ObjectiveThis study introduced self-concept and social support as research variables to establish a research mechanism, in order to encourage college students to participate in sports better, relieve or overcome depression.MethodsThe survey was conducted among 1,200 college students in Jiangxi, China. Serial mediation models were used to examine whether self-concept and social support mediated in the effect of physical exercise on depression.ResultsPhysical exercise significantly negatively predicted college depression. Moreover, Self-concept and social support mediate the relationship between physical exercise and depression in (...) college students.ConclusionThis study reveals how physical exercise affects college students’ depression and its mechanism, and the results have certain enlightenment significance for maintaining and promoting college students’ physical and mental health. (shrink)

The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network and Convolutional Neural Network is constructed. The method’s effectiveness is verified by comparing the (...) prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities. (shrink)