In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

This study was aimed to isolate and identify Saccharomyces boulardii from Mangosteen fruits (Garcinia mangostana L.) by traditional and molecular identification methods To get safe and healthy foods probiotics for use, The isolates and two commercial strains were subjected to cultural, morphological and biochemical tests, The colonies of the isolates were spherical, smooth, mucoidal, dull and white to cream colour on SD agar media .The shape of cells was globose to ovoid and sometimes with budding, in a single form or clustered like a beehive. The isolates and two commercial strains were unable to metabolized galactose and lactose , Results shows that all isolates were unable to utilize potassium nitrate and not grow in the presence of (

This study is an approach to assign the land area of  Kirkuk city [ a city located in the northern of Iraq, 236 kilometers north of  Baghdad  and 83 kilometers  south of  Erbil [ Climatic atlas of  Iraq, 1941-1970  ]  into different  multi zones by using Satellite image and Arc Map10.3,  zones of different traffic noise pollutions. Land zonings process like what achieved in this paper will help and of it’s of a high interest point for the future of Kirkuk city especially urban

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

         It is clear that correct application of antibiotic prophylaxis can reduce the incidence of infection  resulting from the bacterial  inoculation in a variety of clinical situations; it cannot   prevent  all  infections  any  more  than it  can   eliminate  all  established infections. Optimum  antibiotic   prophylaxis  depends on:  rational  selection  of the drug(s),  adequate  concentrations  of the  drug  in  the  tissues that  are at risk, and attention to  timing  of  administration.  Moreover,  the  risk  of

         It is clear that correct application of antibiotic prophylaxis can reduce the incidence of infection  resulting from the bacterial  inoculation in a variety of clinical situations; it cannot   prevent  all  infections  any  more  than it  can   eliminate  all  established infections. Optimum  antibiotic   prophylaxis  depends on:  rational  selection  of the drug(s),  adequate  concentrations  of the  drug  in  the  tissues that  are at risk, and attention to  timing  of  administration.  Moreover,  the  risk  of  infection  in  some situations  does not outweigh  the risks which  attend the administration of even the safest antibiotic drug. The aim of this study was to comp

Samples (4th) reviewed are deposited and stored in the Iraqi Natural History Museum (INHM), and there are 4th of them. Sciurus anomalous (Güldenstädt, 1785) species are preserved and mummified. It is a Caucasian squirrel (S. anomalus) that was medium in size, with a grayish-to-chestnut color, a golden gray back, and a golden tail. It is found in the forests of East and Southeast Asia. The variety possessed for the study was previously registered in the vertebrate literature by several authors and was stored by scientific methods in the museum. As a result of the multiplication and growth of these species, and to know the environmental changes that occurred in them, they were compared with models and samples found throughout Iraq