This study investigates seismic risk and potential impacts of future earthquakes in the Sunda Strait region, known for its susceptibility to significant seismic events due to the subduction of the Indo-Australian Plate beneath the Eurasian Plate. The aim is to assess the likelihood of major earthquakes, estimate their impact, and propose strategies to mitigate associated risks. The research uses historical seismic data and probabilistic models to forecast earthquakes with magnitudes ranging from 6.0 to 8.2 Mw. The Gutenberg-Richter model helps project potential earthquake occurrences and their impacts. The findings suggest that the probability of a major earthquake could occur as early as 2026–2027, with a more significant event estimated to likely occur around 2031. Economic estimates for a 7.8–8.2 Mw earthquake suggest potential damage of up to USD 1.255 billion with significant loss of life. The study identifies key vulnerabilities, such as inadequate building foundations and ineffective disaster management infrastructure, which could worsen the impact of future seismic events. In conclusion, the research highlights the urgent need for comprehensive seismic risk mitigation strategies. Recommendations include reinforcing infrastructure to comply with seismic standards, implementing advanced early warning systems, and enhancing public education on earthquake preparedness. Additionally, government policies must address these issues by increasing funding for disaster management, enforcing building regulations, and incorporating traditional knowledge into construction practices. These measures are essential to reducing future earthquake impacts and improving community resilience.

This study examines the impact of structured cultural educational activities on various dimensions of student well-being in primary education. Using a randomized controlled trial design, 120 third- and fourth-grade students from Arad County, Romania, were assigned to either an experimental group, which participated in cultural educational activities, or a control group, which received no intervention. Well-being and social behavior were assessed using the Strengths and Difficulties Questionnaire (SDQ) and the EPOCH Measure of Adolescent Well-Being, administered before and after the intervention. The SDQ evaluated emotional symptoms, hyperactivity, conduct problems, peer relationship issues, and prosocial behavior, while the EPOCH scale measured engagement, perseverance, optimism, connectedness, and happiness. Analysis revealed statistically significant improvements (p < 0.05) in the experimental group compared to the control group. Students in the experimental group exhibited reduced hyperactivity and peer relationship problems, alongside notable increases in engagement, perseverance, optimism, connectedness, and happiness. These findings highlight the efficacy of integrating cultural educational activities into the primary school curriculum as a strategy for enhancing emotional and social development. The study underscores the importance of such interventions in fostering positive developmental outcomes and offers a foundation for further research into their long-term effects and adaptability across diverse educational contexts.

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.

Tourist visits to a destination or attraction as a result of the destination being featured on television, video, or the cinema screen were the ones, that stimulated the creation and development of film tourism, which quickly established itself in global conditions. The main objective of the paper was focused on the identification and the perception of the conditions of film tourism development in Slovak republic. So far, a lot of film production has been realized in the country, but this potential has not yet been properly used for the creation of tourism products. Implementation of the study from a methodological point of view took place using several research methods. The pilot scientific abstraction of the issue was followed by the analysis of film conditions in the territory of Slovak Republic and their categorization. The given starting points were followed by the implementation of questionnaire research, the results of which were verified using several research methods such as Doornik-Hansen test, Kruskal-Wallis test. The results of the questionnaire research show a significant positive perception of the potential of filmmaking as a significant factor in the creation of new tourism products. At the same time, they identify key destinations that could potentially become objects of product realization. Due to the fact that this issue has not received adequate attention in domestic conditions, the study brings a new, more comprehensive view of the topic and emphasizes the power of the potential for further development.

Artificial Intelligence (AI) in education has both positive and negative impacts, particularly in term of increasing plagiarism. This research analyzes Indonesia’s plagiarism regulations and offers solutions. It uses doctrinal methods with legislative, case, and comparative studies, revealing that plagiarism is regulated but not specifically for AI involvement. The results show that plagiarism in scientific work has actually been regulated through several regulations. On the other hand, there is no regulation governing the involvement of AI in the process of preparing scientific articles. Comparative studies show that the US, Singapore, and the EU have advanced regulations for AI in education. The US has copyright laws for AI works and state regulations, Singapore’s Ministry of Education has guidelines for AI integration and ethics, and the EU has the Artificial Intelligence Act. To tackle AI-related plagiarism in Indonesia, the study suggests enacting AI-specific laws and revising existing ones. Ministerial and Rector statutes should address technical aspects of AI use and plagiarism checks. The Ministry should issue guidelines for universities to develop Standard Procedures for Writing and Checking Scientific Work, using reliable AI-checking software. These measures aim to prevent plagiarism in Indonesia’s educational sector.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.