In a narrow sense, polymer composites refer to multiphase materials composed of polymers and other substances with different compositions, shapes, and properties. They can be divided into structural composites and functional composites. In a broad sense, polymer composite materials also include polymer blend systems, collectively referred to as “polymer alloys.” The purpose of this paper is at studying how to study the impact of polymer composite materials on the adaptation and comfort of sports and fitness facilities. This paper puts forward the problem of comfort, which is based on the construction materials of sports equipment, and then elaborates on the polymer composite materials and makes a case design and analysis of the applicability of polymer composite materials in sports equipment being carried out. The experimental results show that 34.81% of the people are very satisfied and relatively satisfied with the quality identification of sports equipment and only 41.99% of the people are very satisfied and relatively satisfied with the feeling of sports equipment when exercising. Both are less than half of the total sample size, which shows that the current state of sports facilities is worrying.
For football players who participate in sports, the word “health” is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. The collected football player health dataset is used in this paper to conduct experiments. The simulation results prove the reliability and superiority of the proposed algorithm. Furthermore, the algorithm presented in this paper can serve as a foundation for the football team’s and coaches’ scientific training plans.
The physical health of adolescents is directly related to the rise and fall of the country, the hope of the nation, and their own healthy growth. It is a focal issue that the country, society, schools, and families have always paid close attention to. Based on big data, this paper explores the intelligent service system of youth health and national traditional sports. The national traditional sports culture is based on the national traditional sports as the carrier to reflect the sum of the educational wisdom and sports connection and practical ability of all ethnic groups. This article first introduces health big data and the traditional sports culture of the nation. And a health status evaluation model based on multivariate Gaussian distribution is proposed, which adopts Gaussian distribution theory. Based on the multivariate Gaussian distribution model, the probability distribution of physiological big data features is calculated, and then according to the probability of feature points. It constructs a health status assessment model by dividing characteristic probability intervals. Finally, the influence of traditional sports on the physical and mental health of college students is analyzed and studied. The experimental results show that college students must insist on traditional sports more than 2 times a week, preferably no less than 1 hour a day, which has a significant effect on improving the mental health of college students.
ObjectiveTo explore the relationship between physical exercise and college students’ social adaptability, as well as the mediating role of social-emotional competency and self-esteem.MethodsOne thousand two hundred thirty college students were investigated by physical exercise questionnaire, social-emotional competency scale, self-esteem scale, and social adaptability scale. Data were analyzed by Pearson correlation analysis, structural equation model test and deviation-corrected percentile Bootstrap method.Results(1) Physical exercise was positively correlated with social adaptability (r = 0.397, p < 0.01), and the direct path of physical exercise on social adaptability was significant (β = 0.397, t = 15.174, p < 0.01). (2) Physical exercise positively predicted social-emotional competency (β = 0.399, t = 15.235, p < 0.01) and self-esteem (β = 0.305, t = 10.570, p < 0.01). Social-emotional competency positively predicted self-esteem (β = 0.130, t = 4.507, p < 0.01) and social adaptability (β = 0.169, t = 6.104, p < 0.01). Self-esteem positively predicted social adaptability (β = 0.189, t = 6.957, p < 0.01). (3) Social-emotional competency and self-esteem play a significant mediating role between physical exercise and social adaptability. The mediating effect includes three paths: physical exercise→social-emotional competency→social adaptability (the mediating effect value: 0.068); physical exercise→self-esteem→social adaptability (the mediating effect value: 0.059). Physical exercise→social-emotional competency→self-esteem→social adaptability (the mediating effect value: 0.010).ConclusionPhysical exercise can not only directly affect social adaptability of college students, but also indirectly affect social adaptability through the independent intermediary role of social-emotional competency and self-esteem. Furthermore, physical exercise also affect social adaptability through the chain mediation of social-emotional competency and self-esteem.
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