This study proposes a new distribution that is more flexible than the corresponding four-parameter distributions called the Odd Burr XII Gompertz (OBXIIGo) distribution. Then various basic statistical properties of the OBXIIGo distribution are investigated and to estimate its parameters MLE is used. In addition, a Monte Carlo simulation study is conducted to evaluate the performance of parameter estimation using the MLE method, and the OBXIIGo distribution is applied to illustrate its uses on two real data sets, demonstrating its adaptability in various application fields. The results demonstrate the flexibility of the OBXIIGo distribution and its ability for auditing modeling and analysis.
During 1937 Beurling Showed that any positive infinitely increasing real sequence such that the its first element precisely greater than one, called a Beurling’s primes. Furthermore, the series of Beurling integers (or generalized integers) can be constructed using the fundamental theorem of arithmetic. During the seventieth of the last century, Diamond showed that majority of the arithmetical functions were generalized to deal with the generalization of the primes and integers. This work aims to create some weird numbers from a large enough reals So, the reader has to be familiar with Mobius inversion formula of the Pci function. The challenging of this work is the dealing with an algorithm for generating a weird numbers (or maybe a primitive weird numbers) from a large enough real numbers x. The idea of this work can be used for an application of modeling, data simulation and security subjects.
The stress-strength S-S method employs a variety of estimation techniques, including maximum likelihood, shrinkage and least square, to determine and estimate the reliability of a particular system R = P (Y1 < X < Y2) while the system contains a single component with strength X subject to two stresses, Y1 and Y2. With the consumption of Monte Carlo simulation and the statistical measurement Mean Squared Error (MSE), the various estimation methods have been evaluated according to the Restricted Generalized Weibull Distribution (RGWD), the stresses Y1 and Y2 and the strength X constitute independent, non-identical random variables in our S-S model.
Recently it has been proven that the multiplication module is a Weak-multiplication module (W −M M ). In this paper, we present some applications of W − M M in neutrosophy theory. n-refined neutrosophic module M denoted by Mn(I). We proved that Mn(I) is regular and C.P.module. Also, every n-refined neutrosophic finitely generated ideal is a principal ideal of n-refined the ring R (Rn(I)). We proved that if Mn(I) is multiplication module over ring Rn(I) and Mn(I) is of type S2; then every f. generated An(I) of Mn(I) is n-refined multiplication module. Also, if Rn(I) is n-refined neutrosophic ring, so Rn(I) is principal ideal ring if and only if every n-refined neutrosophic multiplication module Mn(I) is of type S. Finally, several results and applications have been presented in this paper with some new definitions, examples, and other properties.
Time series are always preoccupied with maintaining their stability to make precise forecasts about the future. So, a plethora of statistical models are emerged, by some focusing on mean stability and others on variance stability. Neural networks formation that resemble the structure of the nervous system in humans subsequently followed this. Now, in order to break down the time series into its constituent parts, our hybrid model will integrate the time series’ structure. the neural network performs the sub-network prediction procedure after receiving the divided sub-series. These predictions are then concatenated to provide the original network prediction. Time series data display a wide range of patterns. It is frequently helpful to dissect a time series into its trend, seasonality, cyclic variation, and irregular components in order to identify underlying patterns. The time series model is in charge of classifying the data series into four patterns such as: seasonality, trend, dispersion, and the remainder which are expressed in the model by S, T, D, and R respectively. By this work, we will introduce a hybrid model called STDR-MNN that combines a time series with a neural network. The four segmented series are sent to the MNN neural network, which uses them to forecast each segmented series individually. Finally, we will use the MATLAB2022A program to test a realistic application on the generated hybrid model in order to gauge its effectiveness with Real data.
The motivation for this study is to develop the new family of continuous distributions called Odd Lomax-G (OLG). Also present a new flexible three-parameter distribution according to the developed family called the Odd Lomax-G Exponential (OLE) distribution. Using binomial series expansion, logarithmic, and expo-nential expansions, the new OLG family and OLE distribution are expanded. We find the derivative of the moments, the mgf , quantity function, ordered statistics and R´enyi entropy. Then use the MLE method to estimate the OLE model parameters. Finally, an importance of the new family is made clear experimentally through two real data applications. Then explore the performance of OLE distribution inferred from family OLG based on certain goodness of fit criteria.
The present paper introduces the novel subfamilies of regular and bi-univalent functions Σ. The prominent group of Fibonacci polynomial ˜Vn(x, y) is utilized with subordination between regular functions in order to shape these subfamilies. In addition we derive coefficients inequalities for functions belonging to these subfamilies. Various results are exposed as separate cases of the current conclusions.
The problem of complete data is considered one of the most important problems that hinder statistical analysis. Therefore, this problem must be solved by finding sound solutions to it by using some methods that lead to accurate results or close to accuracy. The research aims to compare methods of statistical data analysis. Estimating incomplete data using the filling algorithms R-Estimators, L estimators, EM, and and the W method and the case of the normal distribution of data according to patterns, machines, and the method of losing these observations. A comparison was made between the robust methods and the maximum Likelihood method, and the efficiency of the above-mentioned methods was noted. The imitation method was used in addition to practice. the data on the function variables, which represent the demand for cash and its relationship with the gross domestic product, government consumer spending, and the consumer price index, were discussed for the period from 2000-2022.The process was to compare these methods, and the results were drawn to reach the experimental side , through the experimental and applied part, it is preferable to use robust methods over classical methods in the case of incomplete data.
The peristaltic pump of a fluid which is of non-Newtonian type over a curved tube is the main focus of this study. The mathematical formulation of the problem is presented. A small Reynolds number (Re) and a large wavelength (δ) are used to simplify the calculation of the problem. The final expression of the stream function is found by applying the perturbation procedure for different values k, we derive a numerical solution to the nonlinear differential equation. An extensive review of the influence of various physics parameters, such that Hartman number, non-Newtonian fluid number and many others, in two cases, small and large values of the curvature value k, on the axial velocity profile, the pressure rise, the pressure gradient, and the streamlines. This discussion is supported by the inclusion of pictures and illustrations.
In this article, an effective neural network is created using unconstrained optimization the brand-new BFGS training algorithm. The fourth order nonlinear partial differential equation is mathematically modeled with feed-forward artificial neural network with some adaptive parameters. The network is trained by new modification of BFGS method to avoid some troubles occurs when the network trained by current BFGS. The conventional updated Hessian approximations approach needed significant memory, storage, and cost computing for each iteration. One of these update’s novel features is its ability to estimate the 2nd order curvature of the goal function (energy functions) with high order precision while using the provided gradient and function value data. It is shown that the global convergence properties of the suggested modification, there is a parameter ρ in the update formulae which ranges from zero to one. The numerical experiments demonstrate that the improved BFGS update will be more accurate and more effective than the traditional BFGS methods. The proposed algorithm has well properties such: it has global convergence for energy function which is convex functions; also to get optimal step length we used a nonmonotone line search technique to modify the effectiveness of the proposed algorithm. Finally, used suggested training algorithm, to learned an appropriate neural network for accurately solving any non-linear PDEs.
The properties of capturing of peristaltic flow to a chemically reacting couple stress fluid through an inclined asymmetric channel with variable viscosity and various boundaries are investigated. we have addressed the impacts of variable viscosity, different wave forms, porous medium, heat and mass transfer for peristaltic transport of hydro magnetic couple stress liquid in inclined asymmetric channel with different boundaries. Moreover, The Fluid viscosity assumed to vary as an exponential function of temperature. Effects of almost flow parameters are studied analytically and computed. An rising in the temperature and concentration profiles return to heat and mass transfer Biot numbers. Noteworthy, the Soret and Dufour number effect result on temperature and concentration profiles respectively. An incompressible couple stress fluid occupies the porous medium. Stream function that appear under closed form as well as pressure gradient, temperature and the equation of concentration. In a variety of involved parameters, the results are developed. Mathematical analysis is prefer through large wavelength and low Reynolds number. Additionally, the numerical integration is the technique that used to calculate the concentration, velocity, pressure and temperature profiles respectively. Finally, Via using "MATHEMATICA" software we obtain the explanation of physical Zparameters that graphically by a series of figures when apply variety of wave shape.
This article investigates the nonlocal inverse initial boundary-value problem in a rectangular domain, hyperbolic second order inverse problem. The main objective is to find the unidentified coefficient and offer a solution to the problem. The hyperbolic second-order, nonlinear equation is solved using finite difference method (FDM). However, the inverse problem was successfully solved by the MATLAB subroutine lsqnonlin from the optimization toolbox after being reformulated as a nonlinear regularized least-square op-timization problem with a simple bound on the unknown quantity. Given that the studied problem is often ill-posed and that even a minor error in the input data can have a large impact on the output. Tikhonov’s regularization technique is used to generate stable andaccurate results.
In this research, a comparison was made between two methods for estimating a semiparametric regression model with the presence of an autocorrelation problem, based on a semiparametric partial linear regression model, which contains a parametric component and a nonparametric component. The two components were estimated using two methods, the first methods is semi parametric generalized least squares estimators(SGLSE) , the second methods is least squares estimators method(LSEM) .Simulation study show the first method is beast than the second method by using mean squares of errors (MSE).
In this article Tanh method is considered as effective approach to get solutions of some types of non-linear partial differential equations. Then we suggested new modification for extended Tanh method as highly effective approach to obtaining precise traveling wave solutions to these types of equations. Then using both methods, to solve the (3+1) D- new Hirote bilinear equation (NHBE) and then we compare between the results to illustrate the effectiveness of suggested modification. The interpretation of these solutions presented graphical to illustrate behaviors of the solutions gives us some popular shapes include solutions of type solitary wave, the singular wave, the kink wave, and the singular kink wave.
The present work investigates the impacts of the Lorentz forces, porosity factor, viscous dissipation and radiation in thermo-Marangoni convective flow of a nanofluids (comprising two distinct kinds of carbon nanotubes (CNTs)), in water (H2O). Heat transportation developed by Marangoni forces happens regularly in microgravity situations, heat pipes, and in crystal growth. Therefore, Marangoni convection is considered in the flow model. A nonlinear system is constructed utilizing these assumptions which further converted to ordinary differential equations (ODEs) by accurate similarity transformations. The homotopic scheme is utilized to compute the exact solution for the proposed system. The study reveals that higher estimations of Hartmann number and Marangoni parameter speed up the fluid velocity while the opposite behavior is noted for porosity factor. Further, the rate of heat transfer shows upward trend for the Hartmann number, Marangoni parameter, nanoparticle solid volume fraction, radiation parameter whereas a downward trend is followed by the Brinkman number and porosity factor. It is fascinating to take observe that contemporary analytical outcomes validate the superb convergence with previous investigation.
Bell polynomials have already been used in many different fields, ranging from number theory to operator theory. Here another application in Laplace Transform (LT) theory is shown, describing a method for computing the LT of nested functions. A table containing the first few values of complete Bell polynomials is shown, and a program for deriving the next ones is given. A program for approximating the Laplace Transform of composed analytic functions is also presented.
Most researchers in the field of time series follow one approach, which is to rely on time series models in general. Several models have emerged in engineering, economics, and health applications. These models are known as neural networks, which imitate human nerve cells. This study suggests a hybrid algorithm called STL-FNN. It combines two methods - seasonal trend decomposition STL and feed-forward neural network (FNN). The STL method analyzes the original data into three subseries: seasonality, trend, and residual. To predict each of the three sub-series, the FNN neural network uses the seasonal component and separates them. Finally, all predicted outputs are combined as a total time series product. The results indicate that the STL-FNN can check the performance of the hybrid model. It uses the relative absolute error (MAE) criterion. We analyzed actual data to test the hybrid model's ability to predict. The model used the average monthly spending of foreign visitors in the United Kingdom from January 1986 to February 2020
The paper discusses the inverse problem of determining an unknown source term in a fractional elliptic equation in bounded domain. In order to solve the considered problem, a fractional Tikhonov is used. Applying this method, having a regularized solution is constructed. An a priori and a posteriori error estimates are obtained, and the the terminal data has a random data is considered.
The problem of existence and uniqueness of solutions of initial value problems associated with a nonlinear fractional dynamic equation of Caputo type on an arbitrary time scale of order α > 0 is stated as a fixed point problem on a metric-like space. The initial conditions are assummed to be homogeneous. A theorem on the existence and uniqueness of a solution of the problem is stated and proved. Examples on two different time scales verifying the theoretical findings are presented and numerical computation of several initial terms of the iterative sequence of approximations is included.
The main goal of this paper is to introduce a new abstract structure (so called, interpolative metric space) as a generalization of a standard metric space. We shall consider the analog of Banach Mapping Principle in the context of this new structure.
