Fractional Dynamics of Stuxnet Virus Propagation in Industrial Control Systems

The designed fractional order Stuxnet, the virus model, is analyzed to investigate the spread of the virus in the regime of isolated industrial networks environment by bridging the air-gap between the traditional and the critical control network infrastructures. Removable storage devices are commonly used to exploit the vulnerability of individual nodes, as well as the associated networks, by transferring data and viruses in the isolated industrial control system. A mathematical model of an arbitrary order system is constructed and analyzed numerically to depict the control mechanism. A local and global stability analysis of the system is performed on the equilibrium points derived for the value of α = 1. To understand the depth of fractional model behavior, numerical simulations are carried out for the distinct order of the fractional derivative system, and the results show that fractional order models provide rich dynamics by means of fast transient and super-slow evolution of the model’s steady-state behavior, which are seldom perceived in integer-order counterparts.

Consensus and swarming behaviors for a proportional-derivative system with a cut-off interaction

This paper presents a proportional-derivative protocol for the consensus problem of a class of linear second-order multi-agent systems with local information transmission. The communication topology among the agents is switching and agents receive information within a critical bounded distance. As new observations, we show that the desired protocol system undergoes consensus and swarming behaviours when 1 is a simple eigenvalue of the adjacency matrix. In this case, both final velocity and final relative position are formulated. Simulation results show the effectiveness of the proposed protocol.

A canonical Hamiltonian analysis of Podolsky’s generalized electrodynamics in first-order formalism

We give a canonical Hamiltonian analysis of Podolsky’s generalized electrodynamics by introducing two sets of new variables which help us transform the Lagrangian into an equivalent first-order formalism. After eliminating the unphysical sector, we calculate the physical degrees of freedom of the higher derivative system and obtain the Dirac brackets in the reduced phase space. Then with the aid of the first-class constraints, we construct the independent gauge generator which is closely connected with the BRST charge and the BRST-invariant Hamiltonian. Finally, by choosing appropriate gauge-fixing fermion, we evaluate the path integral of this higher derivative constrained system in BRST quantization scheme with the generalized Lorenz gauge condition.

Expression Analysis System

Expression is the most basic personality trait of an individual. Expressions, ubiquitous to humans from all cultures, can be pivotal in analyzing the personality which is not confined to boundaries. Analyzing the changes in the expression of the individual can bolster the process of deriving his/her personality traits underscoring the paramount reactions like anger, happiness, sadness and so on. This paper aims to exercise Neural Network algorithms to predict the personality traits of an individual from his/her facial expressions. In this paper, a methodology to analyze the personality traits of the individual by periodic monitoring of the changes in facial expressions is presented. The proposed system is intended to analyze the expressions by exploiting Neural Networks strategies to first analyze the facial expressions of the individual by constantly monitoring an individual under observation. This monitoring is done with the help of OpenCV which captures the facial expression at an interval of 15 secs. Thousands of images per expression are used to train the model to aptly distinguish between expression using prominent Neural Network Methodologies of Forward and Backward Propagation. The identified expression is then be fed to a derivative system which plots a graph highlighting the changes in the expression. The graph acts as the crux of the proposed system. The project is important from the perspective of serving as an alternative to manual monitoring which are prone to errors and subjective in nature.

SELECTION OF EXTRACTIVE AGENTS FOR THE SEPARATION OF CHLOROFORM - METHANOL - TETRAHYDROFURAN MIXTURE

Variants of the extractive distillation of chloroform - methanol - tetrahydrofuran equimolar mixture with industrial separating agents are considered. The basic system shows opposite deviations from the ideal behavior, because it contains binary azeotropes with minimum and maximum boiling points (3.3.1-4 system according to Serafimov’s classification). The choice of selective substances for extractive distillation was carried out taking into account the concentration dependences of the excess molar Gibbs energy of the binary constituents of the derivative system “chloroform - methanol - tetrahydrofuran - industrial test agent (ethylene glycol (EG), dimethyl sulfoxide (DMSO), N-methylpyrrolidone (N-MP))” at 101.32 kPa. Based on the results of the evaluation of the thermodynamic criterion, DMSO and N-MP are recommended. Both agents show selective effect when separating two binary constituents. EG is selective only with respect to chloroform-tetrahydrofuran mixture. Since the tested agents show different selective effects, the final agent choice determines the qualitative composition of the product flows in the column for the extractive distillation of the three-component mixture (the first column of the flowsheet) and, accordingly, the structure of the total flowsheet. The schemes consist of two two-column complexes for extractive distillation (for the basic three-component mixture and for the binary mixture). The maximum contribution to the total reboiler energy consumption of the distillation columns is made by the first extractive distillation column: 65% (EG), 53% (N-MP) and 24% (DMSO). The use of the most selective agent reduces the energy consumption of this column: the reboiler load is maximal in the case of EG, in comparison with which the load is 47% lower in the case of N-MP and 76% lower in the case of DMSO.

Ordinary differential equations

This chapter surveys the ordinary differential equations (ODEs) that occur in classical and quantum mechanics, and describes both numerical algorithms and appropriate software design for solving them. Systems of ordinary differential equations, together with a few constants of integration, can in most cases be regarded as a means of defining a function (the “solution”). In this chapter, we develop an object-oriented architecture that applies integrators of the Runge-Kutta family to create these functions. Together with an automatic derivative system for generating partial derivatives from functions of one or more variables, the differential equation solver becomes a powerful tool for solving a variety of few-body problems in classical Hamiltonian systems. This chapter presents a blend of numerical algorithms, physics, and computing techniques. The phenomenon of energy drift is discussed and used to motivate symplectic solvers. Techniques such as adaptive step size and possible problems with stability and multiple scales are also discussed.