Journal of Health and Safety at Work

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Introduction: Construction industry plays a major role in the economic development of all countries and among the various occupations, this industry is one of the most dangerous industries, particularly respiratory contaminants, around the world. The aim of this study was to evaluate the respiratory capacity of construction workers, working in different workshops in Tehran city and developing a regression model to examine the relationship between pulmonary capacities with the type of occupation, work experience and tobacco smoking.
Material and Method: This study was a cross-sectional descriptive study conducted among 628 construction workers in Tehran city in 2017. After data collection, data analyses were performed using statistical independent t-test, one way ANOVA and correlation tests by SPSS software version 22. Also, multiple backward regression was used to check the effect of independent variables on lung function.  
Result: According to the results of this study, a significant relationship was found between age and work history with the pulmonary function indexes (FVC, FEV1, FEV1/FVC and FEF25-75%) (P-value<0.001). The average of FEV1/FVC% was significantly different among various occupational groups (p-value<0.001). In the analysis of the findings of the pulmonary function test in the exposed group a separate model was made using multiple linear regression for each of the pulmonary functions, and the independent variables including age, work experience, job type and cigarette addiction were entered into the model.
Conclusion: The present study showed a significant change in the pulmonary function parameters of the construction workers and the chance of pulmonary disorders might be high among these individuals.

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Introduction: Natural materials are more efficient and attractive than synthetic materials. In this study, the sound absorption behavior by natural kenaf composite and Micro-Perforated Panel (MPP) at low and medium frequency region was investigated.
Material and Methods: Initially, the results of kenaf fibers with a thickness of 10 mm were validated by the Finite Element Method (FEM) based on COMSOL Multiphysics 5.3a. The studied combined panel is consisting kenaf fibers with micro-perforated plates and an air layer. This study examined the varying arrangement of the behind layers of the MPP, the different thickness of the layers, and the structural parameters of MPP. The structure with the best absorption coefficient was chosen for the following stage and was considered constant at each stage.
Results: The arrangement of composite layers indicated a strong direct effect on the sound absorption performance; as we discovered that kenaf fibers behind MPP led to better performance in frequencies below 2500 Hz. In addition to the chamber depth behind the MPP, the material and macroscopic properties of the layers, at the same depth, are also important determinants of the exact point of the resonant frequency. Furthermore, configurations in which air layer depth is more than the absorption layer, with the same diameter (hole) and depth (chamber), maximum resonant absorption peak is achieved.
Conclusion: Low-frequency sounds can be successfully dissipated by combining MP plates with kenaf fibers as reinforcing absorber in combined panel. In general, choosing the optimum structural parameters (Composite panel according to structure A with 0.5 mm hole diameter and 2% perforation percentage) allows a significant absorption at a specific frequency range. In this context, the use of numerical estimation to assess the sound absorption behavior can be meticulously substituted the difficult methods and laboratory costs.

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Introduction: Numerous studies have been conducted on the development of modern insulators, including nano-insulators. However, a comprehensive study has yet to be performed to review and investigate the thermal properties of these insulators. Consequently, this study aimed to examine the effect of nanomaterials on thermal insulation function.
Material and Methods: In this review, articles were searched for in English databases (PubMed, Web of Science, and ScienceDirect), Persian databases (Magiran, SID), and Google Scholar. The keywords used in the search were Nano Material, Nano Insulation, Thermal Insulation, Thermal Insulator Stability, and Thermal Conductivity in both English and Persian.
Results: Of the 4068 studies identified through search databases, 15 were selected according to the entry criteria. Among the studies, the three types of silicone, composite, and aerogel insulation had the highest frequency (each 26.67%), and SiO2 nanoparticles were the most prevalent nanomaterial (26.67%). According to the studies, the type of nanomaterial used in insulation will improve its properties such as thermal resistance, mechanical strength, dielectric strength, tensile strength, elasticity, and hardness.
Conclusion: The results of this study showed that using nanotechnology could be an effective step in improving the properties of insulation materials, the most important of which is increased thermal resistance. Moreover, nanotechnology insulators can prevent thermal energy loss, reduce costs, and provide safety and comfort.

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Introduction: The prevalence of COVID-19 has significantly impacted work environments and the workforce. Therefore, identifying the most important preventive and control strategies, as well as assessing their effectiveness, is of paramount importance. Various studies have shown that machine learning algorithms can be used to predict complex and nonlinear issues, including predicting the behavior of various diseases such as COVID-19 and the parameters affecting it, and can be beneficial. The purpose of this study has been to examine the importance of preventive measures and hygiene behaviors in preventing COVID-19 in the oil refining industry using various machine learning models.
Material and Methods: For this purpose, demographic information and health behaviors of individuals were collected. Subsequently, a multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models were compared to enhance the analysis of the effects of preventive measures on COVID-19 infection. Finally, the most influential factors affecting the likelihood of COVID-19 infection were determined using sensitivity analysis.
Results: The results showed that the accuracies achieved in predicting the impact of preventive measures and health behaviors on COVID-19 in occupational settings were 78.1%, 81.2%, and 78.1% by MLP, RBF, and SVM respectively. The RBF model was identified as the most accurate model for predicting the impact of health behaviors on COVID-19 disease Additionally, the level of social distancing with customers, handwashing frequency and disinfection, the availability of cleansing and disinfecting agents for hands and surfaces in the workplace, and gatherings for eating meals and snacks were identified as the most significant health behaviors influencing the prevalence of COVID-19 in the workplace.
Conclusion: Studies of this nature can underscore the importance of attention to preventive measures and health behaviors in unprecedented circumstances. Furthermore, the utilization of artificial intelligence models and tools such as DSS (Decision Support Systems) can serve as powerful tools for optimizing control measures in work environments.
 

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Introduction: The use of antineoplastic drugs in cancer treatment, while essential, poses risks due to their non-selective action on both cancerous and healthy cells. Assessing and controlling environmental contamination with these drugs in workplaces is crucial. This study aimed to evaluate the efficacy of various commercial wipes in sampling the antineoplastic drug 5-fluorouracil from surfaces to develop standardized sampling methods.
Material and Methods: This study assessed the efficiency of commonly used commercial wipes (Whatman cellulose filter, cotton swab, Millipore™ filter, sterile gauze pad, and alcohol pad) for sampling 5-fluorouracil from different surfaces (stainless steel, vinyl, and ceramic). The sampling area was defined using disposable cardboard frames, and 1000 microliters of a 1 µg/mL 5-fluorouracil solution were applied to each surface. Sampling and extraction were conducted following NIOSH guidelines. The frame dimensions were 10 × 10 cm, limiting the sampling area to 100 square centimeters. Analysis was performed using high-performance liquid chromatography (HPLC), and results were analyzed using Prism GraphPad software, version 8.
Results: The sampling efficiency varied across wipes and surfaces, ranging from 11.2% to 86.2%. Alcohol pads showed the highest efficiency on stainless steel surfaces, while the Millipore™ filter had the lowest efficiency across all surfaces. Extraction efficiency ranged from 43.8% to 98.8%, with alcohol pads providing the highest recovery. Sample stability was maintained over 15 days.
Conclusion: Alcohol pads were most effective in collecting and extracting 5-fluorouracil, particularly from hard, smooth surfaces such as stainless steel and ceramic. These findings may improve sampling methods, thereby reducing occupational exposure to antineoplastic drugs. Further research on different wipes and extraction parameters could refine drug analysis techniques.