The spatio-temporal evolution of wall-bounded turbulence is characterized by high nonlinearity, multi-scale dynamics, and chaotic nature, making its accurate prediction a significant challenge for ...

New research reveals that "foundation models" trained on vast, general time-series data may be able to forecast river flows accurately, even in regions with little or no local hydrological records.

New research reveals that 'foundation models' trained on vast, general time‑series data may be able to forecast river flows accurately, even in ...

Cognitive warfare technologies now model and simulate human behavior at scale, raising concerns about autonomous digital ...

GNSS receivers combined with inertial navigation systems (INS) have been widely applied to various mobile platforms.

Google-Tesla MagNet Challenge is an annual competition. It’s designed to accelerate innovation in magnetic modeling using ...

Researchers report that the integration of machine learning and Internet of Things (IoT) technologies is enabling a new generation of intelligent industrial environments capable of real-time ...

This study presents a deep learning model for breast cancer detection, achieving 99.24% accuracy and improving clinical ...

Introduction Climate-sensitive mortality in rapidly urbanising tropical Africa is poorly characterised, and how pandemics disrupt established seasonal patterns remains underexplored. We analysed ...

Overview: Machine learning skills improve when concepts are applied through regular, structured, hands-on projects.Working on ...

Non-model organisms are organisms that have not been selected by the research community for extensive study either for historic reasons, or because they lack the features that make model organisms ...

A disease model is an animal or cells displaying all or some of the pathological processes that are observed in the actual human or animal disease. Studying disease models aids understanding of how ...