Thermostat for non-equilibrium multiparticle collision dynamics simulations

Multiparticle collision dynamics (MPC), a particle-based mesoscale simulation technique for com- plex fluid, is widely employed in non-equilibrium simulations of soft matter systems. To maintain a defined thermodynamic state, thermalization of the fluid is often required for certain MPC variants. We investigate the influence of three thermostats on the non-equilibrium properties of a MPC fluid under shear or in Poiseuille flow. In all cases, the local velocities are scaled by a factor, which is either determined via a local simple scaling approach (LSS), a Monte Carlo-like procedure (MCS), or by the Maxwell-Boltzmann distribution of kinetic energy (MBS). We find that the various scal- ing schemes leave the flow profile unchanged and maintain the local temperature well. The fluid viscosities extracted from the various simulations are in close agreement. Moreover, the numerically determined viscosities are in remarkably good agreement with the respective theoretically predicted values. At equilibrium, the calculation of the dynamic structure factor reveals that the MBS method closely resembles an isothermal ensemble, whereas the MCS procedure exhibits signatures of an adi- abatic system at larger collision-time steps. Since the velocity distribution of the LSS approach is non-Gaussian, we recommend to apply the MBS thermostat, which has been shown to produce the correct velocity distribution even under non-equilibrium conditions.Comment: 12 pages, 5 figures in Phys. Rev. E, 201

Hydrodynamic correlations in shear flow: A Multiparticle--Collision--Dynamics simulation study

The nonequilibrium hydrodynamic correlations of a Multiparticle-Collision-Dynamics (MPC) fluid in shear flow are studied by analytical calculations and simulations. The Navier-Stokes equations for a MPC fluid are linearized about the shear flow and the hydrodynamic modes are evaluated as an expansion in the momentum vector. The shear-rate dependence and anisotropy of the transverse and longitudinal velocity correlations are analyzed. We demonstrate that hydrodynamic correlations in shear flow are anisotropic, specifically, the two transverse modes are no longer identical. In addition, our simulations reveal the directional dependence of the frequency and attenuation of the longitudinal velocity correlation function. Furthermore, the velocity autocorrelation functions of a tagged fluid particle in shear flow are determined. The simulations results for various hydrodynamic correlations agree very well with the theoretical predictions.Comment: 8 pages, 5 figure

A Study on E-Taiwan Promotion Information Security Governance Programs with E-government Implementation of Information Security Management Standardization

Abstract The promotion of Information Security Governance (ISG) has become an important factor in the implementation of e-government and information security management within the "National Information and Communications Technology Security Development Program (2009˜2012)" in continuing the "Plan for Establishment of Information and Communication Technology Infrastructure Security Mechanism (2001˜2008)" in Taiwan; in July 2013, the working outline of the project was adjusted. And, it was asked all departments of Executive Yuan and local government to process aggressively by regulation on December 25, 2013. This study examines information security development program, and strategies for meeting e-government and information security management requirements within the implementation of information security development programs through information security management systems (ISMS). Moreover, an action program for improved ISMS performance, using an approach combining ISG and ISMS, is proposed. Based on this, this research employs history analysis and in-depth interview methodologies to develop insights into e-Taiwan information security management. Furthermore, the research objective is to examine the relevance between the execution of e-government and information security management framework and ISMS implementation by using the ISG project approach

Integrating SPC and EPC for Multivariate Autocorrelated Process

Statistical process control (SPC) is a widely employed quality control method in industry. SPC is mainly designed for monitoring single quality characteristic. However, as the design of a product/process becomes complex, a process usually has multiple quality characteristics related to it. These characteristics must be monitored by multivariate SPC. When the autocorrelation is present in the process data, the traditional SPC may mislead the results. Hence, the autocorrelated data must be treated to eliminate the autocorrelation effect before employing SPC to detect the assignable causes. Besides, chance causes also have impact on the processes. When the process is out of control but no assignable cause is found, it can be adjusted by employing engineering process control (EPC). However, only using EPC to adjust the process may make inappropriate adjustments due to external disturbances or assignable causes. This study presents an integrated SPC and EPC procedure for multivariate autocorrelated process. The SPC procedure constructs a predicting model using group method of data handling (GMDH), which can transfer the autocorrelated data into uncorrelated data. Then, the Hotelling’s T2 and multivariate cumulative sum control charts are constructed to monitor the process. The EPC procedure constructs a controller utilizing data mining technique to adjust the multiple quality characteristics to their target values. Industry can employ this procedure to monitor and adjust the multivariate autocorrelated process

Data mining of the GAW14 simulated data using rough set theory and tree-based methods

Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple correlations among four consequential layers of genetic data (disease-related loci, endophenotypes, phenotypes, and one disease trait). When information of one layer was blocked and uncertainty was created in the correlations among these layers, the correlation between the first and last layers (susceptibility genes and the disease trait in this case), was not easily directly detected. In this study, we proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait. During the first stage, based on phenotypes of subjects and their parents, decision trees were built to predict trait values. Phenotypes retained in the decision trees were then advanced to the second stage, where rough set theory was applied to discover the minimal subsets of genes associated with the disease trait. For comparison, decision trees were also constructed to map susceptible genes during the second stage. Our results showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease trait. The decision trees and rough set theory failed to identify the true disease-related loci

Replication Attack Detection in Mobile Wireless Sensor Network with LEACH-ME Routing Protocol

Because the Wireless Sensor Network (WSN) nodes are low-cost devices, attackers may capture some nodes in this network and then duplicate these nodes to eavesdrop the transmitted messages or even control the network gradually without difficulty. This is the so-called node replication attack. This type of attacks could cause the huge threat to information security of WSNs. Therefore, in this paper, we would like to suggest a detection approach which can offer good performance but with higher energy consumption. Hence, it can provide an alternative solution for some specific applications that need better precision but do not care energy or lifespan too much