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This study aims to understand the relationship between politics and pandemics in shaping the characteristics and themes of people’s Tweets during the US 2020 presidential election. Additionally, the purpose is to detect misinformation and extremism, not only to help online social networks (OSN) to target such content more rapidly but also to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN. This could help authorities to identify the intents behind them and find out how and when they should address such content.

This study focuses on extracting and verifying knowledge from large-scale OSN data, at the intersection of the Coronavirus pandemic and the US 2020 presidential election. More specifically, this study makes manual, statistical and automatic inferences and extracts knowledge from over a million Tweets related to the two aforementioned major events. On the other hand, disinformation operations intensified in 2020 with the coincidence of the Coronavirus pandemic and presidential election. This study applies machine learning to detect misinformation and extreme opinions on OSN. Over one million Tweets have been collected by our server in real-time from the beginning of April 2020 to the end of January 2021, using six keywords, namely, Covid, Corona, Trump, Biden, Democrats and Republicans. These Tweets are inspected with regard to their topics, opinions, news, and political affiliation, along with misinformation and extremism.

Our analyses showed that the majority of these Tweets concern death tolls, testing, mask, drugs, vaccine, and travel bans. The second concern among these Tweets is reopening the economy and schools, unemployment, and stimulus bills. The third concern is related to the Coronavirus pandemic’s impacts on politics, voting, and misinformation. This highlights the topics that US voters on Twitter were most concerned about during this time period, among the multitude of other topics that politicians and news media were reporting or discussing. Automatic classification of these Tweets using a long short-term memory network revealed that Tweets containing misinformation formed between 0.5% and 1.1% of Coronavirus-related Tweets every month and Tweets containing extreme opinions formed between 0.5% and 3.1% of them every month, with its pick in October 2020, coinciding with the US presidential election month.

The originality of this study lies in establishing a framework to collect, process, and classify OSN data to detect misinformation and extremism and to provide a close to real-time picture of trending topics, misinformation, and extremism flowing on OSN.

The purpose of this study is to develop an algorithm to accurately detect faults in series capacitor compensated (SCC) power transmission lines. The line fault must be distinguished from stable power swing, compensating unit malfunction and defects on other lines sharing the same bus (external faults).

In this regard, an effective fault feature extractor based on the cumulative sum (CUSUM) of the amplified second harmonic of the phase currents is suggested. The features are then applied to an artificial neural network for classification. No-fault cases include stable power swing and several disturbances. Due to the independent analysis of each phase, faulty phase detection is also a by-product.

Various fault scenarios are defined, and the algorithm success rate is compared with some newly published methods. Extensive simulations performed over a single-machine infinite bus, a 3-machine, 9-bus and the large-scale New England IEEE 39-Bus networks all indicate that the proposed algorithm can trip the faulty line more quickly and accurately than the contestant algorithms.

Suggestion of a new algorithm based on the CUSUM of the amplified second harmonic of the phase current for the fault feature extraction that is able to isolate the transmission line internal faults from stable poser swing, line compensating unit malfunction and faults on the adjacent lines connected to the same bus.

The primary objective of this study is to examine the correlation between financial statement comparability and conditional and unconditional conservatism within companies listed on the Tehran Stock Exchange (TSE).

To achieve this, a sample of 193 companies, resulting in 1,546 firm-year observations, were listed on the TSE between 2014 and 2021. The study’s research hypotheses are assessed by applying multiple regression models.

The findings reveal a notable positive association between financial statement comparability and conditional conservatism. Additionally, the research results indicate a significant and negative connection between financial statement comparability and unconditional conservatism.

According to the findings, corporate managers may prioritize financial statement comparability to enhance conditional accounting conservatism, which might be translated as a suitable benchmark for competitors. Equity owners may decrease the agency problems associated with CEOs by emphasizing comparable financial reports, as it improves the quality of financial figures and facilitates stakeholders’ evaluation and comparison of various companies’ performances.

A review of the relevant literature underscores the absence of research focusing on the relationship between financial statement comparability and conditional and unconditional conservatism within emerging markets. Consequently, this study aims to address this gap by investigating this relationship in the context of emerging markets and contributing to the existing body of literature in this field.

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the electronic commerce. To evaluate the validity of the model, the structural equation modeling technique is employed.

A method has been suggested to evaluate the impact of customer history, product classification and prices on the success of the recommender systems in electronic commerce. After that, the authors investigated the relationship between these factors. To achieve this goal, the structural equation modeling technique was used for statistical conclusion validity. The results of gathered data from employees of a company in Iran is indicated the impact of the customer history on the success of recommender systems in e-commerce which is related with the user profile, expert opinion, neighbors, loyalty and clickstream. These factors positively influence the success of recommender systems in ecommerce.

The obtained results demonstrated the efficiency and effectiveness of the proposed model in term of the success of the recommender systems in the electronic commerce.

In this paper, the effective factors of success of recommender systems in electronic commerce are pointed out and the approach to increase the efficiency of this system is applied into a practical example.

The present study aims to examine the effects of the Islamic sacred months, namely, Muḥarram, Rajab, Dhu al-Qaʿdah and Dhu al-Ḥijjah, on stock prices on the Iran and Iraq Stock Exchanges.

Using the infrastructure models of the capital market, the daily stock prices were calculated for the sacred and non-sacred months. As the data of this study are non-stationary, the AMIRA time-series model was used for better understanding of the model or future projections. The dependent variables of this study are the daily stock indexes for Iranian and Iraqi Stock Exchanges, and independent ones are the sacred and non-sacred months of a lunar year. Data were gathered daily from the financial statements of Iranian and Iraqi Stock Exchanges websites. To test the hypotheses under study, a five-year period from 2012 to 2016 was considered for both Iraqi and Iranian Stock Exchanges, which corresponds with the lunar calendar from 1433-1437AH.

The obtained results indicated that there is no significant difference in stock prices between the sacred months of Muḥarram, Rajab, Dhu al-Qaʿdah and Dhu al-Ḥijjah and other non-sacred months. However, the stock price in the Iranian Stock Exchange has a significant difference in Rajab and Dhu al-Qaʿdah with other non-sacred months.

The results of this study will reveal more than ever the role of Islamic sacred months for society and users of financial statements to make better financial decisions especially in Islamic emerging markets.

The main purpose of this paper is presenting Thin Plates Spline-based Differential Quadrature (TPS-DQM) as a meshless numerical method to solve the steady and transient groundwater equation in complex geometry.

The computational nodes are randomly distributed in domain, and the governing equations of groundwater flow are solved, relying on the capability of present model for solving the partial differential equations (PDEs) in irregular domains. To show the accuracy of the proposed model, several seepage problems in both homogenous and non-homogenous soils are solved, and the results are compared with those existing analytical solution and well-known finite element-based software SEEP/W.

The results indicate that the present meshless method is capable of simulating steady-state and unsteady seepage problems, especially in complex geometry and it provides sufficient accuracy and reliability, despite the low computational effort and no need for additional parameters like shape factor.

The main advantage of the method is its meshless characteristic, which does not require structured grid generation and able to solve governing equation in arbitrary geometry.

The purpose of this paper is to analyze the effect of managers’ incentive bonuses on both accrual and real earnings management.

First, the authors investigate the relationship between managers’ bonuses and both accrual earnings management (measured by a modified Jones model) and real earnings management (measured by Roychowdhury proxies). Next, the authors examine whether management has any preferences for earnings management methods to enhance its bonuses. Finally, the authors investigate the possible effects of earnings management on future operating performance. The sample consists of compositional data in the period from 2006 to 2012.

The authors find a negative relationship between real earnings management and managers’ bonuses and detect that managers prefer to use accrual earnings management to earn more bonuses. The results also show that real earnings management will reduce a firm’s performance in future periods, and on the other hand that increasing managers’ bonuses links to improvement of the firm’s future performance. The results suggest that managers are typically aware of the negative effects of real earnings management on the firm’s future performance and thus prefer to improve the firm’s performance in securing their bonuses when their ability to manage accruals is constrained.

The implications of this paper provide further evidence on how managers’ bonuses affect their discretion in using accrual and real earnings management. This finding is important to investors and regulators.

This study aims to assess the relationship between managerial entrenchment, social responsibility and risk-taking of the firm and shareholders’ activity.

The study is carried out based on the disclosed information of listed firms on Tehran and Iraq Stock Exchanges during 2011–2017 from a sample of 121 firms on the Iranian side and 37 firms on the Iraqi side. The hypothesis testing is performed using panel estimators of the adjusted regression models.

The obtained results from hypothesis testing show that there is a significant relationship between managerial entrenchment, social responsibility disclosure, social responsibility growth of the firm and risk-taking and shareholders’ activity in the Iranian Stock Exchange firms. Moreover, in the case of Iraqi firms, a significant relationship is observed between managerial entrenchment, social responsibility disclosure, social responsibility growth of the firm but the relationship between firm risk-taking and shareholders’ activity was not evident.

The current study is almost is the first study conducted on two Islamic countries and the outcomes of the study may help other Muslim countries on the subject of the study.

The study's main objective is to identify business sustainability performance indicators and analyze the mutual relationship between different business sustainability components in Iran.

To achieve research objectives, the 125 indicators of Business Sustainability Performance in Arianpoor and Salehi (2020) were used. For data collection, a questionnaire is designed and developed. Moreover, the Delphi method is used to determine the indicators related to business sustainability performance. Accordingly, we attempted to send the questionnaire to 346 experts and qualified opinion-leaders in the study area to utilize their opinions in our project. Finally, 108 questionnaires were analyzed statistically.

In this study, the confirmatory factor analysis (CFA), binomial test, one sample t-test, one sample Kolmogorov–Smirnov test and Kruskal–Wallis test are used. The results of statistical tests show that among 125 proposed indicators, 11 indicators were eliminated. Hence, to assess business sustainability performance in the listed firm on the Tehran Stock Exchange, 114 indicators were analyzed. To achieve the study's objective, the relationship between financial and non-financial sustainability performance and their effect is analyzed using the Smart PLS Software. Findings indicate that there is a mutual relationship between financial and non-financial sustainability performance in Iran. There is also a relationship between the operational component and research component and non-financial sustainability performance. In contrast, as for the growth component and non-financial sustainability performance, there is no significant relationship. Also, root means squared error (RMSE) values suggest a reasonable model-data fit.

The type and characteristics of different regions have a significant role in the reporting and differ according to different economic conditions. The discussion of business sustainability and its reporting is important; therefore, essential indicators were identified in this study. In addition, all aspects of sustainability performance are considered cohesively to analyze the mutual relationship between different components of sustainability performance and to be able to make more appropriate decisions in future studies about performance evaluation and reporting using the results of this paper.

The purpose of this study is to examine the impact of information technologies and technology-enabled social networks on the efficiency of knowledge management processes in the Middle Eastern audit and consulting companies.

Scientific literature analysis, structural equation modeling and expert evaluation (structured questionnaire) were used to develop the research model, collect data from the audit and consulting companies’ experts and test the research hypotheses.

The empirical results of this research supported the hypotheses stating that information technologies and social networks positively affect the knowledge management cycle, including five processes (acquisition, creation, storage, sharing and application) within the Middle Eastern audit and consulting companies.

The research results were generated from the Middle Eastern audit and consulting companies, which form a limitation concerning the geographical area and the business sector.

From the results of this study, audit and consulting companies, as well as organizations and society broadly, would benefit via the positive effect of information technologies and technology-enabled social networks on the whole knowledge management cycle, which has a further impact on organizational performance. These practical implications are related to a more open, sharing culture that drives organizational performance to the members and stakeholders of organizations, which, in turn, benefits society.

This research analyzes information technologies and technology-enabled social networks’ impact on knowledge management processes, particularly in the context of the Middle Eastern audit and consulting companies. While the phenomena have received some attention in the prior scientific research, the studied context so far remained under-researched, where a gap is found in studying the knowledge management cycle as a whole.