Results 161 to 170 of about 978,255 (310)

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Digital heart initiative: an ecosystem for digital discovery and precision medicine in cardiology. [PDF]

Natl Sci Rev
Ni H, Cao C, Zhou Y, Bai Y, Sheng J, Wang Y, Gu N.   +6 more
europepmc   +1 more source

A Numerical–Experimental Approach for Multi‐Matrix Fiber‐Reinforced Plastics Characterization Using Finite Element Model Updating

Advanced Engineering Materials, EarlyView.
A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora, Tales V. Lisbôa, Axel Spickenheuer, Rogério J. Marczak, Jakson M. Vassoler   +4 more
wiley   +1 more source

Building the future of biophotonics through experiential education and seasonal schools. [PDF]

J Biomed Opt
Mthunzi-Kufa P, Maphutha J, Mcotshana Z, Mcoyi P.   +3 more
europepmc   +1 more source

Air‐Pressure–Actuated Vibroacoustic Metamaterial With Tunable Bandgap: Design, Modeling, and Characterization

Advanced Engineering Materials, EarlyView.
This article presents the design, modeling, and characterization of air‐pressure–actuated programmable vibroacoustic metamaterials (PVAMM). The study focuses on leveraging air pressure to dynamically tune resonance frequencies for effective noise attenuation.
William Kaal, Michael M. Becker, Sarah C. L. Fischer   +2 more
wiley   +1 more source

Publisher Correction: Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning. [PDF]

NPJ Digit Med
Naghavi E, Wang H, Ziaei-Rad V, Guccione J, Kassab G, Boddeti V, Baek S, Lee LC.   +7 more
europepmc   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

GEC-DTSP: A GNN-RL-based Edge-Cloud Digital Twin framework for real-time traffic forecasting and adaptive signal control. [PDF]

PLoS One
Alanazi F, Armghan A, Al-Gburi AJA, Yousef A.   +3 more
europepmc   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Correction: Khan et al. Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models. <i>Polymers</i> 2022, <i>14</i>, 2270. [PDF]

Polymers (Basel)
Khan K, Iqbal M, Salami BA, Amin MN, Ahamd I, Alabdullah AA, Arab AMA, Jalal FE.   +7 more
europepmc   +1 more source