International Journal Of Nonlinear Analysis And Applications
Volume:14 Issue: 3, Mar 2023

In this paper, we introduce a new iterative method for finding the fixed point of a nonlinear function. In fact, we want to offer a new way to obtain the fixed point of various functions using the Grey Wolf Optimizer algorithm. This method is new and very efficient for solving a nonlinear equation. We explain this method with three benchmark functions and compare results with other methods, such as ALO, MVO, MFO and SCA.

The Zakharov equation is a nonlinear plasma fluid model, used for ion-acoustic waves in a magnetized plasma. In the present study, Langmuir waves of the dimensionless Zakharov equation are investigated by using the Sardar-subequation method. The obtained solutions lead to a variety of exact solutions in the form of dark, bright, periodic singular,  singular, and combined dark-bright type solutions. These acquired solutions are depicted graphically by the 2D, contour and 3D plots which show the physical behaviour of obtained solutions. All the graphs confirm the validity of the obtained solutions. These types of solutions have a large range of applications in mathematical and applied sciences.

The mixed fuzzy relation programming with a nonlinear objective function and two operators of max-product and max-min composition is studied in this paper. Its feasible domain structure is investigated and some simplification procedures are presented to reduce the dimension of the original problem. We intend to modify the assimilation and revolution operators of the imperialist competitive algorithm in order to prevent the generation of infeasible solutions. The modified imperialist competitive algorithm (MICA) is compared with a real-value genetic algorithm to solve the original problem. Several test problems are presented to compare its performance with respect to the performance of the genetic algorithm. Their results show the superiority of the proposed algorithm over the genetic algorithm.

The main object of this manuscript is to achieve the solutions of a time-fractional nonlinear system of equations describing the unsteady flow of a polytropic gas using two different approaches based on the combination of new general integral transform in the sense of Caputo fractional derivative and homotopy perturbation method and variational iteration method, respectively. The solutions are obtained in the form of rapidly convergent infinite series with easily computable terms.  Numerical results reveal that the proposed approaches are very effective and simple to obtain approximate and analytical solutions for nonlinear systems of fractional partial differential equations.

The volume and diversity of data in the world are unprecedented in human history. It is growing at an unprecedented rate. Internet and social media technologies as they permeate every stage of our lives and even our mobile phones, people have become a source of data even in their daily activities. So, a new concept emerged: "Big Data". Big data is produced with high volume, speed, structured diversity, and semi-structured and unstructured data. Many industrial areas release big data by creating new data or digitizing existing data models so that organizations can gain a competitive advantage. In order to extract economic value from big data, it should be processed with advanced analytical methods. This research aims to examine the use of Hadoop in analyzing big data according to the Map-Reduce model. and distributed file systems such as Processing, PIG, Mahout, NoSQL, and Cassandra, and the study concluded that advanced analytical methods protect the privacy of personal information, and through them, security gaps can also be filled, and the phenomenon of big data was discussed in terms of its components and resources, and it was emphasized on the advantages of big data in the areas of application.

In this research work, we demonstrate the Hyers-Ulam stability for functions that are homogeneous of degree $k$, in multi-Banach lattice by fixed point method.

The current uncertainties regarding the fragile recovery of the global economy continue to highlight the importance of studying and accurately predicting the path of leading indicators, especially macroeconomic indicators. Nevertheless, for all developed and developing countries, one of the basic goals of macroeconomic policies is to ensure economic stability and stability. The purpose of this research is to investigate the impact of monetary, financial, foreign exchange and commercial policies on a sustainable economy. In this regard, the variables of wide money supply, credits allocated to the private sector, exchange rate, trade openness, cash liabilities, government expenditures and the market value of companies admitted to the stock exchange are used. The data were extracted from the World Bank database and applied to time series data econometric techniques. Also, the autoregressive model with distributed lags (ARDL) analyzed the data for the years 1990-2020. The findings of the research show that the real exchange rate, wide money supply, cash liabilities and commercial openness has a negative and significant impact on economic stability in the short and long term, but the variable of credits allocated to the private sector has a positive effect on the variable. has been affiliated Also, government spending and the market value of listed companies have a negative effect in the short term, but have a positive effect on the stable economy in the long term. Therefore, in addition to active macroeconomic policies, countries should use a wide range of supportive political interventions to achieve a stable economy, which include a specific combination of commercial, financial and currency policies.

Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices.  The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the prediction process when conducting simulations for the time series and for different sample sizes and when applying them on the daily crude oil price data, it was more efficient than the single models, as the comparison between the single models and the proposed hybrid models was done by means of the comparison scale, the mean square error (MSE), the results showed that the proposed hybrid models gave more accurate and efficient results, in addition to its ability to predict crude oil prices well.

Data Envelopment Analysis (DEA) determines the efficiency of decision-making units. Weight restrictions in a model with weight restrictions (WR) are considered to determine the efficiency of units, depending on the importance of indicators (inputs and outputs). Since weight plays an important role in the efficiency and ranking of options, in this paper we examine the effect of the type of weighting method of indices in the calculation of the efficiency of decision-making units. It should be noted that change is not applied in decision-making units but in the weighting method in order to understand the effect of different weighting methods in the calculation of efficiency: that is, the efficiency of a unit is calculated with a variety of weighting methods and the impact of the type of weighting method on the indicators is evaluated in the calculation of the efficiency of that unit. In this study, we showed that the efficiency of each unit is affected by weighting methods and that the efficiency of each unit at each change in the weighting method assigns a different value to itself.

Let $\mathbb{R}$ be the set of real numbers and $\big(Y,\|\cdot\|\big)$  be a real quasi-$\beta$-Banach space. In this paper, we prove the Hyers-Ulam stability on a  restricted domain in quasi-$\beta$-spaces for the following two radical functional equations$$f\big(\sqrt{x^{2}+y^{2}}\big)=f(x)+f(y)$$and$$ f\big(\sqrt{x^{2}+y^{2}}\big)=g(x)+f(y)$$where $f,g:\mathbb{R}\to Y$. Also, we discuss an asymptotic behavior for these equations.

This work proposed a CNN classification model that aims to classify the faces by three stages applied to a real data set. The first stage shows the effects of the augmentation technique on the real data set where these effects include online, offline, and without augmentation. At this stage, the proposed CNN model is a built-from-scratch that has low computational complexity, low layers and the smallest filter sizes.  The second stage involved denoising the images in the real data set, where the images are preprocessed by applying the median, Gaussian, and mean filters to render the images more smooth and compare the effects of these filters based on the classification accuracy. The third stage involved a multi-class proposed model that contained 12 classes of images that were trained on the applied real data set, in addition to a benchmark set of images that was collected from the Internet. The findings reveal that the model accuracy reached $98.81\%$ when the offline augmentation model or the median filter was applied to the real data set, while it reached $97.48\%$ when the CNN multi-class proposed model was applied to identify the non-permission class. These processes were found to improve the performance parameters such as precision, recall, F1 score, and area under the curve (AUC).  Finally, to enhance the test prediction accuracy and test time, pre-training and fine-tuning (transfer learning) are applied on the real data set so as test accuracy and test time of our proposed model are better as compared with other models reached $99.7\%$ and 4 seconds respectively.

The present study aimed to identify and prioritize determinants of the interaction between lean production management stakeholders in the National Iranian Oil Company. The statistical population consisted of all key experts in the field of National Iranian Oil Company and the statistical sample included 25 experts of the National Iranian South Oil Company who were selected using the purposive sampling method. To analyze the data obtained from interviews and questionnaires, the most important criteria and key sub-criteria affecting the supply chain were first localized using Saaty's Delphi method, and then the relationships of the factors were determined using the Fuzzy DEMATEL method and with the help of experts, and the criteria and sub-criteria were ranked. MATLAB software was also utilized for data analysis. In this research, the initial framework of the stakeholder interaction management model was created using the dynamic game theory approach in the lean management process, and their solutions were compared in three game scenarios, namely Nash, Stackelberg, and cooperative scenarios. Based on the research results, the correct layout was the most effective criterion, and up-to-date and efficient equipment was the most affectable sub-criterion. Among the main criteria, logistics ability was the most effective criterion, and financial ability was the most affectable criterion. In terms of interaction, new production items had the highest interaction and correct layout had the lowest interaction. The highest interaction of the main criteria was related to experience and the least was related to production ability. Based on the results, the producer preferred to choose the Stackelberg game with the suppliers and act as a leader and make decisions independently from the suppliers in the present study, thereby, obtaining more profit and production, and then more popularity among people.

We consider a type of Volterra integro-differential equations of the parabolic type that arise naturally in the study of heat flow in materials with memory. We present a simple and accurate numerical method for problems with a weakly singular kernel subject to an initial condition and given boundary conditions. In this method, both the space and time discretizations are based on the Muntz-Legendre collocation method that converts the problem to a system of algebraic equations. For numerical stability purposes, the Muntz-Legendre polynomials and their partial derivatives are stated in terms of Jacobi polynomials. Moreover, to deal with the weakly singular integral term of the problem, two efficient schemes based on the integration by parts and nonclassical Gaussian quadrature are derived. Comparisons between the two proposed schemes and other methods in the literature are made to demonstrate the efficiency, convergence and superiority of our method in the space and time directions.

Prolonged exposure to gases in enclosed spaces, can cause health problems that may not be easily eliminated, Several methods have been developed to determine the concentration of aromatic hydrocarbons. But these methods have certain limitations, which complicate the titration. Regression-based methods can be used using the software and applying numerical methods to the data obtained to determine the concentration of gases. The main idea of this paper: is to keep up with the ideal balance, and limit the deficiency of necessary to obtained from spectroscopic data, and the effect of mutilations presented by different noise decreases and autofluorescence background elimination algorithms was determined from the comparison data. And these changes ratios were in remove background fluorescence(benzene, toluene, xylene), for the (PolyFit) method they were 3% and 5% and 2%, and for the (ModPoly) method they were 1% and 2% and 2%, and for the gas processor method, they were 2% and 5% and 2 %, respectively. So it was noticed it has been noticed here that the proposed method (GasesProcessors) is better in terms of filter performance and autofluorescence background removal compared to other methods.

The aim of this study was to provide an audit quality model based on general health, spiritual intelligence and auditor's locus of control with a structural equation modeling approach among auditors who are members of the Iranian Society of Certified Public Accountants working in Tehran-based auditing firms. The sample size was 182 people who had fully answered the questionnaire. Also, the sampling adequacy test based on the factor analysis method to determine the adequacy of the statistical sample was at a desirable level. The data collected by the questionnaires were analyzed by SPSS and Smart PLS software using structural equation modeling. Analysis of research hypotheses using structural equation modeling at a 95% confidence level showed whether the auditors' general health dimensions including physical symptoms, anxiety symptoms, social functioning and depressive symptoms have a positive and significant effect on the audit quality in the proposed model. The dimensions of auditors' spiritual intelligence including critical existential thinking, constructing personal meaning, transcendent awareness and expanding the quality of awareness have a positive and significant effect on the quality of auditing in the proposed model. The dimensions of the auditor's locus of control, including the internal locus of control and the external locus of control, have a positive and significant effect on the quality of the audit in the proposed model. Finally, it can be said that with the promotion of public health, spiritual intelligence and the locus of control, the quality of auditing increases.

Let $\mathcal{S}^{\ast}_{L}(\uplambda)$ and $\mathcal{CV}_L(\uplambda)$ be the classes of functions$f$, analytic in the unit disc $\Updelta=\{z\colon|z|<1\}$, with thenormalization $f(0)=f'(0)-1=0$, which satisfies the conditions\begin{equation*}\frac{zf'(z)}{f(z)}\prec \left(1+z\right)^{\uplambda}\quad\text{and}\quad \left(1+\frac{zf''(z)}{f'(z)}\right)\prec \left(1+z\right)^{\uplambda}\qquad \left(0<\uplambda\le 1 \right),\end{equation*}where $\prec$ is the subordination relation, respectively. The classes$\mathcal{S}^{\ast}_{L}(\uplambda)$ and $\mathcal{CV}_L(\uplambda)$ are subfamilies of the known classes of strongly starlike and convex functions of order $\uplambda$. We consider  the relations between $\mathcal{S}^{\ast}_{L}(\uplambda)$, $\mathcal{CV}_L(\uplambda)$ and other classes geometrically defined. Also, we obtain the sharp radius of convexity for functions belonging to $\mathcal{S}^{\ast}_{L}(\uplambda)$ class. Furthermore,  the norm of pre-Schwarzian derivatives  and univalency of functions $f$ which satisfy the condition\begin{equation*}\Re\left\{1+\frac{zf''(z)}{f'(z)}\right\}<1+\frac{\uplambda}{2}\qquad\myp{z \in \Updelta}, \end{equation*}are considered.

Modelling the stress-strain relationship of FRP confined concrete is of vital importance in predicting the structural behaviour of confined concrete columns. In recent years the axial stress-strain behaviour of confined concrete under concentric loading is well established, but the behaviour under eccentric loading when axial and bending loads are combined is not well understood. Adding FRP materials to upgrade deficiencies of structural components can save lives by preventing collapse, reducing the damage, and the need for their costly replacement. The retrofit with FRP materials with desirable properties provides an excellent replacement for traditional materials, such as steel jackets, to strengthen the reinforced concrete. Existing studies have shown that the use of FRP materials restores or improves the column's original design strength for possible axial, shear, or flexure and in some cases allows the structure to carry more load than it was designed for. This paper summarizes the results of a research program to study the fundamental stress-strain behaviour of concrete confined by various types of fibre-reinforced polymer (FRP) composite jackets. By using three different types of FRP sheets with different mechanical characteristics, it can be concluded that the e/d ratio in all cases has the most effect on the square column behaviour.

Because of an increase in the frequency of encephalon tumors in each age group, the mortality rate has grown in recent years. In medical imaging, tumors are hard to see because of their complicated structure and noise, which makes it hard and time-consuming for specialists to find them. It is very important to find and pinpoint the tumor's location at an early stage, so this is very important. Medical scans can be used to look for and predict cancerous spots at different levels. These scans can be combined with segmentation and relegation methods to help doctors make an early diagnosis, which can save a lot of time. Physical tumor identification has become a challenging and time-consuming process for medical practitioners due to the intricate structure of tumors and the involution of noise in magnetic resonance (MR) imaging data. As a result, detecting and pinpointing the site of the tumour at an early stage is critical. Medical scans can be used in conjunction with segmentation and relegation procedures to deliver an accurate diagnosis at an early stage in cancer tumour locations at various levels. This research offers a system based on machine learning for segmenting and classifying MRI images for brain tumor identification. The K* classifier, Additive Regression, Bagging, Input Mapped Classifier, and Decision Table algorithms are used in this framework for image preprocessing, image segmentation, feature extraction, and classification.

Tensor completion has numerous applications in digital image processing such as image recovery and video overlay. In this paper, we consider two new approaches to tensor completion. Efficient low-rank tensor with tensor train and tensor ring for image recovery, some basic concepts about tensor algebra and completion problems are presented, after that Tensor completion based on the tensor train and tensor ring are offered and implemented on some examples for image recovery with different observed ratios. The results of these implementations are compared and final results are proposed.

In this paper, we prove some fixed point theorems for self-mappings on an algebraic cone metric space. These results are related to the product of the cone, and improve some well-known results by inserting an algebraic cone $\PP$ instead of $\mathbb{R}^{+}$.

This paper studies the nonlinear quantum-probability based Schrodinger type, Ivancevic options pricing model using the method of Lie symmetries to determine its point symmetries, invariant analytical solutions and conversation laws. In our analysis, we consider a non-zero and zero adaptive market potential model. We demonstrate that this model is invariant under a five-dimensional Lie algebra for the former, and invariant under a seven-dimensional Lie algebra for the latter case. These symmetries allow for a progressive reduction of the equation and thus facilitate a solution. We obtain reductions, exact solutions and conservation laws for both the non-zero and zero adaptive market potential models. We show that many exact solutions are expressible in terms of two transcendental functions, the Fresnel sine and cosine integrals. Graphical solutions are provided in certain cases. This analysis and solutions to such a financial derivatives pricing model are unique, providing novel insights.

The purpose of this article is to investigate the effect of emotional intelligence on the relationship between The research. self-interpretation and auditor objectivity. The method of data collection is library and field method is descriptive-correlational. In order to collect data by field method, interviews with experts, accountants and auditors were conducted using a questionnaire. The statistical population of the study is companies listed on all companies listed on the stock exchange in 1398. In order to determine the 107 sample size, stratified random sampling method has been used. Based on this and according to Krejcie and Morgan table, 86 companies have been selected as a sample. Data analysis using SPSS was software variables was above 0.7, so research The results showed that the Cronbach’s alpha value of all performed. The results showed that there is a positive and it indicates that the questionnaire had good reliability significant effect between the self-interpreting variable with auditors ’objectivity and emotional intelligence and emotional intelligence on auditors’ objectivity at the level of 0.1%. Value R2, 0.7, 0.46 The results are respectively. The results of the analysis of the main hypothesis showed that there is a positive and, 0.23 significant effect between all factors. And their value is higher than 0.3, which shows a favorable relationship. The results of the fit indices show that the model has a good fit. The ratio of chi-square to degree of freedom is 2.71. The root mean square root of the approximation error RMSEA is 0.04. Other fitness indicators such as good fit index GFI, respectively, which confirm the model fit and adjusted goodness fit index AGFA were 0.92 and 0.93.

In this paper, we present a counterexample for the nonstability of multicubic mappings. In other words, we show that Corollary 3.5 of [A. Bodaghi and B. Shojaee, On an equation characterizing multi-cubic mappings and its stability and hyperstability, Fixed Point Theory. 22 (2021), No. 1, 83--92] does not hold when $\alpha=3n$.

This article consists of a new concept of generalized metric space, called $\phi$-metric space which is developed by making a suitable modification in the `triangle inequality. The notion of $\phi$-metric generalizes the concept of some existing metrizable generalized spaces like S-metric, b-metric, etc.  It is shown that one can easily construct a $\phi$-metric from those generalized metric functions and the notion of convergence of a sequence on those generalized metric spaces are identical with the respective induced $\phi$-metric spaces. Moreover, $\phi$-metric space is metrizable and its properties are pretty similar to the metric functions. So $\phi$-metric functions substantially may play the role of metric functions. Also, the structure of $\phi$-metric space is studied and some well-known fixed point theorems are established.

Today, export growth for countries is a key to re-creating the economy. Export development is at the top of government priorities and policies in almost all developing countries. Furthermore, there is an ever-increasing awareness and attention to the importance of exports in developed countries. Accordingly, this paper attempts to identify and design the export marketing model of Iranian saffron. The statistical population consisted of business management and management experts. Sampling was performed by the purposive sampling method. A sample of 15 subjects was selected at the saturation stage. Data was collected through an open interview. The question items were determined based on the research objectives. The interview analysis showed that the model presented qualitatively includes nine main categories of marketing measures, macro strategy, micro strategy, limiting factors, facilitators, internal factors, external factors, and short-term and long-term consequences. The results show that the presented model can be used as a basis for Iranian saffron export marketing.

In recent decades, the coupled Steel plate shear wall system is considered as an efficient lateral force resisting system by engineers and researchers. Different parameters affect the nonlinear behaviour of Coupled Steel plate shear wall system. Other models are produced according to the base model. After verifying the nonlinear behaviour of this system by cross stiffeners on the Coupled Steel plate shear wall, an increase in the thickness of the stiffeners and the thickness of the Coupled Steel plate shear wall have been evaluated. In this study, the effect of the parameters on the initial stiffness, ultimate strength, and energy absorption by the samples have been compared with each other and the base model. The cross stiffeners on the Coupled Steel plate shear wall have also increased the sample's initial stiffness, ultimate strength, and energy absorption. However, the use of stiffeners does not significantly affect the energy absorption by the samples. Stiffeners increase the sample's initial stiffness, ultimate strength, and energy absorption, which has little effect on the energy absorption by the sample.

Facial system estimation is a mature and in-depth research technique in age and gender. Estimation accuracy is an important indicator for evaluating algorithms. By using deep learning-based learning (DL) and machine learning, this work provides a robust approach to estimating the type and age of different external environment changes based on two different algorithms, comparing the results, and analyzing the performance of the two algorithms. The algorithm was evaluated using a data set that is considered the basis in this area of the face estimation system, namely (IMDB-WIKI) an image. The basis of the work depends on the external appearance and the front section. The results obtained: DL(Effacint-B3) AGE Accuracy=0.99 Gender Accuracy=0.97 ML(SVM) AGE Accuracy=0.87 Gender Accuracy=0.97.

Improving people’s quality of life and increasing the level of public welfare are among the goals defined by different governments around the world. In meeting such goals, reducing income inequality is one of the factors that play a major role. Therefore, it is of high significance to figure out the factors related to income inequality. The present study was conducted to investigate the relationship between income inequality, economic growth and misery index in Iran, and in this regard, time series data related to the years 1971-2019 have been used. The income inequality index in the present study is the Gini coefficient and to estimate the research model, Autoregressive Distributed Lag (ARDL) method has been used. The results indicated that increasing as the misery index increases, income inequality in Iran will increase as well. Moreover, the ratio of real government spending to real GDP and economic growth has a positive effect on increasing inequality and the ratio of total real investment to real GDP and a dummy variable for revolution have a negative effect on income inequality.

In this paper, the application of the Fifth-order Meshless Local Petrov-Galerkin Method in solving the linear partial differential-algebraic equations (PDAEs) was surveyed. The Gaussian quadrature points in the domain and on the boundary were determined as centers of local sub-domains. By governing the local weak form in each sub-domain, the compactly supported radial basis functions (CS-RBFs) approximation was used as the trial function and the Heaviside step function was considered as the test function. The proposed method was successfully utilized for solving linear PDAEs and the numerical results were obtained and compared with the exact solution to investigate the accuracy of the proposed method. The sensitivity to different parameters was analyzed and a comparison with the other methods was done.

Let $X$ be a compact Hausdorff space, $A$ be a (commutative) Banach algebra and $\mathcal{A}$ be a Banach $A$-valued function algebra on $X$. Let $\mathfrak{A}$ be the function algebra on $X$, consisting of scalar-valued functions in $\mathcal{A}$. We study and characterize various amenability properties of the algebra $\mathcal{A}$ in terms of cohomological properties of $\mathfrak{A}$ and $A$. Containing some well-known examples, such as $C(X,A)$ and $Lip(X,A)$, the class of vector-valued function algebras also includes, in some sense, the tensor products $\mathfrak{A} \hat \otimes_\gamma A$. As consequences, some known results in this area are covered.