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Use the robust RFCH method with a polychoric correlation matrix in structural equation modeling When you are ordinal data
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Publication Date
Mon Feb 14 2022
Journal Name
Journal Of Educational And Psychological Researches
The Linguistic and Conceptual Equivalence of Conner’s Revised Scales When Applied on a Sudanese Sample
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This study investigates the Linguistic and Conceptual equivalence of Conner’s Revised Scales when applied on a Sudanese sample. Sudanese parents and teachers completed behavior-rating scales on a stratified sample of 200 children. These instruments were based on Conner’s parent -48 and teacher-28 questionnaires. Following a reliable translation into Sudanese Arabic the test-retest reliability of the items and the internal consistency of the original Conner’s' revised scales were explored. The associations between scale scores and between parents and teachers scores were also examined. Both instruments displayed good reliability and the original Conners scales had satisfactory internal consistency. The inter-correlation sugg

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Publication Date
Sun Mar 01 2020
Journal Name
Journal Of Petroleum Research And Studies
Modeling of Oil Viscosity for Southern Iraqi Reservoirs using Neural Network Method
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The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperatu

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Publication Date
Sun Apr 06 2014
Journal Name
Journal Of Economics And Administrative Sciences
Modeling Absolute Deviations Method by using Numerical Methods to measure the dispersion of the proposal for error
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Is in this research review of the way minimum absolute deviations values ​​based on linear programming method to estimate the parameters of simple linear regression model and give an overview of this model. We were modeling method deviations of the absolute values ​​proposed using a scale of dispersion and composition of a simple linear regression model based on the proposed measure. Object of the work is to find the capabilities of not affected by abnormal values by using numerical method and at the lowest possible recurrence.

 

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Publication Date
Fri Jan 01 2010
Journal Name
Conference Proceedings
Assessing the accuracy of 'crowdsourced' data and its integration with official spatial data sets
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Publication Date
Thu Aug 31 2023
Journal Name
Journal Of Legal Sciences
Industrial Property Licensing Contracts are Exempted from Ban in Accordance with USA and European Law
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Intellectual property rights of all kinds، and their nature، are considered a tool in the hands of their owner that enables him to monopolize the benefits that you confer on them without any dispute or mediation from anyone. Intellectual property on its intellectual product، and preventing others from exploiting it without obtaining the permission of its owner All of this is reflected positively on the progress of the industrial and commercial field، and this justifies the protection provided by the laws regulating intellectual property rights to its owner، whether at the national or international level، and with our recognition of the right of the owner of intellectual property rights to enjoy the exclusive use of his right، and

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho

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Publication Date
Mon Jul 04 2022
Journal Name
Indian Journal Of Forensic Medicine & Toxicology
The Correlation between Insulin Resistance and Urotensin II in Patients with Gestational Diabetes Mellitus
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Gestational diabetes mellitus is glucose intolerance of varying degree with onset or first detection duringpregnancy,it can causelong and short term morbidities in both the mother and the child, such as shoulder dystocia,preeclampsia, and high blood pressure. The most powerful endogenous vasoconstrictor peptide, urotensin II, andits receptor are involved in the etiology of gestational diabetes mellitus.Aim of the study: The study’s goal was to see if there is a link between Urotensin II levels and insulin resistancein pregnant women with gestational diabetes.Patients and method: A case-control study that was conducted in obstetrics and gynecology department atBaghdad Teaching hospital from the first of January 2019 to the end of D

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
A multivariate Bayesian model using Gibbs sampler with real data application
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In many scientific fields, Bayesian models are commonly used in recent research. This research presents a new Bayesian model for estimating parameters and forecasting using the Gibbs sampler algorithm. Posterior distributions are generated using the inverse gamma distribution and the multivariate normal distribution as prior distributions. The new method was used to investigate and summaries Bayesian statistics' posterior distribution. The theory and derivation of the posterior distribution are explained in detail in this paper. The proposed approach is applied to three simulation datasets of 100, 300, and 500 sample sizes. Also, the procedure was extended to the real dataset called the rock intensity dataset. The actual dataset is collecte

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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Perceptually Important Points-Based Data Aggregation Method for Wireless Sensor Networks
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The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the

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