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

Objective: Synthesis, Characterization of formazan derivatives and studies the antioxidant activity of prepared compounds and molecular docking. Methods: In this study, formazan compounds (III–XIV) were produced by combining Schiff base compounds (I), (II) with diazonium salts resulting from reactions of different aromatic amines with sodium nitrate in the presence of Con.HCl at 0–5°C. When isonicotinic acid hydrazide reacts with (N,N-dimethylbenzaldehyde, 4-hydroxy-3-methoxybenzaldehyde) in the presence glacial acetic acid as a solvent Schiff base compounds are created. Results: The prepared compounds were identified by FT-IR, 1H NMR, 13C NMR, then the antioxidant activity of the derivatives and molecular docking were studied. D

By unusual method for separating two isomers of a substituted nitro-coumarin using a soxhlet extractor and in controlling temperature to get a selective nitration reaction, several new Schiff base coumarins were synthesized from nitro coumarins as starting material, which were reduced by Fe in glacial acetic acid to produce corresponding amino coumarin derivatives. Then the latter was reacted with different aromatic aldehydes to produce the desired Schiff bases derivatives. After characterization by Fourier transform infrared (FT-IR), Proton nuclear magnetic resonance (1HNMR) and Carbon-13 nuclear magnetic resonance (C-NMR), all these compounds were evaluated as potential Antimicrobial and Antioxidant Agents.

Thirty swabes of medical implants were collected from Al-Yarmouk's hospital which were cultured on manitole agar to isolate Staphelococcus aureus . Only four samples gave positive results with this media. It was used ten types of antibiotics to test the sensitivity of this bacterium against them. All isolates of S. aureus were recorded as multidrug resistant and were considered as MRSA. One pledge alternative therapy is the utilize of certain pure bacterocin MIC (32.5 to 62.5 μg/ml) and it was compared with vancomycin (200-400 μg/ml) with average of (8 – 15) mm diameter of inhibition zones recpectively. The first reduction of biofilm formation ability has been proved in catheters when treatedby pure bacterocin. The test shows the highes

Oral fast dissolving films (OFDFs) are the most innovative oral solid pharmaceutical dosage form, especially for elderly and pediatric patients who may have dysphagia. Bilastine (BLA), is a second – generation antihistamine used to manage allergy symptoms; it is very slightly soluble in water. The main objective of this research was to enhance the solubility and dissolution rate of BLA by complexation technique. Ternary complex of BLA: methyl β- cyclodextrin (M-β-CD): soluplus® 5% w/w was prepared via solvent evaporation technique as a trial to enhance its solubility to be prepared as OFDF by incorporated into aqueous polymeric solution. Seven formulas of OFDFs were prepared using the solvent casting method using

This paper suggests a novel optimal inverse Radial Basis Function (RBF) neural network model for the control of Twin Rotor Aerodynamic Systems (TRAS), such as Multi-Input–Multi-Output (MIMO) systems with high nonlinearity and coupling effects between channels. After analyzing and linearizing the dynamic model, TRAS is decoupled into two Single Input Single Output (SISO) systems, thereby creating vertical (pitch model) and horizontal (yaw model) systems. The relationship between the output angle of each subsystem and the input voltage is modeled using the inverse RBF neural network. The weights, biases, centers and widths of the Gaussian function are unknown parameters of the proposed inverse neural model, and they are obtained usi

Although allowable amounts of glycol contamination in diesel engine oil, no research has been conducted on how these levels and varying loads affect engine performance. The research used a four-stroke diesel engine to investigate the effect of different glycol contamination levels (0, 120, and 220 ppm) under two engine loads (4.5 and 9 kW). Brake specific fuel consumption, brake thermal efficiency, friction power, and exhaust gas temperature were measured to determine the engine performance. The experiment used the factorial arrangement in a completely randomized design (CRD) with three replicates. Increasing the contamination levels from 0 to 120 and then to 220 ppm under constant engine load significantly increased brake specific fuel con

Research in consumer science has proven that grocery shopping is a complex and distressing process. Further, the task of generating the grocery lists for the grocery shopping is always undervalued as the effort and time took to create and manage the grocery lists are unseen and unrecognized. Even though grocery lists represent consumers’ purchase intention, research pertaining the grocery lists does not get much attention from researchers; therefore, limited studies about the topic are found in the literature. Hence, this study aims at bridging the gap by designing and developing a mobile app (application) for creating and managing grocery lists using modern smartphones. Smartphones are pervasive and become a necessity for everyone tod

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co