Impact of Exercise Atmosphere on Adolescents’Exercise Behavior:Chain Mediating Effect of Exercise Identity and Exercise Habit

Appropriate physical exercise has a positive impact on adolescents’physical and mental health,but there is a serious lack of physical exercise among Chinese adolescents.How to shape their exercise behavior(EB)has become an important task in promoting their development.A questionnaire survey was conducted using stratified cluster random sampling on three middle schools by class in Zhejiang Province,China to investigate the impact of exercise atmosphere(EA)on adolescents’exercise behavior and the mediating role of exercise identity(EI)and exercise habit(EH).806 adolescents were investigated by the Exercise Atmosphere Scale(EAS),Exercise-Identity Scale(EIS),Self-Report Habit Index(SRHI),and Physical Activity Rating Scale(PARS-3).The results show that:There is a significant positive correlation between each two of exercise atmosphere,exercise identity,exercise habit,and exercise behavior(p<0.05).Exercise atmosphere could not only directly affect adolescents’physical exercise behavior but can also indirectly affect their physical exercise behavior through the mediating effect of exercise identity and exercise habit,involving three mediating pathways,namely,the mediating path through exercise identity,the mediating pathway through exercise habit and the chain mediating pathway through exercise identity and exercise habit.The direct effect of exercise atmosphere on exercise behavior was 0.459(p<0.01),accounting for 62.62% of the total effect of 0.733,and its indirect effect was 0.274,accounting for 37.28% of the total effect.To a certain extent,the mediating effect model reveals the mechanism of exercise atmosphere affecting exercise behavior and has a certain reference value for promoting adolescents’exercise behavior.We should start by creating an exercise atmosphere,cultivating exercise identity,and enhancing exercise habits to help teenagers form active physical exercise behaviors.

Deep transformer and few‐shot learning for hyperspectral image classification

Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)classification.However,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical tasks.Existing few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of HSI.To solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot instances.Specifically,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of classes.Next,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling capability.Furthermore,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution alignment.On three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.