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Prediction of The Chemical Composition and Physical properties of Aged Asphalt Cement
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In the present work a modification was made on three equations to represent the
experiment data which results for Iraqi petroleum and natural asphalt. The equations
have been developed for estimating the chemical composition and physical properties
of asphalt cement at different temperature and aging time. The standard deviations of
all equations were calculated.
The modified correlation related to the aging time and temperature with penetration
index and durability index of aged petroleum and natural asphalts were developed.
The first equation represents the relationship between the durability index with aging
time and temperature.

loge(DI)=a1+0.0123(2loge T-a3(1/30.t2+1/2.t))


The second equation represents the relationship between the penetration index with
aging time and temperature.

Log(PI)e=b1-0.2013(T-(b3+b4t))2
The third equation represents the relationship between the durability index with
penetration index.

Logeloge(PI)=a-0.5627loge(DI)
The values of penetration index and durability index for all aged samples were
compared with predicted values. These correlations give a percent of error in the
range of 1.2 to 7.4%.

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Publication Date
Sun Feb 03 2019
Journal Name
Iraqi Journal Of Physics
Studying the effect of silica (SiO2) addition on the adhesive properties of polyvinyl alcohol
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The work concerned with studying the effect of (SiO2) addition as a
filler on the adhesive properties of (PVA). Samples were prepared as
sheets by using casting method. The mechanical properties showed
that increase in tensile strength from (34MPa) to (68MPa) when
(SiO2) added to (PVA). The adhesive strength showed that joint
properties depend upon specific adhesive characteristic of material
(PVA) and (SiO2\PVA)composites at different concentrations (1.5%,
2.5%, 3.5%, 4.5wt%), the cohesive strength of the adhesive material,
the joint design, and adherent type (Sponge Rubber(SR), Natural
leather (NL), Vulcanized Rubber(VR), and Cartoon). The results
proved the tensile strength increased with (SiO2) ratio, so

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Publication Date
Sun Sep 04 2011
Journal Name
Baghdad Science Journal
Study the Effect of Ocimum basilscm oil in the Sensory properties of Laboratory Biscuit
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The study aims at showing the effect of basil oil on the sensory properties of the laboratory biscuits. the results show the sensory valuation before and after the storage. the (A4) equation exceeds 15% of T exchange (3 , 4 , 5 g. basil 19 , 55 g . fats )in most of the studied sensuous qualities. Then, the (A5) equation of 20% exchange percentage (4.60g. basil oil + 18.40g. fats). Then, the (A3) equation of 10% exchange percentage (2.30g. basil oil + 20.70g. fats). Then the (A2) equation of 5% exchange percentage (1.5g. basil oil + 21.85g. fats). Finally, the control equation (A1) received the lower value of sensuous evaluation and general acceptance. Abstract differences also appeared at denotation level 0.05 between the (A2)

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Publication Date
Fri Jan 11 2019
Journal Name
Iraqi Journal Of Physics
Effect of thickness on the optical properties of ZnO thin films prepared by pulsed laser deposition technique (PLD)
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Zinc Oxide (ZnO) thin films of different thickness were prepared
on ultrasonically cleaned corning glass substrate, by pulsed laser
deposition technique (PLD) at room temperature. Since most
application of ZnO thin film are certainly related to its optical
properties, so the optical properties of ZnO thin film in the
wavelength range (300-1100) nm were studied, it was observed that
all ZnO films have high transmittance (˃ 80 %) in the wavelength
region (400-1100) nm and it increase as the film thickness increase,
using the optical transmittance to calculate optical energy gap (Eg
opt)
show that (Eg
opt) of a direct allowed transition and its value nearly
constant (~ 3.2 eV) for all film thickness (150

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Publication Date
Tue Jan 01 2019
Journal Name
Aip Conference Proceedings
Studying the partial substitution of barium with cadmium oxide and its effect on the electrical and structural properties of HgBa2Ca2Cu3O8+δ superconducting compound
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Publication Date
Sat Jan 01 2022
Journal Name
The 2nd Universitas Lampung International Conference On Science, Technology, And Environment (ulicoste) 2021
Study the effect of size variation and stability on the electronic and spectroscopic properties of BN wurtzoids- diamantane nanostructure via density functional theory
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Publication Date
Thu Nov 29 2018
Journal Name
Al-khwarizmi Engineering Journal
Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
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Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Permeability Prediction for Nahr-Umr Reservoir / Subba field by Using FZI Method
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 The permeability determination in the reservoirs that are anisotropic and heterogeneous is a complicated problem due to the limited number of wells that contain core samples and well test data. This paper presents hydraulic flow units and flow zone indicator for predicting permeability of rock mass from core for Nahr-Umr reservoir/ Subba field. The Permeability measurement is better found in the laboratory work on the cored rock that taken from the formation. Nahr-Umr Formation is the main lower cretaceous sandstone reservoir in southern of Iraq. This formation is made up mainly of sandstone. Nahr-Umr formation was deposited on a gradually rising basin floor. The digenesis of Nahr-Umr sediments is very important du

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Publication Date
Sun Nov 01 2020
Journal Name
Journal Of Engineering
Convolutional Multi-Spike Neural Network as Intelligent System Prediction for Control Systems
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The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

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Publication Date
Thu Oct 31 2024
Journal Name
Iraqi Geological Journal
Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log
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Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

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Publication Date
Mon Mar 31 2025
Journal Name
International Journal Of Advanced Technology And Engineering Exploration
Breast cancer survival rate prediction using multimodal deep learning with multigenetic features
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Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

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