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Objective: To understand the prevalence of home-related anxiety among adolescent athletes during the novel coronavirus pandemic and to ascertain the factors influencing this anxiety. Methods: We employed cluster sampling to select 1150 adolescent athletes (aged 8–18 years) from six sports training schools in Yantai City, Shandong Province. Mental health status was assessed and recorded. Chi-square tests and multivariable logistic regression were used to analyze the factors contributing to athletes' anxiety. Results: The survey revealed a COVID-19 infection rate of 38.23% (437 individuals) with an anxiety score of 40.98 ± 8.20 and an anxiety detection rate of 11.29% (129 individuals) during the COVID-19 epidemic. Female athletes exhibited a higher anxiety rate of 14.40% compared to 8.40% in male athletes. Multivariate analysis identified female gender as a risk factor for anxiety (OR = 1.64), while participation in aquatics emerged as a protective factor (OR = 0.24, 95% CI: 1.08–2.48). Professional training duration exceeding three years increased anxiety risk (OR = 3.05, 95% CI: 1.67–5.58), as did not seeking help during difficulties (OR = 2.59, 95% CI: 1.33–5.01). Interestingly, parental care was linked to increased anxiety risk (OR = 2.44, 95% CI 1.34–4.44), while care from friends was protective (OR = 0.60, 95% CI: 0.36–1.01), which was possibly due to the pressure associated with parental expectations. Conclusions: Adolescent athletes, particularly females and those with extended training durations, exhibit a heightened susceptibility to anxiety. This study also highlights that athletes who proactively seek assistance during challenging situations tend to experience lower anxiety levels. Additionally, a lack of COVID-19 infection and the involvement of concerned parents contribute to reduced anxiety among these young athletes. [ABSTRACT FROM AUTHOR]

Climate change is a serious challenge facing the world today. Countries are already working together to control carbon emissions and mitigate global warming. Improving energy efficiency is currently one of the main carbon reduction measures proposed by the international community. Within this context, improving energy efficiency in manufacturing systems is crucial to achieving green and low-carbon transformation. The aim of this work is to develop a new preventive maintenance strategy model. The novelty of the model is that it takes into account energy efficiency, maintenance cost, product quality, and the impact of recycling defective products on energy efficiency. Based on the relationship between preventive maintenance cost, operating energy consumption, and failure rate, the correlation coefficient is introduced to obtain the variable preventive maintenance cost and variable operating energy consumption. Then, the cost and energy efficiency models are established, respectively, and finally, the Pareto optimal solution is found by the nondominated sorting genetic algorithm II (NSGAII). The results show that the preventive maintenance strategy proposed in this paper is better than the general maintenance strategy and more relevant to the actual situation of manufacturing systems. The scope of the research in this paper can support the decision of making energy savings and emission reductions in the manufacturing industry, which makes the production, maintenance, quality, and architecture of the manufacturing industry optimized. [ABSTRACT FROM AUTHOR]

Green manufacturing is becoming an important emerging field in the new round of global industrial revolution and scientific and technological competition. Scientific evaluation of China's regional green manufacturing level has far-reaching significance for promoting the transformation and upgrading of the manufacturing industry and enhancing international competitiveness. This paper defines the connotation of green manufacturing in China in the new era, proposes four dimensions of green production, green emission, green technology, and green benefit as the framework of the evaluation system, and constructs the evaluation index system. By taking 30 provincial administrative regions as research samples, based on the data from 2017 to 2020, the combination weighting method and TOPSIS are used for evaluation from the perspective of "static" and "dynamic" to identify the current situation and development of green manufacturing level in each region. It was found that the important factors affecting the green manufacturing level in each region are green products, green invention patents, sulfur dioxide, green factories, and coal consumption. The "static" evaluation results show that the green manufacturing level varies significantly among regions. The eastern area is generally better than the central and western areas, and only six regions are at high-level and medium to high-level. In addition, the four dimensions in most regions are imbalanced, with an obvious Matthew effect. The "dynamic" evaluation results show that the green manufacturing level in 30 regions appears to have six different types of dynamic trends. There is a small change in the ranking of most regions, indicating that the dynamic development of green manufacturing level has clear regional dependence, which is difficult to change in the short term. The research results show that the index system and model can effectively evaluate the regional green manufacturing level. Finally, combined with the important influencing factors, some suggestions are proposed to enhance the regional green manufacturing level. [ABSTRACT FROM AUTHOR]

Enterprises need sustainable development in order to reduce costs and increase income. The cold chain logistics industry needs to promote sustainable supply chains more. As the beginning of the supply chain, the choice of suppliers is particularly important. Considering the risky attitude of decision-makers, an improved hesitant fuzzy TODIM approach is adopted to select suppliers. In order to calculate a more objective indicator weight, the generalized Shapley function of the hesitant fuzzy measure is adopted by analyzing the relationships among indicators. The uncertain supplier evaluation information given by decision-makers is obtained by using hesitant fuzzy information. The improved Interactive and Multi-criteria Decision-Making (TODIM) method based on hesitant fuzzy numbers is used to analyze the psychological behavior of decision-makers under different market prospects and comprehensively rank the candidate suppliers. Finally, a case study of selecting cold chain logistics suppliers is provided to verify the effectiveness and feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

In the face of demand disruptions, dual-channel supply chains (SCs) that lack resilience may be more vulnerable. Reaching moderate SC resilience through coordination is essential for dealing with disruptions. This paper investigates the operation management of a dual-channel fresh-food SC (FSC) under disruption. The centralized and decentralized decision models propose joint quality efforts based on the consideration of quality preference and loss. From the perspective of SC resilience, we analyze how SC members can optimally make price, quality, and quantity decisions resiliently and robustly under the disruption of quality preference. The results show that (1) no matter the kind of decision model, considering quality preference disruptions can significantly increase the SC profit; (2) there is a resilience range in decisions with the influence of the disruption cost. The original optimal decisions in the resilience range are robust and sustain SC performance without change; and (3) the disruption significantly impacts offline channel retailers, who are at a disadvantage when competing with online channels. A centralized decision model can achieve higher profits and quality levels in response to demand disruptions. This paper extends the concept of resilience to the FSC and provides suggestions for fresh-food enterprises to conduct quality efforts and cope with demand interruption. [ABSTRACT FROM AUTHOR]

The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Production scheduling of semiconductor wafer manufacturing is a challenging research topic in the field of industrial engineering. Based on this, the green manufacturing collaborative optimization problem of the semiconductor wafer distributed heterogeneous factory is first proposed, which is also a typical NP-hard problem with practical application value and significance. To solve this problem, it is very important to find an effective algorithm for rational allocation of jobs among various factories and the production scheduling of allocated jobs within each factory, so as to realize the collaborative optimization of the manufacturing process. In this paper, a scheduling model for green manufacturing collaborative optimization of the semiconductor wafer distributed heterogeneous factory is constructed. By designing a new learning strategy of initial population and leadership level, designing a new search strategy of the predatory behavior for the grey wolf algorithm, which is a new swarm intelligence optimization algorithm proposed in recent years, the diversity of the population is expanded and the local optimum of the algorithm is avoided. In the experimental stage, two factories' and three factories' test cases are generated, respectively. The effectiveness and feasibility of the algorithm proposed in this paper are verified through the comparative study with the improved Grey Wolf Algorithms—MODGWO, MOGWO, the fast and elitist multi-objective genetic algorithm—NSGA-II. [ABSTRACT FROM AUTHOR]