An intelligent monitoring pipe combines optical sensing with machine learning algorithms to monitor and predict 3D soil settlement, which could help provide early warnings of risks from soil settlement and prevent pipeline displacement and accidents from building cracks.

University of Tennessee researchers James Ostrowski and Rebekah Herrman are developing quantum-computing tools to tackle multi-stage stochastic decision problems in fields like energy, logistics, and healthcare. Their NSF-funded project combines quantum and classical methods, creates scenario-encoding strategies, benchmarks performance, trains PhD students, and releases open-source libraries so non-experts can test quantum optimization in real-world workflows.

JMIR Publications today announced the release of a timely new article in its News and Perspectives section, showcasing the urgent need to modernize the scientific record. The article, “Our AI-Powered Discoveries Are Trapped in a Predigital System,” details how shifting from a static, paper-based model to a data-native ecosystem can bridge the widening gap between rapid AI innovation and slow formal validation.

A new study maps the rapidly evolving field of intelligent colonoscopy. 

A Texas A&M University biomimicry design course shows that scaffolded, nature‑inspired, inquiry‑based learning significantly boosts biomedical engineering students’ engagement, creativity and design skills.

In this editorial, National Academy of Engineering President Tsu-Jae Liu presents a forward-looking perspective on the role of artificial intelligence in engineering.