This paper has the interest of finding the approximate solution (APPS) of a nonlinear variable coefficients hyperbolic boundary value problem (NOLVCHBVP).  The given boundary value problem is written in its discrete weak form (WEFM) and proved  have a unique solution, which is obtained via the mixed Galerkin finite element with implicit method that reduces the problem to solve the Galerkin nonlinear algebraic system  (GNAS). In this part, the predictor and the corrector techniques (PT and CT, respectively) are proved at first convergence and then are used to transform  the obtained GNAS to a linear GLAS . Then the GLAS is solved using the Cholesky method (ChMe). The stability and the convergence of the method are stud

The purpose of this study is aimed to lay down an arranged platform suited to Iraqi constructional associations which in charge to carry out multi constructional projects, as it fulfilled management requirements and supervising, so that low - cost projects will be controlled in due term and quality. Based on primary info and observed data collected, the study thesis has been formulated in this way: Iraqi constructional sector bodies which are in charge to implement simultaneously multi constructional projects in need to reformulate its organized structure so that it will be more fitted to management and control of these projects. This thesis includes a
theoretical part contained presenting the most important resources locally and int

     The conventional procedures of clustering algorithms are incapable of overcoming the difficulty of managing and analyzing the rapid growth of generated data from different sources. Using the concept of parallel clustering is one of the robust solutions to this problem. Apache Hadoop architecture is one of the assortment ecosystems that provide the capability to store and process the data in a distributed and parallel fashion. In this paper, a parallel model is designed to process the k-means clustering algorithm in the Apache Hadoop ecosystem by connecting three nodes, one is for server (name) nodes and the other two are for clients (data) nodes. The aim is to speed up the time of managing the massive sc

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

ZnO organic hybrid junction (electroluminescence EL device) was fabricated using phase segregation method. ZnO-nanoparticle (NPs) was prepared as a colloidal by self–assembly method of Zinc acetate solution with KOH solution. Nanoparticle is employed to form organic-inorganic hybrid film and generate white light emission, while N,N’–diphenyl-N,N’ –bis(3-methylphenyl)-1,1’-biphenyl 4,4’-diamine (TPD) and polymethyl methacrylate (PMMA) are adopted as the organic matrices. ZnO NPs was used to fabricate TPD: PMMA: ZnO NPs hybrid junction device. The photoluminescence (PL) and electroluminescence (EL) spectra of the TPD: PMMA: ZnO NPs hybrid device provided a broad emission band covering entirely the visible spectrum (∼350-∼700

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121