As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionising the modelling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.

To achieve a deep understanding of the CMAS corrosion mechanism and lifetime prediction of high-performance thermal/environmental barrier coating materials, the (Er_1/4Y_1/4Lu_1/4Yb_1/4)₂Si₂O₇ and (Er_1/6Tm_1/6Y_1/15Gd_1/15Lu_4/15Yb_4/15)₂Si₂O₇ high-entropy rare-earth disilicates designed in this study exhibit approximately 70% reduction in CMAS corrosion depth compared to their single-principal-component counterparts, demonstrating excellent CMAS corrosion resistance. The research further reveals that lattice distortion induced by multi-cation doping can inhibit the penetration of CMAS melt, while large-radius rare-earth ions reduce the corrosion activity by consuming Ca²⁺ in the melt. Additionally, it elucidates the temperature-dependent transition of corrosion mechanisms—dominantly governed by thermodynamics–kinetics competition at 1300 °C, whereas shifting to a dissolution–reprecipitation mechanism at 1500 °C. On this basis, an extended Kalman filter model incorporating physical mechanisms was developed for the first time, enabling high-precision prediction of long-term corrosion depth and rate, thereby providing a reliable tool for coating lifetime assessment.

Through a unique collaboration between the Senckenberg Ocean Species Alliance (SOSA), Pensoft Publishers, and science communicator Ze Frank, a new species of deep-sea chiton has officially been named by the public. Following a week-long viral campaign that invited global name suggestions, the species’ formal scientific description was published today in the Biodiversity Data Journal.

Current robots are severely limited in their intelligence by the constraints inherent in rigid electronic hardware frameworks. The advancement of flexible electronic technology has driven a component innovation which penetrates every constituent of robotic systems. Scientists from Nanyang Technological University and Harbin Institute of Technology conducted a systematic review and outlook on this technological revolution. The review provided a detailed summary of existing research, and offered forward-looking guidance for future research.