A research team from the Xinjiang Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences has made strides in the theoretical design of nonlinear optical (NLO) materials by ...

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of ...

Making high-performance proteins for medicines or consumer products can take trial after trial of tweaks, experiments and fine-tuning. A new machine learning framework squeezes all that into a single ...

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential roughly 28–34 times greater over a 100-year timescale. Major sources include ...

For his research in machine learning-based electron density prediction, Michigan Tech researcher Susanta Ghosh has been recognized with one of the National Science Foundation's highest honors. The NSF ...

This article examines the work of data scientist Sai Prashanth Pathi in AI for credit risk, focusing on explainable machine ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

Neuroblastoma is the most common solid tumor in infants and accounts for nearly 15% of all pediatric cancer-related deaths. Despite decades of progress in surgery, chemotherapy, and stem cell ...

Can you use the new M4 Mac Mini for machine learning? The field of machine learning is constantly evolving, with researchers and practitioners seeking new ways to optimize performance, efficiency, and ...