Results 231 to 240 of about 4,282,960 (349)
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
Time to Marketing of Generic Drugs After Patent Expiration in Canada.
Lexchin J.
europepmc +1 more source
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri +6 more
wiley +1 more source
Reasons for the Underutilization of Generic Drugs by US Ophthalmologists: A Survey. [PDF]
Dietze J, Priluck A, High R, Havens S.
europepmc +1 more source
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley +1 more source
Clinical Utilization of Generic Drugs and Biosimilars for Ulcerative Colitis Treatment: Insights from a Nationwide Database Study in Japan. [PDF]
Moroi R +7 more
europepmc +1 more source
Generic Drugs, Used Textbooks, and the Limits of Liability for Product Improvements
Timothy J. Muris +1 more
openalex +1 more source
Regulation of Generic Drugs in Japan: the Current Situation and Future Prospects
R. Kuribayashi +2 more
semanticscholar +1 more source
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan +8 more
wiley +1 more source

