درخت حوزه‌های تخصصی

The article proposes a methodical approach to assessing the competitiveness of enterprises in the logistics infrastructure of the Black Sea region in the context of the strategy of the development of the Black Sea. The article is presented is the author's interpretation of the steel development of the territory and injected into the logistic infrastructure. It was proposed of the methodical approach to assessing the competitiveness of enterprises-moving enterprises in the development of the territory, as one of the stages of the methodology for assessing the development of the territory in the region. It have been identified Indicators for assessing sustainable development in assessing the competitiveness of key enterprises in the region and in terms of using the TOPSIS model. The application of the TOPSIS model allows to assess the value of the aggregate indicator of sustainable development of a region or territory, taking into account the impact of the level of competitiveness of its key enterprises. In the context of this approach, it is studied and evaluated the level of competitiveness of powerful enterprises of the Black Sea region, which form the logistics infrastructure of the region. It was  determined their influence on the strategy of sustainable development of the territory. This approach allowed to expand the methodology for assessing the assessment of sustainable development of the territory through the methodology of assessing the competitiveness of enterprises-engines of development of the territory, as one of the stages of the overall methodology.

An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly

This study examines trading strategies based on trading systems by analyzing the performance of 11 technical indicators. The data used for analysis were financial data of all firms listed on the Teh-ran Stock Exchange in the period from 2010 to 2020. Excluding the firms whose data were not available for the period under study, 135 firms were selected as the research sample. The results showed that the signals containing three indicators of moving average, exponential moving average, and relative strength over a weekly up to six-month period to buy or sell stocks (as a strategy) could be used more confidently compared to other indicators to achieve higher returns and profitability. As a result, investors can use the signals that these three indicators in weekly (EMA) and monthly (MA, RSI) periods and the quarterly (MA) and six-month (RSI, EMA) periods to determine buying and selling strategies with the lowest investment risks. It is also recommended that investors use a combination of these three indicators to invest, and extend their investment period over a longer period of time to bear less risk, and more returns.

Organizational anomie (OA) is defined as the lack of law, as well as violations or weaknesses in the implementation of laws and norms in the organization, which can have detrimental effects on the performance of the organization and its members. The main focus of this study is to establish and validate an organizational anomie scale that can be employed to implement and operate this tool in field studies. The present research is applied-exploratory in terms of purpose and descriptive-survey in terms of method. To conduct this research, issues related to organizational anomie using factor analysis approach and content analysis method of scientific texts as well as from the perspective of qualified and experienced thinkers and experts and a researcher-made questionnaire on organizational anomie in COVID-19 pandemic are analyzed. it placed. Completed by experts and managers. The reliability coefficient of the test was calculated to be 0.85 using Cronbach's alpha test. Exploratory factor analysis of organizational questions revealed that the components of this questionnaire may explain more than 78.512 % of the desired variance. After one rotation of Varimax factors, the correlation between each item with each component was determined in the best situation and accordingly, twelve components were selected and extracted with the most weight: conflicting goals of the individual and organization, weakness in communication, standard selection ,ambiguity in goals, level of trustworthiness, negative attitude of employees towards themselves, feeling useless, understanding justice, weak control, common norms, organizational indifference, lack of socialization. According to the research findings, it can be concluded that organizational anomie is a multidimensional concept with many dimensions and components and cannot be studied with limited dimensions and components.