The purpose of this study is to address the issue of low local participation in ecotourism management in Indonesia, specifically at the Malela Waterfall ecotourism site in Cicadas Village, Rongga District, West Bandung Regency, West Java, Indonesia. The research method is action research, which includes observation data gathering, in-depth interviews, and Focus Group Discussions. The findings of the study show that by carrying out the process of developing social infrastructure, namely development that prioritizes strengthening human resources in carrying out social service functions in ecotourism activities such as skill training of residents in the field of ecotourism, massive ecotourism outreach, and strengthening social communities—Non-Governmental Organizations (NGOs) and youth organizations as ecotourism actors. This type of development serves to raise awareness and participation among local inhabitants in Malela Waterfall ecotourism in West Bandung Regency. This promotes harmony and mutually beneficial partnerships among all Malela Waterfall ecotourism stakeholders. Furthermore, increasing community participation benefits the well-being of residents in the tourist region.

Background: Kangyang tourism, a wellness tourism niche in China, integrates health preservation with tourism through natural and cultural resources. Despite a growing interest in Kangyang tourism, the factors driving tourist loyalty in this sector are underexplored. Methods: Using a sample of 413 tourists, this study employed Covariance-Based Structural Equation Modeling (CB-SEM) to examine the influence of destination image, service quality, tourist satisfaction, and affective commitment on tourist loyalty. Results: The findings reveal that destination image and service quality positively affect tourist satisfaction, affective commitment, and loyalty. Tourist satisfaction and affective commitment are identified as critical drivers of tourist loyalty. Notably, affective commitment plays a stronger role in fostering loyalty compared to satisfaction. Conclusion: These results highlight the importance of a positive destination image and high service quality in enhancing tourist loyalty through increased emotional and psychological attachment. The findings inform strategies for stakeholders to improve Kangyang tourism’s growth by focusing on emotionally engaging experiences and service excellence.

Personal information is a vital productive commodity in the digital economy, and its processing has seen unparalleled transformations in both breadth and depth. This article proposes to enhance the legal remedies for personal information rights in contemporary China. Research has revealed multiple practical challenges in China’s judicial practices, such as hesitance to prosecute owing to an absence of substantial legal foundation, improper distribution of the burden of proof, and inadequate integration of criminal-civil judicial safeguards for personal information. This paper advocates for China to elucidate the definition of personal information rights via legislation, enable the litigation of personal information infringement cases, and establish explicit criteria for their acceptance into judicial proceedings. Furthermore, China must develop an appropriate structure for distributing the burden of evidence. It must also use discretionary judgment to properly tackle the problems related to evaluating damages in instances of personal information violations.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.