A research paper by scientists at The Chinese University of Hong Kong proposed a 3D radar-based control scheme that realizes the navigated locomotion of microswimmers in 3D space with multiple static and dynamic obstacles.

The new research paper, published on Jun. 2 in the journal Cyborg and Bionic Systems, presented a 3D hierarchical radar with a motion sphere and a detection sphere is firstly developed. Using the radar-based avoidance approach, the desired motion direction for the microswimmer to avoid obstacles can be obtained, and the coarse-to-fine search is used to decrease the computational load of the algorithm. Three navigation modes of the microswimmer in 3D space with dynamic conditions are realized by the radar-based navigation strategy that combines the global path planning algorithm and the radar-based avoidance approach.

A new study from Tel Aviv University has predicted, for the first time, the groundbreaking results that can be obtained from detecting radio waves coming to us from the early Universe. The findings show that during the cosmic dark ages, dark matter formed dense clumps throughout the Universe, which pulled in hydrogen gas and caused it to emit intense radio waves. This leads to a novel method to use the measured radio signals to help resolve the mystery of dark matter.

A recent study published in National Science Review conducted change detection in the youngest, topographically steepest, and theoretically most unstable regions on the lunar surface, revealing a large number of new landslides formed since 2009. Endogenic moonquakes rather than new impacts are the primary trigger, and the Imbrium basin may host an active seismic zone.

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.