Sentence examples for modeling approaches from from inspiring English sources

The absorption and scattering coefficients and the corresponding fits (using all the three modeling approaches) from a representative tissue phantom is shown in Fig. 1.

Common engineering approaches and modeling approaches from software engineering are brought together.

In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters.

The hydrodynamic modeling approach from [2] that is considered in this paper, takes into account both the linear and the nonlinear drag forces (resistive fluid forces), the added mass effect (reactive fluid forces), the fluid moments and current effects.

This study was conducted to employ a process modeling approach from a life cycle perspective to construct and evaluate six anaerobic/anoxic/oxic wastewater treatment systems that include a water line, sludge line and bioenergy recovery system and was designed to meet different treatment standards in China.

Different modeling approaches for pushover analyses, from lower to higher levels of accuracy, have been applied in order to investigate the applicability of the calculation methods adopted to the study of buildings having non-rectangular structural walls.

The predicted values from both modeling approaches for all sub-districts of Thailand were averaged and mapped.

Figure 1 Two approaches for modeling habitat suitability: (a) correlative modeling, and (b) mechanistic modeling approaches (adopted from Kearney[2006]).

This paper reviews the numerous modeling approaches ranging from simple first-order models to more complex dynamic models of treatment behaviour in CWs.

Research aimed at understanding whether the different markers identified converge at the level of a common underlying mechanism, or if they reflect factors acting independently, will benefit greatly from more sophisticated statistical modeling approaches and from innovative use of brain imaging.

Here we show how modeling approaches borrowed from the field of machine learning can accurately recapitulate known higher order chromatin structure, using only data representing features (histone modifications, DNA binding proteins, etc).