Sentence examples for representation of variable from inspiring English sources

Based on user informed data, the developed application uses a dynamic representation of variable size as a chromosome and the cluster executes the evolutionary algorithm using the Island model paradigm.

An important gap in existing capabilities identified in this study is the representation of fluvial systems over time scales relevant for managing the immediate impacts of global climatic change; 101 – 102 years, the representation of variable forcing (e.g. storms), and the representation of biological processes.

The proposed method is based on the shifted Legendre collocation procedure and a matrix form representation of variable-order Caputo fractional derivative.

The suggested method is based on the shifted Legendre collocation procedure and a matrix form representation of variable-order Caputo fractional derivative.

The symbolic representation of variables is presented in Table 1.

Subsequently, the clr-transformed data set was explored by biplots, a graphical representation of variables and cases projected on to principal component planes.

The methodology considers the construction of a superstructure that includes alternatives to be analyzed, the representation of variables by distribution functions, a Monte Carlo simulation, the incorporation of indicators and a multi-objective analysis.

The present study focuses on the vulnerability of residential building stock, which is determined at building scale according to the RISK-UE LM1 methodology (Milutinovic and Trendafiloski 2003), and relies on the 3D representation of variables, results and seismic scenarios using a specially generated 3DCM.

Tested in 1989 and 1990, The Vic Ooyama Barotropic (VICBAR) model used a cubic-B spline representation of variables for the objective analysis of observations and solutions to the shallow-water prediction equations on nested domains, with the boundary conditions defined as the global forecast model.

The procedures are related to the following aspects: 1) representation of variables; 2) linear combination of representations of variables; 3) data integration and representation of variables; and 4) revealing the interpretability of input variables.

We apply kernel PCA and representation of variables to the genomic data and FA data.