From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect o...From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect of the status quo maintenance and the moderating effect of the prevention focus orientation.The study re‐sults show that PEBs in the private domain significantly promote individuals’PEBs in the public domain.The status quo maintenance partially mediates the relationship between PEBs in the private and public domains.Specifically,individuals with a high-level prevention focus orientation strengthen the relationship between the PEBs in the private domain and the status quo maintenance,and that of the PEBs in the public domain.There‐fore,individuals with a high-level prevention focus will more likely engage in subsequent PEBs in the public domain after their initial PEBs in the private domain due to their increased status quo maintenance degree.Policymakers and practitioners should pay attention to the prevention-repetition effect and use the PEBs in the private domain to promote those in the public domain.展开更多
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization...Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.展开更多
基金support provided by the Zhejiang Province Planning Project of Philosophy and Social Science[Grant No.22NDJC107YB]Zhejiang Provincial Natural Science Foundation of China[Grant No.LY21G020009].
文摘From the perspective of regulatory focus theory,the influencing mechanism of pro-environmental behaviors(PEBs)in the private domain on behaviors in the public domain were analyzed by revealing the mediating ef‐fect of the status quo maintenance and the moderating effect of the prevention focus orientation.The study re‐sults show that PEBs in the private domain significantly promote individuals’PEBs in the public domain.The status quo maintenance partially mediates the relationship between PEBs in the private and public domains.Specifically,individuals with a high-level prevention focus orientation strengthen the relationship between the PEBs in the private domain and the status quo maintenance,and that of the PEBs in the public domain.There‐fore,individuals with a high-level prevention focus will more likely engage in subsequent PEBs in the public domain after their initial PEBs in the private domain due to their increased status quo maintenance degree.Policymakers and practitioners should pay attention to the prevention-repetition effect and use the PEBs in the private domain to promote those in the public domain.
基金supported by National Natural Science Foundation of China(Grant No.51175086)
文摘Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
