Blastocystis is a ubiquitous human and animal protozoa that inhabit the gastrointestinal tract. Metronidazole is considered the standard drug for the treatment of Blastocystis infection; however, there is growing evidence of treatment failure, hazardous side effects, and appearance of strains resistant to metronidazole. In the last era, many studies have been implicated in the quest for new treatments for Blastocystis infection, especially natural products. Attention has been focused on the effect of Amygdalin (B17) and pumpkin seed on eradicating parasitic infections. The current work was built up to explore the in vitro efficacy of two natural compounds, Amygdalin (B17) and pumpkin seeds against

Abstract

The study presents a mathematical model with a disaggregating approach to the problem of production planning of a fida Company; which belongs to the ministry of Industry. The study considers disaggregating the entire production into 3 productive families of (hydraulic cylinders, Aldblatt (dampers), connections hydraulics with each holds similar characteristics in terms of the installation cost, production time and stock cost. The Consequences are an ultimate use of the available production capacity as well as meeting the requirements of these families at a minimal cost using linear programming. Moreover, the study considers developing a Master production schedule that drives detailed material and production requi

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

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