Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

      Strong and ∆-convergence for a two-step iteration process utilizing asymptotically nonexpansive and total asymptotically nonexpansive noneslf mappings in the CAT(0) spaces have been studied. As well, several strong convergence theorems under semi-compact and condition (M) have been proved. Our results improve and extend numerous familiar results from the existing literature.

This study aims to find out the effect of the mediator on scaffolding fourth yearstudent- teachers' teaching competencies and their self-efficacy. The present study combines scaffolding and self-efficacy by using a mediator on scaffolding students affects teaching competencies and selfefficacy and from the results of which the existence of student-teachers’ selfawareness was ensured as an effect of the same independent variable. The model affects their teaching competencies and led them to be aware of the needs of their pupils and themselves. 

Acute decompensated heart failure (ADHF) is a leading cause of hospital admission and many factors are known to precipitate decompensation. We aimed to assess the decompensating factors of heart failure and the management of patients admitted to the emergency department (ED). A total of 107 patients were examined, all diagnosed with ADHF in the ED of the Baghdad Teaching Hospital, from June 2017 to December 2017, and presenting with decom¬pensation (pulmonary oedema, peripheral oedema, and fatigue). The mean patient age was 62.5 ± 9.8 years (range: 43–85 years); the majority of them were in their 7th decade (37.4%), and men were slightly more than women. Hy¬pertension was the most commonly associated comorbidity (68.2%), follow

The kindergarten teacher play a role in fixing the children behavior so she must plant the value and the habits that make a positive behavior and accepted by the society so the teacher must know all the right educational psychological styles to fix the children behavior and make them accepted psychologically and socially so the problem of the research start from knowing the relation between the methods of dealing with the kindergarten’s teachers and the non right behavior appearance for the kindergarten children. The current research aims to measure the negative behavior appearance of the children of kindergarten and distinguish it according to (sex and levels) and to distinguish the most using styles by the teachers of kinderg

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci