Results 221 to 230 of about 258,548 (313)
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 +5 more
wiley +1 more source
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 +6 more
wiley +1 more source
Seizing Learning Opportunities in Everyday Life: Infants Are Attentive During Non-Child-Directed Activity. [PDF]
Kaplan BE, Monroy C, Yu C.
europepmc +1 more source
Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari +3 more
wiley +1 more source
Simulating object recognition with the Standard Operating Procedures (SOP) model. [PDF]
Galarce SN +4 more
europepmc +1 more source
A Dislocation Perspective on Strength and Toughness in Ceramics
Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley +1 more source
A predictive map learned from diverse entorhinal inputs explains the role of context-dependent reorganization of hippocampal place cells. [PDF]
Kuniyoshi Y, Yamazaki T.
europepmc +1 more source
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 +4 more
wiley +1 more source
A method for compiling satellite image map geographic objects based on vector map data via deep learning. [PDF]
Du J, Zeng D, Cai K, Qiu Y.
europepmc +1 more source
Stretching the Printability Metric in Direct‐Ink Writing with Highly Extensible Yield‐Stress Fluids
This study introduces “drawability” as a new metric for assessing printability in direct‐ink writing, focusing on gap‐spanning performance and speed robustness. By designing yield‐stress fluids with high extensibility, we demonstrate that extensional strain‐to‐break significantly enhances printability.
Chaimongkol Saengow +9 more
wiley +1 more source

