On February 11, the team from the Data Darkness Lab (DDL) at the Medical Imaging Intelligence and Robotics Research Center of the University of Science and Technology of China (USTC) Suzhou Institut introduced a new out-of-core mechanism, Capsule, for large-scale GNN training, which can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems. This work published on ACM Journals.

MIT engineers have fabricated a metamaterial that is not only strong but also stretchy. Their new method could enable stretchable ceramics, glass, and metals, for tear-proof textiles or stretchy semiconductors.

Given the multitude of conditions that must be optimized in synthesis routes, chemical synthesis remains a complex and multidimensional challenge. The rapid development of computational guidelines and machine learning (ML) techniques has brought exciting hope to this dilemma. A new study published in the journal National Science Review highlights the advancement of computationally guided and ML-assisted approaches in inorganic material synthesis.

Against the backdrop of accelerating global climate change and urbanization processes, urban transportation systems are confronting increasingly complex multi-hazard risks. Spatiotemporal big data, characterized by its high precision and information density, has demonstrated growing significance in transportation system resilience studies. Nevertheless, the current comprehension of the evolutionary trajectory of spatiotemporal big data applications in this domain remains fragmented. In this context, our study conducts a systematic review of global research, elucidating the practical implementations of spatiotemporal big data in transportation system resilience studies. The investigation reveals that multi-source big data with high spatiotemporal resolution has not only catalyzed methodological innovations in resilience assessment but has also potential to facilitate a paradigm shift in the field - transitioning from macro-scale to micro-scale analyses, from static evaluations to dynamic monitoring approaches, and from post-disaster emphasis to comprehensive lifecycle investigations. Journal of Geo-Information Science has published the study's results.