Various types of storing tanks are used to store municipal water at homes. This study was carried out to examine several water physical and chemical variables such as pH, EC, TSS and TDS after different storing periods. The obtained results showed no significant differences between the mean of pH values which ranged from 7.27 ± 0.04 in a water sample of galvanized tank after six days of storing to 8.10 ± 0.12 in water sample of aluminum tank after nine storing days. In case of electric conductivity, Highest mean values (1.85±0.09 μS/cm and 1.44 ± 0.21μS/cm) were found in galvanized tank samples for different storing periods while the lowest mean values (0.58±0.06 μS/cm and 1.04±0.06 μS/cm) were recorded in aluminum tank samples

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

The thermodynamic constanting of “crude and partially purified” Paraxonase(PON) was evaluated in the sera of “healthy and ectopic” pregnant women in order to characterize the reaction of PON with diethyl para-nitro phenyl phosphate as substrate.This study was performed on (17) women with ectopic pregnancy (EP) whose age between (25-55) years  and (25) normal pregnant women with  a mean age of (25 -55) years as  a control group . Samples were collected from the Medical City, AL-Yarmook and Fatema AL-Zahraa hospitals during the period from Sep.2011 to April 2012.The study included the evaluation  of “paraxonase activity, specific activity and total protein” in the (crude and partially purified) sera of EP pa

    Glaucoma is one of the most dangerous eye diseases. It occurs as a result of an imbalance in the drainage and flow of the retinal fluid. Consequently, intraocular pressure is generated, which is a significant risk factor for glaucoma. Intraocular pressure causes progressive damage to the optic nerve head, thus leading to vision loss in the advanced stages. Glaucoma does not give any signs of disease in the early stages, so it is called "the Silent Thief of Sight". Therefore, early diagnosis and treatment of retinal eye disease is extremely important to prevent vision loss. Many articles aim to analyze fundus retinal images and diagnose glaucoma. This review can be used as a guideline to help diagnose glaucoma. It presents 63 artic

This paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors. This system is built using a personal computer and  two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor. The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique. Results showed that the motor current signature analysis (MCSA) technique, and measurement of vibration technique have high possibility in the detection and diagnosis of most mechanical faults with high accuracy. Subsequently, diagnosi

      In this paper, the optical emission spectrum (OES) technique was used to analyze the spectrum resulting from the (CdO:CoO)  plasma in air, produced by Nd:YAG laser with λ=1064 nm, τ=10 ns, a focal length of 10 cm, and a range of energy of 200-500 mJ. We identified laser-induced plasma parameters such as electron temperature (T_e) using Boltzmann plot method, density of electron (n_e), length of Debye (λ_D), frequency of plasma (f_p), and number of Debye (N_D), using two-Line-Ratio method. At a mixing ratio of X= 0.5, the (CdO:CoO) plasma spectrum was recorded for different energies. The results of plasma parameters caused by laser showed that, with t

     A global pandemic has emerged as a result of the widespread coronavirus disease (COVID-19). Deep learning (DL) techniques are used to diagnose COVID-19 based on many chest X-ray. Due to the scarcity of available X-ray images, the performance of DL for COVID-19 detection is lagging, underdeveloped, and suffering from overfitting. Overfitting happens when a network trains a function with an  incredibly high variance to represent the training data perfectly. Consequently, medical images lack the availability of large labeled datasets, and the annotation of medical images is expensive and time-consuming for experts. As the COVID-19 virus is an infectious disease, these datasets are scarce, and it is difficult to get large datasets