The effect of the thermophoretic force on diffusion of dust particles in cryogenic plasmaAt cryogenic temperatures of atoms, the thermophoretic force (atomic drag force) is stronger than at room temperature. The temperature difference between the surface of the dust particles and the surrounding gas was previously evaluated. This difference was the reason of the manifestation of the so-called thermophoretic force between dust particles. This paper presents the results of a study obtained using molecular dynamics modeling of the effect of thermophoretic force on the mean-square displacement of charged dust particles in a two-dimensional layer. The mean-square displacement (MSD) characterizes the diffusion of particles. By changing parameters that describe thermophoretic force, there were identified cases when this force leads to significant changes in the properties of the cryogenic complex plasma. Also the data on computer simulation are provided. MSD was calculated for different values of the coupling parameter. The ratios MSD curves with and without thermophoretic force are given. It was found that the thermophoretic force can drastically influence the diffusion of dust particles if the characteristic interaction radius due tothermophoretic force exceeds the average distance between dust particles. The aforementioned effect can take place at low density of neutral and temperatures relevant to cryogenic conditions. The force with increasing value of cutoff raduis causes deviation of the curves from data obtained on the basis neglecting thermophoretic force. Deviation becomes more visible, with increasing coupling parameter, due to the force.

In the course of recent years, progresses in sensor innovation has lead to increments in the interest for automated strategies for investigating seismological signals. Fundamental to the comprehension of the components creating seismic signals is the information on the phases of seismic waves. Having the option to indicate the kind of wave prompts better performing seismic forecasting frameworks. In this article, we propose another strategy for the characterization of seismic waves quantification from a three-channel seismograms. The seismograms are isolated into covering time windows, where each time-window is mapped to a lot of multi-scale three-dimensional unitary vectors that portray the direction of the seismic wave present in the window at a few physical scales. The issue of arranging seismic waves gets one of ordering focuses on a few two-dimensional unit circles. We take care of this issue by utilizing kernel based machine learning that are remarkably adjusted to the geometry of the circle. The grouping of the seismic wave depends on our capacity to gain proficiency with the limits between sets of focuses on the circles related with the various kinds of seismic waves. At each signal scale, we characterize a thought of vulnerability connected to the order that considers the geometry of the dissemination of tests on the circle. At long last, we join the grouping results acquired at each scale into a unique label.