This research includes theoretical and evaluation design of a polarizer filter of high transmission in the near IR region of (900-1200nm) for different incidence angles to obtain a long wave and short wave pass filter using analytical calculations. Results refer to a new configuration design in fewer layers than used in previous studies in the long wave pass at incidence angles (45o,50o,55o). Adopted Hafnium dioxide (HfO2) and Magnesium fluoride (MgF2) as coating material at design wavelength (933nm), the study also included design short wave pass polarizer by using the same coating material.

Introduction: The use of screw-retained hybrid arch bars (HABs) is a relatively recent development in the treatment of mandibular fractures. The purpose of this study is to compare the clinical outcome between HAB and the conventional Erich arch bar (EAB) in the closed treatment of mandibular fractures. Materials and methods: This study included 18 patients who were treated for mandibular fractures with maxillomandibular fixation (MMF), patients were randomly assigned into a control group (n = 10) in which EAB was used and study group (n = 8) in which HAB was used. The outcome variables were time required for application and removal, gingival inflammation scores, postoperative complications, and incidence of wire-stick injury or gloves perf

Objectives: Small field of view gamma detection and imaging technologies for monitoring in vivo tracer uptake are rapidly expanding and being introduced for bed-side imaging and image guided surgical procedures. The Hybrid Gamma Camera (HGC) has been developed to enhance the localization of targeted radiopharmaceuticals during surgical procedures; for example in sentinel lymph node (SLN) biopsies and for bed-side imaging in procedures such as lacrimal drainage imaging and thyroid scanning. In this study, a prototype anthropomorphic head and neck phantom has been designed, constructed, and evaluated using representative modelled medical scenarios to study the capability of the HGC to detect SLNs and image small organs. Methods: An anthropom

Skills learning is considered as an important factor in learning any subject as well as mathematics . Mathematical skills have a number of steps that should be learned and understood faster and with more accuracy . The practical or applied skills are type of learning which includes educational preparation and hand on skills is acquired which conducted by organized educational institutions. The sample included (120) students (males and females) first year / dept.of electrical technigues . The mathematical skills are implemented to wire up the electrical circuts. Test is implemented with questions concerned with the skills .statistical operations were conducted as well as the validity and standard deviation for the test .The results showed

The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Cloud computing provides huge amount of area for storage of the data, but with an increase of number of users and size of their data, cloud storage environment faces earnest problem such as saving storage space, managing this large data, security and privacy of data. To save space in cloud storage one of the important methods is data deduplication, it is one of the compression technique that allows only one copy of the data to be saved and eliminate the extra copies. To offer security and privacy of the sensitive data while supporting the deduplication, In this work attacks that exploit the hybrid cloud deduplication have been identified, allowing an attacker to gain access to the files of other users based on very small hash signatures of

This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time [Formula: see text]. The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integ

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall