High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations.

Metals are made of randomly oriented crystals at the microscopic-length scale. The alignment of the crystal faces creates an infinite number of configurations and complex patterns, making simulations ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

SANTA CLARA, CA - February 12, 2026 - - Interview Kickstart has launched a new Data Science course designed for working ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Cristani, C. and Tessera, D. (2026) A Foundational Protocol for Reproducible Visualization in Multivariate Quantum Data. Open Access Library Journal, 13, 1-13. doi: 10.4236/oalib.1114704 .

Sophelio Introduces the Data Fusion Labeler (dFL) for Multimodal Time-Series Data - The only labeling and harmonization studio built for multimodal time-series with full provenance you can replay “dFL ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

The growth and impact of artificial intelligence are limited by the power and energy that it takes to train machine learning models. So how are researchers working to improve computing efficiency to ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

Abstract: Data preparation is essential for boosting machine learning model's performance by increasing data quality and lowering noise. This study evaluates the impact of preprocessing on key ...

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter adjustments. It started with the ...