Electric field generation by the electron beam filamentation instability: Filament size effects

The filamentation instability (FI) of counter-propagating beams of electrons is modelled with a particle-in-cell simulation in one spatial dimension and with a high statistical plasma representation. The simulation direction is orthogonal to the beam velocity vector. Both electron beams have initially equal densities, temperatures and moduli of their nonrelativistic mean velocities. The FI is electromagnetic in this case. A previous study of a small filament demonstrated, that the magnetic pressure gradient force (MPGF) results in a nonlinearly driven electrostatic field. The probably small contribution of the thermal pressure gradient to the force balance implied, that the electrostatic field performed undamped oscillations around a background electric field. Here we consider larger filaments, which reach a stronger electrostatic potential when they saturate. The electron heating is enhanced and electrostatic electron phase space holes form. The competition of several smaller filaments, which grow simultaneously with the large filament, also perturbs the balance between the electrostatic and magnetic fields. The oscillations are damped but the final electric field amplitude is still determined by the MPGF.Comment: 14 pages, 10 plots, accepted for publication in Physica Script

On the use of cluster analysis for interpretation of soil pollution monitoring data obtained near mining enterprise

Assessment of soil contamination in areas affected by mining facilities is a necessary part of the research during the local environmental monitoring. Different methods of statistical analysis can be used to process and analyze monitoring data. This paper presents the cluster analysis outcomes of the chemical composition of soil samples collected in the area of a copper mine during the annual monitoring. As a result of cluster analysis, all soil sampling points were divided into three clusters, which seem to be characterized by different mechanisms of pollution (background points without pollution, points in the sanitary protection zone of the quarry with aerogenic dust pollution, intermediate points with mixed pollution type). Based on the study outcomes, one can conclude that the application of cluster analysis in the soil monitoring data processing makes it possible to assess the boundaries of the influence zone of the dust pollution source with a little number of soil sampling points. © 2018 Author(s)

On the application of cluster analysis for vegetation pollution assessment in the area of mining enterprise

The assessment of vegetation contamination in the influence area of mining enterprises is an important part of the research during the environment monitoring. There are different statistical methods that can be used for the analysis of data obtained in environmental monitoring. The article presents the results of cluster analysis of the chemical composition of agricultural vegetation samples collected in the area of copper-pyrite ore deposit location. During the analysis, all samples were divided into three clusters. One can suggest that this separation may be due to different mechanisms of pollutants entry into the particular sampling sites, as well as to the location of these sampling sites relatively to the enterprise industrial area. According to the results of the study, it can be concluded that cluster analysis is an effective tool for distinguishing the zones being characterized by different pollution mechanisms of grassy vegetation, when there are a small number of measurements and relatively low levels of the samples pollution. © 2019 Author(s)

Classification of online toxic comments using the logistic regression and neural networks models

The paper addresses the questions of abusive content identification in the Internet. It is presented the solving of the task of toxic online comments classification, which was issued on the site of machine learning Kaggle (www.Kaggle.com) in March of 2018. Based on the analysis of initial data, four models for solving the task are proposed: logistic regression model and three neural networks models - convolutional neural network (Conv), long shortterm memory (LSTM), and Conv + LSTM. All models are realized as a program in Python 3, which has simple structure and can be adapted to solve other tasks. The results of the classification problem solving with help of proposed models are presented. It is concluded that all models provide successful solving of the task, but the combined model Conv + LSTM is the most effective, so as it provides the best accuracy. © 2018 Author(s)

Ab Initio Exchange Interactions and Magnetic Properties of Intermetallic Compound Gd(2)Fe(17-x)Ga(x)

Intermetallic compounds R2Fe17 are perspective for applications as permanent magnets. Technologically these systems must have Curie temperature Tc much higher than room temperature and preferably have easy axis anisotropy. At the moment highest Tc among stoichiometric R2Fe17 materials is 476 K, which is not high enough. There are two possibilities to increase Tc: substitution of Fe ions with non-magnetic elements or introduction of light elements into interstitial positions. In this work we have focused our attention on substitution scenario of Curie temperature rising observed experimentally in Gd(2)Fe(17-x)Ga(x) (x=0,3,6) compounds. In the framework of the LSDA approach electronic structure and magnetic properties of the compounds were calculated. Ab initio exchange interaction parameters within the Fe sublattice for all nearest Fe ions were obtained. Employing the theoretical values of exchange parameters Curie temperatures Tc of Gd(2)Fe(17-x)Ga(x) within mean-field theory were estimated. Obtained values of Tc agree well with experiment. Also LSDA computed values of total magnetic moment coincide with experimental ones.Comment: 4 pages, 4 figures, 4 tables, Proceedings for EASTMAG-2010, June 28 - July 2 2010, Ekaterinburg, Russi