The purpose of the research is to explore how to reach a consensus on the development of cultural tourism and the sustainability of the entire rural environment from the perspective of different rights holders. Using Beigang Township in Taiwan as a case study, we first conducted a questionnaire survey and analyzed 600 respondents by statistical verifications method, then used an interview method to compile suggestions from experts and scholars, and finally conducted a field survey to collect actual information. After summarizing, organizing, and analyzing all the data, the study was examined in a multivariate manner. This study concludes that creating parking spaces, providing a comfortable resting place, facilitating the exchange of ideas, and improving the environmental literacy of the public will increase the public attention to issues such as village visibility, people interaction, ancient architecture, culture and totems, public health and transportation, and entrepreneurial development, as well as address the concerns of local residents and some men and people over 31–40 years old. By doing so, we can improve community building and security, enrich cultural resources, build and develop sufficient industries, stabilize prices, obtain a safe and hygienic village environment, increase the desire to revisit, become a recommendation for family travel, and achieve the goal of sustainable development of rural environment and health.

Underwater images often come with blurriness, lack of contrast, and low saturation due to the physics of light propagation, absorption, and scattering in seawater. To improve the visual quality of underwater images, many have proposed image processing methods that vary based on different approaches. We use a generative adversarial network (GAN)-based solution and generate high-quality underwater images equivalent to given raw underwater images by training our network to specify the differences between high-quality and raw underwater images. In our proposed method, which is called dilated GAN (DGAN), we add an additional loss function using structural similarity. Moreover, this method can not only determine the realness of the entire image but also functions with classification ability on each constituent pixel in the discriminator. Finally, using two different datasets, we compare the proposed model with other enhancement methods. We conduct several comparisons and demonstrate via full-reference and nonreference metrics that the proposed approach is able to simultaneously improve clarity and correct color and restores the visual quality of the images acquired in typical underwater scenarios.

This study aims to investigate the effect of river improvement and greening projects on people and the urban happiness index. First, the quantitative method was adopted, and data collected from 734 questionnaires were analyzed using SPSS 26.0 software. Then, the qualitative method was used, and semi-structured interviews were conducted to collect the opinions of 12 interviewees, including scholars, government employees, citizens, and practitioners. Finally, we discuss multiple comparison analysis testing. The survey results indicate that river improvement and greening projects could be conducted to take advantage of diverse ecological environments, urban transportation planning, and geographical location. Such projects can help people relieve stress, even during the COVID-19 pandemic; improve their physical and mental health; and enhance their environmental awareness. However, due to poor traffic flow, a low space utilization rate, and inflexible management practices, visiting these environments has posed a risk of infection. As a result, most respondents indicated that the leisure benefits of green fields are limited and not helpful for improving their physical and mental health or having fun. Additionally, survey responses by people from different backgrounds (p < 0.01) varied. Therefore, we believe that by providing a safe living environment, strengthening disaster prevention skills and cooperation against epidemics, reducing accident risks, improving leisure safety and fluency, and planning diverse leisure activities, we can improve people’s perception of environmental experiences, promote leisure participation, improve leisure satisfaction, and enhance well-being.

This study aims to analyze the impact of attractiveness, satisfaction, and willingness to revisit in the perception of cultural tourism tourists and whether cultural tourism could be a remedy to revitalize rural economic development under the epidemic, using the Hakka settlement in Hukeng town as a case study. In this study, a mixed research method is applied to collect 670 questionnaires by snowball sampling and used SPSS 26.0 statistical software to conduct basic statistical validation and Pearson product-moment correlation coefficient (PPMCC) validation; the semi-structured is also used semi-structured interviews to collect the opinions of six experts and scholars, and the final results were examined by multivariate validation. The researchers found the following dimensions to be attractive for tourists to experience cultural tourism: rural scenery, heritage museums and cultural goods, low spending, use of media marketing, planning for proper tourism information, and public transportation construction effectiveness. However, there is also a need to improve the current situation of rural tourism transportation and transportation facilities, inject young development manpower, improve the knowledge and skills of the elderly to start their businesses, develop business opportunities, and stabilize prices, and improve the quality and satisfaction of tourists to promote the desire to travel. The higher the attractiveness, the higher the satisfaction, and the stronger the willingness to revisit.

The rise of marine recreational activities has led to a growing use of marine recreational powered platforms, raising safety concerns related to navigation. In Taiwan, the current regulatory system for such platforms remains fragmented and under debate. This study aims to support policy development by identifying key safety management priorities. This study utilized the four core components of the SHELL model, which include Software, Hardware, Environment, and Liveware, as the analytical foundation and identified 20 preliminary safety criteria through an extensive review of relevant literature. A Modified Delphi Method and DEMATEL analysis were applied to gather expert insights and prioritize 10 representative indicators. The resulting Influence Network Relation Map revealed that “Comprehensive Management Regulations” had the highest causal influence across all dimensions. Additionally, “Basic Navigation Concepts” and “Emergency Response and Safety Knowledge” were found to be the most central elements. Based on these findings, the study recommends targeted measures including enhanced regulation, improved training, radar monitoring, and spatial planning to reduce navigation risks and promote safer marine recreation. Building on the above findings, this study confirms the effectiveness of an innovative integration of the SHELL model and the DEMATEL method, which provides a structured and adaptive framework capable of systematically identifying systemic navigational risks in marine recreational activities.

In the heat treatment of steel, decarburization is a serious issue that leads to poor wear resistance and low fatigue life. At present, the decarburization depth was determined using a visual estimation by the human eye, and the software estimation was determined through traditional image analysis. Therefore, decarburization depth analysis remains limited in experts and traditional algorithms. Artificial intelligence is a general-purpose technology that has a multitude of applications. This paper uses the concept of deep learning to propose a decarburization layer detector (DLD) that can determine the depth of decarburized layers. This DLD system boasts high performance, real-time, low learning, and computation costs. In addition, we used several kinds of decarburized layers images to compare the proposed method with other deep learning network architectures. The experimental results show that the proposed method yields a detection accuracy of 92.97%, which is higher than existing methods and boasts computational demands which are far lower than other network architectures. Therefore, we propose a novel system for automatic decarburization depth determination as an application for metallographic analysis.