Sentence examples for reduction variables from inspiring English sources

Empirical models were developed for the degree of reduction and for the metallic area of the Ni/SiO2 catalysts as functions of the reduction variables (final reduction temperature, heating rate, flow rate and hydrogen concentration of the reducing mixture) for each precursor.

The advantages of using this regression tree approach are that: by aggregating the data over time we achieve a computationally significant dimension reduction; variables of different types are easily included; regions of very low or zero frequency are grouped together and are given low (but non-zero) expected values; and interactions are naturally included.

A number of strategies are proposed for the approach, including strategies for design variables reduction, variable dependency graph development, constraint propagation, and dynamic verification of a design over an assigned set of attributes (variables).

The parameters on the fuel attributes other than fuel price are randomized, while the parameters on price and on the interactions of the emission reduction variable (Reduce) with the demographic and attitudinal variables are held fixed.

In multivariate analysis we found that after correcting for having an HIV-positive steady partner and a smaller number of years since starting injection (both factors being associated independently with HIV seroconversion), the combined harm reduction variable remained associated independently with HIV seroconversion (Table 4).

In terms of statistical accuracy, dimension reduction and variables selection play pivotal roles in analyzing high dimension data.

In this paper, the evaluation, combining and initial reduction of variables is presented, along with assessment of the hypotheses in the preliminary statistical testing.

The same approach of reduction of variables has been also applied to a graded potential system.

This reduction was variable at different vertical positions due to the difference in the canopy density.

In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA) methods are preferred.

(ii) The post-orogenic time required for achieving advanced stages of relief reduction is variable, ranging from 3 to 70 Ma.