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The purpose of this paper is to cover human reliability analysis of the Tehran research reactor using an appropriate method for the representation of human failure probabilities. In the present work, the technique for human error rate prediction and standardized plant analysis risk-human reliability methods have been utilized to quantify different categories of human errors, applied extensively to nuclear power plants. Human reliability analysis is, indeed, an integral and significant part of probabilistic safety analysis studies, without it probabilistic safety analysis would not be a systematic and complete representation of actual plant risks. In addition, possible human errors in research reactors constitute a significant part of the associated risk of such installations and including them in a probabilistic safety analysis for such facilities is a complicated issue. Standardized plant analysis risk-human can be used to address these concerns; it is a well-documented and systematic human reliability analysis system with tables for human performance choices prepared in consultation with experts in the domain. In this method, performance shaping factors are selected via tables, human action dependencies are accounted for, and the method is well designed for the intended use. In this study, in consultations with reactor operators, human errors are identified and adequate performance shaping factors are assigned to produce proper human failure probabilities. Our importance analysis has revealed that human action contained in the possibility of an external object falling on the reactor core are the most significant human errors concerning the Tehran research reactor to be considered in reactor emergency operating procedures and operator training programs aimed at improving reactor safety

Early diagnosis is crucial in the treatment of heart diseases. Researchers have applied a variety of techniques for cardiovascular disease diagnosis, including the detection of heart sounds. It is an efficient and affordable diagnosis technique. Body organs, including the heart, generate several sounds. These sounds are different in different individuals. A number of methodologies have been recently proposed to detect and diagnose normal/abnormal sounds generated by the heart. The present study proposes a technique on the basis of the Mel-frequency cepstral coefficients, fractal dimension, and hidden Markov model. It uses the fractal dimension to identify sounds S1 and S2. Then, the Mel-frequency cepstral coefficients and the first- and second-order difference Mel-frequency cepstral coefficients are employed to extract the features of the signals. The adaptive Hemming window length is a major advantage of the methodology. The S1-S2 interval determines the adaptive length. Heart sounds are divided into normal and abnormal through the improved hidden Markov model and Baum-Welch and Viterbi algorithms. The proposed framework is evaluated using a number of datasets under various scenarios.

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