Objective Investors, in their pursuit to maximize expected returns, minimize risks in their stock portfolios, and achieve the desired benefits, require suitable methods and criteria to select stocks for their portfolios and allocate capital. One of the most important things in stock portfolio optimization is the use of a suitable optimization algorithm. The function of the multi-objective portfolio optimization model is quadratic. Quadratic functions are a special class of nonlinear programming problems in which the objective function is quadratic and the constraints are linear. Common algorithms for quadratic programming require certain parameters with fixed values. Such algorithms are extensively employed for solving real-world problems, particularly in financial contexts. The major objective of this research is to apply the inner point mathematical algorithm to optimize the stock portfolio and to use this algorithm to address the multi-objective portfolio optimization problem. With the GlueVaR risk measurement criterion, the problem of portfolio optimization takes into account the two objectives of maximizing returns during the research period and reducing investment risk, reassuring investors to make better and more accurate decisions about the final object of this research. Methods The necessary information for this study was provided by 50 active companies listed on the Tehran Stock Exchange. However, due to the availability of their daily prices during the study period, the final number of companies considered was reduced to 33. The inner point mathematical approach was utilized to optimize the model with the dual objectives of increasing efficiency and reducing risk. To demonstrate the effectiveness and capability of the algorithm in solving the problem of two-objective optimization, its output was compared with other risk measurement criteria such as variance, and value at risk (VaR). The investment risk in the stock portfolio was also calculated using the GlueVaR criterion. Comparing conditional (CVaR) was also done. The GlueVaR criterion has the advantage over the other criteria since it takes the investor's attitude toward risk into account. This advantage formed the basis of the calculation method in this research according to the mentioned algorithm. Results According to the research, value at risk (VaR) and conditional value at risk (CVaR) perform better than other variance risk measures in the portfolio optimization model with the GlueVaR risk measure and the internal point optimization method for determining the most effective border. Additionally, when applied to optimization problems, the internal point method discovers the optimal point with fewer iterations, providing strong evidence of the algorithm's effectiveness. Conclusion Based on the current findings, it is evident that the internal point algorithm is effective in resolving stock portfolio optimization issues. Additionally, the GlueVaR risk measurement criterion outperforms VaR, variance, and CVaR for most investors with diverse risk and return preferences.

AbstractInternal controls are a process that is performed in order to ensure reasonable achievement of organizational goals. Proper design and deployment of internal control systems in economic units is one of the most important factors in achieving the effectiveness and efficiency of operations, promoting accountability and financial transparency, compliance with laws and regulations and helping to prevent fraud and financial abuse. Internal control is desirable and provides a desirable model of internal control. Using these criteria of the management system and among 167 listed companies, the correlation between the criteria of the management system and the desirability of internal control was analyzed using the neighborhood analysis algorithm. The audit committee was desirable to explain the internal control. The results of the research indicate the closeness of the opinion of experts and also the statistical analysis of the artificial intelligence algorithm with historical data. These results show that considering the role of corporate leadership in the optimal use of resources, accountability, compliance with stakeholder rights and financial transparency, increasing trust in internal control is considered

The waves of Islamic awakening have always become longer and wider since two hundred years ago. The most important achievement of this historical movement is the bringing of Islamic rules from margin to the focal point and later, gradual restoration of confidence to the Muslim world. In order to make this goal operational with the rapid growth of Islamic awakening, many of today's autocratic puppet rulers of Muslim countries feel the loosening of the foundations of their governments. However, this process of waking and awakening is now facing several obstacles from its interior. Accordingly, the question raised in this articleis that "In Imam Khomeini’s view, what are the internal factors in habiting the Islamic awak eningin Muslim countries? “Our results show that in Imam Khomeini’s view, continuation of Islamic awak ening process in volves removing barriers and providing back ground f or the restoration of scientific and knowledge confidence and avoidings cattering and fanaticism. In this study, we examine the Islamic awakening in Imam Khomeini’s view using a pathological process and investigate the internal barriers of this political-social phenomenon.

Graph coloring is a way of coloring the vertices of a graph such that no two adjacent vertices have the same color. Graph coloring problem (GCP) is about finding the smallest number of colors needed to color a given graph. The smallest number of colors needed to color a graph G, is called its chromatic number. GCP is a well-known NP-hard problems and, therefore, heuristic algorithms are usually used to solve it. GCP has many applications such as: bandwidth allocation, register allocation, VLSI design, scheduling, Sudoku, map coloring and so on. We try genetic algorithm (GA) and chaos theory to solve GCP. We proposed a heuristic algorithm called CMHn to implement multi-point crossover operation in GA. To generate initial population, a fast greedy algorithm is used. In this algorithm, the degree of each node and the number colors in its neighbor is used to assign a color to each node. Mutation operation in GA is used to explore the search space and scape from the local optima. In this study, a chaotic mutation operation is presented to select some vertices and change their color. The crossover and mutation parameters in the proposed algorithm is tuned based on some experiment. To evaluate the proposed algorithm, some experiment is conducted on DIMACS data set. Among DIMACS sample graphs, DSJ, Queen, Le450, Wap are well-known challenging samples for graph coloring. The proposed algorithm is executed 10 times on each sample and the best, worst and mean results are reported. Results show that the proposed algorithm can effectively solve GCP and have comparable outcome with the recent studies in this field. The proposed method outperforms other algorithms on very large graphs (Wap graphs).