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Kick tolerance control during well drilling in southern Iraqi deep wells
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The importance of kick tolerance in well operations has recently increased due to its implications in well design, in drilling and well control. To study a simple method for the application of kick tolerance concept in an effective way on the basis of field data, this research purpose is to improve knowledge about Kick Tolerance and represents a technical basis for the discussion on revision of standard procedure. The objective of this work is to review and to present a methodology of determination the kick tolerance parameters using the circulation kicks tolerance concepts. The proposed method allows to know, to evaluate and to analyze the kick tolerance problem in order to make the drilling execution safer and more economical by reducing the probability to have an incident. The calculations of presented methodologies were based upon calculated input values such as ppore and pfrac. and not upon measured leak-off test and RFT (less accurate) input values as in traditional methods. The paper also analyses the calculations not with KT parameters only, but it has continued to give the killing operation procedure to such high pressure high temperature (HPHT) wells.

Publication Date
Sun Nov 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
3D scenes semantic segmentation using deep learning based Survey
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Abstract<p>Semantic segmentation realization and understanding is a stringent task not just for computer vision but also in the researches of the sciences of earth, semantic segmentation decompose compound architectures in one elements, the most mutual object in a civil outside or inside senses must classified then reinforced with information meaning of all object, it’s a method for labeling and clustering point cloud automatically. Three dimensions natural scenes classification need a point cloud dataset to representation data format as input, many challenge appeared with working of 3d data like: little number, resolution and accurate of three Dimensional dataset . Deep learning now is the po</p> ... Show More
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Publication Date
Mon Jan 01 2024
Journal Name
Computers, Materials &amp; Continua
Credit Card Fraud Detection Using Improved Deep Learning Models
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Publication Date
Sat Mar 01 2025
Journal Name
Al-khwarizmi Engineering Journal
Deep-Learning-Based Mobile Application for Detecting COVID-19
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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

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Publication Date
Mon Jan 09 2023
Journal Name
2023 15th International Conference On Developments In Esystems Engineering (dese)
Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform
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Publication Date
Mon Apr 01 2019
Journal Name
Journal Of Engineering
Rehabilitation of Reinforced Concrete Deep Beam by Epoxy Resin
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This investigation presents an experimental and analytical study on the behavior of reinforced concrete deep beams before and after repair. The original beams were first loaded under two points load up to failure, then, repaired by epoxy resin and tested again. Three of the test beams contains shear reinforcement and the other two beams have no shear reinforcement. The main variable in these beams was the percentage of longitudinal steel reinforcement (0, 0.707, 1.061, and 1.414%). The main objective of this research is to investigate the possibility of restoring the full load carrying capacity of the reinforced concrete deep beam with and without shear reinforcement by using epoxy resin as the material of repair. All be

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Publication Date
Thu Sep 15 2022
Journal Name
Romanian Neurosurgery
Cervical carotid artery vasospasm during cerebral angiography
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Background: Vasospasm occurs commonly in the intracranial arteries as a complication of subarachnoid haemorrhage. On the other hand, extracranial Internal carotid artery (ICA) vasospasm is scarce, and it may occur due to mechanical manipulation during cerebral angiography. We report a case of cervical carotid artery vasospasm during diagnostic cerebral angiography, which caused anterior cerebral artery territory hypoperfusion, to discuss potential risk factors. Case description: For a 22-year-old female with a ten-year history of epilepsy on multiple drugs, brain magnetic resonance imaging (MRI) showed frontal periventricular developmental venous anomaly. Diagnostic catheter cerebral angiography was used to better identify the vas

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Publication Date
Fri Apr 30 2021
Journal Name
Al-kindy College Medical Journal
Outcome of Perforated Gallbadder during Laparoscopic Cholecystectomy
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Background: Laparoscopic cholecystectomy (LC) has become the standard treatment for symptomatic cholelithiasis. Bile duct injury and accidental gallbladder perforation with spillage of bile and stone are common complications of LC. This study was carried out to assess the early complications of gallbladder perforation during LC, and identify the risk factor of that perforation.

Objectives: to evaluate the early complications which may occur after the perforation of the gallbladder during laparoscopic cholecystectomy and to determine the risk factors which are associated with the perforation of the gall bladder. 

Subjects and methods: A prospective comparative study o

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Publication Date
Tue Jan 14 2025
Journal Name
South Eastern European Journal Of Public Health
Deep learning-based threat Intelligence system for IoT Network in Compliance With IEEE Standard
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The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

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Publication Date
Mon Jan 01 2024
Journal Name
Fifth International Conference On Applied Sciences: Icas2023
A modified Mobilenetv2 architecture for fire detection systems in open areas by deep learning
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This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

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Publication Date
Sat Dec 31 2022
Journal Name
Iraqi Geological Journal
Geochemical Criteria for Discriminating Shallow and Deep Environments in Oligocene-Miocene Succession, Western Iraq
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The geochemical study of the Oligocene-Miocene succession Anah, Euphrates, and Fatha formations, western Iraq, was carried out to discriminate their depositional environments. Different major and trace patterns were observed between these formations. The major elements (Ca, Mg, Fe, Mn, K, and Na) and trace elements (Li, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Zr, Cs, Ba, Hf, W, Pb, Th, and U) are a function of the setting of the depositional environments. The reefal facies have lower concentrations of MgO, Li, Cr, Co, Ni, Ga, Rb, Zr, and Ba than marine and lagoonal facies but have higher concentrations of CaO, V, and Sr than it. Whereas dolomitic limestone facies are enriched V, and U while depletion in Li, Cr, Ni, Ga, Rb, Sr, Zr, Ba, an

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