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Introduction: Nowadays, the statistics prove that the underground construction projects in the country are increasing, as well as the number of accidents arising from the unsafe condition of these projects. The purpose of this study was to create a framework of safety and health risk management in the construction phase of the Tehran Metro Line 7 tunnel, using IoT technology. Material and Methods: In the first stage, the national safety and health standards, laws and requirements related to the mentioned hazards were collected. In the second stage, the criteria and permissible limits of exposure to occupational hazards were determined. Next, sensors with optical, auditory, gas detection, and visual capabilities connected to the network were examined, and computer programming and comparing sensor information with the specified standards were carried out. Finally, intelligent warning and control systems related to the determined hazards were proposed. Results: In this study, a combined model of risk management utilizing IoT for controlling and monitoring safety and health hazards such as sound, light, explosive and toxic gases was proposed. According to the model, sensors for detecting the mentioned hazards were determined and coded based on the permissible limit of each of the harmful factors. Conclusion: This study has shown that by employing specialized IT and safety knowledge and utilizing relevant software and hardware, the concept of the Internet of Things can be utilized in precise monitoring of the concentration levels of flammable and toxic gases, as well as monitoring of physical agents such as noise and light in various workplace, such as metro tunnel construction sites.

Noise pollution in hospitals has increased over the last few years to a level that can threaten the health and productivity of staff and patient safety. There are many control measures to reduce hospital noise. However, there is still no consensus on the best measures. This study aims to prioritize the control measures for reducing hospital noise. The work is divided into three phases. The first phase identifies and categorizes noise sources in hospitals through a review of the state-of-the art literature using Scopus®, ProQuest, PubMed, Google Scholar, Embase,™ and Web of Science™. The second phase identifies possible strategies for reduction of hospital noise and the best criteria for their adoption using findings from the literature review and interviews with corresponding experts. The third phase uses Fuzzy Analytic Hierarchy Process (FAHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) method to weigh the criteria and to prioritize the control measures. Based on the results, hospital noise sources were classified into four groups: outdoor noise sources (29.7%), noise produced by domestic facilities (20.8%), indoor noise from human activities (27.5%), and noise produced by diagnostic and treatment equipment (22%). The study further arrives at a set of 9 criteria and 22 alternatives ranked using FAHP and fuzzy TOPSIS. The criteria’s weights were determined using the FAHP method, with feasibility (0.175), effectiveness (0.143), and interference with staff activities (0.140) being the most important criteria. It was found that engineering controls such as substitution of noisy equipment (rank = 1), using acoustic enclosures (rank = 2), using double-glazed windows (rank = 2), and soundproofing walls, doors, and windows (rank = 3) have priority for reducing hospital noise.

Objectives: The prevalence of multiple health hazards in various industries is on the rise, with noise and heat being common harmful factors in industrial environments in Iran. This study aimed to examine the immediate effects of simultaneous exposure to thermal stress and noise on human brain waves and perceived workload.Methods: Seventy-two students (36 men and 36 women) participated voluntarily in this study. Participants were subjected to varying levels of noise exposure, including four noise levels and heat stress (three levels of Wet Bulb Globe Temperature) for 30 minutes each. EEG recordings were conducted for 10 minutes, with participants allowed a 30-minute rest period between each test condition.Results: The combined exposure to noise and heat at two high levels (SPL95+WBGT34 and SPL95+WBGT29) resulted in a significant increase in perceived mental workload compared to the baseline state (P

This study aims to predict how COVID-19 anxiety affects productivity determinants by examining the role of perceived workload and individual job performance as mediators using Bayesian mediation analysis. To achieve the study's objectives, all nurses working at Imam Reza Hospital were recruited to participate. To gather data, all eligible and voluntary nurses were asked to complete questionnaires that included the Corona Disease Anxiety Scale (CDAS), Individual Job Performance Questionnaire (IJPQ), NASA Task Load Index (NASA-TLX), and Workforce Productivity Questionnaire. Of the total number of nurses employed, 222 (126 women and 96 men) nurses fully completed the questionnaires. Out of these, 140 individuals were married and 82 were single. The mean ± standard deviation (SD) of the age and experience were 28.56 ± 5.78 and 8.49 ± 6.50, respectively. GeNIe software version 2.0 utilized to analyze the Bayesian network. The results showed that for the high COVID-19 anxiety and high workload states, with a probability of 100 %, the probability of high evaluation and high environment increased by 16.6 % and 16.3 %, respectively, but low evaluation and low environment decreased by 22.4 % and 22 %, respectively. In the high COVID-19 anxiety and low IJP states with a probability of 100 %, most change was related to the high ability (6 % increase), low evaluation (3.9 % increase), high incentive (3.8 % increase), low ability (4.6 % decrease) and low support (2 % decrease). Regarding COVID-19 anxiety (high 100 %), workload (high 100 %) and IJP (low 100 %), most of the increase was related to high environment and high evaluation by an increase of 17.3 and 17.2 %, respectively. Also, the value of low evaluation and low environment was reduced by 24 and 23.4 %, respectively. The study's findings confirm that nurses have been more productive during the COVID-19 pandemic, despite facing high workloads and the COVID-19 anxiety.

Noise is one of the most important environmental stressors, which, along with the prevalence of exposure in the work and living environment, has various effects. Noise exposure can create positive or negative consequences at different levels. Based on this, in the present study, brain wave changes in exposure to different levels of noise were investigated. In this experimental study, 72 student volunteers with an average age of 26.27 years old, participated in this study after meeting the exclusion criteria and receiving sufficient training regarding the implementation procedure. Before starting the test, demographic questionnaires, noise sensitivity, and audiometry were completed for the participants. For exposure, a noise level of 45 dB was chosen as the background sound, and three levels of 75, 85, and 95 dB were selected for exposure. The participants were exposed to the determined facial pressure level for half an hour and rested for half an hour after that. During exposure to the noise, the brain waves of the volunteers were used by the portable EEG device Ewave, 16 channels. And the results were analyzed by the mat lab v12 software. The results of the study show that exposure to two levels of 75 and 85 dB of absolute power alpha has increased in 14 brain channels, including CZ and O2 channels. The results showed that the noise level of 75 dB compared to the background state did not change significantly in any of the channels (P>0.05). At the level of 85 and 95 dB, the absolute power of the beta frequency band has increased. At the level of 95 dB, this increase is statistically noticeable and significant in almost all brain regions (P

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.

Background: Nowadays, a large proportion of work-related activities are performed manually, which has led to an increase in the prevalence of musculoskeletal disorders (MSDs). This study aims to assess the risk of manual material handling (MMH) and estimate the prevalence of MSDs among workers in rice mills in the northern part of the country. Methods: The study was conducted on 100 workers employed in 36 rice mills in Gilan Province using the census method. The Nordic questionnaire was used to determine the prevalence of musculoskeletal disorders, while the NIOSH equation, Snook table, KIM-LHC and MAC assessment methods were used to evaluate the risk of related activities. Data were then analysed using chi-squared test and Spss23 software. Results: The results of the KIM-LHC and MAC lifting methods showed a high prevalence of musculoskeletal disorders in the lower limb during lifting. Similarly, there was a statistically significant relationship (P=0.001 and P=0.002) between the Mac and Snook lifting methods. However, there was no significant relationship between the Niosh method and either the Kim-LHC or Mac-Lifting methods (P=0.10 and P=0.28). Conclusion: The results of the risk assessment using different methods showed a consistent and critical condition of the working environment in the rice mills studied. Therefore, it is imperative to take the necessary engineering and management control measures to improve the working conditions.

Introduction: Many industries have multiple factors harmful to health, leading to simultaneous exposure of these factors to each other. Noise is one of the most common physical parameters in the work environment. On the other hand, heat is also increasing due to various energy processes in industries. Therefore, this study was conducted with the aim of determining changes in physiological parameters and visual-auditory attention in acute exposure to heat and noise. Material and Methods: In this experimental study, 72 individuals (36 men and 36 women) aged between 23 and 33 years participated according to the inclusion criteria. In total, 12 different test modes were performed (3 temperature levels and 4 noise pressure levels). The test was conducted for each person in 4 steps during a specific day. The duration of exposure to each condition was half an hour, and a half-hour rest was given between each test condition. Saliva samples of each subject were collected before and immediately after exposure to noise and heat. Also, The Integrated Visual and Auditory (IVA) was recorded by the participants simultaneously. Results: The results of combined exposure to noise and heat on visual and auditory attention showed that only two modes of combined exposure, SPL95+WBGT34 and SPL95+WBGT29, caused a significant increase (P < 0.05) in mental workload and a significant decrease (P < 0.05) in visual and auditory attention. In addition, the results showed that independent exposure to noise at the levels of 85 and 95 dB and exposure to heat at higher temperature levels of 34 and 29 degrees Celsius cause a significant increase (P < 0.05) in salivary cortisol after exposure. The results of the effect of combined exposure to noise and heat on salivary cortisol showed that three experimental modes of combined exposure (SPL85+WBGT34, SPL95+WBGT29, and SPL95+WBGT34) caused a noticeable and significant (P

Introduction: Musculoskeletal disorders are one of the main problems of industries and administrative jobs, which are caused by various factors. The study aimed to determine the role of each individual, physical, psychosocial, and environmental factors in causing musculoskeletal disorders; Identifying and controlling risk factors, is an important step in reducing and preventing these disorders. Materials and methods: The present study is a cross-sectional study of 342 employees working in different government offices in Saveh city in 2021. In this study, the Nordic summary questionnaire and ROSA checklist were used to investigate the prevalence of musculoskeletal disorders. Also, to investigate individual and psychosocial factors of the work environment, a personal information questionnaire and a general Nordic questionnaire were used. In addition, the evaluation of conditions of work environment was done by Hagner model E1 lux meter devices, TIS10 model thermal stress measuring device, and TES1358 sound meter. Finally, all the resulting data were entered into SPSS software and tested after coding. Results: The results showed that body mass index and gender have a significant effect on suffering from musculoskeletal disorders (p-value

Abstract Background Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care. Methods This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses’ noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses’ individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used. Results For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283). Conclusion Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care.

Cognitive workload has been known as a key factor in traffic accidents, which can be highly increased by talking on the phone while driving. A wide range of studies around the world investigated the effects of mobile phone conversations on driving performance and traffic accidents. But less noticed is the durability of cognitive effects of mobile phone conversations. This study aimed to determine the effects of different types of mobile phone conversations on physiological response and driving performance during and after the conversation. Heart rate, heart rate variability (physiological response), Standard deviation of lane position (SDLP), and the relative distance between two cars (driving performance) of 34 samples (male and female) in the driving simulator were recorded. In this study, three types of conversations (neutral, cognitive, and arousal) were used. Neutral conversation did not pursue specific purpose questions. Cognitive conversations were simple mathematical problem-solving questions and arousal conversations aimed at arousing participant emotions. Each conversation was used as a secondary task in a condition. The study had three conditions; in each condition the participant drove for 15 min. Each condition consisted of 5 min of driving (Background), 5 min of driving and conversation (dual tasks) and 5 min of driving after conversation to trace the effects of the conversation. Vehicle speed was 110 km/h in each of the three conditions using car-following scenario. The results showed that neutral conversations had no significant effects on physiological response. Though, arousal conversations had significant effects on physiological responsiveness and driving performance during conversations, where it was even more significant after disconnection. Therefore, the content of the conversation determines the amount of cognitive load imposed on the driver. Considering the persistence of cognitive effects caused by conversation, the risk of traffic accidents is still high even after disconnection.

Background: This study aimed to investigate the association between demographic, social, and economic factors of employees and hearing loss. Methods: In this cross-sectional study, the required data were extracted from the periodic medical examination of 987 workers in an automotive industry. Kruskal-Wallis and Mann-Whitney tests were used to study the associations. The data were analyzed in the SPSS software. Results: In this study, most of the subjects were in the age group of 30-49 years and also had less than 10 years of working experience. In terms of education level, most of them had a diploma. The relationship between work experience and hearing loss indicated profound hearing loss among a workers with 10 to 20 years of work experience. According to age, the subjects over the age of 55 years had the highest prevalence of hearing loss (p = 0.04). Conclusion: Age and work experience, education, income, and type of job are among the factors that could predict the at risk population for hearing loss.

Introduction: Determining methods for assessing heat stress in different work environments is one of the major challenges for researchers in this field. The purpose of this study was to validate WBGT index and heat pressure assessment (HPA) by some physiological responses in Iranian South Oil Company. Material and Methods: This descriptive-analytical study was carried out on 154 employees of Kharg, Asalouyeh and Mahshahr oil terminals recruited from three different working conditions including indoor, outdoor and rest environments in the summer. The amount of heat stress in the workplace was evaluated by WBGT index HPA method. To meet this purpose, the environmental parameters i.e., temperature, wet temperature, radiation temperature, relative humidity, water vapor pressure and air flow rate were determined. In addition to the direct reading method by the WBGT meter, ISO 7243 was used to calculate the WBGT index. In order to validate the heat stress indicators, physiological parameters of oral temperature, tympanic temperature and work metabolism were measured. Results: The results of paired sample t-test showed a significant difference between WBGT index and HPA in indoor and outdoor environment (P

Introduction and aim: Electrocardiography is one of the most useful and common tools for examining heart function. In studies in the 1960s, heart rate variability (HRV) was introduced and used as a non-invasive tool to evaluate the functions of the autonomic nervous system and other functional disorders of the heart under various conditions. In addition to the clinical applications of heart rate variability, this physiological parameter is also widely used in ergonomics and occupational health. In the study of mental workload, exposure to industrial noise, shift work, stress, and other parameters related to the workplace benefit heart rate variability. Heart rate variability is regulated by the autonomic nervous system and by the sinoatrial (SA) node. The autonomic nervous system is divided into sympathetic and parasympathetic branches, the autonomic nervous system is divided into sympathetic and parasympathetic branches, thereby affecting the heart rate and heart rate variability. Sympathetic activity tends to increase heart rate and decrease heart rate variability, while parasympathetic tend to reduce heart rate and increase heart rate variability. The most prominent component of the variable heart rate period is a respiratory arrhythmia (RSA), which is considered to be 0.15 to 0.4 Hz. The high-frequency component is only affected by parasympathetic neural activity. Another component of heart rate variability is the low-frequency (LF) component in the frequency range, 0.04 to 0.15 Hz. This component is affected by sympathetic and parasympathetic neural activity. Many computational methods have been developed for HRV analysis, each of which is associated with strengths and weaknesses. Although there may still be difficulties in interpreting this index, it provides researchers and health experts with reliable non-invasive physiological information. HRV parameters can be classified in terms of time, frequency, and nonlinear methods. The commercially available ECG equipment does not usually include HRV features due to the lack of standard diagnostic protocols. As an alternative to this commercial software, many simple, online and free, device-independent, and portable software has been developed for HRV analysis and cardiovascular research. Heart Rate Variability Quantification: Heart rate variability can be measured in long-term (24 hours), short-term (5 minutes), and very short-term periods (less than 5 minutes) and can be analyzed as time- -domain, frequency-domain, and non-linear. 1. Time-domain measures The simplest method to analyze heart rate variability is time-domain. Analysis of heart rate variability in the time-domain is performed through both statistical and geometric analysis, both of which are based on heart rate or RR intervals between successive QRS series. Using these methods, the heart rate can be assessed at any point in time or between normal consecutive sets. Statistical parameters recommended by the European Society of Cardiology and the American Society of Pacing Electrophysiology include: 1) SDNN 2) NN50 3) SDSD 4) SDANN 5) RMSSD 6) SDSD 7) NN50 count (Table 1). Table 1: time-domain measures of HRV Variable Units Description [1]SDNN ms Standard deviation of all NN intervals SDRR[2] ms standard deviation of RR interval series SDANN ms Standard deviation of the averages of NN intervals in all 5 min segments of the entire recording SDNN index ms Mean of the standard deviations of all NN intervals for all 5 min segments of the entire recording pNN50 % NN50 count divided by the total number of all NN intervals HR Max –HR Min bmp The average difference between the highest and lowest HRs during each respiratory cycle rMSSD ms Root mean square of successive RR interval difference HRV triangular index Integral of the density of the RR interval histogram divided by its height TINN ms Baseline width of the RR interval histogram 2. Frequency-domain measures: Time-domain measurements information about the overall change in time series or maximum variable amplitude, but no information about periodic heart rate fluctuations. Data frequency domain analysis provides information on how power distribution is a function of frequency. According to the European Society of Cardiology, the power range of a healthy person is usually divided into four main frequency bands. The range of components used is usually: high-frequency (0.4 - 0.15 Hz), low-frequency (0.15-0.04 Hz), very low frequency (0.04 - 0.003 Hz) and infinitely low frequency (

Abstract: Background and aims: Performance assessment and also performance indicators have a great importance in all aspects of life. By these indicators appropriate responses to what is happening for make some improvement, could be determined. In the past, the safety and health performance of organizations was measured with using of some indicators from the USA Occupational Health and Safety Administration (OSHA) such as the rate of recorded injuries, the rate of lost days, the rate of workerschr('39') complaints, and so on. Determining of this information were done to compare the health and safety status of contractors and industries and ranking them. However, improvement or weakening the safety and health status is cleared by the OSHA indicators, but the ways were not determined by these indicators. The main objective of measuring safety and health performance is to provide the necessary information on the developments and current status of strategies, processes and activities that an organization is conducting to control health and safety risks. Safety performance indicators are divided into two main categories: the leading and the lagging indicators. The leading indicators are the organizationchr('39')s actions for predicting and preventing incidents and accidents before they occur, while the lagging indicators are shown the organizationchr('39')s performance after the occurrence of the accidents to reduce its consequences. Sustainable urban development is a high-profile goal, and its realization in metropolitan areas requires the use of efficient management practices and extensive planning. The extent and complexity of urban issues and the urban ever- increasing development have made urban management a difficult task. In addition to issues such as transportation, urban planning and management is one of the important factors that has a main influence on urban constructive factors. In organizations such as the municipality, and on a wider dimension in societies, various plans and policies such as environmental plans, safety plans, crisis management, educational policies, occupational health plans, as well as some international standards for sustainable urban development were used. The Integrated Health, Safety and Environment Management System (HSE-MS), by providing the appropriate structure and model, manages all these policies and plans, and, in addition to creating a specific order in the implementation of these programs helps to centralize costs and improve the economic status of the organization and the achievement of sustainable urban development. Performance measurement is the main issue at all levels of the organization and its improvement needs to be measured, so an organization cannot be conceived without a performance evaluation system. Measuring the performance of an organization in the HSE field is a precondition for continuous improvement. Different organizations and industries need to design performance measurement indicators to assessment and monitor the performance of the HSE management system and determine the effectiveness of themselves performance. Determining the performance assessment indicators of HSE in addition to measuring of the management system performance can lead to systematic identification of improvement points and reduction of HSE related risk factors and ultimately lead to increased organizational productivity. The research shows that the applied, comprehensive and structured model has not yet been designed and implemented to ensure the efficiency and effectiveness of the HSE management system in different metropolitan areas. Although there are some retrospective and prospective HSE performance assessment indicators in municipalities but these indicators are purely result-oriented and the creation and institutionalization of processes related to the seven components of the health, safety and environmental management system cannot be measured by them. What matters here is to create an appropriate method with sufficient accuracy to determine the effectiveness of the system. This research, while introducing an innovative model for quantitative ranking of different municipality areas of metropolitans according to the HSE performance assessment with using of AHP technique, the final goal is the determine of key indicators of urban HSE performance assessment. Methods: This descriptive-applied study was conducted in four steps. Step 1: Selecting HSE Performance Measurement Indicators and Urban Management: In the first step, a variety of safety and health management systems (HSE-MS, OHSAS 18001, ISO 14001, etc.), a variety of existing national and international health and safety assessment methods, guidelines, and requirements for safety and health management systems for determining the performance measurement indicators were studied. At this step, through the field visits of the different areas of the municipality and observing work processes, interviewing and organizing various meetings with HSE managers and experts, and studying documentation, the HSE performance assessment indicators in the three areas including process-oriented, result-oriented and urban management were categorized. Step 2: Selecting the components of the performance monitoring system: The findings suggest that the use of a systematic approach in different domains, in addition to integrating activities, has improved efficiency and effectiveness and increased operational efficiency. Accordingly, many organizations have set up various health, safety and environmental management systems as the basis of themselves operations. The use of the main components of an integrated management system to identify weaknesses or strengths of system is one of the effective measures for measuring performance. At this step, after determining the assessment axes, the performance assessment components were identified for each of the performance monitoring axes. Step 3: Development of Urban HSE Performance Assessment Indicators: At this step, the indicators for assessing the performance of the urban HSE management system were determined based on the components of each of the performance monitoring axes. According to the main components, the HSE management system was included 18 components such as commitment and leadership, policy, resources, etc. Also in the urban management part 6 components including economic sustainability, social sustainability, architecture and urbanization, demographics, political, legal, and industrial was considered. Indicators related to the result performance were also developed based on the components of the HSE performance output monitoring system in the six areas including safety, health, environment, education, culture and HSE of citizenship. Step 4: Select Key Performance Indicators: To create an effective HSE-MS performance measurement method, it is necessary to reduce the number of LPIs to fewer or the most important KPIs. This means that among the existing indicators, the best and most important indicators are selected based on the considered criteria. At this step, key performance indicators were selected from the sum of the extracted indicators based on the elements of the HSE management system using the AHP method. All indicators were compared in pairs and each of them was prioritized as a number between 1-9, 1 as lowest priority and 9 as most preferred. Couple comparisons were conducted based on SMART criteria and expert’s opinion. In this study, the paired comparison of axes and performance indicators and determining the weight vector of the indicators was performed using Expert Choice11 software. Results: In addition to examining the HSE management systems deployed in the municipalities, as well as using previous studies, the axes and indicators for assessing the performance of the urban HSE management system were determined. Of the 30 components and 155 indicators of performance assessment, 18 components and 98 indicators were related to monitoring of management system of HSE based on the seven elements and sub elements of the Health, Safety and Environment Management System (HSE-MS). Performance output and urban management axes with 40 and 17 indicators, respectively, have a smaller share of the total indicators developed. Between the process-oriented performance axes, the commitment and leadership axes (0.198) and the HSE records of occupations (0.016) have the highest and lowest weight, respectively. The safety components (0.247) and training (0.150) had the highest and lowest weight in the result-oriented performance axes respectively and economic sustainability (0.301) and architecture and urban planning (0.122) had the highest and lowest weight in among the urban management functional axes respectively. After weighting the indicators for measurement of performance, 70 indicators that had the highest weight than other indicators were selected as key performance indicators. Of the 70 indicators, 47, 16 and 7 indicators are related to the HSE process, the result and urban management respectively. Between the functional sub axes of the HSE process, accident management, inspection, and audit each with five key performance indicators, has the highest measurement index compared to other HSE process sub axes. In the functional axis of the HSE result, the sub axes of ecology with 4 key performance indicators and the sub-axes of safety and health, each with 3 indicators, had the highest number of indicators in this axis. In the urban management axis, sub axes including the socio-cultural and architecture and urbanism axes, each with two key performance indicators, had 4 indicators of total 70 selected key performance indicators. Conclusion: The results of this study indicate that the determination of HSE performance indicators in addition to the components of the HSE management system should be done based on the type of macro management structure and other influential parameters of the organization. The study of the municipals management structure shows that the extracted indicators based on the components of the HSE management system alone cannot cover the urban HSE performance because of factors such as economic sustainability, socio-cultural, demographic, architectural, urban, and industrial-industrial can significantly affect the performance status of the HSE management system of city. Therefore, it is essential to design and codification of indicators as a tool for measuring the performance of management systems, all aspects of the management system in order to achieve the more accuracy and reliability be considered.

Cognitive activities and the operation of tasks in the workplaces lead to the creation of a cognitive workload and specifically mental workload employee in the work environment. In order to measure these cognitive variables, physiological parameters are used. Electroencephalography is a non-invasive and relatively inexpensive method that can be used to evaluate neurophysiology and cognitive functions. Electroencephalography the electrical activity records a large number of brain neurons on the surface of the skull or the brain. The purpose of this article is to provide generalizations of imaging methods in neuroergonomic, brain wave bases, equipment and requirements for participants. It also describes the brainwave recording protocol step by step. Due to the expansion of interdisciplinary studies, the acquisition of brainwave recording skills is essential for researchers in the field of ergonomics and occupational health

Background and aims: The safety and health performance measurement, designed to provide the necessary information on the concern to progress and the current state of the organizationchr('39')s strategies, processes and activities. The purpose of this study is to present a new model for the development of safety performance indicators using the probability risk assessment model and applying expertschr('39') opinions. Methods: This descriptive-analytic study was carried out in 3 steps: categorize of the construction face activities and its related hazards identification; formation of the accident causal network occurred in the oil and gas refinery construction phase using the Bayesian network and the selection of key performance indicators using AHP method. Results: The statistical analysis of the recorded accidents showed that 21% of the incidence rate is related to the falling. Among all the construction phase activities, using the WBS analysis of the project, 27 activities with work at height risk of were identified. 18 active performance indicators were extracted the accident causal network that using SMART criteria and occurrence probability rate Calculated from the Bayesian network was selected as 5 active key performance indicators. Conclusion: Determining the leading performance indicators is influenced by various organizational, managerial, operational and other factors. As the project progresses, the nature and level of risk of the operation of construction projects is changing. Therefore, indicators of safety performance measurements in these projects should be sensitive to rapid changes. For this reason, active indicators with a short-term measurement period are more effective in measuring the safety performance of construction operations.

Background and aims: One of the most important and preventable hazard related to Healthcare Workers (HCWs) are Needle Stick Injuries (NSI). Biologic outcomes of these injuries can be so hazardous and can cause to transmission of disease such as AIDS, Hepatitis B and C. The aim of this study was determination of occupational exposure to needle stick and its risk factors in one of Sabzevar’s hospital. Methods: This is a cross sectional study on HCWs that by the help of related questionnaire, rate and characteristics of exposure of healthcare personnel was determined. Sampling method was in form of consensus and finally collected data with the help of STATA software and using descriptive and chi-square test were analyzed. Result: Occupational exposure to needle stick among participated HCWs with at least one injury in 12 months before study was close to 32.99%. Among the different variables that might have influenced on the needle stick injuries only training, education level and work experience had significant relationship. (p= 0.001, 0.009,0.009). About organizational factors, the result showed there is a good guideline for reporting injuries and about 79% of employees said they are familiar with how to report. In the case of cause of needle stick injuries, carelessness and high work load pressure were the most frequent respectively.(40.68, 38.98). Conclusion: regarding to high rate of needle stick injurie, its a very serious threat for health care workers to suffer from infectious diseases.detailed inspection in order to achieve to hidden causes and providing control solution for hazardous factors can help managers in improving the level of health and safety in health care system.

Introduction: driving is such a complex cognitive task that requires a high level of vigilance and response to an emergency. Anything that leads to cognitive performance degradation in the driverchr('39')s mental processing, increases crash risks. Traffic noise as one of the most destructive elements of cognitive function was examined in this study. Methods: In this study, the sample consisted of 80 patients (40 cases and 40 controls) who were students of Tehran University of medical sciences that were selected through systematic sampling and then the average sound pressure level recorded traffic noise was played for participants in the acoustics room. To Measure reaction time and error in time movement anticipation, the RT and ZBA softwares were used ,respectively. Results: The results revealed that reaction time increased significantly after exposure to traffic noise. The error time movement after exposure to traffic noise has not significant difference. Conlusions: According to the results it can be concluded that mental activities such as speed and power of information processing are affected by traffic noise and in a confrontation with traffic noise show different changes.

Background: Traffic noise is one of the main important sources in urban noise pollution, which causes various physiological and psychological effects that can cause disturbs in performance, sleep disturbances, hearing loss and impact on job performance. This study was conducted to verify the impact of road traffic noise on reaction time in terms of extraversion and sex. Methods: Traffic noise was measured and recorded in 10 arterial streets in Tehran, and then the rec­orded noise was emitted towards participants in an acoustic room. The participants were 80 (40 cases and 40 controls) students. Personality type was determined by Eysenck Personality Inventory (EPI) questioner. Reaction time before and after exposure to traffic noise was measured. Results: Reaction time before exposure to traffic noise did not differ (P=0.437) significantly between introverts and extraverts. However, it was increased significantly in both groups after exposure to traf­fic noise (P

Aims: The present study aimed to investigate the relationship between traffic noise and drivers′ reaction time (RT) and their error in estimates of movement time. Materials and Methods: In over all, 80 university students with at least 3 years postlicense experience of driving were divided into two groups including traffic noise exposure and control group. S5 test of Vienna test system package was used for assessment of RT. In addition, time-movement anticipation was employed for measuring the rate of error in estimation of movement time before exposing to the traffic noise. After a 1-month interval, participants in the experimental group were exposed to traffic noise about 2 h and the tests were performed again for both groups. Results: Accordingly, RT was statistically higher after traffic noise exposure for male and female. However, there was no significant difference between time of movement before and after exposure to traffic noise both for male and female (P > 0.05). In addition, no difference was shown between movement time, number of accurate estimates, and number of estimates with gross errors before and after of traffic noise exposure in experimental and control group. Conclusion: The results suggest that traffic noise may be associated with poorer mental processing, which can result in longer RT during driving. Therefore, traffic noise would expose drivers to consequent accidents and incidents.