Artificial intelligence (AI) has quickly moved from experimentation to boardroom priority across the airline industry. Carriers worldwide are investing in data science, automation and machine learning ...

It’s a messy, fragmented world of financial and regulatory caveats for modern data center energization. Design once, deploy ...

A class action lawsuit against UnitedHealthcare claims that an AI system was used to unfairly deny post-acute rehabilitation ...

Summary: Workplace attrition threatens operational continuity and clinical trial timelines for life science professionals. The recent shift to remote and decentralized trials presents unique ...

Artificial intelligence is transforming food innovation by accelerating ingredient discovery, optimizing formulations, and enabling personalized nutrition strategies to improve public health outcomes.

New research was motivated by "high-potential societal impacts" of successfully disabling autonomous drones, a study author ...

Company launches initiative to become a dedicated AI infrastructure partner for the biotechnology industry — to deploy secure, on-premises AI systems directly within client environments where ...

Artificial intelligence has quickly moved from experimentation to executive priority across Australian organisations. Boards are asking how AI can reduce cost, improve productivity, and deliver better ...

For a 168-year-old institution, transformation rarely happens overnight, particularly in the banking sector, which is well-known for its legacy systems. But at Bendigo Bank, the past eight years have ...

Objective A panel of antibodies against three antigens, University Hasselt (UH)-rheumatoid arthritis (RA).305, 318 and 329, has been associated with lack of response to first-line therapy in the Care ...

Introduction Older adult loneliness is associated with depression, disability and higher mortality rates. As a public health concern, it contributes to increased medical and nursing care costs.

To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...