Results 41 to 50 of about 25,591 (263)

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Modeling long-run global agricultural price dynamics: a trend-cycle decomposition and unobserved components approach

Frontiers in Sustainable Food Systems
IntroductionSince the 1960s, global agricultural prices have exhibited significant episodic volatility. This study aims to conduct a trend-cycle decomposition of these prices to identify the cyclical patterns of their long-run dynamic evolution ...
Yujia Li, Bingjian Zhao, Ming Che
doaj   +1 more source

Health condition and job status interactions: econometric evidence of causality from a French longitudinal survey

Health Economics Review, 2019
This article investigates the causal links between health and employment status. To disentangle correlation from causality effects, the authors leverage a French panel survey to estimate a bivariate dynamic probit model that can account for the ...
Eric Delattre, Richard K. Moussa, Mareva Sabatier   +2 more
doaj   +1 more source

Unobserved component time series models with Arch disturbances [PDF]

Journal of Econometrics, 1992
Abstract This paper considers how ARCH effects may be handled in time series models formulated in terms of unobserved components. A general model is formulated, but this includes as special cases a random walk plus noise model with both disturbances subject to ARCH effects, an ARCH-M model with a time-varying parameter, and a latent factor model with
Harvey, A. C., Ruiz Ortega, Esther, Sentana, E.   +2 more
openaire   +2 more sources

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Detection of unobserved heterogeneity with growth mixture models

Revista de Matemática: Teoría y Aplicaciones, 2009
Latent growth curve models as structural equation models are extensively discussed in various research fields (Duncan et al., 2006). Recent methodological and statistical extension are focused on the consideration of unobserved heterogeneity in ...
Jost Reinecke, Luca Mariotti
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

REGCMPNT – A Fortran Program for Regression Models with ARIMA Component Errors

Journal of Statistical Software, 2011
RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models.
William R. Bell
doaj  

Forecasting Oil Price by Hierarchical Shrinkage in Dynamic Parameter Models

Discrete Dynamics in Nature and Society, 2020
The aim of this paper is to forecast monthly crude oil price with a hierarchical shrinkage approach, which utilizes not only LASSO for predictor selection, but a hierarchical Bayesian method to determine whether constant coefficient (CC) or time-varying ...
Yuntong Liu, Yu Wei, Yi Liu, Wenjuan Li
doaj   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source