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Reduced mechanisms were applied in computational fluid dynamics (CFD) simulations to correct the temperature profiles which were adopted in kinetic modelling.
To determine whether other regions also showed significant group differences we also ran simulations to correct for multiple comparisons on the entire cortical surface.
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In the CN group, cluster-wise statistical significance of vertex-level regression coefficients was assessed by Monte Carlo simulation to correct for capitalization on multiple comparisons.
The objective of such a simulation is to correct the relative permeability curves used in our Lattice Boltzmann model previously in the last three sections by providing model properties very similar to those of the real experimental conditions.
The functional uncertainty quantification approach can be used to estimate the uncertainties associated with constitutive models used in the simulation and to correct predictions if a more accurate representation becomes available.
Monte Carlo simulations were run to correct for multiple testing to achieve an overall corrected mapwise p = 0.05.
Therefore, a correction factor (CF) and corrected simulation (CS) were recalculated to correct the simulation results.
Monte Carlo simulation was implemented to correct for multiple comparisons (threshold of 53 contiguous voxels with a z-value ≥ 2.25).
Contrasting with other traditional surrogate methods where the whole physical model is replaced by a soft computing estimator acting as a black box, in the proposed approach the ANNs are trained to generate an estimation of the error produced by the low cost simulation in order to correct its results.
Again, a Monte Carlo simulation was conducted to correct for multiple comparisons and an alpha level of p<0.02 was achieved with a per voxel threshold of p<0.02 and a cluster size of 12 contiguous voxels (768 mm).
Per future improvements for the model, Monte Carlo simulations could be employed to correct for the assumption that photon pathlengths through the near-surface tissues are the same for all source-detector separations; we expect these corrections to be relatively small, but their implementation should certainly improve upon the model employed in the present paper.
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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