Results 251 to 260 of about 22,823,923 (369)
Corrigendum: Mathematical modeling in autoimmune diseases: from theory to clinical application. [PDF]
Ugolkov Y+6 more
europepmc +1 more source
IN VIVO GAMMA LUNG MEASUREMENTS--A MATHEMATICAL MODEL
Peter Ammann+2 more
openalex +2 more sources
Biomechanical‐to‐electrical energy conversion devices are uniquely suited for self‐driven physiological information monitoring and powering human–computer interaction systems. These devices based on micro‐/nanoarchitectured inorganic dielectric materials (MNIDMs) have shown ultrahigh electromechanical performance and thus great potential for practical ...
Jia‐Han Zhang+12 more
wiley +1 more source
Superior Adhesion of Monolayer Amorphous Carbon to Copper
The adhesion energy of monolayer amorphous carbon on copper substrate is 85 J m−2, 13 times higher than that of graphene due to covalent‐like bonding between the sp2 carbon structure to copper. X‐ray photoelectron spectroscopy (XPS), near‐edge X‐ray absorption (NEXAFS), and (density functional theory) DFT calculations are used to elucidate the ...
Hongji Zhang+27 more
wiley +1 more source
The role of host mobility in the transmission and spread of Echinococcus granulosus: A Chile-based mathematical modeling approach. [PDF]
Lagos R+4 more
europepmc +1 more source
Glaphene: A Hybridization of 2D Silica Glass and Graphene
‘Glaphene’, a novel hybrid material combining 2D silica glass and graphene, is synthesized via a scalable liquid precursor‐based vapor‐phase growth. This study reveals interlayer hybridization beyond van der Waals interactions, leading to emergent semiconducting behavior.
Sathvik Ajay Iyengar+10 more
wiley +1 more source
Mathematical Modeling and Artificial Intelligence to Explore Connections Between Glaucoma and the Gut Microbiome. [PDF]
Rocks MC+8 more
europepmc +1 more source
Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators
This study presents a DeepONet‐based machine learning framework for designing stochastic mechanical metamaterials with tailored nonlinear mechanical properties. By leveraging sparse but high‐quality experimental data from in situ micro‐mechanical tests, high predictive accuracy and enable efficient inverse design are achieved.
Hanxun Jin+7 more
wiley +1 more source