Human cytomegalovirus (CMV) is the globally highly prevalent herpesvirus worldwide. CMV infects populations of all ages according to the Center for Disease Control and Prevention (CDC) and World Health Organization (WHO). CMV infections remain the most common viral complication potentially multiple in humans and are a major cause of congenital normality in women, which is why they are critical for diagnosis in several times when it happens during pregnancy. Pregnant women with CMV infection can be in charge of abortion or congenital expandaedby. This study involves the collection a total of (90) samples taken from each aborted and pregnant woman (70 with abortion cases and 20 of pregnant without history of abortion as control subjects) r

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

The process of accurate localization of the basic components of human faces (i.e., eyebrows, eyes, nose, mouth, etc.) from images is an important step in face processing techniques like face tracking, facial expression recognition or face recognition. However, it is a challenging task due to the variations in scale, orientation, pose, facial expressions, partial occlusions and lighting conditions. In the current paper, a scheme includes the method of three-hierarchal stages for facial components extraction is presented; it works regardless of illumination variance. Adaptive linear contrast enhancement methods like gamma correction and contrast stretching are used to simulate the variance in light condition among images. As testing material

An experimental study on a KIA pride (SAIPA 131) car model with scale of 1:14 in the wind tunnel was made beside the real car tests. Some of the modifications to passive flow control which are (vortex generator, spoiler and slice diffuser) were added to the car to reduce the drag force which its undesirable characteristic that increase fuel consumption and exhaust toxic gases. Two types of calculations were used to determine the drag force acting on the car body. Firstly, is by the integrating the values of pressure recorded along the pressure taps (for the wind tunnel and the real car testing), secondly, is by using one component balance device (wind tunnel testing) to measure the force. The results show that, the avera

In this work, results from an optical technique (laser speckle technique) for measuring surface roughness was done by using statistical properties of speckle pattern from the point of view of computer image texture analysis. Four calibration relationships were used to cover wide range of measurement with the same laser speckle technique. The first one is based on intensity contrast of the speckle, the second is based on analysis of speckle binary image,  the third is on size of speckle pattern spot, and the latest one is based on characterization of the energy feature of the gray level co-occurrence matrices for the speckle pattern. By these calibration relationships surface roughness of an object surface can be evaluated within the

An experimental study on a KIA pride (SAIPA 131) car model with scale of 1:14 in the wind tunnel was made beside the real car tests. Some of the modifications to passive flow control which are (vortex generator, spoiler and slice diffuser) were added to the car to reduce the drag force which its undesirable characteristic that increase fuel consumption and exhaust toxic gases. Two types of calculations were used to determine the drag force acting on the car body. Firstly, is by the integrating the values of pressure recorded along the pressure taps (for the wind tunnel and the real car testing), secondly, is by using one component balance device (wind tunnel testing) to measure the force. The results show that, the average drag estimated on

There many methods for estimation of permeability. In this Paper, permeability has been estimated by two methods. The conventional and modified methods are used to calculate flow zone indicator (FZI). The hydraulic flow unit (HU) was identified by FZI technique. This technique is effective in predicting the permeability in un-cored intervals/wells. HU is related with FZI and rock quality index (RQI). All available cores from 7 wells (Su -4, Su -5, Su -7, Su -8, Su -9, Su -12, and Su -14) were used to be database for HU classification. The plot of probability cumulative of FZI is used. The plot of core-derived probability FZI for both modified and conventional method which indicates 4 Hu (A, B, C and D) for Nahr Umr forma

The determination of river pollution impact on the performance of water treatment plants is achieved by two main objectives. The first is to study raw and treated water qualities and comparing them with standards and the second is to evaluate the treatment plants efficiency. The analyzed data were those water quality parameters in relation to physical, chemical and bacteriological characteristics for river water and produced water by seven water treatment plants located on Tigris River passing through Baghdad City.
The results of this study indicated that all raw water characteristic are within the surface water standards established by Iraqi and USA criteria except Bacterial Counts.
Tigris River water is of good quality to be trea

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a