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Removal of Vanadium and Nickel Ions from Iraqi Atmospheric Residue by Using Solvent Extraction Method
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Iraqi crude Atmospheric residual fraction supplied from al-Dura refinery was treated to remove metals contaminants by solvent extraction method, with various hydrocarbon solvents and concentrations. The extraction method using three different type solvent (n-hexane, n-heptane, and light naphtha) were found to be effective for removal of oil-soluble metals from heavy atmospheric residual fraction. Different solvents with using three different hydrocarbon solvents (n-hexane, n-heptane, and light naphtha) .different variables were studied solvent/oil ratios (4/1, 8/1, 10/1, 12/1, and 15/1), different intervals of perceptual (15, 30-60, 90 and 120 min) and different temperature (30, 45, 60 and 90 °C) were used. The metals removal percent were found depending on the yield of asphaltene. The solvent-oil ratio had important effects on the amount of metal removal. The metals removal was increased at increasing temperatures from 30 to 90 0C increases the metal ion precipitated. The highest Ni precipitated was 79.23 ppm using heptane at 90 0C while for V the highest value was 64.51 ppm using also heptane at 90 0C, while the mixing time decreased metals removal. With increasing asphalt yield, the removal of metal was more selective. Among the solvents used in the extraction treatment method, the highest Ni precipitated was 76 ppm using hexane at 150 ml solvent and showed the most promising results. Increasing mixing time increases metals removal for V, the highest value was 65.51 ppm using either heptane or light naphtha.    The highest Ni precipitated was 78 ppm using heptane at 120 min while for V the highest value was 67 ppm using either heptane or light naphtha after 120 min.

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Publication Date
Mon Oct 31 2022
Journal Name
International Journal Of Intelligent Engineering And Systems
Robot Path Planning in Unknown Environments with Multi-Objectives Using an Improved COOT Optimization Algorithm
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Publication Date
Tue Jun 14 2016
Journal Name
Transactions Of The Asabe
Using Terahertz Time-Domain Spectroscopy to Discriminate among Water Contamination Levels in Diesel Engine Oil
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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Intelligent Systems
Trip generation modeling for a selected sector in Baghdad city using the artificial neural network
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Abstract<p>This study is planned with the aim of constructing models that can be used to forecast trip production in the Al-Karada region in Baghdad city incorporating the socioeconomic features, through the use of various statistical approaches to the modeling of trip generation, such as artificial neural network (ANN) and multiple linear regression (MLR). The research region was split into 11 zones to accomplish the study aim. Forms were issued based on the needed sample size of 1,170. Only 1,050 forms with responses were received, giving a response rate of 89.74% for the research region. The collected data were processed using the ANN technique in MATLAB v20. The same database was utilized to</p> ... Show More
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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
Thu Oct 20 2022
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Turbidity Changes in the Tigris River Using Satellite Data During the COVID-19 Related Lockdown
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In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity during lockdown associated with COVID-19. Iraq announced the longest-ever lockdown on 25 March 2020 due to COVID-19 pandemic. 

In the absence of ground observations, remote sensing data was adopted, especially during this period. The change in the visible region's spectral reflectance of water in part of the river has been analyzed using the Landsat 8 OLI multispectral remote sensing data at Tigris River, Salah al-Din province (Bayji / near the refinery), Iraq. It was found that the green and red bands are most sensitive and can be used to estimate turbidity. Furthermore, the temporal variation in turbidity was a

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Publication Date
Tue Jan 30 2024
Journal Name
Iraqi Journal Of Science
Predicting COVID-19 in Iraq using Frequent Weighting for Polynomial Regression in Optimization Curve Fitting
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     The worldwide pandemic Coronavirus (Covid-19) is a new viral disease that spreads mostly through nasal discharge and saliva from the lips while coughing or sneezing. This highly infectious disease spreads quickly and can overwhelm healthcare systems if not controlled. However, the employment of machine learning algorithms to monitor analytical data has a substantial influence on the speed of decision-making in some government entities.        ML algorithms trained on labeled patients’ symptoms cannot discriminate between diverse types of diseases such as COVID-19. Cough, fever, headache, sore throat, and shortness of breath were common symptoms of many bacterial and viral diseases.

This research focused on the nu

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Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Comparison Some Robust Estimators for Estimate parameters logistic regression model to Binary Response – using simulation)).
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 The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
Enhancement Digital Forensic Approach for Inter-Frame Video Forgery Detection Using a Deep Learning Technique
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    The digital world has been witnessing a fast progress in technology, which led to an enormous increase in using digital devices, such as cell phones, laptops, and digital cameras. Thus, photographs and videos function as the primary sources of legal proof in courtrooms concerning any incident or crime. It has become important to prove the trustworthiness of digital multimedia. Inter-frame video forgery one of common types of video manipulation performed in temporal domain. It deals with inter-frame video forgery detection that involves frame deletion, insertion, duplication, and shuffling. Deep Learning (DL) techniques have been proven effective in analysis and processing of visual media. Dealing with video data needs to handle th

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Publication Date
Tue Aug 01 2023
Journal Name
Iop Conference Series: Earth And Environmental Science
Modis Satellite Data Evaluation for Detecting the Dust Storm Using Remote Sensing Techniques Over Iraq
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Abstract<p>The phenomena of Dust storm take place in barren and dry regions all over the world. It may cause by intense ground winds which excite the dust and sand from soft, arid land surfaces resulting it to rise up in the air. These phenomena may cause harmful influences upon health, climate, infrastructure, and transportation. GIS and remote sensing have played a key role in studying dust detection. This study was conducted in Iraq with the objective of validating dust detection. These techniques have been used to derive dust indices using Normalized Difference Dust Index (NDDI) and Middle East Dust Index (MEDI), which are based on images from MODIS and in-situ observation based on hourly wi</p> ... Show More
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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition
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