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For meaningful model design we require physical characteristics of real materials undergoing granulation.
Coherent with our model design, we distinguished two types of control variables.
Next, in the section on "data model design", we discuss data model design issues.
In section "Paratuberculosis model design" we describe the model of paratuberculosis infection dynamics that has been designed for this study, and present its behaviour for a fixed set of parameters.
We designed ParaSAM with an easy and manageable client-server application model that can be easily deployed in most research laboratories, using the same model design we implemented in ParaKMeans (Kraj et al., 2008).
Before creating a model design, we identified the model factors (Transformed. Time) and levels to edgeR as [N.0 h, N.1 h, N.12 h, Y.0 h, Y.1 h, Y.12 h] where N = no TGFβ transformation, Y = transformation with TGFβ and hr = time post TNFα treatment in hours.
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We also note that, although the model design progressed iteratively, we carefully investigated the necessity of including each individual mechanism a posteriori.
From the original Matlab model of the design, we extracted the hardware model and in the meantime simultaneously generated an equivalent Matlab model for the hardware model of the design.
We describe the synergistic readjustments, drastic reconfiguration and mismatch between business model and technology design we observed.
The insights from the model guide our empirical design: we use the model's closed-form solutions to calculate the implied importance (benefit-cost ratio) of voting from observable variables, such as total voting participation.
Based upon detailed memory device modeling and ASIC design, we carried out simulations to demonstrate the potential effectiveness of this design method and evaluate the involved trade-offs.
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