Golmohamadi, Mohammadfam, Shafie Motlagh, Faradmal,
Volume 3, Issue 3 (12-2013)
Abstract
Introduction: Every year many people around the world lose their lives or suffer from injuries and serious damages in industrial fire. This study aims at evaluating fire risk using an suitable method and determining endangered humane, financial and environmental capitals in various parts of a chemical industries.
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Material and Method: In this analytical study the developed Frank and Morgan method was used to evaluate the risk of fire in all units of a chemical company. Improved checklists validity was confirmed by experts and then, its reliability was determined by test-retest analyzing method. Human, financial and environmental probable losses were calculated in the case of fire. A risk factor was determined for each unit and all of them were prioritized accordingly.
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Result: The study of developed checklists’ validity showed that there was a high conformity (homogeneity) between results of two measured loads (ICC=0.87 %95CI: 0.699-0.952). Mean value of risk in units was 115.45 and research and development (R&D) and sparse part store units have the highest and lowest risk values, respectively. Endangered humane, financial and environmental capitals had the highest to lowest score, respectively.
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Conclusion: Results showed that the developed Frank and Morgan method can be a suitable tool for evaluating industrial fire risk and prioritizing units in general level of an industrial complex especially chemicals company. According to the findings in this study, the investigation of likely damages to environment in the case of fire has high importance.
I. Nasiri , M. Motamedzade, R. Golmohammadi, J. Faradmal,
Volume 5, Issue 2 (7-2015)
Abstract
Introduction: The bank employees usually require the use of computers for long duration in a static position to get the work done. The present study aimed to evaluate the risk factors for musculoskeletal disorder using the ROSA method among the employees of Sepah Bank. An ergonomic intervention was also performed in order to improve the working conditions.
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Material and Methods: This interventional descriptive-analytical study was carried out among 165 office employees of central building of Sepah Bank. Using random sampling, the subjects were initially divided into two groups of case and control. Before and after the intervention, ROSA method and Nordic questionnaire was respectively used to evaluate the risk factors that cause musculoskeletal disorders and the prevalence of musculoskeletal disorders. The data were collected two weeks prior the interventions and 9 months after the interventions. SPSS software version 16 was utilized for data analysis and the effectiveness of intervention was determined.
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Results: Before the intervention, the mean ROSA scores of all groups' workstations were above 5 with high risk. The results obtained 9 months after the interventions manifested a statistically significant decrease (P<0.001) in the ROSA mean scores and its components in the groups who received the interventions. 9 months after the intervention, the prevalence of musculoskeletal disorders among the subjects who had received intervention showed a significant reduction, as well (0.001> p).
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Conclusion: Using the ROSA technique was seemed to be beneficialto assess the ergonomic risk factors of office works, and the deficiencies in the workstation can be identified through this method. Moreover,by design and implementation of an educational intervention program along with engineering interventions which comply with the elements of this technique, the defects can be eliminated.
Neda Mahdavi, Hasan Khotanlou, Mahdi Darvishi, Javad Faradmal, Iman Dianat, ,
Volume 13, Issue 2 (6-2023)
Abstract
Introduction: Physical fatigue is one of the major risk factors for work-related musculoskeletal disorders and has many life and financial costs. The impact of physical/biomechanical, psychosocial, environmental, and individual risk factors on muscle fatigue is undeniable. The aim of this study is to model the phenomenon of muscle fatigue (as output) in the hand in work environments based on these risk factors (as input) using soft computing methods.
Material and Methods: In the first step, associated risk factors of fatigue for 156 subjects (in three job categories) were assessed using Copenhagen environmental, psychosocial, demographic, and Man-TRA tools. Then, the Roman-Liu equation and mean square amplitude of acceleration waves were used to measure fatigue with a dynamometer and a three-axis accelerometer, respectively. Finally, according to the nature of risk factors and the phenomenon of fatigue, six categories (24 methods) of supervised machine learning (SML) based on classification were selected. MatLab software (MatLab R2017b, The Mathworks Inc., MA, U.S.A.) was used to fit the models using SML.
Results: The best-fitted models in the first and second half of the work shift were obtained using support vector machine methods. Physical risk factors had a significant impact on physical fatigue. After filtering low-priority risk factors, in the first half of the work shift, the most optimal model had an accuracy of 71.8%, precision of 72.5%, sensitivity of 76.9%, specificity of 70.8%, and discrimination power equal to 73%. In the second half of the work shift, the accuracy, precision, sensitivity, and specificity of the optimized model were 60.3%, 57.5%, 50%, and 46.9%, respectively, and the discrimination power was obtained at about 62%.
Conclusion: The fitted models for hand fatigue had acceptable performance in both sections of the shift but can still be optimized. Therefore, it is necessary for future studies to improve the quality of input and output data and include other dimensions affecting fatigue such as cognitive workload and type of work shift in future models.
