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We present PICsar – a new Particle in Cell code geared towards efficiently simulating the magnetosphere of the aligned rotator.
Therefore, it was decided to implement an integrated processing system to generate cross sections libraries for the ECCO cell code, as well as covariance data.
In each case the predicted water depths compare favourably to analytical solutions or to simulation results from the diffusive storage cell code of Hunter et al. (2005).
In this paper both the underlying algorithms as well as a comparison with a fully three dimensional particle in cell code are reported.
The 2D object-oriented particle in cell code OOPIC Pro (Tech-X Corp., Boulder, CO, USA) has been used to study the electrical as well as the kinetic behaviour of the DBDs[18].
The previously available two group library used with the QUABOX/CUBBOX-HYCA code, adapted to model the physical and nuclear processes in a RBMK-1500 reacore core, was created using the freely available WIMSD reactor physics cell code and its associated nuclear data library.
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(A) Log10TE vs. log10mRNA RPKM of host cell coding genes and EV71 polypeptide coding gene were plotted.
Particle in Cell codes are the state-of-the-art tools to study the beam-plasma interaction mechanism, but due to their requirements in terms of number of cores and computational time makes them unsuitable for quick parametric scans.
An alternative possibility is that independent single cell coding is sufficient to account for population activity.
CD4 count was modelled as a time varying covariate using reference cell coding for 3 categories (<50, 50 199, ≥ 200).
In contrast, prediction performance improved for only 32% (±11%) of the experimentally observed place cells when peer information was included after accounting for single cell coding properties.
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