مطالب

The current research was conducted with the aim of providing a sustainable marketing model in the insurance industry with a focus on artificial intelligence. According to the type of data, the research method is mixed (qualitative-quantitative) of exploratory nature. In the statistical population, 12 people were selected from among the experts until the theoretical saturation was reached. The experts included professors of the faculty of business management and senior managers of Alborz insurance company, who were selected by non-probability, targeted and chain sampling. Quantitative part of 400 managers were insurance industry experts, using Cochran's formula, 196 people were selected by stratified random method. To collect data, semi-structured interviews and researcher-made questionnaires were used. The analysis method has been carried out in several consecutive steps. In the first stage, to identify the components of the model, the qualitative method and semi-structured interviews using the grounded theory have been used. In the next section, using interpretative structural modeling, the identified indicators were leveled. Finally, in order to fit the designed model in the qualitative part, a quantitative method with structural equation modeling approach was used with PLS4 software. The findings showed that 6 components were identified and there is a significant relationship between all components and sustainable marketing and the designed model has an acceptable fit. It can be concluded that AI can be incorporated as an essential component of a sustainable marketing strategy, enabling marketing professionals and businesses to make positive changes while increasing their brand reputation and profitability.

The present study aims to examine the relationship between cost stickiness and conservatism levels according to the type of industry. This purpose is achieved using the combined data technique and three models of non-operating accruals, the Basu model, and the market value model with a sample consisting of 113 companies listed on the Tehran Stock Exchange (TSE) over the period 2011 to 2020. Findings show that based on the model of non-operating accruals in the automotive, pharmaceutical, chemical, and food industries, cost stickiness decreases the level of conservatism. However, in the machinery industry, the relationship is significantly based on this model, but cost stickiness does not reduce the level of conservatism. According to the Basu model in the chemical, food, and basic metals industries, cost stickiness reduces the level of conservatism. But in the pharmaceutical industry and machinery and equipment, there is a significant relationship according to this model, but cost stickiness does not decrease the level of conservatism in companies. Also, based on the market value model in the automotive, chemical, machinery, and basic metal industries, there is a significant association, but cost stickiness does not decrease the level of conservatism in companies.

This study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) method to model the fluctua-tions of financial variables. Additionally, the smooth transition regression (STR) method was applied to explore the relationships between the variables. The results reveal significant positive correlations between BTC prices and gold, as well as oil, and a negative correlation with USD prices. We observed volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators. Notably, a negative relationship was identified between Bitcoin and the USD in both linear and non-linear aspects, with a larger coefficient in the second regime. Furthermore, a posi-tive relationship was found between Bitcoin and the variables of gold and oil prices, with coefficients being larger in the second regime compared to the first.

The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be.

The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .

The main purpose of this study is to present and evaluate a model to execute successful On the Job Training Course for Iranian EFL teachers. In order to achieve the goal of the study, 441 teachers of different language institutes in several cities of Iran were asked to complete a self-made questionnaire which was designed based on some face-to-face interviews and observation documents of 26 teachers. As factor analysis was the most crucial concern of this study, the 43 items of the questionnaire were piloted on a sample of 147 EFL teachers. Regarding this matter, the scale in this study enjoyed having a sufficient communality value greater than 0.5 for 25 questions in the questionnaire out of 43 questions. Pearson’s correlation coefficient and structural equation modeling (SEM) were run to analyze the relationships among the components. The result revealed that the research model was in the domain of acceptance. The findings of this study can provide fruitful information for English teaching and learning stakeholders, educational policy makers, supervisors, and those involved in second language teacher education as well as English language teachers in EFL contexts.

Hot and humid climate is one of the robust climates in the world. Vernacular architecture in this area answer to climate conditions with solutions, one of these solutions are windcatchers. Nowadays most of these windcatchers are closed because they are not responsive to current thermal comfort conditions. The reduction of wind speed and the lack of control over the entering hot weather can be considered as two main weaknesses of the windcatchers in these areas. This research suggested to install Damper and Fan to improve the performance of windcatchers in vernacular houses of Bandar-Kong. five windcatchers have been selected as case studies and their performance has been simulated and measured in 24 different modes: open windcatcher (past method), closed windcatcher (current state), windcatcher with valve and windcatcher with fan are four general modes wich are divided to 6 subset. Design Builder 7.0.0.116 is used for simulations. operative temperature is calculated for each space in 24 different modes. The results show that using fan and damper, increase thermal comfort hours from about 43 to 52 percent. Besides using fans and dampers can obviously reduce temperatures over 35degrees.

The Arg or Toopkhāneh Square was an essential part of Karim Khān's citadel complex in his new capital, Shīrāz, Iran, in the mid-18th century, which was destroyed in the early 1920s. However, the studies conducted regarding its form and functions have not been accurate enough, and their hypotheses are inconsistent with reality. This paper calls into question previous studies and takes a new look at the design, geometry, and spatial relations of the Square in the days of the Zand dynasty (17511794) and studies the changes of the Square in the Qājār (1789-1925) and Pahlavi (1925-1979) eras. This study analyzed European travelogues and historical photographs with little existing evidence by applying a historical research method. The study revealed that the Arg, later named Toopkhāneh Square, was rectangular, about 130 by 185 meters, with an east-west orientation. The Arg square and its adjacent buildings should be considered a governmental-military open space with various internal relations. The architecture of the main gate and the Naqqāra Khāneh mansion were the prominent features of this Square, and its location has always been questionable. The previous studies did not have a correct understanding of the location of the Naqqāra Khāneh, the number of entrances to the Square, the height of the walls of the Square, as well as its layout. Finally, it is crucial to notice that The design of this Square differs from the example of Naqsh-e Jahan Square in Safavid Isfahan and Toopkhāneh Square in Qājār Tehran.