Numerical Modeling Of Electron’s Trajectories In Cold Plasma By PIC Method

This study is a contribution to the modeling of plasma discharges. The numerical model proposed is particle type, applied to argon plasma generated by a continuous discharge. A microscopic particle model is used for solving the Boltzmann equation by considering a finite number of particles to represent the charged species. The study of the electrical behavior of plasma is performed using a PIC (Particle-In-Cell) model whitch is well suited for low-pressure no-collision plasmas. This model provides the plasma characteristics (potential, charge densities). The principle of the PIC method is based on sampling (mesh) in a 1D of the space of the reactor between two flat and parallel electrodes in which particles move under the action of electric field (applied). This method makes it possible to determine the values of electric fields (steady state and time) at every point of contact for any interpolation from the numerical values obtained by the method of finite differences.This study is a contribution to the modeling of plasma discharges. The numerical model proposed is particle type, applied to argon plasma generated by a continuous discharge. A microscopic particle model is used for solving the Boltzmann equation by considering a finite number of particles to represent the charged species. The study of the electrical behavior of plasma is performed using a PIC (Particle-In-Cell) model whitch is well suited for low-pressure no-collision plasmas. This model provides the plasma characteristics (potential, charge densities). The principle of the PIC method is based on sampling (mesh) in a 1D of the space of the reactor between two flat and parallel electrodes in which particles move under the action of electric field (applied). This method makes it possible to determine the values of electric fields (steady state and time) at every point of contact for any interpolation from the numerical values obtained by the method of finite differences

Development Of A One Dimensional Fluid Model, Application To Electropositive And Electronegative Gases In DC Discharge

The objective of the work presented in this paper is to develop a numerical calculation program which simulates the behavior of charged species in deposition reactor by cold plasma in DC glow discharge. After applying some simplifying assumptions, we developed a model of fluid type in MATLAB using the numerical method of finite differences. We applied the model to simulate the plasma in the case of an electropositive (He) and an electronegative (SF6) gases in terms of spatial distribution of charged particles, electric field and electric potential between electrodes space.The objective of the work presented in this paper is to develop a numerical calculation program which simulates the behavior of charged species in deposition reactor by cold plasma in DC glow discharge. After applying some simplifying assumptions, we developed a model of fluid type in MATLAB using the numerical method of finite differences. We applied the model to simulate the plasma in the case of an electropositive (He) and an electronegative (SF6) gases in terms of spatial distribution of charged particles, electric field and electric potential between electrodes space

Model for phishing websites classification using artificial neural network

Internet users might be exposed to various forms of threats that can create economic harm, identity fraud, and lack of faith in e-commerce and online banking by consumers as the internet has become a necessary part of everyday activities. Phishing can be regarded as a type of web extortions described as the skill of imitating an honest company's website aimed at obtaining private information for example usernames, passwords, and bank information. The accuracy of classification is very significant in order to produce high accuracy results and least error rate in classification of phishing websites. The objective of this research is to model a suitable neural network classifier and then use the model to class the phishing website data set and evaluate the performance of the classifier. This research will use a phishing website data set which was retrieved from UCI repository and will be experimented using Encog Workbench tool. The main expected outcome from this study is the preliminary ANN classifier which classifies the target class of the phishing websites data set accurately, either phishy, suspicious or legitimate ones. The results indicate that ANN (9-5-1) model outperforms other models by achieving the highest accuracy and the least MSE value which is 0.04745

Tactile perception of randomly rough surfaces

Most everyday surfaces are randomly rough and self-similar on sufficiently small scales. We investigated the tactile perception of randomly rough surfaces using 3D-printed samples, where the topographic structure and the statistical properties of scale-dependent roughness were varied independently. We found that the tactile perception of similarity between surfaces was dominated by the statistical micro-scale roughness rather than by their topographic resemblance. Participants were able to notice differences in the Hurst roughness exponent of 0.2, or a difference in surface curvature of 0.8 mm−1 for surfaces with curvatures between 1 and 3 mm−1. In contrast, visual perception of similarity between color-coded images of the surface height was dominated by their topographic resemblance. We conclude that vibration cues from roughness at the length scale of the finger ridge distance distract the participants from including the topography into the judgement of similarity. The interaction between surface asperities and fingertip skin led to higher friction for higher micro-scale roughness. Individual friction data allowed us to construct a psychometric curve which relates similarity decisions to differences in friction. Participants noticed differences in the friction coefficient as small as 0.035 for samples with friction coefficients between 0.34 and 0.45

Tactile perception of randomly rough surfaces

Most everyday surfaces are randomly rough and self-similar on sufficiently small scales. We investigated the tactile perception of randomly rough surfaces using 3D-printed samples, where the topographic structure and the statistical properties of scale-dependent roughness were varied independently. We found that the tactile perception of similarity between surfaces was dominated by the statistical micro-scale roughness rather than by their topographic resemblance. Participants were able to notice differences in the Hurst roughness exponent of 0.2, or a difference in surface curvature of 0.8 mm−1 for surfaces with curvatures between 1 and 3 mm−1. In contrast, visual perception of similarity between color-coded images of the surface height was dominated by their topographic resemblance. We conclude that vibration cues from roughness at the length scale of the finger ridge distance distract the participants from including the topography into the judgement of similarity. The interaction between surface asperities and fingertip skin led to higher friction for higher micro-scale roughness. Individual friction data allowed us to construct a psychometric curve which relates similarity decisions to differences in friction. Participants noticed differences in the friction coefficient as small as 0.035 for samples with friction coefficients between 0.34 and 0.45

Rapid Independent Trait Evolution despite a Strong Pleiotropic Genetic Correlation

This is the publisher's version. It can also be found here:http://dx.doi.org/10.1086/661907Genetic correlations are the most commonly studied of all potential constraints on adaptive evolution. We present a comprehensive test of constraints caused by genetic correlation, comparing empirical results to predictions from theory. The additive genetic correlation between the filament and the corolla tube in wild radish flowers is very high in magnitude, is estimated with good precision, and is caused by pleiotropy. Thus, evolutionary changes in the relative lengths of these two traits should be constrained. Still, artificial selection produced rapid evolution of these traits in opposite directions, so that in one replicate relative to controls, the difference between them increased by six standard deviations in only nine generations. This would result in a 54% increase in relative fitness on the basis of a previous estimate of natural selection in this population, and it would produce the phenotypes found in the most extreme species in the family Brassicaceae in less than 100 generations. These responses were within theoretical expectations and were much slower than if the genetic correlation was zero; thus, there was evidence for constraint. These results, coupled with comparable results from other species, show that evolution can be rapid despite the constraints caused by genetic correlations