Results 181 to 190 of about 304,469 (259)

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

CAGs-Net: A Novel Adjacent-Context Network With Channel Attention Gate for 3D Brain Tumor Image Segmentation. [PDF]

Int J Biomed Imaging
Ye Q, Shi Y, Guo S.
europepmc   +1 more source

On the Effect of Creep on Local Lattice Orientation in Single‐Crystal Ni‐Based Superalloy Microstructures

Advanced Engineering Materials, EarlyView.
During creep of a single‐crystal Ni‐based superalloy, the overall crystal orientation is observed to remain constant while the microstructure evolves. Despite the lack of macroscopic rotation, small (<1°) rotations are observed on the submicron size scale and are accommodated by counteracting rotations over the scale of several micrometers.
E. J. Payton, E. B. Dursun, G. Eggeler, L. Agudo Jácome   +3 more
wiley   +1 more source

Predicting circRNA-disease associations with shared units and multi-channel attention mechanisms. [PDF]

Bioinformatics
Zhang X, Zou Q, Niu M, Wang C.
europepmc   +1 more source

Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

Advanced Engineering Materials, EarlyView.
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma, Sarath Menon, Fritz Körmann, Thomas Hammerschmidt, Ralf Drautz   +4 more
wiley   +1 more source

DCAN: Dynamic Channel Attention Network for Multi-Scale Distortion Correction. [PDF]

Sensors (Basel)
Zhang J, Peng S, Liu J, Guo A.
europepmc   +1 more source

Superlubricity and Corrosion Inhibition Properties of Solvate Ionic Liquids in Hybrid Carbon Fiber Reinforced Plastic–Steel Interfaces

Advanced Engineering Materials, EarlyView.
Solvate ionic liquids lubrication reduced the coefficient of friction by ∼60% compared to dry sliding, reaching steady‐state values as low as 0.04–0.05. Corrosion weight‐loss measurements in 1 M HCl further demonstrated significant inhibition behavior, with only 100 ppm of [Li(G3)][TFSI] (∼68.5 μL/L) reducing corrosion‐product weight loss by 63 ...
Sameh Dabees, Timothy Harte, Bhagya Dharmasiri, Y. Athulya Wickramasingha, David J. Hayne, Daniel J. Eyckens, Luke C. Henderson   +6 more
wiley   +1 more source

Improved Grain Boundary Reconstruction Method Based on Channel Attention Mechanism. [PDF]

Materials (Basel)
Duan X, Chen Y, Duan X, Rong Z, Nie W, Gao J.   +5 more
europepmc   +1 more source

Enhanced Ultrasound Transmission Through Aberration Layers Using Space‐Coiling Acoustic Metamaterials

Advanced Engineering Materials, EarlyView.
Enhancing ultrasound transmission through an aberration layer exhibiting mismatched impedance with the surrounding medium possesses great implications in imaging and treatment. In this study, we show that a space‐coiling acoustic metamaterial with judiciously tailored structural profile can help improve impedance matching.
Maral Ghanami, Hengzhe Ren, Erqian Dong, Ashkan Ghanbarzadeh‐Dagheyan, Chen Shen   +4 more
wiley   +1 more source

Point-Level Fusion and Channel Attention for 3D Object Detection in Autonomous Driving. [PDF]

Sensors (Basel)
Shen J, Fang Z, Huang J.
europepmc   +1 more source