Recovery colleges (RCs) support personal recovery through education, skill development and social support for people with mental health problems, carers and staff. Guided by co-production and adult learning principles, RCs represent a recent mental health innovation. Since the first RC opened in England in 2009, RCs have expanded to 28 countries and territories. However, most RC research has been conducted in Western countries with similar cultural characteristics, limiting understanding of how RCs can be culturally adapted. The 12-item Recovery Colleges Characterisation and Testing (RECOLLECT) Fidelity Measure (RFM) evaluates the operational fidelity of RCs based on 12 components, but cultural influences on these components remain underexplored. The authors aimed to assess associations between Hofstede’s cultural dimensions and RFM items to identify cultural influences on fidelity components.

Artificial intelligence is now better than humans at identifying many patterns, but evolutionary relationships have always been difficult for it. A team from the Bioinformatics Department at Ruhr University Bochum, Germany, working under Professor Axel Mosig has trained a neuronal network to tackle this issue. The AI can relate any data from different species in an evolutionary relationship and identify which characteristics have developed in what manner throughout the course of evolution. “Our approach lets artificial intelligence look at data through the lense of evolution, in a way,” explains Vivian Brandenburg, lead author of the report published in Computational and Structural Biotechnology Journal on August 22, 2025.

University of Tennessee, Knoxville’s Katie Schuman and her team developed NODES, a neuromorphic computing system for dual-fuel engines, improving fuel efficiency by adapting to real-time conditions. Tested successfully at Oak Ridge National Laboratories, this innovation could revolutionize energy use in shipping and other industries with large combustion engines.

In this study, an integrated artificial intelligence (AI)-atomic force microscopy (AFM) platform is introduced that enables label-free, mechanophenotyping of macrophages at single-cell resolution.

A new AI-powered tool has reduced astronomers’ workload by 85% - filtering through thousands of data alerts to identify the few genuine signals caused by supernovae (powerful explosions from dying stars). The findings have been published today (10 Sept) in The Astrophysical Journal.