Results 111 to 120 of about 553 (265)

Interpolation method for evaluating weakly singular kernels

Journal of Mathematical and Computational Science, 2021
openaire   +1 more source

Machine Learning‐Assisted Design of BaTiO3‐Based Superparaelectric High‐Entropy Ceramics with Superior Energy Storage

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This study employed an adaptive iterative strategy combining machine learning algorithms, domain knowledge, experimental design, and experimental feedback to aim to precisely and quickly discover high‐entropy ceramics with excellent energy storage performance.
Haowen Liu, Xiaoyan Zhang, Zhiyuan Ma, Kailong Ma, Xiwei Qi   +4 more
wiley   +1 more source

Analytical and Numerical Treatment of Evolutionary Time-Fractional Partial Integro-Differential Equations with Singular Memory Kernels

Fractal and Fractional
Evolution equations with fractional-time derivatives and singular memory kernels are used for modeling phenomena exhibiting hereditary properties, as they effectively incorporate memory effects into their formulation.
Kamel Al-Khaled, Isam Al-Darabsah, Amer Darweesh, Amro Alshare   +3 more
doaj   +1 more source

Confined Hot Carrier Cooling Via Excited‐State Energy Gap Engineering in Graphyne Carbon Allotropes

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The excited‐state energy gap (EEG), regulated by the concentration of sp‐hybridized carbon, provides an effective handle for tuning electron–phonon coupling behavior. This design enables long‐lived confinement of hot carriers near the top of the EEG with controllable energy levels.
Fulong Dai, Zhaozhao Xiong, Yingcong Liu, Feiyu Cheng, Haochun Sun, Zhenfa Zheng, Jin Zhao, Zhuojian Liang, Zhuo Kang, Yue Zhang   +9 more
wiley   +1 more source

A Fast Chebyshev Spectral Collocation Method for a Coupled System of Nonlinear Klein–Gordon Equations with Caputo Fractional Memory

Axioms
We develop a fast Chebyshev spectral collocation method for a coupled system of nonlinear Klein–Gordon equations augmented by Caputo-type fractional memory integrals.
Yertay Kazez, Zhanars A. Abdiramanov, Nauryzbay Adil, Abdumauvlen S. Berdyshev   +3 more
doaj   +1 more source

Lignocellulosic Triboelectric Materials for Energy Harvesting and Emerging Applications: A Review on Lignin‐Molecular Engineering toward Improved Performance

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Contrasting sustainable biomass with depleting petroleum resources, this review charts the evolutionary timeline of lignin‐based TENGs. We systematically evaluate their fundamental mechanisms, enhancement strategies, and applications in green self‐powered electronics.
Yuxin Yang, Boyu Du, Yuwen Wang, Tian Tang, Yuxuan Xie, Jiajun Zhang, Yajun Zhang, Jingyu Xu, Xing Wang, Chuanyin Xiong   +9 more
wiley   +1 more source

Machine Learning Unveils Isolated‐Surrounded Pt Motifs in High‐Entropy Alloys for Superior Low‐Temperature Ammonia Oxidation

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
A physics‐informed machine learning approach successfully decodes the complex catalytic activity of high‐entropy alloys for ammonia oxidation. By revealing a synergistic mechanism involving lattice and electronic couplings, the study identifies a superior “isolated‐surrounded” platinum motif.
Shangfeng Jiang, Ting Tao, Kexiang Guo, Xinyu Fan, Xu Zhang, Zhen Zhou   +5 more
wiley   +1 more source

Occipital irregular delta activity in focal epilepsy

Epileptic Disorders, EarlyView.
Abstract Objective Nonspecific occipital irregular delta activity (OID) is a common finding in focal epilepsy (FE). However, the significance of OID and its relationship to the underlying etiology of FE remain largely unstudied. This study aimed to investigate the relationship between OID and the etiology of FE, as well as the relationship between OID ...
Mónika Bessenyei, Miklós Emri, Johanna Dömötör, Béla Clemens   +3 more
wiley   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen, Christian Málaga‐Chuquitaype   +1 more
wiley   +1 more source

Tree‐Boost–Guided CNN–BiLSTM–Transformer for Solar Irradiance Forecasting: Cross‐Regional Evidence for Sustainable Energy Planning

Energy Science &Engineering, EarlyView.
This graphical abstract illustrates a reproducible pipeline that combines gradient‐boosting‐based feature selection with a CNN–BiLSTM–Transformer model to forecast solar irradiance across multi‐site satellite and ground datasets, delivering robust, high‐accuracy predictions that support sustainable grid planning and reliable PV integration.
Muhammad Farhan Hanif, Usama Hassan, Javid Hussain, Irfan Ali Khan, Yuldasheva Gulora, Syed Masood Arif Bukhari   +5 more
wiley   +1 more source