In humans, Pseudomonas aeruginosa is the second most frequent gram negative nosocomial pathogen in hospitals and has the highest case-fatality rate of all hospital-acquired bacteremia because of the hardy resistance of these bacteria to mechanical cleansing as well as to disinfectant, and many antibiotics. The susceptibility of bacteria against the antibiotics is modulated by several local factors such as temperature which modified drug efficacy, so this study was carried out to evaluate the effect of different temperature (20,42,45)Ċon the susceptibility of Pseudomonas aeruginosa to the minimum inhibitory concentrations (MIC) of the antimicrobial agents before and after irradiation. The samples collected from 150 persons suffering from

This paper presents the effect of relativistic and ponderomotive nonlinearity on cross-focusing of two intense laser beams in a collisionless and unmagnetized plasma. It should be noted here that while considering the self-focusing due to relativistic electron mass variation, the electron ponderomotive density depression in the channel may also be important. Therefore/these two nonlinearties may simultaneously affect the self-focusing process. These nonlinearities depend not only on the intensity of one laser but also on the second laser. Therefore, one laser beam affects the dynamics of the second beam and hence the process of cross-focusing takes place. The electric field amplitude of the excited electron plasma wave (EPW) has been cal

Introduction: This study was performed to compare the effect of Fractional CO2 laser or Q switched Nd:YAG laser of surface treatment on the shear bond strength of zirconia-porcelain interface. Methods: Fractional CO2 laser at 30 W, 2 ms, time interval 1 ms, distance between spots 0.3 mm, and number of scans is (4) or Q switched Nd:YAG laser at 30 J/mm2 and 10 Hz were used to assess the shear bond strength of zirconia to porcelain. Pre-sintered zirconia specimens were divided into three groups (n = 10) according to the surface treatment technique used: (a) untreated (Control) group; (b) CO2 group; (c) Nd:YAG group. All samples were then sintered and veneered with porcelain according to the manufacturer’s instructions. Surface morph

The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr

CdO films were deposited on substrates from glass, Silicon and Porous silicon by thermal chemical spray pyrolysis technique with different thicknesses (130 and 438.46) nm. Measurements of X-ray diffraction of CdO thin film proved that the structure of the Polycrystalline is cubic lattice, and its crystallite size is located within nano scale range where the perfect orientation is (200). The results show that the surface’s roughness and the root mean square increased with increasing the thickness of prepared films. The UV-Visible measurements show that the CdO films with different thicknesses possess an allowed direct transition with band gap (4) eV. AFM measurement revealed that the silicon porosity located in nano range. Cadmium oxide f

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca