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Analytical approximate solutions of random integro differential equations with laplace decomposition method
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An efficient combination of Adomian Decomposition iterative technique coupled with Laplace transformation to solve non-linear Random Integro differential equation (NRIDE) is introduced in a novel way to get an accurate analytical solution. This technique is an elegant combination of theLaplace transform, and the Adomian polynomial. The suggested method will convert differential equations into iterative algebraic equations, thus reducing processing and analytical work. The technique solves the problem of calculating the Adomian polynomials. The method’s efficiency was investigated using some numerical instances, and the findings demonstrate that it is easier to use than many other numerical procedures. It has also been established that (LTADM) is a trustworthy technique for solving differential equations. Using the Mathematica 13.3 programme, the graphs of the approximate solutions and consecutive error are presented. Two applications are presented as examples of how the proposed technique can be utilised to obtain analytical or numerical solutions for certain kinds of Random Integro Differential Equations (RIDEs) in order to demonstrate its efficacy and potential.

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Publication Date
Fri Nov 01 2024
Journal Name
Process Safety And Environmental Protection
Optimized ensemble deep random vector functional link with nature inspired algorithm and boruta feature selection: Multi-site intelligent model for air quality index forecasting
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Publication Date
Sun Sep 04 2016
Journal Name
Baghdad Science Journal
The Removal of Zinc from Aqueous Solutions Using Malvaparviflora
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In this study, the adsorption of Zn (NO3)2 is carried out by using surfaces of malvaparviflora. The validity of the adsorption is evaluated by using atomic absorption Spectrophotometry through determination the amount of adsorbed Zn (NO3)2. Various parameters such as PH, adsorbent weight and contact time are studied in terms of their effect on the reaction progress. Furthermore, Lagergren’s equation is used to determine adsorption kinetics. It is observed that high removal of Zn (NO3)2 is obtained at PH=2. High removal of Zn (NO3)2 is at the time equivalent of 60 min and reaches equilibrium,where 0.25gm is the best weight of adsorbant . For kinetics the reaction onto malvaparviflora follows pseudo first order Lagergren’s equation.

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Publication Date
Sat Sep 30 2023
Journal Name
Iraqi Geological Journal
Purification of Aqueous Solutions from Nickel Using Ceramic Waste
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This study aims to test ceramic waste's capacity to remove nickel from aqueous solutions through adsorption. Ceramic wastes were collected from the Refractories Manufacturing Plant in Ramadi. Through a series of lab tests, the reaction time (5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 minutes, and Ni concentrations (20, 40, 60, and 80) were tested using ceramic wastes with a solid to liquid ratio of 2g/30ml. At a temperature of 30ºC, the pH, total dissolved solids (TDS), and electrical conductivity (EC) were all measured. The equilibrium time was set at 30 min. Thereafter, the sorption (%) somewhat increased positively with the Ni concentration. Freundlich's equation showed that the adsorption intensity is 1.1827 and the Freundlich c

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Publication Date
Sat Dec 01 2012
Journal Name
Journal Of Economics And Administrative Sciences
Comparison between the empirical bayes method with moments method to estimate the affiliation parameter in the clinical trials using simulation
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In this research the Empirical Bayes method is used to Estimate the affiliation parameter in the clinical trials and then we compare this with the Moment Estimates for this parameter using Monte Carlo stimulation , we assumed that the distribution of the observation is binomial distribution while the distribution with the unknown random parameters is beta distribution ,finally we conclude that the Empirical bayes method for the random affiliation parameter is efficient using Mean Squares Error (MSE) and for different Sample size .

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Publication Date
Fri May 01 2020
Journal Name
Journal Of Physics: Conference Series
New Approach for Solving (1+1)-Dimensional Differential Equation
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Publication Date
Sun Mar 02 2008
Journal Name
Baghdad Science Journal
Orthogonal Functions Solving Linear functional Differential EquationsUsing Chebyshev Polynomial
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A method for Approximated evaluation of linear functional differential equations is described. where a function approximation as a linear combination of a set of orthogonal basis functions which are chebyshev functions .The coefficients of the approximation are determined by (least square and Galerkin’s) methods. The property of chebyshev polynomials leads to good results , which are demonstrated with examples.

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of College Of Education
Some of the Probability Characteristics Functions of the Solution of a System of Random Linear Inequalities
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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition
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Publication Date
Sat Aug 25 2012
Journal Name
Wireless Personal Communications
Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy Efficient Coverage in Wireless Sensor Networks
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Publication Date
Wed Dec 27 2017
Journal Name
Al-khwarizmi Engineering Journal
The Determination of Critical-Sampling Scheme of Preprocessing for Multiwavelets Decomposition as 1st and 2nd Orders of Approximations.
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One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued input and a vector-valued output. A scalar-valued input signal must somehow be converted into a suitable vector-valued signal. This conversion is called preprocessing. Preprocessing is a mapping process which is done by a prefilter. A postfilter just does the opposite.

The most obvious way to get two input rows from a given signal is to repeat the signal. Two rows go into the multifilter bank. This procedure is called “Repeated Row” which introduces oversampling of the data by a factor of 2.

 For data compression, where one is trying to find compact transform representations for a

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