To improve the self-stabilization performance of flapping-wing micro-aircraft, Prof. Wu Xuezhong and Xiao Dingbang's team at the National University of Defense Technology (NUDT), has proposed a cylindrical air damper with a symmetric structure.

In machine learning tasks for applications as diverse as facial recognition and movie recommendations, neural networks have been used extensively. To address the complexities of neural networks and issues such as overfitting, loss of generalization power, and excessive hardware cost, Qing et al. developed a general compression scheme based on automatically differentiable tensor networks that considerably reduces the number variational parameters needed while maintaining or improving the network’s performance. Their work was published on May 15, 2025, in an article titled “Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters” in Intelligent Computing, a Science Partner Journal.

Researchers have developed an AI-driven framework to optimize urban space use in megacities. 

A new deep-learning model, AngleNet, significantly improves the retrieval accuracy of atmospheric relative humidity (RH) profiles from ground-based microwave radiometers (MWRs).

Macrophages rely on lysosomes to balance microbial killing with self-protection, generating reactive oxygen and nitrogen species during phagocytosis. Using platinum nanoelectrodes, researchers at Wuhan University tracked these reactive molecules in real time, revealing that lysosomal pH acts as a chemical dial. Acidic conditions favor hydrogen peroxide, while mild alkalinization promotes nitric oxide, peroxynitrite, and nitrite. This precise pH-dependent control shapes immune responses, oxidative stress, and inflammatory signaling, offering insights for therapies targeting macrophage function.