The interaction between comet Hale-Bopp tail with the solar wind is investigated in the present paper using magneto-hydrodynamic (MHD) numerical simulation, which accounts for the presence of the interplanetary magnetic field (IMF). The simulation is based on three-dimensional Lax-Wendroff explicit scheme, providing second-order accuracy in space and time. The ions produced from the nucleus of the comet will add considerable effects on the microstructure of the solar wind, thus severely altering its physical properties. The present simulation focuses on careful analysis of these properties by means of simulating the behavior of the comet Hale-Bopp’s tail at 1 AU from the sun. These properties include the changes of the plasma density, particle velocity, IMF changes, pressure, and internal energy. The results indicated that comet tail will highly disturb the physical properties of the solar wind for a considerable distance. These changes reflect the effect of including the source term in the present simulation. It is shown that the comet tail will retain its original shape faster as it approaches the sun. Practical comparisons are also presented in the present research with earlier work. The present simulation was made using MATLAB program.
Secure information transmission over the internet is becoming an important requirement in data communication. These days, authenticity, secrecy, and confidentiality are the most important concerns in securing data communication. For that reason, information hiding methods are used, such as Cryptography, Steganography and Watermarking methods, to secure data transmission, where cryptography method is used to encrypt the information in an unreadable form. At the same time, steganography covers the information within images, audio or video. Finally, watermarking is used to protect information from intruders. This paper proposed a new cryptography method by using thre
... Show MoreThe researcher wanted to make an attempt to identify the foundations of social solidarity, to strengthen the bonds of brotherhood among society, and spread the causes of compassion in the hearts of its members.
The researcher has taken a short course in the hearts of the beloved to hearts.
The aim of this paper is to analyzed unsteady heat transfer for magnetohydrodynamic (MHD) flow of a second grade fluid in a channel with porous medium. The equations which was used to describe the flow are the momentum and energy, these equations were written to get thier non dimentional form. Homotopy analysis method (HAM) is employed to obtain a semi-analytical solutions for velocity and heat transfer fields. The effect of each dimensionless parameter upon the velocity and temperature distributions is analyzed and shown graphically by using MATHEMATICA package.
The aim of this paper is the study of the influence of magnetic field on unsteady
flow of the second-grade fluid with constant viscosity. The equations which
controlled this type of fluid flow are complicated, so finding an analytical solution is
not easy, because it is a system of partial differential equations.We obtained an
expression for the velocity by using homotopy analysis method HAM.
It is found that the equations motion are controlled by many dimensionless
parameter, namely magnetic field parameter M and material constant α,
dimensionless film thickness β and unsteadiness parameter S.We have been studied
the influence of all the physical parameters, that mentioned above on the velocity
field, also a
In this paper, we introduce and discuss an algorithm for the numerical solution of two- dimensional fractional partial differential equation with parameter. The algorithm for the numerical solution of this equation is based on implicit and an explicit difference method. Finally, numerical example is provided to illustrate that the numerical method for solving this equation is an effective solution method.
This paper is concerned with the numerical solutions of the vorticity transport equation (VTE) in two-dimensional space with homogenous Dirichlet boundary conditions. Namely, for this problem, the Crank-Nicolson finite difference equation is derived. In addition, the consistency and stability of the Crank-Nicolson method are studied. Moreover, a numerical experiment is considered to study the convergence of the Crank-Nicolson scheme and to visualize the discrete graphs for the vorticity and stream functions. The analytical result shows that the proposed scheme is consistent, whereas the numerical results show that the solutions are stable with small space-steps and at any time levels.
Echocardiography is a widely used imaging technique to examine various cardiac functions, especially to detect the left ventricular wall motion abnormality. Unfortunately the quality of echocardiograph images and complexities of underlying motion captured, makes it difficult for an in-experienced physicians/ radiologist to describe the motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this study, we present a method to analyze left ventricular wall motion, by using optical flow to estimate velocities of the left ventricular wall segments and find relation between these segments motion. The proposed method will be able to present real clinical help to verify the left ventricular wall motion diagnosis.
In this paper the use of a circular array antenna with adaptive system in conjunction with modified Linearly Constrained Minimum Variance Beam forming (LCMVB) algorithm is proposed to meet the requirement of Angle of Arrival (AOA) estimation in 2-D as well as the Signal to Noise Ratio (SNR) of estimated sources (Three Dimensional 3-D estimation), rather than interference cancelation as it is used for. The proposed system was simulated, tested and compared with the modified Multiple Signal Classification (MUSIC) technique for 2-D estimation. The results show the system has exhibited astonishing results for simultaneously estimating 3-D parameters with accuracy approximately equivalent to the MUSIC technique (for estimating elevation and a
... Show MoreAutism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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