Results 61 to 70 of about 10,032,701 (265)

Best Linear Unbiased Prediction for Multifidelity Computer Experiments

, 2018
Recently it becomes a growing trend to study complex systems which contain multiple computer codes with different levels of accuracy, and a number of hierarchical Gaussian process models are proposed to handle such multiple-fidelity codes.
Weiyan Mu, Qiuyue Wei, Dongli Cui, Shifeng Xiong   +3 more
core   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

The Empirical Best Linear Unbiased Prediction and The Emperical Best Predictor Unit-Level Approaches in Estimating Per Capita Expenditure at the Subdistrict Level

Scientific Journal of Informatics
Purpose: This study aims to estimate and evaluate per capita expenditure at the subdistrict level in Garut Regency by employing unit-level Small Area Estimation (SAE) techniques, specifically utilizing the Empirical Best Linear Unbiased Predictor (EBLUP) and the Empirical Best Predictor (EBP) methods.
Ghina Fauziah, Anang Kurnia, Anik Djuraidah   +2 more
openaire   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers

Genetics Selection Evolution, 2009
Background Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS.
Tier Bruce, Moser Gerhard, Crump Ron E, Khatkar Mehar S, Raadsma Herman W   +4 more
doaj   +1 more source

Integration of Liquid Handling Robotics, Turbidimetry, and Machine Learning Toward Inverse Design of Lower Critical Solution Temperature Polymer Formulations

Advanced Intelligent Systems, EarlyView.
An integrated OT‐2/plate reader platform automates LCST cloud point measurement of NIPAM‐co‐DMAEMA copolymers across high‐dimensional formulation spaces. Machine learning trained on the resulting high‐throughput datasets achieves R2 up to 0.96 and enables inverse design of polymer formulations to target transition temperatures with <1% error.
Lachlan Alexander, Vianna F. Jafari, Tanja Junkers   +2 more
wiley   +1 more source

Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats

The Plant Genome, 2011
Genomic selection (GS) is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from five traits on 446 oat ( L.) lines genotyped with 1005 Diversity Array Technology (
Franco G. Asoro, Mark A. Newell, William D. Beavis, M. Paul Scott, Jean-Luc Jannink   +4 more
doaj   +1 more source

Improving access to new technologies: An experiment with Kenyan input sellers

American Journal of Agricultural Economics, EarlyView.
Abstract Agrodealers play a critical role in rural economies, selling essential inputs for agricultural production, stocking new products, and providing farmers with technical advice. However, uncertainty can discourage them from stocking newer, less familiar products, limiting the availability of innovative technologies and leading to market failures ...
Sarah W. Kariuki, Francisca N. Muteti, Annemie Maertens, Michael Ndegwa, Hope Michelson, Mercy Mbugua, Jason Donovan   +6 more
wiley   +1 more source

Multivariate Analysis of Hematological Stress Biomarkers and Welfare Domains in Organic and Intensive Pig Farming in Italy: A Pilot Study

Animal Research and One Health, EarlyView.
Intensive or organic farming systems may expose pigs to management or environmental challenges. Our preliminary results concluded that organic farming might enhance adaptive immune function, showing that improvements in welfare can translate into measurable immunological benefits, based on the correlation between welfare assessment and physiological ...
Dorotea Ippolito, Talal Hassan, Antonio Marco Maisano, Stefania Bergagna, Cecilia Guasco, Barbara Miniscalco, Martina Tarantola, Elena Colombino, Claudio Caruso, Ahsan Muhammad, Silvia Mioletti, Laura Tomassone, Massimiliano Tursi, Cristina Vercelli, Giovanni Loris Alborali, Maria Teresa Capucchio   +15 more
wiley   +1 more source

Understanding out‐of‐field teacher self‐efficacy beliefs: A person‐centred approach

British Educational Research Journal, EarlyView.
Abstract Teaching out‐of‐field (OOF) has been a widespread global phenomenon and a cause of concern for school leaders and policymakers due to the potential impact it has on teaching quality and student learning. However, research on how the nature of OOF teaching impacts various teachers' psychological attributes such as teacher self‐efficacy (TSE ...
Hoi Vo, David Turner, Tony Yeigh, David Lynch, Lana McCarthy, Declan Forester   +5 more
wiley   +1 more source