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In this paper we propose an optimized adaptive 'minimal' modeling approach for predicting glycemia of critically ill patients and a corresponding Model based Predictive Control MPCC) setting for controlling glycemia.

The type of analysis (within trial or model based), the setting and the source of effectiveness data were extracted for cost-effectiveness studies.

Using soft sets as the granulation structures, Feng et al. [8] defined soft approximation spaces, soft rough approximations and soft rough sets, which are generalizations of Pawlak's rough set model based on soft sets.

In this work, a fully physically-based model, based on a set of conservation partial differential equations, is introduced.

Authors in [5] presented a continuous Markov chain model based on transmission set approach to check if the given input rate can be supported by a network.

The frequency droop block in Fig. 5, decides the ΔP c for the aggregated WPP model, based on the droop setting.

To compare the performance of IGGAUCS and G L1Cox, we build the model based on training data set and evaluate the model with the test data set.

At first, a 3D-to-2D general shell model based on a set of unit-cell problems is derived.

In this paper, we propose a multiple criteria decision-making (MCDM) model based on fuzzy-set theory.

For face modeling, we first generate a global model through deforming a generic model based on a set of facial landmarks.

For the isothermal hydrogenation of cinnamaldehyde over Pt/C/monolithic catalysts three kinetic models have been evaluated, each model based on different sets of assumptions.

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