In this study, we examined the keelboat industry on Penghu Island in Taiwan as an example to discuss sustainable development strategies for the marine environment and villages. First, three experts were consulted to compile questionnaires. A snowball sampling method was used to collect 278 samples of residents and tourists living in the coastal area. Opinions were collected from 8 residents, crew members, tourists, and scholars. The data were finally summarized and compared by triangulation method and then examined. We found that introducing the keelboat industry could preserve maritime culture, increase local popularity and leisure options for people, create business opportunities, and improve the economy. It could also lead to a loss of coastal architectural features, increased the amount of trash in the community, around the harbor, and on the sea, no improvement in public facilities and medical care, and decreased the willingness of young people to return to their hometowns. Encouraging men to work in tourism-related industries, assisting in balancing job opportunities, strengthening villagers’ communication, improving tourists’ environmental literacy, adding onboard guides, improving women’s professional knowledge of marine ecology and working opportunities for boat maintenance, as well as actively participating in community development planning, can improve the current situation and achieve the goal of sustainable development.

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The goal is to explore the impact of motivation of participation, involvement, and satisfaction rate of sports App on physical and mental health and analyze the current status of development and trend of sports App. Six hundred and eighty valid questionnaires were analyzed using test statistic, descriptive statistics, and Pearson Product-Moment correlation followed by semistructured interviews to gather the views of the interviewees. Finally, information is integrated, using induction, organization, and analysis in order to explore in a multiple ways of examining. The result discovered that sports App has the characteristics of customization, being topical, having less interference, and able to increase professionalism, fortify skills, promote interaction, enhance confidence, improve mood, and reduce stress. There are also problems of low interactivity, low stimulation, attenuated fun, fatigue accumulation, and increased pressure. If the motivation, involvement, and degree of satisfaction of the participant can be satisfied, the mental feel will be enhanced.

The purpose of the study was to examine the leisure constraints and job satisfaction of middle-aged and elderly health care workers. The study employed a mixed research method, utilizing SPSS 22.0 and AMOS 23.0 statistical software to analyze 260 questionnaires using basic statistical tests, t-tests, ANOVA tests, and structural equation models, and then interviewed medical and public health workers and experts in the field, and the results were analyzed using multivariate verification analysis. The results showed that there was a significant low correlation between leisure constraints and job satisfaction among middle-aged and elderly health care workers (p < 0.01); interpersonal constraints and external job satisfaction factors were the main influencing factors; improving promotion opportunities and receiving appreciation increased job satisfaction; poor working environment and facilities, as well as the lack of achievement, were the main factors that reduced satisfaction; health factors, a lack of family support, no exercise partner, and a lack of extra budget are the key to leisure constraints. If the organization can provide nearby sports facilities for middle and high-age medical workers, improve welfare, and increase willingness to participate in leisure activities, physical and mental health can be improved. Finally, interpersonal interaction in leisure obstacles is the main reason for improving job satisfaction.

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.