درخت حوزه‌های تخصصی

The purpose of this paper is investigating the effect of JCPOA on the network behavior analysis of Tehran Stock Exchange indexes using the minimum spanning tree (MST) and hierarchical clustering. By simplifying a complex system, network analysis allows for the extraction of important and essential information from that system. In this paper, using network analysis the simultaneous behavior of 38 industry indexes in Tehran Stock Exchange in manufacturing, service and invest-ment sectors during 2012-2017 was investigated. These analysis included identi-fying the main indexes in the direction of moving other indexes using the MST, providing a classification using hierarchical clustering for the behavioral similarity of the indexes as well as examining the degree of integration (behavioral similarity) of market indexes over time. The results showed that investment, automobile, industry and medicine indexes in the research period had a major role in guiding other indexes and indexes can be classified into six groups in terms of behavioral similarity. The market has also been moving toward integration of indexes since early 2015 and beginning the executive steps of Joint Comprehensive Plan of Action (JCPOA). This reflects the investors' hope for the promotion of all indexes.

With increasing trade among different countries The exchange rate fluctuations, consumption, inflation, and market portfolios are considered as major risk factors in financial markets. Hence this study aimed to examine the relationship between the exchange rate fluctuations and asset returns within a theoretical and empirical model, i.e. Consumption-based Capital Asset Pricing Model (CCAPM). To this end, a basic CCAPM was extended and imported consumables were included in Epstein and Zin’s recursive utility function. The research sample encompassed eight portfolios and monthly data from 2003 to 2014. The pricing model parameters were estimated using Euler's equations and Hansen and Singleton’s generalized method of moments (GMM). An estimation of the parameters of Euler's equations indicates the risk aversion and tolerance of economic factors, low elasticity of substitution for domestic consumables and imported consumables, and high elasticity of intertemporal substitution. In the next step, using Euler’s linearized equations as asset pricing model and Fama and Macbeth's two-step regression method, the effects of exchange rate risk premium, inflation, market efficiency, and consumption growth on return premium on assets were investigated. The results indicated the positive impact of the exchange rate risk premium, inflation, and market returns on the return premium on assets.

Evaluating the performance of companies using their financial ratios is a challenging task that is expected to become more straightforward by reducing the dimensionality of the data. The purpose of this study is to evaluate the performance of companies using a hybrid model for investment-related decision making through which the mean value of various financial ratios are calculated based on the investor's risk-taking behavior so that the number of all criteria is reduced to one single value for each alternative. To do so, a sample of 172 companies listed in Tehran Stock Ex-change was selected from 2008 to -2018. Firstly, the financial ratios were prioritized using decision trees regression analysis (type CART) and TOPSIS Technique. The results showed that Gross Profit Margin and Debt to Equity Ratio are the most and the least important factors, respectively. Then, using OWA (Ordered Weighted Averaging Aggregation) operator, the role of investor’s risk-taking behavior was investigated, and the results showed that investor’s risk-taking behavior changes the outcome of the decision-making process significantly.

Resource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance and comparative advantage. According to this view, capa-bilities and resources of the company are the main factors in explaining the func-tional results or competitive advantage. Resources can be considered as inputs which enable companies to do their activities. The purpose of this research is to introduce a resource-based theory for calculating the efficiency score of accepted companies in Tehran Securities Exchange by using Data Envelopment Analysis (DEA). In this regard, the financial statements of 190 companies accepted in the exchange for the 2009 – 2018 period have been analysed. Efficiency indicators, which include 4 categories of resources (10 inputs) and 5 outputs, have formed the axis of the mentioned technique. The results of implementing this model for compa-nies with the efficiency score of one, indicates first a minimal input consumption compared to competing companies in the same industry while producing more out-put, and second, using the resource-based view theory (integration of tangible and intangible resources) enables the company to push the boundaries of efficiency. Finally, it can be said that utilizing minimum and maximum resources simultaneous-ly leads to a focus strategy-type competitive advantage.

Several theories have been presented about the corporate capital structure; some researchers believe that the structure of capital and firm value depend on each other. Moreover, others believe that the firm value is not influenced by any changes in the structure of capital, and with the change in the structure of capital, the value of the firm remains constant. The present research studies the historical theories of capital structure and presents the classification of existing theories in terms of the optimal capital structure. This classification is subdivided into two categories: the presence of an optimal capital structure and the absence of an optimal capital structure. With the historical review of theories of capital structure and the presence of an optimal capital structure or the lack of an optimal capital structure, it seems that none has been a complete model and so far, no one has been able to provide an optimal capital structure. In this research, from the system dynamics approach and using VENSIM software, the capital structure and share price of the company have been studied and the results of model simulation with real data are to ensure the correct performance of the model behavior. And the results show that financing through debt in the capital structure of the company has a significant effect on the debt cost rate and capital cost rate and does not have a significant effect on stock prices. Finally, the share price shows little sensitivity to the company's financial structure.

Now a day's software is the baseline for the success of any organization. There is a huge demand of quality software in the customer-oriented market. Regression testing makes it possible but it’s a costly affair. Regression test suite minimization is way to reduce this cost but it is NP hard problem. This paper proposes an effective approach for regression test suite minimization using Artificial Ecosystem Optimization algorithm. To improve its performance a modified Artificial Ecosystem Optimization algorithm is proposed for Test case minimization. To evaluate the performance of proposed approach experiment is conducted in controlled parameter setting on open-source subject program from SIR repository. The results are collected and analyzed in comparison to existing approaches using statistical test. The test results reflect the superiority of proposed approach.

The data stored in data warehouse is used for making strategic decisions by integrating heterogeneous data from multiple sources at a single storage place, where data is used for querying and analysis purposes. With the advancement in the technology, Business Analytics and Business intelligence are being increasingly used in the financial sector for forecasting business decisions. Many On-Line Analytical Processing (OLAP) tools are being largely explored that can contribute to business decision making. Banking operation handles a lot of data as they operate daily. Subsequently, preparing of this tremendous volume of information requires instant and quick tools that can process the information at high processing speeds. Through this research paper, we represent the OLAP cube as one of the tools which can be used for business analysis. A case study of a bank and loan approval process is considered as one of the areas for implementation and analysis of business decisions using business intelligence which can serve as a key factor for increasing intelligence in the banking sector to make reliable business decisions. Higher management can forecast and predict various outcomes from the bank data warehouse using On-Line Analytical Processing technology which provided a multidimensional view of the data. Analysts can make business decisions by analyzing the reports and pattern trends in the graphs. Management can modify existing policies and procedures to increase the growth of the bank and can have a healthy competition with their competitors.

For the last three decades, research on Information Technology Business Value (ITBV) has highlighted how organizations can create greater value from IT investments in different industries, including the healthcare sector. There has been some research investigating the business value of Health Information Systems (HIS). However, the current body of knowledge regarding the challenges in front of proper business value realization of these systems is limited. This study investigates the challenges of HIS business value creation using a combination of the Fuzzy Analytic Hierarchy Process (FAHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL). First, this study reviews the literature to identify the main challenges in the field. The outcome of this step revealed three main categories and 22 challenges, including technological (ten challenges), organizational (eight challenges), and environmental (four challenges). Then, this study first uses FAHP to evaluate the weighting for each criterion and then adopts the fuzzy DEMATEL method to establish contextual relationships among those criteria. We find out that technological challenges are the most crucial dimension. Moreover, we observed that “infrastructure costs” has the highest priority. Under the technological dimension, “maintenance costs” and “systems compatibility” challenges are the most critical since they are in the cause area and directly influence the HIS outcomes. Under the organizational dimension, “Change in strategic objectives” is the most important challenge. Moreover, “inter-departmental coordination”, “training costs”, “proficiency”, and “users’ knowledge” are affected by each other as well as influenced by the net causes. The outcome of this study shows that to handle HIS business value creation challenges, healthcare executives should start with infrastructure costs, maintenance costs, and systems compatibility challenges.

The motivation of this paper is to examine empirically the effect of social media marketing on value equity, relationship equity and brand equity on fashion apparel brand among Universiti Malaysia Kelantan (UMK) students who have high attributes of internet savvy’s and take delight in undertaking social media marketing. This study is backed by the use and gratification theory that creates desire for amusement and enhancement of information while people use social media marketing.  Data was collected by means of Google Form online survey from a total of 361 respondents. The simple random sampling approach was utilized to collect data from the respondents and data analyzed using the SPSS. The findings indicated that social media marketing activities, which are entertainment, interaction, trendiness, and customization had a positive effect on customers’ equity among students. The results confirm that social media marketing is one of the key success factors in enhancing customer equity. Further, the results showed that interaction is the first concern among the respondents. In other words, the communication between the apparel fashion brands industry with customers is important and helps to create relationships so that customers can get more information about them.

In many significant characteristics of the modern world, including particular industry, agriculture, and military applications, Wireless Sensor Networks (WSNs) have begun to emerge. Even though energy consumption is the greatest challenge confronting WSNs, it is essential to analyse the feasibility of the use of mobile components for the data collection on WSN networks. To collect information from their sensing terrain, WSNs use significant numbers of wireless sensor nodes. The method of acquiring data from the sensor nodes and forwarding this data to the node or base station is Data Collection (DC). In such methods, data collection is effective and efficient in data transfer for an extended WSN lifetime. The scientific and investigational analysis demonstrated that Routing Path Selection-DC has the robustness and low energy, is required to reduce the mobile sink's energy demand, which provides better results, and is adaptable dynamically to multiple situations, including the variance between the ground robot and the cost of energy. The research paper developed an approach that creates a data collection schedule that is collision-free where appropriate power limits are allocated and verifies its overall performance of virtual environment network latency.

In the current paper, we have assimilated fuzzy techniques and optimization techniques, namely differential evolution, to put forward a modern archive-based fuzzy evolutionary algorithm for multi-objective optimization using clustering. The current work account for the application of a cluster associated approach. Specific quantitative cluster validity measures, i.e., J-measure and Xie-Beni, have been referenced to carry out the appropriate partitioning. The proposed algorithm introduces a new form of strategy which attempts to benefit the feasible search domain of the algorithm by minimizing the analysis and exploration of less beneficial search scope. This clustering method yields a group of trade-off solutions on the ultimate optimal pare to front. Eventually, these solutions are united and maintained in an archive for further evaluation. The current work summarizes and organizes an archive concerned with excellent and diversified solutions in an effort to outline comprehensive non-dominated solutions. The degree of efficiency is revealed with respect to partitioning on gene expression and real-life datasets. The proposed algorithm seeks to reduce the function assessment analysis and maintains a very small working population size. The effectiveness of the proposed method is presented in comparison with some state-of-art methods.

Multicasting is an important operation in software-defined wireless sensor networks (SDWSNs). In this operation, a group of nodes specified by their unique node identification numbers is supposed to receive the same multicast message at the approximately same time, if possible. These nodes are termed as multicast members or multicast destinations. They need not be physically close to one another to form a group. The present article proposes an energy-efficient scheduler exclusively for multicast operation in the SDWSN environment. Based on the advantages provided by underlying network architecture, a router can efficiently schedule multicast packets belonging to various multicast sessions. This promotes greenery in the network and significantly increases the packet delivery ratio. These claims are supported and justified by the experimental results presented in this paper. As far as the authors know, there is no multicast packet scheduler in the literature of wireless sensor networks or WSN. SDWSN is a more advanced version, and no multicast protocol has yet been proposed for these kinds of networks. Therefore, while designing the present fuzzy scheduler, we kept in mind all standard multicast protocols in the WSN environment.

The article considers the main aspects of the use of digital platforms as a tool for the transformation of strategic consulting in public administration. The study substantiates the feasibility of using the results of consulting activities in the implementation of strategic objectives by the executive authorities at any level of government. The ways and methods of organizing consulting activities on the basis of digital platforms for the purpose of informational and analytical support of management technologies of public administration and civil service reform analyzed. Based on the results of the study, a model of using consulting services in the public administration system was developed. The criteria of efficiency of realization project management consulting in the public sector, based on digital platforms highlighted.