In this study, an efficient novel technique is presented to obtain a more accurate analytical solution to nonlinear pantograph differential equations. This technique combines the Adomian decomposition method (ADM) with the homotopy analysis method concepts (HAM). The whole integral part of HAM is used instead of an integral part of ADM approach to get higher accurate results. The main advantage of this technique is that it  gives a large and more extended convergent region of iterative approximate solutions for long time intervals that rapidly converge to the exact solution. Another advantage is capable of providing a continuous representation of the approximate solutions, which gives  better information over whole time interv

     Link failure refers to the failure between two connections/nodes in a perfectly working simulation scenario at a particular instance. Transport layer routing protocols form an important basis of setting up a simulation, with Transmission Control Protocol and User Datagram Protocol being the primary of them. The research makes use of Network Simulator v2.35 to conduct different simulation experiments for link failure and provide validation results. In this paper, both protocols, TCP and UDP are compared based on the throughput of packets delivered from one node to the other constrained to the condition that for a certain interval of time the link fails and the simulation time remains the same for either of the protocols. Overall,

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

Background: Evaluation and measurement of primary stability could be achieved by several methods, including the resonance frequency analysis (RFA) and implant insertion torque (IT) values. The need for a sufficient primary stability, guaranteed by an adequate insertion torque and implant stability quotient values, increased its importance mainly in one stage implants or in immediate loading protocols. The aims of this study was to find if there is a correlation between the peak insertion torque (PIT) and ISQ values of implants inserted in the jaws of different bone quality which regarded as two important clinical determinant factors for prediction of implant primary stability, and to evaluate and compare whether an experienced clinician cou