Collective traffic-like movement of ants on a trail: dynamical phases and phase transitions

The traffic-like collective movement of ants on a trail can be described by a stochastic cellular automaton model. We have earlier investigated its unusual flow-density relation by using various mean field approximations and computer simulations. In this paper, we study the model following an alternative approach based on the analogy with the zero range process, which is one of the few known exactly solvable stochastic dynamical models. We show that our theory can quantitatively account for the unusual non-monotonic dependence of the average speed of the ants on their density for finite lattices with periodic boundary conditions. Moreover, we argue that the model exhibits a continuous phase transition at the critial density only in a limiting case. Furthermore, we investigate the phase diagram of the model by replacing the periodic boundary conditions by open boundary conditions.Comment: 8 pages, 6 figure

Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system

Distribution of dwell times of a ribosome: effects of infidelity, kinetic proofreading and ribosome crowding

Ribosome is a molecular machine that polymerizes a protein where the sequence of the amino acid residues, the monomers of the protein, is dictated by the sequence of codons (triplets of nucleotides) on a messenger RNA (mRNA) that serves as the template. The ribosome is a molecular motor that utilizes the template mRNA strand also as the track. Thus, in each step the ribosome moves forward by one codon and, simultaneously, elongates the protein by one amino acid. We present a theoretical model that captures most of the main steps in the mechano-chemical cycle of a ribosome. The stochastic movement of the ribosome consists of an alternating sequence of pause and translocation; the sum of the durations of a pause and the following translocation is the time of dwell of the ribosome at the corresponding codon. We derive the analytical expression for the distribution of the dwell times of a ribosome in our model. Whereever experimental data are available, our theoretical predictions are consistent with those results. We suggest appropriate experiments to test the new predictions of our model, particularly, the effects of the quality control mechanism of the ribosome and that of their crowding on the mRNA track.Comment: This is an author-created, un-copyedited version of an article accepted for publication in Physical Biology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher authenticated version is available online at DOI:10.1088/1478-3975/8/2/02600

Flow properties of driven-diffusive lattice gases: theory and computer simulation

We develop n-cluster mean-field theories (0 < n < 5) for calculating the flow properties of the non-equilibrium steady-states of the Katz-Lebowitz-Spohn model of the driven diffusive lattice gas, with attractive and repulsive inter-particle interactions, in both one and two dimensions for arbitrary particle densities, temperature as well as the driving field. We compare our theoretical results with the corresponding numerical data we have obtained from the computer simulations to demonstrate the level of accuracy of our theoretical predictions. We also compare our results with those for some other prototype models, notably particle-hopping models of vehicular traffic, to demonstrate the novel qualitative features we have observed in the Katz-Lebowitz-Spohn model, emphasizing, in particular, the consequences of repulsive inter-particle interactions.Comment: 12 RevTex page

Probing the superconducting ground state of the noncentrosymmetric superconductors CaTSi3 (T = Ir, Pt) using muon-spin relaxation and rotation

The superconducting properties of CaTSi3 (where T = Pt and Ir) have been investigated using muon spectroscopy. Our muon-spin relaxation results suggest that in both these superconductors time-reversal symmetry is preserved, while muon-spin rotation data show that the temperature dependence of the superfluid density is consistent with an isotropic s-wave gap. The magnetic penetration depths and upper critical fields determined from our transverse-field muon-spin rotation spectra are found to be 448(6) and 170(6) nm, and 3800(500) and 1700(300) G, for CaPtSi3 and CaIrSi3 respectively. The superconducting coherence lengths of the two materials have also been determined and are 29(2) nm for CaPtSi3 and 44(4) nm for CaIrSi3.Comment: 6 pages, 7 figure

Decision trees and multi-level ensemble classifiers for neurological diagnostics

Cardiac autonomic neuropathy (CAN) is a well known complication of diabetes leading to impaired regulation of blood pressure and heart rate, and increases the risk of cardiac associated mortality of diabetes patients. The neurological diagnostics of CAN progression is an important problem that is being actively investigated. This paper uses data collected as part of a large and unique Diabetes Screening Complications Research Initiative (DiScRi) in Australia with data from numerous tests related to diabetes to classify CAN progression. The present paper is devoted to recent experimental investigations of the effectiveness of applications of decision trees, ensemble classifiers and multi-level ensemble classifiers for neurological diagnostics of CAN. We present the results of experiments comparing the effectiveness of ADTree, J48, NBTree, RandomTree, REPTree and SimpleCart decision tree classifiers. Our results show that SimpleCart was the most effective for the DiScRi data set in classifying CAN. We also investigated and compared the effectiveness of AdaBoost, Bagging, MultiBoost, Stacking, Decorate, Dagging, and Grading, based on Ripple Down Rules as examples of ensemble classifiers. Further, we investigated the effectiveness of these ensemble methods as a function of the base classifiers, and determined that Random Forest performed best as a base classifier, and AdaBoost, Bagging and Decorate achieved the best outcomes as meta-classifiers in this setting. Finally, we investigated the meta-classifiers that performed best in their ability to enhance the performance further within the framework of a multi-level classification paradigm. Experimental results show that the multi-level paradigm performed best when Bagging and Decorate were combined in the construction of a multi-level ensemble classifier

Performance and cryptographic evaluation of security protocols in distributed networks using applied pi calculus and Markov Chain

The development of cryptographic protocols goes through two stages, namely, security verification and performance analysis. The verification of the protocol’s security properties could be analytically achieved using threat modelling, or formally using formal methods and model checkers. The performance analysis could be mathematical or simulation-based. However, mathematical modelling is complicated and does not reflect the actual deployment environment of the protocol in the current state of the art. Simulation software provides scalability and can simulate complicated scenarios, however, there are times when it is not possible to use simulations due to a lack of support for new technologies or simulation scenarios. Therefore, this paper proposes a formal method and analytical model for evaluating the performance of security protocols using applied pi-calculus and Markov Chain processes. It interprets algebraic processes and associates cryptographic operatives with quantitative measures to estimate and evaluate cryptographic costs. With this approach, the protocols are presented as processes using applied pi-calculus, and their security properties are an approximate abstraction of protocol equivalence based on the verification from ProVerif and evaluated using analytical and simulation models for quantitative measures. The interpretation of the quantities is associated with process transitions, rates, and measures as a cost of using cryptographic primitives. This method supports users’ input in analysing the protocol’s activities and performance. As a proof of concept, we deploy this approach to assess the performance of security protocols designed to protect large-scale, 5G-based Device-to-Device communications. We also conducted a performance evaluation of the protocols based on analytical and network simulator results to compare the effectiveness of the proposed approach