مطالب

Background : The role, duty and responsibility of professional accountants towards the society, capital owners and other interested parties requires that they observe the general principles of good ethics in all aspects and adhere to the coded and consistent professional conduct. Therefore, the main goal of this research is to find a suitable method for meaningful (long-term) learning of ethics concepts in accounting. Method : This research was applied and semi-experimental and was carried out in a quasi-experimental manner. The statistical population was accounting master students, 50 of whom were selected by in access sampling and randomly divided into two experimental and control groups. The instrument was accounting ethics questionnaire and finally the data were analyzed by covariance method. Results : In this research, traditional teaching methods were compared with alternative methods (use of conceptual diagrams) by comparing the learning rate of two groups of accounting students. The findings of the research indicated that the average scores of the students of the experimental group (who were trained using conceptual diagrams) were significantly higher than the control group (who were trained using the traditional method). Conclusion : Nowadays, in many cases, traditional teaching methods are not able to understand the complex topics of ethics, and therefore students try to learn these topics like parrots. Conceptualization allows a person to communicate between different aspects of moral issues and to understand the relationship between ideas and constituent parts of thoughts. Therefore, using the conceptual diagram prepared by the lecturer or relevant experts in ethics education can increase the quality and quantity of participation in learning among students. Article number: 10

The Coronavirus disease (Covid-19) is one of the infectious and contagious ones called 2019-nCoV acute respiratory disease. Its outbreak was first reported on December 31, 2019, in the Chinese city of Wuhan that quickly spread throughout the country within a few weeks and spread to several other countries, including Italy, the United States, and Germany, within a month. This disease was officially reported in Iran on February 19, 2020. It is important to detect and analyze high risk zones and establish regulations according to the data and the analyses of Geographic Information System (GIS) in epidemiological situations. Meanwhile, the GIS, with its location nature, can be effective in preventing the breakdown of Covid-19 by displaying and analyzing the dangerous zones where people infected with the disease. In fact, recognizing regions based on the risk of getting the disease can influence social restriction policies and urban movement rules in order to prepare daily and weekly plans in different urban regions. In this applied and analytical research, high and low risk zones of Tehran have been identified by using the random forest algorithm which is used for both classification and regression. The algorithm builds decision trees on data samples and then predicts data from each of them, and finally chooses the best solution. In this research, 7 effective criteria have been used in the level of risk of regions toward Covid-19 virus, which is: subway paths and bus for rapid transits, hospitals, administrative and commercial complexes, passageways, population densities and urban traffic. After providing the map of high-risk zones of Covid-19, the Receiver Operating Characteristic curve (ROC) has been used for evaluation. The area under the curve (AUC) obtained from ROC shows an accuracy of 98.8%, which means the high accuracy of this algorithm in predicting high and low zones toward getting the Covid-19 disease.