This paper presents an analytical study for the magnetohydrodynamic (MHD) flow of a generalized Burgers’ fluid in an annular pipe. Closed from solutions for velocity is obtained by using finite Hankel transform and discrete Laplace transform of the sequential fractional derivatives. Finally, the figures are plotted to show the effects of different parameters on the velocity profile.

Abstract

This research aims to develop a unit as part of a sixth-grade Arabic language textbook and measure its effectiveness in the development of twenty-first-century skills of female students. The author adopted the experimental approach with a quasi-experimental design of the pre-post single-group. A list of the major skills was derived from the framework for the 21st-century skills of the mother tongue that was developed by the Partnership for 21st-Century Skills and reviewed and adjusted by some specialists. According to their views, the unit was developed. The study targeted 15 sub-skills falling under three main skills. The results of the study showed the effectiveness of the developed unit in the develo

In this study, a proposed process for the utilization of hydrogen sulphide separated with other gases from omani natural gas for the production of sulphuric acid by wet sulphuric acid process (WSA) was studied. The processwas simulated at an acid gas feed flow of   5000 m³/hr using Aspen ONE- V7.1-HYSYS software. A sensitivity analysis was conducted to determine the optimum conditions for the operation of plant. This included primarily the threepacked bed reactors connected in series for the production of sulphur trioxidewhich represented the bottleneck of the process. The optimum feed temperature  and catalyst bed volume for each reactor were estimated and then used in the simulation of the whole process for tw

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul