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The biodegradation process of phenol in a fluidized bed bioreactor (FBR) has been simulated using genetic algorithm trained feedforward neural network.
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In this study the performance of a granule-based H2-producing upflow anaerobic sludge blanket (UASB) reactor was simulated using neural network and genetic algorithm.
The phenotype was simulated using three different models of genetic effects: additive, recessive, and overdominant (a special case of epistasis effect) models.
Genetic data was simulated using each of these pedigrees under several models.
To understand quantitative trait and neutral marker divergence, hypothetical population genetic scenarios were simulated using parameters resembling the wild salmon population/hatchery strain differentiation.
In this way, the variance-covariance matrix in the multivariate normal distribution determines the correlation between variants, which take values 0, 1, 2. Data for three genetic variants were simulated using the following model: 5where 1. is an indicator function.
In such a case, the data were simulated using an animal model with pedigree but the genetic evaluation was done using a sire model.
Genetic perturbations of metabolic networks can be simulated using the function geneDeletion.
The genetic signatures from the six alternative demographic hypotheses were simulated using coalescent analysis implemented in the software ByeSSC [ 29].
Their competition can be simulated using game theory models.
Spatial coordinates were simulated using Gaussian distributions.
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