Milovan Suvakov

Kinetic phenomena in charged particle transport in gases, swarm parameters and cross section data*

In this review we discuss the current status of the physics of charged particle swarms, mainly electrons. The whole field is analysed mainly through its relationship to plasma modelling and illustrated by some recent examples developed mainly by our group. The measurements of the swarm coefficients and the availability of the data are briefly discussed. More time is devoted to the development of complete electron?molecule cross section sets along with recent examples such as NO, CF4 and HBr. We extend the discussion to the availability of ion and fast neutral data and how swarm experiments may serve to provide new data. As a point where new insight into the kinetics of charge particle transport is provided, the role of kinetic phenomena is discussed and recent examples are listed. We focus here on giving two examples on how non-conservative processes make dramatic effects in transport, the negative absolute mobility and the negative differential conductivity for positrons in argon. Finally we discuss the applicability of swarm data in plasma modelling and the relationship to other fields where swarm experiments and analysis make significant contributions.

Three Classes of Newtonian Three-Body Planar Periodic Orbits

We present the results of a numerical search for periodic orbits of three equal masses moving in a plane under the influence of Newtonian gravity, with zero angular momentum. A topological method is used to classify periodic three-body orbits into families, which fall into four classes, with all three previously known families belonging to one class. The classes are defined by the orbits geometric and algebraic symmetries. In each class we present a few orbits initial conditions, 15 in all; 13 of these correspond to distinct orbits.

Modeling collective charge transport in nanoparticle assemblies

Mapping the assembled patterns of nanoparticles onto networks (mathematical graphs) provides a way for quantitative analysis of the structure effects on the physical properties of the assembly. Here we review the network modeling of the conduction with single-electron tunneling mechanisms in the assembled nanoparticle films. Simulations of the conduction predict the nonlinear current-voltage curves in different classes of the nanoparticle networks. Furthermore, the numerical analysis reveals how the I(V) nonlinearity is related to the collective charge fluctuations along the conducting paths through the sample, and stresses the role of the topology and quenched charge disorder.

Monte Carlo simulation of non-conservative positron transport in pure argon

The main aim of this paper is to apply modern phenomenology and accurate Monte Carlo simulation techniques to obtain the same level of understanding of positron transport as has been achieved for electrons. To this end, a reasonably complete set of cross sections for low energy positron scattering in argon has been used to calculate transport coefficients of low energy positrons in pure argon gas subject to an electrostatic field. We have analyzed the main features of these coefficients and have compared the calculated values with those for electrons in the same gas. The particular focus is on the influence of the non-conservative nature of positronium formation. This effect is substantial, generally speaking much larger than any comparable effects in electron transport due to attachment and/or ionization. As a result several new phenomena have been observed, such as negative differential conductivity (NDC) in the bulk drift velocity, but with no indication of any NDC for the flux drift velocity. In addition, there is a drastic effect on the bulk longitudinal diffusion coefficient for positrons, which is reduced to almost zero, in contrast to the other components of the diffusion tensor, which have normal values. It is found that the best way of explaining these kinetic phenomena is by sampling real space distributions which reveal drastic modification of the usual Gaussian profile due to pronounced spatial differentiation of the positrons by energy.

Co-Evolutionary Mechanisms of Emotional Bursts in Online Social Dynamics and Networks

Collective emotional behavior of users is frequently observed on various Web portals; however, its complexity and the role of emotions in the acting mechanisms are still not thoroughly understood. In this work, using the empirical data and agent-based modeling, a parallel analysis is performed of two archetypal systems—Blogs and Internet-Relayed-Chats—both of which maintain self-organized dynamics but not the same communication rules and time scales. The emphasis is on quantifying the collective emotions by means of fractal analysis of the underlying processes as well as topology of social networks, which arise and co-evolve in these stochastic processes. The results reveal that two distinct mechanisms, which are based on different use of emotions (an emotion is characterized by two components, arousal and valence), are intrinsically associated with two classes of emergent social graphs. Their hallmarks are the evolution of communities in accordance with the excess of the negative emotions on popular Blogs, on one side, and smooth spreading of the Bot’s emotional impact over the entire hierarchical network of chats, on the other. Another emphasis of this work is on the understanding of nonextensivity of the emotion dynamics; it was found that, in its own way, each mechanism leads to a reduced phase space of the emotion components when the collective dynamics takes place. That a non-additive entropy describes emotion dynamics, is further confirmed by computing the q-generalized Kolmogorov-Sinai entropy rate in the empirical data of chats as well as in the simulations of interacting emotional agents and Bots.

How the online social networks are used: dialogues-based structure of MySpace.

Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as users psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipfs distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting ‘weak-ties’ hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.

Transport processes on homogeneous planar graphs with scale-free loops

We consider the role of network geometry in two types of diffusion processes: transport of constant-density information packets with queuing on nodes, and constant voltage-driven tunneling of electrons. The underlying network is a homogeneous graph with scale-free distribution of loops, which is constrained to a planar geometry and fixed node connectivity k=3. We determine properties of noise, flow and return-times statistics for both the processes on this graph and relate the observed differences to the microscopic process details. Our main findings are: (i) through the local interaction between packets queuing at the same node, long-range correlations build up in traffic streams, which are practically absent in the case of electron transport; (ii) noise fluctuations in the number of packets and in the number of tunnelings recorded at each node appear to obey the scaling laws in two distinct universality classes; (iii) the topological inhomogeneity of betweenness plays the key role in the occurrence of broad distributions of return times and in the dynamic flow. The maximum-flow spanning trees are characteristic of each process type.

Positron transport: the plasma-gas interface

Motivated by an increasing number of applications, new techniques in the analysis of electron transport have been developed over the past 30 years or so, but similar methods had yet to be applied to positrons. Recently, an in-depth look at positron transport in pure argon gas has been performed using a recently established comprehensive set of cross sections and well-established Monte Carlo simulations. The key novelty as compared to electron transport is the effect of positronium formation which changes the number of particles and has a strong energy dependence. This coupled with spatial separation by energy of the positron swarm leads to counterintuitive behavior of some of the transport coefficients. Finally new results in how the presence of an applied magnetic field affects the transport coefficients are presented.

Efficient hidden-variable simulation of measurements in quantum experiments.

We prove that the results of a finite set of general quantum measurements on an arbitrary dimensional quantum system can be simulated using a polynomial (in measurements) number of hidden-variable states. In the limit of infinitely many measurements, our method gives models with the minimal number of hidden-variable states, which scales linearly with the number of measurements. These results can find applications in foundations of quantum theory, complexity studies, and classical simulations of quantum systems.

Topology of cell-aggregated planar graphs

We present new algorithm for growth of non-clustered planar graphs by aggregation of cells with given distribution of size and constraint of connectivity k = 3 per node. The emergent graph structures are controlled by two parameters—chemical potential of the cell aggregation and the width of the cell size distribution. We compute several statistical properties of these graphs—fractal dimension of the perimeter, distribution of shortest paths between pairs of nodes and topological betweenness of nodes and links. We show how these topological properties depend on the control parameters of the aggregation process and discuss their relevance for the conduction of current in self-assembled nanopatterns.