International Journal Of Nonlinear Analysis And Applications
Volume:14 Issue: 1, Jan 2023

In the present paper, we prove common fixed point theorem for F-contraction by considering one map is orbitally continuous map in $\Omega-$Extended rectangular b-metric space. In addition, we find fixed point for Banach and Kannan type contraction inequality without consideration of orbitally continuity. Also at the fixed point maps does not force to be continuous. An examples is also provided for the utility of our results.

In this paper, we first introduce the Halpern iteration process for approximating the solution of the fixed point problem of a finite family of $k$-strictly pseudo-contractive mappings in Hadamard spaces. We also propose an extra gradient Halpern iterative algorithm for approximating a common solution of a finite family of $k_j$-strictly pseudocontractive mappings and a pseudomonotone equilibrium problem in Hadamard space. We prove a strong convergence result without imposing any strict (compactness) conditions for approximating the solutions to the aforementioned problems. We state some consequences of our results and display some numerical examples to show the performance of our results. Our results improve and generalize many recent results in the literature.

In this paper, the classical Fekete-Szego problem is studied regarding a class of univalent functions generated using a generalized fractional differential operator. The results presented in the main theorem are new generalizations for well-known results.

In this paper, we introduce the $(G,\psi)$-Ciric-Reich-Rus contraction on metric space endowed with a graph, such that $(X,d)$ is a metric space, and $V (G)$ is the vertices of $G$ coincides with $X$. We give an example to show that our results generalize some known results

This research uses the Analytic Hierarchy Process (AHP) to design a model of factors affecting the failure of manufacturing contracts in the field of oil and gas. The present study is applied based on the purpose and from the point of view of how information is collected; it is a type of mixed (qualitative-quantitative) research. In this research, the statistical population consists of two parts, the quality department includes 21 in-university experts and specialists and experts aware of the subject of construction contracts and the quantitative part includes 384 contractors, consultants, employers, project stakeholders, and people related to the construction contracts in the field of oil and gas. The theme analysis method was used to analyze the data of this research. The hierarchical analysis (AHP) method was used to rank the components and Spss and Expert Choice software were used to analyze the data. The results of the Hierarchical Method (AHP) showed that; comparing the five main dimensions, the technical dimension is in the first place with 32.42% Therefore, the technical dimension has the greatest impact and the political dimension has the least impact on the failure of construction contracts in the field of oil and gas.

Despite the fact that algorithmic strategies for dealing with Combinatorial Optimization (CO) have been available for a long time, the further application of Evolutionary Algorithms (EAs) to such problems provides a vehicle for dealing with MOPs of tremendous scope.  BAT Algorithm with Many Objectives several BAT algorithms based on R2 Distance (MaBAT/R2) are described, which blend the predominance notion with the R2 marker technique. While the R2 Indicator simplifies the multi-objective problem (MOP) by rewriting it as a series of Tchebycheff Approach problems, since this leader decision making uses the Tchebycheff Approach as a criterion, tackling these issues at the same time inside the BAT framework may lead to early converging. Predominance is important in constructing the leader's collection because it allows the chosen leaders to encompass fewer dense regions, avoiding local optima and producing a more diverse approximated Pareto front. 9 non-linear standard functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all of the findings (R2020b).

There are many integral transforms that are widely used to solve many problems arising in applied mathematics and engineering. The main goal of this paper is to use a new general integral transform to solve fractional differential equations with the fractional derivative in the sense of Caputo. In addition, several illustrative examples are given to demonstrate the accurateness and effectiveness of this approach. The obtained results confirm the applicability and high accuracy of the proposedapproach to solving fractional diffrential equations.

Sustainable achievement and survival in the challenging business of modern life depend on the ability of employees to perform the tasks assigned to them. Thus, recruiting the best employees with a fitting job with target posts may play an important role in organizations. The present study aims to provide a model for recruiting and hiring competency-based human resources based on structural equations in the Social Security Organization of Iran. This study was applicable in terms of aim and " combined" in terms of the method. In the qualitative stage, data-based strategies were used to design a model; in contrast, in the quantitative stage, structural equations of smart pls were used. The statistical population was managers and experts in human resources of the Social Security Organization of Iran. They were selected by the purposeful sampling method; in the quantitative stage, this sampling continued until we reached theory saturation. In the quantitative stage, 250 employees were selected from the section of human resources. After collecting data and extracting concepts, categories, the final model was determined. Given fitness indexes in the quantitative section, it became clear that data was fit with underlying structural factors and they were consistent with fit indexes. After collecting data and extracting concepts and categories, the final model was determined. Given fitness indexes in the quantitative section, it became clear that data fitted with underlying structural factors, consistent with fit indexes. Given the dimensions, the Social Security Organization of Iran needs this model, thus implementing this model based on its capacities and recruiting employees as important organization resources to improve the organization and decrease costs.

Let $(\Omega, \mathcal{M}, \mu)$ be a measure space. In this paper, we establish the set of Gateaux differentiability for the usual norm of $L^{1}(\Omega, \mu)$ and the corresponding derivative formulae at each point in this set.

The dominating problem is one of the topics in graph theory because of its applications in different areas. In this paper, some dominating sets are applied in the discrete topological graph. Such as independent domination, connected domination, doubly connected domination, total domination, restrained domination, strongly domination and co-neighborhood domination with their inverses.

This study examines the individual and psychological factors influencing investor decision-making except in the stock market. For this purpose, first, by using the meta-combined method, the factors affecting investment decisions are extracted from previous research and then, using two Delphi techniques, the identified factors were evaluated and approved by experts. Then, using the Dematel method, the relationships between the factors analyzed and the effect of each factor on each other were determined. Based on the results obtained from this method, among individual factors, the type of job has the greatest effect and having education and financial literacy has the least effect on other factors. Income level and savings level are the most affected. Among psychological factors, mass behavior has the most influential factor and representative intuition has the least effect on other factors. And risk-taking, empowerment and optimism are the most affected by other factors, respectively.

The purpose of this article is to make a connection between the Pascal distribution series and some subclasses of normalized analytic functions whose coefficients are probabilities of the Pascal distribution. To be more precise, we investigate such connections with the classes of parabolic starlike and uniformly convex functions with positive coefficients in the open unit disk $\mathbb{U}.$

A contraction mapping is generalized by defining an ambient space under consideration or by altering the contraction condition. In this study, we first define a new space called quasi-$b_v(s)$ metric space and verify that this space is a generalization of $b_v(s)$ metric spaces. We also define a new control function which is a generalization of the altering distance function. Finally, we prove the existence of a fixed point for $\xi$-generalized Meir-Keeler type contractions on quasi-$b_v(s)$-metric spaces. Many famous results in the field have been improved, generalized, and unified by the results presented here. The main result is used to drive several corollaries and an example is presented to back up the claim.

In any country, economic growth and the GDP are among the key most important indicators of progress as well as economic growth/development of that nation. Consequently, it is crucially significant to study and assess factors affecting the GDP. Therefore, the primary objective of this study is to investigate and analyze the impact of exchange rate and inflation on GDP. The objective of this article is to assess and analyze the impact of exchange rates and inflation on the GDPs of Iran, Iraq and Turkey during the period between 2005-2020. This research is descriptive in nature and applied in purpose. The relationships between the variables have been studied utilizing the data panel method compliant with statistical data from Iran, Iraq and Turkey (from 2005-2020). Data from the Central Bank of Iran as well as the World Bank were used for collect testing the hypotheses. Findings revealed that the exchange rate has no effect on GDP in these countries, while inflation has a significant/meaningful plus negative relationship with GDP.

This paper gives a study of various types of cone metric spaces and their topological characterizations. Contrarily to the case of cone metric space $X$, the paper shows with examples that the limit of a sequence may not be unique in the topology generated by partial cone metric $T_{p}$ and $(X, T_{p})$ is not generally Hausdorff topological space and also the cone valued partial metric mapping $p$ may not generally be continuous. Hence $T_{p}$ is not equivalent to any topology generated by any metric on $X$. Furthermore, the paper considers some generalized contraction types of mappings on $\theta$-complete cone metric-like spaces and then generalizes some coupled fixed point theorems of some previous results in this setting.

Many advanced problems in science and engineering can represent mathematically in the form of linear Volterra integro-differential equations or their system. In this paper, we present the solution of the linear system of Volterra integro-differential equations of the second kind by Kamal transform. Some numerical problems have been given and solved by Kamal transform for illustrating the applicability of the Kamal transform. Results of numerical problems assert that the Kamal transform is very effective for obtaining the exact solution of the linear system of Volterra integro-differential equations of the second kind.

In this article, we introduce a new generalized Euler-Lagrange $(m,n)$-cubic functional equation with its general solution. Also, we investigate its various stabilities related to the Ulam problem in fuzzy normed linear spaces using Felbin's concept. Also, we provide a counter-example for the non-existence of stability of a generalized Euler-Lagrange $(m,n)$-cubic functional equation.

this goal, first the factors and indicators affecting the independent audit assurance services were defined based on the field analysis technique and then these factors and indicators were classified. According to this goal, this project is an innovative combination of consecutive and instrumental model type, design, and development model based on the design criteria of this model. Members of the Iranian Society of Certified Public Accountants have led to the definition of research model criteria by using the case study research method and semi-structured interviews of managers and partners of auditing firms and auditing firms. Based on interviews and analysis of previous studies, factors affecting the pricing model of independent audit assurance services were identified. 50 codes were examined which were classified into 9 groups based on axial coding. These factors include corporate governance, corporate ownership structure, internal factors of the company, external factors of the company, factors of the auditing firm, characteristics of managers and owners, financial and accounting factors, audit characteristics, and competitive market auditing. To determine the degree of importance of the identified criteria, the demitel phase group decision-making technique was used. Based on the obtained results, financial and accounting factors are at the highest level of impact (9.91). Financial and accounting factors refer to the criteria of free float, the percentage of ownership of major shareholders, audit risk, the ratio of total liabilities to total assets.

Following the concept of parametric metric space introduced by N. Hussain et al., the definition of generalized parametric metric space is given. A decomposition theorem from a generalized metric into a family of crisp metrics is established. Another decomposition theorem from a family of the crisp metric into a generalized parametric metric identical to the first one under certain conditions is proved. Some basic results are studied and develop a Banach type fixed point theorem with an application to an integral equation.

In many applications, indexing of high-dimensional data has become increasingly important. High-dimensional data is characterized by multiple dimensions. There can be thousands, if not millions, of dimensions in applications. Classic methods cannot analyse this kind of data set. So, we need the appropriate alternative methods to analyse them. In high-dimensional data sets, since the number of predictors is greater than the sample size, it is generally impossible to apply classical methods to fit a efficient  model. A popular method for combating the challenge of the high-dimensionality curse is to solve a penalized least squares optimization problem, which combines the residual sum of squares loss function measuring the goodness of the fitted model to the data sets with some penalization terms that promote the underlying structure. So, the penalized methods can analyse and provide a good fit for the high-dimensional data sets. In this paper, we express some of these approaches and then, apply them to the eye data set for investigating the computational performance of the proposed methods.

Different multiple access methods in wireless communications allow multiple contented stations to share the wireless medium. It can be either orthogonal or non-orthogonal. In orthogonal multiple access (OMA), the channel allocation between the contented stations is orthogonal (either by time, frequency, space or code). This is done to avoid interference between the wireless stations. However, it decreases the performance because the number of simultaneous connections is limited by the orthogonal channel resources, which minimizes the degree of freedom per station. On the other hand, non-orthogonal multiple access (NOMA) allows multiple stations to transmit and receive at the same channel resource time and frequency by using another domain. For example, different power levels for different stations or what is known as PD-NOMA. This paper shows that NOMA can improve the performance of wireless communications even with the presence of interference because each station has a full degree of freedom per resource. Besides, PD-NOMA and MIMO have a good combination by taking advantage of MIMO array gain with non-orthogonal access to improve spectral efficiency. By allocating different power to different stations, different optimization objectives are proposed to maximize the fairness, spectral efficiency, and energy efficiency in the downlink channel.

We consider a nonlinear initial boundary value problem in a two-dimensional rectangle. We derive  variational  formulation of the problem which is in the form of an evolutionary variational inequality in a product Hilbert space. Then, we establish the existence of a unique weak solution  to the problem and prove the  continuous dependence of the solution with respect to some parematers. We proceed with the study of an associated control problem for which we prove the existence of an optimal pair. Finally, we consider a perturbed optimal control problem for which we prove a convergence result.

Over the past few decades, a lot of meta-heuristics have been developed to solve N-P hard problems. Genetic algorithm, ant colony optimization, simulated annealing, electromagnetism algorithm and tabu search are some examples of meta-heuristics algorithms. These kinds of algorithms have two main classes: population-based and Trajectory. Many of these algorithms are inspired by various phenomena of nature. In this research, the author introduces a new population-based method inspired by the lifestyle of lions and the genetic algorithm’s structure called the new genetic algorithm (NGA). The social behaviour of lions and genetic operators like mutation and cross-over is the main structure of NGA. Finally, the NGA is compared with the hybrid genetic and hybrid ant colony optimization as the best existing algorithms in the literature. The experimental results have revealed that the NGA is competitive in terms of solution quality to solve the vehicle routing and scheduling problems as two main categories of N-P hard problems.

In this paper, We studied an application of the Haar wavelet basis in solving a particular class of delay differential equations. We have extended the Haar wavelet series(HWS) method to develop a numerical technique to solve linear and nonlinear Dirichlet boundary value problems of proportional delay nature. Some problems are presented to test the efficiency of the proposed technique, where a remarkable agreement between approximate and analytic solutions is obtained. The numerical simulation indicates that error drops with the increase in the level of resolution. Also, it is observed that the rate of convergence tends to be 2.

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermore, various uses in the real world, Data distributions in intrusion detection systems, for example, are non-stationary, which produce concept drift over time or non-stationary learning. The word "concept drift" is used to describe the process of changing one's mind about something in an online-supervised learning scenario, the connection between the input data and the target variable changes over time. We define adaptive learning, classify existing concept drift strategies, evaluate the most typical, distinct, and widely used approaches and algorithms, describe adaptive algorithm assessment methodology, and show a collection of examples, all of this is based on the assumption that you have a basic understanding of supervised learning. The survey examines the various aspects of concept drift in a comprehensive manner in order to think about the current fragmented "state-of-the-art". As a result, which intends to give scholars, industry analysts, and practitioners a comprehensive introduction to idea drift adaptability.

In this paper, we consider some new classes of general bivariational inclusions. It is shown that the general bivariational inclusions are equivalent to the fixed point problems, resolvent equations and dynamical systems. We have discussed the existence of a solution of the general bivariational inequalities. Some new iterative methods for solving general bivariational inclusions and related optimization problems are suggested by using resolvent methods, resolvent equations and dynamical systems coupled with finite difference technique. Convergence analysis of these methods is investigated under monotonicity. Some special cases are also discussed as applications of the main results.

Despite some studies in the analysis of technological innovation systems and factoryless manufacturing, these two issues have been analyzed comprehensively and at the same timeless, while entrepreneurship and employment development are among the first functions of technological innovation systems in countries. The aim of this study is to provide an integrated model for technological innovation in factoryless manufacturing in Iran. In this research, an integrated approach based on Grounded theory and structural-interpretive modeling has been used. The statistical population includes experts and experts in the field of factoryless producing. Using the purposive sampling approach, a total of 11 people were selected as participants in the study to the extent of theoretical saturation. Data were collected through interviews under the guidance of generalities and in a semi-structured manner and based on the data theory process, during the three stages of open, axial and selective coding, 71 concepts, 19 sub-categories and 6 main categories were identified and categorized. Then the final variables were graded by interpretive structural modeling at 4 levels of the main phenomenon, infrastructure, implementation and outcome. The results show that technological innovation acts as the main and effective basis for determining successful strategies in the field of factoryless manufacturing, which will lead to the interaction of the socio-economic environment in Iran.

The scenario of this article is to introduce the space $\mathfrak{R}^{t}_s(p, \Delta)$  based on a general Riesz sequence space. Its completeness property is derived and its linear isomorphism property with $\ell(p)$ is proved. The K\"{o}the-dual property of the space $\mathfrak{R}^{t}_s(p, \Delta)$ is also derived. Furthermore, its basis is constructed and some characterization of infinite matrices are given.

The increasing difficulty of actual-world optimization problems has prompted computer researchers to produce process improvement techniques regularly. Metaheuristic and evolutionary computation are popular in nature-inspired optimization methods. This paper introduces hybrid dolphin and sparrow optimization (DSO), which is a modification of a new metaphorical algorithm based on the natural behavior of sparrows and dolphins. Various adaptive and arbitrary variables are combined within this algorithm to indicate the exploitation and investigation of the exploration area in various discoveries of optimization. Multiple test strategies are used to calculate DSO performance. Initially, a collection of experiment events, including unimodal, multimodal, and composite functions, is applied to examine the exploitation, exploration, local optima avoidance, and convergence of DSO. Furthermore, unique metrics, such as the most suitable solution through optimization and search history, are applied to qualitatively and quantitatively examine and verify the achievement of DSO on turned 2D inspection functions. The effects of analysis functions and achievement metrics show that the proposed method can search various regions of a search space, provide local optima avoidance, converge toward the global optimum, and utilize encouraging areas of a search range while optimization proceeds efficiently. The DSO algorithm achieves a regular frame for an airfoil with a low drag, which explains that the methods are efficient in improving physical difficulties, including restrained plus unknown search spaces.

In this article, we first establish weighted identities based on twice partially differentiable mappings. Moreover, utilizing this equality, we derive  the weighted Hermite-Hadamard type inequalities via co-ordinated convex mappings in a rectangle from the plane $\mathbb{R}^{2}$. More specifically, we establish new inequalities using the Holder and power-mean inequalities. In addition, we obtain new results with special choices.

Mental accounting is a description of the ways in which they do these things. Based on the concept of mental accounting, people in their financial decisions to evaluate each decision, open a separate account in mind and try to examine the consequences of each decision (positive or negative) alone. This research seeks modeling through grounded theory method. Because we are looking to provide a model. This research is based on the systematic method of grounded theory. In this method, after identifying the research problem and reviewing the previous literature, sampling is done. Data were collected through interviews with 15 experts in the field of research. The method of selecting experts was through the method of snowball. Studies show that people's decisions are not always logically consistent with the decision models introduced in the financial texts. Investors should consider their vision as an account for a specific purpose and have a long-term vision. Because short-term goals usually outweigh long-term goals and lead to the use of mental accounts. Having an empty mind means doing something empty-handed and having mental accounting means having enough information about a subject or a subject. Therefore, planning is not possible without mental accounting.

In this paper, we consider an initial-boundary problem for a wave equation containing nonlinear integral terms. By the linear approximate method associated with the Faedo-Galerkin method, the existence and uniqueness of solutions for the proposed problem are proved. Moreover, a high-order asymptotic expansion in a small parameter of the weak solution is also discussed.

In this paper, we investigate the Cauchy problem for long water wave equations for the existence and nonuniqueness of global classical solutions. We give sufficient conditions on the initial data of the considered equations that guarantee the existence and multiplicity of nonnegative global classical solutions. For this goal, a new topological approach to the fixed point theory of the sum of two operators in Banach spaces is used.

The significant impact of the extreme values on the estimations of the statistical distributions leads to the fact that the adoption of these estimations in the estimation of many statistical functions such as (reliability, survival) and others is not strictly acceptable because of the high difference between the real and estimated values of these functions. The research included comparing several estimation methods for the Lomax distribution parameter according to different values of the sample size, the value of the distribution parameter and the estimation method. The simulation results showed the effect of the estimator values on the sample size, the value of the distribution parameter and the estimation method. Other estimation methods can be relied upon, as well as other statistical distributions to know the effect of extreme values on them.

The present study aimed at investigating the importance-performance analysis (IPA) affecting the improvement of business and prevention of corporate bankruptcy that utilized banking resources according to the IPA matrix. Initially, the relevant indices were extracted from the existing literature on the subject, and thereafter, utilizing the Delphi fuzzy method, important factors in four main dimensions (assessment of proposals to obtain financing, liabilities of banks as business partners, rates of interest, and banking penalties, and the way of devising partnership contracts) were identified. The statistical population of this research consisted of relevant legal, banking, and commercial experts. Ultimately, 12 individuals with at least 15 years of experience in the above subjects were selected as the samples. Compliant to the importance-performance matrix output (among 36 influential factors), effective strategies to assist bank managers/executives toward optimal allocation of resources and proper and efficient granting of facilities/loans to strengthen businesses were identified. Furthermore, the following were prioritized and focused on for improvement/enhancement: components and details of proposed reforms to prevent potential losses, level of education/expertise of managers offering to finance, the relationship between the education/expertise of the individual proposing the project to the bank, the proportion of the bank's human resources in the context of the project implementation process, the relationship between the bank's interests to the profit from the partnership, acceptance of roles in the project's profit and loss distribution, establishing as a basis for the realized profits in the partnership contract, the bank's carefulness in the implementation of the project by the customer, adherence to the final actualized profit.

In this paper, we study the growth of solutions to complex higher order linear differential equations, where the coefficients are analytic in the closed complex plane except at a finite singular point. We obtain some results on the [p,q]-order and on the lower [p,q]-order which improve and extend those of Long and Zeng.

In this paper, we study the concept of L-normal fuzzy filters of distributive lattices in terms of annulates. We also study the special class of fuzzy filters called L-normal fuzzy filters. Further, a set of equivalent conditions are derived for an $ L $-fuzzy filter to be an $ L $-normal fuzzy filter, which leads to a characterization of disjunctive lattices. We observe that every L-normal fuzzy filter is the intersection of all L-prime normal fuzzy filters containing it. Finally, we study the space of $ L $-prime normal fuzzy filters and investigate some of their properties.

In this manuscript, we introduce generalized ($\mathfrak{f^{*}},\psi)$-contraction of kind (S) and use this concept to establish $\psi$-fixed point theorems in the frame of complete metric space. Secondly, we introduce new notion of generalized($\mathfrak{f^{*}}, \psi)$ expansive mapping of kind (S) and utilize the same to prove some fixed point results for surjective mapping satisfying certain conditions. Our results improve the results of [8], [10] and [14] by omitting the continuity condition of $F\in \Im$ with the aid of $\psi$-fixed point. We also give an example which yields the main result. Also, many existing results in the frame of metric spaces are established.

The present study aimed to evaluate the use of business process reengineering (BPR) to improve the business-IT alignment in knowledge-based businesses in Tehran by MATLAB programming software. The research issues included the ambiguity and fatigue of decision-makers and managers of knowledge-based businesses in Tehran due to the combination of different methods of evaluating the use of BPR to improve the business-IT alignment. Using the intelligent system, the status of "improving the alignment of IT with knowledge-based businesses of Tehran" can be examined numerically and more accurately: From the perspective of ideal importance, if the state of "IT service business strategy process reengineering (X1)" is good, i.e. 0.813, and "IT service business design process reengineering (X2)" is good, i.e. 0.824, and "IT service business transfer process reengineering (X3)" is good, i.e. 0.819, and "IT service business operation process reengineering (X4)", i.e. 0.812, and "IT service business continuous improvement process reengineering (X5)" is good, i.e. 0.815, then the state of "improving the alignment of IT with Tehran knowledge-based businesses" is at the "excellent (fifth level)" level, i.e. 0.952. According to the membership functions of linguistic variables by experts, the value of 4.76 within a 5-value range in the range defined for the "excellent" linguistic variable, i.e. the state of improving the alignment of IT with knowledge-based businesses in Tehran was obtained equal to 0.952 with a programming code [0.813; 0.824; 0.819; 0.812; 0.815].

In this investigation, we studied a class of Bazilevic type close-to-star functions which is defined by a generalized differential operator. The new class generalizes many known and new subclasses of close-to-star functions. Some of the investigated properties are the coefficient bounds and the Fekete-Szego functional. Our results extend some known and new ones.

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishrif Formation; MA, MB11, MB12, MB21, MC1, and MC2. The precise facies modelling is constructed by using Petrel software while applying different appropriate scale-up methods. Consequently, the petrophysical properties such as porosity, water saturation and permeability are distributed within each unit depending on facies modelling. The Net to a gross parameter which has a significant impact on determining original oil in place (OIIP) also calculated and distributed using facies modelling. The facies modelling is performed to obtain an accurate estimation of OIIP. Finally, the results of the facies investigation have a significant effect on petrophysical properties and therefore affect the estimation of OIIP by 2\% for the whole Mishrif Formation.

The purpose of this article is to introduce an implicit iterative algorithm which contains several well known existing iterative methods. We study the strong convergence of the proposed iteration process to the common fixed points of a finite family of uniformly L-Lipschitzian total asymptotically demicontraction semigroup in Banach spaces. The results presented in this article extend, generalize, unify and  improve the corresponding results of several authors in the existing literature.

The present study aimed to model and present a model of human resource productivity with a system dynamics approach in the health sector of East Azerbaijan province using a systemic approach. The research method was developmental in terms of purpose and had a qualitative approach. The questionnaires and open interviews with experts in addition to the literature review were used to collect data. Data were analyzed according to the systems dynamics approach by drawing cause and effect loops, and flow diagrams in Vensim DSS software, and thus the system behavior and the impact of different policies on it were tested. In the present research, the important factors in calculating the human resource productivity included the labor number in the organization, education and especially in-service education, the existence of appropriate geographical coverage of health facilities in the province, and staff research activities. After creating a stock and flow diagram and determining quantitative and mathematical relationships for variables with the help of experts in the health sector, the human resource productivity model was first tested and validated in terms of overall structure and relationships between components. Finally, the model was simulated to examine the dynamics hypothesis, and scenarios were presented for improving the level of human resource productivity in the health sector compared to the current status.

In the present research, we state and prove a common fixed point theorem for fuzzy mappings that satisfy the contractive condition in $\mathfrak{F}$-metric spaces. An example of a fixed point result is presented.

Many generalizations have been considered on how to construct the exact solutions of one-component Reaction-Diffusion (RD) equations. Two-component RD systems of equations allow for the study of a wider range of physical phenomena as well as dynamical processes than their counterpart one-component RD equations. The most suitable and best way to study certain models is by using two-component RD systems of equations. Moreover, the presence of delay in nonlinear Partial Differential Equations (PDEs) makes them more difficult to study than those without delay. This study introduces some new exact solutions associated with a generalized form of two-component RD systems of equations with delay. The exact solutions for more complex multidimensional reaction-diffusion systems of equations are also derived. Solutions to RD systems of equations with a delay which are presented in this study are applicable for the formulation of test problems to verify the efficiency of numerical methods which are being used to obtain the solutions of nonlinear delay PDEs.

In this paper, the property of fuzzy sets is used as approach to instrument for the construction and finding the value of “multi-objective linear fractional programming problem” (MOLFPP) and applied complementarity condition on such problems which is one of regulation of order problems which cover by fuzzy dealings. Introduce a technique to convert and solve the problem by symmetric fuzzy approach. Suggest an algorithm and show how fuzzy LFPP can be answered without rising the Arithmetic potency. Our technique is used to convert complementarity multi-objective linear fractional programming problem (CMOLFPP) to LP through symmetric fuzzy linear fractional problem (SFLFP). To demonstrate the efficacy of the suggested technique, a numerical example is presented. then compared the result with other techniques fuzzy linear fractional programming (FLFP) which are solved by using computer applications to test the algorithm of the above method to indicate that the results obtained by such approach are promising.

The following system of additive functional equations 2f(x+y)=g(x)+g(y), g(x+y)=2f(y-x)+4f(x) in a Banach algebra was investigated by Paokanta et al. [S. Paokanta, M. Dehghanian, C. Park and Y. Sayyari, System of additive functional equations in Banach algebras (preprint)]. By using the system (A), Paokanta et al. [S. Paokanta, M. Dehghanian, C. Park and Y. Sayyari, System of additive functional equations in Banach algebras (preprint)] also investigated the notion of an f-hom-der in Banach algebras. In this note, we first show that the system of additive functional equations (A) is equivalent to an additive functional equation and then some aspects related to f-hom-ders are discussed. Finally, we present stability results of f-hom ders associated with the system of additive functional equations (A).

Two problems with Particle Filters (PF) are particle impoverishment and degeneracy. Resampling is introduced to solve degeneracy problem which happens when the majority of the particles have very small weight and a few particles have large weights and sample’s weight variance is too high. Resampling ignores the less informative particles by replacing them with the better ones but it can results in sample impoverishment or diversity loss problem in the particles if there is no controlling mechanism. BrunsVigia Optimization Algorithm (BVOA) is applied in this paper as an extra step to Pf in order to avoid these problems. Operators of BVOA balance between exploration and exploitation and as the result the optimized PF will put much emphasize on more informative particles while keeping the diversity among them. The optimized PF using BVOA, namely BVOA\_PF, is tested in localization problem in a simulating environment. Application of BVOA\_PF in localization and comparing the simulation results with two well-known optimization algorithms as PSO and GWO verify the efficiency of BVOA in real applications like robot localization.

Using a generalized translation operator, we obtain an analogue of Titchmarsh's theorem for the second Hankel-Clifford transformation for functions satisfying the second Hankel-Clifford  Lipschitz condition in the space $L_{\mu}^{2}((0,+\infty),x^{\mu})$.

The aim of this manuscript is to introduce the concept of intuitionistic fuzzy $b$-metric-like spaces and discuss some fixed point results to certify the existence and uniqueness of a fixed point. Non-trivial examples are imparted to illustrate the viability of the proposed method.

In this paper, we define the binomial transform of the generalized Jacobsthal-Padovan sequence and as special cases, the binomial transform of the Jacobsthal-Padovan, Jacobsthal-Perrin, adjusted Jacobsthal-Padovan, modified Jacobsthal-Padovan sequences will be introduced. We investigate their properties in details.

Adaptive noise cancellation (ANC) for all intents and purposes is one of the most very common methods of canceling noise from EEG signals, which definitely is quite significant. However, there are two actually main problems with the adaptive noise canceling method of EEG signals: 1, or so they generally thought. The reference sort of signal for all intents and purposes is not available for the adaptive filter which should for the most part be an estimate of pollutant noise, demonstrating that adaptive noise cancellation (ANC) mostly is one of the most kind of common methods of canceling noise from EEG signals in a really major way. 2, or so they really thought. The MSE particularly standard specifically is usually used to specifically reduce the error of the adaptive filter, fairly further showing how adaptive noise cancellation (ANC) literally is one of the most really common methods of canceling noise from EEG signals, or so they essentially thought. Since the EEG particularly signal and EOG artifact kind of are non-Gaussian, it actually is not kind of appropriate to use the MSE criterion that only considers second-order error in a subtle way. We employed an adaptive noise definitely removal method in this research, which is fairly significant and used DWT to create an estimate of the EOG noise, which was then fed into the ANC reference's input. To decrease the error signal, the error entropy criteria is also employed instead of MSE. In terms of RRMSE, SNR, and coherence studies, the simulation results show that the proposed system outperforms previous methodologies.

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO ("You only look once") neural network algorithm, which is an efficient real-time object identification algorithm, an intelligent system was developed in this thesis to distinguish which faces are wearing a mask and who is not wearing a wrong mask. The proposed system was developed based on data preparation, preprocessing, and adding a multi-layer neural network, followed by extracting the detection algorithm to improve the accuracy of the system. Two global data sets were used to train and test the proposed system and worked on it in three models, where the first contains the AIZOO data set, the second contains the MoLa RGB CovSurv data set, and the third model contains a combined data set for the two in order to provide cases that are difficult to identify and the accuracy results that were obtained. obtained from the merging datasets showed that the face mask (0.953) and the face recognition system were the most accurate in detecting them (0.916).

The present work is concerned with a class of perturbed second-order differential inclusions driven by state-dependent maximal monotone operators, in the context of an infinite-dimensional framework. To prove our existence theorem, we make crucial use of a compactness type assumption on the domain of the operators and succeed in adapting to the second-order setting a discretization approach. An application of our main result to the theory of quasi-variational evolution inequalities is provided.

Let $\mathcal R_{n}$ be the set of all rational functions of the type $r(z) = f(z)/w(z)$, where $f(z)$ is a polynomial of degree at most $n$ and  $w(z) = \prod_{j=1}^{n}(z-\beta_j)$, $|\beta_j|>1$ for $1\leq j\leq n$. In this paper, we prove some results concerning the growth of rational functions with prescribed poles by involving some of the coefficients of polynomial $f(z)$. Our results not only improve the results of N. A. Rather et al. [8], but also give the extension of some recent results concerning the growth of polynomials by Kumar and Milovanovic [3] to the rational functions with prescribed poles and we obtain the analogous results for such rational functions with restricted zeros.

The present study aimed to design and explain the IoT commercialization model in Iranian organizations (Telecommunication Company of Iran). A mixed-methods approach with the exploratory design method was used in the study. In the qualitative phase, the content qualitative analysis method was used by interviewing 21 telecommunications managers as well as IT professors and experts. In the quantitative phase, a questionnaire with a statistical sample of 400 individuals was used using the Interpretive Structural Modeling (ISM) approach. In the qualitative phase, the paradigm model of IoT commercialisation is detected in the roles of causal, contextual, intervening conditions, strategies, main phenomena and consequences, design and development of the country's telecommunication infrastructure, full utilization of all communication power with real and legal customers, increasing the power of analysis in the dimensions of telecommunication business, moving towards integration of telecommunication services, strengthening the understanding of customers and their needs, collecting data, transmitting the selected data through communication networks, evaluating and estimating data, and responding to access information for the main phenomenon and were confirmed in the quantitative phase.

This paper concentrates on the parameter estimation for the Generalized Exponential Distribution (GED) in the presence of interval-censored data with covariate. Interval-censored data usually arises in clinical and epidemiological studies. This research attempts to investigate a conservative imputation technique to deal with interval censored data. This is achieved by comparing the bias, standard error (SE) and root mean square error (RMSE) of the maximum likelihood estimation (MLE) obtained without using imputation with the proposed imputation method at various censoring proportion and sample sizes. The results indicate that the proposed imputation method performs better than the traditional method at all sample sizes and censoring proportions.

Android smartphone platforms have grown rapidly and become ubiquitous due to the fact that they have opened up new possibilities in every aspect of modern life. However, as a consequence of their widespread, such systems suffer an increasing frequency of malware attacks. New malware or modifications to existing malware are increasingly being used by attackers in such operations. Security researchers deal with a continually evolving threat environment that is broad, complicated, and ever-changing. Conventional detection methods failed to adequately protect such systems and so the security industry has started to detect systems that use machine learning techniques are becoming more common. In this work, we used an evolutionary algorithm to identify malware targeting Android smartphones. In order to evaluate our system, we compared it to different state-of-the-art algorithms. Finally, we demonstrated our proposed method's ability to detect zero-day malware. Obtained results show that our method achieved 99.11\% detection accuracy, and is well-suited for detecting zero-day malware.

In this paper, inspired by the concept of generalized weakly contractive mappings in metric spaces, we introduce the concept of generalized weakly contractive mappings in rectangular $b$-metric spaces to study the existence of fixed points for the mappings in these spaces.

The final objective of the Recommender Systems (RSs) is to offer recommendations to the user that are close to his/her taste. When the user enters the system, the most similar data cluster to the user’s taste can be selected, and by creating a neighborhood of the users similar to him/her within the selected cluster, the proposal generation can be followed. Determining the appropriate number of neighbors of the user can lead to increased accuracy of the recommendations made. Due to the enormous size of the datasets, this requires more time. This study aimed to propose recommendations with the highest accuracy and in the shortest possible time through finding the best number of neighbors for the user applying the NSGA-II Multi-Objective Evolutionary Algorithm (MOEA). Here two objects of accuracy and time of recommendations are in a multi-objective state, thus a balance should be created between the two conflict objects. The simulation results on the MovieLens 100K, MovieLens 1M, Netflix and FilmTrust standard datasets indicated that the proposed MOEA was capable of providing recommendations with greater accuracy and at the proper time, hence it could improve the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coverage, Precision, Recall and Fmeasure criteria.

The aim of the current research is to analyze the transient thermoelastic bending analysis of a rectangular plate with a simply supported edge under the heat source.  Initially, the plate is kept at a constant temperature. For $t > 0$, the heat is produced in the plate at the rate $g (W.m^{-3})$ and the surfaces at $x = 0, a$ and $y = 0,  b$ are kept at zero temperature, while the surfaces $z = 0, c$ are subjected to heat convection. Using Green’s function approach and integral transform technique, the analytical solution of the rectangular plate with the simply supported edge is derived. As a preeminent finding from this investigation, it can be deduced that the accuracy,  reliability, and simplicity of these methods are excellent. Accurate bending solutions to title problems are then obtained using the transform technique. The approach used in this paper is more reasonable than conventional methods. Numerical results are presented to demonstrate the validity and efficiency of the approach as compared with those reported in other literature. The outcomes demonstrate that the temperature profile and the thermal deflection are maximum at the middle part of the plate, due to the heat source located in the middle, however, the direction of heat flow and the body deformation is the same.

This paper's aim is to discuss the influence of nanoparticles and porous media, and magnetic field on the peristaltic flow transport of a couple of stress fluids in an asymmetric channel with different waveforms of non-Newtonian fluid. Initially, Mathematical modelling of the 2-D and two directional flows of a couple of stress fluids with a nanofluid is first given and then simplified beneath the hypothesis of long wavelength and low Reynolds number approximation. After making these approximations, we will obtain associated nonlinear differential equations. Then the exact solutions of the temperature distribution, nanoparticle concentration, velocity, stream function, and pressure gradient will be calculated. Finally, the results that we obtained illustrate graphically.

We discuss some new results for Gronwall’s type inequality on time scales. An analysis of the behavior of the solutions of some hyperbolic partial differential equations as applicable to our results is considered.

This paper utilized a newly proposed algorithm based on meta-heuristics for the training of multilayer perceptrons (MLP) that was developed using the idea of artificial gorilla troops optimizers. The precision and consistency of the proposed method's convergence as performance metrics. The Artificial Gorilla Troops Optimizer (GTO) was recently proposed for use in training MLP, and it employs the five most common classification data sets currently available( XOR, balloon, heart Iris, breast cancer) in the California University at Irvine UCI Repository. The newly Optimizers (GTO) are used for the first time as a Multi-Layer Perceptron (MLP) trainer, and its results are compared to those obtained using the more established grey wolf optimization (GWO), the whale optimization algorithm (WOA), and the sine cosine algorithm (SCA). Previously, GTO was used to determine the best weights and biases for the optimal solutions.

The purpose of the present paper is to find the necessary and sufficient conditions and inclusion relations for Pascal distribution series to be in some subclasses of analytic univalent functions of κ--starlike and κ--uniformly convex functions of order ϑ in the open unit disk U. Further, we consider an integral operator related to Pascal distribution series, and several corollaries and consequences of the main results are also considered.

This work elucidates the sufficient conditions for establishing some existence and uniqueness results for a Musca Domestica model that is governed by a first-order nonlinear differential equation with iterative terms resulting from a time and state-dependent delay. The existence of at least one positive periodic solution is proved by using Schauder's fixed point theorem with the help of some properties of an obtained Green's function. Furthermore, under an additional condition, the Banach contraction principle is applied to guarantee the existence, uniqueness and stability of solutions. Finally, the validity of our main findings is demonstrated by two examples. Our findings are completely new and generalize previous ones to some degree.

This work is intended to probe the connections between the diffeology and gyrogroup by introducing the notion of diffeological gyrogroup and proceeding with the study of some basic facts about it. The theory is developed by the study of smooth action of a diffeological gyrogroup.

Early detection and early management of children with Autism Spectrum Disorder (ASD) are crucial to their developmental outcomes. Vocalization refers to the noises that children create before they begin to talk. Studies have indicated that non-speech-like and speech-like vocalizations are the most prevalent cause of ASD children before the age of 2 possible identifying markers. Theoretical reasons for aberrant vocalization in children with ASD largely comprise motivation-oriented, neuromotor-oriented, sensory-oriented and social feedback-oriented to the theory. Future research may be explored to study the potential of aberrant vocalization as a unique early identification flag in children with ASD, boosting sobbing in children with ASD. Research on early screening, examining automatically trained classification models based on the best-predicted set of auditory characteristics, exploring the impact of intrinsic and social motivation on ASD The effect of children's speech-like vocalization and further research on the brain mechanism of ASD children's speech-like speech abnormalities. Thus, early detection and early action gives a more objective theoretical grounding.

Pneumonia is a very serious infectious disease that affects one or two sides of the human lungs. The disease is caused by infectious agents such as bacteria, viruses and fungi. In general, pneumonia is caused by Streptococcus bacteria. In present paper, we developed and analyzed the optimal control and cost-effectiveness strategies for pneumonia with the inclusion of a drug resistance compartment. The basic reproduction number that governs disease transmission has been obtained as the largest eigenvalue of the next-generation matrix. Both local and global stabilities of the disease-free equilibrium and endemic equilibrium points of the model equations were established using basic reproduction numbers. It is found in this research that the control strategies work well and thus the infective population sizes of both asymptomatic and symptomatic classes reduce drastically within a short period of time. Also, the analysis of cost-effectiveness is depicted. Finally, based upon the simulation values of optimal controls, the combination of Prevention, Treatment and Screening of infectious humans is the most efficient and less costly strategy to eradicate pneumonia diseases from the community.

The costs of fraud, both socially and financially, affect citizenship trust. The shadow of such distrust lies on the stock exchange and other investment institutions, and it will take a long way to get out of this dreadful shadow. Therefore, considering the importance and significant costs of financial fraud, this study seeks to predict fraud in financial statements using court accounting components. The statistical population includes three groups of accounting professions (official expert of justice in the field of accounting and auditing, auditors and financial managers of Tehran province), academics (all universities in Tehran province that have accounting and law), law and jurisprudence (lawyers and judges) is in Tehran province. Due to the infinity of the population, with the help of Cochran's formula, the sample size was 384 people. The results of structural equation method showed that each of the variables of essential traits of court accountants, basic skills of court accountants, advanced skills of court accountants and Islamic moral values of court accountants have a positive and significant effect on fraud detection.

In this work, we explore the consequences of fear and time delay on the intraguild predation model. Also, the predator consumes its prey in the form of a ratio-dependent type of interaction. We consider the fear in the prey population and the gestation effect on the predator population. We analyze the existence and the local stability of the proposed model without delay near all non-negative equilibrium points. Furthermore, by taking the fear parameter, the condition to satisfy the existence of Hopf-bifurcation near the coexisting equilibrium is derived. Moreover, we also examine the local stability property and Hopf-bifurcation investigation for the corresponding model in the presence of time delay. Some simulation results were also done to support the primary analytical findings.

There is a growing trend in early detection and diagnosis of COVID-19 for effective and accurate treatment. Several specialized studies have been conducted to develop programs that help in accurate diagnosis and reduce the burden on experts and specialists in this field. This paper describes an automated detection method for COVID-19 using deep learning techniques and computerized tomography images of the chest region. The images were initially optimized as a first step, and then a diagnostic process was performed to determine whether the lung had pneumonia, COVID-19, or healthy using the CNN algorithm. In addition to diagnosing the infection, the lung area was subsequently separated from the CT images for use in performing the final stage of determination of the ratio of COVID-19 infection in the lung and classified according to the ratio of infection rate to three stages (mild, moderate, severe). It is worth mentioning that the proposed system was trained on a database that contained 10,000 images of COVID-19, 10,000 pneumonia, and 10,000 healthy lungs. The proposed system diagnosed COVID-19 with an accuracy of 99.7 and an F1 score of 99.7.

The main purpose of this study is to present and explain the pattern of financial statements in accordance with the entrepreneurial marketing of companies (Case study: National Company of South-Ahvaz Oil Fields). This research is applied in terms of purpose and descriptive-survey in terms of nature. Data were collected using a researcher-made questionnaire that was approved by experts. The statistical community in the qualitative sector consists of experts and knowledgeable individuals and managers aware of marketing and financial management in the oil industry. The number of these people is 20. A small part of the statistical population includes experts, managers and staff members of the National Company for the Southern Oilfields of Yudha, numbering 570 people. The number of samples in the quantitative section was selected according to Morgan table 234 people. The results of data analysis showed that the ten dimensions of financial information location, financial information interconnection, financial information classification management, cash flow statement preparation, separation and separation of financial information, comparative table, liquidity and flexibility Risk-taking, value-creating, and resource leveraging by experts were obtained in semi-structured interviews and the Delphi method. The coefficients obtained from structural equations show that the above variables (Location information, Interconnectedness of financial information, Financial information classification management, Cash Flow Statements, Separation of financial information, Comparative table, Liquidity and flexibility, Risk taking, Risk taking, Create value, Leverage resources) have a positive and significant effect on Financial statements.

In this paper, first, we introduce $ (\alpha,\psi,\varphi) $-Geraghty and generalized $ (\alpha,\psi,\varphi) $-Geraghty contraction mappings in b-metric spaces and then we prove the existence and uniqueness of fixed point after exploring all the conditions which guarantee the existence of fixed point. Our results extend and generalize related fixed point results in the existing literature. We also provide examples in support of our main findings.

In this paper, we considered investigating some properties of zero near rings and novel results in this regard are obtained. In addition, we defined a zero divisor near ring and studied its relation to the zero near ring. In particular, we investigated some conditions on characteristics of elements of a near ring under which it becomes a zero near ring.

In this paper, we propose a new spectral approach based on alternative Legendre polynomials for solving nonlinear fractional integro-differential equations with weakly singular kernel. To do this, by the help of operational matrices of fractional integration and product based on these polynomials, we reduce the considered problem to a system of algebraic equations. Also, we investigate the error analysis of the proposed scheme. Finally, we present some numerical examples to show the high accuracy and validity of the new work.

In this study, the Aboodh transform homotopy perturbation method has been used to solve fractional differential equations. The Swift–Hohenberg equation accurately explains the creation and evolution of patterns in a wide range of systems. The Swift-Hohenberg (S-H) model is linked to fluid dynamics, temperature, and thermal convection. The problem's convergence analysis is displayed. All of the Surface representation graphs are also represented using MAPLE. ATHPM solution has been compared with the exact solution and LADM to check the accuracy of the proposed method.

In this article, we give generalizations and renements of weighted majorization theorem, weighted Faward and weighted Berwald inequalities for functions with nondecreasing increments of convex type.

Today, organizations face competitive environments and always need radical and fundamental change. This competitive environment forces organizations to abandon their fixed work methods and replace them with new ones to make fundamental changes. Industrial clustering is conducted in almost all countries worldwide, and Iran tends to consider industrial clusters in scientific and decision-making meetings in decision-making and development programs. Competitive necessity is the essential issue of clustering and cluster-based policies. Therefore, this study aimed to identify and prioritize the effective factors in the industrial export clusters of Iran to the Eurasian Economic Union (case study: Gilan Province). This applied research employed a field implementation method and questionnaire and interview tools as exploratory research with quantitative and qualitative data. The validity and reliability of the questionnaires were confirmed. The population was industry experts and some professors in the industrial export clusters who were selected as a statistical sample through convenient judgment sampling (15 people) for localization of the model. Data collection was prepared and adjusted through a researcher-made questionnaire based on pair-wise comparisons of the mentioned components. Fuzzy DEMATEL methods and fuzzy network process analysis were used to analyze the data in Excel. First, the criteria were identified, and then its content validity was confirmed by university professors by studying and reviewing scientific texts. In addition, the reliability of the indices was confirmed by Cronbach's alpha greater than 0.7. Then, seven main factors and 21 important sub-factors were identified using the scoring technique. In the next step, the effectiveness of each factor was determined using the fuzzy DMATEL technique (F.DMATEL). The "governmental factor" with an impact value of 1.49 was the most influential, and the "Company structure factor" with a net impact value of -1.174 was the most impressive. The identified factors were prioritized with Fuzzy Network Analysis Process (F.ANP) technique. The results showed that the maximum weight is related to the company structure criterion. The factors of company structure, suppliers, resources, markets, infrastructure, and economy were in the next ranks, respectively. Finally, some recommendations were provided to senior managers and experts of Iran's export industrial clusters to the Eurasian Economic Union, especially in Gilan province.

The aims of this work are first solving set-valued functional inequalities involving additive and quadratic set-valued mappings, and second investigations of their stability by using the fixed point method.

In this article, some integral equations are obtained and based on these integral equations, new integral inequalities are obtained for convex functions that satisfy certain convexity conditions.

Let $A$ be a unital Banach algebra and $X$ be a unital $A$-bimodule. In this paper, among other things, we characterize $n$-multipliers $T: A\longrightarrow X$ by applying zero products preserving bilinear maps.We also describe $n$-multipliers from $C^*$-algebra $A$ into $X$ through the action on zero products.

The COVID-19 pandemic of the coronavirus is a serious health threat. Governments are taking specific protections, including lockdowns and the need that face masks to be used. Wearing a protective cover is one of the most efficient ways to fight the disease. Due to this reason, offerings are the detection of face masks that can be utilized by specialists to create moderation, prevention, and assessment. According to the recommendations of the World Health Organization (WHO), the best important prevention strategy is using a facial mask. Hence, the need of wearing a mask is so important to save our lives and also protect others appropriately in open places including shopping malls and general stores. Reports demonstrate that face mask wearing whereas at work clearly diminishes the chance of spread. This paper presents a rearranged approach to obtain this reason by utilizing machine learning for detecting face masks. A dataset is utilized to construct this detector of the face mask. Via computer vision techniques and algorithms using deep learning the goal can be achieved. It includes the architecture of the MobileNet model that is trained with Tensorflow and Keras libraries. In this paper, Jetson tx2 was utilized to implement a real-time face masks automatic detection that is embedded and powerful, running on an embedded system at a higher frames-per-second rate (FPS) based on IoT. The proposed system aids in monitoring, taking images, and identifying persons who were not wearing masks. Additionally, we employed IoT strategies to transmit the images and alerts to the closest police station so that forfeit could be applied when it discovered unmasked persons. We used an actual dataset to train our model, and this improvement makes the recommended approach possible to seek a high level of accuracy rate of unmasking people our model trained on the real datasets and this improvement makes the proposed model urge a high accuracy in the detection of unmasking persons.

The numerical investigation on the flow of Carreau nanofluid past a radially stretching sheet close to a stagnation point along with convective boundary conditions has been considered. Moreover, the radiation effects and magnetic field are examined. In addition to this, the effects of heat generation/absorption are also explored. The conversion of non-linear partial differential equations describing the proposed flow problem to a set of ordinary differential equations has been carried out by employing appropriate similarity transformations. The numerical solution of the proposed flow equations is derived by the shooting method. The impact of pertinent flow parameters on the non-dimensional velocity, temperature and concentration profiles have been illustrated via tables and graphs. The limiting case of the present study affirms that the obtained numerical results reflect a very good agreement with those from open literature.

This paper is concerned with the following quasilinear elliptic system\[\begin{cases}-\operatorname{div}(a(|D u|) D u)= f(x,u,Du) \qquad \text { in }\; \Omega \\u =0 \qquad \qquad\;\qquad\qquad\qquad\;\;\qquad\text { on }\; \;\partial \Omega,\end{cases}\]where $\Omega$ is a bounded open subset of $\mathbb{R}^n$. By means of the Young measure and the theory of Sobolev spaces, we obtain the existence of a weak solution $u\in W_{0}^{1,p}(\Omega;\mathbb{R}^{m})$.

In this survey, the main and dominant point of pursuit is reviewing some aspects and approaches of networks security that are more appropriate for wireless sensors, by applying effective changes to some details and divisions, and it has been attempted to make them exclusive for the certain wireless sensors network that can be utilized in any particular or crucial industrial fields, like oil and gas pipeline and medical devices for monitoring the patient. Security network algorithms and processing trends are usually accessible and ready to use for everyone, however, it also increases the potential risk and concern of attack for industries that use wireless sensor networks. In this study, basic data has been altered and actually disintegrated before securing; additionally, common algorithms have been employed in order to reduce the risk involved with using wireless sensor networks for maintaining and protecting basic and major industries, and in particular for monitoring pipelines.

The analytic description of holomorphic mappings is coupled with the functions which map the unit disk to the right half plane. Also, a natural extension of the idea of inequalities between real-valued functions is the concept of subordination between functions of a complex variable. In this study, we make use of some first-order differential subordination and superordination conditions on functions associated with 2j,k-symmetrical points, and determine some already known classes of analytic functions. We're also come up with some sandwich theorems based on specific assumptions about the parameters that are involved in our major findings.

The purpose of studying the discipline of nonlinear wave processes is to knowledge of the current state of nonlinear wave problems in mechanics and an understanding of its main problems, as well as to develop general skills for solving these problems using new methods of mathematical. We consider a class of initial boundary value problems for one-dimensional nonlinear wave processes. A new topological approach is applied to prove the existence of at least two nonnegative classical solutions.  The arguments are based on a recent theoretical result.

Financial reporting is the end product of the financial accounting process, providing financial information to various users, both internal and external users from business unit, in the form of accounting reports. According to this fact that the financial reports and book (accounting) value of companies have played a significant role in the life of different groups, today it is more important to pay attention to issues related to honest, relevant, reliable and transparent financial reporting. Therefore, according to its importance, in this paper we study the financial reporting of companies by focusing on the market value of companies and accounting value in inflationary conditions. For this, the information of the member companies of the stock exchange over a ten-year period 2009-2020 are collected, then the hypotheses are tested after performing the necessary statistical tests using linear regression by Eviews 10 and SPSS 26 software. In this research, multivariate regression method is used as a statistical method. The results indicate that there is no significant relationship between the quality of accruals and cash flow with the accounting value of companies in inflation condition, but there is a significant relationship between total assets and net income with the accounting value of companies in inflation conditions. Also, there is no significant relationship between the quality of accruals, cash flow and net income with the market value of companies in inflationary conditions, however, there is a significant relationship between total assets and accounting value of companies in inflationary conditions.

In this research, a semilinear fractional system involving a new operator is tackled. The existence of a distributional solution is demonstrated and the Leray-Schauder degree method is used to deal with the existence of this system. For the uniqueness of the solution, we use the contraction principle with some assumptions made on the semilinear term $\Phi_{1}$ and $\Phi_{2}$. Then, using an example and the finite difference method a numerical investigation of this system is conducted.

Competitive advantage is one of the components that ensure organizational viability. Gaining a competitive advantage is not achieved randomly and without a plan; Rather, organizations should move in this direction by thinking and designing scientific frameworks and creating appropriate structures. There are several strategies that organizations must use to gain a competitive advantage. Smart businesses are recognized in most countries as important elements in socio-economic development. These businesses are especially important in creating job opportunities with low investment, regional development, organizational development of companies based on the principles of technology, product innovation and the creation of new methods. The research method in this paper is the modelling of system dynamics in Tehran Oil Refining Company, the results of which have been analyzed by descriptive statistical methods. The data collection method of this research in the quantitative part is prepared with questionnaires that managers, experts and experts complete sustainable competitive advantage through business intelligence using system dynamics modelling in Tehran Oil Refining Company, and data collection tools to The statistical population of this research is the staff and customers of Tehran Oil Refining Company and the method of data analysis is the use of Vensim software version 19 and modelling method, dynamics. It is a system. The purpose of this study is to design a model for achieving sustainable competitive advantage through business intelligence using system dynamics modelling in Tehran Oil Refining Company. The results of this study showed that managers of different industries can use this model and consider other variables, using the documents in their organization, the effectiveness of each of the criteria for achieving sustainable competitive advantage through business intelligence on the criteria. And predict their desired indicators such as brand equity, brand market share, profitability, demand, etc.

Hilfer-Katugampola-type fractional stochastic differential equations with nonlocal conditions are considered in this paper. By using the fixed point theorem, the existence and uniqueness of solutions for the considered problem are proved. Ulam-Hyers stability for the considered problem is studied. Finally, an example is presented to show our main results.

This article examines the nonlinear effect of inflation on Iran's economic growth. So far, many studies have been conducted on Iran's economic growth in which different estimation methods have been used. The dependent variable of this research is GDP per capita as an indicator of economic growth and the variables of inflation rate, investment, labor force, oil export revenue and government expenditures have been used as explanatory variables. In the present study, the effect of inflation rate on Iran's economic growth has been investigated asymmetrically using the nonlinear distribution interrupt (NARDL) model with a boundary approach. The research period is 1989 to 1397 and the data are annual. The results of the boundary test indicate the existence of a long-term relationship between the research variables. Investment variables and government expenditures have positive and significant effects, labor has a negative and significant effect, and oil export earnings do not have a significant effect on economic growth. Also, negative changes in inflation have a negative and significant effect, while positive changes in inflation are meaningless, and thus the asymmetric effects of inflation on Iran's economic growth are revealed.

Heritage cities suffer today from many challenges and urban transformations represented by economic and social stagnation, loss of urban identity, demographic changes, technological developments, and environmental and health challenges. It is necessary to deal with heritage cities in accordance with the principles of sustainable urban design and in a manner that suits the conditions of cities by choosing the appropriate and correct strategy from sustainable urban design strategies for the purpose of making them sustainable and vibrant in the future and branding place globally while preserving the imprint of the place in it. Thus, the research problem was identified (difficulties related to the appropriate and correct selection of sustainable urban design strategies in the future of heritage cities). The study dealt with the case study with the analysis of the Hafsia quarter in Tunisia, with the possibility of benefiting from it in other heritage cities, including the Iraqi cities, due to the challenges and urban transformations they are exposed to, and to conserve their cultural identity and place an imprint on them. The research found the possibility of mixing sustainable urban design strategies according to the economic, social, environmental, cultural and physical conditions of each area, with the effectiveness of the sustainable urban regeneration strategy for being a comprehensive and complete approach and providing effective solutions appropriate to the conditions of heritage cities.

We familiarize AM and SAM−contractions involving rational terms to prove the best proximant for discontinuous set-valued maps in partially ordered metric spaces. In the sequel, we demonstrate that completeness of space or subspace is not mandatory for the survival of the best proximant of set-valued maps. Obtained outcomes are unifications, extensions, improvements, and generalizations of some of the widely known results. We provide non-trivial illustrations to exhibit the importance of our explorations.

‎In this note‎ , ‎we present some operator ‎ine‎qualities via convexity property‎. ‎In the end‎, ‎refinements of mixed mean inequalities are given‎.

Color images, in spite of it, are quick and simple to understand (interpret) by our minds, but digitally suffer from huge byte consumptions that directly affect storage (space) and/or transmission. Color data compression systems are vital to control the enormous size of color data by losing insignificant color data that implies redundancies and manipulating significant color information. This research is concerned with reducing or packing the storage of color visual natural images using color transformation of $YC_bC_r$ base, based on a hybrid spatial-frequency compression technique. The essential part of the source band that proposed compression system constitutes of exploiting the adaptive lossy 1-D polynomial coding for coefficients and Minimize Matrix Size Algorithm (MMSA) for residual. On the other hand, for the non-source bands, the improved hierarchal scalar quantization method called double scalar uniform quantization scheme (DSUQS) is exploited along C421. For testing the performance of the suggested system, four natural color images from two datasets were adopted, the first called (Miscellaneous) and the second called (Kodak) of square sizes either of $(512\times 512)$ pixels. The experimental results showed the superiority of $YC_bC_r$ compared to the well-known standard joint photographic expert group (JPEG) that achieved higher performances where CR between (21-27) and PSNR between (36-38) dB, where CR of JPEG between (16-21) and PSNR (30-33) dB that implicitly means that the proposed system of color transformation base improved on average compared to JPEG with better-perceived quality for the compressed image.

We introduce a new fractional derivative which obeys classical properties including linearity, product rule, power rule, vanishing derivatives for constant functions, chain rule, quotient rule, Rolle's Theorem and the Mean Value Theorem:$$D^\alpha(f)(t)=\lim _{\epsilon \rightarrow 0} \frac{f\left(t e^{\frac{1}{\Gamma(1-\alpha)}} e^{-\alpha}\right)-f(t)}{\epsilon},$$this definition is comfortable with the classical definition of the Caputo Fractional Operator.

‎In this paper‎, ‎we prove that any surface corresponding to linear second-order ODEs‎ ‎as a submanifold is minimal in the class of third-order ODEs $y'''=f(x‎, ‎y‎, ‎p‎, ‎q)$ as a Riemannian manifold‎ ‎where $y'=p$ and $y''=q$‎, ‎if and only if $q_{yy}=0$‎.‎Moreover‎, ‎we will see the linear second-order ODE with general form $y''=\pm y+\beta(x)$ is the only case that is defined a minimal surface‎ ‎and is also totally geodesic‎.

As of June 24, 2020, the coronavirus infection (COVID-19) has infected more than 9.3 million people and resulted in more than 0.47 million deaths globally. COVID-19 cannot be diagnosed or treated without chest imaging procedures such as computed tomography and X-rays. Due to the highly infectious nature of this disease, radiologists are under continual pressure to diagnose and treat patients. As a possible solution to these issues and to enhance diagnostic accuracy, artificial intelligence (AI)-based image analysis techniques are being investigated. This assessment focuses on the present state of chest imaging analysis techniques using artificial intelligence for COVID-19. Note in particular the imaging analysis methodologies of two common viral types of pneumonia that may be used as a reference for assessing the illness utilizing chest pictures. Examine in deeper depth the progress of AI-assisted illness diagnosis and assessment, finding that AI approaches in this application are extremely useful. Focus on the role of artificial intelligence in the Corona epidemic and analyze the feme dataset utilized in prior studies as well as any relevant articles.

In this paper, we study the existence of the solutions of a class of functional integral equations which contain a number of classical nonlinear integral equations as special cases. The investigations are placed in the Banach space of real functions defined, continuous on the real half-axis and vanishing at infinity. The method used in our considerations depends on suitable conjunction of the technique of measures of noncompactness with the classical Schauder fixed point theorem.  The results obtained in this paper extend and improve essentially some known results in the recent literature. Besides, three examples showing the generality and applicability of our results are presented.

This paper aims to present a useful method for magnifying images, for which it is necessary to group the pixels and define the borders. In the proposed method, images are first partitioned using suitable segmentation algorithms and then artificial neural networks (ANNs) are applied to magnify each segment individually. In the ANNs applied, training is performed using, as input, a down-sampled form of the same image to be magnified. This type of training results in a high quality zoom in each segment since the pixels in an individual segment have very close features. Evaluation results on several images verifies the higher efficiency of the proposed method than other recently developed image zooming methods.

A code smell is any display in a program's source code that may signal a broader issue, impeding software development and maintenance. Developers struggle to recognize code smells, which has resulted in several detection tactics, tools, and software analysis tools, commonly referred to as code smell detection approaches. These techniques are generally used to uncover programming abnormalities and poor practices. Code smell detection techniques are typically used to raise developer awareness of the internal quality of a development program, as different tools have different implications.

In this work, we define an iterative scheme for a generalized spectral problem associated with two operators defined on a Banach space of infinite dimension. We show that under the norm convergence, the generalized approximated eigenvalues and eigenvectors converge to the exact eigenpairs. As a numerical application, we tackle a generalized eigenvalue problem associated with integral operators, where the accuracy and efficiency are illustrated in some numerical examples.

The purpose of this research was a meta-analysis of factors affecting the consequences of organizational anomie (the study of Islamic Azad University). For this purpose, after studying the research literature and identifying the factors affecting organizational anomie, they were classified and separated and the components were extracted. Based on the purpose, this research is among applied research and based on descriptive and exploratory nature. In terms of the method, the meta-analysis technique was used to extract the results, which is a type of qualitative study. In this research, articles published in domestic and Latin magazines have been used. In the first stage, 27 studies were collected from reliable databases including Google Scholar, Science Direct and other databases and those articles that had p-value; t-value and other statistics were separated from each other. This number was 25 acceptable articles that were used. According to the findings of this research, one of the most frequent factors affecting organizational anomie is the lack of common norms at work. The second factor is poor leadership and management. The unethical atmosphere in the workplace, social capital, negative attitude of employees towards their job, poor implementation of the employee socialization process and poor communication, organizational culture, the inability of the organization to show its true values, lack of meritocracy in the organization, lack of evaluation system Appropriateness, nepotism and discrimination, non-commitment of managers to the organization's values, technological turbulence, strategic invasion, central competitor and intensity of competition have also been identified as factors affecting organizational anomie. Finally, according to the research results, some suggestions have been provided.

The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as 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 long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed hybrid models in the prediction process when conducting simulation for different sample sizes and when applied to the daily crude oil price time series data, were more efficient than the single models, and the comparison between the single models and the proposed hybrid models was made by comparison scale, mean square error (MSE), and the results showed that the proposed hybrid models have the ability to predict crude oil prices, as they gave more accurate and efficient results.

In this work, we have considered a discrete-time discrete prey-predator. The discrete-time model is formulated in terms of difference equations and is obtained by applying a nonstandard finite difference scheme of Mickens type. We have discussed the existence and the local dynamics of the fixed points. Analytically, we demonstrated that the system undergoes a Neimark-Sacker bifurcation. Under a parametric condition, all chaotic features are justified numerically. Finally, we use two chaos control strategies to control the Neimark-Sacker bifurcation.

The purpose of this study was to identify the key factors affecting the tax avoidance process in private companies. The present study is in the category of basic research in terms of purpose and in terms of nature and method, is a descriptive and case study with private companies in Iran. The statistical population of this research is experts familiar with the topic, which includes professors and experts in the field of taxation. A sample number of 20 experts answered the questions through purposeful judgmental sampling. In order to present the model and identify the key factors, the interpretive structural modelling approach with the help of Matlab software has been used as a qualitative approach. In the quantitative part, to weight and prioritize the factors, the multi-criteria decision approach has been used with the help of a network analysis process and Super Decision software. The results of interpretive modelling showed that the factors of financial leverage, company size, product market competition and financial constraints that are adjacent to the strategic red line, in addition to being strategic research variables, are classified as linked variables. The findings of the network analysis process also showed that the V10 criterion, which is "social trust", is the most important and therefore the most important sub-criterion in presenting the tax avoidance model. The V6 benchmark, which is "financial constraints", is then the second priority. Factors of competition in the product market and corporate social responsibility are ranked third and fourth.

Nowadays, the IoT attracts a multitude of research and industrial interests. Smaller and smarter devices are being implemented daily in multiple IoT domains. However, protecting IoT devices from cyber-attacks is critical to their operation. Confidential data is leaked as a result of malicious acts. As a result, device performance becomes crucial. Security risks are frequently made in IoT-based structures that impact their standard work. Therefore, to eliminate and mitigate these issues (attacks), the Intrusion Detection System (IDS) was proposed to fulfill this purpose. This paper aims to study the state of the art of the proposed IDS. Moving on, we critically review the proposed IDS-based machine learning algorithms. Based on this evaluation criteria, the solutions covering architectures, intelligent prediction and algorithms are critically reviewed. To achieve our goals, the paper presents challenges and open research areas in IoT design.

In this paper, we investigate a class of initial value problems of nonlinear fractional differential equations with state deviating arguments, delayed impulses and supremum on the half line. A global existence-uniqueness result is obtained using the theory of fixed points in uniform spaces, which is different from what is commonly used in such studies. Our result is obtained in a setting where classical variants of the fixed point theorems frequently used in the literature are inapplicable, and moreover without any bounding restriction with respect to time, neither on the solution nor on the reaction part of the problem. Two examples illustrating our main findings are also given.

Suppose that $G$ is a finite group and $X=\textit{t}^{G}$   be a conjugacy class of an elements of order 3, $ \textit{t}\in G $.The A4-graph, is a simple undirected graph stand for $\textit{A}{_{4}}(G,X)$, whose vertex set X and two vertices $ \textit{x, y}\in X $ are adjacent if they are different and satisfy  $\textit{xy}^{-1} = \textit{yx}^{-1}$. In this article, the orbits under the action of $C_{G}(\textit{t})$ on X are analyzed, along with  the description of the algebraic structure of the subgroup $<\textit{t,x}>$  such that $ \textit{x} $ is a $C_{G}(\textit{t})$ -orbit representative is provided.

Nowadays, due to the increasing trend of environmental changes and developments, knowledge-based companies need strategic entrepreneurship to respond effectively to their environmental needs. Therefore, the present research, which has a mixed research approach (qualitative-quantitative) and has an application-development orientation, tries to investigate in two stages the effect of strategic entrepreneurship components on the performance of knowledge-based companies by applying new dimensions of balanced score card. The first stage, which is qualitative and based on the purpose is the fundamental type and based on the method of research is the descriptive-analytical type, in order to identify the components of strategic entrepreneurship using the Meta-synthesis method, the findings of studies in this regard, as They were systematically reviewed and analyzed based on the formation process and output achievement. Then, by referring to the valid documents available on the sites and after reviewing the information of English sources from 2000 to 2019 and Persian sources from 1380 to 1399, out of a large number of initial articles, 88 suitable articles were identified. In order to analyze the selected articles, constituent concepts and influential components, the content analysis method was used and in order to validate the extracted concepts, the Delphi panel method was used with the presence of 8 experts and specialists in the field of university and industry. Then, the research findings were identified and classified in the form of 49 concepts, 10 sub-components and 5 components, and the conceptual model obtained from the meta-synthesis based on the findings was presented. Also, the rank and importance of experts' opinions regarding strategic entrepreneurship components based on the results of the Friedman ranking test were the components of leadership, opportunism, innovative learning, attitude and resources, respectively. In the quantitative stage of the research, which is applied based on the purpose and descriptive survey based on the research method, in order to investigate the effect of strategic entrepreneurship components on the performance of knowledge-based companies by applying new dimensions of a balanced scorecard, the model method Structural equation modelling (SEM) was used to test research hypotheses using Excel 2013, SPSS 21, and Smart pls3 software. The statistical population studied at this stage is 102 senior managers of knowledge-based companies located in Yazd Science and Technology Park. In order to select a sample by simple random method, 80 samples were selected from the Morgan table and questionnaires were collected. The results of the hypothesis test showed that the components of strategic entrepreneurship affect the six dimensions of the balanced scorecard of knowledge-based companies including financial, customers, learning and growth, internal processes, employees satisfaction and the environment and society dimensions.

In this article, we study a new numerical approach to solve some particular class of delay integro-differential equations. The considered problem is a singularly perturbed Volterra integro-differential equation with a pure delay term. To solve such equations numerically we adopt the standard Adomian decomposition method followed by a first-order truncated Taylor approximation. The most appealing advantage of the present method is that it provides an adequate result for a wide scale of values to the perturbation parameter. The efficiency of the proposed method is illustrated with an example. Moreover, a vivid realization of the treatment is described by the theoretical study related to error analysis. Under some relevant assumptions boundedness of solution, and stability analysis are also established in the agreement of the current method. To strengthen our findings, a comparative study between the proposed technique and the well-renowned spline method is presented in the manuscript. Moreover, outcomes suggest the prior efficiency of the method which is also supported by the theoretical results.

Epilepsy is one of the widespread diseases of the central nervous system around the world, which is characterized by seizures with different periods and symptoms among people. Expecting incoming seizures is important and necessary to organize the patient's life and take the necessary precautions to preserve his life. We used a seizure prediction algorithm based on four stages. In the first round, one of the person's Electroencephalogram (EEG) channels is selected to be the input channel based on a statistical analysis of the person's EEG recordings. In the second round, the preictal period length (PIL) and the length of the sample segment (SEG) used are calculated. In the third round, several features from the selected channel’s data are extracted, while in the fourth round, the simulated annealing method is used to find a set of features that achieve the best performance in differentiating between the Preictal and Interictal periods. The results showed an average sensitivity of 77\% and a low false prediction rate of 0.2 by testing the algorithm with the CHB-MIT scalp dataset. Conclusion and Significance: Comparing our results with the results of recent research showed the superiority of our results in terms of using one channel compared to multiple channels in other research.

Many audit failures are due to ethical slips and unethical behaviours, and auditors’ understanding of the ethical climate can affect auditors’ behaviour and attitudes. The purpose of this study is to provide a model of auditors’ professional ethics behaviour with emphasis on the ethical climate and it has been conducted based on two qualitative and quantitative approaches by selecting 10 experts from the community of certified public accountants through purposive sampling. In the first part, we identified the indicators using library studies and specialized interviewing. Theme analysis and fuzzy Delphi technique were applied to analyse the qualitative data and screen the indicators respectively. In the next step, a model was developed using the quantitative method of ISM. According to our findings, the most fundamental constructs influencing auditors’ professional ethics are ethical climate, economic conditions, social culture, ethical ideology, organizational structure, management and leadership, policy and policy-making, monitoring and evaluation, skills and potentials, personal characteristics, recruitment and retention, Professional commitments and job ethics.Accordingly, the audit organization can create and develop an ethical climate and use other identified factors to add more dynamism to the auditors’ professional ethics, improve ethical and professional standards and enhance the auditors’ capabilities and public trust.

In this study, we consider complex-valued cellular neural networks (CVCNNs) models on time scales. In contrast to earlier research, we employ a straightforward approach to arrive at our theoretical conclusion rather than breaking the model down into real-valued or complex-valued systems. Firstly, we use the Stepanov almost automorphy on time scales, the theory of time scale calculations, the Banach fixed point theorem, and by constructing an appropriate Lyapunov function to establish the existence, uniqueness, and Stepanov-stability of Stepanov almost automorphy solution for this class of CVCNNs on time scales via a direct method. Finally, an example with simulations is given to illustrate the feasibility of our results.

Over the last several years, sentiment analysis has emerged as one of the most popular applications of machine learning. It enables the identification of a user's attitude from a remark, document, or review. As a result of the development of Big Data, recommender systems (RS) are also finding more use in many aspects of day-to-day living. There are three basic kinds of RS: collaborative filtering, content-based, and hybrid. This article presents a quick description of the recommender systems supplemented with a sentiment analysis module. Sentiment Analysis systems may help recommender systems improve by assessing Web-based reviews.

The purpose of this study is to provide a favorable model of a corporate governance system to prevent companies from falling into the trap of money laundering and effective on the decisions of respondents (people and their legal representatives). The research method is exploratory development. The statistical population of this study is Tehran capital market companies. The research period is from 2016 to 2016. From the statistical population of the research, 120 companies were selected as the sample size by the elimination method. In-house and extra-organizational mechanisms were used to lead the company. In this research, correlation tests and regression analysis, and Eviews software have been used to test the research hypotheses and determine whether they are significant or not. Findings showed that the results of testing the hypotheses indicate that the management system with good, average and poor performance has a significant effect on preventing companies from falling into the trap of money laundering and it can be concluded that corporate governance systems avoid companies from Getting caught in the trap of money laundering is effective and the main research hypothesis that states that corporate governance systems are effective in avoiding companies from getting caught in the trap of money laundering (money laundering) is confirmed.

In  this paper, we are concerned with the  existence  of renormalized solution for a  nonlinear periodic  parabolic problem associated to the equation\begin{equation}\frac{\partial u}{\partial t}-A(u)+g(x,t,u,\nabla u)=f\in L^{1} \end{equation}where  $A(u)$ is the  m-Laplacian operator defined on $ W^{1,x}_{0}L_{M}(\mathcal{Q})$.

The most significant issue in remote sensing is the extraction of roads from very high-resolution satellite (VHR) photos. The authors of this research provide an effective Densenet121 block that can understand relationships between global features. As a result, road segmentation is more precise due to the ability of each geographical version available as a pointer to other data that is collected. In the specific, our single model outperformed every other contemporary aggregation model that has been presented in the official Densenet121, our suggested model offers a shorter training convergence time, fewer parameters, and fewer Giga floating-point operations per second (GFLOPs). The authors also provide empirical evaluations on how non-local blocks should be used appropriately for the base model. Theoretically, the applied methodology provided a DenseNet-121 and an unpublished best alternative identification method DenseNet-121, a really sizable dataset provided by the CNN model's training phase. Additionally, because the implemented architecture takes advantage of data augmentation, no custom data extraction method is necessary.

In this paper, we consider ``uniquely remotal center" and `` relative uniquely remotal center" in normal spaces. We define relative sun sets and sunrise sets in normal spaces. We show that every uniquely remotal set has zero Chebyshev radius and every remotal set in strictly convex space has at most a singleton Chebyshev center.

Facial gender recognition is an important and attractive research topic due to its extensive use cases, including gender demographic scanning, targeted advertising, access control, and visitor profile identification. The framework of the proposed method for face detection face consists of Adaboost, Integral Image, Haar- features, and Cascade Classifier. The LFW dataset containing 13,233 facial images was used.

Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology, monetary units and blockchain. This paper reveals the effect of neural networks (NNs) by analyzing the time series of the Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. In this paper, the purpose of Bitcoin price prediction is to use the combined method of signal decomposition into intrinsic components (EMD) and support vector regression (SVR) algorithms. The proposed method uses the intrinsic component decomposition method as a denoising step in the training data. We conduct an empirical study that compares the proposed method with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that NN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price also the Mean Square Error (MSE) of the proposed method is calculated and compared with previous works.

In the present paper, we establish some generalized cyclic contraction results through $p$-number of subsets by using two different types of t-norm, viz. Hadzic type t-norm and minimum t-norm in the setting of 2- probabilistic metric spaces. Our results generalize some existing fixed point theorems in 2-Menger spaces. Some illustrative examples and an application to the existence of a solution to Airy’s type differential equation are also provided.

In this paper, we introduce and study $ij$-somewhere dense sets in bitopological spaces. We present $ij$-somewhere density properties by using some generalized open sets which are used for generalizing the operators of interior, closure and boundary. In this manner, we also investigate several properties of $ij$-somewhere dense sets and present several examples for the main results. We also introduce $\omega$-hyperconnectedness as an extension of hyperconnectedness by using $ij$-somewhere dense sets. We later present some separation axioms based on these types of sets.

Internal controls and their improvement are essential issues in organizations and companies. Therefore, this mixed research was conducted to optimize the quality of internal controls of companies listed on the Iranian Stock Exchange using the imperialist competitive algorithm. This mixed study was conducted on 29 variables in the qualitative section by interviewing 18 experts to optimize the quality of internal controls of companies listed on the Iranian Stock Exchange. Qualitative and quantitative data analysis reached five stages of implementation. In the implementation of the fifth stage, 1550 repetitions were obtained, and the quality level of internal controls was reduced by 1.3473, which is the best possible value for the objective function. Paying severe attention to the systemic approach in the internal control system of companies can lead to a significant improvement in the internal controls of listed companies and enhance their performance. In addition, the effect of the systemic approach affects the level of shareholder satisfaction and the quality of risk detection and evaluation.

Nowadays, it's a very significant way for researchers and other individuals to achieve their interests because it provides short solutions to satisfy their demands. Because there are so many pieces of information on the internet, news recommendation systems allow us to filter content and deliver it to the user in proportion to his desires and interests. RSs have three techniques: content-based filtering, collaborative filtering, and hybrid filtering. We will use the MIND dataset with our system, which was collected in 2019, the big challenge in this dataset because there is a lot of ambiguity and complex text processing. In this paper, will present our proposed recommendation system. The core of our system we have used the GloVe algorithm for word embeddings and representation. Besides, the Multi-head Attention Layer calculates the attention of words, to generate a list of recommended news. Finally, we achieve good results more than some other related works in AUC 71.211, MRR 35.72, nDCG@5 38.05, and nDCG@10 44.45.

In this paper, we obtain explicit forms of the sum of entries, the maximum column sum matrix norm, the maximum row sum matrix norm, Euclidean norm, eigenvalues and the determinant of a k-circulant matrix with the generalized 3-primes numbers. We also study the spectral norm of this k-circulant matrix.

Pipelines are the safest as well as the most economical way to transport gas and condensate over long distances. Radiographic images are provided to commentators as a tool to diagnose welding defects in metal lines, so the study of welding in gas and oil pipelines has always been one of the most important areas of non-destructive testing. Expert interpreters are now used in many countries to interpret radiographic films from non-destructive tests. Interpreters can detect the number of pores on the weld surface by viewing radiographic images due to the limited number of these people and their unavailability. In some cases, there are many problems. For human interpretation, radiographic videos must be collected and sent to the interpreter's place of work or residence. The purpose of this article is to provide a method that can be used to interpret radiographic films quickly using conventional image processing methods and identify the welding defects in them and determine whether these defects are duplicates or not. The method of image segmentation is the area growth method. The main feature of this method is its proper performance in images such as radiographic images that have less subject variety. This method separates a part of the image from the rest by determining a pixel in the image as the starting point and expanding the area around this point due to the similarity between the pixels. In this paper, based on the histogram, the start and end image of the welding range is determined automatically. Then a combination of different standard algorithms is applied to identify defects in the image. Then, the key points of the image are extracted, and using them, the corresponding complex dynamic network is drawn and its calculations are performed. The simulation results show that the proposed method covers the shortcomings of the previous methods and in addition to bringing the detection of welding defects by computer closer to human diagnosis and in some cases works better than human performance, it has also made it possible to identify duplicate images.

The main goal of this study was to determine the weights of variables affecting financial reporting quality in Iran using the analytic hierarchy process based on fuzzy logic. First, the cause and effect relationship between factors was identified using the DEMATEL method. According to this analysis, the most important variables of reporting quality are information disclosure, management ability, corporate governance, audit committee, audit quality, information asymmetry, internal control system, stock exchange rules and regulations, and conflict of interest. The importance of these variables was determined using the fuzzy analytic hierarchy process. In general, management ability is the most important financial reporting variable, and audit committee and conflict of interest are also important. Management ability is also very important to audit quality, information disclosure, corporate governance, and internal control system. The audit committee and conflict of interest are very important to information disclosure and the internal control system, respectively. A noteworthy point in this study is that the role of network relationships was such that the corporate governance variable showed itself to be completely dependent on management ability.

Blockchain technology has drawn in a ton of consideration from specialists, designers, and organizations lately, and its execution has revived interest in various applications, including e-finance, e-medical care, savvy homes, the Internet of Things, government-managed retirement, coordinated factors, and others. Most of the blockchain-related examinations in the writing have been viewed as designing execution centred, with hypothetical framework investigation getting undeniably less consideration; in any case, the current work is simply ready to show the mining system. For better comprehension of the functional and hypothetical elements of the blockchain, a  model in view of the lining hypothesis is advanced in this review. In this article, we have explored the joining of IoT with blockchain technology and gave a top to bottom investigation of the blockchain-empowered IoT and IIoT frameworks. The cutting-edge research is arranged into information capacity and the executive's method, enormous information and distributed computing procedure (money and information inspecting), and modern areas (store network, energy, and medical services area). The sagacious conversation in view of the various classifications is likewise introduced in the paper.

Investors, creditors, government and other users of financial statements rely on financial information given by the managers of firms to make logical and reasonable decisions. In many cases, the purposes of providers are contradictory to the users’ ones. Therefore, auditing is a tool to enhance the reliability of financial statements presented by firms. In the current research, the selection of an optimal method to predict the report type of independent auditor has been addressed and two approaches of vector machine and neural network have been compared. It was conducted during 2008-2017. 84 firms were reviewed. To train and test the research variables, Voka software has been implemented. The dependent variable is the report type of auditor. Results indicated that the accuracy of the support vector machine algorithm was computed as 66.13% and 56.74% for the training and testing sections, respectively. As well, the accuracy of the neural network model was 61.24% and 55.02% in the training and testing sections, respectively. The support vector machine model was more effective than the neural network.

Let $\mathcal{R}_{n}$ be the set of rational functions with prescribed poles. It is known that if $r \in \mathcal{R}_{n},$ such that $r(z)\neq 0$ in $ |z|<1,$ then     \begin{align*}     \sup_{|z|=1}|r^{'}(z)|\leq \frac{|\mathcal{B}^{'}(z)|}{2}\sup_{|z|=1}|r(z)|     \end{align*}     and in case $r(z)=0$ in $|z|\leq 1,$ then     \begin{align*}     \sup_{|z|=1}|r^{'}(z)|\geq \frac{|\mathcal{B}^{'}(z)|}{2}\sup_{|z|=1}|r(z)|,     \end{align*}where $\mathcal{B}(z)$ is the Blashke product. The main aim of this paper is to relax the condition that all poles of $r(z)$ lie outside the unit circle and instead assume their location anywhere off the unit circle in the complex plane $\mathbb{C}. $ The results so obtained besides the above inequalities generalize some other well-known estimates for the derivative of rational functions $r \in \mathcal{R}_{n}$ with prescribed poles and restricted zeros.

The aim of this study was to investigate the nonlinear relationship between financial ratios and capital structure in companies listed on the Tehran Stock Exchange over a period of six years, from 2014 to 2020. The samples of the present study include 134 eligible companies listed on the stock exchange. Tehran and the research method is descriptive-correlational. Data and theoretical foundations of the research have been collected through library studies. Nonlinear regression was not used for data analysis using Ives and Stata econometric software. The results of this study show that a significant non-linear relationship was observed between the financial relations of the capital structure, so it can be justified by referring to the process of political spending, which is one of the components of accounting theories. Also, other research results are based on the non-linear relationship between the variable components of financial ratios and capital structure.

We are concerned with an analysis performed to simulate the steady-state Walter's B' viscoelastic fluid in a 3-D space across the ground and a porous elliptic plate. We study the effect of viscoelasticity and with the help of a suitable resemblance transformation for components of velocity, fundamental equations are then reduced to a set of ODEs which are then solved by the Homotopy analysis method (HAM). Impacts of elasticity and cross-flow Reynolds number are discussed.

The Compressible MHD resistance equations were written for the IR T1 tokamak configuration with one or two singular surfaces for density mass profiles. And they are linearized around equilibrium state. The eigenvalues and eigenfunctions of the resistive MHD modes were obtained for poloidal and toroidal mode numbers numerically by Mathematica software for homogene and inhomogene plasma. The values obtained show that the eigenvalues are complex, and they are placed on certain curves. Also by drawing a diagram of eigenfunctions, it was determined that they were damped.

In this paper, we consider a class of nonlinear fourth-order wave equation with damping and source terms of variable-exponent types. First, by the Faedo-Galerkin approximation method with positive initial energy and suitable conditions on the variable exponents $m(.)$ and $r(.) $, we established the local existence. We also prove that the local solution is global. Finally, the stability estimate of the solution was obtained by using the Komornik inequality

Car accidents are an important phenomenon because of their direct relationship to the living conditions of different population centres in cities; it is known that a single accident causes increased human and material losses. For the purpose of researching the topic of predicting the number of accidents, the zero-inflated Poisson distribution was studied. In this thesis, several methods were searched, namely (maximum likelihood estimation, and moments) methods, in order to estimate the zero-inflation parameter of the Poisson regression. In this research, a number of simulation experiments were carried out according to the assumed distribution (Poisson's zero inflation) and the methods for estimating the assumed zero inflation parameter. And for a number of sample sizes (60, 80,100) according to different values of the zero inflation parameters (0.1, 0.2) and the second parameter of the zero-inflated Poisson distribution (1, 2). The comparison between the results of the different simulation experiments was done through the mean square error due to the estimations of each of the two parameters of zero inflation and the second parameter of the zero-inflated Poisson distribution according to each of (estimation method, distribution parameter and sample size).

This study aimed to provide an audit quality model based on organizational structure, operational mechanism and characteristics of audit firms. The sample size in order to collect the research required data was considered as 386 independent auditors from auditing firms, and the same number of questionnaires were distributed and collected after being answered. 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 99% confidence level showed that; the size of auditing firms, legal system, internal and external quality control of auditing firms and corporate governance mechanisms are among the factors positively affecting audit quality. Therefore, it can be said that with increasing each of the factors of the size of auditing firms, legal system, internal and external quality control of auditing firms and corporate governance mechanisms, the quality of auditing increases. The legal system and internal and external quality control are among the factors that enhance and affect the organizational structure of auditing firms. The size of audit firms and internal and external quality control are factors affecting the operational mechanism of the audit firm. Therefore, it can be said that by increasing the size of auditing firms and internal and external quality control of auditing firms, the operational mechanism of auditing will be improved. Finally, the results showed that the organizational structure of auditing firms and the audit operating mechanism have a positive and significant effect on audit quality.

The problem of determining the zeros of regular polynomials of a quaternionic variable with quaternionic coefficients is addressed in this study. We derive new bounds of the Enestrom-Kakeya type for the zeros of these polynomials by virtue of a maximum modulus theorem and the structure of the zero sets in the newly developed theory of regular functions and polynomials of a quaternionic variable. Our findings generalise several newly proven conclusions concerning the distribution of zeros of a quaternionic polynomial.

In this paper, we prove stability of multi-cubic functional equations in Lipschitz spaces by property multi-symmetric left invariant mean. Indeed, we prove under certain Lipschitz condition a family of Lipschitz mappings can be approximated by multi-cubic mappings.

In this paper, introduced a new accelerated iterative algorithm in \((\lambda,\rho )\) -quasi firmly nonexpansive multi-valued mappings in modular function spaces and present some results for convergence to a fixed point in this mapping, we use faster convergence theorem to comparison our iteration with some other iterations and introduced numerical example. As an application, we have referred to previous work by other researchers.

Today, in all countries, the prosperity of the capital market is recognized as one of the indicators of economic dynamism. The extension of the capital market leads to an increase in the depth of this market and in the long run, will lead to financial development, such that this financial development ultimately will lead to stable economic growth. Therefore, the main purpose of this study is to investigate the effect of economic policy uncertainty on the ratio of current stock value to GDP as an indicator of the development of the Iran capital market during the years 1991-2018. To achieve this goal, first, using the Gregory-Hansen co-integration approach, the existence of a long-term relationship between the variables of the model was tested and then the experimental research model was estimated using the dynamic least squares method. The results of model estimation showed that there was a long-term equilibrium relationship between the variables, and the inflation rate and the uncertainty of economic policies have a negative impact. Also, other variables of the research, i.e., degree of economic openness, GDP, and private sector investment had a positive and significant effect on the Iran capital market development index during the study period.

In this article, using the concept of q-analogue, we define a new class of analytic functions associated with Chebyshev polynomial of second kind. Then with the help of symmetric q-Chebyshev polynomial, we introduce and estimating first two Maclaurin coefficients for new subclasses of analytic functions. Also as application of the results, we estimate the relevant connection to the famous classical Fekete- Szegö inequality belonging to the class $\tilde{S}_{\Sigma}^{q}(\lambda,\gamma,s).$

In recent years, technology has developed very dramatically in all fields, such as imaging technology, which has become crucial in most medical fields. Currently, widely medical images are used to diagnose and treat various illnesses. This research describes a novel waveform developed from the principle of torsion in Haar with Cosine and Sine waves (CAS) for compressing color and medical images such as Magnetic Resonance Imaging (MRI) and computed tomography (CT). This research proposes a method that discusses medical image decomposition and color images using novel waveform analysis for image compression, and how wavelet is utilized for image restoration. The experiments demonstrate that our method is more accurate and compressive than the Haar wavelet Transform (Haar) when comparing numerical results of  Compressed file size, CR (Compression Ratio), PSNR (Peak Signal Noise Ratio) and RMSE (Root Mean Square Error). Furthermore, the method achieves better image quality.

The research had a mixed-methods type and was qualitative and quantitative. In the qualitative phase, the statistical population consisted of 15 faculty members in sports management and economics and all people with experience in the management of martial arts teams as well as senior managers of the Martial Arts Federation. The samples were selected using the non-random (judgmental) purposive and snowball sampling methods. To answer the questionnaire of the present study, the statistical population consisted of 100 athletes, club managers, and managers of the Martial Arts Federation according to Morgan's table, and 80 final samples were selected to answer the questionnaires. The reliability of the questionnaire was used using Cronbach's alpha of 0.83. The questionnaire had 7 components and 27 indices, including coaches (4 questions), administration (5 questions), women (4 questions), finance (5 questions), athletes (3 questions), foresight (3 questions), and information technology (3 questions). SPSS 25 was used to perform descriptive and inferential statistics calculations, and the PLS ver.3 software was utilized for structural equation modeling.  The index of rent in selecting coaches with $71\%$ correlation in the component of coaches, the index of unfavorable training courses with $81\%$ correlation in the administrative component, the index of training restrictions in the women's hall with $81\%$ correlation in the women component, the index of economic problems of athletes and their lack of total compensation with $87\%$ correlation in the financial component, the index of non-commitment of the Sports Medicine Federation to support athletes after injury with $86\%$ correlation in the component of athletes, the non-implementation of projects with strategies of revenue-generating from athletes' championship with $90\%$ correlation in the forecast component, the index of recording the information about championship databases and provincial delegations in the database for easy access to information with $71\%$ correlation in the information technology component had the highest correlation with the above-mentioned components. Furthermore, the financial component was the highest effective component with a path coefficient of 0.853, and the administrative component was the least effective component with a path coefficient of 0.519. All components had a positive and significant effect on the success of the Martial Arts Federation.  Given the above-mentioned factors, the managers of the Martial Arts Federation must implement strategies such as new economic, motivational, educational, information technology, and support programs for athletes and coaches to achieve organizational success and goals in domestic and foreign fields.

In this paper, we discuss some conditions of a greedy basis for Banach space $X$ under a standard $\varepsilon$-isometry mapping. We show that if $X$ and $Y$ are Banach spaces, $\left(x_n\right)$ is a greedy basis for $X$, and $f:X\to Y$ is a standard $\varepsilon$-isometry, then $\left(f\left(x_n\right)\right)$ is a greedy basis for a subspace of $Y$. As a result, if $f$ is a surjective standard $\varepsilon$-isometry, then $\left(f\left(x_n\right)\right)$ is a greedy basis for $Y$. We also show that ${span\left\{\left(f\left(x_n\right)\right)\right\}}^*$ is isomorphic with $\mathrm{\Psi }\subset Y^*$ where $\mathrm{\Psi }$ is defined as\begin{equation*}    \mathrm{\Psi }\mathrm{:=}\overline{span}\left\{{\psi }_n:\ {\psi }_n\in Y^*\ and\ \left|\left\langle x^*_n,x\right\rangle -\left\langle {\psi }_n,f\left(x\right)\right\rangle \right|<3\varepsilon a\right\}\end{equation*} where $\left\|{\psi }_n\right\|=a=\left\|x^*_n\right\|$.

The motive of this paper is to provide an understanding of the role of generalizations of metric spaces in a fixed point perspective. For this purpose, the concept of the pseudo non-triangular metric is introduced. Further, we study and analyze the structure of open sets, closed sets, and other topological properties of the new metric. Then, we compare it with JS-metric, strong JS-metric as well as non-triangular metric and observe that pseudo non-triangular metric becomes the bare minimum metric structure required to prove a new fixed point theorem for contractive type mappings. Finally, we establish the Caristi type fixed point theorem, which generalizes some well-known results, including recent developments by Karapinar et al. [15].

The behavior and growth of cancerous tumor is an interesting research subject and it has been widely analyzed from theoretical and empirical aspects. Various models have been applied to determine the growth pattern of cancerous tumor. In one of the current models, which we refer to as the competitive model, the tumor growth rate is determined based on the competition between the healthy and cancer cells. In this model, the competition matrix between the healthy and cancer cells is a type of prisoner's dilemma game matrix, so the growth curve of tumor has a sigmoid or S shape. According to the effective application of this model in determining the tumor growth rate, some methods to get rid of the model restrictions are presented so that it can be used for tumor progression pattern. Finally, in order to evaluate the efficiency of the developed model, it has been implemented in some empirical examples.

his paper is devoted to the study of the asymptotic behavior of a viscoelastic problem with short memory in a three-dimensional thin domain  $\Omega^\varepsilon$. We prove some convergence results when the thickness tends to zero. The contact is modeled with the Tresca friction law. We derive a variational formulation of the problem and prove its unique weak solution. Then we prove some convergence results when the small parameter $\varepsilon$ tends to zero. Finally, the specific Reynolds limit equation and the limit of Tresca-free boundary conditions are obtained.

Business and Information Technology Alignment (BITA) is a preferred condition in which the relationship between business and IT is optimized to maximize the business value of IT. BITA is a critical discipline allowing businesses to propose appropriate IT-enabled business solutions. A BITA method needs a well-designed process for identifying business problems, needs, and opportunities. Strategic BITA methods that are based on requirements engineering (RE) techniques can enable consideration of all business aspects and therefore fulfill expectations for the design of a suitable IT-enabled business solution. This study introduces a Strategy-oriented and Quality-aware BITA ethod (SQBITAM) to facilitate alignment between business requirements for business systems and the business strategies. SQBITAM consists of a business requirements engineering method, using a combination of goal-oriented and scenario-based requirements engineering approaches and includes an early quality evaluation phase to assess the quality and performance of the designed IT-enabled business solution. Analytical evaluation of the qualities and performance factors enables assessment of the business strategy and TO-BE business process models. An ICT unit in a sample Power Distribution Company is used as a case study to demonstrate, exemplify and validate SQBITAM’s steps. The results show a higher capability of SQBITAM than the currently existing BITA techniques.

In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. Imaging techniques (like Chest X-rays) play an essential role in diagnosing many diseases (such as COVID-19). There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. In this paper, we start by overviewing the status of COVID-19 from a historical standpoint and diagnosis updates. Moving on, provide an overview of the convolutional neural networks. Then, we elaborate Transfer learning method and its main approaches. Next, we provide a critical literature review on implementing Deep learning techniques: 1) Novel deep learning architecture; 2) Direct use of deep learning; 3) Transfer learning fine-tuning technique, and 4) Transfer learning feature extraction technique. For each of these, we evaluate and compare very recent studies published in highly ranked journals. The experiments show that all techniques achieve closer accuracy, ranging from (98-100 \%). Along with all, the direct use of the deep learning technique records the highest accuracy and is less time-consuming and resource spending. Therefore, establishing such a technique is useful to predict the outbreak early, which in turn can aid in controlling the pandemic effectively.

Environmental degradation, global warming, and climate change have become dangerous factors and pose a serious threat to global security. One of these factors is the threat of greenhouse gasses, mainly caused by the emission of CO2 carbon dioxide. In an environment of uncertainty in economic policies, energy efficiency policies are also not effectively implemented due to weak regulations. Incompatible economic policies may also lead to higher energy consumption and thus greater environmental impact. Therefore, this study investigates the role of economic policy uncertainty on carbon dioxide (CO2) emissions in Iran during the period from 1979 to 2018 using the time series method of the factor-augmented vector autoregressive (FAVAR) model and the time-varying parameter (TVP) model proposed by Koop and Korobilis (2013). In addition to investigating the impact of economic policy uncertainty, the impact of globalization on CO2 emissions is also examined. The experimental results show that first, more energy consumption leads to more pollution; second, economic policy uncertainty reduces the quality of the environment; and third, globalization increases the destructive effect of energy consumption on CO2 emissions.

In this paper, we consider the boundary value problem related to the quasilinear hyperbolic equation with nonlinear source terms. We start by showing the local existence theorem. Then, we prove the global existence and the decay of the energy by using Nakao's inequality, finally we get the finite time blow up of solutions.

In recent years, Machine Learning (ML) algorithms, especially Artificial Neural Networks (ANNs), have achieved remarkable success in various fields such as Pattern Recognition, Computer Vision, and Voice Recognition. Where ANNs algorithms have proven their superiority over traditional ML algorithms like (Support Vector Machines, Decision Trees, and Naïve Bayes) in various fields. Multi-layer Perceptron (MLP) network is one of the popular ANNs types and is used in various fields. The field of healthcare pattern recognition is considered one of the most important fields in our modern age, as this field is concerned with patterns extracted from raw data. There are many studies that dealt with MLP networks to detect and classify patterns in such a scope. In this study, a body of work deals with using the MLP networks for healthcare pattern recognition in five different topics (Diabetes, Heart disease, Liver, Breast cancer, and Parkinson's disease). The goal of the research is to identify strengths and weaknesses, and to identify the latest developments of adapting MLP network to recognize patterns in different data sets in the Healthcare field.

The effect of the characteristics of the board of directors in choosing the debt financing model of the company by evidence from the Tehran Stock Exchange was investigated in this study. The statistical population in this study included all companies active on Tehran Stock Exchange. The method of the present research was applied in terms of purpose  and the results can be useful for managers, shareholders, investors, researchers and developers of standards. This research was descriptive exploratory in terms of data  analysis and field one in terms of data collection. A statistical sample of 146 companies for an 8-year period (2011-2018) was selected from Tehran Stock Exchange. Findings  showed that the significance level of the independent variable (characteristics of the board) at the specified confidence level was significantly related to choosing the debt financing mode of the company; Since the significance level of the independent variable (characteristics of the board) was less than the specified error level.

In this work, we first introduce the modified S-iteration method to approximate common fixed points of three multi-valued generalized $( \alpha -\beta )$-nonexpansive mappings in the setting of Banach spaces. Then we prove some convergence theorems for these mappings by using it. Also, a numerical example is given to illustrate the main results. Furthermore, we introduce a one-step iterative process for generalized $( \alpha -\beta )$-nonexpansive mappings and we prove strong convergence results for the one-step iterative process involving such mappings. Thus our results extend and generalise corresponding results in the contemporary literature.

We consider a fractional-order prey-predator model with Holling type IV functional response and the effect of harvesting on both populations. The sufficient condition for the existence, uniqueness, non-negativity and boundedness of the solutions are discussed. Moreover, we found all possible equilibrium points and the local and global stability behavior are investigated. Finally, numerical results are presented with some examples.

In this paper, we establish two new refinements for integral forms of Jensen’s and Holder’s inequalities. Several applications are given on special means.

In this research, models of electrical loads were developed after transferring the data of the substations by using the SCADA system and calculating the electrical loads accurately for a period of 24 months taking into consideration the high and low weather temperatures in Babil Governorate in 2016 and 2020 and indicating the amount of growing loads Electricians and finding ways to handle control or reduce the value of this growth, especially since the poor consumption of electric energy causes great financial losses in light of the economic crisis that rocked the world countries.

In this paper, we introduce and study a system of generalized quasi-variational inequalities involving nonlinear, nonconvex and nondifferentiable terms in uniformly smooth Banach space. By means of the retraction mapping technique, we prove the existence of solutions for this system of quasi-variational inequalities. Further, we suggest an iterative algorithm for finding the approximate solution of this system and discuss the convergence criteria of the sequences generated by the iterative algorithm under some suitable conditions.

Due to the emergence of cryptocurrencies in the world, many people save their capital and assets like cryptocurrencies. Cryptocurrencies are associated with prices of gold and oil, and stock market indices. On this basis, the present study aimed to evaluate the association between cryptocurrencies with oil and gold prices. To this end, the study performed an evaluation using the BEKK multivariate GARCH method. Therefore, two regression models were estimated to evaluate the association between cryptocurrencies and oil and gold prices. Based on the results, the mutual relationship between cryptocurrency volatility and gold and oil prices was confirmed. In general, volatility in oil and gold prices has a positive effect on cryptocurrency volatility. Given that volatility in oil and gold prices has a positive effect on cryptocurrency volatility, and these effects will be more manifested in future periods, cryptocurrency investors are recommended to examine oil and gold prices, especially oil prices in the last 10 years, before purchasing cryptocurrencies.

Earthquakes are one of the most devastating and costly natural risks that a country faces, as they occur without notice and can result in major injuries or the loss of human lives as a result of damage to the destruction of a large number of houses, buildings, and other rigid structures. The point of this review is to assess the exhibition of Artificial Intelligence strategy in foreseeing the following event tremor utilizing the seismic wave signals. We present a three-layer feed forward BP neural organization model to find factors related to quake greatness M and seven other numerically determined boundaries. As info and target vectors, seismicity markers are utilized.

The present paper is mainly concerned with the existence, uniqueness, interval of existence and continuous dependence of solutions on initial conditions for nonlinear implicit Caputo-Fabrizio fractional hybrid differential equations. The results are obtained by using fractional calculus, contraction principle theorem and the Gronwall's inequality to show the estimate of the solutions. An example is given to illustrate the effectiveness of our main results.

Cloud computing and the Internet of Things have both experienced rapid and independent evolution. These two technologies are very different from each other and most of their features complement each other. These properties, which complement other technologies, are the main reason why researchers propose to integrate these two technologies, which in certain cases can be of great benefit. Therefore, the present study aimed to provide an algorithm for the connection of smart objects based on bootstrapping with the cloud, using asymmetric cryptography. In terms of purpose, this research is in the category of applied research. All relevant documents from written sources, including books, articles published in reputable scientific-research journals, research reports on the subject and academic dissertations, and valid Internet resources, and Related to the subject are studied and used. Using the public key on a resource-limited smart device with CoAP, we learn how to obtain a 3GPP-based public bootstrap architecture to ensure authentication and connectivity across a variety of devices. Then, with the help of standard protocols, including RADIUS and EAP, without the need to install new software on the phone, it is possible to automatically set up a wireless network with content to communicate with the cloud. Improved protocols meet the requirements of key IoT security services such as privacy, publicity, and credibility, and can achieve better performance with lower communication costs. On the other hand, the goal of most methods is to increase the scalability and performance of the system and control access to prevent unauthorized access.

Message and node authentication is one among the foremost effective ways to prevent nodes from being propagated, forwarded, and corrupted, unauthorized messages over wireless sensor networks (WSNs). In order to alleviate those challenges, several message authentication schemes based on symmetric or public key cryptosystems have been developed. Despite various benefits, many challenges still exist in the attack mitigating in the wireless sensor network such as considering the reputation of a source and updating it dynamically by considering node transmission characteristics on packet data with the source and intermediate node acts neighbour. In addition, most of them have the constants of communication overhead, high computations, resilience and scalability. To address these issues, a novel polynomial-based scheme has been developed and it has been named Spatial Temporal based Distributed Message and Source Security Authentication Protocol. However, this scheme address all the weakness of built-in threshold-based schemes employed for message authentication in determining the degree of the polynomial. The puzzle-based node authentication has been enabled for intermediate nodes; the proposed scheme allows unlimited message transmission by any node without the struggling problem of the threshold.

We propose a beta exponential distribution that is formed from the logarithm of a random variable with a beta value, as well as a thorough investigation of the distribution's mathematical characteristics. Because we dealt with the beta exponential distribution, we have a clear and understandable way to comprehend the equation and apply it to the actual problem. To do this, we gathered the questions that statisticians find interesting and studied the most significant properties and statistics related to the distribution. Future research will utilize this data to identify specific industrial flaws or inefficiencies as a function of survival. Included are system statistics, BE distributions for graphs, generation functions, moments, and Hazard functions.

In this paper, the solution of the general integral equation of the second kind is approximated using polynomials. These polynomials are obtained based on the Remez algorithm and the minimization of residual function. The nature of the use of the Remez algorithm in the proposed method will lead to the conversion of the integral equation to a system of algebraic equations and obtaining the best polynomial approximation for the solution of an integral equation.  Also, the convergence analysis of the approach is discussed. Finally, some numerical examples and comparisons with previous results confirm the efficiency and high accuracy of the presented method.

The research aimed to identify the role of the website of the Agricultural Extension and Training Department, one of the formations of the Iraqi Ministry of Agriculture in agricultural ideas diffusion,  As well as know its relationship with the degree of diffusion of ideas through the website. The researcher used the descriptive approach in conducting the research and used the content analysis method for the website of the Agricultural Extension and Training Department on the social network "Facebook". It is one of the formations of the Iraqi Ministry of Agriculture on the link (https://www.facebook.com/iraqirshad). The content of the website was analyzed for one year the period from (1/3/2020 to 2/28/2021), The site data were recorded during the research period using a special form, including the fields of agricultural ideas, the pattern of their diffusion, the time, date and day of each idea. The degree of diffusion of each idea was measured by the extent to which followers of the site interacted with those ideas in terms of (liking or commenting and sharing them) and given numerical values (1, 2, 3), respectively. The data were analyzed by using a number of statistical methods, including the range, category length, Spearman correlation coefficient, and t-test. The results showed that (638) agricultural ideas were diffused, of which (474) ideas were diffused as agricultural information, while (164) ideas were diffused as agricultural activities carried out by Agricultural Extension and Training Department. The idea of (determining dates for planting palm seedlings) came first in the degree of diffusion, followed by second rank (beekeepers' work during the month of October). The results also showed that the most agricultural ideas that were diffused through the website were in the field of animal production, at a rate (28.8%) of the total agricultural ideas, followed by the field of horticulture and landscapes at a rate (12.5%) of the total agricultural ideas. The results showed that the website administrators used a (text and image) as style for diffusion with (67%) of the agricultural ideas on the website, while the less used style was (link). It also turned out that Tuesday and Monday were the days during which agricultural ideas were most diffused, while Friday was the least diffusion day for ideas. Most of the agricultural ideas were diffused during the period from (twelve until six)O'clock in the morning, while the month of November ranked first in the number of agricultural ideas that were circulated through the website, while it was found that the month of April of 2020 did not publish any agricultural idea. The results showed a significant correlation between the degree of diffusion of agricultural ideas on Facebook and the type of diffusion of the agricultural idea and the date of its diffusion, while the results showed none a significant correlation between the degree of diffusion of agricultural ideas through Facebook and the time of its diffused, and the field of those ideas. The researcher recommends that those in charge of managing the websites of agricultural institutions publish agricultural ideas in the form of a picture supported by text, as well as focus on the diffusion of ideas in the form of information and not as agricultural activities.

In this paper, we introduce the notions of generalized $\ast$-derivations, generalized Jordan $\ast$-derivations and Jordan triple $\ast$-derivations with the associated Hochschild $\ast$-2-cocycles and then it is proved that if $\mathcal{R}$ is a prime $\ast$-ring and $f:\mathcal{R} \rightarrow \mathcal{R}$ is a nonzero generalized $\ast$-derivation with an associated Hochschild $\ast$-2-cocycle $\beta$, then $\mathcal{R}$ is commutative. Some other results regarding generalized Jordan $\ast$-derivations are also established.

This paper establishes sufficient conditions to ensure the stability and boundedness of zero solution and square integrability of solutions and their derivatives to neutral-type nonlinear differential equations of fourth order by constructing Lyapunov functionals.

In terms of data transparency, provenance, authenticity, verification, data provenance, propositions, reliability, transparency, and safety, today's healthcare data management systems face significant hurdles. Blockchain refers to a decentralized network of computer nodes that may survive together without needing to rely on one another and preserve identical data. The use of blockchain technologies for the provision of safe and confidential healthcare managing data had also attracted considerable interest. In terms of accurate diagnosis and care through safe and secure intelligence sharing, blockchain is also converting traditional care methods into more dependable methods. Blockchain technology has the potential to contribute to personalized, authentic, and secure healthcare in the future by combining every actual relevant data to a particular patient and displaying it in a modern, safe healthcare environment. By using blockchain as a framework, we examine both the recent and current changes in the healthcare industry in this article. Together with problems and answers. This paper identifies challenges and opportunities for implementing blockchain technology in healthcare and summarizes health-related blockchain products and key players offering solutions across different applications. In doing this, our research extends and complements existing blockchain research in healthcare.

Today, Environmental protection and sustainable development are two critical issues. Sustainability is now playing a significant role in almost every aspect of life. Information and communication technology is an integral element in the global economy and international development, with extensive applications in almost all industries. Similarly, software development is an essential component of a rapidly evolving technology community. It is also an important starting point for reducing resource consumption and carbon emissions. Nevertheless, compared to hardware, the software has received inconsiderable attention. The aim of this study is to identify, rank, and determine the importance of factors influencing green software development using a hybrid approach. Five criteria and four subcritical were identified from qualitative content analysis of relevant studies and interviews with twelve academic and industry experts. FDEMATEL was employed to determine the interaction and interrelationships among the identified criteria and sub-criteria while utilizing FANP to calculate the weights of the criteria and their relevant sub-criteria to determine their priorities. The following ranked first to fifth as the most influential factors: operational factor “$ 0.2153 $”, infrastructure factor “$ 0.2046 $”, technological factor “$ 0.2006 $”, individual factor “$ 0.1945 $”, and the organizational factor “$ 0.1849 $”. Among the criteria, knowledge, and awareness had the greatest weight of “$ 0.2908 $“, and ethical factors had the lowest weight of “$ 0.2308 $”. Given the insignificant difference between the highest and lowest factors, it can be concluded that all factors are almost equally effective. And it can be concluded developing green software requires the creation of a trusted ecosystem.

In this paper, the authors investigate the bi-univalency of the generalized distribution series associated with quasi-subordination and remodelled $s$-sigmoid function. The early few coefficients are obtained to achieve our goal. The results obtained are new to the history of bi-univalency.

The counts of various types of white blood cells offer important information that can be used in the diagnostic process for a wide variety of disorders. The automation of this procedure allows for time savings and eliminates the possibility of counting mistakes. In this study, the authors make an attempt to categorize the white blood cells that are found in the peripheral blood based on the shapes of the nuclei and the characteristics that they exhibit. The authors put in place a system and make use of it to automatically identify and analyze White Blood Cells (WBCs). A blood cell can be segmented, scanned, have its features extracted, and then be classified using the system that was proposed. These are the four processes that make up the process. To begin, the authors used segmenting the cell images, which involves grouping white blood cells into their respective clusters. The second part of the process consists in scanning each image that has been segmented and producing the dataset. The third phase involves the form and texture of an image that has been scanned. In the final stage, the authors apply various machine-learning techniques to classify the outcome based on these criteria. These methods include Naïve Bayes, Random Tree, and K-star.

This synthesis research aimed to design an optimal model for measuring the human resources productivity in the East Azarbaijan Gas Company. The designed model was provided to experts for validation. The data were collected from related articles, books, and documents using databases and written resource centers. The statistical population included all valid scientific articles measuring human resource productivity. A total of 54 research articles were selected for final analysis based on inclusion and exclusion criteria due to regular searches in databases. The worksheet form designed by the researcher was used to collect the research information. The findings were analyzed using the seven-step model for the research synthesis by Marsh (1991) and open and axial coding methods. The views of experts, managers and employees of the gas department were used, and the content validity was 0.847 to determine the validity of the human resource efficiency measurement model. The results showed that the optimal model for measuring human resource productivity in East Azerbaijan Gas Company included five indicators of measuring services provided, customer perception and satisfaction, community improvement, unwanted results, and efficiency, each of which includes different components.

By using Halpern’s type iteration process, an iterative algorithm is proposed to study the split inclusion problem and fixed points of a relatively nonexpansive mapping in Banach spaces. This method uses dynamic stepsize that is generated at each iteration by simple computations, which allows it to be easily implemented without the prior information of the operator norm. Then, the main result is used to study the fixed points of a countable family of relatively nonexpansive mappings and the semigroup of relatively nonexpansive mappings. Finally, a numerical example is provided to illustrate the main result.

The Frechet distribution is one of the important statistical distributions and it has many applications, especially in the field of distributing the maximum extent of disease precipitation and river drainage, and estimating the parameters of the Frechet distribution is very important, and for that research came in an attempt to compare several methods of estimating the parameter of Frechet distribution based on different Bayesian methods with (square loss, Linux and Unix) functions. In this research, several simulation experiments were conducted according to the difference in (sample size, value of distribution parameters and estimation methods) and the results were compared based on mean square error criteria, it is possible to use other estimation methods such as (moments and percentile), for other distributions such as (Gumbel and Lindley).

Metaheuristic algorithms are one of the most effective methods for solving optimization problems and are modeled on the behavior of living things or biological phenomena. The swarm behavior of animals in nature to survive is a good way to create metaheuristic algorithms with a group intelligence approach. The swarm hunting mechanism is one of the most interesting meta-behavioral behaviors observed in a large number of organisms, and the chances of success in prey hunting by swarm behaviors will increase. In this paper, a new metaheuristic algorithm with a swarm intelligence approach is presented by using the human hunting mechanism and whale. In this type of behavior, whales and humans participate in hunting in such a way that whales and humans benefit from each other. Implementation and analysis of the proposed method provided less error than 82.60% of the experiments of other algorithms such as particle swarm optimization(PSO), firefly algorithm(FA), grasshopper optimization algorithm(GOA), and butterfly optimization algorithm(BOA). Experiments show that the proposed method converges in complex functions with a probability of 4.36% in local optimizations, which is less than the comparable algorithms. Experiments show that the proposed method can be implemented on a wide range of functional optimization problems and reduces the optimization error due to the simultaneous local and global search of the intelligent algorithm.

Hyperthermia is a method of cancer treatment wherein the temperature of the tumor is increased to over 315 K (42$^{\circ}$C) for a specific duration, ultimately leading to cell death by inducing apoptosis or necrosis. Magnetic Particle Hyperthermia (MPH) is a non-invasive method of cancer treatment in which magnetic nanoparticles are introduced to the tumor at its center and are then subject to a magnetic field. Magnetic nanoparticles upon being exposed to a magnetic field exhibit a heating effect by conversion of magnetic field energy into heat energy. Owing to high acidity, tumor cells are more sensitive to heating than healthy cells, thus if the tumor is heated to 315-319K (42$^{\circ}$–46$^{\circ}$ C), it will be destroyed with minimal to no damage to healthy tissues surrounding the tumor. During the application of hyperthermia as a cancer treatment, blood perfusion protects the healthy tissues surrounding the tumor region from damage due to heat by dissipating any excess heat.  In this paper, the temperature profiles were estimated inside a presumably spherical hepatic tumor mass by solving Pennes' Bio-heat Equation containing the power term. The results were obtained using analytic as well as numerical methods using MPH with mineral oil as a carrier liquid and Magnetite ($Fe_3O_4$) nanoparticles with a mean diameter of 10.9 nm subject to Alternating Magnetic Field (AMF) and Rotating Magnetic Field (RMF).

The research aims to know the extent of the influence of Ambidextrous leadership on the Perma model for the well-being of workers and to indicate which of the two companies is the best in directing and investing the dimensions of the concepts of (Ambidextrous leadership) and (Perma Model), but the problem faced by organizations working in the field of communications in the contemporary business environment with The digital trend and the network economy from the intensity of competition and the acceleration of electronic developments and the poor access to resources, so the research focused on (the General Company for Communications and Informatics, the General Salam Company) as a community for research, as an intentional sample of (273) people from the senior leadership was chosen. The General Company for Communications and Informatics and Al-Salam Company, as it included a sample of administrative leaders (47) people according to the law (Herbert Arkin), and the comparative analytical descriptive approach was followed and information and data were collected through the questionnaire. The most important results, which is the interest of the senior leaders in the sample surveyed in the dimensions of Ambidextrous leadership, as well as the presence of a significant effect of Ambidextrous leadership in the Perma model of the two companies, which is a direct effect and that the Ambidextrous leadership explains a higher percentage in the Perma model of the General Peace Company, while the Ambidextrous leadership explains a lower percentage in the Perma model of the General Company for Communications and Informatics.

A great volume of financial resources, human resources, and production materials of countries are utilized in construction projects in developing countries. Construction processes have become a threat to the survival of future generations and business quality of active organizations in this field due to their detrimental effects on the environment, economy, and human societies such as excessive consumption of natural resources, increasing pollution, loss of human dignity, and lack of sufficient motivation among project staff. Sustainability has been considered a challenging option to overcome today's threats with the ultimate goal of balancing the economic, social, and environmental conditions of projects without compromising the necessary resources for future generations. Furthermore, many studies have proven the effects of innovation and learning components on improving sustainability in different countries. The present study aimed to investigate the effects of innovation and learning components on dimensions of sustainability in Iranian construction industry projects. A questionnaire was designed based on the conceptual model of the research and 85 valid samples were collected. Data analysis was performed using SPSS22 and Smart PLS3. According to the results, learning and innovation had positive and significant effects on the social, economic, and environmental dimensions of projects.

This work is carried out to determine various risk factors which cause delays in the design and development of state-owned irrigation projects in Turkey. Various factors such as management problems, design complexities, financial matters, labor attitude, equipment scarcities, and external risks were qualitatively studied. Data was collected through direct interviews with persons in state-owned irrigation projects and by a questionnaire survey (GAP project as a case study). The questionnaire was designed as on various questions and responses generated during direct interviews. It can be realized that major risk factors which arise through, management, design complexities, and financial matters, are mainly linked to the clients (i.e., Government and consulting firms) causing delayed completion of state-owned projects. During the execution cycle of the project, the contractors also produce a significant portion of risks related to common labor and equipment scarcity. This study can provide, and help in determining a suitable way forward for the completion of projects in time, by omitting delays caused by the government, and processing financial and other matters should be processed in due course for fruitful completion. Contractors should also be bound to follow the given time frame of each project and complete it within a stipulated time. Although external factors such as natural calamities put a strain on project completion time, management should be forecasting these factors and maintain a corrective course of action in case of any.

The present study aimed to investigate the relationship between trade openness and exchange rate volatility in Iran. To this end, we estimated our model through the autoregressive distributed lag (ARDL) method using data from 1979 to 2019. Findings indicated the positive effect of money supply and government expenditures and the negative and favorable effects of trade openness, economic growth, currency system, and oil prices on exchange rate volatility in the short and long term. Even though the impact of economic growth and government expenditures were insignificant, increasing the money supply had the highest adverse effect on the foreign exchange market, and increasing trade openness and the Fixed Exchange Rate System decreased the exchange rate volatility. The currency system and monetary discipline of the Central Bank, along with the trade openness policy can have significant effects on reducing exchange rate volatility and economic stability in Iran.

In this paper, we derive some inequalities for linear canonical curvelet transform (LCCT). At the outset, the basic properties of LCCT including the admissibility condition, and Moyal's principle are stated. Thereafter, some notable inequalities and results related to the well-known Heisenberg- type inequalities are derived for linear canonical curvelet transform.

The present study aims to assess a combined method for face detection and gender recognition using deep learning by evaluating the shortcomings of face detection methods accurately. Deep learning algorithms can learn high-level features and have attracted a lot of attention for use in the field of machine vision. The present study names a hybrid method called Hyper-Yolo-face and utilizes a clear image using deep Convolution Neural Networks (CNNs), Yolo algorithm, and local binary patterns (LBPs) to identify the face and recognize the gender. Reducing the number of parameters is regarded as an extremely important challenge in deep networks in terms of memory consumption and the amount of computing in the network. The proposed method is based on the AlexNet model and generalization in the loss function of version 3 of the Yolo algorithm, which leads to improved precision. The present study focuses on applying small filters in transfer learning and fine-tuning network layers and using a new regression loss function in the Yolo algorithm to make it more appropriate for multiscale face detection. The face images are detected and cut by the presented Yolo in the proposed method. Then, an LBP operator is applied so that richer information and images enter the AlexNet network to estimate other parameters including gender recognition. Based on the experiments on the AFLW, FDDB, and PASCAL datasets, the proposed method improves recognition precision significantly.

In this paper, a deterministic compartmental model of COVID-19 was formulated to describe the transmission dynamics of the disease. The theory of the stability of differential equations is used to study the qualitative behavior of the system. The basic reproduction number representing the epidemic indicator is obtained using the next generation matrix. The local and global stability of the disease-free equilibrium and the endemic equilibrium point of the model equation was established. The results show that if the basic reproduction number is less than one, then the solution converges to a disease-free equilibrium state and the disease-free equilibrium is asymptotically stable. The endemic states are considered to exist when the basic reproduction number for each disease is greater than one. Numerical simulation carried out on the model revealed that an increase in level of transmission levels in individuals has an effect on reducing the prevalence of COVID19 and COVID19 disease. In addition, a sensitivity analysis of the model equation was performed on the key parameters to find out their relative significance and potential impact on the transmission dynamics of COVID-19.

It is no secret to anyone the essential steps of mathematics in image processing and especially in image watermarking techniques. Transformations are one of the most important mathematical tools used in image processing. It is possible to reduce the size of the image very efficiently and extract important information from it. On the other hand,  transformations enable us to move smoothly from the world of image processing (spacial domain) to the world of mathematics and matrices (frequency domain). In this paper, to build a zero-watermarking algorithm, the integer wavelet transform (IWT), is used alone to show the mathematical effect of the results obtained from the proposed zero-watermarking algorithms. One level of IWT is applied to the concealment image and the watermark is XORed with the features chosen from the crucial information obtained from the LL channel to generate the secret share. The results were very satisfactory and the proposed algorithm proved its resistance to different attacks through the robustness metric NC.

The financial technologies currently known as Fintech refer as a significant innovation to the firms that combine financial services with innovative technologies. More precisely, Fintech means innovative financial solutions enabling novel and creative technologies and methods. Based on the reports by credible international institutions, global investments in Fintech in 2019 have undergone an increase of 120% in contrast to 2018. Therefore, the technological innovation development strategy and the related mechanism and process should gradually create new competitive advantages. Fintech innovations are strategic decisions a given company makes to enhance profitability and win customers’ loyalty. The present study investigates the effect of Fintech companies’ profitability enhancement on winning the customers’ loyalty through a random forest algorithm. The study uses a descriptive-applied research method. The study population included the customers of Asan Pardakht Company, reaching ten thousand individuals with 700000 transactions in number. These individuals (customers) were separated based on clustering operation and classified for being subjected to various tests.Moreover, the cross-industry standard process (CRISP) Method was used, and its various stages were implemented, such as business perception, data perception, data preparation, modeling, evaluation, and expansion. After the explication of the data and, also, purging them in various stages, and following the data preparation, the data clustering operation resulted in six data clusters that were subsequently subjected to various examinations; the largest cluster (cluster no.4) was finally identified. Afterward, the various artificial intelligence-based optimization algorithms were implemented in the modeling stage. This is usually done with the determination of the accuracy and error rates. The results indicated that implementing the artificial intelligence-based optimization algorithm could enhance the companies’ profitability, which affects the companies’ winning of the customers’ loyalty and satisfaction with the managers of these companies being eventually envisioned as more efficient.

The main objective of this paper is to introduce and study a new type of iterative method to approximate a common solution of split variational inclusion problem and a finite family of fixed point problems in real Hilbert spaces. Furthermore, we show that the sequence generated by the proposed iterative method converges strongly to a common solution to these problems. The method and results presented in this paper extend and unify some recent known results in this field. Finally, a numerical example is used to demonstrate the convergence analysis of the sequences generated by the iterative method.

In this article, we proposed a new lifetime model the so-called Slash Lindley- Rayleigh model. The new model is yielded as a ratio of 2-independent random variables, namely, a Lindley-Rayleigh model (numerator) divided by a special case of Beta distribution (denominator), specifically, an exponent to the uniform model. This proposed distribution may be theorized as stretching for a Lindley Rayleigh model, which is more flexible as concerns the kurtosis of the model. Certain important probabilistic and statistical characteristics have been founded, and the parameters of the model were estimated depending on the maximum likelihood approach. The simulation study technique has been implemented to assess the performance of the ML method by using the Monte Carlo technique. Also, real sample data of lifetimes were applied to state the usefulness and flexibility of the new model to model a positive date with surplus kurtosis.

We-Energy (WE), as an important energy unit with full duplex and multi-energy carriers in the integrated energy system (IES), uses the coupling matrix to connect the network-side and demand-side energy. However, the coupling matrix of WE is very hard to be formulated directly due to the complicated internal structure and the flexibility of operation mode. This paper proposes a multi-step method for the modeling of WE. According to the method, the conversion process in the WE can be separated into several steps and the WE model can be built by coupling matrix in each step. Then, the WE model is extended by considering the renewable energy, location of storage and different types of demand response. Because of the non-linearity caused by the dispatch factors, the computational complexity increases greatly for solving the optimal scheduling issue of the WE. In order to reduce the computational burden, the variable substitution is added in the proposed modeling method. The results of simulation cases are presented to demonstrate the performance of proposed modeling and calculation method.

In this study, a parametric spline approach is used to evaluate the solution of differential-difference equations with delay and advanced parameters having twin layers. Using the continuity condition of the first-order derivative of the spline at the interior node, the difference scheme is derived. Thenon-standard finite differences for the first derivatives are employed in the scheme to increase the precision of the solution. According to an analysis of the suggested approach, its fourth-order convergence is established.  Maximum absolute errors for the examples chosen from the literature are tabulated. To demonstrate the effectiveness of the method, numerical results are shown along with comparisons to other methods.

In this article, two estimation methods are used to estimate the interval of the parameters for the inverse Weibull distribution in the case of fuzzy data. These two methods are based on, the maximum likelihood method and the relative maximum likelihood method. In addition, we compare the Maximum likelihood intervals with relative maximum likelihood intervals for both real and fuzzy data. The results of the comparison showed that the fuzziness interval estimation is better than the real one. Examples of applications are given.

The wide significance of the problem of finding conservation laws in a great number of applications in mechanics and physics is beyond any doubt. The aim of this paper is to obtain conservation laws of the Sine-Gordon equation via the concept of moving frames and the variational principle. For this purpose, we first present a Lagrangian whose Euler-Lagrange equation is the Sine-Gordon equation, and then by Noether’s First Theorem and Mansfield’s method, we obtain the space of conservation laws in terms of invariants and the adjoint representation of a moving frame, for that Lagrangian, which is invariant under HRT group action.

In the last two years, the coronavirus (COVID-19) pandemic put healthcare systems around the world under tremendous pressure. There have been intelligent systems (Machine Learning (ML) and Deep Learning (DL)) able to identify COVID-19 from similar normal diseases. The algorithms use Imaging techniques (like Chest X-Rays) in classifying COVID-19. Therefore, many global COVID-19 datasets have been released. However, so far, no public local Iraqi dataset has been developed. Therefore, our contribution is two folds. First, we investigate the techniques of deep learning techniques in COVID-19 classification. Second, we develop a new COVID-19 dataset, namely, “Covid-19IraqKirkukDataset” collected from hospitals in Kirkuk, Iraq. To the best of our knowledge, our dataset is the first COVID-19 dataset. Then, the evaluation of Covid19IraqKirkukDataset using Convolutional Neural Networks (CNNs) demonstrates promising classification outcomes.

This paper deals with the oscillatory and asymptotic properties of third-order semi-canonical difference equations with positive and negative terms of the form$$\Delta\left(\varphi(\ell)\Delta\left(\psi(\ell)\Delta \mu(\ell) \right) \right)+\xi(\ell)f(\mu(\sigma(\ell)))-\chi(\ell)g(\mu(\tau(\ell)))=0.$$Using a canonical transformation technique, we offer new criteria which imply that the solutions of the studied equation are almost oscillatory. Some examples are provided to support our results.

Today, due to widespread competition and the presence of interconnected manufacturing environments, supplier performance is an effective factor in the success or failure of companies. The decision to select a supplier is also an important factor in the production and support management of many companies. The supply chain has a special and important position in the strategic management literature. The supply chain is referred to activities related to suppliers of raw materials or services and consumers or customers and producers of the product or the service itself. Evaluation and selection of suppliers is one of the key elements in industrial purchase processes and seems to be one of the main activities of specialized industries. Selecting suppliers is multi-criteria decision-making. To solve this problem, several methods have been proposed in the theoretical literature of the study. This paper presents a conceptual framework for selecting the best supplier by considering several criteria. The proposed framework includes the weights obtained for the criteria by the network analysis process method. Also, by identifying the effective quantitative and qualitative criteria in suppliers' prioritization in the supply chain, the integrated VIKOR-ANP approach is used to provide a method to prioritize suppliers in the supply chain with a combination of qualitative and quantitative criteria. The ANP method is used to evaluate the alternatives to the quality criteria and to obtain the relative weight of the criteria and the VIKOR is used for the final ranking of the alternatives.

We introduce and study an inertial-based iterative algorithm for solving the split common fixed point problem involving a finite family of Bregman quasi-strictly pseudocontractive mappings in real reflexive Banach spaces. Strong convergence of the proposed algorithm is obtained under mild assumptions.

This mixed study aimed to propose a cooperation model between the labor market and technical and vocational education using an exploratory method and structural equation model. The qualitative population included university professors and experts in education and business management, as well as experts in technical and professional organizations, who were selected by a non-random and purposive snowball sampling method (10 people). The statistical population was 1371 principals of technical and vocational schools in West Azerbaijan province, employers, and managers of production and service units located in West Azerbaijan province. The sample size was estimated as many as 300 people using Morgan Krejcie's table. The interview and a researcher-made questionnaire according to the outputs of the interview section were used to collect data. A total of eight components and 70 indicators were identified and finally approved by experts to present the model. Technology, human resources, skills training, entrepreneurial attitude, job training, entrepreneurial behaviors, job opportunities in the labor market, and career path in the labor market were identified as dimensions in developing a suitable model for measuring the cooperation between the labor market and technical and vocational education. According to the results, all the relationships between the model variables were confirmed because the significant number of paths between all variables was greater than 1.96, and the significant levels were less than 0.05 and even 0.01.

We sometimes need to hide data such as texts or images of international paintings or important manuscripts of a high degree of confidentiality and confidentiality, and this is done through several tasks, including archiving or transferring the image of the painting or manuscript to another place. Of course, archiving and preservation are twofold: the first is archiving, documentation and preservation of paper. The second: archiving, documenting and electronic preservation, which is currently the widest due to the digital development in transactions in various fields. In our algorithms, we will hide texts or an image of a global painting located within the abstract art of the artist Dr Luqman Al-Mudhaffar within other images. This is done by hiding the numerical values of the text characters or the color values of the image matrix within the image matrix in which we will hide the text or the image of the global panel that we will call the warehouse image. So that when you look at the image, you see the image of the warehouse, and there is no reference to the text or the image of the global plate. This work, which is considered the secret of hiding the text or the image, will depend on two basic conditions (the first is that the image of the warehouse matches the image of the global board in terms of the number of rows and the number of columns. The second is the use of a secret key to hide and show that only authorized persons know). These two algorithms, which we will call (MKTEXT-7) \& (MK-12), are considered a new method for encoding text and images, and it is one of the products of the great digital development in computer science. Masking and comparisons with readings of previous algorithms. We hope that the results will be at the required level so that we can develop them into more complex algorithms. We will also later combine this algorithm with previous algorithms to come up with a more complex algorithm that intruders cannot decipher.

Stock and price index prediction are among the main challenges for market players, traders, and economic analysts. Pattern recognition is one of the most common methods for analyzing complex data such as financial data. Elliot waves are used as one of the most robust models for predicting many markets, and it works based on a hypothesis that argued that upward and downward market price action always showed up in the same repetitive patterns. The need for expert knowledge and skills to detect these waves makes using it difficult for many traders. So far, little research has been done on the automatic identification of these waves. In this paper, we have attempted to recognize these patterns automatically and use them in predicting future upward/downward trends in prices. For this purpose, twelve patterns have been selected as representing Elliot waves. These patterns are stored in a self-organized map neural network and the network is used to identify the waves in the target stock. The proposed algorithm has been tested with several stocks from the Forex financial market. The results have an average accuracy of 93.94 percent in predicting stock trends and it indicates an improvement in prediction accuracy compared to other works.

We offer some corrections to the paper {\it``Forms of $\varpi-$continuous functions between bitopological spaces"} [Int. J. Nonlinear Anal. Appl. 13 (1) (2022), 2219-2225].

Wireless sensor networks (WSNs) are attractive technologies that have been highly regarded by researchers in recent years due to their wide applications in various fields. Due to the high cost of implementing wireless sensor networks, one of the most important issues in their use is simulation and extraction of the results to achieve maximum efficiency before implementing them. Therefore, simulation is essential to studying WSN. A large number of WSN simulators available nowadays differ in their design, goals, and characteristics. Of course, there are still challenges in simulating wireless sensor networks, the most important of which are the lack of ease of use and lack of full access to the relevant parameters in the existing cases. In this article, we first introduce the various available simulators for WSNs, together with guidelines for selecting an appropriate simulator. In the following, we present our proposed simulator based on NS2 with the aim of achieving an effective and efficient simulation by considering the ease of use and providing a complete list of relevant parameters.

Set theory is one of the tools that plays an effective role in both applied and practical fields. It was circulated and used to facilitate and treat many important scientific problems. It should be noted that we mention the fuzzy sets, which is a subset of $X\times [0,1]$, the soft sets, which are a subset of $E\times p(x)$, center sets, which are subsets of $p(x)\times p(x)$ and the dual soft sets are subsets of $E\times p(x)\times p(y)$, where $E$ is the parameters of elements of $X$ and $Y$. Here in this paper, the concepts of ideal and local function have been generalized to dual soft sets and the study of their impact within this field.

We study a new (3+1)-dimensional novel KP-like equation. We show that this equation admits topological soliton solutions. These will be attained via the aid of ansatz methods. Furthermore, mixed solutions consisting of singular and periodic solutions and others are derived. Moreover, other analytical solutions based on modern group analysis are derived.  In addition,  low-order conservation laws are constructed.

Decision makers need to prioritize the organization’s activities in order to allocate resources optimally. Prioritization of activities is a multi-criteria problem that includes both quantitative and qualitative factors. The C4ISR framework is a well-known and widely used framework that describes the activities of the organization using the Activity Model (OV-5) product. In this paper, a new fuzzy hybrid methodology is proposed to describe and prioritize the activities of the organization in fuzzy conditions. First, the activities of the organization are described in a fuzzy format. Then, Activities are prioritized by the use of a hybrid method based on fuzzy DEMATEL and fuzzy AHP. Fuzzy AHP is used to calculate the weight of each activity and Fuzzy DEMATEL is used to calculate the interdependencies between activities. Finally, the Science and Technology Parks of Iran as an empirical study is presented to illustrate the application of the proposed method.

Software was created as a result of the importance of measuring programmer performance. The amount of time and storage space required to implement a programmer largely determines its performance. This project used automation entrenched reliable rules to estimate the executive time. Our applied similar sophisticated criteria and software in this study to achieve the same automation results automatically and rapidly. Aside from time and storage space, the prepared software evaluates a program's performance using other criteria such as dependability, documentation, and others. All of these standards help to make sound performance judgments. This study concluded the performance testing of programmer samples written in Python as simple structures that provide a clear and easy starting point for testing programmer performance.

The vehicle routing problem has attracted much attention in the recent decade. Considering the real-world constraints, many extensions have been developed. This paper develops a new model for the green vehicle routing problem with simultaneous pickup and delivery under demand uncertainty. Due to the problem's complexity, the standard solvers are only able to solve small-scale instances. To solve the large-scale problems, a two-stage algorithm based on the modified AVNS is proposed. Extensive computational experiments are conducted using modified versions of Solomon’s benchmark instances to show the performance of the algorithm. The results affirm that the two-stage algorithm is capable of generating optimal solutions for small-size instances and the planned routes generated for large-size instances were significantly more robust against the increase of uncertainty parameters.

In this paper, we developed a mathematical model which describes the dynamics of malaria transmission with treatment based on the SIRS-SI framework, using the system of ordinary differential equations (ODE). In addition, we derive a condition for the existence of equilibrium points of the model and investigate their stability and the existence of backward bifurcation for the model.  Our result shows that if the reproduction number $R_0$ is less than 1 the disease-free equilibrium point is stable so that the disease dies out. If $R_0$ is greater than 1, then the disease-free equilibrium point is unstable. In this, the endemic state has a unique equilibrium and the disease persists within the human population. A qualitative study based on bifurcation theory reveals that backward bifurcation may occur. The stable disease-free equilibrium of the model coexists with the stable endemic equilibrium when the basic reproduction number is less than one. Numerical simulations were carried out using a mat lab to support our analytical solutions. And these simulations show how treatment affects the dynamics of the human and mosquito population.

One of the most common problems in the context of project management is the project delay issue. Delay in projects has been caused by many factors, and most of them can be controlled by proper management. One of the most important measures which reflect projects success is its scheduling performance. The capabilities and limitations of an organization for advancing projects are under continuous changes and transformations. Meanwhile, environmental and technological changes over time provide the basis for changes in organizational strategy. Today, the world is moving toward planning and execution of projects by considering factors such as time and cost from the beginning, and these factors are considered simultaneously. In the management of any project, cost, time, risk and quality are critical and essential factors. All of these factors should be in their best condition, so the project can be handled in the best possible way. This paper is looking for a method that includes quality, risk and cost measures in the time-cost tradeoff problem. The "resources" aspect is effective for each of these factors. These resources consist of human resources, machines and financial resources. There are various methods for establishing a tradeoff between time and cost. In this article, a heuristic algorithm based on data envelopment analysis had been developed for multi-criteria time-cost tradeoff. The results of applying this heuristic method to a numerical case study have been reported.

Image segmentation is an essential task in images analyzing images. At the pixel level, the images characterize as:1) an object from its background, or 2) the rest of the objects from each other. Therefore, image segmentation refers to applying a vast diffusion over several domains. This article proposes an improved image segmentation method. To get the segmented image, we determine the multi-thresholds by minimizing a higher-order polynomial curve fitting for an image and choosing the best threshold. The method can be utilized on grey images. The findings demonstrate that our method is effective and robust in image segmentation.

In the present article, our aim is to obtain new Ostrwoski type inequalities by using Taylor's formula with weighted Montgomery identity and the Green's function. The results we acquire containing the identities for the sum $\sum_{i=1}^m p_i f_{1}(\lambda_ i)$ and the integral $\int^{\nu}_{\mu} p(\xi)  f_{1}(g(\xi) )\, d\xi$. We also estimate the difference of two weighted integral means for the result obtained by the Taylor formula with weighted Montgomery identity.

The energy consumption of a residential building is considered in terms of energy use and efficiency. Therefore, forecasting the energy consumption of buildings has been raised as a challenge in recent decades. In a residential home, electricity consumption can have recognizable patterns daily, monthly, or yearly depending on living conditions and daily habits and events. In this research, artificial neural network (ANN) and adaptive fuzzy-neural inference system (ANFIS) have been performed using MATLAB software to predict building energy consumption. Also, random data collected based on the criteria obtained from the hourly electricity consumption of conventional residential buildings in Tehran has been used. In order to evaluate and measure the performance of this model, statistical indicators have been used. According to the applied settings (type of learning, number of steps, and error tolerance), the system error rate is calculated based on MSE, RMSE, μ, σ, and R statistical indicators and the results of energy consumption forecast in three buildings with high accuracy and correlation coefficient. R is more than 98%. The output of this research is an intelligent combined system of ANN and ANFIS. The obtained values well show the ability of this model to estimate energy consumption in the mentioned buildings with high accuracy.

The information data obtained from the spectra of the sensors were combined, which allowed us to build a matrix including thousands of spectrum-simulating gas mixtures, taking into account the presence of different components in several concentrations. Signal process technique and experimental design were utilized all the way for simulating spectral datasets. Particularly, the purpose of the simulation is for database creation for the evaluation of the final detection system for mixtures of gases (aromatic hydrocarbons). The dependable components of a fluorescent read platform are a light source for fluorescence excitation and for isolating the filtered fluorescent light from the excitement light and image detector. However, this technique has several limitations because of the need to utilize many chemo-sensor elements at the same time, which make titration complicated. This is needed to search for another way to overcome complications. In our study, certain filters were used to solve the problem of fluorescence spectroscopy.

The aim of this paper is to analyze a numerical method for solving the time fractional Benjamin-Bona-Mahony (BBM) equation. The time variable has been discretized by using the finite forward difference procedure. The unconditionally stable semi-discrete formula has been proven. Then we apply the Sinc collocation method to approximate the solution of the semi-discrete scheme. The exponential convergence rate of the Sinc method has also been proven. To show the efficiency of the proposed method, two examples were given. Numerical results verified the theoretical results and illustrate the efficiency and accuracy of the method compared with other methods.

In this paper, we introduced a hybrid iterative method for finding the set of common solutions for a system of generalized mixed quasi-equilibrium problems, the set of common fixed points for nonexpansive semigroup and the set of solutions of quasi-variational inclusion problems with multi-valued maximal monotone mappings and inverse strongly monotone mappings in Hilbert spaces. Under suitable assumptions, we prove some strong convergence theorems for the iteration.

This study is aimed at presenting a Model for Social Responsibility Information Disclosure through a data theory approach in companies listed on the Tehran Stock Exchange. This research is an exploratory mix (qualitative and quantitative) in terms of applied purpose, and it is of the database type (qualitative stage) and cross-sectional survey (quantitative stage) in terms of research method. The statistical population in the qualitative section includes 12 experts in the field of accounting, auditing, and financial management who are chosen through the snowball sampling method and the quantitative section of 2184 managers and experts of companies mentioned on the Tehran Stock Exchange (petrochemical companies) in 2020. 317 people were selected by simple random sampling using Cochran's formula. In the qualitative part of the exploratory interview and in the quantitative part, the data collection tool was semi-structured and a questionnaire, respectively. Data analysis in the qualitative domain which is based on open, axial, and selective coding, and in the quantitative domain was done through factor analysis and the Friedman test. The results of the qualitative part concerning the Social Responsibility Information Disclosure indicated that the most vital causal conditions which led to the social responsibility disclosure information included 4 components; i.e. economic utilitarianism, legal requirements, political influence, and managerial personality. The most important contextual conditions that led to the social responsibility disclosure of information included the four components, research areas, educational areas, government regulations, and technological factors. On the other hand, the most important intervening conditions which led to the social responsibility disclosure of information contained 6 components; i.e. market factors, media climate, economic-political factors, cultural-social factors, international factors, and environmental factors. Also, the most important strategies that led to the social responsibility disclosure of information included the three components of cultural affairs, social affairs, and the implementation of appropriate government policies. Finally, the most important consequences that led to the social responsibility disclosure information contained the five components: credit sustainability, corporate sustainability, environmental sustainability, sustainability of political influence, and capital growth. In the quantitative part, the results showed that in social responsibility disclosure information, the highest rank is related to the causal factor with an average rank of 4.34 and the lowest average rank is related to the intervening condition with an average rank of 2.50.

Exponential regression is one of the most common and widely used models in several fields to estimate the parameters of the exponential regression model using ordinary nonlinear least square but this method is not effective in the presence of outlier values so robust methods were used to treat outlier values in this research exponential regression model are used to estimate the parameters using robust method (Median-of-Means, Forward search, M-Estimation), and the simulation method was used to compare the estimation methods with different sample sizes and assuming four percentages of the outliers of the data (10%, 20%, 30%, 40%). And through the mean square error (MSE) was made to reach the best estimation method for the parameters, where the results obtained using the simulation method showed that the forward search is the best because it gives the lowest mean of error squares.

The capital market is considered a subset of financial markets so the long-term financial resources required by manufacturing companies are provided from this part of the financial system. Therefore, the purpose of this study is to investigate the money supply (liquidity) and inflation rate as components of measuring macroeconomic variables on the development of the Iranian capital market with emphasis on the liberalization of justice stocks. Also, we have tried to study the relationship between macroeconomic variables (inflation and liquidity) with capital market development in the years 1384 to 1399 with quarterly data. The method used in this research is the co-integration method and error correction models and implicit reaction functions and analysis of variance. The hypotheses of this study have also been tested using the Distributed Interrupt Autoregression (ARDL) model. Research findings indicate that economic variables such as inflation, liquidity, exchange rates, etc. are stimulus and effective in changes in stock prices has been accepted as a theory. One of the characteristics of developed countries is the existence of efficient markets and financial institutions that, while playing an important role in the economy of these countries, are also the basis for the economic growth and development of these countries. Tehran Stock Exchange, as one of the main pillars of the capital market in the country, is able to accelerate the movement towards growth and development while equipping and pouring stagnant savings in the country and directing them towards production.

Mobile networking refers to the technology utilized to transmit speech and data between specific mobile network nodes across wireless channels. In general, "mobile" refers to the purposeful, lightweight, and portable technologies that moviegoers may carry. To enhance the MANET routing protocols, a hybrid swarm optimization model has been suggested in this study. The suggested optimization establishes the MANET network as the ideal setting. The suggested approach combines cat swarm optimization (CSO) with particle swarm optimization (PSO). MANT network also called mobile sensor network and utilizing the research's technique, the improvement mechanism (s) that could be employed to end degraded routing concerns and enhance act may be identified. Compared to both PSO and CSO, the results produced by the suggested model are the best.

It is necessary to use language inappropriate discourses and establish a proper intercultural connection, to be familiar with cultural messages and trans-messages. In every culture, language speakers use melody, tone of voice, speed, pause, and loudness for their verbal discourse. And gestures) are also cases that should be considered. Given that existing research on the impact of general and specific cultures on language learners has not been done, the present study has examined and analyzed this. At present, the issue of human communication is one of the most important and important issues that have been considered by linguists, sociologists, psychologists, and philosophers. Cultural education is not only effective in learning a foreign language and its appropriate application in different contexts, but also in inevitable cases and its interaction is so important that it has attracted a lot of attention. In this research, the library method is used for theoretical sections and the field method and distribution of questionnaires are used. To analyze the data, one of the multi-criteria decision-making techniques called fuzzy ANP is used. The results show that in the process of learning foreign languages, general culture with a weight of 0.313 is in the first place and special culture with a weight of 0.241 is in the second place.

In the present manuscript, we prove some new fixed point results for multivalued mappings in the setting of b-metric space. Also, we obtain some results for the data dependence of fixed points, the directed graph endowed with a b-metric and for the fractals of an iterated multifunction system. The proven results extend and generalize some of the results in the literature.

Sustainable consumption has become important in society due to numerous challenges such as climate change, environmental diversity reduction, environmental pollution, and resource depletion. The financial and banking sector is among the influential sectors in sustainable consumption.  This study was conducted to design a sustainable marketing model in the banking industry. This statistical research population includes two academic and industrial experts with aware of finance and banking. This study methodology is descriptive and qualitative based on its content and ontology. Thirty-three Delphi panel members were selected to collect data using the judgmental sampling method and snowball. The questionnaire included 61 open and closed questions. Experts were given the questionnaire in two stages to determine the models affecting sustainable marketing in banking.   According to the results, innovative, lean, strategy-oriented, and spherical marketing models through internal marketing models and green, social, public, altruistic, and network-based marketing models through cultural marketing influence sustainable marketing in banking.  The comprehensiveness and versatility of the concept of sustainable marketing can be the achievement of this research. Based on the studied models, sustainable marketing is achieved when different types of marketing are realized in a set. Their establishment can be expected to achieve sustainable marketing.

The process of controlling railway systems is one of the important and effective topics in the process of maintaining the flow of trains’ movement and organizing the travel process, as well as providing early readings of any defect or problem that occurs in the railway network to avoid, treat accidents and ensure a safe environment for the movement of train cars across the geographical area.  Therefore, a continuous follow-up of the railway condition must be provided to ensure that services continue to be provided. Intelligent railway maintenance improves safety and efficiency. This work presents the design and implementation of a real-time monitoring system for railways based on WSN. This study proposes a system consisting of a base station server and a Rail controller. The Base Station (BS) can automatically monitor and control most railway paths. For that, a classification-based deep learning model for object detection near the railway and making appropriate decisions were proposed. To improve classification-model performance, the yolov3 algorithm for object detection was proposed. On the Rail controller side, the Raspberry Pi 4 was utilized as a low-cost processing unit that can be used as a control unit to control some processes, such as streaming video from a camera, gathering information from railway sensors, and sending data to the central station server (PC) by using WiFi protocol. The model can detect and control railway issues in real time by receiving streaming data and directly detecting, classifying issues, and making the best decisions. By alarming or controlling the desired train and stopping it.

This article is arranged to introduce three different algorithms for computing the matrix exponential, cosine and sine functions $At$ for $0\leq t \leq b$,  for all $b \in \mathbb{R^+}$. To achieve this purpose, we deal with the spectral method based on Bernstein polynomials. Bernstein polynomials are briefly introduced and utilized to approximate the functions. The operational matrix of integration of Bernstein polynomials is stated and employed to reduce the dynamic systems to the linear algebraic systems. It is required to solve $n$  linear algebraic systems for evaluating the matrix functions. By presenting the CPU time, it is displayed that the methods require a low amount of running time. Also, error analysis is discussed in detail. The outstanding point of this method is that the approximate exponential, cosine and sine matrix  $At_0$,  for all $t_0\in[0, L]$  can be obtained with only one execution of the algorithm. These three different algorithms have common parts that can be used to practically reduce the computational volume. Some examples are provided to show the high performance of the methods.

In this article, based on the content analysis technique and using an interview tool, we identified the factors affecting the financial behavior model based on psychological factors with the approach of capital efficiency. The identified agents were screened using the fuzzy Delphi technique. Finally, the relevant structural model was presented. The statistical population includes professors and experts in the field of accounting, of which 12 people were sampled through the snowball technique. To collect data, the field method was used with the help of specialized interviews with experts and the fuzzy Delphi questionnaire. Data were analyzed by theme analysis method using MAXQDA software. Based on the library studies of scientific articles and books and reflection on past research, it seems that two categories of internal and external factors (dimensions) can affect the behavioral process of individual investors in the stock market. Three economic, political, and psychological components in the external dimension and two components of the stock market and company conditions were expressed as factors of the internal dimension. Accordingly, it seems that these are the main influencing factors. Psychological characteristics (optimism, overconfidence, risk aversion, and emotion) were identified in this section. The Personality factor was added to the model based on theoretical foundations and studies. According to modern behavioral finance theories, many behavioral issues are involved in investors' decision-making, the most important of which are psychological and Personality factors. People do not always act rationally and often behave unexpectedly and unpredictably. Personal characteristics play an important role in decision-making.

In this paper, we establish the global well-posedness result of the viscous primitive equations of geophysics in the critical Fourier-Besov-Morrey space with variable exponents $\mathcal{F \dot{N}}_{p(\cdot),k(\cdot),q}^{2-\frac{3}{p(\cdot)}}(\mathbb{R}^{3}),$ when the initial data are small and Pandtl number $P=1,$ we also show the Gevrey class regularity of the solution.

Let $G$ be a finite group and $\psi(G)=\sum_{g\in G}o(g)$, where $o(g)$ denotes the order of $g\in G$. We give a criterion for nilpotency of finite groups $G$ based on the sum of element orders of $G$. We prove that if $\psi(G)>\frac{13}{21}\psi(C_n)$ then $G$ is a nilpotent group.

In this study, hybrid classification methods are proposed, and they are used to predict the results of future football matches. Our hybrid classification methods are introduced by using clustering and classification algorithms together. By developing a web scraping tool, data on 6396 football matches played in European leagues are collected. Unlike similar studies, the data includes fans’ opinions gathered from social media platforms in addition to statistical information about the teams and players. The raw data is transformed into suitable datasets through a software developed by authors, and the processed data is used in the classification analysis. The match result variable (dependent variable) is considered as three types denoted by MR-1, MR-2 and MR-3, respectively: The first one has three classes with Home, Draw and Away, the second one has two classes with Home and Draw-Away, the last one has also two classes Home-Draw and Away. The performances of the proposed hybrid methods are compared with the classification algorithms frequently used in the literature. As a result, our hybrid methods are more successful than classical classification algorithms. The prediction successes are 65.46% in the case of MR-1, 81.76% in the case of MR-2, and 77.8% in the case of MR-3.

Due to the pollution of energy related to fossil fuels and the cost of production and consumption, today most developed countries are moving towards the use of renewable energy such as wind and solar energy through wind turbines and photovoltaic systems. Solar energy tends to be a global phenomenon. They have clean and effective modern technology that provides a beacon of hope for a future based on sustainable and pollution-free technology. One of the leading challenges in using these resources is energy source changes, in other words, the wind and flux of the sun change during the day and seasons. If it is possible to find the maximum power and extract the appropriate commands for each of the mentioned power plant units, the maximum power can be extracted according to temporal and spatial changes. For this purpose, in this article, in the beginning, in the first part, the problem and the activities taken are described. Then, in the second part, the required formulations and relations are expressed according to the production power in terms of wind, radiation intensity, environmental conditions, and the characteristics of turbines and solar arrays. Then, considering that the pursuit of maximum power requires the use of optimization algorithms, in the third part, the formulation and implementation process of the colonial competition algorithm is fully described. Section 4 presents the results of implementing the algorithm and solving the model in MATLAB. In the simulation section, the solar position for the province of Khorasan Razavi - Iran is considered. Then, using the information of the Meteorological Organization, the average wind speed is approximated. In wind speed modelling, wind speed information of Khorasan Razavi province - Iran has been used for a period of 72 hours. Also, modelling for a combined power plant with a maximum capacity of 225 has been modelled. For the required power, 200 kW is considered in relation to the capacity of power plants, and it also has the ability to be stored in the home battery, which can be used as a storage power plant. Which a good proposed approach has been able to provide the required power in a 24-hour period based on weather and solar conditions. Also, control variables such as the amount of screw angle, torque and rotor to the generator as well as voltage-current at both ends of the cells are optimally obtained. The results during the simulation indicate the achievement of maximum power based on the required power and atmospheric conditions and solar radiation.

The purpose of the current research was to develop an organizational leadership brand model and to investigate and rank the factors affecting the leadership brand in agricultural products (Case Study: Citrus of Hormozgan Province). This research consists of two main parts: in the first part, in order to select the variables affecting the leadership brand in the field of agriculture, the theoretical foundations of research and library studies have been used and after reviewing the articles published in domestic and foreign scientific sites, four elements of personal characteristics of leadership, cultural elements, brand elements and agricultural infrastructure elements were identified as variables affecting the leadership brand in the field of agriculture, Then, using group decision-making technique in three stages, four elements with 80 indicators were identified as factors affecting the leadership brand in the field of agriculture. The final model was constructed for modeling using the technique of the structural equation. The research plan was a combination that was obtained through qualitative strategies (foundation data) and interviews with 35 managers and experts of the Research Department of the Ministry of Agriculture, the Farmer's House and the Chamber of Commerce, and then, through quantitative strategy, the research hypotheses of the research were calculated as a good test for fitting the model. In the qualitative section, after purposeful sampling, data collection tools were used. In the quantitative part, a questionnaire was distributed among 315 managers and experts in order to investigate the viewpoints of managers and experts of the mentioned organizations and to analyze the data, structural equations model with the PLS approach was used. Four general elements of leadership brand variables including personal elements and characteristics, cultural elements, brand elements and agricultural infrastructure elements were identified and categorized as variables affecting the leadership brand in the field of agriculture. The results of quantitative sector findings showed that each of the four elements of leadership brand has a positive effect on leadership brand in the field of agriculture.

The essential purpose of this paper is to obtain the fixed point of different functions by using a modern repetitive method. We incorporate concepts suggested in the Bisection method and the Moth-Flame Optimization algorithm. This algorithm is more impressive for finding fixed point functions. We also implement this method for four functions and finally compare the current method with other methods such as ALO, MVO, SSA, SCA algorithms. the proposed method shows a decent functionality than the other four methods.

In this paper, the Hyers--Ulam--Rassias stability of Jensen's functional equation is discussed in detail on fuzzy normed linear spaces. It is shown through the analysis that every fuzzy approximate Jensen-type mapping can be approximated by an additive mapping. The obtained results are the fuzzy versions of some classical theorems.

By making use of the Tremblay operator, we introduce new subclasses of analytic functions, for which we obtain some sufficient coefficients estimates, and the consequences are some subordination properties and partial sums inequalities.

Appropriate modeling of the coupled neurons helps us understand neurons' natural functions. In this paper fuzzy logic controller has been designed to synchronize two coupled neuron models. The fractional-order neurons are based on the FitzHugh-Nagumo (FHN) model. An optimized fuzzy controller is designed to synchronize the behavior of two neurons with each other in the presence of external disturbances. This controller overcomes the disturbance. The simulation example shows the performance of the proposed method.

The purpose of this paper is to investigate the existence and uniqueness of solutions to the Caputo sequential fractional differential equations and inclusions with integral boundary conditions. When it comes to proving the existence of solutions, the Krasnoselskii's fixed point theorem is employed. Further, the Banach's contraction principle and the Leray-Schauder alternative are employed to prove the uniqueness of the results. Further, for the multi-valued case, we employ the nonlinear alternative for Kakutani maps, and Convitz and Nadler's fixed point theorem. We emphasize our results with numerical examples.

In traditional optimization models, classical computers have been used to solve mathematical linear or non-linear models. In addition, by data envelopment analysis (DEA) models one can evaluate the relative efficiency of the decision-making units originally in the form of mathematical non-linear models. In this paper, a new attitude is presented in which quantum computers and quantum algorithms can be used to solve DEA models by two different methods. This suggested attitude is worthy to mention as this subject is new and will attract researchers to discuss and improve it.

In this paper, to establish general and shortest confidence interval, for a linear combination of parameters of the two-parameter exponential distribution, we introduce a pivotal quantity. In the case of two populations, we use the method of variance estimate recovery and generalized pivotal quantities to construct a confidence interval for the difference of them. Based on the shortest confidence interval, we present a simple method to obtain its percentiles, and which a shorter confidence interval can be constructed. Also, the performances of the presented methods are studied by real data examples and simulations.

In this research, we want to separate carbon dioxide gas from methane gas. Polymer (poly-ether-block-amide) has properties that absorb carbon dioxide well. Zeolite 4 A nanoparticles have fine pores and are proportional to the kinetic diameter of carbon dioxide, so they can act as a molecular sieve in the membrane, this nanoparticle was added to the polymer to obtain Pebax/4A nanocomposite membrane. Nanoparticles can improve carbon dioxide emissions and the thermal and mechanical stability of membranes. In this research, we want to investigate the effect of nano zeolite loading on the permeability and selectivity of carbon dioxide. By adding nanoparticles to the polymer, its structure was changed, so the nanocomposite membrane was evaluated by FESEM, BET, FTIR, and analyses. Finally, permeability, selectivity, diffusion coefficient, and solubility coefficient of carbon dioxide were calculated.

In this paper, we are concerned with the existence of multiplicity solutions for a Dirichlet impulsive differential equation. The approach is based on variational methods.

Nowadays, speech recognition, which simply refers to the process of converting an audio signal into its equivalent text, has become one of the most important research topics. Although many studies have been conducted in the field of speech recognition for many languages of the world, but can be said that no more study has been conducted in the Persian language and therefore it is necessary to conduct more studies in this field. Since Persian is a rich language that can create many new words by adding a suffix (prefix) to its main root, so it can be said that the success rate of voice recognition programs in this language has also increased with the increase in the number of phonemes and therefore can have a significant improvement. Therefore, in this study, a practical approach to Persian speech recognition based on syllables, which are a unit between phonemes and words, has been used and done by the hidden Markov model. After obtaining syllable utterances, multiple coefficients are calculated for all syllables. Finally, suitable models were created and the success rate was calculated by conducting tests for the systems. To measure the performance of the system, the error rate criterion was used. The results of this study show that the word error rate for the hidden Markov model was 18.3% and increased the system performance by approximately 16% after post-processing.

Economic growth and development are macroeconomic objectives for increasing welfare and economic-social justice. Several factors affect economic growth but their effects can be better examined using composite indices which are suitable indicators in the economic literature for performance evaluation and knowledge about the gap between the current status and goals of economic programs and policies. Therefore, the present manuscript aimed to provide and measure the composite indices, macroeconomic and social variables, and their effects on economic growth with the economic jurisprudence approach. To this end, the vector error correction model (VECM) was used for the period 1996-2019. The results of the impulse response function (IRF) test indicated that the economic growth of Iran had a very slight negative response to the impulse of the composite indices, including economic security, production and productivity, and property rights in the long term. Therefore, the composite indices, including economic security, productivity, and property rights could not significantly affect economic growth in Iran in the long term while the economic growth showed a positive and stable reaction to the impulse of the economic-social justice index in the long term. Furthermore, the increase in the cooperation, participation and presence of the active population with the ability to work in the field of production and economic activities increased economic growth.

Graph operations are an essential tool in graph theory, as we can develop large graphs from small graphs. The aim of this paper is to compute the values of K-Banhatti indices for some graph operations, more precisely join and corona product of two graphs. Path, cycle, and complete graphs are taken for the graph operations. The result found may be applicable in locating some buried information on large graphs.

In this paper, we introduce a new subclass of the class of \textit{m}-fold symmetric bi-univalent functions and obtain estimates of the Taylor-Maclaurin coefficients  $|a_{m+1}|,|a_{2m+1}|$ and Fekete-Szeg\H{o} functional problem for functions in this new subclass. The results in this paper generalize some of the results of Huo Tang et al. [18] Altinkaya and Yalcin [3].

The current research was conducted with the aim of designing a model to obtain a tool for measuring adequacy limits with a heuristic mixture. This, is due to the selection of exploratory mixes of qualitative data, in addition to the fact that more researchers have collected qualitative data first and then collected quantitative data. The required information has been collected by interviewing experts. In this research, "a model to obtain a tool for measuring inadequacy" was chosen as the central category. A total of 37 categories and 132 concepts were identified and selected. In the proposed model, three personality codes "individual factors", "organizational factors", and "social factors" are considered as causal conditions. Private individual factors of personal experiences, personality patterns and individual values, organizational factors of spirituality at work, organizational and organizational values, and in the consequences of social factors, social solidarity and social accountability are expressed. In the next step, the identified codes were investigated. For this purpose, they were identified and screened as the main factors using the Delphi technique, and in the next step, they were analyzed using the structural techniques of the proposed model. The factor load of causal factors in the main category is 0.74 and its t-statistic is 9.75, the factor load of contextual factors on strategies is 0.56 and its t-statistic is 2.10. The factor load of the intervening factors on strategies is 0.59 and its t-statistic is 2.06, the factor load of the main category on strategies is 0.52 and its t-statistic is 6.07. Finally, the factor load of the strategies on the outcomes was 0.87 and the Tian statistic was 9.06. Therefore, it can be said that the research model is approved.

The significant development in devices based on communication technologies requires the provision of advanced generations of communications to provide these requirements. The current hot topics are moving towards providing that meets the requirement of modern systems for industrial and technical requirements. This paper developed the GFDM system with Polar Code (PC) and interleaver, which presents the BER of GFDM at AWGN and the channel effect. Then GFDM transceiver system is built with the PC and interleaver. At each step, test and compare the BER performance. The performance computed under the effect of multipath frequency selective channel. The BER at 22 SNR of GFDM, PC-GFDM, and PC-GFDM with random interleaver is 0.1096, 0.0970, and 0.0680. The BER at 28 SNR of GFDM, PC-GFDM, and PC-GFDM with random interleaver is 0.1047, 0.0914, and 0.0607. This result presents the enhancement of using PC and interleaver. It also showed a distinct enhancement over the other methods mentioned in the researcher's survey.

Developmental disorders are regularly observed in developmental writing skills (developmental dysgraphia) with considerable concerns. Physicians do their diagnosis on the basis of the juvenile’s written products as well as the attitudes and feedback taken from their teachers. This is a very laborious process and yet subjective in nature. Consequently, many juveniles suffering from this defect, particularly those with lower levels of the disorder remain undiagnosed. The aim of the present work was to find a new method for the automatic identification of dysgraphia even at minute levels. Utilizing the most sensitive pen tablet available to gather the desired dataset, we could extract all the considered datasets, i.e. temporal, spatial, kinematic, and pressure parameters with the greatest possible accuracy. On the whole, 102 students (both male and female) from the second, third, and fourth grades of primary schools were participated in the data collection phase by being asked to write a short paragraph. 51 students, in an age range of eight to ten years, were participated in each group, i.e. dysgraphic and non-dysgraphic. Next, a huge set of features (more than two thousand features) was extracted in the preprocessing phase. In the feature selection phase, we eventually ended up with sixteen features that proved to be the most effective in diagnosing dysgraphia. To distinguish between the dysgraphic and non-dysgraphic students, three different types of classifiers, i.e. random forests, AdaBoost classifiers, and support vector machines (SVM) were considered and compared. For the prediction of dysgraphia based on incessant handwriting features, the SVM was revealed to be the best model with a classification performance accuracy of 93.65%. Our work exhibited that online handwriting features including time, jerk, and altitude/azimuth may be utilized to automatically reveal dysgraphia in juveniles with this writing disorder.

In this paper, we investigate the global behavior of positive solutions of the system of difference equations \begin{equation*} x_{n+1}=\alpha+ \dfrac{y^p_{n}}{y^p_{n-2}},\y_{n+1}=\alpha+ \dfrac{x^q_{n} }{x^q_{n-2}},\ n=0, 1, 2, ...\end{equation*}where parameters $\alpha, p, q \in (0, \infty)$ and the initial values $x_{i}$, $y_{i}$ are arbitrary positive numbers for $ i= -2,-1, 0$. Moreover, the rate of convergence of positive solutions is established and some numerical examples are given to demonstrate our theoretical results.

The paper study the existence of at least one weak solutions for Dirichlet boundary value problem involving the $\big(p(x),q(x)\big)$-Laplacian-like operators of the following form:\begin{equation*}\displaystyle\left\{\begin{array}{ll}\displaystyle-\Delta^{l}_{p(x)}-\Delta^{l}_{q(x)}=\lambda g(x, u, \nabla u) & \mathrm{i}\mathrm{n}\ \Omega,\\\\u=0 & \mathrm{o}\mathrm{n}\ \partial\Omega,\end{array}\right.\end{equation*}where $\Delta^{l}_{r(x)} $ is the $r(x)$-Laplacian-like operators, $\Omega$ is a smooth bounded domain in $\mathbb{R}^{N}$, $\lambda$ is a real parameter and $g$ is Carath\'eodory function satisfies the assumption of growth. The existence is proved by using Berkovits' topological degree.