دو ماهنامه سلامت کار ایران
سال شانزدهم شماره 1 (فروردین و اردیبهشت 1398)

The aim of this study was to assess exposure to extremely low frequency electromagnetic fields (ELF-EMFs) and its effects on secretion of thyroid hormones associated with symptoms of fatigue on electric arc welding workers. This study was carried out on two groups including 35 welders (as exposed) and 35 non-welders (as control). Extremely low frequency electromagnetic fields were measured according to guideline compiled by the National Institute for Occupational Safety and Health (NIOSH) using a calibrated ELF meter. The level of Triiodothyronine hormone (T3), thyroxine hormone (T4) and thyroid stimulating hormone (TSH) secretion were studied on both exposed and unexposed groups through the enzyme-linked immunosorbent assay (ELISA). Samn-Perelli fatigue scale questionnaire was completed by two groups. Analysis was performed using SPSS V 20 software and analytical statistics including t-test, chi-squared test, Analysis of variance (ANOVA) and the Pearson correlation coefficient.  The thyroid hormones had lower level in the exposed group than the non-welders. Mean difference of T4 and TSH hormones between the two groups was statistically significant (p <0.05). The prevalence of the hormone disruption compared with their normal range and the levels of fatigue in welders group were higher than those in non-welders. There was a significant weak positive correlation between exposure level to ELF magnetic field and thyroid hormones level (r < 0.12; p> 0.05) and also, fatigue score increased with decreasing   T3 and T4 levels (p <0.05). A statistically significant direct relationship between level exposure to ELF –EMFs and fatigue score was found (r = 0.54; p <0.05). Exposure to ELF-EMFs may disrupt the thyroid hormone secretion such as T4 and TSH levels and may increase the fatigue level. This study is only a starting point for such researches, many issues need to be addressed for drawing the definite conclusions.

Adipose tissue is biologically superior to energy storage and an active tissue and secretes proteins such as vaspin, called adipokine. The relationship between fatty tissue and other biological systems of the body is through the expression of some bioactive molecules called adipocytokines. Adipokines are involved in various metabolic processes, including appetite regulation, insulin sensitivity and insulin secretion, energy expenditure, cardiovascular function, and inflammation. Vaspin can be considered as an adipokine derived from internal adipose with insulin sensitivities. In humans, vaspin serum concentrations are associated positively with age, body mass index, and insulin sensitivity disorder. Vaspin is a serine protease inhibitor family discovered in 2005 and has a composition of 392 to 395 amino acids. This adipokine was found in the visceral fat tissue of rat, a model of obese animals with type 2 diabetes. Vaspin plays a very important role in the pathogenesis of nonalcoholic fatty liver disease. This substance not only regulates the amount of insulin sensitivity, but also acts as a mediator of inflammatory processes. It has also been reported that vaspin circulation is associated with gender, body fat percentage, glucose, insulin, and insulin resistance. It has been reported that vaspin by reacting oxygen inhibitors can inhibit the inflammatory state of smooth muscle cells. Increasing blood glucose results in oxidative stress, which in turn results in the activation of the nuclear factor and thus an increase in the level of pre-inflammatory cytokines in the bloodstream. Consumption of vaspin in obese rats with high fat diet resulted in improved glucose tolerance and insulin sensitivity and inversely altered expression of genes that are dependent on insulin resistance. Animal studies suggest that effecting vaspin on insulin sensitivity increases its effect on adipose tissue. Therefore, it is suggested that the incremental regulation of vaspin suggests a compensatory mechanism against insulin resistance. Sports activities and healthy living today are topics that we have heard or read much more in a variety of ways about their importance. In fact, most people in the world do sports and physical activity in different ways; however, with the mechanization of lifestyle communities, more people go to inertial and sedentary environments. The results of the researches confirm the theory that all people are convinced that exercise is essential for health and well-being and also regular physical activity is essential for themselves and their children. Few studies have been conducted on the effect of sports activity on vaspin and glycemic indices, which have contradictory results, some suggesting that sports activities increase, decrease or without changes in the levels of vaspin. Therefore, due to the limited and controversial studies and the importance of not engaging in sports activities, this study aimed to investigate the effect of eight weeks of aerobic training and two weeks of detraining on levels of vaspin, glucose, insulin and insulin resistance in inactive men.

In this quasi-experimental and applied study, a design of two-group with pre-test- post-test with repeated measurement with control group was used. To conduct the research, 22 non-active male employees of Islamic Azad University, Fars Science and Research Branch, were selected as statistical sample. In order to conduct this research, a first announcement was made at the level of Islamic Azad University, Fars Science and Research Branch, and all volunteers were invited to participate in this study. Subsequently, during the meeting for all volunteers, the method of conducting research, potential injury and health benefits of the contribution in this study was explained. Then, 22 subjects were selected as the statistical sample among the volunteers. Among the criteria for entering this study, we can point out the lack of drug addiction, the absence of specific diseases such as high blood pressure, diabetes and cardiovascular diseases and the ability to perform exercises until the end of the study period. Further, considering that aerobic power is effective on the variables of the research, aerobic power (maximum oxygen consumption) of all subjects was first measured using Cooper field test, and the subjects regarding the aerobic power were divided into two equal groups including experimental and control groups. It is worth noting that in the present study; the statistical sample was selected based on the reported studies. Subsequently, 48 hours later, all subjects attended the laboratory at 8 AM, and from all of them received 7 CC of fasting blood. The experimental group then ran a treadmill for a period of eight weeks, three sessions per week, and each session for 60 minutes with speed of 70% of the heart rate reserve on the treadmill. The control group was asked to do just their daily activities during this time. After eight weeks, blood sampling measured from experimental and control groups at 8 AM similar to the pretest at the same time. The experimental group then interrupted their trainings for two weeks. After two weeks, blood samples gathered again from both experimental and control groups. For analysis of the research findings, Kolmogorov- Smirnov, analysis of variance with repeated measures and independent sample t- tests were used (α = 0.05). SPSS / 20 and Excell / 2010 software were also used.

The results of analysis of variance with repeated measures test showed that there were no significant difference in changes of vaspin levels in the pre-test, first post-test and second post-test between experimental and control groups (P = 0.50); changes in fasting blood glucose levels in pretest, first post-test and second post-test between experimental and control groups (P = 0.85); changes in insulin levels in pre-test, first post-test and second post test between experimental and control groups (P = 0.95) and changes in insulin resistance levels in pre-test, first post-test and second post-test between experimental and control groups (P = 0.65). Also, there were no significant difference in the changes of vaspin levels in pre-test, first post-test and second post-test in experimental (P = 0.79) and control (P = 0.61) groups; changes in fasting blood glucose levels in the pre-test, first post test and second post test in experimental (P = 0.06) and control (p = 0.23) groups; changes in insulin levels in pretest, first post test and second post test in experimental (P = 0.55) and control (P = 0.43) groups and changes in insulin resistance levels in pretest, first post test and second post test in experimental (P = 0.98)  and control (P = 0.58) groups. Therefore, eight weeks of aerobic training, as well as two weeks of detraining, have no significant effect on serum levels of vaspin, glucose, insulin and insulin resistance in inactive men.

Regarding the contradiction in the results of the reported studies, it cannot be determined with certainty that the mechanism of the effect of sports activities on vaspin, however, the exercise and weight loss in collaboration and through mechanisms that are completely separate but related, improve the cardiovascular and metabolic risk factors; So that exercise has important role due to reduced fat storage or changes in the function of adipose tissue cells as an endocrine organ which secreting adipocytokines such as interleukin-6, C- reactive protein and vaspin. Thus, if the exercise do not reduce the number of fat cells or improve the function of these cells, the ability of exercise is limited to adjusting the levels of adipokines, insulin resistance, and inflammation, or that it is not generally seen. According to the results of this study, eight weeks, three sessions per week and each session for 45 minutes running on a treadmill with intensity of 60-70% heart rate reserve had not significant effect on changes of serum levels of vaspin and glycemic indices in staff male; it concluded that eight weeks of aerobic trainings and two weeks of detraining have no significant effect on the levels of vaspin and glycemic indices in staff male.

Mining is categorized with considerable exposure to airborne contaminations. Regarding the high prevalence of diseases and the high population of workers in iron ore mines, the aim of this study was to investigate the effect of miners’ exposure to respirable dust compounds on the serum Malondialdehyde (MDA) levels. In this study, 92 non-smoker healthy workers of Gol-e-Gohar Iron Ore mine was selected as exposed and 48 office workers were selected as the control group. Personal sampling of the workers was carried out to determine the amount of respirable dust and iron compounds according to standard methods such as NIOSH0600 and OSHA-Id121 respectively. Serum MDA was measured by an assay ZellBio GmbH kit. The correlation between exposures to respirable dust and iron compounds with serum MDA levels was determined by the statistical test. The exposure levels of respirable dust and iron compounds, as well as serum serum MDA level in the exposed group, were significantly higher than the control group(p <0.001). The result of this study showed that the positive correlation between exposure to respirable dust and iron compounds in exposed group but there were no significant correlation between exposure to respirable dust and iron compounds with serum MDA. Despite the excessive exposure of workers to respirable dust and iron compounds, there was no statistical correlation of either exposures to mentioned compounds with serum MDA. Due significantly statistically higher serum MDA levels in the exposed group compared to those in the control group, a more comprehensive study investigating larger population is recommended ...

lack of thermal comfort in outdoor environments, can cause disorders and diseases, negative impact on productivity and performance, and even can cause death. Therefore, it is important to evaluate it. Various indices and models have been used to assess the level of comfort of humans. In most evaluations has been tried to estimate the comfort level using meteorological data such as temperature, humidity, Wind speed, solar radiation or a combinations. These indices provide climate data in a way that  reflects individual’s response to climate conditions and, in a numerical classification, range from very appropriate to very inappropriate. These indices facilitate interpreting the complex effects of atmospheric elements on human comfort and allow comparison of different places from climatic comfort point of view. Today, bioclimatic studies, from human comfort point of view, are the basis of many management plans, especially health and treatment. Therefore, in this study, Becker and neurotic pressure bio climate index were used to evaluate discomfort in three different climates of Iran. In this study, thermal comfort was assessed in three different climates of Iran based on the Köppen climate classification including the semi-arid and cold climate (Arak), the humid subtropical climate (Sari) and the hot desert climates (Bandar Abbas) were done using of Becker and neurotic pressure bio climate index.
Becker index known as Cooling Power index (CP) originated in the inclusion of only two climate parameters in the bioclimatic analysis by air temperature and wind speed. Cooling Power index has the advantage of being computed very easily; they are easily understood not only by specialists, but also by the wide public and have a good temporal applicability and availability for the studied territory. The cooling power of the environment depends on the
difference between human body temperature and air temperature and also on the wind speed.
The Neurotic pressure index determines the physiological pressure to provide comfort between absorption and heat release. The purpose of this index is to describe the comfort level using temperature, humidity and wind speed. This index offers separate equations for temperatures below and greater than 20 ° C.Meteorological data between 2014-2000 was used from the Meteorological Organization, including temperature, relative humidity and wind speed, to calculate the indices. Then the mean of the mentioned parameters was calculated daily for 15 years. Excel software and SPSS 22, were used for data analysis, descriptive statistics and linear regression were used. The highest and lowest air temperature monthly mean values in Sari were recorded for August (27.55 ± 0.57°C)and January (7.9 ± 0.68°C), respectively.The highest and lowest monthly mean relative humidity values were recorded forDecember (81 ± 2.6 %) and August (73.7 ± 1.94%), respectively. In Arak and during the surveyed years, the highest average  temperature (28.1 ± 0.65) was observed in July, and the lowest average temperature (-1.14 ± 1.94) was observed in January. Also, the highest humidity rate (70.67 ± 3.87) was observed in January and the lowest humidity rate (22.96 ± 1.25) was observed in July. Also in Bandar Abbas, the highest average temperature (34.27 ± 0.26) was observed in July and the lowest average temperature (17.44 ± 0.62) was observed in January. The highest humidity (68.39 ± 2.41)was recorded in August and the lowest humidity rate (57 ± 4) was in December. The mean of Becker index in Arak,Sari and Bandar Abbas was respectively 15.76 ± 6.3, 13.73 ± 5.2 and 8.93 ± 5.5µcal/cm2/s. According to the results of this index, the three climates covered cool to hot, warm and sultry. The mean of neurotic pressure index in months with temperatures below 20 ° C in Arak was 672 ± 148, in the city of Sari, 605.115.6 and in Bandar Abbas, 567.66 ± 46.76 kcal/hr/m2 and in months with a temperature above 20 ° C, respectively, was 3.37 ± 2. 5.42 ± 4.9 and 11.64 ± 7 kcal/hr/m2. Interpretation of this index showed that the studied areas have cold conditions (Arak) to fully uncomfortable condition (Bandar Abbas). Also, there was a high correlation between the two indexes in three different climates (R2> 0.9). The results of this study showed that the comfort indices reveal the comfort and discomfort periods in three different climates of the country, and despite some differences, show relatively similar manifestations of the comfort climate of under study cities. Results have shown that the studied areas are characterized by biochemical variation from hot to very cold conditions throughout the year. Regarding the advantageous of using these indices, it should be noted that the data measured by meteorological organizations can be used to forecast and timely announcement of heat and cold stresses as well as thermal comfort in different regions. It should be noted that thermal comfort varies with respect to the race, age, type of activity, clothing, metabolism rate, accommodation, etc. Evaluating the trend of thermal comfort indices can identify high-risk areas, which in this context presence of comprehensive and innovative policy making and planning, according to the climate changes and the ability to forecast and control the risks resulting from these changes, seems necessary for community members.

Road traffic accident annually lead to the death of 1.2 million people and also the disability of some 50 million people in the world. Iran is one of the countries with the highest rates of road accidents in the world. According to the annual statistics by the Iranian Legal Medicine Organization, 15,932 people have lost their lives in road traffic accidents in 1395 sh. According to figures in this report, over 75% of road deaths occur among men. Driving and guiding a vehicle is required to assign different levels of attention. In this regard, also can be pointed to the role of the mental workload. Mental workload known as a requirement that a duty impose on the available mental and cognitive resources of people. Mental workload is too high or too low can lead to incomplete understanding, inadequate attention and information processing to be inappropriate. A variety of factors can be effect on individuals driving performance and their mental workload such as age, gender, driving character and fatigue. According to studies, mistakes that can lead to accidents, in many cases, are the result of high mental workloads. Measurement of mental workload while driving can reflect the cognitive needs imposed on the driver. Various behavioral, self-reporting and physiological methods have been successfully used so far in order to measure the mental workload. One of the best ways to measure the mental workload is the evaluation of the individuals' reaction time. Reaction time is the time interval between the emergence of a stimulus and the individual's response. Driver’s gender is one of the possible effective factors that can affect driving performance. This study aimed to assess the effect of gender on young drivers' mental workload and driving performances using a driving simulator. This cross-sectional study was conducted on 40 young drivers (20-29 years) including 20 women and 20 men in Tehran at 2017. The inclusion criteria for the study were aged 20 to 29 years, filling informed consent to participate in the study, sleeping enough for 8 hours at night before the test, having at least two years of driving experience, driving at least once In the week, lack of driving experience with the driving simulator, having adequate or improved vision for the eyes, or for each eye, did not use psychotropic drugs. Exclusion criteria were included the request of the participants to leave the study, having signs of discomfort caused by the simulator, expressing a physical discomfort after the start of the test for any reason. The driving simulator used in this study is a half-body Pride made by the specialists of the virtual reality group of Khajeh Nasir Tusi Industrial University. The simulator has a computer and graphics card and software tools for simulating intra-city and inter-city roads, freeways, night driving, and driving in snowy, rainy, and foggy conditions. The drivers' performances were evaluated using the PDT reaction time and vehicle lateral deviation variables under two conditions: driving with and without mental calculations. The PDT reaction time test is one of the most common and suitable methods for assessing the performance of drivers and the response time of mental workload in simulator studies. Its validity and reliability have been confirmed in various studies. The Rating Scale Mental Effort (RSME) and the Integrated Workload Scale (IWS) self-report scales were used to estimate the individuals' workload. The main scenario of the study included driving a distance of 20 km on a highway. The mean lateral deviation obtained for female and male drivers was 0.55 and 0.51 m respectively, in the driving phase without mental calculations. Moreover in this phase the mean reaction time was 482 ms for females and 450 ms for males. In the driving phase with mental calculation, the mean lateral deviation obtained for female and male drivers was 0.67 and 0.62 m and mean reaction time for females and males was 606 ms and 569 ms, respectively. Based on the RSME and the IWS scales, women perceived more mental workload than men. For most variables of the study, however, the difference between male and female values was not significant based on the independent t-test (P-value> 0.05). Also, performing mental calculations while driving caused a significant increase (P-value = 0.05) in the values of the vehicle's lateral deviation, the drivers' reaction time, and the RSME and the IWS scales. Although there were no statistically significant differences in the study variables between males and females; the mean value of the lateral deviation for women was slightly higher than men, and their driving performance was slightly weaker than men. More lateral deviation of the vehicle should be considered in the process of doing mental calculation, because the high lateral deflection, especially automotive deviation to the left can be dangerous and even fatal accidents.

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 workers' 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 organization's actions for predicting and preventing incidents and accidents before they occur, while the lagging indicators are shown the organization'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. 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. 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. 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.

Anthropometry is the branch of human science that considers the physical measurement of the human body, especially size and shape. One application of anthropometrical data in ergonomics is the design of working space and the development of industrialized products. So that the tools, equipment and workstations, which designed based on the physical dimensions of the workers, can increase their productivity.
According to statistics, 32% of workers possess awkward postures, and 15% of them use improper tools while working. These lead to increases the possibility of work-related injuries, particularly musculoskeletal disorders. Therefore, most countries have made great efforts to build own anthropometrics databases for various groups of citizens.
Due to the lack of comprehensive databases in Iran, often it has been referred to information from the Western countries, especially the United States. The western nations have a big difference in anthropometric dimensions with the Iranian population, and their manufacturers also have been designing and developing tools and machines based on own mental criteria. The mismatching of these designed tools and workstations with the dimensions of the Iranian user's body can cause complications such as fatigue or other physical damage. So, Iranian researchers have suggested that at least one comprehensive, up-to-date and general database needed from the population of the country. Consequently, the building of anthropometric databases is necessary.
The results of several studies have shown that there is a correlation between the dimensions of the body with each other. These correlations can be used to create regression equations for estimating anthropometric dimensions. Identifying, categorizing, and determining the type of relationship between anthropometric dimensions play a significant role in treatment, fitness talent, and clothing production, etc. But, the relationship between these dimensions is affected by the environmental, economic and social factors change. The important affecting physical factors include age, sex, race, body structure, occupation, diet, and physical activity. Among these factors, the race has a very critical role in the variation of body size. So, the differences among diverse races are more than the variations between different nations. Ethnic diversity is a crucial factor that can affect anthropometric data and its application areas. For example, this variety in body dimensions between people with different sex and races can produce many problems in product design. Therefore, due to the necessity of having a comprehensive database (anthropometric data from different races) in the country, extracting appropriate information from massive data and transforming them into knowledge on one hand, and  time-consuming  and high expense process  for collecting data on the other hand, especially in the Iranian population with many races, the use of modern methods is essential. Data mining is a new method used to extract useful and unknown information from raw data. Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. Decision Tree is one of the easiest and popular classification algorithms to understand and interpret. It can be utilized for both classification and regression kind of problem. It can also remove part of the consumed time and expense of collecting anthropometric data. Therefore, this study was intended to investigate and identify the most important factors of ethnic differences in anthropometric dimensions of Iranian workers using the decision tree. The present research is a methodological study using the classification systems in the field of data mining. The sample of the study was anthropometrics raw data (37 dimensions) of 3720 subjects (3,000 men and 720 women) from six races (Fars, Turks, Kurds, Lars, Baluch, and Arabs) of Iranian workers. 
The decision tree (DT) method was used to identify the most important factors of racial differences in anthropometric dimensions of Iranian workers. The WEKA software (version 3.6.12) was used to analyze data and implement data mining algorithms. In the case of the WEKA system, the data is extracted and translated into a standard format we call ARFF, (Attribute-Relation File Format).
In the current study, the preprocessing operation on raw data performed by using a classification. Data preprocessing is one of the most critical steps in a data mining process which deals with the preparation and transformation of the initial dataset. Data preprocessing methods are divided into, data cleaning, data integration, data transformation, data reduction categories. Data cleaning routines work to clean the data by filling in missing values, smoothing noisy data, identifying or removing outliers, and resolving inconsistencies. Dirty data can cause confusion for the mining procedure, thus removing these data is a more appropriate method.
For data preparation, first of all, missing data values identified by using statistical methods and then to replacing the lost data, the PLS Filter and Missing Class Values algorithms were used in the WEKA software. These algorithms first classify the data and compute the average value for each class and fill the missing data of each class with the mean value of that class. Distorted data also eliminated by using the Remove Useless algorithm. Later data preparation, Principal Components Analysis (PCA), one of the most used methods, implemented. It is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The primary motivation behind PCA is to reduce, or summarize, a large number of variables into a smaller number of derived variables that may be readily visualized in 2- or 3-dimensional space. The new set of variables created by PCA can be used in other analyses, but most commonly as a new set of axes on which to plot your multivariate data.
After data preparation, the data are standardized and then the KMO factor is calculated. The KMO factor measure of sampling adequacy and compares the observed correlations and partial correlations among the original variables. The KMO increases with an increase of the number of variables and the correlation coefficients between them but does not much depend on the sample size. Specifically, correlation matrices with KMO< 0.5 are entirely inappropriate whereas those with KMO below 0.6–0.7 must be treated with caution.
Then after the decision tree technique is applied. This technique is a tree-like structure that describes the set of rules that led to the decision, and the ease of interpretation is one of its most important features. This technique is used for categorization and is a graphical method for comparing alternative competition and assigning value to them by combining uncertainties, costs, and repayments by specific number values, which usually consists of several nodes called nodes Inputs and outputs are known. The rules created in the decision tree are also "if" and "then". In each node, you can also split more than two. The CHAID algorithm used to splits the target into two or more categories that are called the initial, or parent nodes. for the analysis of the main factors, the 37 variables (anthropometric dimensions) converted to standard values. KMO value for the total data was 0.947. Since the obtained KMO (0.95) value was greater than the considered criterion (0.7). The existence of the necessary correlation between the input variables confirms the main factors analysis. The result of the analysis indicated, of the 37 anthropometric input variables, only 21 variables had a coefficient of above 0.7 in the seven extracted factors. The remaining 16 variables had little importance due to their high correlation with the main variables and excluded from the analysis cycle. In the decision tree, the race variable selected as the target variable or dependent variable, and its relation with 21 anthropometric variables examined. The optimal number of clusters obtained was 7 clusters. After entering and determining the type of data and dividing them into two sets: one with 70 percent of the source data, for training the model, and one with 30 percent of the source data, for testing the model. This default was chosen because a 70-30 ratio is often used in data mining. The results of the study disclosed that the distinctive factors in categorizing and creating ethnic differences among men were the Shoulder height in sitting position, the eye height in sitting position, the elbow- fingers length, bi-acromial breadth, the width of foot and length of the head, and among the women, Face Breadth, bi-acromial breadth and the length of the elbow-tip of the fingers. Identification of distinctive factors in the classification of racial differences is one of the main findings of the present research in various ethnic groups. According to the results of this study, these factors in Iranian women and men were different and regardless of race, the following conclusions are made; the distinguishing factors in men are related to anatomical factors (temple) and in women related to facial aesthetics (beauty).
The results of this study also confirm the utility of using the decision tree method to investigate the interactions between predictor variables that can be identified the most important determinants of race by combining different nodes in the overall structure. The results from this study verified the using decision tree as an effective method of identifying important variables for classification and detection of racial differences in the anthropometric field.
Furthermore, this finding could be used to designing optimal ergonomic workstations for Iranian workers with different Races, moreover to applications in the manufactures and design process, can be used in other cases such as forensic medicine for diagnosis and making orthopedic products.

Undoubtedly, noise and vibration are major problems of industrial world and lot of people are very exposed to these phenomena in their workplace or residential areas. Researchers have shown that exposure to noise may increase the risks related to personal health, like nervous frailty, extreme irritability, muscle cramps, stress and anxiety, dizziness, headache and migraine, anger, loss of body balance, vomiting, pain, hypertension, high blood pressure, cardiovascular problems, deterioration of sleep quality, mental stress, etc. However, the purpose of this study was to assess and control and reduce the risks resulting from noise in the reduction unit 2 of a steel industry. This study was conducted in two steps. 1) Evaluation of noise pollution in the compressor room and its surrounding: For this purpose, total sound pressure level (SPL) and SPLpeak were measured in the places where workers worked or were traveled. The parameters were measured in the mentioned places according to a grid pattern in both the compressor turn on/off. Because the workers exposed to a noise fluctuating, in addition to SPL analysis on the octave-band frequencies to determine critical frequency when the compressor was switched on, the value of Leq was also measured. The critical frequency is very important in noise control issues. The measurements were performed by using sound level meter, model of “B&K 2231.” The apparatus was calibrated before and after measurements by using “B&K 4230 calibrator.” 2) Noise control measurements: After identifying noise leakage paths to outside of the compressor room, noise was controlled in door, wall-fans and roof window area through installing a silencer on the wall-fans and redesigning the door and the roof window using the previous step data. Inside the compressor room, the total sound pressure level and the critical frequency was estimated 106.2 dB (A) and of 250 Hz in the Lin network, respectively. The total sound pressure level in the area around the compressor and the critical frequency were also estimated 94.3 dB (A) and 2000 Hz respectively. Moreover, the value of Leq was more than the threshold value (<85 dB) at all measured stations. In order to control the noise in the area of ​​the fans, considering the noise assessment and the critical frequency, there was used an absorption silencer with glass wool as adsorbent material. To control noise at the door and roof window, there was designed and installed a steel door with dimensions of 3.9 * 2.13 meter and thickness of 1.5 mm which was filled by a noise-adsorbent 40 mm of glass wool and attached to a metal grid. Moreover, to control noise in the roof window was applied a structure with the same specifications, but with the different dimensions (4.2 * 4.2 m and 7 mm mesh diameter). However, the results related to before and after implementation of the control measurements showed an acceptable attenuation in the SPL (91.8%). The average SPL before and after implementation of the control measurements was 95 and 87.2 dBA, respectively which this value was an acceptable level in comparison with background noise, i.e., 86.4 dBA. On the other hand, The slight difference between the mean value of the background sound pressure level and the average of controlled sound pressure level (0.8 dB) is due to factors such as temperature, variations in sound at different positions, generated noise sources (off or on and so no) and the movement of fluids in the pipes and so on.  

The results of the field study indicated that door, window and roof window were identified as the main paths of noise leakage to outside of the compressor room. Therefore, the implementation of the control measurements could be reduced to an acceptable level, but due to high background noise level even after the implementation of the control measurements, the noise level was less higher than the maximum permissible level (85 dB). Therefore, there were presented a few recommendations such as controlling the other sources of noise (e.g. moving fluid through pipes), utilizing the hearing protection devices and how to choose them properly to reduce background noise and the protection of workers against noise.