Results 181 to 190 of about 2,073,624 (303)

Letter to the Editor: Commentary on Feasibility Study of Parkinson's Speech Disorder Evaluation With Pre-Trained Deep Learning Model for Speech-to-Text Analysis (<i>Korean J Neurotrauma</i> 2024;20:168-179). [PDF]

Korean J Neurotrauma
Kim KH.
europepmc   +1 more source

A Scoping Review and Meta‐Analysis of Proportions of Central Nervous System Manifestations Reported in Patients with Sjögren's Disease

Arthritis Care &Research, Accepted Article.
Objective The objective of this scoping review was to synthesize evidence on the proportion of individuals living with Sjögren's disease who experience central nervous system (CNS) manifestations. Methods We searched MEDLINE (via PubMed) and Embase from 1980 through January 29, 2026, and the ECRI Guidelines Trust from 2020 through January 29, 2026 ...
Arun Varadhachary, Ghaith Noaiseh, Julie Frantsve‐Hawley, Frederick B. Vivino, Jennifer A. McCombe, Shamik Bhattacharyya, E. Sherwood Brown, Andrew R. Carey, Robert Fox, Thomas Grader‐Beck, Janet E. Lewis, Stephen Maitz, Steven Mandel, Astrid Rasmussen, George Sarka, R. Hal Scofield, Daniel J. Wallace, Rochelle Zak, Kristie Cox, Katherine M. Hammitt, Steven Carsons, Nancy Carteron   +21 more
wiley   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

The PRIMA Thesaurus for Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa, Alexey Boubnov, Dario De Angelis, Catriona Eschke, Maximilian Kabbe, Felix Kraus, Mirco Panighel, Richard Thelen, Davide Valentinis   +8 more
wiley   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Aminoglycoside-associated nonsyndromic deafness and speech disorder in mitochondrial A1555G mutation in a family: A case report. [PDF]

Medicine (Baltimore), 2018
Ou YH, Chen AW, Fan JY, Cheng WL, Lin TT, Chen MK, Liu CS.   +6 more
europepmc   +1 more source

Historical Foundation and Practical Guideline for Ferroelectric Switching Kinetic Studies

Advanced Functional Materials, EarlyView.
The P and U pulses in the conventional PUND measurements are not identical because of the interplay between switching current and the measurement circuit components. This circuit effect can lead to a shift in polarization transients and misinterpreted physics in the switching kinetics.
Yi Liang, Pat Kezer, John T. Heron
wiley   +1 more source

Thermoresponsive Liquid Crystalline Polymer Coatings Programmed by Field‐Induced Topological Defects for Tunable Optical and Topographical Responses

Advanced Functional Materials, EarlyView.
Resonant standing waves generate large‐area lattices of liquid‐crystal topological defects that are permanently encoded into polymer coatings by photopolymerization. The preserved director architecture produces thermally reconfigurable surface topographies while maintaining programmable optical functionality, demonstrating a scalable strategy for ...
Jacques A. Peixoto, Mufeeduzaman Chavilkkadan, Alexander Fusi, Marko Beslać, Dirk J. Broer, Etienne Brasselet, Danqing Liu   +6 more
wiley   +1 more source

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source