Researchers from Osaka Metropolitan University and Japan System Techniques Co., Ltd. evaluated the 2019 Osaka-Higashi Line station openings using a natural experiment and health insurance claims. While the overall rail-line effect was not significant, Shigino Station, a well-connected transfer hub, showed an estimated cumulative reduction of about JPY 62,500 per medically treated person over four years, highlighting that station connectivity and local context may determinewhether new stations deliver healthcare cost benefits.

Non-fading and non-toxic structural color can now be applied to flat or 3D surfaces using an inkjet printer. The Kobe University development also opens possibilities for novel display and anti-counterfeiting technologies.

A new study from Sultan Qaboos University shows how artificial intelligence can map hidden connections within legal systems, revealing that key provisions in Oman’s Labour Law of 2023 act as highly influential “hubs” whose amendment could trigger wide-ranging legal effects. Published in The Journal of Engineering Research, the research uses Arabic-language NLP and network analysis to offer policymakers a powerful, evidence-based tool for anticipating the real impact of legal reforms — with implications extending to legal systems worldwide.

Okayama University of Science (OUS), in collaboration with ANA’s Maintenance Center, launched a joint talent development program focused on aircraft maintenance. As part of the program, award-winning students participated in a two-day experiential visit to ANA facilities at Haneda and Okayama airports.

Students explored ANA’s training center, including a flight simulator, and observed large-scale aircraft maintenance operations such as engine inspections and component replacements. The experience provided valuable hands-on insight into real-world aviation maintenance and deepened students’ understanding of the field.

Through this initiative, OUS continues to promote industry-academic collaboration and experiential learning in science and engineering education.

General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introducing reasoning tokens does not yield significant performance gains. To address this gap, we introduce the MMAI Gym for Science, a one-stop shop molecular data formats and modalities as well as task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules' in order to solve practical drug discovery problems. We use MMAI Gym to train an efficient Liquid Foundation Model (LFM) for these applications, demonstrating that smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks. Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining more efficient and broadly applicable in the domain.

A new study provides a rigorous theoretical and numerical analysis of the accuracy of the method of characteristics (MoC), a key computational tool in nuclear reactor simulations. The results clarify how different source approximations affect error behavior, offering guidance for improving high-fidelity nuclear modeling. Importantly, the research findings serve as a theoretical foundation for code verification of MoC programs.

A new study published in Big Earth Data introduces the Cloud-Aware Mixture-of-Experts Linear Transformer U-Net (CA-MTransU-Net) for precise forest burned area (FBA) detection. By integrating Sentinel-1 SAR and Sentinel-2 optical data, the model overcomes traditional challenges such as cloud contamination and the high computational complexity of standard segmentation models. Achieving the highest mean Intersection-over-Union (mIoU) of 87.00% and an inference speed of 6.26 ms, this architecture significantly outperforms conventional benchmark models, offering a robust and efficient solution for post-fire environmental monitoring.