High-intensity laser-produced plasma has been extensively investigated in many studies. In this demonstration, a new spectral range was observed in the resulted spectra from the laser-plasma interaction, which opens up new discussions for new light source generation. Moreover, the characterizations of plasma have been improved through the interaction process of laser-plasma. Three types of laser were incorporated in the measurements, continuous-wave CW He-Ne laser, CW diode green laser, pulse Nd: YAG laser. As the plasma system, DC glow discharge plasma under the vacuum chamber was considered in this research. The plasma spectral peaks were evaluated, where they refer to Nitrogen gas. The results indicated that the plasma intensity increased from several thousands to several tens of thousands through the process of interaction of the Nd: YAG laser with the plasma. This increase in the intensity of the plasma as laser intensity increased occurs regardless of laser wavelength involved in the interaction or not. According to laser-plasma interaction, the so-called full width at half maximum FWHM of the highest peak in the plasma spectrum was broadened from 1.43 to 2.73. Considering the equation of plasma density computing, the plasma density was increased from 1.07× 1018 to 2.05× 1018 cm-3 with increasing FWHM. As a result of the interaction, the electron temperature of plasma was increased from 0.176 to 0.782 eV. It was also noticed that the position of the highest peak in the plasma spectrum depends on the interacted laser wavelength.
The aim of the present study was to isolated the Enterococcus spp. from milk samples of cow and vaginal swabs from aborted women and patient women in Baghdad during September 2016 to april 2017. All 100 milk sample collecting was carried out on California Mastitis Test (CMT) and the positive Percentage of CMT reactions was 5% and the percentage of Enterococcus isolates from mastitic milk was 60% and 30% from nonmasitic milk. The prevalence of Enterococcus spp was 31% of milk samples and the prevalence of Enterococcus spp. Isolates were 67.74% of the isolates of cow milk samples were Enterococcus faecalis, 25.80% was Group D and 6.45% was non groupable while Enterococcus spp. isolates from aborted women samples were 20% and all isolated was
... Show MoreA many risk challenge in (settings hospital) are multi- bacteria are antibiotic-resistant. Some type strains that ability adhesion surface-attached bio-film census. Fifteen MRSA isolates were considered as high biofilm producers Moreover all MRSA isolates; M3, M5, M7 and M11 produced biofilms but the thickest biofilm seen M7strain. The MIC values of N. sativa oil against clinical isolates of MRSA were between (0.25, 0.5, 0.75, 1.0) μg/ml While MRSAcin (50, 75, 100, 125) µg\ ml. All biofilms treated with MRSAcin and Nigella sativa developed a presence of live cells after cultured on plate agar with inhibition zone between MIC (18 – 15) and (14- 11)mm respectively.Yet, results showed that MRSA supernatant developed a inhibitory ef
... Show MoreImage 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
... Show MoreFace recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security
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