Sentence examples for proximity of the data from inspiring English sources

Figures 8, 9 and 10 compare the relative magnitude of the values for the correlation coefficient (based on the proximity of the data points to the diagonal line in these figures) obtained for BWC-LCCDC with each of the other three combinations of centrality metrics: BWC-DegC, BWC-ClC, and BWC-EVC under each of the three correlation measures.

This could be deduced by observing the relative proximity of the data points to the diagonal line in Fig. 11: the data points corresponding to the Spearman's and Pearson's correlation measures are relatively more closer to the diagonal line when compared to the data points corresponding to the Kendall's and Pearson's correlation measures.

The ECV indicates the proximity of the data to unidimensionality (an ECV value of 1).

The model estimates were strongly correlated with simulated outputs, with the quality of the fit given by the Pearson correlation coefficient of r = 0.991 and displayed by the proximity of the data points to the plotted line of unity.

In cases where level of proximity of the data sources or the reporting period to the data is similar, the mandate and expertise of the reporting body on the specific indicators was used.

Improvements in the quality of the thermal 3D data were gained through the application of a distance filter based on the proximity of these data to the RGB 3D point data.

That is, if the interaction of an ion with the semiconductor substrate occurs in close proximity to the data node of a latch circuit, the resultant excess ionization charge collected at the data node may cause the latch to erroneously change state, a single-event upset (SEU).

The network model is constructed on the basis of proximity of the computed data on its total porosity, pore-size function, and simulated mercury intrusion curve to the respective experimental data.

Combined with a look-up of simulations in the proximity of the measured data and iterative generation of new experimental designs, this provides an accurate and effective approach for constraining model parameters.

The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data.

Especially within the close proximity of the clusterhead, transmitting data directly to the clusterhead (i.e., bypassing the correlators) should be preferable over sending data through the correlator nodes.