Sentence examples for quantitatively linking from inspiring English sources

Micromechanical models allow quantitatively linking fracture properties, microstructure features at multiple lengths scales, and manufacturing conditions.

An end to end simulation model quantitatively linking the user requirements with the instrument design parameters has been developed and applied to the European MSG SEVIRI imagery, allowing an evaluation of the minimum and the maximum detectable active fire area and temperature and giving results in agreement with literature data.

However, the permutation analysis allows the resolution of the snapshot to be understood intuitively by quantitatively linking the number of selected targets to a FDR.

While it is encouraging that large numbers of papers have attempted to look at linking animal and human data, more emphases needs to be in quantitatively linking animal and human data.

In addition, differential equations form the backbone of translational pharmacokinetic-pharmacodynamic (PK-PD) models [ 22, 23], the class of models that describe how drug dose influences response through quantitatively linking the drug dose to exposure (pharmacokinetic [ 24, 25]) and the exposure to response (pharmacodynamic [ 26]) in a living system.

Exposure models that include human concentrations can more fully a) maximize the value of biomonitoring data by quantitatively linking emission rates and exposure pathways to internal doses (Woodruff et al. 2011), and b) complete model evaluations through comparisons with biomonitoring data (Cowan-Ellsberry et al. 2009), that is, hypothesis testing.

Here, we quantitatively link for the first time the function and structure of this remarkable physiological phenotype.

The approach utilizes our basic understanding of the physical processes involved in volcanic arc formation to find intensive variables, such as specific heat and density, which can be used to quantitatively link and compare large-scale datasets from different disciplines.

The deviations are quantitatively linked to the dominance of the surface effect through the use of the Surface Cauchy Born model.

Furthermore, OLRM quantitatively links and evaluates the effects and contribution of each of these factors to the occurrence of different failure types.

We perform global sensitivity analysis to quantitatively link uncertainty in the computed performance metrics to the uncertainties in the underlying reservoir parameters.