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Background: In both developed and developing countries, noise is regarded as the most common occupational hazard in various industries. The present study aimed to examine the effect of sound pressure level (SPL) on serum cortisol concentration in three different times during the night shift. Methods: This case–control study was conducted among 75 workers of an industrial and mining firm in 2017. The participants were assigned to one of the three groups (one control and two case groups), with an equal number of workers (25 participants) in each group. Following the ISO 9612 standard, dosimetry was adopted to evaluate equivalent SPL using a TES-1345 dosimeter. The influence of SPL on serum cortisol concentration was measured during the night shift. The serum cortisol concentration was measured using a radioimmunoassay (RIA) test in the laboratory. Repeated measure analysis of variance and linear mixed models were used with α = 0.05. Results: The results indicated a downward trend in the serum cortisol concentration of the three groups during the night shift. Both SPL and exposure time significantly affected cortisol concentration (p

Background: Noise is one of the most common harmful agents in the workplace. Exposure to excessive noise can lead to complications such as cardiovascular disorders, disturbance of body hormones’ rhythm and hearing loss. This study aimed at weighing and modelling factors influencing serum cortisol and melatonin concentrations of workers that are exposed to various sound pressure levels using neural network algorithm. Methodology: A case-control design was adopted in the current research. The required data were collected from 75 industrial and mining firm staff members. They were assigned to three groups with equal sample sizes (25 workers). In developing the conceptual model in regard to variables that may affect workers’ serum cortisol and melatonin concentration, SPL, age, weight, and height were included. The influence of SPL on serum cortisol concentration as assessed in the three shifts. Moreover, radioimmunoassay (RIA) was utilized to assess serum cortisol and melatonin concentrations. Neural network algorithm was subsequently exploited to weigh and model predictor factors. IBM SPSS Modeler 18.0 was the software program used for data analysis. Results: The average cortisol concentration values for administrative, condensing, and pelletizing units respectively were 10.24 ± 2.35, 12.15 ± 3.46, and 14.91 ± 4.16μgdl. On the other hand, the average melatonin concentration values for administrative, condensing, and pelletizing units respectively were 37 ± 12.52, 34 ± 13.15, and 27 ± 9.54μgdl. According to the results of the developed model for cortisol, SPL3 (32%) and age (5%) respectively had the highest and lowest impact. On the other hand, considering the model developed for melatonin, height (27%) and SPL1 (10%) were the most and least influential factors in that order. The accuracy rates of the model were also found to be 95% for cortisol and 97% for melatonin. Conclusion: Comparing cortisol concentrations during various shifts revealed a significant reduction (from the beginning to the end of the shift) in all the three groups. Further, the rise of SPL would result in higher secretion of cortisol. Moreover, in all the three groups, the average serum melatonin concentration went up from the beginning to the middle of the shift and then declined to the end of the shift. Considering the accuracy rates of the models developed to predict hormones, neural network algorithm is a suitable and powerful tool for weighing and modelling factors influencing serum cortisol and melatonin concentrations.

Background: Night shift work is associated with many problems such as sleep deprivation, sleepiness, decreased cognitive performance, increased human errors, and fatigue. This study set out to measure cognitive performance, melatonin rhythms, and sleep after different consecutive night shifts (7 vs. 4) among control room operators (CORs). Methods: The participants included 60 CORs with a mean age of 30.2 years (standard deviation, 2.0) from a petrochemical complex located in Southern Iran. Cognitive performance was assessed using the n-back task and continuous performance test. To evaluate melatonin, saliva was collected and tested by enzyme-linked immunosorbent assay. To assess sleep and sleepiness, the Pittsburgh Sleep Quality Index and Karolinska Sleepiness Scale were used, respectively. Results: Individuals who worked 7 consecutive night shifts had a significantly better cognitive performance and sleep quality than those who worked 4 consecutive night shifts. However, salivary melatonin profile and sleepiness trend were not affected by shift type. Conclusion: The main duty of CORs working night shifts at the studied industry included managing safety-critical processes through complex displays; a responsibility that demands good cognitive performance and alertness. It is suggested that an appropriate number of consecutive night shifts in a rotating shift system should be planned with the ultimate aim of improving CROs performance/alertness and enhancing safety. Keywords: melatonin, night shifts, performance, sleepiness

Heat stress negatively influences human health and performance, and leading to lower efficiency in daily activities. The present study sought to examine the relationship between UTCI, other heat indices (SET, PET, PMV, PPD, and WBGT), and environmental parameters. Daily data, encompassing a 12 month period in 2016 (from 6 a.m. to 9 p.m. for each day), were retrieved from the Meteorological Organization of Kerman. The data were fed into SPSS 20, followed by conducting Pearson product moment correlation and linear regression to find the association between UTCI and other heat indices/environmental parameters. Excel 2016 was also utilized to draw the relevant diagrams. Significant correlations were detected between UTCI and other heat indices (SET, PET, PMV, and WBGT). UTCI also was measurably correlated with environmental parameters like dry temperature (P

Background: Fire service workers often implement multiple duties in the emergency conditions, with such duties being mostly conducted in various ambient temperatures. Methods: The aim of the current study was to assess the firefighters' physiological responses, information processing, and working memory prior to and following simulated firefighting activities in three different hot environments. Seventeen healthy male firefighters performed simulated firefighting tasks in three separate conditions, namely (1) low heat (LH; 29â31°C, 55â60% relative humidity), (2) moderate heat (MH; 32â34°C, 55â60% relative humidity), and (3) severe heat (SH; 35â37°C, 55â60% relative humidity). It took about 45â50 minutes for each firefighter to finish all defined firefighting activities and the paced auditory serial addition test (PASAT). Results: At the end of all the three experimental conditions, heart rate (HR) and tympanic temperature (TT) increased, while PASAT scores as a measure of information processing performance decreased relative to baseline. HR and TT were significantly higher at the end of the experiment in the SH (159.41±4.25 beats/min; 38.22±0.10°C) compared with the MH (156.59±3.77 beats/min; 38.20±0.10°C) and LH (154.24±4.67 beats/min; 38.17±0.10°C) conditions (p0.05). Nonetheless, there was a measurable difference in PASAT scores between LH and SH (p

Heat is one of the harmful agents in many workplaces. The current study aimed at investigating the correlation between heat stress indices (Universal Thermal Climate Index (UTCI), wet-bulb globe temperature (WBGT), wet-bulb dry temperature (WBDT), Tropical Summer Index (TSI)) and Physiological parameters among mine workers. This cross-sectional study was conducted among workers of one of the mines located in southeast Iran in July–August 2017. Workplace environmental parameters and workers' physiological parameters were measured in thirty different ways from 10 a.m. to 2 p.m. The collected data were fed into SPSS 20 followed by conducting Pearson product moment correlation to detect the relationship between heat stress indices and workers' physiological parameters. Attempts were also made to examine the associations among heat stress indices themselves. The results of data analysis indicated that the mean scores of UTCI, WBGT, WBDT, and TSI were 34.59 ± 1.51 °C, 24.59 ± 0.62 °C, 28.37 ± 0.82 °C, and 31.51 ± 0.7 °C, respectively. On the other hand, the results of Pearson correlation revealed that UTCI and WBDT had the strongest association (r = 0.93, p 0.05). The only statistically significant correlation coefficients were recorded between skin temperature and the two indices of WBGT and TSI. According to the value obtained for UTCI, the workers were exposed to severe heat stress. However, based on WBGT value and ISO 7243 standards for an adapted person, workers were not exposed to heat stress. Keywords: Heat stress, Indices, Physiological parameters, Mine