Hierarchical linear modeling was used to model growth in reading comprehension and assess the contribution of three dimensions of reading motivation - self-efficacy, intrinsic, and extrinsic motivation - to intercepts and slopes of reading comprehension.
While modelling growth in availability is necessary for meaningful decision support in developing complex systems such as offshore wind farms, we also discuss the relative value of explicitly articulating epistemic uncertainties.
The proposed mixture models could be useful to model growth in disease-infested environments.
We have used some mathematical tools to model growth in the neural tube.
Modelling growth in IPN survivors as a mixture trait improved the predictive ability of the model compared with a classical linear model.
Hence, in the immunodeficient fly model, growth in complex medium was a good indicator, but not sufficient to fully predict disease and death in flies.
This was due to the maximization of biomass growth rate and also by not including an explicit cellular death rate, assumptions that are reasonable for modeling growth in exponential and early stationary stages (see section "Development of the Mathematical Framework").
In spite of the limitations related to detection thresholds, correlation with the parameters derived from viable count growth curves, and inability to model growth in turbid liquid foods and in solid food matrixes, numerous techniques and mathematical growth models have been used in recent years for estimation of growth rates and lag times from absorbance data [ 5, 7, 8, 12– 12].
The complete dataset allows investigation of the role of HGF/SF as a model growth factor in collective cell migration.
Although economists have long realised that productivity growth is a prime determinant of general economic growth, models of growth in the past made no attempt to explain productivity growth, but assumed that it was "exogenous" (caused by outside factors).
