Researchers have taken inspiration from nature to create a robotic wing that can sense and adapt to changes in water to deliver unparalleled stability.  

Drawing on the adaptive movements of birds and fish, the wing senses disturbances in the flow of water and automatically changes its shape to adjust to these.

The team, led by the University of Southampton, hope the soft robotics and e-skin they’ve pioneered could help close the gap in manoeuvrability and efficiency between robots and animals.  

A research team at The Hong Kong University of Science and Technology (HKUST) has analyzed 40 years of data covering around 1,500 tropical cyclones and discovered that average rain rates surge by more than 20% in the 60 hours before landfall. The study is also the first to clearly identify the physical mechanisms behind this increase, showing that rising humidity over coastal areas and enhanced land-sea frictional contrasts strengthen convection, intensifying rainfall ahead of landfall. The results provide valuable insights for improving coastal disaster preparedness and early‑warning systems.

A research team led by Professor Jo-Woon Chong of SKKU, in collaboration with Texas Tech University, has identified—through EEG analysis—the impact of fragrance on consumers’ emotions, memory, and deep emotional connections with luxury brands.

Researchers at Hokkaido University studied 56 japonica rice cultivars from across Japan and discovered that they contain unique beneficial fats.

In theTibetan Plateau, organophosphate tri-esters (tri-OPEs) and di-OPEs in soil and biota of a typical terrestrial food chain (plant–plateau pika–eagle) have been simultaneously identified with trophic dilution behavior. Differential metabolism—weak in plants versus strong in plateau pika and eagle—likely drove this pattern, highlighting metabolism's key role in OPE trophic transfer.

An Osaka Metropolitan University-led research team used hourly precipitation data from 1981 to 2020 and spatial modeling to predict extreme precipitation events for the next 100 years in Japan.

A research team from The Hong Kong University of Science and Technology (HKUST) has developed GrainBot, an AI-enabled toolkit that automatically extracts and quantifies multiple microstructural features from microscopy images. Designed to meet the growing need for data-driven and autonomous research workflows in materials science, the tool provides a systematic method for converting complex image information into quantitative data, thereby accelerating the discovery and development of next-generation materials.