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Evaluated the level density for proton induced nuclear resonances in (P+48Ti) reaction using different models
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The experimental proton resonance data for the reaction P+48Ti have been used to calculate and evaluate the level density by employed the Gaussian Orthogonal Ensemble, GOE version of RMT, Constant Temperature, CT and Back Shifted Fermi Gas, BSFG models at certain spin-parity and at different proton energies. The results of GOE model are found in agreement with other, while the level density calculated using the BSFG Model showed less values with spin dependence more than parity, due the limitation in the parameters (level density parameter, a, Energy shift parameter, E1and spin cut off parameter, σc). Also, in the CT Model the level density results depend mainly on two parameters (T and ground state back shift energy, E0), which are approximately constant in their behavior with the proton energy compared with GOE model. The RMT estimation used to calculate the corrections of the incompleteness proton resonance measurement data by using two methods; the conventional analysis method, which depends on the resonance widths and the updated, developed, tested and applied a new analysis method, which depends mainly on the resonance spacings. The spacing analysis method is found much less sensitive to non-statistical phenomena than is the width analysis method. Where the analysis of a given data set via these two independent analysis methods indicated the increasing in the reliability of the determination of the missing fraction of levels, the observed fraction f between 0.87+0.13−0.11 – 0.68+0.12−0.12 for different spin-parity of this reaction and then the distinguishability in the level density calculations can be achieved. The modified Porter Thomas distribution along with the maximum likelihood function have been used to get the missing levels corrections for 5 proton resonance sequences in the present reaction. To estimate the present long-range correlations for pure sequence of levels the mean square of the deviation of the cumulative number of levels from a fitted straight line represented by the Dyson and Mehta Δ3 statistic has been employed for spin parity 12+, and calculated the <Δ3> against the cumulative number of proton levels.

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Publication Date
Thu Jun 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Comparing Some of Robust the Non-Parametric Methods for Semi-Parametric Regression Models Estimation
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In this research, some robust non-parametric methods were used to estimate the semi-parametric regression model, and then  these methods were compared using the MSE comparison criterion, different sample sizes, levels of variance, pollution rates, and three different models were used. These methods are S-LLS S-Estimation -local smoothing, (M-LLS)M- Estimation -local smoothing, (S-NW) S-Estimation-NadaryaWatson Smoothing, and (M-NW) M-Estimation-Nadarya-Watson Smoothing.

The results in the first model proved that the (S-LLS) method was the best in the case of large sample sizes, and small sample sizes showed that the

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Publication Date
Tue Dec 01 2020
Journal Name
Journal Of Engineering
Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index
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Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of eac

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Publication Date
Thu Sep 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Methods for Estimating Mixture of Linear Regression Models with Application
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 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

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Publication Date
Thu Sep 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Methods for Estimating Mixture of Linear Regression Models with Application
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 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

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Publication Date
Tue Oct 20 2020
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Performance Enhancement of Face Recognition under High-Density Noise Using PCA and De-Noising Technique
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       There are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to im

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Publication Date
Tue Apr 22 2025
Journal Name
Journal Of Studies And Researches Of Sport Education
The effect of using the educational bag on the level of learning some offensive skills with the epee weapon
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Publication Date
Wed Aug 16 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Study the improvement of thermo - mechanical properties for polyester adhesive by using different fibers (glass, kevelar, and carbon).
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The   mechanical    properties   of   fiber-reinforced-polymer   (FRP)

composites are dependent on the type amount, and orientation of fiber that is selected for a particular service. There are many commercially available reinforcement forms to meet the design requirements of the user. The ability of failure in the fiber architecture allows for optimized performance of a product that saves both weight and cost ( 12).

A modem technology is adopted to produce fibers (glass, kevelar,

and carbon) reinforced composite by using unsaturated polyester, where different volume fraction  of these fibers are used  (0, 0.2, 0.4, 0.6, 0.8,  I)

reinfor

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Publication Date
Tue Apr 22 2025
Journal Name
Karbala Journal Of Physical Education Sciences
Training of different ranges using the (HC-SR04 ultrasonic sensor ) and its effect in developing some of special abilities for young boxers
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Publication Date
Sun Aug 06 2023
Journal Name
Journal Of Economics And Administrative Sciences
Probit and Improved Probit Transform-Based Kernel Estimator for Copula Density
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Copula modeling is widely used in modern statistics. The boundary bias problem is one of the problems faced when estimating by nonparametric methods, as kernel estimators are the most common in nonparametric estimation. In this paper, the copula density function was estimated using the probit transformation nonparametric method in order to get rid of the boundary bias problem that the kernel estimators suffer from. Using simulation for three nonparametric methods to estimate the copula density function and we proposed a new method that is better than the rest of the methods by five types of copulas with different sample sizes and different levels of correlation between the copula variables and the different parameters for the function. The

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Publication Date
Fri Jun 01 2018
Journal Name
Kerbala Journal Of Medicine
Polymerase Chain Reaction Testing in Comparison to Culture of Cerebrospinal Fluid for Diagnosis of Bacterial Meningitis in Children
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