The half-life of an effective mechanistic model to control human action is quite short.
In this study, by choosing a mouse model to control sample-to-sample variation, we successfully concluded that the diversity of MSCs is influenced by tissue origin.
Once again, we employed a statistical model to control for demographic variables like education and race as well as geographic and political factors.
We used a regression model to control for the effects of density dependence shifts (i.e., based on starting abundances of stems) and temporal autocorrelation.
We thus developed a model to control information flows among cooperating object-oriented applications.
In order to estimate the wage difference attributable to union coverage, we use a regression model to control for industry and worker demographic characteristics (see Appendix 2 for a detailed description of data sources and methods).
These data are directly linked to a physicochemical process model to control the extraction process in real time and to perform predictive simulations while processing.
Economic characteristics are also considered in the model to control for their effects on growth.
In [16], a decentralized algorithm based on this model to control intersystem fairness between two wireless systems is proposed.
Arianezhad et al. (2013) presented a new two-echelon model to control the inventory of perishable goods.
Zheng et al. [21, 22] used the aforementioned model to control the engine mount system with concurrent consideration of random road input and engine excitation.
