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The properties of biomorphic ceramics depend decisively on the synthesis parameters, that were optimized using a multivariate methodology for the design of experiments.
In the present article the synthesis as well as the decomposition of these materials is optimized using a multivariate methodology for the design of experiments.
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Multivariate methodology was employed for finding optimum remediation conditions for electrodialytic remediation of harbour sediment from an Arctic location in Norway.
The optimization step was performed using a univariate methodology for 200 mg samples and a multivariate methodology, using the Box-Behnken design, for other variables, such as solvent (HNO3:HCl), final concentration (1.7 mol L−1) and time (26 min).
To solve this problem, a multivariate methodology that was originally developed in the field of geosciences, was proposed.
Zhu et al. [ 73] adapted the multivariate spline methodology for GWAS: Specifically, the procedure starts by starting with a model containing only intercept (the simplest model) and then gradually growing the model by adding terms (e.g., individual SNPs, SNP-SNP interaction) that minimizes a weighted least squares criteria.
Fast Monte Carlo methodology for multivariate particulate systems-I: point ensemble Monte Carlo.
While the methodology for multivariate frequency analysis is well established, the estimation of the associated uncertainty is rarely studied.
In this paper, a multivariate copula based modeling methodology for dynamic reliability modeling of weighted-k-out-of-n systems is applied.
The aim of our work was to elaborate a sampling optimization methodology for multivariate DSM considering the demands on economic efficiency.
This paper describes a multivariate zero-inflated Poisson (MZIP) regression model as an alternative methodology for modeling multivariate crash count data by severity.
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