Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat
... Show MoreThis study involves the investigation of the effect of nitrogen laser with 337.1 nm wavelength on the sensitivity of Staphylococcus aureus bacteria by using local therapeutic due to burns. Thirty six isolate of Staphylococcus aureus bacteria were isolated from 25 patients suffering from sever burns, each isolate of bacteria was irradiated with nitrogen laser at (5, 10, 15 and 30) pulses/second repetition rates for 1, 5, 10, 20 and 30 minutes for each repetition rate. The effects of nitrogen laser on the local therapeutics sensitivity of bacteria were obtained using Kirby Baur method. Changes in the sensitivity of bacteria to local therapeutics (Tetracyclin, Chloramphenicol, Flumizin and Fucidin) occur at high repetition rate(30 pulses/seco
... Show MoreThe purpose of this study was to evaluate the anesthetic effectiveness of a buccal infiltration technique combined with local massage (using 2% lidocaine) in the extraction of mandibular premolars to be utilized as an alternative to the conventional inferior alveolar nerve block.
Patients eligible included any subject with a clinical indication for tooth extraction of the mandibular 1st or 2nd premolars. All patients were anesthetized buccally by local infiltration technique followed by an external pressure applied for 1 min directly over the injection area. In each case, another local
The subject of the Internet of Things is very important, especially at present, which is why it has attracted the attention of researchers and scientists due to its importance in human life. Through it, a person can do several things easily, accurately, and in an organized manner. The research addressed important topics, the most important of which are the concept of the Internet of Things, the history of its emergence and development, the reasons for its interest and importance, and its most prominent advantages and characteristics. The research sheds light on the structure of the Internet of Things, its structural components, and its most important components. The research dealt with the most important search engines in the Intern
... Show MoreEach phenomenon contains several variables. Studying these variables, we find mathematical formula to get the joint distribution and the copula that are a useful and good tool to find the amount of correlation, where the survival function was used to measure the relationship of age with the level of cretonne in the remaining blood of the person. The Spss program was also used to extract the influencing variables from a group of variables using factor analysis and then using the Clayton copula function that is used to find the shared binary distributions using multivariate distributions, where the bivariate distribution was calculated, and then the survival function value was calculated for a sample size (50) drawn from Yarmouk Ho
... Show MoreDiabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
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