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.

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.

By applying conditional variance modeling and Granger causality test, we specified some important determinants of real and nominal UNCERTAINTY in Iran (1960-2006). In addition we investigated the negative effects of these uncertainties on output growth. Our results show that an increase in the rate of inflation and a decrease in the level of oil revenues can raise nominal UNCERTAINTY, while real UNCERTAINTY is arisen by oil revenues. This study could not reject the hypothesis that nominal and real UNCERTAINTY has no effect on output growth.

In this study we have introduced new time series measures of macroeconomic UNCERTAINTY for Iran's economy. We attempt to provide superior econometric estimates of macro UNCERTAINTY and consider its dynamics during 1370-1393. This measure is extracted as a common latent stochastic process from many series, sectors, and markets at the same time, and then its robustness has been evaluated. Dynamics of macro UNCERTAINTY reflect the most important economic episodes of the Iran's economy. According to the baseline estimates, macro UNCERTAINTY has striking rise during the 1370-1372, 1373-1374 and 1390-1392 recession periods, so that the baseline estimate reaches its highest level in spring 1372, spring 1374 and fall 1391. Furthermore, it turns out that, among the dataset, real exchange rate and government expenditure uncertainties are highly correlated with macro UNCERTAINTY. Thus, these uncertainties played a crucial role in shaping macro UNCERTAINTY shocks in Iran.

Objective (s): Consumption expenses and unemployment can be considered as macroeconomic indicators that affect in health. Therefore, considering the inflationary conditions and its fluctuation which leads to UNCERTAINTY in the economy, the purpose of this study was to investigate the effects of inflation fluctuation on consumption and unemployment as key indicators in public’ s health. Methods: In this study, the fluctuation and UNCERTAINTY of inflation was estimated using Stochastic Differential Equation (SDE). For surveying the shocks of inflation fluctuation on consumption and unemployment, the impulse respond (IR) that is so useful in structural Vector Auto Regressive (SVAR) models were used. The source of quarterly data was Statistical Center of Iran (S. C. I) from 2001 to 2020. Results: The results indicated the fluctuation of inflation was effective and meaningful on unemployment by 0. 04 and consumption by (-0073). Conclusion: Based on these two indicators, it can be concluded that inflation fluctuation reduces health in the community.