23-Mar-2026 Neural networks: Directional wiring shapes biologically relevant activity patterns in engineered networks Advanced Institute for Materials Research (AIMR), Tohoku University Researchers at AIMR engineered cultured neuronal networks with brain-like hierarchical modularity and directional connectivity using microfluidic devices. Integrating experiments with computational modeling, they demonstrated that directional connections suppress excessive synchrony and enhance balanced network dynamics. This platform advances understanding of brain organization and enables applications in drug discovery and brain-inspired biocomputing systems. Journal Neural Networks
23-Mar-2026 Cao’s digital twin expertise helps global farmers University of Tennessee at Knoxville University of Tennessee, Knoxville Electrical Engineering and Computer Science associate professor Charles Cao leads UT’s role in HARVEST, an NSF AI‑ENGAGE project building AI-powered “digital twins” of farms. Combining drone, sensor, and satellite data, the system runs real-time and “what-if” simulations to reduce pest losses 20–30% and cut water and fertilizer use 20%, while preserving cross-country data privacy.
20-Mar-2026 Reconstructing food webs to reveal a dynamic Gulf of Maine Bigelow Laboratory for Ocean Sciences At Bigelow Laboratory, we know that the smallest things can have the biggest impact on the ocean ecosystem. A multi-institutional team, led by URI and featuring our researchers, is helping show just how important some of these tiny animals called copepods are for shuttling energy and nutrients across the ocean food web. The team has spent the last several years using advanced laboratory and field methods to understand how the biological communities of the surface and seafloor are connected in the Gulf of Maine and how that relationship — and the larger marine food web — adapts as environmental conditions change. This work is challenging oceanographers' assumptions about how the Gulf of Maine ecosystem works and highlighting the challenges of using historical data to understand our rapidly changing future. Journal Progress In Oceanography
20-Mar-2026 ETRI expands computing resources with light...opening a new era for AI datacenters National Research Council of Science & Technology A team of Korean researchers has developed the world’s first technology that can freely connect and disconnect core computing resources such as memory and accelerators with “light” in next-generation artificial intelligence (AI) datacenters. Electronics and Telecommunications Research Institute (ETRI) announced the development of a new optical switch based datacenter resource interconnection technology (Optical Disaggregation, OD). This technology is regarded as a core next-generation optical network technology that is designed to resolve the shortage of computing resources due to the increasing AI services and that enables faster and more efficient operation of future datacenters. Funder Ministry of Science and ICT
20-Mar-2026 Uncovering links between early Earth’s carbon cycle and ballooning oxygen levels University of Victoria Combining data from ancient rocks with computer modelling shows big changes in Earth’s carbon cycle were linked to rising oxygen levels Journal Proceedings of the National Academy of Sciences Funder Natural Sciences and Engineering Research Council of Canada
19-Mar-2026 £7 million funding for remarkable tool to drive next-generation AI University of Edinburgh The new technology ensures that the Scottish Microelectronics Centre (SMC), based at the University of Edinburgh's School of Engineering, will continue to be a leading centre for semiconductor prototyping and production for the next 50 years.
19-Mar-2026 Behaviors behind high‑impact AI use University of Texas at Austin A landmark study of 1.4 million real workplace interactions with artificial intelligence reveals teachable differences between routine and sophisticated AI use that offer organizations a concrete road map for identifying and scaling high-impact AI capability. The joint study by KPMG LLP, the U.S. audit, tax, and advisory firm, and the McCombs School of Business at The University of Texas at Austin identifies distinct, observable patterns in how high‑impact users frame problems, guide AI reasoning, and apply AI across complex tasks that KPMG is applying internally and in its work for clients. The study is published today in Harvard Business Review.
19-Mar-2026 A better method for identifying overconfident large language models Massachusetts Institute of Technology A new technique for assessing the reliability of a large language model’s predictions is better at identifying when the model is confident, but wrong. This more accurate method for uncertainty quantification could help users know whether to trust the model’s outputs. Funder MIT-IBM Watson AI Lab
19-Mar-2026 Ask the Expert: Colorectal cancer and its recent prevalence among young people University of Texas Health Science Center at Houston Welcome to "Ask the Expert," a UTHealth Houston newsroom series where our leading physicians examine pressing health challenges. In this edition, we address colorectal cancer, why the diagnosis has aggressively increased among young people, and how to look out for and treat it.
19-Mar-2026 Mind over metal: Staying wary of metal-related toxicities Texas A&M University Dr. Christine Rutter, a clinical associate professor at the Texas A&M University College of Veterinary Medicine and Biomedical Sciences, discusses the health risks associated with different metals and how owners can keep their pets safe.
