A research team has developed a method for improving crop growth monitoring by accurately estimating the fraction of absorbed photosynthetically active radiation (fAPAR) throughout the entire rice growth cycle.

Humans possess a remarkable balance between stability and flexibility, enabling them to quickly establish new plans and adjust goals even in the face of sudden changes. However, "Model-Free reinforcement learning," which is widely used in robotics and exemplified by AlphaGo’s famous match against Lee Sedol, struggle to achieve these two capabilities simultaneously. KAIST's research team has discovered that the secret lies in the unique information processing method within the prefrontal cortex, a principle that could serve as the foundation for developing "Brain-like AI” that is both flexible and stable.

Layered double hydroxides (LDHs) are emerging as promising electrocatalysts for the oxygen evolution reaction (OER), a key barrier in clean hydrogen production. However, their catalytic performance has long been restricted by limited active sites, sluggish electron transfer, and structural instability under operational conditions. A new review summarizes how electronic defect engineering, including vacancy creation, heteroatom doping, single-atom incorporation and lattice modulation, which reconfigures electron distribution, enhances active site exposure, accelerates reaction kinetics, and strengthens catalytic durability. The work highlights strategies that effectively lower OER overpotential and improve stability by tuning LDH atomic coordination environments, providing a unified framework for the design of next-generation high-performance water-splitting catalysts.

Medical image segmentation is a fundamental component of many clinical applications such as computer-aided diagnosis, radiotherapy planning, and preoperative planning. Its accuracy and stability directly affect subsequent quantitative analysis and clinical decision-making. Based on a systematic analysis of feature utilization in U-shaped architectures, Prof. Zhao Qi’s team at Liaoning University of Science and Technology and Prof. Shuai Jianwei’s team at the Wenzhou Institute of the University of Chinese Academy of Sciences have developed an E-shaped medical image segmentation framework that differs from conventional symmetric decoders.

What if artificial intelligence could turn centuries of scientific literature—and just a few lab experiments—into a smarter, faster way to produce clean energy from waste? That’s exactly what Dr. Yeqing Li and Dr. Junting Pan have achieved with their innovative “knowledge-based machine learning loop framework” (KMLLF), a breakthrough now published in the open-access journal Carbon Research (Volume 4, Article 71, December 16, 2025). Their work redefines how scientists design biochar—the charcoal-like material increasingly used to turbocharge anaerobic digestion (AD), a key process for turning organic waste into renewable biogas.

NIMS, in joint research with the University of Tokyo, AIST , the University of Osaka, and Tohoku University, proposed a novel method for actively controlling heat flow in solids by utilizing the transport of magnons—quasiparticles corresponding to the collective motion of spins in a magnetic material—and demonstrated that magnons contribute to heat conduction in a ferromagnetic metal and its junction more significantly than previously believed. The creation of new principles “magnon engineering” for modulating thermal transport using magnetic materials is expected to lead to the development of thermal management technologies. This research result was published in Advanced Functional Materials on October 1, 2025.