Effects of Nordic walking on muscle activation and stride length of middle-aged women

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Therefore, the purpose of this study was compared the changes in electromyography and gait parameters achieved through Nordic and usual walking at three stride frequencies. Method: Fourteen middle-aged women with a regular exercise habit were enrolled as participants. The participants were instructed to walk a set 14m distance three times using Nordic or usual walking depend on random. Three stride frequencies (i.e., low [88 steps/min], normal [110 steps/min], and high [132 steps/min]) frequency) were applied three times. The following muscle groups were tested: the biceps brachii and triceps brachii of the upper limb; the vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius of the lower limb. Statistical verification was conducted through a two-factor repeated measures analysis of variance (significance level α set to .05); the independent variables were walking types and stride frequency, and the dependent variables were EMG signals and gait parameters. Results: The biceps and triceps brachii experienced greater activation through Nordic walking than through usual walking ( p < .05). The upper limb muscle groups experienced the most activation at a low stride frequency, followed by a normal and high stride frequency; this trend was not observed for the lower limb muscle groups. No significant difference was identified for stride length. Conclusion: Nordic walking is a full-body physical activity that can effectively facilitate upper limb muscle activation; this effect is particularly pronounced in slow walking scenarios. Health sciences/Health care/Geriatrics Health sciences/Health care/Health policy walking types muscular activity gait analysis Figures Figure 1 Introduction The convenience of modern life in the 21st century has substantially altered our exercise behavior and habits, resulting in a constant increase in exercise participation. The statistics released by the Taiwan Sports Administration, Ministry of Education (2018) reveal that 33.6% of Taiwan’s population exercise regularly, and the total number of people who exercise is increasing annually at a steady pace. In addition, although the proportion of women who exercise (82.5%) is marginally lower than that of men who exercise (84.8%), this gap is gradually decreasing each year. Because of aging and technological advances, the various functions of the human body are gradually exhibiting signs of degradation. In addition, the incidence of chronic disease symptoms (e.g., obesity, hyperlipidemia, hypertension, and diabetes) has been increasing (Wen & Chang, 2013). Studies have indicated that improving physical fitness is conducive to the prevention of various chronic diseases. Nonetheless, engagement in high-intensity exercise may overburden the body and cause adverse effects. Taiwanese people run and cycle, which are activities that require intense knee movement. The lack of proper exercise intensity management and application of improper exercise techniques both increase the risk of lower limb injuries (Wang, 2012). Running mainly works the lower limbs. Because most people today prefer simple and efficient exercises, walking has become popular exercise that does not require any equipment, can be performed at any time, and can achieve a target exercise intensity without substantial effort. The American Heart Association (2011) reported that walking improves the physical health of people and increase their physical activity level. Weight-bearing exercise helps to maintain bone density and places less stress on the joints. The American College of Sports Medicine (2011) describes walking exercise as follows: walking exercises the major muscle groups, which include the knee extensor, gluteus extensor, and muscles in the lower extremity (e.g., gastrocnemius, soleus, and tibialis anterior). facilitates blood flow back to the heart, thereby improving body circulation. improves both muscle endurance and dynamic equilibrium. Nordic walking is an innovative walking types that originated from the technique used by cross-country skiers for summer training. Nordic walking first received attention in 1930, and thereafter, people started to study the advantages and benefits of this walking types (Jódar Reverte, 2019). Nordic walking differs from usual walking in that poles are used to propel the body forward. As an emerging fitness technique, Nordic walking has gradually become a leisure and health/fitness improvement option for people, particularly older adults. Individuals can maximize the benefits of exercise through walking. The Federación Española de Deportes de Montañay Escalada 〔FEDME〕(2018) recognized Nordic walking as a dynamic and rhythmic exercise that is suitable for people of all ages. According to FEDME regulations, Nordic walking competitions are usually conducted on routes measuring between 10 and 42 km. Nordic walking is an activity that involves the use of poles mainly to support the upper and lower body and ensure joint alignment, thereby reducing the impact of walking on the joints (Padulo et al., 2018). It requires a walker to hold a pair of poles at approximately handshake height in front of them. Approximately 50% of the human muscles are in the upper body, and the primary focus of Nordic walking is on exercising the upper body muscles. With each step, people push off with the poles (in opposite directions) to work and tighten the arm muscles, abdomen, back, and chest muscles, thereby performing a full-body exercise (Huang, 2016). Nordic walking effectively works 90% of the body’s muscles. Therefore, it is a full-body aerobic exercise (Wen & Chang, 2013). Nordic walking involves walking while using poles in both hands for support, which sets it apart from conventional power walking. The two poles are used for supporting, stretching, and extending both arms. Therefore, the upper body muscles are exercised, and arm strength is applied. In a comparison of Nordic and usual walking on a treadmill, Hansen and Smith (2009) discovered that older adults exercise more upper body muscles and consume more calories during Nordic walking than during usual walking. Parkatti et al. (2012) also proposed that relative to usual walking, Nordic walking is approximately 20% more effective at strengthening the upper body muscles and improving the circulation of older adults because it involves the use of poles. In addition, Nordic walking can improve gait balance and stability. Willson et al. (2001) reported that the use of walking poles can effectively increase stride length and frequency, thereby increasing walking speed by approximately 3.6%. Moreover, relative to usual walking, Nordic walking can reduce the impact of the ground reaction force on the feet and knee joints by 4.4%, which effectively reduces knee joint load. Dalton and Nantel (2016) implemented a Nordic walking intervention for older adults (8 female and 4 male participants) that comprised two weekly sessions over 8 weeks. Each session involved two 6-min walk tests and six 5-m walk trials (three with the use of poles and three without the use of poles). After the intervention, participants exhibited increased stride length, higher stride frequency, and enhanced strength in hip and knee joint muscles. The literature has mostly focused on health improvement among older adults and special populations including people with diabetes (Balady et al., 2010；Pippi et al., 2020) , cardiovascular diseases (Launois et al., 2018), obesity or overweight (Hagner-Derengowska et al., 2015), and neurological disorders (e.g., Parkinson’s disease) (Monteiro et al., 2017). Most studies have indicated that Nordic walking is a relatively simple exercise that can offset the negative effects of inactivity and assist patients in improving their physical health (Ben Mansour et al., 2018). Few studies have discussed the benefits of Nordic walking for healthy middle-aged people. In addition, most studies conducted treadmill-based experiments. The present study enrolled healthy middle-aged women as participants to explore the effects of walking types on muscle activation and gait parameters. Research Method Participants Fourteen middle-aged women (≥35 years) with a regular exercise habit (playing badminton three times a week for at least 30 min per session) were enrolled as participants. None of the participants had damage to their nerves, muscles, bones, tendons, and ligaments of the upper and lower limbs; they also had no history of cardiovascular diseases in the 6 months preceding the experiment. The basic attributes of the participants were as follows: age = 39.36 ± 1.78 years, height = 164.26 ± 3.41 cm, and weight = 62.53 ± 5.2 kg. The date of data collection was 19~20 June 2020. We confirm that the methods employed in the experiment were conducted in accordance with relevant guidelines/regulations, and informed consent has been obtained from all experimental participants and/or their legally authorized guardians. The ethical approval was approved by ethics committee of Antai Tian-Sheng Memoral Hosptial and approval number was 20-050-B (approval date: 18 May 2020). Test Instruments 1. Noraxon EMG (Noraxon, AZ, USA) In this experiment, Noraxon EMG was used to measure the electromyographic (EMG) signals of participants with varying walking types and stride frequencies. EMG sensors were attached to the participants’ upper limb muscles (i.e., biceps and triceps) and lower limb muscles (i.e., vastus lateralis, biceps femoris, gastrocnemius, and tibialis anterior). 2. GoPro HERO 7 BLACK (GoPro, San Mateo, CA, USA) A GoPro HERO (60Hz) was used to capture gait images of the participants. 3. Pro Metronome app for stride frequency control Stride frequency is the number of steps taken per minute, and the mean stride frequency for usual walking is 110 steps per minute (Shumway-Cook & Woollacott, 2007). We used the app to manage stride frequency; specifically, low, normal, and high stride frequencies were defined, respectively, as 88, 110, and 132 steps/min. Experimental Procedure 1. Prior to the start of the experiment, the 15-min warm-up and an introduction to Nordic walking were conducted. After the researchers reminded the participants of the characteristics of Nordic walking(NW), they accompanied the participants during the intervention to ensure that the participants understood and adapted to Nordic walking, and that the EMG sensors were securely attached and fixed. 2. At the beginning of the experiment, the participants were randomly assigned and instructed to perform either Nordic walking or usual walking(UW) three times; the 3-min rest was provided between each session. During each experiment, the Noraxon EMG was used to collect muscle activation data, and the GoPro was used to capture gait data. 3. One full gait cycle begins with the heel strike of one foot and ends with the next heel strike of the same foot in preparation for the next step. Stride length is the distance between successive points of foot-to-floor contact of the same foot. Changes in stride length can serve as indicators for analyzing body stability and assessing fall risk. Data Processing and Analysis Use of SPSS Statistics V22.0 for Windows for data analysis 1. Descriptive statistics Muscle activation data were collected from upper limb muscles (i.e., biceps brachii and triceps brachii) and lower limb muscles (i.e., vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius). 2. Data processing The sampling frequency of the EMG signals was 1500 Hz, and the original signals were full-wave rectified and smoothed using a root mean square algorithm and a moving-average filter with a window size of 75 ms. Subsequently, the signals underwent electrocardiogram reduction and bandpass filtering at a range of between 80 Hz (low frequency) and 250 Hz (high frequency). 3. Changes in EMG signals and gait parameters The present study compared Nordic and usual walking in terms of EMG signal responses and gait parameter changes at various stride frequencies. The independent variables were walking types(Nordic and usual walking) and stride frequency(88, 110, and 132 steps/min), and the dependent variables were EMG signal and stride length. The two-factor repeated measures analysis of variance was conducted to identify dependent variable differences. The simple main effect test was conducted to determine if an interaction represented a significant difference, whereas a main effect test was conducted to determine if an interaction was nonsignificant. 4. For all statistical tests conducted in the present study, the significance level was set to α = .05. Results The results are divided into two parts, that is, the effects of walking types on EMG signal response and the effects of walking types on stride length, which are explained as follows. Effects of Walking Types on EMG Signal Response (unit: uV) Table 1 summarizes the effects of walking types on EMG signal response, which indicated nonsignificant differences in EMG signal responses between the two walking types and among the three stride frequencies. Accordingly, differences in walking speed did not result in significant differences in EMG signals pertaining to the biceps brachii, triceps brachii, vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius. And the figure1 shows that Nordic walking increased the activation of the biceps brachii muscles (by 92.1% [88 steps/min], 63.2% [110 steps/min], and 53.1% [132 steps/min]) and triceps brachii muscles (by 15.7% [88 steps/min], 14.9% [110 steps/min], and 10.68% [132 steps/min]). Compared with usual walking, Nordic walking increased the activation of the lower limb’s vastus lateralis by 3.2% and 7.2% at the stride frequencies of 88 and 110 steps/min, respectively, but reduced it by 4.2% at the stride frequency of 132 steps/min. Compared with usual walking, Nordic walking increased the activation of the biceps femoris by 13.8%, 2.4%, and 6.35% at the stride frequencies of 88, 110, and 132 steps/min, respectively. Compared with usual walking, Nordic walking increased the activation of the tibialis anterior by 17.1% and 0.7% at the stride frequencies of 88 and 110 steps/min, respectively, but reduced it by 2.1% at the stride frequency of 132 steps/min. Compared with usual walking, Nordic walking increased the activation of the gastrocnemius by 13.1% and 6.9% at the stride frequencies of 88 and 110 steps/min, respectively, but reduced it by 2.5% at the stride frequency of 132 steps/min. Table 2 indicates that the interaction between walking types and stride frequency was nonsignificant, indicating that it had no effect on the EMG signals and gait of each muscle groups. Therefore, this study conducted a post hoc comparison of the main effect of walking types. The post hoc comparison indicated that the main effect on the biceps brachii and triceps brachii was significant (Table 3). Subsequently, we compared the mean EMG signals in Nordic walking (biceps brachii = 16.85 uV, triceps brachii = 10.17 uV) and usual walking (biceps brachii = 1.62 uV, triceps brachii =1.42 uV) and discovered that Nordic walking was more effective in activating the biceps brachii and triceps brachii. Effects of Walking Types on Stride Length Table 4 indicates that Nordic walking can increased stride length by 5.74%, 0.79%, and 1.03% at the stride frequencies of 88, 110, and 132 steps/min, respectively. According to the analysis of variance table, the interaction between walking speed and walking types was nonsignificant ( F = 1.560, p = .220), indicating a nonsignificant difference between the effects of walking types and those of stride frequency on participants. Discussion Comparison between the Effects of Nordic Walking and of Usual Walking on Myoelectric Responses For the stride frequency of 88 steps/min, Nordic walking was more effective in activating the myoelectric signal response in each muscle group relative to usual walking. However, decreased activation was observed in the vastus lateralis at a stride frequency of 110 steps/min and in the tibialis anterior at a stride frequency of 132 steps/min. Figure 1 shows the various muscle groups activation increase or decrease of Nordic walking. The biceps brachii and triceps brachii were significantly activated by Nordic walking; for the lower limb muscle groups, the muscle activation through Nordic walking was nonsignificant, but more muscles were activated relative to usual walking. That is, Nordic walking provides greater exercise intensity for the human body. This finding is consistent with those of other studies. Shim et al. (2013) studied the effects of Nordic and usual walking on upper limb EMG signals; Pellegrini et al. (2015) compared the EMG signals generated through Nordic and usual walking with varying slopes (level and inclined plane) on the treadmill; Pellegrini et al. (2018) conducted five experiments to compare the effects of Nordic walking; and Fujita et al. (2018) conducted a full-body EMG experiment in which older adults performed Nordic and usual walking on a level bituminous surface. The aforementioned studies reported significant differences between Nordic and usual walking in terms of the mean EMG signals generated by the biceps and triceps brachii; this finding is consistent with that of the present study. The increase in the upper limb’s muscle activity, particularly the biceps and triceps brachii, can be attributed to the use of poles to push off and move one’s body forward. By contrast, the vastus lateralis and tibialis anterior exhibited a compensated decrease in activity, which aligns with the results of other studies (Shim et al., 2013; Stahl et al., 2016). Several studies have reported conflicting findings; for example, Yamamoto (2007) did not observe a decrease in lower limb muscle activity. Therefore, the activity of the lower limb muscles during Nordic walking warrants further clarification. Comparison of Effects of Nordic and Usual Walking on Gait In terms of post-intervention stride length, the influences of both Nordic and usual walking on stride length were nonsignificant. However, Table 4 indicates that stride length increased marginally following the completion of a Nordic walking intervention. This finding is inconsistent with those of other studies. Dalton and Nantel (2016) studied older adults with a mean age of 68 years and reported the significant effects, after an 8-week Nordic walking intervention, has the longer strides and faster strides frequency. Skiba et al. (2019) conducted a 10-week Nordic walking training involving 22 patients who had Down syndrome and were aged between 25 and 40 years; they reported a significant improvement in step and stride length. De Santis and Kaplan (2020) implemented a 24-week Nordic training intervention involving 318 patients with Parkinson’s disease and reported an improvement in their gait (i.e., step and stride length). The aforementioned studies have demonstrated that the gait performance of older adults and patients with specific diseases can be improved through long-term Nordic walking, whereas short-term Nordic walking cannot immediately improve their gait performance. Conclusion After comparing the benefits of Nordic walking and usual walking at various stride frequencies, we discovered that as a full-body physical activity, Nordic walking can effectively increase the activation of upper limb muscles. This effect was particularly prominent in slow walking scenarios, but it was not present in lower limb muscles. Furthermore, the stride length of the gait parameters indicated no significant improvement. When Nordic walking is performed as a full-body exercise with no change in gait and at a slow stride frequency, it increases upper limb activity more effectively relative to usual walking. The literature has mainly focused on individuals with specific diseases and older adults. Healthy middle-aged women were enrolled as participants in the present study, which yielded experimental results that were consistent with those of other studies, thereby verifying that Nordic walking can increase upper limb activity and stride length among middle-aged women. Suggestion The results indicated that Nordic walking had no significant effect on the activation of lower limb muscles and changes in stride length, but it had a significant effect on the activation of upper limb muscles. Researchers should increase the scope of research by examining trunk muscles to explore the correlation between Nordic walking and body stability. A comparison of the benefits of 8-week and 10-week training for middle-aged women should be conducted. Declarations Author Contribution 1.TI WU:Data collection, Result analysis, Writing the first draft.2.Jui-Hung TU:Experimental design, Host and guide research work.3.Hsin-Huan WANG:Data collection, Result analysis.4.Chia-Hsiang CHEN:Result analysis and interpretation, Manuscript editing.5.Chun-Ta LIN:Data collection, Experimental design.6.Hung-Sheng HSIEH:Ensure the accuracy and quality of the paper.All authors discussed and formed up this study, and certify that all authors have seen and approved the final version of the manuscript being submitted. ACKNOWLEDGMENTS We would like to thank the Ministry of Science and Technology Project(109-2410-H-153-022), for providing support in terms of research personnel, equipment, consumables, and other forms of assistance. However, we hereby declare that all experimental procedures comply with the current laws of the region where the laboratory is located. Data Availability Statement- The datasets generated and/or analysed during the current study are not publicly available, but are available from the corresponding author on reasonable request. References Balady, G. J. et al . Clinician’s guide to cardiopulmonary exercise testing in adults: A scientific statement from the American Heart Association. Circulation. 122 (2), 191–225(2010). Ben Mansour, K., Gorce, P., & Rezzoug, N. The impact of Nordic walking training on the gait of the elderly. Journal of Sports Sciences. 36 (20), 2368–2374(2018). Dalton, C., & Nantel, J. Nordic walking improves postural alignment and leads to a more normal gait pattern following weeks of training: A pilot study. Journal of Aging and Physical Activity. 24 (4), 575–582(2016). De Santis, K. K., & Kaplan, I. The motor and the non-motor outcomes of Nordic Walking in Parkinson's disease: A systematic review. Journal of Bodywork and Movement Therapies , 24 (2), 4–10 (2020). 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Tables Table 1 The Results of Different Walking Types in Response to Electromyography Signals (Unit: uV) Table 2 Significance of the Interaction Between Walking Types and Stride Frequency Muscle group F Significance Interaction Effect Biceps brachii .242 .786 - Triceps brachii 2.048 .139 - Vastus Lateralis .339 .603 - Biceps femoris .281 .756 - Tibialis anterior 1.453 .244 - Gastrocnemius 1.146 .326 - Step length .402 .671 - Stride length 1.560 .220 - Table 3 Comparison of Walking Types Afterwards Muscle F Significance Simple Effect Biceps brachii 7.451 .011* *(NW＞UW) Triceps brachii 13.485 .011* *(NW＞UW) Vastus lateralis .038 .846 - Biceps femoris .230 .636 - Tibialis anterior .059 .810 - Gastrocnemius .094 .761 - Step length .631 .434 - Stride length .959 .336 - Note:* p < .05 Table 4 The Influence of Different Walking Types on Stride Length (Unit: m) Stride Frequency Walking Style 88/steps 110/steps 132/steps NW UW NW UW NW UW Stride length 1.62±.13 1.53±.10 1.72±.10 1.71±.13 1.79±.13 1.78±.18 Additional Declarations No competing interests reported. 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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-4039719","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":284220156,"identity":"68fb6e88-dc17-429a-adfa-354a334c8db9","order_by":0,"name":"TI WU","email":"","orcid":"","institution":"National Taiwan Sport University","correspondingAuthor":false,"prefix":"","firstName":"TI","middleName":"","lastName":"WU","suffix":""},{"id":284220157,"identity":"b28c08c6-b7c7-403f-bbb7-04dfb5f6bd36","order_by":1,"name":"Jui-Hung TU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACAwaDA0CKGURIyBBUzoPQwpYA0sJDlBYgAGnhMUASwAPsJRIYinkK7iRu51/z+dWNGgseBvbDRzfgtQWoxZjH4Fnizhlvt1nnHAM6jCct7QYRWg4nbrhxdptxDhtQiwSPGbFazjwzzvlHkpbzPcyPc9uI0XLmYYPhHIPDxhtusJkx5/ZJ8LAR8gt7e/Ixgzd/DstuOH/48eecb3Vy/OyHj+HVwsDA2AaODwaJBDYJEM2GXzkYMD8AU/wHmD8QoXoUjIJRMApGIAAAkG9ICAN8ztMAAAAASUVORK5CYII=","orcid":"","institution":"National Pingtung University","correspondingAuthor":true,"prefix":"","firstName":"Jui-Hung","middleName":"","lastName":"TU","suffix":""},{"id":284220158,"identity":"016f7eff-9024-41d0-bb4e-4d3f94fcbab9","order_by":2,"name":"Hsin-Huan WANG","email":"","orcid":"","institution":"National Taiwan Sport University","correspondingAuthor":false,"prefix":"","firstName":"Hsin-Huan","middleName":"","lastName":"WANG","suffix":""},{"id":284220159,"identity":"3c313e12-58e3-4e2f-a6c5-2f3ad670e54b","order_by":3,"name":"Chia-Hsiang CHEN","email":"","orcid":"","institution":"National Pingtung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chia-Hsiang","middleName":"","lastName":"CHEN","suffix":""},{"id":284220160,"identity":"fa426d26-6712-4d11-b6b8-5297e99cf00b","order_by":4,"name":"Chun-Ta LIN","email":"","orcid":"","institution":"National Pingtung University","correspondingAuthor":false,"prefix":"","firstName":"Chun-Ta","middleName":"","lastName":"LIN","suffix":""},{"id":284220161,"identity":"4286762a-8148-44ee-81f2-1dc9dc6497e2","order_by":5,"name":"Hung-Sheng HSIEH","email":"","orcid":"","institution":"National Pingtung University","correspondingAuthor":false,"prefix":"","firstName":"Hung-Sheng","middleName":"","lastName":"HSIEH","suffix":""}],"badges":[],"createdAt":"2024-03-08 08:35:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4039719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4039719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53660037,"identity":"70f043fc-895b-4463-ac3c-1257fcf80921","added_by":"auto","created_at":"2024-03-28 16:05:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228147,"visible":true,"origin":"","legend":"\u003cp\u003eThe activation increase or decrease of various muscle groups in Nordic walking (unit: %)\u003c/p\u003e\n\u003cp\u003eNote: M(muscles group), F(steps/min); The formula of muscle groups’ activation(%)=〔Nordic waling electromyography signals(uV) -Normal walking electromyography signals(uV) 〕/ Nordic waling electromyography signals(uV)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4039719/v1/ceac41ab8ec757796bed40ff.png"},{"id":58023638,"identity":"af84e96e-1e87-45a4-b551-6be6d980939a","added_by":"auto","created_at":"2024-06-10 05:52:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":706724,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4039719/v1/eefc34f3-7454-4563-b99e-5b5df383ccd0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Nordic walking on muscle activation and stride length of middle-aged women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe convenience of modern life in the 21st century has substantially altered our exercise behavior and habits, resulting in a constant increase in exercise participation. The statistics released by the Taiwan\u0026nbsp;Sports Administration, Ministry of Education (2018) reveal that 33.6% of Taiwan\u0026rsquo;s population exercise regularly, and the total number of people who exercise is increasing annually at a steady pace. In addition, although the proportion of women who exercise (82.5%) is marginally lower than that of men who exercise (84.8%), this gap is gradually decreasing each year.\u003c/p\u003e\n\u003cp\u003eBecause of aging and technological advances, the various functions of the human body are gradually exhibiting signs of degradation. In addition, the incidence of chronic disease symptoms (e.g., obesity, hyperlipidemia, hypertension, and diabetes) has been increasing (Wen \u0026amp; Chang, 2013). Studies have indicated that improving physical fitness is conducive to the prevention of various chronic diseases. Nonetheless, engagement in high-intensity exercise may overburden the body and cause adverse effects. Taiwanese people run and cycle, which are activities that require intense knee movement. The lack of proper exercise intensity management and application of improper exercise techniques both increase the risk of lower limb injuries (Wang, 2012).\u003c/p\u003e\n\u003cp\u003eRunning mainly works the lower limbs. Because most people today prefer simple and efficient exercises, walking has become popular exercise that does not require any equipment, can be performed at any time, and can achieve a target exercise intensity without substantial effort. The American Heart Association (2011) reported that walking improves the physical health of people and increase their physical activity level. Weight-bearing exercise helps to maintain bone density and places less stress on the joints. The American College of Sports Medicine (2011) describes walking exercise as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ewalking\u0026nbsp;exercises the major muscle groups, which include the knee extensor, gluteus extensor, and muscles in the lower extremity (e.g., gastrocnemius, soleus, and tibialis anterior).\u003c/li\u003e\n \u003cli\u003efacilitates blood flow back to the heart, thereby improving body circulation.\u003c/li\u003e\n \u003cli\u003eimproves both muscle endurance and dynamic equilibrium.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNordic walking is an innovative walking types that originated from the technique used by cross-country skiers for summer training. Nordic walking first received attention in 1930, and thereafter, people started to study the advantages and benefits of this walking types (J\u0026oacute;dar Reverte, 2019).\u003c/p\u003e\n\u003cp\u003eNordic walking differs from usual walking in that poles are used to propel the body forward. As an emerging fitness technique, Nordic walking has gradually become a leisure and health/fitness improvement option for people, particularly older adults. Individuals can maximize the benefits of exercise through walking. The\u0026nbsp;Federaci\u0026oacute;n Espa\u0026ntilde;ola de Deportes de Monta\u0026ntilde;ay Escalada\u0026nbsp;〔FEDME〕(2018)\u0026nbsp;recognized\u0026nbsp;Nordic walking as a dynamic and rhythmic exercise that is suitable for people of all ages. According to FEDME regulations, Nordic walking competitions are usually conducted on routes measuring between 10 and 42 km.\u003c/p\u003e\n\u003cp\u003eNordic walking is an activity that involves the use of poles mainly to support the upper and lower body and ensure joint alignment, thereby reducing the impact of walking on the joints (Padulo et al., 2018). It requires a walker to hold a pair of poles at approximately handshake height in front of them. Approximately 50% of the human muscles are in the upper body, and the primary focus of Nordic walking is on exercising the upper body muscles. With each step, people push off with the poles (in opposite directions) to work and tighten the arm muscles, abdomen, back, and chest muscles, thereby performing a full-body exercise (Huang, 2016).\u003c/p\u003e\n\u003cp\u003eNordic walking effectively works 90% of the body\u0026rsquo;s muscles. Therefore, it is a full-body aerobic exercise\u0026nbsp;(Wen \u0026amp; Chang, 2013).\u0026nbsp;Nordic walking involves walking while using poles in both hands for support, which sets it apart from conventional power walking. The two poles are used for supporting, stretching, and extending both arms. Therefore, the upper body muscles are exercised, and arm strength is applied. In a comparison of Nordic and usual walking on a treadmill, Hansen and\u0026nbsp;Smith (2009) discovered that older adults exercise more upper body muscles and consume more calories during Nordic walking than during usual walking.\u003c/p\u003e\n\u003cp\u003eParkatti et al. (2012) also proposed that relative to usual walking, Nordic walking is approximately 20% more effective at strengthening the upper body muscles and improving the circulation of older adults because it involves the use of poles. In addition, Nordic walking can improve gait balance and stability. Willson et al. (2001) reported that the use of walking poles can effectively increase stride length and frequency, thereby increasing walking speed by approximately 3.6%. Moreover, relative to usual walking, Nordic walking can reduce the impact of the ground reaction force on the feet and knee joints by 4.4%, which effectively reduces knee joint load.\u003c/p\u003e\n\u003cp\u003eDalton and Nantel (2016) implemented a Nordic walking intervention for older adults (8 female and 4 male participants) that comprised two weekly sessions over 8 weeks. Each session involved two 6-min walk tests and six 5-m walk trials (three with the use of poles and three without the use of poles). After the intervention, participants exhibited increased stride length, higher stride frequency, and enhanced\u0026nbsp;strength in hip and knee joint muscles.\u003c/p\u003e\n\u003cp\u003eThe literature has mostly focused on health improvement among older adults and special populations including people with diabetes (Balady et al., 2010；Pippi\u0026nbsp;et al., 2020) , cardiovascular diseases (Launois et al., 2018), obesity or overweight (Hagner-Derengowska et al., 2015), and neurological disorders (e.g., Parkinson\u0026rsquo;s disease) (Monteiro et al., 2017). Most studies have indicated that Nordic walking is a relatively simple exercise that can offset the negative effects of inactivity and assist patients in improving their physical health (Ben Mansour\u0026nbsp;et al., 2018).\u003c/p\u003e\n\u003cp\u003eFew studies have discussed the benefits of Nordic walking for healthy middle-aged people. In addition, most studies conducted treadmill-based experiments. The present study enrolled healthy middle-aged women as participants to explore the effects of walking types on muscle activation and gait parameters.\u003c/p\u003e"},{"header":"Research Method","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFourteen\u0026nbsp;middle-aged women (\u0026ge;35 years) with a regular exercise habit (playing badminton three times a week for at least 30 min per session) were enrolled as participants. None of the participants had damage to their nerves, muscles, bones, tendons, and ligaments of the upper and lower limbs; they also had no history of cardiovascular diseases in the 6 months preceding the experiment. The basic attributes of the participants were as follows:\u0026nbsp;age = 39.36 \u0026plusmn; 1.78 years, height =\u0026nbsp;164.26 \u0026plusmn; 3.41 cm, and weight = 62.53 \u0026plusmn; 5.2 kg. The date of data collection was 19~20 June 2020.\u0026nbsp;We confirm that the methods employed in the experiment were conducted in accordance with relevant guidelines/regulations, and informed consent has been obtained from all experimental participants and/or their legally authorized guardians. The ethical approval was approved by ethics committee of Antai Tian-Sheng Memoral Hosptial and approval number was 20-050-B (approval date: 18 May 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Noraxon EMG (Noraxon, AZ, USA)\u003c/p\u003e\n\u003cp\u003eIn this experiment, Noraxon EMG was used to measure the electromyographic (EMG) signals of participants with varying walking types and stride frequencies. EMG sensors were attached to the participants\u0026rsquo; upper limb muscles (i.e., biceps and triceps) and lower limb muscles (i.e., vastus lateralis, biceps femoris, gastrocnemius, and tibialis anterior).\u003c/p\u003e\n\u003cp\u003e2. GoPro HERO 7 BLACK (GoPro, San Mateo, CA, USA)\u003c/p\u003e\n\u003cp\u003eA GoPro HERO (60Hz) was used to capture gait images of the participants.\u003c/p\u003e\n\u003cp\u003e3. Pro Metronome app for stride frequency control\u003c/p\u003e\n\u003cp\u003eStride frequency is the number of steps taken per minute, and the mean stride frequency for usual walking is 110 steps per minute (Shumway-Cook \u0026amp; Woollacott, 2007). We used the app to manage stride frequency; specifically, low, normal, and high stride frequencies were defined, respectively, as 88, 110, and 132 steps/min.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Prior to the start of the experiment, the 15-min warm-up and an introduction to Nordic walking were conducted. After the researchers reminded the participants of the characteristics of Nordic walking(NW), they accompanied the participants during the intervention to ensure that the participants understood and adapted to Nordic walking, and that the EMG sensors were securely attached and fixed.\u003c/p\u003e\n\u003cp\u003e2. At the beginning of the experiment, the participants were randomly assigned and instructed to perform either Nordic walking or usual walking(UW) three times; the 3-min rest was provided between each session. During each experiment, the Noraxon EMG was used to collect muscle activation data, and the GoPro was used to capture gait data.\u003c/p\u003e\n\u003cp\u003e3. One full gait cycle begins with the heel strike of one foot and ends with the next heel strike of the same foot in preparation for the next step. Stride length is the distance between successive points of foot-to-floor contact of the same foot. Changes in stride length can serve as indicators for analyzing body stability and assessing fall risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Processing and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUse of SPSS Statistics V22.0 for Windows for data analysis\u003c/p\u003e\n\u003cp\u003e1. Descriptive statistics\u003c/p\u003e\n\u003cp\u003eMuscle activation data were collected from upper limb muscles (i.e., biceps brachii and triceps brachii) and\u0026nbsp;lower limb\u0026nbsp;muscles (i.e., vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius).\u003c/p\u003e\n\u003cp\u003e2. Data processing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sampling frequency of the EMG signals was 1500 Hz, and the original signals were full-wave rectified and smoothed using a root mean square algorithm and a moving-average filter with a window size of 75 ms. Subsequently, the signals underwent electrocardiogram reduction and bandpass filtering at a range of between 80\u0026thinsp;Hz (low frequency) and 250\u0026thinsp;Hz (high frequency).\u003c/p\u003e\n\u003cp\u003e3. Changes in EMG signals and gait parameters\u003c/p\u003e\n\u003cp\u003eThe present study compared Nordic and usual walking in terms of EMG signal responses and gait parameter changes at various stride frequencies. The independent variables were walking types(Nordic and usual walking) and stride frequency(88, 110, and 132 steps/min), and the dependent variables were EMG signal and stride length. The two-factor repeated measures analysis of variance was conducted to identify dependent variable differences. The simple main effect test was conducted to determine if an interaction represented a significant difference, whereas a main effect test was conducted to determine if an interaction was nonsignificant.\u003c/p\u003e\n\u003cp\u003e4. For all statistical tests conducted in the present study, the significance level was set to \u0026alpha; = .05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe results are divided into two parts, that is, the effects of walking types on EMG signal response and the effects of walking types on stride length, which are explained as follows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of Walking Types on EMG Signal Response (unit: uV)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes the effects of walking types on EMG signal response, which indicated nonsignificant differences in EMG signal responses between the two walking types and among the three stride frequencies. Accordingly, differences in walking speed did not result in significant differences in EMG signals pertaining to the biceps brachii, triceps brachii, vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius.\u003c/p\u003e\n\u003cp\u003eAnd the figure1 shows that Nordic walking increased the activation of the biceps brachii muscles (by 92.1% [88 steps/min], 63.2% [110 steps/min], and 53.1% [132 steps/min]) and triceps brachii muscles (by 15.7% [88 steps/min], 14.9% [110 steps/min], and 10.68% [132 steps/min]). Compared with usual walking, Nordic walking increased the activation of the lower limb\u0026rsquo;s vastus lateralis\u0026nbsp;by 3.2% and 7.2% at the stride frequencies of 88 and 110 steps/min, respectively, but reduced it by 4.2% at the stride frequency of 132 steps/min. Compared with usual walking, Nordic walking increased the activation of the biceps femoris by 13.8%, 2.4%, and 6.35% at the stride frequencies of 88, 110, and 132 steps/min, respectively. Compared with usual walking, Nordic walking increased the activation of the tibialis anterior\u0026nbsp;by 17.1% and 0.7% at the stride frequencies of 88 and 110 steps/min, respectively, but reduced it by 2.1% at the stride frequency of 132 steps/min. Compared with usual walking, Nordic walking increased the activation of the gastrocnemius by 13.1% and 6.9% at the stride frequencies of 88 and 110 steps/min, respectively, but reduced it by 2.5% at the stride frequency of 132 steps/min.\u003c/p\u003e\n\u003cp\u003eTable 2 indicates that the interaction between walking types and stride frequency was nonsignificant, indicating that it had no effect on the EMG signals and gait of each muscle groups. Therefore, this study conducted a post hoc comparison of the main effect of walking types.\u003c/p\u003e\n\u003cp\u003eThe post hoc comparison indicated that the main effect on the biceps brachii and triceps brachii was significant (Table 3). Subsequently, we compared the mean EMG signals in Nordic walking (biceps brachii = 16.85 uV, triceps brachii = 10.17 uV) and usual walking (biceps brachii = 1.62 uV, triceps brachii =1.42 uV) and discovered that Nordic walking was more effective in activating the biceps brachii and triceps brachii.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of Walking Types on Stride Length\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 indicates that Nordic walking can increased stride length by 5.74%, 0.79%, and 1.03% at the stride frequencies of 88, 110, and 132 steps/min, respectively. According to the analysis of variance table, the interaction between walking speed and walking types was nonsignificant (\u003cem\u003eF\u003c/em\u003e = 1.560,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = .220), indicating a nonsignificant difference between the effects of walking types and those of stride frequency on participants.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison between the Effects of Nordic Walking and of Usual Walking on Myoelectric Responses\u003c/h2\u003e \u003cp\u003eFor the stride frequency of 88 steps/min, Nordic walking was more effective in activating the myoelectric signal response in each muscle group relative to usual walking. However, decreased activation was observed in the vastus lateralis at a stride frequency of 110 steps/min and in the tibialis anterior at a stride frequency of 132 steps/min.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the various muscle groups activation increase or decrease of Nordic walking. The biceps brachii and triceps brachii were significantly activated by Nordic walking; for the lower limb muscle groups, the muscle activation through Nordic walking was nonsignificant, but more muscles were activated relative to usual walking. That is, Nordic walking provides greater exercise intensity for the human body. This finding is consistent with those of other studies. Shim et al. (2013) studied the effects of Nordic and usual walking on upper limb EMG signals; Pellegrini et al. (2015) compared the EMG signals generated through Nordic and usual walking with varying slopes (level and inclined plane) on the treadmill; Pellegrini et al. (2018) conducted five experiments to compare the effects of Nordic walking; and Fujita et al. (2018) conducted a full-body EMG experiment in which older adults performed Nordic and usual walking on a level bituminous surface. The aforementioned studies reported significant differences between Nordic and usual walking in terms of the mean EMG signals generated by the biceps and triceps brachii; this finding is consistent with that of the present study. The increase in the upper limb\u0026rsquo;s muscle activity, particularly the biceps and triceps brachii, can be attributed to the use of poles to push off and move one\u0026rsquo;s body forward. By contrast, the vastus lateralis and tibialis anterior exhibited a compensated decrease in activity, which aligns with the results of other studies (Shim et al., 2013; Stahl et al., 2016). Several studies have reported conflicting findings; for example, Yamamoto (2007) did not observe a decrease in lower limb muscle activity. Therefore, the activity of the lower limb muscles during Nordic walking warrants further clarification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eComparison of Effects of Nordic and Usual Walking on Gait\u003c/h2\u003e \u003cp\u003eIn terms of post-intervention stride length, the influences of both Nordic and usual walking on stride length were nonsignificant. However, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e indicates that stride length increased marginally following the completion of a Nordic walking intervention. This finding is inconsistent with those of other studies. Dalton and Nantel (2016) studied older adults with a mean age of 68 years and reported the significant effects, after an 8-week Nordic walking intervention, has the longer strides and faster strides frequency. Skiba et al. (2019) conducted a 10-week Nordic walking training involving 22 patients who had Down syndrome and were aged between 25 and 40 years; they reported a significant improvement in step and stride length. De Santis and Kaplan (2020) implemented a 24-week Nordic training intervention involving 318 patients with Parkinson\u0026rsquo;s disease and reported an improvement in their gait (i.e., step and stride length). The aforementioned studies have demonstrated that the gait performance of older adults and patients with specific diseases can be improved through long-term Nordic walking, whereas short-term Nordic walking cannot immediately improve their gait performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAfter comparing the benefits of Nordic walking and usual walking at various stride frequencies, we discovered that as a full-body physical activity, Nordic walking can effectively increase the activation of upper limb muscles. This effect was particularly prominent in slow walking scenarios, but it was not present in lower limb muscles. Furthermore, the stride length of the gait parameters indicated no significant improvement.\u003c/p\u003e \u003cp\u003eWhen Nordic walking is performed as a full-body exercise with no change in gait and at a slow stride frequency, it increases upper limb activity more effectively relative to usual walking. The literature has mainly focused on individuals with specific diseases and older adults. Healthy middle-aged women were enrolled as participants in the present study, which yielded experimental results that were consistent with those of other studies, thereby verifying that Nordic walking can increase upper limb activity and stride length among middle-aged women.\u003c/p\u003e "},{"header":"Suggestion","content":"\u003cp\u003eThe results indicated that Nordic walking had no significant effect on the activation of lower limb muscles and changes in stride length, but it had a significant effect on the activation of upper limb muscles. Researchers should increase the scope of research by examining trunk muscles to explore the correlation between Nordic walking and body stability. A comparison of the benefits of 8-week and 10-week training for middle-aged women should be conducted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1.TI WU:Data collection, Result analysis, Writing the first draft.2.Jui-Hung TU:Experimental design, Host and guide research work.3.Hsin-Huan WANG:Data collection, Result analysis.4.Chia-Hsiang CHEN:Result analysis and interpretation, Manuscript editing.5.Chun-Ta LIN:Data collection, Experimental design.6.Hung-Sheng HSIEH:Ensure the accuracy and quality of the paper.All authors discussed and formed up this study, and certify that all authors have seen and approved the final version of the manuscript being submitted.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eWe would like to thank the Ministry of Science and Technology Project(109-2410-H-153-022), for providing support in terms of research personnel, equipment, consumables, and other forms of assistance. However, we hereby declare that all experimental procedures comply with the current laws of the region where the laboratory is located.\u003c/p\u003e\u003ch2\u003eData Availability Statement-\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available, but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalady, G. J. \u003cem\u003eet al\u003c/em\u003e. Clinician\u0026rsquo;s guide to cardiopulmonary exercise testing in adults: A scientific statement from the American Heart Association.\u003cem\u003e Circulation. \u003c/em\u003e\u003cstrong\u003e122\u003c/strong\u003e(2), 191\u0026ndash;225(2010). \u003c/li\u003e\n\u003cli\u003eBen Mansour, K., Gorce, P., \u0026amp; Rezzoug, N. 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Bulletin of Asaigakuen. \u003cem\u003eUniversity School of Lifelong Learning Support Systems\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e, 108\u0026ndash;110(2007).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Results of Different Walking Types in Response to Electromyography Signals (Unit: uV)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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9OSbJ+QOJmACJmACJmACJmACJmACJjB3AlvndDMg/KjZ8ccfn57//Oen22+/fTJjdP3116fzzz8/HXnkkem4445Lb3zjG9PXv/71yehnRUzABEzABEzABEzABEzABEzABIYlsJVOtxC9+MUvXjxh/vznP6+kycQf/ehH0wtf+MJ0n/vcZ/G36Jdeemn6yU9+Mhn9rIgJmIAJmIAJmIAJmIAJmIAJmMDqBLba6QYPvyZ+z3veM11++eWr0xpBAk/i3/Wud6VTTjkl3eMe90innXZauuqqq0ZoySJNwARMwARMwARMwARMwARMwATWTWDrnW6AfupTn0pHH310eulLX7puvr3a++53v5ve+ta3Ll6Nf+ADH5jOOeecdO211/aS4cImYAImYAImYAImYAImYAImYALTIbATTje4v/e976WnP/3p6eSTT07f+c53pjMCFU2++MUvJn6N/ZGPfGR69KMfnV796lenG2+8sVLaySZgAiZgAiZgAiZgAiZgAiZgAlMksDNOt+C/4hWvSA972MPSxz72MSVNPv70pz+dzjrrrHS/+90vnXDCCeniiy9e3ESYvOJW0ARMwARMwARMwARMwARMwAR2nMDOOd2M93ve857FD5i9+c1vnt3wX3nllenZz352Ovzww9Mzn/nM9N73vnd2fbDCJmACJmACJmACJmACJmACJrArBHbS6WZweX37iU98YnrBC14wy7H+4Q9/mN7xjnekE088Me3ZsyfxS+0f//jHZ9kXK20CJmACJmACJmACJmACJmAC20pgZ51uDeiLXvSixf+Z/YUvfEFJs4u/8pWvpNe//vXp8Y9//OIH417+8penKf43abMDu8MKX3HFFemMM85YisBJJ520VL11VNq3b1/iM3S46KKL0mGHHZZW6fvevXvTgQMHhlbN8kygkcA111yztN1i99SfYti/f//ihvTQujFHudF91FFHpZtvvnkp8ejG+nrLLbcsVd+VTGBsAtg2NjrU/MbWx9h7h+LAnGZeDh04E3A2YK1cNvhssCy56dXbeaebIeEXw+9973sv/uuu6Q1RP42uu+66dN5556WHPvSh6QlPeEL6q7/6q/TNb36znxCX3lkCbIxsEhwob7jhht4c2LTYYKYY6Bu6cUNh6IBcfZY9pOgwv+zNjqH7ZHnbT+CCCy5YOJDLzAnWB2x+WXsfmy4H1THmkg7R9H3Zg7QcEA76y6yzY7Oz/N0mwHqAbbI+DBVwuFe5KT2UHiU57L3M52VvopVkksbaqHMB8bLBZ4NlyU2v3vJWML2+rKTRJz7xifTwhz980EVmJYUGqHz11Ven008/Pf3yL//y4lfbL7vssvTTn/50AMkWsY0EOAhyUGWz5bpviBtM37rrKM9Bgo1vmb616TfEk27aEMMxnIW2Pjh/twhgY8yHZZxm5hDrxLL1xybN4RndOKwOHXQAXuVJt3TCCbHjLRqOp0BA++SyN5RKfRhqfyzJHiKNtZCzzxhBN+lW5emzwRijs36ZdroDcw4Sv//7v5+e9rSnpf/8z/8MOfO+vO2229Lll1+efu/3fi/d6173Ss973vNGOYzMm5K15642B9VlNgcdRKnPZ4phrFfLh+6rnKExXnUbWlfLmycB5ivzdJknTzyZ5YCqub6M0z42NeYOzuzUg24O4MCPcTNw6v23ftMigD0yb/gMZY9yuJddb9ZBiP7OYb/12WAd1jBuG9M8HY/b51bpf/7nf56OPPLI9C//8i+tZedW4Nvf/nZ6y1vesvgRuQc/+MHp3HPPTZ/97Gfn1g3rOzAB3UVlYyxttnoNlc2JO+F812Fbr5TrEB5jlUFdDvo6rHPIjM59bF/1aYdyfCfmex6iA4ADwaGh5EjQJ+RIRjwIkE6d2B79lH7EUY/8dVDpq1h9ztug71EWbZZYyyEa8uCTc/P33SWAzWFb2Cu2loc4D5jnlNFcwLZVV/auWGWQRxscEFWWG15x3qiOYtYQrQ3Uod08IBM51GE+au7n5fiOLNpXUDuK6aMOsKQxF5HHR22QnutBOckgVp9r65f6JH2lT4xVRrJinq9NYJ0EZN+53TfpQFnmLB/mFd+1B8a5FOcN1zGMcTZoWsfyttGHswOBeRh1hUmURT81V4lrZ4PSmqB2xVntsBbF9bLG32cDEZxvfKjlz7cfg2v+7ne/e/Fa9l//9V8PLnsqAv/93/898f+W//qv/3p67GMfm1772temm266aSrqWY81EtABlANgHthw2By00cjJ1sZK+XyDqclgg+LwrI1NMimvDSVuRJRV+6RzHQPy2AQpx6aJ/mxoeZAMyimwsaktySZfmyhpyKJvHMZVlvwYqKM84sgltoGeyIn9hHspSN4c7r6X9HfadAloLmBjcT6gMXOIdM0hbDkeMtUr2SdxtHfykSlHEvvlOzI0T1UmyqA8bTM/KEdePje0RtEeMvVdOilWH5hnCpTP2yNNaxl5zGvmK+nRWcjXHPWNOk3rF/Viu/m6Id20FtJvBxPYFAHNG+w6zp0mfbSWaB5oPsU1QfaNXK0rUaZkMD+YLyovmZSNeyZymPuUVV3SuFZQX9RebR2jPLKY0zHEfVuyaQ8d+c6ny9kg1zu2EdcRrpEf2439ifXUvs8Gkcp8ru10N4wVTik/RvbHf/zHDaW2I+uTn/xkOvPMM9MRRxyRnvKUp6S3ve1t6fvf//52dM69aCWghZzDZh50AI2bIBtV3Fi5lgziGNgAdZDWRhHLs9kQYhoyqKeg+vFgKkeYNMmgjjZa1SWmD3nftLnTVqzDtfrCJqigtFw38mNe5FJqI/Yz9kftEGtDzg8DsYyvTWAZArLvku3p0BznA4e/OPdps2bv5OngGOWrzdp8igdMrTe0wRxXQB5pcX6VHFn6ENtW/ZLOcS6Sr3UkzlvWuhjUF8pHLrks1Yntxv4oPx7Muzo7quvYBIYiEG2+ZKeldjRX4zzIzwZRblxXkDfW2aDrOoYOrBU6l6iPNZ3j3K+tZfHcUlsTaCfKEr/Ybn5ekW4+G4jEPONDT8fz7MPoWuN043zjhO9C+Id/+If0h3/4h4sDDjHfHbaXQNwYtPjH3sbNIW40sUyUwSEzBm2ApFOOEMvroBnTchlRh5IMDt+1gwIHaeTFgz06xA0O+QqxrcgDGfpIB9VROnHMK7XR1E/JizoozbEJDEFAthptXnJze5UTqnzFkpHbO/lyjqN82XO8iVSTEXUozT/k53NZehHTRu4ok15qrzYXow6xH8hRX5AX9avJKrUb9Y31orxYxtcmMDaBaNdd24p1ameDprk01tkgb7O2jumGV3SU6XteXzxif+Ncrc3xOLcpE0NJVq3dWr2Y7ut5EDjUCuah80a05DVzfgWc1853JfCkmyfePPnmCThPwnki7rBdBOLGEDcS9ZIDbtxUONTmm1iUkW8uuhseZcRrtdkko7RBoV983QuZpcO49M91rm1wtbaizugaQy2v1EZTPyUz6lC7maCyjk2gDwHZKjaWBw6fyifGwS3ZXywT5wJlY17pWm3GvCijNGeogyMd65Qca+mvG3lqizjWVXu1uVjTATlxblJOoSar1K7qEMd6tadbsbyvTWAMAtGuu8rX3iobL50NmubSWGcDrQPSq7aOsYbEG4Hqd03nyCjOfbVDrLUFWXFukxdDSVat3Vq90tocy/p6egQOtYLp6TcpjfhhtV/91V9NL3/5yyel1zqU4W+9+Ztv/vabvwHnb8F35cn/Ovhuso24McSNJOqUH3hxdqMTG2V02VyibF0vI4NNR0/WtPHlr4qxsZcOs7UNrrQZoqPkE6NrDLW8UhtN/ZTMqEPelso4NoFlCMhWsbFSyA/SzK/cBiUjnwtdbFtt1mSU5gx1WG84IMd6+bxm7qNvKcR66k9N35oOyI1zk3IKNVmldlWHONarjUks72sTGINAtOs+8tvOBsvMpbz9OEeYTzFEveN87LKOsVbk5wVk13SutVWb4331rrVb6y/yHeZF4FDrnZfuG9GW/0qM/1KM/1osOh0bUWZDjfJr5/zqOb9+/sQnPjHxFgC/iu4wTwJxY4ibVt4bXh+Lm0t80hRldN0Uc/nLysDxzp9463Ux5ij6sAHnobbB9d1YkRu5xI2w1EZTP6Vj1MF3s0XF8RAEZKvYWC0wX+LNLOZXDJJBHO29i21LTk1Gac6oDvM5zg1kxLmNUx7XJdUjLrVX07dJh9g+5RRqskrtqg5xrJffRIjlfG0CYxKIdt23naazwTJzKW8/zhHmUwxR7zgfKdO0jtVeLadeTedaW7U53lfvWru1/vpsEMnM4/pQ652HzpPQkkXm4Q9/ePrEJz4xCX02pQQLF//v973vfe/F/wPO/wfO/wvuMB8CcWPIN628F2xi2mDYgBSijHxTjBtyrKO6iptkxM2OcnnIn4KpjPQt3SCrbXCxrchD/SaWfOlRyyu10dRPyYs6KM2xCQxBQLbaNBdpJ3+LJLYtGflc0E0u5efzpIuMOGfi/It149M1ldErpaVXy6krnaLOtbkYdcg5xbmptpFfk1VqN/Yl1ovyYhlfm8DYBKJdL9OW9lrsPc6Zprm0jrNBbR1jDSm9Wk7fazpHRnGu1uZ4nNuUiaEkq9ZurV5M9/U8CBxqBfPQeTJavutd71o4m29961sno9OmFPnpT3+aLrvssvS7v/u7i799P/3009M//dM/bUodt9uTgJ5qlZ60kKYnx4jVRhnLNm0ubHpxU4qyqMeHkMuIjrKeZMdXRymPLgrxwK87wOgY9VRZ4toGV9oMKR/7IJ0lr5ZXaiPvp2TEWK/R1g4FsayvTaAPAeYD9hrnkupjr9Fp1dOgvGzN3pEj+ZTJnzojXyHKiG1Gh1rzmDr5XNAc0euhTa+WUz+2p/lbm4uleSu9a+tDTVapXckiFmPKRQ6xjK9NYGwCzB/Zapx3Te0y1+N+XjobNM2lsc4GXdYx1jStHXkfazrX5r64EWttQWZtTSCvJKvWbtRP616+HsYyvp4uATvdK47NF77whXTcccelF73oRStK2p7q3/zmN9Ob3/zmxS++P/ShD01/9md/lq677rrt6eAW9kQH3dJCzuZAujYTHarjARGHl01Mmw8bMRuIguSTjyzKUyZuevkGRTuUi4fSUnnaoZw2cPWBNNrjDnwp6IAgnVSG+uqHnHrJUnred6UTx7xSG7E/lEd2HiQv9jcv4+8msAyB+EQqtz3mEvNYcwb7wxY1D9RePDBizzGfeRjXgigrHtBl48TMOeqRr7rM/xgop0M+enMjjrKSiYzcyVf92vytzcXSvJWs0vpAXklWrV3JItZBm744mMCmCDCPNCex5S6hy9kg7uua51oTaGOMs0HbOqa5qrUj72tt/pfmftMcVzviSlmFkqxau6pDLFk+G0Qq87m20z3QWL3gBS9Y/H3zF7/4xYEkboeYz3/+84sfnvu1X/u1dOyxx6bXv/716Stf+cp2dG6LehE33Lgx0EU2VvK1OXLYjZumMLC56ok0dXI5bITKJ+Z7DHFzZmPhEK6NifL5JkN5Nik2NsmVo45cdEROrgd5OuhqAyNGZz4xjetSWdJpn5CXV16tXqn8QtDP/xEHDuEl3WNZX5vAMgQ0X/LDNTZLGjH2x4c5lgfWA80VZDFXY+A7c1G2znVeRnnEtKnyajO3fR3Y1a6+067Wr7w/0im2peva/Cyl0yZBbUsGMeU1Z2N67VrrhnSTTOQ4mMAmCWiPL835kl7YbpezAXs385o5UZKN7WtNIs7nQj6/2s4G1G9ax+gn60cpUDefu/RT8zTmlcqSj765zqpHmyVZyo8x5WKQTFjm62Ms5+vpErDTPeDY8HT3Pve5T3rPe94zoNTtEfXxj388vfjFL14svieeeGJ65zvfmX74wx9uTwdn3hM2Qxb8fMNbV7e0oWjTWVe7U2tHB5/8JsPU9LQ+8yXAgZR5lh/q1tkjzXNi5v4uBt0swNHwIXoXLWBafcYeceim5tT5bHCHnfhsMK35sow2drqXodZQ50vcvpUAACAASURBVGMf+1h62MMetvgvtRqK7XzWe9/73vTMZz4zHX744ek5z3lOuvLKK3eeyaYBcOjj7i8bLpvvuoM31l/8DRibq4MJjElAB7ja0+Ex20a2ne47nnix3uZvAYzN3vJNoEZAb4iVnkjX6oyd7rOBzwZj29i65NvpHoH0d77znXTyySenpz/96el73/veCC1sj0j4XHzxxemEE05Iv/Irv5LOOuus9OlPf3p7OjiznuB48/Qrvrq5ri7s+saK88MB3A73uizO7XCwxuY24XjvstPNOsvr9Ha4PQenSADHG9vc1FtvOROfDXw2yG1irt/tdI84ci996UvT0UcfnT71qU+N2Mr2iL7xxhvTq1/96vToRz86PfKRj0wXXnhh8t/Ib2Z82XTX7fzplVcdxjfxtH0ztO9olRsdm3B+Ntlnt715Ahxo1/2aOU6n5jkx680uBf50hPUVDg4mMEUC7L/YKOvDpoPPBj4bbNoGh2rfTvdQJCty+H+r73nPe6a//du/rZRwconAtddem84555z0wAc+MP3Wb/1W4r9l++53v1sq6jQTMAETMAETMAETMAETMAETmCwBO91rGBr90iI/IubQn8BVV12VTjvttMXNi1NOOSXx/6Pffvvt/QW5hgmYgAmYgAmYgAmYgAmYgAmsmYCd7jUBx0l8/vOfn44//vj0pS99aU2tblczP/nJT9Kll16afud3fmfxK/EvfOEL00c/+tHt6qR7YwImYAImYAImYAImYAImsFUE7HSveTjf9KY3pfve977pfe9735pb3q7mvv71r6c3vvGN6bjjjktHHnlkOv/887erg+6NCZiACZiACZiACZiACZjAVhCw072BYfzIRz6SHvKQhyx+KGwDzW9dk5/73OfSy172svTJT37ykB/niT/U4+vDzOYwM/A8sA3YBmwDtgHbgG3ANmAbaLaBMZwlO91jUO0g81vf+lZ66lOfmp7xjGek73//+x1quIgJmIAJmIAJmIAJmIAJmIAJmMDcCNjp3vCIveQlL0nHHHPMJP5bhg2jcPMmYAImYAImYAImYAImYAImsHUE7HRPYEgvu+yydI973CNdfPHFE9DGKpiACZiACZiACZiACZiACZiACQxFwE73UCRXlMPfJR977LHpzDPPXFGSq5uACZiACZiACZiACZiACZiACUyFgJ3uqYxESum2225Lz33uc9OTnvSkdOONN65Ns2uuuebgj2wdddRR6Yorrjj4nR9auPnmmw+mnXHGGUW9qEdZZJ100kmLa+TkgXIXXXRRnuzvJmACJmACJmACJmACJmACJrCVBOx0T3BY3/CGN6Q9e/ak97///WvTTo63HGJ9j042abUgR50yqmunu0bL6SZgAiZgAiZgAiZgAiZgArtCwE73REf66quvTg960IPSK1/5yrVpyJNqnkQr6Om1vpec6JinJ91NTrfKOzYBEzABEzABEzABEzABEzCBXSBgp3vCo/yNb3xj8ar2s571rPSDH/xgdE15qq3XyXGc9Z1rHG5eMyfo9XHKyhEnLjndpPGhDk/RuUZuvFaZknzVHb3zbsAETMAETMAETMAETMAETMAERiBgp3sEqEOLPO+889IjHvGI9JnPfGZo0YfIk+OMQ8y1nljrO4WVRsyTcJxpguqSrjJyzmMjcrpJk7Ot8rFNriVTzniU42sTMAETMAETMAETMAETMAETmAMBO91zGKWU0qWXXprudre7pf3794+qsZ4s4/AS+I5zre9qXA5zk9OtJ+U47QrUI52g6+h0ky5nnXzJV33HJmACJmACJmACJmACJmACJjAnAna6ZzRa119/fXrc4x6XzjrrrNG0lsOLI0yQ46zvcpAVyynWU2nSlUearqWwHG2+61pl5NhLpuo4NgETMAETMAETMAETMAETMIG5ErDTPbOR+9nPfpZOO+209OQnPznddNNNg2uP4xt/TC3/LgdZT6GJSdOPruEwy3HXE3LKyMHW9fnnn79I47vqql2VUSxnfPDOWqAJmIAJmIAJmIAJmIAJmIAJjExgsk43f8crp0vOWGQR8+Pry7FM12s9zV1VTtf2hiiHrkcccUT6wAc+MIS4ychgXOOTbhx6vY4+GSWtiAmYgAmYgAmYgAmYgAmYgAl0JDBZp1v6y/HG+YpBjvIQT0HlwM/J6YbFgQMH0gMe8ID0qle9KqKZ/bWefGvsGR8HEzABEzABEzABEzABEzABE5gjgck73XpVOX/aaaf7DnP72te+lk488cS0b9++9KMf/WiONmidTcAETMAETMAETMAETMAETGBrCUze6ebps5586oknafrbYp5060k1T0b5TqxXlCmrJ6Z6Kh7TkKP6pBNi/lxG/txzz02PetSj0rXXXjsXla2nCZiACZiACZiACZiACZiACWw9gVk43XKk5RTzPTrdjJKefHONky6nm5iy1In1SCOPetHpllzSkKM252AJl1xySTr88MPT29/+9jmoax1NwARMwARMwARMwARMwARMYOsJzMLpZhR4Wo0TjFOMQyznGEeaUHO6ycO5pi6B8shChkJ0upWvp+Ny3lV26vF1112XHvOYx6Szzz577apGdjDtc8MCzjDXeK5deTdoAiZgAiZgAiZgAiZgAiZgAiMQmI3TLadaTnBXpzs628hQvegQlpxuys013HrrrenUU09NJ5xwQvryl7+8tm7gNIsb3OGtgDMdb3QoXbHGxU63iDg2ARMwARMwARMwARMwARPYBgKTdbrlCOPIRWcZ50wOmp5G40DHp6xKp5ycddLk0MU0ruN3BlVPXdX2XAf6da97Xbrf/e6XPvShD62lC/CKNzNio+TZ6Y5EfG0CJmACJmACJmACJmACJrALBCbrdO8C/HX08aqrrkr3v//902te85rRm4s3L6LzzVNvnG4+p5xyysFrnHDyuMmhGyn5jRG9qTC68m7ABEzABEzABEzABEzABEzABEYgYKd7BKhTE/nVr341PeUpT0nPec5z0v/+7/+Oql58QwFnmu9yxvWkW99RRE52dLr11gL5OOoqM6riExDOjQrdnFC8Z8+edMMNNxyinVipDHG8cRHTdY2Aknzlx5skeTnac1gfAY1JKWacFbjOyzBWpXTKaYzzOvG7ZBPH9NhuLOPr1QlEzrpmjcxDaVxLa4FkkNeUH8c0LydbyXXw9+UJaFxKcRwLj/PyjOdSM99jsQnv9XMZvc3qWVof4poi7Uo2Rlqtvtb8KCu/lmzimBfXr1jG13cmYKf7zky2NuWcc85Jv/mbv5k++9nPjt7H6Dhr8svplmNOuia6Dn3UU3lN6tIBdPQObKiBW2655eBNCvV/7969ifQ8wImNGnYK4hjrKo8Y9vHNA8qVZOPok6cxizJ8PT4B8dc4MmalcdJ4MkeUT7xv375DNsUDBw4conQ+xy644IJD8vUFObs0/9TvdcfMW+ayxpu4dLOxNN5Ki3VlC/Sjiz2oHHZWanfdPLa1vVXmtcd5u6yCecnaGuet9/rtGuOxeoPt5Oe4/OEMbWvtx65YP2JatDufD8YaqTvLtdN9ZyZbnfLOd75zscgTDxmY0PFuFwc3FgWCDvia9KRps5EOchaj0x3Lq9wuxGIRF0WcnzywoEbmyhdb1c85ssAqj7h0yKZOqU214Xh8AvGOdGmcpQEOMzYTA+MXx7jkOLMRq4zmapTBNfVKm3lezt9XJxDHW+NSYl8ab+aw6hDncxp7iE59yR7oATbhMC6BOM5957XHedyxWbd07/XrJr497elcrXW/1jPOe5SNweeDSGO913a618t7Eq3xpJsn3jz5HiowieNhgoWANEI8KKg9Npt48FNdHf7jXbx8wZCMbY1hw8E6OkXwLHEoHdryBbVUL/ItOdeMTX5w31beU+2X5gRjXxpn6c345k43eYyhNmQcLm7SxBDnJeVyBw870nyM9Xw9DgHGOB+T0rjVxjvO6ZK95PaQ94K245qc5/v7MARWndce52HGYQpSvNdPYRTmqQP7gPZ34lrAxkpnwHw/8PmgRnDY9PpIDduOpU2MAH/bzd9487fe/M33ugIHOw4NLARyytfV9lza0SKJExSfTrGwkhdD6XBNfjzYlRynuOAiNx+L0mE/tuvr8QnEMayNM1rUnDBsJW7KzL0Y8pszucO1f//+xc2fWMfX4xHQGOdzU+lquTbe+SEsn9O5Q5/bAzff8tcM1abj4QisOq89zsONxaYlea/f9AjMt/18Haj1RDaW5/t8kBNZz3c73evhPNlW+FVzft38wx/+8Fp01ETPD5JraXwmjcRFMn8VHGc4HqZrHPMDdn6Yzp15HCwFypaefivf8XoIrHo4R8v4VCx/dZgxj045NhED5fOn3zHf18MSiHM5f8sl/s19zenOb6LEOmjKnI7jHec4Tzny8R+2d5YmAqvOa4+zSM4/9l4//zHcVA9WdbrR2+eD9Y+ene71M59cix/84AcX/583/6+3w+YJxI0YbfLFlQO5XgWKB/Vc8+hYx6eYOOQcuOMhPDpklM2d9Fy2v49PYNXDORrmjnW8YcOGW7s5Q7loE+P31i3EuSwnODrJejJdc7ohGOd0fMOF8WQ9yO1B6wiycyfdIzIOgSHmtcd5nLFZt1Tv9esmvj3t5efCWs9yG4vl8v3A54NIZ5zrSTvdGEA8dHDN4UB3Z2R0pPFRWTkY2txiHjL1+p7kUE+hlKa8bY6//OUvpxNOOCGdeuqp6dZbb93mrk6+b6VFMh6ysNdo47UOyc41L3TARhZOdZwXlNGCGw/rNdlOH5+A1i/GJjpkectNTli+hsqx4gm2nOq45smu2Iz5OKyPQD7GjJHmLjFOM2lN453Pad08YywZ+9weKE9gTfBbDesZ6yHmtcd5PWM1dive68cmvL3y5f9oj6j1tGRjKpvvBz4fiMx48S+8zfHaWFqynAYEsFHpUKINh++UOf/88xeHE9JlRFxjbBgk1zJQ8lVGB0zKcE26HA61tbTyM6149tlnp8c85jHpuuuum2kP5q92aZHEYcZJ0gJLzEG6yU7zQ7vmh2wcUvFpOAsuZTQv5k9y3j1gbDXeTePc5IRBIN6w0dgzxow1ATtSO8TYGuVYDx3WR6A0xoxRHBvGhXnKGlELcU5rLsfxjPbAmhL3vZpMpw9HYKh57XEebkw2Jcl7/abIz79d+TTaH2o9KtlYLBv3A58PIplxriftdMs5llHpkKiDiA4exJQhXXV0EFW6DJT8WAasGJoOPHneONinLfXtb397ustd7pIuueSSaSu6pdrVFkmc6HjQwrZltzUU0VHnmnnAR4FDueYX84AFWPNKZRxvhsBQh3OedmqMtR4y1nrzQWueyuDUtdnVZohsd6s15nGOaoya5ijjp3LEjH+Urf1TZSgf14Ttprz53g01rz3Omx/LVTXwXr8qwd2tL59G63iNRM3GVN7nA5FYTzxpp7tmLDo06OChQ2N0urkmH4PkWgZKWZXXQYMDKAcbyuvJABuj5K9nKKbVyrXXXpse9ahHpXPPPXdaiu2ANtidbDPvbr5AxsN0Xpbv+VNMnHbsXyGXx1xwmAaBrodz1iw50DXN480arrXOqXy8OaM1U3mO10OgaS7n49O0N+VvuDDe7IEK2IoOaorjmqByjschMNS89jiPMz7rlOq9fp20t6st+TRaw2u9Y+3nnNcUfD5oojNs3qSdboxFBqWY7uMY8D0eUmLZeKBUPcXkyelWGjGBBTBuiMOinp+0H/3oR4snnyeeeGL62te+Nr8OzFTjpo2YLkVHOs6BUnfzAzZPsvMQF1yenjhMg0B87atpnHHI2kL+VIwDewzRplgP25z4WNfXwxBoGmPGQ/se49PkdKNNdNKZ33mIttXFfvL6/r48gci+acy7jIvHeflxmEJN7/VTGIV56pDv2bVesPe37Rc+H9ToDZ8+aaebOznxDj0Oc5vxdEEkp7v2NLGLjF0q86pXvSo94AEPaL1btktMxuwrdyXbbJO5wOG76dAmHVWW8qU7njE/d8Ykw/H6CcQbiThOJUeY9ZDxawta87CB0mE+5pduzLTJd/7qBErjEqUyN3WDrG0fjLZTupEW8zm8OayPQGS/6ryOsjzO6xvDoVryXj8Uyd2TE/fs2o1Yzgxt+wrkoqxS+Zjv88FqtjZpp5uDBcakTxcHowuO6GS0HV66yNuFMldeeWU64ogjWp3BXWAxZh9Z3LBznmpxXQtaTLvMCTZ25lDt1XG9pljLr+ng9PEJxLWKzVA3RRh/DtyMf8kZL2lGfeyAeqWgJ3C1/FIdpw1DQM5T2802lWvbt7AJ7Zu1dUQOfC1/mJ5ZSonAUPPa41yiO4807/XzGKcpa6n9gLWe9Tw+VOGa84HODG398PmgjdAw+ZN2uofpoqUMReCmm25KT37yk9Npp52Wfvaznw0l1nJ+ToADtw7KipsO4WzaXe864lA3ySLfT7ymaYpsrGyesgliNkjGs6vDTc+QU3uyFvOnSWF7tYrjquum3nZ5XZD6OHZNN+XI77p+NOnjvOUIDDWvPc7L8d9kLe/1m6S/XW1zA5Z1XDdR2UM4z7Eu9Lmh6vPBeuzCTvd6OG9VK2eddVZ63OMel66//vqt6pc7YwImYAImYAImYAImYAImYAJDE7DTPTTRHZHHU9G73vWu6dJLL92RHrubJmACJmACJmACJmACJmACJtCfgJ3u/sxc4+cEPvOZz6RHPOIR6bzzzjMTEzABEzABEzABEzABEzABEzCBAgE73QUoTupO4Ac/+EF61rOetfjbwW984xvdK7qkCZiACZiACZiACZiACZiACewAATvdOzDI6+jiK1/5yvSgBz0oXX311etozm2YgAmYgAmYgAmYgAmYgAmYwCwI2OmexTDNQ8n3v//9i19QfMMb3jAPha2lCZiACZiACZiACZiACZiACYxMwE73yIB3TfyNN96YnvSkJ6XnPve56bbbbtu17ru/JmACJmACJmACJmACJmACJnAIATvdh+Dwl6EInHnmmenYY49Nn/vc54YSaTkmYAImYAImYAImYAImYAImMDsCdrpnN2TzUfjv/u7v0t3vfvd02WWXzUdpa2oCJmACJmACJmACJmACJmACAxKw0z0gTIu6M4FrrrkmHXPMMeklL3nJnTOdYgImYAImYAImYAImYAImYAJbTsBO95YP8BS69z//8z/pGc94RnrqU5+avvWtb01BJetgAiZgAiZgAiZgAiZgAiZgAmshYKd7LZjdCAQuvPDC9JCHPCR95CMfMRATMAETMAETMAETMAETMAET2AkCdrp3Ypin08n3ve996b73vW9605veNB2lrIkJmIAJmIAJmIAJmIAJmIAJjETATvdIYC22TuBLX/pSOv7449Pzn//8dPvtt9cLOscETMAETMAETMAETMAETMAEZk7ATvfMB3DO6r/4xS9Oe/fuTZ///Ofn3A3rbgImYAImYAImYAImYAImYAJVAna6q2icsQ4C/+///b90z3veM11++eXraM5tmIAJmIAJmIAJmIAJmIAJmMBaCdjpXituN1Yi8KlPfSodffTR6aUvfWkp22kmYAImYAImYAImYAImYAImMFsCdrpnO3Tbpfh///d/p6c//enp5JNPTt/5zne2q3PujQmYgAmYgAmYgAmYgAmYwM4SsNO9s0M/zY6/4hWvSA972MPSxz72sWkqaK1MwARMwARMwARMwARMwARMoAcBO909YLnoegi85z3vSaeffvp6GnMrJmACJmACJmACJmACJmACJjAigUGd7sMOOyz5Ywa2AduAbcA2YBuwDdgGbAO2AduAbcA2MEcbGMP3HtTpHkNByzQBEzABEzABEzABEzABEzABEzCBuRKw0z3XkbPeJmACJmACJmACJmACJmACJmACkydgp3vyQ2QFTcAETMAETMAETMAETMAETMAE5krATvdcR856m4AJmIAJmIAJmIAJmIAJmIAJTJ6Ane7JD5EVNAETMAETMAETMAETMAETMAETmCsBO90jjdy+ffvSnj170s033zxSCxZrAiZgAiZgAiZgAiZgAiZgAiYwdQJ2ugsjdNJJJzX+12c40zjVV1xxRaH2HUl2uqtonGECJmACJmACJmACJmACJmACO0PATndlqG+55ZZ01FFHLZzviy666GAp0g8cOJD27t27yDvjjDMO5vnCBEzABEzABEzABEzABEzABEzABCIBO92RRnaNs81/6B6dbhWJTvn+/fuV7NgETMAETMAETMAETMAETMAETMAEDhKw030QxZ0vmpxuSiuf19EdTMAETMAETMAETMAETMAETMAETCAnYKc7JxK+y6kuPemmmPJzp5sfT7vgggsWP6QWxB285G/B9Xo6fx/OK+o8OY+B76STz9N2yl9zzTWxyOJvymkbPW644Yakv0XntXhegc8DT+SjPP7uPJeZ1/F3EzABEzABEzABEzABEzABEzCB5QnY6W5gJ6e65nTLcY75OMt8l3Obi8eRpp5+1RxHWE61yiKDMpKLQ62/L+eaQJ7al0zScKSRxyc61DjhyFC7OP55GbXv2ARMwARMwARMwARMwARMwARMYBgCdrobOOLE4pjy1DoGHF85tzi++VNqyuqpc6yHE5w7utTFQcchVqDd/Ok5TjN1aVdBjnOuA/pSNsqgXt4PvkfHXHIdm4AJmIAJmIAJmIAJmIAJmIAJDEPATncDRzndOLClD06tnhznYkpON45vdK7zOvqOE57/OBuOvnRQOTnx6BkDjrzK6oaAXlXXk3LKc22nO5LztQmYgAmYgAmYgAmYgAmYgAkMS8BOdwNPOd25U4ujqifdOMil/6+75HTjCMenz6WmceLlMNdiOfo1pxu5al9ONQ62XnnHAZeMkg5OMwETMAETMAETMAETMAETMAETGIaAne4GjjWnW1WUj3OcO7FyelWWuIvTLUe69ENoURbXKpvfFCBP7cvpJg0dlY4upXp5G/5uAiZgAiZgAiZgAiZgAiZgAiawPAE73Q3s5FTXnNP4Gnf+Oric2yheT65jWn6t18jzv7/Oy/G9i9Od3wxQPf0IW5d2Sm07zQRMwARMwARMwARMwARMwARMoJ2Ane4GRm1ON1XlSHdxuuWIl15Hx4HW31vrh9X099hRxVi3yenGqW76+3Fkk4/+DiZgAiZgAiZgAiZgAiZgAiZgAuMQsMfVwLXN6dZ/94Xjmj9RloMdxevXxnGq5WCTzzVOsgJ/c41M0qJcysVfL5fTnT+tpg714xN6ZOZOvPqndh2bgAmYgAmYgAmYgAmYgAmYgAkMS8BOd4VnfBIcnVeK4/zKYcW5jU+fyY918zw549TjGsc6d8KpTzplmsrJ6aa+2sHhpi6fGNSWnH3K8aQ7d9hjHV+nBVc4wbprkG10LV8qx3jxKQXdfJF9oF9uo6V6y6RhV9zowcZor0tQWekXY/JKAb70d6x+lNpcJg32sT/5dbwp1iYftnn9fN6WZAxhX7lcvfUS9enyuxKSww1IyWActc4oX3Fuu/S3z9ySnDHjZWyRNRvb7mO/1NH/KgF36pdujqqvrNnxBzz7tCUZTfGy9iiZ0Qa6sKD/9KG2JkjuuuLIl/FosuOaTvSJPZU+IaNpPKMMbI6ymkNDzgl0Ymzoj/oV265d9+HR15ZrbW4inXWOdQg2fJhj9L0pMFcYq2XGCZvXOGMn2Av88jDkXpPL1ve+c1b1FKO31iSxq639lI1zAwZ99hi1ObUYW2HuMr+agmwGTvR9mfU72k4bb2wz2nXXtaipD8rrszaoTlPctv4N3V6TLmPndTtFj63FxORrc8Koax8mDYtNvujyPa8TJ1e+8NQ2dsoxSSSLtvLFTG2Rp8nFIl6aXJSJ/aJc1GtiQ7BxdWAd+efjXFMwbpS1Mm3pbISMe2kRZ/HJxxjdtHm3ye6aj/3JXrAdNkfS2gLlpDv140d6Rxn0Bzskj3pTt0kdGmK/dI3+bKxdA3NWdRW3HUKGsK9cP3TOdWHMuwbmCeOnOaLxzNcrvlOOtZP+asz7cuuqV99yq9givPraL8z50C6BWGm57mKntZ2ycOT7UCG3AcaozR7Vdm4DWpOQUQrIJQ9mfDYdcr5x/5Vdt+lIHY0f48N37IK0prWTNQUGlGMuyh7a2uuST7+wE+TTTte+9OUR+41eTbbcRe91lYG32Ed7ZG2CQR5Iw9apw6crT8lRXdqCmeSUbGTIvUbtx7jvnI11uZa9I4c1X/2BXW7vKksZ2bfOOVPf8/N+x+/xpkVtraM85eg7fRV3xr5P32UPsjmYSpaYSjfZNe0SNB9LY6M6XeO+a0Ob3Lb1b+j22vQZO3/zu93YPdxi+Uy+vhN3i3GM0jUWtT6bKwuvDjnLKMTiqM2/tIijD/LzwOKNnvlmF8tRRgt2TM+vtUAjj8W7T0C/0mEFGSz4tUO8Nok+m1AfvYYqy5iUGNPnNv5RB/q7TF/72FfJfqIOui4d+JTXFmsNyu0EG0VuDDggOgSQrkMD3LCNqYS+tohN01f60XVM1UZ+WBLPfJ5o3CMjycjLxjLI66ITsrqUi7J1XdNZ+jXJXWWtVPtDxPDN7RW56Nf1oEo/sYE4plyTxsGyFHSzhnWza+g6rxkXdOdTW5NrbfbhoXGO/UZuzS5qbW4infFFTwX6oLnM2NRC33MBchgDZEdOGiNsJK6NlGcMlt1rsMXYr7wftbHRWDbNWcnCpnO7Qmf6gpwYNDfy/izDMcqdwrXmeG1ekp/Pb/HH/roEtZFzpW6+vsCYOZ+vOZJR01N6dMlfda1UW13WP/QZqj21u8nYTvcm6a/YtiZulwVyxaZ2tro2i6YNTHAoy8bJIsFCuEygrhy40uJHWmmh1mbJwloL6NfWDxZsHTpKC3xNNunUrdXBMWAjqIU52DJ9qPFlg2s6pOX9ZgxrsvKy+t7Xvkr2I1mKGa/8QKC8LjFtYOv5YUrzJjqEpJUCLJDR/r59WgAAIABJREFUZpulumOk9bFFzRf6SR9qfcz1FJ/cBviOnDiPpE9+iEImZUsHErVH3S46LWOPakMHJ32PMfqV1iuVkf3o+yZirbclThqnOB4lHbED1rfSWKiP+RyRw9F3/nWZ1/QJffjkjlFJ/5jWl4cYdbHl2M6mr5kbfPKgOYjt1oL6XKpfq8PZILcBymJbtBXHddW9Bv2adFtlzqp/pb5oHcxvIGCHsX+SgY70ve8cUP2pxPn4Rb1KnMhnrYBLlyBOpXWI9TXygz36lMZfa1E+V6MOpXFSft+1QfVKcZf1b8j2SjpsIq2+qmxCG7fZi4AWOBZYh3EIdN1cWRx04NLC1lcjFksdrGuLOPnk5ZsaC1jTYokubRsxZSQ/LuJ9+1Eqj7wmmdpU5mrLbHylDbHEQocsbZalzTGvt4x9tdkDbaCDbA2bqh0Qcn34Tlnqlg4OGs+mMZdMzRf6OIUg3bvYIodX1uE+deij1u78Rg22Ac84DpqT+ZxHjtjVDlHo1daPZewxjpN0iGm6Vl5tbJWv8puIm8ZOefk45XrWxpNy8GeexPVB5Zl/fQPM2gJ7Ud5mWx3lq88lu1Fe5KG+xDRklWxZbUw9rq1r0ltjCo9VQ4lpk8wuew36NenWNO+UV5uzTbqxRsEuX49Iq9ltG+um9qaS19S/mo6s8132R+prr2Xs496gG0RxrJtsU3mlvUR61saJfNkqcvKgvHwdyMvxXWtG2/onmau2V9JhU2l2ujdFfoB2MW4mexcjH6C5nRShRSouaiUQHHK0SWnTKpWrpbF4IkMLam0RpxyLNfly0DncxLq1NuhLUz+0sCM73zRrMrumozMLbS00La61OlNJZ9xhprFr04uNls2GOvqQ1lR/Gftq2jzREeayVenBODXZSOybxqzUjvLQuy1Qv23zbZMxZL50L230sR3sWWtv1zqxPmzgTv8Ze+wIDlpHVFZjRBt5UF5tblGnrR/L2GPUQzrkelNGeSXdY36Ut+5rjZ3GMravPPrRFLRPlFgrT+s1cjT20RFvkh/z2nTpeqCNMuO1+tyHh/qDbk22HNuZ8jXzsskh0pjW7LpP38S7iy103WvQr0k3zctl5mytb4w761fJoYMne0spaO8p5c0lTet4V31hBKumPT+XpZuv2iOoy7zL7Ua2WRoH5RHXQtP6IlvtszaU2tF6keuelx2qvVzuJr/b6d4k/RXa1kIV4xXEuWqFgBappg2MMnER04ZWEVlMpk5so2kRZ6OU480CHA9zReE/T0TH2EZelkWadlkQudbCSFuxf3m9tu/St6mcFtdV2mmSP2YerEqbUFubbJr0V2NZ2+woE7l0ta+avJJe8JdcbKDJTlQfnWp2qvEkvy1gw7F/beXHzpfuTTrpgKlDU5c6ud46NMEIG8CGJC+WJZ9PaUw0ZjVdqVPLi21wTduUbbPHvB7OCfqV2pF+Jd2Ro/xc5jq/62kR/c75a1zRsymoHyUGpMFHMuQ4kcahk3Hnmk/bzTd0kJyaPpKHLPYG5heyiWs3Z6KsZXh0teXYzlSv1f+azaK3xrSpTNf+YQMl2yvV77rXoF+TbqvM2ZJe2BXnBT75HKK8bLCkk2y/JHcuafShbV7SF2xLttPmcJb6rnFTe6WbJshVfi5DbRPXQlM/NDdK9srY1tqNbfVZ/4ZoL7Y9hev2E9EUtLQOJrAhAlqkSpsFKrGAsNHEwKLF4tM1sJHmjnPb4qWFlXIsgPniS3rXjxZZHdbojw5nLHp8R1auY9f+UY/NoilowW7aDJrqbzIPPstsoNI5Ms7ldLEvses63k2MsUXkcEhqC5obsp9YPuoU0/Nr+pvPn7zMur9L9yZOzBXNEfTrUqfUDx0qNHb5+FNHebSRB6016KprlW+Lc1n63mSPKhNjyrMG0R72QyBN9kF6vj6pvnTW903FrFHoiS2iO0GOBOmMd1NQP0o2Iw6UIWiOwYzxxknhowO15oNsiva7fNS2xgKdxR1ZSo92W+vTMjzgFvUs2XKtvSmlMw5te53GtDQn+/al680Q5Jb2msi87Vo2uMqczfsn21fb9Ed2p7LYHPnYoJhRRmcO0ucc6JvY1vpRms9tdlaSpRsYtFk7V2Enytf6wnzUGsAaJBumXJePxm2ZtSH2o+v6pzqrtic5U4m7ewZT0dh6mMAaCWhh0oKTN83ilm8w2oTysqXvbH7IyO8OswjWFnEWT+pQVwc1FtNcj7w9+lLrB2Wld34oi4cprvuGLocKbUjoOKcgNvn49e0DY8eY54f7VeyrZj9tuukg1MWeanaq8SS/FmBW6l+t/LrSpXvNFpkf+Ti11SnpDl8dUHUQgVfurJDGhzbyoDlb05U6tbxcVvxes8dYJl5TXrqwFrEuqU9NB2rVibI2dQ0nHUqxS62zsKcvTUH9KLEmDRmUIeh76cCtA3VuA7FtyYlp8Vr2kq9JyCSPNrqEPjy62nKXdjdZhn4w9jm7XCeNYWlO5mWbvmNXNccpr9dnr0G/Nt2WnbO5XnyHF/Yl+y3ZKOum8onRUeeXfD0ttTHltDi/2/SEu/pNvbZxkjztl3Cmjtaqkv1QNrYBX934oE10qIXS2OVl+6wNpbro0Gf9W6W9vP1Nf6+fiDatmds3gQkQYLLXFkby+OSBRYs6XQJlS4su9UuLH2XJi86vdGQRbjosUK7UlvSU3qUycsSaDoOSE2MW96ZDt8qqXyWeKjPFmEPTUAcG+Mcxh0WJh8apjUeU1VY25mssSnZQKldqRzJKeZLBoaCvPanumLF0L7FnfnFgzOdZU52SrtRnXsT+cyjSQSqy13jHNMlsyqMMdUr9UP2mGNlN49dUlzwd+kqHQtWV/vo+pVhOTr7elnSEMeVKrPO8/HuUpxsVbcxinfwaPfiUguwr7h+lcqW0Go8+tlySO5U0+oE9Njkk0lVjWJqTKtMW0w7t5WtJrV6fvQb9ltGty5yt6Ue6bKHLnKG8nPC4DjbJn2oe/e27VsqGmuZ67C/yY1nshxtEtI2stgBjysK8KfTtB7Jqa0OpHfW7pHOX9a9veyUdNplWXpk3qZHbNoEJEdACUdrAWMC6fGrdQWaX+nERxMGL3yVbr+A0bV70pdQPyZBjXSojDiyKfQJ6xY2iVlcsSgtxrc4U0vVEbAhd6Hsc2y62QZlaiLJqZWrpyC3ZQSyvjRYGedAGXxt78ptsNZe3zu9Ntqh50DY2bXasw0XeLz2NiNy4pj3y8qBDa82Joi9tuuQy9T23R6V3iTkQojNOXk035GCjlJti0HpYeiKT6yt7L5XV+MneNfalcZHtNd3Ia5vXcqxzHfku3l0cy7x+jYf6k5cv2XJeZkrfGbuuXBg77LZtjaz1D+cUu+jqcCOnz16Dfn116zpna31SOhxh08ZSdlPaPyRrLjH9bZuXeV8Ye+o1zXXV0djkY8raynxvc6RpS3tFaR9RO8R9+0Gd2toQ5epa477s+te3PbU7lXiau91U6FiPnSfQtLmSV/pocVNeDSILpsrkMYsxckjXYQ05tcVdCzjla4G8fNGOZXVwLMkgjbbbNtIoj2v6EPXP8/UdvZBfaltlphYzfujc5FT00ZmNK7KCRenT1b6W2TzRlzGmjS6BAxMM8sOjDl6lDZ4+xn52aWedZZpskbzSmMixgjn5TfOMvlAObqUA0zh2cl5yh05zvunQKn1L7bSl5fbYVl756CW7aBvnJg6St4kYvRkf+pHbdkmfprEQC8nRuhHHWDJlexxMa6FUL5aVLZZskLoc0vuGJh5NY0jf2/Ttq8sY5fs43LTPHMc+Sozb9MMO+jrcspmue02XNSjqiU6y07Y5G+uVrmm7zcbYYyjDp++ZotTmptOwhWXsnHpNc139arI37bUqW4q1JhC3hb79aFobSm3JlkvtdFn/+rZX0mGTaeVdf5MaTaBtFgEZ6QTUOUQFDA5jZRJ2CRycWNjyxVp3m0qH4i5yd6VM02JXY8D4sJiWAotKPhalcrVFXOOpA5zqaiHTIYD6XT9a/JCJreBw5fJLhyfmSdOGSR465LKkc4y12Ha161h3U9fMIbg0ha7jDas2WWonty+x6zrebYyxsdK6UBpvOYT5QQ0bKvWHcrnzqH51ORCo7JixeLZxijo01WFukh+D1t88nTKwy9uGZX4jRIcPjZXsoqsdRH3y65o9MpfRuTanSZcetXGObalsTNv0tWwa3nDIQ42BDr9xfde6nLNQv3P5lGMNVhtdx5JyshnZIvM4BmTGcsorzWvlEbfx6GvLUfYUrll3NIeiPvDKx035sIZlaf6qDHnRFkhHJmOfp5PHOMCyFJr2mj42Qtt5kE7IqfW3zUaiTOxOthjTdY0s5hZ2nu8bKjO3GHYltk39gIPmeizHeORrLGVpo8QV+21qmzGVfshWkA2T1+VTsvW2tYG2SvMAfWmTfsUQ17+Yrusu7ansVOOyZzBVbdegF0YgI8UophRYoFjQ0Ks0+Uq6Up6JnS/y2ihLm01Jzi6mxc2oK284aUHJmemQnB+e83J8Z4xLC6kWag7hGlPS+D6E08ICSdvIov98uMaG4gJJ25TjIz3yfjCP8oNfXkbfNecoHzcG5U8xhnntkIS+pfHWvKOf4sYcZKy79rtmX30ZMaZ8dPBBH8Za36O8pvGO9lGzl8gD/fMPemADUwjL2KLmTWmdYL4zTyJXOGE/9FuHGfEnPbcFzXtYE6hDXX1flltfe6Q9rQ+xTXSnf30O09ShD8gTgyhz3dfooP41zUeVydlrTDV+9I9rfY/9oSx9J49yBI1FtJNYp8+1DtRan2q6kA5/PtJD7XTloX53tWXJn0LMGKJ3vh7xHSal8xH9VX5pvtMvxpD6ca8XJ9Ly9rADylOmFMjXWJbyl0ljvLvM2ZqNwI2+yF7RHZ61PZ81DF6qF88Ty+g/lTrREcznEDpqXsNG+cwt2JXWPcphC8QxKF12AG+u4ZmzJE/nCmQNvbd2XRtK84A+oR96d13/urYXeU31elpe5YQoYah8phYwPvSqLfZT03eu+ujQIjtQ3KU/2pDzsvnBOc+P32kPOaWAHDY26cTiPaQ9YGM6BNBGdBClD4smZfhwXQpxQy7lkyZ7Vl8Ukz7loIOINtGSrqXxhpXsg77CT5toSUYpTfVLeX3S2BDZ+NCDmE0937wlr228dZhCVsledDDR+JbiWtvSYex4FVtU3dI8hGvtYKS8OAa1+QQfjT1zq6/dlPj1tUcdonTQRqbWSmyZw13TnJAO6ke0A9I2FcQf2419K+lTYqBy8NThmDGHR2084RTXcfgNue5hH9iJ+lbSBd1ol0/UU3W68KDv6ne+nkSZYjSVGB7R/vJr+pIH2XpeNi9XWvthnNeL32FdCl32mlK9pjT1A53a5mzNRnJ+zN/a3NF8J8Yup2wXTdzyvDh+us73AGxB85AyXLNG1NbJpvUF2VEWNpPvm9qL1M6Qawr9pw/MjS5rQ2keiGHX9a9Pe5I95Xh6XuVEaGkCTUSdg2poQuUT+2ABX5iACZiACZiACZiACZiACZiACUyGgJ3uylDY6a6AcbIJmIAJmIAJmIAJmIAJmIAJmEBnAna6K6jkdPMaDK/R8DoFH67jqzFc6zUunkLzagjl4utaypdMXssovVqCrPj6CK+H5E+045NuysdX2fJXe2hDusdu5jrHPF+bgAmYgAmYgAmYgAmYgAmYgAkMR8BOd4VldJBxbOW8kh7/9gaHWn+rg4NMnv7mgr9nIJ86coj19xr536/hCCOHtrgmIIu6tK0gp5s0yvK3knx0U0B1idGHdGTEEHVGnoMJmIAJmIAJmIAJmIAJmIAJmMA4BA71xsZpY5ZScVT5xB8p4MmxnNjorOLcUlaONA6v6ukHJCIEOekxTQ6ynGby5GDjXCsoLT5JJ086RL1IL7XfVF7tODYBEzABEzABEzABEzABEzABE1idgJ3uCkM53Xm2nlzHp881h5e6OMw4yDGUHGGc+ehcx/LxWk43bcYgHdAvhlJb5Kt87qTHur42ARMwARMwARMwARMwARMwARNYjYCd7gq/mtMtpxdnVqGrA0tdvTKOfAXJzB1p5ce4VlY65DLsdEd6vjYBEzABEzABEzABEzABEzCB9RL4hee33nYn39qQTjeOMn/njQMc/8N6Qag50sqPca2sne5IydcmYAImYAImYAImYAImYAImMA0Cdror49DmdMdXweXw4hDnQT+cph9SIz9/+ixHOj49z+Xou8rmT7SlQ56etyU5Kl/SWWUcm4AJmIAJmIAJmIAJmIAJmIAJrEbATneFX83p5kk1edGJrjmw/PAaZeOvndNcyRFWe6X/Siz+nbad7sqAOdkETMAETMAETMAETMAETMAEJkjATndlUOQE59k4zPkPo8npxiGPQQ5y7nSXfr2cJ+e0mf8qOc59fKoumfkTbemQp5ccfHRUeT/pjiPmaxMwARMwARMwARMwARMwARMYloCd7gpP/ddgOLw8fea/8uKadP13YFQlXT+ORhz/yy+uJYen1TjllNH/441DrafYsSx1cJYplzvhlMc5Jz+2FXVQl8iPbSmdWOWjQx/zfW0CJmACJmACJmACJmACJmACJrA6ATvdFYY4rDwNltOKI4yDGh1dquqJeIyjSBxrOd6qr6fM/LdjUR7OvZxh5Km85OmpdWxLT75jGuVK6XoKHstyTXkHEzABEzABEzABEzABEzABEzCB4QnY6R6eqSWagAmYgAmYgAmYgAmYgAmYgAmYwIKAnW4bggmYgAmYgAmYgAmYgAmYgAmYgAmMRMBO90hgLdYETMAETMAETMAETMAETMAETMAE7HTbBkzABEzABEzABEzABEzABEzABExgJAJ2ukcCa7EmYAImYAImYAImYAImYAImYAImMKjTnf8qtr8fVvx1c3MxF9uAbcA2YBuwDdgGbAO2AduAbcA2MD0bGOMWwaBO9xgKWqYJmIAJmIAJmIAJmIAJmIAJmIAJzJWAne65jpz1NgETMAETMAETMAETMAETMAETmDwBO92THyIraAImYAImYAImYAImYAImYAImMFcCdrrnOnLW2wRMwARMwARMwARMwARMwARMYPIE7HRPfoisoAmYgAmYgAmYgAmYgAmYgAmYwFwJ2OkeeeRuueWWdNRRR6W9e/eO3JLFm4AJmIAJmIAJmIAJmIAJmIAJTI2Ane4wIieddFKv/+LrmmuuCbXLl3a6y1ycagImYAImYAImYAImYAImYAK7QMBOdxhlnO4zzjgj4SgryBGPDjbXe/bsSTFN5R2bgAmYgAmYgAmYgAmYgAmYgAmYgAjY6RaJlBYOd/i6uCw53WRcccUVdrpzWP5uAiZgAiZgAiZgAiZgAiZgAiZwCAE73QEHjnQeak43T8NL5fP6/m4CJmACJmACJmACJmACJmACJrC7BOx0t4x9zelWNV4x55V0yuGIq7wc8pivOoopf9FFFy1+aO2www5bxHzPww033JD27dt38O/Nub755psPFkPO/v37F/Vp78CBAwdlog/1Y6D8BRdccFAeZfg4mIAJmIAJmIAJmIAJmIAJmIAJDEvATncLTznRpb/fxpmV80o5nGEccBxorvP82BSOL79oHv+GXI41MhWQwa+f40gTiOWgIwPnG0edvzEnnbroQhrySSOPsgqUoS3S+KgPyndsAiZgAiZgAiZgAiZgAiZgAiYwDAE73S0cm5xuVcWx5SPHHEc5OrnkIScGOcqxHPUpiyOuIAda34nlJPN0W0HOfqxLnhzv+AQdJ1xOvOr7vzQTCccmYAImYAImYAImYAImYAImMBwBO90tLLs63blTHcWWnG4c39xBjnW4xnmnLnEMONu5TJxq0uT4q7yejEenWv9veHT4owOvuo5NwARMwARMwARMwARMwARMwARWI2Cnu4XfGE63nmjHp88lNfi7cBzp2gfnWaHmdJOv+ioruTj+1IvOt8o4NgETMAETMAETMAETMAETMAETWJ2Ane4Whpt0uuVId3GKVTZ/0k33cqebNMrhtJNXet28BYuzTcAETMAETMAETMAETMAETMAEOhCw090CaUynu+mVdNTSa+T531+XVG5zuuNT8Vifp9443TjfXdqJdX1tAiZgAiZgAiZgAiZgAiZgAibQTMBOdzOfg/8FWOkJsqrisDY50KV80vjE//pL8vT31fqbbn5pvBT035KRV3O6eUpOO01/P652mvpQat9pJmACJmACJmACJmACJmACJmACzQTsdDfz6ex0xx8qy0WWnG792jj14uvjONLRQdYr4KTl5XC0FeR050+r9ffb8aZByYnH4bbTLZqOTcAETMAETMAETMAETMAETGAYAna6GzjqCXDTk2KcWfL5RMdWYqOM+FQbB1qvdRPj8OJg50449buUk9NNfelBTF3+i7EY1B/poz7kDnus4+t2AtzgYAzFv73GoSV0g0T2RJzfzIn2FMvpunRD5dBWVvvWpY+yV+kUY/K2OTCnuEG2zA0s1gTGT7y4Zrybgm7eqQ5xPt+b6i+T17WPrEm6aah1KN44XKbtqdXpyqJNb7jwhhN2wxjW7Geo9tr0ifnLtMk6gf1qLYjy5ni9DINaP7Xf1vYJ2tI6AL94c70mc4j0Zfqoc8y6dByin0PI6LIPdmkHG2AN1zoZbWIOe736KP3jPuTzpOj8Ima+MFdWOQf12fOHaO8X2ne76tqm5hA2g/3syhpip7tiRzr8xEWE67goYiR5fjScUn6sHzdX5DCZMNg8sPhKHyZrqZzaigt4zZBJx5mT7nz3AplT7/6d8YkLYRzj7lLu+D/VGef4yccFhwobiGV0zXiykI0RuvYRfdFDOsVYtjuGflOQidPEXFL/++jEvGdOYkfMZc1PmJXWBGSzftBeZMw16WOFrn3UfEAf9QUuXNf6M5bOY8ntyqKtfeaW7Ib5XVs/hmqvTZ+Y37dNxpYxZ6xxHFkP5j7efRlEfvk1LDTWpXHGFrROUlZznPk0Zli2j7o5EM89Y+q5adld98EuejLXtSayb+fr9tT3evUR3VnX4z6EXTgcSoC1UGsj475M0HoQWXOd2w6yh2ivr45d22S9wWZYN1jbWPNgsgvryHIj33ckXH50AhgrRlvayEdv3A0sCMjRWGYM2Li6LDgssKVDLIcBxr+Up+FB/jK6qT5xWx/JR5dSYGFlUd7mwObHODBOfQIHrJwbMpBVu5EC62XGs69ueT/a+kg/2FDjQQA9tbGy4W5LaGPR1k9xgU0+/qW6fdtDfpd1pdSW0rq2STnGvclmJXNucVcGbf1inotRae4yN3HKY2D+w7Rp7VznOEs39FFfVrUxyZxL3LYPtvVDNyuQUwvYQmk/Z53AHkp5krWOvV5tYQNNuqic4zsI6KbbMjyW2fP7trfq+YB+NbXJWprbPesXNp2vfcswmnodO91TH6GO+rHIYrSljbyjCBdbkcAqY8Biw2LUFDjk1MpwmGu7uzzERtzURzbemoOI7jgWuxCYh303rtKhBWbIKjmp2MGyG1Rf3Upj1tRH9C31R87DEO2XdNpUWhOLJp04PDMnujrcktWnvSGcMdpta5PxlgNWWwOk/1zjNgZt/WIsWKNra6gOnqzleaBt+NbCusZZ7Wu8tUbRp10KtTHswkAOe+54xLpT3+ulK3O9qR8q5/gXBNj/mM99w7J7ft/2htifm9osnQ1gwfq2C2fE/iPf11Jcfi0E2KiZyGy+DpshsOxGLGcEJ4oNbJkxpG7bYRf9lpEdaS7bR/q1K5vzqodz8cZ5RVbpRgssyWOjYu6XykhOHg+xqS7TR2yPem03h3J9p/59GRb0aVkntU97MGfOrhra2tT+s81zvI1BE2M5qczT2hoqhqWbbDrE1ub5usZZfWQO4xhqTg9hY5I9h7g2hm266ybFsjdMkT+lvR5dNC9qN1vbmOxavuZy334vu+f3bW+I80HfNmGBw73N+4fG2063SMw4ZkPX4lfasGfctVmpvuxGzEKj8WMD40Na7Y5gDqXL62bUQT8OSauEZfvIgsqBYxeCDiGr9FVzujSfyWNTg6nshbhUtqTDEJvqMn3UAb3t5lBJ5ymnLcNilcN3n/bW4Yxhj+jEp+YUTnn8uurWh3suE4da87O2huqgWlqjlVdbQ9cxzuoTOujGmeY0fdqlUBvDNgbL3miT3Cnt9Yy97FLznz2pZL/S33E6yKwPi1X2fI1R1/Yov2ro2yZrI2dg+rntwU73zEdYm54WvVUOBjNHsXH1l92IpTgLDjLkTHVd/FiwdAiSrFKM7FU3xGX6qNdoSzptY9qqc5BDLYczPm2bEE6O7oDTLuPTFrraVZOcZfqIs41tt/Wpqd0p5i3DgvlKPcYOh0w33YhrjpX63qc95nsXm5DsWtzUJusP+dgr13IsGOsh2q7ptO70JgZNujAGcc7V1lDk8ymt0TrE1niuY5zpI3M3Ho5pF51rejVxmXNebQyb+iSHGV6shVoD+N71JvtU93rZOH3hU7LhJja7lKe5vGyf++75fduLa9WyOnZtk75oLm3bzfgaOzvdNTJON4GeBLR4rLrhsBDp4NplIaJsXk6bX5e4zyK7TB9xKjhU7EqAeR+mkYs2K40bB1wOa20Bm9PNGuyHoLGSrLa4j90u08cuDmVbP6eYvwwLjRUHb41vHMMmx7vWXm47bePdh2WtTWTIeWAdkt5xDWP+b0NoYlDrH04qXDQnKad5mc83jVeeTh2NLXV1rfJtcU23UnpbHxlrjTH10ZU66LVLoTaGTQx0c4q5z36NbfDRTVPspC1Mfa9XH1nrHcoENH/Lud1T434R15dcQq092XDb+qH80rqUt6XvtTaVT6y1Q/KJt2WviP3Mr+1050T83QSWJKBFrM/iVGtKd8U55DQFFlsWKzbvtoB+q+q2TB+31dmq8WY82HSWDYwlhzK49ZGlAw9xU1hFN8ntoxd10Glbb7z0ZQEP6vDJ5y3wTpHjAAAgAElEQVTjTnrTobVPe8x35uyqoalNHbCiM0Z7Wpuo23QoXFW3ddVvYlDTgUNkPh9rayjy+ZTWaDGujeU6xpnxzfcj2kXnml41LnNPr41hU79Up+RYaK3Pb55HeZpP+ZoRy+iatkp2pPwusfTtK0c34XQzsUtbu1RGc3mIPnfZ8/u2R/lVQ582sRPdeKqtf6vqM6X6drqnNBrWZdYElt2kap1m4WpbAFl084NQTd4mNmIWVO7s71Jg42gbty48OFzpiWhXp6XLAXgI3fr0ERugzS6HxS5cplamDwvpTh0+pdA25n3aW4czxtiiU+lwrgN4kzNRYjDFtD7c0R8epblW2yeaODblqS3krhpqfWTu4hjmc5g+UmeItlfVfZ31a2PYpIPqlFjJeWq6MTn1vV59l02U1gOV2eVYc3koBm3zr297lF819G2T9jQ/mubAqnpNoX5515+CZtbBBGZGQIvGUJsN8toWwNLrZjVsyFtVt7595K7+ti+iOe/awTUv1+U7/JDX9akB9sIYNYU2m2qqq7yufeSQzvjnh3XJ2Ya4K4vYVznWMU3XOrDUxrxPe8z3NntQu01xU5tyrEtri9YLHIa5hyYGpb5pHKnX9BE35gnl8jcGkK0nobWbb2OPs8axqR/kDWFrJZZTSxMPjV0X/eRYlxghB35NN9CnvtdHBvSlD5tYd9uvtS4M1U/klWxK8vu2R/lVQ982aY8zQtscWFWvKdS30z2FUbAOW0FgmY24qeNswE1PiPS6We0glstGv1U3wr595LDY1Idcx234zsYxxMYFC3j3eVOAsm32MIRuXfq4Cw43Y9SFRW7XcrBK85HxaRrzPu0hHxtaNTS1yfwmv9SO1ovaDYRV9Vpn/SYGJT3gQv/zjw6k2AB5mq8427SRv36sw2jT3/yOPc6Sn/dFdkyfyCvZc4nN3NPoK2PVp7/ar0vrL3KQV7s5pbqylTZ+Q4zFMn1EL+Y6+75DmYDmfzm3f2rbnt+3vZJ99tWqb5uS3zQHVGbusZ3uwgiyaGgzKWRvNImNHINmQewScNxKk1J3XUt31bvIdZk7E+iySbG5dtk4scGmQxatM4a1MixeXT9xkaVdPrXQpY+qixx02OannOprjOlzZBrzuGb8ux7WmL9d5zr2EA/sGquudiCdGC+um8atrY/UhUHJ1rGL2uEyZzWH720sSuMNX+rlT7bgRnrTmNfagzd5XT9iW9JPeYprbZKPzuwxpdePWZ+a5oLkzyFuYoD+XThSTvNS8y32HV65w6KbGtqrNzXOUU9dy46b7FVltyluGkP1Ezb5+qexy/dY1m3mUG3Nnfperz4Ts6bJVmO6r+8gIBuo8cAGsJ2aLcR6+Z4f83Rda0823HW/0HrVRb9am9KpFDMnmuZAqc4c0+x0Z6PGwOuVToxxSoHNlwUNvbpucpS30z3+KLIQaaGpjY0OT/FQxaLJeDJO2qDZsJDVtuhyQBvSeaF9LcDSJZLr0sdYnnlEv3YpMHYwZIxLDGFBHmWwBwU5LUqDNTf+SvxIoz52Rjk+2AHlhwi64ViT19ZH9JHzgB3HD+noTpltCG0s6GNpvEnXoUdzGHuBD58any7t9eVa009yurQp5wubQXc+XGPXuYMhuXOKuzBo46j+atx1iFU6sQ6emnuUgaG+x7J9r9v069LHvE2Ne23Py8tvw3dse5m9nr5Tl/Fkjmt/0BlAa3+JEeW1TpTy+6bRNuswH+kRZXTpI/3gI72Rg53qe5Tn6zsIwAhmcC/Nf0rBkPw455fd87u013dsSvpFGW1tyt6RQ1kCLFifakyi/LlfT8urnBBNLUgTUmmhyi5uclMbg1wfHaJkM4rzcvmBivy4uVGv6+bKYkV5LVp5W8t8Rxfa58N1DF37GOuwiO7SBqxxj3HpMMpmw8YbnZF4o4/6HOpq7KinAzRl2ZA5MA8VaBe5pfZj33Sd9xH7UV4pLt1IGEr3dcop9S1ngT6l8ZaeHEA0ltgEdpDPPZXt2p7Kd42b9OvTJntTHHvGecj1qWt/hi7XlUETx6iT1tLaAZP5LacO2xjK2WrSr2sfYz+43rXziMYu55VzKe31KsOckBOFHOZMzRaoM9W9nv1BDiQx9hX3NPXX8R0ENKej7ZCWh9L+u8ye37W9vP227yX9VKdLm3lfWOOwnW3YK8ShKbbTXaGjiVHJ3ljyrm1yGwPthk3ABEzABEzABEzABEzABExgAAJ2uisQ7XRXwDjZBEzABEzABEzABEzABEzABEygMwE73RVUcrp51Y9X/nh9pvT6H/l6RZCn0HrlJr6iq3zJrL12hyxeX9LrhsT564rxSTfleS0DuehG2zHwuoZ0j+m5zjHP1yZgAiZgAiZgAiZgAiZgAiZgAsMRsNNdYRkdZBxbOa+kx79JxKHW37HhIJMnp5m/XSCfOnKI9fcQ+d9y4Agjh7a4JujvfmhbQU43aZTlbzn56KaA6hKjD+m0H0PUGXkOJmACJmACJmACJmACJmACJmAC4xA41Bsbp41ZSpXTHX8YgifHcmKjs4pzS3k50ji8qqcfFogQ5KTHNDnIcprJk4ONc62gtPgknTzpEPUivdR+U3m149gETMAETMAETMAETMAETMAETGB1Ana6KwzldOfZenIdnz7XHF7q4jDjIMdQcoRx5qNzHcvHazndtBmDdEC/GEptka/yuZMe6/raBEzABEzABEzABEzABEzABExgNQJ2uiv8ak63nF6cWYWuDix19co48hUkM3eklR/jWlnpkMuw0x3p+doETMAETMAETMAETMAETMAE1kvgF57fetudfGtDOt04yvydNw4wf3+dO8I1R7oEqVbWTneJltNMwARMwARMwARMwARMwARMYLME7HRX+Lc53fFVcDm8OMR50A+n6YfUyK853aS3BTvdbYScbwImYAImYAImYAImYAImYALTIWCnuzIWNaebJ9XkRSe65nTzw2uUjb92TnO5002a2qNOHuLfadvpzun4uwmYgAmYgAmYgAmYgAmYgAlMl4Cd7srYyAnOs3GY8x9Gk9ONQx6DHOTc6S79ejlPzmkz/1VynPv4VF0yaTMG6ZCnlxx86qk88hxMwARMwARMwARMwARMwARMwATGIWCnu8JV/zUYDi9Pn/mvvLgmXf8dGFVJ14+jEcf/8otryeFpNU45ZfT/eONQ6yl2LEsdnGXK5U445XHOyY9tRR3UJfJjW0onVvno0Md8X5uACZiACZiACZiACZiACZiACaxOwE53hSEOK0+D5bTiCOOgRkeXqnoiHuMoEsdajrfq6ykz/+1YlIdzL2cYeSoveXpqHdvSk++YRrlSup6Cx7JcU97BBEzABEzABEzABEzABEzABExgeAJ2uodnaokmYAImYAImYAImYAImYAImYAImsCBgp9uGYAImYAImYAImYAImYAImYAImYAIjEbDTPRJYizUBEzABEzABEzABEzABEzABEzABO922ARMwARMwARMwARMwARMwARMwARMYiYCd7pHAWqwJmIAJmIAJmIAJmIAJmIAJmIAJDOp057+K7e+HFX/d3FzMxTZgG7AN2AZsA7YB24BtwDZgG7ANTM8GxrhFMKjTPYaClmkCJmACJmACJmACJmACJmACJmACcyVgp3uuI2e9TcAETMAETMAETMAETMAETMAEJk/ATvfkh8gKmoAJmIAJmIAJmIAJmIAJmIAJzJWAne65jpz1NgETMAETMAETMAETMAETMAETmDwBO92THyIraAImYAImYAImYAImYAImYAImMFcCdrrnOnLW2wRMwARMwARMwARMwARMwARMYPIE7HSHIbrmmmuK/8XXSSedFErdcXnRRRfdqSz1HUzABEzABEzABEzABEzABEzABExABOx0i8TP45tvvjnt3bv3oEPd5EiTt2fPnkV56g0ZkN3U9pBtWZYJmIAJmIAJmIAJmIAJmIAJmMA4BOx0F7jiQOs/qm9zpnG6b7jhhoKU1ZJ4um6nezWGrm0CJmACJmACJmACJmACJmACmyZgp7syAji9ON68Rl4LBw4cSKVXz2vlu6afccYZi7btdHcl5nImYAImYAImYAImYAImYAImME0Cdror44LDi9PNk+xbbrmlWGrfvn0Jx1uBOvHVdK5zxxlZF1xwwcEn6Tjt0XGXs68n7XL88+9qM6bnbe3fv3+hP2XQBX3zMpLj2ARMwARMwARMwARMwARMwARMYHgCdrobmB511FEL5/iKK664UyleOydfgVfMcW55Sk3gOw577rTjcOP84nxHB1xyiPUjbbmDLGc9f/qOPNqO5bkZgH56PZ4+5GVim742ARMwARMwARMwARMwARMwARMYnoCd7gamclSjc63iOMA8SVYoOcpykqMzjBMen45Tn6fQMZRkkY8cHOfc6S6VxxGn/Rj4HnWJeb42ARMwARMwARMwARMwARMwARMYnoCd7hampafdPKHOn2DjoJOmJ8uILTnDyMPJRoZCdN5r9Ujv43TzxB194o+8cW2nW9Qdm4AJmIAJmIAJmIAJmIAJmMD4BOx0tzCW4xz/7hoHW6+Rl6rjeMvpzV/p1tNzHGJkR+dbstRm7iD3cbr1ejvto0u8GaB2HJuACZiACZiACZiACZiACZiACYxLwE53C1891Y7OM0+r4xNkicCxxTknH+e69Ho5ZXGe9QQd5zt/3XwIp5t2pA+680GugwmYgAmYgAmYgAmYgAmYgAmYwPoI2OnuwJonxTit+vXv+NRb1fVkOT4BrznPqqNX0pEdHe9avT5PutUGMfV4pZ128r/zjuV8bQImYAImYAImYAImYAImYAImMCwBO90dePLEGIeVD84rznIeeHLNU+sYas5zLIOzjtzoyNfqLet00x5P7PV0PbbvaxMwARMwARMwARMwARMwARMwgfEI2OnuyFZPu3FcSwHHOXe6S6+X87Q8DzjcTU63/h5bTnf+tFr/ZVi8GYC++d+Ly5lX++QjMy+nfMcmYAImYAImYAImYAImYAImYAKrEbDT3ZGfHF4c11LAacbxxtmlLHF8pVv1VCZ3pOPr5TjPlMOZJl11aRfHng9lyMMBl3NPe/oldPThu/72nPa4YRAddt1IIHYoE4Az3BjTUtCbCoxX6VO6yVKSw1jKXpDDdbSJWIc2daOFsugXbSSW7Xsd5Zb6I3vqIhdm2GGTbtzwiW1y3aeNLnqsWoa5wxyJN8ZymbEPy3Lrw4Ky6MRaIBvQ3M91W/U7/WfdkH02jWfeFnpSPr8hmZfju9a9yI821xXyeUXbtXkvnbSGRp3jGqtybTGcGD/tI7mtDWFfXXToOlbIalsbh26vTV7X/GXGWbLpM2Oheaf0plhlo43oujQvGAPWdJUZck3EnjWPkY/9YntdQx87xJ7Vh1I81nrVtS8qhz2ICePRxIS1MDJgnvbdr+L40h7rRdMYwEn2sEx76mct7rK/5XWXmQeSob5Em6iddVRnqjFjp/mNDXXtB/MQOxOLtn0m9r/rntPnTBnl97nusweor6uMe5/2+vRjnWXtdPegzYJXWxxZeGVUWhiZSBgY31nYCJTRAk8e30sTlTrk5xsAMtUOiz/6sIhTHoNUIE8ykMPCQLkYKE9erBfzd/ma8YyLW21R1KIL6/zTlS3jT9l4CNGBJbcN7Cg/GKCbNu9VxgxbQg/sM+8LNsenS0BHbE2bUW53kkF7tAVnymheUK82z1R3XXF+4Cm1OwS3PixUFk6Mk9YDxg6WQwbWBtrhA4s+B0xsF/3Qi09bKNldbv9tMpbNp1/0EZbozLX0rq2P2LnKx/mitb6rLnHvYD3J15oh7KtNlz5j1XVtbGqzT3tNcvrmLTPOtMEYyJbZW9GftLZAOewo2oeusbF8vmo/kQ3QBmUo29euct2032ufQR5zDtld+kIZ+lKap8wDPjFQrjQ/SEfOqv2JbS17rbkHdz7oq/HKZcp2GA9YaGwor/HK6+TfqSv54sB3rktjoLGXfO2r6DJE6LK/xXbQcZl5IBnYIH3VHCBmPs0xoDcfxoSYcexiC9iZxhwefeYBZUtzKpehdUdzHb5c0y55qwbsT7bcpc+rjnvf9lbt35j1209CY7Zu2SYwcQJaWLTp5eqyaZQ2SxYJFqNSXi6DRZR28oBsNqgYKEf5PLDwt7VHmVo/kMeizOJYCmwqbBZ9ArLQiXZLAXn54YE+U6emR0nO2GlsaOiEbqUwBLc+LGgPO4i2pQ0VPXOmuc61fuTlJLN2IMzL177rIFvLJ53xrtlJU72h8rBv+qsAW819nJJSIL9pPpXq5GnURz6f2rihV20+tM1L5Pfh2mWs1AfxWYVBn/bU7irxMuPM/GcOLLMuwag2rox5PABrnSmNNW03rb9t44w9014uQ212WRP62CF9pu+lgJx8XyuVW0ca9hDXUXGCN2xigFFJb2wYtlFOrKdrmFA/ytX8p724/lCHvJLN0V5JD7VD3LbXx7JdbYBytFvSKcpruqZ+G6em+lPJY47m8xTmsKnZPbpjb21lmvqI7C7rLTZS0qNmw7HNLmuBytMG/WnTaahx79qe9JtibKd7iqNinSZDQAtpaVHhwBQ30Kg0hxsW2C6htgizwLB4xsCCmKeRzwaAnJo+lGnbiPNNX+2ySSK7dnhUuTzWoYF2S6G0+Zbu0JbqrjuN/tc2oyG49WFR46kNqZYvZrV+KJ9Y9jTEZkl78GsK2HST7TbVHSKvxgy90D2f/+hamod9dGE+cVjn0zS3VrEv9K71raRrl7FSPeSW2Ci/S9ynvS7y2srUWNTGmXm5rKNB3fxgLv1Y5xj3GLReluqgH/O7FtrGGRuqjZXGoG3+9bHDWln0h2dTfq2PY6SX1l32bljFOck1aSX70TwojVvUmT6X2tNayzjEoHHJ66i9eMMm1uOaMvmalZeJ3+lb3n7MR4dl54Hk0M8mG1a5OcT5mKAzaXCsnfu0Py/LoM+egx6ldkhr27ea7CAfG9lik60NOe5d2st1nNr35pPQ1LS1PiawZgLLTnIWtrZNWF3RZhY3efKQQfsx6ECQH1pYTNsWS2Q1LY6xnXhNP9oW6lhe17RVO6ioTB7TL+q0HQDzemN/R6c2vrkOy3KTnL4sZKu5bUie4rZ+wB6HYKhxoD1k1QKcyMfGsONlbLQme9V06Z7PTfREZ+Yuc3IZe9W877pO5H3pYl+wzNeQXE78rv7GtNq17G2V8erTXk2PIdKlRz7OWm8Z7yED8nKZOrQzD+KhHtvC1po4t41z01gpr23dqPW/ix2qrvqyzHyRjLFjnKZ8v4MvY1CaS8qrOVtt+pbqyxbyGzPIUvncfmI76Em5roG+MQdqYYh5AFO1g61FG6+1O6d03Zgpred6mJDbVZ/+Md7w67LnaG/J1zPaL9lw1KPJDmI5rrV2NNnakOP+/9k7Gytbap3bfikQAymQAyHcGEiBDMiADIjgRkACJEAG5HDemDwmVwi5yrV/unf3WR5j4ypb1s+SLFu9uw878rqOr/a+vgm9mqbRJwi8AwK3bHKT7+6hYkLmgDVhT5cyzOeyYkHEQUhjDUn2TB62HCXHFbxcJpS1opnGvRwgd6ehPwn61svfjoxbabwsXFl/K27IuAULY/XsQnt2qD7iglVxsqCpY/WZWPdgBmc+jJ3Fc+XxrGd0R7fa0Itx96E6X4lb93znXeWcPe/EF3twd/8h78xXVSfj7ZacIp8r8lzzjH7lZ317tqeu6kTsTN9UuveIC84RYo3c7rmwknPmZ301xahzV+Kk6rETh9IjH3tetXl292LFswxbe3OOGLqlub762LGJp3NHOOJL6HYbcT7JYj0xeO8+QBf4y4eePXBFx11b3ouO2JjiA33wFTZXH1/R8+qZ4/kCxspc3Sm7Hqs46HS8mztWfny038/kTTq+2liK7lfzSPR5KQRu2eRcLFbJd2UcidEDiQvXdCFzLRcCL/zQ7hbEVw9i5SGrX0KcO+pJuNiE3LOGvRxMfDhgXq1hx5XDCP1vxe1WLDhUd3Q8ozG28Bs8fWfdLXHAOvA7a/gdmVXe2Zpnz7O/juKXYgyM3LtHtFVX8gNrWMv+RQ7vZ3u/8tiJL/bgrk7w3vUVtPBF59WFq+q6er4ib8XjEeOTn8nj2EdOsljkHdyvYNr1M3/3cd9rPO3uuTM/e77Arzf9eKtNO3GoTOIeLF+tkXvACFvAvzf2ub7v55Pn3IRt5zO9K7fy1ScTT+Whz6qx/sq+hNckC/6P3gfohSxk8rmi58re9xxnPxPXq33AvLbia3M/Y8Ra9fuOHbtnjnseOVfOlVUcTLoZpzs+fITfr8ib9H2FsfWufQXtokMQeGcEbtnkXNJIeFdbvWydFZ81oU7J3iS/0x8lWQpAEvYtjSSLfDA8avUA9oC4pbg7knHvHHod4dT534rbrVhwEBMH9DZjdycGoMFf9YLAevl58eoylHXUa9MRTZ1DJvGPTrfso8rrnmdko8dOAzuwQWcxO1onLRcwY73yIH6O2hRf4rzr74m/PKa5PmZ8ofet7Yq8W2WcrVv52csxMaA/amzu/rCzy2fdVNhVOnKufuy0Yub8WS9f95QX/VpowuOWYniKQ+X1HnnI2dkffe2z3zuGYNyLIfwGHThqA/aLK/FyS5sKIvcWevTGflNf5nze6Sd+8ljNPWsfeKbcer/ouLzHu34Se/J6P7O00zniio/3PeLnllbPC+Ox81EG+iGnxnTXXRtW/ZTn5THNdV18F49b/H6LPOW+Sp+i+1U8ET1eEoGrm5zkR9KqyW3HMJIjH9Z7iPckKR8vidCaVEnoXt6l6z22XEmOrIf/rZdLZIEFcs8aeGGXl83VBeCMz7PmseOKTvfgdgsWXIz6Yb/C4sgOfTZdBIw1+isNeeB3pVn833qRvSJrosUHYHC2p+paLxP0Zw08+PQ8gQ8ZP7uQ7MYX/tzZf+p7xVdXc6Myan9FXl33qOcjP6ubBbcyzfH4ieerDd92nvJQH+IA3/nDmbM9t+NneLuH0Z29hR4887kS6+q7G4fQY9OUV+T13j2+JKbFfNo3dR5btAn8dvZ9t5E1k2/dW8Rgb+ZoZK4a66HbbfCaZLH+WfsA3hb0t8Term1vQcc+Eifip+Z1fTndo7zvEEe3tKMzh7jiQ1wTq/iYvuo2yVzFwUSrbVdiDT63+v1WeZPu7zW23rXvpVHkBoEXQuDqJicJXi0UkFEv2SRFk1JPgB649bKnjj3Zdxihu5oc4XnrgaiuyN1t2O6lp9q4u/5ZdEeXkknmPbjJbxcLDuzp4iaf3veYqvP6bKIhDsChxmpdu3qGF+uuNtZNelzlcws9eN5yEcLOnXiHboXJTvzvxhf+3NFHjK74Cr7YcDWnKIv+iry67lHPR35Wt8k+8/PVGGEP4btVQ2bdy9B7YT7y41U/Kx/98eHVPe363TiEHsxuKUyV9VY9WO5iwhkFLZ+r5xW+xd9TEaQOzPd2NCctsQLdbkP/SRbrGWd+4nfrPlAvbZl4S/ORerGqecE8Oe1fi+a656/ai286b97rnj66U3Z5qzjodLxr21X/3er3W+VNur/X2PWb0HtpGrlB4B0QuLrJ/en3FVW5uCCnNy9b9TDnkJuSor/6VpN954eMK8mRn97WxN35nb2bWCfbjtZqC5eSV2lHl5Ku4724VX5nWIDR9BP0yqM/T/EjDfyObGXuqGiQT+29iNSxnWfi5kjXHR630LCHjvbREU/03Yl3MATLqYnXKv6vxBd7cEcf9VC270c9fLHhSk7p/K7I62vvfT/zswXFZJ+2Xy0i2aurC7Z7r8sj/xMvR7n4qp/Bjos4PPEhMXW1XYlDZCGnnmVX5b0lPWfvTp4zRq7mYPAgDuinBk7ghR69EbfMreIIeuKzx1HnU9/hx16cmjZO/G7dB1UOsifeleajPLMnsKfmBQtrsOoNu6EH41sbfuu8id0+Bv/pTtnlruKg0/Gu/2/x3y1+v0fepP97jM2n/ntoEplB4AURuLLJPSivXCxMulOCNIHXhEaimpKil5qJj7AyV3k5vuo51I8O9tU6x49sk2bq0XPnwjOtfdbYCvdJ3r24VZ5HWNxScMN7ip8q04t4HfP5lgsC8lh3tXERubX4vSpL+rNCTLpVT9zu7H9iBEym/QheR/F/Jb7gf5QTuh1XfAXflQ2d7+r9irwVj1vGd/xscTPhp+2rH4ysdGJvrWJanlNM+MO3Fd+rfoaPMUh/S7sSh9h89EODW+Q/cw1xeVYIGR8UMqviedLxrOB2jQVS520sHP2ghFia4kjevWcfY/PUtPOR+0A57J+PFBfqverBHCxrXvBeOOErfS3SV7xX4/3Mkefkr+lO2flOenYa349yljRTf6vfb5U36fBeY9dvQu+laeReRoBkyQbqm48kTqKbfop6WcgnX3Blk5M4zzAlIfZLOUlzSnT4j7l66HIR6GO4wMQOfxqJf/czyYYHco4OdhJnPVz+FFz+c5T8C9m/HrGxx+y/iN544OhS0lU5ww1f6ae+tr+vsAD3ld+IQ+aN3d04UCfX9YvAdGATm6yrMdptQE90uNLQ/2wvXeG3Q8t+W31jtVOYgNe0fvK3e6Nf7MERrI7ifxVf4rzr7wkTeUxzfcw4MW76PO/M9XxX6a7Iq+vued71M74Aa87KHt/EJrrXNvm5zhPT+Kbzksb5yffEn/LE7B4/W7jBa9KHsbN9vYpD7ak9cT7tjUrzKs/Yjs+P4tpcCB1+m9oU+/AG82lPwKfmXGUQr7X1u9tuHEBnDFV+PB/NofMj90GVTVwc3TEq7Ud4xn8TxoyBcY8V9gTYgrGN57O9J+3qzIHnpEe9U5q/d+Nn2g/ymObUcepXfgefjlFdf6u8yuO9n6/dhN5b2wfJXwXZdNg9SOSbsyGREdjY2u1iU/fE/eYKfgCB4GSy7BhO6nMRq4dmpyHh4Q+wr81xLlYmX/xH4uwHLgmJcWR5cDPG+1hAIZIAACAASURBVE5hUOUePSt/RYNs95F6dFovdsShdlUa7AALbYQGG6B/pQYW+m1lq/qe4QYdNsNPuxm7gkWNAeKzfh61r+GJTh6m9Lz3GOMdW/q4eIAX66CRl3P07Bfm8LnYgiHyp5ipax/57B6sWPqM/rVgqHkVHflgxwqDyd/o7gXCnIH97GM+K9t34utWXM58VfmiH/jgu1VuFFPsn9oVedP6W8bUSd/WvvsZ/sSs8a2v8TO0/XK48rN6EkNnuc39ZEwYW5M8+e728HJvYVON6c5DPVYxfSUOkYu8jleX+dbv6INexIC5yT0o/l0n6MSGddg2NeOsxj607G3GatzxzDi6dH7I0vfM1fdJ7i1j+BLZ6IX9U7t3H2ADH3ChIQdbfJ9kvvIY+xh7yH36jJjBjxOG0EBf56EH946B8UVvu3rmGH/wUD/3bJenjKs9fIldbFidAVf8Dm7w4rPC8EzeVRveg/6rLLoB2kSDgzl8DMxHOQF+q8T9KBlnfEyUqw1xtv5rngczE0DtV5iYMKZk4RqLpZpMnSMePXiRxzNjU4OPSRhaDstH+xgdJz3Vh/hGRz597xh3FTeeGa+NfVdpSKiPOhCqnHueq34+H2F9hhu6QMNhVC+hu1gQX6xVl6l/RN7Bp+ikLGJs4uvhPvnNA7LqyFhtyKl0xNMkp6559HM9C6qu9bn6imfwcJ69uNqr6Dr5WxuwVV5gfXYW7cSXvK/01Qfa1X0lv93cCE7YhM69XZHX1976ftXPyiFvEZfigr+nPH/kZ3jh52mfKMcefI0JZCKvxp90V3pzMnzRs+fizutoX0N7JQ7hhdxXaz33EKtgvcIGX0hz5scp9msMGUu1R/bUiAfkGgtT7E3rdsaqfJ9X59s9+wC8tIGe+Lk3pnfsexZNtQfc8C249btQlY/f8LE4s2aKNXhDU2MMrHpOODpzkMt8jTmez9ZUfY+esVU7at/XVJzO/A526Min47grr8t/xfevtujGGQbLI5OYTiZI+LxnY0Nj43vr8Z4YRHYQCAJBIAgEgSAQBIJAEAgCQeA9EUjRffFvDXec5U+q3rvYTdG9463QBIEgEASCQBAIAkEgCASBIBAEnodAiu6LRXf9dUC+RebXReo35RTafoNu339NDxp/VYS+/zolRTtroPPXSvjVDH8dp/+aysSDkFkV3Yzz6z1dL/j6a3/Iwzbo0oJAEAgCQSAIBIEgEASCQBAIAkHgNgRSdF8ouimOKaT9Wwu/0e7F66rYxUUUsRSz/M0CH4tcC2/+5sK/++Dv+5ivNKyhyPbvHnjnGb2QW9ukB4W7fz/a9YaP385bgHeayj/PQSAIBIEgEASCQBAIAkEgCASBIHCMQIruC0W3xW+F1IK3jk3FLvOM8w0yhbKNZwpmxm2ur7SM0ZyzOGaMQh0edWxF+yeT4X8RQTEOj65bim4RSx8EgkAQCAJBIAgEgSAQBIJAELiOQIruC0U331JTZNc2FeJTYcwavsHm0xvFdf2merWedRTH0Nd/hXBFvxqHD/JqQc0324z1Xyf3G/iuc96DQBAIAkEgCASBIBAEgkAQCAJB4ByBFN0Xiu4KJwWtvwZOsVrbqtj177ihnz7+2vpqfZXBM99K8+22fO/5pht+FNzoBT916TLzHgSCQBAIAkEgCASBIBAEgkAQCAL7CPyzWtxf9ykoLXyvGENBTFHKt8R823zlm27k8ffUZ+2s6KbYhg/feFNo+7fl9xbd6MU3237zzrf6/uNtZzpnPggEgSAQBIJAEAgCQSAIBIEgEAT+jUCK7vYt9b8h+t+IxW39FvhK0U0x2389/X/c//d0VHRTcMODj39/vaJfjSOJHwDUXy//n/T/fYMODTrzq+dpQSAIBIEgEASCQBAIAkEgCASBIHAdgRTdJ0U3BSffaPs3z/1vsq8U3f46+vTtMfwpkmlHxbK/Al55rOhX48g4KroNI/+19v4NuvPpg0AQCAJBIAgEgSAQBIJAEAgCQeAYgRTdJ0U3hTLfKFvA9qKbb5wpYGuT1mKV9Xz8ppxvj6GxMUfxTk9z/fSr6Bb5teh+xL9ejszpH03DNu1Q3/RBIAgEgSAQBIJAEAgCQSAIBIEgsIfAP6vFvTWfgspClaKS594oaimwLbIpiP1bZ4pT1jDnP2JGQW3R6rfizFHM1n8R3G+qkUvBbhFdf2WdIpd51sOrNudYC2/e5UGPDq7hWTkW9PDCNsb5SGuhzxpp4Z1fL6/o5zkIBIEgEASCQBAIAkEgCASBIHANga+y6Lbg3OlrMcyzhTfFM8WpRTDfSlus4gLHKYTrt9LOyYfiuRb9FtBVNwpiGzKkoShnLWPws8iHVprOR736ODLQBR7OTbqrR/ogEASCQBAIAkEgCASBIBAEgkAQOEfgqyy6z2EJRRAIAkEgCASBIBAEgkAQCAJBIAgEgfsRSNF9P4bhEASCQBAIAkEgCASBIBAEgkAQCAJBYEQgRfcISwaDQBAIAkEgCASBIBAEgkAQCAJBIAjcj0CK7vsxDIcgEASCQBAIAkEgCASBIBAEgkAQCAIjAim6R1gyGASCQBAIAkEgCASBIBAEgkAQCAJB4H4EHlp0+69ep////zuu4BAcEgOJgcRAYiAxkBhIDCQGEgOJgcTAx4mB+0vsf3N4aNH9b/YZCQJBIAgEgSAQBIJAEAgCQSAIBIEg8PUikKL76/V9LA8CQSAIBIEgEASCQBAIAkEgCASBJyOQovvJAId9EAgCQSAIBIEgEASCQBAIAkEgCHy9CKTo/np9H8uDQBAIAkEgCASBIBAEgkAQCAJB4MkIpOh+MsBhHwSCQBAIAkEgCASBIBAEgkAQCAJfLwIpup/k+//85z9fvvnmmy+///77kySEbRAIAkEgCASBIBAEgkAQCAJBIAi8OgIpugcPff/991+O/ll/immK6l9++WVY/f+HUnQvoclEEAgCQSAIBIEgEASCQBAIAkHgq0EgRffC1X/88ceXb7/99s/i+6effvqbivH//ve/X7777rs/53744Ye/5/IQBIJAEAgCQSAIBIEgEASCQBAIAkGgIpCiu6LRnim2+ca7Ft2S1KL8559/djh9EAgCQSAIBIEgEASCQBAIAkEgCASBvxFI0f03FP9+OCq6oXaeX0dPCwJBIAgEgSAQBIJAEAgCQSAIBIEg0BFI0d0RKe8W1dM33ZA534tu/vG0H3/88c9/SK2w+/uRvwX319P5+3B+RZ1vzmvjnXHm+bYd+l9//bWS/Pk35chGj99+++2Lf4vOr8XzK/C98Y185cffnXeefU3eg0AQCAJBIAgEgSAQBIJAEAgCQeB2BFJ0H2BnUb0qui2c6zzFMu8Wt509hTTr/FfNKYQtqqWFBzTypaD278t5pjGnfHkyRiHtPwJXC2qKcHgol8Ifukqj/PRBIAgEgSAQBIJAEAgCQSAIBIEg8BgEUnQf4EgRS2HKt9a1Ufha3FL49m+pofVb57qOArcXuqylQKcgtiG3f3tO0cxa5NosnLsO6Att5cG6bgfvKbpFM30QCAJBIAgEgSAQBIJAEAgCQeDxCKToPsDUopsCdvpQ1PrNcWczFd0UvrW47mt8pwjv/zgbhb46SGcRj561UchL6w8E/FV1vymHnucU3RW5PAeBIBAEgkAQCAJBIAgEgSAQBB6LQIruAzwtuntRS6HqN90UyNP/r3squimE67fPk2iKeAvmVW+hvyq64at8i2oKbH/lnQJcHpMOGQsCQSAIBIEgEASCQBAIAkEgCASBxyCQovsAx1XR7RLnKY57EWvRKy39TtFtIT39Q2iVF8/S9h8KMKd8i27G0NFxdJnWdRl5DwJBIAgEgSAQBIJAEAgCQSAIBIHbEUjRfYCdRfWqOK2/xt1/HdzitrL3m+s61p/9NfL+99edjvedorv/MMB1/iNsO3Im2RkLAkEgCASBIBAEgkAQCAJBIAgEgXMEUnQfYHRWdLPUQnqn6LYQn34dnQLav7f2H1bz77GrinXtUdFNUX309+PwZh7904JAEAgCQSAIBIEgEASCQBAIAkHgOQik4jrA9azo9n/3ReHav1G2wK7s/dfGKaotsJnnmSLZxt9cw5Oxyhe6+q+XW3T3b6tZw/r6DT08exGvfcpNHwSCQBAIAkEgCASBIBAEgkAQCAKPRSBF9wLP+k1wLV4hp/i1YKW4rd8+M1/X9jmLcdbxTGHdi3DWMw7NEZ1FN+uVQ8HNWj61KctiHzq+6e4Fe13zNT/zN/XVB/ywo/4ApGLjD03wFb6YfsBR6Xee8Ref3pBlXEx9/aFMX7v7Tmz7WxDIgKdxs8uD2BQ/eJ39GwXQY2/fa7vynkHHPsSX+BQcsKP/RkuVS3yAFbSsuccWZCMLTODXY+GqblXPq8/YRZ7Qn7fYhX/B0rjivba+35B1FjN1/SOeiXH9B+bo0PWscvCP9tzib3wIrjW+zmwGQ3Srn0flcPTBt+iz0zhzsP8IoxWf9/T3W/v5qjww+yx+NkaIV2LlltyxiqFHjeMfcxuxD/bshZ2GPdh2pbFflMfaI3n1TIGWcwB9H9mQgQ79jDmSAT5Xcx5rkGO+u4r1kT6vOncltnpO5CzCN1faldiCbz3D3jO26jn4Hmf/FYzvpb2WLe6V9kHWE3wkuKMPBwibol84eO/r6kFD4qkBtgp0E5S8kNWTrbKYM4mvEhk01a6rCfODuO4hanJRAHcw7Zh1H/BOLOBTPjyzlnW3NhLhioexA//+YY0/fLlVtvyNa+OQeNk9AMQPO2jG6aQbPL3oo3/dK7fa8Ih12I3/sRuc9Ss6cnHojTioew+7WDPR9rX93ZhCFv7QF9Jd1c11t/T4DLv44M8e/zs8sQFbwBN+PY64bDBvvMDTPXBWhO7I36HRf/gMf2MvOvGZ4tbLo76hZw1rd5o+BBPx0ObVHjCm+r53/Y7cFQ04w1ebV3SMgxX2SysGR2vq3Hv6+639fFUeOH0WPxPPxDfx7H4hZlbxXWPkrZ7xD3ueHMXHPL+zj1nrHtjVl1zCGnMdvvacISfUZuyAHXN83HdX91zlW5/R44rNruU+edWX2FnznbYz9hnbldgyLsCi5mHOFPjsNHnsxBb83JPGEvvyirwznXZji1jig3zjithSrzM5H20+RfdH81jRl6C8mvjK8jwuEOAQqhvewwGsSQq18V4PS569sLPuamONSXc6+Bmr8uTvBWCak4akVu1y3B7Z2DgVGYxzKTlr2k9Cr80Ev8LEA+NVLmQcGOhc8WQMHPj0gxC/EDe1adOVwhH/ED9Hh99V3apOPE9x1Wl4114uAhWHiXY15iHa46HSg9s0j55vdSFDT+y1YS864Wt8UZt5t/tVf+/EMDTw7rgqc9qnzE3jVbf+DP2OPq7zAu77UX+k69G69/T3W/v5ijwx+wx+Js9jR23um54nK81bP+Ofugd53j2/zfns450m736Oeu72vLzKf+CHjlXvLp89v5srVvI7T9/Je+Rl7N7NLebGfv4bEz2XKusj91diC59Wf4GTGMPnrF2NLXHHL7Whx9mZ+8jYQn7XAf7EVs8fVc+P/LyXLT6yhZ9YdzfObuL7xFA8zDQwrclPxh5M/YCdDj4OVeh6YSavo56D1gK6H8IcTP3Qkhcyz5LzWbI0nnoSRAbJeCcJmjD7ISrvfuFQf+dfJZZXeoBBv2yo+2QbtGeHmBjgdy5TfI5i54pu8q59j6s65zMxoO5TjEt31IvVWdwgZ6JhjLh7i7bCFPnoh49t7DPGpsb4js74ePKDsdTxYN/v8O06wW9lW6flHZ1WtnV6+HZsOs30zppuH3Rv4e8VFs/y8xV5YPBZ/LzKGeRCYv9V2qTnzvmNX/kh3ZX94g8xay4RB/l4vnsHmOKHMfbQdE7LD5pJjvO9h9+UjzodeOFDznfWTPr1Nbyrs/ZJw/uZLdJ+tH43tvDT5CuxAZ+zdiW24GW8dR31U7+/VfmPjK0uHzmMYfPZfbbq9JGez735kaz5ynS9mvi+Mngebi6JYOfCQLK45YJM4rRwQ9bOIaiRyDs6hKE7S5YmO3jVZGjynw4G5dtzIKP7VDQyvsIF3syj4ys3DyV8ZfOSVsec83Drlw3nay92Z36sa+rzpFud9/ksrtCVOMcfO3rLt/bmppW/K61295hh7XvHg/6rujlWbfDZuUrvXO3BduUH5nqeoSBlHKyIt12/sK+uYKj+VdfVs/G2kxcqj1f0t3ZXvzlWdffZuUrv3E6/Wv+Z/DzhQFxPP3CZaN9r7Oz8xufEME0/7uh6tF+c8ww5Og+dOypK4HdlXx7lo2obMsnt6oCcneZ50HXmrCMm6n1jh99HpTmLrW7XdBZ0Gt6Nn8nnzhlb3vP6GQMf/Xq0R58VW9rF/sLuW+9B8nnVPkX3q3pmQy82MMHZE9nG0pDcgABYHyUjWJowrl7GuERzkHv4IIsDfacp07WrNTvJ0gKSgge+8ESv3QSI3nym5tykp8keHV+5eYDVoseL13TgOcel46h5KdkpUld8Jt0m2rO4MgbOYn3i7ZiF1U7caDuXAOmRfY989bi3B6vuE3067XHnpliourAXpksPNO4T6dkv8IXeOXovUdJNPXpc2VPqP/HqY8bbma193Sv6G7uf4eduu++TvM/mZ221J17BeMr/0rx371k67W11I7c5f8t+mfate8m9yp5ij093O+eQvWrwubIvkXXEDznsW/VRB/Vd6VHHPROQQwyAIfEglpX2Mz5jJzhfsRf6nXPQ+LkSW5O/9Su+WrVnxFaVRYwZZ3X8szzPt+PPYt0ntoPN2D+f2Nx3N40iC7xXBxmHCAUDF+OdJNkNIgFW3siakmJfxzuJdidJ7SZL9De20GH3kDBhs3Zq8qx2SufaK4e4a9+yB5vulyO7oGX+zC78Bx38KXq5jPBOz2Vnp026Teu6/p3G4g6d4en7bix4uUB/9oS2ad906YaO+as2d90f/Q7+3Xdggp59HNn6e4rxqpv+nejEodL7TB5S/koHaenhP+lZaeqz+tex1TN80WGyYbXG8Vfz97P8rL29n+RVms/iZ2zCFmMFv79i2z2/saPupyv7xZhnTW/iI2/vG+Teni/Zb+y7iY984XNlX57xQwdiVl3UQX2Ve9Sz1sIbuzgX5He07qPPYSO+x2Zy924zBnb8eCW2jLUpfvQr8bBqj44t5XBvICbAaffO6dqP1K+R/UhWRNcg8GQESJYUQ6tGkqofEtrugULR3HmfHYJVDw6yfpmpupw9T8nXooC1uwfFWcJWj+kQce2VQ7xi8BbPHIIcCPS1HdkFtsyf2QVf6Dh0PHDAxPGzwnvSzcNV/c565NWCmfXaSoyyfrK/YsFzpSUu2Qd8LBZXP0V3HjnQ7O6fLv9R7+g+6SrW6ImtNMYq3vpwpUv9ttf9wBp/QAHOR63Ghj4y1s787PzEXx7TXB/TXvXv82fvr+LvZ/p5wmAlb6L96H5Gf+PNvp91k91vPaZu9uyDnn/Ynz0fXNkv2MR6ZBD75kXiwTxvPoEWnKBljXucvCEPcgVNnXd69J0aa1dz0COrnkH6lRxwpWFH1RPbP3ur9opzj60JA2Lkyl4xLs5iy7w9+Vu/oiet6370PvGTx2pOu9VJ/uyHzxobKbr1evogsEDAw/YsUXrx9gDdOZBYQ7LsvEk+Z4kKdT3E+vrJFPQhqR41+KAPCQ9abSGRn7WesDu9CXXSwbU7mHW+b/XOxWM6CI7swofMn9klj+5H5DHHD0GO2kq3ac1RXOkHYqA3YgBdzmIBW6GbLgz+MKfjCE8+7gfWT/ui6/Ssd/fBqnhmXN+yR9C9/rBhRy8useJBD27wwXYv1Ed8lFcv6p0ef57FXl2jTXVs9ayfp/28WuP4q/j7LfyszfRn8iqtzx/Zz9rAfjG2ie9bYkZez+rPzm/yUc8HV/YLeuP/ioPFLJjw6fzZY57Bnsv00B7te9ZdwRh+q3OBPNXzEbxZcyW3YBt5jt6Yhkc/C57l3/fkexZbXTcwunr+7caWeXvyt37FL6v2yNjqMog19xRxj02fra2R/WyWxp4gcAMCbHqSQD8Mj1iZuDhgzhq8p8Px6BCsPDm8+oFY5+vzTrJEHy4FNpP/7gEL3SphH82J2ZVDXB3foudiUHGpMj0kJj8ezVUeR9h46eLgntqRbhM9Oq2afphoiAX0PItrD/XJl162KpbQVZ7sOWIaWZMeK90fOY5+Vy+DrEHnattVncABHruyoZ1wVi7+PJqXzt549f2ohy/yp7g/W/cq/n5rP98iDyw/op+nGDBm7tkjE99HjpkDa4yi97SPruyXlY7sdfxb5a1oOQOg5bM6D1iLrlf2JfymXEsuRq9e/IjRhMmkO+s5x2peo8DybLui68T/o4yJ25GvwQpfXLlzruyfYksdJn8fzSnjUbElv6l3X9V4meg+4liK7o/otej8Zgjwbd0tyY+fUnKgHDUTnIfoqp+So3z96bfvR/1ZsrSoQq/aONyx5eigkB4a7OiHtJeFFQ+x2D3ElfcWPbhM39oqmwskNnOJ6E08ji5IrPHy0dfz7gE0xeGZbit+0zhjxsAq5rDzLK4trCdf6uf6gyL4TbR+o3OG3cqWW8c56K8e9uKGLbfqK27Yvdvw04Sd68H7aF46e2PN96MevsRDzxdHa5h7FX+/tZ9vkSeWH9HP6l57zgVipu7/Ov8qz/38Ruedz1X9wcMzYjo/Oj9/GHl0HrGGvXllX2LblPPd42e2n+UYc1u3B5vh/co/hOk63/veY6vzu/XO2fmsYsu72HTOkKPO/PGo2Or61nfjgrj5bC1F92fzaOx5GAL3JD8OsLOLBcmPBDZ9SHwcxsytCgCT5+4l/yxZMo/c6bAGC+bOmnT9AmESZX5qyIQ/OrxS2ylqV7Z5wZwOt26jhfuEPbE0Fbo7unU5vE+Xq0rnJbCO+YyPduIaukmOfvYw9X3yu7hOmKjPo/tbCiP8jI+xebVXz/TEl/iYD8+7Dfqj/Q92E7Yr/vgMO3baUb5YrX8Vf7+1n2+RVzH8aH6uuvdn4sv93+de5Z19UPMcsT59zJXOXdXfvL9TdBJDYEeuIeccNfS5kjfhu8rX2lZ79WbNjqyjvII9k+wj+z7yHLbW2Kq23HPnrHx41kdTbHle9Tha3d8q7x1/V/pVbFWa/uw5ceUs7Dxe9X3vdH1V7Z+kF4FIoHLQETAk1l5EPEn0w9liC/qzydL2EcD/k8/Bc1U4yl3Mp0OPsaNLsjx2EhUXl5VfWb/78cAjwbGGpNobeEjnHPQ9KWIb+6YnevfTynaT7CRbeW/dY1u3WR3AvtqOH9hntXlJ6nEEBj02tL8fxsTS5JMd3cByNwagUyfX9YvxVASjH+voawM3eFaMmGfvEB+VnvcJZ/DrtFXGo5+Rt9rbPZ6VjR3aulo7+dv19GBE7GArOuw2/FNlqseuzyc58pjm+phxYtz0ed6Z63v+vf391n6+RV7F8qP6udrgM7H+lntauVd69jT78Siu5Xe0X6bYdx09e5e9Co+aDyuNz+Ze9Oo5dXe/K0uetT+aq3Q+YxtrpvN6ynfEMPQTptg08VHWZ+qPYoszpt8VsJ01Nc8zdm9sGU/9vMEX9U6Jz3Y/xPHUWL+am+gZI16urlnxerXxFN3NIwQ4QYfTaQQ37xwUH7G5yetG+oh2vKXOFohs+v4hgZgYOfxMKB4mHDg1fqreJDjoSWxnTb5HdCs5R2vO5rAd2cY/8cMz8V8Pe+yEjk+/VGunCZ310Pk+6eAFhKITme/dvByCcY+BfjChq/QWZ8QDmPle7WH9hIdFjNgbS+hQMVHWrm5V9u4zNqO/cb2yx3jpdqIv69HR+FjFgfECD+1kj7H+KGZ2bdmhU4fua3Holx5sYg2+PNNz5W/8iM9ZDw3vU2NPEC/Qgg8fsOyYT2uvjGETuiBLv6/WowPYqNdEJ6bYVpvj7+FvZb+Vn6/I+0x+dq/jY/c/MUUsnMVWjZVnPrPfiF9iQZ3QlZxlDj6T7x7odPq9xz77htzmup5XOh/0AkP1ZP2jmwUYuuqrMxnotdr78GEODGzoDa7kl4o1tjH+DLuU/R791dgCB7CZ8hJYeufElkfFljLRFfzr+6MwO4stch52e7Yhl71X7w2P0uVV+KTobp7wsPhsSaCZmdcFAhZ/JLrpQ4KwESMentAyRxLxUJHOnuQGDcntrMEP3qvG4QjN7iG54jONkwA9OJGBTeheG7aTGFcHJslWHtCsMPHwRk79rOirDs96BlP8VPXpz9OlDIyMB2yfaND56HBjjbihA/FYc9Gtul3FCpnIFoeVPV4A6gVLWehqIQF+R3FAvDAvzjzXi4Y8n9F7MVD21Nf4Z39oDxid7cHJ38YJPT6vPu42ItuYQC6YPhob9am2MzY17a+0PPeG3qt89x7+fms/X5X3mfzcbSF+2Qdne6XH0DPf2XM17onVo/N70sX1fW6Kfc86sTg749hT6jTl1y7zlve+h3lnf581bZlop3wHP/B2Ttt4P8p9Z3q86vyV2Lpy58TeR8SWuOE/z3hi/5H7cye2iGvlQ8+5j06fMSbE/N8npTNfae8l8Ss1P2YHgSAQBIJAEAgCQSAIBIEgEASCwAMRSNHdwFz95LKR5TUIBIEgEASCQBAIAkEgCASBIBAEgsApAim6/4LIYrv/SkT99Rl+rcNvwqHrv47Br0Tw6xL8+hC/flN/vZIxf0UIPspjnPepIZt5ZNFPv3aBHH5FB371V1qUVeerDGjrr7Wwnk9aEAgCQSAIBIEgEASCQBAIAkEgCDwOgRTdDUuL4Tb8Z2FM4evf0vn3Woz59wf8bZ5/l8gzRTl09e8WKIL7OH/T0Bt8+Pg3FvDzbx6UR7Fu4Yze8KUA9wcCfb7KYB308KoFeKXJcxAIAkEgCASBIBAEgkAQCAJBIAjch0CK7obfquhmvH7rzTILXgpiGzQUvRS/tUnbxy2SKcZtPlfPmwAAIABJREFU8rDgdlzargfy+MiDYtvCnLXMoX9tFPr+AMFxivy0IBAEgkAQCAJBIAgEgSAQBIJAEHgcAim6G5ZT0U0RS+FKX5vfPteC1oLZAlj6K+N8ez4VwBTh6NG/GZ+KauXST/PKqMV5/eFBXZ/nIBAEgkAQCAJBIAgEgSAQBIJAELgNgRTdDbep6ObXwylcVx8KWNuV4po1nd4Cvxby8qb3n9evRf1UVNc107w2wQ8davFd1+Y5CASBIBAEgkAQCAJBIAgEgSAQBG5HIEV3w24qui2MdwpTaWtRjIjdcdZNRbJqql/lf0TPutU8PPiBAfPTr5srM30QCAJBIAgEgSAQBIJAEAgCQSAI3IZAiu6Gm0VtHfbXyPvfQFcan3eL6xW9RXf/FXLp1a/+vfeqqHbN2Xz9h952bJRv+iAQBIJAEAgCQSAIBIEgEASCQBA4RiBFd8PHorYO+yvf/GvfU6Notd1bdMPHXyHvf0POHH/rXX+dnbGzovpsHh7aiP1pQSAIBIEgEASCQBAIAkEgCASBIPAYBFJ0NxynohsSfw2bf0G8/po5BTeFts2iu39j7Hj9tXDWTOOO9X/p3H9IrRb58KConv7hNXWaiu7pBwjYnqJb1NIHgSAQBIJAEAgCQSAIBIEgEATuRyBFd8GQb3v9lrn/S951DhqKUwpxit1ahFPMUuT2gnkaZ53j/C/FbIzDFz7qQcHNWC+W/XV0aHtBDz+/wWa+/0o6Ojomn/7DAnVKHwSCQBAIAkEgCASBIBAEgkAQCALXEUjR/RdmfrtMcVo/tZClgPWbcArv/q13Xecz7H2u/TRev2Wm8Ia/PwSgwEfH2iadK800rz3+wECdeE/BXdHNcxAIAkEgCASBIBAEgkAQCAJB4H4EUnTfj2E4BIEgEASCQBAIAkEgCASBIBAEgkAQGBFI0T3CksEgEASCQBAIAkEgCASBIBAEgkAQCAL3I5Ci+34MwyEIBIEgEASCQBAIAkEgCASBIBAEgsCIQIruEZYMBoEgEASCQBAIAkEgCASBIBAEgkAQuB+Bhxbd/qNc6f/5j7EFj+CRGEgMJAYSA4mBxEBiIDGQGEgMJAZePwbuL7H/zeGhRfe/2WckCASBIBAEgkAQCAJBIAgEgSAQBILA14tAiu6v1/exPAgEgSAQBIJAEAgCQSAIBIEgEASejECK7icDHPZBIAgEgSAQBIJAEAgCQSAIBIEg8PUikKL76/V9LA8CQSAIBIEgEASCQBAIAkEgCASBJyOQovvJAId9EAgCQSAIBIEgEASCQBAIAkEgCHy9CKTovtP3v/7665dvvvnmy48//vg3p99///3LTz/99Oc48zvtP//5z5/0rL3aWIN89Ojt559//sK/kvjf//63T+U9CASBIBAEgkAQCAJBIAgEgSAQBJ6MQIruvwCmSL7yT/hDT+tF9x9//PF3wQ2/ZxfdXV6PlxTdHZG8B4EgEASCQBAIAkEgCASBIBAE3g6BFN1/YU0R/d13332p3zRbiFtgQ8o8dHVsctf333//ZxG/W3RPPK6MKe/KmtAGgSAQBIJAEAgCQSAIBIEgEASCwHMRSNH9F74U0bXgZngquhmHLkX3cwMz3INAEAgCQSAIBIEgEASCQBAIAp8BgRTdf3nxl19++Zc/V0U3hPza9lHzm+d8032EUuaCQBAIAkEgCASBIBAEgkAQCAKfG4EU3Qf+PSq6XUaxToHdv/muRTcF+rfffvvnr5vzD6b1b9R5X/1DaBTt/Dq7f2/O81TIK0+96Pl7b2X3NbyrE/0PP/zwLxsqrzwHgSAQBIJAEAgCQSAIBIEgEASCwHUEUnQfYHZWdPMvglNEUxCvim6KYWiYr0UuBTHt6B9C++233/7kTUFM451/oZyP6/+c+PLlz8IfPWqj4LZgr0U3RT48HLMA7zZUXnkOAkEgCASBIBAEgkAQCAJBIAgEgesI/LNKu77+U684K7oxnoL1qOjuv4ZuEdwL3OmbauVbHCOPb8SRV8cYn9YzPvGwGGfexg8Quk7OpQ8CQSAIBIEgEASCQBAIAkEgCASB2xBI0X2AmwXrUTF6VnT34thvrym+a5uKZn51nW+k66+jq1PnO62H/0QPXwr3/nfs/QcEVb88B4EgEASCQBAIAkEgCASBIBAEgsB1BFJ0H2BmwfrIohtxFLx8alsVzdJQePNr5hTh937Tza+m+407cnsBr8z0QSAIBIEgEASCQBAIAkEgCASBIHAfAv+s/O7j9elWv0LRTbFNYczfg/PN9CN+vRxHUXjLiyKevzvvfyf+6Rwag4JAEAgCQSAIBIEgEASCQBAIAm+MQIruA8CfWXRTRNc2fdPtP5zmP6QGvTr1b6en9Uf0yqao9x+D67/yLk36IBAEgkAQCAJBIAgEgSAQBIJAELgNgRTdB7hZ4NKvGsUv3xR3GovgXhzzbTL0tZCGt/RVDoU5v05emzp1vtN61q3oK0+eLbw7306X9yAQBIJAEAgCQSAIBIEgEASCQBDYRyBF9wFWFqy9oK5Lrhbd8Or/OBr8pqKZ4rwX3f5KeC+Op/Xw1YZKzxjfotemHZWuzuc5CASBIBAEgkAQCAJBIAgEgSAQBK4jkKJ7gRnfSPv/1ebXrld/78y/+E1x3Gn85phxC1xp+78aXmXVOQtpvhWnGKaHH/Iovv1hwGo9pqlH/Wbdwp//TRiN9dDB+2tvOz98AEt8UD/4Y6eBNbT+g3jEmH5Yrfcf0SMeHtmIA2McfdAL/VaN+JUeXYzrFX0fB1vjF+zA8UgedleMfT5a02Xe+159he5Hvqr4gKf7c1cH8HS/YivyVj8EAwPwq3GE/Ec38hE+X+mhvGr7W8SGcp/RG+PGG/3k9yuxMemp76ocn5nrrf4p0C3x1fnV9yu5ACzqPiZm0e3WRmwRy+J+Fmu3yunrlCfmz/Ize7Xua553cydrH+1reHoH6JjU9xpvYHPLvq783svPVYezZ2xmXxvfPYc/ChPyao07npHZG3GiLsTB2ZnZ1x+933OGPCIuiQfsfqv9foTFLXN1T3df8j7t8d29t9Knxt9OXrhX3kqPK2cAek74MMbc1Xa2R6/ye2v6FN0D4iTaKUh6AuYQ6nSVHYnVhAnddNiaeCof5bBpvRh44EnPO8Hne13PHK2O8ew4/Hmum+GRybxi8JGeSVDivToIwBwa8Ksfxs8a/IkHPtJTrOAbfd559GKmz9/6jr+NiRqjPKNnb9ATL+KCvrxPB0tfy7uXDAtD7EcWPCZ5rEFmxZjnFU6TzHvH2K98kFkPWDGo/Ds+0GAbOu80cITe2OLZ/Qt2tRlH8jdmoUePRzT0gZc6TDYrp9v+FrGh7Ef35uwad/i+tyux0dfyzqXF/Vdl8Yxfux+ND8bxv3mo002yzsbgoS5nucB9DB26Gh/ovJsLqj4UOPCAH7zNi5XmGc9v5Wf3KhizL8QXvFZ5r9pr3nlU3iPuqt+qrPrc4w1djZOjXFB51Of38nPV4eyZmMAvfDinejw/EhP3T937+KY25JHbwY6PeZ419zbjElvhJ2/24k5OuTcuka/MW+LpXvvvXY/+5q3qQ7HEtt52915f53uPv7Mz4F55yu39lTMAHcCpY8Q7sbcTa1X+2R6ttK/6nKL7VT0Tvd4cAQ42L0Wrg4AksZo7U5iLEwmIhF3b2WWG5GriqutWz+h3dEkjeWNnveCyhiSIHAtj+TPHOAmvNg4W+Jw17IU3+NZ2ZBdzJOb3atja7dV//aAQn35pYj24HflC+7jEVNzBzLgAu9qgY67GEWPI4oN/j9oVXNUBG6em7R2rZ8bGpMejxojniuvE90psTOsZA9eVn/B3jyV81i9yxlenrTLxz1H8Xc0F6FBjgX1qziSGrzQv7mDx1u2t/EzO637Gl1M+7RjgV7E98iHrzvzceeNHdFg1dER2b6wjPs/2SF33nn6uehw9mz+P4uJRmLBvz/yJruBWceYZ7PFbPbsnu+Bf92mnuecMuRKXXa7vO3ctaV+xB79+5qknfut3Hefoz/Zepa3Pt54BV+Uh56jBr8bW0Rlw9Zw7kruzR4/Wv8rcOuu+iobRIwi8AQIkEZIlhxWHWk0qiie5kHBubRyYU0JDFjKPLp/MT2snXeB3dKiTvOphLg8v8V0O78jva8Tq6NIPb5PlhKm8+yUCLFaHmvo+s++2IosxcCBOauOd8akxvhMzK395YFbsVrRghrzVvPp1/zo+9fq4yq90+q/j5bpnxEaV/8hn4u1oDyqr28r4KjZcU3toV7ENXuSJ2sAev04XOcan4sj1rD2Khyu5AF5THLB30YPPbjNWd/De5blL91Z+Rp8pVvAxWIH9qrEOv0p75EN4nPm5y3Hf9nHe+SEB+k0y3der+O383tPPXZfVO7Zg71HB/UhMyOn9vJt0m2KHHICu/Qc5fT1+mvaqdJNvmdNfq/mrcam82qPX2V2r0r/i82rvgs+Zf4723spWMIPvLWfAVXnQrxp6THE1nQFgscoT0zm3ksn4zh49Wv9Kc/un5CtpHV2CwAMR8CAhcXipmBKLBxKHM8lv5+CsapI0VwmNuX7Z3l1b6XhG99Wh2WnrO+vQoxaVHiKTbtKfXZyPMHWuHmImcGSiyypxV93f4tmLV9fn6FBz7uyStNL/yvoJy4nvKgYnWnlO++E9YmPS8VFj/oADfFbF6ErWKjZW9Ktx9lLfT1606x5xvfGxykWPzAXKnPqz/FXXWEju/DCqrnvU83v7GT+C18pn2EneAydzLPvwqF31s3Ez8TyS6Vw9IyYejL23n1d61XF8wDlz5g/tnvzg3A4mFg/EIPuctVcaMnb2DXpe5Y0e5vsp1zB/NS67bbt3rb7uo7zj3zP/HO29lZ33nAFX5UF/S7tyBkzn3Erm7h5drX+18RTdr+aR6PPmCJDQPGQ8dPqBxWFBMvKAJsHwcd2O0kdJSX4rPszvJkN0ny4HK96Osw45tah0bJLt3NE3bfAW0wkr56q+0MFTTOh5P7qkasMzey4c08UKbNBxKqydA6tbGuvPDnH5iuUZTpMv5dF7eU766/+Jn3OPjo2u36Pe0VdfGXfs9cnuSeYqNibaozFkUqzUpl6TLs71Na5nTd1bjp/1rAOHmguO1kDbf1iwondv7/Je8bllHLvE7D38zDnCfp5yofbgS/OMfjjz4VU/i4Eya69MdZjmWH/W3tPPZ7o5bzFzFruPwgQ5+N/Yc98QF2fNH+xN50xfS7yg89Vmvp/OkFvissvfuWv1NR/pnT2DjUftaO+t1rlm8qlzqzPA+RXvPg79Lc1Y3lk7nXOrdbt7dLX+1cZTdL+aR6LPmyJAEqtJxkNnSm4qxoHE4enBeXYhcp2H7cRbXtL2nvmqZ5+v7/Df1amu4xJMMqwXAPGYZCPnTG/4w3elv/xX+rJW3LpuVfdnPnPJ4TBF/nThMRYmG8AN2yef7+iM7RPfaS16TH7qtDs0rtE/k/7OTfzeIjbU8dE9uuu3M9+dxcYV3eBFjPXmHpt8oJ6rGGHNaq7Lqe9TLqjz9Zl8eIaT9NioPchgX/lO/Nbc45pn9W/tZy7FFKJ8VnYyzp53Hh3B58yHV/1s3EzY6s8p36rPtOcrr1fyc9WrP2Oj+BJ/vmMfNtgegYm86PEvPq3y6nx9htb9iI47Dd746mpbnSG3xmWVjz41btAR7G/Rs/J9pWf8WeNm0u1o7030jJkjJ6zkt8oRzq949/Hqoz63end/TPr1NeADTrvNPYJ9R3t0l99706Xofm8PRP67IcBBwgWIhGG7chCQYEwIlYe8es+li+TJGpMTCciL51EiYt2UDE2oJuWzvutU37ns9Z+WisckGxuUV/lMz37rQdIE93qRgMfRNz/Qsg46+rds2q+d+IgLUG343jjQDsbq2rODuPLzGTngttPUocZhla/+R70xWeXJ42juPWOj6vroZ3wJXuyLqYmNmE6xMa1bjfET/Sm+5T/5wP2PLj5Lf9av9GB8ygUrenQ++3bHtWIqVuYC9/duvMvvEb06PdPP3TfImnICZ0HNweZY/GvrvG7xszzk2Xu/Xarnoz80QB56HjUxfSU/d33BX+zA19xZdXeMtfdi0uXzDn/Pxn6uSK+O9viOfWNzfKdn7aqhC/6qNku7E5fSTj361liCxvw55bWJx6uPsT9WOaTqfrb3Kq3P+nbCSn41R7iO3vk6xrP4y/usn2TL88oZsDrn5FX7q3u0rn3V5xTdr+qZ6PV0BNj8HLC1mYiOEkyl94DufCpNfTYxk+BI0Mjzwnl0kYH+6MCsMtB9lYArXX1Gf/ToTTwm2cgxUfd1/b0WzqzxEHf9dAGtPFgPXlwK3qPhNw8vdKiXHvRB/zoPlsbGLTp7STnDRSzAc3Vpk8Z+8qVzvdf/035wbuL3lrHRdX7kO7gSo0d+OIuNXX2Ib3j15h6ZfGDMrfY7a1ZzXY7vq1zgfO3Bhct03w+Vpj4bM+Te3rAfW3fjuK+/5/0t/AxG2Kadfd/ge/SozX105sOrfjZuqqz+jExyFz7Bx+hugXh23r2qn6uNYotNvXkm9zPxHky6DN/ZQ2Dcfe88PYVwlc3zUWMe+6405E977564VP4j7lryetWeWJnyWtd3Z+/1NfecAVfl9bzUdenvV8+A1TnX+fJ+yx6d+LzSWIruV/JGdHkzBNjMU3LhsCLBXTmwoD87BI8M8xI2HXiuQ8akr/O1R/cr+pA04T1dnE16k+yjuarP6hl7sQv7d5q+2aF9Fg04oPORr5S9urg5f9SzdkcGPKDrl8Mj3pMvV/RiPu2HI/8fza1k1fGrsVHXPvJZOyb7u5wrsdHXsgdXP5yR76TD0RwyWPOoXNB1Jl8gH913m/E06eQPqa7E8q7cM7q38jN6gJvFrN8qMkYe7DlYvSa8qk1X/WzcVB5nz+hK7uOj3qs1r+rnqq/YgkVvxPTO2XQFky6jvqPDpEel4Vmdz85M8Id2t63OkHvjUp0n24yRK3ru2vMedOzpnVwIFsTWleaaCaujOWQ4vysP+t1GfEC/Yzc8oVudc5NM433SaXePTnzfc+ya599T08gOAg9EwETkJWLVT0muqwGvs0tRX+O7F83pp+3S0KPflHgqjc/ovKsPSZNLbr/syctLxaSfhdEtl2QPc+yavt1Tfu1NwHXsrZ/RFZ3PvunxQOCAObugdhvAlc9OQ87OT9crr904Ys3RxeiVYqPa9+hn/L2TB3ZjY9IPH672EeOrfeIP7FYx9shc0PVGZ+LvSjPfTfnJ/X30jd8VWVdp38LP6gR2yBM/9xljR58JN3he8TP0nn/qs9PjF3TbyTev7GdtNUev8iG2nhUIVzBR7tTj15UenZ6z+Ewv+O3kLHgfnSH3xiX8jbWjuGZuV9+Oxyu8k/vPfhCinuLh+05/zxlwVd5uHKL31TMA+tU5N+HwiD068X3PsRTd74l+ZL8bAhQ1HCj9Y4IiMTC3usxWxW8prFhPQmEtH56PGofSbjLcvYCdFdzqwyGP/F6Ye3HcLZrlR+8hciUBc5G7Ql/lPeoZbMHiyF/gJGa7xbP6PbvgRs5uHEHrpWt1IdLO944N8Xt0j593L1M7sbHSDxmrWLGY78UOmBOL+GDVHp0LlHP1suU6f1AzxaD4nf1AS16P7N/Kz+rMvqqFk37q55F5EryYg25qrp/mpjH4ETu7jdg01vpen3i8qp+7rv7Qqo/zjr1HPwC6iskkwzHkrPa/NPb47kgv6I5iRT70xH3PK3XeuLo1LuGFXX0978YgMc77zl2r6vZKz9iwezfR7iv633MGXJUH/U675Qw4OudWMu/Zoyue7zm+n3XfU8vIHhEgIXJwHyXNcWEGlwiQ/DlsV5ebvpAL4oQ/B8gRDy95+G/nsEWnKRmaUJnf+ag/FyfWTgcdutWLrwm/60kynC782D3xVTZ4ac/OBY510CHriK/8n9mDy+QHZYor9k1xAd0qNsB3taYf6PhopQc6Mm8s78QFNFO8ymOaw5ZXiA2xf0bP5RYbd9oqNlb+lie+Av+jvUDss99qI15Yp35vkQuQTywqs+qD/j1+p1ygnthdG2vJh0c4VPpHPr+Fn6u+yGNvnTXww8eVVvwY3/lMMuQxzfUx9zjx130m7Ufxs/ram9/qececNp/lvauYKLf2YDqdo5XGZ/YGMqteOzEgDX63Ibe+O07vGVLH6vMUl86f5TvpxL7a4txH68lbU06c7Djbe/gYTHoe3DkDrsgTf2PjrK9+unIGqBPxhoxul/P00PCpTT2v7tHK45WeU3S/kjcu6sImSNF9EbQTcjd4TTAs4ZJEwmCepMGHJNCLIdlzMELfC1USCjzw29GBLR96LwDQP6LoRHcTOAdA/TA+JUbsRGf0Z319r7paCKBrbazBDg+cfjmXFv7IRw99wBjr6N+q4W/s1d/Ixd/oNfmAMWzHbtZ1v1e9oemxIW7VFz7Dr+IFDoyhizT28Gb8EQ2fwRddwWHVaiw8MzZW8h8xDp7Vb/gTuyY/Xo2Nyd9VZ3wLz6Omz9GJZu73/Wjt0Rz+Il7Q0RiyZ7znAn0tTe2hrRdPYxretSHT+HUvsbf6nqhrHvX81n5GHvYbR+6PM39rL34+23/S7vZgjl7wNcdOa5nD39DhZ3Sf2iv6edJzNYZt4CEW9LxPe+seTIxxfG/ceyZ2bNnv4q5erGFPwufeZj6BX93DPBOvjB81dFrFJeuZM+ZXfDhToNO+Fd2rj+ND4mWn7ew991yPP33mOLit4lRdduRJu9sjH7k9bnjHn/UMqDzPzjl0ZT0fnmuDNzKNlR3b6/pXek7R/WRvEBwGypNFhf0DEFgdBCQ8DxOSAgfnKrmghomJdTaTEj0HZz9opau9Saj2RwVQXbt69jJdedbn1YUQuSQ+aOvFocrpBwNzxD9rwA9czvYDyVl9WMP7DlZVj3ufuTBoK7qAGfZPehgz0KBrPzC6Lj02iCPtXfXGUT1EV7SPuJRpU5fRbfH9rWJDeY/uq7/xOz4S8y6r0oLPUWywtvu78yPGzy6orEEfcwhrHuHnK7mg7sseF7yDW21TLnCeOCaHyAc9zvKCa+/pq+/ews8dM/y342ttNHeyvx7RjB9xp2esN/2Jj870fUU/d3uO3snp+Ml8v9pb92KCnIo/Mb/aw50W3fDFI/bII86Qo7g8y3f6wjPmETbJ8z167OVz1qrv3X/T3mO/MT/tuytnwK68M73rfM9n2mHfz4C69uycI+bZE3x4ro33nT1a17zqc4ruJ3uGwP/oSeXJEIV9EAgCQSAIBIEgEASCQBAIAkHg0yKQovuJruWnX/wEKEX3E0EO6yAQBIJAEAgCQSAIBIEgEASCwAsjkKL7L+f0X1/g1yT4dYb6aw488+tA/JoEhXT/VQ/ebdOvdtRfEYOXv4ZDYT79eh2/XgIf1sEbueilnDqvXHto6q/vTb+a1O2BH/ynX++Qb/ogEASCQBAIAkEgCASBIBAEgkAQ2EcgRfdfWFGU1oLWb6lroUzBTUFKkcwzNBTfPDNGgVzb6m9WKHbhI28LanhYUPN3nhbNFP/wtpBHXp2Xj7L9G1HoaPwNj3rXvxNh3nF4II/Cvuohz/RBIAgEgSAQBIJAEAgCQSAIBIEgcB2BFN1/YdaLZgrjPgaphTSFcG0WxHVM2v7r5Yz3At1CmcLXxjp04IcB6EOrvJyHnw066PmBQG0U3p0X8+qoPqy38K/r8xwEgkAQCAJBIAgEgSAQBIJAEAgC1xFI0f0XZnzD2wvpo6K7Fr+wsOiu4xa0dQxaimK/hdZlFLrI42Obimrn6Kd5vsmGB0V8b357X2WvdOxr8x4EgkAQCAJBIAgEgSAQBIJAEAgC1xH4X4V3fe2nXUHh6q9d+w2wxq6K1N2i22+cLbCnHhraVFSrx2reX0nvhT70FNvIqzat7Kly8hwEgkAQCAJBIAgEgSAQBIJAEAgCtyGQorvgRlHqr2b7zXMtUCFdFam7RbeF9PRNdFHlz0dpkTm1aX7Sw7XSV5tW9rgmfRAIAkEgCASBIBAEgkAQCAJBIAjcjkCK7r+w41evKbjr3zP3b4UhXRWpU7E70VrM919ln1xokXxL0V1/hVze8qt/7z3pKH36IBAEgkAQCAJBIAgEgSAQBIJAELgPgRTdX7588e+ge6H6jKIbd1Hc8zfk/uNo1YX1Xxe3SL5SdPsr5PUfZJO//1hblZGiW3TSB4EgEASCQBAIAkEgCASBIBAEHo9Aiu7y7XUtuimIH110+7fa/oNm/N24Y7iWb8FrsWzRvfpW3PlalPuvl6N75Q1/5FLs12bRvfPr7nVdnoNAEAgCQSAIBIEgEASCQBAIAkHgHIEU3eUfLOMbaIpPvgn2HyRzzKLU8VoIU+j6/7euhbvfoLOG9RbH0PsPtVnY895/vd2CGN69gMa1fqvNWnjalFuLemjhT6FuY4320Fce0qQPAkEgCASBIBAEgkAQCAJBIAgEgdsRSNH9F3YU0RTAFKYWx/ydNu8W0sz3j9821/H6D5X5t958y1yLWp79xpu1FL3178ldV/nWgnmar2FAkV8L+84f2srb58ojz0EgCASBIBAEgkAQCAJBIAgEgSBwHwIpuu/DL6uDQBAIAkEgCASBIBAEgkAQCAJBIAgsEUjRvYQmE0EgCASBIBAEgkAQCAJBIAgEgSAQBO5DIEX3ffhldRAIAkEgCASBIBAEgkAQCAJBIAgEgSUCKbqX0GQiCASBIBAEgkAQCAJBIAgEgSAQBILAfQik6L4Pv6wOAkEgCASBIBAEgkAQCAJBIAgEgSCwROChRbf/Anb6f/8r58EkmCQGEgOJgcRAYiAxkBhIDCQGEgOJgdeOgWXlfMfEQ4vuO/TI0iAQBIJAEAgCQSAIBIEgEASCQBAIAp8OgRTdn86lMSgIBIEgEASCQBAIAkEgCASBIBAEXgWBFN2v4onoEQSCQBAIAkEgCASBIBAEgkAQCALorOrpAAAgAElEQVSfDoEU3Z/OpTEoCASBIBAEgkAQCAJBIAgEgSAQBF4FgRTdr+KJ6BEEgkAQCAJBIAgEgSAQBIJAEAgCnw6BFN2fzqUxKAgEgSAQBIJAEAgCQSAIBIEgEAReBYEU3a/iiegRBIJAEAgCQSAIBIEgEASCQBAIAp8OgRTdn86lMSgIBIEgEASCQBAIAkEgCASBIBAEXgWBFN2v4onoEQSCQBAIAkEgCASBIBAEgkAQCAKfDoEU3Z/OpTEoCASBIBAEgkAQCAJBIAgEgSAQBF4FgRTdr+KJ6BEEgkAQCAJBIAgEgSAQBIJAEAgCnw6BFN2fzqUxKAgEgSAQBIJAEAgCQSAIBIEgEAReBYEU3a/iiegRBIJAEAgCQSAIBIEgEASCQBAIAp8OgRTdn86lMSgIBIEgEASCQBAIAkEgCASBIBAEXgWBFN2v4okPpsevv/765Ztvvvny448/fjDNo24QCAJBIAgEgSAQBIJAEAgCQeDtEHjJopuC7ocffvjy7bfffvm///u/Pz/ffffdnwXe77//nkLv7eJjKSlF9xKaTASBIBAEgkAQCAJBIAgEgSAQBP5G4OWKboptCu3//Oc/XyjsbBTbP/30099F+B9//OHUU3pkpQWBIBAEgkAQCAJBIAgEgSAQBIJAELgHgZcqui24f/nll6VNzFGU14J8SXzjBLy///77G1dnWRAIAkEgCASBIBAEgkAQCAJBIAgEgf+PwMsU3T///PPf33CfOYfi/FlF92+//fbn3yqn6D7zQuaDQBAIAkEgCASBIBAEgkAQCAJB4AyBlyi6+VVx/lEuvsGm6D1r0PSiGx78o17y8R/56r+Gzjr/VpyeAt5fJf/vf//793r/lpye5t+Sw5eGLOZYXxu8Kn/euw7QMzbRVl58q0/xLw9/EwAdVr8N0Hnyw4zasLHyFDPxUrczWVU31iC3Y9bH+w8ywJQx1iGfPynoeFbdn/GMH5Cr7jzvxCC6sBa9sX23sQYbWafdvDPeW9VL/Wq/q2fnW9+Ri/7os9vcC/w7C+izY78/VKv61+cep+pCnLGf+n53/tm9+wNdsZf9s2rgos+uxgU8r8RGpwWjFYYrfY/Gqy3Yzj69Gm/4zBiBxyrO0QNcK+0Z1ke6P2oO/dF7ir1H4G8OQEb/9P1oXHU636/6ZsJoNxcgq+pD7O3kgEmmY8RXPx+ce3Z/5Gdk171wy76GR90L4PXMPHKG166ftd0Yqz08dpr5m7WPiJMdmbfQ4ONqn88rO7Vryg1n8uFZz5WjvMheMy8Se0e0Z3JX89gO37P9x/mCD8Hmln3Q8wZ23YLfyo73Ggc748Ue//ZGjqz4QbOKr77W9xo3O2ckcapO9qx7VNvZBz3ez/LfkW478o7Wv8LcSxTdBka/aFwBiEsA6718uBHqZYDkAo0bnX46CAjOmoAIGviwljme4c87HxvBzAc5NAsNxurm4pkxeDjuJcbNyqFcx6BljI9yXat8aFjDOB9sQF8v450ntGDPOPpAy/s0Lq7IqnwmfOFTG2sZq5gyj0zXgxnznabyefQzGOkH9BAD8O3YTrL1jzZMNH0MGXyMEXrHKi3ywYw5cbEnZvnc2/AjPJHTfbbiTXwYf8RVjYvVGsaxA521wZ5xZIuHPOBLPKube9b5t+jxLx/8q6/RZ9IFfcHFPY092Mv7bjMOxOIoNqBFHjgiR5yuyFvp1W0hFvXFZPvEx5xu7tGWaW8Rh+gvLfx4Zoy592jYLK7dZubuxV+b3Qe1N460+5VyAX5UP/SigQ9jnl3qvdvrazB463bkZ3TpewH7r+7rvhfAi9hmvLdHyOs86/vVnM++r7HJ8+55h1/ZJ9DDhxjB7t31Ve9nP+/aiX/MhdjSc8OZnuYOcCGWeOds4d39JA9kEWvsKz7mo0fuE3y0w1f/aa97flcX4xpZrDEWVvtADF69Nx9gU/0wXpsxAw2+xm4+k9/ruvp85T7iOvhXvXh+xJm6uw96vKOX+f5KHtiVp92v3P+zOnonTWtA3qoCAUxA2XB2H/MQkIaeAOzO7+ukhz9z0rOx3FyMMee7a7TNNYzzTNKpSdaDGHqbY8ittMoyAUJvEqx0YoAsmzz7ZvcS2Me9MFT9lVexkL8Y+W7fMWUTMdb1rT507bN6DjL0qE39pwtRpQMvsJowqHT1WSx7jOiTmgyJ1ZUOJN+jCy78ur+qHv3ZQ7eP93eTZY+RTtffTZh9nPdpT1Y69w82vWUD+46/+67uUXUibsCxNv1d/Vrn67O0u7GBDnXv6Bviscd0lcPz2R5jHh/3hn09b3Ua3tELuh6j2NbzAPTwXWE66THJfPQYuru/e+yB9T34oyvrV34CuxozyOuxqL1vnQvQu8c5urg3akyq41FPTJhzz+LyiM+tc0d+hic6dXvdq9VHK/nuhR7fvOPnvt9vlfeMnK9vVrYdjbO224yO7P+O5xGft5i7xU5sw5aeG870dZ9Uv/MMr54v2dt1PxlL0Nb1k0zk7Oqm/NX+02893t0HyDpr2EIes2GLGLIPPmrDhjOcyfOcJdVnrMFufFlxWeEA1v0MMJb6PpMH9Du+kd5+FQfO114frjBQxxrHrD9bV2XU51vXVR7v/fwSRTdOJviOnK3zoKsfASSR96TVeZokevD2oO/rlKGevtce+dMF0YRWEwvPq41SeZrs+sYRizpOUuPTmxvbTbHi+ajxFUYdU3HpOHRfdHse+d4TAbw5WND1SA/W4Wtpqx+O9NNvNflCLxY1LlfykY1+qws7/PDlrk7Qr3xWbXHvYPeEW6XtzytboIPf0byYGb+d97PeJxvFvu8z907PP+iGr6a80PXWzp3YWPnWA2k1r8yjPEtcofPEQx1rnMqz9vgTHpPPjLVqJ7Q9D8CPsfe4oKM3PtbebseEjfqusKv4EEcrDMkp9axg3Wp/GI9vmQvw3+QT80P1a7V59Qw/Y+4oLlfr7xk/8zPz+PMR+7oXLBPvaUz70OMoj7B2FZfyqL37sI71Z/bfKk47bX+f8ic02NDju6996/db7FzlhiPdwQTbJz/qj4pbfZYvsUgsHO15aNGPmNht8FztP3Ih81NjfMoHnXYVm6yFxxVdO+/3eifX7dhO/p58ac5c4V7tmtYzBnb9PuI6dLuaj1m7o48yzvYB8T7xM9dN5768p/5M3rTm1cbmnfTGWuIUgmdKRlUVg/wo0KAnmOEFXXU4QVrHVxu9r1MH9fTd/uzSQODBE3kG2yoJyZN+RWvgVR4mL+RMHw/PFc9Hja8wmjBlwzGO7upX7X+PZwuGo2RFkuMStcJspbdFek+S2E6MTIm184L2LNGjV42NzqO/r3wmHVgYw0e4SL/bwwv/H/E01rHpvZv7vMeqF6GpOBLbIxux6xGxIVaTHhU7dFq1o5h2rsdv56Uek8+cqzqak/tFkjiH/i2bZwT+UtfJjkkn6attE93RGDlx9yLyHrlgFevofBRXk83gZEE7nQ/TmkeN7fh5ZSs67O7rVWzDw7NPm+6RR4xe2Svqr+zem5vJ++z3nvM6/e47/Hbje5fnPXS32ule380N6LjK8czJ7wxnfHF2/svvim5H++8oVpzruXvXJ/eu35XzDDriGNzY4+zdszO+64B/WH92nvZ1vq/uI8wTR+YX9LwSC/hktxm3K/5HccUc+eBKO5N3hdd70b5E0S2QOOGs8HCTsqY3DnGTugHZAwj+Hm7II+C7zFWgKLvLdfN0WdK5DjppJ/2lt1/Rilflgc7YddZWPB81rq1djxWm+ox5ktetybvLu+WdOOBAO7o0c3CaJFeYHcn2EgZOyMNeZO7ajewzP6NXjY0jfZhb+cx17pdHX5bAGTyOmrGOTe/dwF7fV13Eb9LROeLmrN0bG2J1dvij06oZ05Odzh2th696TPvIOXqbl1Fyt5dOYu3R8aa8o55YV291nfw68ZD+DP9prWNgsBMr0L9HLsA2dKznDT4jdvs5qk1TD5+6ZnU+TGsfMbbjZ/fu5H/nznyFXXym5py4yfMWeaype2qSV8eUVcfqs7lZHenx1z2xDU/OOu2t8t7r+VY73euTr1a2uGbyk3NHZ7t32p27Avyu6IZ/iYmpGSuTXOeuyKoyWL/zQ4S65hWeiWF0Nxe6Tzw7dnQEM9Z55u2sqTSr+wg0nJ3gql70jO3sPezabcbtyv/IBaOpqds0txo7k7da90rj82nwxhqaTHDCWQC6yQG/NgIK59bEAL9VAHF4ELTQcJjUtlqn7ErLs5tnFVyuQ6a0K70qb2m7rQZeHUd2t6Py8nnF81Hj2qo8+xWmzJMItAk77jnYlXe15/IEfnxWiYnxemlYYXYkGx7IAA9sJQZX8iY+rKkxPtGgV42NiaaOrXwmDTLRF57uM/15pos8ph7bzw4p4wKb3qthI7qusAcLPpOOYrvjj3tjA9/s5JUjGvYetmBrj0vj/Wg9PiKHGx/dZ/qz4+Ea1rHHzoqZzvcR79hXbVPXya+TvF38p7WMEWfgvttW8VjXo3vHus73Z+zHB0dNPfXVUaGw4oOciusqXlbr7xnf9TM68al6KlecjrBlnTxcV3vn5N/fK+2ZPHgc6VJ58Sy/Pj69sze9wE95YVpTx8gp7qWz+11d99bPV+zUHn23o6uYT36SHzS9kYfRDezJMTsNfld0I/Ym2chCJvOT3tp0RVbVn7ia+FaaV38mvsVohdNkgz7t5+xEW8fO7iOVFt7gS+wc+biuWcVBpfEZ3vBd+d+8Mc2zjs+VdibvCq/3or1m8RO1tADGSUdB6CavG5XgxXn9As/YWQAptwbFap2yJxgMajZEbxRZ2GWDP5+puKw2oBN01VZ4GHh1XDsm+cjRvhXPR42vMFphKib02D7ZW2me8azOyOaDryYcwbgWAyvMznTEH8qiJ353GrJrHLGm6175Ts+THHlMc+AgH+LNmNVXxL1j0/rVGHscvmdrjXXjd8XvWePKFwPs7f5ybtJRbOtePdL11thgXfdF1109V736U0RBQ97SP/5QinH2wVnzB0tcRvA1Hy8Z8Kh5Tl714sL6o3PANY/qkVXtha/4icuRrAn/I/ppDtzBYKe9Ry6oennm4kvibsqXlb4+4/teqMOHvfLsdsXP6MRn8v/OvmadPCa7nJN/f69rqjyfpT/rKx+f5eH7WQ9u7s/dGIVnxUA9u+/PZL/l/K6dV3KD+ov5dBbID5rexM0eGvS0Ob7TT/zhw9rVnLkNGvM2Y+rM+JX9r97kEHLuZ2nEOrkQPDw3j2zjLlfvk0e0zlXMkTPdR6StPfp4Jnt/6bzgd/QxT1W+8pjmoMM+9ZSGWLFeQf8r7UzeFV7vRfsyRbeHIQ46SuokBmgA36YjTAiMww+6mkig68mBQIDOgGBtX+cGUnZNeF2Hrjtr4WegQ+/h1S+W0NT16lZtZb321nHWIocgrragK3qrszz7wed45Ymsq+MTRmCObrVghG/1lzhC13Vw7pk9+IAhOqJDjRvkkjx6sbHC5khPsEAGPfYji0+Nj9V6YqP7baJFrysY6rMVL/SbDkfjuMbsxGMaw96JZ6fFDuRj03s2/C9O7DH3Ezrpw0lH1+z4457YIDZ3Ygh9e2xPuKIvdup7/cX7tG87D/AxPliDfh7AvGNrbdDyqZeDnh8r/aOf2VfdriuxdwX/le5XLmFg9da5QL2NBXylj4mV7lPpa69/6/5hnpjYicvK65bnK35GJz637mvWyWPS1Tn59/e65iyPwGMnx8hTfr7v9PiMGMXXVxuxYaysML3K81n0O3ZeyQ3qKeaTn+S32gPsG2jMyRMP5dAzb1zV8dUzPlnJZg3+U390wJfeX26JBzAmv+/kjJXOrzguJv0s6boyD4a3tqP7yIonWOPnfo/t9Edx0GmN26NYQ1fv1fSsMRec6XKLvL7m1d5fpugGGDaiGxvncLgz5hzvOq8mHRxOMLH5cTB0OLOOMc4aaeQJXb/8K4M1HNIkPD6OV9l/KveX7vBBphvOC0YPLGxCD/XDZnj3S6YbmHlxQJ62db4GMnzhJZbgYZPnSlYfx1b4dR3O+KCbFwFwhAcf8GTz6zP4aJv+Abf3atU36sEY/lFPddOGKR6kqb28qz/AxliA31GDbueQEvcjXnXOOKljPmsjNL2ZxMHmaiM+8P1ZM/7OsDnj86h5sao+dGzS8Wiu6nRPbKDLlQN88mXVZXpmL7iH3RcT3dEYesKjxws+rmNgYY67RdcjHaY5/DbJ2Y29q/hPOrCX2N+77T1yAbqZD2oMiBM69RzZ7QHnaZ8QF5MP+vp73q/6GX3Qa9L3aK7q6J6pYz73uSOeR3PwQ0f8sNvkt0svnb72/Wrv+iv56qqMR9Cr54qX81NsnK1hbW/ym+YqLfKIm5ov67zP8LmiGzyJiSvN++YtvmRNPUOvyH11WrA88iO5HqzPcuWOne7jXSyhP/Pz2XzVy7i9EmusJ37BaVdvZd4qz/Wv0L9U0S0gOJBNqWNwDh8KQsYnB1PMQcPBb8ATPLx7uWfcMXnCrwe/hRDyeUae9PZTYMIHfsiEjvXqom32XFq8WELb9YC/suwnPZirDXnKBy/0t614XhmH10SvDHpsQS/sd1Ohkz/AgAZb0K/6GL47RWWV9YxnY0ldwFQfHPUrX6ujP6jw3R4fwRfcVg0asNppYHumS+WjP+uYz2CAbtBMjTl8e6WxT1hXL+6r9WKPTa/Q9JU5BZ2M97rX1NX4PrP11tjAP8Trlbby5REPc9VVWfLE52LRcSJ+pnglP+zGiXJu6Y1/ZB19phi8Bf9JR3A92v91zXvlAnQgDqb4MWea76u+PoPfEb7OTfzlcU9/1c+P2NfGfL9jkA+wt+b0e+Q9MucfYawPj2iO5sz9xNErtzM7bzmX2Bv4fMqh+v5o/4gXefHszEU/bNht6HVl33kvQI+zs63rgI07dvZ1H+UdHKfzDP2Jf3zd88Gttk33kSNe6HXm57P5yv+WfeBdhzi+2m6Rd1XGs+n/WbE9W1r4B4EPgAAbux5qXmgYrx8PSpPs2SEHHYfb1EhAR8kOWXx2mvru0EJzpBfzXhwnfthz9QLFgVsvmxNfx14tyYItNnPpsHnw9cuUF8ydw+XIB6vYuLXgO4ozbao9/sJm9Lj1suBe6TEsnvi5N3E921d93dV37Kv72md9gs6M9cvlrfhP+rEfdi+i6NNxnHgy9uhcQBxM8WOsT35UN/AT297DFwwY38VBvrv9VT8bf/fsa38YAa/aJt7TGGvE9iiPPNrPVdf6zIV5N/bquvqMr+sPLevcqzyf2UmcYseV3HTkR3wLv538yv47O3PR74puyJ729eQPdFTfq3sV+qtrJh1eeWz1gwhwY+/s+HjXPnyM7+p95GgtcXOG/24cIOfqPkBP8OGzq3O156q8uvZVnucK4FW0ix5B4B0QIDGxuc+aCW+i5YLZDz1/wtfHkeOFcyWTJNUvbtCSIEm6u5+JvzymOcZMdP2i5CWx28N7L1Aqb/DtF9k6X5+V3WVUmrd8BoPpUOIS0n+QwOGGX7rfHhEbHFiTHmCBjsyL3W5sTBjrY2ybDkkuEKw7ukhYeKDvREdsT7aAH3PTmrfwufhNuOzgr46Tv52jhxc+2rXzPXMBe3fyCTZiQ8eK96NcIA6snWLA+Wf2R36+d19jO3j1QpV3xjs2O/LAaXdPQzc1eUxzqzHiE/26ztDv+plYn+JnJfM9xo/sVJ+jmJFmwsRcWDHkGT/tnInoRi6u++xKLKz2GDxWc9pDj3xjZ6Uv9lT9XE8+X63p+8M1H63n7J1sFLfqd21jT9S7FbTgR3/WVveRaR1y2L82Y3g3fiafymOaU4498old9v+q8IeGz6pdkbfi8d7jc0Z+b60iPwi8AQJsfpKACYAkR/LnYrnTSDQkLBJBb/BlTt7Mw5+kh1yTFEkYmYyvkiyFD2ue0ZAPb3RVp0kOB23VG1re+2GJvfDC/qlhI/NHidV10HrATxhL94ze4gK5+oUDbnXpxJ6KxwofdL03NpSFLuBTP/Bm/N6G/vgWX8FfDDpfaXocQE/c6r/pIiIvYwYeyjHm6/6R/q361QF/Ff/J39UGsNnNOeJS1z/qeScXVNu9QDJGzPUY0K+rXFD1Ns7q2Fs9r/yMfO3Vtqv7Gh7iYCyTR7DX92rnFXl13ZXnMz+jA/rhU+ylMcZepu9N+6qftRHcjBN4QSPPzuet36/aqX7kKPPa6lyaMGE9a8GVD89gU9+VoW7IES9pwfaRjZyCv/GNvur8GccmaDjnpth1DTQ9vsUDe/oHfkfng3xfqSdfY6P3A3yJX8wTVVd9Di7ddnwPH2hs8GCs8rpyH3HvsUZ/ehZXOcq7tYcX9ojDig+xDE74GQymHMJadIUXH/WuPHfl1TWv+Jyi+xW9Ep3eBAESvZucngRydJh0pTgMWTcdvCRMkkxPMCQO51gLDe9HyZB5Po9uJsyOwSQH/cALfaEneU6HP/Zq08THg3uaq2NgWvXyudI88xk9tRXZHI4esCu52C6mK3xYq/9viQ0Oo6qXuNR+8stK52kcXsjg0D7bD8xDX+ncF2CArV4aJ1mOcSnwAgI/nhl7z2YMVv1vwX/lb20Dp4qf41MPLz6PbsZtjSPGpkbceumEHv2nHHiWCypv+KzkVbpnPE9+rnLu3dfwIpbBydiuMVVl8bwrr6/bed/1cz0b0Zv31Rk1+Zkx7TVGiNvpMr2j97NortiJDsYKNtVP12/CRBrvAKwnz07YQlN9ZT4+ihv5X+mrDT73vazN5GR0PfNhz3fEvrxXPXh9pNbjm3y4Oq/quTbZz9rapjOVsXruwxO/THuyxw60994Jqn48GxPdnk5nDNOjw6Sva5hDVz6dbleevF65T9H9yt6JbkEgCASBIBAEgkAQCAJBIAgEgSDwoRFI0f2h3Rflg0AQCAJBIAgEgSAQBIJAEAgCQeCVEUjR/creiW5BIAgEgSAQBIJAEAgCQSAIBIEg8KERSNH9od0X5YNAEAgCQSAIBIEgEASCQBAIAkHglRFI0f3K3oluQSAIBIEgEASCQBAIAkEgCASBIPChEUjR/aHdF+WDQBAIAkEgCASBIBAEgkAQCAJB4JURSNH9yt6JbkEgCASBIBAEgkAQCAJBIAgEgSDwoRFI0f2h3Rflg0AQCAJBIAgEgSAQBIJAEAgCQeCVEUjR/creiW5BIAgEgSAQBIJAEAgCQSAIBIEg8KERSNH9od0X5YNAEAgCQSAIBIEgEASCQBAIAkHglRFI0f3K3oluQSAIBIEgEASCQBAIAkEgCASBIPChEUjR/aHdF+WDQBAIAkEgCASBIBAEgkAQCAJB4JURSNH9yt6JbkEgCASBIBAEgkAQCAJBIAgEgSDwoRFI0f2h3Rflg0AQCAJBIAgEgSAQBIJAEAgCQeCVEUjR/creiW5BIAgEgSAQBIJAEAgCQSAIBIEg8KERSNH9od0X5YNAEAgCQSAIBIEgEASCQBAIAkHglRFI0f3K3oluQSAIBIEgEASCQBAIAkEgCASBIPChEUjR/aHdF+WDQBAIAkEgCASBIBAEgkAQCAJB4JUReGjR/X//939f8gkGiYHEQGIgMZAYSAwkBhIDiYHEQGIgMfARY+AZxftDi+5nKBieQSAIBIEgEASCQBAIAkEgCASBIBAEPioCKbo/queidxAIAkEgCASBIBAEgkAQCAJBIAi8PAIpul/eRVEwCASBIBAEgkAQCAJBIAgEgSAQBD4qAim6P6rnoncQCAJBIAgEgSAQBIJAEAgCQSAIvDwCKbpf3kVRMAgEgSAQBIJAEAgCQSAIBIEgEAQ+KgIpuj+q56L3l59//vnLr7/++iGQ+OOPP/7U99tvv/3y008//UPn//znP1+++eabL7///vs/xj/CC/j/8MMPX77//vt/qPvLL7/8OdZt/QdRecGX/OuW//3vf8vocx4/Utw8AgFiDz98xPiq9q9irdK8x/NKr6t74C3zwI8//vjh4+E9fB2ZQSAIBIEgEARuRSBF90Xkvvvuu7//t2gUSlxoe+OC2/95/LcoJroeZ++7OnKp7LQUWdM4dIyv5moRNtGc6ew8l8bffvvtz9eu2wrrSV4vFuX/6B67kYWuFQPkvOVl+5F2gT9+wKaKI/hj02TrSv5bFt3oQEGE7vc0/dnjr2IB/ynuWHM0vpqrsTPRdHvIT/xQxDzVdfW98oUH787Zd7u6rGe+r2LtmTJ3eK/0umUPvHUeIP7ZB2lBIAgEgSAQBILA8xFI0X0Dxly0LL5Xlxa+VeKyWi+8N4h6+hIuh/zwAF3psW3VuIh3Gi7zYsG3uF7u4QEGqzlluB66utb5qQdTirTa7rWj8nrWs4VlL3CeJe+t+BI7vSCzIHxlWyly7tWPmCXuwaDHf8cfWdCBjc34n9bfu3/k3fc078jjAwarxnrmsetVviVH5x5rK/3fcnzS69X3gLFb4/EtMYusIBAEgkAQCAJfEwIpum/0NpdRLlpcSKfGhYZ5+nsaF6JVYX8P37qWghVd+RwVv+gyXXhdP2HhxZNifdXguXvxo3Cd5MBbPW61Y6Xfo8bF4t5C71H6PIoPePe4+Ai2sjeJy93YW+FlMX3mV+Swv3ozbqe4Fsdb9g85ih9QTQ1/4bcz27Hp3t8ImOTfOjbF2q28Hrlu0kvfncXFI/W4ygsdia17z6mrckMfBIJAEAgCQeBrQyBF940e56LChXZ1ceUiffQt0q5YLunP/paJixdyuPRjz0pv6HpxpR3gscIC3sz1b9xYi21TsSHf2lsk9W+5pXmEHfJ6Ro9+4PDKl/Bb7MamHhcfxVb28G78rbDBnzt+fcv9I/7TnsOOK0X3K8XrFGsrv7zl+KSXPngl/CZMiP/VD2cm+owFgSAQBIJAEAgC1xFI0X0dsz+LRy6tXqqmCwtjq+JwRyTFKMXvvQXBjiyLAS7oFs+T7tJNPP3b3gmLoznk7H6TZk42fiEAAA77SURBVHGzKiTU7x47+GGJPyTwByv9hx68ozPztJV96Iv/uJDTS1cv4Z1Xx5bfcuj6nH0rhbz6gadFVh/vc1U3fKP+rCMeOxasZ26n6O46uAZ7lIUPbR0bfKM+rJ1wYL14VVt9lrc9+DIH71ubcVmxm3gZn9OcsfGo/QM+xuckT19UvCc6bOp2gbv66nt9CT+xppd/H++y9L9rr8Ra58W7fOwZ02bH6G11Tnu105xIj91T3MFLDOSpzfKbdHBNj/XKg5iQDl7qgy5TQ+6VPQB/9J/smvhnLAgEgSAQBIJAELiOwP9uHdfXfrUruPh4kbII6MUI431MwGohxWWHS0+98NQLLfN8lCcPeLPOCxjyVsWoa1Y9lzQvdRQfymS8tkpXx3lGNuvQpzcxmua4HK5wWvHp475X/W6xA9zRUbvpeecjtvipXnx51g/VPsfkRVEhrvqy89IOe3hUfOTB2FFTb+SpN/Suxx813piDJ/Js0vqnDRanxol09Mjp4+jAuLZCh0zGekGFLOQzJ14dG+TDi3n07Lzh32Xyjk+g1Y6qd11Tbe80Z+/oNenT16FPx0maR+4f9hP6rGQhkzlo0OmoYVv1IbTsE3yIj/iYr+TD2MSfcXOBtPT3xlrl5XP1/a17ABvr3l/FHTInvNGhxwUYMFb3AGNgbKxqA3qLLXjyTBzDt+8X13SZFYfVHjB+V/PyTh8EgkAQCAJBIAjcjkCK7huw4wLkRY6LSr9Ycenlctmbl1EuV1yGaBaH8KzNcfreqkx4esnmIndLQ5cq30sYetaCuNN1WV4E6+UN/dGLDzjVOXizZqd5WT2i7/pdsYO1XT/0EuuKD+O8V79ji1i5Bp61rS7t8qq06lN5gAE+mWKrruXZy3qPH9bCozdwhb9t0kn/SmMPDh0f9ccHNuyv/necXl9VexlXj27HJBP9um3yPSqq4dXXVd3OnpVRbZ3WYFvHqdKJb8Xolv3DGmwiBlZNXDvenR6bul1g1f2B7rWJSeev3Eo7jYlFpeN58nun8f3ePdBlmYMmH3ZadMB2xit+R3tgwkGbwdz8suLN+C17QD1vPT+QmxYEgkAQCAJBIAgcI5Ci+xifcbZe0KdCiG9upku+F8l+EXW8Fj1eGOsYyljQ1Ys5l7F+uRsVXwyiT79IWiSjmzpMdJWl31jVy5uXTPWuc9jImp3mxbDrWddO+u3agZ5gqK2Vr9/O+YMW5lYXZObAbCqMxaBewle80HviUfU6ekZX7OlxqJ01fsBtosOO2lY2I6f7RX9hK5gy3wu1yvtKkcY6ZPKp7UiPrl9f13nV+bNnde9+7eum+Kw0j9o/O/roS3Q6avDqdhGXNS+wvu9jdej8lVtlEnv3xFrlVZ/v3QPY2X9wMcUYMqfxq3tgwuaMd4/rIz06rVip52peuvRBIAgEgSAQBILA7Qj889Z6O5+vZqXfPFWDeyFDwdQLDC+hvbiBDxdOLkv1gspYv4hSXFPwVx4UNNJOBWPVc/WM3H7hki96KW+iqzwt/lmjLrVwRHfm/MaGOZ8rn+l552I46bdrh7pNsi3ca/EBXtjSmxh0PKHThsqH8YkXvCceXd7Ru3GlL6DVFnob/q0/UHDcHr1dN9k86aqtrEOPXrzI2979wbraJmyYR2bXhfcab9BVPSrf+iyvLrvSHD2re/drXwP/I58aO+ijz6o9xqh7ZrV/dvQR1zOb4dXt8odH6MOculZ71aHzV26lrc/QX421ur4/P2oPYLO8Jh/isz6OLYzv7oEVNke8u0xoa8yAR9Wj41PnWZsWBIJAEAgCQSAIPAeBnLIXcZ2+nfWy7AWIy2i/iPYLcxXLZYePa+TXL7sUR9BRIHGRQhf4cqlzbeW7+wwvda9rkKPefJO1oqtrvDBDzw8eLNihUX95QbvbvDhOespjpd+OHfpAXrW3gKj+QA/W9Hakp3OVD+snXvA+srXLnd7BGT5+q02vTxgnzogbiompoS8XePRg3aQn6yZdtZUYtVjpP4iqMsWYdbUdyURubcaeRSlzyKwYVHqfmefTZTt/1qt792tfB/8zn2rDPftnRx9xPbMZXpNdrCM2wI0c0X2rDp2/cidsbo21zqu+37sHWI99/mAKeycfTuPYzvjuHlhhc8S762L8XNkD6omctCAQBIJAEAgCQeA5COSUvYgrBUS90LjcCxOFTb8IeamZihuLgjoHDy5A/cJqAUyPDC62R99QqttZj5yus2vUHX24gK7opFd37OGiWi/j1VbmoN1t6nEk/x47sI/PhKcFRNUXPaDv7UhP53oRM/FSn87/yjsFNXzEzF4/oAc24dfe9OOOzVWGfKqt/tCDuJ32DmvEmHW1TdgwP+GjHNZguz9QqHur8vZ54uXcTq/uxPRRwzZ9sKIT93v2j/rQr5q4Vv9OtPA44sN681Ld6+qw409trrqoX9dpirVOU9/v2QP4E9tqTljJn8av7oErNsu7x9Mte2DFq+KY5yAQBIJAEAgCQeA+BP5dNdzH71Ov5gLXf3VPg704cvnql1SLnH5BYq0XrXrh5NsKLnu9wZvP1NDt1sala9JNfn5bNF0spam9l/AJK78do7+is5fniaey77HDb4imApRvqrC9Foz6Tdn26gl9t8/LbY+PiZdjNS6UAZ9aCDg+9dWuKhcfgSVyql3w4B39WVubOtUxnqe46La6PygmOy7wuFKkKRO5veE/bcZGCqdJXl0Hn2m/VZqjZ/f3WXGPbsTSWbt3/6jP0Q8BjOkzfcCyFtOT7sRij4Fdfz4i1iad6pjxAP67e8B47fmg26mcafzqHrhlf7Gmt6t7QD37fu988x4EgkAQCAJBIAjcjsC/b6238/r0K7mIHV1kLSh7QeSlpl+QuBxxWeuXHS7djlVe0PLpRQQ00t/iBPTrunU+2D1dLDsd79JOWHnZn+YmXnWMogYdVu0eO/TRVNQz1vX1gtx9gW7O9TUWQ/XiX+mrXV76iYUaAzyfFXeVjzLhU3XVD1PciEWfW+E/xYU8qq3K7Lig726Rpm3I7LFwtj9dW3vwhE+3tdKcPYOrhXL1VV0HDXEELmcNfNBpwukIQ/kii/VHe1r/oFONC3nQY0uPG8YnrJBV5enPWrCLAbr5gx716DyvxFrVeXq+ZQ+ofy26j3Cd8Na23T1g3ug2HPGumLPulj3gOVRthRf666euU96DQBAIAkEgCASBawisK5hrfD49NZePqfiqhnO54pI6NS6RzHGRoXnR4bJZL70WAfDiAlUvrfUy7hr4dB7o2S9jk06OecFcFQzQIQ8bdvhqw8TPuWqXepz1YjbxZe29dnR8sZmxXpgYC1yGkdkb+lmE4Rt8ji+9VIOjF1xkwB9e0NQmvZduY2hlf11bn9GlF3D6octkHTqpP3riK+yoeqq/fNCxXtD1FTrDzwYdH/Sp4/B3XNqKTY2XKrNigX7ojU/8YN9R8QBf5Fb+PMMHftUm9Zp617AOmdrGet4dn9b2Me2rtknjXNXXudqDJzKPmvsFH1V+yGCO9ZMO+klswLdjqJ7yBgN4GtPoh0xwQg7rb421IxudQ8aVPaBNrENP9DdGHRMzbcUGMUHulT1QYx1ZNnkjc+JNjBprrLllD/iDnMoHHbAHfmlBIAgEgSAQBILA/Qik6N7AkIsHFxA/XICmxqWIi9nUuNB4aYOPl85OCw/4czn1UicNPLg4qgc09YImHfoyd9a8WMqP/mjdkX1d1hEfLuK3NOwHGws+eTzSDgoD/Y2sXhzuyuKyDAZgCh/4shbe+AxbJl7Q2aDhQsx6fQPfqw0e07ojP6CjcsUA3dADfujmO2N+GNNux+ixdTVe6XiGbsKG8ZVMMLF46Px8n/YKtuGT2th3rAEfeO429of8lAmG7HXsudKwddWO/OYa8Zv8Ls3/a+eOdhuFgSiA/v8H5H+r+zASSp1MmYQVsAdpRRtjxz6YKBfTzT7HpX91rtPveGQcGc9qS3n6UGPM78+fVamX6zTt5l85Zlxpe+vxyVxb9W/1Wt5/ZfHOsuZT+p95ly39z+/1GfRqPua48ql9xrx6/fF4/Do27e5tO/2rPtd7Pu9X10Cd761brDLOnCsbAQIECBAg8LmA0P25oRb+oUC+7OZLoo3As0BCTYJK9gkXFVyyr2C5rVM3cbYBcFueIFlhcfv6VX7OmIWmq5yt7/Rz7zWQ4xOucy3YCBAgQIAAgeMEhO7jbLV8kECCRK00HfQWmr2YQFbmsor4bnsur1C+qpMQkuNfrfau6pzttYwhK7mrFd6z9VV/PheYXAOZ469uOn3eIy0QIECAAAECJSB0l4T9pQQSvFePSl5qEDr7NYGs6r57VDiBJCG7tsydV6vYdeyVA3eNs24eCN4lct/93msg899n6H3ng5ERIECAwLkEhO5znQ+92SGQL4xWaXaA3fjQ+lvW/OlBwnUeDc/cyBzJDZrtY9YpW/0N8l15ErzzZMgdbiLc9Rx9Y1x7roHMBzdivqGuDQIECBAg8DcBoftvTo4iQODkAgnYeVx2+59HZfXvfwrYJz9FunewgGvgYGDNEyBAgACBoYDQPYRTjQABAgQIECBAgAABAgQIdAJCdyeknAABAgQIECBAgAABAgQIDAWE7iGcagQIECBAgAABAgQIECBAoBMQujsh5QQIECBAgAABAgQIECBAYCggdA/hVCNAgAABAgQIECBAgAABAp2A0N0JKSdAgAABAgQIECBAgAABAkMBoXsIpxoBAgQIECBAgAABAgQIEOgEhO5OSDkBAgQIECBAgAABAgQIEBgKCN1DONUIECBAgAABAgQIECBAgEAnIHR3QsoJECBAgAABAgQIECBAgMBQQOgewqlGgAABAgQIECBAgAABAgQ6AaG7E1JOgAABAgQIECBAgAABAgSGAkL3EE41AgQIECBAgAABAgQIECDQCQjdnZByAgQIECBAgAABAgQIECAwFBC6h3CqESBAgAABAgQIECBAgACBTkDo7oSUEyBAgAABAgQIECBAgACBoYDQPYRTjQABAgQIECBAgAABAgQIdAJCdyeknAABAgQIECBAgAABAgQIDAWE7iGcagQIECBAgAABAgQIECBAoBMQujsh5QQIECBAgAABAgQIECBAYCjwA3NxgSRBjV2pAAAAAElFTkSuQmCC\" style=\"width: 986px;\" width=\"986\" height=\"490\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSignificance of the Interaction Between Walking Types and Stride Frequency\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteraction Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eBiceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e2.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eVastus Lateralis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eBiceps femoris\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eTibialis anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eGastrocnemius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eStep length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eStride length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparison of Walking Types Afterwards\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMuscle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSimple Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eBiceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 7.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e*(NW＞UW)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eTriceps brachii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e13.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e*(NW＞UW)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eVastus lateralis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eBiceps femoris\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eTibialis anterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eGastrocnemius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eStep length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.551020408163264%\"\u003e\n \u003cp\u003eStride length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.591836734693878%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote:* p \u0026lt; .05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Influence of Different Walking Types on Stride Length (Unit: m) \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.3265306122449%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eStride Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWalking Style\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e88/steps\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e110/steps\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e132/steps\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.580246913580247%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.28395061728395%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003eStride length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e1.62\u0026plusmn;.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.53\u0026plusmn;.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.72\u0026plusmn;.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.71\u0026plusmn;.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.79\u0026plusmn;.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e1.78\u0026plusmn;.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"walking types, muscular activity, gait analysis","lastPublishedDoi":"10.21203/rs.3.rs-4039719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4039719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Walking is a popular contemporary exercise that has gradually diversified over time, and Nordic walking is a variant. Therefore, the purpose of this study was compared the changes in electromyography and gait parameters achieved through Nordic and usual walking at three stride frequencies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eFourteen middle-aged women with a regular exercise habit were enrolled as participants. The participants were instructed to walk a set 14m distance three times using Nordic or usual walking depend on random. Three stride frequencies (i.e., low [88 steps/min], normal [110 steps/min], and high [132 steps/min]) frequency) were applied three times. The following muscle groups were tested: the biceps brachii and triceps brachii of the upper limb; the vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius of the lower limb. Statistical verification was conducted through a two-factor repeated measures analysis of variance (significance level α set to .05); the independent variables were walking types and stride frequency, and the dependent variables were EMG signals and gait parameters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eThe biceps and triceps brachii experienced greater activation through Nordic walking than through usual walking (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05). The upper limb muscle groups experienced the most activation at a low stride frequency, followed by a normal and high stride frequency; this trend was not observed for the lower limb muscle groups. No significant difference was identified for stride length.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eNordic walking is a full-body physical activity that can effectively facilitate upper limb muscle activation; this effect is particularly pronounced in slow walking scenarios.\u003c/p\u003e","manuscriptTitle":"Effects of Nordic walking on muscle activation and stride length of middle-aged women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 16:05:07","doi":"10.21203/rs.3.rs-4039719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54ffe1a7-d053-482e-ad9c-ab14bd8c97cf","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29921298,"name":"Health sciences/Health care/Geriatrics"},{"id":29921299,"name":"Health sciences/Health care/Health policy"}],"tags":[],"updatedAt":"2024-06-10T05:44:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 16:05:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4039719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4039719","identity":"rs-4039719","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}