The present study is an attempt to explore any significant relationships between learners’ preferences for error correction, demotivation, and language proficiency (LP). One hundred Iranian EFL students, including both males and females, studying at the departments of foreign languages of Shahid Bahonar University of Kerman and Tehran University took part in this study. In order to obtain the required data, two questionnaires and a proficiency test were utilized: the learners’ preferences for error correction questionnaire (Fukuda, 2004) to measure learners’ preferences for error correction, the demotivation questionnaire (Sakai & Kikuchi, 2009) to measure demotivation, and Michigan Test (1997) to measure the learners’ language proficiency level. The findings of this study revealed that first, there was a significant negative relationship between the learners’ preferences for error correction and demotivation (- 0.79): the more satisfied learners are with the error corrections they receive, the less demotivated they will be; second, there was a significant positive relationship between learners’ preferences for error correction and LP (0.69): the higher the learners’ satisfaction with error corrections they receive, the higher their level of LP; third, there was a significant negative relationship between demotivation and LP (- 0.59): the more demotivated learners are, the less their scores of LP will be.

In this paper, of digital error correction algorithm in of capacitor mismatch error and finite and nonlinear gain of Op-Amp has increased significantly by the use of DLMS, an evolutionary search algorithm. To this end, a 16-bit pipelined analog to digital converter was modeled. The obtained digital model is FIR filter with 16 adjustable weights. To adjust of , was divided into three stages and in each stage, the number of filter weights by DLMS algorithm and totally the error correction algorithm is converged through 3000 repetitions in three stages. The DLMS algorithm was simulated using synthesizable RTL code in Verilog HDL and may be implemented. The division of the error correction algorithm into three stages led to improve the error correction and reduce the power consumption. Moreover, an optimum MDAC circuit has been proposed for designing pipelined converter and based on this circuit the error correction algorithm has been designed.

Weather forecasting and monitoring systems based on numerical weather forecasting models have been increasingly used to manage issues related to meteorology and agriculture. Using more accurate minimum and maximum temperature forecasts can be helpful in this regard. But systematic and random errors in the model affect the accuracy of forecasts. In this study, the model errors during the 5 and 14 days training period in the same climate areas on the points of the network where the observations are available are calculated.Then the errors are generalized on all points of the network using the cokriging interpolation method. This, preserves the model forecasts for other points of the network and only error values are applied to them. To better evaluate the model, the spatial and temporal distribution of the maximum and minimum temperature forecast errors are also investigated in the country. Observed daily maximum and minimum temperatures data from 560 meteorological stations for the period 1/11/2019 to 1/2/2021 are used to evaluate the WRF model. The WRF model is run daily at 12UTC, with a forecast time of 120 hours. And first 12 hours of each run is consider as the model spin-up and is not used in errors calculation. In order to correct the maximum and minimum temperature forecast errors for next three days (forecasts of 36, 60 and 84 hours), the forecasts for each day in the period of 11/1/ 2019 to 1/2/2021, is extracted from the model outputs. In order to evaluate the error correction method, the skill score index is used. The validation results of the error correction method shows that the absolute mean error value, correlation coefficient and RMSE are improved after the error correction compared to results that were before the error correction. This shows that the error correction method can be used for other network points that do not contain observational data. The results shows that the RMSE of the raw model maximum (minimum) temperatures forecasts for next three days is approximately 6 degrees Celsius (5 degrees Celsius), which after error correction reaches 2 degrees Celsius (4 degrees Celsius). Also the value of correlation coefficient, after correcting for the model error, has a significant increase compared to the raw model output. The average skill score for the raw minimum and maximum temperature forecast for more than 50% of the days is more than -1 and -1.9, respectively, but after correction, the model skill scores become closer to one and for more than 75 percentage of days that reach above zero. Without exception, all climatic regions after error correction have a higher skill score than before error correction, so that the model skill score for most climatic regions after error correction reaches above zero for more than 75% of the days. Before error correction, the warm semi-humid zone has the lowest average skill score for forecasting maximum and minimum temperatures among climatic zones, but after error correction it reaches the highest value among other zones. In general, for areas with hot and dry climates, the raw output skill score for predicting the minimum temperature in July, August, and September is minimized. The 14-day error correction method did not improve the modeling skill score much compared to the 5-day error correction method, and they acted almost similarly. In areas with high elevation gradient, the model error increases. In general, model underestimates the maximum and minimum temperatures in most areas. Knowing the spatial and temporal distribution of model forecast error can be helpful for researchers to have an overview of the areas (and months) where the model forecast error is high.

In this paper, a new scheme for completely reliable transmission of the information (with an error probability tends to zero) in a wireless communication link will be proposed in which to compensate the effects of fading and multipath, adaptive modulation and coding is used. Obviously, by the practical forward error correction it is impossible to achieve error free communication. Removing the residual error is by an auto-forwarding system. Of course, if error correction coding capability is weak, number of retransmissions will be increased to the much needed and it severely undermines the system throughput. On the other hands, strong error correction capability needs high block length codes and high transmission power which are limited in practice. In this paper, a method for optimum combination of error correction and auto forwarding is provided. In this paper, link adaptation is based on imperfect channel state information. Numerical results demonstrate efficiency of designed method.

Although many studies have focused on the language learners’ beliefs and attitudes regarding error correction, less has been done to investigate whether and how student characteristics influence their preferences. The present investigation explores how socio-economic status affects the error correction views of 140 upperintermediate/ advanced students, ranging from 23 to 31, in an EFL context. The participant's social class was determined by MacArthur scale of subjective socioeconomic status. A questionnaire and a follow-up interview were employed to obtain the student's overall preferences about different aspects of oral corrective feedback (OCF). The results showed that the students unanimously favored teachers as the best provider of feedback and highly expected both local and global errors to be treated; nevertheless, whereas middle-class students would rather their errors to be corrected at the end of the class while the teacher addressed the whole class, high-class students did not mind if teachers corrected them individually as soon as they finished speaking. Besides, although predominantly the students preferred direct error correction, highclass students had a more positive view toward elicitation and self-error correction in general. The findings of this study highlight the influence of language learner's ocioeconomic status on how they expect their teachers to treat their oral errors.

Weather forecasting and monitoring systems based on numerical weather forecasting models have been increasingly used to manage issues related to meteorology and agriculture. Using more accurate daily average wind speed (10m) and relative humidity forecasts can be helpful in this regard. But systematic and random errors in the model affect the accuracy of forecasts. In this study, the model errors during the 5 and 14 days training period in the same climate areas on the points of the network where the observations are available were calculated. Then the errors were generalized on all points of the network using the cokriging interpolation method. This preserves the model forecasts for other points of the network and only error values are applied to them. To better evaluate the model, the spatial and temporal distribution of daily average wind speed (10m) and relative humidity forecast errors were also investigated over Iran. Observed daily wind speed and relative humidity data from 560 meteorological stations for the period 1/11/2019 to 1/2/2021 were used to evaluate the WRF model performance. The WRF model was run daily at 12UTC, with a forecast time of 120 hours, and first 12 hours of each run was consider as the model spin-up time and was not used in errors calculation. In order to correct wind speed and relative humidity forecast errors for next three days (forecasts of 36, 60 and 84 hours), the forecasts for each day in the period of 11/1/ 2019 to 1/2/2021, was extracted from the model outputs. In order to evaluate the error correction method, the skill score index was used. The validation results of the error correction method showed that the absolute mean error value, correlation coefficient and RMSE improved after the error correction compared to results that were before the error correction, which showed that the error correction method can be used for other network points that did not contain observational data. In general after correction, the RMSE for wind speed and relative humidity forecasts could decrease by 13% and 18%, and the skill score could increase to a maximum of 160% and 308%, respectively. Value of correlation coefficient, after correcting the model error, was significantly increased, compared to the raw model output. In general skill score for the raw wind speed and relative humidity forecast for more than 50% of the days was more than -0.5 and -0.3, but after corrections were  increased to 0.2, 0.4 respectively. Without exception, all climatic regions after error correction have higher skill scores than before error correction, so that the model skill score for most climatic regions after error correction was reached above zero for more than 75% of the days. The results showed that errors of the model in different months, places and climatic zones did not have a uniform distribution. In general, the model underestimated the wind speed and overestimated the relative humidity in most areas. In general, the lowest skill scores for relative humidity forecasts occurred in the colder months of November to February in most climatic zones. The 14-day error correction method did not improve the modeling skill score much compared to the 5-day error correction method, and they acted almost similarly. Knowing the spatial and temporal distribution of model forecast error can be helpful for researchers to have an overview of the areas (and months) where the model forecast error can be high or low.

Grammar instruction and error correction are among the most hotly debated issues in second as well as foreign language education. Second language researchers and language educators have expressed different and sometimes contradictory ideas about them. Some believe error correction and grammar instruction are not only beneficial, but they are also necessary. Some others believe that only appropriate incorporation of them in the syllabus can lead to improvement in learning. And still a third group conceives of them as a waste of time and detrimental to the learning process. To gain a better understanding of teachers'' and learners'' perceptions regarding error correction and the role of formal grammar instruction on learning, opinions of 51 teachers and 627 adolescent and adult learners were surveyed by means of two equivalent questionnaires. The participants received two different kinds of treatment in terms of materials, grammar instruction and error correction moves. In one group, learners received more explicit grammar instruction and systematic error correction, while in the other group the focus was on meaning and no systematic correction was provided. The analysis of the obtained data from the questionnaires revealed that differences in the methods of instruction did not lead to a difference in the participants'' attitudes about error orrection and/or grammar instruction on learning. Also learners'' and teachers'' views about these two were close in many respects; however, error correction status diminished in the learners’ views as they improved their proficiency levels. On the other hand, more proficient learners considered more credence for grammar instruction in their learning.

This study examines the long run relationship between the efficiency component (good market efficiency and labor market efficiency) in the global competitiveness index and the variables of economic success (economic growth and unemployment) by using new econometric methods in selected countries of Asia with the average upward Global Competitiveness Index. This study, in the framework of the Panel Vector Error Correction Model (PVECM), examines the long run relationship between variables over the period 2008-2016. Estimation of long run coefficients by using Dynamic Ordinary Least Squares (DOLS) and estimating error correction temr coefficients by using the Pool Mean Group Method (PMG) and Panel Vector Error Correction Model has been done. Estimations of the coefficients of the variables of the good market efficiency and labor market efficiency by using DOLS method show that the effects of good market efficiency and labor markets efficiency on the economic growth in the long run are positive and significant. The impacts of good market efficiency and labor market efficiency on unemployment in the long run are negative and significant. Also, the results of estimating logarithmic coefficients in the DOLS method show that the most effective variable on economic success variables (economic growth and unemployment) is related to good market efficiency. The estimation of the coefficients of error correction term by using the PMG and PVECM method show that when the economic growth rate is dependent variable, since the coefficient of error correction term for this variable is negative and significant, therefore, There is a long run relationship between the rate of economic growth, good market efficiency and labor market efficiency. When the unemployment rate is dependent variable, since the coefficient of error correction term is negative and significant for this variable, there is a long run relationship between the unemployment rate, good market efficiency and labor market efficienc

An extremely important issue in any approach of teaching and learning second/foreign language is that students receive feedback on their activities in second/foreign language learning milieu. There are many strategies that teachers adopt to provide studentswith evidence that what they have just produced in their writing activities is incorrect. However, there has not been much attempt to investigate the effect of considering the students'' attitudes in adopting these error correction strategies. The present study was an attempt to explore the effect of considering student''s attitudes towards error correction on the grammatical accuracy of their English writing. For this purpose twogroups, experimental and control, passing writing essay course as partial requirement of their study at university, were selected. The students in both groups took part in an original IELTS test as a pre-test. For the first half of the term (8 sessions) the students in both groups received similar writing instruction. They also received feedback on their writing activities. Then, a questionnaire was given to the teachers to find out howcorrelated their methods of error correction are. The results showed that the methods used by them were different. After that, an early version of a questionnaire developed by Icy Lee (2005), after some modifications, was given to both experimental and control groups to check the students'' attitudes towards writing error correction strategies. The data obtained from this questionnaire was analyzed to highlight thoseerror correction strategies prefered by most students.Then, the teachers in both groups were informed aboutthe result of the questionnaires. The teacher of theexperimental group adjusted and modified his errorcorrection methods according to those preferred by thestudents for the second half of the term. The teacher inthe control group, on the other hand, ignored thestudents preferences in writing error corrections.Finally, both groups took part in IELTS test as posttest.The result showed that the students in theexperimental group outperformed the students in thecontrol group on grammatical accuracy in Englishwriting.

The rank-k numerical range has a close connection to the construction of quantum error correction code for a noisy quantum channel. For noisy quantum channel, a quantum error correcting code of dimension k exists if and only if the associated joint rank-k numerical range is non-empty. In this paper the notion of joint rank-k numerical range is generalized and some statements of [2011, Generalized numerical ranges and quantum error correction, J. Operator Theory, 66: 2, 335-351.] are extended.