US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and ...

The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

For decades, cybersecurity relied heavily on signature-based detection and static rule systems. These tools were effective ...

IND Technology has raised $50 million to scale its early fault detection technology, aimed at preventing power outages and ...

Electron Microscopy Market Electron Microscopy Market Dublin, Dec. 16, 2025 (GLOBE NEWSWIRE) -- The "Electron Microscopy ...

Even as quantum navigation emerges as a legitimate alternative to satellite-based navigation, the satellites themselves are ...

Walk the floor of any modern plant, and you’ll sense a quiet shift. The old soundtrack of clanking metal and shouted ...

For end users, Cerence xUI delivers a seamless, intuitive, and engaging experience. The platform’s multi-step conversational threads, multi-modal capabilities, and enhanced personalization transform ...

While patching is positioned as the primary fix, interim risk reduction measures cited by advisories include limiting administrative interfaces to trusted networks and, where feasible, temporarily ...

Through a novel combination of machine learning and atomic force microscopy, researchers in China have unveiled the molecular ...

Researchers from Politecnico di Milano propose a data-driven water leak detection method that treats leaks as anomalies in ...