The Hyades Kinematical Structure with Gaia Era

268-273In this work, we have improved the Hyades members with crossmatch between Hipparcos and the recent Gaia EDR3 source, the obtained members with highly probable are about 186 candidates. Considering the classical convergent point and depending on proper motions and radial velocities, we have computed the apex position A, D 93. 36 0. 046, 7. 43 0. 713 which is in line with others. The internal structural parameters of the Hyades open cluster are demonstrated here with space spatial velocities; i.e., , V, V; km s (-5.97±0.41, 45.54±6.75, 5.52±0.43) and , V, W ; km s (-42.11±6.50, -19.09±4.37, -1.32±0.44) and on basis of matrix elements μ, the Velocity Ellipsoid Parameters were achieved, e.g., λ, λ, λ; km s 2137.36 23.12, 6.06 0.41, 2.53 0.63 and σ, σ, σ; km s 46.23 6.80, 2.47 0.64, 1.59 0.80. For the observational quantities, we have deduced a correlation coefficient of about 0.83 for the kinematical property of proper motions on both sides μ cos δ , μ; mas yr and the physical property with the angular distances λ from the vertex, and those prove that the attributes are completely related linearly

Numerical Approximation of Higher-Order Solutions of the Quadratic Nonlinear Stochastic Oscillatory Equation Using WHEP Technique

This paper introduces higher-order solutions of the stochastic nonlinear differential equations with the Wiener-Hermite expansion and perturbation (WHEP) technique. The technique is used to study the quadratic nonlinear stochastic oscillatory equation with different orders, different number of corrections, and different strengths of the nonlinear term. The equivalent deterministic equations are derived up to third order and fourth correction. A model numerical integral solver is developed to solve the resulting set of equations. The numerical solver is tested and validated and then used in simulating the stochastic quadratic nonlinear oscillatory motion with different parameters. The solution ensemble average and variance are computed and compared in all cases. The current work extends the use of WHEP technique in solving stochastic nonlinear differential equations

Magneto-Exothermic Catalytic Chemical Reaction along a Curved Surface

In the current study, the physical behavior of the boundary layer flows along a curved surface owing exothermic catalytic chemical reaction, and the magnetic field is investigated. The mathematical model comprised of a part of momentum, energy, and mass equations, which are solved using a finite difference method along with primitive variable formulation. Numerical solutions, using the method of quantitative differentiation, are made with the appropriate choice of dimensionless parameters. Analysis of the results obtained shows that the field temperature and flow of fluids are strongly influenced by the combined effects of catalytic chemical reactions and the magnetic field. The effects of skin friction, heat transfer, mass transfer, mass concentration, and temperature distribution along the curved surface are illustrated in the plots and in the form of tables. By setting the controlling parameters at the boundaries, the boundary conditions at the surface and away from the surface are determined in each graph. With a larger range of body shape parameter n, skin friction and heat transfer are improved, but mass transfer is reduced. Due to the increasing values of the exothermic parameter, the fluid velocity and mass concentration are decreased gradually and the temperature distribution is increased dramatically

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%)

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%)