Results 121 to 130 of about 40,161 (298)

Interpolation method for evaluating weakly singular kernels

open access: yesJournal 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

open access: yesENERGY &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   +4 more
wiley   +1 more source

Three models for rectilinear particle motion with the Basset history force

open access: yesElectronic Journal of Differential Equations, 2015
We consider three model problems that describe rectilinear particle motion in a viscous fluid under the influence of the Basset history force. These problems consist of sedimentation starting from rest, impulsive motion in a quiescent fluid, and ...
Shujing Xu, Ali Nadim
doaj  

Application of the homotopy perturbation method for weakly singular Volterra integral equations

open access: yesJournal of New Results in Science
In this paper, we study a weakly singular Volterra integral equation of the second kind with the kernel $\displaystyle K(x,t) = \left (\frac{t}{x}\right )^\nu\frac{1}{t}$, for some $\nu >0$ and $x\in[0,X]$.
Ahmet Altürk
doaj   +1 more source

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

open access: yesENERGY &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   +9 more
wiley   +1 more source

Numerical Study of Fourth-Order Volterra Partial Integrodifferential Equation With Weakly Singular Kernel via Subdivision Collocation Approach

open access: yesJournal of Function Spaces
In the present article, an emerging subdivision-based technique is developed for the numerical solution of linear Volterra partial integrodifferential equations (LVPIDEs) of order four with a weakly singular kernel. To approximate the spatial derivatives,
Zainab Iqbal   +4 more
doaj   +1 more source

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

open access: yesENERGY &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   +9 more
wiley   +1 more source

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

open access: yesENERGY &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   +5 more
wiley   +1 more source

Occipital irregular delta activity in focal epilepsy

open access: yesEpileptic 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   +3 more
wiley   +1 more source

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

open access: yesEarthquake 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   +1 more
wiley   +1 more source

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