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In the Cloud Computing model, the computing resources are provided as a service in an on-demand and dynamic fashion.
In Reference [115], this research proposes a performance approximation approach FiM to model the computing performance of iterative, multi-stage applications running on a master-compute framework.
Indeed, in order to add a species to a model, the computing capabilities must be multiplied by two.
With a set of whole-genome markers (say 50K SNP markers or higher density) in a model, the computing task is highly challenging, particularly with sophisticated Bayesian models via MCMC implementation [ 1, 11- 13].
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In order to confirm the appropriate turbulence model, the computed results are validated against indigenous experimental data.
For the Laub and Loomis model, the computed maximum allowed variation is tighter than what was obtained with previous multiparametric analysis methods.
In this paper we present the main characteristics of the model and the computing procedure.
More covariates in the prediction models increase the computing times.
The procedure is as follows: compute the first coefficients of the regression model, compute the residuals and weights, and then compute the new coefficients by the equation.
For each time step, the model computes the head in the river and in the groundwater in a reach.
Before starting the Latent period, the model computed the duration for each of the subsequent four stages of illness.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com