Magnetic Field Line Random Walk and Solar Energetic Particle Path Lengths

<p>In 2020 May-June, six solar energetic ion events were observed by the Parker Solar Probe/ISoIS instrument suite at ~0.35 AU from the Sun.  From standard velocity-dispersion analysis, the apparent ion path length is ~0.625 AU at the onset of each event. We develop a formalism for estimating the path length of random-walking magnetic field lines, to explain why the apparent ion path length at event onset greatly exceeds the radial distance from the Sun for these events. We developed analytical estimates of the average increase in path length of random-walking magnetic field lines, relative to the unperturbed mean field. Both a simple estimate and a rigorous theoretical formulation are obtained for field-lines' path length increase as a function of path length along the large-scale field. Monte Carlo simulations of field line and particle trajectories in a model of solar wind turbulence are used to validate the formalism and study the path lengths of particle guiding-center and full-orbital trajectories. From these simulated trajectories, we find that particle guiding centers can have path lengths somewhat shorter than the average field line path length, while particle orbits can have substantially larger path lengths due to their gyromotion with a nonzero effective pitch angle. The formalism is also implemented in a global solar wind model, and results are compared with ion path lengths inferred from ISoIS observations. The long apparent pathlength during these solar energetic ion events can be explained by 1) a magnetic field line path length increase due to the field line random walk, and 2) particle transport about the guiding center with nonzero effective pitch angle due to pitch angle scattering. This research partially supported by the PSP /ISOIS project.</p>

Fractal signature of coronaviruses related to severe acute respiratory syndrome

Coronavirus is the agent which causes the severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), emergent infections such as the present Pandemic of COVID-19. Understanding its genome pattern is important for developing new and faster ways of testing for identifying the genome of the virus, and also for better understanding of its origin and evolution. The aim of this work was to investigate the genome of SARS-CoV, MERS-CoV and SARS-CoV-2 by the paradigm of chaos theory and fractal geometry. For that, it was calculated the alpha coefficient by detrended fluctuation analysis (DFA) for the sequences of these genomes converted to binary numbers in order to determine if it is a chaotic or a random series of data. Also, it was applied the random walking for obtaining a fractal map of the whole genome, and calculated the fractal dimension (FD) by box-counting of this map by two different softwares. With this, it was found that the alpha coefficient of the first SARS viruses was > 0.5, indicating that the series is chaotic or fractal, and has a persistent long-range memory or self-similarity along its sequence. This is not the case for MERS virus, which showed to have a completely random sequence (alpha < 0.5). For the fractal dimension, SARS viruses presented a FD around 1.5, and for MERS the fractal dimension decreases (FD < 1.5). The images generated by random walking of the entire RNA genome are by itself a fractal signature of the virus, which may be applied for studying its origin and for faster diagnostic of COVID-19.

Searching for the Random Walking microorganism cells

In this paper, we present a complex cooperative search technique for finding the Random Walking microorganism cells on one of [Formula: see text]-intersect real lines at the origin. We have 2[Formula: see text] unit speed searchers starting together from the origin. Furthermore, proving the existence of a finite search plan, we are discussing the existence of optimality for this search plan which minimizes the expected value of the first meeting time between one of the searchers and the microorganism cells.

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

Social networks have become an indispensable part of modern life. Signed networks, a class of social network with positive and negative edges, are becoming increasingly important. Many social networks have adopted the use of signed networks to model like (trust) or dislike (distrust) relationships. Consequently, how to rank nodes from positive and negative views has become an open issue of social network data mining. Traditional ranking algorithms usually separate the signed network into positive and negative graphs so as to rank positive and negative scores separately. However, much global information of signed network gets lost during the use of such methods, e.g., the influence of a friend’s enemy. In this paper, we propose a novel ranking algorithm that computes a positive score and a negative score for each node in a signed network. We introduce a random walking model for signed network which considers the walker has a negative or positive emotion. The steady state probability of the walker visiting a node with negative or positive emotion represents the positive score or negative score. In order to evaluate our algorithm, we use it to solve sign prediction problem, and the result shows that our algorithm has a higher prediction accuracy compared with some well-known ranking algorithms.