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.

Background and objective: It is necessary to identify factors influencing smoking in order to design effective interventions to reduce this behavior. This study aimed to determine the predictive factors of smoking among adults of Arak city based on Transtheoretical model (TTM).Methods: This analytical- cross sectional study was carried out on 328 smokers 30 years of age and older in the city of Arak in 2014. Samples were selected randomly from family file numbers of Arak. Data were collected using a valid and reliable questionnaire including demographic information and questions related to Transtheoretical structure Model (stages of change, processes of change, temptation, Pros and Cons of smoking) and were analyzed using oneway ANOVA, Pearson correlation and linear regression. Results: Most of the subjects (52.7%) were in the precontemplation stage of smoking cessation. Linear regression revealed that variables of change stages (R2=-0.46, p > 0.001), temptation (R2=-0.18, p>0.001), hazards of smoking (R2=-0.12, p>0.001), early age of smoking (R2=-0.14, p>0.006), dramatic relief process of change (R2=-0.12, p>0.015) and the number of friends who smoke (R2=-0.12, p>0.048) were the predictor of daily smoking rates. The model was able to predict 37% of smoking rate variance.Conclusion: Most subjects were in the precontemplation stage of quitting smoking and according to predictive factors of smoking it is necessary to plan an effective educational program for quitting smoking.

Background and objective: Terrorism has led to the spread of violence, threats to security, negative effects on the economic, cultural, social and political dimensions of different societies, growing insecurity in the world, and as a result, the sensitivity of criminal justice activists, including the police, to this issue. In recent years, the police, with a predictive approach in the framework of criminological crime risk management, mainly are seeking to predict terrorist crime and identify high-risk variables, including people or places that are most prone to crime. Methods: The research method in this research is descriptive-analytical, the primary data of which has been collected through library sources. After analyzing the initial data, the results of the theoretical study were obtained. Accordingly, an attempt has been made to explain the issue more precisely by presenting some models. Findings: The findings of the study showed that the police with the policies of controlling terrorist crimes today are more towards forecasting through statistical and information models and in its new form through artificial intelligence. Also, high-risk groups and places, such as immigrants and crowded places, are a priority for police approaches to terrorist crimes. Results: Crime prediction and identification of dangerous terrorist variables play an effective role in identifying the context, in which the terrorist crime was committed, by the police. Accordingly, the spread of terrorist acts and, subsequently, securityism has led populist policies to influence police approaches to identifying certain variables, such as the placement of immigrants or crowded places in the group of high-risk variables. Finally, the focus of the Iranian police should be on reducing predictive approaches to forecasting and identifying terrorist threats with information and statistical methods and models that can be effective in reducing terrorist crimes.

This paper proposes an MPC-based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid-connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro-grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set-points provided to MPC are forecast for future instances by a linear extrapolation to gain smooth active and reactive power exchange under various loading conditions (e. g. balanced / imbalanced, nonlinear and dynamic loading) and voltage imbalance imposed by the upstream grid. In this scheme active and reactive power control change to current control and the references of the currents are forecast. The stability of the proposed control scheme is analyzed and discussed. The effectiveness of the proposed scheme is demonstrated by extensive time-domain simulations using PSCAD / EMTDC for various conditions (various loads, voltage imbalance, parallel operation with other DGs, parameter uncertainties and measurement noises) in several case studies. Comparing the obtained results with those of the two other schemes (PI-based and convectional MPC) shows the superiority of the proposed scheme.

Unfalsified Adaptive Control (UAC) is a recently proposed robust adaptive control strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-Model UAC is followed as an intermediate between UAC and multiple model control. Different approaches in UAC and MMUAC are studied. Also, Multi-Model Unfalsified Generalized predictive control (MMUGPC) is proposed, which is a new control design strategy in the UAC framework. For an uncertain system, by utilizing several generalized predictive controllers and discrete switching between them with unfalsified control, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-Model Unfalsified Generalized predictive control.