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To assess the robustness of the modeling technique, we performed a preliminary sensitivity analysis on the rabbit models by generating perturbed models and comparing activation sequences.
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MD simulations were performed with Gromacs and served as a tool to generate perturbed states of proteins.
TPV is given as, T P V = ∑ n = 1 L l o g k n k n 0, K n = randomly generated perturbed model parameter value and k n 0 is the corresponding model parameter value in the unperturbed system.
Because we considered it likely that recalled event times were accurate to within ± 1 year, we generated perturbed data sets by adding −1, 0, or +1 with equal probability to each observed event time.
For generating a perturbed genome, VarSim samples small variants and SVs from existing databases (e.g. dbSNP, DGV) and/or a provided VCF file.
The data have associated errors which are used to generate a perturbed data ensemble.
Now, we discuss the convergence analysis of iterative sequences generated by perturbed projection iterative Algorithms 6.1-6.5 6.1-6.5
Pattern recognition dependency upon variation of activation energy and desorption heat was evaluated using data generated with perturbed EA.
In this section, we establish the strong convergence of the sequences generated by perturbed projection iterative Algorithms 4.1 and 4.2, under some suitable conditions.
Proof Suppose that the sequence { x n } is the sequence generated by perturbed Ishikawa-hybrid quasi-proximal point Algorithm 5.1, and that ( x ∗, b 1 ∗, b 2 ∗, b 3 ∗, b 4 ∗ ) ∈ X 5 is a solution of problem (3.1).
Monte Carlo analysis is also performed using CODEV to generate 1000 perturbed optical systems.
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