Results 111 to 120 of about 111,413 (237)

A Dirichelet–Neumann m-point BVP with a p-Laplacian-like operator

Nonlinear Analysis: Theory, Methods & Applications, 2005
The authors are concerned with the existence of solutions of the m-point boundary value problem \[ (\phi(u'))'=f(t,u,u'),\quad t\in (a,b),\quad u(a)=0,\quad \theta (u'(b))=\sum_{i=1}^{m-2}\alpha_i\theta (u'(\xi_i)), \] where \(\phi\) and \(\theta\) are odd increasing homeomorphisms from \(\mathbb R\) onto \(\mathbb R\) with \(\phi(0)=\theta(0)=0\), \(f:
García-Huidobro, Marta, Gupta, Chaitan P., Manásevich Tolosa, Raúl   +2 more
openaire   +5 more sources

Mesoscale and microphysical processes leading to extreme hourly rainfall prior to the merger of two mesoscale convective systems in Central China

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Short‐term extreme rainfall can be produced by the variation of low‐level warm moist airflow during mesoscale convective systems (MCSs) approaching another. The cold outflow of the rapidly moving MCS intensifies the warm moist airflow in front, enhancing the convergence and ascending motion in the quasi‐stationary MCS.
Xiaoyu Gao, Yali Luo, Jinfang Yin, Chong Wu   +3 more
wiley   +1 more source

Existence and uniqueness of solutions for nonlocal p-Laplacian problems

Electronic Journal of Differential Equations, 2016
We study the existence and uniqueness of positive solutions to a class of nonlocal boundary-value problems involving the p-Laplacian. Our main tools are a variant of the Schaefer's fixed point theorem, an inequality which suitably handles the p ...
Behrouz Emamizadeh, Amin Farjudian
doaj  

The non‐hydrostatic option of the ECMWF global weather forecast model: Improvements for kilometre‐scale modelling

Quarterly Journal of the Royal Meteorological Society, EarlyView.
This study presents improvements to the non‐hydrostatic version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), enabling stable global simulations at 1.4‐km resolution. A systematic comparison with the hydrostatic version at resolutions from 9 to 1.4 km shows that non‐hydrostatic effects emerge in ...
Jozef Vivoda, Inna Polichtchouk, Michail Diamantakis, Filip Váňa   +3 more
wiley   +1 more source

Distributed Optimization of Finite Condition Number for Laplacian Matrix in Multi‐Agent Systems

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT This paper addresses the distributed optimization of the finite condition number of the Laplacian matrix in multi‐agent systems. The finite condition number, defined as the ratio of the largest to the second smallest eigenvalue of the Laplacian matrix, plays an important role in determining the convergence rate and performance of consensus ...
Yicheng Xu, Faryar Jabbari
wiley   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source

Short‐Term Multi‐Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention‐GCN‐LSTM

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Accurately predicting line loss rates is crucial for effective management in distribution networks, particularly for short‐term multihorizon forecasts ranging from 1 hour to 1 week. In this study, we propose attention‐GCN–LSTM, a novel method that integrates graph convolutional networks (GCN), long short‐term memory (LSTM) and a three‐level ...
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng   +4 more
wiley   +1 more source

Robust Partial Multi‐Label Learning Under Dual Noise via Joint Subspace Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Partial Multi‐label Learning (PML) deals with the ambiguity where each instance is annotated with a set of candidate labels, and only a subset of which is valid. While existing PML methods focus primarily on label disambiguation, they often rely on the assumption of a clean feature space.
Yuanjian Zhang, Zhanbo Fang, Tianna Zhao, Duoqian Miao, Witold Pedrycz   +4 more
wiley   +1 more source

Explicit iteration of an unbounded solution of turbulent flow model involving ψ-Riemann–Liouville fractional derivatives

Alexandria Engineering Journal
This paper is concerned for the first time an explicit iteration of an unbounded solution for a turbulent flow model involving ψ-Riemann–Liouville fractional derivatives with the p-Laplacian operator on the infinite interval [a,∞),a≥0.
Sabri T.M. Thabet, Imed Kedim, Bahaaeldin Abdalla, Thabet Abdeljawad   +3 more
doaj   +1 more source