Yanran Li's AI-driven Marketing Mix Modeling (MMM) framework revolutionizes enterprise resource allocation by integrating ...

High-entropy alloys (HEAs) are rewriting the rules of materials science, and machine learning is accelerating their design. By predicting phase stability and performance from large datasets, ...

The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and ...

Mechanical engineering has traditionally relied on physics, mathematics, and empirical knowledge to design and optimize systems. Machine learning (ML) introduces powerful tools that can complement ...

Spatially distributed prediction of streamflow and nitrogen (N) export dynamics is essential for precision management of agricultural watersheds. To address this need, a team of researchers led by the ...

Machine learning has moved past its initial experimental phase. In earlier years, development often focused on creating the largest possible models to see what capabilities might appear. Today, the ...

The Adriatic Sea and its coastal regions are increasingly exposed to climate variability and extreme events, posing significant challenges to communities, ...

Researchers have combined genome-scale metabolic modeling, machine learning, and thermodynamic analysis to identify cost-efficient strategies for producing the biodegradable plastic ...

Background and Goal: This study examined whether machine learning could predict the risk and contributing factors of no-shows and late cancellations in primary care practices. Study Approach: ...

Machine learning (ML) has emerged as a promising tool for tackling challenges in aquatic environmental research, especially ...