درخت حوزه‌های تخصصی

Organizational DNA expresses a method of investigation, thinking, reflection and thought about organizations, which examines the patterns of organizations, management practices, leadership style and other concepts related to the organization. Organizational DNA uses very diverse approaches to understand and recognize the organization, instead of organizational models and forms, by examining issues such as teamwork, decision-making, and development and improvement of human resources as separate or at least independent variables. In this research, by using the theoretical framework and using quantitative and qualitative methods, it has been done to identify the dominant DNA in Tocafulad Holding companies and determine the relationship model of hidden components using covariance analysis. The statistical population includes 435 employees of Isfahan Tokafulad companies and 6 company experts who used a mixed or mixed design to conduct the research. The technique of structural equations has been used to improve the model and evaluate the components and indicators to check and identify the model. In order to answer the research question, the steps are: 1) presentation of the conceptual model of the relationship between the corporate DNA and the investigated components, 2) quantitative evaluation of the components and indicators of the model, 3) determining the mutual influence of the hidden variables and the obvious variables of the model and 4) the relationship Between the apparent variables of the components with each other and the verification of the initial conceptual model has been carried out and then the model has been improved and finally the dominant DNA has been determined. The research showed that semantic DNA with a factor load of 0.93 is the dominant DNA in 4 out of 6 companies under investigation. It was also found that planning and payment system are the least important and change processes are the most important in line with the improvement model. In the improved model based on covariance analysis, the mutual effects of payment system and planning with mission, leadership style with teamwork, performance management with decision-making, interpersonal relations with change processes and change processes with work group should be considered.

There has not been presented any comprehensive index for Corporate Governance in spite of its crucial role in resolution and responsiveness. Although diverse methods have been sought for measuring the Corporate Governance in the previous studies, in this study, a comprehensive index is proposed for measuring Corporate Governance taking other indexes into account through mathematical equations. For testing the designed model’s functionality, a set of data related to 149 firms from 2014 to 2019 has been used jointly, too. To do so, a questionnaire, which asks the experts their idea about the weight and significance of the criteria of measuring the Corporate Governance of the firm, is made and distributed and the weight and significance of the criteria were determined through one method of multi-criteria decision model (Shannon Entropy). To measure the changeable hidden relationship which exists in this study of the Corporate Governance, provided with the measurement items, a confirmatory factorial analysis was taken, too; results show that the supposed model has been a proper index for Corporate Governance. This mixed index includes criteria such as the board of directors' size, independence, meetings, and its education, the CEO's permanence, the size, independence, financial specialty, financial experience, tenure and the meetings of the auditors hip committee, ownership concentration, organizational ownership, governmental ownership, deals with the dependent persons, the quality of the disclosure, proper information and weaknesses if the internal controls,

The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of 137 companies listed in Tehran Stock Exchange during (2009-2017) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with 15 predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).

The article presents the results of the analysis of the indicators of the level of the shadow economy in Ukraine in the period from 2010 to 2020. The level of shadow economy calculated on the basis of such methods as: unprofitable enterprises, monetary, population expenditures - retail trade - services and electricity was used for the analysis. The causes and consequences of shadow economic activity in Ukraine are given. The study found that the downward trend in the shadow economy persists despite the spread of the negative effects of the COVID - 19 pandemic and declining real GDP. In particular, three of the four methods used to assess the level of the shadow economy recorded a decrease in the level of the shadow economy (the method of "population expenditure - retail trade and services"; the electric method; the monetary method). At the same time, the method of enterprise losses showed an increase in the shadow economy, which is largely due to a significant deterioration in the financial situation of enterprises under the restrictions imposed to prevent the rapid spread of the coronavirus pandemic in the world and Ukraine, as well as logistical problems. The practical value of the results is determined by the fact that the conclusions and proposals can be used to more accurately and objectively calculate the level of the shadow economy, which in turn can be the basis for effective decisions to de-shadow and legalize Ukraine's economy.

Diabetes is a type of metabolic disorder with a high level of blood glucose. Due to the high blood sugar, the risk of heart-related diseases like heart attack and stroke got increased. The number of diabetic patients worldwide has increased significantly, and it is considered to be a major life-threatening disease worldwide. The diabetic disease cannot be cured but it can be controlled and managed by timely detection. Artificial Intelligence (AI) with Machine Learning (ML) empowers automatic early diabetes detection which is found to be much better than a manual method of diagnosis. At present, there are many research papers available on diabetes detection using ML techniques. This article aims to outline most of the literature related to ML techniques applied for diabetes prediction and summarize the related challenges. It also talks about the conclusions of the existing model and the benefits of the AI model. After a thorough screening method, 74 articles from the Scopus and Web of Science databases are selected for this study. This review article presents a clear outlook of diabetes detection which helps the researchers work in the area of automated diabetes prediction.