Houston Methodist neuroscientists have developed a first-of-its-kind method to rapidly produce synchronized, human brain wave-like activity in lab-grown neural networks that can communicate over long distances. This innovation offers researchers a powerful tool to study how brain connectivity is affected in neurodegenerative diseases such as Alzheimer’s and Parkinson’s and to explore potential treatments. 

SAN ANTONIO — November 26, 2025 — Engineers at Southwest Research Institute (SwRI) have developed a reliable testing methodology to study stochastic pre-ignition (SPI) in hydrogen-fueled internal combustion engines (H₂-ICEs), offering the automotive industry tools to address challenges associated with hydrogen fuel and advance cleaner engine technologies.

The presence of robots in schools is no longer surprising. How do elementary school children treat humanoid robots? Are they polite to them, and willing to attribute human-like qualities to them? Researchers from SWPS University have shown that in most cases, children treat robots politely, and younger children and girls are more likely to perceive them as possessing human-like qualities.

Image reconstruction—the process of recovering clear images from incomplete or noisy data—has been advancing rapidly through deep learning. Yet most existing approaches rely on costly supervised training and lack theoretical transparency. A new survey maps the rise of unsupervised deep learning for image reconstruction, from traditional denoising-based priors to modern diffusion models. These methods learn structured visual information directly from unlabeled data, and have achieved impressive performance across various fields, including biomedical imaging and remote sensing. The study shows how unsupervised learning based image reconstruction unites neural network efficiency with solid mathematical foundations to achieve both interpretability and flexibility, offering a blueprint for next-generation imaging systems.

Researchers at the University of Melbourne have developed a new AI-based traffic signal control system called M²SAC that improves both fairness and efficiency at urban intersections. Unlike traditional systems focused only on cars, M²SAC accounts for pedestrians, buses, and other users. A key innovation is the phase mask mechanism, which dynamically adjusts green light timings to reduce delays. Tested on real Melbourne traffic data, the model outperformed existing methods, cutting congestion and balancing traffic flow more equitably. The approach supports smarter, fairer, and more inclusive transport systems for modern cities.