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Guided by the Transtheoretical Model (Prochaska & DiClemente, 1982), this study investigated the differences of physical activity levels and correlates (i.e., self-efficacy, decisional balance, process of change) across different stages of change levels among Chinese college students. The relationships between students' physical activity correlates and physical activity behavior was also examined. The participants were 887 college students (365 males; M[subscript age] = 20.51, SD = ± 1.67) recruited from four universities in south and south-canter China. Participants completed a battery of established questionnaires assessing their physical activity correlates (self-efficacy, decisional balance, process of change) and 1-week physical activity levels. Results suggested that Chinese college students in the high stage of change group reported significantly higher physical activity levels and correlates than those in the low stage of change group. Pearson correlation analyses suggested that students' self-efficacy was moderately related to other correlates and physical activity behavior. Yet, decisional balance and process of change were only modestly associated with physical activity levels for both groups. Regression analyses further revealed students' self-efficacy emerged as the only significant contributor of their daily PA levels across the two groups. However, decision balance and process of change failed to predict physical activity levels. The implications for practice and direction of future research were discussed.

To improve the reliability of the dynamic system including physical and control design, the reliability‐based control co‐design (RB‐CCD) problem has been studied to account for the uncertainty stemming from the random physical design. However, when encountering RB‐CCD in the sophisticated system in which the dynamic model simulation is time‐consuming or the state equation is expressed implicitly, the available RB‐CCD methods will consume significant computational effort to perform numerous system simulations for the reliability analysis and deterministic optimization. Therefore, this work proposes a Dendrite Net‐based decoupled framework for RB‐CCD to alleviate the computational burden. Specifically, the Dendrite (DD) model constructed by the suggested training scheme integrated with an adaptive sampling strategy is used to approximate the state equation in the dynamic system. After that, the sequential optimization and reliability assessment method decouples RB‐CCD into the control co‐design (CCD) problem and time‐dependent reliability assessment problem, which are solved sequentially based on the cheap estimations of DD model, rather than the expensive simulations of the original system. Furthermore, two numerical examples and an engineering example of 3‐DOF robot system are applied to demonstrate the feasibility and efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

In sampling-based reliability analysis, a constraint with multiple failure points may lead to an inefficient iteration process and inaccurate results. To examine this problem, a novel analysis method is proposed in this article, which achieves multiple failure point-based constraint model construction. In the proposed method, a radial sampling-based subregion partition method is presented to locate the potential failure subregions that may have a failure point and to construct a subregion partition model to find the model refinement points in parallel. In addition, a new machine learning algorithm, the dendrite network, is adopted to construct the constraint model and the subregion partition model, and a network-matched learning function is designed to assist dendrite network-based model refinement. Test results demonstrate that the number of training samples is decreased compared with other citation methods. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID‐19) has already devastated the world, and the economy becomes the most critical challenge for any country worldwide. The increasing uncertainty of the COVID‐19 outbreak has made stock markets in China more turbulent and less predictable. Under the current exceptional circumstances, the hospitality industry suffered the most due to the travel restrictions. This research thus assesses the dynamic relationship among the COVID‐19 outbreak, macroeconomic fluctuations and hospitality stock returns based on a structural VAR framework from 13 January to 11 May 2020, in China. Evidence reveals that macroeconomic fluctuations and hospitality stock returns are significantly affected by shocks from the COVID‐19 outbreak. An unanticipated positive change of the COVID‐19 explosion triggers an addition in exchange rates and causes a reduction in the stock market and hospitality industry returns. For the impacts of the exchange rate, findings reveal that a surprise increase in exchange rates (currency depreciation) exerts a significant negative influence on stock market returns. Additionally, a positive change of stock market returns is linked to a decline in exchange rates and a rise in hospitality industry returns. Therefore, knowledge of these relationships can enable policymakers to evaluate and implement effective policies to stabilize the stock markets and help investors to make appropriate investment strategies. [ABSTRACT FROM AUTHOR]

This research assesses the effect of short sales on investment efficiency in China by applying the DID method. Results show that short sales can improve investment efficiency and that the effect is more pronounced for companies with low information transparency, low governance capacity, low audit quality, and high management performance pressure. Evidence reveals that the impact of short sales varies under different market sentiments. Our findings support the beneficial role of short sales on the information and governance environment. Governments and investors can thus pay more attention to short sales, as they help in their role of accelerating information dissemination. [ABSTRACT FROM AUTHOR]