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jih-1044
Studying of Optical System Includes Elliptical Aperture Point Spread Function
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In this work, optical system with elliptical aperture using point spread function was studied. This is due to its comparison with an optical system with a circular aperture. The present work deals with the theoretical study of intensity distribution within the image. In this work, a special formula was derived which is called the point spread function (PSF) by using a pupil function technique. The work deals with the limited optical system diffraction only (ideal system), and the system with focal shift. Also a graphic relation was founded between eccentricity and the best of focal depth given to at least (80%) of intensity.

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
New White Method of Parameters and Reliability Estimation for Transmuted Power Function Distribution
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        In this paper, an estimate has been made for parameters and the reliability function for Transmuted power function (TPF) distribution through using some estimation methods as proposed new technique for white, percentile, least square, weighted least square and modification moment methods. A simulation was used to generate random data that follow the (TPF) distribution on three experiments (E1 , E2 , E3)  of the real values of the parameters, and with sample size (n=10,25,50 and 100) and iteration samples (N=1000), and taking reliability times (0< t < 0) . Comparisons have been made between the obtained results from the estimators using mean square error (MSE). The results showed the

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Publication Date
Fri Nov 01 2013
Journal Name
European Chemical Bulletin
Thermodynamic function of valineacid in n-n dimethyl formamide mixtures from conductance measurement
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sanaa tareq, Baghdad Science Journal, - Cited by 1

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Publication Date
Fri Jul 28 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Estimation of the Two Parameters for Generalized Rayleigh Distribution Function Using Simulation Technique
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     In this paper, suggested formula as well a conventional method for estimating the twoparameters (shape and scale) of the Generalized Rayleigh Distribution was proposed. For different sample sizes (small, medium, and large) and assumed several contrasts for the two parameters a percentile estimator was been used. Mean Square Error was implemented as an indicator of performance and comparisons of the performance have been carried out through data analysis and computer simulation between the suggested formulas versus the studied formula according to the applied indicator. It was observed from the results that the suggested method which was performed for the first time (as far as we know), had highly advantage than t

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Publication Date
Sun May 26 2019
Journal Name
Iraqi Journal Of Science
Bayesian Estimation for Two Parameters of Gamma Distribution under Generalized Weighted Loss Function
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This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).

Scopus (6)
Crossref (4)
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Publication Date
Sun Dec 01 2013
Journal Name
Baghdad Science Journal
Thermodynamic function of valineacid in n-n dimethyl formamide mixtures from conductance measurement
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Some physical properties enthalpy (?H), entropy (?s), free energy (?G),capacities(?cp?) and Pka values) for valine in dimethyl foramideover the temperature range 293.15-318.15K, were determined by direct conductance measurements. The acid dissociation at six temperature was examined at solvent composition x2) involving 0.141 of dimethyl foramide . As results, calculated values have been used to determine the dissociation constant and the associated thermodynamic function for the valine in the solvent mixture over temperatures in the range 293.15-318.15 k. The Pka1, and Pka2 were increased with increasing temperature.

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Publication Date
Sun Jun 11 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Comparison of Bayes' Estimators for the Exponential Reliability Function Under Different Prior Functions
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 In this study, we derived the estimation for Reliability of the Exponential distribution based on the Bayesian approach. In the Bayesian approach, the parameter of the Exponential distribution is assumed to be random variable .We  derived  posterior distribution the parameter of the Exponential distribution under four types priors distributions for the scale parameter of the Exponential distribution is: Inverse Chi-square distribution, Inverted Gamma distribution, improper distribution, Non-informative distribution. And the estimators for Reliability is obtained using the two proposed loss function in this study which is based on the natural logarithm for Reliability function .We used simulation technique, to compare the

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Publication Date
Sun Jun 05 2011
Journal Name
Baghdad Science Journal
Correlation between Paris function parameters to crack velocity for Alumina ceramics
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The question about the existence of correlation between the parameters A and m of the Paris function is re-examined theoretically for brittle material such as alumina ceramic (Al2O3) with different grain size. Investigation about existence of the exponential function which fit a good approximation to the majority of experimental data of crack velocity versus stress intensity factor diagram. The rate theory of crack growth was applied for data of alumina ceramics samples in region I and making use of the values of the exponential function parameters the crack growth rate theory parameters were estimated.

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Publication Date
Mon Jun 30 2008
Journal Name
Iraqi Journal Of Science
On the Greedy Ridge Function Neural Networks for Approximation Multidimensional Functions
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The aim of this paper is to approximate multidimensional functions f∈C(R^s) by developing a new type of Feedforward neural networks (FFNS) which we called it Greedy ridge function neural networks (GRGFNNS). Also, we introduce a modification to the greedy algorithm which is used to train the greedy ridge function neural networks. An error bound are introduced in Sobolov space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result in [1]).

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Publication Date
Wed Jul 20 2022
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
An Application Model for Linear Programming with an Evolutionary Ranking Function
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One of the most important methodologies in operations research (OR) is the linear programming problem (LPP). Many real-world problems can be turned into linear programming models (LPM), making this model an essential tool for today's financial, hotel, and industrial applications, among others. Fuzzy linear programming (FLP) issues are important in fuzzy modeling because they can express uncertainty in the real world. There are several ways to tackle fuzzy linear programming problems now available. An efficient method for FLP has been proposed in this research to find the best answer. This method is simple in structure and is based on crisp linear programming. To solve the fuzzy linear programming problem (FLPP), a new ranking function (R

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Publication Date
Mon Oct 22 2018
Journal Name
Journal Of Economics And Administrative Sciences
Using simulation to compare between parametric and nonparametric transfer function model
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In this paper, The transfer function model in the time series was estimated using different methods, including parametric Represented by the method of the Conditional Likelihood Function, as well as the use of abilities nonparametric are in two methods  local linear regression and cubic smoothing spline method, This research aims to compare those capabilities with the nonlinear transfer function model by using the style of simulation and the study of two models as output variable and one model as input variable in addition t

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