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We thus consider four methods to perform this simulation.
A rectangular grid of 3,648 × 1,264 = 4,611,072 cells with a resolution of 4 m × 7.5 m has been designed in order to perform this simulation.
A SED-ML script to perform this simulation is included within the packaged SourceForge code.
To perform this simulation, we used our regression model and held all covariates at their mean level.
The script used to perform this simulation and lift over annotations to the modified sequence is available at https://sourceforge.net/projects/kitchen-drawer/files/sim_SV.py/download.
The unique feature that enables QSSPN to perform this simulation is Monte Carlo sampling of the feasible dynamic sequences of molecular transitions in the qualitative quasi-steady state model.
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To perform this large-scale simulation effectively, we employed a parallel 3D simulation code based on a domain-partitioning procedure that utilizes a large number of processors with a message-passing interface (based on the work of Furumura and Chen 2004).
To perform this required adaption simulation-based optimization is utilized.
We conclude remarking that unlike what shown in most of the literature [3], [4], [5], [8], no supercomputing techniques have to be employed to perform this kind of simulations on a national scale (57,000,000 of individuals), making them feasible for a standard workstation; our implementation of the five model takes less than 3Gb RAM and a single simulation takes just a few minutes.
To perform this study, a large eddy simulation (LES) model is used, which has been improved by the introduction of a drag force which is representative of a typical Amazon forest canopy.
The simulation software written to perform the simulations in this article is available for download and installation from http://www.uwplatt.edu/biology/ryan-haasl/ (last accessed June 29 , 2014.
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