Results 71 to 80 of about 452,503 (268)

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Bayesian Semiparametric Multivariate Density Deconvolution [PDF]

, 2016
We consider the problem of multivariate density deconvolution when the interest lies in estimating the distribution of a vector-valued random variable but precise measurements of the variable of interest are not available, observations being contaminated
Carroll, Raymond J., Mallick, Bani K., Pati, Debdeep, Sarkar, Abhra   +3 more
core  

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

USING TRAJECTORIES FROM A BIVARIATEGROWTH CURVE OF COVARIATES IN A COXMODEL ANALYSIS [PDF]

, 2004
In many maintenance treatment trials, patients are first enrolled into an open treatmentbefore they are randomized into treatment groups. During this period, patients are followedover time with their responses measured longitudinally. This design is very
Dang, Qianyu
core   +1 more source

The asymptotic normality of an adjusted least squares estimator in a multivariate vector errors-in-variables regression model [PDF]

Theory of Probability and Mathematical Statistics, 2014
Summary: An adjusted least squares estimator in a linear multivariate vector error-in-variables regression model is considered in this paper. Conditions for the asymptotic normality of this estimator are given. A modification of the estimator is constructed whose asymptotic properties are the same as those of the adjusted least squares estimator and ...
openaire   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Using PSO and Genetic Algorithms to Optimize ANFIS Model for Forecasting Uganda’s Net Electricity Consumption

Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020
Uganda seeks to transform its society from a peasant to a modern and largely urban society by the year 2040. To achieve this, electricity as a form of modern and clean energy has been identified as a driving force for all the sectors of the economy.
Kürşat Ayan, Abdal Kasule
doaj   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

Algorithmic Design of Disordered Networks With Arbitrary Coordination: Application to Biophotonics

Advanced Functional Materials, EarlyView.
Predictive Design of Disordered Networks: Disordered network‐like morphologies are abundant in nature, from cytoskeletal networks to bone structures and chalcogenide glasses. These structures are naturally hard to characterize. A new algorithmic tool extends the established Wooten–Weaire–Winer (WWW) algorithm to valencies above 4.
Florin Hemmann, Vincent Glauser, Ullrich Steiner, Matthias Saba   +3 more
wiley   +1 more source

Correcting the estimator for the mean vectors in a multivariate errors-in-variables regression model

, 2015
The multivariate errors-in-variables regression model is applicable when both dependent and independent variables in a multivariate regression are subject to measurement errors. In such a scenario it is long established that the traditional least squares approach to estimating the model parameters is biased and inconsistent.
Lutzeyer, Johannes, Cohen, Edward A. K.
openaire   +2 more sources