Neuroscientists develop human brain-like technology that can accelerate understanding of Alzheimer's, other neurodegenerative diseases
Peer-Reviewed Publication
Updates every hour. Last Updated: 2-Jan-2026 08:11 ET (2-Jan-2026 13:11 GMT/UTC)
A new evidence brief, based on a study by the Juno Evidence Alliance conducted in collaboration with CABI’s One Health Hub, has highlighted that a One Health approach is needed in research into zoonotic disease risks around the world.
Researchers at Imperial College London, developed a new method to combine infrastructure-based traffic data with vehicle-based data. They demonstrate that adding traffic covariates increases accuracy and the use of the No-U-Turn Sampler (NUTS) reduces the computational running time.
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.