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Rutting Prediction of Asphalt Mixtures Containing Treated and Untreated Recycled Concrete Aggregate
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Rutting is a crucial element of the mechanical performance characteristics of asphalt mixtures, which was the primary target of this study. The task involved substituting various portions of virgin coarse aggregate with recycled concrete aggregate materials that had been treated or left untreated at rates ranging from 25 to 100%, with a constant increase of 25%. The treatment process of recycled concrete aggregate involved soaking in acetic acid, followed by a mechanical process for a short time inside a Los Angeles machine without the balls. This research utilized two primary tests: the standard Marshall test to identify the optimal asphalt contents and the volumetric characteristics of asphalt mixtures. The other one was the wheel tracking test, which involved manufacturing 11 slabs with a dimension of 30 x 40 × 5 cm and testing them under repeated wheel loads of 700 N at 55°C to investigate rutting resistance. The results demonstrate that using RCA had a negative impact on rutting resistance, where rut depth increased by 21.64% when using 100% pre-soaked treated RCA.

 

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Publication Date
Sat May 01 2010
Journal Name
Journal Of Baghdad College Of Dentistry
Comparison of microleakage Restorative Dentistry Comparison of microleakage in three different retrograde cavity preparations with Mineral Trioxide Aggregate as filling material
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Background: The apical seal is the single most important factor in determining the success of surgical endodontics, the aim of this study was to compare the sealing ability of Mineral Trioxide Aggregate in three different cavity designs. Materials and Methods: Thirty extracted human single-rooted teeth were divided into three groups of ten teeth per group, a retrograde cavity preparation was carried out using a low speed handpiece and round bur with parallel walls in the first group, ultrasonic retrotip and unit in the second group and a low speed handpiece with a carbide inverted cone bur with undercuts in the third group, all the cavities were filled with MTA. microleakage was measured by dye penetration technique using methylene blue. Re

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Publication Date
Sat Sep 30 2017
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Permeability Prediction By Classical and Flow Zone Indictor (FZI) Methods for an Iraqi Gas Field
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The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.

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Publication Date
Mon Oct 08 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
TOTAL ORGANIC CARBON (TOC) PREDICTION FROM RESISTIVITY AND POROSITY LOGS: A CASE STUDY FROM IRAQ
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     The open hole well log data (Resistivity, Sonic, and Gamma Ray) of well X in Euphrates subzone within the Mesopotamian basin are applied to detect the total organic carbon (TOC) of Zubair Formation in the south part of Iraq. The mathematical interpretation of the logs parameters helped in detecting the TOC and source rock productivity. As well, the quantitative interpretation of the logs data leads to assigning to the organic content and source rock intervals identification. The reactions of logs in relation to the increasing of TOC can be detected through logs parameters. By this way, the TOC can be predicted with an increase in gamma-ray, sonic, neutron, and resistivity, as well as a decrease in the density log

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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Sun Dec 30 2007
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Prediction of Fractional Hold-Up in RDC Column Using Artificial Neural Network
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In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
Evaluation of a fire safety risk prediction model for an existing building
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Abstract<p>Fire is one of the most critical risks devastating to human life and property. Therefore, humans make different efforts to deal with fire hazards. Many techniques have been developed to assess fire safety risks. One of these methods is to predict the outbreak of a fire in buildings, and although it is hard to predict when a fire will start, it is critical to do so to safeguard human life and property. This research deals with evaluating the safety risks of the existing building in the city of Samawah/Iraq and determining the appropriateness of these buildings in terms of safety from fire hazards. Twelve parameters are certified based on the National Fire Protection Association (NFPA20</p> ... Show More
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Publication Date
Fri Jan 01 2021
Journal Name
Ieee Access
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
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Publication Date
Sun Sep 05 2021
Journal Name
Journal Of Arab Statistical Union (jasu)
Using Quality Control and 6-Sigma to Determine The Quality of The Treated Wastewater Discharged From Some Water Purification Plants
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Publication Date
Thu Aug 10 2017
Journal Name
Journal Of The College Of Basic Education
Detection of some major elements and protein in Fusarium wilt disease infected tomato(Lycopersicon esculentum) treated with Arbuscular mycorrhizal fung
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The present study was Conducted to evaluate the effect of amixture of three species of arbuscular mycorrhizal fungi ( Glomus etunicatum , G. leptotichum and Rhizophagus intraradices ) in Influence on the percentage of the components of NPK and protein of tomato leaves and roots infected with Fusarium oxysporum f.sp. Lycopersici wich cause Fusarial wilt disease , planted for 8 weeks in the presence of the organic matter ( peatmose) , using pot cultures in aplastic green house , Results indicated significant increase in the percentage of the elements of NK and protein of tomato leaves and roots In the control treatment (C), While the percentage of the element P was after infection with the pathogen 4 weaks after mycorrhizal colonization in al

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Publication Date
Tue Jun 24 2025
Journal Name
Food And Bioprocess Technology
Classification of Apple Slices Treated by Atmospheric Plasma Jet for Post-harvest Processes Using Image Processing and Convolutional Neural Networks
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Abstract<p>Apple slice grading is useful in post-harvest operations for sorting, grading, packaging, labeling, processing, storage, transportation, and meeting market demand and consumer preferences. Proper grading of apple slices can help ensure the quality, safety, and marketability of the final products, contributing to the post-harvest operations of the overall success of the apple industry. The article aims to create a convolutional neural network (CNN) model to classify images of apple slices after immersing them in atmospheric plasma at two different pressures (1 and 5 atm) and two different immersion times (3 and again 6 min) once and in filtered water based on the hardness of the slices usin</p> ... Show More
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