This study investigated the parallel mediating effects of positive and negative mood states on the relationship between psychological resilience and emotional stability among first- through third-year senior high school students in China during the challenges of the COVID-19 pandemic. Of 408 questionnaires distributed from April 11 to April 22, 2022, to students at a high school located in Changzhou, Jiangsu, China, 360 were completed correctly and analyzed using a cross-sectional study design. The questionnaire included items from the modified Chinese version of the Psychological Resilience Scale, the Profile of Mood States scale, and the Eysenck Personality Questionnaire Short Scale in Chinese, the latter to assess emotional stability. The mediating effects of mood states on the relationship between psychological resilience and emotional stability were explored by using structural equation modeling and bootstrapping methods. The results indicated that psychological resilience directly affected emotional stability but also indirectly affected emotional stability through the mediating effects of positive and negative mood states. The mediating effect of negative mood states was greater than that of positive mood states. This result differs from that of research conducted prior to the pandemic, which found that compared with the damage caused by negative moods to emotional stability, positive moods more strongly promoted emotional stability. Our findings indicate that high school officials in China should consider strengthening mental health support for students who are taking courses online during home quarantine.
The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.
IntroductionThis study explored the effects of coping style and two potential intermediately factors (cognitive reappraisal and psychological resilience) on the mental health of middle school students during the normalization of epidemic prevention and control in China.MethodsAnswers on questionnaires designed to assess coping style, cognitive reappraisal, psychological resilience, and mental health among 743 middle school students (386 boys, 357 girls, 241 first graders, 235 second graders, and 267 third graders) were analyzed using structural equation modeling.ResultsThe results showed that coping style, cognitive reappraisal, and psychological resilience directly predicted mental health. The negative effect of a negative coping style on mental health was significantly stronger than the positive effect of a positive coping style. Coping style affected mental health through the independent mediating effects of cognitive reappraisal and psychological resilience and through their chain mediation.DiscussionThe use of positive coping styles by most students led to greater cognitive reappraisal, strengthened psychological resilience, and thus few mental health problems. These findings provide empirical evidence and may guide educators in the prevention and intervention of mental health problems among middle school students.
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