Researchers from Nanjing University of Science and Technology have developed a novel computational method called BlastGraphNet, which uses graph neural networks to predict the distribution of blast loads on complex 3D buildings. This data-driven approach offers significant improvements in accuracy and efficiency compared to traditional methods, with potential applications in structural design, civil defense, and safety assessments. The study, published in Engineering, demonstrates the model’s ability to provide rapid and precise predictions of blast loads, supporting more effective risk management and engineering solutions.

The University of Tennessee Institute of Agriculture dean of AgResearch, Hongwei Xin, has been awarded the Excellence in Leadership Award from agInnovation South, the coalition of directors of state agricultural experiment stations in Southern states. The group is a regional coalition of the national Association for Public and Land-grant Universities (APLU).

At the UT Institute of Agriculture, Xin is responsible for the research programs of approximately 530 agricultural and natural resource faculty and professional scientists that study disciplines spanning seven academic departments and one School of Natural Resources from agricultural and resource economics to plant sciences, animal sciences, and biosystems engineering and soil sciences. Xin also oversees the management of ten research and education centers that conduct field research, demonstrations and education programs in strategic locations across Tennessee.

Professor Hong Chul Moon's research team at KAIST's Department of Chemical and Biomolecular Engineering announced the development of a 'smart window technology' that allows users to control the light and heat entering through windows according to their intent, and effectively neutralize glare from external sources.

Researchers are developing a living material that actively extracts carbon dioxide from the atmosphere. Photosynthetic cyanobacteria grow inside it, forming biomass and solid minerals and thus binding CO₂ in two different manners.

Biliary atresia (BA) is the leading cause of pediatric liver transplants and often goes undiagnosed until it’s too late.

NIMS and its collaborators (IHI Corporation, Kawasaki Heavy Industries, Ltd., Kansai Electric Power Co., Inc., Kobe Steel, Ltd., Electric Power Development Co., Ltd., the Japan Atomic Energy Agency, Mitsubishi Heavy Industries, Ltd. and Elix, Inc.) have developed a model designed to predict the long-term durability of a range of heat-resistant steel materials by performing machine learning while preserving the confidentiality of each organization’s data. This research was published online in Tetsu-to-Hagané on February 6, 2025.