Socially and economically marginalized people and environmentally vulnerable areas are disproportionately affected by natural hazards. Identifying populations and places vulnerable to disasters is important for disaster management, and crucial for mitigating their economic consequences. From the fields of geography, emergency management, and urban planning, several approaches and methodologies have been used to identify significant vulnerability factors affecting the incidence and impact of disasters. This study performs a regression analysis to examine several factors associated with disaster damage in 230 local communities in South Korea, using ten vulnerability indicators for social, economic, and environmental aspects, and a single indicator for disaster characteristics. A Lagrange Multiplier diagnostic test-based spatial autoregressive model (SAM) was applied to assess the potential spatial autocorrelation in the ordinary least squares (OLS) residuals. This study compared the OLS regression results with those of a spatial autoregressive model, for both presence of spatial autocorrelation, and model performance. The conclusion of this study is that Korean communities with a higher vulnerability to disasters, as a result of their socioeconomic and environmental characteristics, are more likely to experience economic losses from natural disasters.

The rapid advancement of artificial intelligence (AI) has brought about significant societal changes, necessitating robust AI governance frameworks. This study analyzed the research trends in AI governance within the framework of the European Union Artificial Intelligence Act (EU AI Act). This study conducted a bibliometric analysis to examine the publications indexed in the Web of Science database. Our findings reveal that research on AI governance, particularly concerning AI systems regulated by the EU AI Act, remains relatively limited compared to the broader AI research landscape. Nonetheless, a growing interdisciplinary interest in AI governance is evident, with notable contributions from multi-disciplinary journals and open-access publications. Analysis of publications per country revealed that while the United States and China dominate AI governance research, European countries, along with the United Kingdom, also contribute significantly with a focus on specific systems restricted by the Act. Dominant research themes include ethical considerations, privacy concerns, and the growing impact of generative AI, such as ChatGPT. Notably, education, healthcare, and worker management are prominent application domains. Keyword network analysis highlights education, ethics, and ChatGPT as central keywords, underscoring the importance of these areas in current AI governance research. Subsequently, a comprehensive literature review was undertaken based on the bibliometric analysis findings to identify research trends, challenges, and insights within the categories of the EU AI Act. This review revealed critical gaps in research concerning regulated AI systems, highlighting the need for more focused research aligned with the Act’s regulatory framework. The findings provide valuable insights for researchers and policymakers, informing future research directions and contributing to developing comprehensive AI governance frameworks beyond the EU AI Act. Crucially, the study identifies a significant lag between AI technological advancement and the development of policy and regulation, especially concerning specific AI systems categorized as high-risk by the EU AI Act.

The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE). The proposed method employs simulation data from nine accelerometers. The accuracies of CAEs for multi-vehicle are 79.5%–85.8% for 100% and 75% damage severities with all error levels and 50% damage severity without error. However, the accuracies for 50% damage severity with 5% and 10% error levels drop to 69.4%–73.3%. The accuracies of CAEs for single-vehicle ranges from 90.1%–95.1% for all damage severities and error levels that are satisfactory. The findings indicate that the CAE approach for multi-vehicle can be effective when the damages are severe, but not when moderate. Meanwhile, if acceleration data can be obtained for single-vehicle, then the CAE approach can provide a highly accurate and robust method of tendon damage detection in PSC bridges in use, even if the measurement errors are significant.