Results 151 to 160 of about 2,944,876 (211)

Low‐Field Giant Dielectric Tunability by Polaron‐Mediated Interfacial Polarization in Pulsed‐Laser‐Deposited‐Nanocrystalline/Amorphous Composite BaTiO3 Thin Films

Advanced Electronic Materials, EarlyView.
Comparison of tunability efficiency between vacuum‐deposited PLD‐BTO films grown at Td = 550°C in the present study and representative previous reports. ABSTRACT Electrically tunable dielectric thin films are essential components for next‐generation low‐voltage electronic devices.
Shinya Kondo, Taichi Murakami, Loick Pichon, Joël Leblanc‐Lavoie, Takashi Teranishi, Akira Kishimoto, Tomoaki Yamada, My Ali El Khakani   +7 more
wiley   +1 more source

Deep-Pose-Tracker: an automated behavioural analysis framework for Caenorhabditis elegans. [PDF]

Sci Rep
Saha D, Chaudhary S, Vyas D, Ghosh-Roy A, Sharma R.   +4 more
europepmc   +1 more source

Reliable Evaluation of Na‐Ion Battery Materials: Eliminating Na‐Metal Distortions

Advanced Energy Materials, EarlyView.
Na metal is highly reactive in carbonate electrolytes, forming unstable interphases and triggering parasitic reactions that distort measurements and undermine reproducibility. To enable accurate evaluation of materials for Na‑ion batteries, this work suggests that NASICON‑type counter electrodes provide a practical and stable platform for distortion ...
Jinkwang Hwang, Shaoning Zhang, Seongjae Ko, Tomoki Takahashi, Kosei Araya, Duanfei Han, Masashi Okubo, Atsuo Yamada, Shinichi Komaba, Kazuhiko Matsumoto   +9 more
wiley   +1 more source

Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI. [PDF]

Imaging Neurosci (Camb)
Velut S, Thielen J, Chevallier S, Corsi MC, Dehais F.   +4 more
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

DynamicAtlas: a morphodynamic atlas for Drosophila development. [PDF]

Nat Methods
Lefebvre MF, Jain-Sharma V, Claussen N, Mitchell NP, Raich MK, Gustafson HJ, Streichan FE, Bausch AR, Streichan SJ.   +8 more
europepmc   +1 more source

A Breadth‐First Pruned‐Enriched Rosenbluth Method for Force–Extension Simulations of Confined Semiflexible Chains

Advanced Intelligent Discovery, EarlyView.
The behaviors of semiflexible polymers such as DNA and protein are often reshaped by coupled interactions. Monte Carlo simulations assist in studying these systems. This work recasts the traditional chain‐growth strategy into a new framework: a fixed number of chains grow synchronously, while less relevant chains to the target system are removed and ...
Yihan Zhao, Jizeng Wang
wiley   +1 more source

Systematic discovery of enzyme promiscuity in Escherichia coli using in vitro metabolomics. [PDF]

Commun Biol
Gruber CH, Sévin DC, Noor E, Zanotelli VRT, Schmitz J, Zamboni N, Sauer U.   +6 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Geometric and quantum kernel methods for predicting skeletal muscle outcomes in experimental chronic obstructive pulmonary disease. [PDF]

Sci Rep
Alavi A, Khalili H, Chan SMH, Kouchmeshki F, Usman M, Vlahos R.   +5 more
europepmc   +1 more source