Abstract The study investigates how plantar pressure distribution during walking can inform injury prevention and recovery strategies among footballers. Given soccer's high injury risk, the research emphasizes using modern computer based pressure analysis to assess foot stress patterns unique to individual athletes. By integrating this data into motion models, the study aims to offer personalized, evidence based recommendations to reduce overtraining and enhance rehabilitation. The methodology... The study investigates how plantar pressure distribution during walking can inform injury prevention and recovery strategies among footballers. Given soccer's high injury risk, the research emphasizes using modern computer based pressure analysis to assess foot stress patterns unique to individual athletes. By integrating this data into motion models, the study aims to offer personalized, evidence based recommendations to reduce overtraining and enhance rehabilitation. The methodology involved collecting gait data from football players and non athlete college students using the RSCAN gait analysis system. The gait cycle was divided into four phases, with findings showing that footballers spend significantly more time in the initial contact and full foot support phases 88% of the cycle than students. This prolonged forefoot contact is associated with increased stress and an increased risk of injury, particularly among players with prior ankle injuries, who exhibit reduced protective gait adaptations. Results from real time data collection on 300 athletes demonstrate the model's efficiency in processing foot pressure data quickly (under 2 seconds), validating its potential for practical use in training environments. The study concludes that plantar pressure analysis is a reliable predictor of injury severity and a valuable tool for tailoring recovery protocols. It underscores the need for large scale, ethnically representative datasets to refine injury prevention strategies and to support the professional development of football in China. Read More Read Less

Abstract The paper examines the integration of sports and tourism as a driver of economic development and public health improvement in China. It outlines how the fusion of physical activity with leisure and travel meets evolving consumer demands and supports national strategic goals. The study proposes leveraging search algorithms particularly hash function based approaches to optimize sports tourism planning, market prediction, and digital platform integration.... The paper examines the integration of sports and tourism as a driver of economic development and public health improvement in China. It outlines how the fusion of physical activity with leisure and travel meets evolving consumer demands and supports national strategic goals. The study proposes leveraging search algorithms particularly hash function based approaches to optimize sports tourism planning, market prediction, and digital platform integration. Methodologically, the research evaluates algorithm performance on datasets of varying sizes (50,000 to 200,000 records), demonstrating high reliability (up to 0.94) and improved computational efficiency, reducing processing time from 100 to 30 seconds for 3,000 complex data points. The analysis also evaluates tourist satisfaction factors such as comfort, ease of search, and acceptability across multiple teams. Drawing on recent literature, the paper affirms that data-driven strategies, supported by AI and big data, can significantly enhance destination management and market responsiveness. Despite promising results, the author acknowledges a gap in assessing the algorithm's adaptability across diverse contexts. Overall, the study presents a practical, technology enabled framework to advance sports tourism through intelligent search systems, contributing to both economic growth and sustainable tourism development. Read More Read Less

Abstract The paper explores the application of the Group Method of Data Handling (GMDH) algorithmenhanced with fuzzy inference to analyze and optimize the value chain in the sports industry. The value chain framework encompasses enterprise relationships, value creation processes, and structural dynamics, all of which are critical to sustainable industrial growth. Given the sports industry's limited historical data and inherent uncertainty, traditional modeling approaches face... The paper explores the application of the Group Method of Data Handling (GMDH) algorithmenhanced with fuzzy inference to analyze and optimize the value chain in the sports industry. The value chain framework encompasses enterprise relationships, value creation processes, and structural dynamics, all of which are critical to sustainable industrial growth. Given the sports industry's limited historical data and inherent uncertainty, traditional modeling approaches face challenges. The GMDH algorithm offers advantages such as self organization, global optimization, and effectiveness with small datasets. However, its susceptibility to overfitting and structural instability prompted the integration of fuzzy logic to handle imprecise and noisy data better. The fuzzy GMDH model improves prediction accuracy by incorporating fuzzy reasoning into the basic processing units, enabling more robust modeling of nonlinear and uncertain systems. The study applies this optimized model to the sports industry in "M Province," comparing performance between standard GMDH and fuzzy GMDH. Results indicate that the fuzzy variant provides superior fit and predictive capability, supporting more accurate evaluation of the industry's value and development potential. Despite these improvements, the authors acknowledge room for further refinement of the algorithm's architecture. The research underscores the importance of advanced data mining techniques, such as fuzzy GMDH, in enabling evidence based decision making in emerging sectors, including sports, where data scarcity and volatility are common challenges. Read More Read Less