Results 121 to 130 of about 19,076,274 (291)

High throughput screening

High Throughput Screening (HTS) is a drug discovery process that allows automated detection of large numbers of chemical and/or biological compounds for a specific biological target. It speed up target analysis, as large scale compound libraries can be quickly screened in a cost-effective way.
Nuwan Perera, Rangika S. Hikkaduwa Koralege   +1 more
openaire   +3 more sources

Artificial Intelligence‐Driven Extraction of Patient‐Reported Pain Outcomes in Osteoarthritis Using Large Language Models

Arthritis Care &Research, Accepted Article.
Objective Patient‐reported outcome measures (PROMs) are central to assessing symptom burden and treatment response in Osteoarthritis (OA) but are often embedded in unstructured clinical notes. We evaluated whether prompt‐engineered large language models (LLMs) could accurately extract or infer pain scores on a 0‐10 scale from routine OA clinical ...
Jainesh Doshi, Stephen Batter, Yiyuan Wu, William Salerno, Alice Santilli, Sandhya Kannayiram, Susan Goodman, Bella Mehta   +7 more
wiley   +1 more source

High throughput preparation and analysis of plastically shaped material samples [PDF]

, 2003
A rapid throughput method for the preparation, analysis or both of libraries of material samples is provided. According to the method, a plurality of samples is provided. The plurality of samples is then formed into a plurality of films.
Hajduk, Damian, Kolosov, Oleg
core   +2 more sources

A Simplified Laminar Flow Model for the Pultrusion of Glass Fiber/Polyethylene Terephthalate Commingled Yarns

Advanced Engineering Materials, EarlyView.
A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares, Nadège Bouchonneau, Christian Jochum, Donald Radford   +3 more
wiley   +1 more source

Catalyst Optimization in Olefin Polymerization using an HTE/QSAR workflow: Intuitive 3D-Steric Descriptors to Tackle the Database Size Problem

, 2021
Catalyst screening in olefin polymerization is an inherently complex problem. Even using high-throughput experimentation (HTE) tools, databases are usually tiny (at least when compared to those in pharma), rarely comprised of more than several dozen ...
Roberta Cipullo, Georgy P. Goryunov, Peter H. M. Budzelaar, Dmitry V. Uborsky, Vyatcheslav V. Izmer, Antonio Vittoria, Christian Ehm, Mikhail I. Sharikov, Pavel S. Kulyabin, Vincenzo Busico, Alexander Z. Voskoboynikov   +10 more
core  

Microstructure Modification of Additively Manufactured Mo–9Si–8B by Annealing and Its Effects on High‐Temperature Mechanical Properties

Advanced Engineering Materials, EarlyView.
Postbuild annealing systematically modifies the phase fractions and morphology of EB‐PBF processed Mo–9Si–8B. Quantitative microstructure–property correlations reveal how controlled phase evolution enhances high‐temperature compressive strength and creep resistance.
Christopher Schmidt, Yong Chen, Georg Hasemann, Benjamin Wahlmann, Carolin Körner, Manja Krüger   +5 more
wiley   +1 more source

High-Throughput Screening Approach to Identify Thyroperozidase Inhibitors in ToxCast: Data Associated with Publication

, 2018
Data associated with publication on High-Throughput Screening Approach to Identify Thyroperozidase Inhibitors in ToxCast.
EPA's National Center for Computational Toxicology (5039467)
core   +1 more source

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source