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Fault Location of Doukan-Erbil 132kv Double Transmission Lines Using Artificial Neural Network ANN
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's performance was evaluated, and tests were run. Line-to-ground faults were examined. The study demonstrates how effective, rapid, and precise this method is at locating faults. The neural network's performance was examined, and tests were run on it. The overall performance of the mean square error in the trained network execution was 0.11792 at 35 epochs. The correlation coefficient at the entire target was 0.99987 percent of an error on the Doukan-Erbil double transmission lines.

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Publication Date
Fri Feb 08 2019
Journal Name
Iraqi Journal Of Laser
Human Skin WoundWelding Using 980 nm Diode Laser: an in Vitro Experimental Study
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Laser assisted skin wound closure offers many distinct advantages over conventional closure
techniques. The objective of this in vitro experimental study, carried out at the Institute of Laser for
Postgraduate Studies/Baghdad University, was to determine the effectiveness of 980 nm diode laser in
welding of human skin wounds. Multiple 3-4 cm long full thickness incisions in a specimen of human
skin obtained from the discarded panniculus of an Abdominoplasty operation were tried to be laser
welded using a 4 mm spot diameter laser beam from a 980 nm diode laser at different laser parameters
and modes of action. The tensile strength at the weld site was analyzed experimentally. Although laser
assisted wound welding did

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Publication Date
Wed Jun 04 2025
Journal Name
Engineering, Technology & Applied Science Research
Damage Assessment for Refinement Towers in Oil Installations using 3D Laser Scanning Technique
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Terrestrial Laser Scanning (TLS) can be used to detect damage and analyze volumetric changes in large structural test specimens. Compared to traditional imaging and surface analysis methods, the primary reason for using 3D scanning is the difficulty of attaching targets to towers of varying heights, as well as the fact that some towers are covered with insulating materials that obscure key landmarks. In addition, the large number of incoming and outgoing pipes and stairs attached to the towers further complicates traditional assessment methods. This research focuses on assessing the damage to the three refining towers at the Baiji Refinery facility. Scanning was performed using a 3D laser scanner with HDR color imaging, capturing da

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
Concrete strength development by using magnetized water in normal and self-compacted concrete
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Abstract<p>The main objective of this work was to adopt an environmentally friendly technology with enhanced results. The technology of magnetic water (MW) treatment system can be used in concrete mixture production instead of potable water (PW) to improve both workability and strength. Two types of concrete were adopted: normal concreter production with two grades 25 and 35 MPa and the self-compacted concrete (SCC) with 35 MPa grade. The concrete mixes containing MW instead of PW results showed that, for 25 MPa grade, an improvement in a compressive strength of 15.1, 14.8, and 10.2% was achieved for 7, 28, and 90 days, respectively. For 35 MPa grade, an improvement of 13.6, 11.5, and </p> ... Show More
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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset
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Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

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Publication Date
Tue Sep 14 2021
Journal Name
Egyptian Journal Of Chemistry
Real wastewater Treatment by Electrocoagulation- Electro-oxidation Combined System: Optimization using Taguchi Approach
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In this study, the optimum conditions for COD removal from petroleum refinery wastewater by using a combined electrocoagulation- electro-oxidation system were attained by Taguchi method. An orthogonal array experimental design (L18) which is of four controllable parameters including NaCl concentration, C.D. (current density), PH, and time (time of electrolysis) was employed. Chemical oxygen demand (COD) removal percentage was considered as the quality characteristics to be enhanced. Also, the value of turbidity and TDS (total dissolved solid) were estimated. The optimum levels of the studied parameters were determined precisely by implementing S/N analysis and analysis of variance (ANOVA). The optimum conditions were found to be NaCl = 2.5

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Industrial And Engineering Chemistry
Petroleum refinery wastewater treatment using a novel combined electro-Fenton and photocatalytic process
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Publication Date
Mon Mar 20 2023
Journal Name
2023 International Conference On Information Technology, Applied Mathematics And Statistics (icitams)
Hybrid Color Image Compression Using Signals Decomposition with Lossy and Lossless Coding Schemes
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Publication Date
Tue Sep 01 2020
Journal Name
Clinical Plasma Medicine
Breast cancer treatment using cold atmospheric plasma generated by the FE-DBD scheme
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Background Cold atmospheric plasma (CAP) is widely used in the cancer therapy field. This type of plasma is very close to room temperature. This paper illustrates the effects of CAP on breast cancer tissues both in vivo and in vitro. Methods The mouse mammary adenocarcinoma cell line AN3 was used for the in vivo study, and the MCF7, AMJ13, AMN3, and HBL cell lines were used for the in vitro study. A floating electrode-dielectric barrier discharge (FE-DBD) system was used. The cold plasma produced by the device was tested against breast cancer cells. Results The induced cytotoxicity percentages were 61.7%, 68% and 58.07% for the MCF7, AMN3, and AMJ13 cell lines, respectively, whereas the normal breast tissue HBL cell line exhibited very li

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Publication Date
Fri Mar 31 2017
Journal Name
Al-khwarizmi Engineering Journal
Big-data Management using Map Reduce on Cloud: Case study, EEG Images' Data
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Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG r

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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