The Context-Guided Segmentation Network (CGS-Net) developed by University of Maine researchers introduces a deep learning architecture designed to interpret microscopic images of tissue with greater precision than conventional AI models. Powered by a dual-encoder model that mirrors the workflow of a pathologist examining a slide, one branch of the network processes a high-resolution image patch to capture cell-level details, while the other examines a lower-resolution patch encompassing the surrounding tissue. A system of interconnected encoders and decoders uses data from both the high and low resolution images for a complete analysis. 

A team of Iowa State researchers is establishing a year-long program to recruit and train students for the semiconductor industry. It's all about giving students a head start on a semiconductor career and building a U.S. chip workforce.

Horizon, the largest U.S. open-science supercomputer arriving in 2026, is being designed around the Characteristic Science Applications program: 11 cutting-edge software projects tackling the scientific challenges that will define the facility’s future.

Through a partnership with industry, researchers at the FAMU-FSU College of Engineering and Florida State University’s Center for Advanced Power Systems and the National High Magnetic Field Laboratory have supported the development of a design that uses multiple strands of superconducting tape to create a cable, minimizing the chance of failure from defective spots within a wire.

A new study co-authored by University of Notre Dame researcher Helge-Johannes Marahrens shows that analyzing social media posts can help experts predict when people will move during crises, supporting faster and more effective humanitarian aid delivery. Marahrens, assistant professor of computational social science in Notre Dame’s Keough School of Global Affairs, said that this research highlights how powerful computational tools can help address major global challenges to human dignity.

A new study led by researchers at the University of Hawai‘i at Mānoa revealed Kīlauea’s magma system started behaving anomalously about a year before the massive 2018 eruption began. This discovery, made using a unique seismic monitoring method, suggests that tracking these hidden processes could aid eruption prediction and volcanic hazard mitigation. 

A new material developed at UVA could lead to breakthroughs in transplant treatments, drug delivery systems, even battery storage.