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Predominantly, two modeling frameworks are implemented to simulate granulation processes: population balance modeling (PBM) and discrete element methods (DEM).
The cell population balance, ensemble and continuum modeling frameworks are widely used for the mathematical description of microbial populations.
Several modeling frameworks are applicable to the National Children's Study.
For biochemical processes, lifeline modeling frameworks are highlighted as powerful means to reconcile the competing needs for high speed and high quality in biochemical engineering, both in the design and implementation stages, thereby enabling significant growth of the bio-based economy.
Modeling frameworks are particularly useful, including modeling of the biophysical system, the dynamics of human use, and the management process [167].
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The two modeling frameworks were tested over a homogeneous landscape with randomly located sources of infestation.
Each of these modeling frameworks is available for review (U.S. EPA 2009) and has been extensively evaluated (Ambrose et al. 2005; Brown et al. 2007; Knightes 2008).
As discussed in the Background section, collaboration amongst laboratory scientists working in different areas of complex biological processes and the accessibility to modeling frameworks is key to new discoveries using the systems approach.
Uncertainties within the modeling framework are captured using fuzzy numbers.
In [ 31], risk networks in an agent-based modeling framework are used to simulate the spread of HIV.
The two major ingredients of the modeling framework are the concentration fields of the guidance molecules and the finite-dimensional state vectors representing the growth cones and target neurons.
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