Simulations by a team of scientists, including University of Groningen Professor of Artificial Intelligence Davide Grossi, show that an unrestricted flow of information can amplify incorrect ideas amongst like-minded people.

A new artificial intelligence model, PLGMamba, improves hyperspectral image super-resolution by combining local spectral similarity with global feature modeling. 

In a major advance for trauma medicine, researchers at Case Western Reserve University, the University of Pittsburgh and Haima Therapeutics have demonstrated that synthetic platelets can be freeze-dried and remain stable and effective for at least a year at room temperature and at least two months at high temperatures. This unlocks the potential for administering synthetic platelets on a battlefield or on site at a car accident or mass casualty event, which may save more lives.

A research paper by scientists at Sichuan University presented a water-responsive self-curling adhesive conduit to achieve adaptive wrapping and suture-free repair for peripheral nerve injury.

The research paper, published on Mar 27, 2026 in the journal Cyborg and Bionic Systems.

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.