A significant earthquake impacted the Noto Peninsula on January 1, 2024, and recovery efforts have been ongoing since then. News media articles offer valuable perspectives on the recovery process. To better understand the recovery situation and its evolution from a news media perspective, this study analyzes Yahoo! Japan News articles on the Noto Peninsula earthquake posted between August 2024 and July 2025. Using natural language processing (NLP) by keyword-based and Generative Pre-trained Transformer-based approaches with statistical analysis, the Seven Critical Elements of Life Recovery, as well as sentiments and city names, are identified in the articles. Further studies, including one-way analysis of variance with Tukey’s honestly significant difference test and ordinary least squares regression, are conducted to identify differences and changes in volume and sentiment toward recovery elements and locations. Results reveal that both the volume and sentiment vary across recovery elements and differ between cities. However, most cases do not demonstrate a significant trend in either volume or sentiment over time. This suggests that there may be diversity in recovery-related news coverage within the affected region, while most exhibit no changes or linear trends. Overall, this study develops a process to extract structured disaster recovery data from news texts using NLP, providing a comprehensive understanding of disaster recovery in the Noto Peninsula from a news perspective.

Long COVID has emerged as a persistent health challenge, particularly among older adults. Its diverse symptoms often affect physical, psychological, and social well-being, making effective management strategies essential. This study aims to explore the symptoms experienced by older adults with Long COVID, examine their impact on daily life, and identify the strategies employed for symptom management within a community setting. A phenomenological design was applied. Data were collected between January and April 2024 through in-depth interviews with a purposive sample of 14 older adults with Long COVID residing in a rural community in Thailand. Content analysis was used to interpret the data. Six categories of symptom experiences were identified: (1) neuropsychiatric (insomnia), (2) sensory (ageusia), (3) musculoskeletal (muscle pain), (4) upper respiratory (cough), (5) lower respiratory (dyspnea), and (6) general symptoms (fatigue). To manage these symptoms, older adults reported strategies including praying and taking medication for insomnia; using supplements for ageusia and fatigue; taking Thai herbs for muscle pain and cough; and using oxygen therapy, breathing exercises, lung exercises, and rest for dyspnea. Older adults with Long COVID experience multiple symptom categories and employ diverse management strategies that combine traditional and modern approaches. These findings emphasize the importance of personalized clinical care and the integration of cultural and alternative remedies in supporting older adults with Long COVID.

This study developed a system and teaching material creation support model by applying the instructional design (ID) framework to facilitate the development of disaster education teaching materials tailored to regional characteristics. First, a teaching material creation support model based on ID theory was proposed, demonstrating the model’s potential for standardizing the development of disaster education teaching materials and its applicability in practice. Second, a prototype system was developed to create lesson plans aligned with actual regional conditions by identifying regional characteristics and referencing past cases. Third, through presentation to schoolteachers and empirical evaluation, the usefulness of the search function and of the linkage between cases and lesson plans was confirmed, highlighting the effectiveness of sharing disaster education knowledge and supporting instruction. Future challenges include strengthening quality assurance for teaching materials, expanding reference cases through comprehensive data integration, and designing flexibility to preserve teachers’ discretion.

Urban flooding threatens service continuity, livelihoods, and social well-being in coastal megacities. We propose and test a framework that integrates high-resolution, Geographic Information System-based exposure mapping (inundation hotspots, drainage proximity, land cover, and critical facilities) with community-level capacity indicators (preparedness, adaptive capacity, and recovery) derived from a mixed-methods design. Using routinely available spatial data and rapid community assessment, we generate a composite index of flood resilience, identify chronic ponding clusters, and prioritize low-regret interventions. Results reveal spatially coherent hotspots that coincide with high built-up density and limited adaptive capacity; neighborhoods with weaker social preparedness also report longer recovery times. A short list of interventions, including micro-drain maintenance, targeted blue–green retrofits near drainage bottlenecks, and neighborhood-level early-warning and risk communication, offers achievable near-term impact within realistic municipal resource constraints. Sensitivity checks indicate index stability with respect to weighting choices. The approach is transferable to other flood-prone cities by coupling GIS layers with community metrics, providing a transparent basis for investment sequencing and policy design. Findings highlight that combining exposure and capacity yields more actionable prioritization than either dimension alone and aligns with global disaster risk reduction principles.

The spatial zoning of disaster-prone areas in mainland China presents a compelling yet complex challenge. Despite significant strides in disaster management and risk reduction, a comprehensive and region-specific disaster zoning that considers spatial contiguity remains largely unexplored. This study seeks to address this gap by applying and comparing four clustering methods: k-means, spatially constrained k-means, k-means++, and spatially constrained k-means++. These methods were evaluated based on their ability to categorize regions by the extent of areas affected by five major disaster types: floods, droughts, low temperatures, typhoons, and hailstorms. The spatially constrained k-means++ algorithm emerged as the most effective, as it addressed spatial discontinuity inherent in disaster zoning and mitigated the initial value problem linked to traditional k-means methods. Using this approach, mainland China was divided into four distinct disaster-prone clusters. Cluster 1, encompassing 25 provinces, exhibited a complex and overlapping hazard profile, while Clusters 2 and 3 (Hebei–Jilin–Xinjiang and Jiangxi–Hubei, respectively) reflected more specific regional disaster patterns. Hainan formed an independent cluster because of its unique typhoon dominance. These spatially coherent zoning results provide a robust foundation for developing differentiated disaster management strategies, from integrated approaches in multi-disaster regions to specialized interventions in areas facing dominant threats.