基于健康心理学研究领域内的"健康行为过程理论"(Health Action Process Approach;HAPA),采用纵向研究设计,历时4个月对331名高校教职员工的体育锻炼行为及其相关社会认知变量进行3次调查。研究结果表明,积极结果期待和行动...基于健康心理学研究领域内的"健康行为过程理论"(Health Action Process Approach;HAPA),采用纵向研究设计,历时4个月对331名高校教职员工的体育锻炼行为及其相关社会认知变量进行3次调查。研究结果表明,积极结果期待和行动自我效能对锻炼意向的预测作用显著,锻炼意向能够很好地预测锻炼计划;恢复自我效能、锻炼意向和锻炼计划是体育锻炼行为的近轴预测因子;行动自我效能通过维持自我效能预测恢复自我效能。该研究验证了HAPA模型的连续性特征,为今后制定有效的体育锻炼干预措施,促进高校教职员工参与和坚持体育锻炼,提升健康水平提供了理论依据。展开更多
Geological structures often exhibit smooth characteristics away from sharp discontinuities. One aim of geophysical inversion is to recover information about the smooth structures as well as about the sharp discontinui...Geological structures often exhibit smooth characteristics away from sharp discontinuities. One aim of geophysical inversion is to recover information about the smooth structures as well as about the sharp discontinuities. Because no specific operator can provide a perfect sparse representation of complicated geological models, hyper-parameter regularization inversion based on the iterative split Bregman method was used to recover the features of both smooth and sharp geological structures. A novel preconditioned matrix was proposed, which counteracted the natural decay of the sensitivity matrix and its inverse matrix was calculated easily. Application of the algorithm to synthetic data produces density models that are good representations of the designed models. The results show that the algorithm proposed is feasible and effective.展开更多
文摘基于健康心理学研究领域内的"健康行为过程理论"(Health Action Process Approach;HAPA),采用纵向研究设计,历时4个月对331名高校教职员工的体育锻炼行为及其相关社会认知变量进行3次调查。研究结果表明,积极结果期待和行动自我效能对锻炼意向的预测作用显著,锻炼意向能够很好地预测锻炼计划;恢复自我效能、锻炼意向和锻炼计划是体育锻炼行为的近轴预测因子;行动自我效能通过维持自我效能预测恢复自我效能。该研究验证了HAPA模型的连续性特征,为今后制定有效的体育锻炼干预措施,促进高校教职员工参与和坚持体育锻炼,提升健康水平提供了理论依据。
基金Projects(41174061,41374120)supported by the National Natural Science Foundation of China
文摘Geological structures often exhibit smooth characteristics away from sharp discontinuities. One aim of geophysical inversion is to recover information about the smooth structures as well as about the sharp discontinuities. Because no specific operator can provide a perfect sparse representation of complicated geological models, hyper-parameter regularization inversion based on the iterative split Bregman method was used to recover the features of both smooth and sharp geological structures. A novel preconditioned matrix was proposed, which counteracted the natural decay of the sensitivity matrix and its inverse matrix was calculated easily. Application of the algorithm to synthetic data produces density models that are good representations of the designed models. The results show that the algorithm proposed is feasible and effective.
