A research team led by Prof. Guo-Yong Xiang and Prof. Wei Yi from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, has reported the experimental observation of chiral switching between collective steady states in a dissipative Rydberg gas. This phenomenon, underpinned by a unique "Liouvillian exceptional structure" inherent to non-Hermitian physics, allows the state of the system to be controlled by the direction in which it is tuned through the parameter space, much like a revolving door that only allows exit in one direction. The results were published in Science Bulletin.

A research team from the South China University of Technology has developed an innovative statistical modeling approach that accelerates the development of advanced rare-earth-doped laser glasses. Applying neighboring glassy compounds (NGCs) model, the team accurately predicted the local structural environments and luminescence properties of complex glass systems, reducing experimental trial-and-error. The NGCs model was used to establish the composition-structure relationship and populate the composition-property space. Finally, multi-luminescence property charts are generated to select compositions that satisfy multiple constraints, thus facilitating the rational design of chemically complex laser glasses for targeted applications. This versatile methodology paves the way for discovering next-generation laser materials with superior performance, expanding the horizons of glass science and technology.

During the preliminary design phase of flapping-wing micro air vehicles (FWMAVs), there currently exists a deficiency in rapid prediction method for the aerodynamic characteristics of flexible flapping wings. A novel aerodynamic prediction method for flexible flapping wings has recently achieved significant breakthroughs. This method innovatively employs conical surface to mimic wing deformation, combined with an unsteady panel method for aerodynamic force computation, enabling rapid and accurate prediction of both aerodynamic characteristics and control moments of flexible flapping wings.

Unmanned Swarm Systems (USS) have transformed key fields like disaster rescue, transportation, and military operations via distributed coordination, yet trajectory prediction accuracy and interaction mechanism interpretability remain major bottlenecks—issues that existing methods fail to address by either ignoring physical constraints or lacking explainability. A recent breakthrough from Northwestern Polytechnical University solves this: Dr. Shuheng Yang and Prof. Dong Zhang developed the Swarm Relational Inference (SRI) model, an unsupervised end-to-end framework integrating swarm dynamics with dynamic graph neural networks. This model not only enhances interpretability and physical consistency but also drastically reduces long-term prediction errors, marking a critical step toward reliable autonomous collaboration for real-world USS applications.

For decades, aerospace engineers have confronted the life-threatening challenge of reigniting aircraft engines in high-altitude, low-pressure environments. Traditional spark ignition systems, limited by short discharge time and low energy efficiency, consistently fail to ignite lean kerosene-air mixtures in some difficult conditions. Although, gliding arc plasma ignitor offers improvements, its dependence on external gas sources prevents compatibility with combustor wide flight envelopes. This critical bottleneck has impeded next-generation aerospace propulsion systems.

Performance of Global Navigation Satellite Systems (GNSS) in providing positioning, velocity estimation, and timing services in urban environments often suffers significant degradation due to multipath effects and Non-Line-of-Sight signal reception. Traditional Fault Detection and Exclusion methods face technical bottlenecks, including high computational complexity and insufficient exclusion accuracy caused by the complex and diverse nature of fault modes. This study proposed a novel fault detection and correction method for Doppler-observable-based velocity estimation: GS-LASSO (Grouping-Sparsity Least Absolute Shrinkage and Selection Operator). Experiment results demonstrated that the GS-LASSO method could provide high-precision velocity estimates at the decimeter-per-second (dm/s) level in complex urban environments with limited computational resources.