJudQ5ZlmmikQxCbPIfyYYoop4vnnn49XX321biw2gmeddVaUbv/994/f/va3deOmZyKQCLQfgSR67ccuUyYCQ0YgQxOBBkMAWTv88MPj9NNPj7fffruwobPpwlctVl999XBGXv/+/WPppZeOU089Ne65555CVeuQZQTP7lxVsivXly4+/fTTJts+hO6LL74oDkRGGNdYY4148cUXY999942HH344HnzwweLAZBLF+++/PxDB5ZZbLkrnsOVRRx1V9ukSgUSgAxFIoteBYGZWiUAikAg0MgKkd1tvvXWwl3Nkig0Z//rXv4rjU7bbbrsYccQRC4mdA5Sdr2ejxnzzzRc33HBDcdDytNNOW+zS5YfYnX/++bHrrruGzR2ObHnvvfdCGkQQYRR26623Fseu3HTTTTHVVFMVBG+88caL/JcI9EUEuqPOSfS6A/V8ZiKQCCQC3YCAc/GWWWaZWHfddYujU3ySzKfMkL1RRhmlqURUqAcffHA4LoU0DvFD8hDEKaecspDQUf/ayEHl+re//S1I+PhRD5PMjTTSSLHhhhsGWz9+u+++e7EBhKRQHk0Pyx+JQCLQqQgk0etUeDPzRCARSASGBYFMmwgkAonAsCGQRG/Y8MvUiUAi0EMRuPPOOwt15EcffdRDa5DFTgQi3n333eK7xaSuLeFB0ur4HNLbluKkf+9FIIleL3u3WZ1EoLMRsEtyttlmi+mnn75Fxwi/s8sxrPk/88wzcdVVVxWbDYY1r0yfCLSEgCNmqK0dH+PAaJ+Ws7iwIaalNK31pzp/+OGHwzmIl1xySYvJfI7u4osvDm2+xUgZ0GsRSKLXa19tViwR6BwE1lxzzbjyyiuLHZUTTjhh3HzzzfHUU08V7vbbbw+G+nZdtvbpvp6w0047tTZ6u+M5t+3ZZ59tSr/++uvH9ddfX3z5ockzfyQCHYgAIrbiiisWn5GzqLB7+cMPPyzaHPvHYX3UGGOMEYsttlhMN910LX6HOCLi17/+ddx3333F5++G9ZmZvuchkESv572zLHEi0O0IIHgIXW1BGPFvs802Mf7449cG1b33BYYLLrigxW+o1k3UDk9nx/nWajuSZpJEoN0IWPg8+eST4ZNydho70+4H0AAAEABJREFUvmbnnXdu+gpJuzPOhIlAGxBIotcGsDJqItAnEBiGSr711lthx6UdnJ988kn885//bHIkf6QapZ8z3S6//PLYa6+9wllum222WVx99dXFIbvOervxxhuDBGS33XYLUhBHdxx11FGx6aabxlZbbRWXXnppfP31102l/eCDD+LMM8+MLbfcMkgISTAEOtZjo402KiSQ8vJpLmWT/wknnFDsFhWPe/3114NqzS7UvffeOx555JGmL0XYOXryyScXUkDSmB133DH22GOP8FkwadMlArUI+JqIw6ePOOKIZl8IWW+99Zo+DVem8dWS//73v0V/Oemkk5q1SxJyCyKLKP1HO9QfyrTV13vvvbfIQzzt/c0334znnnsu2PDpg+K+8847ccYZZxR9CBHdZZddQnsmmRfOsevT/qmd5VXteoJphjqk+wmBJHo/4ZB/E4FEYBgRoIIdMGBAManIyvEac801V0GMkKpZZpklxhxzzKA+nX/++WP44YcPNku+tjDBBBMUk5Nz1qhTGY6bmE499dQwSSJxJCEO3TVJjj322OE8OPeexTnHTTwH/zrjbf1BqlmTpyNFllpqqRh55JGL8+LmnXfeePzxxwPJO+WUU0Ia6U2c8mTPJC9qt7XXXrtQuwm/4447wkSMbF533XXFmXDOl1M+n/sSJ10iUI0Auzztbb/99gsHSFsgCPfpt//7v//zs3AWOhYX+oj2i+g5suaVV14pws8777yiHzhc2vmGFjqkhEVgzR/H4Fgwae8rrLBCsBFkm/fvf/+7UN+K/tBDDxVtWdsVd4YZZggE8YADDoj3339flEAAna046aSTxj/+8Y/iHlH885//HPprESn/9AgEhuuAUmYWiUAi0EcRoJr6wx/+UKhqJ5544kDGStLDLsh5bSYwUoVLB0ngSAxI7lZbbbWoVCrFlxiQP5MSO6NpppkmFl988eITXCYTBuTUrpNNNlkgXgji73//+9hkk02C9NAECXoSQATS5IlckuCRSPhyA1JIbUay4msM1Gcm35VXXrn4MoT0HCmJqzyQz2233TaoqA877LDiaw8mWBO3MpJuqNsqq6xSHCbsgGBp0yUC1Qho28cff3xo80iShQ3JmcXFaKONVkS1QNIWfYYOkVtwwQWLz8I5aNpB1Mwb7KzVdrV/XxCxeCGpLjL4+Q+pH5KGAGqzTBUQx6mnnjqWXHLJQAAtYkRfdNFFw3OQOIRSf/z73/9eLGo8S7zbbrutOFhbWgdr69sWUL6QYhEnn3Q9A4Ekej3jPWUpE4GGRMDEQ72DdFF7HnTQQVFOYGWBESeEiBTCAb2ka6R5ZXhLV7t6RxhhhIJsIWlUTXPOOWcxCVInmYxKUmknMAJIZSw/9oNIpav71jgSRGS1zIOh+5/+9KdiswmpSJnHiCOOWJBUhwcjqKV/XqsRyN8lAvoDiZ4NTKRhFhTasUWMxQhC9fLLLxdErEzTr1+/0PYscpgtMFfYc889C1WrPqYf1S4uLKaQSAsZzynzGtKVallfrFQqhclFdVz9C8lURv5MMix8xHefrucgkESv57yrLGki0NAIIGMLLLBAceRKdUFNdKuvvnogbezZfGO1Ory1v31ui6pJflRK1emoXavv2/PbZMtusEyrPjaXkCSWk10ZltdEoDUIUL1qP+JaFPnaiDasnVGTanPs5YST7LmWjkTbt4P5I3wkaiSBpG8WNWW88oqUsSOl5q0lgWWc1l4tYkgN5enZSB91L6kg6Xhr88l4jYFAEr3GeA99vhQJQO9AgFrnd7/7XbGJoqyRye7uu+8udh6S/lEtmUDK8NZcSSuQO5IQdkcmwep07u+5554wMZb+pH1s9Mr7oV1NYCZKGzWq41LVknxU++XvRKA1CJByswct45ISr7TSSkEijbRZ9FhMULsiVGW88qpdkzCzGRVnnXXWGWwhVcalhmVu4Fw99qmlf3uvzsokfbcpiSkDVbFNG8rU3jwzXfcgkESve3DPpyYCPRoBEol6ZM3Kn+G3yUYF2Qw5GJbqtn///oGskWg4oV9ccTgSM/khZ36b1DzDVbgJkQqLlI2Eg60Qf7+l++tf/1rsUrRRgl0eZ2KqJW3ifvXVVyFfaZXBb3mxUTIp2+DBT1l8v5XdFCkiv+oyuZe+dPJIlwhUI6BtsNHTdrU3YdqQ3eLs9yyM5plnnqCqveiii4o2rF1pe9KQAopjB7m2Lz9tmwTbb/noL/KuVCohvo1Ml156aey7775Nu9LF9VxX+Zfl8JtzLx/hnHvPJG1kMuErMjaIIHzCusjlYzoIgSR6HQRkZpMI9BUEHNVwzDHHFIbbJG1sjaiLOPZHNiqwryNd23XXXYsz8tj1mFAYcbvazSo+zBiZk/o51oSfYyQQNEevMEgXh0G5zRriIIq+AsBmCCmzIWOttdaKv/zlL2FSdaAzyQZVk80T0pN2KIPyIYD/+9//giqYBM+OYOTPphHG6/I3uYnLMJ00RbnF9ywT4C233BKebdetPBzrYmL2rHSJQImANkcaTBJmB7n2zcbO4d2O5tGmLSL0Ifae+gt7U3H0CwskebFz1W6l0cbt2rX4cVSQ+HbMUgM7vmjGGWcMG5BsxmCzp53aXc6uz+YptqvKoQ9Y2MjXbmB9iq2tdq7d65OlXaFFGkmkTVBsWS2YlCtdz0AgiV7PeE9ZykSgYRAgRUB8jj766OJMO/Y81FAcyYIdhHb1KbBJZ+GFFy42LyB41KM77LBD8ckmccWhZmJobmct6YYr43UTCymDONRXJkqki30S6ZvnS+sZVGKHHXZYSLf88suHCRLZs3FCejsV2UfNPffcYVcu6YZdtLvttlvxlQJlIzkRB1FEUscdd9ziCBdHvsiDpMOnpqiOYaD88iVBgYc8xUuXCJQITDnllHHIIYeEo020fX1kookmCpLnpZdeuowW7OGOPfbYYONKYqed6ifatUiO9aHuFc8iibROW7TjXRtn7+f8SH3RogVhlMbztVVtV9vWN+THbtCZfBZI2r44s88+e+y///6hnNqyjUkbbrhh0cf1NwspEj3k7/nnn5dNuh6CQBK9HvKispiJQKMgYLB3JIOztVpyJgsSijKcRM5xKwhg6cfeSJ0QKkeVmEgYe5vIyjh2HorDIXQbbLBBIGikJMjcMsssE6Qbwk2KyKGyIWDi8Of8dnwFx4ZQePkMfohapVIpdvjKA6GUP0mg9FwZ39WuRpOx36Wz21G8dIlAiYA2YZGiHSFZ2op2T3JcximvpHvCxPFZs7JdC9dmhWmr+ovFigWJ/sJPGk5/kLffpbN4Kn+7OjPPtXTaO3u88t6V3SCTC/1Pf+LHbbHFFoV62KJIudL1DAQ6negRNbNFYF9gt5DVRjU0RNbEzAZiV6uS6vBO/J1ZJwKJQCKQCNQgYIwubcBqgobplmqbpLSlTEhIP/roo0i1YEsIdZ0/CeCAAQOKczGd5XfFFVcENTHJO5KZGzK67l10xJM6neixEWDH4qBInzMifiY2LgtvNeEcoWWXXbaw+SHmLsPymggkAolAIjDsCJik2V5VO8SrXs42BTgg10aUeuHt9XMQNvuyeukdB2LRb55g/1gvTtf45VMgQPDCRtbB4ggfG1S2ftTNDloWJ13PQaDTiR5j1D/+8Y/BToftDtsERs7VKztiabuOqHt6DnRZ0kQgEUgEegYCbMNOPfXUoFVhmM+Qv7R/rK2B8ZrdYUer5+y4pjKvfZ57EiTnLJIksg/jl677EKhUKoHs+2Y0kmfzBiGNtsEEo/tKlk9uDwKdTvTKQmkcBg+rAbva7KbTucvw2isiaOcRMbEt4wyvGYzWxsv7RKA7EchnJwI9AQGbVmhXlJUBvh3GFtjua51zA9dbb71gW1YbNiz3Fvo20tTLw85TNpiu9cLTLxFIBNqPQJcRPUVkLG1nEcNnu4IczcC/1vncEAPQl19+Oa699tpg50cS6HuBudqrRSvvE4FEIBEYOgKlcb9dmpVKpW4COzCd4UYCWD3WWpQbl0ncOKpWJjjiiOu8NbtFZSrMvXilvR37O0fmCBOndBbv1ek9vwzLayLQQxFouGJ3KdFTe9/KI6ljH4K41ZI9Hd25PkgeMkh9QNxvx5GzhxA/+aRLBBKBRCAR6DgEjL1Uuo7qcGxH+aUGZO7qq68uvuZgs5wz3TbaaKMgoTNOOwbEkSHbb799URhx7Rq1+/OOO+4I5M4B2b6ysNdeexVx/EEG2WTbzen4Dp/4csC2sHSJQCLQcQh0OdFzzk///v3DoHDdddfFbrvtFkhfWSUrQEafRPjUvaW/YxlIApHA0i+viUAikAj0eAQaqALs5BjiI2FlsRys68DfFVZYIU444YRwaPWzzz4bSJlz2hBDR+mU8R1Lw7arvGcLyAbbot74Xvqz/UImnffms3iIox2/ZXheE4FEoGMQ6HKip9hUB84AQvLOPvvs4jR7q0ZhVATs8wwifvPj2IsgidQH7tMlAolAIpAIdBwClUql+KICGz2b6ORMtepLCDbTzTfffGHsnmSSScK5cMI5i/JKpbkq2GG7wjgqY/HHH398t00OabSr0xmL8pV/bZymyPkjEUgE2o1AlxK9auKmYzuZ2zcqDSS+pacWzttzWKPPtZQ2H/ypFTibOdzLqySH7vuQy6omAolAItAlCFhwk8Qhax35QKpc47tP5VUqP5HESqUSxv+OfE7mlQgkAhFdQvQY4jLc9Z3IWtAdwEiVi7gJo65lkzf55JOHz7xQ6xLnGxhs93aOj3g333xzHHrooWGwcJ8uEUgEEoFEoP0I+GZvS6mN3y2FtccfobPpo6PP6mtPWXpHmqxFItAyAp1O9Jx0zgbDYcl2zzqLp7o4PhHD3ydYKpVKEcSI10aMp556Khjp+k4gWw7HspD2ieR7e/Kys8t9ukQgEUgEEoH2IfDSSy/F3XffPVhiJyX4YpEDl0u7PQvv2nHXp7gsyssM3nrrrfJn3esUU0wR1MM2a3Q0iaz7wPRMBPowAp1O9EYdddSwY/aSSy4pDHnZ5lXjXalUig8677333k3frGQQ7FuSjHURPmc+OQOKRK9M64PO999/f1TbgpRheU0EEoEhI5ChfQ+B66+/vqg0G2hmMG7Y4N1zzz2xxhprhJ22/J577rlgFvPqq68WZ+ltsMEGQX3r60V2yToPlTRO3NLNOeecce+994ZPZRm3kULHqfi6AqkdMvf888+Ho1TYWVuwb7zxxnHWWWeF/IzlDuX1nNtvvz3qaX/KZ+U1EUgE2oZApxM9IvqpppoqHIbJ+V1bRCs7kj3XMkw6R7FIY7XocM9K5SeJnzi+osGfqtd9ukQgEUgEEoH6CEw33XSx3377hXF26623DuPnuOOOGzZWLLfccsG8xqYIGyQOPPDAGHPMMcPi2o5b4T6dxl6PZI/5TO0Cm9bGeLz99tuHMXnWWWcNJjkIJHJXHpRME+PLHHbistF2XIsdt45YMTdY4PT0t48AABAASURBVM8+++zF4r9+TdI3EUgE2opAK4leW7PN+IlAIpAIJAKNgoDNbaRsLTkSNWXdcMMNo4zDXGaBBRbgHaR5L7zwQgwYMCB8rahS+WXRLQJieNVVV8UTTzxRfGaNhI80zyezZppppnDeXpnvpptuWqhtbfBgfiPsvvvuCxvzTj/99OJ8PicsyDddIpAIDDsCSfSGHcPMIRFIBBKBPoNAaaP37bffBpVsn6l4VrTPIGBzKFODIW1QagsY8mMS8frrr7clWYfFTaLXYVBmRolAItBZCPgqDhUiNWBnPSPzHToCpHI+T8kez8RF5frKK68MPWHGaBcC7BzZSNqI2K4MMlG7EDjxxBPj0ksvLY77efLJJ+Mvf/lLYYrgy1zMDmozdRD4RRddFOxLkbra8EqlEjY8+TIME4ja8M6+T6LX2Qhn/iUCeU0EmiHgGCU7OtmKcZVKpTiQ12+OXe7+++9fpLGBwABZbxAtIuSfLkHAe7HZwmYMkj0H3jsMuUse3kseQiXuHFntG552NlcqlXB1z9/XScTT3j/66KOwgaWXVL9hq2GDkkULEwKmCGxZmSuwSV1ttdUC2b7ggguKjUnVlUDybCqysWn++eePemYHlUqlIIre66GHHlqdvEt+J9HrEpjzIYlAIlCLgGOUfO6QlIgT7jNYfnMnn3xy+NY1f5Ijh6qbBN2nSwR6MgK+60siqp0fe+yxUalUwocD3PP3PWH1QzguvvjicMqE+97nGqNGSN7jjz9efN7vn//8Z6y66qoFuT7ppJOCJsG9OKR7n3/+eVOhHSlkseOkEBuPmgLq/BDHCSIkhd5znSid5pVEr9OgzYwTgURgSAgwyu/Xr1+LUZZaaqngWoyQAYlAD0TAaRLafrmIqa0Cf2fLilcblvedg8C7774bFpaLL754+CyfpzgrkrqVhI+61pE/pK4++SccyXOckM/21R4bJ7ye821o7qabbqoX3Gl+SfQ6Ddphy9guuO222y7OPffcIiOrjYUWWigOP/zw4r63/GFzddRRR0WpoqOm8AH1jTbaqLdUsakejpawkifF4nnDDTfEMsssE7feeqvbPucmn3zyIdbZMR2O8TCgWlmvvvrqMXDgwKDGveWWW2LdddeNa665JhzXYXenFbVz2Ki8qFoc03H88ccH1Ur5INIS0kFnxpEeOj6kDMtrItAVCDhD0I7jIT2L5Fo87dXXo7RZ8dnskfAZN4wjhx12WNjhTApFvesLUssvv3w4zobkSBqONAq5EGYXtLmlozYayL+nO8cHOXrIHFse84bQId3uH3rooTCW2H3u3RiDSPKmmWaaWHTRRdtUfe/VmNamRMMYOYneMALYGck1qs0337wQIxPn2/3j3pdCdObymexkrDo0utKvp10dlO28LkaqJmSrqqOPPjrUq6wLUbkVF1JY+vW0q4Fk/fXXLz7bh1z4IgAy4lBa9VYfg7FdjGzRrCD59XUHm5dffjnYhfnsoXtfXfDb13ZMeiOPPHJYHDjOg2rEESAmRuSPXc2jjz5awCgd+xiEURwD7mabbRYPP/xwEZ5/EoFGQgCps2g59dRTi+NplM04qN3bFODKPnK99dYLmweQNwKCrbbaqjB5IBVEFKUz5ohDTexgajZlvjqVnxCNgOnBBx8cq6yySnEGJLw4hM4HG9jqrbTSSkH44Pghh4nzR7AdNcQ2Tx7eF5tKaVtywkkJJ5tsspaidIp/Lyd6nYJZp2fqsFFEwAri2WefLWw3dthhh3j77bfDtSyAFZoDRq0sSr+ednU464ABA2KCCSYIpE+nQWxN4OqC9JicrVjnnnvuQIz49zTHxoO0yY7Fq6++upDUIhhIyGKLLVZUxwCw++67FzYhvlZQePbxP+xa2PKR1pVQ+HyWdu9QdapdhJkdDfWJwdag3L9//8KuyeKAkxYRdLyBQVnfIdXzDvQ1A7A46RKBRkGA1E9brlbhUvuRKlUqlWIn6IorrhjGxumnnz4IApA3bd84Qihg/HQY9hZbbBEOzWbnKh7JFXsz5xc2Sn27qxy0A/BzYHhtGYwRdtLapLH+oIU62zoaKKSQxsA4DVsEENG2G92CvTaf8n7gwIFBa4Ukln5dcU2i1xUot/MZtnRbVZjsHFxaqTQ/pFSHt3PRqqKdj2iIZFQJditRzxm0GCCXBSOtJBq322meeeYJA1UZ1tOuiAlCorMjflbj1XUwiPhaAImTwaA6LH8PjoDdbXaxlSEkdDDmSr/qK3InDckeh9yR+BmwhzQ4V+eRv3sBAr2kCtVtf/zxxy+OAjFX1FaPitaCspSGa/vG0p122qn4Mkpt/L52T4Nm/hlavQkZLNLNR4ga8ket7osuJKSItM8MwrleXiSBvixjXqcmrhens/yS6HUWsh2Qry34GhF1nkmpXpa33XZb+PRQvbCe4mditqIiaantJBNNNFHMMcccRVV8Ds9xHMVND/2jg48yyihBHV1bhdlmmy0MHkiegaQ2PO+HDQHSDYMx9W61IylmhzNsuWfqRKAxEaCerVQqxcam6nZvZ289KVZj1qLzSsUWmPRzSE+gCfDVFiSPlE9cBNF8tfbaaxckG6GmkWpp0UiSSuJnkS99V7okel2JdhufZZePb1KSAJmkrAiqs0DyEIJ6q7jqeI3+WycaaaSRiu9tUqUhtWVnIYExCbNHZHxcqjm7sE4d9ih2d1aFiF6pMuFXPsAqW/28z55OaMs6NdJ15plnDriz+SvbF3W5dlXbtxqp3FmWRGBYELCz3Rh6ySWXNNuY5Iw44+2w5N0b0i644IKFjaNxobY+xgnzk+Nt1llnnaA6NyeJRyVuTLET1ycG2U3SrhHQCK92b7zxRuyzzz5B1c70pDqsK34n0esKlNvwDNI7k9EzzzwTjzzySDAqR/LYrbFlq86KMa6dg9V+Pem3jmGiZXCsA9jBdOONNxa2JqRaZV1szKDWLXcgl/495epdsrX0HVADLvUt42i2Y1QqZT0YUrO3dDW4lP594YoAq6cVMud3tdMHDLoIP38rcAuCasmovoOwleSZ2l9eJBrSrrDCCsEuadttty1269rQwTBdfFJl+aZLBLoSAW1PX9c+tenaZ2vDCIUwV+Has6swV/7C5eU3P/3Ebzardo8yiaFetImACQMVJDtopkHi92VHSkcqx0acHXyJhfHFaRcnnHBC2Cw4ySSTlEEREcF2Er42ih100EHFZo6FF164WRwSPuZG0lvEm+fMAc0idcFNEr0uALktjyD+3X777QPh0Sio+thfMCSvtl3TwExU7Nvakn8jxTXhOh6A1FI9GbQif9SXxOnKalBjLMuOyn1PdI5V8U5ffPHFYGtp1Wc1zZ/BdVmnSqUS1LfsRfoS8TjnnHPCJhU4UG1ssskmxU449xzyS22iLSD85cBqomMYfffdd8duu+0WJi9EzhFEjl1hg+QYA22M1A7u/AzeDKsN7L5E0JP7EHzS9UwEtE02ok5WUAO7YrVn/u4tBG1Es8i3GNaur7rqqtAHkBA7RR3RpB1r01SDRx55ZHEe3CGHHBJs8fbbb78w7hh/2IbpO8bZM844I+xKd3yRZ/V1Zy5C3CzGSyyQbwtHKu5akicO/NZaa63wLmwW23LLLQOpFsbB37u67rrrwkYxcct5TXhXuiR6XYl2K57FJk/H13Cc4YMA6bxbbLFFVJMdO4GciUQN2IpsGzIKVYKjAIi/ERsDkGNIiNJHHXXU0NEcSEmVaTu689IasiJDKdR6660XBtwll1wyHAXCJgwRcU8VUJ2chNNg4b1X+/fm33YAWhGbzJBfRwlRh5R1Fq5dkHQKs2qGJxsZEyMbVf1FWzGoIo36ip2HVFOO69GvqFQQPITQIsnEavBlNlA+q7OumW8iUIuAMY6U2Tio7WuPzsfjL65xQLu20KUBQNSQC0dQ0QSQzs0yyywFibBz3EYAwgGSIySQZsgix4kGFjRUh9q9PsbfOOs56SLgYyOcxWCJB8mbEzDqkTxxbIYxNtG6Gcu9L/4caSri7f0ggA5h7i6SpzxJ9KDQQE7jMSkheYqlsdmQoLFVKhVexfl6DoslUrYbtfDsgX+QHDtPkTzF11FM2iV5tcoysZu4GcCapMXrac5uUEbPiIaye8fU1AYX90i7A06ROytxW/dLTIT3dmclTapW7ZD7st7afxmmzbifYYYZovTTV+BZ3us/sC3vXUvi6B1YNNjY069fv6h+Tvm8vCYCXYGAiV8b1D5L556/57u6rw6rbtc2sBkztPfqOOaK8t7V4lJ+tAfGIX1HPqWtmbB0EeYdriOw8O4sSBG8McccsyOyHKY8kugNE3ydlXjI+do5ZYcqhxQMOXbPDT3ggAMKFZ56cj23JkMuOVWtFbo6XnTRRWFAHnKKDE0EGhsBtpOkq9RXjV3SLF0i0PsRSKLX+99x1jARSAQSgWYIsNnyvdVqxzyEHRiVE7OJZgnaeGPR4qsjbCPbmDSjt4RA+icC7UQgiV47gctkiUAikAj0VASYfjjmgf0v1Z8vKrDtYr/FFoz9Y1vqZueow2DLNNTx8rL5qPTLayKQCHQPAkn0ugf3fGoi0NkIZP6JQIsIsO9yrIQIpU2XjQAM0h3VscsuuwhqlWN4bufnRx991BSf7SN7MHa3TZ75IxFIBLoFgSR63QJ7PjQRSAQSgcZDgPG/45zs4KwuneOcbBpiQ+qIDwfECncUyDHHHFMc+WFX9K233hqIIgmf74DaRS4e55w393Y9y8c5oWz5hKVLBBKBzkPgF6LXec/InBOBRCARSAR6AAI2Tzi/zW7B6uLuuOOOBZlzppsz2OyEf+edd4qv2ThrTFwSPYebI3nInDjOeRPGOTjWBis2gM59s6v++OOPD+cjCk+XCCQCnYNAEr3OwTVzTQQSgUSgRyHgHEJnszkAFiFTeI7UTZjjIqh2nQvmfELntDmmY6mllgrHAVEFs+8jFXSem4PPpS/dHnvsEc4ls0nDJ6VWWmml4rNQJIXDuvmjfEZeE4FEYHAEkugNjkn6JAKJQCLQZxBwoLdvKyNerg7ndTBvCYCzxQYOHFh8p9MXexC2r776KkjvyjjVV+dE1n5aywHhzz33XDgc3RljbPgWX3zxYB/oEF9q3eo88ncikAh0HAJJ9DoOy8wpEoJEIBHoaQiceuqp8cwzz8Scc84Z77//flDd1krYbrvttthhhx3igQceiI022igcPN2WesrfAb1IYJnOF0l8tQFhJEUs/fOaCCQCHYtAEr2OxTNzSwQSgUSgxyHgCwvUtR9//HGwx0P4ykr4LrOv0tip++9//zt8haRS+ekrPWWcoV19HYAKmGSwNq7NH0hgrX/e9xIEshrdjkASvW5/BVmARCARSAS6HwESPZ8cvPfee8NGinKTxJNPPhlvvvlm+EwftSu1ba3Eb2ilX2SRRUK6AQMGFFfxHcsiH1+GYePHL10ikAh0PAJJ9Doe08wxEUgE2o9ApuwCBKis8m/YAAAQAElEQVRn2eJ5FLs7RA7ZWn311cNnFR2hsvLKK4edsiRu7PT23HPPOPnkk+Poo48udts6PsVnzoYffvhClUu9e9ppp4WjV/gjds8++2yQ5E066aSx//77h0OVzz333MK+z47baaedNhZccEHFSJcIJAKdhEASvU4CNrNNBBKBRKBREdhqq63CJgi7ZB2T4lNoysr2buedd47/+7//C4cdH3zwwcF+zll51K/OyLNjlioXAbRpgyr3oIMOim+++SZswqD2PfXUU2O55ZYLqlrHqcjb1zgOPPDA4rkbb7xx+DKHZ3mO8HSJQCLQOQj0fKLXObhkrolAIpAI9FoEELELL7wwql1ZWUemHHvssU1hc801V5DukcQ5GgVBE+7rGXbpSofEOV/P1zXmm2++OOecc5rSzzHHHKIUUj/5nH766UUYsjjWWGMVYfknEUgEOg+BJHqdh23mnAgkAm1AgM3WXXfdFa+99lobUnVcVKpMR4B0XI6ZU3ch0BOfS8LqwOruKLsvlXRXv+uO+va1ZybR62tvPOubCDQYAs5Qe/XVV8OXEhzDYQdoW4soPTswEqq2pi3js1FzILAylH55TQQ6EwFfFXnsscdigw02CDueqcPb+jy2jqSw7CzbmraM76gbx+Yk2SsR6V3XJHq9631mbdqFQCbqLgS++OKLuPbaa4Oh/yabbBIO760+a6215TJR+ULDRBNN1Nokg8WjYvzTn/4Up5xySijXYBHSIxHoQATYO9qcQuW9zz77FAdStyf7ySefPKjM2Ve2J700SyyxRLEp5sQTT8y2D5Be5pLo9bIXmtVJBHoKAlS1Pq1FosHea6qppmp30W0COO6442KeeeZpdx6ODjFhOjduWKQj7S5AJuxTCJx99tlBVUuKPN5447W77osttlixOBkWe0c7p22eIRnPtv/zq+hFlyR6vehlZlUSgZ6EgK8skOattdZaUUriBgwYEPPOO2/84Q9/iOeffz4eeuihWHXVVeOQQw4pqoaE2RFKAlJ4DPqz6667FvFJ40ychx12WMw+++zh7LaXX345uH/84x/hW62O+xiUpMX//fr1CxLFcqdoixEzIBEYBgQuuOCCom2vs846Qe0qK0faaPflYsU3gC08zjvvPMHx+OOPx1//+tewkYbHG2+8ERtuuGHR9u2EdpzN3nvvXdxr/+JIY9OLtu8LJPxactTGFl92TbcUJ/17JgJJ9Hrme8tSJwI9GgG2SXvttVest956MfHEEzfVpX///uGoDuGjjz56+Hi+4zv222+/+Oijj8KH9Oeee+7iTLYy0e677x6rrbZacWuycujvvvvuG6QT8rn++uvDvbQ33XRTEa+lP77QwJnwWorTgn96JwKtQgCR+te//lXYpFYfLYPU7bHHHjH11FOHhYaNQdNNN12ccMIJxSHTt9xySzjOhqrXgyaccMJw/A2VLX9Xn6lja8dela2pNq+PIY3OPZSuJafdO8CaaylO+vdMBJLo9cz3lqVOBHo0AiR5DtJ1dEdtRRzSS7Jxxx13RHnOGzJIukcywWCcmrVM9+WXX8ZZZ50Vq6++eukV559/fiy77LLx4osvxvLLLx8ffvhhmMiqJ9amyFU/TMLK1W+QZK/KO38mAh2GgONlVlhhhWBbV52pRcn9998fjqxhziDOu+++G1SyduSuvfbaxY70SuWXz8+Rimuv1LfyItW7+uqrizMMEcWVVlop9A9hQ2v7voRCmj3qqKOKnq4XIZBEr7teZj43EejDCLDNI4UjhaiGgYE6Kd7II49cqHA/+eST4tBdhHCyySYLBO+RRx4JGy/KdA8++GCYEEs/v0k/5EXqwf6JVM/XHWy4KNPVu1LzUqWRiNQLT79EYFgQICnWFtdbb73BsrHIQM7Yy1mcuB84cGAh4fN5Ou2ZpK9///5FWrvVSexmnHHG4nBrntIzb2DCMNNMM8UYY4wRN9xwQ9jJbvEkTkuOKli/E7elOOnfMxFIotcz31uWOhHo0QjcfffdMfPMMw9WB0SLyokaC0F76aWXCls9qikH9Zr4+LHbKxOT/M0666xNKmCTn098zTLLLDH99NMX0exuNPENacOHLzuQnCiXCbJImH8SgQ5EwCKEK23oqrO2CYINqbZvAYR4kexZ5NhsJFzbL4meRdCjjz4aCy20UFM22v7bb79dLJL6DZJKO7KFhNxn7YbUppFGn8LTjzyrKcP80SsQSKLXK15jViIR6FkIkLSxoastNSJHxYrYCTOxOd+uVE25J/H4/e9/LzhMdvfee2+x8cKnutgXube5w3Et8pInKSHDdWmLhDV/pBOHs4nDM2ui5G0iMMwIkJhRj9poVJsZSZzPzE055ZShPVLLzjbbbIF8ifvkk08Wm5aYMbgntdO2kUbxLVQQv8UXXzxKMigNYqntS1PPIXm33nprsOFzzIo+Uy9e+vVcBJLo9dx3lyXv8wj0XADWXHPNYoOEyaqsBbUWOzyTmwmPbZHJjhqL3dLXX38d7qmgSpKI+LHZo9qi0iXJkweSV+aL+CF4NnXY0FH6l1e2UaQZdvIyZGcAX4blNRHoSAS0a0Rs6623LhYp1XnbMKEdk2TzJ/VeYIEFwgYjRO62224LEmk2ecJJ/xBHbZ4jyWOTWm2rqr9Ir29p59JVOzZ9Dhm3WYmzAKsOz9+9A4Eker3jPWYtEoEehQBVkmMk7Lw1QSk8YkciQeVqAnNoMfKG2I044ohhoiKdYKdXGozbZCEdNZc0r7zySrBtMkHKk0MEJ5hggjBB1krqGKCfdNJJwWbQlzmq1WDSpksEOhoBixBSNLvLtV/5I3I2YpRtXVst+8III4xQSPhef/31wkZVOxffvcWPPNifUu2y45t22mllGZ6hX7A55U/iXQT8/Meix45eC6Gjjjoq2Pr9HJSXnoxAnbIn0asDSnolAolA5yJAfbX77rsX592VRM9kRarA/o76iE2RiYhEr1KphK9fODPP4crlpETFa4ctlZMJjtTOly1cyxrYrXjmmWcWz6reeUgq6IscpBrbbbfdMB22XD4rr4nA0BBgA3faaaeFDT+lRLtSqRTHqLDPk97C5uKLL45FF13UbbFj/Jhjjokdd9yxSFepVIojhRwSrn+IP8MMMxR5kPpJpA9tuummceCBBxY2ewgjf86mjp122in0OW3fkS780/VOBJLo9c73mrVKBBoeAbv77D4s7e2oV224oN5SeNI3ExDC597ExT6JE5efiQrBM3mW99KQ+rnnPIefydB96ezWpap17h6bvtK/j12zut2AAAmzg7/LzUKKwGTA2ZF+a/vCSsk1v8knnzykK9v+b3/729D2yziu7PtKQlepVIr4+lSZJn7+5zzJnXfeOTbYYIOmA5t/DspLL0QgiV4vfKlZpUQgERg6An/+85/DxDj0mBkjEehdCDiwubQF7F01y9rUQyCJXj1U0q9lBDIkEUgEeiUCjuFgG9aVlbN54J133unKR+azEoE+h0ASvT73yrPCiUAikAj8ggByx/aRapvt5C8hnf/LrmnO5oLOf1o+obMQyHwbG4Ekeo39frJ0iUAikAh0GgLOIdxvv/2Kz8U51oZtWKc9rE7GNhA4DsfxH3WC0ysRSAQ6AIEkeh0AYmaRCCQCbUEg4zYCAo6WOeKII4Kxfnkob1eXy+YCKmNH4nT1s/N5iUBfQSCJXl9501nPRCARSASqEHB8x+OPP158VYRkz/eBfSrO4btlNN9OdV6be5I3562V4Yiio2x8UYHqVbyrrroqfMvVGW7ScD5R5wxEUrtLL700HNBbHe4cOIcBi5suEUgEOh6BHkH0Or7amWMikAgkAn0XAQfpOsdwvvnmC8fV+L3WWmuFcwVffvnlAhjfST399NPDhgkeDrA+77zzwqe6yvurr746fGYL0dtyyy3DkSGHHXZY+ByXOIiisxDLe2cXnnzyyYH0CS9dmWd5n9dEIBHoOASS6HUclplTIpAIJAI9AgEH7VYqleJAXucTImdHHnlkIGK+mKASvvvL/7333nMbvtqwzTbbNJ275niOCy64INZff/3iQF+k0LmIzzzzTPgKAzJ3ySWXxLnnnlt8pcHZhiuuuGKsu+66UZ7rRppnM4j4xUNa/ydjJgKJQCsRSKLXSqAyWiKQCCQCvQEBkjkqV5+ac0hvWSebMXwXFdEjgaOSnWOOOUJ8EjuEbKyxxoqWDpe2a5eEUHyqWcem+EQXiSHVsDzY4nluuenDZ7uQvZbyLMuW10QgEWg/Akn02o9dpuxJCGRZE4FEoEBg4MCBQS3r01mFx89/HK1CSoecsceba665ClLH9o7E7eVBKl1fYihJ2s/Jml3GHnvs8IUGZO+VV14Jn6jztYdnn302EEX5IHWVSqVI9/TTTweJ4fzzz1/c559EIBHoeASS6HU8ppljIpAIJAINiwCiR+LmE1vVhaROJZV7/vnnC287cUnwEDS2diOPPHKT2raIUOePz29R0fq8HMLn+BTfKCYlpBb22S7+kvrG8AMPPBC+zTrJJJPwSpcIdCkCfeVhSfT6ypvOeiYCiUAiMAgBO2CpUeecc85Bd83/I3sOMKZe9Zv9ns0YpG4zzTRTU2Qk7bPPPgv5NHkO+iE+9+ijj8bMM88cpH+VSiXce+6kk046KNZP/20IGTBgQGGzR5r4k2/+TQQSgY5GIIleRyOa+SUCiUAvRaB3VIsEDQGrrQ2J3dxzzx1//vOfg1QO+aJmHW200QoVbHX8Aw44IJZccsnCfq/an43fPPPME6uttlrhPeaYYxZSwFFGGaUgfoXnz38cw4L4yadSqfzsm5dEIBHoaASS6HU0oplfIpAIJAINjEC/fv1isskmiy+//LJZKaldV1555ejfv3+xixYZZMdnp62w6siLL754bLbZZlHrP8YYY8TWW28d8hcfedxiiy1i4403dtvkqI+dr+dIFuSwKSB/JAKJQIcjkESvwyH9JcP8lQgkAolAoyGAfK2//vrxxBNPNCsaKZ6vZNg8UQbYfMGvUmkucbNRY9VVVy0kf2VcV/Z30lDfuucWXHDBYoOG3xybv8MPPzy23377sKuXX7pEIBHoPASS6HUetplzIpAIJAINiQCid9dddw1mY9fZhWXXd9ppp8W2224bM844YyE57OxnNlj+WZxEoMsRSKLX5ZDnAxOBRCAR6F4E2M5NM800gXh1VUkcoGyjxyqrrBKpru0q1PM5iUBEEr1sBYlAIyOQZUsEOgmBJZZYImy06KTsB8uWathO3HHGGWewsPRIBBKBzkMgiV7nYZs5JwKJQCKQCCQCiUAi0KEItDWzJHptRSzjJwKJQCKQCCQCiUAi0EMQSKLXQ15UFjMRSAQSgfYhkKkSgUSgLyOQRK+db//kk0+OrbbaKl1ikG0g20C2gWwD2QayDTRsG0ii106id8UVV8QhhxzSK13WK99rtoFsA9kGsg1kG+gdbSCJXjuJ3iWXXFKcQeVbj+l+TCx+Z2mHhgAAEABJREFUTAyyH2QbyDbQa9tAjvE9eIxPotdOopfJEoFEIBFIBBKBRCARaHQEkug1+hvK8iUCPRGBLHMikAgkAolAQyCQRK8hXkMWIhFIBBKBRCARSAQSgY5HoFGIXsfXLHNMBBKBRCARSAQSgUSgjyOQRK+PN4CsfiKQCCQCjYlAlioRSAQ6AoEkeh2BYuaRCCQCiUAikAgkAolAAyKQRK8BX0oWqX0IZKpEIBFIBBKBRCARaI5AEr3meORdIpAIJAKJQCKQCPQOBLIWgxBoOKL30ksvxSOPPBLffPPNoOLlfwh8++238cwzz4Svcdxyyy3x/vvv8+4T7rnnnosnn3wyvvvuu15d33feeScGDBgQn376aa+uZ2srp80//fTT8eijjw6WxNjA//LLL4/rr78+jBk//PBDs3jSP/7443H11VfHnXfeGZ9//nmz8LwZHAGHHRtb/ve//wX8Bo9R30ca49LHH39cP8LPvgMHDoxbb701vLfe3p9/rnKHXt58882444474sorr4wPPvigKe9PPvkkbrvttrjqqqtCm2/Lu2vKpA//0G61S2PFU089NdS5Br4PP/xwMbZo9+++++5g6Bm75HfdddfFq6++Olh4V3s0DNH78MMP4957741NNtkk9t133wB+V4PRiM8zIF5zzTVx4IEHhsZlkNxvv/3irbfeasTidliZ3nvvvWKC3njjjePoo4/utRP1V199FU888UTsvvvuscYaazTEoNBhL7GdGZnQkN711luv+MRgdTb6w7nnnhv6wLHHHhtbbrll/O1vf4u77747qv+deOKJsffee8cDDzwQfu+///7x5ZdfVkep/7sP+yLPRx11VKy66qqt6m8mPBPaAQccUKR58cUX66Jn8XLkkUfGjjvuGJdeemlB3pHKupHTczAEjBEXXnhhbLvttnH++efH/fffH/xERDL23HPPuOCCC4r5wbswXwhLN3QEzKM77bRTXHzxxcVYYcy4+eabW0xokXnyySfHSSedFI899licc8458Z///KfZfIw0mq/NW//4xz9iww03jFdeeaXFPLsioGGI3iijjBJTTjlljDfeeDkgV7351157LQ466KCYc845Y/vtt4/NN988SDAQvlopRvSif6ONNlpMPfXUMfbYYzcNar2oek1V+fWvfx0TTzxxTDbZZGFl3hTQh3+MOeaY8Yc//KFYWSN21VCQCn300UfF4GrAPfTQQwsJ91ZbbdUUjdRjn332idVXXz2222672HrrreOMM84oJOJNkfLHYAhog9qiyaw1RGz44YePCSecMPr169eiJNoC3js47bTTiglv5513LsYx7X6wAqTHYAggdHvttVfR3v/yl78E/HbYYYcCd31DuybN23XXXQsi2L9//2KBk4KSwaAczMNC5fjjjy+IM/y003nmmScQv5bG4ptuuqkgeeZi44q4iLd52QOMTeZn5O+EE06I//73v0X+QyKP0nW2axiiN8III8Rvf/vb+N3vftfZde7I/Ds9LxIJK+J55503DI4G4plnnjlOP/30NqlXOr2gHfyA3/zmN0VbQPQ6OOuGys5kOcYYYxQLnIYqWDcWZqSRRopxxx03Rh111MFKUalUYskll4yZZpopxh9//FhsscViqaWWioceeqiIi6Bcdtllof0sv/zyYVyZccYZY5ZZZgkTZm9eHBUADMOfMcccsxiDf/WrX9XN5YsvvigWXTAWYbjhhovRRx+9eFeVSoVXM2ciPfXUU2PAgAFxySWXRP9BJER/bin/ZonzJuBM0kR6BL8VVlihGBO1afCQ5iF5Sy+9dOFvfph//vkL0n322WeLkm4ICNAc3H777bHyyisXAgW4aqPMaEhQa5N6HwQsxpaJJpootONJJ520SH/DDTfEgw8+WMzRa6+9diG0mmCCCQoBDaGFMa02v668H64rH5bPajsCt956a9GJy0mvbFzULFbLbc8xUyQCPRcBUv9pppmmWQWQh+mnn77wM/mx6Zx22mmL+/LPHHPMEW+//XZhw1T65bVtCCyxxBKFRqG19o4kGwgHVfALL7xQ2JYh5KRUbXtyT4vdMeWlzUGUSfI+++yzAj8mCgi3J1A7Uh8iG+455IJ27Mwzz3SbbggIUKeyfZ988smbYhGkjDjiiIUqvMnz5x+kpOxRLWB+9iouFpGkq6+//nrA3uIdKTTe3HjjjbHsssvGaqutVsTtrj9J9LoL+VY+l3HoWGON1Uy6oTFpjM8++2wrc8loiUDvRODrr78uTBn+/ve/FxWkOrEAqiV6tAUGaJNjETH/tBmBFVdcMRZeeOFCatGaxDbBDBw4sJBMIS3XXntt/Otf/yrs9FKyOnQEkbj77rsvSFptSmP4/+9//zuoBKVGPN54442oJnoEAiRTBAHipGsZAaQNGevXr19TJBJqwhR2002eP/8glTMXs0U1xvzsHfXask0x7Mt33333QrvgOWX87rgm0esO1NvwTCs5K4RKZXDVCFuaNmSVURsUgSxW+xEwoBqckRC5IHP6BTWW+2pnlW3lXe2Xv1uPwKabbhqkS1RXrUnl3ZCWUKHbaGTSY3bCxiylekNHkESUWQdThb/+9a+FvReibcMA4qctwxGxq83NAqjWL++bI2CsgFO99sy/eewoCNtyyy1XLFy22WabYuczSbUNGcwYvKsyDfKN6C2wwALFZjC2fGVYd1yT6HUH6m14psZjgqpO4t4qQli1f0/7/f333xf2JGwQS6dePa0eQyuvOlF3lXV0NcgMLV2GDxkBOLKxQfLY64lt0jNww9x96bQ1gzFpeOmX17YhgDxzrU2ljbNlmmSSSaJSqRT2fyY+0hBHaLU2n74aT5u1QQaGpEwjjzxyLLjgggXhIN3T1vlVt3W/zQ8k2H0Vt9bW2zhBMwazMo3f8GMOUvpVX6lhjzvuuLjnnnti1llnDZuMSPhIUmebbbamqOZmpg52Q88333zBbvjll19uCm/Fjw6NkkSvQ+Hs+MzmmmuuYDRKJSV3DdFqzm8G6a491bHXUb/SzT333FHWrafWqV652W6wUyrr6arj14ubfq1DgKTbUR1skuBZprKJw05QRzWVfq42BBiM7eR2n67zEUCq7V4sbco80cSKLHLu07WMAFUh9WL1sUDwo+HhbFycaqqpiuOZylyQDipdGzRKv7zWR8DYQW1bvehgDmVRbpNX/VQRK620UmHrS+K60EILhc0bu+22WzPzquq05YkAiHu1f1f+TqLXlWi341mLLrpoIHmlbZFG6ADGVVZZpdW2Mu14bJckMelaEZWO4TFj2C55eBc+ZJxxxglnQ5b1dCXZ6MIi9KpH6Q+nnHJKmAhJ88rK2fVmJW3nHCPr0i4GKWTravJDAsv4veraBZVxtiUS0dpHaeMM3qm3SEmkQ1psqLET2n26lhGwkKdC1HbLWPBDoKlzSfoscqoXNRaVJKnObivT5LU+ApNPPnkhlXMcUxmD1E1bXWeddQovZiDm2+Km6o847CCdz4n4rbXWWlWhzX+as3//+99HqXVoHto1dw1H9KxIrAKB0zUQNPZT7Ohhl2Fi04Ft4zaJ2cJNnN/YpR9y6UzKf/zjH6PaGcSqU5koqHrYolT796TfI444YrBNqq6n8yKr68BonbS2JPTVYX3xN7UsSbaxoJpcuHdGnp21frOPcdQQmxltBVZrrrlmzDDDDLHLLrsEckLVQgIijvB09REgcbBrGZlAzmpj2TnIPgxxrg5DLrRd12p/Z5JZqB5++OEh3+effz6cQ+YQWWqz6rj5e3AEjBnOgnRWJE0HyZFjPzbYYINA8timrr/++sVByeYF4c6WRALtMh88x/SpRoB61iYuanBO+3W4OsypZcX1tRFEztU9Z6x2/p6POzj2zJl5xnhhTslgk3reeecFSTatlR3Q3iE1uzjd4RqG6GnIxKgarAMIMeByRd4dwDTKM53B40R5jZJayqRlFaGBDUMZGz6pXU+IoNWWNkH61/CFbkcBkViDtUOxEfcVVlghfEWgHVn1miSOJND/ET0D8OKLL16YLzA+R+ycK3bWWWfFFltsUezitJPTwEuSBwTt5qKLLiqMpqebbrrijD1q3immmEJwuhYQYDBeqpkWWWSR4qsj1VHtOPQO2Drytxi3C9T4hLjZcGGjhTBuzDHHLDYQsNFzJI6NHEiIjQVlHuKlq48AUwN4Isuzzz57GPMROEb+UlQqlUCmzQkWMciJ3ea+lGEsESddywhUKpWgekWObTSCJefwZAtDKbVri8VSAOFLPMYmhO7ggw8OcanQxeVobxA8ElVHPtHemK9JZ4V3l2sYomcix5St0ktXK/XoLpC6+7kakk8+IQW+nUcy1N1l6uznU+2U7aC8dvYzuyN/kydpbVlHV8SlO8rSKM80sbF/gQXH8Jk9jcnLCtzAy7/WIRZlHfxG/sS1oraILMPyWh8BktJqTA855JBmEUmTLEhKyYTJzye3qtOQ+FUnMnZZuFC3s4UiFUz7vGqEhvybeQLJNIxJRBGIEv8ypa8mwZZECjEkHCjD2nbtm7ERaDvEaQQsdKrxZe5hLCoXkfqEd+F8SGrzWsRoEixUxaEGJslrhLGnYYheLWB5nwgkAolAIpAIJAKJQCIwbAgk0Rs2/DJ1ItBnEciKJwKJQCKQCDQ+Akn0Gv8dZQkTgUQgEUgEEoFEIBFoFwJdSPTaVb5MlAgkAolAIpAIJAKJQCLQTgSS6LUTuEyWCCQCiUAiMIwIZPJEIBHodASS6HU6xPmARCARSAQSgUQgEUgEugeBJHrdg3s+tX0IZKpEIBFIBBKBRCARaAMCSfTaAFZGTQQSgUQgEUgEEoFGQiDLMjQEkugNDaEMTwQSgUQgEUgEEoFEoIcikESvh764LHYikAi0D4FMlQgkAolAX0IgiV5fettZ10QgEUgEEoFEIBHoUwgk0Rvq684IiUAikAgkAolAIpAI9EwEkuj1zPeWpU4EEoFEIBHoLgTyuYlAD0IgiV4PellZ1EQgEUgEEoFEIBFIBNqCQBK9tqCVcROB9iGQqRKBRCARSAQSgW5BIIlet8CeD00EEoFEIBFIBBKBvotA19W8YYjeu+++G8cdd1xsu+22cdBBB8XTTz8dP/zwQ9ch0eBP+vTTT+Oyyy4rXIMXdZiK9+2338aDDz4Yp5122jDl0+iJP/744zj99NNj++23jzPOOCO830Yvc1eW78svv4zrrrsuLrroohYfu/feexfjhTGD++ijj5riSq+/7LjjjsW48v777zeF5Y/6CBhvX3rppTjiiCMCfvVj/eL7zTffxE033RS77LJL8R5uuOGG+OKLL36JMOjXV199Ffy18wMPPDBeeOGFQb75v7UIGBeuvPLKAt8999wzHnvsscHmxTfffDMOO+yw0NYvv/zyVr271j6/r8Z766234txzz42nnnqqVRB8//33cffddxfzc20faFUGnRypIYieQXj33XeP2267Lfw+6pEiOXcAABAASURBVKijYrXVVms1yJ2MUbdnP3DgwIDJP/7xj7j55pu7vTydWQAT+3bbbRcnnHBCZz6mW/NG8jbddNO45557YqGFFopbb701DOLdWqgGevjzzz9fELwNNtggTFz1inb77bfHhRdeGP/73/8KB9ORRx65Kep//vOfOPXUU2P++eePJ598MjbZZJMeT6abKtdJPwYMGBD77rtvwA5BG9pjEHHkzSL92muvjf/7v/8rCEeZ1qLNYuaUU06J/v37x69+9atiYeP9Di3vDI+CNBv3tfVVVlklfvOb3xRk7uGHH26C55VXXom///3v8fnnnxdjydlnn93rF8lNle+kH3feeWcxHu+6666tWpgg4+edd15ss802cc4550TZ/jupeO3KtiGInoa7wAILFA30pJNOihNPPDE++eST2H///dtVqd6WqF+/fsUAaTLrbXWrrc/qq68e8803X5gkasN6y73BoCR3Sy21VCB9F1xwQUFYeksdh6UeU001Vay11lox9dRTDya9kC/J08knn1wseu66667gaANGGGEEwUECQkq68847B3xdrbZPO613S4mLyg/Dn0UWWSSWXnrpGGWUUYaay48//hi33HJLeA/HHHNMPProo/H73/++kII8++yzRXrSwaOPPjr+8pe/xBJLLBHrrrtuIOPnn39+kIAUkfJPiwg89NBDMWAQ+d5www3jj3/8Y2y00Ubxu9/9LnbbbbeAP4nqscceGyTZO+ywQyy++OJFvzn44IPjnXfeaTHfDBgyAvPOO28hpcZBhhzzp9DRRhstVl555VhsscXCO/nJt7H+djvRM2hPMcUUsfDCCxcrPvDMPffc8Yc//CGIpN2naw8CmaYRETAok0ItuOCCMdZYYxVFnG666WLccccNqsjCI/8MEQHEjgSDWoWKxRhSJrBAIGmaYIIJYrbZZgv/TI4WkqQj0vFL13YE7rvvvnj88ccLkvbZZ58VpALOZU5bbLFF8C/VviTWpHjTTz99VCqVGH300WOmmWYK0o9GnRDLunT3FZEzTriS5CnPGGOMURA+BJskT9t/4IEHisWMcA6+4h9//PFu0yUCBQLDFX+78c9www0Xk08+eTHRlcWwMv/1r38ds846a+mV10SgVyBgpU3SQVpVVsjAPO2008Ydd9wRH3zwQemd1zoIfPfdd0G1cuONN8aqq65aqGQvvfTSppX0e++9F88991zMOOOMzVJbODILQQ6bBeRNqxGgIjzkkEMKlSIpRkmkywxIAscff/ymBQyV4zjjjFMQPHGGH374mGiiiQJJef3113mlGwICxgrtvXohM/mguRLhe+211wqpHbMEGp8yG/h7DzQEpV+za970SQS6nejVQ/2NN94oVo1sPuqFp18i0FMRoH43gFdLQtTFxGlAF+Y+XX0EkAVqXZsA9ttvvzDh/fOf/4yrr766SEBiyk022WTFffkHvtSFiGDpl9e2IcAej/qQ+rVeSupbNqcl8XjmmWcKkjfSSCM1RbeosYh/+eWXm/zyx+AIVCqVQgACw+rFn/Hj66+/LhJQLZLqTTjhhMW9PyOOOGKhGSvV5/zSJQINR/SsYBj2sumg0s1XlAj0JgSoYhAOkux69RJez7+b/BrusZVKJZDkWWaZJdZYY41g7zXzzDPH5ptvXtgtIctcPXxhyzVcpXpIgdjwzTXXXIFs1xb57bffLnYcsjelkRFOjV6pVAq1rftq5x1V3+fvwRH405/+FCSiNgW8+uqrxaYim5MQPJJRGJov670PYYPnmD59FYGGI3qOVWHjsdJKKxUrk776YrLevRMBK262SrVb8D/88MNiQhx77LF7Z8U7qVYkd0ienW+kdaRNHFux6kfCl73YmGOOWe2dvzsAARInR2LZOY6El1lSI7LFQ0ZKP2M7v+p4ZVhemyPAnMNmCxI7Jy6wPdXO7STv169fkJRqz9VjCXzhrV80zy3v+jICHUv0hhHJJ554ItjeLLPMMsVKZhizy+SJQMMhYNMFOxvnYZWFM1AzcmdHNt5445XeeW0lAiZExIFaEH6TTjppcXRNdXIbOEyKNr5U++fvYUPA0SqXXHJJsVu31mbP7nkb6pBsT0FA2KdazKS2BiJDdiR1JKh2NdtJbqMF047NNtusSGgs0Z7Nm4XHoD/sH0n8VlxxxUF3+T8R+AmBhiF6Jj67sZwX5HgFxdNgGaj7nS4R6A0IUMXYcWtHXVkf5yTaDerQ39Ivr61HAEkm8SApJc1zRAgbMKRCLogGo/W11167aaMA/3RtQ4D6ELErU5EuUSVOOeWUoU3zZz9mdy5SR/VIwlRuvNDGtXVnpJbqXWnSDR0BtnoORHaEClyloL6dZ555ik1c7jn4Mg1xJIv7dB2PAK0jbtLxOXdejg1B9ABnaz6bjlNPPTX22muvwhFRpwg6io0pdguyu3DYqImrt9oa2RnJwN6k4mrQ6rzm3/U5W6Wvt956Qa2lzZsEGbmTfiy//PJdX6AGfKJ3ThKEqFHH2pxVtnc7OUk+nRWGONiBi8StueaaTTVxFqNNAezF5GE8Ielz3xQpfwyGAFKGKMDVsR1UrNWRjMdbbbVV2BDgHfmChjMhLcb32WefYsxef/31i13PbCQdq4LUiWfMcsC18ctmGmr06rzz9+AIIMsInnMgnUChjTvMetRRRy0iW9RY4Lz44otx5plnBmxh7bzCUlhSRMw/bUJAu/d1JgsZ/aH6AGTjD1vV2kV5GdcCU1s3V7fpoZ0cuduJHhAHDBgQI444YnHopoG7dFaJE088cSdD0PjZUzvtscceoYEprd2GjJ/97k3OZP7f//63OD/RwOY4B5NIb6qjulBdGZgdg4B8IC4GaGHpIhygjlBQVSEM//3vf8PgCRvH0vz1r38NY4b2YZLz1YvyTEJxuPJrDMi0MCSD6lZYuvoIaJPXX399IMmOUan9Oo0FCgkSFbkdtvqm3+V47VqpVIqDk703Ujs7oh1avc466xRfOnKwde3RN/VLk752km+//fYFEPfff3+x4YhdXuHx8x/9wdcwnK2HBFrkaPM/B+elHQhoo8Zj/eCaa64pSHSZzSSTTFIcjFzOxaW/9k2tbgFPtY50l2GNcO12oofgbbzxxnHVVVcN5o488shGwKjby2AlbeVcYuQzRRpUtxesgwtQqVTi0EMPbdYOkP0OfkxDZEftQtJ08cUXFwM4tWNDFKwBCjH77LMXnxIq2zsbJeRY0UjmTH5UhogJ6VA9Akfq4ZNE5fdue2N/gUdHOuq+EnNXBLo6/9133z0QN2O2hZg4te6ss84qDkUu0znzzZEsV1xxRZBcsykrw/I6ZARI+Nk/WujMMMMMLUaGqS9KOTtPfyDpazFyTw3ownJbfFe367/97W9NT6dh9IUdZ3g2eQ76UR3f72mmmWaQb+P873ai1zhQZEkSgUQgEUgEEoFEIBHoXQgk0etd7zNrkwgkAhGJQSKQCCQCicDPCCTR+xmIvCQCiUAikAgkAolAItDbEEii542mSwQSgUQgEUgEEoFEoBcikESvF77UrFIikAgkAonAsCGQqROB3oJAEr3e8iazHolAIpAIJAKJQCKQCNQgkESvBpC8TQTah0CmSgQSgUQgEUgEGg+BJHqN906yRIlAIpAIJAKJQCLQ0xFokPIn0WuQF5HFSAQSgUQgEUgEEoFEoKMRSKLX0YhmfolAIpAItA+BTJUIJAKJQIcjkESvwyHNDBOBRCARSAQSgUQgEWgMBJLoNcZ7aF8pMlUikAgkAolAIpAIJAJDQCCJ3hDAyaBEIBFIBBKBRKAnIZBlTQRqEUiiV4tI3icCiUAikAgkAolAItBLEEii10teZFYjEWgfApkqEUgEEoFEoDcjkESvN7/drFsikAgkAolAIpAI9GkE2kz0Ogutt956K3bYYYeYe+65469//Ws8+OCD8eOPP3bW43pUvp999lmccMIJMe+88zZh06Mq0IbC3nfffbHGGmvEIossEmeeeWYbUvasqO+++2785z//ifnnn7+4fvDBB5H/fkHg008/jeOOOy723XffXzx//vXRRx/F/vvvX4wVyy67bFx44YXxzTff/Bz600X6gw8+OPr37x+bbLJJvPbaaz8F5N8WEfj+++/j4Ycfjv/7v/8LY06LEWsCpDFmP/vsszUhEc8//3xsvvnmMc8888Riiy0WF1xwQXz11VeDxUuP+gi8//77ccQRRxRtfbnllotbbrklvvvuu2aRYbzeeuvFQgstFIccckho+80i5M1QEbjnnnti9913j2eeeWaocZ9++ulYe+21i3fyj3/8I5566qnB0uhLd955Z8Fp3nnnncHCu9pjuK5+YL3nffzxx7HbbrsFcJZYYom49dZbY/311w8Der34fcnPoGhSu/baa+OSSy6JlVZaKTbbbLN49NFHex0MOtk666wTK6+8chxzzDGx9dZbh8m6txF+JO/Pf/5zMWCfd9558cknn4QB44cffuh177Q9FXriiSfitNNOi3/+85/hd1T90x+23377uPfee2OFFVYIY8dqq60Wp556alMsOFosDBgwoFgs/Pa3vw1xTZpNkfLHYAhceeWVsccee8Qpp5wS33777WDhtR5ffvll3HDDDYhcaMeff/55sygW797VFFNMEbfffntBQpD3yy67LBfxzZCqf2Nc2H0Q+Xj77bfjrrvuKsjFdtttF7fddltTAiRD215wwQXj/PPPjzvuuCMOPPDAxLcJoSH/0Gbh9q9//Ssuuuii+OKLL4aY4Lnnnos111wzfve73xWCF22ZcOrFF19slu7ss88O70p47SK0WcQuumkIomfi22KLLcLqfdddd4399tsvXn/99cFW6V2ESUM95sknn4zLL788Ntxwwxh33HEDEZ5++umLCax2ZddQBW9jYb7++uuiA80xxxyxzDLLxLTTThtnnHFGnHTSSYNN9m3MuqGiW8wgJVbdu+yyS0w00UTFREmCbaBpqMJ2U2FmnHHGMPAusMACg5XgpZdeKiRD2sb2gwjfueeeG0sttVTAsox8zjnnBMmwCW/iiScuFgxIiUVDGSevgyNg8bHuuuvGOOOMM3hgHZ+RRhqpeBckdr/5zW8Gi4GgfPjhh7H00kvH8MMPH8YtknpkpDeNXYNVvIM8SIRISTfYYIOoVCrFuDjXXHOFORIRR0oQ5ymnnDLEGW+88YoFI9KNAHZQMXp1NqOMMkqsuuqqBaZDq6gF5AMPPFBoGkhOjSfw//Wvfx2HHXZYs+QkfhaqzTy78aYhiF6/fv1i6qmnLhozLH71q1/Fn/70pzCQuO/LzqAIh8knn7zAZ9RRRy0GTKSgxw6WdV4oCaVO1L9//ygnjfnmmy+oNP/3v//VSdEzvdSHmmDOOeeM0UYbraiEd4vwkdwWHvmnRQRMZtqIPiES3KyoSfrcI3Q333xzTDjhhDHNNNPwijHGGCOQRhPge++9V/jln7YjYIHSFgmFCRDe2rtJ0juK4VyqAAAQAElEQVRCTmaYYYaC+LW9BH0nBS0G3FxHGGGEouJIyayzzhqPPfZYvPDCC/Hmm2+GMVPbLiIM+jPddNPFiCOOWJj6DLrN/x2IgHfB1GbmmWduynXRRReNscceu9DKNHk24I+GIHqInRUffBCYa665plBPIjX8+rLT2UnyRh999AKG4YYbrpjEqLWJkQvPXvDn/vvvL4gswl9Wx2RuYqfSNYGX/j35auJ7+eWXC4llWQ8TIlLC9uONN94ovfNaBwGDKlVsdVClUikWhvxKfGeZZRa3TY6UkJqXhKTJM3+0CYF99tknLr744lbb2CHhFms77rhjHHnkkXHFFVeEcewvf/lLGMfa9PA+GFl7pfajBSir369fv2LhwgxBW9eeSfTKcOPlyCOPHAh56ddTro1eThyFhqAURJTlrVQqhX1ked+I14YgeiUw1AZEnvTbDFDbYhBc5tHbrlZuVnLVjUuD46hGekt9SxuHscYaq6lKlUolrGa1AwNeU0AP/sHuxgBdS1bU04oRge/B1evyosOTNJj60MPdmyAtjtyXDr4mzMS3RKTtV7ZhyB6y1prUJKlszGwiY2+L7P39739vtWq4Nc/orXEqlUpYnJDekdwZG7hXXnmlkB5VKpVi0wVD/2pVO6EJEp2bj7qmZRh7jDXrrbde1zywnU8Zrp3pOiUZA2w2af/+97/juuuuS/HzIJR13EGXuv+HFFY3QQN7DqkuBq5KpdLApW990dSFaykFAt9SWPpXIxDF5i1qfaortp1CK5VKi9KiSqWSKsNo/z/SIirx1ubAtITWgb0twoeUrLXWWsGvtXn05XjsGW2yYH9qZ/lNN91UzIvU4Da4GEe4ehjREtTzT7+OQ4BU1QamnmBy01BEzyvQgLfddttYfPHFQ8Pm15fdpJNOWuwEqpZoMXC2W2jyySfvNdAQiauMurlyVrBWpqR81Lj8erojnVUftnrVdbFSN2iPP/741d75ewgIkHZT66+44opBXSUqu0euVtoNX4uJaumH+Ok6DwEb6uyeX3LJJYtjJg4//PCwE/fYY48tdpx33pN7R86TTTZZcYyQzQI0HsZGEmubj4wT2rzdnwhHWWNx2EKy1Sv98trxCFjE2EnOZnL22Wfv+Ad0cI4NR/TUz0RIbN2Rg7J8e6JzNhK7LR1Y+RE+kxabLp2dX29wjFwROxLdsj7qbTcu49feskIlFWFTQ+Rf1pOqceDAgbHwwgsX9jelf15bRgBeSISdomzBypj6hMWi41dKP1c2oCZFGwHcp+t8BO6+++5CzYiweBrDdSquRx55JIkeQIbiLPymmmqq+Nvf/lYc1WF8pMbddNNNi5TaunmyeoetMZMQwJFDRaT80ykI0Dg6LYRAillIpzykAzNtCKKHHSMwRNLqpqE6BNKWcfd92Zn84WNigwNSYHXHlrG3kB/1+sMf/lAcH3DVVVeFdmBQs3sSKUJ2xekNjm2eweHWW28tJjv1pMrS5h0Y3hvq2BF1gEu1K/Pk54gVBv4cEqF/OG7irLPOCn2CsT81ITItvsnRZOgIJzsSy7zyOjgC8OJbXv0unUWYjUTlfXkt45bX0p/0mmS1lF4zS2DPREtRqTSEKUZZ1Ia9wlT7RpqZNCF9jlhRYAscZgsOUWZ/Kq7NGcLEc03XOgRgJ2Z59ZsjQXXyBXzdm5scd8ZkxKLFeOL96BfOOhSndGVe5bX0745rtxM9IJ188snxxz/+Mfbee++48cYbwz2C4zT17gClkZ5pl5UV3Omnnx5IELWHxmUys+JrpLIOa1l8/aNSqRQHqzrKgcQG+ZlkkkmGNeuGSk8V42wxhyQ7BFs9bUQqB/CGKmw3FMZmFTs07UJGgh0WTrJrwEQ2nCnpHEIHkjLu5ywGEAjFddYkP7ZNjlTZa6+9ivMnfb1BeLr6CDicmrmMyc2hybCujukgc2e4Cedv8kOgvR/juB253pkwzvhNrXXAAQcUY5cv3SAlpLAIuTjpWkYAebAxcZtttoktt9yyODzeV0uYIEgFw4022ijGHHPMsElGW3f4r/Ml7U4XJ93QEaCCRd6YFWjLpKJlKruXy6/vGIPg64xP0jxjTOkc0O6cyDId0qcvMV+QNy1cGdYd124nehotCcfGG28cfrO5MTgQPfcEkWhnvzSdGRZWaAZidnl77rlnkGR09rO7Ov8JJpigONXdsTrO20KAVlhhha4uRsvP66AQNmQWNc6KJJ0ygVqtd1D2PT4bJI4E2+TG/s7AS2KH6LE/YvNlJ6ejeEq3/PLLF4vFsvI77bRTofJ69dVXi6NXkA3tqgzP6+AImOhIiZA5eLOPrY61++67F58nLO1lSTdMZKTu//3vfwO+3lWZhvTae0C8H3/88eJMzPXXXz+8v962SC3r3JFXZMI7QZh9Os7ivvr0Bc/yvhzcy8yJ5Nrn/nJBA5nWO5xDG95qq62KjVylmZQc5p577uLTaBbh+gSptIOQy3GnvNr1j3BLwxnXhSHdxjOLV/7d5bqd6Kk4qRXpBlA0VIM4Q1Nh6aIwNEcKbFKxa60tO996Gn5sD62StIPas9B6Wl2GVF4r7tVXX734agPj6nLyHFKavhJmMeMzf8YDjhQDiUAOLAJJOPjXOpLuEiO/rcQRaGSxehAu4+S1OQLGmGpMLcCrY6yyyirFJjmLT/6u7O6q09gpKqx0Fm+IB+mryZDmppaslHHz2hwBO8kdS2OhX25Wax7jpztkz5hpYcTWOQUkP+HS2r/Gl+o2zO6xTIusGYtwFGOQPlAd12+OlqZM40rCx790s802G+9ucw1B9Lqt9vngRCARSAQSgUQgEUgEejECSfR68cvNqiUCiUCjIZDlSQQSgUSgaxFIote1eOfTEoFEIBFIBBKBRCAR6DIEkuh1GdTte1CmSgQSgUQgEUgEEoFEoL0IJNFrL3KZLhFIBBKBRCAR6HoE8omJQJsQSKLXJrgyciKQCCQCiUAikAgkAj0HgSR6PeddZUkTgfYhkKkSgUQgEUgE+iwCSfT67KvPiicCiUAikAgkAolAb0egHtHr7XXO+iUCiUAikAgkAolAItAnEEii1ydec1YyEUgEEoFhQSDTJgKJQE9FIIleT31zWe5EIBFIBBKBRCARSASGgkASvaEAlMHtQyBTJQKJQCKQCCQCiUD3I5BEr/vfQZYgEUgEEoFEIBHo7Qhk/boJgSR63QR8PjYRSAQSgUQgEUgEEoHORiCJXmcjnPknAolA+xDIVIlAIpAIJALDjEASvWGGMDNIBBKBRCARSAQSgUSgMRFoSKJ39913xwwzzBBvvfVWW1DrtXE/+OCD2GWXXWLkkUeO3//+93HTTTfFjz/+2Cvr+/nnn8dpp50WK6ywQq+sX1mpH374IZ544olYZ5114qKLLiq98zoIAW1bm99hhx3i73//+yCf5v+NC//85z/jV7/6VUw00URx2GGHxRdffNEs0nvvvRebbrppjDXWWLHooovG008/3Sw8bwZH4Ntvv40BAwbEXHPNFR999NHgEWp8Pv300/jvf/8bv/vd72K44YaLnXfeOd55551msbzHXXfdNUYZZZSYeeaZ48Ybbwzvt1mkvGlCADavvvpq0e632267Jv/yx4svvhirrrpq0fYnmWSSOP300wdr+6+88kqsttpqBeYrrrhiuC/T57U5AvC+9957Y/bZZ4+xxx47tt9++/j4449jSP9eeumlgOtvfvObmHDCCWOvvfaK77//vimJ9q4/DD/88FE692uttVZTnK7+MVxXP3BozzNAn3DCCfH8888PLWqfCDfg7rTTTgGX1157LQ4++ODYe++947bbbuuVA6aJwOD15ptv9ur3+9BDD8X5558f1157ba+uZ3sqN3DgwLj11lvj2GOPHWwS+/DDD+Oggw4qBmXtZOmll47ddtutGGzLZ+krf/nLX4rbZ599NlZZZZVi4kuyV0DS4p977rmnaJMPP/xwq8aW0047LV5//fU45JBDYrPNNovjjz8+ttpqq2Ks8hATpgUq8odsHHroobHPPvsUZNIEK07nu571hJdffjkuu+yyMAfWlvztt9+OY445JmacccY466yzYp555oktt9wyTjnllKaozzzzTEEEhcF87rnnLkijeaQpUv4oELDYvv7664vF9r777huw01a1WYueIlLNH/PS8ssvH8sss0yxGLr//vvj9ttvD4tSUb2/Rx55JP71r38Fol46AprFFltMlG5xDUf0Lr744mJ1OOaYY3YLII320McffzzuvPPOYqKy4tCBdXST3HfffddoxR3m8vz5z3+OBRZYYJjzafQMrCC32WabRi9mt5Rv8sknL1bMBsfaApjsllhiiULCvcYaa8R+++0XyJ4JsIyLQL/wwgvF4DvOOOPE2muvHSOOOGLsv//+ZZS81kFg/vnnD9i2Zuz98ssvi3H6gAMOKPBF4v7xj3+Eic9kJ3sSa5Mg6dJvf/vbgph4p2eccUb0xrFLnYfV9evXryAJ9fKxyFlkkUWKdg1TmM8777xxySWXFNGRkwsvvDAqlUpB7mBOivTGG2/EySefXMTJP78ggPxarCy00EKBhBkraFgQ7fvuu++XiFW/LBbffffdWHPNNWOkkUYqJHrLLbdcIIyikdwdffTRcfjhhxcCGUIZZE977855raGIHumG1fiSSy5ZiDwB19cdyd2oo45aNChYUN9OO+20ceWVV+ZgCZAOdpldYyNgIpxvvvlihBFGKApq8YM8UJHwoE5ELqi1JphgAl6FyQMSQ1pMLVZ45p82I2BhQpr61Vdfxa9//etAtEcbbbSmfJZaaqkgJSmldcYuKlvqLZFMjNNMM01cddVV8c033/BK1wYEtH2kBPaSjT/++DHTTDM1qQ3ff//9eOCBBwo/WIujD0h34IEHuk1XhYAFyWOPPVaYKpTe5lb4WoyUftXXcccdtxh7/v3vfxfaBgse7fxvf/tbEc24U7b3wmPQnzvuuKN4xmSTTTbornv+NwzRY3eDXROBWn13DxyN99QHH3wwdOgxxhijKFylUgmNzWD66KOPFn75JxHoKwiMOEgyV05iZZ2//vrrWGGFFYpbC0XqFRLTwuPnPwgGO740CfkZkHZc2JIac5A09pG1kj/4mijLiQ7pGG+88aKMV6nk2NUO2JuSaPsW+qUHuzDvYuGFFy68SPyQF++g8Bj0h4RpyimnLNTp7PsGeeX/nxGgHaDenm666X72iUCM2d6xg2/yjGj6SZu20UYbFWYK//d//1fYqP7pT3+KTTbZpClO9Q/zNNMGktVyMVod3lW/G4LoWYVj0Awhu6riPeU5bGBILwystWWGW61f3icCfQkBpI5xNLWheiMbn332WZCCu692pE1W4NV++bv1CCBubIRHH330wRJRG5KYso0sid6Qxi7vaLBM0qNNCAwcODBIV9mgSqhtU0eSorqvdfpGrV9fvtcGP/nkkyiFKNVYsC+tvq/+jacYb2655ZbCPg/5LgY5EQAAEABJREFUqw6v/v3UU0+FvlGS8eqwrvzd7UQPCHZ6WXXYaduVle8Jz7K6aKmcQwprKU36JwK9BQFjB7WJXbXlYGvVzNWrY6VSKVSO9cI61K+XZkZNzu6rXvWuuOKKQqWF6JXhQxqfLF7LeHltOwIkRaRO/fv3j6mmmqrIQLuvJxAoAgf9oZIcdMn/PyMwJLxIT3+ONtjlzDPPLCR/dvvD1AkAt95662DxeCCD7F797k7X7USPXQHDRTvnsF62NNtuu23Ylm93C2Pr7gSou5+tE1tdWK2VZSHFsHKbfvrpS6+8JgJ9DgH2NWzujBMlqSBtoiok5asGxKYmgzLVTLV//h52BOwyJO3bfPPNo9pmb+qppy6OqqiWJDHRybFr2DEnPSWhtvtTu5YjKTazHvOD+9LZFFOpVKI7bcTKsjTS1dFLNmBQ35blIoUmJXUUUOlXfdXW7RzfYIMNYuWVV46jjjqqOMJJ26c6r45L4yZ+Er1BqBAz//Wvfy22OLOz4ZzjZMXHuJeh9aBovfn/EOtG/2/XVNmI2CO5t9vKpDbExBmYCPRCBEgzTF52HW688cZN5MLER23IRsnZWOKV1UcKhTFeL/3y2jYE4MlVp7IL0UkJjoBCsIWVcYxdSIdFO39jl4nUyQE5dkGk7Q62N9xwQ7EZjySptNnT9tlDWvxr62XOsGe3apfukKRUZfy+dEV82edZBJb11l61U3iVfjAvf//vf/8rbE4RahJB6Xffffew4HQ0SxnPlU0kfuOsT/fd6bpdomcFiB3b0VU64n+rE3pwZK87AeruZ9th6EBSE5fOTFLhbLB11123ODSzu8vX0c9nYKyjWVVRzXV0/o2UHxuRsr7Vg0kjlbG7yuI4grINVLcDv5l6ONaD5N8kxlbJyvmCCy4oisvwmRH65ZdfHtqSoxKcQel8rCJC/qmLgDYIL21S26yNdN555xXnG3o3wkyQjnmyE5Rhu/dg85jTE8QxdpkQy7FLuLPKHGFhIS+PdIMjQAL6448/FvZ33kUZw8YLpy0ges7PgydHbeh4D3Om46nYhSHg3sFdd91VHLfiiI8yn7z+hMDEE08cTviwCxxJozWDpeOdCJzEckzTcccdFwige5u6jCXieR/GKG3fbltEWxxOX9LWzd31bADF6UrX7USvKyvbE59lNbD11luHlYRV85FHHhmzzTZbLLvsssU5Vj2xTkMqs/oZtBBaRq92+Q0pfk8NY9P0n//8J4j37TY3gEf+KxB47rnnYo899giTFZWgDQBwstBxVIFJjkSPusTON87XAkgzZPDHP/4xnE7PlsYufgcvWzQuueSSgtO1gIAJz+G7DNR33HHHQJSrozpaBaYmRLuX4Y/8+TKAd8DZfYickHZQkxu7vDNj1xFHHBGzzDJLOHcMEa/OO3//hICD1H1hxB31rPPvmO6U98YMdl+wLp3+MPfcc4sSpKjaOimTfJyx5zDr0oa1iJR/CgSovNdff/2YY445QnuHF3KmPRNAiWSRaAxxmLh7pmXwZFKGPMPZfGVn7ZhjjilK4RBAhJtGshEkqQ1J9KglDTKMfwvU+vAfA6bOSxqB3PkklAGWbUFvhIVxMZvNa665JlZaaaWm8wN7W10NvBtuuGEYtL3bYVUp9iZ8tG1tHfE49dRTY/HFFw82eJVKJahlHY5MbYsMls6EWL2ZyxlvBmx2NM68QkAaYcBt5PekTSJk2qRxxn11eS1ITHrUUSQVsCfRcy2dvrvgggsWUiRjlwN+tW/vU55bbLFFcTxUdb75+xcELOwtWpwF6Usj5sLyOCGkgXSptu2feOKJhTpRLto4nL1HtqvICNMoYekGR8BYo30yAbF72W+S6DKmsYeE2rzEj6oc0bPoMbb4FBqsF/75iBtxOBJrWjfSbvfd7RqS6DGStCoHVncD1AjPt/IgTtbpGYmWq41GKFtHl+EPf/hD6GilM6F09DMaIb8pppiiWT2930YoVyOUwcp4zjnnbMJn1llnLXZ0ViqVYrcbG6+yfVRfq3cc+k3NIi47GhNgI9Stkcsw+eSTN2EOVychVJcXkdZuSeO8I3FqnXG72v6uL41d1Vi19zdVNxv1Eldku5wHqRpL/+orDU/187R16bR9G2K8g+rw/N0cAe3VuZtwh7H2XcYgbDIWae+lH+JNe+AdCGPrV4aVV4ucfv36hbxLv264Nj2yIYleU+nyRyKQCCQCiUAikAgkAolAuxFIotdu6DJhIpAIJAK9CIGsSiKQCPRKBJLo9crXmpVKBBKBRCARSAQSgUQgIoletoL2IpDpEoFEIBFIBBKBRKDBEUii1+AvKIuXCCQCiUAikAj0DASylI2IQBK9RnwrWaZEIBFIBBKBRCARSAQ6AIEkeh0AYmaRCCQC7UMgUyUCiUAikAh0LgJJ9DoX38w9EUgEEoFEIBFIBBKBbkOghxG9bsMpH5wIJAKJQCKQCCQCiUCPQyCJXo97ZVngRCARSAQSgSYE8kcikAgMEYEkekOEJwMTgUQgEUgEEoFEIBHouQgk0eu57y5L3j4EMlUikAgkAolAItBnEEii12dedVY0EUgEEoFEIBFIBAZHoHf7JNHr3e83a5cIJAKJQCKQCCQCfRiBJHp9+OVn1ROBRKB9CGSqRCARSAR6CgINR/S+//77qHY9BciuLucPP/wQP/74Y1c/ttOfp07qpg343ekP7OYHqGtfqOewwgwnbYJrCa8yjuuwPq+vpodtW/Er07jW4sZPft4b5742Tt4PjgDMuMFDIvjDkmsJzzKOa7080u8XBGAIJ3i6/hJS/5d49Zx8yhR+l3Fak2eZrrOuDUX07rzzzhhvvPGa3JJLLtlZ9e7ifDvucR9//HFcc801Me+888bNN9/ccRk3QE5ffvllHH300bHCCivE4osvHv/617/i1VdfbYCSdXwRPvjgg7jqqqviz3/+c1x88cUd/4BekqNB8oknnojtt98+lltuuVhiiSVit912i7feequpht9++21cccUVseaaa8aiiy4a66yzTtx3331N4fmjdQjA9KyzzoqZZpopPvroo6Em8m7ef//9OO2002LKKaeMhx9+uFmab775Ji6//PJYa621ivdmPPfuXnnllWbx8uYXBD777LMwD64waAz8z3/+80vAoF/fffdd3HbbbbH66qvHdNNNV2C+yy67xOuvvz4o9Kf/+sINN9xQ9IE//elPscYaa8SAAQN+Csy/gyHw+eefF+0X3trnZpttFk8//fRg8UqPZ599NqaZZpomjlLyld/+9rdNOL/33nuxzz77xKqrrhrewUYbbRQvvPBCmUW3XIfrlqfWeahB4fTTT4/DDz+8masTtU97ffLJJ/H888/HwIEDe51E7/jjj48zzzwz9txzzzjvvPPi008/DQMZctvbXrqJlLvnnnt6W9U6tD6I/r777hszzzxzXHTRRXHUUUfF/fffH3vssUfTc2699dbYYostYu21147LLrss5p577th8882bTYBNkfNHiwhojyYkRIxEosWIPwcgetI8+eSTdbH27k466aSC6CEfZ5xxRrz22mtx7rnnFlqbn7Ppu5c6Nf/iiy/CeHfllVcOFvrGG28Ui/sVV1yxICfrrrtunHzyyWHcLCNb4GyzzTax0korFYufpZZaqhhDvdMyTl5/QoDE7cILL4xDDz20mHOML6ONNlrsvPPOQejwU6xf/or/0EMPxYYbbtiMo2y11VYF8ZttttnC+zNneVfey/nnnx+VSiX+/ve/d6vQomGI3t133x0zzjhjsQKxCuGmn376X1DOXwUCk0wyScw111wx1lhjFfe95Y9V0O67715I8kgUfve73xUd7qabboreSIammGKKWH755XvL6+u0eiB1jz/+eCyyyCIx0kgjxdRTTx1LL710GEAffPDBYkA+55xzwlhBCjz66KMXK2kkwyQY+a/VCJASzTLLLDHKKKO0Ks2vfvWrQqqEWA8//PCDpbEoJfEj7atUKsVk6Blvv/12r1ukDlb5dnqMO+64gZzVS458aOMkRTQ6FjP6xS233FJE//rrrwsJKimTsQVpWXbZZYPU6ogjjiji5J9fEDDnXHrppcV48vvf/z6MHUg0smze+SXmT78qlUoxDm299dbNeIr+Au8xxhijkGpfcsklhdSVlM/73HTTTeOOO+6ICy644KeMuuFvQxC9r776Kk488cRiVa4R33vvvTkQdENj6M5HUleQDpiwhxvup2ZpgrCKsgjozrI1yLP7XDGooZ555plwrVQqRf21DQsBVwsAhI6qhR/iIZLBdaqppgrSJPfpOgYBBHuTTTYpiENrckQ0mCiccMIJQWPz7rvvBskSdVY9YtiaPPtynGmnnbYw2SmxG3vssWOyySaLEUYYoYAFvo8++mixGCr7ArJhHKUpKyLlnyYEBg7SillEzjDDDE1+FuDwJH1u8vz5hzFnnHHGiV//+tc/+0TBU84+++xA5niSbiPW8nDPIZEjjjhiPPDAA267xf00o3bLo395KHGnFYrVioGdLQ7W/OGHH/4SKX/1agR0EBU0QbtylUolJppoonjzzTeD7Qq/dH0HgUqlEiOPPHIgC9XvnzS7nMisyk1wFgjVyJgAkYuBgwbzav/83X4ETFYmutbmQPuwxSCVOlUttfqRRx4ZCy20UCG1r1R+Iu6tzSvjDY6AeZN5CymUUCpf9nrVYyj/iSeeuCAkzz33nNt0PyNg7DC30BL87BU0SUgaqV7pN6TrgAEDCqn2pJNOWkQzXiHiL7/8cnFf/jEelb879tq63BqC6FmZbLzxxsUmAyqZDTbYIKhjGAa3rhoZq6cjQMWjDr/5zW9cmjkSHZK9Zp550+sRQOZmn332QmVLKkTiSx144403FsSfqoU6y4RHrVsLCDszZK/WP+/bhwAbagtwqqrW5KAv22xkA81TTz0V0lcqlbTPaw14rYhjYwApE/Ws6FS3FkQIuftaJ7zWry/fGztI30YdddTBYOA/mGeNhzmJ2rzEX/Ccc84ZFjjHHntsvPjii4XNHg3lY4891mqTCPl0tGsIolddKeJpO+wWW2yxwg6nOix/914E6k3UZW2Jyq2Syvu89h0E5phjjth1110LI/RlllkmDjrooEIFwtxj/vnnL9Qo2kc9RCqVSpNaq154X/QbljqbEKljW5uHyZI0b8EFFyw2V5HmMVy3MQMJb20+GW9wBGyEue6664pd+yR2YlgYkUb5Xc+1RADrxe0LfsYNrl5dtfV6/tV+NAkkqDZhlP6TTz55HHLIIcViZuWVVw5cBhmEPRJYxuvq63Bd/cDWPI9R4yqrrBJW8K2J35viWJE5VsRKrXSOjaC+6k31rK1LqXqrVrWZDIjQJ5xwwmhNx6vNM+97PgIWAI5LYd/CoJmNGBXVaqutVtgnsUGiTiTdqK6tYyhMev369av2zt9diIDTAa699tpAyPVvuxuRPUcopaS1/S/CuOhUArao/fv3j3IRTFSRt+sAABAASURBVMJtrHzppZeaZc6W1VxSq9JtFqkP3hg7JphggmAuVlbfrnMagj/+8Y+lV4tXOCPZ448/flMc74INKpWuzWJsI41N3o0NG00Ru/hHQxI9GBjMnRfkd19y1B22zTPwLN0///nPbhL7dh3yJgAqfLssy6c6l4vtg119pV9e+y4CFkGOLKC+JeWDhPZht/5dd93ltnAM/hEJxyAUHvmnQxCg6mqL+s/GABMfaYYCsK0sd5QiK/zStQ0BfcCRIKRJ66+/fpHY+XrvvPNOsat51llnDUeAFAGD/rBBI1m1Q3fQbf6vQmDyQdI3GyUQstLbsSjwtLjkRz1rvPG72vGH85hjjtmiEILWgbmCjYauiHl1Hl35uyGIHsBs/8Z8Aciew246BrxdCUYjPIso2WoCyS3dfPPNFwhgWT6dXSPS2Uu/nn61Et1tt92C/ZV24AiGgw8+uDgAtztF3v/P3lnAWVF9cfwMFmJhY9EtjUpYIAYKIgoiSIsCIigqqISkKCWCGJRK8ydUkBARJRVEpBGQblCkO//7vXgfs++9Xd4uuy8PH+7OzI2Zub9759xT97zUxJU+4n+IQzWmmNR8ViTfG6EPrQSx8/B3YZ5ApOkTpkQC8rLzlpAILGrEw8L52S6E1NN0fgSYg8xF5iSMg3cL4lviO+3N7LFpDsaNo7sNuxm5H980dB2rBBoTTF0wgO66en4OAXACTyxajIUtge7DMBCFAAxnz54tpL59+wobLdB+Yy5kXSAYO2sERyxkWInsffR4FgE0cUT5wAQOz8H8JQ5niRIlpHTp0qYSioeWLVsKSgeT8d8fhB42XOBDjMn8v2xzAHeCvGPCJRg+cflwRTOFIfoTFowehHzgwIHCJK1Xr54gnTMA7LgU/RcPAX4BgOjdSB5MIDaxxKsQwReMfaVKlcxWdbSaOLWyuEOoIrhbfl8dMxbuCSyELKAIOn4rxngm/lw483ft2lWQiMEN7a+bUcAHrFu3btKrVy8TmxCmjx2eCA8xDl+Suk8wamJZosHANI6g5b4BpnOYDMt8sNiB+fvvv2/CpxBignvYNgS5Jg8NFPHemO8wk3Xq1BHvxdG2ifUjTAc4gQPx2N5++22BcYPxY6Mi44PfIxonm3D8h+GgDT6tjAeblwiHw1rKNbSUck3nEICGsN7wyxXMU3Anr1OnTmYDGDVtgG/WW67/S4IghNCCS4LN44irEfds3ry5J+A/vAxloUxpQvlw++xSpUoZB2tU/TB87Lol9g9+BbaOHs8iQOgZYv8gVeMDw0d+tiTy/yKR4qwNscO3h5+RiVZmn7ATaLIxEzCOalrxP3+RhGEu0NIR8Z84VxBjd2204PwSAD+3hVYPDQcmLHcdPT8/Aix2aIaYk2gw+BbdraZPny7QG+svy/fKvGXBg4FDi4EPpW2DjySLJ8Ipv17C2PCrAfiKOY6GV7E4uY8INeAEYwczAbOND6rjOMK6iBWADQDs6LSJHZ3WPA4DDYNHIGB+IpNYkliE3M/Q83MIEA4FFw+0zqQuXboI89PWgGkjDAuY2jyO0CEEGMaGa5uwxqFFnTRpksBgo3llTGx5qI5hweiFqvP6XEVAEVAEUh0BfYAioAgoAiFEQBm9EIKvj1YEFAFFQBFQBBQBRSA1EVBGLzXRTd69tZUioAgoAoqAIqAIKAIpgoAyeikCo95EEVAEFAFFQBFILQT0vopA8hFQRi/52GlLRUARUAQUAUVAEVAEwhoBZfTCenj05RSB5CGgrRQBRUARUAQUARBQRg8UNCkCioAioAgoAoqAIhCFCPzH6EVhz7RLioAioAgoAoqAIqAIxDgCyujF+ATQ7isCioAi4BcBzVQEFIGoQEAZvagYRu2EIqAIKAKKgCKgCCgCvggoo+eLieYkDwFtpQgoAoqAIqAIKAJhhoAyemE2IPo6ioAioAgoAopAdCCgvQgHBJTRC4dR0HdQBBQBRUARUAQUAUUgFRBQRi8VQNVbKgKKQPIQ0FaKgCKgCCgCKYuAMnopi6feTRFQBBQBRUARUAQUgbBBIMIZvbDBUV9EEVAEFAFFQBFQBBSBsEMg7Bi9M2fOyL///iuLFi2SGTNmyPbt28MOtGC/0MmTJ2XDhg0Gj99//1327t0b7FcI2vP27Nkj9HHWrFmyefPmoD032A86duyYLF++XGbOnGmOx48fD/YrhPXzmPPr16+Xv/76y+c9T5w4IatXr5bp06fLnDlzZOvWrXL69Ol49Wi/Zs0aYR4tXrxYjhw5Eq9cL3wRgPbu27dPFixYIODnW8N/Dm34Zg8ePOhT4dChQ7JkyRJDu3755RczVqdOnfKpl6IZUXQz6MLatWs9c/2ff/4RxsndRTD+448/zFxnzidl7Nz3ieXz3bt3y4oVK8TfHPbG5cCBAzJ//nwzJvApXHvXYYzgY6Dx0Hrv8mBfhx2jN23aNHn33Xelb9++8s0338Q8o8dH+9NPP0nnzp1l4sSJ8tVXX8mHH34of//9d7DnSqo/DyavY8eOMmjQIBk7dqw0btxY5s6dm+rPDfYDYDo+//xzM47jx4+XLl26yIgRI4L9GmH7POb27NmzpU6dOmbeu18UJgG60L59e2Gu1KtXT+rXry8sdOL6N2TIEGnXrp35ZvhePv74Yzl69Kirhp56I/Dnn39Knz59pEKFCgEteDDcMBY9evSQ8uXLG+bbfU8WzS+//NLQcug64/b+++/L0qVL3dX0PAEEYPK+++476devnyDU9O/fX8APod82gZno2rWrqTNp0iTp1KmTTJ061Rbr8TwIMIeXLVsmH3zwgbz22ms+c9i7OQxht27dDO15++23pXr16tKqVStB2HHXXbhwoVCPNQzm3F0WivM0oXhoQs9kQjdv3lwKFSpkwIO5KVKkSELVYyJ/27ZtZsIUKFDATK433nhDIMgTJkzw0WJEOiBt2rQRPpB33nnH9LVo0aLmyMKfin0L+q0nT54sn3zyiTRp0kTee+89qVq1qrBYIv0F/WXC8IFXXHGFZM+e3RBPCLH7FTdu3Ch8ExBlmAhoxKZNm6RZs2aear/99puZN0899ZTAEL7++uuehdBTSU98ELj55pvlmmuukcOHD/tojXwqx2VcdNFFct1118n111/v18qARp65XqtWLWnbtq1JadOmldGjRydJYxj3qJj8v3LlShk+fLhhohFaWrduLVu2bJGPPvrI4IESAAERjClnrpcoUcIwe/v37zd19E/iCKRJk0ZuuukmyZgxo+zYsSPxynGlCCy33367dO/eXYYOHSoVK1Y0igmUE+L6d+utt8oNN9wQNgqZsGH00Fq99dZbQkJCB8zLL7/cBV1snmJGQWp74IEH5LLLLpPMmTNL/vz5zeTyXgQjGSEWb5gfFmfGPl26dPLyyy8bFTnmt0jum/vdWUTRzBYuXFhIjGmpUqVMFYiHOYnxPzB6zIH06dP7IIFG7/HHH5e7775bMmXKZLRP5cqVMyZcW/nbb78VCPizzz5rvhlwzpcvn3To0MFW0aMfBFiYbrnlFrnkkkv8lPpmgTGMHmPFuXcNNNe7du0yzKPjOHL11VcLC2A00S3vPqfkNVo8zLJZsmQxt82aNauULFlS0OyxJpB+/vlneeKJJ4Rxu/TSSwVagtZp5MiRpo3+SRwBhBUYPYvxudq+Z2hYEYaqxgnmOXLkEBJC5I033hiP/tAyQ4YMctttt3EaFiksGD046V69egnMTN68eY2vAZodNbWI8LFDgK+66iozYS6++GKzwIFPNEltMHOO4wiMrOlo3B80BSz6+EGwwMdlRfx/5jrmrmzZsnn6AlObK1cu+frrr9W86EHF/wnENXfu3PEK+T5y5sxp8jCToBm11yYz7g+MIdhjpom71P/JQKBUqVLSoEGDgMy63J4FEOYDBhtfSywRO3fulKefflpYYKmjyT8C+HhhosX3lHNbC4YErSvaPvzXMYPfcccdttgwfNBMXBc8mXqSIggwl++77z4jsNgbXnvttcKazLjYvHA8hgWjh7M0misIOA67+HKgzenZs2fML3w4iCI18/HaCXTllVcKJhA+dpsX6cdVq1aZLqBCNydxfxzH8ai/0YTFZUX8/7179wqLnZuhpVNIlRB1zF1cawoMAaTsDRs2GD89WuDniUYDWsK1TTAdaJJYHG1eSh5j4V5lypQxGqVANX5oNfBjgvF+4YUXpHfv3kYDe88994jjOLEA2QX1kTmLqdbt7A+D7DhnscMvjHI02/ZBKARgSBCObZ4eUw8B6DVWGdwTUu8pF37nsGD0mKxI21WqVJG6desaHwPMMZiy0PRceDcj9w5o7fi4/ZlGwmE3T0oha4mZv0UEbR5MUEo9K5T3gdlAU+2vn7wX5Rw1BYYATAQuHpUrVzYNwA/mj8XOZLj+oBmh3JWlp0lAAKYNsxULWyDNmOP42eI3xniMGjXKmLiY/4G0j+U6juPIgw8+aHwZ8V1nHWCdnDJliiDMYO1gLoOlv/EgP5bxC0bfwR9/04YNG4q3YBmM5yflGWHB6OFUilSCloNJiymLXVws7t9//31S+hMhdQN/TSS0hGonVpZQm3DNR0uZ0LsxJ1CPJ1QeSfksfvQnoXeGaUmoTPPjI4CWl/A0zzzzjPH9ohR8YSo4904IS9AW73y9DgwBcE1s7nrfBeaETUb4VOKDzYLIhqsxY8ZE3UYy776nxDXuBuD3448/Cv6q7M7HlxkzIYwFcx3Ljr9nYVL0l695KYcAYYNYl9h5m3J3TZ07hQWjBwFhKz6E23YTguw4Tsyr+DFzYG7C5Ac2ML/EEENrwU5c8qIhIfnTD0zVHEk4c2PWxwnZbbqmLFIT/mQINW5fMSRDXBYyZMggEPFI7Vsw3xtaQegJ8ERjZJ+NuQvH9Hnz5tksc2S3HHOI3bwmQ/+kOgL4j6FxLViwoKHj7KZnox07RZnzqf4CkfYAr/eFkXvyySdNnE3oPuZvfFBfffVVUxOtHvPZTUuIucdaUbZsWVNH/6QOAphsCekEzmwySp2npNxdw4LRw3EaiWXGjBmenkHIMbUw0T2ZMXjy8MMPG1U9fl10n11YqPDZUQghIC8aErvJ2KAAw2P7QxBWfPbuvfdemxXxR3Ztwby74wNCNJjvTZs2jfj+BaMDaIpwNsekz3dgnwljgSaDTV34r7IoUsY3gwDxyCOPCDtEydOUdATwfbQuFoG0Zl4jkDqOY6pjgeA7R2sNbTeZ+icgBBD2cWVC6K1UqZJpw1yGlhC812TE/SF4OEz0Sy+9FHel/y8UAeYvmLrvg6IFN4S77rpL8uTJY4r4Lpjv5iIM/4QFo0esPJwZCfaIBgeCQowspD+3tB6G+KX6KxEagp0+xOzBxI1mgo0LNWvWjKqda2w4wVkbn0yYoHXr1gk+QfhegUGqAx2kB2D6qlGjhkBAPv30U4FpIQg2kjnfQBJeI6qrouHHbxcCCka2s1yz4LHrHC0HO5XREBFF9aHPAAAQAElEQVRknd3M1ANfhEcCKkNLvvjiC7JN6CZzon/8IgDjTDgU/Lv4VRLvSrVr1zYxPWGc3WWYExkLju58vlvuOWzYMONrxoLJtw1NjyYh1d3nlD5HwB84cKDZxIK2GtM3R57D7lvGhI2MrAsINoMHDxaUA8WKFaOKpgARIBYnNAdtqLvJDz/8INWqVROO5LP2EsuWb4RzaA+JuJ58A9QhMe8ZO+5HIi+UKSwYPSQ8okszaXFAhUtGWmnfvr2JgxVKgEL9bD5mPm4IIzgRbRuGGK2F45yVlEP9jin1fNTgBAWFiaV/bMwhYjnMUUo9IxzuQygViMPYsWONbxnSOsQ8EkwAwcAPoormAkI6btw4QaMLE4GgQ2BSfgmAX86BXuAfQyLwNOE/eD+EBmLp4bRO3DYsBVzD/FGuyT8CBKtHEwRjjbaiUaNG8SqykBG7zWbC8GFGJCG4YH158803bbEwzxkr/PPwJcPVhFAg+OqxwcxTUU/8IsCvufAd/O9//zNBkolCwbXjnKX7juOYkGQIjIwboYeI3QbNZL3we1PN9EGAuLT8ggXCDTvLUarYSmiesQYwX6HTzG9+rADBErpjE/QJlxzbDusBDCH5zPvPPvvMFqXy0f/tw4LRs6/WokULgfuFE0abE20LvO1nUo+E3uAjRyWPxqt48eJJvUXE1MexHtU45mlidkXMiyfxRWE6cLJGihwwYIBYKT2Jt4nK6o899piJKA+RJWGShWHD8RmCjDRNPoyHTVxjtrWAcI55l7po/VggbZke/SOA4z94giVH78WJQN8wFfg6cgeOMCPUtW34uTnKbGJDAYweTDpMIoufCjQWncSPMNAw0/zyBVadhGqjIUWbzdqJckTxTQgp//nQF/ccxiJga/JzgGhK0ZLi+wuTZ+c6bWxijGwbjgSypszW9RaaqBPMFFaMXjA7rs9SBBQBRSDWEdD+KwKKQPQjoIxe9I+x9lARUAQUAUVAEVAEYhQBZfRidOCT121tpQgoAoqAIqAIKAKRhIAyepE0WvquioAioAgoAopAOCGg7xL2CCijF/ZDpC+oCCgCioAioAgoAopA8hBQRi95uGkrRUARSB4C2koRUAQUAUUgiAgooxdEsPVRioAioAgoAoqAIqAIBBOB8Gf0gomGPksRUAQUAUVAEVAEFIEoQkAZvSgaTO2KIqAIKAKxgID2URFQBAJHQBm9wLHSmoqAIqAIKAKKgCKgCEQUAsroRdRw6csmDwFtpQgoAoqAIqAIxCYCyujF5rhrrxUBRUARUAQUgdhFIIZ6roxeDA22dlURUAQUAUVAEVAEYgsBZfRia7y1t4qAIpA8BLSVIqAIKAIRiYAyehE5bPrSioAioAgoAoqAIqAInB8BZfTOj1HyamgrRUARUAQUAUVAEVAEQoxAWDB6x44dk//973/SoUMHn7Rw4cIQQxR+j9+1a5cMHz5c1q9fH34vd4FvdPLkSVmyZImMHDnyAu8U3s0PHDggo0ePlk6dOpnjoUOHwvuFQ/h2x48fl2nTpknXrl3lo48+EmjCqVOn4r3Rvn37DA15//335dtvvxVoSrwKenFeBE6fPi2bNm2SL7/8Uo4ePXre+ozLrFmzzLhAu2fMmCFHjhyJ145xIJ95/vnnn8uGDRvilcfaRVL7e/DgQZk6dapZF3v27CkrVqwQxsl9n507d8qAAQPkgw8+kB9++CGgsXO3j/Xz7du3S79+/aRz587y888/C/M6MUz4Nr777jtTnzm9bt06n+rTp0835R9++KEsXrzYpzzYGWHB6DF5mcRjx46VCRMmeNJ77713XtCDDVion7ds2TKz2L3xxhvib4KF+v0u9Pnjxo2TFi1ayCeffHKhtwrb9vv375dmzZoZopIzZ06ZPHmydOnSJWzfN5QvduLECRk8eLCMGjVK8ubNa16lVatW8vvvv5tz/uzdu1deeeUV+fXXXyV79uwyZswY6datG0WakoDA7NmzDW6vv/66D8Pm7zY//fSTmberVq0yglm9evWEhY+FkPqMHQJp//79JVeuXIJw07p166gUUOlvSieYZhgQGL377rtPYPreffddWbp0qedRW7ZskSZNmgjH3Llzy8CBA40SwFNBTxJFYMOGDfLSSy/J33//bWjHZ599Jl9//XWCbZjbHTt2NILnnXfeaQSjhg0bxhNgoD/Meb6Lvn37SrVq1WTlypUJ3jMYBSFn9JDM9+zZI3369JE//vhDfvvtN5O4BshixYoFA4eIeUa+fPmkQoUKkj59ejlz5kzEvHegL/rMM8/IXXfdFdUMPtrKSZMmCYLMs88+K40bN5ahQ4fK3LlzvWDSS7R3YPP2229L+fLlpVGjRnL77bdL9erVPeB89dVXAtPRrl07qVKlirz66qvSu3dvmT9/vqeOnpwfgfvvv1/KlCkjadOmPX/luBpoj6DTaJNgPrJly2aY8tWrV8eVimzcuFE+/vhjQ68qVaokL774ojiOYzTY0H1TSf8kiACaoClTpggM9EMPPWToxJVXXint27c3bWCkYSS2bt0qbdu2laefflqqVq1qmO/du3ebOvonYQTQ3GEhQABBeKxcubKQOIcn8dfSKqN69OghTz75pFAXrJ9//nlTHWsbDDo0C804DB/laApNhRD9CTmjd9FFF0np0qWlUKFChgg4jmOOM2fOlBo1aoQIlvB+rOOcxSi83zJ5b+c40ds3EMHEyNx+4IEH5PrrrydL8uTJIzfeeKNgdjQZ+scggCADAwHxvPTSS03eZZddJo888ohAPCdOnGiO06dPlyeeeEKuu+46U6dAgQKSPk4QghibDP0TEAKOk/i3t3z5clm7dq3ApKGVZnGD6XYcR9KkSSNvvvmmkH/48GHh37x580w+ArvjOHLNNdcIgipuOiyy1NHkHwHmPnSC4+WXX24qMafvuecegflDg4fJFmGmXLlyZs2kEnOfbwVNINeaEkZg8+bNsmjRIkG54DiOqVikSBEB80GDBplr9x/cihAor776ag/eMN7PPfecMamjpLriiiuMEOo4jqkDXWfcsDS473VB58loHHJGz987w00zAE899ZS/Ys1TBCIWAUwEmNxz5Mjh6QMaFEy4MCx79+715Mf6CYSVxQzNBcTX4oHmCMzwJ2PBg2BjGrTlLIwwz/iPwXjYfD1eGAJ16tQxfmAwcix2MB3uO5J38803GyabfPBnoSOfa4R6GEN8i/GLIk9Twgjs2LFD+AbcPnlZsmQxDDPaUr6NP//8UzJnzuy5SYYMGQRmA1cHT6ae+EUA2rFmzRqBntgKzE9oC5o7m2ePmM4R1DHf2jyOKKkQfqDt0B6EHsaMeyPU1KpVSx599FGqhiyFJaOH5HjLLbcIkzZkyOiDFYFUQABCAUG49dZb490dbQfEAeIdryCGLy6++GK57bbbBKYAZs5CgakFwso1mj3SHXfcwaUnIUWzSIK1JzM2T1Ks123atBEWLRYzfzfFfxhNtWU88EuCyUuXLp2neto4s/All1wi+EZ5MvXEBwHHcSRjxowCs4Diw1aA2UDw4RohZtu2beKmJYwN3w3YU0dTwghAN6APbtrBXEUgse4H7tZo72666SbB985Np7mPux7nc+bMMWbgXr16mc0YGzduJDtkKewYPQg4Kn8YPSZtyJDRBysCqYAA8xsGBGLi7/aU+8uPxTzHcaREiRKCOapBgwbG8Rl/pBEjRhgTIUwgeCWGJ8xzLGKXGn3GJwlGDkbC+/4sdmiR8I/EvE45O27RbjiOw2W8xLjFy9ALHwQefvhhgbnAJ+/ff/8VmA82q8H4sT6CIXPf33iQ73NDzYiHQFLxA2d8gNGY1qxZ02zgQCmFmRd6bl1xeEjJkiXNplI2YrBDF99i8kOVwo7RQ1qHyy5UqJAAXqiACdVzmXz4wWD2sAnpLGgfbqg6HiPPRXiBeHur/5HOHceRa6+9NkaQCKybmLTZgY2ZENMhzs0sevjj4duLBA7hxQHafUc0p9APNKXufD1PeQTAmtAebC6C+bZPwIyL9gmaZvNg/sijzObp0T8C7KLF1w56gR/ZN998Y3YuI/xkzZrVbJpBY+qmJeDLWuHWUvm/u+ZCO6666qp4O8zBkvnqnsdupBB0hg0bZtwT6tata0LfoN0Db3gWW9dxHLNpjM1K+PARJSCUWuywY/TwS0BigcBb0GLpyIdKiBmkOJvYro1fTCzhEK19xWcJPxs2Gdg+wqQQO5DwIUjqNl+PZxGAgMLg4QhdtmxZE0qC3W4QaQgyfjX49J6tLcK3wm5d/PSUobCopM4Rpht/JrRPMOPup6DVwOwOPSefBRT/VJiTUDun8z7hntAg3XvvvWYn84wZMwR8UYKgNeXdMSPCDKJV4pqEiRBlSUr7t3PvaEvQDhhmdjfbvqFkgX6wucvmeR/ZAY32Gk0d9Jr5jZAD4+hdl2sYQo6O46vZJj8YKewYPXYRsRiSggFAuD0DHxZ2G3/xxRdiE2YrtBbh9q76PklHgHlNGAu0tbY1kh6+N2+99ZbN0qMfBHCebt++vQm/w45PquCfxGLITkSuSfg1IZkTX4xrTSmDAGZzdkDbuzFnEUpZMNkJTT67aWGy0SrB/LFo0o4y6sOIoOFgZyh5mgJDgDmNj2SpUqU8jv3gXrx4cRM/0t4FWgL29evXt1l6TAABmLyiRYsKm+BsFRg9x3Gkdu3aJgs6gk+euXD9YXPYL7/8IgREJjzW448/7iqNf4ryCk0gTGH8kuBdhR2jRwR8dqhgdgkeDOHzJHxasPVnypRJbII5cOOBqhhzCSbd8HnzlHkTFgM+DBYHtAUpc9fwuQtSOhIeJkXCUeDb1L17d8O8YJ4Jnze9kDdJ2bYweJ9++qncfffdJhQNv7JgTdzgCaHluyGYLBoPyqEh+JSl7JtE993QuOHcz6+0uLVEttcw1ARqh/aQh1kKf0kYOxY8glQTK48A+IwHGmrmNLH0WDDR/HFfBFnGjXtoShwBdpYj3KCdJn4qcxyNKK0Q/vH9AlN+ZQeaifM/fmFo+qijKWEEsAgQHByhm7mJtrlnz54m+LqNisD8hpkeMmSI50YwfvwSDBuToOXQHzYYUQEtH22hV6xl3Js4k8Q7DKVwE1aMHipnwClYsCCYafKDwPjx4030ekwfqI/R9vmpFpFZSEksJIQMQNpikcBcF5GdSeSlb7jhBvNLAmwUIMApoUEgBI4TOtV+Iq8b0iI0dWjvYB5wYSA2nl3o7IthwkKzBAMC8YUww4Q4juJpMQrkyK8CsLARS6xly5YC1u52OKIXLlxY2GyBzxGLGho7aJJNjAFx82D0qIeZEVMXGg/ijGGCZ3ON+7567h8B5vsLL7xgNh4tWLBAmjdvLt7mQWgHAdj5NQcCKxOYmnr+76i53ggQ15ENLvyCCxYAMORo6+FCxq+SwGiTh0DDL2lYFyt+FcPNwOXPn1/QZDMm0HbmPJs1UN7QPlQpLBg923k4LqUo3QAAEABJREFUbABC22Hz9BgfAbQUBNK0CQYhfo3IvXIcx/zmoO0bfilE6o/cHiX85jjvEpUdRgaTrc55/1ihmWM+sCEDguu/lhjtN5ojfk4OKd17QUyoneafQ4AFDqxtQug6VyrStWtXAVvcS9Cu2nruI4umW1AnzA33xVIDIwkT6L6nnieMAL/SwM+fwVzAQCRUE0yJ18bchzFE05dQXc33RYD5itKEAOz84g7z29ZCi40Qw681kYcVhvlOcHuYRPLcKUuWLOZnAKmD8MlPXeJe4q4TivOwYvRCAYA+UxFQBBQBRSDsENAXUgQUgRRCQBm9FAJSb6MIKAKKgCKgCCgCikC4IaCMXriNiL5P8hDQVoqAIqAIKAKKgCLgg4Ayej6QaIYioAgoAoqAIqAIRDoC+v5nEVBG7ywO+lcRUAQUAUVAEVAEFIGoQ0AZvagbUu2QIqAIJA8BbaUIKAKKQPQhoIxe9I2p9kgRUAQUAUVAEVAEFAGDgDJ6Bobk/dFWioAioAgoAoqAIqAIhDMCyuiF8+jouykCioAioAhEEgL6ropA2CGgjF7YDYm+kCKgCCgCioAioAgoAimDgDJ6KYOj3kURSB4C2koRUAQUAUVAEUhFBJTRS0Vw9daKgCKgCCgCioAioAgkBYGUrquMXkojqvdTBBQBRUARUAQUAUUgTBBQRi9MBkJfQxFQBBSB5CGgrRQBRUARSBgBZfQSxkZLFAFFQBFQBBQBRUARiGgElNGL6OFL3strK0VAEVAEFAFFQBGIDQTChtHbu3evfPTRR/Loo49KxYoV5fPPP5djx47Fxiicp5eHDh2SwYMHS9myZeWll16SJUuWnKdF5BYvWLDA9PGpp56SUaNGRW5HzvPmu3btkg4dOsjjjz9ujnv27DlPi9gqPnjwoAwcOFB69erl0/F9+/ZJ7969Da2oVq2ajBs3To4fPx6vHu0/++wzqVChgjRv3ly2bdsWr1wvfBE4ffq0LF26VF599VUBP98a/nOWLVtmvtm1a9f6VFi3bp20aNFCHnvsMXnmmWfMWCld94EpwYzdu3dLv379zFx//vnnZdasWXLq1Kl49cH4lVdekSeffNKsm0kZO9eNYvr0jz/+kK5du8qaNWvOi8Pq1aulYcOGZkzefPNN+euvv+K1AX/4F2jPs88+a+Z8vAohuAgLRu/o0aPy+uuvGwZm2LBhhoj/8MMP0rlz5xBAEl6PhCj26NFDvv32W+nfv7+ULl1a+KiXL18eXi+aAm8DwapZs6Y88sgj0qlTJ9NPFvQUuHVY3QLizaLHEYKwdetWadKkSVi9YyhfBsbhyy+/lHr16sn8+fPjvQrfwzvvvCNTp06V++67T9avXy+VKlWS4cOHi/tfrVq1ZMKECYZRvOSSSwyTsXfvXncVPfdC4LvvvpM2bdrIJ598IidOnPAq9b08cuSITJkyRRo1aiSM1/79++NV2rlzp2HybrjhBrPYvfvuu4a2My5nzpyJV1cvfBGAYXjvvffMHAezcuXKSbNmzeTXX3/1VIbpePrpp6VAgQKGyfvxxx+lZ8+ennI9SRwBlCgjR46U+vXrC7zHgQMHEm2AMPPcc8/JxRdfLDly5JChQ4dKiRIlZNOmTZ52MH8jRoyQggULyoYNGwRmr1u3bp7yUJyEBaMHeBB3NBw33nij3HHHHVK9enX56quvQoFJWD1zxYoV8vXXXxuJ+fbbbxc+9ly5cplJ6S3ZhdWLJ/Fl0MggsfJxoM3Lly+fDBo0yEizf/75ZxLvFr7V0ZoMjNNUWY1e5syZjZDz22+/ydixY8P3xb3fLBWvGXu0Svfff7/PU6AVCDv/+9//DFMyevRoIxigNbKVIdy//PKLfPjhh5IlSxZ56623BC2gP+2gbaNHESwpderUEWhwIHhcfvnlRqvRtGlTufTSS32a7NixQ/7991+jVU2bNq1hRhi7GTNmyMmTJ33qa0Z8BObMmSPQPqw44AtdvOuuu8y8Bz8YbQRF1oUGDRoIx5dfflmGDBnio2WKf2e9sghcccUVAuPWvn17OZ/wQTl0uk+fPkYY+vTTT4Vz7oU1kiPjlT9/fpk5c6Z07NhRJk6cKIULF5bu3buH1KoQFozeZZddJkjdTFC0e0ziLVu2yEMPPQR2MZ2Q3iCS2bJlE8dx5KqrrpI8efLImDFjApK6IwU8TEZ8RIw584H3fuCBBwSGCHMF19GQ0OJBwIsVKyZXX3216RJje8sttxjTgcnQPwkicPPNNxutNkwGlRAKS5YsKYcPH+ZSoB9o+2699VbznZCZPn16o/1D+gZ/8jQlHQEYaCwtgWj7uDtaD75f3DEQcNDGMj45c+aUNGnCYunhNcMywVRAJzjC5PGSV155pWEawBPrx/bt2wWTY6lSpSg2ibWB9QLrj8nQPymGAHP43nvvNZo6e1NcEq6//nqxrjfp0qWTF1980RbLTTfdZBhJ2sKYewqCfBKsry3RbkGsy5cvb3z08KfB5wYzC6r+RBvGQOHcuXPNZLFMAQSSRQxJGe1GtEDw+++/G0Y2e/bsni7x0aBdWLlypVnAPQURfPLPP//Ixo0bBa2s7QZCDosfzC5aEJuvR18EIKrMCXfJRRddJHaxg7HAXIIpy10HKRuasmrVKne2nicBAbQemKQCXbBuu+02ufvuu6VVq1bG7WTSpEmCBqVy5crCmCXh0TFZlfmKpcNtuckSp6G+5pprBDpCwj8sa9asHnwQhMBYrQMeSFLshDmbKVMmsYoIbsx67DiOWOtD5syZBUabMpug72hb+R5sXrCPYcHoARx+N03jTACoQ/FJQ53KpA42IOH2PJxDkeTAyL4bkjKTLpqYAiRU+sdCzpHkOI75aPCbgOCRF+mJvkCgvZkViAPSu2qckjbC+DEtXrxYXnvtNdMQEy0JDanJ+O8P3w8LppW8/8vWQxIQQJuHe40VOs/XFE0q5qsiRYpI48aNjbkL/+IMGTL811QPCSHgOI7kzp3bbI6BXth6bCpiznMNLWENgLnjmgRTAfOBMMm1ptRFAF/5a6+9VurWrZvgg/CvRGkFjU+wUioXhAWjRx+RTJAU2fmCJIP6E789ymI58dEm1P/EyhJqE675ifUlsbJw7U9i75VYfxIrS+yesViGOWT27NnGGRpTOBg4jmM0w5x7J8dx1GQoyf+H5SVjxowB3wDGGhcctNcI8mycqV27tnAM+CYxXLFMmTJyzz33SOvWrWXy5MnG7wutKKZzlCCO4yQ4n1EGiP5LVQQQGr/55hvjf+ePbuOCNn78eKPVZvNdqr7MeW4eFowe5hYkRdSf7L4dMGCAUY/iWOq9k+s8/Ym6YogrDLBbo4XGAp8k1MjR0mGr1qZvtk9ouPBDSZ8+vdHs2fxIPmJWoT8IM+5+sPMWYuGWzt3leu6LAAwDzs8QUXClBtpvEjSFa5vAl8WPHaA2T4+piwCYs2uesFDQd84Zs759++pmjACgz5w5s2DdIgQTfsrs7ESLRwgy3HeY81hAYDjs7aCf+ELiCmLz9JjyCMDEseGrUKFChpHzfgJrFxspca9iV7p3ebCvw4LRwzcJvxpU/Jgkkc7x1SNeXKyroNmQgLreMgZIc2CCX4ZljoI9aVLjeTi58nGwcNv7w+RhpmAXpnVItmWReoSRY/MFc9v2AWEGgoCggxnA5usxYQQ2b95sdmSzCLq1TBnizIJoO9jY426NDyiLYt68ed3Zep6KCMybN0+gW4yH4zgmZiS7etlAwEKZio+OiluzFmK+xS2BcFP4LGOqZUc6HYSWMJ/dNBPmGppJSA/qxEAKSRd//vlnYSxgunEL8X4J1i523CLkeLuReNcNxnVYMHpI2kjg1hcBzQaMDPloP4IBRLg+g12o7FRjYeMdkeiQimvUqGF2KpMXDYkt6MTPwzRh+4MUyzx48MEHbVbEH9EoYZKZNm2apy/4J0KcW7Zs6cnTk4QRwByIgz/CILsMbU02CiAQEFcM4YhNPJRBV/ClwU+MxZI8TUlHAH9hcA20JWNBLD0EGdrgP4ZTOtoox3HI0hQgAosWLRJ82Ikzyi5zmiHoIyBPnz6dS5MQGDGZExfOZOifC0KAOHvesTwxo8PovfDCC8LGGB4AY8fGSc4Rboilxz4DGHXHcUxUADfNp14wU1gwemjyML8QC2js2LFC0MePP/5YAAvTZTABCbdnweig+sWcTWweoqSj+WLnGgxxuL1vct/HcRwTNxEzNRtyCOXQpUsXefvttyWaTNTgw685lC5dWpjvzHVMWlWrVhWINuWxniCUEFJ2yMIEM+9xXWDe48tLXDFcF9q2bWuCauPgjzBgJWdiTaI5Yu4gVWM2RFvKIimxDm4i/YdJwOcRYZI4hWDvrk5kBOIVWsYNhgKhk7FCQ/f9998LlhnbBobkzjvvNGGDENoI+s585xccYPpsPT36RwDmgfWQdZAA4PyAQMOGDT0CPow0NIQjAXlx+idEGQKQ92Yv/0/QXBCAiWajEUIJMR4RDMknEcMWBQTfAzSIOYxLGd8IQe6hPSSCtuOLijCEoIk5ndh5lJGgR+77cu9gprBg9PA1wFkX/zxMLMQPQiVKFPBgghGOz4Igor3jQycK/XXXXSd88DCA4fi+F/JOSKj4pKDBZAGBqa1SpcqF3DIs27JrkWC+uCjg58ECioYqLF82BC8FQYQGIDETNmXhwoUmvA6MHmGFCNkBUUXbbxO/kgGe9nX5RQG+G0y4RK6HmSYGpS3Xoy8CaEBhGqDDaE1xqXHXgsljQbO7B2H00JSi1SAoNdoPtxkR7TUhWRBg0IJQF2YbRi+ahFQ3Ril5Dg1k7uO6glM/AeW9zYS4LTC3HccR4uvB+LFWpOR7RPu9YO6gyWwAhfYw922foTXwIRzxfUTIYRws3bFHNKi43WA6x92qaNGiYss4Eu4JYdPeN9jHsGD06DRgoNXDF4Gf4alYsaLfaOvUDWIKi0fBCKOlYPFiQkWzlhMNABLsO++849fJNSwGJAVegkUQRqZdu3aCBMgYp8Bto+IW/LQQjC+0gISPEoQY5gCmDQ0d+d7JBlEGBM7xU6IuhBkBiXxNCSMAjXFjyi8xuGuzY5bf74QZJJ8jQoq7zRNPPEGRJ2Gq5afsqMPuUZg+b2bFU1lP4iGARhRGmdAdiVk1YPZgRlg3cQtRfOPBeN4L6Avz0ybciGwjXEOgRfhVIyjyDdh67mOdOnVMEywLjJm7jHPmvrU4mIpB/hM2jF6Q+62PUwQUAUVAEVAEkoGANlEEIgsBZfQia7z0bRUBRUARUAQUAUVAEQgYAWX0AoZKKyoCyUNAWykCioAioAgoAqFCQBm9UCGvz1UEFAFFQBFQBBSBWEQgqH1WRi+ocOvDFAFFQBFQBBQBRUARCB4CyugFD2t9kiKgCCgCyUNAWykCioAikEwElNFLJnDaTBFQBBQBRUARUAQUgXBHQBm9cB+h5L2ftlIEFAFFQBFQBBQBRUCU0QvJZX8AABAASURBVNNJoAgoAoqAIqAIRD0C2sFYRUAZvVgdee23IqAIKAKKgCKgCEQ9AsroRf0QawcVgeQhoK0UAUVAEVAEIh8BZfQifwy1B4qAIqAIKAKKgCKgCPhFIAUZPb/310xFQBFQBBQBRUARUAQUgRAhoIxeiIDXxyoCioAiEPUIaAcVAUUg5AgooxfyIdAXUAQUAUVAEVAEFAFFIHUQUEYvdXDVuyYPAW2lCCgCioAioAgoAimIQNgwegcPHpRWrVrJzTffLDly5JBhw4bJyZMnU7CrkX2rnTt3yptvvmlSZPck8bf/+eefpWjRonLjjTfKRx99lHjlCC9dtWqVvPTSSzJ27NgI70nKv/7evXulffv20rhx4wRvnitXLrn88ss9afPmzZ66tOd7uf7666Vs2bKydu1aT5me+Efg1KlTMnv2bHnggQdk3759/iv5yf3ll1+kSJEisnjxYp/SZcuWyZNPPilXXHGF3HrrrdKnTx85fPiwTz3NOIfA9u3b5dVXX5XWrVufy/zvbNOmTVKjRg0z57Nnzy4jR46Uo0eP/ld69kB76lx11VVSpUoV2bZt29mCiP2bui++aNEiKVasmNxwww3y7rvvCrxIYk/k22jQoIGpz7xnzXLXh/Y0b97clN9xxx3y+eefu4tDch4WjN6///4rAHPZZZfJX3/9Jf3795eePXvKoEGDQgJKuD1048aNMnz4cOndu3dUM78//fSTvPHGG9KlSxeZP3++DB061BC748ePh9uQXPD7/Pbbb/LFF1/IuHHjhAX2gm8YRTeAKfvhhx+kR48eCTIcgwcPluLFiwsEl/TJJ58IRBUYILTPPPOMHDhwQFasWCHlypWTSpUqyZo1ayjWlAACs2bNMjR37ty5cvr06QRqncs+duyYUBdavWTJEp82W7ZskTZt2hjsGYuZM2cK4zpixAifuufuGttnzH2UHMznEydOxAPj77//ll69esntt98u/fr1kzx58kjDhg2Fb8FWpD1zP3/+/LJu3TrhWL9+ffMt2Dp6PIvAmTNnZNq0aYYZbtu2rSxfvlzWr19vBMyElEzM6eeff97gCtbvvPOOvPjiizJ+/HhzU5hu8pjvH3zwgTz44INGOdO0aVPxHk/TIEh/woLRg8D88ccf0qhRI7nmmmvkvvvuk0cffVRatmwpbik9SJiE3WMyZcokr7/+ulxyySVh924p9UJIUYz/vffeK3wc9BliN2rUKEHiSqnnhMt9kCCRHsPlfcLpPbJlyybPPfecFC5c2O9r7dmzR2CU0Q71jBMISfXq1fPUZc6gLYXJuOmmm+SFF14w30737t09dfTEF4FSpUpJ+fLl5dprr/Ut9JODYA6zXa1aNYOvd5Vdu3bJ7t27pWTJkpImTRrJmjWr0ZwsXbpUhRtvsP67Zu43a9bsv6v4BxQijFGHDh2kZs2aRlNUokQJGT16tKkIc8Lch0lv0qSJsYrUqVNH0AKq0sRAFO8PAiHC9v333y9PPPGEsSbWrVvX4Llw4cJ4de0FChcYbiwN8CoVK1aUe+65RxgzrG4rV64ULAjQJqw1n376qTz88MNGUYOm1d4n2MeQM3pwuXDSSIcQAwC4+OKLhQl85MgRY0ogT1N0IwCjjzaXj8YytKjFYQAxDUV377V3SUFg0qRJgrkERo65AQ2x7ZkvaI7QepDIx2yIADF58mTZunUrWZqSgQB4s3ChtQikedq0aQXm5PvvvzeMHVoOru+66y656KKLArmF1nEhgPBbunRpufTSS03uLbfcIgUKFPBoisAWS0i+fPkkXbp0pg7mctp169bNXOufcwhsjLOUIXQgiNhctKTwH24tqS07dOiQoLlmXts8xgKl1I4dO0wZWCMs2XKYQcq5DqXlJuSMHtIHhAMCzTmAkPA/YMEHXK5TL+mdwwEBJCjHcQS/K/s+fES33XabMbnpPLCoxPYRgRAtP9I4pt3HHntMMJXY+YEWCWYOP083Urlz5za+N6tXr3Zn63kSEEArNGfOHGEMAmmWMWNGoyFs0aKFccHAZAvzXblyZaPhC+QeWuccAjBvV155pSeD9RItHhYQMtF0w7ww17kmwVCzlqJNwixJnqazCMCcgZd7zYF5hpH78ccfz1Zy/UWgxI0I2uPKNsw2ZmD4GLThMIrucsYITW3mzJnd2UE9Dzmjh/q/YMGCRsUPuAAJIcE0w5HFPqiI6MNCggCLMw9G+8LRJsdxzMLCx2Lz9Bi7CEAvYOxYuDCT4C8zYMAA+fDDDw0oMHwIjVdffbW5tn8cxzF+YboRwCKS9OOCBQvMBim0FIG0hjHB5QT/SMaob9++xiUHAT6Q9iGtEwEPR+BhvrPhgtdlbsPsuZlB8h3H4SDUNSf6xyAAnWBjBcyZyYj74zhnsQLHuMt4/9OnTy/wKgiL+PbB+IE51gUqejN45KHFnjp1qvE7d5yz9yY/2CnkjB4dZpcXDo044Xfs2FG++eYb47QLowew1NEU3QgkxtAjlVqzfnSjoL1LCgLszodm1KlTRz777DOz+5B5wnzxdx/Hcfz6kvmrq3m+CLCDmd3wviX+c6DfaAALFSpkNhEgxEHn0d6jAfHfSnMDQQD82LzG2sl3QBvmvj9mgzKSMtigcC5BJ0jncs6dIVCeuzp3Vr16dXn22WfN5tGuXbvKhAkTBNcE1i80p+dqiqFHRFRgMxjj5C4L9nlYMHo4TCOl4/8BIKj3ceLFHyFv3rzBxkSfFwIEsmTJYp6KQ6s5ifsDMcNvj8UF7UBcVrj+1/cKEQJI44SS4PHQDLRNJEwy5NnE7lsWugwZMtgsPaYyAmwCGDJkiJQpU0bQvH788cfGd4xwE2hDUvnxUX37GTNmGEsHoWtgMugsmjxoJSZJrm1C8+04jmdXus2P9WP6OA0deLGT1mKBpQABBT9Hm+c+Zs6cWfB3xIJQvHhxgXchfM3jjz8udg2jPmsXfudoUdmsRF4oU1gwegCAtIjD9EMPPSSoRtm6zG46iDPlmqIbAfxM+DjYmGN7ik8JkhUagYQkL1tXj7GLAAQbAox/DQkNx7x58+IBQow3YnQmRMDjVdaLFEGAjTI4oDMm3JBd1OwW3bBhg9mcQZ6mpCOA2RCr18svvywwd/YOzG/884hdaPMwQSI8E3JFhWWLytkjPqT457nxgmnD1w6t3dlavn8RFlmv2K1LuCDmOP7Cbm0qa9fEiRMF3KFPvncJbk7YMHpw0UgiY8aMMXH02rVrF88xP7iwhN/TwIe34ogTLufRlFiciYc2ceJEwdkV3wdCBcDk8UFFU19tX+gjY4l2AybX5utRDCPAXMc3k2QxweeFnXIsXuThZ4N2g5ATjuOI4ziCeQXiCxEGW3bKoV0iAK3jODTT5AcB5iA+0sxJIh54VyFWGHHzwNZdxjjRlqM7n4WUcUKjZMu5PzsbVXBzIxX/HEYDvMCKsbClzGXmNKZCNrgQ5gMNFEINdJMAyWj4+D5gpvlu2IXLPahv76PHswgwPwl9wi5+GGLmL8IJGwDxK6UWdIMduGzy4prE+OAfiQUS2vPVV1+ZUDaUgTW+rOANs8i3whjxDTBu1AlFCgtGjzhpMHbvvfeeEAKB4IWApMTg7JQgJljnzp2FD5/QEZhDYIbOlkbP306dOhn1N4s2wSYhYMSaQ1KNnl6e7QkOuvQTxoXdiFyfLdG/SMOYRpj30AZiUsHQgQyafn41AG0GcRZhPjC/uIUBQjMRg5PA69AUAo0TH8sd9oB7hVsK9fvg80WcsP379wu+0lOmTIn3StBoMEVAoQDGg3lLYHOYChY+65hOOWFUMGlhqoV+vf/++8KCx+YBtdSAkG/CogFOlIAltAEawfX06dMFFyeYEeJG2sS3wJynDqE8yOdXpvBf5ZczKCcMC+WaziHAHCTWXe7cuU1gb3CH3rAO2c1cYE2wY9ZdWvJNIDCyPkGL+PUmd3gWflmGuJ1YJRkDxoJEjFiYd+4RihQWjB5mW3zzatWqJTg4li1b1hMrKBSghNsz8TkiiPR3331ndhcyMa1fRri964W8D/MA5uepp54STPj49NDvC7lnuLYlvhWMB866DRs2NNHuw/Vdg/1eaCYIj8IvBGASQasLUeY9MLVAXNH+4r8LMwG94KfQKLcJxo64bxBhiCy/uOJdx9bV41kE0GTwKwrMyQoVKgjXZ0vO/mXXLD8rZ02AOP8zjwkeC8ONHx7XZ2uLsFjClIM9ZltoPHUZE8dRzarFyX3EzAdOaJlgjnPmzOnZQETAaZgRGA0CItvUs2dPue6668xtmOPkw5zcfffd8tprr0nt2rVNmf7xRQAlAkINGjyERbAtVaqUpyL+pQSkxlRLJt8EeWwqoh2B790KKfYXwMPALDIONsH0hXItS8PLhzrx00VIfgTLRToP9fuE2/PxCWBygRGJyZUu3dmAmOH2rhf6Pnx4fFRsxGFeXOj9wrU9TApjaRPmrHB912C/F7856Z7vEEh8NXkPFjIYP+LnIQxg8rdllNsEY4gWg3owGZY5seV69EWAOWjnI8c777wzXiWYauatXdg4ck1dmxBC3Y0QUqHrMOOYyfCldPsyuevquRjmGpwsnjBrxHUDG2Kx2Xz3EQaFcpuY67RDu8dPoPn7PmxdPYphkmHuwB1mmnltcWGzBbQIJQR5fBNgTzB/NoKR5060B3fquBP3QPBx1w3meVgwesHssD5LEVAEFAFFQBGISgS0U4qAHwSU0fMDimYpAoqAIqAIKAKKgCIQDQgooxcNo6h9UASSh4C2UgQUAUVAEYhyBJTRi/IB1u4pAoqAIqAIKAKKQOwikDRGL3Zx0p4rAoqAIqAIKAKKgCIQcQgooxdxQ6YvrAgoAopA+CCgb6IIKALhjYAyeuE9Pvp2ioAioAgoAoqAIqAIJBsBZfSSDZ02TB4C2koRUAQUAUVAEVAEgoWAMnrBQlqfowgoAoqAIqAIKAK+CGhOqiKgjF6qwqs3VwQUAUVAEVAEFAFFIHQIKKMXOuz1yYqAIpA8BLSVIqAIKAKKQIAIKKMXIFBaTRFQBBQBRUARUAQUgUhDIDYYvUgbFX1fRUARUAQUAUVAEVAEUgABZfRSAES9hSKgCCgCikBkIaBvqwjECgLK6MXKSGs/FQFFQBFQBBQBRSDmEFBGL+aGXDucPAS0lSKgCCgCioAiEHkIhIzRO3z4sKxevVpq164tTZs29UGO8qFDh8pjjz0mJUuWlObNm8vWrVsl1v8dPHhQfv75Z7nvvvvkp59+iio4jh07Jl988YUZ8+LFi8sbb7whO3fujKo+enfm1KlTsnfvXhkwYIDkzJlT/vzzT+8qMX195swZWbNmjbz99tvywAMPyL333iudOnWSXbt2eXA5efKk+Raee+45ufvuu6VGjRqydOlST7meBIYAmI4ePVry589v5uT5Wp0+fVr27dsnI0aMkGzZssnChQvjNTlx4oRMnjxZqlSpIsWKFTN0vGPHjrLlsPRoAAAQAElEQVRt27Z49fQicQSgERs2bJCPPvpIKlasKEWLFpXFixebRkePHpWxY8fK448/LtDMRo0aycqVK02Z/jk/AkeOHJFRo0ZJ2bJlpUSJEvLaa6/JunXrJKF/zPlp06ZJ1apVzTg8+OCD0qxZM/n77789TVizyMuePbvceuut0rZtWzl06JCnPBQnIWP0WNy2b99umBYmq7vzEO5hw4bJe++9J127dpXx48fL/v37pWXLlgIz4K4ba+cQYwjqX3/9JSyC0dT/gQMHSv/+/eWDDz6Q7777TrZs2SLvvvuuGfto6qe7LyyGLHy//vqrLoBuYP47R7iDscuRI4dhGvr16yczZ840c+S/KvLLL79IgwYNDPH98ccfpUiRIvLKK6/Ijh07bBU9BoAAeMFAsNAFQltgQGjz22+/yaZNm3yesHnzZvn8888N4z1nzhwZM2aMrF27Vv73v/8JbX0aaIYPAqyF0MIXXnhBNm7cKO+8844RagoWLCgwHd9//73APLdr106Y+9dff71wDgPjczPNiIcAc/Dbb781fAY0BiwvueQSs+Z48yS2IQz3yy+/LDVr1hRo9jfffCPwMS1atDBVwH1sHOOdL18++eyzz4yQ8+GHH5oxMxVC9CdkjB6cLhJ65syZfboO8YDLRkJnQjN5q1WrJhMnTjRE3adBDGWA1/333y9gEk3d3r17t/nAypQpI4z5TTfdJO3bt5cpU6bI77//Hk1djdeXtGnTSt68eeXBBx+Ml68XZxFg7GE+0FikS5dO8uTJI+XKlZPBgwfLkiVLBMKKUIj0XL58eUmfPr08//zzsn79ekFwOHuXsPkb1i/C4oRG9MorrwzoPVkUc+XKZTStF198sU8btH3//vuvUCdNmjRyyy23mLkO8x4II+lzwxjMmD17tqGLVeM0SDB0xeMsHcxxoEBZAqOCJgqN6VVXXSVPP/200fZNnTqVKpoSQYC5+fXXXxsLElpScK1cubLhMaZPn+63JXQF3KHXl112mVmH4WOgUTRAcH/iiSekTp068uijj8pLL70k1157rbC+UR6qFDJGL7EOr1q1yph1kcxtvdy5c4vjOEbNavP0GD0IIPGjrWSxueiii0zHMGXy4VBmMvRPTCHA2EMLjh8/br59Og/DUKBAAXPNvIBpwNyNcADjQZ2bb75ZsmbNarTDXGtKGQQqVapk3ClwqwnkjjCMLKaDBg0SNFOco6UvVaqU2G88kPvEah2YA1xZcF168cUXBUbOjQWWAKw7CD82P1OmTHLFFVcYVxCbp0f/CKCdW7ZsmXFVsDVwQbj00ktlyJAhNiveMWPGjIJwjuIJNypMsmj1cC2h4tVXXy133HGH2H979uwxLjm4k9i8UBzDktFDFfrPP/8Y6d2CkiFDBkMc/vjjD5sV/ccY6uHy5ctNb9HMmJO4Pyzqt912m/HN5KOKy9L/MYSA4zgC0UWCdjMXN9xwg6BBwnQFncA/xr3YOY4jWbJkMdo+zF0xBFmqdpVvkPEA90AexKKIzxjMSt26dY0ZF+0T/lCO4wRyi5iugwCDReO6666Txo0bCwLOww8/LLNmzTK48F0wv900E0sPjAiaQFNJ/ySIALQDZhmNs6104403Gpozd+5cmxXviAtJ69atBT4E7R+uRfXq1ZNnn302Xj2Emp49exqXkoYNGwpCa7wKQb5IE+TnBfQ4CAqEHe7YuwEctHeeXkc+Anx09AIixdGd+Ejwp3Dn6Xn0IwAzh0mFI1oh/HShDThDc0TDAZ0gYdb1RgTzYKz79HpjciHX+NjhJ42mLpD7YNpiAcTUPn/+fMGUDk1nXAJpH+t12FQBVoULF5bu3bsb1yVcnpo0aSLQS/zIDhw4YDR43liR752n1/ERwO0DOuJvPpMfv/a5K/zzYPI2bNhg9hicKzl3hvsZfvRYGdhU+NVXX50rDMFZWDJ6gANx94eHP4Lur57mRRYCl19+eYIvzHxQU0+C8ER1wV133WU2YeGfC9Pw8ccfC87/LHJoh6ATzA9/IDiOY6Rzf2Wal3QEYKxh1AJtCQOOWYud0vhR4v9HhAWiBsDABHqfWK0Hfrgg8A2w7mESxAcPJgIfMmgiGlZ/+PhjXvzVi+W8xGgH5u+EsGGDKFrUL7/8UnApw2zLbnV3/TvvvFN69OghRA7BlYQd01ge3HWCeZ4mmA8L9FkAg3lmxYoVniZwx2h2ChUq5MmLxhMkCSQAPlSbMHtg64/G/to+WfW5e/ceiwEq8gxxZnuwsHUj7Yh5BX9T+mATGqrg9COynwLBxTTCzlqYhlq1apmQHs8884zxw8NUBa0gBIu7p9RnEcRnyZ2v58FDgB22REzAWZ3536tXL4FpYTcifpfBe5PIfRI0kGR7wFxHKN64caPZeIRrC7ukbTn0E20emzNsnh79IwDtYG0hzJutwWYLGGzmqc1zH9lp27dvX8ElAd9JdpVjUkeAQcvqrot1Cp9zzLuY2UO5hoclo4e/DbZwdtxZ4Niqz4RHZWrzovFozR0QQ5vq1KkjfNzR2F/bJ3YxoS2AsbN5xELDVBHpRCt9+vTSpk0bs93ejmmk98mOUTCOaC6Y/47jyPDhw00MvQ4dOpgNGfiBIVW7fWpw+Mcsw3fjOOoLllJjhBYVYTvQ+7FRgLGDWacNAnyFChUEJg9aTp6mhBFA+GXdYz7bWriwoIl65JFHTIw2XBugk7YcrRHjhHBk8/ToH4EsWbIIjBjz1NbAZ4+NQ3bzBP6obncxdvrDbNv67Kglxi8uIuwyt/nuIz6WMJVEknDnB/Pch9EL5sOZkPjdQJQByj4bKZwdXoRYQVrBsZHYNA899JDAENh60XjEDIVJCs2FTfQZ6cD2l4kHXuBi8yL9ePvttwtOrsSCYsyRjjDTlS5d2gQCjeT+XXPNNUKgUzueHGFO3H1ihx2LH9K4O1/PRcAEYozJBJMVAUjZHQc2CAcQZXbP4aSOND5u3DhB06GLHQgFnljUsCjAzLED3rsl8cDw04P2uMtY4Ji7HN35zHHyGDPujVYDTRQ+ZzCA7rp67otAoUKFhM0X7ACFHjI2CDQ2BBUMB5pttEzsTuc7IRYcihJCfPjeUXPcCBDuBz7jhx9+MFE+mJ8TJkwwWmcYaepCd9q1ayd2syC7+5nTuCJAa8CcOuSjHcSyQCzYBQsWGIEGJh16hKAPU8g9Q5FCxujNmzdP2KKM2pTFnYjUgAYISCyYK4mjh+Mp50iFEHoke+rEaiLS/FtvvWWC6xJXCTNvtGCBOhwC9frrr0v9+vUFBolYemjEoqWP3v1gzjOvP/30U0HwefXVV02ke+96sXrNxguY5M6dO5udtjAbLH5uRgFhgG8BnxjijRHTih1v+DTFKm7J6Te/zgLOCN/8YhHa5//uYw4siDBtMIJkMF8xY3Xr1s0sam+++Wa8sB6YtAhkPXLkSHnyySdNkFnaIOhA47mHpoQRQAPE5hdqwJCgoQZ7NEjk8Q2gIWWsWAeIH4nwD83Ep486mhJGAPz41Zbq1atL06ZNhXmJtpngyZbPwJSLLx4bL7gTGlQC+iPwPPXUUwL2uJVBv3HLQWDv06ePuRf3pS1xb/Evpn2oUsgYPXw2cFRE1QxQEGmAskAgqTN50erxEzsQchxTbXmsHpHmWPyQuJHumJTRggXECSLGeDM3unTpYsJkREv//PWDOQ9zt2jRIuN7xs/asUvRX91YzINIIhGzY5NfBUBb4c0k4IuH0IhZl7nTu3fviNcCh2KsWbSITYhmg7Ae3hpR8tCyM2d5P9xMYD4Q2mEOZ8yYYZg5ykhYIWA++CUMmD2OrVq1EjR9jqMmdTBKLDmOY7DCD2zSpEmCXy/fgHsdRAHCOLEZAHwRGmGwE7uvlp1DgA1GKBjAD9oBEweNsTUQMvF7JCQQedAamEPqE6wabSvCJ3Oacnz7CG3D2gzNYqMGQg6WOspDlULG6EGsmaQQDRKLvOPE//jhuKlDCjVQoRog7+eCA3jZZCUP73ohvb6AhzMvmAuxMuaO45gAnHY8OTLGFwBhVDVlPoAJ85zzhDpHzEXqMG9gQBwnPi1JqJ3mn0MA3MDaJq7PlYqZp+48x3GEa1ufI9fuNnZcKGNsmNuOo2Pjxuh85zAXieHHOmlpJnUdR/E9H6bucm/83GXQHOYtdWw+cxohxo4JmLvLoEO2jO+B+rY8VMc0oXqwPlcRUAQUAUVAEVAEYgMB7WXoEFBGL3TY65MVAUVAEVAEFAFFQBFIVQSU0UtVePXmioAikDwEtJUioAgoAopASiCgjF5KoKj3UAQUAUVAEVAEFAFFIAwRiBpGLwyx1VdSBBQBRUARUAQUAUUgpAgooxdS+PXhioAioAgoAqmEgN5WEVAE4hBQRi8OBP2vCCgCioAioAgoAopANCKgjF40jqr2KXkIaCtFQBFQBBQBRSDKEFBGL8oGVLujCCgCioAioAgoAimDQDTcRRm9aBhF7YMioAgoAoqAIqAIKAJ+EFBGzw8omqUIKAKKQPIQ0FaKgCKgCIQXAsrohdd46NsoAoqAIqAIKAKKgCKQYggoo5diUCbvRtpKEVAEFAFFQBFQBBSB1EJAGb3UQlbvqwgoAoqAIqAIJB0BbaEIpCgCyuilKJx6M0VAEVAEFAFFQBFQBMIHgZAyeqdOnZKtW7fKli1bEkRk7969snHjRjl69GiCdaK9AJy2bdsmCxYskD///FMOHDgQtV3ev3+/6eOiRYtk586dUdvPEydOyLp168yYcuQ62Z2NwoZ2zm/atMmndydPnhTy//jjD1myZIn8888/cvr06Xj1aL9582ZZuHCh/PXXX3Ls2LF45Xrhi8CZM2fk4MGDsnLlSgE/3xr+c2gDXTp8+LBPhSNHjsjq1avNPF+8eLHfsfJppBkeBKALrI/M9aVLl8qePXuEcfJUiDsB4xUrVpi5zpxPytjFNdf/fhAA0w0bNsi+ffv8lMbPOn78uKxdu1YYI74DeJb4NUJ/FTJG7++//5Y5c+ZIzZo1pUuXLj5IAN7y5culU6dO8tprrwmg+1SKgQw+2hkzZsh7770nX3zxhfTo0UN69eol//77b9T1ng+kc+fO0rNnT+nbt680btzYLBDR1lGEFsayY8eOMmDAAGnbtq18/fXX0dbNZPdn165dMnfuXKldu7a0b98+3n34HiZMmCCtW7c2dKFKlSrSoEEDw/C5K44aNUpatWolX375pXTo0EH69OmjzJ4bID/nMMT9+/eXxx57LCBhEoZ7Q9xi+PHHH0uZMmVk1apV8e4K4zdkyBDp3r27DBs2zIwBdIzFMF5FvfCLAEze999/L71795bhw4cLtJEEM2cbwPhBL7t16yZfffWVoSWsF7Zcj0lHYP369TJ48GBDf2bNmpXoDRgjvpkWLVpIo0aN5JlnnpE33njDCKKJNgxCofsRIWP00qZNK7feeqvR2gCW+6U4T5MmjVx99dVy5ZVXmjrkxWJCk9e1a1fJA+Ha4gAAEABJREFUlSuXfPjhh9K8eXMjOUycONFHixHp+MDU//rrr9KyZUuBcOXIkcMs9GhsIr1v7vefOnWqYdhhUGDcK1WqJIwxmhR3vVg9v+yyy+SWW26RHTt2CAKfGwcWuTVr1siLL75oFjaYOHDju7D1kKzbtGkjjz76qMG1SZMm8sknn8iUKVNsFT36QQB6e8kllwhadW+tkZ/q4jiOpEuXTi699FLZvXu3TxXGaty4cVKrVi3D7H3wwQemDUw4TKJPA82IhwCMNwwHjDe0v127dsLcR9CnIhiOHDlSxo4dK5RBM4sWLWqUAtFs9aHvqZmuuOIKyZYtm7EEnO85aKlRusDoDRw4UF555RX59ttvDcN9vrbBLA8ZowdRyZo1q1x33XV++3vxxRfLHXfcISz2fivESCamJ0yYpUuXFphjMMufP78MGjRI+NCjBQYWdSTT8uXLS+bMmQ2D/+qrr8q8efPkl19+iZZuClqO8ePHS758+aRYsWJmTB9++GEzljB9UdPRC+jIVVddJczz66+/3ucuMH6PP/643H///YY2VK5cWcqVKyczZ8701P3mm2+MEFStWjW5/PLL5Z577pHcuXMbzZ6nkp54ISCGuYbmwrj5FPrJuOiii+Smm24yY4Vg7l3l0KFDxvLAODqOI+nTpzc0HbOYd1299kVg2rRpRrNq18Ds2bNLyZIlpV+/foaxhrlGaESgyZgxoyAgoVmFlo4ZM8b3hpoTEALM6QIFCgRUF3qOJq9w4cKSJ08eo9UrUqSI/PjjjwG1D1alkDF6wepgpD9n+vTpAqGEMaYvSNwwQjBA0SS1oSLHLMcCTz9JfHAQL3wTvX2wKI/EBNOOpA7RdhzHdAGtNRrb//3vfz4aLFNB/3gQyJkzp9x5551GM0QmDMbNN98sWbJk4VIw++LLxOIII0Km4ziG2UPDhDsIeZqSjsBDDz0kDRs2FBi4QFpDtxzHMSbHjRs3Gl+97du3S4UKFcSOTSD3icU6aFTx3eVo++84jmGq0TjhkweW+KhmypTJVjFWMsrRBHoy9STVEHjggQfkhhtu8NyfeQ3TjcDkyQyDE2X0wmAQEnsF/FkgmDADth4aDzQVlNm8YB9T+nmY37inm2g5jmM0BvhzBrq4cI9wTnv37jUmScuY2HeFWYGZZUG0eXo8PwJo+GDgXnrpJVMZnyU0HUjXJuO/PwgNuIjgCvFflh6SiACaUTQXLGaBNMU1p1mzZvL7779L3bp1ja/ZE088YbRSjnNWyAnkPrFaB2sX85XNLhYDtK0IN1xDS9iogeDPNQmFgBWOudYUXATQVrMxA2tUcJ+c+NOU0Uscn5CXsusHwmo/bvcLRdNOQvyC6BuEjKM7YaKGCXLnReo5jAmbMfz1kz7BjHDUFBgCCAi4ebApgxbgy3fBYse1O6EdUXzdiCTtnI0xbJDBhSSQllgfMDXed999Au5orNGoMj6BtI/lOo7jyIMPPmiiTbDJAusNTB+mWoQZmEAwhbHwNx7khwC/mH/kTz/9JIUKFZKKFSuGFRbK6IXVcPi+jFuT512aWJl33XC/TqwvMEUwu+Heh0Dej8XPHxNi26Kpted6TBwBFjN88yCqt99+u6kMvswXc+H1B2FJ8fUCJQmXzFt/TEVCt0B4YxMMPmQ//PCD2ZTBTmic1aNFcEuo7ymRjwaVDSzsvGV+syGDsEKYBnH1YK4nNB7p06dPiVfQeyQBASwLbM546623jG9wEpqmelVl9FId4gt7wF133WVMfWj2uBNaCXZescgVLFiQrKhIheKkIDriDtGA5ovNKJg5E2MEaRcpCTM8/htuszsaS3YbU0ZfI6UvF/yeF3AD5sakSZMEfyQ2ZjjOWVMg/jKYwefPnx/v7jNmzDA7RNlNF69AL1INAbR3+NfinM7uXHZCE05r6NChRsOXag+OkhvDWD/77LOCzyn0v06dOmZzCzs7EVrQ6uHT7KYl+PVR95FHHokSFCKjG2A+efJks0kMpjzc3loZvXAbEa/3YUcmPkf4qVGErxp+Gew2xGRFXjSke++9V/DPI7ai7Q9hMsjD/GPzIv0IEwLz/ttvv3m6wngyrk2bNvXk6UnCCGDGwgyIj9Lzzz/vqYgZl8UPUyHO6nw3FCIUcV26dGlBG0KepqQjAN5uf7Hz3WHDhg0m8LLVxuM/hvM6GlcE1vO11/JzCED/P/roI7ntttvEuinAUKD1g07amph3cV+wPqs2X48phwBjgaBp7widIV4kIaHsWoXwjvbV1gn1MaSMHkSDGGmo+Fno/IHBxKX84MGD/oqjPg9puHjx4sLCxuRhdypaLyTjaGL00MRAyNC8oI1hkSAoLmY5MIiWgcbUUr16dRNmhaDJMC0QCTR5SOzR0s8L7Qf0AIIKjQAjez+uCdCLQICWl3iShFN5//33zS+qUK9GjRpmdyLBZfFnYgciO7oxqVCuyT8CmFNZtPCh87dIEfcR7Akp4b4D4Txoy9Gdj8UBPzIbN49donzb0LNool3uPqf0OevjiBEj5LnnnjOhmN59911BWOQ511xzjTDXYfRmz55ttH0EVsa3zzIc1NOUdASY/wiIfA/MbXsHtKf169c3wb/JI4YeMTzZgMF38d133wn0CNrDPagTDilkjB5qTvwM+HkcYv6gaoYou0EhlAKLPZJ6qVKlBF8Fd3ksnF977bXCx81CdeONN5pAwlyDh+OcNVdFCw6EXXjzzTelTp06JtgtUcZZwKPNr4rwIAMHDhQWQMKAsNOWX8hgrKNlLC+kHzicE2cQgYZfwUCrDROBoMMvLOC3BH5sDECjR+LXRdAW8Vx22IItQiL4cj8Wy7x581KsKQEE8J8jJhiCNRp2Ivy7q2KeQvtstXEw4zDPfLNokQj8TbBz24adz/z6C/55CHJon2BSWChTjtGzT4u+I7/mwpxFECQgL3FGWTMd5yzdx3yLlppAyTDhxH5Dow3NRGsafYgEp0f8EhfMMt8BuLLe2iezDuOOwNwnQXcYn88//9z4oEKLSFwT79a2C/UxZIxe2bJlZevWrUa1D5HAR8l7oSPeGCpSwEWSJ1BqqAELxfNR1yNJwwj//PPPZjcWH3ko3iU1n0mfCHK7bNkyE5WcLerRSLAcxxGIMgsgDAxMHmaY1MQ2ku4NY8fPEPHdoxHCzJ0hQwaBOYDwQgvQOkEbbKIezITtJ2YUfMGIqzd69Oh4sfdsHT3GRwAGGi0GGgww9g7gjXD+2WefGb9IWuIfyS+60AbmD40GTAZlJMdxBDM6wWMx++KsTrgVfFEp15Q4AsQsRKOHLyobWqCFjnOWybMtoZkwe/hDsp7yE3Pu78DW02PgCPDLIzBxfAfMbX6xybaGeUNTh1DE/GezEfyLpUP2yFigcbXtQn0MGaMX6o7r82MTAe21IqAIKAKKgCIQSwgooxdLo619VQQUAUVAEVAEFAE3AlF/roxe1A+xdlARUAQUAUVAEVAEYhUBZfRideS134qAIpA8BLSVIqAIKAIRhIAyehE0WPqqioAioAgoAoqAIqAIJAUBZfSSglby6morRUARUAQUAUVAEVAEQoKAMnohgV0fqggoAoqAIhC7CGjPFYHgIaCMXvCw1icpAoqAIqAIKAKKgCIQVASU0Qsq3PowRSB5CGgrRUARUAQUAUUgOQgoo5cc1LSNIqAIKAKKgCKgCCgCoUMg4CcroxcwVFpREVAEFAFFQBFQBBSByEJAGb3IGi99W0VAEVAEkoeAtlIEFIGYREAZvZgcdu20IqAIKAKKgCKgCMQCAsroxcIoJ6+P2koRUAQUAUVAEVAEIhwBZfQifAD19RUBRUARUAQUgeAgoE+JRASU0YvEUdN3VgQUAUVAEVAEFAFFIAAEQsroHTt2TGbPni0zZ870edXDhw/L1KlTpWfPnvLFF1/I0qVL5fTp0z71YiEDnObOnSsff/yxDBkyRLZs2RK13T558qSsXLlSxo8fH7V99NexjRs3ytChQ2X37t3+iiM2L7kvzpyfM2eO/PTTTz63OHLkiEybNs3QhgEDBsjixYvl1KlT8erRfvr06fLpp5/K119/LXv37o1Xrhe+CJw5c0a2bt0q//vf/wT8fGvEzzlx4oTMmzdPPv/8czMW0KijR4/Gq8R9yId2DR48OKppV7yOp8LF6tWrZfTo0WYd2LZtm+cJu3btkhEjRpi5PmPGjIDGztNYT+Tvv/+WYcOGyWeffSa//PKLMK8Tg4U5/eOPPxq8WY83bdrkU/348ePyzTffmO8CHqZPnz6yfft2n3rByggZo7d+/XqZMGGCvPDCCzJy5Mh4/QXo3r17S//+/Q0h6dWrl9SqVcsvQxivYRRegAUfN8T0mmuuEXBr166d+Jtc0dD977//Xlq0aCHdu3ePhu6ctw8INCyWb7/9trzxxhuyY8eO87aJ9gob45jeiRMnSt26dY1g4+4v30Pfvn0Fwvn7778LdALa4M0Qdu7c2RDiK664wgiMzKlDhw65b6XnXgjAkPXo0UMaNWokzEuvYp9LmIoPPvhAfv31VzMe9erVM0I5CyGVGasxY8YYRvDqq68WxrVt27bmSLmmwBBgLJjvr776qhFqrrzySkmbNq1pDMP35ptvyrJlywSM+/XrZ5hBU6h/zovA5s2bpXHjxrJq1SpJly6dwGuMGzdOEvqHINOtWzeBPl133XVmPJo0aSLcx91m3bp10qVLF0G4IU2aNEkuu+wyd5WgnoeM0cuYMaOUL19err/+epc0frbvaHSuvfZaw2F/+eWXQqLk5Zdf5hBTCeLYs2dPKVu2rNSsWVMaNGggLFhIC9Go4Xz00UclT548wgcVCwN9+eWXS+HCheWhhx4KaHGNBUxuv/12KVeunNxwww0+tGHNmjUCZjB4aPoHDRpkriG2FhssAWjyWBhhAlu1aiUQWm+B0tbX41kE7rrrLilevLhcfPHFZzPO8xdMu3btagTy+fPnS4YMGeSrr74SNE80ZfHrGUe7+KYt7eK7RsPqrYGlviZfBNBeN2vWTNq3by8wyW3atJE6deoITAaMNBpt8KasRo0a8vTTTwtCzp49e3xvpjnxEEDrhqYZTVvr1q2NMunJJ58U6EVCFgCUU8xfBJyqVauauljYateuHe/eU6ZMkY8++sgop7BYQnsYs3iVgngRMkbvoosuMhyuP6KCFP74448bJjBtnOQCAWIhRJslMfYPbQ9dLliwoKRJk8Ysfvny5TOqZj50yqIpIfVceuml0dSlRPviOI5ccskl5ltItGIMFZ6PNjz22GNy0003Ga1GoUKF5OGHH/ZouGEgkLYRIO+//37zzcA4lihRQtBWWW1TDMEZcFeZh3x7juP4bYOWgkUNAXP//v1SuXJlyZEjhxkHtExvvfWWcT1AEOUG0C7GgzFiTBmT/Pnzy/Dhw4VFljoBpRithBsL7hyYCWfMmGGYcMaHdQBIMDn+9ttvRigi33EcIzRShhDEUVPCCDCXEeMe8qsAABAASURBVFAqVaok4Aeu99xzj5mb4O7dkrkMA8dcR9h0HEdQSFWpUkUWLlxorI+02bBhg4waNUpuvPFGI6jCz1CfslClkDF6iXU4a9ascscdd8SrgloazUe8zBi4mDVrlmHurrrqKtNbCCbYIMXt3LnT5OkfRSBWEMASkDlz5njd5duAmSDzn3/+MWYYtMJc21SkSBGh7M8//7RZekwiAtWqVTOaJUyJ0OOSJUvGuwOuJSxulFEA7eKa8eEa2gXTjVZWaReIJJ5wz8EHDKXH8uXLjW/et99+KzDZtMTNg/xMmTJxadItt9wiMBb4WZoM/ZMgAmicWUfhN2wl5ifKJSxmNs8eDxw4YHx9vYVFaA9MoJ3TY8eOFcYFgRR3HCwM9h6hOoYlo+cNBoQFGzqgeZdF+zX9Tp8+vfEfsH1FOkACQcK2eXr0IKAnMYQApkCINX5ldBuTC2YrN/EmHyYEDQlaEK41JR0B6C/aC7Tu/lrDRKNFzZIliynGBQfcYTxMRtwfS7ti0ToT1/0k/WcDIhuN0BrhP4YFB/8w678Mw4ePHsyJvTH10MyuWLHCZukxAQSgE9ADhEdbhbmKQPLXX3/ZLM+RMtxJoDcIjbYABtCec8TNCkYbmoQfccOGDc3GDMpClSKC0UM9jYkAySZUQIXqufhooFJ2HMfnFVi4fDI1QxGIIQQWLFggaDHw96XbSNZ8F/5cQthVSjn1NCUdgWeffVbKlCljXA28W7NosvECx3Y0IpQr7QKF5Cc0emhEK1asKPg5Nm3aVDjv2bOn0Rgxl2H+/M118pP/5Ghs6dsn6AQ4wRh7l5LvnUc93BUQdNh4BLMHQ81mC9Zo64OXO3duQZv3+uuvC98Ewg3n1Pe+Z7Cuw57RQz2NVPPcc88JtvFgARMuz0GC4IPGL8a+E/4tTFKIgM3TY/giwHih3WAe26RhVC58vAgrgR8YxBetB3eEyYCwounj2iasAkjqaJhsnh6ThgCLGcm7FT5577//vuDQ7jarQ7ugU/5oF2Xe99Hr+AggmNx6663GD8xxHGH+Fi1aVLDwYBaH4cAsDn2R//5xznpBu/+y9JAAAtAJtHRuUyz4MV8RHv01Y68AG8CY1/j2sTkDrSr1vV3LGK+cOXOaXecHDx702Znr7/6plRfWjB6LITtccODNmzdvamEQ1vfFiZyJhEmKF+UjxmSLij5XrlxkaQpzBJDkOnToIK+88oonET8yzF87rF8P2sBONqRnNirZl7355psFUxYMoM3jyMKI7xiEl+vzJS0PDAE0efgkYbL19tnjGkGdOtzN0i4WWKVdIJJ4gonD7wtG2taEuYOBoAxBn/mMP5gtJ0oDJt0KFSrYLD0mgADMMC4eCN+2Cv6jCIWYX22e9xFtHTvOieWJpfHff/+Vjh07xnOvcrfh23Acx2xacucH8zxsGT0WR1TUBQoUkAcffNCDCQENPRcxcIKpBB8AmD26i2TAx4yG05/KnjqawgsBCDIaD0JP2PTAAw+E10tG0Nugyfvkk0/kzjvvNDtu7asTxgCtP2autWvXeoLzsvBhYsH0yFjY+npMGgKEoYDBtq2gRSx4mKwIh0M+GpElS5aY3YbshqaOpV0wLNAu/PzwMaa+poQRQMEBUweethZ4wuTBiCDQEA6HwOK2HHzRNr300ks2S48JIACTxyYtdjTbKihROCcsE0csAzB/nLsT2lZcyvCZBOunnnrKXRzv/I8//jBhb7JlyxYvP5gXIWX00FKxxRnisW/fPk+/ucbxlzx8cIhYTbyb559/XmjjqRg2J6n3IjC6EFHiguE3wC9GsEGDuFTRyOixGOAgy+LAPEg9ZIN3ZxY1TFpIfzZhfnG/Ac6/aDz4Htz5sXrOt4+PEt+71QiBBdcEP0bTgSRuaQMxxGy96tWrCyGZ8IuhHsQYDTg0hXto8o8A8w9mDo0GjLF3LYROAnvDOFNGfFN+kQG3BOgTNJp4hmiYHMcxjDiaJcowj2HmYsNGtNIuMEnJBKNHfDbi4oEpY4M/2GuvvWZCjyHU4OjPpg1240IzwRpXBkJwpeS7ROO9cOMg1iYCIiGZNmzYYAKww7jZXfv8WEGpUqXMr8VYDNiMQTDkunXrCkILvpP471EO042VgR97gCkn7AqhbtD4oY2lTihSyBg9wIWBAWwWOcCFuCONwMxAPOC00YBAUEgQiVjThGDmIPI5C1exYsXMT0KxuBUqVCgU8yVVn4mUVL9+fRNpn5hbLCyTJ09O1WeG+ubMeRZPCA3mLGKREVIh1O8VyufzSwtIyJhaCYHw4osvmtAoMCLMh0WLFpmfKnLTBn4ZoHTp0ua1IbowIJi12KQB40K0e2LvmQr6xy8CBHjlFxjQPLBjsFOnTvHq4Z+EFgR8WdCgyQgmgwYNMkHtuUb7wTzGlw/aBbN99913C2bcH374wQTBR4sS78bRenGB/YIxgKljbUR4IfFdoPCwt4ah4+e7YCbQWKPNbtmypTiO7+Y920aP5xBgLrJhAgYZLR6WMtZbxzmLH6HMsmfPLnYnOd8EOCNAEiuP8UGItHfk+2AMuF/FihVNbD0ETYKJ2zqhOIaM0YNhwwwL0SYBGkwfWiqkGLYlk++dUFuHAqhQPhPTSPPmzQXtJosbkl4o3ye1nu04jgkEbcccaQgTRWo9Lxzuy5xHOrR95ojGIxzeLVTvAFMwffp0AQsSfrqYXDFjEY0+IdoAY2jfmXOIMnU//PBD47dny/ToHwF+gQG8beIXAtw1MZejTYWBw3fY1nMf+Sk6Fk/bjk0y0C7MVzCEWChsmR7PjwCbi9jJDMY///yzwIiAv7slmKItZT3l16PYYOAu1/PEEWC+4oKA8omfZAVz2wJtHrQIJQt5fBOMBUKR2zeYMhI+wmi2qUP8PH7JKhzGI2SMHqBoUgQUAQ8CeqIIKAKKgCKgCKQ4AsropTikekNFQBFQBBQBRUARUAQuFIGUaa+MXsrgqHdRBBQBRUARUAQUAUUg7BBQRi/shkRfSBFQBBSB5CGgrRQBRUAR8EZAGT1vRPRaEVAEFAFFQBFQBBSBKEFAGb0oGcjkdUNbKQKKgCKgCCgCikA0I6CMXjSPrvZNEVAEFAFFQBFICgJaN+oQUEYv6oZUO6QIKAKKgCKgCCgCisBZBJTRO4uD/lUEFIHkIaCtFAFFQBFQBMIYAWX0wnhw9NUUAUVAEVAEFAFFQBG4EASCz+hdyNtqW0VAEVAEFAFFQBFQBBSBgBFQRi9gqLSiIqAIKAKKQGogoPdUBBSB1ENAGb3Uw1bvrAgoAoqAIqAIKAKKQEgRUEYvpPDrw5OHgLZSBBQBRUARUAQUgUAQUEYvEJS0jiKgCCgCioAioAiELwL6ZgkioIxegtBogSKgCCgCioAioAgoApGNQEgZvYMHD8rQoUNl4MCBPigePXpUhgwZIpUrV5YGDRrIjBkz5NSpUz71YjFj69at0qlTJ1m6dGnUd/+bb76RwYMHy+HDh6Oqr7t375Zu3brJc889J927d5e9e/dGVf9SsjPQghEjRkitWrXkpZdekqlTp8rJkyfjPWLXrl3SuXNnqVq1qvTq1SuQ+RKvvV6InD59Wv78809p0aKFHDp0KGBIVqxYIa+//rqsX78+0TZTpkyRZs2aGZrOmCZaWQsNAnv37jX0j3Wwfv36MnfuXJ91cOPGjfLWW2/J888/L1999ZWwrprG+icgBNatW2fmZY0aNWTYsGFyvrnJt/Hpp59K9erVpWnTprJkyRKf5/AtLViwQD788EP5999/fcqDnZEm2A+0z1u2bJmZwHby2nx7ZPGbNWuWZM2aVThWqVLlvITEto3m47x586Rt27by/vvvy86dO6O2qywaAwcOlJdfftmM/4kTJ6Kmr3v27JFq1arJ5s2b5d1335W//vrLEJqo6WAKduT48ePSo0cP+emnn8y8f/TRRw0jMn36dM9TIKRPPfWUwDy3bNlS+EZgKDwV9CQgBCZNmiQdOnQwgge4n68RC+LPP/8sr732mnzyyScJCit///23lC9fXt555x3JnTu3wJBfdtll57t9zJfDUIAVjHTfvn2lePHihqH77bffPNisXbvWCIt33HGHtGnTRiZOnCifffaZp1xPEkdg5cqVUqFCBcmRI4e8/fbbMnz4cBkwYECCjVA4sCaxPrVv317AHWF91apV8dqMHDlSmjdvbnicI0eOxCsLxUXIGL18+fJJo0aNpGjRoj79RkIpW7asmbBdu3YVuOcrrrhCvMH0aZiaGWFy73vuuUdgjjNmzBgmb5Q6r5ElSxapXbu2VKxYUc6cOZM6DwnBXZH00FBu2LBBOnbsKHwHSIUwLhDpELxSWD+SRQ0GhEUsW7ZsAkN33333yQsvvGA0F+D5+eefC8xE69atpUCBAmYxRBP8448/hnXfwu3lYMYQQK677rqAXi1t2rTy0EMPScOGDeXiiy/2acN3u2bNGqP5uPTSS2Xy5Mny4osvSvbs2cVxHJ/6mhEfAeb+okWLjBb7+uuvl2effVbuvPNOI/Bg3YLR7tOnj6RPn16aNGlimGg03l988YXAiMS/m155IwDT1rNnT8mcObOxGubPn99gjRYOHsS7PtdYFlBSoYhiHsPD3HLLLUZwdzN0fEfMdW/LA/cIRQoZo5dYZ2+77TYpVKiQh3hAUPLkyWMGJLF2WqYIhDsCaPN+/fVXKVGihFxzzTXmdSEYEIsuXbqYa/1zFgGYuOXLl8vevXs9tACG4cEHHzRMHqbAf/75R8CzTJkycvXVV5uGaI1uvPFGY8I1GfonRRAYP368caEJVLuOCRHtUrp06YyW5KabbkqR9winm6TWu8AkM685Mud5zlVXXSVFihQRGEAYuR07dsj8+fMNs005iXWS9bJfv35cakoEgU2bNsnixYvliSee8NQqWLCgEUIGDRrkybMnx44dk9mzZwtKJ5t3+eWXS8U4ZQTmX8bC5ofbMSwZPaRDEmBhhoGLxn6eK1cusjQpAhGLAIzJhjhtnnsuQ8hz5swpCxcuNJqpiO1cCr84WgvMsWguYPrs7dHssZjhurBt2zbBZzVv3ry2WDALsuChDcF3z1OgJxeEAL57AwcOPK8Pk30IFphvv/1WmNv4o+I/2bt3b2Nit3X0mDACzH2Yar4DWytznPYJAZG5jxYbjHFvsuUZMmQQGGtwt3l69I8AdAMGDUHb1kDJBG2ZMGGCzfIc0dhhTid5MuNOChUqZPxb9+7dG3cVnv/DktGzUKGCfvjhhwX1NOcQdVumR0Ug8hAQ2b9/v8DsoXFyvz+SIdI7/mbu/Fg+v+SSS4yPriXIFovt27eL9SFDQ8qCePPNN9tic2SxY4Gk3GTonwtG4OuvvxZ8IK+88sqA7jVt2jRhcYT5wF8JXyZMZaRwMWkF1JEQVHIcxzDImAndwgoMHiZHXunAgQOCVs899xEaL7roIjXdAtB5EowZtPjWW2/11AS/NGnSCNo+T+YFMJd6AAAQAElEQVR/JzDYMIJYGfCv/i9bYBbtebgew5rRq1evnvHLw2EdbQfHcAVS30sRSAoCjuPfR8lx/Ocn5d7RVBcTd7ly5YwPEj537GQbNWqUMd2iKbJ9dRz/uDmO/3zbTo+BI4AWGqd1xwkM0y1btgia1WeeeUbw+2Mc8buF0du3b1/gD47Rmrgj4DfGuofJkB23+Kui4c6UKZMHFcfxHQ+YFU8FPUkUAccJDD/HcaRmzZrGr5odt4wJiQ0cWCBhAhN9UEKFQcgPa0aP/uO7hDSI6TYW1NGo6mfMmCGYOGz64YcfBP8A8IimhIbW9pEjO/eiXaOFpil9+vTivdAhmTuOI96avmga7+T0hQWNnYd8/yxy+MGgpcNcBROI3xJp79698W6PFhDiixN7vAK9CCoC+E2ireahaEtgztHUohUhT1PCCDDHCRXE5iOEG0LfYBGAAbz99tuNiRYG2k1L0PKh7XabIxN+QmyXoJmGFrtpB1iibWYzoD908JHs37+/8YvEvGt9JdkExkYZf23CIS/sGT1AwmbOZL/hhhu4jOrEJEMV/Pvvv4tNq1evFhjAaOs4H5jtI0f8MSFU0dZPd38ws0DA3TEQ6TMhVkqWLCnKmLjROnvOooWwR5gVCDDfR/v27c1Cx4LHDnQ0/mdrnzWPg2exYsXk2muvtdnRcoyYfmCyxY+MxdO+NEwepsXMmTPbLD0mgAA4sSsfc/nHH39saAOm21dffdW0gJbgmwoDaDLi/qBFZRMMcffiLvV/IggQGgXa4o6Dx25b3A3Y3Z9QU5i9du3ayXvvvWcsC/jsIYziG5xQm1DnRwSjB0hwzg0bNuQ0qhNMLWph4ibZRDgV906faAEAU5Dtoz2yaEdL//z1A2GFkBTTp0/3FDO3Ic44u3sy9cQHAWKGERgWIkwoECrATICnOzQNTB7mLcLWUEdTyiAAg43mOdC7Pf744wLjgfYO/1PawfQh0MCkcK0pMARgRogNiZ/j/fffbxphKgRLLEAmI+4P3wjKAtaMuEv9nwgCCNx33323sHvfVmNzC2ZvXAzIwx/SLUSSR2JzGOGCcEMgpi1CJfnhmkLG6KHFwOeAiQkBAUycpwGKbfz4JiC144w6evRoE6n9lVdeoTiqk+M4gokDc4dNXDvOWT8CzFZovjBxEqwU7BIFJIwLkVhtH+2Rj4xXZqFm7JkXzBGO5Ed6chzHRLCHwBCDac6cOQKxYIt+qVKlIr17Kf7+aH35JQx+CYY4YkT/R7vHfOFhjuOY2FdoPvh1Bnxm0PzVrVtXsAJQR1NgCGDuxjQObYYG41rhbvnYY4+ZoLKYD8lnsWNjDGFAYC4QXtyMIGFu3njjDRPmho0ZMOMzZ840v6IBTeMemhJGABrP3Ee4wc+xVatW0rhxY7OrnFZokFB+YKrF9QX8+WUMAljj8kQdTQkjgFKF+YnWmVic+AAT4xSfSCuIwHuULl3aBFLmTnwTfBsI5R988IHR6lWqVMmEZKHcJqxwrNN8H9B4NozZslAc04TioTwTFSm7uAAJzQ7csfVDK1y4sECo2W3ExHUcR5o2bRovfg33iMVElHSkDiQ7CDKBYaMRBz4MYkEhKbGIM1eipZ/4heCPiNllzJgxws5yfpEgWvqXkv2AYI4bN858+xBhmDxv7Tbm2S+//FIwB44dO1aefvppaRdnWknJ94iFexGfDcaaTXC4UvCLRO5+syiyoYIFknzcSX755RfjPwzDgdAJ00cZiXoE8OUnLBkXmEiEdRhwx3GooikRBHDvwC8VP1W0TnXq1BEwdTdhzmMNgSmEkWbsSO46/s417ywChGoaNGiQ2b0MU41ZHH/gs6Vi4hYyt+0PO/BNMBaYfIcMGWKCWOMLbOvb4/fffy8IP1jnEG7QatuyUBxDxuixeBNbCUmEBIeMozog4HcDUSGfKNX4G0DMKYv1hKoeXGxCnR+NmLDl3fbRHqOpnwSPRTpnfqOlwjE4mvqXUn1B88n4I+ih5U/ovphw0WQQsR7Nn/eCmFA7zT+HAFojsLYJYfJcqZifI+SnKK02Do0StNnW58g93G2Y18xvfMz46UbGE02+u46e+0egVKlS5uf/YI4xM/qvJZIlSxZBsGE9Jfivzv2EkPKfD9OG9ZCA9Y888oixqNma0Bz871BGkcc3wTxHgElsTGAYqWeTNbdzj1CkkDF6oeisPlMRUASSg4C2UQQUAUVAEYhUBJTRi9SR0/dWBBQBRUARUAQUAUXgPAikCqN3nmdqsSKgCCgCioAioAgoAopAEBBQRi8IIOsjFAFFQBGIcQS0+4qAIhAiBJTRCxHw+lhFQBFQBBQBRUARUARSGwFl9FIbYb1/8hDQVoqAIqAIKAKKgCJwwQgoo3fBEOoNFAFFQBFQBBQBRSC1EdD7Jw8BZfSSh5u2UgQUAUVAEVAEFAFFIOwRUEYv7IdIX1ARUASSh4C2UgQUAUVAEVBGT+eAIqAIKAKKgCKgCCgCUYqAMnqugdVTRUARUAQUAUVAEVAEogkBZfSiaTS1L4qAIqAIKAIpiYDeSxGIeASU0Yv4IdQOKAKKgCKgCCgCioAi4B8BZfT846K5ikDyENBWioAioAgoAopAGCGgjF4YDYa+iiKgCCgCioAioAhEFwKh7o0yeqEeAX2+IqAIKAKKgCKgCCgCqYRASBm9vXv3SseOHeXdd99NtHtbtmyRChUqyA8//JBovWgu/Pvvv6Vly5bSqlWraO6mzJw5Ux544AHJmjWrfPrpp1HdV3fnpk2bZvr9119/ubNj9nzfvn3SuXNnadasWYIYFClSRG666SZPgk7YyrTne8mcObM888wzsn79elsUAcfQvOKpU6dk7ty5UrZsWQG/QN+CNvfff78sXbrUp8mff/4pVapUkQwZMkjOnDnliy++kCNHjvjU04zEEThz5oyMHz9ennzySbn11ltl3rx5ngZg/Mgjj0imTJmkdevWwrrqKdST8yKwZMkSeeihhyRLlizy3nvvyaFDhxJtw7fx2muvSeY42sK8nzFjhk/9zz//XO68805Dm55//nlx0yafykHICBmjt3LlShk3bpwh5v/880+CXT127JgMGzZMfvrpJ+E8wYpRXMAi9eWXX0q3bt3k4MGDUdvT2bNnS+PGjQ0zy3jzsbRv315OnDgRtX1mPGHy6OeCBQvk5MmTUdvXQDu2evVq+e677wzR3blzp99mw4cPl+zZs8vTTz9tEvjdfvvtpi6YVq5cWbZu3WoYl/vuu88wexs3bjTl+sc/AszDPn36GFp7+vRp/5VcudBjvtkPPvhAfv31V5+5C/4wHo8++qgZiwkTJsjYsWNl5MiREsj9XY+K2VNwWrVqldSoUcOslTVr1hToxD333CMwfzB8VatWlQYNGsgff/wh27Ztkw4dOghMe8yCFmDHwY/5W6lSJYFxmzNnjhFWOnXqlCB+O3bsMGORMWNGWbRokTRs2FDq1KkjP/74o+eprNMDBw4U5js0p1ixYlKqVClJjM/xNE6lk5Axerlz55batWsLUnliffv9999l3bp1cttttyVWLarLkDTeeecdufTSS/32MxoykaIgViVKlJDSpUsb6QpGb8SIEbJ48eJo6KLfPlx55ZWmv3Xr1vVbHouZOXLkEBa0u+66y2/3kagh0BDTvn37Cunll1/21IWRWL58uVnw0CTVr19fHMeR7t27e+roiS8CDz/8sGGar7vuOt9CPzmXXXaZwERDx/3RJha2PXv2yIMPPigXXXSRYcxLlixpFkhlRPwA6icLJo+5nTZtWhk9erRHO0pVBJpBgwZJ3rx5BcHmhhtuMEzHpEmTDDMo+i9RBKAj/fr1E+bkU089JdCKF198URAi0fL5a0wZzN6bb74p6dOnN7gXLVpUXn31Vdm1a5dpgmKqWrVqZg27/PLLBfrz77//CppXUyEEf0LG6AXSV9SdLPSYXtKlSxdIE60ToQgsXLjQfAgwenbRuPvuu2X//v3yyy+/RGiv9LVTA4HJkyebOdG1a1djwnJrfBEYZsyYIWj3MGXxfJhpGBIk7O3bt5OlKRkIYErH9IomL5DmMIIsflOnTjUaEhgTzIqFChUyjF8g94jlOgcOHDCm7iuuuEI+/PBDY7J14wHDgRYPjZHNR0iCuejfv7/N0mMCCGzatMlo8KANtkqePHnk4osvlsGDB9sszxHawjqVNo7ptpnMcVwd0KSi4SM/c+bMRmtt3XBow7hggaA8FCmsGb127doZU97NN98cCmz0mUFEAHOE4zjGj8c+lo/olltukTVr1sjhw4dtth5jGIHjx48LrgyYdN9//30pX768tG3b1uNXA2OBgFi4cOF4KEHAWTgxC8cr0IuAEUBzOn369IBdaDBvsQi2aNHC+GKPGjVK0JqgfUqTJqyXnoAxSc2K0L0xY8ZIrly5jGkWqxZmWuY3z2WuU8fNQLBWohSZOHEiVTQlggBCH6ZV8LXV8H9k3ZkyZYrN8hwRKBFy0AR6MuNOChQoIJiBre9pmzZtZMWKFcZi+dVXXwnuEGhjuXdc9ZD8D8uvDT8lOGqIOAQ6JMjoQ4OKwObNm83zrrrqKnO0fzD58AHxkdk8PaYCAhFyS7S9uDGgzcD5/7HHHpNevXrJJ598YnqA1giG7tprrzXX9g+MBf5OlNs8PSYNAdxo0CxdffXVATVEE9W8eXPDjDNGLHiYyGBEArpBjFdifu/evVvuuOMO456AJhtz+AsvvCBHjx41wg0mQfdcZ547jhNSf7BIGTboBK4F119/veeVLX4w0Z7M/07Sx5lq2WCBOR1/PvgUGD9rcWKtoiruaN9++61Ao5o2bSrMeQQcx3EoDkkKO0YPzhhVJwOAE29IUNGHBh2BSy65JMFn8gHxASZYQQtiEgEk8Z49exrJmSNEl3nCfPEHiOM4xizjr0zzzo8APmDscj5/zbM10L5iWsyfP7/xj3QcR2BS8H+Czp+tpX8TQgAzN9EH0IrCZIAj/pDz58+XWbNmSWJzHa1UQvfV/LMIQCdIZ6/i/0WgjJ9z9qp69eomAgibN4gKgeaU3dCsX9myZTOVMOPiQ/z666/Lc889Z3z0EERRWJgKIfiTXEYv1V4ViRttHmp+drMA1Ntvvy0bN240u07feOONVHu23jh0CGTOnNk8HInVnMT9YTFYu3atIHHhdxKXFZH/CY1jP3rmM+nnn3+OyL6E20szN2rVqmV2caLdQNtEshpi+774y0CMMW3ZPD2mLgL4QA0cOFAI/VGvXj3p3bu32RTDJiu0Ian79Oi4O9pP5q3tDX6nzG8c+/E9ZT6jObLl0E8YCphCm6dH/whcc801Zm2BMbM1cAlBYGSDi81zHzGT9+jRw7iLMBZYoGjPRibL6OFKgrawUaNGwg5eNmrg34rvsPtewTwPO0YPDrt48eJSpkwZYWcuUjuAwmHjo5AlS5Zg4qPPChICxM6DsWO3pH0kzD3zoVChQhGtiUHyzpw5eiH30QAAEABJREFUs/G1YT6TMGvZfurxwhCA2QNfFj18OiHGhJ1w3xVHabRR+fLlc2freYoiEP9mhFxBqwfdpoSQIDDl+JXprlsQSTyxoQissG7ZmtBDx3GkYMGCwlxnPhOqzJbDqLBpgNhtNk+P/hHAhxRavGzZMk8FQgJhFseP1JPpdYIpvVy5cvLEE08YzSpCC8wfmzjwH2bzEYwf1zfeeKPgo8qzMMV73SpolyFl9PjY8b0CKM7pNRIM6lFiAdkEZ8ykZvI2adKEajGXwIlOQzjxNeI8mhJMPWYJVOFodZGq8HNAMiUoZST3FZMXqn47nzm6d8rRN/oLo2vHmbxYTtADsOBIsliwKQctL75L5KG9wIzVrl07s5MTpprQBhDr6dOnm9huLISEaILgslDSTpMvAsw/aDFH5qN3DRYw3Gq86Q/jRF175JwEzYbxYLy4J/d2nLMbrhgn6mhKGAE2FEH/vv/+e+OTB34bNmwwG9aIywa+MBTEPyQ6AfhjKkfLRNy9hO+sJSCAAql06dJCOBo0cKytCIgwZwT5pg4bNtgQ496AQT3mNTvQadu/f39P+DdM7AiebOaA4eZbgUYRsojncc9QpJAxekxYbNyYVNhxOWTIkIB3c4UCqFA+E0KJMzMfMo6fX3/9tQk7Esp3Solne9+DOGcZMmQw6m4kJIImE3AVguZdN1quWVAJtsmYMr4QjWiOGxjIuGHyw3EfBg0pmFhhEE3a4ghNzMFXXnlF2NGGfwymLGK1UU5CMGATAKbCjz76SEgIiThFU67JPwL4FdlFjaCv/EqNuya/UgKmdiyYr7QhbiHnAwYMMGFvbBs0ePziAP5J0HrGgbmNtsRtjrT19RgfAbRAuC3h+M8vSIEjG2IwDVITjRHCMRsW28UJOqwRMH38ehIMB3U0JYwAc5CAx+Dcvn17wc8XLTS7+THr0hI8iYOHkMM13wTmWMYDX0naYI2ijMTGGBv2iToff/yxoT+MEUw5dUKRQsboYYpFBQpBZxcdW48dx/+uFMwygJdQANVQABfMZ+JYSxyeoUOHmp+4QeKIRs0Emi8+IhZqTBIsKkhcwcQ62M9yHEeQwPFhYnwhGpHsj5gS+EEbMFvhy8XPI2L6sxogfkarS5cuQjR75gu74NidjyXA/WwI+FtvvWWClrIY8nNo3nXc9fVczDzkl0YQutE44wPmxoXvkV+usfPTcRzBBaFq1aomyCwLGde2DYslPtUslJjVMZMR/Jc57jj+ab1tq0cxGmpcmPDvwlTLxgwsWjDQFh/cEWBSwB4fMQQczIq2PAKPQX1llAjgyzoDbYFO4FNqXwLNKQKljbXH/IbmYDWADpHvvRYj3EC7CMQMX8MY8tNqaPXsfYN9DBmjBwAQFVSkJHbYQuD9AYB0Qjlt/JVHex6LHtoIcCIx+Zhw0dhvFnV8H/hNRwhbNPbR3SfmPD6pjKtNEBx3nVg7R6vrnu+EULHMBfOeoNpohZgjEN20rgCmFitwhVmhHgSXdrZMj/4RwBfWzkGOhIlw10QAIw9NEvkcMS9S1ybuQZlNLG7Qq2effVYqVqxozI5oUmy5HhNHACYC8y34VqhQwfy6CLi7W2EqhGaynvI9KL5udM5/juIE5pj5iQsRmNtW8BzQIgQV8vgVDMbi3nvvFQRN8vwlxoHxoi7fDVYHf/WClRcyRi9YHdTnKAKKQBQjoF1TBBQBRUARSBQBZfQShUcLFQFFQBFQBBQBRUARiFwEYo3Ri9yR0jdXBBQBRUARUAQUAUUgiQgoo5dEwLS6IqAIKAKKQDQhoH1RBKIbAWX0ont8tXeKgCKgCCgCioAiEMMIKKMXw4OvXU8eAtpKEVAEFAFFQBGIFASU0YuUkdL3VAQUAUVAEVAEFIFwRCCs30kZvbAeHn05RUARUAQUAUVAEVAEko+AMnrJx05bKgKKgCKQPAS0lSKgCCgCQUJAGb0gAa2PUQQUAUVAEVAEFAFFINgIKKMXbMST9zxtpQgoAoqAIqAIKAKKQJIRUEYvyZBpA0VAEVAEFAFFINQI6PMVgcAQUEYvMJy0liKgCCgCioAioAgoAhGHgDJ6ETdk+sKKQPIQ0FaKgCKgCCgCsYeAMnqxN+baY0VAEVAEFAFFQBGIEQQSYfRiBAHtpiKgCCgCioAioAgoAlGKQMgYvaNHj8qGDRvkpZdekqZNm/qF9+DBg5ItWza58sorTbrxxhtlx44dfutGc+bx48dl+fLlUqtWLXnttdeiuauevtHnPXv2yJtvvmnSvn37PGWRfnLq1CmZN2+eVK1aVfLkySPPPfecLFmyJNK7lWLvf+zYMdm4caM0atRI6tev7/e+0Ibs2bMbugB9gDZs377dU/fPP/+Ul19+WfLnzy/lypWTmTNnCrh7KuiJDwJ79+6VsWPHSsGCBYVznwpkuNKZM2fk0KFD8s0330iOHDlk4cKFrtJzpydPnpRNmzbJBx98IGXKlJGcOXPK4cOHz1XQM78InD592tD95s2bGzpRpEgR6devnxw4cMBTHzo5efJkefjhh81cf+WVV2TdunWecj1JHAEw3r9/vwwdOtTQ4YTmsPsurEtt2rSRe+65Rx544AEZMmSIQLNsHb6dd99915TnypVLKlWqJL/88ovwLFsn2Mc0wX6gfd6uXbtk7dq1MmHCBIHps/nuY9++faVOnTqGQEAkBg0aJBkyZHBXiYnz3bt3mw9+5MiRMdFfOgljt3TpUpk2bZpwTl60JIjJiy++KOXLl5c5c+bIvffea4SdnTt3RksXL6gfljaMGzdOjhw54vdeX375pRF8oAukgQMHyi233GLqwlTUrVtXYP5g8GrWrGmYPnA3FfSPXwTWr19v5uPq1asFJs5vJVcmDNyGOGF96tSpwtFV5Dll/L7++muziLJAvv/++4YhTJcunaeOnvhHYMuWLdK1a1fJly+frFixQnr37i1gyTpICxiHKVOmSMuWLaVdu3aGmbj66qsFJiShNZV2ms4hcOLECVm1apURBMH4XIn/M5jCZs2aCWvTd999Jx9++KF0795dRo0a5WnQsWNHs16PHz/eCPDVqlWT559/PsFvxNMwFU9CxujdfvvtRrpDKvfXv23bthlG8O2335YmTZqY9MQTT/irGvV5MLdVqlSRiy++ONL6muz3ZZG+//775e677072PcKxIZLf6NGj5YYbbhDGNH369OaIprp///7h+MpBf6fbbrtNHnroIaMl8vdwsEJIfOeddwxdgD6gtbN1x4wZI2j3sBRce+218vTTT8v1118vnTt3tlX06AeBwoULS8mSJeWqq67yU+qbdckll8idd95ptEn+aBOMyI8//ihdunQx49S+fXspVqyYXHHFFb430xwfBBBSmOulSpUyZWiQihcvLj169BA02gjAzHUwve+++wQmD+3RH3/8YQRk00j/JIrAZZddZtaYChUqJFrPFs6dO9cop9566y2jdGJ9Klu2rJnjCKjUg/lGeL/55puF+3NvGER4GspDkULG6J2vs2iv/ve//xlOGOBQUZ+vjZYrAuGOwN9//y2LFy+WvHnzyqWXXmpeF4KQNWtWI7GbDP2TKAKYCocPH260RJMmTfIxm/z666+SMWNGue6668x9ILYQXrSnatYykCTrT40aNaRFixYBm12Z65gaWegqV64sl19+ebKeG4uN0KhCJ+j7RRddxEFgrHH1gGnAlQfGAS01zLapEPcnc+bMgrZ0wIABcVfh+j9y3+vnn38WLGww3bYX0BasCL///rvJQmD66KOPZP78+eb6+++/F8zu7nEyBUH8kyaIzwr4UWg97KQGLDQf+NtYjjngG2lFRSDMEMB8BYHOnTu3580cxxEINP5Oyoh4YPF7gsCH9gj8Fi1aZARB/HzR4NEAIvzPP/8IxJZrm8AXupKQidHW02PCCIAdJkE0dQnXOleyYMECgelG84T5HH/rZ599VpYtWxaQafjcnWLzDAYPpo55axG46aabjEYUszm0hDFxW8WwFMBQz5gxwzbRYwohgG8kDB1COmNjb4sFwpqAycOMfs011wiCUfs4LTaM3rfffitYFygPRQpLRg8JvHHjxsbngAn76quvysSJE+W9994LBUb6TEUgxRDAZwmCATH2d1MWUn/5sZzn7jtaUDZozJo1S0hs1vnpp588tAF8cfT3Zx6EQVF83Wgm7Rztadu2bc0GmEBaYuayLhgjRowQfJpgxN+JM7nDsAdyj1it4ziOMaPjp8f8Zt7CMKOVBkO0duTBCLIZyRsn8r3z9PrCEIDhhr5gIve+ExpYysjHOvPxxx8bDewnn3xiTLyh3ggWloweYNmUKVMmwR7+wgsvyODBgwM2G9j2elQEwgkBJEG01Qm9E4xMQmWaHx8B/HzfeOMNszMXZ2g0ouCLxi9+zbNXjuN4zOVnc/RvUhBggcOnNNA2MCZom/AhS5Mmjdk5ig8ZDLpqrs+PIn53DRo0EDYeoR1i/cOci7aIXc7M9YRoiT9B5/xP1BqJIcAcBnN/dRzHMf54lLGpA8GGjRrwLp9++qmwczqUzHcaXizcExILO1d4T9TVHBNOWhIpCBBi5NZbbxWbUIGvXLkyUl4/We/JQon5hfAh7hvgogARyZIliztbz8+DAAsa5kAWPDQd4IuJhJ107qbgCxMNc+jO1/PURcBxHHEcxzyEhRJ/VCw2W7duNXn6J2EE8DFlhye7N9lxS2ga1j+YPtZEGD5o5oYNGzw3QQOIxYBNAp5MPUkRBKAtmMZh5Nw3xBUB4dLSbjaH4VryyCOPmHBovXr1MrtyiTDibhfM84hg9AAEn4SiRYsKE5trTZGPAOEwOnToIDbhzwBxi/yeJdwDdlATLgEnaluLsCoQZ7TWMHs2X4+BIYBZBJ+8O+64w5hJChQoIPiHWfMg5cTVI6YVBDiwu2otbwTAEzrsnZ/QNeOwceNGswPa1sF8jtmxRIkSNis4xwh9Stq0aU2YIIQXwg1hrsV1ge4gIDPvYTS4JkFLqAMt4VrThSHAfAVP7oKg8uCDD5p4eG5mj3PWLTTXhCgiPBGbZhzHMRYEQqtA8/HN5j6hSGlC8VD7TGzebBMHSM5tPtIephgAxPbNFnO2mhODydaJtSOMAFhwTAqxDWecWJiJJ+dOaLvc74z5B9+HaOkzkjg7ENesWWNiljHv2VVOSAskQXffY/kcXJjrHEkWCzZdEJ6GxY3vgZ2d0IbWrVvbKsYJGpyHDRtmXD3YKcdGLncdT2U98SAAnpi/+dbQjnoK/jvBBIWfHQzff1nmgEmKthxNxn9/Hn30UeF7JggzdJ57Mm5opNCu/ldND4kgAG5oo/FPZ54TIw93Jprg/1ixYkWZPXu2CUXG2BHOBnP5k08+SRVNASKwd+9eYV6z1ribMF+J0/nXX3+ZbAJTs8u2T58+Jr4rlgNivbKnAMabMSHgOEGU4WPYpAGth0lE8DE3CcGfkDF6SNws8AAIIWbbPpMaDMP33DEAABAASURBVCDKbMuvW7euACDEBVU00gvlsZZ+++03E/CViQhTQEBGFrhoxoHdTUhCfERsyGGuBLG/qfoo4gMS5BQfDuY4/UObiRNvqj44Qm6OH1LDhg1NIFMiyuPngsmK1+f4xRdfSL169cwvZ7CbrVChQoK2n3IS0jPY8osBtWvXNlHvYfKIYk+5Jv8IsDgRFgKGDfzxDXPXJGYbmEKHyIcBJ3gvpikWNDDmFwYoI7HwMa9xx+BXffCnZBMSGilM7dTRlDACYA19YEzAiyPxJa3WnyMxInFdwAcMGglzwa5PXBoSvrOWuBGAvhCbcPPmzdKqVSuzycuWw8ixoYIjeTByBEiGMYQGMb/5ZSPmNOUImN26dTPuCvAu1atXN3EPEW5gEKkTihQyRo+YMgACtwsjw8KHSh8QINRI7RBxdnmx4BMoEjs45bGWkBBYuJiIxOphFzIBYKMZBxYJPj5CaNDnzz77LGq6i48SPqcIM/SRscWfI2o6eIEdwbxKcGNMIGgzYCDYCMBtMb/i6IwZCyILbcAM6K0hQpvEL+vg29SzZ08ThsW7DvfTdA4BtEMI1QhZOP6zceJcqQi+YtBsy0SAJ4wGu3HxDcMKQ8w82wZGhADMxHRDG8ivPPATjmjybR09JowAAiHzl4QihO8CTN0tsAQQegwNE4wgVq+77rrLXUXPz4MATDJRPeBFiM/pxg/NKG4f0BORszeCBsH8kdhdiyUGf8mzpSKUE/yeMUEIQhsLE26/G1svmMeQMXosdvgr4adFwskR9SadZzLjk0A+qn+4ZFtGeawl/DRwYgYPEkweGEUzDjD19NWdoqm/9A+/Dr4BiEQsz2/vcYU2uOc7UrSd7xyhDeAGbWCh84cdeThPU4/6tPN+jl7HRwBm2v29MS/dNZiv5DnO2c0VjuOYX2Nwt+Ee7jaMA22ow3ih0XOcs+3d9fTcFwEYA+YvuMNU+9Y4m0MZ2FKX78FxFN+zyAT2F/rC/LSJOWpb2rUXmmTzOIIzeMO3oG0lz53gWSwNY/yg9+7yYJ+HjNELdkf1eYqAIqAIBBMBfZYioAgoAuGAgDJ64TAK+g6KgCKgCCgCioAioAikAgLK6KUCqMm7pbZSBBQBRUARUAQUAUUgZRFQRi9l8dS7KQKKgCKgCCgCKYOA3kURSAEElNFLARD1FoqAIqAIKAKKgCKgCIQjAsroheOo6DspAslDQFspAoqAIqAIKALxEFBGLx4ceqEIKAKKgCKgCCgCikC0ICCijF70jKX2RBFQBBQBRUARUAQUgXgIKKMXDw69UAQUAUUgthHQ3isCikB0IaCMXnSNp/ZGEVAEFAFFQBFQBBQBDwLK6Hmg0JPkIaCtFAFFQBFQBBQBRSBcEVBGL1xHRt9LEVAEFAFFQBGIRAT0ncMKAWX0wmo49GUUAUVAEVAEFAFFQBFIOQSU0Us5LPVOioAikDwEtJUioAgoAopAKiGgjF4qAau3VQQUAUVAEVAEFAFFINQIhJTRO336tOzatUv++eefRHE4evSo7NixQ9asWSN///23SKK1o68QnP7991/T/40bN8qRI0eir5MJ9Gj37t1mjoBBAlUiNvv48eOyc+dOOXjwYMT2IbVenPFmzvv73k+dOmXowOrVq2X9+vWyd+9eOXPmTLxXoT10Ze3atbJ161Y5ceJEvHK98I8AtGXz5s0Cfv5r+ObSBrp07Ngx30JXzr59+2TTpk3CuCXl/q5bxMTpyZMnzfxmbfTuMGWshWDI3D9w4IDP3KfO9u3bzXrBkWvv++j1OQSYt8xLaAWYn29ugie4Mgbr1q0T1qhzdzt7xrhs2LBB7D3P5obub8gYPYj477//LjVr1pSOHTsmiADM3YcffijvvvuuvPPOOzJ58uQE60ZjAYvar7/+Ku3atZMOHTpI69at5dNPP5U9e/ZEY3c9fdq/f7+sWLFCGjZsaPrNh+MpjIKTLVu2yKhRo+T555+XH374IQp6lHJdgHDOnz9fateuLS1atIh3Y76HKVOmSPPmzQ12jz32mDRq1EiWL18u7n/fffedqfPee+9Js2bN5KuvvhIIuruOnsdHgEXryy+/lAcffFD4/uKX+l6x4DGPP//8cylZsqT8+eefvpXicmAEx48fb2gX9At6nxqMd9yjIv4/c5/5/dxzz0m3bt3i9QfBcNKkSfLqq69KjRo15KGHHjJznDXSVmSOjxw50qyVrBd8J+PGjbPFevRCgHW0Z8+e0qpVK4FWQG/++OMPr1rnLpnzQ4YMMfi+//77QnrrrbfizX0EJbBn/OBbmjRpIvA65+4S/LOQMXpp0qSRa665xkjkTGB/XQfwV155xWj8WPD79esntWrV8lc1avOQ3jp37iyZMmWSvn37mgk2a9Ys+f7775MkdUcaQI7jyEUXXSRoAQ4fPizR9o++3XTTTbJ06dJo69oF9wfacPXVVwuMhzdtQDu3ZMkSw+TBvEGgoRMIgfbBixcvNt/J/fffb4QiGMHu3bvLtGnTbBU9+kHgkksuMcwwQri3htRPdXEcx3yjWFzQhIiffwhoYM8ievfdd0v79u1lwIABctlll/mprVnM/RtvvFFmzJjhAwZM9bJly6R+/fpGcIGJYx2A0baVWRtgPurWrWvWi2eeecYwjH/99Zetosf/EIBpGzp0qIwePdrMSxQoWbJkMQLJoUOH/qsV/7Bq1SrDEDZt2lT69+8vH330kVx++eUegZT5jmBJ3gcffCCfffaZ3HbbbdK4cWOjyY5/t+BdhYzRu/baayV37tzCpPbXXSZ11apVBeCZuEWKFJHrrrvOX9Wozlu0aJExPT388MNmQuXIkUPy588vgwYNEiZqtHb+qquuEvqaOXPmVOpiaG97yy23SIkSJUL7EmH69PTp0xvaACPs/YowFY8//rg8+uijki9fPqlevbqUK1dOpk6d6qn6zTffCAwi1oJ06dLJvffeK9mzZ0/UcuBpHMMnd9xxh2TLlk3Spk0bEAoIK8xj6DgMincjtK9orYcNG2aYDsYKen7ppZd6V9Xr/xBg7sMQ/3cZ7wC9L1u2rJQpU0by5s1rrGF8B3PnzjX1+DYmTpxo1swHHnjArBesG4wDVjFTSf94EMAtBGtKxYoVJWvWrAKteOKJJwRmDhw9FV0n27ZtM6421L/44ouFdapAgQKCQoZqWCKmT58u0CiEVcYTJRXCJ5pW6oQipQnFQ8/3TCZ0jx49jHT55ptvGhDxAcEEcL620VaOFgIGF+0nfYNIQiwx50ajpos+alIEEkIgZ86cAmF1HMdUgdgiMd9+++3mGm3UwoULDcPCt0ImTEixYsWMhhB3API0JR0BmIaXX35ZEtJ2eN8RE9bAgQMNQ4Jgj08ZPqnQd++6en1+BJj7KDwc5+zcv/LKKwVhiPWB1jAumM9h1pnz5MFsIDAzDmouB5FzacOGDcY9CCHQ5maKs5yhbR4xYoTNinfMlSuX3HzzzUa4xP+OOQ5T2KlTJ1OPPPgUxzk7RmRyf9bvUNKesGT0UDOjgmZST5gwwaieK1eubOzhe/fuBbuYSXy4119/vZEcbKf5eFENo8a3eXpUBGIRARYvtP8vvvii6T4+TqQ777zTXNs/GTJkMFo+TL82T49JQyBPnjxCskzE+Vr/9ttvZiFlsxFaPYR2fMt++umnqHY7OR8uKVUOw40vZaVKlcwtOUfjhLbJZPz3h7mPKR5G+78sPcQhgFDIpgoYsbhL8x+BBBcGhEWT4fUnY8aMhg/BRIt/NW5VuIYgBFEVhg7hEx6Ga5u4rz0PxTEsGT0IN5IfTsH16tUTtHsNGjSQTz75RBLitEMBXjCeibMoE8cfcUVVH4x30GcoAuGKAGYW3q1atWocDDOHQ7o/8yOLHSZdUzFl/sTUXbp27SovvfSSMQkG0nEWS8zBmBtxcscHClcdzhmjQO6hdRJGgA1IaJ/Al1rMbaw8VpNNnjtR7r6O9XPmINo3lCbeWJDvnWev2QSDaZw1GR9Jt8KlePHixgzcu3dvs0EMhhC3EnbooqCx9wj2MSwZPaR0Elwy4DAQaPTgtGON0UM9n9CkSKwsoTaarwhECwIQYzT/FSpUECRt+gWNSGihgzBDS6inKekIgB0p0JaMD5vIMHc5jmOc0vEpY5MNTEqg99F6vggQAoQIFFWrVhU0dtRAIQDjx7m/lJSx89c+2vISoxXwHQn1t2nTplKwYEGziaN8+fJmcwZKKOrjQsLO9RtuuEEeeeQRIxix4xbs8RWmTihSWDJ6OPmS0GZZUFB9QqgxA9i8iDsm44ULFy5sYq2hlqc5Wgn8AMCByUZepCaciPkgbELax1Qdqf3R9w4eAmgnkJShE6VLlzY7QHk6bg7MI7RJXNs0c+ZMo4nKkiWLzdJjKiPAQgqzh9DOoxzHMS4oMOLkk6cp6QiAHZvxcE+46667PHOfNRKBx202ZCMGdJZNA/juJf1p0dsC/0Y0zm6NHJo3TOLs2PfXc9wOYLBxFYGpw3T71FNPmV27mIEdxzE+xGj6MKMTGYAoAQg8bNDwd89g5IUlo4ePASDin2dBYHLD5Fh/BJsf7Ue0mvgS4GhLX5mE+BmBAxIceZGaIDxsb7eJ+EQ41kdqf/S9g4MA38DXX39tgu8Sa88+lXhiMHrEdENjZP15cXFYuXKl3HfffRKtu7gtBql5RNjENBjoM9hVziY6nN5tGxh0duoWLVrUZsX8MSkA4H9KOBA0d1i5aAumaEkRcGD8CDdEPglmA6XAG2+8waUmFwLwGShL5s2b58nFbQxtqQ3jxsYhAq/bCuB8xRVXmI2i5LFTF3cGeBPoDHk2gTsh4XAv+eKLLwTaZMuCfQwpowfBhonBju0mIEjd+OaNGTNG4J5h8mAG0G7VqVMn2BiF9Hl8uBBFsGDSzZkzx2z/JnREpDN6ECb8S9wJZ1Y34GxbZ4GBmLnzo+GcHVv4iaC5hrhEQ59Sqg/QA2KzQSMgmPa+XBM3jN3oMAw//vijIBAS9BTJmXo4/COpE1ICZg93D+bP22+/TbGmBBBgDjIXmZMseN7VMFmBPfTYXYYQSluO7nz8mKDZmLJgUJjvRAtAGwKj4q6r5+cQwD8dxoH5z7y1JcQU7dOnjyxatEgwLTLvSQSgBt+0adNKlSpVzC8nDR482OyOJrwNu0SJvWfvo8ezCLD+sI6ipSMsCvMXWoF7AcIitWbMmGGC9s+ePZtLwQePuY4mj3UJ3Nl1i1UBekQlaBfMIzE+sSRQl3aUhSqFjNHD7MKuWtTM3377reBns3fvXoMDKn8kdaJKs1MLYoGUAsGINY0P6niiyUNc0YARfBRMmFiOc24LtwEuiv6ggcEMQZwjouojeUVR98wvvTCGCDkEPm3fvn2A3Yv+atOnTxfCoTAHMIE8+eSTxn0BMxSxqAhlAEF+4YUXzC8EwNi1bNnSxMsDnVtvvVXY5Yn0XahQISMsYkIhLAvlmvwjAMPw+uuvm1/FIFYJQOAbAAAQAElEQVQbmLprwvzhNsJCRz4LWps2bYRg1ZhniZPHLwJQRsIlgxio1Ad7NFB8xwjrmNypoyk+Amiq2YRILnMcrDAJwvgNHDhQmPsE6mXO20TcSOY5bYgtSZBevhOwRrON/xhMDeWaziGAKxgxOKG9aOXAHdoBxrgXUBP6DLOHgMk15nJoyYIFCyR37tyCMAMzThBwGG0YO5i61157TTJkyGACKmOVo20oU8gYPTqPShMtFQQDxo/gghYMgCa6NxMVgo8kwyDY8lg5Oo5jAmCyiwczCIwPP/sEMxzNGPARoQFAHQ6TiwkimvrLoorkziKIFgViE039u5C+lCpVyvxiCLSBsUd7h1YC5gDmDrwgwAiGNkFDqGOfi1kGZg+zIYsemnEIuy3Xoy8Cbdu2NT+tyJxEuwGT5q7FT3PBRGC6Ih+zFYwdY0AbNFAwfpSRHOcs7cKfDCaRcCto8/AXo1yTLwK45LDewdgxp4cPHy5oihzHEZgHGA7vuc8aaa07zHECLqNlwpUB5QibYXyfpDkgwDqKjx0+vcS5a9eunYlNSBmJYMrMa9ZcrsEXbR8/K8eahH8fP3VmLVEwflgWsLyxuxxlBW1om6rpPDcPGaN3nvfSYkVAEVAEFAFFQBFQBBSBC0RAGb0LBFCbKwKKgCIQYQjo6yoCikAMIaCMXgwNtnZVEVAEFAFFQBFQBGILAWX0Ymu8k9dbbaUIKAKKgCKgCCgCEYmAMnoROWz60oqAIqAIKAKKQOgQ0CdHDgLK6EXOWOmbKgKKgCKgCCgCioAikCQElNFLElxaWRFQBJKHgLZSBBQBRUARCAUCyuiFAnV9piKgCCgCioAioAgoAkFAIGwZvSD0XR+hCCgCioAioAgoAopAVCOgjF5UD692ThFQBBSBqEFAO6IIKALJQEAZvWSApk0UAUVAEVAEFAFFQBGIBASU0YuEUdJ3TB4C2koRUAQUAUVAEYhxBJTRi/EJoN1XBBQBRUARUARiBYFY7KcyerE46tpnRUARUAQUAUVAEYgJBJTRi4lh1k4qAopA8hDQVoqAIqAIRDYCyuhF9vjp2ysCioAioAgoAoqAIpAgAiFl9I4dOybz58+XefPm+bzgb7/9JgMHDpQvv/wyXvrll1986oZTRmq8y/Hjx2XhwoUGj2+//VZ27NiRGo8Ji3tu27ZN6OOQIUNk6dKlYfFOqfEShw8flp9//tmMKccjR46kxmMi9p7M+QULFsivv/7q04ejR4/KnDlzDF343//+JytWrJBTp07Fq0f7uXPnyuDBg+X777+X/fv3xyvXC18Ezpw5Y2jL+PHjBfx8a/jP2blzp/lm//33X58Ku3fvlh9++EG++uorGTZsmBmrkydP+tTTDP8IQBd+//13M9dHjhwpGzZskNOnT8erDMbfffedmeusm6yr8SroxXkR2Lp1q8ycOVP8zWHvxtQZO3asGZNJkybJrl27vKsItIs1bPjw4bJ69Wqf8mBnhIzR27RpkyEAdevWlUGDBsXrN4SjU6dO0q1bN+nZs6cnvf7664YxjFc5yi8gikyqjz/+WA4ePGj6/9577wkTM9q6/vfff0uLFi3MR/LPP/9I48aNZcqUKdHWTTOOHTt2FJiUAwcOCATh888/j7p+JrdDW7ZsMbShTp060r9//3i34XuAaeB7gMi+//77Qj2ItLtizzi68dFHHxkGD8GhXbt2cujQIXcVPfdCAKG7V69e8sILLwSEFcwgwhj0iDbQdPct9+3bJ59++qn8+OOPkiZNGtm+fbt06dJFYFxgKt11o/w8Wd1DoIE5HjNmjFx88cWG9rdv315WrVrluR9Cf6tWrWTWrFlmzHr37i3jxo3zlOtJ4gjAFCMQtmnTRkjec9i7NWtU69atZcSIESa99tpr8vLLL8dj9hBsoE8w3++++66hT+vWrfO+VVCvQ8boZciQQUqXLi2XX365jzS+efNmadCggfz0008ydepUk5i8l112mZQvXz6oAIX6YUw8FiywYkI1atRIYIJYvLwlu1C/64U+n4+GxQCGvkmTJlKlShVhEUHLd6H3Dqf2Y+OkQQgFTC3jySLZr18/w+CG03uG6l1uvvlmKVWqlFxxxRUCY+d+j7Vr1wpMQufOnY1EDW5o85g7tt706dONcPjSSy8ZIvzOO+/IN998Y5Kto0dfBO68807Jly+fb0ECOZdcconkyJFD7rrrLkHz5F0NOo5Gtnbt2kJ65ZVX5LbbbhMYF8bMu75ex0dg+fLlAq14/vnnpVatWvLmm28aTStzn5p8Gwg9ixYtkrZt20r9+vWlXLly8sEHH8jevXupouk8CFx66aVSoEABeeihhwTN6HmqG8XDww8/LH369JFRo0ZJw4YNTR7XtOUeMN8oqQYMGCA9evSQv/76y1hvKA9VChmjB8BXXXWVwLx5dx7CwYSFGbzpppuEtCFOZX3PPfdItmzZvKtH9TXSL5Jz0aJF5aKLLhIwyZ8/vwwdOlROnDgRNX1fv3690XA9+eSTct111wmLSLVq1cxH4q2tieROo8FDeClevLhkyZLFjCnzHabGEnCJ5A6mwLsz9gnRBujFY489JpkyZZKrr75awJFriCmPRvjB9EgZBJlvJmvWrHL33XdL165do+qbob8pmdKlSydXXnml0b75uy8LGGYqGG3KHceRtGnTCmPlOA5Z8RKMCPXRTNEGms+4MNfjVdQLHwTAC4EFGn/99debcmg/8x1GmbFAuwQjzVrJuDHXmee0GThwoGmjfxJHwHEcYd5fc801iVeMK2Udzp07t0Bvrr32WiFhkbzxxhuNS0JcFfM91KhRQ8jjntTneMMNN1AcshQyRi8pPWbiTp48WdB+JKVdNNSFyWGSQEzpDx/zHXfcYSYWRJS8aEgQLMdxhEXZ9id9+vRmEVm8eLGPZsfWibQjbglopbJnz+55dbTauXLlEuY4jKCnQE98EMicObOPsAchRfihMtrulStXCgSWa5sQlFgY0ZLYPD0mDYGnn37auFYEagJHQIdJaRdnNsd37I8//jCm9GeeecYIOEl7euzVxj0HzSdMn+09wiHzHRoCs7ds2TLJmDGjLZZbbrnFaMKxGHgy9SRFEEBQQSiHqbY3RHDBrJ4nTx6TBdPIGs2Y4YKC0Fm5cmWpWLGiKb+QPxfSNiIYPSY8BLxIkSIX0teIbMuiheTgloKZTGg21qxZE5F98vfSViNz6623eoodxzFSE86v/kxDnooRdILfEgwHzLr7tdFioo3CdO3O1/PEEUBbxHeAWwM1MVnt2bNH3Iw0+XxDaJjAnmtNSUfgxRdflAoVKvi1wvi7Gyb4li1bCuOBGf2TTz4x7hiYyhzHVwPo7x6xnActxNLBnLY4oFWC+eOaDUa4tbhpCesEGnEVaEAo9RPjw3qMa4L7aWzGIA8rDfMfHsZdHuzziGD0lixZIkxmJJlgAxTq57E7E0dmx/EljGg6Q/1+KfV8+sm9kI44uhMMEMmdF6nnMBsQa3/9pE+Uc9QUGAJoezGTwIDQAvz4Lvzhi5RNOfWiP6V8D+vUqSNPPPGEcasI5O5oNmC4CxcuLGhiv/76a+PAbr/1QO4Rq3UcxzF+Y2iRunfvbphlmAo2IME4wEQzl6ElMHbeOJHvnafXKYsAGLNZpnnz5oY/cd89b968ApP3+OOPCztvqeMuD/Z52DN67IqBmOObhz9IsAEK9fOspodFyr4LHzhSHWYRmxfpRzQu9IG+cSTRZ8YfickfMaNOpCU0sfTH3U/6gGbKcRzjd8a1pvMjwILHjrlKlSoJ7g20AF/oBMwe1zYxj2A8rAuEzddj4AiAHynQFmji2WWLDxk7ENkwgIP6oEGDfEKEBHrPWKqH5hMtKCZafO+++OILE/6jYMGCRmPNXMeM6J7r0BWE4gwZMsQSVEHvK2sTu9RhuPEr934B3HEYMyIq4NOHXzb7DLzrBes67Bk9TF0kJrfj+Gq1ggVUqJ6D8y3qeau+h8HDVIWk5+2HdCHvGOq2bLThHYiJxpGEvxoxiNjZB3NEXqQntE9oNwhLYfsCEwLRyJIli9x+++02W4+JIMD3MHr0aOPT6XbpwC+MnZ34hLmb4+sKk5czZ053tp6nIgII6NAqdvI6jiPsusX8C9OHNiQVHx0Vt0YrXaZMGRNqCBzRpuJ6QEQCOohwA210m2nR+rFexlp0CvAIZsKlCv/IsmXLmg1MiT07HPYWhD2jx8RlyzKLY2JgRmsZOwfxxbC+WzhCE7aA0CPRouVi7IoVK2ZCO8yYMYNLk1is2V153333meto+AMjUrJkSXEH/t64caOJgdWsWbNo6GKq9wF60LdvX2FuYBqxD2RDD87RfDMsjAhIlCEw/Pnnn8YhGgmcPE1JR4CNRGhRA22JRg8zLcIpbfAfgylnjLjWFDgC0AhCTbGpyGqQEApRBEAn7Z1YG9Dq4RNp8y7gGPNNEcK9Y+AhwBD2hpAsCOeAhOCJUoJz70QZ2r1QallDyugheeCkCPGAmfEGiGvCizC5Y5U4oL5H9cvChooeHw2kiZo1a5ogmmAUDQltC7+CwmLNL0XQRyQh+gkG0dBH+gBzjpMuGtkOHToIRICgsuwafe6556iiKQ4B6AG71qARYBSXZf5zbQObsvMQUyAxrFjY2LBFperVq0uhQoXkrbfeEjQgBE/GnEscMso1+UcAhgxmDubMHZTX1sYESwBYGGebx5FYn7TlyLVNLG74F1vahekKExaCG9+BrafHhBFgfSSOaokSJUyIIPBPnz69aYDZlnmPD/uECRNMQGq+hYoVKwoWMFNJ/wSEALQEmoNw4m5AvFrWX/xLyQdrNn45jmPi+4I3iViHCDIE+Gd+E94GpQy/+oWrAiZ4aBD3CEUKGaNHMOT777/f9BlmrmrVquIm6KYg7g9l1HOc2DPbxnXfbJVnwcLkxBZuAr8SdZtt3pRHU6JPOB7zqxF169YVjuzaS4pfUCTggSTOr2IgAd57770maDgMCz43kfD+Cb5jChVgZi1durTx48I8Am2AAYGZIHA6zAI/a8ZcITYeCXpSqlQp8wb4xxDMFO0ptAPNHu0w6ZoK+scvAgTaxYEc9wIWLpgKd0UELnyGMSmSD0PYvn17gR4xpwl0zi8aUUbKnDmzwKSgZcLNhPAsaO7r1aun4VUA6DwJZgHtNAGRMXeDNWPjOGfXQsdxhI0u1ONbgCHBWsC4wWCf5/Za/B8C4MYvwoAZQadh7v4rMr6/KJnQxiE0Ug+fSQK1Q3dsgtawS5o5T12EeGgPdIdfK7GaP3vfYB9DxujhewB3jFqUhKbKSipuEOCGITDuvFg7Z9MFHy/mKBYwtBXRigEf3bRp08xvmRIwOVr7yYePoy7+NSyuLKDR2tek9uuBBx4QYq5BF0jEF8TkCsPPLwSg7SXfO7l35UOciUqPZoqfl2PXflLfI9bq89NObkwRtNwYoHFnAYORJh+/WTZYuNvwc1yU2QTtOBn5LwAAAbpJREFUZmciGhN+rxsmD02ULddjwgjUqVPHxEtlDUQITqgmzN706dOF9bRp06bn9RlL6D6xms9PlrnnMAKJxQJGG1qEQA4zx4YYd117DrNNGxQyBLQmnxArCD7uOIfUCUUKGaMnIqHorz5TEVAEFAFFQBFQBBSBmEFAGb2YGWrtqCKgCCgC4Y6Avp8ioAikNALK6KU0ono/RUARUAQUAUVAEVAEwgQBZfTCZCD0NZKHgLZSBBQBRUARUAQUgYQRUEYvYWy0RBFQBBQBRUARUAQiCwF9Wy8ElNHzAkQvFQFFQBFQBBQBRUARiBYE0vDLE5rWi2KgGOgciNE5sF77rXNf54DOgeidA2myZs1qfi9Sj4qDzgGdAzoHdA7oHNA5oHMguuaAmm6TrpvVFoqAIqAIKAKKgCKgCEQEAmq6VbONmq11Dugc0Dmgc+CC5kD0mv3UpBv5Y5uG32bTlFkyZ86sSTHQOaBzQOeAzgGdAzoHomoOqOk2IhSv+pLRhoD2RxFQBBQBRUARCAYCyugFA2V9hiKgCCgCioAioAgoAgkjkGolyuilGrR6Y0VAEVAEFAFFQBFQBEKLwP8BAAD///NmuXgAAAAGSURBVAMAQF+K7CJGhEwAAAAASUVORK5CYII=\" width=\"634\" height=\"677\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2Analysis of Treatment and Response Optimization\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the extraction process data for patchouli essential oil obtained from response surface experimental design, analysis was conducted using Design-Expert 10.0 software. The results of the variance analysis for the quadratic polynomial model characterizing essential oil yield are listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, while the regression coefficients of the model equation are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a scatter plot comparing experimental observations with model predictions, indicating good consistency between the constructed model and experimental data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance Analysis of Polynomial Model of Herba Pogostemonis Essential Oil Yield\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e 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colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1 \u003cem\u003e*\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1 \u003cem\u003e*\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2 \u003cem\u003e*\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1111.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDispersion term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.997, \u003cem\u003ePre R\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: **. Highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01); *. Significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study established a predictive model for patchouli oil yield using response surface analysis. The model exhibits overall highly significant statistical significance (F\u0026thinsp;=\u0026thinsp;236.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a highly significant linear relationship between influencing factors and yield. The model exhibits high reliability, with excellent agreement between predicted and experimental values. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, data points are uniformly and symmetrically distributed on both sides of the reference line, and the coefficient of determination R\u0026sup2; reaches 0.997, confirming minimal deviation between measured and predicted values. The established polynomial model reliably describes changes in patchouli oil yield under experimental conditions, demonstrating good goodness-of-fit and predictive accuracy.\u003c/p\u003e \u003cp\u003eAccording to the ANOVA results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, both \u003cem\u003ex\u003c/em\u003e₁ (extraction time) and \u003cem\u003ex\u003c/em\u003e₃ (soaking time) have a highly significant effect on essential oil yield when considered individually. The influence of the factors on yield can be ranked as follows: \u003cem\u003ex\u003c/em\u003e₁ (extraction time)\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ex\u003c/em\u003e₃ (soaking time)\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003ex\u003c/em\u003e₂ (material-to-liquid ratio). The interaction terms do not show significant effects, whereas the quadratic terms exhibit high significance, indicating notable curvature in the response surface for these variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Variance for Polynomial Models of Patchouli Essential Oil Yield\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower 95% confidence limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper 95% confidence limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1 \u003cem\u003e*\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1 \u003cem\u003e*\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e2 *\u003c/em\u003e\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBased on the regression coefficients listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the quadratic polynomial model relating the yield of patchouli essential oil to extraction time (\u003cem\u003ex\u003c/em\u003e₁), material-to-liquid ratio (\u003cem\u003ex\u003c/em\u003e₂), and soaking time (\u003cem\u003ex\u003c/em\u003e₃) can be expressed as follows:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\begin{gathered} {\\text{Y=1}}{\\text{.210+0}}{\\text{.078}}{{\\text{x}}_{\\text{1}}}{\\text{+}}0.005{{\\text{x}}_{\\text{2}}}{\\text{-}}0.027{{\\text{x}}_{\\text{3}}}{\\text{-}}0.005{{\\text{x}}_{\\text{1}}}{{\\text{x}}_{\\text{3}}} \\hfill \\\\ {\\text{-}}0.010{{\\text{x}}_{\\text{2}}}{{\\text{x}}_{\\text{3}}}{\\text{-}}0.120{\\text{x}}_{1}^{2}{\\text{-}}0.089{\\text{x}}_{2}^{2}{\\text{-}}0.210{\\text{x}}_{3}^{2} \\hfill \\\\ \\end{gathered}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Process Optimization Analysis\u003c/h2\u003e \u003cp\u003eA response surface plot is a three-dimensional representation used to illustrate the relationship between the response variable and the independent factors. Such plots provide a visual means to evaluate the effects of individual factors as well as their interactions on the response. The response surface for the yield of patchouli essential oil is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing Design-Expert 10.0 software for response surface analysis, the optimal process parameters for steam distillation extraction of patchouli essential oil were predicted as follows: extraction time of 129.77 minutes, solid-to-liquid ratio of 1:18.06, and soaking time of 87.94 minutes. Under these conditions, the theoretical maximum oil yield can reach 1.23%. For practical operation and considering instrument precision, conditions were appropriately adjusted to extraction time 130 minutes, solid-liquid ratio 1:18, and soaking time 88 minutes. After three replicate experiments, the average oil yield was 1.21%, closely matching the model prediction of 1.23% with minimal deviation.\u003c/p\u003e \u003cp\u003eThe experimental results demonstrate good agreement between the actual oil yield and the model prediction, indicating that the established response surface model possesses high predictive accuracy and reliability, accurately reflecting the influence of each factor on the oil yield. This result not only confirms the effectiveness and stability of the optimized process conditions but also further demonstrates that the response surface analysis method has successfully optimized the steam distillation extraction process for patchouli essential oil, providing reliable theoretical basis and practical reference for actual production.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eResponse surface methodology was employed to determine the optimal conditions for steam distillation extraction of patchouli essential oil, with the particle size of raw material fixed at 0.2 mm. The optimized parameters were as follows: extraction time of 130 min, material-to-liquid ratio of 1:18, and soaking time of 88 min. Under these conditions, the experimentally measured essential oil yield reached 1.21%, closely matching the model-predicted value and thereby validating the accuracy and reliability of the response surface model.\u003c/p\u003e \u003cp\u003eThe analysis of the response surface indicates that the relative influence of the factors on essential oil yield follows the order: extraction time (\u003cem\u003ex\u003c/em\u003e₁)\u0026thinsp;\u0026gt;\u0026thinsp;soaking time (\u003cem\u003ex\u003c/em\u003e₃)\u0026thinsp;\u0026gt;\u0026thinsp;material-to-liquid ratio (\u003cem\u003ex\u003c/em\u003e₂). Furthermore, the quadratic terms exhibit highly significant effects, while the interaction terms are not significant, suggesting the absence of notable synergistic effects between factors. The established response surface model demonstrates excellent predictive capability and stability, effectively capturing the nonlinear relationships between process parameters and essential oil yield. The optimized process balances extraction efficiency and economic considerations and exhibits strong practical feasibility, providing a scientific basis and technical support for the industrial-scale production of patchouli essential oil.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.S.andH.wrote the main manuscript text and Z.prepared figures 1-7andY.prepared tables1-5. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis research was funded by (1) Science Start-up Foundational of Xi\u0026rsquo;an University of Architecture and Technology [grant number 1960324008]; (2) National Fund Cultivation Project of Xi\u0026rsquo;an University of Architecture and Technology [grant number 019/1960524133].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSrivastava S, Lal RK, Singh VR, Rout PK, Padalia RC, Yadav AK, Bawitlung L, Bhatt D, Maurya AK, Pal A, Bawankule DU, Mishra A, Gupta P, Chanotiya CS (2022) Chemical investigation and biological activities of Patchouli (Pogostemon cablin (Blanco) Benth) essential oil. 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J Food Eng 78(3):836\u0026ndash;845. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jfoodeng.2005.11.024\u003c/span\u003e\u003cspan address=\"10.1016/j.jfoodeng.2005.11.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"heat-and-mass-transfer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hamt","sideBox":"Learn more about [Heat and Mass Transfer](https://www.springer.com/journal/231)","snPcode":"231","submissionUrl":"https://submission.nature.com/new-submission/231/3","title":"Heat and Mass Transfer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Patchouli essential oil, steam distillation, response surface methodology, extraction process","lastPublishedDoi":"10.21203/rs.3.rs-8450235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8450235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo enhance the extraction efficiency of patchouli essential oil via steam distillation, this study systematically investigated the effects of key process parameters on extraction yield. Single-factor experiments were first conducted to analyze the influence of distillation time, solid-liquid ratio, soaking time, and raw material particle size on overall mass transfer performance during extraction. After determining the optimal ranges for each parameter, the three significantly influential variables\u0026mdash;distillation time, solid-liquid ratio, and soaking time\u0026mdash;were selected for focused optimization. A Box\u0026ndash;Behnken design combined with response surface methodology was employed to construct a quadratic polynomial regression model describing the individual and interactive effects of these parameters on essential oil yield. Statistical analysis confirmed the model's high predictive accuracy, with all factors exhibiting significant individual and interactive effects. The response surface optimization yielded optimal extraction conditions: distillation time of 130 minutes, solid-to-liquid ratio of 1:18 g/mL, maceration time of 88 minutes, and a fixed raw material particle size of 0.2 mm to ensure stable heat and mass transfer conditions. Under these optimized conditions, the actual patchouli essential oil yield reached 3.87% (w/w), closely matching the predicted value and validating the model's reliability and practicality. These findings provide scientific basis and technical guidance for enhancing steam distillation efficiency, holding positive implications for advancing the industrial production of patchouli essential oil in fragrance, pharmaceutical, and cosmetic sectors.\u003c/p\u003e","manuscriptTitle":"Study on the Process of Extracting Patchouli Essential Oil by Steam Distillation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 18:39:51","doi":"10.21203/rs.3.rs-8450235/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-12T04:49:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T08:20:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-07T02:44:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298904611565728090565958760271125819937","date":"2026-02-01T19:28:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161602965966670090409526987274781439946","date":"2026-01-31T13:01:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159055953502814303096218382787916059352","date":"2026-01-31T06:05:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27230622871958344914144879159790056774","date":"2026-01-29T07:10:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T18:43:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T13:19:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-26T09:37:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Heat and Mass Transfer","date":"2025-12-25T16:38:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"heat-and-mass-transfer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hamt","sideBox":"Learn more about [Heat and Mass Transfer](https://www.springer.com/journal/231)","snPcode":"231","submissionUrl":"https://submission.nature.com/new-submission/231/3","title":"Heat and Mass Transfer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b8fceeab-b698-42a3-ab55-a53c90c071d9","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T12:10:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 18:39:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8450235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8450235","identity":"rs-8450235","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}