Results 91 to 100 of about 28,119 (261)

Electrode‐Engineered Dual‐Mode Multifunctional Lead‐Free Perovskite Optoelectronic Memristors for Neuromorphic Computing

Advanced Electronic Materials, EarlyView.
A lead‐free perovskite memristive solar cell structure that call emulate both synaptic and neuronal functions controlled by light and electric fields depending on top electrode type. ABSTRACT Memristive devices based on halide perovskites hold strong promise to provide energy‐efficient systems for the Internet of Things (IoT); however, lead (Pb ...
Michalis Loizos, Konstantinos Rogdakis, Konstantinos Chatzimanolis, Katerina Anagnostou, Emmanuel Kymakis   +4 more
wiley   +1 more source

AN MLPEL machine learning model for bathymetry retrieval based on ensemble learning

Egyptian Journal of Remote Sensing and Space Sciences
Machine learning models have made rapid progress in recent years. As a feedforward neural network, the multilayer perceptron (MLP) is widely used in bathymetry because of its simple structure and good nonlinear fitting ability.
Jinshan Zhu, Cong Jiao, Ruifu Wang, Yuquan Wen, Yu Wang, Bopeng Liu, Yina Han   +6 more
doaj   +1 more source

Recent Advances in Programmable Metasurfaces and Meta‐Devices

Advanced Electronic Materials, EarlyView.
Programmable metasurfaces enable various novel functionalities by dynamically tuning electromagnetic wavefronts. This article provides a comprehensive review of recent advances in microwave and terahertz programmable metasurfaces, covering electrical, thermal, optical, and mechanical control mechanisms.
Linda Shao, Chong He, Nan Wang, Liming Si, Weiren Zhu   +4 more
wiley   +1 more source

Linear and Programmable Long‐Term Plasticity in PECVD Amorphous SiC Memristors

Advanced Electronic Materials, EarlyView.
Stoichiometry‐engineered PECVD amorphous SiC memristors exhibit highly linear and programmable long‐term synaptic plasticity with a nonlinearity as low as 0.08. By controlling the local bonding environment, stable multilevel conductance updates are achieved, enabling robust neural‐network classification on MNIST and CIFAR‐10 and highlighting amorphous ...
Qin Liu, Omesh Kapur, Bohao Ding, Fangnan Chen, Bingkai Ding, Ruomeng Huang, C. H. Kees de Groot   +6 more
wiley   +1 more source

Comparative Analysis of the Predictive Performance of an ANN and Logistic Regression for the Acceptability of Eco-Mobility Using the Belgrade Data Set

Data
To solve the problem of environmental pollution caused by road traffic, alternatives to vehicles with internal combustion engines are often proposed.
Jelica Komarica, Draženko Glavić, Snežana Kaplanović   +2 more
doaj   +1 more source

Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors

AIChE Journal, EarlyView.
Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Principles of Constructing Layered Neural Networks Based on Pulse Neurons

Моделирование и анализ информационных систем, 2011
In the article we describe principles of pulse implementation of multilayer neural networks using biologically plausible neurons. It is shown that the multilayer perceptron can be modeled with a neural network composed of pulse neurons using impulse ...
O. A. Dunaeva
doaj  

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source