Inexpensive multifunctional composite paves the way to a circular economy
Peer-Reviewed Publication
Updates every hour. Last Updated: 7-Oct-2025 23:11 ET (8-Oct-2025 03:11 GMT/UTC)
Performance, support and trust: three factors driving the use of AI in the creative industries
A large cross-sectional study of 8,412 first-grade children in Shanghai, led by Southeast University researchers, found that dietary preferences may influence asthma risk. Children who favored pickled and smoked foods were nearly twice as likely to develop asthma, and girls preferring fried foods showed a particularly strong association. Conversely, seafood preference in normal-weight children was linked to reduced asthma risk. The findings highlight diet as a potential target for childhood asthma prevention strategies.
Tokyo faces severe risks due to soil liquefaction, a phenomenon where the ground behaves like a liquid during strong seismic events. To improve existing hazard maps, researchers from Japan developed a new framework that combines extensive borehole data with artificial neural networks. Their model can accurately predict soil properties, producing high-resolution 3D liquefaction hazard maps, helping to improve earthquake risk management in Tokyo and other vulnerable megacities.
The Hebrew University team has developed the first binder-free method for 3D printing glass, using light to trigger a chemical reaction that directly forms silica structures without the need for organic additives or extreme heat. This breakthrough makes glass printing faster, cleaner, and more precise, with potential to revolutionize fields from optics to medicine by enabling custom, high-performance glass components that were previously impossible to manufacture.
An automatic diagnosis system based on wearable augmented reality (AR) glasses and an artificial intelligence (AI)model was developed to assess leafminer damage levels, and it achieved 92.38% accuracy. The DeepLab-Leafminer model incorporated an edge-aware module and the Canny loss function into the DeepLabv3+ model, which enhanced its ability to segment the leafminer damaged area in leaves.A mobile application and a web platform were developed to display the diagnostic results of leafminer damage levels for surveyors to guide their scientific decisions for leafminer prevention and control.