Xinxin Zhang

Objective: Based on the theory of perceived organizational support (POS), conservation of resource (COR) and job demands-resources (JD-R) model, this study establishes a moderated mediation model to test the role of job satisfaction in mediating the relationship between perceived organizational support and job burnout, as well as the role of regulatory emotional self-efficacy in moderating the above mediating process. Method: A total of 784 police officers were surveyed with the Perceived Organizational Support Scale, the Job Burnout Questionnaire, the Regulatory Emotional Self-Efficacy Scale, and the Minnesota Job Satisfaction Questionnaire. Results: (1) After controlling for gender, seniority, age, police classification, education, and marital status, regression analysis showed a significant negative correlation between perceived organizational support and burnout (r = -0.42, p < 0.01), and the former had a significant negative predictive effect on job burnout (β = -0.42, p < 0.001). (2) The mediating effect test shows that job satisfaction plays a partial role in mediating the relationship between perceived organizational support and job burnout. (3) Through the analysis of the moderated mediation model test, regulatory emotional self-efficacy moderates the first half of the path of “perceived organizational support → job satisfaction → job burnout". Conclusion: Perceived organizational support not only directly affects police job burnout but also indirectly affects police job burnout through job satisfaction. Regulatory emotional self-efficacy enhances the influence of organizational support on job satisfaction. This study indicates the combined effect of perceived organizational support, job satisfaction and regulatory emotional self-efficacy on job burnout and has certain guiding significance for alleviating police job burnout. Keywords: Perceived organizational support, Job burnout, Job satisfaction, Regulatory emotional self-efficacy, Moderated mediation.

With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different.

Product market competitiveness is positively influenced by the aesthetic value of product form, which is closely related to product complexity. By measuring the cognitive complexity of the product, this research establishes the relationship between the complexity and aesthetics of the product using an artificial neural network. Hence the prediction of product beauty is achieved, which guides design decisions. In this article, the complexity of product form is first measured through a combination of hesitant-fuzzy theory and information axiom. Afterward, the result is weighted by exponential entropy and dimensionally compressed. This method makes data more suitable for the prediction with small samples, obtaining an accuracy improvement of up to 40% compared with traditional approaches. Finally, the importance order of the design elements which affect morphological complexity is acquired. Results show that three of the six complexity features are more significant, impacting the aesthetic feeling of product form. The method increases the attractiveness of products to customers, providing valuable design support for enterprises and designers in the early days when a new product is designed, and reducing research and development risks.