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Deep Learning Approach for Oil Pipeline Leakage Detection Using Image-Based Edge Detection Techniques
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Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are sent to the base station. Using deep learning approaches such as holistically-nested edge detection (HED) and extreme inception (Xception) networks, images are analyzed at the base station to identify contours using dense extreme inception networks for edge detection (DexiNed). This algorithm is capable of finding many contours in images. Moreover, the CIELAB color space (LAB) is employed to locate black-colored contours, which may indicate oil spills. The suggested method involves eliminating smaller contours to calculate the area of larger contours. If the contour's area exceeds a certain threshold, it is classified as a spill; otherwise, it is stored in a database for further review. In the experiments, spill sizes of 1m2, 2m2, and 3m2 were established at three separate test locations. The drone was operated at three different heights (5 m, 10 m, and 15 m) to capture the scenes. The results show that efficient detection can be achieved at a height of 10 meters using the DexiNed algorithm. Statistical comparison with other edge detection methods using basic metrics, such as perimage best threshold (OIS = 0.867), fixed contour threshold (ODS = 0.859), and average precision (AP = 0.905), validates the effectiveness of the DexiNed algorithm in generating thin edge maps and identifying oil slicks. © 2023 Lavoisier. All rights reserved.

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Enhancing Nitrate Ion Removal from Water using Fixed Bed Columns with Composite Chitosan-based Beads
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    Water contamination is a pressing global concern, especially regarding the presence of nitrate ions. This research focuses on addressing this issue by developing an effective adsorbent for removing nitrate ions from aqueous solutions. two adsorbents Chitosan-Zeolite-Zirconium (Cs-Ze-Zr composite beads and Chitosan-Bentonite-Zirconium Cs-Bn-Zr composite beads were prepared. The study involved continuous experimentation using a fixed bed column with varying bed heights (1.5 and 3 cm) and inlet flow rates (1 and 3 ml/min). The results showed that the breakthrough time increased with higher bed heights for both Cs-Ze-Zr and Cs-Bn-Zr composite beads. Conversely, an increase in flow rate led to a decrease in breakthrough time. Notab

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Publication Date
Tue Dec 01 2020
Journal Name
Baghdad Science Journal
Real-Time Cloth Simulation on Virtual Human Character Using Enhanced Position Based Dynamic Framework Technique
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     Cloth simulation and animation has been the topic of research since the mid-80's in the field of computer graphics. Enforcing incompressible is very important in real time simulation. Although, there are great achievements in this regard, it still suffers from unnecessary time consumption in certain steps that is common in real time applications.   This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing. This research achieves success in cloth simulation on the VHC through enhancing the position-based dynamics (PBD) framework by computing a series of positional constraints which implement constant densities. Also, the self-collision and collision wit

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Evaluating Filtration and Thermal Stability of Water-Based Mud Using Green Synthesized Zinc Oxide Nanoparticles
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   Nanoparticles (NPs) have unique capabilities that make them an eye-opener opportunity for the upstream oil industry. Their nano-size allows them to flow within reservoir rocks without the fear of retention between micro-sized pores. Incorporating NPs with drilling and completion fluids has proved to be an effective additive that improves various properties such as mud rheology, filtration, thermal conductivity, and wellbore stability. However, the biodegradability of drilling fluid chemicals is becoming a global issue as the discharged wetted cuttings raise toxicity concerns and environmental hazards. Therefore, it is urged to utilize chemicals that tend to break down and susceptible to biodegradation. This research presents the pra

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Publication Date
Thu Jul 31 2025
Journal Name
Computers, Materials & Continua
A Secure Audio Encryption Method Using Tent-Controlled Permutation and Logistic Map-Based Key Generation
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The exponential growth of audio data shared over the internet and communication channels has raised significant concerns about the security and privacy of transmitted information. Due to high processing requirements, traditional encryption algorithms demand considerable computational effort for real-time audio encryption. To address these challenges, this paper presents a permutation for secure audio encryption using a combination of Tent and 1D logistic maps. The audio data is first shuffled using Tent map for the random permutation. The high random secret key with a length equal to the size of the audio data is then generated using a 1D logistic map. Finally, the Exclusive OR (XOR) operation is applied between the generated key and the sh

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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Study the Chemical Bonding of Heterometallic Trinuclear Cluster Containing Cobalt and Ruthenium: [(Cp*Co) (CpRu)2 (μ3-H) (μ-H)3] using QTAIM Approach
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The topological parameters of the metal-metal and metal-ligand bonding interactions in a trinuclear tetrahydrido cluster [(Cp*Co) (CpRu)2 (μ3-H) (μ-H)3]1 (Cp* = η5 -C5Me4Et), (Cp = η5 -C5Me5), was explored by using the Quantum Theory of Atoms-in-Molecules (QTAIM). The properties of bond critical points such as the bond delocalization indices δ (A, B), the electron density ρ(r), the local kinetic energy density G(r), the Laplacian of the electron density ∇2ρ(r), the local energy density H(r), the local potential energy density V(r) and ellipticity ε(r) are compared with data from earlier organometallic system studies. A comparison of the topological processes of different atom-atom interactions has become possible than

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Publication Date
Sun Jan 01 2017
Journal Name
Kufa Journal For Nursing Sciences
Effectiveness of Simulation Techniques on the Nursing Students Knowledge toward Cardiopulmonary Resuscitation for Adults at College of Nursing/University of Baghdad
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MM Abdulwahhab, kufa Journal for Nursing sciences, 2017 - Cited by 1

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Publication Date
Sun Feb 10 2019
Journal Name
Journal Of The College Of Education For Women
The Influence of Teacher and Students Errors Correction Techniques on Students’ Achievement in English at College of Physical Education for Women
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The research is an attempt to investigate experimentally the influence of teacher’s errors correction and students’ errors correction on teaching English at the College of Physical Education for Women. Errors are seen as a natural way for developing any language but teachers are puzzled the way they can correct these errors. So, this research gives some idea of using two types of errors correction. The sample of the research is female students of the first year stage at the College of Physical Education for Women of the academic year 2009-2010. The whole population of the research is (94) students while the sample is (64). Thus, the sample represents 68% from the population of the research. The sample represents It is hypothesized th

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Publication Date
Sun Oct 19 2025
Journal Name
Lecture Notes In Networks And Systems
Artificial Intelligence Techniques in the Geographical Analysis of Under-Five Child Mortality in the Central Euphrates Provinces for the Year 2022
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Abstract This research adopted a novel mixed-method approach combining quantitative analysis and geospatial statistics with artificial intelligence (AI) techniques to evaluate U5MR in the Middle Euphrates provinces of Iraq in 2022. We investigated spatial patterns, socioeconomic determinants and health system factors related to child mortality, Using data from the Iraqi Ministry of Health in 2022. We uncovered significant disparities across the regions, where the highest death rate in Al Diwaniyah province was 33.1 per 1000 and dropped to 16.6 per 1000 live births in al-Muthanna province. An exhaustive statistical analysis brought to the fore the main factors that accounted for such a variation, that is to say population density in rural a

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Publication Date
Thu Jan 01 2015
Journal Name
Journal Of Traumatic Stress Disorders & Treatment
Randomised Controlled Study Comparing Two Psychological Therapies for Posttraumatic Stress Disorder (PTSD): Emotional Freedom Techniques (EFT) vs. Narrative Exposure Therapy (NET)
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Publication Date
Sun Jun 28 2026
Journal Name
Journal Of Physical Education
The Learning Gap between Classroom Education and Artificial Intelligence-Based Education in Curriculum Design among Students of the College of Physical Education and Sports Sciences from the Students' Perspective
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       The present study aimed to identify the learning gap between classroom-based education and artificial intelligence (AI)-based education among students of Colleges of Physical Education and Sports Sciences. It also sought to determine the effectiveness of classroom education and AI-based education in reducing the learning gap among these students. The research problem addressed the following questions: Are there statistically significant differences in the learning gap between students who learn through traditional classroom education and those who learn through AI-based education in Colleges of Physical Education and Sports Sciences? What is the nature of the learning gap between classroom education and AI-based educati

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