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In the modeling paradigm described in [7], a biological or biochemical system is described by n time varying quantities s1 t),..,sn(t), which represent the state of the system at the point in time t.
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Superficial cells were imaged using the paradigm described earlier.
However, qualitative modeling is more appropriate modeling paradigm due to its ability to infer unknown parameters ([56] of rehan sepsis).
In this work, a new modeling paradigm for evaluating the cardioembolic risk of MHVs is described.
Problems characterized by qualitative uncertainty described by expert judgments can be addressed by the fuzzy logic modeling paradigm, structured within a so-called fuzzy expert system (FES) to handle and propagate the qualitative, linguistic assessments by the experts.
First, agent-based modeling paradigm is discussed giving details of why it is well suited for modeling and studying complex consumer networks.
We present an experimentally-driven modeling paradigm towards this goal.
For example, agent-based thinking challenges could also be considered as modeling and simulation challenges, but specific to the agent-based modeling paradigm, we have employed in CTSiM.
Following the same latent variable modeling paradigm, we proposed the i-supervector as an uncompressed form of i-vector representation.
However, application of the traditional modeling paradigm, based on systems of specific equations, faces some principal difficulties in these systems.
Qualitative modeling paradigm offers process systems engineering a potential for developing effective tools for handling issues related to Process Safety.
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