Researchers from Wuhan University have developed a new Uncertainty-Aware Filtering (UAF) algorithm that significantly enhances spacecraft autonomous navigation in deep-space environments characterized by weak gravity, strong perturbations, and limited prior knowledge, which is published in the Chinese Journal of Aeronautics (https://doi.org/10.1016/j.cja.2025.103971). The method shows clear advantages over conventional Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) approaches and has been validated using simulations of China’s Tianwen-2 mission to asteroid 311P/PANSTARRS.

A new direct correlation between coordination geometry and catalytic performance is established through electronic modulation, offering a generalizable design principle for high-performance single-atom catalysts in CO₂ conversion.

Manvydas Mikulėnas, a PhD student at Kaunas University of Technology (KTU), supervised by Professor Lina Šeduikytė, is investigating whether digital building models contain enough data to assess whole-life carbon footprints and circularity – the potential to keep materials and components in use. As part of the research, the team developed a prototype that identifies gaps in model data. Missing information already makes climate impact calculations more difficult and could make it harder to reuse building components in the future.