Results 141 to 150 of about 11,165 (303)

Improper Double Integrals with a Logarithmic Kernel Calculated by Methods of Elasticity Theory

, 2019
© 2019, Allerton Press, Inc. The aim of this paper is to calculate some improper double integrals. We consider the plane deformation of an elastic half-space under the action of load applied to its surface.
Nikonenkova T., Skvortsov E.
core  

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

On the Kramers–Kronig transform with logarithmic kernel for the reflection phase in the Drude model

, 2010
We use the Kramers-Kronig transform (KKT) with logarithmic kernel to obtain the reflection phase and, subsequently, the complex refractive index of a bulk mirror from reflectance.
Le Guen, K., Philippe Jonnard, Stefano Nannarone, Jean Michel André, Nicola Mahne, Angelo Giglia, Jonnard, Philippe, Jean-Michel André, Giglia, Angela, Karine Le Guen, Mahne, N., André, J.-M., NANNARONE, Stefano, Nannarone, S.   +13 more
core   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Volume growth and heat kernel estimates for the continuum random tree

, 2008
In this article, we prove global and local (point-wise) volume and heat kernel bounds for the continuum random tree. We demonstrate that there are almost-surely logarithmic global fluctuations and log-logarithmic local fluctuations in the volume of balls
Croydon, David A.
core   +1 more source

Positivity of Solutions to Integral Equations with Logarithmic Kernels [PDF]

Indiana University Mathematics Journal, 1970
openaire   +2 more sources

In‐situ asphaltene capture with iron oxide nanoparticles in steam‐assisted gravity drainage (SAGD) for improved recovery and partial upgrading of bitumen

The Canadian Journal of Chemical Engineering, EarlyView.
Key improvements obtained by incorporating nanoparticles into a lab‐scale SAGD process configuration. The bar plots show enhanced oil recovery, reduced viscosity, and increased asphaltene adsorption in nanoparticle‐assisted scenarios compared with conventional SAGD.
Luis Prada, Saleh S. Baakeem, Mabkhot BinDahbag, M. Daniela Contreras‐Mateus, Jesus Botett, Afif Hethnawi, Nashaat N. Nassar   +6 more
wiley   +1 more source

New Hardy-Hilbert-type integral inequalities involving special inhomogeneous kernel functions

Annals of the West University of Timisoara: Mathematics and Computer Science
In this article, new Hardy-Hilbert-type integral inequalities are established. Our main result is based on a special inhomogeneous two-parameter kernel function.
Chesneau Christophe
doaj   +1 more source

On subset least squares estimation and prediction in vector autoregressive models with exogenous variables

Canadian Journal of Statistics, EarlyView.
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne, Thanh‐Liem Huynh, Mylène Oscar   +2 more
wiley   +1 more source

A goodness‐of‐fit test for regression models with discrete outcomes

Canadian Journal of Statistics, EarlyView.
Abstract Regression models are often used to analyze discrete outcomes, but classical goodness‐of‐fit tests such as those based on the deviance or Pearson's statistic can be misleading or have little power in this context. To address this issue, we propose a new test, inspired by the work of Czado et al.
Lu Yang, Christian Genest, Johanna G. Nešlehová   +2 more
wiley   +1 more source