Background and Objective: Incidental events due to human error in the maintenance activities of molecular sieve replacement in the dehydration unit of gas refineries indicate the inadequate effectiveness of the techniques available to identify human errors. This study aimed to identify and analyze human errors in this maintenance activity using the predictive human error analysis (PHEA) technique and provide appropriate control strategies to prevent or reduce the consequences of identified errors. Materials and Methods: In this study, all activities and tasks performed to achieve the main goals were identified using hierarchical task analysis (HTA) technique. Subsequently, possible types of human errors in each phase of the above processes were predicted and identified using the PHEA method and a series of guidance keywords. After determining the consequences of each error, appropriate control methods were also provided. Results: Out of 11 operations and 111 sub task occupations analyzed in the PHEA worksheets, a total of 337 errors were identified, of which 246, 13, 28, 33, and 17cases were regarded as action, checking, retrieval, information transfering and planning errors, respectively. The most significant identified errors were forgetting the task, doing the task incorrectly, incompletely, at the wrong time, making the right decision on the wrong option, and doing the task more or less than necessary. Conclusion: According to the obtained results, it can be concluded that functional errors were the most prevalent type of errors. Theses results emphasize the necessity of using control strategies in terms of adequate and effective training for personnel, the preparation and modification of instructions, the monitoring of implementation of work and hardware changes.

Error is an inevitable part of life and cannot be completely eliminated, but it can be minimized. A root cause analysis is a technique for understanding the systematic error causes that is involved beyond a person or people to implement an errors and including field and environmental causes of errors when occur in this situation too. An important factor of an error occurrence is a root cause (causes) in causal factors that its revision or removing caused to prevent the recursion of a situation such as an error when is occurring in a process. The process of root cause analysis is consist of six steps: the beginning of the process, Data collection and mapping information, Identifying the problems, Analyzing Information, Solution Providing, Implementing the solutions (action plans), Writing the report.

Background and aims: Control room is a palpitating heart of a system, and any error in operator’s duties has irreparable consequences. Nowadays, in many work places such as nuclear industry, military and chemical parts human error can cause catastrophic event that led many events around the world, so the purpose of this study is analyzing human error in a utility unit control room of a Petrochemical Company using HEART method. Methods: Data collection was done by direct observation methods, interview with "Bordman", senior shift, boss of Fuel vapor, related experts and analysis of previous incidents that occur. Then job analysis and human error identified respectively by HTA and TRACEr technique. Additionally, the error probability determined with HEART techniques which is a one of the tools of human reliability assessment. Results: The most important factors in incidence of human error at the control room operators were: fatigue, experience, consciousness, complexity of information, focus error and Error producing conditions were diagnosed: mental stress, High workload, focus, clear guidelines, lack of training and the task adaptation,. The most calculated likely error in the duties: reload bugs, boiler commissioning, control of production, maintenance, warning signs control.Conclusion: the results can be acknowledged that supervisor duties "Nobatkar" control room because of a high sensitivity range of error likely to have occurred and solutions must be proposed.This technique for this group should be priorited.

AbstractBehavioral finance is one of the main factors of market defects, which focuses on the systematic occurrence of decision-making errors by investors and managers, and studies how investors and managers make systematic and mental errors in their judgments, and expresses the lack of rational behavior of investors in the capital market, which leads to The creation of stock price differences is due to its intrinsic value. The purpose of the current research is to analyze the anatomy of noisy transactions and pricing errors caused by the entry of uninformed traders. The statistical population of this research is the companies accepted in the Tehran Stock Exchange and its statistical sample includes the data of 113 companies. The sampling method was systematic elimination. The method used to estimate the model is the multivariate regression method using the combined data method. The research results show that noise trading has a positive and significant effect on the level of stock pricing error. Also, the pricing error is different at different levels of noise trading; That is, the more the noise, the more the pricing error. This is despite the fact that in addition to the fluctuations of noise transactions, the B/M ratio also affects it. In other words, the pricing error is different at different levels of B/M ratio. As a result, according to the results of the research, the entry of uninformed traders in the stock market creates noise and causes the deviation of the stock price.

In this study, a comprehensive meta-analysis was conducted using a random-effects model to examine the relationship between money supply and inflation and to address inconsistencies in previous research. The overall effect of money supply on inflation was estimated at 0/578, providing a broad measure of its impact. However, an assessment using a funnel plot and Egger's test revealed publication bias. To correct for this bias, the "trim and fill" method was applied, reducing the adjusted overall effect to 0/475. Additionally, meta-regression and subgroup analyses were performed to investigate moderating variables. The results showed that the effect of money supply on inflation differs across fixed and floating exchange rate regimes, time periods before and after 2000, and between static and dynamic models. These findings contribute to a clearer understanding of the impact of monetary and economic policies on inflation and provide a foundation for future research.