In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
MIT researchers have designed a printable aluminum alloy that’s five times stronger than cast aluminum and holds up at ...
A research team has developed a new model, PlantIF, that addresses one of the most pressing challenges in agriculture: the ...
Pharmaceutical Separation Science Session Day two of HPLC 2025 concluded with a session on pharmaceutical separations chaired ...
Reverse Logistics, Artificial Intelligence, Circular Economy, Supply Chain Management, Sustainability, Machine Learning Share and Cite: Waditwar, P. (2026) De-Risking Returns: How AI Can Reinvent Big ...
A research team has developed a new hybrid artificial intelligence framework that can accurately estimate leaf nitrogen ...
Antimicrobial resistance (AMR) is an increasingly dangerous problem affecting global health. In 2019 alone, ...
There is more than one way to describe a water molecule, especially when communicating with a machine learning (ML) model, says chemist Robert DiStasio. You can feed the algorithm the molecule's ...
Abstract: As semiconductor manufacturing approaches the diffraction limit of lithography, traditional model-based optical proximity correction (MBOPC) techniques face accuracy bottlenecks due to ...
1 School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA. 2 Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA. As cloud ...
No audio available for this content. High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, ...
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