Journal of Health and Safety at Work

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Introduction: Occupational accidents are of the main issues in industries. It is necessary to identify the main root causes of accidents for their control. Several models have been proposed for determining the accidents root causes. FTA is one of the most widely used models which could graphically establish the root causes of accidents. The non-linear function is one of the main challenges in FTA compliance and in order to obtain the exact number, the meta-heuristic algorithms can be used.

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Material and Method: The present research was done in power plant industries in construction phase. In this study, a pattern for the analysis of human error in work-related accidents was provided by combination of neural network algorithms and FTA analytical model. Finally, using this pattern, the potential rate of all causes was determined.

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Result: The results showed that training, age, and non-compliance with safety principals in the workplace were the most important factors influencing human error in the occupational accident.

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Conclusion: According to the obtained results, it can be concluded that human errors can be greatly reduced by training, right choice of workers with regard to the type of occupations, and provision of appropriate safety conditions in the work place.

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Introduction: As a metropolitan area in Iran, Tehran is exposed to damage from air pollution due to its large population and pollutants from various sources. Accordingly, research on damage induced by air pollution in this city seems necessary. The main purpose of this study was to forecast ozone in the city of Tehran. Considering the hazards of ozone (O3) gas on human health and the environment and its ascending trend over the past decades, it is also essential to study and predict its quantities in the air. Forecasting ozone in the air can be further used to prevent and control pollution by authorities.
Material and Methods: Using an analytical-applied research method, this study was to predict ozone gas in this metropolitan area via daily ozone data of air quality measurement stations, traffic variables, green space, as well as time factors such as one-day time delay. In this regard, an artificial neural network (ANN) model was employed to forecast ozone concentration using the MATLAB software.
Results: The results of the ANN model were compared with a linear regression one. Correlation coefficient and root-mean-square error (RMSE) of the ANN model were subsequently compared with R2=0.734 and RMSE=0.56 as well as R2=0.608 and RMSE=11.69 regression equations.
Conclusion: It was concluded that the error in the ANN model was smaller than that in the regression one. According to the results of the sensitivity analysis of the season parameters, the length of sunshine hours had the most significant effect on the amount of ozone gas in Tehran air.

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Introduction: These days, there is a consensus that emotional intelligence plays an important role in the success of individuals in different areas of life. Persons with higher emotional intelligence had lower stress in dealing with demands and pressures in the workplace. The purpose of this study was to use artificial neural network to predict job stress and to compare the performance of this model with the multivariate regression model.
Material and Methods: In order to do that, 892 participants were selected randomly in different job categories. Then, 15 dimensions of Bar-On questionnaire, 10 job categories, age and education were considered as input variables and 7 dimensions of health and safety executive HSE were determined as output variables in models.
Results: The results revealed that an artificial neural network with hyperbolic tangent and sigmoid transfer functions respectively in hidden and output layers with 375 hidden neurons had significantly better performance than multivariate regression. So that, correlation of predicted values and job stress were only between 0.192-0.364 in regression model, but neural network had at least correlation 0.527 in all dimensions of job stress.
Conclusion: In predicting job stress using emotional intelligence, artificial neural network method was much better than multivariate regression model.

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Introduction: Working with a computer and workplace conditions expose people to risk factors of musculoskeletal disorders (MSDs). This study aimed to assess posture, examine MSDs, and determine, weigh and prioritize the risk factors among computer users by a neural network algorithm. 
Material and Methods: This descriptive-analytical cross-sectional study was conducted in six phases on computer users in 2019. The status of MSDs was determined via Nordic musculoskeletal questionnaire (NMQ). The factors affecting these disorders were determined by the ROSA method, and then these factors were weighed by the neural network algorithm. The data were analyzed in IBM SPSS Modeler.
Results: The mean age and work experience of the users were 34 ± 6.9 and 1.5 ± 0.7 years, respectively. Most of years were observed at the lower back, neck, and upper back, respectively. The final mean scores of the chair, telephone-monitor, and mouse-keyboard were 3.7 ± 1, 3.6 ± 1.1, and 3.65 ± 1.2, respectively and the final mean score of ROSA was 4.4 ± 0.9. The greatest correlation with the ROSA score was observed in chair (R2 = 0.46), followed by telephone-monitor (R2 = 0.43), and mouse-keyboard (R2 = 0.42). The highest predictor importance of the effective factors based on the neural network algorithm prioritization belonged to the chair (48%), followed by telephone-monitor (28%) and mouse-keyboard (24%). The accuracy of the neural network algorithm in examining the effect of factors on musculoskeletal disorders was 98% based on the ROSA score.
Conclusion: Factors affecting years due to working with computers are the chair, telephone-monitor, and mouse-keyboard, respectively, as prioritized by the neural network algorithm. These disorders can be prevented by ergonomic modification of users’ chairs and correct placement of the monitor and telephone.

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Introduction: Due to the increase in the provision of electronic services to citizens in government offices, the number of computer users and the occurrence of musculoskeletal disorders have increased. Therefore, this study aimed to predict and model the complex relationships between the risk factors of musculoskeletal disorders in computer users working in government offices by an artificial neural network.
Material and Methods: The current cross-sectional study was conducted in 2020 on 342 employees of various government offices in Saveh city. First, the researcher visited the work environment to identify the problems and measure the environmental factors. Then, ergonomic risk assessment and psychosocial factors were evaluated using the Nordic questionnaire and the ROSA method. The effect of various factors in causing musculoskeletal disorders was investigated using a logistic regression test.Then the resulting data were collected and modeled by one of the neural network algorithms. Finally, artificial neural networks presented an optimal model to predict the risk of musculoskeletal disorders.
Results: The results showed that by increasing the level of social interactions, the level of demand, control, and leadership in the job, musculoskeletal disorders in men and women decrease. There was a significant relationship between the prevalence of musculoskeletal disorders and job demand, job control levels, social interaction levels, leadership levels, organizational climate levels, job satisfaction levels, and stress levels, in addition between reports of pain in the neck and shoulder and wrist/hand region. There was a significant relationship with the overall ROSA score. Also, there was a significant relationship between the report of pain or discomfort in the neck area with the phone screen risk score, wrist/hand with the keyboard-mouse risk score, and shoulder, upper back, elbow, and lower back with the chair risk score. The accuracy of the presented model for predicting musculoskeletal disorders was also about 88.5%, which indicates the acceptability of the results.
Conclusion: The results showed that several factors play a role in causing musculoskeletal disorders, which include individual, environmental, psychosocial, and workstation factors. Therefore, in the design of an ergonomic workstation, the effects of the mentioned factors should be investigated. Also, predicting the effectiveness of each of the mentioned factors using an artificial neural network showed that this type of modeling can be used to prevent musculoskeletal disorders or other multifactorial disorders.