مطالب

Although there is an increasing amount of research examining the efficacy of Witten Corrective Feedback (WCF) in enhancing the grammatical accuracy of EFL learners, only a few studies have explored the perspectives of EFL teachers regarding Formulaic-oriented Witten Corrective Feedback (FWCF) in EFL settings. Using a mixed-method approach, this interpretive exploratory study sought to understand the attitudes of Iranian EFL teachers toward FWCF. The analyses were based on quantitative data from a 13-item anonymous bespoke online survey, qualitative data from semi-structured interviews, and an open-ended question at the end of the survey. EFL teachers (n =137) responded to the online survey, and 7 participants participated in semi-structured interviews. The findings indicated that female teachers' favorable opinions about the effectiveness of WCF for improving EFL learners' writing performance resulted from their belief that the learners' capacity to use more formal and courteous language in their writing assignments was positively impacted by using these formulaic sequences. Moreover, the findings demonstrated that EFL teachers preferred direct WCF methods over indirect tactics. The most essential component that affected teachers' practices in the classroom was training designed to change their perspective of the valuelessness of WCF. The teachers' lack of satisfactory understanding of formulaic sequences stemmed from their lack of information. In addition, teachers' hesitancy to use them in the lower proficiency levels emanated from their misconceptions of their uselessness in the language learners' writing tasks.

Detecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in companies has become a big prob-lem. Companies and regulatory agencies must continuously develop their mechanisms to detect fraud. Machine learning and data mining techniques are currently commonly used to solve this problem. However, these tech-niques still need to be improved in terms of computational cost, memory cost, and dealing with big data that is becoming a feature of current financial transactions. In this research, machine learning techniques including logistic regression, neural network, and Bayesian linear regression were used to de-tect financial frauds in the Iranian stock market. According to the obtained results, the support vector machine model with radial kernel has the lowest RMSE and the highest accuracy criterion, and the support vector machine model with linear kernel and Bayesian linear regression has the highest RMSE and the lowest accuracy criterion for modeling the financial fraud of companies in they were Tehran stock market. Also, the models of artificial neural network model, Bayesian linear regression and support vector ma-chine model with linear kernel respectively had the lowest characteristic values and did not perform relatively well in detecting the existence of fi-nancial fraud in the companies present in the Tehran stock market.

The most basic step in managing an organization is to develop appropriate and practical strategies. Developing a detailed and complete plan that will determine the long-term direction of the organization and guarantee the prof-itability and survival of the organization. In this research, using multi-criteria decision-making techniques, a new SWOT model has been created to formulate organizational strategies (financial, commercial, production and similar). The upcoming research method is of a mixed type (qualitative-quantitative) which will be used in the qualitative part of the method (de-scriptive-survey) and in the quantitative part of the fuzzy Delphi technique and multi-criteria decision making. In order to target more the results of this research and its output model, Iralco Company (Iran Aluminium) has been selected as a strategic and widely used global product manufacturer to study and implement the mentioned models and techniques.

The purpose of this research is to investigate the reasons for tax evasion in companies, which uses two independent variables (financial constraints and tax planning opportunities) and tow dependent variable (firm value and tax evasion by tax difference method) in the form of 13 models. The 11 indicators have been considered for the variable of financial constraints of companies, and the model is implemented for all these indicators. The research was conducted in the 5-year period from 2015 to 2019 in the Tehran Stock Exchange, and Eviews software was used to analyse the data and fit them for 3 research hypotheses. The results of the research show that the opportunity for tax planning has a negative effect on the value of the company, and the increase in the opportunity for tax planning and subsequently tax evasion causes a decrease in the value of the company. Also, the research results showed that there is a significant relationship between tax planning opportunity and tax evasion (by tax differences method) of companies, while there is no positive relationship between financial constraints and tax evasion (by tax difference method) in companies that have tax planning opportunities.