Results 221 to 230 of about 127,829 (293)

Integrating Environmental Drivers and Trophic Interactions to Predict Spatial Distribution of High-Risk Marine Organisms at Nuclear Power Plant Cooling Water Intake. [PDF]

Animals (Basel)
Zhang Y, Hu X, Cao L, Liu G, Song C, Jin Y.   +5 more
europepmc   +1 more source

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

Cross-Dataset Generalization of Deep Learning-Based Detectors for Intracranial Hemorrhage Subtype Localization on Noncontrast Head CT: A Comparative Study. [PDF]

Diagnostics (Basel)
Lee CH, Muzakky H, Juan CE, Chang CC, Li YH, Lee TY, Juan CH, Tsai MT, Juan CJ.   +8 more
europepmc   +1 more source

Enhancing Bubble Removal in Geometry‐Optimized Electrodes

Advanced Engineering Materials, EarlyView.
3D‐printed lattice electrodes outperform stochastic foams in alkaline water electrolysis despite 20%–25% lower surface area. Straight flow channels generate Venturi‐like bubble entrainment, suppressing gas accumulation that renders foam interiors electrochemically inactive.
Florian Wiesner, Peter Förster, Alexander Limper, Anselm Brodersen, Matthias Wessling, John Linkhorst   +5 more
wiley   +1 more source

Integrated clinical and multi-omics analysis links composite inflammatory indices to macrophage-associated molecular programs in aortic dissection. [PDF]

Front Genet
Zhang B, Zhang Y, Zheng R, Zhang Y, Wang L.   +4 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Arterial stenosis and electrophysiological nerve dysfunction in type 2 diabetes: clinical evidence from GEE models and transcriptomic pathway analysis. [PDF]

Front Endocrinol (Lausanne)
Zhao XQ, Huang LJ, Yao K, Zeng WC, Zu HB, Wu JF, Zhang JF.   +6 more
europepmc   +1 more source

Experimental Characterization of Mycelium‐Based Composites Under Multiple Loading Conditions

Advanced Engineering Materials, EarlyView.
This study examines the mechanical response of mycelium‐based composites under compression, shear, and tension using mechanical testing and imaging methods. The comparison between unpressed and hot‐pressed specimens shows that hot pressing is associated with higher compression and shear stiffnesses.
Shaghayegh Elahi, Kimiya Tajik, Harvey Shaw, Matti Isakov, Sami Pajunen, Lauri Rautkari   +5 more
wiley   +1 more source

Psychometric properties of the Questionnaire for the Use of Public Transportation and Well-Being: Bayesian reliability, factorial invariance, and structural equation modeling evidence. [PDF]

Front Sociol
Baños-Chaparro J, Caycho-Rodríguez T, Valencia-Pecho D, Sarmiento-Suarez E, Bernabel-Tarazona D, Rivera-Álvarez G.   +5 more
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source