Results 191 to 200 of about 9,582 (250)

Design of a Three‐Phase PWM Rectifier Based on Improved Whale Optimization Algorithm Optimized Fuzzy Control

Advanced Control for Applications, Volume 8, Issue 3, September 2026.
Schematic diagram of the whale optimization algorithm (WOA)–optimized fuzzy proportional–integral–derivative (PID) controller. The WOA is employed to dynamically tune the scaling factor expressions and fuzzy control parameters (a(e)a(e), a(ec)a(ec), βpβp, βiβi), as well as the initial PID gains (kp0kp0, ki0ki0), based on the error (ee) and its ...
Xiaosong Tian, Meiguang Jiang, Congguo Jiang, Guangya Liu, Jun Zhang, Xiangwen Lv, Weijiang Yang, Ruoyu Yang   +7 more
wiley   +1 more source

GABA+‐Edited Magnetic Resonance Spectroscopy Deep Learning Quality Assessment Framework

Magnetic Resonance in Medicine, Volume 96, Issue 3, Page 1028-1041, September 2026.
ABSTRACT Purpose Motivated by the need to improve GABA+‐edited magnetic resonance spectroscopy (MRS) quality, we developed a three‐module framework to improve transient averaging based on quality. We hypothesized that training a deep learning (DL) model to differentiate spectrum quality could improve transient averaging compared to traditional ...
Hanna Bugler, Roberto Souza, Ashley D. Harris   +2 more
wiley   +1 more source

Collisionless Shocks: Contemporary State After Three Quarters of a Century of Research

Reviews of Geophysics, Volume 64, Issue 3, September 2026.
Abstract The collisionless shock research started in the late 1950s. Since then, plenty of data have been accumulated due to the large number of missions in the heliosphere, providing in situ measurements of the fields and particles. The quality of these measurements has vastly improved, especially in the last two decades.
M. Gedalin, M. Balikhin, V. Krasnoselskikh   +2 more
wiley   +1 more source

Advancing forward osmosis predictions: A deep learning‐based surrogate modeling approach

Journal of the Science of Food and Agriculture, Volume 106, Issue 11, Page 6766-6778, 30 August 2026.
Abstract BACKGROUND This study presents a deep learning‐based surrogate model for the rapid and accurate prediction of forward osmosis (FO) performance under diverse operating conditions. To assess the applicability of data‐driven approaches, several machine learning models – decision tree, random forest, support vector machine, and deep neural network
Hyeon Woo Park, Woo‐Ju Kim
wiley   +1 more source

Statistical Study of Betatron and Fermi Electron Acceleration at Dipolarization Fronts

Geophysical Research Letters, Volume 53, Issue 15, 16 August 2026.
Abstract Magnetic reconnection jets in Earth's magnetotail are regions of electron heating and acceleration. They are often associated with dipolarization fronts (DFs), which are characterized by sharp increases in the northward magnetic field component Bz ${B}_{z}$.
A. Kolokotronis, D. B. Graham, Yu. V. Khotyaintsev, C. Norgren, K. Steinvall, L. Richard   +5 more
wiley   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, Volume 13, Issue 43, 3 August 2026.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

Impedance of Nonelectroneutral Solid Electrolyte Interphases With Nanopores: A Theoretical Model

Advanced Science, Volume 13, Issue 43, 3 August 2026.
Physical modeling reveals that often‐neglected non‐electroneutrality and nanopores in the solid‐electrolyte interphase (SEI) govern the impedance behavior. Under nonreactive conditions, the low‐frequency constant‐phase element (CPE) phenomenon can be attributed to the nonelectroneutral local conditions in the SEI.
Chenkun Li, Jun Huang
wiley   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Transfer learning for subject-independent motor imagery EEG classification using convolutional relational networks. [PDF]

Front Neurosci
Otarbay Z, Kyzyrkanov A.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source