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Diagnostic Evaluation of Uterine Artery Doppler Imaging for the Prediction of Early Abnormal Pregnancy
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Objective: to assess the predictive value of Doppler imaging of the uterine artery in the identification of early intrauterine abnormal pregnancy as compared to a normal intrauterine pregnancy.

Subjects and methods: one hundred and twenty pregnant ladies, at their 6-12 weeks of gestation, with a singleton pregnancy were included in this population-based case-control study. Thirty women with a missed miscarriage, 30 with hydatidiform mole, 30 with a blighted ovum, and 30 as a control group, without risk factors, underwent Doppler interrogation of the uterine arteries. Resistive index (RI), pulsatility index (PI), and the systolic/diastolic ratio (S/D) were measured for both sides. The t-test, or ANOVA test when appropriate, was used to analyze the relationship between the variables.

Results: there was a significant reduction of RI mean, PI mean, and S/D ratio among women with different types of abnormal pregnancy compared with the control group. RI and PI mean levels were significantly lower in women with hydatidiform mole and significantly higher in women with missed miscarriage. Lower left S/D mean level was significantly associated with hydatidiform mole and upper left S/D level was associated significantly with control women. For prediction of missed miscarriage; right and left uterine artery RI shows a sensitivity of 80%, 73.3%, a specificity of 68%, 71.1%, and the highest AUC was 0.78 for both.For prediction of molar pregnancy, right and left uterine artery RI showed a sensitivity of 63% for both, a specificity of 54.4%, 60%, and the highest AUC was 0.58, 0.61 respectively.

Conclusions: Uterine artery Doppler ultrasonography at 6-12 weeks of gestation is predictive for early pregnancy complications such as missed abortion, hydatidiform mole, and blighted ovum.

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Publication Date
Mon Nov 01 2021
Journal Name
Energy Reports
Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model
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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
PREDICTION OF BEARING CAPACITY, ANGLE OF INTERNAL FRICTION, COHESION, AND PLASTICITY INDEX USING ANN (CASE STUDY OF BAGHDAD, IRAQ)
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Investigation of Reservoir Flow Unit and Rock Types of Mishrif Formation in Amara Oil Field and Prediction of Performance
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Amara oil field is located at south eastern Iraq in Missan governorate. The Mishrif Formation in Amara field is one of the most important reservoirs in southern Iraq. Identifying and characterizing petrophysical flow units are the key to understanding and improving reservoir description, exploitation, production and predicting the performance of carbonate reservoirs to represent them as combinations of different flow units, each with uniform pore throat size distribution and similar performance. Mishrif Formation in Amara oil field was divided into seven reservoir units (MA.MB11,MB12,MB13,MB21,MC1, and MC2) separated between them barrier beds. The present work is a reservoir flow unit identification for (MA) and (MB11) reservoir units of

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
PREDICTION OF BEARING CAPACITY, ANGLE OF INTERNAL FRICTION, COHESION, AND PLASTICITY INDEX USING ANN (CASE STUDY OF BAGHDAD, IRAQ).
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Civil Engineering And Technology
Prediction of bearing capacity, angle of internal friction, cohesion, and plasticity index using ANN (case study of Baghdad, Iraq)
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In the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and

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Publication Date
Fri Aug 13 2021
Journal Name
Neural Computing And Applications
Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction
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Publication Date
Thu Nov 21 2019
Journal Name
Al-kindy College Medical Journal
The The Role of Breast Sonography and Ductography in the Evaluation of Different Causes of the Nipple Discharge
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ABSTRACT

 

Background : The aim of this work is to assess the role of breast sonography  and ductography in the evaluation of different causes of nipple discharge.

Methods :  The study will be carried out on twenty-five female patients referred to the Radiodiagnosis department at Alexandria Main University Hospital presenting with nipple discharge.

They were divided into two groups:

Group I include 10 patients (40%) with surgically significant nipple discharge who were the patients with unilateral, uniorificial surgically significant colour type nipple discharge .They were investigated by mammography, sonography, and ductography.

Group II include 15 patients

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Publication Date
Sat Jun 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Evaluation of Integrated Information Technology System in Organizations “Exploratory Comparative Search for opinions of sample of Workers in the Faculties of Science & Education AL- Asma'I at the University of Diyala
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       The subject of the information technology system ( ITS ) of the important issues And contemporary thought in management, and various types of organizations seeking to apply and try to

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Publication Date
Thu Jun 01 2017
Journal Name
Iosr Journal Of Computer Engineering
Lossy Image Compression Using Wavelet Transform, Polynomial Prediction And Block Truncation Coding
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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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