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.

US courts have ruled against platform providers for failing to protect children, and the debate over age restrictions for social media has gained momentum. An international group of experts from academia, children’s rights organizations and non-profit institutions is convinced that bans would be the wrong approach. In the journal Science they advocate for new strategies for the digital safety of children and youths aged 13 and older. Prof. Sandra Cortesi and Prof. Urs Gasser from the Technical University of Munich (TUM) explain when artificial intelligence could intervene on smartphones, what role peer groups can play and why children should be involved in shaping their digital education.

JMIR Publications, a leading open-access digital health research publisher, and Jisc, the United Kingdom's not-for-profit organization providing digital services for research and education, are pleased to announce the renewal of their Flat-Fee Unlimited Open Access Publishing Agreement for 2026-2027.

Existing datasets for medical QA cannot comprehensively assess the proficiency of LLMs in pediatrics. To fill this problem, a research team led by Hui LI and Yanhao WANG published their new research on the benchmark of LLMs for pediatric QA in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

Automated lesion segmentation is essential for DR screening, but current deep learning models often lack robustness, generating false positives in low-contrast or artifact-heavy regions. This instability largely stems from a lack of anatomical understanding. While incorporating vessel structures can guide the model, obtaining pixel-level vessel annotations for training is notoriously expensive and scarce.

To address this dilemma, the research team proposed MedFuse on 15 March 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.