When it comes to Earth’s climate system, water is often at the center of the story — whether it’s too much, too little or arriving at the wrong time. And while today’s climate models can tell us how much rain might fall or how humid the air might be, they often can’t answer the simpler, and perhaps more important, question: Where did this water come from? A new project led by Rice University and the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR) is changing that. Backed by a grant from the National Science Foundation, the initiative — called SCI-SWIM, short for sustainable community infrastructure for stable water isotope modeling — will build a new and improved version of the Community Earth System Model (CESM), which can trace water across the entire planet from the clouds in the sky to the thick ice sheets deep underground.

A patent-pending algorithm developed by Purdue University researchers recovers spectral information from photographs taken by conventional cameras. The technology could open up new possibilities for sectors like agriculture, defense and security, environmental monitoring, food quality analysis, industrial quality control, and medical diagnostics.

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.