This study tried to examine EFL translators‟ Uncertainty and Uncertainty management strategies through employing think aloud procedures. The participants of this study were some MA andBA translators selected from several universities in Iran. To this aim, a proficiency test was firstly administered among the volunteers. Then, think aloud protocol and retrospective interview were used to collect data. Meanwhile, Angelone coding system was used to categorize the data. To identify the significance of differences, chi-square nonparametric test was utilized. The findings indicated that MA translators had greater tendency to show Uncertainty at larger chunks of language such as collocation and sentence, while BA ones were more inclined to show Uncertainty in textual level. At the same time, behavioral and locus options were compared and contrasted between MA andBA translators. It was also found out that look-up and rereading strategies were frequently used to manage Uncertainty. The findings of this study can be helpful for translators to improve their translation ability by being more aware of what is happening inside their minds. Awareness of a stockpile of strategies helps them have fewer difficulties while translating a text.

Objective: The concept of this study is related to different stakeholders which include policy makers, financial market participants, asset managers and company managers. First, policymakers and government agencies should be aware that Uncertainty related to economic policies (monetary policies, fiscal spending, regulations, changes in taxes, national debt, etc.) can have decisive effects on investor sentiment and asset prices. Government agencies should provide uninterrupted communication with elected officials to help clarify the government's policy stance on economic matters. This limits Uncertainty about the future direction of certain government policies, which should lead to less volatile and better priced financial assets. Second, financial market participants and asset managers should be aware of changes and direction of change in policy Uncertainty and position their portfolios in response to changes in policy Uncertainty, which can have short-term and long-term effects on the stock market. Asset managers and investors with long-term mandates and time horizons should be equally concerned about policy Uncertainty and its asymmetric effects. Finally, corporate managers may use policy Uncertainty to help them time their capital raising activities. Therefore, by taking advantage of policy Uncertainty, firm managers may time their firm's capital raising activities to minimize their firm's cost of capital and maximize firm value. Therefore, considering the importance of the capital market in the country's economy on the one hand and the occurrence of numerous shocks in the field of monetary policy, the main purpose of this study is to investigate the effect of monetary policy Uncertainty on stock market Uncertainty in Iran.Method: In this study, according to the type and purpose of the research, it is applied and in terms of controlling the variables, it is a non-experimental and descriptive-correlation research. Conducting research in the form of inductive analogy and its information is event type. In this study, information on Iran's economy during the years 1368-1400 has been examined. The source of the data used is the database of the World Bank and the Central Bank of the Islamic Republic of Iran, and ARDL and NARDL methods have been used to estimate the desired models. It should be mentioned that Eviews11 software was used to estimate the model. The research model with Elha is specified from the two studies of Battabial and Killins (2021) and Fu and Lu (2021).Findings: The results of the ARDL linear model show that in the long term, the Uncertainty of monetary policies causes the intensification of Uncertainty in the stock market. On the other hand, in the non-linear ARDL model, the results showed that monetary policy Uncertainty shocks do not have an asymmetric effect on stock market Uncertainty in the first place, and secondly, negative shocks have a positive effect on stock market Uncertainty in the long term. Other results showed that government size and economic growth do not play a role in stock market Uncertainty. While, the exchange rate, inflation rate and interest rate have an effect on the Uncertainty of the stock market.Conclusion: This research was conducted with the aim of investigating the effect of monetary policy Uncertainty on stock market Uncertainty in Iran using the NARDL approach. In this study, in addition to using the NARDL approach, the ARDL approach has also been used, and in some ways, the linear and non-linear approach has been compared. In the nonlinear model, the positive and negative shocks of monetary policy Uncertainty were calculated using the NARDL approach and included in the model. The results of linear and non-linear models showed that the Uncertainty of monetary policies affects the Uncertainty of the stock market in the long term. In other words, the Uncertainty in the application and implementation of monetary policies increases Uncertainty in the stock market. The results in the non-linear approach showed that; First, the effect of negative and positive Uncertainty shocks is not asymmetric. Second, only the negative shock of monetary policy Uncertainty has an effect on stock market Uncertainty. In other words, with the occurrence of a negative shock in the Uncertainty of monetary policies, the Uncertainty in the stock market increases. The results of other variables showed that economic growth and government size, despite their importance in the formation of trust in the capital market, have an insignificant effect on stock market Uncertainty. On the other hand, the occurrence of inflationary conditions in the country, both due to the inflation rate and the increase in the exchange rate, causes an increase in Uncertainty in the stock market. However, an increase in interest rates will reduce Uncertainty in the stock market.

Purpose: The future of the construction industry is increasingly influenced by new technologies. In order to adopt appropriate strategies in facing new technologies, it is necessary to know the possible futures of the construction industry. This research was done with the aim of explaining the technological uncertainties and compiling the future scenarios of the construction industry.  Method: The research method is applied and was carried out with a combination of quantitative and qualitative methods. First, the library study was used to determine the technological drivers, then the Structural Analysis was used to explain the technological uncertainties, and finally, the Schwartz method was used to compile the scenarios. The statistical population is experts of construction industry. Findings: Nine technological uncertainties affecting the future of the construction industry have been identified and for each of them, three states of decline, stagnation and progress have been considered. Data analysis by Scenario Wizard shows eight probable scenarios. The portfolio of scenarios including four groups of progress scenarios, towards progress, towards stagnation, and towards wane has been compiled. Conclusion: In the progress scenario, the 89% of uncertainties have developed. In  towards progress, 56% of the factors are in the development status, which indicates the development of the technological factor application. In the stagnation scenario, no progress has been made in the application of uncertainties and they are in a static state. In towards wane, uncertainties have been placed in a situation of reduced use.

In this paper, target differentiation based on the pattern of data which are obtained by a set of two ultrasonic sensors is considered. A neural network based target classifier is applied to these data to categorize the data of each sensor. Then the results are fused together by Dempster–Shafer theory (DST) and Dezert–Smarandache theory (DSmT) to make a final decision. The Generalized Aggregated Uncertainty measure named GAU1, as an extension to the Aggregated Uncertainty (AU), is used to evaluate DSmT. Then the GAU1 and AU as the Uncertainty measures are applied to the obtained results of the decision makers to evaluate DSmT and DST accordingly. The introduced configuration for decision making has enough flexibility and robustness to use as a distributed sensor network.