ABSTRACT
Background: Considering the necessity of health risk management, the present study conducted to provide a comprehensive model for identifying, evaluating, and prioritizing occupational health risks in an oilfield. Methods: We conducted this descriptive-analytical cross-sectional study in 2022 at the North-Azadegan oil field in Iran. The occupational health risk was assessed using the "Harmful Agents Risk Priority Index" (HARPI) method. Results: Among the employees for the office section in all job groups, ergonomic risks due to people's posture while working has the highest risk score and is the most critical risk for implementing corrective actions. In the operational section, for the HSE group, benzene, the production group, Electromagnetic Fields (EMFs), and other groups, undesirable lighting has the highest risk score, and exposure to Toluene, Ethylbenzene, Xylenes (TEX) has the lowest risk score. In this oil field, controlling exposure to benzene, correcting ergonomic conditions, and controlling noise exposure, with scores of 81.3,74.85 and 71.36, have the highest priority, respectively. Sequentially, Toluene, Xylene, and ethylbenzene, with scores of 10.25,11.61, and 11.61, have the lowest control priority. Conclusion: The proposed model can prioritize the workplaces' harmful agents based on the HARPI score due to exposure to chemicals, physical factors, and analysis posture.
ABSTRACT
BACKGROUND: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator. METHODS: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers' levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. RESULTS: Eye closure duration and blink frequency have a direct ratio of drivers' levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. CONCLUSION: The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers' drowsiness by considering the dynamic changes of the eyes.
ABSTRACT
BACKGROUND: Low back pain caused by work, ranked the second after cardiovascular diseases, are among the most common reasons of patients' referral to the physicians in Iran. This study aimed to determine the changes in back compressive force when measuring maximum acceptable weight of lift in Iranian male students. METHODS: This experimental study was conducted in 2015 on 15 young male students were recruited from Tehran University of Medical Science. Each participant performed 18 different lifting tasks involving three lifting frequencies, with three lifting heights, and two box sizes. Each set of experiments was conducted during the 20 min work period using free-style lifting technique. The back compressive force evaluated with hand-calculation back compressive force method. Finally, Pearson correlation test, analysis of variance (ANOVA) and t-test were used for data analysis. RESULTS: The mean of back compressive force (BCF) for the small and large boxes at a frequency of 1lift/min at heights of F - K height, were 1001.02 (±86.74), 1210.57 (±93.77) Ib, respectively. There was a significant difference between mean BCF in terms of frequencies of lifts (P=0.02). The result revealed significant difference between frequencies of 1 lift/min and 6.67 lift/min (P=0.01). There was a significant difference between mean BCF in terms of the sizes of the two boxes (P=0.001). There was a significant relationship between the BCF and maximum acceptable weight of lift in all test conditions (P=0.001). CONCLUSION: BCF is affected by box size, lifting frequency and weight of load.
ABSTRACT
BACKGROUND: Driver fatigue is one of the major implications in transportation safety and accounted for up to 40% of road accidents. This study aimed to analyze the EEG alpha power changes in partially sleep-deprived drivers while performing a simulated driving task. METHODS: Twelve healthy male car drivers participated in an overnight study. Continuous EEG and EOG records were taken during driving on a virtual reality simulator on a monotonous road. Simultaneously, video recordings from the driver face and behavior were performed in lateral and front views and rated by two trained observers. Moreover, the subjective self-assessment of fatigue was implemented in every 10-min interval during the driving using Fatigue Visual Analog Scale (F-VAS). Power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. RESULTS: The findings showed a significant increase in the absolute alpha power (P = 0.006) as well as F-VAS scores during the final section of driving (P = 0.001). Meanwhile, video ratings were consistent with subjective self-assessment of fatigue. CONCLUSION: The increase in alpha power in the final section of driving indicates the decrease in the level of alertness and attention and the onset of fatigue, which was consistent with F-VAS and video ratings. The study suggested that variations in alpha power could be a good indicator for driver mental fatigue, but for using as a countermeasure device needed further investigations.
