Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

High-resolution medical images are critical for preserving intricate anatomical details essential for accurate diagnosis, effective surgical planning, and creating precise digital twins. However, acquiring such images often requires expensive equipment, specialized personnel, considerable time, and significant financial investment. This research addresses these challenges by proposing an AI-driven specialized super-resolution (SR) framework tailored for medical imaging. Our approach leverages transfer learning by fine-tuning high-performing general-domain SR models (BSRGAN, DPSR, HAT, RealESRGAN, and SwinIR) using approximately 190,000 images from KISTI’s Digital Korean dataset. On average, the fine-tuned SR models exhibited a 3.28% improvement in PSNR and a 0.6% increase in SSIM compared to their zero-shot counterparts, underscoring the effectiveness of transfer learning in enhancing both image quality and structural fidelity for medical applications. Notably, the fine-tuned HAT model achieved the highest performance, recording a Peak Signal-to-Noise Ratio (PSNR) of 39.95 and a Structural Similarity Index Map (SSIM) of 0.9461. The DPSR model also demonstrated robust detail preservation, with a PSNR of 39.60 and an SSIM of 0.9421. Qualitative evaluations by anatomy and medical imaging specialists confirmed the enhanced quality of the high-resolution outputs. These advancements highlight the transformative potential of specialized SR models to overcome the limitations of general-domain approaches in medical imaging. By laying the groundwork for developing high-resolution medical datasets, the proposed framework supports more precise diagnostics and advances the creation of accurate 3D human models and digital twins. Overall, this work underscores the vital role of AI-driven innovation in shaping the future of medical imaging and precision healthcare.

This study analyses the structure and collaboration patterns of co-authorship networks in library and information science (LIS) journals published in South Korea and internationally from 2015 to 2024. South Korean journals were categorised into Korean-language and English-language publications, while international journals included all articles and a subset article authored by Korean researchers. This classification enabled a comparative analysis of the structures of collaboration and the impact of co-authorship. The results show that international journals have more authors per article and higher research collaboration than South Korean journals. Among the international journals, articles by Korean researchers showed a high degree of international co-authorship, indicating a growing trend towards global collaboration. Korean journals had more single-authored articles and collaborations within the same institution, with limited international participation. An analysis of the impact of co-authorship on the number of citations found that articles involving collaborations across multiple institutions and disciplines tended to receive more citations. An analysis of the Simpson Diversity Index (SDI) demonstrated higher interdisciplinary diversity in international journals with Korean authors than in Korean journals, reflecting broader academic engagement. A correlation analysis of institutional productivity rankings between Korean and international journals found no significant relationship, suggesting different patterns of collaboration. These findings emphasise the need to expand international co-authorship and promote interdisciplinary collaboration in Korean LIS research. Building a more open and inclusive research environment in Korean journals is crucial to attract diverse authors. This study provides a basic insight into the trends of co-authorship and offers strategies to improve the international visibility and impact of Korean LIS research.