This article expands on the article by Estaghvirou et al. (2013) by contaminating the phenotypic datasets simulated for that article with outliers to assess their influence on the methods for accuracy estimation in genomic prediction.
The impact of train positioning error on link BER performance is also simulated in this article.
Here, we add Table one (Table 1 here) to illustrate the sizes of rectangular graphene films simulated in the article [1].
The CNTs geometries simulated in this article were generated using the program Tubegen [39], while water molecules were introduced using the SPC/E model implemented by the genbox package available in GROMACS [38].
To confirm the validity of the method proposed in this article, we simulated a set of one-dimensional signals with relatively high carrier frequency, as shown in Fig. 1a.
This work is an extended version of conference ACIIDS 2017 paper: [15] in that article, we simulated a quantum circuit based on kNN algorithm which role was to classify the Hermitian and non-Hermitian matrices.
"To design the back-loaded horn, for example," he continued — he just had to use the phrase I'd denigrated in the already written draft of this article — "I simulated an ideal horn given the driver's parameters and enclosure size.
The models presented in this article were simulated numerically in Python (using the freely available packages weave, numpy, scipy, and matplotlib).
The Python scripts used to generate and analyze datasets for this article, the simulated data sets, and the raw parameter estimates and their associated uncertainties are available online in the LabArchives respository http://dx.doi.org/10.6070/H4GQ6VQ2.org/10.6070/H4GQ6VQ2
The structures discussed in this article simulate a part of the cortex of the mammalian visual system.
However, this article simulates a project in which interventions are mainly safety-based.
