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The calculated results show that those models can reveal complex nonlinear behaviors.
GWR models can reveal detailed site information on the different roles of related factors in different parts of the study area.
The comparison made between the established models and experimental results shows that both the models can reveal the real system performance sufficiently.
Discontinuous cambium models can reveal characteristics of the individual stages of TE differentiation in a unique manner, but they are not directly comparable to a continuous cambium (Pesquet et al. [2011]).
Moreover, two-dimensional discrete models can reveal a plethora of complicated asymptotic behaviors, from convergence to a fixed point or a periodic cycle until a quasi-periodic orbit along a closed invariant curve or even an erratic motion inside a bounded chaotic attractor (see e.g. [1 5]).
According to theoretical studies [29], MVAR models can reveal an independent causal index between time-series X1 and X2 even if the other variables, X3.XN, mediate the causal flow between X1 and X2.
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By the analysis of the example this model can reveal chaotic stochastic behaviour.
Genome-scale metabolic modeling can reveal limitations in cyanobacteria metabolism and guide genetic engineering strategies to increase chemical production.
Model comparisons indicate that the proposed model can reveal the essential relationships of wind power forecasting uncertainty, and describe the various dependences more accurately.
Specifically, the Indian buffet game model can reveal how players learn the uncertainty through social learning and make optimal decisions to maximize their own expected utilities by considering negative network externality [7].
As the proposed models relate the properties of fluids to the structural parameters of porous media, the various expressions of the developed model can reveal the phenomena of all types of fluid flow in a variety of porous media.
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