A collaborative research team from Peking University and Hunan University has demonstrated a novel approach to generating powerful, structured terahertz (THz) pulses with programmable polarization textures. The study, titled “Generation of Strong THz Pulses with Topologically Tunable Polarization Features”, has been published in Ultrafast Science.

Two-dimensional (2D) metals, similar to graphene, are one-atom-thick elemental metals that exhibit unique properties due to their ultrathin structure. Unlike graphene, metals have nondirectional metallic bonds, making exfoliation from 3D bulk metals difficult without a layered structure. The concept of 2D metals has been extended to include a few-layer thickness, akin to few-layer graphene. 2D metals, such as antimonene (Sb) and stanene (Sn), offer significant potential for applications that require large specific surface areas, such as in electronics, electrochemistry, and catalysis, due to their enhanced surface and edge conductivity, quantum optics, and plasmonic properties.

Typhoons and their Atlantic counterparts—hurricanes—can develop into massively destructive storms that can take a severe toll on both infrastructure and human life. To date, collecting in situ tropical cyclone data has been too dangerous and cost prohibitive to routinely collect on a larger scale. Researchers have just developed a submersible vehicle, the “Blue Whale,” designed to withstand the adverse conditions of these storms and collect the in situ data necessary for more accurate typhoon intensity forecasts and marine condition warnings.

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.