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

An optimization study was conducted to determine the optimal operating pressure for the oil and gas separation vessels in the West Qurna 1 oil field. The ASPEN HYSYS software was employed as an effective tool to analyze the optimal pressure for the second and third-stage separators while maintaining a constant operating pressure for the first stage. The analysis involved 10 cases for each separation stage, revealing that the operating pressure of 3.0 Kg/cm2 and 0.7 Kg/cm2 for the second and third stages, respectively, yielded the optimum oil recovery to the flow tank. These pressure set points were selected based on serval factors including API gravity, oil formation volume factor, and gas-oil ratio from the flow tank.    To impro

This research aims to develop transdermal patches of Ondansetron hydrochloride (OSH) with different types of polymers, ethyl cellulose and, polyvinyl pyrrolidone k30 in a ratio (3:0.5,3:1,3:2,2:1,1:1) with propylene glycol 20%w/w as a plasticizer. Prepared transdermal patches were evaluated for physical properties. The compatibility between the drug and excipients was studied by Differential scanning calorimetry (DSC), where there is no interaction between the drug and polymers. From the statistical study, there is a statistical difference between all the prepared formulations p<0.05. In-vitro Release study of transdermal patches was performed by using a paddle over the disc. The release profile of OSH follow

Ag nanoparticles were prepared using Nd:YAG laser from Ag matel in distilled water using different energies laser (100 and 600) mJ using 200 pulses, and study the effect of the preparation conditions on the structural characteristics of and then study the effect of nanoparticles on the rate of killing the two types of bacteria particles (Staph and E.coli). The goal is to prepare the nanoparticle effectively used to kill bacteria.