In the latest issue of Engineering, researchers from Donghua University and the University of British Columbia present a new design methodology for 3D rotary braiding machines. This innovative approach, based on an average cutting circle strategy, allows for the creation of complex geometric textile composites with enhanced flexibility and precision. The study details how varying the number of incisions on horn gears and combining different cut-circles can significantly expand the capabilities of 3D braiding technology. The findings offer a practical solution for producing intricate 3D braided structures, with potential applications in aerospace, automotive, medical, and emerging fields like nanogenerators and sensors.

Even the smartest machines can’t match young minds at language learning. Researchers share new findings on how children stay ahead of AI - and why it matters.

If a human learned language at the same rate as ChatGPT, it would take them 92,000 years. While machines can crunch massive datasets at lightning speed, when it comes to acquiring natural language, children leave artificial intelligence in the dust.

A newly published framework in Trends in Cognitive Sciences by Professor Caroline Rowland of the Max Planck Institute for Psycholinguistics, in collaboration with colleagues at the ESRC LuCiD Centre in the UK, presents a novel framework to explain how children achieve this remarkable feat.

City of Hope is using generative artificial intelligence to create operational efficiency, enable AI-driven patient personalization, improve access to clinical trials and empower breakthrough research

This study published in Engineering offers new insights into the impact of alcoholic liver disease (ALD) on post-transplant outcomes in patients with hepatocellular carcinoma (HCC) and hepatitis B virus (HBV) infection. Researchers found that ALD significantly increases the risk of HBV reactivation and worsens survival outcomes following liver transplantation. Using advanced machine learning techniques, the study identified key metabolic factors associated with HBV reactivation and developed a novel risk stratification index to better predict patient outcomes. The findings highlight the importance of considering ALD in the management of liver transplant recipients with HBV-related HCC.

Researchers have developed a new hybrid earthquake early warning system called HEWFERS, which leverages advanced machine learning techniques and seismological principles to provide real-time predictions of ground shaking intensity. The system integrates a domain-informed variational autoencoder, a feed-forward neural network, and Gaussian process regression to offer both on-site and regional early warnings. Validated with a large database of ground motion records, HEWFERS demonstrates enhanced accuracy and reliability in seismic risk mitigation.