Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples of a quarter of a cycle of the signal are recorded. Then, discrete wavelet transform (DWT) is applied to the recorded signals, and the details of the wavelet transform output are extracted. Because of the existence of the high-frequency transients in the internal fault current signals, the wavelet transform outputs of the internal fault signals have more fluctuations than that of the inrush current signals. By calculating the standard deviation of the wavelet transform output, the fluctuations can be quantified. Therefore, the standard deviation of the wavelet transform output can be used as a criterion to discriminate between the internal faults and the magnetizing inrush currents. The proposed algorithm has a very low computational burden, and it uses only a quarter of a cycle of the differential current signals. This guarantees the high speed of the proposed algorithm. The proposed algorithm is tested by different conditions of the internal faults and the inrush situations, and it successfully identifies the true situation with high accuracy in all conditions. The simulation results show the superior specifications of the proposed algorithm.

Power transformers are the most important components of a power system, so their protection is a critical issue. This paper proposes a novel and efficient algorithm based on the high-frequency components of the differential current signal to discriminate between the magnetizing inrush currents and the internal faults. After detecting the over-current in the differential current signals, samples of a quarter of a cycle of the signal are recorded. Then, discrete wavelet transform (DWT) is applied to the recorded signals, and the details of the wavelet transform output are extracted. Because of the existence of the high-frequency transients in the internal fault current signals, the wavelet transform outputs of the internal fault signals have more fluctuations than that of the inrush current signals. By calculating the standard deviation of the wavelet transform output, the fluctuations can be quantified. Therefore, the standard deviation of the wavelet transform output can be used as a criterion to discriminate between the internal faults and the magnetizing inrush currents. The proposed algorithm has a very low computational burden, and it uses only a quarter of a cycle of the differential current signals. This guarantees the high speed of the proposed algorithm. The proposed algorithm is tested by different conditions of the internal faults and the inrush situations, and it successfully identifies the true situation with high accuracy in all conditions. The simulation results show the superior specifications of the proposed algorithm.

The mean value is commonly used as a measure of central tendency. It is frequently reported along with either the standard deviation (SD) or the standard error of the mean (SEM). While the SD reflects the dispersion of the data in both the sample and population, SEM indicates the precision of the mean. SEM is not commonly used in reporting science; however, the 95% confidence interval, which is calculated based on SEM, is frequently reported in scientific literature.

Purpose: To report a case of Joubert’ s syndrome with alternating skew deviation and lateral skew deviation. Case report: A 18-year-old girl was referred with the chief complaint of abnormal eye movements since infancy. Some degree of oculomotor apraxia was revealed as the girl had difficulties in initiating horizontal saccades and subsequent head thrust. Periodic alternating vertical misalignment of eyes was also evident, known as alternating skew deviation. After 3 to 5 seconds, the vertical divergence of the eyes reversed direction. Gaze to either side also resulted in hypertropia of abducting eye, known as lateral skew deviation. MRI demonstrated hypoplasia of superior cerebellar vermis along molar tooth signs associated with Joubert syndrome. Conclusion: This report shows that alternating skew deviation and lateral skew deviation can be manifestations of Joubert’ s syndrome.

Independence of observations is a fundamental assumption in designing an X chart for individual observations. But unfortunately, the assumption of existence of independent data is not even approximately satisfied. In fact, there is one kind of autocorrelation in the data. This paper demonstrates, through simulating an AR (1) process, that the autocorrelation in the data has deep effect on X chart. Moreover, estimators of standard deviation, mean moving range and median moving range to estimate process standard deviation are introduced. Among the cited estimators, the best of them is considered to estimate process standard deviation using autocorrelated data. Finally, two new estimators are presented to estimate process standard deviation using autocorrelated data.

The use of variance as a risk measure is limited by its non-coherentnature. On the other hand, standard deviation has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of standard deviation in portfolio optimization problems with cardinalityconstraints and short selling, specifically in the mean-conditional value-at riskframework. It is shown that, subject to certain conditions, this approach leadsto lower standard deviation. Empirical results obtained from experiments onthe SP index data set from 2016-2021 using various numbers of stocks andconfidence levels indicate that the proposed model outperforms existing modelsin terms of Sharpe ratios.

The aim of this study was to evaluate the impact of non-interest income on asset returns, standard deviation of return on assets, return on assets adjusted, is. This study literature study and analytical reason and based on panel data analysis (panel data) is. In this study, 117 financial companies listed on the Tehran Stock Exchange during the period 1389 to 1393. Software to analyze the results of the study, 7 Eviews used. The results confirming the first hypothesis suggests that the non-interest income and a return on assets of the company, there was a significant relationship. Also according to the analysis made in connection with the second hypothesis study came to the conclusion that Non-interest income and standard deviation of returns between corporate assets, there was a significant relationship. Following the results of verification third hypothesis suggests that the non-interest income and adjusted return on assets of the company, there was a significant relationship.

Outlier detection is a technique to identify and remove significantly different data from the more correct and consistent data in a data set. Outlier data can have negative impact on classification and clustering performance; that should be identified and removed to improve the classification efficiency. Regardless of whether a classifying technique classifies an outlier correctly, the very notion of identifying a data as outlier is of great significance.   In this paper, a new approach is proposed for outlier data detection within a test data set along with unsupervised training set selection. The selected training set is used for two-step classification. After unsupervised clustering the training set, the closest cluster to a test sample is selected using the Euclidean distance measure. Then, the outlier in the test sample is identified with the concepts of standard deviation and mean value.  The results showed by evaluating the distance of each sample of the test set with the new selected data set. the accuracy of the classifiers is enhanced after detection and elimination of outlier data.

Meta-analysis is known as a statistical analysis that combines the results of multiple scientific studies. Meta-analysis can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error (1). . .