Results 241 to 250 of about 708,932 (315)
Machine Learning-Based Prediction of Textural Properties and Nonlinear Regulatory Pattern Analysis of 3D-Printed Dough Containing Konjac Glucomannan. [PDF]
Leng W, Sun Y, Xie J, Pang J.
europepmc +1 more source
A nanoporous SiO2 memristor enabling reconfigurable volatile and non‐volatile switching within a single device is demonstrated. The dual‐mode functionality supports both physical reservoir dynamics and synaptic weight storage, allowing unified hardware implementation of reservoir computing for temporal information processing, including image and ...
Bohao Ding +5 more
wiley +1 more source
A Discrete Informational Framework for Classical Gravity: Ledger Foundations and Galaxy Rotation Curve Constraints. [PDF]
Simons M, Allahyarov E, Washburn J.
europepmc +1 more source
An exciting Approach to Theoretical Spectroscopy
ABSTRACT Theoretical spectroscopy, and more generally, electronic‐structure theory, are powerful concepts for describing the complex many‐body interactions in materials. They cover methods from ground‐state properties to lattice excitations and light‐matter interaction, including time‐resolved variants.
Martí Raya‐Moreno +29 more
wiley +1 more source
KT-YOLO: A multi-convolution kernel collaboration model for dense Hu sheep behavior detection. [PDF]
Zhang S, Chang H, Wu Z, Wu G, Ji R.
europepmc +1 more source
Thin‐Film Transistor Based Active Taxel for Multimode Tactile Perception and Fused Processing
Skin serves as the largest‐area organ for human and embodied intelligent robots, providing tactile interaction. An active multimode fused (AMF) artificial skin is developed using standard TFT processes, featuring 2T‐1C taxels with optical and electrostatic capacitive receptors for cross‐modal sensing.
Sihao Wu +13 more
wiley +1 more source
Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel. [PDF]
Zhao W, Sutherland DJ, Dao Duc K.
europepmc +1 more source
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source

