The present study aimed at identifying the role of supervisory and training practices in the field education program in achieving course objectives from the perspective of the student teachers in the Faculty of Science and Arts in sharourah, and its relationship to some variables (gender, specialization,estimation). To achieve this, the researcher used the descriptive-analytical method, and the study tool was designed, which consists of (24) items distributed on two axes: the objectives of field education and supervisory and training practices.Then, the study tool was applied to the research sample represented in the entire study community, which consists of (65) male and female students in the Faculty of Sciences and Arts in Sharurah Governorate in the academic year 1437-1438 AH. The results of the study indicated that the objectives of the Field Education have been achieved to a large extent , In addition, the field education program and the supervisory and training practices it included have helped in achieving these objectives, with a difference in the role from one practice to another,It was also found that there were no significant differences due to the difference in gender and estimation variables, while there were statistically significant differences due to the difference in the specialty variable in favor of the major of Islamic studies.The study recommended expanding the fields of practice in field education by updating the traditional methods.
In this study, He's parallel numerical algorithm by neural network is applied to type of integration of fractional equations is Abel’s integral equations of the 1st and 2nd kinds. Using a Levenberge – Marquaradt training algorithm as a tool to train the network. To show the efficiency of the method, some type of Abel’s integral equations is solved as numerical examples. Numerical results show that the new method is very efficient problems with high accuracy.
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In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete
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This study investigates the application of hydraulic acid fracturing to enhance oil production in the Mishrif Formation of the Al-Fakkah oilfield due to declining flow rates and wellhead pressures resulting from asphaltene deposition and inadequate permeability. Implementing acid fracturing, an established technique for low-permeability carbonate reserves, was essential due to the inadequacy of prior solvent cleaning and acidizing efforts. The document outlines the protocols established prior to and following the treatment, emphasizing the importance of careful oversight to guarantee safety and efficacy. In the MiniFrac treatment, 150 barrels of #30 cross-linked gel were injected at 25 barrels per minute, followed by an overflush wi
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MDS code is a linear code that achieves equality in the Singleton bound, and projective MDS (PG-MDS) is MDS code with independents property of any two columns of its generator matrix. In this paper, elementary methods for modifying a PG-MDS code of dimensions 2, 3, as extending and lengthening, in order to find new incomplete PG-MDS codes have been used over . Also, two complete PG-MDS codes over of length and 28 have been found.
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The aim of this paper, is to design multilayer Feed Forward Neural Network(FFNN)to find the approximate solution of the second order linear Volterraintegro-differential equations with boundary conditions. The designer utilized to reduce the computation of solution, computationally attractive, and the applications are demonstrated through illustrative examples.
In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.
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These search summaries in building a mathematical model to the issue of Integer linear Fractional programming and finding the best solution of Integer linear Fractional programming (I.L.F.P) that maximize the productivity of the company,s revenue by using the largest possible number of production units and maximizing denominator objective which represents,s proportion of profits to the costs, thus maximizing total profit of the company at the lowest cost through using Dinkelbach algorithm and the complementary method on the Light industries company data for 2013 and comparing results with Goal programming methods results.
It is clear that the final results of resolution and Dinkelbac
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