درخت حوزه‌های تخصصی

The present study is a methodological synthesis aiming to evaluate the adherence of Iranian L2 papers to the study quality standards. Ten Iranian journals were selected based on the latest ranking of Iran’s Ministry of Science, Research, and Technology (MSRT), and all experimental papers (N = 367) published from their beginnings were explored for study quality with regards to sampling, design, statistical tests, reporting practices and data sharing, and visual presentation. In the evaluation of the papers, the protocols proposed by Gass and Plonsky (2011) and Pagout and Plonsky (2017) were moderated and some recent issues proposed by APA’s (2018) Journal Article Reporting Standards and some scholars (e.g., Hu & Plonsky, 2019; Khany & Tazik, 2019; Larson-Hall, 2017) were added. The results showed that while there were issues, like acceptable sample size, use of pre-testing, reporting descriptive and inferential statistics, and ensuring the reliability of instruments, that were acceptably adhered to quality standards, problematic areas existed in all five facets of quality, and the majority of them stayed constant or changed slightly over time. The shortcomings caused by such lack of adherence are discussed to identify the challenges in the way of improving the papers’ quality. Although the results are obtained from a specific context, the implications may be generalizable to other contexts where English is taught and researched as a foreign language .

The study has two main objectives: firstly, to examine the opportunity of the Momentum and Contrarian investment strategy for three different monetary systems to trade currencies in Forex markets using Symlet wavelet decomposition approach. Secondly, to examine the co-movements between the three monetary systems using wavelet coherence analysis. The findings indicate that an investor with momentum strategy can consider investing in Bitcoin and Gold market, while the contrarian investment strategy is more advisable for the fiat money market during crisis period. Furthermore, the wavelet coherence analysis indicates that Bitcoin currency is the most leading monetary system during the Covid-19 pandemic crisis, followed by gold. However, US dollar mostly leads Bitcoin during non-crisis periods, while Gold is found to lead the US dollar throughout the sample period of the study. This suggests that the cryptocurrency system or gold standard should be considered as the alternative monetary system for better economic stability specially during the crisis period. Moreover, Bitcoin and gold had an anti-phase correlation before the Covid-19 pandemic crisis, which implies better benefits of hedging in the non-crisis period, while during a crisis they are moving together across different horizons. In contrast, Bitcoin and Fiat Money are strongly correlated during non-crisis periods, while during covide-19 pandemic crisis the correlation is statistically insignificant. Overall, the outcomes offer significant guidance for policymakers in understanding which monetary system leads to better economic stability during the crisis period and provides many implications for market players such hedging and Diversification investments strategy in forex markets.

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.