A nano-sensor for nitrotyrosine (NT) molecule was found by studying the interactions of NT molecule with new B24N24 nanocages. It was calculated using density functionals in this case. The predicted adsorption mechanisms included physical and chemical adsorption with the adsorption energy of −2.76 to −4.60 and −11.28 to −15.65 kcal mol−1, respectively. The findings show that an NT molecule greatly increases the electrical conductivity of a nanocage by creating electronic noise. Moreover, NT adsorption in the most stable complexes significantly affects the Fermi level and the work function. This means the B24N24 nanocage can detect NT as a Φ–type sensor. The recovery time was determined to be 0.3 s. The sensitivity of pure BN na

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

The study conducted to investigate the association between Helicobacter pylori infection and eye diseases (Glaucoma, Cataract, CSR and Uveitis). One hundred and four patients with multiple eye disorders (10-80) years were observed from 10/9/2020 to 18/11/2020 and compared to thirty-one healthy people (19 female and 12 male). Each participant was tested for H. pylori CagAAbs and TNF-α using an enzyme-linked immunosorbent assay (ELISA). The results have shown that there was a non-significant difference (p≥0.05) in the concentration of CagAantibodies in sera of patients with eye diseases except in the case of CSR (central serous chorioretinopathy), which was a significant difference (P≤0.05) compared to the control group. Also, the result

Background: Hyperlipidemia is an elevated fat (lipids), mostly cholesterol and triglycerides, in the blood. These lipids usually bind to proteins to remain circulated so-called lipoprotein. Aims of the study: To determine taste detection threshold and estimate the trace elements (zinc) in serum and saliva of those patients and compare all of these with healthy control subjects. Methods: Eighty subjects were incorporated in this study, thy were divided into two groups: forty patients on simvastatin treatment age between (35-60) years, and forty healthy control of age range between (35-60) years. Saliva was collected by non-stimulated technique within 10 minutes. Serum was obtained from each subject. Zinc was estimated in serum and saliva

A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

User confidentiality protection is concerning a topic in control and monitoring spaces. In image, user's faces security in concerning with compound information, abused situations, participation on global transmission media and real-world experiences are extremely significant. For minifying the counting needs for vast size of image info and for minifying the size of time needful for the image to be address computationally. consequently, partial encryption user-face is picked. This study focuses on a large technique that is designed to encrypt the user's face slightly. Primarily, dlib is utilizing for user-face detection. Susan is one of the top edge detectors with valuable localization characteristics marked edges, is used to extract

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve