Results 101 to 110 of about 69,128 (288)

Semantic Modeling in Materials Science and Engineering With Platform MaterialDigital Core Ontology 3.0

Advanced Engineering Materials, EarlyView.
The community‐driven Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a Basic Formal Ontology‐aligned semantic backbone for the processing–structure–properties paradigm in Materials Science and Engineering. Modular engineering, automated releases, and validation workflows are highlighted and key semantic patterns for materials ...
Markus Schilling, Philipp von Hartrott, Jörg Waitelonis, Thomas Hanke, Henk Birkholz, Hossein Beygi Nasrabadi, Khashayar Razghandi, Kamilla Zaripova, Felix Thonagel, Fabian Neuhaus, Martin Glauer, Lars Vogt, Harald Sack, Lutz Mädler, Bernd Bayerlein, Chris Eberl   +15 more
wiley   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

A Multi-Chain Blockchain Framework for Trusted Data Management and Efficient Traceability in Fruit and Vegetable Supply Chains

Computers
Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information ...
Weiqiang Chen, Zhiyao Zhao, Haisheng Li, Jiping Xu, Chongxuan Liu, Xin Zhang   +5 more
doaj   +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

Dynamically Actuated Reconfigurable Topographical Surface Enables Active Control of Implant‐Associated Infections

Advanced Functional Materials, EarlyView.
A dynamically actuated reconfigurable topographical surface (DARTS) integrates contact‐mediated bactericidal nanotopography with programmable mechanical actuation to actively disrupt bacterial biofilms. Dynamic surface reconfiguration enhances bacterial killing and suppresses implant‐associated infections in vivo, providing a new strategy for active ...
Mohammad Asadi Tokmedash, Jihun Lee, J. Scott VanEpps, Sungmin Nam, Jouha Min   +4 more
wiley   +1 more source

Venturing into the labyrinth: the information retrieval challenge of human digital memories [PDF]

, 2007
Advances in digital capture and storage technologies mean that it is now possible to capture and store one’s entire life experiences in a Human Digital Memory (HDM).
Kelly, Liadh, Jones, Gareth J.F.
core   +2 more sources

Enhancing retrieval and discovery of desktop documents

, 2009
Personal computers provide users with abilities to create, organize, store and access large amounts of information. Most of this information is in the form of documents in files organized in the hierarchical folder structures provided by the operating ...
Mosweunyane, Gontlafetse
core   +1 more source

Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles [PDF]

, 2007
Over the last decade, storage of non text-based data in databases has become an increasingly important trend in information management. Image in particular, has been gaining popularity as an alternative, and sometimes more viable, option for information ...
Chung, Kien- Ping
core  

An Integrated Oxygen‐Supplying Device and Predictive Multiphysics Model for Extended Ambient Transport of Insulin‐Producing Cells

Advanced Functional Materials, EarlyView.
Cell therapies typically rely on cold‐chain logistics and cryopreservation, limiting access and compromising cell quality. Here, a dual‐chamber device separates an oxygen‐supplying chamber from a hyaluronic acid cargo chamber, sustaining oxygen delivery for over 70 h and enabling ambient‐temperature shipment.
Daniel A. Domingo‐Lopez, Ruth Tarpey, Benjamin Brennan, Bryan D. Boyle, Amir M. Alsharabasy, Abhik Mallick, Gabriella Bellavia, Abhay Pandit, Garry P. Duffy, Ruth E. Levey   +9 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