Results 51 to 60 of about 2,604,808 (287)

The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry

Agribusiness, EarlyView.
ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley   +1 more source

The Whale Optimization Algorithm and Markov Chain Monte Carlo-Based Approach for Optimizing Teacher Professional Development in Creative Learning Design with Technology

Algorithms
In this article, we present a hybrid optimization methodology using the whale optimization algorithm and Markov Chain Monte Carlo sampling technique in a teachers’ training development program regarding creativity in technology-enhanced learning design ...
Kalliopi Rigopouli, Dimitrios Kotsifakos, Yannis Psaromiligkos   +2 more
doaj   +1 more source

On the containment condition for adaptive Markov Chain Monte Carlo algorithms [PDF]

, 2009
This paper considers ergodicity properties of certain adaptive Markov chain Monte Carlo (MCMC) algorithms for multidimensional target distributions, in particular Adaptive Metropolis and Adaptive Metropolis-within-Gibbs.
Rosenthal, Jeffrey S. (Jeffrey Seth), Bai, Yan, Roberts, Gareth O.   +2 more
core  

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Approaches to evaluating measurement uncertainty [PDF]

International Journal of Metrology and Quality Engineering, 2012
The Guide to the expression of measurement uncertainty, (GUM, JCGM 100) and its Supplement 1: propagation of distributions by a Monte Carlo method, (GUMS1, JCGM 101) are two of the most widely used documents concerning ...
Forbes A.B.
doaj   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Phase space sampling with Markov Chain Monte Carlo methods [PDF]

EPJ Web of Conferences
The efficient exploration of the high-dimensional and multi-modal phase space of scattering events at high-energy particle colliders presents a severe computational challenge. We here discuss the application of Markov Chain Monte Carlo (MCMC) techniques,
La Cagnina Salvatore, Grunwald Cornelius, Janßen Timo, Kröninger Kevin, Schumann Steffen   +4 more
doaj   +1 more source

Linking community structure and climate vulnerability in desert plant assemblages of southern California

American Journal of Botany, EarlyView.
Abstract Premise Desert plant assemblages in southern California provide an opportunity to link patterns of community structure with climate‐driven vulnerability in a rapidly changing environment. California sustains an exceptionally diverse flora of approximately 4300 plant species, with 31% identified as endemic.
Hector Zumbado‐Ulate, Luis E. Barrios, Danelle A. Baronia, G. Darrel Jenerette, Lynn C. Sweet   +4 more
wiley   +1 more source

Interacting Particle Markov Chain Monte Carlo

, 2016
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to
Doucet, A, Rainforth, T, Naesseth, C, Lindsten, F, Paige, B, Wood, F, van de Meent, J   +6 more
openaire   +4 more sources

SMCTC : sequential Monte Carlo in C++ [PDF]

, 2009
Sequential Monte Carlo methods are a very general class of Monte Carlo methods for sampling from sequences of distributions. Simple examples of these algorithms are used very widely in the tracking and signal processing literature.
Johansen, Adam M., Adam M. Johansen
core   +1 more source