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The good agreement between the model and the field data shows that the complete denitrification process can be efficiently modeled as a sequence of first order reactions.
Jointly, these temporal effects can be efficiently modeled as a multistage process in which benzene exposure affects the penultimate stage in disease induction.
In the case of DNA sequences, the finite set of nucleotide symbols {A, C, G, T} can be efficiently modeled as a corresponding set of binary values {00, 01, 10, 11} (9).
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It has been demonstrated in [ 23, 26, 27] that the enzymatic activity of RISC can be efficiently modeled in-vitro as a classic Michaelis-Menten reaction, where the target mRNA is the substrate, the siRNA-loaded RISC is the active enzyme (at a constant concentration), and the product is the degraded mRNA.
The proposed ground motion model can be efficiently applied in simulations as well as random vibration vibration and reliability studies of inelastic structures.
Second, our model can be efficiently solved by a MIP model.
Complex biological processes such as cell proliferation or cellular stress can be efficiently described by causal network models [ 16, 17].
Subsequently the models can be efficiently applied to the analysis of new data, using fast dynamic programming algorithms such as the Viterbi algorithm.
The optimization model can be efficiently solved to optimality using state-of-the-art solvers.
A second-order model can be efficiently constructed with CCD.
This modeling can be efficiently performed using Markov chains.
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