The Jeribe Formation, the Jambour oil field, is the major carbonate reservoir from the tertiary reservoirs of the Jambour field in northern Iraq, including faults. Engineers have difficulty organizing carbonate reserves since they are commonly tight and heterogeneous. This research presents a geological model of the Jeribe reservoir based on its facies and reservoir characterization data (Permeability, Porosity, Water Saturation, and Net to Gross). This research studied four wells. The geological model was constructed with the Petrel 2020.3 software. The structural maps were developed using a structural contour map of the top of the Jeribe Formation. A pillar grid model with horizons and layering was designed for each zone. Followin

In this paper an estimator of reliability function for the pareto dist. Of the first kind has been derived and then a simulation approach by Monte-Calro method was made to compare the Bayers estimator of reliability function and the maximum likelihood estimator for this function. It has been found that the Bayes. estimator was better than maximum likelihood estimator for all sample sizes using Integral mean square error(IMSE).

 Abstract

The study of oxygen mass transfer was conducted in a laboratory scale 5 liter stirred bioreactor equipped with one Rushton turbine impeller. The effects of superficial gas velocity, impeller speed, power input and liquid viscosity on the oxygen mass transfer were considered. Air/ water and air/CMC systems were used as a liquid media for this study. The concentration of CMC was ranging from 0.5 to 3 w/v. The experimental results show that volumetric oxygen mass transfer coefficient increases with the increase in the superficial gas velocity and impeller speed and decreases with increasing liquid viscosity. The experimental results of k_la were correlated with a mathematical correlation des

In this paper, we build a fuzzy classification system for classifying the nutritional status of children under 5 years old in Iraq using the Mamdani method based on input variables such as weight and height to determine the nutritional status of the child. Also, Classifying the nutritional status faces a difficult challenge in the medical field due to uncertainty and ambiguity in the variables and attributes that determine the categories of nutritional status for children, which are relied upon in medical diagnosis to determine the types of malnutrition problems and identify the categories or groups suffering from malnutrition to determine the risks faced by each group or category of children. Malnutrition in children is one of the most

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

Hepatitis is one of the diseases that has become more developed in recent years in terms of the high number of infections. Hepatitis causes inflammation that destroys liver cells, and it occurs as a result of viruses, bacteria, blood transfusions, and others. There are five types of hepatitis viruses, which are (A, B, C, D, E) according to their severity. The disease varies by type. Accurate and early diagnosis is the best way to prevent disease, as it allows infected people to take preventive steps so that they do not transmit the difference to other people, and diagnosis using artificial intelligence gives an accurate and rapid diagnostic result. Where the analytical method of the data relied on the radial basis network to diagnose the

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Background: Penetrating neck injuries are common problem in our country due to increasing violence, terrorist bombing and military operations.
These injuries are potentially life threating and need great attention and proper management.
Objective: The aim of this study is to focus on the importance of anatomical zonal classification of the neck in the management of penetrating injuries of the visceral compartment of the Neck.
Methods :70 patients with various injuries who were managed at causality unit and Otolaryngology department in Al-Kindy Teaching Hospital during aperiod from January 1st 2015 to October 31st 2015.
The study carried on those patient depending on proper clinical examination and their urgent management.

Error control schemes became a necessity in network-on-chip (NoC) to improve reliability as the on-chip interconnect errors increase with the continuous shrinking of geometry. Accordingly, many researchers are trying to present multi-bit error correction coding schemes that perform a high error correction capability with the simplest design possible to minimize area and power consumption. A recent work, Multi-bit Error Correcting Coding with Reduced Link Bandwidth (MECCRLB), showed a huge reduction in area and power consumption compared to a well-known scheme, namely, Hamming product code (HPC) with Type-II HARQ. Moreover, the authors showed that the proposed scheme can correct 11 random errors which is considered a high