Results 121 to 130 of about 140,549 (266)
The Biofuels Blueprint: Understanding the U.S. Renewable Fuel Standard
ABSTRACT We provide a comprehensive review of the U.S. Renewable Fuel Standard (RFS), synthesizing nearly two decades of program evolution, market outcomes, and economic analysis. The RFS mandates minimum volumes of renewable fuel blending through a nested structure based on life‐cycle greenhouse gas reductions, enforced via tradeable Renewable ...
Maria Gerveni +3 more
wiley +1 more source
ABSTRACT EU member states have exhibited varying rates of apple production growth. Technical efficiency (TE) estimation is suitable for identifying best‐practice farm performance. This study examined whether the development of the apple sector in Germany, Italy, and Poland was influenced by production efficiency, access to technology, as well as ...
Anika Muder, Jakub Staniszewski
wiley +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi +5 more
wiley +1 more source
Artificial Intelligence for Bone: Theory, Methods, and Applications
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan +3 more
wiley +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng +4 more
wiley +1 more source
Machine learning-driven discovery of host genetic factors for paratuberculosis in goats within the one health framework. [PDF]
Yaman Y +10 more
europepmc +1 more source
A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley +1 more source
Molecular Complexity Constrained Early Amino Acid Recruitment into the Genetic Code. [PDF]
Hashmi SA, Chok H, Cabrera R, Blanco C.
europepmc +1 more source

