The stress concentration and damage evolution of ultra-high performance concrete (UHPC) under long-term dynamic loading are difficult to monitor in real time, as conventional sensors suffer from poor durability, high cost, and incompatibility with matrix deformation. Recently, a team from the University of Shanghai for Science and Technology developed a machine learning framework that significantly improves dynamic compressive stress prediction in high-sensitivity ultra-high performance concrete (HS-UHPC) by incorporating electrical resistivity as a key input parameter. Using three machine learning algorithms—double-layer neural network, boosting tree, and squared exponential Gaussian process regression (SE-GPR)—the team demonstrated that adding resistivity measurements alongside traditional displacement data enhances predictive accuracy, with the SE-GPR model achieving an R² of 0.85 and reducing mean absolute error by 41.1% compared to displacement-only models. The core innovation is using electrical resistivity to directly capture load‑induced microstructural changes, overcoming the damage‑detection limitations of traditional strain or displacement measurements. This provides a new theoretical basis for intelligent monitoring of self‑sensing concrete.

A research team investigated how high-volume fly ash replacement affects concrete’s early-age characteristics and hardening properties. Tests with 0%–60% fly ash replacing cement show that fly ash improves flowability but delays early hydration. While early strength drops with higher fly ash, 10%–40% replacement delivers superior long-term mechanical performance. The team recommends 40% fly ash as the optimal cement replacement, balancing environmental sustainability, cost efficiency, and structural performance for green concrete engineering.

Published in Mycology, research by an international team details the isolation (guided by bioactivity and DeepSAT), structural elucidation, and biological evaluations for metabolites from the endophytic fungus Stagonosporopsis sp. YZHH-J-1, including three new anthraquinone derivatives stagonoquinones A−C (1−3), along with eleven known analogues.

These findings establish a critical proinflammatory axis involving matrix metalloproteinase 12 (MMP12) and interleukin-17A (IL-17A) that mediates community-acquired respiratory distress syndrome (CARDS) toxin-induced pulmonary pathology, uncovering potential therapeutic targets for mitigating Mycoplasma pneumoniae -associated respiratory disease.