This study explored whether gender differences exist in social support, optimism/pessimism, and psychological well-being among university student athletes and examined the relationship among these three variables and the mediating effect of optimism/pessimism. A total of 322 university student athletes (159 men and 163 women) who were Division 1 athletes participated in this study. The research instruments used in this study comprised the revised Athletes’ Received Support Questionnaire, the Life Orientation Test, and the Psychological Well-Being Scale. The results demonstrated the influence of gender differences for some variables. Regarding pessimistic tendency and autonomy (a dimension of the Psychological Well-Being Scale), the average scores of men were significantly higher than those of women. Regarding the other three dimensions of the Psychological Well-Being Scale (purpose in life, positive relationships with others, and personal growth), the average scores were higher for women than for men. Moreover, significant positive correlations were observed among social support provided by coaches, optimism, and psychological well-being. Optimism mediated the relationship between social support and psychological well-being, and pessimism was negatively correlated with psychological well-being; however, the mediating path was not significant. Finally, suggestions for future research and practical implications are proposed for researchers, educators, and supervisors in the field of sports.

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As the application of big data becomes more and more popular, machine learning algorithms are changing with each passing day, and the models produced by machine learning are increasingly diversified. The focus of big data applications has gradually shifted to the prediction and inference of models. How to choose the most suitable model for enterprise application scenarios among many machine learning models has become a topic of research that has attracted much attention. Ensemble methods have been proposed to discover best model by multiple training phase. Studies of finding best combination within multiple modes are still few. Configuring different machine learning models with appropriate parameters and looking for parameters is an NP-hard problem, which requires an optimization algorithm. This study proposes to apply differential evolution algorithm to integrate multiple trained machine learning models into an appropriate model. In this paper, the regression model is taken as an example and the differential evolution algorithm is compared with the particles swarm optimization algorithm. The results show that the differential evolution algorithm has better performance.